Book Image

Hadoop Blueprints

By : Anurag Shrivastava, Tanmay Deshpande
Book Image

Hadoop Blueprints

By: Anurag Shrivastava, Tanmay Deshpande

Overview of this book

If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level. Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book. The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space.
Table of Contents (14 chapters)
Hadoop Blueprints
About the Authors
About the Reviewers

Tree-structure models for classification

When we build our model, we have to find the informative attributes or the features contained in our data. A feature that never changes in our data set is not a very informative attribute. Some features will be more informative and they will influence the outcome more than the others. In other words, we can say that such features provide more information gain than the others. Once we have identified the features providing the information and sorted them in order of information gain, we can arrange them in the form of an inverted tree to create our model. The leaf nodes of the tree denote the outcome. Other nodes denote the features or attributes that provide information gain to reach the final outcome.

In the tree-based learning approach, we recursively divide the historical data. During the training of the model, various dividing criteria will be tried. If a feature has a non-numeric value then it is converted into binary. A classification tree can...