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.
The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects.
Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.
Preface
Part I : Purpose and Process
Chapter 1 : Database Marketing and Data Mining
Chapter 2 : A Process Model for Data Mining - CRISP-DM
Part 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 : Grouping Methods
Chapter 10 : Ward’s Method of Cluster Analysis and Principal Components
Chapter 11 : K-Centroids Partitioning Cluster Analysis
Bibliography
Index
