ISLR-Python

This repository contains the 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. The solutions are implemented in Python.

About the Book

"Introduction to Statistical Learning" provides an introduction to statistical learning methods and their applications. It covers a wide range of topics in statistical learning, including linear regression, classification methods, resampling methods, tree-based methods, and more. The book presents both theoretical concepts and practical examples to help readers understand the principles and techniques of statistical learning.

Repository Structure

The repository is organized as follows:

• Exercises: This directory contains exercises from the book. Each chapter has its own directory named "Chapter X," where X represents the chapter number. Inside each chapter directory, you will find Jupyter Notebook files (e.g., Exercise_X_Y.ipynb) corresponding to specific exercises.
• Notebooks: This directory contains the lab notebooks from the book. Each lab notebook is named "Lab_X.ipynb," where X represents the lab number.
• Data: This directory contains the datasets used in the exercises and labs. The data files are provided in various formats, such as CSV or Excel, depending on the requirements of each exercise or lab.
• Figures: This directory contains images and visualizations generated during the analysis of the datasets and the implementation of statistical learning methods. These images are included in the solutions and can help illustrate the concepts discussed.

Getting Started

If you want to explore the solutions in this repository, follow these steps:

1. Clone the repository to your local machine using the following command:

   git clone https://github.com/ogulcancicek/ISLR-Python.git
   

2. Navigate to the cloned repository:

   cd ISLR-Python
   

3. The Exercises directory contains exercise solutions organized by chapter. Each chapter directory contains Jupyter Notebook files (e.g., Exercise_X_Y.ipynb) corresponding to specific exercises. Open the desired file to view the solution implementation.

4. The Notebooks directory contains lab notebooks from the book. Open the desired notebook (e.g., Lab_X.ipynb) to view the lab content and implementation.

5. The Data directory contains the datasets used in the exercises and labs. You may need to download the necessary data files and place them in the appropriate directories when running the code.

6. Feel free to explore the code and experiment with it. You can modify the solutions or create your implementations based on the provided exercises and labs.

Disclaimer

The solutions in this repository are intended for educational purposes. They should be used as a reference to aid in understanding the concepts presented.