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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
11. Other Books You May Enjoy

Understanding customer segmentation

Customer segmentation, or market segmentation, at a basic level, is the partitioning of a broad range of potential customers in a given market into specific subgroups of customers, where each of the subgroups contains customers that share certain similarities. The following diagram depicts the formal definition of customer segmentation where customers are identified into three groups:

Illustration depicting customer segmentation definition

Customer segmentation needs the organizations to gather data about customers and analyze it to identify patterns that can be used to determine subgroups. The segmentation of customers could be achieved through multiple data points related to customers. The following are some of the data points:

  • Demographics: This data point includes race, ethnicity, age, gender, religion, level of education, income, life...
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