The correct answer is: B. To prepare data for analysis.
Data preprocessing is the process of cleaning and transforming raw data into a format that can be used by machine learning algorithms. This process can involve a variety of tasks, such as removing duplicate data, filling in missing values, and normalizing the data. The goal of data preprocessing is to ensure that the data is accurate, complete, and consistent, so that the machine learning algorithm can produce accurate results.
Option A is incorrect because the goal of data preprocessing is not to increase data complexity. In fact, the opposite is often true: data preprocessing can involve simplifying the data by removing irrelevant or redundant information.
Option C is incorrect because the goal of data preprocessing is not to create visualizations of the data. This is the job of data visualization, which is a separate process that can be used to explore and understand data.
Option D is incorrect because the goal of data preprocessing is not to reduce the size of the dataset. In fact, data preprocessing can sometimes involve increasing the size of the dataset by adding new features or by splitting the data into training and testing sets.