Librería Portfolio

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