linear_regression.py

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

diabetes = datasets.load_diabetes()

diabetes_X = diabetes.data

diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-30:]

diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.target[-30:]

model = linear_model.LinearRegression()

model.fit(diabetes_X_train, diabetes_y_train)

diabetes_y_predicted = model.predict(diabetes_X_test)

print("Mean squared error is: ", mean_squared_error(diabetes_y_test, diabetes_y_predicted))

print("Weights: ", model.coef_)
print("Intercept: ", model.intercept_)

plt.scatter(diabetes_X_test, diabetes_y_test)
plt.plot(diabetes_X_test, diabetes_y_predicted)

plt.show()

# Mean squared error is:  3035.0601152912695
# Weights:  [941.43097333]
# Intercept:  153.39713623331698