Summary

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Summary#

These are my solutions to the exercises in the book An Introduction to Statistical Learning with Python (ISLP).

I attempt to tackle both conceptual and applied exercises in these notebooks.

Read the Book#

If you’re reading this from github, you can explore the full Jupyter Book here: ISLP Solutions

Notes:#

  • Questions that involve sketching are done using matplotlib’s xkcd theme.

  • Mathematical proofs are mostly written using mathjax inside the markdown cells by wrapping the mathjax code in $...$ like so \(\alpha^2 + \beta_0 = \gamma\), though some were updated to use LaTeX align environments to be compatible with jupyter-book.

  • I typed the questions in the notebooks for chapters 2 and 3, but it was taking too much time so I stopped doing that from chapter 4 and on.

Usage#

  • You can either read the jupyter book online, or just view the notebooks on github.

  • If you’re viewing it through the jupyter book, you can launch the notebooks using the rocket icon on the top right in google colab or binder to experiment with them, keep in mind that you might have to run a few pip installs for the missing libraries on colab.

You can also just clone the repo and run it locally

git clone https://github.com/Mohamed-Badry/islp-solutions.git
cd islp-solutions
pip install -r requirements.txt
jupyter notebook

or build the book with

pip install -r requirements-book.txt
jupyter-book build .

Note: Preferrably do both in a venv or conda environemnt.

Credits:#

A big thank you to the authors of the original book An Introduction to Statistical Learning with Python for making such a great learning resource free, easily accessible, and creating a great course to accompany it.

The notebooks are rendered using the wonderful Jupyter Book project.

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