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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
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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

Why to Choose R for Your Data Mining and Where to Start

 Since this is our first step on the journey to R knowledge, we have to be sure to acquire all the tools and notions we will use on our trip. You are probably already an R enthusiast and would like to discover more about it, but maybe you are not so sure why you should invest time in learning it. Perhaps you lack confidence in defining its points of strength and weakness, and therefore you are not sure it is the right language to bet on. Crucially, you do not actually know where and how to practically begin your journey to R mastery. The good news, is you will not have to wait long to solve all of these issues, since this first chapter is all about them.

In particular, within this chapter we will:

  • Look at the history of R to understand where everything came from
  • Analyze R's points of strength, understanding why it is a savvy idea to learn this programming language
  • Learn how to install the R language on your computer and how to write and run R code
  • Gain an understanding of the R language and the foundation notions needed to start writing R scripts
  • Understand R's points of weakness and how to work around them

By the end of the chapter, we will have all the weapons needed to face our first real data mining problem.

You have been reading a chapter from
R Data Mining
Published in: Nov 2017
Publisher: Packt
ISBN-13: 9781787124462
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