%pylab inline

from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.naive_bayes import GaussianNB

digits = datasets.load_digits()

train_X, test_X, train_y, test_y = train_test_split(digits.data, digits.target, test_size=0.33)

nb_estimator = GaussianNB()
nb_estimator.fit(train_X, train_y)
pred = nb_estimator.predict(test_X)

print 'Predicted labels:', pred[0:8]
print 'Actual labels:', test_y[0:8]

from sklearn import linear_model
import matplotlib.pyplot as plt

houses = datasets.load_boston()
# Use only one feature
houses_X = houses.data[:, np.newaxis]
houses_X_temp = houses_X[:, :, 2]

X_train, X_test, y_train, y_test = train_test_split(houses_X_temp, houses.target, test_size=0.33)

lreg = linear_model.LinearRegression()
lreg.fit(X_train, y_train)

plt.scatter(X_test, y_test, color='black')
plt.plot(X_test, lreg.predict(X_test), color='green', linewidth=3)

plt.show()