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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 wholesale customer dataset and the segmentation problem

The UCI Machine Learning Repository offers the wholesale customer dataset at https://archive.ics.uci.edu/ml/datasets/wholesale+customers. The dataset refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories. The goal of these projects is to apply clustering techniques to identify segments that are relevant for certain business activities, such as rolling out a marketing campaign. Before we actually use the clustering algorithms to get clusters, let's first read the data and perform some EDA to understand the data using the following code block:

# 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...
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