Book Image

R Data Mining

Book Image

R Data Mining

Overview of this book

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
14
Epilogue

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.

Describing the population distribution

First...