This project was developed to predict the outcomes of UFC (Ultimate Fighting Championship) fights using fight data from UFC Stats and a Random Forest Classifier. The model is built using Python and the scikit-learn module.
The dataset used in training the machine learning algorithm contains data on UFC fights, including fight statistics such as Significant Strikes, Submission Attempts, and Takedowns and fight outcomes. Data processing involves handling missing values and standardizing data for analysis. Additionally, some new features like fighters' win/loss ratios and average fight duration are added.
The dataset is split into training and testing sets. A Random Forest Classifier from the scikit-learn library is trained on the data.
To run this project, clone the repository and install the required dependencies:
git clone https://github.com/b-temirov/UFC-fight-prediction.git cd UFC-fight-prediction pip install -r requirements.txt
To use the prediction system, ensure the dataset is available and run the Jupyter notebook or the Python script:
jupyter notebook ufc_fight_prediction.ipynb