principal-component-analysis-from-scratch

Introduction

This repository contains an implementation of Principal Component Analysis (PCA) from scratch using Python. PCA is a powerful technique used for dimensionality reduction, which helps in reducing the complexity of data while preserving as much variance as possible. This implementation does not rely on any machine learning libraries, providing a clear understanding of the inner workings of PCA.

Features

• PCA implementation from scratch using NumPy and custom functions
• Step-by-step explanation of the algorithm
• Easy-to-follow code with comments
• Example usage with sample datasets