In this section, we will cover the broad topic of data transformation. The main idea of data transformation is to take the input data and transform it in careful ways so as to clean it, extract the most relevant information from it, and to turn it into a usable form for further analysis and learning. During these transformations, we must only use methods that are designed while keeping in mind not to add any bias or artifacts that would affect the integrity of the data.
In the case of some datasets, we need to create more features from features we are already given. Typically, some form of aggregation is done using common aggregators such as average, sum, minimum, or maximum to create additional features. In financial fraud detection, for example, Card Fraud datasets usually contain transactional behaviors of accounts over various time periods during which the accounts were active. Performing behavioral synthesis such as by capturing...