Stepwise Regression using Python

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 Python 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.
The module also covers various various selection procedures in Regression like Forward Selection, Backward Elimination, Best Subset regression & stepwise Regression.
The excel sheets comes accompanying with a PPT explaining all the steps.