Repository: minsuk-heo/kaggle-titanic Branch: master Commit: 96c14e776090 Files: 4 Total size: 649.7 KB Directory structure: gitextract_awgsv_c6/ ├── input/ │ ├── gender_submission.csv │ ├── test.csv │ └── train.csv └── titanic-solution.ipynb ================================================ FILE CONTENTS ================================================ ================================================ FILE: input/gender_submission.csv ================================================ PassengerId,Survived 892,0 893,1 894,0 895,0 896,1 897,0 898,1 899,0 900,1 901,0 902,0 903,0 904,1 905,0 906,1 907,1 908,0 909,0 910,1 911,1 912,0 913,0 914,1 915,0 916,1 917,0 918,1 919,0 920,0 921,0 922,0 923,0 924,1 925,1 926,0 927,0 928,1 929,1 930,0 931,0 932,0 933,0 934,0 935,1 936,1 937,0 938,0 939,0 940,1 941,1 942,0 943,0 944,1 945,1 946,0 947,0 948,0 949,0 950,0 951,1 952,0 953,0 954,0 955,1 956,0 957,1 958,1 959,0 960,0 961,1 962,1 963,0 964,1 965,0 966,1 967,0 968,0 969,1 970,0 971,1 972,0 973,0 974,0 975,0 976,0 977,0 978,1 979,1 980,1 981,0 982,1 983,0 984,1 985,0 986,0 987,0 988,1 989,0 990,1 991,0 992,1 993,0 994,0 995,0 996,1 997,0 998,0 999,0 1000,0 1001,0 1002,0 1003,1 1004,1 1005,1 1006,1 1007,0 1008,0 1009,1 1010,0 1011,1 1012,1 1013,0 1014,1 1015,0 1016,0 1017,1 1018,0 1019,1 1020,0 1021,0 1022,0 1023,0 1024,1 1025,0 1026,0 1027,0 1028,0 1029,0 1030,1 1031,0 1032,1 1033,1 1034,0 1035,0 1036,0 1037,0 1038,0 1039,0 1040,0 1041,0 1042,1 1043,0 1044,0 1045,1 1046,0 1047,0 1048,1 1049,1 1050,0 1051,1 1052,1 1053,0 1054,1 1055,0 1056,0 1057,1 1058,0 1059,0 1060,1 1061,1 1062,0 1063,0 1064,0 1065,0 1066,0 1067,1 1068,1 1069,0 1070,1 1071,1 1072,0 1073,0 1074,1 1075,0 1076,1 1077,0 1078,1 1079,0 1080,1 1081,0 1082,0 1083,0 1084,0 1085,0 1086,0 1087,0 1088,0 1089,1 1090,0 1091,1 1092,1 1093,0 1094,0 1095,1 1096,0 1097,0 1098,1 1099,0 1100,1 1101,0 1102,0 1103,0 1104,0 1105,1 1106,1 1107,0 1108,1 1109,0 1110,1 1111,0 1112,1 1113,0 1114,1 1115,0 1116,1 1117,1 1118,0 1119,1 1120,0 1121,0 1122,0 1123,1 1124,0 1125,0 1126,0 1127,0 1128,0 1129,0 1130,1 1131,1 1132,1 1133,1 1134,0 1135,0 1136,0 1137,0 1138,1 1139,0 1140,1 1141,1 1142,1 1143,0 1144,0 1145,0 1146,0 1147,0 1148,0 1149,0 1150,1 1151,0 1152,0 1153,0 1154,1 1155,1 1156,0 1157,0 1158,0 1159,0 1160,1 1161,0 1162,0 1163,0 1164,1 1165,1 1166,0 1167,1 1168,0 1169,0 1170,0 1171,0 1172,1 1173,0 1174,1 1175,1 1176,1 1177,0 1178,0 1179,0 1180,0 1181,0 1182,0 1183,1 1184,0 1185,0 1186,0 1187,0 1188,1 1189,0 1190,0 1191,0 1192,0 1193,0 1194,0 1195,0 1196,1 1197,1 1198,0 1199,0 1200,0 1201,1 1202,0 1203,0 1204,0 1205,1 1206,1 1207,1 1208,0 1209,0 1210,0 1211,0 1212,0 1213,0 1214,0 1215,0 1216,1 1217,0 1218,1 1219,0 1220,0 1221,0 1222,1 1223,0 1224,0 1225,1 1226,0 1227,0 1228,0 1229,0 1230,0 1231,0 1232,0 1233,0 1234,0 1235,1 1236,0 1237,1 1238,0 1239,1 1240,0 1241,1 1242,1 1243,0 1244,0 1245,0 1246,1 1247,0 1248,1 1249,0 1250,0 1251,1 1252,0 1253,1 1254,1 1255,0 1256,1 1257,1 1258,0 1259,1 1260,1 1261,0 1262,0 1263,1 1264,0 1265,0 1266,1 1267,1 1268,1 1269,0 1270,0 1271,0 1272,0 1273,0 1274,1 1275,1 1276,0 1277,1 1278,0 1279,0 1280,0 1281,0 1282,0 1283,1 1284,0 1285,0 1286,0 1287,1 1288,0 1289,1 1290,0 1291,0 1292,1 1293,0 1294,1 1295,0 1296,0 1297,0 1298,0 1299,0 1300,1 1301,1 1302,1 1303,1 1304,1 1305,0 1306,1 1307,0 1308,0 1309,0 ================================================ FILE: input/test.csv ================================================ PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 898,3,"Connolly, Miss. 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Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C 829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q 830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28, 831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C 832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S 833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C 834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S 835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S 836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C 837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S 838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S 839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S 840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C 841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S 842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S 843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C 844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C 845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S 846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S 847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S 848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C 849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S 850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C 851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S 852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S 853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C 854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S 855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S 856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S 857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S 858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S 859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C 860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C 861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S 862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S 863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S 864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S 889,0,3,"Johnston, Miss. 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: titanic-solution.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "6a09d4fb-60c5-4f45-b844-8c788a50c543", "_uuid": "8e892e637f005dd61ec7dcb95865e52f3de2a77f" }, "source": [ "# Titanic: Machine Learning from Disaster\n", "### Predict survival on the Titanic\n", "- Defining the problem statement\n", "- Collecting the data\n", "- Exploratory data analysis\n", "- Feature engineering\n", "- Modelling\n", "- Testing" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4af5e83d-7fd8-4a61-bf26-9583cb6d3476", "_uuid": "65d04d276a8983f62a49261f6e94a02b281dbcc9" }, "source": [ "## 1. Defining the problem statement\n", "Complete the analysis of what sorts of people were likely to survive. \n", "In particular, we ask you to apply the tools of machine learning to predict which passengers survived the Titanic tragedy." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(url= \"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3f529075-7f9b-40ff-a79a-f3a11a7d8cbe", "_uuid": "64ca0f815766e3e8074b0e04f53947930cb061aa" }, "source": [ "## 2. Collecting the data\n", "\n", "training data set and testing data set are given by Kaggle\n", "you can download from \n", "my github [https://github.com/minsuk-heo/kaggle-titanic/tree/master](https://github.com/minsuk-heo/kaggle-titanic) \n", "or you can download from kaggle directly [kaggle](https://www.kaggle.com/c/titanic/data) \n", "\n", "### load train, test dataset using Pandas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "e58a3f06-4c2a-4b87-90de-f8b09039fd4e", "_uuid": "46f0b12d7bf66712642e9a9b807f5ef398426b83", "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "train = pd.read_csv('input/train.csv')\n", "test = pd.read_csv('input/test.csv')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "836a454f-17bc-41a2-be69-cd86c6f3b584", "_uuid": "1ed3ad39ead93977b8936d9c96e6f6f806a8f9b3" }, "source": [ "## 3. Exploratory data analysis\n", "Printing first 5 rows of the train dataset." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "749a3d70-394c-4d2c-999a-4d0567e39232", "_uuid": "b9fdb3b19d7a8f30cd0bb69ae434e04121ecba93" }, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.0010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.0010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.0000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.001011380353.1000C123S
4503Allen, Mr. William Henrymale35.00003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.00001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.003134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.000234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.001023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale4.0011PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale58.000011378326.5500C103S
121303Saundercock, Mr. William Henrymale20.0000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale39.001534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14.00003504067.8542NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female55.000024870616.0000NaNS
161703Rice, Master. Eugenemale2.004138265229.1250NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female31.001034576318.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
202102Fynney, Mr. Joseph Jmale35.000023986526.0000NaNS
212212Beesley, Mr. Lawrencemale34.000024869813.0000D56S
222313McGowan, Miss. Anna \"Annie\"female15.00003309238.0292NaNQ
232411Sloper, Mr. William Thompsonmale28.000011378835.5000A6S
242503Palsson, Miss. Torborg Danirafemale8.003134990921.0750NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.001534707731.3875NaNS
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
272801Fortune, Mr. Charles Alexandermale19.003219950263.0000C23 C25 C27S
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
293003Todoroff, Mr. LaliomaleNaN003492167.8958NaNS
.......................................
505103Panula, Master. Juha Niilomale7.0041310129539.6875NaNS
515203Nosworthy, Mr. Richard Catermale21.0000A/4. 398867.8000NaNS
525311Harper, Mrs. Henry Sleeper (Myna Haxtun)female49.0010PC 1757276.7292D33C
535412Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...female29.0010292626.0000NaNS
545501Ostby, Mr. Engelhart Corneliusmale65.000111350961.9792B30C
555611Woolner, Mr. HughmaleNaN001994735.5000C52S
565712Rugg, Miss. Emilyfemale21.0000C.A. 3102610.5000NaNS
575803Novel, Mr. Mansouermale28.500026977.2292NaNC
585912West, Miss. Constance Miriumfemale5.0012C.A. 3465127.7500NaNS
596003Goodwin, Master. William Frederickmale11.0052CA 214446.9000NaNS
606103Sirayanian, Mr. Orsenmale22.000026697.2292NaNC
616211Icard, Miss. Ameliefemale38.000011357280.0000B28NaN
626301Harris, Mr. Henry Birkhardtmale45.00103697383.4750C83S
636403Skoog, Master. Haraldmale4.003234708827.9000NaNS
646501Stewart, Mr. Albert AmaleNaN00PC 1760527.7208NaNC
656613Moubarek, Master. GeriosmaleNaN11266115.2458NaNC
666712Nye, Mrs. (Elizabeth Ramell)female29.0000C.A. 2939510.5000F33S
676803Crease, Mr. Ernest Jamesmale19.0000S.P. 34648.1583NaNS
686913Andersson, Miss. Erna Alexandrafemale17.004231012817.9250NaNS
697003Kink, Mr. Vincenzmale26.00203151518.6625NaNS
707102Jenkin, Mr. Stephen Curnowmale32.0000C.A. 3311110.5000NaNS
717203Goodwin, Miss. Lillian Amyfemale16.0052CA 214446.9000NaNS
727302Hood, Mr. Ambrose Jrmale21.0000S.O.C. 1487973.5000NaNS
737403Chronopoulos, Mr. Apostolosmale26.0010268014.4542NaNC
747513Bing, Mr. Leemale32.0000160156.4958NaNS
757603Moen, Mr. Sigurd Hansenmale25.00003481237.6500F G73S
767703Staneff, Mr. IvanmaleNaN003492087.8958NaNS
777803Moutal, Mr. Rahamin HaimmaleNaN003747468.0500NaNS
787912Caldwell, Master. Alden Gatesmale0.830224873829.0000NaNS
798013Dowdell, Miss. Elizabethfemale30.000036451612.4750NaNS
\n", "

80 rows × 12 columns

\n", "
" ], "text/plain": [ " 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", "10 11 1 3 \n", "11 12 1 1 \n", "12 13 0 3 \n", "13 14 0 3 \n", "14 15 0 3 \n", "15 16 1 2 \n", "16 17 0 3 \n", "17 18 1 2 \n", "18 19 0 3 \n", "19 20 1 3 \n", "20 21 0 2 \n", "21 22 1 2 \n", "22 23 1 3 \n", "23 24 1 1 \n", "24 25 0 3 \n", "25 26 1 3 \n", "26 27 0 3 \n", "27 28 0 1 \n", "28 29 1 3 \n", "29 30 0 3 \n", ".. ... ... ... \n", "50 51 0 3 \n", "51 52 0 3 \n", "52 53 1 1 \n", "53 54 1 2 \n", "54 55 0 1 \n", "55 56 1 1 \n", "56 57 1 2 \n", "57 58 0 3 \n", "58 59 1 2 \n", "59 60 0 3 \n", "60 61 0 3 \n", "61 62 1 1 \n", "62 63 0 1 \n", "63 64 0 3 \n", "64 65 0 1 \n", "65 66 1 3 \n", "66 67 1 2 \n", "67 68 0 3 \n", "68 69 1 3 \n", "69 70 0 3 \n", "70 71 0 2 \n", "71 72 0 3 \n", "72 73 0 2 \n", "73 74 0 3 \n", "74 75 1 3 \n", "75 76 0 3 \n", "76 77 0 3 \n", "77 78 0 3 \n", "78 79 1 2 \n", "79 80 1 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.00 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.00 1 \n", "2 Heikkinen, Miss. Laina female 26.00 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 \n", "4 Allen, Mr. William Henry male 35.00 0 \n", "5 Moran, Mr. James male NaN 0 \n", "6 McCarthy, Mr. Timothy J male 54.00 0 \n", "7 Palsson, Master. Gosta Leonard male 2.00 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.00 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.00 1 \n", "10 Sandstrom, Miss. Marguerite Rut female 4.00 1 \n", "11 Bonnell, Miss. Elizabeth female 58.00 0 \n", "12 Saundercock, Mr. William Henry male 20.00 0 \n", "13 Andersson, Mr. Anders Johan male 39.00 1 \n", "14 Vestrom, Miss. Hulda Amanda Adolfina female 14.00 0 \n", "15 Hewlett, Mrs. (Mary D Kingcome) female 55.00 0 \n", "16 Rice, Master. Eugene male 2.00 4 \n", "17 Williams, Mr. Charles Eugene male NaN 0 \n", "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.00 1 \n", "19 Masselmani, Mrs. Fatima female NaN 0 \n", "20 Fynney, Mr. Joseph J male 35.00 0 \n", "21 Beesley, Mr. Lawrence male 34.00 0 \n", "22 McGowan, Miss. Anna \"Annie\" female 15.00 0 \n", "23 Sloper, Mr. William Thompson male 28.00 0 \n", "24 Palsson, Miss. Torborg Danira female 8.00 3 \n", "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.00 1 \n", "26 Emir, Mr. Farred Chehab male NaN 0 \n", "27 Fortune, Mr. Charles Alexander male 19.00 3 \n", "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n", "29 Todoroff, Mr. Lalio male NaN 0 \n", ".. ... ... ... ... \n", "50 Panula, Master. Juha Niilo male 7.00 4 \n", "51 Nosworthy, Mr. Richard Cater male 21.00 0 \n", "52 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.00 1 \n", "53 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... female 29.00 1 \n", "54 Ostby, Mr. Engelhart Cornelius male 65.00 0 \n", "55 Woolner, Mr. Hugh male NaN 0 \n", "56 Rugg, Miss. Emily female 21.00 0 \n", "57 Novel, Mr. Mansouer male 28.50 0 \n", "58 West, Miss. Constance Mirium female 5.00 1 \n", "59 Goodwin, Master. William Frederick male 11.00 5 \n", "60 Sirayanian, Mr. Orsen male 22.00 0 \n", "61 Icard, Miss. Amelie female 38.00 0 \n", "62 Harris, Mr. Henry Birkhardt male 45.00 1 \n", "63 Skoog, Master. Harald male 4.00 3 \n", "64 Stewart, Mr. Albert A male NaN 0 \n", "65 Moubarek, Master. Gerios male NaN 1 \n", "66 Nye, Mrs. (Elizabeth Ramell) female 29.00 0 \n", "67 Crease, Mr. Ernest James male 19.00 0 \n", "68 Andersson, Miss. Erna Alexandra female 17.00 4 \n", "69 Kink, Mr. Vincenz male 26.00 2 \n", "70 Jenkin, Mr. Stephen Curnow male 32.00 0 \n", "71 Goodwin, Miss. Lillian Amy female 16.00 5 \n", "72 Hood, Mr. Ambrose Jr male 21.00 0 \n", "73 Chronopoulos, Mr. Apostolos male 26.00 1 \n", "74 Bing, Mr. Lee male 32.00 0 \n", "75 Moen, Mr. Sigurd Hansen male 25.00 0 \n", "76 Staneff, Mr. Ivan male NaN 0 \n", "77 Moutal, Mr. Rahamin Haim male NaN 0 \n", "78 Caldwell, Master. Alden Gates male 0.83 0 \n", "79 Dowdell, Miss. Elizabeth female 30.00 0 \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 \n", "10 1 PP 9549 16.7000 G6 S \n", "11 0 113783 26.5500 C103 S \n", "12 0 A/5. 2151 8.0500 NaN S \n", "13 5 347082 31.2750 NaN S \n", "14 0 350406 7.8542 NaN S \n", "15 0 248706 16.0000 NaN S \n", "16 1 382652 29.1250 NaN Q \n", "17 0 244373 13.0000 NaN S \n", "18 0 345763 18.0000 NaN S \n", "19 0 2649 7.2250 NaN C \n", "20 0 239865 26.0000 NaN S \n", "21 0 248698 13.0000 D56 S \n", "22 0 330923 8.0292 NaN Q \n", "23 0 113788 35.5000 A6 S \n", "24 1 349909 21.0750 NaN S \n", "25 5 347077 31.3875 NaN S \n", "26 0 2631 7.2250 NaN C \n", "27 2 19950 263.0000 C23 C25 C27 S \n", "28 0 330959 7.8792 NaN Q \n", "29 0 349216 7.8958 NaN S \n", ".. ... ... ... ... ... \n", "50 1 3101295 39.6875 NaN S \n", "51 0 A/4. 39886 7.8000 NaN S \n", "52 0 PC 17572 76.7292 D33 C \n", "53 0 2926 26.0000 NaN S \n", "54 1 113509 61.9792 B30 C \n", "55 0 19947 35.5000 C52 S \n", "56 0 C.A. 31026 10.5000 NaN S \n", "57 0 2697 7.2292 NaN C \n", "58 2 C.A. 34651 27.7500 NaN S \n", "59 2 CA 2144 46.9000 NaN S \n", "60 0 2669 7.2292 NaN C \n", "61 0 113572 80.0000 B28 NaN \n", "62 0 36973 83.4750 C83 S \n", "63 2 347088 27.9000 NaN S \n", "64 0 PC 17605 27.7208 NaN C \n", "65 1 2661 15.2458 NaN C \n", "66 0 C.A. 29395 10.5000 F33 S \n", "67 0 S.P. 3464 8.1583 NaN S \n", "68 2 3101281 7.9250 NaN S \n", "69 0 315151 8.6625 NaN S \n", "70 0 C.A. 33111 10.5000 NaN S \n", "71 2 CA 2144 46.9000 NaN S \n", "72 0 S.O.C. 14879 73.5000 NaN S \n", "73 0 2680 14.4542 NaN C \n", "74 0 1601 56.4958 NaN S \n", "75 0 348123 7.6500 F G73 S \n", "76 0 349208 7.8958 NaN S \n", "77 0 374746 8.0500 NaN S \n", "78 2 248738 29.0000 NaN S \n", "79 0 364516 12.4750 NaN S \n", "\n", "[80 rows x 12 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head(80)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Dictionary\n", "- Survived: \t0 = No, 1 = Yes \n", "- pclass: \tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd \t\n", "- sibsp:\t# of siblings / spouses aboard the Titanic \t\n", "- parch:\t# of parents / children aboard the Titanic \t\n", "- ticket:\tTicket number\t\n", "- cabin:\tCabin number\t\n", "- embarked:\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5ebc1e0e-2b5a-4d92-98e0-defa019d4439", "_uuid": "1892fbb34b26d775d1c428fdb7b6254449286b28" }, "source": [ "**Total rows and columns**\n", "\n", "We can see that there are 891 rows and 12 columns in our training dataset." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n", "
" ], "text/plain": [ " PassengerId Pclass Name Sex \\\n", "0 892 3 Kelly, Mr. James male \n", "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", "2 894 2 Myles, Mr. Thomas Francis male \n", "3 895 3 Wirz, Mr. Albert male \n", "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked \n", "0 34.5 0 0 330911 7.8292 NaN Q \n", "1 47.0 1 0 363272 7.0000 NaN S \n", "2 62.0 0 0 240276 9.6875 NaN Q \n", "3 27.0 0 0 315154 8.6625 NaN S \n", "4 22.0 1 1 3101298 12.2875 NaN S " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "ed1e7849-d1b6-490d-b86b-9ca71dfafc7d", "_uuid": "5a641beccf0e555dfd7b9a53a17188ea6edef95b" }, "outputs": [ { "data": { "text/plain": [ "(891, 12)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(418, 11)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "418b8a69-f2aa-442d-8f45-fa8887190938", "_uuid": "4ee2591110660a4a16b3da7a7530f0945e121b46" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", "PassengerId 891 non-null int64\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.6+ KB\n" ] } ], "source": [ "train.info()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 418 entries, 0 to 417\n", "Data columns (total 11 columns):\n", "PassengerId 418 non-null int64\n", "Pclass 418 non-null int64\n", "Name 418 non-null object\n", "Sex 418 non-null object\n", "Age 332 non-null float64\n", "SibSp 418 non-null int64\n", "Parch 418 non-null int64\n", "Ticket 418 non-null object\n", "Fare 417 non-null float64\n", "Cabin 91 non-null object\n", "Embarked 418 non-null object\n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 36.0+ KB\n" ] } ], "source": [ "test.info()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "abc3c4fc-6419-405f-927a-4214d2c73eec", "_uuid": "622d4d4b2ba8f77cc537af97fc343d4cd6de26b2" }, "source": [ "We can see that *Age* value is missing for many rows. \n", "\n", "Out of 891 rows, the *Age* value is present only in 714 rows.\n", "\n", "Similarly, *Cabin* values are also missing in many rows. Only 204 out of 891 rows have *Cabin* values." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "0663e2bb-dc27-4187-94b1-ff4ff78b68bc", "_uuid": "3bf74de7f2483d622e41608f6017f2945639e4df" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 177\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 86\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 1\n", "Cabin 327\n", "Embarked 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "176aa52d-fde8-42e6-a3ee-db31f8b0ca49", "_uuid": "b48a9feff6004d783960aa1b32fdfde902d87e21" }, "source": [ "There are 177 rows with missing *Age*, 687 rows with missing *Cabin* and 2 rows with missing *Embarked* information." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c8553d48-c5e0-4947-bd13-1b38509c850c", "_uuid": "1a28e607e9ed63cefe0f35a4e4d72f2f36299323" }, "source": [ "### import python lib for visualization" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "b1d8a6d2-c22d-435c-8c98-973e8f41b138", "_uuid": "26411c710f69b29939c815d5f5ab01d9177df7d0", "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "sns.set() # setting seaborn default for plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar Chart for Categorical Features\n", "- Pclass\n", "- Sex\n", "- SibSp ( # of siblings and spouse)\n", "- Parch ( # of parents and children)\n", "- Embarked\n", "- Cabin" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def bar_chart(feature):\n", " survived = train[train['Survived']==1][feature].value_counts()\n", " dead = train[train['Survived']==0][feature].value_counts()\n", " df = pd.DataFrame([survived,dead])\n", " df.index = ['Survived','Dead']\n", " df.plot(kind='bar',stacked=True, figsize=(10,5))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bar_chart('Sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Chart confirms **Women** more likely survivied than **Men**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bar_chart('Pclass')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Chart confirms **1st class** more likely survivied than **other classes** \n", "The Chart confirms **3rd class** more likely dead than **other classes**" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bar_chart('SibSp')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Chart confirms **a person aboarded with more than 2 siblings or spouse** more likely survived \n", "The Chart confirms ** a person aboarded without siblings or spouse** more likely dead" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bar_chart('Parch')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Chart confirms **a person aboarded with more than 2 parents or children** more likely survived \n", "The Chart confirms ** a person aboarded alone** more likely dead" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bar_chart('Embarked')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Chart confirms **a person aboarded from C** slightly more likely survived \n", "The Chart confirms **a person aboarded from Q** more likely dead \n", "The Chart confirms **a person aboarded from S** more likely dead" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "810cd964-24eb-44fb-9e7b-18bbddd4900f", "_uuid": "fd86ccdf2d1248b79c68365444e96e46a50f3f5a" }, "source": [ "## 4. Feature engineering\n", "\n", "Feature engineering is the process of using domain knowledge of the data \n", "to create features (**feature vectors**) that make machine learning algorithms work. \n", "\n", "feature vector is an n-dimensional vector of numerical features that represent some object. \n", "Many algorithms in machine learning require a numerical representation of objects, \n", "since such representations facilitate processing and statistical analysis." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", "
" ], "text/plain": [ " 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", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \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 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 how titanic sank?\n", "sank from the bow of the ship where third class rooms located \n", "conclusion, Pclass is key feature for classifier" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(url= \"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
\n", "
" ], "text/plain": [ " 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 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "5 Moran, Mr. James male NaN 0 \n", "6 McCarthy, Mr. Timothy J male 54.0 0 \n", "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 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 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Name" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_test_data = [train, test] # combining train and test dataset\n", "\n", "for dataset in train_test_data:\n", " dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Mr 517\n", "Miss 182\n", "Mrs 125\n", "Master 40\n", "Dr 7\n", "Rev 6\n", "Col 2\n", "Major 2\n", "Mlle 2\n", "Countess 1\n", "Ms 1\n", "Lady 1\n", "Jonkheer 1\n", "Don 1\n", "Mme 1\n", "Capt 1\n", "Sir 1\n", "Name: Title, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train['Title'].value_counts()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Mr 240\n", "Miss 78\n", "Mrs 72\n", "Master 21\n", "Col 2\n", "Rev 2\n", "Dona 1\n", "Ms 1\n", "Dr 1\n", "Name: Title, dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test['Title'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Title map\n", "Mr : 0 \n", "Miss : 1 \n", "Mrs: 2 \n", "Others: 3\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n", " \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n", " \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n", "for dataset in train_test_data:\n", " dataset['Title'] = dataset['Title'].map(title_mapping)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedTitle
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS0
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C2
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS1
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S2
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS0
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" ], "text/plain": [ " 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", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked Title \n", "0 0 A/5 21171 7.2500 NaN S 0 \n", "1 0 PC 17599 71.2833 C85 C 2 \n", "2 0 STON/O2. 3101282 7.9250 NaN S 1 \n", "3 0 113803 53.1000 C123 S 2 \n", "4 0 373450 8.0500 NaN S 0 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedTitle
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ0
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS2
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ0
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS0
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS2
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" ], "text/plain": [ " PassengerId Pclass Name Sex \\\n", "0 892 3 Kelly, Mr. James male \n", "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", "2 894 2 Myles, Mr. Thomas Francis male \n", "3 895 3 Wirz, Mr. Albert male \n", "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked Title \n", "0 34.5 0 0 330911 7.8292 NaN Q 0 \n", "1 47.0 1 0 363272 7.0000 NaN S 2 \n", "2 62.0 0 0 240276 9.6875 NaN Q 0 \n", "3 27.0 0 0 315154 8.6625 NaN S 0 \n", "4 22.0 1 1 3101298 12.2875 NaN S 2 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.head()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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48963female22.011310129812.2875NaNS2
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" ], "text/plain": [ " PassengerId Pclass Sex Age SibSp Parch Ticket Fare Cabin \\\n", "0 892 3 male 34.5 0 0 330911 7.8292 NaN \n", "1 893 3 female 47.0 1 0 363272 7.0000 NaN \n", "2 894 2 male 62.0 0 0 240276 9.6875 NaN \n", "3 895 3 male 27.0 0 0 315154 8.6625 NaN \n", "4 896 3 female 22.0 1 1 3101298 12.2875 NaN \n", "\n", " Embarked Title \n", "0 Q 0 \n", "1 S 2 \n", "2 Q 0 \n", "3 S 0 \n", "4 S 2 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 Sex\n", "\n", "male: 0\n", "female: 1" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sex_mapping = {\"male\": 0, \"female\": 1}\n", "for dataset in train_test_data:\n", " dataset['Sex'] = dataset['Sex'].map(sex_mapping)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bar_chart('Sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.4 Age" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.4.1 some age is missing\n", "Let's use Title's median age for missing Age" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
0103022.0010A/5 211717.2500NaNS0
1211138.0010PC 1759971.2833C85C2
2313126.0000STON/O2. 31012827.9250NaNS1
3411135.001011380353.1000C123S2
4503035.00003734508.0500NaNS0
56030NaN003308778.4583NaNQ0
6701054.00001746351.8625E46S0
780302.003134990921.0750NaNS3
8913127.000234774211.1333NaNS2
91012114.001023773630.0708NaNC2
10111314.0011PP 954916.7000G6S1
111211158.000011378326.5500C103S1
121303020.0000A/5. 21518.0500NaNS0
131403039.001534708231.2750NaNS0
141503114.00003504067.8542NaNS1
151612155.000024870616.0000NaNS2
16170302.004138265229.1250NaNQ3
1718120NaN0024437313.0000NaNS0
181903131.001034576318.0000NaNS2
1920131NaN0026497.2250NaNC2
202102035.000023986526.0000NaNS0
212212034.000024869813.0000D56S0
222313115.00003309238.0292NaNQ1
232411028.000011378835.5000A6S0
24250318.003134990921.0750NaNS1
252613138.001534707731.3875NaNS2
2627030NaN0026317.2250NaNC0
272801019.003219950263.0000C23 C25 C27S0
2829131NaN003309597.8792NaNQ1
2930030NaN003492167.8958NaNS0
.......................................
707102032.0000C.A. 3311110.5000NaNS0
717203116.0052CA 214446.9000NaNS1
727302021.0000S.O.C. 1487973.5000NaNS0
737403026.0010268014.4542NaNC0
747513032.0000160156.4958NaNS0
757603025.00003481237.6500F G73S0
7677030NaN003492087.8958NaNS0
7778030NaN003747468.0500NaNS0
78791200.830224873829.0000NaNS3
798013130.000036451612.4750NaNS1
808103022.00003457679.0000NaNS0
818213029.00003457799.5000NaNS0
8283131NaN003309327.7875NaNQ1
838401028.000011305947.1000NaNS0
848512117.0000SO/C 1488510.5000NaNS1
858613133.0030310127815.8500NaNS2
868703016.0013W./C. 660834.3750NaNS0
8788030NaN00SOTON/OQ 3920868.0500NaNS0
888911123.003219950263.0000C23 C25 C27S1
899003024.00003432758.0500NaNS0
909103029.00003432768.0500NaNS0
919203020.00003474667.8542NaNS0
929301046.0010W.E.P. 573461.1750E31S0
939403026.0012C.A. 231520.5750NaNS0
949503059.00003645007.2500NaNS0
9596030NaN003749108.0500NaNS0
969701071.0000PC 1775434.6542A5C0
979811023.0001PC 1775963.3583D10 D12C0
989912134.000123191923.0000NaNS2
9910002034.001024436726.0000NaNS0
\n", "

100 rows × 12 columns

\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n", "0 1 0 3 0 22.00 1 0 A/5 21171 \n", "1 2 1 1 1 38.00 1 0 PC 17599 \n", "2 3 1 3 1 26.00 0 0 STON/O2. 3101282 \n", "3 4 1 1 1 35.00 1 0 113803 \n", "4 5 0 3 0 35.00 0 0 373450 \n", "5 6 0 3 0 NaN 0 0 330877 \n", "6 7 0 1 0 54.00 0 0 17463 \n", "7 8 0 3 0 2.00 3 1 349909 \n", "8 9 1 3 1 27.00 0 2 347742 \n", "9 10 1 2 1 14.00 1 0 237736 \n", "10 11 1 3 1 4.00 1 1 PP 9549 \n", "11 12 1 1 1 58.00 0 0 113783 \n", "12 13 0 3 0 20.00 0 0 A/5. 2151 \n", "13 14 0 3 0 39.00 1 5 347082 \n", "14 15 0 3 1 14.00 0 0 350406 \n", "15 16 1 2 1 55.00 0 0 248706 \n", "16 17 0 3 0 2.00 4 1 382652 \n", "17 18 1 2 0 NaN 0 0 244373 \n", "18 19 0 3 1 31.00 1 0 345763 \n", "19 20 1 3 1 NaN 0 0 2649 \n", "20 21 0 2 0 35.00 0 0 239865 \n", "21 22 1 2 0 34.00 0 0 248698 \n", "22 23 1 3 1 15.00 0 0 330923 \n", "23 24 1 1 0 28.00 0 0 113788 \n", "24 25 0 3 1 8.00 3 1 349909 \n", "25 26 1 3 1 38.00 1 5 347077 \n", "26 27 0 3 0 NaN 0 0 2631 \n", "27 28 0 1 0 19.00 3 2 19950 \n", "28 29 1 3 1 NaN 0 0 330959 \n", "29 30 0 3 0 NaN 0 0 349216 \n", ".. ... ... ... ... ... ... ... ... \n", "70 71 0 2 0 32.00 0 0 C.A. 33111 \n", "71 72 0 3 1 16.00 5 2 CA 2144 \n", "72 73 0 2 0 21.00 0 0 S.O.C. 14879 \n", "73 74 0 3 0 26.00 1 0 2680 \n", "74 75 1 3 0 32.00 0 0 1601 \n", "75 76 0 3 0 25.00 0 0 348123 \n", "76 77 0 3 0 NaN 0 0 349208 \n", "77 78 0 3 0 NaN 0 0 374746 \n", "78 79 1 2 0 0.83 0 2 248738 \n", "79 80 1 3 1 30.00 0 0 364516 \n", "80 81 0 3 0 22.00 0 0 345767 \n", "81 82 1 3 0 29.00 0 0 345779 \n", "82 83 1 3 1 NaN 0 0 330932 \n", "83 84 0 1 0 28.00 0 0 113059 \n", "84 85 1 2 1 17.00 0 0 SO/C 14885 \n", "85 86 1 3 1 33.00 3 0 3101278 \n", "86 87 0 3 0 16.00 1 3 W./C. 6608 \n", "87 88 0 3 0 NaN 0 0 SOTON/OQ 392086 \n", "88 89 1 1 1 23.00 3 2 19950 \n", "89 90 0 3 0 24.00 0 0 343275 \n", "90 91 0 3 0 29.00 0 0 343276 \n", "91 92 0 3 0 20.00 0 0 347466 \n", "92 93 0 1 0 46.00 1 0 W.E.P. 5734 \n", "93 94 0 3 0 26.00 1 2 C.A. 2315 \n", "94 95 0 3 0 59.00 0 0 364500 \n", "95 96 0 3 0 NaN 0 0 374910 \n", "96 97 0 1 0 71.00 0 0 PC 17754 \n", "97 98 1 1 0 23.00 0 1 PC 17759 \n", "98 99 1 2 1 34.00 0 1 231919 \n", "99 100 0 2 0 34.00 1 0 244367 \n", "\n", " Fare Cabin Embarked Title \n", "0 7.2500 NaN S 0 \n", "1 71.2833 C85 C 2 \n", "2 7.9250 NaN S 1 \n", "3 53.1000 C123 S 2 \n", "4 8.0500 NaN S 0 \n", "5 8.4583 NaN Q 0 \n", "6 51.8625 E46 S 0 \n", "7 21.0750 NaN S 3 \n", "8 11.1333 NaN S 2 \n", "9 30.0708 NaN C 2 \n", "10 16.7000 G6 S 1 \n", "11 26.5500 C103 S 1 \n", "12 8.0500 NaN S 0 \n", "13 31.2750 NaN S 0 \n", "14 7.8542 NaN S 1 \n", "15 16.0000 NaN S 2 \n", "16 29.1250 NaN Q 3 \n", "17 13.0000 NaN S 0 \n", "18 18.0000 NaN S 2 \n", "19 7.2250 NaN C 2 \n", "20 26.0000 NaN S 0 \n", "21 13.0000 D56 S 0 \n", "22 8.0292 NaN Q 1 \n", "23 35.5000 A6 S 0 \n", "24 21.0750 NaN S 1 \n", "25 31.3875 NaN S 2 \n", "26 7.2250 NaN C 0 \n", "27 263.0000 C23 C25 C27 S 0 \n", "28 7.8792 NaN Q 1 \n", "29 7.8958 NaN S 0 \n", ".. ... ... ... ... \n", "70 10.5000 NaN S 0 \n", "71 46.9000 NaN S 1 \n", "72 73.5000 NaN S 0 \n", "73 14.4542 NaN C 0 \n", "74 56.4958 NaN S 0 \n", "75 7.6500 F G73 S 0 \n", "76 7.8958 NaN S 0 \n", "77 8.0500 NaN S 0 \n", "78 29.0000 NaN S 3 \n", "79 12.4750 NaN S 1 \n", "80 9.0000 NaN S 0 \n", "81 9.5000 NaN S 0 \n", "82 7.7875 NaN Q 1 \n", "83 47.1000 NaN S 0 \n", "84 10.5000 NaN S 1 \n", "85 15.8500 NaN S 2 \n", "86 34.3750 NaN S 0 \n", "87 8.0500 NaN S 0 \n", "88 263.0000 C23 C25 C27 S 1 \n", "89 8.0500 NaN S 0 \n", "90 8.0500 NaN S 0 \n", "91 7.8542 NaN S 0 \n", "92 61.1750 E31 S 0 \n", "93 20.5750 NaN S 0 \n", "94 7.2500 NaN S 0 \n", "95 8.0500 NaN S 0 \n", "96 34.6542 A5 C 0 \n", "97 63.3583 D10 D12 C 0 \n", "98 23.0000 NaN S 2 \n", "99 26.0000 NaN S 0 \n", "\n", "[100 rows x 12 columns]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head(100)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n", "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n", "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 30.0\n", "1 35.0\n", "2 21.0\n", "3 35.0\n", "4 30.0\n", "5 30.0\n", "6 30.0\n", "7 9.0\n", "8 35.0\n", "9 35.0\n", "10 21.0\n", "11 21.0\n", "12 30.0\n", "13 30.0\n", "14 21.0\n", "15 35.0\n", "16 9.0\n", "17 30.0\n", "18 35.0\n", "19 35.0\n", "20 30.0\n", "21 30.0\n", "22 21.0\n", "23 30.0\n", "24 21.0\n", "25 35.0\n", "26 30.0\n", "27 30.0\n", "28 21.0\n", "29 30.0\n", " ... \n", "861 30.0\n", "862 35.0\n", "863 21.0\n", "864 30.0\n", "865 35.0\n", "866 21.0\n", "867 30.0\n", "868 30.0\n", "869 9.0\n", "870 30.0\n", "871 35.0\n", "872 30.0\n", "873 30.0\n", "874 35.0\n", "875 21.0\n", "876 30.0\n", "877 30.0\n", "878 30.0\n", "879 35.0\n", "880 35.0\n", "881 30.0\n", "882 21.0\n", "883 30.0\n", "884 30.0\n", "885 35.0\n", "886 9.0\n", "887 21.0\n", "888 21.0\n", "889 30.0\n", "890 30.0\n", "Name: Age, Length: 891, dtype: float64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head(30)\n", "train.groupby(\"Title\")[\"Age\"].transform(\"median\")" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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GN2nSJB544AGeffZZ/u3f/o0ZM2bwzW9+87zr+v1+fvrTn1JcXMy3vvUtamtr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\nIsD/AZK2a7RJ9MnMAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", " \n", "plt.show() " ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 20)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2ZwdgjBEANVlD3B9D3F+HuD+G+kAcjYEGNATiUOWZ+dUqly/i1BmjNETUCYQF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\niIiIiIiIAPw/aP8Q3mwhHY8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(0, 20)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20, 30)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TJyVJb775plfXsizv8eLFi/XjH/9Yhw4d0p/+6Z8WHXP58uX693//d506dUqX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\nAABEOAQAAAAAiHAIAAAAAJD0/wNcstWFCvwzggAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(20, 30)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(30, 40)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QBNQE3KgJuPEnrbUAgOFYylk64/zlCN58L4w338tNcrNwnr9gvUW/l5PcEBEREc0WDIdE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\nHZvfdY0NAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(30, 40)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(40, 60)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T64Sd8blOeDiVQFoJipXwGAxaHrYtPP76xXKvgv4jKkfl5HreU+c7nnJuXg0j\nBcvRlquCp+/4nGILABlHOASAs5DjmPi00rEMm+HJjWgObg8Cq96+SEeKgYLTDJu+Z6pGLoeOXvq+\nUUuhqCgM4+DpDQTQyrZ+2hbPu+7EBZfqQNp4is99HYlNbu5UHSBLUVnlcGBa21ZSOQpUCktpAC1F\n8Tr9YVnFcq/KYfm0rh01Msq7OTX6Dco5uZoQmXdzSbAcfbnBHQigjG4CwNgiHAIARjXcqbSnI7JW\n4WkEzcq0rz/U0Z5AwUk+XnQ4jlMJnc6wgdJPQmR87ebw/ZU2349Pq/V9I88d+2s4R2OMiZ9/6rjS\nGI2HxqOdkcpRSeUwSAJlWaVK0Bw0rWmrCqSB4tNrfxmVVY5Or3Ce8ZT3Rh7dHBw0Gd0EgOMjHAIA\nJpxjjBxf8v2x+Yu4tcn1l+FAcPRzvo4e7U/bg5obBKn2ZkHJdmEYX8PZeyzSe8Hpn04rDVzDWR0Y\nBwJl7UhmGjyr+ivbDMwn643DjYNGfh8meZxIoxpP428P1deCRjZSEAVDRjUro5bHH+ksj+vo5uAQ\nOWQU080r7w1arllvIJA65hQfmgsAdUA4BACc8Ywx8pOw2Zi0FQp5tTSd3nV31sbXYtYGy1Nv6ytF\nKvbGbWNxq3DPNfFjUbw4LMYvJ1323IF2zxsY4ay0p21V+xiubbxCqGOc9E6sY3GnooHRzerwWDrp\n0c1SWNbRUlGHk+s5T0fl7rLHC5TxyGcSRAedOjtcKPUcj9FNAOOGcAgAwHEYY+R78cjfWLI2vqNs\nEFiF4cANgapHMoeMcIaDAmfSFkZSGFgd64tUDOP1x/ohVa6r+BrNmiDqpOHU92pD6HBtlW2nHpX6\n+/qHhFDPNXKc0/ucB0Y3XTV6Y3MtZ+XazWGv00xPr61cr3mc4BmW1Rf262i5qFJ4ejcLcoyTBMWG\nYQNl3hs0mpm055JR0MojUaqnOefcfh4ugAGEQwAAJpgxRp4bj/yNhyhKTpEN4/AZhHGoDAObPDZF\nafvg9WqX4/XCqu0rITQMraJxCqGDRz4r4TEdKU3nnfhzTG4oVNM/eHqcNsfRiCNxlTCWd8fm7k6V\nO9UOGc08TtisPn128DbvlY6ofKysYAye1Vn9KJQ0NLrJMzidXDp6masJlr7yThxIB29bHTwZ6QTO\nHIRDAADOMo4Th56xuqbzeIYLoTXLg0JoGFo5rqveY+Wa9YYE0sCqrz9K58c6hFYzRmlQdD0jPwmP\nrmPSwOkcZpEYAAAPCElEQVQmQd51q9uGm468XhpgXcl1feXcnBr90x81DW2oIAyOEyJLKkWBgrCc\nPjqlHFXmBx6FEilUX7mk/rBfxXKPgqis0J7GA1Mrn6+MfMePn72ZhEXf9ZVzBi/HYXPourlk3Urf\nwHL1Op5xCaHAGCAcAgCAU3IqIbRQaFCx2HdSPyeyVlEohdFAyKydH6YtXb+qv7otmR+8375SpPDY\nQN845tIaxsTPO3UcxUFy0Hw8jT/zynza5xg5bmV7I9f15Dp+7Xauke8YNSTrxdsYuV68n0mTGnSs\nt79mn8aJFCqQNaEiBckrVGDLKttAQRhUBc1yenOhyuNTyuHQZ3QeC46ly2P6+Q0TQuNAORA6fceL\np27VvOMly8O1VZaHX49HqeBsRDgEAACZ5hgjx5M8TfzIUBRZRdFAgIyiOGxGldBZ6Q8HpkPaBm1T\nGXGtWc8O/KzKNkEolcpR3FbVnwWu68p1PLlOYxwyHaVh1BkUXF1X8it9Jg64xrEyxso4oeSGkhNK\nJpKcQLZqak0gq1DWxK/IhLIK06A6MA0V2kBhFOpYWFJRvQpt3DZeHDny3ZEDZKUtfq6pK8/xBl7G\nk+96x+lzNT2apOKRkvxB23iOJ79qfe6Ii7FEOAQAADiOyuhoPYLpcKyNbzgUJSEzqpqvBM+oemqH\naYuSZ41WtXmeq76+YNB+q9aPrMLj7SsJu+Ugqm076SDrSBqbazurPrEkdEaSE8okU5loYN6J4pBq\n4mXHjWTcuD+eVrar3UYmVNmJVDah5JQkcywJsVG8rwli5MiVK8e48fNNjSvXeDXznonDvJe0x8HS\nTcNmHDiTqTuwXHl5rld1aq8bnwqctg1sQ1g98xEOAQAAzhDGGBkjOY6kMQysp3K674mI7NAwmQbc\nJGxaqzTw2mT9uN9WtSfLSeAdsl7aPrDPyEo2Cbq2qi9droTgZD4N3UHVMVXvIzrOMSV9tacg2zRA\npqGyElLN2C+HNcslGacvXTbOxA43W2ukyJGxjmQdybrpvLGOJEfGujKKl40cGblyrCNjHDnWjZfl\nylHSVllOAq8jJ5lWhWDHjdcyXjyfhmI3uYtx3O44Jj4bwRkYyR5Yjv8xqHa5qt0x+t+tLRP6eU40\nwiEAAADGhWPi6yE1TnfmzRJrB0KktVXLdmDZ2jhEDmkbZr2Ghpx6e0sDAXS4fVW/VBWKg+oQbpNj\niU+/jWykSJEiGyWn6IZKWmRtpEihrCJZRclpvFE8Ipq0WROlbapZjsNwOnJqonTeOkGynIzkmgm6\nmtdKqrqZr7WKA2sUh1UbOZI1tW3WkaKkLV2n0m/0vz9078Qce50QDgEAAIDTZIyJM3B6VuXpBeJC\nIa9icaJuiTSxbFVArYTV0IZJYI0URqFCOzCN7EB/fC1ppCgKk+2q9mPDQfutCsNKwrCJFDlJ+E1+\nnk2uXa0E4IycRV4XhEMAAAAAE8aY5ETRjIaw6tAa2bBm/mxHOAQAAACAhGOcc/bGOufmuwYAAAAA\n1CAcAgAAAAAIhwAAAAAAwiEAAAAAQIRDAAAAAIBOIBxGUaQ1a9aovb1dy5cv18GDB2v6t2/frra2\nNrW3t+u55547oW0AAAAAANkyajjctm2bSqWStmzZorvuuksbN25M+8rlsjZs2KCnnnpKmzdv1pYt\nW/Tuu++OuA0AAAAAIHtGfc7h3r17tXDhQknSvHnztG/fvrTvwIEDmjlzpiZPnixJWrBggXbv3q2u\nrq7jbnM8s6fPULffe0pvAuNjyuQmapJB1CWbqEv2UJNsoi7ZRF2yh5qgHkYNh8ViUYVCIV12XVdB\nEMjzPBWLRbW0tKR9zc3NKhaLI25zPP/zopnSRaf6NjBuqEk2UZdsoi7ZQ02yibpkE3XJHmqCCTZq\nOCwUCurp6UmXoyhKQ97gvp6eHrW0tIy4zUgOHTp6UgeP8dXa2kJNMoi6ZBN1yR5qkk3UJZuoS/ZQ\nk2xqbW0ZfaUz2KjXHM6fP187duyQJHV1dWnu3Llp35w5c3Tw4EF1d3erVCppz549uvzyy0fcBgAA\nAACQPaMO5y1evFg7d+5UR0eHrLVav369tm7dqt7eXrW3t2vVqlXq7OyUtVZtbW2aMWPGsNsAAAAA\nALLLWGttvQ+igqHzbOF0hmyiLtlEXbKHmmQTdckm6pI91CSbzvnTSgEAAAAAZz/CIQAAAAAgW6eV\nAgAAAADqg5FDAAAAAADhEAAAAABAOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQJI3UT/o8OHD\nuuGGG/TUU0/J8zytWrVKxhj9xm/8hj772c/KcQZyahRFWrt2rfbv369cLqd169Zp1qxZE3Wo55Tq\nupRKJd1///1yXVe5XE4PPPCApk+fXrP+9ddfr0KhIEm68MILtWHDhnoc9lmtuib9/f267bbbdPHF\nF0uSli5dqiVLlqTr8rsycarr8pWvfEXvvvuuJOmtt97Sb/3Wb+mhhx6qWZ/flfE3+DNesWIF3y0Z\nMLgut9xyC98tdTb4812+fDnfLRkwuC59fX18t2TAY489pu3bt6tcLmvp0qX60Ic+dG59t9gJUCqV\n7Kc//Wl75ZVX2p/85Cf2tttus9/73vestdbed9999tvf/nbN+t/61rfsPffcY6219gc/+IFdsWLF\nRBzmOWdwXZYtW2Z//OMfW2utfeaZZ+z69etr1u/r67PXXnttPQ71nDG4Js8995x98sknj7s+vysT\nY3BdKrq7u+2f/Mmf2HfeeadmfX5Xxt9wnzHfLfU3XF34bqmv4T5fvlvqb6Q/93y31M/3vvc9e9tt\nt9kwDG2xWLRf/vKXz7nvlgk5rfSBBx5QR0eHzj//fEnSj370I33oQx+SJF1xxRV65ZVXatbfu3ev\nFi5cKEmaN2+e9u3bNxGHec4ZXJe//du/1aWXXipJCsNQ+Xy+Zv3XX39dx44d06233qpbbrlFXV1d\nE37MZ7vBNdm3b5++853vaNmyZbr33ntVLBZr1ud3ZWIMrkvFww8/rE9+8pND2vldGX/DfcZ8t9Tf\ncHXhu6W+hvt8+W6pv5H+3PPdUj8vv/yy5s6dq9tvv10rVqzQRz/60XPuu2Xcw+ELL7ygadOmpR+a\nJFlrZYyRJDU3N+vo0aM12xSLxXTIXJJc11UQBON9qOeU4epS+Z/Qv/3bv+lrX/ua/vRP/7Rmm4aG\nBnV2durJJ5/U5z73Od19993UZQwNV5MPfvCDWrlypZ5++mlddNFFeuSRR2q24Xdl/A1XFyk+zXTX\nrl264YYbhmzD78r4G+4z5rul/oary7Rp0yTx3VIvw32+H/jAB/huqbPj/bnnu6W+fvWrX2nfvn36\n0pe+dM5+t4z7NYdf//rXZYzRrl279Nprr+mee+7RL3/5y7S/p6dHkyZNqtmmUCiop6cnXY6iSJ43\nYZdHnhOGq8umTZu0e/dubdq0SY8//nj6hV4xe/ZszZo1S8YYzZ49W1OmTNGhQ4d0wQUX1OldnF2O\nV5PW1lZJ0uLFi3X//ffXbMPvyvg7Xl2+/e1v6+Mf/7hc1x2yDb8r42+4z/hHP/pR2s93S30c78/+\nD37wA75b6mS4z3fhwoXp58t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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(40, 60)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(40, 60)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T64Sd8blOeDiVQFoJipXwGAxaHrYtPP76xXKvgv4jKkfl5HreU+c7nnJuXg0j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\nD37wA75b6mS4z3fhwoXp58t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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(40, 60)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(60, 80.0)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Age',shade= True)\n", "facet.set(xlim=(0, train['Age'].max()))\n", "facet.add_legend()\n", "plt.xlim(60)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", "PassengerId 891 non-null int64\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Sex 891 non-null int64\n", "Age 891 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "Title 891 non-null int64\n", "dtypes: float64(2), int64(7), object(3)\n", "memory usage: 83.6+ KB\n" ] } ], "source": [ "train.info()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 418 entries, 0 to 417\n", "Data columns (total 11 columns):\n", "PassengerId 418 non-null int64\n", "Pclass 418 non-null int64\n", "Sex 418 non-null int64\n", "Age 418 non-null float64\n", "SibSp 418 non-null int64\n", "Parch 418 non-null int64\n", "Ticket 418 non-null object\n", "Fare 417 non-null float64\n", "Cabin 91 non-null object\n", "Embarked 418 non-null object\n", "Title 418 non-null int64\n", "dtypes: float64(2), int64(6), object(3)\n", "memory usage: 36.0+ KB\n" ] } ], "source": [ "test.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.4.2 Binning\n", "Binning/Converting Numerical Age to Categorical Variable \n", "\n", "feature vector map: \n", "child: 0 \n", "young: 1 \n", "adult: 2 \n", "mid-age: 3 \n", "senior: 4" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for dataset in train_test_data:\n", " dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0,\n", " dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1,\n", " dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2,\n", " dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3,\n", " dataset.loc[ dataset['Age'] > 62, 'Age'] = 4" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n", "0 1 0 3 0 1.0 1 0 A/5 21171 \n", "1 2 1 1 1 3.0 1 0 PC 17599 \n", "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n", "3 4 1 1 1 2.0 1 0 113803 \n", "4 5 0 3 0 2.0 0 0 373450 \n", "\n", " Fare Cabin Embarked Title \n", "0 7.2500 NaN S 0 \n", "1 71.2833 C85 C 2 \n", "2 7.9250 NaN S 1 \n", "3 53.1000 C123 S 2 \n", "4 8.0500 NaN S 0 " ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bar_chart('Age')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.5 Embarked" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.5.1 filling missing values" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()\n", "Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()\n", "Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()\n", "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n", "df.index = ['1st class','2nd class', '3rd class']\n", "df.plot(kind='bar',stacked=True, figsize=(10,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "more than 50% of 1st class are from S embark \n", "more than 50% of 2nd class are from S embark \n", "more than 50% of 3rd class are from S embark\n", "\n", "**fill out missing embark with S embark**" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for dataset in train_test_data:\n", " dataset['Embarked'] = dataset['Embarked'].fillna('S')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
010301.010A/5 211717.2500NaNS0
121113.010PC 1759971.2833C85C2
231311.000STON/O2. 31012827.9250NaNS1
341112.01011380353.1000C123S2
450302.0003734508.0500NaNS0
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" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n", "0 1 0 3 0 1.0 1 0 A/5 21171 \n", "1 2 1 1 1 3.0 1 0 PC 17599 \n", "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n", "3 4 1 1 1 2.0 1 0 113803 \n", "4 5 0 3 0 2.0 0 0 373450 \n", "\n", " Fare Cabin Embarked Title \n", "0 7.2500 NaN S 0 \n", "1 71.2833 C85 C 2 \n", "2 7.9250 NaN S 1 \n", "3 53.1000 C123 S 2 \n", "4 8.0500 NaN S 0 " ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n", "for dataset in train_test_data:\n", " dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.6 Fare" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitle
010301.010A/5 211717.2500NaN00
121113.010PC 1759971.2833C8512
231311.000STON/O2. 31012827.9250NaN01
341112.01011380353.1000C12302
450302.0003734508.0500NaN00
560302.0003308778.4583NaN20
670103.0001746351.8625E4600
780300.03134990921.0750NaN03
891312.00234774211.1333NaN02
9101210.01023773630.0708NaN12
10111310.011PP 954916.7000G601
11121113.00011378326.5500C10301
12130301.000A/5. 21518.0500NaN00
13140303.01534708231.2750NaN00
14150310.0003504067.8542NaN01
15161213.00024870616.0000NaN02
16170300.04138265229.1250NaN23
17181202.00024437313.0000NaN00
18190312.01034576318.0000NaN02
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21221202.00024869813.0000D5600
22231310.0003309238.0292NaN21
23241102.00011378835.5000A600
24250310.03134990921.0750NaN01
25261313.01534707731.3875NaN02
26270302.00026317.2250NaN10
27280101.03219950263.0000C23 C25 C2700
28291311.0003309597.8792NaN21
29300302.0003492167.8958NaN00
30310103.000PC 1760127.7208NaN13
31321112.010PC 17569146.5208B7812
32331311.0003356777.7500NaN21
33340204.000C.A. 2457910.5000NaN00
34350102.010PC 1760482.1708NaN10
35360103.01011378952.0000NaN00
36371302.00026777.2292NaN10
37380301.000A./5. 21528.0500NaN00
38390311.02034576418.0000NaN01
39401310.010265111.2417NaN11
40410313.01075469.4750NaN02
41420212.0101166821.0000NaN02
42430302.0003492537.8958NaN10
43441210.012SC/Paris 212341.5792NaN11
44451311.0003309587.8792NaN21
45460302.000S.C./A.4. 235678.0500NaN00
46470302.01037037115.5000NaN20
47481311.000143117.7500NaN21
48490302.020266221.6792NaN10
49500311.01034923717.8000NaN02
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n", "0 1 0 3 0 1.0 1 0 A/5 21171 \n", "1 2 1 1 1 3.0 1 0 PC 17599 \n", "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n", "3 4 1 1 1 2.0 1 0 113803 \n", "4 5 0 3 0 2.0 0 0 373450 \n", "5 6 0 3 0 2.0 0 0 330877 \n", "6 7 0 1 0 3.0 0 0 17463 \n", "7 8 0 3 0 0.0 3 1 349909 \n", "8 9 1 3 1 2.0 0 2 347742 \n", "9 10 1 2 1 0.0 1 0 237736 \n", "10 11 1 3 1 0.0 1 1 PP 9549 \n", "11 12 1 1 1 3.0 0 0 113783 \n", "12 13 0 3 0 1.0 0 0 A/5. 2151 \n", "13 14 0 3 0 3.0 1 5 347082 \n", "14 15 0 3 1 0.0 0 0 350406 \n", "15 16 1 2 1 3.0 0 0 248706 \n", "16 17 0 3 0 0.0 4 1 382652 \n", "17 18 1 2 0 2.0 0 0 244373 \n", "18 19 0 3 1 2.0 1 0 345763 \n", "19 20 1 3 1 2.0 0 0 2649 \n", "20 21 0 2 0 2.0 0 0 239865 \n", "21 22 1 2 0 2.0 0 0 248698 \n", "22 23 1 3 1 0.0 0 0 330923 \n", "23 24 1 1 0 2.0 0 0 113788 \n", "24 25 0 3 1 0.0 3 1 349909 \n", "25 26 1 3 1 3.0 1 5 347077 \n", "26 27 0 3 0 2.0 0 0 2631 \n", "27 28 0 1 0 1.0 3 2 19950 \n", "28 29 1 3 1 1.0 0 0 330959 \n", "29 30 0 3 0 2.0 0 0 349216 \n", "30 31 0 1 0 3.0 0 0 PC 17601 \n", "31 32 1 1 1 2.0 1 0 PC 17569 \n", "32 33 1 3 1 1.0 0 0 335677 \n", "33 34 0 2 0 4.0 0 0 C.A. 24579 \n", "34 35 0 1 0 2.0 1 0 PC 17604 \n", "35 36 0 1 0 3.0 1 0 113789 \n", "36 37 1 3 0 2.0 0 0 2677 \n", "37 38 0 3 0 1.0 0 0 A./5. 2152 \n", "38 39 0 3 1 1.0 2 0 345764 \n", "39 40 1 3 1 0.0 1 0 2651 \n", "40 41 0 3 1 3.0 1 0 7546 \n", "41 42 0 2 1 2.0 1 0 11668 \n", "42 43 0 3 0 2.0 0 0 349253 \n", "43 44 1 2 1 0.0 1 2 SC/Paris 2123 \n", "44 45 1 3 1 1.0 0 0 330958 \n", "45 46 0 3 0 2.0 0 0 S.C./A.4. 23567 \n", "46 47 0 3 0 2.0 1 0 370371 \n", "47 48 1 3 1 1.0 0 0 14311 \n", "48 49 0 3 0 2.0 2 0 2662 \n", "49 50 0 3 1 1.0 1 0 349237 \n", "\n", " Fare Cabin Embarked Title \n", "0 7.2500 NaN 0 0 \n", "1 71.2833 C85 1 2 \n", "2 7.9250 NaN 0 1 \n", "3 53.1000 C123 0 2 \n", "4 8.0500 NaN 0 0 \n", "5 8.4583 NaN 2 0 \n", "6 51.8625 E46 0 0 \n", "7 21.0750 NaN 0 3 \n", "8 11.1333 NaN 0 2 \n", "9 30.0708 NaN 1 2 \n", "10 16.7000 G6 0 1 \n", "11 26.5500 C103 0 1 \n", "12 8.0500 NaN 0 0 \n", "13 31.2750 NaN 0 0 \n", "14 7.8542 NaN 0 1 \n", "15 16.0000 NaN 0 2 \n", "16 29.1250 NaN 2 3 \n", "17 13.0000 NaN 0 0 \n", "18 18.0000 NaN 0 2 \n", "19 7.2250 NaN 1 2 \n", "20 26.0000 NaN 0 0 \n", "21 13.0000 D56 0 0 \n", "22 8.0292 NaN 2 1 \n", "23 35.5000 A6 0 0 \n", "24 21.0750 NaN 0 1 \n", "25 31.3875 NaN 0 2 \n", "26 7.2250 NaN 1 0 \n", "27 263.0000 C23 C25 C27 0 0 \n", "28 7.8792 NaN 2 1 \n", "29 7.8958 NaN 0 0 \n", "30 27.7208 NaN 1 3 \n", "31 146.5208 B78 1 2 \n", "32 7.7500 NaN 2 1 \n", "33 10.5000 NaN 0 0 \n", "34 82.1708 NaN 1 0 \n", "35 52.0000 NaN 0 0 \n", "36 7.2292 NaN 1 0 \n", "37 8.0500 NaN 0 0 \n", "38 18.0000 NaN 0 1 \n", "39 11.2417 NaN 1 1 \n", "40 9.4750 NaN 0 2 \n", "41 21.0000 NaN 0 2 \n", "42 7.8958 NaN 1 0 \n", "43 41.5792 NaN 1 1 \n", "44 7.8792 NaN 2 1 \n", "45 8.0500 NaN 0 0 \n", "46 15.5000 NaN 2 0 \n", "47 7.7500 NaN 2 1 \n", "48 21.6792 NaN 1 0 \n", "49 17.8000 NaN 0 2 " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fill missing Fare with median fare for each Pclass\n", "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n", "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n", "train.head(50)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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H2yiesTUBJk8McuCQS23mQwqIIiIiIiIyIvUaDjdu3Egmk2HdunXccccdrFmz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\nIiIoHIqIiIiIiAgKhyIiIiIiIgL8/4XauFMKAuOVAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Fare',shade= True)\n", "facet.set(xlim=(0, train['Fare'].max()))\n", "facet.add_legend()\n", " \n", "plt.show() " ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 20)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rB0800REaAmBZnpsta/NZvzIHh33uD5jWt4jpSeOSnjQu6Udjkp40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\nAJL+BYFgQ6iEUtgRAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Fare',shade= True)\n", "facet.set(xlim=(0, train['Fare'].max()))\n", "facet.add_legend()\n", "plt.xlim(0, 20)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 30)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WrVqFVatW4fPPPwcAvPvuu3jwwQfxjW98Y9ijhgBHDqOKoqjY8cUF/HnXafQ4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\nVReUAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Fare',shade= True)\n", "facet.set(xlim=(0, train['Fare'].max()))\n", "facet.add_legend()\n", "plt.xlim(0, 30)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 512.32920000000001)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8oJNM1iUYsE7rWiIiIiIiImeq/o31HEZHEl2zlZ5ez2EsorUORUREREREelOy4bAtmSEc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\naVJuggvjl1ARqOr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'Fare',shade= True)\n", "facet.set(xlim=(0, train['Fare'].max()))\n", "facet.add_legend()\n", "plt.xlim(0)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for dataset in train_test_data:\n", " dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0,\n", " dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,\n", " dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,\n", " dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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450302.0003734500.0NaN00
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" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n", "0 1 0 3 0 1.0 1 0 A/5 21171 \n", "1 2 1 1 1 3.0 1 0 PC 17599 \n", "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n", "3 4 1 1 1 2.0 1 0 113803 \n", "4 5 0 3 0 2.0 0 0 373450 \n", "\n", " Fare Cabin Embarked Title \n", "0 0.0 NaN 0 0 \n", "1 2.0 C85 1 2 \n", "2 0.0 NaN 0 1 \n", "3 2.0 C123 0 2 \n", "4 0.0 NaN 0 0 " ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.7 Cabin" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "C23 C25 C27 4\n", "G6 4\n", "B96 B98 4\n", "D 3\n", "C22 C26 3\n", "E101 3\n", "F2 3\n", "F33 3\n", "B57 B59 B63 B66 2\n", "C68 2\n", "B58 B60 2\n", "E121 2\n", "D20 2\n", "E8 2\n", "E44 2\n", "B77 2\n", "C65 2\n", "D26 2\n", "E24 2\n", "E25 2\n", "B20 2\n", "C93 2\n", "D33 2\n", "E67 2\n", "D35 2\n", "D36 2\n", "C52 2\n", "F4 2\n", "C125 2\n", "C124 2\n", " ..\n", "F G63 1\n", "A6 1\n", "D45 1\n", "D6 1\n", "D56 1\n", "C101 1\n", "C54 1\n", "D28 1\n", "D37 1\n", "B102 1\n", "D30 1\n", "E17 1\n", "E58 1\n", "F E69 1\n", "D10 D12 1\n", "E50 1\n", "A14 1\n", "C91 1\n", "A16 1\n", "B38 1\n", "B39 1\n", "C95 1\n", "B78 1\n", "B79 1\n", "C99 1\n", "B37 1\n", "A19 1\n", "E12 1\n", "A7 1\n", "D15 1\n", "Name: Cabin, Length: 147, dtype: int64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.Cabin.value_counts()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for dataset in train_test_data:\n", " dataset['Cabin'] = dataset['Cabin'].str[:1]" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()\n", "Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()\n", "Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()\n", "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n", "df.index = ['1st class','2nd class', '3rd class']\n", "df.plot(kind='bar',stacked=True, figsize=(10,5))" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cabin_mapping = {\"A\": 0, \"B\": 0.4, \"C\": 0.8, \"D\": 1.2, \"E\": 1.6, \"F\": 2, \"G\": 2.4, \"T\": 2.8}\n", "for dataset in train_test_data:\n", " dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# fill missing Fare with median fare for each Pclass\n", "train[\"Cabin\"].fillna(train.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)\n", "test[\"Cabin\"].fillna(test.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.8 FamilySize" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train[\"FamilySize\"] = train[\"SibSp\"] + train[\"Parch\"] + 1\n", "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 11.0)" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZUdlKhTcyi6UXEREREZFSUDicgrfaD+Is2oNpu7i57g48pqfcRSqp+tpCNWg/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\nD5GO45DK5Ikns8RSWWKJLLHkqVs8mSO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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n", "facet.map(sns.kdeplot,'FamilySize',shade= True)\n", "facet.set(xlim=(0, train['FamilySize'].max()))\n", "facet.add_legend()\n", "plt.xlim(0)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}\n", "for dataset in train_test_data:\n", " dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitleFamilySize
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121113.010PC 175992.00.8120.4
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" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n", "0 1 0 3 0 1.0 1 0 A/5 21171 \n", "1 2 1 1 1 3.0 1 0 PC 17599 \n", "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n", "3 4 1 1 1 2.0 1 0 113803 \n", "4 5 0 3 0 2.0 0 0 373450 \n", "\n", " Fare Cabin Embarked Title FamilySize \n", "0 0.0 2.0 0 0 0.4 \n", "1 2.0 0.8 1 2 0.4 \n", "2 0.0 2.0 0 1 0.0 \n", "3 2.0 0.8 0 2 0.4 \n", "4 0.0 2.0 0 0 0.0 " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarkedTitleFamilySize
010301.010A/5 211710.02.0000.4
121113.010PC 175992.00.8120.4
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" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n", "0 1 0 3 0 1.0 1 0 A/5 21171 \n", "1 2 1 1 1 3.0 1 0 PC 17599 \n", "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n", "3 4 1 1 1 2.0 1 0 113803 \n", "4 5 0 3 0 2.0 0 0 373450 \n", "\n", " Fare Cabin Embarked Title FamilySize \n", "0 0.0 2.0 0 0 0.4 \n", "1 2.0 0.8 1 2 0.4 \n", "2 0.0 2.0 0 1 0.0 \n", "3 2.0 0.8 0 2 0.4 \n", "4 0.0 2.0 0 0 0.0 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "features_drop = ['Ticket', 'SibSp', 'Parch']\n", "train = train.drop(features_drop, axis=1)\n", "test = test.drop(features_drop, axis=1)\n", "train = train.drop(['PassengerId'], axis=1)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((891, 8), (891,))" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data = train.drop('Survived', axis=1)\n", "target = train['Survived']\n", "\n", "train_data.shape, target.shape" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PclassSexAgeFareCabinEmbarkedTitleFamilySize
0301.00.02.0000.4
1113.02.00.8120.4
2311.00.02.0010.0
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" ], "text/plain": [ " Pclass Sex Age Fare Cabin Embarked Title FamilySize\n", "0 3 0 1.0 0.0 2.0 0 0 0.4\n", "1 1 1 3.0 2.0 0.8 1 2 0.4\n", "2 3 1 1.0 0.0 2.0 0 1 0.0\n", "3 1 1 2.0 2.0 0.8 0 2 0.4\n", "4 3 0 2.0 0.0 2.0 0 0 0.0\n", "5 3 0 2.0 0.0 2.0 2 0 0.0\n", "6 1 0 3.0 2.0 1.6 0 0 0.0\n", "7 3 0 0.0 1.0 2.0 0 3 1.6\n", "8 3 1 2.0 0.0 2.0 0 2 0.8\n", "9 2 1 0.0 2.0 1.8 1 2 0.4" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Modelling" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Importing Classifier Modules\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.svm import SVC\n", "\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 9 columns):\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Sex 891 non-null int64\n", "Age 891 non-null float64\n", "Fare 891 non-null float64\n", "Cabin 891 non-null float64\n", "Embarked 891 non-null int64\n", "Title 891 non-null int64\n", "FamilySize 891 non-null float64\n", "dtypes: float64(4), int64(5)\n", "memory usage: 62.7 KB\n" ] } ], "source": [ "train.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2 Cross Validation (K-fold)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import KFold\n", "from sklearn.model_selection import cross_val_score\n", "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2.1 kNN" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.82222222 0.76404494 0.80898876 0.83146067 0.87640449 0.82022472\n", " 0.85393258 0.79775281 0.84269663 0.84269663]\n" ] } ], "source": [ "clf = KNeighborsClassifier(n_neighbors = 13)\n", "scoring = 'accuracy'\n", "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n", "print(score)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "82.6" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# kNN Score\n", "round(np.mean(score)*100, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2.2 Decision Tree" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.76666667 0.82022472 0.78651685 0.76404494 0.88764045 0.76404494\n", " 0.82022472 0.82022472 0.74157303 0.79775281]\n" ] } ], "source": [ "clf = DecisionTreeClassifier()\n", "scoring = 'accuracy'\n", "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n", "print(score)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "79.69" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# decision tree Score\n", "round(np.mean(score)*100, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2.3 Ramdom Forest" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.77777778 0.80898876 0.82022472 0.76404494 0.86516854 0.82022472\n", " 0.79775281 0.80898876 0.76404494 0.83146067]\n" ] } ], "source": [ "clf = RandomForestClassifier(n_estimators=13)\n", "scoring = 'accuracy'\n", "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n", "print(score)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "80.59" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Random Forest Score\n", "round(np.mean(score)*100, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2.4 Naive Bayes" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.85555556 0.73033708 0.75280899 0.75280899 0.70786517 0.80898876\n", " 0.76404494 0.80898876 0.86516854 0.83146067]\n" ] } ], "source": [ "clf = GaussianNB()\n", "scoring = 'accuracy'\n", "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n", "print(score)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "78.78" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Naive Bayes Score\n", "round(np.mean(score)*100, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2.5 SVM" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.83333333 0.80898876 0.83146067 0.82022472 0.84269663 0.82022472\n", " 0.84269663 0.85393258 0.83146067 0.86516854]\n" ] } ], "source": [ "clf = SVC()\n", "scoring = 'accuracy'\n", "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n", "print(score)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "83.5" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "round(np.mean(score)*100,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Testing" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clf = SVC()\n", "clf.fit(train_data, target)\n", "\n", "test_data = test.drop(\"PassengerId\", axis=1).copy()\n", "prediction = clf.predict(test_data)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "submission = pd.DataFrame({\n", " \"PassengerId\": test[\"PassengerId\"],\n", " \"Survived\": prediction\n", " })\n", "\n", "submission.to_csv('submission.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " PassengerId Survived\n", "0 892 0\n", "1 893 1\n", "2 894 0\n", "3 895 0\n", "4 896 1" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submission = pd.read_csv('submission.csv')\n", "submission.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "This notebook is created by learning from the following notebooks:\n", "\n", "- [Mukesh ChapagainTitanic Solution: A Beginner's Guide](https://www.kaggle.com/chapagain/titanic-solution-a-beginner-s-guide?scriptVersionId=1473689)\n", "- [How to score 0.8134 in Titanic Kaggle Challenge](http://ahmedbesbes.com/how-to-score-08134-in-titanic-kaggle-challenge.html)\n", "- [Titanic: factors to survive](https://olegleyz.github.io/titanic_factors.html)\n", "- [Titanic Survivors Dataset and Data Wrangling](http://www.codeastar.com/data-wrangling/)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }