Credit Card Fraud Detection using Python & Machine Learning Algorithms

• Published in: International journal for research in applied science and engineering technology Vol. 11; no. 5; pp. 3120 - 3128
• Main Author: Mangal, Ekta
• Format: Journal Article
• Language: English
• Published: 31.05.2023
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2023.52242

Browsing and many other online sites have increased the digital payment modes through which risk of frauds during transactions got increased. It is necessary to have a look on fraud transactions so that the customers does not pay for what they haven’t done. Such complications may be intercept with Data mining through Machine Learning. It aims to display the customization of a data set by applying machine learning with Credit Card Fraud Detection. The CCFD complications comprise of analyzing previous transactions through credit card along the data of the unauthorized users. These models are then applied to analyze whether the new transaction is authorized or not. In this project, we have concentrated on examining and pre-refining the data sets in addition to the deployment of numerous inconsistency observation methods such as Logical Regression, Random Forest, Decision tree, XG Boost on Credit Card Transaction data.