Feature Engineering and Model Tuning: Practical Data Science Techniques

This book provides a comprehensive exploration of feature engineering, hyperparameter tuning, model interpretability, and production-level machine learning practices. Designed for practitioners and advanced learners, it delves into the techniques, tools, and strategies required to build robust, scalable, and explainable machine learning systems. From automated pipelines to drift detection and continuous monitoring, each chapter bridges theory with practical implementation to equip readers with the skills needed to deploy and maintain high-performing models in real-world environments.