Data Analysis - PCA (Principal Component Analysis) TPs

Welcome to this repository containing my Practical Work (TPs) on Principal Component Analysis (PCA / ACP), completed as part of my Data Analysis course.

Repository Contents

This repository contains 6 TPs, implemented in Jupyter Notebooks:

1. TP1 to TP5:

   • PCA implemented from scratch on simple datasets (matrices).
   • Very beginner-friendly, with detailed interpretation of the results.

2. TP6:

   • PCA applied to a real face image dataset.
   • More complex, including interpretation of principal components on real data.

All TPs are documented in French.

Objective

• Understand how PCA works.
• Implement PCA from scratch.
• Interpret results on simple datasets and on a real dataset.

Prerequisites

• Python 3
• Jupyter Notebook
• Python libraries: numpy, matplotlib, pandas....

Instructions

• To clone the repository:
  git clone https://github.com/fatmazohrasaidi/TP-ANA-DON.git