天池--泰坦尼克机器学习入门

#数据分析库
import pandas as pd
#科学计算库
import numpy as np 
from pandas import Series,DataFrame

data_train = pd.read_csv("datalab/1386/titanic_train.csv")
data_test = pd.read_csv("datalab/1386/titanic_test.csv")

我们可以使用data_train.info()函数来获取更多关于数据的信息，如下所示：

data_train.info()

从上面数据我们可以看到Age,Cabin和Embarked列的数据有缺失，Age列共有714条数据，缺失117条数据；Cabin列有204条数据，缺失687条数据；Embarked列有889条数据，缺失2条数据，其他列都是891条数据；

data_train.describe()

mean字段告诉我们，大概0.383838的人最后获救了，2/3等舱的人数比1等舱要多，平均乘客年龄大概是29.7岁(计算这个时候会略掉无记录的)等等…
泰坦尼克号乘客生存预测

1. 案例背景
   泰坦尼克号沉船事故是世界上最著名的沉船事故之一。1912年4月15日，在她的处女航期间，泰坦尼克号撞上冰山后沉没，造成2224名乘客和机组人员中超过1502人的死亡。这一轰动的悲剧震惊了国际社会，并导致更好的船舶安全法规。 事故中导致死亡的一个原因是许多船员和乘客没有足够的救生艇。然而在被获救群体中也有一些比较幸运的因素；一些人群在事故中被救的几率高于其他人，比如妇女、儿童和上层阶级。 这个Case里，我们需要分析和判断出什么样的人更容易获救。最重要的是，要利用机器学习来预测出在这场灾难中哪些人会最终获救；

GitHub

2.数据样本
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list datalab file

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!ls datalab/1386/
titanic_test.csv titanic_train.csv
此项目数据集分为2份数据集titanic_train.csv和titanic_test.csv

titanic_train.csv: 训练集，共计891条数据

titanic_test.csv: 测试集，共计418条数据

数据特征
字段 字段说明
PassengerId 乘客编号
Survived 存活情况（存活：1 ; 死亡：0）
Pclass 客舱等级
Name 乘客姓名
Sex 性别
Age 年龄
SibSp 同乘的兄弟姐妹/配偶数
Parch 同乘的父母/小孩数
Ticket 船票编号
Fare 船票价格
Cabin 客舱号
Embarked 登船港口
PassengerId 是数据唯一序号；Survived 是存活情况，为预测标记特征；剩下的10个是原始特征数据。

说明
这个项目练习主要是刚刚入门机器学习的同学，旨在通过这个实战案例初步理解机器学习需要做什么工作，理解简单的机器学习算法。

3. 数据处理
   先看看项目的数据具体长什么样吧。在Data下我们titanic_train.csv和titanic_test.csv两个文件，分别存着官方给的训练和测试数据。 使用python语句分别读取文件里面的数据并观察数据。

#数据分析库
import pandas as pd
#科学计算库
import numpy as np
from pandas import Series,DataFrame

data_train = pd.read_csv(“datalab/1386/titanic_train.csv”)
data_test = pd.read_csv(“datalab/1386/titanic_test.csv”)
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#数据分析库
2
import pandas as pd
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#科学计算库
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import numpy as np
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from pandas import Series,DataFrame
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​
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data_train = pd.read_csv(“datalab/1386/titanic_train.csv”)
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data_test = pd.read_csv(“datalab/1386/titanic_test.csv”)
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data_train.head(10)
No (safe) renderer could be found for output. It has the following MIME types: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 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这个是训练集中前10条数据，典型的dataframe格式，如果你没接触过这种格式，完全没有关系，你就把它想象成Excel里面的列,共计12列，其中Survived表示是否存活（存活：1 ; 死亡：0），是标签列，其他列为特征列。

1
data_test.head(10)
No (safe) renderer could be found for output. It has the following MIME types: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 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这个是测试集中前10条数据，共计11列，需要根据测试集中的特征数据，以及出来的模型来进行预测Survived标签列的值，来预测乘客是否存活。

逐条往下看，要看完这么多条，眼睛都有一种要瞎的赶脚。我们可以使用data_train.info()函数来获取更多关于数据的信息，如下所示：

data_train.info()
1
data_train.info()

RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId 891 non-null int64
Survived 891 non-null int64
Pclass 891 non-null int64
Name 891 non-null object
Sex 891 non-null object
Age 714 non-null float64
SibSp 891 non-null int64
Parch 891 non-null int64
Ticket 891 non-null object
Fare 891 non-null float64
Cabin 204 non-null object
Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
从上面数据我们可以看到Age,Cabin和Embarked列的数据有缺失，Age列共有714条数据，缺失117条数据；Cabin列有204条数据，缺失687条数据；Embarked列有889条数据，缺失2条数据，其他列都是891条数据；

data_train.describe()
1
data_train.describe()
No (safe) renderer could be found for output. It has the following MIME types: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233
mean字段告诉我们，大概0.383838的人最后获救了，2/3等舱的人数比1等舱要多，平均乘客年龄大概是29.7岁(计算这个时候会略掉无记录的)等等…

4. 特征选取
   不做复杂的特征工程，采用最快速的方法预测一个结果

数据空值处理
客舱号Cabin列由于存在大量的空值，如果直接对空值进行填空，带来的误差影响会比较大，先不选用Cabin列做特征
年龄列对于是否能够存活的判断很重要，采用Age均值对空值进行填充
PassengerId是一个连续的序列，对于是否能够存活的判断无关，不选用PassengerId作为特征

#Age列中的缺失值用Age中位数进行填充
data_train["Age"] = data_train['Age'].fillna(data_train['Age'].median())  
data_train.describe()

线性回归算法

#线性回归
from sklearn.linear_model import LinearRegression   

#训练集交叉验证，得到平均值
#from sklearn.cross_validation import KFold 
from sklearn.model_selection import KFold
 
#选取简单的可用输入特征
predictors = ["Pclass","Age","SibSp","Parch","Fare"]     
 
#初始化现行回归算法
alg = LinearRegression()
#样本平均分成3份，3折交叉验证
#kf = KFold(data_train.shape[0],n_folds=3,random_state=1)   
kf = KFold(n_splits=3,shuffle=False,random_state=1) 

predictions = []
for train,test in kf.split(data_train):
	#The predictors we're using to train the algorithm.  Note how we only take then rows in the train folds.
	train_predictors = (data_train[predictors].iloc[train,:])
	#The target we're using to train the algorithm.
	train_target = data_train["Survived"].iloc[train]
	#Training the algorithm using the predictors and target.
	alg.fit(train_predictors,train_target)
	#We can now make predictions on the test fold
	test_predictions = alg.predict(data_train[predictors].iloc[test,:])
	predictions.append(test_predictions)
	import numpy as np
 
#The predictions are in three aeparate numpy arrays.	Concatenate them into one.
#We concatenate them on axis 0,as they only have one axis.
predictions = np.concatenate(predictions,axis=0)
 
#Map predictions to outcomes(only possible outcomes are 1 and 0)
predictions[predictions>.5] = 1
predictions[predictions<=.5] = 0
accuracy = sum(predictions == data_train["Survived"]) / len(predictions)
print ("准确率为: ", accuracy)

准确率为: 0.7037037037037037
逻辑回归算法

from sklearn import model_selection
#逻辑回归
from sklearn.linear_model import LogisticRegression   

#初始化逻辑回归算法
LogRegAlg=LogisticRegression(random_state=1)
re = LogRegAlg.fit(data_train[predictors],data_train["Survived"])

#使用sklearn库里面的交叉验证函数获取预测准确率分数
scores = model_selection.cross_val_score(LogRegAlg,data_train[predictors],data_train["Survived"],cv=3)

#使用交叉验证分数的平均值作为最终的准确率
print("准确率为: ",scores.mean())

准确率为: 0.699214365881

增加特征Sex和Embarked列，查看对预测的影响
对性别Sex列和登船港口Embarked列进行字符处理

#Sex性别列处理：male用0，female用1
data_train.loc[data_train["Sex"] == "male","Sex"] = 0
data_train.loc[data_train["Sex"] == "female","Sex"] = 1
#缺失值用最多的S进行填充
data_train["Embarked"] = data_train["Embarked"].fillna('S') 
#地点用0,1,2
data_train.loc[data_train["Embarked"] == "S","Embarked"] = 0    
data_train.loc[data_train["Embarked"] == "C","Embarked"] = 1
data_train.loc[data_train["Embarked"] == "Q","Embarked"] = 2

增加2个特征Sex和Embarked，继续使用逻辑回归算法进行预测

predictors = ["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked"]  

LogRegAlg=LogisticRegression(random_state=1)
#Compute the accuracy score for all the cross validation folds.(much simpler than what we did before!)
re = LogRegAlg.fit(data_train[predictors],data_train["Survived"])
scores = model_selection.cross_val_score(LogRegAlg,data_train[predictors],data_train["Survived"],cv=3)
#Take the mean of the scores (because we have one for each fold)
print("准确率为: ",scores.mean())

准确率为: 0.787878787879
通过增加了2个特征，模型的准确率提高到78.78%，说明好的特征有利于提升模型的预测能力。

#新增：对测试集数据进行预处理，并进行结果预测
#Age列中的缺失值用Age均值进行填充
data_test["Age"] = data_test["Age"].fillna(data_test["Age"].median())
#Fare列中的缺失值用Fare最大值进行填充
data_test["Fare"] = data_test["Fare"].fillna(data_test["Fare"].max()) 

#Sex性别列处理：male用0，female用1
data_test.loc[data_test["Sex"] == "male","Sex"] = 0
data_test.loc[data_test["Sex"] == "female","Sex"] = 1
#缺失值用最多的S进行填充
data_test["Embarked"] = data_test["Embarked"].fillna('S') 
#地点用0,1,2
data_test.loc[data_test["Embarked"] == "S","Embarked"] = 0    
data_test.loc[data_test["Embarked"] == "C","Embarked"] = 1
data_test.loc[data_test["Embarked"] == "Q","Embarked"] = 2

test_features = ["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked"] 
#构造测试集的Survived列，
data_test["Survived"] = -1

test_predictors = data_test[test_features]
data_test["Survived"] = LogRegAlg.predict(test_predictors)

使用随机森林算法

from sklearn import model_selection
from sklearn.ensemble import RandomForestClassifier
 
predictors=["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked"]
 

#10棵决策树，停止的条件：样本个数为2，叶子节点个数为1
alg=RandomForestClassifier(random_state=1,n_estimators=10,min_samples_split=2,min_samples_leaf=1) 

#Compute the accuracy score for all the cross validation folds.  (much simpler than what we did before!)
#kf=cross_validation.KFold(data_train.shape[0],n_folds=3,random_state=1)
kf=model_selection.KFold(n_splits=3,shuffle=False, random_state=1)


scores=model_selection.cross_val_score(alg,data_train[predictors],data_train["Survived"],cv=kf)
print(scores)
#Take the mean of the scores (because we have one for each fold)
print(scores.mean())

[ 0.75420875 0.8013468 0.8013468 ]
0.785634118967
增加决策树的个数到30棵决策树，交叉验证方法采用10折交叉验证

#30棵决策树，停止的条件：样本个数为2，叶子节点个数为1
alg=RandomForestClassifier(random_state=1,n_estimators=30,min_samples_split=2,min_samples_leaf=1) 

#Compute the accuracy score for all the cross validation folds.  (much simpler than what we did before!)
#kf=cross_validation.KFold(data_train.shape[0],n_folds=10,random_state=1)
kf=model_selection.KFold(n_splits=10,shuffle=False,random_state=1)

scores=model_selection.cross_val_score(alg,data_train[predictors],data_train["Survived"],cv=kf)
 
print(scores)
#Take the mean of the scores (because we have one for each fold)
print(scores.mean())

[ 0.74444444 0.80898876 0.78651685 0.82022472 0.85393258 0.85393258
0.7752809 0.7752809 0.84269663 0.85393258]
0.81152309613
保留问题：
线性回归算法中 等到的预测结果 predictions.append(test_predictions) 顺序怎么保证与 data_train相同 accuracy = sum(predictions == data_train[“Survived”]) / len(predictions)
Age用的众数填补的缺失值，所以逻辑回归准确率只有0.69，而用均值替代，准确率为0.79
数据未进行归一化处理
这里不再赘述数据归一化的概念，数据归一化在机器学习种普遍广泛地运用于提升机器学习效率以及提升模型的收敛速度，加快梯度下降算法速度。在线性回归和逻辑回归中一般需要进行数据归一化操作，而在随机森林，决策树，朴素贝叶斯，XGBoost，lightGBM等算法种并不需要，原因在于这些算法普遍并不关注与数据之间量纲的差别。