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R Data Mining

You're reading from R Data Mining

Product type Book

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Pages 442 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

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

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

How to build a data mining architecture in R

Until now, we have been treating the data mining architecture topic at a general level, defining its components and their role within the system; but how do we build such kinds of architecture in R? This is what we are going to discover here. At the end of the paragraph, you will then be able not just to understand how a data mining architecture is composed but even how to build one for your own purposes.

To be clear, we have to specify from the beginning here that we are not going to build a firm-wide data mining architecture, but rather a small architecture like the ones needed to develop your first data mining projects with R. Once this is set, we can proceed with looking at each of the aforementioned components and how to implement them with our beloved R language.

Data sources

As seen earlier, this is where everything begins: the data. R is well-known for being able to treat different kinds of data coming from a great variety of data sources...

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