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@article{179098,
        author = {Yuvraj Singh Pawar and Harsh Pawar and Vilas Khedekar},
        title = {Credit Card Fraud Detection System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7013-7019},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179098},
        abstract = {In this era of digital world, credit card usage has become as a preferred mode of payment for both online and offline transactions. However, this growth was followed by an alarming rise of credit card fraud, which not only poses a significant financial loss to the customer but also damages the credibility of the financial institution. Credit card fraud detection includes analyzing patterns in transaction data to flag irregular activities that may indicate fraudulent behavior. This paper explores machine learning-based approach for detecting fraudulent transactions which uses Random Forest Classifier. The system, built with a Streamlit interface, enables users to upload two datasets, visualize transaction patterns, and identify suspicious activity with high accuracy. Performance metrics including accuracy, precision, recall, and F1-score were used to assess the effectiveness of the model.},
        keywords = {Credit Card Fraud, Fraud Detection, Machine Learning, Random Forest Classifier},
        month = {May},
        }