Which of the following best describes the main purpose of Exploratory Data Analysis (ED

EDA is an essential step in data analysis, as it involves analyzing data to reveal its main characteristics, patterns, anomalies, and relationships before moving on to complex modeling. It is particularly valuable because it allows analysts to gain insights and understand the data's distribution, which informs the subsequent steps in the data science workflow. Through techniques like visualizations (histograms, scatter plots) and summary statistics, EDA helps highlight trends and outliers, enabling better decision-making in model selection and feature engineering. Option A is incorrect because EDA is exploratory, not confirmatory; it does not "prove" hypotheses but rather helps formulate them. Option B is incorrect since EDA involves understanding data characteristics, not just organizing it into categories. Option D is incorrect because EDA complements, but does not replace, machine learning models. Option E is incorrect as while EDA involves some cleaning, its primary purpose is to explore data characteristics.