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Books > Computing & IT > Applications of computing > Databases > Data mining
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Predictive Data Mining Models (Hardcover, 2nd ed. 2020)
Loot Price: R3,279
Discovery Miles 32 790
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Predictive Data Mining Models (Hardcover, 2nd ed. 2020)
Series: Computational Risk Management
Expected to ship within 10 - 15 working days
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This book provides an overview of predictive methods demonstrated
by open source software modeling with Rattle (R') and WEKA.
Knowledge management involves application of human knowledge
(epistemology) with the technological advances of our current
society (computer systems) and big data, both in terms of
collecting data and in analyzing it. We see three types of analytic
tools. Descriptive analytics focus on reports of what has happened.
Predictive analytics extend statistical and/or artificial
intelligence to provide forecasting capability. It also includes
classification modeling. Prescriptive analytics applies
quantitative models to optimize systems, or at least to identify
improved systems. Data mining includes descriptive and predictive
modeling. Operations research includes all three. This book focuses
on prescriptive analytics. The book seeks to provide simple
explanations and demonstration of some descriptive tools. This
second edition provides more examples of big data impact, updates
the content on visualization, clarifies some points, and expands
coverage of association rules and cluster analysis. Chapter 1 gives
an overview in the context of knowledge management. Chapter 2
discusses some basic data types. Chapter 3 covers fundamentals time
series modeling tools, and Chapter 4 provides demonstration of
multiple regression modeling. Chapter 5 demonstrates regression
tree modeling. Chapter 6 presents autoregressive/integrated/moving
average models, as well as GARCH models. Chapter 7 covers the set
of data mining tools used in classification, to include special
variants support vector machines, random forests, and boosting.
Models are demonstrated using business related data. The style of
the book is intended to be descriptive, seeking to explain how
methods work, with some citations, but without deep scholarly
reference. The data sets and software are all selected for
widespread availability and access by any reader with computer
links.
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