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

Data understanding


Now that the goals and success criteria of our activity are clear, we can start gathering relevant data for our project. Where should we look for this data? Within the resources, we listed the project plan, of course. The first task of this phase will, therefore, be to actually start acquiring from your resources.

Data collection

A core principle to be respected during these activities is replicability—you should carefully take note of all  the steps and criteria employed within the data acquisition phase, so that it can be replicated by a third-party, and also by yourself in future, if needed. The typical output of this phase is a data collection phase, where steps and filtering criteria are listed.

How to perform data collection with R

If your data mining project is going to be performed with R, which we could infer from you reading this book, the data collection phase will essentially be performed by downloading your data from its original source, and importing it within...