Book Image

Apache Spark Machine Learning Blueprints

By : Alex Liu
Book Image

Apache Spark Machine Learning Blueprints

By: Alex Liu

Overview of this book

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
Table of Contents (18 chapters)
Apache Spark Machine Learning Blueprints
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Chapter 7. Recommendations on Spark

In this chapter, we will switch our focus to SPSS on Apache Spark as SPSS is a widely used tool for machine learning and data science computing.

Specifically, in this chapter, with a process similar to what we used in previous chapters, we will start with discussing setting up our SPSS on a Spark system for a recommendation project, together with a full description of this real-life project. Then, we will select machine learning methods and prepare the data. With SPSS Analytic Server, we will estimate models on Spark and then evaluate models with a focus on using error ratios. Finally, we will deploy the models for our client. Here are the topics that will be covered in this chapter:

  • Spark for a recommendation engine

  • Methods for recommendation development

  • Data treatment

  • Model estimation

  • Model evaluation

  • Recommendation deployment