Reading-Assigment

📘 PCA Reading Assignment

An educational resource for learning Principal Component Analysis (PCA) — a fundamental dimensionality reduction technique in machine learning and data science.

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📖 Description

This repository is a reading assignment for the Advanced Big Data Analytics course (24K-8001). It provides a comprehensive exploration of PCA through two complementary learning resources:

1. Interactive PCA Guide — A web-based tutorial with step-by-step explanations, interactive visualizations, and mathematical notation.
2. Practical PCA Implementation — A Jupyter Notebook demonstrating PCA in Python, comparing manual calculations with scikit-learn.

✨ Key Features

• Interactive Learning — Step-by-step PCA walkthrough with clickable buttons and dynamic charts
• Mathematical Rigor — Full LaTeX-rendered equations for standardization, covariance, eigendecomposition
• Hands-On Code — Complete Python implementation using NumPy and scikit-learn
• Visual Explanations — Scatter plots, bar charts, and data transformation visualizations
• Self-Contained — No build step required; open HTML files directly in any browser

🛠️ Tech Stack

Category	Technologies
Languages	Python, HTML, CSS, JavaScript
Data Science	NumPy, scikit-learn, Pandas
Visualization	Matplotlib, Seaborn, Chart.js
Math Rendering	MathJax
Styling	Tailwind CSS
Notebooks	Jupyter Notebook

📂 Project Structure

Reading-Assigment/
├── src/                                    # Main content files
│   ├── pca-interactive-guide.html          # Interactive web-based PCA tutorial
│   └── pca-practical-implementation.html   # Jupyter Notebook export with Python code
├── docs/                                   # Developer documentation
│   ├── setup.md                            # Installation and setup guide
│   ├── architecture.md                     # Project architecture overview
│   └── development.md                      # Development workflow
├── .github/                                # GitHub configuration
│   ├── workflows/ci.yml                    # CI pipeline
│   ├── ISSUE_TEMPLATE/                     # Issue templates
│   └── pull_request_template.md            # PR template
├── README.md                               # This file
├── CONTRIBUTING.md                         # Contribution guidelines
├── LICENSE                                 # MIT License
├── CHANGELOG.md                            # Version history
├── requirements.txt                        # Python dependencies
├── .gitignore                              # Git ignore rules
└── .editorconfig                           # Editor configuration

🚀 Installation

View Online

Visit the live site: https://mnoumanhanif.github.io/Reading-Assigment/

View Locally

# Clone the repository
git clone https://github.com/mnoumanhanif/Reading-Assigment.git
cd Reading-Assigment

# Open the interactive guide in your browser
open src/pca-interactive-guide.html        # macOS
xdg-open src/pca-interactive-guide.html    # Linux
start src/pca-interactive-guide.html       # Windows

📋 Usage

Learning Path

1. Start with theory — Open src/pca-interactive-guide.html to understand PCA concepts, the mathematical process, and real-world applications.
2. Move to practice — Open src/pca-practical-implementation.html to see PCA implemented in Python with NumPy and scikit-learn.
3. Experiment — Set up the Python environment and try modifying the code yourself.

Python Environment (Optional)

To run or modify the Jupyter notebook:

python -m venv venv
source venv/bin/activate    # macOS/Linux
pip install -r requirements.txt
jupyter notebook

🧪 Testing

The CI pipeline validates:

• HTML file existence and structure
• Required documentation files
• Project directory structure

Run the checks locally:

# Verify HTML files exist
ls src/*.html

# Check HTML structure
for f in src/*.html; do grep -q "<html" "$f" && echo "✅ $f"; done

🤝 Contributing

Contributions are welcome! Please read CONTRIBUTING.md for guidelines on:

• Reporting issues
• Submitting pull requests
• Content and code style standards

📚 Additional Resources

• Principal Component Analysis — Wikipedia
• A Complete Guide to PCA in Machine Learning — Medium
• Step-By-Step Guide to PCA — Turing

📄 License

This project is licensed under the MIT License.

👤 Author

mnoumanhanif — GitHub Profile

Course: Advanced Big Data Analytics (24K-8001)

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