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


Can you feel your backpack becoming heavy? This chapter was a big boost for your R knowledge: nearly 30 pages earlier you were only just aware of how to print "Hello World" with R, and now you have discovered useful insights from your real banking data.

We have learned the following:

  • Installing additional packages in the base version of R 
  • Importing data into your R environment
  • Creating pivot tables in R
  • Discovering and showing information through data visualization techniques
  • Plotting data with ggplot2

I am tempted to accelerate further in the next chapter, immediately showing you how to implement data mining algorithms with the powerful weapon we have at our disposal. But, we have to be prudent and firmly cover the foundations to let you soundly build upon them. In the next chapter, we'll learn how to organize and conduct a data mining project through the CRISP-DM methodology.

That said, if you are really reckless, you could always skip to Chapter 4Keeping the Home Clean – The Data Mining...