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Predictive Analytics with KNIME: Analytics for Citizen Data Scientists

Autor Frank Acito
en Limba Engleză Hardback – 30 noi 2023
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.
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Specificații

ISBN-13: 9783031456299
ISBN-10: 3031456297
Pagini: 314
Ilustrații: XIII, 314 p. 155 illus., 130 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.64 kg
Ediția:1st ed. 2023
Editura: Springer Nature Switzerland
Colecția Springer
Locul publicării:Cham, Switzerland

Cuprins

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

Notă biografică

Frank Acito is Professor emeritus, Indiana University, Bloomington


Textul de pe ultima copertă

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.

Caracteristici

Uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding Integrates with R and several examples are used with R nodes, in a KNIME workflow Provides graphic representations of each step taken in analyses, making the analyses self-documenting'/