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.