How do you handle missing values in a dataset? Explain the concept and any framework you might use.

Missing values can be handled by deletion, imputation, or using models that support missing data. If the missingness is random and the proportion is small, I may drop rows or columns. For numeric features, I often impute with the mean or median, or use more advanced methods like K‑Nearest Neighbors or regression imputation. For categorical features, I replace missing values with the mode or a special category. Libraries such as pandas provide simple methods (df.fillna), while scikit‑learn’s SimpleImputer or IterativeImputer offer more flexibility. I also check for patterns in missingness to ensure I’m not introducing bias. After imputation, I validate the impact on downstream models by comparing performance metrics.