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Lab18/Dataset/breast-cancer-wisconsin.data
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606140,1,1,1,1,2,?,2,1,1,2
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721482,4,4,4,4,6,5,7,3,1,2
730881,7,6,3,2,5,10,7,4,6,4
733639,3,1,1,1,2,?,3,1,1,2
733639,3,1,1,1,2,1,3,1,1,2
733823,5,4,6,10,2,10,4,1,1,4
740492,1,1,1,1,2,1,3,1,1,2
743348,3,2,2,1,2,1,2,3,1,2
752904,10,1,1,1,2,10,5,4,1,4
756136,1,1,1,1,2,1,2,1,1,2
760001,8,10,3,2,6,4,3,10,1,4
760239,10,4,6,4,5,10,7,1,1,4
76389,10,4,7,2,2,8,6,1,1,4
764974,5,1,1,1,2,1,3,1,2,2
770066,5,2,2,2,2,1,2,2,1,2
785208,5,4,6,6,4,10,4,3,1,4
785615,8,6,7,3,3,10,3,4,2,4
792744,1,1,1,1,2,1,1,1,1,2
797327,6,5,5,8,4,10,3,4,1,4
798429,1,1,1,1,2,1,3,1,1,2
704097,1,1,1,1,1,1,2,1,1,2
806423,8,5,5,5,2,10,4,3,1,4
809912,10,3,3,1,2,10,7,6,1,4
810104,1,1,1,1,2,1,3,1,1,2
814265,2,1,1,1,2,1,1,1,1,2
814911,1,1,1,1,2,1,1,1,1,2
822829,7,6,4,8,10,10,9,5,3,4
826923,1,1,1,1,2,1,1,1,1,2
830690,5,2,2,2,3,1,1,3,1,2
831268,1,1,1,1,1,1,1,3,1,2
832226,3,4,4,10,5,1,3,3,1,4
832567,4,2,3,5,3,8,7,6,1,4
836433,5,1,1,3,2,1,1,1,1,2
837082,2,1,1,1,2,1,3,1,1,2
846832,3,4,5,3,7,3,4,6,1,2
850831,2,7,10,10,7,10,4,9,4,4
855524,1,1,1,1,2,1,2,1,1,2
857774,4,1,1,1,3,1,2,2,1,2
859164,5,3,3,1,3,3,3,3,3,4
859350,8,10,10,7,10,10,7,3,8,4
866325,8,10,5,3,8,4,4,10,3,4
873549,10,3,5,4,3,7,3,5,3,4
877291,6,10,10,10,10,10,8,10,10,4
877943,3,10,3,10,6,10,5,1,4,4
888169,3,2,2,1,4,3,2,1,1,2
888523,4,4,4,2,2,3,2,1,1,2
896404,2,1,1,1,2,1,3,1,1,2
897172,2,1,1,1,2,1,2,1,1,2
95719,6,10,10,10,8,10,7,10,7,4
160296,5,8,8,10,5,10,8,10,3,4
342245,1,1,3,1,2,1,1,1,1,2
428598,1,1,3,1,1,1,2,1,1,2
492561,4,3,2,1,3,1,2,1,1,2
493452,1,1,3,1,2,1,1,1,1,2
493452,4,1,2,1,2,1,2,1,1,2
521441,5,1,1,2,2,1,2,1,1,2
560680,3,1,2,1,2,1,2,1,1,2
636437,1,1,1,1,2,1,1,1,1,2
640712,1,1,1,1,2,1,2,1,1,2
654244,1,1,1,1,1,1,2,1,1,2
657753,3,1,1,4,3,1,2,2,1,2
685977,5,3,4,1,4,1,3,1,1,2
805448,1,1,1,1,2,1,1,1,1,2
846423,10,6,3,6,4,10,7,8,4,4
1002504,3,2,2,2,2,1,3,2,1,2
1022257,2,1,1,1,2,1,1,1,1,2
1026122,2,1,1,1,2,1,1,1,1,2
1071084,3,3,2,2,3,1,1,2,3,2
1080233,7,6,6,3,2,10,7,1,1,4
1114570,5,3,3,2,3,1,3,1,1,2
1114570,2,1,1,1,2,1,2,2,1,2
1116715,5,1,1,1,3,2,2,2,1,2
1131411,1,1,1,2,2,1,2,1,1,2
1151734,10,8,7,4,3,10,7,9,1,4
1156017,3,1,1,1,2,1,2,1,1,2
1158247,1,1,1,1,1,1,1,1,1,2
1158405,1,2,3,1,2,1,2,1,1,2
1168278,3,1,1,1,2,1,2,1,1,2
1176187,3,1,1,1,2,1,3,1,1,2
1196263,4,1,1,1,2,1,1,1,1,2
1196475,3,2,1,1,2,1,2,2,1,2
1206314,1,2,3,1,2,1,1,1,1,2
1211265,3,10,8,7,6,9,9,3,8,4
1213784,3,1,1,1,2,1,1,1,1,2
1223003,5,3,3,1,2,1,2,1,1,2
1223306,3,1,1,1,2,4,1,1,1,2
1223543,1,2,1,3,2,1,1,2,1,2
1229929,1,1,1,1,2,1,2,1,1,2
1231853,4,2,2,1,2,1,2,1,1,2
1234554,1,1,1,1,2,1,2,1,1,2
1236837,2,3,2,2,2,2,3,1,1,2
1237674,3,1,2,1,2,1,2,1,1,2
1238021,1,1,1,1,2,1,2,1,1,2
1238464,1,1,1,1,1,?,2,1,1,2
1238633,10,10,10,6,8,4,8,5,1,4
1238915,5,1,2,1,2,1,3,1,1,2
1238948,8,5,6,2,3,10,6,6,1,4
1239232,3,3,2,6,3,3,3,5,1,2
1239347,8,7,8,5,10,10,7,2,1,4
1239967,1,1,1,1,2,1,2,1,1,2
1240337,5,2,2,2,2,2,3,2,2,2
1253505,2,3,1,1,5,1,1,1,1,2
1255384,3,2,2,3,2,3,3,1,1,2
1257200,10,10,10,7,10,10,8,2,1,4
1257648,4,3,3,1,2,1,3,3,1,2
1257815,5,1,3,1,2,1,2,1,1,2
1257938,3,1,1,1,2,1,1,1,1,2
1258549,9,10,10,10,10,10,10,10,1,4
1258556,5,3,6,1,2,1,1,1,1,2
1266154,8,7,8,2,4,2,5,10,1,4
1272039,1,1,1,1,2,1,2,1,1,2
1276091,2,1,1,1,2,1,2,1,1,2
1276091,1,3,1,1,2,1,2,2,1,2
1276091,5,1,1,3,4,1,3,2,1,2
1277629,5,1,1,1,2,1,2,2,1,2
1293439,3,2,2,3,2,1,1,1,1,2
1293439,6,9,7,5,5,8,4,2,1,2
1294562,10,8,10,1,3,10,5,1,1,4
1295186,10,10,10,1,6,1,2,8,1,4
527337,4,1,1,1,2,1,1,1,1,2
558538,4,1,3,3,2,1,1,1,1,2
566509,5,1,1,1,2,1,1,1,1,2
608157,10,4,3,10,4,10,10,1,1,4
677910,5,2,2,4,2,4,1,1,1,2
734111,1,1,1,3,2,3,1,1,1,2
734111,1,1,1,1,2,2,1,1,1,2
780555,5,1,1,6,3,1,2,1,1,2
827627,2,1,1,1,2,1,1,1,1,2
1049837,1,1,1,1,2,1,1,1,1,2
1058849,5,1,1,1,2,1,1,1,1,2
1182404,1,1,1,1,1,1,1,1,1,2
1193544,5,7,9,8,6,10,8,10,1,4
1201870,4,1,1,3,1,1,2,1,1,2
1202253,5,1,1,1,2,1,1,1,1,2
1227081,3,1,1,3,2,1,1,1,1,2
1230994,4,5,5,8,6,10,10,7,1,4
1238410,2,3,1,1,3,1,1,1,1,2
1246562,10,2,2,1,2,6,1,1,2,4
1257470,10,6,5,8,5,10,8,6,1,4
1259008,8,8,9,6,6,3,10,10,1,4
1266124,5,1,2,1,2,1,1,1,1,2
1267898,5,1,3,1,2,1,1,1,1,2
1268313,5,1,1,3,2,1,1,1,1,2
1268804,3,1,1,1,2,5,1,1,1,2
1276091,6,1,1,3,2,1,1,1,1,2
1280258,4,1,1,1,2,1,1,2,1,2
1293966,4,1,1,1,2,1,1,1,1,2
1296572,10,9,8,7,6,4,7,10,3,4
1298416,10,6,6,2,4,10,9,7,1,4
1299596,6,6,6,5,4,10,7,6,2,4
1105524,4,1,1,1,2,1,1,1,1,2
1181685,1,1,2,1,2,1,2,1,1,2
1211594,3,1,1,1,1,1,2,1,1,2
1238777,6,1,1,3,2,1,1,1,1,2
1257608,6,1,1,1,1,1,1,1,1,2
1269574,4,1,1,1,2,1,1,1,1,2
1277145,5,1,1,1,2,1,1,1,1,2
1287282,3,1,1,1,2,1,1,1,1,2
1296025,4,1,2,1,2,1,1,1,1,2
1296263,4,1,1,1,2,1,1,1,1,2
1296593,5,2,1,1,2,1,1,1,1,2
1299161,4,8,7,10,4,10,7,5,1,4
1301945,5,1,1,1,1,1,1,1,1,2
1302428,5,3,2,4,2,1,1,1,1,2
1318169,9,10,10,10,10,5,10,10,10,4
474162,8,7,8,5,5,10,9,10,1,4
787451,5,1,2,1,2,1,1,1,1,2
1002025,1,1,1,3,1,3,1,1,1,2
1070522,3,1,1,1,1,1,2,1,1,2
1073960,10,10,10,10,6,10,8,1,5,4
1076352,3,6,4,10,3,3,3,4,1,4
1084139,6,3,2,1,3,4,4,1,1,4
1115293,1,1,1,1,2,1,1,1,1,2
1119189,5,8,9,4,3,10,7,1,1,4
1133991,4,1,1,1,1,1,2,1,1,2
1142706,5,10,10,10,6,10,6,5,2,4
1155967,5,1,2,10,4,5,2,1,1,2
1170945,3,1,1,1,1,1,2,1,1,2
1181567,1,1,1,1,1,1,1,1,1,2
1182404,4,2,1,1,2,1,1,1,1,2
1204558,4,1,1,1,2,1,2,1,1,2
1217952,4,1,1,1,2,1,2,1,1,2
1224565,6,1,1,1,2,1,3,1,1,2
1238186,4,1,1,1,2,1,2,1,1,2
1253917,4,1,1,2,2,1,2,1,1,2
1265899,4,1,1,1,2,1,3,1,1,2
1268766,1,1,1,1,2,1,1,1,1,2
1277268,3,3,1,1,2,1,1,1,1,2
1286943,8,10,10,10,7,5,4,8,7,4
1295508,1,1,1,1,2,4,1,1,1,2
1297327,5,1,1,1,2,1,1,1,1,2
1297522,2,1,1,1,2,1,1,1,1,2
1298360,1,1,1,1,2,1,1,1,1,2
1299924,5,1,1,1,2,1,2,1,1,2
1299994,5,1,1,1,2,1,1,1,1,2
1304595,3,1,1,1,1,1,2,1,1,2
1306282,6,6,7,10,3,10,8,10,2,4
1313325,4,10,4,7,3,10,9,10,1,4
1320077,1,1,1,1,1,1,1,1,1,2
1320077,1,1,1,1,1,1,2,1,1,2
1320304,3,1,2,2,2,1,1,1,1,2
1330439,4,7,8,3,4,10,9,1,1,4
333093,1,1,1,1,3,1,1,1,1,2
369565,4,1,1,1,3,1,1,1,1,2
412300,10,4,5,4,3,5,7,3,1,4
672113,7,5,6,10,4,10,5,3,1,4
749653,3,1,1,1,2,1,2,1,1,2
769612,3,1,1,2,2,1,1,1,1,2
769612,4,1,1,1,2,1,1,1,1,2
798429,4,1,1,1,2,1,3,1,1,2
807657,6,1,3,2,2,1,1,1,1,2
8233704,4,1,1,1,1,1,2,1,1,2
837480,7,4,4,3,4,10,6,9,1,4
867392,4,2,2,1,2,1,2,1,1,2
869828,1,1,1,1,1,1,3,1,1,2
1043068,3,1,1,1,2,1,2,1,1,2
1056171,2,1,1,1,2,1,2,1,1,2
1061990,1,1,3,2,2,1,3,1,1,2
1113061,5,1,1,1,2,1,3,1,1,2
1116192,5,1,2,1,2,1,3,1,1,2
1135090,4,1,1,1,2,1,2,1,1,2
1145420,6,1,1,1,2,1,2,1,1,2
1158157,5,1,1,1,2,2,2,1,1,2
1171578,3,1,1,1,2,1,1,1,1,2
1174841,5,3,1,1,2,1,1,1,1,2
1184586,4,1,1,1,2,1,2,1,1,2
1186936,2,1,3,2,2,1,2,1,1,2
1197527,5,1,1,1,2,1,2,1,1,2
1222464,6,10,10,10,4,10,7,10,1,4
1240603,2,1,1,1,1,1,1,1,1,2
1240603,3,1,1,1,1,1,1,1,1,2
1241035,7,8,3,7,4,5,7,8,2,4
1287971,3,1,1,1,2,1,2,1,1,2
1289391,1,1,1,1,2,1,3,1,1,2
1299924,3,2,2,2,2,1,4,2,1,2
1306339,4,4,2,1,2,5,2,1,2,2
1313658,3,1,1,1,2,1,1,1,1,2
1313982,4,3,1,1,2,1,4,8,1,2
1321264,5,2,2,2,1,1,2,1,1,2
1321321,5,1,1,3,2,1,1,1,1,2
1321348,2,1,1,1,2,1,2,1,1,2
1321931,5,1,1,1,2,1,2,1,1,2
1321942,5,1,1,1,2,1,3,1,1,2
1321942,5,1,1,1,2,1,3,1,1,2
1328331,1,1,1,1,2,1,3,1,1,2
1328755,3,1,1,1,2,1,2,1,1,2
1331405,4,1,1,1,2,1,3,2,1,2
1331412,5,7,10,10,5,10,10,10,1,4
1333104,3,1,2,1,2,1,3,1,1,2
1334071,4,1,1,1,2,3,2,1,1,2
1343068,8,4,4,1,6,10,2,5,2,4
1343374,10,10,8,10,6,5,10,3,1,4
1344121,8,10,4,4,8,10,8,2,1,4
142932,7,6,10,5,3,10,9,10,2,4
183936,3,1,1,1,2,1,2,1,1,2
324382,1,1,1,1,2,1,2,1,1,2
378275,10,9,7,3,4,2,7,7,1,4
385103,5,1,2,1,2,1,3,1,1,2
690557,5,1,1,1,2,1,2,1,1,2
695091,1,1,1,1,2,1,2,1,1,2
695219,1,1,1,1,2,1,2,1,1,2
824249,1,1,1,1,2,1,3,1,1,2
871549,5,1,2,1,2,1,2,1,1,2
878358,5,7,10,6,5,10,7,5,1,4
1107684,6,10,5,5,4,10,6,10,1,4
1115762,3,1,1,1,2,1,1,1,1,2
1217717,5,1,1,6,3,1,1,1,1,2
1239420,1,1,1,1,2,1,1,1,1,2
1254538,8,10,10,10,6,10,10,10,1,4
1261751,5,1,1,1,2,1,2,2,1,2
1268275,9,8,8,9,6,3,4,1,1,4
1272166,5,1,1,1,2,1,1,1,1,2
1294261,4,10,8,5,4,1,10,1,1,4
1295529,2,5,7,6,4,10,7,6,1,4
1298484,10,3,4,5,3,10,4,1,1,4
1311875,5,1,2,1,2,1,1,1,1,2
1315506,4,8,6,3,4,10,7,1,1,4
1320141,5,1,1,1,2,1,2,1,1,2
1325309,4,1,2,1,2,1,2,1,1,2
1333063,5,1,3,1,2,1,3,1,1,2
1333495,3,1,1,1,2,1,2,1,1,2
1334659,5,2,4,1,1,1,1,1,1,2
1336798,3,1,1,1,2,1,2,1,1,2
1344449,1,1,1,1,1,1,2,1,1,2
1350568,4,1,1,1,2,1,2,1,1,2
1352663,5,4,6,8,4,1,8,10,1,4
188336,5,3,2,8,5,10,8,1,2,4
352431,10,5,10,3,5,8,7,8,3,4
353098,4,1,1,2,2,1,1,1,1,2
411453,1,1,1,1,2,1,1,1,1,2
557583,5,10,10,10,10,10,10,1,1,4
636375,5,1,1,1,2,1,1,1,1,2
736150,10,4,3,10,3,10,7,1,2,4
803531,5,10,10,10,5,2,8,5,1,4
822829,8,10,10,10,6,10,10,10,10,4
1016634,2,3,1,1,2,1,2,1,1,2
1031608,2,1,1,1,1,1,2,1,1,2
1041043,4,1,3,1,2,1,2,1,1,2
1042252,3,1,1,1,2,1,2,1,1,2
1057067,1,1,1,1,1,?,1,1,1,2
1061990,4,1,1,1,2,1,2,1,1,2
1073836,5,1,1,1,2,1,2,1,1,2
1083817,3,1,1,1,2,1,2,1,1,2
1096352,6,3,3,3,3,2,6,1,1,2
1140597,7,1,2,3,2,1,2,1,1,2
1149548,1,1,1,1,2,1,1,1,1,2
1174009,5,1,1,2,1,1,2,1,1,2
1183596,3,1,3,1,3,4,1,1,1,2
1190386,4,6,6,5,7,6,7,7,3,4
1190546,2,1,1,1,2,5,1,1,1,2
1213273,2,1,1,1,2,1,1,1,1,2
1218982,4,1,1,1,2,1,1,1,1,2
1225382,6,2,3,1,2,1,1,1,1,2
1235807,5,1,1,1,2,1,2,1,1,2
1238777,1,1,1,1,2,1,1,1,1,2
1253955,8,7,4,4,5,3,5,10,1,4
1257366,3,1,1,1,2,1,1,1,1,2
1260659,3,1,4,1,2,1,1,1,1,2
1268952,10,10,7,8,7,1,10,10,3,4
1275807,4,2,4,3,2,2,2,1,1,2
1277792,4,1,1,1,2,1,1,1,1,2
1277792,5,1,1,3,2,1,1,1,1,2
1285722,4,1,1,3,2,1,1,1,1,2
1288608,3,1,1,1,2,1,2,1,1,2
1290203,3,1,1,1,2,1,2,1,1,2
1294413,1,1,1,1,2,1,1,1,1,2
1299596,2,1,1,1,2,1,1,1,1,2
1303489,3,1,1,1,2,1,2,1,1,2
1311033,1,2,2,1,2,1,1,1,1,2
1311108,1,1,1,3,2,1,1,1,1,2
1315807,5,10,10,10,10,2,10,10,10,4
1318671,3,1,1,1,2,1,2,1,1,2
1319609,3,1,1,2,3,4,1,1,1,2
1323477,1,2,1,3,2,1,2,1,1,2
1324572,5,1,1,1,2,1,2,2,1,2
1324681,4,1,1,1,2,1,2,1,1,2
1325159,3,1,1,1,2,1,3,1,1,2
1326892,3,1,1,1,2,1,2,1,1,2
1330361,5,1,1,1,2,1,2,1,1,2
1333877,5,4,5,1,8,1,3,6,1,2
1334015,7,8,8,7,3,10,7,2,3,4
1334667,1,1,1,1,2,1,1,1,1,2
1339781,1,1,1,1,2,1,2,1,1,2
1339781,4,1,1,1,2,1,3,1,1,2
13454352,1,1,3,1,2,1,2,1,1,2
1345452,1,1,3,1,2,1,2,1,1,2
1345593,3,1,1,3,2,1,2,1,1,2
1347749,1,1,1,1,2,1,1,1,1,2
1347943,5,2,2,2,2,1,1,1,2,2
1348851,3,1,1,1,2,1,3,1,1,2
1350319,5,7,4,1,6,1,7,10,3,4
1350423,5,10,10,8,5,5,7,10,1,4
1352848,3,10,7,8,5,8,7,4,1,4
1353092,3,2,1,2,2,1,3,1,1,2
1354840,2,1,1,1,2,1,3,1,1,2
1354840,5,3,2,1,3,1,1,1,1,2
1355260,1,1,1,1,2,1,2,1,1,2
1365075,4,1,4,1,2,1,1,1,1,2
1365328,1,1,2,1,2,1,2,1,1,2
1368267,5,1,1,1,2,1,1,1,1,2
1368273,1,1,1,1,2,1,1,1,1,2
1368882,2,1,1,1,2,1,1,1,1,2
1369821,10,10,10,10,5,10,10,10,7,4
1371026,5,10,10,10,4,10,5,6,3,4
1371920,5,1,1,1,2,1,3,2,1,2
466906,1,1,1,1,2,1,1,1,1,2
466906,1,1,1,1,2,1,1,1,1,2
534555,1,1,1,1,2,1,1,1,1,2
536708,1,1,1,1,2,1,1,1,1,2
566346,3,1,1,1,2,1,2,3,1,2
603148,4,1,1,1,2,1,1,1,1,2
654546,1,1,1,1,2,1,1,1,8,2
654546,1,1,1,3,2,1,1,1,1,2
695091,5,10,10,5,4,5,4,4,1,4
714039,3,1,1,1,2,1,1,1,1,2
763235,3,1,1,1,2,1,2,1,2,2
776715,3,1,1,1,3,2,1,1,1,2
841769,2,1,1,1,2,1,1,1,1,2
888820,5,10,10,3,7,3,8,10,2,4
897471,4,8,6,4,3,4,10,6,1,4
897471,4,8,8,5,4,5,10,4,1,4
lab_guides/Lab_10.md
+3 -83
View File
@@ -23,27 +23,6 @@ Exploring Your Data
===================
If you are running your project by following the CRISP-DM methodology,
the first step will be to discuss the project with the stakeholders and
clearly define their requirements and expectations. Only once this is
clear can you start having a look at the data and see whether you will
be able to achieve these objectives.
After receiving a dataset, you may want to make sure that the dataset
contains the information you need for your project. For instance, if you
are working on a supervised project, you will check whether this dataset
contains the target variable you need and whether there are any missing
or incorrect values for this field. You may also check how many
observations (rows) and variables (columns) there are. These are the
kind of questions you will have initially with a new dataset. This
section will introduce you to some techniques you can use to get the
answers to these questions.
For the rest of this section, we will be working with a dataset
containing transactions from an online retail store.
Our dataset is an Excel spreadsheet. Luckily, the `pandas`
package provides a method we can use to load this type of file:
`read_excel()`.
@@ -155,17 +134,8 @@ df.dtypes
You should get the following output:
![Caption: Description of the data type for each column of the
DataFrame ](./images/B15019_10_03.jpg)
![](./images/B15019_10_03.jpg)
Caption: Description of the data type for each column of the
DataFrame
From this output, we can see that the `InvoiceDate` column is
a date type (`datetime64[ns]`), `Quantity` is an
integer (`int64`), and that `UnitPrice` and
`CustomerID` are decimal numbers (`float64`). The
remaining columns are text (`object`).
The `pandas` package provides a single method that can display
all the information we have seen so far, that is, the `info()`
@@ -228,18 +198,7 @@ You should get the following output:
Caption: Displaying the first five rows using the head() method
The output of the `head()` method shows that the
`InvoiceNo`, `StockCode`, and `CustomerID`
columns are unique identifier fields for each purchasing invoice, item
sold, and customer. The `Description` field is text describing
the item sold. `Quantity` and `UnitPrice` are the
number of items sold and their unit price, respectively.
`Country` is a text field that can be used for specifying
where the customer or the item is located or from which country version
of the online store the order has been made. In a real project, you may
reach out to the team who provided this dataset and confirm what the
meaning of the `Country` column is, or any other column
details that you may need, for that matter.
With `pandas`, you can specify the number of top rows to be
displayed with the `head()` method by providing an integer as
@@ -294,23 +253,7 @@ df.sample(n=5, random_state=1)
You should get the following output:
![Caption: Displaying five random sampled rows using the sample()
method ](./images/B15019_10_08.jpg)
Caption: Displaying five random sampled rows using the sample()
method
In this output, we can see an additional value in the
`Country` column: `Germany`. We can also notice a
few interesting points:
- `InvoiceNo` can also contain alphabetical letters (row
`94,801` starts with a `C`, which may have a
special meaning).
- `Quantity` can have negative values: `-2` (row
`94801`).
- `CustomerID` contains missing values: `NaN` (row
`210111`).
![](./images/B15019_10_08.jpg)
@@ -382,12 +325,6 @@ The following steps will help you to complete this exercise:
![](./images/B15019_10_09.jpg)
Caption: List of columns in the housing dataset
We can infer the type of information contained in some of the
variables by looking at their names, such as `LotArea`
(property size), `YearBuilt` (year of construction), and
`SalePrice` (property sale price).
7. Print out the type of each variable contained in this DataFrame
using the `dtypes` attribute from the `pandas`
@@ -706,17 +643,6 @@ You should get the following output:
-80995
```
The lowest value in this variable is extremely low. We can think that
having negative values is possible for returned items, but here, the
minimum (`-80995`) is very low. This, again, will be something
to be confirmed with the relevant people in your organization.
Now, we are going to have a look at the central tendency of this column.
**Central tendency** is a statistical term referring to the central
point where the data will cluster around. The most famous central
tendency measure is the average (or mean). The average is calculated by
summing all the values of a column and dividing them by the number of
values.
If we plot the `Quantity `column on a graph with its average,
it would look as follows:
@@ -770,12 +696,6 @@ You should get the following output:
3.0
```
The median value for this column is 3, which is quite different from the
mean (`9.55`) we found earlier. This tells us that there are
some outliers in this dataset and we will have to decide on how to
handle them after we\'ve done more investigation (this will be covered
in *Lab 11*, *Data Preparation*).
We can also evaluate the spread of this column (how much the data points
vary from the central point). A common measure of spread is the standard
deviation. The smaller this measure is, the closer the data is to its
lab_guides/Lab_7.md
-165
View File
@@ -133,17 +133,6 @@ The following steps will help you complete the exercise:
two new DataFrames called `training_df` and
`eval_df`.
You specify a value of `0.8` for `train_size` so
that `80%` of the data is assigned to
`training_df`.
`random_state` ensures that your experiments are
reproducible. Without `random_state`, the data is split
differently every time using a different random number. With
`random_state`, the data is split the same way every time.
We will be studying `random_state` in depth in the next
lab.
8. Check the information of `training_df`:
```
@@ -635,13 +624,6 @@ The following steps will help you complete the exercise:
```
In this step, you the compute cross-validation scores and store the
result in a Python list, which you call `_scores`. You do
this using `cross_cal_score`. The function requires the
following four parameters: the model to make use of (in our case,
it\'s called `_lr`); the features of the dataset; the
labels of the dataset; and the number of cross-validation splits to
create (five, in our case).
7. Now, display the scores as shown in the following code snippet:
@@ -662,16 +644,6 @@ Caption: Printing the cross-validation scores
LogisticRegressionCV
====================
`LogisticRegressionCV` is a class that implements
cross-validation inside it. This class will train multiple
`LogisticRegression` models and return the best one.
Exercise 7.06: Training a Logistic Regression Model Using Cross-Validation
--------------------------------------------------------------------------
@@ -859,137 +831,6 @@ The following steps will help you complete the exercise:
![](./images/B15019_07_26.jpg)
Caption: Computing the R2 score
Hyperparameter Tuning with GridSearchCV
=======================================
`GridSearchCV` will take a model and parameters and train one
model for each permutation of the parameters. At the end of the
training, it will provide access to the parameters and the model scores.
This is called hyperparameter tuning and you will be looking at this in
much more depth in *Lab 8, Hyperparameter Tuning*.
The usual practice is to make use of a small training set to find the
optimal parameters using hyperparameter tuning and then to train a final
model with all of the data.
Before the next exercise, let\'s take a brief look at decision trees,
which are a type of model or estimator.
Decision Trees
--------------
A decision tree works by generating a separating hyperplane or a
threshold for the features in data. It does this by considering every
feature and finding the correlation between the spread of the values in
that feature and the label that you are trying to predict.
Consider the following data about balloons. The label you need to
predict is called `inflated`. This dataset is used for
predicting whether the balloon is inflated or deflated given the
features. The features are:
- `color`
- `size`
- `act`
- `age`
The following table displays the distribution of features:
![](./images/B15019_07_27.jpg)
Caption: Tabular data for balloon features
Now consider the following charts, which are visualized depending on the
spread of the features against the label:
- If you consider the `Color` feature, the values are
`PURPLE` and `YELLOW`, but the number of
observations is the same, so you can\'t infer whether the balloon is
inflated or not based on the color, as you can see in the following
figure:
![](./images/B15019_07_28.jpg)
Caption: Barplot for the color feature
- The `Size` feature has two values: `LARGE` and
`SMALL`. These are equally spread, so we can\'t infer
whether the balloon is inflated or not based on the color, as you
can see in the following figure:
![](./images/B15019_07_29.jpg)
Caption: Barplot for the size feature
- The `Act` feature has two values: `DIP` and
`STRETCH`. You can see from the chart that the majority of
the `STRETCH` values are inflated. If you had to make a
guess, you could easily say that if `Act` is
`STRETCH`, then the balloon is inflated. Consider the
following figure:
![](./images/B15019_07_30.jpg)
Caption: Barplot for the act feature
- Finally, the `Age` feature also has two values:
`ADULT` and `CHILD`. It\'s also visible from the
chart that the `ADULT` value constitutes the majority of
inflated balloons:
![](./images/B15019_07_31.jpg)
Caption: Barplot for the age feature
The two features that are useful to the decision tree are
`Act` and `Age`. The tree could start by considering
whether `Act` is `STRETCH`. If it is, the prediction
will be true. This tree would look like the following figure:
![](./images/B15019_07_32.jpg)
Caption: Decision tree with depth=1
The left side evaluates to the condition being false, and the right side
evaluates to the condition being true. This tree has a depth of 1. F
means that the prediction is false, and T means that the prediction is
true.
To get better results, the decision tree could introduce a second level.
The second level would utilize the `Age` feature and evaluate
whether the value is `ADULT`. It would look like the following
figure:
![](./images/B15019_07_33.jpg)
Caption: Decision tree with depth=2
This tree has a depth of 2. At the first level, it predicts true if
`Act` is `STRETCH`. If `Act` is not
`STRETCH`, it checks whether `Age` is
`ADULT`. If it is, it predicts true, otherwise, it predicts
false.
The decision tree can have as many levels as you like but starts to
overfit at a certain point. As with everything in data science, the
optimal depth depends on the data and is a hyperparameter, meaning you
need to try different values to find the optimal one.
In the following exercise, we will be making use of grid search with
cross-validation to find the best parameters for a decision tree
estimator.
Exercise 7.07: Using Grid Search with Cross-Validation to Find the Best Parameters for a Model
@@ -1091,12 +932,6 @@ The following steps will help you complete the exercise:
```
In this step, you import `numpy`. NumPy is a numerical
computation library. You alias it as `np`. You also import
`DecisionTreeClassifier`, which you use to create decision
trees. Finally, you import `GridSearchCV`, which will use
cross-validation to train multiple models.
9. Instantiate the decision tree:
```
lab_guides/Lab_8.md
+4 -160
View File
@@ -38,6 +38,7 @@ You should get the following output:
'metric_params': None, 'n_jobs': None, 'n_neighbors': 5,
'p': 2, 'weights': 'uniform'}
```
A dictionary of all the hyperparameters is now printed to the screen,
revealing their default settings. Notice `k`, our number of
nearest neighbors, is set to `5`.
@@ -135,37 +136,6 @@ cancer based on cell measurements sourced from the affected breast
sample.
These are the important attributes of the dataset:
- ID number
- Diagnosis (M = malignant, B = benign)
- 3-32)
10 real-valued features are computed for each cell nucleus as follows:
- Radius (mean of distances from the center to points on the
perimeter)
- Texture (standard deviation of grayscale values)
- Perimeter
- Area
- Smoothness (local variation in radius lengths)
- Compactness (perimeter\^2 / area - 1.0)
- Concavity (severity of concave portions of the contour)
- Concave points (number of concave portions of the contour)
- Symmetry
- Fractal dimension (refers to the complexity of the tissue
architecture; \"coastline approximation\" - 1)
The following steps will help you complete this exercise:
1. Create a new notebook in Jupyter.
@@ -381,15 +351,7 @@ The output will be as follows:
Caption: Average precisions for all folds
We can see from the output that `k = 5` is the best
hyperparameterization found, with a mean precision score of roughly 94%.
Increasing `k` to `7` didn\'t significantly improve
performance. It is important to note that the only parameter we are
changing here is k and that each time the k-NN estimator is initialized,
it is done with the remaining hyperparameters set to their default
values.
To make this point clear, we can run the same loop, this time just
We can run the same loop, this time just
printing the hyperparameterization that will be tried:
```
@@ -511,27 +473,6 @@ The output will be as follows:
'metric_params': None, 'n_jobs': None, 'n_neighbors': 7,
'p': 2, 'weights': 'distance'}
```
This implementation, while great for demonstrating how the grid search
process works, may not practical when trying to evaluate estimators that
have `3`, `4`, or even `10` different
types of hyperparameters, each with a multitude of possible settings.
To carry on in this way will mean writing and keeping track of multiple
`for` loops, which can be tedious. Thankfully,
`scikit-learn`\'s `model_selection` module gives us
a method called `GridSearchCV` that is much more
user-friendly. We will be looking at this in the topic ahead.
GridSearchCV
============
`GridsearchCV` is a method of tuning wherein the model can be
built by evaluating the combination of parameters mentioned in a grid.
In the following figure, we will see how `GridSearchCV` is
different from manual search and look at grid search in a muchdetailed
way in a table format.
@@ -882,48 +823,10 @@ and what we mean by a distribution.
Random Variables and Their Distributions
----------------------------------------
A random variable is non-constant (its value can change) and its
variability can be described in terms of distribution. There are many
different types of distributions, but each falls into one of two broad
categories: discrete and continuous. We use discrete distributions to
describe random variables whose values can take only whole numbers, such
as counts.
An example is the count of visitors to a theme park in a day, or the
number of attempted shots it takes a golfer to get a hole-in-one.
We use continuous distributions to describe random variables whose
values lie along a continuum made up of infinitely small increments.
Examples include human height or weight, or outside air temperature.
Distributions often have parameters that control their shape.
Discrete distributions can be described mathematically using what\'s
called a probability mass function, which defines the exact probability
of the random variable taking a certain value. Common notation for the
left-hand side of this function is `P(X=x)`, which in plain
English means that the probability that the random variable
`X` equals a certain value `x` is `P`.
Remember that probabilities range between `0` (impossible) and
`1` (certain).
By definition, the summation of each `P(X=x)` for all possible
`x`\'s will be equal to 1, or if expressed another way, the
probability that `X` will take any value is 1. A simple
example of this kind of distribution is the discrete uniform
distribution, where the random variable `X` will take only one
of a finite range of values and the probability of it taking any
particular value is the same for all values, hence the term uniform.
For example, if there are 10 possible values the probability that
`X` is any particular value is exactly 1/10. If there were 6
possible values, as in the case of a standard 6-sided die, the
probability would be 1/6, and so on. The probability mass function for
the discrete uniform distribution is:
![](./images/B15019_08_15.jpg)
Caption: Probability mass function for the discrete uniform
distribution
Caption: Probability mass function for the discrete uniform distribution
The following code will allow us to see the form of this distribution
with 10 possible values of X.
@@ -966,34 +869,7 @@ The output will be as follows:
![](./images/B15019_08_16.jpg)
Caption: Visualizing the bar chart
In the visual output, we see that the probability of `X` being
a specific whole number between 1 and 10 is equal to 1/10.
Note
Other discrete distributions you commonly see include the binomial,
negative binomial, geometric, and Poisson distributions, all of which we
encourage you to investigate. Type these terms into a search engine to
find out more.
Distributions of continuous random variables are a bit more challenging
in that we cannot calculate an exact `P(X=x)` directly because
`X` lies on a continuum. We can, however, use integration to
approximate probabilities between a range of values, but this is beyond
the scope of this course. The relationship between `X` and
probability is described using a probability density function,
`P(X)`. Perhaps the most well-known continuous distribution is
the normal distribution, which visually takes the form of a bell.
The normal distribution has two parameters that describe its shape, mean
(`𝜇`) and variance (`𝜎`[2]). The
probability density function for the normal distribution is:
![](./images/B15019_08_17.jpg)
Caption: Probability density function for the normal distribution
The following code shows two normal distributions with the same mean
(`𝜇`` = 0`) but different variance parameters
@@ -1158,15 +1034,6 @@ The output will be as follows:
![](./images/B15019_08_21.jpg)
Caption: Visualization of the sample distribution
A model will then be fitted for each value of 𝛼 sampled and assessed for
performance. As we have seen with the other approaches to hyperparameter
tuning in this lab, performance will be assessed using k-fold
cross-validation (with `k =10`) but because we are dealing
with a regression problem, the performance metric will be the test-set
negative MSE.
Using this metric means larger values are better. We will store the
results in a dictionary with each 𝛼 value as the key and the
corresponding cross-validated negative MSE as the value:
@@ -1503,36 +1370,13 @@ The following steps will help you complete the exercise.
The output will be as follows:
![Caption: Visualizing the test scores of the top-performing
models ](./images/B15019_08_27.jpg)
![](./images/B15019_08_27.jpg)
Caption: Visualizing the test scores of the top-performing models
Advantages and Disadvantages of a Random Search
-----------------------------------------------
Because a random search takes a finite sample from a range of possible
hyperparameterizations (`n_iter` in
`model_selection.RandomizedSearchCV`), it is feasible to
expand the range of your hyperparameter search beyond what would be
practical with a grid search. This is because a grid search has to try
everything in the range, and setting a large range of values may be too
slow to process. Searching this wider range gives you the chance of
discovering a truly optimal solution.
Compared to the manual and grid search strategies, you do sacrifice a
level of control to obtain this benefit. The other consideration is that
setting up random search is a bit more involved than other options in
that you have to specify distributions. There is always a chance of
getting this wrong. That said, if you are unsure about what
distributions to use, stick with discrete or continuous uniform for the
respective variable types as this will assign an equal probability of
selection to all options.
Activity 8.01: Is the Mushroom Poisonous?
-----------------------------------------
lab_guides/Lab_9.md
-64
View File
@@ -65,21 +65,6 @@ The output will be as follows:
![](./images/B15019_09_03.jpg)
Caption: Coefficients of the linear regression model
A large positive or a large negative number for a feature coefficient
means it has a strong influence on the outcome. On the other hand, if
the coefficient is close to 0, this means the variable does not have
much impact on the prediction.
From this table, we can see that column `s1` has a very low
coefficient (a large negative number) so it negatively influences the
final prediction. If `s1` increases by a unit of 1, the
prediction value will decrease by `-792.184162`. On the other
hand, `bmi` has a large positive number
(`519.839787`) on the prediction, so the risk of diabetes is
highly linked to this feature: an increase in body mass index (BMI)
means a significant increase in the risk of diabetes.
@@ -1246,55 +1231,6 @@ The output will be as follows:
![](./images/B15019_09_35.jpg)
Caption: Output of LIME
Note
Your output may differ slightly. This is due to the random sampling
process of LIME.
There is a lot of information in the preceding output. Let\'s go through
it a bit at a time. The left-hand side shows the prediction
probabilities for the two classes of the target variable. For this
observation, the model thinks there is a 0.85 probability that the
predicted value will be malignant:
![](./images/B15019_09_36.jpg)
Caption: Prediction probabilities from LIME
The right-hand side shows the value of each feature for this
observation. Each feature is color-coded to highlight its contribution
toward the possible classes of the target variable. The list sorts the
features by decreasing importance. In this example, the mean perimeter,
mean area, and area error contributed to the model to increase the
probability toward class 1. All the other features influenced the model
to predict outcome 0:
![](./images/B15019_09_37.jpg)
Caption: Value of the feature for the observation of interest
Finally, the central part shows how each variable contributed to the
final prediction. In this example, the `worst concave points`
and `worst compactness` variables led to an increase of,
respectively, 0.10 and 0.05 probability in predicting outcome 0. On the
other hand, `mean perimeter` and `mean area` both
contributed to an increase of 0.03 probability of predicting class 1:
![](./images/B15019_09_38.jpg)
Caption: Contribution of each feature to the final prediction
It\'s as simple as that. With LIME, we can easily see how each variable
impacted the probabilities of predicting the different outcomes of the
model. As you saw, the LIME package not only computes the local
approximation but also provides a visual representation of its results.
It is much easier to interpret than looking at raw outputs. It is also
very useful for presenting your findings and illustrating how different
features influenced the prediction of a single observation.
Exercise 9.05: Local Interpretation with LIME
---------------------------------------------