9780133886016

Modeling Techniques in Predictive Analytics Business Problems and Solutions with R, Revised and Expanded Edition

by
  • ISBN13:

    9780133886016

  • ISBN10:

    0133886018

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 10/1/2014
  • Publisher: Pearson FT Press
  • View Upgraded Edition
  • Purchase Benefits
  • Free Shipping On Orders Over $59!
    Your order must be $59 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • Get Rewarded for Ordering Your Textbooks! Enroll Now
List Price: $79.99 Save up to $12.00
  • Buy New
    $67.99
    Add to Cart Free Shipping

    CURRENTLY AVAILABLE, USUALLY SHIPS IN 24-48 HOURS

Supplemental Materials

What is included with this book?

  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
  • The eBook copy of this book is not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

Summary

To succeed with predictive analytics, you must understand it on three levels:

 

Strategy and management

Methods and models

Technology and code

 

This up-to-the-minute reference thoroughly covers all three categories.

 

Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have.

 

Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more.

 

Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value.

 

Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively.

 

All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller

 

If you want to make the most of predictive analytics, data science, and big data, this is the book for you. 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 cases and challenges, 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 spatio-temporal data.

 

You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights.

 

You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance.

 

This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods.

 

Gain powerful, actionable, profitable insights about:

  • Advertising and promotion
  • Consumer preference and choice
  • Market baskets and related purchases
  • Economic forecasting
  • Operations management
  • Unstructured text and language
  • Customer sentiment
  • Brand and price
  • Sports team performance
  • And much more

 

 

Author Biography

THOMAS W. MILLER (Evanston, IL), faculty director of Northwestern University’s Predictive Analytics program, has designed and taught courses in predictive analytics, predictive modeling, marketing analytics, and advanced modeling. Also owner of Research Publishers LLC, he has worked with predictive models for 30+ years, and consults on retail site selection, product positioning, segmentation, and pricing. He holds a Ph.D. in psychology (psychometrics); and M.S. degrees in statistics, business, and economics. His books include Data and Text Mining: A Business Applications Approach; Research and Information Services: An Integrated Approach for Business, and Without a Tout: How to Pick a Winning Team. He previously directed the A.C. Nielsen Center for Marketing Research in the School of Business, U. of Wisconsin-Madison.

Table of Contents

1. Introduction

2. Brand Equity Analysis

3. Competitive Analysis

4. Customer Satisfaction, Loyalty, and Churn

5. Financial Market Analysis

6. Investment Analysis

7. Market Segmentation

8. New Product Research

9. Pricing Research

10. Product Design

11. Product Positioning

12. Recommender Systems

13. Risk Analytics

14. Sales Forecasting

15. Sales Promotion

16. Sentiment Analysis

17. Site Selection

18. Social Network Analysis

19. Target Marketing

20. Transportation Planning

21. Work Force Scheduling

 

Today's most accessible guide to predictive analytics for managers, analysts, and programmers -- now updated and restructured for more effective learning!

Rewards Program

Write a Review