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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Length 458 pages
Edition 3rd Edition
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (16) Chapters Close

Preface 1. Introducing Machine Learning FREE CHAPTER 2. Managing and Understanding Data 3. Lazy Learning – Classification Using Nearest Neighbors 4. Probabilistic Learning – Classification Using Naive Bayes 5. Divide and Conquer – Classification Using Decision Trees and Rules 6. Forecasting Numeric Data – Regression Methods 7. Black Box Methods – Neural Networks and Support Vector Machines 8. Finding Patterns – Market Basket Analysis Using Association Rules 9. Finding Groups of Data – Clustering with k-means 10. Evaluating Model Performance 11. Improving Model Performance 12. Specialized Machine Learning Topics Other Books You May Enjoy
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Index

Finding teen market segments using k-means clustering


Interacting with friends on a social networking service (SNS) such as Facebook, Tumblr, and Instagram has become a rite of passage for teenagers around the world. Having a relatively large amount of disposable income, these adolescents are a coveted demographic for businesses hoping to sell snacks, beverages, electronics, and hygiene products.

The many millions of teenage consumers using such sites have attracted the attention of marketers struggling to find an edge in an increasingly competitive market. One way to gain this edge is to identify segments of teenagers who share similar tastes, so that clients can avoid targeting advertisements to teens with no interest in the product being sold. For instance, sporting apparel is likely to be a difficult sell to teens with no interest in sports.

Given the text of teenagers' SNS pages, we can identify groups that share common interests such as sports, religion, or music. Clustering can automate...

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