This AI project leverages third-party APIs such as Riot Games API and web scraping techniques to extract extensive champion statistics that are key to developing an accurate AI model from League of Legends. All these data will then be organized and preprocessed in the Jupyter notebooks and fed into various AI models, including XGBoost, Random Forest, and Neural Networks, to predict match outcomes based on champion selections and other relevant features. The trained AI models are then integrated into a Flask web application, providing users with match predictions and easy access to player and champion data for personal or experimental purposes.
- Programming Languages: Python
- Web Development: Flask, Jinja2, HTML, CSS, JavaScript
- Data Collection: Riot Games API, Web Scraping (BeautifulSoup, Requests)
- Data Processing: Pandas, NumPy
- Machine Learning: XGBoost, Random Forest, Neural Networks (TensorFlow)
- Model Evaluation: Scikit-learn
- Database Management: SQLAlchemy, PostgreSQL
- Task Management: Celery, Redis (To be implemented)
- Version Control: Git, GitHub
