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Apache Spark 2.x Machine Learning Cookbook

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
Authors (5):
Mohammed Guller Mohammed Guller
Profile icon Mohammed Guller
Siamak Amirghodsi Siamak Amirghodsi
Profile icon Siamak Amirghodsi
Shuen Mei Shuen Mei
Profile icon Shuen Mei
Meenakshi Rajendran Meenakshi Rajendran
Profile icon Meenakshi Rajendran
Broderick Hall Broderick Hall
Profile icon Broderick Hall
View More author details

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 movies data details for the recommendation system in Spark 2.0


In this recipe, we will begin to explore the data file by parsing data into a Scala case class and generating a simple metric. The key here is to acquire an understanding of our data, so in the later stages, if nebulous results arise, we will have some insight to make an informed conclusion about the correctness of our results.

This is the first of the two recipes which explore the movie dataset. Data exploration is an important first step in statistical analysis and machine learning.

One of the best ways to understand the data quickly is to generate a data visualization of it, and we will use JFreeChart to do that. It is very important to make sure you feel comfortable with the data and understand firsthand what is in each file, and the story it tries to tell.

We must always explore, understand, and visualize the data before we do anything else. Most performances and misses with ML and others systems can be traced...

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