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

Random forest


Hey there, did you hear the discussion between Mr. Clough and Mr Sheene? And who do you think was the next person Mr. Sheene talked to after Mr. Clough? Yeah, you are guessing right, it was me: Andy, I want the list on my desk in two hours. Mr. Sheene was actually quite upset by Mr. Clough suggesting that one of us spread the word about the analyses

That said, what we have to do now? Well, first of all, we still have to fit random forest on our data, in order to complete our data modelling strategy. Finally, we will employ all of our estimated valid models on the full list of customers pertaining to the Middle East area. 

The result of this application will be the list of customers enriched with our model prediction.

What? How are we going to merge predictions from our different models? We are going to leverage ensemble learning techniques for that. But let's keep things in their order—we still have to fit two more models, and time is running out. 

Time to hurry up now and fit...