import matplotlib.pyplot as plt

import numpy as np

from sklearn import datasets, linear_model, metrics

from sklearn.model_selection import train_test_split as tts

boston = datasets.load_boston()

x = boston.data

y = boston.target

x_train, x_test, y_train, y_test = tts(x, y, test_size=1, random_state=1)

regr = linear_model.LinearRegression()

regr.fit(x_train, y_train)

print('Coefficients: {}'.format(regr.coef_))

print('Variance Score: {}'.format(regr.score(x_test, y_test)))

plt.scatter(regr.predict(x_train), regr.predict(x_train) -

y_train, color='green', label='Train Data')

plt.scatter(regr.predict(x_test), regr.predict(

x_test)-y_test, color='blue', label='Test Data')

plt.hlines(y=0, xmin=0, xmax=0, linewidth=2)

plt.show()