Packt+ | Advance your knowledge in tech

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

View More author details

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

Summary

This is the author speaking here. What a great chapter! Yeah, I know I should not say that since I am the author of the book, nevertheless, I think the one you just completed was a relevant step towards your discovery of R for data mining. You are now able to:

Fit a linear model in R, both having a single explanatory variable and multiple explanatory variables (univariate and multivariate) through the lm() function and assess its estimates through the summary() function
Evaluate whether the linear regression model assumptions are met, through the durbinWatsonTest() and NCVtest() functions
Perform principal component regression on your data through the pcr() function
Perform stepwise regression through the stepAIC() function and evaluate its output
Compare and interpret the output and performance of different regression models and evaluate whether your model is a reasonable way to describe the observed phenomenon

It's now time to take a closer look at what a model performance, introducing...