Predictive Analytics with KNIME: Analytics for Citizen Data Scientists

Author:   Frank Acito
Publisher:   Springer International Publishing AG
Edition:   2023 ed.
ISBN:  

9783031456299


Pages:   314
Publication Date:   30 November 2023
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Our Price $284.60 Quantity:  
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Predictive Analytics with KNIME: Analytics for Citizen Data Scientists


Overview

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.

Full Product Details

Author:   Frank Acito
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2023 ed.
Weight:   0.658kg
ISBN:  

9783031456299


ISBN 10:   3031456297
Pages:   314
Publication Date:   30 November 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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

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Author Information

Frank Acito is Professor emeritus, Indiana University, Bloomington

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Latest Reading Guide

NOV RG 20252

 

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