📌 Project Overview

This project implements a complete machine learning pipeline to detect fraudulent transactions using the IEEE-CIS dataset. It includes:

• Deep EDA in R and Python
• Robust preprocessing and feature engineering
• Ensemble modeling (XGBoost, LightGBM, CatBoost)
• A FastAPI deployment for real-time predictions
• Unit testing with pytest for pipeline robustness

🚀 Check the full repository: GitHub

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🧠 Dataset and Preprocessing

The dataset contains transaction and identity features, most of them anonymized.

Key steps:

• Merged identity and transaction data
• Imputed missing values using statistical strategies
• Encoded categorical variables using LabelEncoder
• Scaled numerical features using StandardScaler
• Saved transformers and models using joblib for reuse in deployment

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🤖 Model Training and Evaluation

We trained and compared multiple models:

• Logistic Regression
• Random Forest
• XGBoost
• LightGBM
• CatBoost
• Stacking Ensemble with Logistic Regression as meta-learner

All models were evaluated using:

• Accuracy, Precision, Recall, F1-score
• ROC-AUC
• Confusion matrices
• ROC curves

📊 Results are available in the reports section.

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🧪 Testing

We implemented tests to ensure pipeline integrity:

• ✅ Preprocessing pipeline does not crash with valid data
• ✅ API responds with expected output structure
• ✅ Unit tests managed with pytest

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🖥️ Deployment

The final model was deployed using FastAPI. Key features:

• /predict endpoint returns prediction and fraud probability
• Interactive Swagger UI available at /docs
• Can be run locally via Uvicorn or deployed in a Docker container

uvicorn src.main:app --reload

Or with Docker

docker pull alexmatiasastorga/fraud-api:latest
docker run -d -p 8000:8000 alexmatiasastorga/fraud-api

📌 Conclusion

This project demonstrates a real-world machine learning workflow from raw data to deployment. Future improvements may include:

• DAG automation with Apache Airflow
• Cloud deployment (Render or AWS)
• Monitoring with MLFlow or Prometheus

🔗 Links

• 🔍 Project Repository
• 🐳 Docker Image