[
  {
    "path": "input/gender_submission.csv",
    "content": "PassengerId,Survived\r\n892,0\r\n893,1\r\n894,0\r\n895,0\r\n896,1\r\n897,0\r\n898,1\r\n899,0\r\n900,1\r\n901,0\r\n902,0\r\n903,0\r\n904,1\r\n905,0\r\n906,1\r\n907,1\r\n908,0\r\n909,0\r\n910,1\r\n911,1\r\n912,0\r\n913,0\r\n914,1\r\n915,0\r\n916,1\r\n917,0\r\n918,1\r\n919,0\r\n920,0\r\n921,0\r\n922,0\r\n923,0\r\n924,1\r\n925,1\r\n926,0\r\n927,0\r\n928,1\r\n929,1\r\n930,0\r\n931,0\r\n932,0\r\n933,0\r\n934,0\r\n935,1\r\n936,1\r\n937,0\r\n938,0\r\n939,0\r\n940,1\r\n941,1\r\n942,0\r\n943,0\r\n944,1\r\n945,1\r\n946,0\r\n947,0\r\n948,0\r\n949,0\r\n950,0\r\n951,1\r\n952,0\r\n953,0\r\n954,0\r\n955,1\r\n956,0\r\n957,1\r\n958,1\r\n959,0\r\n960,0\r\n961,1\r\n962,1\r\n963,0\r\n964,1\r\n965,0\r\n966,1\r\n967,0\r\n968,0\r\n969,1\r\n970,0\r\n971,1\r\n972,0\r\n973,0\r\n974,0\r\n975,0\r\n976,0\r\n977,0\r\n978,1\r\n979,1\r\n980,1\r\n981,0\r\n982,1\r\n983,0\r\n984,1\r\n985,0\r\n986,0\r\n987,0\r\n988,1\r\n989,0\r\n990,1\r\n991,0\r\n992,1\r\n993,0\r\n994,0\r\n995,0\r\n996,1\r\n997,0\r\n998,0\r\n999,0\r\n1000,0\r\n1001,0\r\n1002,0\r\n1003,1\r\n1004,1\r\n1005,1\r\n1006,1\r\n1007,0\r\n1008,0\r\n1009,1\r\n1010,0\r\n1011,1\r\n1012,1\r\n1013,0\r\n1014,1\r\n1015,0\r\n1016,0\r\n1017,1\r\n1018,0\r\n1019,1\r\n1020,0\r\n1021,0\r\n1022,0\r\n1023,0\r\n1024,1\r\n1025,0\r\n1026,0\r\n1027,0\r\n1028,0\r\n1029,0\r\n1030,1\r\n1031,0\r\n1032,1\r\n1033,1\r\n1034,0\r\n1035,0\r\n1036,0\r\n1037,0\r\n1038,0\r\n1039,0\r\n1040,0\r\n1041,0\r\n1042,1\r\n1043,0\r\n1044,0\r\n1045,1\r\n1046,0\r\n1047,0\r\n1048,1\r\n1049,1\r\n1050,0\r\n1051,1\r\n1052,1\r\n1053,0\r\n1054,1\r\n1055,0\r\n1056,0\r\n1057,1\r\n1058,0\r\n1059,0\r\n1060,1\r\n1061,1\r\n1062,0\r\n1063,0\r\n1064,0\r\n1065,0\r\n1066,0\r\n1067,1\r\n1068,1\r\n1069,0\r\n1070,1\r\n1071,1\r\n1072,0\r\n1073,0\r\n1074,1\r\n1075,0\r\n1076,1\r\n1077,0\r\n1078,1\r\n1079,0\r\n1080,1\r\n1081,0\r\n1082,0\r\n1083,0\r\n1084,0\r\n1085,0\r\n1086,0\r\n1087,0\r\n1088,0\r\n1089,1\r\n1090,0\r\n1091,1\r\n1092,1\r\n1093,0\r\n1094,0\r\n1095,1\r\n1096,0\r\n1097,0\r\n1098,1\r\n1099,0\r\n1100,1\r\n1101,0\r\n1102,0\r\n1103,0\r\n1104,0\r\n1105,1\r\n1106,1\r\n1107,0\r\n1108,1\r\n1109,0\r\n1110,1\r\n1111,0\r\n1112,1\r\n1113,0\r\n1114,1\r\n1115,0\r\n1116,1\r\n1117,1\r\n1118,0\r\n1119,1\r\n1120,0\r\n1121,0\r\n1122,0\r\n1123,1\r\n1124,0\r\n1125,0\r\n1126,0\r\n1127,0\r\n1128,0\r\n1129,0\r\n1130,1\r\n1131,1\r\n1132,1\r\n1133,1\r\n1134,0\r\n1135,0\r\n1136,0\r\n1137,0\r\n1138,1\r\n1139,0\r\n1140,1\r\n1141,1\r\n1142,1\r\n1143,0\r\n1144,0\r\n1145,0\r\n1146,0\r\n1147,0\r\n1148,0\r\n1149,0\r\n1150,1\r\n1151,0\r\n1152,0\r\n1153,0\r\n1154,1\r\n1155,1\r\n1156,0\r\n1157,0\r\n1158,0\r\n1159,0\r\n1160,1\r\n1161,0\r\n1162,0\r\n1163,0\r\n1164,1\r\n1165,1\r\n1166,0\r\n1167,1\r\n1168,0\r\n1169,0\r\n1170,0\r\n1171,0\r\n1172,1\r\n1173,0\r\n1174,1\r\n1175,1\r\n1176,1\r\n1177,0\r\n1178,0\r\n1179,0\r\n1180,0\r\n1181,0\r\n1182,0\r\n1183,1\r\n1184,0\r\n1185,0\r\n1186,0\r\n1187,0\r\n1188,1\r\n1189,0\r\n1190,0\r\n1191,0\r\n1192,0\r\n1193,0\r\n1194,0\r\n1195,0\r\n1196,1\r\n1197,1\r\n1198,0\r\n1199,0\r\n1200,0\r\n1201,1\r\n1202,0\r\n1203,0\r\n1204,0\r\n1205,1\r\n1206,1\r\n1207,1\r\n1208,0\r\n1209,0\r\n1210,0\r\n1211,0\r\n1212,0\r\n1213,0\r\n1214,0\r\n1215,0\r\n1216,1\r\n1217,0\r\n1218,1\r\n1219,0\r\n1220,0\r\n1221,0\r\n1222,1\r\n1223,0\r\n1224,0\r\n1225,1\r\n1226,0\r\n1227,0\r\n1228,0\r\n1229,0\r\n1230,0\r\n1231,0\r\n1232,0\r\n1233,0\r\n1234,0\r\n1235,1\r\n1236,0\r\n1237,1\r\n1238,0\r\n1239,1\r\n1240,0\r\n1241,1\r\n1242,1\r\n1243,0\r\n1244,0\r\n1245,0\r\n1246,1\r\n1247,0\r\n1248,1\r\n1249,0\r\n1250,0\r\n1251,1\r\n1252,0\r\n1253,1\r\n1254,1\r\n1255,0\r\n1256,1\r\n1257,1\r\n1258,0\r\n1259,1\r\n1260,1\r\n1261,0\r\n1262,0\r\n1263,1\r\n1264,0\r\n1265,0\r\n1266,1\r\n1267,1\r\n1268,1\r\n1269,0\r\n1270,0\r\n1271,0\r\n1272,0\r\n1273,0\r\n1274,1\r\n1275,1\r\n1276,0\r\n1277,1\r\n1278,0\r\n1279,0\r\n1280,0\r\n1281,0\r\n1282,0\r\n1283,1\r\n1284,0\r\n1285,0\r\n1286,0\r\n1287,1\r\n1288,0\r\n1289,1\r\n1290,0\r\n1291,0\r\n1292,1\r\n1293,0\r\n1294,1\r\n1295,0\r\n1296,0\r\n1297,0\r\n1298,0\r\n1299,0\r\n1300,1\r\n1301,1\r\n1302,1\r\n1303,1\r\n1304,1\r\n1305,0\r\n1306,1\r\n1307,0\r\n1308,0\r\n1309,0\r\n"
  },
  {
    "path": "input/test.csv",
    "content": "PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\r\n892,3,\"Kelly, Mr. James\",male,34.5,0,0,330911,7.8292,,Q\r\n893,3,\"Wilkes, Mrs. James (Ellen Needs)\",female,47,1,0,363272,7,,S\r\n894,2,\"Myles, Mr. Thomas Francis\",male,62,0,0,240276,9.6875,,Q\r\n895,3,\"Wirz, Mr. Albert\",male,27,0,0,315154,8.6625,,S\r\n896,3,\"Hirvonen, Mrs. Alexander (Helga E Lindqvist)\",female,22,1,1,3101298,12.2875,,S\r\n897,3,\"Svensson, Mr. Johan Cervin\",male,14,0,0,7538,9.225,,S\r\n898,3,\"Connolly, Miss. Kate\",female,30,0,0,330972,7.6292,,Q\r\n899,2,\"Caldwell, Mr. Albert Francis\",male,26,1,1,248738,29,,S\r\n900,3,\"Abrahim, Mrs. Joseph (Sophie Halaut Easu)\",female,18,0,0,2657,7.2292,,C\r\n901,3,\"Davies, Mr. John Samuel\",male,21,2,0,A/4 48871,24.15,,S\r\n902,3,\"Ilieff, Mr. Ylio\",male,,0,0,349220,7.8958,,S\r\n903,1,\"Jones, Mr. Charles Cresson\",male,46,0,0,694,26,,S\r\n904,1,\"Snyder, Mrs. John Pillsbury (Nelle Stevenson)\",female,23,1,0,21228,82.2667,B45,S\r\n905,2,\"Howard, Mr. Benjamin\",male,63,1,0,24065,26,,S\r\n906,1,\"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)\",female,47,1,0,W.E.P. 5734,61.175,E31,S\r\n907,2,\"del Carlo, Mrs. Sebastiano (Argenia Genovesi)\",female,24,1,0,SC/PARIS 2167,27.7208,,C\r\n908,2,\"Keane, Mr. Daniel\",male,35,0,0,233734,12.35,,Q\r\n909,3,\"Assaf, Mr. Gerios\",male,21,0,0,2692,7.225,,C\r\n910,3,\"Ilmakangas, Miss. Ida Livija\",female,27,1,0,STON/O2. 3101270,7.925,,S\r\n911,3,\"Assaf Khalil, Mrs. Mariana (Miriam\"\")\"\"\",female,45,0,0,2696,7.225,,C\r\n912,1,\"Rothschild, Mr. Martin\",male,55,1,0,PC 17603,59.4,,C\r\n913,3,\"Olsen, Master. Artur Karl\",male,9,0,1,C 17368,3.1708,,S\r\n914,1,\"Flegenheim, Mrs. Alfred (Antoinette)\",female,,0,0,PC 17598,31.6833,,S\r\n915,1,\"Williams, Mr. Richard Norris II\",male,21,0,1,PC 17597,61.3792,,C\r\n916,1,\"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)\",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C\r\n917,3,\"Robins, Mr. Alexander A\",male,50,1,0,A/5. 3337,14.5,,S\r\n918,1,\"Ostby, Miss. Helene Ragnhild\",female,22,0,1,113509,61.9792,B36,C\r\n919,3,\"Daher, Mr. Shedid\",male,22.5,0,0,2698,7.225,,C\r\n920,1,\"Brady, Mr. John Bertram\",male,41,0,0,113054,30.5,A21,S\r\n921,3,\"Samaan, Mr. Elias\",male,,2,0,2662,21.6792,,C\r\n922,2,\"Louch, Mr. Charles Alexander\",male,50,1,0,SC/AH 3085,26,,S\r\n923,2,\"Jefferys, Mr. Clifford Thomas\",male,24,2,0,C.A. 31029,31.5,,S\r\n924,3,\"Dean, Mrs. Bertram (Eva Georgetta Light)\",female,33,1,2,C.A. 2315,20.575,,S\r\n925,3,\"Johnston, Mrs. Andrew G (Elizabeth Lily\"\" Watson)\"\"\",female,,1,2,W./C. 6607,23.45,,S\r\n926,1,\"Mock, Mr. Philipp Edmund\",male,30,1,0,13236,57.75,C78,C\r\n927,3,\"Katavelas, Mr. Vassilios (Catavelas Vassilios\"\")\"\"\",male,18.5,0,0,2682,7.2292,,C\r\n928,3,\"Roth, Miss. Sarah A\",female,,0,0,342712,8.05,,S\r\n929,3,\"Cacic, Miss. Manda\",female,21,0,0,315087,8.6625,,S\r\n930,3,\"Sap, Mr. Julius\",male,25,0,0,345768,9.5,,S\r\n931,3,\"Hee, Mr. Ling\",male,,0,0,1601,56.4958,,S\r\n932,3,\"Karun, Mr. Franz\",male,39,0,1,349256,13.4167,,C\r\n933,1,\"Franklin, Mr. Thomas Parham\",male,,0,0,113778,26.55,D34,S\r\n934,3,\"Goldsmith, Mr. Nathan\",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S\r\n935,2,\"Corbett, Mrs. Walter H (Irene Colvin)\",female,30,0,0,237249,13,,S\r\n936,1,\"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)\",female,45,1,0,11753,52.5542,D19,S\r\n937,3,\"Peltomaki, Mr. Nikolai Johannes\",male,25,0,0,STON/O 2. 3101291,7.925,,S\r\n938,1,\"Chevre, Mr. Paul Romaine\",male,45,0,0,PC 17594,29.7,A9,C\r\n939,3,\"Shaughnessy, Mr. Patrick\",male,,0,0,370374,7.75,,Q\r\n940,1,\"Bucknell, Mrs. William Robert (Emma Eliza Ward)\",female,60,0,0,11813,76.2917,D15,C\r\n941,3,\"Coutts, Mrs. William (Winnie Minnie\"\" Treanor)\"\"\",female,36,0,2,C.A. 37671,15.9,,S\r\n942,1,\"Smith, Mr. Lucien Philip\",male,24,1,0,13695,60,C31,S\r\n943,2,\"Pulbaum, Mr. Franz\",male,27,0,0,SC/PARIS 2168,15.0333,,C\r\n944,2,\"Hocking, Miss. Ellen Nellie\"\"\"\"\",female,20,2,1,29105,23,,S\r\n945,1,\"Fortune, Miss. Ethel Flora\",female,28,3,2,19950,263,C23 C25 C27,S\r\n946,2,\"Mangiavacchi, Mr. Serafino Emilio\",male,,0,0,SC/A.3 2861,15.5792,,C\r\n947,3,\"Rice, Master. Albert\",male,10,4,1,382652,29.125,,Q\r\n948,3,\"Cor, Mr. Bartol\",male,35,0,0,349230,7.8958,,S\r\n949,3,\"Abelseth, Mr. Olaus Jorgensen\",male,25,0,0,348122,7.65,F G63,S\r\n950,3,\"Davison, Mr. Thomas Henry\",male,,1,0,386525,16.1,,S\r\n951,1,\"Chaudanson, Miss. Victorine\",female,36,0,0,PC 17608,262.375,B61,C\r\n952,3,\"Dika, Mr. Mirko\",male,17,0,0,349232,7.8958,,S\r\n953,2,\"McCrae, Mr. Arthur Gordon\",male,32,0,0,237216,13.5,,S\r\n954,3,\"Bjorklund, Mr. Ernst Herbert\",male,18,0,0,347090,7.75,,S\r\n955,3,\"Bradley, Miss. Bridget Delia\",female,22,0,0,334914,7.725,,Q\r\n956,1,\"Ryerson, Master. John Borie\",male,13,2,2,PC 17608,262.375,B57 B59 B63 B66,C\r\n957,2,\"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)\",female,,0,0,F.C.C. 13534,21,,S\r\n958,3,\"Burns, Miss. Mary Delia\",female,18,0,0,330963,7.8792,,Q\r\n959,1,\"Moore, Mr. Clarence Bloomfield\",male,47,0,0,113796,42.4,,S\r\n960,1,\"Tucker, Mr. Gilbert Milligan Jr\",male,31,0,0,2543,28.5375,C53,C\r\n961,1,\"Fortune, Mrs. Mark (Mary McDougald)\",female,60,1,4,19950,263,C23 C25 C27,S\r\n962,3,\"Mulvihill, Miss. Bertha E\",female,24,0,0,382653,7.75,,Q\r\n963,3,\"Minkoff, Mr. Lazar\",male,21,0,0,349211,7.8958,,S\r\n964,3,\"Nieminen, Miss. Manta Josefina\",female,29,0,0,3101297,7.925,,S\r\n965,1,\"Ovies y Rodriguez, Mr. Servando\",male,28.5,0,0,PC 17562,27.7208,D43,C\r\n966,1,\"Geiger, Miss. Amalie\",female,35,0,0,113503,211.5,C130,C\r\n967,1,\"Keeping, Mr. Edwin\",male,32.5,0,0,113503,211.5,C132,C\r\n968,3,\"Miles, Mr. Frank\",male,,0,0,359306,8.05,,S\r\n969,1,\"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)\",female,55,2,0,11770,25.7,C101,S\r\n970,2,\"Aldworth, Mr. Charles Augustus\",male,30,0,0,248744,13,,S\r\n971,3,\"Doyle, Miss. Elizabeth\",female,24,0,0,368702,7.75,,Q\r\n972,3,\"Boulos, Master. Akar\",male,6,1,1,2678,15.2458,,C\r\n973,1,\"Straus, Mr. Isidor\",male,67,1,0,PC 17483,221.7792,C55 C57,S\r\n974,1,\"Case, Mr. Howard Brown\",male,49,0,0,19924,26,,S\r\n975,3,\"Demetri, Mr. Marinko\",male,,0,0,349238,7.8958,,S\r\n976,2,\"Lamb, Mr. John Joseph\",male,,0,0,240261,10.7083,,Q\r\n977,3,\"Khalil, Mr. Betros\",male,,1,0,2660,14.4542,,C\r\n978,3,\"Barry, Miss. Julia\",female,27,0,0,330844,7.8792,,Q\r\n979,3,\"Badman, Miss. Emily Louisa\",female,18,0,0,A/4 31416,8.05,,S\r\n980,3,\"O'Donoghue, Ms. Bridget\",female,,0,0,364856,7.75,,Q\r\n981,2,\"Wells, Master. Ralph Lester\",male,2,1,1,29103,23,,S\r\n982,3,\"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)\",female,22,1,0,347072,13.9,,S\r\n983,3,\"Pedersen, Mr. Olaf\",male,,0,0,345498,7.775,,S\r\n984,1,\"Davidson, Mrs. Thornton (Orian Hays)\",female,27,1,2,F.C. 12750,52,B71,S\r\n985,3,\"Guest, Mr. Robert\",male,,0,0,376563,8.05,,S\r\n986,1,\"Birnbaum, Mr. Jakob\",male,25,0,0,13905,26,,C\r\n987,3,\"Tenglin, Mr. Gunnar Isidor\",male,25,0,0,350033,7.7958,,S\r\n988,1,\"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)\",female,76,1,0,19877,78.85,C46,S\r\n989,3,\"Makinen, Mr. Kalle Edvard\",male,29,0,0,STON/O 2. 3101268,7.925,,S\r\n990,3,\"Braf, Miss. 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Andre\",male,1,0,2,S.C./PARIS 2079,37.0042,,C\r\n829,1,3,\"McCormack, Mr. Thomas Joseph\",male,,0,0,367228,7.75,,Q\r\n830,1,1,\"Stone, Mrs. George Nelson (Martha Evelyn)\",female,62,0,0,113572,80,B28,\r\n831,1,3,\"Yasbeck, Mrs. Antoni (Selini Alexander)\",female,15,1,0,2659,14.4542,,C\r\n832,1,2,\"Richards, Master. George Sibley\",male,0.83,1,1,29106,18.75,,S\r\n833,0,3,\"Saad, Mr. Amin\",male,,0,0,2671,7.2292,,C\r\n834,0,3,\"Augustsson, Mr. Albert\",male,23,0,0,347468,7.8542,,S\r\n835,0,3,\"Allum, Mr. Owen George\",male,18,0,0,2223,8.3,,S\r\n836,1,1,\"Compton, Miss. Sara Rebecca\",female,39,1,1,PC 17756,83.1583,E49,C\r\n837,0,3,\"Pasic, Mr. Jakob\",male,21,0,0,315097,8.6625,,S\r\n838,0,3,\"Sirota, Mr. Maurice\",male,,0,0,392092,8.05,,S\r\n839,1,3,\"Chip, Mr. Chang\",male,32,0,0,1601,56.4958,,S\r\n840,1,1,\"Marechal, Mr. Pierre\",male,,0,0,11774,29.7,C47,C\r\n841,0,3,\"Alhomaki, Mr. Ilmari Rudolf\",male,20,0,0,SOTON/O2 3101287,7.925,,S\r\n842,0,2,\"Mudd, Mr. Thomas Charles\",male,16,0,0,S.O./P.P. 3,10.5,,S\r\n843,1,1,\"Serepeca, Miss. Augusta\",female,30,0,0,113798,31,,C\r\n844,0,3,\"Lemberopolous, Mr. Peter L\",male,34.5,0,0,2683,6.4375,,C\r\n845,0,3,\"Culumovic, Mr. Jeso\",male,17,0,0,315090,8.6625,,S\r\n846,0,3,\"Abbing, Mr. Anthony\",male,42,0,0,C.A. 5547,7.55,,S\r\n847,0,3,\"Sage, Mr. Douglas Bullen\",male,,8,2,CA. 2343,69.55,,S\r\n848,0,3,\"Markoff, Mr. Marin\",male,35,0,0,349213,7.8958,,C\r\n849,0,2,\"Harper, Rev. John\",male,28,0,1,248727,33,,S\r\n850,1,1,\"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)\",female,,1,0,17453,89.1042,C92,C\r\n851,0,3,\"Andersson, Master. Sigvard Harald Elias\",male,4,4,2,347082,31.275,,S\r\n852,0,3,\"Svensson, Mr. Johan\",male,74,0,0,347060,7.775,,S\r\n853,0,3,\"Boulos, Miss. Nourelain\",female,9,1,1,2678,15.2458,,C\r\n854,1,1,\"Lines, Miss. Mary Conover\",female,16,0,1,PC 17592,39.4,D28,S\r\n855,0,2,\"Carter, Mrs. Ernest Courtenay (Lilian Hughes)\",female,44,1,0,244252,26,,S\r\n856,1,3,\"Aks, Mrs. Sam (Leah Rosen)\",female,18,0,1,392091,9.35,,S\r\n857,1,1,\"Wick, Mrs. George Dennick (Mary Hitchcock)\",female,45,1,1,36928,164.8667,,S\r\n858,1,1,\"Daly, Mr. Peter Denis \",male,51,0,0,113055,26.55,E17,S\r\n859,1,3,\"Baclini, Mrs. Solomon (Latifa Qurban)\",female,24,0,3,2666,19.2583,,C\r\n860,0,3,\"Razi, Mr. Raihed\",male,,0,0,2629,7.2292,,C\r\n861,0,3,\"Hansen, Mr. Claus Peter\",male,41,2,0,350026,14.1083,,S\r\n862,0,2,\"Giles, Mr. Frederick Edward\",male,21,1,0,28134,11.5,,S\r\n863,1,1,\"Swift, Mrs. Frederick Joel (Margaret Welles Barron)\",female,48,0,0,17466,25.9292,D17,S\r\n864,0,3,\"Sage, Miss. Dorothy Edith \"\"Dolly\"\"\",female,,8,2,CA. 2343,69.55,,S\r\n865,0,2,\"Gill, Mr. John William\",male,24,0,0,233866,13,,S\r\n866,1,2,\"Bystrom, Mrs. (Karolina)\",female,42,0,0,236852,13,,S\r\n867,1,2,\"Duran y More, Miss. Asuncion\",female,27,1,0,SC/PARIS 2149,13.8583,,C\r\n868,0,1,\"Roebling, Mr. Washington Augustus II\",male,31,0,0,PC 17590,50.4958,A24,S\r\n869,0,3,\"van Melkebeke, Mr. Philemon\",male,,0,0,345777,9.5,,S\r\n870,1,3,\"Johnson, Master. Harold Theodor\",male,4,1,1,347742,11.1333,,S\r\n871,0,3,\"Balkic, Mr. Cerin\",male,26,0,0,349248,7.8958,,S\r\n872,1,1,\"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)\",female,47,1,1,11751,52.5542,D35,S\r\n873,0,1,\"Carlsson, Mr. Frans Olof\",male,33,0,0,695,5,B51 B53 B55,S\r\n874,0,3,\"Vander Cruyssen, Mr. Victor\",male,47,0,0,345765,9,,S\r\n875,1,2,\"Abelson, Mrs. Samuel (Hannah Wizosky)\",female,28,1,0,P/PP 3381,24,,C\r\n876,1,3,\"Najib, Miss. Adele Kiamie \"\"Jane\"\"\",female,15,0,0,2667,7.225,,C\r\n877,0,3,\"Gustafsson, Mr. Alfred Ossian\",male,20,0,0,7534,9.8458,,S\r\n878,0,3,\"Petroff, Mr. Nedelio\",male,19,0,0,349212,7.8958,,S\r\n879,0,3,\"Laleff, Mr. Kristo\",male,,0,0,349217,7.8958,,S\r\n880,1,1,\"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)\",female,56,0,1,11767,83.1583,C50,C\r\n881,1,2,\"Shelley, Mrs. William (Imanita Parrish Hall)\",female,25,0,1,230433,26,,S\r\n882,0,3,\"Markun, Mr. Johann\",male,33,0,0,349257,7.8958,,S\r\n883,0,3,\"Dahlberg, Miss. Gerda Ulrika\",female,22,0,0,7552,10.5167,,S\r\n884,0,2,\"Banfield, Mr. Frederick James\",male,28,0,0,C.A./SOTON 34068,10.5,,S\r\n885,0,3,\"Sutehall, Mr. Henry Jr\",male,25,0,0,SOTON/OQ 392076,7.05,,S\r\n886,0,3,\"Rice, Mrs. William (Margaret Norton)\",female,39,0,5,382652,29.125,,Q\r\n887,0,2,\"Montvila, Rev. Juozas\",male,27,0,0,211536,13,,S\r\n888,1,1,\"Graham, Miss. Margaret Edith\",female,19,0,0,112053,30,B42,S\r\n889,0,3,\"Johnston, Miss. Catherine Helen \"\"Carrie\"\"\",female,,1,2,W./C. 6607,23.45,,S\r\n890,1,1,\"Behr, Mr. Karl Howell\",male,26,0,0,111369,30,C148,C\r\n891,0,3,\"Dooley, Mr. Patrick\",male,32,0,0,370376,7.75,,Q\r\n"
  },
  {
    "path": "titanic-solution.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"6a09d4fb-60c5-4f45-b844-8c788a50c543\",\n    \"_uuid\": \"8e892e637f005dd61ec7dcb95865e52f3de2a77f\"\n   },\n   \"source\": [\n    \"# Titanic: Machine Learning from Disaster\\n\",\n    \"### Predict survival on the Titanic\\n\",\n    \"- Defining the problem statement\\n\",\n    \"- Collecting the data\\n\",\n    \"- Exploratory data analysis\\n\",\n    \"- Feature engineering\\n\",\n    \"- Modelling\\n\",\n    \"- Testing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"4af5e83d-7fd8-4a61-bf26-9583cb6d3476\",\n    \"_uuid\": \"65d04d276a8983f62a49261f6e94a02b281dbcc9\"\n   },\n   \"source\": [\n    \"## 1. Defining the problem statement\\n\",\n    \"Complete the analysis of what sorts of people were likely to survive.  \\n\",\n    \"In particular, we ask you to apply the tools of machine learning to predict which passengers survived the Titanic tragedy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\\\"/>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from IPython.display import Image\\n\",\n    \"Image(url= \\\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"3f529075-7f9b-40ff-a79a-f3a11a7d8cbe\",\n    \"_uuid\": \"64ca0f815766e3e8074b0e04f53947930cb061aa\"\n   },\n   \"source\": [\n    \"## 2. Collecting the data\\n\",\n    \"\\n\",\n    \"training data set and testing data set are given by Kaggle\\n\",\n    \"you can download from  \\n\",\n    \"my github [https://github.com/minsuk-heo/kaggle-titanic/tree/master](https://github.com/minsuk-heo/kaggle-titanic)  \\n\",\n    \"or you can download from kaggle directly [kaggle](https://www.kaggle.com/c/titanic/data)  \\n\",\n    \"\\n\",\n    \"### load train, test dataset using Pandas\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"_cell_guid\": \"e58a3f06-4c2a-4b87-90de-f8b09039fd4e\",\n    \"_uuid\": \"46f0b12d7bf66712642e9a9b807f5ef398426b83\",\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"train = pd.read_csv('input/train.csv')\\n\",\n    \"test = pd.read_csv('input/test.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"836a454f-17bc-41a2-be69-cd86c6f3b584\",\n    \"_uuid\": \"1ed3ad39ead93977b8936d9c96e6f6f806a8f9b3\"\n   },\n   \"source\": [\n    \"## 3. Exploratory data analysis\\n\",\n    \"Printing first 5 rows of the train dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"_cell_guid\": \"749a3d70-394c-4d2c-999a-4d0567e39232\",\n    \"_uuid\": \"b9fdb3b19d7a8f30cd0bb69ae434e04121ecba93\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>26.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>35.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Allen, Mr. William Henry</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Moran, Mr. James</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330877</td>\\n\",\n       \"      <td>8.4583</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>McCarthy, Mr. Timothy J</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>54.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>17463</td>\\n\",\n       \"      <td>51.8625</td>\\n\",\n       \"      <td>E46</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Palsson, Master. Gosta Leonard</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>2.00</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>349909</td>\\n\",\n       \"      <td>21.0750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>27.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>347742</td>\\n\",\n       \"      <td>11.1333</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>14.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>237736</td>\\n\",\n       \"      <td>30.0708</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Sandstrom, Miss. Marguerite Rut</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>4.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>PP 9549</td>\\n\",\n       \"      <td>16.7000</td>\\n\",\n       \"      <td>G6</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Bonnell, Miss. Elizabeth</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>58.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113783</td>\\n\",\n       \"      <td>26.5500</td>\\n\",\n       \"      <td>C103</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Saundercock, Mr. William Henry</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>20.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5. 2151</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Andersson, Mr. Anders Johan</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>39.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>347082</td>\\n\",\n       \"      <td>31.2750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>14.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>350406</td>\\n\",\n       \"      <td>7.8542</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>55.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>248706</td>\\n\",\n       \"      <td>16.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Rice, Master. Eugene</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>2.00</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>382652</td>\\n\",\n       \"      <td>29.1250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>18</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Williams, Mr. Charles Eugene</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>244373</td>\\n\",\n       \"      <td>13.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>19</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>31.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>345763</td>\\n\",\n       \"      <td>18.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Masselmani, Mrs. Fatima</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2649</td>\\n\",\n       \"      <td>7.2250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>21</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Fynney, Mr. Joseph J</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>239865</td>\\n\",\n       \"      <td>26.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Beesley, Mr. Lawrence</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>34.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>248698</td>\\n\",\n       \"      <td>13.0000</td>\\n\",\n       \"      <td>D56</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>23</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>McGowan, Miss. Anna \\\"Annie\\\"</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>15.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330923</td>\\n\",\n       \"      <td>8.0292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>24</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Sloper, Mr. William Thompson</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>28.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113788</td>\\n\",\n       \"      <td>35.5000</td>\\n\",\n       \"      <td>A6</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Palsson, Miss. Torborg Danira</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>8.00</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>349909</td>\\n\",\n       \"      <td>21.0750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>26</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>347077</td>\\n\",\n       \"      <td>31.3875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>27</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Emir, Mr. Farred Chehab</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2631</td>\\n\",\n       \"      <td>7.2250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>28</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Fortune, Mr. Charles Alexander</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>19.00</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>19950</td>\\n\",\n       \"      <td>263.0000</td>\\n\",\n       \"      <td>C23 C25 C27</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>29</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>O'Dwyer, Miss. Ellen \\\"Nellie\\\"</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330959</td>\\n\",\n       \"      <td>7.8792</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Todoroff, Mr. Lalio</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>349216</td>\\n\",\n       \"      <td>7.8958</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50</th>\\n\",\n       \"      <td>51</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Panula, Master. Juha Niilo</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>7.00</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3101295</td>\\n\",\n       \"      <td>39.6875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>51</th>\\n\",\n       \"      <td>52</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Nosworthy, Mr. Richard Cater</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>21.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/4. 39886</td>\\n\",\n       \"      <td>7.8000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>52</th>\\n\",\n       \"      <td>53</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Harper, Mrs. Henry Sleeper (Myna Haxtun)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>49.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17572</td>\\n\",\n       \"      <td>76.7292</td>\\n\",\n       \"      <td>D33</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>53</th>\\n\",\n       \"      <td>54</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>29.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2926</td>\\n\",\n       \"      <td>26.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>54</th>\\n\",\n       \"      <td>55</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Ostby, Mr. Engelhart Cornelius</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>65.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>113509</td>\\n\",\n       \"      <td>61.9792</td>\\n\",\n       \"      <td>B30</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>55</th>\\n\",\n       \"      <td>56</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Woolner, Mr. Hugh</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>19947</td>\\n\",\n       \"      <td>35.5000</td>\\n\",\n       \"      <td>C52</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>56</th>\\n\",\n       \"      <td>57</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Rugg, Miss. Emily</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>21.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>C.A. 31026</td>\\n\",\n       \"      <td>10.5000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>57</th>\\n\",\n       \"      <td>58</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Novel, Mr. Mansouer</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>28.50</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2697</td>\\n\",\n       \"      <td>7.2292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>58</th>\\n\",\n       \"      <td>59</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>West, Miss. Constance Mirium</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>5.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>C.A. 34651</td>\\n\",\n       \"      <td>27.7500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>59</th>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Goodwin, Master. William Frederick</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>11.00</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>CA 2144</td>\\n\",\n       \"      <td>46.9000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>60</th>\\n\",\n       \"      <td>61</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Sirayanian, Mr. Orsen</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2669</td>\\n\",\n       \"      <td>7.2292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>61</th>\\n\",\n       \"      <td>62</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Icard, Miss. Amelie</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113572</td>\\n\",\n       \"      <td>80.0000</td>\\n\",\n       \"      <td>B28</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>62</th>\\n\",\n       \"      <td>63</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Harris, Mr. Henry Birkhardt</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>45.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>36973</td>\\n\",\n       \"      <td>83.4750</td>\\n\",\n       \"      <td>C83</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>63</th>\\n\",\n       \"      <td>64</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Skoog, Master. Harald</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>4.00</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>347088</td>\\n\",\n       \"      <td>27.9000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>64</th>\\n\",\n       \"      <td>65</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Stewart, Mr. Albert A</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17605</td>\\n\",\n       \"      <td>27.7208</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>65</th>\\n\",\n       \"      <td>66</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Moubarek, Master. Gerios</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2661</td>\\n\",\n       \"      <td>15.2458</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>66</th>\\n\",\n       \"      <td>67</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Nye, Mrs. (Elizabeth Ramell)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>29.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>C.A. 29395</td>\\n\",\n       \"      <td>10.5000</td>\\n\",\n       \"      <td>F33</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>67</th>\\n\",\n       \"      <td>68</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Crease, Mr. Ernest James</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>19.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>S.P. 3464</td>\\n\",\n       \"      <td>8.1583</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>68</th>\\n\",\n       \"      <td>69</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Andersson, Miss. Erna Alexandra</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>17.00</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>3101281</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>69</th>\\n\",\n       \"      <td>70</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Kink, Mr. Vincenz</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>26.00</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>315151</td>\\n\",\n       \"      <td>8.6625</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>70</th>\\n\",\n       \"      <td>71</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Jenkin, Mr. Stephen Curnow</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>32.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>C.A. 33111</td>\\n\",\n       \"      <td>10.5000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>71</th>\\n\",\n       \"      <td>72</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Goodwin, Miss. Lillian Amy</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>16.00</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>CA 2144</td>\\n\",\n       \"      <td>46.9000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>72</th>\\n\",\n       \"      <td>73</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Hood, Mr. Ambrose Jr</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>21.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>S.O.C. 14879</td>\\n\",\n       \"      <td>73.5000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>73</th>\\n\",\n       \"      <td>74</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Chronopoulos, Mr. Apostolos</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>26.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2680</td>\\n\",\n       \"      <td>14.4542</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>74</th>\\n\",\n       \"      <td>75</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Bing, Mr. Lee</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>32.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1601</td>\\n\",\n       \"      <td>56.4958</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75</th>\\n\",\n       \"      <td>76</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Moen, Mr. Sigurd Hansen</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>25.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>348123</td>\\n\",\n       \"      <td>7.6500</td>\\n\",\n       \"      <td>F G73</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>76</th>\\n\",\n       \"      <td>77</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Staneff, Mr. Ivan</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>349208</td>\\n\",\n       \"      <td>7.8958</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>77</th>\\n\",\n       \"      <td>78</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Moutal, Mr. Rahamin Haim</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>374746</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>78</th>\\n\",\n       \"      <td>79</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Caldwell, Master. Alden Gates</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>0.83</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>248738</td>\\n\",\n       \"      <td>29.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>79</th>\\n\",\n       \"      <td>80</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Dowdell, Miss. Elizabeth</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>30.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>364516</td>\\n\",\n       \"      <td>12.4750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>80 rows × 12 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0             1         0       3   \\n\",\n       \"1             2         1       1   \\n\",\n       \"2             3         1       3   \\n\",\n       \"3             4         1       1   \\n\",\n       \"4             5         0       3   \\n\",\n       \"5             6         0       3   \\n\",\n       \"6             7         0       1   \\n\",\n       \"7             8         0       3   \\n\",\n       \"8             9         1       3   \\n\",\n       \"9            10         1       2   \\n\",\n       \"10           11         1       3   \\n\",\n       \"11           12         1       1   \\n\",\n       \"12           13         0       3   \\n\",\n       \"13           14         0       3   \\n\",\n       \"14           15         0       3   \\n\",\n       \"15           16         1       2   \\n\",\n       \"16           17         0       3   \\n\",\n       \"17           18         1       2   \\n\",\n       \"18           19         0       3   \\n\",\n       \"19           20         1       3   \\n\",\n       \"20           21         0       2   \\n\",\n       \"21           22         1       2   \\n\",\n       \"22           23         1       3   \\n\",\n       \"23           24         1       1   \\n\",\n       \"24           25         0       3   \\n\",\n       \"25           26         1       3   \\n\",\n       \"26           27         0       3   \\n\",\n       \"27           28         0       1   \\n\",\n       \"28           29         1       3   \\n\",\n       \"29           30         0       3   \\n\",\n       \"..          ...       ...     ...   \\n\",\n       \"50           51         0       3   \\n\",\n       \"51           52         0       3   \\n\",\n       \"52           53         1       1   \\n\",\n       \"53           54         1       2   \\n\",\n       \"54           55         0       1   \\n\",\n       \"55           56         1       1   \\n\",\n       \"56           57         1       2   \\n\",\n       \"57           58         0       3   \\n\",\n       \"58           59         1       2   \\n\",\n       \"59           60         0       3   \\n\",\n       \"60           61         0       3   \\n\",\n       \"61           62         1       1   \\n\",\n       \"62           63         0       1   \\n\",\n       \"63           64         0       3   \\n\",\n       \"64           65         0       1   \\n\",\n       \"65           66         1       3   \\n\",\n       \"66           67         1       2   \\n\",\n       \"67           68         0       3   \\n\",\n       \"68           69         1       3   \\n\",\n       \"69           70         0       3   \\n\",\n       \"70           71         0       2   \\n\",\n       \"71           72         0       3   \\n\",\n       \"72           73         0       2   \\n\",\n       \"73           74         0       3   \\n\",\n       \"74           75         1       3   \\n\",\n       \"75           76         0       3   \\n\",\n       \"76           77         0       3   \\n\",\n       \"77           78         0       3   \\n\",\n       \"78           79         1       2   \\n\",\n       \"79           80         1       3   \\n\",\n       \"\\n\",\n       \"                                                 Name     Sex    Age  SibSp  \\\\\\n\",\n       \"0                             Braund, Mr. Owen Harris    male  22.00      1   \\n\",\n       \"1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.00      1   \\n\",\n       \"2                              Heikkinen, Miss. Laina  female  26.00      0   \\n\",\n       \"3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.00      1   \\n\",\n       \"4                            Allen, Mr. William Henry    male  35.00      0   \\n\",\n       \"5                                    Moran, Mr. James    male    NaN      0   \\n\",\n       \"6                             McCarthy, Mr. Timothy J    male  54.00      0   \\n\",\n       \"7                      Palsson, Master. Gosta Leonard    male   2.00      3   \\n\",\n       \"8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.00      0   \\n\",\n       \"9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.00      1   \\n\",\n       \"10                    Sandstrom, Miss. Marguerite Rut  female   4.00      1   \\n\",\n       \"11                           Bonnell, Miss. Elizabeth  female  58.00      0   \\n\",\n       \"12                     Saundercock, Mr. William Henry    male  20.00      0   \\n\",\n       \"13                        Andersson, Mr. Anders Johan    male  39.00      1   \\n\",\n       \"14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.00      0   \\n\",\n       \"15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.00      0   \\n\",\n       \"16                               Rice, Master. Eugene    male   2.00      4   \\n\",\n       \"17                       Williams, Mr. Charles Eugene    male    NaN      0   \\n\",\n       \"18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.00      1   \\n\",\n       \"19                            Masselmani, Mrs. Fatima  female    NaN      0   \\n\",\n       \"20                               Fynney, Mr. Joseph J    male  35.00      0   \\n\",\n       \"21                              Beesley, Mr. Lawrence    male  34.00      0   \\n\",\n       \"22                        McGowan, Miss. Anna \\\"Annie\\\"  female  15.00      0   \\n\",\n       \"23                       Sloper, Mr. William Thompson    male  28.00      0   \\n\",\n       \"24                      Palsson, Miss. Torborg Danira  female   8.00      3   \\n\",\n       \"25  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.00      1   \\n\",\n       \"26                            Emir, Mr. Farred Chehab    male    NaN      0   \\n\",\n       \"27                     Fortune, Mr. Charles Alexander    male  19.00      3   \\n\",\n       \"28                      O'Dwyer, Miss. Ellen \\\"Nellie\\\"  female    NaN      0   \\n\",\n       \"29                                Todoroff, Mr. Lalio    male    NaN      0   \\n\",\n       \"..                                                ...     ...    ...    ...   \\n\",\n       \"50                         Panula, Master. Juha Niilo    male   7.00      4   \\n\",\n       \"51                       Nosworthy, Mr. Richard Cater    male  21.00      0   \\n\",\n       \"52           Harper, Mrs. Henry Sleeper (Myna Haxtun)  female  49.00      1   \\n\",\n       \"53  Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...  female  29.00      1   \\n\",\n       \"54                     Ostby, Mr. Engelhart Cornelius    male  65.00      0   \\n\",\n       \"55                                  Woolner, Mr. Hugh    male    NaN      0   \\n\",\n       \"56                                  Rugg, Miss. Emily  female  21.00      0   \\n\",\n       \"57                                Novel, Mr. Mansouer    male  28.50      0   \\n\",\n       \"58                       West, Miss. Constance Mirium  female   5.00      1   \\n\",\n       \"59                 Goodwin, Master. William Frederick    male  11.00      5   \\n\",\n       \"60                              Sirayanian, Mr. Orsen    male  22.00      0   \\n\",\n       \"61                                Icard, Miss. Amelie  female  38.00      0   \\n\",\n       \"62                        Harris, Mr. Henry Birkhardt    male  45.00      1   \\n\",\n       \"63                              Skoog, Master. Harald    male   4.00      3   \\n\",\n       \"64                              Stewart, Mr. Albert A    male    NaN      0   \\n\",\n       \"65                           Moubarek, Master. Gerios    male    NaN      1   \\n\",\n       \"66                       Nye, Mrs. (Elizabeth Ramell)  female  29.00      0   \\n\",\n       \"67                           Crease, Mr. Ernest James    male  19.00      0   \\n\",\n       \"68                    Andersson, Miss. Erna Alexandra  female  17.00      4   \\n\",\n       \"69                                  Kink, Mr. Vincenz    male  26.00      2   \\n\",\n       \"70                         Jenkin, Mr. Stephen Curnow    male  32.00      0   \\n\",\n       \"71                         Goodwin, Miss. Lillian Amy  female  16.00      5   \\n\",\n       \"72                               Hood, Mr. Ambrose Jr    male  21.00      0   \\n\",\n       \"73                        Chronopoulos, Mr. Apostolos    male  26.00      1   \\n\",\n       \"74                                      Bing, Mr. Lee    male  32.00      0   \\n\",\n       \"75                            Moen, Mr. Sigurd Hansen    male  25.00      0   \\n\",\n       \"76                                  Staneff, Mr. Ivan    male    NaN      0   \\n\",\n       \"77                           Moutal, Mr. Rahamin Haim    male    NaN      0   \\n\",\n       \"78                      Caldwell, Master. Alden Gates    male   0.83      0   \\n\",\n       \"79                           Dowdell, Miss. Elizabeth  female  30.00      0   \\n\",\n       \"\\n\",\n       \"    Parch            Ticket      Fare        Cabin Embarked  \\n\",\n       \"0       0         A/5 21171    7.2500          NaN        S  \\n\",\n       \"1       0          PC 17599   71.2833          C85        C  \\n\",\n       \"2       0  STON/O2. 3101282    7.9250          NaN        S  \\n\",\n       \"3       0            113803   53.1000         C123        S  \\n\",\n       \"4       0            373450    8.0500          NaN        S  \\n\",\n       \"5       0            330877    8.4583          NaN        Q  \\n\",\n       \"6       0             17463   51.8625          E46        S  \\n\",\n       \"7       1            349909   21.0750          NaN        S  \\n\",\n       \"8       2            347742   11.1333          NaN        S  \\n\",\n       \"9       0            237736   30.0708          NaN        C  \\n\",\n       \"10      1           PP 9549   16.7000           G6        S  \\n\",\n       \"11      0            113783   26.5500         C103        S  \\n\",\n       \"12      0         A/5. 2151    8.0500          NaN        S  \\n\",\n       \"13      5            347082   31.2750          NaN        S  \\n\",\n       \"14      0            350406    7.8542          NaN        S  \\n\",\n       \"15      0            248706   16.0000          NaN        S  \\n\",\n       \"16      1            382652   29.1250          NaN        Q  \\n\",\n       \"17      0            244373   13.0000          NaN        S  \\n\",\n       \"18      0            345763   18.0000          NaN        S  \\n\",\n       \"19      0              2649    7.2250          NaN        C  \\n\",\n       \"20      0            239865   26.0000          NaN        S  \\n\",\n       \"21      0            248698   13.0000          D56        S  \\n\",\n       \"22      0            330923    8.0292          NaN        Q  \\n\",\n       \"23      0            113788   35.5000           A6        S  \\n\",\n       \"24      1            349909   21.0750          NaN        S  \\n\",\n       \"25      5            347077   31.3875          NaN        S  \\n\",\n       \"26      0              2631    7.2250          NaN        C  \\n\",\n       \"27      2             19950  263.0000  C23 C25 C27        S  \\n\",\n       \"28      0            330959    7.8792          NaN        Q  \\n\",\n       \"29      0            349216    7.8958          NaN        S  \\n\",\n       \"..    ...               ...       ...          ...      ...  \\n\",\n       \"50      1           3101295   39.6875          NaN        S  \\n\",\n       \"51      0        A/4. 39886    7.8000          NaN        S  \\n\",\n       \"52      0          PC 17572   76.7292          D33        C  \\n\",\n       \"53      0              2926   26.0000          NaN        S  \\n\",\n       \"54      1            113509   61.9792          B30        C  \\n\",\n       \"55      0             19947   35.5000          C52        S  \\n\",\n       \"56      0        C.A. 31026   10.5000          NaN        S  \\n\",\n       \"57      0              2697    7.2292          NaN        C  \\n\",\n       \"58      2        C.A. 34651   27.7500          NaN        S  \\n\",\n       \"59      2           CA 2144   46.9000          NaN        S  \\n\",\n       \"60      0              2669    7.2292          NaN        C  \\n\",\n       \"61      0            113572   80.0000          B28      NaN  \\n\",\n       \"62      0             36973   83.4750          C83        S  \\n\",\n       \"63      2            347088   27.9000          NaN        S  \\n\",\n       \"64      0          PC 17605   27.7208          NaN        C  \\n\",\n       \"65      1              2661   15.2458          NaN        C  \\n\",\n       \"66      0        C.A. 29395   10.5000          F33        S  \\n\",\n       \"67      0         S.P. 3464    8.1583          NaN        S  \\n\",\n       \"68      2           3101281    7.9250          NaN        S  \\n\",\n       \"69      0            315151    8.6625          NaN        S  \\n\",\n       \"70      0        C.A. 33111   10.5000          NaN        S  \\n\",\n       \"71      2           CA 2144   46.9000          NaN        S  \\n\",\n       \"72      0      S.O.C. 14879   73.5000          NaN        S  \\n\",\n       \"73      0              2680   14.4542          NaN        C  \\n\",\n       \"74      0              1601   56.4958          NaN        S  \\n\",\n       \"75      0            348123    7.6500        F G73        S  \\n\",\n       \"76      0            349208    7.8958          NaN        S  \\n\",\n       \"77      0            374746    8.0500          NaN        S  \\n\",\n       \"78      2            248738   29.0000          NaN        S  \\n\",\n       \"79      0            364516   12.4750          NaN        S  \\n\",\n       \"\\n\",\n       \"[80 rows x 12 columns]\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head(80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Data Dictionary\\n\",\n    \"- Survived: \\t0 = No, 1 = Yes  \\n\",\n    \"- pclass: \\tTicket class\\t1 = 1st, 2 = 2nd, 3 = 3rd  \\t\\n\",\n    \"- sibsp:\\t# of siblings / spouses aboard the Titanic  \\t\\n\",\n    \"- parch:\\t# of parents / children aboard the Titanic  \\t\\n\",\n    \"- ticket:\\tTicket number\\t\\n\",\n    \"- cabin:\\tCabin number\\t\\n\",\n    \"- embarked:\\tPort of Embarkation\\tC = Cherbourg, Q = Queenstown, S = Southampton  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"5ebc1e0e-2b5a-4d92-98e0-defa019d4439\",\n    \"_uuid\": \"1892fbb34b26d775d1c428fdb7b6254449286b28\"\n   },\n   \"source\": [\n    \"**Total rows and columns**\\n\",\n    \"\\n\",\n    \"We can see that there are 891 rows and 12 columns in our training dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>892</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Kelly, Mr. James</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>34.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330911</td>\\n\",\n       \"      <td>7.8292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>893</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Wilkes, Mrs. James (Ellen Needs)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>47.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>363272</td>\\n\",\n       \"      <td>7.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>894</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Myles, Mr. Thomas Francis</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>62.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>240276</td>\\n\",\n       \"      <td>9.6875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>895</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Wirz, Mr. Albert</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>27.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>315154</td>\\n\",\n       \"      <td>8.6625</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>896</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3101298</td>\\n\",\n       \"      <td>12.2875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Pclass                                          Name     Sex  \\\\\\n\",\n       \"0          892       3                              Kelly, Mr. James    male   \\n\",\n       \"1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \\n\",\n       \"2          894       2                     Myles, Mr. Thomas Francis    male   \\n\",\n       \"3          895       3                              Wirz, Mr. Albert    male   \\n\",\n       \"4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \\n\",\n       \"\\n\",\n       \"    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \\n\",\n       \"0  34.5      0      0   330911   7.8292   NaN        Q  \\n\",\n       \"1  47.0      1      0   363272   7.0000   NaN        S  \\n\",\n       \"2  62.0      0      0   240276   9.6875   NaN        Q  \\n\",\n       \"3  27.0      0      0   315154   8.6625   NaN        S  \\n\",\n       \"4  22.0      1      1  3101298  12.2875   NaN        S  \"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"_cell_guid\": \"ed1e7849-d1b6-490d-b86b-9ca71dfafc7d\",\n    \"_uuid\": \"5a641beccf0e555dfd7b9a53a17188ea6edef95b\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(891, 12)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(418, 11)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"_cell_guid\": \"418b8a69-f2aa-442d-8f45-fa8887190938\",\n    \"_uuid\": \"4ee2591110660a4a16b3da7a7530f0945e121b46\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 891 entries, 0 to 890\\n\",\n      \"Data columns (total 12 columns):\\n\",\n      \"PassengerId    891 non-null int64\\n\",\n      \"Survived       891 non-null int64\\n\",\n      \"Pclass         891 non-null int64\\n\",\n      \"Name           891 non-null object\\n\",\n      \"Sex            891 non-null object\\n\",\n      \"Age            714 non-null float64\\n\",\n      \"SibSp          891 non-null int64\\n\",\n      \"Parch          891 non-null int64\\n\",\n      \"Ticket         891 non-null object\\n\",\n      \"Fare           891 non-null float64\\n\",\n      \"Cabin          204 non-null object\\n\",\n      \"Embarked       889 non-null object\\n\",\n      \"dtypes: float64(2), int64(5), object(5)\\n\",\n      \"memory usage: 83.6+ KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 418 entries, 0 to 417\\n\",\n      \"Data columns (total 11 columns):\\n\",\n      \"PassengerId    418 non-null int64\\n\",\n      \"Pclass         418 non-null int64\\n\",\n      \"Name           418 non-null object\\n\",\n      \"Sex            418 non-null object\\n\",\n      \"Age            332 non-null float64\\n\",\n      \"SibSp          418 non-null int64\\n\",\n      \"Parch          418 non-null int64\\n\",\n      \"Ticket         418 non-null object\\n\",\n      \"Fare           417 non-null float64\\n\",\n      \"Cabin          91 non-null object\\n\",\n      \"Embarked       418 non-null object\\n\",\n      \"dtypes: float64(2), int64(4), object(5)\\n\",\n      \"memory usage: 36.0+ KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"abc3c4fc-6419-405f-927a-4214d2c73eec\",\n    \"_uuid\": \"622d4d4b2ba8f77cc537af97fc343d4cd6de26b2\"\n   },\n   \"source\": [\n    \"We can see that *Age* value is missing for many rows. \\n\",\n    \"\\n\",\n    \"Out of 891 rows, the *Age* value is present only in 714 rows.\\n\",\n    \"\\n\",\n    \"Similarly, *Cabin* values are also missing in many rows. Only 204 out of 891 rows have *Cabin* values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"_cell_guid\": \"0663e2bb-dc27-4187-94b1-ff4ff78b68bc\",\n    \"_uuid\": \"3bf74de7f2483d622e41608f6017f2945639e4df\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"PassengerId      0\\n\",\n       \"Survived         0\\n\",\n       \"Pclass           0\\n\",\n       \"Name             0\\n\",\n       \"Sex              0\\n\",\n       \"Age            177\\n\",\n       \"SibSp            0\\n\",\n       \"Parch            0\\n\",\n       \"Ticket           0\\n\",\n       \"Fare             0\\n\",\n       \"Cabin          687\\n\",\n       \"Embarked         2\\n\",\n       \"dtype: int64\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.isnull().sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"PassengerId      0\\n\",\n       \"Pclass           0\\n\",\n       \"Name             0\\n\",\n       \"Sex              0\\n\",\n       \"Age             86\\n\",\n       \"SibSp            0\\n\",\n       \"Parch            0\\n\",\n       \"Ticket           0\\n\",\n       \"Fare             1\\n\",\n       \"Cabin          327\\n\",\n       \"Embarked         0\\n\",\n       \"dtype: int64\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test.isnull().sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"176aa52d-fde8-42e6-a3ee-db31f8b0ca49\",\n    \"_uuid\": \"b48a9feff6004d783960aa1b32fdfde902d87e21\"\n   },\n   \"source\": [\n    \"There are 177 rows with missing *Age*, 687 rows with missing *Cabin* and 2 rows with missing *Embarked* information.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"c8553d48-c5e0-4947-bd13-1b38509c850c\",\n    \"_uuid\": \"1a28e607e9ed63cefe0f35a4e4d72f2f36299323\"\n   },\n   \"source\": [\n    \"### import python lib for visualization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"_cell_guid\": \"b1d8a6d2-c22d-435c-8c98-973e8f41b138\",\n    \"_uuid\": \"26411c710f69b29939c815d5f5ab01d9177df7d0\",\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set() # setting seaborn default for plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Bar Chart for Categorical Features\\n\",\n    \"- Pclass\\n\",\n    \"- Sex\\n\",\n    \"- SibSp ( # of siblings and spouse)\\n\",\n    \"- Parch ( # of parents and children)\\n\",\n    \"- Embarked\\n\",\n    \"- Cabin\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def bar_chart(feature):\\n\",\n    \"    survived = train[train['Survived']==1][feature].value_counts()\\n\",\n    \"    dead = train[train['Survived']==0][feature].value_counts()\\n\",\n    \"    df = pd.DataFrame([survived,dead])\\n\",\n    \"    df.index = ['Survived','Dead']\\n\",\n    \"    df.plot(kind='bar',stacked=True, figsize=(10,5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAlQAAAFMCAYAAAAN9SJCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10da75c90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bar_chart('Sex')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Chart confirms **Women** more likely survivied than **Men**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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EMTVAAAhQQVAEAhQQUAUEhQAQAUElQAAIWGfFI6AKPDX3S/seoRGCE+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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x110e2cc10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bar_chart('Pclass')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Chart confirms **1st class** more likely survivied than **other classes**  \\n\",\n    \"The Chart confirms **3rd class** more likely dead than **other classes**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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m2VlpYqPj7+QrwHAAAAR4UMqsTERD322GOnPL5hw4ZTHissLFRhYWF4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\\nAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1110b6b10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bar_chart('SibSp')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Chart confirms **a person aboarded with more than 2 siblings or spouse** more likely survived  \\n\",\n    \"The Chart confirms ** a person aboarded without siblings or spouse** more likely dead\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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o+P12OPPXbO4+vXrz/nsby8POXl5YVmMgAAgAjBjT0BAAAMEVQAAACG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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1110c1290>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bar_chart('Parch')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Chart confirms **a person aboarded with more than 2 parents or children** more likely survived  \\n\",\n    \"The Chart confirms ** a person aboarded alone** more likely dead\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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3Tp0lQqld2v9/b2pq+vL21tbXv8vb6+vv1++KhRw9PU1FgyPxzWxoxp\\n2/+bAN4ix5a3br8P9vzVr36Vyy+/PB0dHfnoRz+ar3zlK7tf6+/vz4gRI9La2pr+/v49tv92YL2R\\nLVu2vs2xgSTp6emt9QjAIcix5fXtKzT3+Vt+L774Yi666KL86Z/+ac4///wkyQknnJA1a9YkSVav\\nXp2TTz45kydPztq1azMwMJDe3t50d3dn4sSJg7gLAAD1a59nqP7yL/8yr7zySm655ZbccsstSZIv\\nfOELueGGG9LZ2Znx48dn5syZaWxszOzZs9PR0ZFqtZp58+Zl2LBhB2UHAABqrVKtVqu1+nCnFF/f\\nRUt+WOsRGCL+er4v8+PNc2zhzXJseX1v+5IfAAD7J6gAAAoJKgCAQoIKAKCQoAIAKCSoAAAKCSoA\\ngEKCCgCgkKACACgkqAAACgkqAIBCggoAoJCgAgAoJKgAAAoJKgCAQoIKAKCQoAIAKCSoAAAKCSoA\\ngEKCCgCgkKACACgkqAAACgkqAIBCggoAoJCgAgAoJKgAAAoJKgCAQoIKAKCQoAIAKCSoAAAKCSoA\\ngEKCCgCgkKACACgkqAAACgkqAIBCggoAoJCgAgAoJKgAAAoJKgCAQm8qqH7+859n9uzZSZJnnnkm\\ns2bNSkdHRxYtWpRdu3YlSVasWJHzzjsv7e3tuf/++w/cxAAAdWa/QbVs2bJcd911GRgYSJIsXrw4\\nc+fOzbe//e1Uq9WsWrUqPT096erqyvLly3PHHXeks7Mz27dvP+DDAwDUg/0G1dixY3PzzTfv/nn9\\n+vWZOnVqkmTGjBl56KGHsm7dukyZMiXNzc1pa2vL2LFjs2HDhgM3NQBAHdlvUM2cOTNNTU27f65W\\nq6lUKkmSlpaW9Pb2pq+vL21tbbvf09LSkr6+vgMwLgBA/Wna/1v21NDwnw3W39+fESNGpLW1Nf39\\n/Xts/+3AeiOjRg1PU1PjWx0B+P/GjNn/v2cAb5Vjy1v3loPqhBNOyJo1azJt2rSsXr06v/d7v5fJ\\nkyfna1/7WgYGBrJ9+/Z0d3dn4sSJ+11ry5atb2to4N/19PTWegTgEOTY8vr2FZpvOaiuvvrqLFiw\\nIJ2dnRk/fnxmzpyZxsbGzJ49Ox0dHalWq5k3b16GDRtWNDQAwFDxpoLq2GOPzYoVK5Ikxx13XO66\\n66693tPe3p729vbBnQ4AYAjwYE8AgEKCCgCgkKACACgkqAAACgkqAIBCggoAoJCgAgAoJKgAAAoJ\\nKgCAQoIKAKCQoAIAKCSoAAAKCSoAgEKCCgCgkKACACgkqAAACgkqAIBCggoAoJCgAgAoJKgAAAoJ\\nKgCAQoIKAKCQoAIAKCSoAAAKCSoAgEKCCgCgkKACACgkqAAACgkqAIBCggoAoJCgAgAoJKgAAAoJ\\nKgCAQoIKAKCQoAIAKCSoAAAKCSoAgEKCCgCgkKACACjUNJiL7dq1K1/84hfz6KOPprm5OTfccEPG\\njRs3mB8BAFB3BvUM1X333Zft27fnu9/9bq666qosWbJkMJcHAKhLgxpUa9euzWmnnZYkOfHEE/Pw\\nww8P5vIAAHVpUIOqr68vra2tu39ubGzMa6+9NpgfAQBQdwb1HqrW1tb09/fv/nnXrl1panrjjxgz\\npm0wP/6Q8b+/ek6tRwAOQY4tcOAM6hmqk046KatXr06S/OxnP8vEiRMHc3kAgLpUqVar1cFa7D9+\\ny++xxx5LtVrNjTfemPe9732DtTwAQF0a1KACADgcebAnAEAhQQUAUEhQAQAUElQAAIUEFQBAoUF9\\nsCcMln/91399w9c++MEPHsRJgEPJxo0b3/C1Y4455iBOwqFGUFGXvvOd7yRJnn322ezYsSO/+7u/\\nm0ceeSQtLS3p6uqq8XTAUDVv3rwkycsvv5z+/v5MmDAhTzzxRN71rnfl7rvvrvF0DGWCirrU2dmZ\\nJLnkkktyyy23pKmpKTt37swll1xS48mAoey73/1ukuTyyy/P0qVL09ramq1bt+bKK6+s8WQMde6h\\noq719PTs/vPOnTvz61//uobTAIeKTZs2pbW1NUkyfPjwPY418HY4Q0VdO//88/PhD384EydOzOOP\\nP55Pf/rTtR4JOASceuqp+cQnPpEPfOADWbduXc4888xaj8QQ56tnqHsvvfRSnn322YwbNy6jR4+u\\n9TjAIeLhhx/O008/neOPPz6TJk2q9TgMcYKKuvb4449n0aJFeeWVV/LHf/zHmTBhQk4//fRajwUM\\ncc8880zuueee7NixI0myefPmXH/99TWeiqHMPVTUtRtuuCGLFy/OqFGjcv755+fmm2+u9UjAIeCq\\nq65Kkvz0pz/Nc889l5dffrnGEzHUCSrq3rhx41KpVDJ69Oi0tLTUehzgEDB8+PB85jOfydFHH50l\\nS5bkxRdfrPVIDHGCirr2zne+M8uXL8+2bdvy/e9/PyNGjKj1SMAhoFKppKenJ/39/dm6dWu2bt1a\\n65EY4gQVde3GG2/Mc889l1GjRuXhhx/On//5n9d6JOAQcMUVV+QHP/hBzjnnnJx55pk55ZRTaj0S\\nQ5yb0qlrN954Y9rb23P88cfXehTgENPX15fnnnsu733ve91OQDFBRV279957873vfS/9/f0577zz\\ncvbZZ+eII46o9VjAEHfvvffm1ltvzc6dO3PWWWelUqnksssuq/VYDGEu+VHXZs6cmdtuuy2dnZ15\\n4IEHcuqpp9Z6JOAQ8K1vfSsrVqzIyJEjc9lll+W+++6r9UgMcZ6UTl3buHFj7r777vzLv/xLTjjh\\nhCxbtqzWIwGHgIaGhjQ3N6dSqaRSqeTII4+s9UgMcS75Udc+/vGP54ILLshHPvKR3d+7BVCqs7Mz\\nzz//fB5++OFMmzYtw4cPz/z582s9FkOYoKIubdq0Ke9+97vz5JNPplKp7PHacccdV6OpgEPBhg0b\\ncs899+See+7JRz/60YwYMSKzZ8+u9VgMcYKKurR48eJcc801ex3kKpVK/vZv/7ZGUwFD3T//8z9n\\n2bJlmTVrVkaPHp2NGzdmxYoV+dznPucLkikiqKhr9913X84444w0NPj9CaDcrFmzcscdd2T48OG7\\nt/X19WXOnDnp6uqq4WQMdf4rRV378Y9/nHPOOSc33XRTfvnLX9Z6HGCIa2pq2iOmkqS1tTWNjY01\\nmohDhd/yo64tWLAg27dvz6pVq3L99ddnx44dufPOO2s9FjBE/dd7Mv/Drl27DvIkHGoEFXVv3bp1\\n+dGPfpSXXnopM2fOrPU4wBD2xBNP5KqrrtpjW7VaTXd3d40m4lDhHirq2tlnn51Jkyblggsu8F1b\\nQLGf/OQnb/ja1KlTD+IkHGoEFXXt9ttvz8UXX1zrMQBgn9yUTl1bvXp1du7cWesxAGCf3ENFXduy\\nZUtOO+20HHvssbu/ImL58uW1HgsA9uCSH3Xt+eef32vbe97znhpMAgBvzBkq6trdd9+917Yrrrii\\nBpMAwBsTVNS1d73rXUn+/deaH3nkEc+KAaAuCSrq2oUXXrjHz37jD4B6JKioa0899dTuP2/evDkb\\nN26s4TQA8PoEFXVt4cKFqVQq+c1vfpORI0dm/vz5tR4JAPbiOVTUpfXr1+fcc8/NHXfckU984hPZ\\nvHlzNm3alB07dtR6NADYi6CiLn35y1/OkiVL0tzcnK997Wu5/fbbs3LlyixbtqzWowHAXlzyoy7t\\n2rUrkyZNygsvvJBt27bld37nd5IkDQ3+HwCA+uO/TtSlpqZ/b/0HHnhg95ci79ixI/39/bUcCwBe\\nlzNU1KVTTjklF154YTZt2pRbb701zz77bK6//vqcffbZtR4NAPbiq2eoW93d3Wltbc3RRx+dZ599\\nNo8++mj+8A//sNZjAcBeBBUAQCH3UAEAFBJUAACFBBUAQCFBBQBQSFABABT6f5JSGSlQLivwAAAA\\nAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11109e150>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bar_chart('Embarked')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The Chart confirms **a person aboarded from C** slightly more likely survived  \\n\",\n    \"The Chart confirms **a person aboarded from Q** more likely dead  \\n\",\n    \"The Chart confirms **a person aboarded from S** more likely dead\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"_cell_guid\": \"810cd964-24eb-44fb-9e7b-18bbddd4900f\",\n    \"_uuid\": \"fd86ccdf2d1248b79c68365444e96e46a50f3f5a\"\n   },\n   \"source\": [\n    \"## 4. Feature engineering\\n\",\n    \"\\n\",\n    \"Feature engineering is the process of using domain knowledge of the data  \\n\",\n    \"to create features (**feature vectors**) that make machine learning algorithms work.  \\n\",\n    \"\\n\",\n    \"feature vector is an n-dimensional vector of numerical features that represent some object.  \\n\",\n    \"Many algorithms in machine learning require a numerical representation of objects,  \\n\",\n    \"since such representations facilitate processing and statistical analysis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Allen, Mr. William Henry</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex   Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male  22.0      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female  26.0      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male  35.0      0   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  \\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S  \\n\",\n       \"1      0          PC 17599  71.2833   C85        C  \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S  \\n\",\n       \"3      0            113803  53.1000  C123        S  \\n\",\n       \"4      0            373450   8.0500   NaN        S  \"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.1 how titanic sank?\\n\",\n    \"sank from the bow of the ship where third class rooms located  \\n\",\n    \"conclusion, Pclass is key feature for classifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\\\"/>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Image(url= \\\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Allen, Mr. William Henry</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Moran, Mr. James</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330877</td>\\n\",\n       \"      <td>8.4583</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>McCarthy, Mr. Timothy J</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>54.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>17463</td>\\n\",\n       \"      <td>51.8625</td>\\n\",\n       \"      <td>E46</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Palsson, Master. Gosta Leonard</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>349909</td>\\n\",\n       \"      <td>21.0750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>27.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>347742</td>\\n\",\n       \"      <td>11.1333</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>14.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>237736</td>\\n\",\n       \"      <td>30.0708</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"5            6         0       3   \\n\",\n       \"6            7         0       1   \\n\",\n       \"7            8         0       3   \\n\",\n       \"8            9         1       3   \\n\",\n       \"9           10         1       2   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex   Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male  22.0      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female  26.0      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male  35.0      0   \\n\",\n       \"5                                   Moran, Mr. James    male   NaN      0   \\n\",\n       \"6                            McCarthy, Mr. Timothy J    male  54.0      0   \\n\",\n       \"7                     Palsson, Master. Gosta Leonard    male   2.0      3   \\n\",\n       \"8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \\n\",\n       \"9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  \\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S  \\n\",\n       \"1      0          PC 17599  71.2833   C85        C  \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S  \\n\",\n       \"3      0            113803  53.1000  C123        S  \\n\",\n       \"4      0            373450   8.0500   NaN        S  \\n\",\n       \"5      0            330877   8.4583   NaN        Q  \\n\",\n       \"6      0             17463  51.8625   E46        S  \\n\",\n       \"7      1            349909  21.0750   NaN        S  \\n\",\n       \"8      2            347742  11.1333   NaN        S  \\n\",\n       \"9      0            237736  30.0708   NaN        C  \"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.2 Name\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_test_data = [train, test] # combining train and test dataset\\n\",\n    \"\\n\",\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\\\.', expand=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Mr          517\\n\",\n       \"Miss        182\\n\",\n       \"Mrs         125\\n\",\n       \"Master       40\\n\",\n       \"Dr            7\\n\",\n       \"Rev           6\\n\",\n       \"Col           2\\n\",\n       \"Major         2\\n\",\n       \"Mlle          2\\n\",\n       \"Countess      1\\n\",\n       \"Ms            1\\n\",\n       \"Lady          1\\n\",\n       \"Jonkheer      1\\n\",\n       \"Don           1\\n\",\n       \"Mme           1\\n\",\n       \"Capt          1\\n\",\n       \"Sir           1\\n\",\n       \"Name: Title, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train['Title'].value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Mr        240\\n\",\n       \"Miss       78\\n\",\n       \"Mrs        72\\n\",\n       \"Master     21\\n\",\n       \"Col         2\\n\",\n       \"Rev         2\\n\",\n       \"Dona        1\\n\",\n       \"Ms          1\\n\",\n       \"Dr          1\\n\",\n       \"Name: Title, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test['Title'].value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Title map\\n\",\n    \"Mr : 0  \\n\",\n    \"Miss : 1  \\n\",\n    \"Mrs: 2  \\n\",\n    \"Others: 3\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"title_mapping = {\\\"Mr\\\": 0, \\\"Miss\\\": 1, \\\"Mrs\\\": 2, \\n\",\n    \"                 \\\"Master\\\": 3, \\\"Dr\\\": 3, \\\"Rev\\\": 3, \\\"Col\\\": 3, \\\"Major\\\": 3, \\\"Mlle\\\": 3,\\\"Countess\\\": 3,\\n\",\n    \"                 \\\"Ms\\\": 3, \\\"Lady\\\": 3, \\\"Jonkheer\\\": 3, \\\"Don\\\": 3, \\\"Dona\\\" : 3, \\\"Mme\\\": 3,\\\"Capt\\\": 3,\\\"Sir\\\": 3 }\\n\",\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset['Title'] = dataset['Title'].map(title_mapping)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Braund, Mr. Owen Harris</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Heikkinen, Miss. Laina</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Allen, Mr. William Henry</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  \\\\\\n\",\n       \"0            1         0       3   \\n\",\n       \"1            2         1       1   \\n\",\n       \"2            3         1       3   \\n\",\n       \"3            4         1       1   \\n\",\n       \"4            5         0       3   \\n\",\n       \"\\n\",\n       \"                                                Name     Sex   Age  SibSp  \\\\\\n\",\n       \"0                            Braund, Mr. Owen Harris    male  22.0      1   \\n\",\n       \"1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \\n\",\n       \"2                             Heikkinen, Miss. Laina  female  26.0      0   \\n\",\n       \"3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \\n\",\n       \"4                           Allen, Mr. William Henry    male  35.0      0   \\n\",\n       \"\\n\",\n       \"   Parch            Ticket     Fare Cabin Embarked  Title  \\n\",\n       \"0      0         A/5 21171   7.2500   NaN        S      0  \\n\",\n       \"1      0          PC 17599  71.2833   C85        C      2  \\n\",\n       \"2      0  STON/O2. 3101282   7.9250   NaN        S      1  \\n\",\n       \"3      0            113803  53.1000  C123        S      2  \\n\",\n       \"4      0            373450   8.0500   NaN        S      0  \"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Name</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>892</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Kelly, Mr. James</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>34.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330911</td>\\n\",\n       \"      <td>7.8292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>893</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Wilkes, Mrs. James (Ellen Needs)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>47.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>363272</td>\\n\",\n       \"      <td>7.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>894</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Myles, Mr. Thomas Francis</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>62.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>240276</td>\\n\",\n       \"      <td>9.6875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>895</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Wirz, Mr. Albert</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>27.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>315154</td>\\n\",\n       \"      <td>8.6625</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>896</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3101298</td>\\n\",\n       \"      <td>12.2875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Pclass                                          Name     Sex  \\\\\\n\",\n       \"0          892       3                              Kelly, Mr. James    male   \\n\",\n       \"1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \\n\",\n       \"2          894       2                     Myles, Mr. Thomas Francis    male   \\n\",\n       \"3          895       3                              Wirz, Mr. Albert    male   \\n\",\n       \"4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \\n\",\n       \"\\n\",\n       \"    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Title  \\n\",\n       \"0  34.5      0      0   330911   7.8292   NaN        Q      0  \\n\",\n       \"1  47.0      1      0   363272   7.0000   NaN        S      2  \\n\",\n       \"2  62.0      0      0   240276   9.6875   NaN        Q      0  \\n\",\n       \"3  27.0      0      0   315154   8.6625   NaN        S      0  \\n\",\n       \"4  22.0      1      1  3101298  12.2875   NaN        S      2  \"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAlQAAAFMCAYAAAAN9SJCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1112cce10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bar_chart('Title')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# delete unnecessary feature from dataset\\n\",\n    \"train.drop('Name', axis=1, inplace=True)\\n\",\n    \"test.drop('Name', axis=1, inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>38.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>26.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>35.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\\\\n\",\n       \"0            1         0       3    male  22.0      1      0   \\n\",\n       \"1            2         1       1  female  38.0      1      0   \\n\",\n       \"2            3         1       3  female  26.0      0      0   \\n\",\n       \"3            4         1       1  female  35.0      1      0   \\n\",\n       \"4            5         0       3    male  35.0      0      0   \\n\",\n       \"\\n\",\n       \"             Ticket     Fare Cabin Embarked  Title  \\n\",\n       \"0         A/5 21171   7.2500   NaN        S      0  \\n\",\n       \"1          PC 17599  71.2833   C85        C      2  \\n\",\n       \"2  STON/O2. 3101282   7.9250   NaN        S      1  \\n\",\n       \"3            113803  53.1000  C123        S      2  \\n\",\n       \"4            373450   8.0500   NaN        S      0  \"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>892</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>34.5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330911</td>\\n\",\n       \"      <td>7.8292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>893</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>47.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>363272</td>\\n\",\n       \"      <td>7.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>894</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>62.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>240276</td>\\n\",\n       \"      <td>9.6875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>895</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>27.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>315154</td>\\n\",\n       \"      <td>8.6625</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>896</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>22.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3101298</td>\\n\",\n       \"      <td>12.2875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Pclass     Sex   Age  SibSp  Parch   Ticket     Fare Cabin  \\\\\\n\",\n       \"0          892       3    male  34.5      0      0   330911   7.8292   NaN   \\n\",\n       \"1          893       3  female  47.0      1      0   363272   7.0000   NaN   \\n\",\n       \"2          894       2    male  62.0      0      0   240276   9.6875   NaN   \\n\",\n       \"3          895       3    male  27.0      0      0   315154   8.6625   NaN   \\n\",\n       \"4          896       3  female  22.0      1      1  3101298  12.2875   NaN   \\n\",\n       \"\\n\",\n       \"  Embarked  Title  \\n\",\n       \"0        Q      0  \\n\",\n       \"1        S      2  \\n\",\n       \"2        Q      0  \\n\",\n       \"3        S      0  \\n\",\n       \"4        S      2  \"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.3 Sex\\n\",\n    \"\\n\",\n    \"male: 0\\n\",\n    \"female: 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sex_mapping = {\\\"male\\\": 0, \\\"female\\\": 1}\\n\",\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset['Sex'] = dataset['Sex'].map(sex_mapping)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAlQAAAFMCAYAAAAN9SJCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1113eef90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bar_chart('Sex')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.4 Age\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.4.1 some age is missing\\n\",\n    \"Let's use Title's median age for missing Age\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>22.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>38.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>26.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>35.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n    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<td>0</td>\\n\",\n       \"      <td>54.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>17463</td>\\n\",\n       \"      <td>51.8625</td>\\n\",\n       \"      <td>E46</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.00</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>349909</td>\\n\",\n       \"      <td>21.0750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>27.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>347742</td>\\n\",\n       \"      <td>11.1333</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>14.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>237736</td>\\n\",\n       \"      <td>30.0708</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>4.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>PP 9549</td>\\n\",\n       \"      <td>16.7000</td>\\n\",\n       \"      <td>G6</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>58.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113783</td>\\n\",\n       \"      <td>26.5500</td>\\n\",\n       \"      <td>C103</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>20.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5. 2151</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>39.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>347082</td>\\n\",\n       \"      <td>31.2750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>14.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>350406</td>\\n\",\n       \"      <td>7.8542</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>55.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>248706</td>\\n\",\n       \"      <td>16.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.00</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>382652</td>\\n\",\n       \"      <td>29.1250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>18</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>244373</td>\\n\",\n       \"      <td>13.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>19</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      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<td>1</td>\\n\",\n       \"      <td>15.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330923</td>\\n\",\n       \"      <td>8.0292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>24</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>28.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113788</td>\\n\",\n       \"      <td>35.5000</td>\\n\",\n       \"      <td>A6</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      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\"      <td>0</td>\\n\",\n       \"      <td>26.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2680</td>\\n\",\n       \"      <td>14.4542</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>74</th>\\n\",\n       \"      <td>75</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>32.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1601</td>\\n\",\n       \"      <td>56.4958</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75</th>\\n\",\n       \"      <td>76</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>25.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>348123</td>\\n\",\n       \"      <td>7.6500</td>\\n\",\n       \"      <td>F G73</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>76</th>\\n\",\n       \"      <td>77</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>349208</td>\\n\",\n       \"      <td>7.8958</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>77</th>\\n\",\n       \"      <td>78</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>374746</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>78</th>\\n\",\n       \"      <td>79</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.83</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>248738</td>\\n\",\n       \"      <td>29.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>79</th>\\n\",\n       \"      <td>80</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>30.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>364516</td>\\n\",\n       \"      <td>12.4750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>80</th>\\n\",\n       \"      <td>81</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>22.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>345767</td>\\n\",\n       \"      <td>9.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>81</th>\\n\",\n       \"      <td>82</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>29.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>345779</td>\\n\",\n       \"      <td>9.5000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>82</th>\\n\",\n       \"      <td>83</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330932</td>\\n\",\n       \"      <td>7.7875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Q</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>83</th>\\n\",\n       \"      <td>84</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>28.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113059</td>\\n\",\n       \"      <td>47.1000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>84</th>\\n\",\n       \"      <td>85</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>17.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>SO/C 14885</td>\\n\",\n       \"      <td>10.5000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>85</th>\\n\",\n       \"      <td>86</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>33.00</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3101278</td>\\n\",\n       \"      <td>15.8500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>86</th>\\n\",\n       \"      <td>87</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>16.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>W./C. 6608</td>\\n\",\n       \"      <td>34.3750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>87</th>\\n\",\n       \"      <td>88</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>SOTON/OQ 392086</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>88</th>\\n\",\n       \"      <td>89</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>23.00</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>19950</td>\\n\",\n       \"      <td>263.0000</td>\\n\",\n       \"      <td>C23 C25 C27</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>89</th>\\n\",\n       \"      <td>90</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>24.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>343275</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>90</th>\\n\",\n       \"      <td>91</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>29.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>343276</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>91</th>\\n\",\n       \"      <td>92</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>20.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>347466</td>\\n\",\n       \"      <td>7.8542</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>92</th>\\n\",\n       \"      <td>93</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>46.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>W.E.P. 5734</td>\\n\",\n       \"      <td>61.1750</td>\\n\",\n       \"      <td>E31</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>93</th>\\n\",\n       \"      <td>94</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>26.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>C.A. 2315</td>\\n\",\n       \"      <td>20.5750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>94</th>\\n\",\n       \"      <td>95</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>59.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>364500</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>95</th>\\n\",\n       \"      <td>96</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>374910</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>96</th>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>71.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17754</td>\\n\",\n       \"      <td>34.6542</td>\\n\",\n       \"      <td>A5</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>97</th>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>23.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>PC 17759</td>\\n\",\n       \"      <td>63.3583</td>\\n\",\n       \"      <td>D10 D12</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>98</th>\\n\",\n       \"      <td>99</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>34.00</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>231919</td>\\n\",\n       \"      <td>23.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>99</th>\\n\",\n       \"      <td>100</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>34.00</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>244367</td>\\n\",\n       \"      <td>26.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>100 rows × 12 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    PassengerId  Survived  Pclass  Sex    Age  SibSp  Parch            Ticket  \\\\\\n\",\n       \"0             1         0       3    0  22.00      1      0         A/5 21171   \\n\",\n       \"1             2         1       1    1  38.00      1      0          PC 17599   \\n\",\n       \"2             3         1       3    1  26.00      0      0  STON/O2. 3101282   \\n\",\n       \"3             4         1       1    1  35.00      1      0            113803   \\n\",\n       \"4             5         0       3    0  35.00      0      0            373450   \\n\",\n       \"5             6         0       3    0    NaN      0      0            330877   \\n\",\n       \"6             7         0       1    0  54.00      0      0             17463   \\n\",\n       \"7             8         0       3    0   2.00      3      1            349909   \\n\",\n       \"8             9         1       3    1  27.00      0      2            347742   \\n\",\n       \"9            10         1       2    1  14.00      1      0            237736   \\n\",\n       \"10           11         1       3    1   4.00      1      1           PP 9549   \\n\",\n       \"11           12         1       1    1  58.00      0      0            113783   \\n\",\n       \"12           13         0       3    0  20.00      0      0         A/5. 2151   \\n\",\n       \"13           14         0       3    0  39.00      1      5            347082   \\n\",\n       \"14           15         0       3    1  14.00      0      0            350406   \\n\",\n       \"15           16         1       2    1  55.00      0      0            248706   \\n\",\n       \"16           17         0       3    0   2.00      4      1            382652   \\n\",\n       \"17           18         1       2    0    NaN      0      0            244373   \\n\",\n       \"18           19         0       3    1  31.00      1      0            345763   \\n\",\n       \"19           20         1       3    1    NaN      0      0              2649   \\n\",\n       \"20           21         0       2    0  35.00      0      0            239865   \\n\",\n       \"21           22         1       2    0  34.00      0      0            248698   \\n\",\n       \"22           23         1       3    1  15.00      0      0            330923   \\n\",\n       \"23           24         1       1    0  28.00      0      0            113788   \\n\",\n       \"24           25         0       3    1   8.00      3      1            349909   \\n\",\n       \"25           26         1       3    1  38.00      1      5            347077   \\n\",\n       \"26           27         0       3    0    NaN      0      0              2631   \\n\",\n       \"27           28         0       1    0  19.00      3      2             19950   \\n\",\n       \"28           29         1       3    1    NaN      0      0            330959   \\n\",\n       \"29           30         0       3    0    NaN      0      0            349216   \\n\",\n       \"..          ...       ...     ...  ...    ...    ...    ...               ...   \\n\",\n       \"70           71         0       2    0  32.00      0      0        C.A. 33111   \\n\",\n       \"71           72         0       3    1  16.00      5      2           CA 2144   \\n\",\n       \"72           73         0       2    0  21.00      0      0      S.O.C. 14879   \\n\",\n       \"73           74         0       3    0  26.00      1      0              2680   \\n\",\n       \"74           75         1       3    0  32.00      0      0              1601   \\n\",\n       \"75           76         0       3    0  25.00      0      0            348123   \\n\",\n       \"76           77         0       3    0    NaN      0      0            349208   \\n\",\n       \"77           78         0       3    0    NaN      0      0            374746   \\n\",\n       \"78           79         1       2    0   0.83      0      2            248738   \\n\",\n       \"79           80         1       3    1  30.00      0      0            364516   \\n\",\n       \"80           81         0       3    0  22.00      0      0            345767   \\n\",\n       \"81           82         1       3    0  29.00      0      0            345779   \\n\",\n       \"82           83         1       3    1    NaN      0      0            330932   \\n\",\n       \"83           84         0       1    0  28.00      0      0            113059   \\n\",\n       \"84           85         1       2    1  17.00      0      0        SO/C 14885   \\n\",\n       \"85           86         1       3    1  33.00      3      0           3101278   \\n\",\n       \"86           87         0       3    0  16.00      1      3        W./C. 6608   \\n\",\n       \"87           88         0       3    0    NaN      0      0   SOTON/OQ 392086   \\n\",\n       \"88           89         1       1    1  23.00      3      2             19950   \\n\",\n       \"89           90         0       3    0  24.00      0      0            343275   \\n\",\n       \"90           91         0       3    0  29.00      0      0            343276   \\n\",\n       \"91           92         0       3    0  20.00      0      0            347466   \\n\",\n       \"92           93         0       1    0  46.00      1      0       W.E.P. 5734   \\n\",\n       \"93           94         0       3    0  26.00      1      2         C.A. 2315   \\n\",\n       \"94           95         0       3    0  59.00      0      0            364500   \\n\",\n       \"95           96         0       3    0    NaN      0      0            374910   \\n\",\n       \"96           97         0       1    0  71.00      0      0          PC 17754   \\n\",\n       \"97           98         1       1    0  23.00      0      1          PC 17759   \\n\",\n       \"98           99         1       2    1  34.00      0      1            231919   \\n\",\n       \"99          100         0       2    0  34.00      1      0            244367   \\n\",\n       \"\\n\",\n       \"        Fare        Cabin Embarked  Title  \\n\",\n       \"0     7.2500          NaN        S      0  \\n\",\n       \"1    71.2833          C85        C      2  \\n\",\n       \"2     7.9250          NaN        S      1  \\n\",\n       \"3    53.1000         C123        S      2  \\n\",\n       \"4     8.0500          NaN        S      0  \\n\",\n       \"5     8.4583          NaN        Q      0  \\n\",\n       \"6    51.8625          E46        S      0  \\n\",\n       \"7    21.0750          NaN        S      3  \\n\",\n       \"8    11.1333          NaN        S      2  \\n\",\n       \"9    30.0708          NaN        C      2  \\n\",\n       \"10   16.7000           G6        S      1  \\n\",\n       \"11   26.5500         C103        S      1  \\n\",\n       \"12    8.0500          NaN        S      0  \\n\",\n       \"13   31.2750          NaN        S      0  \\n\",\n       \"14    7.8542          NaN        S      1  \\n\",\n       \"15   16.0000          NaN        S      2  \\n\",\n       \"16   29.1250          NaN        Q      3  \\n\",\n       \"17   13.0000          NaN        S      0  \\n\",\n       \"18   18.0000          NaN        S      2  \\n\",\n       \"19    7.2250          NaN        C      2  \\n\",\n       \"20   26.0000          NaN        S      0  \\n\",\n       \"21   13.0000          D56        S      0  \\n\",\n       \"22    8.0292          NaN        Q      1  \\n\",\n       \"23   35.5000           A6        S      0  \\n\",\n       \"24   21.0750          NaN        S      1  \\n\",\n       \"25   31.3875          NaN        S      2  \\n\",\n       \"26    7.2250          NaN        C      0  \\n\",\n       \"27  263.0000  C23 C25 C27        S      0  \\n\",\n       \"28    7.8792          NaN        Q      1  \\n\",\n       \"29    7.8958          NaN        S      0  \\n\",\n       \"..       ...          ...      ...    ...  \\n\",\n       \"70   10.5000          NaN        S      0  \\n\",\n       \"71   46.9000          NaN        S      1  \\n\",\n       \"72   73.5000          NaN        S      0  \\n\",\n       \"73   14.4542          NaN        C      0  \\n\",\n       \"74   56.4958          NaN        S      0  \\n\",\n       \"75    7.6500        F G73        S      0  \\n\",\n       \"76    7.8958          NaN        S      0  \\n\",\n       \"77    8.0500          NaN        S      0  \\n\",\n       \"78   29.0000          NaN        S      3  \\n\",\n       \"79   12.4750          NaN        S      1  \\n\",\n       \"80    9.0000          NaN        S      0  \\n\",\n       \"81    9.5000          NaN        S      0  \\n\",\n       \"82    7.7875          NaN        Q      1  \\n\",\n       \"83   47.1000          NaN        S      0  \\n\",\n       \"84   10.5000          NaN        S      1  \\n\",\n       \"85   15.8500          NaN        S      2  \\n\",\n       \"86   34.3750          NaN        S      0  \\n\",\n       \"87    8.0500          NaN        S      0  \\n\",\n       \"88  263.0000  C23 C25 C27        S      1  \\n\",\n       \"89    8.0500          NaN        S      0  \\n\",\n       \"90    8.0500          NaN        S      0  \\n\",\n       \"91    7.8542          NaN        S      0  \\n\",\n       \"92   61.1750          E31        S      0  \\n\",\n       \"93   20.5750          NaN        S      0  \\n\",\n       \"94    7.2500          NaN        S      0  \\n\",\n       \"95    8.0500          NaN        S      0  \\n\",\n       \"96   34.6542           A5        C      0  \\n\",\n       \"97   63.3583      D10 D12        C      0  \\n\",\n       \"98   23.0000          NaN        S      2  \\n\",\n       \"99   26.0000          NaN        S      0  \\n\",\n       \"\\n\",\n       \"[100 rows x 12 columns]\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head(100)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\\n\",\n    \"train[\\\"Age\\\"].fillna(train.groupby(\\\"Title\\\")[\\\"Age\\\"].transform(\\\"median\\\"), inplace=True)\\n\",\n    \"test[\\\"Age\\\"].fillna(test.groupby(\\\"Title\\\")[\\\"Age\\\"].transform(\\\"median\\\"), inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0      30.0\\n\",\n       \"1      35.0\\n\",\n       \"2      21.0\\n\",\n       \"3      35.0\\n\",\n       \"4      30.0\\n\",\n       \"5      30.0\\n\",\n       \"6      30.0\\n\",\n       \"7       9.0\\n\",\n       \"8      35.0\\n\",\n       \"9      35.0\\n\",\n       \"10     21.0\\n\",\n       \"11     21.0\\n\",\n       \"12     30.0\\n\",\n       \"13     30.0\\n\",\n       \"14     21.0\\n\",\n       \"15     35.0\\n\",\n       \"16      9.0\\n\",\n       \"17     30.0\\n\",\n       \"18     35.0\\n\",\n       \"19     35.0\\n\",\n       \"20     30.0\\n\",\n       \"21     30.0\\n\",\n       \"22     21.0\\n\",\n       \"23     30.0\\n\",\n       \"24     21.0\\n\",\n       \"25     35.0\\n\",\n       \"26     30.0\\n\",\n       \"27     30.0\\n\",\n       \"28     21.0\\n\",\n       \"29     30.0\\n\",\n       \"       ... \\n\",\n       \"861    30.0\\n\",\n       \"862    35.0\\n\",\n       \"863    21.0\\n\",\n       \"864    30.0\\n\",\n       \"865    35.0\\n\",\n       \"866    21.0\\n\",\n       \"867    30.0\\n\",\n       \"868    30.0\\n\",\n       \"869     9.0\\n\",\n       \"870    30.0\\n\",\n       \"871    35.0\\n\",\n       \"872    30.0\\n\",\n       \"873    30.0\\n\",\n       \"874    35.0\\n\",\n       \"875    21.0\\n\",\n       \"876    30.0\\n\",\n       \"877    30.0\\n\",\n       \"878    30.0\\n\",\n       \"879    35.0\\n\",\n       \"880    35.0\\n\",\n       \"881    30.0\\n\",\n       \"882    21.0\\n\",\n       \"883    30.0\\n\",\n       \"884    30.0\\n\",\n       \"885    35.0\\n\",\n       \"886     9.0\\n\",\n       \"887    21.0\\n\",\n       \"888    21.0\\n\",\n       \"889    30.0\\n\",\n       \"890    30.0\\n\",\n       \"Name: Age, Length: 891, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head(30)\\n\",\n    \"train.groupby(\\\"Title\\\")[\\\"Age\\\"].transform(\\\"median\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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m41s2LheMYXeVNd1rAzd3IuVtXEuvdPEI0lU12OEEIIIYYxCYdCiCF3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\\nIsD/AZK2a7RJ9MnMAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x110f03650>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Age',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Age'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \" \\n\",\n    \"plt.show() \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0, 20)\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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OEPT/qcGQ6JiIiIiOYp2xboPqPjjROD+FVvF44Pd8PUhiCHU5BCaUjL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\\niIiIiIiIAPw/aP8Q3mwhHY8AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1114b3210>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Age',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Age'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(0, 20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(20, 30)\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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8gLeN4y6KguYCsUcBQKZsJgKGgr6Lf5Hj8AAM4B4RAApjjXNYoPpIoC\\n3FDTNL0pnX1J9SXSIz5HwPGpLuioKRxUKOCoLjg41GWW2cAXdFTnt/muPgAAxhHhEABqWCrtKpFM\\nayDpKpFKK5HMrCeSaQ2kco9dDSTTJeWuBlJp73GubCCZHlQ+0umakuTYluoCjsL1fs1oDhWN3g0Z\\n+AI2UzcBAJgACIcAcA6MMUqmXCVSufA2REhLFQeyQSEtVRr+MuXJtKv+RFpuhQ9TORuWlRnBc2yf\\n/I5P9UFHTQ0BObZPQb9ddhQvFCje5ncIeQAATFaEQwBVYYxR2s3+pI1cY5ROu9421zVKZZdp1/Xq\\npctsy+ybO55bsm/uHG5Jvdyx3My2bFnRPq6rVMotGKErHsEbTbbPkr8guDWFgpomeeuOnSn32z45\\nJUu/48vva/uKjpNZWvLbPvl8Fu/FAwBMKMYYuXJljCuT+y+7TZLcou2uV577zy3Zz6h8HSnz98Sg\\nY5Xst0gt1f2FjLGK4dB1Xa1bt05HjhxRIBDQhg0bNGfOHK987969eu655+Q4jtra2nTjjTcqnU7r\\n4Ycf1q9//WtZlqXHH39c8+fPH9MLAcaaazLBIRce3Gy4MQVhJp2rY7JPZq6RMZl988vhtmf3K9ju\\nGiPjFjwu3M/N7mdK9htq+4iOVeEcrpGR8tecdocMVkUhLV0Q6LLXP1EUhrKg31ZDyMmEsFwAKxPW\\ncuEsv92S37GLwpq3Xza4FWpurld3d7xKVwwAqDW5QJQJO5mla1wvOOXKBtfJBJu0See3ZcNP5nE6\\n83dByXGMcZUu3VZyjtJtxpjy5xmmvaXXUnrMWnOb/l+1mzCmKobDPXv2KJFIaMeOHero6NDmzZu1\\nZcsWSVIymdSmTZu0c+dOhUIhrVy5UosXL1ZHR4ckafv27Xr99df17LPPevtgckq7rpIpV6l0Zqpd\\nMpXOLNNudt1VKjsqVBiu3IJglQtVhSND+boqDmYF4WzIsGaKj50ue77iY0iZj9kv17YJlGXGnc9n\\nyWdlRr8syxq0DDg++fzZej5LPiu/tH2WLJ9kW5YsnyU7V1ZUT+X3y56ztKx4/4JjVzimb4g6jLYB\\nQO3Ijfjkw0S6ZL1SWCofQkxJWT7EGK9u2qSHDWnljpkudx5l/9Yw6TLhLrO99NwT+S8RK/ef5ZMv\\nu7RkySefLCtTZvsc+bPrudJMmS+7b+4okmX5So7rlQyuq9zruFVUv7iuZCl7TMuSVVK38NiTXcVw\\neOjQIS1atEiS1NraqsOHD3tlR48eVUtLi5qamiRJCxcu1IEDB3TNNdfoL/7iLyRJb7/9thobG8eg\\n6cgxxiiVdpVMmWwYS3uBLJnOTIsrDWq5x4PKhtpeEPCKj5HOhqlq/xYqsywV/dFvFYaCbAiws++9\\n8lmSVRISLCsfNnyW5e1vWSZb38iyJPmMLCvzI+/Hlc9SwXp+uyVJVvbOmOXKZMssK3OnT5aRZJR7\\nPrIKlt7j7AZL2Tbknr6skvq5bbKyx8ztY+WPXXjMkvOo6BjK75ctzncDU7JebtvgTmNM6R7F6/kn\\n9PxTvGTJWJbSktyCp28VPLnLa2vxi4VyLwjGkowly7W8349Xyyqo7y0LXyzy7Rpcp+BxmfVcuwrP\\nkDuWGehXb7J/0LWU+x0UvZCVfQEEMJHlp9UVj+6UCymmIEy4BSHEDFGv4nFzoz9F+xecwwtVpSNP\\nlc5ZPKo0+FpM0TUUhrCJHJIyEST3vJ2NQFbx0rZsOZZTHKKyS0u+zN8g2bLCcJUvyy9z5yk8p3cs\\nyysZFNqGO4+v8LgF5yk8VnGbeS2aSCqGw2g0qnA47K3btq1UKiXHcRSNRhWJRLyyhoYGRaPRzIEd\\nRw888ID+67/+S//8z/88osbMnBmpXGmCSabSiven1DeQKlgmB21LpvIfTJEsWSayYS33PqdkKv/B\\nFrmgNtYsS95UudxPvd8vxw7IKXivk2P75DhWUb3CbbmRJMsyMlZals+VUVquLy0pLWNlf7KPMxMa\\nUnKVzgen7PxvKbeemxOefcGwcvPEvZezQS8q3l2+0qkRpVMfsnf8koUvkoOmU4zzi5QpWQIVDAqR\\nJWFyqIDpKxc6y+7rG7TNV+Fcg+7uVjxu4ZXIC/ZeqLYGR/hcqPa2WMU1CkP2kNtKwnvxeQvbNUxb\\nyrS9/DmG2VZyB6bc807ptko3W0z+YMPWOXaq6KAluwz9KH/68vsUnWmI4xaue+8PKghKQ763yGRe\\nK9yiOvl9yr6vaKjjmpJ6wxx3uLYZ7/Wn3PEL21b6PqfJ+WSfCxj5oFIyouSzZMkpeE7JB5DCx4VB\\nJB+MfIP2sazBwSlzozcffErblNk/3y5vOdTxC/bxygfVIyShtlUMh+FwWLFYzFt3XVeO45Qti8Vi\\nRWHxySef1N///d/rxhtv1Le//W3V19cPe67Ozt6zvoCxkHYznxLYP5BWXyKl/oG0+hMp9SXS6htI\\nZctSmbJB2wrWE6mz+oj44di+XODKTHuzbZ8a6pxs4Mpsd2yfbNuS48subZ+cbN1ceWb/THlmOp6R\\nz3YlX1qWz0i+TAiTLyVjuZKVD2kpk1LKDCht0kq5yczSJJU2KaW8x2n1e2UppUwqW55S2k0plc6s\\n19Yc8vxdPJ/lk4xVcsct98Jky2c5mSf9whcr74/EwXfKCu/O5e8QFh67ZFuuHUNMpci3ePAjb4s1\\nTFmZbSM4YpljF5eVP+pZlJV5sRzcovyfSLk//EzR9tz7GAu3m4L6ZfeQTH6tqKzgj7Li+kUtGdE5\\n8u0q3E9F5y5dOo6tZDI1uKSkXaXXUHq+/O+m9Hoy9bzHBb8PN/v7TXvHH3Q075z5f3PnKL1WYLIq\\nvG1QeINj6NkLRevZ1wivnlV6I6HwGLmbF+VHZLKvOGVeu3wlN3nKjRjl9/GV1isJRYXnLLd/9owl\\nN3uKR61y+5ytcLhO0Wj/KP2/OwcjvDFrCqqkvbVa+psHGF7FcLhgwQK99tprWrZsmTo6Ooo+WGbe\\nvHk6duyYuru7VV9fr4MHD2r16tV69dVXdeLECX3uc59TKBTKPIn4xvbjz11jNJBIe6GsrzTYZQNb\\n8bZ86MsFu/5E6j19CmHQ75PfsRX024rU+xVwbAX9PgX8tgL+7GPHViC7Lei38x9aYWfvlPnScq2U\\nXCsp10oqraSSJqGkSSrt5sJWsiiQpU1KKTcfxpImpf7CcJYty63ngtpY8skn27Lls2zZli3b5yhg\\nBWXLLt5e8NgnW7blk2058mWXtuWTT5k6+SBV8IJT+ELkbRvqRbR8GMup+osPkDUZ+mJxYM7+a0zp\\nlqJ6w4bgTEFJmC/8NxdkS7YVLAeHew16nGtjuaNkmzCopOQIJTcqyrRkmPLiaxn6L9GyN3usocqG\\nullT+cZMXZ1f/QPJIW8hjWytZPs53MTKP18XhKWKISwXSkpDXOa2YOFxC0Pb4OnZKlrPnQcAJhvL\\nlM47KZH7tNK33npLxhht3LhRb7zxhuLxuNrb271PKzXGqK2tTTfffLPi8bjWrl2rrq4upVIp3Xbb\\nbbrqqquGbUg67erXvzujaDyh3nhS0b6kevuS2VCXDXIFAS8X7HKjewOJ9Dnfp/bbPi+sBfw+BR07\\n/9hvK+Dkg1zR4+w+ftsnn5OWfJnQljADSrgJJdyEkm7B44LtCXdAyewyU5Z/PNp33DMBzPHCWtlg\\npuJthduHDnL58JYPc/mQNxFfOCfDH+SYHOiLqBX0RdQC+iFqxcrsZ7FMVhXD4XhZft+3zqq+7bOG\\nHIkrH+aKR+/ygS6tlBKDAlphcEuYXJArDHWJorJzDXSWLDmWX36fX47lyLH8cnx++S2/HF923Xvs\\nVAxspWXnOn1jquLFB7WCvohaQV9ELaAfolZM9nBYcVrpeJl7YaMCtk+hoKNQ0FF90FZd0FFdwPam\\nZvodn3yOK/mScq1UQXgrGaHLhriYSai7MOQlB5QcyAY8k3wPgc7nhbk6X0hhp1H+gmDnWE423GWD\\nneVk6+cDX66cNycDAAAAqAU1Ew4/9qmUzvS+q363T/3pfnW6fepP92nA7Vd/X7/6Y31KuAPnfHyf\\nfF5wq7PrFSk3Umc5XqDzF43aFZfblj2KVw4AAAAA1Vcz4XDXb14pu922bAWsoOp8IUWcxrJTMIcd\\nqSPQAQAAAEBFNRMOPzHrkzIJnwK+gAK+oPy+gAK+gGyrZpoIAAAAAJNWzSSv9zd9gDcaAwAAAECV\\njO2XDwIAAAAAJgTCIQAAAACAcAgAAAAAIBwCAAAAAEQ4BAAAAIBRl06n9fjjj+tv/uZvtHLlSq1d\\nu1aJROKcjnX//fefcztWrVqlzs7OEdUlHAIAAADAKPvv//5vGWP0la98RV//+tc1bdo0feMb3zin\\nY33xi18c5daVRzgEAAAAgFE2a9YsHTx4UN/73vcUi8V077336lOf+pRWr17t1bn66qslSZ/+9Kf1\\nt3/7t3rsscd00003eeXt7e2KRqO6+uqr9cYbb+iee+6RJCWTSV1//fVyXVcvvPCCVqxYoRUrVuiH\\nP/yhJGnXrl26/vrrdfvtt4941FCqoe85BAAAAIDJ4rLLLtP999+v7du368EHH1Rra6s+97nPla3b\\n3d2tf/qnf9JFF12k22+/Xb/73e/U39+v2bNnKxwOS5Iuv/xyHT9+XLFYTP/zP/+jRYsW6Ze//KUO\\nHjyor3/964rH47rpppv0yU9+Ul/+8pe9Ucq//Mu/HHGbCYcAAAAAMMqOHDmiyy+/XM8//7xSqZRe\\neOEFPfvsswoEApIkY4xX1+/366KLLpIkXXfdddq9e7f6+/t13XXXFR1z6dKl2rNnj/bt26c77rhD\\nv/jFL/SrX/1Kt9xyiyRpYGBAp06d0vTp01VXVydJmj9//ojbzLRSAAAAABhlP/rRj/Qv//IvkiTH\\ncfSBD3xAc+fO1cmTJyVJb775plfXsizv8eLFi/XjH/9Yhw4d0p/+6Z8WHXP58uX693//d506dUqX\\nXHKJLr74YrW2tmrbtm168cUXtWzZMjU2Nqqzs1OxWEyJREJHjx4dcZsZOQQAAACAUXbzzTfriSee\\n0LXXXqtQKKTp06dr/fr1evrpp/WZz3xGl112maZNmzZov0AgoEsuuUT19fWybbuo7Pzzz5cxRkuW\\nLJGUmWo6b9483XTTTYrH42pra1MgENA999yjv/7rv9aMGTPKnmMolikcz6yi3f97QNFof7WbgSku\\nHK6jH6Im0BdRK+iLqAX0Q9SKlYsWVbsJY4pppQAAAAAAwiEAAAAAgHAIAAAAABDhEAAAAAAgwiEA\\nAAAAQCP4KgvXdbVu3TodOXJEgUBAGzZs0Jw5c7zyvXv36rnnnpPjOGpra9ONN96oZDKpBx98UMeP\\nH1cikdDnP/95XXnllWN6IQAAAACAc1cxHO7Zs0eJREI7duxQR0eHNm/erC1btkiSksmkNm3apJ07\\ndyoUCmnlypVavHixfvCDH6i5uVlPPfWUuru7dd111xEOAQAAAGAMVRrYq6RiODx06JAWZb/Po7W1\\nVYcPH/bKjh49qpaWFjU1NUmSFi5cqAMHDujqq6/W0qVLJUnGmEFf3ggAAAAAGF3DDeyNRMVwGI1G\\nFQ6HvXXbtpVKpeQ4jqLRqCKRiFfW0NCgaDSqhoYGb98777xTd99994gaEw7XjbjhwFihH6JW0BdR\\nK+iLqAX0Q0w0L+7+ufb/7PioHvOT/9/7dOvyDw1ZPtzA3khUDIfhcFixWMxbd11XjuOULYvFYl5Y\\n/MMf/qA77rhDN910k5YvXz6ixkSj/WfVeGC0hcN19EPUBPoiagV9EbWAfgiMzHADeyNRsdaCBQv0\\n2muvadmyZero6ND8+fO9snnz5unYsWPq7u5WfX29Dh48qNWrV6urq0u33nqrHn30Uf3Jn/zJOVwW\\nAAAAAExcty7/0LCjfGNhuIG9kahYc8mSJdq/f79WrFghY4w2btyo3bt3Kx6Pq729XWvWrNHq1atl\\njFFbW5tmzZqlDRs2qKenR88//7yef/55SdLWrVtVV8d0AAAAAAAYC8MN7I2EZYwxY9S2s7L7fw8w\\nXQBVx7QV1Ar6ImoFfRG1gH6IWrEy+36+WpX7tNK33nrLG9ibN2/eiPcf+RgjAAAAAKBm+Xw+/cM/\\n/MO57z+KbQEAAAAATFCEQwAAAAAA4RAAAAAAQDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAEwq\\nP/vZz7SSRQWxAAAMRklEQVRq1aqz3o/vOQQAAACASWLr1q3atWuXQqHQWe9LOAQAAACAUbat4xv6\\nye9+OqrH/MRFC7SqtW3YOi0tLfrSl76k+++//6yPz7RSAAAAAJgkli5dKsc5tzFARg4BAAAAYJSt\\nam2rOMpXaxg5BAAAAAAQDgEAAAAAhEMAAAAAmFRmz56tl19++az3IxwCAAAAAAiHAAAAAADCIQAA\\nAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAADSCcOi6rh599FG1t7dr1apVOnbsWFH53r17\\n1dbWpvb29kHfpfGzn/1Mq1atGt0WAwAAAABGnVOpwp49e5RIJLRjxw51dHRo8+bN2rJliyQpmUxq\\n06ZN2rlzp0KhkFauXKnFixdrxowZ2rp1q3bt2qVQKDTmFwEAAAAAeG8qjhweOnRIixYtkiS1trbq\\n8OHDXtnRo0fV0tKipqYmBQIBLVy4UAcOHJAktbS06Etf+tIYNRsAAAAAMJoqjhxGo1GFw2Fv3bZt\\npVIpOY6jaDSqSCTilTU0NCgajUqSli5dqt///vdn1ZhwuO6s6gNjgX6IWkFfRK2gL6IW0A+BsVcx\\nHIbDYcViMW/ddV05jlO2LBaLFYXFsxWN9p/zvsBoCIfr6IeoCfRF1Ar6ImoB/RAYHxWnlS5YsED7\\n9u2TJHV0dGj+/Ple2bx583Ts2DF1d3crkUjo4MGDuuKKK8autQAAAACAMVFx5HDJkiXav3+/VqxY\\nIWOMNm7cqN27dysej6u9vV1r1qzR6tWrZYxRW1ubZs2aNR7tBgAAAACMIssYY6rdCEna/b8HmC6A\\nqmPaCmoFfRG1gr6IWkA/RK1Ymf2gzsmq4rRSAAAAAMDkRzgEAAAAABAOAQAAAACEQwAAAACACIcA\\nAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcA\\nAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcA\\nAAAAABEOAQAAAAAaQTh0XVePPvqo2tvbtWrVKh07dqyofO/evWpra1N7e7tefvnlEe0DAAAAAKgt\\nFcPhnj17lEgktGPHDt13333avHmzV5ZMJrVp0ya9+OKL2rZtm3bs2KGurq5h9wEAAAAA1B6nUoVD\\nhw5p0aJFkqTW1lYdPnzYKzt69KhaWlrU1NQkSVq4cKEOHDigjo6OIfcZytwZs9Ttj5/TRQCjpbmp\\nnn6ImkBfRK2gL6IW0A+B8VExHEajUYXDYW/dtm2lUik5jqNoNKpIJOKVNTQ0KBqNDrvPUD58UYt0\\n0bleBjCK6IeoFfRF1Ar6ImoB/RAYcxXDYTgcViwW89Zd1/VCXmlZLBZTJBIZdp/hdHb2nlXjgdE2\\nc2aEfoiaQF9EraAvohbQD1ErZs6MVK40gVV8z+GCBQu0b98+SVJHR4fmz5/vlc2bN0/Hjh1Td3e3\\nEomEDh48qCuuuGLYfQAAAAAAtaficN6SJUu0f/9+rVixQsYYbdy4Ubt371Y8Hld7e7vWrFmj1atX\\nyxijtrY2zZo1q+w+AAAAAIDaZRljTLUbkcN0AVQb01ZQK+iLqBX0RdQC+iFqxZSfVgoAAAAAmPwI\\nhwAAAACA2ppWCgAAAACoDkYOAQAAAACEQwAAAAAA4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAA\\nAEnOeJ8wmUzqwQcf1PHjx5VIJPT5z39el156qdasWSPLsvT+979fjz32mHw+civGVrm+eOGFF2r9\\n+vWybVuBQEBPPvmkZsyYUe2mYpIr1xevvPJKSdLu3bv10ksvaceOHVVuJSa7cv2wtbVVDz/8sHp6\\nepROp/XFL35RLS0t1W4qJrmhXp8fe+wx2batiy++WE888QR/K2LMpdNpPfzww/r1r38ty7L0+OOP\\nKxgMTurcMu7hcNeuXWpubtZTTz2l7u5uXXfddfrgBz+ou+++Wx//+Mf16KOP6nvf+56WLFky3k3D\\nFFOuL86ePVuPPPKILrvsMm3fvl1bt27V2rVrq91UTHLl+uKVV16pN954Qzt37hRfR4vxUK4ffuIT\\nn9Dy5cu1bNky/eQnP9H//d//EQ4x5sr1xQ996EO644479Od//ue677779P3vf1+LFy+udlMxyb32\\n2muSpO3bt+v111/Xs88+K2PMpM4t4x5zr776at11112SJGOMbNvWz3/+c33sYx+TJP3Zn/2ZfvSj\\nH413szAFleuLzzzzjC677DJJmbtFwWCwmk3EFFGuL545c0bPPPOMHnzwwSq3DlNFuX7405/+VCdO\\nnNBnP/tZ7d6923utBsZSub542WWXqbu7W8YYxWIxOc64j29gCrrqqqu0fv16SdLbb7+txsbGSZ9b\\nxj0cNjQ0KBwOKxqN6s4779Tdd98tY4wsy/LKe3t7x7tZmILK9cXzzz9fkvTTn/5UL730kj772c9W\\nt5GYEkr74l133aWHHnpIa9euVUNDQ7Wbhymi3HPi8ePH1djYqK9+9au64IILtHXr1mo3E1NAub6Y\\nm0p6zTXX6NSpU/r4xz9e7WZiinAcRw888IDWr1+v5cuXT/rcUpUJsn/4wx90yy236Nprr9Xy5cuL\\n5unGYjE1NjZWo1mYgkr7oiT9x3/8hx577DG98MILmj59epVbiKmisC9efPHFOnbsmNatW6d7771X\\nv/rVr/TEE09Uu4mYAkqfE5ubm72pe4sXL9bhw4er3EJMFaV98YknntDXvvY1fec739F1112nzZs3\\nV7uJmEKefPJJffe739UjjzyigYEBb/tkzC3jHg67urp066236gtf+IJuuOEGSdLll1+u119/XZK0\\nb98+ffSjHx3vZmEKKtcXv/Wtb+mll17Stm3bdNFFF1W5hZgqSvviRz7yEX3729/Wtm3b9Mwzz+jS\\nSy/VQw89VO1mYpIr95y4cOFC/eAHP5AkHThwQJdeemk1m4gpolxfbGpqUjgcliSdf/756unpqWYT\\nMUW8+uqr+vKXvyxJCoVCsixLH/7whyd1brHMOH/SwYYNG/Sf//mfuuSSS7xtDz30kDZs2KBkMqlL\\nLrlEGzZskG3b49ksTEGlfTGdTuuXv/ylLrzwQu8u0B//8R/rzjvvrGYzMQWUe17cunWr6urq9Pvf\\n/1733nuvXn755Sq2EFNBuX64efNmPfzww+rr61M4HNY//uM/qqmpqYqtxFRQri/eddddevrpp+U4\\njvx+v9avX6/Zs2dXsZWYCuLxuNauXauuri6lUinddtttmjdvnh555JFJm1vGPRwCAAAAAGrP5PlS\\nDgAAAADAOSMcAgAAAAAIhwAAAAAAwiEAAAAAQIRDAAAAAIAIhwCACe6tt97SBz7wAX33u9+tdlMA\\nAJjQCIcAgAntlVde0dKlS7V9+/ZqNwUAgAnNqXYDAAA4V6lUSrt27dLXvvY1rVixQr/97W/V0tKi\\n119/3fti4tbWVh09elTbtm3TsWPHtG7dOnV3d6uurk6PPPKILr/88mpfBgAANYGRQwDAhPX9739f\\nF154oebOnaurrrpK27dvVzKZ1P3336+nnnpKr776qhwnfx/0gQce0Be+8AV985vf1Pr163XPPfdU\\nsfUAANQWwiEAYMJ65ZVX9Fd/9VeSpGXLlumb3/ym3nzzTZ133nn64Ac/KEm64YYbJEmxWEyHDx/W\\n2rVrde211+q+++5TPB7XmTNnqtZ+AABqCdNKAQAT0qlTp7Rv3z4dPnxY//Zv/yZjjHp6erRv3z65\\nrjuovuu6CgQC+ta3vuVte+edd9Tc3DyezQYAoGYxcggAmJB27dqlT3ziE9q3b5/27t2r1157Tbff\\nfrt++MMfqqenR0eOHJEk7d69W5IUiUR08cUXe+Fw//79uvnmm6vWfgAAao1ljDHVbgQAAGdr+fLl\\nuueee7R48WJv26lTp7R48WL967/+qzZs2CCfz6e5c+eqp6dHW7du1dGjR70PpPH7/Vq3bp0+8pGP\\nVPEqAACoHYRDAMCk4rqunn76af3d3/2d6uvr9ZWvfEUnTpzQmjVrqt00AABqGu85BABMKj6fT83N\\nzbrhhhvk9/v1vve9T0888US1mwUAQM1j5BAAAAAAwAfSAAAAAAAIhwAAAAAAEQ4BAAAAACIcAgAA\\nAABEOAQAAAAAiHAIAAAAAJD0/wNcstWFCvwzggAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x110e72e90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Age',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Age'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(20, 30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(30, 40)\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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f7hZKYLqt0VteNMLzrO9AKww2VrU8BePmNBCK1NAbhV/pFBRERENFvx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\\nHZvfdY0NAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11149d850>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Age',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Age'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(30, 40)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(40, 60)\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Oa69ZoPjDjKNx5GGtg7EaOuuXjxYu3cuVMdHR2y1mr9+vXaunWrent7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\\nRK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1118d28d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Age',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Age'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(40, 60)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(40, 60)\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Oa69ZoPjDjKNx5GGtg7EaOuuXjxYu3cuVMdHR2y1mr9+vXaunWrent7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\\nRK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x111d0e650>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Age',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Age'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(40, 60)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(60, 80.0)\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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RoaGlJzc7Pa29vV1tYmY4yampo0e/Zsbdy4UcePH9cTTzyhJ554QpL0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\\ngg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1118ff150>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Age',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Age'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(60)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 891 entries, 0 to 890\\n\",\n      \"Data columns (total 12 columns):\\n\",\n      \"PassengerId    891 non-null int64\\n\",\n      \"Survived       891 non-null int64\\n\",\n      \"Pclass         891 non-null int64\\n\",\n      \"Sex            891 non-null int64\\n\",\n      \"Age            891 non-null float64\\n\",\n      \"SibSp          891 non-null int64\\n\",\n      \"Parch          891 non-null int64\\n\",\n      \"Ticket         891 non-null object\\n\",\n      \"Fare           891 non-null float64\\n\",\n      \"Cabin          204 non-null object\\n\",\n      \"Embarked       889 non-null object\\n\",\n      \"Title          891 non-null int64\\n\",\n      \"dtypes: float64(2), int64(7), object(3)\\n\",\n      \"memory usage: 83.6+ KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 418 entries, 0 to 417\\n\",\n      \"Data columns (total 11 columns):\\n\",\n      \"PassengerId    418 non-null int64\\n\",\n      \"Pclass         418 non-null int64\\n\",\n      \"Sex            418 non-null int64\\n\",\n      \"Age            418 non-null float64\\n\",\n      \"SibSp          418 non-null int64\\n\",\n      \"Parch          418 non-null int64\\n\",\n      \"Ticket         418 non-null object\\n\",\n      \"Fare           417 non-null float64\\n\",\n      \"Cabin          91 non-null object\\n\",\n      \"Embarked       418 non-null object\\n\",\n      \"Title          418 non-null int64\\n\",\n      \"dtypes: float64(2), int64(6), object(3)\\n\",\n      \"memory usage: 36.0+ KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.4.2 Binning\\n\",\n    \"Binning/Converting Numerical Age to Categorical Variable  \\n\",\n    \"\\n\",\n    \"feature vector map:  \\n\",\n    \"child: 0  \\n\",\n    \"young: 1  \\n\",\n    \"adult: 2  \\n\",\n    \"mid-age: 3  \\n\",\n    \"senior: 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0,\\n\",\n    \"    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1,\\n\",\n    \"    dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2,\\n\",\n    \"    dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3,\\n\",\n    \"    dataset.loc[ dataset['Age'] > 62, 'Age'] = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\\\\n\",\n       \"0            1         0       3    0  1.0      1      0         A/5 21171   \\n\",\n       \"1            2         1       1    1  3.0      1      0          PC 17599   \\n\",\n       \"2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \\n\",\n       \"3            4         1       1    1  2.0      1      0            113803   \\n\",\n       \"4            5         0       3    0  2.0      0      0            373450   \\n\",\n       \"\\n\",\n       \"      Fare Cabin Embarked  Title  \\n\",\n       \"0   7.2500   NaN        S      0  \\n\",\n       \"1  71.2833   C85        C      2  \\n\",\n       \"2   7.9250   NaN        S      1  \\n\",\n       \"3  53.1000  C123        S      2  \\n\",\n       \"4   8.0500   NaN        S      0  \"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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jU1NXriiSdkWZbmzZuncePGacmSJdq2bZtGjx6tiooKbdq0Sa2trcrL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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x111220150>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bar_chart('Age')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.5 Embarked\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.5.1 filling missing values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1113ee790>\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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u/zltcWj88mH+x56NAhTZkyRZmZmRo5cqRWrFjR8LNAIKDExET5fD4F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\\nAGCIoAIAADBEUAEAABj6/1rB0sKWPvsgAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1111ee050>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()\\n\",\n    \"Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()\\n\",\n    \"Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()\\n\",\n    \"df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\\n\",\n    \"df.index = ['1st class','2nd class', '3rd class']\\n\",\n    \"df.plot(kind='bar',stacked=True, figsize=(10,5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"more than 50% of 1st class are from S embark  \\n\",\n    \"more than 50% of 2nd class are from S embark  \\n\",\n    \"more than 50% of 3rd class are from S embark\\n\",\n    \"\\n\",\n    \"**fill out missing embark with S embark**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset['Embarked'] = dataset['Embarked'].fillna('S')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\\\\n\",\n       \"0            1         0       3    0  1.0      1      0         A/5 21171   \\n\",\n       \"1            2         1       1    1  3.0      1      0          PC 17599   \\n\",\n       \"2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \\n\",\n       \"3            4         1       1    1  2.0      1      0            113803   \\n\",\n       \"4            5         0       3    0  2.0      0      0            373450   \\n\",\n       \"\\n\",\n       \"      Fare Cabin Embarked  Title  \\n\",\n       \"0   7.2500   NaN        S      0  \\n\",\n       \"1  71.2833   C85        C      2  \\n\",\n       \"2   7.9250   NaN        S      1  \\n\",\n       \"3  53.1000  C123        S      2  \\n\",\n       \"4   8.0500   NaN        S      0  \"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"embarked_mapping = {\\\"S\\\": 0, \\\"C\\\": 1, \\\"Q\\\": 2}\\n\",\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.6 Fare\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>7.2500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>71.2833</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>7.9250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>53.1000</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330877</td>\\n\",\n       \"      <td>8.4583</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>17463</td>\\n\",\n       \"      <td>51.8625</td>\\n\",\n       \"      <td>E46</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>349909</td>\\n\",\n       \"      <td>21.0750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>347742</td>\\n\",\n       \"      <td>11.1333</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>237736</td>\\n\",\n       \"      <td>30.0708</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>PP 9549</td>\\n\",\n       \"      <td>16.7000</td>\\n\",\n       \"      <td>G6</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113783</td>\\n\",\n       \"      <td>26.5500</td>\\n\",\n       \"      <td>C103</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5. 2151</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>347082</td>\\n\",\n       \"      <td>31.2750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>350406</td>\\n\",\n       \"      <td>7.8542</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>248706</td>\\n\",\n       \"      <td>16.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>382652</td>\\n\",\n       \"      <td>29.1250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>18</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>244373</td>\\n\",\n       \"      <td>13.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>19</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>345763</td>\\n\",\n       \"      <td>18.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2649</td>\\n\",\n       \"      <td>7.2250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>21</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>239865</td>\\n\",\n       \"      <td>26.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>248698</td>\\n\",\n       \"      <td>13.0000</td>\\n\",\n       \"      <td>D56</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>23</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330923</td>\\n\",\n       \"      <td>8.0292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>24</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113788</td>\\n\",\n       \"      <td>35.5000</td>\\n\",\n       \"      <td>A6</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>349909</td>\\n\",\n       \"      <td>21.0750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>26</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>347077</td>\\n\",\n       \"      <td>31.3875</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>27</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2631</td>\\n\",\n       \"      <td>7.2250</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>28</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>19950</td>\\n\",\n       \"      <td>263.0000</td>\\n\",\n       \"      <td>C23 C25 C27</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>29</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330959</td>\\n\",\n       \"      <td>7.8792</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>349216</td>\\n\",\n       \"      <td>7.8958</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>30</th>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17601</td>\\n\",\n       \"      <td>27.7208</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>31</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17569</td>\\n\",\n       \"      <td>146.5208</td>\\n\",\n       \"      <td>B78</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>32</th>\\n\",\n       \"      <td>33</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>335677</td>\\n\",\n       \"      <td>7.7500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>33</th>\\n\",\n       \"      <td>34</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>C.A. 24579</td>\\n\",\n       \"      <td>10.5000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>34</th>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17604</td>\\n\",\n       \"      <td>82.1708</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>35</th>\\n\",\n       \"      <td>36</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113789</td>\\n\",\n       \"      <td>52.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>36</th>\\n\",\n       \"      <td>37</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2677</td>\\n\",\n       \"      <td>7.2292</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>37</th>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A./5. 2152</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>38</th>\\n\",\n       \"      <td>39</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>345764</td>\\n\",\n       \"      <td>18.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39</th>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2651</td>\\n\",\n       \"      <td>11.2417</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>40</th>\\n\",\n       \"      <td>41</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>7546</td>\\n\",\n       \"      <td>9.4750</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>41</th>\\n\",\n       \"      <td>42</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>11668</td>\\n\",\n       \"      <td>21.0000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>42</th>\\n\",\n       \"      <td>43</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>349253</td>\\n\",\n       \"      <td>7.8958</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>43</th>\\n\",\n       \"      <td>44</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>SC/Paris 2123</td>\\n\",\n       \"      <td>41.5792</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>44</th>\\n\",\n       \"      <td>45</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>330958</td>\\n\",\n       \"      <td>7.8792</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>45</th>\\n\",\n       \"      <td>46</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>S.C./A.4. 23567</td>\\n\",\n       \"      <td>8.0500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>46</th>\\n\",\n       \"      <td>47</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>370371</td>\\n\",\n       \"      <td>15.5000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>47</th>\\n\",\n       \"      <td>48</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>14311</td>\\n\",\n       \"      <td>7.7500</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48</th>\\n\",\n       \"      <td>49</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2662</td>\\n\",\n       \"      <td>21.6792</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>49</th>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>349237</td>\\n\",\n       \"      <td>17.8000</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\\\\n\",\n       \"0             1         0       3    0  1.0      1      0         A/5 21171   \\n\",\n       \"1             2         1       1    1  3.0      1      0          PC 17599   \\n\",\n       \"2             3         1       3    1  1.0      0      0  STON/O2. 3101282   \\n\",\n       \"3             4         1       1    1  2.0      1      0            113803   \\n\",\n       \"4             5         0       3    0  2.0      0      0            373450   \\n\",\n       \"5             6         0       3    0  2.0      0      0            330877   \\n\",\n       \"6             7         0       1    0  3.0      0      0             17463   \\n\",\n       \"7             8         0       3    0  0.0      3      1            349909   \\n\",\n       \"8             9         1       3    1  2.0      0      2            347742   \\n\",\n       \"9            10         1       2    1  0.0      1      0            237736   \\n\",\n       \"10           11         1       3    1  0.0      1      1           PP 9549   \\n\",\n       \"11           12         1       1    1  3.0      0      0            113783   \\n\",\n       \"12           13         0       3    0  1.0      0      0         A/5. 2151   \\n\",\n       \"13           14         0       3    0  3.0      1      5            347082   \\n\",\n       \"14           15         0       3    1  0.0      0      0            350406   \\n\",\n       \"15           16         1       2    1  3.0      0      0            248706   \\n\",\n       \"16           17         0       3    0  0.0      4      1            382652   \\n\",\n       \"17           18         1       2    0  2.0      0      0            244373   \\n\",\n       \"18           19         0       3    1  2.0      1      0            345763   \\n\",\n       \"19           20         1       3    1  2.0      0      0              2649   \\n\",\n       \"20           21         0       2    0  2.0      0      0            239865   \\n\",\n       \"21           22         1       2    0  2.0      0      0            248698   \\n\",\n       \"22           23         1       3    1  0.0      0      0            330923   \\n\",\n       \"23           24         1       1    0  2.0      0      0            113788   \\n\",\n       \"24           25         0       3    1  0.0      3      1            349909   \\n\",\n       \"25           26         1       3    1  3.0      1      5            347077   \\n\",\n       \"26           27         0       3    0  2.0      0      0              2631   \\n\",\n       \"27           28         0       1    0  1.0      3      2             19950   \\n\",\n       \"28           29         1       3    1  1.0      0      0            330959   \\n\",\n       \"29           30         0       3    0  2.0      0      0            349216   \\n\",\n       \"30           31         0       1    0  3.0      0      0          PC 17601   \\n\",\n       \"31           32         1       1    1  2.0      1      0          PC 17569   \\n\",\n       \"32           33         1       3    1  1.0      0      0            335677   \\n\",\n       \"33           34         0       2    0  4.0      0      0        C.A. 24579   \\n\",\n       \"34           35         0       1    0  2.0      1      0          PC 17604   \\n\",\n       \"35           36         0       1    0  3.0      1      0            113789   \\n\",\n       \"36           37         1       3    0  2.0      0      0              2677   \\n\",\n       \"37           38         0       3    0  1.0      0      0        A./5. 2152   \\n\",\n       \"38           39         0       3    1  1.0      2      0            345764   \\n\",\n       \"39           40         1       3    1  0.0      1      0              2651   \\n\",\n       \"40           41         0       3    1  3.0      1      0              7546   \\n\",\n       \"41           42         0       2    1  2.0      1      0             11668   \\n\",\n       \"42           43         0       3    0  2.0      0      0            349253   \\n\",\n       \"43           44         1       2    1  0.0      1      2     SC/Paris 2123   \\n\",\n       \"44           45         1       3    1  1.0      0      0            330958   \\n\",\n       \"45           46         0       3    0  2.0      0      0   S.C./A.4. 23567   \\n\",\n       \"46           47         0       3    0  2.0      1      0            370371   \\n\",\n       \"47           48         1       3    1  1.0      0      0             14311   \\n\",\n       \"48           49         0       3    0  2.0      2      0              2662   \\n\",\n       \"49           50         0       3    1  1.0      1      0            349237   \\n\",\n       \"\\n\",\n       \"        Fare        Cabin  Embarked  Title  \\n\",\n       \"0     7.2500          NaN         0      0  \\n\",\n       \"1    71.2833          C85         1      2  \\n\",\n       \"2     7.9250          NaN         0      1  \\n\",\n       \"3    53.1000         C123         0      2  \\n\",\n       \"4     8.0500          NaN         0      0  \\n\",\n       \"5     8.4583          NaN         2      0  \\n\",\n       \"6    51.8625          E46         0      0  \\n\",\n       \"7    21.0750          NaN         0      3  \\n\",\n       \"8    11.1333          NaN         0      2  \\n\",\n       \"9    30.0708          NaN         1      2  \\n\",\n       \"10   16.7000           G6         0      1  \\n\",\n       \"11   26.5500         C103         0      1  \\n\",\n       \"12    8.0500          NaN         0      0  \\n\",\n       \"13   31.2750          NaN         0      0  \\n\",\n       \"14    7.8542          NaN         0      1  \\n\",\n       \"15   16.0000          NaN         0      2  \\n\",\n       \"16   29.1250          NaN         2      3  \\n\",\n       \"17   13.0000          NaN         0      0  \\n\",\n       \"18   18.0000          NaN         0      2  \\n\",\n       \"19    7.2250          NaN         1      2  \\n\",\n       \"20   26.0000          NaN         0      0  \\n\",\n       \"21   13.0000          D56         0      0  \\n\",\n       \"22    8.0292          NaN         2      1  \\n\",\n       \"23   35.5000           A6         0      0  \\n\",\n       \"24   21.0750          NaN         0      1  \\n\",\n       \"25   31.3875          NaN         0      2  \\n\",\n       \"26    7.2250          NaN         1      0  \\n\",\n       \"27  263.0000  C23 C25 C27         0      0  \\n\",\n       \"28    7.8792          NaN         2      1  \\n\",\n       \"29    7.8958          NaN         0      0  \\n\",\n       \"30   27.7208          NaN         1      3  \\n\",\n       \"31  146.5208          B78         1      2  \\n\",\n       \"32    7.7500          NaN         2      1  \\n\",\n       \"33   10.5000          NaN         0      0  \\n\",\n       \"34   82.1708          NaN         1      0  \\n\",\n       \"35   52.0000          NaN         0      0  \\n\",\n       \"36    7.2292          NaN         1      0  \\n\",\n       \"37    8.0500          NaN         0      0  \\n\",\n       \"38   18.0000          NaN         0      1  \\n\",\n       \"39   11.2417          NaN         1      1  \\n\",\n       \"40    9.4750          NaN         0      2  \\n\",\n       \"41   21.0000          NaN         0      2  \\n\",\n       \"42    7.8958          NaN         1      0  \\n\",\n       \"43   41.5792          NaN         1      1  \\n\",\n       \"44    7.8792          NaN         2      1  \\n\",\n       \"45    8.0500          NaN         0      0  \\n\",\n       \"46   15.5000          NaN         2      0  \\n\",\n       \"47    7.7500          NaN         2      1  \\n\",\n       \"48   21.6792          NaN         1      0  \\n\",\n       \"49   17.8000          NaN         0      2  \"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# fill missing Fare with median fare for each Pclass\\n\",\n    \"train[\\\"Fare\\\"].fillna(train.groupby(\\\"Pclass\\\")[\\\"Fare\\\"].transform(\\\"median\\\"), inplace=True)\\n\",\n    \"test[\\\"Fare\\\"].fillna(test.groupby(\\\"Pclass\\\")[\\\"Fare\\\"].transform(\\\"median\\\"), inplace=True)\\n\",\n    \"train.head(50)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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EROrdfkZZom9957b4/n6urqCvtz585l7ty5PcrLysr40Y9+dFoVG4ye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\\nIiIoHIqIiIiIiAgKhyIiIiIiIgL8/4XauFMKAuOVAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1112cc750>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Fare',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Fare'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \" \\n\",\n    \"plt.show()  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0, 20)\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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dYX52DqeEIRYSQobizgzo0FdPcNU9PQzdmGEO/UdvFObRd28yybSvPY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\\nAJL+BYFgQ6iEUtgRAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x111024ed0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Fare',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Fare'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(0, 20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0, 30)\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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I7HmcbL+P/+dAz/tvkwjp9uh6qq4W4iERERjXBDjhxu27YNDocDW7Zs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\\nVReUAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1112ae450>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Fare',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Fare'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(0, 30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0, 512.32920000000001)\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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EROrdfkZZom9957b4/n6urqCvtz585l7ty5PcrLysr40Y9+dFoVG4ye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\\nIiIoHIqIiIiIiAgKhyIiIiIiIgL8/4XauFMKAuOVAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x111212b50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'Fare',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['Fare'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0,\\n\",\n    \"    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,\\n\",\n    \"    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,\\n\",\n    \"    dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>C85</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>C123</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\\\\n\",\n       \"0            1         0       3    0  1.0      1      0         A/5 21171   \\n\",\n       \"1            2         1       1    1  3.0      1      0          PC 17599   \\n\",\n       \"2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \\n\",\n       \"3            4         1       1    1  2.0      1      0            113803   \\n\",\n       \"4            5         0       3    0  2.0      0      0            373450   \\n\",\n       \"\\n\",\n       \"   Fare Cabin  Embarked  Title  \\n\",\n       \"0   0.0   NaN         0      0  \\n\",\n       \"1   2.0   C85         1      2  \\n\",\n       \"2   0.0   NaN         0      1  \\n\",\n       \"3   2.0  C123         0      2  \\n\",\n       \"4   0.0   NaN         0      0  \"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.7 Cabin\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"C23 C25 C27        4\\n\",\n       \"G6                 4\\n\",\n       \"B96 B98            4\\n\",\n       \"D                  3\\n\",\n       \"C22 C26            3\\n\",\n       \"E101               3\\n\",\n       \"F2                 3\\n\",\n       \"F33                3\\n\",\n       \"B57 B59 B63 B66    2\\n\",\n       \"C68                2\\n\",\n       \"B58 B60            2\\n\",\n       \"E121               2\\n\",\n       \"D20                2\\n\",\n       \"E8                 2\\n\",\n       \"E44                2\\n\",\n       \"B77                2\\n\",\n       \"C65                2\\n\",\n       \"D26                2\\n\",\n       \"E24                2\\n\",\n       \"E25                2\\n\",\n       \"B20                2\\n\",\n       \"C93                2\\n\",\n       \"D33                2\\n\",\n       \"E67                2\\n\",\n       \"D35                2\\n\",\n       \"D36                2\\n\",\n       \"C52                2\\n\",\n       \"F4                 2\\n\",\n       \"C125               2\\n\",\n       \"C124               2\\n\",\n       \"                  ..\\n\",\n       \"F G63              1\\n\",\n       \"A6                 1\\n\",\n       \"D45                1\\n\",\n       \"D6                 1\\n\",\n       \"D56                1\\n\",\n       \"C101               1\\n\",\n       \"C54                1\\n\",\n       \"D28                1\\n\",\n       \"D37                1\\n\",\n       \"B102               1\\n\",\n       \"D30                1\\n\",\n       \"E17                1\\n\",\n       \"E58                1\\n\",\n       \"F E69              1\\n\",\n       \"D10 D12            1\\n\",\n       \"E50                1\\n\",\n       \"A14                1\\n\",\n       \"C91                1\\n\",\n       \"A16                1\\n\",\n       \"B38                1\\n\",\n       \"B39                1\\n\",\n       \"C95                1\\n\",\n       \"B78                1\\n\",\n       \"B79                1\\n\",\n       \"C99                1\\n\",\n       \"B37                1\\n\",\n       \"A19                1\\n\",\n       \"E12                1\\n\",\n       \"A7                 1\\n\",\n       \"D15                1\\n\",\n       \"Name: Cabin, Length: 147, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.Cabin.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset['Cabin'] = dataset['Cabin'].str[:1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1121b2d10>\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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tsdsDuAA4WHh5T9TcBlnC9wAs7zyuOmP3rGx+dfi1r5+fmqXbu2goOD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\\nAAxRqAAAAAz9P7ZN3zLpSp8aAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1123f8390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()\\n\",\n    \"Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()\\n\",\n    \"Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()\\n\",\n    \"df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\\n\",\n    \"df.index = ['1st class','2nd class', '3rd class']\\n\",\n    \"df.plot(kind='bar',stacked=True, figsize=(10,5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"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\",\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# fill missing Fare with median fare for each Pclass\\n\",\n    \"train[\\\"Cabin\\\"].fillna(train.groupby(\\\"Pclass\\\")[\\\"Cabin\\\"].transform(\\\"median\\\"), inplace=True)\\n\",\n    \"test[\\\"Cabin\\\"].fillna(test.groupby(\\\"Pclass\\\")[\\\"Cabin\\\"].transform(\\\"median\\\"), inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.8 FamilySize\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train[\\\"FamilySize\\\"] = train[\\\"SibSp\\\"] + train[\\\"Parch\\\"] + 1\\n\",\n    \"test[\\\"FamilySize\\\"] = test[\\\"SibSp\\\"] + test[\\\"Parch\\\"] + 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0, 11.0)\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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HOR4zi098SIhjz4PJPm7VnXoOsORURERERmVT6f5+/+7u/40z/9Ux58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\\nAgAAAABEcggAAAAAkPR/nzjIhIgY754AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1121ef8d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"facet = sns.FacetGrid(train, hue=\\\"Survived\\\",aspect=4)\\n\",\n    \"facet.map(sns.kdeplot,'FamilySize',shade= True)\\n\",\n    \"facet.set(xlim=(0, train['FamilySize'].max()))\\n\",\n    \"facet.add_legend()\\n\",\n    \"plt.xlim(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"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\",\n    \"for dataset in train_test_data:\\n\",\n    \"    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>FamilySize</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.8</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\\\\n\",\n       \"0            1         0       3    0  1.0      1      0         A/5 21171   \\n\",\n       \"1            2         1       1    1  3.0      1      0          PC 17599   \\n\",\n       \"2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \\n\",\n       \"3            4         1       1    1  2.0      1      0            113803   \\n\",\n       \"4            5         0       3    0  2.0      0      0            373450   \\n\",\n       \"\\n\",\n       \"   Fare  Cabin  Embarked  Title  FamilySize  \\n\",\n       \"0   0.0    2.0         0      0         0.4  \\n\",\n       \"1   2.0    0.8         1      2         0.4  \\n\",\n       \"2   0.0    2.0         0      1         0.0  \\n\",\n       \"3   2.0    0.8         0      2         0.4  \\n\",\n       \"4   0.0    2.0         0      0         0.0  \"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>SibSp</th>\\n\",\n       \"      <th>Parch</th>\\n\",\n       \"      <th>Ticket</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>FamilySize</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>A/5 21171</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>PC 17599</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.8</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>STON/O2. 3101282</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>113803</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>373450</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\\\\n\",\n       \"0            1         0       3    0  1.0      1      0         A/5 21171   \\n\",\n       \"1            2         1       1    1  3.0      1      0          PC 17599   \\n\",\n       \"2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \\n\",\n       \"3            4         1       1    1  2.0      1      0            113803   \\n\",\n       \"4            5         0       3    0  2.0      0      0            373450   \\n\",\n       \"\\n\",\n       \"   Fare  Cabin  Embarked  Title  FamilySize  \\n\",\n       \"0   0.0    2.0         0      0         0.4  \\n\",\n       \"1   2.0    0.8         1      2         0.4  \\n\",\n       \"2   0.0    2.0         0      1         0.0  \\n\",\n       \"3   2.0    0.8         0      2         0.4  \\n\",\n       \"4   0.0    2.0         0      0         0.0  \"\n      ]\n     },\n     \"execution_count\": 68,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"features_drop = ['Ticket', 'SibSp', 'Parch']\\n\",\n    \"train = train.drop(features_drop, axis=1)\\n\",\n    \"test = test.drop(features_drop, axis=1)\\n\",\n    \"train = train.drop(['PassengerId'], axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((891, 8), (891,))\"\n      ]\n     },\n     \"execution_count\": 70,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_data = train.drop('Survived', axis=1)\\n\",\n    \"target = train['Survived']\\n\",\n    \"\\n\",\n    \"train_data.shape, target.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Pclass</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Fare</th>\\n\",\n       \"      <th>Cabin</th>\\n\",\n       \"      <th>Embarked</th>\\n\",\n       \"      <th>Title</th>\\n\",\n       \"      <th>FamilySize</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.8</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1.6</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1.6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.8</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1.8</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Pclass  Sex  Age  Fare  Cabin  Embarked  Title  FamilySize\\n\",\n       \"0       3    0  1.0   0.0    2.0         0      0         0.4\\n\",\n       \"1       1    1  3.0   2.0    0.8         1      2         0.4\\n\",\n       \"2       3    1  1.0   0.0    2.0         0      1         0.0\\n\",\n       \"3       1    1  2.0   2.0    0.8         0      2         0.4\\n\",\n       \"4       3    0  2.0   0.0    2.0         0      0         0.0\\n\",\n       \"5       3    0  2.0   0.0    2.0         2      0         0.0\\n\",\n       \"6       1    0  3.0   2.0    1.6         0      0         0.0\\n\",\n       \"7       3    0  0.0   1.0    2.0         0      3         1.6\\n\",\n       \"8       3    1  2.0   0.0    2.0         0      2         0.8\\n\",\n       \"9       2    1  0.0   2.0    1.8         1      2         0.4\"\n      ]\n     },\n     \"execution_count\": 71,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_data.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5. Modelling\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Importing Classifier Modules\\n\",\n    \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.naive_bayes import GaussianNB\\n\",\n    \"from sklearn.svm import SVC\\n\",\n    \"\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"RangeIndex: 891 entries, 0 to 890\\n\",\n      \"Data columns (total 9 columns):\\n\",\n      \"Survived      891 non-null int64\\n\",\n      \"Pclass        891 non-null int64\\n\",\n      \"Sex           891 non-null int64\\n\",\n      \"Age           891 non-null float64\\n\",\n      \"Fare          891 non-null float64\\n\",\n      \"Cabin         891 non-null float64\\n\",\n      \"Embarked      891 non-null int64\\n\",\n      \"Title         891 non-null int64\\n\",\n      \"FamilySize    891 non-null float64\\n\",\n      \"dtypes: float64(4), int64(5)\\n\",\n      \"memory usage: 62.7 KB\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train.info()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.2 Cross Validation (K-fold)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import KFold\\n\",\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"k_fold = KFold(n_splits=10, shuffle=True, random_state=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.2.1 kNN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.82222222  0.76404494  0.80898876  0.83146067  0.87640449  0.82022472\\n\",\n      \"  0.85393258  0.79775281  0.84269663  0.84269663]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = KNeighborsClassifier(n_neighbors = 13)\\n\",\n    \"scoring = 'accuracy'\\n\",\n    \"score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\\n\",\n    \"print(score)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"82.6\"\n      ]\n     },\n     \"execution_count\": 78,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# kNN Score\\n\",\n    \"round(np.mean(score)*100, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.2.2 Decision Tree\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.76666667  0.82022472  0.78651685  0.76404494  0.88764045  0.76404494\\n\",\n      \"  0.82022472  0.82022472  0.74157303  0.79775281]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = DecisionTreeClassifier()\\n\",\n    \"scoring = 'accuracy'\\n\",\n    \"score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\\n\",\n    \"print(score)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"79.69\"\n      ]\n     },\n     \"execution_count\": 80,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# decision tree Score\\n\",\n    \"round(np.mean(score)*100, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.2.3 Ramdom Forest\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.77777778  0.80898876  0.82022472  0.76404494  0.86516854  0.82022472\\n\",\n      \"  0.79775281  0.80898876  0.76404494  0.83146067]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = RandomForestClassifier(n_estimators=13)\\n\",\n    \"scoring = 'accuracy'\\n\",\n    \"score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\\n\",\n    \"print(score)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"80.59\"\n      ]\n     },\n     \"execution_count\": 82,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Random Forest Score\\n\",\n    \"round(np.mean(score)*100, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.2.4 Naive Bayes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.85555556  0.73033708  0.75280899  0.75280899  0.70786517  0.80898876\\n\",\n      \"  0.76404494  0.80898876  0.86516854  0.83146067]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = GaussianNB()\\n\",\n    \"scoring = 'accuracy'\\n\",\n    \"score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\\n\",\n    \"print(score)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"78.78\"\n      ]\n     },\n     \"execution_count\": 84,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Naive Bayes Score\\n\",\n    \"round(np.mean(score)*100, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.2.5 SVM\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.83333333  0.80898876  0.83146067  0.82022472  0.84269663  0.82022472\\n\",\n      \"  0.84269663  0.85393258  0.83146067  0.86516854]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = SVC()\\n\",\n    \"scoring = 'accuracy'\\n\",\n    \"score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\\n\",\n    \"print(score)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"83.5\"\n      ]\n     },\n     \"execution_count\": 86,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"round(np.mean(score)*100,2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 7. Testing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"clf = SVC()\\n\",\n    \"clf.fit(train_data, target)\\n\",\n    \"\\n\",\n    \"test_data = test.drop(\\\"PassengerId\\\", axis=1).copy()\\n\",\n    \"prediction = clf.predict(test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"submission = pd.DataFrame({\\n\",\n    \"        \\\"PassengerId\\\": test[\\\"PassengerId\\\"],\\n\",\n    \"        \\\"Survived\\\": prediction\\n\",\n    \"    })\\n\",\n    \"\\n\",\n    \"submission.to_csv('submission.csv', index=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>PassengerId</th>\\n\",\n       \"      <th>Survived</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>892</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>893</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>894</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>895</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>896</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   PassengerId  Survived\\n\",\n       \"0          892         0\\n\",\n       \"1          893         1\\n\",\n       \"2          894         0\\n\",\n       \"3          895         0\\n\",\n       \"4          896         1\"\n      ]\n     },\n     \"execution_count\": 89,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"submission = pd.read_csv('submission.csv')\\n\",\n    \"submission.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## References\\n\",\n    \"\\n\",\n    \"This notebook is created by learning from the following notebooks:\\n\",\n    \"\\n\",\n    \"- [Mukesh ChapagainTitanic Solution: A Beginner's Guide](https://www.kaggle.com/chapagain/titanic-solution-a-beginner-s-guide?scriptVersionId=1473689)\\n\",\n    \"- [How to score 0.8134 in Titanic Kaggle Challenge](http://ahmedbesbes.com/how-to-score-08134-in-titanic-kaggle-challenge.html)\\n\",\n    \"- [Titanic: factors to survive](https://olegleyz.github.io/titanic_factors.html)\\n\",\n    \"- [Titanic Survivors Dataset and Data Wrangling](http://www.codeastar.com/data-wrangling/)\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  }
]