This notebook details the implementation of a comprehensive anomaly detection system for API access behaviors. It involves data preprocessing, extensive feature engineering, and the training of multiple machine learning models including Random Forest, XGBoost, LightGBM, and Isolation Forest. The analysis identifies key behavioral patterns and discovers thousands of new potential anomalies missed by existing algorithmic classifications.