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Data mining for business analytics : concepts, techniques, and applications with XLMiner / Galit Shmueli, Peter C. Bruce and Nitin R. Patel

By: Contributor(s): Material type: TextTextPublication details: Hoboken, New Jersey : John Wiley & Son, Inc., c2016Edition: THIRD EDITIONDescription: xxx, 514 pages : illustrations ; 26 cmISBN:
  • 9781118729274
Subject(s): LOC classification:
  • HF 5548.2 .S44 2016
Contents:
Part 1. Preliminaries -- 1. Introduction -- 2. Overview of the data mining process -- Part 2. Data exploration and dimension reduction -- 3. Data visualization -- 4. Dimension reduction -- Part 3. Performance evaluation -- 5. Evaluating predictive performance -- Part 4. Prediction and classification methods -- 6. Multiple linear regression -- 7. k-Nearest-Neighbors (k-NN) -- 8. teh Naive Bayes classifier -- 9. Classification and regression trees -- 10. Logistic regression -- 11. Neural nets -- 12. Discriminant analysis -- 13. Combining methods: ensembles and uplift modeling -- Part 5. Mining relationships among records -- 14. Association rules and collaborative filtering -- 15. Cluster analysis -- Part 6. Forecasting time series -- 16. Handling time series -- 17. Regression-based forecasting -- 18. Smoothing methods -- Part 7. Data analytics -- 19. Social network analytics -- 20. text mining -- Part 8. Cases -- 21. Cases.
Summary: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
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Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Graduate Studies General Circulation Doctor of Education - Educational Management GC HF 5548.2 .S44 2016 (Browse shelf(Opens below)) c.1 Available NULIB000013108
Browsing LRC - Graduate Studies shelves, Shelving location: General Circulation, Collection: Doctor of Education - Educational Management Close shelf browser (Hides shelf browser)
GC BF 713 .V36 2009 Human development / GC HB 171.5 .A695 2014 Economics / GC HF 5548.2 .S44 2016 Data mining for business analytics : concepts, techniques, and applications with XLMiner / GC HF 5515.I56 .V35 2000 c.1 The Power of goals : how to achieve outstanding success / GC H 61 .C74 2018 Qualitative inquiry & research design : choosing among five approaches / GC H 62 .C747 2014 Research design : Qualitative, quantitative and mixed methods approaches /

Includes bibliographical references and index.

Part 1. Preliminaries -- 1. Introduction -- 2. Overview of the data mining process -- Part 2. Data exploration and dimension reduction -- 3. Data visualization -- 4. Dimension reduction -- Part 3. Performance evaluation -- 5. Evaluating predictive performance -- Part 4. Prediction and classification methods -- 6. Multiple linear regression -- 7. k-Nearest-Neighbors (k-NN) -- 8. teh Naive Bayes classifier -- 9. Classification and regression trees -- 10. Logistic regression -- 11. Neural nets -- 12. Discriminant analysis -- 13. Combining methods: ensembles and uplift modeling -- Part 5. Mining relationships among records -- 14. Association rules and collaborative filtering -- 15. Cluster analysis -- Part 6. Forecasting time series -- 16. Handling time series -- 17. Regression-based forecasting -- 18. Smoothing methods -- Part 7. Data analytics -- 19. Social network analytics -- 20. text mining -- Part 8. Cases -- 21. Cases.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.

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