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@article{194314,
        author = {E.Nagaraju and M.Sai krishna and K. Prem Kumar and Ms. S.Lavanya},
        title = {Credit Card Fraud Transaction Detection Using Hybrid Random Forest and Logistic Regression Ensemble Machine Learning Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4604-4611},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194314},
        abstract = {The recent surge in online and digital payments has resulted in a substantial increase in credit card frauds, making fraud detection a critical task for financial organizations. The task is complex as the number of fraudulent transactions is extremely low compared to legitimate transactions, resulting in most machine learning classifiers becoming biased and thus inefficient in detecting frauds. In this paper, a hybrid machine learning technique is presented using SMOTE to handle class imbalance, Random Forest to identify key features, and Logistic Regression for classification, making the detection process more efficient and reliable. The system also identifies and stores the fraudulent transactions in a separate CSV file, which can be used for further analysis and auditing, thus making the system useful for real-time banking and financial analysis applications.},
        keywords = {Credit Card Fraud Detection, Handling Class Imbalance, SMOTE Oversampling, Random Forest Feature Selection, Logistic Regression Classification.},
        month = {March},
        }