SentinelPay

SentinelPay is a comprehensive fraud detection system that interfaces with a machine learning backend to identify suspicious transactions in real-time. The application consists of a React frontend and a FastAPI backend, designed for scalability and ease of use.

Features

• Real-time Fraud Detection: Uses an XGBoost model to classify transactions.
• Interactive Dashboard: A React-based UI to visualize transaction data and alerts.
• AutoML Capabilities: Includes components for automated model retraining.
• Alert Management: System to manage and review flagged transactions.

Tech Stack

• Frontend: React, Vite, CSS
• Backend: Python, FastAPI, Pandas, XGBoost
• Machine Learning: Scikit-learn, XGBoost

Getting Started

Prerequisites

• Node.js (v18 or higher)
• Python (v3.10 or higher)

Installation

1. Clone the repository:

   git clone https://github.com/ankit/SentinelPay.git
   cd SentinelPay

2. Backend Setup:

   Navigate to the backend directory and install dependencies.

   cd backend
   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -r requirements.txt

3. Frontend Setup:

   Navigate to the frontend directory and install dependencies.

   cd ../frontend
   npm install

Usage

Running the Application

1. Start the Backend:

   From the backend directory (with virtual environment activated):

   uvicorn main:app --reload

   The API will be available at http://localhost:8000.

2. Start the Frontend:

   From the frontend directory:

   npm run dev

   The application will be available at http://localhost:5173.

File Structure

SentinelPay/
├── backend/            # FastAPI application and ML logic
│   ├── main.py         # App entry point
│   ├── ml_engine.py    # Machine learning inference engine
│   └── ...
├── frontend/           # React application
│   ├── src/            # Source code
│   └── ...
└── README.md           # Project documentation