This paper is published in Volume-7, Issue-3, 2021
Area
Machine Learning
Author
Vadlamudi Tony Titus, Lakshmi Choudari, Shashank, Gunavardhan
Org/Univ
Gandhi Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, India
Pub. Date
24 May, 2021
Paper ID
V7I3-1433
Publisher
Keywords
Credit Card Transactions, Fraudster, SMOTE, Machine Learning

Citationsacebook

IEEE
Vadlamudi Tony Titus, Lakshmi Choudari, Shashank, Gunavardhan. Credit card fraud detection, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Vadlamudi Tony Titus, Lakshmi Choudari, Shashank, Gunavardhan (2021). Credit card fraud detection. International Journal of Advance Research, Ideas and Innovations in Technology, 7(3) www.IJARIIT.com.

MLA
Vadlamudi Tony Titus, Lakshmi Choudari, Shashank, Gunavardhan. "Credit card fraud detection." International Journal of Advance Research, Ideas and Innovations in Technology 7.3 (2021). www.IJARIIT.com.

Abstract

Credit card Fraud has emerged as a bigger problem with an increase in online transactions and e-commerce. A credit card fraud happens when a fraudster uses your credit card information to make unauthorized purchases in your name. Credit card companies need to address these fraudulent transactions so that customers are not charged for the goods they did not buy. In this paper, we aim to tackle such fraudulent transactions by identifying them using various machine learning algorithms like Logistic Regression, Random Forest and, eXtreme Gradient Boosting. The results of the algorithms are based on accuracy, precision, recall, and F1-score. The algorithms are compared, and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect fraudulent transactions.
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