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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
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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 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

The Data Mining Process - CRISP-DM Methodology

At this point, our backpack is quite full of exciting tools; we have the R language and an R development platform. Moreover, we know how to use them to summarize data in the most effective ways. We have finally gained knowledge on how to effectively represent our data, and we know these tools are powerful. Nevertheless, what if a real data mining problem suddenly shows up? What if we return to the office tomorrow and our boss finally gives the OK: Yeah, you can try using your magic R on our data, let's start with some data mining on our customers database; show me what you can do. OK, this is getting a bit too fictional, but you get the point—we need one more tool, something like a structured process to face data mining problems when we encounter them.

When dealing with time and resource constraints, having a well-designed...

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