#資料查驗
import pandas as pd

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


#使用決策樹模型預測泰坦尼克號乘客的生還情況
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)


#決策樹模型的預測效能
from sklearn.metrics import classification_report 

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