Welcome to the UFC Fight Winner Prediction System project! This repository contains the code and resources for a machine learning model that predicts the outcome of UFC fights based on historical data. The project utilizes a Kaggle dataset spanning from 1993 to 2021, employs Logistic Regression for prediction, integrates fighter images using the www.fightingtomatoes.com API, and offers a user-friendly web app powered by Streamlit for fight outcome predictions.
- Utilizes a Kaggle dataset covering UFC fights from 1993 to 2021 for training and prediction.
- Implements Logistic Regression to predict fight outcomes based on historical data.
- Enhances user experience by fetching fighter images through the fightingtomatoes API.
- Presents a user-friendly web interface with Streamlit for easy fight winner predictions.
Try out the live demo of the UFC Fight Winner Prediction System and test its accuracy in predicting fight outcomes!
Follow these steps to set up the project locally:
- Clone the repository: git clone https://github.com/heisenberg3376/UFC-fight-winner-Prediction-System.git
- Install the required Python packages: pip install -r requirements.txt
- Run the Streamlit app: streamlit run app.py
- Open your web browser and navigate to
http://localhost:8501to access the app.
- Open the Streamlit app in your web browser.
- Input the names of two fighters competing in a UFC match.
- Click the "Predict" button to receive the predicted outcome of the fight.
The project relies on the following key dependencies:
- Python (>=3.6)
- Streamlit (>=1.0)
- Pandas (>=1.3)
- Scikit-learn (>=0.24)
- Streamlit-option-menu
For a complete list of dependencies, please refer to the requirements.txt file.
Contributions are welcome! If you find a bug or want to enhance the project, feel free to submit a pull request. For major changes, please open an issue first to discuss the proposed changes.
This project is licensed under the MIT License.