Principal Component Analysis (PCA) to Address Multicollinearity

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.

To view the video

Click here to view the video.

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