from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score

# Load the data from the boston house-prices dataset 
boston_data = load_boston()
x = boston_data['data']
y = boston_data['target']
z = boston_data['DESCR']
#print(boston_data)
print(z)
# Make and fit the linear regression model
# TODO: Fit the model and assign it to the model variable
model = LinearRegression()
model.fit(x,y)

# Make a prediction using the model
sample_house = [[2.29690000e-01, 0.00000000e+00, 1.05900000e+01, 0.00000000e+00, 4.89000000e-01,
                6.32600000e+00, 5.25000000e+01, 4.35490000e+00, 4.00000000e+00, 2.77000000e+02,
                1.86000000e+01, 3.94870000e+02, 1.09700000e+01]]
# TODO: Predict housing price for the sample_house
prediction = model.predict(sample_house)
y_pred=model.predict(x)
print(r2_score(y, y_pred) )