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Hadoop Real-World Solutions Cookbook- Second Edition

You're reading from   Hadoop Real-World Solutions Cookbook- Second Edition Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784395506
Length 290 pages
Edition 2nd Edition
Tools
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Author (1):
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Tanmay Deshpande Tanmay Deshpande
Author Profile Icon Tanmay Deshpande
Tanmay Deshpande
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with Hadoop 2.X FREE CHAPTER 2. Exploring HDFS 3. Mastering Map Reduce Programs 4. Data Analysis Using Hive, Pig, and Hbase 5. Advanced Data Analysis Using Hive 6. Data Import/Export Using Sqoop and Flume 7. Automation of Hadoop Tasks Using Oozie 8. Machine Learning and Predictive Analytics Using Mahout and R 9. Integration with Apache Spark 10. Hadoop Use Cases Index

Using Predictive analytics on Bank Data using Mahout


In this recipe, we are going to take a look at how to use Mahout to generate a predictive model and validate how good this model is against some sample data. Here, we will be using the sample data collected by a bank during their marketing operations.

Getting ready

To perform this recipe, you should have a running Hadoop cluster as well as the latest version of Mahout installed on it.

How to do it...

In this recipe, we are going to use Logistic Regression in order to predict the occurrence of an event. It uses predictors from the given data in order to calculate the probability. The Mahout implementation uses the Stochastic Gradient Descent (SGD) algorithm for logistic regression. You can learn more about SGD for logistic regression at http://blog.trifork.com/2014/02/04/an-introduction-to-mahouts-logistic-regression-sgd-classifier/.

SGD is, by default, a sequential algorithm so we cannot run any parallel activities on it. Even though it is...

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