DAT 2.MACHINE LEARNING IN PYTHON [ LINEAR REGRESSION CODE ,GRAPH PLOT ]

 import matplotlib.pyplot as plt

import numpy as np
from sklearn import datasets ,linear_model
from sklearn.metrics import mean_squared_error


diabetics =datasets.load_diabetes()
# print(diabetics.keys())
#dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])

# diabetics_x = diabetics.data[ :,np.newaxis, 2]
# print(diabetics_x)
diabetics_x = diabetics.data

diabetics_x_train = diabetics_x[ :-30]
diabetics_x_test = diabetics_x[-30: ]
diabetics_y_train = diabetics.target[ :-30]
diabetics_y_test = diabetics.target[-30: ]

model =linear_model.LinearRegression()   # data fit
model.fit(diabetics_x_train,diabetics_y_train)

diabetics_y_predicted = model.predict(diabetics_x_test)

print("mean square error is:", mean_squared_error(diabetics_y_test , diabetics_y_predicted))
print("weights: ", model.coef_)
print("intercept: ", model.intercept_)

# plt.scatter(diabetics_x_test , diabetics_y_test)#scatter plot
# plt.plot(diabetics_x_test, diabetics_y_predicted)
# plt.show()


# mean square error is: 3035.0601152912686
# weights:  [941.43097333]
# intercept:  153.39713623331698

# mean square error is: 1826.5364191345425
# weights:  [  -1.16924976 -237.18461486  518.30606657  309.04865826 -763.14121622
#   458.90999325   80.62441437  174.32183366  721.49712065   79.19307944]
# intercept:  153.05827988224112

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