PCA Code for Principal Component Analysis with Implementation Guide

We provide a beginner-friendly Principal Component Analysis (PCA) code implementation specifically tailored for face recognition applications. PCA is a fundamental dimensionality reduction algorithm widely used for feature extraction and data compression in pattern recognition. This implementation includes practical code demonstrations covering key steps: data normalization, covariance matrix computation, eigenvalue decomposition, and principal component selection. The code features clear variable naming, detailed inline comments explaining mathematical operations, and modular functions for covariance calculation and eigenvector sorting. Beginners can study how PCA transforms high-dimensional facial data into meaningful lower-dimensional representations while preserving critical variance. This hands-on approach helps learners understand core face recognition mechanisms and gain practical experience with statistical pattern recognition techniques. Our well-documented code ensures easy adaptation for different datasets and serves as an educational foundation for advanced machine learning concepts.