Statistics, Regression, Data Science, Machine Learning, AI.
X_test.drop(features_to_remove, axis =1) # Fitting model model = sm.OLS(y_train, new_X_train).fit() print(model.summary()) We can start it out by noticing that R-Squared, at 0.502, is lower than the previous one. This may indicate that this model is able to explain a lesser percentage of the