Detect suspicious financial transactions using SQL and Python. Build user-level behavioral features in SQLite, apply Isolation Forest for anomaly detection, and visualize high-risk patterns. Demonstrates unsupervised fraud analytics and SQL-driven data science workflow.

🕵️♂️ Detect fraud in bank transactions using SQL for feature engineering and Python's Isolation Forest for unsupervised anomaly detection.

End-to-end fraud detection using SQL & Python — logistic regression + unsupervised anomaly scoring

End-to-end financial fraud detection pipeline using SQL, Python, unsupervised anomaly detection, and XGBoost. Includes 1M-transaction dataset, feature engineering, risk scoring, and model interpretation.