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

Spark for attrition prediction


In this section, we will start with a real use case and then describe how to prepare Apache Spark for this attrition prediction project.

The use case

NIY University is a private university and wants to improve its student retention using predictive modeling with Big Data. According to ACT's research (refer to http://www.act.org/research/policymakers/pdf/retain_2015.pdf), the average retention rate for American colleges was only about 68% in 2015, and it is even lower for two-year public colleges at 54.7% and for private two-year colleges at 63.4%. That is, about 32% of students left school before graduation, and the attrition is even at greater for two-year public colleges at 45.3% and for two-year private colleges at 36.6%. As student attrition costs both colleges and students a lot, using Big Data to predict students' attrition and designing interventions to prevent them has a lot of value.

The university has a lot of information about student demographics and...