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CONTRIBUTING.md

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Getting Started 🚀

To contribute to Health Learning, follow these steps:

  1. Fork the repository: Click the "Fork" button on the top right corner of this repository to create your own copy.

  2. Clone your fork: Clone the repository to your local machine using git clone https://github.com/your-username/HealthLearning.git.

  3. Create a new branch: Create a new branch for your changes using git checkout -b disease/issue-name.

  4. Make your changes: Make your desired changes to the codebase. You can explore the datasets, improve existing models, develop new algorithms, update documentation, or fix bugs.

  5. Commit your changes: Once you've made your changes, stage them using git add . and commit them using git commit -m "Your commit message here".

  6. Push your changes: Push your changes to your forked repository using git push origin disease/issue-name.

  7. Submit a Pull Request (PR): Go to your forked repository on GitHub and click the "New pull request" button. Provide a brief description of your changes and submit the PR for review.

Alternatively, contribute using GitHub Desktop

  1. Open GitHub Desktop: Launch GitHub Desktop and log in to your GitHub account if you haven't already.

  2. Clone the Repository:

    • If you haven't cloned the HealthLearning repository yet, you can do so by clicking on the "File" menu and selecting "Clone Repository."
    • Choose the HealthLearning repository from the list of repositories on GitHub and clone it to your local machine.

  3. Switch to the Correct Branch:

    • Ensure you are on the branch that you want to submit a pull request for.
    • If you need to switch branches, you can do so by clicking on the "Current Branch" dropdown menu and selecting the desired branch.

  4. Make Changes: Make your changes to the code or files in the repository using your preferred code editor.

  5. Commit Changes:

    • In GitHub Desktop, you'll see a list of the files you've changed. Check the box next to each file you want to include in the commit.
    • Enter a summary and description for your changes in the "Summary" and "Description" fields, respectively. Click the "Commit to " button to commit your changes to the local branch.

  6. Push Changes to GitHub: After committing your changes, click the "Push origin" button in the top right corner of GitHub Desktop to push your changes to your forked repository on GitHub.

  7. Create a Pull Request:

  • Go to the GitHub website and navigate to your fork of the HealthLearning repository.
  • You should see a button to "Compare & pull request" between your fork and the original repository. Click on it.

  1. Review and Submit:

    • On the pull request page, review your changes and add any additional information, such as a title and description, that you want to include with your pull request.
    • Once you're satisfied, click the "Create pull request" button to submit your pull request.

  2. Wait for Review: Your pull request will now be available for review by the project maintainers. They may provide feedback or ask for changes before merging your pull request into the main branch of the HealthLearning repository.

Assigning Issues 📝

If you would like to work on a specific issue, comment on the issue or convey in the meetups expressing your interest in contributing. The project maintainers will review your request and may assign the issue to you if they believe you are a suitable candidate for the task.

Code Style Guidelines 📋

  • Use meaningful variable names and comments to improve code readability.
  • Ensure your code is well-documented and includes docstrings where necessary.

Code Review 👀

All contributions are subject to code review. A mentor/maintainer will review your PR, provide feedback, and merge it once it meets the project's standards.

Help and Support 🤝

If you need any help or have questions about contributing, feel free to reach out to us through GitHub issues or Discord.

Thank you for contributing to Health Learning! Together, we can make a positive impact on healthcare through machine learning and deep learning. 🌟👩‍⚕️👨‍💻