Hybrid Ai model for online payment fraud detection using machine learning
Objective
The project aims to develop a scalable, modular fraud detection system using a hybrid AI approach that integrates SVM, Random Forest, XGBoost, and Voting Classifier to predict fraudulent online transactions, providing real-time alerts, enhanced accuracy, and adaptability across various platforms, with performance evaluated through metrics like accuracy, precision, recall, and F1-score.
Abstract
Keywords : Support Vector Machine (SVM), Random Forest (RF), XGBoost, Voting Classifier.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
1 SOFTWARE REQUIREMENS
Operating System : Windows 7/8/10
Server side Script : HTML, CSS, Bootstrap & JS
Programming Language : Python
Libraries Flask, Pandas, Torch, Sklearn, Librosa,Numpy , Seaborn, Matplotlib
IDE/Workbench : VSCode
Server Deployment : Xampp Server
Database : MySQL
2 HARDWARE REQUIREMENTS
Processor - I3/Intel Processor
RAM - 8GB (min)
Hard Disk - 128 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three
Demo Video
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