📘 An Introduction to Statistical Learning — Notes, Labs, and Exercises

This repository is my personal companion to the book An Introduction to Statistical Learning (ISL), where I take structured notes, replicate labs, and solve the end-of-chapter exercises using both Python and R.

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📂 Repository Structure

The repo is organized into two main directories:

islp/ (ISL with Python)

• Contains notes, labs, and exercise solutions for each chapter implemented in Python
• Follows the structure of the ISLP (Introduction to Statistical Learning with Python) adaptation of the book
• Utilizes libraries such as pandas, numpy, matplotlib, seaborn, scikit-learn, and statsmodels

islr/ (ISL with R)

• Contains notes, labs, and exercise solutions for each chapter implemented in R
• Closely follows the original code and methods from the ISLR (Introduction to Statistical Learning with R) edition
• Uses R packages such as ISLR, ggplot2, caret, MASS, and base R functions

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✅ Goals

• Build a strong foundational understanding of statistical learning concepts
• Compare and practice implementing models in both R and Python
• Maintain a clear and reusable reference for revision and future projects

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📚 Chapters Covered

Each folder inside islp/ and islr/ is named after a corresponding chapter in the book. Inside each chapter folder, you’ll typically find:

• 📓 notes.md – Conceptual summaries
• 💻 lab.ipynb / lab.R – Code from the chapter labs
• 📝 exercises.ipynb / exercises.R – Solutions to end-of-chapter exercises

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🚧 Work in Progress

This is a living repository that I update as I progress through the book. Contributions, feedback, or suggestions are welcome!