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R Data Mining

You're reading from   R Data Mining Implement data mining techniques through practical use cases and real-world datasets

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787124462
Length 442 pages
Edition 1st Edition
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Author (1):
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Andrea Cirillo Andrea Cirillo
Author Profile Icon Andrea Cirillo
Andrea Cirillo
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Table of Contents (16) Chapters Close

Preface 1. Why to Choose R for Your Data Mining and Where to Start FREE CHAPTER 2. A First Primer on Data Mining Analysing Your Bank Account Data 3. The Data Mining Process - CRISP-DM Methodology 4. Keeping the House Clean – The Data Mining Architecture 5. How to Address a Data Mining Problem – Data Cleaning and Validation 6. Looking into Your Data Eyes – Exploratory Data Analysis 7. Our First Guess – a Linear Regression 8. A Gentle Introduction to Model Performance Evaluation 9. Don't Give up – Power up Your Regression Including Multiple Variables 10. A Different Outlook to Problems with Classification Models 11. The Final Clash – Random Forests and Ensemble Learning 12. Looking for the Culprit – Text Data Mining with R 13. Sharing Your Stories with Your Stakeholders through R Markdown 14. Epilogue
15. Dealing with Dates, Relative Paths and Functions

Visualizing your data with ggplot2

It is beyond the scope of this book to provide a comprehensive and exhaustive explanation of the data visualization principles and techniques, but in the remaining sections of this chapter, we are going to learn the basic elements of this powerful discipline and how to apply them to our data through the means of the ggplot2 package.

Basic data visualization principles

As is often the case, when dealing with data visualization we should start from the final objective to work out the best way to accomplish it. The main objective of data visualization is to effectively communicate an insight contained within a given set of data. We can elaborate a bit more on this. The point here...

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