What is the purpose of exploratory data analysis (EDA)?

The purpose of exploratory data analysis (EDA) is fundamentally to summarize the main characteristics of data sets. This involves a variety of techniques, including graphical representations, statistical measures, and data visualization, which allow data scientists to understand the underlying patterns, trends, and relationships within the data.

Through EDA, one can identify anomalies or outliers in the data, ascertain the distribution of variables, and discern how different variables relate to each other. This process is crucial as it often informs further analysis or the appropriate modeling techniques that will be applied later on. By providing a comprehensive overview of the data, EDA helps in setting the stage for effective decision-making in subsequent steps of data analysis and model building.

While other choices touch upon aspects related to data handling and machine learning, they do not capture the primary aim of EDA, which is centered on understanding and summarizing the data itself rather than directly creating models or optimizing algorithms.