AmirhosseinHonardoust/Fraud-Detection-SQL-Supervised
Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.
This project helps financial analysts and fraud prevention teams automatically identify suspicious transactions. It takes your historical transaction data, including which transactions were confirmed fraud, and generates new insights by calculating user spending patterns. The output is a list of transactions ranked by their probability of being fraudulent, along with performance metrics and visualizations to show how well the system works.
Use this if you have past transaction data with known fraud cases and need to build a system to predict future fraudulent activity.
Not ideal if you don't have labeled data indicating which past transactions were fraudulent, as this system requires that information to learn.
Stars
34
Forks
1
Language
Python
License
MIT
Category
Last pushed
Oct 21, 2025
Commits (30d)
0
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