Data Science, Analytics and Machine Learning with R

Data Science, Analytics, and Machine Learning with R is a book that explains the principles of data mining and machine learning techniques and emphasizes the importance of applied and multivariate modeling. It provides step-by-step codes and real-world examples with data from various areas, and includes an entire chapter on spatial analysis. Other chapters cover ensemble and uplift modeling, GLMM estimations, and more.

Format: Paperback / softback
Length: 660 pages
Publication date: 01 July 2022
Publisher: Elsevier Science Publishing Co Inc

Data Science, Analytics, and Machine Learning with R is a comprehensive guide that delves into the principles of data mining and machine learning techniques, emphasizing their practical applications and multivariate modeling. The book provides a solid foundation for understanding these concepts, with step-by-step codes and real-world examples drawn from diverse fields such as medicine, health, biology, engineering, technology, and related sciences.

To enhance the learning experience, the book employs the latest R language syntax, utilizing widely recognized and up-to-date packages. Extensively commented code scripts clarify the actions performed in each command, making the content accessible to readers of all levels. Additionally, the book offers valuable insights into data collection, guiding readers on how to build their own robots from scratch.

Furthermore, an entire chapter is dedicated to spatial analysis, enabling readers to create maps using geo-referenced data, a technique commonly employed in epidemiologic research. Basic statistical techniques are also introduced to enhance the understanding of spatial data. Other chapters cover ensemble and uplift modeling, as well as GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.

By comprehensively covering these topics, Data Science, Analytics, and Machine Learning with R equips readers with the skills and knowledge necessary to leverage data for meaningful insights and decision-making in various domains. Whether you are a data scientist, analyst, or engineer, this book is a valuable resource for advancing your understanding of these cutting-edge technologies.

Dimension: 235 x 191 (mm)
ISBN-13: 9780128242711