• Sheetal

With the fastest growing development in technology and improvement in communication channels. There is a large number of online transactions that take place each and every day which is paid by credit card is targeted by fraudulent activities. If these transactions could be identified, detected automatically they would be very helpful in fraud detection. For this classification, the technique is used to classify data of different kinds. It will predict the class labels for the new data. In this paper, a comparative analysis of these classification techniques such as J48 which is a type of decision tree classifier and naïve Bayes classifier is used. Three open source data mining tools: Orange, Weka, and Rapid miner to predict and classify the customer’s credit card dataset (Good/Bad) to know which technique and tool work better in predicting fraud values with the highest accuracy parameter. Parameters to be used are accuracy and error rate. Three credit card fraud detection data set is sourced from UCI Machine learning repository, kaggle.com, and OpenMl. The result shows that the orange tool with the naïve Bayes algorithm shows the highest accuracy and lowest error rate for all three datasets. Section 1 of this paper presents Introduction, Section2 presents Literature survey, section3 Objectives, and Problem statement, Section 4 presents Methodology, Section 5 Results, and Discussion and Section 6 Present Conclusion and future scope.

Data Mining, credit card fraud datasets, Classification, Datamining tools.

"Comparative Analysis of Different Credit Card Fraud Detection Datasets using Data Mining Tools and Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.911-917, June-2019, Available :http://www.jetir.org/papers/JETIR1906564.pdf

"Comparative Analysis of Different Credit Card Fraud Detection Datasets using Data Mining Tools and Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp911-917, June-2019, Available at : http://www.jetir.org/papers/JETIR1906564.pdf