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


Our journey has begun within this chapter. Leveraging the knowledge gained within previous chapters, we have started facing a challenge that suddenly appeared: discover the origin of a heavy loss our company is suffering.

We received some dirty data to be cleaned, and this was the occasion to learn about data cleaning and tidy data. This was the first set of activities to make our data fit the analyses' needs, and the second a conceptual framework that can be employed to define which structure our data should have to fit those needs. We also learned how to evaluate the respect of the three main rules of tidy data (every row has a record, every column has an attribute, and every table is an entity).

We also learned about data quality and data validation, discovering which metrics define the level of quality of our data and a set of checks that can be employed to assess this quality and spot any needed improvements.

We applied all these concepts to our data, making our data through the...