Linear regression using sklearn models

linear_model.py

#import libraries needed
import numpy as np
from sklearn import linear_model
if __name__=="__main__":
    #load Dataset, here i am creating one for Single variable Linear regression
    x = np.array([2, 4, 3, 6, 7, 6, 9, 10 , 12])
    x=x.reshape(-1,1) #reshaping it
    #slope of the line is 3 and intercept is 6
    np.random.seed(18)
    y = 4.5*x+3*np.random.uniform(0, 100, x.shape)*0.01
    #Create an object for linear regression
    linear = linear_model.LinearRegression()
    #Train model
    linear.fit(x, y)
    print("Slope: ", linear.coef_)
    print("Intercept: ", linear.intercept_)
    '''
    If you want to plot:
    from matplotlib import pyplot as plt
    plt.plot(x, linear.coef_*x+linear.intercept_, label='predicted')
    plt.scatter(x, y, label='y')
    plt.xlabel('X (independant)')
    plt.ylabel('Ans (dependant)')
    plt.legend()
    plt.savefig('linear.png')
    plt.show()
    '''
    #predict for test data
    print("x=5 => y=", linear.predict([[5]]))