Getting Started

1. Clone the repository and navigate to the project folder.

2. Download IPL datasets (CSV files) and place them in the data/ directory.

3. Install dependencies (recommended: create a virtual environment):

   pip install pandas scikit-learn matplotlib seaborn opencv-python

4. Open the notebook:
   Launch Jupyter Lab or Notebook and open notebooks/ipl_data_exploration.ipynb.

5. Run the notebook cells to load data, explore statistics, visualize results, and try out analysis/model scripts.

Modules Overview

• Preprocessing:
  Load and preview IPL datasets using src/preprocessing/load_data.py.

• Analysis:
  Analyze match and player statistics with functions in src/analysis/basic_analysis.py.

• Models:
  Train machine learning models to predict outcomes or generate insights, using src/models/model_training.py.

• OpenCV:
  Use image utilities in src/opencv/image_utils.py for processing match/event images or player photos.

• RAG:
  Retrieve relevant documents or information using simple utilities in src/rag/retrieval.py.

Contribution

Feel free to fork the repository, add new features, or improve existing modules. Pull requests are welcome!

License

This project is licensed under the MIT License.

Acknowledgments

• IPL dataset sources: Kaggle IPL datasets
• Python, Pandas, Scikit-learn, OpenCV, Matplotlib, Seaborn