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

Introducing summary EDA

Have you ever heard about summary EDA? Since you are new to the job, I guess the answer is no. I will tell you something about this while I download the data you sent me, and open it within the RStudio project I prepared for the occasion. I hope you don't mind if I tell you something you already know.

Summary, EDA encompasses all the activities that are based on the computation of one or more indexes useful to describe the data we are dealing with. What differentiates this branch of the EDA from its relatives is the non-graphical nature of this set of measures: here, we are going to compute just a bunch of numbers, while with the graphical EDA we will perform later, plot and visualization will be the core of our techniques.

While we were talking, our data became ready, so we can start working on it. I will start looking at the cash_flows report, since it probably has enough info to reveal to us where this drop is coming from.