Python Computing Supplement

Preface

This is a computing supplement to the main website that uses python, and in particular scikit-learn for modeling. The structure is similar to the website, but the content here shows how to use this software for each topic.

License

As this computing supplement will largely be adapting the book, we adopt here the same license, CC BY-NC-SA 4.0

Intended Audience

Readers should have used python before, but do not have to be experts. If you are new to python, we suggest taking a look at the Python Data Science Handbook.

You do not have to be a modeling expert either. We hope that you have used a linear or logistic regression before and understand basic statistical concepts such as correlation, variability, probabilities, etc.

How can I ask questions?

If you have questions about the content, it is probably best to ask on a public forum, like Stack Overflow for programmatic questions, or the data science or statistics Stack Exchange sites. You’ll most likely get a faster answer there if you take the time to ask the questions in the best way possible.

If you want a direct answer from us, you should follow what Max calls Yihui’s Rule: add an issue to GitHub (labeled as “Discussion”) first. It may take some time for us to get back to you.

Can I contribute?

There is a contributing page with details on how to get up and running to compile the materials and suggestions on how to help.

If you just want to fix a typo, you can make a pull request to alter the appropriate .qmd file.

Please feel free to improve the quality of this content by submitting pull requests.

Computing Notes

Quarto version 1.4.538 was used to compile and render the materials.

Python version 3.11.7 was used for computations. For the list of python packages used and their versions, see the Pipfile in the source repository.