Paper Key : IRJ************832
Author: Konijeti Sri Vyshnavi,Maddala H S M Krishna Karthik,Methuku Samhitha,Suravarapu Ankith
Date Published: 23 Oct 2023
Abstract
Abstract: Fraud has risen dramatically as a result of advances in technology and global connections. Detection and prevention are two approaches for preventing fraud. The dataset used to detect credit card fraud is heavily skewed and different sorts of misclassification errors may incur different costs, so it's critical to keep track of them. Classification techniques have promise for detecting both fraudulent and non-fraudulent transactions. The major goal of this research is to improve the XGBoost (eXtreme Gradient Boosting) strategy by applying resampling approaches to handle a class imbalance in datasets. XGBoost is a popular machine learning model that is utilized in areas such as fraud detection.