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

Deployment


As discussed before, MLlib supports model export to Predictive Model Markup Language (PMML). Therefore, we export some developed models to PMML for this project as some other departments of the university are interested in our analytical results and use other systems such as SPSS.

However, for practical purposes, the users of this project are more interested in rule-based decision making to use some of our insights and also in score-based decision making to reduce student attrition.

Specifically, as for this project, the client is interested in applying our results to, firstly, decide which interventions to use for a combination of course adjustments or counseling services with a special student segment, and, secondly, when the university needs to start some interventions as per the student attrition score.

Therefore, we need to turn some of our results into rules and also produce a student attrition risk score for this university.

Rules

All the algorithms either in MLlib or R can...