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SPAM Detector ML

A fraud detection application with both web frontend and Python machine learning backend for analyzing and predicting fraudulent transactions.

Features

  • Real-time Fraud Detection: Upload CSV files and get instant fraud predictions
  • Interactive Dashboard: Modern web interface with dark/light mode
  • Machine Learning Backend: Python-based Dash application with trained models
  • Data Visualization: Charts and analytics for prediction results
  • Risk Assessment: Color-coded risk levels for transaction analysis

Live Demo

Frontend: Deployed on Netlify

Technology Stack

Frontend:

  • Vite + JavaScript
  • Tailwind CSS
  • Chart.js

Backend:

  • Python + Dash
  • Scikit-learn
  • Pandas, NumPy
  • Plotly

Quick Start

Frontend Development

npm install
npm run dev

Python Backend

cd python-backend
pip install dash scikit-learn pandas numpy plotly
python app.py

Deployment

Frontend (Netlify)

The frontend is configured for automatic deployment to Netlify:

  • Build command: npm install --legacy-peer-deps && npm run build
  • Publish directory: dist
  • Node version: 18

Backend Deployment

The Python backend can be deployed to platforms like:

  • PythonAnywhere
  • Heroku
  • Railway
  • DigitalOcean

Usage

  1. Upload CSV: Drag and drop transaction data
  2. View Results: Analyze predictions with confidence scores
  3. Interpret Charts: Review distribution and correlation visualizations
  4. Risk Assessment: Examine color-coded transaction risk levels

Project Structure

├── src/                 # Frontend source code
├── python-backend/      # Python ML backend
│   ├── app.py          # Dash application
│   ├── main.py         # Additional scripts
│   ├── requirements.txt # Python dependencies
│   └── static/         # ML model files
├── dist/               # Built frontend files
└── netlify.toml        # Netlify configuration

License

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