Principal Component Analysis Demystified

Have you used or thought of using Principal Component Analysis (PCA) as a feature extraction method in your machine learning pipelines, but wished for a better understanding of what a principal component is and how it’s obtained? We take a deep dive into a small dimensional data set, present a visual explanation of the role played by eigenvalues and eigenvectors when PCA is applied, and illustrate how what you start with leads to what you end with, what the advantages are, and what could get lost along the