Exploratory data analysis (EDA) is the process of analyzing data distributions, identifying outliers, and understanding feature correlations prior to machine learning (ML). EDA oversees feature selection, preprocessing of the data, and model selection during machine learning development. Pattern recognition is automated, EDA results are validated, and outcomes are forecasted by machine learning techniques. The iterative strategy includes enhancing both the EDA and ML phases in order to improve model performance, interpret findings, and get a deeper understanding of the data.

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