This repo contains solution attempts for the conceptual and applied exercises of the Introduction to Statistical Learning with Python.
So far, I was able to work through the exercises until the end of Chapter 4 (Classification). I will try to add more as I read and work through the rest of the book.
The main goal of this project is to learn through practice in order to get more familiar with Python's data science stack. ISLP book is perfect for this purpose as it provides an heuristic approach while keeping a good dosage of theoretical/mathematical background for the materials covered.
The project is developed using Python 3.10 on jupyter notebooks in VS Code. In addition to the standard Python Data Science stack: numpy, pandas, matplotlib, seaborn, statsmodels and scikit-learn, you need to install ISLP library that accompanies the book. It has several built-in functions, for example allowing to load some of the data needed to tackle exercises without requiring it to be present on your local directory. You can install it using your jupyter notebooks by simply running the following command:
pip install ISLP