Book Image

Learning Predictive Analytics with R

By : Eric Mayor
Book Image

Learning Predictive Analytics with R

By: Eric Mayor

Overview of this book

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.
Table of Contents (23 chapters)
Learning Predictive Analytics with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Exercises and Solutions
Index

Summary


In this chapter, we discovered some important concepts regarding association rules. In particular, we examined how important support, confidence, and lift measures are in the assessment of association rules, and that high support and confidence do not necessarily mean that an association rule is useful. We uncovered the efficient working of the apriori algorithm for mining association rules and discovered the use of apriori in R in mining several datasets. We have also seen that it is often necessary to recode some variables before being able to analyze the data. Finally, we have discovered Grouped matrix-based visualization.

In next chapter, we will examine statistical distributions and correlations.