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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
Why to Choose R for Your Data Mining and Where to Start A First Primer on Data Mining Analysing Your Bank Account Data The Data Mining Process - CRISP-DM Methodology Keeping the House Clean – The Data Mining Architecture How to Address a Data Mining Problem – Data Cleaning and Validation Looking into Your Data Eyes – Exploratory Data Analysis Our First Guess – a Linear Regression A Gentle Introduction to Model Performance Evaluation Don't Give up – Power up Your Regression Including Multiple Variables A Different Outlook to Problems with Classification Models The Final Clash – Random Forests and Ensemble Learning Looking for the Culprit – Text Data Mining with R Sharing Your Stories with Your Stakeholders through R Markdown Epilogue
Dealing with Dates, Relative Paths and Functions

Summary

In this chapter, we started our journey in the best possible way.

We discovered what our powerful weapon is, where it comes from, and what its powers are, learning the main points of strength of the R language. We started to learn how to handle it, understanding the installation process and basic building blocks such as vectors, lists, data frames, and functions. Finally, we acknowledged the main weaknesses of R and how to reduce their impact.

In the next chapter, we will have a kind of training session, applying R to real data from our own personal data, specifically, from our banking data. Take time to recollect what you have discovered in the previous pages; you will need all of it when you take on the next chapter.

 

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R Data Mining
Published in: Nov 2017 Publisher: Packt ISBN-13: 9781787124462
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