A personal hub to organize every phase of your Kaggle journeys—from raw data to polished submissions.
Kaggle-Works enforces a clean, reproducible workflow for tabular-data competitions:
- Competition-Submits/
Store your CSV/JSON submissions and keep a history of top leaderboard runs. - Zipped-Data/
Untouched archive downloads (ZIP, TAR, etc.)—your “source-of-truth.” - Extracted-Data/
Unpacked & lightly cleaned CSV/Parquet files ready for analysis. - My_Works/
Jupyter notebooks, Python scripts, utility modules, write-ups, and visualizations.
Whether you’re chasing that gold medal or simply honing your data chops, this repo structure helps you stay organized and shareable.
- Clone the repo
git clone https://github.com/Kratugautam99/Kaggle-Works.git cd Kaggle-Works
- Install dependencies
Create a virtual environment and install your favorite libs:python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt
- Add raw data archives
Drop your.zip
/.tar
files intoZipped-Data/
. - Unpack & clean
Extract intoExtracted-Data/
and run your preprocessing pipelines (inMy_Works/
). - Train & submit
Develop models inMy_Works/
, save finalsubmission.csv
underCompetition-Submits/
.
Kaggle-Works/
├── Competition-Submits/ ← Final submission files
├── Zipped-Data/ ← Raw dataset archives
├── Extracted-Data/ ← Unpacked & cleaned data
└── My_Works/ ← Notebooks, scripts, analyses
- Keep raw data immutable in
Zipped-Data/
. - Version your cleaned exports in
Extracted-Data/
. - Isolate EDA & experiments in
My_Works/
. - Never overwrite prior submissions—store each attempt in
Competition-Submits/
.
Feel free to fork, propose improvements, or share new templates:
- Fork the repo
- Create a feature branch (
git checkout -b feature/my-awesome-pipeline
) - Commit your changes (
git commit -m "Add my new data-cleaning recipe"
) - Push to your branch (
git push origin feature/my-awesome-pipeline
) - Open a Pull Request
This project is licensed under the MIT License. See LICENSE for details.