Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data

Authors

  • N.Adityasundar
  • T.SaiAbhigna
  • B.Lakshman

DOI:

https://doi.org/10.46243/jst.2020.v5.i3.pp138-146

Keywords:

Credit card, machine learning, fake, transactions

Abstract

:Most online customers use cards to pay for their purchases. As charge cards become the most mainstream strategy for installment, instances of misrepresentation relationship with it too increases. The primary goal of this venture is to be ready to perceive false exchanges from non-fake exchanges. In request to do so,primarily,data mining methods are utilized to examine the examples and attributes of deceitful and non-fake transactions.Then,machine learning systems are utilized to foresee the fake and non-fake exchanges automatically. Algorithms LR (Logistic Regression) is used. Therefore, the blend of AI and information mining procedures are utilized to distinguish the fake and non-fake exchanges by learning the examples of the information. Models are made utilizing these calculations and afterward precision,accuracy,recall are determined and an examination is made.

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Published

2020-05-14

How to Cite

N.Adityasundar, T.SaiAbhigna, & B.Lakshman. (2020). Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data. Journal of Science & Technology (JST), 5(3), 138–146. https://doi.org/10.46243/jst.2020.v5.i3.pp138-146

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