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

What is classification and why do we need it?


We don't have much time left, and we still need to try at least four models to get the best one and produce the list of customers potentially involved in our profit drop. We are now going to try classification models.

We have already talked about these models in brief, but let me tell you something more structured about them before actually applying them to our data.

Classification models are the ones employed to predict a categorical output. We have already seen that regression models are a good tool when dealing with quantitative output, that is numerical response variables. A typical example is the revenues we were thinking about before. But, what if you don't have a numerical response variable, but categorical variables?

Linear regression limitations for categorical variables

Let's try to face a problem involving categorical variables with the only model you know—linear regression.

Let's imagine, for instance that you are dealing with a dataset...