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


In the past, rankings were mostly reviewed as a reference by users. With this project, we found we are also in a position to assist our users in integrating our results with their decision-making tools, to help them utilize rankings better and also make their lives easier. For this, producing rules from rankings and also making scores behind rankings easily accessible became very important.

Because of the preceding reason, our deployment is still on to develop a set of rules and also to make all the scores available for decision makers, which include schools and some parents. Specifically, the main task of sending out a rule to alert users when some ranking changes dramatically, especially when a ranking drops down dramatically. Users of this project also have a need to obtain all the scores and rankings for their management performance.

Another purpose for this project is to produce good predictive models for the users to forecast possible changes of school rankings as per population...