FINANCIAL FRAUD DETECTION USING LOGISTIC ALGORITHM

Abstract

This paper presents the Gaussian Naïve Bayes for Fraud Detection of the credit card. The aim is to boost the accuracy and enhance the flexibleness of the algorithm. The first target of this project is to perform an investigation on the credit cards fraud detection dataset utilizing ML procedures and distinguish the deceitful exchanges from the given dataset. Diverse examining methods are executed to handle the category irregularity exchange issue and arrangement of ML calculations like Logistic Regression and Gaussian naïve Bayes are going to be actualized on the dataset, and the outcomes are going to be accounted for with estimating the data credited utilizing proposed calculations with more exactness and adequacy. During this paper, fraud classification using ML algorithms is proposed. This technique uses logistic regression to make the classifier stop frauds in credit card transactions. To handle unwanted data and to make sure a high degree of detection accuracy, a pre-processing step is used.