Credit Card Fraud Detection using Ensemble Learning
Handles extreme class imbalance in transaction data using SMOTE and an XGBoost ensemble, catching fraudulent transactions with a recall of 96% while keeping false positives low.
- Project code
- AUX-AML-205
- Domain
- AI & ML
- Type
- Software project
- Level · Year
- Advanced · 2024-25
Technologies and components
What is included
- Complete source code
- Database / dataset and setup guide
- Project report (college format)
- PPT for review and viva
- Installation and demo support
- Viva question guidance
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View project →Frequently asked questions
Can I get the Credit Card Fraud Detection using Ensemble Learning project for my final year?
Yes. This project (code AUX-AML-205) is available as a software project kit for the 2024-25 academic year. Contact us on WhatsApp to request it.
Can this project be customised?
Yes. Features, technology and documentation can be modified to suit your guide and college requirements.
Which departments is this project suitable for?
CSE, IT, AI & DS, BCA and MCA students, and ECE students interested in software.
Get this project with full support
Ask about customisation, delivery time and viva support.
