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@article{178836,
        author = {P. Sethupathi and Mr. M. Asan nainar},
        title = {CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6427-6432},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178836},
        abstract = {This project focuses on detecting credit card fraud using machine learning techniques. The system is built using Python and the Flask web framework for the backend, allowing real-time predictions through a user-friendly interface. The frontend is designed with HTML, CSS, and JavaScript to make the application easy to use. The model is trained on a real-world dataset of credit card transactions and uses features like transaction amount, time, and user behavior patterns to identify fraud. We used the Random Forest algorithm because of its high accuracy and ability to handle large datasets. The model is saved using joblib and integrated into the web application to provide instant feedback on whether a transaction is fraudulent or not. This system can help banks and financial institutions quickly detect and prevent fraud, protecting users from unauthorized activities. In the future, this system can be improved by adding live data monitoring, better visualization of results, and support for multiple types of fraud detection.},
        keywords = {Machine Learning, Flask, Credit Card Fraud, Python, Random Forest, Fraud Detection System, Web Application, Financial Security, Real-time Prediction, Data Analysis},
        month = {May},
        }