Kaggle泰坦尼克利用决策树算法预测代码

def Dectree():
    #获取数据
    train=pd.read_csv('./train.csv')
    test=pd.read_csv('./test.csv')
    true=pd.read_csv('./gender_submission.csv')
    #处理数据(找特征值。目标值)
    # print(data[0:10])
    x_train=train[['Pclass','Sex','Age']]
    y_train=train['Survived']
    x_test = test[['Pclass', 'Sex', 'Age']]
    y_true=true['Survived']
    #填补缺失值
    x_train['Age'].fillna(x_train['Age'].mean(),inplace=True)
    x_test['Age'].fillna(x_test['Age'].mean(), inplace=True)
    #数据集分割
    # x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25)
    #特征工程数据处理，针对类别属性（即非数值类型）使用one-hot编码
    #字典处理
    dict=DictVectorizer(sparse=False)
    X_train=dict.fit_transform(x_train.to_dict(orient='record'))
    X_test=dict.transform(x_test.to_dict(orient='record'))
    print(X_train)
    print('names',dict.feature_names_)#获取特征名称
    print(X_test)
    #用决策树预测
    dec=DecisionTreeClassifier(max_depth=5)
    dec.fit(X_train,y_train)
    y_predict = dec.predict(X_test)
    print('预测准确率',dec.score(X_test,y_true))
    print(classification_report(y_true,y_predict))
    print('y_predict', y_predict)
    # 创造提交文件
    ids = test["PassengerId"]
    submission_df = {"PassengerId": ids,"Survived": y_predict}
    submission = pd.DataFrame(submission_df)
    submission.to_csv("submission_Titanic.csv", index=False)
if __name__ == '__main__':
    Dectree()

结果如下：