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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

Our journey has begun within this chapter. Leveraging the knowledge gained within previous chapters, we have started facing a challenge that suddenly appeared: discover the origin of a heavy loss our company is suffering.

We received some dirty data to be cleaned, and this was the occasion to learn about data cleaning and tidy data. This was the first set of activities to make our data fit the analyses' needs, and the second a conceptual framework that can be employed to define which structure our data should have to fit those needs. We also learned how to evaluate the respect of the three main rules of tidy data (every row has a record, every column has an attribute, and every table is an entity).

We also learned about data quality and data validation, discovering which metrics define the level of quality of our data and a set of checks that can be employed to assess this quality and spot any needed improvements.

We applied all these concepts to our data, making our data through the...

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