This repository contains my personal notes and exercises as I work through the new Python version of the book "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
"An Introduction to Statistical Learning" is a popular textbook that provides an introduction to statistical learning methods, including linear regression, classification, resampling methods, tree-based methods, support vector machines, unsupervised learning, and more. This repository focuses on the Python version of the book, which offers practical implementations of the concepts using Python libraries such as scikit-learn, pandas, and matplotlib.
To get started with this repository and take advantage of a custom GitHub code space, follow these steps:
- Launch the repository in the GitHub code space by clicking the green "Code" button and selecting "Open with Code Spaces".
- Explore the code, notes, and exercises within the code space environment.
Alternatively, you can also follow these steps to work with the repository on your local machine:
- Clone the repository to your local machine using
git clone. - Install the necessary dependencies by running
pip install -r requirements.txt. - Explore the code, notes, and exercises within the repository.