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


How was your estimation activity? Andy is showing you both theoretical and practical aspects of what you will come to do, letting you keep them all together.

Within this chapter, you actually learned a lot and you are now able to estimate a simple (one variable) linear regression model and check whether its assumptions are satisfied. This is not to be underestimated for two main reasons:

  • Simple linear models are quite often an oversimplification of the real relationship between two variables. Nevertheless, they tend to be considered good enough for the level of accuracy requested within many fields, and this is why they are very popular.
  • You will find that a lot of models estimate without checking for assumptions, and you should remember that estimates coming from an invalid model are invalid estimates, at least for descriptive purposes (more on this within Chapter 8, A Gentle Introduction to Model Performance Evaluation). Knowing which are the assumptions behind this popular model...