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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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Identifying the customer segments in the wholesale customers data using AGNES

AGNES is the reverse of DIANA in the sense that it follows a bottom-up approach to clustering the dataset. The following diagram illustrates the working principle of the AGNES algorithm for clustering:

Working of agglomerative hierarchical clustering algorithm

Except for the bottom-up approach followed by AGNES, the implementation details behind the algorithm are the same as for DIANA; therefore, we won't repeat the discussion of the concepts here. The following code block clusters our wholesale dataset into three clusters with AGNES; it also creates a visualization of the clusters thus formed:

# setting the working directory to a folder where dataset is located
setwd('/home/sunil/Desktop/chapter5/')
# reading the dataset to cust_data dataframe
cust_data = read.csv(file='Wholesale_customers_...
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