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Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Title
Predictive analytics with KNIME : analytics for citizen data scientists / Frank Acito.
ISBN
9783031456305 (electronic bk.)
3031456300 (electronic bk.)
9783031456299
3031456297
Published
Cham : Springer, [2023]
Copyright
©2023
Language
English
Description
1 online resource (xiii, 314 pages) : illustrations (chiefly color)
Item Number
10.1007/978-3-031-45630-5 doi
Call Number
QA76.9.Q36
Dewey Decimal Classification
001.4/2
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed December 12, 2023).
Available in Other Form
Print version: 9783031456299
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.
