IPL-Match-Predictor

Overview

The IPL-Match-Predictor is a machine learning-based application designed to predict the outcomes of Indian Premier League (IPL) matches. Leveraging historical match data and various statistical features, this predictor aims to provide insights into the likely winners of upcoming IPL matches. The application is deployed using Streamlit for a user-friendly web interface.

Features

• Match Outcome Prediction: Predicts the winning team for a given IPL match based on historical data and team statistics.
• Data Visualization: Visualizes key statistics and trends from past matches.
• Streamlit Web Interface: Simple and intuitive interface for inputting match details and viewing predictions.

Installation

1. Clone the repository:

   git clone https://github.com/iminsgineark/IPL-Match-Predictor-
   cd IPL-Match-Predictor

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

Usage

1. Prepare the dataset: Ensure you have a dataset of historical IPL matches in a CSV format. You can use publicly available datasets or create your own.

2. Configure the application: Update the configuration file (config.py) with the path to your dataset and other relevant settings.

3. Train the model:

   python scripts/train_model.py

4. Run the Streamlit app:

   streamlit run app.py

5. Access the web interface: Open your web browser and navigate to http://localhost:8501 to use the IPL-Match-Predictor's Streamlit interface.

Project Structure

IPL-Match-Predictor/
│
├── data/
│   ├── raw/               # Raw dataset files
│   ├── processed/         # Processed dataset files
│
├── models/                # Trained models
│
├── notebooks/             # Jupyter notebooks for exploration and analysis
│
├── scripts/
│   ├── train_model.py     # Script for training the machine learning model
│   ├── predict.py         # Script for making predictions
│
├── app.py                 # Streamlit application script
├── config.py              # Configuration file
├── requirements.txt       # Python dependencies
├── README.md              # Project documentation

Contributing

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -am 'Add some feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request. .

Acknowledgements

• IPL Data Source for providing the historical IPL match data.
• The contributors and maintainers of the various open-source libraries used in this project.

Contact

For any queries or feedback, please contact Utkrist Ark.

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Happy Predicting! 🏏