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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

2. A First Primer on Data Mining Analysing Your Bank Account Data FREE CHAPTER

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

Data sources

Data sources are everywhere. As the following picture tries to suggest, we can find data within all the realms of reality. This hyperbolic sentence is becoming more true thanks to the well-known trend of the internet of things, and now that every kind of object is getting connected to the internet, we are starting to collect data from tons of new physical sources. This data can come in a form already feasible for being collected and stored within our databases, or in a form that needs to be further modified to become usable for our analyses:

We can, therefore, see that between our data sources and the physical data warehouse where they are going to be stored, a small components lies, which is the set of tools and software needed to make data coming from sources storable.

We should note something here—we are not talking about data cleaning and data validation. Those activities will be performed later on by our data mining engine retrieving the data from our data warehouse. For...