Merge pull request #116 from adi271001/regression

Regression Techniques SImple Linear Regression

Author: himanshu-03

Regression-Techniques/simple-linear-regression.py

@@ -0,0 +1,54 @@
+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()