Problem Statement Crypto traders lack interpretable, short-horizon price signals that combine market sentiment with price momentum. Most sentiment tools are disconnected from live trading data and have no production ML lifecycle behind them.

Solution & Approach Built a production-grade ML system that fetches live Bitcoin price data and news every 15 minutes via GitHub Actions, engineers 40+ features combining VADER and FinBERT sentiment scores with price and temporal signals, and trains/evaluates an ensemble of XGBoost, LightGBM, Random Forest, and Logistic Regression models. PSI drift detection gates model promotion; rollback restores the last stable model automatically. A Next.js frontend visualises live predictions with confidence intervals.

Key Highlights 15-minute fully automated pipeline via GitHub Actions Dual sentiment engine: VADER (speed) + FinBERT (accuracy) compared head-to-head 40+ engineered features combining sentiment, price, and temporal signals PSI drift detection with automatic rollback to last stable model Sub-200ms inference latency on Google Cloud Run Note : This project has backend that is deployed on free-tier service which may cause the backend to be suspended GitHub Link : https://github.com/AhmedIsmailKhalid/Enhanced-News-Sentiment-Analysis-for-Bitcoin-Price-Prediction