python中利用决策树实现泰坦尼克死亡和生还人生的分类

最新推荐文章于 2024-05-14 18:16:33 发布

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本文使用Python的pandas和scikit-learn库对泰坦尼克号乘客数据进行预处理和特征提取，并采用决策树分类器对乘客是否生还进行了预测。通过训练测试集划分、缺失值填充、特征向量化等步骤提升模型准确性。

import pandas as pd;

titanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')

print titanic.info()

X = titanic[['pclass', 'age', 'sex']]
y = titanic['survived']

print X.info()

X['age'].fillna(X['age'].mean(), inplace = True)

print X.info()

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 33)

from sklearn.feature_extraction import DictVectorizer

vec = DictVectorizer(sparse = False)

X_train = vec.fit_transform(X_train.to_dict(orient = 'record'))

print vec.feature_names_

X_test = vec.transform(X_test.to_dict(orient = 'record'))

from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier()

dtc.fit(X_train, y_train)

y_predict = dtc.predict(X_test)

from sklearn.metrics import classification_report

print dtc.score(X_test, y_test)
print classification_report(y_predict, y_test, target_names = ['died', 'survived'])