Real-Time Anomaly Detection Framework

Develop a scalable Python anomaly detection framework using scikit-learn and numpy that can process streaming data in real-time, identifying statistical outliers across multiple domains. The solution must support multiple detection algorithms (Isolation Forest, Local Outlier Factor, Z-Score), provide configurable sensitivity thresholds, and generate actionable alerts with comprehensive metadata about detected anomalies. Include a modular design allowing easy integration with message queues and logging systems.