PCA-Formative

Principle Component Analysis

PCA on African Data

This repository contains a Google Colab notebook demonstrating Principal Component Analysis (PCA) on a dataset of African data. The notebook walks through the entire PCA workflow, from data loading and standardization to dimensionality reduction and visualization.

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📁 Repository Contents

• AfricanData.csv – The dataset used for analysis (numeric columns only).
• PCA_Analysis.ipynb – Google Colab notebook implementing PCA step by step.
• README.md – This file.

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📝 About PCA

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variance as possible. It helps to:

• Identify patterns in data
• Remove redundant features
• Visualize high-dimensional data in 2D or 3D

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🚀 Steps Covered in the Notebook

1. Load and Standardize Data – Convert data into a standardized numeric form.
2. Compute Covariance Matrix – Understand relationships between features.
3. Perform Eigendecomposition – Find eigenvalues and eigenvectors.
4. Sort Principal Components – Rank components based on explained variance.
5. Project Data – Reduce dimensions by projecting onto top principal components.
6. Visualize Data – Compare original and reduced datasets.

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💻 How to Run

1. Open Google Colab.
2. Click File → Open notebook → GitHub.
3. Paste the repository URL or search for this repo.
4. Run all cells to perform PCA and visualize results.

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📊 Results

• The notebook produces a reduced dataset with top principal components.
• Visualizations show before-and-after dimensionality reduction.

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📌 Requirements

• Python 3.x
• NumPy
• Pandas
• Matplotlib
• Google Colab (optional, but recommended)

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📬 Contact

For questions or suggestions, please open an issue in this repository.

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