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Hadoop Blueprints

Hadoop Blueprints

By : Sudheesh Narayan, Anurag Shrivastava, Deshpande
5 (1)
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Hadoop Blueprints

Hadoop Blueprints

5 (1)
By: Sudheesh Narayan, Anurag Shrivastava, 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 (9 chapters)
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Machine learning for fraud detection

Fraud detection is a machine learning problem wherein we use historical data to build a model, which we will use to predict the outcome for future situations.

With the help of our cleaned historical transaction data, we will build a fraud detection model. To build the fraud detection model, we will apply the concepts of machine learning. A definition of machine learning is as follows:

"A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E."

                                                                  –Tom Mitchell, Carnegie Mellon University

Please note that the detailed explanation of machine learning is beyond the scope of this book...

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