Principal Component Analysis: Mathematical derivation of PCA and application on the breast cancer dataset
Hello ladies and gentle ladies(men)!!I have the honor and pleasure to present to you the beautiful, the useful, the undisputed champion of linear dimensional reduction, welcome PCA. In this article we would be explaining some major concepts related to PCA. We will further describe the mathematical concept behind the PCA algorithm, i.e covariance, eigen decomposition, and singular value decomposition and look at some limitations of PCA as a dimensional reduction technique. We would conclude this article by looking at the application of PCA in the dimensional reduction of the breast cancer dataset.
Prerequisite :
In partaking in this article we hope you have a basic understanding of the following
- covariance and correlation
- Eigenvalue and EigenVector
- Matrix multiplication
- python
Assumptions:
For the success and understanding of this article we would make some basic assumption,
- Our dataset has been Explored, clean and ready to be served.
- Our prerequisite have been meet
