Machine Learning for Credit Card Fraud detection

Volume 20 No 10 (2022)

Soppadandi Nikhila and Dr. U. Poorna Laxmi

DOI: 10.14704/nq.2022.20.10.NQ55901

Abstract

Information about credit cards is lost in credit card fraud. For detection, a variety of machine-learning techniques can be applied. This essay determines whether the transaction is legitimate or fake. After a feature selection process, the dataset was divided into training and test data. Naive Bayes, Logistic Regression, and Random Forest were the algorithms employed in the experiment. Results demonstrate that credit card fraud may be detected with high accuracy using Random Forest Regression

Keywords

Fraud, Logistic Regression, Random Forest, Naïve Bayes

Copyright © Neuroquantology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.