Credit Card Fraud Detection Using XGBoost Algorithm

Credit card is one of the modern payment methods widely spread all over the world. It provides excellent facilities in purchasing as well as selling operations. However, it suffers from fraud problems, causing considerable economic losses to banks, institutions, and individuals, amounting to billions of dollars annually. That has made great interest in finding systems and means with outstanding capabilities to confront fraud, whose patterns in addition to methods are increasing dramatically. One of the most prominent techniques used by researchers in this field is Machine Learning (ML) techniques. In this paper, we proposed some of the classification ML algorithms such as Logistic regression(LR), Linear Discriminant Analysis (LDA), and Naïve Bayes(NB), additionally, the boosting algorithm XGBoost to create models capable of detecting fraud. The dataset from Kaggle. We used performance metrics such as accuracy, precision, f1, recall, AUC confusion matrix to evaluate the models' performance. The XGBoost model presented the best results compared to other models.