README.md

metadata

title: E-Commerce Fraud Detection
emoji: 🛡️
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false
license: mit
tags:
  - fraud-detection
  - machine-learning
  - streamlit
  - e-commerce
  - security
datasets:
  - fraud-detection-dataset

🛡️ E-Commerce Fraud Detection System

A Streamlit app for real-time e-commerce fraud detection using machine learning and explainable AI.

🚀 Features

• Real-time fraud risk assessment
• Explainable AI (feature impact)
• Interactive analytics dashboard
• Modular, production-ready code

🏗️ Project Structure

app.py
pages/
  home.py
  fraud_detection.py
  model_insights.py
  analytics_dashboard.py
utils/
  model_utils.py
  preprocessing.py
  visualization.py
requirements.txt
lightgbm_model.pkl
customer_loc.pkl

⚙️ Configuration

Hugging Face Space Configuration

For optimal deployment on Hugging Face Spaces, ensure your repository includes:

Space Metadata (README.md)

---
title: E-Commerce Fraud Detection
emoji: 🛡️
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false
license: mit
---

Space Configuration (README.md)

Add this section to your README for better Space discovery: ```yaml

tags: - fraud-detection - machine-learning - streamlit - e-commerce - security datasets: - fraud-detection-dataset

## 🧑‍💻 Local Development
1. Install dependencies:
   ```bash
   pip install -r requirements.txt

2. Run the app:

   streamlit run app.py
   

🐳 Docker Deployment

You can also run this app in a Docker container:

1. Build the Docker image:

   docker build -t fraudlens-app .
   

2. Run the container:

   docker run -p 8501:8501 fraudlens-app
   

The app will be available at http://localhost:8501.

🌐 Deploy on Hugging Face Spaces

1. Push this repo (with all files, including .pkl models) to a public GitHub repository.
2. Create a new Space on Hugging Face Spaces and select Streamlit as the SDK.
3. In "Repository URL", enter your GitHub repo URL.
4. The app will build and deploy automatically!

Note: For Docker-based Spaces, select the Docker SDK and ensure your Dockerfile is present in the repo.

Space Configuration Files

• app.py: Main Streamlit application entry point
• requirements.txt: Python dependencies
• Dockerfile: For Docker-based deployment
• README.md: Space metadata and documentation

📦 Requirements

All dependencies are listed in requirements.txt.

📄 License

MIT