{"id":24647382,"url":"https://github.com/deepaksilaych/ipl-win-predictor","last_synced_at":"2025-04-30T17:42:51.986Z","repository":{"id":248368060,"uuid":"828503029","full_name":"DeepakSilaych/ipl-win-predictor","owner":"DeepakSilaych","description":"Welcome to the \"IPL Win Predictor\" project! 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This machine learning model, built using logistic regression, predicts the probability of a team winning an IPL match based on the current match situation. Get ready to make data-driven predictions!\n\n## About This Project\n\nThe \"IPL Win Predictor\" leverages logistic regression to provide insights into the probability of a team winning an IPL match. This model analyzes various match features, team performance, and player statistics to offer real-time predictions.\n\n### Features\n\n- **Real-Time Predictions**: Get live predictions for IPL match outcomes based on the current match situation.\n\n- **Interactive Interface**: The predictor is deployed on Streamlit, offering a user-friendly interface for exploring match scenarios.\n\n- **Customizable Inputs**: Adjust the match parameters and teams to simulate different match scenarios.\n\n- **Deployment**: Hosted on Streamlit Cloud for easy access and sharing.\n\n## Usage\n\nTo make predictions, provide the following parameters when prompted:\n\n- **Batting Team**: The team currently at bat.\n- **Bowling Team**: The team currently bowling.\n- **City**: The location of the match.\n- **Current runs**: The current score of batting team.\n- **Overs Completed**: The number of overs completed.\n- **Wickets**: The number of wickets lost.\n- **Target Runs**: The total runs scored by a bowling team.\n\nThe predictor will calculate the probability of the batting team winning based on these parameters and the current match situation.\n\n\n## Technologies Used\n\nThis project leverages the following technologies:\n\n- [Python](https://www.python.org/)\n- [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n- [NumPy](https://numpy.org/)\n- [pandas](https://pandas.pydata.org/)\n- [Streamlit](https://www.streamlit.io/)\n\n## Installation\n\nTo run this project locally, follow these steps:\n\n1. Clone the repository to your local machine using this command:\n\n   ```shell\n   git clone https://github.com/DeepakSilaych/ipl-win-predictor.git\n   ```\n\n2. Navigate to the project directory:\n\n   ```shell\n   cd ipl-win-predictor\n   ```\n\n3. Install the required Python libraries:\n\n   ```shell\n   pip install -r requirements.txt\n   ```\n\n4. Run the Streamlit app locally:\n\n   ```shell\n   streamlit run app.py\n   ```\n\n5. Open the provided local URL in your web browser to access the IPL Win Predictor.\n\n## Usage\n\nTo make predictions, provide the current match situation including team performance, player statistics, and match conditions. The predictor will calculate the probability of a team winning.\n\n\n\n## Contribute\n\nIf you'd like to contribute to this project or have suggestions for improvement, please feel free to submit issues or pull requests on [GitHub](https://github.com/DeepakSilaych/ipl-win-predictor).\n\nThank you for exploring the \"IPL Win Predictor\"! We hope this tool assists your IPL match predictions. 🏏🌟\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepaksilaych%2Fipl-win-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeepaksilaych%2Fipl-win-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepaksilaych%2Fipl-win-predictor/lists"}