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. It explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. The authors also give insight into some of the challenges faced when deploying these tools. Readers can access a powerful, GUI-enhanced customized R package online as well as example data sets on the book's website.
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. It explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. The authors also give insight into some of the challenges faced when deploying these tools. Readers can access a powerful, GUI-enhanced customized R package online as well as example data sets on the book's website.
Dr. Daniel S. Putler is a Data Artisan in Residence at Alteryx, a business intelligence/analytics software company. Dr. Robert E. Krider is a professor of marketing in the Beedie School of Business at Simon Fraser University. He has also taught in Hong Kong, Shanghai, Portugal, and Germany. His research tackles questions of customer and competitor behavior in retailing and media industries.
Inhaltsangabe
I Purpose and Process: Database Marketing and Data Mining. A Process Model for Data Mining-CRISP-DM. II Predictive Modeling Tools: Basic Tools for Understanding Data. Multiple Linear Regression. Logistic Regression. Lift Charts. Tree Models. Neural Network Models. Putting It All Together. III Grouping Methods: Ward's Method of Cluster Analysis and Principal Components. K-Centroids Partitioning Cluster Analysis. Bibliography. Index.
I Purpose and Process: Database Marketing and Data Mining. A Process Model for Data Mining-CRISP-DM. II Predictive Modeling Tools: Basic Tools for Understanding Data. Multiple Linear Regression. Logistic Regression. Lift Charts. Tree Models. Neural Network Models. Putting It All Together. III Grouping Methods: Ward's Method of Cluster Analysis and Principal Components. K-Centroids Partitioning Cluster Analysis. Bibliography. Index.
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