In this article we will try to understand what PCA is all about, why do we need to perform PCA on a given dataset. We also look into the Mathematical Principles or the concepts underlying PCA and how these concepts help us in determining the Principal Components for a given dataset. Finally, we will look into the steps involved in performing PCA using the Air Quality Index dataset using Python as a tool.
Before, getting into what PCA is all about and the steps involved in it. First, let us try to understand why do we need to perform PCA as part of any Data Science Project especially when we have a large dataset involved.
- Why PCA and Why Dimensionality Reduction?:
- Mathematical Concepts Underlying PCA:
- Steps Involved In PCA:
- Scree Plot to decide on the Number of Principal Components:
- Advantages and Limitations of PCA.