• P.T.S. PRIYA
• SIRIYALA MOHAN KRISHNA

Detection of credit card fraud is currently the issue that arises most frequently in the modern world. Due to the growth of e-commerce platforms as well as online transactions, this has happened. Fraudulent usage of a credit card typically occurs when the card is taken for any unauthorised use, or even when the fraudster utilises the card's information for his own objectives. Currently, there are many credit card issues that we must deal with. A technique for detecting credit card fraud [3] was devised in order to catch fraudulent actions. Machine learning [4] algorithms are the primary focus of this project. Both the Random Forest [2] and Adaboost [1] algorithms are employed. On accuracy, precision, recall, and F1-score, the two algorithms' outputs are compared. On the basis of the confusion matrix [5], the ROC curve [6] is plotted. The algorithms from Random Forest and Adaboost are compared, and the method with the highest accuracy, precision, recall, and F1-score are regarded as the optimal approach for use in fraud detection.

Adaboost, ROC Curve, fraudulent, credit card fraud, Random Forest

"ENHANCING CREDIT CARD FRAUD DETECTION THROUGH MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.j661-j668, June-2023, Available :http://www.jetir.org/papers/JETIR2306978.pdf

"ENHANCING CREDIT CARD FRAUD DETECTION THROUGH MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppj661-j668, June-2023, Available at : http://www.jetir.org/papers/JETIR2306978.pdf