Book Image

Hadoop Real-World Solutions Cookbook - Second Edition

By : Tanmay Deshpande
Book Image

Hadoop Real-World Solutions Cookbook - Second Edition

By: Tanmay Deshpande

Overview of this book

Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization. Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book. This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business.
Table of Contents (18 chapters)
Hadoop Real-World Solutions Cookbook Second Edition
Credits
About the Author
Acknowledgements
About the Reviewer
www.PacktPub.com
Preface
Index

Conducting predictive analytics using Spark MLib


Spark has a very rich machine learning library called MLib. This is a collection of various algorithms that are used for classification, clustering, recommendations, and so on. In this recipe, we are going to take a look at how to build a predictive model using MLib.

Getting ready

To perform this recipe, you should have Hadoop and Spark installed. You also need to install Scala. Here, I am using Scala 2.11.0.

How to do it...

For this recipe, we are going use the classic example dataset of iris flowers; you can find out more about this at https://en.wikipedia.org/wiki/Iris_flower_data_set.

Here, based on the petal length and width and the sepal length and width, we need to classify the flowers into species. First, we build a model, and then run tests on it to predict the output.

To start with, we first download iris.txt from https://github.com/deshpandetanmay/hadoop-real-world-cookbook/blob/master/data/iris.txt.

Next, save it in HDFS.

We start the...