Overview

Practical XGBoost is a hands-on, step-by-step guide for mastering one of the most powerful machine learning algorithms available today. This book shows how to build, tune, and deploy gradient boosting models using Python, making complex concepts approachable and actionable for anyone working with data. Inside, you'll discover how to transform tabular data into predictive models that deliver real results. From handling missing values to engineering meaningful features, optimizing hyperparameters, and scaling models for large datasets, every concept is paired with runnable Python examples that bring learning directly into practice. What this book helps you achieve: Build accurate classification and regression models efficiently using XGBoost. Engineer features and interpret model predictions for clear, trustworthy results. Tune hyperparameters, implement distributed training, and leverage GPU acceleration. Deploy models in real-world pipelines using FastAPI, MLflow, and cloud platforms. Explore practical case studies in finance, healthcare, and energy to see XGBoost applied to real problems. This book stands out by focusing on practical, end-to-end implementation. Every step is explained clearly, with actionable tips and code that works out of the box, bridging the gap between understanding the algorithm and applying it in production-ready systems. Take control of your machine learning projects. Start building faster, smarter, and more accurate models with Practical XGBoost.

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