Author(s): Praveen Bhavani

Originally published on Towards AI.

Principal Component Analysis (PCA) is one of the most widely used techniques for dimensionality reduction and feature extraction. PCA transforms correlated variables into a smaller set of uncorrelated variables called principal components, while preserving as much information (variance) as possible.

PCA is fundamentally a linear algebra and statistical method rooted in:

Covariance structure analysis