注：本案例為黑馬的課堂案例，上傳僅為方便查看

# 1.獲取數(shù)據(jù) # 2.數(shù)據(jù)基本處理 # 2.1 確定特征值,目標(biāo)值 # 2.2 缺失值處理 # 2.3 數(shù)據(jù)集劃分 # 3.特征工程(字典特征抽取) # 4.機(jī)器學(xué)習(xí)(決策樹) # 5.模型評(píng)估 import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.feature_extraction import DictVectorizer from sklearn.tree import DecisionTreeClassifier, export_graphviz # 1.獲取數(shù)據(jù),此數(shù)據(jù)連接已失效 data = pd.read_csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt") data row.namespclasssurvivednameageembarkedhome.destroomticketboatsex01234567891011121314151617181920212223242526272829...128312841285128612871288128912901291129212931294129512961297129812991300130113021303130413051306130713081309131013111312

1	1st	1	Allen, Miss Elisabeth Walton	29.0000	Southampton	St Louis, MO	B-5	24160 L221	2	female
2	1st	0	Allison, Miss Helen Loraine	2.0000	Southampton	Montreal, PQ / Chesterville, ON	C26	NaN	NaN	female
3	1st	0	Allison, Mr Hudson Joshua Creighton	30.0000	Southampton	Montreal, PQ / Chesterville, ON	C26	NaN	(135)	male
4	1st	0	Allison, Mrs Hudson J.C. (Bessie Waldo Daniels)	25.0000	Southampton	Montreal, PQ / Chesterville, ON	C26	NaN	NaN	female
5	1st	1	Allison, Master Hudson Trevor	0.9167	Southampton	Montreal, PQ / Chesterville, ON	C22	NaN	11	male
6	1st	1	Anderson, Mr Harry	47.0000	Southampton	New York, NY	E-12	NaN	3	male
7	1st	1	Andrews, Miss Kornelia Theodosia	63.0000	Southampton	Hudson, NY	D-7	13502 L77	10	female
8	1st	0	Andrews, Mr Thomas, jr	39.0000	Southampton	Belfast, NI	A-36	NaN	NaN	male
9	1st	1	Appleton, Mrs Edward Dale (Charlotte Lamson)	58.0000	Southampton	Bayside, Queens, NY	C-101	NaN	2	female
10	1st	0	Artagaveytia, Mr Ramon	71.0000	Cherbourg	Montevideo, Uruguay	NaN	NaN	(22)	male
11	1st	0	Astor, Colonel John Jacob	47.0000	Cherbourg	New York, NY	NaN	17754 L224 10s 6d	(124)	male
12	1st	1	Astor, Mrs John Jacob (Madeleine Talmadge Force)	19.0000	Cherbourg	New York, NY	NaN	17754 L224 10s 6d	4	female
13	1st	1	Aubert, Mrs Leontine Pauline	NaN	Cherbourg	Paris, France	B-35	17477 L69 6s	9	female
14	1st	1	Barkworth, Mr Algernon H.	NaN	Southampton	Hessle, Yorks	A-23	NaN	B	male
15	1st	0	Baumann, Mr John D.	NaN	Southampton	New York, NY	NaN	NaN	NaN	male
16	1st	1	Baxter, Mrs James (Helene DeLaudeniere Chaput)	50.0000	Cherbourg	Montreal, PQ	B-58/60	NaN	6	female
17	1st	0	Baxter, Mr Quigg Edmond	24.0000	Cherbourg	Montreal, PQ	B-58/60	NaN	NaN	male
18	1st	0	Beattie, Mr Thomson	36.0000	Cherbourg	Winnipeg, MN	C-6	NaN	NaN	male
19	1st	1	Beckwith, Mr Richard Leonard	37.0000	Southampton	New York, NY	D-35	NaN	5	male
20	1st	1	Beckwith, Mrs Richard Leonard (Sallie Monypeny)	47.0000	Southampton	New York, NY	D-35	NaN	5	female
21	1st	1	Behr, Mr Karl Howell	26.0000	Cherbourg	New York, NY	C-148	NaN	5	male
22	1st	0	Birnbaum, Mr Jakob	25.0000	Cherbourg	San Francisco, CA	NaN	NaN	(148)	male
23	1st	1	Bishop, Mr Dickinson H.	25.0000	Cherbourg	Dowagiac, MI	B-49	NaN	7	male
24	1st	1	Bishop, Mrs Dickinson H. (Helen Walton)	19.0000	Cherbourg	Dowagiac, MI	B-49	NaN	7	female
25	1st	1	Bjornstrm-Steffansson, Mr Mauritz Hakan	28.0000	Southampton	Stockholm, Sweden / Washington, DC	NaN		D	male
26	1st	0	Blackwell, Mr Stephen Weart	45.0000	Southampton	Trenton, NJ	NaN	NaN	(241)	male
27	1st	1	Blank, Mr Henry	39.0000	Cherbourg	Glen Ridge, NJ	A-31	NaN	7	male
28	1st	1	Bonnell, Miss Caroline	30.0000	Southampton	Youngstown, OH	C-7	NaN	8	female
29	1st	1	Bonnell, Miss Elizabeth	58.0000	Southampton	Birkdale, England Cleveland, Ohio	C-103	NaN	8	female
30	1st	0	Borebank, Mr John James	NaN	Southampton	London / Winnipeg, MB	D-21/2	NaN	NaN	male
...	...	...	...	...	...	...	...	...	...	...
1284	3rd	0	Vestrom, Miss Hulda Amanda Adolfina	NaN	NaN	NaN	NaN	NaN	NaN	female
1285	3rd	0	Vonk, Mr Jenko	NaN	NaN	NaN	NaN	NaN	NaN	male
1286	3rd	0	Ware, Mr Frederick	NaN	NaN	NaN	NaN	NaN	NaN	male
1287	3rd	0	Warren, Mr Charles William	NaN	NaN	NaN	NaN	NaN	NaN	male
1288	3rd	0	Wazli, Mr Yousif	NaN	NaN	NaN	NaN	NaN	NaN	male
1289	3rd	0	Webber, Mr James	NaN	NaN	NaN	NaN	NaN	NaN	male
1290	3rd	1	Wennerstrom, Mr August Edvard	NaN	NaN	NaN	NaN	NaN	NaN	male
1291	3rd	0	Wenzel, Mr Linhart	NaN	NaN	NaN	NaN	NaN	NaN	male
1292	3rd	0	Widegren, Mr Charles Peter	NaN	NaN	NaN	NaN	NaN	NaN	male
1293	3rd	0	Wiklund, Mr Jacob Alfred	NaN	NaN	NaN	NaN	NaN	NaN	male
1294	3rd	1	Wilkes, Mrs Ellen	NaN	NaN	NaN	NaN	NaN	NaN	female
1295	3rd	0	Willer, Mr Aaron	NaN	NaN	NaN	NaN	NaN	NaN	male
1296	3rd	0	Willey, Mr Edward	NaN	NaN	NaN	NaN	NaN	NaN	male
1297	3rd	0	Williams, Mr Howard Hugh	NaN	NaN	NaN	NaN	NaN	NaN	male
1298	3rd	0	Williams, Mr Leslie	NaN	NaN	NaN	NaN	NaN	NaN	male
1299	3rd	0	Windelov, Mr Einar	NaN	NaN	NaN	NaN	NaN	NaN	male
1300	3rd	0	Wirz, Mr Albert	NaN	NaN	NaN	NaN	NaN	NaN	male
1301	3rd	0	Wiseman, Mr Phillippe	NaN	NaN	NaN	NaN	NaN	NaN	male
1302	3rd	0	Wittevrongel, Mr Camiel	NaN	NaN	NaN	NaN	NaN	NaN	male
1303	3rd	1	Yalsevac, Mr Ivan	NaN	NaN	NaN	NaN	NaN	NaN	male
1304	3rd	0	Yasbeck, Mr Antoni	NaN	NaN	NaN	NaN	NaN	NaN	male
1305	3rd	1	Yasbeck, Mrs Antoni	NaN	NaN	NaN	NaN	NaN	NaN	female
1306	3rd	0	Youssef, Mr Gerios	NaN	NaN	NaN	NaN	NaN	NaN	male
1307	3rd	0	Zabour, Miss Hileni	NaN	NaN	NaN	NaN	NaN	NaN	female
1308	3rd	0	Zabour, Miss Tamini	NaN	NaN	NaN	NaN	NaN	NaN	female
1309	3rd	0	Zakarian, Mr Artun	NaN	NaN	NaN	NaN	NaN	NaN	male
1310	3rd	0	Zakarian, Mr Maprieder	NaN	NaN	NaN	NaN	NaN	NaN	male
1311	3rd	0	Zenn, Mr Philip	NaN	NaN	NaN	NaN	NaN	NaN	male
1312	3rd	0	Zievens, Rene	NaN	NaN	NaN	NaN	NaN	NaN	female
1313	3rd	0	Zimmerman, Leo	NaN	NaN	NaN	NaN	NaN	NaN	male

1313 rows × 11 columns

data.describe() row.namessurvivedagecountmeanstdmin25%50%75%max

1313.000000	1313.000000	633.000000
657.000000	0.341965	31.194181
379.174762	0.474549	14.747525
1.000000	0.000000	0.166700
329.000000	0.000000	21.000000
657.000000	0.000000	30.000000
985.000000	1.000000	41.000000
1313.000000	1.000000	71.000000

# 2.數(shù)據(jù)基本處理 # 2.1 確定特征值,目標(biāo)值 x = data[["pclass", "age", "sex"]] x.head() pclassagesex01234

1st	29.0000	female
1st	2.0000	female
1st	30.0000	male
1st	25.0000	female
1st	0.9167	male

y = data["survived"] y.head() 0 1 1 0 2 0 3 0 4 1 Name: survived, dtype: int64 # 2.2 缺失值處理 x["age"].fillna(value=data["age"].mean(), inplace=True) x.head() pclassagesex01234

1st	29.0000	female
1st	2.0000	female
1st	30.0000	male
1st	25.0000	female
1st	0.9167	male

# 2.3 數(shù)據(jù)集劃分，test_size是測(cè)試集所占的比例 x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=22, test_size=0.2) # 3.特征工程(字典特征抽取) x.head() pclassagesex01234

1st	29.0000	female
1st	2.0000	female
1st	30.0000	male
1st	25.0000	female
1st	0.9167	male

x_train = x_train.to_dict(orient="records") x_test = x_test.to_dict(orient="records") x_train [{'pclass': '3rd', 'age': 45.0, 'sex': 'female'},{'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},{'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'}, ........] transfer = DictVectorizer()x_train = transfer.fit_transform(x_train) x_test = transfer.fit_transform(x_test) x_train <1050x6 sparse matrix of type '<class 'numpy.float64'>'with 3150 stored elements in Compressed Sparse Row format> # 4.機(jī)器學(xué)習(xí)(決策樹) estimator = DecisionTreeClassifier(max_depth=5)estimator.fit(x_train, y_train) DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,max_features=None, max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0, presort=False, random_state=None,splitter='best') # 5.模型評(píng)估 y_pre = estimator.predict(x_test) print(y_pre) [0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0 00 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 01 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 10 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 11 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 00 0 0 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 00 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 00 1 0 1] ret = estimator.score(x_test, y_test) ret 0.7984790874524715 # 決策樹可視化 export_graphviz(estimator, out_file="./data/tree.dot", feature_names=['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', '女性', '男性'])

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