from sklearn import datasets
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn import metrics
boston = datasets.load_boston()
x = boston.data
y = boston.target
df = pd.DataFrame(data = np.c_[x,y],columns=np.append(boston.feature_names,['MEDV']))
print(df)
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.4,random_state=12345)
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)
linreg = LinearRegression()
model = linreg.fit(x_train,y_train)
print(metrics.mean_squared_error(y_train,model.predict(x_train)))
print(metrics.r2_score(y_train,model.predict(x_train)))
print(model.coef_)
print(model.intercept_)
