• Electa Alice Jayarani A
• Vairajyothi.C
• Ramani N
• Sakthi Priyanka.G
• M.B.Sakthi Selva Deebika

Credit must card organizations can distinguish false Mastercard exchanges with the goal that clients are not charged for things that they didn't buy. Such issues can be handled with Data Science and its significance, alongside Machine Learning, couldn't possibly be more significant. This project means to show the displaying of an informational index utilizing AI with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem incorporates displaying past credit card exchanges with the information of the ones that ended up being extortion. This model is then used to perceive whether a new exchange is false or not. Our goal here is to identify 100 percent of the false exchanges while limiting the wrong misrepresentation arrangements. Charge card Fraud Detection is an average example of arrangement. In this cycle, we have centered on breaking down and pre-handling informational collections as well as the organization of different abnormality identification calculations, for example, Neighborhood Outlier Factor and Isolation Forest calculation on the PCA changed Credit Card Transaction information.

Credit card, Data science, R programming

"CREDIT CARD EXTORTION DETECTION USING R", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.a431-a435, January-2023, Available :http://www.jetir.org/papers/JETIR2301059.pdf

"CREDIT CARD EXTORTION DETECTION USING R", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppa431-a435, January-2023, Available at : http://www.jetir.org/papers/JETIR2301059.pdf