📚 An Introduction to Statistical Learning — Python Solutions

This repository contains Python solutions to the exercises and labs from the book
"Introduction to Statistical Learning, Second Edition"
by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.

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📖 About the Book

"Introduction to Statistical Learning" introduces key statistical learning methods and their practical applications.
Topics include:

• 📈 Linear Regression
• 🔢 Classification Methods
• 🔄 Resampling Methods
• 🌳 Tree-Based Methods
• ...and more!

Both theory and hands-on examples are presented to help readers master statistical learning.

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🗺️ Repository Structure

An-Introduction-to-Statistical-Learning-Python/
│
├── Exercises/    📁 Exercise solutions, organized by chapter ("Chapter X")
├── Notebooks/    📁 Lab notebooks ("Lab_X.ipynb")
├── data/         📁 Datasets for exercises and labs
├── figures/      📁 Images & visualizations from notebooks
├── LICENSE       📜 MIT License
└── README.md     📘 Project documentation

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🚀 Getting Started

1. Clone the repository

   git clone https://github.com/mdzaheerjk/An-Introduction-to-Statistical-Learning-Python.git
   cd An-Introduction-to-Statistical-Learning-Python

2. Explore Exercise Solutions

   • Go to the Exercises/ folder.
   • Each chapter has its own directory (Chapter X/).
   • Open Jupyter Notebooks (e.g., Exercise_X_Y.ipynb) for solutions.

3. Explore Lab Notebooks

   • Find labs in the Notebooks/ directory (e.g., Lab_X.ipynb).
   • Open with Jupyter Notebook or JupyterLab.

4. Datasets & Figures

   • Datasets are in data/ (formats: CSV, Excel, etc.).
   • Visualizations and images are in figures/.

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💡 Usage Notes

• Feel free to experiment and modify solutions.
• Datasets may need to be downloaded or placed in the appropriate directories.
• All solutions are intended for educational purposes and as a reference.

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⚠️ Disclaimer

Solutions are provided for learning and reference only.
Use them to deepen your understanding of statistical learning concepts.

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👤 Author

• mdzaheerjk

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📄 License

MIT License. See LICENSE for details.

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Emoji Key:
📁 = Folder  📘 = Docs  📜 = License  📈 = Data/Analysis