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

Logistic regression


Logistic regression was developed during the 19th century to study the growth of population and some specific types of chemical reactions, and the first person to formally define it was the Belgian statistician Pierre François Verhulst, who published in 1837 four pages about it within his mentor's publication, Correspondance Mathématique et Physique. 

Starting from this first publication, a lot of others followed, paired with extensive use of the model in real-life domains far from the original one, such as fraud detection and the estimation of the probability of default.

The intuition behind logistic regression

The intuition behind logistic regression starts exactly where linear regression stops—solving the problem of estimated values outside the natural domain of our response variable. It start from the typical problem of having a response variable that can pertain to two alternative categories, either zero or one, and the need for some record to one of those categories...