Statistical Learning with Python

This repository contains Python implementations and solutions to exercises from the book An Introduction to Statistical Learning: with Applications in Python (ISLP).

Description

The book provides a comprehensive introduction to statistical learning methods, covering both supervised and unsupervised learning techniques. This project implements the concepts and exercises using Python libraries such as NumPy, Pandas, and Matplotlib.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/statistical-learning.git
   cd statistical-learning

2. Install dependencies using uv (recommended):

   uv sync

   Or using pip:

   pip install -r requirements.txt

Usage

The solutions are organized as Jupyter notebooks. Each chapter has its own notebook containing:

• Conceptual exercises with solutions
• Applied exercises with Python implementations
• Data analysis and visualizations

To run the notebooks:

jupyter notebook

Chapters

• Chapter 2: Statistical Learning (In Progress)

Data

The data/ directory contains datasets used in the book's exercises, including:

• Advertising.csv
• Auto.csv
• Boston.csv
• College.csv
• And more...

Requirements

• Python >= 3.14
• Dependencies: NumPy, Pandas, Matplotlib, IPython kernel

License

This project is licensed under the MIT License - see the LICENSE file for details.