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R Machine Learning Projects

You're reading from   R Machine Learning Projects Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Length 334 pages
Edition 1st Edition
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Author (1):
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Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Author Profile Icon Dr. Sunil Kumar Chinnamgari
Dr. Sunil Kumar Chinnamgari
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Table of Contents (12) Chapters Close

Preface 1. Exploring the Machine Learning Landscape FREE CHAPTER 2. Predicting Employee Attrition Using Ensemble Models 3. Implementing a Jokes Recommendation Engine 4. Sentiment Analysis of Amazon Reviews with NLP 5. Customer Segmentation Using Wholesale Data 6. Image Recognition Using Deep Neural Networks 7. Credit Card Fraud Detection Using Autoencoders 8. Automatic Prose Generation with Recurrent Neural Networks 9. Winning the Casino Slot Machines with Reinforcement Learning 10. The Road Ahead
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Customer Segmentation Using Wholesale Data

In today's competitive world, the success of an organization largely depends on how much it understands its customers' behavior. Understanding each customer individually to better tailor the organizational effort to individual needs is a very expensive task. Based on the size of the organization, this task can be very challenging as well. As an alternative, organizations rely on something called segmentation, which attempts to categorize customers into groups based on identified similarities. This critical aspect of customer segmentation allows organizations to extend their efforts to the individual needs of various customer subsets (if not catering to individual needs), therefore reaping greater benefits.

In this chapter, we will learn about the concept and importance of customer segmentation. We'll then deep dive into...

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