Credit Card Fraud Detection Using Naive Bayes and Robust Scaling Techniques

The Internet is an important element of our life. Due to the wide use of the internet, the status of online shopping is increasing day by day. The Credit Card is the easiest method for online shopping and paying bills. Therefore, Credit Card becomes popular and appropriate approach for online money transaction and it is growing very quickly. In this paper, machine learning algorithms are utilized for the detection of credit card fraud. Firstly, common type of models is used. After that, hybrid methods which can use to Ada Boost and majority voting methods are activated. Ada Boost method is able to develop the individual results from different algorithms. For finding efficiency so we are used Kaggle dataset. Using we are calculated the result.In this paper, to group the main factors that can manual for unrivaled precision in Visa false exchange location procedure. Moreover, we clarify the presentation of various directed AI calculations that are existed in writing against the great classifier that it executed in this paper. The end-product of this framework have emphatically distinguished that most of casting a ballot technique acquires better quality, precision proportions in getting extortion cases in Mastercard for recognizable proof of genuine Mastercard exchange information.