1 # coding: utf-8
2
3 # In[3]:
4
5
6 import numpy as np
7 import matplotlib.pyplot as plt
8
9 def estimate_coefficients(x, y):
10 # size of the dataset OR number of observations/points
11 n = np.size(x)
12
13 # mean of x and y
14 # Since we are using numpy just calling mean on numpy is sufficient
15 mean_x, mean_y = np.mean(x), np.mean(y)
16
17 # calculating cross-deviation and deviation about x
18 SS_xy = np.sum(y*x - n*mean_y*mean_x)
19 SS_xx = np.sum(x*x - n*mean_x*mean_x)
20
21 # calculating regression coefficients
22 b_1 = SS_xy / SS_xx
23 b_0 = mean_y - b_1*mean_x
24
25 return(b_0, b_1)
26
27 # x,y are the location of points on graph
28 # color of the points change it to red blue orange play around
29
30
31
32 def plot_regression_line(x, y, b):
33 # plotting the points as per dataset on a graph
34 plt.scatter(x, y, color = "m",marker = "o", s = 30)
35
36 # predicted response vector
37 y_pred = b[0] + b[1]*x
38
39 # plotting the regression line
40 plt.plot(x, y_pred, color = "g")
41
42 # putting labels for x and y axis
43 plt.xlabel('Size')
44 plt.ylabel('Cost')
45
46 # function to show plotted graph
47 plt.show()
48
49
50
51
52
53 def main():
54 # Datasets which we create
55 x = np.array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
56 y = np.array([300, 350, 500, 700, 800, 850, 900, 900, 1000, 1200])
57
58 # estimating coefficients
59 b = estimate_coefficients(x, y)
60 print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1]))
61
62 # plotting regression line
63 plot_regression_line(x, y, b)
64
65
66 if __name__ == "__main__":
67 main()
