dav1d001/PCA-African-Development

# PCA on African Development Indicators ## Overview This project implements Principal Component Analysis (PCA) from scratch using NumPy and Pandas. It analyzes World Bank development data from Sub-Saharan African countries to reduce dimensionality while retaining 95% of the variance. ## Contents * `PCA_Analysis.ipynb`: The main notebook containing the implementation, visualization, and analysis. * `africa_data.csv`: The raw dataset sourced from World Bank Indicators (2022). * `requirements.txt`: List of Python dependencies. ## Key Findings * The analysis successfully reduced **39 original indicators** down to **9 Principal Components**. * These 9 components capture **95.3%** of the information in the dataset. * Visualizations (Scree Plot, Correlation Heatmap, and PCA Scatter) are included in the notebook. ## How to Run 1. Clone this repository. 2. Install dependencies: ```bash pip install -r requirements.txt