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


In this chapter, we took some time to better understand how we should structure our data mining activities. Even if it felt like a pause in our exciting riding to R mastery, it was quite a useful one. You are now able to address any kind of data mining task as it shows up. All you have to do is to follow the logical and chronological path you have learned:

  • Business understanding (what is the problem, what are the relevant questions?)
  • Data understanding (which data is at my disposal to solve the problem?)
  • Data preparation (get my data ready to work)
  • Data modeling (try to get knowledge from the data to solve the problem)
  • Evaluation (see if the problem found a question with your analysis)
  • Deployment (place it in production, if needed)

You have a framework and a methodology to help you get started with your project, and this is really powerful. Ready to start riding again? Well, even if you are ready, you will have to wait for a few more pages: in the next chapter, we are going to take a close...