sm add constant

1from pandas import DataFrame
2import statsmodels.api as sm
3
4Stock_Market = {'Year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016],
5                'Month': [12, 11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1],
6                'Interest_Rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75],
7                'Unemployment_Rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1],
8                'Stock_Index_Price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719]        
9                }
10
11df = DataFrame(Stock_Market,columns=['Year','Month','Interest_Rate','Unemployment_Rate','Stock_Index_Price']) 
12
13X = df[['Interest_Rate','Unemployment_Rate']] # here we have 2 variables for the multiple linear regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example
14Y = df['Stock_Index_Price']
15
16X = sm.add_constant(X) # adding a constant
17
18model = sm.OLS(Y, X).fit()
19predictions = model.predict(X) 
20
21print_model = model.summary()
22print(print_model)
23