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@article{171010,
        author = {Shivabasayya Kulkarni and Doddamani Basavaraj and Bhagyashri M},
        title = {RANDOM FOREST FOR CREDIT CARD FRAUD DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2026-2030},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171010},
        abstract = {The proposed framework is introduced using real-world credit card transactions for fraud detection. As credit card transaction increases, fraudulent transactions also increase exponentially. Banks also seek to improve the identification of fraudulent transactions to reduce losses. The fraudulent transaction primarily occurs because the card or cardholder needs not to be present during the transaction process and the seller has no way to check whether the payment is made by the cardholder or not. The proposed method uses the algorithm, Random Forest, to locate fraud transactions and precision for such transactions. Decision trees are used for dataset classification. Random forest output is a confusion matrix used to evaluate results.},
        keywords = {Random Forest, Credit Card, Fraud Detection, Fraudulent Transaction.},
        month = {December},
        }