Data Pre-Processing and an Example of Data Classification With RapidMiner

Abstract

In this book, the focus is on data mining with RapidMiner. However, it is important to note that there are other essential steps to consider when delving into the realm of data mining. This chapter serves as an introduction to the process of data pre-processing using RapidMiner, allowing readers to practice with a dataset example available on the platform. With RapidMiner, data pre-processing begins with exploring the data visually and then selecting the features that will be analyzed with each data mining technique. Managing missing values in a feature is also a crucial step in this process, which can be achieved by either removing them or replacing them with appropriate values. In addition, RapidMiner allows data scientists to detect outliers and normalize features easily using diagram design without requiring any computer programming skills. To help readers become familiar with the tools offered by RapidMiner, a classification technique will be demonstrated step-by-step.