Chapman & Hall/CRC data mining and knowledge discovery series.
Front Cover; Contents; List of Figures; List of Tables; Preface; 1. Introduction; 2. Properties of Statistical Distributions; 3. Important Matrix Relationships; 4. Linear Modeling and Regression; 5. Nonlinear Modeling; 6. Time Series Analysis; 7. Data Preparation and Variable Selection; 8. Model Goodness Measures; 9. Optimization Methods; 10. Miscellaneous Topics; Appendix A: Useful Mathematical Relations; Appendix B: DataMinerXL --;Microsoft Excel Add-In for Building Predictive Models; Bibliography
Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts. The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish--Fisher expansion and o.