Credit Card Fraud Detection using Imbalance Resampling Method with Feature Selection

Abstract

Many fraud transactions exist in the online world that affects various financial institutions but Credit Card Fraud transaction is the most occurring problem in the world. Credit Card fraud is the situation in which fraudsters misuse credit cards for illegal purposes. Hence, detection of fraudulent transactions is essen-tial. Several researchers have worked on detecting fraud transactions and also provide solutions whose surveys are given in this paper. This study makes a major contribution to research on the detection of Credit Card fraud transactions through Machine Learning Algorithms suchas Decision Tree and Naive Bayes. The data have been selected from Kag-gle and categorize into training (80%) and testing (20%) data. The whole experiment was performed on the Jupyter Notebook tool for which the Anaconda Navigator has been installed. The Heatmap is used for visualization and colorfully represents the data. The main aim of this work is to balance the dataset with Near-Miss Under-sampling Method. The information gain method is applied for feature selection. The best algorithm founded in this paper is Decision Tree with 97% accuracy as compared to Naïve Bayes with 90%. The results are achieved based on Accuracy, Recall, Precision, and F1-score. We have also shown the ROC Curve and Precision-Recall Curve of the algorithm in this paper.