Credit Card Fraud Detection

The banking industry is seriously threatened by credit card fraud, as illegal transactions result inbillions of dollars being lost annually worldwide.This study presents a machine learning-based method fordetecting credit card fraud in real-time. The suggested method makes use of an interactive web applicationcreated with Streamlit that incorporates a pre-trained classification model. Thirty numerical features thatreflect transaction characteristics are used to train the model using anonymised transaction data. Throughthe interface, users enter transaction details, and the system classifies the transaction as either legitimate orfraudulent based on its prediction of the likelihood of fraud. By spotting obscure patterns in the featurespace, the model shows excellent accuracy in finding anomalies. The purpose of this application is to helpanalysts and financial institutions quickly determine the legality of transactions and reduce the risksassociated with fraud. The system is a useful tool for both industry and research because it is portable, easyto use, and deployable across multiple platforms.