Predictive Analytics with Python

Build Models That Survive Beyond the Notebook.

Key Features ? Get a free one-month digital subscription to www.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 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. ? 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 Contents 1. From Notebooks to Systems 2. The Modern Python Environment 3. High-Performance ETL with Polars 4. Defensive Data Programming with Pandera 5. Feature Engineering as Software 6. Handling Real-World Messiness 7. The Baseline: Linear Pipelines 8. Productionizing Gradient Boosting (XGBoost) 9. The Tuning Lifecycle and Experiment Tracking 10. Model Evaluation and Interpretation 11. Engineering Time-Series Features 12. Modern Forecasting with Nixtla 13. The Deployment Gap: Serialization and Packaging 14. Serving Predictions with APIs 15. Monitoring and Model Governance 16. Capstone: Building the Enterprise AVM Index

About the Author Rahul Kumar Thatikonda is an Analytics Manager and Business Transformation Leader. He specializes in bridging experimental notebook code with production-grade software to upgrade critical industrial infrastructure. Rahul is dedicated to building resilient AI systems that deliver transformative, societal value.