Predictive Analytics with Python: Build Production-Ready Models Polars, Pandera, Scikit-Learn, XGBoost, MLflow, FastAPI, Docker, and Python (English Edition)

Build Models That Survive Beyond the Notebook. Book Description Data Science Finds the Signal. Engineering Turns It into Business Value. Moving a model from a Jupyter Notebook to a production system requires engineering discipline, not just data science skills. Predictive Analytics with Python is the definitive guide for the engineering-first era of data science, helping you transition from fragile notebook workflows to resilient, production-ready predictive systems built for real-world infrastructure. You begin by replacing slow legacy workflows with a modern technical stack, high-performance ETL with Polars, data contract enforcement with Pandera, and resilient Scikit-Learn and XGBoost pipelines with rigorous feature engineering, cross-validation, and experiment tracking using MLflow. The book then advances into time-series forecasting with Nixtla before covering model serialisation, REST API deployment with FastAPI, Docker containerisation, and production monitoring as well as governance. The book culminates in an end-to-end capstone project building an enterprise-grade Automated Real Estate Valuation Model. By the end, you will engineer predictive systems that prioritize stability, auditability, and transformative business value. What you will learn ● Transition fragile notebook workflows into robust production-grade software engineering practices. ● Execute high-performance ETL and data processing using the Polars library at scale. ● Enforce rigorous data contracts using Pandera to validate pipeline inputs automatically. Table of Contents From Notebooks to Systems The Modern Python Environment High-Performance ETL with Polars Defensive Data Programming with Pandera Feature Engineering as Software Handling Real-World Messiness The Baseline: Linear Pipelines Productionizing Gradient Boosting (XGBoost) The Tuning Lifecycle and Experiment Tracking Model Evaluation and Interpretation Engineering Time-Series Features Modern Forecasting with Nixtla The Deployment Gap: Serialization and Packaging Serving Predictions with APIs Monitoring and Model Governance Capstone: Building the Enterprise AVM Index