This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data.
You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further.
The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages.

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Spis treści

Learning Predictive Analytics with R. Get to grips with key data visualization and predictive analytic skills using R eBook -- spis treści

• 1. Setting GNU R for predictive modeling
• 2. Basic data visualization with tools built-in in R
• 3. Data visualization with lattice
• 4. Unsupervized learning: clustering with k-means
• 5. Unsupervized learning: Hierarchical clustering
• 6. Unsupervized learning: Principal Component Analysis
• 7. Unsupervized learning: market basket analyses with Apriori (association rules)
• 8. Probability Distributions, Covariance, and Correlation
• 9. Regression
• 10. Classification with naïve Bayes and k-nn
• 11. Decision trees
• 12. Multilevel regression in R
• 13. Text Analytics with R
• 14. PMML
• 15. Appendix, Solution to exercises
• 16. References