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Data mining and business analytics with R / Johannes Ledolter.

By: Material type: TextTextPublication details: New Jersey : John Wiley, c2013.Description: xi, 351 p. : illustrations (some color) ; 25 cmISBN:
  • 9781118447147 (cloth)
Subject(s): DDC classification:
  • 006.312 23 L474
Contents:
1. Introduction-- 2. Processing the information and getting to know your data-- 3. Standard linear regression-- 4. Local polynomial regression: a nonparametric regression approach-- 5. Importance of parsimony in statistical modeling-- 6. Penalty-based variable selection in regression models with many parameters (LASSO)-- 7. Logistic regression-- 8. Binary classification, probabilities, and evaluating classification performance-- 9. Classification using a nearest neighbor analysis-- 10. The naive Bayesian analysis: a model for predicting a categorical response from mostly categorical predictor variables-- 11. Multinomial logistic regression-- 12. More on classification and a discussion on discriminant analysis-- 13. Decision trees-- 14. Further discussion on regression and classification trees, computer software, and other useful classification methods-- 15. Clustering-- 16. Market basket analysis: association rules and lift-- 17. Dimension reduction: factor models and principal components-- 18. Reducing the dimension in regressions with multicollinear inputs: principal components regression and partial least squares-- 19. Text as data: text mining and sentiment analysis-- 20. Network data-- Appendix A: Exercises-- Appendix B: References-- Index.
Summary: Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets.
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Includes bibliographical references and index.

1. Introduction--
2. Processing the information and getting to know your data--
3. Standard linear regression--
4. Local polynomial regression: a nonparametric regression approach--
5. Importance of parsimony in statistical modeling--
6. Penalty-based variable selection in regression models with many parameters (LASSO)--
7. Logistic regression--
8. Binary classification, probabilities, and evaluating classification performance--
9. Classification using a nearest neighbor analysis--
10. The naive Bayesian analysis: a model for predicting a categorical response from mostly categorical predictor variables--
11. Multinomial logistic regression--
12. More on classification and a discussion on discriminant analysis--
13. Decision trees--
14. Further discussion on regression and classification trees, computer software, and other useful classification methods--
15. Clustering--
16. Market basket analysis: association rules and lift--
17. Dimension reduction: factor models and principal components--
18. Reducing the dimension in regressions with multicollinear inputs: principal components regression and partial least squares--
19. Text as data: text mining and sentiment analysis--
20. Network data--

Appendix A: Exercises--
Appendix B: References--
Index.

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets.

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