The correct answer is: B. To prepare the data for analysis.
Data preprocessing is the process of cleaning and transforming raw data into a format that can be used for machine learning. This includes tasks such as removing duplicate data, filling in missing values, and converting data types. The goal of data preprocessing is to make the data more consistent and easier to work with, so that machine learning models can learn from it more effectively.
Option A is incorrect because the goal of data preprocessing is not to make the data more complex. 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 reduce the size of the dataset. In some cases, data preprocessing may involve increasing the size of the dataset by adding new features or by splitting the data into training and testing sets.
Option D is incorrect because the goal of data preprocessing is not to create visualizations of the data. Data visualization is a separate step in the machine learning process that can be used to explore and understand the data, but it is not a necessary part of data preprocessing.