Cover for Data Science, Analytics and Machine Learning with R

Data Science, Analytics and Machine Learning with R

Book2023

Authors:

Luiz Paulo Fávero, Patrícia Belfiore and Rafael de Freitas Souza

Data Science, Analytics and Machine Learning with R

Book2023

 

Cover for Data Science, Analytics and Machine Learning with R

Authors:

Luiz Paulo Fávero, Patrícia Belfiore and Rafael de Freitas Souza

About the book

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Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariat ... read full description

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  1. Full text access
  2. Book chapterNo access

    Answers

    Pages 605-638

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    References

    Pages 639-640

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    Index

    Pages 641-648

About the book

Description

Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning.

In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.

Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning.

In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.

Key Features

  • Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience
  • Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R
  • Teaches readers how to apply machine learning techniques to a wide range of data and subject areas
  • Presents data in a graphically appealing way, promoting greater information transparency and interactive learning
  • Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience
  • Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R
  • Teaches readers how to apply machine learning techniques to a wide range of data and subject areas
  • Presents data in a graphically appealing way, promoting greater information transparency and interactive learning

Details

ISBN

978-0-12-824271-1

Language

English

Published

2023

Copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Imprint

Academic Press

Authors

Luiz Paulo Fávero

Patrícia Belfiore

Rafael de Freitas Souza