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

Data preparation

Now that we know a bit more about our data, we can skip to the next step: preparing our data for the modeling activity. This main objective involves a lot of tasks that usually go under the names of data cleaning, data validation, data munging, and data wrangling. The reason behind the need for these activities is quite simple: the modeling techniques we are going to apply to our data will have specific requirements, which could include, for instance, the absence of null values or a specific type of variable as an input (categorical, numerical, and many more). It is, therefore, critical to prepare our data in a way that is suitable for our models. Moreover, we may need to perform basic transformation activities on our raw data, such as merging or appending tables. Finally, we may even need to draw a sample from our data, for instance, to address a matter of resource constraints.

We are going to look closer at how to perform these tasks with R in Chapters 5, How to Address...