What is the process of collecting, cleaning, and visualizing data before building a model?

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Exploratory Data Analysis is the process of examining data to understand its structure, quality, and relationships before building a model. It involves loading the data, checking distributions and summaries, spotting outliers, and identifying missing values. By visualizing the data with histograms, box plots, scatter plots, and other charts, you gain intuition about what preprocessing steps are needed and how the data might behave under different models. This stage informs decisions like whether imputation is necessary for missing values, whether scaling is required, or how to encode categoricals. While imputation, scaling, and one-hot encoding are important preprocessing techniques, they are tools used within the broader practice of exploring and preparing data, which is why Exploratory Data Analysis best fits the described process.