Tennis Data & Model Project

What this is

• A small project that collects tennis match data and includes a lightweight prediction model UI/build.
• Meant for exploration, quick experiments, and sharing a reproducible dataset + model bundle.

What's inside

• A dataset folder with recent Roland Garros match data and engineered CSVs for modelling.
• A small frontend/build in pred_model containing the production build and source.
• Scripts and examples to run experiments locally.

Quick start

1. Explore the data: open the dataset CSVs to see match features and engineered columns.
2. Inspect the frontend: the pred_model folder contains a React app build under pred_model/build and the source in pred_model/src.
3. Run locally (optional):
   • If you want to run the React app from source, install dependencies in pred_model and start the dev server.
   • For quick checks, open the prebuilt pred_model/build/index.html in a browser.

How to use these files

• Datasets: use the CSVs for training, analysis, or feature experiments.
• Model/frontend: the build is ready to serve; if you want to modify the UI, edit the source in the pred_model/src folder and re-build.

Good practices

• Keep a copy of raw CSVs and a separate copy for engineered features.
• Track experiments (parameters, train/validation splits) in a small log or notebook.
• If you modify the frontend, bump the version and note major interface changes.

Need help?

• I can:
  • Generate a short requirements.txt or package.json updates.
  • Create a simple script to load a CSV and show sample rows. Tell me which next step you'd like.