Predictive Analytics with Python Build Production-Ready Predictive Models with Polars, Pandera, Scikit-Learn, XGBoost

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Predictive Analytics with Python: Build Production-Ready Predictive Models with Polars, Pandera, Scikit-Learn, XGBoost, MLflow, FastAPI, Docker, and Python
English | September 18, 2026 | ISBN: 8169646596 | 491 pages | EPUB (True) | 99.79 MB
Build Models That Survive Beyond the Notebook.Key Features● Get a free one-month digital subscription towww.avaskillshelf.com.●Production-first data pipeline engineering using Polars for high-performance ETL and Pandera for data contracts.● Full MLOps lifecycle coverage with CRISP-DM, Scikit-Learn, XGBoost, MLflow experiment tracking, and hyperparameter tuning.● Enterprise-grade capstone project building an Automated Valuation Model deployed with Docker and FastAPI.Book DescriptionData 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.● Build resilient predictive pipelines using Scikit-Learn and XGBoost with production patterns.● Automate hyperparameter tuning and track model experiments using MLflow effectively.● Deploy production predictive models as REST APIs using Docker and FastAPI.Table of Contents1. From Notebooks to Systems2. The Modern Python Environment3. High-Performance ETL with Polars4. Defensive Data Programming with Pandera5. Feature Engineering as Software6. Handling Real-World Messiness7. The Baseline: Linear Pipelines8. Productionizing Gradient Boosting (XGBoost)9. The Tuning Lifecycle and Experiment Tracking10. Model Evaluation and Interpretation11. Engineering Time-Series Features12. Modern Forecasting with Nixtla13. The Deployment Gap: Serialization and Packaging14. Serving Predictions with APIs15. Monitoring and Model Governance16. Capstone: Building the Enterprise AVM Index​

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