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

Dataset reorganizing


In this section, we will cover dataset reorganization techniques. Then, we will discuss some of Spark's special features for data reorganizing and also some of R's special methods for data reorganizing that can be used with the Spark notebook.

After this section, we will be able to reorganize datasets for various machine learning needs.

Dataset reorganizing tasks

Reorganizing datasets sounds easy but could be very challenging and also often very time consuming.

Two common data reorganizing tasks are—firstly, to obtain a subset of the data for modeling and, secondly, to aggregate data to a higher level. For example, we have students' data, but we need to have a dataset at the classroom level. For this, we will need to calculate some attributes for students and then reorganize it into new data.

To work with data reorganizing, data scientists and machine learning professionals often utilize their familiar SQL or R programming tools. Fortunately within the Spark environment,...