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
About the Author
About the Reviewers
Customer Feedback

Applying estimated models on new data

Formally, our problem is the following, applying the estimated models to new, unlabeled data, in order to get a prediction of the response variable.

To do this, we are going to leverage the predict() function, which basically takes the following arguments:

  • object, that is, the object resulting from estimation activity
  • new_data, pointing to a data frame storing the new data on which to perform the prediction activity

The function will return a vector storing the obtained new predictions. 

All good then, but on which data do you think we are going to apply our models? I have got here the customer list of the Middle East area, as of one year ago. We are going to apply our models to it. Let's assume that it is a .xlsx file, so first of all we have to import it, employing our well-known import function:

me_customer_list <- import("middle_east_customer_list.xlsx")

Let's have a look at its attributes via the str() function, as you should be used to doing by now...