In this chapter, the goal was to provide an introduction to how to use R in order to build and test association rule mining (market basket analysis) and recommendation engines. Market basket analysis is trying to understand what items are purchased together. With recommendation engines, the goal is to provide a customer with other items that they will enjoy based on how they have rated previously viewed or purchased items. It is important to understand the R package that we used (recommenderlab
) for recommendation is not designed for implementation but rather to develop and test algorithms.
Mastering Machine Learning with R
By :
Mastering Machine Learning with R
By:
Overview of this book
Table of Contents (20 chapters)
Mastering Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
A Process for Success
Linear Regression – The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks
Cluster Analysis
Principal Components Analysis
Market Basket Analysis and Recommendation Engines
Time Series and Causality
Text Mining
R Fundamentals
Index
Customer Reviews