Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (23 chapters)
Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
16
Sources

Chapter 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis

It's much easier to double your business by doubling your conversion rate than by doubling your traffic.                                                                           - Jeff Eisenberg, CEO of BuyerLegends.com

I don't see smiles on the faces of people at Whole Foods.                                                                            - Warren Buffett

One would have to live on the dark side of the moon in order to not observe each and every day the results of the techniques that we are about to discuss in this chapter. If you visit www.amazon.com, watch movies on www.netflix.com, or visit any retail website, you will be exposed to terms such as "related products", "because you watched...", "customers who bought x also bought y", or "recommended for you", at every twist and turn. With large volumes of historical real-time or near real-time information, retailers utilize the algorithms discussed...