Due to the ongoing pandemic, there is a sudden boom in the E-commerce industry and hence many online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. The main aim of the project is to design and develop a fraud detection model based on the given Dataset.

Credit Card Fraud Detector is a React web app that predicts if a credit card transaction is fraudulent using a Python machine learning model. Users can input transaction data, and Flask facilitates communication between the backend and frontend. Backend files are located in the res folder. This app provides simple UI for user interaction.

The Credit Card Fraud Detector project uses machine learning algorithms to detect and prevent fraudulent credit card transactions. By analyzing patterns in transaction data and applying anomaly detection techniques, it helps financial institutions identify and mitigate potential fraud, protecting cardholders and minimizing financial losses.

Credit Fraud Detector is a Machine Learning project designed to identify fraudulent credit card transactions. The system uses a pre-trained ML model to analyze transaction features and classify them as fraudulent or legitimate. The model is deployed using Flask and provides predictions through a simple web interface built with HTML and CSS.