Credit Card Fraud Identification Using Logistic Regression and Local Outlier Factor

Abstract

Credit cards are a crucial financial tool that enables its users to make purchases and pay at a later date. Issued by financial customs, credit cards give users a pre-agreed credit limit that they can use for their purchases. MasterCard extortion is a type of data fraud where crooks make buys utilising a Visa account which doesn't have a place with them. The two primary tactics for reducing frauds and losses caused by fraudulent conduct are fraud detection systems and fraud prevention. Fraud detection is tracking the behaviours of large groups of people in order to estimate, perceive, or identify obnoxious activity, such as fraud, intrusion, or defaulting. The Local Outlier Factor is a technique for detecting aberrant data points by comparing a data point's local variability to that of its neighbours. Under the Supervised Learning approach, one of the most prominent Machine Learning algorithms is logistic regression