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

Data treatment with SPSS


There are always many common data and feature issues to work with for any machine learning project, including this movie recommendation project for which we can use SPSS Modeler.

In comparison with other projects in this book, the data structure here is relatively simple; however, one special issue for the data to be used for this project is about missing values because some users do not rate some movies. To deal with this, SPSS Modeler has a few super nodes to deal with the issue. In other words, we need to develop a special SPSS Modeler Stream, which include nodes for missing value treatments. After this job, we need to separate the data into parts to train and test.

Missing data nodes on SPSS modeler

To deal with missing values and build a special data stream, we need to start with a Type Node with some Super Nodes to handle missing values to be filled with imputed values.

Specifically, you can do this from the Data Audit report, which allows you to specify options...