UPI Fraud Detection Details 

Detects fraudulent UPI transactions in real time using a machine learning–based classification model

Project Description 

This project focuses on building a machine learning–powered web application to identify fraudulent UPI transactions. A classification model is trained on transaction data to distinguish between legitimate and suspicious transactions based on key patterns and features. The trained model is integrated with a Flask-based web application that allows users to input transaction details and instantly receive fraud prediction results. 

Features 

• Machine learning–based fraud detection system

• Web application with simple and interactive UI

• Data preprocessing and feature engineering using Pandas

• Model evaluation and visualization using charts

• Well-structured and easy-to-understand codebase

• Ideal for academic and final-year projects

Tech Stack

• Python

• Flask (Backend)

• Pandas

• NumPy

• Scikit-learn

• Matplotlib (Data Visualization)

• HTML, JavaScript (Frontend)

System Requirements

• Python 3.10+

• Anaconda

• Libraries: numpy, pandas, scikit-learn, matplotlib

• No GPU required

Deliverables 

• Jupyter Notebook

• Dataset

• Setup over video call

• WhatsApp support