Part I I Purpose and Process -- chapter 1 Database Marketing and Data Mining -- chapter 2 A Process Model for Data Mining--CRISP-DM -- part II II Predictive Modeling Tools -- chapter 3 Basic Tools for Understanding Data -- chapter 4 Multiple Linear Regression -- chapter 5 Logistic Regression -- chapter 6 Lift Charts -- chapter 7 Tree Models -- chapter 8 Neural Network Models -- chapter 9 Putting It All Together -- part III III Grouping Methods -- chapter 10 Ward's Method of Cluster Analysis and Principal Components -- chapter 11 K-Centroids Partitioning Cluster Analysis
Summary
Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations
Bibliography
Includes bibliographical references (pages 283-285)
