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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
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Understanding the attrition problem and the dataset

HR analytics helps with interpreting organizational data. It finds out the people-related trends in the data and helps the HR department take the appropriate steps to keep the organization running smoothly and profitably. Attrition in a corporate setup is one of the complex challenges that the people managers and HR personnel have to deal with. Interestingly, machine learning models can be deployed to predict potential attrition cases, thereby helping the appropriate HR personnel or people managers take the necessary steps to retain the employee.

In this chapter, we are going to build ML ensembles that will predict such potential cases of attrition. The job attrition dataset used for the project is a fictional dataset created by data scientists at IBM. The rsample library incorporates this dataset and we can make use of this...

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