🏆 Kaggle-Works

A personal hub to organize every phase of your Kaggle journeys—from raw data to polished submissions. Kratugautam99 Account Below

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📖 Overview

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.

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🚀 Getting Started

1. Clone the repo

   git clone https://github.com/Kratugautam99/Kaggle-Works.git
   cd Kaggle-Works

2. Install dependencies
   Create a virtual environment and install your favorite libs:

   python3 -m venv .venv
   source .venv/bin/activate
   pip install -r requirements.txt

3. Add raw data archives
   Drop your .zip/.tar files into Zipped-Data/.
4. Unpack & clean
   Extract into Extracted-Data/ and run your preprocessing pipelines (in My_Works/).
5. Train & submit
   Develop models in My_Works/, save final submission.csv under Competition-Submits/.

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📁 Repository Structure

Kaggle-Works/
├── Competition-Submits/   ← Final submission files
├── Zipped-Data/           ← Raw dataset archives
├── Extracted-Data/        ← Unpacked & cleaned data
├── My_Works/              ← Notebooks, scripts, analyses
└── README.md              ← This File

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💡 Usage Tips

• 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/.

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🤝 Contributing

Feel free to fork, propose improvements, or share new templates:

1. Fork the repo
2. Create a feature branch (git checkout -b feature/my-awesome-pipeline)
3. Commit your changes (git commit -m "Add my new data-cleaning recipe")
4. Push to your branch (git push origin feature/my-awesome-pipeline)
5. Open a Pull Request

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Made with 💪 by Kratu Gautam
_{Organize. Reproduce. Compete. Share.}

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