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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

Multi-arm bandit – real-world use cases

We encounter so many situations in the real world that are similar to that of the MABP we reviewed in this chapter. We could apply RL strategies to all these situations. The following are some of the real-world use cases similar to that of the MABP:

  • Finding the best medicine/s among many alternatives
  • Identifying the best product to launch among possible products
  • Deciding the amount of traffic (users) that we need to allocate for each website
  • Identifying the best marketing strategy for launching a product
  • Identifying the best stocks portfolio to maximize profit
  • Finding out the best stock to invest in
  • Figuring out the shortest path in a given map
  • Click-through rate prediction for ads and articles
  • Predicting the most beneficial content to be cached at a router based upon the content of articles
  • Allocation of funding for different departments...
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