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| # Building the optimal model using backward elimination | |
| import statsmodels.formula.api as sm | |
| #Appending the contant as first col of the dataset for readability. | |
| X = np.append(arr = np.ones((50, 1)).astype(int), values = X, axis = 1) | |
| #X_optimized initialized with all features. | |
| X_opt = X[:, [0,1,2,3,4,5]] | |
| #Fitting using OLS | |
| regressor_OLS = sm.OLS(endog = y, exog= X_opt).fit() | |
| #Summary for results, any feature who can be eliminated ? | |
| regressor_OLS.summary() | |
| #Eliminated feature 2, refitting.. | |
| X_opt = X[:, [0,1,3,4,5]] | |
| regressor_OLS = sm.OLS(endog = y, exog= X_opt).fit() | |
| regressor_OLS.summary() | |
| #Eliminated feature 1, refitting.. | |
| X_opt = X[:, [0,3,4,5]] | |
| regressor_OLS = sm.OLS(endog = y, exog= X_opt).fit() | |
| regressor_OLS.summary() | |
| #Eliminated feature 4, refitting.. | |
| X_opt = X[:, [0,3,5]] | |
| regressor_OLS = sm.OLS(endog = y, exog= X_opt).fit() | |
| regressor_OLS.summary() | |
| #Eliminated feature 5, refitting.. | |
| X_opt = X[:, [0,3]] | |
| regressor_OLS = sm.OLS(endog = y, exog= X_opt).fit() | |
| regressor_OLS.summary() | |
| #Finished, optimal model found. |
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