Fraud Detection in E-Commerce Using Machine Learning

Abstract

A rise in transactions is being caused by an increase in online customers. We observe that the prevalence of misrepresentation in online transactions is also increasing. Device learning will become more widely used to avoid misrepresentation in online commerce. The goal of this investigation is to identify the best device learning calculation using decision trees, naive Bayes, random forests, and neural networks. The realities to be utilized have not yet been modified. Engineered minority over-testing stability information is made utilizing the strategy framework. The precision of the brain not entirely settled by the disarray network appraisal is 96%, trailed by naive Bayes (95%), random forest (95%), and decision tree (92%).