A thorough introduction that covers both theoretical ideas and real-world applications to the most significant machine learning techniques used in predictive data analytics. In order to create prediction models, machine learning frequently uses massive datasets to extract patterns. Applications for predictive data analytics that use these models include document classification, risk assessment, pricing prediction, and consumer behaviour forecasting. The most significant machine learning techniques used in predictive data analytics are covered in-depth and specifically in this beginning textbook, which also covers theoretical concepts and real-world applications.Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.