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


As usual, this is the author speaking at the end of the chapter. How was your first experience with internal audit? It seems you actually got a lot out of those PDFs.

You first learned how to iteratively read text from PDFs and store it in a single data frame.

Then you discovered how to prepare the data frame for text mining activities, removing irrelevant words and transforming it from a list of sentences into a list of words. Finally, you learned how to perform sentiment analysis, wordcloud development, and n-gram analysis on it.

From these analyses, you discovered that the companies you predicted being defaulted are actually considered bad customers by your colleagues in the commercial department.

This helped you gain knowledge from unstructured data. 

Moving to more structured data contained in the same PDFs, you learned how to transform the data into an edge list in order to perform network analysis, which mainly consisted of the computation of the nodes' degrees. This resulted in...