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%matplotlib inline
import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.feature_selection import SelectKBest, f_regression 
from sklearn.linear_model import LinearRegression 
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor


    

In [3]:

    

boston_dataset = datasets.load_boston()
X_full = boston_dataset.data
Y = boston_dataset.target
print (X_full.shape)
print (Y.shape)


    

    




(506, 13)
(506,)

In [5]:

    

selector = SelectKBest(f_regression, k=1)
selector.fit(X_full, Y)

X = X_full[:, selector.get_support()]
print (X.shape)


    

    




(506, 1)

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plt.scatter(X, Y, color='black')
plt.show()


    

In [7]:

    

regressor = LinearRegression(normalize=True)
regressor.fit(X, Y)
plt.scatter(X, Y, color='black')
plt.scatter(X, regressor.predict(X), color='blue', linewidth=3)
plt.show()


    

In [8]:

    

regressor = SVR()
regressor.fit(X, Y)
plt.scatter(X, Y, color='black')
plt.scatter(X, regressor.predict(X), color='blue', linewidth=3)
plt.show()


    

In [9]:

    

regressor = RandomForestRegressor()
regressor.fit(X, Y)
plt.scatter(X, Y, color='black')
plt.scatter(X, regressor.predict(X), color='blue', linewidth=3)
plt.show()


    

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