• Jisha .M.V
• Dr. D .Vimal Kumar

Credit card payment has now become popular .Credit card is an easiest way to pay directly through your bank account. In every mode of transaction method ,there is pros and cons, there exists many credit cards fraud committed online , via web ,phone shopping or card holder-not-present. However ,there is very limited information available to distinguish dynamic fraud from genuine customer behavior in such an extremely sparse and imbalanced data environment, which makes the instant and effective detection become more and more important and challenging. In this paper , a comparative analysis is done using four data mining classification algorithms, namely, Random Forest, Logistic Regression ,K-nearest neighbor and Support Vector Machine, to detect the efficient algorithm which is having high accuracy in detecting the credit card fraud, as the transaction increases day by day.

Random Forest, Logistic Regression , K-nearest neighbor and Support Vector Machine, confusion matrix.

"An Efficient Credit Card Fraud Classifier Of The Four Data Mining Classification Algorithms-A Comparative Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.371-378, November-2018, Available :http://www.jetir.org/papers/JETIR1811656.pdf

"An Efficient Credit Card Fraud Classifier Of The Four Data Mining Classification Algorithms-A Comparative Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp371-378, November-2018, Available at : http://www.jetir.org/papers/JETIR1811656.pdf