{"repo":"AmirhosseinHonardoust/Fraud-Detection-SQL-Unsupervised","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Fraud-Detection-SQL-Unsupervised","clone":"git clone https://github.com/AmirhosseinHonardoust/Fraud-Detection-SQL-Unsupervised.git","description":"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.","language":"Python","stars":27,"topics":["anomaly-detection","banking-data","data-analysis","data-science","financial-analytics","fraud-detection","isolation-forest","machine-learning","portfolio-project","python"],"license":"MIT","category":"machine-learning","readme_excerpt":"Fraud Detection [SQL + Python (Unsupervised)] Detect potentially fraudulent bank transactions using SQL (SQLite) for feature engineering and Python for unsupervised anomaly detection with Isolation Forest. --- Overview This project demonstrates a practical fraud detection workflow where no labeled data is available. It integrates SQL-based data aggregation with machine learning anomaly detection , showing how data engineers and analysts can uncover unusual transaction patterns in banking or financial systems. --- Workflow 1. Load transaction data into SQLite 2. Run SQL feature engineering - Compute user-level metrics (average amount, total amount, number of transactions) - Compute daily activity (daily totals and transaction counts) 3. Apply Isolation Forest to detect anomalies based on aggregated behavioral features 4. Generate outputs - Ranked anomaly scores - Summary tables - Distribution chart --- Project Structure --- Dataset Schema Column Description --------- -------------- tx id Transaction ID user id Unique user identifier date Transaction date region User region merchant Merchant name or type amount Transaction amount --- SQL Feature Engineering Feature generation is handled by src/queries.sql . It builds temporary SQL views to calculate user statistics and daily activity. --- Machine Learning The unsupervised model uses Isolation Forest from scikit-learn. - Detects outliers based on feature deviation - Flags top anomalies (typically 2–3% of all transactions) - Prod","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Fraud-Detection-SQL-Unsupervised/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}