Comparative Analysis of Credit Card Fraud Detection using Logistic regression with Random Forest towards an Increase in Accuracy of Prediction
M.Vamsi Krishna, J. Praveenchandar
Abstract
The study aims to identify the frauds committed using a payment card such as credit cards, debit cards, and also an experiment is performed to find the best suitable algorithm among Random forest and Logistic Regression. Materials and Methods: To stop the fraud detections using Random forest (N=10) and Logistic regression (N=10) with supervised learning that gives insights from the previous data. Results: The precision of the random forest is 76.29% compared with Logistic regression with accuracy of 74.65% with statistical significance value p=0.03 (p<0.05) using Independent sample t test. Conclusion: This results proved that Random forest was significantly better for Fraud detection than Logistic regression within the study’s limits.