Stepwise Regression using R

We all know the importance of Regression in Machine Learning but none of us actually know the real challenges involved in developing a Regression Model and running it stepwise on real life data.
Stepwise linear regression is a method of regressing multiple variables while simultaneously removing those that aren’t important.
This Module covers Regression in R starting from Removing problems of Multicollinearity, Selecting Important Variables, Correcting Problems of autocorrelation, Checking problems of Non Linearity and then correcting the Problems of Heteroskedasticity if any.