This repository contains code and resources for performing Multiple Correspondence Analysis (MCA), also known as Analyse Factorielle en Composante Multiple (AFCM) in French, using R. MCA is a multivariate statistical technique used to analyze categorical data by exploring relationships among multiple variables simultaneously. This project is part of my school course on Data analysis.
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Importing the Dataset: Begin by importing your dataset, ensuring to remove non-qualitative (quantitative) columns if necessary.
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Generating the Barplot: Utilize the imported data to create a barplot showing the top 8 modalities in each facet.
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Transforming Data into Disjunctive Table: Transform the dataset into a disjunctive table to perform Multiple Correspondence Analysis (MCA).
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Performing MCA: Use the prepared data to conduct Multiple Correspondence Analysis (MCA).
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Visualizing Results: Visualize the results of MCA by generating plots such as eigenvalue diagram, variable and individual contributions, and biplot.
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Identifying Key Variables: Identify the variables best represented by MCA by examining their relative contributions.
Contributions to this project are welcome! If you have suggestions, bug fixes, or additional features, feel free to fork the repository, make your changes, and submit a pull request.
This project is licensed under the MIT License, allowing both commercial and non-commercial use with proper attribution to the original authors.