The correct answer is D. To create new features from existing data.
Feature engineering is the process of using domain knowledge to extract features from raw data that make it more suitable for machine learning. This can involve combining existing features, transforming them, or creating new ones altogether. The goal of feature engineering is to improve the performance of machine learning models by making them more relevant to the task at hand.
Option A is incorrect because data aggregation is the process of combining data from multiple sources into a single dataset. This can be done for a variety of reasons, such as to increase the size of the dataset, to improve the accuracy of the data, or to make it easier to analyze.
Option B is incorrect because data visualization is the process of representing data in a graphical or pictorial format. This can be done to make the data easier to understand, to identify patterns or trends, or to communicate the results of an analysis to others.
Option C is incorrect because hypothesis testing is a statistical method used to determine whether there is a significant difference between two or more groups. This can be done to test the validity of a hypothesis, to compare the effectiveness of different treatments, or to make predictions about future events.