README.md

metadata

title: fraud-pattern-detector
emoji: 🕵️
colorFrom: red
colorTo: yellow
sdk: docker
app_port: 7860
pinned: false

Fraud Pattern Detector (XGBoost + Network Patterns) — FastAPI + Streamlit

Fraud detection demo trained on Kaggle's IEEE-CIS Fraud Detection dataset.

What you get

• XGBoost baseline with compact feature engineering
• Per-transaction explanations (SHAP-style drivers)
• Lightweight transaction-network view (cluster patterns around shared identifiers)
• FastAPI inference service (/score)
• Streamlit UI with 2 tabs:
  • Single transaction scorer
  • Network visualization with flagged clusters
• Docker image compatible with Hugging Face Spaces

Local setup

Requires Python 3.11+.

python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Download data (Kaggle)

1. Place your Kaggle credentials at ~/.kaggle/kaggle.json (recommended), or ensure a kaggle.json exists in your workspace root.
2. Ensure you've accepted the competition rules on Kaggle.

python -m scripts.download_data

Train (XGBoost baseline)

python -m scripts.train_xgb --seed 42 --max_rows 200000

Run locally

Terminal 1:

uvicorn app.api:app --host 0.0.0.0 --port 8000

Terminal 2:

streamlit run app/streamlit_app.py --server.port 7860 --server.address 0.0.0.0