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You're reading from Apache Spark 2.x Machine Learning Cookbook

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781783551606

Pages 666 pages

Edition 1st Edition

Languages

Scala

Concepts

Machine Learning

Authors (5):

Mohammed Guller

Siamak Amirghodsi

Shuen Mei

Meenakshi Rajendran

Broderick Hall

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Table of Contents (20) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Practical Machine Learning with Spark Using Scala

2. Just Enough Linear Algebra for Machine Learning with Spark

3. Spark's Three Data Musketeers for Machine Learning - Perfect Together

4. Common Recipes for Implementing a Robust Machine Learning System

5. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I

6. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II

7. Recommendation Engine that Scales with Spark

8. Unsupervised Clustering with Apache Spark 2.0

9. Optimization - Going Down the Hill with Gradient Descent

10. Building Machine Learning Systems with Decision Tree and Ensemble Models

11. Curse of High-Dimensionality in Big Data

12. Implementing Text Analytics with Spark 2.0 ML Library

13. Spark Streaming and Machine Learning Library

Exploring the ratings data details for the recommendation system in Spark 2.0

In this recipe, we explore the data from the user/rating perspective to the nature and property of our data file. We will start to explore the ratings data file by parsing data into a Scala case class and generating visualization for insight. The ratings data will be used a little later to generate features for our recommendation engine. Again, we stress that the first step in any data science/machine learning exercise should be visualization and exploration of the data.

Once again, the best way of understanding data quickly is to generate a data visualization of it, and we will use a JFreeChart scatterplot to do this. A quick look at the chart of users by ratings produced by the JFreeChart plot shows a resemblance to a multinomial distribution with outliers, and an increasing sparsity when ratings are increased in magnitude.