Start by clarifying the dataset characteristics (e.g., time series, spatial, or tabular) and the nature of missingness (MCAR, MAR, MNAR). Then discuss interpolation methods ranging from simple (linear, polynomial) to advanced (splines, kriging, ML-based), and justify your choice based on data properties and business goals.
Pro tip: Mention that interpolation is not always the best approach—sometimes imputation or model-based methods are more appropriate. Also, emphasize the importance of validating the filled data to avoid introducing bias.
Identify the data type (time series, spatial, etc.) and analyze the missingness pattern (random vs. systematic). This guides the choice of interpolation method.
Select a method based on data characteristics: linear for simple trends, spline for smooth curves, kriging for spatial data, or ML models for complex patterns.
Apply the interpolation, then validate using cross-validation or hold-out data to ensure the filled values are reasonable and do not introduce bias.
Discuss when interpolation might fail (e.g., large gaps, non-stationary data) and alternative approaches like multiple imputation or model-based prediction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.