link to training dataset: https://www.kaggle.com/datasets/kartik2112/fraud-detection?select=fraudTrain.csv

FraudSentry FraudSentry is an advanced fraud detection system designed to analyze credit card statements and identify potential fraudulent activities. The system is built using a combination of machine learning for fraud detection and a user-friendly React frontend for data input and visualization.

FraudSentry

FraudSentry is an advanced fraud detection system designed to analyze credit card statements and identify potential fraudulent activities. The system is built using a combination of machine learning for fraud detection and a user-friendly React frontend for data input and visualization.

Table of Contents

• Features
• Installation
• Usage
• Contributing
• License
• Acknowledgements

Features

• Fraud Detection: Utilizes machine learning models to identify potentially fraudulent transactions.
• User-Friendly Interface: React-based frontend allows users to input data and receive results intuitively.
• Dynamic Search: Live search functionality for job and city inputs, enhancing user experience.
• Data Privacy: Secure handling of user data and sensitive information.

Installation

Prerequisites

• Node.js: Version 12 or higher
• Python: Version 3.7 or higher
• npm: Package manager for Node.js
• pip: Package manager for Python
• Virtual Environment: Recommended for Python dependencies

Backend Installation

1. Clone the Repository

   git clone https://github.com/yourusername/FraudSentry.git
   cd FraudSentry/backend
   

2. Set Up Python Virtual Environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install Backend Dependencies

   pip install -r requirements.txt
   

4. Run Backend Server

   uvicorn api:app --reload
   

Frontend Installation

1. Navigate to Frontend Directory

   cd ../frontend
   

2. Install Frontend Dependencies

   npm install
   

3. Run Frontend Server

   npm start
   

Usage

1. Start the backend and frontend servers as described in the installation section.
2. Navigate to the frontend URL, typically http://localhost:3000.
3. Use the application to input credit card statement data.
4. View the fraud detection results displayed after submission.

License

This project is licensed under the MIT License - see the LICENSE file for details.