DecisionTree.py

import pandas as pd 
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction import DictVectorizer
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
titanic=pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')
#print(titanic.head())
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())
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=33)
vec=DictVectorizer(sparse=False)
X_train=vec.fit_transform(X_train.to_dict(orient='record'))
X_test=vec.transform(X_test.to_dict(orient='record'))
print(vec.feature_names_)
dtc=DecisionTreeClassifier()
dtc.fit(X_train,y_train)
y_predict=dtc.predict(X_test)
#print(y_predict)
print(dtc.score(X_test,y_test))
print(classification_report(y_predict,y_test,target_names=['died','survived']))