Python framework for online learning in machine learning with streaming data pipelines, concept drift detection, and prequential evaluation.

Client-server backend for real-time Activity of Daily Living (ADL) classification. Handles window ingestion, prediction, label requests, and incremental model updates.

Complete system for the classification of Activities of Daily Living (ADL) by collecting inertial data from smartphones and evaluating supervised models (RF, SVM) under a Stream Learning approach, including online architecture for real-time classification.

Real-time NYC subway anomaly detection: GTFS-RT, online ML (River), live Mapbox command center tracking 1000+ stations.