PCA_SVM: Dimensionality Reduction with Principal Component Analysis

This code implements Principal Component Analysis (PCA) for dimensionality reduction, retaining 90% of the original data's variance by calculating eigenvalues and eigenvectors from the covariance matrix. The algorithm automatically determines the optimal number of components needed to achieve this threshold. Detailed code comments explain each processing step, including data standardization, covariance computation, and component selection. PCA dimensionality reduction simplifies complex datasets while maintaining essential patterns, making subsequent machine learning operations like SVM classification more computationally efficient. Our implementation includes visualization of variance retention and component significance analysis. The package provides sample datasets for immediate testing, with clear data loading interfaces that allow seamless integration of custom datasets. Users can modify the data input section to accommodate different file formats (CSV, MATLAB .mat files) and data structures. We recommend testing with domain-specific data to optimize performance for particular applications, as proper data preprocessing significantly impacts the algorithm's effectiveness in real-world scenarios.