PCA_SVM: Dimensionality Reduction with Principal Component Analysis

This implementation utilizes PCA for dimensionality reduction, preserving 90% of critical data variance through eigenvalue decomposition. The code includes comprehensive comments and comes with sample datasets, while allowing users to integrate custom data for flexible testing.

Face Recognition Program with PCA-SVM Algorithm Implementation

Face Recognition Program with accessible source code, achieving high recognition accuracy through PCA dimensionality reduction and SVM classification