PCA Homework (HW PCA)

This repository hosts all the materials and code for the mandatory project on Principal Components Aanalysis, for the course Computational Linear Algebra for Large Scale Problems (A.Y. 2024/2025). It features Principal Component Analysis through scikit-learn module to reduce a 150-feature survey dataset to at most 5 components (preserving at least 33% variance) and k-Means clustering to identify the profiles described by the coefficients of the 5 PCs.

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Contents

• HW_PCA.pdf: Original homework instructions.
• HWpca_Lolli.ipynb: My Jupyter Notebook with code and analysis.
• Data: Survey dataset files (responses_hw.csv and columns_hw.csv).

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Task Overview

1. Dimensionality Reduction

   • Use PCA to reduce the dataset to at most 5 principal components while retaining at least 33% of the total variance.

2. Clustering

   • Apply k-Means clustering on the PCA-transformed data to identify 3–10 distinct customer profiles.

3. Analysis & Interpretation

   • Analyze the principal components and interpret the clusters in the context of the survey data.

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Libraries & Tools

• NumPy / Pandas: For data manipulation and numerical operations.
• scikit-learn: For PCA and k-Means clustering.
• Matplotlib / Seaborn: For visualizations.

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Getting Started

1. Install Requirements: Refer to requirements.txt.
2. Run Notebook: Open and execute HWpca_Lolli.ipynb using Jupyter Notebook.
3. Explore: Adjust PCA and clustering parameters to further experiment with the analysis.

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Contributions & Contact

This project is shared for academic purposes. For questions or suggestions, please open an issue or contact me directly.