From Kaggle Competition to Production-Grade ML Service: A Practical Guide to Engineering the Titanic Project

This project upgrades the classic Kaggle Titanic survival prediction challenge into a complete production-ready machine learning service, demonstrating professional data science and ML engineering methodologies. It covers the entire workflow including data exploration, feature engineering, model training, interpretability, and FastAPI deployment, following industry best practices.

• Original author/maintainer: thibaultclement
• Source platform: GitHub
• Original title: titanic-ml-service
• Original link: https://github.com/thibaultclement/titanic-ml-service
• Release date: 2026-06-11

The Titanic survival prediction is a classic entry point for data science, but most solutions stop at Kaggle scores. The goal of this project is to build a production-grade ML service—beyond just pursuing accuracy, it demonstrates professional methodologies with each step following best practices.

The project follows a complete data science workflow:

1. Business Understanding: Predict passenger survival probability (binary classification problem; evaluation metrics are accuracy or AUC-ROC)
2. Data Collection and Validation: Use Kaggle training/test sets, check data quality, missing values, and outliers
3. EDA: Conduct exploratory analysis via Jupyter notebooks, visualize feature distributions and relationships with the target variable
4. Feature Engineering: A key step including feature combination (e.g., family size = number of siblings/spouses + number of parents/children +1), encoding (one-hot/target encoding), scaling, missing value handling, and feature selection.

• Model Training and Comparison: Train models like Logistic Regression (baseline), Random Forest, XGBoost/LightGBM, SVM, and Neural Networks; tune hyperparameters using cross-validation and grid search
• Evaluation: Use multiple metrics including accuracy, precision/recall, F1 score, AUC-ROC, and confusion matrix
• Interpretability: Use SHAP values to explain feature contributions to predictions, aiding debugging and business decisions.

• API Design: Provides endpoints such as health check (GET /health), prediction (POST /predict), explanation (POST /explain), what-if analysis (POST /what_if), and model information (GET /model)
• Input/Output: Uses Pydantic models to define strict validation and formatting, with automatic documentation generation
• Containerization: Includes a Dockerfile, supporting one-click build and deployment to ensure environment consistency.

• Code Quality: Modular design, type hints, docstrings, and unit tests covering core functions
• Configuration Management: Centralized management of hyperparameters, paths, etc., via config.py to avoid hardcoding
• Version Control: DVC-ready data version control, model version management, and experiment tracking
• Automation: Makefile defines tasks like installing dependencies, running tests, and starting the service to improve efficiency
• Model Card: Includes model_card.md, which records model purpose, training data, performance, limitations, and ethical considerations.

Learning Value: Provides references for those transitioning from data science to ML engineering, including production-grade project structure, code organization (converting notebooks to Python packages), API design templates, testing strategies, and complete deployment pipelines

Conclusion: This project proves that entry-level datasets can also demonstrate professional ML engineering capabilities. By following best practices, focusing on code quality and interpretability, it serves as an excellent reference template for ML engineers and is worth in-depth study by data scientists.