Data Mining and Predictive Analytics
eBook - ePub

Data Mining and Predictive Analytics

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Data Mining and Predictive Analytics

About this book

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, www.dataminingconsultant, 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.

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Yes, you can access Data Mining and Predictive Analytics by Daniel T. Larose in PDF and/or ePUB format, as well as other popular books in Informatik & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2015
Print ISBN
9781118116197
eBook ISBN
9781118868706
Edition
2
Subtopic
Data Mining

Table of contents

  1. Cover
  2. Series
  3. Title Page
  4. Copyright
  5. Table of Contents
  6. Dedication
  7. Preface
  8. Acknowledgments
  9. Part I: Data Preparation
  10. Part II: Statistical Analysis
  11. Part III: Classification
  12. Part IV: Clustering
  13. Part V: Association Rules
  14. Part VI: Enhancing Model Performance
  15. Part VII: Further Topics
  16. Part VIII: Case Study: Predicting Response to Direct-Mail Marketing
  17. Appendix A: Data Summarization and Visualization
  18. Index
  19. End User License Agreement