决策树

# encoding:utf-8

import pandas as pd
from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn import *
from sklearn.datasets import *
from sklearn.model_selection import *
from sklearn.neighbors import *
from sklearn.preprocessing import *
from sklearn.naive_bayes import *
from sklearn.tree import DecisionTreeClassifier,export_graphviz

def decision():
    '''
    决策树对泰坦尼克号进行预测生死
    :return:
    '''
    # 获取数据
    titan = pd.read_csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt")

    # 处理数据,找出特征值和目标值
    x = titan[['pclass','age','sex']]
    y = titan['survived']
    print(x)

    # 缺失值处理 以平均值填补
    x['age'].fillna(x['age'].mean(),inplace=True)

    # 分割数据集到训练集测试集
    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25)

    # 进行处理（特征工程） 特征-》类别-》one_hot编码
    dict = DictVectorizer(sparse=False)
    x_train = dict.fit_transform(x_train.to_dict(orient="records"))
    print(dict.get_feature_names())
    x_test = dict.transform(x_test.to_dict(orient="records"))
    print(x_train)

    # 用决策树进行预测
    dec = DecisionTreeClassifier()
    dec.fit(x_train,y_train)

    # 预测准确率
    print("预测的准确率：",dec.score(x_test,y_test))

    # 导出决策树的结构
    # export_graphviz(dec,out_file="./tree.dot",feature_names=['年龄','pclass1','pclass2','pclass3','女性','男性'])
    return None

if __name__ == '__main__':
    decision()