- Learning Predictive Analytics with R
- Table of Contents
- Learning Predictive Analytics with R
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Setting GNU R for Predictive Analytics
- 2. Visualizing and Manipulating Data Using R
- 3. Data Visualization with Lattice
- 4. Cluster Analysis
- 5. Agglomerative Clustering Using hclust()
- 6. Dimensionality Reduction with Principal Component Analysis
- 7. Exploring Association Rules with Apriori
- 8. Probability Distributions, Covariance, and Correlation
- 9. Linear Regression
- 10. Classification with k-Nearest Neighbors and Naïve Bayes
- 11. Classification Trees
- 12. Multilevel Analyses
- 13. Text Analytics with R
- 14. Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
- A. Exercises and Solutions
- Exercises
- Chapter 1 – Setting GNU R for Predictive Modeling
- Chapter 2 – Visualizing and Manipulating Data Using R
- Chapter 3 – Data Visualization with Lattice
- Chapter 4 – Cluster Analysis
- Chapter 5 – Agglomerative Clustering Using hclust()
- Chapter 6 – Dimensionality Reduction with Principal Component Analysis
- Chapter 7 – Exploring Association Rules with Apriori
- Chapter 8 – Probability Distributions, Covariance, and Correlation
- Chapter 9 – Linear Regression
- Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes
- Chapter 11 – Classification Trees
- Chapter 12 – Multilevel Analyses
- Chapter 13 – Text Analytics with R
- Solutions
- Chapter 1 – Setting GNU R for Predictive Modeling
- Chapter 2 – Visualizing and Manipulating Data Using R
- Chapter 3 – Data Visualization with Lattice
- Chapter 4 – Cluster Analysis
- Chapter 5 – Agglomerative Clustering Using hclust()
- Chapter 6 – Dimensionality Reduction with Principal Component Analysis
- Chapter 7 – Exploring Association Rules with Apriori
- Chapter 8 – Probability Distributions, Covariance, and Correlation
- Chapter 9 – Linear Regression
- Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes
- Chapter 11 – Classification Trees
- Chapter 12 – Multilevel Analyses
- Chapter 13 – Text Analytics with R
- Exercises
- B. Further Reading and References
- Preface
- Chapter 1 – Setting GNU R for Predictive Modeling
- Chapter 2 – Visualizing and Manipulating Data Using R
- Chapter 3 – Data Visualization with Lattice
- Chapter 4 – Cluster Analysis
- Chapter 5 – Agglomerative Clustering Using hclust()
- Chapter 6 – Dimensionality Reduction with Principal Component Analysis
- Chapter 7 – Exploring Association Rules with Apriori
- Chapter 8 – Probability Distributions, Covariance, and Correlation
- Chapter 9 – Linear Regression
- Chapter 10 – Classification with k-Nearest Neighbors and Naïve Bayes
- Chapter 11 – Classification Trees
- Chapter 12 – Multilevel Analyses
- Chapter 13 – Text Analytics with R
- Chapter 14 – Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
- Index
Becker, R. A., Cleveland, W. S., and Shyu, M. J. The visual design and control of trellis display. Journal of Computational and Graphical Statistics, 5. 123-155. 1996.
Campbell, J. A. Health insurance coverage in Department of Commerce (Ed.) Current population reports. 15-44. Department of Commerce, Washington. 1999.
Cleveland, W. S. Visualizing data. Hobart Press, Summit. 1993.
Sarkar, D. Lattice: multivariate data visualization with R. Springer, New York. 2008.
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