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


The author speaking again: how was your EDA? You found where the drop came from, didn't you? This is great, and we are going to see what the boss thinks about it in a few pages, but let me just summarize here which topic you have been working on with your colleague and what you have learned.

First of all, you were introduced to the concept of EDA and how it can be included within the data analysis process.

You then learned about summary EDA and actually performed it on real data, focusing on quartiles and median, mean, variance, standard deviation, and skewness. For all of them, you first got a sense of what these summary statistics are about and how they work. Finally, you learned what the relevant functions are and how you have to employ them in order to compute each and every one of these statistics.

As a last step within the summary EDA field, you discovered the Anscombe quartet, which is composed of four different datasets sharing a lot of identical summary statistics, even if...