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Practical Machine Learning with R

You're reading from   Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781838550134
Length 416 pages
Edition 1st Edition
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Authors (3):
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Brindha Priyadarshini Jeyaraman Brindha Priyadarshini Jeyaraman
Author Profile Icon Brindha Priyadarshini Jeyaraman
Brindha Priyadarshini Jeyaraman
Ludvig Renbo Olsen Ludvig Renbo Olsen
Author Profile Icon Ludvig Renbo Olsen
Ludvig Renbo Olsen
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
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Toc

Table of Contents (8) Chapters Close

About the Book 1. An Introduction to Machine Learning FREE CHAPTER 2. Data Cleaning and Pre-processing 3. Feature Engineering 4. Introduction to neuralnet and Evaluation Methods 5. Linear and Logistic Regression Models 6. Unsupervised Learning 1. Appendix

Applications of Clustering

Clustering is useful in a variety of fields. We will look at two of them in detail:

  • Market segmentation: Segmentation is the process of splitting a heterogeneous group of consumers or customers into smaller homogeneous groups. These smaller groups can be targeted differently based on their characteristics and behavior. Segmentation is important to businesses because it shapes both marketing efforts and product development. Businesses designing products must decide which product is targeted to which segment of consumers or customers. They must consequently decide what features to include, how to price the product, and how to take it to market. All these actions are heavily influenced by the characteristics of the different segments.

    Most businesses start off not knowing how to group their customers. Clustering is useful in helping these businesses to identify segments in their data.

  • Document clustering and information retrieval: In the information and knowledge...
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