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@article{175861,
        author = {Padala Gowri Shankar RamSai and G Srinivasa Raju and Medam Siva Reddy and Mohammad Roshan Ali and Penmetsa Vishnu Sai Varma},
        title = {E-COMMERCE FRAUD DETECTION USING MACHINE LEARNING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5252-5261},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175861},
        abstract = {The ”E-Commerce Fraud Detection Based on Machine Learning” project is confronted with the critical issue of identifying fraudulent transactions on an on- line shopping site. Deployed on Python as backend computation and HTML, CSS, and JavaScript for frontend user interface, the system is packaged in the Flask web framework for the purpose of presenting an interactive and dynamic user interface. The project employs two high-performing machine learning algorithms: an XGB Classifier and a Stacking Classifier. High-performing metrics determine the models’ capability to distinguish legitimate versus fraudulent transactions.
Dataset has been generated via 23,634 simulated transactions that have been generated while working with Python’s Faker library along with some additional custom logic to mimic real-life transaction behavior and fraud status. Data includes 16 features like Transaction ID, Customer ID, Transaction Amount, Payment Method, and binary fraud flag, etc. All the features combined aim to capture the richness of customer profiles and the transaction behavior so that the fraud can be identified. The outcomes of the project demonstrate the efficiency of machine learning techniques to enhance security and trust in e-commerce sites and providing a useful tool to prevent financial loss due to fraudulent activities.},
        keywords = {Fraud Detection, Machine Learning, Synthetic Data Generation, Faker Library, Anomaly Detection, Online Transaction Security, Stacking Classifier, XGBoost (XGB) Classifier.},
        month = {April},
        }