Comparison of Logistic Regression and Decision Tree method for Credit Card Fraud Detection

Abstract

The use of Credit cards has drastically increased as they become one of the vital and most used modes of payment. Along with this the Credit Card. Fraud has also grown to a greater extent. Thus, it becomes vital for Credit card companies or banks to identify fraudulent transactions. Machine learning algorithms are used to analyze and identify suspicious transactions. Credit Card Fraud detection is a typical case of classification and data analysis, in this, we have focused on analyzing and comparing the data on parameters like accuracy, precision on the basis of two supervised learning classification algorithm Logistics Regression and Decision tree.

Fraud Prediction in Movie Theater Credit Card Transactions using Machine Learning

“…This study achieved the best prediction performance result. [35] Logistic Regression 77.97% [36] Logistic Regression 99.26% [37] Logistic Regression 91.20% [38] Logistic Regression 74.65% This study Logistic Regression 99.88%…”

“…The accuracy results of this study were compared with similar studies that used LR to detect fraudulent credit card transactions, as shown in Table II. The study in [35] used a credit card transaction dataset from the UCI Machine Learning Data Repository to detect frauds in credit card transactions, while studies [36][37][38] used the same dataset as this study. All these studies used the LR method to achieve high accuracy and predictive performance, regardless of their individual approaches.…”