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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

Product type Paperback

Published in Jan 2019

Publisher Packt

ISBN-13 9781789807943

Length 334 pages

Edition 1st Edition

Languages

R

Tools

H2O

Concepts

Machine Learning

Author (1):

Dr. Sunil Kumar Chinnamgari

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Table of Contents (12) Chapters

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

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Understanding the Amazon reviews dataset

We use the Amazon product reviews polarity dataset for the various projects in this chapter. It is an open dataset constructed and made available by Xiang Zhang. It is used as a text classification benchmark in the paper: Character-level Convolutional Networks for Text Classification and Advances in Neural Information Processing Systems 28, Xiang Zhang, Junbo Zhao, Yann LeCun, (NIPS 2015).

The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, 4 and 5 as positive. Samples of score 3 are ignored. In the dataset, class 1 is the negative and class 2 is the positive. The dataset has 1,800,000 training samples and 200,000 testing samples.

The train.csv and test.csv files contains all the samples as comma-separated values. There are three columns in them, corresponding to class index (1 or 2), review title...

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Authors (1)

Dr. Sunil Kumar Chinnamgari

Dr. Sunil Kumar Chinnamgari

Dr. Sunil Kumar Chinnamgari has a Ph.D. in computer science and specializes in machine learning and natural language processing. He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meetups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.

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