Multiple Linear Regression model using Python: Machine Learning

If we look at the p-values of some of the variables, the values seem to be pretty high, which means they aren't significant. That means we can drop those variables from the model. Before dropping the variables, as discussed above, we have to see the multicollinearity between the variables. We do that by calculating the VIF value. Variance Inflation Factor or VIF is a quantitative value that says how much the feature variables are correlated with each other. It is an extremely important parameter to test our linear model.