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@article{149915,
        author = {Mohan kumar B and Bharanidharan K and Chennakesavan RB and Guhan kumar P},
        title = {CREDIT CARD TRANSACTION CLASSIFICATION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {1},
        pages = {1052-1055},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=149915},
        abstract = {In our day by day life Credit cards are sued for buying products and ventures with the assistance of virtual card for online exchange or physical card for disconnected exchange. In a physical-card based buy, the card holder presents his card genuinely to a dealer for making an installment. To do deceitful exchanges in this sort of procurement; an aggressor needs to take the Credit card. On the off chance that the card holder doesn't understand the loss of card, it can prompt a generous money related misfortune to the Credit card organization. In online installment mode, aggressors need just little data for doing deceitful exchange (secure code, card number, lapse date and so on.). In this buy technique, essentially exchanges will be done through Internet or phone. To submit misrepresentation in these kinds of buys, a fraudster essentially has to realize the card subtleties. More often than not, the certified card holder doesn't know that another person has seen or taken his card data. The best way to recognize this sort of extortion is to examine the examples on each card and make sense of any irregularity as for the typical example. The most ordinarily realized classifier Support Vector Machine alongside Radial premise work part is utilized with AdaBoost a boosting calculation so as to enhance the exactness.},
        keywords = {Support Vector Machine, Adaboost, Credit card, Machine Learning},
        month = {},
        }