• Ch. Vishwanath Reddy
• P.Kavya Sri
• T.Rishitha
• Dr.M.I.Thariq Hussan

It is compelling that credit card able to pick out fraudulent credit card transactions so users can be delinquent and can be assure of their assets. Those hurdles can be address with Data Science and its eminence, along with Machine Learning, cannot be exaggerate. This project envisage to portray the modelling of a data set using machine learning and XG Boost with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem involves customizing past credit card transactions with the data matching to be fraud. This model is then used to recognize whether a new transaction is illicit or not. We precisely lean towards finding deceitful transactions and minimizing the fraud transactions using XG boost. Credit Card Fraud Detection is a complex swatch of classification. In this complex system , we have focused on pre-processing and analyzing labelled data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Random forest, XG Boost, and Decision factor on the PCA transformed Credit Card Transaction data.

Credit card fraud, applications of machine learning, local outlier factor, automated fraud detection, Random forest, XG Boost, and Decision factor

"Credit Card Fraud Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g409-g415, April-2024, Available :http://www.jetir.org/papers/JETIR2404654.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.11, Issue 4, page no. ppg409-g415, April-2024, Available at : http://www.jetir.org/papers/JETIR2404654.pdf