dive into linear regression (3-way implementation) Python

 Code 1

(.env) [boris@fedora35server LINEAREGRS]$ cat linearRegr1.py

import numpy as np 

import matplotlib.pyplot as plt 

def estimate_coef(x, y): 

    n = np.size(x) 

    m_x, m_y = np.mean(x), np.mean(y) 

    SS_xy = np.sum(y*x) - n*m_y*m_x 

    SS_xx = np.sum(x*x) - n*m_x*m_x 

    theta_1 = SS_xy / SS_xx 

    theta_0 = m_y - theta_1*m_x 

    return(theta_0, theta_1) 

def plot_regression_line(x, y, theta): 

    plt.scatter(x, y, color = "b",marker = "o", s = 30) 

    y_pred = theta[0] + theta[1]*x 

    plt.plot(x, y_pred, color = "r") 

    plt.xlabel('x') 

    plt.ylabel('y') 

    plt.show() 

x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) 

y = np.array([11 ,13, 12, 15, 17, 18, 18, 19, 20, 22]) 

theta = estimate_coef(x, y) 

print("Estimated coefficients:\ntheta_0 = {} \ntheta_1 = {}".format(theta[0], theta[1])) 

plot_regression_line(x, y, theta) 

print(round(theta[0]+ theta[1]*11,4))

References

https://www.educative.io/edpresso/a-deep-dive-into-linear-regression-3-way-implementation