#!/usr/bin/python3
from sklearn.datasets import *
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import train_test_split
import numpy as np

SEED = 42
np.random.seed(SEED)

data = load_boston()

X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, train_size=0.85)

model = LinearRegression()
model.fit(data.data, data.target)

p=model.predict(X_test)
squares=[]
for n in range(len(p)):
    squares.append((y_test[n] - p[n])**2)

print(np.mean(squares))