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Modeling Techniques In Predictive Analytics by Thomas Miller

US $16.95
Approximately£12.64
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Good
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eBay item number:363927656638
Last updated on 30 Apr, 2025 23:45:09 BSTView all revisionsView all revisions

Item specifics

Condition
Good: A book that has been read, but is in good condition. Minimal damage to the book cover eg. ...
Edition
1
Publish Year
2013
Updated ISBN1
0133886018
Updated ISBN2
9780133886016
ISBN
9780133412932

About this product

Product Identifiers

Publisher
Pearson Education
ISBN-10
0133412938
ISBN-13
9780133412932
eBay Product ID (ePID)
160065349

Product Key Features

Number of Pages
348 Pages
Publication Name
Modeling Techniques in Predictive Analytics : Business Problems and Solutions with R
Language
English
Subject
Mathematical & Statistical Software, Sociology / General, Data Processing, Databases / Data Mining, Statistics
Publication Year
2013
Type
Textbook
Author
Thomas W. Miller
Subject Area
Computers, Social Science, Business & Economics
Series
Ft Press Analytics Ser.
Format
Hardcover

Dimensions

Item Height
1 in
Item Weight
26.4 Oz
Item Length
9.6 in
Item Width
7.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2013-946325
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
658.40355015118
Synopsis
Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you've identified it, and then how to successfully model that data. You'll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today's #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it's easy to find for those who want it (and easy to skip for those who don't).
LC Classification Number
HD30.27.M55 2014

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