Credit Card Fraud Detection using Logistic Regression Compared with t-SNE to Improve Accuracy

Abstract

Aim: The principle objective of this article is to improve the accuracy of Credit card fraud detection using Novel logistic regression compared with the t-SNE. Materials and Methods: The categorizing is performed by adopting a sample size of n = 10 in novel logistic regression and sample size n = 10 in t-SNE with a sample size = 10, obtained using the G-power value 80%. Results: The analysis of the results shows that the Novel logistic regression has a high accuracy of (99.89) in comparison with the t-SNE(60.99). There is a statistically significant difference between the study groups with (p< 0.05). Conclusion: For Credit card fraud detection it shows that the Logistic Regression appears to generate more accuracy than the t-SNE