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The model was developed as part of the final project for the online course PyTorch Basics for Machine Learning. The primary objective of this project was to apply the machine learning techniques I learned throughout the course to a real-world dataset, using PyTorch to predict the outcomes of League of Legends (LoL) matches based on historical data.\n\n## Files\n- LoL_Match_Outcome_Predictor.ipynb: Jupyter notebook with all code, explanations, and visualizations.\n- league_of_legends_data_large.csv: The used dataset\n- requirements.txt: List of required libraries and dependencies for running the project.\n- LICENSE: Open-source license.\n\n## How to Use\n1. Clone the repository:\n```bash\ngit clone https://github.com/codedby-mozz/LoL-match-predictor.git\n```\n\n2. Install dependencies\n```bash\npip install -r requirements.txt\n```\nor run the setup code in the Jupyter notebook.\n\n3. Open the notebook and run the cells to see the results, modify the model, and tune parameters as needed.\n\nThe notebook includes detailed explanations and visualizations of the model's performance, including:\n- Confusion Matrix\n- ROC Curve\n- Classification Report\n\n## Requirements\n- Python 3.x\n- torch\n- sklearn\n- matplotlib\n- pandas\n- numpy\n\n## License\n\n[MIT](https://choosealicense.com/licenses/mit/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodedby-mozz%2Flol-match-outcome-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodedby-mozz%2Flol-match-outcome-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodedby-mozz%2Flol-match-outcome-predictor/lists"}