In [10]:

    

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

#Load the data:
boston_data = load_boston()

x = boston_data['data']
y = boston_data['target']


    

In [11]:

    

#train model on data
housemodel = LinearRegression()
housemodel.fit(x, y)


    

    
Out[11]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [12]:

    

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]]

print housemodel.predict(sample_house)


    

    




[ 23.68420569]

In [ ]: