Applied Machine Learning: Practical Models for Solving Real-World Business Problems

Rheinwerk Publishing, Inc · Jason Hodson

Jul 2026 · Packt Publishing Ltd

Ebook

443

Pages

Tools

About this ebook

Turn business data into reliable machine learning models through practical use cases. Prepare data, select algorithms, evaluate results, and monitor performance to measure real-world impact.Key Features

Business-focused use cases link data preparation, model selection, and measurable outcomes
Model decision guidance clarifies when regression, trees, boosting, or clustering fit best
End-to-end coverage connects evaluation, interpretability, deployment, and monitoring plans

Book DescriptionBusiness-focused machine learning begins with a clear view of the data and the decision it must support. The opening material establishes practical tools such as GitHub and Anaconda, introduces three use cases with dedicated datasets, and shows how visualization, descriptive statistics, correlation analysis, cleaning, and dummy coding shape dependable inputs. The discussion then moves through a structured model-selection process. Readers compare regression, decision trees, random forests, gradient boosting, and clustering, while learning when each approach fits a business need. Validation metrics, interpretability, and iterative feature engineering provide a disciplined way to judge results, expose weak assumptions, and refine performance without treating the model as a black box. The final stage connects analysis to operations through implementation, monitoring, prediction workflows, and impact measurement. Readers see how model quality must be maintained after launch and how outcomes can be linked to business value. By the end of this journey, readers can prepare data, choose and evaluate suitable models, and manage machine learning solutions from initial idea through long-term use.What you will learn

Prepare datasets for machine learning analysis
Compare models using a structured decision framework
Build regression, tree, boosting, and clustering models
Evaluate predictions with appropriate validation metrics
Improve interpretability through feature engineering
Deploy and monitor models for measurable business impact

Who this book is for

Designed for administrators, DevOps teams, and professionals who want to apply machine learning to business problems. It suits readers seeking a practical path through data preparation, model selection, evaluation, implementation, and ongoing monitoring.

Turn business data into reliable machine learning models through practical use cases. Prepare data, select algorithms, evaluate results, and monitor performance to measure real-world impact.Key Features Business-focused use cases link data preparation, model selection, and measurable outcomes Mod...

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About the author

The Rheinwerk Computing series offers new and established professional comprehensive guidance to enrich the readers' skillsets and enhance their career prospects. Their publications are written by leading experts in the fields of programming, administration, security, analytics, and more. Each book is detailed and hands-on to help readers develop essential, practical skills that they can apply to their daily work.

Jason Hodson has worked in data-centric roles for nearly a decade. He currently works as an HR analytics manager, and he has prior experience in a forecasting role using the full range of applied machine learning. In a previous role, Jason wrote the end-to-end code for an enterprise hiring manager and candidate experience process, collaborating with recruiting leaders to understand and leverage data from a company-wide survey. He's built large data models and dashboards and taught nontechnical users how to adopt and use them. Jason has been a technical mentor in all his roles, helping others develop their analytics and programming skill set. The common thread across Jason's career is his ability to be a translator for stakeholders, peers, and junior team members. His learning journey also gives him a unique perspective: Before earning a master's degree in business analytics, he was entirely self-taught. This has made his approach to teaching more practical, allowing concepts to translate better (and faster) into the business world.

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