Create linear_reg.py

linear_reg.py

@@ -0,0 +1,53 @@
+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, m_y = np.mean(x), 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 
+	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()