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


Here I am again. You just took another major leap on your journey to machine learning discovery. If you took the right time to acquire and practice what Andy just showed you, you should have now added to your toolbox two of the most employed classification models: logistic regression and support vector machines. Both of them are employed to perform classification exercises.

The logistic regression predicts the probability of a given outcome occurring, estimating the level of contribution to this output provided by all of the explanatory variables. This makes this model quite useful when interpretability is one of the objectives of the analysis.

On the other side, you have support vector machines, which are based on the concept of a hyperplane, a sort of blade of different possible shapes able to divide our population into two or more groups, and by that, mean perform the desired classification task. This algorithm shows pretty high performance, especially with a non-linear hyperplane...