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Fraud Detection Inference Service

Real-time fraud detection inference service.
Built with Celery + RabbitMQ as message broker, results and transaction data stored in PostgreSQL.
Uses XGBoost model with strong class imbalance handling via SMOTE.
Hyperparameters were optimized using Optuna.

confusion matrix feature importance

Architecture

  • Backend: Celery workers
  • Message Broker: RabbitMQ
  • Database: PostgreSQL
  • ML Model: XGBoost (trained with SMOTE + Optuna tuning)
  • Deployment: Docker + Docker Compose

Main Components

  • app/app.py — Celery configuration and task registration
  • worker/tasks.py — main task run_fraud_inference(**data_dict) for model inference
  • models/ — serialized XGBoost model (xgb_final.joblib) + preprocessing pipeline
  • docker-compose.yml — launches RabbitMQ, PostgreSQL and Celery workers

Quick Start (Local)

Requirements

  • Docker
  • Docker Compose

Run

docker-compose up --build
Default flower localhost:5555