├── README.md
└── multiplelinear.py


/README.md:
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1 | # Visualize-Multiple-Linear-Regression


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/multiplelinear.py:
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 1 | # -*- coding: utf-8 -*-
 2 | """
 3 | Created on Tue Jun 18 12:38:44 2019
 4 | 
 5 | @author: krish.naik
 6 | """
 7 | 
 8 | ## Visualization for Multiple Linear Regression
 9 | 
10 | import numpy as np
11 | X= [[150,100],[159,200],[170,350],[175,400],[179,500],[180,180],[189,159],[199,110],[199,400],[199,230],[235,120],[239,340],[239,360],[249,145],[249,400]]
12 | Y= [0.73,1.39,2.03,1.45,1.82,1.32,0.83,0.53,1.95,1.27,0.49,1.03,1.24,0.55,1.3]
13 | 
14 | 
15 | 
16 | 
17 | ## Prepare the Dataset
18 | 
19 | import pandas as pd
20 | df2=pd.DataFrame(X,columns=['Price','AdSpends'])
21 | df2['Sales']=pd.Series(Y)
22 | df2
23 | 
24 | 
25 | ## Apply multiple Linear Regression
26 | import matplotlib.pyplot as plt
27 | import statsmodels.formula.api as smf
28 | model = smf.ols(formula='Sales ~ Price + AdSpends', data=df2)
29 | results_formula = model.fit()
30 | results_formula.params
31 | 
32 | 
33 | 
34 | ## Prepare the data for Visualization
35 | 
36 | x_surf, y_surf = np.meshgrid(np.linspace(df2.Price.min(), df2.Price.max(), 100),np.linspace(df2.AdSpends.min(), df2.AdSpends.max(), 100))
37 | onlyX = pd.DataFrame({'Price': x_surf.ravel(), 'AdSpends': y_surf.ravel()})
38 | fittedY=results_formula.predict(exog=onlyX)
39 | 
40 | 
41 | 
42 | ## convert the predicted result in an array
43 | fittedY=np.array(fittedY)
44 | 
45 | 
46 | 
47 | 
48 | # Visualize the Data for Multiple Linear Regression
49 | 
50 | fig = plt.figure()
51 | ax = fig.add_subplot(111, projection='3d')
52 | ax.scatter(df2['Price'],df2['AdSpends'],df2['Sales'],c='red', marker='o', alpha=0.5)
53 | ax.plot_surface(x_surf,y_surf,fittedY.reshape(x_surf.shape), color='b', alpha=0.3)
54 | ax.set_xlabel('Price')
55 | ax.set_ylabel('AdSpends')
56 | ax.set_zlabel('Sales')
57 | plt.show()
58 | 
59 | 


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