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@article{152074,
        author = {Varun Sharma  and Rishabh Ladhani and Vivek Srivastava and Mrs. Charu Tyagi and Mrs. Richa Gupta},
        title = {Credit Card Fraud Transaction Detection using Machine Learning Algorithms And Data Science},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {353-358},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152074},
        abstract = {Mastercard cheats are simple and cordial targets. Web based business and numerous other online locales have expanded the online installment modes, expanding the danger for online fakes. Expansion in extortion rates, analysts began utilizing distinctive AI techniques to distinguish and break down cheats in online exchanges. The principle point of the paper is to plan and foster a novel misrepresentation discovery technique for Streaming Transaction Data, with a goal, to break down the past exchange subtleties of the clients and concentrate the standards of conduct. Where cardholders are bunched into various gatherings dependent on their exchange sum. Then, at that point utilizing sliding window methodology [1], to total the exchange made by the cardholders from various gatherings so the personal conduct standard of the gatherings can be extricated individually. Later various classifiers [3],[5],[6],[8] are prepared over the gatherings independently. And afterward the classifier with better evaluating score can be picked to be perhaps the best technique to foresee cheats. Along these lines, trailed by an input component to tackle the issue of idea float [1]. In this paper, we worked with European Mastercard misrepresentation dataset},
        keywords = {Card-Not-Present frauds, Card-Present-Frauds, Concept Drift},
        month = {},
        }