Predictive Data Mining Models

✍ Scribed by David L. Olson, Desheng Wu

• Publisher: Springer Singapore
• Year: 2020
• Tongue: English
• Leaves: 131
• Series: Computational Risk Management
• Edition: 2nd ed. 2020
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xi
Knowledge Management (David L. Olson, Desheng Wu)....Pages 1-9
Data Sets (David L. Olson, Desheng Wu)....Pages 11-20
Basic Forecasting Tools (David L. Olson, Desheng Wu)....Pages 21-44
Multiple Regression (David L. Olson, Desheng Wu)....Pages 45-56
Regression Tree Models (David L. Olson, Desheng Wu)....Pages 57-77
Autoregressive Models (David L. Olson, Desheng Wu)....Pages 79-93
Classification Tools (David L. Olson, Desheng Wu)....Pages 95-121
Predictive Models and Big Data (David L. Olson, Desheng Wu)....Pages 123-125

✦ Subjects

Business and Management; Big Data/Analytics; Data Mining and Knowledge Discovery; Risk Management