predictive data mining models von olson david (37 Ergebnisse)

Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Hardcover. Zustand: Fine. No Jacket. 1st Edition. 2017.Hardcover.Fine condition.102 pages.Ships from Japan.Usually ships in 1-2 working days.

Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.

Zustand: New.

Zustand: New.

Zustand: New.

Paperback. Zustand: new. Paperback. This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The books main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access. This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Zustand: New.

Paperback. Zustand: new. Paperback. 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 editionprovides 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. This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R) and WEKA. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Zustand: New. In.

Zustand: As New. Unread book in perfect condition.

PF. Zustand: New.

Zustand: New.

Zustand: New. pp. XI, 125 77 illus., 69 illus. in color. 2nd ed. 2020 edition NO-PA16APR2015-KAP.

Zustand: New. In.

Zustand: As New. Unread book in perfect condition.

Hardcover. Zustand: new. Hardcover. 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 editionprovides 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. This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R) and WEKA. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PF. Zustand: New.

Zustand: New.

Zustand: New. pp. 102.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book's main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.

Hardcover. Zustand: Brand New. 2nd edition. 125 pages. 9.25x6.25x0.50 inches. In Stock.

Paperback. Zustand: New. New. book.

Paperback. Zustand: new. Paperback. 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 editionprovides 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. This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R) and WEKA. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Zustand: new. Paperback. This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The books main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access. This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Zustand: new. Hardcover. 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 editionprovides 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. This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R) and WEKA. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Zustand: New. Print on Demand pp. XI, 125 77 illus., 69 illus. in color.