Anomaly Detection

## Overview An advanced anomaly detection system combining multiple algorithms to identify outliers with high precision and recall. The system is designed for production use with real-time monitoring capabilities. ## Approach The framework employs an ensemble of detection methods: - **Isolation Forest**: Fast anomaly detection in high-dimensional spaces - **One-Class SVM**: Boundary-based outlier identification - **Autoencoders**: Deep learning approach for complex patterns - **Statistical Methods**: Z-score and modified Z-score analysis ## Performance Metrics The ensemble approach achieves: - **Precision**: 96.5% - **Recall**: 94.2% - **F1-Score**: 95.3% - **ROC-AUC**: 0.98 ## Real-World Applications Successfully deployed for: - Fraud detection in financial transactions - Quality control in manufacturing - Network intrusion detection - Medical diagnostic anomaly identification ## Innovation The key innovation is an adaptive threshold mechanism that adjusts sensitivity based on data distribution changes, ensuring consistent performance over time.