Online Payment Fraud Detection using Logistic Regression A Machine Learning Approach

Authors

  • Umesh Shelare UG Student, Yeshwantrao Chavan College of Engineering, Nagpur - 441110, Maharashtra, India
  • Pradyunya Chunchwar UG Student, Yeshwantrao Chavan College of Engineering, Nagpur - 441110, Maharashtra, India
  • Yash Jambhulkar UG Student, Yeshwantrao Chavan College of Engineering, Nagpur - 441110, Maharashtra, India
  • Aman Verma UG Student, Yeshwantrao Chavan College of Engineering, Nagpur - 441110, Maharashtra, India

Keywords:

Machine learning, Logistic regression, classification, Fraud detection

Abstract

In today's world, online transactions have gotten to be an integral part of people's lives, advertising benefits such as ease of utilize, feasibility, and speedier installments. In any case, along with these points of interest, online transactions moreover come with the hazard of fraud, phishing, and data misfortune. Commercial banks and insurance companies contribute altogether in developing transaction detection frameworks to anticipate high-risk transactions and relieve related dangers. In this study, we present a machine learning-driven fraud detection model for transactions, which involves feature engineering. The model leverages calculations that can learn from expansive amounts of data to progress stability and performance.

References

A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective - Samaneh Sorournejad, Zojah, Atani et.al - November 2016

Support Vector machines and malware detection - T.Singh,F.Di Troia, C.Vissagio , Mark Stamp - San Jose State University - October 2015

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Published

29-04-2023

How to Cite

Umesh Shelare, Pradyunya Chunchwar, Yash Jambhulkar, & Aman Verma. (2023). Online Payment Fraud Detection using Logistic Regression A Machine Learning Approach. International Journal for Research Publication and Seminar, 14(3), 228–234. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/495

Issue

Vol. 14 No. 3 (2023): Vol 14 | Issue 3 | NCASIT 2023

Section

Original Research Article

License

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