Credit CardE-Commerce Fraud Detection Using Machine Learning

Abstract

The rapid growth of digital payments and e-commerce has increased the risk of fraudulent transactions, making traditional rule-based systems ineffective. This project proposes a machine learning-based fraud detection system using historical transaction data. Data preprocessing and feature engineering are applied to improve data quality and extract meaningful features. Supervised learning algorithms such as Logistic Regression, Random Forest are used to classify transactions as legitimate or fraudulent. The model learns fraud patterns from past data and adapts to new fraud techniques. Overall, the system enables early fraud detection and ensures secure and reliable digital transactions. KEYWORDS- shopping cart, product catalog, payment gateway, online payment, digital wallet, management, logistics, delivery, customer reviews, SEO, digital marketing, CRM, security, cloud computing, AI, chatbots.