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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
Languages
Tools
Concepts
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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 2. A First Primer on Data Mining Analysing Your Bank Account Data FREE CHAPTER 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

A bit of history

When Ross Ihaka and Robert Gentleman published R: A Language for Data Analysis and Graphics in 1996, they probably didn't imagine the success the language would achieve between then and now. R was born at the University of Auckland in the early 1990s. In the beginning, it was supposed to be a user-friendly data analysis tool, employed by students to perform their research activities. Nevertheless, the points of strength we will look at in the following paragraphs quickly made it very popular among the wider community of researchers and data analysts, finally reaching the business realm in recent years and being used within major financial institutions.

R language development is currently led by the R core team, which releases updates of the base R language on a regular basis. You can discover more about the bureaucratic side of R by visiting the official R website at https://www.r-project.org/about.html.

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