DeepShot

🚀 Introducing DeepShot: An NBA Game Prediction Model 🏀 Hey devs, sports fans, and data nerds! After weeks of work, I'm excited to share DeepShot – an advanced NBA game predictor powered by historical data from Basketball Reference, machine learning, and a clean NiceGUI-powered web interface. 🔍 What it does: DeepShot uses team-level rolling averages (including Exponentially Weighted Moving Averages) and an Elo rating system to accurately predict NBA game outcomes. All predictions are visualized in real time through a sleek, responsive UI. 🧠 Key Features: 📊 Data-Driven Predictions using past performance & rolling trends ⚡️ EWMA-based Weighted Stats Engine 🏀 Elo Ratings for contextual team strength 🌐 Cross-platform interface built with NiceGUI 🔎 Key stats highlight to visualize matchup advantages at a glance 👨‍💻 Tech Stack: Python Pandas, Scikit-learn, XGBoost BeautifulSoup, Requests NiceGUI for the frontend Hosted locally, runs on Windows/macOS/Linux 📥 Clone it here → github.com/saccofrancesco/deepshot 🧠 Want to see how predictive modeling and sports analytics come together? This is for you. 🔁 Feedback, stars, forks, and PRs are more than welcome! Let me know what you think, or drop your ideas for improvements — always open to suggestions! 🧠 #NBA #Python #MachineLearning #SportsAnalytics #OpenSource #NiceGUI #PredictiveModeling #GitHub #XGBoost #EWMA #EloRating #Basketball