Book Image

Apache Spark 2.x Machine Learning Cookbook

By : Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
Book Image

Apache Spark 2.x Machine Learning Cookbook

By: Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Overview of this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Setting up the required data for a scalable recommendation engine in Spark 2.0


In this recipe, we examine the MovieLens public dataset and take a first exploratory view of the data. We will use the explicit data based on customer ratings from the MovieLens dataset. The MovieLens dataset contains 1,000,000 ratings of 4,000 movies from 6,000 users.

You will need one of the following command line tools to retrieve the specified data: curl (recommended for Mac) or wget (recommended for Windows or Linux).

How to do it...

  1. You can start with downloading the dataset using either of the following commands:
wget http://files.grouplens.org/datasets/movielens/ml-1m.zip

You can also use the following command:

curl http://files.grouplens.org/datasets/movielens/ml-1m.zip -o ml-1m.zip
  1. Now you need to decompress the ZIP:
unzip ml-1m.zip
creating: ml-1m/
inflating: ml-1m/movies.dat
inflating: ml-1m/ratings.dat
inflating: ml-1m/README
inflating: ml-1m/users.dat

The command will create a directory named ml-1m with...