uva-machine-learning-25f-projects

For those who haven't submitted your project code yet, please follow the instructions below to upload your work to the course repository.

Step 1: Set up your local branch

• Go to the course repository and click Fork: https://github.com/Qdata4Capstone/uva-machine-learning-25f-projects
• Go to your new forked repository and clone it to your local environment:
  • git clone https://github.com//uva-machine-learning-25f-projects.git
• Navigate into the cloned folder and add the original repository as an upstream remote:
  • git remote add upstream https://github.com/Qdata4Capstone/uva-machine-learning-25f-projects.git

Step 2: Prepare your code:

• For each team, please create a folder named team-XX corresponding to your team ID (e.g., team-1, team-11, team-111). 
• Inside this folder, include the following:
  • src/: A subfolder containing all source code.
  • data/: A subfolder with the data required to reproduce results.
    • Note: If the data cannot be uploaded, include a markdown file describing how to collect it.
  • requirements.txt: A file listing required packages. (Format reference)
  • README.md: A markdown file describing the folder content. You can view an example here. Your README should include:
    • Project Title
    • Team ID and Members
    • Overview: A brief introduction to the project.
    • Usage: How to run the code to get core results.
    • (Optional) Setup: Instructions for environment setup (if non-trivial).
    • (Optional) Video: A link to your demo video with a brief description.
• You are also welcome to include additional files or documentation in the folder or README.md if they help people better understand your project and code.

Step 3: Upload your code

• Commit your changes (no requirements on the commit message)
  • git add .
  • git commit -m "upload project code by Team-XX"
• Push the changes to your fork
  • git push origin main
• On GitHub, navigate to your fork and open a pull request via: Pull requests → New pull request