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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 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

Keeping the House Clean – The Data Mining Architecture

In the previous chapter, we defined the dynamic part of our data mining activities, understanding how a data mining project should be organized in terms of phases, input, and output. In this chapter, we are going to set our scene, defining the static part of our data mining projects, the data mining architecture.

How do we organize data bases, scripts, and output within our project? This is the question this chapter is going to answer. We are going to look at:

  • The general structure of data mining architecture
  • How to build such kind of structure with R

This is a really useful chapter, especially if you are approaching the data mining activity for the first time, and no matter the programming language, since it will let you gain a first view on what you will typically find in a data mining environment. No matter whether...

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