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Data Mining for Business Analytics: Concepts, Techniques and Applications in R by Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel and Kenneth C. Lichtendahl

By: Shmueli, Galit.
Contributor(s): Bruce, Peter C | Yahav, Inbal | Patel, Nitin R | Lichtendahl, Kenneth C.
Material type: materialTypeLabelBookPublisher: Hoboken John Wiley & Sons, Inc. 2018Description: 544p.ISBN: 9781118879368.Subject(s): Business mathematics - Computer programsDDC classification: 658.40380
List(s) this item appears in: R Books
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Part I: Preliminaries
1. Introduction
2. Overview of the data mining process

Part II: Data Exploration and Dimension Reduction
3. Data visualization
4. Dimension reduction

Part III: Performance Evaluation
5. Evaluating predictive performance

Part IV: Prediction and Classification Methods
6. Multiple linear regression
7. k-Nearest Neighbors (kNN)
8. The 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 V: Mining Relationships Among Records
14. Association rules and collaborative filtering
15. Cluster analysis

Part VI: Forecasting Time Series
16. Handling time series
17. Regression-based forecasting
18. Smoothing methods

Part VII: Data Analytics
19. Social network analytics
20. Text mining

Part VIII: Cases
21. Cases

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