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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

Summary

Can you feel your backpack becoming heavy? This chapter was a big boost for your R knowledge: nearly 30 pages earlier you were only just aware of how to print "Hello World" with R, and now you have discovered useful insights from your real banking data.

We have learned the following:

Installing additional packages in the base version of R
Importing data into your R environment
Creating pivot tables in R
Discovering and showing information through data visualization techniques
Plotting data with ggplot2

I am tempted to accelerate further in the next chapter, immediately showing you how to implement data mining algorithms with the powerful weapon we have at our disposal. But, we have to be prudent and firmly cover the foundations to let you soundly build upon them. In the next chapter, we'll learn how to organize and conduct a data mining project through the CRISP-DM methodology.

That said, if you are really reckless, you could always skip to Chapter 4, Keeping the Home Clean – The Data Mining...

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