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CONTRIBUTING.md

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Contributing to Data Science Projects

We appreciate your interest in contributing to this data science project! Please follow the guidelines below to ensure that your contributions are aligned with the project's standards.

How to Contribute

  1. Fork the Repository:
    • Click on the "Fork" button at the top right corner of the repository page. This will create a copy of the repository in your GitHub account.
  2. Clone Your Fork:
    • Clone your forked repository to your local machine: 
      git clone https://github.com/Mubashir-Ul-Hassan/data-science-projects.git
  3. Create a Branch:
    • Create a new branch for your feature or bug fix: 
      git checkout -b main
  4. Make Your Changes:
    • Add new code or make improvements. Be sure to follow the code quality guidelines mentioned below.
  5. Commit Your Changes:
    • Commit your changes with a clear and descriptive commit message: 
      git add .
git commit -m
  6. Push to Your Fork:
    • Push the changes to your forked repository: 
      git push origin main
  7. Submit a Pull Request:
    • Go to the original repository on GitHub and submit a pull request (PR) from your forked branch.
    • Provide a clear description of what changes you’ve made and link to any related issue, if applicable.

Guidelines for Submitting Valid Contributions

To ensure a smooth collaboration, please adhere to the following guidelines:

  1. Code Quality:

    • Write clear, maintainable code with meaningful comments where necessary.
    • Follow Python's PEP 8 coding style guide.
    • Use docstrings to document your functions and classes.

  2. Commenting:

    • Comment on complex sections of code to explain their purpose and logic.
    • Keep your comments concise and relevant.

  3. Testing:

    • If applicable, ensure that your code passes all tests. If the project includes unit tests, please add tests for any new functionality or modifications you introduce.

Specific Instructions for Data Science Projects

  1. Running Scripts:

    • Before submitting a PR, ensure that all scripts run without errors.
    • If you add a new script, include instructions in the README.md file on how to execute it.

  2. Dependencies:

    • Include any new libraries required for your changes in the requirements.txt file.
    • Use the following command to add new dependencies: 
      pip freeze > requirements.txt

  3. Datasets:

    • If you're adding new data, include a brief description in the relevant folder or file explaining the dataset and its source.
    • Large datasets should not be directly uploaded to the repository. Instead, provide links to where they can be downloaded.

  4. Submission Requirements:

    • Ensure your scripts are well-documented.
    • If you're submitting data analysis, provide visualizations and summaries where applicable.
    • Clearly explain any new models, algorithms, or techniques used in your contribution.

Code of Conduct

Please be respectful and considerate in all communications. We expect contributors to follow our Code of Conduct to maintain an inclusive and welcoming environment.