Solutions to Exercises from "An Introduction to Statistical Learning with Applications in Python"

This repository contains my solutions to the exercises from the textbook:

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.

(Note: The original English version was published in 2013, but this repository is based on the Python version.)

Important: The exercise texts are not included in this repository. Please refer to the textbook for the problem statements. All solutions were developed independently by me.

Table of Contents

• Linear Regression - ML00.ipynb
• Linear Regression - ML01.ipynb
• Classification
• Exercises ML

File Format

All files are in Jupyter Notebook format (.ipynb), allowing for easy execution and testing of the code. You will need to install Jupyter or use platforms like Google Colab to work with these notebooks.

How to Run

1. Ensure you have the necessary libraries installed (e.g., numpy, pandas, matplotlib, scikit-learn). You can typically install these using pip:

   pip install numpy pandas matplotlib scikit-learn

2. Open Jupyter Notebook:

   jupyter notebook

License

This project is licensed under the MIT License.