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

Supervised learning

In Chapter 3, Building a Fraud Detection System, we built a fraud detection model using clustering, which is an unsupervised learning method.

In this chapter, we can see a difference between supervised and unsupervised machine more clearly. In the case of credit card approval data, we have a clearly defined target variable, the result. We are only interested in the value of this target variable for new customers. The historical card approval data may be captured from a manual or automated card approval process.

You should keep in mind when a supervised learning method predicts the value of the target variable, it can never be 100% certain about the outcome. The process of building the model produces a model which provides a probabilistic estimate for the outcome for the data which has not been seen previously or the future data.

Supervised learning has two distinct steps. In the first step, we build the model by mining the historical data. Once the model is ready, it can...