Statistical and Machine-Learning Data Mining:
Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition
Nonfiction, Computers, Advanced Computing, Theory, Science & Nature, Mathematics, Statistics, Database Management
| Author: | Bruce Ratner | ISBN: | 9781351652384 |
| Publisher: | CRC Press | Publication: | July 12, 2017 |
| Imprint: | Chapman and Hall/CRC | Language: | English |
| Author: | Bruce Ratner |
| ISBN: | 9781351652384 |
| Publisher: | CRC Press |
| Publication: | July 12, 2017 |
| Imprint: | Chapman and Hall/CRC |
| Language: | English |
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.
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.
