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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

Chapter 14. Epilogue

It's a quiet day at the Hippalus inc. like it was three months ago when the amazing drop in revenues was found. You are sitting at your desk looking at the intranet news when suddenly an email pops-up in your inbox:

Extraordinary meeting @ the main meeting room at 9.00.

You ask some of your colleagues if they know what is going to happen during the meeting. Nobody actually knows, and since the time is getting closer to 9, you decide to get to the meeting room. At the time you get there, a small chat is going on, but as soon as you come into the room the chat almost stops. And it definitely stops once Andy also gets in the room.

Dear colleagues, I have suddenly organized this meeting to share with you some good news. A sound of relief fills the room as soon as Mr. Sheen completes this pleasant sentence.

I was recently notified that the investigations on the revenues drop came to an end, and that end was a bad one. There is actually no great meaning in this talking, nevertheless...

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