Commercial Data Mining :Processing, Analysis and Modeling for Predictive Analytics Projects ( The Savvy Manager's Guides )

Publication subTitle :Processing, Analysis and Modeling for Predictive Analytics Projects

Publication series :The Savvy Manager's Guides

Author: Nettleton   David  

Publisher: Elsevier Science‎

Publication year: 2014

E-ISBN: 9780124166585

P-ISBN(Paperback): 9780124166028

P-ISBN(Hardback):  9780124166028

Subject: F224-39 computer applications;TP39 computer application

Language: ENG

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Description

Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.

Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book.

  • Illustrates cost-benefit evaluation of potential projects
  • Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools
  • Approachable reference can be read from cover to cover by readers of all experience levels
  • Includes practical examples and case studies as well as actionable business insights from author's own experience

Chapter

Front Cover

pp.:  1 – 4

Copyright

pp.:  5 – 6

Contents

pp.:  6 – 12

Acknowledgments

pp.:  12 – 14

Chapter 1: Introduction

pp.:  14 – 20

Chapter 2: Business Objectives

pp.:  20 – 30

Chapter 3: Incorporating Various Sources of Data and Information

pp.:  30 – 62

Chapter 4: Data Representation

pp.:  62 – 80

Chapter 5: Data Quality

pp.:  80 – 92

Chapter 6: Selection of Variables and Factor Derivation

pp.:  92 – 118

Chapter 7: Data Sampling and Partitioning

pp.:  118 – 132

Chapter 8: Data Analysis

pp.:  132 – 150

Chapter 9: Data Modeling

pp.:  150 – 172

Chapter 10: Deployment Systems

pp.:  172 – 184

Chapter 11: Text Analysis

pp.:  184 – 194

Chapter 12: Data Mining from Relationally Structured Data, Marts, and Warehouses

pp.:  194 – 208

Chapter 13: CRM - Customer Relationship Management and Analysis

pp.:  208 – 222

Chapter 14: Analysis of Data on the Internet I - Website Analysis and Internet Search

pp.:  222 – 224

Chapter e14: Analysis of Data on the Internet I - Website Analysis and Internet Search

pp.:  224 – 238

Chapter 15: Analysis of Data on the Internet II - Search Experience Analysis

pp.:  238 – 240

Chapter e15: Analysis of Data on the Internet II - Search Experience Analysis

pp.:  240 – 252

Chapter 16: Analysis of Data on the Internet III - Online Social Network Analysis

pp.:  252 – 254

Chapter e16: Analysis of Data on the Internet III - Online Social Network Analysis

pp.:  254 – 270

Chapter 17: Analysis of Data on the Internet IV - Search Trend Analysis over Time

pp.:  270 – 272

Chapter e17: Analysis of Data on the Internet IV - Search Trend Analysis over Time

pp.:  272 – 284

Chapter 18: Data Privacy and Privacy-Preserving Data Publishing

pp.:  284 – 296

Chapter 19: Creating an Environment for Commercial Data Analysis

pp.:  296 – 306

Chapter 20: Summary

pp.:  306 – 308

Appendix: Case Studies

pp.:  308 – 352

Bibliography

pp.:  352 – 354

Index

pp.:  354 – 362

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