What is the primary objective of Exploratory Data Analysis (ED

Explanation: EDA is a critical step in the data analysis process, primarily focused on understanding the dataset's structure, identifying patterns, detecting anomalies, and gaining initial insights. By utilizing graphical and statistical techniques such as histograms, scatter plots, and summary statistics, EDA helps analysts identify correlations, outliers, and potential biases in the data. This process enables better-informed decisions for subsequent modeling or hypothesis testing, ensuring a smoother analysis workflow. Option A: While data cleaning is essential for EDA, it is only a preparatory step rather than its primary objective. Option C: Machine learning deployment follows EDA as it requires a well-understood dataset for optimal performance. Option D: Hypothesis validation falls under inferential statistics, which often uses EDA insights but is not EDA’s core function. Option E: Data privacy is vital but unrelated to the specific goals of EDA.