Repository: justmarkham/DAT5 Branch: master Commit: 87aa6d195393 Files: 78 Total size: 2.7 MB Directory structure: gitextract_q__99hvo/ ├── .gitignore ├── README.md ├── code/ │ ├── 00_python_beginner_workshop.py │ ├── 00_python_intermediate_workshop.py │ ├── 01_chipotle_homework_solution.py │ ├── 01_reading_files.py │ ├── 03_exploratory_analysis_pandas.py │ ├── 04_apis.py │ ├── 04_visualization.py │ ├── 05_iris_exercise.py │ ├── 05_sklearn_knn.py │ ├── 07_glass_id_homework_solution.py │ ├── 08_web_scraping.py │ ├── 10_logistic_regression_confusion_matrix.py │ ├── 13_naive_bayes.py │ ├── 15_kaggle.py │ ├── 17_ensembling_exercise.py │ ├── 18_clustering.py │ ├── 18_regularization.py │ ├── 19_advanced_sklearn.py │ ├── 19_gridsearchcv_exercise.py │ ├── 19_regex_exercise.py │ ├── 19_regex_reference.py │ ├── 20_sql.py │ └── 21_ensembles_example.py ├── data/ │ ├── SMSSpamCollection.txt │ ├── airline_safety.csv │ ├── auto_mpg.txt │ ├── chipotle_orders.tsv │ ├── default.csv │ ├── drinks.csv │ ├── homicides.txt │ ├── imdb_movie_ratings_top_1000.csv │ ├── imdb_movie_urls.csv │ ├── kaggle_tweets.csv │ ├── titanic_train.csv │ ├── vehicles_test.csv │ └── vehicles_train.csv ├── homework/ │ ├── 02_command_line_hw_soln.md │ ├── 03_pandas_hw_soln.py │ ├── 04_visualization_hw_soln.py │ ├── 06_bias_variance.md │ ├── 07_glass_identification.md │ ├── 11_roc_auc.md │ ├── 11_roc_auc_annotated.md │ ├── 13_spam_filtering.md │ └── 13_spam_filtering_annotated.md ├── notebooks/ │ ├── 06_bias_variance.ipynb │ ├── 06_model_evaluation_procedures.ipynb │ ├── 09_linear_regression.ipynb │ ├── 11_cross_validation.ipynb │ ├── 11_roc_auc.ipynb │ ├── 11_titanic_exercise.ipynb │ ├── 13_bayes_iris.ipynb │ ├── 13_naive_bayes_spam.ipynb │ ├── 14_nlp.ipynb │ ├── 16_decision_trees.ipynb │ ├── 17_ensembling.ipynb │ └── 18_regularization.ipynb ├── other/ │ ├── peer_review.md │ ├── project.md │ ├── public_data.md │ └── resources.md └── slides/ ├── 01_course_overview.pptx ├── 02_Introduction_to_the_Command_Line.md ├── 02_git_github.pptx ├── 04_apis.pptx ├── 04_visualization.pptx ├── 05_intro_to_data_science.pptx ├── 05_machine_learning_knn.pptx ├── 08_web_scraping.pptx ├── 10_logistic_regression_confusion_matrix.pptx ├── 11_drawing_roc.pptx ├── 13_bayes_theorem.pptx ├── 13_naive_bayes.pptx ├── 15_kaggle.pptx ├── 18_clustering.pptx └── 20_sql.pptx ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ .ipynb_checkpoints/ .DS_Store *.pyc ================================================ FILE: README.md ================================================ ## DAT5 Course Repository Course materials for [General Assembly's Data Science course](https://generalassemb.ly/education/data-science/washington-dc/) in Washington, DC (3/18/15 - 6/3/15). **Instructors:** Brandon Burroughs and Kevin Markham ([Data School blog](http://www.dataschool.io/), [email newsletter](http://www.dataschool.io/subscribe/), [YouTube channel](https://www.youtube.com/user/dataschool)) Monday | Wednesday --- | --- | 3/18: Introduction and Python 3/23: Git and Command Line | 3/25: Exploratory Data Analysis **3/30:** Visualization and APIs | 4/1: Machine Learning and KNN **4/6:** Bias-Variance and Model Evaluation | 4/8: Kaggle Titanic 4/13: Web Scraping, Tidy Data, Reproducibility | 4/15: Linear Regression 4/20: Logistic Regression and Confusion Matrices | 4/22: ROC and Cross-Validation **4/27:** Project Presentation #1 | 4/29: Naive Bayes 5/4: Natural Language Processing | 5/6: Kaggle Stack Overflow 5/11: Decision Trees | 5/13: Ensembles **5/18:** Clustering and Regularization | 5/20: Advanced scikit-learn and Regex **5/25:** *No Class* | 5/27: Databases and SQL 6/1: Course Review | **6/3:** Project Presentation #2 ### Key Project Dates * **3/30:** Deadline for discussing your project idea(s) with an instructor * **4/6:** Project question and dataset (write-up) * **4/27:** Project presentation #1 (slides, code, visualizations) * **5/18:** First draft due (draft of project paper, code, visualizations) * **5/25:** Peer review due * **6/3:** Project presentation #2 (project paper, slides, code, visualizations, data, data dictionary) ### Key Project Links * [Course project requirements](other/project.md) * [Public data sources](other/public_data.md) * [Kaggle competitions](http://www.kaggle.com/) * [Examples of student projects](https://github.com/justmarkham/DAT-project-examples) * [Peer review guidelines](other/peer_review.md) ### Logistics * Office hours will take place every Saturday and Sunday. * Homework will be assigned every Wednesday and due on Monday, and you'll receive feedback by Wednesday. * Our primary tool for out-of-class communication will be a private chat room through [Slack](https://slack.com/). ### Submission Forms * [Homework submission form](http://bit.ly/dat5homework) (also for project submissions) * [Gist](https://gist.github.com/) is an easy way to put your homework online * [Feedback submission form](http://bit.ly/dat5feedback) (at the end of every class) ### Before the Course Begins * Install the [Anaconda distribution](http://continuum.io/downloads) of Python 2.7x. * Install [Git](http://git-scm.com/book/en/v2/Getting-Started-Installing-Git) and create a [GitHub](https://github.com/) account. * Once you receive an email invitation from Slack, join our "DAT5 team" and add your photo. * Choose a [Python workshop](https://generalassemb.ly/education?format=classes-workshops) to attend, depending upon your current skill level: * Beginner: [Saturday 3/7 10am-2pm](https://generalassemb.ly/education/introduction-to-python-programming/washington-dc/11137) or [Thursday 3/12 6:30pm-9pm](https://generalassemb.ly/education/introduction-to-python-programming/washington-dc/11136) * Intermediate: [Saturday 3/14 10am-2pm](https://generalassemb.ly/education/python-for-data-science-intermediate/washington-dc/11167) * Practice your Python using the resources below. ### Python Resources * [Codecademy's Python course](http://www.codecademy.com/en/tracks/python): Good beginner material, including tons of in-browser exercises. * [DataQuest](https://dataquest.io/missions): Similar interface to Codecademy, but focused on teaching Python in the context of data science. * [Google's Python Class](https://developers.google.com/edu/python/): Slightly more advanced, including hours of useful lecture videos and downloadable exercises (with solutions). * [A Crash Course in Python for Scientists](http://nbviewer.ipython.org/gist/rpmuller/5920182): Read through the Overview section for a quick introduction to Python. * [Python for Informatics](http://www.pythonlearn.com/book.php): A very beginner-oriented book, with associated [slides](https://drive.google.com/folderview?id=0B7X1ycQalUnyal9yeUx3VW81VDg&usp=sharing) and [videos](https://www.youtube.com/playlist?list=PLlRFEj9H3Oj4JXIwMwN1_ss1Tk8wZShEJ). * Code from our [beginner](code/00_python_beginner_workshop.py) and [intermediate](code/00_python_intermediate_workshop.py) workshops: Useful for review and reference. ----- ### Class 1: Introduction and Python * Introduction to General Assembly * Course overview ([slides](slides/01_course_overview.pdf)) * Brief tour of Slack * Checking the setup of your laptop * Python lesson with [airline safety data](https://github.com/fivethirtyeight/data/tree/master/airline-safety) ([code](code/01_reading_files.py)) **Homework:** * Python exercises with [Chipotle order data](https://github.com/TheUpshot/chipotle) (listed at bottom of [code](code/01_reading_files.py) file) ([solution](code/01_chipotle_homework_solution.py)) * Work through GA's excellent introductory [command line tutorial](http://generalassembly.github.io/prework/command-line/#/) and then take this brief [quiz](https://gahub.typeform.com/to/J6xirf). * Read through the [course project requirements](other/project.md) and start thinking about your own project! **Optional:** * If we discovered any setup issues with your laptop, please resolve them before Monday. * If you're not feeling comfortable in Python, keep practicing using the resources above! ----- ### Class 2: Git and Command Line * Any questions about the course project? * Command line ([slides](slides/02_Introduction_to_the_Command_Line.md)) * Git and GitHub ([slides](slides/02_git_github.pdf)) **Homework:** * Command line exercises with [SMS Spam Data](https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection) (listed at the bottom of [Introduction to the Command Line](slides/02_Introduction_to_the_Command_Line.md)) ([solution](homework/02_command_line_hw_soln.md)) * **Note**: This homework is not due until Monday. You might want to create a GitHub repo for your homework instead of using Gist! **Optional:** * Browse through some [example student projects](https://github.com/justmarkham/DAT-project-examples) to stimulate your thinking and give you a sense of project scope. **Resources:** * This [Command Line Primer](http://lifehacker.com/5633909/who-needs-a-mouse-learn-to-use-the-command-line-for-almost-anything) goes a bit more into command line scripting. * Read the first two chapters of [Pro Git](http://git-scm.com/book/en/v2) to gain a much deeper understanding of version control and basic Git commands. * Watch [Introduction to Git and GitHub](https://www.youtube.com/playlist?list=PL5-da3qGB5IBLMp7LtN8Nc3Efd4hJq0kD) (36 minutes) for a quick review of a lot of today's material. * [GitRef](http://gitref.org/) is an excellent reference guide for Git commands, and [Git quick reference for beginners](http://www.dataschool.io/git-quick-reference-for-beginners/) is a shorter guide with commands grouped by workflow. * The [Markdown Cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) covers standard Markdown and a bit of "[GitHub Flavored Markdown](https://help.github.com/articles/github-flavored-markdown/)." ----- ### Class 3: Pandas * Pandas for data exploration, analysis, and visualization ([code](code/03_exploratory_analysis_pandas.py)) * [Split-Apply-Combine](http://i.imgur.com/yjNkiwL.png) pattern * Simple examples of [joins in Pandas](http://www.gregreda.com/2013/10/26/working-with-pandas-dataframes/#joining) **Homework:** * Pandas practice with [Automobile MPG Data](https://archive.ics.uci.edu/ml/datasets/Auto+MPG) (listed at the bottom of [Exploratory Analysis in Pandas](code/03_exploratory_analysis_pandas.py)) ([solution](homework/03_pandas_hw_soln.py)) * Talk to an instructor about your project * Don't forget about the Command line exercises (listed at the bottom of [Introduction to the Command Line](slides/02_Introduction_to_the_Command_Line.md)) **Optional:** * To learn more Pandas, review this [three-part tutorial](http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/), or review these two excellent (but extremely long) notebooks on Pandas: [introduction](http://nbviewer.ipython.org/github/fonnesbeck/Bios8366/blob/master/notebooks/Section2_5-Introduction-to-Pandas.ipynb) and [data wrangling](http://nbviewer.ipython.org/github/fonnesbeck/Bios8366/blob/master/notebooks/Section2_6-Data-Wrangling-with-Pandas.ipynb). * Read [How Software in Half of NYC Cabs Generates $5.2 Million a Year in Extra Tips](http://iquantny.tumblr.com/post/107245431809/how-software-in-half-of-nyc-cabs-generates-5-2) for an excellent example of exploratory data analysis. ----- ### Class 4: Visualization and APIs * Visualization ([slides](slides/04_visualization.pdf) and [code](code/04_visualization.py)) * APIs ([slides](slides/04_apis.pdf) and [code](code/04_apis.py)) **Homework:** * Visualization practice with [Automobile MPG Data](https://archive.ics.uci.edu/ml/datasets/Auto+MPG) (listed at the bottom of [the visualization code](code/04_visualization.py)) ([solution](homework/04_visualization_hw_soln.py)) * **Note**: This homework isn't due until Monday. **Optional:** * Watch [Look at Your Data](https://www.youtube.com/watch?v=coNDCIMH8bk) (18 minutes) for an excellent example of why visualization is useful for understanding your data. **Resources:** * For more on Pandas plotting, read this [notebook](http://nbviewer.ipython.org/github/fonnesbeck/Bios8366/blob/master/notebooks/Section2_7-Plotting-with-Pandas.ipynb) or the [visualization page](http://pandas.pydata.org/pandas-docs/stable/visualization.html) from the official Pandas documentation. * To learn how to customize your plots further, browse through this [notebook on matplotlib](http://nbviewer.ipython.org/github/fonnesbeck/Bios8366/blob/master/notebooks/Section2_4-Matplotlib.ipynb) or this [similar notebook](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb). * To explore different types of visualizations and when to use them, [Choosing a Good Chart](http://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf) and [The Graphic Continuum](http://www.coolinfographics.com/storage/post-images/The-Graphic-Continuum-POSTER.jpg) are handy one-page references, or check out the [R Graph Catalog](http://shinyapps.stat.ubc.ca/r-graph-catalog/). * For a more in-depth introduction to visualization, browse through these [PowerPoint slides](http://www2.research.att.com/~volinsky/DataMining/Columbia2011/Slides/Topic2-EDAViz.ppt) from Columbia's Data Mining class. * [Mashape](https://www.mashape.com/explore) and [Apigee](https://apigee.com/providers) allow you to explore tons of different APIs. Alternatively, a [Python API wrapper](http://www.pythonforbeginners.com/api/list-of-python-apis) is available for many popular APIs. ----- ### Class 5: Data Science Workflow, Machine Learning, KNN * Iris dataset * [What does an iris look like?](http://sebastianraschka.com/Images/2014_python_lda/iris_petal_sepal.png) * [Data](http://archive.ics.uci.edu/ml/datasets/Iris) hosted by the UCI Machine Learning Repository * "Human learning" exercise ([solution](code/05_iris_exercise.py)) * Introduction to data science ([slides](slides/05_intro_to_data_science.pdf)) * [Quora: What is data science?](https://www.quora.com/What-is-data-science/answer/Michael-Hochster) * [Data science Venn diagram](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) * [Quora: What is the workflow of a data scientist?](http://www.quora.com/What-is-the-work-flow-or-process-of-a-data-scientist/answer/Ryan-Fox-Squire) * Example student project: [MetroMetric](https://github.com/justmarkham/DAT-project-examples/blob/master/pdf/bus_presentation.pdf) * Machine learning and KNN ([slides](slides/05_machine_learning_knn.pdf)) * [Reddit AMA with Yann LeCun](http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun) * [Characteristics of your zip code](http://www.esri.com/landing-pages/tapestry/) * Introduction to scikit-learn ([code](code/05_sklearn_knn.py)) * Documentation: [user guide](http://scikit-learn.org/stable/modules/neighbors.html), [module reference](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors), [class documentation](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) **Homework:** * Complete your visualization homework assigned in class 4 * [Reading assignment on the bias-variance tradeoff](homework/06_bias_variance.md) * A write-up about your [project question and dataset](other/project.md) is due on Monday! ([example one](https://github.com/justmarkham/DAT4-students/blob/master/jason/jk_project_idea.md), [example two](https://github.com/justmarkham/DAT4-students/blob/master/alexlee/project_question.md)) **Optional:** * For a useful look at the different types of data scientists, read [Analyzing the Analyzers](http://cdn.oreillystatic.com/oreilly/radarreport/0636920029014/Analyzing_the_Analyzers.pdf) (32 pages). * For some thoughts on what it's like to be a data scientist, read these short posts from [Win-Vector](http://www.win-vector.com/blog/2012/09/on-being-a-data-scientist/) and [Datascope Analytics](http://datascopeanalytics.com/what-we-think/2014/07/31/six-qualities-of-a-great-data-scientist). * For a fun (yet enlightening) look at the data science workflow, read [What I do when I get a new data set as told through tweets](http://simplystatistics.org/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/). * For a more in-depth introduction to data science, browse through these [PowerPoint slides](http://www2.research.att.com/~volinsky/DataMining/Columbia2011/Slides/Topic1-DMIntro.ppt) from Columbia's Data Mining class. * For a more in-depth introduction to machine learning, read section 2.1 (14 pages) of Hastie and Tibshirani's excellent book, [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/). (It's a free PDF download!) * For a really nice comparison of supervised versus unsupervised learning, plus an introduction to reinforcement learning, watch this [video](http://work.caltech.edu/library/014.html) (13 minutes) from Caltech's [Learning From Data](http://work.caltech.edu/telecourse.html) course. **Resources:** * Quora has a [data science topic FAQ](https://www.quora.com/What-is-the-Data-Science-topic-FAQ) with lots of interesting Q&A. * Keep up with local data-related events through the Data Community DC [event calendar](http://www.datacommunitydc.org/calendar) or [weekly newsletter](http://www.datacommunitydc.org/thenewsletter/). ----- ### Class 6: Bias-Variance Tradeoff and Model Evaluation * Brief introduction to the IPython Notebook * Exploring the bias-variance tradeoff ([notebook](notebooks/06_bias_variance.ipynb)) * Discussion of the [assigned reading](homework/06_bias_variance.md) on the bias-variance tradeoff * Model evaluation procedures ([notebook](notebooks/06_model_evaluation_procedures.ipynb)) **Resources:** * If you would like to learn the IPython Notebook, the official [Notebook tutorials](http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/Notebook/Index.ipynb) are useful. * To get started with Seaborn for visualization, the official website has a series of [tutorials](http://web.stanford.edu/~mwaskom/software/seaborn/tutorial.html) and an [example gallery](http://web.stanford.edu/~mwaskom/software/seaborn/examples/index.html). * Hastie and Tibshirani have an excellent [video](https://www.youtube.com/watch?v=_2ij6eaaSl0&t=2m34s) (12 minutes, starting at 2:34) that covers training error versus testing error, the bias-variance tradeoff, and train/test split (which they call the "validation set approach"). * Caltech's Learning From Data course includes a fantastic [video](http://work.caltech.edu/library/081.html) (15 minutes) that may help you to visualize bias and variance. ----- ### Class 7: Kaggle Titanic * Guest instructor: [Josiah Davis](https://generalassemb.ly/instructors/josiah-davis/3315) * Participate in Kaggle's [Titanic competition](http://www.kaggle.com/c/titanic-gettingStarted) * Work in pairs, but the goal is for every person to make at least one submission by the end of the class period! **Homework:** * Option 1 is to do the [Glass identification homework](homework/07_glass_identification.md). This is a good option if you are still getting comfortable with what we have learned so far, and prefer a very structured assignment. ([solution](code/07_glass_id_homework_solution.py)) * Option 2 is to keep working on the Titanic competition, and see if you can make some additional progress! This is a good assignment if you are feeling comfortable with the material and want to learn a bit more on your own. * In either case, please submit your code as usual, and include lots of code comments! ----- ### Class 8: Web Scraping, Tidy Data, Reproducibility * Web scraping ([slides](slides/08_web_scraping.pdf) and [code](code/08_web_scraping.py)) * [HTML Tree](http://www.openbookproject.net/tutorials/getdown/css/images/lesson4/HTMLDOMTree.png) * Tidy data: * [Introduction](http://stat405.had.co.nz/lectures/18-tidy-data.pdf) * Example datasets: [Bob Ross](https://github.com/fivethirtyeight/data/blob/master/bob-ross/elements-by-episode.csv), [NFL ticket prices](https://github.com/fivethirtyeight/data/blob/master/nfl-ticket-prices/2014-average-ticket-price.csv), [airline safety](https://github.com/fivethirtyeight/data/blob/master/airline-safety/airline-safety.csv), [Jets ticket prices](https://github.com/fivethirtyeight/data/blob/master/nfl-ticket-prices/jets-buyer.csv), [Chipotle orders](https://github.com/TheUpshot/chipotle/blob/master/orders.tsv) * Reproducibility: * [Introduction](http://www.dataschool.io/reproducibility-is-not-just-for-researchers/), [Tweet](https://twitter.com/jakevdp/status/519563939177197571) * [Components of reproducible analysis](https://github.com/jtleek/datasharing) * Examples: [Classic rock](https://github.com/fivethirtyeight/data/tree/master/classic-rock), [student project 1](https://github.com/jwknobloch/DAT4_final_project), [student project 2](https://github.com/justmarkham/DAT4-students/tree/master/Jonathan_Bryan/Project_Files) **Resources:** * This [web scraping tutorial from Stanford](http://web.stanford.edu/~zlotnick/TextAsData/Web_Scraping_with_Beautiful_Soup.html) provides an example of getting a list of items. * If you want to learn more about tidy data, [Hadley Wickham's paper](http://www.jstatsoft.org/v59/i10/paper) has a lot of nice examples. * If your co-workers tend to create spreadsheets that are [unreadable by computers](https://bosker.wordpress.com/2014/12/05/the-government-statistical-services-terrible-spreadsheet-advice/), perhaps they would benefit from reading this list of [tips for releasing data in spreadsheets](http://www.clean-sheet.org/). (There are some additional suggestions in this [answer](http://stats.stackexchange.com/questions/83614/best-practices-for-creating-tidy-data/83711#83711) from Cross Validated.) * Here's [Colbert on reproducibility](http://thecolbertreport.cc.com/videos/dcyvro/austerity-s-spreadsheet-error) (8 minutes). ----- ### Class 9: Linear Regression * Linear regression ([notebook](notebooks/09_linear_regression.ipynb)) * Simple linear regression * Estimating and interpreting model coefficients * Confidence intervals * Hypothesis testing and p-values * R-squared * Multiple linear regression * Feature selection * Model evaluation metrics for regression * Handling categorical predictors **Homework:** * If you're behind on homework, use this time to catch up. * Keep working on your project... your first presentation is in less than two weeks!! **Resources:** * To go much more in-depth on linear regression, read Chapter 3 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/), from which this lesson was adapted. Alternatively, watch the [related videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) or read my [quick reference guide](http://www.dataschool.io/applying-and-interpreting-linear-regression/) to the key points in that chapter. * To learn more about Statsmodels and how to interpret the output, DataRobot has some decent posts on [simple linear regression](http://www.datarobot.com/blog/ordinary-least-squares-in-python/) and [multiple linear regression](http://www.datarobot.com/blog/multiple-regression-using-statsmodels/). * This [introduction to linear regression](http://people.duke.edu/~rnau/regintro.htm) is much more detailed and mathematically thorough, and includes lots of good advice. * This is a relatively quick post on the [assumptions of linear regression](http://pareonline.net/getvn.asp?n=2&v=8). ----- ### Class 10: Logistic Regression and Confusion Matrices * Logistic regression ([slides](slides/10_logistic_regression_confusion_matrix.pdf) and [code](code/10_logistic_regression_confusion_matrix.py)) * Confusion matrices (same links as above) **Homework:** * Video assignment on [ROC Curves and Area Under the Curve](homework/11_roc_auc.md) * Review the notebook from class 6 on [model evaluation procedures](notebooks/06_model_evaluation_procedures.ipynb) **Resources:** * For more on logistic regression, watch the [first three videos](https://www.youtube.com/playlist?list=PL5-da3qGB5IC4vaDba5ClatUmFppXLAhE) (30 minutes total) from Chapter 4 of An Introduction to Statistical Learning. * UCLA's IDRE has a handy table to help you remember the [relationship between probability, odds, and log-odds](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/odds_ratio.htm). * Better Explained has a very friendly introduction (with lots of examples) to the [intuition behind "e"](http://betterexplained.com/articles/an-intuitive-guide-to-exponential-functions-e/). * Here are some useful lecture notes on [interpreting logistic regression coefficients](http://www.unm.edu/~schrader/biostat/bio2/Spr06/lec11.pdf). * Kevin wrote a [simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) that you can use as a reference guide. ----- ### Class 11: ROC Curves and Cross-Validation * ROC curves and Area Under the Curve * Discuss the [video assignment](homework/11_roc_auc.md) * Exercise: [drawing an ROC curve](slides/11_drawing_roc.pdf) * Calculating AUC and plotting an ROC curve ([notebook](notebooks/11_roc_auc.ipynb)) * Cross-validation ([notebook](notebooks/11_cross_validation.ipynb)) * Discuss this article on [Smart Autofill for Google Sheets](http://googleresearch.blogspot.com/2014/10/smart-autofill-harnessing-predictive.html) **Homework:** * Your first [project presentation](other/project.md) is on Monday! Please submit a link to your project repository (with slides, code, and visualizations) before class using the homework submission form. **Optional:** * Titanic exercise ([notebook](notebooks/11_titanic_exercise.ipynb)) **Resources:** * scikit-learn has extensive documentation on [model evaluation](http://scikit-learn.org/stable/modules/model_evaluation.html). * For more on cross-validation, read section 5.1 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (11 pages) or watch the related videos: [K-fold and leave-one-out cross-validation](https://www.youtube.com/watch?v=nZAM5OXrktY) (14 minutes), [cross-validation the right and wrong ways](https://www.youtube.com/watch?v=S06JpVoNaA0) (10 minutes). ----- ### Class 12: Project Presentation #1 * Project presentations! **Homework:** * Read these [Introduction to Probability](https://docs.google.com/presentation/d/1cM2dVbJgTWMkHoVNmYlB9df6P2H8BrjaqAcZTaLe9dA/edit#slide=id.gfc3caad2_00) slides (from the [OpenIntro Statistics textbook](https://www.openintro.org/stat/textbook.php)) and try the included quizzes. Pay specific attention to the following terms: probability, sample space, mutually exclusive, independent. * Reading assignment on [spam filtering](homework/13_spam_filtering.md). ----- ### Class 13: Naive Bayes * Conditional probability and Bayes' theorem * [Slides](slides/13_bayes_theorem.pdf) (adapted from [Visualizing Bayes' theorem](http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/)) * [Visualization of conditional probability](http://setosa.io/conditional/) * Applying Bayes' theorem to iris classification ([notebook](notebooks/13_bayes_iris.ipynb)) * Naive Bayes classification * [Slides](slides/13_naive_bayes.pdf) * Example with spam email ([notebook](notebooks/13_naive_bayes_spam.ipynb)) * Discuss the reading assignment on [spam filtering](homework/13_spam_filtering.md) * [Airport security example](http://www.quora.com/In-laymans-terms-how-does-Naive-Bayes-work/answer/Konstantin-Tt) * Classifying [SMS messages](https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection) ([code](code/13_naive_bayes.py)) **Homework:** * Please download/install the following for the NLP class on Monday * In Spyder, `import nltk` and run `nltk.download('all')`. This downloads all of the necessary resources for the Natural Language Tool Kit. * We'll be using two new packages/modules for this class: textblob and lda. Please install them. **Hint**: In the Terminal (Mac) or Git Bash (Windows), run `pip install textblob` and `pip install lda`. **Resources:** * For other intuitive introductions to Bayes' theorem, here are two good blog posts that use [ducks](https://planspacedotorg.wordpress.com/2014/02/23/bayes-rule-for-ducks/) and [legos](http://www.countbayesie.com/blog/2015/2/18/bayes-theorem-with-lego). * For more on conditional probability, these [slides](https://docs.google.com/presentation/d/1psUIyig6OxHQngGEHr3TMkCvhdLInnKnclQoNUr4G4U/edit#slide=id.gfc69f484_00) may be useful. * For more details on Naive Bayes classification, Wikipedia has two excellent articles ([Naive Bayes classifier](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) and [Naive Bayes spam filtering](http://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering)), and Cross Validated has a good [Q&A](http://stats.stackexchange.com/questions/21822/understanding-naive-bayes). * If you enjoyed Paul Graham's article, you can read [his follow-up article](http://www.paulgraham.com/better.html) on how he improved his spam filter and this [related paper](http://www.merl.com/publications/docs/TR2004-091.pdf) about state-of-the-art spam filtering in 2004. * If you're planning on using text features in your project, it's worth exploring the different types of [Naive Bayes](http://scikit-learn.org/stable/modules/naive_bayes.html) and the many options for [CountVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html). ----- ### Class 14: Natural Language Processing * Natural Language Processing ([notebook](notebooks/14_nlp.ipynb)) * NLTK: tokenization, stemming, lemmatization, part of speech tagging, stopwords, Named Entity Recognition, LDA * Alternative: TextBlob **Resources:** * [Natural Language Processing with Python](http://www.nltk.org/book/): free online book to go in-depth with NLTK * [NLP online course](https://www.coursera.org/course/nlp): no sessions are available, but [video lectures](https://class.coursera.org/nlp/lecture) and [slides](http://web.stanford.edu/~jurafsky/NLPCourseraSlides.html) are still accessible * [Brief slides](http://files.meetup.com/7616132/DC-NLP-2013-09%20Charlie%20Greenbacker.pdf) on the major task areas of NLP * [Detailed slides](https://github.com/ga-students/DAT_SF_9/blob/master/16_Text_Mining/DAT9_lec16_Text_Mining.pdf) on a lot of NLP terminology * [A visual survey of text visualization techniques](http://textvis.lnu.se/): for exploration and inspiration * [DC Natural Language Processing](http://www.meetup.com/DC-NLP/): active Meetup group * [Stanford CoreNLP](http://nlp.stanford.edu/software/corenlp.shtml): suite of tools if you want to get serious about NLP * Getting started with regex: [Python introductory lesson](https://developers.google.com/edu/python/regular-expressions) and [reference guide](https://github.com/justmarkham/DAT3/blob/master/code/99_regex_reference.py), [real-time regex tester](https://regex101.com/#python), [in-depth tutorials](http://www.rexegg.com/) * [A good explanation of LDA](http://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) * [Textblob documentation](http://textblob.readthedocs.org/en/dev/) * [SpaCy](http://honnibal.github.io/spaCy/): a new NLP package ----- ### Class 15: Kaggle Stack Overflow * Overview of how Kaggle works ([slides](slides/15_kaggle.pdf)) * Kaggle In-Class competition: [Predict whether a Stack Overflow question will be closed](https://inclass.kaggle.com/c/dat5-stack-overflow) ([code](code/15_kaggle.py)) **Optional:** * Keep working on this competition! You can make up to 5 submissions per day, and the competition doesn't close until 6:30pm ET on Wednesday, May 27 (class 20). **Resources:** * For a great overview of the diversity of problems tackled by Kaggle competitions, watch [Kaggle Transforms Data Science Into Competitive Sport](https://www.youtube.com/watch?v=8w4UY66GKcM) (28 minutes) by Jeremy Howard (past president of Kaggle). * [Getting in Shape for the Sport of Data Science](https://www.youtube.com/watch?v=kwt6XEh7U3g) (74 minutes), also by Jeremy Howard, contains a lot of tips for competitive machine learning. * [Learning from the best](http://blog.kaggle.com/2014/08/01/learning-from-the-best/) is an excellent blog post covering top tips from Kaggle Masters on how to do well on Kaggle. * [Feature Engineering Without Domain Expertise](https://www.youtube.com/watch?v=bL4b1sGnILU) (17 minutes), a talk by Kaggle Master Nick Kridler, provides some simple advice about how to iterate quickly and where to spend your time during a Kaggle competition. * Kevin's [project presentation video](https://www.youtube.com/watch?v=HGr1yQV3Um0) (16 minutes) gives a nice tour of the end-to-end machine learning process for a Kaggle competition. (Or, just check out the [slides](https://speakerdeck.com/justmarkham/allstate-purchase-prediction-challenge-on-kaggle).) ----- ### Class 16: Decision Trees * Decision trees ([notebook](notebooks/16_decision_trees.ipynb)) **Resources:** * scikit-learn documentation: [Decision Trees](http://scikit-learn.org/stable/modules/tree.html) **Installing Graphviz (optional):** * Mac: * [Download and install PKG file](http://www.graphviz.org/Download_macos.php) * Windows: * [Download and install MSI file](http://www.graphviz.org/Download_windows.php) * **Add it to your Path:** Go to Control Panel, System, Advanced System Settings, Environment Variables. Under system variables, edit "Path" to include the path to the "bin" folder, such as: `C:\Program Files (x86)\Graphviz2.38\bin` ----- ### Class 17: Ensembles * Ensembles and random forests ([notebook](notebooks/17_ensembling.ipynb)) **Homework:** * Your [project draft](other/project.md#may-18-first-draft-due) is due on Monday! Please submit a link to your project repository (with paper, code, and visualizations) before class using the homework submission form. * Your peers and your instructors will be giving you feedback on your project draft. * Here's an example of a great [final project paper](https://github.com/justmarkham/DAT-project-examples/blob/master/pdf/nba_paper.pdf) from a past student. * Make at least one new submission to our [Kaggle competition](https://inclass.kaggle.com/c/dat5-stack-overflow)! We suggest trying Random Forests or building your own ensemble of models. For assistance, you could use this [framework code](code/17_ensembling_exercise.py), or refer to the [complete code](code/15_kaggle.py) from class 15. You can optionally submit your code to us if you want feedback. **Resources:** * scikit-learn documentation: [Ensembles](http://scikit-learn.org/stable/modules/ensemble.html) * Quora: [How do random forests work in layman's terms?](http://www.quora.com/How-do-random-forests-work-in-laymans-terms/answer/Edwin-Chen-1) ----- ### Class 18: Clustering and Regularization * Clustering ([slides](slides/18_clustering.pdf) and [code](code/18_clustering.py)) * Regularization ([notebook](notebooks/18_regularization.ipynb) and [code](code/18_regularization.py)) **Homework:** * You will be assigned to review the project drafts of two of your peers. You have until next Monday to provide them with feedback, according to [these guidelines](other/peer_review.md). **Resources:** * [Introduction to Data Mining](http://www-users.cs.umn.edu/~kumar/dmbook/index.php) has a thorough [chapter on cluster analysis](http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf). * The scikit-learn user guide has a nice [section on clustering](http://scikit-learn.org/stable/modules/clustering.html). * Wikipedia article on [determining the number of clusters](http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set). * This [K-means clustering visualization](http://shiny.rstudio.com/gallery/kmeans-example.html) allows you to set different numbers of clusters for the iris data, and this [other visualization](http://asa.1gb.ru/kmeans/1.html) allows you to see the effects of different initial positions for the centroids. * Fun examples of clustering: [A Statistical Analysis of the Work of Bob Ross](http://fivethirtyeight.com/features/a-statistical-analysis-of-the-work-of-bob-ross/) (with [data and Python code](https://github.com/fivethirtyeight/data/tree/master/bob-ross)), [How a Math Genius Hacked OkCupid to Find True Love](http://www.wired.com/2014/01/how-to-hack-okcupid/all/), and [characteristics of your zip code](http://www.esri.com/landing-pages/tapestry/). * An Introduction to Statistical Learning has useful videos on [K-means clustering](https://www.youtube.com/watch?v=aIybuNt9ps4&list=PL5-da3qGB5IBC-MneTc9oBZz0C6kNJ-f2&index=3) (17 minutes), [ridge regression](https://www.youtube.com/watch?v=cSKzqb0EKS0&list=PL5-da3qGB5IB-Xdpj_uXJpLGiRfv9UVXI&index=6) (13 minutes), and [lasso regression](https://www.youtube.com/watch?v=A5I1G1MfUmA&index=7&list=PL5-da3qGB5IB-Xdpj_uXJpLGiRfv9UVXI) (15 minutes). * Caltech's Learning From Data course has a great video introducing [regularization](http://work.caltech.edu/library/121.html) (8 minutes) that builds upon their video about the [bias-variance tradeoff](http://work.caltech.edu/library/081.html). * Here is a longer example of [feature scaling](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb) in scikit-learn, with additional discussion of the types of scaling you can use. * [Clever Methods of Overfitting](http://hunch.net/?p=22) is a classic post by John Langford. ----- ### Class 19: Advanced scikit-learn and Regular Expressions * Advanced scikit-learn ([code](code/19_advanced_sklearn.py)) * Searching for optimal parameters: [GridSearchCV](http://scikit-learn.org/stable/modules/grid_search.html) * [Exercise](code/19_gridsearchcv_exercise.py) * Standardization of features: [StandardScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) * Chaining steps: [Pipeline](http://scikit-learn.org/stable/modules/pipeline.html) * Regular expressions ("regex") * Motivating example: [data](data/homicides.txt), [code](code/19_regex_exercise.py) * Reference guide: [code](code/19_regex_reference.py) **Optional:** * Use regular expressions to create a list of causes from the homicide data. Your list should look like this: `['shooting', 'shooting', 'blunt force', ...]`. If the cause is not listed for a particular homicide, include it in the list as `'unknown'`. **Resources:** * scikit-learn has an incredibly active [mailing list](https://www.mail-archive.com/scikit-learn-general@lists.sourceforge.net/index.html) that is often much more useful than Stack Overflow for researching a particular function. * The scikit-learn documentation includes a [machine learning map](http://scikit-learn.org/stable/tutorial/machine_learning_map/) that may help you to choose the "best" model for your task. * In you want to build upon the regex material presented in today's class, Google's Python Class includes an excellent [lesson](https://developers.google.com/edu/python/regular-expressions) (with an associated [video](https://www.youtube.com/watch?v=kWyoYtvJpe4&index=4&list=PL5-da3qGB5IA5NwDxcEJ5dvt8F9OQP7q5)). * [regex101](https://regex101.com/#python) is an online tool for testing your regular expressions in real time. * If you want to go really deep with regular expressions, [RexEgg](http://www.rexegg.com/) includes endless articles and tutorials. * [Exploring Expressions of Emotions in GitHub Commit Messages](http://geeksta.net/geeklog/exploring-expressions-emotions-github-commit-messages/) is a fun example of how regular expressions can be used for data analysis. ----- ### Class 20: Databases and SQL * Databases and SQL ([slides](slides/20_sql.pdf) and [code](code/20_sql.py)) **Homework:** * Read this classic paper, which may help you to connect many of the topics we have studied throughout the course: [A Few Useful Things to Know about Machine Learning](http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf). * Your [final project](other/project.md#june-3-project-presentation-2) is due next Wednesday! * Please submit a link to your project repository before Wednesday's class using the homework submission form. * Your presentation should start with a recap of the key information from the previous presentation, but you should spend most of your presentation discussing what has happened since then. * Don't forget to practice your presentation and time yourself! **Resources:** * [SQLZOO](http://sqlzoo.net/wiki/SQL_Tutorial), [Mode Analytics](http://sqlschool.modeanalytics.com/), and [Code School](http://campus.codeschool.com/courses/try-sql/contents) all have online SQL tutorials that look promising. * [w3schools](http://www.w3schools.com/sql/trysql.asp?filename=trysql_select_all) has a sample database that allows you to practice your SQL. * [10 Easy Steps to a Complete Understanding of SQL](http://tech.pro/tutorial/1555/10-easy-steps-to-a-complete-understanding-of-sql) is a good article for those who have some SQL experience and want to understand it at a deeper level. * [A Comparison Of Relational Database Management Systems](https://www.digitalocean.com/community/tutorials/sqlite-vs-mysql-vs-postgresql-a-comparison-of-relational-database-management-systems) gives the pros and cons of SQLite, MySQL, and PostgreSQL. * If you want to go deeper into databases and SQL, Stanford has a well-respected series of [14 mini-courses](https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about). ----- ### Class 21: Course Review * Pipelines ([code](code/19_advanced_sklearn.py)) * Class review * Creating an ensemble ([code](code/21_ensembles_example.py)) **Resources:** * [Data science review](https://docs.google.com/document/d/1XCdyrsQwU5OC5os7RHdVTEtS-tpHBbsoKKWLpYI6Svo/edit?usp=sharing): A summary of key concepts from the Data Science course. * [Comparing supervised learning algorithms](https://docs.google.com/spreadsheets/d/15_QJXm6urctsbIXO-C_eXrsSffbHedio8z0E5ozxO-M/edit?usp=sharing): Kevin's table comparing the machine learning models we studied in the course. * [Choosing a Machine Learning Classifier](http://blog.echen.me/2011/04/27/choosing-a-machine-learning-classifier/): Edwin Chen's short and highly readable guide. * [Machine Learning Done Wrong](http://ml.posthaven.com/machine-learning-done-wrong) and [Common Pitfalls in Machine Learning](http://danielnee.com/?p=155): Thoughtful advice on common mistakes to avoid in machine learning. * [Practical machine learning tricks from the KDD 2011 best industry paper](http://blog.david-andrzejewski.com/machine-learning/practical-machine-learning-tricks-from-the-kdd-2011-best-industry-paper/): More advanced advice than the resources above. * [An Empirical Comparison of Supervised Learning Algorithms](http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf): Research paper from 2006. * [Many more resources for continued learning!](other/resources.md) ----- ### Class 22: Project Presentation #2 * Presentations! **Class is over! What should I do now?** * Take a break! * Go back through class notes/code/videos to make sure you feel comfortable with what we've learned. * Take a look at the **Resources** for each class to get a deeper understanding of what we've learned. Start with the **Resources** from Class 21 and move to topics you are most interested in. * You might not realize it, but you are at a point where you can continue learning on your own. You have all of the skills necessary to read papers, blogs, documentation, etc. * GA Data Guild * [8/24/2015](https://generalassemb.ly/education/data-science-guild/washington-dc/13274) * [9/21/2015](https://generalassemb.ly/education/data-science-guild/washington-dc/13275) * [10/19/2015](https://generalassemb.ly/education/data-science-guild/washington-dc/13276) * [11/9/2015](https://generalassemb.ly/education/data-science-guild/washington-dc/13277) * Follow data scientists on Twitter. This will help you stay up on the latest news/models/applications/tools. * Participate in [Data Community DC](http://www.datacommunitydc.org/) events. They sponsor meetups, workshops, etc, notably the [Data Science DC Meetup](http://www.meetup.com/Data-Science-DC/). Sign up for their [newsletter](http://www.datacommunitydc.org/newsletter/) also! * Read blogs to keep learning. I really like [District Data Labs](http://districtdatalabs.silvrback.com/). * Do Kaggle competitions! This is a good way to continue and hone your skillset. Plus, you'll learn a ton along the way. And finally, don't forget about [graduation](https://generalassemb.ly/education/graduation-april-may-june-courses/washington-dc/12892)! ================================================ FILE: code/00_python_beginner_workshop.py ================================================ ''' Multi-line comments go between 3 quotation marks. You can use single or double quotes. ''' # One-line comments are preceded by the pound symbol # BASIC DATA TYPES x = 5 # creates an object print type(x) # check the type: int (not declared explicitly) type(x) # automatically prints type(5) # assigning it to a variable is not required type(5.0) # float type('five') # str type(True) # bool # LISTS nums = [5, 5.0, 'five'] # multiple data types nums # print the list type(nums) # check the type: list len(nums) # check the length: 3 nums[0] # print first element nums[0] = 6 # replace a list element nums.append(7) # list 'method' that modifies the list help(nums.append) # help on this method help(nums) # help on a list object nums.remove('five') # another list method sorted(nums) # 'function' that does not modify the list nums # it was not affected nums = sorted(nums) # overwrite the original list sorted(nums, reverse=True) # optional argument # list slicing [start:end:stride] weekdays = ['mon','tues','wed','thurs','fri'] weekdays[0] # element 0 weekdays[0:3] # elements 0, 1, 2 weekdays[:3] # elements 0, 1, 2 weekdays[3:] # elements 3, 4 weekdays[-1] # last element (element 4) weekdays[::2] # every 2nd element (0, 2, 4) weekdays[::-1] # backwards (4, 3, 2, 1, 0) days = weekdays + ['sat','sun'] # concatenate lists # FUNCTIONS def give_me_five(): # function definition ends with colon return 5 # indentation required for function body give_me_five() # prints the return value (5) num = give_me_five() # assigns return value to a variable, doesn't print it def calc(x, y, op): # three parameters (without any defaults) if op == 'add': # conditional statement return x + y elif op == 'subtract': return x - y else: print 'Valid operations: add, subtract' calc(5, 3, 'add') calc(5, 3, 'subtract') calc(5, 3, 'multiply') calc(5, 3) # EXERCISE: Write a function that takes two parameters (hours and rate), and # returns the total pay. def compute_pay(hours, rate): return hours * rate compute_pay(40, 10.50) # EXERCISE: Update your function to give the employee 1.5 times the hourly rate # for hours worked above 40 hours. def compute_more_pay(hours, rate): if hours <= 40: return hours * rate else: return 40*rate + (hours-40)*(rate*1.5) compute_more_pay(30, 10) compute_more_pay(45, 10) # STRINGS # create a string s = str(42) # convert another data type into a string s = 'I like you' # examine a string s[0] # returns 'I' len(s) # returns 10 # string slicing like lists s[:6] # returns 'I like' s[7:] # returns 'you' s[-1] # returns 'u' # split a string into a list of substrings separated by a delimiter s.split(' ') # returns ['I','like','you'] s.split() # same thing # concatenate strings s3 = 'The meaning of life is' s4 = '42' s3 + ' ' + s4 # returns 'The meaning of life is 42' s3 + ' ' + str(42) # same thing # EXERCISE: Given a string s, return a string made of the first 2 and last 2 # characters of the original string, so 'spring' yields 'spng'. However, if the # string length is less than 2, instead return the empty string. def both_ends(s): if len(s) < 2: return '' else: return s[:2] + s[-2:] both_ends('spring') both_ends('cat') both_ends('a') # FOR LOOPS # range returns a list of integers range(0, 3) # returns [0, 1, 2]: includes first value but excludes second value range(3) # same thing: starting at zero is the default # simple for loop for i in range(5): print i # print each list element in uppercase fruits = ['apple', 'banana', 'cherry'] for i in range(len(fruits)): print fruits[i].upper() # better for loop for fruit in fruits: print fruit.upper() # EXERCISE: Write a program that prints the numbers from 1 to 100. But for # multiples of 3 print 'fizz' instead of the number, and for the multiples of # 5 print 'buzz'. For numbers which are multiples of both 3 and 5 print 'fizzbuzz'. def fizz_buzz(): nums = range(1, 101) for num in nums: if num % 15 == 0: print 'fizzbuzz' elif num % 3 == 0: print 'fizz' elif num % 5 == 0: print 'buzz' else: print num fizz_buzz() # EXERCISE: Given a list of strings, return a list with the strings # in sorted order, except group all the strings that begin with 'x' first. # e.g. ['mix', 'xyz', 'apple', 'xanadu', 'aardvark'] returns # ['xanadu', 'xyz', 'aardvark', 'apple', 'mix'] # Hint: this can be done by making 2 lists and sorting each of them # before combining them. def front_x(words): lista=[] listb=[] for word in words: if word[0]=='x': lista.append(word) else: listb.append(word) return sorted(lista) + sorted(listb) front_x(['mix', 'xyz', 'apple', 'xanadu', 'aardvark']) ================================================ FILE: code/00_python_intermediate_workshop.py ================================================ ## QUIZ TO REVIEW BEGINNER WORKSHOP a = 5 b = 5.0 c = a/2 d = b/2 ''' What is type(a)? int What is type(b)? float What is c? 2 What is d? 2.5 ''' e = [a, b] f = range(10) ''' What is type(e)? list What is len(e)? 2 What is type(f)? list What are the contents of f? integers 0 through 9 What is 'range' called? a function How do I get help on 'range'? help(range) ''' g = ['mon','tues','wed','thurs','fri'] ''' How do I slice out 'mon'? g[0] How do I slice out 'mon' through 'wed'? g[0:3] What are two ways to slice out 'fri'? g[4] or g[-1] How do I check the type of 'mon'? type(g[0]) ''' g.remove('wed') sorted(g) h = sorted(g, reverse=True) ''' What are the contents of g? ['mon','tues','thurs','fri'] What are the contents of h? ['tues','thurs','mon','fri'] What is 'remove' called? a list method How do I get help on 'remove'? help(g.remove) What is 'reverse=True' called? an optional argument ''' i = 'Hello' j = 'friend' k = i + j l = i + 3 m = i[0] ''' What is 'k'? 'Hellofriend' What is 'l'? undefined (due to error) What is 'm'? 'H' ''' ## FOR LOOPS AND BASIC LIST COMPREHENSIONS # print 1 through 5 nums = range(1, 6) for num in nums: print num # for loop to create a list of cubes cubes = [] for num in nums: cubes.append(num**3) # equivalent list comprehension cubes = [num**3 for num in nums] # [1, 8, 27, 64, 125] ''' EXERCISE: Given that: letters = ['a','b','c'] Write a list comprehension that returns: ['A','B','C'] Hint: 'hello'.upper() returns 'HELLO' [letter.upper() for letter in letters] BONUS EXERCISE: Given that: word = 'abc' Write a list comprehension that returns: ['A','B','C'] [letter.upper() for letter in word] ''' ## LIST COMPREHENSIONS WITH CONDITIONS nums = range(1, 6) # for loop to create a list of cubes of even numbers cubes_of_even = [] for num in nums: if num % 2 == 0: cubes_of_even.append(num**3) # equivalent list comprehension # syntax: [expression for variable in iterable if condition] cubes_of_even = [num**3 for num in nums if num % 2 == 0] # [8, 64] ## DICTIONARIES # dictionaries are similar to lists: # - both can contain multiple data types # - both are iterable # - both are mutable # dictionaries are different from lists: # - dictionaries are unordered # - dictionary lookup time is constant regardless of dictionary size # dictionaries are like real dictionaries: # - dictionaries are made of key-value pairs (word and definition) # - dictionary keys must be unique (each word is only defined once) # - you can use the key to look up the value, but not the other way around # create a dictionary (and open Variable Explorer in Spyder) family = {'dad':'homer', 'mom':'marge', 'size':6} # examine a dictionary family[0] # throws an error (there is no ordering) family['dad'] # returns 'homer' len(family) # returns 3 family.keys() # returns list: ['dad', 'mom', 'size'] family.values() # returns list: ['homer', 'marge', 6] family.items() # returns list of tuples: # [('dad', 'homer'), ('mom', 'marge'), ('size', 6)] # modify a dictionary family['cat'] = 'snowball' # add a new entry family['cat'] = 'snowball ii' # edit an existing entry del family['cat'] # delete an entry family['kids'] = ['bart', 'lisa'] # value can be a list # accessing a list element within a dictionary family['kids'][0] # returns 'bart' ''' EXERCISE: Given that: d = {'a':10, 'b':20, 'c':[30, 40]} First, print the value for 'a' Then, change the value for 'b' to be 25 Then, change the 30 to be 35 Finally, append 45 to the end of the list that contains 35 and 40 d['a'] d['b'] = 25 d['c'][0] = 35 d['c'].append(45) BONUS EXERCISE: Write a list comprehension that returns a list of the keys in uppercase [key.upper() for key in d.keys()] ''' ## APIs # API Providers: https://apigee.com/providers # Echo Nest API Console: https://apigee.com/console/echonest # Echo Nest Developer Center: http://developer.echonest.com/ import requests # import module (make its functions available) # use requests to talk to the web r = requests.get('http://www.google.com') r.text type(r.text) # request data from the Echo Nest API r = requests.get('http://developer.echonest.com/api/v4/artist/top_hottt?api_key=KBGUPZPJZS9PHWNIN&format=json') r.text r.json() # decode JSON type(r.json()) top = r.json() # pretty print for easier readability import pprint pprint.pprint(top) # pull out the artist data artists = top['response']['artists'] # list of 15 dictionaries # reformat data into a table structure artists_data = [artist.values() for artist in artists] # list of 15 lists artists_header = artists[0].keys() # list of 2 strings ## WORKING WITH PUBLIC DATA # List of data sources: https://github.com/justmarkham/DAT5/blob/master/other/public_data.md # FiveThirtyEight: http://fivethirtyeight.com/ # FiveThirtyEight data: https://github.com/fivethirtyeight/data # NFL ticket prices data: https://github.com/fivethirtyeight/data/tree/master/nfl-ticket-prices # Question: What is the average ticket price for Ravens' home vs away games? # open a CSV file from a URL import csv r = requests.get('https://raw.githubusercontent.com/fivethirtyeight/data/master/nfl-ticket-prices/2014-average-ticket-price.csv') data = [row for row in csv.reader(r.iter_lines())] # list of lists # open a downloaded CSV file from your working directory with open('2014-average-ticket-price.csv', 'rU') as f: data = [row for row in csv.reader(f)] # list of lists # examine the data type(data) len(data) data[0] data[1] # save the data we want data = data[1:97] # step 1: create a list that only contains events data[0][0] data[1][0] data[2][0] events = [row[0] for row in data] # EXERCISE # step 2: create a list that only contains prices (stored as integers) prices = [int(row[2]) for row in data] # step 3: figure out how to locate the away teams events[0] events[0].find(' at ') stop = events[0].find(' at ') events[0][:stop] # step 4: use a for loop to make a list of the away teams away_teams = [] for event in events: stop = event.find(' at ') away_teams.append(event[:stop]) # EXERCISE # step 5: use a for loop to make a list of the home teams home_teams = [] for event in events: start = event.find(' at ') + 4 stop = event.find(' Tickets ') home_teams.append(event[start:stop]) # step 6: figure out how to get prices only for Ravens home games zip(home_teams, prices) # list of tuples [pair[1] for pair in zip(home_teams, prices)] # iterate through tuples and get price [price for team, price in zip(home_teams, prices)] # better way to get price [price for team, price in zip(home_teams, prices) if team == 'Baltimore Ravens'] # add a condition # step 7: create lists of the Ravens home and away game prices ravens_home = [price for team, price in zip(home_teams, prices) if team == 'Baltimore Ravens'] ravens_away = [price for team, price in zip(away_teams, prices) if team == 'Baltimore Ravens'] # EXERCISE # step 8: calculate the average of each list float(sum(ravens_home)) / len(ravens_home) float(sum(ravens_away)) / len(ravens_away) ================================================ FILE: code/01_chipotle_homework_solution.py ================================================ ''' SOLUTION FILE: Homework with Chipotle data https://github.com/TheUpshot/chipotle ''' ''' PART 1: read in the data, parse it, and store it in a list of lists called 'data' Hint: this is a tsv file, and csv.reader() needs to be told how to handle it ''' import csv # specify that the delimiter is a tab character with open('chipotle_orders.tsv', 'rU') as f: data = [row for row in csv.reader(f, delimiter='\t')] ''' PART 2: separate the header and data into two different lists ''' header = data[0] data = data[1:] ''' PART 3: calculate the average price of an order Hint: examine the data to see if the 'quantity' column is relevant to this calculation Hint: work smarter, not harder! (this can be done in a few lines of code) ''' # count the number of unique order_id's # note: you could assume this is 1834 because that's the maximum order_id, but it's best to check num_orders = len(set([row[0] for row in data])) # 1834 # create a list of prices # note: ignore the 'quantity' column because the 'item_price' takes quantity into account prices = [float(row[4][1:-1]) for row in data] # strip the dollar sign and trailing space # calculate the average price of an order and round to 2 digits round(sum(prices) / num_orders, 2) # $18.81 ''' PART 4: create a list (or set) of all unique sodas and soft drinks that they sell Note: just look for 'Canned Soda' and 'Canned Soft Drink', and ignore other drinks like 'Izze' ''' # if 'item_name' includes 'Canned', append 'choice_description' to 'sodas' list sodas = [] for row in data: if 'Canned' in row[2]: sodas.append(row[3][1:-1]) # strip the brackets # create a set of unique sodas unique_sodas = set(sodas) ''' PART 5: calculate the average number of toppings per burrito Note: let's ignore the 'quantity' column to simplify this task Hint: think carefully about the easiest way to count the number of toppings Hint: 'hello there'.count('e') ''' # keep a running total of burritos and toppings burrito_count = 0 topping_count = 0 # calculate number of toppings by counting the commas and adding 1 # note: x += 1 is equivalent to x = x + 1 for row in data: if 'Burrito' in row[2]: burrito_count += 1 topping_count += (row[3].count(',') + 1) # calculate the average topping count and round to 2 digits round(topping_count / float(burrito_count), 2) # 5.40 ''' PART 6: create a dictionary in which the keys represent chip orders and the values represent the total number of orders Expected output: {'Chips and Roasted Chili-Corn Salsa': 18, ... } Note: please take the 'quantity' column into account! Advanced: learn how to use 'defaultdict' to simplify your code ''' # start with an empty dictionary chips = {} # if chip order is not in dictionary, then add a new key/value pair # if chip order is already in dictionary, then update the value for that key for row in data: if 'Chips' in row[2]: if row[2] not in chips: chips[row[2]] = int(row[1]) # this is a new key, so create key/value pair else: chips[row[2]] += int(row[1]) # this is an existing key, so add to the value # defaultdict saves you the trouble of checking whether a key already exists from collections import defaultdict dchips = defaultdict(int) for row in data: if 'Chips' in row[2]: dchips[row[2]] += int(row[1]) ''' BONUS: think of a question about this data that interests you, and then answer it! ''' ================================================ FILE: code/01_reading_files.py ================================================ ''' Lesson on file reading using Airline Safety Data https://github.com/fivethirtyeight/data/tree/master/airline-safety ''' # read the whole file at once, return a single string (including newlines) # 'rU' mode (read universal) converts different line endings into '\n' f = open('airline_safety.csv', 'rU') data = f.read() f.close() # use a context manager to automatically close your file with open('airline_safety.csv', 'rU') as f: data = f.read() # read the whole file at once, return a list of lines with open('airline_safety.csv', 'rU') as f: data = f.readlines() # use list comprehension to duplicate readlines with open('airline_safety.csv', 'rU') as f: data = [row for row in f] # use the csv module to create a list of lists import csv with open('airline_safety.csv', 'rU') as f: data = [row for row in csv.reader(f)] # alternative method that doesn't require downloading the file import requests r = requests.get('https://raw.githubusercontent.com/fivethirtyeight/data/master/airline-safety/airline-safety.csv') data = [row for row in csv.reader(r.iter_lines())] # separate the header and data header = data[0] data = data[1:] # EXERCISE: # create a list of airline names (without the star) # create a list of the same length that contains 1 if there's a star and 0 if not airlines = [] starred = [] for row in data: if row[0][-1] == '*': starred.append(1) airlines.append(row[0][:-1]) else: starred.append(0) airlines.append(row[0]) # EXERCISE: # create a list that contains the average number of incidents per distance [(int(row[2]) + int(row[5])) / float(row[1]) for row in data] ''' A few extra things that will help you with the homework ''' # 'in' statement is useful for lists my_list = [1, 2, 1] 1 in my_list # True 3 in my_list # False # 'in' is useful for strings (checks for substrings) my_string = 'hello there' 'the' in my_string # True 'then' in my_string # False # 'in' is useful for dictionaries (checks keys but not values) my_dict = {'name':'Kevin', 'title':'instructor'} 'name' in my_dict # True 'Kevin' in my_dict # False # 'set' data structure is useful for gathering unique elements set(my_list) # returns a set of 1, 2 len(set(my_list)) # count of unique elements ''' Homework with Chipotle data https://github.com/TheUpshot/chipotle ''' ''' PART 1: read in the data, parse it, and store it in a list of lists called 'data' Hint: this is a tsv file, and csv.reader() needs to be told how to handle it ''' ''' PART 2: separate the header and data into two different lists ''' ''' PART 3: calculate the average price of an order Hint: examine the data to see if the 'quantity' column is relevant to this calculation Hint: work smarter, not harder! (this can be done in a few lines of code) ''' ''' PART 4: create a list (or set) of all unique sodas and soft drinks that they sell Note: just look for 'Canned Soda' and 'Canned Soft Drink', and ignore other drinks like 'Izze' ''' ''' PART 5: calculate the average number of toppings per burrito Note: let's ignore the 'quantity' column to simplify this task Hint: think carefully about the easiest way to count the number of toppings Hint: 'hello there'.count('e') ''' ''' PART 6: create a dictionary in which the keys represent chip orders and the values represent the total number of orders Expected output: {'Chips and Roasted Chili-Corn Salsa': 18, ... } Note: please take the 'quantity' column into account! Advanced: learn how to use 'defaultdict' to simplify your code ''' ''' BONUS: think of a question about this data that interests you, and then answer it! ''' ================================================ FILE: code/03_exploratory_analysis_pandas.py ================================================ """ CLASS: Pandas for Data Exploration, Analysis, and Visualization About the data: WHO alcohol consumption data: article: http://fivethirtyeight.com/datalab/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/ original data: https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption files: drinks.csv (with additional 'continent' column) """ """ First, we need to import Pandas into Python. Pandas is a Python package that allows for easy manipulation of DataFrames. You'll also need to import matplotlib for plotting. """ #imports import pandas as pd import matplotlib.pyplot as plt import numpy as np ''' Reading Files, Summarizing, Selecting, Filtering, Sorting ''' # Can read a file from a local file on your computer or from a URL drinks = pd.read_table('drinks.csv', sep=',') # read_table is more general drinks = pd.read_csv('drinks.csv') # read_csv is specific to CSV and implies sep="," # Can also read from URLs drinks = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/drinks.csv') ''' Key Concept: Dot notation In Python, you can think of an object as an entity that can have both attributes and methods. A dot following an object indicates that you are about to access something within the object, an attribute or a method. Attributes contain information about the object. They are usually a single "word" following the dot. A method is somethng the object can do. They are usually a "word" with parentheses following the dot. ''' # examine the drinks data drinks # print the first 30 and last 30 rows type(drinks) # DataFrame drinks.head() # print the first 5 rows drinks.head(10) # print the first 10 rows drinks.tail() # print the last 5 rows drinks.describe() # summarize all numeric columns drinks.describe(include='all') # includes non numeric columns; new in pandas 0.15.0 drinks.index # "the index" (aka "the labels") drinks.columns # column names (which is "an index") drinks.dtypes # data types of each column drinks.shape # number of rows and columns drinks.values # underlying numpy array drinks.info() # concise summary (includes memory usage as of pandas 0.15.0) # Print the 'beer_servings' Series (a single column) drinks.beer_servings drinks['beer_servings'] type(drinks.beer_servings) # Print two columns drinks[['beer_servings','wine_servings']] cols = ['beer_servings','wine_servings'] drinks[cols] # Calculate the average 'beer_servings' for the entire dataset drinks.describe() # summarize all numeric columns drinks.beer_servings.describe() # summarize only the 'beer_servings' Series drinks.beer_servings.mean() # only calculate the mean drinks.beer_servings.max() # only calculate the max drinks.beer_servings.min() # only calculate the min # Other aggregation functions drinks.beer_servings.sum() drinks.beer_servings.count() float(drinks.beer_servings.sum())/drinks.beer_servings.count() # Count the number of occurrences of each 'continent' value drinks.continent.value_counts() # Simple logical filters # Print all columns, but only show rows where the country is in Europe # Let's look at each piece of this. drinks.continent # Returns all of the continent values drinks.continent=='EU' # Returns True/False list drinks[drinks.continent=='EU'] # Returns all rows where True # Other logical filters drinks[drinks.beer_servings > 158] drinks[drinks.beer_servings <= 10] type(drinks[drinks.beer_servings <= 10]) # DataFrame drinks[drinks.beer_servings <= 10][['country','beer_servings']] # Calculate the average 'beer_servings' for all of Europe drinks[drinks.continent=='EU'].beer_servings.mean() # More complex logical fitering # Only show European countries with 'wine_servings' greater than 300 # Note: parentheses are required for each condition, and you can't use 'and' or 'or' keywords drinks[(drinks.continent=='EU') & (drinks.wine_servings > 300)] # Show European countries or countries with 'wine_servings' greater than 300 drinks[(drinks.continent=='EU') | (drinks.wine_servings > 300)] # Show countries who have more than the mean beer_servings drinks[drinks.beer_servings > drinks.beer_servings.mean()] ########################################## ############ Exercise 1 ############ ########################################## # Using the 'drinks' data, answer the following questions: # 1. What is the maximum number of total litres of pure alcohol? drinks.total_litres_of_pure_alcohol.max() # 2. Which country has the maximum number of total litres of pure alcohol? drinks[drinks.total_litres_of_pure_alcohol == drinks.total_litres_of_pure_alcohol.max()]['country'] # 3. Does Haiti or Belarus consume more servings of spirits? drinks.spirit_servings[drinks.country=='Haiti'] > drinks.spirit_servings[drinks.country=='Belarus'] # 4. How many countries have more than 300 wine servings OR more than 300 # beer servings OR more than 300 spirit servings? drinks[(drinks.wine_servings > 300) | (drinks.beer_servings > 300) | (drinks.spirit_servings > 300)].country.count() # 5. For the countries in the previous question, what is the average total litres # of pure alcohol? drinks[(drinks.wine_servings > 300) | (drinks.beer_servings > 300) | (drinks.spirit_servings > 300)].mean() # sorting drinks.beer_servings.order() # only works for a Series drinks.sort_index() # sort rows by label drinks.sort_index(by='beer_servings') # sort rows by a specific column drinks.sort_index(by='beer_servings', ascending=False) # use descending order instead drinks.sort_index(by=['beer_servings', 'wine_servings']) # sort by multiple columns # Determine which 10 countries have the highest 'total_litres_of_pure_alcohol' drinks.sort_index(by='total_litres_of_pure_alcohol').tail(10) # Determine which country has the highest value for 'beer_servings' drinks[drinks.beer_servings==drinks.beer_servings.max()].country # Use dot notation to string together commands # How many countries in each continent have beer_servings greater than 182? # i.e. a beer every two days drinks[drinks.beer_servings > 182].continent.value_counts() # add a new column as a function of existing columns # note: can't (usually) assign to an attribute (e.g., 'drinks.total_servings') drinks['total_servings'] = drinks.beer_servings + drinks.spirit_servings + drinks.wine_servings drinks['alcohol_mL'] = drinks.total_litres_of_pure_alcohol * 1000 drinks.head() ''' Split-Apply-Combine ''' # for each continent, calculate mean beer servings drinks.groupby('continent').beer_servings.mean() # for each continent, calculate mean of all numeric columns drinks.groupby('continent').mean() # for each continent, count number of occurrences drinks.groupby('continent').continent.count() drinks.continent.value_counts() ''' A little numpy ''' probs = np.array([0.51, 0.50, 0.02, 0.49, 0.78]) # np.where functions like an IF statement in Excel # np.where(condition, value if true, value if false) np.where(probs >= 0.5, 1, 0) drinks['lots_of_beer'] = np.where(drinks.beer_servings > 300, 1, 0) ########################################## ############ Exercise 2 ############ ########################################## # 1. What is the average number of total litres of pure alcohol for each # continent? drinks.groupby('continent').total_litres_of_pure_alcohol.mean() # 2. For each continent, calculate the mean wine_servings for all countries who # have a spirit_servings greater than the overall spirit_servings mean. drinks[drinks.spirit_servings > drinks.spirit_servings.mean()].groupby('continent').wine_servings.mean() # 3. Per continent, for all of the countries that drink more beer servings than # the average number of beer servings, what is the average number of wine # servings? drinks[drinks.beer_servings > drinks.beer_servings.mean()].groupby('continent').wine_servings.mean() ''' Advanced Filtering (of rows) and Selecting (of columns) ''' # loc: filter rows by LABEL, and select columns by LABEL drinks.loc[0] # row with label 0 drinks.loc[0:3] # rows with labels 0 through 3 drinks.loc[0:3, 'beer_servings':'wine_servings'] # rows 0-3, columns 'beer_servings' through 'wine_servings' drinks.loc[:, 'beer_servings':'wine_servings'] # all rows, columns 'beer_servings' through 'wine_servings' drinks.loc[[0,3], ['beer_servings','spirit_servings']] # rows 1 and 4, columns 'beer_servings' and 'spirit_servings' # iloc: filter rows by POSITION, and select columns by POSITION drinks.iloc[0] # row with 0th position (first row) drinks.iloc[0:3] # rows with positions 0 through 2 (not 3) drinks.iloc[0:3, 0:3] # rows and columns with positions 0 through 2 drinks.iloc[:, 0:3] # all rows, columns with positions 0 through 2 drinks.iloc[[0,2], [0,1]] # 1st and 3rd row, 1st and 2nd column # mixing: select columns by LABEL, then filter rows by POSITION drinks.wine_servings[0:3] drinks[['beer_servings', 'spirit_servings', 'wine_servings']][0:3] ########################################## ############# Homework ############# ########################################## ''' Use the automotive mpg data (https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.csv) to complete the following parts. Please turn in your code for each part. Before each code chunk, give a brief description (one line) of what the code is doing (e.g. "Loads the data" or "Creates scatter plot of mpg and weight"). If the code output produces a plot or answers a question, give a brief interpretation of the output (e.g. "This plot shows X,Y,Z" or "The mean for group A is higher than the mean for group B which means X,Y,Z"). ''' ''' Part 1 Load the data (https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.txt) into a DataFrame. Try looking at the "head" of the file in the command line to see how the file is delimited and how to load it. Note: You do not need to turn in any command line code you may use. ''' ''' Part 2 Get familiar with the data. Answer the following questions: - What is the shape of the data? How many rows and columns are there? - What variables are available? - What are the ranges for the values in each numeric column? - What is the average value for each column? Does that differ significantly from the median? ''' ''' Part 3 Use the data to answer the following questions: - Which 5 cars get the best gas mileage? - Which 5 cars with more than 4 cylinders get the best gas mileage? - Which 5 cars get the worst gas mileage? - Which 5 cars with 4 or fewer cylinders get the worst gas mileage? ''' ''' Part 4 Use groupby and aggregations to explore the relationships between mpg and the other variables. Which variables seem to have the greatest effect on mpg? Some examples of things you might want to look at are: - What is the mean mpg for cars for each number of cylindres (i.e. 3 cylinders, 4 cylinders, 5 cylinders, etc)? - Did mpg rise or fall over the years contained in this dataset? - What is the mpg for the group of lighter cars vs the group of heaver cars? Note: Be creative in the ways in which you divide up the data. You are trying to create segments of the data using logical filters and comparing the mpg for each segment of the data. ''' ================================================ FILE: code/04_apis.py ================================================ ''' CLASS: APIs Data Science Toolkit text2sentiment API ''' ''' APIs without wrappers (i.e. there is no nicely formatted function) ''' # Import the necessary modules import requests # Helps construct the request to send to the API import json # JSON helper functions # We have a sentence we want the sentiment of sample_sentence = 'A couple hundred hours & several thousand lines of code later... thank you @GA_DC!! #DataScience #GAGradNight' # We know end URL endpoint to send it to url = 'http://www.datasciencetoolkit.org/text2sentiment/' # First we specify the header header = {'content-type': 'application/json'} # Next we specify the body (the information we want the API to work on) body = sample_sentence # Now we make the request response = requests.post(url, data=body, headers=header) # Notice that this is a POST request # Let's look at the response response.status_code response.ok response.text # Let's turn that text back into JSON r_json = json.loads(response.text) r_json r_json['score'] # 2.0 ########################################## ############ Exercise 1 ############ ########################################## # Turn the above code into a function # The function should take in one argument, some text, and return a number, # the sentiment. Call your function "get_sentiment". def get_sentiment(text): url = 'http://www.datasciencetoolkit.org/text2sentiment/' #specify header header = {'content-type': 'application/json'} # Next we specify the body (the information we want the API to work on) body = text # Now we make the request response = requests.post(url, data=body, headers=header) # Notice that this is a POST request r_json = json.loads(response.text) sentiment = r_json['score'] # 2.0 return sentiment # Now that we've created our own wrapper, we can use it throughout our code. # We now have multiple sentences sentences = ['I love pizza!', 'I hate pizza!', 'I feel nothing about pizza!'] # Loop through the sentences for sentence in sentences: sentiment = get_sentiment(sentence) print sentence, sentiment # Print the results ''' APIs with wrappers (i.e. there is a nicely formatted function) ''' # Import the API library import dstk # Remember our sample sentence? sample_sentence # Let's try our new API library # Instantiate DSTK object dstk = dstk.DSTK() dstk.text2sentiment(sample_sentence) # 2.0 # We can once again loop through our sentences for sentence in sentences: sentiment = dstk.text2sentiment(sentence) print sentence, sentiment['score'] ================================================ FILE: code/04_visualization.py ================================================ """ CLASS: Visualization """ # imports import pandas as pd import matplotlib.pyplot as plt # import the data available at https://raw.githubusercontent.com/justmarkham/DAT5/master/data/drinks.csv drinks = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/drinks.csv') ''' Visualization ''' # bar plot of number of countries in each continent drinks.continent.value_counts().plot(kind='bar', title='Countries per Continent') plt.xlabel('Continent') plt.ylabel('Count') plt.show() # show plot window (if it doesn't automatically appear) plt.savefig('countries_per_continent.png') # save plot to file # bar plot of average number of beer servings (per adult per year) by continent drinks.groupby('continent').beer_servings.mean().plot(kind='bar', title='Average Number of Beer Servings By Continent') plt.ylabel('Average Number of Beer Servings Per Year') plt.show() # histogram of beer servings (shows the distribution of a numeric column) drinks.beer_servings.hist(bins=20) plt.title("Distribution of Beer Servings") plt.xlabel('Beer Servings') plt.ylabel('Frequency') plt.show() # density plot of beer servings (smooth version of a histogram) drinks.beer_servings.plot(kind='density', xlim=(0,500)) plt.title("Distribution of Beer Servings") plt.xlabel('Beer Servings') plt.show() # grouped histogram of beer servings (shows the distribution for each group) drinks.beer_servings.hist(by=drinks.continent) plt.show() drinks.beer_servings.hist(by=drinks.continent, sharex=True) plt.show() drinks.beer_servings.hist(by=drinks.continent, sharex=True, sharey=True) plt.show() drinks.beer_servings.hist(by=drinks.continent, sharey=True, layout=(2, 3)) # change layout (new in pandas 0.15.0) plt.show() # boxplot of beer servings by continent (shows five-number summary and outliers) drinks.boxplot(column='beer_servings', by='continent') plt.show() # scatterplot of beer servings versus wine servings drinks.plot(kind='scatter', x='beer_servings', y='wine_servings', alpha=0.3) plt.show() # same scatterplot, except point color varies by 'spirit_servings' # note: must use 'c=drinks.spirit_servings' prior to pandas 0.15.0 drinks.plot(kind='scatter', x='beer_servings', y='wine_servings', c='spirit_servings', colormap='Blues') plt.show() # same scatterplot, except all European countries are colored red colors = np.where(drinks.continent=='EU', 'r', 'b') drinks.plot(x='beer_servings', y='wine_servings', kind='scatter', c=colors) plt.show() # Scatter matrix pd.scatter_matrix(drinks) plt.show() ########################################## ############ Exercise 1 ############ ########################################## # 1. Generate a plot showing the average number of total litres of pure alcohol # by continent. drinks.groupby('continent').total_litres_of_pure_alcohol.mean().plot(kind='bar') plt.show() # 2. Illustrate the relationship between spirit servings and total litres of # pure alcohol. What kind of relationship is there? drinks.plot(kind='scatter', x='spirit_servings', y='total_litres_of_pure_alcohol', alpha=0.4) plt.show() # 3. Generate one plot that shows the distribution of spirit servings for each # continent. drinks.spirit_servings.hist(by=drinks.continent, sharex=True, sharey=True) plt.show() ########################################## ############# Homework ############# ########################################## ''' Use the automotive mpg data (https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.txt) to complete the following parts. Please turn in your code for each part. Before each code chunk, give a brief description (one line) of what the code is doing (e.g. "Loads the data" or "Creates scatter plot of mpg and weight"). If the code output produces a plot or answers a question, give a brief interpretation of the output (e.g. "This plot shows X,Y,Z" or "The mean for group A is higher than the mean for group B which means X,Y,Z"). ''' ''' Part 1 Produce a plot that compares the mean mpg for the different numbers of cylinders. ''' ''' Part 2 Use a scatter matrix to explore relationships between different numeric variables. ''' ''' Part 3 Use a plot to answer the following questions: -Do heavier or lighter cars get better mpg? -How are horsepower and displacement related? -What does the distribution of acceleration look like? -How is mpg spread for cars with different numbers of cylinders? -Do cars made before or after 1975 get better average mpg? (Hint: You need to create a new column that encodes whether a year is before or after 1975.) ''' ================================================ FILE: code/05_iris_exercise.py ================================================ ''' EXERCISE: "Human Learning" with iris data Can you predict the species of an iris using petal and sepal measurements? TASKS: 1. Read iris data into a pandas DataFrame, including column names. 2. Gather some basic information about the data. 3. Use groupby, sorting, and/or plotting to look for differences between species. 4. Come up with a set of rules that could be used to predict species based upon measurements. BONUS: Define a function that accepts a row of data and returns a predicted species. Then, use that function to make predictions for all existing rows of data. ''' import pandas as pd import numpy as np import matplotlib.pyplot as plt ## TASK 1 # read the iris data into a pandas DataFrame, including column names col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] iris = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', names=col_names) ## TASK 2 # gather basic information iris.shape iris.head() iris.describe() iris.species.value_counts() iris.dtypes iris.isnull().sum() ## TASK 3 # use groupby to look for differences between the species iris.groupby('species').sepal_length.mean() iris.groupby('species').mean() iris.groupby('species').describe() # use sorting to look for differences between the species iris.sort_index(by='sepal_length').values iris.sort_index(by='sepal_width').values iris.sort_index(by='petal_length').values iris.sort_index(by='petal_width').values # use plotting to look for differences between the species iris.petal_width.hist(by=iris.species, sharex=True) iris.boxplot(column='petal_width', by='species') iris.boxplot(by='species') # map species to a numeric value so that plots can be colored by category iris['species_num'] = iris.species.map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2}) iris.plot(kind='scatter', x='petal_length', y='petal_width', c='species_num', colormap='Blues') pd.scatter_matrix(iris, c=iris.species_num) ## TASK 4 # If petal length is less than 3, predict setosa. # Else if petal width is less than 1.8, predict versicolor. # Otherwise predict virginica. ## BONUS # define function that accepts a row of data and returns a predicted species def classify_iris(row): if row[2] < 3: # petal_length return 0 # setosa elif row[3] < 1.8: # petal_width return 1 # versicolor else: return 2 # virginica # predict for a single row classify_iris(iris.iloc[0, :]) # first row classify_iris(iris.iloc[149, :]) # last row # store predictions for all rows predictions = [classify_iris(row) for row in iris.values] # calculate the percentage of correct predictions np.mean(iris.species_num == predictions) # 0.96 ================================================ FILE: code/05_sklearn_knn.py ================================================ ''' CLASS: Introduction to scikit-learn with iris data ''' # read in iris data import pandas as pd col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] iris = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', names=col_names) # create numeric column for the response # note: features and response must both be entirely numeric! iris['species_num'] = iris.species.map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2}) # create X (features) three different ways X = iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']] X = iris.loc[:, 'sepal_length':'petal_width'] X = iris.iloc[:, 0:4] # create y (response) y = iris.species_num # check the shape of X and y X.shape # 150 by 4 (n=150, p=4) y.shape # 150 (must match first dimension of X) # scikit-learn 4-step modeling pattern: # Step 1: import the class you plan to use from sklearn.neighbors import KNeighborsClassifier # Step 2: instantiate the "estimator" (aka the model) # note: all unspecified parameters are set to the defaults knn = KNeighborsClassifier(n_neighbors=1) # Step 3: fit the model with data (learn the relationship between X and y) knn.fit(X, y) # Step 4: use the "fitted model" to predict the response for a new observation knn.predict([3, 5, 4, 2]) # predict for multiple observations at once X_new = [[3, 5, 4, 2], [3, 5, 2, 2]] knn.predict(X_new) # try a different value of K ("tuning parameter") knn = KNeighborsClassifier(n_neighbors=5) knn.fit(X, y) knn.predict(X_new) # predicted classes knn.predict_proba(X_new) # predicted probabilities of class membership knn.kneighbors([3, 5, 4, 2]) # distances to nearest neighbors (and identities) # calculate Euclidian distance manually for nearest neighbor import numpy as np np.sqrt(((X.iloc[106, :] - [3, 5, 4, 2])**2).sum()) ================================================ FILE: code/07_glass_id_homework_solution.py ================================================ ''' HOMEWORK: Glass Identification (aka "Glassification") ''' # TASK 1: read data into a DataFrame import pandas as pd df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data', names=['id','ri','na','mg','al','si','k','ca','ba','fe','glass_type'], index_col='id') # TASK 2: briefly explore the data df.shape df.head() df.tail() df.glass_type.value_counts() df.isnull().sum() # TASK 3: convert to binary classification problem (1/2/3/4 maps to 0, 5/6/7 maps to 1) import numpy as np df['binary'] = np.where(df.glass_type < 5, 0, 1) # method 1 df['binary'] = df.glass_type.map({1:0, 2:0, 3:0, 4:0, 5:1, 6:1, 7:1}) # method 2 df.binary.value_counts() # TASK 4: create a feature matrix (X) features = ['ri','na','mg','al','si','k','ca','ba','fe'] # create a list of features features = df.columns[:-2] # alternative way: slice 'columns' attribute like a list X = df[features] # create DataFrame X by only selecting features # TASK 5: create a response vector (y) y = df.binary # TASK 6: split X and y into training and testing sets from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=99) # TASK 7: fit a KNN model on the training set using K=5 from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=5) knn.fit(X_train, y_train) # TASK 8: make predictions on the testing set and calculate accuracy y_pred = knn.predict(X_test) from sklearn import metrics print metrics.accuracy_score(y_test, y_pred) # 90.7% accuracy # TASK 9: calculate null accuracy 1 - y.mean() # 76.2% null accuracy # BONUS: write a for loop that computes test set accuracy for a range of K values k_range = range(1, 30, 2) scores = [] for k in k_range: knn = KNeighborsClassifier(n_neighbors=k) knn.fit(X_train, y_train) y_pred = knn.predict(X_test) scores.append(metrics.accuracy_score(y_test, y_pred)) # BONUS: plot K versus test set accuracy to choose on optimal value for K import matplotlib.pyplot as plt plt.plot(k_range, scores) # optimal value is K=1 ================================================ FILE: code/08_web_scraping.py ================================================ ''' CLASS: Web Scraping We will be using two packages in particular: requests and Beautiful Soup 4. ''' ''' Introduction to Beautiful Soup ''' # imports import requests # How Python gets the webpages from bs4 import BeautifulSoup # Creates structured, searchable object import pandas as pd import matplotlib.pyplot as plt # First, let's play with beautiful soup on a "toy" webpage html_doc = """ Brandon's Homepage!

Brandon's Homepage

My name is Brandon. I'm love web scraping!

I'm originally from Louisiana. I went to undergrad at Louisiana Tech and grad school at UNC.

I currently work as a Product Manager of Linguistics and Analytics at Clarabridge.

My Hobbies

""" type(html_doc) # Beautiful soup allows us to create a structured object out of this string b = BeautifulSoup(html_doc) type(b) # Let's look at "b" b # The most useful methods in a Beautiful Soup object are "find" and "findAll". # "find" takes several parameters, the most important are "name" and "attrs". # Let's talk about "name". b.find(name='body') # Finds the 'body' tag and everything inside of it. body = b.find(name='body') type(body) #tag # You can search tags also h1 = body.find(name='h1') # Find the 'h1' tag inside of the 'body' tag h1 h1.text # Print out just the text inside of the body # Now let's find the 'p' tags p = b.find(name='p') # This only finds one. This is where 'findAll' comes in. all_p = b.findAll(name='p') all_p type(all_p) # Result sets are a lot like Python lists all_p[0] # Access specific element with index all_p[1] # Iterable like list for one_p in all_p: print one_p.text # Print text # Access specific attribute of a tag all_p[0] # Specific tag all_p[0]['id'] # Speific attribute of a specific tag # Now let's talk about 'attrs' # Beautiful soup also allows us to choose tags with specific attributes b.find(name='p', attrs={"id":"intro"}) b.find(name='p', attrs={"id":"background"}) b.find(name='p', attrs={"id":"current"}) ########################################## ############ Exercise 1 ############ ########################################## # 1. Extact the 'h3' element from Brandon's webpage. b.find(name='h3') # 2. Extract Brandon's hobbies from the html_doc. Print out the text of the hobby. hobbies = b.findAll(name='ul') for hobby in hobbies: print hobby.text # 3. Extract Brandon's hobby that has the id "my favorite". b.find(name='li', attrs={'id':'my favorite'}) ''' Beautiful Soup from the web ''' # We see data on a web page that we want to get. First we need the HTML. # This downloads the HTML and puts it into the variable r r = requests.get('http://www.imdb.com/title/tt1856010/') # But when we look at it, it's just one giant string. type(r.text) # Unicode string r.text[0:200] # Beautiful soup allows us to create a structured object out of this string b = BeautifulSoup(r.text) type(b) ''' "find" and "findAll" with the 'name' parameter in Beautiful Soup ''' b.find(name='body') # Find a specific HTML tag body = b.find(name='body') # Store the output of your "find" type(body) # Let's look at the type # Can we still run another "find" command on the output? img = body.find('img') # Find the image tags img type(img) # Yes, but it only finds one of the "img" tags. We want them all. imgs = body.findAll(name='img') imgs # Now we get them all. type(imgs) # Resultsets are a lot like Python lists # Let's look at each individual image imgs[0] imgs[1] # We're really interested is the 'src' attribute, the actual image location. # How do we access attributes in a Python object? Using the dot notation or the # brackets. With Beautiful Soup, we must use the brackets imgs[0]['src'] # Now we can look through each image and print the 'src' attribute. for img in imgs: print img['src'] # Or maybe we want to create a list of all of the 'src' attributes src_list = [] for img in imgs: src_list.append(img['src']) len(src_list) ''' "find" and "findAll" with the 'attrs' parameter in Beautiful Soup ''' # Now let's talk about 'attrs' # Beautiful soup also allows us to choose tags with specific attributes title = b.find(name="span", attrs={"class":"itemprop", "itemprop":"name"}) title # Prints HTML matching that tag, but we want the actual name title.text # The "text" attribute gives you the text between two HTML tags star_rating = b.find(name="div", attrs={"class":"titlePageSprite star-box-giga-star"}) # How do I get the actual star_rating number? star_rating.text # How do I make this star_rating a number instead of a string? float(star_rating.text) ########################################## ############ Exercise 2 ############ ########################################## ''' We've retrieved the title of the show, but now we want the show's rating, duration, and genre. Using "find" and "find all", write code that retrieves each of these things Hint: Everything can be found in the "infobar". Try finding that first and searchng within it. ''' infobar = b.find(name="div", attrs={"class":"infobar"}) # Retrieve the show's content rating content_rating = infobar.find(name='meta', attrs={"itemprop":"contentRating"})['content'] # Retrieve the show's duration duration = infobar.find(name='time', attrs={"itemprop":"duration"}).text # Retrieve the show's genre genre = infobar.find(name='span', attrs={"itemprop":"genre"}).text ''' Looping through 'findAll' results ''' # Now we want to get the list of actors and actresses # First let's get the "div" block with all of the actor info actors_raw = b.find(name='div', attrs={"class":"txt-block", "itemprop":"actors", "itemscope":"", "itemtype":"http://schema.org/Person"}) # Now let's find all of the occurences of the "span" with "itemprop" "name", # meaning the tags with actors' and actresses' names. actors = actors_raw.findAll(name="span", attrs={"itemprop":"name"}) # Now we want to loop through each one and get the text inside the tags actors_list = [actor.text for actor in actors] ''' Creating a "Web Scraping" Function The above code we've written is useful, but we don't want to have to type it everytime. We want to create a function that takes the URL and outputs the pieces we want everytime. ''' def getIMDBInfo(url): r = requests.get(url) # Get HTML b = BeautifulSoup(r.text) # Create Beautiful Soup object # Get various attributes and put them in dictionary results = {} # Initialize empty dictionary # Get the title results['title'] = b.find(name="span", attrs={"class":"itemprop", "itemprop":"name"}).text # Rating results['star_rating'] = float(b.find(name="div", attrs={"class":"titlePageSprite"}).text) # Actors/actresses actors_raw = b.find(name='div', attrs={"class":"txt-block", "itemprop":"actors", "itemscope":"", "itemtype":"http://schema.org/Person"}) actors = actors_raw.findAll(name="span", attrs={"class":"itemprop", "itemprop":"name"}) results['actors_list'] = [actor.text for actor in actors] # Content Rating infobar = b.find(name="div", attrs={"class":"infobar"}) results['content_rating'] = infobar.find(name='meta', attrs={"itemprop":"contentRating"})['content'] # Show duration results['duration'] = int(infobar.find(name='time', attrs={"itemprop":"duration"}).text.strip()[:-4])#infobar.find(name='time', attrs={"itemprop":"duration"}).text # Genre results['genre'] = infobar.find(name='span', attrs={"itemprop":"genre"}).text # Return dictionary return results # Let's see if it worked # We can look at the results of our previous web page, "House of Cards" getIMDBInfo('http://www.imdb.com/title/tt1856010/') # Now let's try another one: Interstellar getIMDBInfo('http://www.imdb.com/title/tt0816692/') # Now let's show the true functionality list_of_title_urls = [] with open('imdb_movie_urls.csv', 'rU') as f: list_of_title_urls = f.read().split('\n') # Let's get the data for each title in the list data = [] for title_url in list_of_title_urls: imdb_data = getIMDBInfo(title_url) data.append(imdb_data) column_names = ['star_rating', 'title', 'content_rating', 'genre', 'duration', 'actors_list'] movieRatings = pd.DataFrame(data, columns = column_names) movieRatings # Now we have some data we can begin exploring, aggregating, etc. ''' Bonus material: Getting movie data for the top 1000 movies on IMDB ''' # Or let's build another webscraper to get the IMDB top 1000 movie_links = [] # Create empty list # Notice that we are creating a list [1,101,201,...] and changing the URL slightly each time. for i in range(1,1000,100): # Get url r = requests.get('http://www.imdb.com/search/title?groups=top_1000&sort=user_rating&start=' + str(i) + '&view=simple') # Get HTML b = BeautifulSoup(r.text) # Create Beautiful Soup object links = b.findAll(name='td', attrs={'class':'title'}) # Find all 'td's with 'class'='title' for link in links: a_link = link.find('a') # Find liks movie_links.append('http://www.imdb.com' + str(a_link['href'])) # Add link to list # Create dataframe of the top 1000 movies on IMDB # NOTE: This could take 5-10 minutes. You can skip this part as I've already # pulled all of this data and saved it to a file. data = [] j=0 # Loop through every movie title for movie_link in movie_links: try: imdb_data = getIMDBInfo(movie_link) # Get movie data data.append(imdb_data) # Put movie data in list except: pass j += 1 if j%50 == 0: print 'Completed ' + str(j) + ' titles!' # Print progress # Create data frame with movies column_names = ['star_rating', 'title', 'content_rating', 'genre', 'duration', 'actors_list'] movieRatingsTop1000 = pd.DataFrame(data, columns = column_names) # Read in the reated dataframe movieRatingsTop1000 = pd.read_csv('imdb_movie_ratings_top_1000.csv') # Now you're ready to do some analysis movieRatingsTop1000.describe() movieRatingsTop1000.groupby('genre').star_rating.mean() movieRatingsTop1000.groupby('content_rating').star_rating.mean() movieRatingsTop1000.plot(kind='scatter', x='duration', y='star_rating') plt.show() ================================================ FILE: code/10_logistic_regression_confusion_matrix.py ================================================ ''' CLASS: Logistic Regression and Confusion Matrix ''' ############################################################################### ### Logistic Regression ############################################################################### # Imports import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.cross_validation import train_test_split from sklearn import metrics from math import exp import numpy as np import matplotlib.pyplot as plt # Read in data data = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/default.csv') data.head() # Change column to number data['student_bin'] = data.student.map({'No':0, 'Yes':1}) # Let's do some cursory analysis. data.groupby('default').balance.mean() data.groupby('default').income.mean() # Set X and y feature_cols = ['balance', 'income','student_bin'] X = data[feature_cols] y = data.default # Train test split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 2) # Fit model logreg = LogisticRegression() logreg.fit(X_train, y_train) y_pred = logreg.predict(X_test) # Predict # Access accuracy print metrics.accuracy_score(y_test, y_pred) ############################################################################### ### Null Accuracy Rate ############################################################################### # Compare to null accuracy rate. The null accuracy rate is the accuracy if I # predict all the majority class. If there are more 1's, I predict all 1's. # If there are more 0's, I predict all 0's. There are several ways to do this. # 1. Create a vector of majority class and use the accuracy_score. # "If I predicted all 0's, how accurate would I be? print metrics.accuracy_score(y_test, [0]*len(y_test)) # 2. Calculate the mean of y_test (AKA the percentage of 1's) y_test.mean() # One minus that number will be the percentage of 0's. This means that if you # predict all 0's, you will be correct 1-y_test-mean() percent of the time. 1 - y_test.mean() # This puts our accuracy score into context a bit. We can now see that we # actually didn't do so great! ############################################################################### ### Intepretting Logistic Regression Coefficients ############################################################################### # Let's look at the coefficients for col in zip(feature_cols, logreg.coef_[0]): print col[0], col[1] # Let's interpret those. for col in zip(feature_cols, logreg.coef_[0]): print 'A unit increase in', col[0], 'equals a', exp(col[1]), 'increase in odds.' ############################################################################### ### Confusion Matrix ############################################################################### # Let's look at the confusion matrix con_mat = metrics.confusion_matrix(y_test, y_pred) print con_mat # Let's define our true posititves, false positives, true negatives, and false negatives true_neg = con_mat[0][0] false_neg = con_mat[1][0] true_pos = con_mat[1][1] false_pos = con_mat[0][1] # Sensitivity: percent of correct predictions when reference value is 'default' sensitivity = float(true_pos)/(false_neg + true_pos) print sensitivity print metrics.recall_score(y_test, y_pred) # Specificity: percent of correct predictions when reference value is 'not default' specificity = float(true_neg) / (true_neg + false_pos) print specificity ############################################################################### ### Logistic Regression Thresholds ############################################################################### # Logistic regression is actually predicting the underlying probability. # However, when you clal the "predict" function, it returns class labels. You # can still predict the actual probability and set your own threshold if you'd # like. This can be useful in cases where the "signal" from the model isn't # strong. # Predict probabilities logreg.predict_proba(X_test).shape probs = logreg.predict_proba(X_test)[:, 1] # The natural threshold for probabilility is 0.5, but you don't have to use # that. # Use 0.5 thrshold for predicting 'default' and confirm we get the same results preds_05 = np.where(probs >= 0.5, 1, 0) print metrics.accuracy_score(y_test, preds_05) con_mat_05 = metrics.confusion_matrix(y_test, preds_05) print con_mat_05 # Let's look at a histogram of these probabilities. plt.hist(probs, bins=20) plt.title('Distribution of Probabilities') plt.xlabel('Probability') plt.ylabel('Frequency') plt.show() # Change cutoff for predicting default to 0.2 preds_02 = np.where(probs > 0.2, 1, 0) delta = float((preds_02 != preds_05).sum())/len(X_test)*100 print 'Changing the threshold from 0.5 to 0.2 changed %.2f percent of the predictions.' % delta # Check the new accuracy, sensitivity, specificity print metrics.accuracy_score(y_test, preds_02) con_mat_02 = metrics.confusion_matrix(y_test, preds_02) print con_mat_02 # Let's define our true posititves, false positives, true negatives, and false negatives true_neg = con_mat_02[0][0] false_neg = con_mat_02[1][0] true_pos = con_mat_02[1][1] false_pos = con_mat_02[0][1] # Sensitivity: percent of correct predictions when reference value is 'default' sensitivity = float(true_pos)/(false_neg + true_pos) print sensitivity print metrics.recall_score(y_test, preds_02) # Specificity: percent of correct predictions when reference value is 'not default' specificity = float(true_neg) / (true_neg + false_pos) print specificity ############################################################################### ### Exercise/Possibly Homework ############################################################################### ''' Let's use the glass identification dataset again. We've previously run knn on this dataset. Now, let's try logistic regression. Access the dataset at http://archive.ics.uci.edu/ml/datasets/Glass+Identification. Complete the following tasks or answer the following questions. ''' ''' 1. Read the data into a pandas dataframe. ''' # Taken from Kevin's 07 HW solution df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data', names=['id','ri','na','mg','al','si','k','ca','ba','fe','glass_type'], index_col='id') '''' 2. Explore the data and look at what columns are available. ''' # Taken from Kevin's 07 HW solution df.shape # 214 x 10 df.head() df.tail() df.glass_type.value_counts() df.isnull().sum() # No nulls in our data '''' 3. Convert the 'glass type' column into a binary response. * If type of class = 1/2/3/4, binary=0. * If type of glass = 5/6/7, binary=1. ''' # Taken from Kevin's 07 HW solution df['binary'] = np.where(df.glass_type < 5, 0, 1) # method 1 df['binary'] = df.glass_type.map({1:0, 2:0, 3:0, 4:0, 5:1, 6:1, 7:1}) # method 2 df.binary.value_counts() ''' 4. Create a feature matrix and a response vector. ''' # Taken from Kevin's 07 HW solution features = ['ri','na','mg','al','si','k','ca','ba','fe'] # create a list of features features = df.columns[:-2] # alternative way: slice 'columns' attribute like a list X = df[features] # create DataFrame X by only selecting features y = df.binary ''' 5. Split the data into the appropriate training and testing sets. ''' # Taken from Kevin's 07 HW solution X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=99) ''' 6. Create and fit a logistic regression model. ''' logreg = LogisticRegression() # Instatiate estimator logreg.fit(X_train, y_train) # Fit data ''' 7. Make predictions with your new model. ''' y_pred = logreg.predict(X_test) # Create predictions ''' 8. Calculate the accuracy rate of your model and compare it to the null accuracy. ''' # Calculate accuracy of model metrics.accuracy_score(y_test, y_pred) # Calculate null accuracy metrics.accuracy_score(y_test, [0]*len(y_test)) ''' 9. Generate a confusion matrix for your predictions. Use this to calculate the sensitivity and specificity of your model. ''' # Let's look at the confusion matrix con_mat = metrics.confusion_matrix(y_test, y_pred) print con_mat # Let's define our true posititves, false positives, true negatives, and false negatives true_neg = con_mat[0][0] false_neg = con_mat[1][0] true_pos = con_mat[1][1] false_pos = con_mat[0][1] # Sensitivity: percent of correct predictions when reference value is 'default' sensitivity = float(true_pos)/(false_neg + true_pos) print sensitivity # Specificity: percent of correct predictions when reference value is 'not default' specificity = float(true_neg) / (true_neg + false_pos) print specificity ================================================ FILE: code/13_naive_bayes.py ================================================ ''' CLASS: Naive Bayes SMS spam classifier DATA SOURCE: https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection ''' ## READING IN THE DATA # read tab-separated file using pandas import pandas as pd df = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/SMSSpamCollection.txt', sep='\t', header=None, names=['label', 'msg']) # examine the data df.head(20) df.label.value_counts() df.msg.describe() # convert label to a binary variable df['label'] = df.label.map({'ham':0, 'spam':1}) df.head() # split into training and testing sets from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(df.msg, df.label, random_state=1) X_train.shape X_test.shape ## COUNTVECTORIZER: 'convert text into a matrix of token counts' ## http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html from sklearn.feature_extraction.text import CountVectorizer # start with a simple example train_simple = ['call you tonight', 'Call me a cab', 'please call me... PLEASE!'] # learn the 'vocabulary' of the training data vect = CountVectorizer() vect.fit(train_simple) vect.get_feature_names() # transform training data into a 'document-term matrix' train_simple_dtm = vect.transform(train_simple) train_simple_dtm train_simple_dtm.toarray() # examine the vocabulary and document-term matrix together pd.DataFrame(train_simple_dtm.toarray(), columns=vect.get_feature_names()) # transform testing data into a document-term matrix (using existing vocabulary) test_simple = ["please don't call me"] test_simple_dtm = vect.transform(test_simple) test_simple_dtm.toarray() pd.DataFrame(test_simple_dtm.toarray(), columns=vect.get_feature_names()) ## REPEAT PATTERN WITH SMS DATA # instantiate the vectorizer vect = CountVectorizer() # learn vocabulary and create document-term matrix in a single step train_dtm = vect.fit_transform(X_train) train_dtm # transform testing data into a document-term matrix test_dtm = vect.transform(X_test) test_dtm # store feature names and examine them train_features = vect.get_feature_names() len(train_features) train_features[:50] train_features[-50:] # convert train_dtm to a regular array train_arr = train_dtm.toarray() train_arr ## SIMPLE SUMMARIES OF THE TRAINING DATA # refresher on NumPy import numpy as np arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) arr arr[0, 0] arr[1, 3] arr[0, :] arr[:, 0] np.sum(arr) np.sum(arr, axis=0) np.sum(arr, axis=1) # exercise: calculate the number of tokens in the 0th message in train_arr sum(train_arr[0, :]) # exercise: count how many times the 0th token appears across ALL messages in train_arr sum(train_arr[:, 0]) # exercise: count how many times EACH token appears across ALL messages in train_arr np.sum(train_arr, axis=0) # exercise: create a DataFrame of tokens with their counts train_token_counts = pd.DataFrame({'token':train_features, 'count':np.sum(train_arr, axis=0)}) train_token_counts.sort('count', ascending=False) ## MODEL BUILDING WITH NAIVE BAYES ## http://scikit-learn.org/stable/modules/naive_bayes.html # train a Naive Bayes model using train_dtm from sklearn.naive_bayes import MultinomialNB nb = MultinomialNB() nb.fit(train_dtm, y_train) # make predictions on test data using test_dtm y_pred = nb.predict(test_dtm) y_pred # compare predictions to true labels from sklearn import metrics print metrics.accuracy_score(y_test, y_pred) print metrics.confusion_matrix(y_test, y_pred) # predict (poorly calibrated) probabilities and calculate AUC y_prob = nb.predict_proba(test_dtm)[:, 1] y_prob print metrics.roc_auc_score(y_test, y_prob) # exercise: show the message text for the false positives X_test[y_test < y_pred] # exercise: show the message text for the false negatives X_test[y_test > y_pred] ## COMPARE NAIVE BAYES AND LOGISTIC REGRESSION ## USING ALL DATA AND CROSS-VALIDATION # create a document-term matrix using all data all_dtm = vect.fit_transform(df.msg) # instantiate logistic regression from sklearn.linear_model import LogisticRegression logreg = LogisticRegression() # compare AUC using cross-validation # note: this is slightly improper cross-validation... can you figure out why? from sklearn.cross_validation import cross_val_score cross_val_score(nb, all_dtm, df.label, cv=10, scoring='roc_auc').mean() cross_val_score(logreg, all_dtm, df.label, cv=10, scoring='roc_auc').mean() ## EXERCISE: CALCULATE THE 'SPAMMINESS' OF EACH TOKEN # create separate DataFrames for ham and spam df_ham = df[df.label==0] df_spam = df[df.label==1] # learn the vocabulary of ALL messages and save it vect.fit(df.msg) all_features = vect.get_feature_names() # create document-term matrix of ham, then convert to a regular array ham_dtm = vect.transform(df_ham.msg) ham_arr = ham_dtm.toarray() # create document-term matrix of spam, then convert to a regular array spam_dtm = vect.transform(df_spam.msg) spam_arr = spam_dtm.toarray() # count how many times EACH token appears across ALL messages in ham_arr ham_counts = np.sum(ham_arr, axis=0) # count how many times EACH token appears across ALL messages in spam_arr spam_counts = np.sum(spam_arr, axis=0) # create a DataFrame of tokens with their separate ham and spam counts all_token_counts = pd.DataFrame({'token':all_features, 'ham':ham_counts, 'spam':spam_counts}) # add one to ham counts and spam counts so that ratio calculations (below) make more sense all_token_counts['ham'] = all_token_counts.ham + 1 all_token_counts['spam'] = all_token_counts.spam + 1 # calculate ratio of spam-to-ham for each token all_token_counts['spam_ratio'] = all_token_counts.spam / all_token_counts.ham all_token_counts.sort('spam_ratio') ================================================ FILE: code/15_kaggle.py ================================================ ''' CLASS: Kaggle Stack Overflow competition ''' # read in the file and set the first column as the index import pandas as pd train = pd.read_csv('train.csv', index_col=0) train.head() ''' What are some assumptions and theories to test? PostId: unique within the dataset OwnerUserId: not unique within the dataset, assigned in order OwnerCreationDate: users with older accounts have more open questions ReputationAtPostCreation: higher reputation users have more open questions OwnerUndeletedAnswerCountAtPostTime: users with more answers have more open questions Tags: 1 to 5 tags are required, many unique tags PostClosedDate: should only exist for closed questions OpenStatus: 1 means open ''' ## OPEN STATUS # dataset is perfectly balanced in terms of OpenStatus (not a representative sample) train.OpenStatus.value_counts() ## USER ID # OwnerUserId is not unique within the dataset, let's examine the top 3 users train.OwnerUserId.value_counts() # mostly closed questions, all lowercase, lots of spelling errors train[train.OwnerUserId==466534] # fewer closed questions, better grammar, high reputation but few answers train[train.OwnerUserId==39677] # very few closed questions, lots of answers train[train.OwnerUserId==34537] ## REPUTATION # ReputationAtPostCreation is higher for open questions: possibly use as a feature train.groupby('OpenStatus').ReputationAtPostCreation.describe() # not a useful histogram train.ReputationAtPostCreation.hist() # much more useful histogram train[train.ReputationAtPostCreation < 1000].ReputationAtPostCreation.hist() # grouped histogram train[train.ReputationAtPostCreation < 1000].ReputationAtPostCreation.hist(by=train.OpenStatus, sharey=True) ## ANSWER COUNT # rename column train.rename(columns={'OwnerUndeletedAnswerCountAtPostTime':'Answers'}, inplace=True) # Answers is higher for open questions: possibly use as a feature train.groupby('OpenStatus').Answers.describe() # grouped histogram train[train.Answers < 50].Answers.hist(by=train.OpenStatus, sharey=True) ## USER ID # OwnerUserId is assigned in numerical order train.sort('OwnerUserId').OwnerCreationDate # OwnerUserId is lower for open questions: possibly use as a feature train.groupby('OpenStatus').OwnerUserId.describe() ## TITLE # create a new feature that represents the length of the title (in characters) train['TitleLength'] = train.Title.apply(len) # Title is longer for open questions: possibly use as a feature train.TitleLength.hist(by=train.OpenStatus) ## BODY # create a new feature that represents the length of the body (in characters) train['BodyLength'] = train.BodyMarkdown.apply(len) # BodyMarkdown is longer for open questions: possibly use as a feature train.BodyLength.hist(by=train.OpenStatus) ## TAGS # Tag1 is required, and the rest are optional train.isnull().sum() # there are over 5000 unique tags len(train.Tag1.unique()) # calculate the percentage of open questions for each tag train.groupby('Tag1').OpenStatus.mean() # percentage of open questions varies widely by tag (among popular tags) train.groupby('Tag1').OpenStatus.agg(['mean','count']).sort('count') # create a new feature that represents the number of tags for each question train['NumTags'] = train.loc[:, 'Tag1':'Tag5'].notnull().sum(axis=1) # NumTags is higher for open questions: possibly use as a feature train.NumTags.hist(by=train.OpenStatus) ''' Define a function that takes in a raw CSV file and returns a DataFrame that includes all created features (and any other modifications). That way, we can apply the same changes to both train.csv and test.csv. ''' # define the function def make_features(filename): df = pd.read_csv(filename, index_col=0) df.rename(columns={'OwnerUndeletedAnswerCountAtPostTime':'Answers'}, inplace=True) df['TitleLength'] = df.Title.apply(len) df['BodyLength'] = df.BodyMarkdown.apply(len) df['NumTags'] = df.loc[:, 'Tag1':'Tag5'].notnull().sum(axis=1) return df # apply function to both training and testing files train = make_features('train.csv') test = make_features('test.csv') ''' Use train/test split to compare a model that includes 1 feature with a model that includes 5 features. ''' ## ONE FEATURE # define X and y feature_cols = ['ReputationAtPostCreation'] X = train[feature_cols] y = train.OpenStatus # split into training and testing sets from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) # fit a logistic regression model from sklearn.linear_model import LogisticRegression logreg = LogisticRegression() logreg.fit(X_train, y_train) # examine the coefficient to check that it makes sense logreg.coef_ # predict response classes and predict class probabilities y_pred = logreg.predict(X_test) y_prob = logreg.predict_proba(X_test)[:, 1] # check how well we did from sklearn import metrics metrics.accuracy_score(y_test, y_pred) # 0.538 (better than guessing) metrics.confusion_matrix(y_test, y_pred) # predicts closed most of the time metrics.roc_auc_score(y_test, y_prob) # 0.602 (not horrible) metrics.log_loss(y_test, y_prob) # 0.690 (what is this?) # log loss is the competition's evaluation metric, so let's get a feel for it true = [0, 0, 1, 1] prob = [0.1, 0.2, 0.8, 0.9] metrics.log_loss(true, prob) # 0.164 (lower is better) # let's try a few other predicted probabilities and check the log loss prob = [0.4, 0.4, 0.6, 0.6] # 0.511 (predictions are right, but less confident) prob = [0.4, 0.4, 0.4, 0.6] # 0.612 (one wrong prediction that is a bit off) prob = [0.4, 0.4, 0.1, 0.6] # 0.959 (one wrong prediction that is way off) prob = [0.5, 0.5, 0.5, 0.5] # 0.693 (you can get this score without a model) ## FIVE FEATURES # define X and y feature_cols = ['ReputationAtPostCreation', 'Answers', 'TitleLength', 'BodyLength', 'NumTags'] X = train[feature_cols] y = train.OpenStatus # split into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) # fit a logistic regression model logreg.fit(X_train, y_train) # examine the coefficients to check that they make sense logreg.coef_ # predict response classes and predict class probabilities y_pred = logreg.predict(X_test) y_prob = logreg.predict_proba(X_test)[:, 1] # check how well we did metrics.accuracy_score(y_test, y_pred) # 0.589 (doing better) metrics.confusion_matrix(y_test, y_pred) # predicts open more often metrics.roc_auc_score(y_test, y_prob) # 0.625 (tiny bit better) metrics.log_loss(y_test, y_prob) # 0.677 (a bit better) # let's see if cross-validation gives us similar results from sklearn.cross_validation import cross_val_score scores = cross_val_score(logreg, X, y, scoring='log_loss', cv=10) scores.mean() # 0.677 (identical to train/test split) scores.std() # very small ''' Use the model with 5 features to make a submission ''' # make sure that X and y are defined properly feature_cols = ['ReputationAtPostCreation', 'Answers', 'TitleLength', 'BodyLength', 'NumTags'] X = train[feature_cols] y = train.OpenStatus # train the model on ALL data (not X_train and y_train) logreg.fit(X, y) # predict class probabilities for the actual testing data (not X_test) y_prob = logreg.predict_proba(test[feature_cols])[:, 1] # sample submission file indicates we need two columns: PostId and predicted probability test.index # PostId y_prob # predicted probability # create a DataFrame that has 'id' as the index, then export to a CSV file sub = pd.DataFrame({'id':test.index, 'OpenStatus':y_prob}).set_index('id') sub.to_csv('sub1.csv') ''' Create a few more features from Title ''' # string methods for a Series are accessed via 'str' train.Title.str.lower() # create a new feature that represents whether a Title is all lowercase train['TitleLowercase'] = (train.Title.str.lower() == train.Title).astype(int) # check if there are a meaningful number of ones train.TitleLowercase.value_counts() # percentage of open questions is lower among questions with lowercase titles: possibly use as a feature train.groupby('TitleLowercase').OpenStatus.mean() # create features that represent whether Title contains certain words train['TitleQuestion'] = train.Title.str.contains('question', case=False).astype(int) train['TitleNeed'] = train.Title.str.contains('need', case=False).astype(int) train['TitleHelp'] = train.Title.str.contains('help', case=False).astype(int) ''' Build a document-term matrix from Title using CountVectorizer ''' # define X and y X = train.Title y = train.OpenStatus # split into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) # use CountVectorizer with the default settings from sklearn.feature_extraction.text import CountVectorizer vect = CountVectorizer() # fit and transform on X_train, but only transform on X_test train_dtm = vect.fit_transform(X_train) test_dtm = vect.transform(X_test) # try a Naive Bayes model from sklearn.naive_bayes import MultinomialNB nb = MultinomialNB() nb.fit(train_dtm, y_train) y_prob = nb.predict_proba(test_dtm)[:, 1] metrics.log_loss(y_test, y_prob) # 0.659 (a bit better than our previous model) # try tuning CountVectorizer and repeat Naive Bayes vect = CountVectorizer(stop_words='english') train_dtm = vect.fit_transform(X_train) test_dtm = vect.transform(X_test) nb.fit(train_dtm, y_train) y_prob = nb.predict_proba(test_dtm)[:, 1] metrics.log_loss(y_test, y_prob) # 0.637 (even better) # try switching to logistic regression logreg.fit(train_dtm, y_train) y_prob = logreg.predict_proba(test_dtm)[:, 1] metrics.log_loss(y_test, y_prob) # 0.573 (much better!) ''' Create features from BodyMarkdown using TextBlob ''' # examine BodyMarkdown for first question train.iloc[0].BodyMarkdown # calculate the number of sentences in that question using TextBlob from textblob import TextBlob len(TextBlob(train.iloc[0].BodyMarkdown).sentences) # calculate the number of sentences for all questions (raises an error) train.BodyMarkdown.apply(lambda x: len(TextBlob(x).sentences)) # explicitly decode string to unicode to fix error (WARNING: VERY SLOW) train['BodySentences'] = train.BodyMarkdown.apply(lambda x: len(TextBlob(x.decode('utf-8')).sentences)) ================================================ FILE: code/17_ensembling_exercise.py ================================================ # Helper code for class 17 exercise # define the function def make_features(filename): df = pd.read_csv(filename, index_col=0) df.rename(columns={'OwnerUndeletedAnswerCountAtPostTime':'Answers'}, inplace=True) df['TitleLength'] = df.Title.apply(len) df['BodyLength'] = df.BodyMarkdown.apply(len) df['NumTags'] = df.loc[:, 'Tag1':'Tag5'].notnull().sum(axis=1) return df # apply function to both training and testing files train = make_features('train.csv') test = make_features('test.csv') # define X and y feature_cols = ['ReputationAtPostCreation', 'Answers', 'TitleLength', 'BodyLength', 'NumTags'] X = train[feature_cols] y = train.OpenStatus ############################################################################### ##### Create some models with the derived features ############################################################################### ############################################################################### ##### Count vectorizer ############################################################################### # define X and y X = train.Title y = train.OpenStatus # split into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) # use CountVectorizer with the default settings from sklearn.feature_extraction.text import CountVectorizer vect = CountVectorizer() # fit and transform on X_train, but only transform on X_test train_dtm = vect.fit_transform(X_train) test_dtm = vect.transform(X_test) ############################################################################### ##### Create a model with the text features ############################################################################### ================================================ FILE: code/18_clustering.py ================================================ ''' THE DATA We have data about cars: things like MPG, acceleration, weight, etc. However, we don't have logical groupings for these cars. We can construct these manually using our domain knowledge (e.g. we could put all of the high mpg cars together and all of the low mpg cars together), but we want a more automatic way of grouping these vehicles that can take into account more features. ''' # Imports from sklearn.cluster import KMeans # K means model import matplotlib.pyplot as plt import pandas as pd import numpy as np # Read in data data = pd.read_table('auto_mpg.txt', sep='|') # All values range from 0 to 1 data.drop('car_name', axis=1, inplace=True) # Drop labels from dataframe data.head() ''' CLUSTER ANALYSIS How do we implement a k-means clustering algorithm? scikit-learn KMeans documentation for reference: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html ''' # Standardize our data from sklearn.preprocessing import StandardScaler scaler = StandardScaler() data_scaled = scaler.fit_transform(data) # Set random seed for reproducibility np.random.seed(0) # Run KMeans est = KMeans(n_clusters=2, init='random') # Instatiate estimator est.fit(data_scaled) # Fit your data y_kmeans = est.predict(data_scaled) # Make cluster "predictions" # Inspect the data by looking at the means for each cluster data.groupby(y_kmeans).mean() # This can be compared to the overall means for each variable data.mean() # We can get the coordiantes for the center of each cluster centers = est.cluster_centers_ ''' VISUALIZING THE CLUSTERS ''' # We can create a nice plot to visualize this upon two of the dimensions colors = np.array(['red', 'green', 'blue', 'yellow', 'orange']) plt.figure() plt.scatter(data_scaled[:, 0], data_scaled[:, 5], c=colors[y_kmeans], s=50) plt.xlabel('MPG') plt.ylabel('Acceleration') plt.scatter(centers[:, 0], centers[:, 5], linewidths=3, marker='+', s=300, c='black') plt.show() # We can generate a scatter matrix to see all of the different dimensions paired pd.scatter_matrix(data, c=colors[y_kmeans], figsize=(15,15), s = 100) plt.show() ''' DETERMINING THE NUMBER OF CLUSTERS How do you choose k? There isn't a bright line, but we can evaluate performance metrics such as the silhouette coefficient across values of k. Note: You also have to take into account practical limitations of choosing k also. Ten clusters may give the best value, but it might not make sense in the context of your data. scikit-learn Clustering metrics documentation: http://scikit-learn.org/stable/modules/classes.html#clustering-metrics ''' # Create a bunch of different models k_rng = range(2,15) k_est = [KMeans(n_clusters = k).fit(data) for k in k_rng] # Silhouette Coefficient # Generally want SC to be closer to 1, while also minimizing k from sklearn import metrics silhouette_score = [metrics.silhouette_score(data, e.labels_, metric='euclidean') for e in k_est] # Plot the results plt.figure() plt.title('Silhouette coefficient for various values of k') plt.plot(k_rng, silhouette_score, 'b*-') plt.xlim([1,15]) plt.grid(True) plt.ylabel('Silhouette Coefficient') plt.show() ================================================ FILE: code/18_regularization.py ================================================ ############################################################################### ##### Regularization with Linear Regression ############################################################################### ## TASK: Regularized regression ## FUNCTIONS: Ridge, RidgeCV, Lasso, LassoCV ## DOCUMENTATION: http://scikit-learn.org/stable/modules/linear_model.html ## DATA: Crime (n=319 non-null, p=122, type=regression) ## DATA DICTIONARY: http://archive.ics.uci.edu/ml/datasets/Communities+and+Crime ########## Prepare data ########## # read in data, remove categorical features, remove rows with missing values import pandas as pd crime = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/communities/communities.data', header=None, na_values=['?']) crime = crime.iloc[:, 5:] crime.dropna(inplace=True) crime.head() # define X and y X = crime.iloc[:, :-1] y = crime.iloc[:, -1] # split into train/test from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) ########## Linear Regression Model Without Regularization ########## # linear regression from sklearn.linear_model import LinearRegression lm = LinearRegression() lm.fit(X_train, y_train) lm.coef_ # make predictions and evaluate import numpy as np from sklearn import metrics preds = lm.predict(X_test) print 'RMSE (no regularization) =', np.sqrt(metrics.mean_squared_error(y_test, preds)) ########## Ridge Regression Model ########## # ridge regression (alpha must be positive, larger means more regularization) from sklearn.linear_model import Ridge rreg = Ridge(alpha=0.1, normalize=True) rreg.fit(X_train, y_train) rreg.coef_ preds = rreg.predict(X_test) print 'RMSE (Ridge reg.) =', np.sqrt(metrics.mean_squared_error(y_test, preds)) # use RidgeCV to select best alpha from sklearn.linear_model import RidgeCV alpha_range = 10.**np.arange(-2, 3) rregcv = RidgeCV(normalize=True, scoring='mean_squared_error', alphas=alpha_range) rregcv.fit(X_train, y_train) rregcv.alpha_ preds = rregcv.predict(X_test) print 'RMSE (Ridge CV reg.) =', np.sqrt(metrics.mean_squared_error(y_test, preds)) ########## Lasso Regression Model ########## # lasso (alpha must be positive, larger means more regularization) from sklearn.linear_model import Lasso las = Lasso(alpha=0.01, normalize=True) las.fit(X_train, y_train) las.coef_ preds = las.predict(X_test) print 'RMSE (Lasso reg.) =', np.sqrt(metrics.mean_squared_error(y_test, preds)) # try a smaller alpha las = Lasso(alpha=0.0001, normalize=True) las.fit(X_train, y_train) las.coef_ preds = las.predict(X_test) print 'RMSE (Lasso reg.) =', np.sqrt(metrics.mean_squared_error(y_test, preds)) # use LassoCV to select best alpha (tries 100 alphas by default) from sklearn.linear_model import LassoCV lascv = LassoCV(normalize=True, alphas=alpha_range) lascv.fit(X_train, y_train) lascv.alpha_ lascv.coef_ preds = lascv.predict(X_test) print 'RMSE (Lasso CV reg.) =', np.sqrt(metrics.mean_squared_error(y_test, preds)) ############################################################################### ##### Regularization with Logistic Regression ############################################################################### ## TASK: Regularized classification ## FUNCTION: LogisticRegression ## DOCUMENTATION: http://scikit-learn.org/stable/modules/linear_model.html ## DATA: Titanic (n=891, p=5 selected, type=classification) ## DATA DICTIONARY: https://www.kaggle.com/c/titanic-gettingStarted/data ########## Prepare data ########## # Get and prepare data titanic = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/titanic_train.csv') titanic['Sex'] = titanic.Sex.map({'female':0, 'male':1}) titanic.Age.fillna(titanic.Age.mean(), inplace=True) embarked_dummies = pd.get_dummies(titanic.Embarked, prefix='Embarked').iloc[:, 1:] titanic = pd.concat([titanic, embarked_dummies], axis=1) # define X and y feature_cols = ['Pclass', 'Sex', 'Age', 'Embarked_Q', 'Embarked_S'] X = titanic[feature_cols] y = titanic.Survived # split into train/test X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) # standardize our data from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) ########## Logistic Regression Model Without Regularization ########## # logistic regression from sklearn.linear_model import LogisticRegression logreg = LogisticRegression() logreg.fit(X_train_scaled, y_train) logreg.coef_ y_pred = logreg.predict(X_test_scaled) # Access accuracy print 'Accuracy (no penalty) =', metrics.accuracy_score(y_test, y_pred) ########## Logistic Regression With L1 Penalty ########## # logistic regression with L1 penalty (C must be positive, smaller means more regularization) logreg_l1 = LogisticRegression(C=0.1, penalty='l1') logreg_l1.fit(X_train_scaled, y_train) logreg_l1.coef_ y_pred_l1 = logreg_l1.predict(X_test_scaled) # Access accuracy print 'Accuracy (L1 penalty) =', metrics.accuracy_score(y_test, y_pred_l1) ########## Logistic Regression With L2 Penalty ########## # logistic regression with L2 penalty (C must be positive, smaller means more regularization) logreg_l2 = LogisticRegression(C=0.1, penalty='l2') logreg_l2.fit(X_train_scaled, y_train) logreg_l2.coef_ y_pred_l2 = logreg_l2.predict(X_test_scaled) # Access accuracy print 'Accuracy (L2 penalty) =', metrics.accuracy_score(y_test, y_pred_l2) ================================================ FILE: code/19_advanced_sklearn.py ================================================ ## TASK: Searching for optimal parameters ## FUNCTION: GridSearchCV ## DOCUMENTATION: http://scikit-learn.org/stable/modules/grid_search.html ## DATA: Titanic (n=891, p=5 selected, type=classification) ## DATA DICTIONARY: https://www.kaggle.com/c/titanic-gettingStarted/data # read in and prepare titanic data import pandas as pd titanic = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/titanic_train.csv') titanic['Sex'] = titanic.Sex.map({'female':0, 'male':1}) titanic.Age.fillna(titanic.Age.mean(), inplace=True) embarked_dummies = pd.get_dummies(titanic.Embarked, prefix='Embarked').iloc[:, 1:] titanic = pd.concat([titanic, embarked_dummies], axis=1) # define X and y feature_cols = ['Pclass', 'Sex', 'Age', 'Embarked_Q', 'Embarked_S'] X = titanic[feature_cols] y = titanic.Survived # use cross-validation to find best max_depth from sklearn.tree import DecisionTreeClassifier from sklearn.cross_validation import cross_val_score # try max_depth=2 treeclf = DecisionTreeClassifier(max_depth=2, random_state=1) cross_val_score(treeclf, X, y, cv=10, scoring='roc_auc').mean() # try max_depth=3 treeclf = DecisionTreeClassifier(max_depth=3, random_state=1) cross_val_score(treeclf, X, y, cv=10, scoring='roc_auc').mean() # use GridSearchCV to automate the search from sklearn.grid_search import GridSearchCV treeclf = DecisionTreeClassifier(random_state=1) depth_range = range(1, 21) param_grid = dict(max_depth=depth_range) grid = GridSearchCV(treeclf, param_grid, cv=10, scoring='roc_auc') grid.fit(X, y) # check the results of the grid search grid.grid_scores_ grid_mean_scores = [result[1] for result in grid.grid_scores_] # plot the results import matplotlib.pyplot as plt plt.plot(depth_range, grid_mean_scores) # what was best? grid.best_score_ grid.best_params_ grid.best_estimator_ # search a "grid" of parameters depth_range = range(1, 21) leaf_range = range(1, 11) param_grid = dict(max_depth=depth_range, min_samples_leaf=leaf_range) grid = GridSearchCV(treeclf, param_grid, cv=10, scoring='roc_auc') grid.fit(X, y) grid.grid_scores_ grid.best_score_ grid.best_params_ ## TASK: Standardization of features (aka "center and scale" or "z-score normalization") ## FUNCTION: StandardScaler ## DOCUMENTATION: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html ## EXAMPLE: http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb ## DATA: Wine (n=178, p=2 selected, type=classification) ## DATA DICTIONARY: http://archive.ics.uci.edu/ml/datasets/Wine # fake data train = pd.DataFrame({'id':[0,1,2], 'length':[0.9,0.3,0.6], 'mass':[0.1,0.2,0.8], 'rings':[40,50,60]}) oos = pd.DataFrame({'length':[0.59], 'mass':[0.79], 'rings':[54.9]}) # define X and y X = train[['length','mass','rings']] y = train.id # KNN with k=1 from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=1) knn.fit(X, y) # what "should" it predict? what does it predict? knn.predict(oos) # standardize the features from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X) X_scaled = scaler.transform(X) # compare original to standardized X.values X_scaled # figure out how it standardized scaler.mean_ scaler.std_ (X.values-scaler.mean_) / scaler.std_ # try this on real data wine = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None, usecols=[0,10,13]) wine.columns=['label', 'color', 'proline'] wine.head() wine.describe() # define X and y X = wine[['color', 'proline']] y = wine.label # split into train/test from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) # standardize X_train scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) # check that it worked properly X_train_scaled[:, 0].mean() X_train_scaled[:, 0].std() X_train_scaled[:, 1].mean() X_train_scaled[:, 1].std() # standardize X_test X_test_scaled = scaler.transform(X_test) # is this right? X_test_scaled[:, 0].mean() X_test_scaled[:, 0].std() X_test_scaled[:, 1].mean() X_test_scaled[:, 1].std() # compare KNN accuracy on original vs scaled data knn = KNeighborsClassifier(n_neighbors=3) knn.fit(X_train, y_train) knn.score(X_test, y_test) knn.fit(X_train_scaled, y_train) knn.score(X_test_scaled, y_test) ## TASK: Chaining steps ## FUNCTION: Pipeline ## DOCUMENTATION: http://scikit-learn.org/stable/modules/pipeline.html ## DATA: Wine (n=178, p=2 selected, type=classification) ## DATA DICTIONARY: http://archive.ics.uci.edu/ml/datasets/Wine # here is proper cross-validation on the original (unscaled) data X = wine[['color', 'proline']] y = wine.label knn = KNeighborsClassifier(n_neighbors=3) cross_val_score(knn, X, y, cv=5, scoring='accuracy').mean() # why is this improper cross-validation on the scaled data? scaler = StandardScaler() X_scaled = scaler.fit_transform(X) cross_val_score(knn, X_scaled, y, cv=5, scoring='accuracy').mean() # fix this using Pipeline from sklearn.pipeline import make_pipeline pipe = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=3)) cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean() # using GridSearchCV with Pipeline neighbors_range = range(1, 21) param_grid = dict(kneighborsclassifier__n_neighbors=neighbors_range) grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy') grid.fit(X, y) grid.best_score_ grid.best_params_ ================================================ FILE: code/19_gridsearchcv_exercise.py ================================================ ''' EXERCISE: GridSearchCV with Stack Overflow competition data ''' import pandas as pd # define a function to create features def make_features(filename): df = pd.read_csv(filename, index_col=0) df.rename(columns={'OwnerUndeletedAnswerCountAtPostTime':'Answers'}, inplace=True) df['TitleLength'] = df.Title.apply(len) df['BodyLength'] = df.BodyMarkdown.apply(len) df['NumTags'] = df.loc[:, 'Tag1':'Tag5'].notnull().sum(axis=1) return df # apply function to both training and testing files train = make_features('train.csv') test = make_features('test.csv') # define X and y feature_cols = ['ReputationAtPostCreation', 'Answers', 'TitleLength', 'BodyLength', 'NumTags'] X = train[feature_cols] y = train.OpenStatus ''' MAIN TASK: Use GridSearchCV to find optimal parameters for KNeighborsClassifier. - For "n_neighbors", try 5 different integer values. - For "weights", try 'uniform' and 'distance'. - Use 5-fold cross-validation (instead of 10-fold) to save computational time. - Remember that log loss is your evaluation metric! BONUS TASK #1: Once you have found optimal parameters, train your KNN model using those parameters, make predictions on the test set, and submit those predictions. BONUS TASK #2: Read the scikit-learn documentation for GridSearchCV to find the shortcut for accomplishing bonus task #1. ''' # MAIN TASK from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier() from sklearn.grid_search import GridSearchCV neighbors_range = [20, 40, 60, 80, 100] weight_options = ['uniform', 'distance'] param_grid = dict(n_neighbors=neighbors_range, weights=weight_options) grid = GridSearchCV(knn, param_grid, cv=5, scoring='log_loss') grid.fit(X, y) grid.grid_scores_ grid.best_score_ grid.best_params_ # BONUS TASK #1 knn = KNeighborsClassifier(n_neighbors=100, weights='uniform') knn.fit(X, y) y_prob = knn.predict_proba(test[feature_cols])[:, 1] sub = pd.DataFrame({'id':test.index, 'OpenStatus':y_prob}).set_index('id') sub.to_csv('sub.csv') # BONUS TASK #2 y_prob = grid.predict_proba(test[feature_cols])[:, 1] sub = pd.DataFrame({'id':test.index, 'OpenStatus':y_prob}).set_index('id') sub.to_csv('sub.csv') ================================================ FILE: code/19_regex_exercise.py ================================================ ''' Regular Expressions Exercise ''' # open file and store each line as one row with open('../data/homicides.txt', 'rU') as f: raw = [row for row in f] ''' Create a list of ages ''' import re ages = [] for row in raw: match = re.search(r'\d+ years old', row) if match: ages.append(match.group()) else: ages.append('0') # split the string on spaces, only keep the first element, and convert to int ages = [int(element.split()[0]) for element in ages] # check that 'raw' and 'ages' are the same length assert(len(raw)==len(ages)) # simplify process using a lookahead ages = [] for row in raw: match = re.search(r'\d+(?= years)', row) if match: ages.append(int(match.group())) else: ages.append(0) ================================================ FILE: code/19_regex_reference.py ================================================ ''' Regular Expressions (regex) Reference Guide Sources: https://developers.google.com/edu/python/regular-expressions https://docs.python.org/2/library/re.html ''' ''' Basic Patterns: Ordinary characters match themselves exactly . matches any single character except newline \n \w matches a word character (letter, digit, underscore) \W matches any non-word character \b matches boundary between word and non-word \s matches single whitespace character (space, newline, return, tab, form) \S matches single non-whitespace character \d matches single digit (0 through 9) \t matches tab \n matches newline \r matches return \ match a special character, such as period: \. Rules for Searching: Search proceeds through string from start to end, stopping at first match All of the pattern must be matched Basic Search Function: match = re.search(r'pattern', string_to_search) Returns match object If there is a match, access match using match.group() If there is no match, match is None Use 'r' in front of pattern to designate a raw string ''' import re s = 'my 1st string!!' match = re.search(r'my', s) # returns match object if match: # checks whether match was found print match.group() # if match was found, then print result re.search(r'my', s).group() # single-line version (without error handling) re.search(r'st', s).group() # 'st' re.search(r'sta', s).group() # error re.search(r'\w\w\w', s).group() # '1st' re.search(r'\W', s).group() # ' ' re.search(r'\W\W', s).group() # '!!' re.search(r'\s', s).group() # ' ' re.search(r'\s\s', s).group() # error re.search(r'..t', s).group() # '1st' re.search(r'\s\St', s).group() # ' st' re.search(r'\bst', s).group() # 'st' ''' Repetition: + 1 or more occurrences of the pattern to its left * 0 or more occurrences ? 0 or 1 occurrence + and * are 'greedy': they try to use up as much of the string as possible Add ? after + or * to make them non-greedy: +? or *? ''' s = 'sid is missing class' re.search(r'miss\w+', s).group() # 'missing' re.search(r'is\w+', s).group() # 'issing' re.search(r'is\w*', s).group() # 'is' s = '

my heading

' re.search(r'<.+>', s).group() # '

my heading

' re.search(r'<.+?>', s).group() # '

' ''' Positions: ^ match start of a string $ match end of a string ''' s = 'sid is missing class' re.search(r'^miss', s).group() # error re.search(r'..ss', s).group() # 'miss' re.search(r'..ss$', s).group() # 'lass' ''' Brackets: [abc] match a or b or c \w, \s, etc. work inside brackets, except period just means a literal period [a-z] match any lowercase letter (dash indicates range unless it's last) [abc-] match a or b or c or - [^ab] match anything except a or b ''' s = 'my email is john-doe@gmail.com' re.search(r'\w+@\w+', s).group() # 'doe@gmail' re.search(r'[\w.-]+@[\w.-]+', s).group() # 'john-doe@gmail.com' ''' Lookarounds: Lookahead matches a pattern only if it is followed by another pattern 100(?= dollars) matches '100' only if it is followed by ' dollars' Lookbehind matches a pattern only if it is preceded by another pattern (?<=\$)100 matches '100' only if it is preceded by '$' ''' s = 'Name: Cindy, 30 years old' re.search(r'\d+(?= years? old)', s).group() # '30' re.search(r'(?<=Name: )\w+', s).group() # 'Cindy' ''' Match Groups: Parentheses create logical groups inside of match text match.group(1) corresponds to first group match.group(2) corresponds to second group match.group() corresponds to entire match text (as usual) ''' s = 'my email is john-doe@gmail.com' match = re.search(r'([\w.-]+)@([\w.-]+)', s) if match: match.group(1) # 'john-doe' match.group(2) # 'gmail.com' match.group() # 'john-doe@gmail.com' ''' Finding All Matches: re.findall() finds all matches and returns them as a list of strings list_of_strings = re.findall(r'pattern', string_to_search) If pattern includes parentheses, a list of tuples is returned ''' s = 'emails: joe@gmail.com, bob@gmail.com' re.findall(r'[\w.-]+@[\w.-]+', s) # ['joe@gmail.com', 'bob@gmail.com'] re.findall(r'([\w.-]+)@([\w.-]+)', s) # [('joe', 'gmail.com'), ('bob', 'gmail.com')] ''' Option Flags: Options flags modify the behavior of the pattern matching default: matching is case sensitive re.IGNORECASE: ignore uppercase/lowercase differences ('a' matches 'a' or 'A') default: period matches any character except newline re.DOTALL: allow period to match newline default: within a string of many lines, ^ and $ match start and end of entire string re.MULTILINE: allow ^ and $ to match start and end of each line Option flag is third argument to re.search() or re.findall(): re.search(r'pattern', string_to_search, re.IGNORECASE) re.findall(r'pattern', string_to_search, re.IGNORECASE) ''' s = 'emails: nicole@ga.co, joe@gmail.com, PAT@GA.CO' re.findall(r'\w+@ga\.co', s) # ['nicole@ga.co'] re.findall(r'\w+@ga\.co', s, re.IGNORECASE) # ['nicole@ga.co', 'PAT@GA.CO'] ''' Substitution: re.sub() finds all matches and replaces them with a specified string new_string = re.sub(r'pattern', r'replacement', string_to_search) Replacement string can refer to text from matching groups: \1 refers to group(1) \2 refers to group(2) etc. ''' s = 'sid is missing class' re.sub(r'is ', r'was ', s) # 'sid was missing class' s = 'emails: joe@gmail.com, bob@gmail.com' re.sub(r'([\w.-]+)@([\w.-]+)', r'\1@yahoo.com', s) # 'emails: joe@yahoo.com, bob@yahoo.com' ''' Useful, But Not Covered: re.split() splits a string by the occurrences of a pattern re.compile() compiles a pattern (for improved performance if it's used many times) A|B indicates a pattern that can match A or B ''' ================================================ FILE: code/20_sql.py ================================================ ############################################################################### ##### Class 20: SQL ############################################################################### """ Accessing the data from a database is just another way to get data. This has no affect on how you model the data or do anything else; it's just a different repository for storing data. We're used to getting data from "flat files" like CSV or TXT files. The method for getting the data is different, but the result is the same. """ ############################################################################### ##### Accessing Data from a SQLLite Database ############################################################################### # Python package to interface with database files import sqlite3 as lite ##### Connecting to a Database ##### # Connect to a local database (it's basically just a file) con = lite.connect('sales.db') con # Create a Cursor object. This let's you browse your database cur = con.cursor() cur ##### What Tables are in our Database? ###### # Let's look at what tables we have available. Let's not worry about what the # command is that it's executing. We'll cover that more later. cur.execute("SELECT name FROM sqlite_master WHERE type='table'") # Note that this doesn't explicitly return anything. It only stores the # results in your cursor. You have to 'fetch' the results to get them back. # There are several different ways to do this. However, once you 'fetch' a # result, it is no longer there # Fetch all results at once cur.fetchall() # One at a time cur.fetchone() # Some specified number at a time cur.fetchmany(4) # Also note that the results weren't stored anywhere, only printed out. To # keep them, we must put them in a variable cur.execute("SELECT name FROM sqlite_master WHERE type='table'") tables = cur.fetchall() tables ##### Getting Data from our Database ##### # Select all of the data from the table 'Orders' cur.execute("SELECT * FROM Orders") orders_table = cur.fetchall() orders_table # This is a list of tuples, not the most convenient thing to work with, but # managable. We know how to access these elements. orders_table[0] orders_table[0][0] orders_table[0][3] # We could also put these into a dataframe. import pandas as pd orders_data = pd.DataFrame(orders_table) orders_data # However, pandas has a nice funtion to read the results of your SQL query into # a pandas data frame. This is the best thing ever! orders_data = pd.read_sql_query("SELECT * FROM Orders", con=con) orders_data # Let's look at our other data to see what is contained in it. for table in tables: print 'Table %s' % table[0] print ' ' print pd.read_sql_query("SELECT * FROM %s" % table[0], con=con).head() print ' ' print ' ' print ' ' # NOTE: Be careful doing this if there are a lot of tables in your database. # Here we only have five, so it's okay. # Let's look at the sales database schema to get a better idea of the layout. # https://raw.githubusercontent.com/justmarkham/DAT5/master/slides/20_sales_db_schema.png ############################################################################### ##### Exploring, Discovering, and Aggregating Data ############################################################################### """ Everything that is done in the following queries could be done in pandas. However, it is at times easier to do it in SQL, so it's important to be aware of how to do it. I've included the pandas ways of doing things below the SQL queries for easy of comparison. """ ##### Selecting Data ##### # Return all of the data (* means all columns) pd.read_sql_query("SELECT * FROM Orders", con=con) orders_data # Return specific columns by name pd.read_sql_query("SELECT CustomerID, EmployeeID, OrderDate, FreightCharge FROM Orders", con) orders_data[['CustomerID','EmployeeID','OrderDate','FreightCharge']] ##### Segmenting Data ##### # Return only the more recent orders (order date more recent than 2013) pd.read_sql_query("SELECT * FROM Orders WHERE OrderDate > '2013-01-01'", con) orders_data[orders_data.OrderDate > '2013-01-01'] # Return only orders shipped via '1' pd.read_sql_query("SELECT * FROM Orders WHERE ShipVia = 1", con) orders_data[orders_data.ShipVia == 1] # Combine conditions with AND and OR pd.read_sql_query("SELECT * FROM Orders WHERE ShipVia = 1 AND OrderDate > '2013-01-01'", con) orders_data[(orders_data.ShipVia == 1) & (orders_data.OrderDate > '2013-01-01')] pd.read_sql_query("SELECT * FROM Orders WHERE ShipVia = 1 OR OrderDate > '2013-01-01'", con) orders_data[(orders_data.ShipVia == 1) | (orders_data.OrderDate > '2013-01-01')] ##### Ordering Data ##### # We can return the rows in a specific order. pd.read_sql_query("SELECT * FROM Orders ORDER BY OrderDate", con) orders_data.sort_index(by="OrderDate") # Ascending pd.read_sql_query("SELECT * FROM Orders ORDER BY FreightCharge", con) orders_data.sort_index(by="FreightCharge") # Descending pd.read_sql_query("SELECT * FROM Orders ORDER BY FreightCharge DESC", con) orders_data.sort_index(by="FreightCharge", ascending=False) ##### Aggregating Data ##### # Count the number of rows in the order dataset pd.read_sql_query("SELECT COUNT(*) FROM Orders", con) orders_data.OrderID.count() pd.read_sql_query("SELECT COUNT(*) AS row_count FROM Orders", con) # Alias column # Compute the minimum, maximum, and average freight charge pd.read_sql_query("""SELECT MIN(FreightCharge) AS min, MAX(FreightCharge) AS max, AVG(FreightCharge) AS avg FROM Orders""", con) (orders_data.FreightCharge.min(), orders_data.FreightCharge.max(), orders_data.FreightCharge.mean()) ##### Group By ##### # Let's look at the average freight cost by the method of shipping # What are all of the ShipVia values? pd.read_sql_query("SELECT DISTINCT ShipVia FROM Orders", con) # Note DISTINCT orders_data.ShipVia.unique() # We can write a query for each one of the ShipVia values pd.read_sql_query("SELECT ShipVia, AVG(FreightCharge) AS avg FROM Orders WHERE ShipVia = 1", con) orders_data[orders_data.ShipVia == 1].FreightCharge.mean() pd.read_sql_query("SELECT ShipVia, AVG(FreightCharge) AS avg FROM Orders WHERE ShipVia = 2", con) orders_data[orders_data.ShipVia == 2].FreightCharge.mean() pd.read_sql_query("SELECT ShipVia, AVG(FreightCharge) AS avg FROM Orders WHERE ShipVia = 3", con) orders_data[orders_data.ShipVia == 3].FreightCharge.mean() pd.read_sql_query("SELECT ShipVia, AVG(FreightCharge) AS avg FROM Orders WHERE ShipVia = 4", con) orders_data[orders_data.ShipVia == 4].FreightCharge.mean() # However, this is pretty verbose. Also, what if there were 20 values? Should # we write 20 queries? Of course not! This is where GROUP BY comes in. pd.read_sql_query("SELECT ShipVia, AVG(FreightCharge) AS avg FROM Orders GROUP BY ShipVia", con) orders_data.groupby('ShipVia').FreightCharge.mean() # You can use any aggregation or other metric with a group by pd.read_sql_query("SELECT ShipVia, MAX(FreightCharge) AS max FROM Orders GROUP BY ShipVia", con) orders_data.groupby('ShipVia').FreightCharge.max() # However, we don't know what any of these "ShipVia" values mean. We can # probably look in the Shippers table and figure it out. pd.read_sql_query("SELECT * FROM Shippers", con) # But it's always better to have all of this info together. ############################################################################### ##### Joining Tables ############################################################################### """ But surely there's a better way to look at it all at once. This is where JOIN's come in. As the name suggests, JOIN's allow you to JOIN two (or more) tables together. There are several types of joins: -INNER JOIN: Returns all rows when there is at least one match in BOTH tables -LEFT JOIN: Return all rows from the left table, and the matched rows from the right table -RIGHT JOIN: Return all rows from the right table, and the matched rows from the left table -FULL JOIN: Return all rows when there is a match in ONE of the tables http://i.stack.imgur.com/GbJ7N.png These have different use cases (please read more about them). In our case, we want to join the Shippers table (with the ShipVia ids) to the corresponding ids in our Orders table. So, we want to LEFT JOIN Shippers to Orders based upon the matching id. NOTE: You can also JOIN pandas dataframes using the "merge" function. """ # Let's look at the tables separately to evaluate how to join. pd.read_sql_query("SELECT * FROM Orders", con) pd.read_sql_query("SELECT * FROM Shippers", con) # Let's look at the join pd.read_sql_query("""SELECT * FROM Orders LEFT JOIN Shippers ON Orders.ShipVia = Shippers.ShipperID""" , con) # Note that any time we want to refer to a column in a particular table, we # have to type the table name. That would get old. Instead, we can give each # table an alias or nickname. We get the same result. pd.read_sql_query("""SELECT * FROM Orders a LEFT JOIN Shippers b ON a.ShipVia = b.ShipperID""" , con) # We can also return specific columns from each table. pd.read_sql_query("""SELECT b.CompanyName, a.FreightCharge FROM Orders a LEFT JOIN Shippers b ON a.ShipVia = b.ShipperID""" , con) # We can get our result from before, but with the compnay name instead of just # their id. pd.read_sql_query("""SELECT b.CompanyName, AVG(a.FreightCharge) AS avg FROM Orders a LEFT JOIN Shippers b ON a.ShipVia = b.ShipperID GROUP BY b.CompanyName""" , con) # Finally, we can order our data by average freight charge. pd.read_sql_query("""SELECT b.CompanyName, AVG(a.FreightCharge) AS avg FROM Orders a LEFT JOIN Shippers b ON a.ShipVia = b.ShipperID GROUP BY b.CompanyName ORDER BY avg""" , con) ############################################################################### ##### Nested Queries ############################################################################### """ Nested queries are exactly what they sound like, queries within queries. These can be convenient in a number of different places. They allow you to use the result from one query in another query. """ # Let's say we want to figure out what percentage of orders get shipped by each # shipper. We can count the number of occurences of each shipper. pd.read_sql_query("SELECT ShipVia, COUNT(ShipVia) AS count FROM Orders GROUP BY ShipVia", con) # We can calculate the number of total orders there are. pd.read_sql_query("SELECT COUNT(*) FROM Orders", con) # We can divide each of those by the number of orders total. pd.read_sql_query("""SELECT ShipVia, 1.0*COUNT(ShipVia)/20*100 AS percent FROM Orders GROUP BY ShipVia""", con) # But what happens when we get a new order. We have to update the "20" # manually. That's not optimal. That's where nested queries help. pd.read_sql_query("""SELECT ShipVia, 1.0*COUNT(ShipVia)/(SELECT COUNT(*) FROM Orders)*100 AS percent FROM Orders GROUP BY ShipVia""", con) # You can nest any number of queries in a full query. ############################################################################### ##### Case Statements ############################################################################### """ CASE statements are similar to if else statements in Python. They allow you to specify conditions and what the results are if the condition is true. They also allow you to specify what happens when none of the conditions are met (similar to the else statement in Python). """ # Let's say you want to determine whether the average freight charge has # changed from year to year. A CASE statement allows you to create conditions # for each year. pd.read_sql_query("""SELECT CASE WHEN OrderDate > '2012-01-01' AND OrderDate < '2013-01-01' THEN 2012 WHEN OrderDate > '2013-01-01' AND OrderDate < '2014-01-01' THEN 2013 ELSE 'Not a date!' END AS year, OrderDate FROM Orders""", con) # Now we can use this to calcualte the average freight charge per year. pd.read_sql_query("""SELECT CASE WHEN OrderDate > '2012-01-01' AND OrderDate < '2013-01-01' THEN 2012 WHEN OrderDate > '2013-01-01' AND OrderDate < '2014-01-01' THEN 2013 ELSE 'Not a date!' END AS year, AVG(FreightCharge) AS avg FROM Orders GROUP BY year""", con) # Close the connection con.close() ############################################################################### ##### Normal Data Science Process with a Database ############################################################################### """ Finally, just to reiterate that getting data from databases is nothing more than another way to get data (and thus, has no effect upon the rest of the data science process), here is some code we used in a previous class. Instead of reading data from a CSV file, we get it from a database. """ ##### Training ##### # Open new connection con = lite.connect('vehicles.db') # Get training data from database train = pd.read_sql_query('SELECT * FROM vehicle_train', con=con) # Encode car as 0 and truck as 1 train['type'] = train.type.map({'car':0, 'truck':1}) train.head() # Create a list of the feature columns (every column except for the 0th column) feature_cols = train.columns[1:] # Define X (features) and y (response) X = train[feature_cols] y = train.price # Import the relevant class, and instantiate the model (with random_state=1) from sklearn.tree import DecisionTreeRegressor treereg = DecisionTreeRegressor(random_state=1) treereg.fit(X, y) # Use 3-fold cross-validation to estimate the RMSE for this model from sklearn.cross_validation import cross_val_score import numpy as np scores = cross_val_score(treereg, X, y, cv=3, scoring='mean_squared_error') np.mean(np.sqrt(-scores)) ##### Testing ##### # Get testing data from database test = pd.read_sql_query('SELECT * FROM vehicle_test', con=con) con.close() # Encode car as 0 and truck as 1 test['type'] = test.type.map({'car':0, 'truck':1}) # Print the data test # Define X and y X_test = test[feature_cols] y_test = test.price # Make predictions on test data y_pred = treereg.predict(X_test) y_pred # Calculate RMSE from sklearn import metrics np.sqrt(metrics.mean_squared_error(y_test, y_pred)) # Calculate RMSE for your own tree! y_test = [3000, 6000, 12000] y_pred = [3057, 3057, 16333] np.sqrt(metrics.mean_squared_error(y_test, y_pred)) ================================================ FILE: code/21_ensembles_example.py ================================================ ''' Imports ''' import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.cross_validation import cross_val_score from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.pipeline import make_pipeline ''' Define a function that takes in a raw CSV file and returns a DataFrame that includes all created features (and any other modifications). That way, we can apply the same changes to both train.csv and test.csv. ''' # Define the function def make_features(filename): # Read in dataframe df = pd.read_csv(filename, index_col=0) #Rename columns df.rename(columns={'OwnerUndeletedAnswerCountAtPostTime':'Answers'}, inplace=True) # Get length of title of post df['TitleLength'] = df.Title.apply(len) # Get length of body of post df['BodyLength'] = df.BodyMarkdown.apply(len) # Number of tags for post df['NumTags'] = df.loc[:, 'Tag1':'Tag5'].notnull().sum(axis=1) # Is the title lowercase? df['TitleLowercase'] = (df.Title.str.lower() == df.Title).astype(int) # Create features that represent whether Title contains certain words df['TitleQuestion'] = df.Title.str.contains('question', case=False).astype(int) df['TitleNeed'] = df.Title.str.contains('need', case=False).astype(int) df['TitleHelp'] = df.Title.str.contains('help', case=False).astype(int) return df # Apply function to the training data train = make_features('train.csv') X = train.drop('OpenStatus', axis=1) y = train.OpenStatus # Read in test data test = make_features('test.csv') # Split into training and testing sets #X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) ''' Five feature logistic regression model ''' # Define feature cols feature_cols_logreg = ['ReputationAtPostCreation', 'Answers', 'TitleLength', 'BodyLength', 'NumTags'] # Perform cross validation to get an idea of the performance of the model logreg = LogisticRegression() -cross_val_score(logreg, X[feature_cols_logreg], y, scoring="log_loss", cv=5).mean() # Predict class probabilities for the actual testing data logreg.fit(X[feature_cols_logreg], y) y_prob_logreg = logreg.predict_proba(test[feature_cols_logreg])[:, 1] ''' Five feature random forest model ''' # Define feature cols feature_cols_rf = ['TitleLowercase', 'TitleQuestion', 'TitleNeed', 'TitleHelp'] # Perform cross validation to get an idea of the performance of the model rf = RandomForestClassifier() -cross_val_score(rf, X[feature_cols_rf], y, scoring="log_loss", cv=5).mean() # Predict class probabilities for the actual testing data rf.fit(X[feature_cols_rf], y) y_prob_rf = rf.predict_proba(test[feature_cols_rf])[:, 1] ''' Text logistic regression model on 'Title' using pipeline ''' # Make pipleline pipe = make_pipeline(CountVectorizer(stop_words='english'), LogisticRegression()) # Perform cross validation to get an idea of the performance of the model -cross_val_score(pipe, X['Title'], y, scoring="log_loss", cv=5).mean() # Predict class probabilities for the actual testing data pipe.fit(X['Title'], y) y_prob_pipe = pipe.predict_proba(test['Title'])[:, 1] ''' Create submission ''' # Ensemble predictions y_prob_combined = (y_prob_logreg + y_prob_rf + 2*y_prob_pipe) / 3 # Create a DataFrame that has 'id' as the index, then export to a CSV file sub = pd.DataFrame({'id':test.index, 'OpenStatus':y_prob_combined}).set_index('id') sub.to_csv('sub_ensemble.csv') ================================================ FILE: data/SMSSpamCollection.txt ================================================ ham Go until jurong point, crazy.. 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But liked ones will be Remembered Everytime... BSLVYL ham Joy's father is John. Then John is the NAME of Joy's father. Mandan spam Hi 07734396839 IBH Customer Loyalty Offer: The NEW NOKIA6600 Mobile from ONLY £10 at TXTAUCTION!Txt word:START to No:81151 & get Yours Now!4T& ham Hi this is yijue... It's regarding the 3230 textbook it's intro to algorithms second edition... I'm selling it for $50... spam SMS AUCTION You have won a Nokia 7250i. This is what you get when you win our FREE auction. To take part send Nokia to 86021 now. HG/Suite342/2Lands Row/W1JHL 16+ ham K, want us to come by now? ham How. Its a little difficult but its a simple way to enter this place ham Ha... Both of us doing e same thing. But i got tv 2 watch. U can thk of where 2 go tonight or u already haf smth in mind... ham Dont show yourself. How far. Put new pictures up on facebook. ham Watching tv now. I got new job :) ham Good afternoon sexy buns! How goes the job search ? 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More exciting prizes soon, so keep an eye on ur mobile or visit www.win-82050.co.uk ham You bad girl. I can still remember them ham How much i gave to you. Morning. ham I hope your alright babe? I worry that you might have felt a bit desparate when you learned the job was a fake ? I am here waiting when you come back, my love ham Hey, can you tell me blake's address? Carlos wanted me to meet him there but I got lost and he's not answering his phone ham Can i get your opinion on something first? ham That one week leave i put know that time. Why. ham If we hit it off, you can move in with me :) ham excellent. I spent <#> years in the Air Force. Iraq and afghanistan. I am stable and honest. do you like traveling? ham I wanna watch that movie ham Ok lor thanx... Ü in school? ham I'm in class. Did you get my text. ham The bus leaves at <#> ham God bless.get good sleep my dear...i will pray! spam Todays Voda numbers ending 1225 are selected to receive a £50award. If you have a match please call 08712300220 quoting claim code 3100 standard rates app ham Do have a nice day today. I love you so dearly. ham Aiyo a bit pai seh ü noe... Scared he dun rem who i am then die... Hee... But he become better lookin oredi leh... ham Aight, I'll ask a few of my roommates ham Now, whats your house # again ? And do you have any beer there ? ham Do ü all wan 2 meet up n combine all the parts? How's da rest of da project going? ham "Getting tickets 4 walsall tue 6 th march. My mate is getting me them on sat. ill pay my treat. Want 2 go. Txt bak .Terry" ham Yes we are chatting too. ham HI ITS JESS I DONT KNOW IF YOU ARE AT WORK BUT CALL ME WHEN U CAN IM AT HOME ALL EVE. XXX ham Sian... Aft meeting supervisor got work 2 do liao... U working now? ham Are you going to write ccna exam this week?? ham Well i will watch shrek in 3D!!B) ham Am i that much dirty fellow? ham Dunno dat's wat he told me. Ok lor... ham I'll probably be by tomorrow (or even later tonight if something's going on) ham I couldn't say no as he is a dying man and I feel sad for him so I will go and I just wanted you to know I would probably be gone late into your night ham If you're thinking of lifting me one then no. ham Same as u... Dun wan... Y u dun like me already ah... Wat u doing now? Still eating? ham Sent me ur email id soon ham Wat makes some people dearer is not just de happiness dat u feel when u meet them but de pain u feel when u miss dem!!! ham Dude. What's up. How Teresa. Hope you have been okay. When i didnt hear from these people, i called them and they had received the package since dec <#> . Just thot you'ld like to know. Do have a fantastic year and all the best with your reading. Plus if you can really really Bam first aid for Usmle, then your work is done. ham Hey gorgeous man. My work mobile number is. Have a good one babe. 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I got a list with only u and Joanna if I'm feeling really anti social ham I am in your office na. ham "Are you comingdown later?" ham Super da:)good replacement for murali ham Da is good good player.why he is unsold. ham Hi. || Do u want | to join me with sts later? || Meeting them at five. || Call u after class. ham Its on in engalnd! But telly has decided it won't let me watch it and mia and elliot were kissing! Damn it! spam FREE NOKIA Or Motorola with upto 12mths 1/2price linerental, 500 FREE x-net mins&100txt/mth FREE B'tooth*. Call Mobileupd8 on 08001950382 or call 2optout/D3WV ham I dont want to hear philosophy. Just say what happen ham You got job in wipro:)you will get every thing in life in 2 or 3 years. ham Then cant get da laptop? My matric card wif ü lei... ham Dunno da next show aft 6 is 850. Toa payoh got 650. spam This is the 2nd time we have tried 2 contact u. U have won the 750 Pound prize. 2 claim is easy, call 08718726970 NOW! Only 10p per min. 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The fact that you even felt like i would do it to hurt you shows you really don't know me at all. It was messy wednesday, but it wasn't bad. The problem i have with it is you HAVE the time to clean it, but you choose not to. You skype, you take pictures, you sleep, you want to go out. I don't mind a few things here and there, but when you don't make the bed, when you throw laundry on top of it, when i can't have a friend in the house because i'm embarassed that there's underwear and bras strewn on the bed, pillows on the floor, that's something else. You used to be good about at least making the bed. ham I'll let you know when it kicks in ham You call him now ok i said call him ham Call to the number which is available in appointment. And ask to connect the call to waheed fathima. ham Or ü go buy wif him then i meet ü later can? ham Mmmm ... Fuck ... Not fair ! You know my weaknesses ! *grins* *pushes you to your knee's* *exposes my belly and pulls your head to it* Don't forget ... 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Even my Colleagues did not wish. As I entered my cabin my PA said, '' Happy B'day Boss !!''. I felt special. She askd me 4 lunch. After lunch she invited me to her apartment. We went there. She said,'' do u mind if I go into the bedroom for a minute ? '' ''OK'', I sed in a sexy mood. She came out 5 minuts latr wid a cake...n My Wife, My Parents, My Kidz, My Friends n My Colleagues. All screaming.. SURPRISE !! and I was waiting on the sofa.. ... ..... ' NAKED...! ham I think you should go the honesty road. Call the bank tomorrow. Its the tough decisions that make us great people. spam FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+ ham No. Its not specialisation. Can work but its slave labor. Will look for it this month sha cos no shakara 4 beggar. ham Is she replying. 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In the same way friendship has one law; Never make ur friend feel alone until you are alive.... Gud night spam PRIVATE! Your 2003 Account Statement for 07808247860 shows 800 un-redeemed S. I. M. points. Call 08719899229 Identifier Code: 40411 Expires 06/11/04 ham Apo all other are mokka players only ham Perhaps * is much easy give your account identification, so i will tomorrow at UNI ham Wait . I will msg after <#> min. ham What i told before i tell. Stupid hear after i wont tell anything to you. You dad called to my brother and spoken. Not with me. ham God's love has no limit. God's grace has no measure. God's power has no boundaries. May u have God's endless blessings always in ur life...!! Gud ni8 ham I want to be inside you every night... ham Machan you go to gym tomorrow, i wil come late goodnight. ham Lol they were mad at first but then they woke up and gave in. ham I went to project centre ham It‘s reassuring, in this crazy world. ham Just making dinner, you ? ham Yes. 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Its true to its name ================================================ FILE: data/airline_safety.csv ================================================ airline,avail_seat_km_per_week,incidents_85_99,fatal_accidents_85_99,fatalities_85_99,incidents_00_14,fatal_accidents_00_14,fatalities_00_14 Aer Lingus,320906734,2,0,0,0,0,0 Aeroflot*,1197672318,76,14,128,6,1,88 Aerolineas Argentinas,385803648,6,0,0,1,0,0 Aeromexico*,596871813,3,1,64,5,0,0 Air Canada,1865253802,2,0,0,2,0,0 Air France,3004002661,14,4,79,6,2,337 Air India*,869253552,2,1,329,4,1,158 Air New Zealand*,710174817,3,0,0,5,1,7 Alaska Airlines*,965346773,5,0,0,5,1,88 Alitalia,698012498,7,2,50,4,0,0 All Nippon Airways,1841234177,3,1,1,7,0,0 American*,5228357340,21,5,101,17,3,416 Austrian Airlines,358239823,1,0,0,1,0,0 Avianca,396922563,5,3,323,0,0,0 British Airways*,3179760952,4,0,0,6,0,0 Cathay Pacific*,2582459303,0,0,0,2,0,0 China Airlines,813216487,12,6,535,2,1,225 Condor,417982610,2,1,16,0,0,0 COPA,550491507,3,1,47,0,0,0 Delta / Northwest*,6525658894,24,12,407,24,2,51 Egyptair,557699891,8,3,282,4,1,14 El Al,335448023,1,1,4,1,0,0 Ethiopian Airlines,488560643,25,5,167,5,2,92 Finnair,506464950,1,0,0,0,0,0 Garuda Indonesia,613356665,10,3,260,4,2,22 Gulf Air,301379762,1,0,0,3,1,143 Hawaiian Airlines,493877795,0,0,0,1,0,0 Iberia,1173203126,4,1,148,5,0,0 Japan Airlines,1574217531,3,1,520,0,0,0 Kenya Airways,277414794,2,0,0,2,2,283 KLM*,1874561773,7,1,3,1,0,0 Korean Air,1734522605,12,5,425,1,0,0 LAN Airlines,1001965891,3,2,21,0,0,0 Lufthansa*,3426529504,6,1,2,3,0,0 Malaysia Airlines,1039171244,3,1,34,3,2,537 Pakistan International,348563137,8,3,234,10,2,46 Philippine Airlines,413007158,7,4,74,2,1,1 Qantas*,1917428984,1,0,0,5,0,0 Royal Air Maroc,295705339,5,3,51,3,0,0 SAS*,682971852,5,0,0,6,1,110 Saudi Arabian,859673901,7,2,313,11,0,0 Singapore Airlines,2376857805,2,2,6,2,1,83 South African,651502442,2,1,159,1,0,0 Southwest Airlines,3276525770,1,0,0,8,0,0 Sri Lankan / AirLanka,325582976,2,1,14,4,0,0 SWISS*,792601299,2,1,229,3,0,0 TACA,259373346,3,1,3,1,1,3 TAM,1509195646,8,3,98,7,2,188 TAP - Air Portugal,619130754,0,0,0,0,0,0 Thai Airways,1702802250,8,4,308,2,1,1 Turkish Airlines,1946098294,8,3,64,8,2,84 United / Continental*,7139291291,19,8,319,14,2,109 US Airways / America West*,2455687887,16,7,224,11,2,23 Vietnam Airlines,625084918,7,3,171,1,0,0 Virgin Atlantic,1005248585,1,0,0,0,0,0 Xiamen Airlines,430462962,9,1,82,2,0,0 ================================================ FILE: data/auto_mpg.txt ================================================ mpg|cylinders|displacement|horsepower|weight|acceleration|model_year|origin|car_name 18|8|307|130|3504|12|70|1|chevrolet chevelle malibu 15|8|350|165|3693|11.5|70|1|buick skylark 320 18|8|318|150|3436|11|70|1|plymouth satellite 16|8|304|150|3433|12|70|1|amc rebel sst 17|8|302|140|3449|10.5|70|1|ford torino 15|8|429|198|4341|10|70|1|ford galaxie 500 14|8|454|220|4354|9|70|1|chevrolet impala 14|8|440|215|4312|8.5|70|1|plymouth fury iii 14|8|455|225|4425|10|70|1|pontiac catalina 15|8|390|190|3850|8.5|70|1|amc ambassador dpl 15|8|383|170|3563|10|70|1|dodge challenger se 14|8|340|160|3609|8|70|1|plymouth 'cuda 340 15|8|400|150|3761|9.5|70|1|chevrolet monte carlo 14|8|455|225|3086|10|70|1|buick estate wagon (sw) 24|4|113|95|2372|15|70|3|toyota corona mark ii 22|6|198|95|2833|15.5|70|1|plymouth duster 18|6|199|97|2774|15.5|70|1|amc hornet 21|6|200|85|2587|16|70|1|ford maverick 27|4|97|88|2130|14.5|70|3|datsun pl510 26|4|97|46|1835|20.5|70|2|volkswagen 1131 deluxe sedan 25|4|110|87|2672|17.5|70|2|peugeot 504 24|4|107|90|2430|14.5|70|2|audi 100 ls 25|4|104|95|2375|17.5|70|2|saab 99e 26|4|121|113|2234|12.5|70|2|bmw 2002 21|6|199|90|2648|15|70|1|amc gremlin 10|8|360|215|4615|14|70|1|ford f250 10|8|307|200|4376|15|70|1|chevy c20 11|8|318|210|4382|13.5|70|1|dodge d200 9|8|304|193|4732|18.5|70|1|hi 1200d 27|4|97|88|2130|14.5|71|3|datsun pl510 28|4|140|90|2264|15.5|71|1|chevrolet vega 2300 25|4|113|95|2228|14|71|3|toyota corona 19|6|232|100|2634|13|71|1|amc gremlin 16|6|225|105|3439|15.5|71|1|plymouth satellite custom 17|6|250|100|3329|15.5|71|1|chevrolet chevelle malibu 19|6|250|88|3302|15.5|71|1|ford torino 500 18|6|232|100|3288|15.5|71|1|amc matador 14|8|350|165|4209|12|71|1|chevrolet impala 14|8|400|175|4464|11.5|71|1|pontiac catalina brougham 14|8|351|153|4154|13.5|71|1|ford galaxie 500 14|8|318|150|4096|13|71|1|plymouth fury iii 12|8|383|180|4955|11.5|71|1|dodge monaco (sw) 13|8|400|170|4746|12|71|1|ford country squire (sw) 13|8|400|175|5140|12|71|1|pontiac safari (sw) 18|6|258|110|2962|13.5|71|1|amc hornet sportabout (sw) 22|4|140|72|2408|19|71|1|chevrolet vega (sw) 19|6|250|100|3282|15|71|1|pontiac firebird 18|6|250|88|3139|14.5|71|1|ford mustang 23|4|122|86|2220|14|71|1|mercury capri 2000 28|4|116|90|2123|14|71|2|opel 1900 30|4|79|70|2074|19.5|71|2|peugeot 304 30|4|88|76|2065|14.5|71|2|fiat 124b 31|4|71|65|1773|19|71|3|toyota corolla 1200 35|4|72|69|1613|18|71|3|datsun 1200 27|4|97|60|1834|19|71|2|volkswagen model 111 26|4|91|70|1955|20.5|71|1|plymouth cricket 24|4|113|95|2278|15.5|72|3|toyota corona hardtop 25|4|97.5|80|2126|17|72|1|dodge colt hardtop 23|4|97|54|2254|23.5|72|2|volkswagen type 3 20|4|140|90|2408|19.5|72|1|chevrolet vega 21|4|122|86|2226|16.5|72|1|ford pinto runabout 13|8|350|165|4274|12|72|1|chevrolet impala 14|8|400|175|4385|12|72|1|pontiac catalina 15|8|318|150|4135|13.5|72|1|plymouth fury iii 14|8|351|153|4129|13|72|1|ford galaxie 500 17|8|304|150|3672|11.5|72|1|amc ambassador sst 11|8|429|208|4633|11|72|1|mercury marquis 13|8|350|155|4502|13.5|72|1|buick lesabre custom 12|8|350|160|4456|13.5|72|1|oldsmobile delta 88 royale 13|8|400|190|4422|12.5|72|1|chrysler newport royal 19|3|70|97|2330|13.5|72|3|mazda rx2 coupe 15|8|304|150|3892|12.5|72|1|amc matador (sw) 13|8|307|130|4098|14|72|1|chevrolet chevelle concours (sw) 13|8|302|140|4294|16|72|1|ford gran torino (sw) 14|8|318|150|4077|14|72|1|plymouth satellite custom (sw) 18|4|121|112|2933|14.5|72|2|volvo 145e (sw) 22|4|121|76|2511|18|72|2|volkswagen 411 (sw) 21|4|120|87|2979|19.5|72|2|peugeot 504 (sw) 26|4|96|69|2189|18|72|2|renault 12 (sw) 22|4|122|86|2395|16|72|1|ford pinto (sw) 28|4|97|92|2288|17|72|3|datsun 510 (sw) 23|4|120|97|2506|14.5|72|3|toyouta corona mark ii (sw) 28|4|98|80|2164|15|72|1|dodge colt (sw) 27|4|97|88|2100|16.5|72|3|toyota corolla 1600 (sw) 13|8|350|175|4100|13|73|1|buick century 350 14|8|304|150|3672|11.5|73|1|amc matador 13|8|350|145|3988|13|73|1|chevrolet malibu 14|8|302|137|4042|14.5|73|1|ford gran torino 15|8|318|150|3777|12.5|73|1|dodge coronet custom 12|8|429|198|4952|11.5|73|1|mercury marquis brougham 13|8|400|150|4464|12|73|1|chevrolet caprice classic 13|8|351|158|4363|13|73|1|ford ltd 14|8|318|150|4237|14.5|73|1|plymouth fury gran sedan 13|8|440|215|4735|11|73|1|chrysler new yorker brougham 12|8|455|225|4951|11|73|1|buick electra 225 custom 13|8|360|175|3821|11|73|1|amc ambassador brougham 18|6|225|105|3121|16.5|73|1|plymouth valiant 16|6|250|100|3278|18|73|1|chevrolet nova custom 18|6|232|100|2945|16|73|1|amc hornet 18|6|250|88|3021|16.5|73|1|ford maverick 23|6|198|95|2904|16|73|1|plymouth duster 26|4|97|46|1950|21|73|2|volkswagen super beetle 11|8|400|150|4997|14|73|1|chevrolet impala 12|8|400|167|4906|12.5|73|1|ford country 13|8|360|170|4654|13|73|1|plymouth custom suburb 12|8|350|180|4499|12.5|73|1|oldsmobile vista cruiser 18|6|232|100|2789|15|73|1|amc gremlin 20|4|97|88|2279|19|73|3|toyota carina 21|4|140|72|2401|19.5|73|1|chevrolet vega 22|4|108|94|2379|16.5|73|3|datsun 610 18|3|70|90|2124|13.5|73|3|maxda rx3 19|4|122|85|2310|18.5|73|1|ford pinto 21|6|155|107|2472|14|73|1|mercury capri v6 26|4|98|90|2265|15.5|73|2|fiat 124 sport coupe 15|8|350|145|4082|13|73|1|chevrolet monte carlo s 16|8|400|230|4278|9.5|73|1|pontiac grand prix 29|4|68|49|1867|19.5|73|2|fiat 128 24|4|116|75|2158|15.5|73|2|opel manta 20|4|114|91|2582|14|73|2|audi 100ls 19|4|121|112|2868|15.5|73|2|volvo 144ea 15|8|318|150|3399|11|73|1|dodge dart custom 24|4|121|110|2660|14|73|2|saab 99le 20|6|156|122|2807|13.5|73|3|toyota mark ii 11|8|350|180|3664|11|73|1|oldsmobile omega 20|6|198|95|3102|16.5|74|1|plymouth duster 19|6|232|100|2901|16|74|1|amc hornet 15|6|250|100|3336|17|74|1|chevrolet nova 31|4|79|67|1950|19|74|3|datsun b210 26|4|122|80|2451|16.5|74|1|ford pinto 32|4|71|65|1836|21|74|3|toyota corolla 1200 25|4|140|75|2542|17|74|1|chevrolet vega 16|6|250|100|3781|17|74|1|chevrolet chevelle malibu classic 16|6|258|110|3632|18|74|1|amc matador 18|6|225|105|3613|16.5|74|1|plymouth satellite sebring 16|8|302|140|4141|14|74|1|ford gran torino 13|8|350|150|4699|14.5|74|1|buick century luxus (sw) 14|8|318|150|4457|13.5|74|1|dodge coronet custom (sw) 14|8|302|140|4638|16|74|1|ford gran torino (sw) 14|8|304|150|4257|15.5|74|1|amc matador (sw) 29|4|98|83|2219|16.5|74|2|audi fox 26|4|79|67|1963|15.5|74|2|volkswagen dasher 26|4|97|78|2300|14.5|74|2|opel manta 31|4|76|52|1649|16.5|74|3|toyota corona 32|4|83|61|2003|19|74|3|datsun 710 28|4|90|75|2125|14.5|74|1|dodge colt 24|4|90|75|2108|15.5|74|2|fiat 128 26|4|116|75|2246|14|74|2|fiat 124 tc 24|4|120|97|2489|15|74|3|honda civic 26|4|108|93|2391|15.5|74|3|subaru 31|4|79|67|2000|16|74|2|fiat x1.9 19|6|225|95|3264|16|75|1|plymouth valiant custom 18|6|250|105|3459|16|75|1|chevrolet nova 15|6|250|72|3432|21|75|1|mercury monarch 15|6|250|72|3158|19.5|75|1|ford maverick 16|8|400|170|4668|11.5|75|1|pontiac catalina 15|8|350|145|4440|14|75|1|chevrolet bel air 16|8|318|150|4498|14.5|75|1|plymouth grand fury 14|8|351|148|4657|13.5|75|1|ford ltd 17|6|231|110|3907|21|75|1|buick century 16|6|250|105|3897|18.5|75|1|chevroelt chevelle malibu 15|6|258|110|3730|19|75|1|amc matador 18|6|225|95|3785|19|75|1|plymouth fury 21|6|231|110|3039|15|75|1|buick skyhawk 20|8|262|110|3221|13.5|75|1|chevrolet monza 2+2 13|8|302|129|3169|12|75|1|ford mustang ii 29|4|97|75|2171|16|75|3|toyota corolla 23|4|140|83|2639|17|75|1|ford pinto 20|6|232|100|2914|16|75|1|amc gremlin 23|4|140|78|2592|18.5|75|1|pontiac astro 24|4|134|96|2702|13.5|75|3|toyota corona 25|4|90|71|2223|16.5|75|2|volkswagen dasher 24|4|119|97|2545|17|75|3|datsun 710 18|6|171|97|2984|14.5|75|1|ford pinto 29|4|90|70|1937|14|75|2|volkswagen rabbit 19|6|232|90|3211|17|75|1|amc pacer 23|4|115|95|2694|15|75|2|audi 100ls 23|4|120|88|2957|17|75|2|peugeot 504 22|4|121|98|2945|14.5|75|2|volvo 244dl 25|4|121|115|2671|13.5|75|2|saab 99le 33|4|91|53|1795|17.5|75|3|honda civic cvcc 28|4|107|86|2464|15.5|76|2|fiat 131 25|4|116|81|2220|16.9|76|2|opel 1900 25|4|140|92|2572|14.9|76|1|capri ii 26|4|98|79|2255|17.7|76|1|dodge colt 27|4|101|83|2202|15.3|76|2|renault 12tl 17.5|8|305|140|4215|13|76|1|chevrolet chevelle malibu classic 16|8|318|150|4190|13|76|1|dodge coronet brougham 15.5|8|304|120|3962|13.9|76|1|amc matador 14.5|8|351|152|4215|12.8|76|1|ford gran torino 22|6|225|100|3233|15.4|76|1|plymouth valiant 22|6|250|105|3353|14.5|76|1|chevrolet nova 24|6|200|81|3012|17.6|76|1|ford maverick 22.5|6|232|90|3085|17.6|76|1|amc hornet 29|4|85|52|2035|22.2|76|1|chevrolet chevette 24.5|4|98|60|2164|22.1|76|1|chevrolet woody 29|4|90|70|1937|14.2|76|2|vw rabbit 33|4|91|53|1795|17.4|76|3|honda civic 20|6|225|100|3651|17.7|76|1|dodge aspen se 18|6|250|78|3574|21|76|1|ford granada ghia 18.5|6|250|110|3645|16.2|76|1|pontiac ventura sj 17.5|6|258|95|3193|17.8|76|1|amc pacer d/l 29.5|4|97|71|1825|12.2|76|2|volkswagen rabbit 32|4|85|70|1990|17|76|3|datsun b-210 28|4|97|75|2155|16.4|76|3|toyota corolla 26.5|4|140|72|2565|13.6|76|1|ford pinto 20|4|130|102|3150|15.7|76|2|volvo 245 13|8|318|150|3940|13.2|76|1|plymouth volare premier v8 19|4|120|88|3270|21.9|76|2|peugeot 504 19|6|156|108|2930|15.5|76|3|toyota mark ii 16.5|6|168|120|3820|16.7|76|2|mercedes-benz 280s 16.5|8|350|180|4380|12.1|76|1|cadillac seville 13|8|350|145|4055|12|76|1|chevy c10 13|8|302|130|3870|15|76|1|ford f108 13|8|318|150|3755|14|76|1|dodge d100 31.5|4|98|68|2045|18.5|77|3|honda accord cvcc 30|4|111|80|2155|14.8|77|1|buick opel isuzu deluxe 36|4|79|58|1825|18.6|77|2|renault 5 gtl 25.5|4|122|96|2300|15.5|77|1|plymouth arrow gs 33.5|4|85|70|1945|16.8|77|3|datsun f-10 hatchback 17.5|8|305|145|3880|12.5|77|1|chevrolet caprice classic 17|8|260|110|4060|19|77|1|oldsmobile cutlass supreme 15.5|8|318|145|4140|13.7|77|1|dodge monaco brougham 15|8|302|130|4295|14.9|77|1|mercury cougar brougham 17.5|6|250|110|3520|16.4|77|1|chevrolet concours 20.5|6|231|105|3425|16.9|77|1|buick skylark 19|6|225|100|3630|17.7|77|1|plymouth volare custom 18.5|6|250|98|3525|19|77|1|ford granada 16|8|400|180|4220|11.1|77|1|pontiac grand prix lj 15.5|8|350|170|4165|11.4|77|1|chevrolet monte carlo landau 15.5|8|400|190|4325|12.2|77|1|chrysler cordoba 16|8|351|149|4335|14.5|77|1|ford thunderbird 29|4|97|78|1940|14.5|77|2|volkswagen rabbit custom 24.5|4|151|88|2740|16|77|1|pontiac sunbird coupe 26|4|97|75|2265|18.2|77|3|toyota corolla liftback 25.5|4|140|89|2755|15.8|77|1|ford mustang ii 2+2 30.5|4|98|63|2051|17|77|1|chevrolet chevette 33.5|4|98|83|2075|15.9|77|1|dodge colt m/m 30|4|97|67|1985|16.4|77|3|subaru dl 30.5|4|97|78|2190|14.1|77|2|volkswagen dasher 22|6|146|97|2815|14.5|77|3|datsun 810 21.5|4|121|110|2600|12.8|77|2|bmw 320i 21.5|3|80|110|2720|13.5|77|3|mazda rx-4 43.1|4|90|48|1985|21.5|78|2|volkswagen rabbit custom diesel 36.1|4|98|66|1800|14.4|78|1|ford fiesta 32.8|4|78|52|1985|19.4|78|3|mazda glc deluxe 39.4|4|85|70|2070|18.6|78|3|datsun b210 gx 36.1|4|91|60|1800|16.4|78|3|honda civic cvcc 19.9|8|260|110|3365|15.5|78|1|oldsmobile cutlass salon brougham 19.4|8|318|140|3735|13.2|78|1|dodge diplomat 20.2|8|302|139|3570|12.8|78|1|mercury monarch ghia 19.2|6|231|105|3535|19.2|78|1|pontiac phoenix lj 20.5|6|200|95|3155|18.2|78|1|chevrolet malibu 20.2|6|200|85|2965|15.8|78|1|ford fairmont (auto) 25.1|4|140|88|2720|15.4|78|1|ford fairmont (man) 20.5|6|225|100|3430|17.2|78|1|plymouth volare 19.4|6|232|90|3210|17.2|78|1|amc concord 20.6|6|231|105|3380|15.8|78|1|buick century special 20.8|6|200|85|3070|16.7|78|1|mercury zephyr 18.6|6|225|110|3620|18.7|78|1|dodge aspen 18.1|6|258|120|3410|15.1|78|1|amc concord d/l 19.2|8|305|145|3425|13.2|78|1|chevrolet monte carlo landau 17.7|6|231|165|3445|13.4|78|1|buick regal sport coupe (turbo) 18.1|8|302|139|3205|11.2|78|1|ford futura 17.5|8|318|140|4080|13.7|78|1|dodge magnum xe 30|4|98|68|2155|16.5|78|1|chevrolet chevette 27.5|4|134|95|2560|14.2|78|3|toyota corona 27.2|4|119|97|2300|14.7|78|3|datsun 510 30.9|4|105|75|2230|14.5|78|1|dodge omni 21.1|4|134|95|2515|14.8|78|3|toyota celica gt liftback 23.2|4|156|105|2745|16.7|78|1|plymouth sapporo 23.8|4|151|85|2855|17.6|78|1|oldsmobile starfire sx 23.9|4|119|97|2405|14.9|78|3|datsun 200-sx 20.3|5|131|103|2830|15.9|78|2|audi 5000 17|6|163|125|3140|13.6|78|2|volvo 264gl 21.6|4|121|115|2795|15.7|78|2|saab 99gle 16.2|6|163|133|3410|15.8|78|2|peugeot 604sl 31.5|4|89|71|1990|14.9|78|2|volkswagen scirocco 29.5|4|98|68|2135|16.6|78|3|honda accord lx 21.5|6|231|115|3245|15.4|79|1|pontiac lemans v6 19.8|6|200|85|2990|18.2|79|1|mercury zephyr 6 22.3|4|140|88|2890|17.3|79|1|ford fairmont 4 20.2|6|232|90|3265|18.2|79|1|amc concord dl 6 20.6|6|225|110|3360|16.6|79|1|dodge aspen 6 17|8|305|130|3840|15.4|79|1|chevrolet caprice classic 17.6|8|302|129|3725|13.4|79|1|ford ltd landau 16.5|8|351|138|3955|13.2|79|1|mercury grand marquis 18.2|8|318|135|3830|15.2|79|1|dodge st. regis 16.9|8|350|155|4360|14.9|79|1|buick estate wagon (sw) 15.5|8|351|142|4054|14.3|79|1|ford country squire (sw) 19.2|8|267|125|3605|15|79|1|chevrolet malibu classic (sw) 18.5|8|360|150|3940|13|79|1|chrysler lebaron town @ country (sw) 31.9|4|89|71|1925|14|79|2|vw rabbit custom 34.1|4|86|65|1975|15.2|79|3|maxda glc deluxe 35.7|4|98|80|1915|14.4|79|1|dodge colt hatchback custom 27.4|4|121|80|2670|15|79|1|amc spirit dl 25.4|5|183|77|3530|20.1|79|2|mercedes benz 300d 23|8|350|125|3900|17.4|79|1|cadillac eldorado 27.2|4|141|71|3190|24.8|79|2|peugeot 504 23.9|8|260|90|3420|22.2|79|1|oldsmobile cutlass salon brougham 34.2|4|105|70|2200|13.2|79|1|plymouth horizon 34.5|4|105|70|2150|14.9|79|1|plymouth horizon tc3 31.8|4|85|65|2020|19.2|79|3|datsun 210 37.3|4|91|69|2130|14.7|79|2|fiat strada custom 28.4|4|151|90|2670|16|79|1|buick skylark limited 28.8|6|173|115|2595|11.3|79|1|chevrolet citation 26.8|6|173|115|2700|12.9|79|1|oldsmobile omega brougham 33.5|4|151|90|2556|13.2|79|1|pontiac phoenix 41.5|4|98|76|2144|14.7|80|2|vw rabbit 38.1|4|89|60|1968|18.8|80|3|toyota corolla tercel 32.1|4|98|70|2120|15.5|80|1|chevrolet chevette 37.2|4|86|65|2019|16.4|80|3|datsun 310 28|4|151|90|2678|16.5|80|1|chevrolet citation 26.4|4|140|88|2870|18.1|80|1|ford fairmont 24.3|4|151|90|3003|20.1|80|1|amc concord 19.1|6|225|90|3381|18.7|80|1|dodge aspen 34.3|4|97|78|2188|15.8|80|2|audi 4000 29.8|4|134|90|2711|15.5|80|3|toyota corona liftback 31.3|4|120|75|2542|17.5|80|3|mazda 626 37|4|119|92|2434|15|80|3|datsun 510 hatchback 32.2|4|108|75|2265|15.2|80|3|toyota corolla 46.6|4|86|65|2110|17.9|80|3|mazda glc 27.9|4|156|105|2800|14.4|80|1|dodge colt 40.8|4|85|65|2110|19.2|80|3|datsun 210 44.3|4|90|48|2085|21.7|80|2|vw rabbit c (diesel) 43.4|4|90|48|2335|23.7|80|2|vw dasher (diesel) 36.4|5|121|67|2950|19.9|80|2|audi 5000s (diesel) 30|4|146|67|3250|21.8|80|2|mercedes-benz 240d 44.6|4|91|67|1850|13.8|80|3|honda civic 1500 gl 33.8|4|97|67|2145|18|80|3|subaru dl 29.8|4|89|62|1845|15.3|80|2|vokswagen rabbit 32.7|6|168|132|2910|11.4|80|3|datsun 280-zx 23.7|3|70|100|2420|12.5|80|3|mazda rx-7 gs 35|4|122|88|2500|15.1|80|2|triumph tr7 coupe 32.4|4|107|72|2290|17|80|3|honda accord 27.2|4|135|84|2490|15.7|81|1|plymouth reliant 26.6|4|151|84|2635|16.4|81|1|buick skylark 25.8|4|156|92|2620|14.4|81|1|dodge aries wagon (sw) 23.5|6|173|110|2725|12.6|81|1|chevrolet citation 30|4|135|84|2385|12.9|81|1|plymouth reliant 39.1|4|79|58|1755|16.9|81|3|toyota starlet 39|4|86|64|1875|16.4|81|1|plymouth champ 35.1|4|81|60|1760|16.1|81|3|honda civic 1300 32.3|4|97|67|2065|17.8|81|3|subaru 37|4|85|65|1975|19.4|81|3|datsun 210 mpg 37.7|4|89|62|2050|17.3|81|3|toyota tercel 34.1|4|91|68|1985|16|81|3|mazda glc 4 34.7|4|105|63|2215|14.9|81|1|plymouth horizon 4 34.4|4|98|65|2045|16.2|81|1|ford escort 4w 29.9|4|98|65|2380|20.7|81|1|ford escort 2h 33|4|105|74|2190|14.2|81|2|volkswagen jetta 33.7|4|107|75|2210|14.4|81|3|honda prelude 32.4|4|108|75|2350|16.8|81|3|toyota corolla 32.9|4|119|100|2615|14.8|81|3|datsun 200sx 31.6|4|120|74|2635|18.3|81|3|mazda 626 28.1|4|141|80|3230|20.4|81|2|peugeot 505s turbo diesel 30.7|6|145|76|3160|19.6|81|2|volvo diesel 25.4|6|168|116|2900|12.6|81|3|toyota cressida 24.2|6|146|120|2930|13.8|81|3|datsun 810 maxima 22.4|6|231|110|3415|15.8|81|1|buick century 26.6|8|350|105|3725|19|81|1|oldsmobile cutlass ls 20.2|6|200|88|3060|17.1|81|1|ford granada gl 17.6|6|225|85|3465|16.6|81|1|chrysler lebaron salon 28|4|112|88|2605|19.6|82|1|chevrolet cavalier 27|4|112|88|2640|18.6|82|1|chevrolet cavalier wagon 34|4|112|88|2395|18|82|1|chevrolet cavalier 2-door 31|4|112|85|2575|16.2|82|1|pontiac j2000 se hatchback 29|4|135|84|2525|16|82|1|dodge aries se 27|4|151|90|2735|18|82|1|pontiac phoenix 24|4|140|92|2865|16.4|82|1|ford fairmont futura 36|4|105|74|1980|15.3|82|2|volkswagen rabbit l 37|4|91|68|2025|18.2|82|3|mazda glc custom l 31|4|91|68|1970|17.6|82|3|mazda glc custom 38|4|105|63|2125|14.7|82|1|plymouth horizon miser 36|4|98|70|2125|17.3|82|1|mercury lynx l 36|4|120|88|2160|14.5|82|3|nissan stanza xe 36|4|107|75|2205|14.5|82|3|honda accord 34|4|108|70|2245|16.9|82|3|toyota corolla 38|4|91|67|1965|15|82|3|honda civic 32|4|91|67|1965|15.7|82|3|honda civic (auto) 38|4|91|67|1995|16.2|82|3|datsun 310 gx 25|6|181|110|2945|16.4|82|1|buick century limited 38|6|262|85|3015|17|82|1|oldsmobile cutlass ciera (diesel) 26|4|156|92|2585|14.5|82|1|chrysler lebaron medallion 22|6|232|112|2835|14.7|82|1|ford granada l 32|4|144|96|2665|13.9|82|3|toyota celica gt 36|4|135|84|2370|13|82|1|dodge charger 2.2 27|4|151|90|2950|17.3|82|1|chevrolet camaro 27|4|140|86|2790|15.6|82|1|ford mustang gl 44|4|97|52|2130|24.6|82|2|vw pickup 32|4|135|84|2295|11.6|82|1|dodge rampage 28|4|120|79|2625|18.6|82|1|ford ranger 31|4|119|82|2720|19.4|82|1|chevy s-10 ================================================ FILE: data/chipotle_orders.tsv ================================================ order_id quantity item_name choice_description item_price 1 1 Chips and Fresh Tomato Salsa NULL $2.39 1 1 Izze [Clementine] $3.39 1 1 Nantucket Nectar [Apple] $3.39 1 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 2 2 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream]] $16.98 3 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 3 1 Side of Chips NULL $1.69 4 1 Steak Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 4 1 Steak Soft Tacos [Tomatillo Green Chili Salsa, [Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 5 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 5 1 Chips and Guacamole NULL $4.45 6 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 6 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 7 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole]] $11.25 7 1 Chips and Guacamole NULL $4.45 8 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 8 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Cheese, Sour Cream]] $8.49 9 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 9 2 Canned Soda [Sprite] $2.18 10 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 10 1 Chips and Guacamole NULL $4.45 11 1 Barbacoa Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream, Lettuce]] $8.99 11 1 Nantucket Nectar [Pomegranate Cherry] $3.39 12 1 Chicken Burrito [[Tomatillo-Green Chili Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 12 1 Izze [Grapefruit] $3.39 13 1 Chips and Fresh Tomato Salsa NULL $2.39 13 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 14 1 Carnitas Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Sour Cream, Lettuce]] $8.99 14 1 Canned Soda [Dr. Pepper] $1.09 15 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 15 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 16 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Sour Cream]] $8.99 16 1 Side of Chips NULL $1.69 17 1 Carnitas Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 17 1 Bottled Water NULL $1.09 18 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, Rice] $8.75 18 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Cheese, Lettuce]] $8.75 18 1 Chips and Guacamole NULL $4.45 18 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 19 1 Barbacoa Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 19 1 Chips NULL $2.15 20 1 Chips and Guacamole NULL $4.45 20 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 20 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 20 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Lettuce]] $8.75 21 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 21 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Fajita Veggies, Cheese]] $8.99 21 1 Izze [Blackberry] $3.39 22 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Sour Cream]] $8.99 22 1 Chips and Guacamole NULL $3.99 23 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 23 2 Canned Soda [Mountain Dew] $2.18 24 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 24 1 Canned Soda [Sprite] $1.09 25 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.99 25 1 Chips and Fresh Tomato Salsa NULL $2.39 26 1 Barbacoa Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Lettuce]] $9.25 26 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 27 1 Barbacoa Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 27 1 Chips NULL $2.15 28 1 Chips and Guacamole NULL $4.45 28 1 Steak Soft Tacos [Fresh Tomato Salsa, Cheese] $9.25 28 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 28 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 29 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 29 1 Steak Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 30 1 Izze [Blackberry] $3.39 30 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese]] $8.99 30 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 31 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream]] $8.99 31 1 Side of Chips NULL $1.69 32 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 32 1 Chips and Guacamole NULL $3.99 33 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 33 1 Chips and Guacamole NULL $4.45 34 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 34 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Lettuce]] $8.75 34 1 Chips NULL $2.15 34 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 35 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce, Guacamole]] $11.25 35 1 Chips NULL $2.15 36 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 36 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 37 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $8.75 37 1 Steak Soft Tacos [Tomatillo Red Chili Salsa] $9.25 38 1 Veggie Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 38 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 38 1 Bottled Water NULL $1.09 39 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese]] $9.25 39 1 Chips and Fresh Tomato Salsa NULL $2.95 40 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 40 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream, Guacamole]] $11.75 40 1 Steak Crispy Tacos [Fresh Tomato Salsa, Sour Cream] $9.25 41 1 Carnitas Burrito [Roasted Chili Corn Salsa, [Sour Cream, Guacamole]] $11.75 41 1 Chips and Guacamole NULL $4.45 42 1 Barbacoa Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream]] $9.25 42 1 Chips and Guacamole NULL $4.45 43 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 43 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 44 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Sour Cream, Lettuce]] $8.75 44 1 Chips and Guacamole NULL $4.45 45 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 45 1 Steak Bowl [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 45 1 Chips and Guacamole NULL $3.99 46 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Sour Cream, Lettuce]] $8.49 46 1 Nantucket Nectar [Pineapple Orange Banana] $3.39 47 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Fajita Veggies, Sour Cream]] $8.99 47 1 Canned Soda [Dr. Pepper] $1.09 48 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 48 1 Chips and Guacamole NULL $4.45 49 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 49 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 49 1 Chips and Guacamole NULL $4.45 50 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 50 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 51 1 Barbacoa Bowl [[Tomatillo-Red Chili Salsa (Hot), Tomatillo-Green Chili Salsa (Medium)], [Rice, Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 51 1 Chicken Burrito [[Tomatillo-Red Chili Salsa (Hot), Tomatillo-Green Chili Salsa (Medium)], [Rice, Pinto Beans, Cheese, Lettuce]] $8.49 51 1 Canned Soda [Diet Dr. Pepper] $1.09 52 1 Steak Soft Tacos [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Cheese, Sour Cream, Lettuce]] $8.99 52 1 Chips and Guacamole NULL $3.99 53 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 53 1 Barbacoa Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $9.25 53 1 Chips and Guacamole NULL $4.45 54 1 Chicken Bowl [Fresh Tomato Salsa, [Guacamole, Cheese, Sour Cream, Fajita Vegetables, Rice]] $11.25 54 1 Chips and Guacamole NULL $4.45 55 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Fajita Veggies, Sour Cream]] $8.99 55 1 Canned Soda [Coca Cola] $1.09 56 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 56 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 56 1 Chips and Guacamole NULL $4.45 57 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.75 57 1 Chips and Guacamole NULL $4.45 58 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 58 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 59 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream, Guacamole]] $10.98 60 2 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Sour Cream, Cheese, Guacamole]] $22.50 61 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 61 1 Chips and Guacamole NULL $4.45 62 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Guacamole]] $11.25 62 1 Chips and Guacamole NULL $4.45 63 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 63 1 Chips and Guacamole NULL $4.45 64 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 64 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 65 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.75 65 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese]] $9.25 65 1 Chips and Guacamole NULL $4.45 66 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Black Beans, Sour Cream, Guacamole]] $11.48 67 2 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $17.98 67 1 Side of Chips NULL $1.69 68 2 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $17.50 68 1 Chips and Guacamole NULL $4.45 69 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 69 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 70 2 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $17.50 70 1 Chips and Guacamole NULL $4.45 71 1 Chips and Guacamole NULL $4.45 71 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 71 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.75 72 1 Chicken Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 73 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 73 1 Chicken Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Guacamole, Lettuce]] $10.98 73 2 Canned Soda [Diet Coke] $2.18 74 1 Carnitas Bowl [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 74 1 Veggie Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 74 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 75 1 Chips and Guacamole NULL $4.45 75 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 75 1 Barbacoa Crispy Tacos [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 75 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream]] $8.75 76 1 Chicken Burrito [[Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Cheese, Lettuce]] $8.49 76 2 Canned Soda [Diet Dr. Pepper] $2.18 77 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 77 1 Nantucket Nectar [Apple] $3.39 78 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), Lettuce] $8.49 78 1 Steak Soft Tacos [Roasted Chili Corn Salsa (Medium), Lettuce] $8.99 79 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 80 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Rice, Black Beans, Sour Cream, Guacamole]] $11.48 81 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 81 1 Canned Soda [Coca Cola] $1.09 81 1 Canned Soda [Dr. Pepper] $1.09 82 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 82 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream]] $8.75 82 1 Chips and Fresh Tomato Salsa NULL $2.95 83 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream]] $9.25 83 1 Chips and Guacamole NULL $4.45 83 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 84 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Guacamole, Lettuce]] $11.48 84 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 84 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Lettuce]] $8.49 85 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Fajita Veggies, Cheese, Lettuce, Sour Cream, Rice]] $8.99 85 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 86 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 86 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 86 1 Chips and Fresh Tomato Salsa NULL $2.95 87 1 Canned Soda [Coca Cola] $1.09 87 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream]] $8.99 88 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 88 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 89 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Guacamole, Lettuce]] $10.98 89 1 Canned Soda [Diet Coke] $1.09 89 1 Chips and Guacamole NULL $3.99 90 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 90 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $8.75 91 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 91 1 Nantucket Nectar [Peach Orange] $3.39 92 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 92 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 92 1 Chips NULL $2.15 93 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 93 1 Veggie Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 93 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 93 1 Chips and Guacamole NULL $4.45 94 2 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Guacamole]] $22.50 95 1 Chips and Guacamole NULL $4.45 95 1 Steak Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 96 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 96 2 Chicken Crispy Tacos [Tomatillo Red Chili Salsa, [Cheese, Sour Cream, Lettuce]] $17.50 96 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 97 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $9.25 97 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 97 1 Chips NULL $2.15 98 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 98 2 Chips NULL $4.30 98 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 99 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Guacamole]] $11.25 99 1 Chips NULL $2.15 100 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream]] $8.99 100 1 Canned Soda [Mountain Dew] $1.09 101 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 101 1 Chips and Guacamole NULL $3.99 102 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 102 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 102 1 Chips and Roasted Chili Corn Salsa NULL $2.95 103 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 103 2 Chips and Tomatillo Green Chili Salsa NULL $5.90 103 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $9.25 103 1 Carnitas Soft Tacos [Tomatillo Green Chili Salsa, [Fajita Vegetables, Pinto Beans, Cheese]] $9.25 103 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 104 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese]] $9.25 104 1 Chips and Fresh Tomato Salsa NULL $2.95 105 2 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $17.50 106 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $8.75 106 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream]] $9.25 107 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 108 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 108 1 Canned Soda [Mountain Dew] $1.09 108 1 Canned Soda [Dr. Pepper] $1.09 108 1 Canned Soda [Mountain Dew] $1.09 108 1 Barbacoa Burrito [[Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Pinto Beans, Cheese]] $8.99 109 1 Chicken Salad [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 109 1 Canned Soda [Diet Dr. Pepper] $1.09 110 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $8.75 110 1 Veggie Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $8.75 110 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 110 1 Barbacoa Crispy Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce]] $9.25 111 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 111 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 112 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.99 112 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.99 113 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream]] $8.99 113 1 Canned Soda [Mountain Dew] $1.09 114 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 114 1 Canned Soft Drink [Coke] $1.25 115 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice]] $8.99 115 1 Chips and Fresh Tomato Salsa NULL $2.39 116 1 Steak Soft Tacos [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $9.25 116 1 Chips and Fresh Tomato Salsa NULL $2.95 117 1 Barbacoa Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.99 117 1 Chips and Guacamole NULL $3.99 118 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 118 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 119 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 119 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 120 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese]] $8.49 120 1 Side of Chips NULL $1.69 121 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream]] $8.49 121 1 Chips and Guacamole NULL $3.99 122 1 Steak Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Lettuce]] $8.99 122 1 Side of Chips NULL $1.69 122 1 Canned Soda [Coca Cola] $1.09 123 2 Steak Salad Bowl [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Guacamole]] $23.78 124 2 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $17.50 124 1 Chips NULL $2.15 124 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 125 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Lettuce]] $9.25 125 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 125 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 125 1 Chips and Guacamole NULL $4.45 126 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 126 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.25 127 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.25 127 1 Canned Soft Drink [Sprite] $1.25 128 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream]] $9.25 128 1 Chips and Guacamole NULL $4.45 128 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce, Guacamole, Sour Cream, Cheese, Black Beans, Rice]] $11.25 129 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Cheese, Guacamole]] $11.75 129 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Cheese, Guacamole]] $11.75 129 1 6 Pack Soft Drink [Sprite] $6.49 130 1 Steak Soft Tacos [Tomatillo-Red Chili Salsa (Hot), Lettuce] $8.99 130 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 131 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 131 1 Chips and Fresh Tomato Salsa NULL $2.39 132 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 132 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 132 1 Chips NULL $2.15 133 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream]] $8.99 133 1 Side of Chips NULL $1.69 134 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 134 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 134 1 Chips NULL $2.15 135 1 Chips and Guacamole NULL $4.45 135 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 136 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), Fajita Veggies] $8.49 136 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Cheese, Lettuce]] $8.99 137 2 Chicken Salad Bowl [Fresh Tomato Salsa, Fajita Vegetables] $17.50 138 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese]] $8.49 138 1 Canned Soda [Diet Coke] $1.09 138 1 Bottled Water NULL $1.09 139 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream]] $8.75 139 1 Canned Soft Drink [Coke] $1.25 139 1 Chips NULL $2.15 140 1 Steak Burrito [Fresh Tomato (Mild), [Lettuce, Guacamole, Rice, Cheese]] $11.08 141 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 141 1 Chicken Soft Tacos [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 142 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), Rice] $8.99 142 1 Chips and Fresh Tomato Salsa NULL $2.39 143 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese]] $8.75 143 1 Chips NULL $2.15 143 1 Bottled Water NULL $1.50 144 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 144 1 Chips NULL $2.15 145 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 145 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream]] $8.49 146 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 146 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $9.25 147 1 Steak Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Cheese, Guacamole, Lettuce]] $11.75 147 1 Canned Soft Drink [Coke] $1.25 148 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 148 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $8.75 148 1 Chips and Guacamole NULL $4.45 148 1 6 Pack Soft Drink [Diet Coke] $6.49 149 1 Steak Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 149 1 Chips and Fresh Tomato Salsa NULL $2.95 149 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Guacamole, Lettuce]] $11.25 149 1 Chips and Guacamole NULL $4.45 149 1 Canned Soft Drink [Lemonade] $1.25 149 1 Canned Soft Drink [Sprite] $1.25 150 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 150 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 150 2 Canned Soda [Diet Coke] $2.18 151 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese]] $8.49 151 2 Canned Soda [Coca Cola] $2.18 152 2 Steak Burrito [Fresh Tomato (Mild), [Lettuce, Guacamole, Rice, Cheese]] $22.16 153 2 Chicken Soft Tacos [Fresh Tomato Salsa, Sour Cream] $17.50 154 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 154 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 154 1 6 Pack Soft Drink [Coke] $6.49 155 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Cheese, Sour Cream]] $8.99 155 1 Izze [Blackberry] $3.39 155 1 Izze [Grapefruit] $3.39 156 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Cheese, Sour Cream, Lettuce]] $8.99 156 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.49 157 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 157 1 Chips NULL $2.15 158 1 Chicken Crispy Tacos [Tomatillo Green Chili Salsa, [Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 158 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 159 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 159 1 Canned Soft Drink [Diet Coke] $1.25 160 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Cheese, Sour Cream, Lettuce]] $8.99 160 1 Canned Soda [Diet Coke] $1.09 161 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 161 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 162 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese]] $9.25 162 1 Chips and Fresh Tomato Salsa NULL $2.95 163 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.75 163 1 Bottled Water NULL $1.50 164 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream]] $8.99 164 1 Canned Soda [Mountain Dew] $1.09 165 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 165 1 Canned Soft Drink [Coke] $1.25 165 1 Canned Soft Drink [Coke] $1.25 166 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 166 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 166 1 Chips NULL $2.15 167 1 Barbacoa Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 167 1 Side of Chips NULL $1.69 168 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 168 1 6 Pack Soft Drink [Diet Coke] $6.49 169 1 Chicken Burrito [Fresh Tomato Salsa, [Cheese, Sour Cream]] $8.75 169 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream]] $9.25 170 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 170 1 Chips and Guacamole NULL $4.45 170 1 Canned Soft Drink [Coke] $1.25 171 1 Veggie Burrito [Tomatillo Green Chili Salsa, Guacamole] $11.25 171 1 Chips and Guacamole NULL $4.45 172 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 172 1 Barbacoa Burrito [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 173 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese]] $8.49 173 1 Carnitas Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Cheese, Lettuce]] $8.99 174 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 174 1 Canned Soft Drink [Sprite] $1.25 175 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 175 1 Canned Soft Drink [Coke] $1.25 176 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.75 176 1 Carnitas Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 176 1 Chips and Guacamole NULL $4.45 177 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 177 1 Chips NULL $2.15 178 3 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Guacamole, Lettuce]] $32.94 179 1 Canned Soft Drink [Coke] $1.25 179 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Sour Cream, Cheese, Guacamole]] $11.25 180 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 180 1 Side of Chips NULL $1.69 180 1 Canned Soda [Dr. Pepper] $1.09 181 2 Chicken Bowl [Tomatillo Red Chili Salsa] $17.50 182 1 Chips and Guacamole NULL $4.45 182 1 6 Pack Soft Drink [Diet Coke] $6.49 182 1 Barbacoa Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $9.25 183 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 183 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 184 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 184 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Lettuce]] $8.75 184 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 184 1 Chips NULL $2.15 184 1 Chips and Fresh Tomato Salsa NULL $2.95 185 2 Chicken Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Lettuce]] $17.50 186 1 Chicken Soft Tacos [[Tomatillo-Red Chili Salsa (Hot), Roasted Chili Corn Salsa (Medium)], [Guacamole, Cheese, Sour Cream]] $10.98 186 1 Steak Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream, Guacamole]] $11.48 186 1 Barbacoa Crispy Tacos [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Cheese, Sour Cream, Guacamole]] $11.48 186 1 Izze [Grapefruit] $3.39 187 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 187 1 Side of Chips NULL $1.69 188 1 Carnitas Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 188 1 Canned Soda [Coca Cola] $1.09 189 1 Veggie Burrito [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Pinto Beans, Fajita Veggies, Guacamole, Lettuce]] $10.98 189 1 Nantucket Nectar [Pomegranate Cherry] $3.39 190 1 Steak Crispy Tacos [[Roasted Chili Corn Salsa (Medium), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Cheese, Sour Cream]] $8.99 190 1 Canned Soda [Dr. Pepper] $1.09 191 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 191 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 192 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 192 1 Chips and Guacamole NULL $4.45 192 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 192 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese]] $9.25 193 3 Bowl [Braised Carnitas, Pinto Beans, [Sour Cream, Cheese, Cilantro-Lime Rice]] $22.20 194 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese]] $8.49 194 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 195 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 195 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 195 1 Barbacoa Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 195 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream]] $8.75 195 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 195 1 Carnitas Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Lettuce]] $9.25 195 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 195 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 196 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 196 1 Chicken Burrito [Fresh Tomato Salsa, [Black Beans, Cheese, Guacamole, Lettuce]] $11.25 196 1 Chips and Fresh Tomato Salsa NULL $2.95 196 1 Canned Soft Drink [Coke] $1.25 197 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 197 1 Side of Chips NULL $1.69 198 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 198 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 199 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 199 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 199 1 Chips and Guacamole NULL $4.45 200 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 200 1 Chips and Guacamole NULL $3.99 201 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Sour Cream, Cheese]] $8.49 201 1 Chips and Guacamole NULL $3.99 202 1 Barbacoa Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Guacamole]] $11.48 202 1 Side of Chips NULL $1.69 202 1 Canned Soda [Diet Dr. Pepper] $1.09 203 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce, Guacamole]] $11.25 203 1 Bottled Water NULL $1.50 204 1 Chicken Burrito [Fresh Tomato (Mild), [Guacamole, Lettuce, Rice, Fajita Veggies, Sour Cream, Cheese]] $10.58 204 1 Side of Chips NULL $1.69 205 1 Carnitas Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 205 1 Veggie Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 205 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $8.75 205 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 205 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 205 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 205 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $8.75 205 1 Barbacoa Crispy Tacos [Fresh Tomato Salsa, Guacamole] $11.75 205 1 Chicken Burrito [Fresh Tomato Salsa, Cheese] $8.75 205 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 205 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 205 1 Chips NULL $2.15 206 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole]] $11.25 206 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole]] $11.75 206 2 Canned Soft Drink [Diet Coke] $2.50 206 1 Chips and Guacamole NULL $4.45 207 1 Steak Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Cheese]] $9.25 207 1 Chips and Guacamole NULL $4.45 207 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Rice, Lettuce, Guacamole, Fajita Vegetables, Cheese, Sour Cream, Black Beans]] $11.25 208 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Cheese]] $8.49 208 1 Chips and Guacamole NULL $3.99 209 1 Steak Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Lettuce]] $9.25 209 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $9.25 210 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 210 1 Barbacoa Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 211 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Lettuce, Guacamole]] $11.75 211 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce, Guacamole]] $11.25 212 1 Canned Soft Drink [Lemonade] $1.25 212 2 Carnitas Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $23.50 212 1 Canned Soft Drink [Coke] $1.25 213 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), Rice] $8.99 213 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), Rice] $8.49 214 1 Burrito [Adobo-Marinated and Grilled Chicken, Pinto Beans, [Sour Cream, Salsa, Cheese, Cilantro-Lime Rice, Guacamole]] $7.40 214 1 Burrito [Braised Barbacoa, Vegetarian Black Beans, [Sour Cream, Salsa, Cheese, Cilantro-Lime Rice]] $7.40 215 1 Veggie Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $8.75 215 1 Canned Soft Drink [Diet Coke] $1.25 215 1 Chips and Guacamole NULL $4.45 215 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 215 1 Chips NULL $2.15 216 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 216 1 Chips and Guacamole NULL $4.45 217 1 Burrito [Braised Barbacoa, Pinto Beans, [Sour Cream, Salsa, Cheese, Cilantro-Lime Rice, Guacamole]] $7.40 217 1 Crispy Tacos [Adobo-Marinated and Grilled Steak] $7.40 217 1 Crispy Tacos [Adobo-Marinated and Grilled Chicken] $7.40 218 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Sour Cream]] $8.99 218 1 Chips and Guacamole NULL $3.99 219 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 219 1 Side of Chips NULL $1.69 220 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 220 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $8.75 221 1 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Black Beans, Pinto Beans, Lettuce]] $8.75 221 1 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 222 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 222 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 222 1 Chips and Fresh Tomato Salsa NULL $2.95 223 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 223 2 Steak Burrito [Fresh Tomato Salsa, [Sour Cream, Lettuce, Cheese, Rice]] $18.50 224 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 224 1 Chips and Guacamole NULL $3.99 225 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 225 1 Carnitas Bowl [Tomatillo Green Chili Salsa, [Pinto Beans, Black Beans]] $9.25 225 1 Steak Burrito [Tomatillo Green Chili Salsa, Cheese] $9.25 225 2 Bottled Water NULL $3.00 225 1 Canned Soft Drink [Diet Coke] $1.25 226 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 226 1 Barbacoa Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Pinto Beans, Sour Cream, Cheese, Lettuce]] $9.25 226 1 Chips and Guacamole NULL $4.45 227 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 227 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Lettuce, Guacamole]] $11.25 227 1 Chips and Guacamole NULL $4.45 228 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Fajita Veggies, Cheese, Guacamole, Lettuce]] $10.98 229 2 Steak Burrito [Fresh Tomato (Mild), [Lettuce, Guacamole, Rice, Cheese]] $22.16 230 1 Chips and Guacamole NULL $4.45 230 1 Chicken Burrito [Tomatillo Red Chili Salsa] $8.75 230 1 Steak Burrito [Tomatillo Green Chili Salsa] $9.25 230 1 6 Pack Soft Drink [Diet Coke] $6.49 230 1 Carnitas Crispy Tacos [Roasted Chili Corn Salsa] $9.25 231 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 231 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 231 1 Barbacoa Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 231 1 Chips NULL $2.15 231 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 231 1 Chips and Guacamole NULL $4.45 232 1 Barbacoa Burrito [Roasted Chili Corn Salsa] $9.25 232 1 Chips and Roasted Chili Corn Salsa NULL $2.95 233 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $9.25 233 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 233 1 Carnitas Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese]] $9.25 233 1 Canned Soft Drink [Diet Coke] $1.25 233 1 Canned Soft Drink [Diet Coke] $1.25 234 1 Chicken Salad Bowl [Fresh Tomato Salsa, Fajita Vegetables] $8.75 234 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Black Beans, Sour Cream, Cheese]] $8.75 234 1 Steak Soft Tacos [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese]] $9.25 234 1 Chips and Guacamole NULL $4.45 235 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Lettuce, Guacamole]] $11.75 235 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 235 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 236 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 236 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $9.25 237 2 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $16.98 237 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 237 1 Izze [Clementine] $3.39 237 1 Izze [Grapefruit] $3.39 238 1 Steak Crispy Tacos [Tomatillo Green Chili Salsa, [Fajita Vegetables, Cheese, Lettuce]] $9.25 238 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 239 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 239 1 Chips NULL $2.15 240 1 Chicken Salad Bowl [Fresh Tomato Salsa, Fajita Vegetables] $8.75 240 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $9.25 240 1 Chips and Guacamole NULL $4.45 241 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Black Beans, Fajita Veggies, Cheese, Guacamole, Lettuce]] $10.98 242 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 242 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa] $9.25 243 2 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $22.50 244 1 Barbacoa Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce]] $9.25 244 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 244 1 Bottled Water NULL $1.50 245 1 Steak Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream, Guacamole]] $11.48 246 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 246 1 Side of Chips NULL $1.69 247 1 Chicken Crispy Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 247 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 247 1 Chicken Crispy Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.49 247 2 Nantucket Nectar [Pineapple Orange Banana] $6.78 248 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Lettuce, Guacamole]] $11.75 248 1 Chips and Guacamole NULL $4.45 249 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $8.75 249 1 Chips and Guacamole NULL $4.45 250 1 Steak Salad Bowl [Fresh Tomato Salsa, [Pinto Beans, Cheese, Guacamole, Lettuce]] $11.89 250 1 Steak Salad Bowl [Fresh Tomato Salsa, Lettuce] $9.39 251 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 251 1 Chips and Fresh Tomato Salsa NULL $2.95 251 1 Canned Soft Drink [Nestea] $1.25 252 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 252 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream]] $8.75 253 2 Steak Salad Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $23.78 254 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $8.75 254 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 254 1 Chips NULL $2.15 254 1 Chips NULL $2.15 254 1 Canned Soft Drink [Diet Coke] $1.25 255 1 Veggie Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 255 1 Chips and Guacamole NULL $3.99 256 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 256 1 Canned Soda [Dr. Pepper] $1.09 257 1 Veggie Burrito [Tomatillo Green Chili Salsa] $8.75 257 1 Chips and Guacamole NULL $4.45 258 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce]] $8.75 258 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Sour Cream, Cheese, Guacamole]] $11.75 258 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 258 1 Chips and Guacamole NULL $4.45 259 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 259 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 260 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 260 1 Chips and Guacamole NULL $3.99 261 2 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Black Beans, Cheese, Guacamole]] $22.50 262 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Cheese, Guacamole]] $11.25 262 1 Canned Soft Drink [Coke] $1.25 263 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $8.75 263 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 264 2 Steak Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $18.50 264 1 6 Pack Soft Drink [Diet Coke] $6.49 265 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 265 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Black Beans, Cheese, Sour Cream]] $8.75 265 1 Chips and Guacamole NULL $4.45 266 1 Veggie Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 266 1 Chips and Guacamole NULL $3.99 267 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream]] $8.99 267 1 Canned Soda [Sprite] $1.09 268 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 268 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 269 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream, Guacamole]] $11.48 269 1 Side of Chips NULL $1.69 270 2 Canned Soft Drink [Coke] $2.50 270 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese]] $9.25 270 1 Bottled Water NULL $1.50 271 2 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $17.50 271 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 272 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Lettuce]] $8.75 272 1 Chips and Guacamole NULL $4.45 273 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 273 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 274 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 274 1 Side of Chips NULL $1.69 275 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 275 1 Chips and Guacamole NULL $3.99 276 1 Steak Salad [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 276 1 Steak Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 276 1 Chips and Guacamole NULL $3.99 276 1 Carnitas Soft Tacos [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 277 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 277 1 Chips and Guacamole NULL $4.45 277 1 Canned Soft Drink [Lemonade] $1.25 278 1 Chips and Guacamole NULL $3.99 278 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 279 1 Bowl [Adobo-Marinated and Grilled Steak, [Sour Cream, Salsa, Cheese, Cilantro-Lime Rice, Guacamole]] $7.40 279 1 Chips and Mild Fresh Tomato Salsa NULL $3.00 279 1 Burrito [Adobo-Marinated and Grilled Chicken, [Sour Cream, Cheese, Cilantro-Lime Rice]] $7.40 279 1 Chips and Guacamole NULL $4.00 280 1 Veggie Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 280 1 Chips and Guacamole NULL $3.99 281 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $9.25 281 1 Chips and Guacamole NULL $4.45 282 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Guacamole, Cheese]] $11.25 282 1 Canned Soft Drink [Coke] $1.25 282 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Guacamole]] $11.75 282 1 Canned Soft Drink [Coke] $1.25 283 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 283 1 Chips NULL $2.15 284 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole]] $11.25 284 1 Chips and Guacamole NULL $4.45 284 3 Canned Soft Drink [Diet Coke] $3.75 284 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 285 1 Carnitas Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 285 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 286 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Sour Cream, Cheese, Lettuce]] $8.75 286 1 Chips and Guacamole NULL $4.45 286 1 Canned Soft Drink [Diet Coke] $1.25 287 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 287 1 Chicken Soft Tacos [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.49 287 2 Canned Soda [Coca Cola] $2.18 288 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 288 2 Canned Soda [Coca Cola] $2.18 288 1 Bottled Water NULL $1.09 289 1 Chicken Salad Bowl [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Lettuce]] $8.75 289 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 289 1 Canned Soft Drink [Lemonade] $1.25 290 1 Bottled Water NULL $1.50 290 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 291 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 291 1 Chips and Roasted Chili Corn Salsa NULL $2.95 291 1 Canned Soft Drink [Lemonade] $1.25 292 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 292 1 Chips and Guacamole NULL $3.99 293 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 294 1 Chips and Guacamole NULL $3.99 294 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 295 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 295 1 Canned Soft Drink [Diet Coke] $1.25 296 1 Barbacoa Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 296 1 Side of Chips NULL $1.69 297 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 297 1 Canned Soft Drink [Coke] $1.25 298 1 6 Pack Soft Drink [Nestea] $6.49 298 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 299 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 299 1 Chicken Burrito [Fresh Tomato Salsa] $8.75 299 1 Chips and Fresh Tomato Salsa NULL $2.95 300 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $8.75 300 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.75 301 1 Steak Burrito [Roasted Chili Corn (Medium), [Sour Cream, Cheese]] $8.69 301 1 Steak Crispy Tacos [Fresh Tomato (Mild), [Lettuce, Cheese]] $8.69 301 1 Chips and Fresh Tomato Salsa NULL $2.29 302 1 Chips and Guacamole NULL $3.99 302 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 303 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $8.75 303 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 304 1 Canned Soft Drink [Sprite] $1.25 304 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.75 304 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 304 1 Veggie Soft Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 305 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Guacamole, Cheese]] $11.25 305 1 Canned Soft Drink [Coke] $1.25 305 1 Steak Burrito [Roasted Chili Corn Salsa] $9.25 306 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole]] $11.75 306 1 6 Pack Soft Drink [Coke] $6.49 307 1 Chips and Guacamole NULL $3.99 307 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Cheese, Rice, Pinto Beans, Sour Cream]] $8.49 308 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 308 1 Chips NULL $2.15 309 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 309 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce, Guacamole]] $11.89 310 1 Barbacoa Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 310 1 Side of Chips NULL $1.69 311 1 Steak Salad [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 311 1 Steak Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Cheese, Fajita Veggies, Lettuce, Sour Cream, Rice, Black Beans]] $8.99 311 1 Chips and Guacamole NULL $3.99 311 1 Carnitas Soft Tacos [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Lettuce, Black Beans]] $8.99 312 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 312 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 312 1 Chips NULL $2.15 313 1 Burrito [White Rice, Adobo-Marinated and Grilled Steak, [Salsa, Cheese]] $7.40 313 1 Burrito [White Rice, Adobo-Marinated and Grilled Steak, Pinto Beans, [Sour Cream, Salsa, Cheese, Cilantro-Lime Rice]] $7.40 314 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $8.75 314 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce]] $8.75 315 1 Barbacoa Crispy Tacos [Fresh Tomato Salsa, [Sour Cream, Lettuce]] $9.25 315 1 Chips and Guacamole NULL $4.45 316 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 316 1 Canned Soft Drink [Sprite] $1.25 317 1 Carnitas Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Guacamole]] $11.75 317 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Guacamole]] $11.25 317 1 Canned Soft Drink [Coke] $1.25 318 1 Chips and Guacamole NULL $3.99 318 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Cheese, Rice, Pinto Beans, Sour Cream]] $8.49 319 1 Chicken Bowl [Tomatillo-Green Chili Salsa (Medium), Black Beans] $8.49 319 1 Chicken Bowl [Tomatillo-Green Chili Salsa (Medium), Black Beans] $8.49 319 1 Steak Crispy Tacos [Tomatillo-Green Chili Salsa (Medium), Pinto Beans] $8.99 320 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 320 1 Chips NULL $2.15 320 1 Canned Soft Drink [Sprite] $1.25 321 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 321 1 Canned Soda [Diet Coke] $1.09 321 1 Bottled Water NULL $1.09 322 1 Veggie Soft Tacos [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 322 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 322 1 Canned Soft Drink [Nestea] $1.25 323 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese]] $9.25 323 1 Canned Soft Drink [Lemonade] $1.25 323 1 Chips NULL $2.15 324 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 324 1 Canned Soft Drink [Diet Coke] $1.25 325 1 Chips and Guacamole NULL $4.45 325 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 325 2 Canned Soft Drink [Coke] $2.50 326 2 Chips and Guacamole NULL $8.90 326 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 326 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 326 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 327 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 327 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole, Lettuce]] $11.25 328 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Sour Cream, Guacamole, Lettuce]] $10.98 328 1 Chips and Guacamole NULL $3.99 329 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 329 1 Chips and Guacamole NULL $3.99 330 1 Side of Chips NULL $1.69 330 1 Barbacoa Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 331 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese]] $9.25 331 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese]] $8.75 332 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Sour Cream, Lettuce]] $8.99 332 1 Canned Soda [Mountain Dew] $1.09 333 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $9.25 333 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 334 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 334 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 335 1 Chips and Guacamole NULL $3.99 335 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Cheese, Rice, Pinto Beans, Sour Cream]] $8.49 336 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 336 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 337 2 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Black Beans, Cheese, Guacamole]] $22.50 338 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $10.98 338 3 Bottled Water NULL $3.27 338 1 Chips and Guacamole NULL $3.99 338 1 Carnitas Soft Tacos [Fresh Tomato Salsa (Mild), [Rice, Pinto Beans, Cheese, Sour Cream]] $8.99 338 1 Canned Soda [Coca Cola] $1.09 339 1 Carnitas Bowl [Fresh Tomato (Mild), [Guacamole, Sour Cream, Cheese]] $11.08 340 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 340 1 Chips and Guacamole NULL $4.45 341 1 Steak Bowl [Fresh Tomato (Mild), [Guacamole, Lettuce, Pinto Beans, Rice, Sour Cream, Cheese]] $11.08 342 1 Chips and Guacamole NULL $3.99 342 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Cheese, Pinto Beans, Sour Cream, Rice]] $8.49 343 2 Chips NULL $4.30 343 1 Barbacoa Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Cheese]] $9.25 343 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 343 1 Veggie Bowl [Roasted Chili Corn Salsa, [Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 343 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 343 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 344 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Sour Cream]] $8.49 344 1 Carnitas Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Fajita Veggies, Sour Cream]] $8.99 344 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Sour Cream]] $8.49 345 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 345 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 345 1 Chips and Guacamole NULL $4.45 346 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 346 1 Chips and Roasted Chili Corn Salsa NULL $2.95 346 1 Steak Bowl [Tomatillo Green Chili Salsa] $9.25 346 1 Veggie Bowl [Roasted Chili Corn Salsa, Fajita Vegetables] $8.75 347 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Guacamole]] $11.25 347 1 Chips and Guacamole NULL $4.45 348 2 Veggie Bowl [Fresh Tomato Salsa (Mild), [Rice, Sour Cream, Cheese, Pinto Beans]] $16.98 349 1 Veggie Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese]] $8.49 349 1 Chips and Fresh Tomato Salsa NULL $2.39 350 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 350 3 Canned Soft Drink [Sprite] $3.75 351 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Lettuce]] $9.25 351 1 Chips and Guacamole NULL $4.45 352 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese]] $9.25 352 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $9.25 352 1 Chips and Guacamole NULL $4.45 353 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $8.75 353 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 354 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 354 1 Barbacoa Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $9.25 355 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $9.25 355 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese]] $9.25 356 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Sour Cream, Guacamole, Lettuce]] $11.48 356 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Sour Cream, Lettuce]] $8.99 357 2 Chicken Salad Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Guacamole]] $22.50 358 1 Carnitas Burrito [Roasted Chili Corn Salsa, [Cheese, Rice, Guacamole]] $11.75 358 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Guacamole]] $11.25 358 1 Canned Soft Drink [Coke] $1.25 359 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 359 1 Chips NULL $2.15 359 1 Canned Soft Drink [Diet Coke] $1.25 360 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 360 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 360 1 Canned Soft Drink [Sprite] $1.25 361 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Guacamole]] $11.25 361 1 Bottled Water NULL $1.50 362 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 362 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Pinto Beans, Sour Cream, Guacamole]] $11.25 363 1 Barbacoa Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream]] $9.25 363 1 6 Pack Soft Drink [Coke] $6.49 363 2 Chips and Guacamole NULL $8.90 364 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 364 1 Chips and Guacamole NULL $4.45 365 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce, Guacamole]] $11.25 365 1 Canned Soft Drink [Diet Coke] $1.25 366 2 Chicken Crispy Tacos [Tomatillo Red Chili Salsa] $17.50 366 1 Steak Burrito [Tomatillo Red Chili Salsa] $9.25 367 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 367 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 367 2 Bottled Water NULL $3.00 368 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 368 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 369 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Sour Cream, Guacamole]] $10.98 369 1 Steak Salad [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 369 1 Chips and Guacamole NULL $3.99 370 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 370 1 Chips and Guacamole NULL $4.45 371 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Sour Cream]] $8.99 371 1 Side of Chips NULL $1.69 372 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 372 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 373 1 Veggie Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 373 1 Chips and Guacamole NULL $4.45 374 1 Barbacoa Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.75 374 1 Canned Soft Drink [Coke] $1.25 375 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole]] $11.75 375 1 Chips and Roasted Chili Corn Salsa NULL $2.95 376 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 376 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 376 2 Canned Soda [Mountain Dew] $2.18 377 2 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese]] $17.98 377 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese]] $8.99 378 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 378 1 Side of Chips NULL $1.69 379 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 379 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 379 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 379 3 Canned Soft Drink [Lemonade] $3.75 379 1 Steak Crispy Tacos [Roasted Chili Corn Salsa, [Cheese, Sour Cream, Lettuce]] $9.25 380 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 380 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 380 1 Chips NULL $2.15 380 1 Bottled Water NULL $1.50 381 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), Cheese] $8.49 381 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), Cheese] $8.49 381 1 Nantucket Nectar [Pomegranate Cherry] $3.39 382 1 Steak Bowl [Fresh Tomato (Mild), [Lettuce, Black Beans, Rice, Cheese]] $8.69 382 1 Chips and Guacamole NULL $3.89 383 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 383 1 Chips and Guacamole NULL $4.45 384 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 384 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.25 384 1 Chips NULL $2.15 384 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 385 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 385 1 Side of Chips NULL $1.69 386 1 Veggie Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese]] $8.49 386 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 387 1 Canned Soft Drink [Lemonade] $1.25 387 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $8.75 387 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce, Guacamole]] $11.75 387 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Sour Cream, Lettuce, Guacamole]] $11.75 388 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 388 1 Bottled Water NULL $1.09 389 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream]] $8.75 389 1 Chips and Guacamole NULL $4.45 389 1 Chicken Crispy Tacos [Fresh Tomato Salsa, Lettuce] $8.75 389 1 Chips and Fresh Tomato Salsa NULL $2.95 389 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 389 1 Chips NULL $2.15 390 1 Chips and Guacamole NULL $3.99 390 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 391 1 Steak Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 391 1 Chips and Guacamole NULL $3.99 392 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese]] $8.49 392 1 Side of Chips NULL $1.69 392 1 Canned Soda [Coca Cola] $1.09 393 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 393 1 Canned Soda [Sprite] $1.09 394 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $9.25 394 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce]] $8.75 395 1 Steak Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Cheese, Sour Cream, Lettuce]] $8.99 395 1 Chips and Fresh Tomato Salsa NULL $2.39 396 1 Canned Soft Drink [Diet Coke] $1.25 396 1 Canned Soft Drink [Coke] $1.25 396 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Sour Cream, Cheese, Guacamole]] $11.25 396 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Cheese, Guacamole]] $11.25 397 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 397 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream]] $9.25 398 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 398 1 Bottled Water NULL $1.50 399 1 Chips and Guacamole NULL $3.99 399 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Rice, Pinto Beans, Cheese, Sour Cream]] $8.49 400 1 Veggie Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Pinto Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 400 1 Carnitas Burrito [Tomatillo Red Chili Salsa] $9.25 400 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 401 2 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $17.50 402 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 402 2 Chips and Guacamole NULL $8.90 402 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 402 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 403 1 Barbacoa Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream]] $8.99 403 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 404 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 404 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 404 1 Chips NULL $2.15 405 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Cheese, Guacamole]] $11.25 405 1 Canned Soft Drink [Diet Coke] $1.25 406 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 406 1 Canned Soft Drink [Diet Coke] $1.25 407 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 407 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.75 408 1 Canned Soft Drink [Coke] $1.25 408 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 408 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 409 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.49 409 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Sour Cream, Guacamole]] $10.98 410 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans]] $8.75 410 1 Canned Soft Drink [Diet Coke] $1.25 410 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Black Beans, Lettuce]] $8.75 411 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Cheese]] $8.49 411 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 412 1 Bottled Water NULL $1.50 412 1 Bottled Water NULL $1.50 412 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce]] $8.75 412 1 Chips and Guacamole NULL $4.45 413 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 413 1 Canned Soft Drink [Coke] $1.25 413 1 Carnitas Crispy Tacos [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 414 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 414 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 415 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 415 1 Chips and Guacamole NULL $4.45 416 1 Steak Burrito [Tomatillo Red Chili Salsa] $9.25 416 1 Chicken Bowl [Fresh Tomato Salsa] $8.75 417 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream]] $8.99 417 1 Side of Chips NULL $1.69 418 2 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Black Beans]] $17.50 419 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 419 1 Barbacoa Crispy Tacos [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Cheese, Sour Cream, Lettuce]] $8.99 419 1 Steak Crispy Tacos [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.99 420 1 Chicken Burrito [Fresh Tomato Salsa, [Black Beans, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 420 1 Chips and Guacamole NULL $4.45 421 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 421 1 Chips and Guacamole NULL $4.45 422 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 422 1 6 Pack Soft Drink [Sprite] $6.49 423 1 Chicken Crispy Tacos [Fresh Tomato Salsa (Mild), [Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 423 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 424 1 Chicken Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese]] $8.49 424 1 Canned Soda [Diet Dr. Pepper] $1.09 424 1 Side of Chips NULL $1.69 425 1 Barbacoa Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream]] $9.25 425 1 Chips and Guacamole NULL $4.45 426 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 426 1 Canned Soft Drink [Coke] $1.25 426 1 Chips and Fresh Tomato Salsa NULL $2.95 427 1 Chips and Guacamole NULL $3.99 427 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Sour Cream, Cheese]] $8.49 428 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Lettuce, Guacamole]] $11.75 428 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Guacamole]] $11.75 428 1 Chips NULL $2.15 429 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Rice, Guacamole]] $11.48 430 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 430 1 Chips NULL $2.15 430 1 Canned Soft Drink [Diet Coke] $1.25 431 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 431 1 Chips NULL $2.15 431 1 Canned Soft Drink [Sprite] $1.25 432 1 Chicken Burrito [Fresh Tomato Salsa, Rice] $8.75 432 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese]] $8.75 432 1 6 Pack Soft Drink [Coke] $6.49 433 1 Side of Chips NULL $1.69 433 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 434 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Lettuce]] $8.75 434 1 Barbacoa Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Guacamole]] $11.75 435 1 Chicken Burrito [[Tomatillo-Green Chili Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 435 1 Canned Soda [Mountain Dew] $1.09 436 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.49 436 1 Izze [Clementine] $3.39 437 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 437 1 Chips NULL $2.15 437 1 Canned Soft Drink [Diet Coke] $1.25 438 1 Carnitas Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $11.48 438 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 439 1 Chicken Salad [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 439 1 Chicken Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 440 1 Chips and Roasted Chili Corn Salsa NULL $2.95 440 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 440 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 441 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 441 1 Chips and Guacamole NULL $4.45 442 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Sour Cream, Guacamole]] $11.48 443 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 443 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 443 1 Chips and Guacamole NULL $4.45 444 1 Side of Chips NULL $1.69 444 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 445 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $8.75 445 1 Chips and Guacamole NULL $4.45 445 1 Bottled Water NULL $1.50 446 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Guacamole]] $11.75 446 1 Chips NULL $2.15 447 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Sour Cream]] $8.99 447 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 448 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Lettuce]] $8.75 448 1 Chips and Fresh Tomato Salsa NULL $2.95 448 1 Canned Soft Drink [Coke] $1.25 449 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 449 1 Chips NULL $2.15 449 1 Canned Soft Drink [Diet Coke] $1.25 450 2 Canned Soda [Dr. Pepper] $2.18 450 2 Canned Soda [Coca Cola] $2.18 450 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Guacamole]] $11.48 451 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $8.75 451 1 Chips and Fresh Tomato Salsa NULL $2.95 451 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole]] $11.75 452 1 Carnitas Burrito [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 452 1 Canned Soft Drink [Diet Coke] $1.25 453 1 Chicken Burrito [Fresh Tomato (Mild), [Guacamole, Lettuce, Rice, Fajita Veggies, Cheese]] $10.58 454 1 Barbacoa Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream]] $8.99 454 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 455 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 455 1 Chips and Guacamole NULL $4.45 456 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 456 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 457 2 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $17.50 457 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Lettuce]] $8.75 458 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 458 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Lettuce]] $8.75 459 2 Chicken Salad Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Guacamole]] $22.50 460 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 460 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 460 1 Chips and Guacamole NULL $4.45 461 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.75 461 1 Canned Soft Drink [Diet Coke] $1.25 462 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 462 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 462 1 Chips and Fresh Tomato Salsa NULL $2.95 463 1 Barbacoa Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 463 1 Side of Chips NULL $1.69 464 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 464 1 Chips and Guacamole NULL $4.45 465 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 465 1 6 Pack Soft Drink [Coke] $6.49 466 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 466 1 Chips and Guacamole NULL $4.45 466 1 Carnitas Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 467 1 Chips and Guacamole NULL $4.45 467 1 Barbacoa Soft Tacos [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 467 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 468 1 Chips and Guacamole NULL $4.45 468 1 Carnitas Salad Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.89 468 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 469 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 469 1 Chips and Fresh Tomato Salsa NULL $2.95 469 1 Bottled Water NULL $1.50 470 1 Chips and Guacamole NULL $4.45 470 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $8.75 471 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 471 1 Bottled Water NULL $1.09 471 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 471 1 Canned Soda [Dr. Pepper] $1.09 471 1 Chips and Guacamole NULL $3.99 472 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Fajita Veggies, Cheese, Guacamole]] $11.48 472 1 Veggie Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 473 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $9.25 473 1 Chips and Guacamole NULL $4.45 474 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Fajita Veggies, Cheese, Guacamole, Lettuce]] $11.48 475 1 Barbacoa Soft Tacos [Tomatillo Green Chili Salsa, [Sour Cream, Cheese, Lettuce]] $9.25 475 1 Chips and Roasted Chili Corn Salsa NULL $2.95 475 1 Barbacoa Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $9.25 476 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 476 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 477 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream]] $8.99 477 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 478 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce]] $8.75 478 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 478 1 Chips and Guacamole NULL $4.45 478 1 Steak Salad Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.89 479 1 Chicken Burrito [Fresh Tomato (Mild), [Guacamole, Rice, Fajita Veggies, Sour Cream, Cheese]] $10.58 479 1 Chicken Burrito [Fresh Tomato (Mild), [Lettuce, Rice, Fajita Veggies, Sour Cream, Cheese]] $8.19 479 1 Side of Chips NULL $1.69 480 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 480 1 Chips NULL $2.15 480 1 Canned Soft Drink [Diet Coke] $1.25 481 2 Chicken Burrito [Fresh Tomato Salsa, Rice] $17.50 481 1 6 Pack Soft Drink [Coke] $6.49 481 1 Steak Crispy Tacos [Tomatillo Red Chili Salsa, Rice] $9.25 481 1 Chips NULL $2.15 482 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 482 1 Chips and Guacamole NULL $4.45 482 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 482 1 Canned Soft Drink [Coke] $1.25 483 1 Barbacoa Bowl [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Guacamole]] $11.48 483 1 Barbacoa Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 484 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream]] $8.99 484 2 Bottled Water NULL $2.18 485 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies]] $8.49 485 1 Barbacoa Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies]] $8.99 485 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 485 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 485 2 Bottled Water NULL $2.18 486 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $8.75 486 1 Chips and Guacamole NULL $4.45 487 1 Chicken Burrito [Fresh Tomato Salsa, [Lettuce, Guacamole, Rice, Cheese, Fajita Vegetables, Sour Cream]] $11.25 487 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa] $9.25 488 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Cheese]] $9.25 488 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 488 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 488 1 Chips and Guacamole NULL $4.45 488 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 488 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 489 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 489 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Cheese, Sour Cream]] $8.49 490 1 Carnitas Crispy Tacos [Tomatillo-Green Chili Salsa (Medium), [Fajita Veggies, Cheese, Lettuce]] $8.99 490 1 Side of Chips NULL $1.69 491 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream]] $9.25 491 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 491 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 491 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream]] $9.25 491 1 Chicken Burrito [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $8.75 491 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Guacamole, Lettuce]] $11.75 491 1 Chicken Burrito [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $8.75 491 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 491 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 491 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 492 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 493 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 493 1 Chips and Guacamole NULL $4.45 494 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 494 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Sour Cream]] $8.49 495 1 Carnitas Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 495 1 Izze [Clementine] $3.39 496 1 Chips and Guacamole NULL $3.99 496 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Lettuce]] $8.99 496 1 Canned Soda [Diet Coke] $1.09 496 1 Canned Soda [Diet Dr. Pepper] $1.09 496 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 497 2 Chicken Burrito [Tomatillo Red Chili Salsa, [Cheese, Rice, Black Beans]] $17.50 497 1 Chips and Guacamole NULL $4.45 498 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Guacamole]] $11.25 498 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.25 498 1 Chips and Guacamole NULL $4.45 499 1 Steak Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream]] $9.25 499 1 Chips and Guacamole NULL $4.45 500 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 500 1 Chips NULL $2.15 500 1 Canned Soft Drink [Diet Coke] $1.25 501 1 Barbacoa Salad Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.89 501 1 Chips and Fresh Tomato Salsa NULL $2.95 502 1 Steak Burrito [Roasted Chili Corn Salsa, [Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 502 1 Canned Soft Drink [Diet Coke] $1.25 503 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 503 1 Chips and Guacamole NULL $4.45 504 1 Barbacoa Bowl [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Guacamole]] $11.48 504 1 Barbacoa Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 505 1 Chips and Fresh Tomato Salsa NULL $2.95 505 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 506 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $8.75 506 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 506 1 Chips and Fresh Tomato Salsa NULL $2.95 507 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 507 1 Canned Soft Drink [Lemonade] $1.25 507 1 Carnitas Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.75 507 1 Canned Soft Drink [Lemonade] $1.25 508 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese]] $8.99 508 1 Chips and Fresh Tomato Salsa NULL $2.39 509 1 Canned Soft Drink [Diet Coke] $1.25 509 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Cheese, Guacamole]] $11.25 509 1 Canned Soft Drink [Coke] $1.25 509 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Guacamole]] $11.25 510 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 510 1 Chips and Guacamole NULL $3.99 511 4 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $35.00 511 3 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $27.75 511 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 511 4 Chips and Fresh Tomato Salsa NULL $11.80 511 2 Chips and Guacamole NULL $8.90 511 2 Chips and Tomatillo Green Chili Salsa NULL $5.90 511 1 6 Pack Soft Drink [Coke] $6.49 512 1 Veggie Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 513 2 Steak Burrito [[Tomatillo-Green Chili Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Sour Cream]] $17.98 513 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 514 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 514 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans]] $8.75 514 1 Chips NULL $2.15 515 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 515 1 Chips and Guacamole NULL $4.45 516 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Cheese, Lettuce]] $8.49 516 1 Side of Chips NULL $1.69 517 1 Chicken Burrito [Tomatillo Red Chili (Hot), [Lettuce, Rice, Cheese]] $8.19 517 1 Steak Burrito [Tomatillo Red Chili (Hot), [Lettuce, Rice, Sour Cream, Cheese]] $8.69 518 1 Chicken Burrito [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 519 1 Chips and Guacamole NULL $3.99 519 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 519 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 520 1 Chips and Guacamole NULL $4.45 520 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce]] $8.75 520 1 6 Pack Soft Drink [Sprite] $6.49 521 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 521 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 522 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese]] $9.25 522 1 Canned Soft Drink [Diet Coke] $1.25 522 1 Chips and Guacamole NULL $4.45 523 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese]] $8.49 523 1 Side of Chips NULL $1.69 524 1 Carnitas Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 524 1 Chips and Guacamole NULL $4.45 525 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Lettuce, Black Beans, Sour Cream, Cheese, Rice, Fajita Veggies, Pinto Beans]] $8.99 525 1 Canned Soda [Sprite] $1.09 526 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 526 1 Chips and Roasted Chili Corn Salsa NULL $2.95 527 1 Steak Bowl [Fresh Tomato (Mild), [Guacamole, Lettuce, Pinto Beans, Rice, Sour Cream, Cheese]] $11.08 527 1 Carnitas Bowl [Fresh Tomato (Mild), [Guacamole, Lettuce, Rice, Sour Cream, Cheese]] $11.08 527 1 Chips and Guacamole NULL $3.89 528 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Black Beans, Fajita Veggies, Cheese, Guacamole, Lettuce]] $10.98 529 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 529 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 529 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Guacamole]] $11.25 530 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 530 2 Chips NULL $4.30 530 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 531 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.49 531 1 Chips and Guacamole NULL $3.99 532 1 Side of Chips NULL $1.69 532 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Lettuce, Cheese, Sour Cream]] $8.99 533 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese]] $8.49 533 1 Side of Chips NULL $1.69 534 1 Steak Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 534 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 534 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.89 535 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $9.25 535 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 535 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 535 1 Chips and Fresh Tomato Salsa NULL $2.95 536 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 536 1 Chips and Guacamole NULL $4.45 537 1 Chips and Guacamole NULL $4.45 537 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 538 1 Chicken Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 539 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream, Guacamole]] $11.48 539 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese]] $8.49 540 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $9.25 540 1 Chips and Guacamole NULL $4.45 541 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 541 1 Barbacoa Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.89 542 1 Steak Crispy Tacos [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Lettuce]] $8.99 542 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Lettuce]] $8.49 543 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Sour Cream]] $8.75 543 1 Steak Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 544 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 544 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 545 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 545 1 Barbacoa Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.75 545 1 Chips and Guacamole NULL $4.45 546 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 546 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 546 2 Canned Soft Drink [Coke] $2.50 547 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 547 1 Side of Chips NULL $1.69 548 1 Carnitas Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Guacamole]] $11.75 548 1 Chips NULL $2.15 549 1 Canned Soft Drink [Coke] $1.25 549 1 Carnitas Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 549 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream, Lettuce]] $8.75 549 2 Chips and Guacamole NULL $8.90 550 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 550 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 551 1 Veggie Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 551 1 Chicken Burrito [Tomatillo Green Chili Salsa, Guacamole] $11.25 552 2 Chips and Guacamole NULL $8.90 552 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 552 1 Steak Soft Tacos [Tomatillo Green Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 553 1 Steak Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 553 1 Side of Chips NULL $1.69 553 1 Nantucket Nectar [Pomegranate Cherry] $3.39 554 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 554 1 Chips and Roasted Chili Corn Salsa NULL $2.95 555 1 Carnitas Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 555 1 Side of Chips NULL $1.69 555 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Cheese, Sour Cream, Lettuce]] $8.49 556 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 557 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 557 1 Chips and Fresh Tomato Salsa NULL $2.95 558 1 Steak Soft Tacos [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 558 1 Side of Chips NULL $1.69 559 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 559 1 Chips NULL $2.15 559 1 Canned Soft Drink [Diet Coke] $1.25 560 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $8.75 560 1 Chips and Roasted Chili Corn Salsa NULL $2.95 560 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 560 2 Canned Soft Drink [Coke] $2.50 561 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 561 1 Canned Soft Drink [Coke] $1.25 561 1 Carnitas Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.75 561 2 Canned Soft Drink [Lemonade] $2.50 561 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Pinto Beans, Guacamole, Lettuce]] $11.25 561 1 Bottled Water NULL $1.50 561 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $8.75 561 1 Canned Soft Drink [Coke] $1.25 561 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 562 1 Barbacoa Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies, Guacamole, Lettuce]] $11.48 562 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 563 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream]] $8.75 563 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 563 1 Chips NULL $2.15 564 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole]] $11.75 564 1 Canned Soft Drink [Diet Coke] $1.25 565 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 565 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 566 1 6 Pack Soft Drink [Diet Coke] $6.49 566 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 567 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 567 1 Veggie Soft Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 567 1 Canned Soda [Coca Cola] $1.09 568 1 Chicken Bowl [Tomatillo-Green Chili Salsa (Medium), [Fajita Veggies, Cheese, Guacamole, Lettuce]] $10.98 569 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 569 1 Canned Soft Drink [Diet Coke] $1.25 569 1 Chips NULL $2.15 570 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 570 1 Bottled Water NULL $1.50 571 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 571 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Guacamole]] $11.25 572 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Cheese]] $8.49 572 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 573 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Lettuce]] $8.75 573 1 Bottled Water NULL $1.50 573 1 Canned Soft Drink [Diet Coke] $1.25 573 1 Bottled Water NULL $1.50 574 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese]] $8.75 574 2 Canned Soft Drink [Diet Coke] $2.50 574 1 Chips NULL $2.15 575 1 Salad [Brown Rice, Adobo-Marinated and Grilled Chicken, Vegetarian Black Beans] $7.40 575 1 Salad [White Rice, Adobo-Marinated and Grilled Chicken, Vegetarian Black Beans] $7.40 575 1 Chips and Guacamole NULL $4.00 576 1 Barbacoa Bowl [Tomatillo Red Chili Salsa] $9.25 576 1 Barbacoa Salad Bowl [Roasted Chili Corn Salsa] $9.39 576 1 Barbacoa Bowl [Roasted Chili Corn Salsa] $9.25 576 1 Barbacoa Bowl [Roasted Chili Corn Salsa] $9.25 576 1 Barbacoa Salad Bowl [Roasted Chili Corn Salsa] $9.39 577 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 577 2 Chips and Guacamole NULL $8.90 577 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Guacamole, Lettuce]] $11.25 577 4 Bottled Water NULL $6.00 577 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Guacamole]] $11.25 577 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole]] $11.25 577 1 Veggie Bowl [Fresh Tomato Salsa, [Black Beans, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 578 2 Chicken Bowl [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole]] $22.50 579 1 Carnitas Bowl [[Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 579 1 Canned Soda [Sprite] $1.09 580 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies]] $8.49 580 1 Veggie Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies, Lettuce]] $8.49 581 2 Chicken Burrito [Fresh Tomato Salsa, Rice] $17.50 582 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 582 1 Bottled Water NULL $1.50 583 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 583 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce]] $8.75 584 1 Chips and Guacamole NULL $3.99 584 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 585 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 585 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Sour Cream, Lettuce]] $8.75 585 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 586 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables]] $8.75 586 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Sour Cream]] $8.75 587 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 587 1 Bottled Water NULL $1.50 588 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 588 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.25 588 1 Chips and Roasted Chili Corn Salsa NULL $2.95 589 1 Steak Crispy Tacos [Fresh Tomato Salsa (Mild), [Rice, Cheese]] $8.99 589 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 590 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $9.25 590 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Cheese]] $8.75 590 1 Chips and Guacamole NULL $4.45 591 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 591 1 Canned Soda [Sprite] $1.09 591 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 592 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $8.75 592 1 Chicken Salad Bowl [Fresh Tomato Salsa, Lettuce] $8.75 593 1 Steak Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 594 1 Carnitas Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 594 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 595 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 595 1 Chips and Fresh Tomato Salsa NULL $2.39 596 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 596 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 596 1 Chips and Guacamole NULL $3.99 597 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 597 1 Chips and Guacamole NULL $4.45 598 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice]] $8.75 598 1 Chips NULL $2.15 598 1 Canned Soft Drink [Diet Coke] $1.25 599 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Sour Cream, Guacamole]] $10.98 599 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Cheese]] $8.99 600 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce, Guacamole]] $11.25 600 1 Bottled Water NULL $1.50 601 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 601 1 Chips NULL $2.15 601 1 Canned Soft Drink [Diet Coke] $1.25 602 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 602 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 602 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream]] $8.75 603 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 603 1 Bottled Water NULL $1.50 604 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 604 1 Canned Soft Drink [Coke] $1.25 604 1 Chips and Guacamole NULL $4.45 605 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Fajita Veggies, Cheese, Sour Cream, Guacamole]] $10.98 606 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 606 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 606 1 Canned Soft Drink [Nestea] $1.25 607 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 607 1 Chips and Fresh Tomato Salsa NULL $2.95 607 1 Canned Soft Drink [Sprite] $1.25 608 1 Chicken Soft Tacos [Roasted Chili Corn Salsa (Medium), Cheese] $8.49 608 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 608 1 Chips and Fresh Tomato Salsa NULL $2.39 609 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Guacamole, Lettuce]] $11.25 609 1 Canned Soft Drink [Diet Coke] $1.25 610 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 610 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 611 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 611 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 612 1 Steak Salad Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.89 612 1 Canned Soft Drink [Diet Coke] $1.25 613 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 613 1 Chips NULL $2.15 613 1 Canned Soft Drink [Lemonade] $1.25 614 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Sour Cream]] $8.99 614 1 Canned Soda [Mountain Dew] $1.09 615 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 615 1 Chips and Guacamole NULL $4.45 616 3 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $26.25 617 1 Chicken Bowl [Fresh Tomato Salsa, [Sour Cream, Lettuce, Rice, Cheese]] $8.75 617 1 Chips and Guacamole NULL $4.45 618 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 618 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 619 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 619 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 620 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 620 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 621 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 621 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 622 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 622 1 Chips and Guacamole NULL $4.45 622 1 Canned Soft Drink [Coke] $1.25 623 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 623 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 623 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 623 1 Barbacoa Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 624 1 Barbacoa Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.99 624 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 624 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 624 1 Chips and Guacamole NULL $3.99 625 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 625 1 Chips and Guacamole NULL $4.45 626 1 Chips NULL $2.15 626 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 626 1 Canned Soft Drink [Sprite] $1.25 627 1 Chicken Burrito [Fresh Tomato Salsa, [Guacamole, Lettuce, Sour Cream, Fajita Vegetables, Cheese, Rice]] $11.25 627 1 Steak Soft Tacos [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole]] $11.75 628 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 628 1 Chips and Guacamole NULL $4.45 628 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole]] $11.25 628 1 Chips NULL $2.15 628 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream]] $8.75 628 1 Chips and Guacamole NULL $4.45 629 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans]] $8.75 629 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans]] $8.75 629 1 Canned Soft Drink [Diet Coke] $1.25 630 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Guacamole, Lettuce]] $11.25 630 1 Chips NULL $2.15 631 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Lettuce, Black Beans]] $8.75 631 2 Chips and Roasted Chili Corn Salsa NULL $5.90 632 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.75 632 1 Canned Soft Drink [Coke] $1.25 633 1 Steak Burrito [Roasted Chili Corn Salsa, [Black Beans, Cheese, Lettuce]] $9.25 633 3 Canned Soft Drink [Coke] $3.75 634 1 Chicken Soft Tacos [Fresh Tomato Salsa] $8.75 634 1 Canned Soft Drink [Lemonade] $1.25 634 1 Chips and Guacamole NULL $4.45 635 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 635 1 Chips and Guacamole NULL $4.45 635 1 Chicken Burrito [Fresh Tomato Salsa, Rice] $8.75 635 2 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $23.50 636 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Cheese, Lettuce]] $8.75 636 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 636 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 637 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Cheese, Lettuce]] $8.75 637 1 Steak Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.89 637 1 Chips and Guacamole NULL $4.45 637 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 637 1 Canned Soft Drink [Diet Coke] $1.25 638 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce]] $8.75 638 1 Chips and Guacamole NULL $4.45 638 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 638 1 Canned Soft Drink [Sprite] $1.25 639 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 639 1 Chips NULL $2.15 639 1 Canned Soft Drink [Diet Coke] $1.25 640 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Sour Cream, Lettuce, Guacamole]] $11.75 640 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 641 1 Barbacoa Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 641 1 Nantucket Nectar [Peach Orange] $3.39 642 1 Steak Burrito [Roasted Chili Corn Salsa, [Black Beans, Cheese, Lettuce]] $9.25 642 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Cheese, Lettuce]] $9.25 643 2 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Lettuce]] $17.50 643 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 644 1 Steak Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.89 644 1 Bottled Water NULL $1.50 645 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 645 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 646 1 Carnitas Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 646 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 647 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce, Guacamole]] $11.25 647 1 Chips and Roasted Chili Corn Salsa NULL $2.95 648 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 648 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 648 2 Chips NULL $4.30 649 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 649 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 649 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 649 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans]] $8.75 649 2 Chips NULL $4.30 650 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 650 1 Canned Soft Drink [Diet Coke] $1.25 650 1 Chips NULL $2.15 651 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 651 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Fajita Veggies, Sour Cream, Lettuce]] $8.49 651 1 Izze [Blackberry] $3.39 651 1 Izze [Blackberry] $3.39 652 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Sour Cream, Guacamole]] $10.98 652 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Guacamole, Lettuce]] $10.98 652 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 653 1 Chicken Burrito [Fresh Tomato Salsa, [Guacamole, Cheese, Rice, Sour Cream, Fajita Vegetables, Lettuce]] $11.25 653 1 Steak Soft Tacos [Tomatillo Green Chili Salsa, [Sour Cream, Cheese, Guacamole, Rice, Fajita Vegetables]] $11.75 654 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.75 654 1 Canned Soft Drink [Lemonade] $1.25 654 2 Chips and Tomatillo Red Chili Salsa NULL $5.90 654 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $8.75 654 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 655 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 655 1 Chips and Guacamole NULL $4.45 655 1 Bottled Water NULL $1.50 656 1 Nantucket Nectar [Pineapple Orange Banana] $3.39 656 1 Carnitas Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 656 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 657 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Guacamole]] $11.25 657 1 Chips NULL $2.15 658 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 658 1 Carnitas Crispy Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 659 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.75 659 1 Chips and Guacamole NULL $4.45 660 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 660 1 Chips and Guacamole NULL $4.45 661 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 661 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 662 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 662 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 663 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 663 1 Barbacoa Soft Tacos [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.99 664 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 664 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 665 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.49 665 1 Chips and Guacamole NULL $3.99 666 1 Chips and Guacamole NULL $3.89 666 1 Barbacoa Burrito [Fresh Tomato (Mild), [Guacamole, Rice, Black Beans]] $11.08 667 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 667 1 Barbacoa Bowl [[Tomatillo-Green Chili Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Lettuce]] $8.99 667 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Lettuce]] $8.49 668 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 668 1 Veggie Crispy Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 669 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 669 1 Chips NULL $2.15 670 2 Chicken Burrito [Tomatillo Red Chili Salsa, Rice] $17.50 670 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, Rice] $8.75 670 1 Chips and Roasted Chili Corn Salsa NULL $2.95 671 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 671 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 672 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Fajita Veggies, Guacamole, Lettuce]] $10.98 672 1 Canned Soda [Diet Coke] $1.09 673 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 673 1 Chips NULL $2.15 674 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 674 1 Canned Soft Drink [Sprite] $1.25 674 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese]] $8.75 675 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 675 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 676 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 676 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 677 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 677 1 Chips NULL $2.15 677 1 Canned Soft Drink [Lemonade] $1.25 678 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 678 1 Chips and Guacamole NULL $3.99 679 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 679 1 Chips NULL $2.15 680 1 Carnitas Bowl [Tomatillo-Red Chili Salsa (Hot), [Lettuce, Sour Cream, Cheese, Fajita Veggies, Rice, Black Beans]] $8.99 680 1 Izze [Clementine] $3.39 681 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $9.25 681 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $8.75 682 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 682 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 682 1 Chips and Guacamole NULL $4.45 683 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 683 1 Chips NULL $2.15 683 1 Canned Soft Drink [Diet Coke] $1.25 684 1 Steak Burrito [Fresh Tomato Salsa, [Sour Cream, Guacamole, Rice, Lettuce]] $11.75 684 1 Chips and Guacamole NULL $4.45 685 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $9.25 685 1 Chips and Fresh Tomato Salsa NULL $2.95 686 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies]] $8.49 686 1 Veggie Salad [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies, Lettuce]] $8.49 687 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Lettuce, Guacamole]] $11.25 687 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 687 1 Chips and Guacamole NULL $4.45 688 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole]] $11.25 688 1 Veggie Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 688 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 688 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 688 1 Chips and Guacamole NULL $4.45 689 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.49 689 1 Side of Chips NULL $1.69 690 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 690 1 Nantucket Nectar [Apple] $3.39 691 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream]] $8.75 691 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Guacamole, Lettuce]] $11.25 691 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 691 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 691 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 691 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 691 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.75 691 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 691 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Guacamole, Lettuce]] $11.75 691 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 691 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 692 2 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Lettuce, Guacamole, Sour Cream, Cheese, Rice, Black Beans]] $21.96 693 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 693 1 Side of Chips NULL $1.69 694 1 Steak Soft Tacos [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 694 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream]] $8.99 695 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Cheese, Lettuce]] $8.49 695 1 Side of Chips NULL $1.69 696 1 Steak Bowl [Fresh Tomato Salsa, Rice] $9.25 696 1 Chips NULL $2.15 696 1 Bottled Water NULL $1.50 697 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 697 1 Bottled Water NULL $1.50 698 1 Chicken Bowl [Fresh Tomato Salsa, [Sour Cream, Cheese]] $8.75 698 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 698 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 699 1 Chicken Bowl [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Fajita Veggies, Lettuce]] $8.49 699 1 Side of Chips NULL $1.69 700 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 700 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 700 1 Canned Soft Drink [Lemonade] $1.25 701 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Guacamole, Lettuce]] $11.25 701 1 Chips NULL $2.15 702 1 Carnitas Bowl [Fresh Tomato (Mild), [Guacamole, Sour Cream, Cheese]] $11.08 703 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese]] $8.49 703 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese]] $8.49 703 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese]] $8.49 704 1 Steak Bowl [Fresh Tomato Salsa, Guacamole] $11.75 704 1 Canned Soft Drink [Sprite] $1.25 705 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 705 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Black Beans, Lettuce]] $8.75 706 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 706 1 Veggie Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 706 1 Chips and Guacamole NULL $4.45 707 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Sour Cream, Guacamole, Lettuce]] $11.25 707 1 Bottled Water NULL $1.50 708 2 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $23.50 708 1 Barbacoa Soft Tacos [Tomatillo Green Chili Salsa, [Pinto Beans, Sour Cream, Guacamole]] $11.75 708 1 6 Pack Soft Drink [Coke] $6.49 709 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $8.75 709 1 Chips and Guacamole NULL $4.45 709 1 6 Pack Soft Drink [Diet Coke] $6.49 710 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese]] $8.75 710 2 Canned Soft Drink [Diet Coke] $2.50 710 1 Chips NULL $2.15 711 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 711 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 712 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 712 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 712 1 Chips and Guacamole NULL $4.45 713 1 Carnitas Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream]] $8.99 713 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 714 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $9.25 714 1 Chips and Fresh Tomato Salsa NULL $2.95 715 1 Steak Burrito [Roasted Chili Corn Salsa, [Lettuce, Black Beans, Cheese]] $9.25 715 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Cheese, Lettuce]] $9.25 716 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 716 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 717 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $8.75 717 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 718 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 719 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 719 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 720 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 720 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $8.75 720 1 Chips NULL $2.15 721 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Guacamole]] $11.25 721 1 Chips NULL $2.15 722 1 Chips and Guacamole NULL $4.45 722 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 723 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.49 723 1 Chips and Fresh Tomato Salsa NULL $2.39 724 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 724 1 Canned Soft Drink [Coke] $1.25 724 1 Canned Soft Drink [Coke] $1.25 724 1 Canned Soft Drink [Coke] $1.25 725 1 Canned Soft Drink [Coke] $1.25 725 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 725 1 Chips NULL $2.15 726 1 Chicken Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 726 1 Side of Chips NULL $1.69 727 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 727 1 Bottled Water NULL $1.50 728 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream]] $8.75 728 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream]] $8.75 729 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.49 729 1 Chips and Guacamole NULL $3.99 730 1 Barbacoa Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 730 1 Canned Soda [Sprite] $1.09 731 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Black Beans, Lettuce, Guacamole]] $11.25 731 1 Canned Soft Drink [Diet Coke] $1.25 732 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 732 1 Chips and Fresh Tomato Salsa NULL $2.39 732 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Cheese, Sour Cream]] $8.49 733 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Lettuce]] $8.75 733 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 734 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole]] $11.25 734 1 Chips and Guacamole NULL $4.45 734 1 Chicken Salad Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Guacamole, Lettuce]] $11.25 734 1 Steak Salad Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.89 735 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 735 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 736 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $8.75 736 1 Chips and Guacamole NULL $4.45 737 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 737 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 737 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 737 2 Chips and Guacamole NULL $8.90 738 1 Barbacoa Salad Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.39 738 1 Chips and Fresh Tomato Salsa NULL $2.95 739 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 739 1 Chips and Guacamole NULL $3.99 740 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 740 1 Chips NULL $2.15 740 1 Canned Soft Drink [Diet Coke] $1.25 741 2 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Sour Cream, Cheese, Fajita Veggies, Guacamole, Rice, Pinto Beans]] $21.96 742 1 Veggie Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 742 1 Side of Chips NULL $1.69 743 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 743 1 Chips and Guacamole NULL $4.45 744 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 744 1 Carnitas Crispy Tacos [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 745 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Fajita Veggies, Cheese, Sour Cream]] $8.49 745 1 Chips and Guacamole NULL $3.99 746 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 746 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 746 1 Chips and Fresh Tomato Salsa NULL $2.95 746 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Rice, Black Beans, Lettuce]] $8.75 747 2 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream]] $17.50 748 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 748 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 749 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $8.75 749 1 6 Pack Soft Drink [Coke] $6.49 750 2 Chips NULL $4.30 750 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 751 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 751 1 Chips NULL $2.15 751 1 Bottled Water NULL $1.50 752 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 752 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Lettuce]] $8.49 752 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 753 1 Steak Burrito [Tomatillo Red Chili Salsa, Sour Cream] $9.25 753 1 Chips and Guacamole NULL $4.45 754 1 6 Pack Soft Drink [Diet Coke] $6.49 754 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 754 1 Chips and Guacamole NULL $4.45 755 1 Veggie Bowl [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 755 1 Side of Chips NULL $1.69 756 1 Carnitas Salad Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $9.39 756 1 Barbacoa Crispy Tacos [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $9.25 757 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 757 1 Chips and Fresh Tomato Salsa NULL $2.95 758 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 758 1 Chips NULL $2.15 758 1 Canned Soft Drink [Sprite] $1.25 759 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Guacamole, Lettuce]] $11.25 759 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 759 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $8.75 759 1 Veggie Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 759 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 759 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Guacamole, Lettuce]] $11.25 759 2 Canned Soft Drink [Coke] $2.50 759 2 Canned Soft Drink [Diet Coke] $2.50 759 4 Bottled Water NULL $6.00 759 2 Chips and Guacamole NULL $8.90 759 2 Chips and Fresh Tomato Salsa NULL $5.90 760 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Guacamole, Lettuce]] $11.25 760 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 761 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 761 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Sour Cream, Guacamole]] $10.98 762 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 762 1 Chips NULL $2.15 762 1 Bottled Water NULL $1.50 763 1 Steak Bowl [Fresh Tomato Salsa, Rice] $9.25 763 1 Canned Soft Drink [Coke] $1.25 763 1 Bottled Water NULL $1.50 763 1 Chips NULL $2.15 764 1 Canned Soft Drink [Lemonade] $1.25 764 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 764 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 765 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Cheese, Sour Cream, Lettuce]] $8.49 765 1 Chips and Guacamole NULL $3.99 766 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Black Beans, Lettuce, Guacamole]] $11.25 766 2 Chips NULL $4.30 767 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Lettuce, Guacamole]] $11.75 767 1 Chips NULL $2.15 768 2 Barbacoa Crispy Tacos [Fresh Tomato Salsa, [Sour Cream, Cheese, Rice]] $18.50 769 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.49 769 1 Chips and Fresh Tomato Salsa NULL $2.39 770 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 770 1 Chips and Guacamole NULL $4.45 770 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 771 1 Carnitas Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream]] $8.99 771 1 Chips and Guacamole NULL $3.99 772 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $9.25 772 1 Steak Soft Tacos [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $9.25 773 1 Bottled Water NULL $1.50 773 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 773 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 774 1 6 Pack Soft Drink [Diet Coke] $6.49 774 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce, Guacamole]] $11.75 775 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Rice, Fajita Veggies, Sour Cream, Lettuce]] $8.49 775 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 776 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $8.75 776 1 Chips and Guacamole NULL $4.45 776 1 6 Pack Soft Drink [Coke] $6.49 777 2 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Black Beans, Cheese, Guacamole]] $22.50 778 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 778 1 Chips and Roasted Chili Corn Salsa NULL $2.95 779 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 779 1 Side of Chips NULL $1.69 780 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Black Beans, Fajita Veggies, Cheese, Guacamole, Lettuce]] $10.98 781 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 781 1 Chips and Guacamole NULL $4.45 782 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Guacamole]] $11.25 782 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 783 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 783 1 Chicken Bowl [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream]] $8.49 783 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 784 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 784 1 6 Pack Soft Drink [Diet Coke] $6.49 784 1 Chicken Soft Tacos [Tomatillo Green Chili Salsa, [Sour Cream, Guacamole]] $11.25 785 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Lettuce, Guacamole]] $11.75 785 1 Chips and Fresh Tomato Salsa NULL $2.95 786 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 786 1 Side of Chips NULL $1.69 787 1 Steak Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 787 2 Canned Soda [Dr. Pepper] $2.18 787 1 Canned Soda [Coca Cola] $1.09 787 1 Steak Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 787 1 Carnitas Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 788 1 Canned Soda [Dr. Pepper] $1.09 788 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 789 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 789 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 790 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Sour Cream, Guacamole]] $10.98 790 1 Canned Soda [Diet Coke] $1.09 791 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.75 791 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 791 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Guacamole, Lettuce]] $11.25 791 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole]] $11.25 791 1 Chips NULL $2.15 792 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Lettuce, Guacamole]] $11.25 792 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 792 1 Chips and Guacamole NULL $4.45 792 1 Chips and Roasted Chili Corn Salsa NULL $2.95 793 1 Carnitas Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Guacamole]] $11.75 793 1 Bottled Water NULL $1.50 794 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 794 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 794 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 795 1 Chicken Burrito [Tomatillo Red Chili Salsa, Lettuce] $8.75 795 1 Chips NULL $2.15 795 1 Barbacoa Bowl [Tomatillo Red Chili Salsa] $9.25 796 1 Bottled Water NULL $1.50 796 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 796 1 Chips NULL $2.15 797 1 Chips and Guacamole NULL $3.99 797 1 Steak Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Fajita Veggies, Cheese, Sour Cream]] $8.99 798 1 6 Pack Soft Drink [Diet Coke] $6.49 798 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $8.75 798 2 Chips and Guacamole NULL $8.90 799 1 Steak Soft Tacos [Tomatillo-Green Chili Salsa (Medium), [Cheese, Sour Cream]] $8.99 799 1 Chips and Guacamole NULL $3.99 800 2 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $17.50 801 1 Veggie Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole, Lettuce]] $11.25 801 1 Canned Soft Drink [Diet Coke] $1.25 802 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $10.98 803 1 Barbacoa Soft Tacos [Fresh Tomato Salsa, [Black Beans, Cheese, Lettuce]] $9.25 803 1 Chips and Guacamole NULL $4.45 803 1 Canned Soft Drink [Coke] $1.25 803 2 Bottled Water NULL $3.00 804 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 804 1 Chips and Guacamole NULL $4.45 805 1 Chips and Guacamole NULL $4.45 805 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 805 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 806 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 806 1 Chips NULL $2.15 806 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 806 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 806 1 Chips and Guacamole NULL $4.45 807 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 807 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Lettuce]] $9.25 807 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Guacamole, Lettuce]] $11.25 807 1 Canned Soft Drink [Coke] $1.25 808 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 808 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 808 1 Canned Soft Drink [Sprite] $1.25 809 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Lettuce]] $8.75 809 1 Chips and Guacamole NULL $4.45 810 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Guacamole, Lettuce]] $11.48 810 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Guacamole, Lettuce]] $11.48 811 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Black Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 811 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 812 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Guacamole]] $11.25 812 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 812 1 Chips NULL $2.15 813 1 Bottled Water NULL $1.50 813 1 Chips NULL $2.15 813 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 814 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 814 2 Chips and Guacamole NULL $8.90 814 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 815 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 815 1 Canned Soft Drink [Diet Coke] $1.25 816 1 Chips and Guacamole NULL $4.45 816 1 Steak Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream]] $9.25 816 1 Steak Crispy Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 816 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 817 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $9.25 817 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 817 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 818 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $9.25 818 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Lettuce]] $8.75 818 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 818 1 Carnitas Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 818 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 818 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 818 2 Chips and Guacamole NULL $8.90 818 1 Chips and Fresh Tomato Salsa NULL $2.95 819 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 820 1 Veggie Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 820 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 821 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Guacamole]] $10.98 822 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 822 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream]] $8.99 823 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 823 2 Chicken Burrito [Fresh Tomato Salsa, Rice] $17.50 823 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 824 1 Chips and Guacamole NULL $3.99 824 1 Chicken Soft Tacos [Roasted Chili Corn Salsa (Medium), [Cheese, Sour Cream]] $8.49 825 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Guacamole]] $10.98 825 1 Side of Chips NULL $1.69 826 2 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $17.50 827 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 827 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 828 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 828 1 Chips and Fresh Tomato Salsa NULL $2.39 829 1 Canned Soft Drink [Sprite] $1.25 829 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Lettuce]] $8.75 829 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 830 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Cheese, Sour Cream, Guacamole, Lettuce, Rice]] $11.75 830 1 Chicken Soft Tacos [Tomatillo Green Chili Salsa, [Guacamole, Sour Cream]] $11.25 831 2 Chicken Burrito [Fresh Tomato Salsa, [Lettuce, Rice, Black Beans, Cheese]] $17.50 832 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies]] $8.49 832 1 Veggie Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies]] $8.49 832 1 Chips and Fresh Tomato Salsa NULL $2.39 833 1 Chicken Bowl [Fresh Tomato Salsa, [Sour Cream, Guacamole, Lettuce]] $11.25 833 1 Bottled Water NULL $1.50 834 1 Veggie Burrito [Fresh Tomato Salsa, [Cheese, Rice, Pinto Beans]] $8.75 834 2 Bottled Water NULL $3.00 834 1 Canned Soft Drink [Diet Coke] $1.25 835 1 Chicken Bowl [Fresh Tomato Salsa, Cheese] $8.75 835 1 Chips and Guacamole NULL $4.45 836 1 Steak Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole]] $11.75 836 1 Barbacoa Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Pinto Beans, Sour Cream, Guacamole, Lettuce]] $11.75 836 1 Chips and Guacamole NULL $4.45 837 2 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Cheese, Sour Cream, Rice, Fajita Veggies, Guacamole]] $21.96 838 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $8.75 838 1 Chips and Guacamole NULL $4.45 838 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $8.75 839 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Guacamole]] $11.25 839 1 Carnitas Soft Tacos [Tomatillo Green Chili Salsa] $9.25 840 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 840 1 Chips and Fresh Tomato Salsa NULL $2.39 841 1 Barbacoa Soft Tacos [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream]] $9.25 841 1 Veggie Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 842 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 842 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Sour Cream, Lettuce]] $8.49 842 1 Chips and Guacamole NULL $3.99 842 1 Izze [Clementine] $3.39 843 1 Steak Bowl [Tomatillo Red Chili Salsa, [Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 843 1 Bottled Water NULL $1.50 844 1 Chips and Guacamole NULL $4.45 844 1 Carnitas Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $9.25 844 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 845 1 Canned Soft Drink [Coke] $1.25 845 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.75 846 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $8.75 846 1 Bottled Water NULL $1.50 846 1 Chicken Burrito [Fresh Tomato Salsa, [Cheese, Lettuce]] $8.75 847 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 847 1 Chips NULL $2.15 847 1 Bottled Water NULL $1.50 848 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 848 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 848 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans]] $8.75 848 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Sour Cream]] $8.75 848 2 Chicken Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream]] $17.50 848 1 Chicken Burrito [Fresh Tomato Salsa, [Pinto Beans, Cheese, Sour Cream]] $8.75 849 1 6 Pack Soft Drink [Coke] $6.49 849 1 Chips and Guacamole NULL $4.45 849 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.25 850 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Black Beans, Lettuce, Guacamole]] $11.25 850 2 Chips NULL $4.30 850 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 851 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole, Lettuce]] $11.25 851 1 Chips and Guacamole NULL $4.45 852 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Sour Cream, Lettuce]] $8.99 852 1 Chips and Fresh Tomato Salsa NULL $2.39 853 1 Steak Burrito [Roasted Chili Corn Salsa, [Cheese, Black Beans, Lettuce]] $9.25 853 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Lettuce, Cheese]] $9.25 854 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 854 1 Chips and Guacamole NULL $4.45 855 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.75 855 1 Veggie Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 855 1 Chips and Fresh Tomato Salsa NULL $2.95 855 1 Chips NULL $2.15 856 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 856 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 857 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 857 1 Chips NULL $2.15 857 1 Bottled Water NULL $1.50 858 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 858 1 Chips and Guacamole NULL $4.45 859 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Guacamole]] $10.98 859 1 Steak Burrito [Fresh Tomato Salsa (Mild), Black Beans] $8.99 859 2 Canned Soda [Diet Coke] $2.18 860 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 860 1 Chips and Guacamole NULL $4.45 860 1 Canned Soft Drink [Diet Coke] $1.25 861 1 Steak Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Black Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 861 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 862 1 Chips and Guacamole NULL $4.45 862 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 863 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Guacamole]] $10.98 864 1 Steak Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 864 1 Bottled Water NULL $1.50 865 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Guacamole, Rice, Black Beans, Cheese, Sour Cream]] $10.98 866 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 866 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 866 1 Chips and Guacamole NULL $3.99 867 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 867 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 868 1 Chips and Guacamole NULL $3.99 868 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese]] $8.49 868 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 868 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Lettuce]] $8.99 869 1 Veggie Salad Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole]] $11.25 869 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Guacamole, Lettuce]] $11.25 870 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 871 1 Chicken Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 872 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 872 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 872 1 Nantucket Nectar [Pineapple Orange Banana] $3.39 873 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 873 1 Canned Soft Drink [Sprite] $1.25 873 1 Chips and Fresh Tomato Salsa NULL $2.95 874 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 874 1 Bottled Water NULL $1.50 875 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Cheese, Guacamole]] $10.98 875 1 Side of Chips NULL $1.69 876 1 Chips and Guacamole NULL $4.45 876 1 Chicken Burrito [Roasted Chili Corn Salsa] $8.75 877 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Lettuce]] $8.75 877 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $8.75 878 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 878 1 Chips and Guacamole NULL $4.45 879 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream]] $9.25 879 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 879 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole]] $11.25 879 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 880 1 Chips and Guacamole NULL $3.99 880 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 880 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream]] $8.49 881 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 881 1 Chicken Burrito [Fresh Tomato Salsa, [Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 881 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans]] $8.75 882 1 Chips and Guacamole NULL $4.45 882 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Lettuce]] $8.75 883 1 Chips and Guacamole NULL $4.45 883 1 Canned Soft Drink [Diet Coke] $1.25 883 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Pinto Beans, Cheese, Lettuce]] $8.75 884 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 884 1 Bottled Water NULL $1.50 884 1 Chips NULL $2.15 885 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce, Guacamole]] $11.75 885 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Cheese, Lettuce, Guacamole]] $11.75 886 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 886 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 887 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.75 887 1 Bottled Water NULL $1.50 888 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 888 1 Carnitas Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $9.25 888 1 Canned Soft Drink [Sprite] $1.25 888 1 Chicken Burrito [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream]] $8.75 888 1 Chips NULL $2.15 889 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 889 1 Canned Soda [Mountain Dew] $1.09 890 1 Chips and Guacamole NULL $4.45 890 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.25 890 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 891 1 Chips NULL $2.15 891 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 891 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans]] $8.75 892 1 Canned Soft Drink [Lemonade] $1.25 892 1 Steak Bowl [Fresh Tomato Salsa] $9.25 892 1 Chips NULL $2.15 893 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 893 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole, Lettuce]] $11.25 894 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Lettuce]] $8.75 894 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 894 1 Chips and Guacamole NULL $4.45 895 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Cheese, Lettuce]] $8.49 895 1 Side of Chips NULL $1.69 896 1 Veggie Salad Bowl [Roasted Chili Corn Salsa, Fajita Vegetables] $8.75 896 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Pinto Beans, Sour Cream, Cheese]] $8.75 897 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 897 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 898 1 Steak Crispy Tacos [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $9.25 898 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Guacamole]] $11.25 898 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 899 1 Chips and Guacamole NULL $4.45 899 1 Steak Crispy Tacos [Fresh Tomato Salsa, Guacamole] $11.75 899 1 6 Pack Soft Drink [Coke] $6.49 900 1 Carnitas Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Guacamole, Lettuce]] $11.48 900 1 Steak Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Black Beans, Rice, Cheese, Sour Cream]] $8.99 901 4 Canned Soda [Sprite] $4.36 901 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Sour Cream, Guacamole, Lettuce]] $11.48 901 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Lettuce]] $8.99 901 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 902 1 Carnitas Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $11.48 903 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 903 1 Chips NULL $2.15 903 1 Bottled Water NULL $1.50 904 1 Barbacoa Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream]] $8.99 904 1 Side of Chips NULL $1.69 905 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 905 1 Canned Soft Drink [Nestea] $1.25 906 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 906 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 907 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 907 1 Steak Soft Tacos [Roasted Chili Corn Salsa, [Black Beans, Sour Cream, Lettuce]] $9.25 907 2 Chips and Guacamole NULL $8.90 907 1 Chips NULL $2.15 907 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream]] $9.25 908 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 908 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 909 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 909 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 909 2 Chips NULL $4.30 910 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 910 1 Chicken Bowl [Fresh Tomato Salsa] $8.75 911 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese]] $8.75 911 1 Chips NULL $2.15 911 1 Canned Soft Drink [Diet Coke] $1.25 912 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $8.75 912 1 Canned Soft Drink [Lemonade] $1.25 912 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 912 2 Chips and Fresh Tomato Salsa NULL $5.90 913 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 913 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Lettuce]] $8.75 913 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $9.25 913 1 Chips and Guacamole NULL $4.45 914 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 914 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 915 2 Canned Soft Drink [Coke] $2.50 915 1 Steak Bowl [Fresh Tomato Salsa, Rice] $9.25 915 1 Chips NULL $2.15 916 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 916 1 Bottled Water NULL $1.50 916 1 Canned Soft Drink [Coke] $1.25 916 1 Veggie Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 916 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 916 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $8.75 916 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Lettuce]] $8.75 916 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $8.75 917 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 918 1 Chicken Burrito [Fresh Tomato Salsa, Rice] $8.75 918 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Rice, Cheese]] $8.75 919 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 919 1 Chips NULL $2.15 919 1 Canned Soft Drink [Sprite] $1.25 920 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 921 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 921 1 Chips NULL $2.15 921 1 Bottled Water NULL $1.50 922 1 Chicken Bowl [Roasted Chili Corn Salsa, [Cheese, Lettuce, Fajita Vegetables, Rice]] $8.75 922 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 922 1 Canned Soft Drink [Sprite] $1.25 923 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 923 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 924 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 924 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 925 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 925 1 Chips and Guacamole NULL $4.45 926 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Lettuce]] $9.25 926 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 926 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream]] $8.75 926 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 926 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Lettuce]] $9.25 926 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 926 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 926 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 926 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Lettuce]] $8.75 926 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 926 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Rice, Sour Cream]] $8.75 926 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 926 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 926 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream]] $9.25 926 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 926 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese]] $9.25 926 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 926 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 926 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 926 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 926 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 926 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $9.25 926 1 Veggie Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 927 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 927 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 928 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 928 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 928 1 Canned Soft Drink [Nestea] $1.25 929 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 929 1 Bottled Water NULL $1.50 930 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 930 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 931 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 931 1 Bottled Water NULL $1.50 931 1 Chips NULL $2.15 932 2 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies]] $16.98 933 1 Steak Bowl [Fresh Tomato Salsa, [Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 933 1 Bottled Water NULL $1.50 934 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 934 2 Chips NULL $4.30 934 1 Bottled Water NULL $1.50 935 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 935 1 Chips and Roasted Chili Corn Salsa NULL $2.95 935 1 Canned Soft Drink [Nestea] $1.25 936 1 Canned Soft Drink [Coke] $1.25 936 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $9.25 936 1 Chips and Guacamole NULL $4.45 937 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 937 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 937 1 Chips and Guacamole NULL $4.45 938 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 938 1 Chips NULL $2.15 938 1 Canned Soft Drink [Sprite] $1.25 939 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Guacamole, Lettuce]] $10.98 939 1 Side of Chips NULL $1.69 940 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 940 1 Canned Soft Drink [Coke] $1.25 940 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 940 1 Canned Soft Drink [Coke] $1.25 941 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 941 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 941 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 942 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 942 1 Side of Chips NULL $1.69 943 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 943 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 944 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 944 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 945 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 945 1 Chips and Roasted Chili Corn Salsa NULL $2.95 945 1 6 Pack Soft Drink [Sprite] $6.49 945 1 Chicken Crispy Tacos [Tomatillo Red Chili Salsa, Cheese] $8.75 946 1 Veggie Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 946 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $9.25 946 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 947 2 Nantucket Nectar [Peach Orange] $6.78 947 2 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Cheese, Lettuce]] $17.98 947 1 Nantucket Nectar [Apple] $3.39 948 1 Carnitas Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 948 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 948 1 Veggie Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Pinto Beans, Cheese, Lettuce]] $8.75 949 1 Chips and Guacamole NULL $4.45 949 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Lettuce, Guacamole]] $11.75 949 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Sour Cream, Cheese, Lettuce]] $9.25 949 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese]] $8.75 949 1 6 Pack Soft Drink [Coke] $6.49 950 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 950 1 Chips and Fresh Tomato Salsa NULL $2.39 951 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Lettuce]] $8.75 951 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 952 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 952 1 Chips and Guacamole NULL $4.45 952 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 952 1 Chips NULL $2.15 953 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 953 1 Barbacoa Bowl [Roasted Chili Corn Salsa, [Cheese, Lettuce]] $9.25 953 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 953 1 Steak Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Guacamole]] $11.89 953 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Guacamole]] $11.75 953 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Guacamole]] $11.25 953 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $9.25 953 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Lettuce]] $9.25 954 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 954 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 955 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 955 1 Side of Chips NULL $1.69 955 1 Bottled Water NULL $1.09 956 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 956 1 Bottled Water NULL $1.50 957 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 957 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 958 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 958 1 Bottled Water NULL $1.50 959 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Cheese, Lettuce]] $8.49 959 1 Steak Crispy Tacos [Tomatillo-Red Chili Salsa (Hot), [Cheese, Lettuce]] $8.99 960 2 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Black Beans, Cheese, Guacamole]] $22.50 961 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice]] $8.75 961 2 Chips NULL $4.30 962 1 Chicken Crispy Tacos [Fresh Tomato Salsa, Fajita Vegetables] $8.75 962 1 Chicken Salad Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole]] $11.25 962 1 Chips NULL $2.15 963 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 963 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 964 1 Chips and Guacamole NULL $4.45 964 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 965 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 965 1 Nantucket Nectar [Pomegranate Cherry] $3.39 965 1 Chips and Fresh Tomato Salsa NULL $2.39 966 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 966 1 Chips NULL $2.15 966 1 Barbacoa Soft Tacos [Roasted Chili Corn Salsa, [Cheese, Sour Cream, Guacamole]] $11.75 967 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 967 1 Chips and Fresh Tomato Salsa NULL $2.39 968 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $8.75 968 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 969 1 Steak Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.89 969 1 Canned Soft Drink [Diet Coke] $1.25 970 5 Bottled Water NULL $7.50 970 1 Barbacoa Salad Bowl [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Pinto Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.89 971 1 Canned Soda [Coca Cola] $1.09 971 1 Veggie Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 971 1 Chips and Fresh Tomato Salsa NULL $2.39 972 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 972 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 972 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 973 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 973 1 Steak Soft Tacos [Tomatillo Red Chili Salsa, [Cheese, Sour Cream]] $9.25 973 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 973 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $8.75 973 2 Chips and Guacamole NULL $8.90 974 1 Carnitas Burrito [Fresh Tomato Salsa, [Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 974 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $8.75 975 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 975 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 975 2 Bottled Water NULL $3.00 976 1 Bottled Water NULL $1.50 976 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 976 1 Steak Crispy Tacos [Fresh Tomato Salsa, [Cheese, Guacamole]] $11.75 977 1 Barbacoa Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Lettuce]] $8.99 977 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Sour Cream, Guacamole]] $10.98 978 1 Chips and Guacamole NULL $4.45 978 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream]] $8.75 978 1 Chips NULL $2.15 979 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 979 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 979 1 Chips and Guacamole NULL $4.45 980 1 Side of Chips NULL $1.69 980 1 Veggie Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Guacamole, Lettuce]] $10.98 981 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 981 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 982 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 982 1 Chips and Guacamole NULL $4.45 983 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 983 1 Chips and Guacamole NULL $3.99 983 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 984 1 Canned Soft Drink [Coke] $1.25 984 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Pinto Beans, Sour Cream, Lettuce]] $9.25 984 1 Chips and Guacamole NULL $4.45 985 1 Veggie Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Guacamole, Lettuce]] $10.98 986 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 986 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 987 1 Steak Burrito [Tomatillo Green Chili Salsa, [Lettuce, Guacamole, Sour Cream, Cheese, Fajita Vegetables, Rice]] $11.75 987 1 Chicken Burrito [Roasted Chili Corn Salsa, [Guacamole, Sour Cream, Rice, Fajita Vegetables, Lettuce]] $11.25 988 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Cheese, Lettuce]] $8.49 988 1 Carnitas Bowl [[Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Pinto Beans, Cheese, Guacamole, Lettuce]] $11.48 989 1 Chips and Guacamole NULL $4.45 989 1 Canned Soft Drink [Diet Coke] $1.25 989 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 990 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 990 1 Canned Soda [Coca Cola] $1.09 990 1 Chips and Fresh Tomato Salsa NULL $2.39 991 1 Chicken Bowl [Fresh Tomato Salsa, [Guacamole, Cheese, Sour Cream, Fajita Vegetables, Rice]] $11.25 991 1 Chips NULL $2.15 992 1 Chicken Bowl [Fresh Tomato Salsa, Cheese] $8.75 992 1 Chips and Guacamole NULL $4.45 993 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Cheese, Lettuce]] $8.49 993 1 Side of Chips NULL $1.69 994 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Guacamole, Lettuce]] $10.98 994 1 Side of Chips NULL $1.69 995 1 Steak Burrito [Fresh Tomato Salsa, [Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 995 1 Chips and Guacamole NULL $4.45 995 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.25 996 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Lettuce]] $8.75 996 1 Chicken Crispy Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 996 1 Steak Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 996 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Black Beans, Cheese, Guacamole, Lettuce]] $11.25 997 2 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Guacamole]] $22.50 998 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 998 1 Chips and Fresh Tomato Salsa NULL $2.39 999 2 Canned Soft Drink [Sprite] $2.50 999 1 Chicken Soft Tacos [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce]] $8.75 999 1 Chicken Crispy Tacos [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce]] $8.75 999 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce]] $9.25 1000 1 Barbacoa Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1000 1 Chicken Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1001 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 1001 1 Canned Soda [Coca Cola] $1.09 1002 1 Barbacoa Burrito [[Tomatillo-Green Chili Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1002 1 Side of Chips NULL $1.69 1003 1 Carnitas Burrito [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 1003 1 Canned Soft Drink [Diet Coke] $1.25 1004 2 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Fajita Veggies, Pinto Beans, Sour Cream, Cheese, Guacamole]] $21.96 1005 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1005 1 Chips NULL $2.15 1005 1 Canned Soft Drink [Coke] $1.25 1006 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Lettuce]] $8.75 1006 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1006 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1006 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1006 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1006 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1006 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 1006 1 Chips NULL $2.15 1007 1 Carnitas Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese]] $8.99 1007 1 Canned Soda [Diet Dr. Pepper] $1.09 1007 1 Side of Chips NULL $1.69 1008 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 1008 1 Chips NULL $2.15 1008 1 Canned Soft Drink [Sprite] $1.25 1009 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 1009 1 Canned Soda [Diet Coke] $1.09 1009 1 Bottled Water NULL $1.09 1009 1 Side of Chips NULL $1.69 1010 1 Carnitas Bowl [[Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Pinto Beans, Cheese, Guacamole]] $11.48 1010 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Black Beans, Cheese, Lettuce]] $8.49 1010 1 Chicken Crispy Tacos [Fresh Tomato Salsa (Mild), [Rice, Cheese, Lettuce]] $8.49 1010 2 Chips and Tomatillo-Red Chili Salsa NULL $4.78 1011 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Lettuce, Sour Cream, Cheese, Rice]] $9.25 1011 1 Canned Soft Drink [Coke] $1.25 1011 1 Canned Soft Drink [Coke] $1.25 1011 1 Canned Soft Drink [Coke] $1.25 1012 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1012 1 Chicken Crispy Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1013 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1013 1 Chips and Guacamole NULL $4.45 1013 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1013 1 Chips and Guacamole NULL $4.45 1014 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Cheese, Rice, Pinto Beans, Sour Cream]] $8.99 1014 1 Canned Soda [Coca Cola] $1.09 1015 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Cheese, Guacamole, Lettuce]] $11.25 1015 1 Chips NULL $2.15 1016 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1016 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $9.25 1016 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1017 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 1017 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole]] $11.25 1018 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream]] $9.25 1018 1 Chips and Fresh Tomato Salsa NULL $2.95 1019 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1019 1 Chips and Guacamole NULL $4.45 1020 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 1020 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1020 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1021 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 1021 1 Bottled Water NULL $1.50 1022 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Guacamole]] $10.98 1023 2 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Fajita Veggies]] $16.98 1024 2 Carnitas Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $18.50 1025 1 Chicken Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $10.98 1025 1 Chips and Guacamole NULL $3.99 1025 1 Canned Soda [Mountain Dew] $1.09 1026 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1026 1 Carnitas Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1026 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 1026 1 Chips and Guacamole NULL $4.45 1027 2 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $17.50 1028 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1028 1 Chips and Guacamole NULL $4.45 1029 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1029 1 Canned Soft Drink [Diet Coke] $1.25 1029 1 Chips and Guacamole NULL $4.45 1030 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Cheese]] $9.25 1030 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1030 1 Chicken Bowl [Tomatillo Red Chili Salsa, Fajita Vegetables] $8.75 1031 2 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $17.50 1031 1 Chips and Guacamole NULL $4.45 1032 1 Steak Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce]] $9.39 1032 1 Canned Soft Drink [Diet Coke] $1.25 1032 1 Chips and Roasted Chili Corn Salsa NULL $2.95 1033 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Guacamole, Lettuce]] $11.25 1033 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce]] $8.75 1033 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1034 1 Chicken Bowl [Fresh Tomato Salsa, [Sour Cream, Fajita Vegetables, Rice, Guacamole, Cheese]] $11.25 1034 1 Chips NULL $2.15 1035 2 Chips and Guacamole NULL $8.90 1035 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1035 1 Carnitas Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.89 1036 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 1037 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 1037 1 Chips and Guacamole NULL $4.45 1038 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1038 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 1039 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 1039 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Black Beans, Fajita Veggies, Guacamole]] $10.98 1040 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Lettuce]] $8.49 1040 1 Izze [Clementine] $3.39 1041 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $9.25 1041 1 Chicken Bowl [Tomatillo Red Chili Salsa, Rice] $8.75 1041 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1042 2 Bottled Water NULL $3.00 1042 1 Steak Salad Bowl [Fresh Tomato Salsa, [Black Beans, Sour Cream, Cheese, Lettuce]] $9.39 1043 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Cheese, Guacamole]] $11.75 1043 1 Chips and Fresh Tomato Salsa NULL $2.95 1044 1 Bottled Water NULL $1.50 1044 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1044 1 Steak Crispy Tacos [Fresh Tomato Salsa, [Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1045 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1045 1 Chips NULL $2.15 1045 1 Bottled Water NULL $1.50 1046 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Lettuce]] $8.49 1046 1 Izze [Clementine] $3.39 1047 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.75 1047 1 Canned Soft Drink [Diet Coke] $1.25 1048 2 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $17.50 1049 1 Veggie Burrito [Fresh Tomato Salsa, [Cheese, Sour Cream, Guacamole]] $11.25 1049 1 Canned Soft Drink [Nestea] $1.25 1050 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1050 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1050 1 Canned Soft Drink [Lemonade] $1.25 1051 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1051 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 1051 3 Chips and Guacamole NULL $13.35 1051 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1051 1 Carnitas Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1052 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 1052 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1052 1 Chips and Guacamole NULL $4.45 1052 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream, Guacamole]] $11.25 1053 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1053 1 Nantucket Nectar [Pineapple Orange Banana] $3.39 1054 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1054 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1055 2 Chips and Tomatillo-Green Chili Salsa NULL $4.78 1055 1 Chips and Fresh Tomato Salsa NULL $2.39 1055 1 Chips and Guacamole NULL $3.99 1056 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1056 1 Barbacoa Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1057 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1057 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1057 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 1058 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1058 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1058 1 Chips and Guacamole NULL $4.45 1059 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 1059 1 Chicken Crispy Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese]] $8.75 1059 1 Chips and Guacamole NULL $4.45 1060 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Fajita Veggies, Lettuce]] $8.49 1060 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1061 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Black Beans, Sour Cream, Guacamole]] $10.98 1062 1 Veggie Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 1062 1 Chips and Guacamole NULL $3.99 1063 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 1063 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 1064 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.75 1064 1 Chips and Guacamole NULL $4.45 1064 1 Bottled Water NULL $1.50 1064 1 Canned Soft Drink [Sprite] $1.25 1065 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese, Lettuce]] $8.49 1065 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1066 1 Veggie Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Lettuce]] $8.75 1066 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1067 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1067 1 Carnitas Burrito [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1068 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $8.75 1068 1 Chips and Fresh Tomato Salsa NULL $2.95 1068 1 Canned Soft Drink [Diet Coke] $1.25 1069 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole]] $11.25 1069 1 Bottled Water NULL $1.50 1070 1 Chicken Burrito [Fresh Tomato Salsa, [Sour Cream, Cheese, Guacamole, Rice]] $11.25 1070 1 Chips and Guacamole NULL $4.45 1070 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $9.25 1070 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Cheese]] $8.75 1071 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 1071 1 Carnitas Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1071 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1071 1 Chips and Guacamole NULL $4.45 1071 1 Bottled Water NULL $1.50 1072 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $8.75 1072 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Lettuce]] $8.75 1073 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1073 1 Side of Chips NULL $1.69 1074 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 1074 1 Chips NULL $2.15 1074 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 1074 1 Chips NULL $2.15 1075 1 Bottled Water NULL $1.50 1075 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1076 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.75 1076 1 Chips NULL $2.15 1076 1 6 Pack Soft Drink [Coke] $6.49 1077 1 Steak Burrito [Roasted Chili Corn Salsa, [Cheese, Black Beans, Lettuce]] $9.25 1077 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Cheese, Lettuce]] $9.25 1078 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1078 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1079 1 Barbacoa Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1079 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1080 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1080 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1081 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1081 1 Chips NULL $2.15 1081 1 Canned Soft Drink [Nestea] $1.25 1082 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1082 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $9.25 1082 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Guacamole, Lettuce]] $11.25 1083 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Cheese, Lettuce]] $8.75 1083 2 Chips and Fresh Tomato Salsa NULL $5.90 1084 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1084 1 Chips and Guacamole NULL $4.45 1085 1 Steak Soft Tacos [Fresh Tomato Salsa, Sour Cream] $9.25 1085 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1085 1 Chips and Guacamole NULL $4.45 1086 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 1086 1 Chips and Guacamole NULL $4.45 1087 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1087 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1087 1 Chips NULL $2.15 1088 1 Steak Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole]] $11.89 1088 1 Chicken Bowl [Fresh Tomato Salsa, [Pinto Beans, Cheese, Guacamole]] $11.25 1089 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 1089 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1089 1 Bottled Water NULL $1.50 1090 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice]] $8.75 1090 2 Chips NULL $4.30 1091 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1091 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.25 1091 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1092 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.75 1092 1 Chips NULL $2.15 1092 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.25 1093 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1093 1 Bottled Water NULL $1.09 1094 1 Veggie Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1094 1 Veggie Salad [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1095 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1095 1 Izze [Blackberry] $3.39 1095 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Fajita Veggies, Cheese]] $8.99 1096 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 1096 1 Chips and Guacamole NULL $3.99 1097 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1097 1 Chips NULL $2.15 1097 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 1097 1 Chips and Guacamole NULL $4.45 1097 2 Canned Soft Drink [Diet Coke] $2.50 1098 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Guacamole]] $11.48 1098 1 Canned Soda [Sprite] $1.09 1099 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1099 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1100 2 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Fajita Veggies]] $16.98 1101 1 Chips and Guacamole NULL $3.99 1101 1 Veggie Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 1101 1 Canned Soda [Sprite] $1.09 1102 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1102 1 Chips and Fresh Tomato Salsa NULL $2.95 1102 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans]] $8.75 1103 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream]] $8.75 1103 1 Chips and Fresh Tomato Salsa NULL $2.95 1103 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1104 1 Chicken Bowl [Fresh Tomato Salsa, [Cheese, Guacamole, Sour Cream, Fajita Vegetables, Rice]] $11.25 1104 1 Chips NULL $2.15 1105 1 Steak Burrito [Fresh Tomato Salsa, [Pinto Beans, Cheese, Sour Cream]] $9.25 1105 1 Chips and Guacamole NULL $4.45 1106 1 Chips and Guacamole NULL $4.45 1106 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 1106 1 Barbacoa Crispy Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce, Guacamole]] $11.75 1107 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.75 1107 1 Chips NULL $2.15 1108 1 Carnitas Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1108 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1109 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole, Lettuce]] $11.25 1109 1 6 Pack Soft Drink [Diet Coke] $6.49 1110 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1110 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1110 1 Chips and Fresh Tomato Salsa NULL $2.95 1111 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 1111 1 Chips and Guacamole NULL $3.99 1112 1 Bottled Water NULL $1.50 1112 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $9.25 1112 1 Chips NULL $2.15 1113 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Lettuce]] $8.75 1113 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.25 1114 1 Steak Salad Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.89 1114 1 Chips and Guacamole NULL $4.45 1114 1 Bottled Water NULL $1.50 1114 1 Canned Soft Drink [Coke] $1.25 1115 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1115 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1115 1 Chips and Guacamole NULL $4.45 1116 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1116 1 Steak Crispy Tacos [Roasted Chili Corn Salsa, [Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1117 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 1117 1 Canned Soda [Diet Dr. Pepper] $1.09 1117 1 Bottled Water NULL $1.09 1117 1 Side of Chips NULL $1.69 1118 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1118 1 Nantucket Nectar [Apple] $3.39 1119 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1119 1 Side of Chips NULL $1.69 1120 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1120 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1121 3 Bottled Water NULL $3.27 1121 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Lettuce]] $8.99 1122 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1122 1 Canned Soda [Coca Cola] $1.09 1123 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 1123 1 Side of Chips NULL $1.69 1124 1 Chips and Guacamole NULL $3.99 1124 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Cheese, Sour Cream]] $8.49 1124 1 Canned Soda [Coca Cola] $1.09 1125 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1125 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1126 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 1126 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 1127 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $9.25 1127 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 1128 1 Nantucket Nectar [Peach Orange] $3.39 1128 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.99 1128 1 Steak Soft Tacos [Tomatillo-Green Chili Salsa (Medium), [Cheese, Sour Cream]] $8.99 1128 1 Chips and Fresh Tomato Salsa NULL $2.39 1129 1 Steak Crispy Tacos [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1129 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1130 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream]] $9.25 1130 1 Steak Burrito [Tomatillo Red Chili Salsa, [Cheese, Sour Cream]] $9.25 1131 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Cheese, Lettuce]] $8.99 1131 1 Chips and Guacamole NULL $3.99 1131 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1132 1 Veggie Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1132 1 Canned Soda [Diet Coke] $1.09 1132 1 Veggie Soft Tacos [Roasted Chili Corn Salsa (Medium), [Black Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1133 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1133 1 Nantucket Nectar [Apple] $3.39 1134 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Lettuce, Guacamole]] $11.25 1134 1 Canned Soft Drink [Diet Coke] $1.25 1135 1 Steak Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Fajita Veggies, Cheese, Sour Cream]] $8.99 1135 1 Chips and Guacamole NULL $3.99 1136 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream, Lettuce]] $8.75 1136 2 Chips and Guacamole NULL $8.90 1136 1 6 Pack Soft Drink [Diet Coke] $6.49 1137 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1137 1 Steak Crispy Tacos [Tomatillo Red Chili Salsa, [Cheese, Sour Cream]] $9.25 1138 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Guacamole, Lettuce]] $11.25 1138 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole, Lettuce]] $11.25 1139 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Guacamole, Lettuce]] $11.25 1139 1 Canned Soft Drink [Sprite] $1.25 1140 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream]] $8.75 1140 1 Steak Bowl [Roasted Chili Corn Salsa, [Black Beans, Pinto Beans, Cheese, Sour Cream]] $9.25 1140 1 Chips and Guacamole NULL $4.45 1140 1 Canned Soft Drink [Coke] $1.25 1141 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream]] $8.75 1141 1 Canned Soft Drink [Coke] $1.25 1141 1 Chips NULL $2.15 1142 2 Steak Crispy Tacos [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Cheese, Sour Cream, Lettuce]] $17.98 1142 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.99 1143 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $8.75 1143 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1143 1 Canned Soft Drink [Coke] $1.25 1144 1 Steak Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1144 1 Bottled Water NULL $1.50 1144 1 Bottled Water NULL $1.50 1145 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans]] $8.75 1145 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 1146 1 Carnitas Burrito [Fresh Tomato (Mild), [Lettuce, Black Beans, Guacamole, Rice, Sour Cream, Cheese]] $11.08 1147 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1147 1 Side of Chips NULL $1.69 1148 1 Carnitas Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1148 1 Side of Chips NULL $1.69 1149 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Cheese, Guacamole]] $10.98 1149 1 Chips and Guacamole NULL $3.99 1149 1 Izze [Blackberry] $3.39 1150 1 Steak Bowl [Roasted Chili Corn Salsa, [Pinto Beans, Rice, Cheese, Sour Cream, Guacamole]] $11.75 1150 1 Chips NULL $2.15 1151 1 Steak Burrito [Fresh Tomato Salsa, [Sour Cream, Rice, Guacamole, Lettuce]] $11.75 1151 1 Chips and Fresh Tomato Salsa NULL $2.95 1152 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1152 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, Cheese] $8.75 1153 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1153 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1154 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1154 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1154 1 Chips and Guacamole NULL $4.45 1155 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1155 1 Canned Soft Drink [Diet Coke] $1.25 1156 2 Canned Soda [Coca Cola] $2.18 1156 2 Canned Soda [Sprite] $2.18 1156 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1156 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa (Medium), [Cheese, Sour Cream, Lettuce]] $8.49 1156 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1156 1 Chips and Fresh Tomato Salsa NULL $2.39 1157 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.25 1157 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 1158 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce, Guacamole]] $11.75 1158 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Lettuce, Guacamole]] $11.75 1159 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1159 1 Chips and Guacamole NULL $4.45 1159 1 Chips NULL $2.15 1160 1 Chicken Bowl [Fresh Tomato (Mild), [Rice, Sour Cream, Cheese]] $8.19 1160 1 Chicken Bowl [Fresh Tomato (Mild), [Guacamole, Rice]] $10.58 1161 1 Chips and Guacamole NULL $4.45 1161 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $9.25 1162 1 Bottled Water NULL $1.09 1162 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Pinto Beans, Fajita Veggies, Lettuce]] $8.99 1163 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1163 1 Bottled Water NULL $1.50 1164 1 Carnitas Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1164 1 Chips and Guacamole NULL $3.99 1165 1 Chips and Guacamole NULL $4.45 1165 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.75 1165 1 Chips NULL $2.15 1166 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1166 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $8.75 1166 1 Chips and Guacamole NULL $4.45 1167 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1167 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $9.25 1167 3 Chips NULL $6.45 1167 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 1168 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1168 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1169 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1169 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1170 2 Chicken Bowl [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $21.96 1170 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese]] $8.99 1170 3 Side of Chips NULL $5.07 1170 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1171 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $9.25 1171 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 1171 1 Chips and Guacamole NULL $4.45 1172 1 Nantucket Nectar [Peach Orange] $3.39 1172 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1173 1 Carnitas Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1173 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1174 2 Chicken Burrito [Fresh Tomato Salsa, [Cheese, Rice, Black Beans, Lettuce]] $17.50 1175 2 Steak Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $22.96 1176 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1176 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1176 1 Steak Salad Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Guacamole, Lettuce]] $11.89 1176 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Guacamole]] $11.25 1177 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese]] $8.49 1177 1 Side of Chips NULL $1.69 1177 1 Canned Soda [Sprite] $1.09 1178 1 Barbacoa Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1178 1 Steak Crispy Tacos [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1179 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $9.25 1179 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1180 1 Barbacoa Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $9.25 1180 1 Chips and Roasted Chili Corn Salsa NULL $2.95 1181 1 Chicken Salad [Fresh Tomato Salsa (Mild), Black Beans] $8.49 1181 1 Chips and Guacamole NULL $3.99 1182 1 Steak Bowl [Tomatillo-Green Chili Salsa (Medium), [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.99 1182 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Black Beans, Cheese, Lettuce]] $8.49 1182 3 Chips and Tomatillo-Red Chili Salsa NULL $7.17 1182 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Black Beans, Cheese, Lettuce]] $8.49 1182 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Black Beans, Cheese]] $8.99 1183 2 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $17.50 1184 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1184 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1185 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1185 1 Canned Soft Drink [Coke] $1.25 1186 1 Carnitas Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Lettuce]] $8.99 1186 1 Bottled Water NULL $1.09 1187 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1187 1 Chips and Guacamole NULL $4.45 1188 1 Chicken Bowl [Fresh Tomato Salsa, [Sour Cream, Fajita Vegetables, Rice, Guacamole, Cheese]] $11.25 1188 1 Chips NULL $2.15 1188 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1189 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Guacamole]] $11.25 1189 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Guacamole]] $11.25 1189 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 1190 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream]] $8.75 1190 1 Chips and Guacamole NULL $4.45 1190 1 Canned Soft Drink [Diet Coke] $1.25 1191 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 1191 1 Steak Burrito [Fresh Tomato Salsa, [Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 1191 2 Canned Soft Drink [Sprite] $2.50 1192 1 Veggie Salad [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1192 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream]] $8.99 1192 2 Bottled Water NULL $2.18 1193 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1193 1 Chips and Guacamole NULL $4.45 1193 1 Chicken Salad Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Guacamole, Lettuce]] $11.25 1193 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1194 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1194 1 Carnitas Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1194 1 Side of Chips NULL $1.69 1195 1 Canned Soda [Coca Cola] $1.09 1195 1 Steak Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1195 1 Barbacoa Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1196 1 Chicken Bowl [Fresh Tomato Salsa, [Sour Cream, Guacamole, Lettuce]] $11.25 1196 1 6 Pack Soft Drink [Diet Coke] $6.49 1196 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1197 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1197 1 Chips and Guacamole NULL $4.45 1198 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1198 1 Side of Chips NULL $1.69 1199 1 Steak Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Guacamole]] $11.48 1200 1 Barbacoa Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1200 2 Canned Soft Drink [Diet Coke] $2.50 1201 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Cheese, Lettuce, Rice, Black Beans]] $8.49 1201 1 Canned Soda [Diet Dr. Pepper] $1.09 1201 1 Bottled Water NULL $1.09 1201 1 Side of Chips NULL $1.69 1202 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1202 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1203 1 Barbacoa Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Guacamole]] $11.75 1203 1 Canned Soft Drink [Sprite] $1.25 1204 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 1204 1 Side of Chips NULL $1.69 1205 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1205 1 Side of Chips NULL $1.69 1206 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Guacamole]] $11.75 1206 1 Chips and Guacamole NULL $4.45 1206 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Cheese, Lettuce]] $8.75 1207 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1207 1 Chips and Guacamole NULL $4.45 1208 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1208 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1209 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.25 1209 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1210 2 Chips and Guacamole NULL $7.98 1210 1 Chicken Crispy Tacos [Fresh Tomato Salsa (Mild), Fajita Veggies] $8.49 1210 1 Bottled Water NULL $1.09 1211 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Cheese, Sour Cream, Lettuce]] $8.49 1211 1 Chips and Guacamole NULL $3.99 1212 1 Chicken Salad [Fresh Tomato Salsa (Mild), Black Beans] $8.49 1212 1 Chips and Guacamole NULL $3.99 1213 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1213 1 Chips and Guacamole NULL $3.99 1214 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Guacamole]] $11.25 1214 1 Chips and Guacamole NULL $4.45 1215 1 Chicken Bowl [[Tomatillo-Red Chili Salsa (Hot), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Lettuce]] $8.49 1215 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 1216 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1216 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1217 1 Chips and Guacamole NULL $3.99 1217 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1218 1 Canned Soft Drink [Coke] $1.25 1218 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Sour Cream, Cheese, Guacamole]] $11.25 1218 1 Chips and Guacamole NULL $4.45 1219 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1219 1 Chips and Guacamole NULL $4.45 1220 1 Canned Soda [Dr. Pepper] $1.09 1220 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese]] $8.99 1220 1 Chips and Guacamole NULL $3.99 1221 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1221 2 Chips and Guacamole NULL $8.90 1221 1 Carnitas Soft Tacos [Tomatillo Green Chili Salsa, [Cheese, Sour Cream, Lettuce]] $9.25 1222 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1222 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Lettuce]] $8.75 1223 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.25 1223 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1223 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1224 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1224 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1225 1 Steak Crispy Tacos [Fresh Tomato Salsa (Mild), [Cheese, Sour Cream, Lettuce]] $8.99 1225 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1226 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Cheese, Sour Cream]] $8.75 1226 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Cheese, Sour Cream, Lettuce]] $8.75 1226 1 Chips and Guacamole NULL $4.45 1227 2 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Fajita Veggies]] $16.98 1228 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1228 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.25 1228 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 1228 1 Chips and Guacamole NULL $4.45 1229 1 Steak Crispy Tacos [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1229 1 Chips and Fresh Tomato Salsa NULL $2.95 1230 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Fajita Veggies, Lettuce, Rice, Black Beans]] $8.49 1230 1 Side of Chips NULL $1.69 1231 3 Canned Soft Drink [Diet Coke] $3.75 1231 1 Chips and Guacamole NULL $4.45 1231 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1232 1 Bottled Water NULL $1.50 1232 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Sour Cream, Cheese, Guacamole, Lettuce, Black Beans, Rice]] $11.25 1233 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 1234 1 Veggie Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole, Lettuce]] $11.25 1234 1 Chips NULL $2.15 1235 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1235 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.39 1236 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese]] $8.49 1236 1 Chips and Guacamole NULL $3.99 1237 1 Chicken Bowl [Fresh Tomato (Mild), [Lettuce, Fajita Veggies, Black Beans, Rice, Sour Cream, Cheese]] $8.19 1237 1 Chips and Fresh Tomato Salsa NULL $2.29 1238 1 Carnitas Bowl [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Rice, Cheese, Guacamole, Lettuce]] $11.48 1238 1 Side of Chips NULL $1.69 1239 1 Steak Soft Tacos [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Fajita Veggies, Cheese, Guacamole, Lettuce]] $11.48 1239 1 Chips and Guacamole NULL $3.99 1240 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1240 1 Side of Chips NULL $1.69 1241 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1241 1 Chips and Fresh Tomato Salsa NULL $2.95 1242 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1242 1 Side of Chips NULL $1.69 1243 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.25 1243 1 Chips and Guacamole NULL $4.45 1243 1 Carnitas Salad Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.39 1243 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1243 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1244 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese]] $8.75 1244 1 Chips and Guacamole NULL $4.45 1244 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.89 1244 1 Chips and Guacamole NULL $4.45 1245 1 Chips and Guacamole NULL $4.45 1245 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1246 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 1246 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1246 1 Chips NULL $2.15 1247 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Sour Cream, Cheese, Rice]] $8.75 1247 3 Canned Soft Drink [Diet Coke] $3.75 1248 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 1248 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 1248 1 Chips and Guacamole NULL $4.45 1248 1 6 Pack Soft Drink [Diet Coke] $6.49 1249 2 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Fajita Veggies, Rice]] $16.98 1250 2 Steak Burrito [Tomatillo Red Chili Salsa, [Cheese, Black Beans, Rice, Lettuce, Sour Cream]] $18.50 1250 2 Canned Soft Drink [Coke] $2.50 1250 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1251 1 Chips and Guacamole NULL $3.99 1251 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1252 1 Chicken Burrito [[Rice, Cheese]] $8.19 1252 1 Steak Burrito [Fresh Tomato (Mild), [Black Beans, Guacamole, Rice, Sour Cream, Cheese]] $11.08 1253 1 6 Pack Soft Drink [Lemonade] $6.49 1253 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.75 1254 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1254 1 Steak Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.99 1254 1 Canned Soda [Diet Dr. Pepper] $1.09 1255 1 Veggie Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 1255 1 Chips and Guacamole NULL $4.45 1255 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 1256 1 Barbacoa Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1256 1 Side of Chips NULL $1.69 1257 2 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $21.96 1258 2 Canned Soda [Dr. Pepper] $2.18 1258 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Sour Cream]] $8.99 1259 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1259 1 Chips and Fresh Tomato Salsa NULL $2.39 1260 1 Barbacoa Burrito [[Black Beans, Rice, Cheese]] $8.69 1260 1 Side of Chips NULL $1.69 1261 2 Chips and Guacamole NULL $8.90 1261 2 Canned Soft Drink [Diet Coke] $2.50 1261 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 1262 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1262 1 Canned Soda [Coca Cola] $1.09 1263 1 Veggie Salad [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1263 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1264 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 1264 1 Chips and Guacamole NULL $4.45 1265 2 Chicken Salad Bowl [Fresh Tomato Salsa, [Pinto Beans, Cheese, Lettuce]] $17.50 1266 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Lettuce]] $8.75 1266 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Lettuce]] $9.25 1267 1 Chicken Burrito [[Tomatillo-Red Chili Salsa (Hot), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Cheese, Lettuce]] $8.49 1267 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 1268 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1268 1 Chips and Guacamole NULL $4.45 1269 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Lettuce, Pinto Beans, Black Beans, Guacamole, Cheese]] $11.25 1269 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Pinto Beans, Cheese, Sour Cream, Guacamole, Fajita Vegetables, Rice, Black Beans, Lettuce]] $11.25 1270 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 1270 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese]] $8.75 1271 1 Chicken Bowl [Fresh Tomato Salsa (Mild), Lettuce] $8.49 1271 1 Chicken Bowl [Fresh Tomato Salsa (Mild), Cheese] $8.49 1271 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), Cheese] $8.49 1271 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), Lettuce] $8.99 1272 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1272 1 Chips and Guacamole NULL $4.45 1273 1 Barbacoa Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Guacamole]] $11.48 1274 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1274 1 Canned Soft Drink [Coke] $1.25 1274 1 Chips NULL $2.15 1275 1 Steak Burrito [Fresh Tomato Salsa, [Pinto Beans, Cheese, Sour Cream]] $9.25 1275 1 Chips and Guacamole NULL $4.45 1276 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.75 1276 1 Chips NULL $2.15 1276 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 1277 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Pinto Beans, Cheese, Lettuce]] $8.99 1277 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1278 1 Barbacoa Bowl [Fresh Tomato (Mild), [Lettuce, Black Beans, Rice]] $8.69 1278 1 Steak Bowl [Fresh Tomato (Mild), [Lettuce, Black Beans, Rice]] $8.69 1279 1 Steak Burrito [Fresh Tomato Salsa, [Guacamole, Lettuce, Sour Cream, Rice]] $11.75 1279 1 Chips and Fresh Tomato Salsa NULL $2.95 1280 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 1280 1 Bottled Water NULL $1.50 1281 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1281 1 Chips and Guacamole NULL $4.45 1281 1 Bottled Water NULL $1.50 1282 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $11.48 1282 1 Steak Soft Tacos [Fresh Tomato Salsa (Mild), [Cheese, Sour Cream]] $8.99 1282 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1283 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 1283 1 Barbacoa Salad Bowl [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Sour Cream, Guacamole]] $11.89 1283 1 Chips NULL $2.15 1283 1 Canned Soft Drink [Diet Coke] $1.25 1284 1 Barbacoa Soft Tacos [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1284 1 Chips and Guacamole NULL $4.45 1285 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Lettuce]] $8.75 1285 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Lettuce]] $8.75 1286 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1286 1 Chips and Guacamole NULL $4.45 1287 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1287 1 Chips NULL $2.15 1287 1 Chips and Guacamole NULL $4.45 1288 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Sour Cream]] $8.49 1288 1 Canned Soda [Mountain Dew] $1.09 1288 1 Chips and Guacamole NULL $3.99 1289 1 Veggie Salad Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole]] $11.25 1289 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1290 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 1290 1 Chips and Fresh Tomato Salsa NULL $2.39 1291 1 Steak Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 1291 1 Chips and Fresh Tomato Salsa NULL $2.39 1292 1 Chicken Bowl [Fresh Tomato Salsa, [Guacamole, Cheese, Rice, Sour Cream, Fajita Vegetables]] $11.25 1292 1 Chips NULL $2.15 1293 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1293 1 Chips and Fresh Tomato Salsa NULL $2.95 1293 1 Canned Soft Drink [Coke] $1.25 1294 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1294 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1295 2 Chicken Burrito [Fresh Tomato Salsa, [Sour Cream, Cheese, Pinto Beans, Rice]] $17.50 1296 1 Steak Burrito [Fresh Tomato Salsa, [Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1296 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1297 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1297 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1298 1 Chips and Guacamole NULL $4.45 1298 2 Canned Soft Drink [Diet Coke] $2.50 1298 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 1299 1 Carnitas Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Fajita Veggies, Lettuce]] $8.99 1299 1 Barbacoa Bowl [[Tomatillo-Red Chili Salsa (Hot), Tomatillo-Green Chili Salsa (Medium)], [Rice, Pinto Beans, Fajita Veggies, Lettuce]] $8.99 1299 1 Side of Chips NULL $1.69 1300 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1300 1 Side of Chips NULL $1.69 1301 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1301 1 Canned Soft Drink [Diet Coke] $1.25 1302 1 Steak Burrito [Fresh Tomato (Mild), [Lettuce, Guacamole, Pinto Beans, Rice, Sour Cream, Cheese]] $11.08 1302 1 Carnitas Burrito [Fresh Tomato (Mild), [Lettuce, Rice]] $8.69 1303 1 Barbacoa Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1303 1 Canned Soda [Sprite] $1.09 1304 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.25 1304 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.25 1304 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1304 1 Canned Soft Drink [Sprite] $1.25 1305 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Lettuce]] $8.75 1305 1 Canned Soft Drink [Diet Coke] $1.25 1305 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce]] $8.75 1306 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 1306 1 Chips NULL $2.15 1306 1 Canned Soft Drink [Sprite] $1.25 1307 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1307 1 Chips and Guacamole NULL $3.99 1308 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1308 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1309 1 Chicken Bowl [[Tomatillo-Red Chili Salsa (Hot), Tomatillo-Green Chili Salsa (Medium)], [Rice, Black Beans, Cheese, Lettuce]] $8.49 1309 3 Chips and Tomatillo-Red Chili Salsa NULL $7.17 1310 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1310 1 Chips and Guacamole NULL $4.45 1311 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Guacamole, Lettuce, Sour Cream, Rice, Cheese, Black Beans]] $11.48 1312 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1312 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese]] $8.75 1313 1 Barbacoa Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 1314 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1314 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1315 1 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Cheese, Lettuce]] $8.49 1315 1 Chips and Guacamole NULL $3.99 1316 1 Bottled Water NULL $1.50 1316 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.25 1317 1 Chicken Bowl [Fresh Tomato Salsa, [Lettuce, Fajita Vegetables, Guacamole, Rice, Black Beans]] $11.25 1317 1 Chicken Bowl [Fresh Tomato Salsa, [Lettuce, Cheese, Pinto Beans, Rice]] $8.75 1318 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $8.75 1318 1 Chips and Guacamole NULL $4.45 1319 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1319 1 Chips and Guacamole NULL $4.45 1320 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $8.75 1320 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Cheese]] $8.75 1320 2 Chips NULL $4.30 1320 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1321 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1321 3 Bottled Water NULL $4.50 1322 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce, Pinto Beans]] $9.25 1322 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Guacamole, Cheese, Sour Cream]] $11.25 1322 1 Chips and Guacamole NULL $4.45 1323 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1323 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1323 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1323 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Black Beans, Cheese, Sour Cream]] $9.25 1324 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 1324 1 Chips NULL $2.15 1324 1 Canned Soft Drink [Sprite] $1.25 1325 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1325 1 Chips NULL $2.15 1325 1 Canned Soft Drink [Nestea] $1.25 1326 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese]] $8.99 1326 1 Chicken Crispy Tacos [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Cheese]] $8.49 1327 1 Chicken Salad Bowl [Fresh Tomato Salsa] $8.75 1327 1 Chicken Bowl [Fresh Tomato Salsa] $8.75 1327 1 Canned Soft Drink [Sprite] $1.25 1328 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.75 1328 3 Bottled Water NULL $4.50 1329 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Lettuce]] $8.75 1329 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 1330 1 Chips and Guacamole NULL $3.99 1330 1 Nantucket Nectar [Peach Orange] $3.39 1330 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 1330 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1330 1 Barbacoa Crispy Tacos [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1331 1 Bottled Water NULL $1.50 1331 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1331 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1332 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1332 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1332 1 Chips NULL $2.15 1333 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 1333 1 Chips NULL $2.15 1333 1 Canned Soft Drink [Sprite] $1.25 1334 2 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $21.96 1335 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1335 1 Side of Chips NULL $1.69 1336 3 Steak Bowl [Tomatillo Green Chili (Medium), [Rice, Black Beans, Sour Cream, Cheese]] $26.07 1337 1 Steak Bowl [Fresh Tomato Salsa, [Cheese, Black Beans, Pinto Beans, Guacamole, Sour Cream]] $11.75 1337 1 Bottled Water NULL $1.50 1338 1 Steak Bowl [[Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Lettuce]] $8.99 1338 1 Carnitas Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Rice, Pinto Beans, Lettuce]] $8.99 1339 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 1340 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Cheese, Sour Cream, Lettuce]] $9.25 1340 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1341 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1341 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese]] $8.75 1341 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Lettuce]] $8.75 1341 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1341 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice]] $8.75 1342 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Lettuce, Cheese]] $8.75 1342 1 Chips NULL $2.15 1342 1 Canned Soft Drink [Sprite] $1.25 1343 1 Steak Salad Bowl [Fresh Tomato Salsa, [Cheese, Guacamole, Lettuce]] $11.89 1343 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1344 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 1344 1 Chips and Guacamole NULL $4.45 1345 2 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $18.50 1346 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Lettuce]] $9.25 1346 1 Barbacoa Salad Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.39 1347 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1347 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 1348 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1348 1 Side of Chips NULL $1.69 1348 1 Bottled Water NULL $1.09 1349 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Cheese, Sour Cream]] $8.99 1349 1 Chicken Salad [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 1349 2 Canned Soda [Coca Cola] $2.18 1350 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.25 1350 1 Canned Soft Drink [Nestea] $1.25 1351 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1351 1 Nantucket Nectar [Pineapple Orange Banana] $3.39 1352 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1352 1 Chips and Guacamole NULL $3.99 1353 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1353 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1354 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1354 1 Side of Chips NULL $1.69 1355 2 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream]] $16.98 1356 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $9.25 1356 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $9.25 1356 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole]] $11.75 1356 1 Canned Soft Drink [Diet Coke] $1.25 1357 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Cheese, Guacamole, Lettuce]] $11.25 1357 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Cheese, Lettuce]] $8.75 1358 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Black Beans, Cheese, Lettuce]] $8.49 1358 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Rice, Black Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1359 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $8.75 1359 1 Canned Soft Drink [Diet Coke] $1.25 1359 1 Canned Soft Drink [Lemonade] $1.25 1359 1 Canned Soft Drink [Nestea] $1.25 1360 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole, Lettuce]] $11.25 1360 2 6 Pack Soft Drink [Diet Coke] $12.98 1360 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1360 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1360 1 Chips and Guacamole NULL $4.45 1361 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1361 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1362 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 1362 1 Chicken Bowl [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Sour Cream, Lettuce]] $8.49 1363 1 Veggie Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Cheese, Sour Cream, Lettuce]] $8.49 1363 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1364 1 Steak Burrito [Fresh Tomato Salsa, [Pinto Beans, Lettuce, Cheese, Rice]] $9.25 1364 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Guacamole, Cheese, Sour Cream]] $11.25 1365 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Cheese, Lettuce, Rice, Pinto Beans]] $8.75 1365 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1365 1 Bottled Water NULL $1.50 1366 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1366 1 Chips and Fresh Tomato Salsa NULL $2.95 1367 1 Steak Crispy Tacos [Fresh Tomato Salsa, Lettuce] $9.25 1367 1 Chips and Fresh Tomato Salsa NULL $2.95 1367 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1367 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1368 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Guacamole, Lettuce]] $11.25 1368 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Pinto Beans, Lettuce]] $8.75 1369 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 1369 1 Chips and Guacamole NULL $4.45 1370 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1370 1 Canned Soft Drink [Coke] $1.25 1371 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $8.75 1371 1 Chips and Fresh Tomato Salsa NULL $2.95 1371 2 Canned Soft Drink [Diet Coke] $2.50 1372 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Cheese]] $9.25 1372 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1373 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 1373 1 6 Pack Soft Drink [Coke] $6.49 1374 2 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $17.50 1375 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Green Chili Salsa (Medium)], [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1375 1 Side of Chips NULL $1.69 1376 1 Chicken Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 1376 1 Chips and Guacamole NULL $3.99 1377 1 Chicken Bowl [Fresh Tomato Salsa, [Cheese, Black Beans, Rice]] $8.75 1377 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1377 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1378 1 Chicken Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Cheese, Lettuce]] $8.49 1378 1 Steak Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Lettuce]] $8.99 1379 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream]] $8.99 1379 1 Canned Soda [Sprite] $1.09 1380 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1380 1 Chips and Guacamole NULL $4.45 1380 1 Canned Soft Drink [Coke] $1.25 1381 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1381 1 Steak Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1382 1 Chicken Bowl [Roasted Chili Corn Salsa, [Black Beans, Cheese, Guacamole]] $11.25 1382 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 1383 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Guacamole, Lettuce]] $11.25 1383 1 Bottled Water NULL $1.50 1384 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Guacamole]] $11.25 1384 1 Chips NULL $2.15 1385 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 1386 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 1386 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.75 1387 1 Chips and Roasted Chili Corn Salsa NULL $2.95 1387 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1388 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 1388 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Sour Cream, Lettuce]] $9.25 1389 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce]] $8.75 1389 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese]] $9.25 1389 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese]] $8.75 1390 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese]] $8.75 1390 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese]] $8.75 1390 1 Canned Soft Drink [Coke] $1.25 1391 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 1391 1 Bottled Water NULL $1.50 1391 1 Chips NULL $2.15 1392 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1392 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1392 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.25 1393 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 1393 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Lettuce]] $8.75 1394 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Guacamole]] $11.25 1394 1 Chips and Guacamole NULL $4.45 1395 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Sour Cream, Lettuce]] $8.49 1395 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies, Sour Cream]] $8.49 1395 1 Veggie Salad [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1396 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa (Medium), [Cheese, Sour Cream, Lettuce]] $8.49 1396 1 Steak Soft Tacos [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream]] $8.99 1396 1 Side of Chips NULL $1.69 1396 1 Chips and Fresh Tomato Salsa NULL $2.39 1396 1 Canned Soda [Coca Cola] $1.09 1396 1 Canned Soda [Dr. Pepper] $1.09 1397 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1397 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1398 3 Carnitas Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $35.25 1399 1 Chicken Bowl [Fresh Tomato Salsa, [Cheese, Rice, Black Beans]] $8.75 1399 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1399 1 Canned Soft Drink [Sprite] $1.25 1400 1 Chicken Bowl [Fresh Tomato Salsa, [Guacamole, Sour Cream, Cheese, Rice, Fajita Vegetables]] $11.25 1400 1 Chips NULL $2.15 1401 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Lettuce]] $9.25 1401 1 Chips and Guacamole NULL $4.45 1402 1 Chicken Burrito [Roasted Chili Corn Salsa, Fajita Vegetables] $8.75 1402 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 1402 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Sour Cream]] $8.75 1402 1 Chips and Guacamole NULL $4.45 1403 1 Canned Soft Drink [Diet Coke] $1.25 1403 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole, Lettuce]] $11.89 1404 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 1404 1 Chips NULL $2.15 1404 1 Canned Soft Drink [Diet Coke] $1.25 1405 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 1405 1 Canned Soda [Diet Dr. Pepper] $1.09 1405 1 Bottled Water NULL $1.09 1405 1 Side of Chips NULL $1.69 1406 1 Steak Burrito [[Lettuce, Fajita Veggies, Rice]] $8.69 1406 1 Steak Salad [[Lettuce, Fajita Veggies]] $8.69 1407 1 Steak Crispy Tacos [Fresh Tomato Salsa] $9.25 1407 1 Chips and Fresh Tomato Salsa NULL $2.95 1408 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Guacamole, Lettuce]] $10.98 1408 1 Chips and Fresh Tomato Salsa NULL $2.39 1409 2 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Black Beans, Cheese, Guacamole]] $22.50 1410 2 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $21.96 1411 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Fajita Veggies, Guacamole]] $10.98 1411 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1411 1 Side of Chips NULL $1.69 1412 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.75 1412 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1413 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese]] $8.75 1413 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1414 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Sour Cream]] $8.75 1414 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1414 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Sour Cream]] $8.75 1415 1 Chicken Burrito [[Rice, Cheese]] $8.19 1415 1 Chicken Burrito [Fresh Tomato (Mild), [Guacamole, Rice, Sour Cream, Cheese]] $10.58 1416 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Cheese, Lettuce, Pinto Beans, Rice]] $8.75 1416 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1416 1 Bottled Water NULL $1.50 1417 2 Canned Soft Drink [Coke] $2.50 1417 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $9.25 1417 1 Chips and Guacamole NULL $4.45 1418 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans]] $9.25 1418 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1419 1 Carnitas Burrito [Fresh Tomato (Mild), [Lettuce, Rice]] $8.69 1419 1 Steak Burrito [Fresh Tomato (Mild), [Lettuce, Pinto Beans, Rice, Sour Cream, Cheese]] $8.69 1419 1 Chips and Guacamole NULL $3.89 1420 1 Steak Burrito [[Lettuce, Fajita Veggies, Rice]] $8.69 1420 1 Side of Chips NULL $1.69 1421 1 Chips and Guacamole NULL $4.45 1421 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1422 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1422 1 Chips and Guacamole NULL $4.45 1422 1 Chips NULL $2.15 1423 1 Chicken Burrito [[Lettuce, Fajita Veggies, Rice]] $8.19 1423 1 Steak Burrito [[Lettuce, Fajita Veggies, Rice]] $8.69 1424 1 Chips and Guacamole NULL $4.45 1424 1 Veggie Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1425 1 Canned Soft Drink [Coke] $1.25 1425 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1425 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1426 1 Barbacoa Salad Bowl [Fresh Tomato Salsa, Guacamole] $11.89 1426 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1426 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Guacamole, Lettuce]] $11.75 1426 1 Barbacoa Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1427 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1427 1 Chips NULL $2.15 1427 1 Bottled Water NULL $1.50 1428 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.75 1428 1 Chips and Guacamole NULL $4.45 1429 1 Chips and Guacamole NULL $3.99 1429 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.99 1429 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Guacamole]] $10.98 1429 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 1430 1 Canned Soft Drink [Sprite] $1.25 1430 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1430 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 1431 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1431 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1431 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1431 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 1431 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1431 1 Chips NULL $2.15 1432 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $9.25 1432 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1433 1 Nantucket Nectar [Pineapple Orange Banana] $3.39 1433 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Pinto Beans, Rice, Cheese, Sour Cream]] $8.49 1433 1 Chicken Soft Tacos [Tomatillo-Green Chili Salsa (Medium), [Cheese, Sour Cream]] $8.49 1433 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1434 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $10.98 1434 1 Canned Soda [Dr. Pepper] $1.09 1434 1 Canned Soda [Coca Cola] $1.09 1434 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream]] $8.49 1435 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 1435 1 Canned Soft Drink [Diet Coke] $1.25 1436 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1436 1 Bottled Water NULL $1.09 1437 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 1437 1 Chips NULL $2.15 1437 1 Bottled Water NULL $1.50 1438 1 Chips and Guacamole NULL $3.99 1438 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1439 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1439 1 Chips NULL $2.15 1439 1 Bottled Water NULL $1.50 1440 1 Chicken Burrito [Tomatillo-Green Chili Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1440 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Guacamole, Lettuce]] $10.98 1440 2 Canned Soda [Diet Coke] $2.18 1441 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 1441 1 Chips NULL $2.15 1441 1 Bottled Water NULL $1.50 1442 1 Chips and Guacamole NULL $4.45 1442 1 Steak Soft Tacos [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 1443 15 Chips and Fresh Tomato Salsa NULL $44.25 1443 7 Bottled Water NULL $10.50 1443 1 6 Pack Soft Drink [Coke] $6.49 1443 3 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $33.75 1443 4 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $35.00 1443 3 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $27.75 1443 2 Bottled Water NULL $3.00 1444 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.25 1444 1 Steak Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1445 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Guacamole, Lettuce]] $10.98 1445 1 Chips and Fresh Tomato Salsa NULL $2.39 1446 2 Canned Soft Drink [Diet Coke] $2.50 1446 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese]] $8.75 1446 1 Chips and Guacamole NULL $4.45 1447 2 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Cheese, Sour Cream, Fajita Veggies, Pinto Beans, Rice]] $16.98 1448 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1448 1 6 Pack Soft Drink [Nestea] $6.49 1449 2 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese]] $16.98 1449 2 Steak Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese]] $17.98 1449 2 Veggie Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $16.98 1449 2 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Cheese]] $16.98 1449 1 Carnitas Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Lettuce]] $8.99 1449 1 Carnitas Soft Tacos [Fresh Tomato Salsa (Mild), [Rice, Cheese, Lettuce]] $8.99 1449 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Cheese, Sour Cream, Lettuce]] $8.49 1450 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Lettuce]] $9.25 1450 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Lettuce]] $8.75 1450 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1451 1 Steak Soft Tacos [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium)], [Rice, Fajita Veggies, Cheese, Guacamole, Lettuce]] $11.48 1451 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1452 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Pinto Beans, Lettuce]] $8.99 1452 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1453 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 1453 1 Chips and Guacamole NULL $4.45 1453 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1453 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1454 1 6 Pack Soft Drink [Coke] $6.49 1454 3 Chicken Burrito [Fresh Tomato Salsa] $26.25 1454 1 Chicken Soft Tacos [Fresh Tomato Salsa, Rice] $8.75 1454 3 Chicken Burrito [Fresh Tomato Salsa, Rice] $26.25 1454 1 Chicken Soft Tacos [Fresh Tomato Salsa, Rice] $8.75 1454 1 Chicken Crispy Tacos [Fresh Tomato Salsa, Rice] $8.75 1455 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1455 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Guacamole, Lettuce]] $11.25 1455 1 Chips NULL $2.15 1456 1 Canned Soft Drink [Sprite] $1.25 1456 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1457 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1457 1 Chips NULL $2.15 1457 1 Bottled Water NULL $1.50 1458 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Fajita Vegetables]] $11.25 1458 1 Chips NULL $2.15 1459 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 1459 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 1460 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.25 1460 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Guacamole]] $11.25 1460 1 Chips NULL $2.15 1461 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 1461 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1461 1 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Lettuce]] $8.75 1462 2 Chips and Tomatillo-Green Chili Salsa NULL $4.78 1462 2 Chicken Soft Tacos [[Tomatillo-Green Chili Salsa (Medium), Fresh Tomato Salsa (Mild)], [Cheese, Sour Cream, Guacamole]] $21.96 1463 1 Veggie Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1463 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 1464 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $9.25 1464 1 Chips and Fresh Tomato Salsa NULL $2.95 1465 2 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Fajita Veggies]] $16.98 1466 1 Chicken Bowl [Fresh Tomato (Mild), [Lettuce, Fajita Veggies, Pinto Beans, Rice, Sour Cream, Cheese]] $8.19 1466 1 Chips and Guacamole NULL $3.89 1467 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1467 1 Bottled Water NULL $1.09 1468 1 Steak Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 1468 1 Izze [Blackberry] $3.39 1469 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1469 1 Chips and Guacamole NULL $4.45 1470 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $10.98 1470 1 Chicken Bowl [[Tomatillo-Green Chili Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $10.98 1471 2 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $17.50 1472 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1472 1 Canned Soft Drink [Coke] $1.25 1472 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Lettuce]] $8.75 1473 1 Steak Burrito [Fresh Tomato Salsa, [Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1473 1 Chips and Guacamole NULL $4.45 1474 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.25 1474 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Rice, Guacamole]] $11.25 1475 1 Steak Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.99 1475 1 Canned Soda [Coca Cola] $1.09 1476 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Black Beans, Fajita Veggies, Guacamole]] $10.98 1476 1 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot), Fresh Tomato Salsa (Mild)], [Rice, Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1476 1 Side of Chips NULL $1.69 1477 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Black Beans, Guacamole]] $11.25 1477 1 Carnitas Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1478 2 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Sour Cream, Cheese, Black Beans, Rice]] $16.98 1479 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 1479 1 Chips and Guacamole NULL $4.45 1480 1 Chicken Burrito [Roasted Chili Corn Salsa, [Guacamole, Lettuce, Rice, Fajita Vegetables, Sour Cream]] $11.25 1480 1 6 Pack Soft Drink [Diet Coke] $6.49 1480 1 Steak Crispy Tacos [Tomatillo Green Chili Salsa, [Cheese, Sour Cream, Guacamole]] $11.75 1481 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1481 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1482 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1482 1 Chips and Guacamole NULL $4.45 1482 2 Canned Soft Drink [Diet Coke] $2.50 1483 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1483 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1483 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Cheese]] $8.75 1483 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1483 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1483 1 Steak Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans]] $9.25 1483 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1483 1 Steak Bowl [Roasted Chili Corn Salsa, [Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1483 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream]] $8.75 1483 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1483 1 Steak Soft Tacos [Fresh Tomato Salsa, Guacamole] $11.75 1483 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Lettuce]] $9.25 1483 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1483 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1484 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream]] $8.49 1484 1 Canned Soda [Dr. Pepper] $1.09 1484 1 Canned Soda [Dr. Pepper] $1.09 1485 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 1485 1 Canned Soda [Diet Dr. Pepper] $1.09 1485 1 Side of Chips NULL $1.69 1486 1 Chicken Bowl [Fresh Tomato Salsa, [Guacamole, Lettuce, Rice, Cheese, Sour Cream, Black Beans]] $11.25 1486 1 Canned Soft Drink [Lemonade] $1.25 1487 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables]] $8.75 1487 1 Chips NULL $2.15 1487 1 Bottled Water NULL $1.50 1488 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1488 1 Side of Chips NULL $1.69 1489 1 Canned Soft Drink [Diet Coke] $1.25 1489 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 1489 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1490 1 Chicken Salad [Fresh Tomato Salsa (Mild), Black Beans] $8.49 1490 1 Chips and Guacamole NULL $3.99 1491 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice]] $8.49 1491 1 Chicken Salad [Fresh Tomato Salsa (Mild), [Black Beans, Cheese, Lettuce]] $8.49 1492 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1492 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1492 1 Chips NULL $2.15 1493 1 Bottled Water NULL $1.50 1493 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1494 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Rice, Cheese, Sour Cream]] $8.99 1494 1 Canned Soda [Sprite] $1.09 1495 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 1496 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Fajita Veggies, Guacamole, Lettuce]] $10.98 1497 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1497 1 Canned Soft Drink [Sprite] $1.25 1498 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 1498 1 Chips and Guacamole NULL $4.45 1499 1 Chips and Guacamole NULL $4.45 1499 1 Canned Soft Drink [Sprite] $1.25 1499 1 Carnitas Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.89 1500 1 Carnitas Salad [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Sour Cream]] $8.99 1500 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Pinto Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.99 1501 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1501 1 Chips and Guacamole NULL $4.45 1502 2 Steak Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $23.50 1503 1 Veggie Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1503 1 Chips and Guacamole NULL $3.99 1504 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1504 1 Chips and Guacamole NULL $4.45 1505 1 Chips and Guacamole NULL $4.45 1505 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $9.25 1506 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1506 1 Chips and Guacamole NULL $4.45 1507 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1507 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1508 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1508 1 6 Pack Soft Drink [Coke] $6.49 1509 2 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $17.50 1509 1 Chips and Guacamole NULL $4.45 1510 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1510 1 Chips and Fresh Tomato Salsa NULL $2.95 1510 1 Bottled Water NULL $1.50 1511 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $8.75 1511 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice]] $8.75 1511 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1511 1 Chips and Guacamole NULL $4.45 1511 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1512 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1512 1 Steak Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1513 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1513 1 Chips and Roasted Chili Corn Salsa NULL $2.95 1514 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1514 1 Chips and Guacamole NULL $4.45 1514 2 Bottled Water NULL $3.00 1514 1 Canned Soft Drink [Diet Coke] $1.25 1515 1 Chips and Fresh Tomato Salsa NULL $2.95 1515 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Pinto Beans, Sour Cream, Cheese]] $9.25 1516 1 Steak Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.99 1516 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1517 1 6 Pack Soft Drink [Diet Coke] $6.49 1517 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream, Lettuce]] $8.75 1517 2 Chips and Guacamole NULL $8.90 1518 1 Carnitas Bowl [Fresh Tomato Salsa (Mild), [Fajita Veggies, Cheese, Sour Cream, Guacamole]] $11.48 1518 1 Canned Soda [Diet Coke] $1.09 1519 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $10.98 1520 1 Barbacoa Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Pinto Beans, Sour Cream, Cheese]] $9.25 1520 1 Steak Soft Tacos [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $9.25 1521 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Guacamole, Lettuce]] $11.25 1521 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce]] $8.75 1522 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.75 1522 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole]] $11.25 1523 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1523 1 Canned Soft Drink [Nestea] $1.25 1524 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream]] $9.25 1524 1 Chips and Guacamole NULL $4.45 1525 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1525 1 6 Pack Soft Drink [Sprite] $6.49 1526 1 Steak Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1526 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1527 1 Barbacoa Burrito [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream]] $8.99 1527 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1528 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 1528 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1529 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1529 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1530 1 Canned Soft Drink [Coke] $1.25 1530 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1530 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1531 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.75 1531 1 Chips NULL $2.15 1532 1 Veggie Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1532 1 Veggie Burrito [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1533 1 Barbacoa Bowl [Fresh Tomato (Mild), [Lettuce, Rice, Cheese]] $8.69 1533 2 Chicken Burrito [[Lettuce, Rice]] $16.38 1533 1 Chicken Burrito [Rice] $8.19 1533 1 Chips and Guacamole NULL $3.89 1533 1 Chips and Fresh Tomato Salsa NULL $2.29 1534 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole]] $11.75 1534 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1534 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1535 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce]] $8.75 1535 1 Chips and Guacamole NULL $4.45 1536 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1536 1 Chicken Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1537 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 1537 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1537 1 6 Pack Soft Drink [Coke] $6.49 1537 1 Chips and Fresh Tomato Salsa NULL $2.95 1538 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1538 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1538 1 Chips NULL $2.15 1539 1 Steak Burrito [Fresh Tomato Salsa, [Pinto Beans, Cheese, Sour Cream]] $9.25 1539 1 Steak Burrito [Fresh Tomato Salsa, [Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 1540 2 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Rice, Pinto Beans, Fajita Veggies, Cheese, Sour Cream, Guacamole]] $21.96 1541 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 1541 1 Nantucket Nectar [Peach Orange] $3.39 1541 1 Side of Chips NULL $1.69 1541 1 Veggie Burrito [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1542 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1542 1 Chips and Guacamole NULL $4.45 1543 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1543 1 Chips and Guacamole NULL $3.99 1544 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 1544 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 1545 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1545 1 Chips NULL $2.15 1546 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1546 1 Chips and Guacamole NULL $4.45 1547 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1547 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $9.25 1547 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese]] $8.75 1548 2 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $18.50 1549 1 Bottled Water NULL $1.50 1549 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1549 1 Chips NULL $2.15 1550 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Guacamole, Lettuce]] $11.48 1550 2 Canned Soda [Mountain Dew] $2.18 1550 1 Chips and Guacamole NULL $3.99 1551 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1551 1 Bottled Water NULL $1.50 1552 1 Steak Soft Tacos [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Cheese, Sour Cream]] $8.99 1552 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1553 1 Steak Burrito [Roasted Chili Corn Salsa, [Pinto Beans, Rice, Guacamole, Sour Cream, Cheese]] $11.75 1553 1 Bottled Water NULL $1.50 1554 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1554 1 6 Pack Soft Drink [Diet Coke] $6.49 1554 1 Chips and Guacamole NULL $4.45 1554 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1555 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1555 1 Chips and Guacamole NULL $3.99 1556 2 Canned Soft Drink [Coke] $2.50 1556 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1556 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1557 1 Chips and Guacamole NULL $3.99 1557 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Pinto Beans, Cheese]] $8.99 1558 1 Chicken Bowl [Fresh Tomato Salsa, Cheese] $8.75 1558 1 6 Pack Soft Drink [Diet Coke] $6.49 1559 8 Side of Chips NULL $13.52 1559 2 Chicken Soft Tacos [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Lettuce]] $16.98 1559 2 Veggie Soft Tacos [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Lettuce]] $16.98 1559 2 Carnitas Crispy Tacos [Fresh Tomato Salsa (Mild), [Cheese, Lettuce]] $17.98 1559 2 Chicken Crispy Tacos [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sour Cream, Lettuce]] $16.98 1560 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1560 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1561 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1561 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1562 1 Chicken Burrito [[Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1562 1 Canned Soda [Mountain Dew] $1.09 1562 1 Side of Chips NULL $1.69 1563 1 Canned Soft Drink [Lemonade] $1.25 1563 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1563 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1564 1 Barbacoa Burrito [Fresh Tomato (Mild), [Black Beans, Rice, Sour Cream, Cheese]] $8.69 1564 1 Chicken Burrito [Fresh Tomato (Mild), [Black Beans, Rice, Sour Cream, Cheese]] $8.19 1565 1 Chicken Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1565 1 Chips and Roasted Chili-Corn Salsa NULL $2.39 1566 1 Chicken Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1566 1 Chips and Fresh Tomato Salsa NULL $2.39 1567 1 Chicken Bowl [[Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium)], [Rice, Fajita Veggies, Guacamole, Lettuce]] $10.98 1568 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1568 1 Steak Soft Tacos [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.99 1569 1 Side of Chips NULL $1.69 1569 1 Veggie Burrito [Fresh Tomato Salsa (Mild), [Black Beans, Fajita Veggies, Cheese, Sour Cream, Lettuce]] $8.49 1570 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1570 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1571 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Pinto Beans, Cheese, Lettuce, Guacamole]] $11.25 1571 1 Chips and Guacamole NULL $4.45 1572 1 Chips and Guacamole NULL $4.45 1572 2 Chicken Burrito [Tomatillo Red Chili Salsa, [Cheese, Black Beans, Rice]] $17.50 1573 1 Chicken Burrito [Fresh Tomato Salsa, [Sour Cream, Cheese, Rice, Black Beans]] $8.75 1573 1 Chips and Guacamole NULL $4.45 1574 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1574 1 Chips NULL $2.15 1574 1 Bottled Water NULL $1.50 1574 1 Bottled Water NULL $1.50 1575 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies]] $8.99 1575 1 Canned Soda [Dr. Pepper] $1.09 1576 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $8.75 1576 1 Chips and Guacamole NULL $4.45 1577 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1577 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce]] $8.75 1577 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 1577 2 Chips NULL $4.30 1578 1 Chicken Bowl [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.49 1578 1 Side of Chips NULL $1.69 1578 1 Bottled Water NULL $1.09 1578 1 Canned Soda [Diet Dr. Pepper] $1.09 1579 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 1579 1 Chips NULL $2.15 1579 1 Bottled Water NULL $1.50 1580 1 Carnitas Soft Tacos [Tomatillo-Red Chili Salsa (Hot), [Cheese, Sour Cream, Lettuce]] $8.99 1580 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1580 1 Chips and Guacamole NULL $3.99 1581 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1581 1 Chips NULL $2.15 1581 1 Canned Soft Drink [Nestea] $1.25 1582 1 Steak Crispy Tacos [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1582 1 Chips and Fresh Tomato Salsa NULL $2.95 1583 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1583 1 Chips and Guacamole NULL $4.45 1584 1 Steak Crispy Tacos [Fresh Tomato Salsa, [Sour Cream, Lettuce, Rice, Cheese]] $9.25 1584 1 Chips and Fresh Tomato Salsa NULL $2.95 1585 1 Chips and Guacamole NULL $3.99 1585 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.99 1585 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Fajita Veggies, Cheese, Lettuce]] $8.49 1586 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $8.75 1586 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1586 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Pinto Beans, Guacamole, Lettuce]] $11.25 1586 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1586 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Lettuce]] $8.75 1586 1 Chips and Guacamole NULL $4.45 1587 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Guacamole]] $11.25 1587 1 Canned Soft Drink [Coke] $1.25 1588 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Cheese]] $9.25 1588 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1588 1 Chips and Guacamole NULL $4.45 1588 1 Chips and Fresh Tomato Salsa NULL $2.95 1589 1 Chicken Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Sour Cream, Guacamole]] $10.98 1590 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Fajita Vegetables, Lettuce, Cheese, Sour Cream]] $8.75 1590 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1591 2 Steak Burrito [Fresh Tomato Salsa, [Lettuce, Sour Cream, Cheese, Fajita Vegetables, Pinto Beans, Rice]] $18.50 1592 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $9.25 1592 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $9.25 1592 4 Canned Soft Drink [Coke] $5.00 1592 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Lettuce]] $8.75 1592 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1593 2 Chicken Bowl [[Roasted Chili Corn Salsa (Medium), Fresh Tomato Salsa (Mild)], [Rice, Fajita Veggies]] $16.98 1594 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.25 1594 1 Chips and Fresh Tomato Salsa NULL $2.95 1595 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1595 1 Chips NULL $2.15 1595 1 Bottled Water NULL $1.50 1596 1 Chicken Salad [Fresh Tomato Salsa (Mild), [Pinto Beans, Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1596 1 Chips and Fresh Tomato Salsa NULL $2.39 1597 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese]] $9.25 1597 1 Chips NULL $2.15 1597 1 Canned Soft Drink [Nestea] $1.25 1598 1 Chicken Burrito [Tomatillo Green Chili (Medium), [Lettuce, Black Beans, Rice, Sour Cream, Cheese]] $8.19 1598 1 Steak Burrito [Tomatillo Red Chili (Hot), [Lettuce, Black Beans, Rice, Sour Cream, Cheese]] $8.69 1598 1 Chicken Salad [Roasted Chili Corn (Medium), [Lettuce, Black Beans, Cheese]] $8.19 1598 1 Chicken Burrito [Roasted Chili Corn (Medium), [Lettuce, Black Beans, Rice, Sour Cream, Cheese]] $8.19 1599 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1599 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1599 1 Canned Soft Drink [Nestea] $1.25 1600 1 Steak Crispy Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1600 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1601 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1601 1 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Lettuce]] $8.75 1601 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1601 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1602 1 Steak Burrito [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Pinto Beans, Fajita Veggies, Lettuce]] $8.99 1602 1 Bottled Water NULL $1.09 1603 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1603 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1603 1 Chips and Guacamole NULL $4.45 1604 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Tomatillo-Green Chili Salsa (Medium), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1604 1 Chips and Guacamole NULL $3.99 1604 1 Chips and Tomatillo-Green Chili Salsa NULL $2.39 1604 1 Canned Soda [Diet Coke] $1.09 1605 1 Steak Burrito [[Fresh Tomato Salsa (Mild), Tomatillo-Red Chili Salsa (Hot)], [Pinto Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.99 1605 1 Chicken Bowl [Tomatillo-Green Chili Salsa (Medium), [Rice, Cheese, Sour Cream, Lettuce]] $8.49 1606 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1606 1 Chicken Salad Bowl [Fresh Tomato Salsa] $8.75 1607 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1607 1 Steak Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1608 1 Chips and Guacamole NULL $4.45 1608 2 Canned Soft Drink [Diet Coke] $2.50 1608 1 Chicken Soft Tacos [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1609 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1609 1 Nantucket Nectar [Pineapple Orange Banana] $3.39 1610 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 1610 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $9.25 1610 1 Chips and Guacamole NULL $4.45 1610 1 Canned Soft Drink [Lemonade] $1.25 1611 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1611 1 Chips NULL $2.15 1611 1 Chips and Guacamole NULL $4.45 1611 2 Canned Soft Drink [Coke] $2.50 1612 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese, Lettuce]] $8.99 1612 1 Chips and Guacamole NULL $3.99 1613 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, Cheese] $8.75 1613 1 Chips NULL $2.15 1613 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce, Guacamole]] $11.75 1614 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1614 1 Canned Soft Drink [Coke] $1.25 1615 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream]] $8.75 1615 1 Steak Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream]] $9.39 1615 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1615 1 Steak Soft Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1616 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese]] $9.25 1616 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1617 1 Carnitas Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $9.25 1617 1 Chips and Guacamole NULL $4.45 1618 1 Canned Soft Drink [Diet Coke] $1.25 1618 1 Carnitas Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Pinto Beans, Sour Cream, Cheese, Lettuce]] $9.25 1618 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 1619 1 Barbacoa Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Guacamole]] $11.75 1619 1 Bottled Water NULL $1.50 1620 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $8.75 1620 1 Chips and Guacamole NULL $4.45 1621 1 Steak Bowl [Roasted Chili Corn Salsa (Medium), [Rice, Black Beans, Fajita Veggies]] $8.99 1621 1 Canned Soda [Sprite] $1.09 1622 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1622 1 Chips and Fresh Tomato Salsa NULL $2.95 1623 1 Steak Bowl [Fresh Tomato Salsa (Mild), [Sour Cream, Lettuce, Cheese, Black Beans, Rice]] $8.99 1623 1 Side of Chips NULL $1.69 1624 1 Barbacoa Bowl [[Rice, Cheese]] $8.69 1624 1 Side of Chips NULL $1.69 1625 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.25 1625 1 Chips and Guacamole NULL $4.45 1626 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1626 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1626 1 Chips and Guacamole NULL $4.45 1627 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1627 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1627 1 Chips and Fresh Tomato Salsa NULL $2.95 1627 1 Bottled Water NULL $1.50 1628 1 Chicken Burrito [[Lettuce, Rice, Fajita Veggies]] $8.19 1628 1 Steak Burrito [[Lettuce, Fajita Veggies, Rice]] $8.69 1629 1 Bottled Water NULL $1.09 1629 1 Side of Chips NULL $1.69 1629 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Cheese]] $8.49 1630 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1630 1 Chips and Fresh Tomato Salsa NULL $2.95 1630 1 Canned Soft Drink [Coke] $1.25 1631 1 Chicken Bowl [Roasted Chili Corn Salsa (Medium), [Black Beans, Rice, Fajita Veggies, Cheese, Sour Cream]] $8.49 1631 1 Chips and Guacamole NULL $3.99 1632 1 Chicken Bowl [Fresh Tomato Salsa (Mild), [Black Beans, Rice, Sour Cream, Guacamole]] $10.98 1632 1 Nantucket Nectar [Peach Orange] $3.39 1633 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1633 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1633 1 Chips NULL $2.15 1634 2 Steak Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $18.50 1635 1 Veggie Burrito [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1635 1 Veggie Salad [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium), Tomatillo-Red Chili Salsa (Hot)], [Black Beans, Rice, Fajita Veggies, Lettuce]] $8.49 1636 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Guacamole]] $11.25 1636 1 Bottled Water NULL $1.50 1637 1 Chicken Bowl [[Fresh Tomato Salsa (Mild), Roasted Chili Corn Salsa (Medium)], [Black Beans, Rice, Cheese, Sour Cream, Lettuce]] $8.49 1637 1 Chips and Tomatillo-Red Chili Salsa NULL $2.39 1638 1 Steak Burrito [Tomatillo-Red Chili Salsa (Hot), [Black Beans, Rice, Cheese, Lettuce]] $8.99 1638 1 Chips and Fresh Tomato Salsa NULL $2.39 1639 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.75 1639 1 Chips and Guacamole NULL $4.45 1640 2 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $17.50 1641 1 Steak Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1641 1 Carnitas Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1642 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 1642 1 Chips and Guacamole NULL $4.45 1642 3 Canned Soft Drink [Diet Coke] $3.75 1643 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1643 1 Chips and Guacamole NULL $4.45 1643 1 Steak Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1643 1 Chips and Fresh Tomato Salsa NULL $2.95 1644 1 Canned Soft Drink [Lemonade] $1.25 1644 1 Chips and Guacamole NULL $4.45 1644 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $8.75 1645 1 Canned Soft Drink [Coke] $1.25 1645 1 Bottled Water NULL $1.50 1645 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1646 1 Veggie Salad Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1646 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole, Lettuce]] $11.25 1647 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1647 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1648 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $9.25 1648 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Guacamole]] $11.25 1648 1 Chips and Guacamole NULL $4.45 1648 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Cheese]] $8.75 1649 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 1649 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 1650 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream]] $8.75 1650 1 Chips and Guacamole NULL $4.45 1651 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $8.75 1651 1 Canned Soft Drink [Lemonade] $1.25 1651 1 Chips NULL $2.15 1652 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 1652 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $8.75 1652 2 Chips NULL $4.30 1653 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $8.75 1653 1 Canned Soft Drink [Sprite] $1.25 1653 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1654 1 Steak Soft Tacos [Fresh Tomato Salsa] $9.25 1654 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1655 1 Chips and Guacamole NULL $4.45 1655 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1655 1 Veggie Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1655 1 Barbacoa Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1655 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1655 3 Bottled Water NULL $4.50 1656 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.75 1656 1 Canned Soft Drink [Lemonade] $1.25 1657 1 Barbacoa Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1657 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.75 1657 1 Canned Soft Drink [Coke] $1.25 1657 1 Canned Soft Drink [Nestea] $1.25 1658 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans]] $9.25 1658 1 Chips and Guacamole NULL $4.45 1659 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1659 1 Carnitas Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole]] $11.75 1660 2 Chicken Burrito [Fresh Tomato Salsa, Rice] $17.50 1660 1 Chips NULL $2.15 1660 1 Canned Soft Drink [Coke] $1.25 1660 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1660 10 Bottled Water NULL $15.00 1660 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Guacamole]] $11.25 1660 1 Chips and Guacamole NULL $4.45 1660 1 Chips and Guacamole NULL $4.45 1660 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1661 1 Chicken Bowl [Fresh Tomato Salsa, [Guacamole, Sour Cream, Cheese, Rice, Fajita Vegetables]] $11.25 1661 1 Chips NULL $2.15 1662 1 Canned Soft Drink [Sprite] $1.25 1662 1 Bottled Water NULL $1.50 1662 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Pinto Beans, Sour Cream, Cheese]] $8.75 1662 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Sour Cream, Cheese, Guacamole]] $11.25 1662 1 Chips and Fresh Tomato Salsa NULL $2.95 1663 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1663 1 Canned Soft Drink [Sprite] $1.25 1664 1 Chips NULL $2.15 1664 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1664 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $9.25 1664 1 6 Pack Soft Drink [Diet Coke] $6.49 1665 1 Canned Soft Drink [Diet Coke] $1.25 1665 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese]] $8.75 1665 1 Chips NULL $2.15 1666 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1666 1 6 Pack Soft Drink [Coke] $6.49 1667 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole]] $11.25 1667 1 Bottled Water NULL $1.50 1668 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1668 1 Chips and Guacamole NULL $4.45 1669 1 Chicken Burrito [Fresh Tomato Salsa, Rice] $8.75 1669 1 Chips and Guacamole NULL $4.45 1670 1 Steak Soft Tacos [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1670 1 Barbacoa Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $9.25 1670 1 Canned Soft Drink [Coke] $1.25 1670 1 Canned Soft Drink [Sprite] $1.25 1671 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1671 3 Bottled Water NULL $4.50 1672 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 1672 1 Canned Soft Drink [Coke] $1.25 1673 1 Bottled Water NULL $1.50 1673 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $9.25 1673 1 Chips NULL $2.15 1673 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Lettuce, Guacamole]] $11.25 1673 1 Chips and Guacamole NULL $4.45 1674 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1674 1 Chips and Guacamole NULL $4.45 1675 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream]] $8.75 1675 1 Chips and Guacamole NULL $4.45 1676 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese]] $9.25 1676 1 Chips and Guacamole NULL $4.45 1676 1 Canned Soft Drink [Coke] $1.25 1677 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Guacamole]] $11.25 1677 1 Chips and Guacamole NULL $4.45 1678 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1678 1 Chips and Guacamole NULL $4.45 1679 1 Chicken Salad Bowl [Roasted Chili Corn Salsa, [Black Beans, Cheese, Lettuce]] $8.75 1679 1 Chips NULL $2.15 1679 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream]] $8.75 1679 1 Chips and Guacamole NULL $4.45 1679 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Lettuce]] $8.75 1680 1 Steak Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1680 1 Bottled Water NULL $1.50 1681 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 1681 1 Chips and Guacamole NULL $4.45 1682 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 1682 1 Chips NULL $2.15 1682 1 Bottled Water NULL $1.50 1683 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Lettuce]] $8.75 1683 1 Chips and Guacamole NULL $4.45 1684 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Lettuce]] $8.75 1684 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Guacamole, Lettuce]] $11.75 1685 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 1685 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Cheese, Sour Cream, Lettuce]] $8.75 1686 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1686 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1687 1 Bottled Water NULL $1.50 1687 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $8.75 1687 1 Chips NULL $2.15 1688 1 Steak Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Guacamole]] $11.75 1688 1 Chips NULL $2.15 1689 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1689 1 Chips and Fresh Tomato Salsa NULL $2.95 1690 2 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $18.50 1690 1 Canned Soft Drink [Coke] $1.25 1690 1 Chips and Guacamole NULL $4.45 1691 1 Chicken Bowl [White Rice] $8.50 1691 1 Chicken Bowl [White Rice] $8.50 1691 2 Chips and Guacamole NULL $8.50 1692 1 Chicken Salad Bowl [Tomatillo Green Chili Salsa, [Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1692 1 Carnitas Salad Bowl [Tomatillo Green Chili Salsa, [Black Beans, Cheese, Guacamole]] $11.89 1692 1 Canned Soft Drink [Diet Coke] $1.25 1693 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.39 1693 1 Chips and Guacamole NULL $4.45 1694 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1694 1 Chips NULL $2.15 1694 1 Bottled Water NULL $1.50 1695 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce, Guacamole]] $11.75 1695 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, Cheese] $8.75 1695 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $9.25 1696 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1696 1 Chips and Guacamole NULL $4.45 1696 1 Canned Soft Drink [Coke] $1.25 1696 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1697 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1697 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1698 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 1698 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1698 1 Chips NULL $2.15 1699 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1699 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole]] $11.25 1699 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1700 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Guacamole]] $11.25 1700 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1701 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1701 1 Chips NULL $2.15 1701 1 Veggie Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1701 1 Chips NULL $2.15 1702 1 Steak Bowl [Rice, Tomatillo-Red Chili Salsa (Hot)] $8.99 1702 1 Steak Burrito [Rice, Tomatillo-Red Chili Salsa (Hot)] $8.99 1703 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1703 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 1703 1 Barbacoa Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1703 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1704 1 Chicken Bowl [Fresh Tomato Salsa, [Pinto Beans, Rice, Lettuce, Cheese]] $8.75 1704 1 Chips and Guacamole NULL $4.45 1705 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1705 1 Canned Soft Drink [Diet Coke] $1.25 1705 1 Chips and Guacamole NULL $4.45 1706 1 Veggie Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.25 1706 1 Canned Soft Drink [Coke] $1.25 1707 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1707 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1708 1 Veggie Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1708 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1709 1 Canned Soft Drink [Diet Coke] $1.25 1709 1 Canned Soft Drink [Sprite] $1.25 1709 1 Canned Soft Drink [Nestea] $1.25 1709 1 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $9.25 1710 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Guacamole]] $11.75 1710 1 Chips NULL $2.15 1711 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1711 1 Chips and Guacamole NULL $4.45 1712 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Lettuce]] $8.75 1712 1 Chips and Guacamole NULL $4.45 1713 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole]] $11.25 1713 1 Steak Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $9.25 1714 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $8.75 1714 1 Steak Soft Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Cheese, Sour Cream, Lettuce]] $9.25 1714 1 Chips and Guacamole NULL $4.45 1715 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1715 1 Chips and Guacamole NULL $4.45 1715 1 Canned Soft Drink [Coke] $1.25 1716 1 Chips and Guacamole NULL $4.45 1716 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1717 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]] $9.25 1717 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Pinto Beans, Guacamole, Lettuce]] $11.25 1717 1 Chips NULL $2.15 1718 2 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $23.50 1718 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Cheese]] $9.25 1718 1 Chips NULL $2.15 1719 2 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Lettuce, Sour Cream]] $18.50 1719 1 Chicken Soft Tacos [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1719 2 Chips and Tomatillo Red Chili Salsa NULL $5.90 1720 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole]] $11.89 1720 1 Chips and Guacamole NULL $4.45 1721 1 Veggie Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]] $8.75 1721 1 Chips and Guacamole NULL $4.45 1722 1 Steak Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.25 1722 1 Chips and Fresh Tomato Salsa NULL $2.95 1723 1 Veggie Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole, Lettuce]] $11.25 1723 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1724 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 1724 1 Chicken Crispy Tacos [Tomatillo Red Chili Salsa, [Black Beans, Cheese, Lettuce, Guacamole]] $11.25 1725 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Guacamole]] $11.25 1725 1 Chips and Fresh Tomato Salsa NULL $2.95 1726 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1726 1 Carnitas Soft Tacos [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1727 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $9.25 1727 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1728 1 Chicken Bowl [Fresh Tomato Salsa, [Guacamole, Lettuce]] $11.25 1728 1 Chips NULL $2.15 1729 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1729 2 Chips and Guacamole NULL $8.90 1730 1 Chicken Salad Bowl [Tomatillo Red Chili Salsa, [Rice, Cheese]] $8.75 1730 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1731 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1731 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1732 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream]] $8.75 1732 1 Chips NULL $2.15 1732 1 Canned Soft Drink [Coke] $1.25 1733 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1733 1 Canned Soft Drink [Sprite] $1.25 1734 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Lettuce]] $8.75 1734 1 Chips and Guacamole NULL $4.45 1735 1 Canned Soft Drink [Coke] $1.25 1735 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Guacamole]] $11.25 1735 1 Chicken Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1736 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1736 1 Canned Soft Drink [Coke] $1.25 1737 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Guacamole]] $11.25 1737 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Guacamole]] $11.25 1737 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Guacamole]] $11.25 1738 1 Chicken Salad Bowl [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Cheese, Lettuce]] $8.75 1738 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Lettuce]] $8.75 1738 2 Steak Soft Tacos [Fresh Tomato Salsa, [Cheese, Lettuce]] $18.50 1739 1 Barbacoa Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole]] $11.75 1739 1 Canned Soft Drink [Coke] $1.25 1740 1 Chips and Guacamole NULL $4.45 1740 1 Barbacoa Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1741 1 6 Pack Soft Drink [Coke] $6.49 1741 1 Chips and Guacamole NULL $4.45 1741 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Sour Cream, Cheese, Lettuce]] $8.75 1742 1 Chicken Bowl [Fresh Tomato Salsa, [Pinto Beans, Rice, Cheese, Lettuce, Guacamole, Sour Cream, Fajita Vegetables]] $11.25 1742 1 6 Pack Soft Drink [Diet Coke] $6.49 1742 1 Steak Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1743 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1743 1 Steak Bowl [Roasted Chili Corn Salsa, [Black Beans, Cheese, Sour Cream]] $9.25 1744 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.25 1744 1 Chips NULL $2.15 1745 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]] $8.75 1745 1 Chips and Guacamole NULL $4.45 1745 1 Chicken Crispy Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1745 1 Chips and Guacamole NULL $4.45 1746 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1746 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese]] $9.25 1746 1 Chips and Guacamole NULL $4.45 1746 1 Canned Soft Drink [Lemonade] $1.25 1746 1 Canned Soft Drink [Sprite] $1.25 1747 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Black Beans, Sour Cream, Lettuce]] $8.75 1747 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1748 1 Barbacoa Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1748 1 Chips NULL $2.15 1748 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1749 1 Chips and Guacamole NULL $4.45 1749 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1749 1 Canned Soft Drink [Coke] $1.25 1750 1 Carnitas Burrito [Roasted Chili Corn Salsa, [Rice, Pinto Beans, Guacamole, Lettuce]] $11.75 1750 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1751 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole]] $11.25 1751 2 Chips NULL $4.30 1751 1 Barbacoa Salad Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $9.39 1752 1 Steak Salad Bowl [Fresh Tomato Salsa, [Black Beans, Pinto Beans, Cheese]] $9.39 1752 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Pinto Beans, Cheese, Sour Cream]] $8.75 1753 1 Canned Soft Drink [Coke] $1.25 1753 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese]] $9.25 1753 1 Chips and Guacamole NULL $4.45 1754 1 Chicken Bowl [Fresh Tomato Salsa, [Black Beans, Rice, Sour Cream, Cheese, Guacamole, Lettuce]] $11.25 1754 1 Canned Soft Drink [Sprite] $1.25 1755 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese]] $8.75 1755 1 Canned Soft Drink [Coke] $1.25 1755 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1756 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole]] $11.25 1756 1 Chips and Guacamole NULL $4.45 1757 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1757 1 Bottled Water NULL $1.50 1757 1 Chips and Guacamole NULL $4.45 1758 1 Chips and Guacamole NULL $4.45 1758 1 Steak Burrito [Fresh Tomato Salsa, [Cheese, Black Beans, Rice]] $9.25 1759 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 1759 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1759 1 Canned Soft Drink [Diet Coke] $1.25 1760 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.25 1760 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1761 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream, Lettuce]] $8.75 1761 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Lettuce]] $8.75 1761 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1761 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Cheese]] $8.75 1761 2 Chips NULL $4.30 1762 1 Steak Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1762 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Cheese, Lettuce]] $9.39 1763 1 Chips and Guacamole NULL $4.45 1763 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1763 1 Canned Soft Drink [Coke] $1.25 1764 2 Chicken Bowl [Fresh Tomato Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $22.50 1764 1 Chips and Guacamole NULL $4.45 1764 1 Chips and Fresh Tomato Salsa NULL $2.95 1764 1 Chips NULL $2.15 1764 2 Steak Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $23.50 1765 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1765 1 Chips NULL $2.15 1765 1 Canned Soft Drink [Coke] $1.25 1766 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1766 1 Chips and Guacamole NULL $4.45 1766 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 1766 1 Steak Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Sour Cream, Guacamole]] $11.75 1767 2 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Lettuce]] $17.50 1768 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1768 1 Carnitas Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1768 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 1769 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1769 1 Chips and Guacamole NULL $4.45 1770 1 Barbacoa Bowl [Tomatillo Green Chili Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.75 1770 1 Bottled Water NULL $1.50 1771 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1771 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream]] $8.75 1772 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce, Guacamole]] $11.75 1772 1 Steak Burrito [Tomatillo Red Chili Salsa, [Rice, Black Beans, Cheese]] $9.25 1772 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1773 1 Chips and Guacamole NULL $4.45 1773 1 Steak Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.75 1774 1 Carnitas Crispy Tacos [Fresh Tomato Salsa, [Pinto Beans, Sour Cream, Cheese, Lettuce]] $9.25 1774 1 Chips and Guacamole NULL $4.45 1775 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1775 1 Chicken Crispy Tacos [Tomatillo Red Chili Salsa, [Black Beans, Cheese]] $8.75 1775 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1776 1 Chips NULL $2.15 1776 1 Bottled Water NULL $1.50 1776 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 1777 1 Chips and Guacamole NULL $4.45 1777 1 Steak Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $9.25 1778 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese]] $8.75 1778 1 Bottled Water NULL $1.50 1778 1 Canned Soft Drink [Lemonade] $1.25 1778 1 Canned Soft Drink [Lemonade] $1.25 1779 1 Canned Soft Drink [Diet Coke] $1.25 1779 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $8.75 1779 1 Chips and Tomatillo Red Chili Salsa NULL $2.95 1780 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1780 1 Veggie Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1781 1 Bottled Water NULL $1.50 1781 1 Steak Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1782 1 Veggie Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1782 1 Chips NULL $2.15 1783 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $9.25 1783 1 Chicken Soft Tacos [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Sour Cream, Cheese, Lettuce]] $8.75 1784 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1784 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1785 1 Bottled Water NULL $1.50 1785 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 1785 1 Bottled Water NULL $1.50 1785 1 Canned Soft Drink [Diet Coke] $1.25 1786 1 Chicken Bowl [Fresh Tomato Salsa, Rice] $8.75 1786 1 Carnitas Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1786 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1786 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1786 1 Barbacoa Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Guacamole, Lettuce]] $11.75 1786 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1786 1 Carnitas Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Guacamole, Lettuce]] $11.75 1786 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Sour Cream, Lettuce]] $8.75 1786 4 Chips and Guacamole NULL $17.80 1786 4 Canned Soft Drink [Coke] $5.00 1786 4 Canned Soft Drink [Sprite] $5.00 1787 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1787 1 Chips and Tomatillo Green Chili Salsa NULL $2.95 1787 1 Canned Soft Drink [Lemonade] $1.25 1788 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Pinto Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1788 1 Canned Soft Drink [Sprite] $1.25 1788 1 Chips NULL $2.15 1789 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Sour Cream, Guacamole]] $11.25 1789 2 Canned Soft Drink [Coke] $2.50 1789 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $9.25 1790 1 Canned Soft Drink [Sprite] $1.25 1790 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Cheese, Guacamole, Lettuce]] $11.25 1790 1 Chips and Fresh Tomato Salsa NULL $2.95 1791 1 Carnitas Bowl [Fresh Tomato Salsa, [Rice, Sour Cream, Guacamole, Lettuce]] $11.75 1791 1 Chips NULL $2.15 1792 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 1792 1 Bottled Water NULL $1.50 1793 1 Steak Burrito [Brown Rice] $8.99 1793 1 Chips NULL $1.99 1793 1 Barbacoa Bowl [Guacamole] $11.49 1794 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Fajita Vegetables, Cheese, Lettuce]] $8.75 1794 1 Chips and Guacamole NULL $4.45 1794 1 Canned Soft Drink [Diet Coke] $1.25 1795 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1795 1 Chips and Guacamole NULL $4.45 1795 1 Canned Soft Drink [Sprite] $1.25 1796 1 Bottled Water NULL $1.50 1796 1 Steak Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.75 1797 1 Veggie Burrito [Fresh Tomato Salsa, [Rice, Pinto Beans, Cheese]] $8.75 1797 1 Chicken Salad Bowl [Fresh Tomato Salsa, Lettuce] $8.75 1798 1 Chicken Burrito [Roasted Chili Corn Salsa, [Guacamole, Lettuce, Rice, Fajita Vegetables, Sour Cream]] $11.25 1798 1 6 Pack Soft Drink [Diet Coke] $6.49 1798 1 Steak Crispy Tacos [Tomatillo Green Chili Salsa, [Cheese, Sour Cream, Guacamole]] $11.75 1799 1 Chicken Burrito [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese]] $8.75 1799 1 Chips NULL $2.15 1799 1 Canned Soft Drink [Sprite] $1.25 1800 1 6 Pack Soft Drink [Diet Coke] $6.49 1800 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 1800 1 Chips and Guacamole NULL $4.45 1801 1 Chicken Burrito [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Lettuce]] $8.75 1801 1 Chips and Guacamole NULL $4.45 1802 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1802 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1803 1 Chips and Guacamole NULL $4.45 1803 1 6 Pack Soft Drink [Lemonade] $6.49 1803 1 Steak Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Cheese]] $9.25 1804 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1804 1 Canned Soft Drink [Coke] $1.25 1804 1 Chips and Guacamole NULL $4.45 1805 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 1805 1 Veggie Salad Bowl [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1806 1 Bottled Water NULL $1.50 1806 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 1806 1 Bottled Water NULL $1.50 1806 1 Canned Soft Drink [Diet Coke] $1.25 1807 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese]] $9.25 1807 1 Steak Salad Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Cheese]] $9.39 1808 1 Chips and Guacamole NULL $4.45 1808 1 Steak Soft Tacos [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Cheese, Sour Cream]] $9.25 1809 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Fajita Vegetables, Cheese, Sour Cream]] $8.75 1809 1 Chips and Guacamole NULL $4.45 1809 1 Canned Soft Drink [Sprite] $1.25 1810 1 Chicken Bowl [Roasted Chili Corn Salsa, [Black Beans, Sour Cream, Cheese, Guacamole]] $11.25 1810 1 Steak Crispy Tacos [Roasted Chili Corn Salsa, [Fajita Vegetables, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1811 1 Chicken Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Lettuce]] $8.75 1811 1 Chicken Burrito [Tomatillo Green Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1811 1 Carnitas Soft Tacos [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1812 1 Chicken Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $8.75 1812 1 Chicken Burrito [Tomatillo Red Chili Salsa, [Rice, Cheese, Guacamole, Lettuce]] $11.25 1812 2 Canned Soft Drink [Coke] $2.50 1813 2 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Lettuce]] $17.50 1814 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream]] $9.25 1814 1 Chips and Guacamole NULL $4.45 1815 1 Chicken Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Pinto Beans, Cheese, Guacamole, Lettuce]] $11.25 1815 1 Canned Soft Drink [Sprite] $1.25 1816 1 Chicken Burrito [Roasted Chili Corn Salsa, [Pinto Beans, Cheese, Sour Cream]] $8.75 1816 1 Chips and Guacamole NULL $4.45 1817 1 Bottled Water NULL $1.50 1817 1 Chicken Burrito [Fresh Tomato Salsa, [Fajita Vegetables, Rice]] $8.75 1817 1 Bottled Water NULL $1.50 1817 1 Canned Soft Drink [Diet Coke] $1.25 1818 1 Steak Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce]] $9.39 1818 1 Veggie Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Lettuce]] $8.75 1819 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Cheese, Lettuce]] $8.75 1819 1 Chips and Guacamole NULL $4.45 1820 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.25 1820 1 Canned Soft Drink [Coke] $1.25 1821 1 Carnitas Burrito [Tomatillo Green Chili Salsa, [Rice, Pinto Beans, Sour Cream]] $9.25 1821 1 Chips and Guacamole NULL $4.45 1822 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Black Beans, Cheese, Guacamole]] $11.25 1822 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Pinto Beans, Cheese, Sour Cream, Lettuce]] $8.75 1822 2 Bottled Water NULL $3.00 1823 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans, Sour Cream, Lettuce]] $8.75 1823 1 Chips NULL $2.15 1823 1 Canned Soft Drink [Diet Coke] $1.25 1824 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] $11.25 1824 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole]] $11.25 1824 1 Chips and Guacamole NULL $4.45 1825 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.25 1825 1 Chicken Bowl [Roasted Chili Corn Salsa, [Rice, Black Beans, Cheese, Lettuce, Guacamole]] $11.25 1825 1 Chicken Bowl [Tomatillo Red Chili Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce]] $8.75 1825 1 Barbacoa Burrito [Tomatillo Red Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Sour Cream, Cheese, Guacamole]] $11.75 1825 1 Carnitas Bowl [Roasted Chili Corn Salsa, [Rice, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1825 1 Barbacoa Bowl [Roasted Chili Corn Salsa, [Pinto Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1826 1 Chicken Bowl [Tomatillo Green Chili Salsa, [Rice, Black Beans]] $8.75 1826 1 Chips and Guacamole NULL $4.45 1826 1 Canned Soft Drink [Nestea] $1.25 1826 1 Bottled Water NULL $1.50 1827 1 Chicken Bowl [Roasted Chili Corn Salsa, [Cheese, Lettuce]] $8.75 1827 1 Chips and Guacamole NULL $4.45 1827 1 Canned Soft Drink [Diet Coke] $1.25 1827 1 Barbacoa Burrito [Tomatillo Green Chili Salsa] $9.25 1827 1 Barbacoa Burrito [Tomatillo Green Chili Salsa] $9.25 1828 1 Chicken Bowl [Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] $8.75 1828 1 Chips and Guacamole NULL $4.45 1828 1 Canned Soft Drink [Coke] $1.25 1829 1 Steak Burrito [Tomatillo Green Chili Salsa, [Rice, Cheese, Sour Cream, Guacamole]] $11.75 1829 1 Veggie Burrito [Tomatillo Red Chili Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Guacamole, Lettuce]] $11.25 1829 1 Canned Soft Drink [Sprite] $1.25 1830 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1830 1 Veggie Burrito [Tomatillo Green Chili Salsa, [Rice, Fajita Vegetables, Black Beans, Guacamole]] $11.25 1831 1 Carnitas Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Lettuce]] $9.25 1831 1 Chips NULL $2.15 1831 1 Bottled Water NULL $1.50 1832 1 Chicken Soft Tacos [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]] $8.75 1832 1 Chips and Guacamole NULL $4.45 1833 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Black Beans, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1833 1 Steak Burrito [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese, Lettuce, Guacamole]] $11.75 1834 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Guacamole, Lettuce]] $11.25 1834 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Lettuce]] $8.75 1834 1 Chicken Salad Bowl [Fresh Tomato Salsa, [Fajita Vegetables, Pinto Beans, Lettuce]] $8.75 ================================================ FILE: data/default.csv ================================================ default,student,balance,income 0,No,729.5264952,44361.62507 0,Yes,817.1804066,12106.1347 0,No,1073.549164,31767.13895 0,No,529.2506047,35704.49394 0,No,785.6558829,38463.49588 0,Yes,919.5885305,7491.558572 0,No,825.5133305,24905.22658 0,Yes,808.6675043,17600.45134 0,No,1161.057854,37468.52929 0,No,0,29275.26829 0,Yes,0,21871.07309 0,Yes,1220.583753,13268.56222 0,No,237.045114,28251.69534 0,No,606.7423433,44994.55585 0,No,1112.968401,23810.17405 0,No,286.2325601,45042.41304 0,No,0,50265.31235 0,Yes,527.5401841,17636.53962 0,No,485.9368642,61566.10612 0,No,1095.072735,26464.63139 0,No,228.9525496,50500.1822 0,No,954.2617928,32457.50908 0,No,1055.956605,51317.88308 0,No,641.9843888,30466.10326 0,No,773.2117245,34353.31431 0,No,855.0085225,25211.33216 0,No,642.9997385,41473.5118 0,No,1454.863272,32189.09495 0,No,615.7042766,39376.39462 0,Yes,1119.569353,16556.07021 0,No,494.8162288,54384.78284 0,Yes,448.8806563,15799.47041 0,Yes,584.9048947,22429.93505 0,No,913.5871726,46907.2254 0,Yes,1423.938917,22634.48809 0,Yes,1499.724657,13190.65272 0,No,692.0341671,47806.61177 0,No,351.4534715,35087.48865 0,No,742.6276208,37864.82394 0,No,653.1184001,39489.5947 0,No,872.1386795,41787.56727 0,No,837.2626228,51471.77209 0,No,1151.630566,42917.46956 0,Yes,220.5556084,16872.94756 0,Yes,1690.23441,19052.57222 0,No,408.7729143,54206.93921 0,No,1238.610321,50066.68053 0,No,1228.30835,37408.50387 0,No,820.9192041,47746.54207 0,No,857.4851178,31688.34597 0,No,563.6181772,42641.25327 0,Yes,1282.972534,13120.63647 0,Yes,1505.782675,26557.14145 0,Yes,904.0402591,16882.30061 0,No,0,49956.58057 0,Yes,1294.497347,10464.32073 0,Yes,1275.550633,15887.46849 0,No,1536.594601,48766.90746 0,No,1332.522644,39143.13707 0,No,492.0797678,33379.0946 0,No,766.2343793,46478.29426 0,No,690.1272462,63432.98425 0,No,0,32481.54005 0,No,1480.655309,36866.15707 0,No,989.055597,45344.25132 0,No,1302.341835,33695.42623 0,No,1044.552343,54696.76598 0,No,0,43658.22772 0,No,866.0284364,38363.42163 0,No,690.8007924,48140.47134 0,No,597.757142,31577.61513 0,No,429.4854023,51311.2181 0,No,1398.156201,39644.94219 0,Yes,1578.064099,19886.49395 0,No,857.0690531,26655.68897 0,Yes,752.459946,16211.27579 0,Yes,774.738427,15193.73314 0,No,728.3732513,45131.71826 0,No,76.99129053,28392.09341 0,No,196.4569131,41346.78591 0,Yes,948.7479184,14297.61905 0,No,431.9208853,39369.88156 0,No,461.6381822,46221.21149 0,No,572.0157602,33046.31296 0,No,335.5681447,51258.44423 0,No,510.2375932,60397.71865 0,Yes,1005.161306,24038.65204 0,Yes,162.4544511,13241.75206 0,No,932.5331557,50537.46565 0,Yes,893.3304676,11905.68179 0,No,1332.782564,44495.16276 0,No,1404.383069,35043.10603 0,Yes,1148.965139,16578.19212 0,No,368.2234257,57596.82584 0,No,449.4666288,45950.66672 0,No,820.0171126,51584.65732 0,Yes,619.7518686,15750.62208 0,No,1047.718124,46416.97099 0,No,243.8413283,47193.88813 0,No,186.5003869,45430.55027 0,No,1422.018488,38224.03152 0,No,383.798659,61425.28639 0,No,1159.435096,45802.10494 0,No,882.9919126,17181.73797 0,No,1463.232892,65457.85419 0,Yes,1722.355846,19311.42294 0,Yes,773.9218613,14558.91214 0,No,958.1694185,32816.2848 0,No,1207.53273,68037.57513 0,No,1056.61083,30546.60103 0,No,117.6424839,46550.26727 0,No,596.9641192,58088.36045 0,No,0,47729.62576 0,Yes,541.6627553,16335.70344 0,No,1119.178303,45020.01912 0,Yes,810.3209365,20498.66581 0,No,271.5514377,32850.12014 0,No,1114.403237,47714.02211 0,No,867.0308281,33540.97998 0,Yes,1176.789402,17632.7121 0,No,610.2278187,42465.36296 0,No,272.6156617,55449.33864 0,No,449.7350858,49643.42644 0,Yes,827.7672094,10673.52623 0,No,430.0000994,33925.2196 0,No,0,31825.76497 0,Yes,1557.344637,18404.59419 0,No,928.2369663,33722.15894 0,No,1373.450163,49101.79821 0,Yes,951.0729075,18601.53324 0,Yes,1292.210869,23065.85098 0,No,582.1887007,24760.79573 0,Yes,339.4246383,19307.98029 0,No,409.9823246,44641.09012 0,No,1216.45151,53639.84683 0,No,598.4613921,44124.26811 1,Yes,1486.998122,17854.39703 0,No,943.7963387,59976.83987 0,No,0,49525.74502 0,Yes,996.2760868,20883.24077 0,Yes,748.3013404,11248.67075 0,No,324.7379218,15411.51857 0,Yes,575.3821585,16005.68132 0,No,441.7382849,36012.2359 0,No,1188.64218,39526.56127 0,No,95.14768019,51371.19991 0,No,1015.614837,43218.79438 0,No,1258.567393,44931.67492 0,Yes,1178.244414,7750.289221 0,No,731.5326563,43956.06008 0,No,350.0887921,44455.19952 0,No,418.8483454,36901.78333 0,No,533.2294652,39393.54248 0,No,95.29697413,32359.38903 0,Yes,1453.005932,15933.00211 0,No,352.644414,39404.74437 0,Yes,736.2389355,14107.99397 0,No,1306.484572,41224.62502 0,No,1149.145627,45194.79466 0,No,703.2268907,57679.27914 0,Yes,1360.800435,18864.30849 0,No,31.44508054,53287.3503 0,No,988.2144581,48570.36346 0,No,360.0190736,23927.54661 0,No,647.4835286,33226.27754 0,Yes,0,9816.789438 0,No,445.2418192,31974.62451 0,Yes,878.5420645,17496.74148 0,Yes,335.8341186,21759.87027 0,No,666.6088814,30438.46883 0,No,813.876647,40737.45978 0,No,836.3000716,54607.17874 0,No,967.4826837,33055.11027 1,Yes,2205.799521,14271.49225 0,Yes,1258.185219,11672.222 0,No,1239.801699,40107.17221 0,Yes,0,13760.29846 0,Yes,927.8877323,22473.37961 0,Yes,1390.030783,21299.09923 0,No,410.4977048,53471.21363 0,No,73.07654218,30681.73754 0,No,790.5977592,45303.10331 0,No,789.495097,32579.90021 0,No,791.3604221,46319.23612 0,No,479.042379,37255.88033 0,No,888.6132726,28899.79175 0,No,1284.386361,42831.95462 0,Yes,712.2763513,23341.95023 0,Yes,1290.620169,18408.04908 0,No,126.3626854,44280.92979 0,No,779.3762577,61158.88511 0,No,697.4909575,49476.7596 0,No,606.9746135,43610.03302 0,Yes,1802.903334,21411.42395 0,No,900.0853326,41276.56319 0,No,966.280288,33440.9793 0,No,671.0613596,35689.11187 0,No,171.559116,23505.99277 0,No,1482.4803,45260.6116 0,No,640.6627517,46464.56593 0,Yes,706.1610275,18078.35179 1,Yes,1774.694223,20359.50609 0,No,916.4993169,25768.40745 0,No,1271.270812,36053.50295 0,No,110.5729098,47339.10875 0,No,1747.258882,37819.28909 1,No,1889.59919,48956.17159 0,No,27.20139465,21051.84013 0,No,746.2297751,35159.85023 1,Yes,1899.390626,20655.2 0,Yes,813.9945655,20391.52895 0,No,499.4578151,38899.16046 0,No,853.1613317,36272.45178 0,No,474.7273191,32064.33511 0,No,1235.876113,35838.77702 0,No,620.6751526,44213.7701 0,No,220.0285698,37880.02965 0,Yes,1304.914299,18074.07542 0,Yes,1608.15256,22358.0467 0,No,256.4712276,37657.05592 0,Yes,1153.604998,19224.97523 0,No,771.8958745,31170.08528 0,Yes,1099.630422,17064.04283 0,No,782.8830297,40357.13343 0,No,444.2649062,35924.33879 0,No,504.0283787,24780.96124 0,No,1762.503402,42124.66007 0,Yes,850.0961166,18618.98791 0,No,1412.238721,40785.05977 0,No,715.4968296,47482.56538 0,No,1363.528599,55713.04161 0,No,493.2418898,42495.20308 0,No,1354.752537,33375.13169 0,Yes,571.293911,15416.26935 0,No,956.1513522,44086.70773 0,No,964.820253,34390.74604 0,No,552.634366,50513.16424 0,No,338.2662016,37767.23986 0,No,709.1233514,44041.63009 0,No,978.9782004,57839.05373 0,No,371.0101278,15702.70708 1,Yes,1572.856481,14930.17833 0,No,0,40150.21798 1,No,1964.476872,39054.58914 0,No,548.0377834,33605.64947 0,No,1257.055222,34045.00882 0,No,1189.918057,38016.8939 0,No,687.683759,39373.57975 0,No,845.6989658,39899.51268 0,Yes,957.6848338,19676.34565 0,No,233.5168463,43326.10538 0,Yes,1126.948744,14298.26457 0,Yes,488.8551765,15159.49485 0,No,767.9035598,44323.57966 0,No,561.9257991,31388.50565 0,Yes,1092.270013,17454.60184 0,No,71.788683,31965.74471 0,Yes,1839.143682,13625.39793 0,Yes,876.0007779,22474.57409 0,No,554.1078115,30357.02598 0,No,891.3178981,43945.04165 0,No,464.8363331,27324.89969 0,Yes,563.5426611,24082.03934 1,No,1530.353157,30003.81715 0,No,990.5670262,41690.25262 0,No,590.3997865,42769.20388 0,Yes,715.1641096,21153.51101 0,No,1540.377727,41852.07844 0,No,1243.326518,41639.68038 0,Yes,235.3284999,20909.70475 0,No,528.2543232,49394.34077 0,No,592.2538956,30731.2016 0,No,705.204972,42683.18724 0,No,456.515644,48195.77584 0,Yes,470.7175747,18052.105 0,Yes,1503.07513,10600.41354 0,No,1362.965595,44217.91604 0,No,908.7715737,45032.84599 0,Yes,1096.820272,16148.06001 0,No,369.2219425,47835.09105 0,No,673.399899,50256.29021 0,No,1038.500008,51173.14138 0,No,0,28762.92378 0,No,527.1940866,32431.11826 0,No,817.4693368,32974.42735 0,Yes,1549.042755,19702.29321 0,Yes,566.7137894,11853.95317 0,No,1496.056465,24935.91874 0,No,862.6566564,42844.834 0,No,1259.149418,31263.64108 0,No,1025.038833,43510.04964 0,Yes,703.2777871,14793.74268 0,Yes,1082.391497,15175.36121 0,No,910.4490235,44054.91642 0,Yes,996.5438622,16995.6604 0,No,353.778226,58514.31808 0,Yes,782.7108275,13300.29747 0,No,1716.595418,51056.86829 0,Yes,502.8986384,15153.96576 0,No,1033.468469,59203.96766 0,No,0,34925.36541 0,Yes,1087.083499,19263.76958 0,No,1047.244249,43899.75925 0,No,1056.927353,38367.86795 0,Yes,979.2244246,26010.8878 0,Yes,847.8187063,15978.63363 0,No,781.7712848,50958.79051 0,Yes,402.4888868,15904.05592 0,No,204.7401817,17534.29054 0,Yes,2022.674643,18336.74317 0,No,748.9370638,44186.61776 0,No,838.2756878,35917.49061 0,No,386.0160493,44694.03096 0,No,812.3992965,49623.10098 0,Yes,1809.926564,6985.135945 0,No,0,30360.54894 0,Yes,1328.183804,24688.47372 0,Yes,1464.936049,26716.68637 0,No,1200.565138,39718.69266 0,No,989.68642,27856.57706 0,No,870.7345799,35539.27022 0,Yes,1200.84739,19849.83141 0,Yes,1428.832047,23204.68146 0,No,0,33951.28873 0,Yes,359.4588665,21783.26949 0,No,265.6839498,40862.75401 0,No,1027.770532,44406.83691 0,No,729.0474335,35042.133 0,No,1042.80492,25867.14772 0,No,230.2305879,37875.61482 0,No,367.4660122,30611.27227 0,Yes,737.7053539,20863.79967 0,No,18.26128536,30349.46042 0,No,1257.760209,30834.77137 0,No,455.5215132,40907.39835 0,No,56.96756035,35458.48156 0,Yes,723.9319853,12213.03714 0,No,612.3831577,41379.76781 0,Yes,1220.177606,15308.12707 0,No,1222.347692,32363.37105 0,No,1165.491138,47684.21945 1,No,1642.819997,46856.94704 0,No,944.17356,56088.6838 0,No,749.6080039,41423.94117 0,No,370.3751868,31231.00784 1,No,1991.64912,42133.37318 0,No,773.9152266,44562.33812 0,Yes,817.2498113,20480.88706 0,No,1060.71622,50260.46437 1,No,1550.449264,56273.51361 0,Yes,454.4669622,15092.43931 0,Yes,1256.239325,17784.55894 0,No,1484.395393,36731.30869 0,Yes,1050.477328,12661.61163 0,No,986.4006189,45430.47221 0,No,717.341927,44266.37654 0,No,692.1432227,47038.09032 1,No,1328.892725,34710.06237 0,No,673.4681911,32930.06936 0,No,218.639297,48991.39538 0,No,1218.166966,43954.27749 0,Yes,13.51680255,19322.54503 0,Yes,531.4786999,21726.55897 0,No,165.5381517,33436.45574 0,Yes,1351.849722,25890.50399 0,No,107.282083,54143.96275 0,No,947.1935714,35604.11719 0,Yes,434.5602592,14812.64524 0,No,1020.06724,37458.87925 0,No,662.305318,31597.89762 0,Yes,754.4206129,20767.68302 0,No,289.4895757,44412.8951 0,No,198.0384092,36270.93812 0,Yes,516.0604917,16615.02196 0,No,697.563809,41755.39815 0,Yes,502.3247835,16947.42293 0,Yes,1459.687083,15575.41923 0,No,716.9987227,34418.26022 0,Yes,1037.972705,18324.03006 0,No,673.0161172,29735.9616 0,No,371.9217847,41099.77244 0,No,89.13495796,55604.60753 0,No,1273.280181,40895.28277 0,No,600.723303,18680.51677 0,No,905.0381646,40711.7989 0,No,1061.441562,54437.01032 0,No,1672.639248,46334.78776 0,Yes,883.7026456,19750.094 0,No,373.78033,53936.24178 0,No,1287.795187,52382.6647 0,No,1229.742571,48739.92739 0,Yes,510.613847,22457.05175 0,No,775.6301687,27205.85739 0,No,1152.982887,50206.57693 0,Yes,1303.66259,7986.000336 0,No,1675.278852,28244.50348 0,No,398.277733,30001.79506 0,Yes,739.9017594,25023.87386 0,No,226.4176965,32018.71363 0,No,1217.321712,25173.61961 0,No,639.8205582,29902.00199 0,Yes,883.1669769,12717.49223 0,No,2.287610953,38186.52427 0,No,419.6373477,55967.99317 0,Yes,1544.14025,11102.53474 0,No,1101.015018,32153.01359 1,No,1700.599913,30488.98341 0,No,311.8245869,48301.05966 0,No,442.8302223,55074.13083 0,No,728.8149601,17909.58107 0,No,278.8779025,37188.66837 0,No,758.7023275,36625.98955 0,No,620.3396794,22803.74103 0,Yes,1890.167411,18402.46488 0,No,0,28951.4519 0,Yes,1329.198842,23184.01555 0,No,1075.863772,44128.62826 0,No,618.5275092,54956.99054 0,No,1111.086576,27466.89368 0,No,864.0397435,45881.93021 0,No,895.2298096,48512.45564 0,Yes,954.0304337,21908.52716 0,No,947.6349327,46813.51631 0,No,267.3037277,23117.92867 0,Yes,1366.805783,23913.2088 0,No,624.7318835,38325.17376 0,No,13.67938267,25221.63767 0,No,785.8664857,44778.01578 0,Yes,1353.093846,18933.49619 0,No,480.3212639,25553.26285 0,No,0,29507.31325 0,Yes,1221.971243,17916.85609 0,Yes,1184.936523,25108.75671 0,Yes,478.5720556,27512.3449 0,No,221.6904619,34786.10135 0,No,578.3876217,41054.13866 0,Yes,1361.458432,19576.95713 0,Yes,977.8480956,19731.96099 0,No,1184.425245,36887.43997 1,Yes,1118.701039,21848.4429 1,No,1119.097245,37224.56781 0,No,776.3499793,31329.48485 0,No,1322.297091,51792.40237 0,No,878.0264576,27438.58512 0,No,1268.379515,16921.18088 0,No,0,32611.37307 0,No,0,37262.57409 0,Yes,366.7869573,25833.0631 0,No,747.5610364,39898.76909 0,No,1787.285144,37499.09355 0,Yes,1929.44698,14995.49217 0,No,987.142891,27961.31961 0,Yes,856.3573684,20758.21469 0,No,1101.149707,18153.84432 0,No,704.18452,31515.98168 0,No,555.6234367,40837.73164 0,No,1073.498261,44764.11055 0,No,1126.223023,32842.65237 0,Yes,649.7733186,15708.29638 0,No,977.657621,43743.49673 0,No,1049.889964,42637.51106 0,Yes,1383.566333,15764.34588 0,Yes,1095.46574,14065.16757 0,No,1089.371791,30224.59613 0,No,605.2209683,21792.32152 0,No,1033.885399,49262.04533 0,No,1604.720554,38186.98507 0,No,826.3270259,24679.71504 0,Yes,1176.146823,16670.68079 0,Yes,1275.023199,19937.09803 0,Yes,1290.28314,23538.94414 0,No,1133.474199,51414.49596 0,No,837.4125669,37400.49288 0,Yes,379.0821667,18280.36703 0,Yes,1134.166278,12453.36359 0,No,0,39120.08479 0,No,1491.174803,59394.38308 0,Yes,1695.359586,9582.941897 0,No,705.5733276,54589.23987 0,No,239.1933663,33743.22655 0,Yes,1183.710641,12428.61451 0,No,898.7618389,38267.08158 0,Yes,1524.2904,11719.59348 0,No,777.0237374,35115.01855 0,No,1394.795819,26074.93436 0,Yes,0,20476.15572 0,No,62.57157516,47946.76709 1,No,1981.451815,28127.89547 0,No,514.0540348,50053.26193 0,No,722.788039,53714.18607 0,No,152.8663042,33716.86406 0,No,293.1847729,35390.2876 0,No,0,38613.6205 0,Yes,645.3668572,17878.36431 0,Yes,1515.152833,26046.77208 0,Yes,767.4395055,18846.48922 0,Yes,491.8797471,21715.2263 0,No,0,41389.43527 0,No,0,34589.48806 0,No,509.1551641,40132.65921 0,Yes,965.5873701,16440.0997 0,Yes,1868.540072,20489.59811 0,No,403.4972058,41803.77816 0,No,593.1436251,37670.93502 0,No,0,44547.86555 0,No,742.2900159,39367.19331 0,Yes,1110.100457,13013.61057 0,No,771.5512953,49440.40129 0,No,624.3388915,42492.75307 0,Yes,499.9572298,22884.95257 0,No,1088.63752,60650.03613 0,No,1556.491928,49669.66887 0,Yes,797.0843357,23558.68308 0,No,415.9925026,38215.67804 0,Yes,538.2326812,26833.03963 0,Yes,1181.934702,21086.08201 0,Yes,836.0206886,9271.789924 0,No,104.4432151,46928.00549 0,No,564.2094786,53196.62302 0,Yes,804.5394022,25091.46077 0,No,1028.95364,21218.30352 0,No,748.7465837,8983.856902 0,No,66.63208073,28735.97411 0,No,452.8022289,48034.23771 0,Yes,798.4605101,27724.06575 0,Yes,1941.902928,23467.12697 0,No,1446.101012,22996.4323 0,No,761.0640983,61580.03413 0,No,1350.172728,43147.45558 0,No,380.9502006,36943.36199 0,Yes,1602.849607,15906.4668 0,No,147.6101155,37588.56261 0,No,0,36283.08013 0,Yes,733.0725124,18818.16738 0,Yes,1082.747976,26096.33703 0,No,779.6573003,40804.4757 0,No,994.1587122,65254.0758 0,No,769.1813741,50070.78155 0,Yes,684.0985191,4985.169113 0,No,442.5633017,27898.46539 1,No,1717.071593,38408.89092 0,No,0,36302.52844 0,No,1092.345865,44717.01258 0,No,497.3349541,35146.79877 0,Yes,1149.680655,16907.70074 1,No,1465.210164,58699.9832 0,No,565.3176042,41789.611 0,Yes,922.1378905,12224.18517 0,Yes,175.5993806,11510.05787 0,No,1024.946819,49675.56042 0,No,596.8854323,54091.62476 0,No,750.4104842,53084.90748 0,Yes,790.8700274,14183.97003 0,No,112.3278725,42386.04856 0,Yes,738.3944873,16093.28455 0,No,1157.855561,54419.80857 0,No,1213.243149,45149.51632 0,No,1339.626204,41656.00289 0,No,588.3124302,56520.96251 0,No,721.8081325,56375.72199 0,No,680.820719,43843.47412 0,Yes,815.4065709,12071.7625 0,No,1042.507112,37170.10905 0,Yes,663.2498681,20454.61655 0,Yes,1478.618545,18026.4711 0,No,337.756754,33655.57243 0,Yes,1273.55072,23126.6362 0,No,114.1797259,49562.98927 0,No,0,52295.60093 0,Yes,1516.317689,26377.59349 0,Yes,898.1146731,24164.7511 0,No,790.5905821,48219.59447 0,No,0,29072.72149 0,No,1273.24483,49136.4742 0,No,1279.200448,36063.11411 0,Yes,732.7193809,14568.74741 1,No,1763.579088,46227.07454 0,Yes,1280.418448,16310.90553 0,No,1529.937995,36912.8973 0,Yes,345.4676365,18251.49456 0,No,197.9894038,33723.98576 1,Yes,1770.969441,15975.5372 0,Yes,639.3719435,17037.0548 0,No,238.8146439,23526.71091 0,No,390.5558431,45338.35721 0,Yes,830.1715111,19476.58758 0,Yes,1378.876076,21613.57847 0,No,242.4034493,37364.42309 0,No,1236.547494,36211.49036 0,No,1063.774937,34914.27525 0,No,126.923179,33819.14332 0,No,758.4655013,38018.14569 0,No,908.9314586,68758.8784 0,No,558.5183424,46297.42541 0,No,1819.242333,36969.55252 0,Yes,1110.681744,19327.57504 0,No,723.2433747,43459.01185 0,No,972.031864,18510.94603 0,No,696.0144306,40741.42024 0,Yes,1178.248909,29362.60461 0,Yes,329.5585109,19371.81209 0,Yes,792.5292385,14546.30177 0,No,1246.037758,33691.72131 0,No,591.7607765,41670.25738 0,No,244.0811388,24854.24982 0,No,1038.54836,49944.86515 0,No,679.0745868,49488.56398 0,No,1061.415836,40585.22164 0,Yes,1268.240691,11914.57827 0,Yes,955.5336858,14736.72088 0,No,1115.82063,27882.80926 0,Yes,601.1632606,22029.19352 0,No,1097.600039,52286.43182 0,No,1042.783073,45982.96632 0,Yes,1321.443053,8624.110689 0,No,1052.39326,37637.65931 0,No,687.9517011,27604.07927 0,Yes,566.4605126,24828.26598 0,No,769.414224,35071.16654 0,No,955.1353421,26372.98582 0,No,643.4095265,28660.14017 0,No,0,42745.30201 0,No,437.6360626,45384.04745 0,No,1278.865642,34673.17099 0,No,1639.390572,30624.77593 0,No,891.6437373,36470.82834 0,Yes,316.4590483,18813.94021 0,No,622.9295074,48874.55577 0,No,107.2775268,42287.30286 0,Yes,990.5432158,12398.4906 0,No,0,45659.9957 0,No,0,33119.95533 0,No,591.2184873,35287.82458 0,Yes,928.0600783,21121.84483 0,No,304.5992239,40785.98918 0,No,235.9394977,31336.51012 0,Yes,1108.053518,10853.23094 0,No,687.306397,24563.75768 0,No,60.18603613,39864.86355 0,No,0,34648.9726 0,No,272.8932571,58730.57286 1,No,1531.716459,43930.4001 0,No,949.6086485,47702.57375 0,No,561.9161785,35080.57924 0,No,468.1325222,25231.34623 0,Yes,993.10817,12215.94682 0,No,0,31314.69509 0,Yes,759.4783692,12710.72092 0,No,1340.787209,36440.22832 0,Yes,1521.463396,15210.47407 0,No,133.7433443,30457.01637 1,No,780.1725692,51656.87406 0,No,839.9154583,22884.79302 0,No,913.5058729,36670.85422 0,No,924.8814415,35712.63791 0,No,1197.831505,54652.3093 0,No,49.2096659,30451.15286 0,No,970.7160966,48387.64862 0,No,783.0574005,54082.42408 0,Yes,465.0628886,20379.06209 0,No,0,39337.74998 0,Yes,732.2642676,21986.04545 0,No,576.0912613,37679.69859 0,Yes,588.9910563,16309.87545 0,No,863.1407178,43779.47785 0,No,857.575734,49675.37032 0,No,361.5017311,33674.89024 0,No,891.1554271,36023.08501 0,No,918.122206,44738.55603 0,No,1335.028018,21150.85594 0,No,421.4008434,39873.52174 0,Yes,743.6361445,18883.97151 0,No,773.7403725,41872.49476 0,No,731.3934722,45251.88192 0,No,964.1889227,36330.3946 0,Yes,350.0151843,17657.17266 0,No,1206.661362,37125.59135 0,No,1051.947573,52888.1851 0,No,454.7603707,45572.17248 0,No,932.6653075,42758.6007 0,No,535.7458465,36259.46376 0,No,241.8052522,41186.18281 0,No,1026.50671,42042.16794 0,Yes,187.6798856,18278.83397 0,No,197.5383215,58646.9278 0,No,585.7080883,43288.40038 0,Yes,1947.022401,12147.04641 0,No,829.5475975,39717.53761 0,No,196.9384595,35488.44888 0,No,591.863619,31438.77601 0,No,0,39742.10546 0,Yes,1008.291619,11939.29526 0,Yes,1052.227478,17410.75159 0,No,331.6805267,49756.17101 0,No,913.107204,29414.65218 0,Yes,561.3916122,21747.26318 0,No,246.955463,47692.89425 0,No,578.9786101,46304.71748 0,No,557.8087771,62352.84607 0,Yes,788.2517516,10418.18286 0,No,1060.807429,39174.05636 0,Yes,1247.907029,19816.72014 0,No,1184.360723,34259.9902 0,No,1024.578238,33071.54992 0,No,1080.291656,43367.89957 0,No,540.2790448,26267.15635 0,No,1448.835465,33835.73626 0,Yes,561.1940353,27421.11126 0,No,1191.480307,30040.57215 0,No,565.1389875,38202.58394 0,No,482.9821952,35845.19283 0,No,950.3500933,37486.24955 0,Yes,350.7029135,21235.36042 1,Yes,1551.023469,19027.50863 0,No,981.5962751,37747.91335 0,No,1442.129805,10921.61503 0,No,1060.33586,56607.25397 0,No,1154.890792,61794.34633 0,Yes,544.1397693,20056.82854 0,No,1137.175454,27588.93331 0,No,414.0840495,47811.41892 0,Yes,436.0083144,17504.44777 0,No,987.8147997,25809.98028 0,No,679.3918418,46603.54641 0,No,1377.772007,51633.32955 0,No,856.8119605,32437.13183 0,No,1036.676833,44923.80275 0,No,0,46826.80447 0,No,961.4720707,51936.75989 0,No,275.9880913,35622.82945 0,No,426.6935421,30769.34477 0,No,290.2519082,46214.15923 0,No,934.9697035,42325.74999 0,Yes,668.9911556,26342.04846 0,Yes,2004.727568,27136.53798 0,No,1312.86573,29938.25946 0,Yes,662.2735565,15092.05102 0,No,524.639806,36120.89226 0,Yes,1212.589568,21058.34866 0,No,961.3157694,30290.8065 1,Yes,1504.290178,13965.18604 0,Yes,613.656318,25040.48845 0,Yes,1233.343605,13402.07329 0,No,1221.385959,36961.31512 0,No,1217.056807,46256.78049 0,No,628.7576984,43205.1753 0,Yes,819.0973161,15957.94364 0,Yes,843.4934551,12710.02571 0,No,1173.559815,29141.36573 0,Yes,1195.590283,13329.5964 0,No,1202.883124,12288.12708 0,No,24.87182349,29316.97033 0,No,853.2414216,29484.05238 0,No,398.7757053,40223.90981 0,Yes,1521.172396,18149.88619 0,Yes,1160.221793,15941.05088 0,No,985.6140627,49948.46711 0,No,583.8207193,38215.96476 0,Yes,638.6919826,18148.30171 0,No,384.9977195,25380.70345 0,No,1050.764231,31558.89842 1,Yes,1871.938387,18077.48709 0,No,793.7617176,35157.73998 0,Yes,591.6606513,21790.50243 0,No,419.4764413,24001.5122 0,Yes,514.2838902,18185.4797 0,No,1027.895035,21551.61109 0,Yes,1029.681549,15977.32114 0,Yes,953.6263238,18363.0685 0,Yes,1463.337765,11579.15945 0,No,1172.459499,34690.13828 0,Yes,548.2728639,12048.82289 0,No,1752.883789,48250.10462 0,Yes,555.9206802,23909.70649 0,Yes,1560.931752,13621.56916 0,Yes,839.8835566,16883.33886 0,Yes,1112.817978,13634.90763 0,No,1309.253772,43278.05928 0,No,940.5908748,41560.47099 0,No,1019.248977,41195.37012 0,No,433.6690195,32904.69772 0,No,1015.115425,42050.46652 0,No,75.17766307,52765.97828 0,No,1050.316376,39585.62422 0,No,48.91185684,35155.17247 0,No,1721.64778,48236.12633 0,No,1607.35144,48700.84805 0,No,1180.447542,43914.41982 0,No,609.8771346,40875.94654 0,Yes,1054.200208,19440.97318 0,Yes,793.8204657,19074.03173 0,No,879.6756179,30021.38335 0,No,1237.647422,29151.23466 0,Yes,1412.764358,18234.45922 0,Yes,1174.716483,13025.76945 0,No,363.3448038,44325.78885 0,No,919.4857763,44928.18464 0,Yes,1195.05967,21648.65668 0,Yes,794.1377878,15721.79966 0,No,10.18881851,33776.82546 0,No,0,31083.22146 0,Yes,498.5059792,16967.61976 0,Yes,1227.161107,18459.41861 1,No,1902.612991,53394.07623 0,Yes,1059.204637,11742.37986 0,Yes,450.3053908,21177.61108 0,No,418.5398704,55002.73341 0,No,1070.480677,32939.36671 0,No,1306.770573,56640.63403 0,Yes,1315.337298,8431.175004 0,No,74.5334252,19146.24563 0,No,66.15233049,38927.12451 0,No,56.42071165,54217.33825 0,No,1056.596875,25123.85962 0,No,259.2375778,51355.37462 0,Yes,1143.692578,23500.90128 0,Yes,0,15682.0685 0,No,877.7939338,30239.72072 0,No,919.077555,45315.55745 0,No,94.93247587,27212.95124 0,No,333.3741889,29413.14899 0,No,197.9107165,30971.19725 0,No,624.910088,41047.55579 0,No,138.2522518,54406.50225 0,No,787.5517717,46185.552 0,No,1030.927564,36271.33456 0,No,1049.747643,30159.20989 0,No,0,35738.98757 0,Yes,1130.056787,18467.10219 0,No,795.5412235,51303.14014 0,Yes,1503.596238,18710.61448 0,No,436.1350124,46071.66052 0,No,800.7055933,36314.43633 1,Yes,1881.049952,16580.45056 0,Yes,1445.45803,18632.61606 0,No,1166.100236,23994.40391 0,No,163.7471037,38970.0289 0,No,0,46353.80212 0,No,1322.052937,47814.1742 0,No,1283.523253,61525.69618 0,No,1000.898434,43059.0486 0,No,249.860323,47602.1165 0,No,500.2221784,38991.01248 0,No,568.0944851,36374.43392 0,Yes,1047.085154,13714.27938 0,Yes,186.6683743,15976.63786 0,No,782.778332,39224.40927 0,No,388.5483505,18007.83296 0,Yes,842.5722263,17279.52182 0,No,556.1156313,44197.90846 0,No,1234.578618,48112.48054 0,No,412.8943419,42123.21721 0,No,683.3573938,29269.33352 0,No,872.2261716,48101.61169 0,No,415.7654285,24740.7536 0,Yes,1518.018809,16740.44308 0,No,254.3944552,44612.14757 0,No,1507.249195,24057.51794 0,No,900.5675031,40848.69179 0,No,595.1451336,46011.94663 0,Yes,163.9398231,21083.00451 0,No,389.6132519,43472.6743 0,Yes,1334.97118,14834.86429 0,Yes,1961.728657,17864.09925 0,No,1173.161493,36439.67873 0,No,1102.418242,23218.0838 0,No,1228.907919,46411.82006 1,No,1505.831475,29525.7494 0,Yes,1217.623913,21160.4293 0,Yes,843.159091,12042.89422 0,No,1004.532904,59403.93001 0,No,294.0629444,57500.11105 0,No,484.7234795,24569.56729 0,No,775.5561388,34586.25334 0,Yes,1339.312769,21031.85983 0,No,801.8550344,39156.62749 0,No,551.8871003,37573.14139 0,Yes,522.3811806,23440.06422 0,Yes,1237.454723,20660.46938 0,No,320.0059169,57563.48923 0,No,960.6700306,30093.72754 0,No,272.0195177,29773.60932 0,No,927.1659182,33781.81511 0,No,75.79525931,55189.68599 0,No,1285.851381,37635.95408 0,No,1471.775073,32478.04391 0,Yes,808.0147191,14485.46844 0,No,747.3535557,42900.02495 0,No,327.7957317,23865.3281 0,Yes,851.3417919,18786.58034 0,No,110.8543651,27370.30437 0,No,541.7555693,57322.10502 0,No,0,36269.29254 0,No,1201.360264,51740.89005 0,No,57.2198379,18982.55571 0,Yes,785.0934846,32945.82807 0,Yes,383.6396643,17445.18244 0,No,1107.304157,52765.05866 0,No,396.9859854,55454.63104 0,No,0,44807.37827 0,No,924.2415348,45207.38763 0,No,602.4937249,33202.53438 0,Yes,828.739825,17962.31109 0,Yes,1.611175572,20837.33546 0,Yes,894.0057891,18002.32519 0,No,1234.476479,31313.37458 0,No,406.0139804,42642.23737 0,Yes,1051.998449,12376.09598 0,Yes,1085.83808,18890.22808 0,No,1656.173272,41133.12685 0,Yes,242.6526089,20426.79397 0,Yes,602.3064955,12927.7128 0,No,187.5973026,59660.99704 0,No,331.2970819,34497.74268 0,No,911.3877951,57131.32037 0,No,391.4082005,39761.56184 0,No,1450.349184,33957.18196 0,No,819.9169885,28960.11281 0,Yes,1590.176413,18666.41221 0,Yes,1256.61487,22493.50126 1,Yes,1889.33211,22652.10963 0,No,743.6853749,10774.97191 0,No,343.1944994,29555.90547 0,No,534.9731547,44079.33287 0,No,1207.697906,34857.54031 0,No,564.8138972,39829.41683 0,Yes,495.3652633,22938.13687 0,No,902.5138231,38647.52189 0,Yes,1741.273354,18544.05165 0,No,1166.642625,52700.10969 0,Yes,631.15977,23245.07076 0,Yes,1076.977966,14668.38071 1,No,1243.554025,37634.3464 0,No,2113.019023,34747.75578 0,No,1296.060997,43313.28299 0,No,1088.674077,47147.72595 0,No,824.7317041,42313.51575 0,No,684.2948414,37011.18328 0,No,37.3648865,26221.5179 0,Yes,878.1490055,18414.57007 0,No,904.2415222,40683.46035 0,Yes,942.042215,22181.39455 0,Yes,957.4925363,16323.36476 0,No,285.2908758,17606.63593 0,Yes,1468.382748,19846.06475 0,No,322.7130195,27668.5761 0,No,414.1340631,40877.22645 0,No,1105.002812,36812.70609 0,No,0.445756769,31934.83798 0,No,619.7013996,13902.179 0,No,293.8693202,56676.80175 0,Yes,637.3827283,19704.91942 0,No,1109.528617,69325.07982 0,No,1223.112944,52903.46137 0,Yes,1183.69295,13081.66106 0,No,569.7908726,54335.2241 0,No,625.5319229,46026.48791 0,No,833.6552993,16908.7781 0,No,866.0459226,44827.26588 0,Yes,945.4798492,17279.28681 0,No,1554.845545,43901.80532 0,No,575.7683453,51927.7468 0,No,1141.603687,38722.96119 0,No,1146.64022,45376.55648 0,No,0,34305.91868 0,No,434.7147713,35454.75542 0,No,1026.13443,39135.63696 0,Yes,1302.797206,29252.36143 0,No,690.7058706,59561.9052 0,No,574.4516329,40727.64293 0,No,0,43346.62226 0,Yes,251.3060237,13058.60666 0,Yes,1252.113954,16490.70693 0,No,207.8952069,52249.31234 1,No,1753.084389,48965.34697 0,Yes,1279.935227,21107.52024 0,No,862.8765137,36461.90113 0,No,1143.541405,33394.9791 0,No,1253.18164,71238.5506 0,No,624.0839722,25557.63967 0,Yes,1027.86107,14322.08836 1,No,1964.014684,50553.53452 0,No,752.4261091,32539.64501 0,No,953.9298177,32640.14405 0,No,773.2042127,66749.43344 0,No,432.7910885,52238.08744 0,No,797.7340162,31616.80024 0,No,240.8415528,56089.15425 0,No,662.3158574,31815.34656 0,Yes,1056.36555,15432.39599 0,No,741.0875961,43015.53915 0,No,0,42284.68217 0,No,981.9544898,41123.21247 0,No,1569.835666,42781.3833 0,No,136.5055213,45596.10783 0,Yes,731.9517153,18117.42791 0,No,717.8107678,32040.93126 0,Yes,1005.176223,18262.17614 0,No,561.9283609,31192.91272 1,No,2033.19179,44998.28744 0,No,638.8174557,46704.738 0,Yes,1104.949687,11528.99966 0,No,0,29514.03361 0,No,633.7666725,34290.60492 0,Yes,1526.025134,20894.07666 0,No,522.7662715,43026.32913 0,No,853.1031276,47381.72423 0,No,1225.225152,39338.86212 0,Yes,1559.627869,22047.78161 0,No,260.3399357,34932.49376 0,No,1320.943245,50505.3669 0,No,1443.64839,45089.38954 0,No,791.6382334,30303.60678 0,No,922.6664691,33445.48969 0,No,80.75286364,35887.54636 0,No,193.3585386,34728.02143 0,No,939.0985018,45519.01898 0,No,1336.803015,30787.15572 1,No,1488.779562,49803.29308 0,Yes,375.1967469,13709.20533 0,Yes,773.3811632,18978.18845 0,No,581.4231695,44600.6628 0,No,461.0502494,43147.77203 1,Yes,1424.559323,25398.19744 0,Yes,1139.396214,17139.84809 0,No,1032.20327,41673.73415 0,No,96.64183869,44556.21942 0,No,338.7794082,42678.71319 0,Yes,1331.425462,13793.18518 0,No,0,34479.62349 0,No,616.25397,42436.68644 0,No,599.8008625,30146.63574 0,Yes,1466.56289,20904.44788 0,No,593.3038909,49925.32392 0,No,786.98126,49432.95276 0,No,926.9853267,48753.31287 0,No,1175.420433,37462.64333 0,Yes,1697.749667,23295.84328 0,No,575.5143833,54203.19021 0,No,1386.302444,54404.48169 0,Yes,1222.245092,23152.7231 0,No,1556.194194,26905.58075 0,No,1103.92188,52821.53485 1,No,1496.07211,40214.62083 0,Yes,975.642874,25920.32191 0,Yes,781.1742571,23403.06377 0,Yes,940.4354663,18939.84279 0,No,375.32962,32531.21576 0,No,627.8761333,51770.45835 0,No,1304.383209,49371.95691 0,No,1496.23237,37534.32924 0,Yes,1005.593829,15851.48265 0,No,828.8577543,46060.85293 0,Yes,1144.35852,18903.37844 0,No,1386.191949,42537.99079 0,Yes,1321.805206,11453.61153 0,No,1121.151251,35765.70649 0,No,547.8633422,45966.35106 0,No,813.9973412,42461.56142 0,No,75.18398526,52166.00597 0,No,598.2192978,28621.12542 0,Yes,63.03632327,12308.01832 0,No,0,48642.25451 0,No,1553.703309,35929.16355 0,No,813.7160939,46419.99708 0,No,1386.176753,42875.00602 0,No,933.7673154,43693.52116 0,Yes,1240.0956,20134.64729 0,No,0,33781.65631 0,No,613.5818624,30833.80206 0,No,253.5167956,53125.48345 0,Yes,947.8858454,17094.47889 0,Yes,112.8119095,16100.57083 0,Yes,827.0413054,18892.82129 0,No,484.679499,43900.09156 0,Yes,271.2497049,25157.73142 0,No,1018.248906,52509.74244 0,No,0,43730.76665 0,Yes,0,16421.48992 0,No,1101.803715,36555.4627 0,Yes,1471.897822,18913.03822 0,No,1057.71318,40707.61877 0,No,352.5505372,17626.16713 0,No,1029.819308,50635.89081 0,No,1308.663094,35964.79193 0,Yes,1581.790489,10537.52984 0,No,918.1189019,53710.2153 0,No,637.8002911,40323.55907 0,No,369.041788,38468.24465 0,Yes,985.0926986,18499.96154 0,No,306.9569266,32428.81291 0,No,1205.913239,43715.41376 0,No,1079.502963,40504.91545 0,No,555.2884886,40046.42198 0,No,905.0116944,39293.98251 0,Yes,1348.956074,20870.26798 0,Yes,1461.833249,19252.23729 0,No,443.4469923,29811.36345 1,No,2024.105018,51508.86888 0,No,1516.551152,39368.14187 0,Yes,520.5576465,18256.28839 0,No,850.5480987,44501.91504 0,No,926.4896733,49919.96729 0,Yes,1275.824225,9611.963157 0,No,249.5983832,20684.23741 0,Yes,1564.471411,19372.8216 0,No,443.1755645,37639.98243 0,No,323.5469083,33991.99111 0,Yes,512.3202025,24949.62161 0,Yes,524.7606336,14154.91347 0,No,741.4204609,38660.99965 0,Yes,1994.049188,14305.11147 0,No,1175.715193,24000.89494 0,No,284.0487757,41243.85479 0,No,565.5053845,39109.30967 0,No,47.74566544,31150.37469 0,No,776.9548658,45083.3202 0,No,353.7390971,65526.80974 0,No,76.13224922,53594.42001 0,No,839.6035976,45994.64473 0,Yes,754.4840226,22425.53548 0,Yes,833.1207736,23630.27367 0,No,277.2949179,27548.9503 0,No,1594.697189,37091.87383 0,No,0,40393.47543 0,Yes,1281.617091,14236.09199 0,No,1469.703786,40174.95035 0,No,0,38165.68831 0,No,891.403241,46611.73164 0,No,493.7649099,51518.05525 0,Yes,884.6140116,24306.65035 0,No,0,32582.74556 0,No,1028.520386,47944.60096 0,Yes,425.8317403,13275.91441 0,No,1132.358062,52489.76422 0,No,965.2738039,46218.46056 1,No,2499.01675,51504.29396 0,Yes,1435.181264,11346.84187 0,Yes,1391.19636,8637.963307 0,Yes,1804.413369,17665.54905 0,Yes,321.7950672,19229.22587 0,No,1506.669829,41931.70448 1,Yes,1402.267516,12104.31561 1,No,1379.430717,38881.96935 0,No,1232.978729,57182.33212 0,No,134.8580208,35755.86662 0,Yes,422.9140174,18787.33703 0,No,1373.564213,21785.25275 0,No,661.4258876,55591.55703 0,No,1427.718421,27425.67495 0,No,938.6378621,39459.76704 0,No,608.4289941,56483.0569 0,No,1477.532394,46965.13826 0,Yes,1470.588741,17876.29269 0,Yes,1488.598859,20739.62679 0,No,0,49608.00617 0,Yes,328.0383342,19566.26724 0,Yes,1333.806128,21087.32352 0,No,764.5347169,40593.28542 0,No,110.326679,52106.2049 1,Yes,2502.684931,14947.51975 0,No,604.3155154,36592.75684 0,Yes,1595.288158,22645.04006 0,No,0,50076.26357 0,No,440.6376501,50902.13614 0,Yes,876.9186246,13372.51292 0,No,1366.890314,35566.95637 0,No,1129.975937,32795.04634 0,No,1077.020824,56862.06694 0,No,1411.074241,26040.00459 0,No,0,45788.49211 0,No,861.652948,27355.80502 0,No,1185.661115,56483.5372 0,No,901.5539184,57100.41767 0,Yes,773.7302157,14841.98962 0,No,1387.973385,30716.64306 0,No,1207.694726,37355.94434 0,Yes,455.7164377,12587.40145 0,Yes,550.8950488,19110.07447 0,Yes,1484.805423,22965.17668 0,Yes,441.6261268,15261.70803 0,No,810.4120752,28056.78391 0,No,1137.039899,49810.89999 0,Yes,1402.407716,13870.96968 0,No,1541.812806,29374.92888 0,No,898.9807259,63041.22478 0,Yes,700.3311593,18702.956 0,Yes,895.4580635,23795.5515 0,No,770.1321919,48489.37866 0,No,841.951731,53779.01825 0,No,44.31361993,30449.25019 0,No,377.3003603,42257.49319 0,No,264.8011505,49216.24815 0,No,522.0216391,37341.89878 0,No,1681.916005,41866.55806 0,No,1069.539368,55421.55279 0,No,375.7009493,37925.97462 0,Yes,670.991526,19835.96905 0,No,318.9125682,49305.55317 0,Yes,955.1202176,25314.85309 0,No,1123.793439,24901.99634 0,No,993.5808787,34836.25272 0,No,278.8052603,54559.11666 0,No,1553.289604,44150.16209 0,No,0,34522.88556 0,No,483.5913011,36497.46846 0,Yes,311.5305491,18534.43072 0,No,536.2538616,32994.15136 0,No,127.7079183,27483.895 1,Yes,1507.333948,23898.87823 0,No,1351.03593,40946.60552 0,No,723.1989866,42751.76524 0,Yes,1041.202799,22618.42415 0,No,1102.110109,34246.69491 0,Yes,420.3702643,16419.71297 1,No,1278.4073,36675.60688 0,No,542.469658,52741.81067 0,No,401.8290422,47175.61452 0,No,506.5058716,51114.31972 0,No,1160.499604,23498.3002 0,No,1277.795913,56605.59886 0,Yes,1200.624471,18973.90768 0,Yes,1418.586072,11556.69531 0,Yes,480.3397036,14592.8404 0,No,370.5300188,31139.72184 0,No,737.6852405,51138.26546 0,No,501.921446,41366.67789 0,No,1682.201224,30441.55485 0,Yes,813.4527916,21073.98883 0,No,683.8262514,46937.62287 0,Yes,1036.600444,15197.24851 0,Yes,471.1433887,24945.03775 0,No,336.4196404,39707.04233 0,Yes,443.7230439,13136.83042 0,No,967.1388884,35269.12297 0,No,549.2759696,15706.21509 0,No,1601.160056,17974.74118 0,No,902.1378198,59695.29046 0,No,0,37553.55105 0,No,1433.183548,41744.31508 0,Yes,502.7977355,13376.05334 0,No,433.4528187,35858.9302 0,No,406.4267183,36750.49898 0,No,935.481005,56610.81557 0,Yes,1545.830641,12160.84228 0,No,93.94056279,19811.33054 0,Yes,192.4111547,24050.37172 0,No,716.9202275,24015.51912 0,Yes,1157.139538,21222.41554 0,No,72.16974028,39560.63498 0,Yes,1417.268258,20464.02574 0,No,1195.483956,38452.64134 0,No,559.1186503,55007.2651 0,No,0,37499.68927 0,No,359.2257409,25243.09848 1,Yes,2123.369217,23836.46426 0,No,1316.542384,20353.49883 0,No,1172.30089,34513.61085 0,No,1453.083637,36828.32719 0,Yes,323.9570867,12327.24399 0,No,1152.550664,27346.03061 0,No,1333.998166,57662.12193 0,No,709.6586991,37992.21295 0,No,731.3091222,33126.84977 0,Yes,279.3155744,19742.03706 0,No,0,17059.36832 0,Yes,630.704463,16501.809 0,No,543.0371297,31268.09516 0,No,618.881502,27906.58962 0,Yes,1053.161735,25222.97884 0,Yes,0,19622.57717 0,Yes,1762.352183,17032.34235 0,No,0,43943.24491 0,No,469.1519745,30366.7196 0,No,699.7625588,32881.30451 0,No,446.1427252,42611.58355 0,No,571.1799476,39682.80287 0,No,952.3348213,44864.15486 0,No,584.6670228,55682.4667 0,No,174.3678659,47750.12061 0,Yes,671.8017009,15299.25989 0,No,1339.55551,53585.76133 0,No,0,49232.70655 0,Yes,1579.070977,21101.20461 0,No,917.3235409,40193.8381 0,No,251.4282215,25962.05919 0,No,998.3620611,38385.29131 0,No,828.2325106,42246.21736 0,Yes,1500.283089,16943.02902 0,No,1142.637976,26731.09036 0,Yes,1415.198823,16737.51871 0,No,198.7309179,33512.93037 0,No,341.7692502,41662.74261 0,Yes,146.7353634,12716.21283 0,No,1576.306916,30547.79997 0,No,1830.471547,24053.47692 0,No,368.8007286,34526.03537 0,Yes,1026.576284,23636.55277 0,No,0,36225.50725 0,No,241.3360311,40122.40162 0,No,1272.053891,44895.5933 0,No,361.2007027,49395.02674 0,No,1464.009916,32023.59552 0,Yes,815.0774084,18445.6597 0,Yes,1026.128247,16430.64112 0,No,630.7255595,30466.37852 0,No,1624.126432,16054.30421 0,No,744.7326369,44965.04641 0,No,1439.296779,36170.59837 0,Yes,1491.050998,13033.12274 0,Yes,0,13334.24013 0,No,1162.699796,27206.66185 0,No,1463.593161,34064.87872 0,Yes,1309.697543,15536.23876 0,No,920.6474223,37936.08914 0,Yes,6.318864439,18912.61406 0,No,128.9911619,56946.4364 0,Yes,1323.289063,21149.31369 0,No,383.2157693,15233.31635 0,No,267.380973,32265.11891 0,No,429.074698,48109.80825 0,No,711.003891,51035.74577 0,Yes,309.5241479,12135.03813 0,Yes,1317.926258,14070.16706 0,No,427.3328427,55503.07363 0,No,872.8569657,31471.54539 0,No,1105.556173,38886.03581 0,No,678.0189216,59416.77886 0,Yes,1164.63073,17929.65298 0,No,214.9016261,33904.57772 0,No,721.5867078,50952.48482 0,Yes,224.338769,18282.2166 0,Yes,1199.237774,13809.35049 0,No,580.0545844,37278.27931 0,No,821.4936019,34739.39481 0,No,602.8629316,37849.13424 0,Yes,377.7676037,14021.5889 0,No,727.632558,38431.15806 0,Yes,600.4308598,18538.30171 0,No,993.5916771,26682.32857 0,No,1096.587037,47235.45313 0,No,1603.333397,43474.6376 0,No,1146.971428,30637.2973 0,No,1342.17534,58049.99404 0,Yes,1663.687014,19847.69861 0,No,1005.247966,41308.25485 0,No,962.8949273,25063.49298 0,Yes,1092.620656,27128.16725 0,No,919.3433464,43287.90231 0,Yes,176.8405782,20021.69339 0,No,460.6044655,38818.18164 0,Yes,782.5448463,20593.52669 0,No,613.7429339,56951.3597 0,No,362.1034983,39658.95045 0,No,861.6819237,49672.34718 0,Yes,936.4734896,19389.48003 0,No,1222.737042,68564.99068 0,No,794.4867872,44145.21585 0,No,0,13573.5624 1,No,2220.966201,40725.09621 0,No,523.2162452,38063.40167 1,No,1907.377311,42346.82846 0,Yes,1809.463468,18804.72386 0,No,1392.261315,32283.38838 0,No,1349.466456,35870.01951 0,No,621.7362508,51595.75172 0,Yes,1273.581981,18475.19238 0,No,636.2405203,17355.7575 0,No,680.4518871,33192.4018 0,No,836.8498663,27936.51892 0,Yes,1115.10674,19169.07158 0,Yes,799.7433408,16147.57662 0,Yes,996.3868788,20070.81392 0,No,1632.808045,33455.90111 0,No,439.8374803,24452.6466 0,No,276.7453134,50523.67789 0,No,1715.315063,22824.09762 0,No,848.6936359,29042.5594 0,Yes,323.5033574,19144.81572 0,No,793.6122324,24973.20848 0,No,612.0784475,39443.95741 0,Yes,1418.429004,14907.07001 0,Yes,1466.939439,19139.62963 0,Yes,440.7900554,19896.02967 0,No,0,53432.61549 0,No,885.180222,46571.77169 0,No,635.9697054,42451.61434 0,No,664.8163017,32894.80407 0,No,1100.238779,34281.25517 0,Yes,1606.320366,13422.30712 0,Yes,1704.427857,20892.31412 0,No,1295.991633,39799.99754 0,No,468.412124,22943.12569 0,No,303.7832118,44745.11955 0,No,971.709025,33774.89191 1,No,1758.420947,49787.45657 0,Yes,1334.701119,24621.96305 0,No,982.6992113,40194.46538 0,No,52.03888628,14680.10005 0,No,401.5035397,25010.80009 0,No,1456.849378,40186.53088 0,No,772.9245771,42678.38808 0,Yes,1844.361718,14238.11325 0,Yes,1015.511397,21362.93892 0,No,988.6780269,31210.24323 0,No,1072.744106,45704.83957 0,No,1576.900672,39835.34872 0,No,840.1051582,33976.70754 0,Yes,1210.438929,19022.19227 0,Yes,714.5496128,13431.84165 0,Yes,607.094029,16373.93446 0,No,413.3635225,43071.18631 0,No,1184.212015,34990.58061 0,No,273.6022741,27996.5771 0,Yes,871.8152195,9788.57126 0,No,1724.556817,46397.45231 0,Yes,1107.40038,15661.57973 0,Yes,1161.407345,18420.29889 0,Yes,107.6025084,17941.73917 0,No,136.4220356,42719.89406 0,No,682.035984,30288.9418 0,Yes,791.5303591,18342.73735 0,No,457.8690747,43134.54801 0,No,1514.963236,36403.03614 0,No,816.0938872,37763.03651 0,No,1241.954243,41952.11577 0,No,932.2025461,49214.70512 0,No,608.1084466,46962.78701 0,Yes,627.5932466,15423.20763 0,Yes,593.0552412,22786.91671 0,No,187.3101926,31140.74672 0,No,736.4757788,39538.3308 0,Yes,838.0811589,23698.70725 0,No,511.5159962,43737.18181 0,No,355.3596461,65798.92003 0,No,1138.396686,24098.34886 0,No,828.0049554,53806.31628 0,No,1218.553005,51224.91731 0,No,316.0121309,34250.5216 0,Yes,480.4697653,14866.61225 0,No,152.5030074,39502.8368 0,No,1129.316466,46689.9794 0,Yes,887.29064,14958.55147 0,No,938.4369137,34184.94288 0,No,1076.665439,43972.69386 1,No,1575.487818,35735.45501 0,No,148.1459807,43104.38609 1,No,1865.635779,49604.88213 0,Yes,633.7691889,14109.82181 0,Yes,0,23422.13722 0,No,922.817168,50800.35901 0,No,4.712141697,28521.36688 0,No,1198.175927,42968.63988 0,No,1726.618698,43814.96127 0,No,1075.219721,30485.05974 0,Yes,883.9235437,14740.13868 0,No,1099.292985,47625.30546 0,No,948.9651331,45456.9288 0,Yes,814.6300753,22948.15776 0,No,687.2929387,35989.22765 0,No,693.6715894,32578.60631 0,No,814.8431184,28526.21683 0,No,1031.974601,59555.86581 0,No,1319.483579,62054.98639 0,No,1366.026022,51551.10832 0,No,1033.455043,55487.21989 0,No,0,50231.44734 0,No,857.5823611,54372.89419 0,No,1156.289558,33959.73853 0,No,1713.26359,34005.8614 0,Yes,902.9807197,15076.33854 0,Yes,212.0421829,16179.30775 0,No,632.8039803,51149.42525 0,Yes,378.0211154,19571.3923 0,No,999.5380168,43934.85626 0,No,970.8525458,49043.78232 0,No,1319.187613,35466.24186 0,No,286.3299636,59576.65032 0,No,830.5718756,39723.76866 0,Yes,657.5321356,19395.73893 0,No,786.6410085,57704.64461 0,No,111.0619105,40675.32738 0,No,298.2365837,27838.68412 0,Yes,819.8586726,19802.46107 1,No,1790.674983,44607.42902 0,No,883.4719996,44207.33303 0,Yes,384.8313281,17844.51751 1,No,1567.610704,38785.35768 0,Yes,927.7727236,18148.06928 0,No,796.3261454,28616.70904 0,No,652.5533166,32438.0581 0,No,315.0122984,44690.49774 0,No,1609.797453,38756.45469 0,No,851.7962751,57950.77175 0,No,460.2078669,44527.27275 0,No,1203.828964,41484.93943 1,No,2074.807589,38988.85925 0,No,1243.708322,37926.10524 0,Yes,1373.037835,19457.6093 0,No,449.4126863,52892.25015 0,No,126.5585946,69541.9486 0,No,387.2361429,24243.94446 1,Yes,2332.878254,11770.23412 0,No,1359.438493,24411.13474 0,No,469.3317131,58360.38912 0,No,111.2949539,40086.69012 0,No,1443.875165,54355.72614 0,No,396.820316,51560.0281 0,Yes,146.1511448,15160.48947 0,No,607.2000058,36615.82944 0,No,1708.149967,46416.94558 0,No,956.533337,53702.54156 0,Yes,676.6132706,16148.18637 0,Yes,238.3181069,17194.15414 0,Yes,0,15967.61421 0,Yes,740.7379512,13065.56594 0,Yes,933.7116933,11810.55457 0,No,860.0343646,33740.69911 0,No,1032.940825,31152.14069 0,No,0,36949.94989 0,No,835.2734997,51471.14295 0,No,188.4087559,35073.5146 0,No,506.3249831,43631.29653 0,No,496.7518122,35955.63928 0,Yes,739.1794564,15819.73057 0,Yes,916.5369368,20130.91526 0,No,1559.752146,37227.26882 0,No,392.2395399,44983.2018 0,No,1065.82556,39718.94925 0,No,523.7088979,48091.74453 0,No,789.564771,39199.02953 0,No,805.1953048,38222.25523 0,No,1255.543375,45142.27359 0,No,654.808846,44255.7672 0,No,716.7649108,51528.49866 0,No,686.0723086,32318.42224 0,No,1530.370017,29479.02245 0,No,879.3738243,29697.14649 0,No,738.3278043,37273.17115 0,No,1647.426764,49156.15931 0,Yes,503.2166226,21371.24632 0,No,215.0253294,40399.40994 0,Yes,820.656301,24820.04824 0,No,990.6748042,56141.11376 0,Yes,914.1064962,15546.78367 0,Yes,1021.011613,8970.912036 0,No,1143.431379,35773.40185 0,Yes,763.3973889,15224.26346 1,No,1532.3263,42152.36171 0,No,1860.741148,39551.03583 0,No,1101.296311,27781.45239 0,No,541.9968025,43873.28941 0,Yes,696.5835269,2981.279548 0,No,1536.232276,30635.57179 0,No,1333.197313,33782.90385 0,Yes,869.509201,18056.45478 0,No,1048.55214,40916.12455 0,No,12.07926653,32670.41964 0,No,699.3825817,34276.5925 0,No,855.4263748,48653.58256 0,Yes,562.3041982,20164.31101 0,No,62.17004972,28660.74751 0,No,977.8897501,45005.25642 0,No,1685.450623,60890.3648 0,No,1257.79532,21679.4905 0,No,671.784325,53374.954 0,No,63.09747039,38493.08725 0,No,686.3844319,28953.27033 0,No,582.944764,40368.54382 0,No,808.6251972,32316.37274 0,No,1077.980307,17705.21896 0,Yes,1716.089774,22755.26192 0,No,417.7275813,31206.29045 0,No,1300.995166,42493.95555 0,No,896.678116,46328.5442 0,No,136.82832,13585.9975 0,No,29.17509908,38871.47869 0,No,282.9095623,35445.91861 0,No,858.5426304,19071.84843 0,No,864.0471984,27690.11354 0,No,1225.975008,42203.20816 0,No,696.4287643,38028.29972 0,Yes,1840.217987,26480.71927 0,Yes,1227.485273,16717.62059 0,No,1716.675134,39543.29108 0,No,330.6224277,11732.25138 0,Yes,915.6568136,17710.8547 0,No,133.0970308,34578.99685 0,No,420.611587,33293.46686 0,No,1058.058796,27196.25858 1,No,1972.16682,34362.63501 0,No,790.2389859,43646.14313 0,No,131.6247137,40158.70898 0,No,0,37468.48077 0,Yes,106.2506074,17638.79973 0,Yes,712.2537066,21407.04362 0,No,906.2232258,44626.94267 0,No,127.4099032,49735.64164 0,No,1698.071916,48595.70465 0,Yes,1448.035606,18989.39705 0,No,711.4394514,40507.82124 0,No,326.6289568,52696.72518 0,No,352.9438765,59372.71187 0,Yes,1247.067238,15522.58415 0,No,439.068967,47899.16968 0,No,1030.210058,40711.45317 0,No,0,33168.05805 0,No,1113.276529,51457.53906 0,No,1341.077777,40199.84141 1,Yes,2269.946966,18021.10595 0,No,1384.264924,27737.60852 0,No,0,32724.50637 0,No,745.8132048,42762.47499 0,No,909.6336239,41915.52066 0,No,196.3740083,57397.2772 0,No,866.1746688,41365.45638 0,Yes,1460.747667,24960.35938 0,No,760.7767006,44851.56026 0,Yes,1165.283215,10354.93762 0,No,1085.425572,39274.83387 0,Yes,724.385576,18641.49842 0,No,114.8844751,27365.4222 0,No,794.5875684,52507.47963 0,No,1093.256619,28811.27349 0,No,943.9378335,48198.40061 1,No,1861.060871,55671.72366 0,No,900.8387591,33130.70934 0,Yes,1682.796142,23344.07394 0,No,196.1954156,26612.5603 0,No,1181.123749,29181.839 0,Yes,1101.15809,13142.18149 0,Yes,1506.860473,14914.82336 0,Yes,1213.840554,17374.06395 0,Yes,547.2463251,17445.90172 0,No,1708.574079,34890.71713 0,No,248.0414824,36257.16145 0,No,386.2650712,45776.20668 0,No,527.4632744,35708.16925 0,No,1546.842501,51161.9795 0,No,0,47214.4336 0,No,0,55781.20484 0,No,955.4664338,47520.17558 0,Yes,750.8620345,16932.32771 0,Yes,1255.748135,12209.57707 0,No,818.0608834,35902.4718 0,Yes,1469.366668,11337.42299 0,No,331.7207347,46853.74585 1,No,1456.546056,51508.57434 0,Yes,1290.854293,19532.73153 0,Yes,711.0304958,12685.82486 0,No,734.3172028,33240.03903 0,No,329.2188974,48133.82442 0,No,1231.506211,35068.79074 0,No,847.054148,28539.1079 0,No,811.4289115,55119.89696 0,Yes,1656.857598,15359.43651 0,Yes,1547.153248,14344.28959 0,Yes,590.718726,13041.19846 0,Yes,893.9664662,15092.77603 0,No,0,34066.43811 0,No,800.15468,51730.70461 0,Yes,685.5225351,12361.8634 0,No,1262.757445,68579.10466 0,No,770.1150264,49856.69012 0,No,1181.000359,35123.74007 0,Yes,527.653898,17819.96534 0,No,441.4899198,39209.90013 0,No,921.9744134,38340.56889 0,Yes,875.3623525,14637.17693 0,No,1033.121155,66304.77382 0,No,1046.743543,40822.44741 0,Yes,1319.706449,23373.9312 0,Yes,698.5122051,26768.91312 0,No,570.9814914,47021.00672 0,No,842.5417747,33897.92651 0,No,0,47256.06211 0,No,850.3848881,44547.48551 0,Yes,963.1879064,12799.50938 0,No,1136.331132,42149.86899 0,Yes,1945.490483,14941.31796 0,Yes,1442.543601,13049.31938 0,No,135.6290155,39925.70017 0,No,423.8769467,36783.05143 0,No,299.7795953,28917.53781 0,Yes,701.5393706,17607.45782 0,No,283.207207,39263.39202 0,Yes,1357.849734,11402.54063 0,Yes,1327.864163,11020.90643 0,Yes,946.4499823,15179.21205 0,No,663.5052822,53102.24551 0,No,115.840084,47519.84972 0,No,800.4862112,37715.83633 0,No,640.0638938,52413.85897 0,No,877.2830136,27405.1355 0,Yes,1382.087692,15552.25205 0,No,751.3945389,50910.24562 0,No,0,39893.27193 0,No,1120.703239,29613.88758 0,Yes,1312.051431,28483.13551 0,No,389.0553278,22581.62365 0,No,861.6904462,58029.64023 0,No,1079.396005,38864.1448 1,No,1893.289792,31821.72558 0,No,337.4959039,55853.26513 0,No,1142.351706,45580.57481 0,Yes,847.0564853,13741.32707 0,Yes,1575.238116,24469.4914 0,Yes,403.3470535,19690.34551 0,Yes,566.5231194,25398.39838 1,Yes,2009.685744,25694.49616 0,No,954.5981678,50139.09299 0,No,1057.940415,45514.67253 0,No,381.3874762,28265.08609 0,No,793.1868556,35555.78028 0,No,1078.910385,54419.78655 0,Yes,1311.098483,21189.2384 0,No,38.29557625,37186.55159 0,No,756.2166996,36513.92121 0,No,56.86937772,56245.0654 0,No,1189.748039,30315.88513 0,No,1337.390877,51507.50092 0,Yes,1255.499215,16703.01737 0,No,1211.867926,42777.58234 0,No,318.8772708,50069.02433 0,No,336.0705452,38595.91155 0,No,934.4904926,31043.40833 0,No,812.4882979,42721.3007 0,No,1184.683904,37937.95265 0,Yes,1737.607593,7153.539666 0,No,94.12072071,36303.94158 0,No,994.6744382,33002.33905 0,No,0,21809.21851 0,No,1006.46883,44563.7708 0,No,331.9228185,47779.92656 0,No,967.1352554,23278.66591 0,No,132.158429,26878.47213 0,No,813.7409929,30975.21409 0,Yes,0,15986.24197 0,No,529.264959,38814.6067 0,No,743.0559317,40944.5638 0,No,27.7011327,45540.9439 0,No,1299.924669,44214.54091 0,No,906.5417626,27074.77474 0,No,1451.179476,53556.5521 0,No,1027.566531,48558.08728 0,No,1047.227421,40974.72813 0,No,1452.603293,28989.04395 0,Yes,1190.544112,20291.17198 1,No,1858.904515,35525.21393 0,Yes,1152.520224,12699.40587 0,No,831.5951866,37372.82827 0,No,922.6966762,34622.61934 0,Yes,469.4564151,13944.8934 0,No,474.8292191,41288.8291 0,Yes,1485.272118,10300.85544 0,Yes,943.3475559,19977.89177 0,No,1789.464512,27427.00457 0,No,659.5431977,21835.1718 0,Yes,1319.534005,24032.54412 0,Yes,514.3682658,18934.68347 0,No,1244.465198,40036.00808 0,No,946.8100507,41237.76925 0,Yes,535.6860201,11019.60693 0,No,682.8184588,33026.00374 0,No,1190.48596,38553.57479 0,No,171.0011892,42749.13071 0,Yes,1238.016616,26854.36628 0,No,1037.157699,36034.57837 0,Yes,464.8353104,19121.22865 0,No,124.699032,65211.07421 0,No,667.2030608,44682.83156 0,No,1111.060998,50301.47568 0,No,1142.39654,33740.44298 0,No,778.8301656,44257.33103 0,No,1412.024755,47727.56463 0,No,986.081816,52009.39965 0,No,1251.784019,32721.14404 0,No,149.0244884,33398.46237 0,No,465.0855346,43480.56849 0,No,1031.126796,41022.09564 1,No,1478.124069,31515.34449 0,No,789.0007057,35798.47264 0,Yes,790.2959403,14086.44837 0,Yes,1123.595047,16082.90571 0,No,1370.109496,40101.37913 0,No,396.5135872,41969.74677 0,No,682.0783655,30736.88765 0,No,338.735981,44923.04553 0,Yes,1056.329476,16000.84177 0,No,927.211781,44368.88309 0,No,634.6961045,32594.69223 0,No,833.533113,40526.30562 0,Yes,1345.230627,18518.71569 0,No,550.8989669,45347.78162 0,No,495.7089542,24845.43297 0,No,661.4261522,22610.3103 0,No,136.1995896,20935.61911 0,No,480.1925853,26084.40714 0,No,230.8689248,32798.78259 0,No,93.5701987,42930.77918 0,Yes,508.8248517,22663.70983 0,Yes,970.5159466,16584.82829 0,No,1324.296671,46875.39427 0,No,932.9638468,32105.44192 0,No,0,31418.36914 0,Yes,984.7631618,17039.97332 0,No,725.1205004,47549.20075 0,No,246.8238097,40953.34276 0,No,1051.321091,23859.4318 0,Yes,926.8926126,19445.93717 0,Yes,1024.024885,18545.89964 0,No,699.8420242,41985.25767 0,Yes,1036.323514,14108.45452 0,No,321.1088067,36275.14289 0,No,0,41152.17395 0,Yes,1299.187324,19878.74735 0,No,763.3753093,40978.14118 0,Yes,808.7427719,9775.581396 0,No,808.5075036,34543.0188 0,Yes,677.5528258,20745.01533 0,Yes,904.0089267,13996.5019 0,Yes,102.1667716,26266.36282 0,No,1218.346515,43930.98989 0,Yes,774.4138371,12289.86437 0,No,1309.564374,47432.43278 0,No,921.9989908,31553.73405 0,No,629.6962085,60230.21901 0,No,77.50293863,31050.15582 1,No,1772.855484,42080.10684 0,No,960.2376921,53418.51083 0,No,664.6529879,56677.62141 0,No,109.918734,33419.00689 1,Yes,1790.360537,14306.8236 0,No,1425.801629,34015.84987 0,Yes,1167.620124,23502.21282 0,No,891.1921462,35862.1673 0,Yes,1124.259312,23168.92345 0,No,1796.114376,31862.57529 0,No,237.9761856,38229.51786 0,No,1353.264924,43888.88306 0,Yes,1176.55006,18662.59883 0,Yes,1229.441498,12158.04466 0,No,270.548125,34258.83123 0,Yes,722.0554201,14661.52456 1,No,1170.198974,46692.10478 0,No,840.1365052,38738.17715 0,Yes,873.6412372,21810.61199 0,No,434.2433404,57146.4261 0,No,605.2744474,58469.47549 0,No,428.8421619,36980.13927 0,No,1662.762192,36691.56219 0,No,682.7124117,33175.18305 0,No,598.513777,42584.12081 0,No,1088.254765,32617.59348 0,No,911.0522329,38629.51836 0,No,538.1600228,44654.09318 0,No,721.7438218,21162.74272 0,Yes,438.5173671,17400.39085 0,Yes,516.865449,20179.34606 0,No,811.1712029,42185.44357 0,No,775.9818346,68179.7612 0,No,285.4000014,39398.56535 1,No,1554.816319,26430.4894 0,No,1057.015212,54652.71033 0,Yes,673.8124271,17672.28798 0,No,689.6873624,24085.43483 0,Yes,1400.344589,14768.69622 0,No,601.4172905,41490.61584 0,Yes,754.8595587,18996.8367 0,No,639.6652615,49977.29496 0,No,765.7724451,35122.02738 0,No,220.3689577,24019.31056 0,No,47.30266754,38293.31114 0,Yes,780.7223987,23819.78879 0,No,168.8134955,57262.7545 0,No,1557.640951,46030.26209 1,No,1891.109614,34448.69385 0,Yes,688.7475484,26662.3766 0,No,452.0078771,39990.76569 0,No,574.4911928,31632.62424 0,No,572.1661387,43807.21905 0,No,946.770746,26618.52276 0,No,523.9086948,43928.63793 0,No,1146.358385,44840.02496 0,No,1646.613919,39347.4568 0,No,1216.202309,43656.12889 0,No,1184.899625,53896.90225 0,No,1416.065555,40371.28459 0,No,431.9427414,46658.01381 0,Yes,521.0330664,16887.16039 0,Yes,806.9961112,15964.99698 0,No,775.4439275,33781.78107 0,No,942.2799083,24683.38081 0,Yes,1261.257138,26085.07612 0,No,509.3708922,39546.47264 0,No,1389.386452,39707.98955 1,Yes,1836.161765,18944.80788 0,No,196.4829904,41972.98495 0,No,1348.888866,40548.75271 0,No,661.6020253,43157.11928 0,No,718.9867228,21044.67383 0,No,451.1293241,39849.71324 0,Yes,114.4182416,12540.90728 0,Yes,889.0123127,10382.9368 0,No,1046.135324,47218.81679 0,Yes,1263.874831,18520.06367 0,No,1109.034634,29759.6363 0,No,698.4053812,33265.43584 0,No,1328.967126,51721.37161 0,Yes,1199.809244,19538.02749 0,No,1261.778453,26767.92328 0,No,605.3656943,39002.22964 0,No,550.6398862,35648.39063 0,No,894.2750955,36513.5392 0,No,1163.534574,46173.76824 0,Yes,1061.755086,17502.81964 0,Yes,1204.54963,19058.98454 0,No,1666.415624,18916.37486 0,No,412.9020609,49060.92374 0,No,595.0659734,29842.88605 0,Yes,836.0057683,7404.715843 0,No,178.1545157,28785.9621 0,Yes,627.6453208,17868.44485 0,No,1004.255095,36895.86555 0,No,1195.399568,34333.92991 0,No,515.6356355,51072.84702 0,No,1242.110378,37980.75837 0,Yes,153.2610397,14027.79074 0,No,1086.740286,42451.99308 0,No,280.9590497,29998.8573 0,No,955.8814143,48657.79037 0,No,1093.282258,18844.25039 0,No,734.0879454,22342.55035 0,No,793.8293877,45089.55206 0,Yes,410.4961872,25838.19736 0,Yes,677.6531966,18930.0772 0,No,814.5538745,39148.55837 0,No,800.1773456,43547.39817 0,Yes,300.7955043,19673.67406 1,Yes,1748.680849,13715.42438 0,No,0,54901.82221 1,No,1807.684491,42308.95445 0,No,833.0705629,36330.26209 0,No,794.464327,32431.82055 0,No,631.5980113,46841.11096 0,No,108.2494526,45264.56479 0,No,1040.396434,41395.28825 0,Yes,1207.052483,24747.88997 0,No,67.23043907,45751.44793 0,No,438.3989038,40576.74212 0,No,708.579795,21728.8202 0,No,775.4022116,41857.57803 0,No,0,49459.27548 0,No,610.5067355,52124.23357 0,No,845.3432645,34216.75436 0,No,181.9197785,37725.24736 0,No,272.9878407,63566.36599 0,No,805.0963327,40236.89963 0,No,226.8557048,33014.89333 0,No,0,39576.43465 0,No,691.9288288,23957.71488 0,No,1674.19783,54658.07415 0,Yes,715.7174174,27285.33324 0,No,339.4334297,44889.87491 0,Yes,767.3371317,21979.64653 0,No,1088.488096,38171.37207 0,No,28.56739084,35414.57828 0,Yes,792.5775616,21347.27345 0,Yes,1093.11502,22593.08161 0,No,408.4892466,35984.56168 0,No,1214.830968,38224.96205 0,Yes,850.1598866,26764.29246 0,Yes,1078.662963,25945.17896 0,No,403.6464529,55458.89125 0,No,1378.169887,48152.98785 0,No,306.9952026,43504.30524 0,No,1569.380835,42414.62536 0,Yes,1377.269683,17077.24211 0,Yes,656.379057,26578.88356 0,Yes,1654.632735,23687.35721 0,No,702.0847515,47811.75807 0,No,1110.060616,40236.50149 0,Yes,718.211754,18788.74812 0,No,737.1504318,50544.78419 0,No,672.3800476,40236.8732 0,No,1298.002763,42219.91124 0,No,489.3993741,37338.58292 0,Yes,636.104269,20763.82786 0,Yes,1406.799827,16686.62801 0,No,997.809843,50287.87374 0,No,0,49792.75726 0,Yes,691.2247423,16872.15171 0,No,485.8689097,14213.70558 0,No,730.1796753,42685.18467 0,No,152.1195126,41847.23184 0,Yes,1105.925571,13126.21424 0,No,0,47592.72814 0,No,815.737668,48788.10334 0,No,568.629894,28122.59409 0,No,589.9444194,24854.60458 0,No,1139.567166,36363.6248 0,Yes,344.5228968,21341.65384 1,No,2005.575128,36636.00859 0,No,1391.033809,51881.81867 0,No,1338.884597,32500.58487 0,No,78.78255363,37605.45251 0,No,764.6451796,49100.94838 0,No,602.6168777,56017.12754 0,No,834.6900595,42468.91366 0,No,1391.516591,30152.80387 1,No,1823.751426,53526.35641 0,No,1010.070759,39456.90834 0,Yes,1069.80034,14736.147 0,No,1116.065771,37074.06137 0,No,428.4022839,36340.65107 0,No,147.4885752,44482.26878 0,Yes,689.519195,15600.04737 0,No,939.4207168,55360.03312 0,Yes,591.1402653,19937.02257 0,No,1139.264364,41869.31867 0,No,500.6492512,31353.75002 0,No,1109.408616,64213.19249 0,Yes,733.928449,28105.6062 0,No,687.9567922,40354.21604 0,No,118.5984509,35974.76401 0,No,0,42009.04182 0,No,634.8650438,49681.22453 0,No,615.4712543,38161.6557 0,No,457.9577436,36211.19499 0,Yes,854.1493647,12843.43651 0,No,770.9545178,42673.99315 0,Yes,0,24283.33598 0,No,58.4438978,44838.69499 1,Yes,1893.664334,21526.35063 0,No,768.0436339,35951.12467 0,No,336.2424173,46279.52586 0,No,852.9713399,42773.77146 0,Yes,1300.707924,13675.52018 0,No,743.5160765,33764.57181 0,No,293.4523575,38528.44736 0,Yes,1472.948461,23877.59266 0,No,756.1601309,52778.05363 0,No,465.4956002,42358.16154 0,Yes,1041.438743,24847.8812 0,No,709.7382874,46363.34367 0,No,427.11769,34193.16093 0,No,612.2257762,60323.35727 0,No,1322.152854,47892.88998 0,No,673.7476812,25013.06314 1,No,1135.047349,48982.22585 0,Yes,509.6129401,21928.91099 0,No,113.669024,38513.33229 0,Yes,560.4317644,24511.21021 0,No,1163.73601,32467.74804 0,No,133.5089142,49516.34083 0,No,897.4202657,36296.74814 0,Yes,793.5627844,13446.05629 0,No,942.7868536,27736.64512 0,No,953.8070084,40908.40612 0,No,814.2436789,29631.11007 0,No,1387.704469,26863.17233 0,Yes,601.345719,15897.138 0,No,656.5227013,34229.3741 0,Yes,1790.544092,21938.20923 1,Yes,1925.982795,17763.3503 0,No,0,16834.80271 0,No,310.1186425,31445.77108 0,Yes,956.7203919,14464.80738 0,No,205.1731978,47284.25351 0,Yes,1572.803778,24557.35211 0,Yes,1455.684182,14822.83306 0,Yes,1011.458814,28316.75761 0,No,413.5990058,31466.22059 0,Yes,0,19281.71466 0,No,377.1938454,41901.84737 0,No,0,51480.38735 0,Yes,0,13407.75859 0,Yes,527.0560852,22986.28382 0,Yes,1470.330834,15752.68179 0,No,776.5683574,54662.516 0,No,1096.203668,41016.21823 0,No,130.4529103,46094.07715 0,No,1331.607309,27665.67547 0,Yes,598.0818313,12385.82408 0,No,177.0108651,53545.01511 0,No,937.0330528,27174.5267 0,No,0,46581.36178 0,Yes,970.2107314,19245.19816 0,No,268.8794915,48006.15004 0,Yes,708.9778283,15968.91656 0,No,596.0091827,32983.5435 1,Yes,1944.677459,13026.04673 0,Yes,1805.293071,10405.42666 0,No,405.3880438,28493.35072 0,No,849.1290111,26970.73609 0,No,988.4077153,32458.76937 1,Yes,2261.848162,20030.16512 0,No,1221.599764,40865.31289 0,No,1057.3564,45099.09159 0,Yes,292.3000209,17321.22712 0,Yes,940.0797321,18581.57718 0,No,368.1969332,32107.67872 0,No,703.3860084,38419.41383 0,No,1018.22126,39893.30287 0,No,240.2256121,25042.40173 0,Yes,446.6348475,18309.79074 0,No,1277.793381,35620.156 0,No,519.5840264,30546.86437 0,No,933.5212001,40378.42312 0,Yes,1074.577871,18574.35373 0,Yes,1697.753989,19021.0677 0,No,1240.664707,46148.19747 0,No,573.5848026,54427.92115 0,No,486.057014,54794.3922 0,No,455.3484167,41378.99712 0,No,1110.43993,39311.53452 0,No,1225.065552,49090.36931 0,No,286.2582428,28838.32131 0,No,1444.630477,40364.55588 0,No,1031.044588,38199.04679 0,Yes,382.0032763,19951.72172 0,No,1337.313266,41529.55205 0,No,463.9018129,39626.76943 0,No,948.7779787,24094.78649 0,No,626.5161419,49207.0013 0,No,350.0715602,45354.74619 1,Yes,1492.963421,11054.06844 0,No,847.6358955,33199.37538 0,No,241.9407399,47843.07974 0,No,616.5386778,31925.18324 0,No,733.4148453,36724.89331 0,No,1100.972882,50237.25782 0,No,665.6278337,55093.3824 0,No,174.935485,55044.37007 0,No,124.2272969,51265.5462 0,No,344.5450091,38463.68526 0,No,954.9482703,42963.66173 0,No,1629.325936,44911.64353 0,No,913.044919,31841.63497 0,No,1306.843023,49036.7417 0,Yes,2308.893236,19110.26641 0,No,1042.42039,58234.76647 0,No,1264.875626,44359.06062 0,Yes,1348.645456,21789.38222 0,No,1256.892288,23100.0095 0,No,1046.631553,45717.62805 0,No,0,22535.50636 0,No,432.6554265,22986.93175 0,No,563.1501915,43028.5972 0,No,721.0934339,41411.14368 0,No,741.5927598,24393.31595 0,No,1071.305347,19529.03519 0,Yes,1589.638598,22541.89203 0,No,1194.597579,38222.50611 0,No,269.5879188,38650.34609 0,Yes,455.5990263,20549.13483 0,Yes,795.5407915,10257.67249 0,No,595.3688043,31418.81567 0,Yes,539.8347931,13119.68585 1,No,1322.723979,23229.90913 0,No,1267.295791,36784.83699 0,No,1091.999738,34704.51229 0,Yes,951.7855506,14354.63488 0,Yes,559.4983021,16994.57867 0,Yes,1222.141327,9080.1457 0,Yes,1026.104495,20991.39643 0,No,1140.594209,25116.66829 0,No,556.4448383,39707.10879 0,Yes,306.6107704,11511.88422 0,No,1271.250986,42533.26766 0,No,333.0727083,61508.75656 0,No,1174.519506,23942.20577 0,No,473.9614391,35133.07041 1,Yes,1724.963232,22170.68734 0,Yes,1056.770651,14194.659 0,No,841.734663,38023.33074 0,No,590.8781726,52977.64198 0,Yes,1070.666361,19312.8406 0,No,914.9723795,52871.32437 0,No,1130.512899,35071.67338 1,No,1706.956841,21738.79633 0,No,839.3255069,43909.00396 0,Yes,330.9361079,21272.30738 0,Yes,762.5850193,24738.11845 0,No,986.8181659,32386.16236 0,No,916.0730001,42201.8206 0,No,1263.676788,25695.48137 0,No,0,42118.19666 0,No,441.0087864,47707.12518 0,No,349.0544114,46698.02743 0,No,423.9854717,37979.78533 0,Yes,1295.029713,21717.46955 0,No,681.7244755,31153.63754 0,No,1153.02131,20586.27811 0,No,1470.342593,26136.74369 0,Yes,1040.873311,12177.03896 0,No,1007.268679,28245.32008 0,No,667.1445832,44863.06899 0,Yes,634.6799702,17023.68286 0,No,298.7045166,39599.66158 0,No,909.48422,48358.44097 1,Yes,1737.537815,20974.03459 0,No,203.2638172,46518.13241 0,Yes,0,19750.39622 0,Yes,1889.678319,14756.90002 1,No,1558.463471,41648.32617 0,No,1201.927539,45382.61357 0,No,1051.604407,47529.21731 0,No,641.5253832,18350.73342 0,No,883.907073,41668.1062 0,Yes,953.809047,18241.74144 0,Yes,1168.895545,14970.87009 0,No,431.3307401,58551.89326 0,Yes,1291.915263,17910.06461 0,Yes,335.3579479,5118.745551 0,No,30.8244501,35960.60602 0,Yes,1119.237574,15214.11563 1,Yes,1812.079998,17962.27016 0,No,385.6908387,44650.78296 0,Yes,651.5500049,17743.06562 0,No,1717.495025,38791.05764 0,Yes,1181.513221,11784.80226 0,No,745.2795147,34704.62025 0,Yes,1453.309747,15183.72922 0,Yes,1333.097471,29363.97174 0,Yes,571.8863591,15632.76928 0,No,494.6532294,40377.66836 0,No,163.5826085,58220.78047 0,Yes,1371.468372,24248.55196 0,No,1129.202659,40688.63559 0,No,0,53663.16668 0,No,84.85654884,53760.46526 0,No,681.4512395,48892.52469 0,Yes,1366.477534,11268.77433 0,No,82.0884565,27357.59993 0,No,602.9962261,33888.40919 0,No,176.3231998,54798.69875 0,Yes,713.7991565,20676.90519 0,No,137.7246167,44747.32768 0,No,159.9734731,30567.80131 1,Yes,1956.923906,15574.38998 0,No,183.5763822,51149.28157 0,No,975.3354849,50751.03859 0,No,0,44216.92938 0,No,424.285797,35675.7411 0,No,1477.669207,42011.80796 0,Yes,1211.363648,14186.38867 0,No,903.5493047,46971.56217 0,Yes,1076.233587,18096.70148 0,No,988.667818,28486.33975 0,No,1311.088803,32797.98992 0,No,0,40056.69266 0,No,289.3299083,45559.17639 0,No,22.53717523,59536.27562 0,Yes,287.6074133,24440.2252 0,No,25.82837121,22720.09454 0,Yes,725.7933681,23202.96396 0,Yes,1137.028318,25117.76297 0,No,1335.115552,30115.15496 0,No,1193.676973,34360.9095 0,Yes,0,18162.77049 0,No,1047.72339,45181.7486 0,No,0,48271.87044 0,No,327.3225081,29816.38032 0,No,771.6829167,38854.24248 0,Yes,873.6786524,17655.71003 0,No,974.9077893,36763.14597 0,No,309.8239952,28139.62585 0,No,286.8523269,18402.16475 0,No,509.8522537,49345.42564 0,Yes,840.3386901,21097.37031 0,No,223.6869181,23513.29538 0,No,1407.224571,22806.84821 0,No,1178.982625,45545.16842 0,No,1298.484815,39143.29192 0,No,627.3533533,37616.46594 0,No,907.1697759,27631.5678 0,No,872.1518416,40524.33874 0,No,620.3203556,52536.77665 0,No,380.333108,30890.0736 0,No,1073.168533,51668.96083 0,No,883.233754,32310.74502 0,Yes,1558.217831,7364.830078 1,No,2023.733603,32094.62781 0,Yes,803.9611048,13172.87051 0,No,1060.204707,27870.15539 0,No,869.8545396,33589.13955 0,Yes,1306.483031,13984.78526 0,No,1597.898741,36524.71624 0,No,1351.962465,43630.03692 0,No,0,29475.38138 0,No,442.5909249,38947.35437 0,Yes,963.2011987,15938.0661 0,No,1063.797087,41157.35715 0,No,214.4115627,42758.81282 0,No,1258.576399,33571.14452 0,No,506.6271509,49068.87087 0,No,38.40778522,38702.10168 0,No,1237.5474,37387.15415 0,No,0,47437.51224 0,No,555.6652909,46584.7305 0,No,747.9375239,44030.46149 0,Yes,1000.621768,17223.63797 0,No,773.6729475,40427.74193 0,No,744.7661997,35067.64372 0,No,953.1233642,51257.1439 0,No,998.3757695,37816.8439 0,Yes,1098.955685,21025.92746 0,No,521.6873539,48700.15308 0,No,82.73038975,27627.22408 0,No,219.1150032,31490.53342 0,No,276.3798286,38721.86797 0,Yes,1180.363817,24063.31274 0,No,620.9185967,39099.30763 0,No,414.8575624,38030.82462 0,No,1389.890556,48689.73039 0,Yes,703.0119004,21411.93007 0,No,493.5494228,52241.92216 0,Yes,1351.411543,14003.47572 0,Yes,461.7827648,17609.83052 0,No,143.1022619,44846.90101 0,No,1280.003234,30274.3356 0,Yes,1764.68273,17995.72861 1,No,1648.474017,55548.25418 0,No,0,37409.00299 0,No,610.5977516,22635.06647 0,No,355.1485857,55634.33065 0,Yes,472.5111008,19903.89205 0,No,786.0139114,52693.94375 0,No,772.7320741,47161.2767 0,No,751.2972186,25013.61407 0,No,1433.71078,42798.56551 0,No,1037.008599,36622.79223 0,Yes,1827.13495,19616.00973 0,No,764.5059389,37869.21429 0,No,589.5420167,28237.82068 0,No,754.1274899,41341.8026 0,No,911.9264415,41249.28174 0,No,311.918279,39694.48373 0,Yes,933.2872312,20694.58778 1,No,1374.474712,35805.70886 0,Yes,322.3119543,10056.11055 0,Yes,703.8387655,19318.05933 0,Yes,534.6929068,18729.56624 0,No,0,26626.48588 0,No,770.5198985,44509.57539 0,No,1015.725881,31820.94127 0,No,421.1464996,61655.34805 0,No,1125.091402,33564.07555 0,No,857.380822,28755.18076 0,No,363.9414625,46155.53922 0,No,1157.779701,32219.83173 0,No,993.302551,28501.85601 0,Yes,403.4594274,15333.87629 1,Yes,2134.934488,20330.86487 0,Yes,1234.942716,26919.11596 0,Yes,805.1042517,22174.02889 0,No,0,41933.09577 0,Yes,157.5140425,24506.18683 0,No,257.8976572,39477.03186 0,No,1056.915063,53080.58634 0,No,0,48290.85403 0,Yes,1730.291081,19446.67572 0,No,694.5007107,14150.91438 0,No,1546.760526,32928.63613 0,No,428.6653053,45570.79095 0,Yes,1764.532309,18148.52408 0,Yes,1181.979697,17223.36723 0,No,943.1323898,30178.73406 0,No,0,41234.89175 0,No,1087.193767,54961.28848 0,No,14.82095305,32320.26552 0,No,0,47207.05072 0,No,836.0105904,40178.9347 0,No,1494.521135,42280.40785 0,Yes,1318.528957,15821.15235 0,No,431.1279151,37772.80471 0,No,1335.473573,45847.38211 0,No,1141.442176,27914.40225 0,No,795.1192916,38710.38133 0,No,1005.444088,40615.3422 0,No,481.2401636,49010.75551 0,No,427.340143,27260.73546 0,Yes,1157.593925,17996.90333 0,No,890.2130229,43643.5824 0,Yes,1321.329159,20302.21947 0,No,1167.943622,57256.00741 0,Yes,1197.170953,10834.11138 0,Yes,922.8853555,22988.84265 0,No,834.7118946,28553.47188 0,Yes,1264.586941,14649.15502 0,No,1603.665223,25802.30347 0,No,838.2760677,55082.31753 0,Yes,850.6772791,21251.63951 0,Yes,1508.307713,17645.30502 0,No,1272.480579,34570.57066 0,No,981.8397963,23636.97634 0,No,487.6950209,46480.03597 0,No,1046.416673,47598.30737 0,No,1533.917257,42888.64105 0,No,709.9535652,37302.62213 0,Yes,1610.015858,19649.48443 0,Yes,645.6885305,14211.14714 0,No,377.7139958,46729.33299 0,No,994.99017,39315.95589 0,No,1240.665744,57303.79193 0,No,736.5906124,37031.48477 0,No,1201.230618,46478.15132 0,Yes,647.3673882,16897.78966 0,No,1161.239934,52064.47135 0,Yes,1521.912738,21370.79389 0,No,805.5206283,43383.70654 0,No,1155.399548,34539.56438 0,No,613.1111421,38632.13064 0,No,623.9576763,46188.29496 0,No,1000.90104,46469.10252 0,Yes,807.7488274,18760.3452 0,No,1156.040208,38295.57124 0,No,637.283464,32885.70199 0,Yes,1241.690987,20105.068 0,No,741.1930622,31104.00445 0,No,1169.834113,39386.37003 0,No,0,35821.78653 0,No,1088.3032,33673.36068 0,Yes,0,22187.96621 0,No,608.9485801,26463.37476 0,Yes,431.3027892,15606.24584 0,No,512.6534379,39631.69523 0,Yes,1241.61642,12058.59762 0,No,0,53058.25521 0,No,561.4337464,37884.9611 0,No,245.2442839,35132.22117 0,No,1081.063663,34138.32374 0,No,492.3264548,42317.24242 0,No,1349.57175,50645.29057 0,Yes,1150.43168,11666.98975 0,No,357.8658809,42499.03229 0,No,331.1052765,45104.8152 0,Yes,1457.673369,20042.86397 0,Yes,852.3139525,15956.64884 0,No,1378.940514,43998.1414 0,Yes,1005.521508,22128.0802 0,No,1420.993454,34126.42169 0,No,570.2955412,30615.33055 0,Yes,1228.990373,17304.6435 0,Yes,1142.935257,20942.00334 0,No,759.3458148,32066.70673 0,No,846.6835117,41651.21558 1,No,2133.464209,28237.88234 0,No,354.5743684,30954.47668 0,No,0,46450.96604 0,No,1051.401733,30072.74931 0,No,0,41371.66119 0,No,887.4681371,30563.77026 0,No,688.1093445,20098.59346 0,No,1257.34063,40810.17277 0,No,1175.817635,41447.58481 0,No,0,34838.43927 0,No,608.5517388,22555.50859 0,No,0,32608.48884 1,Yes,2110.556858,19345.10473 0,No,696.9001673,38713.31706 0,No,559.4685833,61187.81057 0,No,715.3929218,49473.37638 0,Yes,2026.863631,20469.92949 0,Yes,1187.358891,24267.39648 0,No,785.7906701,42887.59272 0,No,845.9203698,42799.43953 0,Yes,1032.670815,13901.11897 0,No,861.2727392,38111.03287 0,No,1601.87795,27885.16355 0,No,955.9270221,10836.72352 0,Yes,877.8798035,18217.61365 0,No,1043.758264,31523.6869 0,Yes,1080.071527,11239.53116 0,No,589.2178954,47722.17011 0,Yes,769.5312437,23048.54061 0,No,951.0475319,33326.5872 0,No,1189.158934,36223.87056 0,No,1709.344285,35290.52311 0,Yes,1402.553926,16607.56425 0,No,250.8795835,51662.22782 0,No,681.4176227,26426.22921 0,No,1333.248659,39196.66301 0,No,858.7611254,29150.34401 0,Yes,992.7762449,14204.67215 0,No,265.3022839,32276.46294 0,No,754.4775776,31592.19989 0,Yes,584.0812286,24440.02035 0,No,1175.233377,47684.46113 0,No,1388.664149,31021.55008 0,No,410.099265,37328.45502 0,No,59.13249864,34162.23526 0,Yes,1939.71236,13836.98607 0,No,886.1490576,24466.12539 0,No,770.9994019,40990.6215 0,Yes,306.4109271,13594.18823 0,No,1022.101638,37095.21594 0,No,847.9840353,24950.53392 0,No,1596.227808,42993.57653 0,Yes,1635.222942,8531.038085 0,No,0,45217.85066 0,No,1306.796916,48799.51191 0,No,932.9523691,35858.18915 0,Yes,1052.875142,13486.14694 0,Yes,1304.634346,13496.87987 0,Yes,2052.133463,13130.6536 0,No,1216.725616,52377.8489 0,No,526.470865,44241.56562 0,No,153.4479361,36813.19871 0,No,905.6415057,47271.34912 0,No,1068.873957,41690.04867 0,No,0,30049.10026 0,Yes,992.440019,14207.26834 0,No,964.6025631,36676.57165 0,No,493.6354264,43524.24978 0,No,835.6564355,44234.17039 0,No,480.0930869,57068.13046 0,No,637.60187,52347.76202 0,Yes,2134.015627,17897.64661 0,No,1475.207229,42243.17748 0,No,887.3432884,55751.7209 0,No,1376.309911,43234.13992 0,No,1304.700879,45162.65104 0,No,225.506104,54534.70972 0,No,1261.268219,39984.52959 0,Yes,30.58367172,16246.57305 0,Yes,437.0352358,16261.20757 0,No,922.6374027,52205.50687 0,No,412.7468149,44650.07843 0,No,693.1013449,54492.74796 0,Yes,915.4626482,27283.31983 0,Yes,1322.492812,22930.445 0,No,1610.086988,40498.99811 0,No,1113.134257,27110.60301 0,No,0,47803.25837 0,No,1163.146385,44424.82596 0,No,924.2953475,26397.59729 1,Yes,1983.234475,25687.92975 0,No,725.7005868,49337.40235 0,Yes,1030.496155,16662.73157 0,Yes,1038.97702,13739.71928 0,Yes,292.8574108,15220.93587 0,Yes,495.1139113,14881.84062 0,No,659.0404108,47942.24982 0,No,291.9681397,48511.60308 0,No,471.3565174,45573.30811 0,No,855.822381,56219.80884 0,No,918.6817794,48160.52429 0,No,1251.32255,37881.82096 1,No,1066.884084,44918.41232 0,Yes,1427.124037,12553.88673 0,Yes,1263.880058,12418.80765 0,Yes,1128.141493,10159.24188 0,No,694.3985831,36570.42544 0,Yes,805.3980049,17902.57919 0,No,519.5172955,39704.69296 0,Yes,1051.297161,16299.61175 0,No,421.8969022,37323.75941 0,Yes,622.4137203,13072.02796 0,Yes,1005.264248,21394.12401 0,Yes,537.4925429,9421.714858 0,Yes,189.9987142,24716.95613 0,No,309.8628233,56100.05441 0,No,969.4457592,33411.80109 0,Yes,775.5892326,17030.57885 0,No,1296.474737,48682.3735 0,No,1037.155516,38490.38489 0,No,978.6521803,25742.11973 0,No,965.0461321,47987.31975 0,Yes,816.0427981,17894.79377 0,Yes,716.1857509,7997.622944 0,No,1130.608881,43936.64641 0,No,383.7049011,41311.24395 0,No,1008.154522,20326.59218 0,No,652.8174543,41161.85735 0,Yes,1232.794737,16279.1854 0,No,803.224401,52869.80781 0,Yes,1443.431696,22693.65775 0,Yes,1288.44856,21216.96257 0,No,0,43193.23215 0,Yes,1053.332641,10212.50236 0,Yes,744.7879024,22343.09739 0,No,412.6876132,33923.45804 0,No,943.4020371,35281.72849 0,No,1192.974028,39504.47602 0,Yes,1071.511583,11082.0629 0,No,1493.270467,41005.21748 0,Yes,1132.837721,15703.0252 0,Yes,597.4840406,14796.74967 0,Yes,792.9754861,17346.17092 0,No,1376.075401,22317.56559 0,No,1060.016921,39757.43817 0,No,532.3267381,30851.27777 0,No,0,28711.75713 0,Yes,894.1258092,24670.1189 0,No,906.4363882,37309.68181 0,No,0,28727.12016 0,No,506.4259139,35333.18222 0,Yes,943.5017211,14123.70357 0,Yes,0,14874.80021 0,No,442.4895649,54996.84275 0,Yes,380.9633077,20117.83003 0,No,961.999353,37073.19238 1,No,1789.093391,48331.12686 0,No,76.47608896,27283.69174 0,No,1067.139026,53995.7305 0,No,0,39592.43693 0,No,253.1319822,40973.64654 0,No,1056.928348,55409.4596 0,No,179.7000705,37727.53512 0,Yes,990.9357627,18197.22873 0,No,693.7537183,36723.44877 0,No,965.2768178,37705.24322 0,No,300.8762062,51423.69464 0,No,1265.044932,47297.7227 0,No,558.6705138,40335.61099 0,No,1183.983311,53599.74553 0,Yes,1165.840209,20495.1586 0,Yes,1542.191227,17265.91861 0,Yes,587.7677591,22669.42433 0,No,0,31005.03576 0,No,389.9766946,27329.97931 0,No,647.075901,55522.62393 0,No,70.98316299,40012.53291 0,Yes,115.5453555,12670.02762 0,No,90.93344719,42451.73558 0,No,1230.424256,51716.7399 0,No,201.5583994,27924.84656 0,Yes,1562.846155,10353.90391 0,Yes,622.8952091,11019.87756 0,Yes,1142.126955,9184.732762 0,Yes,1796.26764,21126.17721 0,No,947.4239701,38505.78387 0,No,419.4405795,34332.66131 0,No,1660.827702,53754.20072 0,No,391.2404617,24972.24591 0,No,613.4309365,34745.00747 0,Yes,1024.108807,18667.92565 0,Yes,0,25976.06511 0,No,1252.958338,33876.18903 0,No,708.8374818,55675.75986 0,No,266.6470861,16515.30843 0,Yes,1339.363614,19496.16522 0,No,781.5953359,49457.1053 0,Yes,1021.370111,19032.50901 0,No,948.4942785,40739.85993 0,Yes,1312.93378,20208.54461 0,No,0,50861.78885 0,No,0,24566.48337 0,No,0,30922.60421 0,Yes,859.3369637,13993.65454 0,No,646.157668,45696.83161 1,Yes,1707.914634,10591.71742 0,No,133.673455,28492.4003 0,Yes,80.59567582,16145.6377 0,Yes,631.6008477,17845.07415 0,No,1236.617914,40668.7254 0,No,742.5833733,40085.43995 0,Yes,97.03146278,12731.75755 0,No,1054.22211,50900.24256 0,No,959.600287,29850.21207 0,No,1258.852277,33683.18173 0,No,443.2464668,36547.16104 0,No,1605.102415,26126.32944 0,No,0,25964.84434 0,No,832.6459751,25106.37478 0,Yes,443.5370288,19632.17542 0,No,817.4317693,40088.05051 0,No,881.6637997,43725.99753 0,No,1154.094274,48400.24115 0,Yes,961.5755637,9681.493893 0,Yes,516.1219595,17538.46162 0,Yes,1357.411868,22572.79013 0,No,1036.030594,35261.35578 0,Yes,900.6093417,17802.92726 0,No,1516.326025,46393.26454 0,Yes,1015.204535,22208.32892 0,No,232.105744,40368.95685 0,Yes,1082.284133,19451.81628 0,No,682.4396608,17992.97118 0,Yes,1218.13888,18144.98483 0,No,802.4131422,47444.4928 0,No,143.8548983,38014.87349 0,No,1087.465794,32908.52712 0,Yes,895.7539556,14753.29733 0,No,660.2854965,58956.51318 0,Yes,630.9734892,19087.17478 0,Yes,1648.966029,15402.48208 0,Yes,975.9555974,18246.81572 0,No,297.0053783,22955.08212 0,No,0,47328.23291 0,No,0,32629.94485 0,Yes,789.2148949,18231.83026 1,No,1804.036475,31318.29603 0,No,1266.113867,29698.51627 0,Yes,135.1876434,20414.41217 0,No,28.81374346,36941.0634 0,Yes,0,15347.91429 0,No,750.6989964,49150.34585 0,No,805.3051169,57386.1288 0,No,1093.803005,29573.23823 0,No,483.9652713,44335.95811 0,Yes,1534.394884,19592.10939 0,No,1349.187291,48013.6902 0,No,158.5704427,20575.46501 0,Yes,172.4620199,25850.38313 0,No,1280.762861,53675.56755 0,Yes,213.1732752,22811.45698 0,No,0,32248.53657 0,Yes,704.0136486,13903.45573 0,No,708.8139762,38295.23381 0,No,257.3963831,49477.78564 0,Yes,1188.744296,22867.07059 0,No,0,62886.70923 0,No,1321.703891,45581.79478 0,Yes,882.5819675,17749.97205 0,Yes,1418.134148,11964.42509 0,No,1245.422205,36897.78011 0,No,339.5292685,58747.38773 0,Yes,824.5478304,29088.11135 0,No,241.4426903,30171.62449 0,No,1510.79325,47684.23946 0,No,396.64125,52174.95427 0,Yes,1633.697803,15786.31732 0,No,650.4183411,47122.07247 0,Yes,1382.438231,15680.61049 0,No,613.4802945,24367.86142 0,No,1424.351218,37845.99925 0,Yes,914.1081756,19053.00589 0,Yes,802.0089304,15469.65262 0,Yes,539.674903,18852.27385 0,No,337.5759999,52192.27472 0,Yes,813.4948376,16681.3503 0,No,477.882122,39834.03081 0,No,342.1916021,32154.75355 0,No,840.365781,45316.83541 0,Yes,865.5655922,16643.24096 0,No,645.5916366,44402.10623 0,No,1755.38891,35031.532 0,No,498.7494508,51690.06347 0,No,587.7810817,30859.63634 0,Yes,824.6165935,10062.57593 0,No,0,32660.72526 0,Yes,880.803636,20436.43903 0,No,917.1221347,48227.81937 0,Yes,789.7175406,19280.54426 0,No,763.8556247,52938.03547 0,Yes,1062.16651,17383.25855 0,No,944.3325674,44444.14918 0,No,877.501423,25587.74393 0,No,1344.103406,41417.34592 1,Yes,1543.099582,18304.65057 0,No,380.3174501,38864.58415 0,Yes,1176.893303,23634.81573 0,No,733.1999149,34524.91886 0,No,348.9970288,48942.02578 0,Yes,1380.087526,19245.83269 0,No,943.1657194,40369.69803 0,No,1248.885849,51897.39491 0,No,713.223492,51107.8601 0,No,834.2356082,36721.86221 0,No,0,26200.0384 0,No,1558.883862,37489.72211 0,No,1362.186619,37736.7496 0,No,1581.521505,45729.88805 0,No,324.8549714,39752.35419 0,No,1112.73574,47527.12558 0,No,379.5987261,32578.43583 0,No,1549.098151,26239.78187 0,No,1489.415915,46732.76781 0,No,548.2063638,41647.72716 0,No,561.968473,37637.00503 0,No,595.4867014,34470.41803 0,No,267.5139688,27364.86548 0,Yes,1793.303188,21034.87965 0,Yes,1773.153542,10177.86674 0,No,618.8603727,37333.33383 0,No,205.6438081,37766.49369 0,No,1011.431543,36364.75385 0,No,0,38130.64567 0,Yes,1154.432105,15221.17239 0,No,292.5159807,43705.37904 0,Yes,1106.946277,19233.77389 1,No,1665.954621,30070.13526 0,Yes,968.671101,22212.74103 0,No,797.2493693,39928.58818 0,Yes,636.8978227,18620.23895 0,Yes,1011.334848,14101.2903 0,No,791.0352223,47303.67664 0,No,942.4747495,36965.04557 0,No,706.9742779,46165.15453 0,No,767.4348125,55990.87461 0,No,568.8436683,37074.54081 0,Yes,1271.562957,18999.61681 0,No,938.9391417,44280.78635 0,No,1012.194199,34631.70605 0,No,1355.493231,18935.51151 0,No,912.0487219,43486.95708 0,No,289.5988343,25724.77064 1,Yes,2035.861557,14435.84319 0,Yes,1602.179886,17858.06791 0,Yes,0,20067.80976 0,Yes,1202.390705,14036.62088 0,No,554.7076529,28164.19855 0,Yes,229.9887235,2541.200814 0,No,768.979148,35503.90896 0,Yes,1211.040146,16898.12947 0,No,0,35648.38116 0,No,0,27537.73535 0,Yes,859.9366511,15966.71713 0,No,0,47556.30125 0,Yes,1471.538391,14959.66188 0,Yes,582.8738672,17730.5511 0,Yes,463.1193392,25858.28175 0,No,986.1541303,48940.49003 0,No,534.7460545,29685.12923 0,No,1402.690527,39656.6322 0,No,967.748001,46278.07452 0,No,884.0225178,36889.42825 0,Yes,455.6999383,19080.80899 0,No,0,66915.57179 0,No,1141.23821,37917.66454 0,No,365.2358798,21358.00005 0,No,38.37592458,33579.35623 0,Yes,731.9706113,13593.14051 0,No,1006.719786,47784.31847 0,No,1107.801156,34561.94954 0,No,777.5308087,45673.67391 0,No,1501.426087,35398.95712 0,Yes,862.0600385,12085.45308 0,Yes,12.1864203,19910.96897 0,No,0,30208.04644 0,No,1820.32549,31309.99848 0,No,576.0689929,40179.83005 0,No,837.7526213,31017.90114 0,No,399.793885,34875.33233 0,Yes,578.7550989,20909.83537 0,No,1106.540875,46565.05995 0,No,390.7456114,38442.29606 0,Yes,274.1946278,15421.55166 0,No,1476.877642,42271.33341 0,No,578.4607424,20306.93601 0,No,1064.147713,28192.12588 0,No,1598.303417,46404.66469 0,No,863.4518296,33529.88065 0,No,1157.759857,32574.5029 0,Yes,406.3860491,24086.95249 0,No,309.2444877,44202.7075 0,No,943.9512443,28353.63001 0,No,1206.791937,42916.70139 0,Yes,1448.060458,21028.28675 0,No,1166.593989,26977.62275 0,No,800.308672,61281.30081 0,No,714.9260001,31032.8697 0,No,471.2550831,28417.74199 0,No,662.1644451,30390.06007 0,No,770.4319619,53398.31473 0,No,597.3268696,55496.77711 0,No,140.8530225,48183.27674 0,No,1035.552944,29423.23203 0,Yes,784.1087812,11560.5677 0,Yes,442.027425,17072.33664 0,No,888.081432,54072.65702 0,No,704.2029083,44775.12201 0,Yes,1016.450616,15099.5302 0,No,1036.460728,42914.98167 0,Yes,1060.220034,22795.94299 0,No,1243.92806,38075.4383 0,Yes,1291.977346,26571.87456 0,No,69.25247581,20222.86624 0,No,196.3892855,29932.66203 0,Yes,709.0271029,18305.73452 0,No,1034.149962,52106.8387 0,No,868.0060301,50549.53346 0,No,1236.006993,48800.35685 0,No,12.91234955,46282.25773 0,Yes,826.4536365,21342.70303 0,Yes,703.9900094,20752.17544 0,Yes,817.1214384,13152.03413 0,No,1210.014631,45066.13324 0,Yes,766.4062474,22296.9837 0,No,1643.654052,33980.82583 0,No,1053.014778,54009.63194 0,No,240.0774345,41508.9557 0,No,62.65294851,44440.70548 0,Yes,924.2332039,11194.66888 0,Yes,944.0710359,26027.68765 0,No,295.7854428,51837.75233 1,No,2085.586978,35657.22567 0,No,474.3552638,58068.62224 0,No,504.7233526,54297.11366 0,No,292.3841444,33961.10045 0,No,593.2804678,40227.14388 0,No,76.06408834,50054.76235 0,Yes,1270.09281,16809.00645 0,No,877.3436869,40689.47859 0,Yes,932.5609425,20980.8055 0,No,279.9633883,30262.11714 0,No,1155.175006,40398.39935 0,No,0,45431.75759 0,Yes,1216.767562,23378.57282 0,Yes,0,15274.46903 0,Yes,728.5493992,22891.92938 0,No,0,48365.57759 0,Yes,643.5652672,21323.04944 0,No,973.0823643,27289.27132 0,Yes,1177.957425,14385.16707 0,No,196.9871114,41314.99362 0,No,1526.268669,44845.13629 0,No,441.3960846,56060.20361 0,No,1004.641773,56533.31307 0,No,1258.069534,36645.14057 0,No,1563.993419,37119.60197 0,No,367.3515763,29905.52786 0,Yes,1222.768041,18276.43406 0,Yes,1582.202813,19682.87249 0,No,647.130283,32725.38665 0,No,0,33146.49534 0,Yes,1585.333029,15079.48894 0,Yes,837.9723362,25773.18265 0,Yes,686.7821494,10543.55632 0,No,947.842159,37891.03704 0,Yes,1205.055008,18101.67631 0,No,1254.131055,37456.26199 0,No,949.1238463,50528.53387 0,No,530.24602,27610.70309 0,No,833.6899703,54558.14011 0,No,638.4653109,36173.98245 1,Yes,2387.314867,28296.91472 0,Yes,839.3908902,21383.23117 0,No,294.634281,63285.02511 0,No,1178.179438,34720.93616 0,No,1149.720136,57890.51596 0,No,909.7642677,42468.27822 0,Yes,1391.508027,18539.59036 0,Yes,1029.249632,18018.35831 0,No,1504.59694,30773.74284 0,No,1276.4893,35122.85361 0,No,874.9150425,25023.32309 0,No,1104.689463,51516.33629 0,No,0,47056.19919 0,No,698.800444,37466.0203 0,No,1055.410868,33945.14276 0,Yes,131.2846105,14491.84407 0,No,795.903786,44181.67186 0,No,611.9542968,39291.20373 0,No,1670.423008,41409.77115 0,No,1423.149545,33591.23154 0,No,770.6419392,29623.47129 0,No,1315.469609,29808.35223 0,No,599.204105,53218.11086 0,No,584.356741,34128.76699 0,No,27.27187849,51659.87841 0,No,65.72759508,32332.18265 0,No,1338.461116,23909.54695 0,Yes,1120.422968,16151.25285 0,No,726.0293438,41996.12441 0,No,1353.588284,44372.79392 0,No,531.7442107,38122.1924 0,No,1824.641385,36762.80884 0,Yes,894.4654681,14035.49914 0,Yes,1275.538565,16445.62472 0,No,550.6243941,19106.26579 0,Yes,270.3910262,13158.44223 0,No,651.9986041,35266.12844 0,No,718.3511021,50765.89263 0,No,1008.94654,31235.78292 0,Yes,1510.747812,13390.92 0,No,1393.368283,38371.97746 0,No,586.5882744,45519.83771 0,No,793.4139267,45873.17997 0,No,285.9101691,43263.63064 0,Yes,550.3810558,20322.76863 0,Yes,716.491995,16785.38532 0,No,383.6864669,39946.48497 0,No,252.3333091,38431.81869 0,No,1164.10427,40167.02331 0,No,1080.185777,38398.8661 0,No,906.4785442,47101.99063 0,No,457.8000986,26567.24108 0,No,1215.867359,42762.12183 0,No,217.3615206,45083.11137 0,Yes,1076.727705,12502.64492 0,Yes,58.06606208,23639.23362 0,No,1615.225001,55219.52001 0,Yes,1119.001789,22811.3938 0,No,671.9580672,17621.45385 0,No,634.2179264,38672.74965 0,Yes,831.8185363,16164.48461 0,Yes,373.0625165,22018.9821 0,No,776.4548762,58481.54391 0,Yes,1031.303939,12195.37983 0,No,910.6724158,43795.5817 0,No,1010.809065,42163.4536 0,No,1090.35035,48280.94632 0,No,1008.550343,53801.61564 0,No,0,50127.18047 0,Yes,1824.368004,20074.2811 0,No,730.1644185,44411.67827 0,No,991.1317826,30941.54852 0,No,1356.516665,35030.63502 0,No,576.6355769,41699.20979 0,No,1334.554191,43385.82181 0,No,1487.661808,47624.3796 0,No,778.6934925,48551.97108 0,No,444.1963823,37599.42019 0,No,1023.36216,34632.10269 0,Yes,153.4000958,17840.38488 0,No,661.7606164,47169.35685 0,No,312.6147065,34918.61291 0,No,645.3725645,30102.03554 0,No,0,49010.27332 0,No,779.6147234,22975.49302 0,Yes,569.4987908,19755.52114 0,No,1287.765895,38507.11939 0,No,1177.81507,32294.54565 0,No,795.3685571,25704.64664 0,No,877.039197,24902.73549 0,No,823.9919883,36701.53017 0,No,1019.215358,52089.64463 0,No,942.7374821,38908.88112 0,No,6.787861759,40145.33909 0,No,0,55158.15124 0,Yes,1420.204205,22790.79583 0,Yes,1254.662583,18189.52076 0,No,0,37796.40346 0,No,1140.071899,33605.37793 0,Yes,747.9585112,19869.39018 0,No,1598.602553,48717.26315 0,No,1226.370717,21426.30698 0,No,929.8199349,50611.98595 0,No,807.405738,43853.88425 0,No,345.3649412,56965.70211 0,No,982.4509449,42907.02619 0,Yes,1067.670767,14485.94849 0,No,742.5406142,39017.95032 0,No,1060.178447,30948.29027 0,No,523.7533678,33767.05071 0,No,697.982915,29799.3192 1,Yes,1500.894533,15802.23288 0,Yes,1682.897631,14378.8906 0,Yes,1264.04207,6466.513408 0,No,1352.462336,34247.78002 0,No,376.8789166,50671.32275 0,No,629.5612815,37255.36509 0,Yes,709.4142298,11355.11271 0,No,541.3462788,42332.24628 0,No,421.1723819,42721.47777 0,No,500.8042199,47910.96385 0,No,0,41663.34011 0,No,1089.365873,43360.16015 0,No,603.8474825,56468.12224 1,No,1165.539853,50341.5739 0,No,1457.622602,43155.3627 0,No,390.0550264,27108.21366 0,No,584.1412164,35517.22911 0,No,901.0122829,13498.12653 0,Yes,1380.096737,14142.07544 0,No,1146.067689,39902.44189 0,No,1058.025969,43327.02611 0,No,474.3796621,35980.75279 0,Yes,969.1435189,22425.40576 0,Yes,1616.738878,12557.82288 0,No,1185.028651,33298.5004 0,No,472.6332289,41257.38603 0,No,1257.625123,30964.23458 0,No,640.9184388,37306.69399 0,No,105.7442477,42442.84735 0,No,672.4267927,25947.24541 0,No,568.666447,45186.97594 0,Yes,368.1982988,26670.01211 0,No,1546.548506,57266.82959 0,No,230.2203941,28930.25452 0,No,815.0547024,34200.18335 0,Yes,164.3055117,17995.85826 0,Yes,1785.797516,15291.6708 0,No,661.0522978,27873.63791 0,Yes,979.8771549,13522.42151 0,Yes,778.2499052,23805.91057 0,Yes,190.6192783,21744.50932 0,No,523.813717,45284.18935 0,No,175.6988688,40044.47032 0,No,977.9479232,38368.48417 0,No,837.6953592,44260.76441 0,Yes,1436.32082,27658.38667 0,No,638.229501,53812.79788 0,No,714.0116591,48631.36436 0,No,738.2532477,36086.09213 0,No,507.5265349,58027.77092 0,Yes,1033.833494,15818.0614 0,Yes,19.73946092,20514.17137 0,No,434.704864,48898.37012 0,No,0,45334.83933 0,No,629.7640719,53069.32515 0,Yes,522.7677589,26900.28072 0,Yes,294.6564875,11681.46233 0,Yes,469.4256329,14647.00227 0,No,838.4370383,40903.48069 0,No,1407.639515,52746.13315 0,Yes,1417.225499,16053.97426 0,No,363.9940646,38110.44311 0,Yes,128.2518222,17076.5736 1,Yes,1894.168201,28321.68455 0,Yes,1624.795642,10623.59745 0,No,645.9492708,46406.33798 0,No,684.0849049,38184.56838 0,No,1168.284538,46904.60382 0,No,766.1240549,51614.0562 0,Yes,738.2686012,14063.07546 0,No,525.8903383,45530.50912 0,No,631.2535067,38877.57033 0,No,0,65943.78394 0,No,913.9139657,53809.33036 0,No,578.7525663,60498.92546 0,No,1503.849951,43888.39666 0,Yes,555.2657969,11581.22066 1,Yes,1289.24621,13624.54526 0,No,545.5665107,35714.65113 0,No,1112.348992,33468.82218 0,No,564.9074481,35820.88568 0,No,591.8414074,38879.50988 0,Yes,908.1228119,22084.27165 1,Yes,2169.196187,18195.2669 0,Yes,1108.105366,15337.30227 0,No,0,27167.26039 0,No,957.5919029,35330.85535 0,No,448.0399683,29537.31025 0,No,1188.072875,40704.70722 0,Yes,1223.885814,19520.009 0,Yes,959.967042,18413.86385 0,No,734.7218364,35010.02587 0,No,0,47628.41183 0,No,1423.284258,48914.19708 0,No,217.1635013,26975.49181 0,No,672.0451389,36705.34046 0,No,830.7653898,45958.98422 0,No,652.5281789,58750.496 0,No,533.9891482,64930.2398 0,Yes,1626.634079,17721.63006 0,No,1382.120977,30395.68823 0,No,0,38663.20051 0,No,1165.689152,38110.36478 0,No,1190.420507,34198.05133 0,No,435.2146571,39505.41246 0,Yes,1382.422544,17166.04257 0,No,1265.954833,27233.56306 0,No,1303.626132,40010.46516 0,Yes,240.1013667,21142.0419 0,Yes,326.4386248,21109.43181 0,No,562.8019307,41934.59733 0,No,531.4863606,28836.19262 0,Yes,594.5443798,17875.33907 0,No,745.7338929,60180.56113 0,Yes,1218.638905,29899.80685 0,No,266.4604812,37622.05376 1,No,1899.546842,46076.23711 0,Yes,717.601673,13729.05176 0,No,507.3345958,56075.10284 0,Yes,1422.774586,14044.5335 0,No,1253.699614,37645.79996 0,No,1301.650765,38918.99417 1,Yes,2415.316994,17429.50337 0,Yes,1367.013875,11293.46926 0,No,449.0515984,31624.90568 0,No,505.6978729,31971.31291 0,No,1150.458785,54982.75183 0,No,981.4922134,30385.06908 0,Yes,612.2660524,16270.83564 0,Yes,1122.292386,20351.33607 0,No,59.79707128,36000.07269 0,Yes,1353.011845,26207.68628 0,No,1232.233527,50731.86426 0,No,924.0131534,28803.77469 0,No,517.8245459,65501.62076 0,Yes,1795.71687,12547.82909 0,No,983.56056,26050.13005 0,Yes,990.3542603,18998.09078 0,No,1529.517006,52681.70666 0,No,272.8731293,44249.67868 0,Yes,636.2066156,19468.43387 1,No,1751.347088,38381.5858 0,No,721.9203797,46269.83721 0,No,511.4175111,25768.28549 0,No,563.6872028,41354.35016 0,No,1110.720922,47221.44762 0,No,403.0343765,48874.15116 0,No,430.0904233,29246.37709 0,No,278.4739843,33187.76893 1,No,2228.472283,27438.34899 0,No,1201.782617,30099.16237 0,No,490.7984044,44686.10315 0,Yes,792.8559724,8699.598343 0,No,870.6623709,37180.06765 0,Yes,1149.209836,11337.05925 0,No,493.533675,49735.56761 0,Yes,874.3483758,10845.01883 0,No,1097.94754,23224.12783 0,No,976.1491224,29998.36307 0,No,1373.725379,43124.21315 0,Yes,1282.762841,22360.19633 0,No,636.7284738,45649.17081 0,No,1823.231922,24744.85406 0,No,605.8638006,34515.19566 0,No,0,27888.82603 0,No,491.5203106,37128.7743 0,No,248.3623967,37868.55976 0,No,1036.503746,37151.41064 0,No,1130.893933,35235.36568 0,Yes,1048.538983,10392.06411 0,No,1251.888502,50174.32907 1,No,1578.847496,61220.13315 0,No,0,40341.33883 0,No,1531.962958,33595.43278 0,No,935.4652503,30909.93091 0,Yes,1056.181067,16873.51176 0,Yes,1060.776827,21430.79122 0,No,253.8508786,41472.12196 0,No,323.1232557,36119.42882 0,No,1051.224565,31749.63903 0,Yes,925.9470583,14258.26743 0,Yes,831.2419342,13702.99993 0,No,280.8368683,56567.00833 0,Yes,1092.34255,20465.15382 0,No,702.276627,49708.95525 0,No,663.0958877,32756.25991 0,No,224.7422764,25386.99487 0,No,1108.680514,60725.52684 0,Yes,0,20071.82169 0,No,352.7980607,36751.97786 0,Yes,1390.185426,21735.24694 0,No,599.647361,32471.21624 0,No,514.5454923,45763.89983 0,Yes,484.8943167,15883.66443 0,No,278.7204414,49231.1908 0,No,661.0557337,35806.05151 0,No,1110.29634,27501.12265 0,No,1306.257568,29376.44137 1,Yes,1966.062966,17735.7788 0,No,588.5647713,33224.44475 0,No,0,42869.15883 0,No,1289.602237,35164.88976 0,No,1054.278499,50795.20662 0,No,0,43513.11466 0,No,897.8072418,37899.66779 0,Yes,673.7855907,13344.85695 0,No,636.3350235,49487.60089 0,No,412.0716146,48347.29698 1,No,1898.323497,61011.21595 0,No,1606.403675,27382.32539 0,No,0,41040.19512 0,Yes,0,20157.25126 0,No,486.3844235,53051.72368 0,No,1738.110835,38821.6263 0,No,0,62746.83679 0,No,355.9366266,34730.88552 0,No,0,24818.35863 0,Yes,1925.987451,20441.67444 0,No,819.1118002,55716.54873 0,Yes,588.756707,24015.96067 0,No,975.5719981,28804.00025 0,No,127.1619255,40727.555 0,No,713.4368134,48176.42822 0,No,737.7230425,48994.81285 0,Yes,687.0644195,14059.45763 0,No,444.5145836,41957.74494 0,No,1061.284237,32839.34486 0,No,1425.495582,37189.02514 0,Yes,638.2619325,21282.65109 0,Yes,1101.444967,20936.92692 0,No,888.2445875,41489.19125 0,No,951.2087248,20616.0807 0,Yes,1152.096644,15852.42345 0,No,883.1603001,27125.90696 0,No,1126.988011,47378.01918 0,No,742.8212302,34353.48791 0,Yes,1532.116645,19462.15441 0,Yes,266.7686301,28452.59827 0,Yes,310.4305997,16479.99239 0,No,1702.301357,46589.06262 0,No,649.7824955,39655.39818 0,No,648.3214545,45392.28403 0,No,995.2741519,24782.87676 0,No,330.0748824,45757.37558 0,No,1358.573433,20700.70608 1,No,1166.798527,30367.61086 0,No,671.6695725,26142.91161 0,Yes,484.0854836,12045.31042 0,No,1404.627553,47712.18407 0,No,617.001387,54489.74386 0,Yes,964.6248897,21436.55559 0,No,466.579143,24719.39552 0,No,618.801159,25927.57156 0,No,1310.952365,28978.46412 0,No,123.9274635,28311.8309 0,No,786.2642192,45304.79336 0,No,748.2105894,46981.93176 1,Yes,2124.489239,12651.07432 0,Yes,326.3912434,16988.9233 0,Yes,474.8827825,14986.64329 0,No,59.9658271,24889.25459 0,No,1611.333882,47894.27665 0,No,545.6052116,27236.55814 0,No,0,46590.44064 0,No,1249.354139,52232.25119 0,Yes,587.8747541,18448.10027 1,No,1302.734742,43680.06524 0,Yes,908.3727454,27416.62435 0,Yes,1252.133019,13860.93503 0,No,277.1686519,37177.03609 0,No,623.5748091,57483.50421 0,No,943.1993902,41765.1425 0,Yes,1815.445231,18919.41697 0,Yes,927.4246504,20498.52765 1,Yes,2008.032985,18601.40309 0,No,1028.235235,31297.96196 0,No,408.236017,51131.51326 0,No,1355.436847,42630.66866 0,Yes,376.7707988,16042.23892 0,No,856.4791573,69124.2685 0,No,1110.743705,43991.92013 0,No,437.814279,27855.75497 0,No,1013.705407,60574.80587 1,No,1922.829046,56202.5762 0,No,1066.792794,49629.90301 0,No,465.5532801,33998.84758 0,No,899.4833121,36361.97808 0,No,592.8006667,34355.90955 0,No,164.0010194,42695.39792 0,No,0,62975.12874 0,Yes,1335.131324,17044.3654 0,Yes,1549.61638,11927.53195 0,No,1441.070696,33852.6776 0,No,1157.652068,39080.26353 0,No,899.4620065,36335.21952 0,No,331.167032,44752.22929 0,No,439.2017918,42132.01363 0,No,489.7176909,27043.23056 0,No,652.9936792,37143.58123 0,No,206.8208783,49221.14852 0,Yes,932.4874049,14950.09473 0,No,1098.232451,42331.89108 0,Yes,1959.59552,19677.93573 0,Yes,1706.953766,20804.65612 0,No,1255.883711,55971.18547 0,No,537.9544088,54481.308 0,No,349.1618062,38844.34984 0,No,167.6060988,20150.90276 0,No,1612.465315,42281.23114 0,No,551.2540524,48007.99181 0,Yes,1521.496682,16830.56514 0,No,826.9601242,61365.44432 0,No,770.0819346,38010.26111 0,Yes,1109.425497,10973.08825 0,No,559.3240273,53542.41272 0,Yes,390.2781979,9050.002781 0,No,1567.023862,42258.77335 0,No,170.2602366,62708.16202 0,No,528.8502036,46222.28896 0,No,0,49471.7494 0,Yes,1000.415807,14071.00854 0,No,538.7486464,34834.44571 0,No,435.4028043,35168.72709 0,No,110.6605307,64525.9379 0,No,153.563343,49405.26098 0,Yes,856.6247281,19056.86784 0,Yes,934.2520481,20747.21223 0,No,783.1229601,36514.69014 0,Yes,1271.112495,17451.04119 0,No,910.7598227,33660.71897 0,No,748.6520804,40612.21576 0,No,668.9422598,42028.92384 0,Yes,1061.932041,18589.62145 0,No,766.0088421,45155.09721 0,Yes,1396.234603,15837.25338 0,No,265.4358914,34040.54849 1,No,2080.937247,34494.17121 0,No,324.6912643,47103.45782 0,Yes,1135.010767,24005.0051 1,No,1731.680766,56228.92168 0,Yes,1133.232966,16749.46145 0,No,782.6486901,37497.087 0,Yes,745.5654746,12896.6329 0,No,1444.361237,51154.50812 0,Yes,1340.5292,19318.84043 1,No,1903.667219,41173.50399 0,No,317.9035736,42209.48734 0,No,901.3177479,43242.61209 0,No,527.9834823,39950.95852 0,Yes,915.6537558,16888.81242 0,No,1143.550354,42694.80392 1,No,1488.558775,22256.86369 0,No,1024.230284,27104.85794 0,No,619.9038223,29217.35234 0,No,1007.248656,34893.13769 0,No,0,42577.81452 0,No,1342.047594,51442.56558 0,Yes,1478.169785,22564.58488 0,No,149.3577795,52310.41153 0,No,279.7242752,52008.41581 0,No,869.8087827,49312.50649 0,No,1493.287852,56364.25489 0,No,1127.822686,43344.89429 0,No,924.6927054,38676.97044 0,No,311.507988,43379.63468 0,No,696.5100668,28610.19466 0,Yes,295.6334521,25452.47585 0,No,464.762682,17862.22687 0,Yes,750.4126745,17291.52689 0,No,890.4076532,52943.3834 0,No,0,37455.19888 0,Yes,340.697866,12351.82596 0,No,724.2438058,43399.71828 0,No,979.8550462,40374.05156 0,No,33.45702647,30510.03842 0,No,1203.736179,40969.08235 0,No,735.7308567,23225.1771 0,Yes,884.9188466,27208.77127 0,No,825.8499757,48216.71189 0,Yes,1734.58584,14165.27886 0,Yes,670.4934929,16513.04426 0,No,874.8472856,36709.10831 0,No,0,40021.35536 0,No,827.3504736,43833.4237 0,No,1508.841378,31739.27192 0,Yes,0,19095.83037 0,Yes,914.515479,7525.584256 0,No,302.9612445,51030.43953 0,Yes,275.4858932,13448.33419 0,No,589.5580943,42335.44703 0,No,1120.267458,45153.04007 0,Yes,1416.966156,11104.56632 0,No,1270.176678,44185.35771 0,No,848.2344863,35186.5775 0,No,560.9680738,43254.71541 0,No,619.6155048,45463.3009 0,No,1197.579775,18449.02078 1,No,1585.729401,52274.86012 0,No,478.3633261,28262.86813 0,No,1144.912479,41370.51658 0,No,778.7519229,58860.38076 0,Yes,1675.422141,13703.88109 0,No,680.7697735,56759.23459 0,Yes,1113.577978,17593.52985 0,No,339.3513975,38097.01668 0,No,908.8712521,55612.17236 0,No,480.787555,11193.4157 0,Yes,874.5830207,17378.28727 0,No,0,42515.16283 0,No,1353.522203,52048.50412 0,No,1073.950033,28845.74203 0,Yes,1402.287769,13276.24908 0,No,759.1480034,24473.1066 0,No,4.109498145,38326.19836 0,No,194.5543901,38794.14591 0,Yes,597.5996145,16526.67733 0,Yes,1067.781115,18641.72431 0,No,379.2910135,43096.48981 0,Yes,583.5997492,15179.68613 0,No,465.5222142,30792.88359 0,No,1144.122747,33624.94143 0,Yes,1025.087573,29019.32622 0,No,203.9580437,63638.18732 0,Yes,439.5817266,17823.50194 0,No,0,40574.42079 0,No,720.8575686,42753.72169 0,No,1385.79514,44983.29656 0,No,1127.507577,64618.74214 0,No,808.0001678,36076.0798 0,Yes,280.2481537,25307.37708 0,No,378.7230203,15738.33184 0,Yes,885.5168464,21211.3259 0,No,0,37482.48573 0,Yes,1006.942246,19065.69279 0,No,490.645063,37998.71435 0,Yes,141.0265496,11314.39178 0,No,761.8855412,46421.76407 0,No,1787.60382,33312.80668 0,No,964.5413058,33908.05138 0,No,931.5769842,52703.79122 0,No,10.0565726,37673.31655 0,Yes,1971.663236,22040.26274 0,Yes,1171.519916,17642.26775 0,No,784.383692,44927.4957 0,No,503.4985373,20476.07294 0,Yes,295.4807145,17324.04163 1,Yes,1475.057438,20210.27262 0,No,950.4713695,38990.17386 0,Yes,1083.702362,17522.73069 0,Yes,1334.666264,18064.77834 1,No,1332.386178,53517.35049 0,No,314.5480494,43440.60406 0,Yes,683.1898968,12367.69713 0,Yes,1087.412755,17060.59429 0,No,443.6347834,46611.02063 0,No,426.5528803,34191.29522 0,No,532.9847408,55847.28595 0,Yes,1378.217339,20119.8292 0,No,470.0359134,29309.12726 0,Yes,470.3093927,10062.0836 0,No,98.39661755,25308.58993 0,No,400.0591104,30160.28898 0,No,1265.725868,20816.95333 0,Yes,700.3351717,15905.2127 0,No,1266.772636,47991.05013 0,No,116.3537354,30765.3518 0,Yes,1023.638535,15738.16001 0,No,823.6641694,24533.20314 0,Yes,1328.671531,13389.43379 0,No,1032.185178,46541.64947 0,Yes,1093.639058,14790.76919 0,No,587.764496,39618.81736 0,No,1379.184314,48254.60001 0,Yes,444.0253084,23691.98322 0,Yes,597.6024871,21296.6123 0,No,738.1944102,35134.26535 0,No,563.4911731,47142.44985 0,Yes,1518.715646,23877.81985 0,No,1610.719681,30098.82399 0,Yes,542.3896938,14294.59508 0,No,586.1690704,31466.85134 0,Yes,648.8853641,24457.36408 0,Yes,913.0464559,17139.5796 0,No,1650.896019,43478.12821 0,No,758.5061124,37485.90813 0,No,1955.557356,45507.9115 0,No,446.9644659,27626.60257 0,No,874.9416209,39807.67955 0,Yes,1124.567376,28936.90622 0,No,0,29427.91484 0,No,184.5691415,44021.30216 0,No,1133.799783,39860.16322 0,Yes,1148.923139,19717.42629 0,No,1040.272472,35639.5543 0,No,753.9608286,21507.99492 0,No,818.4962932,34903.67755 0,Yes,1228.655816,17460.64216 0,Yes,16.4148836,24291.34942 0,No,763.900776,64967.64045 0,Yes,1667.308827,15614.82091 0,No,637.4918793,48942.61214 0,No,911.1320579,31265.21771 0,No,125.0538441,50059.54712 0,Yes,548.2788232,15308.59999 0,No,795.0348699,34169.66973 0,No,649.1590457,37015.27499 0,No,0,51758.41029 0,No,292.3343828,41036.12926 0,No,1285.991803,31068.12253 0,No,845.8543023,44480.65532 0,Yes,1660.111798,26838.93305 0,No,610.5686385,36145.01702 0,Yes,1137.421085,16177.27291 0,Yes,1115.768562,11496.54181 0,No,302.1642898,28805.06267 0,No,1074.143357,35054.58442 0,No,510.960176,32151.84898 0,No,974.4105653,43861.80192 0,No,46.18328844,42533.71536 0,No,1731.604528,58075.94369 0,No,1429.393379,31387.70033 0,No,503.5138088,62115.10508 1,Yes,2179.221428,15302.7765 0,No,18.78828058,24699.09684 0,No,1047.139689,46007.98061 0,Yes,1049.603574,11137.86104 0,No,1372.935822,35467.75766 0,No,958.3266322,28145.84248 0,No,826.6385929,47358.54902 0,No,1005.163082,42127.99447 0,No,1132.862485,25402.471 0,No,618.610524,45154.74784 0,No,1073.825421,47935.00532 0,No,1108.800997,39676.55456 0,No,583.7120008,42590.12414 0,No,835.5334433,23160.57862 0,No,698.6738292,29580.0691 0,No,929.5761899,40442.06312 0,No,916.5979585,41432.4345 0,No,1324.198769,10892.32916 0,Yes,1219.376677,11361.82386 0,No,265.2958484,45828.01885 0,Yes,1255.425835,19885.76115 0,No,1524.436703,18878.55448 0,No,326.3972934,30156.35542 0,No,1821.003152,54051.98234 0,Yes,1357.423466,25733.3094 0,No,1187.473801,53318.99336 0,Yes,925.9048607,20952.58614 0,No,1451.785006,27853.96778 0,No,32.06807899,38788.66047 0,No,685.1975865,36015.41886 0,No,1071.89915,54791.80557 0,No,874.4902512,50078.45942 0,No,926.3096412,38093.97752 0,No,1235.640809,53171.29688 0,Yes,1155.504667,24840.76175 1,No,1360.385782,28992.00182 0,No,234.9957765,40394.42105 0,No,854.6912433,26672.18171 0,Yes,1405.658927,19701.75543 0,Yes,1161.026177,13913.44196 0,No,329.3476893,20708.98772 0,No,1541.320802,32508.3839 0,No,1456.652456,59361.91835 0,No,0,24565.57165 0,No,83.08037273,45442.82986 0,No,95.8981855,31437.42323 0,No,674.804717,47653.61598 0,No,917.7839803,44466.88503 0,Yes,759.8033264,14217.69888 0,No,379.0630675,14792.51184 1,Yes,2004.394001,18860.84665 0,No,1469.603836,43436.95207 0,No,200.2748084,36926.39755 0,No,843.4296594,39512.06905 0,No,676.7633506,34400.86089 0,No,46.09208958,42025.76231 0,No,542.8420254,48897.75254 0,No,89.90989542,30004.4708 0,No,908.1844428,37319.62257 0,No,0,46306.93629 0,Yes,1326.848392,20790.25397 0,Yes,993.8172666,12531.0255 0,Yes,1084.381324,20881.45652 0,No,1197.71154,30377.25003 0,No,1378.297068,43411.66192 0,No,182.7419949,32359.20921 0,No,529.6503069,35102.27513 0,Yes,1632.354361,15763.58457 0,Yes,1119.202526,20619.50975 0,No,787.1368176,45772.61403 0,Yes,969.7841591,21412.60098 0,No,1256.98265,38083.13036 0,No,1015.045427,31026.41301 0,Yes,1197.608957,17452.00004 0,Yes,847.4580256,16926.35002 0,No,598.4386356,52896.0597 0,Yes,983.0922765,17001.64919 0,No,1296.267423,45226.12102 0,No,412.397142,30206.95882 1,No,1504.755378,26183.24535 0,Yes,367.3634193,16295.56247 0,No,0,27889.3047 0,Yes,894.7254906,19335.63404 0,No,75.83412558,42075.04784 0,No,642.5352176,34893.78594 0,Yes,1777.284555,14567.81398 0,No,370.0332879,44507.21131 0,No,653.373457,52276.5854 0,No,793.9121634,48907.68384 0,No,917.0752778,19003.77498 0,No,509.7784393,38614.41313 0,No,947.8137895,42094.47015 0,Yes,860.8353999,19514.76352 0,No,302.2270486,35232.46044 0,No,891.8873793,35062.53819 0,No,1377.558752,38364.67915 0,Yes,1222.958464,15174.97246 0,No,378.9704244,39492.01195 0,No,0,41523.75248 0,Yes,1103.913001,28552.53733 0,Yes,704.4725219,21140.27564 0,Yes,1402.31057,21566.55688 0,Yes,68.37660782,10996.08786 0,No,409.5960723,30627.50294 0,Yes,655.7009447,19915.23285 0,Yes,654.8146682,14504.39641 0,No,1255.920125,43929.14322 0,No,1217.960888,33443.30641 0,No,691.8931985,35562.60937 0,Yes,548.1362888,19501.34107 0,No,437.5460694,31537.12693 0,No,1067.842439,70700.64784 0,No,521.4397781,31981.85812 0,Yes,1348.99532,25805.80996 0,No,674.6637003,41798.39162 0,No,516.3590418,25891.75965 0,Yes,1470.338563,19647.14233 0,No,859.0079101,43101.87381 0,No,1114.088015,59200.137 0,No,773.3268067,51787.78944 0,Yes,803.968975,16553.77283 0,No,376.2925648,43940.04806 0,No,829.7296771,36164.15418 0,No,299.8906333,34920.18154 0,Yes,148.6570947,15645.77712 0,No,705.113586,47752.15923 0,No,467.8641353,43308.69657 0,No,382.0942862,38348.28619 0,No,450.3090116,46172.72509 0,No,379.9145929,39064.04852 0,Yes,151.7714011,18659.07094 0,No,1535.224473,35282.22802 0,No,1596.87343,34655.88003 0,No,778.0601546,52545.57805 0,No,107.7066719,42199.96763 0,Yes,961.7978206,13389.06941 0,Yes,235.2497893,13360.92519 0,No,935.3381557,57139.30486 0,No,0,34563.04588 0,No,10.30689072,37590.30958 0,Yes,2370.463612,24251.95872 0,No,1447.022281,37322.71022 0,No,829.0233191,25161.89851 0,No,1474.061544,47040.80859 0,No,913.6383267,50351.1552 0,No,1028.886338,34257.63277 0,No,563.8917207,54147.70839 0,Yes,747.8216828,19330.3084 0,Yes,811.5399409,13828.96866 0,Yes,797.3736475,23438.1458 0,No,701.9181566,53151.76073 0,No,1195.359822,42107.364 1,No,1672.506063,66466.46089 0,No,889.9012,20805.61404 0,Yes,579.7213046,18555.75259 0,No,1143.200459,28900.72779 1,Yes,1237.621866,14862.70546 0,No,740.9570805,48683.22038 0,Yes,1470.474586,18621.17043 0,No,507.6786163,45839.13418 0,Yes,452.3023556,19922.64012 0,No,431.9049281,30115.15283 0,No,388.9354653,36932.81012 0,Yes,647.2601142,18905.85384 0,No,1069.130301,42361.04675 0,No,517.9885181,45495.00543 0,No,996.2511833,23720.98747 0,No,995.1223306,47286.83091 0,No,0,55069.81221 0,No,480.5711421,36192.09032 0,No,413.8588246,42453.68188 0,No,730.0720039,47074.22892 0,No,11.30778333,43520.4888 0,Yes,1507.892451,14632.25766 0,Yes,1129.113909,13580.25696 0,No,1159.93658,46789.88063 0,No,817.9824403,40658.58991 0,Yes,901.1752435,16745.06685 0,Yes,1778.121512,17060.92745 0,No,1036.756686,34912.78946 0,No,199.1361487,49292.54565 0,Yes,1295.690551,23191.23653 0,Yes,468.2576618,6365.564576 0,No,688.1054911,27080.74254 0,No,202.3212081,37188.56938 0,No,621.0034435,37249.43644 0,No,963.3324811,30100.76227 0,No,1106.697012,51879.71848 0,No,1391.407689,30427.20258 0,No,198.5947905,58598.66764 0,No,90.68269663,37914.40131 0,No,460.873865,49243.44479 0,No,1182.444832,34784.78813 0,No,748.4344222,43215.82973 0,Yes,1727.554591,11608.12845 0,No,831.4782222,39079.78845 0,No,655.6291501,40257.93299 0,Yes,787.1367086,22434.97645 0,Yes,760.8953501,20070.148 0,No,0,44387.5749 0,No,523.3884426,53115.59936 0,No,481.5910034,35817.76129 0,No,0,26275.25654 0,Yes,1414.657799,17954.85255 0,Yes,1103.216776,22410.9083 0,No,948.6964528,38247.91323 0,Yes,725.5724195,9019.545512 0,No,1727.349001,40791.14292 0,No,891.6073042,43925.96442 0,No,667.290493,66538.34422 0,Yes,1060.26371,15661.409 0,Yes,830.8605751,18259.66422 0,No,319.1745767,44410.53156 0,No,1147.274484,44871.54944 0,No,664.5115204,47878.83383 0,No,1066.899512,35567.76207 0,Yes,374.9767674,19806.51909 0,No,549.1913427,43688.68042 0,Yes,943.5813708,17730.91926 0,No,111.2850398,42292.29777 0,No,504.7395253,59560.17966 0,No,798.4773511,32921.63394 1,No,2008.458596,35145.04762 0,No,965.9265722,29219.07078 0,No,1768.139978,30762.10521 0,No,435.0553769,32198.02488 0,Yes,685.6319445,15892.09254 0,Yes,745.2306213,12877.5095 0,No,161.7594874,43676.05741 0,No,0,32558.47509 0,No,1389.286467,38373.01452 0,No,802.0926754,45052.58045 0,Yes,335.8226355,16123.11335 0,No,1031.786257,39260.00622 0,No,893.2416447,43816.22547 0,No,1007.277995,45124.04044 0,No,786.8023084,38023.61481 0,Yes,1814.558895,24177.27252 0,No,116.2188756,50109.02341 0,No,839.9240942,47347.67671 0,Yes,824.6917018,15158.62479 0,No,239.3844788,25485.17338 0,No,933.7511521,52319.15351 0,No,459.3876653,37630.61332 0,Yes,870.0239927,15955.88515 0,No,232.2075504,11151.04762 0,No,150.7838407,32106.48364 0,No,1215.170473,55230.64819 0,No,1435.360095,33728.89232 0,No,338.0683219,47340.02669 0,Yes,1056.947399,20304.09812 0,No,194.1734166,28743.16437 0,No,298.5096896,50229.77974 0,Yes,938.339329,21911.72721 0,No,1297.392431,47095.22157 0,No,690.2283828,56958.59936 0,No,1013.906431,34166.82649 0,No,1213.794105,48796.84928 0,No,0,39269.83592 0,Yes,1399.950257,11010.21245 0,No,590.99973,50246.71554 0,No,1438.833691,35532.39932 0,No,438.2346852,48859.26102 0,No,783.0015156,37428.22004 0,No,1864.946403,48678.41519 0,No,208.0504439,46840.51927 0,No,621.1651351,32168.5529 0,No,994.1999643,52689.53943 0,No,674.6401585,42463.52497 0,No,537.5871865,46953.37681 0,No,690.3409422,29924.22946 0,Yes,1369.408276,18144.35026 0,No,654.277589,37706.59487 0,No,798.031314,29934.14323 0,No,763.2248929,48170.35992 0,No,1663.616348,52975.26361 0,No,988.3939471,49804.28375 0,No,1419.631892,40295.2688 0,Yes,1196.041822,19665.8292 0,No,618.0513849,16944.34449 0,No,1028.181249,32249.84567 0,No,523.5010994,39168.17425 0,No,517.4596209,39543.5871 0,Yes,764.5629974,5082.992631 0,No,206.9488464,53058.56141 0,No,847.9815066,43023.58501 0,No,0,40540.69441 0,No,631.7471322,37936.04343 0,No,1014.599104,51438.7102 0,No,157.2800282,41415.39603 0,No,525.4851998,43737.90426 0,No,962.9486146,28660.93057 0,No,594.1061337,24069.21303 1,Yes,2321.882221,21331.31478 0,Yes,1128.780156,17231.80847 0,Yes,2118.800574,18791.85208 0,No,290.5648739,13239.87284 0,No,1244.392244,33880.29922 0,No,1177.917268,49061.72291 0,No,0,44225.61586 0,Yes,492.6570685,10154.15626 0,No,0,62689.03303 0,No,530.2512986,39311.32571 0,No,0,45104.09403 0,No,1028.174611,51228.30211 0,Yes,488.2385515,33003.42295 0,No,4.458938299,46970.49642 0,No,1636.316153,35814.79198 0,No,107.9218494,33764.20207 0,Yes,129.184357,20570.8542 0,No,956.9854006,38285.7479 0,Yes,823.0640631,17254.39842 0,No,458.9984676,33163.54535 0,Yes,867.9046789,18949.02734 0,Yes,117.8094413,18752.8302 0,No,474.6661055,42058.77731 0,Yes,1180.529146,19139.30023 0,No,315.8506084,47328.98275 0,Yes,552.2040873,22554.57852 1,No,1342.262874,35691.63736 0,No,407.289756,45376.23591 0,No,1624.956465,47574.55972 0,Yes,548.4547299,21672.53989 0,No,908.8882984,42205.2065 0,Yes,969.8986849,17940.11913 0,Yes,966.350838,11776.52915 0,No,628.5669713,37617.11918 0,Yes,882.9774249,8833.668596 0,No,824.9954903,34277.5259 0,No,762.9128371,41617.81395 0,No,1246.229525,49479.57682 0,No,60.96076167,41548.11727 0,No,1377.978816,34683.61245 0,No,1215.252546,43793.11584 0,No,494.9019178,13980.36118 0,No,839.8737554,39158.23755 0,No,982.3816071,29157.29227 0,Yes,927.5165876,4143.118844 0,No,433.8578193,30581.58287 0,No,1213.03372,17726.67011 0,No,316.0043508,26518.46264 0,Yes,973.9031102,21590.0349 0,No,0,25603.53326 0,No,713.7511068,37230.07372 0,No,997.4654347,22061.79981 0,No,1326.064864,34945.80095 0,Yes,1135.361184,24314.03688 0,No,0,50805.03659 0,No,43.89647321,48543.59909 0,No,666.370177,50105.2676 0,Yes,0,18426.26072 1,Yes,2334.123559,19335.88929 0,No,303.791175,28525.40173 0,No,634.4950473,35131.30153 0,Yes,853.8183574,18750.75063 0,No,747.4553934,43898.75063 0,Yes,1120.030273,19170.45835 0,No,1290.191542,36898.92872 0,No,1348.785018,33488.41292 1,No,1632.894118,44326.58484 0,No,693.6306933,32594.66376 0,No,467.5830885,43435.83661 0,No,618.1253382,27868.79389 0,Yes,1217.667355,20977.23519 0,No,872.0713576,36004.64481 0,No,0,28659.48838 0,No,894.7344842,37903.52646 0,Yes,361.6104669,21685.69223 0,Yes,937.197434,18411.82443 0,No,0,55760.50194 0,No,339.5318647,31092.75765 0,No,423.7145506,32640.29407 0,Yes,1529.061705,17358.04298 0,No,894.1144875,47681.62283 0,No,760.7208238,40854.46907 0,No,883.0162511,37961.15592 0,Yes,1322.585497,15304.26039 0,No,2036.993189,31384.13 0,No,751.3999173,54993.40784 0,No,578.8362774,25522.51998 0,No,829.2816512,58070.11256 0,No,1160.061265,51895.30404 0,No,1197.771881,44047.69118 0,No,417.5922108,48035.60964 0,No,777.8244481,43910.98856 0,No,625.7461988,29644.2083 0,No,1382.775774,36232.31933 0,No,986.3058026,25342.30479 0,No,937.7383255,24932.72113 0,No,0,44525.84935 0,No,131.576673,51106.99485 0,Yes,1096.184606,15535.54866 0,No,499.1739193,44773.57533 0,No,1236.21872,37884.5661 0,No,637.014541,27828.1376 1,No,2147.312578,58271.39083 0,No,516.2165693,42446.04118 0,Yes,113.276476,21068.16403 0,No,352.3277028,40716.17481 0,No,918.014298,26798.07141 0,Yes,1378.913117,14329.77123 0,Yes,482.5452583,21369.27509 0,No,813.2006512,49477.51151 0,Yes,1330.485181,13765.44133 0,No,1373.533033,56878.33506 0,Yes,1411.607365,16677.30692 0,No,313.8050859,37720.7621 1,Yes,1803.944567,29100.81923 0,No,964.5246557,40814.65291 0,No,1139.370702,42759.38253 0,Yes,1009.431089,21904.69277 0,No,615.4653882,25865.18062 0,No,673.6980514,41891.42597 0,Yes,1288.551958,24918.41508 0,Yes,2388.174009,7832.135644 0,No,0,34545.09712 0,Yes,1119.38661,20011.46887 0,No,1352.703443,35743.00396 0,Yes,886.3485401,17534.46708 0,Yes,1468.79522,25038.8165 0,No,727.3734929,46329.02775 0,No,593.4338377,36995.82154 0,Yes,1168.340641,13580.94664 0,No,541.784475,56100.10543 0,No,1055.414789,29967.17338 0,No,399.7235905,43243.6661 0,Yes,552.8777042,14320.85979 0,No,1189.752134,50838.51722 0,No,698.0014773,38135.5587 0,No,431.5360552,46941.05894 0,No,851.1035675,53433.57417 0,No,1457.716054,37453.83977 0,Yes,992.2802505,13958.29148 0,No,387.7613417,40063.72764 0,No,794.1762128,43335.98504 0,No,412.2548906,37088.21233 0,Yes,1163.452512,27308.69093 0,Yes,880.281524,20177.40029 0,No,752.2218482,48764.02273 0,No,40.09310013,33278.36519 0,Yes,1242.5214,18331.00352 0,Yes,1671.813691,20074.50933 0,No,1097.039083,40021.35302 0,No,918.7473435,28327.55036 0,No,1185.288927,39926.60693 0,No,997.4773427,44748.79194 0,Yes,741.4423677,15499.91932 0,Yes,1234.249913,12268.36496 0,No,0,52585.68587 0,Yes,985.2622511,30295.63096 0,No,1023.198467,40866.01884 0,No,423.4304738,46000.79668 0,Yes,1007.139484,17217.50506 0,No,893.3552679,49960.30616 0,No,1008.357462,39610.62537 0,No,691.996757,45428.53932 0,No,410.2368039,36502.37857 0,Yes,1215.38361,15994.04102 0,No,599.0349705,49813.1267 0,No,355.251657,50160.70311 0,No,869.8984352,33540.04952 0,No,1786.463952,54207.16779 0,Yes,1221.813756,12603.30286 0,No,0,39164.02235 0,No,1621.245375,38590.89809 0,No,1121.496586,39395.89936 0,No,736.6375029,55187.91982 0,No,1739.535924,51623.28754 0,No,1478.621444,41986.04773 0,No,903.5644525,35296.4479 0,No,739.6528792,48283.18702 0,No,228.9558548,36783.00525 0,No,1313.559525,39989.00907 0,No,903.9258639,27395.79614 0,No,667.6598304,42078.68486 0,No,539.8879468,33856.45403 0,No,1877.114809,43442.32501 0,No,739.8597477,30853.29309 0,Yes,408.5629744,18471.77433 0,Yes,482.3251509,30588.03024 0,No,1097.817753,43065.61873 0,No,1580.866727,36513.61672 0,Yes,920.2311149,16088.2081 0,No,425.0537454,37351.35552 0,No,0,53273.34166 0,Yes,928.1822759,16968.86752 0,No,617.3487635,38106.78833 1,No,1673.486349,49310.33291 0,No,1023.051552,19354.39792 0,No,1459.561402,44334.61697 0,No,1235.777517,35140.4588 0,No,621.9781679,43472.23346 0,No,280.9443867,50404.03297 0,No,326.652236,45562.57424 0,No,902.762305,52528.23748 0,Yes,672.6932215,13795.96275 0,Yes,710.7799965,17625.96752 0,No,901.2084412,48855.45084 1,Yes,2216.017669,20911.69564 0,No,1210.592999,40182.70682 0,No,828.9707387,33423.93637 0,Yes,533.4927964,14707.29581 0,Yes,768.8230469,22138.2159 0,No,746.6703307,40354.55847 0,Yes,730.7394952,7299.520099 0,Yes,1308.024588,17353.86042 0,No,481.2610496,33500.5653 0,Yes,1409.989102,21569.89077 0,No,524.6079138,40663.04341 0,Yes,1122.1379,13929.47367 0,No,1105.878816,42110.4244 1,No,1319.816454,36548.81835 0,Yes,823.7366728,19041.81838 0,No,172.9757855,47060.63142 0,Yes,1497.364809,10576.34148 1,Yes,1819.940936,19393.48297 0,No,389.3958811,20200.37994 1,Yes,2047.417007,13678.50803 0,No,909.8655536,44879.27758 0,No,686.1632948,33190.42713 0,Yes,0,20565.77765 0,Yes,1676.097684,25687.91439 0,No,971.960011,33543.33493 0,No,887.594632,59416.15976 0,No,1312.847706,64402.60891 0,No,1808.722658,30020.6429 0,No,1170.743464,49832.9514 0,No,439.352384,44620.26515 0,No,103.0423418,45798.6747 0,No,1473.897564,32860.10099 0,No,780.7092515,37023.11715 0,No,1144.242002,28018.99557 0,No,1148.509336,37392.16784 0,No,667.4121637,31150.02654 0,No,1013.785194,50268.99231 0,Yes,1418.38865,12788.71903 0,Yes,1345.288297,23169.40413 0,Yes,1493.106066,14295.66474 0,No,828.2653037,36241.66013 0,Yes,208.8969152,17966.36114 0,No,567.2940844,39841.22835 0,No,686.869876,32359.33746 0,Yes,1926.289008,12283.40851 0,No,814.286312,36613.70709 0,No,1569.518216,43024.97068 0,No,0,42035.33898 0,Yes,0,13226.15496 0,Yes,1828.614325,20225.80611 0,No,777.6460629,50869.72861 1,No,1644.696623,61474.93445 0,No,295.1286576,36539.08483 0,No,781.1237141,22327.67598 0,No,673.9266568,39508.62412 0,No,343.2495905,58411.67754 0,Yes,1302.340309,16292.26714 0,No,225.8196601,54124.20604 0,Yes,1456.858054,14530.3112 0,No,955.0907543,52224.49279 0,No,777.0230001,47369.11683 0,No,0,23616.53901 0,No,527.2520163,49280.30094 0,No,29.84898288,36972.93414 0,No,125.1039485,16157.21831 0,Yes,1095.868822,16127.52466 0,No,0,41194.07139 0,No,973.9988496,48095.06797 0,No,1288.252682,42226.46201 0,No,269.2892196,33846.50864 0,No,1105.890238,35450.72727 0,No,360.8923852,37581.7918 0,No,933.104366,24113.91729 0,No,1184.210495,38409.53551 0,Yes,1850.612146,20409.03805 0,Yes,1517.804161,12958.65092 0,No,969.5504078,41301.97154 0,No,683.8090455,41004.65902 0,No,915.0752059,23682.91332 0,No,0,47895.80492 0,No,606.5810048,32497.44886 0,No,689.9194521,37695.79145 0,No,561.6228589,38894.55088 0,No,303.517761,43791.10542 1,No,1823.404105,14664.13812 0,Yes,1698.291816,11764.86211 0,Yes,1355.313216,11070.85497 0,Yes,1009.911576,18001.8645 0,No,887.9032989,29642.14403 0,No,308.5335358,39121.36406 0,No,1512.256314,35652.76786 0,No,0,39858.12723 0,No,1274.603005,55206.70233 0,Yes,1795.219846,7112.408499 0,No,848.9972626,44379.75335 0,No,588.7801861,58764.36376 0,No,551.1743516,24975.02706 0,Yes,1084.044084,13680.1133 0,No,723.4157186,35938.13978 1,No,698.5673519,31754.52342 0,No,910.7396182,40222.7125 0,Yes,1581.065936,17331.32583 0,No,948.6897016,39671.53388 0,No,199.1745658,21753.35696 0,Yes,1759.412325,11556.18453 0,No,780.6589645,29691.49427 0,Yes,1770.796815,18837.42017 1,Yes,2182.604344,20780.69245 0,No,696.0127587,35920.66202 0,No,563.9177259,47839.61726 0,No,1041.071523,41485.62372 0,No,293.6553752,38893.60459 0,Yes,1489.04815,17934.49337 0,No,237.4898898,44614.71632 0,No,1266.796021,34652.10888 0,No,1059.86583,33567.84503 0,Yes,704.2927042,13472.48319 0,No,0,45701.08516 0,No,0,47846.95657 0,No,499.6227806,42052.87012 0,No,728.8188595,33662.07097 0,No,1238.105177,37544.94213 0,No,776.8998746,37685.4493 0,No,202.0704329,28814.17531 0,No,625.3990249,52074.96822 0,No,1089.302178,28232.14128 0,No,992.9635257,45540.44568 0,No,352.7465359,41548.88295 0,No,416.7476666,69386.90159 0,No,937.9231754,31664.0669 0,Yes,15.15379273,13430.41584 0,No,1128.673594,42566.29755 0,Yes,1924.559775,8097.917975 0,No,211.0755018,50506.22019 0,No,23.52912629,42814.81856 0,No,891.3948219,43535.93994 0,No,707.1074635,18675.91728 0,Yes,1018.099338,18239.2817 0,No,1404.099156,28764.49671 0,No,817.0858884,55892.7261 0,Yes,908.8486338,20332.39106 0,No,1216.195156,47561.71276 0,No,176.9040458,43150.26853 0,No,401.708353,51338.21601 0,No,1214.211192,45778.02671 0,Yes,1835.564112,17807.44952 0,No,66.77272206,43189.66453 0,No,1343.185883,30960.61603 0,No,1309.405746,34469.98017 0,Yes,463.0916614,21248.65294 1,No,1672.031483,50327.36776 0,No,357.7445593,45516.77611 0,No,705.0950458,38297.85495 0,Yes,453.6439697,9770.006437 0,Yes,1799.149349,20215.62382 0,No,748.1381646,21153.48246 0,No,650.0893552,46496.51016 0,Yes,341.4855933,21278.82614 0,No,1476.478933,50701.84532 0,No,1134.344472,41748.18345 0,Yes,852.3586673,20948.97088 0,Yes,930.663263,22919.99717 0,No,1027.017571,47046.01236 0,No,1494.254119,48133.93844 1,No,1396.789181,40371.12474 0,No,44.05939884,50765.69828 0,No,938.2801803,33341.96319 0,No,776.9646792,36704.0519 0,Yes,1589.649633,15262.42162 0,Yes,1247.556321,16410.89037 0,No,0,30143.96371 1,Yes,2291.617688,20837.20945 0,No,676.9595811,33013.82476 0,No,135.9982356,59369.64841 0,No,630.6085404,52491.55867 0,No,315.9532575,38938.97575 0,Yes,564.738467,15411.19159 0,No,663.2441573,25728.86844 0,No,72.12325678,35374.86457 0,No,491.036498,37835.61321 0,No,1331.895676,38903.19798 0,No,755.3396629,37027.2546 0,No,684.1860237,45400.01736 0,Yes,407.3404404,25376.72863 0,No,0,37727.46358 0,No,130.3159693,44559.81729 0,No,1224.454493,46811.61439 0,No,1525.35393,52675.7637 0,No,618.9984399,17122.34756 0,No,353.6383342,43145.17966 0,No,422.6445292,58345.01452 0,No,25.73626917,28163.69297 0,No,697.6213981,48469.8814 0,No,462.1383867,44546.23719 0,No,599.6677199,36584.07624 0,No,1251.235906,43191.31209 0,No,1432.298465,31250.58298 0,Yes,975.9154041,12102.81789 0,Yes,445.9290605,10629.61835 0,No,694.4124308,47705.87128 0,Yes,320.0122023,14704.47769 0,No,1102.302032,38872.12992 0,No,0,39250.09552 0,No,165.9730086,28439.58796 0,No,1258.878806,40858.86844 0,No,1372.761993,30219.15009 0,No,1464.028962,43700.14263 0,Yes,1594.093167,14135.21128 0,No,0,31741.04562 0,No,1595.910033,55712.29374 0,No,901.1760737,55064.63649 0,No,789.7507972,39022.35325 0,No,1203.205839,43809.25995 0,Yes,1399.338053,22736.21907 0,No,1125.876488,49359.34272 0,No,236.3384662,39260.06019 0,No,218.1463145,27166.69754 0,No,413.5765513,49424.31038 0,No,469.0539803,25680.17493 0,No,923.9925164,27818.24687 0,No,182.6280599,31623.93645 0,No,478.4017447,30338.35686 0,No,918.5461513,53331.27843 0,Yes,873.9305469,29870.74737 0,No,748.7048356,48036.0791 0,No,146.8181908,48280.70993 0,No,1266.640989,41930.58627 0,Yes,883.8818318,14888.51811 0,Yes,1353.981432,17431.60535 0,Yes,1777.744671,24820.85627 0,No,695.4933217,48899.34318 0,No,0,33313.31107 0,No,377.7266313,49730.70458 0,No,975.737182,39453.22631 0,Yes,918.590687,14936.70819 0,No,1313.500378,31145.70835 0,No,1302.399854,15468.34474 0,No,1246.626138,41930.155 0,No,198.385708,37085.60371 0,No,395.6077739,27750.8215 0,Yes,638.0655848,17922.40349 0,No,677.2048392,52080.52336 0,No,681.57575,29980.33054 0,No,1114.945775,45832.94832 0,No,558.1784844,12740.86464 0,No,531.4779785,35314.07104 0,Yes,1080.969996,23760.75888 0,No,150.7705446,31216.78586 1,Yes,1665.70832,21024.27165 0,No,1086.193489,52761.41304 0,No,0,41435.43794 0,No,1253.923498,34304.76691 0,No,100.4631161,24687.85307 0,No,961.1524669,51179.46012 0,Yes,1055.861054,9045.241626 0,No,89.4101022,24086.60114 0,No,918.9503478,47761.40271 0,No,995.3870476,44217.68564 0,No,1774.233089,42955.82797 0,No,701.6914324,35335.78263 0,No,1177.920843,30169.29581 0,No,1308.267257,43611.46156 0,No,1114.536871,35044.72982 0,Yes,815.5486691,16908.13976 0,Yes,1010.506784,6786.38809 0,No,752.9859192,45523.49679 0,Yes,729.1897184,18329.72087 0,No,1095.471779,53581.15671 0,No,355.1701761,30440.89519 0,No,1308.987967,31777.20188 0,No,0,40257.7955 0,No,464.0641049,16670.15281 0,No,570.9163985,46569.16773 0,Yes,1045.338813,29601.40394 0,No,0,39134.93192 0,No,642.2562314,29223.99534 0,No,486.8159722,25117.64382 0,No,1082.119828,43256.40056 0,Yes,1224.636872,25717.07064 0,No,953.1473563,29328.03916 0,No,1323.175214,52019.34997 0,Yes,139.1928688,24734.08293 0,No,856.881451,32449.31133 0,No,311.8892887,46116.13153 0,No,0,49959.14101 0,No,498.8541547,47736.23679 0,Yes,465.8710734,14519.47338 0,No,892.82027,45379.84427 0,Yes,456.2406945,17657.60886 0,No,557.261712,45040.94441 0,No,0,28094.15709 0,No,138.6207393,50249.41724 0,No,241.1631501,36581.25912 0,No,763.1772393,42481.49875 0,Yes,1195.813811,19906.73149 0,No,130.9969303,54366.80276 0,No,1253.041317,32362.65251 0,No,1496.098042,44309.9029 0,Yes,1071.955237,19813.99697 0,Yes,724.9047355,16643.12695 0,Yes,194.1850121,20889.852 0,No,1441.743359,45037.16768 0,Yes,1678.382001,8154.239774 0,Yes,2128.434148,22664.21984 0,Yes,1393.464421,15573.79311 0,Yes,1437.830363,23062.49487 0,No,1257.958107,38532.19219 0,Yes,1329.398351,15971.57324 0,No,605.1244322,29769.97835 0,No,601.0816652,45607.34686 0,No,1164.513406,29874.48879 1,No,2054.452527,37366.76505 0,No,701.6328413,43340.9203 0,No,673.0924399,27894.09399 0,Yes,1262.925919,16935.19311 0,No,357.9963055,30217.02129 0,Yes,1052.598123,24418.21395 0,No,1234.512473,50293.43354 0,No,1153.802194,37552.62162 0,No,1375.29736,35810.91421 0,No,249.1036852,51051.33576 0,No,874.031434,39664.93613 0,No,768.1587234,23991.04129 0,Yes,1236.256423,16389.86381 0,Yes,641.2207571,23265.69236 0,Yes,825.5228817,14539.16201 0,No,931.0411701,42225.36714 0,No,650.0755295,32560.76104 0,Yes,0,17577.99634 0,No,682.6045089,57553.97658 0,No,1207.105002,50010.82983 0,Yes,840.2209676,18103.73552 0,No,894.6279058,39027.63028 0,No,711.5258161,35468.38215 0,No,1635.217569,47669.74196 0,No,740.8693707,46541.21117 0,No,773.4830759,38647.37067 0,No,1142.326319,45173.3746 0,No,894.16832,42212.44583 0,Yes,14.03384572,13846.80104 0,No,537.0403329,21364.3105 0,No,1092.524306,19759.12264 0,No,541.0713808,29807.40555 0,No,1049.004118,34088.16979 0,No,463.9325612,37842.04778 0,No,214.2726904,56032.64758 0,Yes,997.7624823,13799.54474 0,Yes,938.684222,18454.39903 1,No,1350.603261,21541.29879 0,Yes,935.7571992,5386.176483 0,No,819.0485492,61602.63667 0,Yes,1669.091038,16830.75295 0,No,395.8518569,33708.59238 0,Yes,810.5424069,17975.15713 0,Yes,62.23712836,20267.49993 0,No,1133.112585,23110.65365 0,No,748.4875955,36453.64512 1,Yes,1337.235187,32761.0534 0,No,1138.430984,36133.61053 0,Yes,1247.455532,15382.60965 0,Yes,875.9797105,17286.69449 0,No,980.7639386,43650.53446 0,No,238.9815065,30412.70202 0,No,185.0101145,40874.83285 0,Yes,478.9618638,19911.51027 0,No,145.4765901,41196.4202 0,No,339.9590471,36132.80569 0,No,303.9037423,43836.70473 0,No,536.2142781,47956.52456 0,Yes,1219.581454,12434.04184 1,Yes,1926.371583,17012.43814 0,Yes,460.202332,14782.45877 0,No,825.2878657,46147.95575 0,No,31.25654574,44970.76801 0,No,535.5848146,51971.70563 0,No,667.7116286,30803.44466 0,No,694.3267591,42041.45656 0,No,81.42293643,51044.40781 0,No,1324.492949,28691.83661 0,Yes,1587.132957,25819.48539 0,No,775.0229447,39137.07673 0,No,0,46542.98528 0,Yes,819.7277126,10175.30995 0,No,1416.224397,31303.16331 0,No,1005.293851,34010.27821 0,Yes,1419.360362,20118.5582 0,No,230.0066559,44232.13848 0,Yes,1559.45787,20707.31861 0,No,672.7082326,47532.58768 0,Yes,457.2357469,5296.710921 0,No,968.4589678,45299.00083 0,No,257.5463322,29967.20428 0,Yes,1130.54464,20862.90449 0,No,486.5760502,27687.34117 0,No,207.3038092,49999.49574 0,No,110.9151208,58185.55984 0,No,678.4851464,30196.04569 0,Yes,515.1499965,10214.70526 0,Yes,837.9118858,20984.17906 0,No,709.6172205,34624.83575 0,Yes,745.2824716,17803.75736 0,Yes,1288.393705,22919.63773 0,Yes,819.7423451,20792.83096 1,No,1708.65972,38203.50947 0,No,1061.613602,38250.77775 0,No,372.7412499,35570.21178 0,No,883.8678853,42866.38395 0,No,0,41864.25913 0,No,657.6876177,26454.58575 0,No,221.0238822,42087.08223 0,Yes,1009.896004,9804.666039 0,No,174.4129448,40282.48755 0,No,358.8483412,44486.90647 0,No,421.7596249,35522.66598 0,Yes,1295.851844,10987.26519 0,No,1005.100951,36275.64879 0,No,268.9614828,40733.46798 1,Yes,1807.666218,18166.32265 0,No,1237.861344,33816.18157 0,Yes,700.1690395,28160.64227 0,Yes,1236.678437,16298.56815 0,No,354.7025871,29893.03973 0,No,657.0219512,47044.33353 0,No,1444.9225,54599.15776 0,No,1456.411254,30332.98599 0,Yes,501.1402608,19467.53765 0,Yes,1155.222175,18442.34093 0,No,804.4697537,46869.32017 0,Yes,1473.633713,17816.62085 0,No,1345.380592,30175.94167 0,Yes,545.6124774,13020.95914 0,No,1134.279848,37658.23096 0,No,212.3613685,32283.40531 0,No,424.7352349,22327.4342 0,No,658.4636449,39242.75542 0,No,1023.795885,42335.73719 0,No,815.5669074,31752.07715 0,Yes,953.42174,14062.05354 0,No,905.6456347,35876.79569 0,No,623.092054,42035.45262 0,No,287.2001519,45952.72892 0,Yes,1310.793503,16742.95252 0,Yes,1509.663402,16777.56234 0,No,1616.108734,33046.18794 0,No,710.1593099,32649.61987 0,No,1213.658672,45662.64661 0,No,1004.116828,52822.40438 0,No,36.35843995,44603.42733 0,No,1349.192332,51189.11227 1,No,1854.604154,38406.35646 0,No,126.1532047,33964.05061 0,Yes,987.1540956,13002.48555 0,Yes,1127.993934,15186.86869 1,No,1436.311549,18810.80414 0,No,0,41564.76983 0,Yes,1068.860742,20804.37868 0,No,0,62128.66663 0,Yes,568.9755524,17291.81469 0,No,1608.745469,41572.2737 0,No,885.0040673,48753.55996 0,Yes,886.9510173,21792.60979 0,No,1358.132472,49903.59708 0,No,215.7527192,22254.58917 0,Yes,410.4667487,25574.43832 0,No,1146.614029,40870.90133 0,Yes,1262.393294,24797.60757 0,No,778.4746661,39360.16409 0,No,444.3040741,43424.93499 0,No,826.9536725,16124.62568 0,No,204.694596,42076.57612 0,No,819.5307335,38456.61504 0,No,1386.296964,31739.51324 0,No,698.9032763,28682.95742 0,No,756.4334686,33296.56712 0,No,739.9018425,47349.53197 0,No,220.2291177,37451.01761 0,Yes,458.9677371,18444.29785 0,No,0,36584.757 0,No,102.2583724,23476.74536 0,No,215.068135,47815.5997 0,No,906.0813599,45720.01304 0,No,735.927001,18373.4865 0,No,944.3217169,34546.11681 0,No,1071.007369,40758.00204 0,No,1345.451737,43322.19302 0,Yes,250.17301,17439.62927 0,No,265.7290045,60182.67719 0,No,1006.053988,27002.06581 0,Yes,1670.126895,14746.89261 0,Yes,291.9623649,7105.292025 0,No,381.5209693,31750.77732 0,Yes,851.7297296,14726.37602 0,No,1375.049224,42557.68909 0,No,816.7337205,40949.36444 0,No,819.018615,49111.39157 0,Yes,1026.15356,25393.7688 0,No,853.4381453,58757.68136 0,Yes,1200.376613,10817.56196 0,No,1037.134803,43368.22337 0,No,0,28363.7137 0,No,357.1381462,35623.46527 0,Yes,970.4260895,20569.64004 0,No,812.3479812,33654.67742 0,No,91.94732281,44337.4059 0,No,1396.037916,46378.73051 0,No,72.65008166,50786.97919 0,No,1168.092556,27823.45422 0,Yes,1098.454046,16837.21032 1,No,1549.894673,47916.29486 0,No,1039.098374,26217.13205 0,No,309.838215,54945.58168 0,No,1477.528611,36709.66844 0,No,79.53894967,34417.37572 0,No,1219.347882,34310.29194 0,No,812.5880324,43980.13997 0,No,557.3749689,35067.26624 0,No,1688.314557,32723.45163 0,No,1137.791157,21103.42962 0,No,856.2829599,40394.04958 0,No,1126.410483,35125.67772 0,No,541.0218143,29519.88015 0,No,682.8796097,43142.05452 0,No,538.9498876,35634.2028 0,Yes,1305.809699,28929.38474 0,No,735.0188559,25305.25395 0,Yes,344.3089096,14256.24175 0,No,1060.695812,36920.68241 0,No,848.6763909,38742.05979 0,Yes,1245.267507,16772.61937 1,No,1563.535198,51704.91997 1,Yes,1433.167635,18087.68637 0,Yes,1238.58951,10514.82864 0,No,625.6150864,53655.27048 0,Yes,1681.572465,20059.67479 1,Yes,1681.481506,10155.32399 0,No,602.6381471,70021.64844 0,No,681.2130022,37287.22978 0,Yes,966.0269814,27960.37189 0,Yes,539.1600365,17824.26071 0,No,921.3042946,25350.4002 0,Yes,1325.346923,21487.6237 0,No,0,46714.55812 0,Yes,900.2746082,22986.61368 0,No,695.3708301,31779.39667 0,Yes,1270.996404,15069.04871 0,No,1388.561139,54935.9412 0,No,635.741696,21297.1199 0,No,484.5157687,39775.24989 0,No,1185.096271,42969.71809 0,No,656.9802829,32225.29529 0,No,1084.925654,36908.68843 0,No,1309.559449,36851.26912 0,No,847.5306542,40982.4198 0,No,0,35600.14937 0,No,78.16808284,56254.17531 0,Yes,251.9717446,11757.96327 0,No,871.2313984,31770.62398 0,Yes,918.7587912,14304.59736 0,No,0,31774.64754 0,No,450.0573591,36121.23739 0,Yes,1079.37288,14223.26681 0,Yes,320.2304819,14185.27463 0,No,799.3700279,34293.72751 0,No,1206.592155,25646.0822 0,No,0,39830.09162 0,No,603.6616705,44663.73612 0,No,672.0628046,33850.97884 0,Yes,959.2223533,16082.44763 0,No,670.4719051,24265.5869 0,No,587.2594598,33732.21623 0,Yes,602.3076763,11019.58695 0,Yes,937.6060593,19962.19804 0,No,1502.757749,53129.78304 0,No,1063.155675,34642.06476 0,No,511.6137514,36796.79478 0,No,926.9698728,61937.56967 0,Yes,1004.364637,13422.37946 0,No,367.6680959,50025.94458 0,Yes,256.7070707,22166.79439 0,Yes,1777.685651,21573.90002 0,No,717.7428338,49347.02181 0,Yes,532.3821353,17704.81438 0,Yes,1346.679507,16104.89497 0,No,334.4664195,40156.24449 0,No,0,36179.82686 0,Yes,774.4984602,10333.80055 0,No,779.2599906,38387.72127 0,No,881.9391119,62222.9807 0,No,1563.230205,31126.32678 0,No,991.0609685,37597.32306 0,No,430.6510942,38372.02125 0,No,806.3776849,40454.06788 0,No,438.7666361,32762.18623 0,Yes,1457.858845,17365.56672 0,No,366.2048882,30631.96311 0,No,707.6469316,35268.01743 0,Yes,244.3821578,19773.07246 0,No,903.6157164,20444.42412 0,Yes,1031.498886,24636.30022 0,No,914.3782799,40659.12596 0,No,1047.25165,39222.05889 0,No,884.4943566,30520.35327 0,No,434.7788265,39099.09185 0,Yes,36.84403824,16942.30534 0,No,189.3622325,36706.81197 0,No,366.3212813,40904.76363 0,Yes,1092.464572,14659.71807 0,No,732.734289,39024.14923 0,No,594.4405256,28723.34186 0,Yes,604.5927,14896.59187 0,No,1732.843605,30322.86931 0,No,577.4333324,43585.04414 0,No,0,38626.42292 0,No,54.67522087,32279.56843 0,No,75.95176394,60826.99403 0,No,550.5327125,27223.85787 0,Yes,1343.874484,17280.43863 0,No,1350.428847,26450.29174 0,No,866.3379875,35141.42764 0,No,6.071892351,46930.57516 0,No,501.3451781,45368.23118 1,No,1488.469925,36457.22328 0,No,1462.786737,60296.39834 1,Yes,1567.679282,19172.67945 0,No,771.7893469,42139.07027 0,Yes,1534.9684,17084.58112 0,Yes,801.0497163,14679.83344 0,No,1724.369132,32610.81033 0,No,0,50258.53894 0,Yes,1277.374387,19675.17784 0,No,786.9874342,33289.85082 0,Yes,231.7707259,13702.47092 0,No,173.9594605,53965.05121 0,No,1184.927422,40958.64399 0,No,1308.206711,32215.53337 0,No,203.3000811,53055.69443 0,No,1092.098834,32122.05118 1,Yes,1902.969565,12464.30304 0,Yes,1004.438273,16456.27725 0,No,366.7015515,60456.11777 0,Yes,1148.73389,23812.62699 0,No,0,33272.15215 0,No,476.2352711,37627.35002 0,Yes,477.7829458,22753.59547 0,Yes,970.832756,19096.88941 0,No,1268.593973,42012.89659 0,No,657.5311129,40807.2813 0,No,0,47447.79657 1,No,1661.752645,21871.46297 1,No,2075.188754,40882.49422 0,No,915.7715639,24243.18016 0,Yes,1048.274763,20659.14486 0,No,987.5662506,49542.40423 0,No,535.1353429,44448.60703 0,No,1057.932601,26628.44074 0,No,1023.147921,43698.81669 0,No,749.0385161,53711.46049 0,Yes,1143.329016,17367.92553 0,Yes,1269.226506,18552.42414 0,No,0,35282.56729 0,Yes,723.3718471,17623.58567 0,No,1000.376782,40272.30202 0,Yes,1069.693058,10722.24946 0,Yes,1391.335002,18309.421 0,No,1075.824171,22982.22663 0,No,548.6453079,38094.42617 1,Yes,2109.999395,17774.52529 0,Yes,649.3601165,10524.3261 0,No,863.138441,58119.7166 0,No,861.6448507,39272.00904 0,No,567.4891943,22304.57395 0,Yes,642.5364898,14763.37718 0,Yes,1045.657466,23335.04633 0,No,738.1441283,43693.06639 0,Yes,0,11537.89323 0,Yes,1137.954162,22110.95576 0,No,462.6746283,50229.58589 0,No,1006.201561,42689.27369 0,No,584.1932422,36676.78915 0,No,1314.765205,30067.41065 0,No,0,30236.45633 0,Yes,1005.707006,10887.49987 0,No,113.5712644,32803.83265 0,No,967.7398523,26210.79214 0,No,0,45486.45868 0,No,651.3251349,51583.2502 0,No,1035.315645,53066.10488 0,Yes,58.9719451,20914.93102 0,No,0,60480.57797 0,Yes,1190.021728,16240.76757 0,No,37.99253638,48739.83538 0,No,652.266674,48639.44148 0,No,1529.687857,40977.41388 0,No,626.9285568,42139.18446 0,Yes,756.9956094,26674.39078 0,Yes,496.7969074,21058.96159 0,Yes,396.348074,19541.0461 0,No,1590.641641,36242.497 0,Yes,1331.980568,20672.33954 0,No,36.08799035,37015.55736 0,No,338.4767539,32666.51253 0,Yes,992.3483203,22324.9956 0,Yes,1123.340012,20785.64023 0,No,743.6231452,37333.57412 0,No,1111.691517,43659.07059 0,No,969.6560183,40142.13415 0,No,974.5078184,25346.77821 0,Yes,285.6496186,26874.75458 0,No,596.6200269,30764.66205 0,No,1417.912113,38015.16636 0,Yes,1055.017327,24188.04975 0,No,1001.221971,44801.401 0,No,771.4410045,44810.29936 0,Yes,672.5680785,22788.36738 1,No,1511.610952,53506.94493 0,No,601.6896467,31522.78485 0,Yes,1316.652076,12165.729 0,No,122.0943088,46609.39748 0,No,840.0150181,51350.63767 0,Yes,1765.893938,18053.49972 0,Yes,1752.73615,15596.89143 0,No,1147.978075,40515.69046 0,No,1839.322208,38104.09111 0,No,1208.395128,32430.18277 0,No,952.1275301,49137.39569 0,No,1251.151035,45205.2458 0,No,215.5414226,55278.43265 0,No,1294.457122,40768.45105 0,No,463.0855991,46760.49589 0,No,815.2441831,26231.46541 0,No,854.5112787,34463.76511 0,No,1062.058711,35781.12549 0,No,1035.160236,67450.68807 0,No,880.396518,60206.08655 0,No,847.432632,37303.5744 0,No,529.1890785,50195.11301 0,No,1163.016893,38902.76362 0,No,570.0246843,40987.06233 0,No,0,29254.86823 0,Yes,673.4736889,20926.49855 0,No,1728.335976,33488.30436 0,No,1285.828594,43507.46606 0,Yes,1511.894197,22794.07105 0,No,676.6846401,24989.83369 0,No,1010.516286,36298.99498 0,Yes,613.1568831,17665.4777 0,Yes,1134.296094,18991.82121 0,No,295.843231,30602.26689 0,No,206.5425226,14909.3671 0,Yes,1194.884549,18943.40411 0,No,1551.092739,44596.74595 0,No,1210.176036,29460.1916 0,No,407.8607445,47826.21377 0,No,1561.933701,56614.52158 0,No,602.5456297,42798.07875 0,Yes,640.364464,14938.76657 0,No,927.6691817,39934.14448 0,Yes,1436.676385,14418.16602 0,No,552.5513185,48267.67339 0,No,839.2218564,43213.69411 0,Yes,1248.954189,14024.90498 0,No,929.9259221,34535.45527 0,No,1198.832807,40275.0895 0,No,479.5462007,24805.9234 0,No,1523.372674,44603.32637 0,No,990.8069149,44966.20673 0,No,890.5655077,14327.99078 0,No,678.4668286,40803.57496 0,No,1462.223173,29574.23457 0,No,0,48781.47896 0,Yes,490.3217974,24403.07667 0,Yes,2216.329753,24737.08176 0,No,1336.363246,22245.51252 0,No,355.0605964,36888.83432 0,Yes,1094.521679,15617.52141 0,Yes,1969.407748,23674.3763 0,No,1274.159685,41943.67752 0,No,224.1700297,33542.30818 0,Yes,781.4523056,18301.5838 0,No,1360.952931,46419.81699 0,No,210.797283,34468.89916 0,No,671.7326056,26431.2726 0,No,275.063482,28266.17198 0,No,306.1895161,30587.00678 0,No,293.614967,35945.66026 0,No,394.4129448,52725.15994 0,No,1002.030641,49319.33865 0,No,357.5171949,29711.35968 0,No,959.0630532,55089.99026 0,No,734.3358294,49128.53775 0,No,325.3312147,32961.65703 0,No,910.4827507,47298.72064 0,No,496.7739601,38316.13544 0,No,416.632532,53256.27963 0,Yes,1525.172306,21427.09362 0,No,124.7541714,45763.7922 0,No,0,52069.29467 0,No,854.9134418,39190.29567 0,No,796.1739999,27278.39687 0,No,1044.863161,41726.23363 0,No,1690.668077,55412.75947 0,No,935.7129441,34067.05422 0,No,306.4591959,36812.62605 0,No,698.4405643,21903.0381 0,No,973.9908207,45941.02201 0,No,455.0177823,45611.47645 0,Yes,1133.699627,26119.30468 0,No,598.1930735,42734.43969 0,No,615.477641,45792.89996 0,No,1270.130906,47817.55662 0,No,956.9226131,50834.40834 0,Yes,1095.076812,18087.71987 0,No,143.5630209,54411.26212 0,Yes,604.0550916,21187.71056 0,No,0,38954.1075 0,No,0,37825.22141 0,Yes,1179.994525,18954.39379 0,No,337.6513682,36787.01961 0,No,855.5967839,40412.72569 0,No,437.5777389,55720.97296 0,Yes,596.8065278,17397.34524 0,Yes,1725.386954,19915.92949 0,No,619.5406416,39049.50042 0,No,627.9877394,56266.1534 0,No,186.5235201,36717.51458 0,Yes,1093.02753,12179.03819 0,No,684.2704638,42437.91476 0,No,963.0926687,33110.30411 0,No,190.5034301,53690.59123 0,No,68.30593198,52323.90214 0,No,1336.96984,33148.76865 0,No,372.4258549,53695.29638 0,No,1095.028241,36282.47457 0,No,813.133978,59309.47668 0,No,31.70412287,42191.65919 0,No,655.8320417,37267.58281 0,No,1340.240429,37464.31876 0,No,881.2565114,41728.8461 0,No,684.7150446,27951.19723 0,No,1158.379804,61054.76393 0,No,945.2682188,38738.53539 0,Yes,465.5836287,15625.63353 0,No,999.6348552,51385.69973 0,No,1143.680795,48044.89701 0,Yes,669.3807882,16805.19372 0,Yes,838.9709429,21407.4524 0,Yes,1384.737597,23083.66709 0,Yes,1401.073191,21811.26825 0,No,1591.193583,37501.48689 0,No,54.05059372,46662.11281 0,No,862.9197102,36774.21591 0,No,712.2077207,29546.48988 0,No,908.9265484,40757.09412 0,No,779.0764379,31710.97405 0,Yes,99.38256245,16725.24507 0,No,1081.001337,32344.57858 0,No,1124.249135,27521.15158 0,No,923.4065669,33122.22004 0,No,1096.21847,30653.26275 0,No,999.6678059,51420.97671 0,No,0,16601.63528 0,Yes,517.8219594,12673.90607 0,No,1461.887463,38559.07348 0,No,699.0538658,41722.80607 0,Yes,1241.061898,4664.565047 0,No,1380.749804,52664.87659 0,No,561.9646064,40555.4076 0,No,1122.746958,47140.829 0,Yes,736.2440566,13756.54552 0,No,643.4811548,45925.88666 0,No,1141.157938,44333.21063 0,Yes,1273.891137,20104.43016 0,Yes,1306.046537,20776.38865 0,Yes,840.9889092,15406.20741 0,No,289.2454385,46550.52551 0,No,758.1342851,33220.57549 0,No,930.7169386,46501.27571 1,Yes,2177.150869,17659.74782 0,No,0,24892.91569 0,Yes,565.8300588,21042.22772 0,No,1076.126584,23632.5203 0,Yes,1031.86993,18668.48355 0,Yes,768.4037418,15417.84154 0,Yes,690.4210492,19273.73239 0,No,469.8442413,51308.31851 0,No,617.8600254,50177.77338 0,Yes,1779.049699,15689.77663 0,No,0,47669.70408 1,No,1928.280283,35492.12823 0,No,1123.71926,56217.6849 0,No,794.6461084,41033.58771 0,Yes,1428.066883,19818.29166 1,No,1028.767207,40346.83327 0,No,1006.202977,50501.76154 0,No,691.7517135,45420.96912 0,No,524.8381501,41268.42365 0,No,493.9141608,37409.18393 0,No,1520.980478,37510.53936 0,No,665.0397566,47062.25309 0,Yes,1520.442101,18462.43066 0,No,1094.780473,34190.87652 0,No,735.0910408,44933.32315 0,No,681.6935764,33327.11303 0,No,376.034544,50748.27174 1,No,2037.943354,43016.07218 0,Yes,1630.199589,14232.66153 0,Yes,0,21881.70591 0,No,184.4275319,36731.62695 0,No,349.6606656,39391.32231 0,No,673.5552703,49169.72854 0,No,82.72452491,42048.4448 0,No,1247.120605,50539.90745 0,Yes,640.639543,29236.63019 0,No,273.4469724,52492.74982 0,No,0,35377.14073 0,No,803.8311868,33417.77241 0,No,1248.375749,37469.86462 0,No,0,40348.31418 0,Yes,1307.204973,19381.54132 0,No,0,45793.39304 0,No,1186.098705,50353.92543 0,Yes,1037.573018,18769.57902 1,Yes,1878.001146,17473.18399 0,No,736.2348369,36313.63355 0,No,260.1621754,33551.7153 0,Yes,1103.681782,14225.72134 0,Yes,886.0593168,10378.64229 0,No,221.1637828,43072.89425 0,No,1098.057751,29402.87314 0,No,0,38686.67529 0,No,826.9498104,46946.05245 0,No,569.9677215,38982.01743 0,No,1316.057411,45308.56384 0,No,86.72551869,51257.58346 0,No,1458.835325,24850.58478 0,No,1243.451348,41184.89891 0,No,790.75576,41909.10767 0,No,1997.17281,50273.60103 0,No,960.9126678,29304.45772 0,No,646.8567437,28836.75713 0,Yes,287.6494893,15441.36675 0,No,630.6653369,56266.7585 0,No,610.500867,28664.04266 0,No,1224.229414,34490.225 0,No,326.8736761,48756.02758 0,Yes,325.2828199,15644.24197 0,Yes,623.7578593,19191.96692 0,No,1394.476776,44092.54868 0,No,929.0854677,35571.77665 0,No,887.743411,43478.88519 0,No,0,26598.64322 0,Yes,127.2227631,9801.500167 0,No,986.358895,41688.06434 0,Yes,1430.325502,20846.96332 1,No,1026.358855,56182.12993 0,No,556.0174984,33705.07865 0,No,1018.436236,53962.27769 0,Yes,1083.711391,17040.5194 0,No,1602.806943,34700.26596 0,No,921.3478541,47610.9552 0,Yes,769.4497618,21717.94372 0,Yes,289.8341427,13914.60372 0,No,955.3391649,46310.10752 0,No,387.7866998,38745.36115 0,Yes,1340.966389,18725.95421 0,No,1252.894039,35587.7236 0,No,435.3931594,32907.33247 0,No,491.0485061,37219.52108 0,No,1112.509304,44298.38564 0,No,828.9802098,55696.8037 0,No,568.3520036,38963.95924 0,Yes,672.410411,16508.42503 0,No,931.7841072,37280.41015 0,Yes,982.7730973,24145.52943 0,Yes,686.5287583,25779.78334 0,No,1069.526911,34694.00075 0,No,863.9548546,17159.16338 0,Yes,976.4514847,10541.57023 1,No,1586.502199,48925.93338 0,No,544.1262485,49900.58402 0,No,198.9291418,50463.77549 0,No,1026.194095,35685.48373 0,No,1317.564864,40731.76397 0,No,0,33322.48236 0,No,358.6137769,47688.80472 0,Yes,1511.37288,14982.59851 0,No,359.9166787,52248.11807 0,Yes,529.3431063,17245.80061 0,Yes,888.564819,21820.21718 1,No,1954.333299,35067.53492 0,No,131.747076,36947.77862 0,No,518.8990204,46192.55437 0,Yes,1593.327492,11570.72862 1,Yes,1473.034561,18108.96167 0,No,1527.924799,44640.74474 0,No,1349.908479,48103.03078 0,No,371.8818634,46682.54695 0,No,758.0210778,55278.59194 0,No,1306.832034,57561.41126 0,No,605.8472399,58892.90548 0,No,1085.243811,21805.79231 0,Yes,1582.23461,20214.68954 0,No,1078.48346,40944.26978 0,No,711.6638177,35741.20216 0,No,1204.459183,39418.70709 0,Yes,885.2098957,18693.72712 0,No,1795.883711,25878.51931 0,No,230.9162266,36139.41058 0,Yes,1038.555598,17055.79504 0,No,731.4771282,18843.27132 0,No,1734.228044,37621.63317 0,No,640.5203498,30327.60774 0,No,218.1148541,38766.10763 0,No,912.3904235,26857.1719 0,No,726.0421485,42272.43784 0,No,408.6190178,35622.59839 0,No,309.7409324,33333.56472 0,No,1392.619372,54027.19947 0,Yes,587.0449399,15939.81952 0,No,898.2726563,48233.8755 0,No,325.8410056,37392.68151 0,No,896.0271359,30452.03684 0,No,803.7645777,48013.61015 0,No,509.1956334,46368.39407 0,Yes,1031.017732,16568.633 0,No,782.4133944,34830.62629 0,No,1168.377486,42388.12102 0,No,561.6230018,36266.80588 0,No,850.5184574,35683.83229 0,No,752.5102185,18960.36558 0,No,245.5059615,31307.24364 1,No,1733.824679,26330.51173 0,No,590.666032,59574.21772 0,Yes,711.5828226,17780.75983 0,Yes,831.8453438,19797.35659 0,No,1344.711281,26215.39184 0,No,224.6911451,55040.61273 0,No,361.7426655,54071.34056 0,No,1322.810964,48016.61734 0,No,1043.374397,28079.26268 0,No,1475.518485,48653.68375 0,No,812.0838869,41821.29978 0,Yes,287.7923623,13060.00857 0,No,775.3097895,38721.24589 0,No,344.6240771,43246.57799 0,Yes,372.4308104,17243.0493 0,Yes,2088.529109,18078.32917 0,No,946.3981669,30770.06673 0,No,675.3767263,36921.57596 0,No,1012.768454,36406.24109 0,Yes,1011.937209,18509.29713 0,No,452.3726509,59429.12314 0,No,1092.491259,30654.68526 0,No,71.57768976,61588.2545 0,No,1319.041718,57768.89998 0,No,266.2777587,41204.29499 0,No,370.963649,25321.35282 0,No,848.7244264,41585.95255 0,No,507.3961316,35353.40553 0,Yes,267.3278291,19698.71388 0,No,420.8993319,39764.70243 0,No,129.5964485,52169.23157 0,No,327.2404373,40061.62451 0,No,678.0711056,63791.8242 0,Yes,199.1270927,19913.6025 0,No,1215.698245,33598.97076 0,No,460.6815058,37129.27082 0,Yes,1300.720433,9266.529731 0,No,534.568708,37281.04021 0,No,1308.529172,57190.87487 0,No,507.065032,42753.19192 0,No,197.3272654,24562.11966 0,Yes,819.834292,16412.99702 0,No,0,57003.59377 0,No,1489.780332,30296.61044 0,No,581.5208186,53100.57248 0,Yes,286.2915646,19553.63561 0,No,371.8652201,45949.91421 0,Yes,992.7802211,16585.32428 0,Yes,758.1385236,23758.57763 0,No,1174.194909,35533.48452 0,Yes,1196.098264,23851.22139 0,No,613.2314024,34664.57133 0,No,968.9723403,33763.19028 0,Yes,1151.298716,14384.76708 0,No,848.7027399,35192.83887 0,Yes,989.0907701,20262.02194 0,No,92.740663,40722.59065 0,No,1654.934175,33179.78392 0,No,212.4979287,53619.47571 0,No,73.89323483,44308.9054 0,No,692.155234,37592.26085 0,No,957.5839622,40315.77284 0,No,69.02387754,41566.58207 0,No,1394.125443,42921.01708 0,No,1317.866468,36610.88611 0,No,602.0963956,53553.72247 0,No,430.1182212,41105.28763 0,No,211.9830282,33448.24478 0,No,544.1431456,33861.5539 0,Yes,943.8430643,16701.78309 1,No,1412.192448,37283.0081 0,No,1416.44477,33099.49688 0,No,193.2831647,37376.64971 0,No,857.2364484,67124.05709 0,No,1132.078482,29715.30494 0,No,1489.357027,37758.41289 0,No,1527.359325,33923.52877 0,Yes,1427.049384,18268.52683 0,No,964.8630373,43744.98335 0,No,326.1509299,32919.97105 0,No,866.2996685,65931.95111 0,No,626.1601542,34007.90835 0,No,836.1928646,43275.14523 0,No,826.5580205,44371.74734 0,No,102.1225226,33300.18752 0,No,1609.297031,52752.96843 0,Yes,140.4556219,11179.49786 0,No,1147.980695,20766.39507 0,No,855.2925657,35749.03279 0,No,1682.201856,36366.99129 0,No,689.8207736,30304.7803 0,No,1706.046305,32900.71144 0,No,479.5219091,43743.62231 0,Yes,765.3633568,11976.40849 0,No,894.3291029,45250.73031 0,No,993.6470928,26768.95668 0,Yes,378.4925197,18659.77213 0,Yes,576.0650075,13536.60904 1,Yes,2113.629761,21100.72179 0,No,542.6962061,40996.21233 0,Yes,493.6295461,20500.21263 0,No,0,29322.63139 0,No,1185.581253,47782.49713 0,Yes,1719.871119,22431.858 0,No,495.7500691,44361.39119 0,No,533.8941544,33785.74614 0,No,150.1427055,38610.37731 0,Yes,1298.901493,14975.26493 0,No,957.2389599,57698.40822 0,No,836.9332916,51031.93983 0,Yes,1400.702824,23083.26016 0,No,857.329142,25742.17377 0,No,876.7655553,49291.16656 0,No,979.3167149,27534.91214 0,Yes,491.7043208,8352.208988 0,No,1621.711643,37970.47402 0,No,643.6155094,44251.01588 0,Yes,343.7986381,19971.77621 1,No,1711.169093,18579.10247 0,No,1170.027406,33332.62578 0,Yes,638.2043997,14641.38373 0,Yes,1457.839411,18496.93206 0,No,383.860155,31749.67842 0,No,618.0316829,44419.54767 0,No,565.2182999,29015.72406 0,No,290.1459587,32862.55529 0,Yes,576.9442788,22800.86705 0,No,633.1271156,54034.35501 0,Yes,1350.629002,11854.98444 0,Yes,893.5582716,23033.81221 0,Yes,895.3189658,20869.35594 0,No,1470.572491,44490.32107 0,No,789.2565112,43835.34027 0,No,218.7690046,52074.66272 0,Yes,531.6938027,15794.58161 0,Yes,1268.537855,21336.8901 0,No,0,34040.31291 0,No,1046.562865,26555.48894 0,No,389.9921752,29032.1127 0,No,642.2346279,35800.08382 0,No,391.9899522,35012.86961 0,No,1483.689011,58310.51235 0,Yes,438.5747709,17439.11495 0,No,897.8616937,47557.5241 0,No,0,41693.38584 0,Yes,776.6390702,15338.8886 0,No,112.4707089,34320.02615 0,No,634.477776,56726.12001 0,No,791.9474961,47886.52327 0,No,1088.285306,43134.53141 0,Yes,2018.358536,15472.84958 0,No,1470.635273,40080.21475 0,Yes,611.2088966,19733.2518 0,No,1117.080028,42388.41555 0,No,1589.990919,42268.27985 0,No,0,57898.41516 0,No,636.2386682,39172.36337 0,Yes,714.3971711,17412.71798 0,Yes,946.7796039,12881.40211 0,No,108.0567758,41840.32581 0,No,816.8883528,52492.34594 0,No,5.88381628,45840.47246 0,No,761.7506343,13889.13253 0,Yes,1173.781343,15823.80548 0,No,1418.475373,38034.14443 0,Yes,864.3571268,10484.7705 0,Yes,1101.160092,14307.00108 0,No,0,46615.69803 0,No,0,43782.25586 0,No,582.3172218,29239.84607 0,No,369.0504876,38418.86652 0,No,966.0518775,10174.7298 0,No,399.4456151,54329.92293 0,No,51.88848659,41839.17339 0,No,460.8794155,40046.02933 0,No,1139.012728,36958.57801 0,No,707.5731029,48790.40225 0,Yes,1034.603151,18614.0375 0,No,118.8698001,42823.57196 0,Yes,461.0043408,15554.02977 0,No,1057.275,42731.87641 0,No,1035.485881,41714.37377 0,No,1010.279513,34188.98465 0,No,0,41199.79936 0,No,656.0237254,36349.43699 0,No,539.1375008,23561.43052 0,No,1377.462061,49406.0727 0,Yes,1322.145996,21272.57594 0,Yes,1393.589491,23261.86324 0,No,1324.013984,45114.74031 0,No,1020.529557,35652.66346 0,No,2033.540235,41629.23729 0,No,888.6429126,19942.60287 0,No,1350.116267,39555.96231 1,Yes,1936.061862,13377.82884 0,Yes,1406.917364,16427.10078 0,No,282.8381245,29478.13801 0,No,601.0825714,25319.3661 0,Yes,1639.426608,14682.77666 0,Yes,48.52809588,16647.30616 0,No,324.5045318,51139.87685 0,Yes,886.4015079,13995.88959 0,No,967.4415738,51674.41937 0,No,711.7376077,45419.88194 0,No,1547.88079,50638.33959 0,Yes,1200.594445,13772.98912 0,No,0,45201.12871 0,No,1454.981589,43821.22135 0,No,355.4423386,46147.51787 0,No,0,46927.78445 0,No,0,49784.33635 0,No,282.8653131,39961.03982 0,No,303.7151177,29048.03524 0,No,157.7630016,36206.28569 0,No,300.0004665,41189.4522 0,No,463.9820725,43330.55244 0,No,1654.234956,39580.12971 0,No,762.5744974,45526.89479 0,Yes,1887.881716,21901.1825 0,No,1206.263492,34621.77331 0,Yes,247.6531036,20444.75243 0,No,576.7961199,39209.83287 0,Yes,1214.408306,18055.23847 0,No,987.7455283,40243.62002 0,Yes,1257.01613,19247.90061 0,No,524.0856189,40319.11814 0,Yes,1236.207092,23364.04868 0,No,1008.675001,68610.41206 0,Yes,1474.779605,17382.46739 0,No,772.3753146,36997.33674 0,No,701.5525937,39526.57719 0,No,0,28798.03997 0,Yes,1165.895511,14969.28725 0,No,0,28226.55795 0,No,1429.221048,40564.88631 0,Yes,806.6692357,17359.67476 0,No,528.0893156,46389.34068 0,No,963.4281482,60084.12648 0,Yes,764.5271667,22578.63567 0,No,573.699137,49465.75485 0,No,1410.36924,62395.03815 0,No,816.7410271,47272.33361 0,Yes,1220.524914,12195.36195 0,No,245.4387533,54133.03831 0,Yes,1083.911282,8584.077577 0,No,485.3488065,14676.29551 0,No,677.758794,50309.06472 0,No,286.6608747,34499.53263 0,Yes,843.9202067,20754.02417 0,No,831.8366199,41247.6422 0,No,1024.326377,33832.48963 0,No,1277.123098,42472.90827 0,Yes,1221.943911,19127.35023 0,No,967.2492817,27663.13407 0,Yes,1556.904418,19272.23648 0,Yes,419.729555,24679.73147 0,No,276.1648843,53625.71409 0,Yes,1407.497272,14688.3889 0,No,1041.088023,39515.84302 0,No,182.3254442,39703.26885 0,No,162.2184867,41381.27071 0,No,405.2739806,52571.18413 0,No,887.1583614,51519.12648 1,No,1844.883839,35508.67542 0,No,792.9815259,37549.95 0,Yes,942.1401049,23835.38547 0,No,938.7858968,14524.35762 0,No,18.90420269,35590.23258 0,Yes,599.4709832,18575.40842 0,Yes,679.809547,19263.12332 0,No,601.2632926,24400.09625 0,No,132.0810393,51694.73956 0,No,743.0329689,25186.4259 0,No,728.1868975,52515.06755 0,No,467.2475637,33644.25784 0,Yes,1104.933348,17377.49815 0,Yes,310.1981818,14672.9257 0,No,1575.712705,45957.24472 0,Yes,1370.248937,13358.2742 0,No,1593.432803,73554.2335 0,No,463.0022177,42297.4578 0,Yes,786.1060239,20954.45157 0,Yes,731.5118011,26505.34452 0,Yes,468.2732193,27054.89382 0,No,219.1457095,37993.04368 0,Yes,846.95907,20659.85327 0,No,702.1592628,57468.40444 0,Yes,1381.644434,11059.56807 0,No,1455.505837,33455.04969 0,No,1357.909718,43377.11218 0,No,1032.627079,23349.83013 0,No,798.8596367,48336.12931 0,Yes,117.9030818,14649.85806 0,No,234.743604,27499.95362 0,No,890.2668722,36631.49842 0,Yes,518.4788393,18635.50971 0,No,1124.49596,31739.56375 0,No,1468.788055,38521.8702 0,No,938.1061788,13405.21094 0,No,722.0291625,26984.25629 0,No,610.641708,42899.84813 0,No,407.7713007,18555.32354 0,No,1057.228817,58133.52784 0,No,1262.404516,57158.75502 0,Yes,1082.674545,22417.55615 1,No,1969.942924,29415.75329 0,No,760.0996428,45921.34313 0,No,929.3962543,23133.65981 0,No,1177.249598,35419.61031 0,No,309.2706808,42226.65131 0,No,981.9439338,38483.53189 0,No,200.162804,24280.13016 0,No,1630.48301,54323.42289 0,No,947.2354163,28310.49378 0,No,537.3963542,40828.14012 0,Yes,1292.568784,14859.24008 0,No,356.8276454,39444.82535 0,No,500.155117,34437.70689 0,No,1345.70698,40802.88652 0,Yes,1091.139756,19990.83533 0,No,1025.527625,22741.3913 0,No,403.2400249,42993.43641 0,No,699.3426727,36957.68057 0,No,1445.805164,15666.05743 0,Yes,963.8992578,16561.25564 0,No,542.7656016,52625.41571 0,No,766.9290949,25376.10376 0,No,808.4291276,29212.90473 0,No,736.5367299,46416.18292 0,No,1076.139823,31845.95153 0,Yes,731.3148595,11510.15147 0,No,236.0037059,38202.51384 0,No,1382.454952,40394.3086 0,Yes,915.5715469,22586.33828 0,No,176.5969458,48957.75638 0,Yes,639.9996263,19900.0064 0,Yes,950.6780086,20435.81781 0,No,1320.711115,29105.44244 0,Yes,1203.029399,15952.45202 0,No,1335.935254,19482.20628 1,Yes,1647.282248,16154.46228 0,Yes,1434.386464,15761.69962 0,No,307.4762385,36120.96859 0,Yes,1277.8949,20649.92999 0,No,998.3930699,46051.92286 0,No,561.343259,44403.99429 0,Yes,1577.083581,15230.8206 0,Yes,412.0714443,12588.97113 1,Yes,1758.406571,14272.27378 0,Yes,251.4681938,19373.88802 0,No,1353.131735,40164.04859 0,No,10.19445669,32641.93256 0,No,933.5547342,33633.14869 0,Yes,1692.752538,23743.84195 0,No,663.2504792,36276.86395 0,No,51.53040142,36851.91287 0,No,797.7426471,53398.92859 0,No,0,50243.93117 0,No,1217.072762,62764.09766 0,No,0,44980.29304 0,No,741.844666,45808.54028 0,No,455.1844905,43513.35063 0,Yes,1788.140822,26228.71044 0,No,1253.773412,28153.01406 0,No,918.0933737,44112.81017 0,No,0,30241.94858 0,Yes,699.1734013,17572.83106 0,No,171.996377,33505.49343 0,No,1326.348854,46587.08029 0,No,1275.495303,40026.28595 1,Yes,2247.421889,17926.72301 0,No,993.7721191,52645.66309 0,No,997.3795041,34824.84806 0,Yes,652.3726691,12139.05712 0,No,1124.886189,41989.0342 0,No,1608.818447,36721.10348 0,Yes,776.606473,4755.25219 0,No,845.0207249,33895.23785 0,No,570.0546987,42157.80771 0,No,186.4381344,13325.12133 0,No,0,48672.95601 0,No,1429.488716,31995.488 0,No,295.0566741,39479.75168 0,No,896.5812766,54840.05787 0,Yes,1562.532324,13754.33407 0,No,889.3087417,33036.24479 0,Yes,1805.682955,20727.64022 0,No,1334.105928,43704.43086 0,No,527.9402237,56820.82074 0,No,1282.076946,52753.07657 0,No,932.8287296,48165.18024 0,Yes,1850.888247,21575.76333 0,No,455.1907842,36488.15785 0,No,748.2866448,45626.99455 0,No,874.4787423,33485.52051 0,Yes,538.7065362,16350.36018 0,No,841.8517272,48615.01457 0,No,1814.168195,28322.83919 0,Yes,365.7044087,15953.97561 0,No,401.1807575,39686.67595 0,No,2096.136391,49992.52981 0,No,1429.878559,33452.58756 0,Yes,1737.347715,19202.71019 0,No,1043.390338,45309.94897 0,Yes,1155.456548,22194.47788 0,Yes,931.649113,19179.44679 0,No,146.7554609,49925.06939 0,No,1406.947652,27667.83603 0,Yes,705.8968256,18447.87609 0,No,641.8039407,32628.64126 0,No,379.6035635,42862.48889 0,No,706.2267919,33920.45697 0,No,577.1319969,43616.09623 0,No,90.99981606,52387.59199 0,No,549.3990418,48424.40438 1,Yes,1102.434982,17391.77965 0,Yes,1228.338796,18129.69406 0,Yes,974.0143849,20108.10358 0,No,263.6560618,50782.8322 0,No,847.0691474,35581.29012 0,No,835.1918091,39977.13147 0,No,757.9077112,37088.83957 0,Yes,1013.665766,13821.09008 0,Yes,643.5770009,28765.91053 0,Yes,757.2727635,18876.08916 0,No,1248.278943,41100.62617 0,No,699.2858502,41475.9821 0,No,866.4026312,18586.72717 0,No,697.6663826,35402.26796 0,No,899.0968928,39630.63879 0,No,674.2046296,46481.95268 0,No,902.9239586,33778.40323 0,No,1591.755806,46259.65998 0,No,484.2173586,30178.57524 0,No,751.6872866,51327.31727 0,No,604.1949471,38292.7691 0,No,640.6334699,46075.41506 0,Yes,782.4451651,23447.21338 0,No,1026.955197,40307.01102 0,Yes,862.9022538,16157.86653 0,Yes,780.5121258,14813.34158 0,No,878.0093852,15262.93511 0,No,481.8620283,32928.239 0,Yes,1579.990032,13274.7315 0,No,1.674025903,23001.66708 0,No,514.8490253,37656.84787 0,No,2087.678741,44997.36435 0,Yes,973.5147102,20770.48403 0,No,528.8724969,45235.49719 0,No,1131.412434,37663.22687 0,No,955.3433705,40368.59789 0,No,759.0322605,45774.38354 0,No,1023.852414,31492.99837 0,No,1096.077756,30374.83472 0,No,829.3296907,48734.16617 0,No,792.3568717,39911.42723 0,No,882.2645345,18379.51486 0,Yes,733.8470899,16400.12834 0,No,553.5194362,45385.31133 0,Yes,822.9595949,8918.702539 0,No,493.9599904,34621.75764 0,No,88.25405671,43927.31589 0,No,0,45077.57458 0,No,611.8257349,21716.53436 0,Yes,1096.692136,20856.58764 0,No,191.6110706,35119.58138 0,No,447.9581612,54044.82311 0,No,1149.689188,39974.56302 0,Yes,463.3000047,16416.61273 0,No,1347.635595,58953.09267 0,Yes,0,16979.89307 0,No,643.0108474,27735.15824 0,Yes,365.2229558,15375.15172 0,No,289.6815694,45991.04432 0,No,578.2906566,48044.38449 0,No,1186.932729,51742.57644 0,No,426.0072838,50874.56518 0,No,1115.738777,48125.64676 0,Yes,932.6958142,18743.31374 0,Yes,1048.487596,22935.59545 0,No,768.372947,44405.30875 0,No,785.4947084,50683.15418 0,Yes,865.2058661,22081.58164 1,No,1801.801183,24152.26483 0,No,950.3412748,64396.16534 0,No,787.0429349,46266.94145 0,No,982.8399888,32419.66505 0,No,1175.389577,35339.55667 0,Yes,887.2848722,13132.90135 0,No,801.9944369,46439.00355 0,No,1096.246733,42685.10729 0,No,628.8553316,40490.42605 0,Yes,567.7225809,14892.32445 0,Yes,843.0087866,22037.47041 0,Yes,1217.309875,10520.04307 0,No,0,10593.92125 0,No,1186.777951,38581.92786 0,No,439.0438931,40148.10805 0,Yes,778.9993533,10489.57353 0,Yes,742.5493235,24505.01935 0,Yes,763.3376238,15849.86396 0,No,408.11603,39388.31756 0,No,1071.258354,36869.05258 0,No,1373.575601,22182.41659 0,No,52.91808918,30506.35885 0,Yes,1140.454363,18991.88604 0,No,1162.299227,47513.12477 0,Yes,1528.49437,22664.34935 0,No,991.9611672,49910.09688 0,No,592.4323214,32283.26412 0,No,848.3276082,30472.71793 0,No,309.5179821,35293.19357 0,Yes,425.8446454,15324.758 0,Yes,1268.486114,19862.04442 0,Yes,898.6319508,15293.87405 0,No,274.9569465,24102.39151 0,No,399.443128,45360.93225 0,Yes,850.3864062,6389.070569 0,No,92.90008897,43955.88137 0,No,854.0233346,32107.30834 0,No,0,29721.34187 0,No,1094.812833,32936.05866 0,No,680.1644594,25391.63308 0,No,1231.351894,29867.87647 0,No,0,38228.26017 0,No,830.0460942,51847.12293 0,No,1267.001444,32752.19374 0,No,1623.690272,36747.90398 0,No,302.7190177,45260.39773 0,No,439.9619588,28215.47884 0,Yes,1016.825478,18703.55523 0,No,1421.204764,47423.79177 0,No,479.6771518,40995.03621 0,Yes,925.8969167,12384.20423 0,Yes,1192.702159,21597.75167 0,No,1649.91703,36091.38475 0,No,808.9991026,26379.8867 0,No,752.0562947,49741.43152 0,No,727.1907763,27282.19836 0,No,845.60731,31633.08725 0,No,445.6618979,46816.10511 0,No,1459.044966,49753.80492 0,Yes,988.6416096,22085.45082 0,No,796.9912085,25159.55486 0,No,903.4591316,33640.09968 0,No,1163.083904,45488.75056 0,No,1217.210527,33865.05058 0,Yes,740.2286772,23469.3875 0,No,1259.450524,28881.72525 0,No,501.391686,42856.19 0,No,808.6118886,48650.43388 0,No,413.8882716,53142.0239 0,Yes,666.4982302,11951.89879 0,Yes,412.1607883,17679.52177 0,Yes,372.6524955,22308.04128 0,No,1235.076654,48687.55716 0,No,853.2462216,41581.19534 0,No,1911.668517,52802.08882 0,No,357.9694607,57012.55088 0,Yes,698.1579817,19582.81616 0,No,918.4610912,32468.45074 1,No,1741.914915,35067.42512 0,No,577.4934767,39015.41684 0,Yes,1415.681994,21856.32093 0,Yes,1524.486833,15845.87125 0,Yes,1171.441401,19213.38573 0,Yes,396.8014841,26061.76425 0,No,1236.158124,19682.7109 0,No,1053.405495,52921.85379 0,No,1207.040625,42171.98897 0,No,528.9619452,27065.70528 0,No,662.703255,48652.16994 0,No,517.2285076,40248.58607 0,No,938.8460043,40914.22823 1,No,1823.636559,44260.15637 0,No,638.7219872,48085.70791 0,No,929.0226144,23889.68006 0,Yes,282.2487063,19809.09867 0,Yes,1429.609328,14827.89454 0,Yes,672.3581632,24495.03762 0,No,752.4667639,39896.10937 0,No,471.346127,30337.14118 0,No,270.5857589,44958.63127 1,No,2075.327892,43817.49942 0,No,96.80389905,31371.72608 0,Yes,568.294951,13286.3505 0,Yes,990.8805619,21164.66399 0,No,284.5609721,45542.1738 0,No,983.8373408,28980.10202 0,No,924.4373405,40987.48615 0,No,857.1531969,31252.65135 0,No,195.0223352,44222.97874 0,No,1046.008996,45573.38377 0,Yes,933.3504051,15557.63256 0,No,0,43646.91172 0,No,691.8920905,32512.19539 0,Yes,1097.969621,16275.68364 0,No,0,45374.99718 0,Yes,402.909163,22752.32695 0,No,1230.903161,44303.198 0,No,959.4820389,31357.44633 0,No,630.2291255,46713.63044 0,Yes,1353.49303,16727.66372 0,Yes,603.57519,15477.35388 0,No,830.2500906,35377.85706 0,No,858.1799645,30892.7358 0,No,380.1735083,37395.71893 0,Yes,1005.811733,12112.65742 0,No,1027.825307,33089.46729 0,No,456.003327,29898.00506 0,No,1043.09783,30516.2627 0,No,586.9688854,50317.65289 0,No,271.9422082,27790.06928 0,No,419.7290517,37444.53606 0,Yes,597.1123802,12660.81457 0,No,342.9573696,43688.53486 0,No,728.2789548,30028.18011 0,Yes,513.8367632,9879.115221 0,No,795.9586091,22570.4834 0,No,1.976691657,51672.36067 0,No,1355.64122,36671.65987 0,Yes,319.2626761,24881.38566 0,No,1719.169241,57866.05876 0,Yes,1191.085574,20895.40819 0,No,984.4391631,25294.86799 0,No,947.7959041,45284.20595 0,No,949.0387135,47760.40017 0,Yes,219.7286195,18401.98242 0,No,1465.743931,25521.26989 0,Yes,645.3388269,20122.20229 0,Yes,856.5644805,23199.97702 0,Yes,912.8325373,19467.97931 0,Yes,774.3476913,15147.40509 0,Yes,1376.340022,17101.42779 0,No,699.6803861,50267.84987 0,Yes,903.4586502,17254.9607 0,Yes,1150.547186,23705.95342 0,No,751.338737,42736.4281 0,No,556.077321,25371.21377 0,No,751.3922405,29875.04689 0,No,425.9869196,39772.75241 0,No,297.6808143,26586.53383 0,No,507.2462937,46106.23331 0,No,865.6970038,33541.04638 0,Yes,1265.527377,14672.25618 0,No,564.4113065,39643.53629 0,No,621.0412193,50804.67531 0,No,1205.59069,25920.86062 0,No,1943.9323,24193.60895 0,No,415.2887519,35790.13089 0,Yes,1169.420444,19879.24817 0,No,685.636884,49260.62537 0,No,781.9190194,54925.50779 0,No,651.3743295,44648.69641 0,No,490.2510328,49222.49138 0,No,976.2895424,35909.43829 0,No,1476.838123,41154.88175 0,No,752.9482339,62329.12133 0,No,511.605907,29914.17626 0,Yes,1384.372132,21059.60487 0,Yes,1031.670446,22440.62198 0,Yes,1491.507493,23636.16481 0,No,672.2363541,46336.05827 0,Yes,644.413564,23319.64913 0,No,539.5934291,9950.229447 0,Yes,0,17648.35555 0,No,919.8126372,44316.35833 0,No,1249.872794,50418.04452 0,No,539.8053161,35924.61001 0,No,1162.11326,49255.14433 0,No,944.4502979,47463.33358 0,No,0,29675.04039 0,Yes,1440.528795,20882.01732 0,Yes,1558.471919,17470.4767 0,No,854.1406227,52239.80024 0,No,468.5494575,53273.91902 0,No,902.4974099,56947.86374 0,No,897.2856085,66547.91535 0,Yes,422.1894101,14366.56905 0,No,194.5759668,44543.781 0,Yes,487.5114038,17713.58867 0,No,1558.860467,42112.3133 0,No,630.0289508,43547.54217 0,No,1056.65463,30711.86121 0,No,1712.470037,40395.34637 0,No,25.60294273,35464.24798 0,No,1384.850571,40131.49389 0,No,961.365709,42619.07773 0,No,229.214974,68145.13373 0,No,778.791565,53219.52735 0,No,653.4362222,41440.92856 1,No,961.4888501,27717.615 0,No,1084.457564,33080.96538 0,Yes,869.6677558,20285.25178 0,No,818.334937,27390.83691 0,No,416.8611818,39672.5173 0,No,250.8352449,58139.02159 0,No,0,48411.0769 0,Yes,481.5883754,19623.46054 0,Yes,1506.911513,23375.64593 0,No,1100.008931,34358.07468 0,No,220.6667528,64467.7334 0,Yes,789.2835878,20370.17371 0,No,41.35917584,47351.83354 0,No,382.9626972,43645.94779 0,No,567.9170514,28875.52181 0,No,244.5090864,33751.24507 0,Yes,815.341565,26064.36562 0,No,529.0726813,43914.67143 0,No,736.4185577,48660.15108 0,Yes,0,17744.92997 0,Yes,1260.341165,11696.67776 0,Yes,708.213553,12092.31447 0,Yes,99.99761123,4376.810337 0,No,1143.30818,48630.03384 0,Yes,892.3838609,17841.5227 0,No,1250.047245,50883.71442 0,No,415.6553667,43389.66075 0,Yes,1058.867384,16059.83275 0,No,857.6996646,43162.67365 0,No,1429.767205,37255.9111 0,Yes,322.266857,19231.93909 0,No,572.1867296,35926.37624 0,No,1438.758473,24061.6476 0,Yes,1742.886938,20344.42801 0,No,325.2675938,39431.53541 0,No,1091.418551,43091.15686 0,No,1252.409529,53237.10354 0,No,607.4312695,34541.302 1,No,1258.764794,26331.3724 0,No,1026.702707,33915.04884 0,Yes,590.5662326,9143.396433 0,Yes,1218.170346,18918.37715 0,No,260.6188019,42125.14867 0,Yes,1498.070591,13274.63056 0,No,909.9468758,53430.89543 0,Yes,1167.110583,18676.56977 0,No,923.6855831,38403.01062 0,Yes,1275.197078,15793.90917 0,No,709.2575197,23249.93691 0,No,682.9880122,41679.75979 0,No,1355.961899,25208.04886 0,Yes,1815.889294,15490.02827 0,No,698.2917247,43876.16188 0,No,189.1298582,28827.21309 0,No,1418.70284,35619.96673 0,No,670.4225823,53666.19234 0,No,206.0163452,38550.37667 0,No,1616.962125,47504.6645 0,No,995.5295997,24126.61036 0,No,591.0368363,30834.71179 0,No,1294.903234,52338.22146 0,Yes,1259.372813,22361.05474 0,Yes,1313.35267,17437.61583 0,No,0,24461.85446 0,Yes,769.4791217,27515.57093 0,Yes,571.5771218,15325.0791 0,No,756.0879108,49163.2233 0,No,183.4286922,39020.22886 0,No,1054.805589,52290.34067 0,No,1386.207726,44536.4873 0,No,596.1879849,45929.44918 0,Yes,1029.916975,16691.80329 0,No,417.5384554,31730.8666 0,No,1218.421391,40449.31171 0,No,210.7807881,46326.76112 1,No,1178.158858,44437.20214 0,No,997.6251137,30670.94715 0,No,637.902181,48425.72265 0,No,0,54245.1198 0,No,350.051852,48411.98668 0,No,1517.918958,49400.17084 0,No,190.6411053,31463.41998 0,No,971.5836736,41278.41903 0,Yes,925.3906782,16427.57101 0,No,1637.839922,34107.08056 0,No,224.5924725,27246.0453 0,No,255.117031,55980.07404 0,No,1201.016548,41585.83205 0,No,1075.570111,51370.2097 0,No,438.4202406,29899.82216 0,Yes,1246.437178,15756.71825 0,Yes,1655.11286,17269.79891 0,No,1063.816802,38457.24929 0,No,1626.563799,43592.8405 0,No,409.40402,48746.43435 0,No,1018.673018,38421.81091 0,Yes,1090.891882,20421.95772 0,No,324.885414,36555.39975 0,Yes,1085.152966,20054.8985 0,Yes,869.772061,18724.40368 0,Yes,608.8406483,24523.8541 0,Yes,1042.972565,9595.587571 0,Yes,1276.667762,19073.10852 0,No,928.0940903,55894.66202 0,No,1592.763904,35084.09317 0,No,0,39653.92321 0,No,340.999798,48058.38318 0,No,81.53186513,40847.81131 1,Yes,2145.607674,23516.19134 0,No,654.4160098,47244.70418 0,Yes,1155.780577,15146.89646 0,No,1745.313528,49090.12856 0,No,534.7848919,46138.43179 0,No,584.8374117,39553.1715 0,No,1365.556851,38511.3198 0,Yes,946.3671498,12532.65752 0,No,263.190295,34112.47197 0,Yes,1090.925059,18935.81822 0,No,835.189928,44678.83175 0,No,1265.535986,35133.21729 0,No,357.5066215,66404.68311 0,Yes,564.0054409,16502.33737 0,No,817.9277097,59431.83918 0,No,1366.278621,41192.46265 0,No,836.3431371,34559.1584 0,No,0,27367.78266 0,Yes,780.2279181,25228.71073 0,No,995.8773192,52860.58494 0,No,0,22827.18449 0,No,554.3445863,31028.9242 0,No,1204.578841,39583.12053 0,No,1053.375879,39612.67194 0,No,821.498847,39318.48939 0,Yes,470.9123472,24074.36465 0,Yes,1534.853735,21678.35097 0,No,1177.976867,50811.75946 0,Yes,1151.899517,15097.51233 0,Yes,906.4390691,24662.29072 0,Yes,1513.542437,13246.37528 0,No,740.8851866,34196.06746 0,No,1176.414512,57318.70197 0,No,208.2950791,37295.80198 0,Yes,375.3063048,19342.15809 0,Yes,953.1305453,18057.78649 0,No,280.9559003,37690.03348 0,No,399.1294716,38004.10498 0,No,1229.858122,58399.49667 0,No,1740.864107,46458.60612 0,Yes,795.1395535,24606.15429 0,No,584.8167761,47449.62424 0,No,1140.748209,39017.07762 0,No,1202.316722,49914.52204 0,Yes,476.7204658,10516.86972 0,No,1059.872271,38698.67273 0,No,1367.108606,36427.42974 0,Yes,1604.777659,23999.76034 0,Yes,1055.682537,25109.50847 0,No,367.6116872,44046.01751 0,Yes,0,18032.07378 0,No,543.8057414,31512.61059 0,No,192.6274629,38042.01839 0,No,1184.839953,34527.6868 0,Yes,1510.777233,19491.69407 0,No,801.6399094,37441.79212 0,No,631.0308358,44525.88999 0,No,279.1168757,30144.4325 0,No,674.0325619,54832.66573 0,Yes,957.6302042,15018.91365 0,No,274.4242206,31292.62745 0,No,1044.449897,43374.0041 0,No,319.807607,45202.6354 0,Yes,429.222463,19442.02909 0,No,835.5850236,41032.37131 0,No,2.788920349,54838.80551 0,Yes,867.4416448,16396.68236 0,Yes,271.7309257,14215.09288 0,No,404.1946521,41668.41958 0,No,1572.811108,18218.47436 0,No,730.6335593,42300.14687 0,No,0,56610.2365 0,No,1046.168559,46078.98231 0,No,514.8717738,50339.1918 0,Yes,1181.586009,22142.05215 0,No,0,45235.29087 0,No,272.2199278,47757.82441 0,No,1175.125526,33284.20607 0,No,812.8896264,48563.79976 0,No,571.0415013,40389.44633 0,Yes,1041.147351,12488.73193 0,No,334.0194949,40916.03325 0,No,1402.601194,37273.59307 0,No,1569.870324,55938.76622 0,Yes,1035.966107,30531.61311 0,No,1376.113241,63073.44514 0,Yes,668.642229,20240.95496 0,No,0,36249.17499 0,No,1112.655684,35159.21546 0,No,718.5798774,12571.68936 0,No,641.5503677,45574.49958 1,Yes,2125.792202,19539.14861 0,Yes,814.3541028,17973.50916 0,No,904.0336286,18898.31133 0,No,795.4973264,25336.32685 0,Yes,1753.598144,21130.91859 0,No,284.3956974,60840.80984 0,Yes,1300.889693,14156.64567 0,No,1237.843391,31880.17559 0,No,563.4665954,31151.18709 0,Yes,6.727869735,21695.70276 0,No,661.5281862,58519.85979 0,No,630.7148446,53546.5706 0,No,721.1416861,40458.95421 0,Yes,360.0091602,18487.43325 0,No,751.0309852,45581.71886 0,No,572.4682971,52797.42938 0,Yes,242.4629888,23413.35959 0,No,199.4547896,57455.21016 0,No,477.5668171,16978.27113 0,Yes,1184.725034,19797.77015 0,Yes,604.7587402,19369.34923 0,No,384.3310694,34232.06923 0,No,1399.573561,23577.76451 0,No,328.7781476,48626.35674 0,No,271.0598661,30465.30757 0,Yes,145.0021215,19927.0921 0,Yes,344.1541116,20439.68811 0,No,985.7527395,22829.58798 0,Yes,1002.41925,17797.34371 0,Yes,1004.844323,20249.20743 0,No,483.4158516,53873.51265 0,No,1034.601162,41252.72833 0,Yes,854.6262002,16322.72259 0,No,796.8367297,29397.88409 0,No,535.6968565,55828.08177 0,Yes,1070.639261,14833.02918 0,No,0,30524.75601 0,No,1333.901206,23261.84654 1,No,1114.832181,39776.99896 0,Yes,793.2480916,24314.68239 0,No,433.93529,26888.08734 0,Yes,1004.273646,17069.52357 0,Yes,1112.701189,20901.21374 0,No,298.7751394,33255.83885 0,No,796.8463462,20078.78923 0,Yes,969.3971625,17686.562 0,No,1075.118346,47566.78403 0,Yes,0,16451.94239 0,Yes,44.65556623,26346.81193 0,Yes,1000.740586,14689.49468 0,No,1311.999313,26377.97395 1,Yes,2086.536165,17893.72137 0,No,697.0468594,38743.09318 0,No,1240.66425,30938.53745 0,Yes,726.7641457,16079.63675 0,No,633.5041872,40571.76652 0,Yes,386.156432,22527.64722 0,No,0,34701.19596 0,No,808.5935854,41222.35167 0,No,1384.392454,35499.52693 0,No,713.067544,44413.75139 0,No,590.6765328,26673.8476 0,No,294.8446533,41591.77996 0,No,208.0077062,42399.52956 0,No,220.4493279,33213.32492 0,No,773.172691,49308.16489 0,No,1248.477058,37204.07352 0,No,634.1866103,32255.98924 0,No,678.1868937,55542.96963 0,No,1252.621497,27295.51793 0,Yes,1297.265043,16864.43489 0,No,0,38870.4968 0,No,1190.761292,27257.12253 0,No,196.515562,44226.63177 0,No,619.5592065,33476.66786 0,No,438.6250849,41454.72128 0,No,676.0988601,38571.41375 0,No,589.914146,49945.38901 0,No,1068.14635,27859.86445 0,No,1210.416122,45113.16252 0,Yes,390.3564738,9114.275289 0,No,1505.707167,35786.32716 0,No,490.0559754,35895.35391 0,No,123.2860681,47467.95895 0,No,563.2864112,51720.91153 0,No,409.1603681,45055.89747 0,Yes,781.6803963,21342.47871 0,No,870.3027256,24263.12744 0,No,509.1560111,47444.15145 0,No,1056.662484,39255.76331 0,No,394.9902144,41574.83351 0,Yes,1592.004647,19982.29006 0,No,0,49250.46411 0,No,1276.403278,52215.13403 1,No,2413.319449,38540.57271 0,No,210.1232594,58413.10367 1,No,2187.224846,42205.12305 0,No,1159.841945,43385.01904 0,No,1339.603177,46442.35377 0,Yes,793.3474509,11495.39314 0,Yes,163.5033913,17545.72664 0,No,867.6294827,43091.93599 0,Yes,1231.025804,13363.97805 0,No,312.4154887,47118.99943 0,Yes,758.4480112,20936.06864 0,Yes,1035.823468,13959.42045 0,No,763.7352796,44125.71873 0,Yes,728.5435561,12475.46073 0,No,1066.296558,42247.77855 0,No,338.7252717,37017.10149 0,No,264.9424906,39317.57391 0,No,1167.325681,43337.13006 0,No,553.7428862,39653.98309 0,No,485.7928417,38281.31479 0,No,485.9448148,46447.40528 0,No,690.4383031,66807.9363 1,No,1588.526206,38014.56885 0,No,460.4301908,56002.2232 0,No,275.660215,31259.9782 0,No,336.9071196,41907.71615 0,No,307.693119,42220.85392 0,Yes,1431.077694,20576.77995 0,Yes,787.6182494,11928.55523 0,Yes,1660.404013,18861.15466 0,No,0,41482.70163 0,No,1744.935803,43978.11235 0,Yes,758.3096627,12662.126 0,No,1173.462466,48396.50483 0,No,1335.541418,53354.84921 0,Yes,1434.849164,17459.03127 0,No,591.066666,43869.3159 0,No,862.7765105,49444.78465 0,Yes,1233.250011,16076.85426 0,Yes,911.7867777,20928.39244 0,No,0,43066.26756 0,No,286.928184,50618.72261 0,No,377.5344919,28045.59512 0,No,811.7924885,36874.61863 0,Yes,1856.605596,17845.33685 0,No,607.8379922,33737.17549 1,Yes,2182.348954,22037.85969 0,Yes,1406.567671,23050.88827 0,Yes,662.0906031,16310.79451 0,No,777.0162482,52797.93905 0,No,0,47236.97377 0,Yes,769.7114996,11262.63855 0,No,970.1847021,38784.45915 0,No,537.9266975,45615.19566 0,No,768.1548836,39701.93491 0,No,1150.040923,19536.16037 0,No,902.5572891,32765.38813 0,No,442.8275798,42004.19673 0,No,100.8668444,31389.45857 0,Yes,904.0563158,23188.71514 0,No,1396.263723,24379.07154 0,No,1001.331745,47640.737 1,No,1631.615617,38906.95641 0,No,790.7405179,45900.29736 0,No,1097.393094,46807.08168 0,No,0,37598.86279 0,Yes,1262.835385,12406.99895 0,No,738.1929286,54491.14267 0,No,655.2677484,28642.58257 0,No,0,32944.99582 0,No,638.5403332,20607.4593 0,No,570.9308876,46840.72126 0,No,1056.443662,31268.737 0,No,1522.855155,35805.25597 0,No,977.3215555,35296.28141 0,No,743.5650136,28893.87601 0,No,352.0332779,38259.22409 0,No,318.2094608,48975.36743 0,Yes,674.8509392,15798.1346 0,No,268.9543495,55892.72711 0,Yes,1023.205479,17694.9656 0,No,718.1569168,42172.00521 0,No,843.2908052,41692.42221 0,No,866.729785,36452.72212 0,No,449.949322,38987.02486 0,No,459.101423,45043.6664 0,Yes,760.2195299,17657.98551 0,No,1160.827632,26022.95259 0,No,1317.512987,49285.54547 0,Yes,561.7818671,18470.28261 0,Yes,176.5338346,15175.58604 0,Yes,1059.236341,12601.47121 0,No,526.7984896,28491.4805 0,No,20.96450385,62862.75183 0,No,528.9424635,42134.27063 0,No,1561.53353,54594.84671 0,No,963.7228112,47605.76859 0,Yes,1227.365273,12128.14817 0,No,1209.377359,35071.31662 0,Yes,671.1758688,17564.95475 0,No,275.6246467,35761.3165 0,No,776.188873,34140.98039 0,Yes,699.303115,19789.53066 0,Yes,606.1251346,16957.64876 0,Yes,1231.212923,20000.06255 1,No,1408.438085,48012.91357 0,No,338.4954707,34332.84324 0,No,1380.48149,24856.64306 0,No,1316.42181,45806.90151 0,No,1046.182045,48752.13176 0,Yes,904.684255,17281.11917 0,No,1281.520648,37665.29394 0,No,0,46597.49508 0,Yes,501.1116447,22393.27308 0,No,1085.95015,36532.71876 0,No,351.9451074,48726.04767 1,No,1803.170259,36192.63022 0,No,666.622349,44931.93976 0,No,154.703584,33162.19321 0,No,360.0891792,44448.9158 0,No,105.4410038,35291.46335 0,No,492.4282925,25848.9885 0,Yes,1232.422469,24093.29644 0,No,1154.398529,20457.22078 0,Yes,1113.326964,14579.65698 0,Yes,699.6980374,22590.66798 0,Yes,1001.785683,24771.72694 0,Yes,1217.226947,16449.999 0,No,1156.933998,45325.34097 0,Yes,1637.450714,18766.85518 0,No,824.2414326,53320.28294 0,No,662.3428972,49885.01912 0,No,617.6407642,37604.93356 0,No,271.4519112,34863.1042 0,Yes,688.1812417,18755.95323 0,No,797.7091656,39677.69721 0,No,464.4043311,53215.93158 0,Yes,976.2022175,23191.7707 0,No,1430.766147,45672.51997 0,Yes,1208.144482,17721.29366 0,No,0,41960.1745 0,Yes,402.3587995,15256.48418 0,Yes,824.5143779,15309.27621 0,Yes,1060.878824,11041.3315 0,No,310.4959193,47427.32909 0,No,691.4184986,40232.65666 0,No,23.15226669,42016.89909 0,Yes,913.2761576,20446.203 0,No,970.8164382,35835.35508 0,No,1148.447538,39662.12241 0,No,1638.687046,38441.15803 0,Yes,0,19134.44475 0,Yes,1448.950207,28273.83539 0,Yes,1286.839756,17464.99353 0,No,762.0735996,48268.59139 0,No,977.5765275,51074.21274 0,No,368.7680524,45647.29761 0,No,267.9593388,46134.62266 0,Yes,503.1563883,18728.29694 0,No,0,48523.33555 0,No,282.6028771,56118.3224 0,No,748.0570433,47775.18082 0,No,999.6540136,27564.68871 0,No,494.660496,47035.75468 0,No,1022.682388,22588.55547 0,Yes,1135.217871,19062.59687 0,No,707.8023303,40140.80045 1,Yes,1954.321689,17137.47372 0,Yes,1282.350409,7230.030471 0,No,808.6169215,23519.67255 0,Yes,1219.933751,21765.38212 0,No,481.8804217,44137.0795 0,No,409.4117491,53624.45775 0,No,1157.277347,31369.49194 1,No,1424.93289,27668.95224 0,No,0,58825.61513 0,No,687.0691307,34434.21823 0,No,1435.662933,31507.08928 0,No,188.544102,49697.23179 0,No,1084.387499,34558.61578 0,No,644.6185908,37452.06721 0,No,1345.563131,38602.11298 0,Yes,733.7194635,27165.47998 0,Yes,744.1780148,20773.6274 0,No,0,29542.0254 0,Yes,1395.961882,23717.85111 1,No,1920.242332,57242.98341 0,No,538.4132679,40773.94146 0,Yes,385.6982623,18352.43625 0,Yes,1350.757577,19692.91878 0,No,1567.763856,39201.24186 0,No,753.6868883,42230.64777 0,No,72.37195337,28717.94062 0,Yes,675.0360244,21115.79312 0,Yes,980.16435,13713.50223 0,Yes,910.3836047,19163.80699 0,Yes,1262.884299,14340.50794 0,No,621.3127192,39372.07719 0,No,643.4026386,50208.82372 0,Yes,728.2592917,13438.98556 0,No,459.2602277,59731.28878 0,No,289.6166642,49066.83365 0,Yes,784.7466141,11642.92229 0,Yes,712.5947732,18441.02612 0,No,1096.003601,52030.9726 0,Yes,435.4855311,19899.75457 0,No,688.0224701,29019.81789 0,No,599.4283586,36654.64817 0,No,68.75629448,45811.74294 0,Yes,765.1369986,14290.26763 0,No,1273.124571,57688.56805 0,No,1078.621061,46060.08477 0,No,1677.348956,44972.70986 0,No,531.7561839,42365.07367 0,Yes,631.1726124,11075.89406 0,No,1081.478534,34072.64381 0,No,368.5153456,60953.82479 0,No,343.7269991,38054.5554 0,No,1715.282689,60137.63368 0,No,450.3355798,20080.10212 0,No,740.5807734,20717.54444 0,No,0,26237.47434 0,No,475.8690387,35621.69112 0,Yes,1383.745059,14633.98588 0,Yes,406.4823849,22708.90108 0,No,857.2615657,34406.61082 0,Yes,1637.235201,16751.16975 0,No,1094.315872,34854.34239 0,No,1213.127074,47848.0479 0,Yes,1074.746406,22729.30865 0,Yes,1010.212633,13469.86665 0,No,416.1282864,21960.62456 0,Yes,675.8413465,17801.47436 0,No,0,36338.8485 0,No,867.3041732,29541.04315 0,No,843.9073034,23956.51937 0,No,766.43043,39078.797 0,No,1286.537233,46337.12389 0,No,1142.935154,34401.66711 0,Yes,925.4112876,15120.33562 0,Yes,1110.453823,14965.43739 0,Yes,938.6059755,24392.10404 0,No,321.0397429,37026.58716 0,No,943.3876407,39415.59565 0,No,511.5987702,54002.51166 0,No,743.8495986,24303.46763 0,No,1089.577061,46890.2154 0,No,0,46275.36347 0,No,0,31511.65746 0,No,1282.321247,37437.71096 0,No,1373.796792,36888.92841 0,No,1228.3343,43577.66949 0,No,1536.788312,34734.86388 0,No,735.4704341,53312.53053 0,Yes,1142.427605,26114.40315 0,No,1490.715076,36417.40004 0,No,880.0328325,60203.88156 0,No,2.84301484,39923.8193 1,Yes,2066.695603,10470.636 1,No,2343.797513,51095.29393 0,No,929.7454817,36071.52426 0,Yes,764.6277907,26188.0695 0,No,504.6044952,31870.01829 0,No,685.2069414,40555.79988 0,No,473.027458,43016.86073 0,No,1302.553499,32235.05245 0,No,435.0041342,30828.86039 0,No,601.8385009,37697.37005 0,No,456.7636577,50160.6337 0,No,1065.042918,37747.10405 0,No,1153.331896,37936.08708 0,No,59.63300148,35709.73846 0,Yes,1106.430674,16960.69412 0,Yes,1106.453525,14840.93813 0,Yes,385.430994,21619.66341 0,No,0,45025.51165 0,No,727.957482,53606.97314 0,No,1028.777609,36276.08514 0,Yes,1035.916689,10607.90614 0,No,974.5977187,25316.85875 0,No,772.8092199,39944.6072 0,No,1014.973979,41993.64061 0,Yes,0,10276.25611 0,Yes,1403.789341,22893.96052 0,No,580.4233413,25926.23509 0,No,499.5507444,45954.67124 0,Yes,748.2362054,11613.22295 0,No,568.0939695,31392.06695 0,No,1366.725774,35425.51208 0,No,511.6788941,32693.85088 0,Yes,678.2178753,19979.69399 0,Yes,1692.42056,7761.342904 0,No,631.8045979,32752.51926 0,No,585.0500546,34297.56527 0,No,1173.490792,44134.74487 0,No,544.2374276,43306.62114 1,Yes,1985.321739,22248.40266 0,No,1011.060904,38001.7121 0,No,1231.214517,48156.35756 0,No,875.842483,45524.5322 0,No,1516.065384,59194.23921 0,No,563.1212611,30226.48272 0,Yes,1244.566927,21717.47587 0,Yes,1215.643468,16403.68576 0,No,1077.747127,30133.25763 0,Yes,1393.916368,16658.48308 0,No,803.5700674,45353.97068 0,No,916.7492378,58469.93356 0,No,147.0493336,52074.5107 0,No,734.4727806,46805.55302 0,Yes,996.205915,13647.53574 0,No,1883.200207,47438.25571 0,No,1271.115698,40578.31619 0,No,173.7334181,32480.53398 0,No,1004.187665,37132.65721 0,Yes,2033.362496,18549.74539 0,No,269.3578618,40883.18547 0,No,441.701436,44227.91027 0,No,1471.608629,61385.59987 0,Yes,1133.822004,16602.02802 0,No,843.3526611,51736.91025 0,No,250.4683325,42309.08282 0,No,1569.728273,30604.28645 0,Yes,995.3471118,11690.74126 1,No,1572.34559,37895.17307 0,No,1570.416179,49501.81038 0,No,645.931623,39695.62726 0,No,1001.853011,32748.63094 0,Yes,905.2312955,15699.89846 0,Yes,859.3796898,19287.5037 0,No,1670.850285,54442.2662 0,No,391.2566277,33651.40556 0,No,670.0082484,35919.79677 0,No,1036.053835,44869.46019 0,No,481.9894913,19240.31429 0,Yes,542.7692556,17664.88143 0,Yes,887.2889243,17409.03112 0,Yes,1465.849554,17678.73631 0,Yes,1545.63746,13959.99367 0,No,262.7913362,28974.75055 0,Yes,793.7963758,21172.40412 0,No,1192.766158,45902.50755 0,Yes,1120.320878,29045.4068 0,Yes,514.1994978,21482.03481 0,No,710.7604275,53050.48538 0,No,1176.243271,27956.7829 1,No,1805.559588,40119.78783 0,No,124.8284595,39478.30007 0,No,1056.993239,45489.89668 0,Yes,904.9946707,19108.74233 0,No,1402.120138,41275.64547 0,No,7.069530292,48711.63749 0,Yes,733.7833904,16124.35425 0,No,612.960653,43392.88709 0,No,998.704699,46697.77777 0,No,1078.457001,33001.12651 0,No,638.4763864,53034.55233 0,Yes,890.0481045,15156.91913 0,No,815.6821325,53817.27975 0,No,1276.98265,40611.34869 0,No,671.1857708,44120.64174 0,No,818.2843466,48490.81219 0,No,1371.330899,35820.62983 0,No,897.4957561,47816.41161 0,Yes,699.6964224,17318.88187 0,No,848.9407381,35534.98389 0,No,0,27297.90952 0,Yes,1660.861064,13694.85645 0,No,1475.503847,44769.12214 0,No,0,22431.45354 0,No,1186.964356,33439.8364 0,No,844.4038026,36854.63634 0,Yes,1540.308824,15241.01588 0,No,679.7075809,29534.82146 1,No,1650.578,31514.25965 0,Yes,614.7838595,18487.23371 0,No,1379.678044,31766.2936 0,Yes,770.1055385,15284.71993 0,No,1209.540282,40805.77763 0,Yes,1380.969819,13633.43347 0,No,1349.106443,28340.43736 0,Yes,1051.393853,23520.426 0,Yes,428.4258221,15446.95803 0,No,1714.18603,52779.08882 0,Yes,1647.801283,15392.21298 0,No,482.9758188,47924.67664 1,No,2124.671313,44520.0001 0,No,297.8382216,34549.46263 0,No,1201.348042,35858.40429 0,No,811.2419476,26448.3285 0,No,0,36201.53887 0,No,585.8041768,30143.32851 0,No,511.321855,38510.16091 0,No,1249.194174,45586.89022 0,No,48.96555026,27069.73978 0,No,863.5747099,32889.38396 0,No,1579.359465,50216.57163 0,No,800.6093328,50324.55493 0,Yes,1356.534691,19480.54377 1,Yes,1704.578529,16887.40514 0,Yes,528.5856887,19621.62922 0,Yes,1390.507666,15885.51078 0,Yes,333.5411962,27321.55783 0,No,1569.556299,36306.01944 0,No,1509.780887,43650.41851 0,No,961.2415545,31647.28748 0,No,1443.507447,47205.34866 0,No,0,37605.37939 0,No,895.3959518,48939.73566 0,No,1355.866843,45537.95492 0,Yes,960.1139856,24326.06027 0,No,1164.4764,36997.71898 0,No,535.1829158,37042.76649 0,Yes,1424.772927,21852.92988 0,No,773.1724915,50069.20882 0,No,685.1925696,33185.01044 0,No,343.7688061,51626.13569 0,Yes,1513.541568,23556.6905 0,No,415.0036341,37859.31446 0,No,1034.36018,25111.21255 0,No,1115.896008,21646.30595 0,Yes,748.7464309,17440.62655 0,No,291.4087289,54408.78898 0,Yes,453.9297986,12665.307 0,No,889.565857,49941.08863 0,No,345.1906244,48111.44622 0,No,393.828578,28362.62463 0,Yes,991.0939963,16842.43614 0,No,4.466457821,45157.90962 0,No,660.0589187,39163.32727 0,Yes,1168.868403,18113.34377 0,No,528.8673882,38881.47626 0,No,89.33404767,36515.98159 0,Yes,196.7767287,15858.3533 0,No,1637.347759,37676.66312 0,No,574.9553577,33517.21103 0,No,969.134944,29705.04132 0,Yes,1049.241311,23332.48384 0,No,772.5312697,23222.73977 0,No,1304.068285,26219.32314 0,No,875.8752694,37160.9608 0,No,587.0745299,34975.97647 0,Yes,495.136133,15519.48512 0,No,131.6339983,42028.0086 0,No,1333.182128,19396.03188 1,No,1819.249095,49621.05617 0,No,953.8160755,45281.36985 0,No,327.0385825,51348.93727 0,No,989.63631,27318.94371 0,No,1109.221483,56680.13753 0,Yes,0,9321.463734 0,Yes,635.394658,18344.07546 0,No,100.021825,48377.64128 0,No,264.9680541,36842.82266 0,No,465.8456638,52714.08886 0,No,1227.2618,49697.04618 0,No,1378.677643,30862.99636 0,No,715.6567217,23266.13569 0,Yes,0,21719.45483 0,No,1335.230281,43660.39171 0,No,1576.903647,35117.80535 0,Yes,1232.590353,22108.58037 0,No,1613.714114,47151.60976 0,No,763.7431661,59856.79667 0,No,741.430159,32951.17936 0,No,325.5865499,45809.98424 1,No,1797.767019,47068.72461 0,No,899.9027004,47093.73838 1,No,1605.214586,26149.14822 0,No,0,42879.39694 0,No,886.6890235,50507.93081 0,No,1008.290635,30801.08975 0,Yes,1034.504453,19105.62822 0,Yes,1125.734507,23410.09941 0,Yes,1070.950734,12707.58041 0,No,920.4084978,40536.64248 0,Yes,734.7331649,10816.94977 0,No,1701.688487,38567.20471 0,No,1453.316173,29830.05674 0,No,294.0243461,49988.14731 0,No,1302.479132,43154.39293 0,No,1135.187984,47075.0323 0,No,1123.633139,17292.30317 0,Yes,483.1097993,15067.31371 0,No,1669.502687,30259.25637 0,Yes,1051.376889,13271.46469 0,Yes,778.3409168,23737.26676 0,No,37.99084851,43787.7819 0,No,599.8796698,51733.82517 0,Yes,1037.642867,22889.48898 0,No,1187.463608,37092.59041 0,No,1287.205895,37135.36394 0,No,599.3803801,58909.34426 0,No,278.0784558,33811.66921 0,No,536.7008086,35786.51482 0,No,626.5147862,55155.07786 0,No,0,54298.85961 0,No,885.5100067,42683.47973 0,Yes,1314.841724,15965.41501 0,No,580.164721,31879.13893 0,No,1144.530511,40998.92734 0,No,0,55435.43055 0,No,321.3085814,33136.33648 0,No,776.3958736,49705.81618 0,No,519.3086916,42861.93266 0,No,892.2810718,40492.10058 0,Yes,945.0549545,21095.53818 0,No,840.2368475,24801.36811 0,No,660.5336718,31480.04082 0,No,213.010074,38950.60007 0,No,506.8241417,50493.09526 0,No,884.6606716,35324.02022 0,Yes,296.462756,20138.24699 0,Yes,700.0753377,14828.55059 0,No,927.468685,44308.28232 0,Yes,1172.273498,22792.62169 0,No,841.5262496,40239.6774 0,No,49.6823973,47026.03855 0,Yes,903.9166521,20246.39323 0,No,600.1827009,50578.05423 0,Yes,1749.122184,21359.13155 1,Yes,1893.023004,16853.32957 0,Yes,1636.519725,17533.3422 0,Yes,1533.911936,16453.88308 0,Yes,1523.722571,15708.95773 0,Yes,1156.6202,11102.56815 1,Yes,2168.453196,24648.97992 0,No,456.8307381,39629.58084 0,No,1616.168586,39654.61886 0,Yes,1311.860099,15565.1977 0,Yes,437.0682956,21638.94784 0,No,683.1146156,31615.36555 0,Yes,1327.282439,15819.35932 0,No,797.6309421,54042.36225 0,No,635.7076076,26084.69605 0,Yes,1312.88532,16221.20289 0,Yes,944.8328467,23323.82273 1,No,1838.871369,32129.33982 0,No,267.3723188,41183.65749 0,Yes,723.5404147,28289.23591 0,No,610.0521709,35855.38793 0,No,1861.317697,46653.58874 0,No,0,34882.22855 0,No,1577.972544,50430.23981 0,Yes,954.1503905,15969.81997 0,No,204.3961996,38979.62388 0,Yes,1109.468217,21663.13247 0,No,771.2248776,47146.43951 0,No,376.350444,32168.44216 0,Yes,1523.880095,19342.98352 0,Yes,1178.211737,12077.01326 0,Yes,1457.575381,19668.15776 0,Yes,1292.447316,8231.037423 0,No,1233.502222,23031.60089 0,No,542.1405751,22394.1911 0,No,1381.819269,48394.28962 0,No,1157.768113,49960.76521 0,No,744.4938745,24506.6782 0,No,1387.490549,40993.59534 1,No,1434.127716,47311.53042 0,No,1483.536563,34156.3742 0,No,1340.893192,31959.9656 0,No,972.0944016,26231.14591 0,No,1511.879387,36381.23765 0,Yes,1111.997722,20549.62035 0,Yes,1555.563352,15957.32883 0,No,1727.365146,41707.4773 0,No,25.7358489,49089.60408 1,Yes,1895.334933,16394.50811 0,Yes,417.478257,17787.94782 0,No,247.1990572,37377.35782 0,No,91.38769809,44091.876 0,Yes,617.089796,22801.66397 0,Yes,793.3337851,10487.93018 0,Yes,1035.720966,20393.27002 0,No,826.9817504,35426.1808 0,No,1734.451564,31578.00495 0,No,1069.62206,40299.31991 0,No,626.8384282,41334.18353 0,No,932.0569663,45505.30722 0,No,1551.027731,31768.03061 0,Yes,315.0902684,22551.89125 0,No,1059.351605,41179.01002 0,No,976.7040479,59291.72257 0,Yes,562.5589369,11524.81035 0,Yes,310.0202771,16382.15293 0,No,936.944765,40240.49711 0,No,0,43033.49225 0,Yes,431.1970473,20301.49367 0,No,1411.548718,42956.63139 0,Yes,1183.452815,16189.28839 0,Yes,1849.603666,16966.88592 0,No,391.4820793,45821.63323 0,No,10.88494131,32866.44524 0,Yes,697.2486326,25730.91758 0,No,175.054337,22644.99588 0,Yes,1094.740579,19638.26543 0,Yes,134.6240411,19464.29871 0,No,1069.748332,32441.28198 0,No,1165.812599,47005.57329 0,No,321.2181021,43257.35349 0,No,178.2750321,35615.92811 0,Yes,953.8584818,18121.11336 0,Yes,1335.812707,12843.44803 0,Yes,853.5943254,25373.46219 0,No,639.6256747,38660.92322 0,No,1051.847749,37160.2733 0,No,335.1071558,36374.21438 0,No,1193.762792,34909.30888 0,No,915.2030835,31422.94555 0,No,426.0870274,15498.86947 1,Yes,1805.618353,19168.02997 0,No,717.4789564,37810.47213 0,No,520.9472873,41039.28245 0,No,1034.892431,34539.84576 0,Yes,1022.598336,11739.09598 0,No,617.5914405,43871.69796 0,Yes,1322.162293,16984.19826 0,No,476.5913949,33760.45831 0,No,1101.091224,43035.56208 0,Yes,1019.579112,15833.79065 0,No,329.4482726,43242.87537 0,Yes,949.2338248,25040.71234 0,Yes,1230.714628,18581.27461 0,No,1252.653058,35382.14033 0,No,0,47019.66323 0,No,642.2693048,37752.38459 0,No,512.5135258,44178.8405 0,No,1114.901552,38155.36839 0,Yes,1742.346535,10778.15487 0,No,763.0223125,34931.34829 0,No,878.4447309,47722.81035 0,Yes,936.2295509,14217.40152 0,No,585.2377164,40585.74654 0,No,347.6140996,40138.0778 0,No,607.8986423,26117.66517 0,Yes,1251.085756,21968.26999 0,No,316.5364349,39995.3188 0,No,336.7970403,42880.4643 0,Yes,871.0667958,13144.36558 0,No,1187.904686,43873.98291 0,No,86.01278996,43467.50265 0,No,737.041709,27879.65685 0,Yes,1419.870721,10401.04666 0,Yes,402.5033656,12255.87074 0,No,191.2150007,41921.68285 0,Yes,1836.780086,15422.23216 0,Yes,1273.186248,20429.62137 0,No,1037.600637,40415.82298 0,No,827.5428855,39655.67701 0,No,915.9135902,34934.48239 0,Yes,212.7086441,13155.67206 0,No,941.1681796,58991.86151 0,No,487.4168943,34439.90331 0,No,1889.524775,32509.61626 0,No,691.2022794,38948.10536 0,Yes,502.6458212,14425.08417 0,No,140.0185757,52099.1163 0,No,0,36484.59415 0,No,519.4614942,44498.01559 0,Yes,1031.869798,19322.55235 0,No,939.6693901,47112.7653 0,No,0,44902.92935 0,No,1093.684902,39684.45193 0,Yes,1623.336605,14338.40878 0,No,851.2040106,44905.32531 0,No,1119.702648,33510.35718 0,No,0,32720.5717 0,Yes,744.9142665,15711.71642 0,Yes,303.3458684,15220.59932 0,No,1522.755927,29165.44319 0,No,361.3676099,36533.71036 0,No,1062.727927,48041.6494 0,Yes,1344.094679,22012.29315 0,No,387.7319185,55163.79694 0,No,860.0282429,56687.14354 0,No,1637.357955,46629.94697 0,No,975.1985123,30573.25805 0,No,487.7974797,21418.69468 0,No,0,49626.41562 1,No,1562.454585,43067.33374 0,No,1399.126163,26505.02013 0,Yes,542.064066,9135.886774 0,No,901.0757265,38613.42665 0,No,295.8705689,19054.0305 0,No,90.56141029,50939.52115 0,No,476.1499315,58837.72311 0,No,382.8850624,42620.53023 0,No,1013.963746,49653.79713 0,Yes,932.6857989,15774.70106 0,No,360.5749488,42217.38292 0,No,727.9636084,40258.70172 0,No,917.9527564,31380.23317 0,No,1567.165357,38255.05057 0,No,1006.680488,33348.57536 0,No,1278.932666,42894.7653 0,No,0,56493.48609 0,No,1107.487867,23530.62273 0,Yes,1719.566851,18696.29875 0,No,1066.174662,33911.56487 0,No,0,30172.81059 0,Yes,1296.103527,15709.45579 0,No,720.2445985,38493.49193 0,No,574.6382233,41914.46952 0,No,241.4300888,34507.77593 0,No,345.1421431,41943.77284 0,No,1341.703442,53122.06896 1,No,1847.791373,30202.84624 0,Yes,1497.067767,9030.402564 0,No,503.39124,29187.44412 0,No,1119.575449,42280.80903 0,No,1172.415172,58067.12982 0,Yes,1594.384586,21514.6028 0,No,566.4438526,36039.39407 0,No,542.4701592,39037.80101 0,No,1086.420939,38656.66313 0,Yes,995.0394301,8750.5588 0,Yes,1200.875976,13833.9822 0,No,0,56895.96198 1,No,1508.649243,37111.3089 0,No,1170.547946,52358.80093 0,No,1356.743836,54489.93878 0,No,850.0364106,31522.23465 0,No,1184.140383,25196.76179 0,No,902.7136645,23847.39229 0,Yes,1597.890914,24006.18039 0,No,1079.076123,36636.28005 0,Yes,1503.578445,18776.53385 1,Yes,1570.65878,16239.15317 0,No,0,41655.68226 0,Yes,735.5783648,13976.68591 0,No,923.3462318,44670.86424 0,Yes,1269.678223,13998.30673 0,No,706.4819315,48339.87899 0,No,1146.875398,64644.90529 0,Yes,1749.549609,20078.36188 0,Yes,263.49132,25363.59525 0,No,468.8699085,44285.32011 0,No,443.9130878,35241.99297 0,No,375.720573,35564.41027 0,No,772.8229977,25556.75717 0,No,750.1395372,17993.82049 0,No,414.8042587,37825.37218 0,No,626.1752277,33930.97505 0,No,645.2012091,44324.22436 0,Yes,1287.287235,13215.97025 0,No,1391.261154,51169.36221 0,Yes,601.8046898,16266.3026 0,No,1061.310874,33942.61975 0,No,272.7504686,42646.70489 0,Yes,1421.740129,11134.9396 0,No,1065.626806,36015.68758 0,No,1167.035059,34467.54286 0,No,949.1290849,28411.65649 0,Yes,565.9887284,18645.93665 1,No,1914.106962,55573.70171 0,No,515.9428051,50678.33694 0,No,1347.46071,32720.44574 0,No,124.3059108,44464.63585 0,No,1007.220296,42183.08973 0,No,218.3379599,44179.9465 0,No,1344.55595,34841.26052 0,No,1063.219526,27887.25053 0,Yes,624.5431657,14820.46719 0,No,559.8320113,39144.47684 0,No,341.3868405,29326.33924 0,No,574.1644925,44072.0608 0,Yes,734.8982568,11868.07421 0,No,875.2953348,46041.75628 0,No,366.0908026,47096.37807 0,Yes,1009.993872,19870.53658 0,No,221.9224906,32988.57136 1,Yes,1957.120295,18805.95213 0,Yes,1050.431291,19246.33309 0,No,612.3249889,55326.41284 1,No,1456.96393,49054.19389 0,Yes,1821.523917,15802.38929 0,Yes,770.3280295,29059.14469 0,Yes,1269.906274,771.9677294 0,Yes,1923.381941,12940.22783 0,No,890.7594972,20257.37999 0,No,1341.054788,33778.16204 0,No,888.2650394,37745.60517 0,Yes,1189.313959,19545.72712 0,No,1820.540494,46251.44869 0,No,481.8409362,36377.64743 0,No,432.0920648,56732.73728 0,No,683.9971778,36578.18465 0,No,354.7019532,44412.19392 0,Yes,669.7372373,17109.63048 0,Yes,713.9228744,26049.97564 0,No,597.0416087,31713.09769 0,Yes,1306.416879,18996.35032 0,No,1991.135514,30809.67457 0,Yes,717.6328948,19590.971 0,No,390.6066769,38083.5473 0,Yes,1862.538659,23839.57252 1,No,1143.680503,37751.01734 0,No,264.372357,45778.30895 0,No,216.8465333,37160.9411 0,No,686.8929286,51120.82998 0,No,1642.192319,24444.31218 0,Yes,0,15587.9006 0,Yes,1218.649212,19206.13346 0,No,1970.859761,37905.25263 0,Yes,1151.733317,23149.54749 0,No,539.0777208,36275.45572 1,Yes,2287.173842,18692.14431 0,No,1218.245802,56816.80894 0,No,700.4531369,54661.11507 0,No,1167.231679,30648.4288 0,No,1492.615028,46174.69799 0,No,666.3947948,34912.3367 0,Yes,636.8988095,23005.57595 0,No,420.727023,37831.57008 0,No,879.0526491,30633.84194 0,No,376.2411614,25821.16106 0,No,961.4024541,25478.81835 0,No,500.7285411,33406.05411 0,Yes,369.6799323,24834.41758 0,No,719.938044,31031.2194 0,No,1168.347005,39275.19354 0,No,600.5380969,39260.38212 0,No,0,18593.91474 0,No,252.9448968,23443.41646 0,No,676.4308888,53378.98852 0,Yes,1066.822188,26348.30364 0,Yes,1004.650577,13869.7521 0,No,1010.421141,46217.80956 0,No,1260.931294,27697.33825 0,No,402.6324273,58828.79225 0,Yes,919.4754847,6100.733202 0,No,1004.653816,42729.18223 0,No,614.5385663,33811.98762 0,No,1358.109779,50662.89279 0,No,160.5248787,29675.83549 0,Yes,1190.188829,15860.32668 0,No,262.5992604,48762.28355 0,No,647.9180241,35494.48529 0,No,1574.651282,30559.5137 0,Yes,1658.602257,14821.00456 0,No,4.814908682,28714.13538 0,No,1220.312282,36190.99361 0,No,802.8571022,45806.19917 0,No,81.87448947,35818.228 0,Yes,70.73361201,17466.13896 0,Yes,962.9057964,22795.0325 0,No,926.694301,44233.64617 1,Yes,1233.445895,12586.47819 0,Yes,644.1117175,9778.219986 0,Yes,1338.659659,8078.176047 0,No,740.3867859,46877.96074 0,Yes,901.1865026,19204.70162 0,No,975.3887418,45597.93769 0,No,298.7196152,49585.87127 0,No,591.4980139,29507.48706 0,No,177.0144011,37553.80181 0,No,292.2719769,60582.22621 0,No,625.1918331,32826.40965 0,No,546.9866501,37513.70038 0,No,727.6753323,23515.61089 0,Yes,531.9764082,21357.63216 0,No,311.2127675,51257.87429 0,Yes,703.0630696,17692.65839 0,No,1239.683068,52241.45566 0,No,810.2788394,51283.14786 0,No,303.4826916,21381.31449 0,No,1372.035706,36188.23978 0,No,1146.375563,37053.82846 0,Yes,730.9627524,20097.40094 0,No,807.2192163,42138.65307 0,No,450.4055188,34206.27365 0,Yes,467.9127389,25424.41007 0,Yes,684.8910355,17582.98554 0,No,1013.469261,38280.91701 0,No,676.6527202,31741.43106 0,No,287.9668874,37409.97776 0,No,816.6489096,52163.78155 0,No,1118.789834,39111.79234 0,No,532.1536093,43789.33519 0,Yes,1364.232684,18423.04766 0,Yes,1464.690871,16542.04507 0,Yes,997.642404,27054.72741 0,No,335.3400825,41942.75251 0,No,1149.012958,35310.12955 0,Yes,905.5657992,24032.80055 0,No,649.6732695,32276.42914 0,No,380.1891882,40689.92044 0,No,0,48303.87151 0,Yes,485.4343175,21278.34081 0,No,649.8419855,54693.15179 0,No,698.5724534,44949.26908 0,Yes,1328.953669,20666.70281 0,No,609.4776899,41798.29235 1,No,1723.187215,43143.16449 0,No,0,36581.8039 0,No,594.6012612,36190.75639 0,Yes,875.7129441,18696.95358 0,No,949.3149767,50404.27876 0,No,252.3767271,39723.48373 0,No,1162.567975,21753.52835 0,Yes,1244.359563,19232.04744 0,No,578.4955654,46030.88128 0,No,443.9009529,50768.44487 0,No,1316.089253,41314.16952 0,No,904.4240809,56062.01653 0,Yes,1037.974595,15580.76888 0,No,842.9856646,42682.14201 0,Yes,1258.528735,10920.96459 0,Yes,297.3907381,19383.79115 1,Yes,1553.349657,18091.94374 0,Yes,684.9353812,21908.57159 0,No,1265.501264,32910.39744 0,No,362.6111511,39296.07799 0,No,1532.916209,22080.92177 0,Yes,1341.615739,26319.01559 0,Yes,1086.161108,23576.78673 0,No,496.0017626,32818.15707 0,No,1597.052084,50827.80754 0,Yes,2092.45853,14514.76996 0,Yes,1019.984293,23795.42248 0,No,765.2338783,29096.41726 0,No,928.9649696,46709.48028 0,Yes,1787.201938,17553.60725 0,No,628.1368037,38436.19145 0,No,351.820396,30513.51793 0,Yes,265.5039229,23775.43557 0,Yes,1886.55057,6467.149663 0,No,57.64704922,38218.18447 0,No,933.827036,57377.43278 0,Yes,996.2320726,10707.94266 0,Yes,1033.056283,15453.0337 0,Yes,824.5184007,20689.72422 0,No,358.6476417,56331.77877 0,No,1393.369463,43439.67407 0,No,332.4138259,37129.15118 0,No,1215.08895,39002.49696 0,Yes,1793.789405,22740.54856 0,No,874.174255,33821.90601 1,Yes,1893.942955,12399.12474 0,No,1119.613426,32213.22903 0,No,234.6993207,28098.89325 0,Yes,874.0171638,24635.31103 0,No,1283.228887,47514.99337 0,Yes,1326.558083,25853.14254 0,No,1263.798242,47203.82028 0,Yes,1145.279018,12906.20414 0,No,1118.851738,30558.38386 0,No,753.8322965,46957.28188 0,No,1039.23791,50653.91379 0,Yes,1947.072679,13157.95656 0,No,864.7774424,32120.29412 0,No,1165.851838,48855.50945 0,No,1101.438796,39045.99391 0,No,438.5165083,36838.88203 0,No,750.7200082,47876.89589 0,Yes,743.4154463,19610.17998 0,No,1371.351016,34955.4079 0,No,673.3842033,34891.54278 0,No,1208.220572,36402.74523 0,Yes,483.4205666,12596.93401 0,No,1394.079754,27179.42878 0,Yes,1405.317174,17933.23701 0,No,736.3926729,32026.96396 0,Yes,1167.34909,16356.93057 0,No,1031.869932,46725.37402 0,Yes,0,17189.3714 0,No,0,51226.76161 0,No,1334.950731,51086.44465 0,Yes,997.9362831,25788.36315 0,No,419.4445698,47155.69222 1,Yes,1806.921846,11506.21775 0,No,474.495396,43522.46238 0,Yes,531.3584198,11420.5687 0,No,880.5810244,49547.17602 0,Yes,1491.166105,18171.62223 0,No,976.9028073,32837.16555 0,Yes,642.3822859,13563.46329 0,Yes,1670.829991,11385.88027 0,No,1285.371279,39925.2034 0,No,199.5945257,27579.35339 0,No,66.51311342,29670.69883 0,No,577.5319564,22111.86691 0,No,1142.616307,42053.57047 0,No,767.1392121,49171.97818 0,Yes,851.3383722,22829.02478 0,No,683.9421408,35232.83518 0,No,485.4404311,38656.45463 0,No,0,49772.97475 0,Yes,915.1476615,21545.71736 0,No,1143.529562,38663.22863 0,No,365.2423298,27027.40806 0,No,661.1041871,42945.57029 0,No,964.7660193,49646.80657 0,No,1044.786889,41148.54006 0,No,201.8074002,71878.77264 0,No,655.6622473,54844.20473 0,No,10.90579922,48181.68915 0,No,528.4044347,41066.7319 0,No,728.3560585,32388.61576 0,No,938.3867636,18594.12798 0,Yes,359.9363755,26500.20874 0,No,1185.149365,32467.41774 0,Yes,811.0466954,21263.09914 0,Yes,1089.831454,15056.07787 0,Yes,288.3188143,18356.56525 0,No,351.9591074,29281.03071 0,No,977.1955021,58658.75015 0,No,919.6724049,40573.17945 0,No,800.2433379,19051.324 0,No,771.8254969,40520.8094 0,No,585.869313,67100.68521 0,No,1077.812379,26518.08028 0,No,907.3067584,28461.29374 0,No,838.017911,42232.94151 0,No,1006.934931,25733.34781 0,No,712.4120489,42352.09149 0,No,508.4532442,31966.40088 1,No,1476.509782,42984.41285 0,No,1224.471239,37088.0532 0,No,636.309344,50040.41979 0,No,1609.698815,60123.6273 0,Yes,885.212532,11774.41657 0,Yes,137.0393722,10994.7941 0,Yes,988.8956298,19449.17918 0,Yes,563.2901332,22323.83355 0,No,309.7823539,33671.56261 0,Yes,821.2572882,17347.43946 0,No,434.1899042,42205.26489 1,Yes,1321.535379,23735.15989 0,No,549.8538372,50084.38951 0,No,1170.019022,48445.21687 0,No,1238.371887,42321.52715 0,Yes,0,15698.98123 0,No,803.946256,42286.28782 0,Yes,1456.005961,15069.88289 0,No,954.33059,27180.54596 0,No,1224.930711,44002.65453 0,No,796.3908026,47600.49452 0,No,461.0262193,15755.64723 0,No,468.2857768,23336.03001 0,No,321.0127629,30275.54188 0,No,1653.434709,44965.03236 0,No,1190.536219,41630.28283 0,No,178.0473468,35401.70897 0,Yes,1447.856782,24669.84203 0,No,0,56043.91022 0,No,282.3076802,38099.14331 0,No,28.73880255,41809.20418 0,Yes,1279.316806,17837.03455 0,No,0,36061.70076 0,No,1470.348149,49018.80185 0,Yes,405.5682901,20523.91231 0,Yes,509.1722947,19962.81788 0,No,1207.150842,29875.37904 0,No,546.3750599,37524.43432 0,No,86.91865797,34638.9039 0,No,418.8238889,56320.63386 0,No,0,56412.00753 0,No,41.65143751,47522.02367 0,No,906.7458077,38881.01107 0,Yes,1499.856349,25156.73644 0,No,1561.631319,32952.84077 0,Yes,1615.504802,19566.15796 0,No,766.0960211,41435.5919 1,Yes,1807.705844,21849.48163 0,No,594.9167826,43152.53324 0,No,1458.675647,57786.32312 0,No,901.288509,44594.34803 0,No,32.9935989,31644.77062 0,No,1050.268525,59401.19567 0,No,1329.096762,60321.61321 0,No,410.9356245,39076.27282 0,No,1591.82829,27668.44177 0,No,561.9897454,40446.00878 0,Yes,592.1700026,18684.23564 0,No,624.6963964,30763.46512 0,No,1433.740497,39445.08409 0,Yes,635.3621561,13912.21853 0,No,982.9687181,50700.17386 0,No,877.4158794,22997.94503 0,No,433.4299707,49209.2564 0,No,951.3130442,25540.81596 0,No,205.0062644,36554.62653 0,Yes,1214.783347,11789.44984 0,No,974.8972056,26921.36588 0,Yes,470.1071813,16014.11331 0,No,0,30684.73055 0,No,1666.22423,34918.43253 0,No,798.6598947,17463.02135 0,No,286.2674216,47960.43821 0,No,908.5726294,48426.92744 0,No,78.74644144,41335.26248 0,Yes,1051.425965,6583.874438 0,No,1301.158296,28673.64598 0,Yes,1506.691675,20680.18265 0,No,0,29901.39942 0,Yes,0,15759.91517 0,No,182.8909632,21286.84999 0,Yes,1468.969355,15977.66255 0,No,1291.244338,42098.47895 0,No,608.9583159,24975.64641 0,No,713.7206102,17753.76732 0,No,1446.646704,28951.72738 0,Yes,889.292843,14273.29372 0,No,1219.63446,47936.30678 0,Yes,533.0075803,20556.41715 0,No,1024.143822,45585.96877 0,Yes,510.2960401,19682.56083 0,No,797.3475885,35177.40999 0,Yes,1686.608874,22175.1316 0,No,530.5473292,44627.56264 0,No,1332.531071,36974.6082 0,No,1401.34681,31403.06548 0,No,586.8113871,40353.35048 0,No,628.4195457,34756.24764 0,Yes,1193.307929,16430.01599 0,Yes,666.424384,19177.67937 0,Yes,1017.66712,13553.09143 0,No,648.3446128,18843.60623 0,No,952.138823,28642.3638 0,Yes,1492.065854,11615.98347 0,Yes,171.5988951,24809.11453 0,Yes,1231.281094,15716.53226 1,No,1833.646548,43539.78433 0,Yes,633.1704436,23694.32524 0,No,655.2077278,30593.14758 0,No,331.4070861,20588.17187 0,No,1213.362642,33527.48881 0,No,881.6498549,50114.70054 0,Yes,1799.361876,14250.19939 0,No,1165.936504,42252.26271 0,Yes,758.6734779,24166.7623 0,No,269.2901413,43778.73288 0,Yes,1043.880984,27380.47009 0,No,893.1145504,43511.65216 0,Yes,728.4671377,15958.747 0,No,413.7521931,44803.36028 0,No,97.26487584,37468.18023 0,No,765.0464862,44870.68429 0,Yes,1054.485054,10997.40407 0,Yes,649.530745,13408.66295 0,No,742.3840999,34265.36231 0,No,1016.463702,49635.10485 0,No,636.3043098,52011.06922 0,No,976.4285332,31897.56603 0,No,1197.818051,37487.65054 0,No,483.4548791,44281.34657 0,No,1239.492389,40312.23023 0,No,942.387355,34685.86796 0,No,712.1312457,48835.99289 0,No,214.9817904,46693.93774 0,No,558.3388574,38284.67553 0,Yes,1134.559731,20964.2127 0,No,523.6476518,37590.81994 0,No,593.1607864,46475.03132 0,Yes,868.7125196,13025.52498 0,No,607.7211365,41791.88774 0,No,1059.804101,64315.02621 0,Yes,0,29106.58374 0,No,1141.743817,45455.16307 0,No,1127.259556,47545.8468 0,No,456.9152179,42506.86315 0,Yes,385.3975215,13739.16614 0,No,705.9967717,53423.1498 0,No,742.5622239,48207.61508 0,Yes,1310.399734,24267.77776 0,No,670.9824681,55003.09132 0,No,441.7254656,39307.50404 0,No,1075.718431,38020.72359 0,Yes,675.2406919,18827.78189 1,No,1377.68002,41435.2695 0,No,302.3999089,32048.74216 0,No,910.2307775,46274.3436 0,No,812.5282385,58338.92578 0,Yes,1298.516266,18755.76764 0,No,679.8276669,43692.53639 0,No,1187.283928,56503.57295 0,Yes,813.1853163,24823.79917 0,Yes,893.7084711,16399.77852 0,Yes,894.6573347,23656.84181 0,Yes,0,18495.77982 0,Yes,1179.161205,16923.40249 0,No,1067.379034,56291.60201 0,Yes,1291.607142,16114.02428 0,No,119.4719259,47761.14999 0,No,343.7242848,53705.45963 0,No,1699.301572,29196.08185 0,No,1558.596985,38880.44487 0,Yes,1455.504623,13555.33569 0,No,1079.372744,66134.71789 0,No,508.1530596,52772.26145 0,No,965.0127556,32549.55151 0,No,553.6490192,47021.49182 0,Yes,920.7789671,14194.86915 0,No,637.3381071,49657.39428 0,No,744.4402059,40280.267 0,No,1529.152652,43826.29127 0,No,755.3569917,53091.51529 0,No,69.56179754,49074.2476 0,Yes,1567.211982,15877.78648 0,No,429.352533,17208.71403 0,No,1348.303554,39165.65917 0,No,1068.107972,37863.44785 0,No,1074.791401,25715.17745 0,No,395.884602,34555.82697 0,No,1047.13677,46567.92504 0,Yes,1518.62964,18925.49001 0,No,964.0462535,46501.9112 0,Yes,1099.542904,25314.84494 0,No,737.1769582,54160.85789 0,No,587.7538164,38885.65921 0,No,1177.088377,29949.49784 0,Yes,1182.000082,18102.83375 0,No,351.2888006,30345.6989 0,No,658.0236327,30391.63176 0,Yes,9.514032891,24581.97711 0,No,515.5802807,43509.04398 0,No,312.127697,37743.50812 0,No,820.6918521,28064.7322 0,No,1498.565036,39948.97799 0,No,1090.54797,20027.75639 0,Yes,717.3391896,18382.18719 0,No,631.5969082,30517.22942 0,No,1118.929803,49988.09133 0,Yes,496.5163137,12862.02656 0,Yes,524.1735101,8272.090321 0,No,1021.2201,47650.00253 0,No,749.8830655,28580.92192 0,No,181.7244389,36858.05867 0,No,1555.836184,30110.96029 0,No,895.746675,35531.83709 0,Yes,400.9045488,20912.28865 0,No,540.237504,32931.87538 0,No,403.8100343,40535.16617 0,Yes,1218.543165,14063.11689 0,Yes,1252.344171,17621.52959 0,Yes,384.5909324,21364.23009 0,No,319.030066,27401.11677 0,Yes,850.8392962,19413.20394 0,No,1072.822621,54527.97928 0,No,1637.319454,43364.95612 0,Yes,600.9563131,21613.91361 0,No,1014.725193,40223.99273 0,No,1311.451279,47805.79785 0,Yes,1084.54298,17410.84658 0,Yes,1302.086748,21949.27691 0,Yes,329.7858539,16476.98691 0,No,625.8463055,34245.84287 0,Yes,777.6427309,18190.64527 0,Yes,1161.881101,13499.83015 0,No,138.5885867,38437.99189 0,Yes,740.057882,24710.119 0,No,824.987977,42408.89554 0,No,1292.160161,39387.68824 0,No,528.0608252,34016.88875 0,No,708.851824,36322.34913 0,Yes,1066.194241,16736.43384 0,No,1072.572774,38483.46172 0,Yes,1315.476272,27959.64347 1,Yes,1988.869747,17762.86591 0,No,416.2293097,35580.55569 1,No,1721.434918,41999.70334 0,Yes,1706.760437,21127.48242 0,No,707.4436697,42656.64914 0,No,249.1121284,46332.36673 0,No,446.8616771,49645.34571 0,No,764.806803,33127.15599 0,Yes,824.1233648,25108.76629 0,Yes,786.9150381,12881.68933 0,No,308.0062759,37361.77514 0,No,1733.895361,47872.62098 0,No,996.8525108,49954.70408 0,No,0,52632.32989 0,Yes,1070.332099,20053.02853 0,No,0,39329.80818 0,No,1250.679653,37012.40758 0,No,1753.915146,43855.83313 0,Yes,425.4199728,18109.32069 0,No,1056.82442,37994.96955 0,Yes,1546.581832,14981.16867 0,No,330.3242892,55734.04574 0,No,1080.155564,35471.98678 0,No,691.1683584,42342.3507 0,No,584.2234605,38683.37488 0,Yes,681.9722899,19925.44439 0,No,999.4856455,37919.02126 1,No,1757.738499,51991.46349 0,No,1185.736827,24265.43592 0,Yes,1770.52557,17447.12692 0,No,149.82255,33948.86629 0,No,0,36708.35734 0,No,771.0160219,47614.73816 0,No,0,56982.50592 0,No,607.0946322,27145.63754 0,No,306.6580976,30135.6288 0,Yes,364.8266997,22122.52108 0,No,814.693522,39020.89769 0,No,1656.038047,29816.05017 0,No,125.9377001,44260.48182 0,Yes,1766.649774,15423.43728 0,No,1593.837919,30570.16108 0,No,0,30131.97581 0,Yes,210.8979227,19075.20696 0,No,449.1416064,40992.30273 0,No,1125.302093,33408.66034 0,No,1529.14314,43125.12328 0,No,483.6375266,47517.655 0,No,570.9834124,40580.90918 0,Yes,989.0542835,17276.4439 0,No,896.7344228,21627.45252 0,Yes,1533.898986,10488.4126 0,No,1357.693251,25389.83071 0,No,40.30111234,33656.71653 0,Yes,1014.891129,15253.3116 0,Yes,594.3105933,14159.64178 0,No,1038.322871,45057.64205 0,No,1423.384402,48315.00731 0,No,601.7770832,15298.21319 0,No,947.5977921,34869.12685 0,No,296.355437,24625.88522 0,No,633.8504298,29213.37583 0,Yes,1154.446367,14301.15712 0,No,504.6153595,56623.01029 0,Yes,1176.934525,19829.22646 0,No,1001.320238,41063.19735 0,Yes,1352.292826,23660.65483 0,Yes,1060.060072,18617.21814 0,No,1371.703275,48298.54454 0,No,1015.969113,37952.96428 0,No,1171.41159,52278.2442 0,No,1021.283608,50389.62074 0,No,1743.099238,31438.59197 0,No,84.34924717,59022.17661 0,No,879.4171517,35228.11056 0,No,820.945303,34064.67368 0,No,1085.898368,29771.06456 0,No,648.3648681,40276.57183 0,No,0,27614.49927 0,Yes,313.3960316,18071.68803 0,No,477.5507621,24273.41864 0,No,403.2683085,38629.94742 0,Yes,864.9479861,8624.220719 0,No,502.0259542,31574.71251 0,No,971.8148887,35583.19054 0,No,0,49606.99523 0,Yes,823.4713732,12331.91623 0,No,969.8063054,60396.78919 0,Yes,881.265405,15275.42058 0,No,1315.191489,42694.97507 0,No,1149.659956,34635.1377 0,No,943.5662471,31377.94282 0,No,270.0725934,36833.64514 1,No,1392.599753,37620.24131 0,No,0,30836.44051 0,No,939.6424061,38395.6138 0,Yes,838.9784353,21176.47917 0,No,142.6144048,34695.11589 0,No,1493.033897,30848.61911 0,No,1193.406554,55017.46259 0,No,0,19572.30011 0,Yes,1078.514259,20073.35524 1,Yes,2461.506979,11878.55704 0,No,614.1406212,17472.91489 0,No,872.1227091,43154.71996 0,No,880.0472807,45092.5924 0,No,433.4001126,25085.07012 0,No,618.1270378,29836.28565 0,No,1115.154313,38623.5482 0,Yes,327.4198831,14938.94435 0,Yes,888.7186748,15037.6083 0,Yes,995.9150546,24897.17325 0,No,1219.390103,42549.91358 0,Yes,984.3611537,16577.83193 0,No,980.3971031,41753.07598 0,No,422.4516417,34797.79654 0,Yes,311.321864,22648.76345 0,Yes,1019.647755,2702.982331 0,Yes,1250.972829,19275.25403 0,No,1042.699189,10693.6216 0,No,436.5756274,22389.46339 0,No,698.074828,37027.81607 0,No,296.8818307,43136.56168 0,Yes,610.1853688,19010.12579 0,No,0,38686.58143 0,Yes,1230.957508,20573.78631 0,Yes,712.0267045,17273.11586 0,No,772.4056333,39023.49164 0,No,175.1677608,53019.42301 0,Yes,124.8712711,20886.49619 0,No,423.3376968,60170.57447 0,No,364.7738836,31857.18073 0,Yes,851.9759048,14170.92231 0,Yes,1619.714151,21781.94056 0,No,1200.946758,47636.85963 0,No,765.1320369,39600.48462 0,No,1343.954922,26365.52625 0,Yes,1669.763112,23741.5106 0,No,266.759321,25864.93903 0,Yes,1039.909419,21212.68268 0,No,246.7564139,37138.3863 0,No,1282.567571,36558.83792 0,Yes,1070.320464,12756.76045 0,No,766.5125244,43426.8308 0,No,1102.924309,57828.17878 0,No,924.9593298,26332.20962 0,Yes,1034.902391,15945.13985 0,No,1807.688667,40731.51641 0,No,520.6546128,39914.91905 0,No,1018.769969,35813.42074 0,No,1281.675172,41481.321 1,No,1621.575785,36577.8462 0,No,0,40772.92686 0,Yes,1848.826618,18128.56959 1,Yes,2117.120614,12143.4748 0,No,1002.144124,44445.52727 0,No,551.1858718,36603.97534 0,Yes,149.1541,22802.81968 0,Yes,1360.865633,22310.92951 0,No,1136.148437,38474.61369 0,No,717.6673898,27956.27352 0,Yes,651.5487845,23374.83297 0,Yes,604.5743501,16153.41429 0,No,759.0904065,41891.85759 0,No,1204.957811,24788.76787 0,No,796.2572608,33760.51826 0,No,506.4623208,36596.12674 0,No,160.0124036,48121.18102 0,No,339.6911806,49109.36138 0,No,0,35288.33508 0,Yes,1344.709359,17318.8399 0,No,368.086167,51601.36084 0,No,1005.129339,43501.69649 0,No,712.8636656,47454.74443 0,No,384.5278841,25950.26756 0,No,512.6564669,43846.97971 0,No,0,37112.5978 0,Yes,721.5173197,18310.94552 0,Yes,1458.893462,21186.56296 0,No,388.6092291,23040.44174 0,No,516.1096447,48303.80104 0,No,575.7546157,26757.57934 0,No,0,26100.75191 0,No,1440.39242,46113.26845 0,Yes,1136.400756,24962.84907 0,No,326.519432,20310.45248 0,No,837.2091498,38032.9563 0,No,1324.84258,41590.33519 0,No,244.935779,27369.01052 1,Yes,1875.458895,17820.46333 0,No,0,30287.49034 0,No,1793.290612,45117.38191 0,Yes,1874.494501,19592.06987 0,No,1036.985599,46388.62453 0,No,91.41528435,46468.78798 0,No,438.8123997,39390.72553 0,Yes,1371.093868,16204.76896 0,Yes,950.7767872,17711.74176 0,No,370.0009349,39286.61366 0,No,688.9057615,43606.92374 0,No,1452.32898,37691.77019 0,No,641.9084171,45107.43169 0,No,618.1391643,50146.42925 0,No,516.4497663,41922.88849 0,Yes,720.4352399,24582.88069 0,Yes,485.0148907,23261.33095 0,No,929.7554006,30908.8982 0,No,591.7871599,46381.23725 0,Yes,753.3835736,12258.60451 0,No,1255.425942,17917.79479 0,No,694.5387626,42855.56531 0,No,431.159894,51727.54032 0,Yes,23.20605171,22196.65337 0,Yes,584.5762328,16639.65584 0,No,877.0294572,35054.9489 0,No,1300.296924,51338.85741 0,No,1049.927427,25445.92007 1,No,1099.057701,42590.89445 0,No,763.7737643,60938.6641 0,No,506.7765142,40625.48747 0,No,41.27972799,52066.14735 0,No,789.6399004,44073.24669 0,Yes,867.4707644,11905.67895 0,No,1352.29093,33077.08882 0,Yes,0,20217.09091 0,No,337.8749093,30090.53051 0,No,660.244842,40885.84695 0,No,1144.481793,45548.38038 0,Yes,1075.092764,7031.294556 0,No,535.8972417,43917.5687 0,Yes,1170.224624,8747.47272 0,No,922.1241069,48137.37167 0,No,1064.087172,34677.93179 0,Yes,1294.285702,17444.60356 0,No,845.7367046,34676.26423 0,No,212.6477409,29592.17085 0,No,501.2866489,36154.65269 0,No,463.0454558,35621.08035 0,Yes,722.2273331,18487.04056 0,No,724.2055922,36489.69055 0,No,209.0427889,40075.98415 0,Yes,1108.624099,18761.7671 0,No,0,60203.59603 0,No,960.0680599,23482.83561 0,No,685.3419782,51856.89098 0,No,1446.604606,45552.0099 0,No,1187.656684,46731.55487 0,No,1073.025819,39553.83126 0,No,1608.68335,40078.22043 0,No,1160.133222,32978.6596 0,No,621.1492608,15546.76898 0,Yes,1367.429631,23617.61236 0,Yes,909.1019937,7283.763145 0,No,524.4167931,41535.32466 0,No,654.4936185,12696.80325 0,Yes,1010.576124,20982.97031 0,Yes,130.1477105,15174.67298 0,No,150.6636179,42800.69692 0,No,584.8383476,41447.41353 0,Yes,771.295014,15594.01185 0,No,1363.481664,35359.75405 0,Yes,414.8064509,18832.03421 0,Yes,1647.004573,16614.80417 0,Yes,562.8425912,19669.76934 0,No,843.7009112,50891.67332 0,No,455.376336,36590.55036 0,No,459.4239213,49958.7568 0,No,967.7206245,31641.67634 0,No,84.34446632,42333.53363 0,No,530.6035028,59740.20997 0,No,1369.925967,49089.81963 0,No,692.1562081,39199.4465 0,Yes,564.638739,24342.03079 0,Yes,1251.186806,11229.64122 0,No,378.445644,30562.09552 0,Yes,0,24000.33023 0,No,0,37361.5408 0,No,820.4850172,35951.11998 0,Yes,246.8670273,16209.53668 0,No,1490.137485,35882.75867 0,No,421.038872,17137.16813 0,No,1147.571897,32012.76535 0,Yes,1087.917744,18053.0839 0,No,295.150456,48387.47245 0,No,543.7942744,51964.75122 0,Yes,1002.443921,14152.66949 0,No,809.478458,51608.54246 0,No,659.7508151,50536.90097 0,Yes,1627.774613,23516.63903 0,No,751.6317328,50562.49861 0,Yes,625.7720225,23122.61103 0,Yes,486.2248233,20289.67472 0,No,1302.134218,64016.7604 0,Yes,1233.735523,15480.20778 0,Yes,1220.276932,11573.84845 0,No,982.5145872,62140.92546 0,Yes,306.0413141,13190.83226 0,Yes,806.699911,19269.68168 0,No,789.3268693,36183.65664 0,Yes,1481.573981,23170.56479 0,No,1228.788529,59100.85115 0,No,464.9391825,58247.12157 0,No,958.0474328,15052.44572 0,Yes,0,21294.14848 0,No,1523.930277,58551.35959 0,No,0,50633.79256 0,No,387.7552719,34086.38704 0,No,1194.205725,45588.18069 0,Yes,1430.824811,10766.93777 0,Yes,1251.994891,18922.03436 0,Yes,207.9259703,19078.8919 0,No,1596.385952,34190.18972 0,No,0,33769.60487 0,No,1213.352955,39344.12403 0,No,407.2634242,40524.33861 0,No,786.5856491,43014.22075 0,Yes,695.4764393,14030.36055 0,Yes,1113.236885,19139.26643 0,Yes,717.2052655,20627.81892 0,No,374.9261906,39167.97268 0,Yes,270.5992974,15728.91071 0,Yes,1334.780808,20468.44084 0,No,280.6148305,35494.59786 0,No,143.9178471,41626.6877 0,No,1027.054579,44268.95873 0,No,127.5453894,33070.49906 1,No,1809.409864,33453.40654 0,No,1626.391087,61438.09441 0,No,628.8755362,48380.51084 0,Yes,385.3503585,22395.88236 0,No,753.9999428,29020.72906 0,No,650.2493011,33475.74424 0,No,232.763666,58488.7903 0,No,1005.623009,43471.78032 0,No,82.42756982,29761.54453 0,Yes,41.38551638,18664.90101 0,No,510.6575133,25855.91702 0,No,1588.916251,54043.0063 0,Yes,1127.435955,23495.56552 0,Yes,1242.722943,23037.41372 0,No,1730.110458,48766.80091 0,No,915.462439,33887.76364 0,No,65.33298386,31992.24222 0,No,180.1714989,21403.07771 0,No,989.2625778,48055.41224 0,No,314.9851768,51112.93787 0,No,607.2770601,36487.99099 0,Yes,724.4378462,15835.62752 0,No,832.2174013,44053.25606 0,Yes,794.1468067,12519.76839 0,No,1806.714877,39082.7861 0,No,394.5018396,29642.91027 0,No,108.0038644,44764.2889 0,No,806.5501813,27622.90415 0,No,845.0777454,38410.84357 0,No,659.8089468,39712.95892 0,No,1248.830649,39317.90418 0,No,852.522041,34237.64567 0,No,878.9279371,47228.34093 0,Yes,819.9121905,16585.0281 0,No,0,34213.49152 0,No,620.6815968,34549.08647 0,No,917.856227,49353.95063 0,No,539.4419088,26164.84468 0,No,747.139342,42318.93328 0,No,864.4607726,44429.06675 0,Yes,391.9982267,9995.041633 0,No,151.2607703,57657.28222 0,No,873.1255251,34352.70032 0,No,28.78977602,55369.20139 0,No,228.5118511,57232.26856 0,No,1190.139323,27734.73898 0,Yes,1845.976439,12107.74182 0,No,107.1547685,42776.30187 0,No,2058.784781,55021.5518 0,No,914.6373126,46492.64883 0,No,799.7327046,45702.40167 0,No,890.248108,34679.48276 0,No,1184.492298,43228.8059 0,No,1143.122864,42672.12915 0,No,718.7431182,31371.78108 0,No,847.6549143,43515.3022 0,No,1067.273201,50647.8724 0,No,791.5425837,32574.34127 0,No,826.3553124,37241.44063 0,Yes,652.7556923,23492.32324 0,No,827.6347367,36439.66094 0,Yes,704.0954634,16509.52293 0,No,773.5937314,29661.46353 0,No,402.1290147,42978.39361 0,No,411.5376269,30065.44435 0,No,340.9643991,27262.05798 0,Yes,1021.56825,14500.62917 0,No,1323.640681,30517.20825 0,Yes,1362.644449,18205.23178 0,No,347.4609165,24735.55069 1,No,1610.484015,35589.73383 0,No,1313.836804,40843.09978 0,Yes,705.3905442,19125.68498 0,No,705.7352329,45280.81923 0,No,0,38968.41579 0,Yes,0,23735.40854 0,No,1562.669858,52422.78022 0,Yes,655.611221,19039.16827 0,Yes,780.2540758,20758.47124 0,No,946.1375736,40038.68311 0,No,1402.512445,46207.77984 0,No,321.5883321,52711.61736 0,No,1824.293748,22471.44572 0,No,0,23562.29061 0,No,602.1950122,26768.91742 0,No,314.4591953,40016.47593 0,No,1223.714012,64440.21753 0,No,487.1168883,55459.453 0,No,324.8515434,52838.84933 0,No,98.22905568,48070.15932 0,No,0,18063.88836 0,Yes,345.7996071,15967.99085 0,No,869.3081193,32571.16926 0,No,437.5552567,37212.26936 0,No,323.5950541,34129.65607 0,No,1286.905557,45569.06752 0,Yes,940.4793543,20578.80723 0,No,1385.303477,35045.43873 0,No,765.8186636,33216.3532 0,No,599.009725,53004.58546 0,No,1153.329251,17598.43403 0,No,352.2783255,34359.84124 0,No,226.2173456,59680.00226 0,Yes,754.968696,30340.1204 0,No,1713.537851,43899.08429 0,No,969.5383098,33293.79472 0,No,1024.612986,34401.01774 0,No,838.6530169,25272.48607 0,No,1072.03377,47029.26037 0,Yes,686.6943789,18551.53006 0,No,715.5440757,50127.95563 0,No,1130.772145,40314.27154 0,No,1286.63632,44825.86584 0,No,937.5011931,33266.51944 0,Yes,1881.256521,14992.38673 0,No,135.9533458,49372.83598 0,Yes,1471.923929,19774.92388 0,No,773.1715664,37877.60452 0,No,552.6725661,50688.49633 0,No,1074.087653,35873.22864 0,Yes,137.4314686,22783.42097 0,Yes,523.8314927,12062.39436 0,No,724.5377902,33254.52849 1,No,2040.590171,50930.91079 0,Yes,1415.890802,22823.62786 0,No,174.4836347,54992.9893 0,No,805.3144881,36864.68929 0,Yes,1008.03313,13547.07854 0,Yes,0,21665.52681 0,No,1078.41039,44474.42029 0,No,768.6940069,32373.77105 0,No,391.1897845,42212.03034 0,No,0,46910.79789 0,Yes,1318.577735,18042.01761 0,No,955.4493408,40698.97613 0,No,1425.53486,33666.10426 0,Yes,1196.491941,14916.44469 1,No,2038.891851,58606.48624 0,Yes,1047.99824,15846.65523 0,No,995.3460999,46702.23907 0,No,1657.2559,29945.56602 0,Yes,396.5826334,18244.16602 0,Yes,594.5589853,13180.50211 0,No,931.7108765,50210.06947 0,No,1318.807066,44778.046 0,No,24.26359999,36070.13885 0,No,713.6733979,48914.5602 0,No,891.7516852,53044.83561 0,No,1794.411575,44037.84752 0,Yes,1412.089967,17049.66001 1,No,2024.820212,64135.43108 0,No,399.597296,42578.04594 1,No,1598.020831,39163.36106 0,No,1045.311352,31754.02378 1,Yes,2578.469022,25706.64777 0,No,1113.289142,42292.49015 0,Yes,645.1186876,16097.21702 0,Yes,861.104127,15873.61286 0,Yes,408.2183522,21432.72643 0,Yes,794.9731014,21590.96187 0,No,1477.637956,36546.82315 0,No,634.3999798,33377.36039 0,No,798.0820907,38080.96234 0,Yes,0,19919.56344 0,Yes,980.1017057,16381.20313 0,No,831.2454231,27706.95385 1,Yes,2083.228376,20103.60274 0,Yes,1436.074826,21511.45781 0,No,1467.225191,28845.0222 0,No,469.4384772,37879.25652 0,No,1166.715877,31032.38597 0,No,844.0412923,32089.62465 0,Yes,1538.973751,15757.93354 0,No,562.7011169,39067.75151 0,No,1112.110885,29229.53168 0,No,246.393764,33647.99323 0,No,562.7238684,60196.18261 0,No,916.5692398,42486.39891 0,No,618.5663094,28477.27953 0,Yes,1604.613738,15467.21891 0,No,847.6149639,42308.05232 0,No,304.4827935,52543.82829 0,No,778.8197433,31986.57121 0,Yes,1464.39479,13968.50801 0,No,221.4799857,37782.99708 0,No,592.0451816,39064.99448 0,No,1010.612455,34055.57159 0,No,1194.456464,49316.99776 0,No,844.3125669,52237.72548 0,Yes,179.5039775,19020.29596 0,No,355.9301126,32120.31724 0,Yes,1776.728974,21280.87661 0,No,27.30188269,25050.82709 0,Yes,631.0198864,20753.51811 0,No,187.9776847,49989.30743 0,Yes,1511.434098,11793.16178 0,No,1008.478231,22094.74808 0,No,820.4930473,43829.69508 0,No,112.03331,40057.07744 0,Yes,1675.658764,20773.1333 0,Yes,592.8931977,14833.28967 0,No,492.3989268,36004.37877 0,No,364.7461927,46096.16015 0,No,1025.327348,44194.51371 0,No,612.6572774,38635.36444 0,No,1456.630985,38676.54566 0,Yes,1762.598246,13099.58889 0,Yes,180.0951424,27351.42882 1,Yes,1721.973027,15179.74933 0,No,0,30822.45419 0,No,1029.786861,69547.43695 0,No,268.443287,55294.9511 0,No,1711.432084,39019.68965 0,No,0,36886.98626 0,Yes,1295.818065,22241.70347 0,No,1722.12592,21623.03645 0,Yes,560.3817531,23546.39634 0,No,676.5720235,41328.42762 0,No,518.9369941,28231.01207 0,No,626.4046872,18130.3221 0,No,524.3801768,38020.63992 0,No,0,33932.85275 0,No,517.4991855,60790.90832 0,No,563.3696936,22083.9086 0,No,865.8865437,59740.73253 0,Yes,618.1192173,24698.82724 0,No,1428.81283,41417.05369 0,No,1195.6287,34701.80249 0,No,218.0141007,29211.39846 0,No,1434.138048,38038.60389 0,No,506.2673922,48843.00181 0,No,1322.367854,34622.68625 0,No,584.8188278,49317.83485 0,No,815.072837,35017.85336 0,No,0,50956.67561 0,Yes,489.9275545,21445.53004 0,No,644.8898301,42211.3598 0,No,1151.203644,36468.7245 0,No,1440.433962,27532.65357 0,Yes,858.2153831,12588.10812 0,No,0,29021.74528 0,No,0,54053.90392 1,Yes,1402.205727,15964.20918 0,Yes,1171.724712,23505.19762 0,No,662.3026321,44664.00591 0,Yes,1142.734792,12893.95909 0,No,785.4309975,37465.3601 0,No,705.5486105,32899.60672 0,Yes,1713.312471,17432.27359 0,No,861.642116,38982.39152 0,No,398.4273199,37094.56787 0,Yes,703.6859705,16617.57009 0,No,366.2601114,52360.91288 0,No,1054.115963,41781.72858 0,No,189.7327424,53266.33832 0,No,944.2042462,41883.4143 0,No,0,36969.17175 0,No,499.9134357,28352.55339 0,No,866.310977,59647.44029 0,No,761.9884906,39172.94524 0,No,1150.999818,50099.29362 0,No,53.43705295,51027.06836 0,No,319.7674269,36057.40111 0,No,0,35116.59096 0,No,222.1054562,41217.34936 0,Yes,1336.99864,21309.25108 0,Yes,1397.504863,26604.55709 0,No,794.584067,37520.25333 0,Yes,883.1573413,18213.07596 0,No,1168.167308,17198.98356 0,No,257.4470251,38275.95337 0,Yes,1273.488513,17105.86705 0,Yes,1934.511142,17084.04858 0,Yes,1704.014193,17986.76358 0,Yes,220.8914188,25667.86594 0,No,323.3051241,51306.26862 0,No,969.4041426,31476.51662 0,No,385.1851205,50316.80152 0,No,995.4084089,40907.49695 0,No,758.3921316,40751.40748 0,Yes,652.8675877,23515.13132 0,Yes,903.1768373,15810.57419 0,No,533.4295863,24857.90653 0,Yes,1151.856308,23250.38451 0,No,1271.328544,43878.81337 0,No,136.1184403,44757.6928 0,Yes,1171.950442,15850.77726 0,No,512.6609456,49989.59958 0,No,172.1575923,28370.86591 0,No,1256.222658,42125.93733 0,No,835.8371832,57505.9653 0,No,1026.633616,61064.38758 0,Yes,1144.274829,16002.35547 0,Yes,682.9542988,15728.04791 0,No,797.6728164,45377.72334 0,No,97.32693534,62335.19685 0,Yes,1436.329318,7761.318943 0,Yes,1325.624926,15880.44715 0,No,1462.69493,50508.74644 0,No,191.9130126,55659.47329 0,No,854.8842799,44173.82773 0,No,864.3437963,34598.43439 0,No,476.5671956,46953.41828 0,No,1155.680389,50955.10653 0,No,1127.700193,32068.14654 0,No,0,62192.99565 0,No,312.9169851,31328.0435 0,Yes,467.8946604,16371.69775 0,Yes,1055.597773,16810.55958 0,Yes,989.235475,13609.63128 0,No,245.7067237,40462.55381 0,No,585.7526132,43627.32792 0,No,610.8543063,39175.08053 0,No,258.0365107,40138.84173 0,No,1092.998159,37351.33966 0,No,1387.873287,27145.70328 0,No,833.0841932,49704.51196 0,No,1184.87087,41802.94433 0,Yes,916.7943938,17152.15796 0,Yes,1427.337578,22909.43047 0,Yes,1413.274427,19064.62219 0,Yes,1592.549635,18024.28172 0,No,0,26941.79774 0,No,1050.204097,34803.53376 0,No,1511.177706,35899.25584 0,No,894.6817393,37232.29375 0,Yes,781.6137295,12195.96995 0,No,0,43311.6407 0,No,1094.886615,38342.71501 0,No,0,59266.96087 0,Yes,875.6700609,18687.04517 0,No,81.91809171,40936.90644 0,No,371.1818581,34679.96073 0,Yes,1438.055497,19200.50183 0,No,674.5989565,67278.94844 0,No,1097.762548,42228.97048 0,No,593.0602869,47787.59592 0,No,1167.101189,35952.19366 0,No,1114.826926,33729.38986 0,No,304.1465978,23866.93586 0,No,353.6311313,40686.76015 0,No,1269.420335,39010.14819 0,Yes,397.5424885,22710.86574 0,No,607.1662709,49547.46582 0,No,1667.480365,47991.58505 0,No,956.3113506,42971.90111 0,No,668.3980286,38637.43388 0,No,290.679874,37603.97217 0,Yes,826.7274558,18776.30124 0,No,1102.111459,48079.1253 0,No,348.6588679,36519.09019 0,Yes,991.2328292,8926.799452 0,No,1224.235782,32751.69692 0,No,146.3658568,44666.9323 0,Yes,697.13558,18377.14971 0,No,956.4469096,40740.32911 0,Yes,1184.909432,13276.22112 0,No,1702.236683,36530.47362 0,No,0,36850.97118 0,No,965.4597617,25045.72554 0,No,331.1643387,25922.41674 0,No,1307.093669,35425.60999 0,No,1637.037557,45027.53816 0,Yes,1091.932103,11703.38056 0,Yes,1499.19013,17560.37184 0,Yes,770.2905437,22114.71826 0,Yes,1147.35491,19188.90827 0,No,1037.274163,28437.77245 0,No,626.9739384,50447.77415 0,No,847.7207929,55260.34981 0,Yes,1199.592686,16454.74984 0,No,1288.285095,54666.37745 0,No,675.2253871,33700.21261 0,No,541.4409621,32502.69684 0,Yes,1165.007391,18389.07904 0,No,1042.791935,26141.00595 0,No,989.2170927,42813.37511 0,No,586.6026083,27798.16832 0,No,931.5910554,33302.24002 0,Yes,978.5385668,16514.32495 0,No,958.9368289,47468.66971 0,No,384.5320414,36019.4104 0,No,645.0751364,43244.52376 0,No,424.7602723,29502.40358 0,No,612.5518758,33036.24132 0,No,1381.442222,47470.01877 0,No,0,45560.91289 0,No,1096.026109,23954.21792 0,No,945.6854495,38714.51829 0,No,722.7680066,20962.10645 0,Yes,887.8808195,20563.14713 0,Yes,1049.953288,19724.83261 0,No,728.0416851,29188.94274 0,No,1114.904032,40084.00147 0,Yes,1451.303542,14185.13792 0,Yes,629.2131895,16007.69295 0,No,630.2307415,49059.26276 0,Yes,1372.978032,10776.44428 0,Yes,746.8638361,13904.42764 0,Yes,1400.545836,16519.08324 0,No,971.3514351,46533.85648 0,No,637.0823864,49097.90067 0,No,1778.429109,34591.23827 0,Yes,678.2050836,12787.09057 0,No,1094.772982,46617.88257 0,Yes,879.852005,23996.7402 0,No,1449.324346,38720.38158 0,No,322.0016252,43774.44211 0,No,1430.57687,44104.95892 0,Yes,1199.385586,18884.12747 0,No,168.3675173,47684.52622 0,Yes,1143.398205,15997.17119 0,No,416.9064901,38104.77323 0,No,1352.613669,42194.3867 0,No,1431.741973,37793.28732 0,No,481.3050408,35747.7451 0,No,774.0731766,46082.04322 0,No,0,34749.79359 0,Yes,1186.891002,14256.72955 0,Yes,978.359336,16679.89397 0,No,1409.11216,30626.22399 0,No,1491.479282,54835.77005 0,Yes,1228.484966,18937.20694 0,No,1553.478985,44425.89062 0,No,1200.041622,56081.08023 0,No,984.8608392,45435.49712 0,No,558.3205402,38014.7601 0,Yes,778.8538719,16007.45698 0,No,163.3531708,45183.14512 0,Yes,574.8182292,16479.53883 0,No,1149.936856,39231.65048 0,No,774.5380741,22691.88244 0,No,866.7724996,30400.56051 0,No,325.859608,36682.28257 0,Yes,879.6270083,14898.31147 0,No,160.5030608,19132.69486 0,No,905.7190025,38210.73377 0,Yes,649.93106,13475.01951 0,No,196.9129257,35737.60363 0,No,488.5663189,25407.21592 0,No,449.2743161,13124.56319 0,No,824.8024322,40957.52227 0,Yes,600.6021718,18630.37913 0,No,912.0655308,62142.06106 0,No,619.1392617,41281.98096 0,No,225.3646951,60275.88336 0,No,369.4309534,63536.77517 0,No,745.6661466,54891.43396 0,Yes,782.3522809,22692.66999 0,Yes,1051.247162,23200.97099 0,Yes,613.601284,9506.460305 0,No,863.6184007,30397.18726 0,No,943.0860284,29622.60774 0,Yes,1102.631262,10671.92358 0,Yes,1175.133331,15444.92692 0,No,491.4493698,51484.24549 0,Yes,1131.085568,21829.08239 0,Yes,717.3718454,16319.11698 0,Yes,517.3005655,17068.33888 1,Yes,2038.54316,12181.90258 0,No,868.4944763,22176.27625 0,No,111.416891,33328.24588 0,Yes,1587.564398,13080.68875 0,Yes,640.9984302,21680.09769 0,Yes,1928.59212,17570.19508 0,Yes,759.6787942,18871.40179 0,No,1306.353964,35652.76198 0,No,908.1742897,46791.03636 0,No,871.6774122,28182.1163 0,No,0,38878.17457 0,No,0,53302.21303 0,Yes,947.5251546,24073.09095 0,No,222.8802756,40849.9209 0,No,1237.534378,30967.58876 0,No,1318.719112,36822.92496 0,Yes,1030.052112,9760.399561 0,No,925.7062398,62764.88429 0,No,425.7797909,42131.53596 0,Yes,1901.653755,21323.16327 1,No,1940.37162,41162.93366 0,Yes,227.317232,24282.22856 0,No,836.5742295,51238.58093 0,Yes,981.9979984,20147.51185 0,No,1186.796231,52689.25014 0,Yes,315.978748,13186.11769 0,Yes,768.4472412,21497.52889 0,Yes,969.9094653,13962.33243 0,No,1175.191473,50921.25628 0,Yes,1684.275341,20344.1782 0,No,1120.623194,48571.26007 0,No,784.4700535,52617.20709 0,No,604.9800026,38885.31032 0,No,517.9789015,41755.34398 0,No,270.2640888,41978.04788 0,No,825.600966,33986.69732 0,No,623.5173805,51677.34542 0,No,529.5083605,38328.61263 0,Yes,1014.769662,12239.88381 0,No,288.5346538,44081.03481 0,No,500.2527238,17104.05425 0,No,565.6759823,37299.07929 0,No,1005.337168,34297.89776 0,No,852.3933685,46320.85489 0,No,613.1501138,34286.11245 0,No,905.5657259,52997.73104 0,No,1086.181729,52507.59729 0,No,1057.178127,35807.02486 0,No,252.8994606,27454.61895 0,Yes,1401.282703,16683.053 0,No,1529.131259,36572.02567 0,Yes,947.453711,21945.79304 0,No,0,33917.59412 0,Yes,1312.907203,15495.02659 0,No,1269.290575,43581.19884 0,Yes,20.25809384,21530.33151 0,No,0,30818.51195 0,No,35.32299975,53407.26963 0,No,897.3126908,31465.96477 0,No,1043.552358,38393.6119 0,No,1165.323966,33433.82892 0,No,1299.47321,55165.47298 0,No,675.776529,21758.02281 0,Yes,1202.38503,20550.6377 0,No,1190.159831,30746.37697 0,Yes,535.0105962,13909.39159 0,No,194.1935548,43150.50173 0,No,430.6203194,46182.66498 0,No,1047.927091,59765.59967 0,No,220.7916993,26453.2893 1,Yes,1493.968539,24126.02979 0,Yes,856.6553992,15523.10294 0,Yes,1635.500024,10596.52689 0,No,666.6138887,49215.02453 1,No,1562.837918,49303.00947 0,No,255.8883718,48833.22877 0,No,602.361702,53800.54721 0,No,1599.314695,28825.47457 0,No,150.9495108,36923.55219 0,No,1388.81659,35460.80124 0,No,87.94865179,47924.69791 0,Yes,1935.827701,17969.79853 0,Yes,1289.315963,7677.896398 0,No,871.9872122,45496.94816 0,No,1333.699981,44361.62087 0,No,181.1793446,37380.14432 0,No,791.1822252,48206.73175 0,No,69.17702365,24556.02178 0,No,940.8944835,54839.12365 0,Yes,346.2155779,17866.52804 0,No,214.846235,39629.20018 0,Yes,676.8313079,16998.65172 0,No,595.9969943,37417.13782 0,Yes,999.3916964,23688.64897 0,No,1447.043074,22433.63677 0,No,464.0984776,44769.86483 0,No,872.6399078,29749.17068 0,No,381.7040992,25011.10339 0,Yes,866.4967607,24262.6939 0,Yes,1243.344102,23634.74858 0,No,1184.308021,37600.07534 0,Yes,757.4071028,25309.77323 0,No,939.2584897,56225.82073 0,No,560.7702336,61472.85869 0,No,971.2788333,26853.3009 0,No,518.6807923,30151.33046 0,No,834.2636789,28022.83682 0,Yes,545.5855799,19454.3258 0,No,1057.350872,39651.12351 0,Yes,720.5164509,20222.94899 0,No,1285.517976,37442.64259 0,Yes,406.3397694,30088.44251 0,No,1023.892032,33392.70978 0,No,356.2441181,21319.08596 0,No,657.0888188,31724.62855 0,Yes,1168.546414,25802.27648 0,No,344.8822415,26227.32928 0,Yes,535.3359894,24268.8285 0,Yes,1222.966587,21373.3501 0,Yes,663.6027281,21436.52164 0,Yes,591.5371447,20961.46526 0,Yes,941.7180089,23362.97717 0,No,434.5449437,35620.0653 1,No,1804.190505,23096.08436 0,No,0,34227.94714 0,No,0,28356.25194 0,No,586.4709775,52203.85893 0,No,707.4361492,42407.75423 0,No,1491.640587,61096.49218 0,Yes,556.1822343,21650.82992 0,No,683.147542,44439.52874 0,Yes,671.1317307,11857.47764 0,No,1020.197756,43592.83414 0,Yes,1183.067702,13210.87763 0,Yes,1127.536131,19322.96966 0,No,1545.982805,32691.92383 0,No,1114.39773,44195.54455 0,No,843.497244,41917.31977 0,No,1047.67337,34413.19591 0,Yes,810.5409457,17252.25425 0,Yes,501.1105625,26425.69801 0,Yes,1031.638361,21432.14705 0,No,360.2150315,44082.18713 0,No,1234.831678,40155.45141 1,No,2236.764215,37113.88307 0,No,750.8405315,32034.71213 0,No,231.9683403,42465.96082 0,No,1616.500194,41825.40398 0,No,981.7993298,49644.91871 0,No,538.0199251,27284.51086 0,No,136.3073425,48799.65491 0,No,614.9409836,30905.47982 0,Yes,824.0407418,11659.19328 0,No,393.0612671,44149.38831 0,Yes,759.3971306,18693.1879 0,Yes,1743.798837,17541.00287 0,Yes,621.1701063,17503.68997 0,No,1360.547173,54705.12698 0,No,253.5218815,37506.20673 0,No,1392.332678,48709.52926 0,No,1508.250859,43525.05277 0,Yes,1823.86706,19447.96656 0,No,0,39423.51252 0,No,728.673461,36557.23484 0,No,1471.198738,56477.13086 0,No,967.2567624,32201.63408 0,No,640.7059824,38792.35719 0,Yes,559.5664108,15720.38246 0,Yes,1258.114533,20010.57627 0,No,1126.887696,32770.57902 0,No,1341.263141,46263.88501 0,No,751.1019308,50290.14411 0,No,325.9276366,57313.2825 0,Yes,905.9019451,20285.22513 0,No,699.6042756,49035.24746 0,No,996.4459056,51393.78872 0,Yes,1034.170213,23825.02851 0,Yes,1464.636242,14763.56268 0,No,280.724169,40382.59926 0,No,725.8676857,41792.67292 0,Yes,1162.590922,15419.11052 0,Yes,193.7197625,18002.54765 0,No,743.181827,50250.91901 0,No,1403.839834,43225.0419 0,No,460.1398044,52708.55985 0,No,1218.219887,36290.16201 0,No,903.9870202,51705.2716 0,Yes,1473.695931,19759.50187 0,No,525.515682,50763.98401 1,Yes,1874.822612,14957.81633 0,No,700.6223878,42023.88878 0,Yes,1164.666966,23297.15789 0,Yes,1803.766349,23201.82698 0,Yes,451.7115231,22483.09581 0,No,1101.98446,36231.85382 0,Yes,1343.777124,28198.00535 0,No,606.4358765,38897.07941 0,No,0,45380.65676 0,Yes,0,20963.38311 0,No,1140.820152,36495.05539 0,No,1597.647402,42515.08887 0,Yes,521.8414793,10316.33674 0,Yes,837.5214935,13215.78581 0,No,390.3103153,42322.2467 0,Yes,806.4819727,22127.08179 0,No,890.2666463,38376.09065 0,Yes,237.3830296,21376.68324 0,Yes,808.396167,11804.03448 0,No,694.3909457,46128.86471 0,No,492.2539455,42143.19844 0,Yes,460.5954148,12939.00909 0,Yes,838.4599545,19805.87523 0,No,675.8425047,38173.92575 0,No,625.4198746,47406.24077 0,No,45.47628178,27599.07233 0,No,474.7692355,30799.94939 0,No,1024.823657,46113.64712 0,No,696.107577,40325.38481 0,Yes,953.5336584,25820.58748 0,No,1143.855231,50690.2577 0,Yes,683.4413004,22595.45396 0,No,644.6044091,30276.57073 1,Yes,2034.674718,17133.96596 0,Yes,1345.491624,12235.38767 0,Yes,1307.087092,18082.55489 0,No,705.5005243,52559.03126 0,No,0,47725.52153 0,No,501.1696197,35664.80539 0,No,991.5209236,30775.52076 0,Yes,956.6922342,19594.54904 0,No,353.6499924,30726.28569 0,No,838.9240601,47864.41892 0,Yes,353.6527609,20531.49654 0,Yes,751.2431402,16475.02756 0,No,991.1584083,42767.69028 0,Yes,1096.489647,17856.26503 1,No,1655.775597,25639.79354 0,No,0,35577.42528 0,Yes,485.6531778,16099.91794 0,No,850.4665937,34367.67653 0,Yes,0,17695.84017 0,Yes,766.6355371,21718.81362 0,No,309.1791625,31232.07089 0,No,244.6623745,29275.11542 1,Yes,1013.216886,19651.26179 0,No,357.5921567,48565.01771 0,Yes,1908.242008,17642.62663 0,No,1449.135708,40495.47218 0,No,464.4163012,38236.74002 0,No,1491.707545,40637.44798 0,No,256.8941868,39019.87574 0,No,0,35025.97139 0,No,0,23910.13966 0,No,817.6210891,43705.82271 0,Yes,888.3063208,22901.63394 0,No,1240.754605,51511.2976 0,No,828.6441617,10948.5045 0,Yes,959.8542276,18481.75266 0,No,1281.448849,48837.37876 0,Yes,1577.675431,11951.47861 0,Yes,1444.114268,21441.01213 0,Yes,647.6702879,13618.34779 0,No,720.1649187,43654.23034 0,No,64.15479336,47969.00511 0,Yes,546.0286641,21615.26908 0,Yes,1102.127277,14009.94299 0,No,1294.52357,28001.04907 0,No,341.3222905,33808.33618 0,No,399.3903814,17348.78423 0,No,710.3946335,31483.3472 0,No,1467.573384,26697.7859 0,Yes,282.8315188,13371.05502 0,No,544.2251816,46149.51022 0,No,1585.860373,42709.49189 0,No,0,28784.46924 0,No,1033.426272,43594.76232 0,No,1131.053646,38834.23625 0,No,544.2872953,24477.47191 0,No,1116.720357,33716.17752 1,No,2155.288986,34787.25268 0,No,732.0566466,39433.48064 0,No,591.6362698,33852.30967 0,No,0,63251.30763 0,No,453.8499581,27865.2848 0,No,170.070917,27005.57712 0,No,916.7291772,37212.80853 0,No,611.3372902,38389.28253 0,No,258.6296725,33327.93778 0,Yes,357.5956653,18141.00282 0,No,813.1738871,49274.38284 1,No,1743.06471,22308.12575 0,No,801.8027115,46182.2571 0,No,674.7366147,26060.4861 0,No,430.4906483,39921.13691 0,No,1352.995532,34089.03318 0,Yes,1040.880212,19427.81424 0,No,1305.774698,34412.11637 0,No,1294.761706,37712.3869 0,Yes,583.295482,23694.36737 0,Yes,1409.299178,21508.50151 0,No,886.2166288,40486.85944 0,No,515.6197997,39638.63963 0,No,1866.304653,58710.88765 0,No,657.7057207,41224.79688 0,No,270.2243329,49128.03714 0,No,953.3917889,20749.33816 0,No,630.2207789,24919.18388 1,Yes,2024.66017,9663.788159 0,Yes,497.0569883,23119.13503 0,No,48.72556296,37957.90214 0,No,440.0245428,49358.42579 0,No,1217.94866,43996.014 0,Yes,1203.98226,21524.31842 0,Yes,1493.873562,15626.65942 0,No,0,48025.61685 0,No,1204.954348,47167.9385 0,No,945.6697175,39928.2439 0,Yes,807.5955718,16926.37334 0,No,514.3815273,20632.26812 0,No,1493.357435,51510.14807 0,No,738.2214298,25184.46731 0,Yes,451.9124615,22316.26132 0,No,997.0278823,35591.38727 0,No,1082.074394,30627.46169 0,Yes,1001.348886,19314.92233 0,No,1058.627514,49380.07843 0,No,245.3464925,42703.01867 0,No,579.3818249,32296.2634 0,No,1180.935714,35352.1518 0,No,969.0830285,45192.33896 0,No,0,44561.32545 0,Yes,714.1754997,19995.8564 0,No,0,36379.51885 0,Yes,100.8507591,17315.13019 0,No,1856.530876,33498.83452 0,No,620.4573093,41059.0965 1,No,1815.79252,31632.58386 0,No,1508.145014,31101.08122 0,No,1170.916976,38712.4201 1,No,2063.571934,37372.75849 0,Yes,284.8415772,20477.83723 0,No,878.4461099,29561.78308 1,Yes,1740.768306,18161.24408 1,No,1994.395066,44794.3375 0,No,1290.179212,42813.97517 0,No,441.3369832,55970.92336 0,Yes,1790.413725,22953.43108 0,No,821.2504797,68721.97447 0,No,341.0403786,40255.4676 0,No,216.9616918,31742.99445 0,No,48.23798999,37536.36143 0,No,501.3606578,49897.30073 0,No,267.416625,24154.5098 0,No,623.6184284,54008.19583 0,No,752.7393782,38671.78599 0,No,0,38665.87024 0,No,413.1145947,63793.55763 0,No,895.4392232,26138.10122 0,Yes,127.6633751,13942.24412 1,No,1361.735791,50322.35532 0,No,592.6115176,55124.8645 0,Yes,947.850997,22047.92056 0,No,784.1715748,47322.68911 0,No,952.3111521,21990.09147 0,No,448.2635314,26350.87102 0,No,316.1240081,37322.93156 0,Yes,1246.117198,9846.702644 0,No,680.7064686,42316.40311 0,No,1208.304376,38461.04654 1,No,1292.430211,50990.57717 0,No,663.6101987,48089.52055 0,No,435.5922002,38830.51802 0,No,579.193281,36181.81276 0,Yes,819.0310647,22092.7223 0,No,603.0203998,51229.78847 1,Yes,2654.322576,21930.38888 0,Yes,0,23274.97616 0,Yes,1487.043545,21412.35155 0,No,1466.772192,46875.08354 0,No,1168.488567,40615.56669 0,Yes,592.2969458,19744.97839 0,Yes,1255.973891,21757.67639 0,No,389.792381,53685.47315 0,No,604.304327,38758.54553 0,No,940.3923997,33874.81453 0,No,329.5189766,38150.63493 0,No,1465.027032,18600.2135 0,Yes,1817.280072,20751.43242 0,Yes,1055.063798,16174.20638 0,Yes,536.0102178,23587.49408 0,No,817.4743281,41994.92534 0,No,1244.563566,36157.81847 0,No,983.1275713,19985.60143 0,No,644.8002553,32298.30857 0,No,896.9814679,35755.74845 0,No,527.2715744,41573.98191 0,No,0,51122.51562 0,Yes,1427.940531,12666.26077 0,Yes,1262.480766,15636.25736 0,No,1283.278483,46335.83606 0,No,489.5199137,22750.69218 0,No,887.2014361,41641.45357 0,No,328.0130111,38500.3996 0,Yes,1521.494949,18018.90434 0,No,326.0381126,25498.01353 0,No,1816.973502,29897.18181 0,No,1534.007005,35566.26707 0,Yes,1657.113859,10852.29313 0,No,1270.456976,36092.5871 0,No,560.5146192,25254.04166 0,No,0,28490.77146 0,No,697.9327582,35706.66135 0,No,1420.50748,37437.7379 0,No,695.6690098,36977.64019 0,Yes,212.8213685,24515.97547 0,Yes,929.41113,23236.75175 0,No,1676.114799,25317.17139 0,No,571.9599418,24932.00592 0,No,355.0163312,36327.87701 0,Yes,1315.421445,21650.41516 0,Yes,1006.994109,17834.06419 0,No,332.6741696,22938.59315 1,No,1622.499039,39228.54143 0,No,0,26394.54536 0,No,1131.602054,19893.77231 0,No,0,44981.15029 0,No,1493.742248,32742.85821 0,No,47.42347928,29567.85743 0,No,153.7126336,54331.52977 0,No,458.997387,21165.86648 0,Yes,1260.828176,22372.2875 0,Yes,1080.477662,18741.31686 0,No,1814.105649,32977.90862 0,Yes,1739.837812,21593.01285 0,No,0,50496.47372 0,No,983.4906792,20828.53065 0,No,0,28176.11886 0,Yes,138.9063092,13005.89515 0,No,870.3479659,42397.09982 0,Yes,584.4864011,14017.39055 0,Yes,1508.701776,25338.26469 0,Yes,1282.474952,25304.58644 0,No,521.736728,28401.49746 0,No,1150.677712,38342.28906 0,Yes,1010.848591,16428.75455 0,Yes,1746.297372,24536.55387 0,No,1382.268993,19023.99621 0,No,499.8186019,31335.96076 0,No,339.6768042,43308.9659 0,No,253.6301022,50236.50518 0,No,690.1857313,32761.22721 0,No,743.0525534,33556.01649 0,No,736.4955216,49119.34782 0,Yes,584.5180962,19624.58866 1,Yes,1276.685083,19282.43592 0,No,399.8771607,47291.01589 0,No,182.23872,45937.97688 0,No,743.6661697,37542.58034 0,No,731.1016272,26655.41471 0,No,615.7266574,38687.11522 0,No,499.1491489,56343.61044 0,No,785.0335169,45598.9154 0,Yes,969.252341,16864.04349 0,No,841.4900639,43371.92444 0,No,181.1714826,35175.90354 0,No,811.5963666,38323.72387 0,No,1625.639876,65026.73893 0,No,962.223838,43974.05687 0,No,617.0250542,33369.08936 0,Yes,478.9236749,19730.18516 0,No,1017.749721,30227.86323 0,No,708.5001616,35086.90492 0,No,423.4079473,40844.34151 0,No,0,29775.15887 0,Yes,935.693635,25362.32188 0,No,0,51719.37487 0,Yes,1757.845459,17426.51971 0,No,733.5453865,30638.21619 0,Yes,1050.409386,16746.06282 0,No,25.80037667,44091.68791 0,No,646.0812402,36686.95945 0,No,695.4717672,25544.89204 0,No,536.5174942,28629.46367 0,No,542.2771433,52447.21934 0,No,811.7699461,46076.30224 0,Yes,911.7608245,21082.55279 0,No,621.3034989,50474.97901 0,No,549.3931277,39131.49488 0,No,929.2971542,42503.40972 0,No,154.6639204,44714.37168 0,No,36.23504123,49075.98156 0,No,718.5952536,39157.78993 0,No,379.1003606,31350.92863 0,No,904.3357112,30890.74555 0,No,1028.964342,18701.40959 0,No,343.4938298,67930.34362 0,No,375.7993192,21297.58462 0,Yes,502.2610869,13572.74891 0,Yes,1231.558825,15727.66963 0,No,456.8715128,54894.16445 0,Yes,1112.129919,22709.43345 0,No,1063.712861,36475.09799 0,Yes,895.8232449,19825.6299 0,No,1407.092142,43677.97642 0,No,418.4983441,29856.47168 0,No,684.9062972,14756.99835 0,No,650.04611,46137.93297 0,No,495.4211542,26740.76901 0,No,698.90513,27001.5539 0,No,847.7729672,42193.59756 0,No,1185.159734,35793.34852 0,No,1315.609131,54833.05552 0,No,0,32809.33479 0,No,629.087771,32296.93165 0,No,326.6700185,50562.49033 0,No,0,35775.31693 0,No,949.2823724,26960.74276 0,No,1077.733083,50334.85412 0,No,1256.18673,31706.17856 0,No,1356.729836,37664.03739 0,No,564.1282921,37695.77537 0,No,793.9709695,41931.58535 0,Yes,412.3886362,9892.127549 0,Yes,810.6685873,19925.74377 0,Yes,1273.835776,15593.64168 0,No,1386.668478,56325.44936 0,Yes,574.6731443,22814.34644 0,Yes,1614.141638,11334.29247 0,No,0,45642.76194 0,No,949.9967068,32676.23075 0,Yes,618.7567523,18297.32777 0,No,1408.176119,47496.16431 0,No,465.9198784,41133.94856 0,No,952.9278193,44894.44293 0,Yes,17.6095776,13739.7546 0,Yes,989.8845974,23416.50047 0,Yes,1817.171176,24601.03749 0,No,746.8365105,41294.45225 0,No,549.0649295,58539.41427 0,Yes,993.0495382,20538.54574 0,Yes,1447.418108,16658.42409 0,Yes,598.2181944,19794.57641 0,Yes,79.9899964,14390.81312 0,No,641.2545874,31306.53484 0,Yes,562.8376983,25068.04645 0,Yes,448.2432107,23540.89103 0,Yes,1134.696572,20068.01855 0,No,1649.211607,42148.74388 0,Yes,817.6766467,22339.88541 0,Yes,948.1513026,18107.35715 0,Yes,1180.018086,19871.20674 0,No,761.1876588,54681.82839 0,No,420.4958319,58264.20582 0,Yes,1841.408534,18000.70468 0,No,102.7214411,13719.35687 0,No,265.5928459,24833.34159 0,No,1558.861075,45255.96711 0,Yes,220.6595334,20148.57976 0,No,325.8701315,35015.26897 0,No,1272.477386,42671.31859 0,No,906.2310971,46715.23468 0,No,1070.810048,35979.47014 0,Yes,0,15572.02126 0,No,722.1334868,22669.2398 0,No,1453.292872,43370.75789 0,No,1207.287852,33671.4595 0,No,1118.219923,32632.91307 0,No,1281.921573,56778.41539 0,Yes,878.9983936,12054.85904 0,No,735.1033377,51403.71597 0,No,914.3410823,43460.16638 0,No,4.151111645,22722.22204 0,No,1063.592329,38057.33357 0,No,331.8428277,37264.69482 0,No,838.5789048,43501.25753 0,No,418.2996428,49670.02198 0,No,538.9427317,23289.28046 0,Yes,2133.745481,19675.50759 0,No,1651.322553,34024.66368 0,Yes,1152.973272,13036.06671 0,Yes,1065.256355,16658.94724 0,No,486.6526399,30838.6297 0,Yes,770.8266665,18896.729 0,No,1202.463489,42818.64311 0,No,323.8001014,34576.49203 1,No,2062.870628,30839.18067 0,No,1058.284332,39314.51347 0,No,796.0250533,38443.89641 0,Yes,1379.770017,15509.4094 0,No,1201.332489,23969.45182 0,No,1114.116007,40359.96721 0,No,821.6071023,50689.19322 0,No,1079.965765,43215.8058 0,Yes,662.995363,12008.33574 0,Yes,1343.701549,27168.40822 0,Yes,553.4557368,21358.42066 0,No,472.954055,63759.78703 0,Yes,1806.551672,17648.1976 0,Yes,855.2269959,15348.70666 0,Yes,790.5543286,20568.82724 0,No,1380.144032,48087.82505 0,No,1522.530802,30765.5638 0,Yes,967.3490473,14725.14091 0,Yes,886.2211404,12796.05899 1,No,1323.628142,18820.79497 0,No,1351.325423,46663.39221 0,No,294.0187903,59128.5034 0,No,1756.050716,52497.5856 0,No,439.496364,46784.94481 0,No,725.7832919,35503.64421 0,No,1049.946768,43172.32719 0,No,1138.628859,36978.45938 0,No,568.2242546,39873.24285 0,No,0,43160.3242 0,No,572.6658576,42285.2853 0,No,1270.393178,43032.27159 0,No,1444.432105,19153.34273 1,No,1926.599082,59224.45276 0,No,101.9934557,27909.63104 0,No,547.1749749,36969.00323 0,No,907.633104,28577.09655 0,No,282.8892177,42132.07381 0,Yes,811.9650452,12853.17561 0,Yes,1305.104453,20573.55387 0,Yes,533.9165429,21390.68662 0,Yes,1420.59614,20197.81153 0,No,89.3129241,39915.45359 0,No,839.04092,36665.70146 0,No,522.1534576,40717.16252 0,No,488.7211263,46503.16615 0,Yes,983.268503,24554.27156 0,No,442.6659006,43991.81237 0,Yes,977.589372,17637.7342 0,Yes,317.2679998,17801.53722 0,No,1033.756658,21837.24283 0,No,629.3201832,52089.63305 0,No,1328.334103,39877.61246 0,Yes,1486.3773,19164.3504 0,No,760.5532935,19957.83914 0,Yes,1204.24147,19302.90148 0,Yes,1367.786515,12449.27904 0,No,226.2992254,34305.25608 0,No,902.4163952,37905.63148 0,No,462.0042896,33157.3622 0,No,1072.379365,19958.54399 0,No,424.04819,25081.84301 0,No,846.1312948,29812.0877 0,Yes,951.5885363,12163.18485 0,No,1079.823299,44390.71605 0,Yes,1395.668987,21374.59212 0,No,828.8895452,52560.90735 0,No,829.8842621,40749.78743 0,No,1626.169419,32827.70286 0,No,1057.633174,37229.80731 0,No,729.2259909,35633.50292 0,Yes,154.8755316,20685.45742 0,No,1583.096101,15350.45605 0,No,0,40766.69534 0,No,528.5692049,42249.357 0,No,809.104674,24820.52559 0,Yes,132.1045587,20065.72248 0,Yes,2149.336812,15093.82753 0,No,497.7257852,26591.11317 0,No,489.1655607,26875.72374 0,Yes,596.539256,9518.832332 0,Yes,1883.065456,23989.89246 0,Yes,748.0592283,17362.80746 0,No,1046.918121,42376.50217 0,No,310.1302239,37697.22019 0,Yes,1065.521076,21129.23446 0,Yes,1097.067481,20564.1258 0,No,1154.858678,57640.91696 1,No,2049.029845,52568.90881 0,No,1398.652467,34194.0824 0,No,130.3468225,53852.75509 0,No,1400.163187,52343.47718 0,No,0,51740.4216 0,Yes,552.3342396,21262.56636 0,No,0,58233.02096 0,No,246.6365453,18953.67194 0,No,0,38249.98238 0,Yes,655.5101263,15771.49243 0,Yes,100.355433,13940.60266 0,Yes,1103.003565,25385.60194 0,Yes,928.2334115,20932.69206 0,No,509.3177986,18221.14656 0,Yes,420.2570639,24703.99805 0,No,307.7305626,48123.55105 0,No,733.0760867,40056.10037 0,No,851.5646599,33211.1755 0,No,1300.337182,47580.54383 0,Yes,203.966127,16433.67429 0,Yes,1557.525571,17132.31236 0,No,0,41359.89161 0,No,1136.455031,47721.27371 0,No,986.1815438,34780.95439 0,No,452.8271077,62912.44434 0,No,775.4935595,46025.8708 0,Yes,926.0781636,17660.80724 0,No,1074.991267,27834.79004 0,No,844.4654791,44518.39527 0,No,391.558358,45822.52436 0,Yes,1283.028556,15638.00659 0,Yes,1343.243517,7032.060009 0,Yes,1049.420036,15815.34397 0,Yes,728.9858382,17360.523 0,Yes,1203.103256,19967.8631 0,No,221.3348565,31764.20291 0,No,149.2232188,36550.33346 0,No,474.6283748,28182.97557 0,Yes,962.4665364,14606.09006 0,No,1166.213186,36324.67886 1,Yes,2207.599054,19780.76352 0,Yes,1244.923511,24291.40798 0,No,913.4535992,52793.18025 1,No,1315.558765,35456.69605 0,No,80.71492577,37651.56783 0,Yes,745.2211412,24689.51848 0,No,1505.094083,43036.12115 0,Yes,903.438812,13028.15086 0,No,250.9888204,33568.53155 0,No,857.1507423,34691.32218 0,No,92.97568856,49608.58644 0,No,1363.310379,35387.69892 0,No,146.8971527,41872.17252 0,Yes,2014.497648,15728.93807 0,No,1112.468524,43201.52049 0,Yes,237.1635864,25219.98306 0,No,1018.36679,47723.32678 0,No,150.3023857,39730.16503 0,No,1074.12404,47517.86692 0,No,0,30098.42154 0,Yes,1205.945041,12950.81643 0,Yes,713.2463934,20549.8519 0,No,1351.852966,40177.95896 0,No,254.7280579,53696.8322 0,Yes,842.2559874,23310.22886 0,No,390.9808235,47083.4315 0,Yes,639.4683036,12323.34984 0,Yes,514.3349873,27443.97525 0,No,791.6633418,48127.62614 0,Yes,1204.917904,11339.15677 0,No,1000.525438,53680.98374 0,Yes,810.3080124,16248.45555 0,No,797.8653933,43717.06841 0,No,0,46461.32012 0,No,990.1447723,48040.60189 0,Yes,284.050552,15352.35978 0,No,1080.321281,48226.7297 0,No,1433.992327,19864.97538 0,No,792.9740677,28674.67063 0,No,1351.957437,24136.07195 0,No,528.6768448,39925.18474 0,No,692.0528224,39586.84902 0,No,1085.520235,36746.32035 0,No,567.7041783,50643.31622 0,No,1190.061303,41965.87995 0,Yes,289.3569474,15715.50065 0,No,1440.132927,28370.10432 0,No,373.1788864,30826.07502 0,No,733.2005691,38592.04202 0,Yes,1095.612002,10450.74488 0,No,1117.629704,61940.01578 0,No,505.0151281,43658.81314 0,Yes,230.1591687,24776.69315 0,No,586.7756504,32622.37614 0,Yes,513.7976777,15540.45438 0,Yes,1212.756662,13722.90083 0,No,578.4836466,45066.20733 0,No,1229.520302,59755.03012 0,Yes,1829.909941,21018.96012 0,Yes,973.26448,16984.61076 0,No,690.6403424,26989.39435 0,No,781.6677859,49427.51772 0,No,652.2027614,36348.29624 0,No,762.9231977,50293.87225 0,Yes,843.6424315,17992.31717 0,Yes,1316.210983,16327.62598 0,Yes,559.9031826,9357.759248 0,No,355.0300657,52926.77776 0,No,1635.202902,31541.09404 0,No,696.8956932,63952.31349 0,No,444.0427027,47857.71461 0,No,1585.525089,49011.41567 0,No,1322.080983,48906.51832 0,No,941.7416323,34559.64654 0,Yes,1426.014177,20779.67893 1,Yes,1359.213947,25548.48741 0,No,1504.569386,36697.40199 0,No,48.26564032,32081.0923 0,No,0,47393.32869 0,Yes,802.3692671,18999.52529 0,No,600.9390903,43846.20889 0,No,1440.025713,41814.80384 0,Yes,1184.204443,23235.65679 0,No,31.86206537,33702.65113 0,No,580.3790561,55724.27123 0,Yes,591.9864699,8815.176376 0,No,915.5920094,37720.18727 1,No,2006.986173,50033.53315 0,Yes,1216.58625,18140.62399 0,No,970.9255522,46051.86837 0,No,664.7189435,36998.56601 0,No,873.8796086,46203.90955 0,No,444.2814001,56881.20657 0,No,641.7604481,44857.70123 0,Yes,1106.323694,21202.03008 0,No,616.5546498,46107.20793 0,No,1164.36214,38759.73055 0,No,828.6628118,28427.67029 0,No,23.29833181,37975.53898 0,No,717.4344202,45267.81985 0,No,167.5841567,49087.25569 0,No,821.8532926,35358.39274 0,No,769.6667019,39401.47506 0,No,0,37343.41681 0,No,1038.700266,33783.71422 0,Yes,1050.151333,16963.81269 0,Yes,927.6321006,20640.7741 0,No,1014.722629,38780.78972 0,Yes,1391.441597,11685.64543 0,No,290.483067,47724.23976 0,No,656.4349437,26551.6009 0,No,1456.642937,24089.09534 0,Yes,1240.724012,19702.7995 0,No,636.6060455,31870.55147 0,No,1665.036973,48295.52515 0,No,825.9559447,31661.62599 0,No,0,61608.53205 0,Yes,1152.124419,18644.40004 0,Yes,711.0398753,22000.10656 0,No,1221.083123,29370.58491 0,No,781.2917026,36744.56826 0,No,0,41671.20946 0,No,919.3001361,33866.36338 0,No,155.9899169,50135.85017 0,No,0,42616.60157 0,Yes,1520.851645,20044.0815 0,No,407.3215382,38285.41491 0,No,555.2299512,22788.96808 0,No,705.1971768,35864.01172 0,Yes,916.1289706,28839.58373 0,No,1083.281187,45260.41668 0,Yes,771.1332614,20655.07107 0,No,1700.294976,53844.57899 0,No,679.2668048,36694.75026 0,No,762.9914907,44162.36289 0,Yes,1196.536996,25651.53638 0,No,627.3837596,47489.28308 0,Yes,1175.799057,18178.08193 0,No,660.2005712,33598.08914 0,Yes,822.6253243,10923.38168 0,No,842.4075391,38161.73866 0,No,1248.399685,41555.81099 0,Yes,1405.876654,27071.3225 0,No,419.7020001,44989.76285 0,Yes,91.7339465,19153.72843 0,No,157.0034532,50131.87888 0,No,666.5299507,51876.48965 0,No,806.7812411,55735.5492 0,No,0,44754.24888 0,No,1378.266436,40228.93417 0,No,354.216391,25427.05443 0,No,596.1826889,55591.05892 0,No,102.4698629,44600.43197 0,No,637.2169994,48461.90944 0,No,1274.79655,18044.82312 0,No,403.0414814,29200.98376 0,No,619.5151739,53747.54914 0,No,1111.690766,31059.46264 0,No,1235.485768,29992.18595 0,No,850.3855684,45530.37706 1,Yes,2352.054949,24067.5481 0,No,1518.352515,53971.73059 0,No,0,47935.2832 0,No,541.9440018,37640.30026 0,Yes,1401.193609,17688.85647 0,No,329.4656843,46028.01545 0,No,530.0938205,21736.80982 0,No,0,27455.909 0,No,1112.06414,27694.72127 0,No,291.0761413,23479.61343 0,No,1133.414939,36456.94903 0,Yes,488.9570121,15912.30458 0,No,926.5478994,25363.01464 0,No,783.7975633,33151.56724 0,Yes,246.081819,20046.77246 0,Yes,860.9177306,11575.70219 0,No,1838.194861,32308.02404 0,Yes,918.8288881,16110.99046 0,No,1040.105665,16905.45794 0,No,951.620226,41689.56837 0,No,749.1147396,46478.10516 0,No,70.18547766,36216.96353 0,No,1002.351218,43048.16992 0,No,948.705393,14095.80961 0,No,983.6190158,24666.68283 0,Yes,0,16156.02912 0,Yes,1209.384198,12830.83284 0,No,527.1727849,28931.12828 0,No,671.76548,40907.8081 0,No,807.2945307,49367.77593 0,Yes,1189.46474,17287.63921 0,No,612.1079216,31367.71308 0,No,1.980734441,42380.87485 0,No,507.9075264,45727.76853 0,No,1678.118466,41813.89234 0,No,0,51054.56902 0,Yes,378.6721749,14328.31189 0,Yes,1202.760711,20973.25965 0,Yes,1038.540997,21839.72003 0,No,333.1800575,29400.47785 0,No,1322.251761,43802.62724 0,Yes,1579.216996,15111.91448 0,No,852.5307769,46662.76795 0,No,1407.869208,43033.93593 0,Yes,0,17791.43323 0,No,350.1960855,46893.73199 0,Yes,1276.350839,27732.26882 0,No,17.03168001,24492.21348 1,No,1288.406997,44253.30874 0,No,861.3638604,47969.67726 0,No,1209.756641,40605.81341 0,No,1067.779229,28077.25468 0,No,831.58733,32203.27184 1,No,1747.983669,18966.10957 0,No,0,58211.53607 0,Yes,1177.327655,13737.05034 0,No,0,50559.47075 0,No,943.4672893,26388.61716 0,No,61.18334462,37604.2178 0,No,1436.647669,38571.32806 0,No,343.6431783,43483.73796 0,No,1018.382871,30846.82094 0,Yes,1173.09812,23811.0003 0,No,979.8510491,44869.04691 0,No,444.6047964,51584.33636 0,No,282.0424337,50501.07724 0,Yes,1420.022982,26424.53908 0,No,654.6613601,32400.48148 0,No,1388.563489,21151.26313 0,Yes,0,13621.81119 0,No,0.023816297,37981.78063 0,Yes,1132.084057,15675.53444 0,No,1330.481097,50459.0469 0,No,815.1740233,37946.9814 0,No,1163.32404,38323.0844 0,No,769.2306365,49590.06658 0,No,698.0788729,38960.26719 0,No,0,41239.02051 1,Yes,1789.477475,17667.78222 0,No,1124.966527,50701.72754 0,No,1012.286208,60697.63362 0,No,0,31722.32967 0,No,481.7778056,34478.9529 0,Yes,1383.569824,17245.02869 0,No,856.7704514,42662.80198 0,No,994.0118889,52188.77527 0,No,203.3281235,40490.14499 1,No,1856.914717,33445.61687 0,No,566.8727192,67018.41961 0,No,1428.725258,51633.43285 0,No,1339.199963,44257.9353 0,Yes,215.6338978,23221.61937 1,No,1125.656562,34758.12378 0,No,514.8594362,57241.70308 0,Yes,564.8027621,21314.77944 0,Yes,705.8830645,11121.69931 0,No,390.0217243,39029.85707 0,No,690.6010978,43511.33727 0,Yes,1596.701055,21581.27226 0,No,1578.463711,51413.84027 0,Yes,1363.992957,18140.36103 0,No,819.3263746,22406.05494 0,No,0,44712.23168 0,No,870.0205679,40535.31012 0,No,629.2718914,35695.08861 0,No,217.5124122,39616.26025 0,Yes,1702.981702,25203.45676 0,No,86.57888823,31893.22336 0,Yes,967.1689244,27077.62677 0,Yes,329.7643313,17762.77388 0,No,594.3644282,24273.56248 0,No,51.07230207,38764.39468 0,No,345.3938681,27012.75388 0,Yes,740.5778507,17700.07207 0,No,1128.833475,45043.93562 0,No,1549.263306,43240.45903 0,Yes,472.0245371,13685.47013 0,No,466.1097686,26598.12135 0,No,633.7831603,37672.1581 0,No,959.0214602,46133.13427 0,No,1634.996685,36995.75116 0,No,155.9930613,37599.76797 0,No,1060.97292,37410.37638 0,No,862.1501957,59240.84503 0,Yes,380.9051066,22042.57001 0,No,184.5304739,35058.34065 0,No,1690.38735,46228.32006 0,No,406.9985219,41505.40605 0,No,626.1681253,35196.07823 0,No,1915.332066,21361.04813 0,Yes,1017.360966,21702.1831 0,No,669.3614789,46007.00527 0,No,574.4698776,46696.18634 0,Yes,256.3256705,15627.66279 0,Yes,1365.79119,22301.70606 0,No,1194.645236,35622.32821 0,No,286.3010545,30026.07076 0,Yes,1260.494009,8549.253176 0,No,659.7830853,42054.91499 0,Yes,583.7742232,12240.66052 0,No,1024.819477,40380.29796 0,No,1045.426886,50639.06009 0,No,522.7744089,37481.68219 0,No,212.9317825,46843.91249 0,No,0,54609.72856 0,No,316.5638722,30293.96333 0,No,625.0173287,58911.60865 0,Yes,789.3772662,21140.50887 0,No,454.4881463,39608.32287 0,No,1293.297606,65128.40453 0,No,964.0904134,48405.84311 0,No,316.0175119,39959.93 0,No,826.0227222,53874.37194 0,No,667.4920413,24613.67552 0,Yes,950.4772929,17932.12509 0,No,0,54659.16319 0,No,650.2901385,44358.65162 0,No,448.8819968,38426.25934 0,No,1326.41237,35158.19033 0,No,1322.093977,51845.1804 0,No,575.1376565,25669.69717 0,No,392.4771819,20674.67281 0,Yes,716.0480967,15149.87577 0,No,358.9239515,50989.7442 0,No,946.8920555,9364.849781 0,No,184.8872983,49716.02041 0,No,600.8200858,49727.48991 0,No,965.1504836,34783.00688 0,No,937.0370554,42169.0434 0,No,1225.922648,43718.69493 0,No,581.3131046,32454.11015 0,No,62.11093385,51206.29252 0,No,1653.551876,23137.56122 0,Yes,994.0907388,17401.84724 0,Yes,1080.759733,16900.3906 0,No,663.6637928,51135.08626 0,No,0,44464.29729 0,No,11.13807006,52195.92052 0,Yes,1403.163526,28207.93167 0,No,1005.525429,34587.24363 0,Yes,831.3044957,17572.31793 0,No,154.2124634,29153.45158 0,No,497.8819052,55543.62469 0,Yes,601.8912206,16948.61323 0,No,832.3445356,51737.37105 0,No,1557.559016,36742.18853 0,No,705.3382099,23952.54451 1,No,1234.102021,43764.86864 0,Yes,816.4436082,19141.73021 0,No,768.3785848,43325.97039 0,Yes,1725.339244,18268.19537 0,No,1004.125407,53397.392 0,Yes,952.8871314,15082.67711 0,No,1965.329358,31804.63422 0,No,930.0813016,21575.42385 0,No,499.6463238,69342.67248 0,No,1140.140114,41775.51811 0,No,518.3699058,27527.2348 0,No,1167.889158,35969.02356 0,No,1402.242717,32742.27061 0,No,0,31363.6495 0,No,42.14999323,31812.89153 0,No,989.0177917,35129.09174 0,No,10.23148531,27237.38077 0,Yes,1298.511809,20491.77034 0,No,987.2500449,50045.76318 0,No,920.5743145,40527.98706 0,No,534.0555448,30948.80675 0,Yes,1249.766474,16251.2847 0,Yes,732.4299418,26433.10544 0,No,655.1998724,38192.53039 0,No,224.2091091,51199.64167 0,No,635.808711,40497.38162 0,Yes,1384.415373,13707.0416 0,No,979.1059379,46319.91199 0,Yes,1463.828111,15657.43448 0,No,1302.783778,50709.50863 0,Yes,50.95148626,16251.61106 0,No,1235.187684,50865.52407 0,Yes,1047.225624,18069.25235 0,Yes,1032.625789,21122.95701 0,Yes,1285.956397,24106.58027 0,No,1265.127739,49385.52186 0,No,1032.513001,42495.46261 0,No,566.3670972,39033.32499 0,No,521.8002069,22209.05521 0,Yes,313.2133614,28399.35086 0,Yes,0,18251.72562 0,Yes,982.8003557,20312.21096 0,No,430.4690603,47462.07099 0,Yes,1255.848888,22022.57658 0,No,1082.169496,54099.91952 0,No,450.3274856,52446.41 0,No,814.6550424,42520.09365 0,Yes,1068.454641,13638.70269 0,No,549.1689893,64952.61264 0,No,928.4096682,41482.44353 0,No,1476.762643,54844.51934 0,Yes,785.8549597,19188.91423 0,No,1493.61038,38744.22826 0,No,1074.01591,50491.26051 0,No,693.3584799,46466.29939 0,No,848.6053871,22749.64768 0,No,1233.711874,72461.30139 0,No,1264.132385,35385.1168 0,No,1474.638036,39872.22777 0,No,700.2448482,25919.03935 0,No,803.9755663,41429.24825 0,No,120.843053,36205.87776 0,No,1032.403484,38691.79393 0,No,563.9920899,60222.36765 0,No,377.3636418,37442.84265 0,No,0,26493.24268 0,Yes,688.6979763,18794.48972 0,Yes,583.2484746,17353.83858 0,No,1509.871131,41246.58701 0,No,43.14350042,34316.89015 0,No,860.7512943,36605.23178 0,No,204.0302467,51108.04496 0,No,1059.244387,59865.02823 0,No,1207.741224,33416.60657 0,No,615.8218494,34590.68358 0,No,1018.56813,34103.87952 0,No,1436.670724,46755.28083 1,No,1800.641733,48708.95993 0,No,0,56943.90944 0,Yes,1006.969158,10364.81853 0,No,868.7298917,40902.42961 0,No,1068.577035,26315.04709 0,No,838.8491505,34358.86667 0,No,1278.429972,25590.65652 0,No,264.0160964,45987.47391 0,No,1168.570735,47561.0981 0,Yes,1600.625534,24223.25141 0,No,456.1387522,43943.49589 0,Yes,1578.289555,12778.5985 0,Yes,1205.310031,19889.57075 0,Yes,1115.54336,22552.71309 0,Yes,950.488156,13299.48696 1,No,1547.995445,37524.25353 0,No,256.4389848,40227.38208 0,Yes,1367.855955,12489.81425 0,No,400.8888732,53900.66446 0,Yes,1020.589033,13094.94573 0,No,596.4297067,12135.54156 0,No,289.531497,36841.99817 0,No,775.7339827,37022.00324 0,No,895.3238349,43554.0744 0,No,1798.778917,57191.63224 0,No,1112.455001,40576.19022 0,Yes,1225.345971,5524.37479 0,No,927.6159508,40618.11065 0,No,252.1069537,38494.65469 0,Yes,1128.356209,15669.09653 0,No,424.4959338,23983.77316 0,Yes,1084.446531,26713.7972 0,No,1360.398571,29868.31119 0,Yes,1009.109251,8868.656745 0,No,0,29097.54484 0,No,336.5035487,40626.99417 0,No,537.140426,52513.66722 0,No,600.134861,39395.9784 0,No,938.0901344,46370.82126 1,No,2095.114817,44647.58648 0,No,1176.791505,30579.24922 0,No,1099.62491,48202.21488 0,No,417.646983,54009.45088 0,No,1175.594953,19030.13944 0,No,1245.086295,40290.50389 0,Yes,1381.274512,23903.85455 0,No,1068.986888,37171.65141 0,No,635.7618482,49414.72656 0,No,347.6015108,49332.9942 0,No,1579.909363,45406.51155 0,No,288.1780643,36514.67592 0,Yes,1626.901421,11702.2835 0,Yes,846.1719595,19577.4272 0,No,1395.602656,54148.84857 0,Yes,339.6950982,27387.26248 0,Yes,1634.269726,18036.63059 0,No,75.39869065,50551.03646 0,Yes,375.9188495,20740.07619 0,Yes,770.4087891,1498.227274 0,No,347.3710782,18488.14732 0,No,771.1737061,45956.78731 0,No,1272.076805,21503.19535 0,Yes,985.2408897,17263.75651 0,No,751.3543919,27179.76165 0,No,829.6896647,35594.22391 0,No,910.0878413,25183.76825 0,No,712.9369924,20810.31895 0,No,977.5153311,33690.64931 0,No,1379.287022,57843.88678 0,No,1322.969042,51956.29183 0,No,460.2344394,47305.21604 0,Yes,596.0562474,16084.90771 1,Yes,1538.015603,21356.93711 0,No,471.918843,60656.05079 0,No,1186.885199,27494.32937 0,Yes,541.3347065,21245.32063 0,Yes,1286.355523,20029.89123 0,Yes,1723.216056,23278.99578 0,No,288.8079496,30470.70701 0,Yes,1595.9068,23308.13628 0,No,1064.975106,45737.78123 0,Yes,1941.054062,14756.21148 0,Yes,889.6422514,16701.36935 0,No,604.8395243,27419.42522 0,No,1003.601427,24978.08472 0,Yes,1464.355897,21826.44148 0,No,322.2886981,19420.39978 0,No,639.8670344,34879.55851 0,No,687.1568293,26902.70259 0,Yes,978.2369035,15073.78785 0,No,337.1756682,35040.94162 0,No,880.7508517,34604.67939 0,No,1285.528038,39530.75968 0,No,939.698674,52383.27522 0,Yes,729.8121989,9871.924219 0,No,0,62160.28622 0,Yes,1471.03354,12665.24189 0,Yes,816.965766,13932.20994 0,Yes,1323.395658,23072.54992 0,Yes,383.823401,11777.53769 0,No,1634.731555,40868.33212 0,No,846.3932928,38352.56514 0,No,364.6630512,10239.97249 0,Yes,833.6144952,11078.39028 0,Yes,1867.552268,16650.04176 0,No,823.6097758,34856.52607 0,No,1255.349977,23297.47496 0,No,1024.408305,45202.26554 0,No,0,36922.86795 0,Yes,1447.970793,16421.4555 0,Yes,1057.599366,21570.75279 0,Yes,1027.132901,16259.38055 0,Yes,1212.737041,18090.47792 1,No,1815.174112,23648.41351 0,Yes,167.6379148,16771.56703 0,No,272.5261417,32687.10959 0,No,0,39462.68829 0,No,1387.288646,29712.77243 0,No,624.5478327,48345.33663 0,No,262.9951148,17822.61335 0,No,986.7600897,44605.44511 0,No,0,43930.90369 0,Yes,884.3118999,17542.91967 0,No,929.8301537,45178.05305 0,No,751.8438613,52663.29299 0,Yes,0,18875.77167 0,Yes,879.7105153,17863.84223 0,Yes,1313.976248,20030.42871 0,No,319.1315969,34296.77412 0,No,996.1819846,57972.50444 0,No,1123.434723,45366.81141 0,No,110.5559311,32419.15079 0,No,581.9607584,31654.55892 0,No,982.2264655,38152.69661 0,No,874.6174204,37143.64487 0,No,650.0070413,41427.87732 0,No,1452.684428,43418.82909 0,Yes,1443.860426,19146.29142 0,Yes,0,13911.44128 0,No,737.3487795,50264.76017 0,No,106.1910377,41514.75847 0,Yes,1273.752339,19635.6615 0,No,863.046051,50667.87676 0,No,1504.682568,38223.24598 0,Yes,1182.679045,17287.74847 0,No,853.0604228,39553.36568 0,Yes,160.8871375,19686.7442 0,No,1118.565977,46578.46551 0,Yes,271.6699001,14080.75407 0,No,584.0902554,24580.85684 0,No,911.9315442,40503.00398 0,No,745.8666182,54677.68335 0,Yes,1291.075852,17136.51334 0,No,572.8895007,43381.91527 0,No,445.8292876,42343.80068 0,No,865.4002135,53798.81921 0,No,886.777433,43410.38154 0,Yes,451.695309,11460.4347 0,No,558.321407,42767.32476 0,No,1602.003893,41827.70125 0,No,503.4992852,36521.43251 0,No,521.9028271,47032.59935 0,No,1214.549654,21630.03783 0,Yes,596.2052136,22324.46841 0,No,926.166134,41501.72338 0,No,522.6443371,35893.2878 0,No,986.2647529,38664.78982 0,Yes,885.9235776,22365.85498 0,No,562.3141784,63794.54207 0,No,503.3018571,37749.52078 0,No,1375.384954,47099.20687 0,Yes,1089.405259,19053.58906 0,No,770.4863341,25445.87768 0,No,797.3166569,35034.80691 0,Yes,389.2132418,22907.76746 0,No,589.3910369,38255.53703 1,No,2073.782846,52335.5506 0,No,1128.411463,51778.17985 0,No,1142.6697,40823.37971 0,Yes,1007.537827,16814.2955 0,Yes,789.0991535,18653.63285 1,No,961.732662,27600.4163 0,Yes,1264.649887,23852.64226 0,Yes,1627.45984,19630.1613 0,Yes,979.0653268,17666.3871 0,No,768.8103024,47003.44058 0,No,714.5124913,48115.5047 0,No,223.3563457,51662.5973 0,No,1659.292191,36641.72723 0,No,1062.28401,50894.10365 0,No,38.67912124,30961.74224 0,No,1216.981452,37967.68808 1,No,1825.970599,39407.91703 0,Yes,293.5195709,10592.84669 0,No,1588.876356,45093.44592 0,No,1926.246876,46535.20199 0,No,892.9195198,9299.868186 0,Yes,1583.418031,23434.70871 0,Yes,968.953961,13867.52431 0,No,258.2552591,38309.98805 0,Yes,492.2459418,23689.7895 0,No,1240.573891,35598.88727 1,No,1658.967689,38204.12262 0,Yes,877.1562292,22046.53855 0,Yes,0,20877.8878 0,Yes,1041.310442,12078.77761 0,No,1806.916531,36306.81763 0,No,0,28705.2025 0,No,1500.594004,27354.50025 0,No,1329.911388,50560.76225 0,No,913.8865735,15220.50377 0,No,898.5668285,37919.75068 0,No,0,47231.27308 0,No,457.6015016,49908.59744 0,No,882.0879636,43549.9904 0,No,602.6228949,25376.4501 0,No,513.3962714,19021.64228 0,No,880.7170052,35579.24262 0,No,815.6912947,48646.54151 0,No,1295.613346,39717.73601 0,No,1740.634123,48311.43958 1,No,1506.191848,47061.39454 0,No,906.416188,49815.65114 0,No,1278.017553,41691.19552 0,No,45.82242658,32525.63844 0,No,629.6055874,50113.82071 0,No,270.5878637,33490.00527 0,No,1486.05481,34705.36122 0,Yes,830.6517981,14420.62263 1,Yes,1719.255655,15752.00801 0,No,935.6365984,21730.86127 0,Yes,482.1352042,17459.00419 0,No,335.8245426,29402.76756 0,No,870.4123797,17685.44824 0,No,527.9403591,47963.17904 0,No,1104.306623,40006.13331 0,Yes,1220.938831,6744.042425 0,No,1338.532457,35072.30326 0,Yes,1129.234293,22689.61359 0,No,915.518056,42331.02163 0,No,833.9577066,34174.64532 0,No,381.5665199,49114.89265 0,No,218.5898068,34512.63347 0,No,1156.994528,53450.27359 1,Yes,2046.639515,17910.63447 0,Yes,1368.383018,16449.72494 0,Yes,1302.516318,18210.80642 0,No,1175.289139,35830.04557 0,Yes,256.3677844,18200.88045 0,No,908.9987947,46174.47106 0,Yes,1107.002413,30618.62035 0,No,618.4607312,49385.78803 0,No,932.872998,61192.89713 0,No,547.7177274,50619.36106 1,Yes,1726.47961,13654.60494 0,No,581.0035694,52917.72944 0,No,356.613168,44473.39649 0,No,644.8630705,38794.67614 0,Yes,737.0822476,24637.61749 1,No,1666.113834,25054.76782 0,Yes,1215.470014,20456.43829 0,Yes,1002.775284,11604.28775 0,No,363.2293301,51821.42683 0,No,0,45178.59169 0,No,29.6164765,26710.00957 0,Yes,1036.117398,15067.74824 1,No,959.1592134,59435.23458 0,No,390.5302949,57525.26648 0,No,451.0760516,38348.49032 0,No,1412.996704,53295.87299 0,No,216.5282622,28859.69462 0,Yes,1027.07059,24346.93975 0,No,143.7547093,48250.30757 0,Yes,887.6564222,16993.2876 0,No,1199.980266,38251.18252 0,Yes,1075.179073,21569.37058 0,No,932.6024332,44714.4841 0,Yes,1434.589977,16613.43145 0,No,444.5863003,51386.2831 0,No,853.2122166,31607.3934 0,Yes,973.0478582,23033.85036 0,Yes,102.2830716,20671.04721 1,No,652.3971344,46155.04387 0,No,132.7309018,33370.83434 0,No,1128.470745,49540.70209 0,No,1315.225239,52556.15383 0,Yes,1025.858241,21561.88893 0,No,785.7866574,48179.38288 0,Yes,1775.181537,20385.87352 0,No,362.0550497,50906.48126 0,No,1154.578914,61089.43087 0,No,18.29458177,43247.90342 0,No,0,37517.24641 0,Yes,1740.87005,17478.03129 0,Yes,1228.993901,18810.38436 0,Yes,1669.723709,20301.31598 0,No,1584.565943,45447.18703 0,No,751.1222244,37304.19899 0,No,1063.336261,39301.28181 0,No,710.136461,46308.26965 0,No,1063.724503,26045.92836 0,No,162.784652,33935.49133 0,No,615.0013946,40236.53356 0,No,1011.367192,28172.91537 0,Yes,1658.373575,22010.48568 0,No,1490.983046,39596.05741 0,Yes,1630.488555,16310.53406 0,No,781.2749578,53178.9268 0,No,476.3012118,25774.48762 0,No,892.4450599,28187.54073 0,Yes,1671.523208,9981.897014 0,No,813.8609406,33357.80882 0,No,1280.23019,23647.3332 0,No,564.3948535,30380.64 0,No,525.8713814,8017.638591 0,No,837.328407,61950.803 0,Yes,1847.469647,19388.68931 0,Yes,717.7596662,19567.70277 0,Yes,1935.50422,9431.293871 0,No,808.4947937,56204.66301 0,No,218.739422,53259.76232 0,Yes,118.2014929,15472.65599 0,No,988.1720461,49057.04192 0,Yes,673.3272479,14431.45949 0,Yes,616.3120185,14663.56388 0,Yes,475.8532047,19993.75594 0,No,221.5467559,43668.39232 0,No,959.1720257,38310.08601 0,No,575.6740578,36958.75241 0,No,1017.655914,37241.79099 0,No,1763.84564,36686.34837 0,No,883.3906599,38408.97671 0,No,830.2341,36523.48118 0,No,163.2610271,40600.41547 0,Yes,2.994461997,18710.2648 0,No,51.1859857,39385.75968 0,No,806.2166632,35259.30464 0,No,1186.485885,50371.72546 0,Yes,357.9512904,14338.52874 0,Yes,1590.823099,21784.11312 0,Yes,822.0901693,20906.21784 0,No,1082.815418,41355.98559 0,No,779.477524,34089.79701 0,No,4.67941444,44571.70622 0,No,58.75120527,26939.03991 0,Yes,966.4818283,23116.58021 0,No,683.9218808,37383.23265 0,Yes,0,18235.67437 0,No,1139.020309,24573.92095 0,No,1293.459682,48039.93645 0,No,304.5443388,47217.87221 0,No,1215.148939,51804.99395 0,Yes,305.7879569,16594.52784 0,No,693.7173326,40148.87111 1,Yes,1502.186739,18603.49553 0,No,1645.42318,36839.39621 0,No,279.670673,58566.88422 0,No,1267.485211,40034.56673 0,No,456.3730188,47633.78544 0,No,962.1342815,44696.29053 0,Yes,698.9785363,25103.00945 0,No,619.0292594,42868.58961 0,No,870.9717257,39653.37672 0,No,217.7382263,47657.30628 0,No,713.3260637,44605.35119 0,Yes,306.4338126,13934.51832 0,No,1314.895386,33706.55936 0,No,1057.679669,39708.03334 0,Yes,625.6011998,16134.21151 0,No,1302.655581,28978.55731 0,No,1408.351474,33697.34596 0,Yes,926.569431,19243.40645 0,No,1018.40964,25394.25463 0,No,0,40737.86681 0,No,724.8873243,27800.95238 0,Yes,325.2836312,15962.70988 0,No,445.7173734,25171.86035 0,No,1367.442766,45919.59224 0,No,1353.918741,39879.15905 0,No,1555.96807,34417.87308 0,No,1267.1658,51197.7996 0,Yes,640.8190409,19055.91653 0,No,658.0537091,41882.58065 0,Yes,808.8337865,18791.55956 0,No,819.1126054,50082.88871 0,Yes,1365.022824,23281.3205 0,No,396.8440851,32656.52284 0,No,421.6981304,32290.32596 0,No,969.3588374,46026.71937 0,Yes,1490.075451,15910.80185 0,Yes,1079.077949,17966.76938 0,No,809.4556899,39077.43281 0,Yes,641.8936456,16400.36475 0,No,872.3208931,44407.79016 0,Yes,1004.630153,9643.366151 0,Yes,1170.911536,19473.69193 0,No,1433.6461,44617.7387 0,No,579.2802194,50813.50925 1,No,2128.795992,42096.50237 0,Yes,842.9107463,14462.27646 0,No,4.10094017,42174.17974 0,No,218.3797414,36044.81506 0,Yes,717.8754839,23406.81977 0,Yes,1146.834987,14336.42855 0,No,968.5158515,41714.98984 0,No,1124.587156,45622.94234 0,No,419.0007993,37186.7894 0,Yes,320.7255697,12589.76756 0,No,426.7641498,38495.79802 0,No,949.1775916,36557.96159 0,No,968.7366195,17352.41033 0,No,1188.965168,42286.48817 0,Yes,1436.829989,22925.46992 0,Yes,843.0178966,12268.93352 0,No,1410.959915,58310.25593 0,Yes,598.7333129,23118.5311 0,No,72.9283697,13300.76955 0,No,1347.78085,29345.95821 0,No,0,41160.62251 0,Yes,1280.848066,20575.24501 0,No,1088.574773,43883.14925 0,No,1111.958143,47254.68959 0,No,1124.141744,46052.18163 0,No,0,36337.35217 0,Yes,711.8512316,15132.39359 0,No,548.8392758,44909.0662 0,No,385.2501833,55268.66055 0,No,1132.174903,56152.42904 0,No,399.1183753,48889.98903 0,Yes,810.1022252,15102.32418 0,No,1036.774746,46239.07322 0,No,927.7661181,38197.37354 0,No,999.8260907,43661.14174 0,No,649.2697755,31039.002 0,No,1391.318301,48414.90574 0,No,1058.5637,38917.83093 0,No,415.5417379,44147.22065 0,No,771.1374169,42418.36801 0,No,727.974547,20456.15639 0,No,239.327165,49328.20926 0,Yes,1219.76035,17208.20886 0,No,953.936465,41250.47804 0,No,143.3753202,17435.09638 0,Yes,1669.278106,13771.3934 0,Yes,719.5540535,17195.74454 0,No,295.3071502,45776.84328 0,No,804.4996588,51014.82404 0,No,619.3688154,45886.0785 0,No,1157.116929,38867.06363 0,No,287.8432009,46617.0634 0,Yes,1070.75212,11383.44265 0,No,452.3679123,29254.87102 0,No,492.3523118,39305.22705 0,Yes,1033.041846,18958.31508 0,No,1191.610584,49004.83618 0,Yes,1463.378451,14380.62531 0,No,152.440267,23366.51744 0,No,182.37046,45507.9337 0,No,732.1370669,42693.94026 0,No,1916.088508,23316.57025 0,Yes,419.7107822,18073.00181 0,No,1418.556174,33960.56774 0,Yes,811.6886759,20016.75253 0,No,214.2364951,38720.81076 0,Yes,799.1592677,19547.41318 0,No,831.4561419,58476.73923 0,Yes,713.5598393,24324.14394 0,Yes,870.7730355,16446.35483 0,No,301.3194028,51539.95232 0,Yes,1235.966487,21706.63031 0,No,572.0040551,42484.57059 0,No,545.9932912,52619.62177 0,Yes,1407.040124,25589.74276 0,No,1171.163313,27338.00271 0,No,322.9823275,25267.39762 0,No,497.5406723,43898.4285 0,No,92.1021951,22726.41318 0,No,395.4755073,45084.10562 0,Yes,184.9252469,11126.26618 0,No,209.6744506,35688.13581 0,No,376.1854981,31373.35639 0,No,1492.189654,39004.50294 0,No,905.5317518,43066.06006 0,Yes,627.6681284,18539.56415 0,Yes,1224.520653,22327.4078 0,No,667.3723112,35694.42878 0,No,278.7031614,30113.85339 0,No,1389.803075,48868.09315 0,No,1060.728841,49396.77194 0,Yes,1080.568922,20439.85299 0,Yes,912.5594287,14262.1285 0,No,534.4322264,34129.47541 0,No,280.4616786,26212.90762 0,Yes,801.7215785,18698.40928 0,No,1125.158736,37864.32598 0,Yes,1569.302757,13711.4924 0,No,755.4604432,33460.92621 0,Yes,860.8286768,27186.50002 0,No,1233.618926,49448.171 0,No,699.5684983,48773.81955 0,No,914.8810755,39789.05544 0,No,1650.032103,49481.71303 0,No,366.5943613,33708.78607 0,No,400.6469289,37173.48479 0,No,0,37538.04505 0,No,1902.1499,35008.66617 0,No,982.6310241,60388.0199 0,No,717.9696372,43826.87888 0,No,874.4512007,24707.49025 0,No,1577.034837,33319.31247 0,Yes,1700.572366,20844.71916 0,No,425.8199081,14540.90464 0,No,667.6561036,52946.365 0,No,0,48112.47827 0,Yes,764.8071634,18347.31137 0,No,1030.191299,34250.87454 0,No,426.2863964,38585.54909 0,No,0,43599.45329 0,No,1039.207255,40028.9042 0,No,603.2531381,50214.8553 0,Yes,1446.64376,8797.283915 0,No,1396.207515,39023.48171 0,No,297.2430337,39081.9229 0,No,613.9770765,34752.08764 0,No,828.840863,39984.27347 0,No,675.1149519,58672.05165 0,Yes,1670.467307,15838.31457 1,Yes,1530.551479,13003.9292 0,Yes,356.1987479,21466.20708 0,No,284.1692103,37463.61751 1,No,1698.826142,27374.88752 0,Yes,0,20195.00266 0,Yes,1038.886748,10414.55435 0,No,392.5664548,45469.72228 0,No,23.35710655,56686.91562 0,Yes,1503.972721,14661.8319 0,Yes,799.5164509,23024.85275 0,No,1534.68321,48592.33383 0,Yes,655.4491646,18265.59543 0,Yes,939.7048867,11331.43252 1,Yes,1867.308569,24720.48056 0,No,399.9018204,40863.11505 0,No,381.2585585,55896.23385 0,No,1509.915664,24427.76851 0,No,1209.652028,58174.92106 0,No,1359.802139,46235.23536 0,No,1267.55074,10233.87975 0,No,890.149841,38350.29242 0,No,1508.749012,30587.34398 0,No,1260.154869,35733.46585 0,No,0,33220.98518 0,No,67.38846229,48792.11744 0,No,1132.647803,33677.78145 0,No,0,22074.49369 0,No,861.5900531,32901.01046 0,No,796.2989755,37923.39362 0,No,1356.159222,42262.93007 1,No,1975.653028,38221.83974 0,No,663.6636338,43951.25306 0,No,0,39043.27743 0,No,398.0231243,33056.67119 0,No,304.0779533,47196.07647 0,No,1292.672763,52541.64925 0,No,1664.095652,54087.6411 0,No,1069.719857,34538.99742 0,Yes,618.6241213,20836.79305 0,Yes,1609.136059,16373.94536 0,Yes,1538.449303,15991.22102 0,No,734.2534108,56253.81026 0,No,931.957796,40913.43497 0,Yes,0,17557.96334 0,No,524.8626184,54738.50665 0,No,1135.493293,37962.9829 0,No,1115.961253,30620.26847 0,No,157.6604751,42125.73361 0,Yes,11.39556822,19387.92064 0,Yes,434.8392811,16775.32048 0,Yes,863.321267,20425.85133 0,Yes,822.8866687,22376.22605 0,Yes,1203.946904,20904.83818 0,No,901.6135616,35283.89564 0,No,1000.764561,36709.08378 0,Yes,715.341171,21309.14054 0,No,325.0912751,41257.56074 0,Yes,948.8804092,8503.602563 0,No,751.340141,68883.39527 0,No,1484.777695,32479.08996 0,No,1253.860477,41767.39706 0,No,1262.168412,29545.50021 0,Yes,46.01022532,13950.08695 0,No,0,39469.92899 0,Yes,906.1238153,19275.80014 0,No,273.639788,21911.63387 0,No,1037.766523,53823.22982 0,Yes,508.0502997,14925.96115 0,No,543.9718971,34390.50259 0,Yes,1219.97548,15039.55362 0,Yes,697.1713264,22254.06196 0,Yes,1066.54576,15553.08841 0,No,761.5537246,46836.67227 1,No,1973.822147,27340.01227 0,No,1021.2796,44718.40772 0,No,783.0870843,36917.10317 0,No,1354.332946,29590.82888 0,No,677.7088241,31158.46821 0,No,317.8555578,63021.74795 0,No,141.1284837,41300.72207 0,No,759.2302682,48560.13624 0,No,1133.945796,47188.94778 0,No,0,43593.9536 0,No,599.7188463,48085.88434 0,Yes,529.2734426,20358.83363 0,No,962.1443608,60455.46313 0,No,675.0227331,44480.01305 0,Yes,1038.620081,12312.89596 0,Yes,656.8083859,19017.4278 0,No,1103.723807,39532.90751 0,No,2391.007739,50302.90956 0,No,440.4870289,39395.96014 0,No,1070.864032,36017.00296 0,No,1275.206852,58961.73304 0,No,763.0399763,35277.90178 0,No,781.9044045,65617.64493 0,No,853.7040154,46291.58114 0,No,323.0349157,33673.80969 0,No,980.0371689,31977.56333 0,No,1077.00754,33071.46853 1,No,1335.612871,37595.34427 0,No,438.0803769,44737.65845 0,No,955.4206455,45575.42099 0,Yes,923.0188229,19136.61492 0,No,728.2125497,50403.55925 0,No,259.5062852,40405.3592 0,Yes,606.9514434,26718.87155 0,Yes,512.4497777,24192.88899 0,No,0,43969.2151 0,No,663.6751712,38338.20573 1,No,2288.408082,52043.56905 0,No,0,34836.3407 0,No,1693.642578,26995.69339 0,Yes,906.0337005,22658.76333 0,No,763.5347298,49201.4347 0,No,1218.864986,45593.86021 0,No,496.3754784,33782.5372 0,No,1695.387238,26623.76815 0,Yes,1128.768728,17718.94174 0,No,978.23929,43410.36332 0,No,1084.909923,15981.68909 0,No,1248.887903,31960.4213 0,No,1071.241432,58134.55507 0,No,834.3197877,33687.7504 0,No,1500.572106,39891.8641 0,Yes,958.874769,25282.24105 0,No,981.0626387,54020.43415 0,No,1017.788252,29828.33034 0,Yes,1106.107201,9610.503423 1,No,2148.898454,44309.91717 0,Yes,1113.752598,17810.67306 0,Yes,1882.84179,15968.03654 0,No,1189.241184,29889.60228 0,No,1092.425957,43578.64698 0,No,748.4870239,40727.14156 0,No,631.5128658,35053.3472 0,No,477.9072924,34318.9832 0,No,1128.791563,29722.17266 1,Yes,1627.898323,17546.99702 0,No,625.8701778,53756.04406 0,No,751.3666276,31574.42116 0,No,1074.514538,41545.68354 0,Yes,501.0582476,23090.99719 0,No,876.4748603,39499.27681 0,Yes,580.3514814,17624.15891 0,No,560.2970822,28419.35438 0,Yes,364.1734146,21487.6441 0,No,898.895728,41268.31261 0,Yes,705.9302321,19891.30378 0,No,846.4193819,36115.76209 0,No,813.3640705,44707.67703 0,No,528.882016,44538.55146 0,No,1486.201068,51021.61436 0,No,696.4657513,37599.62013 0,Yes,1775.581701,13735.19087 0,No,319.6023694,53135.36704 0,No,879.035415,52945.81891 0,Yes,421.9572648,21744.97424 0,No,449.312928,47494.43249 0,No,611.1797439,38712.39426 0,No,461.7521043,37617.04367 0,Yes,1478.60088,13375.01572 0,No,1105.467425,32371.40117 0,Yes,734.4591993,17619.48718 0,No,583.3074437,37118.83066 0,No,456.7108145,21892.17721 1,No,1750.25315,51578.94016 0,No,879.6241282,33682.18616 1,No,1515.606239,48688.51209 0,No,13.94447463,31071.63429 0,Yes,776.54428,14229.72812 0,Yes,865.9063158,18064.24399 0,Yes,692.3563178,18689.05214 0,No,415.7682594,38425.7595 0,No,1765.990895,47642.42212 0,No,1391.033877,53255.02112 0,No,0,53946.23365 0,Yes,826.7412128,18856.90003 0,No,896.7219248,48703.41297 0,Yes,1635.175122,13518.93062 0,No,453.6482324,32178.62217 0,No,789.5511785,30777.839 0,Yes,905.1423693,13485.51424 0,No,1439.118794,20187.78769 0,No,1092.530775,43482.7882 0,Yes,871.7507742,14247.94685 0,No,991.3353944,44445.57892 0,Yes,1294.500408,25687.32605 0,Yes,180.620128,20975.5605 0,No,755.4328007,14455.86536 0,No,876.119027,37668.36679 0,Yes,933.3320248,26051.39832 0,No,908.3159335,21287.94249 0,No,218.4175592,25401.13312 0,Yes,915.4398274,16624.33911 1,No,2202.462395,47287.25711 0,No,173.2491717,30697.24506 0,Yes,770.0157407,13684.78995 0,No,739.4180178,40656.95145 0,No,623.5261189,59441.30998 0,No,506.6254535,49861.00341 0,No,875.2416404,52861.7442 0,No,842.9494293,39957.12786 0,Yes,401.3326735,15332.01783 0,No,1092.906583,45479.46699 0,No,0,41740.6866 0,Yes,999.281112,20013.35064 0,No,372.3792385,25374.89909 0,No,658.7995582,54802.07822 0,No,1111.647317,45490.68246 0,No,938.8362414,56633.44874 0,Yes,172.4129875,14955.94169 0,No,711.5550205,52992.37891 0,No,757.9629184,19660.72177 0,No,845.4119892,58636.15698 0,No,1569.009053,36669.11236 0,Yes,200.9221826,16862.95232 ================================================ FILE: data/drinks.csv ================================================ country,beer_servings,spirit_servings,wine_servings,total_litres_of_pure_alcohol,continent Afghanistan,0,0,0,0,AS Albania,89,132,54,4.9,EU Algeria,25,0,14,0.7,AF Andorra,245,138,312,12.4,EU Angola,217,57,45,5.9,AF Antigua & Barbuda,102,128,45,4.9,NAm Argentina,193,25,221,8.3,SA Armenia,21,179,11,3.8,EU Australia,261,72,212,10.4,OC Austria,279,75,191,9.7,EU Azerbaijan,21,46,5,1.3,EU Bahamas,122,176,51,6.3,NAm Bahrain,42,63,7,2,AS Bangladesh,0,0,0,0,AS Barbados,143,173,36,6.3,NAm Belarus,142,373,42,14.4,EU Belgium,295,84,212,10.5,EU Belize,263,114,8,6.8,NAm Benin,34,4,13,1.1,AF Bhutan,23,0,0,0.4,AS Bolivia,167,41,8,3.8,SA Bosnia-Herzegovina,76,173,8,4.6,EU Botswana,173,35,35,5.4,AF Brazil,245,145,16,7.2,SA Brunei,31,2,1,0.6,AS Bulgaria,231,252,94,10.3,EU Burkina Faso,25,7,7,4.3,AF Burundi,88,0,0,6.3,AF Cote d'Ivoire,37,1,7,4,AF Cabo Verde,144,56,16,4,AF Cambodia,57,65,1,2.2,AS Cameroon,147,1,4,5.8,AF Canada,240,122,100,8.2,NAm Central African Republic,17,2,1,1.8,AF Chad,15,1,1,0.4,AF Chile,130,124,172,7.6,SA China,79,192,8,5,AS Colombia,159,76,3,4.2,SA Comoros,1,3,1,0.1,AF Congo,76,1,9,1.7,AF Cook Islands,0,254,74,5.9,OC Costa Rica,149,87,11,4.4,NAm Croatia,230,87,254,10.2,EU Cuba,93,137,5,4.2,NAm Cyprus,192,154,113,8.2,EU Czech Republic,361,170,134,11.8,EU North Korea,0,0,0,0,AS DR Congo,32,3,1,2.3,AF Denmark,224,81,278,10.4,EU Djibouti,15,44,3,1.1,AF Dominica,52,286,26,6.6,NAm Dominican Republic,193,147,9,6.2,NAm Ecuador,162,74,3,4.2,SA Egypt,6,4,1,0.2,AF El Salvador,52,69,2,2.2,NAm Equatorial Guinea,92,0,233,5.8,AF Eritrea,18,0,0,0.5,AF Estonia,224,194,59,9.5,EU Ethiopia,20,3,0,0.7,AF Fiji,77,35,1,2,OC Finland,263,133,97,10,EU France,127,151,370,11.8,EU Gabon,347,98,59,8.9,AF Gambia,8,0,1,2.4,AF Georgia,52,100,149,5.4,EU Germany,346,117,175,11.3,EU Ghana,31,3,10,1.8,AF Greece,133,112,218,8.3,EU Grenada,199,438,28,11.9,NAm Guatemala,53,69,2,2.2,NAm Guinea,9,0,2,0.2,AF Guinea-Bissau,28,31,21,2.5,AF Guyana,93,302,1,7.1,SA Haiti,1,326,1,5.9,NAm Honduras,69,98,2,3,NAm Hungary,234,215,185,11.3,EU Iceland,233,61,78,6.6,EU India,9,114,0,2.2,AS Indonesia,5,1,0,0.1,AS Iran,0,0,0,0,AS Iraq,9,3,0,0.2,AS Ireland,313,118,165,11.4,EU Israel,63,69,9,2.5,AS Italy,85,42,237,6.5,EU Jamaica,82,97,9,3.4,NAm Japan,77,202,16,7,AS Jordan,6,21,1,0.5,AS Kazakhstan,124,246,12,6.8,AS Kenya,58,22,2,1.8,AF Kiribati,21,34,1,1,OC Kuwait,0,0,0,0,AS Kyrgyzstan,31,97,6,2.4,AS Laos,62,0,123,6.2,AS Latvia,281,216,62,10.5,EU Lebanon,20,55,31,1.9,AS Lesotho,82,29,0,2.8,AF Liberia,19,152,2,3.1,AF Libya,0,0,0,0,AF Lithuania,343,244,56,12.9,EU Luxembourg,236,133,271,11.4,EU Madagascar,26,15,4,0.8,AF Malawi,8,11,1,1.5,AF Malaysia,13,4,0,0.3,AS Maldives,0,0,0,0,AS Mali,5,1,1,0.6,AF Malta,149,100,120,6.6,EU Marshall Islands,0,0,0,0,OC Mauritania,0,0,0,0,AF Mauritius,98,31,18,2.6,AF Mexico,238,68,5,5.5,NAm Micronesia,62,50,18,2.3,OC Monaco,0,0,0,0,EU Mongolia,77,189,8,4.9,AS Montenegro,31,114,128,4.9,EU Morocco,12,6,10,0.5,AF Mozambique,47,18,5,1.3,AF Myanmar,5,1,0,0.1,AS Namibia,376,3,1,6.8,AF Nauru,49,0,8,1,OC Nepal,5,6,0,0.2,AS Netherlands,251,88,190,9.4,EU New Zealand,203,79,175,9.3,OC Nicaragua,78,118,1,3.5,NAm Niger,3,2,1,0.1,AF Nigeria,42,5,2,9.1,AF Niue,188,200,7,7,OC Norway,169,71,129,6.7,EU Oman,22,16,1,0.7,AS Pakistan,0,0,0,0,AS Palau,306,63,23,6.9,OC Panama,285,104,18,7.2,NAm Papua New Guinea,44,39,1,1.5,OC Paraguay,213,117,74,7.3,SA Peru,163,160,21,6.1,SA Philippines,71,186,1,4.6,AS Poland,343,215,56,10.9,EU Portugal,194,67,339,11,EU Qatar,1,42,7,0.9,AS South Korea,140,16,9,9.8,AS Moldova,109,226,18,6.3,EU Romania,297,122,167,10.4,EU Russian Federation,247,326,73,11.5,AS Rwanda,43,2,0,6.8,AF St. Kitts & Nevis,194,205,32,7.7,NAm St. Lucia,171,315,71,10.1,NAm St. Vincent & the Grenadines,120,221,11,6.3,NAm Samoa,105,18,24,2.6,OC San Marino,0,0,0,0,EU Sao Tome & Principe,56,38,140,4.2,AF Saudi Arabia,0,5,0,0.1,AS Senegal,9,1,7,0.3,AF Serbia,283,131,127,9.6,EU Seychelles,157,25,51,4.1,AF Sierra Leone,25,3,2,6.7,AF Singapore,60,12,11,1.5,AS Slovakia,196,293,116,11.4,EU Slovenia,270,51,276,10.6,EU Solomon Islands,56,11,1,1.2,OC Somalia,0,0,0,0,AF South Africa,225,76,81,8.2,AF Spain,284,157,112,10,EU Sri Lanka,16,104,0,2.2,AS Sudan,8,13,0,1.7,AF Suriname,128,178,7,5.6,SA Swaziland,90,2,2,4.7,AF Sweden,152,60,186,7.2,EU Switzerland,185,100,280,10.2,EU Syria,5,35,16,1,AS Tajikistan,2,15,0,0.3,AS Thailand,99,258,1,6.4,AS Macedonia,106,27,86,3.9,EU Timor-Leste,1,1,4,0.1,AS Togo,36,2,19,1.3,AF Tonga,36,21,5,1.1,OC Trinidad & Tobago,197,156,7,6.4,NAm Tunisia,51,3,20,1.3,AF Turkey,51,22,7,1.4,AS Turkmenistan,19,71,32,2.2,AS Tuvalu,6,41,9,1,OC Uganda,45,9,0,8.3,AF Ukraine,206,237,45,8.9,EU United Arab Emirates,16,135,5,2.8,AS United Kingdom,219,126,195,10.4,EU Tanzania,36,6,1,5.7,AF USA,249,158,84,8.7,NAm Uruguay,115,35,220,6.6,SA Uzbekistan,25,101,8,2.4,AS Vanuatu,21,18,11,0.9,OC Venezuela,333,100,3,7.7,SA Vietnam,111,2,1,2,AS Yemen,6,0,0,0.1,AS Zambia,32,19,4,2.5,AF Zimbabwe,64,18,4,4.7,AF ================================================ FILE: data/homicides.txt ================================================ 39.311024, -76.674227, iconHomicideShooting, 'p2', '
Leon Nelson
3400 Clifton Ave.
Baltimore, MD 21216
black male, 17 years old
Found on January 1, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.312641, -76.698948, iconHomicideShooting, 'p3', '
Eddie Golf
4900 Challedon Road
Baltimore, MD 21207
black male, 26 years old
Found on January 2, 2007
Victim died at scene
Cause: shooting
' 39.309781, -76.649882, iconHomicideBluntForce, 'p4', '
Nelsene Burnette
2000 West North Ave
Baltimore, MD 21217
black female, 44 years old
Found on January 2, 2007
Victim died at scene
Cause: blunt force
' 39.363925, -76.598772, iconHomicideAsphyxiation, 'p5', '
Thomas MacKenney
5900 Northwood Drive
Baltimore, MD 21212
black male, 21 years old
Found on January 3, 2007
Victim died at scene
Cause: asphyxiation
' 39.238928, -76.602718, iconHomicideBluntForce, 'p6', '
Edward Canupp
500 Maude Ave.
Baltimore, MD 21225
white male, 61 years old
Found on January 5, 2007
Victim died at scene
Cause: blunt force
' 39.352676, -76.607979, iconHomicideShooting, 'p7', '
Michael Cunningham
5200 Ready Ave.
Baltimore, MD 21212
black male, 46 years old
Found on January 5, 2007
Victim died at JHH
Cause: shooting
' 39.310999, -76.622023, iconHomicideShooting, 'p8', '
Ray Alston
300 West North Ave.
Baltimore, MD 21217
black male, 27 years old
Found on January 5, 2007
Victim died at UMMC
Cause: shooting
' 39.311103, -76.584475, iconHomicideShooting, 'p9', '
Yule Henderson
1800 North Montford Ave.
Baltimore, MD 21213
black male, 21 years old
Found on January 7, 2007
Victim died at JHH
Cause: shooting
' 39.348101, -76.564960, iconHomicideShooting, 'p10', '
Marcus McDowell
5100 Harford Road
Baltimore, MD 21214
black male, 16 years old
Found on January 8, 2007
Victim died at Bayview
Cause: shooting
' 39.315050, -76.568647, iconHomicideShooting, 'p11', '
Rodney Gardner
3100 Ravenwood Road
Baltimore, MD 21213
black male, 21 years old
Found on January 8, 2007
Victim died at JHH
Cause: shooting
' 39.319081, -76.690475, iconHomicideShooting, 'p12', '
Troy Chesley
4500 Fairfax Road
Baltimore, MD 21216
black male, 34 years old
Found on January 9, 2007
Victim died at Sinai
Cause: shooting
' 39.317947, -76.614191, iconHomicideShooting, 'p13', '
Gregory Rochester
200 East 25th Street
Baltimore, MD 21218
black male, 25 years old
Found on January 9, 2007
Victim died at scene
Cause: shooting
' 39.234253, -76.603307, iconHomicideShooting, 'p14', '
Melissa Stefanski
600 Washburn Ave.
Baltimore, MD 21225
white female, 23 years old
Found on January 9, 2007
Victim died at scene
Cause: shooting
' 39.352110, -76.609721, iconHomicideShooting, 'p15', '
Antwaine Curbeam
5200 York Road
Baltimore, MD 21212
black male, 30 years old
Found on January 9, 2007
Victim died at JHH
Cause: shooting
' 39.304176, -76.597963, iconHomicideShooting, 'p16', '
William Davis
1200 North Caroline St.
Baltimore, MD 21213
black male, 26 years old
Found on January 9, 2007
Victim died at JHH
Cause: shooting
' 39.324880, -76.595280, iconHomicideShooting, 'p17', '
Richard Crane
1500 East 29th Street
Baltimore, MD 21218
black male, 36 years old
Found on January 13, 2007
Victim died at scene
Cause: shooting
' 39.367143, -76.571191, iconHomicideStabbing, 'p18', '
Dante Watson
2200 Fleetwood Ave.
Baltimore, MD 21214
black male, 21 years old
Found on January 15, 2007
Victim died at JHH
Cause: stabbing
' 39.303363, -76.640377, iconHomicideShooting, 'p19', '
Bonita Madden
1300 North Calhoun St.
Baltimore, MD 21217
black female, 27 years old
Found on January 18, 2007
Victim died at scene
Cause: shooting
' 39.290452, -76.603138, iconHomicideShooting, 'p20', '
Milan Walker
1000 E. Baltimore St.
Baltimore, MD 21215
black male, 30 years old
Found on January 20, 2007
Victim died at scene
Cause: shooting
' 39.330618, -76.532941, iconHomicideShooting, 'p21', '
Anton Jones
5400 Cedonia Ave.
Baltimore, MD 21206
black male, 19 years old
Found on January 20, 2007
Victim died at scene
Cause: shooting
' 39.305189, -76.669075, iconHomicideShooting, 'p22', '
David Thomas
3100 Brighton St
Baltimore, MD 21216
black male, 31 years old
Found on January 22, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.299613, -76.625618, iconHomicideShooting, 'p23', '
Ronald Lewis
500 West Preston St.
Baltimore, MD 21201
black male, 34 years old
Found on January 23, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.304334, -76.593591, iconHomicideShooting, 'p24', '
Tio Floyd
1700 East Biddle street
Baltimore, MD 21213
black male, 24 years old
Found on January 23, 2007
Victim died at JHH
Cause: shooting
' 39.309478, -76.634801, iconHomicideShooting, 'p25', '
Jermall Ford
2100 Madison Ave.
Baltimore, MD 21217
black male, 31 years old
Found on January 24, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.310920, -76.588600, iconHomicideShooting, 'p26', '
Vernon Dredden
1800 North Chester St.
Baltimore, MD 21213
black male, 33 years old
Found on January 27, 2007
Victim died at JHH
Cause: shooting
' 39.349367, -76.687649, iconHomicideShooting, 'p27', '
Kevin Fowlin
3900 West Rogers Ave.
Baltimore, MD 21215
black male, 24 years old
Found on January 27, 2007
Victim died at Sinai
Cause: shooting
' 39.366298, -76.709231, iconHomicideAsphyxiation, 'p28', '
Sintia Mesa
7200 Brook Crest Way
Baltimore, MD 21208
hispanic female, 25 years old
Found on January 29, 2007
Victim died at scene
Cause: asphyxiation
' 39.295311, -76.675544, iconHomicideShooting, 'p29', '
Stephanie Stevens
700 Edgewood St.
Baltimore, MD 21229
white female, 22 years old
Found on January 31, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.240181, -76.607571, iconHomicideShooting, 'p30', '
Ryan Holliman
3500 Second Street
Baltimore, MD 21225
black male, 23 years old
Found on February 1, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.310694, -76.638573, iconHomicideShooting, 'p31', '
Desmond Tucker
2300 Druid Hill Ave.
Baltimore, MD 21217
black male, 52 years old
Found on February 2, 2007
Victim died at scene
Cause: shooting
' 39.342274, -76.604927, iconHomicideShooting, 'p32', '
Darnell Gaither
800 East 43rd street
Baltimore, MD 21212
black male, 34 years old
Found on February 8, 2007
Victim died at JHH
Cause: shooting
' 39.299847, -76.583709, iconHomicideShooting, 'p33', '
Dwight Evans
2400 East Madison Street
Baltimore, MD 21205
black male, 32 years old
Found on February 10, 2007
Victim died at JHH
Cause: shooting
' 39.288248, -76.645234, iconHomicideShooting, 'p34', '
Alusine Kamara
1800 West Baltimore Street
Baltimore, MD 21223
black male, 26 years old
Found on February 11, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.287917, -76.652864, iconHomicideShooting, 'p35', '
Harold Robinson
2300 West Baltimore
Baltimore, MD 21223
black male, 39 years old
Found on February 11, 2007
Victim died at scene
Cause: shooting
' 39.343423, -76.677113, iconHomicideShooting, 'p36', '
George Baskerville
3700 Oakmont Ave.
Baltimore, MD 21215
black male, 28 years old
Found on February 17, 2007
Victim died at Sinai
Cause: shooting
' 39.301874, -76.595066, iconHomicideShooting, 'p37', '
David Frasier
1000 Branch Water Ct
Baltimore, MD 21205
black male, 29 years old
Found on February 18, 2007
Victim died at JHH
Cause: shooting
' 39.301893, -76.594621, iconHomicideShooting, 'p38', '
Brian Lessane
1000 Broadway
Baltimore, MD 21205
black male, 19 years old
Found on February 19, 2007
Victim died at JHH
Cause: shooting
' 39.294303, -76.584805, iconHomicideShooting, 'p39', '
Darnell Cain
2300 East Fayette St.
Baltimore, MD 21224
black male, 37 years old
Found on February 19, 2007
Victim died at JHH
Cause: shooting
' 39.315515, -76.611204, iconHomicideShooting, 'p40', '
Charles Pace
2300 Barclay St.
Baltimore, MD 21218
black male, 22 years old
Found on February 19, 2007
Victim died at JHH
Cause: shooting
' 39.311964, -76.647326, iconHomicideShooting, 'p41', '
Andre Jones
1800 Clifton Ave.
Baltimore, MD 21217
black male, 27 years old
Found on February 21, 2007
Victim died at shock trauma
Cause: shooting
' 39.308375, -76.585015, iconHomicideShooting, 'p42', '
Daniel Savage
1500 North Bradford St.
Baltimore, MD 21213
black male, 32 years old
Found on February 21, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.306680, -76.647097, iconHomicideShooting, 'p43', '
Antonio Harris
1600 McKean Ave.
Baltimore, MD 21217
black male, 18 years old
Found on February 23, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.285221, -76.645013, iconHomicideStabbing, 'p44', '
William Duck IV
200 South Fulton ave
Baltimore, MD 21223
black male, 21 years old
Found on February 24, 2007
Victim died at Shock Trauma
Cause: stabbing
' 39.297648, -76.645809, iconHomicideShooting, 'p45', '
Vernon Carter
1800 West Lanvale St.
Baltimore, MD 21217
black male, 25 years old
Found on February 26, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.314416, -76.612624, iconHomicideShooting, 'p46', '
Vic Fenner
2200 Guilford Ave.
Baltimore, MD 21218
black male, 17 years old
Found on February 27, 2007
Victim died at JHH
Cause: shooting
' 39.277771, -76.615648, iconHomicideShooting, 'p47', '
Thomas Terry
100 West Hamburg St
Baltimore, MD 21230
black male, 19 years old
Found on March 3, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.299104, -76.575948, iconHomicideShooting, 'p48', '
Anthony Brown
700 North Curley St
Baltimore, MD 21205
black male, 20 years old
Found on March 4, 2007
Victim died at JHH
Cause: shooting
' 39.310810, -76.652745, iconHomicideShooting, 'p49', '
Michael Woods
2000 North Smallwood St.
Baltimore, MD 21216
black male, 28 years old
Found on March 5, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.302166, -76.670222, iconHomicideShooting, 'p50', '
Richard Stuckey
1115 North Ellamont St
Baltimore, MD 21216
black male, 17 years old
Found on March 6, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.302450, -76.668326, iconHomicideShooting, 'p51', '
Anthony Bryan
3000 Rosedale Court
Baltimore, MD 21216
black male, 37 years old
Found on March 9, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.309878, -76.648194, iconHomicideShooting, 'p52', '
Allen Coates
1900 North Monroe St.
Baltimore, MD 21217
black male, 36 years old
Found on March 11, 2007
Victim died at Sinai
Cause: shooting
' 39.346773, -76.674769, iconHomicideShooting, 'p53', '
Damon Smith
5000 Denmore Ave.
Baltimore, MD 21215
black male, 40 years old
Found on March 11, 2007
Victim died at Sinai
Cause: shooting
' 39.295606, -76.661463, iconHomicideShooting, 'p54', '
Mark Robinson
2700 Harlem Ave.
Baltimore, MD 21216
black male, 48 years old
Found on March 12, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.354406, -76.535599, iconHomicideShooting, 'p55', '
Tyrone Jackson
6500 Brook Ave.
Baltimore, MD 21216
black male, 19 years old
Found on March 13, 2007
Victim died at JHH
Cause: shooting
' 39.315233, -76.594642, iconHomicideShooting, 'p56', '
Steven Washington
1600 Cliftview Ave.
Baltimore, MD 21213
black male, 17 years old
Found on March 13, 2007
Victim died at JHH
Cause: shooting
' 39.319077, -76.567534, iconHomicideShooting, 'p57', '
Christopher Clarke
3100 Cliftmont Ave.
Baltimore, MD 21213
black male, 18 years old
Found on March 13, 2007
Victim died at JHH
Cause: shooting
' 39.344856, -76.570641, iconHomicideShooting, 'p58', '
Antwan Askins
2900 List Ave
Baltimore, MD 21214
black male, 27 years old
Found on March 13, 2007
Victim died at Bayview
Cause: shooting
' 39.282398, -76.687493, iconHomicideStabbing, 'p59', '
Michael Stuckey
100 Diener Place
Baltimore, MD 21229
black male, 49 years old
Found on March 14, 2007
Victim died at Shock Trauma
Cause: stabbing
' 39.300416, -76.549294, iconHomicideBluntForce, 'p60', '
Charles Erdman
5800 Erdman Ave.
Baltimore, MD 21205
white male, 65 years old
Found on March 15, 2007
Victim died at JHH
Cause: blunt force
' 39.347934, -76.670281, iconHomicideShooting, 'p61', '
Rodney Easton
4900 Queensberry Ave
Baltimore, MD 21215
black male, 21 years old
Found on March 17, 2007
Victim died at scene
Cause: shooting
' 39.300121, -76.576681, iconHomicideShooting, 'p62', '
Edwin Mathews
2900 East Madison St
Baltimore, MD 21205
black male, 30 years old
Found on March 17, 2007
Victim died at scene
Cause: shooting
' 39.296419, -76.660473, iconHomicideShooting, 'p63', '
Charles Hargrove
2600 Rayner Ave
Baltimore, MD 21216
black male, 19 years old
Found on March 20, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.341096, -76.609483, iconHomicideShooting, 'p64', '
Ricardo Paige
502 East 43rd Street
Baltimore, MD 21212
black male, 54 years old
Found on March 20, 2007
Victim died at scene
Cause: shooting
' 39.299517, -76.624003, iconHomicideShooting, 'p65', '
Shawn Weaver
900 McCulloh St
Baltimore, MD 21201
black male, 17 years old
Found on March 21, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.297212, -76.601856, iconHomicideBluntForce, 'p66', '
Theresa Parker
1200 Treeleaf Court
Baltimore, MD 21202
black female, 39 years old
Found on March 25, 2007
Victim died at scene
Cause: blunt force
' 39.296901, -76.638583, iconHomicideStabbing, 'p67', '
Artesha Moses
700 North Carey St.
Baltimore, MD 21217
black female, 18 years old
Found on March 27, 2007
Victim died at Shock Trauma
Cause: stabbing
' 39.304770, -76.582672, iconHomicideShooting, 'p68', '
Ronald Harmon
2500 East Biddle St.
Baltimore, MD 21213
black male, 17 years old
Found on March 27, 2007
Victim died at JHH
Cause: shooting
' 39.278873, -76.540463, iconHomicideShooting, 'p69', '
Estefany Gonzalez
6300 Toone St.
Baltimore, MD 21224
hispanic female, 16 years old
Found on March 30, 2007
Victim died at Bayview
Cause: shooting
' 39.293053, -76.569438, iconHomicideShooting, 'p70', '
David Johns
3400 Noble St.
Baltimore, MD 21224
black male, 23 years old
Found on March 30, 2007
Victim died at JHH
Cause: shooting
' 39.368493, -76.561814, iconHomicideShooting, 'p71', '
Pelvin Derrien
6800 Sturbridge Drive
Baltimore, MD 21234
black male, 23 years old
Found on March 30, 2007
Victim died at JHH
Cause: shooting
' 39.367743, -76.568454, iconHomicideShooting, 'p72', '
Andre McBride
6600 Knottwood Court
Baltimore, MD 21214
black male, 21 years old
Found on March 31, 2007
Victim died at Sinai
Cause: shooting
' 39.234076, -76.594904, iconHomicideShooting, 'p73', '
Darrell Smith
3600 St Margaret St
Baltimore, MD 21225
black male, 21 years old
Found on April 2, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.281752, -76.634010, iconHomicideStabbing, 'p74', '
Eric Zurawski
1300 West Ostend St.
Baltimore, MD 21223
white male, 39 years old
Found on April 8, 2007
Victim died at Shock Trauma
Cause: stabbing
' 39.284797, -76.676177, iconHomicideShooting, 'p75', '
John Daughtry
100 South Morley St.
Baltimore, MD 21229
black male, 25 years old
Found on April 9, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.300065, -76.578119, iconHomicideShooting, 'p76', '
Tavon Campbell
2800 Madison St
Baltimore, MD 21205
black male, 20 years old
Found on April 11, 2007
Victim died at Hopkins
Cause: shooting
' 39.345123, -76.612623, iconHomicideStabbing, 'p77', '
Brent Flanagan
2600 West Coldspring Lane
Baltimore, MD 21215
black male, 16 years old
Found on April 12, 2007
Victim died at scene
Cause: stabbing
' 39.290684, -76.643912, iconHomicideShooting, 'p78', '
Kevin Randall
200 N.Mount St.
Baltimore, MD 21223
black male, 45 years old
Found on April 18, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.284069, -76.652610, iconHomicideShooting, 'p79', '
Johnnie James
300 South Bentalou St.
Baltimore, MD 21223
black male, 25 years old
Found on April 18, 2007
Victim died at St. Agnes
Cause: shooting
' 39.253198, -76.623167, iconHomicideShooting, 'p80', '
Christopher Wayman
2400 Seabury Road
Baltimore, MD 21225
black male, 23 years old
Found on April 19, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.341365, -76.601631, iconHomicideShooting, 'p81', '
Joseph Ensey Sr.
4100 St. Georges Ave.
Baltimore, MD 21218
white male, 45 years old
Found on April 20, 2007
Cause: shooting
' 39.286176, -76.646850, iconHomicideShooting, 'p82', '
Van Johnson
1900 West Lombard St.
Baltimore, MD 21223
black male, 29 years old
Found on April 22, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.342089, -76.673344, iconHomicideShooting, 'p83', '
Damon Dubose
3500 Woodland Ave.
Baltimore, MD 21215
black male, 23 years old
Found on April 22, 2007
Victim died at Sinai
Cause: shooting
' 39.288265, -76.612197, iconHomicideStabbing, 'p84', '
Ernest Buchanan
31 South Calvert St.
Baltimore, MD 21202
white male, 18 years old
Found on April 26, 2007
Victim died at Shock Trauma
Cause: stabbing
' 39.305936, -76.669110, iconHomicideShooting, 'p85', '
Dewitt Smith
1600 North Rosedale St.
Baltimore, MD 21216
black male, 25 years old
Found on April 27, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.343144, -76.568685, iconHomicideShooting, 'p86', '
Ronald Daniels
4700 Harford Road
Baltimore, MD 21214
black male, 35 years old
Found on April 29, 2007
Victim died at scene
Cause: shooting
' 39.280370, -76.541686, iconHomicideShooting, 'p87', '
Azerwoine Walker
6200 Elliott St
Baltimore, MD 21224
black male, 30 years old
Found on April 29, 2007
Victim died at Bayview
Cause: shooting
' 39.291714, -76.693725, iconHomicideShooting, 'p88', '
Lonnie Plateo
4600 Old Frederick Road
Baltimore, MD 21229
black male, 36 years old
Found on April 29, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.285095, -76.648511, iconHomicideShooting, 'p89', '
Leroy Sanders
2000 West Pratt St.
Baltimore, MD 21223
black male, 22 years old
Found on April 29, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.297583, -76.623702, iconHomicideShooting, 'p90', '
Jamal Knox
500 Half Mile Court
Baltimore, MD 21201
black male, 16 years old
Found on April 30, 2007
Victim died at scene
Cause: shooting
' 39.310787, -76.591604, iconHomicideShooting, 'p91', '
Eric Queen
1900 East Lafayette Ave.
Baltimore, MD 21213
black male, 24 years old
Found on April 30, 2007
Victim died at JHH
Cause: shooting
' 39.328917, -76.660273, iconHomicideShooting, 'p92', '
Deshaun White
3600 Reisterstown Road
Baltimore, MD 21215
black male, 31 years old
Found on April 30, 2007
Victim died at Sinai
Cause: shooting
' 39.254057, -76.620404, iconHomicideUnknown, 'p93', '
Abdul Azzie
3301 Waterview Ave.
Baltimore, MD 21230
black male, 18 years old
Found on May 1, 2007
Victim died at scene
Cause: unknown
' 39.298047, -76.668032, iconHomicideShooting, 'p94', '
Larry Brockington
900 North Rosedale St.
Baltimore, MD 21216
black male, 31 years old
Found on May 1, 2007
Victim died at scene
Cause: shooting
' 39.314478, -76.611136, iconHomicideShooting, 'p95', '
Derius Harmon
2200 Barclay St
Baltimore, MD 21218
black male
Found on May 2, 2007
Victim died at scene
Cause: shooting
' 39.286282, -76.643641, iconHomicideShooting, 'p96', '
Matthew Davis
100 S. Mount St
Baltimore, MD 21223
black male, 23 years old
Found on May 3, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.282153, -76.637361, iconHomicideShooting, 'p97', '
Adrian Beasley
1300 Herkimer St
Baltimore, MD 21223
black male, 23 years old
Found on May 5, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.293636, -76.598033, iconHomicideShooting, 'p98', '
Rocky Bottoms
200 North Spring Court
Baltimore, MD
black male, 24 years old
Found on May 6, 2007
Victim died at JHH
Cause: shooting
' 39.333575, -76.696199, iconHomicideShooting, 'p99', '
Michael Davis
3700 Woodbine Ave.
Baltimore, MD 21207
black male, 25 years old
Found on May 7, 2007
Victim died at Sinai
Cause: shooting
' 39.306709, -76.598118, iconHomicideShooting, 'p100', '
Thomas Mouzon
1400 North Caroline St
Baltimore, MD 21213
black male, 23 years old
Found on May 7, 2007
Victim died at JHH
Cause: shooting
' 39.315647, -76.596416, iconHomicideShooting, 'p101', '
John Graves
1500 Cliftview Ave.
Baltimore, MD 21213
black male, 26 years old
Found on May 7, 2007
Victim died at JHH
Cause: shooting
' 39.318043, -76.559050, iconHomicideShooting, 'p102', '
William Curtis
4000 Raymonn Ave.
Baltimore, MD 21213
black male, 23 years old
Found on May 8, 2007
Victim died at scene
Cause: shooting
' 39.315826, -76.612696, iconHomicideShooting, 'p103', '
Deandre Hatcher
300 East 23 1/2 St
Baltimore, MD 21218
black male, 17 years old
Found on May 10, 2007
Victim died at JHH
Cause: shooting
' 39.300760, -76.587987, iconHomicideShooting, 'p104', '
Gerald Wilson
900 North Chester St
Baltimore, MD 21205
black male, 22 years old
Found on May 11, 2007
Victim died at JHH
Cause: shooting
' 39.314671, -76.601985, iconHomicideShooting, 'p105', '
Antwoine Hawkins
2200 Robb St
Baltimore, MD 21218
black male, 29 years old
Found on May 12, 2007
Victim died at scene
Cause: shooting
' 39.309494, -76.597588, iconHomicideShooting, 'p106', '
Todd Little
1700 North Dallas St.
Baltimore, MD 21213
black male, 29 years old
Found on May 12, 2007
Victim died at scene
Cause: shooting
' 39.318677, -76.598933, iconHomicideShooting, 'p107', '
Nathaniel Hicks
2500 Garrett Ave.
Baltimore, MD 21218
black male, 30 years old
Found on May 14, 2007
Victim died at JHH
Cause: shooting
' 39.281946, -76.651071, iconHomicideShooting, 'p108', '
Deandre Salmond
500 South Smallwood St.
Baltimore, MD 21223
black male, 19 years old
Found on May 15, 2007
Victim died at Shock Traum
Cause: shooting
' 39.350325, -76.609653, iconHomicideShooting, 'p109', '
Earl Cornish III
5100 York Road
Baltimore, MD 21212
black male, 21 years old
Found on May 16, 2007
Victim died at Sinai
Cause: shooting
' 39.288930, -76.596624, iconHomicideShooting, 'p110', '
Robert Perlie
200 North Dallas Court
Baltimore, MD 21231
black male, 16 years old
Found on May 17, 2007
Victim died at JHH
Cause: shooting
' 39.287899, -76.653612, iconHomicideBluntForce, 'p111', '
Alvin Parson
1 Gorman Ave.
Baltimore, MD 21223
black male, 22 years old
Found on May 19, 2007
Victim died at Shock Trauma
Cause: blunt force
' 39.296297, -76.567795, iconHomicideBluntForce, 'p112', '
Tarik Tynes
3600 Pulaski Highway
Baltimore, MD 21224
black male, 35 years old
Found on May 19, 2007
Victim died at scene
Cause: blunt force
Read the article
' 39.294303, -76.584799, iconHomicideShooting, 'p113', '
Alexander Rose
2301 East Fayette St.
Baltimore, MD 21224
black male, 23 years old
Found on May 22, 2007
Victim died at JHH
Cause: shooting
' 39.313356, -76.613934, iconHomicideShooting, 'p114', '
Adrian Smith
200 East 21st St.
Baltimore, MD 21218
black male, 23 years old
Found on May 22, 2007
Victim died at JHH
Cause: shooting
' 39.304683, -76.611988, iconHomicideShooting, 'p115', '
Curtis Taylor
1300 Guilford Ave.
Baltimore, MD 21202
black male, 22 years old
Found on May 22, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.284737, -76.653064, iconHomicideShooting, 'p116', '
Jasman Elmore
2300 Frederick Ave.
Baltimore, MD 21223
black male, 18 years old
Found on May 23, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.305265, -76.647936, iconHomicideBluntForce, 'p117', '
Voltaire Conway
1900 Presstman St.
Baltimore, MD 21217
black male, 40 years old
Found on May 23, 2007
Victim died at Shock Trauma
Cause: blunt force
Read the article
' 39.354392, -76.580746, iconHomicideShooting, 'p118', '
Perry Brooks
1900 Hillenwood Ave.
Baltimore, MD 21239
black male, 49 years old
Found on May 25, 2007
Victim died at scene
Cause: shooting
' 39.339750, -76.685430, iconHomicideStabbing, 'p119', '
Renard Maith
4000 Belvieu Ave.
Baltimore, MD 21215
black male, 52 years old
Found on May 25, 2007
Victim died at scene
Cause: stabbing
' 39.307232, -76.643930, iconHomicideShooting, 'p120', '
Brian Johnson
1600 Vincent Court
Baltimore, MD 21217
black male, 31 years old
Found on May 26, 2007
Victim died at scene
Cause: shooting
' 39.296389, -76.649099, iconHomicideShooting, 'p121', '
Amin Reed
700 North Payson St.
Baltimore, MD 21217
black male, 30 years old
Found on May 26, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.332409, -76.535064, iconHomicideShooting, 'p122', '
Davon Williams
5800 Waycross Road
Baltimore, MD 21206
black male, 19 years old
Found on May 26, 2007
Victim died at Good Sam
Cause: shooting
' 39.292576, -76.608668, iconHomicideStabbing, 'p123', '
David Bishop
200 North Gay St.
Baltimore, MD 21202
black male, 32 years old
Found on May 27, 2007
Victim died at Shock Trauma
Cause: stabbing
' 39.306379, -76.655726, iconHomicideShooting, 'p124', '
Laron Henderson
1600 Moreland Ave.
Baltimore, MD 21216
black male, 27 years old
Found on May 29, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.299868, -76.645943, iconHomicideShooting, 'p125', '
Jourman Parson
1800 West Mosher St.
Baltimore, MD 21217
black male, 17 years old
Found on May 29, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.311024, -76.674227, iconHomicideShooting, 'p126', '
Neil Rather
3400 Clifton Ave.
Baltimore, MD 21216
black male, 18 years old
Found on May 29, 2007
Victim died at Sinai
Cause: shooting
' 39.290361, -76.654431, iconHomicideShooting, 'p127', '
John Drew
2400 West Lexington St.
Baltimore, MD 21223
black male, 26 years old
Found on May 30, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.313997, -76.599438, iconHomicideShooting, 'p128', '
Charles Payne
2100 Sherwood Ave.
Baltimore, MD 21218
black male, 31 years old
Found on June 1, 2007
Victim died at scene
Cause: shooting
' 39.315175, -76.640307, iconHomicideStabbing, 'p129', '
Shirley Cooper
2600 Madison Ave.
Baltimore, MD 21217
black female, 72 years old
Found on June 2, 2007
Victim died at scene
Cause: stabbing
' 39.330675, -76.659828, iconHomicideShooting, 'p130', '
David Washington
2700 Violet Ave.
Baltimore, MD 21215
black male, 24 years old
Found on June 2, 2007
Victim died at Sinai
Cause: shooting
' 39.297589, -76.647474, iconHomicideShooting, 'p131', '
Curtis Alexander
1900 West Lanvale St.
Baltimore, MD 21217
black male, 28 years old
Found on June 3, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.325798, -76.666210, iconHomicideBluntForce, 'p132', '
Virginia Jones
3600 Rosedale Road
Baltimore, MD 21215
black female, 81 years old
Found on June 3, 2007
Victim died at Georgetown Medical Center
Cause: blunt force
Read the article
' 39.325377, -76.591931, iconHomicideShooting, 'p133', '
Tyrone Bonner Jr.
1700 East 30th St.
Baltimore, MD 21218
black male, 30 years old
Found on June 5, 2007
Victim died at JHH
Cause: shooting
' 39.321867, -76.618636, iconHomicideShooting, 'p134', '
Ghulam Mustafa
100 West 28th St.
Baltimore, MD 21218
Other male, 33 years old
Found on June 5, 2007
Victim died at JHH
Cause: shooting
' 39.312491, -76.587502, iconHomicideShooting, 'p135', '
Craig Hunter
1900 North Collington Ave.
Baltimore, MD 21213
black male, 29 years old
Found on June 8, 2007
Victim died at JHH
Cause: shooting
' 39.314411, -76.568089, iconHomicideShooting, 'p136', '
Demetrius Burnette
3600 Bonview Ave.
Baltimore, MD 21213
black male, 31 years old
Found on June 8, 2007
Victim died at Bayview
Cause: shooting
' 39.304962, -76.605071, iconHomicideShooting, 'p138', '
Melvin Jordan
900 East Preston St.
Baltimore, MD 21202
black male, 32 years old
Found on June 9, 2007
Victim died at JHH
Cause: shooting
' 39.291679, -76.589815, iconHomicideStabbing, 'p139', '
Michael Simms
1 South Chapel St.
Baltimore, MD 21231
black male, 18 years old
Found on June 10, 2007
Victim died at JHH
Cause: stabbing
' 39.290478, -76.652988, iconHomicideShooting, 'p140', '
Barbara Griffin
200 North Bentalou St.
Baltimore, MD 21223
black female, 18 years old
Found on June 11, 2007
Victim died at scene
Cause: shooting
' 39.305050, -76.672058, iconHomicideShooting, 'p141', '
Juan Taylor
3300 Brighton St.
Baltimore, MD 21216
black male, 44 years old
Found on June 11, 2007
Victim died at scene
Cause: shooting
' 39.320773, -76.596766, iconHomicideShooting, 'p142', '
Curtis Washington
1600 Montpelier St.
Baltimore, MD 21218
black male, 17 years old
Found on June 12, 2007
Victim died at JHH
Cause: shooting
' 39.314491, -76.671232, iconHomicideShooting, 'p143', '
Sterling Carr Jr.
2400 North Ellamont St.
Baltimore, MD 21216
black male, 28 years old
Found on June 13, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.286956, -76.671618, iconHomicideShooting, 'p144', '
Tyree Williams
3200 Phelps Lane
Baltimore, MD 21229
black male, 22 years old
Found on June 13, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.315230, -76.636146, iconHomicideShooting, 'p145', '
Maurice Gordon
2500 Linden Ave.
Baltimore, MD 21217
black male, 15 years old
Found on June 14, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.285176, -76.646773, iconHomicideShooting, 'p146', '
Richard Jones
1900 West Pratt St.
Baltimore, MD 21223
black male, 37 years old
Found on June 15, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.249850, -76.639488, iconHomicideBluntForce, 'p147', '
Alvis Harris
3300 Annapolis Road
Baltimore, MD 21230
black male, 40 years old
Found on June 16, 2007
Victim died at scene
Cause: blunt force
' 39.317554, -76.660270, iconHomicideShooting, 'p148', '
Marcarian Grimes
2600 Fairview ave.
Baltimore, MD 21215
black male, 23 years old
Found on June 17, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.302220, -76.687530, iconHomicideShooting, 'p149', '
Name not yet released
1300 North Woodington road
Baltimore, MD 21229
black male, 43 years old
Found on June 17, 2007
Victim died at scene
Cause: shooting
' 39.341032, -76.671108, iconHomicideShooting, 'p150', '
David Carter
3400 St. Ambrose Ave.
Baltimore, MD 21215
black male, 26 years old
Found on June 18, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.321284, -76.624068, iconHomicideShooting, 'p151', '
Ronnie Bundy
2900 Miles Ave.
Baltimore, MD 21211
black male, 21 years old
Found on June 19, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.222987, -76.588187, iconHomicideStabbing, 'p152', '
Phillip Airey
4800 Pennington Ave.
Baltimore, MD 21226
white male, 36 years old
Found on June 19, 2007
Victim died at scene
Cause: stabbing
Read the article
' 39.325177, -76.605058, iconHomicideShooting, 'p153', '
George Wilson
3100 Ellerslie Ave.
Baltimore, MD 21218
black male, 24 years old
Found on June 21, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.301968, -76.632700, iconHomicideShooting, 'p154', '
Kyle Lewis
1500 Pennsylvania Ave.
Baltimore, MD 21217
black male, 27 years old
Found on June 23, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.353866, -76.656348, iconHomicideAsphyxiation, 'p155', '
Jewels Cook
2300 Cylburn Ave.
Baltimore, MD 21209
black male, 36 years old
Found on June 27, 2007
Cause: asphyxiation
Read the article
' 39.332963, -76.670198, iconHomicideShooting, 'p156', '
Joseph Johnson
4000 Cedardale Road
Baltimore, MD 21215
black male, 29 years old
Found on June 29, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.289255, -76.600229, iconHomicideShooting, 'p157', '
Paul Cornish
1100 Granby St.
Baltimore, MD 21202
black male, 28 years old
Found on June 30, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.348038, -76.576973, iconHomicideShooting, 'p158', '
Gerald Smith
4800 Herring Run Drive
Baltimore, MD 21214
black male, 25 years old
Found on July 1, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.299805, -76.585188, iconHomicideShooting, 'p159', '
Davon Turner
800 N. Patterson Park Ave.
Baltimore, MD 21205
black male, 21 years old
Found on July 1, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.312447, -76.612506, iconHomicideShooting, 'p160', '
Allen Burton
2000 Guilford Ave.
Baltimore, MD 21218
black male, 39 years old
Found on July 2, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.323701, -76.537282, iconHomicideShooting, 'p161', '
Nathaniel Price
4900 Greencrest Road
Baltimore, MD 21206
black male, 30 years old
Found on July 2, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.307413, -76.596774, iconHomicideShooting, 'p162', '
Christopher Barrett
1500 N. Bond St.
Baltimore, MD 21213
black male, 52 years old
Found on July 2, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.302754, -76.607642, iconHomicideShooting, 'p163', '
Antonio Tracey
1100 Greenmount Ave.
Baltimore, MD 21202
black male, 31 years old
Found on July 2, 2007
Victim died at JHH
Cause: shooting
' 39.309601, -76.595106, iconHomicideStabbing, 'p164', '
Phyllis Johnson
1700 N. Broadway
Baltimore, MD 21213
black female, 40 years old
Found on July 3, 2007
Victim died at JHH
Cause: stabbing
Read the article
' 39.297501, -76.649165, iconHomicideShooting, 'p165', '
George Johnson
800 N. Payson St.
Baltimore, MD 21217
black male, 25 years old
Found on July 6, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.343771, -76.568201, iconHomicideBluntForce, 'p166', '
Mario Mauro
3000 Rosekemp Ave.
Baltimore, MD 21214
white male, 53 years old
Found on July 7, 2007
Victim died at JHH
Cause: blunt force
' 39.298662, -76.614146, iconHomicideBluntForce, 'p167', '
Ashley Bellosi
800 St. Paul St.
Baltimore, MD 21202
white female, 23 years old
Found on July 7, 2007
Victim died at Shock Trauma
Cause: blunt force
' 39.277428, -76.689935, iconHomicideBluntForce, 'p168', '
Clayborn Johnson
4400 Parkton St.
Baltimore, MD 21229
black male, 61 years old
Found on July 8, 2007
Victim died at Shock Trauma
Cause: blunt force
' 39.284275, -76.644950, iconHomicideStabbing, 'p169', '
Christine Richardson
300 S. Fulton Ave.
Baltimore, MD 21223
black female, 15 years old
Found on July 10, 2007
Victim died at scene
Cause: stabbing
Read the article
' 39.289177, -76.595218, iconHomicideStabbing, 'p170', '
JOHN DOE
200 S. Herring Court
Baltimore, MD 21231
male
Found on July 12, 2007
Victim died at JHH
Cause: stabbing
Read the article
' 39.295826, -76.571295, iconHomicideShooting, 'p171', '
Jerry Crosby
400 N. Bouldin St.
Baltimore, MD 21224
black male, 24 years old
Found on July 12, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.338089, -76.667294, iconHomicideShooting, 'p172', '
Yemel McMillian
2800 Boarman Ave
Baltimore, MD 21215
black male, 20 years old
Found on July 14, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.311003, -76.585848, iconHomicideShooting, 'p173', '
Name not yet released
1800 N. Gay St.
Baltimore, MD 21213
black male, 25 years old
Found on July 15, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.329983, -76.536506, iconHomicideShooting, 'p174', '
Maurice White
5700 Radecke Ave.
Baltimore, MD 21206
black male, 22 years old
Found on July 15, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.329983, -76.536506, iconHomicideShooting, 'p175', '
Wayne White
5700 Radecke Ave.
Baltimore, MD 21206
black male, 24 years old
Found on July 15, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.311442, -76.663246, iconHomicideShooting, 'p176', '
Earl Williams
2100 Koko Lane
Baltimore, MD 21216
black male, 26 years old
Found on July 15, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.341032, -76.671108, iconHomicideShooting, 'p177', '
Perry Costley
3400 St. Ambrose Ave.
Baltimore, MD 21215
black male, 18 years old
Found on July 17, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.277722, -76.542933, iconHomicideShooting, 'p178', '
Daniel Santiago
6100 Fortview Ave.
Baltimore, MD 21224
Hispanic male, 29 years old
Found on July 17, 2007
Victim died at Bayview
Cause: shooting
Read the article
' 39.247739, -76.628140, iconHomicideShooting, 'p179', '
Steven Brandon Sr.
2800 Round Road
Baltimore, MD 21225
black male, 41 years old
Found on July 18, 2007
Victim died at Harbor
Cause: shooting
Read the article
' 39.321451, -76.575428, iconHomicideShooting, 'p180', '
Donte Chase
3000 Clifton Park Terrace
Baltimore, MD 21213
black male, 17 years old
Found on July 19, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.364889, -76.583094, iconHomicideBluntForce, 'p181', '
Christopher Robotham
1600 Waverly Way
Baltimore, MD 21239
black male, 37 years old
Found on July 26, 2007
Victim died at scene
Cause: blunt force
Read the article
' 39.288325, -76.643787, iconHomicideShooting, 'p182', '
Samuel Epps
1700 W. Baltimore St.
Baltimore, MD 21223
black male, 30 years old
Found on July 27, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.286862, -76.632053, iconHomicideShooting, 'p183', '
Cynthia Webb
900 W. Lombard St.
Baltimore, MD 21223
black female, 42 years old
Found on July 27, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.284152, -76.649861, iconHomicideShooting, 'p184', '
Demetris Downing Sr.
2100 McHenry St.
Baltimore, MD 21223
black male, 25 years old
Found on July 28, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.299228, -76.665193, iconHomicideShooting, 'p185', '
Kenneth Mitchell
2900 W. Mosher St.
Baltimore, MD 21216
black male, 44 years old
Found on July 28, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.295739, -76.694041, iconHomicideShooting, 'p186', '
Jordan Brown
4600 Rokeby Road
Baltimore, MD 21229
black male, 21 years old
Found on July 31, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.280061, -76.631863, iconHomicideShooting, 'p187', '
Eghosa Miaes
1200 Nanticoke St.
Baltimore, MD 21230
black male, 20 years old
Found on August 1, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.308536, -76.655876, iconHomicideShooting, 'p188', '
Carl Barnes
2400 Westwood Ave.
Baltimore, MD 21216
black male, 35 years old
Found on August 1, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.254359, -76.651593, iconHomicideShooting, 'p189', '
William Johnson
2400 Marbourne Ave.
Baltimore, MD 21216
black male, 18 years old
Found on August 2, 2007
Victim died at St. Agnes
Cause: shooting
Read the article
' 39.316765, -76.567027, iconHomicideShooting, 'p190', '
Keenan McCargo
3700 Lyndale Ave.
Baltimore, MD 21213
black male, 28 years old
Found on August 2, 2007
Victim died at Bayview
Cause: shooting
Read the article
' 39.315438, -76.609477, iconHomicideShooting, 'p191', '
Donte Bracey
500 E. 23rd St.
Baltimore, MD 21218
black male, 20 years old
Found on August 2, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.291400, -76.567459, iconHomicideShooting, 'p192', '
Larado Williams
3600 E. Lombard St.
Baltimore, MD 21224
black male, 27 years old
Found on August 2, 2007
Cause: shooting
Read the article
' 39.292457, -76.569407, iconHomicideShooting, 'p193', '
Eric Ford
1 S. Highland Ave.
Baltimore, MD 21224
black male, 19 years old
Found on August 5, 2007
Victim died at Bayview
Cause: shooting
Read the article
' 39.306906, -76.582873, iconHomicideShooting, 'p194', '
Taavon Mitchell
1400 of N. Milton Ave.
Baltimore, MD 21213
black male, 27 years old
Found on August 7, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.307834, -76.585626, iconHomicideShooting, 'p195', '
Joseph Bryant
2300 E. Oliver St.
Baltimore, MD 21213
black male, 29 years old
Found on August 7, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.341847, -76.671567, iconHomicideShooting, 'p196', '
Troy Richardson
3400 Dupont Ave.
Baltimore, MD 21215
black male, 30 years old
Found on August 9, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.299039, -76.638724, iconHomicideShooting, 'p197', '
Davon McCargo
1300 W. Lafayette Ave.
Baltimore, MD 21217
black male, 20 years old
Found on August 11, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.313481, -76.611069, iconHomicideShooting, 'p198', '
Byron Dickey
2100 Barclay St.
Baltimore, MD 21218
black male, 28 years old
Found on August 12, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.296156, -76.630850, iconHomicideShooting, 'p199', '
Frederick Moore
700 Brune St.
Baltimore, MD 21201
black male, 27 years old
Found on August 15, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.232693, -76.605927, iconHomicideStabbing, 'p200', '
Karen Kutchey
4000 Fifth St.
Baltimore, MD 21225
white female, 51 years old
Found on August 16, 2007
Victim died at scene
Cause: stabbing
Read the article
' 39.270360, -76.597606, iconHomicideBluntForce, 'p201', '
Ramona Bradley
1100 Harper Way
Baltimore, MD 21205
white female, 40 years old
Found on August 18, 2007
Victim died at Bayview
Cause: blunt force
Read the article
' 39.310172, -76.641149, iconHomicideShooting, 'p202', '
Darius Cox
2400 Woodbrook Ave.
Baltimore, MD 21217
black male, 17 years old
Found on August 18, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.301133, -76.640296, iconHomicideShooting, 'p203', '
Ishmael Cooper
1400 Riggs Ave.
Baltimore, MD 21217
black male, 15 years old
Found on August 23, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.320837, -76.674782, iconHomicideShooting, 'p204', '
Dwight Baker
3400 Powhatan Ave.
Baltimore, MD 21216
black male, 35 years old
Found on August 25, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.292523, -76.568468, iconHomicideShooting, 'p205', '
Andrew McNair
3500 E. Baltimore St.
Baltimore, MD 21224
black male, 28 years old
Found on August 26, 2007
Victim died at Bayview
Cause: shooting
Read the article
' 39.311676, -76.605137, iconHomicideShooting, 'p206', '
Matthew Sivells
1900 Cecil Ave.
Baltimore, MD 21218
black male, 26 years old
Found on August 26, 2007
Victim died at Hopkins
Cause: shooting
Read the article
' 39.315648, -76.659174, iconHomicideShooting, 'p207', '
Himank Karki
2600 Gwynns Falls Parkway
Baltimore, MD 21216
Other male, 18 years old
Found on August 27, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.282842, -76.648354, iconHomicideShooting, 'p208', '
Lacy Hazel
400 S. Payson St.
Baltimore, MD 21223
black male, 44 years old
Found on August 28, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.317879, -76.669886, iconHomicideShooting, 'p209', '
Darnell Thomas
3100 Artaban Place
Baltimore, MD 21216
black male, 23 years old
Found on August 30, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.302594, -76.668359, iconHomicideShooting, 'p210', '
Kahlil Taylor
3000 Normount Court
Baltimore, MD 21216
black male, 23 years old
Found on August 30, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.292872, -76.588351, iconHomicideShooting, 'p211', '
Sean Blackwell
2000 E. Fairmount Ave.
Baltimore, MD 21231
black male, 20 years old
Found on August 31, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.306488, -76.669145, iconHomicideShooting, 'p212', '
Gary Watts
1300 N. Rosedale St.
Baltimore, MD 21216
black male, 21 years old
Found on September 1, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.318677, -76.598933, iconHomicideShooting, 'p213', '
Davon Qualls
2500 Garrett Ave.
Baltimore, MD 21218
black male, 17 years old
Found on September 4, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.367481, -76.598899, iconHomicide, 'p214', '
Pauline Borum
6100 Macbeth Dr.
Baltimore, MD 21239
black female, 60 years old
Found on September 4, 2007
Victim died at scene
Read the article
' 39.367481, -76.598899, iconHomicide, 'p215', '
Jasmine Borum
6100 Macbeth Dr.
Baltimore, MD 21239
black female, 17 years old
Found on September 4, 2007
Victim died at scene
Read the article
' 39.285482, -76.639382, iconHomicideShooting, 'p216', '
Barry Newman
200 S. Calhoun St.
Baltimore, MD 21223
black male, 29 years old
Found on September 4, 2007
Cause: shooting
Read the article
' 39.317175, -76.569580, iconHomicideShooting, 'p217', '
Khonji Watton
3600 Elmley Ave.
Baltimore, MD 21213
black female, 27 years old
Found on September 10, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.281203, -76.672253, iconHomicideShooting, 'p218', '
John Christen
3300 Old Frederick Road
Baltimore, MD 21229
black male, 20 years old
Found on September 15, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.307834, -76.585626, iconHomicideShooting, 'p219', '
Brian Smith
2301 E. Oliver St.
Baltimore, MD 21213
black male, 27 years old
Found on September 15, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.251293, -76.624670, iconHomicideStabbing, 'p220', '
Qafim Kaba
2500 Terra Firma Road
Baltimore, MD 21225
black male, 29 years old
Found on September 15, 2007
Cause: stabbing
Read the article
' 39.322411, -76.572593, iconHomicideShooting, 'p221', '
Deion Morris
3400 Belair Road
Baltimore, MD 21213
black male, 23 years old
Found on September 16, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.322411, -76.572593, iconHomicideShooting, 'p222', '
Channing Myrick
3400 Belair Road
Baltimore, MD 21213
black male, 26 years old
Found on September 16, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.306503, -76.640763, iconHomicideShooting, 'p223', '
Tyrone Jones
1500 Woodyear St.
Baltimore, MD 21217
black male, 26 years old
Found on September 16, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.345123, -76.612623, iconHomicideShooting, 'p224', '
Richard Asare
1300 E. Coldspring Lane
Baltimore, MD 21239
black male, 24 years old
Found on September 17, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.246847, -76.627822, iconHomicideShooting, 'p225', '
Glenn Conley
2900 Round Road
Baltimore, MD 21225
black male, 25 years old
Found on September 17, 2007
Cause: shooting
Read the article
' 39.303749, -76.581174, iconHomicideBluntForce, 'p226', '
Darrick Harris
2600 E. Chase St.
Baltimore, MD 21213
black male, 40 years old
Found on September 19, 2007
Victim died at JHH
Cause: blunt force
Read the article
' 39.304722, -76.628978, iconHomicideShooting, 'p227', '
Monea Gorham
1500 Madison Ave.
Baltimore, MD 21217
black female, 16 years old
Found on September 20, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.335441, -76.643257, iconHomicideBluntForce, 'p228', '
William Diven
1700 W. 41st Street
Baltimore, MD 21211
white male, 46 years old
Found on September 22, 2007
Victim died at scene
Cause: blunt force
Read the article
' 39.315297, -76.646090, iconHomicideBluntForce, 'p229', '
Robert Rutledge
2800 Parkwood Ave.
Baltimore, MD 21217
black male, 40 years old
Found on September 22, 2007
Victim died at Shock Trauma
Cause: blunt force
Read the article
' 39.342139, -76.684450, iconHomicideStabbing, 'p230', '
Richard Ray
5200 Fairlawn Ave.
Baltimore, MD 21215
black male, 37 years old
Found on September 25, 2007
Victim died at Sinai
Cause: stabbing
Read the article
' 39.323234, -76.627068, iconHomicideShooting, 'p231', '
Keith Ray
600 Wyman Park Drive
Baltimore, MD 21211
black male, 24 years old
Found on September 25, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.296375, -76.648226, iconHomicideStabbing, 'p232', '
Alfred Smith
700 Appleton St.
Baltimore, MD 21217
black male, 56 years old
Found on September 26, 2007
Cause: stabbing
Read the article
' 39.298027, -76.633882, iconHomicideShooting, 'p233', '
Jason Fortune
800 N. Fremont Ave.
Baltimore, MD 21217
black male, 24 years old
Found on September 28, 2007
Victim died at Shock Trauma
Cause: shooting
Read the article
' 39.311767, -76.635327, iconHomicideShooting, 'p234', '
Tyrone Blanding
2300 Eutaw Place
Baltimore, MD 21217
black male, 30 years old
Found on October 5, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.320402, -76.594780, iconHomicideShooting, 'p235', '
Darwin Kelly
1700 Homestead St.
Baltimore, MD 21218
black male, 20 years old
Found on October 6, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.293969, -76.599529, iconHomicideShooting, 'p236', '
Damon Coleman
1400 May Court
Baltimore, MD 21231
black male, 35 years old
Found on October 6, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.316205, -76.595882, iconHomicideShooting, 'p237', '
Montaz Askew
1600 Normal Ave.
Baltimore, MD 21213
black male, 20 years old
Found on October 9, 2007
Cause: shooting
' 39.305989, -76.675074, iconHomicide, 'p238', '
Kwame Asofo
3400 Winterbourne Road
Baltimore, MD 21216
black male, 21 years old
Found on October 10, 2007
' 39.288655, -76.636015, iconHomicideShooting, 'p239', '
Kevin Ware
1100 W. Baltimore St.
Baltimore, MD 21223
black male, 40 years old
Found on October 11, 2007
Cause: shooting
Read the article
' 39.308984, -76.611542, iconHomicide, 'p240', '
Deron Hope
1700 Latrobe St.
Baltimore, MD 21202
black male, 16 years old
Found on October 13, 2007
Victim died at JHH
Read the article
' 39.337970, -76.661051, iconHomicideShooting, 'p241', '
Dione Biggs
2600 Quantico Ave.
Baltimore, MD 21215
black male, 33 years old
Found on October 14, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.310822, -76.679507, iconHomicideAsphyxiation, 'p242', '
Henry Mazyck
2100 Allendale Road
Baltimore, MD 21216
black male, 39 years old
Found on October 15, 2007
Victim died at scene
Cause: asphyxiation
Read the article
' 39.336123, -76.665704, iconHomicideShooting, 'p243', '
Andre Bryant
2800 Quantico Ave.
Baltimore, MD 21215
black male, 47 years old
Found on October 15, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.310917, -76.646670, iconHomicideShooting, 'p244', '
Darren Mebane
1800 Walbrook Ave.
Baltimore, MD 21217
black male, 21 years old
Found on October 18, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.229726, -76.604934, iconHomicideShooting, 'p245', '
Donte Davis
4200 Audrey Ave.
Baltimore, MD 21225
black male, 25 years old
Found on October 19, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.346019, -76.669900, iconHomicideShooting, 'p246', '
Qur'ron Holloway
3100 Woodland Ave.
Baltimore, MD 21215
black male, 19 years old
Found on October 19, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.235877, -76.600730, iconHomicideShooting, 'p247', '
Christopher Burden
700 E. Patapsco Ave.
Baltimore, MD 21225
black male, 23 years old
Found on October 25, 2007
Cause: shooting
Read the article
' 39.293921, -76.681171, iconHomicideStabbing, 'p248', '
Veronica Fludd
600 Lyndhurst St.
Baltimore, MD 21229
black female, 26 years old
Found on October 26, 2007
Victim died at scene
Cause: stabbing
Read the article
' 39.330744, -76.532041, iconHomicideShooting, 'p249', '
John Doe
5900 Radecke Ave.
Baltimore, MD 21206
Hispanic male
Found on October 29, 2007
Cause: shooting
Read the article
' 39.359007, -76.575498, iconHomicideShooting, 'p250', '
Marlon Beckford
5800 Edgepark Road
Baltimore, MD 21239
black male, 31 years old
Found on October 30, 2007
Cause: shooting
Read the article
' 39.316242, -76.606439, iconHomicideShooting, 'p251', '
Alexander Robertson-El
700 Bartlett Ave.
Baltimore, MD 21218
black male, 24 years old
Found on October 30, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.316268, -76.610406, iconHomicideShooting, 'p252', '
Naim King
2400 Brentwood Ave.
Baltimore, MD 21218
black male, 31 years old
Found on October 31, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.346571, -76.668934, iconHomicideShooting, 'p253', '
Nathaniel Footman
3000 Woodland Ave.
Baltimore, MD 21215
black male, 18 years old
Found on October 31, 2007
Victim died at Sinai
Cause: shooting
' 39.278918, -76.542301, iconHomicideShooting, 'p254', '
Lawrence Jones
1300 Ballard Way
Baltimore, MD 21224
male, 17 years old
Found on November 2, 2007
Victim died at Bayview
Cause: shooting
Read the article
' 39.315886, -76.577181, iconHomicideShooting, 'p255', '
Robert Tyson
3300 Elmora Ave.
Baltimore, MD 21213
male, 24 years old
Found on November 3, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.301795, -76.596298, iconHomicideShooting, 'p256', '
Norman Smith
1600 E. Eager St.
Baltimore, MD 21205
male, 30 years old
Found on November 4, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.342763, -76.589386, iconHomicideShooting, 'p257', '
Terrance Regan
1600 Lochwood Road
Baltimore, MD 21218
male, 16 years old
Found on November 5, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.336719, -76.660419, iconHomicideShooting, 'p258', '
Leonard Hunt
2600 Oswego Ave.
Baltimore, MD 21215
male, 44 years old
Found on November 8, 2007
Victim died at scene
Cause: shooting
' 39.306427, -76.581308, iconHomicideShooting, 'p259', '
Carlos Smithson
2600 Grogan Ave.
Baltimore, MD 21213
black male, 22 years old
Found on November 11, 2007
Cause: shooting
Read the article
' 39.307586, -76.641318, iconHomicideShooting, 'p260', '
John Morris
700 Baker St.
Baltimore, MD 21217
male, 42 years old
Found on November 12, 2007
Cause: shooting
Read the article
' 39.293735, -76.649742, iconHomicideBluntForce, 'p261', '
Frederick Davis
500 Brice St.
Baltimore, MD 21223
male, 39 years old
Found on November 13, 2007
Cause: blunt force
' 39.328984, -76.564472, iconHomicideShooting, 'p262', '
Trent Earll
4400 Belair Road
Baltimore, MD 21206
male, 36 years old
Found on November 13, 2007
Cause: shooting
Read the article
' 39.371610, -76.566968, iconHomicideShooting, 'p263', '
Kendrick Bowman
7100 McClean Blvd.
Baltimore, MD 21234
black male, 17 years old
Found on November 15, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.345841, -76.665568, iconHomicideShooting, 'p264', '
Herbert Lemon
3000 Rosalind Ave.
Baltimore, MD 21215
black male, 31 years old
Found on November 16, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.309773, -76.703097, iconHomicideShooting, 'p265', '
Martay Powell
2100 Crimea Road
Baltimore, MD 21207
black male, 22 years old
Found on November 23, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.315904, -76.593124, iconHomicideUnknown, 'p266', '
Bryanna Harris
1700 E. 25th St.
Baltimore, MD 21213
black female, 2 years old
Found on November 23, 2007
Victim died at JHH
Cause: unknown
Read the article
' 39.287265, -76.633736, iconHomicideShooting, 'p267', '
Robert Gray
1000 Boyd St.
Baltimore, MD 21223
black male, 36 years old
Found on November 23, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.307629, -76.655817, iconHomicideShooting, 'p268', '
Michael Crowder
2400 Presbury St.
Baltimore, MD 21216
black male, 33 years old
Found on November 26, 2007
Victim died at Shock Trauma
Cause: shooting
' 39.348593, -76.688842, iconHomicideBluntForce, 'p269', '
Quentin Reddicks-Flowers
4000 W. Rogers Ave.
Baltimore, MD 21215
black male, 31 years old
Found on November 28, 2007
Cause: blunt force
' 39.343826, -76.679404, iconHomicideStabbing, 'p270', '
Tywonde Jones
5000 Cordelia Ave.
Baltimore, MD 21215
black male, 13 years old
Found on November 29, 2007
Cause: stabbing
Read the article
' 39.310317, -76.707511, iconHomicideShooting, 'p271', '
Eugenio Harrison
N. Forest Park Ave. and Windsor Mill Road
Baltimore, MD 21207
black male, 19 years old
Found on December 2, 2007
Victim died at Sinai
Cause: shooting
' 39.309263, -76.663932, iconHomicideBluntForce, 'p272', '
Lonnie Foote
2800 W. North Ave.
Baltimore, MD 21216
black male, 58 years old
Found on December 3, 2007
Victim died at St. Agnes
Cause: blunt force
' 39.278020, -76.542939, iconHomicideShooting, 'p273', '
Artavius Tubman
6100 Boston St.
Baltimore, MD 21224
black male, 24 years old
Found on December 10, 2007
Victim died at Bayview
Cause: shooting
Read the article
' 39.320131, -76.681762, iconHomicideShooting, 'p274', '
Richard Lawson
2900 Chelsea Terrace
Baltimore, MD 21216
black male, 24 years old
Found on December 14, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.309822, -76.616473, iconHomicideShooting, 'p275', '
Jamal Rowlett
1800 N. Charles St.
Baltimore, MD 21201
black male, 20 years old
Found on December 16, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.310071, -76.671049, iconHomicideAsphyxiation, 'p276', '
Lezli Williams
3200 Walbrook Ave.
Baltimore, MD 21216
black female, 22 years old
Found on December 18, 2007
Victim died at scene
Cause: asphyxiation
Read the article
' 39.261234, -76.648137, iconHomicideShooting, 'p277', '
Michael Weathers
2300 Atlantic Ave.
Baltimore, MD 21230
black male
Found on December 20, 2007
Victim died at scene
Cause: shooting
Read the article
' 39.310254, -76.638359, iconHomicideShooting, 'p278', '
Jason Allen
1300 North Ave.
Baltimore, MD 21217
black male, 25 years old
Found on December 21, 2007
Cause: shooting
' 39.315715, -76.649357, iconHomicideShooting, 'p279', '
Jim Harper
2400 Reisterstown Road
Baltimore, MD 21217
male, 36 years old
Found on December 24, 2007
Victim died at Sinai
Cause: shooting
Read the article
' 39.354681, -76.707924, iconHomicideShooting, 'p280', '
Toby MacCombie
6600 Vincent Lane
Baltimore, MD 21215
black male, 30 years old
Found on December 24, 2007
Cause: shooting
Read the article
' 39.309494, -76.597588, iconHomicideShooting, 'p281', '
Kevin Bacon
1700 N. Dallas St.
Baltimore, MD 21213
black male, 46 years old
Found on December 26, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.300384, -76.597730, iconHomicideShooting, 'p282', '
Todd Dargan
900 N. Caroline St.
Baltimore, MD 21205
black male, 25 years old
Found on December 28, 2007
Victim died at JHH
Cause: shooting
Read the article
' 39.29972400000, -76.67071400000, icon_homicide_shooting, 'p507', '
Petro Taylor
1200 N. Franklintown Road
Baltimore, MD 21216
Race: Black
Gender: male
Age: 20 years old
Found on December 30, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.31537100000, -76.63452600000, icon_homicide_shooting, 'p506', '
Antonio Coby
800 Chauncey Ave.
Baltimore, MD 21227
Race: Unknown
Gender: male
Age: 28 years old
Found on December 29, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.29472400000, -76.66153500000, icon_homicide_shooting, 'p505', '
Edgar Mazyck
2700 Edmondson Ave.
Baltimore, MD 21229
Race: Unknown
Gender: male
Age: 39 years old
Found on December 28, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.31324200000, -76.59201400000, icon_homicide_shooting, 'p504', '
Lisa Bushrod
2000 N. Wolfe St.
Baltimore, MD 21213
Race: Unknown
Gender: female
Age: 42 years old
Found on December 26, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.29521900000, -76.63233200000, icon_homicide_shooting, 'p503', '
David Woodward
900 Bennett Place
Baltimore, MD 21230
Race: Unknown
Gender: male
Age: 30 years old
Found on December 25, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.28308800000, -76.57073400000, icon_homicide_shooting, 'p502', '
Alaina High
800 S. Bouldin st.
Baltimore, MD 21224
Race: Unknown
Gender: female
Age: 22 years old
Found on December 23, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.30968000000, -76.59534100000, icon_homicide_shooting, 'p501', '
Ronald Hall
1700 Broadway
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 46 years old
Found on December 23, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.32333300000, -76.59903000000, icon_homicide_shooting, 'p500', '
Thaddeus McCauley
1400 Homestead St.
Baltimore, MD 21218
Race: Unknown
Gender: male
Age: 19 years old
Found on December 19, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.34918300000, -76.67384000000, icon_homicide_shooting, 'p499', '
Gary Mann
5100 Palmer Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 24 years old
Found on December 16, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.34523400000, -76.68970000000, icon_homicide_shooting, 'p498', '
Warren Davis
5400 Fairlawn Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 41 years old
Found on December 15, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.30398800000, -76.57963500000, icon_homicide_shooting, 'p497', '
Oril Jackson
2700 E. Chase St
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 21 years old
Found on December 15, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.23045740000, -76.59774480000, icon_homicide_shooting, 'p496', '
Larry Gaither
1000 Jack Place
Baltimore, MD 21225
Race: Unknown
Gender: male
Age: 44 years old
Found on December 11, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.29532040000, -76.57464600000, icon_homicide_shooting, 'p495', '
Brian Smith
3000 Puaski Highway
Baltimore, MD 21224
Race: Unknown
Gender: male
Age: 37 years old
Found on December 10, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.29720620000, -76.63037540000, icon_homicide_shooting, 'p494', '
Ronald Jackson
1100 Myrtle Ave.
Baltimore, MD 21217
Race: Black
Gender: male
Age: 14 years old
Found on December 7, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.34672300000, -76.67503000000, icon_homicide_shooting, 'p493', '
Darrell Keith
5000 Denmore Ave
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 24 years old
Found on December 6, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.29746800000, -76.62018700000, icon_homicide_shooting, 'p492', '
Travis Makofski
700 N. Howard St.
Baltimore, MD 21201
Race: White
Gender: male
Age: 28 years old
Found on December 6, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.31867670000, -76.59893340000, icon_homicide_shooting, 'p491', '
Michael Harris
2500 Garrett Ave.
Baltimore, MD 21218
Race: Unknown
Gender: male
Age: 37 years old
Found on December 5, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.32608700000, -76.65792400000, icon_homicide_shooting, 'p490', '
Leroy Boxdale
3400 Park Heights Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 19 years old
Found on December 2, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.31117100000, -76.59059300000, icon_homicide_shooting, 'p488', '
Donell Williams
1800 N. Washington St.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 19 years old
Found on December 1, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.28957200000, -76.57039400000, icon_homicide_shooting, 'p489', '
Alton Alston
200 S. Clinton St.
Baltimore, MD 21224
Race: Unknown
Gender: male
Age: 18 years old
Found on December 1, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.33607300000, -76.68522300000, icon_homicide_shooting, 'p484', '
Darren Davis
4000 Oakford Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 26 years old
Found on November 30, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.33607300000, -76.68522300000, icon_homicide_shooting, 'p485', '
Troy Brown
4000 Oakford Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 45 years old
Found on November 30, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.33607300000, -76.68522300000, icon_homicide_shooting, 'p486', '
Perrish Parker
4000 Oakford Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 14 years old
Found on November 30, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.32974440000, -76.56737310000, icon_homicide_shooting, 'p487', '
Dawn William-Stewart
4000 Parkside Drive
Baltimore, MD 21206
Race: Unknown
Gender: female
Age: 41 years old
Found on November 30, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.31821800000, -76.58768650000, icon_homicide_shooting, 'p483', '
Adrian Andrews
2800 St. Lo Drive
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 17 years old
Found on November 27, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.37061500000, -76.60095000000, icon_homicide_shooting, 'p482', '
Alexander Njuguna
1000 Woodson Road
Baltimore, MD 21212
Race: Black
Gender: male
Age: 29 years old
Found on November 26, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.32386260000, -76.60879110000, icon_homicide_shooting, 'p481', '
Voncelle Blackwell
2900 Limond Place
Baltimore, MD 21218
Race: Black
Gender: male
Age: 26 years old
Found on November 25, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.31560600000, -76.61143900000, icon_homicide_shooting, 'p480', '
Frederick Ward
2300 Barclay St.
Baltimore, MD 21218
Race: Unknown
Gender: male
Age: 24 years old
Found on November 24, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.31169630000, -76.70116640000, icon_homicide_shooting, 'p479', '
Adama Diara
2200 Tucker Lane
Baltimore, MD 21207
Race: Unknown
Gender: male
Age: 22 years old
Found on November 23, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.27795380000, -76.61737770000, icon_homicide_shooting, 'p477', '
Angelo Ford
1000 Leadenhall St.
Baltimore, MD 21230
Race: Unknown
Gender: male
Age: 49 years old
Found on November 23, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.31206700000, -76.60012800000, icon_homicide_shooting, 'p478', '
Richard Green
1300 W. North Ave.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 39 years old
Found on November 23, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.35494790000, -76.71026790000, icon_homicide_shooting, 'p476', '
Isiah Benjamin
6600 Eberle Drive
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 39 years old
Found on November 22, 2008
Victim died at Sinai Hospital
Cause: Shooting
' 39.31188820000, -76.60011480000, icon_homicide_stabbing, 'p475', '
Veronica Williams
1300 E. North Ave.
Baltimore, MD 21202
Race: Black
Gender: female
Age: 29 years old
Found on November 21, 2008
Victim died at Johns Hopkins Hospital
Cause: Stabbing
Read the article
' 39.31481740000, -76.66444650000, icon_homicide_stabbing, 'p474', '
Markel Williams
2800 N. Dukeland St.
Baltimore, MD 21216
Race: Black
Gender: male
Age: 15 years old
Found on November 21, 2008
Victim died at Sinai Hospital
Cause: Stabbing
Read the article
' 39.31032570000, -76.66405570000, icon_homicide_shooting, 'p471', '
Charles Hoggins
2000 N. Dukeland St.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 45 years old
Found on November 18, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.23919680000, -76.60438280000, icon_homicide_shooting, 'p470', '
Steven Graham
3500 4th St.
Baltimore, MD 21225
Race: Unknown
Gender: male
Age: 14 years old
Found on November 18, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30124320000, -76.60280220000, icon_homicide_shooting, 'p469', '
Donte Graham
1100 Abbott Court
Baltimore, MD 21202
Race: Unknown
Gender: male
Age: 21 years old
Found on November 18, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.28322500000, -76.63946800000, icon_homicide_bluntforce, 'p472', '
Lyle Dimeler
400 S. Calhoun St.
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 53 years old
Found on November 18, 2008
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force
Read the article
' 39.28181500000, -76.65127900000, icon_homicide_shooting, 'p468', '
Joseph Robinson
500 S. Smallwood St
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 30 years old
Found on November 17, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.34621400000, -76.67085500000, icon_homicide_shooting, 'p467', '
Charles Norman
4800 Palmer Ave
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 48 years old
Found on November 16, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.35376900000, -76.55508400000, icon_homicide_bluntforce, 'p466', '
Eunice Taylor
3115 Mary Ave.
Baltimore, MD 21214
Race: Black
Gender: female
Age: 69 years old
Found on November 12, 2008
Victim died at Unknown
Cause: Blunt Force
' 39.31467110000, -76.60198470000, icon_homicide_shooting, 'p465', '
Larry Franklin
2200 Robb St.
Baltimore, MD 21218
Race: Unknown
Gender: male
Age: 22 years old
Found on November 12, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.31238500000, -76.63445300000, icon_homicide_shooting, 'p464', '
Gregory Hines
2200 Linden Ave.
Baltimore, MD 21217
Race: Unknown
Gender: male
Age: 30 years old
Found on November 10, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.30335440000, -76.63620720000, icon_homicide_shooting, 'p462', '
Leroy Taylor
600 Sewell St.
Baltimore, MD 21206
Race: Unknown
Gender: male
Age: 45 years old
Found on November 7, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.28858700000, -76.60968900000, icon_homicide_bluntforce, 'p463', '
Takira Johnson
100 Custom House Ave
Baltimore, MD 21202
Race: Black
Gender: female
Age: 27 years old
Found on November 7, 2008
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force
Read the article
' 39.29234240000, -76.63696630000, icon_homicide_shooting, 'p461', '
Ernest McFadden
1200 W. Saratoga St
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 39 years old
Found on November 3, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30175480000, -76.59781840000, icon_homicide_shooting, 'p460', '
Anthony Rainey
1000 N. Caroline St.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 25 years old
Found on November 2, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.30883430000, -76.64722470000, icon_homicide_shooting, 'p459', '
Mark Henson
1800 McKean Ave
Baltimore, MD 21217
Race: Unknown
Gender: male
Age: 18 years old
Found on November 1, 2008
Victim died at Scene
Cause: Shooting
' 39.35377800000, -76.57093800000, icon_homicide_shooting, 'p523', '
Harley Johnson
2200 Echodale Ave
Baltimore, MD 21214
Race: Black
Gender: male
Age: 27 years old
Found on October 31, 2008
Victim died at Scene
Cause: Shooting
' 39.33317000000, -76.60704100000, icon_homicide_shooting, 'p458', '
Mark Vines
600 McKewin Ave.
Baltimore, MD 21218
Race: Unknown
Gender: male
Age: 21 years old
Found on October 29, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.33896810000, -76.65789910000, icon_homicide_shooting, 'p457', '
Jason Betts
2400 Loyola Southway
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 25 years old
Found on October 25, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.31101300000, -76.59431800000, icon_homicide_shooting, 'p456', '
Derrick Phillips
1800 N. Regester St.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 42 years old
Found on October 25, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.33578500000, -76.66417100000, icon_homicide_shooting, 'p455', '
Gregory Boston
2700 Oswego Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 48 years old
Found on October 23, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.32098800000, -76.56505900000, icon_homicide_shooting, 'p454', '
Marshall Nelson
3500 Pelham Ave.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 23 years old
Found on October 22, 2008
Victim died at Scene
Cause: Shooting
' 39.32068100000, -76.60178700000, icon_homicide_shooting, 'p453', '
Dontay Monroe
1400 Exeter Hall Ave.
Baltimore, MD 21218
Race: Black
Gender: male
Age: 18 years old
Found on October 22, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.29850600000, -76.57096100000, icon_homicide_shooting, 'p452', '
Shawn Crosby
600 N. Clinton St.
Baltimore, MD 21205
Race: Unknown
Gender: male
Age: 17 years old
Found on October 21, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.29961320000, -76.62561840000, icon_homicide_shooting, 'p450', '
Carlton Bethea
500 W. Preston St.
Baltimore, MD 21201
Race: Unknown
Gender: male
Age: 29 years old
Found on October 20, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.36652710000, -76.54906900000, icon_homicide_stabbing, 'p524', '
William Hightower
3000 Woodhome Ave
Baltimore, MD 21234
Race: Black
Gender: male
Age: 48 years old
Found on October 15, 2008
Victim died at Unknown
Cause: Stabbing
' 39.28460700000, -76.65146900000, icon_homicide_shooting, 'p449', '
Delvon Butts
200 S. Smallwood St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 15 years old
Found on October 14, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30916170000, -76.66927700000, icon_homicide_shooting, 'p448', '
Rubin Nelson
1900 N. Rosedale Ave.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 26 years old
Found on October 14, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.30116600000, -76.63202200000, icon_homicide_shooting, 'p446', '
Howard Grant
1400 Pennsylvania Ave
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 18 years old
Found on October 12, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30116600000, -76.63202200000, icon_homicide_shooting, 'p447', '
Justin Berry
1400 Pennsylvania Ave
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 19 years old
Found on October 12, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.28729160000, -76.68790040000, icon_homicide_shooting, 'p445', '
Darius Burton
4300 Davis Ave
Baltimore, MD 21229
Race: Unknown
Gender: male
Age: 18 years old
Found on October 12, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.28419600000, -76.64517400000, icon_homicide_shooting, 'p443', '
Clayton Oxendine
300 S. Fulton Ave.
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 24 years old
Found on October 6, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.28055000000, -76.65414900000, icon_homicide_shooting, 'p444', '
Darrell Gulliver
500 S. Catherine St.
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 38 years old
Found on October 6, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.34512600000, -76.66850300000, icon_homicide_shooting, 'p442', '
Craig Colvin
3100 Virginia Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 23 years old
Found on October 6, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.31343900000, -76.63894900000, icon_homicide_shooting, 'p526', '
Cyprian Jackson
2500 Madison Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 28 years old
Found on October 6, 2008
Victim died at Unknown
Cause: Shooting
' 39.31944580000, -76.61550850000, icon_homicide_asphyxiation, 'p541', '
Baby Boy Blevins
2600 St Paul St
Baltimore, MD 21218
Race: White
Gender: male
Age: 1 year old
Found on October 4, 2008
Victim died at Scene
Cause: Asphyxiation
' 39.29135170000, -76.64538430000, icon_homicide_shooting, 'p441', '
William Haskins
1800 Penrose Ave.
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 32 years old
Found on October 4, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30904760000, -76.64304360000, icon_homicide_shooting, 'p440', '
Modesto Smith
700 Allegany Place
Baltimore, MD 21217
Race: Unknown
Gender: male
Age: 21 years old
Found on October 2, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.32027650000, -76.57525400000, icon_homicide_shooting, 'p439', '
Michael Wilson
3100 Belair Road
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 24 years old
Found on September 30, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.28513300000, -76.61363800000, icon_homicide_stabbing, 'p438', '
Helen Reightler
300 Light St.
Baltimore, MD 21202
Race: White
Gender: female
Age: 43 years old
Found on September 29, 2008
Victim died at Sinai Hospital
Cause: Stabbing
Read the article
' 39.30968000000, -76.59534100000, icon_homicide_shooting, 'p538', '
Kevin Rouzer
1700 N. Broadway
Baltimore, MD 21213
Race: Black
Gender: male
Age: 29 years old
Found on September 28, 2008
Victim died at Unknown
Cause: Shooting
' 39.34721130000, -76.68308450000, icon_homicide_shooting, 'p436', '
Jarrell Laws
5300 Cordelia Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 17 years old
Found on September 26, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.31740080000, -76.59810650000, icon_homicide_shooting, 'p435', '
Derrick Reed
2500 Aisquith St.
Baltimore, MD 21218
Race: Black
Gender: male
Age: 15 years old
Found on September 24, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.34084100000, -76.59117200000, icon_homicide_shooting, 'p434', '
Kenneth Harris
1500 Havenwood Rd
Baltimore, MD 21218
Race: Black
Gender: male
Age: 45 years old
Found on September 20, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.29119770000, -76.62813830000, icon_homicide_shooting, 'p433', '
Barry Graham
700 W. Lexington St.
Baltimore, MD 21201
Race: Black
Gender: male
Age: 24 years old
Found on September 14, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.28467200000, -76.58666900000, icon_homicide_shooting, 'p432', '
Lloyd Melton
2100 Boston St.
Baltimore, MD 21231
Race: Black
Gender: male
Age: 21 years old
Found on September 14, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.36302300000, -76.58208300000, icon_homicide_shooting, 'p431', '
Darien Sawyer
1600 Wadsworth Way
Baltimore, MD 21239
Race: Black
Gender: male
Age: 29 years old
Found on September 11, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.30023100000, -76.58265300000, icon_homicide_shooting, 'p428', '
Demarco Brown
800 N. Milton Ave.
Baltimore, MD 21205
Race: Black
Gender: male
Age: 25 years old
Found on September 9, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.31294000000, -76.60862100000, icon_homicide_shooting, 'p430', '
Robert Wilson
2000 Boone St.
Baltimore, MD 21218
Race: Black
Gender: male
Age: 23 years old
Found on September 9, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.28469100000, -76.64938400000, icon_homicide_shooting, 'p539', '
Andre Harris
200 Harmison St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 23 years old
Found on September 6, 2008
Victim died at Unknown
Cause: Shooting
' 39.30094100000, -76.58757600000, icon_homicide_shooting, 'p427', '
Tyrone Bowie
2100 Ashland Ave.
Baltimore, MD 21205
Race: Black
Gender: male
Age: 26 years old
Found on September 4, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.28003580000, -76.66654860000, icon_homicide_shooting, 'p426', '
Reginald Carter
400 S. Longwood St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 20 years old
Found on August 31, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.28335490000, -76.68674190000, icon_homicide_shooting, 'p537', '
Durrell Aldridge
200 S Woodington St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 24 years old
Found on August 31, 2008
Victim died at Unknown
Cause: Shooting
' 39.28335490000, -76.68674190000, icon_homicide_shooting, 'p425', '
Shirley Barnes
200 S. Woodington Road
Baltimore, MD 21229
Race: Black
Gender: female
Age: 44 years old
Found on August 31, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30282100000, -76.60497800000, icon_homicide_shooting, 'p424', '
Levon McCray
900 Valley St.
Baltimore, MD 21202
Race: Black
Gender: male
Age: 23 years old
Found on August 30, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.30333150000, -76.64176110000, icon_homicide_shooting, 'p422', '
Clarence Hamilton
1300 N. Stricker St.
Baltimore, MD 21217
Race: Black
Gender: male
Age: 22 years old
Found on August 27, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31141800000, -76.58402900000, icon_homicide_bluntforce, 'p423', '
Floyd Jones
1800 N. Port St.
Baltimore, MD 21205
Race: Black
Gender: male
Age: 56 years old
Found on August 27, 2008
Victim died at Johns Hopkins Hospital
Cause: Blunt Force
Read the article
' 39.34250000000, -76.60477600000, icon_homicide_shooting, 'p421', '
Wilbert Flowers
800 E. 43rd St.
Baltimore, MD 21212
Race: Black
Gender: male
Age: 31 years old
Found on August 25, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.31000130000, -76.58437790000, icon_homicide_shooting, 'p420', '
Tavon Garrison
2400 E. Lanvale St.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 28 years old
Found on August 22, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29578200000, -76.63434500000, icon_homicide_shooting, 'p419', '
Eric Brown
1000 Edmondson Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 20 years old
Found on August 21, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32040300000, -76.67753500000, icon_homicide_shooting, 'p417', '
John Person
2900 Garrison Blvd.
Baltimore, MD 21216
Race: Black
Gender: male
Age: 17 years old
Found on August 17, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.33075800000, -76.58087800000, icon_homicide_asphyxiation, 'p418', '
Kiuna Jackson
3600 Harford Road
Baltimore, MD 21214
Race: Black
Gender: female
Age: 19 years old
Found on August 15, 2008
Victim died at Scene
Cause: Asphyxiation
Read the article
' 39.34908200000, -76.66572300000, icon_homicide_shooting, 'p536', '
Brandon Robinson
2800 Oakley Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 23 years old
Found on August 14, 2008
Victim died at Unknown
Cause: Shooting
' 39.25111400000, -76.64382500000, icon_homicide_shooting, 'p414', '
Vernon Paige
2100 E. Patapsco St.
Baltimore, MD 21230
Race: Black
Gender: male
Age: 21 years old
Found on August 13, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.29639400000, -76.63450200000, icon_homicide_shooting, 'p413', '
Michael Sewell
1000 Brantley St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 29 years old
Found on August 12, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.33141100000, -76.68719100000, icon_homicide_shooting, 'p412', '
Michael Little
4100 Barrington Road
Baltimore, MD 21215
Race: Black
Gender: male
Age: 28 years old
Found on August 12, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.31008600000, -76.58532000000, icon_homicide_shooting, 'p411', '
George Blaine
1700 N. Bradford St.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 30 years old
Found on August 9, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.29502200000, -76.57089400000, icon_homicide_shooting, 'p416', '
Rudy Guzman
3300 E. Fayette St.
Baltimore, MD 21224
Race: Black
Gender: male
Age: 22 years old
Found on August 8, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.25076780000, -76.63242240000, icon_homicide_shooting, 'p415', '
Shawn Weaver
1000 Shellbanks Road
Baltimore, MD 21225
Race: Black
Gender: male
Age: 38 years old
Found on August 8, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.31723300000, -76.64690100000, icon_homicide_unknown, 'p535', '
Javon Thompson
3200 Auchentoroly Terrace
Baltimore, MD 21217
Race: Black
Gender: male
Age: 1 year old
Found on August 8, 2008
Victim died at Unknown
Cause: Unknown
' 39.37170990000, -76.60706170000, icon_homicide_shooting, 'p410', '
Matthew Scarborough
500 Castle Drive
Baltimore, MD 21212
Race: White
Gender: male
Age: 16 years old
Found on August 7, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.30216320000, -76.66568620000, icon_homicide_shooting, 'p409', '
Robert Buckner
1300 Bloomingdale Road
Baltimore, MD 21216
Race: Black
Gender: male
Age: 19 years old
Found on August 6, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.28961030000, -76.64389960000, icon_homicide_shooting, 'p408', '
Jeffrey Alston
1700 W. Fayette St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 23 years old
Found on August 5, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.34393150000, -76.65822500000, icon_homicide_shooting, 'p534', '
Ralph Hall
4700 Greenspring Ave
Baltimore, MD 21209
Race: Black
Gender: male
Age: 37 years old
Found on July 30, 2008
Victim died at Unknown
Cause: Shooting
' 39.29497990000, -76.60174790000, icon_homicide_shooting, 'p407', '
Troy Wilson
1200 Orleans St.
Baltimore, MD 21202
Race: Black
Gender: male
Age: 17 years old
Found on July 29, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.26464990000, -76.63369520000, icon_homicide_shooting, 'p406', '
Bernard Soloman
2700 Manokin St.
Baltimore, MD 21230
Race: Black
Gender: male
Age: 23 years old
Found on July 29, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.31263100000, -76.59197900000, icon_homicide_shooting, 'p405', '
Brandon Eveline
1900 N. Wolfe St.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 24 years old
Found on July 28, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.35681800000, -76.60436800000, icon_homicide_shooting, 'p404', '
Tyrone McCray
5500 Ivanhoe Ave.
Baltimore, MD 21212
Race: Black
Gender: male
Age: 28 years old
Found on July 27, 2008
Victim died at Scene
Cause: Shooting
' 39.34271800000, -76.69478400000, icon_homicide_shooting, 'p402', '
Quinton Hogan
5800 N. Rogers Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 23 years old
Found on July 25, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.34271800000, -76.69478400000, icon_homicide_shooting, 'p403', '
Donell Rogers
5800 N. Rogers Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 21 years old
Found on July 25, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.33204000000, -76.66105600000, icon_homicide_shooting, 'p401', '
Justin Harrison
3800 Park Heights Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 27 years old
Found on July 21, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.28564090000, -76.63658520000, icon_homicide_stabbing, 'p399', '
Elizabeth Queensbury
1200 W. Pratt St.
Baltimore, MD 21223
Race: Black
Gender: female
Age: 51 years old
Found on July 21, 2008
Victim died at Scene
Cause: Stabbing
Read the article
' 39.28877500000, -76.67263800000, icon_homicide_shooting, 'p398', '
Gary Cooper
200 N. Hilton St.
Baltimore, MD 21229
Race: Black
Gender: male
Age: 29 years old
Found on July 21, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.37213500000, -76.59359700000, icon_homicide_stabbing, 'p400', '
Ruby Mosby
1214 Limit Ave.
Baltimore, MD 21239
Race: Black
Gender: female
Age: 23 years old
Found on July 20, 2008
Victim died at Unknown
Cause: Stabbing
Read the article
' 39.31072100000, -76.63300200000, icon_homicide_shooting, 'p396', '
Omar Phillips
900 W. North Ave.
Baltimore, MD 21217
Race: Black
Gender: male
Age: 35 years old
Found on July 19, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.34949100000, -76.66972000000, icon_homicide_shooting, 'p397', '
Kevin McKnight
5000 Pembridge Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 45 years old
Found on July 18, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.32605000000, -76.67683900000, icon_homicide_stabbing, 'p391', '
Calvin Ray
3700 Liberty Heights Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 17 years old
Found on July 13, 2008
Victim died at Unknown
Cause: Stabbing
Read the article
' 39.31662700000, -76.57837600000, icon_homicide_shooting, 'p390', '
Tony Smith
3200 Lyndale Ave.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 26 years old
Found on July 13, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.30843300000, -76.66117300000, icon_homicide_shooting, 'p392', '
Bryant Wallace
1800 Braddish Ave.
Baltimore, MD 21216
Race: Black
Gender: male
Age: 21 years old
Found on July 11, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.29060200000, -76.69208000000, icon_homicide_unknown, 'p393', '
Brenda Hatfield
4500 Old Frederick Road
Baltimore, MD 21229
Race: Black
Gender: female
Age: 45 years old
Found on July 7, 2008
Victim died at Scene
Cause: Unknown
' 39.36401200000, -76.59610600000, icon_homicide_bluntforce, 'p394', '
Patrice Marable
1100 Gleneagle Road
Baltimore, MD 21239
Race: Black
Gender: female
Age: 28 years old
Found on July 6, 2008
Victim died at Johns Hopkins Hospital
Cause: Blunt Force
Read the article
' 39.30607800000, -76.58088400000, icon_homicide_shooting, 'p389', '
Kenneth Baker
2600 E.Preston St.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 26 years old
Found on July 6, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.29674300000, -76.58636300000, icon_homicide_shooting, 'p540', '
Valando Goodman
2200 Jeffereson St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 32 years old
Found on June 29, 2008
Victim died at Unknown
Cause: Shooting
' 39.32948300000, -76.68233560000, icon_homicide_asphyxiation, 'p386', '
Nicole Sesker
3500 Garrison Blvd.
Baltimore, MD 21215
Race: Black
Gender: female
Age: 39 years old
Found on June 27, 2008
Victim died at Scene
Cause: Asphyxiation
Read the article
' 39.29223900000, -76.58026900000, icon_homicide_bluntforce, 'p532', '
Chicas Santiago
2600 E Baltimore St
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 38 years old
Found on June 26, 2008
Victim died at Unknown
Cause: Blunt Force
' 39.32211900000, -76.59289200000, icon_homicide_shooting, 'p384', '
Philip Gaddy
1700 Abbotston St.
Baltimore, MD 21218
Race: Unknown
Gender: male
Age: 41 years old
Found on June 24, 2008
Victim died at Good Samaritan Hospital
Cause: Shooting
Read the article
' 39.30824500000, -76.63638390000, icon_homicide_shooting, 'p381', '
Romie Ziegler
500 Bloom St.
Baltimore, MD 21217
Race: Black
Gender: male
Age: 21 years old
Found on June 22, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.28441690000, -76.64141670000, icon_homicide_shooting, 'p525', '
Eddie Van Kirk
300 S. Parrish St
Baltimore, MD 21223
Race: White
Gender: male
Age: 45 years old
Found on June 22, 2008
Victim died at Unknown
Cause: Shooting
' 39.32850500000, -76.66719300000, icon_homicide_shooting, 'p531', '
Marcus Caldwell
3200 Sequoia Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 23 years old
Found on June 22, 2008
Victim died at Unknown
Cause: Shooting
' 39.27984400000, -76.63215000000, icon_homicide_asphyxiation, 'p385', '
Amanda Bishop
1300 Nanticoke St.
Baltimore, MD 21230
Race: White
Gender: female
Age: 22 years old
Found on June 22, 2008
Victim died at Scene
Cause: Asphyxiation
Read the article
' 39.28001460000, -76.61120740000, icon_homicide_shooting, 'p383', '
Stephan Waters
200 E. Montgomery St.
Baltimore, MD 21230
Race: Unknown
Gender: male
Age: 24 years old
Found on June 22, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.32850500000, -76.66719300000, icon_homicide_shooting, 'p380', '
Marcus Caldwell
3200 Sequoia Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 23 years old
Found on June 22, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.28068710000, -76.60988090000, icon_homicide_shooting, 'p382', '
Keyva Bluitt
800 Battery Ave.
Baltimore, MD 21230
Race: Black
Gender: male
Age: 35 years old
Found on June 20, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.32066000000, -76.62297100000, icon_homicide_shooting, 'p379', '
Donta Gregory
2800 Huntingdon Ave.
Baltimore, MD 21211
Race: Unknown
Gender: male
Age: 32 years old
Found on June 19, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.30824500000, -76.63638390000, icon_homicide_shooting, 'p378', '
Brian Goodwyn
500 Bloom St.
Baltimore, MD 21217
Race: Unknown
Gender: male
Age: 21 years old
Found on June 19, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.31215600000, -76.64707400000, icon_homicide_shooting, 'p377', '
Ronnie Ware
1800 Clifton Ave.
Baltimore, MD 21217
Race: Black
Gender: male
Age: 18 years old
Found on June 17, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.22952120000, -76.59786050000, icon_homicide_shooting, 'p376', '
Sandy Howard
4100 Mariban Court
Baltimore, MD 21225
Race: Unknown
Gender: female
Age: 23 years old
Found on June 17, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.35345500000, -76.56249500000, icon_homicide_stabbing, 'p373', '
Samuel Bobrow
2900 Hamilton Ave.
Baltimore, MD 21214
Race: Unknown
Gender: male
Found on June 14, 2008
Victim died at Unknown
Cause: Stabbing
Read the article
' 39.32818100000, -76.56495600000, icon_homicide_shooting, 'p370', '
Bryant Price
4200 Shamrock Ave.
Baltimore, MD 21206
Race: Unknown
Gender: male
Age: 23 years old
Found on June 14, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.32818100000, -76.56495600000, icon_homicide_shooting, 'p371', '
Justin Amis
4200 Shamrock Ave.
Baltimore, MD 21206
Race: Unknown
Gender: male
Age: 22 years old
Found on June 14, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.30535200000, -76.66950400000, icon_homicide_shooting, 'p372', '
Andrew Wallace
3100 Brighton St.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 30 years old
Found on June 13, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30476100000, -76.65588200000, icon_homicide_shooting, 'p368', '
Djuan Anderson
1500 Moreland Ave.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 17 years old
Found on June 12, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30476100000, -76.65588200000, icon_homicide_shooting, 'p369', '
Darrius Harrison
1500 Moreland Ave.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 17 years old
Found on June 12, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.32825700000, -76.63841300000, icon_homicide_asphyxiation, 'p367', '
Elizabeth Garrett
3500 Buena Vista Ave.
Baltimore, MD 21211
Race: White
Gender: female
Age: 25 years old
Found on June 11, 2008
Victim died at Scene
Cause: Asphyxiation
' 39.32001030000, -76.69238870000, icon_homicide_shooting, 'p366', '
Kenneth Jones
4500 Bonner Road
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 26 years old
Found on June 9, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.34644600000, -76.68920000000, icon_homicide_shooting, 'p365', '
Tyrone Wells
4100 Rockfield Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 33 years old
Found on June 3, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.31136500000, -76.66966100000, icon_homicide_shooting, 'p364', '
Derrell Smith
3100 Clifton Ave.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 22 years old
Found on June 2, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.24802150000, -76.62443230000, icon_homicide_shooting, 'p533', '
Ralph McKnight
800 Bridgeview Road
Baltimore, MD 21225
Race: Black
Gender: male
Age: 50 years old
Found on June 1, 2008
Victim died at Unknown
Cause: Shooting
' 39.31014800000, -76.58394600000, icon_homicide_asphyxiation, 'p362', '
Josephine House
1700 N. Port St.
Baltimore, MD 21213
Race: Unknown
Gender: female
Age: 63 years old
Found on May 28, 2008
Victim died at Unknown
Cause: Asphyxiation
Read the article
' 39.31189000000, -76.60394400000, icon_homicide_shooting, 'p361', '
Cedric Yarborough
1100 E. North Ave.
Baltimore, MD 21202
Race: Unknown
Gender: male
Age: 19 years old
Found on May 25, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.31619200000, -76.59527800000, icon_homicide_shooting, 'p360', '
Ronald Cromwell Jr.
1600 Normal Ave.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 22 years old
Found on May 24, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.28906000000, -76.59401800000, icon_homicide_shooting, 'p359', '
Carlos Cervantes
200 S. Broadway
Baltimore, MD 21231
Race: Unknown
Gender: male
Age: 37 years old
Found on May 19, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.23435840000, -76.59977140000, icon_homicide_shooting, 'p357', '
Valerie Barnes
800 Pontiac Ave.
Baltimore, MD 21225
Race: Unknown
Gender: female
Age: 18 years old
Found on May 18, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.28749000000, -76.64408900000, icon_homicide_shooting, 'p358', '
Jeffrey Gause
1700 Hollins St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 42 years old
Found on May 18, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.29790400000, -76.68671000000, icon_homicide_shooting, 'p356', '
Michael Morris
4200 Colborne Rd.
Baltimore, MD 21229
Race: Black
Gender: male
Age: 46 years old
Found on May 16, 2008
Victim died at Unknown
Cause: Shooting
' 39.30740280000, -76.64482450000, icon_homicide_shooting, 'p354', '
Marcellus Hall
1600 N. Mount St.
Baltimore, MD 21217
Race: Black
Gender: male
Age: 20 years old
Found on May 15, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.31272900000, -76.58588000000, icon_homicide_shooting, 'p355', '
Tony Allen
2300 E. North Ave.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 53 years old
Found on May 15, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.31272900000, -76.58588000000, icon_homicide_shooting, 'p363', '
Omar Spriggs
2300 E. North Ave.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 27 years old
Found on May 15, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.28747870000, -76.59652950000, icon_homicide_shooting, 'p353', '
Marvin Lucas
300 S. Dallas St.
Baltimore, MD 21231
Race: Unknown
Gender: male
Found on May 14, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.28747100000, -76.62621900000, icon_homicide_stabbing, 'p352', '
Timothy Swann
700 W. Lombard St.
Baltimore, MD 21201
Race: Black
Gender: male
Age: 27 years old
Found on May 11, 2008
Victim died at University of Maryland Medical Center
Cause: Stabbing
Read the article
' 39.32273650000, -76.58868660000, icon_homicide_shooting, 'p351', '
David Henderson
2800 Hillen Road
Baltimore, MD 21218
Race: Unknown
Gender: male
Age: 18 years old
Found on May 9, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.28238500000, -76.63540200000, icon_homicide_shooting, 'p350', '
Robert Johnson
1200 James St.
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 39 years old
Found on May 7, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30742800000, -76.66616100000, icon_homicide_shooting, 'p349', '
Kenneth Carter
2900 Presbury St.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 17 years old
Found on May 6, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30706000000, -76.58266500000, icon_homicide_shooting, 'p347', '
Paris Richardson
2500 E. Hoffman St.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 21 years old
Found on May 5, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.34474820000, -76.68195560000, icon_homicide_bluntforce, 'p348', '
Ellsworth Monroe
3900 W. Belvedere Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 73 years old
Found on May 4, 2008
Victim died at Sinai Hospital
Cause: Blunt Force
Read the article
' 39.29559600000, -76.58748000000, icon_homicide_shooting, 'p345', '
Tyrone Freeman
2100 Orleans St.
Baltimore, MD 21231
Race: Unknown
Gender: male
Found on May 4, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.32083570000, -76.57458390000, icon_homicide_shooting, 'p346', '
Sean Henderson
3200 Belair Road
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 35 years old
Found on May 4, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.27911570000, -76.65922560000, icon_homicide_shooting, 'p344', '
Dawn Shipley
500 S. Brunswick St.
Baltimore, MD 21223
Race: Unknown
Gender: female
Age: 29 years old
Found on May 3, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.34589300000, -76.68856400000, icon_homicide_shooting, 'p343', '
Michael Wilson
4100 Newbern Ave.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 23 years old
Found on May 1, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.29429500000, -76.64150200000, icon_homicide_shooting, 'p341', '
Michael Ellerby
500 N. Stricker St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 24 years old
Found on April 21, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.32434500000, -76.62302650000, icon_homicide_stabbing, 'p340', '
Nancy Schmidt
200 W. 31st St.
Baltimore, MD 21211
Race: Unknown
Gender: female
Age: 74 years old
Found on April 21, 2008
Victim died at Union Memorial Hospital
Cause: Stabbing
Read the article
' 39.24044050000, -76.60017910000, icon_homicide_shooting, 'p339', '
Stanley Johnson
3400 Sixth St.
Baltimore, MD 21225
Race: Unknown
Gender: male
Age: 25 years old
Found on April 20, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.29850600000, -76.57096100000, icon_homicide_bluntforce, 'p338', '
600 N. Clinton St.
Baltimore, MD 21205
Race: Unknown
Gender: male
Found on April 18, 2008
Victim died at Scene
Cause: Blunt Force
Read the article
' 39.32040300000, -76.67753500000, icon_homicide_shooting, 'p337', '
Tavon Smith
2900 Garrison Blvd.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 27 years old
Found on April 16, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.31291600000, -76.58625300000, icon_homicide_shooting, 'p336', '
Shawndreta Griffin
1900 N. Patterson Park Ave.
Baltimore, MD 21213
Race: Unknown
Gender: female
Age: 14 years old
Found on April 14, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.29850600000, -76.57096100000, icon_homicide_stabbing, 'p530', '
James Daniels
600 N Clinton St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 51 years old
Found on April 13, 2008
Victim died at Unknown
Cause: Stabbing
' 39.29084500000, -76.64412600000, icon_homicide_shooting, 'p335', '
Anthony Izzard
1700 W. Lexington St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 25 years old
Found on April 12, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.28224880000, -76.64066430000, icon_homicide_shooting, 'p333', '
Gerrard Meginson
1500 Cole St.
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 20 years old
Found on April 3, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.29574820000, -76.66497650000, icon_homicide_shooting, 'p332', '
Darius Bunch
800 N. Franklintown Road
Baltimore, MD 21216
Race: Unknown
Gender: male
Found on April 1, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30700800000, -76.58405800000, icon_homicide_shooting, 'p331', '
Lamar Skinner
2400 E. Hoffman St.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 20 years old
Found on March 31, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.28693400000, -76.62101400000, icon_homicide_unknown, 'p328', '
Anthony Castillo
110 S. Eutaw St.
Baltimore, MD 21201
Race: Unknown
Gender: male
Age: 6 years old
Found on March 30, 2008
Victim died at Scene
Cause: Unknown
Read the article
' 39.28736460000, -76.62080720000, icon_homicide_unknown, 'p329', '
Austin Castillo
111 S. Eutaw St.
Baltimore, MD 21201
Race: Unknown
Gender: male
Age: 4 years old
Found on March 30, 2008
Victim died at Scene
Cause: Unknown
Read the article
' 39.28692630000, -76.62078240000, icon_homicide_unknown, 'p330', '
Athena Castillo
112 S. Eutaw St.
Baltimore, MD 21201
Race: Unknown
Gender: female
Age: 2 years old
Found on March 30, 2008
Victim died at Scene
Cause: Unknown
Read the article
' 39.34846300000, -76.68595800000, icon_homicide_shooting, 'p327', '
Javon King
5400 Nelson Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 19 years old
Found on March 30, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.30267900000, -76.59714910000, icon_homicide_stabbing, 'p326', '
Ronald Joyner
1000 Billie Holiday Court
Baltimore, MD 21205
Race: Black
Gender: male
Age: 27 years old
Found on March 30, 2008
Victim died at Johns Hopkins Hospital
Cause: Stabbing
Read the article
' 39.28618540000, -76.64593190000, icon_homicide_shooting, 'p325', '
Tyrone Bahia
100 Addison St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 19 years old
Found on March 27, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.28832500000, -76.57258700000, icon_homicide_bluntforce, 'p529', '
Zach Sowers
300 S. Robinson St
Baltimore, MD 21224
Race: White
Gender: male
Age: 28 years old
Found on March 26, 2008
Victim died at Johns Hopkins Bayview Medical Center
Cause: Blunt Force
' 39.28332990000, -76.64067120000, icon_homicide_shooting, 'p323', '
Robert Long
400 S. Stricker St.
Baltimore, MD 21223
Race: Unknown
Gender: male
Age: 36 years old
Found on March 24, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.27311600000, -76.67422900000, icon_homicide_shooting, 'p322', '
Jeffrey Butler
3400 Wilkens Ave.
Baltimore, MD 21229
Race: Black
Gender: male
Age: 18 years old
Found on March 23, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.31552000000, -76.56664300000, icon_homicide_shooting, 'p321', '
Davide Rankin
3700 Ravenwood Ave.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 21 years old
Found on March 23, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.29208800000, -76.62131600000, icon_homicide_shooting, 'p429', '
Dominic Faw
200 N. Eutaw St.
Baltimore, MD 21201
Race: Black
Gender: male
Age: 29 years old
Found on March 22, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30698200000, -76.67913290000, icon_homicide_asphyxiation, 'p334', '
Yolanda Brown
3600 Winterbourne Road
Baltimore, MD 21216
Race: Unknown
Gender: female
Age: 36 years old
Found on March 20, 2008
Victim died at Scene
Cause: Asphyxiation
Read the article
' 39.32040300000, -76.67753500000, icon_homicide_shooting, 'p320', '
Andre Jones
2900 Garrison Blvd.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 18 years old
Found on March 15, 2008
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.30916170000, -76.66927700000, icon_homicide_shooting, 'p319', '
Jamal Harrison
1900 N. Rosedale St.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 22 years old
Found on March 14, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.29505500000, -76.62146100000, icon_homicide_unknown, 'p528', '
Montford April
400 W Franklin St
Baltimore, MD 21201
Race: Black
Gender: female
Age: 41 years old
Found on March 14, 2008
Victim died at Unknown
Cause: Unknown
' 39.24700120000, -76.62872720000, icon_homicide_shooting, 'p318', '
Lemell Barnes
2800 Spelman Road
Baltimore, MD 21225
Race: Black
Gender: male
Age: 22 years old
Found on March 13, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.34171100000, -76.65985300000, icon_homicide_shooting, 'p317', '
Tavon Burks
2500 Edgecombe Circle N.
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 16 years old
Found on March 11, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.32406200000, -76.59054200000, icon_homicide_shooting, 'p316', '
Marcus Hughes
1800 E. 28th St.
Baltimore, MD 21218
Race: Black
Gender: male
Age: 36 years old
Found on March 10, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.28412000000, -76.64691000000, icon_homicide_asphyxiation, 'p374', '
Baby Boy Blackwell
300 S. Monroe St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 1 year old
Found on March 9, 2008
Victim died at Scene
Cause: Asphyxiation
Read the article
' 39.29582500000, -76.68427000000, icon_homicide_shooting, 'p315', '
Levette Johnson
700 Wildwood Parkway
Baltimore, MD 21229
Race: Black
Gender: male
Age: 35 years old
Found on March 9, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.28536430000, -76.69660470000, icon_homicide_shooting, 'p314', '
Latasha Harris
200 Atholgate Lane
Baltimore, MD 21229
Race: Unknown
Gender: female
Age: 30 years old
Found on March 7, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.31669020000, -76.63049020000, icon_homicide_stabbing, 'p313', '
Dalion Stanley
700 Druid Park Lake Drive
Baltimore, MD 21217
Race: Black
Gender: male
Age: 45 years old
Found on March 4, 2008
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
Read the article
' 39.29832600000, -76.66300200000, icon_homicide_shooting, 'p312', '
D'Andre Jackson
2800 W. Lafayette Ave.
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 35 years old
Found on March 3, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.34672300000, -76.67503000000, icon_homicide_shooting, 'p311', '
Anthony Underwood
5000 Denmore Ave.
Baltimore, MD 21215
Race: Black
Gender: male
Age: 29 years old
Found on March 1, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.30216320000, -76.66568620000, icon_homicide_shooting, 'p310', '
Bernard Wallace
1300 Bloomingdale Road
Baltimore, MD 21216
Race: Black
Gender: male
Age: 44 years old
Found on February 29, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.30921900000, -76.62311600000, icon_homicide_shooting, 'p308', '
Edward Baylor
1500 Mount Royal Ave.
Baltimore, MD 21217
Race: Unknown
Gender: male
Age: 32 years old
Found on February 27, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30970300000, -76.62288800000, icon_homicide_shooting, 'p309', '
Rebecca Meekins
1501 Mount Royal Ave.
Baltimore, MD 21217
Race: Unknown
Gender: female
Age: 16 years old
Found on February 27, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.31073210000, -76.62799410000, icon_homicide_shooting, 'p307', '
Eric Jones
700 W. North Ave.
Baltimore, MD 21217
Race: Unknown
Gender: male
Age: 23 years old
Found on February 25, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.30402000000, -76.58289300000, icon_homicide_shooting, 'p306', '
Julius Pressley
1100 N. Milton Ave.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 23 years old
Found on February 24, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.30401400000, -76.58841200000, icon_homicide_shooting, 'p305', '
Henry Davis
1100 N. Chester St.
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 48 years old
Found on February 20, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.27906200000, -76.62471400000, icon_homicide_shooting, 'p304', '
Murriel Chew
1000 Russell St.
Baltimore, MD 21230
Race: Black
Gender: male
Age: 20 years old
Found on February 19, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.34291700000, -76.67324500000, icon_homicide_shooting, 'p303', '
Tyisha Brown
3400 Woodland Ave.
Baltimore, MD 21215
Race: Black
Gender: female
Age: 15 years old
Found on February 16, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.32165500000, -76.69093300000, icon_homicide_stabbing, 'p302', '
Shenera Norris
4400 Fairview Ave.
Baltimore, MD 21216
Race: Black
Gender: female
Age: 31 years old
Found on February 15, 2008
Victim died at Scene
Cause: Stabbing
Read the article
' 39.34768830000, -76.59170150000, icon_homicide_shooting, 'p301', '
Jeff Payne
1500 Pentridge Road
Baltimore, MD 21239
Race: Black
Gender: male
Age: 22 years old
Found on February 15, 2008
Victim died at Good Samaritan Hospital
Cause: Shooting
Read the article
' 39.30926400000, -76.62911600000, icon_homicide_shooting, 'p300', '
Kernia Hair
1800 Bolton St.
Baltimore, MD 21217
Race: Black
Gender: female
Age: 25 years old
Found on February 7, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.29257300000, -76.68271800000, icon_homicide_shooting, 'p299', '
Emmanuel Bryant
500 N. Loudon Ave.
Baltimore, MD 21229
Race: Black
Gender: male
Age: 22 years old
Found on February 7, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30864700000, -76.59639500000, icon_homicide_shooting, 'p298', '
Victor Couther
1600 E. Federal St.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 19 years old
Found on February 2, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.29908700000, -76.64345600000, icon_homicide_shooting, 'p297', '
Cumberland Richardson
1600 W. Lafayette
Baltimore, MD 21217
Race: Black
Gender: male
Age: 62 years old
Found on February 2, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.36848180000, -76.56698110000, icon_homicide_shooting, 'p527', '
Edward Hunt
2300 E. Northern Parkway
Baltimore, MD 21234
Race: Black
Gender: male
Age: 27 years old
Found on January 30, 2008
Victim died at Unknown
Cause: Shooting
' 39.30121500000, -76.64186900000, icon_homicide_shooting, 'p293', '
Sidney Millner
1100 N. Stricker St.
Baltimore, MD 21217
Race: Black
Gender: male
Age: 25 years old
Found on January 25, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.29887300000, -76.64939700000, icon_homicide_shooting, 'p295', '
Michael Johnson
2000 W. Lafayette Ave.
Baltimore, MD 21217
Race: Black
Gender: male
Age: 24 years old
Found on January 25, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.29676710000, -76.62631530000, icon_homicide_shooting, 'p294', '
Irvin Lawson
900 Pennsylvania Ave.
Baltimore, MD 21201
Race: Black
Gender: male
Age: 31 years old
Found on January 25, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.30722900000, -76.60336700000, icon_homicide_shooting, 'p291', '
Effrem Kearney
1500 Holbrook St.
Baltimore, MD 21202
Race: Black
Gender: male
Age: 20 years old
Found on January 19, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.32216100000, -76.59069400000, icon_homicide_shooting, 'p290', '
Collin Mazyck
2700 Tivoly Ave.
Baltimore, MD 21218
Race: Black
Gender: male
Age: 24 years old
Found on January 17, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.31946200000, -76.56589100000, icon_homicide_shooting, 'p289', '
Isaiah McKiever
3500 Cliftmont Ave.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 19 years old
Found on January 17, 2008
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
Read the article
' 39.32687000000, -76.57367300000, icon_homicide_bluntforce, 'p292', '
Lisa Holley
2700 Pelham Ave.
Baltimore, MD 21213
Race: Unknown
Gender: female
Age: 38 years old
Found on January 16, 2008
Victim died at Johns Hopkins Hospital
Cause: Blunt Force
Read the article
' 39.24529030000, -76.62084960000, icon_homicide_shooting, 'p288', '
Edward Smith
800 Bethune Road
Baltimore, MD 21225
Race: Black
Gender: male
Age: 14 years old
Found on January 14, 2008
Victim died at Unknown
Cause: Shooting
Read the article
' 39.31824300000, -76.61096000000, icon_homicide_stabbing, 'p287', '
Willie Joyner
400 E. 25th St.
Baltimore, MD 21218
Race: Black
Gender: male
Age: 18 years old
Found on January 12, 2008
Victim died at Johns Hopkins Hospital
Cause: Stabbing
Read the article
' 39.32853460000, -76.59400400000, icon_homicide_shooting, 'p286', '
Zecariah Hallback
1500 E. 33rd St.
Baltimore, MD 21218
Race: Black
Gender: male
Age: 18 years old
Found on January 9, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30930800000, -76.66770100000, icon_homicide_shooting, 'p285', '
Jerrell Brown
3000 W. North Ave.
Baltimore, MD 21216
Race: Black
Gender: male
Age: 30 years old
Found on January 8, 2008
Victim died at Scene
Cause: Shooting
Read the article
' 39.32522900000, -76.57255600000, icon_homicide_shooting, 'p284', '
Jerome Waters
2800 Kentucky Ave.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 32 years old
Found on January 4, 2008
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.24529030000, -76.62084960000, icon_homicide_shooting, 'p283', '
Linwood Colvin
800 Bethune Road
Baltimore, MD 21225
Race: Black
Gender: male
Age: 28 years old
Found on January 1, 2008
Victim died at Maryland Shock Trauma Center
Cause: Shooting
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' 39.29174700000, -76.56967600000, icon_homicide_stabbing, 'p791', '
Anthony Metzel
12 S. Highland Ave
Baltimore, MD 21224
Race: White
Gender: male
Age: 56 years old
Found on December 31, 2009
Victim died at Unknown
Cause: Stabbing

Metzel was beaten and stabbed Dec. 24, 2007 and died this year at a nursing facility. This date reflects the date he...

' 39.33613740000, -76.67901980000, icon_homicide_shooting, 'p790', '
Jeffrey Ward
3800 Oakford Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 19 years old
Found on December 30, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.31546100000, -76.60952560000, icon_homicide_shooting, 'p789', '
Joseph Beatty
2300 Greenmount Avenue
Baltimore, MD 21218
Race: Black
Gender: male
Age: 38 years old
Found on December 29, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29916700000, -76.57690100000, icon_homicide_shooting, 'p787', '
Harrison Butler
700 N. Linwood Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 44 years old
Found on December 27, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting

Butler was shot on Tuesday, Dec. 22 and taken off life support on Dec. 26. His death was added to the...

' 39.29121880000, -76.62813720000, icon_homicide_shooting, 'p785', '
Ashley Harper
700 W. Lexington St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 53 years old
Found on December 26, 2009
Victim died at Unknown
Cause: Shooting
' 39.36030920000, -76.70776980000, icon_homicide_shooting, 'p784', '
Wayne Ruder
6900 Reisterstown Road
Baltimore, MD 21215
Race: White
Gender: male
Age: 56 years old
Found on December 26, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.28949200000, -76.59148990000, icon_homicide_shooting, 'p786', '
Kinlaw Jones
1800 E. Pratt
Baltimore, MD 21231
Race: White
Gender: male
Age: 21 years old
Found on December 26, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30990900000, -76.59144300000, icon_homicide_asphyxiation, 'p783', '
Trina Johnson
1900 E. Lanvale St
Baltimore, MD 21213
Race: Black
Gender: female
Age: 27 years old
Found on December 24, 2009
Victim died at Scene
Cause: Asphyxiation

Johnson\'s remains were found in a rowhouse in Oct. 2008. This date reflects the date homicide detectives added her to...

' 39.29916020000, -76.60195370000, icon_homicide_shooting, 'p782', '
Danell Freeman
800 Aisquith St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 19 years old
Found on December 22, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.27616890000, -76.69801430000, icon_homicide_shooting, 'p780', '
Clifford Williams
600 S. Wickham Rd
Baltimore, MD 21229
Race: Black
Gender: male
Age: 22 years old
Found on December 20, 2009
Victim died at St. Agnes Hospital
Cause: Shooting

Williams was a solider on leave from Afghanistan for Christmas break.

' 39.33599930000, -76.69773300000, icon_homicide_shooting, 'p779', '
Stephen Makia
5000 Belle Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 28 years old
Found on December 20, 2009
Victim died at Scene
Cause: Shooting

Makia was a Marine Corps veteran.

' 39.32519400000, -76.57259400000, icon_homicide_bluntforce, 'p781', '
Janaya Wallace
2800 Kentucky Ave
Baltimore, MD 21213
Race: Black
Gender: female
Age: 1 year old
Found on December 19, 2009
Victim died at Johns Hopkins Hospital
Cause: Blunt Force
' 39.31211380000, -76.59703440000, icon_homicide_stabbing, 'p778', '
Joshua Hargrove
1600 E. North Ave.
Baltimore, MD 21213
Race: Black
Gender: male
Age: 20 years old
Found on December 18, 2009
Victim died at Johns Hopkins Hospital
Cause: Stabbing

Hargorve was stabbed during a fight at a party at the Great Blacks in Wax museum

' 39.31630600000, -76.60621900000, icon_homicide_shooting, 'p777', '
Darnell Gray
700 Bartlett Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 35 years old
Found on December 16, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.33169000000, -76.69573700000, icon_homicide_shooting, 'p775', '
Dante Johnson-Turner
4700 Liberty Heights Ave.
Baltimore, MD 21207
Race: Black
Gender: male
Age: 29 years old
Found on December 12, 2009
Victim died at Sinai Hospital
Cause: Shooting

Johnson-Turner was shot on Dec. 11 and pronounced dead on Dec. 12.

' 39.33650790000, -76.65635980000, icon_homicide_shooting, 'p773', '
Jamal Keats
2400 Shirley Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 27 years old
Found on December 12, 2009
Victim died at Scene
Cause: Shooting
' 39.32427400000, -76.57706300000, icon_homicide_shooting, 'p774', '
Glen Johnson
3300 Woodstock Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 32 years old
Found on December 12, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting

Johnson was shot during a home invasion, according to police.

' 39.30956400000, -76.63922800000, icon_homicide_shooting, 'p776', '
Dagas McGill
2300 Etting St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 39 years old
Found on December 11, 2009
Victim died at Unknown
Cause: Shooting

McGill was shot on Sept. 17, 1999 and died on Sept. 11, 2009. This date reflects the date his death was added to the...

' 39.35561900000, -76.60208800000, icon_homicide_bluntforce, 'p772', '
Deborah Beard
5500 Midwood Ave
Baltimore, MD 21212
Race: Black
Gender: female
Age: 59 years old
Found on December 8, 2009
Victim died at Johns Hopkins Hospital
Cause: Blunt Force

Beard and her 17-year-old daughter, Qurra Iqbal, were attacked Dec. 3. Iqbal was pronounced dead at the scene; Beard...

' 39.30534800000, -76.59919700000, icon_homicide_shooting, 'p771', '
Shannon Colbert
1400 E. Preston St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 32 years old
Found on December 8, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting

Colbert was shot Aug. 14, 2008 and died from his injuies on Nov. 24, 2009. This date reflects the date his death was...

' 39.29594000000, -76.57885300000, icon_homicide_shooting, 'p770', '
Corey Jones
2700 Orleans St
Baltimore, MD 21224
Race: Black
Gender: male
Age: 20 years old
Found on December 5, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.35561900000, -76.60208800000, icon_homicide_bluntforce, 'p766', '
Qurra Iqbal
5500 Midwood Ave
Baltimore, MD 21212
Race: Unknown
Gender: female
Age: 17 years old
Found on December 3, 2009
Victim died at Unknown
Cause: Blunt Force
' 39.29446300000, -76.58624000000, icon_homicide_shooting, 'p768', '
Wayne Smith
2200 E. Fayette St
Baltimore, MD 21231
Race: Unknown
Gender: male
Age: 42 years old
Found on December 3, 2009
Victim died at Unknown
Cause: Shooting

Smith was shot and paralyzed while riding his bike on Nov. 25, 1998. He died this year and this date reflects the date...

' 39.33808300000, -76.66788690000, icon_homicide_shooting, 'p769', '
Tavon Waters
2800 Boarman Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 26 years old
Found on December 3, 2009
Victim died at Unknown
Cause: Shooting

Walker was shot in Oct 18, 2006 and died this year. This date reflects the day his death was added to the city homicide...

' 39.34364250000, -76.53548290000, icon_homicide_bluntforce, 'p765', '
David Monath
4700 Glenarm
Baltimore, MD 21206
Race: White
Gender: male
Age: 66 years old
Found on December 2, 2009
Victim died at Scene
Cause: Blunt Force
' 39.31252500000, -76.63816600000, icon_homicide_shooting, 'p762', '
Antoine Ennis
1100 Whitelock St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 37 years old
Found on November 30, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30019260000, -76.63882080000, icon_homicide_shooting, 'p767', '
Joshuan Beasley
1000 n. carey st
Baltimore, MD 21217
Race: Black
Gender: male
Age: 26 years old
Found on November 30, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29678400000, -76.62027900000, icon_homicide_shooting, 'p763', '
Glen Footman
600 N Howard St
Baltimore, MD 21201
Race: White
Gender: male
Age: 52 years old
Found on November 30, 2009
Victim died at University of Maryland Medical Center
Cause: Shooting

Footman was shot in Sept. 2008, and died Nov. 9, 2009. This date reflects when police added his death to the 2009...

' 39.27673600000, -76.61646300000, icon_homicide_shooting, 'p764', '
Ralph Taylor
100 W Cross St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 45 years old
Found on November 30, 2009
Victim died at Unknown
Cause: Shooting

Taylor was shot in August 1993 and died in 2009. This date reflects the day police added him to the homicide total.

' 39.30904460000, -76.63738130000, icon_homicide_shooting, 'p759', '
Julius Sullivan
500 Gold St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 36 years old
Found on November 25, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.24648040000, -76.62861670000, icon_homicide_shooting, 'p756', '
Angelo Dangerfield
2800 Spelman
Baltimore, MD 21225
Race: Black
Gender: male
Age: 21 years old
Found on November 25, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30253380000, -76.63755750000, icon_homicide_stabbing, 'p755', '
Larry Staton
1200 Winchester St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 57 years old
Found on November 25, 2009
Victim died at Unknown
Cause: Stabbing

Staton was beaten and stabbed on Dec. 29, 2002; he died June 12, 2009 and was added to the homicide tally on this...

' 39.29504000000, -76.65494900000, icon_homicide_bluntforce, 'p760', '
Alexander Murrell
2400 Edmondson Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 62 years old
Found on November 25, 2009
Victim died at Scene
Cause: Blunt Force
' 39.32418160000, -76.59793010000, icon_homicide_shooting, 'p757', '
Curtis Mitchell
1400 Carswell St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 28 years old
Found on November 24, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30510000000, -76.57650400000, icon_homicide_shooting, 'p753', '
Tavon Fortune
1200 n. curley
Baltimore, MD 21213
Race: Black
Gender: male
Age: 23 years old
Found on November 22, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29826300000, -76.57361400000, icon_homicide_shooting, 'p754', '
Brandon Benjamin
3100 McElderry ST
Baltimore, MD 21205
Race: Black
Gender: male
Age: 22 years old
Found on November 20, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31256590000, -76.64749400000, icon_homicide_shooting, 'p752', '
Virginia McGhee
2140 N. Fulton Ave
Baltimore, MD 21217
Race: Black
Gender: female
Age: 34 years old
Found on November 19, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting

McGhee was shot outside the Joseph H. Brown Jr. Funeral Home, where she was attending a viewing for boyfriend...

' 39.30153390000, -76.60815300000, icon_homicide_bluntforce, 'p751', '
Lykeisha Freeman
600 E. Eager St
Baltimore, MD 21202
Race: Black
Gender: female
Age: 35 years old
Found on November 17, 2009
Victim died at Scene
Cause: Blunt Force
' 39.30990900000, -76.59144300000, icon_homicide_shooting, 'p748', '
Antonio Smith
1900 E Lanvale St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 18 years old
Found on November 15, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32036200000, -76.67765700000, icon_homicide_shooting, 'p749', '
Carron Spruiel
2900 Garrison Blvd
Baltimore, MD 21216
Race: Black
Gender: male
Found on November 15, 2009
Victim died at Scene
Cause: Shooting
' 39.28672300000, -76.62869800000, icon_homicide_shooting, 'p744', '
Anthony Swann
800 lemmon st
Baltimore, MD 21201
Race: Black
Gender: male
Age: 45 years old
Found on November 14, 2009
Victim died at Unknown
Cause: Shooting

Swann and his 63-year-old mother were both shot when a masked man entered their home and opened fire as they were...

' 39.31047100000, -76.63726600000, icon_homicide_shooting, 'p745', '
Sammie McCullough Jr.
1200 W North Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 36 years old
Found on November 14, 2009
Victim died at Unknown
Cause: Shooting
' 39.34315900000, -76.68142400000, icon_homicide_shooting, 'p750', '
Jose Chacon
5100 arbutus ave
Baltimore, MD 21215
Race: Hispanic
Gender: male
Age: 35 years old
Found on November 13, 2009
Victim died at Scene
Cause: Shooting
' 39.29768100000, -76.64938600000, icon_homicide_shooting, 'p747', '
Michael McFadden
2000 W Lanvale
Baltimore, MD 21217
Race: Black
Gender: male
Age: 51 years old
Found on November 12, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29660980000, -76.67741140000, icon_homicide_shooting, 'p740', '
Martin Hawkins
3600 gelston dr
Baltimore, MD 21229
Race: Unknown
Gender: male
Age: 38 years old
Found on November 12, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30750800000, -76.58385300000, icon_homicide_stabbing, 'p738', '
Jason Mattison Jr.
2400 Llewelyn ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 15 years old
Found on November 10, 2009
Victim died at Scene
Cause: Stabbing
' 39.32041100000, -76.65713600000, icon_homicide_shooting, 'p741', '
Kenneth Jones
3000 towanda ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 46 years old
Found on November 9, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.31217830000, -76.61798730000, icon_homicide_shooting, 'p737', '
Charlie Williams
100 W. 20th st
Baltimore, MD 21218
Race: Black
Gender: male
Age: 25 years old
Found on November 8, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.28603130000, -76.64857490000, icon_homicide_shooting, 'p736', '
Johnny Hawkins
100 s. payson st
Baltimore, MD 21223
Race: Black
Gender: male
Age: 40 years old
Found on November 7, 2009
Victim died at Unknown
Cause: Shooting
' 39.37054480000, -76.56207460000, icon_homicide_shooting, 'p742', '
Allen Johnson
2400 pickering dr
Baltimore, MD 21234
Race: Black
Gender: male
Age: 31 years old
Found on November 3, 2009
Victim died at Unknown
Cause: Shooting
' 39.28979460000, -76.63824490000, icon_homicide_shooting, 'p743', '
Joseph Taylor
1300 W Fayette St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 28 years old
Found on November 3, 2009
Victim died at Unknown
Cause: Shooting
' 39.29604100000, -76.64204500000, icon_homicide_stabbing, 'p733', '
Dennis Cornish
1500 Edmondson Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 30 years old
Found on October 30, 2009
Victim died at Scene
Cause: Stabbing
' 39.31324600000, -76.59208800000, icon_homicide_shooting, 'p734', '
Joshua McKinney
2000 N wolfe St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 24 years old
Found on October 29, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting

McKinney was shot Oct. 24, and detectives received word on this date that he had died.

' 39.23341380000, -76.60026480000, icon_homicide_shooting, 'p735', '
Eric Cromwell
800 Washburn Ave
Baltimore, MD 21222
Race: Unknown
Gender: male
Age: 20 years old
Found on October 29, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32185590000, -76.59843320000, icon_homicide_stabbing, 'p732', '
Darren Green
1500 Montpelier St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 24 years old
Found on October 25, 2009
Victim died at Johns Hopkins Hospital
Cause: Stabbing
' 39.29327330000, -76.59965990000, icon_homicide_shooting, 'p730', '
Hubert Bentston
100 N. Central Ave
Baltimore, MD 21231
Race: Black
Gender: male
Age: 38 years old
Found on October 21, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.28106810000, -76.57833520000, icon_homicide_bluntforce, 'p731', '
Constantine Frank
1000 S Lakewood Ave
Baltimore, MD 21224
Race: White
Gender: male
Age: 54 years old
Found on October 19, 2009
Victim died at Scene
Cause: Blunt Force

Frank, a Fells Point businessman, was found July 29 in the vestibule of his business bound by the hands. He had...

' 39.28763250000, -76.63673200000, icon_homicide_shooting, 'p729', '
Brock Tatum
1200 Hollins St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 21 years old
Found on October 18, 2009
Victim died at Unknown
Cause: Shooting
' 39.30993610000, -76.67416260000, icon_homicide_shooting, 'p728', '
Rashaud Coates
2000 Denison St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 31 years old
Found on October 15, 2009
Victim died at Unknown
Cause: Shooting
' 39.30650200000, -76.65633000000, icon_homicide_shooting, 'p726', '
Lewis White
2400 Baker St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 34 years old
Found on October 14, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31771300000, -76.56012800000, icon_homicide_shooting, 'p727', '
Ivan Wood
3900 Elmora Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 28 years old
Found on October 14, 2009
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.28887020000, -76.67424300000, icon_homicide_shooting, 'p725', '
Mark Johnson
3200 W Lexington St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 31 years old
Found on October 13, 2009
Victim died at Scene
Cause: Shooting

Johnson was found slumped in the front seat of a vehicle suffering from a gunshot wound to the head

' 39.32478500000, -76.67988000000, icon_homicide_shooting, 'p723', '
Dion Jones
3700 Springdale Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 35 years old
Found on October 11, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Jones was killed in a drive-by shooting; a second victim was injured

' 39.29910800000, -76.57826400000, icon_homicide_shooting, 'p722', '
Alonzo Key
700 N Kenwood Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 26 years old
Found on October 10, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting

Key was shot along the route of the Baltimore marathon, not long after runners had finished passing through.

' 39.31165410000, -76.60617350000, icon_homicide_shooting, 'p724', '
Damon Jackson
1900 Oakhill Ave
Baltimore, MD 21202
Race: Black
Gender: male
Age: 37 years old
Found on October 10, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31968500000, -76.60824300000, icon_homicide_shooting, 'p721', '
Calvin Taylor
2600 Mathews St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 39 years old
Found on October 7, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting

Taylor was shot Oct. 5 and died Oct. 7.

' 39.28371100000, -76.68253800000, icon_homicide_shooting, 'p715', '
Antoine Rawlings
4000 Massachusetts Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 18 years old
Found on October 3, 2009
Victim died at Unknown
Cause: Shooting
' 39.35862260000, -76.60991750000, icon_homicide_asphyxiation, 'p716', '
Elda Vazquez
500 Benninghaus Rd
Baltimore, MD 21212
Race: Hispanic
Gender: female
Age: 30 years old
Found on October 2, 2009
Victim died at Scene
Cause: Asphyxiation

Vasquez disappeared in January 2008 and was discovered on this date by a Verizon worker in an underground cable vault.

' 39.29781200000, -76.64940900000, icon_homicide_shooting, 'p714', '
Darius Anderson
800 N Payson St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 17 years old
Found on October 2, 2009
Victim died at Unknown
Cause: Shooting
' 39.30361800000, -76.66573690000, icon_homicide_other, 'p713', '
Ethel Henderson
1400 Poplar Grove St
Baltimore, MD 21216
Race: Black
Gender: female
Age: 85 years old
Found on September 29, 2009
Victim died at Johns Hopkins Bayview Medical Center
Cause: Other
' 39.28494000000, -76.64538700000, icon_homicide_shooting, 'p712', '
Jamal Brown
1800 Dover Ct
Baltimore, MD 21223
Race: Black
Gender: male
Age: 26 years old
Found on September 26, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32502000000, -76.68422200000, icon_homicide_shooting, 'p1234', '
Mia Nichols
3901 W. Forest Park Ave
Baltimore, MD 21207
Race: Black
Gender: female
Age: 37 years old
Found on September 26, 2009
Victim died at Unknown
Cause: Shooting
' 39.31434310000, -76.58881110000, icon_homicide_stabbing, 'p711', '
Shantel Brown
2100 Sinclair Ln
Baltimore, MD 21213
Race: Black
Gender: female
Age: 31 years old
Found on September 25, 2009
Victim died at Johns Hopkins Hospital
Cause: Stabbing
' 39.26315060000, -76.64205500000, icon_homicide_shooting, 'p710', '
Kareem Guest
2400 Maisel Ct
Baltimore, MD 21230
Race: Black
Gender: male
Age: 31 years old
Found on September 20, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.22378020000, -76.58825250000, icon_homicide_shooting, 'p709', '
Eugene Chambers
1600 Cypress St
Baltimore, MD 21226
Race: White
Gender: male
Age: 20 years old
Found on September 18, 2009
Victim died at Harbor Hospital
Cause: Shooting
' 39.29455780000, -76.66138840000, icon_homicide_shooting, 'p708', '
Stanley Mooring
600 Ashburton St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 50 years old
Found on September 18, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31894400000, -76.56997100000, icon_homicide_shooting, 'p707', '
Gerard Thomas
3400 Cliftmont Ave
Baltimore, MD 21213
Race: Unknown
Gender: male
Age: 24 years old
Found on September 16, 2009
Victim died at Scene
Cause: Shooting
' 39.30747240000, -76.63542380000, icon_homicide_shooting, 'p705', '
Darrell Neal
500 Presstman St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 31 years old
Found on September 14, 2009
Victim died at Unknown
Cause: Shooting
' 39.33965290000, -76.66641660000, icon_homicide_shooting, 'p706', '
Alvin Alston
2700 w. cold spring ln
Baltimore, MD 21215
Race: Black
Gender: male
Age: 45 years old
Found on September 13, 2009
Victim died at Unknown
Cause: Shooting
' 39.27680700000, -76.61626800000, icon_homicide_shooting, 'p704', '
Andre Williams
100 W Cross St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 35 years old
Found on September 13, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30804340000, -76.63462880000, icon_homicide_shooting, 'p703', '
Isreali Mason
2000 McCulloh St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 18 years old
Found on September 13, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32084790000, -76.57460130000, icon_homicide_shooting, 'p700', '
Name not yet released
3200 Belair Road
Baltimore, MD 21213
Race: Black
Gender: male
Age: 30 years old
Found on September 11, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.36849890000, -76.56697360000, icon_homicide_shooting, 'p701', '
Trenton Marshall
2300 E Northern Parkway
Baltimore, MD 21234
Race: Black
Gender: male
Age: 20 years old
Found on September 11, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.34795030000, -76.60953120000, icon_homicide_shooting, 'p699', '
Kenneth Burley Jr.
500 Radnor Ave
Baltimore, MD 21212
Race: Unknown
Gender: male
Age: 17 years old
Found on September 10, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29445500000, -76.63868700000, icon_homicide_shooting, 'p698', '
Shelton Elliott
500 N Carey ST
Baltimore, MD 21223
Race: Black
Gender: male
Age: 43 years old
Found on September 9, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29522400000, -76.62896900000, icon_homicide_shooting, 'p697', '
Michael Chase
700 Murphy Lane
Baltimore, MD 21201
Race: Black
Gender: male
Age: 47 years old
Found on September 9, 2009
Victim died at Unknown
Cause: Shooting

Chase was shot in 1988 in the 1500 block of Hopewell Court. He became a quadraplegic and died May 19, 2009. Sept. 9,...

' 39.30125500000, -76.57945600000, icon_homicide_bluntforce, 'p696', '
Charles Mack
2700 Ashland Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 47 years old
Found on September 8, 2009
Victim died at Johns Hopkins Hospital
Cause: Blunt Force
' 39.34035710000, -76.66510320000, icon_homicide_shooting, 'p695', '
Kevin Taylor
4400 Pimlico Road
Baltimore, MD 21215
Race: Black
Gender: male
Age: 29 years old
Found on September 4, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.22455320000, -76.58832630000, icon_homicide_shooting, 'p694', '
David Hunt
1600 Elmtree St
Baltimore, MD 21226
Race: White
Gender: male
Age: 23 years old
Found on September 4, 2009
Victim died at Scene
Cause: Shooting
' 39.30543570000, -76.64333950000, icon_homicide_shooting, 'p693', '
Jamaal Holmes
1600 Presstman St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 25 years old
Found on September 4, 2009
Victim died at Unknown
Cause: Shooting
' 39.31027500000, -76.64680700000, icon_homicide_shooting, 'p692', '
Larry Daniels
1900 N. Fulton St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 32 years old
Found on September 1, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30475300000, -76.66744400000, icon_homicide_shooting, 'p691', '
Chris Hester
3000 Presstman St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 38 years old
Found on September 1, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.22627900000, -76.58841900000, icon_homicide_unknown, 'p1235', '
Andrew Giancoli
1600 Hazel St
Baltimore, MD 21226
Race: White
Gender: male
Age: 48 years old
Found on August 28, 2009
Victim died at Unknown
Cause: Unknown

Giancoli was found dead in Anne Arundel County, on East Ordnance Road near Stahl Point Road, on this date. In 2010,...

' 39.30592090000, -76.63749490000, icon_homicide_shooting, 'p690', '
Michael White
598 Presstman St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 24 years old
Found on August 27, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30709280000, -76.57762740000, icon_homicide_shooting, 'p688', '
Erica Carr
1400 Kenhill Ave
Baltimore, MD 21213
Race: Black
Gender: female
Age: 37 years old
Found on August 27, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31662700000, -76.57837600000, icon_homicide_shooting, 'p689', '
Shawn Williams
3200 Lyndale Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 28 years old
Found on August 26, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32749070000, -76.68259740000, icon_homicide_shooting, 'p685', '
Nolan Evans
3900 Liberty Heights Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 40 years old
Found on August 25, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.27551300000, -76.68829400000, icon_homicide_shooting, 'p684', '
David Lumpkins III
600 Yale Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 21 years old
Found on August 22, 2009
Victim died at St. Agnes Hospital
Cause: Shooting
' 39.29674300000, -76.58636300000, icon_homicide_shooting, 'p687', '
Marlon Ringgold
2200 Jefferson St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 31 years old
Found on August 21, 2009
Victim died at Unknown
Cause: Shooting
' 39.29029100000, -76.56903100000, icon_homicide_shooting, 'p683', '
Donte Gunter
125 S Highland Ave
Baltimore, MD 21224
Race: Black
Gender: male
Age: 25 years old
Found on August 20, 2009
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.33343460000, -76.68961560000, icon_homicide_shooting, 'p686', '
David Deans
4200 Belle Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 25 years old
Found on August 20, 2009
Victim died at Unknown
Cause: Shooting
' 39.30823400000, -76.57999800000, icon_homicide_shooting, 'p681', '
Gregory Wilson
2700 E Oliver St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 20 years old
Found on August 11, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.28917660000, -76.59523380000, icon_homicide_shooting, 'p680', '
Thomas Medley
200 S. Herring Court
Baltimore, MD 21231
Race: Black
Gender: male
Age: 32 years old
Found on August 10, 2009
Victim died at Unknown
Cause: Shooting
' 39.25006610000, -76.62282400000, icon_homicide_shooting, 'p679', '
Charles Pratt
600 Cherry Hill Road
Baltimore, MD 21225
Race: Black
Gender: male
Age: 18 years old
Found on August 9, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29522300000, -76.65069600000, icon_homicide_shooting, 'p678', '
Steven Eldridge
2100 Edmondson Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 25 years old
Found on August 2, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29659900000, -76.67870100000, icon_homicide_shooting, 'p677', '
Cortez Smith
800 Allendale St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 18 years old
Found on August 2, 2009
Victim died at Unknown
Cause: Shooting
' 39.34328180000, -76.66110860000, icon_homicide_shooting, 'p676', '
Marcella Lawson
2800 Edgecombe circle north
Baltimore, MD 21215
Race: Black
Gender: female
Age: 64 years old
Found on August 2, 2009
Victim died at Scene
Cause: Shooting
' 39.29894600000, -76.64784700000, icon_homicide_bluntforce, 'p675', '
Stanley Joines
1900 W Lafayette Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 26 years old
Found on August 1, 2009
Victim died at Scene
Cause: Blunt Force
' 39.29280370000, -76.62250730000, icon_homicide_stabbing, 'p674', '
David Reese
300 N Paca St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 18 years old
Found on August 1, 2009
Victim died at Unknown
Cause: Stabbing
' 39.27996900000, -76.65879100000, icon_homicide_shooting, 'p673', '
Lamont Woodard
500 E. Lynn Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 39 years old
Found on July 31, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29510600000, -76.56871100000, icon_homicide_shooting, 'p671', '
Darvin Jones
3500 E Fayette St
Baltimore, MD 21222
Race: Black
Gender: male
Age: 19 years old
Found on July 26, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29534300000, -76.56795300000, icon_homicide_shooting, 'p672', '
Gary Martin
200 N Conkling St
Baltimore, MD 21222
Race: Black
Gender: male
Age: 18 years old
Found on July 26, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31221300000, -76.56684000000, icon_homicide_shooting, 'p670', '
Shonte Ellis
4217 Erdman Ave
Baltimore, MD 21213
Race: Black
Gender: female
Age: 26 years old
Found on July 25, 2009
Victim died at Scene
Cause: Shooting
' 39.28515840000, -76.55283570000, icon_homicide_shooting, 'p669', '
Marcus Sanchez
5100 Foster Ave
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 26 years old
Found on July 25, 2009
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.34077500000, -76.67421600000, icon_homicide_shooting, 'p668', '
Antoine Davis
3600 Woodland AVe
Baltimore, MD 21215
Race: Black
Gender: male
Age: 33 years old
Found on July 23, 2009
Victim died at Scene
Cause: Shooting
' 39.29911900000, -76.57832400000, icon_homicide_shooting, 'p667', '
Jerrod Reed
700 N Kenwood Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 16 years old
Found on July 19, 2009
Victim died at Scene
Cause: Shooting
' 39.32996250000, -76.58359430000, icon_homicide_shooting, 'p666', '
Diandre Cherry
3400 Lake Montebello Drive
Baltimore, MD 21218
Race: Black
Gender: male
Age: 25 years old
Found on July 16, 2009
Victim died at Scene
Cause: Shooting
' 39.28561580000, -76.59242730000, icon_homicide_shooting, 'p663', '
Josephine Lewatowski
500 S Regester St
Baltimore, MD 21231
Race: White
Gender: female
Age: 48 years old
Found on July 14, 2009
Victim died at Unknown
Cause: Shooting
' 39.33820900000, -76.60777700000, icon_homicide_shooting, 'p661', '
John Woodies
4000 Old York Road
Baltimore, MD 21218
Race: Black
Gender: male
Age: 38 years old
Found on July 13, 2009
Victim died at Scene
Cause: Shooting
' 39.32620500000, -76.59434100000, icon_homicide_shooting, 'p662', '
Antwon Witherspoon
2900 The Alameda
Baltimore, MD 21218
Race: Black
Gender: male
Age: 28 years old
Found on July 13, 2009
Victim died at Unknown
Cause: Shooting
' 39.36977300000, -76.58935700000, icon_homicide_shooting, 'p664', '
Curtis Shepherd
6200 Falkirk Road
Baltimore, MD 21239
Race: Black
Gender: male
Age: 36 years old
Found on July 13, 2009
Victim died at Scene
Cause: Shooting
' 39.29815000000, -76.57642100000, icon_homicide_shooting, 'p660', '
Charles Armstead
2900 McElderry St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 21 years old
Found on July 9, 2009
Victim died at Unknown
Cause: Shooting
' 39.30429500000, -76.63377500000, icon_homicide_shooting, 'p659', '
Maurice McFadden
1700 Division St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 18 years old
Found on July 8, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32216500000, -76.68324800000, icon_homicide_shooting, 'p658', '
Arnold Foster
3900 Fairfax Road
Baltimore, MD 21215
Race: Black
Gender: male
Age: 21 years old
Found on July 8, 2009
Victim died at Scene
Cause: Shooting
' 39.28878280000, -76.64948260000, icon_homicide_shooting, 'p656', '
Julius Powell
2100 W Fairmount Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 23 years old
Found on July 3, 2009
Victim died at Union Memorial Hospital
Cause: Shooting
' 39.30179490000, -76.59629790000, icon_homicide_shooting, 'p654', '
Steven Wilson
1600 E Eager St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 24 years old
Found on June 30, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29480820000, -76.65477820000, icon_homicide_stabbing, 'p682', '
Gerrod Finch
600 N. Wheeler St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 21 years old
Found on June 30, 2009
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.30598100000, -76.65165800000, icon_homicide_shooting, 'p657', '
Tavon Walker
2100 Brighton St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 28 years old
Found on June 29, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32207500000, -76.67458200000, icon_homicide_shooting, 'p653', '
Antonio Montgomery
3400 Fairview Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 36 years old
Found on June 28, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.30611500000, -76.66754000000, icon_homicide_stabbing, 'p651', '
James Johnson
3000 Baker St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 52 years old
Found on June 27, 2009
Victim died at Unknown
Cause: Stabbing
' 39.30611500000, -76.66754000000, icon_homicide_stabbing, 'p652', '
Robert Phelps
3000 Baker St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 48 years old
Found on June 27, 2009
Victim died at Unknown
Cause: Stabbing
' 39.28750620000, -76.59585850000, icon_homicide_shooting, 'p650', '
Stevie Ford
300 Mason Court
Baltimore, MD 21231
Race: Black
Gender: male
Age: 19 years old
Found on June 24, 2009
Victim died at Scene
Cause: Shooting
' 39.30995500000, -76.61510100000, icon_homicide_bluntforce, 'p649', '
Edward Davis
1800 St. Paul St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 39 years old
Found on June 24, 2009
Victim died at Scene
Cause: Blunt Force
' 39.31169630000, -76.70116640000, icon_homicide_asphyxiation, 'p648', '
Wanda Hackett
2200 Tucker Lane
Baltimore, MD 21207
Race: Black
Gender: female
Age: 51 years old
Found on June 23, 2009
Victim died at Scene
Cause: Asphyxiation
' 39.31116000000, -76.59122500000, icon_homicide_shooting, 'p646', '
Wesley Nelson
1800 N Chapel St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 29 years old
Found on June 21, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30802500000, -76.58526900000, icon_homicide_shooting, 'p645', '
Edward Patterson
2300 E Oliver St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 39 years old
Found on June 20, 2009
Victim died at Scene
Cause: Shooting
' 39.29452000000, -76.57913370000, icon_homicide_shooting, 'p644', '
Candido Hernandez
2700 E Fayette St
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 36 years old
Found on June 19, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.35667200000, -76.60784400000, icon_homicide_stabbing, 'p643', '
Tyrone Phillips
600 Tunbridge Road
Baltimore, MD 21212
Race: Black
Gender: male
Age: 19 years old
Found on June 18, 2009
Victim died at Johns Hopkins Hospital
Cause: Stabbing
' 39.30984100000, -76.66135500000, icon_homicide_shooting, 'p641', '
Pierre Alford
1900 Braddish Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 24 years old
Found on June 17, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30790100000, -76.64997800000, icon_homicide_shooting, 'p640', '
Darnell Johns
1700 N Payson
Baltimore, MD 21217
Race: Black
Gender: male
Age: 21 years old
Found on June 10, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.33905010000, -76.55495440000, icon_homicide_stabbing, 'p638', '
Lydia Steed
4000 Biddison Lane
Baltimore, MD 21206
Race: Black
Gender: female
Age: 31 years old
Found on June 10, 2009
Victim died at Scene
Cause: Stabbing
' 39.33905010000, -76.55495440000, icon_homicide_stabbing, 'p639', '
Allisha Royster
4000 Biddison Lane
Baltimore, MD 21206
Race: Black
Gender: female
Age: 23 years old
Found on June 10, 2009
Victim died at Scene
Cause: Stabbing
' 39.30918000000, -76.58167100000, icon_homicide_shooting, 'p637', '
Karl Snyder
2526 E. Federal St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 21 years old
Found on June 8, 2009
Victim died at Scene
Cause: Shooting
' 39.31599080000, -76.63618140000, icon_homicide_stabbing, 'p636', '
Dana Richardson
900 Brooks Lane
Baltimore, MD 21217
Race: Black
Gender: male
Age: 44 years old
Found on June 7, 2009
Victim died at Scene
Cause: Stabbing
' 39.27709600000, -76.54575200000, icon_homicide_shooting, 'p635', '
Anthony Benitez
1400 Anglesea St
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 21 years old
Found on June 3, 2009
Victim died at Scene
Cause: Shooting
' 39.23519400000, -76.61844400000, icon_homicide_shooting, 'p634', '
Anthony Geiger
212 Old Riverside Rd, Brooklyn, MD
Baltimore, MD 21225
Race: White
Gender: male
Age: 41 years old
Found on June 2, 2009
Victim died at Unknown
Cause: Shooting
' 39.34109100000, -76.60977100000, icon_homicide_shooting, 'p633', '
Joseph Woah-Tee
4300 York Road
Baltimore, MD 21212
Race: Black
Gender: male
Age: 60 years old
Found on May 31, 2009
Victim died at Unknown
Cause: Shooting
' 39.34836220000, -76.56837710000, icon_homicide_shooting, 'p631', '
Ricardo Montgomery
2800 Alisa Ave
Baltimore, MD 21214
Race: Black
Gender: male
Age: 39 years old
Found on May 30, 2009
Victim died at Scene
Cause: Shooting
' 39.29805700000, -76.58320600000, icon_homicide_shooting, 'p632', '
Douglas Winston
600 N Port St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 35 years old
Found on May 30, 2009
Victim died at Unknown
Cause: Shooting
' 39.28786500000, -76.62060300000, icon_homicide_shooting, 'p630', '
Milton Stepney Sr.
15 S Eutaw St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 32 years old
Found on May 28, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31053000000, -76.63618300000, icon_homicide_shooting, 'p629', '
Curtis Brown
1100 W North Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 32 years old
Found on May 27, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29428870000, -76.58637990000, icon_homicide_shooting, 'p628', '
David Parker
200 N. Collington Ave
Baltimore, MD 21231
Race: Unknown
Gender: male
Age: 25 years old
Found on May 27, 2009
Victim died at Unknown
Cause: Shooting
' 39.31962700000, -76.57545100000, icon_homicide_shooting, 'p627', '
Sean Howard
3102 Cliftmont Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 24 years old
Found on May 25, 2009
Victim died at Unknown
Cause: Shooting
' 39.29314900000, -76.57052200000, icon_homicide_shooting, 'p625', '
Hardy Jones
3300 Noble St
Baltimore, MD 21224
Race: Black
Gender: male
Age: 26 years old
Found on May 25, 2009
Victim died at Unknown
Cause: Shooting
' 39.31434310000, -76.58881110000, icon_homicide_shooting, 'p626', '
Quite Vanterpool
2100 Sinclair Lane
Baltimore, MD 21213
Race: Black
Gender: male
Age: 41 years old
Found on May 24, 2009
Victim died at Unknown
Cause: Shooting
' 39.32406200000, -76.59054200000, icon_homicide_shooting, 'p624', '
Keon Cameron
1800 E. 28th St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 18 years old
Found on May 24, 2009
Victim died at Unknown
Cause: Shooting
' 39.29243900000, -76.63716200000, icon_homicide_shooting, 'p623', '
Chernere Wooten
300 N Carrollton Ave
Baltimore, MD 21223
Race: Black
Gender: female
Age: 18 years old
Found on May 23, 2009
Victim died at Unknown
Cause: Shooting
' 39.28696100000, -76.64702800000, icon_homicide_shooting, 'p622', '
Lee Johnson
1900 Frederick Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 28 years old
Found on May 21, 2009
Victim died at Unknown
Cause: Shooting
' 39.31432890000, -76.59056510000, icon_homicide_shooting, 'p621', '
Tavon Crawford
2700 Saint Lo Drive
Baltimore, MD 21213
Race: Black
Gender: male
Age: 26 years old
Found on May 21, 2009
Victim died at Unknown
Cause: Shooting
' 39.36270500000, -76.71129600000, icon_homicide_bluntforce, 'p620', '
Lucio Solorzano
7000 Reisterstown Road
Baltimore, MD 21215
Race: Hispanic
Gender: male
Age: 84 years old
Found on May 17, 2009
Victim died at Scene
Cause: Blunt Force
' 39.31312900000, -76.60449400000, icon_homicide_shooting, 'p619', '
Christopher Ricks
2000 Kennedy Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 32 years old
Found on May 9, 2009
Victim died at Unknown
Cause: Shooting
' 39.33851200000, -76.56996800000, icon_homicide_shooting, 'p618', '
Anthony Griffin
3100 Montebello Terrace
Baltimore, MD 21214
Race: Black
Gender: male
Age: 23 years old
Found on May 8, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31339100000, -76.66977600000, icon_homicide_shooting, 'p617', '
Deontae Carter
2300 N Rosedale St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 24 years old
Found on May 8, 2009
Victim died at Scene
Cause: Shooting
' 39.33485800000, -76.60753300000, icon_homicide_shooting, 'p616', '
Anthony Egger
601 Parkwyrth Ave
Baltimore, MD 21218
Race: White
Gender: male
Age: 48 years old
Found on May 5, 2009
Victim died at Scene
Cause: Shooting
' 39.28093700000, -76.67221600000, icon_homicide_shooting, 'p615', '
Fabian Palmer
3300 Frederick Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 25 years old
Found on May 3, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29992300000, -76.58239300000, icon_homicide_shooting, 'p614', '
William Jones
800 N Milton Ave
Baltimore, MD 21205
Race: Unknown
Gender: male
Age: 26 years old
Found on May 2, 2009
Victim died at Scene
Cause: Shooting
' 39.28193200000, -76.65119100000, icon_homicide_shooting, 'p612', '
Shawn Williams
500 S. Smallwood
Baltimore, MD 21223
Race: Black
Gender: male
Age: 18 years old
Found on April 27, 2009
Victim died at Unknown
Cause: Shooting
' 39.32345300000, -76.57625700000, icon_homicide_bluntforce, 'p613', '
Frank Swiston
2800 Erdman Ave
Baltimore, MD 21213
Race: White
Gender: male
Age: 53 years old
Found on April 25, 2009
Victim died at Scene
Cause: Blunt Force
' 39.30229700000, -76.67024100000, icon_homicide_shooting, 'p611', '
Maurice Toomer
3200 Normount Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 17 years old
Found on April 25, 2009
Victim died at Unknown
Cause: Shooting
' 39.30179490000, -76.59629790000, icon_homicide_shooting, 'p610', '
Jasmon Jiggets
1600 E. Eager St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 28 years old
Found on April 23, 2009
Victim died at Unknown
Cause: Shooting
' 39.27478800000, -76.68909300000, icon_homicide_bluntforce, 'p609', '
Melody Smith
700 Yale Ave
Baltimore, MD 21229
Race: Black
Gender: female
Age: 54 years old
Found on April 21, 2009
Victim died at Scene
Cause: Blunt Force
' 39.34168500000, -76.55902800000, icon_homicide_shooting, 'p608', '
Kenneth Johnson
5100 Hillburn Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 23 years old
Found on April 21, 2009
Victim died at Unknown
Cause: Shooting
' 39.31243300000, -76.66966000000, icon_homicide_shooting, 'p607', '
Qonta Waddell
3100 Windsor Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 24 years old
Found on April 21, 2009
Victim died at Unknown
Cause: Shooting
' 39.31542400000, -76.59373300000, icon_homicide_shooting, 'p606', '
Calvin Hayes
1700 Cliftview Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 78 years old
Found on April 18, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29313100000, -76.57116900000, icon_homicide_stabbing, 'p605', '
Ernesto Flores
16 N. Clinton St
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 23 years old
Found on April 18, 2009
Victim died at Unknown
Cause: Stabbing
' 39.28895800000, -76.65345100000, icon_homicide_shooting, 'p603', '
Erskine Evans III
41 Gorman Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 28 years old
Found on April 15, 2009
Victim died at Unknown
Cause: Shooting
' 39.31064400000, -76.60113100000, icon_homicide_shooting, 'p604', '
Harold Able Sr
1800 Aiken St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 63 years old
Found on April 15, 2009
Victim died at Scene
Cause: Shooting
' 39.28184230000, -76.64945670000, icon_homicide_shooting, 'p602', '
Russell Day
2100 Christian St
Baltimore, MD 21223
Race: White
Gender: male
Age: 31 years old
Found on April 13, 2009
Victim died at Unknown
Cause: Shooting
' 39.24585670000, -76.62951400000, icon_homicide_shooting, 'p601', '
Quinton Savage
2800 Winwood Court
Baltimore, MD 21225
Race: Black
Gender: male
Age: 22 years old
Found on April 11, 2009
Victim died at Unknown
Cause: Shooting
' 39.28227000000, -76.64678000000, icon_homicide_shooting, 'p599', '
Dominic Baker
1900 Wilkens Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 16 years old
Found on April 8, 2009
Victim died at Scene
Cause: Shooting
' 39.28762400000, -76.59347410000, icon_homicide_shooting, 'p598', '
Doreatha Wright
1700 Gough St
Baltimore, MD 21231
Race: Black
Gender: female
Age: 42 years old
Found on April 5, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32164800000, -76.55589500000, icon_homicide_shooting, 'p600', '
Curtis Pounds
4700 Homesdale Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 31 years old
Found on April 5, 2009
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.34277620000, -76.58796920000, icon_homicide_shooting, 'p597', '
Timothy Hebron
4200 Welbourne Road
Baltimore, MD 21218
Race: Black
Gender: male
Age: 23 years old
Found on April 4, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30155900000, -76.62555920000, icon_homicide_stabbing, 'p596', '
Zachary Thompson
400 Watty Court
Baltimore, MD 21201
Race: Black
Gender: male
Age: 48 years old
Found on April 3, 2009
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.31551600000, -76.59983000000, icon_homicide_shooting, 'p595', '
Dewayne Booker
1200 Bonaparte Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 20 years old
Found on April 2, 2009
Victim died at Unknown
Cause: Shooting
' 39.31717400000, -76.56540200000, icon_homicide_shooting, 'p594', '
Derrick Franklin
3800 Lyndale Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 28 years old
Found on April 1, 2009
Victim died at Scene
Cause: Shooting
' 39.32025000000, -76.68094200000, icon_homicide_shooting, 'p593', '
Darrell Lee
2900 Allendale Road
Baltimore, MD 21216
Race: Black
Gender: male
Age: 19 years old
Found on April 1, 2009
Victim died at Scene
Cause: Shooting
' 39.28985400000, -76.64170800000, icon_homicide_shooting, 'p592', '
David Williams
1500 W. Fayette St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 20 years old
Found on March 31, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30566900000, -76.63195300000, icon_homicide_shooting, 'p591', '
Marcus Nicholson
1700 McCulloh St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 20 years old
Found on March 29, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31073210000, -76.62799410000, icon_homicide_shooting, 'p590', '
Caneil Fullwood
700 W. North Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 29 years old
Found on March 26, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.35900720000, -76.57549770000, icon_homicide_shooting, 'p589', '
Carlos Spence
5800 Edgepark Road
Baltimore, MD 21239
Race: Black
Gender: male
Age: 23 years old
Found on March 26, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29707310000, -76.57365370000, icon_homicide_shooting, 'p588', '
Kara Lawson
500 N Ellwood Ave
Baltimore, MD 21205
Race: Black
Gender: female
Age: 23 years old
Found on March 23, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.24178480000, -76.62669370000, icon_homicide_shooting, 'p587', '
Davon Saunders
600 W. Patapsco Ave
Baltimore, MD 21225
Race: Black
Gender: male
Age: 26 years old
Found on March 22, 2009
Victim died at Scene
Cause: Shooting
' 39.26279510000, -76.63787690000, icon_homicide_shooting, 'p586', '
Martie Williams
2600 Maisel St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 20 years old
Found on March 21, 2009
Victim died at Unknown
Cause: Shooting
' 39.26149850000, -76.53258770000, icon_homicide_stabbing, 'p585', '
William Smith
6500 Cleveland Ave
Baltimore, MD 21222
Race: White
Gender: male
Age: 24 years old
Found on March 17, 2009
Victim died at Johns Hopkins Bayview Medical Center
Cause: Stabbing
' 39.32380600000, -76.60958900000, icon_homicide_shooting, 'p583', '
Adrian Martise
2900 Greenmount Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 24 years old
Found on March 17, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32380600000, -76.60958900000, icon_homicide_shooting, 'p584', '
Anthony Bailey
2900 Greenmount Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 20 years old
Found on March 17, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31008300000, -76.65155100000, icon_homicide_shooting, 'p581', '
Keon Barnes
1900 Pulaski St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 35 years old
Found on March 14, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29175400000, -76.68166700000, icon_homicide_shooting, 'p582', '
Andrew Goodwin
400 Normandy Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 22 years old
Found on March 13, 2009
Victim died at Unknown
Cause: Shooting
' 39.29267300000, -76.56924300000, icon_homicide_shooting, 'p579', '
Wayne Robinson
3400 E. Baltimore St
Baltimore, MD 21224
Race: Black
Gender: male
Age: 22 years old
Found on March 8, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29864500000, -76.62098800000, icon_homicide_shooting, 'p578', '
Sctario Edwards
800 Linden Ave
Baltimore, MD 21201
Race: Black
Gender: female
Age: 25 years old
Found on March 7, 2009
Victim died at Unknown
Cause: Shooting
' 39.29114000000, -76.56580200000, icon_homicide_shooting, 'p577', '
Herbert Carsten Jr.
100 S. Eaton St
Baltimore, MD 21224
Race: Black
Gender: male
Age: 17 years old
Found on March 6, 2009
Victim died at Unknown
Cause: Shooting
' 39.31100770000, -76.62197560000, icon_homicide_shooting, 'p576', '
Roger Dennis
300 W. North Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 25 years old
Found on March 5, 2009
Victim died at Unknown
Cause: Shooting
' 39.32676670000, -76.63663490000, icon_homicide_stabbing, 'p575', '
Nelson Gause
1500 Clipper Road
Baltimore, MD 21211
Race: Black
Gender: male
Age: 29 years old
Found on March 1, 2009
Victim died at Good Samaritan Hospital
Cause: Stabbing
' 39.29688700000, -76.63596100000, icon_homicide_stabbing, 'p574', '
Frederick Archer
700 N Arlington Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 68 years old
Found on February 26, 2009
Victim died at Scene
Cause: Stabbing
' 39.34009900000, -76.66046600000, icon_homicide_shooting, 'p573', '
Ramon Williams
2500 Loyola Northway
Baltimore, MD 21215
Race: Black
Gender: male
Age: 21 years old
Found on February 22, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.28676300000, -76.56800300000, icon_homicide_stabbing, 'p572', '
Jose Pena
3500 Eastern Ave
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 26 years old
Found on February 22, 2009
Victim died at Johns Hopkins Bayview Medical Center
Cause: Stabbing
' 39.28793000000, -76.65660300000, icon_homicide_shooting, 'p571', '
Hubert Dickerson Jr.
2500 W Baltimore St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 32 years old
Found on February 21, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.35664200000, -76.59274400000, icon_homicide_shooting, 'p569', '
Roger Evans
5600 Sagra Road
Baltimore, MD 21239
Race: Black
Gender: male
Age: 24 years old
Found on February 16, 2009
Victim died at Scene
Cause: Shooting
' 39.23542590000, -76.61110560000, icon_homicide_stabbing, 'p570', '
James Flannery
3900 S. Hanover St.
Baltimore, MD 21225
Race: White
Gender: male
Age: 23 years old
Found on February 16, 2009
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.30691200000, -76.55915150000, icon_homicide_shooting, 'p561', '
Michael Davis
5000 Erdman Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 30 years old
Found on February 14, 2009
Victim died at Scene
Cause: Shooting
' 39.35643700000, -76.54993800000, icon_homicide_stabbing, 'p560', '
Daniel Hoeck
6100 Glenoak Ave
Baltimore, MD 21214
Race: White
Gender: male
Age: 62 years old
Found on February 12, 2009
Victim died at Bon Secours Hospital
Cause: Stabbing
' 39.32701200000, -76.59189600000, icon_homicide_shooting, 'p559', '
Tracy Kinchen
1700 E. 32nd St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 34 years old
Found on February 11, 2009
Victim died at Scene
Cause: Shooting
' 39.30795800000, -76.65135000000, icon_homicide_shooting, 'p555', '
Barakaat Faruq
2100 Presbury St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 20 years old
Found on February 10, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.24041420000, -76.61829920000, icon_homicide_asphyxiation, 'p554', '
Eric Pendergrass
200 W. Patapsco Ave
Baltimore, MD 21225
Race: Black
Gender: male
Age: 26 years old
Found on February 9, 2009
Victim died at Scene
Cause: Asphyxiation
' 39.32190000000, -76.59536800000, icon_homicide_shooting, 'p553', '
David Bryan Wright
1600 Gorsuch Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 47 years old
Found on February 4, 2009
Victim died at Unknown
Cause: Shooting
' 39.30193340000, -76.69463730000, icon_homicide_shooting, 'p552', '
Lemuel Wallace
4600 N Franklintown Rd
Baltimore, MD 21207
Race: Black
Gender: male
Age: 37 years old
Found on February 4, 2009
Victim died at Scene
Cause: Shooting
' 39.30987900000, -76.59049900000, icon_homicide_shooting, 'p551', '
Demetrius Saulsbury
1700 N Washington St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 22 years old
Found on February 4, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.26405040000, -76.61701120000, icon_homicide_stabbing, 'p550', '
Kendrick Daney
200 W Dickman St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 38 years old
Found on February 1, 2009
Victim died at Scene
Cause: Stabbing
' 39.29522300000, -76.65069600000, icon_homicide_shooting, 'p549', '
Theodore Moore
2100 Edmondson Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 44 years old
Found on January 30, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29816200000, -76.66532700000, icon_homicide_shooting, 'p548', '
James McKoy
900 Poplar Grove
Baltimore, MD 21216
Race: Black
Gender: male
Age: 46 years old
Found on January 29, 2009
Victim died at Unknown
Cause: Shooting
' 39.32050100000, -76.55003800000, icon_homicide_shooting, 'p547', '
Dwayne Lawrence
4900 Aberdeen Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 17 years old
Found on January 28, 2009
Victim died at Scene
Cause: Shooting
' 39.31249600000, -76.66789100000, icon_homicide_shooting, 'p546', '
Jasmine Harris
3000 Windsor Ave
Baltimore, MD 21216
Race: Black
Gender: female
Age: 24 years old
Found on January 27, 2009
Victim died at Scene
Cause: Shooting
' 39.29358810000, -76.59590120000, icon_homicide_shooting, 'p545', '
Stephen Mauk
200 N. Bond St
Baltimore, MD 21231
Race: White
Gender: male
Age: 47 years old
Found on January 26, 2009
Victim died at Scene
Cause: Shooting
' 39.34022500000, -76.67151600000, icon_homicide_shooting, 'p544', '
Juan Johnson
4600 Reisterstown Rd
Baltimore, MD 21215
Race: Unknown
Gender: male
Age: 14 years old
Found on January 25, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.27924100000, -76.63662300000, icon_homicide_shooting, 'p543', '
Jaiwan Jones
1200 Bayard St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 26 years old
Found on January 17, 2009
Victim died at Scene
Cause: Shooting
' 39.28283540000, -76.70844730000, icon_homicide_shooting, 'p542', '
Ronald Crosby
5400 Jamestowne Court
Baltimore, MD 21229
Race: Black
Gender: male
Age: 56 years old
Found on January 14, 2009
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31162730000, -76.70134460000, icon_homicide_shooting, 'p522', '
Kipton A. Degree Jr.
5000 Dickey Hill Road
Baltimore, MD 21216
Race: Black
Gender: male
Age: 23 years old
Found on January 9, 2009
Victim died at Sinai Hospital
Cause: Shooting
' 39.29158600000, -76.56732300000, icon_homicide_shooting, 'p520', '
Bryant Eldridge
3600 E. Lombard St.
Baltimore, MD 21224
Race: Black
Gender: male
Age: 20 years old
Found on January 9, 2009
Victim died at Scene
Cause: Shooting
Read the article
' 39.30212350000, -76.59643210000, icon_homicide_shooting, 'p518', '
Antron Batts
1000 N Bond St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 35 years old
Found on January 7, 2009
Victim died at Scene
Cause: Shooting
Read the article
' 39.30055810000, -76.60423190000, icon_homicide_shooting, 'p516', '
Tian Zin Wang
800 Webb Court
Baltimore, MD 21205
Race: Asian
Gender: male
Age: 51 years old
Found on January 6, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.37191500000, -76.54922400000, icon_homicide_stabbing, 'p517', '
David Falkinburg
2800 Clearview Ave.
Baltimore, MD 21234
Race: White
Gender: male
Age: 45 years old
Found on January 6, 2009
Victim died at Scene
Cause: Stabbing
Read the article
' 39.32890400000, -76.66052500000, icon_homicide_shooting, 'p515', '
Joshua Harris
3600 Reisterstown Road
Baltimore, MD 21215
Race: Black
Gender: male
Age: 21 years old
Found on January 5, 2009
Victim died at Sinai Hospital
Cause: Shooting
Read the article
' 39.30017600000, -76.57838500000, icon_homicide_shooting, 'p580', '
Andre Thorpe
800 N. Kenwood Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 17 years old
Found on January 5, 2009
Victim died at Unknown
Cause: Shooting
' 39.33771060000, -76.55629230000, icon_homicide_shooting, 'p514', '
Lougene Williams
4000 Chesmont Ave.
Baltimore, MD 21206
Race: Black
Gender: male
Age: 20 years old
Found on January 4, 2009
Victim died at Unknown
Cause: Shooting
Read the article
' 39.35827620000, -76.58943380000, icon_homicide_shooting, 'p512', '
Trevane Ricks
5604 Loch Raven Blvd.
Baltimore, MD 21239
Race: Black
Gender: male
Age: 16 years old
Found on January 4, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.35774900000, -76.58987200000, icon_homicide_shooting, 'p513', '
Mayresa Craft
5600 Loch Raven Blvd.
Baltimore, MD 21239
Race: Black
Gender: female
Age: 15 years old
Found on January 4, 2009
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.29493070000, -76.60302560000, icon_homicide_shooting, 'p508', '
Marcel Mitchell
1100 Orleans St.
Baltimore, MD 21202
Race: Black
Gender: male
Age: 20 years old
Found on January 2, 2009
Victim died at Scene
Cause: Shooting
Read the article
' 39.29493570000, -76.60289110000, icon_homicide_shooting, 'p509', '
Glen Cunningham
1104 Orleans St.
Baltimore, MD 21202
Race: Black
Gender: male
Age: 22 years old
Found on January 2, 2009
Victim died at Scene
Cause: Shooting
Read the article
' 39.29922400000, -76.58114100000, icon_homicide_shooting, 'p510', '
Mario Williams
700 N. Luzerne Ave.
Baltimore, MD 21205
Race: Black
Gender: male
Age: 31 years old
Found on January 2, 2009
Victim died at Unknown
Cause: Shooting
Read the article
' 39.29803230000, -76.58379350000, icon_homicide_shooting, 'p1021', '
Bernard Clowney
600 N Montford Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 50 years old
Found on December 31, 2010
Victim died at Scene
Cause: Shooting
' 39.35157370000, -76.56084320000, icon_homicide_shooting, 'p1020', '
David King
5500 Richard Ave
Baltimore, MD 21224
Race: Unknown
Gender: male
Age: 25 years old
Found on December 29, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.28659900000, -76.65032800000, icon_homicide_shooting, 'p1019', '
Raymond Woodland
2100 Boyd St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 20 years old
Found on December 26, 2010
Victim died at Unknown
Cause: Shooting
' 39.33692500000, -76.68724600000, icon_homicide_shooting, 'p1018', '
Keith L. Robinson
4100 Ridgewood Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 23 years old
Found on December 25, 2010
Victim died at Scene
Cause: Shooting
' 39.29930980000, -76.66167050000, icon_homicide_shooting, 'p1017', '
Issac Joyner
1000 ashburton st
Baltimore, MD 21216
Race: Black
Gender: male
Age: 14 years old
Found on December 23, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Joyner and two other juveniles were shot by at least one gunman while standing in the block.

' 39.32322790000, -76.60779050000, icon_homicide_shooting, 'p1016', '
Mustafa Malik
2900 Mathews st
Baltimore, MD 21218
Race: Black
Gender: male
Age: 43 years old
Found on December 22, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29107300000, -76.56668500000, icon_homicide_shooting, 'p1015', '
Juan Carlos Santos-Hernandez
3700 Mt. Pleasant Ave
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 29 years old
Found on December 22, 2010
Victim died at Scene
Cause: Shooting
' 39.36594500000, -76.60667500000, icon_homicide_shooting, 'p1011', '
Brian Taylor
6000 Majorie Ln
Baltimore, MD 21212
Race: Black
Gender: male
Age: 23 years old
Found on December 18, 2010
Victim died at Good Samaritan Hospital
Cause: Shooting
' 39.32483100000, -76.59053290000, icon_homicide_unknown, 'p1012', '
Karen Ferrell
1800 29th St
Baltimore, MD 21218
Race: Black
Gender: female
Age: 42 years old
Found on December 18, 2010
Victim died at Scene
Cause: Unknown

Ferrell was found wrapped in plastic in the basement of her home. She had been reported missing on Dec. 15. 

' 39.29332600000, -76.57935900000, icon_homicide_shooting, 'p1010', '
Ramon Uceda
100 N. Lakewood Ave
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 26 years old
Found on December 18, 2010
Victim died at Unknown
Cause: Shooting
' 39.32331900000, -76.59904800000, icon_homicide_asphyxiation, 'p1014', '
Ellison McCall
1400 Homestead St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 30 years old
Found on December 18, 2010
Victim died at Scene
Cause: Asphyxiation

This victim died in a fire police and fire investigators believe was intentionally set. 

' 39.29928400000, -76.57370530000, icon_homicide_shooting, 'p1009', '
Alethea Hawkins
3100 Monument St
Baltimore, MD 21205
Race: Black
Gender: female
Age: 38 years old
Found on December 18, 2010
Victim died at Unknown
Cause: Shooting

Police say victim was killed by a man who later killed himself in the 3700 block of Ravenwood Ave. 

' 39.31003400000, -76.64831100000, icon_homicide_other, 'p1013', '
Micha Crane
1900 W. North Ave.
Baltimore, MD 21217
Race: Black
Gender: female
Age: 1 year old
Found on December 17, 2010
Victim died at University of Maryland Medical Center
Cause: Other

Micha was taken to the hospital on Sept. 8 and pronounced dead at 6:40 p.m. On Dec. 17, an autopsy determined she died...

' 39.29930500000, -76.66911100000, icon_homicide_shooting, 'p1007', '
Cherrie Gammon
1300 N. Franklintown Rd
Baltimore, MD 21216
Race: White
Gender: female
Age: 25 years old
Found on December 12, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Woman was found shot in the chest multiple times. 

' 39.34240400000, -76.67245900000, icon_homicide_shooting, 'p1006', '
Travis Baltimore
3400 Virigina Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 24 years old
Found on December 10, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.30819580000, -76.66569130000, icon_homicide_shooting, 'p1005', '
David Carter
2800 Westwood Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 20 years old
Found on December 8, 2010
Victim died at Scene
Cause: Shooting
' 39.35442500000, -76.59047800000, icon_homicide_shooting, 'p1004', '
Dante Sweeney
5300 Loch Raven Blvd
Baltimore, MD 21239
Race: Black
Gender: male
Age: 22 years old
Found on December 7, 2010
Victim died at Scene
Cause: Shooting

Police say a man came to Sweeney\'s apartment and shot him, then demanded money. Investigators believe he was a drug...

' 39.32566000000, -76.58898200000, icon_homicide_shooting, 'p1001', '
Troy Thomas
1900 E. 30th St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 26 years old
Found on December 3, 2010
Victim died at Good Samaritan Hospital
Cause: Shooting
' 39.31637800000, -76.60977400000, icon_homicide_other, 'p1003', '
Tammy Madison
2400 Greenmount Ave
Baltimore, MD 21218
Race: Black
Gender: female
Age: 45 years old
Found on December 3, 2010
Victim died at Johns Hopkins Hospital
Cause: Other

Madison was pushed in front of a delivery truck on Feb. 16 and killed. Investigators determined on this date that she...

' 39.28471600000, -76.64871100000, icon_homicide_shooting, 'p1000', '
Raquan Campbell
200 S. Payson St.
Baltimore, MD 21223
Race: Black
Gender: male
Age: 15 years old
Found on December 1, 2010
Victim died at Unknown
Cause: Shooting
' 39.33999900000, -76.69414800000, icon_homicide_stabbing, 'p998', '
Robin Patterson
4400 Belvieu Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 61 years old
Found on November 26, 2010
Victim died at Unknown
Cause: Stabbing

Police say Robin Patterson tried to intervene in a fight between brother, Harry Patterson Jr., and Harry Patterson\'s...

' 39.28121300000, -76.64492500000, icon_homicide_stabbing, 'p997', '
Davon Douglas
1800 Eagle St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 28 years old
Found on November 24, 2010
Victim died at Unknown
Cause: Stabbing
' 39.32532530000, -76.55793900000, icon_homicide_bluntforce, 'p996', '
Derrick Cross
4400 Plainfield Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 29 years old
Found on November 24, 2010
Victim died at Unknown
Cause: Blunt Force
' 39.29174800000, -76.68273490000, icon_homicide_shooting, 'p994', '
Charles Burrell
400 N. Loudon Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 24 years old
Found on November 23, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31823160000, -76.56735180000, icon_homicide_stabbing, 'p995', '
Patrick Dolan
3500 Juneway
Baltimore, MD 21213
Race: White
Gender: male
Age: 19 years old
Found on November 23, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Stabbing

Police say Dolan was stabbed during a robbery try

' 39.30951940000, -76.60252110000, icon_homicide_shooting, 'p991', '
Sheron Jones
1700 Aisquith St
Baltimore, MD 21202
Race: Black
Gender: female
Age: 28 years old
Found on November 21, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Jones was a corrections officer who worked in the city. 

' 39.29343790000, -76.69387950000, icon_homicide_shooting, 'p990', '
Carlton Sellman
4600 Edmonson Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 19 years old
Found on November 20, 2010
Victim died at Unknown
Cause: Shooting
' 39.30462310000, -76.66552940000, icon_homicide_shooting, 'p992', '
Jerry Thomas
1500 Poplar Grove St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 29 years old
Found on November 19, 2010
Victim died at Unknown
Cause: Shooting

Three people were shot in this incident and the other two were expected to survive.

' 39.31980850000, -76.55438990000, icon_homicide_shooting, 'p993', '
Wilbur Street
4700 Haldane Rd
Baltimore, MD 21206
Race: Black
Gender: male
Age: 52 years old
Found on November 18, 2010
Victim died at Unknown
Cause: Shooting

Street was shot in 1982 and developed a seizure disorder. He died from a seizure in March, while in prison in...

' 39.29768100000, -76.64938600000, icon_homicide_shooting, 'p989', '
Marcus Brown
2000 W. Lanvale St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 20 years old
Found on November 17, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.28733500000, -76.57183100000, icon_homicide_stabbing, 'p988', '
David Hopkins
400 SE Ave
Baltimore, MD 21224
Race: Black
Gender: male
Age: 19 years old
Found on November 13, 2010
Victim died at Unknown
Cause: Stabbing

Police say David Lawrence Hopkins was stabbed to death by his uncle, 44-year-old Dewayne Hopkins

' 39.28838300000, -76.64709490000, icon_homicide_shooting, 'p987', '
Sherrod Mason
1900 W. Baltimore St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 34 years old
Found on November 13, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.33793800000, -76.59337900000, icon_homicide_shooting, 'p986', '
Eric Dozier
1500 Roundhill Rd
Baltimore, MD 21218
Race: Black
Gender: male
Age: 25 years old
Found on November 9, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29112940000, -76.63014190000, icon_homicide_shooting, 'p984', '
Derrius Currie
800 W. Lexington St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 21 years old
Found on November 6, 2010
Victim died at Scene
Cause: Shooting

Currie\'s body was found inside a vacant home by a Department of Pubilc Works crew

' 39.28554920000, -76.63868130000, icon_homicide_shooting, 'p982', '
Kevin Anderson
200 S. Woodyear St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 31 years old
Found on November 4, 2010
Victim died at Unknown
Cause: Shooting
' 39.37123050000, -76.56992740000, icon_homicide_stabbing, 'p981', '
Jerry Harden
7000 McClean Blvd
Baltimore, MD 21234
Race: Black
Gender: male
Age: 21 years old
Found on November 4, 2010
Victim died at Scene
Cause: Stabbing

Man was found stabbed to death inside a home that had been set on fire.

' 39.26218500000, -76.63184500000, icon_homicide_bluntforce, 'p983', '
Wilson Thomas
2200 Kloman St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 44 years old
Found on November 3, 2010
Victim died at Unknown
Cause: Blunt Force

Thomas was beaten Oct. 22 and taken to Shock Trauma, where he died on Nov. 3.

' 39.31839310000, -76.59826180000, icon_homicide_shooting, 'p980', '
Malcolm Hill
2500 Robb St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 53 years old
Found on November 2, 2010
Victim died at Unknown
Cause: Shooting
' 39.30361010000, -76.63054420000, icon_homicide_shooting, 'p978', '
Andrew Joyce
500 Mosher St
Baltimore, MD 21216
Race: White
Gender: male
Age: 23 years old
Found on November 1, 2010
Victim died at Unknown
Cause: Shooting

Joyce was working a shift as a tow truck driver. He was found seated in the car with a gunshot wound to his chest, and...

' 39.31346300000, -76.66172300000, icon_homicide_shooting, 'p977', '
Jovanna Mitchell
2300 Braddish Ave
Baltimore, MD 21217
Race: Black
Gender: female
Age: 20 years old
Found on October 31, 2010
Victim died at Unknown
Cause: Shooting
' 39.32315200000, -76.67794900000, icon_homicide_shooting, 'p985', '
Edwin Green Jr.
3600 Fairview Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 43 years old
Found on October 29, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.28326090000, -76.64278220000, icon_homicide_bluntforce, 'p979', '
Dobia Wright
400 S. Vincent St.
Baltimore, MD 21223
Race: White
Gender: male
Age: 46 years old
Found on October 28, 2010
Victim died at Bon Secours Hospital
Cause: Blunt Force

Victim got into an argument over money and was struck in the face, then fell as he was being led away and hit his head...

' 39.32885100000, -76.68532300000, icon_homicide_shooting, 'p975', '
Ronald Clark
3500 Carsdale Ave
Baltimore, MD 21207
Race: Black
Gender: male
Age: 18 years old
Found on October 26, 2010
Victim died at Unknown
Cause: Shooting
' 39.30798990000, -76.64746400000, icon_homicide_shooting, 'p974', '
William Spears
1700 McKean Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 63 years old
Found on October 26, 2010
Victim died at Unknown
Cause: Shooting
' 39.33140260000, -76.68480140000, icon_homicide_shooting, 'p973', '
Alan Chavis
4000 Barrington Rd
Baltimore, MD 21207
Race: Black
Gender: male
Age: 16 years old
Found on October 26, 2010
Victim died at Sinai Hospital
Cause: Shooting

Chavis had been reported missing by his mother on Oct. 14

' 39.30789070000, -76.59003400000, icon_homicide_stabbing, 'p972', '
Mary Williams
2000 E Oliver St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 48 years old
Found on October 25, 2010
Victim died at Johns Hopkins Hospital
Cause: Stabbing
' 39.30507600000, -76.58293000000, icon_homicide_shooting, 'p971', '
Durrell Burroughs
1200 N Milton Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 19 years old
Found on October 25, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29872610000, -76.58768230000, icon_homicide_shooting, 'p970', '
Calvin McNair
2100 E. Monument St.
Baltimore, MD 21205
Race: Black
Gender: male
Age: 20 years old
Found on October 23, 2010
Victim died at Unknown
Cause: Shooting
' 39.28954800000, -76.65029900000, icon_homicide_shooting, 'p969', '
Monta Hunt
2100 W. Fayette St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 25 years old
Found on October 23, 2010
Victim died at Unknown
Cause: Shooting
' 39.28219070000, -76.57695310000, icon_homicide_bluntforce, 'p968', '
Brian Stevenson
2800 Hudson St.
Baltimore, MD 21224
Race: Black
Gender: male
Age: 37 years old
Found on October 16, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Blunt Force

Police say off-duty Detective Brian Stevenson was killed after being struck in the head with a piece of concrete...

' 39.36535600000, -76.70886500000, icon_homicide_asphyxiation, 'p966', '
Khloe Lewis
3800 Glengyle Ave
Baltimore, MD 21215
Race: Black
Gender: female
Age: 1 year old
Found on October 13, 2010
Victim died at Sinai Hospital
Cause: Asphyxiation

Father Timothy Lewis confessed to strangling infant son because he would not stop crying.

' 39.29782700000, -76.65011500000, icon_homicide_shooting, 'p965', '
Sherman Payne
800 N. Brice St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 56 years old
Found on October 13, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.33506430000, -76.66124930000, icon_homicide_shooting, 'p964', '
Harvey McCall
2600 Shirley Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 42 years old
Found on October 11, 2010
Victim died at Unknown
Cause: Shooting
' 39.30637800000, -76.65730600000, icon_homicide_shooting, 'p963', '
Dennis Waddell
1600 Warwick Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 33 years old
Found on October 10, 2010
Victim died at Unknown
Cause: Shooting
' 39.30735390000, -76.66758100000, icon_homicide_shooting, 'p962', '
James Ingram
3000 Presbury St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 46 years old
Found on October 9, 2010
Victim died at Unknown
Cause: Shooting
' 39.33088820000, -76.61503600000, icon_homicide_shooting, 'p961', '
Travis Lane
3500 N Calvert St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 19 years old
Found on October 9, 2010
Victim died at Union Memorial Hospital
Cause: Shooting
' 39.32336400000, -76.55682100000, icon_homicide_shooting, 'p960', '
Daryll Hood
4700 Shamrock Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 22 years old
Found on October 9, 2010
Victim died at Unknown
Cause: Shooting
' 39.33605500000, -76.67069980000, icon_homicide_shooting, 'p959', '
Michael Morrell
3100 Grantley Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 51 years old
Found on October 9, 2010
Victim died at Unknown
Cause: Shooting
' 39.31439300000, -76.57316230000, icon_homicide_stabbing, 'p958', '
Sterling Palmer
2600 Edison Highway
Baltimore, MD 21213
Race: Black
Gender: male
Age: 78 years old
Found on October 8, 2010
Victim died at Unknown
Cause: Stabbing
' 39.26274700000, -76.63344200000, icon_homicide_shooting, 'p957', '
Kirk Carter
2300 Sidney Ave
Baltimore, MD 21230
Race: Black
Gender: male
Age: 22 years old
Found on October 6, 2010
Victim died at Unknown
Cause: Shooting
' 39.24700440000, -76.62878500000, icon_homicide_shooting, 'p956', '
Randol Bumcombe
2700 Claflin Ct
Baltimore, MD 21225
Race: Black
Gender: female
Age: 24 years old
Found on October 5, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Double-shooting; other victim, an adult male, expected to survive.

' 39.29112940000, -76.63014190000, icon_homicide_shooting, 'p955', '
Arthur Peacock
800 W. Lexington St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 34 years old
Found on September 30, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31924800000, -76.55145600000, icon_homicide_shooting, 'p954', '
Ardrey Murphy
5500 Bowleys Lane
Baltimore, MD 21206
Race: Black
Gender: male
Age: 28 years old
Found on September 29, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.31023800000, -76.64760690000, icon_homicide_stabbing, 'p953', '
Yassmin Lindo
1900 mckean ave
Baltimore, MD 21217
Race: Black
Gender: female
Age: 36 years old
Found on September 28, 2010
Victim died at Unknown
Cause: Stabbing
' 39.32314600000, -76.68337200000, icon_homicide_shooting, 'p952', '
Donnie Martin
3100 Chelsea Terrace
Baltimore, MD 21216
Race: Black
Gender: male
Age: 38 years old
Found on September 25, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30756700000, -76.59523200000, icon_homicide_shooting, 'p951', '
James Schools
1500 N Broadway
Baltimore, MD 21213
Race: Black
Gender: male
Age: 31 years old
Found on September 23, 2010
Victim died at Unknown
Cause: Shooting
' 39.32073910000, -76.57476320000, icon_homicide_shooting, 'p950', '
Sean Cooper
3200 Belair Rd
Baltimore, MD 21213
Race: Black
Gender: male
Age: 20 years old
Found on September 22, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Double shooting, believed to be a robbery

' 39.31554000000, -76.59980800000, icon_homicide_shooting, 'p949', '
George Lewis
1200 Bonaparte Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 25 years old
Found on September 19, 2010
Victim died at Unknown
Cause: Shooting
' 39.32042020000, -76.67701870000, icon_homicide_shooting, 'p948', '
Isaiah White
2900 Edgewood St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 28 years old
Found on September 19, 2010
Victim died at Unknown
Cause: Shooting
' 39.30581300000, -76.64196990000, icon_homicide_bluntforce, 'p947', '
Cecelia Mitchell
1500 N. Stricker St
Baltimore, MD 21217
Race: Black
Gender: female
Age: 66 years old
Found on September 17, 2010
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force
' 39.29728440000, -76.59231210000, icon_homicide_shooting, 'p946', '
Jean Davis
600 N. Wolfe St
Baltimore, MD 21287
Race: Black
Gender: female
Age: 84 years old
Found on September 16, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Davis was shot by her son, Paul Warren Pardus, who was upset about her medical treatment and also shot her doctor, who...

' 39.27295100000, -76.53612600000, icon_homicide_shooting, 'p945', '
Willie Johnson
1700 kane st
Baltimore, MD 21224
Race: Black
Gender: male
Age: 30 years old
Found on September 13, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.30622370000, -76.65919650000, icon_homicide_shooting, 'p943', '
Marcel Burton
2600 Baker St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 33 years old
Found on September 9, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.33783600000, -76.66221800000, icon_homicide_bluntforce, 'p944', '
Robert Lockett
2630 Quantico Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 48 years old
Found on September 9, 2010
Victim died at Sinai Hospital
Cause: Blunt Force

Lockett was found injured on July 22, and died on Aug. 4. On Sept. 7, detectives received information indicating that...

' 39.29659300000, -76.67878100000, icon_homicide_shooting, 'p941', '
Thomas Vas
800 Allendale St
Baltimore, MD 21229
Race: White
Gender: male
Age: 35 years old
Found on September 7, 2010
Victim died at Unknown
Cause: Shooting

Police say man was from West Virginia and shot during apparent drug transaction.

' 39.30656940000, -76.64972190000, icon_homicide_shooting, 'p940', '
Levern Domneys
2000 Baker St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 20 years old
Found on September 3, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.27996600000, -76.65878700000, icon_homicide_shooting, 'p939', '
Datea Scott-Smith
500 East Lynn Ave
Baltimore, MD 21223
Race: Black
Gender: female
Age: 30 years old
Found on September 2, 2010
Victim died at Unknown
Cause: Shooting

Triple shooting, apparent dispute over drugs.

' 39.32946590000, -76.56659700000, icon_homicide_shooting, 'p937', '
Nathaniel Santiago
4100 Harris Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 31 years old
Found on August 31, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Santiago was entering an apartment building when he was approached by two men and got into a fight.

' 39.28472840000, -76.61309780000, icon_homicide_other, 'p938', '
Ankush Gupta
330 S. Calvert St
Baltimore, MD 21201
Race: Unknown
Gender: male
Age: 22 years old
Found on August 31, 2010
Victim died at Scene
Cause: Other

Police say Gupta was pushed into the Inner Harbor on the morning of Aug. 27, 2008. Wayne Black was charged with...

' 39.31435160000, -76.64509170000, icon_homicide_shooting, 'p936', '
Louis Scott
2700 Parkwood Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 29 years old
Found on August 30, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31748050000, -76.65157600000, icon_homicide_bluntforce, 'p942', '
John Lemon Jr.
2600 Reisterstown Road
Baltimore, MD 21215
Race: Black
Gender: male
Age: 44 years old
Found on August 30, 2010
Victim died at Sinai Hospital
Cause: Blunt Force

Lemon was struck and hit his head on a light pole on Aug. 18, at 1:39 a.m. He died Aug. 30, and this date reflects when...

' 39.25038300000, -76.64060000000, icon_homicide_stabbing, 'p935', '
Carrington McNutt
3300 Annapolis Rd
Baltimore, MD 21230
Race: Black
Gender: male
Age: 23 years old
Found on August 28, 2010
Victim died at Maryland Shock Trauma Center
Cause: Stabbing

One of eight people stabbed at a reunion for Walbrook High School

' 39.30137650000, -76.60726170000, icon_homicide_bluntforce, 'p934', '
Albert Bethea
700 E. Eager St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 57 years old
Found on August 24, 2010
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force

Bethea was beaten on Aug. 22 at 3:35 a.m. and pronounced dead on Aug. 24 as a result of his injuries.

' 39.34993200000, -76.67210300000, icon_homicide_bluntforce, 'p933', '
Contray Merchant
5100 Queensberry Rd
Baltimore, MD 21215
Race: Black
Gender: male
Age: 29 years old
Found on August 22, 2010
Victim died at Unknown
Cause: Blunt Force

Beaten in August 2007; died May of this year.

' 39.29378300000, -76.57652800000, icon_homicide_shooting, 'p932', '
Jose Gonzalez-Coreas
118 N. Linwood Ave
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 43 years old
Found on August 21, 2010
Victim died at Unknown
Cause: Shooting

One of two men shot by a 14-year-old girl during a robbery

' 39.29495400000, -76.57803100000, icon_homicide_bluntforce, 'p931', '
Martin Reyes
200 N. Kenwood Ave
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 51 years old
Found on August 21, 2010
Victim died at Unknown
Cause: Blunt Force

Beaten with wood board; police arrest 19 year old man who said he "hates Mexicans."

' 39.29465330000, -76.62736730000, icon_homicide_shooting, 'p928', '
Donald Carter
700 W. Franklin St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 39 years old
Found on August 16, 2010
Victim died at Unknown
Cause: Shooting
' 39.30282400000, -76.61015000000, icon_homicide_shooting, 'p926', '
Isaiah Gordon
400 E Chase St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 20 years old
Found on August 15, 2010
Victim died at Unknown
Cause: Shooting

Gordon was found shot multiple times

' 39.31321400000, -76.60372300000, icon_homicide_shooting, 'p925', '
Westley Lewis
2000 Robb St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 19 years old
Found on August 14, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.23951000000, -76.60545500000, icon_homicide_shooting, 'p924', '
Andre Graham
3500 Horton Ave
Baltimore, MD 21225
Race: Black
Gender: male
Age: 27 years old
Found on August 13, 2010
Victim died at Unknown
Cause: Shooting

Police have charged 21-year-old Dane Shives Jr. in this killing.

' 39.31924160000, -76.57223360000, icon_homicide_shooting, 'p923', '
Gerald Gray
3100 Edison Highway
Baltimore, MD 21213
Race: Black
Gender: male
Age: 35 years old
Found on August 12, 2010
Victim died at Unknown
Cause: Shooting
' 39.32791790000, -76.68281800000, icon_homicide_shooting, 'p922', '
Shelred Carr
3400 Garrison Blvd
Baltimore, MD 21215
Race: Black
Gender: male
Age: 58 years old
Found on August 12, 2010
Victim died at Unknown
Cause: Shooting
' 39.30862590000, -76.59527600000, icon_homicide_shooting, 'p930', '
Shawn Wright
1600 N. Broadway
Baltimore, MD 21213
Race: Black
Gender: male
Age: 30 years old
Found on August 11, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Police say a man jumped out of a minivan and shot Wright multiple times before fleeing on Federal St.

' 39.29796500000, -76.64162100000, icon_homicide_shooting, 'p929', '
Franklin Spencer
1500 W. Lanvale St
Baltimore, MD 21215
Race: Black
Gender: male
Age: 39 years old
Found on August 11, 2010
Victim died at Unknown
Cause: Shooting

Spencer died in 2004 and was added to the homicide rolls on this date after a separate investigation prompted an...

' 39.31488580000, -76.59630070000, icon_homicide_shooting, 'p920', '
Tavon Caldwell
2200 Germania Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 25 years old
Found on August 8, 2010
Victim died at Unknown
Cause: Shooting
' 39.28446700000, -76.64686300000, icon_homicide_bluntforce, 'p921', '
Theodore Corwin
1900 McHenry St
Baltimore, MD 21223
Race: Asian
Gender: male
Age: 30 years old
Found on August 7, 2010
Victim died at Unknown
Cause: Blunt Force
' 39.31181520000, -76.62807590000, icon_homicide_stabbing, 'p919', '
Christopher Miller
700 Lennox St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 24 years old
Found on August 7, 2010
Victim died at Unknown
Cause: Stabbing
' 39.28819740000, -76.65850900000, icon_homicide_shooting, 'p918', '
Martin York
10 Shipley St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 45 years old
Found on August 7, 2010
Victim died at Unknown
Cause: Shooting
' 39.32279400000, -76.57690590000, icon_homicide_shooting, 'p917', '
Jarrod Lee Covel
2800 Clifton Park Terrace
Baltimore, MD 21213
Race: Black
Gender: male
Age: 20 years old
Found on August 6, 2010
Victim died at Unknown
Cause: Shooting
' 39.33812500000, -76.66127300000, icon_homicide_shooting, 'p916', '
Dionndra Dugger
2600 Quantico Ave
Baltimore, MD 21215
Race: Black
Gender: female
Age: 20 years old
Found on August 1, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.31204900000, -76.60227900000, icon_homicide_shooting, 'p915', '
Milton Hill
1200 E North Ave
Baltimore, MD 21202
Race: Black
Gender: male
Age: 70 years old
Found on July 30, 2010
Victim died at Scene
Cause: Shooting
' 39.34770700000, -76.67946000000, icon_homicide_shooting, 'p913', '
Shawn Crawford
5200 Florence Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 36 years old
Found on July 30, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.31002600000, -76.61342990000, icon_homicide_shooting, 'p912', '
Emmanuel Thomas
200 E. Fafayette Ave
Baltimore, MD 21202
Race: Black
Gender: male
Age: 21 years old
Found on July 29, 2010
Victim died at Unknown
Cause: Shooting
' 39.33743900000, -76.66316500000, icon_homicide_bluntforce, 'p914', '
Steven Harris
4200 Pimlico Road
Baltimore, MD 21215
Race: Black
Gender: male
Age: 38 years old
Found on July 29, 2010
Victim died at Scene
Cause: Blunt Force

Harris was found dead July 22 and ruled a shooting victim; an autopsy subsequently showed that he had not been shot,...

' 39.27757880000, -76.70017030000, icon_homicide_shooting, 'p911', '
Jermaine Parker
5100 Williston St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 24 years old
Found on July 27, 2010
Victim died at Scene
Cause: Shooting
' 39.34827900000, -76.56526800000, icon_homicide_stabbing, 'p910', '
Corey Sims
5100 Harford Rd
Baltimore, MD 21214
Race: Black
Gender: male
Age: 19 years old
Found on July 27, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Stabbing
' 39.31943410000, -76.61552280000, icon_homicide_stabbing, 'p908', '
Stephen Pitcairn
2600 St Paul St
Baltimore, MD 21218
Race: White
Gender: male
Age: 23 years old
Found on July 25, 2010
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.30728080000, -76.60098930000, icon_homicide_shooting, 'p907', '
John Hall
1500 Lanhorne Ct
Baltimore, MD 21202
Race: Black
Gender: male
Age: 30 years old
Found on July 25, 2010
Victim died at Unknown
Cause: Shooting
' 39.31000450000, -76.61219880000, icon_homicide_stabbing, 'p906', '
Justin Kendrick
300 E. Lafayette Ave
Baltimore, MD 21202
Race: Black
Gender: male
Age: 24 years old
Found on July 24, 2010
Victim died at Unknown
Cause: Stabbing
' 39.29693400000, -76.57959300000, icon_homicide_shooting, 'p905', '
Juan Hernandez
500 N. Lakewood Ave
Baltimore, MD 21205
Race: Hispanic
Gender: male
Age: 27 years old
Found on July 24, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.37190920000, -76.56712870000, icon_homicide_shooting, 'p909', '
Jamison Ford
2300 Perring Manor Rd
Baltimore, MD 21234
Race: Black
Gender: male
Age: 30 years old
Found on July 23, 2010
Victim died at Unknown
Cause: Shooting
' 39.22827340000, -76.59897200000, icon_homicide_shooting, 'p904', '
Curtis Williams
4200 10th St
Baltimore, MD 21225
Race: Black
Gender: male
Age: 26 years old
Found on July 22, 2010
Victim died at Scene
Cause: Shooting
' 39.29251200000, -76.63401750000, icon_homicide_stabbing, 'p902', '
Lonnie Howie Jr
300 N. Schroeder St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 42 years old
Found on July 20, 2010
Victim died at Maryland Shock Trauma Center
Cause: Stabbing

http://www.marchfh.com/obituaries/Lonnie-Howie/

' 39.33195300000, -76.66125500000, icon_homicide_shooting, 'p901', '
Javon Perry
3800 Park Heights Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 27 years old
Found on July 17, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.32861830000, -76.63110420000, icon_homicide_bluntforce, 'p900', '
John Sandy
3400 Elm Ave
Baltimore, MD 21211
Race: White
Gender: male
Age: 73 years old
Found on July 13, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Blunt Force

Sandy was beaten while returning from work on April 18; he died on July 13.

' 39.31425640000, -76.61872110000, icon_homicide_shooting, 'p899', '
Yolanda Howard
100 W. 22nd St
Baltimore, MD 21218
Race: Black
Gender: female
Age: 35 years old
Found on July 12, 2010
Victim died at Scene
Cause: Shooting
' 39.34656900000, -76.68080700000, icon_homicide_shooting, 'p898', '
Ramah Reid
5200 Cuthbert Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 26 years old
Found on July 7, 2010
Victim died at Scene
Cause: Shooting
' 39.30613890000, -76.60230890000, icon_homicide_shooting, 'p897', '
Vance Williams
1500 E. Hoffman St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 19 years old
Found on July 6, 2010
Victim died at Scene
Cause: Shooting
' 39.31993700000, -76.59797800000, icon_homicide_shooting, 'p896', '
John Crowder
2600 Garrett Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 17 years old
Found on July 5, 2010
Victim died at Unknown
Cause: Shooting

Crowder was a standout basketball player at Towson Catholic and Mount Carmel who was entering his junior year.

' 39.30248500000, -76.58559800000, icon_homicide_shooting, 'p895', '
Jason Rogers
1000 N. Patterson Park Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 30 years old
Found on July 4, 2010
Victim died at Unknown
Cause: Shooting
' 39.30929100000, -76.58385000000, icon_homicide_shooting, 'p893', '
Warren Mitchell
1600 N. Port St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 24 years old
Found on July 3, 2010
Victim died at Unknown
Cause: Shooting
' 39.31154300000, -76.63970900000, icon_homicide_shooting, 'p894', '
Raynard Johnson
2400 Druid Hill Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 24 years old
Found on July 3, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Johnson was shot 6/20 and died on 7/3.

' 39.28988900000, -76.61207100000, icon_homicide_shooting, 'p892', '
Chase Love
200 E. Baltimore ST
Baltimore, MD 21202
Race: Black
Gender: male
Age: 26 years old
Found on July 2, 2010
Victim died at Unknown
Cause: Shooting

Love, a New Orleans native, was shot at a downtown hookah bar during an altercation. He was an active duty Marine out...

' 39.31069630000, -76.62958570000, icon_homicide_shooting, 'p891', '
Renardo Broom
700 W. North Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 16 years old
Found on July 1, 2010
Victim died at Unknown
Cause: Shooting
' 39.32351200000, -76.59648900000, icon_homicide_shooting, 'p890', '
Phillip Bundy
1500 Carswell St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 28 years old
Found on June 29, 2010
Victim died at Unknown
Cause: Shooting
' 39.31324600000, -76.59208800000, icon_homicide_shooting, 'p889', '
Antwan Pullen
2000 N. Wolfe St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 23 years old
Found on June 28, 2010
Victim died at Unknown
Cause: Shooting

Pullen was shot on 6/21, died on 6/23, and was added to the homicide tally on 6/28

' 39.34437500000, -76.68300900000, icon_homicide_bluntforce, 'p888', '
Gloria Harrod
5200 St. Charles Ave
Baltimore, MD 21215
Race: Black
Gender: female
Age: 47 years old
Found on June 28, 2010
Victim died at Unknown
Cause: Blunt Force
' 39.28884990000, -76.65906360000, icon_homicide_shooting, 'p887', '
Eric Williams
2600 W. Fayette ST
Baltimore, MD 21223
Race: Black
Gender: male
Age: 28 years old
Found on June 27, 2010
Victim died at Unknown
Cause: Shooting
' 39.29314900000, -76.57052200000, icon_homicide_shooting, 'p886', '
Evando Minor
3300 Noble St
Baltimore, MD 21224
Race: Black
Gender: male
Age: 23 years old
Found on June 26, 2010
Victim died at Unknown
Cause: Shooting
' 39.34628400000, -76.67007300000, icon_homicide_shooting, 'p885', '
Wesley Lashley
3100 Woodland Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 29 years old
Found on June 24, 2010
Victim died at Unknown
Cause: Shooting

Lashley was shot on Saturday, June 19, and died on this date.

' 39.30440230000, -76.63147300000, icon_homicide_shooting, 'p884', '
Tyree Page
500 Mc Mechen St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 34 years old
Found on June 23, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Double shooting

' 39.29627500000, -76.57230100000, icon_homicide_shooting, 'p882', '
Durell Cartwright
400 N. East Ave
Baltimore, MD 21224
Race: Black
Gender: male
Age: 30 years old
Found on June 21, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30149460000, -76.65828190000, icon_homicide_shooting, 'p881', '
Daniel Payne
2500 Winchester St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 55 years old
Found on June 20, 2010
Victim died at St. Agnes Hospital
Cause: Shooting

Possible robbery

' 39.31247100000, -76.59069000000, icon_homicide_shooting, 'p880', '
Marquell Turner
1900 Washington St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 19 years old
Found on June 19, 2010
Victim died at Unknown
Cause: Shooting
' 39.29655500000, -76.64595900000, icon_homicide_shooting, 'p879', '
Derrick Pinkney
700 N. Fulton Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 33 years old
Found on June 15, 2010
Victim died at Unknown
Cause: Shooting
' 39.24501350000, -76.62740550000, icon_homicide_shooting, 'p883', '
Larry Griffin
2900 Spelman Rd
Baltimore, MD 21225
Race: Black
Gender: male
Age: 25 years old
Found on June 14, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Griffin was shot May 22 in the legs and abdomen and died June 14 at Shock Trauma.

' 39.29476600000, -76.58151200000, icon_homicide_shooting, 'p878', '
Avon Beasley
200 N. Rose St
Baltimore, MD 21224
Race: Black
Gender: male
Age: 25 years old
Found on June 13, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29367780000, -76.60180830000, icon_homicide_shooting, 'p877', '
Spencer Williams
1100 New Hope Circle
Baltimore, MD 21202
Race: Black
Gender: male
Age: 22 years old
Found on June 11, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Williams was shot in the head on June 7 and was pronounced dead on this date.

' 39.29983100000, -76.68557300000, icon_homicide_asphyxiation, 'p875', '
Glennie Reid
1200 N Augusta Ave
Baltimore, MD 21229
Race: Black
Gender: female
Age: 83 years old
Found on June 7, 2010
Victim died at Scene
Cause: Asphyxiation
' 39.30096010000, -76.61661920000, icon_homicide_shooting, 'p876', '
Tyrone Brown
1 W. Eager St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 32 years old
Found on June 5, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Brown was shot 12 times by Officer Gahiji A. Tshamba, who has been charged with first-degree murder.

' 39.31575690000, -76.54165530000, icon_homicide_stabbing, 'p873', '
Mark Zimmerman
6059 Moravia Park Dr
Baltimore, MD 21206
Race: White
Gender: male
Age: 44 years old
Found on June 4, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Stabbing
' 39.27061500000, -76.59056900000, icon_homicide_bluntforce, 'p874', '
Matthew Martin
1300 Richardson St
Baltimore, MD 21230
Race: White
Gender: male
Age: 31 years old
Found on June 4, 2010
Victim died at Scene
Cause: Blunt Force

Killed April 9; body later found in Patapsco State Park. Police determined he was killed at this address, at the home...

' 39.30670200000, -76.64575260000, icon_homicide_shooting, 'p872', '
James Johnson
1601 Bruce Ct
Baltimore, MD 21217
Race: Black
Gender: male
Age: 19 years old
Found on June 1, 2010
Victim died at Unknown
Cause: Shooting
' 39.29389730000, -76.64551940000, icon_homicide_shooting, 'p871', '
Deandre Leeper
500 N. Fulton Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 27 years old
Found on June 1, 2010
Victim died at Unknown
Cause: Shooting
' 39.31732900000, -76.54861800000, icon_homicide_stabbing, 'p870', '
Kevin Belton
4800 Truesdale Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 43 years old
Found on May 31, 2010
Victim died at Unknown
Cause: Stabbing
' 39.33627600000, -76.59652100000, icon_homicide_shooting, 'p869', '
Don Rice
3900 Loch Raven Blvd
Baltimore, MD 21218
Race: Black
Gender: male
Age: 59 years old
Found on May 31, 2010
Victim died at Unknown
Cause: Shooting
' 39.28594780000, -76.64996770000, icon_homicide_shooting, 'p867', '
Damon Chase
68 S. Pulaski St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 30 years old
Found on May 30, 2010
Victim died at Unknown
Cause: Shooting

Double shooting

' 39.28594780000, -76.64996770000, icon_homicide_shooting, 'p868', '
Michael Hatch
68 S. Pulaski St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 32 years old
Found on May 30, 2010
Victim died at Unknown
Cause: Shooting

Double shooting

' 39.28302320000, -76.64978820000, icon_homicide_shooting, 'p866', '
Alvin Martin
2100 Ramsay St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 28 years old
Found on May 30, 2010
Victim died at Unknown
Cause: Shooting
' 39.29575600000, -76.58157300000, icon_homicide_shooting, 'p864', '
Davon Dorsey
400 N. Rose St
Baltimore, MD 21224
Race: Black
Gender: male
Age: 18 years old
Found on May 29, 2010
Victim died at Unknown
Cause: Shooting
' 39.22811200000, -76.58900400000, icon_homicide_shooting, 'p865', '
Ronald Anderson
4100 Pennington Ave
Baltimore, MD 21226
Race: Black
Gender: male
Age: 30 years old
Found on May 29, 2010
Victim died at Unknown
Cause: Shooting
' 39.29908900000, -76.58075100000, icon_homicide_shooting, 'p863', '
Timothy Gaskins
2600 E Monument St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 22 years old
Found on May 29, 2010
Victim died at Unknown
Cause: Shooting
' 39.30142300000, -76.57910100000, icon_homicide_shooting, 'p862', '
Donte Vandiver
900 N. Belnord Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 19 years old
Found on May 24, 2010
Victim died at Unknown
Cause: Shooting
' 39.30933200000, -76.66771200000, icon_homicide_shooting, 'p861', '
Jimmy Elton
3000 W. North Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 19 years old
Found on May 20, 2010
Victim died at Unknown
Cause: Shooting
' 39.34203400000, -76.69185800000, icon_homicide_shooting, 'p860', '
Jamie Hilton-Bey
4300 Elderon Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 34 years old
Found on May 20, 2010
Victim died at Unknown
Cause: Shooting
Read the article
' 39.28905520000, -76.59823660000, icon_homicide_shooting, 'p859', '
Mark Crockett
200 S. Spring Ct
Baltimore, MD 21231
Race: Black
Gender: male
Age: 34 years old
Found on May 17, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32193900000, -76.59034600000, icon_homicide_stabbing, 'p858', '
Michael Pryor
2824 Harford Rd
Baltimore, MD 21218
Race: Black
Gender: male
Age: 32 years old
Found on May 15, 2010
Victim died at Johns Hopkins Hospital
Cause: Stabbing
' 39.34881000000, -76.67739900000, icon_homicide_shooting, 'p857', '
Jerome Booze Jr.
3313 Paton Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 30 years old
Found on May 14, 2010
Victim died at St. Agnes Hospital
Cause: Shooting
' 39.33770030000, -76.67108820000, icon_homicide_shooting, 'p856', '
Michael Carroway
4300 Daytona Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 49 years old
Found on May 12, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.33796930000, -76.60253480000, icon_homicide_shooting, 'p854', '
Jerome Simmons
900 North Hill Rd
Baltimore, MD 21218
Race: Black
Gender: male
Age: 34 years old
Found on May 8, 2010
Victim died at Scene
Cause: Shooting

Two men found shot in a running car.

' 39.33796930000, -76.60253480000, icon_homicide_shooting, 'p855', '
George Toe
900 North Hill Rd
Baltimore, MD 21218
Race: Black
Gender: male
Age: 30 years old
Found on May 8, 2010
Victim died at Scene
Cause: Shooting

Two men found shot in a running car.

' 39.29688190000, -76.58678400000, icon_homicide_shooting, 'p853', '
Anthony Crist
500 N. Collington
Baltimore, MD 21205
Race: White
Gender: male
Age: 42 years old
Found on May 8, 2010
Victim died at Scene
Cause: Shooting
' 39.32662350000, -76.59438460000, icon_homicide_asphyxiation, 'p852', '
Betsy Riggin
3200 The Alameda
Baltimore, MD 21218
Race: White
Gender: female
Age: 29 years old
Found on May 7, 2010
Victim died at Scene
Cause: Asphyxiation
' 39.23667420000, -76.61046920000, icon_homicide_bluntforce, 'p850', '
Wayne Clark
3733 s. hanover st
Baltimore, MD 21225
Race: White
Gender: male
Age: 36 years old
Found on May 5, 2010
Victim died at Harbor Hospital
Cause: Blunt Force
' 39.29100590000, -76.63392740000, icon_homicide_bluntforce, 'p851', '
David Beers
1000 W. Lexington St
Baltimore, MD 21223
Race: White
Gender: male
Age: 49 years old
Found on May 5, 2010
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force
Read the article

Beers was found beaten on April 23 at 4:05 p.m. and died on this date.

' 39.28586390000, -76.65013600000, icon_homicide_shooting, 'p849', '
Jonathan Byrd
2100 Frederick AVe
Baltimore, MD 21223
Race: Black
Gender: male
Age: 29 years old
Found on May 4, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.28989500000, -76.64172000000, icon_homicide_shooting, 'p848', '
Andrew Copeland
1500 W. Fayette St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 16 years old
Found on May 3, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
Read the article
' 39.29112940000, -76.63014190000, icon_homicide_shooting, 'p847', '
David Mitchell
800 W Lexington St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 16 years old
Found on April 29, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.35100000000, -76.60917490000, icon_homicide_shooting, 'p846', '
Dwayne Majett
500 Chateau Ave
Baltimore, MD 21212
Race: Black
Gender: male
Age: 30 years old
Found on April 27, 2010
Victim died at Good Samaritan Hospital
Cause: Shooting
Read the article

Majett, who was convicted of second-degree murder in 1997, was found in a rear alley

' 39.27921500000, -76.63658700000, icon_homicide_shooting, 'p845', '
David Woods
1200 Bayard St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 31 years old
Found on April 26, 2010
Victim died at Unknown
Cause: Shooting
' 39.33221900000, -76.69717300000, icon_homicide_shooting, 'p844', '
Ramie Mays
4800 Liberty Heights Ave
Baltimore, MD 21207
Race: Black
Gender: male
Age: 18 years old
Found on April 25, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.30894700000, -76.64671190000, icon_homicide_shooting, 'p843', '
Kevin Hyslop
1800 N. Fulton Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 33 years old
Found on April 20, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Witnesses told police Hyslop was shot by Calvin Williams, 33, after Hyslop stabbed another man.

' 39.30401300000, -76.58288400000, icon_homicide_shooting, 'p839', '
Jamal Thomas
1100 N. Milton Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 30 years old
Found on April 18, 2010
Victim died at Scene
Cause: Shooting

Killed after argument broke out in liquor store

' 39.34900910000, -76.68442700000, icon_homicide_shooting, 'p842', '
Sean Johnson
5400 Narcissus Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 40 years old
Found on April 18, 2010
Victim died at Scene
Cause: Shooting

Shot in the head inside of a car.

' 39.30677900000, -76.58994400000, icon_homicide_bluntforce, 'p841', '
Melonie Hamber
2000 E. Hoffman St
Baltimore, MD 21213
Race: Black
Gender: female
Age: 2 years old
Found on April 17, 2010
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force

Melonie\'s father, Tyrone Hamber, has been charged in her death. Police say he beat her with a belt.

' 39.31204900000, -76.60227900000, icon_homicide_shooting, 'p840', '
Branden Bowser
1200 E. North Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 23 years old
Found on April 16, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.35493730000, -76.70060670000, icon_homicide_shooting, 'p838', '
Gavin Campbell
4100 Kenshaw Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 24 years old
Found on April 16, 2010
Victim died at Unknown
Cause: Shooting

Double shooting

' 39.31146600000, -76.60336300000, icon_homicide_shooting, 'p837', '
Nelson Armstrong
1101 E. North Ave
Baltimore, MD 21202
Race: Black
Gender: male
Age: 21 years old
Found on April 11, 2010
Victim died at Unknown
Cause: Shooting
' 39.34925630000, -76.67996350000, icon_homicide_shooting, 'p836', '
Anthony Walker
5300 Denmore
Baltimore, MD 21215
Race: Black
Gender: male
Age: 36 years old
Found on April 11, 2010
Victim died at Scene
Cause: Shooting
' 39.32813100000, -76.60892800000, icon_homicide_shooting, 'p835', '
Damon Minor
501 E. 33rd St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 21 years old
Found on April 10, 2010
Victim died at Unknown
Cause: Shooting
' 39.32360200000, -76.60970800000, icon_homicide_shooting, 'p834', '
Charles Bowman
2900 Greenmount Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 72 years old
Found on April 8, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot at Yau\'s Carryout

' 39.35430100000, -76.60922700000, icon_homicide_shooting, 'p833', '
Sean Ramseur
500 Glenwood Ave
Baltimore, MD 21212
Race: Black
Gender: male
Age: 40 years old
Found on April 7, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
Read the article
' 39.36706900000, -76.57140820000, icon_homicide_shooting, 'p832', '
Raymond Langford Jr.
2200 Fleetwood Ave
Baltimore, MD 21214
Race: Black
Gender: male
Age: 25 years old
Found on April 6, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting

Triple shooting, two others wounded

' 39.30955850000, -76.70138730000, icon_homicide_shooting, 'p831', '
Michael Thomas
1900 Eagle Dr
Baltimore, MD 21207
Race: Black
Gender: male
Age: 27 years old
Found on March 29, 2010
Victim died at Scene
Cause: Shooting
' 39.28799840000, -76.65411200000, icon_homicide_shooting, 'p829', '
Antonio Wilson
10 N. Calverton Rd
Baltimore, MD 21223
Race: Black
Gender: male
Age: 29 years old
Found on March 28, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30703900000, -76.58165500000, icon_homicide_shooting, 'p830', '
Charles Bowman
1400 N. Luzerne Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 16 years old
Found on March 27, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.35853160000, -76.57128690000, icon_homicide_shooting, 'p828', '
Phillip Holmes
2200 Roselawn
Baltimore, MD 21214
Race: Black
Gender: male
Age: 22 years old
Found on March 23, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30657000000, -76.58093200000, icon_homicide_shooting, 'p827', '
Michael Holt
2600 Grogan Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 20 years old
Found on March 22, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32605700000, -76.65791290000, icon_homicide_shooting, 'p826', '
Michael Thomas
3400 Park Heights Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 27 years old
Found on March 21, 2010
Victim died at Unknown
Cause: Shooting
' 39.31193300000, -76.60867390000, icon_homicide_shooting, 'p825', '
Carlos Williams
1900 Boone St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 50 years old
Found on March 21, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Williams was shot in an apparent robbery as he was leaving for work, according to police

' 39.32073910000, -76.57476320000, icon_homicide_shooting, 'p824', '
Donte Gee
3200 Belair Rd
Baltimore, MD 21213
Race: Black
Gender: male
Age: 25 years old
Found on March 18, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot when one of two men he was walking with pulled a gun and shot him in the head and chest

' 39.31415280000, -76.53982850000, icon_homicide_shooting, 'p823', '
Daniel Dixon
6510 frankford ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 25 years old
Found on March 15, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting

Dixon was a Johns Hopkins security guard and a former Air Force reservist and was shot and killed during a robbery...

' 39.31776780000, -76.61973650000, icon_homicide_shooting, 'p822', '
Asia Carter
200 W. 25th St
Baltimore, MD 21211
Race: Black
Gender: male
Age: 37 years old
Found on March 15, 2010
Victim died at Scene
Cause: Shooting

Killed in drive-by shooting

' 39.31668950000, -76.63041280000, icon_homicide_bluntforce, 'p821', '
Rajahnthon Haynie
700 Druid Park Lake Drive
Baltimore, MD 21217
Race: Black
Gender: male
Age: 1 year old
Found on March 14, 2010
Victim died at Scene
Cause: Blunt Force

Rajahnthon was found buried in Druid Hill Park, inside of a bag. His mother, Lakesha Haynie, has been charged in his...

' 39.31946500000, -76.61435900000, icon_homicide_shooting, 'p820', '
Donatello Fenner
2600 N Calvert St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 22 years old
Found on March 12, 2010
Victim died at Unknown
Cause: Shooting
' 39.28954800000, -76.65029900000, icon_homicide_shooting, 'p818', '
Jamal Rogers
2100 w. fayette st
Baltimore, MD 21223
Race: Black
Gender: male
Age: 20 years old
Found on March 5, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32870700000, -76.56542900000, icon_homicide_shooting, 'p817', '
Alvin McMiller
4300 Belair Rd
Baltimore, MD 21206
Race: Black
Gender: male
Age: 23 years old
Found on March 4, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot outside Sheldon\'s bar

' 39.28592820000, -76.63512980000, icon_homicide_bluntforce, 'p819', '
Lee McCoy
201 S. Arlington ST
Baltimore, MD 21223
Race: Black
Gender: male
Age: 44 years old
Found on March 3, 2010
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force

Punched in the head at West Baltimore Behaviorial Health center, head hit on floor.

' 39.30677400000, -76.59891100000, icon_homicide_shooting, 'p816', '
Kenly Wheeler
1400 N Caroline St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 29 years old
Found on March 3, 2010
Victim died at Scene
Cause: Shooting

Wheeler\'s body was found on the grounds of Dr. Bernard Harris Sr. Elementary School

' 39.30539000000, -76.64642200000, icon_homicide_shooting, 'p815', '
Dejuan Green
1500 N. Fulton Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 23 years old
Found on February 26, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Green was one of three people shot during this incident.

' 39.30360740000, -76.66369290000, icon_homicide_stabbing, 'p814', '
Timothy Mason
1400 N. Dukeland St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 35 years old
Found on February 23, 2010
Victim died at Scene
Cause: Stabbing
' 39.31950700000, -76.56590500000, icon_homicide_shooting, 'p813', '
Leonard Gee
3500 Cliftmont Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 28 years old
Found on February 21, 2010
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.28920100000, -76.61410200000, icon_homicide_shooting, 'p812', '
James Ball
10 S. Light St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 38 years old
Found on February 20, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Ball was a security guard working downtown outside the Bank of America building was shot during a dispute over a woman...

' 39.30771200000, -76.65523800000, icon_homicide_shooting, 'p811', '
David Scott
1700 Ruxton Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 35 years old
Found on February 17, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29927390000, -76.57973800000, icon_homicide_stabbing, 'p810', '
Adam Couther
700 n. lakewood ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 27 years old
Found on February 17, 2010
Victim died at Johns Hopkins Hospital
Cause: Stabbing

Stabbed while intervening in a dispute between a relative and her boyfriend.

' 39.34520340000, -76.67139180000, icon_homicide_shooting, 'p809', '
Daron Howard
3200 Woodland Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 21 years old
Found on February 16, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.30182900000, -76.65393000000, icon_homicide_shooting, 'p808', '
Rodney Stephens
1200 N. Bentalou St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 38 years old
Found on February 7, 2010
Victim died at Scene
Cause: Shooting
' 39.28369000000, -76.68254390000, icon_homicide_stabbing, 'p807', '
Damien Osacoca
4000 Massachusetts Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 24 years old
Found on February 6, 2010
Victim died at Sinai Hospital
Cause: Stabbing
' 39.33904510000, -76.65776360000, icon_homicide_shooting, 'p806', '
Shaun Henderson
2400 Loyola Southway
Baltimore, MD 21209
Race: Black
Gender: male
Age: 30 years old
Found on February 5, 2010
Victim died at Sinai Hospital
Cause: Shooting
' 39.29525200000, -76.65070800000, icon_homicide_shooting, 'p805', '
Juan Tucker
2100 Edmondson Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 34 years old
Found on February 3, 2010
Victim died at Unknown
Cause: Shooting
' 39.31321600000, -76.64864900000, icon_homicide_shooting, 'p804', '
John England
2200 N. Monroe St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 27 years old
Found on February 1, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting

England was found shot multiple times in the road.

' 39.28997940000, -76.63103100000, icon_homicide_stabbing, 'p803', '
Kevin Davis
851 W. Fayette St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 31 years old
Found on January 29, 2010
Victim died at Maryland Shock Trauma Center
Cause: Stabbing

Victim was found stabbed inside of a vehicle.

' 39.31220600000, -76.60285600000, icon_homicide_shooting, 'p802', '
Michael Manning
1900 Aisquith St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 35 years old
Found on January 25, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Manning was shot multiple times.

' 39.37084390000, -76.56768030000, icon_homicide_stabbing, 'p801', '
Darius Ray
6900 McClean Blvd
Baltimore, MD 21234
Race: Black
Gender: male
Age: 20 years old
Found on January 23, 2010
Victim died at Sinai Hospital
Cause: Stabbing

Ray was a Marine stationed in Washington DC

' 39.30977400000, -76.59861700000, icon_homicide_shooting, 'p800', '
Raymond Gibson
1700 N. Caroline St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 21 years old
Found on January 19, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot while walking down the street.

' 39.29509800000, -76.62147200000, icon_homicide_stabbing, 'p798', '
James Jackson
400 W. Franklin St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 27 years old
Found on January 18, 2010
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.29794050000, -76.62953460000, icon_homicide_shooting, 'p799', '
Darius Goines
600 W Hoffman St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 19 years old
Found on January 18, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30980600000, -76.65426700000, icon_homicide_shooting, 'p797', '
Darnell Taylor
2300 W. North Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 48 years old
Found on January 16, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.33064600000, -76.56697000000, icon_homicide_shooting, 'p795', '
Derrick Taylor
3900 Eierman Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 20 years old
Found on January 10, 2010
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot after leaving a neighborhood bar.

' 39.30147500000, -76.58052100000, icon_homicide_shooting, 'p794', '
Darel Alston
900 N. Glover St.
Baltimore, MD 21205
Race: Black
Gender: male
Age: 38 years old
Found on January 10, 2010
Victim died at Scene
Cause: Shooting

Victim was found inside vehicle.

' 39.30614400000, -76.58302300000, icon_homicide_bluntforce, 'p796', '
Sean Johnson
1300 N. Milton Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 41 years old
Found on January 7, 2010
Victim died at Johns Hopkins Hospital
Cause: Blunt Force

Johnson was beaten on the fourth and died from his injuries on the 7th

' 39.28881700000, -76.66003700000, icon_homicide_shooting, 'p793', '
Antonio Lashley
100 N. Franklintown Rd
Baltimore, MD 21223
Race: Black
Gender: male
Age: 21 years old
Found on January 6, 2010
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32057800000, -76.65560300000, icon_homicide_shooting, 'p792', '
Marcal Walton
2312 Ocala Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 33 years old
Found on January 3, 2010
Victim died at Scene
Cause: Shooting
' 39.35000000000, -76.66580000000, icon_homicide_shooting, 'p1222', '
Tiyon Campbell
2800 W Garrison Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 31 years old
Found on December 28, 2011
Victim died at Scene
Cause: Shooting

Home invasion/robbery

' 39.24972920000, -76.63036560000, icon_homicide_shooting, 'p1221', '
Damond Wallace
2700 Giles Rd
Baltimore, MD 21225
Race: Black
Gender: male
Age: 30 years old
Found on December 24, 2011
Victim died at Unknown
Cause: Shooting
' 39.32989760000, -76.60500810000, icon_homicide_bluntforce, 'p1220', '
Shirley Garrett
800 E 34 St
Baltimore, MD 21218
Race: Black
Gender: female
Age: 67 years old
Found on December 22, 2011
Victim died at Union Memorial Hospital
Cause: Blunt Force

Garrett was beaten with a hammer on March 31 by her 14-year-old grandson Hassanhii Garrett, who police say admitted to...

' 39.28483780000, -76.65613470000, icon_homicide_shooting, 'p1219', '
Dameone Suggs
2500 W Pratt St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 40 years old
Found on December 21, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30259890000, -76.66898810000, icon_homicide_shooting, 'p1218', '
Donte Collins
1500 Rosedale St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 29 years old
Found on December 19, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Double shooting

' 39.29043840000, -76.65167190000, icon_homicide_shooting, 'p1216', '
Dayon Barnes
200 N Smallwood St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 27 years old
Found on December 18, 2011
Victim died at Bon Secours Hospital
Cause: Shooting
' 39.32925090000, -76.56409650000, icon_homicide_shooting, 'p1217', '
Jeffrey Smith
4200 Nicholas Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 32 years old
Found on December 17, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31245700000, -76.67862900000, icon_homicide_shooting, 'p1215', '
Lawrence Edwards
2200 Elsinore Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 23 years old
Found on December 16, 2011
Victim died at Scene
Cause: Shooting
' 39.33135490000, -76.65778650000, icon_homicide_stabbing, 'p1214', '
Dwight Jones
2500 Violet Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 58 years old
Found on December 10, 2011
Victim died at Scene
Cause: Stabbing
' 39.32351700000, -76.61486400000, icon_homicide_shooting, 'p1213', '
Brandon Hudson
2900 N. Calvert St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 22 years old
Found on December 8, 2011
Victim died at Scene
Cause: Shooting

Found inside apartment. 

' 39.29272400000, -76.57214000000, icon_homicide_stabbing, 'p1210', '
Carlos Martinez
1 N. East Ave
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 27 years old
Found on December 6, 2011
Victim died at Unknown
Cause: Stabbing
' 39.31090600000, -76.61640790000, icon_homicide_shooting, 'p1211', '
Ronald Watkins
1 E. North Ave
Baltimore, MD 21201
Race: Black
Gender: male
Age: 35 years old
Found on December 6, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Watkins was shot in 1992 at age 16, and suffered damage to his aorta. Police say he died July 10, 2011, and his death...

' 39.28126900000, -76.65269900000, icon_homicide_shooting, 'p1209', '
Kenneth Davis
500 S Bentalou St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 29 years old
Found on November 29, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29462600000, -76.63229500000, icon_homicide_shooting, 'p1208', '
Tavon Toney
900 W Franklin St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 25 years old
Found on November 22, 2011
Victim died at Unknown
Cause: Shooting

Toney was shot on Nov. 21; this date reflects the date that he died. 

' 39.29683390000, -76.63748690000, icon_homicide_shooting, 'p1207', '
Gregory McFadden
700 N Carrollton Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 25 years old
Found on November 15, 2011
Victim died at Unknown
Cause: Shooting
' 39.30767900000, -76.65608600000, icon_homicide_shooting, 'p1205', '
Steven Pennington
1700 Moreland St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 32 years old
Found on November 14, 2011
Victim died at Unknown
Cause: Shooting
' 39.31544400000, -76.59932900000, icon_homicide_shooting, 'p1206', '
Kevin Lofland
2200 Aiken St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 18 years old
Found on November 12, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Double shooting

' 39.29798890000, -76.62781980000, icon_homicide_shooting, 'p1203', '
Darren Robertson
1000 Pennsylvania Ave
Baltimore, MD 21201
Race: Black
Gender: male
Age: 20 years old
Found on November 12, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32688740000, -76.57558120000, icon_homicide_shooting, 'p1204', '
Lakeishe Player
2600 kentucky ave
Baltimore, MD 21213
Race: Black
Gender: female
Age: 26 years old
Found on November 11, 2011
Victim died at Unknown
Cause: Shooting

Found inside home

' 39.34267000000, -76.59304300000, icon_homicide_shooting, 'p1202', '
Santos Villanueva
4300 Loch Raven Boulevard
Baltimore, MD 21218
Race: Hispanic
Gender: male
Age: 25 years old
Found on November 9, 2011
Victim died at Scene
Cause: Shooting
' 39.34838690000, -76.67475040000, icon_homicide_asphyxiation, 'p1201', '
Shirley Tyler
3200 Spaulding Ave
Baltimore, MD 21215
Race: Black
Gender: female
Age: 67 years old
Found on November 8, 2011
Victim died at Scene
Cause: Asphyxiation

Tyler was found dead in her home on Nov. 5; police added her death to the homicide count on this date and charged her...

' 39.33626300000, -76.55553990000, icon_homicide_shooting, 'p1200', '
Davon Diggs
4100 Parkwood Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 21 years old
Found on November 5, 2011
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting

Originally reported in 5000 Belair Road; later determined to be rear alley of 4100 block Parkwood

' 39.32355530000, -76.60964570000, icon_homicide_shooting, 'p1199', '
Freddie Jones Jr.
2900 Greenmount Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 52 years old
Found on October 31, 2011
Victim died at Unknown
Cause: Shooting
' 39.35441920000, -76.61004470000, icon_homicide_shooting, 'p1198', '
Richard Ford Jr.
5300 York Road
Baltimore, MD 21212
Race: White
Gender: male
Age: 38 years old
Found on October 31, 2011
Victim died at Unknown
Cause: Shooting

Ford and his pregnant girlfriend, Kimberly Smith, were shot on Feb 1, 1998. Smith died, while Ford was critically...

' 39.29580300000, -76.62935220000, icon_homicide_stabbing, 'p1197', '
Eddie Nance
600 George St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 53 years old
Found on October 28, 2011
Victim died at Unknown
Cause: Stabbing
' 39.30319990000, -76.66128800000, icon_homicide_shooting, 'p1196', '
Shyekee Wilson
1300 Braddish Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 18 years old
Found on October 27, 2011
Victim died at Scene
Cause: Shooting
' 39.34001390000, -76.60347000000, icon_homicide_bluntforce, 'p1195', '
Sherry Montgomery-Cantey
900 E. 41st St
Baltimore, MD 21218
Race: Black
Gender: female
Age: 43 years old
Found on October 25, 2011
Victim died at Scene
Cause: Blunt Force
' 39.31099600000, -76.63326900000, icon_homicide_shooting, 'p1194', '
Edward Bardney
2000 Linden Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 38 years old
Found on October 24, 2011
Victim died at University of Maryland Medical Center
Cause: Shooting
' 39.31390870000, -76.64561330000, icon_homicide_shooting, 'p1193', '
Samuel Pinkney
2800 S. Woodbrook AVe
Baltimore, MD 21217
Race: Black
Gender: male
Age: 18 years old
Found on October 21, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30941660000, -76.60026800000, icon_homicide_shooting, 'p1190', '
Antoinne Pratt
1700 Harford Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 23 years old
Found on October 17, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot while inside vehicle

' 39.31544400000, -76.59932900000, icon_homicide_shooting, 'p1189', '
Marquis Jones
2200 Aiken St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 20 years old
Found on October 16, 2011
Victim died at Unknown
Cause: Shooting
' 39.32474800000, -76.59474890000, icon_homicide_shooting, 'p1187', '
Christopher McMillion
1500 E. 29th St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 23 years old
Found on October 14, 2011
Victim died at Unknown
Cause: Shooting
' 39.29276460000, -76.66302040000, icon_homicide_bluntforce, 'p1192', '
Willie Nelson
2800 W. Mulberry St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 52 years old
Found on October 13, 2011
Victim died at Scene
Cause: Blunt Force

Nelson was found dead inside a home on Aug. 16, and an autopsy later determined he had died from blunt force trauma....

' 39.31125600000, -76.58434100000, icon_homicide_shooting, 'p1186', '
Louis Ingram
2400 E. Lafayette St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 24 years old
Found on October 11, 2011
Victim died at Scene
Cause: Shooting
' 39.31284930000, -76.63151430000, icon_homicide_shooting, 'p1191', '
Earl Brown
2200 Callow Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 36 years old
Found on October 11, 2011
Victim died at Union Memorial Hospital
Cause: Shooting

Brown was shot in the head on Sept. 23, 1999 and developed a seizure disorder. He died from a seizure on Oct. 7, and...

' 39.28514000000, -76.63973500000, icon_homicide_shooting, 'p1185', '
Devearl Singletary
1400 Kuper Place
Baltimore, MD 21223
Race: Black
Gender: male
Age: 17 years old
Found on October 10, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29543510000, -76.57856960000, icon_homicide_shooting, 'p1184', '
Kevin Pierre
300 N. Belnord Ave
Baltimore, MD 21224
Race: Black
Gender: male
Age: 20 years old
Found on October 10, 2011
Victim died at Unknown
Cause: Shooting
Read the article
' 39.22919410000, -76.60146580000, icon_homicide_shooting, 'p1183', '
Maricus Perkins
900 Herndon Ct
Baltimore, MD 21225
Race: Black
Gender: male
Age: 20 years old
Found on October 3, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30440190000, -76.63147260000, icon_homicide_shooting, 'p1182', '
Anton Ingram
500 McMechen St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 32 years old
Found on October 3, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32662000000, -76.58916800000, icon_homicide_shooting, 'p1181', '
Aldrick Hamilton
1900 E. 31st St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 32 years old
Found on September 28, 2011
Victim died at Unknown
Cause: Shooting
' 39.29304800000, -76.59816830000, icon_homicide_shooting, 'p1180', '
Antonio Blackwell
1400 E Fayette St
Baltimore, MD 21231
Race: Black
Gender: male
Age: 26 years old
Found on September 28, 2011
Victim died at Unknown
Cause: Shooting
' 39.31160300000, -76.67559400000, icon_homicide_shooting, 'p1179', '
Dwight Montgomery
2200 Garrison Blvd
Baltimore, MD 21216
Race: Black
Gender: male
Age: 45 years old
Found on September 28, 2011
Victim died at Sinai Hospital
Cause: Shooting
' 39.35166700000, -76.69318300000, icon_homicide_shooting, 'p1178', '
Keith Parker
4000 Primrose Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 38 years old
Found on September 27, 2011
Victim died at Scene
Cause: Shooting

Found shot to death in a vehicle. 

' 39.33969200000, -76.68115200000, icon_homicide_shooting, 'p1176', '
Gregory Toles
3900 Dolfield Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 30 years old
Found on September 25, 2011
Victim died at Scene
Cause: Shooting

Double shooting; this victim was shot in the head and back

' 39.33969200000, -76.68115200000, icon_homicide_shooting, 'p1177', '
Tyrone McQueen
3900 Dolfield Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 28 years old
Found on September 25, 2011
Victim died at Scene
Cause: Shooting

Double shooting; this victim was shot in the face. 

' 39.32989760000, -76.60500810000, icon_homicide_shooting, 'p1175', '
Robert James
800 E 34th St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 30 years old
Found on September 21, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot while sitting in driver\'s seat of vehicle

' 39.29627500000, -76.57230100000, icon_homicide_shooting, 'p1173', '
Thomas Powell
400 N East Ave
Baltimore, MD 21224
Race: Black
Gender: male
Age: 20 years old
Found on September 20, 2011
Victim died at Unknown
Cause: Shooting
' 39.33414690000, -76.67352790000, icon_homicide_shooting, 'p1174', '
Isaiah Roane
3500 Dolfield Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 29 years old
Found on September 20, 2011
Victim died at Unknown
Cause: Shooting

Double shooting from Sept. 7; date reflects when vicitm died

' 39.28999640000, -76.63223870000, icon_homicide_stabbing, 'p1172', '
Gregory Parker
100 N Poppleton Ave
Baltimore, MD 21225
Race: Black
Gender: male
Age: 22 years old
Found on September 19, 2011
Victim died at Unknown
Cause: Stabbing
' 39.23511840000, -76.60112860000, icon_homicide_shooting, 'p1171', '
Larry Petty
700 E Pontiac Ave
Baltimore, MD 21225
Race: Black
Gender: male
Age: 25 years old
Found on September 17, 2011
Victim died at Scene
Cause: Shooting
Read the article
' 39.24123310000, -76.62524930000, icon_homicide_asphyxiation, 'p1170', '
Armando Santiago
700 W Patapsco Ave
Baltimore, MD 21225
Race: Hispanic
Gender: male
Age: 42 years old
Found on September 16, 2011
Victim died at Scene
Cause: Asphyxiation

Found in an alley

' 39.28632800000, -76.64696800000, icon_homicide_shooting, 'p1167', '
Bruce Benn
1900 W. Lombard St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 16 years old
Found on September 14, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31600660000, -76.57686800000, icon_homicide_shooting, 'p1169', '
Gerrod Davis
3300 Elmora Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 24 years old
Found on September 13, 2011
Victim died at Unknown
Cause: Shooting

Davis was shot Aug. 31, 2006, died February 9, 2009, and his death was ruled a homicide by the state medical examiner\'s...

' 39.34055340000, -76.60767780000, icon_homicide_shooting, 'p1166', '
Marcus Hopkins
4200 Old York Road
Baltimore, MD 21212
Race: Black
Gender: male
Age: 21 years old
Found on September 8, 2011
Victim died at Unknown
Cause: Shooting
' 39.30063000000, -76.66187200000, icon_homicide_stabbing, 'p1165', '
Dalyrie McFadden
2700 Riggs Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 1 year old
Found on September 7, 2011
Victim died at St. Agnes Hospital
Cause: Stabbing

Child\'s father, Hari Close, charged with boy\'s murder. 

' 39.29652850000, -76.58774930000, icon_homicide_shooting, 'p1164', '
Antonio Laws
500 N Chester St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 31 years old
Found on September 4, 2011
Victim died at Scene
Cause: Shooting

Shot while sleeping in home

' 39.27864630000, -76.61706630000, icon_homicide_bluntforce, 'p1162', '
Janice Drayton
900 Leadenhall St
Baltimore, MD 21230
Race: Black
Gender: female
Age: 51 years old
Found on September 1, 2011
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force
' 39.35724500000, -76.59137800000, icon_homicide_bluntforce, 'p1163', '
Davon Booth Jr.
5600 Woodmont Ave
Baltimore, MD 21239
Race: Black
Gender: male
Age: 1 year old
Found on September 1, 2011
Victim died at Good Samaritan Hospital
Cause: Blunt Force

Davon was pronounced dead on Aug. 8. An autopsy later revealed he had died as a result of head trauma. This date...

' 39.24595530000, -76.62748300000, icon_homicide_shooting, 'p1161', '
Dewayne Jones
2800 Round Rd
Baltimore, MD 21225
Race: Black
Gender: male
Age: 23 years old
Found on August 28, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32098250000, -76.59409020000, icon_homicide_bluntforce, 'p1160', '
Dramiara Johnson
1700 Gorsuch Ave
Baltimore, MD 21218
Race: Black
Gender: female
Age: 4 years old
Found on August 27, 2011
Victim died at Johns Hopkins Hospital
Cause: Blunt Force
' 39.30434700000, -76.60528600000, icon_homicide_shooting, 'p1159', '
Kennard Hailey
1200 Valley St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 26 years old
Found on August 25, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32572400000, -76.67943400000, icon_homicide_shooting, 'p1157', '
Lee Jones III
3700 W. Forest Park Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 24 years old
Found on August 23, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Double shooting

' 39.32231410000, -76.55292900000, icon_homicide_shooting, 'p1156', '
Big Gurung
5200 Bowleys Lane
Baltimore, MD 21206
Race: Asian
Gender: female
Age: 20 years old
Found on August 23, 2011
Victim died at Unknown
Cause: Shooting

Double shooting, police investigating possible robbery motive

' 39.30401300000, -76.58288400000, icon_homicide_shooting, 'p1154', '
Lawrence Rollings
1100 N. Milton Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 25 years old
Found on August 22, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot on 21st; pronounced dead on 22nd. 

' 39.29007950000, -76.67951780000, icon_homicide_shooting, 'p1158', '
Jerome Golphin
200 Mt Holly St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 25 years old
Found on August 22, 2011
Victim died at Unknown
Cause: Shooting
' 39.35683790000, -76.55772720000, icon_homicide_stabbing, 'p1155', '
Alfred Garner Jr.
6000 Harford Rd
Baltimore, MD 21214
Race: Black
Gender: male
Age: 35 years old
Found on August 22, 2011
Victim died at Unknown
Cause: Stabbing

Fight outside Ibis bar; Garner was stabbed on 8/21, date reflects when he died. 

' 39.31104400000, -76.67422710000, icon_homicide_shooting, 'p1153', '
Demetrius Sowers
3400 Clifton Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 33 years old
Found on August 14, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.34265500000, -76.66326100000, icon_homicide_shooting, 'p1152', '
Derrell Burrow
2900 Edgecombe Circle S
Baltimore, MD 21215
Race: Black
Gender: male
Age: 23 years old
Found on August 14, 2011
Victim died at Scene
Cause: Shooting
' 39.32669000000, -76.55243290000, icon_homicide_stabbing, 'p1151', '
Irene Logan
4700 Moravia Road
Baltimore, MD 21206
Race: Black
Gender: female
Age: 91 years old
Found on August 3, 2011
Victim died at Unknown
Cause: Stabbing

Found stabbed to death in home, which had been ransacked. 

' 39.31492900000, -76.59607690000, icon_homicide_shooting, 'p1150', '
Sean Eames
1600 Darley Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 24 years old
Found on August 1, 2011
Victim died at Unknown
Cause: Shooting
' 39.29763100000, -76.71053200000, icon_homicide_shooting, 'p1148', '
Anthony Tarbert
1100 Wedgewood Rd
Baltimore, MD 21229
Race: White
Gender: male
Age: 15 years old
Found on August 1, 2011
Victim died at Scene
Cause: Shooting

Double shooting; two teens killed. Date reflects when he was found. 

' 39.29763100000, -76.71053200000, icon_homicide_shooting, 'p1149', '
Dominic Perry
1100 Wedgewood Rd
Baltimore, MD 21229
Race: Black
Gender: male
Age: 15 years old
Found on July 31, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Double shooting

' 39.31879800000, -76.55122100000, icon_homicide_shooting, 'p1147', '
Gerald Bonner
4800 Bowland Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 31 years old
Found on July 30, 2011
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting

Two additional victims were located at area hospitals. 

' 39.30436800000, -76.59383240000, icon_homicide_shooting, 'p1146', '
Hassan Frank
1200 N. Gay St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 29 years old
Found on July 29, 2011
Victim died at Scene
Cause: Shooting

Body found in trunk of car

' 39.31948200000, -76.56351500000, icon_homicide_shooting, 'p1145', '
Anthony F. Johnson
3600 Chesterfield Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 47 years old
Found on July 27, 2011
Victim died at Scene
Cause: Shooting

Found slumped over behind wheel of car. 

' 39.30142300000, -76.57910100000, icon_homicide_shooting, 'p1144', '
Ryshawn Cox
900 N. Belnord Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 21 years old
Found on July 26, 2011
Victim died at Harbor Hospital
Cause: Shooting
' 39.32804600000, -76.56231500000, icon_homicide_shooting, 'p1140', '
Michael Jones
4300 Nicholas Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 27 years old
Found on July 26, 2011
Victim died at Scene
Cause: Shooting

One of three bodies found in burning home; police say victims were shot first.

' 39.32804600000, -76.56231500000, icon_homicide_shooting, 'p1141', '
Billy Lovitt
4300 Nicholas Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 58 years old
Found on July 26, 2011
Victim died at Scene
Cause: Shooting

One of three bodies found in burning home. Police say victims were shot first.

' 39.32804600000, -76.56231500000, icon_homicide_shooting, 'p1142', '
Tanyika Gibbs
4300 Nicholas Ave
Baltimore, MD 21206
Race: Black
Gender: female
Age: 37 years old
Found on July 26, 2011
Victim died at Unknown
Cause: Shooting

One of three bodies found in burning home. Police say victims were shot first.

' 39.31672010000, -76.60846860000, icon_homicide_other, 'p1143', '
Dwayne Hawkins
600 Cokesbury Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 25 years old
Found on July 25, 2011
Victim died at Scene
Cause: Other

Hawkins died June 13, 2008 in a fire. In March, police received new information that he was killed; this date reflects...

' 39.29773720000, -76.64290870000, icon_homicide_stabbing, 'p1138', '
Marvin Hayes
800 N Gilmor St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 22 years old
Found on July 20, 2011
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.30998100000, -76.65436500000, icon_homicide_shooting, 'p1136', '
Name not yet released
1900 Bentalou St
Baltimore, MD 21216
Race: Unknown
Gender: male
Age: 25 years old
Found on July 19, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.28116900000, -76.63392900000, icon_homicide_shooting, 'p1135', '
Rasheed Abdullah
1200 Ward St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 21 years old
Found on July 17, 2011
Victim died at Unknown
Cause: Shooting
' 39.29938470000, -76.59626680000, icon_homicide_shooting, 'p1134', '
Rodney Johnson
800 N Bond St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 30 years old
Found on July 16, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.28823950000, -76.65926700000, icon_homicide_shooting, 'p1133', '
David McKoy
1 N Franklintown Rd
Baltimore, MD 21223
Race: Black
Gender: male
Age: 29 years old
Found on July 15, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30877790000, -76.65150600000, icon_homicide_shooting, 'p1132', '
Jawan Weeden
1800 N. Pulaski St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 30 years old
Found on July 14, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Weeden was shot on July 12, and died on this date. 

' 39.31399200000, -76.60761590000, icon_homicide_shooting, 'p1137', '
Omar Richardson
2100 Homewood Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 24 years old
Found on July 14, 2011
Victim died at Unknown
Cause: Shooting
' 39.29505960000, -76.57854620000, icon_homicide_bluntforce, 'p1131', '
Patsy Person
200 N Belnord Ave
Baltimore, MD 21224
Race: Black
Gender: female
Age: 43 years old
Found on July 10, 2011
Victim died at Unknown
Cause: Blunt Force
' 39.29893680000, -76.59719190000, icon_homicide_shooting, 'p1129', '
Jerel McFadden
1500 Lester Morton Ct
Baltimore, MD 21205
Race: Black
Gender: male
Age: 22 years old
Found on July 10, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32098250000, -76.59409020000, icon_homicide_stabbing, 'p1130', '
Richard Mills
1700 Gorsuch Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 46 years old
Found on July 10, 2011
Victim died at Johns Hopkins Hospital
Cause: Stabbing

Found seated in a pickup truck

' 39.29066330000, -76.61522780000, icon_homicide_stabbing, 'p1128', '
Tremon Fields
100 N. Charles St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 25 years old
Found on July 9, 2011
Victim died at Maryland Shock Trauma Center
Cause: Stabbing

Police say two groups of people got into fight; victim was stabbed in the leg

' 39.29536100000, -76.67893500000, icon_homicide_shooting, 'p1127', '
Sterling Kelly
3700 Harlem Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 29 years old
Found on July 5, 2011
Victim died at Unknown
Cause: Shooting

Kelly was shot and gravely injured on June 21. This date reflects the date he was added to the city\'s homicide count,...

' 39.28514050000, -76.60393320000, icon_homicide_stabbing, 'p1126', '
Joseph Calo
700 Eastern Ave
Baltimore, MD 21202
Race: White
Gender: male
Age: 26 years old
Found on July 4, 2011
Victim died at Maryland Shock Trauma Center
Cause: Stabbing

Stabbing occurred after Inner Harbor July 4th fireworks display

' 39.28118300000, -76.63641500000, icon_homicide_shooting, 'p1125', '
James Lofton
1100 S. Carey St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 21 years old
Found on July 2, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Shot on 7/1, died at 3 pm on this date

' 39.34960070000, -76.59371150000, icon_homicide_shooting, 'p1124', '
William Daughtry III
1400 Stonewood Rd
Baltimore, MD 21239
Race: Black
Gender: male
Age: 20 years old
Found on July 1, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.26850620000, -76.65037930000, icon_homicide_shooting, 'p1123', '
Omar Johnson
1900 Breitwert Ave
Baltimore, MD 21230
Race: Black
Gender: male
Age: 16 years old
Found on June 29, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30361010000, -76.63054420000, icon_homicide_shooting, 'p1122', '
Christopher Samuel
500 Mosher St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 24 years old
Found on June 28, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31499700000, -76.56815200000, icon_homicide_shooting, 'p1121', '
Chong Wan Yim
3900 Erdman Ave
Baltimore, MD 21213
Race: Asian
Gender: male
Age: 55 years old
Found on June 28, 2011
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting

Shot while making delivery at shopping plaza

' 39.29188100000, -76.57114360000, icon_homicide_stabbing, 'p1120', '
Brittany Devone
3200 Leverton Ave
Baltimore, MD 21224
Race: Black
Gender: female
Age: 22 years old
Found on June 27, 2011
Victim died at Scene
Cause: Stabbing

Police arrest Jose Villatoro at scene

' 39.35665190000, -76.56967310000, icon_homicide_shooting, 'p1119', '
Andre Womack
5500 Grindon Ave
Baltimore, MD 21214
Race: Black
Gender: male
Age: 23 years old
Found on June 22, 2011
Victim died at Unknown
Cause: Shooting
' 39.34265500000, -76.66326100000, icon_homicide_shooting, 'p1118', '
Anthony Carr
2900 Edgecombe Cir S
Baltimore, MD 21215
Race: Black
Gender: male
Age: 52 years old
Found on June 20, 2011
Victim died at Sinai Hospital
Cause: Shooting

Shot while sitting on porch after returning home from work. 

' 39.31545660000, -76.57571990000, icon_homicide_shooting, 'p1288', '
Rayner Veney
3400 Ravenwood Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 51 years old
Found on June 20, 2011
Victim died at Unknown
Cause: Shooting

Shot on his front steps on June 20, 2011. Died on April 27, 2012 after being in a coma. This date reflects the date his...

' 39.29561110000, -76.66144900000, icon_homicide_shooting, 'p1117', '
Troy Fennell
700 Ashburton St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 39 years old
Found on June 17, 2011
Victim died at Unknown
Cause: Shooting
' 39.29287700000, -76.67432000000, icon_homicide_shooting, 'p1116', '
Charles Lassane
500 Denison St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 55 years old
Found on June 16, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

One of six victims after gunman opens fire on porch

' 39.33811090000, -76.60754370000, icon_homicide_shooting, 'p1115', '
Angelo Winston
600 Dumbarton Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 26 years old
Found on June 14, 2011
Victim died at Unknown
Cause: Shooting

Shot while walking past vehicle

' 39.31637800000, -76.60977400000, icon_homicide_shooting, 'p1114', '
Henry Mills
2400 Greenmount Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 40 years old
Found on June 14, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30834400000, -76.66625400000, icon_homicide_shooting, 'p1113', '
Gary Gibson
2900 Westwood Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 23 years old
Found on June 12, 2011
Victim died at Scene
Cause: Shooting
' 39.33880800000, -76.66163200000, icon_homicide_shooting, 'p1112', '
Roy Murray
2600 Park Heights Terrace
Baltimore, MD 21215
Race: Black
Gender: male
Age: 29 years old
Found on June 12, 2011
Victim died at Scene
Cause: Shooting

Double shooting; a woman was also shot in the stomach

' 39.28864800000, -76.64115090000, icon_homicide_shooting, 'p1111', '
James Wright
1500 W. Baltimore St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 25 years old
Found on June 11, 2011
Victim died at Unknown
Cause: Shooting
' 39.34992800000, -76.67235800000, icon_homicide_shooting, 'p1110', '
Chad Anderson
3000 Spaulding Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 26 years old
Found on June 10, 2011
Victim died at Unknown
Cause: Shooting
' 39.36706900000, -76.57140820000, icon_homicide_shooting, 'p1109', '
Durran Banks
2200 Fleetwood Ave
Baltimore, MD 21214
Race: Black
Gender: male
Age: 25 years old
Found on June 3, 2011
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.33466900000, -76.53700300000, icon_homicide_stabbing, 'p1106', '
John Kelly
5800 Moores Run Ct
Baltimore, MD 21206
Race: Black
Gender: male
Age: 25 years old
Found on June 1, 2011
Victim died at Scene
Cause: Stabbing

Body found on fire. 

' 39.30137650000, -76.60726170000, icon_homicide_shooting, 'p1105', '
Maurice Gary
700 E. Eager St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 35 years old
Found on May 31, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29269600000, -76.56925900000, icon_homicide_shooting, 'p1104', '
Payton Rivers
3400 E. Baltimore St
Baltimore, MD 21224
Race: Black
Gender: male
Age: 34 years old
Found on May 31, 2011
Victim died at Unknown
Cause: Shooting
' 39.31345500000, -76.68294200000, icon_homicide_shooting, 'p1103', '
Lois Smyth
3900 Windsor Mill Road
Baltimore, MD 21216
Race: White
Gender: female
Age: 40 years old
Found on May 29, 2011
Victim died at Scene
Cause: Shooting

Body found by jogger on Gwynns Falls Trail. 

' 39.33094700000, -76.69315700000, icon_homicide_shooting, 'p1102', '
Fareed Abdullah
4500 Liberty Heights Ave
Baltimore, MD 21207
Race: Black
Gender: male
Age: 28 years old
Found on May 28, 2011
Victim died at Scene
Cause: Shooting

Victim was shot while sitting in his vehicle, then sped off and struck a pedestrian, who suffered non-life-threatening...

' 39.31532650000, -76.59364540000, icon_homicide_shooting, 'p1101', '
Sean Johnson
1700 Cliftview Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 12 years old
Found on May 26, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Sean was among four young people injured in a shooting May 24 as they watched a basketball game on TV from a porch....

' 39.28095950000, -76.63293030000, icon_homicide_stabbing, 'p1108', '
Kevin Jones
1200 W. Ostend St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 57 years old
Found on May 26, 2011
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.30225810000, -76.59571100000, icon_homicide_shooting, 'p1107', '
Anthony Sherman
1600 Ward Ct
Baltimore, MD 21205
Race: Black
Gender: male
Age: 27 years old
Found on May 26, 2011
Victim died at Unknown
Cause: Shooting

Sherman was shot on 5/25 and died on this date. 

' 39.30550900000, -76.64817700000, icon_homicide_shooting, 'p1100', '
Donte Larkins
1500 N Monroe St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 33 years old
Found on May 25, 2011
Victim died at Scene
Cause: Shooting
' 39.29436370000, -76.66855760000, icon_homicide_stabbing, 'p1099', '
Dashawn Brown
3100 Edmondson Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 17 years old
Found on May 22, 2011
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.30675900000, -76.64657200000, icon_homicide_stabbing, 'p1098', '
Jalil Al-Salim
1600 N Fulton Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 50 years old
Found on May 21, 2011
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.23003200000, -76.59871300000, icon_homicide_shooting, 'p1097', '
Marcus Nickens Jr.
4100 10th St
Baltimore, MD 21225
Race: Black
Gender: male
Age: 19 years old
Found on May 19, 2011
Victim died at Unknown
Cause: Shooting
' 39.34838730000, -76.67475080000, icon_homicide_shooting, 'p1096', '
Raymond Young Jr.
3200 Spaulding Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 29 years old
Found on May 19, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.34506700000, -76.59735400000, icon_homicide_shooting, 'p1095', '
Dawaun Anderson
4500 Marble Hall Rd
Baltimore, MD 21239
Race: Black
Gender: male
Age: 22 years old
Found on May 14, 2011
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting
' 39.29057300000, -76.62142600000, icon_homicide_shooting, 'p1094', '
Willie Elliott
100 N Eutaw St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 31 years old
Found on May 11, 2011
Victim died at Unknown
Cause: Shooting
' 39.34742900000, -76.59841000000, icon_homicide_stabbing, 'p1093', '
Darian Kess
1200 Linworth Ave
Baltimore, MD 21239
Race: Black
Gender: male
Age: 27 years old
Found on May 3, 2011
Victim died at Johns Hopkins Hospital
Cause: Stabbing

Kess was stabbed in the neck during a robbery by three men who followed him inside of his apartment. He died May 2 from...

' 39.29039830000, -76.59383020000, icon_homicide_stabbing, 'p1092', '
Gilberto Gonzalez
1700 E. Lombard St
Baltimore, MD 21231
Race: Hispanic
Gender: male
Age: 22 years old
Found on April 29, 2011
Victim died at Unknown
Cause: Stabbing
' 39.31281300000, -76.58776700000, icon_homicide_shooting, 'p1088', '
Kareem Randall
1900 N. Collington Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 23 years old
Found on April 28, 2011
Victim died at Unknown
Cause: Shooting
' 39.31281300000, -76.58776700000, icon_homicide_shooting, 'p1091', '
Alex Venable
1900 N. Collington Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 26 years old
Found on April 28, 2011
Victim died at Unknown
Cause: Shooting

Triple shooting

' 39.28862700000, -76.64670200000, icon_homicide_asphyxiation, 'p1089', '
Elmore Rembert
1 N. Monroe St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 49 years old
Found on April 27, 2011
Victim died at Scene
Cause: Asphyxiation

Body was found when firefighters responded to extinguish a blaze in a vacant home. The body was found smoldering, and...

' 39.23650600000, -76.60427700000, icon_homicide_shooting, 'p1087', '
Jason Willis
3600 5th St
Baltimore, MD 21225
Race: Black
Gender: male
Age: 23 years old
Found on April 26, 2011
Victim died at Harbor Hospital
Cause: Shooting
' 39.30867600000, -76.65434700000, icon_homicide_shooting, 'p1086', '
John Atkins
1800 N. Bentalou St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 43 years old
Found on April 25, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29922640000, -76.68632450000, icon_homicide_shooting, 'p1081', '
Steven White
4200 Rokeby Rd
Baltimore, MD 21229
Race: Black
Gender: male
Age: 29 years old
Found on April 24, 2011
Victim died at Scene
Cause: Shooting

Two men found shot in vehicle

' 39.29922640000, -76.68632450000, icon_homicide_shooting, 'p1082', '
Damon Griffin
4200 Rokeby Rd
Baltimore, MD 21229
Race: Black
Gender: male
Age: 25 years old
Found on April 24, 2011
Victim died at Scene
Cause: Shooting

Two men found shot in vehicle

' 39.28446000000, -76.69382800000, icon_homicide_shooting, 'p1080', '
Isaac Stevens
4700 Sayer Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 25 years old
Found on April 24, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29434200000, -76.63874900000, icon_homicide_stabbing, 'p1084', '
Tevon Allen
500 N Carey St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 22 years old
Found on April 23, 2011
Victim died at Unknown
Cause: Stabbing

Stabbed inside restaurant

' 39.29682050000, -76.64005990000, icon_homicide_shooting, 'p1083', '
Brandon Littlejohn
700 N Calhoun St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 20 years old
Found on April 23, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32050600000, -76.59455100000, icon_homicide_shooting, 'p1085', '
William Cann
1700 Homestead St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 46 years old
Found on April 22, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Found in alley

' 39.29069590000, -76.61812900000, icon_homicide_shooting, 'p1079', '
Edward Jones
200 W. Fayette St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 50 years old
Found on April 22, 2011
Victim died at Unknown
Cause: Shooting
' 39.28405440000, -76.61503850000, icon_homicide_shooting, 'p1078', '
Keith Cooper
1 W Conway St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 47 years old
Found on April 21, 2011
Victim died at Scene
Cause: Shooting

Found dead in an apartment on the 14th floor of the Hanover Square building for the elderly and disabled. 

' 39.31734500000, -76.60768700000, icon_homicide_shooting, 'p1077', '
Julian Turner
600 Barlett Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 19 years old
Found on April 20, 2011
Victim died at Unknown
Cause: Shooting
' 39.35334370000, -76.70601470000, icon_homicide_asphyxiation, 'p1287', '
Phylicia Barnes
6500 Eberle Dr
Baltimore, MD 21215
Race: Black
Gender: female
Age: 16 years old
Found on April 20, 2011
Victim died at Scene
Cause: Asphyxiation

Phylicia Barnes went missing from her sister\'s apartment on Dec. 28, 2010, and was found dead on April 20, 2011...

' 39.29438820000, -76.63409910000, icon_homicide_shooting, 'p1076', '
Edward Auston
500 N Schroeder St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 31 years old
Found on April 19, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29466780000, -76.57422680000, icon_homicide_shooting, 'p1075', '
Donis Lopez
500 N. Decker Ave
Baltimore, MD 21224
Race: Hispanic
Gender: male
Age: 26 years old
Found on April 16, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31813600000, -76.69086800000, icon_homicide_shooting, 'p1074', '
Chantel Ford
4500 Westchester Rd
Baltimore, MD 21216
Race: Black
Gender: female
Age: 24 years old
Found on April 13, 2011
Victim died at Sinai Hospital
Cause: Shooting
' 39.30817400000, -76.59357700000, icon_homicide_shooting, 'p1073', '
Isiah Callaway
1700 Crystal Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 19 years old
Found on April 12, 2011
Victim died at Scene
Cause: Shooting

Found unresponsive in a vehicle, bleeding from head

' 39.23658100000, -76.61089600000, icon_homicide_stabbing, 'p1072', '
Antonio Miranda
3800 S. Hanover St
Baltimore, MD 21225
Race: Hispanic
Gender: male
Age: 43 years old
Found on April 11, 2011
Victim died at Harbor Hospital
Cause: Stabbing
' 39.29312600000, -76.61831100000, icon_homicide_shooting, 'p1071', '
Dwight Taylor
200 W. Saratoga St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 25 years old
Found on April 9, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Shot in a barbershop

' 39.31993000000, -76.54937800000, icon_homicide_stabbing, 'p1070', '
James Evans
4900 Bowland Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 31 years old
Found on April 9, 2011
Victim died at Unknown
Cause: Stabbing
' 39.31184090000, -76.62698670000, icon_homicide_shooting, 'p1069', '
Keenya Jordan
600 Lennox st
Baltimore, MD 21217
Race: Black
Gender: female
Age: 32 years old
Found on April 6, 2011
Victim died at Unknown
Cause: Shooting
' 39.32118100000, -76.67504500000, icon_homicide_shooting, 'p1067', '
Christopher Bullock
3400 Powhatan Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 24 years old
Found on April 5, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29276460000, -76.66302040000, icon_homicide_shooting, 'p1068', '
Roosevelt Tillman
2800 Mulberry St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 20 years old
Found on April 5, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

One of two men shot while sitting in a car.

' 39.29247580000, -76.61117460000, icon_homicide_stabbing, 'p1066', '
Charles Johnson
300 Guilford St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 24 years old
Found on April 2, 2011
Victim died at Unknown
Cause: Stabbing

One of four people stabbed at the downtown club, Bourbon Street

' 39.30250320000, -76.63898170000, icon_homicide_shooting, 'p1065', '
Derek Jones
1300 Winchester St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 25 years old
Found on March 31, 2011
Victim died at Unknown
Cause: Shooting
' 39.31624100000, -76.54262000000, icon_homicide_shooting, 'p1064', '
Tyrone Slay
6000 Moravia Park Dr
Baltimore, MD 21206
Race: Black
Gender: male
Age: 31 years old
Found on March 31, 2011
Victim died at Unknown
Cause: Shooting
' 39.28352790000, -76.62700800000, icon_homicide_shooting, 'p1063', '
Murrell Hearns
700 Washington Blvd
Baltimore, MD 21230
Race: Black
Gender: male
Age: 31 years old
Found on March 30, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Shot in business parking lot

' 39.31889690000, -76.62154860000, icon_homicide_stabbing, 'p1061', '
Jhoma Blackwell
2600 Huntingdon Ave
Baltimore, MD 21211
Race: Black
Gender: female
Age: 18 years old
Found on March 29, 2011
Victim died at Scene
Cause: Stabbing
' 39.32390120000, -76.54078350000, icon_homicide_shooting, 'p1062', '
Durell Roach
5400 Sinclair Lane
Baltimore, MD 21206
Race: Black
Gender: male
Age: 31 years old
Found on March 29, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.29579500000, -76.63434500000, icon_homicide_shooting, 'p1060', '
Gregory Davenport
1000 Edmondson Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 27 years old
Found on March 29, 2011
Victim died at Unknown
Cause: Shooting
' 39.31718300000, -76.57815100000, icon_homicide_shooting, 'p1059', '
Steven Oglesby
3200 Elmley Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 17 years old
Found on March 27, 2011
Victim died at Unknown
Cause: Shooting

Two 17 year olds were shot while sitting in a vehicle, police said. The other victim suffered non-life-threatening...

' 39.31355700000, -76.61131700000, icon_homicide_stabbing, 'p1058', '
Reginald Wragg
2100 Barclay St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 46 years old
Found on March 25, 2011
Victim died at Scene
Cause: Stabbing

Found in an alley

' 39.31171890000, -76.60932300000, icon_homicide_stabbing, 'p1057', '
Andre Drummond
500 E. North Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 48 years old
Found on March 23, 2011
Victim died at Unknown
Cause: Stabbing

Drummond\'s body was found inside of a vacant home after police received an anonymous tip.

' 39.29354640000, -76.69176220000, icon_homicide_stabbing, 'p1055', '
David McLaughlin Jr.
4500 Edmondson Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 24 years old
Found on March 20, 2011
Victim died at Maryland Shock Trauma Center
Cause: Stabbing

Jumped at a gas station.

' 39.30754590000, -76.62728790000, icon_homicide_shooting, 'p1054', '
Angelo Fitzgerald
300 McMechen St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 21 years old
Found on March 19, 2011
Victim died at Unknown
Cause: Shooting

Triple shooting

' 39.28095950000, -76.63293030000, icon_homicide_bluntforce, 'p1056', '
Darshewn Freeman
1200 W. Ostend St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 44 years old
Found on March 18, 2011
Victim died at Maryland Shock Trauma Center
Cause: Blunt Force

Found bleeding in rear of block. Police investigating as a homicide. 

' 39.31244490000, -76.64566000000, icon_homicide_shooting, 'p1053', '
Brandon Carter
2700 Pennsylvania Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 29 years old
Found on March 17, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Double shooting at carryout

' 39.32579100000, -76.60230400000, icon_homicide_shooting, 'p1052', '
Tanise Ervin
1100 Gorsuch Ave
Baltimore, MD 21218
Race: Black
Gender: female
Age: 19 years old
Found on March 12, 2011
Victim died at Unknown
Cause: Shooting

Triple shooting

' 39.34369230000, -76.68046990000, icon_homicide_stabbing, 'p1050', '
Ronald Gibbs
5000 Nelson Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 17 years old
Found on March 6, 2011
Victim died at Sinai Hospital
Cause: Stabbing

Detectives believe the victim and a 20-year-old woman got into an argument with a man, who stabbed both of them. The...

' 39.28876200000, -76.67278300000, icon_homicide_shooting, 'p1049', '
Paul Simons III
200 N. Hilton St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 23 years old
Found on March 5, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Preliminary investigation has revealed that just after 9pm, three armed suspects entered a home in the 200 block of N...

' 39.25930200000, -76.64534400000, icon_homicide_other, 'p1048', '
George Marshall
2700 Hollins Ferry Road
Baltimore, MD 21230
Race: White
Gender: male
Age: 57 years old
Found on March 5, 2011
Victim died at Unknown
Cause: Other

Marshall was struck and killed by a vehicle being driven by a man attempting to flee after being caught trying to break...

' 39.29814900000, -76.70987400000, icon_homicide_stabbing, 'p1051', '
Charles Hopson
1100 Cooks Lane
Baltimore, MD 21229
Race: Black
Gender: male
Age: 22 years old
Found on March 5, 2011
Victim died at Unknown
Cause: Stabbing

Hopson was stabbed on March 31, 2010 in a domestic related incident. He died on March 5, 2011, and his death is...

' 39.31145890000, -76.60945990000, icon_homicide_shooting, 'p1047', '
James Fields Jr
1901 Greenmount Ave
Baltimore, MD 21202
Race: Black
Gender: male
Age: 47 years old
Found on March 3, 2011
Victim died at Unknown
Cause: Shooting

Fields was shot in 1992 outside a carryout. He survived, but died in January as a result of complications from his...

' 39.31164600000, -76.66432200000, icon_homicide_stabbing, 'p1046', '
Edgar Waylan Wilson
2800 Clifton Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 62 years old
Found on February 27, 2011
Victim died at Scene
Cause: Stabbing

Grandson has been charged with first-degree murder. 

' 39.31537500000, -76.59555300000, icon_homicide_shooting, 'p1044', '
Mark Saunders
1600 Cliftview Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 23 years old
Found on February 20, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Double shooting; 

' 39.34368800000, -76.67171400000, icon_homicide_asphyxiation, 'p1045', '
Anthony Trent
3300 Virginia Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 25 years old
Found on February 19, 2011
Victim died at Scene
Cause: Asphyxiation
Read the article

Victim was a transgendered woman found in the basement of a vacant home. 

' 39.31149710000, -76.60950730000, icon_homicide_stabbing, 'p1043', '
Joshua Mathews
1900 Greenmount Ave
Baltimore, MD 21202
Race: Black
Gender: male
Age: 45 years old
Found on February 17, 2011
Victim died at Unknown
Cause: Stabbing
' 39.32632900000, -76.59338900000, icon_homicide_shooting, 'p1042', '
Martez Hall
1600 E 31st St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 22 years old
Found on February 16, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.28145300000, -76.63041600000, icon_homicide_stabbing, 'p1041', '
Steven Williams
1100 W. Cross St
Baltimore, MD 21230
Race: Black
Gender: male
Age: 53 years old
Found on February 15, 2011
Victim died at Unknown
Cause: Stabbing

Funeral home page: 

' 39.33464980000, -76.66278120000, icon_homicide_shooting, 'p1040', '
Jose Estrella
4000 Park Heights
Baltimore, MD 21215
Race: Black
Gender: male
Age: 19 years old
Found on February 11, 2011
Victim died at Sinai Hospital
Cause: Shooting

Convenience store parking lot

' 39.35927600000, -76.54387800000, icon_homicide_shooting, 'p1039', '
Isaiah McFadden
6600 Alta Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 21 years old
Found on February 8, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.32154180000, -76.59367400000, icon_homicide_shooting, 'p1038', '
Craig Manuel
2700 Polk St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 19 years old
Found on February 7, 2011
Victim died at Unknown
Cause: Shooting
' 39.29780800000, -76.68644500000, icon_homicide_shooting, 'p1037', '
Warren Wilmer
900 N. Woodington Rd
Baltimore, MD 21229
Race: Black
Gender: male
Age: 36 years old
Found on February 2, 2011
Victim died at Scene
Cause: Shooting
' 39.31844700000, -76.54844900000, icon_homicide_shooting, 'p1036', '
Jeffrey Purnell
5700 Eastbury Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 51 years old
Found on January 30, 2011
Victim died at Johns Hopkins Bayview Medical Center
Cause: Shooting

Victim was found in a vehicle

' 39.28601610000, -76.62927180000, icon_homicide_shooting, 'p1035', '
Raynard Benjamin
800 W Pratt St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 30 years old
Found on January 29, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.28211570000, -76.64488280000, icon_homicide_shooting, 'p1034', '
Damon Gilmore
500 S. Fulton Ave
Baltimore, MD 21223
Race: Black
Gender: male
Age: 31 years old
Found on January 24, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29873900000, -76.59841400000, icon_homicide_shooting, 'p1033', '
Antonio Lamont Lee
1400 E Monument St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 35 years old
Found on January 23, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Lee was shot after a white van pulled up to his vehicle at a traffic light and the occupants opened fire, police...

' 39.25001300000, -76.61934590000, icon_homicide_shooting, 'p1032', '
Harry Hicks
400 Swale Ave
Baltimore, MD 21225
Race: Black
Gender: male
Age: 29 years old
Found on January 22, 2011
Victim died at Unknown
Cause: Shooting

Hicks was shot by two men while his wife was loading his daughter into their vehicle, police say. 

' 39.24501350000, -76.62740550000, icon_homicide_shooting, 'p1031', '
Rhidel Price
2900 Denham Circle
Baltimore, MD 21225
Race: Black
Gender: male
Age: 21 years old
Found on January 20, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29559700000, -76.63739800000, icon_homicide_shooting, 'p1029', '
Kevin S. Lewis
600 N Carrollton Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 45 years old
Found on January 10, 2011
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.32537000000, -76.58631200000, icon_homicide_shooting, 'p1028', '
Wendell Woodard
3000 Harford Rd
Baltimore, MD 21218
Race: Black
Gender: male
Age: 26 years old
Found on January 10, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting

Shot inside residence

' 39.31749200000, -76.65011190000, icon_homicide_shooting, 'p1026', '
Willie Davis
1700 Gwynns Falls Pkway
Baltimore, MD 21217
Race: Black
Gender: male
Age: 26 years old
Found on January 9, 2011
Victim died at Scene
Cause: Shooting

Double shooting - victims found in laundry room of apartment complex

' 39.31716000000, -76.64932390000, icon_homicide_shooting, 'p1027', '
Marvin Kelly
1701 Gwynns Falls Pkway
Baltimore, MD 21217
Race: Black
Gender: male
Age: 22 years old
Found on January 9, 2011
Victim died at Scene
Cause: Shooting

Double shooting - two people killed in apartment complex laundry room

' 39.32855200000, -76.61339400000, icon_homicide_shooting, 'p1025', '
Leon D. Smith
300 E. 33rd St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 34 years old
Found on January 9, 2011
Victim died at Unknown
Cause: Shooting

Smith was found shot several times in a vehicle in a rear alley that empties into Union Memorial Hospital.

' 39.32569690000, -76.57517430000, icon_homicide_shooting, 'p1030', '
Deandre Garris
3500 Woodstock Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 34 years old
Found on January 5, 2011
Victim died at Unknown
Cause: Shooting
' 39.35866900000, -76.57164000000, icon_homicide_shooting, 'p1024', '
Hezikah Wilson
5600 Plymouth Rd
Baltimore, MD 21214
Race: Black
Gender: male
Age: 38 years old
Found on January 2, 2011
Victim died at Good Samaritan Hospital
Cause: Shooting
' 39.29693400000, -76.57959300000, icon_homicide_shooting, 'p1023', '
Marquise Hall
500 N. Lakewood Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 16 years old
Found on January 1, 2011
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.22619100000, -76.58992000000, icon_homicide_stabbing, 'p1022', '
David Jones
1500 Hazel St
Baltimore, MD 21226
Race: Black
Gender: male
Age: 21 years old
Found on January 1, 2011
Victim died at Maryland Shock Trauma Center
Cause: Stabbing

One of two men stabbed; surviving victim was 27 years old

' 39.30401300000, -76.58288400000, icon_homicide_shooting, 'p1301', '
Name not yet released
1100 N. Milton Ave
Baltimore, MD 21213
Race: Unknown
Gender: male
Found on May 14, 2012
Victim died at Scene
Cause: Shooting
' 39.32704100000, -76.59371500000, icon_homicide_shooting, 'p1300', '
Lacy Lamb
1600 E. 32nd St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 19 years old
Found on May 13, 2012
Victim died at Unknown
Cause: Shooting
' 39.28749410000, -76.59684890000, icon_homicide_shooting, 'p1299', '
Dominick Brown
1500 Gough St
Baltimore, MD 21231
Race: Black
Gender: male
Age: 22 years old
Found on May 13, 2012
Victim died at Unknown
Cause: Shooting
' 39.29500560000, -76.62138970000, icon_homicide_shooting, 'p1298', '
Derrick Lawson
400 W Franklin St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 33 years old
Found on May 13, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.33735810000, -76.66459790000, icon_homicide_stabbing, 'p1297', '
Thomas Weddington
3600 Park Heights Terrace
Baltimore, MD 21215
Race: Black
Gender: male
Age: 34 years old
Found on May 13, 2012
Victim died at Sinai Hospital
Cause: Stabbing
' 39.32164700000, -76.61250600000, icon_homicide_bluntforce, 'p1296', '
Cornell Booker
300 Whitridge Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 46 years old
Found on May 9, 2012
Victim died at Johns Hopkins Hospital
Cause: Blunt Force

Police say on April 19, Booker was intoxicated and got into an argument with his girlfriend. Their son intervened and...

' 39.31297000000, -76.60412500000, icon_homicide_shooting, 'p1295', '
Shavar Little
1100 E. 20th St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 28 years old
Found on May 8, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31633260000, -76.67449640000, icon_homicide_shooting, 'p1293', '
Christopher Mobley
3400 Piedmont Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 28 years old
Found on May 8, 2012
Victim died at Sinai Hospital
Cause: Shooting

Mobley was shot on May 1 and died on this date. 

' 39.29587680000, -76.62146150000, icon_homicide_shooting, 'p1294', '
Terrence Anderson
600 N Eutaw St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 19 years old
Found on May 8, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Shot on May 6, died on this date

' 39.34801100000, -76.67054700000, icon_homicide_shooting, 'p1290', '
Robert Bond
3000 Oakley Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 21 years old
Found on May 6, 2012
Victim died at Scene
Cause: Shooting

Found shot in the head on porch of vacant home

' 39.31663900000, -76.68442000000, icon_homicide_shooting, 'p1291', '
Quintin Poindexter
4100 Alto Rd
Baltimore, MD 21216
Race: Black
Gender: male
Age: 18 years old
Found on May 5, 2012
Victim died at Scene
Cause: Shooting

Found on school grounds

' 39.30119960000, -76.62501030000, icon_homicide_asphyxiation, 'p1292', '
Cheryl Thomas
400 Cummings Ct
Baltimore, MD 21201
Race: Black
Gender: female
Age: 44 years old
Found on May 3, 2012
Victim died at Unknown
Cause: Asphyxiation
' 39.31545660000, -76.57571990000, icon_homicide_shooting, 'p1289', '
Rayner Veney
3400 Ravenwood Ave
Baltimore, MD 21213
Race: Black
Gender: male
Age: 51 years old
Found on April 28, 2012
Victim died at Unknown
Cause: Shooting

Shot on his front steps on June 20, 2011. Died on April 27, 2012 after being in a coma. This date reflects the date his...

' 39.29290040000, -76.62147800000, icon_homicide_stabbing, 'p1286', '
Cortez Lemon
400 W. Saratoga St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 23 years old
Found on April 27, 2012
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.29640500000, -76.65679900000, icon_homicide_shooting, 'p1285', '
Michael L. Williams
2500 harlem ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 45 years old
Found on April 25, 2012
Victim died at Unknown
Cause: Shooting
' 39.32372990000, -76.68685500000, icon_homicide_shooting, 'p1284', '
Floyd Dorsey
4100 Norfolk Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 59 years old
Found on April 24, 2012
Victim died at Sinai Hospital
Cause: Shooting
' 39.34453120000, -76.55479640000, icon_homicide_bluntforce, 'p1282', '
Somchanh Sipayboun
5400 Hillburn Ave
Baltimore, MD 21206
Race: Asian
Gender: female
Age: 37 years old
Found on April 23, 2012
Victim died at Johns Hopkins Hospital
Cause: Blunt Force

Found inside home

' 39.33876300000, -76.66581430000, icon_homicide_shooting, 'p1281', '
Willie Battle
2700 Boarman Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 19 years old
Found on April 23, 2012
Victim died at Sinai Hospital
Cause: Shooting
' 39.31021280000, -76.64118030000, icon_homicide_shooting, 'p1283', '
Mark Day
1500 W. North Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 43 years old
Found on April 23, 2012
Victim died at Unknown
Cause: Shooting

Shot on Aug 23, 2010, died March 14. Date reflects date he was added to the homicide total.

' 39.32073870000, -76.57476280000, icon_homicide_shooting, 'p1280', '
Derrick Deon Smith
3200 Belair Rd
Baltimore, MD 21213
Race: Black
Gender: male
Age: 33 years old
Found on April 18, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30974400000, -76.59926400000, icon_homicide_shooting, 'p1279', '
Marvin Lee Buckson
1700 N Spring St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 29 years old
Found on April 17, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.33161080000, -76.69589500000, icon_homicide_shooting, 'p1278', '
Torey Garrison
4700 Liberty Heights Ave
Baltimore, MD 21207
Race: Unknown
Gender: male
Age: 34 years old
Found on April 16, 2012
Victim died at Sinai Hospital
Cause: Shooting
' 39.30508170000, -76.60226330000, icon_homicide_shooting, 'p1277', '
Ricardo Mabray
1300 Aisquith St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 27 years old
Found on April 16, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30643800000, -76.63532300000, icon_homicide_shooting, 'p1275', '
Phillip Scott
1900 Etting St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 34 years old
Found on April 14, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29684610000, -76.64966850000, icon_homicide_shooting, 'p1276', '
Clarence Nicholson
2000 Rayner Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 31 years old
Found on April 14, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.30152200000, -76.62553740000, icon_homicide_shooting, 'p1274', '
Brandon Sims
400 Watty Court
Baltimore, MD 21201
Race: Black
Gender: male
Age: 23 years old
Found on April 13, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Shot while eating crabs with friends in courtyard of McCulloh Homes

' 39.23190000000, -76.60086100000, icon_homicide_shooting, 'p1273', '
Leslie Johnson
800 Jack St
Baltimore, MD 21225
Race: Black
Gender: male
Age: 36 years old
Found on April 9, 2012
Victim died at Unknown
Cause: Shooting

Shot with a shotgun on April 14, 1997. Died from complications from those injuries on Jan. 30, 2012. This date reflects...

' 39.30640200000, -76.60332690000, icon_homicide_shooting, 'p1272', '
Justin Marasa
1400 Holbrook St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 22 years old
Found on April 8, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting

Found in a vehicle. 

' 39.32096200000, -76.59962890000, icon_homicide_shooting, 'p1271', '
Robert Laney
2600 Kirk Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 33 years old
Found on April 8, 2012
Victim died at Unknown
Cause: Shooting
' 39.30800230000, -76.64350330000, icon_homicide_shooting, 'p1270', '
Jermaine Shuron
1700 N Calhoun St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 37 years old
Found on April 7, 2012
Victim died at Unknown
Cause: Shooting
' 39.36142700000, -76.59580100000, icon_homicide_shooting, 'p1268', '
Javell Heath
1100 E Belvedere Ave
Baltimore, MD 21239
Race: Black
Gender: male
Age: 19 years old
Found on April 2, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting

Found shot on March 27, died on March 29. This date reflects when his death was added to the city list by homicide...

' 39.34065820000, -76.60329110000, icon_homicide_shooting, 'p1269', '
Dion Brandon
900 Belgian Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 30 years old
Found on April 2, 2012
Victim died at Unknown
Cause: Shooting

Shot in a triple shooting in the 2005, died of complications from his injuries this year and was added to the city...

' 39.31387960000, -76.60493710000, icon_homicide_shooting, 'p1267', '
Marcus Williams
1000 E. 22nd St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 29 years old
Found on April 1, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting

Williams was found sitting in a Dodge Charger that had rear-ended another vehicle. He was suffering from multiple...

' 39.30028430000, -76.60492380000, icon_homicide_shooting, 'p1266', '
Chauncey Hardy
900 Valley st
Baltimore, MD 21202
Race: Black
Gender: male
Age: 59 years old
Found on March 30, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.36145280000, -76.57222700000, icon_homicide_shooting, 'p1264', '
Tyree Cypress
6200 Pioneer Dr
Baltimore, MD 21214
Race: Black
Gender: male
Age: 19 years old
Found on March 27, 2012
Victim died at Unknown
Cause: Shooting
' 39.34078900000, -76.55067300000, icon_homicide_shooting, 'p1263', '
Adam Robinson
4100 Idaho Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 26 years old
Found on March 25, 2012
Victim died at Unknown
Cause: Shooting

Shot answering answering the front door

' 39.30606490000, -76.66754800000, icon_homicide_bluntforce, 'p1265', '
Aaron Evans
3000 Baker St
Baltimore, MD 21216
Race: Black
Gender: male
Age: 1 year old
Found on March 25, 2012
Victim died at University of Maryland Medical Center
Cause: Blunt Force

Non-breathing infant taken to hospital on Jan. 30 - died from brain injuries on March 25

' 39.29020400000, -76.62974500000, icon_homicide_shooting, 'p1262', '
Rodrick Burden
800 W Fayette St
Baltimore, MD 21201
Race: Black
Gender: male
Age: 18 years old
Found on March 24, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Burden was shot outside his home and found by his mother. 

' 39.31360870000, -76.60964600000, icon_homicide_shooting, 'p1261', '
Ronnell Chaney
500 E. 21st St
Baltimore, MD 21218
Race: Black
Gender: male
Age: 20 years old
Found on March 23, 2012
Victim died at Unknown
Cause: Shooting

Chanel was shot on March 20 and died on this date. 

' 39.31083640000, -76.64247450000, icon_homicide_shooting, 'p1260', '
Jonathan Hutchinson
1600 Traction St
Baltimore, MD 21217
Race: Black
Gender: male
Age: 24 years old
Found on March 21, 2012
Victim died at Unknown
Cause: Shooting
' 39.30823500000, -76.66249000000, icon_homicide_shooting, 'p1259', '
Dexter Jones Jr.
1800 Ashburton
Baltimore, MD 21216
Race: Black
Gender: male
Age: 23 years old
Found on March 20, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29125800000, -76.53895600000, icon_homicide_shooting, 'p1258', '
Dominic Thornton
6400 E Pratt St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 37 years old
Found on March 19, 2012
Victim died at Unknown
Cause: Shooting

"Drive up" shooting; suspect vehicle drove up to victim vehicle, opened fire

' 39.34730100000, -76.67918800000, icon_homicide_shooting, 'p1257', '
Fareed Caldwell
3500 W Belvedere Ave
Baltimore, MD 21215
Race: Black
Gender: male
Age: 25 years old
Found on March 16, 2012
Victim died at Sinai Hospital
Cause: Shooting
' 39.30379090000, -76.58498600000, icon_homicide_shooting, 'p1256', '
Gregory Parker
2300 E Chase St
Baltimore, MD 21213
Race: Black
Gender: male
Age: 54 years old
Found on March 16, 2012
Victim died at Unknown
Cause: Shooting
' 39.30125000000, -76.58334300000, icon_homicide_shooting, 'p1255', '
Timothy Harris
900 N Port St
Baltimore, MD 21205
Race: Black
Gender: male
Age: 47 years old
Found on March 13, 2012
Victim died at Unknown
Cause: Shooting
' 39.30616440000, -76.60368770000, icon_homicide_shooting, 'p1254', '
Sherard Houston
1000 E Hoffman St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 38 years old
Found on March 10, 2012
Victim died at Unknown
Cause: Shooting
' 39.30994290000, -76.65286390000, icon_homicide_shooting, 'p1253', '
Javon Johnson
2200 W. North Ave
Baltimore, MD 21216
Race: Black
Gender: male
Age: 21 years old
Found on March 7, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.29188000000, -76.67544470000, icon_homicide_shooting, 'p1252', '
Steven Fields
3500 W. Mulberry St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 19 years old
Found on March 6, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Double-shooting; incident occurred on 3/5 at 9:45 p.m., this date reflects the day the victim died. 

' 39.29196550000, -76.67664780000, icon_homicide_shooting, 'p1251', '
John Edwards
3510 W. Mulberry St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 17 years old
Found on March 5, 2012
Victim died at Scene
Cause: Shooting

Double shooting

' 39.30078090000, -76.62448640000, icon_homicide_shooting, 'p1250', '
Tyrice Forney
400 Oxford Ct
Baltimore, MD 21201
Race: Black
Gender: male
Age: 21 years old
Found on March 4, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting
' 39.31537200000, -76.59555500000, icon_homicide_shooting, 'p1249', '
Monae Turnage
1600 Cliftview Ave
Baltimore, MD 21213
Race: Black
Gender: female
Age: 13 years old
Found on March 4, 2012
Victim died at Scene
Cause: Shooting

Found under trash bags in backyard; had been earlier reported missing

' 39.34506700000, -76.59734900000, icon_homicide_shooting, 'p1248', '
Terrence Joyner
4500 Marble Hall Rd
Baltimore, MD 21239
Race: Black
Gender: male
Age: 42 years old
Found on March 4, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.31107490000, -76.69447160000, icon_homicide_shooting, 'p1247', '
Oswaldo Santana
4700 Windsor Mill
Baltimore, MD 21207
Race: Black
Gender: male
Age: 21 years old
Found on February 29, 2012
Victim died at Sinai Hospital
Cause: Shooting
' 39.32639480000, -76.67892150000, icon_homicide_bluntforce, 'p1246', '
Mya Carr
3400 Callaway Ave
Baltimore, MD 21215
Race: Black
Gender: female
Age: 5 years old
Found on February 28, 2012
Victim died at Scene
Cause: Blunt Force

Mya was found dead on Feb. 27; her death was ruled a homicide on this date. Her father, Matthew Carr, 22, was charged...

' 39.29591900000, -76.57797370000, icon_homicide_stabbing, 'p1245', '
Thomas Atkins
400 N Kenwood Ave
Baltimore, MD 21224
Race: Black
Gender: male
Age: 32 years old
Found on February 26, 2012
Victim died at Johns Hopkins Hospital
Cause: Stabbing

Atkins was found in the 2800 block of Orleans St. It was determined the stabbing occurred in the 400 block of N....

' 39.31344510000, -76.64627920000, icon_homicide_shooting, 'p1244', '
Guy Randall
2200 N. Fulton Ave
Baltimore, MD 21217
Race: Black
Gender: male
Age: 46 years old
Found on February 23, 2012
Victim died at Unknown
Cause: Shooting

Shot in sidewalk near elementary school

' 39.28676900000, -76.63818300000, icon_homicide_stabbing, 'p1243', '
Orville Chamblee
1300 W Lombard St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 47 years old
Found on February 19, 2012
Victim died at Maryland Shock Trauma Center
Cause: Stabbing
' 39.32247040000, -76.60956130000, icon_homicide_shooting, 'p1241', '
Jerry Isaac
2800 Greenmount Ave
Baltimore, MD 21218
Race: Black
Gender: male
Age: 22 years old
Found on February 13, 2012
Victim died at Unknown
Cause: Shooting
' 39.32757500000, -76.53124800000, icon_homicide_shooting, 'p1240', '
Kyndal Staten
5900 Laclede Rd
Baltimore, MD 21206
Race: Black
Gender: male
Age: 27 years old
Found on February 12, 2012
Victim died at Unknown
Cause: Shooting
' 39.31858500000, -76.68770000000, icon_homicide_shooting, 'p1239', '
Johnny McFadden
2900 Haverford Rd
Baltimore, MD 21216
Race: Black
Gender: male
Age: 62 years old
Found on February 9, 2012
Victim died at Scene
Cause: Shooting
' 39.30490100000, -76.60377670000, icon_homicide_shooting, 'p1238', '
Gregory Kearney
931 E. Preston St
Baltimore, MD 21202
Race: Black
Gender: male
Age: 28 years old
Found on February 6, 2012
Victim died at Unknown
Cause: Shooting
' 39.35944500000, -76.58732660000, icon_homicide_shooting, 'p1236', '
Corey Alexander
5700 Nasco Place
Baltimore, MD 21239
Race: Black
Gender: male
Age: 31 years old
Found on February 1, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting

Found shot in the head in a vehicle; second victim walked into Good Samaritan Hospital for treatment. Antomar Jones...

' 39.32466900000, -76.55934300000, icon_homicide_shooting, 'p1237', '
Norris Conley
4300 Greenhill Ave
Baltimore, MD 21206
Race: Black
Gender: male
Age: 26 years old
Found on February 1, 2012
Victim died at Scene
Cause: Shooting

Found shot in the head in basement of home. Robert McLean charged with murder. 

' 39.28336900000, -76.68235700000, icon_homicide_shooting, 'p1233', '
Shayvon Booker
200 S. Loudon Ave
Baltimore, MD 21229
Race: Black
Gender: male
Age: 19 years old
Found on January 29, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Police believe Booker was killed in retaliation for the Jan. 18 killing of James Rice. Charged with murder: Joseph...

' 39.33937890000, -76.66725810000, icon_homicide_shooting, 'p1232', '
Travis Spencer
2700 Coldspring Lane
Baltimore, MD 21215
Race: Black
Gender: male
Age: 24 years old
Found on January 27, 2012
Victim died at Sinai Hospital
Cause: Shooting
' 39.28846940000, -76.64253510000, icon_homicide_shooting, 'p1231', '
Dominic Hope
1600 W. Baltimore St
Baltimore, MD 21223
Race: Black
Gender: male
Age: 32 years old
Found on January 20, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Shot at barber shop; second man also wounded

' 39.33007300000, -76.65826000000, icon_homicide_asphyxiation, 'p1230', '
Audrey Collins
3600 Cottage Ave
Baltimore, MD 21215
Race: Black
Gender: female
Age: 68 years old
Found on January 20, 2012
Victim died at Johns Hopkins Bayview Medical Center
Cause: Asphyxiation

Collins\' son is accused of setting himself on fire, then jumping onto his mother, causing her to catch fire. She died...

' 39.27529000000, -76.68889800000, icon_homicide_shooting, 'p1229', '
James Rice
4300 Cedargarden Rd
Baltimore, MD 21229
Race: Black
Gender: male
Age: 18 years old
Found on January 19, 2012
Victim died at Scene
Cause: Shooting
' 39.29282500000, -76.67573100000, icon_homicide_shooting, 'p1228', '
James Hunter
500 N Edgewood St
Baltimore, MD 21229
Race: Black
Gender: male
Age: 30 years old
Found on January 12, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Shot in the head, found in an alley

' 39.22828720000, -76.59890410000, icon_homicide_shooting, 'p1226', '
Ramon Parrott
1000 Renick Ct
Baltimore, MD 21225
Race: Black
Gender: male
Age: 35 years old
Found on January 8, 2012
Victim died at Maryland Shock Trauma Center
Cause: Shooting

Actual address is 4117 Mariban Ct, but unable to get map to recognize that address.

' 39.30100810000, -76.58366610000, icon_homicide_shooting, 'p1225', '
Doral Hinton
2400 Ashland Ave
Baltimore, MD 21205
Race: Black
Gender: male
Age: 27 years old
Found on January 6, 2012
Victim died at Johns Hopkins Hospital
Cause: Shooting
' 39.30482530000, -76.58122890000, icon_homicide_stabbing, 'p1224', '
Mary Hines
2600 E. Biddle St
Baltimore, MD 21213
Race: Black
Gender: female
Age: 84 years old
Found on January 5, 2012
Victim died at Scene
Cause: Stabbing

Hines was found dead in a fire; an autopsy later determined that she had been stabbed first. 

' 39.33200940000, -76.66912780000, icon_homicide_shooting, 'p1223', '
Bruce Royster
4000 Edgewood Rd
Baltimore, MD 21215
Race: Black
Gender: male
Age: 62 years old
Found on January 3, 2012
Victim died at Sinai Hospital
Cause: Shooting

Found in vehicle

' 39.27904300000, -76.65948890000, icon_homicide_bluntforce, 'p1227', '
Joseph Curtis
500 Brunswich
Baltimore, MD 21223
Race: Black
Gender: male
Age: 65 years old
Found on January 3, 2012
Victim died at Unknown
Cause: Blunt Force

Curtis was beaten on Sept 18, 2006 and died May 28, 2011. The medical examiner informed the police department about the...

' ================================================ FILE: data/imdb_movie_ratings_top_1000.csv ================================================ star_rating,title,content_rating,genre,duration,actors_list 9.3,The Shawshank Redemption,R,Crime,142,"[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunton']" 9.2,The Godfather,R,Crime,175,"[u'Marlon Brando', u'Al Pacino', u'James Caan']" 9.1,The Godfather: Part II,R,Crime,200,"[u'Al Pacino', u'Robert De Niro', u'Robert Duvall']" 9,The Dark Knight,PG-13,Action,152,"[u'Christian Bale', u'Heath Ledger', u'Aaron Eckhart']" 8.9,Pulp Fiction,R,Crime,154,"[u'John Travolta', u'Uma Thurman', u'Samuel L. Jackson']" 8.9,12 Angry Men,NOT RATED,Drama,96,"[u'Henry Fonda', u'Lee J. Cobb', u'Martin Balsam']" 8.9,"The Good, the Bad and the Ugly",NOT RATED,Western,161,"[u'Clint Eastwood', u'Eli Wallach', u'Lee Van Cleef']" 8.9,The Lord of the Rings: The Return of the King,PG-13,Adventure,201,"[u'Elijah Wood', u'Viggo Mortensen', u'Ian McKellen']" 8.9,Schindler's List,R,Biography,195,"[u'Liam Neeson', u'Ralph Fiennes', u'Ben Kingsley']" 8.9,Fight Club,R,Drama,139,"[u'Brad Pitt', u'Edward Norton', u'Helena Bonham Carter']" 8.8,The Lord of the Rings: The Fellowship of the Ring,PG-13,Adventure,178,"[u'Elijah Wood', u'Ian McKellen', u'Orlando Bloom']" 8.8,Inception,PG-13,Action,148,"[u'Leonardo DiCaprio', u'Joseph Gordon-Levitt', u'Ellen Page']" 8.8,Star Wars: Episode V - The Empire Strikes Back,PG,Action,124,"[u'Mark Hamill', u'Harrison Ford', u'Carrie Fisher']" 8.8,Forrest Gump,PG-13,Drama,142,"[u'Tom Hanks', u'Robin Wright', u'Gary Sinise']" 8.8,The Lord of the Rings: The Two Towers,PG-13,Adventure,179,"[u'Elijah Wood', u'Ian McKellen', u'Viggo Mortensen']" 8.7,Interstellar,PG-13,Adventure,169,"[u'Matthew McConaughey', u'Anne Hathaway', u'Jessica Chastain']" 8.7,One Flew Over the Cuckoo's Nest,R,Drama,133,"[u'Jack Nicholson', u'Louise Fletcher', u'Michael Berryman']" 8.7,Seven Samurai,UNRATED,Drama,207,"[u'Toshir\xf4 Mifune', u'Takashi Shimura', u'Keiko Tsushima']" 8.7,Goodfellas,R,Biography,146,"[u'Robert De Niro', u'Ray Liotta', u'Joe Pesci']" 8.7,Star Wars,PG,Action,121,"[u'Mark Hamill', u'Harrison Ford', u'Carrie Fisher']" 8.7,The Matrix,R,Action,136,"[u'Keanu Reeves', u'Laurence Fishburne', u'Carrie-Anne Moss']" 8.7,City of God,R,Crime,130,"[u'Alexandre Rodrigues', u'Matheus Nachtergaele', u'Leandro Firmino']" 8.7,It's a Wonderful Life,APPROVED,Drama,130,"[u'James Stewart', u'Donna Reed', u'Lionel Barrymore']" 8.7,The Usual Suspects,R,Crime,106,"[u'Kevin Spacey', u'Gabriel Byrne', u'Chazz Palminteri']" 8.7,Se7en,R,Drama,127,"[u'Morgan Freeman', u'Brad Pitt', u'Kevin Spacey']" 8.6,Life Is Beautiful,PG-13,Comedy,116,"[u'Roberto Benigni', u'Nicoletta Braschi', u'Giorgio Cantarini']" 8.6,Once Upon a Time in the West,PG-13,Western,175,"[u'Henry Fonda', u'Charles Bronson', u'Claudia Cardinale']" 8.6,The Silence of the Lambs,R,Drama,118,"[u'Jodie Foster', u'Anthony Hopkins', u'Lawrence A. Bonney']" 8.6,L̩on: The Professional,R,Crime,110,"[u'Jean Reno', u'Gary Oldman', u'Natalie Portman']" 8.6,City Lights,PASSED,Comedy,87,"[u'Charles Chaplin', u'Virginia Cherrill', u'Florence Lee']" 8.6,Spirited Away,PG,Animation,125,"[u'Daveigh Chase', u'Suzanne Pleshette', u'Miyu Irino']" 8.6,The Intouchables,R,Biography,112,"[u'Fran\xe7ois Cluzet', u'Omar Sy', u'Anne Le Ny']" 8.6,Casablanca,PG,Drama,102,"[u'Humphrey Bogart', u'Ingrid Bergman', u'Paul Henreid']" 8.6,Whiplash,R,Drama,107,"[u'Miles Teller', u'J.K. Simmons', u'Melissa Benoist']" 8.6,American History X,R,Crime,119,"[u'Edward Norton', u'Edward Furlong', u""Beverly D'Angelo""]" 8.6,Modern Times,G,Comedy,87,"[u'Charles Chaplin', u'Paulette Goddard', u'Henry Bergman']" 8.6,Saving Private Ryan,R,Action,169,"[u'Tom Hanks', u'Matt Damon', u'Tom Sizemore']" 8.6,Raiders of the Lost Ark,PG,Action,115,"[u'Harrison Ford', u'Karen Allen', u'Paul Freeman']" 8.6,Rear Window,APPROVED,Mystery,112,"[u'James Stewart', u'Grace Kelly', u'Wendell Corey']" 8.6,Psycho,R,Horror,109,"[u'Anthony Perkins', u'Janet Leigh', u'Vera Miles']" 8.5,The Green Mile,R,Crime,189,"[u'Tom Hanks', u'Michael Clarke Duncan', u'David Morse']" 8.5,Sunset Blvd.,NOT RATED,Drama,110,"[u'William Holden', u'Gloria Swanson', u'Erich von Stroheim']" 8.5,The Pianist,R,Biography,150,"[u'Adrien Brody', u'Thomas Kretschmann', u'Frank Finlay']" 8.5,The Dark Knight Rises,PG-13,Action,165,"[u'Christian Bale', u'Tom Hardy', u'Anne Hathaway']" 8.5,Gladiator,R,Action,155,"[u'Russell Crowe', u'Joaquin Phoenix', u'Connie Nielsen']" 8.5,Terminator 2: Judgment Day,R,Action,137,"[u'Arnold Schwarzenegger', u'Linda Hamilton', u'Edward Furlong']" 8.5,Memento,R,Mystery,113,"[u'Guy Pearce', u'Carrie-Anne Moss', u'Joe Pantoliano']" 8.5,Taare Zameen Par,PG,Drama,165,"[u'Darsheel Safary', u'Aamir Khan', u'Tanay Chheda']" 8.5,Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb,PG,Comedy,95,"[u'Peter Sellers', u'George C. Scott', u'Sterling Hayden']" 8.5,The Departed,R,Crime,151,"[u'Leonardo DiCaprio', u'Matt Damon', u'Jack Nicholson']" 8.5,Cinema Paradiso,R,Drama,155,"[u'Philippe Noiret', u'Enzo Cannavale', u'Antonella Attili']" 8.5,Apocalypse Now,R,Drama,153,"[u'Martin Sheen', u'Marlon Brando', u'Robert Duvall']" 8.5,The Great Dictator,APPROVED,Comedy,125,"[u'Charles Chaplin', u'Paulette Goddard', u'Jack Oakie']" 8.5,The Prestige,PG-13,Drama,130,"[u'Christian Bale', u'Hugh Jackman', u'Scarlett Johansson']" 8.5,Back to the Future,PG,Adventure,116,"[u'Michael J. Fox', u'Christopher Lloyd', u'Lea Thompson']" 8.5,The Lion King,G,Animation,89,"[u'Matthew Broderick', u'Jeremy Irons', u'James Earl Jones']" 8.5,The Lives of Others,R,Drama,137,"[u'Ulrich M\xfche', u'Martina Gedeck', u'Sebastian Koch']" 8.5,Alien,R,Horror,117,"[u'Sigourney Weaver', u'Tom Skerritt', u'John Hurt']" 8.5,Paths of Glory,APPROVED,Drama,88,"[u'Kirk Douglas', u'Ralph Meeker', u'Adolphe Menjou']" 8.5,Django Unchained,R,Western,165,"[u'Jamie Foxx', u'Christoph Waltz', u'Leonardo DiCaprio']" 8.5,3 Idiots,PG-13,Comedy,170,"[u'Aamir Khan', u'Madhavan', u'Mona Singh']" 8.5,Grave of the Fireflies,UNRATED,Animation,89,"[u'Tsutomu Tatsumi', u'Ayano Shiraishi', u'Akemi Yamaguchi']" 8.5,The Shining,R,Horror,146,"[u'Jack Nicholson', u'Shelley Duvall', u'Danny Lloyd']" 8.4,M,NOT RATED,Crime,99,"[u'Peter Lorre', u'Ellen Widmann', u'Inge Landgut']" 8.4,WALLE,G,Animation,98,"[u'Ben Burtt', u'Elissa Knight', u'Jeff Garlin']" 8.4,Witness for the Prosecution,APPROVED,Crime,116,"[u'Tyrone Power', u'Marlene Dietrich', u'Charles Laughton']" 8.4,Munna Bhai M.B.B.S.,NOT RATED,Comedy,156,"[u'Sunil Dutt', u'Sanjay Dutt', u'Arshad Warsi']" 8.4,American Beauty,R,Drama,122,"[u'Kevin Spacey', u'Annette Bening', u'Thora Birch']" 8.4,Das Boot,R,Adventure,149,"[u'J\xfcrgen Prochnow', u'Herbert Gr\xf6nemeyer', u'Klaus Wennemann']" 8.4,Princess Mononoke,PG-13,Animation,134,"[u'Y\xf4ji Matsuda', u'Yuriko Ishida', u'Y\xfbko Tanaka']" 8.4,Am̩lie,R,Comedy,122,"[u'Audrey Tautou', u'Mathieu Kassovitz', u'Rufus']" 8.4,North by Northwest,APPROVED,Adventure,136,"[u'Cary Grant', u'Eva Marie Saint', u'James Mason']" 8.4,Rang De Basanti,NOT RATED,Drama,157,"[u'Aamir Khan', u'Soha Ali Khan', u'Siddharth']" 8.4,Jodaeiye Nader az Simin,PG-13,Drama,123,"[u'Peyman Moaadi', u'Leila Hatami', u'Sareh Bayat']" 8.4,Citizen Kane,APPROVED,Drama,119,"[u'Orson Welles', u'Joseph Cotten', u'Dorothy Comingore']" 8.4,Aliens,R,Action,137,"[u'Sigourney Weaver', u'Michael Biehn', u'Carrie Henn']" 8.4,Vertigo,APPROVED,Mystery,128,"[u'James Stewart', u'Kim Novak', u'Barbara Bel Geddes']" 8.4,Oldeuboi,R,Drama,120,"[u'Min-sik Choi', u'Ji-tae Yu', u'Hye-jeong Kang']" 8.4,Once Upon a Time in America,R,Crime,229,"[u'Robert De Niro', u'James Woods', u'Elizabeth McGovern']" 8.4,Double Indemnity,PASSED,Crime,107,"[u'Fred MacMurray', u'Barbara Stanwyck', u'Edward G. Robinson']" 8.4,Star Wars: Episode VI - Return of the Jedi,PG,Action,134,"[u'Mark Hamill', u'Harrison Ford', u'Carrie Fisher']" 8.4,Toy Story 3,G,Animation,103,"[u'Tom Hanks', u'Tim Allen', u'Joan Cusack']" 8.4,Braveheart,R,Action,177,"[u'Mel Gibson', u'Sophie Marceau', u'Patrick McGoohan']" 8.4,To Kill a Mockingbird,NOT RATED,Drama,129,"[u'Gregory Peck', u'John Megna', u'Frank Overton']" 8.4,Requiem for a Dream,R,Drama,102,"[u'Ellen Burstyn', u'Jared Leto', u'Jennifer Connelly']" 8.4,Lawrence of Arabia,PG,Adventure,216,"[u""Peter O'Toole"", u'Alec Guinness', u'Anthony Quinn']" 8.4,A Clockwork Orange,X,Crime,136,"[u'Malcolm McDowell', u'Patrick Magee', u'Michael Bates']" 8.4,Bicycle Thieves,NOT RATED,Drama,93,"[u'Lamberto Maggiorani', u'Enzo Staiola', u'Lianella Carell']" 8.4,The Kid,NOT RATED,Comedy,68,"[u'Charles Chaplin', u'Edna Purviance', u'Jackie Coogan']" 8.4,Swades,NOT RATED,Drama,189,"[u'Shah Rukh Khan', u'Gayatri Joshi', u'Kishori Balal']" 8.4,Reservoir Dogs,R,Crime,99,"[u'Harvey Keitel', u'Tim Roth', u'Michael Madsen']" 8.4,Eternal Sunshine of the Spotless Mind,R,Drama,108,"[u'Jim Carrey', u'Kate Winslet', u'Tom Wilkinson']" 8.4,Taxi Driver,R,Crime,113,"[u'Robert De Niro', u'Jodie Foster', u'Cybill Shepherd']" 8.4,Dilwale Dulhania Le Jayenge,NOT RATED,Comedy,181,"[u'Shah Rukh Khan', u'Kajol', u'Amrish Puri']" 8.4,Singin' in the Rain,APPROVED,Comedy,103,"[u'Gene Kelly', u""Donald O'Connor"", u'Debbie Reynolds']" 8.4,All About Eve,APPROVED,Drama,138,"[u'Bette Davis', u'Anne Baxter', u'George Sanders']" 8.4,Yojimbo,UNRATED,Action,110,"[u'Toshir\xf4 Mifune', u'Eijir\xf4 T\xf4no', u'Tatsuya Nakadai']" 8.4,The Sting,PG,Comedy,129,"[u'Paul Newman', u'Robert Redford', u'Robert Shaw']" 8.4,Rash̫mon,UNRATED,Crime,88,"[u'Toshir\xf4 Mifune', u'Machiko Ky\xf4', u'Masayuki Mori']" 8.4,Amadeus,R,Biography,160,"[u'F. Murray Abraham', u'Tom Hulce', u'Elizabeth Berridge']" 8.3,The Treasure of the Sierra Madre,NOT RATED,Action,126,"[u'Humphrey Bogart', u'Walter Huston', u'Tim Holt']" 8.3,Monty Python and the Holy Grail,PG,Adventure,91,"[u'Graham Chapman', u'John Cleese', u'Eric Idle']" 8.3,Full Metal Jacket,R,Drama,116,"[u'Matthew Modine', u'R. Lee Ermey', u""Vincent D'Onofrio""]" 8.3,The Apartment,APPROVED,Comedy,125,"[u'Jack Lemmon', u'Shirley MacLaine', u'Fred MacMurray']" 8.3,Ikiru,NOT RATED,Drama,143,"[u'Takashi Shimura', u'Nobuo Kaneko', u""Shin'ichi Himori""]" 8.3,The Third Man,NOT RATED,Film-Noir,93,"[u'Orson Welles', u'Joseph Cotten', u'Alida Valli']" 8.3,Snatch.,R,Comedy,102,"[u'Jason Statham', u'Brad Pitt', u'Benicio Del Toro']" 8.3,For a Few Dollars More,APPROVED,Western,132,"[u'Clint Eastwood', u'Lee Van Cleef', u'Gian Maria Volont\xe9']" 8.3,Metropolis,NOT RATED,Drama,153,"[u'Brigitte Helm', u'Alfred Abel', u'Gustav Fr\xf6hlich']" 8.3,Dil Chahta Hai,NOT RATED,Comedy,183,"[u'Aamir Khan', u'Saif Ali Khan', u'Akshaye Khanna']" 8.3,2001: A Space Odyssey,G,Mystery,160,"[u'Keir Dullea', u'Gary Lockwood', u'William Sylvester']" 8.3,Some Like It Hot,NOT RATED,Comedy,120,"[u'Marilyn Monroe', u'Tony Curtis', u'Jack Lemmon']" 8.3,L.A. Confidential,R,Crime,138,"[u'Kevin Spacey', u'Russell Crowe', u'Guy Pearce']" 8.3,Batman Begins,PG-13,Action,140,"[u'Christian Bale', u'Michael Caine', u'Ken Watanabe']" 8.3,Inglourious Basterds,R,Adventure,153,"[u'Brad Pitt', u'Diane Kruger', u'Eli Roth']" 8.3,Scarface,R,Crime,170,"[u'Al Pacino', u'Michelle Pfeiffer', u'Steven Bauer']" 8.3,Mr. Smith Goes to Washington,NOT RATED,Drama,129,"[u'James Stewart', u'Jean Arthur', u'Claude Rains']" 8.3,Toy Story,G,Animation,81,"[u'Tom Hanks', u'Tim Allen', u'Don Rickles']" 8.3,Indiana Jones and the Last Crusade,PG-13,Action,127,"[u'Harrison Ford', u'Sean Connery', u'Alison Doody']" 8.3,Unforgiven,R,Western,131,"[u'Clint Eastwood', u'Gene Hackman', u'Morgan Freeman']" 8.3,The Great Escape,UNRATED,Adventure,172,"[u'Steve McQueen', u'James Garner', u'Richard Attenborough']" 8.3,Jagten,R,Drama,115,"[u'Mads Mikkelsen', u'Thomas Bo Larsen', u'Annika Wedderkopp']" 8.3,On the Waterfront,NOT RATED,Crime,108,"[u'Marlon Brando', u'Karl Malden', u'Lee J. Cobb']" 8.3,The General,UNRATED,Action,107,"[u'Buster Keaton', u'Marion Mack', u'Glen Cavender']" 8.3,Raging Bull,R,Biography,129,"[u'Robert De Niro', u'Cathy Moriarty', u'Joe Pesci']" 8.3,Downfall,R,Biography,156,"[u'Bruno Ganz', u'Alexandra Maria Lara', u'Ulrich Matthes']" 8.3,Up,PG,Animation,96,"[u'Edward Asner', u'Jordan Nagai', u'John Ratzenberger']" 8.3,Wild Strawberries,UNRATED,Drama,91,"[u'Victor Sj\xf6str\xf6m', u'Bibi Andersson', u'Ingrid Thulin']" 8.3,The Gold Rush,NOT RATED,Adventure,95,"[u'Charles Chaplin', u'Mack Swain', u'Tom Murray']" 8.3,Ran,R,Action,162,"[u'Tatsuya Nakadai', u'Akira Terao', u'Jinpachi Nezu']" 8.3,Chinatown,R,Drama,130,"[u'Jack Nicholson', u'Faye Dunaway', u'John Huston']" 8.3,My Neighbor Totoro,G,Animation,86,"[u'Hitoshi Takagi', u'Noriko Hidaka', u'Chika Sakamoto']" 8.3,Judgment at Nuremberg,NOT RATED,Drama,186,"[u'Spencer Tracy', u'Burt Lancaster', u'Richard Widmark']" 8.3,Barfi!,NOT RATED,Adventure,151,"[u'Ranbir Kapoor', u'Priyanka Chopra', u'Ileana']" 8.3,The Seventh Seal,NOT RATED,Drama,96,"[u'Max von Sydow', u'Gunnar Bj\xf6rnstrand', u'Bengt Ekerot']" 8.3,Heat,R,Action,170,"[u'Al Pacino', u'Robert De Niro', u'Val Kilmer']" 8.3,Pan's Labyrinth,R,Drama,118,"[u'Ivana Baquero', u'Ariadna Gil', u'Sergi L\xf3pez']" 8.3,The Bridge on the River Kwai,PG,Adventure,161,"[u'William Holden', u'Alec Guinness', u'Jack Hawkins']" 8.3,Die Hard,R,Action,131,"[u'Bruce Willis', u'Alan Rickman', u'Bonnie Bedelia']" 8.3,Good Will Hunting,R,Drama,126,"[u'Robin Williams', u'Matt Damon', u'Ben Affleck']" 8.3,The Wages of Fear,NOT RATED,Adventure,131,"[u'Yves Montand', u'Charles Vanel', u'Peter van Eyck']" 8.3,The Secret in Their Eyes,R,Drama,129,"[u'Ricardo Dar\xedn', u'Soledad Villamil', u'Pablo Rago']" 8.3,Lagaan: Once Upon a Time in India,PG,Adventure,224,"[u'Aamir Khan', u'Gracy Singh', u'Rachel Shelley']" 8.2,The Wolf of Wall Street,R,Biography,180,"[u'Leonardo DiCaprio', u'Jonah Hill', u'Margot Robbie']" 8.2,It Happened One Night,UNRATED,Comedy,105,"[u'Clark Gable', u'Claudette Colbert', u'Walter Connolly']" 8.2,Blade Runner,R,Sci-Fi,117,"[u'Harrison Ford', u'Rutger Hauer', u'Sean Young']" 8.2,Warrior,PG-13,Drama,140,"[u'Tom Hardy', u'Nick Nolte', u'Joel Edgerton']" 8.2,Howl's Moving Castle,PG,Animation,119,"[u'Chieko Baish\xf4', u'Takuya Kimura', u'Tatsuya Gash\xfbin']" 8.2,The Elephant Man,PG,Biography,124,"[u'Anthony Hopkins', u'John Hurt', u'Anne Bancroft']" 8.2,Rebecca,NOT RATED,Mystery,130,"[u'Laurence Olivier', u'Joan Fontaine', u'George Sanders']" 8.2,Incendies,R,Drama,139,"[u'Lubna Azabal', u'M\xe9lissa D\xe9sormeaux-Poulin', u'Maxim Gaudette']" 8.2,"Lock, Stock and Two Smoking Barrels",R,Comedy,107,"[u'Jason Flemyng', u'Dexter Fletcher', u'Nick Moran']" 8.2,V for Vendetta,R,Action,132,"[u'Hugo Weaving', u'Natalie Portman', u'Rupert Graves']" 8.2,The Big Lebowski,R,Comedy,117,"[u'Jeff Bridges', u'John Goodman', u'Julianne Moore']" 8.2,Cool Hand Luke,PG,Crime,126,"[u'Paul Newman', u'George Kennedy', u'Strother Martin']" 8.2,Relatos salvajes,R,Comedy,122,"[u'Dar\xedo Grandinetti', u'Mar\xeda Marull', u'M\xf3nica Villa']" 8.2,Casino,R,Biography,178,"[u'Robert De Niro', u'Sharon Stone', u'Joe Pesci']" 8.2,Gone with the Wind,G,Drama,238,"[u'Clark Gable', u'Vivien Leigh', u'Thomas Mitchell']" 8.2,Gone Girl,R,Drama,149,"[u'Ben Affleck', u'Rosamund Pike', u'Neil Patrick Harris']" 8.2,The Best Years of Our Lives,PASSED,Drama,172,"[u'Fredric March', u'Dana Andrews', u'Myrna Loy']" 8.2,How to Train Your Dragon,PG,Animation,98,"[u'Jay Baruchel', u'Gerard Butler', u'Christopher Mintz-Plasse']" 8.2,Diabolique,UNRATED,Horror,116,"[u'Simone Signoret', u'V\xe9ra Clouzot', u'Paul Meurisse']" 8.2,Gran Torino,R,Drama,116,"[u'Clint Eastwood', u'Bee Vang', u'Christopher Carley']" 8.2,Rush,R,Action,123,"[u'Daniel Br\xfchl', u'Chris Hemsworth', u'Olivia Wilde']" 8.2,Into the Wild,R,Adventure,148,"[u'Emile Hirsch', u'Vince Vaughn', u'Catherine Keener']" 8.2,My Sassy Girl,NOT RATED,Comedy,123,"[u'Tae-hyun Cha', u'Gianna Jun', u'In-mun Kim']" 8.2,The Deer Hunter,R,Drama,182,"[u'Robert De Niro', u'Christopher Walken', u'John Cazale']" 8.2,Mary and Max,NOT RATED,Animation,92,"[u'Toni Collette', u'Philip Seymour Hoffman', u'Eric Bana']" 8.2,Hachi: A Dog's Tale,G,Drama,93,"[u'Richard Gere', u'Joan Allen', u'Cary-Hiroyuki Tagawa']" 8.2,The Maltese Falcon,NOT RATED,Crime,100,"[u'Humphrey Bogart', u'Mary Astor', u'Gladys George']" 8.2,A Beautiful Mind,PG-13,Biography,135,"[u'Russell Crowe', u'Ed Harris', u'Jennifer Connelly']" 8.2,Dial M for Murder,PG,Crime,105,"[u'Ray Milland', u'Grace Kelly', u'Robert Cummings']" 8.2,Trainspotting,R,Drama,94,"[u'Ewan McGregor', u'Ewen Bremner', u'Jonny Lee Miller']" 8.2,Tae Guk Gi: The Brotherhood of War,R,Action,140,"[u'Dong-gun Jang', u'Bin Won', u'Eun-ju Lee']" 8.2,Persona,NOT RATED,Drama,83,"[u'Bibi Andersson', u'Liv Ullmann', u'Margaretha Krook']" 8.2,Touch of Evil,PG-13,Crime,95,"[u'Charlton Heston', u'Orson Welles', u'Janet Leigh']" 8.2,Fargo,R,Crime,98,"[u'William H. Macy', u'Frances McDormand', u'Steve Buscemi']" 8.2,The Avengers,PG-13,Action,143,"[u'Robert Downey Jr.', u'Chris Evans', u'Scarlett Johansson']" 8.2,The 400 Blows,NOT RATED,Crime,99,"[u'Jean-Pierre L\xe9aud', u'Albert R\xe9my', u'Claire Maurier']" 8.2,The Princess Bride,PG,Adventure,98,"[u'Cary Elwes', u'Mandy Patinkin', u'Robin Wright']" 8.2,Hotel Rwanda,PG-13,Drama,121,"[u'Don Cheadle', u'Sophie Okonedo', u'Joaquin Phoenix']" 8.2,Stalker,NOT RATED,Drama,163,"[u'Alisa Freyndlikh', u'Aleksandr Kaydanovskiy', u'Anatoliy Solonitsyn']" 8.2,The Battle of Algiers,NOT RATED,Crime,121,"[u'Brahim Hadjadj', u'Jean Martin', u'Yacef Saadi']" 8.2,Underground,NOT RATED,Comedy,167,"[u'Predrag Manojlovic', u'Lazar Ristovski', u'Mirjana Jokovic']" 8.2,Fanny and Alexander,R,Drama,188,"[u'Bertil Guve', u'Pernilla Allwin', u'Kristina Adolphson']" 8.2,Network,R,Drama,121,"[u'Faye Dunaway', u'William Holden', u'Peter Finch']" 8.2,The Thing,R,Horror,109,"[u'Kurt Russell', u'Wilford Brimley', u'Keith David']" 8.2,Butch Cassidy and the Sundance Kid,M,Biography,110,"[u'Paul Newman', u'Robert Redford', u'Katharine Ross']" 8.2,The Grapes of Wrath,NOT RATED,Drama,129,"[u'Henry Fonda', u'Jane Darwell', u'John Carradine']" 8.2,"Black Cat, White Cat",R,Comedy,127,"[u'Bajram Severdzan', u'Srdjan Todorovic', u'Branka Katic']" 8.2,Life of Brian,R,Comedy,94,"[u'Graham Chapman', u'John Cleese', u'Michael Palin']" 8.2,The Sixth Sense,PG-13,Drama,107,"[u'Bruce Willis', u'Haley Joel Osment', u'Toni Collette']" 8.2,Finding Nemo,G,Animation,100,"[u'Albert Brooks', u'Ellen DeGeneres', u'Alexander Gould']" 8.2,Nausica_ of the Valley of the Wind,PG,Animation,117,"[u'Sumi Shimamoto', u'Mahito Tsujimura', u'Hisako Ky\xf4da']" 8.1,Kingsman: The Secret Service,R,Action,129,"[u'Colin Firth', u'Taron Egerton', u'Samuel L. Jackson']" 8.1,Platoon,R,Drama,120,"[u'Charlie Sheen', u'Tom Berenger', u'Willem Dafoe']" 8.1,Guardians of the Galaxy,PG-13,Action,121,"[u'Chris Pratt', u'Vin Diesel', u'Bradley Cooper']" 8.1,In the Name of the Father,R,Biography,133,"[u'Daniel Day-Lewis', u'Pete Postlethwaite', u'Alison Crosbie']" 8.1,Kill Bill: Vol. 1,R,Action,111,"[u'Uma Thurman', u'David Carradine', u'Daryl Hannah']" 8.1,No Country for Old Men,R,Crime,122,"[u'Tommy Lee Jones', u'Javier Bardem', u'Josh Brolin']" 8.1,Le Samoura,PG,Crime,101,"[u'Alain Delon', u'Nathalie Delon', u'Fran\xe7ois P\xe9rier']" 8.1,12 Years a Slave,R,Biography,134,"[u'Chiwetel Ejiofor', u'Michael Kenneth Williams', u'Michael Fassbender']" 8.1,La leggenda del pianista sull'oceano,R,Drama,165,"[u'Tim Roth', u'Pruitt Taylor Vince', u'Bill Nunn']" 8.1,8_,NOT RATED,Drama,138,"[u'Marcello Mastroianni', u'Anouk Aim\xe9e', u'Claudia Cardinale']" 8.1,Ben-Hur,G,Adventure,212,"[u'Charlton Heston', u'Jack Hawkins', u'Stephen Boyd']" 8.1,Amores Perros,R,Drama,154,"[u'Emilio Echevarr\xeda', u'Gael Garc\xeda Bernal', u'Goya Toledo']" 8.1,Stand by Me,R,Adventure,89,"[u'Wil Wheaton', u'River Phoenix', u'Corey Feldman']" 8.1,What Ever Happened to Baby Jane?,APPROVED,Drama,134,"[u'Bette Davis', u'Joan Crawford', u'Victor Buono']" 8.1,Annie Hall,PG,Comedy,93,"[u'Woody Allen', u'Diane Keaton', u'Tony Roberts']" 8.1,The Imitation Game,PG-13,Biography,114,"[u'Benedict Cumberbatch', u'Keira Knightley', u'Matthew Goode']" 8.1,Laura,APPROVED,Film-Noir,88,"[u'Gene Tierney', u'Dana Andrews', u'Clifton Webb']" 8.1,Departures,PG-13,Drama,130,"[u'Masahiro Motoki', u'Ry\xf4ko Hirosue', u'Tsutomu Yamazaki']" 8.1,Infernal Affairs,R,Crime,101,"[u'Andy Lau', u'Tony Chiu Wai Leung', u'Anthony Chau-Sang Wong']" 8.1,There Will Be Blood,R,Drama,158,"[u'Daniel Day-Lewis', u'Paul Dano', u'Ciar\xe1n Hinds']" 8.1,The Grand Budapest Hotel,R,Adventure,99,"[u'Ralph Fiennes', u'F. Murray Abraham', u'Mathieu Amalric']" 8.1,La Strada,NOT RATED,Drama,108,"[u'Anthony Quinn', u'Giulietta Masina', u'Richard Basehart']" 8.1,Sin City,R,Crime,124,"[u'Mickey Rourke', u'Clive Owen', u'Bruce Willis']" 8.1,Memories of Murder,UNRATED,Crime,132,"[u'Kang-ho Song', u'Sang-kyung Kim', u'Roe-ha Kim']" 8.1,Donnie Darko,R,Drama,113,"[u'Jake Gyllenhaal', u'Jena Malone', u'Mary McDonnell']" 8.1,Who's Afraid of Virginia Woolf?,TV-MA,Drama,131,"[u'Elizabeth Taylor', u'Richard Burton', u'George Segal']" 8.1,Gandhi,PG,Biography,191,"[u'Ben Kingsley', u'John Gielgud', u'Candice Bergen']" 8.1,Solaris,PG,Drama,167,"[u'Natalya Bondarchuk', u'Donatas Banionis', u'J\xfcri J\xe4rvet']" 8.1,Harry Potter and the Deathly Hallows: Part 2,PG-13,Adventure,130,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']" 8.1,"Paris, Texas",R,Drama,147,"[u'Harry Dean Stanton', u'Nastassja Kinski', u'Dean Stockwell']" 8.1,The Wizard of Oz,PASSED,Adventure,102,"[u'Judy Garland', u'Frank Morgan', u'Ray Bolger']" 8.1,3-Iron,R,Crime,88,"[u'Seung-yeon Lee', u'Hyun-kyoon Lee', u'Hyuk-ho Kwon']" 8.1,Boyhood,R,Drama,165,"[u'Ellar Coltrane', u'Patricia Arquette', u'Ethan Hawke']" 8.1,Million Dollar Baby,PG-13,Drama,132,"[u'Hilary Swank', u'Clint Eastwood', u'Morgan Freeman']" 8.1,The Last Picture Show,R,Drama,118,"[u'Timothy Bottoms', u'Jeff Bridges', u'Cybill Shepherd']" 8.1,Strangers on a Train,APPROVED,Crime,101,"[u'Farley Granger', u'Robert Walker', u'Ruth Roman']" 8.1,Cat on a Hot Tin Roof,APPROVED,Drama,108,"[u'Elizabeth Taylor', u'Paul Newman', u'Burl Ives']" 8.1,La Dolce Vita,NOT RATED,Comedy,174,"[u'Marcello Mastroianni', u'Anita Ekberg', u'Anouk Aim\xe9e']" 8.1,Chungking Express,PG-13,Drama,98,"[u'Brigitte Lin', u'Takeshi Kaneshiro', u'Tony Chiu Wai Leung']" 8.1,The Night of the Hunter,APPROVED,Crime,92,"[u'Robert Mitchum', u'Shelley Winters', u'Lillian Gish']" 8.1,La Haine,NOT RATED,Crime,98,"[u'Vincent Cassel', u'Hubert Kound\xe9', u'Sa\xefd Taghmaoui']" 8.1,Yip Man,R,Action,106,"[u'Donnie Yen', u'Simon Yam', u'Siu-Wong Fan']" 8.1,High Noon,PG,Western,85,"[u'Gary Cooper', u'Grace Kelly', u'Thomas Mitchell']" 8.1,Notorious,APPROVED,Drama,101,"[u'Cary Grant', u'Ingrid Bergman', u'Claude Rains']" 8.1,Before Sunrise,R,Drama,105,"[u'Ethan Hawke', u'Julie Delpy', u'Andrea Eckert']" 8.1,Elite Squad: The Enemy Within,UNRATED,Action,115,"[u'Wagner Moura', u'Irandhir Santos', u'Andr\xe9 Ramiro']" 8.1,The Bourne Ultimatum,PG-13,Action,115,"[u'Matt Damon', u'\xc9dgar Ram\xedrez', u'Joan Allen']" 8.1,Castle in the Sky,PG,Animation,124,"[u'Anna Paquin', u'James Van Der Beek', u'Cloris Leachman']" 8.1,The Celebration,R,Drama,105,"[u'Ulrich Thomsen', u'Henning Moritzen', u'Thomas Bo Larsen']" 8.1,"Spring, Summer, Fall, Winter... and Spring",R,Drama,103,"[u'Ki-duk Kim', u'Yeong-su Oh', u'Jong-ho Kim']" 8.1,Shutter Island,R,Mystery,138,"[u'Leonardo DiCaprio', u'Emily Mortimer', u'Mark Ruffalo']" 8.1,Barry Lyndon,PG,Adventure,184,"[u""Ryan O'Neal"", u'Marisa Berenson', u'Patrick Magee']" 8.1,Stalag 17,NOT RATED,Comedy,120,"[u'William Holden', u'Don Taylor', u'Otto Preminger']" 8.1,Three Colors: Red,R,Drama,99,"[u'Ir\xe8ne Jacob', u'Jean-Louis Trintignant', u'Fr\xe9d\xe9rique Feder']" 8.1,X-Men: Days of Future Past,PG-13,Action,131,"[u'Patrick Stewart', u'Ian McKellen', u'Hugh Jackman']" 8.1,Sleuth,PG,Mystery,138,"[u'Laurence Olivier', u'Michael Caine', u'Alec Cawthorne']" 8.1,In the Mood for Love,PG,Drama,98,"[u'Tony Chiu Wai Leung', u'Maggie Cheung', u'Ping Lam Siu']" 8.1,The Man Who Shot Liberty Valance,APPROVED,Drama,123,"[u'James Stewart', u'John Wayne', u'Vera Miles']" 8.1,Arsenic and Old Lace,NOT RATED,Comedy,118,"[u'Cary Grant', u'Priscilla Lane', u'Raymond Massey']" 8.1,The Big Sleep,APPROVED,Crime,114,"[u'Humphrey Bogart', u'Lauren Bacall', u'John Ridgely']" 8.1,Roman Holiday,NOT RATED,Comedy,118,"[u'Gregory Peck', u'Audrey Hepburn', u'Eddie Albert']" 8.1,The Philadelphia Story,NOT RATED,Comedy,112,"[u'Cary Grant', u'Katharine Hepburn', u'James Stewart']" 8.1,Akira,R,Animation,124,"[u'Nozomu Sasaki', u'Mami Koyama', u'Mitsuo Iwata']" 8.1,Anatomy of a Murder,UNRATED,Crime,160,"[u'James Stewart', u'Lee Remick', u'Ben Gazzara']" 8.1,The Cabinet of Dr. Caligari,UNRATED,Crime,67,"[u'Werner Krauss', u'Conrad Veidt', u'Friedrich Feher']" 8.1,The Help,PG-13,Drama,146,"[u'Emma Stone', u'Viola Davis', u'Octavia Spencer']" 8.1,The Sea Inside,PG-13,Biography,125,"[u'Javier Bardem', u'Bel\xe9n Rueda', u'Lola Due\xf1as']" 8.1,Elite Squad,R,Action,115,"[u'Wagner Moura', u'Andr\xe9 Ramiro', u'Caio Junqueira']" 8.1,The Hustler,UNRATED,Drama,134,"[u'Paul Newman', u'Jackie Gleason', u'Piper Laurie']" 8.1,Rio Bravo,NOT RATED,Western,141,"[u'John Wayne', u'Dean Martin', u'Ricky Nelson']" 8.1,Twelve Monkeys,R,Mystery,129,"[u'Bruce Willis', u'Madeleine Stowe', u'Brad Pitt']" 8.1,Harvey,NOT RATED,Comedy,104,"[u'James Stewart', u'Josephine Hull', u'Peggy Dow']" 8.1,A Christmas Story,PG,Comedy,94,"[u'Peter Billingsley', u'Melinda Dillon', u'Darren McGavin']" 8.1,Jaws,PG,Drama,124,"[u'Roy Scheider', u'Robert Shaw', u'Richard Dreyfuss']" 8.1,The Raid 2,R,Action,150,"[u'Iko Uwais', u'Yayan Ruhian', u'Arifin Putra']" 8.1,Rocky,PG,Drama,119,"[u'Sylvester Stallone', u'Talia Shire', u'Burt Young']" 8.1,Wings of Desire,PG-13,Drama,128,"[u'Bruno Ganz', u'Solveig Dommartin', u'Otto Sander']" 8.1,Pirates of the Caribbean: The Curse of the Black Pearl,PG-13,Adventure,143,"[u'Johnny Depp', u'Geoffrey Rush', u'Orlando Bloom']" 8.1,The Killing,APPROVED,Crime,85,"[u'Sterling Hayden', u'Coleen Gray', u'Vince Edwards']" 8.1,Papillon,R,Biography,151,"[u'Steve McQueen', u'Dustin Hoffman', u'Victor Jory']" 8.1,The King's Speech,R,Biography,118,"[u'Colin Firth', u'Geoffrey Rush', u'Helena Bonham Carter']" 8.1,Groundhog Day,PG,Comedy,101,"[u'Bill Murray', u'Andie MacDowell', u'Chris Elliott']" 8.1,A Fistful of Dollars,R,Action,99,"[u'Clint Eastwood', u'Gian Maria Volont\xe9', u'Marianne Koch']" 8.1,"Monsters, Inc.",G,Animation,92,"[u'Billy Crystal', u'John Goodman', u'Mary Gibbs']" 8.1,Dog Day Afternoon,R,Crime,125,"[u'Al Pacino', u'John Cazale', u'Penelope Allen']" 8.1,The Perks of Being a Wallflower,PG-13,Drama,102,"[u'Logan Lerman', u'Emma Watson', u'Ezra Miller']" 8.1,Young Frankenstein,PG,Comedy,106,"[u'Gene Wilder', u'Madeline Kahn', u'Marty Feldman']" 8.1,The Terminator,R,Action,107,"[u'Arnold Schwarzenegger', u'Linda Hamilton', u'Michael Biehn']" 8.1,Harold and Maude,GP,Comedy,91,"[u'Ruth Gordon', u'Bud Cort', u'Vivian Pickles']" 8.1,Before Sunset,R,Drama,80,"[u'Ethan Hawke', u'Julie Delpy', u'Vernon Dobtcheff']" 8.1,A Streetcar Named Desire,PG,Drama,122,"[u'Vivien Leigh', u'Marlon Brando', u'Kim Hunter']" 8.1,Bringing Up Baby,APPROVED,Comedy,102,"[u'Katharine Hepburn', u'Cary Grant', u'Charles Ruggles']" 8.1,The Diving Bell and the Butterfly,PG-13,Biography,112,"[u'Mathieu Amalric', u'Emmanuelle Seigner', u'Marie-Jos\xe9e Croze']" 8.1,His Girl Friday,APPROVED,Comedy,92,"[u'Cary Grant', u'Rosalind Russell', u'Ralph Bellamy']" 8.1,Sling Blade,R,Drama,135,"[u'Billy Bob Thornton', u'Dwight Yoakam', u'J.T. Walsh']" 8.1,All Quiet on the Western Front,UNRATED,Drama,136,"[u'Lew Ayres', u'Louis Wolheim', u'John Wray']" 8.1,Prisoners,R,Crime,153,"[u'Hugh Jackman', u'Jake Gyllenhaal', u'Viola Davis']" 8.1,The Return,UNRATED,Drama,105,"[u'Vladimir Garin', u'Ivan Dobronravov', u'Konstantin Lavronenko']" 8.1,The Manchurian Candidate,APPROVED,Mystery,126,"[u'Frank Sinatra', u'Laurence Harvey', u'Janet Leigh']" 8.1,Duck Soup,PASSED,Comedy,68,"[u'Groucho Marx', u'Harpo Marx', u'Chico Marx']" 8.1,Beauty and the Beast,G,Animation,84,"[u""Paige O'Hara"", u'Robby Benson', u'Richard White']" 8.1,The Truman Show,PG,Drama,103,"[u'Jim Carrey', u'Ed Harris', u'Laura Linney']" 8.1,The Wild Bunch,R,Action,145,"[u'William Holden', u'Ernest Borgnine', u'Robert Ryan']" 8,Rope,PG,Crime,80,"[u'James Stewart', u'John Dall', u'Farley Granger']" 8,The Graduate,APPROVED,Comedy,106,"[u'Dustin Hoffman', u'Anne Bancroft', u'Katharine Ross']" 8,Jurassic Park,PG-13,Adventure,127,"[u'Sam Neill', u'Laura Dern', u'Jeff Goldblum']" 8,Dogville,R,Crime,178,"[u'Nicole Kidman', u'Paul Bettany', u'Lauren Bacall']" 8,Furious 7,PG-13,Action,137,"[u'Vin Diesel', u'Paul Walker', u'Dwayne Johnson']" 8,Patton,GP,Biography,172,"[u'George C. Scott', u'Karl Malden', u'Stephen Young']" 8,The Nightmare Before Christmas,PG,Animation,76,"[u'Danny Elfman', u'Chris Sarandon', u""Catherine O'Hara""]" 8,Being There,PG,Comedy,130,"[u'Peter Sellers', u'Shirley MacLaine', u'Melvyn Douglas']" 8,Persepolis,PG-13,Animation,96,"[u'Chiara Mastroianni', u'Catherine Deneuve', u'Gena Rowlands']" 8,This Is Spinal Tap,R,Comedy,82,"[u'Rob Reiner', u'Michael McKean', u'Christopher Guest']" 8,East of Eden,PG,Drama,115,"[u'James Dean', u'Raymond Massey', u'Julie Harris']" 8,Pink Floyd The Wall,R,Animation,95,"[u'Bob Geldof', u'Christine Hargreaves', u'James Laurenson']" 8,Blood Diamond,R,Adventure,143,"[u'Leonardo DiCaprio', u'Djimon Hounsou', u'Jennifer Connelly']" 8,Big Fish,PG-13,Adventure,125,"[u'Ewan McGregor', u'Albert Finney', u'Billy Crudup']" 8,Slumdog Millionaire,R,Drama,120,"[u'Dev Patel', u'Freida Pinto', u'Saurabh Shukla']" 8,Star Trek,PG-13,Action,127,"[u'Chris Pine', u'Zachary Quinto', u'Simon Pegg']" 8,The Straight Story,G,Biography,112,"[u'Richard Farnsworth', u'Sissy Spacek', u'Jane Galloway Heitz']" 8,Crimes and Misdemeanors,PG-13,Comedy,104,"[u'Martin Landau', u'Woody Allen', u'Bill Bernstein']" 8,Charade,NOT RATED,Comedy,113,"[u'Cary Grant', u'Audrey Hepburn', u'Walter Matthau']" 8,JFK,R,Drama,189,"[u'Kevin Costner', u'Gary Oldman', u'Jack Lemmon']" 8,Dawn of the Dead,UNRATED,Horror,127,"[u'David Emge', u'Ken Foree', u'Scott H. Reiniger']" 8,Magnolia,R,Drama,188,"[u'Tom Cruise', u'Jason Robards', u'Julianne Moore']" 8,Manhattan,R,Comedy,96,"[u'Woody Allen', u'Diane Keaton', u'Mariel Hemingway']" 8,Central Station,R,Drama,113,"[u'Fernanda Montenegro', u'Vin\xedcius de Oliveira', u'Mar\xedlia P\xeara']" 8,Brazil,R,Sci-Fi,132,"[u'Jonathan Pryce', u'Kim Greist', u'Robert De Niro']" 8,The Exorcist,R,Horror,122,"[u'Ellen Burstyn', u'Max von Sydow', u'Linda Blair']" 8,Her,R,Drama,126,"[u'Joaquin Phoenix', u'Amy Adams', u'Scarlett Johansson']" 8,Dancer in the Dark,R,Crime,140,"[u'Bj\xf6rk', u'Catherine Deneuve', u'David Morse']" 8,Rain Man,R,Drama,133,"[u'Dustin Hoffman', u'Tom Cruise', u'Valeria Golino']" 8,Talk to Her,R,Drama,112,"[u'Rosario Flores', u'Javier C\xe1mara', u'Dar\xedo Grandinetti']" 8,The Adventures of Robin Hood,PG,Action,102,"[u'Errol Flynn', u'Olivia de Havilland', u'Basil Rathbone']" 8,"Aguirre, the Wrath of God",NOT RATED,Adventure,93,"[u'Klaus Kinski', u'Ruy Guerra', u'Helena Rojo']" 8,In the Heat of the Night,APPROVED,Crime,109,"[u'Sidney Poitier', u'Rod Steiger', u'Warren Oates']" 8,Nosferatu,UNRATED,Horror,81,"[u'Max Schreck', u'Greta Schr\xf6der', u'Ruth Landshoff']" 8,Black Swan,R,Drama,108,"[u'Natalie Portman', u'Mila Kunis', u'Vincent Cassel']" 8,Ratatouille,G,Animation,111,"[u'Brad Garrett', u'Lou Romano', u'Patton Oswalt']" 8,The Searchers,APPROVED,Adventure,119,"[u'John Wayne', u'Jeffrey Hunter', u'Vera Miles']" 8,Short Term 12,R,Drama,96,"[u'Brie Larson', u'Frantz Turner', u'John Gallagher Jr.']" 8,Doctor Zhivago,PG-13,Drama,197,"[u'Omar Sharif', u'Julie Christie', u'Geraldine Chaplin']" 8,Life of Pi,PG,Adventure,127,"[u'Suraj Sharma', u'Irrfan Khan', u'Adil Hussain']" 8,All the President's Men,R,Drama,138,"[u'Dustin Hoffman', u'Robert Redford', u'Jack Warden']" 8,Battleship Potemkin,UNRATED,History,66,"[u'Aleksandr Antonov', u'Vladimir Barsky', u'Grigori Aleksandrov']" 8,Dances with Wolves,PG-13,Adventure,181,"[u'Kevin Costner', u'Mary McDonnell', u'Graham Greene']" 8,Catch Me If You Can,PG-13,Biography,141,"[u'Leonardo DiCaprio', u'Tom Hanks', u'Christopher Walken']" 8,Cinderella Man,PG-13,Biography,144,"[u'Russell Crowe', u'Ren\xe9e Zellweger', u'Craig Bierko']" 8,Dead Poets Society,PG,Drama,128,"[u'Robin Williams', u'Robert Sean Leonard', u'Ethan Hawke']" 8,Ghost in the Shell,NOT RATED,Animation,83,"[u'Atsuko Tanaka', u'Iemasa Kayumi', u'Akio \xd4tsuka']" 8,Head-On,R,Drama,121,"[u'Birol \xdcnel', u'Sibel Kekilli', u'G\xfcven Kira\xe7']" 8,No Man's Land,R,Drama,98,"[u'Branko Djuric', u'Rene Bitorajac', u'Filip Sovagovic']" 8,Dallas Buyers Club,R,Biography,117,"[u'Matthew McConaughey', u'Jennifer Garner', u'Jared Leto']" 8,Days of Heaven,PG,Drama,94,"[u'Richard Gere', u'Brooke Adams', u'Sam Shepard']" 8,The Sound of Music,G,Biography,174,"[u'Julie Andrews', u'Christopher Plummer', u'Eleanor Parker']" 8,District 9,R,Action,112,"[u'Sharlto Copley', u'David James', u'Jason Cope']" 8,Shadow of a Doubt,APPROVED,Thriller,108,"[u'Teresa Wright', u'Joseph Cotten', u'Macdonald Carey']" 8,Frankenstein,UNRATED,Horror,70,"[u'Colin Clive', u'Mae Clarke', u'Boris Karloff']" 8,Shaun of the Dead,R,Comedy,99,"[u'Simon Pegg', u'Nick Frost', u'Kate Ashfield']" 8,Night of the Living Dead,UNRATED,Horror,96,"[u'Duane Jones', u""Judith O'Dea"", u'Karl Hardman']" 8,Kill Bill: Vol. 2,R,Action,137,"[u'Uma Thurman', u'David Carradine', u'Michael Madsen']" 8,Secrets & Lies,R,Drama,136,"[u'Timothy Spall', u'Brenda Blethyn', u'Phyllis Logan']" 8,The Artist,PG-13,Comedy,100,"[u'Jean Dujardin', u'B\xe9r\xe9nice Bejo', u'John Goodman']" 8,Aladdin,G,Animation,90,"[u'Scott Weinger', u'Robin Williams', u'Linda Larkin']" 8,The Lady Vanishes,PASSED,Comedy,96,"[u'Margaret Lockwood', u'Michael Redgrave', u'Paul Lukas']" 8,The Hobbit: The Desolation of Smaug,PG-13,Adventure,161,"[u'Ian McKellen', u'Martin Freeman', u'Richard Armitage']" 8,Let the Right One In,R,Drama,115,"[u'K\xe5re Hedebrant', u'Lina Leandersson', u'Per Ragnar']" 8,Fiddler on the Roof,G,Drama,181,"[u'Topol', u'Norma Crane', u'Leonard Frey']" 8,The Hobbit: An Unexpected Journey,PG-13,Adventure,169,"[u'Martin Freeman', u'Ian McKellen', u'Richard Armitage']" 8,Three Colors: Blue,R,Drama,98,"[u'Juliette Binoche', u'Zbigniew Zamachowski', u'Julie Delpy']" 8,Rosemary's Baby,R,Drama,136,"[u'Mia Farrow', u'John Cassavetes', u'Ruth Gordon']" 8,Mystic River,R,Crime,138,"[u'Sean Penn', u'Tim Robbins', u'Kevin Bacon']" 8,Serenity,PG-13,Action,119,"[u'Nathan Fillion', u'Gina Torres', u'Chiwetel Ejiofor']" 8,Amarcord,R,Comedy,123,"[u'Magali No\xebl', u'Bruno Zanin', u'Pupella Maggio']" 8,Planet of the Apes,G,Adventure,112,"[u'Charlton Heston', u'Roddy McDowall', u'Kim Hunter']" 8,True Romance,R,Crime,120,"[u'Christian Slater', u'Patricia Arquette', u'Dennis Hopper']" 8,Hannah and Her Sisters,PG-13,Comedy,103,"[u'Mia Farrow', u'Dianne Wiest', u'Michael Caine']" 8,Moon,R,Drama,97,"[u'Sam Rockwell', u'Kevin Spacey', u'Dominique McElligott']" 8,Before Midnight,R,Drama,109,"[u'Ethan Hawke', u'Julie Delpy', u'Seamus Davey-Fitzpatrick']" 8,My Name Is Khan,PG-13,Drama,165,"[u'Shah Rukh Khan', u'Kajol', u'Sheetal Menon']" 8,Scent of a Woman,R,Drama,156,"[u'Al Pacino', u""Chris O'Donnell"", u'James Rebhorn']" 8,Mulholland Dr.,R,Drama,147,"[u'Naomi Watts', u'Laura Harring', u'Justin Theroux']" 8,King Kong,UNRATED,Adventure,100,"[u'Fay Wray', u'Robert Armstrong', u'Bruce Cabot']" 8,The Fault in Our Stars,PG-13,Drama,126,"[u'Shailene Woodley', u'Ansel Elgort', u'Nat Wolff']" 8,The Incredibles,PG,Animation,115,"[u'Craig T. Nelson', u'Samuel L. Jackson', u'Holly Hunter']" 8,Edward Scissorhands,PG-13,Drama,105,"[u'Johnny Depp', u'Winona Ryder', u'Dianne Wiest']" 8,Casino Royale,PG-13,Action,144,"[u'Daniel Craig', u'Eva Green', u'Judi Dench']" 8,How to Train Your Dragon 2,PG,Animation,102,"[u'Jay Baruchel', u'Cate Blanchett', u'Gerard Butler']" 8,In Bruges,R,Comedy,107,"[u'Colin Farrell', u'Brendan Gleeson', u'Ciar\xe1n Hinds']" 8,The Untouchables,R,Crime,119,"[u'Kevin Costner', u'Sean Connery', u'Robert De Niro']" 8,Breathless,UNRATED,Crime,90,"[u'Jean-Paul Belmondo', u'Jean Seberg', u'Daniel Boulanger']" 8,Spartacus,PG-13,Action,197,"[u'Kirk Douglas', u'Laurence Olivier', u'Jean Simmons']" 8,The Iron Giant,PG,Animation,86,"[u'Eli Marienthal', u'Harry Connick Jr.', u'Jennifer Aniston']" 8,Midnight Cowboy,X,Drama,113,"[u'Dustin Hoffman', u'Jon Voight', u'Sylvia Miles']" 8,The Blues Brothers,R,Action,133,"[u'John Belushi', u'Dan Aykroyd', u'Cab Calloway']" 8,Freaks,UNRATED,Drama,64,"[u'Wallace Ford', u'Leila Hyams', u'Olga Baclanova']" 8,Letters from Iwo Jima,R,Drama,141,"[u'Ken Watanabe', u'Kazunari Ninomiya', u'Tsuyoshi Ihara']" 8,Edge of Tomorrow,PG-13,Action,113,"[u'Tom Cruise', u'Emily Blunt', u'Bill Paxton']" 8,Glory,R,Drama,122,"[u'Matthew Broderick', u'Denzel Washington', u'Cary Elwes']" 8,The African Queen,PG,Adventure,105,"[u'Humphrey Bogart', u'Katharine Hepburn', u'Robert Morley']" 7.9,Birdman: Or (The Unexpected Virtue of Ignorance),R,Comedy,119,"[u'Michael Keaton', u'Zach Galifianakis', u'Edward Norton']" 7.9,Big Hero 6,PG,Animation,102,"[u'Ryan Potter', u'Scott Adsit', u'Jamie Chung']" 7.9,Almost Famous,R,Drama,122,"[u'Billy Crudup', u'Patrick Fugit', u'Kate Hudson']" 7.9,The Notebook,PG-13,Drama,123,"[u'Gena Rowlands', u'James Garner', u'Rachel McAdams']" 7.9,The Conversation,PG,Drama,113,"[u'Gene Hackman', u'John Cazale', u'Allen Garfield']" 7.9,The Breakfast Club,R,Comedy,97,"[u'Emilio Estevez', u'Judd Nelson', u'Molly Ringwald']" 7.9,The Pursuit of Happyness,PG-13,Biography,117,"[u'Will Smith', u'Thandie Newton', u'Jaden Smith']" 7.9,The Killer,R,Action,111,"[u'Yun-Fat Chow', u'Danny Lee', u'Sally Yeh']" 7.9,Once,R,Drama,85,"[u'Glen Hansard', u'Mark\xe9ta Irglov\xe1', u'Hugh Walsh']" 7.9,Ying xiong,PG-13,Action,99,"[u'Jet Li', u'Tony Chiu Wai Leung', u'Maggie Cheung']" 7.9,The Remains of the Day,PG,Drama,134,"[u'Anthony Hopkins', u'Emma Thompson', u'John Haycraft']" 7.9,The Bride of Frankenstein,NOT RATED,Horror,75,"[u'Boris Karloff', u'Elsa Lanchester', u'Colin Clive']" 7.9,The Wrestler,R,Drama,109,"[u'Mickey Rourke', u'Marisa Tomei', u'Evan Rachel Wood']" 7.9,Nightcrawler,R,Crime,117,"[u'Jake Gyllenhaal', u'Rene Russo', u'Bill Paxton']" 7.9,Gravity,PG-13,Sci-Fi,91,"[u'Sandra Bullock', u'George Clooney', u'Ed Harris']" 7.9,Hard Boiled,R,Action,128,"[u'Yun-Fat Chow', u'Tony Chiu Wai Leung', u'Teresa Mo']" 7.9,Bonnie and Clyde,APPROVED,Biography,111,"[u'Warren Beatty', u'Faye Dunaway', u'Michael J. Pollard']" 7.9,The Bourne Identity,PG-13,Action,119,"[u'Franka Potente', u'Matt Damon', u'Chris Cooper']" 7.9,Key Largo,UNRATED,Crime,100,"[u'Humphrey Bogart', u'Edward G. Robinson', u'Lauren Bacall']" 7.9,Nine Queens,R,Crime,114,"[u'Ricardo Dar\xedn', u'Gast\xf3n Pauls', u'Leticia Br\xe9dice']" 7.9,Toy Story 2,G,Animation,92,"[u'Tom Hanks', u'Tim Allen', u'Joan Cusack']" 7.9,The Killing Fields,R,Drama,141,"[u'Sam Waterston', u'Haing S. Ngor', u'John Malkovich']" 7.9,All About My Mother,R,Drama,101,"[u'Cecilia Roth', u'Marisa Paredes', u'Candela Pe\xf1a']" 7.9,Cowboy Bebop: The Movie,R,Animation,115,"[u'Beau Billingslea', u'Melissa Fahn', u'Nicholas Guest']" 7.9,Ed Wood,R,Biography,127,"[u'Johnny Depp', u'Martin Landau', u'Sarah Jessica Parker']" 7.9,Stagecoach,NOT RATED,Adventure,96,"[u'John Wayne', u'Claire Trevor', u'Andy Devine']" 7.9,The Man Who Would Be King,PG,Action,129,"[u'Sean Connery', u'Michael Caine', u'Christopher Plummer']" 7.9,The Outlaw Josey Wales,PG,Western,135,"[u'Clint Eastwood', u'Sondra Locke', u'Chief Dan George']" 7.9,Children of Men,R,Drama,109,"[u'Julianne Moore', u'Clive Owen', u'Chiwetel Ejiofor']" 7.9,The Insider,R,Biography,157,"[u'Russell Crowe', u'Al Pacino', u'Christopher Plummer']" 7.9,The Right Stuff,R,Adventure,193,"[u'Sam Shepard', u'Scott Glenn', u'Ed Harris']" 7.9,Badlands,PG,Crime,94,"[u'Martin Sheen', u'Sissy Spacek', u'Warren Oates']" 7.9,A Prophet,R,Crime,155,"[u'Tahar Rahim', u'Niels Arestrup', u'Adel Bencherif']" 7.9,"4 Months, 3 Weeks and 2 Days",NOT RATED,Drama,113,"[u'Anamaria Marinca', u'Vlad Ivanov', u'Laura Vasiliu']" 7.9,Le pass̩,PG-13,Drama,130,"[u'B\xe9r\xe9nice Bejo', u'Tahar Rahim', u'Ali Mosaffa']" 7.9,The Fall,R,Adventure,117,"[u'Lee Pace', u'Catinca Untaru', u'Justine Waddell']" 7.9,Breaking the Waves,R,Drama,159,"[u'Emily Watson', u'Stellan Skarsg\xe5rd', u'Katrin Cartlidge']" 7.9,Mr. Nobody,R,Drama,141,"[u'Jared Leto', u'Sarah Polley', u'Diane Kruger']" 7.9,Crash,R,Drama,112,"[u'Don Cheadle', u'Sandra Bullock', u'Thandie Newton']" 7.9,Avatar,PG-13,Action,162,"[u'Sam Worthington', u'Zoe Saldana', u'Sigourney Weaver']" 7.9,Iron Man,PG-13,Action,126,"[u'Robert Downey Jr.', u'Gwyneth Paltrow', u'Terrence Howard']" 7.9,Do the Right Thing,R,Comedy,120,"[u'Danny Aiello', u'Ossie Davis', u'Ruby Dee']" 7.9,Carlito's Way,R,Crime,144,"[u'Al Pacino', u'Sean Penn', u'Penelope Ann Miller']" 7.9,"Crouching Tiger, Hidden Dragon",PG-13,Action,120,"[u'Yun-Fat Chow', u'Michelle Yeoh', u'Ziyi Zhang']" 7.9,Lilya 4-Ever,R,Crime,109,"[u'Oksana Akinshina', u'Artyom Bogucharskiy', u'Pavel Ponomaryov']" 7.9,The Chorus,PG-13,Drama,97,"[u'G\xe9rard Jugnot', u'Fran\xe7ois Berl\xe9and', u'Jean-Baptiste Maunier']" 7.9,The Boondock Saints,R,Action,108,"[u'Willem Dafoe', u'Sean Patrick Flanery', u'Norman Reedus']" 7.9,Miller's Crossing,R,Crime,115,"[u'Gabriel Byrne', u'Albert Finney', u'John Turturro']" 7.9,Walk the Line,PG-13,Biography,136,"[u'Joaquin Phoenix', u'Reese Witherspoon', u'Ginnifer Goodwin']" 7.9,Shrek,PG,Animation,90,"[u'Mike Myers', u'Eddie Murphy', u'Cameron Diaz']" 7.9,My Fair Lady,APPROVED,Drama,170,"[u'Audrey Hepburn', u'Rex Harrison', u'Stanley Holloway']" 7.9,The Ten Commandments,APPROVED,Adventure,220,"[u'Charlton Heston', u'Yul Brynner', u'Anne Baxter']" 7.9,The Fighter,R,Biography,116,"[u'Mark Wahlberg', u'Christian Bale', u'Amy Adams']" 7.9,Captain Phillips,PG-13,Biography,134,"[u'Tom Hanks', u'Barkhad Abdi', u'Barkhad Abdirahman']" 7.9,Cabaret,PG,Drama,124,"[u'Liza Minnelli', u'Michael York', u'Helmut Griem']" 7.9,Halloween,R,Drama,91,"[u'Donald Pleasence', u'Jamie Lee Curtis', u'Tony Moran']" 7.9,My Left Foot,R,Biography,103,"[u'Daniel Day-Lewis', u'Brenda Fricker', u'Alison Whelan']" 7.9,Miracle on 34th Street,APPROVED,Comedy,96,"[u'Edmund Gwenn', u""Maureen O'Hara"", u'John Payne']" 7.9,The Man from Nowhere,R,Action,119,"[u'Bin Won', u'Sae-ron Kim', u'Tae-hoon Kim']" 7.9,Toki o kakeru sh̫jo,NOT RATED,Animation,98,"[u'Riisa Naka', u'Takuya Ishida', u'Mitsutaka Itakura']" 7.9,Little Miss Sunshine,R,Adventure,101,"[u'Steve Carell', u'Toni Collette', u'Greg Kinnear']" 7.9,Taken,PG-13,Action,93,"[u'Liam Neeson', u'Maggie Grace', u'Famke Janssen']" 7.9,Blue Is the Warmest Color,NC-17,Drama,179,"[u'L\xe9a Seydoux', u'Ad\xe8le Exarchopoulos', u'Salim Kechiouche']" 7.9,Boogie Nights,R,Drama,155,"[u'Mark Wahlberg', u'Julianne Moore', u'Burt Reynolds']" 7.9,Hot Fuzz,R,Comedy,121,"[u'Simon Pegg', u'Nick Frost', u'Martin Freeman']" 7.9,Ferris Bueller's Day Off,PG-13,Comedy,103,"[u'Matthew Broderick', u'Alan Ruck', u'Mia Sara']" 7.9,The World's Fastest Indian,PG-13,Biography,127,"[u'Anthony Hopkins', u'Diane Ladd', u'Iain Rea']" 7.9,Kiki's Delivery Service,G,Animation,103,"[u'Kirsten Dunst', u'Minami Takayama', u'Rei Sakuma']" 7.9,Down by Law,R,Comedy,107,"[u'Tom Waits', u'John Lurie', u'Roberto Benigni']" 7.9,Glengarry Glen Ross,R,Drama,100,"[u'Al Pacino', u'Jack Lemmon', u'Alec Baldwin']" 7.9,Clerks,R,Comedy,92,"[u""Brian O'Halloran"", u'Jeff Anderson', u'Marilyn Ghigliotti']" 7.9,Adam's Apples,R,Comedy,94,"[u'Ulrich Thomsen', u'Mads Mikkelsen', u'Nicolas Bro']" 7.9,The Girl with the Dragon Tattoo,R,Crime,158,"[u'Daniel Craig', u'Rooney Mara', u'Christopher Plummer']" 7.9,The 39 Steps,UNRATED,Mystery,86,"[u'Robert Donat', u'Madeleine Carroll', u'Lucie Mannheim']" 7.9,E.T. the Extra-Terrestrial,PG,Family,115,"[u'Henry Thomas', u'Drew Barrymore', u'Peter Coyote']" 7.8,Tombstone,R,Action,130,"[u'Kurt Russell', u'Val Kilmer', u'Sam Elliott']" 7.8,Predator,R,Action,107,"[u'Arnold Schwarzenegger', u'Carl Weathers', u'Kevin Peter Hall']" 7.8,Tangled,PG,Animation,100,"[u'Mandy Moore', u'Zachary Levi', u'Donna Murphy']" 7.8,Guess Who's Coming to Dinner,UNRATED,Comedy,108,"[u'Spencer Tracy', u'Sidney Poitier', u'Katharine Hepburn']" 7.8,Ordinary People,R,Drama,124,"[u'Donald Sutherland', u'Mary Tyler Moore', u'Judd Hirsch']" 7.8,The Best Offer,R,Crime,131,"[u'Geoffrey Rush', u'Jim Sturgess', u'Sylvia Hoeks']" 7.8,Silver Linings Playbook,R,Comedy,122,"[u'Bradley Cooper', u'Jennifer Lawrence', u'Robert De Niro']" 7.8,Hamlet,PG-13,Drama,242,"[u'Kenneth Branagh', u'Julie Christie', u'Derek Jacobi']" 7.8,Amour,PG-13,Drama,127,"[u'Jean-Louis Trintignant', u'Emmanuelle Riva', u'Isabelle Huppert']" 7.8,I Saw the Devil,NOT RATED,Crime,141,"[u'Byung-hun Lee', u'Min-sik Choi', u'In-seo Kim']" 7.8,The Motorcycle Diaries,R,Adventure,126,"[u'Gael Garc\xeda Bernal', u'Rodrigo De la Serna', u'M\xeda Maestro']" 7.8,Office Space,R,Comedy,89,"[u'Ron Livingston', u'Jennifer Aniston', u'David Herman']" 7.8,Evil,NOT RATED,Drama,113,"[u'Andreas Wilson', u'Henrik Lundstr\xf6m', u'Gustaf Skarsg\xe5rd']" 7.8,The Girl with the Dragon Tattoo,R,Crime,152,"[u'Michael Nyqvist', u'Noomi Rapace', u'Ewa Fr\xf6ling']" 7.8,Withnail & I,R,Comedy,107,"[u'Richard E. Grant', u'Paul McGann', u'Richard Griffiths']" 7.8,The Day the Earth Stood Still,APPROVED,Sci-Fi,92,"[u'Michael Rennie', u'Patricia Neal', u'Hugh Marlowe']" 7.8,Black Book,R,Drama,145,"[u'Carice van Houten', u'Sebastian Koch', u'Thom Hoffman']" 7.8,Evil Dead II,X,Comedy,84,"[u'Bruce Campbell', u'Sarah Berry', u'Dan Hicks']" 7.8,Kaze tachinu,PG-13,Animation,126,"[u'Hideaki Anno', u'Hidetoshi Nishijima', u'Miori Takimoto']" 7.8,Star Trek Into Darkness,PG-13,Action,132,"[u'Chris Pine', u'Zachary Quinto', u'Zoe Saldana']" 7.8,The Triplets of Belleville,PG-13,Animation,78,"[u'Mich\xe8le Caucheteux', u'Jean-Claude Donda', u'Michel Robin']" 7.8,Open Your Eyes,R,Drama,117,"[u'Eduardo Noriega', u'Pen\xe9lope Cruz', u'Chete Lera']" 7.8,The Magnificent Seven,NOT RATED,Action,128,"[u'Yul Brynner', u'Steve McQueen', u'Charles Bronson']" 7.8,Drive,R,Crime,100,"[u'Ryan Gosling', u'Carey Mulligan', u'Bryan Cranston']" 7.8,Mississippi Burning,R,Crime,128,"[u'Gene Hackman', u'Willem Dafoe', u'Frances McDormand']" 7.8,The Lego Movie,PG,Animation,100,"[u'Chris Pratt', u'Will Ferrell', u'Elizabeth Banks']" 7.8,The Day of the Jackal,PG,Crime,143,"[u'Edward Fox', u'Terence Alexander', u'Michel Auclair']" 7.8,Misery,R,Thriller,107,"[u'James Caan', u'Kathy Bates', u'Richard Farnsworth']" 7.8,Willy Wonka & the Chocolate Factory,G,Family,100,"[u'Gene Wilder', u'Jack Albertson', u'Peter Ostrum']" 7.8,Fantasia,G,Animation,125,"[u'Leopold Stokowski', u'Deems Taylor', u'Corey Burton']" 7.8,Gattaca,PG-13,Drama,106,"[u'Ethan Hawke', u'Uma Thurman', u'Jude Law']" 7.8,American Gangster,R,Biography,157,"[u'Denzel Washington', u'Russell Crowe', u'Chiwetel Ejiofor']" 7.8,Porco Rosso,PG,Animation,94,"[u'Sh\xfbichir\xf4 Moriyama', u'Tokiko Kat\xf4', u'Sanshi Katsura']" 7.8,The French Connection,R,Action,104,"[u'Gene Hackman', u'Roy Scheider', u'Fernando Rey']" 7.8,The Boy in the Striped Pajamas,PG-13,Drama,94,"[u'Asa Butterfield', u'David Thewlis', u'Rupert Friend']" 7.8,Empire of the Sun,PG,Drama,153,"[u'Christian Bale', u'John Malkovich', u'Miranda Richardson']" 7.8,About Time,R,Drama,123,"[u'Domhnall Gleeson', u'Rachel McAdams', u'Bill Nighy']" 7.8,Blue Velvet,R,Crime,120,"[u'Isabella Rossellini', u'Kyle MacLachlan', u'Dennis Hopper']" 7.8,In America,PG-13,Drama,105,"[u'Paddy Considine', u'Samantha Morton', u'Djimon Hounsou']" 7.8,The Curious Case of Benjamin Button,PG-13,Drama,166,"[u'Brad Pitt', u'Cate Blanchett', u'Tilda Swinton']" 7.8,The Last of the Mohicans,R,Action,112,"[u'Daniel Day-Lewis', u'Madeleine Stowe', u'Russell Means']" 7.8,Moonrise Kingdom,PG-13,Adventure,94,"[u'Jared Gilman', u'Kara Hayward', u'Bruce Willis']" 7.8,Rebel Without a Cause,PG-13,Drama,111,"[u'James Dean', u'Natalie Wood', u'Sal Mineo']" 7.8,Fantastic Mr. Fox,PG,Animation,87,"[u'George Clooney', u'Meryl Streep', u'Bill Murray']" 7.8,Invasion of the Body Snatchers,APPROVED,Horror,80,"[u'Kevin McCarthy', u'Dana Wynter', u'Larry Gates']" 7.8,October Sky,PG,Biography,108,"[u'Jake Gyllenhaal', u'Chris Cooper', u'Laura Dern']" 7.8,Dirty Harry,R,Action,102,"[u'Clint Eastwood', u'Andrew Robinson', u'Harry Guardino']" 7.8,Ghostbusters,PG,Comedy,105,"[u'Bill Murray', u'Dan Aykroyd', u'Sigourney Weaver']" 7.8,Captain America: The Winter Soldier,PG-13,Action,136,"[u'Chris Evans', u'Samuel L. Jackson', u'Scarlett Johansson']" 7.8,Wreck-It Ralph,PG,Animation,101,"[u'John C. Reilly', u'Jack McBrayer', u'Jane Lynch']" 7.8,The Hangover,R,Comedy,100,"[u'Zach Galifianakis', u'Bradley Cooper', u'Justin Bartha']" 7.8,Back to the Future Part II,PG,Adventure,108,"[u'Michael J. Fox', u'Christopher Lloyd', u'Lea Thompson']" 7.8,Belle de Jour,APPROVED,Drama,101,"[u'Catherine Deneuve', u'Jean Sorel', u'Michel Piccoli']" 7.8,"O Brother, Where Art Thou?",PG-13,Adventure,106,"[u'George Clooney', u'John Turturro', u'Tim Blake Nelson']" 7.8,Repulsion,UNRATED,Drama,105,"[u'Catherine Deneuve', u'Ian Hendry', u'John Fraser']" 7.8,Airplane!,PG,Comedy,88,"[u'Robert Hays', u'Julie Hagerty', u'Leslie Nielsen']" 7.8,Pride & Prejudice,PG,Drama,129,"[u'Keira Knightley', u'Matthew Macfadyen', u'Brenda Blethyn']" 7.8,Zulu,UNRATED,Drama,138,"[u'Stanley Baker', u'Jack Hawkins', u'Ulla Jacobsson']" 7.8,Night on Earth,R,Comedy,129,"[u'Winona Ryder', u'Gena Rowlands', u'Lisanne Falk']" 7.8,From Here to Eternity,NOT RATED,Drama,118,"[u'Burt Lancaster', u'Montgomery Clift', u'Deborah Kerr']" 7.8,Apocalypto,R,Action,139,"[u'Gerardo Taracena', u'Raoul Trujillo', u'Dalia Hern\xe1ndez']" 7.8,Atonement,R,Drama,123,"[u'Keira Knightley', u'James McAvoy', u'Brenda Blethyn']" 7.8,The Dirty Dozen,NOT RATED,Action,150,"[u'Lee Marvin', u'Ernest Borgnine', u'Charles Bronson']" 7.8,X-Men: First Class,PG-13,Action,132,"[u'James McAvoy', u'Michael Fassbender', u'Jennifer Lawrence']" 7.8,Run Lola Run,R,Action,80,"[u'Franka Potente', u'Moritz Bleibtreu', u'Herbert Knaup']" 7.8,The Longest Day,G,Action,178,"[u'John Wayne', u'Robert Ryan', u'Richard Burton']" 7.8,Zelig,PG,Comedy,79,"[u'Woody Allen', u'Mia Farrow', u'Patrick Horgan']" 7.8,The Last Emperor,PG-13,Biography,163,"[u'John Lone', u'Joan Chen', u""Peter O'Toole""]" 7.8,The Goonies,PG,Adventure,114,"[u'Sean Astin', u'Josh Brolin', u'Jeff Cohen']" 7.8,The White Ribbon,R,Drama,144,"[u'Christian Friedel', u'Ernst Jacobi', u'Leonie Benesch']" 7.8,The Fugitive,PG-13,Action,130,"[u'Harrison Ford', u'Tommy Lee Jones', u'Sela Ward']" 7.8,The Color Purple,PG-13,Drama,154,"[u'Danny Glover', u'Whoopi Goldberg', u'Oprah Winfrey']" 7.8,South Park: Bigger Longer & Uncut,R,Animation,81,"[u'Trey Parker', u'Matt Stone', u'Mary Kay Bergman']" 7.8,(500) Days of Summer,PG-13,Comedy,95,"[u'Zooey Deschanel', u'Joseph Gordon-Levitt', u'Geoffrey Arend']" 7.8,Lost in Translation,R,Drama,101,"[u'Bill Murray', u'Scarlett Johansson', u'Giovanni Ribisi']" 7.8,Argo,R,Drama,120,"[u'Ben Affleck', u'Bryan Cranston', u'John Goodman']" 7.8,Blazing Saddles,R,Comedy,93,"[u'Cleavon Little', u'Gene Wilder', u'Slim Pickens']" 7.8,Breakfast at Tiffany's,NOT RATED,Comedy,115,"[u'Audrey Hepburn', u'George Peppard', u'Patricia Neal']" 7.8,Finding Neverland,PG,Biography,106,"[u'Johnny Depp', u'Kate Winslet', u'Julie Christie']" 7.8,The Experiment,R,Drama,120,"[u'Moritz Bleibtreu', u'Christian Berkel', u'Oliver Stokowski']" 7.8,Lucky Number Slevin,R,Crime,110,"[u'Josh Hartnett', u'Ben Kingsley', u'Morgan Freeman']" 7.8,The Theory of Everything,PG-13,Biography,123,"[u'Eddie Redmayne', u'Felicity Jones', u'Tom Prior']" 7.8,Harry Potter and the Prisoner of Azkaban,PG,Adventure,142,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']" 7.8,Kung Fu Hustle,R,Action,99,"[u'Stephen Chow', u'Wah Yuen', u'Qiu Yuen']" 7.8,Being John Malkovich,R,Comedy,112,"[u'John Cusack', u'Cameron Diaz', u'Catherine Keener']" 7.8,The Social Network,PG-13,Biography,120,"[u'Jesse Eisenberg', u'Andrew Garfield', u'Justin Timberlake']" 7.8,3:10 to Yuma,R,Adventure,122,"[u'Russell Crowe', u'Christian Bale', u'Ben Foster']" 7.8,The Name of the Rose,R,Crime,130,"[u'Sean Connery', u'Christian Slater', u'Helmut Qualtinger']" 7.8,Mary Poppins,APPROVED,Comedy,139,"[u'Julie Andrews', u'Dick Van Dyke', u'David Tomlinson']" 7.8,The Game,R,Drama,129,"[u'Michael Douglas', u'Deborah Kara Unger', u'Sean Penn']" 7.8,Changeling,R,Drama,141,"[u'Angelina Jolie', u'Colm Feore', u'Amy Ryan']" 7.8,Donnie Brasco,R,Biography,127,"[u'Al Pacino', u'Johnny Depp', u'Michael Madsen']" 7.8,Serpico,R,Biography,130,"[u'Al Pacino', u'John Randolph', u'Jack Kehoe']" 7.8,What's Eating Gilbert Grape,PG-13,Drama,118,"[u'Johnny Depp', u'Leonardo DiCaprio', u'Juliette Lewis']" 7.8,Goldfinger,APPROVED,Action,110,"[u'Sean Connery', u'Gert Fr\xf6be', u'Honor Blackman']" 7.8,Nebraska,R,Adventure,115,"[u'Bruce Dern', u'Will Forte', u'June Squibb']" 7.8,The Sandlot,PG,Comedy,101,"[u'Tom Guiry', u'Mike Vitar', u'Patrick Renna']" 7.8,Boyz n the Hood,R,Crime,112,"[u'Cuba Gooding Jr.', u'Laurence Fishburne', u'Hudhail Al-Amir']" 7.8,The Bourne Supremacy,PG-13,Action,108,"[u'Matt Damon', u'Franka Potente', u'Joan Allen']" 7.8,Skyfall,PG-13,Action,143,"[u'Daniel Craig', u'Javier Bardem', u'Naomie Harris']" 7.8,Ray,PG-13,Biography,152,"[u'Jamie Foxx', u'Regina King', u'Kerry Washington']" 7.8,300,R,Action,117,"[u'Gerard Butler', u'Lena Headey', u'David Wenham']" 7.8,A Bronx Tale,R,Crime,121,"[u'Robert De Niro', u'Chazz Palminteri', u'Lillo Brancato']" 7.8,The Birds,PG-13,Horror,119,"[u'Rod Taylor', u'Tippi Hedren', u'Suzanne Pleshette']" 7.8,Battle Royale,NOT RATED,Action,114,"[u'Tatsuya Fujiwara', u'Aki Maeda', u'Tar\xf4 Yamamoto']" 7.8,Control,R,Biography,122,"[u'Sam Riley', u'Samantha Morton', u'Craig Parkinson']" 7.8,Kramer vs. Kramer,APPROVED,Drama,105,"[u'Dustin Hoffman', u'Meryl Streep', u'Jane Alexander']" 7.8,Deliverance,R,Adventure,110,"[u'Jon Voight', u'Burt Reynolds', u'Ned Beatty']" 7.8,Ocean's Eleven,PG-13,Crime,116,"[u'George Clooney', u'Brad Pitt', u'Julia Roberts']" 7.8,As Good as It Gets,PG-13,Comedy,139,"[u'Jack Nicholson', u'Helen Hunt', u'Greg Kinnear']" 7.8,Awakenings,PG-13,Biography,121,"[u'Robert De Niro', u'Robin Williams', u'Julie Kavner']" 7.8,The King of Comedy,PG,Comedy,109,"[u'Robert De Niro', u'Jerry Lewis', u'Diahnne Abbott']" 7.8,Man on Fire,R,Action,146,"[u'Denzel Washington', u'Christopher Walken', u'Dakota Fanning']" 7.8,Sabrina,UNRATED,Comedy,113,"[u'Humphrey Bogart', u'Audrey Hepburn', u'William Holden']" 7.8,Dark City,R,Mystery,100,"[u'Rufus Sewell', u'Kiefer Sutherland', u'Jennifer Connelly']" 7.8,Good Bye Lenin!,R,Comedy,121,"[u'Daniel Br\xfchl', u'Katrin Sa\xdf', u'Chulpan Khamatova']" 7.7,Remember the Titans,PG,Biography,113,"[u'Denzel Washington', u'Will Patton', u'Wood Harris']" 7.7,Star Trek II: The Wrath of Khan,PG,Action,113,"[u'William Shatner', u'Leonard Nimoy', u'DeForest Kelley']" 7.7,Road to Perdition,R,Crime,117,"[u'Tom Hanks', u'Tyler Hoechlin', u'Rob Maxey']" 7.7,Rushmore,R,Comedy,93,"[u'Jason Schwartzman', u'Bill Murray', u'Olivia Williams']" 7.7,The Machinist,R,Drama,101,"[u'Christian Bale', u'Jennifer Jason Leigh', u'Aitana S\xe1nchez-Gij\xf3n']" 7.7,Flipped,PG,Comedy,90,"[u'Madeline Carroll', u'Callan McAuliffe', u'Rebecca De Mornay']" 7.7,The Count of Monte Cristo,PG-13,Action,131,"[u'Jim Caviezel', u'Guy Pearce', u'Richard Harris']" 7.7,Detachment,NOT RATED,Drama,98,"[u'Adrien Brody', u'Christina Hendricks', u'Marcia Gay Harden']" 7.7,Love Me If You Dare,R,Comedy,93,"[u'Guillaume Canet', u'Marion Cotillard', u'Thibault Verhaeghe']" 7.7,Dead Man,R,Drama,121,"[u'Johnny Depp', u'Gary Farmer', u'Crispin Glover']" 7.7,The Purple Rose of Cairo,PG,Comedy,82,"[u'Mia Farrow', u'Jeff Daniels', u'Danny Aiello']" 7.7,21 Grams,R,Crime,124,"[u'Sean Penn', u'Benicio Del Toro', u'Naomi Watts']" 7.7,50/50,R,Comedy,100,"[u'Joseph Gordon-Levitt', u'Seth Rogen', u'Anna Kendrick']" 7.7,Kick-Ass,R,Action,117,"[u'Aaron Taylor-Johnson', u'Nicolas Cage', u'Chlo\xeb Grace Moretz']" 7.7,Delicatessen,R,Comedy,99,"[u'Marie-Laure Dougnac', u'Dominique Pinon', u'Pascal Benezech']" 7.7,Barton Fink,R,Drama,116,"[u'John Turturro', u'John Goodman', u'Judy Davis']" 7.7,The Last King of Scotland,R,Biography,123,"[u'James McAvoy', u'Forest Whitaker', u'Gillian Anderson']" 7.7,Adaptation.,R,Comedy,114,"[u'Nicolas Cage', u'Meryl Streep', u'Chris Cooper']" 7.7,A Very Long Engagement,R,Drama,133,"[u'Audrey Tautou', u'Gaspard Ulliel', u'Jodie Foster']" 7.7,Mysterious Skin,NC-17,Drama,105,"[u'Brady Corbet', u'Joseph Gordon-Levitt', u'Elisabeth Shue']" 7.7,Stardust,PG-13,Adventure,127,"[u'Charlie Cox', u'Claire Danes', u'Sienna Miller']" 7.7,Black Hawk Down,R,Drama,144,"[u'Josh Hartnett', u'Ewan McGregor', u'Tom Sizemore']" 7.7,Paprika,R,Animation,90,"[u'Megumi Hayashibara', u'T\xf4ru Emori', u'Katsunosuke Hori']" 7.7,Fear and Loathing in Las Vegas,R,Comedy,118,"[u'Johnny Depp', u'Benicio Del Toro', u'Tobey Maguire']" 7.7,Frost/Nixon,R,Drama,122,"[u'Frank Langella', u'Michael Sheen', u'Kevin Bacon']" 7.7,Enter the Dragon,R,Action,102,"[u'Bruce Lee', u'John Saxon', u'Jim Kelly']" 7.7,Short Cuts,R,Comedy,187,"[u'Andie MacDowell', u'Julianne Moore', u'Tim Robbins']" 7.7,A Hard Day's Night,APPROVED,Comedy,87,"[u'John Lennon', u'Paul McCartney', u'George Harrison']" 7.7,The Last Samurai,R,Action,154,"[u'Tom Cruise', u'Ken Watanabe', u'Billy Connolly']" 7.7,The Station Agent,R,Comedy,89,"[u'Peter Dinklage', u'Patricia Clarkson', u'Bobby Cannavale']" 7.7,Zombieland,R,Comedy,88,"[u'Jesse Eisenberg', u'Emma Stone', u'Woody Harrelson']" 7.7,Sympathy for Mr. Vengeance,R,Crime,129,"[u'Kang-ho Song', u'Ha-kyun Shin', u'Doona Bae']" 7.7,Despicable Me,PG,Animation,95,"[u'Steve Carell', u'Jason Segel', u'Russell Brand']" 7.7,Shane,APPROVED,Drama,118,"[u'Alan Ladd', u'Jean Arthur', u'Van Heflin']" 7.7,Forbidden Planet,PASSED,Action,98,"[u'Walter Pidgeon', u'Anne Francis', u'Leslie Nielsen']" 7.7,Titanic,PG-13,Drama,194,"[u'Leonardo DiCaprio', u'Kate Winslet', u'Billy Zane']" 7.7,Gone Baby Gone,R,Crime,114,"[u'Morgan Freeman', u'Ed Harris', u'Casey Affleck']" 7.7,The Dinner Game,PG-13,Comedy,80,"[u'Thierry Lhermitte', u'Jacques Villeret', u'Francis Huster']" 7.7,Dawn of the Planet of the Apes,PG-13,Action,130,"[u'Gary Oldman', u'Keri Russell', u'Andy Serkis']" 7.7,25th Hour,R,Crime,135,"[u'Edward Norton', u'Barry Pepper', u'Philip Seymour Hoffman']" 7.7,Ponyo,G,Animation,101,"[u'Cate Blanchett', u'Matt Damon', u'Liam Neeson']" 7.7,Shine,PG-13,Biography,105,"[u'Geoffrey Rush', u'Armin Mueller-Stahl', u'Justin Braine']" 7.7,The Blind Side,PG-13,Biography,129,"[u'Quinton Aaron', u'Sandra Bullock', u'Tim McGraw']" 7.7,Brokeback Mountain,R,Drama,134,"[u'Jake Gyllenhaal', u'Heath Ledger', u'Michelle Williams']" 7.7,Cast Away,PG-13,Adventure,143,"[u'Tom Hanks', u'Helen Hunt', u'Paul Sanchez']" 7.7,Malcolm X,PG-13,Biography,202,"[u'Denzel Washington', u'Angela Bassett', u'Delroy Lindo']" 7.7,Show Me Love,UNRATED,Comedy,89,"[u'Alexandra Dahlstr\xf6m', u'Rebecka Liljeberg', u'Erica Carlson']" 7.7,Boy A,R,Drama,106,"[u'Andrew Garfield', u'Peter Mullan', u'Shaun Evans']" 7.7,The Warriors,R,Action,92,"[u'Michael Beck', u'James Remar', u'Dorsey Wright']" 7.7,Close Encounters of the Third Kind,PG,Drama,137,"[u'Richard Dreyfuss', u'Fran\xe7ois Truffaut', u'Teri Garr']" 7.7,Match Point,R,Drama,124,"[u'Scarlett Johansson', u'Jonathan Rhys Meyers', u'Emily Mortimer']" 7.7,Training Day,R,Crime,122,"[u'Denzel Washington', u'Ethan Hawke', u'Scott Glenn']" 7.7,The Butterfly Effect,R,Sci-Fi,113,"[u'Ashton Kutcher', u'Amy Smart', u'Melora Walters']" 7.7,The City of Lost Children,R,Fantasy,112,"[u'Ron Perlman', u'Daniel Emilfork', u'Judith Vittet']" 7.7,Billy Elliot,PG-13,Comedy,110,"[u'Jamie Bell', u'Julie Walters', u'Jean Heywood']" 7.7,Love Actually,R,Comedy,135,"[u'Hugh Grant', u'Martine McCutcheon', u'Liam Neeson']" 7.7,The Producers,PG,Comedy,88,"[u'Zero Mostel', u'Gene Wilder', u'Dick Shawn']" 7.7,Harry Potter and the Deathly Hallows: Part 1,PG-13,Adventure,146,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']" 7.7,The Visitor,PG-13,Drama,104,"[u'Richard Jenkins', u'Haaz Sleiman', u'Danai Gurira']" 7.7,Philadelphia,PG-13,Drama,125,"[u'Tom Hanks', u'Denzel Washington', u'Roberta Maxwell']" 7.7,Sense and Sensibility,PG,Drama,131,"[u'Emma Thompson', u'Kate Winslet', u'James Fleet']" 7.7,Three Colors: White,R,Comedy,91,"[u'Zbigniew Zamachowski', u'Julie Delpy', u'Janusz Gajos']" 7.7,La grande bellezza,NOT RATED,Drama,142,"[u'Toni Servillo', u'Carlo Verdone', u'Sabrina Ferilli']" 7.7,Primal Fear,R,Crime,129,"[u'Richard Gere', u'Laura Linney', u'Edward Norton']" 7.7,Where Eagles Dare,M,Action,158,"[u'Richard Burton', u'Clint Eastwood', u'Mary Ure']" 7.7,Dazed and Confused,R,Comedy,102,"[u'Jason London', u'Wiley Wiggins', u'Matthew McConaughey']" 7.7,Blood Simple.,R,Crime,99,"[u'John Getz', u'Frances McDormand', u'Dan Hedaya']" 7.7,This Is England,UNRATED,Crime,101,"[u'Thomas Turgoose', u'Stephen Graham', u'Jo Hartley']" 7.7,Fearless,PG-13,Action,104,"[u'Jet Li', u'Li Sun', u'Yong Dong']" 7.7,Dangerous Liaisons,R,Drama,119,"[u'Glenn Close', u'John Malkovich', u'Michelle Pfeiffer']" 7.7,Saw,R,Horror,103,"[u'Cary Elwes', u'Leigh Whannell', u'Danny Glover']" 7.7,Snow White and the Seven Dwarfs,APPROVED,Animation,88,"[u'Adriana Caselotti', u'Harry Stockwell', u'Lucille La Verne']" 7.7,Coraline,PG,Animation,100,"[u'Dakota Fanning', u'Teri Hatcher', u'John Hodgman']" 7.7,The Hunger Games: Catching Fire,PG-13,Adventure,146,"[u'Jennifer Lawrence', u'Josh Hutcherson', u'Liam Hemsworth']" 7.7,Sophie's Choice,R,Drama,150,"[u'Meryl Streep', u'Kevin Kline', u'Peter MacNicol']" 7.7,La Vie en Rose,PG-13,Biography,140,"[u'Marion Cotillard', u'Sylvie Testud', u'Pascal Greggory']" 7.7,Star Wars: Episode III - Revenge of the Sith,PG-13,Action,140,"[u'Hayden Christensen', u'Natalie Portman', u'Ewan McGregor']" 7.7,True Grit,PG-13,Adventure,110,"[u'Jeff Bridges', u'Matt Damon', u'Hailee Steinfeld']" 7.7,After Hours,R,Comedy,97,"[u'Griffin Dunne', u'Rosanna Arquette', u'Verna Bloom']" 7.7,Frozen,PG,Animation,102,"[u'Kristen Bell', u'Idina Menzel', u'Jonathan Groff']" 7.7,Lolita,NOT RATED,Drama,152,"[u'James Mason', u'Shelley Winters', u'Sue Lyon']" 7.7,Eastern Promises,R,Crime,100,"[u'Naomi Watts', u'Viggo Mortensen', u'Armin Mueller-Stahl']" 7.7,Midnight in Paris,PG-13,Comedy,94,"[u'Owen Wilson', u'Rachel McAdams', u'Kathy Bates']" 7.7,The Muppet Christmas Carol,G,Comedy,85,"[u'Michael Caine', u'Dave Goelz', u'Steve Whitmire']" 7.7,Who Framed Roger Rabbit,PG,Animation,104,"[u'Bob Hoskins', u'Christopher Lloyd', u'Joanna Cassidy']" 7.7,Sympathy for Lady Vengeance,R,Crime,112,"[u'Yeong-ae Lee', u'Min-sik Choi', u'Shi-hoo Kim']" 7.7,Grindhouse,R,Action,191,"[u'Kurt Russell', u'Rose McGowan', u'Danny Trejo']" 7.7,In a Better World,R,Drama,119,"[u'Mikael Persbrandt', u'Trine Dyrholm', u'Markus Rygaard']" 7.7,Blow-Up,NOT RATED,Drama,111,"[u'David Hemmings', u'Vanessa Redgrave', u'Sarah Miles']" 7.7,The Secret World of Arrietty,G,Animation,94,"[u'Bridgit Mendler', u'Amy Poehler', u'Will Arnett']" 7.7,Kiss Kiss Bang Bang,R,Action,103,"[u'Robert Downey Jr.', u'Val Kilmer', u'Michelle Monaghan']" 7.7,Lost Highway,R,Drama,134,"[u'Bill Pullman', u'Patricia Arquette', u'John Roselius']" 7.7,Zodiac,R,Crime,157,"[u'Jake Gyllenhaal', u'Robert Downey Jr.', u'Mark Ruffalo']" 7.7,Les Mis̩rables,PG-13,Drama,158,"[u'Hugh Jackman', u'Russell Crowe', u'Anne Hathaway']" 7.7,Moulin Rouge!,PG-13,Drama,127,"[u'Nicole Kidman', u'Ewan McGregor', u'John Leguizamo']" 7.7,Whale Rider,PG-13,Drama,101,"[u'Keisha Castle-Hughes', u'Rawiri Paratene', u'Vicky Haughton']" 7.7,End of Watch,R,Crime,109,"[u'Jake Gyllenhaal', u'Michael Pe\xf1a', u'Anna Kendrick']" 7.7,Philomena,PG-13,Biography,98,"[u'Judi Dench', u'Steve Coogan', u'Sophie Kennedy Clark']" 7.7,Fury,R,Action,134,"[u'Brad Pitt', u'Shia LaBeouf', u'Logan Lerman']" 7.7,The Big Blue,PG-13,Action,168,"[u'Jean-Marc Barr', u'Jean Reno', u'Rosanna Arquette']" 7.7,First Blood,R,Action,93,"[u'Sylvester Stallone', u'Brian Dennehy', u'Richard Crenna']" 7.7,Minority Report,PG-13,Action,145,"[u'Tom Cruise', u'Colin Farrell', u'Samantha Morton']" 7.7,Spellbound,UNRATED,Film-Noir,111,"[u'Ingrid Bergman', u'Gregory Peck', u'Michael Chekhov']" 7.7,The Wicker Man,R,Horror,88,"[u'Edward Woodward', u'Christopher Lee', u'Diane Cilento']" 7.7,Seven Pounds,PG-13,Drama,123,"[u'Will Smith', u'Rosario Dawson', u'Woody Harrelson']" 7.7,Midnight Express,R,Biography,121,"[u'Brad Davis', u'Irene Miracle', u'Bo Hopkins']" 7.7,Kelly's Heroes,GP,Action,144,"[u'Clint Eastwood', u'Telly Savalas', u'Don Rickles']" 7.7,MASH,R,Comedy,116,"[u'Donald Sutherland', u'Elliott Gould', u'Tom Skerritt']" 7.7,The Player,R,Comedy,124,"[u'Tim Robbins', u'Greta Scacchi', u'Fred Ward']" 7.7,Traffic,R,Crime,147,"[u'Michael Douglas', u'Benicio Del Toro', u'Catherine Zeta-Jones']" 7.7,Y Tu Mam Tambi̩n,R,Drama,106,"[u'Maribel Verd\xfa', u'Gael Garc\xeda Bernal', u'Daniel Gim\xe9nez Cacho']" 7.7,Stranger Than Fiction,PG-13,Comedy,113,"[u'Will Ferrell', u'Emma Thompson', u'Dustin Hoffman']" 7.7,Inside Man,R,Crime,129,"[u'Denzel Washington', u'Clive Owen', u'Jodie Foster']" 7.6,127 Hours,R,Adventure,94,"[u'James Franco', u'Amber Tamblyn', u'Kate Mara']" 7.6,The Fifth Element,PG-13,Action,126,"[u'Bruce Willis', u'Milla Jovovich', u'Gary Oldman']" 7.6,Milk,R,Biography,128,"[u'Sean Penn', u'Josh Brolin', u'Emile Hirsch']" 7.6,United 93,R,Action,111,"[u'David Alan Basche', u'Olivia Thirlby', u'Liza Col\xf3n-Zayas']" 7.6,Jin l_ng sh_ san chai,R,Drama,146,"[u'Christian Bale', u'Ni Ni', u'Xinyi Zhang']" 7.6,Dracula,APPROVED,Horror,85,"[u'Bela Lugosi', u'Helen Chandler', u'David Manners']" 7.6,High Plains Drifter,R,Western,105,"[u'Clint Eastwood', u'Verna Bloom', u'Marianna Hill']" 7.6,I Am Sam,PG-13,Drama,132,"[u'Sean Penn', u'Michelle Pfeiffer', u'Dakota Fanning']" 7.6,Fried Green Tomatoes,PG-13,Drama,130,"[u'Kathy Bates', u'Jessica Tandy', u'Mary Stuart Masterson']" 7.6,Watchmen,R,Action,162,"[u'Jackie Earle Haley', u'Patrick Wilson', u'Carla Gugino']" 7.6,28 Days Later...,R,Horror,113,"[u'Cillian Murphy', u'Naomie Harris', u'Christopher Eccleston']" 7.6,The Illusionist,PG-13,Drama,110,"[u'Edward Norton', u'Jessica Biel', u'Paul Giamatti']" 7.6,Superbad,R,Comedy,113,"[u'Michael Cera', u'Jonah Hill', u'Christopher Mintz-Plasse']" 7.6,The Godfather: Part III,R,Crime,162,"[u'Al Pacino', u'Diane Keaton', u'Andy Garcia']" 7.6,The Royal Tenenbaums,R,Comedy,110,"[u'Gene Hackman', u'Gwyneth Paltrow', u'Anjelica Huston']" 7.6,The Jungle Book,APPROVED,Animation,78,"[u'Phil Harris', u'Sebastian Cabot', u'Louis Prima']" 7.6,Thank You for Smoking,R,Comedy,92,"[u'Aaron Eckhart', u'Cameron Bright', u'Maria Bello']" 7.6,Man Bites Dog,NC-17,Comedy,95,"[u'Beno\xeet Poelvoorde', u'Jacqueline Poelvoorde-Pappaert', u'Nelly Pappaert']" 7.6,A Few Good Men,R,Drama,138,"[u'Tom Cruise', u'Jack Nicholson', u'Demi Moore']" 7.6,The Legend of Drunken Master,R,Action,102,"[u'Jackie Chan', u'Ho-Sung Pak', u'Lung Ti']" 7.6,The Kite Runner,PG-13,Drama,128,"[u'Khalid Abdalla', u'Ahmad Khan Mahmoodzada', u'Atossa Leoni']" 7.6,When Harry Met Sally...,R,Comedy,96,"[u'Billy Crystal', u'Meg Ryan', u'Carrie Fisher']" 7.6,Munich,R,Drama,164,"[u'Eric Bana', u'Daniel Craig', u'Marie-Jos\xe9e Croze']" 7.6,House of Sand and Fog,R,Drama,126,"[u'Jennifer Connelly', u'Ben Kingsley', u'Ron Eldard']" 7.6,Lethal Weapon,R,Action,110,"[u'Mel Gibson', u'Danny Glover', u'Gary Busey']" 7.6,Animal House,R,Comedy,109,"[u'John Belushi', u'Karen Allen', u'Tom Hulce']" 7.6,Lord of War,R,Crime,122,"[u'Nicolas Cage', u'Ethan Hawke', u'Jared Leto']" 7.6,Blow,R,Biography,124,"[u'Johnny Depp', u'Pen\xe9lope Cruz', u'Franka Potente']" 7.6,Sherlock Holmes,PG-13,Action,128,"[u'Robert Downey Jr.', u'Jude Law', u'Rachel McAdams']" 7.6,Harry Potter and the Goblet of Fire,PG-13,Adventure,157,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']" 7.6,La piel que habito,R,Thriller,120,"[u'Antonio Banderas', u'Elena Anaya', u'Jan Cornet']" 7.6,The Crow,R,Action,102,"[u'Brandon Lee', u'Michael Wincott', u'Rochelle Davis']" 7.6,West Side Story,UNRATED,Crime,152,"[u'Natalie Wood', u'George Chakiris', u'Richard Beymer']" 7.6,The Thin Red Line,R,Drama,170,"[u'Jim Caviezel', u'Sean Penn', u'Nick Nolte']" 7.6,American Psycho,R,Crime,102,"[u'Christian Bale', u'Justin Theroux', u'Josh Lucas']" 7.6,The Blind Swordsman: Zatoichi,R,Action,116,"[u'Takeshi Kitano', u'Tadanobu Asano', u'Yui Natsukawa']" 7.6,The Last Temptation of Christ,R,Drama,164,"[u'Willem Dafoe', u'Harvey Keitel', u'Barbara Hershey']" 7.6,The Naked Gun: From the Files of Police Squad!,PG-13,Comedy,85,"[u'Leslie Nielsen', u'Priscilla Presley', u'O.J. Simpson']" 7.6,The Others,PG-13,Horror,104,"[u'Nicole Kidman', u'Christopher Eccleston', u'Fionnula Flanagan']" 7.6,The Party,PG,Comedy,99,"[u'Peter Sellers', u'Claudine Longet', u'Natalia Borisova']" 7.6,Army of Darkness,R,Comedy,81,"[u'Bruce Campbell', u'Embeth Davidtz', u'Marcus Gilbert']" 7.6,The Hurt Locker,R,Drama,131,"[u'Jeremy Renner', u'Anthony Mackie', u'Brian Geraghty']" 7.6,The Raid: Redemption,R,Action,101,"[u'Iko Uwais', u'Ananda George', u'Ray Sahetapy']" 7.6,Volver,R,Comedy,121,"[u'Pen\xe9lope Cruz', u'Carmen Maura', u'Lola Due\xf1as']" 7.6,Following,R,Mystery,69,"[u'Jeremy Theobald', u'Alex Haw', u'Lucy Russell']" 7.6,Kung Fu Panda,PG,Animation,92,"[u'Jack Black', u'Ian McShane', u'Angelina Jolie']" 7.6,Mad Max 2: The Road Warrior,R,Action,94,"[u'Mel Gibson', u'Bruce Spence', u'Michael Preston']" 7.6,Jsan-nin no shikaku,R,Action,141,"[u'K\xf4ji Yakusho', u'Takayuki Yamada', u'Y\xfbsuke Iseya']" 7.6,The Man Who Wasn't There,R,Crime,116,"[u'Billy Bob Thornton', u'Frances McDormand', u'Michael Badalucco']" 7.6,The Counterfeiters,R,Crime,98,"[u'Karl Markovics', u'August Diehl', u'Devid Striesow']" 7.6,The Hunt for Red October,PG,Action,135,"[u'Sean Connery', u'Alec Baldwin', u'Scott Glenn']" 7.6,Lone Survivor,R,Action,121,"[u'Mark Wahlberg', u'Taylor Kitsch', u'Emile Hirsch']" 7.6,A Fish Called Wanda,R,Comedy,108,"[u'John Cleese', u'Jamie Lee Curtis', u'Kevin Kline']" 7.6,The Impossible,PG-13,Drama,114,"[u'Naomi Watts', u'Ewan McGregor', u'Tom Holland']" 7.6,Garden State,R,Comedy,102,"[u'Zach Braff', u'Peter Sarsgaard', u'Natalie Portman']" 7.6,Escape from Alcatraz,PG,Action,112,"[u'Clint Eastwood', u'Patrick McGoohan', u'Roberts Blossom']" 7.6,Apollo 13,PG,Adventure,140,"[u'Tom Hanks', u'Bill Paxton', u'Kevin Bacon']" 7.6,"Lust, Caution",NC-17,Drama,157,"[u'Tony Chiu Wai Leung', u'Wei Tang', u'Joan Chen']" 7.6,Selma,PG-13,Biography,128,"[u'David Oyelowo', u'Carmen Ejogo', u'Tim Roth']" 7.6,Funny Games,UNRATED,Crime,108,"[u'Susanne Lothar', u'Ulrich M\xfche', u'Arno Frisch']" 7.6,The Abyss,PG-13,Adventure,139,"[u'Ed Harris', u'Mary Elizabeth Mastrantonio', u'Michael Biehn']" 7.6,Robin Hood,G,Animation,83,"[u'Brian Bedford', u'Phil Harris', u'Roger Miller']" 7.6,Enemy at the Gates,R,Drama,131,"[u'Jude Law', u'Ed Harris', u'Joseph Fiennes']" 7.6,Falling Down,R,Crime,113,"[u'Michael Douglas', u'Robert Duvall', u'Barbara Hershey']" 7.6,Little Children,R,Drama,136,"[u'Kate Winslet', u'Jennifer Connelly', u'Patrick Wilson']" 7.6,Hunger,NOT RATED,Biography,96,"[u'Stuart Graham', u'Laine Megaw', u'Brian Milligan']" 7.6,Indiana Jones and the Temple of Doom,PG,Action,118,"[u'Harrison Ford', u'Kate Capshaw', u'Jonathan Ke Quan']" 7.6,Ip Man 2,R,Action,108,"[u'Donnie Yen', u'Xiaoming Huang', u'Sammo Kam-Bo Hung']" 7.6,The Little Mermaid,G,Animation,83,"[u'Jodi Benson', u'Samuel E. Wright', u'Rene Auberjonois']" 7.6,"It's a Mad, Mad, Mad, Mad World",APPROVED,Action,205,"[u'Spencer Tracy', u'Milton Berle', u'Ethel Merman']" 7.6,The Reader,R,Drama,124,"[u'Kate Winslet', u'Ralph Fiennes', u'Bruno Ganz']" 7.6,The Omen,R,Horror,111,"[u'Gregory Peck', u'Lee Remick', u'Harvey Stephens']" 7.6,A Royal Affair,R,Drama,137,"[u'Alicia Vikander', u'Mads Mikkelsen', u'Mikkel Boe F\xf8lsgaard']" 7.6,Braindead,R,Comedy,104,"[u'Timothy Balme', u'Diana Pe\xf1alver', u'Elizabeth Moody']" 7.6,Secondhand Lions,PG,Comedy,109,"[u'Haley Joel Osment', u'Michael Caine', u'Robert Duvall']" 7.6,Disconnect,R,Drama,115,"[u'Jason Bateman', u'Jonah Bobo', u'Haley Ramm']" 7.6,A Single Man,R,Drama,99,"[u'Colin Firth', u'Julianne Moore', u'Matthew Goode']" 7.6,Hoosiers,PG,Drama,114,"[u'Gene Hackman', u'Barbara Hershey', u'Dennis Hopper']" 7.6,Moneyball,PG-13,Biography,133,"[u'Brad Pitt', u'Robin Wright', u'Jonah Hill']" 7.6,The Book Thief,PG-13,Drama,131,"[u'Sophie N\xe9lisse', u'Geoffrey Rush', u'Emily Watson']" 7.6,Die Hard: With a Vengeance,R,Action,131,"[u'Bruce Willis', u'Jeremy Irons', u'Samuel L. Jackson']" 7.6,Interview with the Vampire: The Vampire Chronicles,R,Horror,123,"[u'Brad Pitt', u'Tom Cruise', u'Antonio Banderas']" 7.6,Midnight Run,R,Action,126,"[u'Robert De Niro', u'Charles Grodin', u'Yaphet Kotto']" 7.6,An American Werewolf in London,R,Comedy,97,"[u'David Naughton', u'Jenny Agutter', u'Joe Belcher']" 7.6,The Great Debaters,PG-13,Biography,126,"[u'Denzel Washington', u'Forest Whitaker', u'Kimberly Elise']" 7.6,Tucker and Dale vs. Evil,R,Comedy,89,"[u'Tyler Labine', u'Alan Tudyk', u'Katrina Bowden']" 7.6,Dead Man Walking,R,Crime,122,"[u'Susan Sarandon', u'Sean Penn', u'Robert Prosky']" 7.6,The Piano,R,Drama,121,"[u'Holly Hunter', u'Harvey Keitel', u'Sam Neill']" 7.6,The Town,R,Crime,125,"[u'Ben Affleck', u'Rebecca Hall', u'Jon Hamm']" 7.6,Rise of the Planet of the Apes,PG-13,Action,105,"[u'James Franco', u'Andy Serkis', u'Freida Pinto']" 7.6,Boys Don't Cry,R,Biography,118,"[u'Hilary Swank', u'Chlo\xeb Sevigny', u'Peter Sarsgaard']" 7.6,Ice Age,PG,Animation,81,"[u'Denis Leary', u'John Leguizamo', u'Ray Romano']" 7.6,Of Mice and Men,PG-13,Drama,115,"[u'John Malkovich', u'Gary Sinise', u'Ray Walston']" 7.6,Sideways,R,Comedy,126,"[u'Paul Giamatti', u'Thomas Haden Church', u'Virginia Madsen']" 7.6,High Fidelity,R,Comedy,113,"[u'John Cusack', u'Iben Hjejle', u'Todd Louiso']" 7.6,The Meaning of Life,R,Comedy,107,"[u'John Cleese', u'Terry Gilliam', u'Eric Idle']" 7.6,Hugo,PG,Adventure,126,"[u'Asa Butterfield', u'Chlo\xeb Grace Moretz', u'Christopher Lee']" 7.6,Leaving Las Vegas,R,Drama,111,"[u'Nicolas Cage', u'Elisabeth Shue', u'Julian Sands']" 7.6,The Evil Dead,NC-17,Horror,85,"[u'Bruce Campbell', u'Ellen Sandweiss', u'Richard DeManincor']" 7.6,Collateral,R,Crime,120,"[u'Tom Cruise', u'Jamie Foxx', u'Jada Pinkett Smith']" 7.6,"Planes, Trains & Automobiles",R,Comedy,93,"[u'Steve Martin', u'John Candy', u'Laila Robins']" 7.6,Hodejegerne,R,Crime,100,"[u'Aksel Hennie', u'Synn\xf8ve Macody Lund', u'Nikolaj Coster-Waldau']" 7.6,Field of Dreams,PG,Drama,107,"[u'Kevin Costner', u'James Earl Jones', u'Ray Liotta']" 7.6,Batman,PG-13,Action,126,"[u'Michael Keaton', u'Jack Nicholson', u'Kim Basinger']" 7.6,Tell No One,UNRATED,Crime,131,"[u'Fran\xe7ois Cluzet', u'Marie-Jos\xe9e Croze', u'Andr\xe9 Dussollier']" 7.6,Ghost Dog: The Way of the Samurai,R,Action,116,"[u'Forest Whitaker', u'Henry Silva', u'John Tormey']" 7.6,Chaplin,PG-13,Biography,143,"[u'Robert Downey Jr.', u'Geraldine Chaplin', u'Paul Rhys']" 7.6,Keith,PG-13,Drama,93,"[u'Elisabeth Harnois', u'Jesse McCartney', u'Margo Harshman']" 7.6,Juno,PG-13,Comedy,96,"[u'Ellen Page', u'Michael Cera', u'Jennifer Garner']" 7.6,The Assassination of Jesse James by the Coward Robert Ford,R,Biography,160,"[u'Brad Pitt', u'Casey Affleck', u'Sam Shepard']" 7.6,The Fisher King,R,Comedy,137,"[u'Jeff Bridges', u'Robin Williams', u'Adam Bryant']" 7.6,House of Flying Daggers,PG-13,Action,119,"[u'Ziyi Zhang', u'Takeshi Kaneshiro', u'Andy Lau']" 7.6,The Hours,PG-13,Drama,114,"[u'Meryl Streep', u'Nicole Kidman', u'Julianne Moore']" 7.6,The Damned United,R,Biography,98,"[u'Colm Meaney', u'Henry Goodman', u'David Roper']" 7.6,Star Trek: First Contact,PG-13,Action,111,"[u'Patrick Stewart', u'Jonathan Frakes', u'Brent Spiner']" 7.6,The Natural,PG,Drama,138,"[u'Robert Redford', u'Robert Duvall', u'Glenn Close']" 7.6,The Hobbit: The Battle of the Five Armies,PG-13,Adventure,144,"[u'Ian McKellen', u'Martin Freeman', u'Richard Armitage']" 7.6,Equilibrium,R,Action,107,"[u'Christian Bale', u'Sean Bean', u'Emily Watson']" 7.6,August Rush,PG,Drama,114,"[u'Freddie Highmore', u'Keri Russell', u'Jonathan Rhys Meyers']" 7.6,Saving Mr. Banks,PG-13,Biography,125,"[u'Emma Thompson', u'Tom Hanks', u'Annie Rose Buckley']" 7.6,The Hurricane,R,Biography,146,"[u'Denzel Washington', u'Vicellous Reon Shannon', u'Deborah Kara Unger']" 7.6,Jacob's Ladder,R,Drama,113,"[u'Tim Robbins', u'Elizabeth Pe\xf1a', u'Danny Aiello']" 7.6,The Curse of the Were-Rabbit,G,Animation,85,"[u'Peter Sallis', u'Helena Bonham Carter', u'Ralph Fiennes']" 7.6,Christmas Vacation,PG-13,Comedy,97,"[u'Chevy Chase', u""Beverly D'Angelo"", u'Juliette Lewis']" 7.5,"Good Night, and Good Luck.",PG,Drama,93,"[u'David Strathairn', u'George Clooney', u'Patricia Clarkson']" 7.5,Straw Dogs,UNRATED,Crime,113,"[u'Dustin Hoffman', u'Susan George', u'Peter Vaughan']" 7.5,42,PG-13,Biography,128,"[u'Chadwick Boseman', u'T.R. Knight', u'Harrison Ford']" 7.5,Rudy,PG,Biography,114,"[u'Sean Astin', u'Jon Favreau', u'Ned Beatty']" 7.5,Sleepers,R,Crime,147,"[u'Robert De Niro', u'Kevin Bacon', u'Brad Pitt']" 7.5,The Wind That Shakes the Barley,NOT RATED,Drama,127,"[u'Cillian Murphy', u'Padraic Delaney', u'Liam Cunningham']" 7.5,Gangs of New York,R,Crime,167,"[u'Leonardo DiCaprio', u'Cameron Diaz', u'Daniel Day-Lewis']" 7.5,Elizabeth,R,Biography,124,"[u'Cate Blanchett', u'Geoffrey Rush', u'Christopher Eccleston']" 7.5,Green Street Hooligans,R,Crime,109,"[u'Elijah Wood', u'Charlie Hunnam', u'Claire Forlani']" 7.5,Reign Over Me,R,Drama,124,"[u'Adam Sandler', u'Don Cheadle', u'Jada Pinkett Smith']" 7.5,RoboCop,R,Action,102,"[u'Peter Weller', u'Nancy Allen', u""Dan O'Herlihy""]" 7.5,The Life of David Gale,R,Crime,130,"[u'Kevin Spacey', u'Kate Winslet', u'Laura Linney']" 7.5,Life as a House,R,Drama,125,"[u'Hayden Christensen', u'Kevin Kline', u'Kristin Scott Thomas']" 7.5,Despicable Me 2,PG,Animation,98,"[u'Steve Carell', u'Kristen Wiig', u'Benjamin Bratt']" 7.5,Jackie Brown,R,Crime,154,"[u'Pam Grier', u'Samuel L. Jackson', u'Robert Forster']" 7.5,Menace II Society,R,Crime,97,"[u'Tyrin Turner', u'Larenz Tate', u'June Kyoto Lu']" 7.5,The Class,PG-13,Drama,128,"[u'Fran\xe7ois B\xe9gaudeau', u'Agame Malembo-Emene', u'Ang\xe9lica Sancio']" 7.5,Bullets Over Broadway,R,Comedy,98,"[u'John Cusack', u'Dianne Wiest', u'Jennifer Tilly']" 7.5,American Splendor,R,Biography,101,"[u'Paul Giamatti', u'Shari Springer Berman', u'Harvey Pekar']" 7.5,Perfume: The Story of a Murderer,R,Crime,147,"[u'Ben Whishaw', u'Dustin Hoffman', u'Alan Rickman']" 7.5,Cloud Atlas,R,Drama,172,"[u'Tom Hanks', u'Halle Berry', u'Hugh Grant']" 7.5,Marathon Man,R,Crime,125,"[u'Dustin Hoffman', u'Laurence Olivier', u'Roy Scheider']" 7.5,Pinocchio,APPROVED,Animation,88,"[u'Dickie Jones', u'Christian Rub', u'Mel Blanc']" 7.5,The Painted Veil,PG-13,Drama,125,"[u'Naomi Watts', u'Edward Norton', u'Liev Schreiber']" 7.5,A Simple Plan,R,Crime,121,"[u'Bill Paxton', u'Billy Bob Thornton', u'Bridget Fonda']" 7.5,The Conjuring,R,Horror,112,"[u'Patrick Wilson', u'Vera Farmiga', u'Ron Livingston']" 7.5,The Man Who Knew Too Much,PG,Thriller,120,"[u'James Stewart', u'Doris Day', u'Brenda de Banzie']" 7.5,Quiz Show,PG-13,Drama,133,"[u'Ralph Fiennes', u'John Turturro', u'Rob Morrow']" 7.5,American Graffiti,PG,Comedy,110,"[u'Richard Dreyfuss', u'Ron Howard', u'Paul Le Mat']" 7.5,The Bridges of Madison County,PG-13,Drama,135,"[u'Clint Eastwood', u'Meryl Streep', u'Annie Corley']" 7.5,Gallipoli,PG,Adventure,110,"[u'Mel Gibson', u'Mark Lee', u'Bill Kerr']" 7.5,L'illusionniste,PG,Animation,80,"[u'Jean-Claude Donda', u'Eilidh Rankin', u'Duncan MacNeil']" 7.5,Miracle,PG,Drama,135,"[u'Kurt Russell', u'Patricia Clarkson', u'Nathan West']" 7.5,Maria Full of Grace,R,Crime,101,"[u'Catalina Sandino Moreno', u'Guilied Lopez', u'Orlando Tob\xf3n']" 7.5,Scott Pilgrim vs. the World,PG-13,Action,112,"[u'Michael Cera', u'Mary Elizabeth Winstead', u'Kieran Culkin']" 7.5,Pleasantville,PG-13,Comedy,124,"[u'Tobey Maguire', u'Jeff Daniels', u'Joan Allen']" 7.5,Sherlock Holmes: A Game of Shadows,PG-13,Action,129,"[u'Robert Downey Jr.', u'Jude Law', u'Jared Harris']" 7.5,Doubt,PG-13,Drama,104,"[u'Meryl Streep', u'Philip Seymour Hoffman', u'Amy Adams']" 7.5,The Fly,R,Horror,96,"[u'Jeff Goldblum', u'Geena Davis', u'John Getz']" 7.5,The Orphanage,R,Drama,105,"[u'Bel\xe9n Rueda', u'Fernando Cayo', u'Roger Pr\xedncep']" 7.5,Mal̬na,R,Drama,109,"[u'Monica Bellucci', u'Giuseppe Sulfaro', u'Luciano Federico']" 7.5,Three Days of the Condor,R,Mystery,117,"[u'Robert Redford', u'Faye Dunaway', u'Cliff Robertson']" 7.5,[Rec],R,Horror,78,"[u'Manuela Velasco', u'Ferran Terraza', u'Jorge-Yamam Serrano']" 7.5,A Perfect World,PG-13,Crime,138,"[u'Kevin Costner', u'Clint Eastwood', u'Laura Dern']" 7.5,To Catch a Thief,APPROVED,Mystery,106,"[u'Cary Grant', u'Grace Kelly', u'Jessie Royce Landis']" 7.5,Best in Show,PG-13,Comedy,90,"[u'Fred Willard', u'Eugene Levy', u""Catherine O'Hara""]" 7.5,Everything Is Illuminated,PG-13,Comedy,106,"[u'Elijah Wood', u'Eugene Hutz', u'Boris Leskin']" 7.5,A Nightmare on Elm Street,R,Horror,91,"[u'Heather Langenkamp', u'Johnny Depp', u'Robert Englund']" 7.5,The Ice Storm,R,Drama,112,"[u'Kevin Kline', u'Joan Allen', u'Sigourney Weaver']" 7.5,X2,PG-13,Action,134,"[u'Patrick Stewart', u'Hugh Jackman', u'Halle Berry']" 7.5,We Need to Talk About Kevin,R,Drama,112,"[u'Tilda Swinton', u'John C. Reilly', u'Ezra Miller']" 7.5,Felon,R,Crime,104,"[u'Stephen Dorff', u'Marisol Nichols', u'Vincent Miller']" 7.5,Still Alice,PG-13,Drama,101,"[u'Julianne Moore', u'Alec Baldwin', u'Kristen Stewart']" 7.5,Rust and Bone,R,Drama,120,"[u'Marion Cotillard', u'Matthias Schoenaerts', u'Armand Verdure']" 7.5,Freedom Writers,PG-13,Biography,123,"[u'Hilary Swank', u'Imelda Staunton', u'Patrick Dempsey']" 7.5,Legends of the Fall,R,Drama,133,"[u'Brad Pitt', u'Anthony Hopkins', u'Aidan Quinn']" 7.5,Buffalo '66,R,Comedy,110,"[u'Vincent Gallo', u'Christina Ricci', u'Ben Gazzara']" 7.5,The Constant Gardener,R,Drama,129,"[u'Ralph Fiennes', u'Rachel Weisz', u'Hubert Kound\xe9']" 7.5,My Cousin Vinny,R,Comedy,120,"[u'Joe Pesci', u'Marisa Tomei', u'Ralph Macchio']" 7.5,The Texas Chain Saw Massacre,R,Horror,83,"[u'Marilyn Burns', u'Edwin Neal', u'Allen Danziger']" 7.5,Mulan,G,Animation,88,"[u'Ming-Na Wen', u'Eddie Murphy', u'BD Wong']" 7.5,A History of Violence,R,Crime,96,"[u'Viggo Mortensen', u'Maria Bello', u'Ed Harris']" 7.5,Open Range,R,Action,139,"[u'Kevin Costner', u'Robert Duvall', u'Diego Luna']" 7.5,Total Recall,R,Action,113,"[u'Arnold Schwarzenegger', u'Sharon Stone', u'Michael Ironside']" 7.5,Pi,R,Drama,84,"[u'Sean Gullette', u'Mark Margolis', u'Ben Shenkman']" 7.5,The Devil's Backbone,R,Drama,106,"[u'Marisa Paredes', u'Eduardo Noriega', u'Federico Luppi']" 7.5,The Aviator,PG-13,Biography,170,"[u'Leonardo DiCaprio', u'Cate Blanchett', u'Kate Beckinsale']" 7.5,Devil's Advocate,R,Drama,144,"[u'Keanu Reeves', u'Al Pacino', u'Charlize Theron']" 7.5,"The Adventures of Priscilla, Queen of the Desert",R,Comedy,104,"[u'Hugo Weaving', u'Guy Pearce', u'Terence Stamp']" 7.5,Harry Potter and the Half-Blood Prince,PG,Adventure,153,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']" 7.5,Fruitvale Station,R,Biography,85,"[u'Michael B. Jordan', u'Melonie Diaz', u'Octavia Spencer']" 7.5,In the Bedroom,R,Crime,130,"[u'Tom Wilkinson', u'Sissy Spacek', u'Nick Stahl']" 7.5,Source Code,PG-13,Mystery,93,"[u'Jake Gyllenhaal', u'Michelle Monaghan', u'Vera Farmiga']" 7.5,Biutiful,R,Drama,148,"[u'Javier Bardem', u'Maricel \xc1lvarez', u'Hanaa Bouchaib']" 7.5,From Russia with Love,APPROVED,Action,115,"[u'Sean Connery', u'Robert Shaw', u'Lotte Lenya']" 7.5,Calvary,R,Drama,102,"[u'Brendan Gleeson', u""Chris O'Dowd"", u'Kelly Reilly']" 7.5,The Mission,PG,Adventure,125,"[u'Robert De Niro', u'Jeremy Irons', u'Ray McAnally']" 7.5,In the Loop,NOT RATED,Comedy,106,"[u'Tom Hollander', u'Peter Capaldi', u'James Gandolfini']" 7.5,The Three Burials of Melquiades Estrada,R,Adventure,121,"[u'Tommy Lee Jones', u'Barry Pepper', u'Dwight Yoakam']" 7.5,Babel,R,Drama,143,"[u'Brad Pitt', u'Cate Blanchett', u'Gael Garc\xeda Bernal']" 7.5,Frenzy,R,Thriller,116,"[u'Jon Finch', u'Barry Foster', u'Alec McCowen']" 7.5,Heavenly Creatures,R,Biography,99,"[u'Melanie Lynskey', u'Kate Winslet', u'Sarah Peirse']" 7.5,Sweeney Todd: The Demon Barber of Fleet Street,R,Drama,116,"[u'Johnny Depp', u'Helena Bonham Carter', u'Alan Rickman']" 7.5,Dracula,R,Horror,128,"[u'Gary Oldman', u'Winona Ryder', u'Anthony Hopkins']" 7.5,Looper,R,Action,119,"[u'Joseph Gordon-Levitt', u'Bruce Willis', u'Emily Blunt']" 7.5,The Proposition,R,Crime,104,"[u'Ray Winstone', u'Guy Pearce', u'Emily Watson']" 7.5,Bullitt,PG,Action,114,"[u'Steve McQueen', u'Jacqueline Bisset', u'Robert Vaughn']" 7.5,Harry Potter and the Sorcerer's Stone,PG,Adventure,152,"[u'Daniel Radcliffe', u'Rupert Grint', u'Richard Harris']" 7.5,2046,R,Drama,129,"[u'Tony Chiu Wai Leung', u'Ziyi Zhang', u'Faye Wong']" 7.5,Transamerica,R,Adventure,103,"[u'Felicity Huffman', u'Kevin Zegers', u'Fionnula Flanagan']" 7.5,Smoke,R,Comedy,112,"[u'Harvey Keitel', u'William Hurt', u'Giancarlo Esposito']" 7.5,Suspiria,X,Horror,92,"[u'Jessica Harper', u'Stefania Casini', u'Flavio Bucci']" 7.5,The Judge,R,Drama,141,"[u'Robert Downey Jr.', u'Robert Duvall', u'Vera Farmiga']" 7.5,Bad Education,NC-17,Crime,106,"[u'Gael Garc\xeda Bernal', u'Fele Mart\xednez', u'Javier C\xe1mara']" 7.5,Up in the Air,R,Drama,109,"[u'George Clooney', u'Vera Farmiga', u'Anna Kendrick']" 7.5,Begin Again,R,Drama,104,"[u'Keira Knightley', u'Mark Ruffalo', u'Adam Levine']" 7.5,Running Scared,R,Action,122,"[u'Paul Walker', u'Cameron Bright', u'Chazz Palminteri']" 7.5,Ghost World,R,Comedy,111,"[u'Steve Buscemi', u'Thora Birch', u'Scarlett Johansson']" 7.5,Witness,R,Crime,112,"[u'Harrison Ford', u'Kelly McGillis', u'Lukas Haas']" 7.5,Trading Places,R,Comedy,116,"[u'Eddie Murphy', u'Dan Aykroyd', u'Ralph Bellamy']" 7.5,Mud,PG-13,Drama,130,"[u'Matthew McConaughey', u'Tye Sheridan', u'Jacob Lofland']" 7.5,Across the Universe,PG-13,Drama,133,"[u'Evan Rachel Wood', u'Jim Sturgess', u'Joe Anderson']" 7.5,Les Mis̩rables,PG-13,Crime,134,"[u'Liam Neeson', u'Geoffrey Rush', u'Uma Thurman']" 7.5,Notes on a Scandal,R,Drama,92,"[u'Cate Blanchett', u'Judi Dench', u'Andrew Simpson']" 7.5,Inside Llewyn Davis,R,Drama,104,"[u'Oscar Isaac', u'Carey Mulligan', u'John Goodman']" 7.5,Brick,R,Crime,110,"[u'Joseph Gordon-Levitt', u'Lukas Haas', u'Emilie de Ravin']" 7.5,Clerks II,R,Comedy,97,"[u""Brian O'Halloran"", u'Jeff Anderson', u'Rosario Dawson']" 7.5,Say Anything...,PG-13,Comedy,100,"[u'John Cusack', u'Ione Skye', u'John Mahoney']" 7.4,Man on the Moon,R,Biography,118,"[u'Jim Carrey', u'Danny DeVito', u'Gerry Becker']" 7.4,Mean Streets,R,Crime,112,"[u'Robert De Niro', u'Harvey Keitel', u'David Proval']" 7.4,Harry Potter and the Order of the Phoenix,PG-13,Adventure,138,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']" 7.4,Beetlejuice,PG,Comedy,92,"[u'Alec Baldwin', u'Geena Davis', u'Michael Keaton']" 7.4,"Crazy, Stupid, Love.",PG-13,Comedy,118,"[u'Steve Carell', u'Ryan Gosling', u'Julianne Moore']" 7.4,Excalibur,R,Adventure,140,"[u'Nigel Terry', u'Helen Mirren', u'Nicholas Clay']" 7.4,True Grit,M,Adventure,128,"[u'John Wayne', u'Kim Darby', u'Glen Campbell']" 7.4,Labyrinth,PG,Adventure,101,"[u'David Bowie', u'Jennifer Connelly', u'Toby Froud']" 7.4,Alice in Wonderland,G,Animation,75,"[u'Kathryn Beaumont', u'Ed Wynn', u'Richard Haydn']" 7.4,Predestination,R,Drama,97,"[u'Ethan Hawke', u'Sarah Snook', u'Noah Taylor']" 7.4,Much Ado About Nothing,PG-13,Comedy,111,"[u'Kenneth Branagh', u'Emma Thompson', u'Keanu Reeves']" 7.4,A Bridge Too Far,PG,Drama,175,"[u'Sean Connery', u""Ryan O'Neal"", u'Michael Caine']" 7.4,Raising Arizona,PG-13,Comedy,94,"[u'Nicolas Cage', u'Holly Hunter', u'Trey Wilson']" 7.4,The Bucket List,PG-13,Adventure,97,"[u'Jack Nicholson', u'Morgan Freeman', u'Sean Hayes']" 7.4,Terms of Endearment,R,Comedy,132,"[u'Shirley MacLaine', u'Debra Winger', u'Jack Nicholson']" 7.4,Take Shelter,R,Drama,120,"[u'Michael Shannon', u'Jessica Chastain', u'Shea Whigham']" 7.4,Far from Heaven,PG-13,Drama,107,"[u'Julianne Moore', u'Dennis Quaid', u'Dennis Haysbert']" 7.4,Eraserhead,UNRATED,Drama,89,"[u'Jack Nance', u'Charlotte Stewart', u'Allen Joseph']" 7.4,Frances Ha,R,Comedy,86,"[u'Greta Gerwig', u'Mickey Sumner', u'Adam Driver']" 7.4,Home Alone,PG,Comedy,103,"[u'Macaulay Culkin', u'Joe Pesci', u'Daniel Stern']" 7.4,Bound,R,Crime,108,"[u'Jennifer Tilly', u'Gina Gershon', u'Joe Pantoliano']" 7.4,Sleepy Hollow,R,Drama,105,"[u'Johnny Depp', u'Christina Ricci', u'Miranda Richardson']" 7.4,Pirate Radio,R,Comedy,117,"[u'Philip Seymour Hoffman', u'Bill Nighy', u'Nick Frost']" 7.4,The NeverEnding Story,PG,Adventure,102,"[u'Noah Hathaway', u'Barret Oliver', u'Tami Stronach']" 7.4,X-Men,PG-13,Action,104,"[u'Patrick Stewart', u'Hugh Jackman', u'Ian McKellen']" 7.4,Zero Dark Thirty,R,Drama,157,"[u'Jessica Chastain', u'Joel Edgerton', u'Chris Pratt']" 7.4,Manhattan Murder Mystery,PG,Comedy,104,"[u'Woody Allen', u'Diane Keaton', u'Jerry Adler']" 7.4,National Lampoon's Vacation,R,Comedy,98,"[u'Chevy Chase', u""Beverly D'Angelo"", u'Imogene Coca']" 7.4,My Sister's Keeper,PG-13,Drama,109,"[u'Cameron Diaz', u'Abigail Breslin', u'Alec Baldwin']" 7.4,Deconstructing Harry,R,Comedy,96,"[u'Woody Allen', u'Judy Davis', u'Julia Louis-Dreyfus']" 7.4,The Way Way Back,PG-13,Comedy,103,"[u'Steve Carell', u'Toni Collette', u'Allison Janney']" 7.4,Capote,R,Biography,114,"[u'Philip Seymour Hoffman', u'Clifton Collins Jr.', u'Catherine Keener']" 7.4,Driving Miss Daisy,PG,Comedy,99,"[u'Morgan Freeman', u'Jessica Tandy', u'Dan Aykroyd']" 7.4,La Femme Nikita,R,Action,118,"[u'Anne Parillaud', u'Marc Duret', u'Patrick Fontana']" 7.4,Lincoln,PG-13,Biography,150,"[u'Daniel Day-Lewis', u'Sally Field', u'David Strathairn']" 7.4,Limitless,PG-13,Mystery,105,"[u'Bradley Cooper', u'Anna Friel', u'Abbie Cornish']" 7.4,The Simpsons Movie,PG-13,Animation,87,"[u'Dan Castellaneta', u'Julie Kavner', u'Nancy Cartwright']" 7.4,The Rock,R,Action,136,"[u'Sean Connery', u'Nicolas Cage', u'Ed Harris']" 7.4,The English Patient,R,Drama,162,"[u'Ralph Fiennes', u'Juliette Binoche', u'Willem Dafoe']" 7.4,Law Abiding Citizen,R,Crime,109,"[u'Gerard Butler', u'Jamie Foxx', u'Leslie Bibb']" 7.4,Wonder Boys,R,Drama,107,"[u'Michael Douglas', u'Tobey Maguire', u'Frances McDormand']" 7.4,Death at a Funeral,R,Comedy,90,"[u'Matthew Macfadyen', u'Peter Dinklage', u'Ewen Bremner']" 7.4,Blue Valentine,NC-17,Drama,112,"[u'Ryan Gosling', u'Michelle Williams', u'John Doman']" 7.4,The Cider House Rules,PG-13,Drama,126,"[u'Tobey Maguire', u'Charlize Theron', u'Michael Caine']" 7.4,Tootsie,PG,Comedy,116,"[u'Dustin Hoffman', u'Jessica Lange', u'Teri Garr']" 7.4,Back to the Future Part III,PG,Adventure,118,"[u'Michael J. Fox', u'Christopher Lloyd', u'Mary Steenburgen']" 7.4,Master and Commander: The Far Side of the World,PG-13,Action,138,"[u'Russell Crowe', u'Paul Bettany', u'Billy Boyd']" 7.4,Poltergeist,PG,Horror,114,"[u'JoBeth Williams', u""Heather O'Rourke"", u'Craig T. Nelson']" 7.4,Wall Street,R,Crime,126,"[u'Charlie Sheen', u'Michael Douglas', u'Tamara Tunie']" ================================================ FILE: data/imdb_movie_urls.csv ================================================ http://www.imdb.com/title/tt1856010/ http://www.imdb.com/title/tt0816692/ http://www.imdb.com/title/tt1826940/ http://www.imdb.com/title/tt0993846/ http://www.imdb.com/title/tt0285403/ http://www.imdb.com/title/tt2084970/ http://www.imdb.com/title/tt2980516/ http://www.imdb.com/title/tt0386676/ http://www.imdb.com/title/tt1266020/ ================================================ FILE: data/kaggle_tweets.csv ================================================ "37 spots up. Is anybody else even trying? #kaggle - https://t.co/9GLyaK3OhT" "RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted " "@alexjc There was an Elo-based Kaggle comp recently; the forums might have useful info. https://t.co/NUxbAvTTcO" "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted " "RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted " "RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted " "Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted to our competitions" "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "#kaggle kaggle is really so slow today" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition #R #data http://t.co/6Vor5Rdi5n" "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "Data Science Challenge: predict Taxi trajectories. @Gabriellilor, @siffolone are you in? http://t.co/yO9iCOnTlA" "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "RT @benhamner: The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard " "@benhamner @kaggle building a model and then putting it into production only to see it negatively influence hundreds of customers is worse." "The most brutal way to learn about overfitting? Watching yourself drop hundreds of places when a @kaggle final leaderboard is revealed" "8 https://t.co/PlxfewX5dX" ""What has 64 cores and 128GB RAM? Not me!..." #kaggle" "Amazing. "how far you could go if you do explicitly what everyone is doing implicitly: overfitting" http://t.co/AkF1rbaw1l #datascience" "@t3kcit check out Otto Kaggle competition. Both of them are used and highly discussed in the forum" "@what_mojo @KaggleStatus @kaggle hahah then thanks both!" "@leonardo_noleto @KaggleStatus @kaggle ha!! thank me. i've been bugging them since yesterday to do this." "@what_mojo now they do! check @KaggleStatus, thanks @kaggle!" "How the #Kaggle restaurant contest was hacked http://t.co/0dkV1Ta0CI" "#kaggle is " "You can follow @KaggleStatus for updates on our platform's performance. (Relevant information available now.)" "@gef0rce @kaggle @kdd_news who knows :D" "amar81sb: RT bimalj13: Use #BlueMix DashDB and R to solve a Kaggle Competition #dashdb #R http://t.co/ZT6Euk3IzC" "ReleaseTEAMLTD: RT bimalj13: Use #BlueMix DashDB and R to solve a Kaggle Competition #dashdb #R http://t.co/ZT6Euk3IzC" "@leonardo_noleto @kaggle if only @kaggle would tweet submission status using some sort of technology that could alert people....." "@kaggle can you please tweet server status [as its currently down]? thanks!" "@abhi1thakur @kaggle @kdd_news why not on Kaggle?" "RT @bimalj13: Use #BlueMix DashDB and R to solve a Kaggle Competition #dashdb #R http://t.co/noOgMEU5ia" "RT @bimalj13: Use #BlueMix DashDB and R to solve a Kaggle Competition #dashdb #R http://t.co/noOgMEU5ia" "Use #BlueMix DashDB and R to solve a Kaggle Competition #dashdb #R http://t.co/noOgMEU5ia" "RT @ChicagoCDO: ICYMI: You can help @ChiPublicHealth predict where West Nile-infected mosquitos could be found http://t.co/GzYLQlkQfN" "ICYMI: You can help @ChiPublicHealth predict where West Nile-infected mosquitos could be found http://t.co/GzYLQlkQfN" "how I feel when I am not able to submit my @kaggle submissions #troubles #KeepCalm http://t.co/7JQ24zJCuj" "Kdd cup 2015 website is total ripoff of @kaggle . only difference: it doesnt work :P :P @kdd_news" "Organiser un #concours de #datascientists en 5 tapes http://t.co/akB01nik8u @LUsineDigitale #Kaggle" "The 2nd data science problem of the "On Learning from Taxi GPS Traces" Discovery Challenge is now available on Kaggle:http://t.co/dAlys25mCG" "How the Kaggle restaurant contest was hacked: http://t.co/yaSVLvuKji is a server that organized the ATLAS Higgs... http://t.co/DcvMKvqfO8" "if you're a data scientist, many cool projects to try on kaggle now. itching to have a go (incl. one on west nile virus). but work calls." "Why did nobody tell me #datascience was this easy? #kaggle - https://t.co/KDfukDMxQb" "RT @mariandragt: My first @Kaggle @OttoGroup_Com challenge submission with #AzureML! Lot to improve, but happy with this first step! #Machi" "My first @Kaggle @OttoGroup_Com challenge submission with #AzureML! Lot to improve, but happy with this first step! #MachineLearning" "RT @MLWave: Paper due in Lecture Notes in Computer Science: http://t.co/pLHZoSANJG Thanks co-authors, Kaggle, Wolpert, SKLearn. Soon a bett" "Facebook Recruiting IV: Human or Robot? | Kaggle https://t.co/q3Rzag6wRk" "Kaggle also has to be extra careful with competitions that can be gamed like the NCAA Machine Learning March Madness." "Kaggle has to be careful to avoid the public test data being snoopable (which it was in Restaurant Revenue)." "Why I passed on this Kaggle competition. Our perfect submission - Restaurant Revenue Prediction | Kaggle http://t.co/7NRWpsNbSs" "RT @botheredbybees: "This tutorial willhelp you get started with Word2Vec for natural language processing" http://t.co/zYuNjaWOfo" "RT @botheredbybees: "This tutorial willhelp you get started with Word2Vec for natural language processing" http://t.co/zYuNjaWOfo" ""This tutorial willhelp you get started with Word2Vec for natural language processing" http://t.co/zYuNjaWOfo" "@Rosebxd__ Hey. Any 'update' on Kaggle? I've managed to push the AUC to 0.91621 " "@tompy_ why do you have a Kaggle acc though? Did you study data mining at university?" "@tompy_ Kaggle is cool, I agree. My class assignment on there? Not so fun" "There's a problem with Kaggle though, the higher your mark there, the lower the real life accuracy ughhh" "Are you serious teacher, HE IS GOING TO FRIGGIN PUT THE ASSIGNMENT ON KAGGLE FFFFFFFFFFFFFFF" "RT @ML_Nantes: Kaggle, planctons, et $175,000 au Nantes Machine Learning Meetup, ce soir la Cantine. http://t.co/F1Qmbaocov" "Kaggle, planctons, et $175,000 au Nantes Machine Learning Meetup, ce soir la Cantine. http://t.co/F1Qmbaocov" "RT @SolomonMg: Nice demonstration of overfitting on the Kaggle leaderboard h/t @RexDouglass https://t.co/KFPua4Lrul" "RT datasama "uriellcoleria Cependant un trs fort intrt quantifiable : nombreux MOOCs suivis + comptitions kaggle + projets peuvent suff" "@uriellcoleria Cependant un trs fort intrt quantifiable : nombreux MOOCs suivis + comptitions @kaggle + projets peuvent suffire 2/2" "RT @indizen_insight: Setting up #Python for #machinelearning http://t.co/rYnfLAiMyU via @kaggle" "RT @indizen_insight: Setting up #Python for #machinelearning http://t.co/rYnfLAiMyU via @kaggle" "RT @indizen_insight: Setting up #Python for #machinelearning http://t.co/rYnfLAiMyU via @kaggle" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @indizen_insight: Setting up #Python for #machinelearning http://t.co/rYnfLAiMyU via @kaggle" "RT @kaggle: #scriptoftheweek Heat map of West Nile virus in Chicago. It was forked to create yearly maps https://t.co/31ghCneG70 http://t.c" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "Need more ram to read data from @kaggle , or use #spark..the wonders of the latter" "Wow! Somebody just used some statistically crafty overfitting on a test-data set in a @kaggle challenge! http://t.co/TluTOx19Xo" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "New video series: Intro to machine learning with scikit-learn: http://t.co/HcPSvYfBwl #ML http://t.co/z3ILkiQfcf" "668 places up. A small price to pay for my social life. #kaggle - https://t.co/2tBmLaypGq" "504 places up. Ensemble of RF & GBM using feature selection. Dumped the corpus method using the tm package #kaggle - https://t.co/KKCRLAn13e" "RT @RexDouglass: Understanding overfitting by gaming/breaking the Kaggle leader board 1) http://t.co/br8CteXZdh 2) https://t.co/mgpZjEpGYg" "'Kenreisman/machine-learning' Top: Our perfect submission - Restaurant Revenue https://t.co/rGMQo6fikn, see more http://t.co/VAU8peuLvN" "Facebook Machine Learning Challenge... https://t.co/gJpIiDgDJ9" "Moved up 370 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/hX09tF1CKd" "Moved up 221 places on #kaggle. Top 3% now. Thank you, no-learn and lasagne. (And #python in general.) #deeplearning" "730 places up. A small price to pay for my social life. #kaggle - https://t.co/SKb79Li55R - I am happy now :-)))" "10% I got a score of 1669892.08867. | Moved up 92 spots. Soon the robots will have my job. #kaggle - https://t.co/Gzxy3uzgre" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "178 spots up. Is anybody else even trying? #kaggle - https://t.co/u3maNaDZGU" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "scikit-learn video #2: Setting up Python for machine learning | no free hunch - http://t.co/obc6viljDj" "scikit-learn video #3: Machine learning first steps with the Iris dataset | no free hunch - http://t.co/CURXle7gVP" "scikit-learn video #4: Model training and prediction with K-nearest neighbors | no free hunch - http://t.co/IY2nSbRe7P" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "RT @mrtz: Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "Moved up 46 spots. #kaggle - https://t.co/CEWNH3jBpH" "15 spots up. #kaggle - https://t.co/D1a8LNigyM" "Overfitting is the new cool. Team scores perfect submission in Kaggle competition: https://t.co/eJDpQLPDsR" "Understanding overfitting by gaming/breaking the Kaggle leader board 1) http://t.co/br8CteXZdh 2) https://t.co/mgpZjEpGYg" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/ZYQlJKfqmc" "RT @kronos455: Data scientist is the sexiest job of this century. *winks* #kaggle - https://t.co/EmSeyM0GSh" "Data scientist is the sexiest job of this century. *winks* #kaggle - https://t.co/EmSeyM0GSh" "@benhamner @Azure @kaggle created for meme purposes only." "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "challenge idea : predict winner of next #kaggle contest. Feature : time zone cause nb of submission on hacking evening is 3 or 6. damn utc+2" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "@mariana_farinha @kaggle what would you want an Android app to do?" "RT @curtwehrley: MT @benhamner I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've run a @kaggle challen" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "scikit-learn video #4: Model training and prediction with K-nearest neighbors http://t.co/HFWwKlqNzy" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "MT @benhamner I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've run a @kaggle challenge first" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "@benhamner @kaggle @Azure I also look 69 when I'm wearing sunglasses. But in my mirror shades: a 69-year-old Fat Maverick from Top Gun." "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "@kaggle @EzraPenland Brix beginning of the concept of modern economic power http://t.co/P4LG8xgF9I" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "Moved up 76 spots. #kaggle - https://t.co/vEAYgajuQY" "Our perfect submission - Restaurant Revenue Prediction | Kaggle http://t.co/YzoH9qCf4N" "4 spots up. Is anybody else even trying? #kaggle - https://t.co/Tf6o9wPphc" "RT @rmelody: Data Science projects billed $300/hour on Kaggle http://t.co/COmnwvCc1S http://t.co/x0tsCOlM5s" "Data Science projects billed $300/hour on Kaggle http://t.co/COmnwvCc1S http://t.co/x0tsCOlM5s" "Awesome. Team intentionally overfits and posts a perfect score in #kaggle contest: https://t.co/pIwGR1Zs7X http://t.co/sXzfhU8tsr" "46 places up. A small price to pay for my social life. #kaggle - https://t.co/L3dP7q8A36" "Moved up 440 spots on #kaggle. I'm not addicted... I can quit when I want... really... https://t.co/haMyCDu3d4" "The art of overfitting ! Perfect score @kaggle http://t.co/0yqWFYrP1I" "RT @ewald_zietsman: A forest of random forests. I'm making one. #noobtactics #machinelearning #kaggle" "RT @betatim: Perfect scores on kaggle: http://t.co/jiqkuh0Ht6 on the internet, everything (good & bad) that is technically possible will be" "RT @ewald_zietsman: A forest of random forests. I'm making one. #noobtactics #machinelearning #kaggle" "A forest of random forests. I'm making one. #noobtactics #machinelearning #kaggle" "100 places up. Not unlike my electricity bill. #kaggle - https://t.co/L3dP7q8A36" "10 places up. A small price to pay for my social life. #kaggle - https://t.co/gKuwB1eq5D" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @chris_bour: Everything is possible @kaggle http://t.co/W8Rtu2ojcK" "gus_massa comments on "Kaggle: Restaurant Revenue Prediction has a submission with zero error" - http://t.co/KtD4hiYK2c #sales #marketing" "gus_massa comments on "Kaggle: Restaurant Revenue Prediction has a submission with zero error" - http://t.co/0YTB0F7gqK - By ..." "Jumped 57 spots on #kaggle. I'll sleep when I'm dead. https://t.co/XEl41wClyU. slogging my ass off yet can't reach above 90.6% accuracy. :(" "231 places up. A small price to pay for my social life. #kaggle" "@kaggle Is it normal for your site to be really slow? It seems that most pages take 30+ s to load. Having trouble to upload submissions too" "Thanks @mjcavaretta, I'll be sure to take a look @kaggle @kdnuggets" "@deadlysyntax Have you checked out some of the #opendata sources like @kaggle or @kdnuggets? It might stimulate some ideas." "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "94 places up. Not unlike my electricity bill. #kaggle - https://t.co/XJMUjvkBQH" "RT @KobbyDon1: KobbyDon : @ThugMetricsNews-RT @drkassorla: Top 10 #R Packages to be a Kaggle Champion http://t.co/8RkUcfQkai #bigdata #hpc" "RT @drkassorla: Top 10 #R Packages to be a Kaggle Champion http://t.co/AVXwxru9w5 #bigdata #hpc" "RT @KobbyDon1: KobbyDon : @ThugMetricsNews-RT @drkassorla: Top 10 #R Packages to be a Kaggle Champion http://t.co/8RkUcfQkai #bigdata #hpc" "KobbyDon : @ThugMetricsNews-RT @drkassorla: Top 10 #R Packages to be a Kaggle Champion http://t.co/8RkUcfQkai #bigdata #hpc" "RT @drkassorla: Top 10 #R Packages to be a Kaggle Champion http://t.co/AVXwxru9w5 #bigdata #hpc" "Top 10 #R Packages to be a Kaggle Champion http://t.co/AVXwxru9w5 #bigdata #hpc" "13 spots up, just a few more to go. #kaggle - https://t.co/D1a8LNigyM" "kaggle" "RT @chris_bour: Everything is possible @kaggle http://t.co/W8Rtu2ojcK" "Everything is possible @kaggle http://t.co/W8Rtu2ojcK" "Perfect scores on kaggle: http://t.co/jiqkuh0Ht6 on the internet, everything (good & bad) that is technically possible will be done! #loveit" "www Jumped 259 spots on #kaggle. I'll sleep when I'm dead. https://t.co/ayIkVcvJxL" "RT @manasrnkar: 39 spots up. RAM from @Azure to battle it out ! #MachineLearning #kaggle - https://t.co/WGlFiVAW7M" "RT @manasrnkar: 39 spots up. RAM from @Azure to battle it out ! #MachineLearning #kaggle - https://t.co/WGlFiVAW7M" "RT @manasrnkar: 39 spots up. RAM from @Azure to battle it out ! #MachineLearning #kaggle - https://t.co/WGlFiVAW7M" "39 spots up. RAM from @Azure to battle it out ! #MachineLearning #kaggle - https://t.co/WGlFiVAW7M" "RT @balazskegl: Trouble in the @kaggle public leaderboard. http://t.co/zzRtwIHCch" "0.47 1%validation 56 spots up, just a few more to go. #kaggle - https://t.co/ayIkVcvJxL" "awajeet: CarmenRuppach: RT Armin_Stegerer: Use #Bluemix DashDB and R to solve a Kaggle Competition http://t.co/M4ZWNIoIQj" "CarmenRuppach: RT Armin_Stegerer: Use #Bluemix DashDB and R to solve a Kaggle Competition http://t.co/M4ZWNIoIQj" "Trouble in the @kaggle public leaderboard. http://t.co/zzRtwIHCch" "RT @Armin_Stegerer: Use #Bluemix DashDB and R to solve a Kaggle Competition http://t.co/ortOLBpbUF" "Moved up 426 spots on #kaggle. Still a long way to go... https://t.co/qwL8NK0DdZ" "1p Kaggle: Restaurant Revenue Prediction has a submission with zero error - http://t.co/PtO0wFxAIh - http://t.co/zzNDaOEy5i ..." "1p Kaggle: Restaurant Revenue Prediction has a submission with zero error - http://t.co/P7dx7ulMTM #sales #marketing" "1p Kaggle: Restaurant Revenue Prediction has a submission with zero error - http://t.co/j8PcPdc8Gc #advertising #sales #business #inc" "Kaggle: Restaurant Revenue Prediction has a submission with zero error http://t.co/ADfmNfHM71" "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "Kaggle: Restaurant Revenue Prediction has a submission with zero error http://t.co/qINC9QOx41" ""scikit-learn video #4: Model training and prediction with K-nearest neighbors" http://t.co/ighWxgUQUY #ml #scikit" "How to win a Kaggle competition - Data Science Central http://t.co/V3T0YG4bbi, see more http://t.co/2wg3BbHBNq" "How to win a Kaggle competition - Data Science Central http://t.co/V3T0YG4bbi, see more http://t.co/L1rR6e0Ija" "Who like me is preparing to win a #kaggle competition?. "How to win a Kaggle competition http://t.co/xFevsyWX6H"" "RT @analyticbridge: How to win a Kaggle competition http://t.co/9DnYjq0o9x" ">>> import crystall_ball #kaggle http://t.co/Yrmb9Kytot" "95 places up. A small price to pay for my social life. #kaggle - https://t.co/hX09tF1CKd" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "RT @analyticbridge: How to win a Kaggle competition http://t.co/9DnYjq0o9x" "RT @analyticbridge: How to win a Kaggle competition http://t.co/9DnYjq0o9x" "RT @analyticbridge: How to win a Kaggle competition http://t.co/9DnYjq0o9x" "Moved up 140 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/HI6y10ieHH" "How to win a Kaggle competition http://t.co/9DnYjq0o9x" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "Moved up 618 spots. Soon the robots will have my job. #kaggle - https://t.co/O7QQ4aqiuG" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "103 places up. Not unlike my electricity bill. #kaggle - https://t.co/en8AAMcEai" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "RT @kaggle: #scriptoftheweek Heat map of West Nile virus in Chicago. It was forked to create yearly maps https://t.co/31ghCneG70 http://t.c" "#kaggle - because this RAM isn't going to use itself. https://t.co/9bZezOWKQ4" "RT @vrstartup: Intro to #MachineLearning http://t.co/6FOdja7IMU #DataScience #BigData http://t.co/Nv8Qh1UIjW" "RT @vrstartup: Intro to #MachineLearning http://t.co/6FOdja7IMU #DataScience #BigData http://t.co/Nv8Qh1UIjW" "RT @vrstartup: Intro to #MachineLearning http://t.co/6FOdja7IMU #DataScience #BigData http://t.co/Nv8Qh1UIjW" "RT @vrstartup: Intro to #MachineLearning http://t.co/6FOdja7IMU #DataScience #BigData http://t.co/Nv8Qh1UIjW" "Intro to #MachineLearning http://t.co/6FOdja7IMU #DataScience #BigData http://t.co/Nv8Qh1UIjW" "1 place up. Seriously? Just improved my score by 0.00012. #kaggle - https://t.co/E1GIKF23z6" "577 places up. A small price to pay for my social life. #kaggle - https://t.co/U1NxC3ePs3" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "@kaggle any chance of tweeting when kaggle is down. like right now?" "GPU Powered DeepLearning with NVIDIA DIGITS on EC2 | joy of data http://t.co/8lxDMVWK6C #MachineLearning #DeepLearning #GPU #Kaggle" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "RT @KirkDBorne: Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @k" "Top 10 R Packages to be a Kaggle #DataScience Champion: http://t.co/Erz87vEs1H #Rstats #BigData #Analytics by @hey_anmol @kdnuggets" "Top #MachineLearning Story: scikit-learn video #4: Model training and predictio http://t.co/G8nwzV1jgk, see more http://t.co/v4Ftk6B797" "An N-gram model uses only N-1 words of prior context.#machinelearning , building a language model for @kaggle :-)" "Last day of Data Science bootcamp at @GA. Course project (@kaggle DC bikeshare forecasting) takeaway: stay enamored! http://t.co/1SBkevj0zY" "@kaggle Interesting way to name a subject and tell us more about what the email should do. http://t.co/7Szthj0QMM" "These days I am on kaggle's bike rental prediction exercise." "85 spots up. Is anybody else even trying? #kaggle - https://t.co/Tf6o9wPphc" "Moved up 394 spots. Soon the robots will have my job. #kaggle - https://t.co/QHoG6qJxrf" "372 places up. A small price to pay for my social life. #kaggle - https://t.co/gKuwB1eq5D" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @beingawesom: Moved up 25 spots. Thanks #MIT. Slow climb to master #machinelearning. #kaggle - https://t.co/9gpnDKIL1n" "Moved up 25 spots. Thanks #MIT. Slow climb to master #machinelearning. #kaggle - https://t.co/9gpnDKIL1n" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "32 places up. OK. just more 13 to place in the money. #kaggle - https://t.co/lktR1hKFxh" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "Been staring at this for the past ten minutes. #randomForest #DataScience #R #Kaggle http://t.co/c1moMYbyFo" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @analyticbridge: Data Science projects billed $300/hour on Kaggle http://t.co/g09j8xm0LW" "Big Data Science @analyticbridge :Data Science projects billed $300/hour on Kaggle http://t.co/aiC8zmu064 http://t.co/JHvVV1u31d" "RT @analyticbridge: Data Science projects billed $300/hour on Kaggle http://t.co/g09j8xm0LW" "Data Science projects billed $300/hour on Kaggle http://t.co/g09j8xm0LW" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "New #Facebook #Kaggle recruitment challenge - Facebook Recruiting IV: Human or Robot? http://t.co/1WZtW0d1yB" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "I just made my first #kaggle submission to https://t.co/s0GvNZhuGd" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @rishabh_droy: Doing my part to bring about the singularity. #MachineLearning #kaggle - https://t.co/ArRFtKzuOo" "Doing my part to bring about the singularity. #MachineLearning #kaggle - https://t.co/ArRFtKzuOo" "Just loving it......660 places up. #kaggle - https://t.co/3PiGecpHk3" "1http://t.co/WYYUmY5bNH" "Facebook Recruiting IV: Human or Robot? http://t.co/40NZd8T9Sk" " #myjlab ( Tutorial - Kaggle ) http://t.co/QEgTCJkuvt" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @indizen_insight: Setting up #Python for #machinelearning http://t.co/rYnfLAiMyU via @kaggle" "scikit-learn video #4: Model training and prediction with K-nearest neighbors | no free hunch https://t.co/NicpxfWxXQ" "RT @indizen_insight: Setting up #Python for #machinelearning http://t.co/rYnfLAiMyU via @kaggle" "Top story: Getting Started with R - Facial Keypoints Detection | Kaggle https://t.co/LlwCoLPOpZ, see more http://t.co/4UFmt0m2hK" "RT @indizen_insight: Setting up #Python for #machinelearning http://t.co/rYnfLAiMyU via @kaggle" "gaggle on kaggle" "Setting up #Python for #machinelearning http://t.co/rYnfLAiMyU via @kaggle" "RT @AntarMeir: scikit-learn video #4: Model training and prediction with K-nearest neighbors. #machinelearning http://t.co/Y1MRje0zKw" "scikit-learn video #4: Model training and prediction with K-nearest neighbors. #machinelearning http://t.co/Y1MRje0zKw" "Kaggle: The Home of Data Science http://t.co/KQzIAJrfhu" "Data scientist is the sexiest job of this century. *winks* #kaggle - https://t.co/e7K4sA6Zez" "Looking at http://t.co/giX76QuUYt there is some good stuff for newbs in there! https://t.co/Rp2YL68xAa a nice R tutorial #facialrecognition" "RT @KirkDBorne: Use #MachineLearning cheat sheet + set up #Python in @Kaggle's 2nd #DataScience tutorial: http://t.co/8M2fxpRbrF http://t.c" "RT @KirkDBorne: Use #MachineLearning cheat sheet + set up #Python in @Kaggle's 2nd #DataScience tutorial: http://t.co/8M2fxpRbrF http://t.c" "RT @KirkDBorne: Use #MachineLearning cheat sheet + set up #Python in @Kaggle's 2nd #DataScience tutorial: http://t.co/8M2fxpRbrF http://t.c" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @Farhan_Khwaja: 14 spots up, just a few more to go. #kaggle - https://t.co/PqWbRm5Hjy" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "14 spots up, just a few more to go. #kaggle - https://t.co/PqWbRm5Hjy" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "Interview with team who developed a "#telematic fingerprint" for #drivers http://t.co/23ZPY6nvJI http://t.co/eqIvl13Wfy" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "@yahisus do u kaggle?" "RT @KirkDBorne: Use #MachineLearning cheat sheet + set up #Python in @Kaggle's 2nd #DataScience tutorial: http://t.co/8M2fxpRbrF http://t.c" "RT @kaggle: #scriptoftheweek Heat map of West Nile virus in Chicago. It was forked to create yearly maps https://t.co/31ghCneG70 http://t.c" "RT @KirkDBorne: Use #MachineLearning cheat sheet + set up #Python in @Kaggle's 2nd #DataScience tutorial: http://t.co/8M2fxpRbrF http://t.c" "RT @KirkDBorne: Use #MachineLearning cheat sheet + set up #Python in @Kaggle's 2nd #DataScience tutorial: http://t.co/8M2fxpRbrF http://t.c" "RT @KirkDBorne: Use #MachineLearning cheat sheet + set up #Python in @Kaggle's 2nd #DataScience tutorial: http://t.co/8M2fxpRbrF http://t.c" "RT @KirkDBorne: Use #MachineLearning cheat sheet + set up #Python in @Kaggle's 2nd #DataScience tutorial: http://t.co/8M2fxpRbrF http://t.c" "I am ranked 2566 for my first #Kaggle. Not bad eh? #MachineLearning https://t.co/NPIuRg7rgh" "Submitting my first #Kaggle. #Predict all them bike share demand #machinelearning #DCTech #DataScience" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "Data dorks, check http://t.co/OqRA3HWaHb, where companies crowdsource analysis models. Modelers win money and/or get on a leader board." "Data scientist is the sexiest job of this century. *winks* #kaggle - https://t.co/hIIwhXi8Cn" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "The fredcadena Post-Ledger is out! http://t.co/fBpkHIqW6C Stories via @MsJessLoren @kaggle @sherrirose" "@benhamner @Azure @kaggle Thats on your outside - how about your InnerAge? http://t.co/Wv2Bj6ZOfQ" "@benhamner @kaggle @Azure I think they just wanted a geotagged, targeting cookie bound, dataset of human faces :-)" "Top stories: Top #LinkedIn Gps 4 #Analytics #Big Data #Data Mining; 10 R Packages for #Kaggle http://t.co/ULI23yXUTe http://t.co/KJGRvUHhyF" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @kaggle: #scriptoftheweek Heat map of West Nile virus in Chicago. It was forked to create yearly maps https://t.co/31ghCneG70 http://t.c" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "Moved up 9 spots. Soon the robots will have my job. #kaggle - https://t.co/Tf6o9wPphc" "scikit-learn video #3: Machine learning first steps with the Iris dataset http://t.co/DYC0RyvLYv" "127 places up. A small price to pay for my social life. #kaggle - https://t.co/Tf6o9wPphc" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "RT @benhamner: I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://" "I uploaded 5 recent pics to #HowOldRobot and got 5 decades. Microsoft @Azure should've ran a @kaggle challenge first http://t.co/3qhHWBDOhZ" "RT @Haystack_Data: Top 10 R Packages to be a Kaggle Champion http://t.co/cOwOtA8s2I via @kdnuggets #bigdata #data #R #datascience #analytic" "RT @kaggle: Trip matching, telematics and lots of ensembling. 2nd place team in the AXA challenge shares their story #nofreehunch http://t." "RT @kaggle: We had a lively discussion with @mrtz today about Kaggle's leaderboard. Thanks for coming in! http://t.co/KN7Xpqb2eV" "RT @kaggle: #scriptoftheweek Heat map of West Nile virus in Chicago. It was forked to create yearly maps https://t.co/31ghCneG70 http://t.c" "Nice demonstration of matplotlib'ing on top of a map background. https://t.co/sxiFgp84P6" "I just published What I learnt from my first Kaggle competition. https://t.co/sj0WODeYWW" "scikit-learn video #4: Model training and prediction with K-nearest neighbors http://t.co/ouhavelCTA" "RT @kaggle: Our #scriptoftheweek shows counts of daily bike rentals in Washington DC follow Benford's law https://t.co/sDh7GBPGII http://t." "RT @leonardo_noleto: "Domain knowledge is overrated for predictive modeling competitions" @wzchen http://t.co/plNrEAeTbE #DataScience #Kagg" "RT @kaggle: #scriptoftheweek Heat map of West Nile virus in Chicago. It was forked to create yearly maps https://t.co/31ghCneG70 http://t.c" "RT @leonardo_noleto: "Domain knowledge is overrated for predictive modeling competitions" @wzchen http://t.co/plNrEAeTbE #DataScience #Kagg" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "#scriptoftheweek Heat map of West Nile virus in Chicago. It was forked to create yearly maps https://t.co/31ghCneG70 http://t.co/lHNGMUdmSm" "Thanks to my instructors for sharing your knowledge 41st on Kaggle's TFI! @JHUDataScience @rdpeng @jtleek @bcaffo http://t.co/orcEhpCdA9" "1,528 teams of data scientists in #AXA first #datascience competition on #Kaggle>8th most active competition ever http://t.co/NVdVCLhTYm" "RT @benjamincabanes: Kaggle, le site qui transforme le big data en or http://t.co/i4YmzWQFbM via @lemondefr" "@HeatherOhana Show you can solve problems and think through solutions. Use whatever way you have on hand, be it Kaggle or independent work." "@21t_cog Hi. And thanks. Kaggle scares me a little. There is a meetup group here in town I should hit up for advice though." "scikit-learn video #4: Model training and prediction with K-nearest neighbors http://t.co/raxBnilWnI" "@HeatherOhana The MSPA does have weight. Do some Kaggle comps for experience/resume lines too, I respect that as a hiring mgr. Also, hi :)" "Top 10 on #kaggle. Don't make me use the kernel trick. https://t.co/taVKsuZcsN" "Moved up 688 spots. Soon the robots will have my job. #kaggle - https://t.co/VBcT0nFEVc" "Ever want to volunteer your #datascience #analytics skills? Great podcast w/Datakind, the Kaggle of social projects http://t.co/4OazY9cohX" "RT @benhamner: Machine learning gremlins: dozens of failure modes we've seen in real world machine learning at @kaggle https://t.co/Zjrea1y" "RT @MKTJimmyxu: 10 R Packages to Win Kaggle Competitions by @DataRobot #gbm #glmnet http://t.co/lX6BhgRgH1 via @SlideShare" "Top stories for Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Ch... http://t.co/f5Hkn2wkHB" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @rohanrao: Doing my part to bring about the singularity. #MachineLearning #kaggle - https://t.co/icnG2mxPJb" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @MKTJimmyxu: 10 R Packages to Win Kaggle Competitions by @DataRobot #gbm #glmnet http://t.co/lX6BhgRgH1 via @SlideShare" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "CNNJumped 433 spots on #kaggle. I'll sleep when I'm dead. https://t.co/rrFJJJU4eu" "Old Days :P @codechef @kaggle @codeorg @Codecademy http://t.co/sZ8X6JqBX9" "scikit-learn video #4: Model training and prediction with K-nearest neighbors http://t.co/PH8BvpAAdf #datascience #machinelearning #data" "53 spots up, just a few more to go. #kaggle - https://t.co/K7yWjOulTZ" "Kaggle Competition (Facebook recruiting): Human or Robot? - http://t.co/ktu1qkjEg8" ""scikit-learn video #4: Model training and prediction with K-nearest neighbors" http://t.co/6x3iNDLRXC #analytics #feedly" "Moved up 24 spots on #kaggle, currently in the top 8%. https://t.co/s9PMV0sBbG" "RT @stianlagstad: Dagens epost-subject: "Our A/B tests predict you will open this email" @kaggle" "Doing my part to bring about the singularity. #MachineLearning #kaggle - https://t.co/Jioj8CBLDU" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "scikit-learn video #4: Model training and prediction with K-nearest neighbors http://t.co/QSzpfcB3iv" "RT @leonardo_noleto: "Domain knowledge is overrated for predictive modeling competitions" @wzchen http://t.co/plNrEAeTbE #DataScience #Kagg" ""Domain knowledge is overrated for predictive modeling competitions" @wzchen http://t.co/plNrEAeTbE #DataScience #Kaggle" "Me encanta Kaggle. Participar, perder, y luego ver como los genios se las apaan para ganar. Se aprende un montn. http://t.co/YSsmVt9RGy" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @datasama: Competition Kaggle : Prdire la demande d'un rseau de vlo partag type Vlib http://t.co/89qfANMjdk #prediction #datascienc" "RT @benhamner: What drives demand for DC bike rentals? Nice weather during weekday commute / weekend lunch https://t.co/cpPSaEzkyJ http://t" "RT @datasama: Competition Kaggle : Prdire la demande d'un rseau de vlo partag type Vlib http://t.co/89qfANMjdk #prediction #datascienc" "Top stories for Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Ch... http://t.co/WwDmtNjOk8" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT datasama "Competition Kaggle : Prdire la demande d'un rseau de vlo partag type Vlib http://t.co/MNkSPUuiIj #prediction #datascien" "scikit-learn video #4: Model training and prediction with K-nearest neighbors: Welcome back to my series of vi... http://t.co/SpxDDcxcq3" "Competition Kaggle : Prdire la demande d'un rseau de vlo partag type Vlib http://t.co/89qfANMjdk #prediction #datascience #kaggle" "Moved up 22 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/en8AAMcEai" "Moved up 432 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/Fa6I91sqoj" "RT @AdamTaran: Intro to #machinelearning with scikit-learn by @justmarkham and @kaggle http://t.co/IAL23H5JVI @combine_au http://t.co/A7hjP" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "RT @Farhan_Khwaja: 371 places up. Now accepting donations for more RAM. #kaggle - https://t.co/PqWbRm5Hjy" "RT @Farhan_Khwaja: 69 places up. Not unlike my electricity bill. #kaggle - https://t.co/PqWbRm5Hjy" "RT @kaggle: #DataSciBowl Mini Challenge: 1st 10 to tweet the answer to #36 & @BoozAllen wins a NDSB prize: http://t.co/IFZWVYRLtx http://t." "RT @rohanrao: Doing my part to bring about the singularity. #MachineLearning #kaggle - https://t.co/icnG2mxPJb" "Jumped 21 spots on #kaggle. I'll sleep when I'm dead. https://t.co/icnG2mxPJb" "Doing my part to bring about the singularity. #MachineLearning #kaggle - https://t.co/icnG2mxPJb" "69 places up. Not unlike my electricity bill. #kaggle - https://t.co/PqWbRm5Hjy" "sckit-learn video #1: Intro to machine learning with scikit-learn | no free hunch http://t.co/0gtOmh1a9Z" "Restaurant Revenue Prediction122 places up. A small price to pay for my social life. #kaggle - https://t.co/rpscydhv8n" "RT @kaggle: Our third @scikit_learn #machinelearning tutorial features the famous Iris dataset http://t.co/NiIaQjHooK http://t.co/7nZCBNlBY7" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @AdamTaran: Intro to #machinelearning with scikit-learn by @justmarkham and @kaggle http://t.co/IAL23H5JVI @combine_au http://t.co/A7hjP" "RT @AdamTaran: Intro to #machinelearning with scikit-learn by @justmarkham and @kaggle http://t.co/IAL23H5JVI @combine_au http://t.co/A7hjP" "@AdamTaran @kaggle @combine_au Thanks for the mention, I hope you enjoy the series!" "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "497 places up. Not unlike my electricity bill. #kaggle - https://t.co/wuJLY86Iir" "Jumped 420 spots on #kaggle. I'll sleep when I'm dead. https://t.co/pEYCQvysgk" "204 places up. Not unlike my electricity bill. #kaggle - https://t.co/L6czQfFF68" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "scikit-learn video #4: Model training and prediction with K-nearest ...: In the first three videos, we discuss... http://t.co/ly77yKN7P6" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "Teaching robots to learn #MachineLearning #kaggle - https://t.co/zGUfW9Kzi9" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @AdamTaran: Intro to #machinelearning with scikit-learn by @justmarkham and @kaggle http://t.co/IAL23H5JVI @combine_au http://t.co/A7hjP" "RT @AdamTaran: Intro to #machinelearning with scikit-learn by @justmarkham and @kaggle http://t.co/IAL23H5JVI @combine_au http://t.co/A7hjP" "What drives demand for DC bike rentals? Nice weather during weekday commute / weekend lunch https://t.co/cpPSaEzkyJ http://t.co/VBrilP79p9" "16 places up. Not unlike my electricity bill. #kaggle - https://t.co/PqWbRm5Hjy" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @ultraistter: KaggleTOP25%: TOP10%: TOP10: , : " "Intro to #machinelearning with scikit-learn by @justmarkham and @kaggle http://t.co/IAL23H5JVI @combine_au http://t.co/A7hjPDs0TI" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "@benhamner @kaggle Great visualization. I am now interested in this contest." "Harry's - Sr. Analytics Marketing Manager (New York City) http://t.co/Pw0UM80oT0" "RT @kaggle: Get a #scikitlearn cheat sheet and setup with #Python for #machinelearning in our 2nd tutorial http://t.co/3hGadYd96k http://t." "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @ultraistter: KaggleTOP25%: TOP10%: TOP10: , : " "10 R Packages to Win Kaggle Competitions by @DataRobot #gbm #glmnet http://t.co/lX6BhgRgH1 via @SlideShare" "RT @kaggle: Get a #scikitlearn cheat sheet and setup with #Python for #machinelearning in our 2nd tutorial http://t.co/3hGadYd96k http://t." "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "RT @kaggle: scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co" "scikit-learn #machinelearning video #4: Model training & prediction with k-Nearest Neighbors http://t.co/JtR5945jLU http://t.co/6F9jfBU7XP" "371 places up. Now accepting donations for more RAM. #kaggle - https://t.co/PqWbRm5Hjy" "New post: scikit-learn video #4: Model training and prediction with K-nearest neighbors http://t.co/J5MC3rzhHd" "Moved up 657 spots. Soon the robots will have my job. #kaggle - https://t.co/7lKpBCcGq4" "no free hunch scikit-learn video #4: Model training and prediction with K-nearest neighbors: Welcome back to... http://t.co/eSgtkNCMna" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "Check out scikit-learn video #4: Model training and prediction with K-nearest neighbors : http://t.co/IqesfeRV9c #datascience" "686 places up. Now accepting donations for more RAM. #kaggle - https://t.co/1HtGXMgFhp" "@egkozlov https://t.co/eHmCnuVgUJ " "The Walt Disney Company - Decision Science Project/Technical Manager (Lake Buena Vista, FL) http://t.co/F3Wxtwlm5g" "http://t.co/EPERufncAh - Data Scientist (Various Locations) http://t.co/JMYSJuTG2o" "#BigData predicts stock needed before a #tornado. Enter our @kaggle #competition. http://t.co/iTrUNZ4d2f #TeamWalmart http://t.co/poMkYlDsti" "RT @NYCDataSci: Our students are presenting their Kaggle AXA competition projects. Watch it now at https://t.co/JuggrLAzFI" "RT @MajkWu: Kaggle's Chief Scientist: Specialist Knowledge Is Useless and Unhelpful http://t.co/WZIyYjRU9O #stats #ML" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "46 places up. Now accepting donations for more RAM. #kaggle - https://t.co/Gp86BhI4it" "RT @NotThatLebowski: Jumped 219 spots on #kaggle. I'll sleep when I'm dead. https://t.co/XEl41wClyU. #MachineLearning is fun....:D Friggin " "RT @NotThatLebowski: Jumped 219 spots on #kaggle. I'll sleep when I'm dead. https://t.co/XEl41wClyU. #MachineLearning is fun....:D Friggin " "RT @DataCommunityDC: Great tutorial from @kaggle on how to conduct #machinelearning in @scikit_learn using Iris #dataset http://t.co/rmg1jV" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "Just for the sake of #machinelearning, I opened this mail. Happy @kaggle? http://t.co/oACQLAQdiP" "@benhamner @kaggle You started on that quickly!" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "Use #BigData to predict stock needed before a #tornado. Enter our @kaggle #competition. http://t.co/G1t3ircmwd http://t.co/LyszuECiH6" "Moved up 862 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/ykNqEBg2A0" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "Dagens epost-subject: "Our A/B tests predict you will open this email" @kaggle" "So, that was an interesting experience with #kaggle. Now to beat the French... https://t.co/RFlUsN0WTg" "RT @ML_Nantes: Lundi 4 mai - Nantes ML Meetup est de retour / comptition Kaggle ($175K) / recherche des planctons dans des images. http://" "http://t.co/t9CoqKUmL8 #kaggle #winsolutions" " kaggle , , " "Lundi 4 mai - Nantes ML Meetup est de retour / comptition Kaggle ($175K) / recherche des planctons dans des images. http://t.co/F1Qmbaocov" "Well played, @kaggle. http://t.co/PaBditB9GD" "RT @ravdeepchawla: Well played @kaggle ... Very well played :D http://t.co/HTjOLRiXCo" "RT @DataCommunityDC: Great tutorial from @kaggle on how to conduct #machinelearning in @scikit_learn using Iris #dataset http://t.co/rmg1jV" "This worked Kaggle. http://t.co/4RLB5Oou1U" "scikit-learn video #2: Setting up #Python for #machinelearning | no free hunch http://t.co/8xGDbRkUoj" "Great tutorial from @kaggle on how to conduct #machinelearning in @scikit_learn using Iris #dataset http://t.co/rmg1jV9oWY via @justmarkhams" "27 spots up, just a few more to go. #kaggle - https://t.co/fQdKxmBiie" "@peopleshark @RecruitToolbox #techsourcinglab alerted us to @kaggle Excited to explore various competitions to apply our specialized skills!" "Alternately cringing at and amused by this marvelous bit of #Kaggle meta-marketing. #datascience #ABtests http://t.co/84BkZZ56Kr" "Nice one, @kaggle http://t.co/bc9rURuQ9T" "I LOLed, @kaggle, but I havent clicked (yet). http://t.co/9oK8OH3B2r" "RT @EmmanuelTZ: Discover the winners' interview of the #AXA Driver Telematics competition on Kaggle - great insight on data science https:/" "Well played @kaggle ... Very well played :D http://t.co/HTjOLRiXCo" "#MachineLearning contest @kaggle, the prize: a chance to work @facebook https://t.co/C9VHOnIdwT" "The Felipe Mata Daily is out! http://t.co/PgsHL9wECL Stories via @DAAorg @kaggle @Telefonica" "Bot or Human = Job at Facebook! https://t.co/G6TOxyAY8Y http://t.co/05bsuwTfbn" "RT @EmmanuelTZ: Discover the winners' interview of the #AXA Driver Telematics competition on Kaggle - great insight on data science https:/" "RT @EmmanuelTZ: Discover the winners' interview of the #AXA Driver Telematics competition on Kaggle - great insight on data science https:/" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "kaggle12" "RT @ultraistter: KaggleTOP25%: TOP10%: TOP10: , : " "RT @ultraistter: KaggleTOP25%: TOP10%: TOP10: , : " "RT @kdnuggets: Succeed in @Kaggle #predictive #modeling competitions w/out domain #knowledge, but NOT in real tasks http://t.co/16Gf6DMmJO" "RT @ultraistter: KaggleTOP25%: TOP10%: TOP10: , : " "RT @ultraistter: KaggleTOP25%: TOP10%: TOP10: , : " "KaggleTOP25%: TOP10%: TOP10: , : " "Teaching robots to learn #MachineLearning #kaggle - https://t.co/oOEzgUAGGg" "RT @ultraistter: Kaggle(,TOP10,TOP10%,)4" "RT @ultraistter: Kaggle(,TOP10,TOP10%,)4" "RT @ultraistter: Kaggle(,TOP10,TOP10%,)4" "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "RT @ultraistter: Kaggle(,TOP10,TOP10%,)4" "Kaggle(,TOP10,TOP10%,)4" "RT @NYCDataSci: Our students are presenting their Kaggle AXA competition projects. Watch it now at https://t.co/JuggrLAzFI" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "RT @chris_bour: Next Paris @Kaggle meetup just announced. Hacking session on the Otto Group challenge http://t.co/Lfu5YquSRx" "RT @chris_bour: Next Paris @Kaggle meetup just announced. Hacking session on the Otto Group challenge http://t.co/Lfu5YquSRx" "RT @chris_bour: Next Paris @Kaggle meetup just announced. Hacking session on the Otto Group challenge http://t.co/Lfu5YquSRx" "RT @geoinquiets: Competici d'anlisi de dades geo i meteo per detectar virus a Chicago https://t.co/d4l3mWL0Av via @RealIvanSanchez http:/" "Competici d'anlisi de dades geo i meteo per detectar virus a Chicago https://t.co/d4l3mWL0Av via @RealIvanSanchez http://t.co/Hf0RzEMc70" "RT @DataRobot: The journey from a noob to a Kaggler on #Python: http://t.co/283LZnSNm1 #Kaggle #DataScience http://t.co/1k209HdJFi" "RT @DataRobot: The journey from a noob to a Kaggler on #Python: http://t.co/283LZnSNm1 #Kaggle #DataScience http://t.co/1k209HdJFi" "RT @DataRobot: The journey from a noob to a Kaggler on #Python: http://t.co/283LZnSNm1 #Kaggle #DataScience http://t.co/1k209HdJFi" "The journey from a noob to a Kaggler on #Python: http://t.co/283LZnSNm1 #Kaggle #DataScience http://t.co/1k209HdJFi" "Top stories for Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Ch http://t.co/Ugs524Kl2p" "Jumped 21 spots on #kaggle. I'll sleep when I'm dead. https://t.co/YP4yPceoVE" "RT @ScraperWiki: Adventures in @kaggle: Machine learning in Python on the Forest cover type prediction challenge https://t.co/6syi7gKhQ7" "Adventures in @kaggle: Machine learning in Python on the Forest cover type prediction challenge https://t.co/6syi7gKhQ7" "Kaggle's Chief Scientist: Specialist Knowledge Is Useless and Unhelpful http://t.co/WZIyYjRU9O #stats #ML" "Paper due in Lecture Notes in Computer Science: http://t.co/pLHZoSANJG Thanks co-authors, Kaggle, Wolpert, SKLearn. Soon a better write-up." "I love when companies enlist open innovation tools like kaggle! Good on you AXA. https://t.co/0jFYey6YKy" "RT @myui: TD/data scientist/kaggle master..) https://t.co/h7nsJAkzNt" "RT @myui: TD/data scientist/kaggle master..) https://t.co/h7nsJAkzNt" "Weird, #meta & doomed to fail? @kaggle & Facebook launch a machine learning comp to help humans beat machine learning http://t.co/qeJboFcDat" "ML hyperparameters tuning w/ pysmac, RandomizedSearchCV, hyperopt... Learn & practice at next Paris @Kaggle Meetup http://t.co/Lfu5YquSRx" "RT @treycausey: Facebook is recruiting ML engineers via a Kaggle competition. http://t.co/54ZEswQrXO" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "Jumped 11 spots on #kaggle. I'll sleep when I'm dead. https://t.co/VeC2fp4KHy" "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "231 places up. Now accepting donations for more RAM. #kaggle - https://t.co/ebVG3IqOr5" "It's lonely here at the top. #kaggle - https://t.co/UhPMkGKsFh" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "RT @treycausey: Facebook is recruiting ML engineers via a Kaggle competition. http://t.co/54ZEswQrXO" "RT @aflyax: Nice #machinelearning libraries, including alternatives to #scikitlearn: http://t.co/fSJrbAESz3 #python #kaggle" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "Digging the new scripts functionality on the @kaggle competitions. Great way to learn EDA methodologies." "Pretty concise and good videos on how to start with scikit -learn (on Kaggle blog): http://t.co/vqdWpIVqKv" "Great scikit-learn Python talk of Kaggle competitions in Pycon 2015: https://t.co/p6n5SEvEvS" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "Kaggle1 http://t.co/xeQIQPHPf5" "Top stories for Apr 19-25 for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Champion http://t.co/bXdAvt6u3H via @kdnuggets" "RT @Keiku: User Rankings1BigData Conference 2015Kaggle" "RT @Keiku: User Rankings1BigData Conference 2015Kaggle" "RT @toshi_k_datasci: kaggle300.1%300" "Moved up 1 spot. Soon the robots will have my job. #kaggle - https://t.co/lhRH5P9rcf" "KaggleKaggle Update: Our A/B tests predict you will open this emailSubjectwww https://t.co/Ge8IXdQLtx" "Restaurant Revenue Prediction97 spots up. Is anybody else even trying? #kaggle - https://t.co/rpscydhv8n" "kaggle701" "RT @Keiku: User Rankings1BigData Conference 2015Kaggle" "147 places up. Not unlike my electricity bill. #kaggle - https://t.co/FtI2ls7hce" "Moved up 538 spots. Soon the robots will have my job. #kaggle - https://t.co/u3maNaDZGU" "Tutorial - Kaggle http://t.co/HnEvu1zhHZ" "User Rankings1BigData Conference 2015Kaggle" "RT @benhamner: Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "Rank 92 :) , I am loving it ..... #kaggle - #AnalyticsEdge There is nothing in this world that is not possible . @knock_the_T_out_of_can't" "Yearly maps of West Nile Virus mosquito detections around Chicago https://t.co/sR5ZhO80ii http://t.co/zDq3c6OnvG" "kaggle300.1%300" "RT @Keiku: ForumBeatbenchmarkKaggle0.1%BigData Conference 2015 Spring http://t.co/iugseei" "RT @Keiku: ForumBeatbenchmarkKaggle0.1%BigData Conference 2015 Spring http://t.co/iugseei" "RT @DataRobot: A great tutorial from @kaggle on how to conduct #machinelearning in @scikit_learn using the famous Iris #dataset: http://t.c" "RT @Keiku: ForumBeatbenchmarkKaggle0.1%BigData Conference 2015 Spring http://t.co/iugseei" "ForumBeatbenchmarkKaggle0.1%BigData Conference 2015 Spring http://t.co/iugseeiVRi" "RT @skathirmani: Titanic: Getting Started With R http://t.co/XgYZziWkjY #Kaggle #RStats #DataScience via @trevs" "Titanic: Getting Started With R http://t.co/XgYZziWkjY #Kaggle #RStats #DataScience via @trevs" "Moved up 3 spots. #kaggle - https://t.co/uidJIvkTox" "RT @Nikkei_Bigdata: Kaggle http://t.co/p48L3s1JFB" "Nice #machinelearning libraries, including alternatives to #scikitlearn: http://t.co/fSJrbAESz3 #python #kaggle" "Kaggle http://t.co/p48L3s1JFB" "RT @JulianHi: New Kaggle challenge from Facebook: Predict if an online bid is made by a machine or a human http://t.co/p1kJwHMvUY http://t." "RT @dtosato: scikit-learn video #2: Setting up Python for machine learning http://t.co/BecvR2bn3q" "RT @JulianHi: New Kaggle challenge from Facebook: Predict if an online bid is made by a machine or a human http://t.co/p1kJwHMvUY http://t." "RT @JulianHi: New Kaggle challenge from Facebook: Predict if an online bid is made by a machine or a human http://t.co/p1kJwHMvUY http://t." "RT @KaggleCareers: Microsoft - Senior Data Scientist (Redmond, WA) http://t.co/RlICMzoGZy" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "@kaggle That would free the #kaggle hashtag for actual discussion about Kaggle. My 2 cents." "@kaggle Shouldn't it be better to change it to something like #KaggleChallenge, with the name of the actual challenge?" "@kaggle The bad thing about semi-automatic tweets when one moves up the leaderboard is that they take up almost all #kaggle tagging." "I'm giving up on #kaggle Restaurant Revenue Prediction challenge. Only 5 days to go and the only thing I do is overfit again and again." "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "RT @NYCDataSci: Our students are presenting their Kaggle AXA competition projects. Watch it now at https://t.co/JuggrLAzFI" "#CollaborativeEconomy is out! http://t.co/0l0WskbPuZ Stories via @kaggle @WorkMarket" "Description - Facebook Recruiting IV: Human or Robot? | Kaggle http://t.co/3hUuowH7GI" "RT @YvesMulkers: Top stories for Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Ch http:/" "Facebook is recruiting ML engineers via a Kaggle competition. http://t.co/WGEJn5KRfE @treycausey" "Moved up 891 spots. Soon the robots will have my job. #kaggle - https://t.co/ZjmCRxUTXK" "Nuestros amigos de Kaggle nos presentan un desafo. Se animan a resolverlo? http://t.co/WtyxoEf9s2" "RT @treycausey: Facebook is recruiting ML engineers via a Kaggle competition. http://t.co/54ZEswQrXO" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "Top stories for Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Ch http://t.co/2JDSeKQsGi" "RT @JulianHi: New Kaggle challenge from Facebook: Predict if an online bid is made by a machine or a human http://t.co/p1kJwHMvUY http://t." "New Kaggle challenge from Facebook: Predict if an online bid is made by a machine or a human http://t.co/p1kJwHMvUY http://t.co/77tQknUWaa" "RT @treycausey: Facebook is recruiting ML engineers via a Kaggle competition. http://t.co/54ZEswQrXO" "RT @treycausey: Facebook is recruiting ML engineers via a Kaggle competition. http://t.co/54ZEswQrXO" "RT @benhamner: Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0q" "Nice example of using @scikit_learn to learn ensemble weights for combining multiple machine learning models https://t.co/0qGmFadvBF" "RT @Zecca_Lehn: New 'script feature' on @kaggle allows users to share #rstats and #python code; to run competition datsets in browser--with" "RT @NYCDataSci: Our students are presenting their Kaggle AXA competition projects. Watch it now at https://t.co/JuggrLAzFI" "Our students are presenting their Kaggle AXA competition projects. Watch it now at https://t.co/JuggrLAzFI" "Nuestros amigos de Kaggle nos presentan un desafo. Se animan a resolverlo? http://t.co/WtyxoEf9s2" "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "@kaggle And it's fast / open / free!" "RT @Zecca_Lehn: New 'script feature' on @kaggle allows users to share #rstats and #python code; to run competition datsets in browser--with" "Nuestros amigos de Kaggle nos presentan un desafo. Se animan a resolverlo? http://t.co/WtyxoEf9s2" "New 'script feature' on @kaggle allows users to share #rstats and #python code; to run competition datsets in browser--without downloading!" "I am a giant nerd, but that's okay. #kaggle" "@TakamiGodan Unless there's an algorithm for inferring terrorist risk from Chatroulette behaviour. New @kaggle comp? @ZDNet @zackwhittaker" "Moved up 246 spots. Soon the robots will have my job. #kaggle - https://t.co/1HtGXLZ3SP" "Moved up 8 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/yN8EdCG4C5" "Nuestros amigos de Kaggle nos presentan un desafo. Se animan a resolverlo? http://t.co/WtyxoEf9s2" "@treycausey They're selecting for the perfect FB dev-- smart enough to win Kaggle while not smart enough to avoid working for free." "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "Nuestros amigos de Kaggle nos presentan un desafo. Se animan a resolverlo? http://t.co/VGgp1hQ7O1" "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "@AlokPattaniESPN @skatz23 Zach B, the Kaggle winner?" "288 spots up, just a few more to go. #kaggle - https://t.co/yN8EdCG4C5" "Facebook is recruiting ML engineers via a Kaggle competition. http://t.co/54ZEswQrXO" " http://t.co/PSwi8jf96X #DataScience http://t.co/Ptexlph9Qy" "RT @AXALab: Discover the winners' interview of the hugely popular @AXA Driver Telematics competition on @kaggle https://t.co/kKfHoJFQt5 via" "Kaggle: Big Data als sport, met flink wat prijzengeld Numrush http://t.co/SX1cut6sXx" "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "RT @kaggle: Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t." "Recruiting launch! Compete for the @facebook perks (like data from 1.3B monthly active users) https://t.co/zUtz0sZm0g http://t.co/Ovfwp0EGhN" "My first submittion. #kaggle - https://t.co/7QREYHOpwn" "Discover the winners' interview of the hugely popular @AXA Driver Telematics competition on @kaggle https://t.co/kKfHoJFQt5 via @fredtardy" "Gotta' start somewhere! (No hyperplanes were harmed in the making of this submission). #kaggle - https://t.co/U0nY4SQlEz" "Microsoft - Senior Data Scientist (Redmond, WA) http://t.co/RlICMzoGZy" "630 spots up. Is anybody else even trying? #kaggle - https://t.co/eq13r5BKRB" "RT @TJO_datasci: KagglePythonRR" "Got an email with the title "Our A/B tests predict you will open this email". Good job #kaggle" "AXA Winners' Interview: Learning Telematic Fingerprints From GPS Data http://t.co/BWrBt9srAl via @kaggle" "Moved up 25 spots on #kaggle. Now score is 0.50950. Still could not cross the 0.5 barrier!. A-a-a-a-a!!! https://t.co/E1GIKF23z6" "@fchollet Thanks. Love to see a regression example at https://t.co/tHE1XX77Rp (how about Kaggle TFI demo?)" "RT @kdnuggets: .@Kaggle Competition (#Facebook recruiting): #Human or #bot? http://t.co/NSVyy8v9Zy http://t.co/ANE6k9lM5N" "Doing my part to bring about the singularity. #MachineLearning #kaggle - https://t.co/u3maNaDZGU" "RT @kdnuggets: .@Kaggle Competition (#Facebook recruiting): #Human or #bot? http://t.co/NSVyy8v9Zy http://t.co/ANE6k9lM5N" "RT @kdnuggets: Top stories: Top LinkedIn Groups for #Analytics, #BigData; 10 R Packages for @Kaggle Champion http://t.co/Kx0D8mqp3W http://" "RT @kdnuggets: Top stories: Top LinkedIn Groups for #Analytics, #BigData; 10 R Packages for @Kaggle Champion http://t.co/Kx0D8mqp3W http://" "95 places up. Now accepting donations for more RAM. #kaggle - https://t.co/YP4yPceoVE" "RT @TJO_datasci: KagglePythonRR" "RT @TJO_datasci: KagglePythonRR" "RT @TJO_datasci: KagglePythonRR" "KagglePythonRR" " Kaggle BP: http://t.co/M9lz6dTKPy" "75,000 #datascientists workingon interesting #data sets &Fortune500 problems becoming #startups themselves @kaggle http://t.co/M6lzR0nzDq" "Nextlargestcompetition is GEflightquest. GE is trying to improve the ability to estimate which flights will be delayed JeremyHoward @kaggle" "10 spots up, just a few more to go. #kaggle - https://t.co/ivVzjxdXYP" "Jeremy Howard @kaggle: The hard thing generally is training the algorithms, not so much running them #machinelearning http://t.co/M6lzR0nzDq" "RT @Sophie_Fds: CONIX aime les dfis ! @ConixConsulting #ChallengeAxa #Datascientist #kaggle http://t.co/k626CQsc94 http://t.co/ASD9uSu4Rq" "RT @jhernanper: 1Mby1M: #ThoughtLeaders in #BigData: Interview with Jeremy Howard, President and Chief Scientist of Kaggle htt... http://t." "RT @jhernanper: 1Mby1M: #ThoughtLeaders in #BigData: Interview with Jeremy Howard, President and Chief Scientist of Kaggle htt... http://t." "Top 10 R Packages to be a Kaggle Champion http://t.co/U5TYwL1qrc" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/8gWRFVRvQB via @rbloggers" "RT @NotThatLebowski: Jumped 219 spots on #kaggle. I'll sleep when I'm dead. https://t.co/XEl41wClyU. #MachineLearning is fun....:D Friggin " ".@Kaggle Competition (#Facebook recruiting): #Human or #bot? http://t.co/NSVyy8v9Zy http://t.co/ANE6k9lM5N" "Jumped 219 spots on #kaggle. I'll sleep when I'm dead. https://t.co/XEl41wClyU. #MachineLearning is fun....:D Friggin excited." "1Mby1M: #ThoughtLeaders in #BigData: Interview with Jeremy Howard, President and Chief Scientist of Kaggle htt... http://t.co/s7ZoKmW0JD" "CONIX aime les dfis ! @ConixConsulting #ChallengeAxa #Datascientist #kaggle http://t.co/k626CQsc94 http://t.co/ASD9uSu4Rq" "handwritten_digit_recognition.ipynb http://t.co/cG6LT9sbh2" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "RT @prrgutierrez: Havin fun testing #dataiku #dss 2.0 without a line of python. #kaggle - https://t.co/66qSWTpXxU" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/ax2aOAhtZi" "811 places up. A small price to pay for my social life. #kaggle - https://t.co/VDTssRBbiU" "Fresh from @KDNuggets: Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/7RDWShhCWF" "Ever wonder what it'slike to work at #Facebook?Enter this competition for a chance of an interview there... http://t.co/gceJZxqn9f" "18 spots up. Is anybody else even trying? #kaggle - https://t.co/m2At6Z4fuW" "Jumped 29 spots on #kaggle. I'll sleep when I'm dead. https://t.co/VeC2fp4KHy" "RT @prrgutierrez: Havin fun testing #dataiku #dss 2.0 without a line of python. #kaggle - https://t.co/66qSWTpXxU" "RT @prrgutierrez: Havin fun testing #dataiku #dss 2.0 without a line of python. #kaggle - https://t.co/66qSWTpXxU" "Its very strange that a online coarse taught me how to strive .. embrace failure and keep progressing .. #kaggle #turningInto_dataScientist" "Havin fun testing #dataiku #dss 2.0 without a line of python. #kaggle - https://t.co/66qSWTpXxU" "1,240 spots up, just a few more to go. #kaggle - https://t.co/Fa6I91sqoj" "BRAND NEW: #datascience @docker containers by @kaggle https://t.co/jC0ahVYnv1 #rstats #python #julialang" "1 place up. Not unlike my electricity bill. #kaggle - https://t.co/dGLy1qj6Jh" "5 days to go! Curious about the results. Black box data mining and small test set make this really interesting https://t.co/FmtfRqZI9h" "Allstate Purchase Prediction Challenge on Kaggle http://t.co/hH0C43wkJo" "RT @josselinchevala: data scientist here to find a platform competitions and jobs https://t.co/svD40vMCR7 #BigData #DataScience" " Kaggle , ." "data scientist here to find a platform competitions and jobs https://t.co/svD40vMCR7 #BigData #DataScience" "RT @chris_bour: Paris Kaggle Meetup - hands-on session is full ! http://t.co/Lfu5YquSRx" "Paris Kaggle Meetup - hands-on session is full ! http://t.co/Lfu5YquSRx" "RT @fredtardy: Discover the winners' interview of the hugely popular AXA Driver Telematics competition on Kaggle https://t.co/ZLantB3ISV" "RT @fredtardy: Discover the winners' interview of the hugely popular AXA Driver Telematics competition on Kaggle https://t.co/ZLantB3ISV" "Causing #superintelligence, one submission at a time #kaggle - https://t.co/Fa6I91sqoj" "RT @fredtardy: Discover the winners' interview of the hugely popular AXA Driver Telematics competition on Kaggle https://t.co/ZLantB3ISV" "Facebook and Kaggle (http://t.co/5SFBrxwW5w) are launching an engineering competition for 2015. https://t.co/W1wypnvCmd" "Discover the winners' interview of the hugely popular AXA Driver Telematics competition on Kaggle https://t.co/ZLantB3ISV" " kaggle csv . . 1 1000 " "RT @Haystack_Data: Top 10 R Packages to be a Kaggle Champion http://t.co/cOwOtA8s2I via @kdnuggets #bigdata #data #R #datascience #analytic" "RT @Haystack_Data: Top 10 R Packages to be a Kaggle Champion http://t.co/cOwOtA8s2I via @kdnuggets #bigdata #data #R #datascience #analytic" "RT @Haystack_Data: Top 10 R Packages to be a Kaggle Champion http://t.co/cOwOtA8s2I via @kdnuggets #bigdata #data #R #datascience #analytic" "Kaggle Competition (Facebook recruiting): Human or Robot? Facebook and Kaggle are launchin http://t.co/814ZaJZYe4" "sbumit I got a score of 1681647.17927. | 366 places up. #kaggle - https://t.co/Gzxy3uzgre" "RT @Haystack_Data: Top 10 R Packages to be a Kaggle Champion http://t.co/cOwOtA8s2I via @kdnuggets #bigdata #data #R #datascience #analytic" "RT @Haystack_Data: Top 10 R Packages to be a Kaggle Champion http://t.co/cOwOtA8s2I via @kdnuggets #bigdata #data #R #datascience #analytic" "RT @Haystack_Data: Top 10 R Packages to be a Kaggle Champion http://t.co/cOwOtA8s2I via @kdnuggets #bigdata #data #R #datascience #analytic" "Top 10 R Packages to be a Kaggle Champion http://t.co/cOwOtA8s2I via @kdnuggets #bigdata #data #R #datascience #analytics #kaggle" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/EoD25jOl2b" "note to self. read every kaggle winner's post competition write-up" "RT @adrianplattner: lets try to improve my solution, let the hacking begins. #kaggle #ml #machinelearning" "lets try to improve my solution, let the hacking begins. #kaggle #ml #machinelearning" "Made my first #kaggle submission for https://t.co/ynzrwfSqKM competition ! Find No Chimp Inc. Team at Public Leaderboard." "Moved up 455 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/CvLg59KQmw" "RT @moorejh: #datascience MT @Rbloggers Comparing Tree-Based Classification Methods via #Kaggle http://t.co/qYK3LNMFEo #rstats http://t.co/" "RT @DataScientistFr: Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/gmoSrqgdOA #Analytics" "RT @BigData_Fr: Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/NQHTcOjQ7R #Analytics" "Moved up 17 spots on #kaggle. This is a parameter tuning contest : ) https://t.co/NF03l9Gda7" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/gmoSrqgdOA #Analytics" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/eHMtN857VE #Analytics" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/NQHTcOjQ7R #Analytics" "Facebook + Kaggle Competition! Human v. Robot http://t.co/xiAUoIFVai" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/VLfY6aXzji http://t.co/ZcaRVQDbH1" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/B1FlOCvqrA" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/7CauxRvyTl" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/4Sr7BXjKm9" "Kaggle Competition (Facebook recruiting): Human or Robot? http://t.co/5rp5SIdT1f" "868 places up. Not unlike my electricity bill. #kaggle - https://t.co/0Pyh9vM6Wi" "My preference for testing predictions vs actual outcomes is logloss - https://t.co/FRFVqwkqDT" "@threepointpi For predictions I tend to use Kaggle's preferences. https://t.co/b50SuG9Eyk" "#Hadoop & beyond: A primer on #BigData for the little guy | http://t.co/yWi3PLwVky #DataScience #NewSQL #Kaggle" "A new Facebook Recruiting Challenge on @Kaggle. Hunt down some auction bots and make them fail the Turing test: http://t.co/b0JWKnLgQf" "@nvenkataraman1 Like this, what does this buy me? I could run a one-liner by cleaning up this file, no? https://t.co/U6h2idNlNH" "Should you get a Coursera certificate if they already have Master's or PhD? Coursera/Kaggle projects are great for real-world data/projects" "RT @frenchdata: Paris Kaggle Meetup : les inscriptions vont ouvrir cet aprs-midi http://t.co/rtcHTrE5xw" "There are now over 300.000 Data Scientists on @Kaggle (Let's count the bots as Kagglers too). 100k+ growth in less than a year." "I just discovered the amazing world of #kaggle competitions http://t.co/xqUErctemu" "Facebook uses a new competition on Kaggle to #recruit software engineers on machine learning: http://t.co/OvdlgZXPtl #hrtech #recruiting" "2015/4/27 1. Kaggle Competition ?? 2. 3. #3good" "RT @moorejh: #datascience MT @Rbloggers Comparing Tree-Based Classification Methods via #Kaggle http://t.co/qYK3LNMFEo #rstats http://t.co/" "RT @moorejh: #datascience MT @Rbloggers Comparing Tree-Based Classification Methods via #Kaggle http://t.co/qYK3LNMFEo #rstats http://t.co/" "RT @moorejh: #datascience MT @Rbloggers Comparing Tree-Based Classification Methods via #Kaggle http://t.co/qYK3LNMFEo #rstats http://t.co/" "570 places up. Not unlike my electricity bill. #kaggle - https://t.co/WzNwFGy1ac" "RT @moorejh: #datascience MT @Rbloggers Comparing Tree-Based Classification Methods via #Kaggle http://t.co/qYK3LNMFEo #rstats http://t.co/" ". @sabrieker tabii pratik yapmak mhim, o yzden de misal: https://t.co/d17p94htLs" "Wenn du weit, was #Kaggle ist, solltest du unbedingt teilnehmen oder dich einfach bei uns bewerben. :-) #jobs https://t.co/JVYh4C9joR" "#datascience MT @Rbloggers Comparing Tree-Based Classification Methods via #Kaggle http://t.co/qYK3LNMFEo #rstats http://t.co/ggoOZ2GAdl" " 67 places up. A small price to pay for my social life. #kaggle - https://t.co/vlrCrhkBRr" "RT @kaggle: Our third @scikit_learn #machinelearning tutorial features the famous Iris dataset http://t.co/NiIaQjHooK http://t.co/7nZCBNlBY7" "We are trying a new formula at the kaggle Paris Meetup. RSVP and come to hack the Otto challenge with us: http://t.co/lW6cJQsz7S" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/roKxYiCopY #Classification #analytics #datascience" "RT @PubHealthSoc: MT @reedmonseur @kaggle: Competition! Can u predict when & where mosquitos will test + 4 West Nile virus in Chi? http://t" "Description - Facebook Recruiting IV: Human or Robot? | Kaggle http://t.co/OLRsUp8mxy" "MT @reedmonseur @kaggle: Competition! Can u predict when & where mosquitos will test + 4 West Nile virus in Chi? http://t.co/rMyBoffIr3" "decades of worldwide seismic data readings and live data is now accessible. Earthquake prediction is an ideal open data prize @kaggle" "kaggle5 places up. Not unlike my electricity bill. #kaggle - https://t.co/6OKuIVrTI4" "Kaggle: The Home of Data Science http://t.co/sUYV6rDWzF /cc @gglanzani" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "#dss 1 sql aggregation + 1 click button random forest = top 10 - https://t.co/66qSWTpXxU #kaggle" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "Paris Kaggle Meetup : les inscriptions vont ouvrir cet aprs-midi http://t.co/rtcHTrE5xw" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "BRAND NEW: #datascience @docker containers by @kaggle https://t.co/jC0ahVYnv1 #rstats #python #julialang" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "Moved up 577 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/XEl41wClyU" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning from @kaggle video series http://t.co/tRyO4bOGoG #DataScience #BigData http://t.co/rr70hOkPvh" "RT @KirkDBorne: Intro to #MachineLearning from @kaggle video series http://t.co/kNCtvadjb6 #DataScience #BigData http://t.co/3Ycjk92bqO" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "Data Science Use Cases | Kaggle http://t.co/bNZ5zW95qC" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "Teaching robots to learn #MachineLearning #kaggle - https://t.co/XEl41wClyU Submitted my 1st model. Friggin excited...:D" "91 places up & Top 10% , aww yeah ! Now accepting donations for more RAM. #Otto #MachineLearning #kaggle - https://t.co/WGlFiVAW7M" "[ 4 ] : (Kaggle) http://t.co/5SQBfKyKIe -831680990" "[ 4 ] : (Kaggle) http://t.co/SqZ7UpMhw8 1495974355" "Homework 7 of the #Mooc Analytics Edge from @MITxonedX: 93% This week is dedicated to @Kaggle challenge #DataScience http://t.co/N2PDFDcXVQ" "RT @justmarkham: THRILLED to launch new video series with @kaggle! Intro to #machinelearning with scikit-learn http://t.co/vnSoSoXMTx http:" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition #AMustRead http://t.co/VhwsGwrN7t via @vuabraham" "RT @_nestic: Human or Robot auction bidding? Facebook & Kaggle competition http://t.co/h2GniRfvP9 #MachineLearning #BigData" "RT @_nestic: Human or Robot auction bidding? Facebook & Kaggle competition http://t.co/h2GniRfvP9 #MachineLearning #BigData" "Facebook Recruiting: Human or Robot? If you are interested in Machine Learning, go for it https://t.co/lWIgKnJ0OD" "491 places up. Now accepting donations for more RAM. #kaggle - https://t.co/TuriP3mlg4" "RT @_nestic: Human or Robot auction bidding? Facebook & Kaggle competition http://t.co/h2GniRfvP9 #MachineLearning #BigData" "RT @_nestic: Human or Robot auction bidding? Facebook & Kaggle competition http://t.co/h2GniRfvP9 #MachineLearning #BigData" "93 places up. Score 0.51484. Getting closer to the personal goal of crossing the 0.5 barrier. #kaggle - https://t.co/E1GIKF23z6" "RT @_nestic: Human or Robot auction bidding? Facebook & Kaggle competition http://t.co/h2GniRfvP9 #MachineLearning #BigData" "Human or Robot auction bidding? Facebook & Kaggle competition http://t.co/h2GniRfvP9 #MachineLearning #BigData" "Top stories, Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Champ... http://t.co/yXp3z19379" "Microsoft Kaggle: Bag-of-words Ensemble (Microsoft Malware Classification ... http://t.co/DnRPiC3gTn #Microsoft" "RT @kaggle: Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu" "RT @kaggle: We had a lively discussion with @mrtz today about Kaggle's leaderboard. Thanks for coming in! http://t.co/KN7Xpqb2eV" "RT @kaggle: We had a lively discussion with @mrtz today about Kaggle's leaderboard. Thanks for coming in! http://t.co/KN7Xpqb2eV" "RT @Gruzman: @furrier @rwang0 @kevaldesai @UCSB I would point him to the open competition in date science https://t.co/CIMkt1X4P4 and ask " "@furrier @rwang0 @kevaldesai @UCSB I would point him to the open competition in date science https://t.co/CIMkt1X4P4 and ask to select." "RT @kaggle: Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu" "RT @pieterbuteneers: .@sedielem and #IraKorshunova of #ResLab @ugent_fea will will present our approach to win the #NDSB @kaggle https://t" "Description - Facebook Recruiting IV: Human or Robot? | Kaggle http://t.co/IPuSg6NWyo" "Jumped 80 spots on #kaggle. I'll sleep when I'm dead. https://t.co/D1a8LNigyM" "[ 4 ] : (Kaggle) http://t.co/bCzzFioGgd -184167949" "[ 4 ] : (Kaggle) http://t.co/7wGVYPsiVl -1158589128" "Nice competition - https://t.co/Jak1YpyoqO" "40 places up. A small price to pay for my social life. #kaggle - https://t.co/LcCjOpoUaV" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "Moved up 13 spots. #kaggle - https://t.co/dGLy1qAI7R" "RT @KrishSwamy1: Using #R to fire APIs and get data - neat #DataScience tricks series! http://t.co/7wnWjReIkp" "RT @KrishSwamy1: Using #R to fire APIs and get data - neat #DataScience tricks series! http://t.co/7wnWjReIkp" "RT @KrishSwamy1: Using #R to fire APIs and get data - neat #DataScience tricks series! http://t.co/7wnWjReIkp" "RT @KrishSwamy1: Using #R to fire APIs and get data - neat #DataScience tricks series! http://t.co/7wnWjReIkp" "RT @KrishSwamy1: Using #R to fire APIs and get data - neat #DataScience tricks series! http://t.co/7wnWjReIkp" "RT @KrishSwamy1: Using #R to fire APIs and get data - neat #DataScience tricks series! http://t.co/7wnWjReIkp" "[ 4 ] : (Kaggle) http://t.co/SqZ7UpuGEA 1915059873" "Using #R to fire APIs and get data - neat #DataScience tricks series! http://t.co/7wnWjReIkp" "Loved this comparison of different tree prediction methods #statistics #DataScience http://t.co/7wnWjReIkp" "Kaggle: Bag-of-words Ensemble (Microsoft Malware Classi... [] on @Qiita http://t.co/96m21bxYzb" "RT @kaggle: Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu" "19 places up. Now accepting donations for more RAM. #kaggle - https://t.co/sIF1y81QyS" "Up 126 spots, still crap! Need more skillz. #kaggle - https://t.co/gDukSaTdZk" "Check out "AXA Winners' Interview: Learning Telematic Fingerprints From GPS Data" http://t.co/7kaXPhUm3P (via @pocket)" "57 places up. A small price to pay for my social life. #kaggle - https://t.co/FtI2ls7hce This is me doing #kaggle: http://t.co/mmrddYcK7o" ""Comparing Tree-Based Classification Methods via the Kaggle Otto Competition" http://t.co/rZOVwsBeS2" "About to fall out of the Top 100 http://t.co/mROULUBTVs Time to break out the #neuralnetworks... #kaggle Peace out #gradientboosting " "RT @snipe: Tennessee Leeuwenburg: Interlude: Kaggle Scripting http://t.co/AWqzgwuLfG" "randomforestgbm http://t.co/qFkFkWuVnW" "Description - Facebook Recruiting IV: Human or Robot? | Kaggle http://t.co/NcVI5IQOsU" ".@kaggle challenge kickstarter: develop AI to unsubscribe from Microsoft Store." "Facebook Recruiting IV: Human or Robot? on @kaggle http://t.co/neMqk5WhT8" "RT @langstat: Comparing Tree-Based Classification Methods via the Kaggle Otto Competition R-bloggers http://t.co/i9I1gKRjOh CART, RF, GBM" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition R-bloggers http://t.co/i9I1gKRjOh CART, RF, GBM" "RT @derrickharris: Facebook and Kaggle are launching a machine-learning engineering competition https://t.co/M1AtQHJsDP" "Description - Facebook Recruiting IV: Human or Robot? | Kaggle http://t.co/AGirg0WyUe" "RT @itsmaloy: @facebook recruiting on @kaggle https://t.co/Tv0HRN9UpX #DataScience #machinelearning" "RT @itsmaloy: @facebook recruiting on @kaggle https://t.co/Tv0HRN9UpX #DataScience #machinelearning" "[ 4 ] : (Kaggle) http://t.co/5SQBfKQm6O -1420417080" "RT @derrickharris: Facebook and Kaggle are launching a machine-learning engineering competition https://t.co/M1AtQHJsDP" "RT @derrickharris: Facebook and Kaggle are launching a machine-learning engineering competition https://t.co/M1AtQHJsDP" "RT @derrickharris: Facebook and Kaggle are launching a machine-learning engineering competition https://t.co/M1AtQHJsDP" "RT @justmarkham: THRILLED to launch new video series with @kaggle! Intro to #machinelearning with scikit-learn http://t.co/vnSoSoXMTx http:" "RT @derrickharris: Facebook and Kaggle are launching a machine-learning engineering competition https://t.co/M1AtQHJsDP" "RT @derrickharris: Facebook and Kaggle are launching a machine-learning engineering competition https://t.co/M1AtQHJsDP" "RT @derrickharris: Facebook and Kaggle are launching a machine-learning engineering competition https://t.co/M1AtQHJsDP" "Facebook and Kaggle are launching a machine-learning engineering competition https://t.co/M1AtQHJsDP" "RT @itsmaloy: @facebook recruiting on @kaggle https://t.co/Tv0HRN9UpX #DataScience #machinelearning" "@facebook recruiting on @kaggle https://t.co/Tv0HRN9UpX #DataScience #machinelearning" "RT @kdnuggets: You can succeed in @Kaggle #predictive #modeling competitions w/out domain #knowledge, but NOT in real tasks http://t.co/s90" "[ 4 ] : (Kaggle) http://t.co/SqZ7UpuGEA -1660124685" "Pretty fun piece with some tips about overfitting to the leaderboard. Kagglers beware. :-) https://t.co/fFqpGHD4Jc" "RT @kaggle: We had a lively discussion with @mrtz today about Kaggle's leaderboard. Thanks for coming in! http://t.co/KN7Xpqb2eV" "We had a lively discussion with @mrtz today about Kaggle's leaderboard. Thanks for coming in! http://t.co/KN7Xpqb2eV" "Tutorial - Kaggle http://t.co/L3ygfv8hIc" "A new Facebook Kaggle competition: https://t.co/mILVzZeYXk" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "Aww Yess ! 76 places up. A small price to pay for my social life. #MachineLearning #kaggle - https://t.co/WGlFiVjlge" "RT @kaggle: Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/1I8hdrr984 via @Prismatic" "RT @kaggle: Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu" ""Interlude: Kaggle Scripting" http://t.co/2BE374EAJP" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "RT @DeepLearnIT: Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "Intro to #MachineLearning #deeplearning http://t.co/Noa1XGddLg #DataScience #BigData http://t.co/pd4rx3KKPD" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/jcmgA0CGKg #r #statistics #data science" "The chance to work at Facebook is on again. It worked for me. ;) https://t.co/7WGYBJxXsZ" "RT @kaggle: Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu" "Facebook Recruiting IV: Human or Robot? https://t.co/s9gyVupwiy" "RT @kaggle: Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu" "RT @PhilCulliton: A good set of books / links for folks interested in #BigData. In the advanced section I'd add http://t.co/wjIkHWPsgs http" "RT @kaggle: Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu" "Moved up 119 spots. #kaggle - https://t.co/dgYzMvXNes" "Competing in the @OttoGroup_Com challenge? Check out this blog comparing tree-based classification methods http://t.co/thfiQxKu6u" ""Comparing Tree-Based Classification Methods via the Kaggle Otto Competition | R-bloggers" http://t.co/2oLZzCSrMg" "A good set of books / links for folks interested in #BigData. In the advanced section I'd add http://t.co/wjIkHWPsgs https://t.co/uNPdacS27Y" "@PeterDiamandis hey Peter. I read your book, Bold, and am curious if kaggle is the best platform to crowdsource data mining task?" "Facebook competition on Kaggle: https://t.co/Fg1NDk8xgR" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/E13CHhIcwt via @rbloggers" "1p Kaggle Facebook Recruiting IV: Human or Robot? - http://t.co/y8OY3Bg5jn #sales #marketing" "RT @kdnuggets: You can succeed in @Kaggle #predictive #modeling competitions w/out domain #knowledge, but NOT in real tasks http://t.co/s90" "1p Kaggle Facebook Recruiting IV: Human or Robot? - http://t.co/zrPJsQBdV8 #advertising #sales #business #inc" "Kaggle Facebook Recruiting IV: Human or Robot? http://t.co/VOh4hnuAHR" "Moved up 310 spots on #kaggle. I'm not addicted. I can quit when I want. https://t.co/eq13r5BKRB" "[ 4 ] : (Kaggle) http://t.co/bCzzFi75oF 1095566913" "[ 4 ] : (Kaggle) http://t.co/7wGVYPsiVl 573579792" "Audible GmbH - Data Scientist (m/f) (Berlin, Germany) http://t.co/TNfQtLv45x" "85 places up. #kaggle - https://t.co/eq13r5BKRB" "RT @Rbloggers: Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/UytNI6XnNO #rstats" "I just made my first #kaggle submission to https://t.co/BSTs3x96Ik!" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/C7i8VeMmQO" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/Fi2OfUPJf1" "RT @Rbloggers: Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/UytNI6XnNO #rstats" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/4EVXobCxRJ" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/tio2e8icaD http://t.co/TPZFUknN3d" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/5y65PftAyO" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/5O7erMotZZ" "RT @Rbloggers: Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/UytNI6XnNO #rstats" "From R-Bloggers - Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/GL8Iw8efWm" "New #rstats Post : Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/DTG3HOHMpg" "#RBloggers "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition" http://t.co/0l8h188B7z" "@RBloggers Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/rwW3GmVgHy" "Comparing Tree-Based Classification Methods via the Kaggle Otto Competition http://t.co/UytNI6XnNO #rstats" "RT @KaggleCareers: .@fb_engineering is hiring #machinelearning software engineers! https://t.co/DOV6PyDy8A" ".@fb_engineering is hiring #machinelearning software engineers! https://t.co/DOV6PyDy8A" "[ 4 ] : (Kaggle) http://t.co/SqZ7UpuGEA 46338417" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @dan_s_becker: Love seeing @vikparuchuri's open data science collaboration https://t.co/HhHMhpn5Iz and @Kaggle's scripts. Collaborate to" "HT @kdnuggets: You can succeed in @Kaggle #predictive model competitions wout domain #knowledge but NOT in real tasks http://t.co/Ki6g5KVpWQ" "RT @kdnuggets: You can succeed in @Kaggle #predictive #modeling competitions w/out domain #knowledge, but NOT in real tasks http://t.co/s90" "RT @kdnuggets: You can succeed in @Kaggle #predictive #modeling competitions w/out domain #knowledge, but NOT in real tasks http://t.co/s90" "RT @kdnuggets: You can succeed in @Kaggle #predictive #modeling competitions w/out domain #knowledge, but NOT in real tasks http://t.co/s90" "Love seeing @vikparuchuri's open data science collaboration https://t.co/HhHMhpn5Iz and @Kaggle's scripts. Collaborate to learn from others" "You can succeed in @Kaggle #predictive #modeling competitions w/out domain #knowledge, but NOT in real tasks http://t.co/s909MT1qRv" "My @Quora answer to I'm not old enough to use Kaggle. What should I do? http://t.co/t3v4TQWOXK" "48 places up. A small price to pay for my social life. #kaggle - https://t.co/D1a8LNigyM" "RT @leonardo_noleto: Everything you always wanted to know about Gradient boosting machines http://t.co/ORqRmy1WRm #MachineLearning #kaggle" "Everything you always wanted to know about Gradient boosting machines http://t.co/ORqRmy1WRm #MachineLearning #kaggle" "RT @kaggle: Competition launch! Can you predict when and where mosquitos will test positive for West Nile virus in Chicago? https://t.co/v6" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @myui: TD/data scientist/kaggle master..) https://t.co/h7nsJAkzNt" "6 places up. Now accepting donations for more RAM. #kaggle - https://t.co/GPWQVL90oF" "Top 10 R Packages to be a Kaggle Champion http://t.co/XTh20AixnA" "RT @myui: TD/data scientist/kaggle master..) https://t.co/h7nsJAkzNt" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @myui: TD/data scientist/kaggle master..) https://t.co/h7nsJAkzNt" "[ 4 ] : (Kaggle) http://t.co/5SQBfKQm6O 1110729395" "RT @myui: TD/data scientist/kaggle master..) https://t.co/h7nsJAkzNt" "TD/data scientist/kaggle master..) https://t.co/h7nsJAkzNt" "[ 4 ] : (Kaggle) http://t.co/SqZ7UpuGEA 1355711585" "Moved up 17 spots. Soon the robots will have my job. #kaggle - https://t.co/GPWQVL90oF" "#MachineLearning: so easy I can do it. #kaggle - https://t.co/7jQxFTKpFq" "RT @Armin_Stegerer: Use #Bluemix DashDB and R to solve a Kaggle Competition http://t.co/ortOLBpbUF" "RT @DeepInTheCode: #DataScience and #BigData Weekly is out! http://t.co/OAQxLkGBDE Stories via @HodderBooks @OKFN @kaggle" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @RomeoKienzler: RT @DominoDataLab: How to use R, H2O, and Domino for a Kaggle competition http://t.co/0mRnZ126AZ #Rstats" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "kaggle" "RT @ScraperWiki: Adventures in Kaggle: Forest Cover Type Prediction - a little bit of machine learning https://t.co/6syi7gKhQ7 ^@IanHopkins" "Adventures in Kaggle: Forest Cover Type Prediction - a little bit of machine learning https://t.co/6syi7gKhQ7 ^@IanHopkinson_" "RT @MangoTheCat: Top 10 R Packages to be a Kaggle Champion http://t.co/zGJLyLJ8wA via @kdnuggets" "RT @DeepInTheCode: #DataScience and #BigData Weekly is out! http://t.co/OAQxLkGBDE Stories via @HodderBooks @OKFN @kaggle" "Using Statistical Algorithms for Success in Kaggles Data Science Competitions - Statistics... https://t.co/42tf0gBIwF" "[ 4 ] : (Kaggle) http://t.co/8nrt6xgaAV -1885029448" "Top 10 R Packages to be a Kaggle Champion http://t.co/zGJLyLJ8wA via @kdnuggets" "RT @kdnuggets: Top stories: Top LinkedIn Groups for #Analytics, #BigData; 10 R Packages for @Kaggle Champion http://t.co/Kx0D8mqp3W http://" "RT @DeepInTheCode: #DataScience and #BigData Weekly is out! http://t.co/OAQxLkGBDE Stories via @HodderBooks @OKFN @kaggle" "RT @DeepInTheCode: #DataScience and #BigData Weekly is out! http://t.co/OAQxLkGBDE Stories via @HodderBooks @OKFN @kaggle" "RT @DeepInTheCode: #DataScience and #BigData Weekly is out! http://t.co/OAQxLkGBDE Stories via @HodderBooks @OKFN @kaggle" "#DataScience and #BigData Weekly is out! http://t.co/OAQxLkGBDE Stories via @HodderBooks @OKFN @kaggle" "RT @statsforbios: Nice heat map of West Nile Virus positive mosquito traps. From @kaggle competition. https://t.co/zq8DxhEKH2 http://t.co/g" "RT @DominoDataLab: How to use R, H2O, and Domino for a Kaggle competition http://t.co/0mRnZ126AZ #Rstats" "Nice heat map of West Nile Virus positive mosquito traps. From @kaggle competition. https://t.co/zq8DxhEKH2 http://t.co/gPOUSd7lT2" "[ 4 ] : (Kaggle) http://t.co/bCzzFioGgd 1131999417" "Use #Bluemix DashDB and R to solve a Kaggle Competition http://t.co/ortOLBpbUF" "[ 4 ] : (Kaggle) http://t.co/7wGVYPJTMT -1939318592" "Deseando tener un poco ms de tiempo para aprender a hacer magia en Kaggle y resolver este reto tan chulo https://t.co/i6DDD8xmwR" "RT @t3kcit: Kaggle launches script sharing platform: http://t.co/UVYmpbEb8i" "RT @t3kcit: Kaggle launches script sharing platform: http://t.co/UVYmpbEb8i" "Answer on @Quora by @log0ymxm to How do you get better at kaggle competitions? http://t.co/3TI1nxgnRC" "[ 4 ] : (Kaggle) http://t.co/SqZ7UpMhw8 749117374" "Top stories for Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Ch... http://t.co/ViKQIIWjbm" "Top stories for Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Ch... http://t.co/0IlXJxT7Du" "Top stories for Apr 19-25: Top LinkedIn Groups for Analytics, Big Data, Data Mining; 10 R Packages for a Kaggle Ch... http://t.co/LllFEp78XN" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @kaggle: Get a #scikitlearn cheat sheet and setup with #Python for #machinelearning in our 2nd tutorial http://t.co/3hGadYd96k http://t." "@kaggle need the ability to filter scripts by language #pythonjunkie" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "Tutorial - Kaggle http://t.co/WYd2NQMMpt" "RT @5h15h: Predict travel time of #taxi trips based http://t.co/PVuLfm1Tck #MachineLearning #Portugal #DataScience http://t.co/2ofbwtaLf2" "RT @5h15h: Predict travel time of #taxi trips based http://t.co/PVuLfm1Tck #MachineLearning #Portugal #DataScience http://t.co/2ofbwtaLf2" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @GautamBanrjee: "@KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/CMiCkAwujx #DataScience #BigData http://t" "Moved up 372 spots. Soon the robots will have my job. #kaggle - https://t.co/m2At6Z4fuW" "RT @GautamBanrjee: "@KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/CMiCkAwujx #DataScience #BigData http://t" "RT @GautamBanrjee: "@KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/CMiCkAwujx #DataScience #BigData http://t" "Kaggle" "Tennessee Leeuwenburg: Interlude: Kaggle Scripting http://t.co/JjGRwLp4Jd" ""@KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/CMiCkAwujx #DataScience #BigData http://t.co/88yCtqhY1e"" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "KaggleTennessee Leeuwenburg: Interlude: Kaggle Scripting http://t.co/W8BkuPCI0O #python #feedly" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "#PlanetPython Tennessee Leeuwenburg: Interlude: Kaggle Scripting http://t.co/bcp9UemdpP" "[ 4 ] : (Kaggle) http://t.co/5SQBfKyKIe -2118959773" "Tennessee Leeuwenburg: Interlude: Kaggle Scripting http://t.co/AWqzgwuLfG" "[ 4 ] : (Kaggle) http://t.co/SqZ7UpMhw8 640706170" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "Use #Bluemix DashDB and R to solve a Kaggle Competition http://t.co/H58FBdABfi" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "Top 10 #R Packages to be a Kaggle Champion http://t.co/E6LIFnh9GB gbm randomForest e1071 glmnet tau Matrix SOAR foreach doMC data.table" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "RT @KirkDBorne: Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "Intro to #MachineLearning [new @kaggle video series] http://t.co/ZWwiwp1skB #DataScience #BigData http://t.co/Zc4H3CvPis" "1 spot up, just a few more to go. #kaggle - https://t.co/SKb79LzFXp. 0.00011 improvement on ensemble model random forest + lgm :-S" "RT @kaggle: Get a #scikitlearn cheat sheet and setup with #Python for #machinelearning in our 2nd tutorial http://t.co/3hGadYd96k http://t." ================================================ FILE: data/titanic_train.csv ================================================ PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S 31,0,1,"Uruchurtu, Don. 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Margaret Delia",female,19,0,0,330958,7.8792,,Q 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S 51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S 52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S 53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C 54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S 55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C 56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S 57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S 58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C 59,1,2,"West, Miss. 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Lillian Amy",female,16,5,2,CA 2144,46.9,,S 73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S 74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C 75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S 76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S 77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S 78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S 79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S 80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S 81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S 82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S 83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q 84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S 85,1,2,"Ilett, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q ================================================ FILE: data/vehicles_test.csv ================================================ price,year,miles,doors,type 3000,2003,130000,4,truck 6000,2005,82500,4,car 12000,2010,60000,2,car ================================================ FILE: data/vehicles_train.csv ================================================ price,year,miles,doors,type 22000,2012,13000,2,car 14000,2010,30000,2,car 13000,2010,73500,4,car 9500,2009,78000,4,car 9000,2007,47000,4,car 4000,2006,124000,2,car 3000,2004,177000,4,car 2000,2004,209000,4,truck 3000,2003,138000,2,car 1900,2003,160000,4,car 2500,2003,190000,2,truck 5000,2001,62000,4,car 1800,1999,163000,2,truck 1300,1997,138000,4,car ================================================ FILE: homework/02_command_line_hw_soln.md ================================================ ## Command Line Homework Solution #### The following solution assumes you are working from the class "DAT5" directory. * How many text messages are there? * Answer: 5574 * Code: `wc data/SMSSpamCollection.txt` gives you the line count, word count, and character count * What is the average number of words per text? What is the average number of characters per text? * Answer: Words per text: 15.6 or 16.6 (see below for explanation); Characters per text: 85.7 or 81.9 (see below) * Code: * `wc data/SMSSpamCollection.txt` gives you the line count, word count, and character count. You can divide the word count by the line count (so the number of words in each line which represents one text) to get 92482/5574 = 16.6 words per text. However, if you want to be more technical about it, each line contains an extra word that is not part of the text, the "spam" or "ham" label. You could remove the number of "spam"/"ham" labels (one per line) from the total word count to get (92482 - 5574)/5574 = 15.6. * Similarly, using the line count and character count from the `wc` command, you can divide the character count by the line count to get 477907/5574 = 85.7. If you remove the characters counted for the "spam" and "ham" labels, you get (477907 - 4*(# of hams) - 5*(# of spams) )/5574 = (477907 - 4*(4827) - 5*(747) )/5574 = 81.9. **Note**: The point of this wasn't to necessarily get the exact numbers but to identify that you can use `wc` to get a quick idea of features and labels in your data without having to open it. * How many messages are spam? How many are ham? * Answer: Spam: 4827 Ham: 747 * Code: * `grep -w 'ham' data/SMSSpamCollection.txt | wc` gives you the line count of lines labeled 'ham' in the file. * `grep -w 'spam' data/SMSSpamCollection.txt | wc` gives you the line count of lines labeled 'spam' in the file. * Is there a difference between the number of words per text and characters per text in messages that are spam vs. those that are ham? What are these numbers? * Answer: Yes, there is a difference. It seems that the "spam" texts have a much higher words per text and characters per text. Using the simplified calculations (i.e. not remove the "spam" and "ham" from the word counts), we get the following numbers. ``` Words per Text Char per Text Ham: 15.3 76.6 Spam: 24.9 145.12 ``` * Code: * `grep -w 'ham' data/SMSSpamCollection.txt | wc` gives the line, word, and character count for all of the lines labeled 'ham'. You can divide the word count by line count to get the 'Words per Text' and divide the character count by line count to get the 'Characters per Text'. * `grep -w 'spam' data/SMSSpamCollection.txt | wc` gives the line, word, and character count for all of the lines labeled 'spam'. You can divide the word count by line count to get the 'Words per Text' and divide the character count by line count to get the 'Characters per Text'. * **Bonus**: If you feel that this is too easy, research the `awk` command to learn how to calculate and print out these averages in the console. * Answer: See below * Code: * `grep -w 'spam' data/SMSSpamCollection.txt | wc | awk '{print "Words per text: "$2/$1}'` will give you the words per text. Notice the format of `awk` here. You are telling it to print something and pass it column number labels. `wc` prints out three columns: lines, words, and characters. For the words per text (i.e. line) we want to divide the second column by the first. * `grep -w 'spam' data/SMSSpamCollection.txt | wc | awk '{print "Characters per text: "$3/$1}'` will give you the character per text. * Separate the spam and ham messages into files "spam_messages.txt" and "ham_messages.txt". * Answer: The code below accomplishes this. * Code: * `grep -w 'ham' data/SMSSpamCollection.txt > ham.txt` takes the output of the `grep`, which is all of the lines that have a label ham, and puts it into a file called `ham.txt` using the `>` operator. * `grep -w 'spam' data/SMSSpamCollection.txt > spam.txt` takes the output of the `grep`, which is all of the lines that have a label spam, and puts it into a file called `spam.txt` using the `>` operator. ================================================ FILE: homework/03_pandas_hw_soln.py ================================================ ''' Exploratory Data Analysis Homework Solution ''' ''' Use the automotive mpg data (https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.csv) to complete the following parts. Please turn in your code for each part. Before each code chunk, give a brief description (one line) of what the code is doing (e.g. "Loads the data" or "Creates scatter plot of mpg and weight"). If the code output produces a plot or answers a question, give a brief interpretation of the output (e.g. "This plot shows X,Y,Z" or "The mean for group A is higher than the mean for group B which means X,Y,Z"). ''' ''' Part 1 Load the data (https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.txt) into a DataFrame. Try looking at the "head" of the file in the command line to see how the file is delimited and how to load it. Note: You do not need to turn in any command line code you may use. ''' # Imports import pandas as pd # Reads text file and uses '|' as separator auto = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.txt', sep='|') auto = pd.read_table('auto_mpg.txt', sep='|') # This is if you are reading from you computer # Note: This assumes that '.../DAT5/data' is your working directory. ''' Part 2 Get familiar with the data. Answer the following questions: - What is the shape of the data? How many rows and columns are there? - What variables are available? - What are the ranges for the values in each numeric column? - What is the average value for each column? Does that differ significantly from the median? ''' auto.shape # There are 392 rows and 9 columns auto.columns # This lists the column names that are available auto.info() # This lists the column names as well as their data type. # You can infer the range from the information available in describe auto.describe() # This will give you the five number summary for all numeric variables auto.min(numeric_only=True) # This will give you all of the minimums for numeric variables auto.max(numeric_only=True) # This will give you all of the maximums for numeric variables # You can calculate the range with the above info as shown below. auto.max(numeric_only=True) - auto.min(numeric_only=True) # Range auto.mean() # Means for all numeric variables auto.median() # Medians for all numeric variables # How much greater is the mean than the median? auto.mean() - auto.median() # The means are somewhat greater than the medians. ''' Part 3 Use the data to answer the following questions: - Which 5 cars get the best gas mileage? - Which 5 cars with more than 4 cylinders get the best gas mileage? - Which 5 cars get the worst gas mileage? - Which 5 cars with 4 or fewer cylinders get the worst gas mileage? ''' # 5 cars that get best gas mileage auto.sort_index(by='mpg', ascending=False)[0:5][['car_name','mpg']] # 5 cars with more than 4 cylinders that get the best gas mileage auto[auto.cylinders > 4].sort_index(by='mpg', ascending=False)[0:5][['car_name','mpg']] # 5 cars that get worst gas mileage auto.sort_index(by='mpg')[0:5][['car_name','mpg']] # 5 cars with 4 or fewer cylinders that get the worst gas mileage auto[auto.cylinders > 4].sort_index(by='mpg')[0:5][['car_name','mpg']] ''' Part 4 Use groupby and aggregations to explore the relationships between mpg and the other variables. Which variables seem to have the greatest effect on mpg? Some examples of things you might want to look at are: - What is the mean mpg for cars for each number of cylindres (i.e. 3 cylinders, 4 cylinders, 5 cylinders, etc)? - Did mpg rise or fall over the years contained in this dataset? - What is the mpg for the group of lighter cars vs the group of heaver cars? Note: Be creative in the ways in which you divide up the data. You are trying to create segments of the data using logical filters and comparing the mpg for each segment of the data. ''' # Mean mpg for cars for each number of cylinders auto.groupby(by='cylinders').mpg.mean() # Mpg usually rose over the years contianed in this dataset auto.groupby(by='model_year').mpg.mean() # The mpg for the gorup of lighter cars vs the group of heavier cars # We can divide the dataset in half by the median (the lower half being the # lighter cars and the upper half being the heavier cars). auto[auto.weight <= auto.weight.median()].mpg.mean() # light cars mean mpg auto[auto.weight > auto.weight.median()].mpg.mean() # heavier cars mean mpg # It appears that the lighter cars get better gas mileage than the heavier cars # This question was pretty open ended, but here are some other things you could have looked at # The average mpg for the four quartiles of displacement # We didn't talk about the 'quantile' function in class, but it's a useful one! auto[auto.displacement <= auto.displacement.quantile(0.25)].mpg.mean() auto[(auto.displacement > auto.displacement.quantile(0.25)) & (auto.displacement <= auto.displacement.quantile(0.50))].mpg.mean() auto[(auto.displacement > auto.displacement.quantile(0.50)) & (auto.displacement <= auto.displacement.quantile(0.75))].mpg.mean() auto[auto.displacement > auto.displacement.quantile(0.75)].mpg.mean() # It appears that as engine displacement (size) increases, the average mpg decreases. This makes sense. # Instead of using the somewhat complicated logic of the 'quantile', you can easily divide your dataset # into buckets using the `cut` function. auto.groupby(pd.cut(auto.horsepower,5)).mpg.mean() # It appears that as horsepower increases, the average mpg decreases. This makes sense. auto.groupby(pd.cut(auto.acceleration, 5)).mpg.mean() # It appears that as acceleration increases, the average mpg increases. ''' I'll also include something I found particularly cool from Lloyd's homework. He wanted to look at how MPG has changed over time, but he also wanted to consider how specific groups have changed. He wanted to look at low, mid, and high power cars based upon their horsepower and see how these groups have changed over time. His code is below. In his data, he called the original dataset 'auto'. ''' # Now to look at how efficency has changed over time based on power and weight classes, # two things that we know play a large role in gas mileage. First, we create a table of # efficeincy by power class and year. horsey = pd.DataFrame() # Defines low power as below 100 horsepower horsey['low_power'] = auto[(auto.horsepower < 100)].groupby('model_year').mpg.mean() # Defines mid power as between 100 and 150 (inclusive) horsepower horsey['mid_power'] = auto[(auto.horsepower >= 100) & (auto.horsepower <= 150)].groupby('model_year').mpg.mean() # Defines high power as above 150 horsepower horsey['high_power'] = auto[auto.horsepower > 150].groupby('model_year').mpg.mean() ''' low_power mid_power high_power model_year 70 23.300000 18.333333 13.076923 71 26.357143 17.285714 13.333333 72 23.500000 15.000000 12.857143 73 22.166667 16.352941 12.727273 74 27.312500 15.500000 NaN 75 22.470588 17.500000 16.000000 76 25.750000 17.071429 15.500000 77 28.433333 18.100000 15.666667 78 28.363158 19.350000 17.700000 79 29.225000 20.266667 16.900000 80 34.516667 28.100000 NaN 81 31.372727 25.833333 NaN 82 32.607143 23.500000 NaN We can see from the data here that low power cars have seen much better gains in efficiency than mid or high power cars. I then wanted to see how much car weights have changed in that same time. ''' ================================================ FILE: homework/04_visualization_hw_soln.py ================================================ ''' Visualization Homework Solution ''' ''' Use the automotive mpg data (https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.txt) to complete the following parts. Please turn in your code for each part. Before each code chunk, give a brief description (one line) of what the code is doing (e.g. "Loads the data" or "Creates scatter plot of mpg and weight"). If the code output produces a plot or answers a question, give a brief interpretation of the output (e.g. "This plot shows X,Y,Z" or "The mean for group A is higher than the mean for group B which means X,Y,Z"). ''' # Imports import pandas as pd import numpy as np import matplotlib.pyplot as plt # Reads text file and uses '|' as separator auto = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.txt', sep='|') ''' Part 1 Produce a plot that compares the mean mpg for the different numbers of cylinders. ''' # The first part of creating this plot is to generate the appropriate data. # Since we want mean mpg FOR EACH number of cylinders, we should use a 'groupby'. auto.groupby('cylinders').mpg.mean() # Give us mean mpg for each cylinder count # Now that we have the data we want, we can think about how to plot it. # The keyword 'compare' indiciated that you probably want to use a bar chart. auto.groupby('cylinders').mpg.mean().plot(kind='bar') # Create plot from data plt.title("Comparing Mean MPG for Different Numbers of Cylinders") # Add title plt.xlabel("Number of Cylinders") # Add x label plt.ylabel("Average MPG") # Add y label plt.show() # Show plot # With the exception of the three cylinder car (of which there are only 4), # we can see that mean mpg decreases as number of cylinders increases. ''' Part 2 Use a scatter matrix to explore relationships between different numeric variables. ''' pd.scatter_matrix(auto) # Generate scatter matrix pd.scatter_matrix(auto, c=auto.mpg) # Consider adding color to your scatter matrix too. plt.show() # Show plot ''' There are several things to notice here. First, we can talk about different variables' relationships with mpg. Looking across the top row, where mpg is on the y axis, we see that there is a clearly negative relationship between mpg and number of cylinders, displacement, horsepower, and weight. There is a clearly positive relationship between mpg and model_year. There is a vaguely positive relationships between mpg and acceleration, though it's not a very clear one. There also seems to be a weakly positive relationship between mpg and origin. There are also several other relationships you may notice: * Dipslacement and horsepower have a positive relationship. This makes sense, because horsepower should increase as the engine volume (displacement) gets larger. * Displacement and weight have a positive relationship. This makes sense, because heavier cars tend to need bigger engines. * Horsepower and weight have a positive relationship. This makes sense, because larger cars tend to have higher horsepower engines. There may be other inferences you could draw from this plot as well, but this demonstrates the usefulness of the scatter matrix in understanding your data visually. ''' ''' Part 3 Use a plot to answer the following questions: ''' ''' -Do heavier or lighter cars get better mpg? ''' # Since we want to look at the relationship between two numeric variables, we # can use a scatterplot to see how they "move" with each other. auto.plot(kind='scatter', x='weight', y='mpg', alpha=0.5) # Create scatter plot plt.title('Car MPG by Weight') plt.xlabel('Car weight') plt.ylabel('MPG') plt.show() # From the plot, it appears lighter cars get better mpg. As weight increase, # mpg decreases. ''' -How are horsepower and displacement related? ''' # Once again, since we want to look at the relationship between two numeric # variables, we can use a scatterplot. # Notice that I didn't specify whether displacement or horsepower should be on # the x-axis. However, using my (limited) domain expertise, I would think that # horsepower is affected by the displacement of the engine. So I put # displacement on the x-axis and horsepower on the y-axis. auto.plot(kind='scatter', x='displacement', y='horsepower', alpha=0.5) plt.title('Horsepower by Engine Displacement') plt.xlabel('Engine Displacement') plt.ylabel('Horsepower ') plt.show() # This plot shows that displacement and horsepower have a positive relationship. ''' -What does the distribution of acceleration look like? ''' # Since I'm interested in the distribution of acceleration, I can use a # histogram to investigate that. auto.acceleration.hist() plt.title('Distribution of Acceleration') plt.xlabel('Acceleration') plt.ylabel('Frequency') plt.show() # We can see that acceleration has an almost normal distribution. The most # frequent value of acceleration is around 16. The values of acceleration # range from 8 to 25. ''' -How is mpg spread for cars with different numbers of cylinders? ''' # Since we are interested in the spread (as in the range of different values) # for each of the different cylinder counts, we should use a boxplot as it # illustrates the spread of a numeric variable and accepts the "by" parameter, # which allows us to generate a plot for each value of a variable. auto.boxplot('mpg', by='cylinders') plt.title('Car MPG by Number of Cylinders') plt.xlabel('Number of Cylinders') plt.ylabel('MPG') plt.show() # This plot gives us a lot of information. I'll list a few things to notice: # * The range for 3 cylinders is pretty small, which might be because there are # 4 observations. # * As shown in our earlier plots, mpg decreases as number of cylinders increases. # * Interestingly, there are 4 cylinder cars that get relatively low gas mileage. # * Over half of the 4 cylinder cars get better mpg than all of the 8 cylinders cars. ''' -Do cars made before or after 1975 get better average mpg? (Hint: You need to create a new column that encodes whether a year is before or after 1975.) ''' # There are several different ways to do this one. The most straightforward # way could be to create a new column called 'before_1975' that contains a # 'Before 1975' or 'After 1975'. We'll include 1975 in 'After 1975'. auto['before_1975'] = np.where(auto.model_year < 75,'Before 1975', 'After 1975') # Remember that np.where is like the IF function in Excel: # np.where(, , ) # Now we can get the data we need by use a group by. auto.groupby('before_1975').mpg.mean().plot(kind='bar') plt.title('Average MPG Before and After 1975') plt.xlabel('') plt.ylabel('Average MPG') plt.show() # The labels are a little cut off, so you can use some extra matplotlib for # formatting. 'set_xticklabels' let's you set several things. auto.groupby('before_1975').mpg.mean().plot(kind='bar').set_xticklabels(['After 1975','Before 1975'], rotation=0) plt.title('Average MPG Before and After 1975') plt.xlabel('') plt.ylabel('Average MPG') plt.show() # We can see that the average mpg for cars after 1975 is higher. # This could have been done without creating the extra variable. auto.groupby(auto.model_year < 75).mpg.mean().plot(kind='bar').set_xticklabels(['After 1975','Before 1975'], rotation=0) plt.title('Average MPG Before and After 1975') plt.xlabel('') plt.ylabel('Average MPG') plt.show() # We get the same results but without the intermediate step. ================================================ FILE: homework/06_bias_variance.md ================================================ ## Class 6 Pre-work: Bias-Variance Tradeoff Read this excellent article, [Understanding the Bias-Variance Tradeoff](http://scott.fortmann-roe.com/docs/BiasVariance.html), and be prepared to **discuss it in class** on Monday. **Note:** You can ignore sections 4.2 and 4.3. Here are some questions to think about while you read: * In the Party Registration example, what are the features? What is the response? Is this a regression or classification problem? * Conceptually, how is KNN being applied to this problem to make a prediction? * How do the four visualizations in section 3 relate to one another? Change the value of K using the slider, and make sure you understand what changed in the visualizations (and why it changed). * In figures 4 and 5, what do the lighter colors versus the darker colors mean? How is the darkness calculated? * What does the black line in figure 5 represent? What predictions would an ideal machine learning model make, with respect to this line? * Choose a very small value of K, and click the button "Generate New Training Data" a number of times. Do you "see" low variance or high variance? Do you "see" low bias or high bias? * Repeat this with a very large value of K. Do you "see" low variance or high variance? Do you "see" low bias or high bias? * Try using other values of K. What value of K do you think is "best"? How do you define "best"? * Why should we care about variance at all? Shouldn't we just minimize bias and ignore variance? * Does a high value for K cause "overfitting" or "underfitting"? ================================================ FILE: homework/07_glass_identification.md ================================================ ## Class 7 Homework: Glass Identification Let's practice what we have learned using the [Glass Identification dataset](http://archive.ics.uci.edu/ml/datasets/Glass+Identification). 1. Read the data into a DataFrame. 2. Briefly explore the data to make sure the DataFrame matches your expectations. 3. Let's convert this into a binary classification problem. Create a new DataFrame column called "binary": * If type of glass = 1/2/3/4, binary = 0. * If type of glass = 5/6/7, binary = 1. 4. Create a feature matrix "X". (Think carefully about which columns are actually features!) 5. Create a response vector "y" from the "binary" column. 6. Split X and y into training and testing sets. 7. Fit a KNN model on the training set using K=5. 8. Make predictions on the testing set and calculate accuracy. 9. Calculate the "null accuracy", which is the classification accuracy that could be achieved by always predicting the majority class. **Bonus:** * Write a for loop that computes the test set accuracy for a range of K values. * Plot K versus test set accuracy to help you choose an optimal value for K. ================================================ FILE: homework/11_roc_auc.md ================================================ ## Class 11 Pre-work: ROC Curves and Area Under the Curve (AUC) Before learning about ROC curves, it's important to be comfortable with the following terms: true positive, true negative, false positive, false negative, sensitivity, and specificity. If you aren't yet comfortable, Rahul Patwari has excellent videos on [Intuitive sensitivity and specificity](https://www.youtube.com/watch?v=U4_3fditnWg) (9 minutes) and [The tradeoff between sensitivity and specificity](https://www.youtube.com/watch?v=vtYDyGGeQyo) (13 minutes). Then, watch Kevin's video on [ROC Curves and Area Under the Curve](https://www.youtube.com/watch?v=OAl6eAyP-yo) (14 minutes), and be prepared to **discuss it in class** on Wednesday. (There's a blog post containing the [video transcript and screenshots](http://www.dataschool.io/roc-curves-and-auc-explained/), which might serve as a useful reference.) You can also play with the [visualization](http://www.navan.name/roc/) shown in the video. Optionally, you could also watch Rahul Patwari's video on [ROC curves](https://www.youtube.com/watch?v=21Igj5Pr6u4) (12 minutes). Here are some questions to think about: - If you have a classification model that outputs predicted probabilities, how could you convert those probabilities to class predictions? - What are the methods in scikit-learn that output predicted probabilities and class predictions? - Why are predicted probabilities (rather than just class predictions) required to generate an ROC curve? - Could you use an ROC curve for a regression problem? Why or why not? - What's another term for True Positive Rate? - If I wanted to increase specificity, how would I change the classification threshold? - Is it possible to adjust your classification threshold such that both sensitivity and specificity increase simultaneously? Why or why not? - What are the primary benefits of ROC curves over classification accuracy? - What should you do if your AUC is 0.2? - What would the plot of reds and blues look like for a dataset in which each observation was a credit card transaction, and the response variable was whether or not the transaction was fraudulent? (0 = not fraudulent, 1 = fraudulent) - Let's say your classifier has a sensitivity of 0.95 and a specificity of 0.3, and the classes are balanced. Would it result in more false positives or false negatives? - What's a real-world scenario in which you would prefer a high specificity (rather than a high sensitivity) for your classifier? ================================================ FILE: homework/11_roc_auc_annotated.md ================================================ ## Class 11 Pre-work: ROC Curves and Area Under the Curve (AUC) Before learning about ROC curves, it's important to be comfortable with the following terms: true positive, true negative, false positive, false negative, sensitivity, and specificity. If you aren't yet comfortable, Rahul Patwari has excellent videos on [Intuitive sensitivity and specificity](https://www.youtube.com/watch?v=U4_3fditnWg) (9 minutes) and [The tradeoff between sensitivity and specificity](https://www.youtube.com/watch?v=vtYDyGGeQyo) (13 minutes). Then, watch Kevin's video on [ROC Curves and Area Under the Curve](https://www.youtube.com/watch?v=OAl6eAyP-yo) (14 minutes), and be prepared to **discuss it in class** on Wednesday. (There's a blog post containing the [video transcript and screenshots](http://www.dataschool.io/roc-curves-and-auc-explained/), which might serve as a useful reference.) You can also play with the [visualization](http://www.navan.name/roc/) shown in the video. Optionally, you could also watch Rahul Patwari's video on [ROC curves](https://www.youtube.com/watch?v=21Igj5Pr6u4) (12 minutes). Here are some questions to think about: - If you have a classification model that outputs predicted probabilities, how could you convert those probabilities to class predictions? - Set a threshold, and classify everything above the threshold as a 1 and everything below the threshold as a 0. - What are the methods in scikit-learn that output predicted probabilities and class predictions? - predict_proba and predict. - Why are predicted probabilities (rather than just class predictions) required to generate an ROC curve? - Because an ROC curve is measuring the performance of a classifier at all possible thresholds, and thresholds only make sense in the context of predicted probabilities. - Could you use an ROC curve for a regression problem? Why or why not? - No, because ROC is a plot of TPR vs FPR, and those concepts have no meaning in a regression problem. - What's another term for True Positive Rate? - Sensitivity or recall. - If I wanted to increase specificity, how would I change the classification threshold? - Increase it. - Is it possible to adjust your classification threshold such that both sensitivity and specificity increase simultaneously? Why or why not? - No, because increasing either of those requires moving the threshold in opposite directions. - What are the primary benefits of ROC curves over classification accuracy? - Doesn't require setting a classification threshold, allows you to visualize the performance of your classifier, works well for unbalanced classes. - What should you do if your AUC is 0.2? - Reverse your predictions so that your AUC is 0.8. - What would the plot of reds and blues look like for a dataset in which each observation was a credit card transaction, and the response variable was whether or not the transaction was fraudulent? (0 = not fraudulent, 1 = fraudulent) - Blues would be significantly larger, lots of overlap between blues and reds. - Let's say your classifier has a sensitivity of 0.95 and a specificity of 0.3, and the classes are balanced. Would it result in more false positives or false negatives? - False positives, meaning it falsely predicted positive (the true status is negative). - What's a real-world scenario in which you would prefer a high specificity (rather than a high sensitivity) for your classifier? - Speed cameras issuing speeding tickets. ================================================ FILE: homework/13_spam_filtering.md ================================================ ## Class 13 Pre-work: Spam Filtering Read Paul Graham's [A Plan for Spam](http://www.paulgraham.com/spam.html) and be prepared to **discuss it in class on Wednesday**. Here are some questions to think about while you read: * Should a spam filter optimize for sensitivity or specificity, in Paul's opinion? * Before he tried the "statistical approach" to spam filtering, what was his approach? * What are the key components of his statistical filtering system? In other words, how does it work? * What did Paul say were some of the benefits of the statistical approach? * How good was his prediction of the "spam of the future"? ================================================ FILE: homework/13_spam_filtering_annotated.md ================================================ ## Class 13 Pre-work: Spam Filtering Read Paul Graham's [A Plan for Spam](http://www.paulgraham.com/spam.html) and be prepared to **discuss it in class on Wednesday**. Here are some questions to think about while you read: * Should a spam filter optimize for sensitivity or specificity, in Paul's opinion? * specificity to minimize false positives * Before he tried the "statistical approach" to spam filtering, what was his approach? * hand engineering features and computing a "score" * What are the key components of his statistical filtering system? In other words, how does it work? * scan the entire text (including headers) and tokenize it * count number of occurrences of each token in ham corpus and spam corpus * assign each token a spam score based upon its relative frequency * for new mail, only take 15 most interesting tokens into account * What did Paul say were some of the benefits of the statistical approach? * it works better (almost no false positives) * less work for him because it discovers features automatically * you know what the "score" means * can easily be tuned to the individual user * evolves with the spam * creates an implicit whitelist/blacklist of email addresses, server names, etc. * How good was his prediction of the "spam of the future"? * great! ================================================ FILE: notebooks/06_bias_variance.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:14d04997739722e6bbeb8a37f8f5fc7f30d18a0f0dc4619c08b1982912c9cedf" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring the Bias-Variance Tradeoff" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "# allow plots to appear in the notebook\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Brain and body weight" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a [dataset](http://people.sc.fsu.edu/~jburkardt/datasets/regression/x01.txt) of the average weight of the body and the brain for 62 mammal species. Let's read it into pandas and take a quick look:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.read_table('http://people.sc.fsu.edu/~jburkardt/datasets/regression/x01.txt', sep='\\s+', skiprows=33, names=['id','brain','body'], index_col='id')\n", "df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " brain body\n", "count 62.000000 62.000000\n", "mean 198.789984 283.134194\n", "std 899.158011 930.278942\n", "min 0.005000 0.140000\n", "25% 0.600000 4.250000\n", "50% 3.342500 17.250000\n", "75% 48.202500 166.000000\n", "max 6654.000000 5712.000000" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to focus on a smaller subset in which the body weight is less than 200:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# only keep rows in which the body weight is less than 200\n", "df = df[df.body < 200]\n", "df.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "(51, 2)" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're actually going to pretend that there are only 51 mammal species in existence. In other words, we are pretending that this is the entire dataset of brain and body weights for **every known mammal species**.\n", "\n", "Let's create a scatterplot (using [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/)) to visualize the relationship between brain and body weight:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.lmplot(x='body', y='brain', data=df, ci=None, fit_reg=False)\n", "sns.plt.xlim(-10, 200)\n", "sns.plt.ylim(-10, 250)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "(-10, 250)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There appears to be a relationship between brain and body weight for mammals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making a prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's pretend that a **new mammal species** is discovered. We measure the body weight of every member of this species that we can find, and calculate an **average body weight of 100**. We want to **predict the average brain weight** of this species (rather than measuring it directly). How might we do this?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.lmplot(x='body', y='brain', data=df, ci=None)\n", "sns.plt.xlim(-10, 200)\n", "sns.plt.ylim(-10, 250)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "(-10, 250)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We drew a straight line that appears to best capture the relationship between brain and body weight. So, we might predict that our new species has a brain weight of about 45, since that's the approximate y value when x=100.\n", "\n", "This is known as a \"linear model\" or a \"linear regression model\", which we will study in a future class." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making a prediction from a sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Earlier, I said that this dataset contained every known mammal species. That's very convenient, but **in the real world, all you ever have is a sample of data**. A more realistic situation would be to only have brain and body weights for (let's say) half of the 51 known mammals.\n", "\n", "When that new mammal species (with a body weight of 100) is discovered, we still want to make an accurate prediction for the brain weight, but this task might be more difficult since we don't have all of the data that we would ideally like to have.\n", "\n", "Let's simulate this situation by assigning each of the 51 rows to **either sample 1 or sample 2**:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set a random seed for reproducibility\n", "np.random.seed(12345)\n", "\n", "# randomly assign every row to either sample 1 or sample 2\n", "df['sample'] = np.random.randint(1, 3, len(df))\n", "df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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brainbodysample
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\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " brain body sample\n", "id \n", "1 3.385 44.5 1\n", "2 0.480 15.5 2\n", "3 1.350 8.1 2\n", "5 36.330 119.5 2\n", "6 27.660 115.0 1" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now tell Seaborn to create two plots, in which the left plot only uses the data from **sample 1** and the right plot only uses the data from **sample 2**:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# col='sample' subsets the data by sample and creates two separate plots\n", "sns.lmplot(x='body', y='brain', data=df, ci=None, col='sample')\n", "sns.plt.xlim(-10, 200)\n", "sns.plt.ylim(-10, 250)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "(-10, 250)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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U+MOVbpu0WmXGLl+tZfzu2ctF1zvamunJl2yzkL2kapHNZjl+ZoK+9BAvHB7m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\nXLd18zp6UrkjT+/t2FiGFup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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The line looks pretty similar between the two plots, despite the fact that they used separate samples of data. In both cases, we would predict a brain weight of about 45.\n", "\n", "It's easier to see the degree of similarity by placing them on the same plot:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# hue='sample' subsets the data by sample and creates a single plot\n", "sns.lmplot(x='body', y='brain', data=df, ci=None, hue='sample')\n", "sns.plt.xlim(-10, 200)\n", "sns.plt.ylim(-10, 250)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "(-10, 250)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What was the point of this exercise? This was a visual demonstration of a high bias, low variance model:\n", "\n", "- It's **high bias** because it doesn't fit the data particularly well.\n", "- It's **low variance** because it doesn't change much depending upon which points happen to be in the sample." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's try something completely different" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What would a low bias, high variance model look like? Let's try polynomial regression, with an eighth order polynomial:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.lmplot(x='body', y='brain', data=df, ci=None, col='sample', order=8)\n", "sns.plt.xlim(-10, 200)\n", "sns.plt.ylim(-10, 250)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "(-10, 250)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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XDss0zb+u3rFEJKyOeyc6LDHwbUnGaU7EmE7ntMTCQzW+IhcLw0QHgEgkQltL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\nKWOyw/68y3GxWLbvWBXbgglXweBkrps2LOzx2VlJ3Y6dZnT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ORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "- It's **low bias** because the models match the data quite well!\n", "- It's **high variance** because the models are widely different depending upon which points happen to be in the sample. (For a body weight of 100, the brain weight prediction would either be 40 or 0, depending upon which data happened to be in the sample.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Can we find a middle ground?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps we can create a model that has **less bias than the linear model**, and **less variance than the eighth order polynomial**?\n", "\n", "Let's try a second order polynomial instead:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.lmplot(x='body', y='brain', data=df, ci=None, col='sample', order=2)\n", "sns.plt.xlim(-10, 200)\n", "sns.plt.ylim(-10, 250)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "(-10, 250)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AplscCSHM7vX1FOBomuojSaPKC5sP86373uwJyDcvn5rhGkmS4Cwjyd0jxgBlwKYQwtNA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\neH1bFa9vP8bx+pbTHlOQn8P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"text": [ "" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems better. In both the left and right plots, **it fits the data pretty well, but not too well**.\n", "\n", "This is the essence of the **bias-variance tradeoff**: finding a model that appropriately balances bias and variance, and thus will generalize to new data (known as \"out of sample\" data)." ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/06_model_evaluation_procedures.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:ccea3b12dbd9d0bc247abfb693b91de332ed10a26258dd0dce7138bca03b0fc4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Evaluation Procedures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Motivating question:** Last time, we created classification models on the iris data to predict species using the iris measurements. We used KNN, with K=1 and K=5. But which model is \"better\"? And more importantly, what's the \"best\" value of K?\n", "\n", "To choose between models, we need two things:\n", "\n", "1. **Evaluation procedure:** the process you use to evaluate a model\n", "2. **Evaluation metric:** the numeric calculation you use to compare models\n", "\n", "In supervised learning, we define the \"best\" model as one that generalizes to \"out-of-sample\" data. In other words, we want a model that accurately predicts the future, not the past. Our evaluation procedure should support that goal. We will focus on evaluation procedures in today's class.\n", "\n", "Choosing an evaluation metric is a more subjective decision. The appropriate evaluation metric can depend upon the goal of your problem. We will focus on evaluation metrics in a future class." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation Procedure #1: Train and Test on Entire Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by training on the entire iris dataset, and then testing our model by seeing how well it performs on that same data. That will be our evaluation procedure.\n", "\n", "We'll use **classification accuracy** as our evalutaion metric, which is the percentage of correct predictions. This is called a \"reward function\" because it's something we want to maximize.\n", "\n", "The opposite of classification accuracy is **classification error**, which is the percentage of incorrect predictions. This is called a \"loss function\" because it's something we want to minimize." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# read in the iris data\n", "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "\n", "# create X (features) and y (response)\n", "X = iris.data\n", "y = iris.target" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# KNN with K=5\n", "from sklearn.neighbors import KNeighborsClassifier # import class\n", "knn = KNeighborsClassifier(n_neighbors=5) # instantiate the estimator\n", "knn.fit(X, y) # train on entire dataset\n", "y_pred = knn.predict(X) # make predictions (or \"test\") on entire dataset" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compute the accuracy. This is known as **training accuracy** because we are testing on the same data we used to train the model." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# method 1: use metrics module\n", "from sklearn import metrics\n", "print metrics.accuracy_score(y, y_pred)\n", "\n", "# method 2: use NumPy\n", "import numpy as np\n", "print np.mean(y == y_pred)\n", "\n", "# method 3: use built-in \"score\" method (allows you to skip the knn.predict step)\n", "print knn.score(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.966666666667\n", "0.966666666667\n", "0.966666666667\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "I recommend **method 1** because this pattern can be reused for different evaluation metrics." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# now try KNN with K=1\n", "knn = KNeighborsClassifier(n_neighbors=1)\n", "knn.fit(X, y)\n", "y_pred = knn.predict(X)\n", "print metrics.accuracy_score(y, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's amazing! So, K=1 is the best value for K, right?\n", "\n", "Not so fast...\n", "\n", "Training accuracy rewards overly complex models that do not generalize to out-of-sample data. Setting K=1 caused KNN to effectively memorize the training data. Thus, it will do very well when tested using the in-sample data, but may do very poorly on out-of-sample data. **As such, training accuracy is not a good estimate of out-of-sample accuracy,** and out-of-sample accuracy is what matters.\n", "\n", "Creating an unnecessarily complex model is known as **overfitting**, because it is learning the \"noise\" rather than the \"signal\".\n", "\n", "From Quora: [What is an intuitive explanation of overfitting?](http://www.quora.com/What-is-an-intuitive-explanation-of-overfitting/answer/Jessica-Su)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overfitting\n", "\n", "![Overfitting](images/overfitting.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Underfitting vs. Overfitting\n", "\n", "![Underfitting vs. Overfitting](images/underfitting_overfitting.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation Procedure #2: Train/Test Split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now try an alternative model evaluation procedure known as **train/test split**. (This is also known as the **test set approach** or the **validation set approach**.) Here's our plan:\n", "\n", "1. Split the dataset into two pieces: a **training set** and a **testing set**.\n", "2. Train the model on the training set.\n", "3. Test the model on the testing set, and evaluate how well we did.\n", "4. Repeat steps 2 and 3 using different **tuning parameters** (the value of K, in this case).\n", "\n", "Why do we believe this will allow us to build a model that generalizes? Because we are evaluating the model on **data that was not used to train the model**.\n", "\n", "Before we use this procedure, we first have to understand how the `train_test_split` function works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Understanding the `train_test_split` function" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# create X: 5 observations and 2 features\n", "X = np.arange(1, 11).reshape((5, 2))\n", "print X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1 2]\n", " [ 3 4]\n", " [ 5 6]\n", " [ 7 8]\n", " [ 9 10]]\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# create y: response vector of size 5\n", "y = X.prod(axis=1)\n", "print y" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 2 12 30 56 90]\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# randomly split the rows of X into two pieces\n", "from sklearn.cross_validation import train_test_split\n", "X_split = train_test_split(X) # returns a list with two elements\n", "print X_split[0]\n", "print X_split[1]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1 2]\n", " [ 9 10]\n", " [ 3 4]]\n", "[[5 6]\n", " [7 8]]\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# use random_state parameter to always get the same random split\n", "X_split = train_test_split(X, random_state=2)\n", "print X_split[0]\n", "print X_split[1]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[3 4]\n", " [7 8]\n", " [1 2]]\n", "[[ 5 6]\n", " [ 9 10]]\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# \"unpack\" the list into two separate objects\n", "X_train, X_test = train_test_split(X, random_state=2)\n", "print X_train\n", "print X_test" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[3 4]\n", " [7 8]\n", " [1 2]]\n", "[[ 5 6]\n", " [ 9 10]]\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# randomly split the rows of X and y into two pieces each\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# X has been split into two pieces\n", "print X_train\n", "print X_test" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[3 4]\n", " [7 8]\n", " [1 2]]\n", "[[ 5 6]\n", " [ 9 10]]\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# y has been split into two pieces with the same ordering\n", "print y_train\n", "print y_test" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[12 56 2]\n", "[30 90]\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "![train_test_split](images/train_test_split.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using the Train/Test Split Procedure on iris" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Step 0: load the iris data again\n", "iris = load_iris()\n", "X = iris.data\n", "y = iris.target" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# Step 1: split data into a training set and a testing set\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4)\n", "print X_train.shape\n", "print X_test.shape\n", "print y_train.shape\n", "print y_test.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(112L, 4L)\n", "(38L, 4L)\n", "(112L,)\n", "(38L,)\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# Step 2: train the model on the training set (using K=1)\n", "knn = KNeighborsClassifier(n_neighbors=1)\n", "knn.fit(X_train, y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=1, p=2, weights='uniform')" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# Step 3: test the model on the testing set, and check the accuracy\n", "y_pred = knn.predict(X_test)\n", "print metrics.accuracy_score(y_test, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.947368421053\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# Step 4: repeat this process for K=5\n", "knn = KNeighborsClassifier(n_neighbors=5)\n", "knn.fit(X_train, y_train)\n", "y_pred = knn.predict(X_test)\n", "print metrics.accuracy_score(y_test, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.973684210526\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "K=1 had a higher **training accuracy** than K=5, but K=5 had a higher **testing accuracy** than K=1. Testing accuracy is a much better estimator of out-of-sample accuracy, and thus we would prefer to use K=5 in this situation.\n", "\n", "Training error tends to decrease as model complexity increases, whereas testing error tends to hit a \"sweet spot\" somewhere between minimal and maximal complexity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Training Error](images/training_error.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's pretend we tried **many values of K**, and K=5 turned out to have the highest testing accuracy. (In other words, K=5 was the \"sweet spot\".) What would we conclude?\n", "\n", "- We would declare 5 to be the best value for K when using KNN on the iris dataset.\n", "- We would estimate that when given the measurements of an unknown iris, we would be able to correctly predict its species 97% of the time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making Predictions on Out-of-Sample Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now given the measurements of a (truly) unknown iris, and are asked to predict its species. How do we do it?\n", "\n", "1. We re-train our model on the **entire dataset**, using the **best value for K** found in the previous step.\n", "2. We make our prediction, and are 97% sure that we will make the correct prediction.\n", "\n", "It is important to re-train your model on **all** of the data, otherwise you will be ignoring valuable training data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# train the model on X (not X_train) using K=5\n", "knn = KNeighborsClassifier(n_neighbors=5)\n", "knn.fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=5, p=2, weights='uniform')" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "# make a prediction for an unknown iris\n", "out_of_sample = [5, 4, 3, 2]\n", "knn.predict(out_of_sample)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "array([1])" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What's Wrong with Train/Test Split for Model Evaluation?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What would happen if the `train_test_split` function had split the data differently? Would we get the same exact results as before?\n", "\n", "Let's check!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# use a different random_state than last time\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n", "knn = KNeighborsClassifier(n_neighbors=5)\n", "knn.fit(X_train, y_train)\n", "y_pred = knn.predict(X_test)\n", "print metrics.accuracy_score(y_test, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It turns out that test set accuracy is a **high variance estimate** of out-of-sample accuracy. In a future class, we will learn an alternative model evaluation procedure called **K-fold cross-validation** that will provide a more accurate estimate of out-of-sample accuracy, and thus will be a better procedure for choosing the \"best\" model.\n", "\n", "However, train/test split is still an incredibly useful evaluation procedure because of its **flexibility and speed**, and thus we will use it throughout the course." ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/09_linear_regression.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:029ed166d1b9e896a08ee8d00c3eaa9354ec0607f6b83b74a228b5562d01b430" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Linear Regression\n", "\n", "*Adapted from Chapter 3 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Motivation\n", "\n", "**Regression problems** are supervised learning problems in which the response is continuous. **Classification problems** are supervised learning problems in which the response is categorical. **Linear regression** is a technique that is useful for regression problems.\n", "\n", "So, why are we learning linear regression?\n", "\n", "- widely used\n", "- runs fast\n", "- easy to use (not a lot of tuning required)\n", "- highly interpretable\n", "- basis for many other methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Libraries\n", "\n", "We'll be using [Statsmodels](http://statsmodels.sourceforge.net/) for **teaching purposes** since it has some nice characteristics for linear modeling. However, we recommend that you spend most of your energy on [scikit-learn](http://scikit-learn.org/stable/) since it provides significantly more useful functionality for machine learning in general." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# imports\n", "import pandas as pd\n", "import seaborn as sns\n", "import statsmodels.formula.api as smf\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn import metrics\n", "from sklearn.cross_validation import train_test_split\n", "import numpy as np\n", "\n", "# allow plots to appear directly in the notebook\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Advertising Data\n", "\n", "Let's take a look at some data, ask some questions about that data, and then use linear regression to answer those questions!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# read data into a DataFrame\n", "data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)\n", "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TVRadioNewspaperSales
1230.137.869.222.1
244.539.345.110.4
317.245.969.39.3
4151.541.358.518.5
5180.810.858.412.9
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ " TV Radio Newspaper Sales\n", "1 230.1 37.8 69.2 22.1\n", "2 44.5 39.3 45.1 10.4\n", "3 17.2 45.9 69.3 9.3\n", "4 151.5 41.3 58.5 18.5\n", "5 180.8 10.8 58.4 12.9" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What are the **features**?\n", "- TV: advertising dollars spent on TV for a single product in a given market (in thousands of dollars)\n", "- Radio: advertising dollars spent on Radio\n", "- Newspaper: advertising dollars spent on Newspaper\n", "\n", "What is the **response**?\n", "- Sales: sales of a single product in a given market (in thousands of widgets)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# print the shape of the DataFrame\n", "data.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "(200, 4)" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 200 **observations**, and thus 200 markets in the dataset." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# visualize the relationship between the features and the response using scatterplots\n", "sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', size=7, aspect=0.7)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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RyF5QPayvYN6pDnc6DgB7dKqjvVKH4+Q3APEwogFoYurtXjfn7/aNQtZDA8fX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\nlmyubeUGAFkiZ4bDdQKQlHb5hdwD5BuLQaYs7irVrG4NAOG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"text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Questions About the Advertising Data\n", "\n", "Let's pretend you work for the company that manufactures and markets this widget. The company might ask you the following: On the basis of this data, how should we spend our advertising money in the future?\n", "\n", "This general question might lead you to more specific questions:\n", "1. Is there a relationship between ads and sales?\n", "2. How strong is that relationship?\n", "3. Which ad types contribute to sales?\n", "4. What is the effect of each ad type of sales?\n", "5. Given ad spending in a particular market, can sales be predicted?\n", "\n", "We will explore these questions below!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple Linear Regression\n", "\n", "Simple linear regression is an approach for predicting a **quantitative response** using a **single feature** (or \"predictor\" or \"input variable\"). It takes the following form:\n", "\n", "$y = \\beta_0 + \\beta_1x$\n", "\n", "What does each term represent?\n", "- $y$ is the response\n", "- $x$ is the feature\n", "- $\\beta_0$ is the intercept\n", "- $\\beta_1$ is the coefficient for x\n", "\n", "Together, $\\beta_0$ and $\\beta_1$ are called the **model coefficients**. To create your model, you must \"learn\" the values of these coefficients. And once we've learned these coefficients, we can use the model to predict Sales!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimating (\"Learning\") Model Coefficients\n", "\n", "Generally speaking, coefficients are estimated using the **least squares criterion**, which means we are finding the line (mathematically) which minimizes the **sum of squared residuals** (or \"sum of squared errors\"):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Estimating coefficients](images/estimating_coefficients.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What elements are present in the diagram?\n", "- The black dots are the **observed values** of x and y.\n", "- The blue line is our **least squares line**.\n", "- The red lines are the **residuals**, which are the distances between the observed values and the least squares line.\n", "\n", "How do the model coefficients relate to the least squares line?\n", "- $\\beta_0$ is the **intercept** (the value of $y$ when $x$=0)\n", "- $\\beta_1$ is the **slope** (the change in $y$ divided by change in $x$)\n", "\n", "Here is a graphical depiction of those calculations:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Slope-intercept](images/slope_intercept.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's estimate the model coefficients for the advertising data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "### STATSMODELS ###\n", "\n", "# create a fitted model\n", "lm1 = smf.ols(formula='Sales ~ TV', data=data).fit()\n", "\n", "# print the coefficients\n", "lm1.params" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "Intercept 7.032594\n", "TV 0.047537\n", "dtype: float64" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "### SCIKIT-LEARN ###\n", "\n", "# create X and y\n", "feature_cols = ['TV']\n", "X = data[feature_cols]\n", "y = data.Sales\n", "\n", "# instantiate and fit\n", "lm2 = LinearRegression()\n", "lm2.fit(X, y)\n", "\n", "# print the coefficients\n", "print lm2.intercept_\n", "print lm2.coef_" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "7.03259354913\n", "[ 0.04753664]\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpreting Model Coefficients\n", "\n", "How do we interpret the TV coefficient ($\\beta_1$)?\n", "- A \"unit\" increase in TV ad spending is **associated with** a 0.047537 \"unit\" increase in Sales.\n", "- Or more clearly: An additional $1,000 spent on TV ads is **associated with** an increase in sales of 47.537 widgets.\n", "\n", "Note that if an increase in TV ad spending was associated with a **decrease** in sales, $\\beta_1$ would be **negative**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the Model for Prediction\n", "\n", "Let's say that there was a new market where the TV advertising spend was **$50,000**. What would we predict for the Sales in that market?\n", "\n", "$$y = \\beta_0 + \\beta_1x$$\n", "$$y = 7.032594 + 0.047537 \\times 50$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# manually calculate the prediction\n", "7.032594 + 0.047537*50" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "9.409444" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "### STATSMODELS ###\n", "\n", "# you have to create a DataFrame since the Statsmodels formula interface expects it\n", "X_new = pd.DataFrame({'TV': [50]})\n", "\n", "# predict for a new observation\n", "lm1.predict(X_new)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "array([ 9.40942557])" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "### SCIKIT-LEARN ###\n", "\n", "# predict for a new observation\n", "lm2.predict(50)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "array([ 9.40942557])" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus, we would predict Sales of **9,409 widgets** in that market." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the Least Squares Line\n", "\n", "Let's plot the least squares line for Sales versus each of the features:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', size=7, aspect=0.7, kind='reg')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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uPPnAKPaOsYrBKN/95wlMXkoAAMY39eMvvr5/0eN2uiaI7M7ufRhW0h7pEiKb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\nEREREd8o0SAiIiIiIiIivlGiQURERERER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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Confidence in our Model\n", "\n", "**Question:** Is linear regression a high variance/low bias model, or a low variance/high bias model?\n", "\n", "**Answer:** Low variance/high bias. Under repeated sampling, the line will stay roughly in the same place (low variance), but the average of those models won't do a great job capturing the true relationship (high bias). Note that low variance is a useful characteristic when you don't have a lot of training data!\n", "\n", "A closely related concept is **confidence intervals**. Statsmodels calculates 95% confidence intervals for our model coefficients, which are interpreted as follows: If the population from which this sample was drawn was **sampled 100 times**, approximately **95 of those confidence intervals** would contain the \"true\" coefficient." ] }, { "cell_type": "code", "collapsed": false, "input": [ "### STATSMODELS ###\n", "\n", "# print the confidence intervals for the model coefficients\n", "lm1.conf_int()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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01
Intercept6.1297197.935468
TV0.0422310.052843
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ " 0 1\n", "Intercept 6.129719 7.935468\n", "TV 0.042231 0.052843" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Keep in mind that we only have a **single sample of data**, and not the **entire population of data**. The \"true\" coefficient is either within this interval or it isn't, but there's no way to actually know. We estimate the coefficient with the data we do have, and we show uncertainty about that estimate by giving a range that the coefficient is **probably** within.\n", "\n", "Note that using 95% confidence intervals is just a convention. You can create 90% confidence intervals (which will be more narrow), 99% confidence intervals (which will be wider), or whatever intervals you like." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hypothesis Testing and p-values\n", "\n", "Closely related to confidence intervals is **hypothesis testing**. Generally speaking, you start with a **null hypothesis** and an **alternative hypothesis** (that is opposite the null). Then, you check whether the data supports **rejecting the null hypothesis** or **failing to reject the null hypothesis**.\n", "\n", "(Note that \"failing to reject\" the null is not the same as \"accepting\" the null hypothesis. The alternative hypothesis may indeed be true, except that you just don't have enough data to show that.)\n", "\n", "As it relates to model coefficients, here is the conventional hypothesis test:\n", "- **null hypothesis:** There is no relationship between TV ads and Sales (and thus $\\beta_1$ equals zero)\n", "- **alternative hypothesis:** There is a relationship between TV ads and Sales (and thus $\\beta_1$ is not equal to zero)\n", "\n", "How do we test this hypothesis? Intuitively, we reject the null (and thus believe the alternative) if the 95% confidence interval **does not include zero**. Conversely, the **p-value** represents the probability that the coefficient is actually zero:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "### STATSMODELS ###\n", "\n", "# print the p-values for the model coefficients\n", "lm1.pvalues" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "Intercept 1.406300e-35\n", "TV 1.467390e-42\n", "dtype: float64" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the 95% confidence interval **includes zero**, the p-value for that coefficient will be **greater than 0.05**. If the 95% confidence interval **does not include zero**, the p-value will be **less than 0.05**. Thus, a p-value less than 0.05 is one way to decide whether there is likely a relationship between the feature and the response. (Again, using 0.05 as the cutoff is just a convention.)\n", "\n", "In this case, the p-value for TV is far less than 0.05, and so we **believe** that there is a relationship between TV ads and Sales.\n", "\n", "Note that we generally ignore the p-value for the intercept." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How Well Does the Model Fit the data?\n", "\n", "The most common way to evaluate the overall fit of a linear model is by the **R-squared** value. R-squared is the **proportion of variance explained**, meaning the proportion of variance in the observed data that is explained by the model, or the reduction in error over the **null model**. (The null model just predicts the mean of the observed response, and thus it has an intercept and no slope.)\n", "\n", "R-squared is between 0 and 1, and higher is better because it means that more variance is explained by the model. Here's an example of what R-squared \"looks like\":" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![R-squared](images/r_squared.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that the **blue line** explains some of the variance in the data (R-squared=0.54), the **green line** explains more of the variance (R-squared=0.64), and the **red line** fits the training data even further (R-squared=0.66). (Does the red line look like it's overfitting?)\n", "\n", "Let's calculate the R-squared value for our simple linear model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "### STATSMODELS ###\n", "\n", "# print the R-squared value for the model\n", "lm1.rsquared" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "0.61187505085007099" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "### SCIKIT-LEARN ###\n", "\n", "# print the R-squared value for the model\n", "lm2.score(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "0.61187505085007099" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is that a \"good\" R-squared value? It's hard to say. The threshold for a good R-squared value depends widely on the domain. Therefore, it's most useful as a tool for **comparing different models**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple Linear Regression\n", "\n", "Simple linear regression can easily be extended to include multiple features. This is called **multiple linear regression**:\n", "\n", "$y = \\beta_0 + \\beta_1x_1 + ... + \\beta_nx_n$\n", "\n", "Each $x$ represents a different feature, and each feature has its own coefficient. In this case:\n", "\n", "$y = \\beta_0 + \\beta_1 \\times TV + \\beta_2 \\times Radio + \\beta_3 \\times Newspaper$\n", "\n", "Let's estimate these coefficients:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "### STATSMODELS ###\n", "\n", "# create a fitted model with all three features\n", "lm1 = smf.ols(formula='Sales ~ TV + Radio + Newspaper', data=data).fit()\n", "\n", "# print the coefficients\n", "lm1.params" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "Intercept 2.938889\n", "TV 0.045765\n", "Radio 0.188530\n", "Newspaper -0.001037\n", "dtype: float64" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "### SCIKIT-LEARN ###\n", "\n", "# create X and y\n", "feature_cols = ['TV', 'Radio', 'Newspaper']\n", "X = data[feature_cols]\n", "y = data.Sales\n", "\n", "# instantiate and fit\n", "lm2 = LinearRegression()\n", "lm2.fit(X, y)\n", "\n", "# print the coefficients\n", "print lm2.intercept_\n", "print lm2.coef_" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2.93888936946\n", "[ 0.04576465 0.18853002 -0.00103749]\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# pair the feature names with the coefficients\n", "zip(feature_cols, lm2.coef_)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "[('TV', 0.04576464545539765),\n", " ('Radio', 0.18853001691820462),\n", " ('Newspaper', -0.0010374930424762799)]" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do we interpret these coefficients? For a given amount of Radio and Newspaper ad spending, an **increase of $1000 in TV ad spending** is associated with an **increase in Sales of 45.765 widgets**.\n", "\n", "A lot of the information we have been reviewing piece-by-piece is available in the Statsmodels model summary output:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "### STATSMODELS ###\n", "\n", "# print a summary of the fitted model\n", "lm1.summary()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Sales R-squared: 0.897
Model: OLS Adj. R-squared: 0.896
Method: Least Squares F-statistic: 570.3
Date: Thu, 16 Apr 2015 Prob (F-statistic): 1.58e-96
Time: 13:54:22 Log-Likelihood: -386.18
No. Observations: 200 AIC: 780.4
Df Residuals: 196 BIC: 793.6
Df Model: 3
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 2.9389 0.312 9.422 0.000 2.324 3.554
TV 0.0458 0.001 32.809 0.000 0.043 0.049
Radio 0.1885 0.009 21.893 0.000 0.172 0.206
Newspaper -0.0010 0.006 -0.177 0.860 -0.013 0.011
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 60.414 Durbin-Watson: 2.084
Prob(Omnibus): 0.000 Jarque-Bera (JB): 151.241
Skew: -1.327 Prob(JB): 1.44e-33
Kurtosis: 6.332 Cond. No. 454.
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Sales R-squared: 0.897\n", "Model: OLS Adj. R-squared: 0.896\n", "Method: Least Squares F-statistic: 570.3\n", "Date: Thu, 16 Apr 2015 Prob (F-statistic): 1.58e-96\n", "Time: 13:54:22 Log-Likelihood: -386.18\n", "No. Observations: 200 AIC: 780.4\n", "Df Residuals: 196 BIC: 793.6\n", "Df Model: 3 \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "Intercept 2.9389 0.312 9.422 0.000 2.324 3.554\n", "TV 0.0458 0.001 32.809 0.000 0.043 0.049\n", "Radio 0.1885 0.009 21.893 0.000 0.172 0.206\n", "Newspaper -0.0010 0.006 -0.177 0.860 -0.013 0.011\n", "==============================================================================\n", "Omnibus: 60.414 Durbin-Watson: 2.084\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 151.241\n", "Skew: -1.327 Prob(JB): 1.44e-33\n", "Kurtosis: 6.332 Cond. No. 454.\n", "==============================================================================\n", "\"\"\"" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What are a few key things we learn from this output?\n", "\n", "- TV and Radio have significant **p-values**, whereas Newspaper does not. Thus we reject the null hypothesis for TV and Radio (that there is no association between those features and Sales), and fail to reject the null hypothesis for Newspaper.\n", "- TV and Radio ad spending are both **positively associated** with Sales, whereas Newspaper ad spending is **slightly negatively associated** with Sales. (However, this is irrelevant since we have failed to reject the null hypothesis for Newspaper.)\n", "- This model has a higher **R-squared** (0.897) than the previous model, which means that this model provides a better fit to the data than a model that only includes TV." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Selection\n", "\n", "How do I decide **which features to include** in a linear model? Here's one idea:\n", "- Try different models, and only keep features in the model if they have small p-values.\n", "- Check whether the R-squared value goes up when you add new features.\n", "\n", "What are the **drawbacks** to this approach?\n", "- Linear models rely upon a lot of **assumptions** (such as the features being independent), and if those assumptions are violated (which they usually are), R-squared and p-values are less reliable.\n", "- Using a p-value cutoff of 0.05 means that if you add 100 features to a model that are **pure noise**, 5 of them (on average) will still be counted as significant.\n", "- R-squared is susceptible to **overfitting**, and thus there is no guarantee that a model with a high R-squared value will generalize. Below is an example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "### STATSMODELS ###\n", "\n", "# only include TV and Radio in the model\n", "lm1 = smf.ols(formula='Sales ~ TV + Radio', data=data).fit()\n", "lm1.rsquared" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "0.89719426108289557" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "# add Newspaper to the model (which we believe has no association with Sales)\n", "lm1 = smf.ols(formula='Sales ~ TV + Radio + Newspaper', data=data).fit()\n", "lm1.rsquared" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "0.89721063817895219" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**R-squared will always increase as you add more features to the model**, even if they are unrelated to the response. Thus, selecting the model with the highest R-squared is not a reliable approach for choosing the best linear model.\n", "\n", "There is alternative to R-squared called **adjusted R-squared** that penalizes model complexity (to control for overfitting), but it generally [under-penalizes complexity](http://scott.fortmann-roe.com/docs/MeasuringError.html).\n", "\n", "So is there a better approach to feature selection? **Train/test split** or **cross-validation.** They provide a more reliable estimate of out-of-sample error, and thus are better for choosing which of your models will best **generalize** to out-of-sample data. There is extensive functionality for cross-validation in scikit-learn, including automated methods for searching different sets of parameters and different models. Importantly, cross-validation can be applied to **any model**, whereas the methods described above only apply to **linear models**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Evaluation Metrics for Regression\n", "\n", "For classification problems, we have only used classification accuracy as our evaluation metric. What metrics can we used for regression problems?\n", "\n", "**Mean Absolute Error** (MAE) is the mean of the absolute value of the errors:\n", "\n", "$$\\frac 1n\\sum_{i=1}^n|y_i-\\hat{y}_i|$$\n", "\n", "**Mean Squared Error** (MSE) is the mean of the squared errors:\n", "\n", "$$\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2$$\n", "\n", "**Root Mean Squared Error** (RMSE) is the square root of the mean of the squared errors:\n", "\n", "$$\\sqrt{\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2}$$\n", "\n", "Let's calculate these by hand, to get an intuitive sense for the results:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# define true and predicted response values\n", "y_true = [100, 50, 30, 20]\n", "y_pred = [90, 50, 50, 30]\n", "\n", "# calculate MAE, MSE, RMSE\n", "print metrics.mean_absolute_error(y_true, y_pred)\n", "print metrics.mean_squared_error(y_true, y_pred)\n", "print np.sqrt(metrics.mean_squared_error(y_true, y_pred))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10.0\n", "150.0\n", "12.2474487139\n" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "MSE is more popular than MAE because MSE \"punishes\" larger errors. But, RMSE is even more popular than MSE because RMSE is interpretable in the \"y\" units." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Evaluation Using Train/Test Split\n", "\n", "Let's use train/test split with RMSE to see whether Newspaper should be kept in the model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# include Newspaper\n", "X = data[['TV', 'Radio', 'Newspaper']]\n", "y = data.Sales\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n", "lm2 = LinearRegression()\n", "lm2.fit(X_train, y_train)\n", "y_pred = lm2.predict(X_test)\n", "print np.sqrt(metrics.mean_squared_error(y_test, y_pred))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.40465142303\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "# exclude Newspaper\n", "X = data[['TV', 'Radio']]\n", "y = data.Sales\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n", "lm2 = LinearRegression()\n", "lm2.fit(X_train, y_train)\n", "y_pred = lm2.predict(X_test)\n", "print np.sqrt(metrics.mean_squared_error(y_test, y_pred))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.38790346994\n" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Handling Categorical Features with Two Categories\n", "\n", "Up to now, all of our features have been numeric. What if one of our features was categorical?\n", "\n", "Let's create a new feature called **Size**, and randomly assign observations to be **small or large**:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set a seed for reproducibility\n", "np.random.seed(12345)\n", "\n", "# create a Series of booleans in which roughly half are True\n", "nums = np.random.rand(len(data))\n", "mask_large = nums > 0.5\n", "\n", "# initially set Size to small, then change roughly half to be large\n", "data['Size'] = 'small'\n", "data.loc[mask_large, 'Size'] = 'large'\n", "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TVRadioNewspaperSalesSize
1230.137.869.222.1large
244.539.345.110.4small
317.245.969.39.3small
4151.541.358.518.5small
5180.810.858.412.9large
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ " TV Radio Newspaper Sales Size\n", "1 230.1 37.8 69.2 22.1 large\n", "2 44.5 39.3 45.1 10.4 small\n", "3 17.2 45.9 69.3 9.3 small\n", "4 151.5 41.3 58.5 18.5 small\n", "5 180.8 10.8 58.4 12.9 large" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For scikit-learn, we need to represent all data **numerically**. If the feature only has two categories, we can simply create a **dummy variable** that represents the categories as a binary value:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# create a new Series called Size_large\n", "data['Size_large'] = data.Size.map({'small':0, 'large':1})\n", "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TVRadioNewspaperSalesSizeSize_large
1230.137.869.222.1large1
244.539.345.110.4small0
317.245.969.39.3small0
4151.541.358.518.5small0
5180.810.858.412.9large1
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ " TV Radio Newspaper Sales Size Size_large\n", "1 230.1 37.8 69.2 22.1 large 1\n", "2 44.5 39.3 45.1 10.4 small 0\n", "3 17.2 45.9 69.3 9.3 small 0\n", "4 151.5 41.3 58.5 18.5 small 0\n", "5 180.8 10.8 58.4 12.9 large 1" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's redo the multiple linear regression and include the **Size_large** feature:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# create X and y\n", "feature_cols = ['TV', 'Radio', 'Newspaper', 'Size_large']\n", "X = data[feature_cols]\n", "y = data.Sales\n", "\n", "# instantiate, fit\n", "lm2 = LinearRegression()\n", "lm2.fit(X, y)\n", "\n", "# print coefficients\n", "zip(feature_cols, lm2.coef_)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "[('TV', 0.045719820924362803),\n", " ('Radio', 0.18872814313427874),\n", " ('Newspaper', -0.0010976794483515545),\n", " ('Size_large', 0.057423850854828061)]" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do we interpret the **Size_large coefficient**? For a given amount of TV/Radio/Newspaper ad spending, being a large market is associated with an average **increase** in Sales of 57.42 widgets (as compared to a small market, which is called the **baseline level**).\n", "\n", "What if we had reversed the 0/1 coding and created the feature 'Size_small' instead? The coefficient would be the same, except it would be **negative instead of positive**. As such, your choice of category for the baseline does not matter, all that changes is your **interpretation** of the coefficient." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Handling Categorical Features with More than Two Categories\n", "\n", "Let's create a new feature called **Area**, and randomly assign observations to be **rural, suburban, or urban**:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set a seed for reproducibility\n", "np.random.seed(123456)\n", "\n", "# assign roughly one third of observations to each group\n", "nums = np.random.rand(len(data))\n", "mask_suburban = (nums > 0.33) & (nums < 0.66)\n", "mask_urban = nums > 0.66\n", "data['Area'] = 'rural'\n", "data.loc[mask_suburban, 'Area'] = 'suburban'\n", "data.loc[mask_urban, 'Area'] = 'urban'\n", "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TVRadioNewspaperSalesSizeSize_largeArea
1230.137.869.222.1large1rural
244.539.345.110.4small0urban
317.245.969.39.3small0rural
4151.541.358.518.5small0urban
5180.810.858.412.9large1suburban
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ " TV Radio Newspaper Sales Size Size_large Area\n", "1 230.1 37.8 69.2 22.1 large 1 rural\n", "2 44.5 39.3 45.1 10.4 small 0 urban\n", "3 17.2 45.9 69.3 9.3 small 0 rural\n", "4 151.5 41.3 58.5 18.5 small 0 urban\n", "5 180.8 10.8 58.4 12.9 large 1 suburban" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have to represent Area numerically, but we can't simply code it as 0=rural, 1=suburban, 2=urban because that would imply an **ordered relationship** between suburban and urban, and thus urban is somehow \"twice\" the suburban category. Note that if you do have ordered categories (i.e., strongly disagree, disagree, neutral, agree, strongly agree), you can use a single dummy variable and represent the categories numerically (such as 1, 2, 3, 4, 5).\n", "\n", "Anyway, our Area feature is unordered, so we have to create **additional dummy variables**. Let's explore how to do this using pandas:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# create three dummy variables using get_dummies\n", "pd.get_dummies(data.Area, prefix='Area').head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Area_ruralArea_suburbanArea_urban
1100
2001
3100
4001
5010
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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ " Area_rural Area_suburban Area_urban\n", "1 1 0 0\n", "2 0 0 1\n", "3 1 0 0\n", "4 0 0 1\n", "5 0 1 0" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, we actually only need **two dummy variables, not three**. Why? Because two dummies captures all of the \"information\" about the Area feature, and implicitly defines rural as the \"baseline level\".\n", "\n", "Let's see what that looks like:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# create three dummy variables using get_dummies, then exclude the first dummy column\n", "area_dummies = pd.get_dummies(data.Area, prefix='Area').iloc[:, 1:]\n", "area_dummies.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Area_suburbanArea_urban
100
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510
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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ " Area_suburban Area_urban\n", "1 0 0\n", "2 0 1\n", "3 0 0\n", "4 0 1\n", "5 1 0" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is how we interpret the coding:\n", "- **rural** is coded as Area_suburban=0 and Area_urban=0\n", "- **suburban** is coded as Area_suburban=1 and Area_urban=0\n", "- **urban** is coded as Area_suburban=0 and Area_urban=1\n", "\n", "If this is confusing, think about why we only needed one dummy variable for Size (Size_large), not two dummy variables (Size_small and Size_large). In general, if you have a categorical feature with k \"levels\", you create k-1 dummy variables.\n", "\n", "Anyway, let's add these two new dummy variables onto the original DataFrame, and then include them in the linear regression model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# concatenate the dummy variable columns onto the DataFrame (axis=0 means rows, axis=1 means columns)\n", "data = pd.concat([data, area_dummies], axis=1)\n", "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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TVRadioNewspaperSalesSizeSize_largeAreaArea_suburbanArea_urban
1230.137.869.222.1large1rural00
244.539.345.110.4small0urban01
317.245.969.39.3small0rural00
4151.541.358.518.5small0urban01
5180.810.858.412.9large1suburban10
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ " TV Radio Newspaper Sales Size Size_large Area Area_suburban \\\n", "1 230.1 37.8 69.2 22.1 large 1 rural 0 \n", "2 44.5 39.3 45.1 10.4 small 0 urban 0 \n", "3 17.2 45.9 69.3 9.3 small 0 rural 0 \n", "4 151.5 41.3 58.5 18.5 small 0 urban 0 \n", "5 180.8 10.8 58.4 12.9 large 1 suburban 1 \n", "\n", " Area_urban \n", "1 0 \n", "2 1 \n", "3 0 \n", "4 1 \n", "5 0 " ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "# create X and y\n", "feature_cols = ['TV', 'Radio', 'Newspaper', 'Size_large', 'Area_suburban', 'Area_urban']\n", "X = data[feature_cols]\n", "y = data.Sales\n", "\n", "# instantiate and fit\n", "lm2 = LinearRegression()\n", "lm2.fit(X, y)\n", "\n", "# print the coefficients\n", "zip(feature_cols, lm2.coef_)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "[('TV', 0.04574401036331379),\n", " ('Radio', 0.18786669552525814),\n", " ('Newspaper', -0.0010876977267108706),\n", " ('Size_large', 0.077396607497479411),\n", " ('Area_suburban', -0.10656299015958708),\n", " ('Area_urban', 0.26813802165220019)]" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do we interpret the coefficients?\n", "- Holding all other variables fixed, being a **suburban** area is associated with an average **decrease** in Sales of 106.56 widgets (as compared to the baseline level, which is rural).\n", "- Being an **urban** area is associated with an average **increase** in Sales of 268.13 widgets (as compared to rural)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What Didn't We Cover?\n", "\n", "- Detecting collinearity\n", "- Diagnosing model fit\n", "- Transforming features to fit non-linear relationships\n", "- Interaction terms\n", "- Assumptions of linear regression\n", "- And so much more!\n", "\n", "You could certainly go very deep into linear regression, and learn how to apply it really, really well. It's an excellent way to **start your modeling process** when working a regression problem. However, it is limited by the fact that it can only make good predictions if there is a **linear relationship** between the features and the response, which is why more complex methods (with higher variance and lower bias) will often outperform linear regression.\n", "\n", "Therefore, we want you to understand linear regression conceptually, understand its strengths and weaknesses, be familiar with the terminology, and know how to apply it. However, we also want to spend time on many other machine learning models, which is why we aren't going deeper here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resources\n", "\n", "- To go much more in-depth on linear regression, read Chapter 3 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/), from which this lesson was adapted. Alternatively, watch the [related videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) or read my [quick reference guide](http://www.dataschool.io/applying-and-interpreting-linear-regression/) to the key points in that chapter.\n", "- To learn more about Statsmodels and how to interpret the output, DataRobot has some decent posts on [simple linear regression](http://www.datarobot.com/blog/ordinary-least-squares-in-python/) and [multiple linear regression](http://www.datarobot.com/blog/multiple-regression-using-statsmodels/).\n", "- This [introduction to linear regression](http://people.duke.edu/~rnau/regintro.htm) is much more detailed and mathematically thorough, and includes lots of good advice.\n", "- This is a relatively quick post on the [assumptions of linear regression](http://pareonline.net/getvn.asp?n=2&v=8)." ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/11_cross_validation.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:4aa33b85c25916966326c2f6ba5b3a06c4df0e8ad5ba66461d3b5f66460f3892" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cross-Validation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_iris\n", "from sklearn.cross_validation import train_test_split, cross_val_score\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.linear_model import LogisticRegression, LinearRegression\n", "from sklearn import metrics\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What's Wrong with Train/Test Split for Model Evaluation?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Previously, we studied the **train/test split** procedure for model evaluation. We found that it was a superior alternative to training and testing on the same data because it helps to avoid overfitting.\n", "\n", "However, we also found that it provides a **high variance estimate** of out-of-sample accuracy, since changing which observations happen to be in the training set versus the testing set can make a meaningful difference in the estimated accuracy.\n", "\n", "Let's see this in code:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# load the iris data\n", "iris = load_iris()\n", "X = iris.data\n", "y = iris.target" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# use train/test split with random_state=4\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4)\n", "\n", "# check accuracy of KNN with K=5\n", "knn = KNeighborsClassifier(n_neighbors=5)\n", "knn.fit(X_train, y_train)\n", "y_pred = knn.predict(X_test)\n", "print metrics.accuracy_score(y_test, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.973684210526\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# use train/test split with random_state=1\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n", "\n", "# check accuracy of KNN with K=5\n", "knn = KNeighborsClassifier(n_neighbors=5)\n", "knn.fit(X_train, y_train)\n", "y_pred = knn.predict(X_test)\n", "print metrics.accuracy_score(y_test, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "One **solution** to this problem might be to take a bunch of train/test splits, and then average the results together. That's the essense of cross-validation!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Steps for K-fold Cross-Validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Randomly split the dataset into K equal partitions.\n", "2. Use partition 1 as the test set and the union of the other partitions as the training set.\n", "3. Calculate test set accuracy.\n", "4. Repeat steps 2 and 3 K times, using a different partition as the test set each time.\n", "5. Use the average test set accuracy as the estimate of out-of-sample accuracy.\n", "\n", "Here are two diagrams of **5-fold cross-validation:**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![5-fold cross-validation](images/cross_validation_diagram.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![5-fold cross-validation](images/cross_validation_example.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-Validation Recommendations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any number of folds can be used, but **10-fold cross-validation** is generally recommended because it has been shown experimentally to produce the most reliable estimates of out-of-sample accuracy.\n", "\n", "For classification problems, **stratified sampling** is also recommended, meaning that each response class should be represented with approximately equal proportions in each of the K folds. (scikit-learn does by default when using `cross_val_score`, which we will use below.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing Cross-Validation to Train/Test Split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Advantages of **cross-validation:**\n", "\n", "- More accurate estimate of out-of-sample accuracy\n", "- More efficient use of data (every record is used for both training and testing)\n", "\n", "Advantages of **train/test split:**\n", "\n", "- Runs 10 times faster than 10-fold cross-validation\n", "- Simpler to examine the test set results without writing custom code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-Validation Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try out cross-validation on a few datasets:\n", "\n", "- **iris:** multi-class classification\n", "- **default:** binary classification\n", "- **advertising:** regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iris dataset\n", "\n", "- Multi-class classification\n", "- Using accuracy as evaluation metric\n", "- Using cross-validation to select tuning parameters (aka \"hyperparameters\")" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# load the iris data\n", "iris = load_iris()\n", "X = iris.data\n", "y = iris.target" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10-fold cross-validation with K=5\n", "knn = KNeighborsClassifier(n_neighbors=5)\n", "scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n", "print scores" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 1. 0.93333333 1. 1. 0.86666667 0.93333333\n", " 0.93333333 1. 1. 1. ]\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# use average accuracy as estimate of out-of-sample accuracy\n", "print np.mean(scores)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.966666666667\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# search for an optimal value of K\n", "k_range = range(1, 30)\n", "scores = []\n", "for k in k_range:\n", " knn = KNeighborsClassifier(n_neighbors=k)\n", " scores.append(np.mean(cross_val_score(knn, X, y, cv=10, scoring='accuracy')))\n", "print scores" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0.95999999999999996, 0.95333333333333337, 0.96666666666666656, 0.96666666666666656, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.96666666666666679, 0.96666666666666679, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.97333333333333338, 0.98000000000000009, 0.97333333333333338, 0.98000000000000009, 0.96666666666666656, 0.96666666666666656, 0.97333333333333338, 0.95999999999999996, 0.96666666666666656, 0.95999999999999996, 0.96666666666666656, 0.95333333333333337, 0.95333333333333337]\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# plot the K values (x-axis) versus the 10-fold CV score (y-axis)\n", "plt.plot(k_range, scores)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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yTcPMPgD8H2ATZ7oP7gCeoQXOX47ju5Nwh4RWOH9XEJLBA6KfH7r7\n35jZaEo4fw0ZCEREpHYasWtIRERqSIFARKTNKRCIiLQ5BQIRkTanQCAi0uYUCERE2pwCgYhIm1Mg\nEBFpc/8fRCKt63G6jx4AAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Default dataset\n", "\n", "- Binary classification\n", "- Using AUC as evaluation metric\n", "- Using cross-validation to select between models" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# read in the default data\n", "data = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/default.csv')\n", "X = data[['balance']]\n", "y = data.default" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10-fold cross-validation with logistic regression\n", "logreg = LogisticRegression()\n", "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "0.94819099843061938" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10-fold cross-validation with KNN (K=5)\n", "knn = KNeighborsClassifier(n_neighbors=5)\n", "cross_val_score(knn, X, y, cv=10, scoring='roc_auc').mean()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "0.81417567491604037" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Advertising dataset\n", "\n", "- Regression\n", "- Using RMSE as evaluation metric\n", "- Using cross-validation to select features" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# read in the advertising data\n", "data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10-fold cross-validation with three features\n", "X = data[['TV', 'Radio', 'Newspaper']]\n", "y = data.Sales\n", "lm = LinearRegression()\n", "scores = cross_val_score(lm, X, y, cv=10, scoring='mean_squared_error')\n", "print scores" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[-3.56038438 -3.29767522 -2.08943356 -2.82474283 -1.3027754 -1.74163618\n", " -8.17338214 -2.11409746 -3.04273109 -2.45281793]\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# convert from MSE to RMSE\n", "scores_sqrt = np.sqrt(-scores)\n", "print scores_sqrt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 1.88689808 1.81595022 1.44548731 1.68069713 1.14139187 1.31971064\n", " 2.85891276 1.45399362 1.7443426 1.56614748]\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# calculate the average RMSE\n", "print np.mean(scores_sqrt)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.69135317081\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10-fold cross-validation with two features\n", "feature_cols = ['TV', 'Radio']\n", "X = data[feature_cols]\n", "print np.mean(np.sqrt(-cross_val_score(lm, X, y, cv=10, scoring='mean_squared_error')))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.67967484191\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improvements to Cross-Validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although standard K-fold cross-validation is a very useful model evaluation procedure, there are some common variations that can make cross-validation even better:\n", "\n", "### Repeated cross-validation\n", "\n", "K-fold cross-validation is repeated multiple times (with different random splits of the data into the K folds), and the results are averaged. This provides a more reliable estimate of out-of-sample accuracy by reducing the variance associated with a single trial of cross-validation.\n", "\n", "### Creating a hold-out set\n", "\n", "Instead of running cross-validation on the entire dataset, a portion of the data is \"held out\" and not touched during the model building process. The best model is located and tuned using cross-validation on the remaining data. At the end of this process, the hold-out set is then used to test the best model. The hold-out set accuracy is considered to be a more reliable estimate of out-of-sample accuracy than the cross-validated accuracy, since the hold-out set is truly out-of-sample data.\n", "\n", "### Feature engineering and selection within cross-validation iterations\n", "\n", "Ideally, all feature engineering and selection should be done within each cross-validation iteration. Performing these tasks before cross-validation does not properly mimic the application of the model to out-of-sample data, since those processes will have \"unfair\" knowledge of the entire dataset, and thus the cross-validated estimate of out-of-sample accuracy will be biased upward. Performing these tasks within cross-validation iterations will produce a more reliable estimate." ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/11_roc_auc.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:6ab739bad14c85b70b2ad82a62559e2d9c118d2d5943fe690424af62ccf3caf6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ROC Curves and AUC" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import metrics\n", "from sklearn.dummy import DummyClassifier\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# read in the data\n", "data = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/default.csv')\n", "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
defaultstudentbalanceincome
00No729.52649544361.62507
10Yes817.18040712106.13470
20No1073.54916431767.13895
30No529.25060535704.49394
40No785.65588338463.49588
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ " default student balance income\n", "0 0 No 729.526495 44361.62507\n", "1 0 Yes 817.180407 12106.13470\n", "2 0 No 1073.549164 31767.13895\n", "3 0 No 529.250605 35704.49394\n", "4 0 No 785.655883 38463.49588" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model with One Feature" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# create X and y\n", "X = data[['balance']]\n", "y = data.default" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# split into train and test\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# create logistic regression model\n", "logreg = LogisticRegression()\n", "logreg.fit(X_train, y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# make predictions and calculate accuracy\n", "y_pred = logreg.predict(X_test)\n", "print metrics.accuracy_score(y_test, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.9748\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# calculate null accuracy rate\n", "print y_test.mean()\n", "print 1 - y_test.mean()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.0316\n", "0.9684\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# use a \"dummy classifier\" to calculate null accuracy rate\n", "dumb = DummyClassifier(strategy='most_frequent')\n", "dumb.fit(X_train, y_train)\n", "y_pred = dumb.predict(X_test)\n", "print metrics.accuracy_score(y_test, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.9684\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# predict probability of default\n", "y_prob = logreg.predict_proba(X_test)[:, 1]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# plot ROC curve\n", "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_prob)\n", "plt.plot(fpr, tpr)\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.0])\n", "plt.xlabel('False Positive Rate (1 - Specificity)')\n", "plt.ylabel('True Positive Rate (Sensitivity)')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# calculate AUC\n", "print metrics.roc_auc_score(y_test, y_prob)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.941011926236\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# histogram of predicted probabilities grouped by actual response value\n", "df = pd.DataFrame(data = {'probability':y_prob, 'actual':y_test})\n", "df.probability.hist(by=df.actual, sharex=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "array([,\n", " ], dtype=object)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model with Two Features" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# compare to a model with an additional feature\n", "X = data[['balance', 'income']]\n", "y = data.default\n", "\n", "# split into train and test\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n", "logreg.fit(X_train, y_train)\n", "\n", "# calculate accuracy\n", "y_pred = logreg.predict(X_test)\n", "print metrics.accuracy_score(y_test, y_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.9684\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# predict probability of default\n", "y_prob = logreg.predict_proba(X_test)[:, 1]\n", "\n", "# plot ROC curve\n", "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_prob)\n", "plt.plot(fpr, tpr)\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.0])\n", "plt.xlabel('False Positive Rate (1 - Specificity)')\n", "plt.ylabel('True Positive Rate (Sensitivity)')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Ezz0Hjz4Ka6+ddzRmZo2rbhIDJO0KH/1o3lGYmTW2umljmDULFizIOwozs8ZX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\nZtbB/wddp1FiKQxW1gAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# calculate AUC\n", "print metrics.roc_auc_score(y_test, y_prob)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.641794634501\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# histogram of predicted probabilities grouped by actual response value\n", "df = pd.DataFrame(data = {'probability':y_prob, 'actual':y_test})\n", "df.probability.hist(by=df.actual, sharex=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "array([,\n", " ], dtype=object)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 16 } ], "metadata": {} } ] } ================================================ FILE: notebooks/11_titanic_exercise.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:557342100eb7ce91ca76e7e4f24737943f3625640543427282904a15759174c8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Titanic Exercise" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "from sklearn.cross_validation import train_test_split, cross_val_score\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import metrics\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read in the data and look at the first 10 rows." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check for missing values." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to focus on Pclass, Sex, Age, and Embarked:\n", "\n", "- **Pclass:** leave as-is\n", "- **Sex:** convert \"male\" to 0 and \"female\" to 1\n", "- **Age:** fill in missing values using the mean\n", "- **Embarked:** create dummy variables" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create X and y using the features we have chosen." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train/Test Split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split X and y into training and testing sets." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit a logistic regression model on the training data." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the model's intercept." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the model's coefficients. How do we interpret them?" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Predict the probability of survival for the first person in X_train using scikit-learn." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do this same calculation manually." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretend this person was 10 years older, and calculate their probability of survival (manually)." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretend this person was a woman, and calculate their probability of survival (manually)." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Evaluation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make predictions on the testing data and calculate the accuracy." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare this to the null accuracy." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the confusion matrix. Does this model tend towards specificity or sensitivity?" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the specificity and the sensitivity." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Change the threshold to make the model more sensitive, then print the new confusion matrix." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recalculate the specificity and the sensitivity." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the ROC curve. How can we interpret the results?" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the AUC." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-Validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use cross-validation to check the AUC for the current model." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove Embarked from the model and check AUC again using cross-validation." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] } ================================================ FILE: notebooks/13_bayes_iris.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:8b2fbf4113bc8fefcc6add5899d1725dc3a1a926ebd5d31dadd1876ac74bd349" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Applying Bayes' theorem to iris classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if Bayes' theorem might be able to help us solve a classification task, namely predicting the species of an iris!\n", "\n", "We'll load the iris data into a DataFrame, and round up all of the measurements to the next integer:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_iris\n", "import numpy as np\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# load the iris data\n", "iris = load_iris()\n", "\n", "# round up the measurements\n", "X = np.ceil(iris.data)\n", "\n", "# clean up column names\n", "features = [name[:-5].replace(' ', '_') for name in iris.feature_names]\n", "\n", "# read into pandas\n", "df = pd.DataFrame(X, columns=features)\n", "\n", "# create a list of species using iris.target and iris.target_names\n", "species = [iris.target_names[num] for num in iris.target]\n", "\n", "# add the species list as a new DataFrame column\n", "df['species'] = species" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# print the head\n", "df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
06421setosa
15321setosa
25421setosa
35421setosa
45421setosa
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " sepal_length sepal_width petal_length petal_width species\n", "0 6 4 2 1 setosa\n", "1 5 3 2 1 setosa\n", "2 5 4 2 1 setosa\n", "3 5 4 2 1 setosa\n", "4 5 4 2 1 setosa" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say that I had an out-of-sample observation with the following measurements: 7, 3, 5, 2. I want to predict the species of this iris. How might I do that?\n", "\n", "We'll first take a look at all observations in the training data with those measurements:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# show all observations with features: 7, 3, 5, 2\n", "df[(df.sepal_length==7) & (df.sepal_width==3) & (df.petal_length==5) & (df.petal_width==2)]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
547352versicolor
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757352versicolor
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1237352virginica
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1277352virginica
1467352virginica
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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " sepal_length sepal_width petal_length petal_width species\n", "54 7 3 5 2 versicolor\n", "58 7 3 5 2 versicolor\n", "63 7 3 5 2 versicolor\n", "68 7 3 5 2 versicolor\n", "72 7 3 5 2 versicolor\n", "73 7 3 5 2 versicolor\n", "74 7 3 5 2 versicolor\n", "75 7 3 5 2 versicolor\n", "76 7 3 5 2 versicolor\n", "77 7 3 5 2 versicolor\n", "87 7 3 5 2 versicolor\n", "91 7 3 5 2 versicolor\n", "97 7 3 5 2 versicolor\n", "123 7 3 5 2 virginica\n", "126 7 3 5 2 virginica\n", "127 7 3 5 2 virginica\n", "146 7 3 5 2 virginica" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# count the species for these observations\n", "df[(df.sepal_length==7) & (df.sepal_width==3) & (df.petal_length==5) & (df.petal_width==2)].species.value_counts()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "versicolor 13\n", "virginica 4\n", "dtype: int64" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# count the species for all observations\n", "df.species.value_counts()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "setosa 50\n", "versicolor 50\n", "virginica 50\n", "dtype: int64" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, so how might Bayes' theorem help us here? Perhaps we should frame this as a conditional probability: What is the probability of some particular class, given the measurements 7352?\n", "\n", "$$P(class | 7352)$$\n", "\n", "We could check this probability for each of the three classes, and then predict the class with the highest probability. Let's start with versicolor:\n", "\n", "$$P(versicolor | 7352) = \\frac {P(7352 | versicolor) \\times P(versicolor)} {P(7352)}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's calculate each of the terms on the right side of the equation:\n", "\n", "$$P(7352 | versicolor) = \\frac {13} {50} = 0.26$$\n", "\n", "$$P(versicolor) = \\frac {50} {150} = 0.33$$\n", "\n", "$$P(7352) = \\frac {17} {150} = 0.11$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Therefore, Bayes' theorem says the probability of versicolor given these measurements is:\n", "\n", "$$P(versicolor | 7352) = \\frac {0.26 \\times 0.33} {0.11} = 0.76$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seems reasonable!\n", "\n", "We could also repeat this process for the other two classes, though we already know that versicolor will have the highest probability:\n", "\n", "$$P(virginica | 7352) = \\frac {0.08 \\times 0.33} {0.11} = 0.24$$\n", "\n", "$$P(setosa | 7352) = \\frac {0 \\times 0.33} {0.11} = 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make some hypothetical adjustments to the data, to demonstrate how Bayes' theorem actually makes intuitive sense:\n", "\n", "Pretend that more of the versicolors were 7352, thus increasing $P(7352|versicolor)$. It would make sense that given an iris with 7352, the probability of it being a versicolor would also increase.\n", "\n", "Or, pretend that most irises in general were versicolor, thus increasing $P(versicolor)$. It would make sense that the probability of any iris being a versicolor (regardless of measurements) would also increase.\n", "\n", "Or, pretend that 17 of the setosas were 7352, thus doubling $P(7352)$. It would make sense that given an iris with 7352, the probability of it being a versicolor would be cut in half." ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/13_naive_bayes_spam.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:b7e3a62e1216c53fa3e7d0fa56c5373dfe7f58c3817a1468b0abbc52dfe7b6a7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Applying Naive Bayes classification to spam email" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's pretend we have an email with three words: \"Send money now.\" We want to classify that email as ham or spam.\n", "\n", "We'll use Naive Bayes classification:\n", "\n", "$$P(spam | \\text{send money now}) = \\frac {P(\\text{send money now} | spam) \\times P(spam)} {P(\\text{send money now})}$$\n", "\n", "By assuming that the features (the words) are conditionally independent, we can simplify the likelihood function:\n", "\n", "$$P(spam | \\text{send money now}) \\approx \\frac {P(\\text{send} | spam) \\times P(\\text{money} | spam) \\times P(\\text{now} | spam) \\times P(spam)} {P(\\text{send money now})}$$\n", "\n", "We could calculate all of the values in the numerator by examining a corpus of spam:\n", "\n", "$$P(spam | \\text{send money now}) \\approx \\frac {0.2 \\times 0.1 \\times 0.1 \\times 0.9} {P(\\text{send money now})} = \\frac {0.0018} {P(\\text{send money now})}$$\n", "\n", "We could repeat this process to calculate the probability that the email is ham:\n", "\n", "$$P(ham | \\text{send money now}) \\approx \\frac {0.05 \\times 0.01 \\times 0.1 \\times 0.1} {P(\\text{send money now})} = \\frac {0.000005} {P(\\text{send money now})}$$\n", "\n", "All we care about is whether spam or ham has the higher probability, and so we predict that the email is spam.\n", "\n", "What have we learned from this exercise?\n", "\n", "- The \"naive\" assumption of Naive Bayes (that the features are conditionally independent) is critical to making these calculations simple.\n", "- The normalization constant (the denominator) can be ignored since it's the same for all classes.\n", "- The prior probability is basically irrelevant once you have a lot of features.\n", "- The Naive Bayes classifier can handle a lot of irrelevant features." ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/14_nlp.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:217a675665d9d52af77e2656091e39dd1b522040e8fd3c0e64135e07865179bf" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Natural Language Processing (NLP)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is NLP?\n", "* 'Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human\u2013computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input...' - Wikipedia\n", "* Using computers to process (analyze, understand, generate) natural human languages (as opposed to unnatural computer languages).\n", "* NLP is concerned with the interface between human and computer language. However, language is often ambiguous, so this isn't always a straightforward task.\n", "\n", "Why NLP?\n", "* Most knowledge created by humans is unstructured text, so we need some way to make sense of it.\n", "* Enables quantitative analysis of text data at large scale.\n", "* Provides a repeatable, \"unbiased\" way to look at text." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Motivation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the purpose of this class, we can pretend we are Data Scientists working for Kaggle. Everyone loves Kaggle, but we're interested into digging a little deeper into what the web is saying about Kaggle." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Imports" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import tweepy # Twitter API wrapper\n", "import nltk # Classic NLP package" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Getting Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Variables that contains the user credentials to access Twitter API \n", "consumer_key = \"Consumer_Key\" # Replace with your own consumer_key\n", "consumer_secret = \"Consumer_Secret\" # Replace with your own consumer_secret\n", "\n", "# Create authorization for API\n", "auth = tweepy.auth.OAuthHandler(consumer_key, consumer_secret)\n", "#auth.set_access_token(access_token, access_token_secret)\n", "\n", "# Initialize API object by passing it your credentials\n", "api = tweepy.API(auth)\n", "\n", "# Use the api to search\n", "tweets = api.search(q=\"kaggle\", count=10, result_type=\"recent\")\n", "print tweets[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Status(contributors=None, truncated=False, text=u'37 spots up. 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] } ], "prompt_number": 122 }, { "cell_type": "code", "collapsed": false, "input": [ "tweets[0].text" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 123, "text": [ "u'37 spots up. Is anybody else even trying? #kaggle - https://t.co/9GLyaK3OhT'" ] } ], "prompt_number": 123 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some quick vocab:\n", "* **corpus** - collection of documents\n", "* **corpora** - plural form of corpus\n", "\n", "Let's build our corpus of tweets." ] }, { "cell_type": "code", "collapsed": false, "input": [ "tweets_text = []\n", "for tweet in tweepy.Cursor(api.search, q='kaggle', result_type='recent').items(1000):\n", " tweets_text.append(tweet.text.encode('ascii','ignore'))\n", "print tweets_text[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "37 spots up. Is anybody else even trying? #kaggle - https://t.co/9GLyaK3OhT\n" ] } ], "prompt_number": 121 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since I did not provide my credentials above, I saved all of the tweet text to a CSV file. We can read it back into a list." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Running this will overwrite the current data.\n", "'''\n", "with open('../data/kaggle_tweets.csv','w') as f:\n", " for tweet in tweets_text:\n", " f.write('\"%s\"\\n' % tweet)\n", "'''" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "with open('../data/kaggle_tweets.csv','r') as f:\n", " tweets_text = [tweet.replace('\\n','').replace('\"','') for tweet in f.readlines()]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "tweets_text[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "'37 spots up. Is anybody else even trying? #kaggle - https://t.co/9GLyaK3OhT'" ] } ], "prompt_number": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Tokenization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing we need to do with our corpus of text is to break the documents into smaller units. This is known as **tokenization**. There are two natural ways to go about this: breaking the documents apart into sentences or into words. This gives more structure to the previously unstructured text. This also allows us to more easily perform other tasks upon our corpus.\n", "\n", "**Note**: Breaking documents and paragraphs into sentences and words is easier is some languages than others. English has obvious (to us) breaks in the text for sentences and words. However, this might not be the case for other languages. In addition, there are nuances in the English language with hyphenated words and phrases that are independent clauses but might be part of a larger sentence.\n", "\n", "First let's try breaking our tweets down into sentences." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Tokenize into sentences\n", "sentences = []\n", "for tweet in tweets_text:\n", " for sent in nltk.sent_tokenize(tweet):\n", " sentences.append(sent)\n", "sentences[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "['37 spots up.',\n", " 'Is anybody else even trying?',\n", " '#kaggle - https://t.co/9GLyaK3OhT',\n", " 'RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted ',\n", " '@alexjc There was an Elo-based Kaggle comp recently; the forums might have useful info.',\n", " 'https://t.co/NUxbAvTTcO',\n", " 'RT @benhamner: The most brutal way to learn about overfitting?',\n", " 'Watching yourself drop hundreds of places when a @kaggle final leaderboard ',\n", " 'RT @benhamner: The most brutal way to learn about overfitting?',\n", " 'Watching yourself drop hundreds of places when a @kaggle final leaderboard ']" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's break our tweets into individual words, referred to as tokens." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Tokenize into words\n", "tokens = []\n", "for tweet in tweets_text:\n", " for word in nltk.word_tokenize(tweet):\n", " tokens.append(word)\n", "tokens[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "['37', 'spots', 'up', '.', 'Is', 'anybody', 'else', 'even', 'trying', '?']" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is really messy though. Do we care about analyzing punctuation and other non alphanumeric characters? We will exclude those using regular expressions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Only keep tokens that start with a letter (using regular expressions)\n", "import re\n", "clean_tokens = [token for token in tokens if re.search('^[a-zA-Z]+', token)]\n", "clean_tokens[:20]# Tokenize into words" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "['spots',\n", " 'up',\n", " 'Is',\n", " 'anybody',\n", " 'else',\n", " 'even',\n", " 'trying',\n", " 'kaggle',\n", " 'https',\n", " 'RT',\n", " 'antgoldbloom',\n", " 'Proud',\n", " 'to',\n", " 'share',\n", " 'that',\n", " 'over',\n", " 'the',\n", " 'weekend',\n", " 'Kaggle',\n", " 'passed']" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now perform the \"hello world\" task of text analysis and get a list of the most popular words" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Count the tokens\n", "from collections import Counter\n", "c = Counter(clean_tokens)\n", "c.most_common(25) # Most frequent tokens" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "[('http', 780),\n", " ('kaggle', 482),\n", " ('RT', 471),\n", " ('Kaggle', 364),\n", " ('to', 347),\n", " ('https', 304),\n", " ('a', 236),\n", " ('the', 194),\n", " ('for', 147),\n", " ('I', 142),\n", " ('and', 139),\n", " ('up', 126),\n", " ('of', 120),\n", " ('in', 115),\n", " ('DataScience', 115),\n", " ('on', 111),\n", " ('video', 109),\n", " ('with', 108),\n", " ('MachineLearning', 103),\n", " ('BigData', 99),\n", " ('is', 88),\n", " ('machinelearning', 88),\n", " ('R', 86),\n", " ('benhamner', 85),\n", " ('via', 84)]" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do you notice about this list of words? Are there any duplicated words? What should we do about that?" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Stemming and Lemmatizing (Normalizing)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Stemming** reduces a word to its base (stem) form. It often makes sense to treat multipe word forms the same way. \n", "\n", "Stemming uses a \"simple\" rule-based approach that runs very quickly. The output isn't always the best for irregular words. Stemmed words are not usually shown to users but rather used for analysis/indexing." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Initialize stemmer\n", "from nltk.stem.snowball import SnowballStemmer\n", "stemmer = SnowballStemmer('english')\n", "\n", "# Some exmaples\n", "print 'charge:', stemmer.stem('charge')\n", "print 'charging:', stemmer.stem('charging')\n", "print 'charged:', stemmer.stem('charged')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "charge: charg\n", "charging: charg\n", "charged: charg\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's stem all of our tokens and recompute the count of most popular tokens." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Stem the tokens\n", "stemmed_tokens = [stemmer.stem(t) for t in clean_tokens]\n", "\n", "# Count the stemmed tokens\n", "c = Counter(stemmed_tokens)\n", "c.most_common(25) # all lowercase" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "[(u'kaggl', 848),\n", " (u'http', 780),\n", " ('rt', 471),\n", " ('to', 348),\n", " (u'https', 304),\n", " ('a', 261),\n", " (u'the', 220),\n", " (u'machinelearn', 191),\n", " (u'competit', 172),\n", " (u'for', 148),\n", " ('i', 145),\n", " (u'and', 142),\n", " (u'datasci', 140),\n", " ('up', 127),\n", " ('of', 120),\n", " ('in', 118),\n", " (u'bigdata', 113),\n", " ('on', 113),\n", " (u'predict', 112),\n", " (u'video', 111),\n", " (u'with', 110),\n", " (u'data', 102),\n", " ('is', 99),\n", " (u'model', 92),\n", " (u'facebook', 89)]" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, some of these are still a bit uninterpretable for humans. That's where lemmatizing comes in.\n", "\n", "**Lemmatization**, or normalization, dervies the canonical form (i.e. lemma) of a word. This can be better than stemming in some cases, because it reduces words to a \"normal\" form. This often uses a dictionary based approach and can be slower than stemming. This is the tradeoff for \"better\" results." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Initialize lemmatizer\n", "lemmatizer = nltk.WordNetLemmatizer()\n", "\n", "# Compare stemmer to lemmatizer\n", "print 'dogs - stemmed:', stemmer.stem('dogs'), ', lemmatized:', lemmatizer.lemmatize('dogs')\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "dogs - stemmed: dog , lemmatized: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "dog\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "print 'wolves - stemmed:', stemmer.stem('wolves'), ', lemmatized:', lemmatizer.lemmatize('wolves')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "wolves - stemmed: wolv , lemmatized: wolf\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's lemmatize our Twitter dataset." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Lemmatize the tokens\n", "lemmatized_tokens = [lemmatizer.lemmatize(t).lower() for t in clean_tokens] # I lowercased things too.\n", "\n", "# Count the lemmatized tokens\n", "c = Counter(lemmatized_tokens)\n", "c.most_common(25) # all lowercase" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "[(u'http', 1084),\n", " ('kaggle', 847),\n", " ('rt', 471),\n", " ('to', 348),\n", " ('a', 264),\n", " ('the', 220),\n", " ('machinelearning', 191),\n", " (u'competition', 168),\n", " ('for', 148),\n", " ('i', 145),\n", " ('and', 142),\n", " ('datascience', 140),\n", " ('up', 127),\n", " ('of', 120),\n", " ('in', 118),\n", " ('on', 113),\n", " ('bigdata', 113),\n", " ('video', 111),\n", " ('with', 110),\n", " ('data', 102),\n", " ('is', 99),\n", " ('facebook', 89),\n", " ('r', 87),\n", " ('benhamner', 85),\n", " ('via', 84)]" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lemmatizing didn't do much here since msot of the popular words don't have significantly different normal forms." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# One more example\n", "print 'is - stemmed:', stemmer.stem('is'), ', lemmatized:', lemmatizer.lemmatize('is')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "is - stemmed: is , lemmatized: is\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is not what I learned in grammar school. Why isn't the result \"be\"? \n", "\n", "The lemmatizer assumes everything is a noun unless explicitly told otherwise." ] }, { "cell_type": "code", "collapsed": false, "input": [ "lemmatizer.lemmatize('is',pos='v')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "u'be'" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "nltk.pos_tag(nltk.word_tokenize('Lloyld loves NLP'))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "[('Lloyld', 'NNP'), ('loves', 'VBZ'), ('NLP', 'NNP')]" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Stopword Removal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Stopwords** are common words that will most likely appear in any text. They are \"useless\" words that don't contain much information. For the purpose of word counts and other word frequencies, they are not particularly useful. \n", "\n", "Let's remove the stopwords from our tweets and look at the most popular words." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# View the list of stopwords\n", "stopwords = nltk.corpus.stopwords.words('english')\n", "print stopwords[0:25]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[u'i', u'me', u'my', u'myself', u'we', u'our', u'ours', u'ourselves', u'you', u'your', u'yours', u'yourself', u'yourselves', u'he', u'him', u'his', u'himself', u'she', u'her', u'hers', u'herself', u'it', u'its', u'itself', u'they']\n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have a list of stopwords, we can remove them from our token list." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Stem the stopwords\n", "stemmed_stops = [stemmer.stem(t) for t in stopwords]\n", "\n", "# Remove stopwords from stemmed tokens\n", "stemmed_tokens_no_stop = [stemmer.stem(t) for t in stemmed_tokens if t not in stemmed_stops]\n", "c = Counter(stemmed_tokens_no_stop)\n", "most_common_stemmed = c.most_common(25)\n", "\n", "# Remove stopwords from cleaned tokens\n", "clean_tokens_no_stop = [t.lower() for t in clean_tokens if t.lower() not in stopwords]\n", "c = Counter(clean_tokens_no_stop)\n", "most_common_not_stemmed = c.most_common(25)\n", "\n", "# Compare the most common results for stemmed words and non stemmed words\n", "for i in range(25):\n", " text_list = most_common_stemmed[i][0] + ' ' + str(most_common_stemmed[i][1]) + ' '*25\n", " text_list = text_list[0:30]\n", " text_list += most_common_not_stemmed[i][0] + ' ' + str(most_common_not_stemmed[i][1])\n", " print text_list" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "kaggl 848 kaggle 847\n", "http 780 http 780\n", "rt 471 rt 471\n", "https 304 https 304\n", "machinelearn 191 machinelearning 191\n", "competit 172 competition 147\n", "datasci 140 datascience 140\n", "bigdata 113 bigdata 113\n", "predict 112 video 109\n", "video 111 data 102\n", "data 102 facebook 89\n", "model 92 r 87\n", "facebook 89 benhamner 85\n", "r 87 via 84\n", "benhamn 85 new 80\n", "via 84 prediction 77\n", "new 80 challenge 77\n", "challeng 77 scikit-learn 72\n", "scikit-learn 72 top 69\n", "spot 69 intro 69\n", "top 69 spots 67\n", "intro 69 model 62\n", "learn 68 kirkdborne 61\n", "use 61 recruiting 58\n", "kirkdborn 61 training 57\n" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results are a bit more interesting. You can see the most popular words that occur in the tweets about \"kaggle\". We could dig further into this and look at the specific tweets for some of the more interesting occurences. For instance, it seeems that there are more occurences of \"R\" than \"Python\" (which doesn't even show up in our top list). Does that mean that R is more popular than Python for Kaggle competitions?" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Named Entity Recognition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Named Entity Recognition (NER)** is the automatic extraction of names, places, organizations, etc. This can help you identify \"important\" words. NER classifiers can work in different ways, but the most interesting and relevant ones use some sort of supervised machine learning technique. There is some sort of tagged dataset that has a model/algorithm fit to it. With what we've learned in class so far, you could build your own NER classifier! However, it's often better to use existing classifiers. \n", "\n", "First, let's build a NER extraction function that takes in a sentence." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def extract_entities(text):\n", " entities = []\n", " # tokenize into sentences\n", " for sentence in nltk.sent_tokenize(text):\n", " # tokenize sentences into words\n", " # add part-of-speech tags\n", " # use NLTK's NER classifier\n", " chunks = nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sentence)))\n", " # parse the results\n", " entities.extend([chunk for chunk in chunks if hasattr(chunk, 'label')])\n", " return entities" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's look at all of the words in this dataset and see which named entities are identified.\n", "for entity in extract_entities('Kevin and Brandon are instructors for General Assembly in Washington, D.C.'):\n", " print '[' + entity.label() + '] ' + ' '.join(c[0] for c in entity.leaves())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[PERSON] Kevin\n", "[PERSON] Brandon\n", "[ORGANIZATION] General Assembly\n", "[GPE] Washington\n" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems to work pretty well! But how resilient is it?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for entity in extract_entities('kevin and BRANDON are instructors for @GA_DC, DC'):\n", " print '[' + entity.label() + '] ' + ' '.join(c[0] for c in entity.leaves())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ORGANIZATION] BRANDON\n", "[ORGANIZATION] DC\n" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The accuracy decreased dramatically! There are companies who are working to solve this problem, but as you get into more unstructured, \"wild\" data (like social media), this gets harder to do. \n", "\n", "We could run this on our entire dataset, but it would take a while, so I'll provide the code, but not actually run it.\n", "\n", "``` {python}\n", "named_entities = []\n", "for tweet in tweets_text:\n", " temp_entities = extract_entities(tweet)\n", " for temp_entity in temp_entities:\n", " named_entities.append((temp_entity.label(), temp_entity.leaves()[0][0]))\n", "```\n", "\n", "Let's at least run it on one tweet." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print tweets_text[21]\n", "for entity in extract_entities(tweets_text[21]):\n", " print '[' + entity.label() + '] ' + ' '.join(c[0] for c in entity.leaves())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Data Science Challenge: predict Taxi trajectories. @Gabriellilor, @siffolone are you in? http://t.co/yO9iCOnTlA\n", "[PERSON] Data" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[ORGANIZATION] Science\n", "[GPE] Gabriellilor\n" ] } ], "prompt_number": 25 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Topic Modeling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Topic modeling allows us to discover \"topic grougs\" in our dataset. There are several different versions of this, but we'll talk about one specifically, LDA.\n", "\n", "**Latent Dirichlet Allocation (LDA)** is a topic modeling method that allows us to discover clusters of words that appear together frequently. We can use this to look for clusters of words in our Kaggle corpus.\n", "\n", "While the code below intorduces some new specifics, the overall process is similar to what we've done before." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import lda # Latent Dirichlet Allocation\n", "import numpy as np\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "\n", "# Instantiate a count vectorizer with two additional parameters\n", "vect = CountVectorizer(stop_words='english', ngram_range=[1,3]) \n", "sentences_train = vect.fit_transform(np.array(tweets_text))\n", "\n", "# Instantiate an LDA model\n", "model = lda.LDA(n_topics=10, n_iter=500)\n", "model.fit(sentences_train) # Fit the model \n", "n_top_words = 10\n", "topic_word = model.topic_word_\n", "for i, topic_dist in enumerate(topic_word):\n", " topic_words = np.array(vect.get_feature_names())[np.argsort(topic_dist)][:-n_top_words:-1]\n", " print('Topic {}: {}'.format(i+1, ', '.join(topic_words)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:lda:all zero row in document-term matrix found\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Topic 1: kaggle, http, 10, 10 packages, packages, facebook, packages kaggle, rt, 10 packages kaggle\n", "Topic 2: kaggle, http, data, rt, stories, big, mining, big data, data mining\n", "Topic 3: http, kaggle, model, training, model training, video model, video model training, nearest, prediction nearest\n", "Topic 4: kaggle, http, https, rt, kaggle http, science, kaggle https, places, data science" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Topic 5: kaggle, rt, competition, https, http, kaggle https, kaggle competition, kaggle competition http, machinelearning" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Topic 6: kaggle, http, rt, benhamner, microsoft, azure, ve, challenge, got\n", "Topic 7: http, rt, kaggle, new, datascience, bigdata, intro, intro machinelearning, machinelearning" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Topic 8: prediction, neighbors, prediction nearest neighbors, learning, nearest neighbors http, machine, machine learning, https, kaggle scikit learn\n", "Topic 9: http, kaggle, rt, based, classification, tree, methods, classification methods, based classification" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Topic 10: kaggle, https, spots, spots kaggle, kaggle https, rt, top10, moved, http" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results could be interesting. There are vague clusters about machinelearning/scikit learn, methods of classification, and others. These are not hard and fast clusters or groups, but they are something to investigate.\n", "\n", "Let's try this again on a different corpus." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Imports\n", "import requests\n", "from bs4 import BeautifulSoup\n", "\n", "# Get Data Science Wiki page\n", "r = requests.get(\"http://en.wikipedia.org/wiki/Data_science\")\n", "b = BeautifulSoup(r.text)\n", "paragraphs = b.find(\"body\").findAll(\"p\")\n", "paragraphs_text = [p.text for p in paragraphs]\n", "text = \"\"\n", "for paragraph in paragraphs:\n", " text += paragraph.text + \" \"\n", "\n", "# Data Science corpus\n", "text[:500]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "u'In general terms, Data Science is the extraction of knowledge from data.[1][2] It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information theory and information technology, including signal processing, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression and high'" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "# tokenize into sentences\n", "sentences = [sent for sent in nltk.sent_tokenize(text)]\n", "sentences[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "u'In general terms, Data Science is the extraction of knowledge from data.'" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can try running LDA using the paragraphs as documents." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Instantiate a count vectorizer with two additional parameters\n", "vect = CountVectorizer(stop_words='english', ngram_range=[1,3]) \n", "sentences_train = vect.fit_transform(paragraphs_text)\n", "\n", "# Instantiate an LDA model\n", "model = lda.LDA(n_topics=10, n_iter=500)\n", "model.fit(sentences_train) # Fit the model \n", "n_top_words = 10\n", "topic_word = model.topic_word_\n", "for i, topic_dist in enumerate(topic_word):\n", " topic_words = np.array(vect.get_feature_names())[np.argsort(topic_dist)][:-n_top_words:-1]\n", " print('Topic {}: {}'.format(i+1, ', '.join(topic_words)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:lda:all zero row in document-term matrix found\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Topic 1: business, processing, sciences, finance, subject, research, social, emerging, medical\n", "Topic 2: clinical, clinical data, data scientist, trials, clinical trials, safety, genomics, handling, efficacy novel\n", "Topic 3: areas, models, technical, international, computing, discipline, theory, cleveland, broad" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Topic 4: term, term data, term data science, statistician, data scientist, lecture, entitled statistics data, data, survey\n", "Topic 5: methods, used, computer, applications, published, issues, related, naur, conference\n", "Topic 6: learning, machine, machine learning, information, big, statistics machine learning, knowledge, big data, signal processing\n", "Topic 7: data, scientists, data scientists, large, marketing, structured, software, crucial, work" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Topic 8: data, science, data science, statistics, scientist, statistical, field, analysis, science data\n", "Topic 9: journal, needed, field statistics, april, data collection, jeff, publication, mahalanobis, research\n", "Topic 10: security, fraud, insights, using, using data, security data, statistics machine, security data science, security fraud\n" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use the sentences as documents. While topic modeling usually does better with longer documents (emails vs. tweets), LDA has been shown to do well even with short documents (specifically tweets)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Instantiate a count vectorizer with two additional parameters\n", "vect = CountVectorizer(stop_words='english', ngram_range=[1,3]) \n", "sentences_train = vect.fit_transform(paragraphs_text)\n", "\n", "# Instantiate an LDA model\n", "model = lda.LDA(n_topics=10, n_iter=500)\n", "model.fit(sentences_train) # Fit the model \n", "n_top_words = 10\n", "topic_word = model.topic_word_\n", "for i, topic_dist in enumerate(topic_word):\n", " topic_words = np.array(vect.get_feature_names())[np.argsort(topic_dist)][:-n_top_words:-1]\n", " print('Topic {}: {}'.format(i+1, ', '.join(topic_words)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since topic modeling is more of a clustering technique, it can be difficult to determine which one is \"better\"." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Textblob" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's talk about a new NLP package, Textblob. Textblob's tagline is \"Simplified Text Processing\". You can do many of the same things in Textblob that you can do in NLTK, but it is \"simpler\" to use. That's obviously a subjective thing, but it's good to be aware of both packages." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from textblob import TextBlob, Word\n", "\n", "# Textblob has a different syntax, but it generally performs the same functions as NLTK.\n", "blob = TextBlob('Kevin and Brandon are instructors for General Assembly in Washington, D.C. They both love Data Science.')\n", "print 'Sentences:', blob.sentences\n", "print 'Words:', blob.words\n", "print 'Noun Phrases:', blob.noun_phrases" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Sentences: [Sentence(\"Kevin and Brandon are instructors for General Assembly in Washington, D.C.\"), Sentence(\"They both love Data Science.\")]\n", "Words: ['Kevin', 'and', 'Brandon', 'are', 'instructors', 'for', 'General', 'Assembly', 'in', 'Washington', 'D.C', 'They', 'both', 'love', 'Data', 'Science']\n", "Noun Phrases: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "['kevin', 'brandon', u'general assembly', 'washington', 'd.c', 'data']\n" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Textblob has many useful functionalities: \n", "* Singularizing and pluralizing words\n", "* Spell check\n", "* Word defintions\n", "* Translation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Singularize and pluralize\n", "blob = TextBlob('Put away the dishes.')\n", "print [word.singularize() for word in blob.words]\n", "print [word.pluralize() for word in blob.words]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['Put', 'away', 'the', 'dish']\n", "['Puts', 'aways', 'thes', 'dishess']\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "# Spelling correction\n", "blob = TextBlob('15 minuets late')\n", "print 'Original: 15 minuets late Corrected:', blob.correct()\n", "\n", "# Spellcheck\n", "print 'Original: parot Corrected:', Word('parot').spellcheck()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Original: 15 minuets late Corrected: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "15 minutes late\n", "Original: parot Corrected: [('part', 0.9929478138222849), (u'parrot', 0.007052186177715092)]\n" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "# Definitions\n", "print Word('bank').define()\n", "print ' '\n", "print Word('bank').define('v')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# translation and language identification\n", "blob = TextBlob('Welcome to the classroom.')\n", "print 'English: \"Welcome to the classroom.\" Spanish:', blob.translate(to='es')\n", "print ''\n", "blob = TextBlob('Hola amigos')\n", "print '\"Hola amigos\" is the language', blob.detect_language()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "English: \"Welcome to the classroom.\" Spanish: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Bienvenido a la sala de clase .\n", "\n", "\"Hola amigos\" is the language " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "es\n" ] } ], "prompt_number": 33 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Sentiment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sentiment allows us to convert a limited range of emotion into a number. It gives us an idea of how \"positive\", \"negative\", or \"neutral\" a piece of text is. We built a sentiment API function in our API's class, so we could use that. But for the sake of variety, let's use the built in functionality of Textblob.\n", "\n", "Textblob has two different \"types\" of sentiment, a polarity sentiment (positive/negative) and a subjectivity sentiment." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# The sentiment polarity score is a float within the range [-1.0, 1.0].\n", "print 'I love pizza Sentiment =', TextBlob('I love pizza').sentiment.polarity\n", "print 'I hatee pizza Sentiment =', TextBlob('I hate pizza').sentiment.polarity\n", "print 'I feel nothing about pizza Sentiment =', TextBlob('I feel nothing about pizza').sentiment.polarity" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "I love pizza Sentiment = " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "0.5\n", "I hatee pizza Sentiment = -0.8\n", "I feel nothing about pizza Sentiment = 0.0\n" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "# The subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.\n", "print 'I am a cool person Subjectivity =', TextBlob(\"I am a cool person\").sentiment.subjectivity # Pretty subjective\n", "print 'I am a person Subjectivity =', TextBlob(\"I am a person\").sentiment.subjectivity # Pretty objective" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "I am a cool person Subjectivity = 0.65\n", "I am a person Subjectivity = 0.0\n" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But once again, it's not perfect." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# different scores for essentially the same sentence\n", "print TextBlob('Kevin and Brandon are instructors for General Assembly in Washington, D.C.').sentiment.subjectivity\n", "print TextBlob('Kevin and Brandon are instructors in Washington, D.C.').sentiment.subjectivity" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.5\n", "0.0\n" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this idea of sentiment in mind, let's see how positive, negative, and neutral people are about our Kaggle tweets." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's loop through our tweets and calculate sentiment\n", "sentiments = [TextBlob(tweet).sentiment.polarity for tweet in tweets_text]\n", "print tweets_text[0], sentiments[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "37 spots up. Is anybody else even trying? #kaggle - https://t.co/9GLyaK3OhT 0.0\n" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "# Average sentiment\n", "avg_sentiment = np.sum(sentiments)/len(sentiments)\n", "print avg_sentiment" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.0880874324259\n" ] } ], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This average sentiment is pretty neutral. In my experience, this is usually the case; people don't express as much sentiment as you think and the positives and negatives often cancel each other out.\n", "\n", "Let's look at the distribution of the sentiment to get a better idea." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.distplot(sentiments)\n", "plt.title('Distribution of Sentiment')\n", "plt.xlabel('Sentiment (-1 to 1)')\n", "plt.ylabel('Frequency')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Tv9nMNpSvhLPffxJdSSIiIrIclYR6m7tPnxPd3Y8CWslfRERkjakk1JNm1lq+YmZtQEN0\nJYmIiMhyVDKm/hXgBjP7Z4JD2v4H8KVIqxIREZElWzTU3f0fzOwg8AqCY9X/xd2vjrwyERERWZKK\nztLm7p8HPh9xLSIiIrIClSw+0wu8DTh1xuNL7n55lIWJiIjI0lTSUv8G8CBwA1AMb9PZ2kRERNaY\nSkK9y93/W+SViIiIyIpUckjbL83spMgrERERkRWppKW+EdhtZj8jODsbaExdRERkzakk1L8c/ptJ\nY+oiIiJrTCXHqX9uFeoQERGRFVp0TN0Ct5jZ4+H13zSzK6MuTERERJamkolynwD+DhgOr98HaDxd\nRERkjakk1Dvd/fuE4+juPgXkI61KRERElqySUJ80s1T5Snh421R0JYmIiMhyVNr9/k2g28z+GrgF\n+FCkVYmIiMiSVTL7/fNmthd4GdAMvM7db468MhEREVmSSs/SdjOgIBcREVnDKjlL2y/muLnk7hdE\nUI+IiIgsUyUt9T+fcbkJuAI4GE05IiIislyVjKnfNPO6mV0P/CyqgkRERGR5Kpn9Plsn0FvtQkRE\nRGRlljqmngSegg5pExERWXOWOqY+Cex1d42pi4iIrDFLHlMXERGRtamS7vcBgnXfE3PcXXL3zVWv\nSkRERJasku73fwE2Ap8iCPY3A0PAZyKsS0RERJaoklB/sbs/Y8b1t5nZne7+vqiKEhERkaWr5JC2\nDjPrKV8JL3dEV5KIiIgsRyUt9auAe83suwTd7y8G/j7SqkRERGTJFm2pu/vHgRcBDwC7gRe5+z9H\nXZiIiIgsTUVnaQMeB37m7ncBmFnC3UuRVSUiIiJLtmhL3cxeTNBK/2Z4/XzgmojrEhERkSWqZKLc\n3wAXAIMA7v4L4NQoixIREZGlq+iELu5+aNZN+QhqERERkRWoJNTTZralfMXMLiFYfEZERETWkEom\nyr0HuBbYZWY/AU4HXh5pVSIiIrJkC4a6mSWBCeB3gIvCm2919+GoCxMREZGlWTDU3b1oZl9093MI\nWusiIiKyRlUypv4rMzsl8kpERERkRSoZU+8A7jezW4DR8LaSu18eXVkiIiKyVPOGupl9yN3fBXwR\n+BpPPoxNq8mJiIisMQu11H8HwN0/Z2b3uPt5S924mX0GeAnQH47LY2Ybga8COwmWn71cE+9ERERW\nrqLFZ1bgs8ALZ932buAGdzfgh+F1ERERWaGFWupNZnYWwelWy5enufuDi23c3W82s12zbn458Lzw\n8ueBm1Cwi4iIrNhCod4MfC+8nJhxuWy5M+J73b0vvNwH9C5zOyIiIjLDvKHu7ruifnF3L5mZJt2J\niIhUQaXnU6+mPjPb4u6HzWwr0L/YE3p62lehLNF+jkYqVaStdRCA9rYmkuTp7m6ns1P7Owp6H68O\n7ee1qRahfg3weuAD4f/fXuwJAwOZqGta93p62rWfI5JOZxgdy9HaBpnRCbJjOY4cyZDPRz1Pdf3R\n+3h1aD+vXZGGupl9hWBSXLeZ7QfeB/wj8DUzezPhIW1R1iAiIrJeRBrq7n7FPHe9IMrXFRERWY/U\n/yciIhITCnUREZGYUKiLiIjEhEJdREQkJhTqIiIiMaFQFxERiQmFuoiISEwo1EVERGJCoS4iIhIT\nCnUREZGYUKiLiIjEhEJdREQkJhTqIiIiMaFQFxERiQmFusgqKJVKtS5BRNYBhbpIxJ7oG+Nbtx7i\nQH+m1qWISMwp1EUi9qtfZygWwfcN17oUEYk5hbpIxI6mcwAc6B+tcSUiEncKdZGIlUM9k82TyeZr\nXI2IxJlCXSRiR0Zy05cPHc3WsBIRiTuFukiEJqeKDI3maWoIPmqHFeoiEiGFukiEjo5MUCpB74ZG\nWprqOTyY1eFtIhIZhbpIhPqHxwFoba5n++Z2JvJTjGQna1yViMSVQl0kQv1DQai3NdWxfXNbcNtw\nbqGniIgsm0JdJEID5ZZ6U71CXUQip1AXidB0S725jvaWFB0tDRwZyTNV1Li6iFSfQl0kQgPD4zSl\nkqTqg4/aps4mJqdKpMcKNa5MROJIoS4SkVKpxMDwON0djSQSCQCaG+sByIwr1EWk+hTqIhEZHs2T\nnyyyqbNx+ramVB0Ao+OaAS8i1adQF4lIeZJcd8fMUFdLXUSio1AXiUh5kly3WuoiskoU6iIRKS88\ns2lmS70xDHUtQCMiEVCoi0RkYK5QV/e7iERIoS4Skf6hceqSCTa0paZvU/e7iERJoS4SkYHhcbo7\nm0gmE9O31dclqa9LqKUuIpFQqItEYCI/yeh4gZ6u5uPua2xIqqUuIpFQqItEYDQbtMQ7WlPH3dfY\nkCQzXtApWEWk6hTqIhEod6+3NTccd19Tqo5iEbI5tdZFpLoU6iIRyIQt9faW40O9sSH42KXH8qta\nk4jEn0JdJAKj40Fgz9VSL4d6OfhFRKpFoS4SgfKYelvz3GPqoJa6iFSfQl0kAuUx9bm635sagmPV\nM1mFuohUl0JdJAKjC0yUa0yFLXV1v4tIlSnURSIwWslEObXURaTKFOoiEciMF0gArU0LTJTTmLqI\nVJlCXSQCmWye1uaGJy0RW9bYkCSBut9FpPoU6iIRGB0vzDmeDpBIJGhtrtfsdxGpOoW6SJUVS6Ug\n1OcYTy9ra67X7HcRqTqFukiVZScmKZWgfZ6WOgT3jU1MMjlVXMXKRCTuFOoiVbbQ4Wxlbc31gFaV\nE5HqUqiLVNmxw9mOX02u7FioqwteRKpHoS5SZZkF1n0vK3fN61h1EakmhbpIlS208EzZdEt9TN3v\nIlI99bV6YTN7HEgDU0DB3S+oVS0i1bTQudTL2tRSF5EI1CzUgRJwibsP1rAGkaqbPkPbAi319rCl\nrlAXkWqqdff78cttiZzgymPqCx3S1tai7ncRqb5ahnoJuNHM7jSzt9SwDpGqWuhc6mWaKCciUahl\n9/vF7n7IzHqAG8zsYXe/ea4H9vS0r3Jp65P2c3VMTBapr0tw8vYuEokEqVSRttZglKm9rYkkeU7a\n2kmqPsl4fkr7vcq0P1eH9vPaVLNQd/dD4f8DZvYt4AJgzlAfGMisZmnrUk9Pu/ZzlQylJ2htbuDI\nkVEA0ukMo2M5WtsgMzpBdizH0aOjtLc0MDgyof1eRXofrw7t57WrJt3vZtZiZu3h5VbgMmB3LWoR\nqbbRbGHB8fSy9pYU6WyeUqm0ClWJyHpQqzH1XuBmM7sXuB34rrv/oEa1iFTN5FSRbG5ywcPZyjpa\nUxQmi0zkp1ahMhFZD2rS/e7ujwHn1uK1RaI0Vj5GfYElYsvKi9NksnmaG2s5vUVE4qLWh7SJxEp5\n4ZlKut87wuBP66QuIlIlCnWRKjp2OFtlY+oAmTEd1iYi1aFQF6mi8mlXF1r3vayjVceqi0h1KdRF\nqmh63fdKQl3d7yJSZQp1kSoazZaXiK1kopy630WkuhTqIlVUyRnayjpayy11hbqIVIdCXaSKljKm\nfuyQNnW/i0h1KNRFqiizhNnv9XVJWhrr1VIXkapRqItU0chojubGOlINdRU9vr01pTF1EakahbpI\nFQ2P5ulsbaz48R0tDWTGCxSLWv9dRFZOa1OKVMnkVJHR8QLbe1orfk5HS4pSKRiLL0+cW4pSqUQm\nkz7u9vb2DhKJxJK3JyInNoW6SJWkw270zrbKW+rtM2bALyfUM5k0N9y+h+aWY18kxrNjXHrhaXR0\ndC55eyJyYlOoi1TJ8GgY6ksI547yDPixPPQs73WbW1ppaW1f3pNFJFY0pi5SJSOjOQC6ltBSP3as\nug5rE5GVU6iLVMnwdPf7UlrqWoBGRKpHoS5SJdMt9SV0v888p7qIyEop1EWqZCRsqXcsp/t9TN3v\nIrJyCnWRKhkJJ8p1LaH7ffqkLmqpi0gVKNRFqmR4NDe99GulWprqqUsmNKYuIlWhUBepkpGxPF1t\nqSUt+pJMJGhraSCj7ncRqQKFukgVFEsl0mP5Jc18L+toSamlLiJVoVAXqYLRbIGpYomuJaz7XtbR\n0sBEfop8YSqCykRkPVGoi1TBcHg423Ja6jOXihURWQmFukgVjCxj3feyjukZ8BpXF5GVUaiLVMHw\nMhaeKSsvQJPWedVFZIUU6iJVUD5GfSUtdXW/i8hKKdRFqmA5C8+Ulb8IDGVyVa1JRNYfhbpIFQyP\nlSfKLb2l3ruxGYC+wfGq1iQi649CXaQKRkbzJBMJ2psblvzc7s4m6pIJ+oayEVQmIuuJQl2kCoZH\nc7S3NpBMVr6aXFldMklPVzOHj2YplUoRVCci64VCXWSFSqVSsETsMhaeKduysYVsbpLMuA5rE5Hl\nU6iLrNB4borCZHFZC8+UbdnYAkDfoLrgRWT5FOoiKzQSTpJbzsz3si2bglA/fFShLiLLV/k5IkVk\nTsPlY9RX0P3euyGYAX9Yk+WWrVQqMTIyQjqdmb6tvb1jSWfNEznRKdRFVmhktAot9enudx3WtlyZ\nTJrrf76fYin4szaeHePSC0+jo6OzxpWJrB6FusgK9Q0FQbyps2nZ2+hoTdHcWMdhjamvSEtLK0WW\n/+VK5ESnMXWRFXricNDdu7O3fdnbSCQS9G5ooX8oS7Gow9pEZHkU6iIr9ERfhq621LJWk5tpy8YW\nJqdKHE1PLOl5xWKJPQdGdEIYEVH3u8hKpMfyDGVyPP3UTSveVnlc/fBglp6u5oqekytMceuDgxwe\nypFMwNmnbOTULep+Flmv1FIXWYF9fWHX+5bld72X9c4I9Uqks3k+/p/O4aEcvRuaaW6sZ/feQX5w\n1wCDOjmMyLqkUBdZgSf6Vj6eXrZlCaE+VSzy4a/ey77+LDs3N3Pp+Tt4+bNP4axdG8jmpvjyDx+n\nqCVnRdYdhbrICkxPkqtKS718trbFQ/1nuw+zr2+UZ5y+kWdaF8lkgob6JM94ag/bNjWx5+AoN/5i\n/4prEpETi0JdZAWe6MvQ1tzAhvaVTZIDaErV09WWWjTUJ/KTfOvmvaQakrz8opOetLhKIpHgN0/r\npK25nv/4yV5+PTC64rpE5MShUBdZprGJAgPDE+zc0l61Vcu2bGzhaDpHrjA172Ouv2M/I6N5XnjB\nyXS2Hj8prilVx6sv2cnkVJFPfedBcoUpSqUS6fTIcf/idFa4OP0sIsul2e8iy7SvL2gFV2M8vWzn\nlnYe3jfM/Y8e5fwzNh93/8hojutu30dHa4oXXngy+YmxObdzzildXHLuNm669yCfvfYhrrhkOzfe\n8SjNLa3Tj4nLimuD6Qk+9Z0H2d+fobuzka2b2jhlW0etyxKpCYW6yDJVczy97LlP38b1d+znhjv3\nzxnqX7/pUXKFKV79O6fRlKonv8Ah7X9wqfHrI2Pc8VA/3e31tLS00tJavVrXgj0HRvjYt3aTHsvT\n0VLP/v4s+/uz/OrACM87Z0OtyxNZdep+F1mm6cPZetuqts2tm1p52qmb2HNghMcOpZ903/2PHuXW\nXx5m55Z2nvP0rYtuq74uyZ+88hw2djRy7R0HefTQ2IJd1CdaF/1dj/TzgS/fzWi2wBXPP52/fv3T\n+L3nbucp2zoYyuT4xSPDOgJA1h2FusgyPdGXobmxvuKFYir1gmduB+DGO4/NXh/PTfL56x6mLpng\nTS8+k7pkZR/djtYUb/+9p9HYkOSePSNcf8d+huY5hj2TSXPD7Xu4Zfeh6X833L6HTCY95+Nrac+B\nET55zYPU1yd556ufzqXn7yCRSNDe0sBFv7GF3g3N/ProBNf/4lCtS122ub5krdUvWLJ2KNQjUCqV\njvsn8TKUyXH4aJadvW1VP7Xn2bs2snVTC3c81M9weAa4r/94D0OZHC951k52bF5az8DJve28+zVn\ns21TE/1D43z3Z49z3e37eOTAKIcGx5/Umm0Ou+jL/2aOwZfVOmz6hrJ85Bv3UywWecNlp7B9Yx3p\n9AiZTJoSJZLJBM87bxutTXVcf+ch7nqkf9Vqq6bZX7LW6hcsWVs0pl5lhUKB/7zhNppajo1d5sfT\n/F8vem4Nq1ofSqXScX/0ymEzM3hnn2N7qc8rlUp88QePUAIuOKu32j8GiUSCS5+5g6uvf4Srvn4f\n2YlJjoxMcFJPKy+9aNeytrmhPcVFZ21kcCzBLx8bZGBonP6hcXY/9iCtTc5pJ3XSuyHFcGac7o0N\ntDTW05SGVEWwAAASEElEQVSqm3Nb5bApB352bJRnnd1Le/uTJ6dFcS7zvqEs//S1+xgdL3DOyc0c\nHclyy+7gEMDBI330bN5MY3MjTal6LjprIzfdf4TPXPswO3rb2TxPj8pcv/+o6l+q5hjOg5BoKdSr\nrFQq0dzWSWvHsbXAs7X9u7BuzAybUqnEYKbA4OAgiUQdnZ0ddLU1MJkfP27Gd/l5Tc0t5AtFiiXI\npgeoq2uga2Pwe5w5U/yOh/q551dHOOPkLp779G2R/CzP+o0tfPvmvezrG6WtuYFzT+vmVb99KvV1\nK+tc2765je2b25jIT7L3wFEgyRP9We579OiMRw1PX0rVJ/nZA4N0tTfT2BCEfKFQ4PDQOIXJLPnJ\nIpNTU1x3T5qG+iQtjXW0NtXR0QyvePapnHby5gWDca5AnS9M73y4n89c+xAT+Skue8YWOlqSTwq8\n7NiTj8nvbG3gVc/dyZd/9Dif+PYvee9rn0FD/fH7b/aXFIjPkQGy/ijUJVaKyUYeOZjj0V+PkJ2Y\nLN8KBKHV2VLP4fRjbN7YRntLisx4noMDaZ44PMbYRIbCVBGARAKaGwps6R5l55Z2upqDJVzTY3m+\ndIOTakjyhhedQTKillxjQx3vf+MF5ApT9G5ornqLsSlVz67eFp59zlY6OjoZHs2xZ18/tz7Qz8Rk\ngvHcFBO5SbITBYZGCxwaPH6afVOqjlR9koa6EnXJBFOlJCNjkwyNFgB48CsPsKnjUc4+ZSO/ccpG\nbEcXHbOOq5+r1f/bzzyV5pY2CpNFRsby7Pn1CA8+PsTdPkCqIclbXnoWZ5/czC27Fx8vv+CMTewb\nyHHL7kN85UbnD//LU+fcl2oRS1zUJNTN7IXAVUAd8G/u/oFa1FFtpVKJsfGCzoddA6PjBb51y35+\nurufUgka6pKctr2TZHGCZCJBXaqZI8MTHBmZ4K5fDQKDT3p+XTKYZNXWkqIumWAkkyWbK7L3YJq9\nB9MkEnDtHX0US1CYLHLF809n84aWqtU/V4u1DujdsDpdwF1tjdj2DvqHxma1fjM8+5ytYciWgBKZ\nTIY7H+mjtS3obj/Sf4hkso6N3ZvDz8Akjx88Sq4wxWOHx/npfQf56X0HgWDi3kndraTCFvNELs/h\noXFyhVHyk0WKxRLX3n3PnDVu72njv7/ibLZ1t5JOj1T8s/3Xy4zHD6e56d6DjIzlefNLzqSlqWHe\nx08Vi2ti1vxEfopDgxPUZ4IjGYqTeab0t0UWseqhbmZ1wMeAFwC/Bn5hZte4+0OrXUs1pLN57nq4\nn4eeGML3D5POBq2U+rpBOlpTnNzbzuaW+VcHq6WZQZJKFUmnM2tiHHEp0tk8N939a37wi/1kc5O0\nNtXx9NN62LW1nfq65JMCB2A0k+Y3nrKJyVIjI2M52ltSNNcXuP/RI9MhBUFQJRJJig0d7OvLcOjI\nKC1NDSSTSU49qZPnP2N7Vcdi13oXcEN9HQ3hX4vJfN28P18ikaCtpYEtHSXyuQJnnL+ZwUyBvuEc\nR4fHKRThoSeGnrztugTNTQ20NTdQnCpAqUhjYyN1SWioT9LWWOJlFz+FU3cs3JU/n8aGOv7sNefx\nyWse4J5fHeFvPncnL7t4Fz1dzbS3NPCrfUPc/1ia0Ylh0mMFRscLJBLwk/uPsnlDK2fu3MC5p3dz\nUndr5J+N0fECtz1wmLt9AD8wTLH45PvveGSY88/YzIVn9WI7uk6oz6qsjlq01C8A9rj74wBm9u/A\nK4ATJtTHc5M88NggP3/gMPc/enT62/OG9kbOecoGDh8dZbKYZDiTYzAdzF7efeA2nvHUzTzDetix\nuY1ksvYfxnR6hOtv20NDqpnW1kbSw8Nc9lunr4kQWcjoeAHfP8x9e47w8wf6mJwq0tpUzysu2k4y\nMXXchK2ZkskEG9sbn/QzptMjc/5xTCQS9HQ109PVTHZ783RX9cznVTOIK+kCnv1FIpNJQ2nhx8z3\nuKg1NbfQ2tZBaxvs2Hqs1d/Y3Dbdm5Udy3D7Q33TP/fsL2Hlx2zualpRgHW0pnjXq8/lWzfv5Xs/\nf4JPf2/uPzfNjXX0bmhmcmqSwmSJh54Y4qEnhvjmT/fS3dnEuad1c+7p3Zy+vWvO8fnlyE5M8tAT\ng9z5yAB3PTLA5FSRBLBjcwstjUnaW1uYnCoyksnSP5LnpnsPctO9B9mysYXnPn0bF57VW5VzD8xl\nvi+u3d3VW5tBqqsWoX4SMPP0UQeAC2tQx4IKk1MMpnNM5KcYz00yMDxO39A4jx1K4/uHp4N8x+Y2\nLj5nK+ee3k1PZxOFQoEbbnuY1o5N5AtTHBgY5dH9RzkyPM53b32c7976OI0NSXb2trJraxcbO5rY\n2N5Ic2M9DfVJUg3JsFWUpD6ZqOgP2cyHlEqQn5wil58iXygyUZgkly+SHstxJD3BYDrH0ZEJjqYn\nGBnNEfwYxz60P/rlvUFNHY1s6mia/tfe0kCqoW66vsb6JPX1SUql4INfLJUolTj2f7FEsVgiV5hi\nIl/+N8lIZpSJfJF8YYqJQpFcfopcYYrJYolkIkEiAclEglQqRTK8TAImJvKMjOUZHitwZOTYcdbd\nHY0892mbufDMTeQnxrjvsbmXTY3K7CCuJFArDd35HnfbA/00twZfJAaP9NHS2kFL27EaxrNj/OTu\nwelJfvM9brW/IMy1rYnx0UW3tdwa5nreC87dxFNPamb/QJbBdI50dpINrTCRh229G0iFkwHLX0Dq\nUi3sfvQo9/zqCLv3HuHGuw5w410HSCagu7OR3g3BZ6M5VU9LUx3NqTo2drWTaqgjQfmzGbyvS6XS\n9N+TvqNpjqbz9A9PsL9/jHKv+tZNLTznadt41tm9JIoT3LL70PT7KzuW4llnb+HQcJFbdh/izocH\n+NqP9/C1H+9hW3fQo7BlYwubOpvobE3RUJekri5BfV2S+rokdclE+FkNP5/h5za4DFNTRbITk2TG\nC4xm82SyBY6OjLLnwDCFYoJcvshUsUSxWKS5aTftzSnaWxpob03R0RJcbm1qoKWpnubGeloag/9T\nDUmSiQT19Uk6Wo4/V4FUVy1C/YQYFPr7L9w9fa7s2Xb2tvO0Uzfxm9Zz3BKhiUSCwvgI2VLQ5b6l\nFZo3j5LrbSGTa6B/ZJLhsSJ+IIMfmHv7UUsmoLM1xUndTUzkJkk11FNXl2Q0m6Ohvo7hzAQHj6xu\nOFYqVZ9gU1sdzfU5upphx5Y2JrLD/OSuYYYGj9Da2gEzvuRMjI+RTNaTHQv29Xh2bM6AGM8++edd\n7vOGjvZz3cH9dHYdW6K0XFdbWyPZsdyCj5lZ+0KPK4d6UGt2us6Ztc82+3Gzt7/UGsqPm72v5rtt\nuduqdH9NjI9RXw9TxcSCz0sm6+js2kBjAnpaj21rMt/IZD54XPn33d4OZ+1oZkdXJ62Jo2QLzfSP\nTDKSLXI0PUH/8NwL+SxFV0uSDa1w2TNP4oxdPcEX+eLEce+v8ewY2bEM2zd28JrnbedlF27hLh/k\noX0j7DmYifwzm6pPUFeXJJkMvij0D4+zr39pZwG8/LdP44UXnhxRhQK1CfVfAztmXN9B0FqfU0KD\nRiKyDny01gWsgu9+uNYVxF8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"text": [ "" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like people tend to be more positive about Kaggle than negative.\n", "\n", "Let's look at the really negative tweets! We'll define negative as less than or equal to -0.25. We'll exclude all the tweets with links in them." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Loop through sentiments and look for negative sentiments. \n", "for i in range(len(sentiments)):\n", " if sentiments[i] <= -0.25 and 'http' not in tweets_text[i]:\n", " print tweets_text[i], sentiments[i]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "#kaggle kaggle is really so slow today -0.3\n", "Are you serious teacher, HE IS GOING TO FRIGGIN PUT THE ASSIGNMENT ON KAGGLE FFFFFFFFFFFFFFF -0.333333333333\n", "RT @ewald_zietsman: A forest of random forests. I'm making one. #noobtactics #machinelearning #kaggle -0.5\n", "RT @ewald_zietsman: A forest of random forests. I'm making one. #noobtactics #machinelearning #kaggle -0.5\n", "A forest of random forests. I'm making one. #noobtactics #machinelearning #kaggle -0.5\n", "@kaggle The bad thing about semi-automatic tweets when one moves up the leaderboard is that they take up almost all #kaggle tagging. -0.7\n" ] } ], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the postive ones, too. We'll define positive as greater than or equal to 0.25." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Loop through sentiments and look for positive sentiments. \n", "for i in range(len(sentiments)):\n", " if sentiments[i] >= 0.25 and 'http' not in tweets_text[i]:\n", " print tweets_text[i], sentiments[i]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted 0.4\n", "RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted 0.4\n", "RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted 0.4\n", "RT @antgoldbloom: Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted 0.4\n", "Proud to share that over the weekend Kaggle passed a big milestone: over 1MM machine learning models have been submitted to our competitions 0.4\n", "@what_mojo @KaggleStatus @kaggle hahah then thanks both! 0.25\n", "@what_mojo now they do! check @KaggleStatus, thanks @kaggle! 0.25\n", "You can follow @KaggleStatus for updates on our platform's performance. (Relevant information available now.) 0.4\n", "@gef0rce @kaggle @kdd_news who knows :D 1.0\n", "Kdd cup 2015 website is total ripoff of @kaggle . only difference: it doesnt work :P :P @kdd_news 0.375\n", "if you're a data scientist, many cool projects to try on kaggle now. itching to have a go (incl. one on west nile virus). but work calls. 0.425\n", "RT @mariandragt: My first @Kaggle @OttoGroup_Com challenge submission with #AzureML! Lot to improve, but happy with this first step! #Machi 0.475\n", "My first @Kaggle @OttoGroup_Com challenge submission with #AzureML! Lot to improve, but happy with this first step! #MachineLearning 0.475\n", "@tompy_ Kaggle is cool, I agree. My class assignment on there? Not so fun 0.325\n", "Need more ram to read data from @kaggle , or use #spark..the wonders of the latter 0.25\n", "Moved up 221 places on #kaggle. Top 3% now. Thank you, no-learn and lasagne. (And #python in general.) 0.275\n", "@benhamner @kaggle @Azure I also look 69 when I'm wearing sunglasses. But in my mirror shades: a 69-year-old Fat Maverick from Top Gun. 0.5\n", "Thanks @mjcavaretta, I'll be sure to take a look @kaggle @kdnuggets 0.35\n", "Submitting my first #Kaggle. #Predict all them bike share demand #machinelearning #DCTech #DataScience 0.25\n", "@benhamner @kaggle @Azure I think they just wanted a geotagged, targeting cookie bound, dataset of human faces :-) 0.25\n", "@HeatherOhana The MSPA does have weight. Do some Kaggle comps for experience/resume lines too, I respect that as a hiring mgr. Also, hi :) 0.5\n", "@AdamTaran @kaggle @combine_au Thanks for the mention, I hope you enjoy the series! 0.35\n", "@benhamner @kaggle Great visualization. I am now interested in this contest. 0.525\n", "@benhamner @kaggle You started on that quickly! 0.416666666667\n", "Digging the new scripts functionality on the @kaggle competitions. Great way to learn EDA methodologies. 0.468181818182\n", "@kaggle Shouldn't it be better to change it to something like #KaggleChallenge, with the name of the actual challenge? 0.25\n", "I am a giant nerd, but that's okay. #kaggle 0.25\n", "@treycausey They're selecting for the perfect FB dev-- smart enough to win Kaggle while not smart enough to avoid working for free. 0.329591836735\n", "Got an email with the title Our A/B tests predict you will open this email. Good job #kaggle 0.35\n", "Should you get a Coursera certificate if they already have Master's or PhD? Coursera/Kaggle projects are great for real-world data/projects 0.8\n", "decades of worldwide seismic data readings and live data is now accessible. Earthquake prediction is an ideal open data prize @kaggle 0.352840909091\n", "91 places up & Top 10% , aww yeah ! 0.4375\n", "@PeterDiamandis hey Peter. I read your book, Bold, and am curious if kaggle is the best platform to crowdsource data mining task? 0.411111111111\n" ] } ], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems like there might be some false positives in there. However, it's what we have to work with, so let's get a count of the \"negative\" (-1.00 to -0.25), \"neutral\" (-0.25 to 0.25), and \"positive\" (0.25 to 1.00) tweets." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Loop through all of the sentiments and put them into the appropriate group\n", "pos_neg_neutral = []\n", "for sentiment in sentiments:\n", " if sentiment <= -0.25:\n", " pos_neg_neutral.append('negative')\n", " elif sentiment >= 0.25:\n", " pos_neg_neutral.append('positive')\n", " elif sentiment > -0.25 and sentiment < 0.25:\n", " pos_neg_neutral.append('neutral')\n", "\n", "sns.barplot(np.array(pos_neg_neutral))\n", "plt.title('Positive, Negative, and Neutral Sentiment')\n", "plt.xlabel('Sentiment Category')\n", "plt.ylabel('Frequency')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that most tweets are neutral, but there are far more positive tweets than negative tweets." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've made a few interesting discoveries here, but there is a lot more that could be explored. You could collect more data, look at the sentiment for specific topics, track topic velocity over time, explore more complex topic modeling, and much more. Natural Language Processing is a vast field with many subareas. We've provided some links on the DAT5 readme to help you explore it more deeply." ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/16_decision_trees.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:87008866d7dcc97facb27ccb4d1630e237017383a6de66e047b039f143121da7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Decision Trees\n", "\n", "*Adapted from Chapter 8 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Motivation\n", "\n", "Why are we learning about decision trees?\n", "\n", "- They're useful for both regression and classification problems.\n", "- They're popular (for a variety of reasons).\n", "- They're the basis for more sophisticated modeling approaches.\n", "- They demonstrate a different way of \"thinking\" than the models we have studied so far." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression trees\n", "\n", "Let's look at a simple example to get started.\n", "\n", "Our goal is to **predict a baseball player's Salary** based on **Years** (number of years playing in the major leagues) and **Hits** (number of hits he made in the previous year). Here is the training data, represented visually (low salary is blue/green, high salary is red/yellow):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Salary data](images/salary_color.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How might you \"stratify\" or \"segment\" the feature space into regions, based on salary?** Here are the rules:\n", "\n", "- You can only use straight lines, drawn one at a time.\n", "- Your line must either be vertical or horizontal.\n", "- Your line stops when it hits an existing line.\n", "\n", "Intuitively, you want to **maximize** the similarity (or \"homogeneity\") within a given region, and **minimize** the similarity between different regions.\n", "\n", "*Let's take a minute and do this...*\n", "\n", "Below is a regression tree that has been fit to the data by a computer. (We will talk later about how the fitting algorithm actually works.) Note that Salary is measured in thousands and has been log-transformed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Salary tree](images/salary_tree.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How do we make Salary predictions (for out-of-sample data) using a decision tree?**\n", "\n", "- Start at the top, and examine the first \"splitting rule\" (Years < 4.5).\n", "- If the rule is **True** for a given player, follow the **left branch**. If the rule is **False**, follow the **right branch**.\n", "- Continue until reaching the bottom. The predicted Salary is the number in that particular \"bucket\".\n", "- **Note:** Years and Hits are both integers, but the convention is to label these rules using the midpoint between adjacent values.\n", "\n", "Example predictions:\n", "\n", "- Years=3, then predict 5.11 ($\\$1000 \\times e^{5.11} \\approx \\$166000$)\n", "- Years=5 and Hits=100, then predict 6.00 ($\\$1000 \\times e^{6.00} \\approx \\$403000$)\n", "- Years=8 and Hits=120, then predict 6.74 ($\\$1000 \\times e^{6.74} \\approx \\$846000$)\n", "\n", "**How did we come up with the numbers at the bottom of the tree?** Each number is just the **mean Salary in the training data** of players who fit that criteria.\n", "\n", "Here's the same diagram as before, split into the three regions:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Salary regions](images/salary_regions.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This diagram is essentially a combination of the two previous diagrams. In $R_1$, the mean log Salary was 5.11. In $R_2$, the mean log Salary was 6.00. In $R_3$, the mean log Salary was 6.74. Thus, those values are used to predict out-of-sample data.\n", "\n", "Let's introduce some terminology:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Salary tree annotated](images/salary_tree_annotated.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How might you interpret the \"meaning\" of this tree?**\n", "\n", "- Years is the most important factor determining Salary, with a lower number of Years corresponding to a lower Salary.\n", "- For a player with a lower number of Years, Hits is not an important factor determining Salary.\n", "- For a player with a higher number of Years, Hits is an important factor determining Salary, with a greater number of Hits corresponding to a higher Salary.\n", "\n", "What we have seen so far hints at the advantages and disadvantages of decision trees:\n", "\n", "**Advantages:**\n", "\n", "- Highly interpretable\n", "- Can be displayed graphically\n", "- Prediction is fast\n", "\n", "**Disadvantages:**\n", "\n", "- Predictive accuracy is not as high as some supervised learning methods\n", "- Can easily overfit the training data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building a regression tree by hand\n", "\n", "How do you build a decision tree? You're going to find out by building one in pairs!\n", "\n", "Your training data is a tiny dataset of [used vehicle sale prices](https://raw.githubusercontent.com/justmarkham/DAT5/master/data/vehicles_train.csv). Your goal is to predict **price** for testing data. Here are your instructions:\n", "\n", "- Read the data into Pandas.\n", "- Explore the data by sorting, plotting, or split-apply-combine (aka `group_by`).\n", "- Decide which feature is the most important predictor, and use that to make your first split. (Only binary splits are allowed!)\n", "- After making your first split, you should actually split your data in Pandas into two parts, and then explore each part to figure out what other splits to make.\n", "- Stop making splits once you are convinced that it strikes a good balance between underfitting and overfitting. (As always, your goal is to build a model that generalizes well!)\n", "- You are allowed to split on the same variable multiple times!\n", "- Draw your tree, making sure to label your leaves with the mean Price for the observations in that \"bucket\".\n", "- When you're finished, review your tree to make sure nothing is backwards. (Remember: follow the **left branch** if the rule is **true**, and follow the **right branch** if the rule is **false**.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How does a computer build a regression tree?\n", "\n", "The ideal approach would be for the computer to consider every possible partition of the feature space. However, this is computationally infeasible, so instead an approach is used called **recursive binary splitting:**\n", "\n", "- Begin at the top of the tree.\n", "- For every single predictor, examine every possible cutpoint, and choose the predictor and cutpoint such that the resulting tree has the **lowest possible mean squared error (MSE)**. Make that split.\n", "- Repeat the examination for the two resulting regions, and again make a single split (in one of the regions) to minimize the MSE.\n", "- Keep repeating this process until a stopping criterion is met, such as **maximum tree depth** or **minimum number of samples in a leaf**.\n", "\n", "Below is the regression tree for player salaries grown much deeper, and a comparison of the training, test, and cross-validation errors for trees with different numbers of leaves:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Salary unpruned](images/salary_unpruned.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the **training error** continues to go down as the tree size increases, but the lowest **cross-validation error** occurs for a tree with 3 leaves." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building a regression tree in scikit-learn" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# read the training data into pandas and print it out\n", "import pandas as pd\n", "train = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/vehicles_train.csv')\n", "train" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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priceyearmilesdoorstype
0220002012130002car
1140002010300002car
2130002010735004car
395002009780004car
490002007470004car
5400020061240002car
6300020041770004car
7200020042090004truck
8300020031380002car
9190020031600004car
10250020031900002truck
1150002001620004car
12180019991630002truck
13130019971380004car
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ " price year miles doors type\n", "0 22000 2012 13000 2 car\n", "1 14000 2010 30000 2 car\n", "2 13000 2010 73500 4 car\n", "3 9500 2009 78000 4 car\n", "4 9000 2007 47000 4 car\n", "5 4000 2006 124000 2 car\n", "6 3000 2004 177000 4 car\n", "7 2000 2004 209000 4 truck\n", "8 3000 2003 138000 2 car\n", "9 1900 2003 160000 4 car\n", "10 2500 2003 190000 2 truck\n", "11 5000 2001 62000 4 car\n", "12 1800 1999 163000 2 truck\n", "13 1300 1997 138000 4 car" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# encode car as 0 and truck as 1\n", "train['type'] = train.type.map({'car':0, 'truck':1})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# create a list of the feature columns (every column except for the 0th column)\n", "feature_cols = train.columns[1:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# define X (features) and y (response)\n", "X = train[feature_cols]\n", "y = train.price" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# import the relevant class, and instantiate the model (with random_state=1)\n", "from sklearn.tree import DecisionTreeRegressor\n", "treereg = DecisionTreeRegressor(random_state=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# print the model object to see the default arguments\n", "treereg" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "DecisionTreeRegressor(compute_importances=None, criterion='mse',\n", " max_depth=None, max_features=None, max_leaf_nodes=None,\n", " min_density=None, min_samples_leaf=1, min_samples_split=2,\n", " random_state=1, splitter='best')" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# use 3-fold cross-validation to estimate the RMSE for this model\n", "from sklearn.cross_validation import cross_val_score\n", "import numpy as np\n", "scores = cross_val_score(treereg, X, y, cv=3, scoring='mean_squared_error')\n", "np.mean(np.sqrt(-scores))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "4707.2505884845632" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuning a regression tree\n", "\n", "Let's see if we can reduce the RMSE by tuning the **max_depth** parameter. One way to search for an optimal value would be to try different values, one by one:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# try max_depth=1\n", "treereg = DecisionTreeRegressor(max_depth=1, random_state=1)\n", "scores = cross_val_score(treereg, X, y, cv=3, scoring='mean_squared_error')\n", "np.mean(np.sqrt(-scores))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "4928.1374642038018" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, we could write a loop to try a range of values:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# define a range of values\n", "max_depth_range = range(1, 11)\n", "\n", "# create an empty list to store the average RMSE for each value of max_depth\n", "RMSE_scores = []\n", "\n", "# use cross-validation with each value of max_depth\n", "for depth in max_depth_range:\n", " treereg = DecisionTreeRegressor(max_depth=depth, random_state=1)\n", " MSE_scores = cross_val_score(treereg, X, y, cv=3, scoring='mean_squared_error')\n", " RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))\n", "\n", "# print the results\n", "RMSE_scores" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "[4928.1374642038018,\n", " 4804.3767888427128,\n", " 4592.1554255755254,\n", " 4704.0052694797387,\n", " 4707.2505884845632,\n", " 4707.2505884845632,\n", " 4707.2505884845632,\n", " 4707.2505884845632,\n", " 4707.2505884845632,\n", " 4707.2505884845632]" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# plot the max_depth (x-axis) versus the RMSE (y-axis)\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.plot(max_depth_range, RMSE_scores)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a later class, we'll learn how to use scikit-learn's [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html) to locate these optimal parameters more easily." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# max_depth=3 was best, so fit a tree using that parameter\n", "treereg = DecisionTreeRegressor(max_depth=3, random_state=1)\n", "treereg.fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "DecisionTreeRegressor(compute_importances=None, criterion='mse', max_depth=3,\n", " max_features=None, max_leaf_nodes=None, min_density=None,\n", " min_samples_leaf=1, min_samples_split=2, random_state=1,\n", " splitter='best')" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# compute the \"Gini importance\" of each feature: the (normalized) total reduction of MSE brought by that feature\n", "pd.DataFrame({'feature':feature_cols, 'importance':treereg.feature_importances_})" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
featureimportance
0year0.798744
1miles0.201256
2doors0.000000
3type0.000000
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ " feature importance\n", "0 year 0.798744\n", "1 miles 0.201256\n", "2 doors 0.000000\n", "3 type 0.000000" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a tree diagram" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# create a Graphviz file\n", "from sklearn.tree import export_graphviz\n", "with open(\"tree_vehicles.dot\", 'wb') as f:\n", " f = export_graphviz(treereg, out_file=f, feature_names=feature_cols)\n", "\n", "# At the command line, run this to convert to PNG:\n", "# dot -Tpng tree_vehicles.dot -o tree_vehicles.png" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Tree for vehicle data](images/tree_vehicles.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do we read this decision tree?\n", "\n", "**Internal nodes:**\n", "\n", "- \"samples\" is the number of observations in that node before splitting\n", "- \"mse\" is the mean squared error calculated by comparing the actual response values in that node against the mean response value in that node\n", "- first line is the condition used to split that node (go left if true, go right if false)\n", "\n", "**Leaves:**\n", "\n", "- \"samples\" is the number of observations in that node\n", "- \"value\" is the mean response value in that node\n", "- \"mse\" is the mean squared error calculated by comparing the actual response values in that node against \"value\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting on test data\n", "\n", "How good is scikit-learn's regression tree at predicting the price for test observations?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# read the test data\n", "test = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/vehicles_test.csv')\n", "\n", "# encode car as 0 and truck as 1\n", "test['type'] = test.type.map({'car':0, 'truck':1})\n", "\n", "# print the data\n", "test" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
priceyearmilesdoorstype
03000200313000041
1600020058250040
21200020106000020
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ " price year miles doors type\n", "0 3000 2003 130000 4 1\n", "1 6000 2005 82500 4 0\n", "2 12000 2010 60000 2 0" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# define X and y\n", "X_test = test[feature_cols]\n", "y_test = test.price" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# make predictions on test data\n", "y_pred = treereg.predict(X_test)\n", "y_pred" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "array([ 4000., 5000., 13500.])" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# calculate RMSE\n", "from sklearn import metrics\n", "np.sqrt(metrics.mean_squared_error(y_test, y_pred))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "1190.2380714238084" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "# calculate RMSE for your own tree!\n", "y_test = [3000, 6000, 12000]\n", "y_pred = [3057, 3057, 16333]\n", "np.sqrt(metrics.mean_squared_error(y_test, y_pred))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "3024.3118776563592" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification trees\n", "\n", "Classification trees are very similar to regression trees. Here is a quick comparison:\n", "\n", "|regression trees|classification trees|\n", "|---|---|\n", "|predict a continuous response|predict a categorical response|\n", "|predict using mean response of each leaf|predict using most commonly occuring class of each leaf|\n", "|splits are chosen to minimize MSE|splits are chosen to minimize Gini index (discussed below)|\n", "\n", "Here's an **example of a classification tree**, which predicts whether Barack Obama or Hillary Clinton would win the Democratic primary in a particular county in 2008:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Obama-Clinton decision tree](images/obama_clinton_tree.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**A few questions:**\n", "\n", "- What is the response variable?\n", "- What are the features?\n", "- What is the most predictive feature?\n", "- How would we calculate the total number of counties?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Splitting criteria for classification trees\n", "\n", "Here are common options for the splitting criteria:\n", "\n", "- **classification error rate:** fraction of training observations in a region that don't belong to the most common class\n", "- **Gini index:** measure of total variance across classes in a region\n", "- **cross-entropy:** numerically similar to Gini index\n", "\n", "The goal when splitting is to increase the \"node purity\", and it turns out that the **Gini index and cross-entropy** are better measures of purity than classification error rate. The Gini index is faster to compute than cross-entropy, so it is generally preferred (and is used by scikit-learn by default)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example calculations of Gini index\n", "\n", "Let's say that we are predicting survival on the Titanic. At a particular node, there are **25 individuals**, of whom 10 survived and 15 died. Here's how we calculate the Gini index before making a split:\n", "\n", "$$1 - \\left(\\frac {Survived} {Total}\\right)^2 - \\left(\\frac {Died} {Total}\\right)^2 = 1 - \\left(\\frac {10} {25}\\right)^2 - \\left(\\frac {15} {25}\\right)^2 = 0.48$$\n", "\n", "The **maximum value** of the Gini index is 0.5, and occurs when the classes are perfectly balanced in a node. The **minimum value** of the Gini index is 0, and occurs when there is only one class represented in a node. Thus, a node with a lower Gini index is said to be more \"pure\".\n", "\n", "**When deciding between splits**, the decision tree algorithm chooses the split that maximizes the resulting node purity. Let's pretend that gender was the split being considered, and the resulting nodes are as follows:\n", "\n", "- **Males:** 2 survived, 13 died\n", "- **Females:** 8 survived, 2 died\n", "\n", "To evaluate this split, we calculate the **weighted average of the Gini indices of the resulting nodes:**\n", "\n", "$$\\text{Males: } 1 - \\left(\\frac {2} {15}\\right)^2 - \\left(\\frac {13} {15}\\right)^2 = 0.23$$\n", "$$\\text{Females: } 1 - \\left(\\frac {8} {10}\\right)^2 - \\left(\\frac {2} {10}\\right)^2 = 0.32$$\n", "$$\\text{Weighted Average: } 0.23 \\left(\\frac {15} {25}\\right) + 0.32 \\left(\\frac {10} {25}\\right) = 0.27$$\n", "\n", "Thus, the decrease in Gini index (and gain in purity) from splitting on gender is **0.21**. The decision tree algorithm will choose this split if no other splits result in a larger gain in purity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building a classification tree in scikit-learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll build a classification tree using the [Titanic data](https://www.kaggle.com/c/titanic-gettingStarted/data) provided by Kaggle." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# read in the data\n", "titanic = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/titanic_train.csv')\n", "titanic.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale2210A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female3810PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale2600STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female351011380353.1000C123S
4503Allen, Mr. William Henrymale35003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale23134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female270234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female141023773630.0708NaNC
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "5 6 0 3 \n", "6 7 0 1 \n", "7 8 0 3 \n", "8 9 1 3 \n", "9 10 1 2 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", "2 Heikkinen, Miss. Laina female 26 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", "4 Allen, Mr. William Henry male 35 0 \n", "5 Moran, Mr. James male NaN 0 \n", "6 McCarthy, Mr. Timothy J male 54 0 \n", "7 Palsson, Master. Gosta Leonard male 2 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", "5 0 330877 8.4583 NaN Q \n", "6 0 17463 51.8625 E46 S \n", "7 1 349909 21.0750 NaN S \n", "8 2 347742 11.1333 NaN S \n", "9 0 237736 30.0708 NaN C " ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's choose our response variable and a few features, and review **how to handle categorical features**:\n", "\n", "- **Survived:** This is our response variable, and is already encoded as 0=died and 1=survived.\n", "- **Pclass:** These are the passenger class categories (1=first class, 2=second class, 3=third class). They are logically ordered, so we'll leave them as-is. (If the tree splits on this feature, the splits will occur at 1.5 or 2.5.)\n", "- **Sex:** This is a binary category, so we should encode it as 0=female and 1=male. (If the tree splits on this feature, the split will occur at 0.5.)\n", "- **Age:** This is a numeric feature, but we need to fill in the missing values.\n", "- **Embarked:** This is the port they embarked from. There are three unordered categories, so we should create dummy variables and drop one level as usual." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# encode female as 0 and male as 1\n", "titanic['Sex'] = titanic.Sex.map({'female':0, 'male':1})\n", "\n", "# fill in the missing values for age with the mean age\n", "titanic.Age.fillna(titanic.Age.mean(), inplace=True)\n", "\n", "# create three dummy variables, drop the first dummy variable, and store the two remaining columns as a DataFrame\n", "embarked_dummies = pd.get_dummies(titanic.Embarked, prefix='Embarked').iloc[:, 1:]\n", "\n", "# concatenate the two dummy variable columns onto the original DataFrame\n", "titanic = pd.concat([titanic, embarked_dummies], axis=1)\n", "\n", "# print the updated DataFrame\n", "titanic.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedEmbarked_QEmbarked_S
0103Braund, Mr. Owen Harris122.00000010A/5 211717.2500NaNS01
1211Cumings, Mrs. John Bradley (Florence Briggs Th...038.00000010PC 1759971.2833C85C00
2313Heikkinen, Miss. Laina026.00000000STON/O2. 31012827.9250NaNS01
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)035.0000001011380353.1000C123S01
4503Allen, Mr. William Henry135.000000003734508.0500NaNS01
5603Moran, Mr. James129.699118003308778.4583NaNQ10
6701McCarthy, Mr. Timothy J154.000000001746351.8625E46S01
7803Palsson, Master. Gosta Leonard12.0000003134990921.0750NaNS01
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)027.0000000234774211.1333NaNS01
91012Nasser, Mrs. Nicholas (Adele Achem)014.0000001023773630.0708NaNC00
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "5 6 0 3 \n", "6 7 0 1 \n", "7 8 0 3 \n", "8 9 1 3 \n", "9 10 1 2 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris 1 22.000000 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 38.000000 1 \n", "2 Heikkinen, Miss. Laina 0 26.000000 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 35.000000 1 \n", "4 Allen, Mr. William Henry 1 35.000000 0 \n", "5 Moran, Mr. James 1 29.699118 0 \n", "6 McCarthy, Mr. Timothy J 1 54.000000 0 \n", "7 Palsson, Master. Gosta Leonard 1 2.000000 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 0 27.000000 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) 0 14.000000 1 \n", "\n", " Parch Ticket Fare Cabin Embarked Embarked_Q Embarked_S \n", "0 0 A/5 21171 7.2500 NaN S 0 1 \n", "1 0 PC 17599 71.2833 C85 C 0 0 \n", "2 0 STON/O2. 3101282 7.9250 NaN S 0 1 \n", "3 0 113803 53.1000 C123 S 0 1 \n", "4 0 373450 8.0500 NaN S 0 1 \n", "5 0 330877 8.4583 NaN Q 1 0 \n", "6 0 17463 51.8625 E46 S 0 1 \n", "7 1 349909 21.0750 NaN S 0 1 \n", "8 2 347742 11.1333 NaN S 0 1 \n", "9 0 237736 30.0708 NaN C 0 0 " ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "# create a list of the feature columns\n", "feature_cols = ['Pclass', 'Sex', 'Age', 'Embarked_Q', 'Embarked_S']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "# define X (features) and y (response)\n", "X = titanic[feature_cols]\n", "y = titanic.Survived" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "# fit a classification tree with max_depth=3 on all data\n", "from sklearn.tree import DecisionTreeClassifier\n", "treeclf = DecisionTreeClassifier(max_depth=3, random_state=1)\n", "treeclf.fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n", " max_depth=3, max_features=None, max_leaf_nodes=None,\n", " min_density=None, min_samples_leaf=1, min_samples_split=2,\n", " random_state=1, splitter='best')" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "# create a Graphviz file\n", "with open(\"tree_titanic.dot\", 'wb') as f:\n", " f = export_graphviz(treeclf, out_file=f, feature_names=feature_cols)\n", "\n", "# At the command line, run this to convert to PNG:\n", "# dot -Tpng tree_titanic.dot -o tree_titanic.png" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Tree for Titanic data](images/tree_titanic.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the split in the bottom right. The **same class** is predicted in both of its leaves! Why did this split occur?\n", "\n", "Although that split didn't affect the **classification error rate**, it did increase the **node purity**. Node purity is important because we're interested in the class proportions among the observations in each region." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# compute the feature importances\n", "pd.DataFrame({'feature':feature_cols, 'importance':treeclf.feature_importances_})" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
featureimportance
0Pclass0.242664
1Sex0.655584
2Age0.064494
3Embarked_Q0.000000
4Embarked_S0.037258
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ " feature importance\n", "0 Pclass 0.242664\n", "1 Sex 0.655584\n", "2 Age 0.064494\n", "3 Embarked_Q 0.000000\n", "4 Embarked_S 0.037258" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Wrapping up decision trees\n", "\n", "Here are some advantages and disadvantages of decision trees that we haven't yet talked about:\n", "\n", "**Advantages:**\n", "\n", "- Can be specified as a series of rules, and are thought to more closely approximate human decision-making than other models\n", "- Non-parametric (will do better than linear models if relationship between features and response is highly non-linear)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Trees versus linear models](images/tree_vs_linear.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Disadvantages:**\n", "\n", "- Small variations in the data can result in a completely different tree (high variance)\n", "- Recursive binary splitting makes \"locally optimal\" decisions that may not result in a globally optimal tree\n", "- Can create biased trees if the classes are highly imbalanced\n", "\n", "Note that there is not just one decision tree algorithm; instead, there are many variations. A few common decision tree algorithms that are often referred to by name are C4.5, C5.0, and CART. (More details are available in the [scikit-learn documentation](http://scikit-learn.org/stable/modules/tree.html#tree-algorithms-id3-c4-5-c5-0-and-cart).) scikit-learn uses an \"optimized version\" of CART." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resources\n", "\n", "- scikit-learn documentation: [Decision Trees](http://scikit-learn.org/stable/modules/tree.html)" ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/17_ensembling.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:803dd679ec261442a6f0cc436aae1f7776f20b5d231f8ec1e4d225e813a68f16" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Ensembling\n", "\n", "*Adapted from Chapter 8 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*\n", "\n", "Let's pretend that instead of building a single model to solve a classification problem, you created **five independent models**, and each model was correct 70% of the time. If you combined these models into an \"ensemble\" and used their majority vote as a prediction, how often would the ensemble be correct?\n", "\n", "Let's simulate it to find out!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "# set a seed for reproducibility\n", "np.random.seed(1234)\n", "\n", "# generate 1000 random numbers (between 0 and 1) for each model, representing 1000 observations\n", "mod1 = np.random.rand(1000)\n", "mod2 = np.random.rand(1000)\n", "mod3 = np.random.rand(1000)\n", "mod4 = np.random.rand(1000)\n", "mod5 = np.random.rand(1000)\n", "\n", "# each model independently predicts 1 (the \"correct response\") if random number was at least 0.3\n", "preds1 = np.where(mod1 > 0.3, 1, 0)\n", "preds2 = np.where(mod2 > 0.3, 1, 0)\n", "preds3 = np.where(mod3 > 0.3, 1, 0)\n", "preds4 = np.where(mod4 > 0.3, 1, 0)\n", "preds5 = np.where(mod5 > 0.3, 1, 0)\n", "\n", "# print the first 20 predictions from each model\n", "print preds1[:20]\n", "print preds2[:20]\n", "print preds3[:20]\n", "print preds4[:20]\n", "print preds5[:20]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1]\n", "[1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 0]\n", "[1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1]\n", "[1 1 0 0 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0]\n", "[0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 1 1 1 1 1]\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# add the predictions together\n", "sum_of_preds = preds1 + preds2 + preds3 + preds4 + preds5\n", "\n", "# ensemble predicts 1 (the \"correct response\") if at least 3 models predict 1\n", "ensemble_preds = np.where(sum_of_preds >=3 , 1, 0)\n", "\n", "# print the ensemble's first 20 predictions\n", "print ensemble_preds[:20]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1]\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# how accurate was the ensemble?\n", "ensemble_preds.mean()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "0.84099999999999997" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Amazing, right?\n", "\n", "**Ensemble learning (or \"ensembling\")** is simply the process of combining several models to solve a prediction problem, with the goal of producing a combined model that is more accurate than any individual model. For **classification** problems, the combination is often done by majority vote. For **regression** problems, the combination is often done by taking an average of the predictions.\n", "\n", "For ensembling to work well, the individual models must meet two conditions:\n", "\n", "- Models should be **accurate** (they must outperform random guessing)\n", "- Models should be **independent** (their predictions are not correlated with one another)\n", "\n", "The idea, then, is that if you have a collection of individually imperfect (and independent) models, the \"one-off\" mistakes made by each model are probably not going to be made by the rest of the models, and thus the mistakes will be discarded when averaging the models.\n", "\n", "It turns out that as you add more models to the voting process, the probability of error decreases. This is known as [Condorcet's Jury Theorem](http://en.wikipedia.org/wiki/Condorcet%27s_jury_theorem), which was developed by a French political scientist in the 18th century.\n", "\n", "Anyway, we'll see examples of ensembling below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bootstrapping\n", "\n", "**Some preliminary terminology:** In statistics, \"bootstrapping\" refers to the process of using \"bootstrap samples\" to quantify the uncertainty of a model. Bootstrap samples are simply random samples with replacement:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set a seed for reproducibility\n", "np.random.seed(1)\n", "\n", "# create an array of 0 to 9, then sample 10 times with replacement\n", "np.random.choice(a=10, size=10, replace=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "array([5, 8, 9, 5, 0, 0, 1, 7, 6, 9])" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bagging\n", "\n", "On their own, decision trees are not competitive with the best supervised learning methods in terms of **predictive accuracy**. However, they can be used as the basis for more sophisticated methods that have much higher accuracy!\n", "\n", "One of the main issues with decision trees is **high variance**, meaning that different splits in the training data can lead to very different trees. **\"Bootstrap aggregation\" (aka \"bagging\")** is a general purpose procedure for reducing the variance of a machine learning method, but is particularly useful for decision trees.\n", "\n", "What is the bagging process (in general)?\n", "\n", "- Take repeated bootstrap samples (random samples with replacement) from the training data set\n", "- Train our method on each bootstrapped training set and make predictions\n", "- Average the predictions\n", "\n", "This increases predictive accuracy by **reducing the variance**, similar to how cross-validation reduces the variance associated with the test set approach (for estimating out-of-sample error) by splitting many times an averaging the results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Applying bagging to decision trees\n", "\n", "So how exactly can bagging be used with decision trees? Here's how it applies to **regression trees**:\n", "\n", "- Grow B regression trees using B bootstrapped training sets\n", "- Grow each tree deep so that each one has low bias\n", "- Every tree makes a numeric prediction, and the predictions are averaged (to reduce the variance)\n", "\n", "It is applied in a similar fashion to **classification trees**, except that during the prediction stage, the overall prediction is based upon a majority vote of the trees.\n", "\n", "**What value should be used for B?** Simply use a large enough value that the error seems to have stabilized. (Choosing a value of B that is \"too large\" will generally not lead to overfitting.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Manually implementing bagged decision trees (with B=3)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "\n", "# read in vehicle data\n", "vehicles = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT4/master/data/used_vehicles.csv')\n", "\n", "# convert car to 0 and truck to 1\n", "vehicles['type'] = vehicles.type.map({'car':0, 'truck':1})\n", "\n", "# print out data\n", "vehicles" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
priceyearmilesdoorstype
0 22000 2012 13000 2 0
1 14000 2010 30000 2 0
2 13000 2010 73500 4 0
3 9500 2009 78000 4 0
4 9000 2007 47000 4 0
5 4000 2006 124000 2 0
6 3000 2004 177000 4 0
7 2000 2004 209000 4 1
8 3000 2003 138000 2 0
9 1900 2003 160000 4 0
10 2500 2003 190000 2 1
11 5000 2001 62000 4 0
12 1800 1999 163000 2 1
13 1300 1997 138000 4 0
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " price year miles doors type\n", "0 22000 2012 13000 2 0\n", "1 14000 2010 30000 2 0\n", "2 13000 2010 73500 4 0\n", "3 9500 2009 78000 4 0\n", "4 9000 2007 47000 4 0\n", "5 4000 2006 124000 2 0\n", "6 3000 2004 177000 4 0\n", "7 2000 2004 209000 4 1\n", "8 3000 2003 138000 2 0\n", "9 1900 2003 160000 4 0\n", "10 2500 2003 190000 2 1\n", "11 5000 2001 62000 4 0\n", "12 1800 1999 163000 2 1\n", "13 1300 1997 138000 4 0" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# calculate the number of rows in vehicles\n", "n_rows = vehicles.shape[0]\n", "\n", "# set a seed for reproducibility\n", "np.random.seed(123)\n", "\n", "# create three bootstrap samples (will be used to select rows from the DataFrame)\n", "sample1 = np.random.choice(a=n_rows, size=n_rows, replace=True)\n", "sample2 = np.random.choice(a=n_rows, size=n_rows, replace=True)\n", "sample3 = np.random.choice(a=n_rows, size=n_rows, replace=True)\n", "\n", "# print samples\n", "print sample1\n", "print sample2\n", "print sample3" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[13 2 12 2 6 1 3 10 11 9 6 1 0 1]\n", "[ 9 0 0 9 3 13 4 0 0 4 1 7 3 2]\n", "[ 4 7 2 4 8 13 0 7 9 3 12 12 4 6]\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# use sample1 to select rows from DataFrame\n", "print vehicles.iloc[sample1, :]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " price year miles doors type\n", "13 1300 1997 138000 4 0\n", "2 13000 2010 73500 4 0\n", "12 1800 1999 163000 2 1\n", "2 13000 2010 73500 4 0\n", "6 3000 2004 177000 4 0\n", "1 14000 2010 30000 2 0\n", "3 9500 2009 78000 4 0\n", "10 2500 2003 190000 2 1\n", "11 5000 2001 62000 4 0\n", "9 1900 2003 160000 4 0\n", "6 3000 2004 177000 4 0\n", "1 14000 2010 30000 2 0\n", "0 22000 2012 13000 2 0\n", "1 14000 2010 30000 2 0\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", "# grow one regression tree with each bootstrapped training set\n", "treereg1 = DecisionTreeRegressor(random_state=123)\n", "treereg1.fit(vehicles.iloc[sample1, 1:], vehicles.iloc[sample1, 0])\n", "\n", "treereg2 = DecisionTreeRegressor(random_state=123)\n", "treereg2.fit(vehicles.iloc[sample2, 1:], vehicles.iloc[sample2, 0])\n", "\n", "treereg3 = DecisionTreeRegressor(random_state=123)\n", "treereg3.fit(vehicles.iloc[sample3, 1:], vehicles.iloc[sample3, 0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "DecisionTreeRegressor(compute_importances=None, criterion='mse',\n", " max_depth=None, max_features=None, min_density=None,\n", " min_samples_leaf=1, min_samples_split=2, random_state=123,\n", " splitter='best')" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# read in out-of-sample data\n", "oos = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT4/master/data/used_vehicles_oos.csv')\n", "\n", "# convert car to 0 and truck to 1\n", "oos['type'] = oos.type.map({'car':0, 'truck':1})\n", "\n", "# print data\n", "oos" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
priceyearmilesdoorstype
0 3000 2003 130000 4 1
1 6000 2005 82500 4 0
2 12000 2010 60000 2 0
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " price year miles doors type\n", "0 3000 2003 130000 4 1\n", "1 6000 2005 82500 4 0\n", "2 12000 2010 60000 2 0" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# select feature columns (every column except for the 0th column)\n", "feature_cols = vehicles.columns[1:]\n", "\n", "# make predictions on out-of-sample data\n", "preds1 = treereg1.predict(oos[feature_cols])\n", "preds2 = treereg2.predict(oos[feature_cols])\n", "preds3 = treereg3.predict(oos[feature_cols])\n", "\n", "# print predictions\n", "print preds1\n", "print preds2\n", "print preds3" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 1300. 5000. 13000.]\n", "[ 1300. 1300. 14000.]\n", "[ 2000. 3000. 22000.]\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# average predictions and compare to actual values\n", "print (preds1 + preds2 + preds3)/3\n", "print oos.price.values" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 1533.33333333 3100. 16333.33333333]\n", "[ 3000 6000 12000]\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimating out-of-sample error\n", "\n", "Bagged models have a very nice property: **out-of-sample error can be estimated without using the test set approach or cross-validation!**\n", "\n", "Here's how the out-of-sample estimation process works with bagged trees:\n", "\n", "- On average, each bagged tree uses about two-thirds of the observations. **For each tree, the remaining observations are called \"out-of-bag\" observations.**\n", "- For the first observation in the training data, predict its response using **only** the trees in which that observation was out-of-bag. Average those predictions (for regression) or take a majority vote (for classification).\n", "- Repeat this process for every observation in the training data.\n", "- Compare all predictions to the actual responses in order to compute a mean squared error or classification error. This is known as the **out-of-bag error**.\n", "\n", "**When B is sufficiently large, the out-of-bag error is an accurate estimate of out-of-sample error.**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set is a data structure used to identify unique elements\n", "print set(range(14))\n", "\n", "# only show the unique elements in sample1\n", "print set(sample1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])\n", "set([0, 1, 2, 3, 6, 9, 10, 11, 12, 13])\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "# use the \"set difference\" to identify the out-of-bag observations for each tree\n", "print sorted(set(range(14)) - set(sample1))\n", "print sorted(set(range(14)) - set(sample2))\n", "print sorted(set(range(14)) - set(sample3))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[4, 5, 7, 8]\n", "[5, 6, 8, 10, 11, 12]\n", "[1, 5, 10, 11]\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus, we would predict the response for **observation 4** by using tree 1 (because it is only out-of-bag for tree 1). We would predict the response for **observation 5** by averaging the predictions from trees 1, 2, and 3 (since it is out-of-bag for all three trees). We would repeat this process for all observations, and then calculate the MSE using those predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimating variable importance\n", "\n", "Although bagging **increases predictive accuracy**, it **decreases model interpretability** because it's no longer possible to visualize the tree to understand the importance of each variable.\n", "\n", "However, we can still obtain an overall summary of \"variable importance\" from bagged models:\n", "\n", "- To compute variable importance for bagged regression trees, we can calculate the **total amount that the mean squared error is decreased due to splits over a given predictor, averaged over all trees**.\n", "- A similar process is used for bagged classification trees, except we use the Gini index instead of the mean squared error.\n", "\n", "(We'll see an example of this below.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forests\n", "\n", "Random Forests is a **slight variation of bagged trees** that has even better performance! Here's how it works:\n", "\n", "- Exactly like bagging, we create an ensemble of decision trees using bootstrapped samples of the training set.\n", "- However, when building each tree, **each time a split is considered**, a random sample of m predictors is chosen as split candidates from the full set of p predictors. **The split is only allowed to use one of those m predictors.**\n", "\n", "Notes:\n", "\n", "- A new random sample of predictors is chosen for **every single tree at every single split**.\n", "- For **classification**, m is typically chosen to be the square root of p. For **regression**, m is typically chosen to be somewhere between p/3 and p.\n", "\n", "What's the point?\n", "\n", "- Suppose there is one very strong predictor in the data set. When using bagged trees, most of the trees will use that predictor as the top split, resulting in an ensemble of similar trees that are \"highly correlated\".\n", "- Averaging highly correlated quantities does not significantly reduce variance (which is the entire goal of bagging).\n", "- **By randomly leaving out candidate predictors from each split, Random Forests \"decorrelates\" the trees**, such that the averaging process can reduce the variance of the resulting model." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# read in the Titanic data\n", "titanic = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT4/master/data/titanic.csv')\n", "\n", "# encode sex feature\n", "titanic['sex'] = titanic.sex.map({'female':0, 'male':1})\n", "\n", "# fill in missing values for age\n", "titanic.age.fillna(titanic.age.mean(), inplace=True)\n", "\n", "# create three dummy variables, drop the first dummy variable, and store this as a DataFrame\n", "embarked_dummies = pd.get_dummies(titanic.embarked, prefix='embarked').iloc[:, 1:]\n", "\n", "# concatenate the two dummy variable columns onto the original DataFrame\n", "# note: axis=0 means rows, axis=1 means columns\n", "titanic = pd.concat([titanic, embarked_dummies], axis=1)\n", "\n", "# create a list of feature columns\n", "feature_cols = ['pclass', 'sex', 'age', 'embarked_Q', 'embarked_S']\n", "\n", "# print the updated DataFrame\n", "titanic.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
survivedpclassnamesexagesibspparchticketfarecabinembarkedembarked_Qembarked_S
0 0 3 Braund, Mr. Owen Harris 1 22.000000 1 0 A/5 21171 7.2500 NaN S 0 1
1 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 38.000000 1 0 PC 17599 71.2833 C85 C 0 0
2 1 3 Heikkinen, Miss. Laina 0 26.000000 0 0 STON/O2. 3101282 7.9250 NaN S 0 1
3 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 35.000000 1 0 113803 53.1000 C123 S 0 1
4 0 3 Allen, Mr. William Henry 1 35.000000 0 0 373450 8.0500 NaN S 0 1
5 0 3 Moran, Mr. James 1 29.699118 0 0 330877 8.4583 NaN Q 1 0
6 0 1 McCarthy, Mr. Timothy J 1 54.000000 0 0 17463 51.8625 E46 S 0 1
7 0 3 Palsson, Master. Gosta Leonard 1 2.000000 3 1 349909 21.0750 NaN S 0 1
8 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 0 27.000000 0 2 347742 11.1333 NaN S 0 1
9 1 2 Nasser, Mrs. Nicholas (Adele Achem) 0 14.000000 1 0 237736 30.0708 NaN C 0 0
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ " survived pclass name sex \\\n", "0 0 3 Braund, Mr. Owen Harris 1 \n", "1 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 \n", "2 1 3 Heikkinen, Miss. Laina 0 \n", "3 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 \n", "4 0 3 Allen, Mr. William Henry 1 \n", "5 0 3 Moran, Mr. James 1 \n", "6 0 1 McCarthy, Mr. Timothy J 1 \n", "7 0 3 Palsson, Master. Gosta Leonard 1 \n", "8 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 0 \n", "9 1 2 Nasser, Mrs. Nicholas (Adele Achem) 0 \n", "\n", " age sibsp parch ticket fare cabin embarked \\\n", "0 22.000000 1 0 A/5 21171 7.2500 NaN S \n", "1 38.000000 1 0 PC 17599 71.2833 C85 C \n", "2 26.000000 0 0 STON/O2. 3101282 7.9250 NaN S \n", "3 35.000000 1 0 113803 53.1000 C123 S \n", "4 35.000000 0 0 373450 8.0500 NaN S \n", "5 29.699118 0 0 330877 8.4583 NaN Q \n", "6 54.000000 0 0 17463 51.8625 E46 S \n", "7 2.000000 3 1 349909 21.0750 NaN S \n", "8 27.000000 0 2 347742 11.1333 NaN S \n", "9 14.000000 1 0 237736 30.0708 NaN C \n", "\n", " embarked_Q embarked_S \n", "0 0 1 \n", "1 0 0 \n", "2 0 1 \n", "3 0 1 \n", "4 0 1 \n", "5 1 0 \n", "6 0 1 \n", "7 0 1 \n", "8 0 1 \n", "9 0 0 " ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# import class, instantiate estimator, fit with all data\n", "from sklearn.ensemble import RandomForestClassifier\n", "rfclf = RandomForestClassifier(n_estimators=100, max_features=3, oob_score=True, random_state=1)\n", "rfclf.fit(titanic[feature_cols], titanic.survived)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "RandomForestClassifier(bootstrap=True, compute_importances=None,\n", " criterion='gini', max_depth=None, max_features=3,\n", " min_density=None, min_samples_leaf=1, min_samples_split=2,\n", " n_estimators=100, n_jobs=1, oob_score=True, random_state=1,\n", " verbose=0)" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the most important tuning parameters for Random Forests:\n", "\n", "- **n_estimators:** more estimators (trees) increases performance but decreases speed\n", "- **max_features:** cross-validate to choose an ideal value" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# compute the feature importances\n", "pd.DataFrame({'feature':feature_cols, 'importance':rfclf.feature_importances_})" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
featureimportance
0 pclass 0.166796
1 sex 0.371653
2 age 0.415432
3 embarked_Q 0.011912
4 embarked_S 0.034207
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ " feature importance\n", "0 pclass 0.166796\n", "1 sex 0.371653\n", "2 age 0.415432\n", "3 embarked_Q 0.011912\n", "4 embarked_S 0.034207" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# compute the out-of-bag classification accuracy\n", "rfclf.oob_score_" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "0.80022446689113358" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Use a random forest classifier on the Stack Overflow Kaggle competition data.\n", " * Try changing the tuning parameters, n_estimators and max_features.\n", "* Build an ensemble of your three favorite models. Use this ensemble to make predictions for the Stack Overflow Kaggle competition.\n", " * If you need help deciding, I like logistic regression with our derived features, a random forest classifier with our derived features, and a logistic regression model with the word features.\n", "* Look at the 17_ensembling_exercise.py code for help." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Wrapping up ensembling\n", "\n", "Ensembling is incredibly popular, when the **primary goal is predictive accuracy**. For example, the team that eventually won the $1 million [Netflix Prize](http://en.wikipedia.org/wiki/Netflix_Prize) used an [ensemble of 107 models](http://www2.research.att.com/~volinsky/papers/chance.pdf) early on in the competition.\n", "\n", "There was a recent paper in the Journal of Machine Learning Research titled \"[Do We Need Hundreds of Classifiers to Solve Real World Classification Problems?](http://jmlr.csail.mit.edu/papers/volume15/delgado14a/delgado14a.pdf)\" (**Spoiler alert:** Random Forests did very well.) In the [comments about the paper](https://news.ycombinator.com/item?id=8719723) on Hacker News, Ben Hamner (Kaggle's chief scientist) said the following:\n", "\n", "> This is consistent with our experience running hundreds of Kaggle competitions: for most classification problems, some variation on ensembled decision trees (random forests, gradient boosted machines, etc.) performs the best. This is typically in conjunction with clever data processing, feature selection, and internal validation.\n", "\n", "> One key exception is where the data is richly and hierarchically structured. Text, speech, and visual data falls under this category. In many cases here, variations of neural networks (deep neural nets/CNN's/RNN's/etc.) provide very dramatic improvements.\n", "\n", "But as you can imagine, ensembling may not often be practical in a real-time environment.\n", "\n", "**You can also build your own ensembles:** just build a variety of models and average them together! Here are some strategies for building independent models:\n", "\n", "- using different models\n", "- choosing different combinations of features\n", "- changing the tuning parameters\n", "\n", "Note that there is an entire class of well-known ensembling methods that we did not discuss, namely **boosting**. Instead of building independent models and averaging the predictions, the models are built sequentially on repeatedly modified versions of the data. More information is available in the scikit-learn documentation on Ensemble Methods, namely the sections on [AdaBoost](http://scikit-learn.org/stable/modules/ensemble.html#adaboost) and [Gradient Tree Boosting](http://scikit-learn.org/stable/modules/ensemble.html#gradient-tree-boosting)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resources\n", "\n", "- scikit-learn documentation: [Ensemble Methods](http://scikit-learn.org/stable/modules/ensemble.html)\n", "- Quora: [How do random forests work in layman's terms?](http://www.quora.com/How-do-random-forests-work-in-laymans-terms/answer/Edwin-Chen-1)" ] } ], "metadata": {} } ] } ================================================ FILE: notebooks/18_regularization.ipynb ================================================ { "metadata": { "name": "", "signature": "sha256:283eafa4edacfbbb8b51d404c8feab98319104a044aaa4138d97957373762033" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Overfitting, revisited\n", "\n", "What is overfitting? Here are a few ways of explaining it:\n", "\n", "- Building a model that matches the training set too closely.\n", "- Building a model that does well on the training data, but doesn't generalize to out-of-sample data.\n", "- Learning from the noise in the data, rather than just the signal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overfitting\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Underfitting vs Overfitting\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What are some ways to overfit the data?**\n", "\n", "- Train and test on the same data\n", "- Create a model that is overly complex (one that doesn't generalize well)\n", " - Example: KNN in which K is too low\n", " - Example: Decision tree that is grown too deep\n", "\n", "An overly complex model has **low bias** but **high variance**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Regression, revisited\n", "\n", "**Question:** Are linear regression models high bias/low variance, or low bias/high variance?\n", "\n", "**Answer:** High bias/low variance (generally speaking)\n", "\n", "Great! So as long as we don't train and test on the same data, we don't have to worry about overfitting, right? Not so fast...." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overfitting with Linear Regression (part 1)\n", "\n", "Linear models can overfit if you include irrelevant features.\n", "\n", "**Question:** Why would that be the case?\n", "\n", "**Answer:** Because it will learn a coefficient for any feature you feed into the model, regardless of whether that feature has the signal or the noise.\n", "\n", "This is especially a problem when **p (number of features) is close to n (number of observations)**, because that model will naturally have high variance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overfitting with Linear Regression (part 2)\n", "\n", "Linear models can also overfit when the included features are highly correlated. From the [scikit-learn documentation](http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares):\n", "\n", "> \"...coefficient estimates for Ordinary Least Squares rely on the independence of the model terms. When terms are correlated and the columns of the design matrix X have an approximate linear dependence, the design matrix becomes close to singular and as a result, the least-squares estimate becomes highly sensitive to random errors in the observed response, producing a large variance.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overfitting with Linear Regression (part 3)\n", "\n", "Linear models can also overfit if the coefficients are too large.\n", "\n", "**Question:** Why would that be the case?\n", "\n", "**Answer:** Because the larger the absolute value of the coefficient, the more power it has to change the predicted response. Thus it tends toward high variance, which can result in overfitting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regularization\n", "\n", "Regularization is a method for \"constraining\" or \"regularizing\" the size of the coefficients, thus \"shrinking\" them towards zero. It tends to reduce variance more than it increases bias, and thus minimizes overfitting.\n", "\n", "Common regularization techniques for linear models:\n", "\n", "- **Ridge regression** (also known as \"L2 regularization\"): shrinks coefficients toward zero (but they never reach zero)\n", "- **Lasso regularization** (also known as \"L1 regularization\"): shrinks coefficients all the way to zero\n", "- **ElasticNet regularization**: balance between Ridge and Lasso\n", "\n", "Lasso regularization is useful if we believe many features are irrelevant, since a feature with a zero coefficient is essentially removed from the model. Thus, it is a useful technique for feature selection.\n", "\n", "How does regularization work?\n", "\n", "- A tuning parameter alpha (or sometimes lambda) imposes a penalty on the size of coefficients.\n", "- Instead of minimizing the \"loss function\" (mean squared error), it minimizes the \"loss plus penalty\".\n", "- A tiny alpha imposes no penalty on the coefficient size, and is equivalent to a normal linear model.\n", "- Increasing the alpha penalizes the coefficients and shrinks them toward zero." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bias-variance trade-off\n", "\n", "Our goal is to locate the optimum model complexity, and thus regularization is useful when we believe our model is too complex.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Standardizing features\n", "\n", "It's usually recommended to standardize your features when using regularization.\n", "\n", "**Question:** Why would that be the case?\n", "\n", "**Answer:** If you don't standardize, features would be penalized simply because of their scale. Also, standardizing avoids penalizing the intercept (which wouldn't make intuitive sense)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ridge vs Lasso Coefficient Plots\n", "\n", "Below is a visualization of what happens when you apply regularization. The general idea is that you are restricting the \"space\" in which your coefficients can be fit. This means you are shriking the coefficient space. You still want the coefficients that give the \"best\" model (as determined by you metric, e.g. RMSE, accuracy, AUC, etc), but you are restricting the area in which you can evaluate coefficients.\n", "\n", "In this specific image, we are fitting a model with two predictors, B1 and B2. The x-axis shows B1 and the y-axis shows B2. There is a third dimension here, our evaluation metric. For the sake of example, we can assume this is linear regression, so we are trying to minimize our Root Mean Squared Error (RMSE). B-hat represents the set of coefficients, B1 and B2, where RMSE is minimized. While this is the \"best\" model according to our criterion, we've imposed a penalty that restricts the coefficients to the blue box. So we want to find the point (representing two coeffcients B1 and B2) where RMSE is minimized within our blue box. Technically, the RMSE will be higher here, but it will be the lowest within our penalized box. Due to the shape or space for the regression problem and the shape of our penalty box, many of the \"optimal\" coefficients will be close to zero for Ridge Regression and exactly zero for LASSO Regression.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ridge vs Lasso path diagrams\n", "\n", "Larger alpha (on the left here) means more regularization, which means more coefficients close to zero.\n", "" ] } ], "metadata": {} } ] } ================================================ FILE: other/peer_review.md ================================================ ## Peer Review Guidelines You will be assigned to review the project drafts of two of your peers. You will have one week to provide them with feedback. Expectations: * Read everything they wrote! * If they provided their data, review it and try to understand it. * Read their code and try to understand their thought process. * If their code can be run, try running it. * Spend at least one hour reviewing their project (including the time it takes to write the feedback). Your feedback would ideally consist of: * Strengths of their project (things you particularly like about it) * Comments about things you think could be improved * Questions about things you don't understand * Comments about their code * Suggestions for next steps * Guiding principle: Give feedback that would be helpful to you if it was your project! You should take a quick glance through their project as soon as possible, to make sure you understand what they have given you and what files you should be reviewing. If you're unclear, ask them about it! ================================================ FILE: other/project.md ================================================ # Course Project ## Overview The final project should represent significant original work applying data science techniques to an interesting problem. Final projects are individual attainments, but you should be talking frequently with your instructors and classmates about them. Address a data-related problem in your professional field or a field you're interested in. Pick a subject that you're passionate about; if you're strongly interested in the subject matter it'll be more fun for you and you'll produce a better project! To stimulate your thinking, here is an excellent list of [public data sources](public_data.md). Using public data is the most common choice. If you have access to private data, that's also an option, though you'll have to be careful about what results you can release. You are also welcome to compete in a [Kaggle competition](http://www.kaggle.com/) as your project, in which case the data will be provided to you. You should also take a look at [past projects](https://github.com/justmarkham/DAT-project-examples) from other GA Data Science students, to get a sense of the variety and scope of projects. ## Project Milestones ### March 30: Deadline for Discussing Project Ideas with an Instructor By March 30, you should talk with one of your instructors about your project idea(s). They can help you to choose between different project ideas, advise you on the appropriate scope for your project, and ensure that your project question might reasonably be answerable using the data science tools and techniques taught in the course. ### April 6: Project Question and Dataset Create a GitHub repository for your project. It should include a document that answers these questions: What is the question you hope to answer? What data are you planning to use to answer that question? What do you know about the data so far? Why did you choose this topic? Example: * I'm planning to predict passenger survival on the Titanic. * I have Kaggle's Titanic dataset with 10 passenger characteristics. * I know that many of the fields have missing values, that some of the text fields are messy and will require cleaning, and that about 38% of the passengers in the training set survive. * I chose this topic because I'm fascinated by the history of the Titanic. ### April 27: Project Presentation #1: Data Exploration and Analysis Plan You'll be giving a presentation to the class about the work you have done so far, as well as your plans for the project going forward. Your presentation should use slides (or a similar format). Your slides, exploratory code, and visualizations should be included in your GitHub repository. Here are some questions that you should address in your presentation: What data have you gathered, and how did you gather it? What steps have you taken to explore the data? Which areas of the data have you cleaned, and which areas still need cleaning? What insights have you gained from your exploration? Will you be able to answer your question with this data, or do you need to gather more data (or adjust your question)? How might you use modeling to answer your question? Example: * I've created visualizations and numeric summaries to explore how survivability differs by passenger characteristic, and it appears that gender and class have a large role in determining survivability. * I estimated missing values for age using the titles provided in the Name column. * I created features to represent "spouse on board" and "child on board" by further analyzing names. * I think that the fare and ticket columns might be useful for predicting survival, but I still need to clean those columns. * I analyzed the differences between the training and testing sets, and found that the average fare was slightly higher in the testing set. * Since I'm predicting a binary outcome, I plan to use a classification method such as logistic regression to make my predictions. ### May 18: First Draft Due **At a minimum**, your project repository on GitHub should contain: * A draft of your project paper (in the format specified [below](#june-3-project-presentation-2)) * Code, with lots of comments * Visualizations of your data **Ideally**, you would also include: * Draft slides for presentation #2 * Data and data dictionary Your peers and instructors will provide feedback by May 25, according to [these guidelines](peer_review.md). **Tips for success:** * The work should stand "on its own", and should not depend upon the reader remembering your first project presentation. * The better you explain your project, and the easier it is to follow, the more useful feedback you will receive! * If your reviewers can actually run your code on the provided data, they will be able to give you more useful feedback on your code. (It can be very hard to make useful code suggestions on code that can't be run!) ### June 3: Project Presentation #2 Your **project paper** should be written with a technical audience in mind. Here are the components you should cover: * Problem statement and hypothesis * Description of your data set and how it was obtained * Description of any pre-processing steps you took * What you learned from exploring the data, including visualizations * How you chose which features to use in your analysis * Details of your modeling process, including how you selected your models and validated them * Your challenges and successes * Possible extensions or business applications of your project * Conclusions and key learnings Your **presentation** should cover these components with less breadth and depth. Focus on creating an engaging, clear, and informative presentation that tells the story of your project and is suitable for a non-technical audience. Your project repository on GitHub should contain the following: * **Project paper:** any format (PDF, Markdown, etc.) * **Presentation slides:** any format (PDF, PowerPoint, Google Slides, etc.) * **Code:** commented Python scripts, and any other code you used in the project * **Visualizations:** integrated into your paper and/or slides * **Data:** data files in "raw" or "processed" format * **Data dictionary (aka "code book"):** description of each variable, including units If it's not possible or practical to include your entire dataset, you should link to your data source and provide a sample of the data. (GitHub has a [size limit](https://help.github.com/articles/what-is-my-disk-quota/) of 100 MB per file and 1 GB per repository.) If your data is private, you can either include an "anonymized" version of your data or create a private GitHub repository. ================================================ FILE: other/public_data.md ================================================ ## Public Data Sources * Open data catalogs from various governments and NGOs: * [NYC Open Data](https://nycopendata.socrata.com/) * [DC Open Data Catalog](http://data.dc.gov/) / [OpenDataDC](http://www.opendatadc.org/) * [DataLA](https://data.lacity.org/) * [data.gov](https://www.data.gov/) (see also: [Project Open Data Dashboard](http://data.civicagency.org/)) * [data.gov.uk](http://data.gov.uk/) * [US Census Bureau](http://www.census.gov/) * [World Bank Open Data](http://data.worldbank.org/) * [Humanitarian Data Exchange](http://docs.hdx.rwlabs.org/) * [Sunlight Foundation](http://sunlightfoundation.com/api/): government-focused data * [ProPublica Data Store](https://projects.propublica.org/data-store/) * Datasets hosted by academic institutions: * [UC Irvine Machine Learning Repository](http://archive.ics.uci.edu/ml/): datasets specifically designed for machine learning * [Stanford Large Network Dataset Collection](http://snap.stanford.edu/data/): graph data * [Inter-university Consortium for Political and Social Research](http://www.icpsr.umich.edu/) * [Pittsburgh Science of Learning Center's DataShop](http://www.learnlab.org/technologies/datashop/) * [Academic Torrents](http://academictorrents.com/): distributed network for sharing large research datasets * [Dataverse Project](http://dataverse.org/): searchable archive of research data * Datasets hosted by private companies: * [Quandl](https://www.quandl.com/): over 10 million financial, economic, and social datasets * [Amazon Web Services Public Data Sets](http://aws.amazon.com/datasets/) * [Kaggle](http://www.kaggle.com/) provides datasets with their challenges, but each competition has its own rules as to whether the data can be used outside of the scope of the competition. * Big lists of datasets: * [Awesome Public Datasets](https://github.com/caesar0301/awesome-public-datasets): Well-organized and frequently updated * [Rdatasets](http://vincentarelbundock.github.io/Rdatasets/): collection of 700+ datasets originally distributed with R packages * [RDataMining.com](http://www.rdatamining.com/resources/data) * [KDnuggets](http://www.kdnuggets.com/datasets/index.html) * [inside-R](http://www.inside-r.org/howto/finding-data-internet) * [100+ Interesting Data Sets for Statistics](http://rs.io/2014/05/29/list-of-data-sets.html) * [20 Free Big Data Sources](http://smartdatacollective.com/bernardmarr/235366/big-data-20-free-big-data-sources-everyone-should-know) * [Sebastian Raschka](https://github.com/rasbt/pattern_classification/blob/master/resources/dataset_collections.md): datasets categorized by format and topic * APIs: * [Apigee](https://apigee.com/providers): explore dozens of popular APIs * [Mashape](https://www.mashape.com/): explore hundreds of APIs * [Python APIs](http://www.pythonforbeginners.com/api/list-of-python-apis): Python wrappers for many APIs * Other interesting datasets: * [FiveThirtyEight](https://github.com/fivethirtyeight/data): data and code related to their articles * [The Upshot](https://github.com/TheUpshot/): data related to their articles * [Yelp Dataset Challenge](http://www.yelp.com/dataset_challenge): Yelp reviews, business attributes, users, and more from 10 cities * [Donors Choose](http://data.donorschoose.org/open-data/overview/): data related to their projects * [200,000+ Jeopardy questions](http://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/) * [CrowdFlower](http://www.crowdflower.com/data-for-everyone): interesting datasets created or enhanced by their contributors * [UFO reports](https://github.com/planetsig/ufo-reports): geolocated and time-standardized UFO reports for close to a century * [Reddit Top 2.5 Million](https://github.com/umbrae/reddit-top-2.5-million): all-time top 1,000 posts from each of the top 2,500 subreddits * Other resources: * [Datasets subreddit](http://www.reddit.com/r/datasets/): ask for help finding a specific data set, or post your own * [Center for Data Innovation](http://www.datainnovation.org/category/publications/data-set-blog/): blog posts about interesting, recently-released data sets. This is just the tip of the iceberg; there's a lot of data out there! ================================================ FILE: other/resources.md ================================================ # Resources for Continued Learning ## Blogs * [Simply Statistics](http://simplystatistics.org/): Written by the Biostatistics professors at Johns Hopkins University who also run Coursera's [Data Science Specialization](https://www.coursera.org/specialization/jhudatascience/1) * [yhat's blog](http://blog.yhathq.com/): Beginner-friendly content, usually in Python * [No Free Hunch](http://blog.kaggle.com/) (Kaggle's blog): Mostly interviews with competition winners, or updates on their competitions * [FastML](http://fastml.com/): Various machine learning content, often with code * [Edwin Chen](http://blog.echen.me/): Infrequently updated, but long and thoughtful pieces * [FiveThirtyEight](http://fivethirtyeight.com/): Tons of timely data-related content * [Machine Learning Mastery](http://machinelearningmastery.com/blog/): Frequent posts on machine learning, very accessible * [Data School](http://www.dataschool.io/): Kevin Markham's blog! Beginner-focused, with reference guides and videos * [MLWave](http://mlwave.com/): Detailed posts on Kaggle competitions, by a Kaggle Master * [Data Science 101](http://101.datascience.community/): Short, frequent content about all aspects of data science * [ML in the Valley](http://ml.posthaven.com/): Thoughtful pieces by the Director of Analytics at Codecademy ## Aggregators * [DataTau](http://www.datatau.com/): Like [Hacker News](https://news.ycombinator.com/), but for data * [MachineLearning on reddit](http://www.reddit.com/r/MachineLearning/): Very active subreddit * [Quora's Machine Learning section](http://www.quora.com/Machine-Learning): Lots of interesting Q&A * [Quora's Data Science topic FAQ](https://www.quora.com/What-is-the-Data-Science-topic-FAQ) * [KDnuggets](http://www.kdnuggets.com/): Data mining news, jobs, classes and more ## DC Data Groups * [Data Community DC](http://www.datacommunitydc.org/): Coordinates six local data-related meetup groups * [District Data Labs](http://www.districtdatalabs.com/): Offers courses and other projects to local data scientists ## Online Classes * [Coursera's Data Science Specialization](https://www.coursera.org/specialization/jhudatascience/1): Nine courses (running every month) and a Capstone project, taught in R * [Stanford's Statistical Learning](http://online.stanford.edu/course/statistical-learning): By the authors of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) and [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/), taught in R, highly recommended (preview the [lecture videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)) * [Coursera's Machine Learning](https://www.coursera.org/learn/machine-learning/): Andrew Ng's acclaimed course, taught in MATLAB/Octave * [Caltech's Learning from Data](http://work.caltech.edu/telecourse.html): Widely praised, not language-specific * [Udacity's Data Analyst Nanodegree](https://www.udacity.com/course/nd002): Project-based curriculum using Python, R, MapReduce, MongoDB * [Coursera's Data Mining Specialization](https://www.coursera.org/specialization/datamining/20): New specialization that began February 2015 * [Coursera's Natural Language Processing](https://www.coursera.org/course/nlp): No upcoming sessions, but [lectures](https://class.coursera.org/nlp/lecture) and [slides](http://web.stanford.edu/~jurafsky/NLPCourseraSlides.html) are available * [SlideRule's Data Analysis Learning Path](https://www.mysliderule.com/learning-paths/data-analysis): Curated content from various online classes * [Udacity's Intro to Artificial Intelligence](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271): Taught by Peter Norvig and Sebastian Thrun * [Coursera's Neural Networks for Machine Learning](https://www.coursera.org/course/neuralnets): Taught by Geoffrey Hinton, no upcoming sessions * [statistics.com](http://www.statistics.com/data-science/): Many online courses in data science * [CourseTalk](http://www.coursetalk.com/): Read reviews of online courses ## Online Content from Offline Classes * [Harvard's CS109 Data Science](http://cs109.github.io/2014/): Similar topics as General Assembly's course * [Columbia's Data Mining Class](http://www2.research.att.com/~volinsky/DataMining/Columbia2011/Columbia2011.html): Excellent slides * [Harvard's CS171 Visualization](http://www.cs171.org/2015/index.html): Includes programming in D3 ## Face-to-Face Educational Programs * [Comparison of data science bootcamps](http://yet-another-data-blog.blogspot.com/2014/04/data-science-bootcamp-landscape-full.html): Up-to-date list maintained by a Zipfian Academy graduate * [The Complete List of Data Science Bootcamps & Fellowships](http://www.skilledup.com/articles/list-data-science-bootcamps/) * [Galvanize](http://www.galvanize.com/) (acquired [Zipfian Academy](http://www.zipfianacademy.com/)): Offers Data Science Immersive (Denver, Seattle, San Francisco) * [GalvanizeU](http://www.galvanizeu.com/): Offers Master of Engineering in Big Data (San Francisco) * [Data Science Retreat](http://datascienceretreat.com/): Primarily uses R (Berlin) * [Metis Data Science Bootcamp](http://www.thisismetis.com/data-science): Newer bootcamp (New York) * [Persontyle](http://www.persontyle.com/): Various course offerings (based in London) * [Software Carpentry](http://software-carpentry.org/): Two-day workshops, primarily for researchers and hosted by universities (worldwide) * [Colleges and Universities with Data Science Degrees](http://datascience.community/colleges) ## Conferences * [Knowledge Discovery and Data Mining (KDD)](http://www.kdd.org/): Hosted by ACM * [O'Reilly Strata + Hadoop World](http://strataconf.com/): Big focus on "big data" (San Jose, London, New York) * [PyData](http://pydata.org/): For developers and users of Python data tools (worldwide) * [PyCon](https://us.pycon.org/): For developers and users of Python (Portland in 2016) ## Books * [An Introduction to Statistical Learning with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) (free PDF) * [Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) (free PDF) * [Think Stats](http://www.greenteapress.com/thinkstats/) (free PDF or HTML) * [Mining of Massive Datasets](http://www.mmds.org/) (free PDF) * [Python for Informatics](http://www.pythonlearn.com/book.php) (free PDF or HTML) * [Statistics: Methods and Applications](http://www.statsoft.com/Textbook) (free HTML) * [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) * [Data Smart: Using Data Science to Transform Information into Insight](http://www.amazon.com/gp/product/111866146X/) * [Sams Teach Yourself SQL in 10 Minutes](http://www.amazon.com/Sams-Teach-Yourself-Minutes-Edition/dp/0672336073) ## Other Resources * [Open Source Data Science Masters](https://github.com/datasciencemasters/go): Huge list of resources * [Data Science Trello Board](https://trello.com/b/rbpEfMld/data-science): Another list of resources * [The Hitchhiker's Guide to Python](http://docs.python-guide.org/en/latest/): Online guide to understanding Python and getting good at it * [Python Reference](https://github.com/rasbt/python_reference): Python tips, tutorials, and more * [videolectures.net](http://videolectures.net/Top/Computer_Science/): Tons of academic videos * [Metacademy](http://www.metacademy.org/list): Quick summary of many machine learning terms, with links to resources for learning more * [Terms in data science defined in one paragraph](https://github.com/rasbt/pattern_classification/blob/master/resources/data_glossary.md) ================================================ FILE: slides/02_Introduction_to_the_Command_Line.md ================================================ # Introduction to the Command Line This document outlines some basic commands for the Unix command line. For Linux and OS X users, these should work in **Terminal**. For Windows users, most of these will work in **Git Bash**. **Note**: Most of these commands will not work in the Windows Command Prompt. ### What is the command line? The Command Line Interface (CLI) is a way of interacting with your computer based upon the text commands you type. This is different from the way most people interact with their computers using their mouse and a Graphical User Interface (GUI). ### Why should I use it? Once you become comfortable with the basics, it is a much more powerful way to use your computer. You're able to do many things more quickly and programatically. Also, you look cooler when using the command line. ### General Command Format ` - ` * `` is the actual command * `` (AKA “flags”) specify different ways of or options for executing the command * `` are things that go “into” the command to be operated on ### General Tips * If there are spaces in file/folder names, use a “\” to “escape” the space characters or put the entire file path in quotes. * Type the first few letters of the file or folder name, then hit tab to autocomplete the file or folder name. This often auto-escapes spaces for you. * Use the up and down arrow keys to navigate previously entered commands. ### Getting Help With a Command For Linux/Mac users, the `man` command will give much more detailed information (a short **man**ual) about a command. To use it type `man ` where `` is any command line command. ` --help` may also print out the "help" page about a particular command. ## File Paths ##### Relative File Paths A relative file path specifies the path to a file taking into account what your current working directory is. For example, if you were to give someone directions to your house, you would give the directions with context of leaving their house (the relative path from where they are to where you are). ##### Absolute File Paths An absolute file path specifies the path to a file assuming there is no knowledge of what your current working directory is. This means the file path starts at the root directory. For example, if you were to give someone directions from their house to your house, you would start by telling them to be on earth, then go to your continent, then go to your country, then go to your region, etc. ## Basic Commands ##### `pwd` * **P**rints **w**orking **d**irectory (the directory/folder you are currently in) * Lets you see where you are in your file system * Try it out; type `pwd` into your command line. ##### `ls` * `ls` **l**i**s**ts files/folders in your current directory * Can list files in a specific directory (file path) with `ls ` * Optional arguments * `ls -a` lists **a**ll files, including the hidden files * `ls -l` lists the files in a **l**ist with extra information * File permissions (owner, group, all users) * Number of links * Owner name * Owner group * File size * Created/Last modified date * File/Folder name * `ls *` will list all files and the contents of all folders in your current working directory. ##### `clear` * **Clear**s all output from your console ##### `cd` * `cd ` **c**hanges **d**irectory to the path you specify. You can use a relative path or an absolute path. * `cd ..` moves your working directory "up" one directory. * `cd` moves your working directory to your "home" directory. * Using `cd` and `ls` to navigate and look at your files at the command line is like clicking folders in Finder/Document Explorer. ##### `mkdir ` * `mkdir ` **m**a**k**es/creates a new **dir**ectory/folder named ``. ##### `touch` * `touch ` creates an empty file with the name ``. * This is useful for creating empty files to be edited at a later time. ##### `rm -i` * `rm -i ` **r**e**m**oves a file. The -i flag prompts you to confirm that you really want to delete the file. * **NOTE**: Be careful with `rm`. Once you `rm` a file, it is gone forever. That's why it's smart to always use `rm -i`. * `rm -ir ` removes a folder and everything within it. * **Note**: The `-r` option means **r**ecursive. It's often needed for operations on a folder. ##### `mv` * `mv ` will **m**o**v**e a file from its `` to a ``. * This is the same as dragging and dropping a file from one place to another. * `mv ` renames a file. You "move" it from one location, ``, to another location, ``, though it doesn't actually move locations. ##### `cp` * `cp ` will **c**o**p**y a file from its `` to a ``, leaving the original file untouched. * This is different from `mv` in that you are creating a new file and putting it somewhere rather than just moving the current file. ##### `zip`/`unzip` * `zip ` will zip/compress the `` into a ``. * `zip -r ` will zip/compress the `` and all of its files into a ``. * **Note**: A list of files and folders, separated by spaces, can be zipped into one zipped file with `zip -r ...`. * `unzip ` will unzip/uncompress a file. ## Exercise One * Create an empty directory called `test`. * Change your working directory to this new `test` directory. * Create *three* empty files in your `test` directory using `touch`. * Remove *one* of the previously created files. * Go up one directory and remove the `test` directory with the empty files in it. **NOTE**: Be careful and make sure you are removing the correct files! * Create an empty file called `test.txt`. * Change the name to `data_science_is_cool.txt`. * Create an empty file called `my_secrets.txt`. * Create a new directory called `my_diary`. * Move the two previously created files into this directory. * Create a new directory called `my_blog`. Copy the file `data_science_is_cool.txt` from `my_diary` to `my_blog`. * Examine the files in `my_blog` to confirm that `data_science_is_cool.txt` is there. * Zip the folders `my_diary` and `my_blog` into a new file called `writings.zip`. * Delete the folders `my_diary` and `my_blog`. * Unzip `writings.zip` to get your folders back. * Confirm that the two folders `my_diary` and `my_blog` have been recreated. * Once you finish, you can delete everything: `writings.zip`, `my_diary`, and `my_blog`. ## Advanced Commands ##### `find` * `find -name ` will search the specified path and **find** files and folders of the given ``. * If you want to search your current working diretory (`pwd`), you can use the `.` (a period) instead of a specifc path. `.` means your current working directory. * `` can be the full name, but it can also be a partial match using wild card characters. * \* specifies any number of any characters * `find -name '*.csv'` will find all of the CSV files in your current directory. * ? specifies one character * `find -name '0?_submission.csv'` would find the following files in your current working directory: `01_submission.csv`, `02_submission.csv`, and `03_submission.csv` but not `10_submission.csv`. * This is a very useful and powerful command. ##### `head` * `head ` prints the **head** (first ten lines) of the ``. * This is useful for looking at the beginning of a large file before performing a command on it. ##### `tail` * `tail ` prints the **tail** (last ten lines) of the ``. * This is useful for looking at the end of a large file before performing a command on it. ##### `cat` * `cat ` prints the entire ``. * This is useful sending the contents of the file into another command (more on that below). ##### `less` * `less ` prints out enough of the file to fill up the console window and allows you to "scroll" through the file. * Type `q` or `:q` to get out of the file and return to the command line. ##### `grep` * `grep ` **g**lobally searches for a **r**egular **e**xpression and **p**rints the matches to the console. * Works for partial matches * Returns the line that matched. * `grep ` searches a `` for the `` and prints the matching lines to the console. * If no file is given, `grep` will operate on the output from the previous command. ##### `wc` * `wc ` returns the **c**ount of lines, **w**ords, and characters in a file. * **Note**: A word is any set of characters delimited by space. * If no file is given, `wc` will opearte on the output from the previous command. * Combining `wc` and `grep` is a useful way of determining how many occurences of a specific word there are in a file. ##### `|` * Let’s you **pipe** commands into each other * ` | | ` pipes these three commands into each other. `` completes and the results of it are piped into ``. `` completes and the results of it are piped into ``. `` completes and the results of it are printed to the console. * This concept is a very powerful one on the command line. This allows you to do complex things all from the command line. * Many commands will use the output from a previous command as the input for the next command. ##### `>` * ` > ` takes the output of the `` and saves it in a ``. * This is useful for saving the results of a command to a file. ## Exercise Two * Navigate to the directory where you cloned the DAT5 repository. * Find all of the CSV files in the `data` directory. How many are there? * How many Markdown files are there in the DAT5 directory? * How many lines are in the `airline_safety.csv` data? * Using `airline_safety.csv`, return a list of the airlines that have a '\*' in the name. How many airlines are there in this list? Write these airlines to a file called `starred_airlines.csv`. **Note**: Make sure to put the \* in quotes. * Open the file `starred_airlines.csv` to confirm it was correctly created. Then, remove the file. * Using `chipotle_orders.tsv`, how many 'Steak Burrito's contain 'Black Beans'? Are there more 'Steak Burrito's with 'Pinto Beans'? ## Command Line Utilities ##### `sudo` * `sudo ` runs the `` with admin access. * Stands for **S**uper **u**ser **d**o * Use with caution as this gives your computer unrestricted access when performing the command that follows. This is sometimes necessary to install software or access certain, restricted directories. ##### `nano` * `nano ` will open the file in the NANO text editor. * `Ctrl + X` will e**x**it the editor and prompt you to save the file or exit without saving. * I would not recommend doing a lot of work in NANO, though it is useful to be aware of. ##### `vim` * `vim ` will open the file in the VIM text editor. But you can't start typing right away. * Type `i` to enable **i**nsert mode and start typing. * Use `:` to start a command * `:w` to **w**rite/save a file * `:x` to write/save the file you are working on and e**x**it the text editor * `:q` to quit/exit the text editor. * I would not recommend doing a lot of work in VIM, though it is useful to be aware of. ## Homework * For the following homework, please turn in the code used to generate the answer as well as the answer. * Using the command line, look at the file `SMSSpamCollection.txt` (Source: https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip) in the `data` directory. It contains text messages that are labeled as spam or ham (the opposite of spam). Answer the following questions: * How many text messages are there? * What is the average number of words per text? What is the average number of characters per text? * How many messages are spam? How many are ham? * Is there a difference between the number of words per text and characters per text in messages that are spam vs. those that are ham? What are these numbers? * **Bonus**: If you feel that this is too easy, research the `awk` command to learn how to calculate and print out these averages in the console. * Separate the spam and ham messages into files "spam_messages.txt" and "ham_messages.txt".