E-Commerce Fraud Detection Based on Machine Learning | Python Final Year IEEE Project 2025
E-Commerce Fraud Detection Based on Machine Learning | Python Final Year IEEE Project 2024 - 2025.
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📌Project Title: E-Commerce Fraud Detection Based on Machine Learning
💡Implementation: Python.
🔬Algorithm / Model Used: Stacking Classifier, XGB Classifier.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.3000/
📘Project Abstract:
The project titled "E-Commerce Fraud Detection Based on Machine Learning" addresses the critical challenge of identifying fraudulent transactions in e-commerce platforms. Developed using Python for backend processing and HTML, CSS, and JavaScript for the frontend interface, the system is integrated within the Flask web framework to deliver a responsive and interactive user experience.
The project leverages two advanced machine learning models: a Stacking Classifier and an XGB Classifier. The Stacking Classifier achieves an impressive train accuracy of 100% and a test accuracy of 99%, while the XGB Classifier attains a train accuracy of 96% and a test accuracy of 95%. These high performance metrics underscore the models' effectiveness in distinguishing between legitimate and fraudulent transactions.
📌IEEE Base paper Title: E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review.
📌REFERENCE:
Abed Mutemi, Fernando Bacao, “E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review”, Big Data Mining and Analytics ( Volume: 7, Issue: 2, June 2024), IEEE Journal 2024.
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