Miranics/PCA-Formative-Assignment

Principal Component Analysis on African Development Indicators Dataset # PCA-Formative-Assignment colab.research.google.com Principal Component Analysis (PCA) workflow for the Advanced Linear Algebra formative assignment. The repository contains: - `data/african_development_indicators.csv` — an African-focused dataset with missing values and the non-numeric `Country` column. - `notebooks/pca_formative_assignment.ipynb` — the completed assessment notebook with every template "TO DO" implemented and outputs rendered. - `requirements.txt` — dependencies needed to reproduce the results locally or in Google Colab. ## Dataset highlights - Covers 10 African countries (2019 & 2020) with socio-economic metrics (electricity access, internet adoption, education index, etc.). - Includes intentional `NaN` entries and categorical data to demonstrate imputation and encoding as required by the rubric. ## Getting started 1. **Clone** the repo and create a virtual environment (recommended). 2. **Install dependencies**: ```powershell pip install -r requirements.txt ``` 3. **Open the notebook** in VS Code or Jupyter/Colab and run cells from top to bottom to regenerate outputs. ## 🧭 Notebook roadmap | Section | Description | | --- | --- | | Assignment context | Summarises rubric requirements and dataset compliance. | | Dataset inspection | Loads the CSV, surfaces missing values, and classifies column types. | | Data preparation | Imputes numeric/categorical fields and one-hot encodes `Country`. | | Step 1 | Pure NumPy standardisation matching the provided formula. | | Step 3 | Covariance matrix computation. | | Step 4 | Eigendecomposition using `numpy.linalg.eigh`. | | Step 5 | Sorting eigenpairs, explained variance ratios, and visualisation. | | Step 6–7 | Dynamic component selection (95% threshold) and projection. | | Step 8 | Side-by-side visual comparison (original vs. PCA space). | | Task 3 | Runtime benchmark on a large synthetic dataset (optimisation requi …