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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Toc

Table of Contents (9) Chapters Close

Preface 1. Association Rule Mining 2. Fuzzy Logic Induced Content-Based Recommendation FREE CHAPTER 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Introducing our use case

Our customer owns and operates water theme parks. Last year, he had induced a couple of small-time firms to solicit customers to purchase theme park tickets. The problem at hand now is with one of the small-time firms--let's call it Solit. Solit claims that in the last quarter, it was able to channel close to 15,000 customers to buy tickets at the various theme parks. Neither our customer nor Solit has an exact list of the customers. Both of them have their own database of customers. The problem at hand is how many exact customers were channeled by Solit? The data contains the first name, middle name, last name, and date of birth of the customers. Keep in mind the data fields can differ slightly, for example, two records referring to the same entity, say John, may have a small change in the last name or a slightly different date of birth:

The preceding...
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