Real-Time Anomaly Detection Framework

Build a Python-based anomaly detection system using statistical methods and machine learning techniques that can process streaming data in real-time. Implement z-score, IQR, and isolation forest algorithms to detect statistical and contextual anomalies across numerical and categorical datasets. Create a flexible architecture that supports multiple input streams, configurable detection thresholds, and automatic alerting mechanisms via email or Slack integration.