E-Commerce Fraud Detection Using Machine Learning
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Student Project | Computer Technology | India | Volume 14 Issue 4, April 2025 | Popularity: 4.7 / 10


     

E-Commerce Fraud Detection Using Machine Learning

Jyothi Krishna, Sindhu Daniel


Abstract: The rapid expansion of e-commerce has simultaneously increased the sophistication and frequency of fraudulent activities. This paper presents a machine learning-based approach for detecting e-commerce fraud using ensemble models?Stacking Classifier and XGBoost. A synthetic dataset of 23,634 transactions was generated to simulate realistic user and fraud behavior. The proposed system leverages Python and the Flask framework, offering real-time detection capabilities. Evaluation metrics such as precision, recall, and F1-score were used due to the highly imbalanced nature of fraud data. The results indicate significant improvement in fraud detection performance compared to traditional models, particularly in reducing false positives and negatives. The system shows strong adaptability to evolving fraud patterns and can be effectively integrated into e-commerce platforms for live transaction monitoring.


Keywords: E-commerce, Fraud Detection, Machine Learning, XGBoost, Stacking Classifier, Ensemble Learning, Synthetic Dataset


Edition: Volume 14 Issue 4, April 2025


Pages: 1551 - 1555


DOI: https://www.doi.org/10.21275/SR25416171957


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Jyothi Krishna, Sindhu Daniel, "E-Commerce Fraud Detection Using Machine Learning", International Journal of Science and Research (IJSR), Volume 14 Issue 4, April 2025, pp. 1551-1555, https://www.ijsr.net/getabstract.php?paperid=SR25416171957, DOI: https://www.doi.org/10.21275/SR25416171957

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