In practical scenarios, most of the time you would find that the data available for predictive analysis is not fit for the purpose. This is primarily because of two reasons:
In the real world, data is always messy. It usually has lots of unwanted items, such as missing values, duplicate records, data in different formats, data scattered all around, and so on.
Quite often, data is either required in a proper format or needs some preprocessing so that it is ready before we apply machine learning algorithms to it for predictive analysis.
So, you need to prepare your data or transform your data to make it fit for the required analysis. ML Studio comes with different options to prepare your data, and in this chapter, you will explore options to preprocess data for some of the common scenarios and ways to prepare data when necessary modules are not readily available with ML Studio. This chapter aims to familiarize you with these options to provide you with an overview...