Abstract: Credit Card Fraud detection is a challenging task for researchers as fraudsters are innovative, quick-moving individuals. The credit card fraud detection system is challenging as the dataset provided for fraud detection is very imbalanced. In today’s economy, credit card (CC) plays a major role. It is an inevitable part of a household, business global business. While using CCs can of er huge advantages if used cautiously and safely, significant credit financial damage can be incurred by fraudulent activity. Several methods to deal with the rising credit card fraud (CCF) have been suggested. In this paper, an ensemble learningbased an intelligent approach for detecting fraud in credit card transactions using XGBoost classifier is used to detect credit card fraud, and it is a more regularized form of Gradient Boosting. XGBoost uses advanced regularization (L1 and L2), which increases model simplification abilities. Furthermore, XGBoost has an inherent ability to handle missing values. When XGBoost encounters node at lost value, it tries to split left right hands learn all ways to the highest loss.
Keywords: component, formatting, style, styling, insert
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DOI:
10.17148/IJARCCE.2022.11388
