Data science, analytics and machine learning with R / Luiz Paulo Fávero, Patrícia Belfiore, Rafael de Freitas Souza

By:

Fávero, Luiz Paulo [VerfasserIn]

Contributor(s):

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: London : Academic Press, [2023]Copyright date: ©2023Edition: Fisrt editionDescription: 1 Online-Ressource (558 pages) : illustrationsISBN:

9780323859233
0323859232
9780128242711
012824271X

Subject(s):

Additional physical formats: Erscheint auch als: DATA SCIENCE, ANALYTICS AND MACHINE LEARNING WITH R. [S.l.] : ELSEVIER ACADEMIC PRESS, 2022DDC classification:

658.05631 23/eng/20230206
658.05631 23

LOC classification:

HF5548.2
HF1008

Online resources:

Zugang im Netz des KIT

Summary: 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. --PPN: PPN: 1882889541Package identifier: Produktsigel: ZDB-4-NLEBK | BSZ-4-NLEBK-KAUB

Holdings ( 0 )

No physical items for this record