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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 the Jokes recommendation problem and the dataset

Dr. Ken Goldberg and his colleagues, Theresa Roeder, Dhruv Gupta, and Chris Perkins, introduced a dataset to the world through their paper Eigentaste: A Constant Time Collaborative Filtering Algorithm, which is pretty popular in the recommender-systems domain. The dataset is named the Jester's jokes dataset. To create it, a number of users are presented with several jokes and they are asked to rate them. The ratings provided by the users for the various jokes formed the dataset. The data in this dataset is collected between April 1999 and May 2003. The following are the attributes of the dataset:

  • Over 11,000,000 ratings of 150 jokes from 79,681 users
  • Each row is a user (Row 1 = User #1)
  • Each column is a joke (Column 1 = Joke #1)
  • Ratings are given as real values from -10.00 to +10.00; -10 being the lowest possible...
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