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Analysis of obesity levels using MCA with two approaches for quantitative variables.

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Obesity Analysis

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Summary

This repository contains data analysis on obesity levels based on eating habits and physical activity. It uses Multiple Correspondence Analysis (MCA) with two approaches to handle quantitative variables, exploring relationships between demographic, behavioral, and health-related factors.

Contents

  • Scripts for data preprocessing and MCA
  • Visualizations of results
  • Study report (FRENCH)
  • Presentation slides (FRENCH)

Dataset

The analysis uses the Estimation of Obesity Levels Based on Eating Habits and Physical Condition dataset from the UCI Machine Learning Repository.

Authors

Made by Mohamed El Amine Kherroubi and Bousdjira Nadine. The code and documentation have been refactored and optimized with AI-assisted tools for improved readability and efficiency.

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Analysis of obesity levels using MCA with two approaches for quantitative variables.

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