Table of contents : Cover Front Matter 1. Introduction to Analytics 2. Problem Definition 3. Introduction to KNIME 4. Data Preparation 5. Dimensionality Reduction 6. Ordinary Least Squares Regression 7. Logistic Regression 8. Classification and Regression Trees 9. Naïve Bayes 10. k Nearest Neighbors 11. Neural Networks 12. Ensemble Models 13. Cluster Analysis 14. Communication and Deployment Back Matter