saccofrancesco/deepshot
📦 Open Source Projectsaccofrancesco
AI-powered NBA game outcome predictor leveraging advanced team statistics and trend-based feature engineering.
DeepShot is a specialized machine learning repository tailored for sports analytics, specifically focusing on NBA game outcome prediction. The project emphasizes the importance of feature engineering in sports data, moving beyond basic box scores to incorporate trend-based features that capture team momentum and performance volatility. Technically, the project utilizes a robust stack including Python, pandas for data manipulation, and scikit-learn/XGBoost for predictive modeling. It provides a structured pipeline for data ingestion, preprocessing, and model evaluation, allowing users to experiment with different algorithms to improve forecast accuracy. The repository serves as both a functional tool for sports betting analysis and an educational resource for data scientists interested in time-series forecasting and sports-specific machine learning challenges. By tracking model performance over time, users can identify which statistical indicators are most predictive of success in the modern NBA landscape.
💡Highlights
- ├─XGBoost-based predictive modeling
- ├─Advanced trend-based feature eng
- └─Time-series performance tracking
🎯For
- ├─Sports Data Scientists
- └─Machine Learning Enthusiasts