If you'd like to develop and/or build the data-science book, you should:

1. Clone this repository
2. Launch the devcontainer via vscode dev container extension and run poetry shell to lauch the virtual environment data-mining-book-py3.9
3. (Optional) Edit the books source files located in the data_mining/ directory
4. (Optional) Run jupyter-book clean data_mining/ to remove any existing builds
5. Run sphinx-autobuild --host 0.0.0.0 data_mining data_mining/_build/html for interactive editing and liveview.
6. (Optiona) Run jupyter-book build data_mining/ for an offline build

A fully-rendered HTML version of the book will be built in data_mining/_build/html/.

Please see the Jupyter Book documentation to discover options for deploying a book online using services such as GitHub, GitLab, or Netlify.

For GitHub and GitLab deployment specifically, the cookiecutter-jupyter-book includes templates for, and information about, optional continuous integration (CI) workflow files to help easily and automatically deploy books online with GitHub or GitLab. For example, if you chose github for the include_ci cookiecutter option, your book template was created with a GitHub actions workflow file that, once pushed to GitHub, automatically renders and pushes your book to the gh-pages branch of your repo and hosts it on GitHub Pages when a push or pull request is made to the main branch.

We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab.

This project is created using the excellent open source Jupyter Book project and the executablebooks/cookiecutter-jupyter-book template.