- 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
Author
Eric Mayor
Reviewers
Ajay Dhamija
Khaled Tannir
Matt Wiley
Commissioning Editor
Kunal Parikh
Acquisition Editor
Kevin Colaco
Content Development Editor
Siddhesh Salvi
Technical Editor
Deepti Tuscano
Copy Editors
Puja Lalwani
Merilyn Pereira
Project Coordinator
Kranti Berde
Proofreader
Safis Editing
Indexer
Rekha Nair
Production Coordinator
Aparna Bhagat
Cover Work
Aparna Bhagat
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