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

Summary


Match point! You and Andy finally got the list Mr Clough requested. As you may be guessing from the simple fact that there are still pages left in the book, this is not the end.

All you know at the moment is that the companies that probably produced that dramatic drop in Hippalus revenues are small companies with previous experiences of default and bad ROS values. We could infer that those are not exactly the ideal customers for a wholesale company such as Hippalus. Why is the company so exposed to these kinds of counterparts?

We actually don't know at the moment: our data mining models got us to the entrance of the crime scene and left us there. What would you do next? You can bet Mr Clough is not going to let things remain that unclear, so let's see what happens in a few pages.

In the meantime, I would like to recap what you have learned in this chapter. After learning what decision trees are and what their main limitations are, you discovered what a random forest is and how it overcomes...