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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

Applying linear regression to our data

Linear regression is probably the most famous statistical model. It has been around for a long time, since the first concepts behind its development go back to the 1980. This model mainly owes its popularity to the relative ease of application and its great interpretability.

The intuition behind linear regression

When applying linear regression to a set of data, we are making the following assumption—the relationship between one (or more) explanatory variable and the response variable is known and linear. There are two points to consider:

Known: We are assuming the existence of some kind of law ruling the level of y given the level of x. We are also usually implying that the level of x directly causes the level of y. We know from our discussion about linear correlation that this is not necessarily true and that further evidence is needed to assume causality.
Linear: The relation between the explanatory variables and a response is assumed to be representable...