Open AccessCredit Card Fraud Detection and Prevention using Machine Learning
Author(s) -
S. Abinayaa,
H. Sangeetha,
R. A. Karthikeyan,
Sriram Kailasam,
Duggal Piyush
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d7327.049420
Subject(s) - credit card , credit card fraud , computer science , flexibility (engineering) , random forest , set (abstract data type) , data set , data mining , representation (politics) , machine learning , artificial intelligence , statistics , mathematics , world wide web , politics , political science , law , payment , programming language
This research focused mainly on detecting credit card fraud in real world. We must collect the credit card data sets initially for qualified data set. Then provide queries on the user's credit card to test the data set. After random forest algorithm classification method using the already evaluated data set and providing current data set[1]. Finally, the accuracy of the results data is optimised. Then the processing of a number of attributes will be implemented, so that affecting fraud detection can be found in viewing the representation of the graphical model. The techniques efficiency is measured based on accuracy, flexibility, and specificity, precision. The results obtained with the use of the Random Forest Algorithm have proved much more effective.