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-XXcorresponding 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