Code:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

%matplotlib notebook	

#generating random data with slope=2 and intercept=-2
delta = np.random.uniform(-10,10, size=(20,)) #for generating random data
x = np.arange(20)
y = 2*x-2+delta #y=mx+b; m=slope; b-y-intercept; delta=random scatter
plt.scatter(x,y, c = 'b', label='random data') #scatter plot
#print(x)
#print(y)

#generating the desired line which is y=2x-2
y = 2*x-2 #y=mx+b; m=slope; b-y-intercept
plt.plot(x,y, c='r', linewidth=1, label = 'desired') #plot line

#generating the linear regression from the random data
x = x.reshape(-1, 1)

model = LinearRegression()
model.fit(x, y)

y_predict = model.predict(x)
#print(y_predict)

plt.plot(x, y_predict, 'g', linewidth=3, linestyle=':', dash_capstyle='round', label='output') #plot linear regression

#styling the plot
plt.legend(loc=2)
plt.xticks(np.arange(0, 21, 1))
plt.yticks(np.arange(-10, 50, 10))
plt.show()
plt.savefig('MP4.png')

print("Linear regression coefficient = ", model.coef_[0])
print("Linear regression intercept = ", model.intercept_)
print("coefficient of determination (R\u00b2) = ", r2_score(y, y_predict))
print("root mean squared error (RMSE) = ", np.sqrt(mean_squared_error(y, y_predict)))