EDA

In this segment, we explore the importance of Exploratory Data Analysis (EDA) in machine learning interviews, especially during notebook-based exercises. This is a key moment to distinguish ourselves by demonstrating analytical depth and real-world intuition.

• We move beyond just checking for missing values or printing basic stats, which signals a surface-level approach.
• Instead, we analyze feature distributions, identifying skewness, outliers, and unusual patterns that may affect modeling.
• We examine relationships and interactions between variables, not just computing correlations, but exploring potential dependencies and dynamics.
• We assess data quality, including duplicates, inconsistent categorical values, and signs of data leakage.
• This deeper EDA shows that we can interrogate the data critically, a trait that interviewers associate with strong, production-ready ML practitioners.

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