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Features
One of only two books on big data on Intel´s prestigous recommended reading list
Provides step-by-step solutions to common problems facing data scientists, modelers, and marketers; other books typically provide outlined-solutions.
Illustrations involve real problems, real data, and better solutions.
uniquely introduces two new machine-learning methods specifically tailored to database assessment of optimal model performance.
new edition will add latest methodologies as well as SAS coverage
Summary
The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. is a compilation of new and creative data mining techniques, which address the scaling-up of the framework of classical and modern statistical methodology, for predictive modeling and analysis of big data. SM-DM provides proper solutions to common problems facing the newly minted data scientist in the data mining discipline. Its presentation focuses on the needs of the data scientists (commonly known as statisticians, data miners and data analysts), delivering practical yet powerful, simple yet insightful quantitative techniques, most of which use the ´old´ statistical methodologies improved upon by the new machine learning influence.
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