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

As usual, this is the author speaking at the end of the chapter. How was your first experience with internal audit? It seems you actually got a lot out of those PDFs.

You first learned how to iteratively read text from PDFs and store it in a single data frame.

Then you discovered how to prepare the data frame for text mining activities, removing irrelevant words and transforming it from a list of sentences into a list of words. Finally, you learned how to perform sentiment analysis, wordcloud development, and n-gram analysis on it.

From these analyses, you discovered that the companies you predicted being defaulted are actually considered bad customers by your colleagues in the commercial department.

This helped you gain knowledge from unstructured data.

Moving to more structured data contained in the same PDFs, you learned how to transform the data into an edge list in order to perform network analysis, which mainly consisted of the computation of the nodes' degrees. This resulted in...