• Devendra D. Borse
• Prof. Dr. Suhas. H. Patil

Nowadays, the Internet is an important factor of our life. Due to the wide use of the internet, the status of online shopping is varies 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. To estimate the model efficiency, an openly accessible credit card data set is used. After that, a real-world credit card dataset from a financial organization is evaluated. In addition, noise is added to the examples of data to further evaluate the toughness of the algorithms. In this paper, to classify the most important variables that can guide to superior accuracy in credit card fraudulent transaction detection technique. Additionally, we explain the performance of different supervised machine learning algorithms that are existed in literature against the good classifier that it executed in this paper. The final results of this system have positively identified that the majority of voting method obtains better quality, accuracy ratios in catching fraud cases in credit cards for identification of actual credit card transaction data.

Credit Card, Fraud detection, supervised machine learning, data mining techniques, online shopping, predictive modeling etc.

"CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.352-357, June-2019, Available :http://www.jetir.org/papers/JETIR1906978.pdf

"CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp352-357, June-2019, Available at : http://www.jetir.org/papers/JETIR1906978.pdf