Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Table of Contents:
Preface to Third Edition Preface of Second Edition Acknowledgments Author 1. Introduction 2. Science Dealing with Data: Statistics and Data Science 3. Two Basic Data Mining Methods for Variable Assessment 4. CHAID-Based Data Mining for Paired-Variable Assessment 5. The Importance of Straight Data Simplicity and Desirability for Good Model-Building Practice 6. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data 7. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment 8. Market Share Estimation: Data Mining for an Exceptional Case 9. The Correlation Coefficient: Its Values Range between Plus and Minus 1, or Do They? 10. Logistic Regression: The Workhorse of Response Modeling 11. Predicting Share of Wallet without Survey Data 12. Ordinary Regression: The Workhorse of Profit Modeling 13. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution 14. CHAID for Interpreting a Logistic Regression Model 15. The Importance of the Regression Coefficient 16. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables 17. CHAID for Specifying a Model with Interaction Variables 18. Market Segmentation Classification Modeling with Logistic Regression 19. Market Segmentation Based on Time-Series Data Using Latent Class Analysis 20. Market Segmentation: An Easy Way to Understand the Segments 21. The Statistical Regression Model: An Easy Way to Understand the Model 22. CHAID as a Method for Filling in Missing Values 23. Model Building with Big Complete and Incomplete Data 24. Art, Science, Numbers, and Poetry 25. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling 26. Assessment of Marketing Models 27. Decile Analysis: Perspective and Performance 28. Net T-C Lift Model: Assessing the Net Effects of Test and Control Campaigns 29. Bootstrapping in Marketing: A New Approach for Validating Models 30. Validating the Logistic Regression Model: Try Bootstrapping 31. Visualization of Marketing Models: Data Mining to Uncover Innards of a Model 32. The Predictive Contribution Coefficient: A Measure of Predictive Importance 33. Regression Modeling Involves Art, Science, and Poetry, Too 34. Opening the Dataset: A Twelve-Step Program for Dataholics 35. Genetic and Statistic Regression Models: A Comparison 36. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model 37. A Data Mining Method for Moderating Outliers Instead of Discarding Them 38. Overfitting: Old Problem, New Solution 39. The Importance of Straight Data: Revisited 40. The GenIQ Model: Its Definition and an Application 41. Finding the Best Variables for Marketing Models 42. Interpretation of Coefficient-Free Models 43. Text Mining: Primer, Illustration, and TXTDM Software 44. Some of My Favorite Statistical Subroutines Index