Abstract
Abstract: Credit cards are becoming the most widely utilized form of payment. The numbers of fraud users are growing as quickly as the technology. This paper discusses the performance of three popular Machine Leaning techniques for predicting credit card fraud detection. In this paper we used Machine Learning algorithms such as Decision Tree, Random Forest and Simple Regression tree for handling the imbalanced credit dataset. KNIME is used as data analytical tool for the purpose of simulation. The Comparative Study related to Accuracy is being tested and the Regression tree will give the best result
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