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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

Content-based recommendation engine

A recommendation engine that is solely based on the explicit or implicit feedback received from customers is termed as content-based recommendation system. Explicit feedback is the customer's expression of the interest through filling in a survey about preferences or rating jokes of interest or opting for newsletters related to the joke or adding the joke on the watchlist, and so on. Implicit feedback is more of a mellowed-out approach where a customer visits a page, clicks on a joke link, or just spends time reading a joke review on an e-commerce page. Based on the feedback received, similar jokes are recommended to the customers. It may be noted that content-based recommendations do not take into consideration the preferences and feedback of other customers in the system; instead, it is purely based on the personalized feedback from the...

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