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You're reading from R Data Mining Implement data mining techniques through practical use cases and real-world datasets

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Length 442 pages

Edition 1st Edition

Languages

Tools

ggplot

Concepts

Data Mining

Author (1):

Andrea Cirillo

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Table of Contents (16) Chapters

Preface

1. Why to Choose R for Your Data Mining and Where to Start FREE CHAPTER

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

Match point! You and Andy finally got the list Mr Clough requested. As you may be guessing from the simple fact that there are still pages left in the book, this is not the end.

All you know at the moment is that the companies that probably produced that dramatic drop in Hippalus revenues are small companies with previous experiences of default and bad ROS values. We could infer that those are not exactly the ideal customers for a wholesale company such as Hippalus. Why is the company so exposed to these kinds of counterparts?

We actually don't know at the moment: our data mining models got us to the entrance of the crime scene and left us there. What would you do next? You can bet Mr Clough is not going to let things remain that unclear, so let's see what happens in a few pages.

In the meantime, I would like to recap what you have learned in this chapter. After learning what decision trees are and what their main limitations are, you discovered what a random forest is and how it overcomes...