Description: Data Mining and Predictive AnalyticsAuthor(s): Daniel T. Larose Format: Hardback Publisher: John Wiley & Sons Inc, United States Imprint: John Wiley & Sons Inc ISBN-13: 9781118116197, 978-1118116197 Synopsis Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified "white box" approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, [url] with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
Price: 103.7 GBP
Location: Aldershot
End Time: 2024-12-28T10:42:05.000Z
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Book Title: Data Mining and Predictive Analytics
Item Height: 232 mm
Item Width: 164 mm
Series: Wiley Series on Methods and Applications in Data Mining
Author: Daniel T. Larose
Publication Name: Data Mining and Predictive Analytics
Format: Hardcover
Language: English
Publisher: John Wiley & Sons INC International Concepts
Subject: Computer Science
Publication Year: 2015
Type: Textbook
Item Weight: 1318 g
Number of Pages: 824 Pages
