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Modeling Techniques in Predictive Analytics Business Problems and Solutions with R

ISBN-10: 0133412938

ISBN-13: 9780133412932

Edition: 2014

Authors: Thomas W. Miller

List price: $79.99
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Description:

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, Web and text analytics, 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).
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Book details

List price: $79.99
Copyright year: 2014
Publisher: Pearson Education
Publication date: 8/29/2013
Binding: Hardcover
Pages: 348
Size: 9.00" wide x 10.00" long x 1.00" tall
Weight: 1.628
Language: English

Preface
Figures
Tables
Exhibits
Analytics and Data Science
Advertising and Promotion
Preference and Choice
Market Basket Analysis
Economic Data Analysis
Operations Management
Text Analytics
Sentiment Analysis
Sports Analytics
Brand and Price
Spatial Data Analysis
The Big Little Data Game
There's a Pack' for That
Regression
Classification
Recommender Systems
Product Positioning
Segmentation and Target Marketing
Finance and Risk Analytics
Social Network Analysis
Measurement
Code and Utilities
Bibliography
Index