Aviator Predictor Real-Time Analytics

I need a working Aviator predictor (version 12) that focuses on real-time predictive analytics. All incoming data will arrive through an API I already have, so your first step is to hook the model up to that feed, validate the data stream, and keep it in sync with low latency. Core goals • Ingest real-time game data via my API. • Generate a live prediction for each round with clear confidence scoring. • Display the next outcome locally (CLI or lightweight web view) and expose it through a simple REST or WebSocket endpoint so I can embed it elsewhere. • Log every prediction with a timestamp so we can back-test performance. What I’m looking for • Clean, well-commented code in any mainstream language (Python, Node.js, or similar). • A lightweight model that prioritises speed over heavy compute. If you can incorporate adaptive learning so accuracy improves during the session, even better. • Clear setup instructions and a one-command deployment (Docker compose or comparable). • Brief user guide that shows how to hit the endpoints, interpret the confidence score, and restart the service. This project is only about the live predictive engine—no dashboards, no historical analysis batches. If you have prior experience with rapid-frequency API data, low-latency prediction, or gaming-odds models, mention it; samples or short demos are welcome. I’m ready to move quickly once I see a solid approach.

Project ID: 40423780