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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Issue: January 2023

Volume 8 | Issue 1

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Paper Title: Credit Card Fraud Detection using Machine Learning
Authors Name: G.Sridevi , Swathi.P , Manikanta.P , Sowmya.P , Venkatesh.K, Anand.K
Unique Id: IJSDR2006018
Published In: Volume 5 Issue 6, June-2020
Abstract: “Visa extortion” is a wide-extending term for burglary and misrepresentation submitted utilizing or including an installment card, for example, a Mastercard or plastic, as a false wellspring of assets in an exchange. The Credit Card Fraud Classification issue incorporates demonstrating past Visa exchanges with the information on the ones that ended up being extortion. This model is then used to recognize whether another exchange is false or not. The exhibition of misrepresentation recognition in charge card exchanges is significantly influenced by the examining approach on dataset, choice of factors and location technique(s) utilized. The Credit Card Fraud Detection Problem incorporates displaying past Mastercard exchanges with the information on the ones that ended up being extortion. Thus we are utilizing the procedure of AI for misrepresentation recognition. In this we take the genuine bank dataset and split the dataset into preparing set and testing set and afterward apply the Logistic Regression strategy. Our point here is "to recognize the 100% of the deceitful exchanges while limiting the mistaken misrepresentation characterizations." It is implemented in Python.It checks each exchange for the likelihood of being false and to recognize fraudulent ones. The output will be the total no.of fraudulent and non fraudulent transactions, display each transaction as fraud or non fraud and their plots.
Keywords: Fraud detection, credit card, Logistic regression, python, kaggle.
Cite Article: "Credit Card Fraud Detection using Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 6, page no.113 - 120, June-2020, Available :http://www.ijsdr.org/papers/IJSDR2006018.pdf
Downloads: 000201506
Publication Details: Published Paper ID: IJSDR2006018
Registration ID:191896
Published In: Volume 5 Issue 6, June-2020
DOI (Digital Object Identifier):
Page No: 113 - 120
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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