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Predictive analytics with KNIMEanalytics for citizen data scientists /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive analytics with KNIMEby Frank Acito.
Reminder of title:	analytics for citizen data scientists /
Author:	Acito, Frank.
Published:	Cham :Springer Nature Switzerland :2023.
Description:	xiii, 314 p. :illustrations (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Predictive analytics.
Online resource:	https://doi.org/10.1007/978-3-031-45630-5
ISBN:	9783031456305$q(electronic bk.)

Predictive analytics with KNIMEanalytics for citizen data scientists /
Acito, Frank.

Predictive analytics with KNIMEanalytics for citizen data scientists /[electronic resource] :by Frank Acito. - Cham :Springer Nature Switzerland :2023. - xiii, 314 p. :illustrations (chiefly color), digital ;24 cm.

Chapter 1 Introduction to analytics -- Chapter 2 Problem definition -- Chapter 3 Introduction to KNIME -- Chapter 4 Data preparation -- Chapter 5 Dimensionality reduction and feature extraction -- Chapter 6 Ordinary least squares regression -- Chapter 7 Logistic regression -- Chapter 8 Decision and regression trees -- Chapter 9 Naïve Bayes -- Chapter 10 k nearest neighbors -- Chapter 11 Neural networks -- Chapter 12 Ensemble models -- Chapter 13 Cluster analysis -- Chapter 14 Communication and deployment.

This book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool. The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME.

ISBN: 9783031456305$q(electronic bk.)

Standard No.: 10.1007/978-3-031-45630-5doiSubjects--Uniform Titles:

KNIME (Computer file)
Subjects--Topical Terms:

877232
Predictive analytics.

LC Class. No.: QA76.9.Q36

Dewey Class. No.: 001.42

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