# https://www.coursera.org/learn/machine-learning/programming/jsE7w/week-2-practice-lab-linear-regression/instructions

import numpy as np
import matplotlib.pyplot as plt

def estimate_coef(x, y):
# number of observations/points
n = np.size(x)

``````# mean of x and y vector
m_x = np.mean(x)
m_y = np.mean(y)

# calculating cross-deviation and deviation about x
SS_xy = np.sum(y*x) - n*m_y*m_x
SS_xx = np.sum(x*x) - n*m_x*m_x

# calculating regression coefficients
b_1 = SS_xy / SS_xx
b_0 = m_y - b_1*m_x

return (b_0, b_1)
``````

def plot_regression_line(x, y, b):
# plotting the actual points as scatter plot
plt.scatter(x, y, color = “m”,
marker = “o”, s = 30)

``````# predicted response vector
y_pred = b[0] + b[1]*x

# plotting the regression line
plt.plot(x, y_pred, color = "g")

# putting labels
plt.xlabel('x')
plt.ylabel('y')

# function to show plot
plt.show()
``````

def main():
# observations / data
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12])

``````# estimating coefficients
b = estimate_coef(x, y)
print("Estimated coefficients:\nb_0 = {} \
\nb_1 = {}".format(b[0], b[1]))

# plotting regression line
plot_regression_line(x, y, b)
``````

if name == “main”:
main()

Welcome to the community.

I am curious why you posted your code. Do you have a question?