What is the best way to manage and document your data mining and machine learning code?

For robust management and documentation of data mining and machine learning code, I consider these advanced methods: Version Control: Utilize Git with Git LFS for handling large datasets and model binaries. Data Management: Embrace DVC for data and experiment versioning. Containerization: Leverage Docker for environment consistency, and Kubernetes for distributed tasks. MLOps: Use platforms like MLflow, integrating CI/CD pipelines. Documentation: Implement Sphinx; merge code and narrative with Jupyter. Code Analysis: Employ SonarQube for semantic evaluations. A holistic approach guarantees rigorous, scalable, and transparent codebase maintenance.

Good code management is critical for successful data mining or machine learning projects. Start by clearly stating the problem and goals. This sets the roadmap for your work. Pick a well-known framework like CRISP-DM for structure; it makes your job easier to understand. Use version control tools like Git to keep track of changes and collaborate. Remember metadata and documentation; these are your code's instruction manual. Use simple formats like JSON for metadata and include README files for context. Finally, share your work on platforms like GitHub. It opens doors for feedback and collaboration and helps the community, too. All these steps make your project organized, understandable, and easy to build upon.

In simple language - Create a GitHub repository, and upload a notebook with a well-written explanation. Use # and upload the file as well. JSON and README files are advisable.

In addition to what has been stated by others, one should probably write up a formal document describing the data preparation, model building, and model deployment processes. Toward the end of my time at Salford, I started to use version control to manage my data preparation code. It should also be used for scoring engine code. Since I've always been in the habit of giving models unique names, version control there may be overkill (but definitely back up the files). Any code not dead obvious to people other than you should be commented. No exceptions.

In addition to hosting a repository for code, the data science community uses git as a home to manage the motley collection of files that make up typical projects, which often consist of figures, reports, logs, presentations and packages. Across your team, track changes to the codebase, collaborate with team members, and easily revert to previous states if issues arise. Commit frequently, making small, meaningful submissions with descriptive messages associated with a specific issue in the requirements ticketing system. This helps everyone understand the purpose of each change. Use branches to work on specific features or experiments, & merge them into the main branch when they are validated and stable. Your future self will thank you.

I think of comments as a conversation with your future self or with someone who might be reading the code for the first time, although it's tempting to write complex and complicated code, it's always understandable to prioritize them. As my knowledge, code is read more than it is written, so using analogies or simple explanations can make complex algorithms or logic more digestible and can be more extendable in future.

Descriptive and modular code is essential for code clarity and maintainability. Use meaningful names for variables and functions, and break your code into small, independent units with specific tasks. This enhances readability, reusability, and testability. Tools like pylint and frameworks like scikit-learn help maintain code quality and structure in machine learning projects.

Modularization is vital. No non trivial code segment should appear more than once for the very simple reason that one does not want to have to fix it in more than one place. Routine/macro names should be descriptive and again, anything likely not to be dead obvious to people other than the programmer must be commented.

Use whatever tools your software provides to document permanent datasets and individual fields therein (such as labels in SAS). Even if it seems trivial, label everything if the system supports it, and if it doesn't then use comments and external documentation to do it. At a minimum, there should be names, types, and descriptions for all fields; and descriptions for all tables.

As my knowledge, in the field of data mining and machine learning, the context in which data is collected and processed can be as critical as the data itself. Comprehensive metadata acts as a bridge, providing clarity on how to turn raw data into actionable insights. It's like a recipe that describes not only the ingredients, but also the logic behind each step. By carefully documenting data sources, preprocessing choices, and any changes made, you ensure that the project remains transparent and reliable.