Python Developer Needed for Sports Odds / Player Prop Analytics Dashboard

I need a private sports/player prop analytics dashboard built in Python. This is a private analytics tool, not a gambling app that accepts wagers. I want a working Phase 1 MVP that includes both the live odds dashboard and a starter machine learning projection model. The app should be able to: 1. Pull player prop lines from The Odds API or another sports odds API 2. Store live lines in SQLite or PostgreSQL 3. Display player props in a Streamlit dashboard 4. Track line movement: old line vs new line 5. Compare sportsbook consensus lines to my own projected/model line 6. Send Discord webhook alerts when a line moves or when an edge is found 7. Allow CSV upload for historical player/game data 8. Train a starter machine learning projection model in Python 9. Generate projected lines for players/stats 10. Show projected line, sportsbook line, edge amount, lean, and confidence/probability estimate 11. Include simple backtesting with win rate and error metrics 12. Use .env for API keys, not hard-coded keys 13. Include requirements.txt, clean commented code, and setup instructions so I can run it myself The machine learning model can start with Linear Regression, Random Forest, XGBoost, or LightGBM. It does not need to be perfect on day one, but it needs to be functional and built in a way that can be improved later. Important: - Mock data is okay for testing, but the final app must support real CSV uploads and real odds API data. - I need full source code ownership. - I need clear setup instructions. - I want this built as a private dashboard, not a public betting platform. Phase 1 MVP must include: - Live odds API integration - Database storage - Streamlit dashboard - Line movement tracking - Discord alerts - CSV upload - Starter machine learning projection model - Basic backtesting - Setup instructions If this first version works well, I may hire again for future upgrades like private login, cloud deployment, multi-sport expansion, better modeling, and a subscription-style private dashboard.