Data mining for business analytics : concepts, techniques, and applications with XLMiner /
Shmueli, Galit
Data mining for business analytics : concepts, techniques, and applications with XLMiner / Galit Shmueli, Peter C. Bruce and Nitin R. Patel - THIRD EDITION. - Hoboken, New Jersey : John Wiley & Son, Inc., c2016 - xxx, 514 pages : illustrations ; 26 cm.
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.
9781118729274
COMPUTER FILE
HF 5548.2 .S44 2016
Data mining for business analytics : concepts, techniques, and applications with XLMiner / Galit Shmueli, Peter C. Bruce and Nitin R. Patel - THIRD EDITION. - Hoboken, New Jersey : John Wiley & Son, Inc., c2016 - xxx, 514 pages : illustrations ; 26 cm.
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.
9781118729274
COMPUTER FILE
HF 5548.2 .S44 2016