How do you handle missing values in a dataset?
This question assesses your practical data preprocessing skills and your ability to choose appropriate imputation strategies.
Why Interviewers Ask This
Real-world data is rarely clean. Interviewers ask this to see if you have a systematic approach to data cleaning. They want to know if you understand the impact of missing data on model performance and how different imputation methods affect the data distribution.
How to Answer This Question
Discuss options: dropping rows/columns, mean/median/mode imputation, or using predictive models (like KNN) for imputation. Mention analyzing the pattern of missingness (MCAR, MAR, MNAR). Emphasize that the choice depends on the amount of missing data and the nature of the variable.
Key Points to Cover
- Analyze the pattern of missingness first.
- Dropping is viable for small amounts of MCAR data.
- Imputation preserves sample size but may introduce bias.
- Choice of method depends on data type and distribution.
Sample Answer
Handling missing values depends on the extent and pattern of the missingness. If the data is Missing Completely At Random (MCAR) and the volume is small, I might drop the affected rows. For numerical features, I often use median imputation to avoid skewing the distribution with outliers, while mode imputation works for categorical data. For larger gaps, I might use predictive models like K-Nearest Neighbors to estimate missing values. It is crucial to analyze why data is missing before deciding on a strategy to avoid introducing bias.
Common Mistakes to Avoid
- Always dropping rows without analysis.
- Using mean imputation for skewed distributions.
- Ignoring the potential bias introduced by imputation.
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