Multicollinearity is a serious problem to tackle when you are creating a Linear Regression model.
There are various popular techniques like variance inflation factor (VIF) which can eliminate multicollinearity. However, there is 1 underrated technique which not a lot of people talk about.
Principal Component Analysis or PCA can also be used to address multicollinearity.
So, I decided to create a video which will help you remove multicollinearity using PCA.