Predictive Analytics will teach both art and science, showing when and why to use particular techniques to achieve particular results. And importantly, how, when and why to violate assumptions in the underlying math and stats-based theory. The principles and techniques in this book have been developed by the author over 20 years of consulting and training. His approach is thoroughly pragmatic, focusing on the algorithms and analysis techniques that are supported by commercial and open source software and shows which mix of techniques and real-world tools yield the best results.

· Overview of Predictive Analytics

· Setting Up the Problem

· Data Understanding

· Data Preparation

· Itemsets and Association Rules

· Descriptive Modeling

· Interpreting Descriptive Models

· Predictive Modeling

· Assessing Predictive Models

· Model Ensembles

· Text Mining

· Model Deployment

· Case Studies