As a data scientist, you've realized that your dataset contains missing values. How would you handle this situation as part of your EDA process?
- Always replace missing values with the mean or median
- Choose an appropriate imputation method depending on the nature of the data and the type of missingness
- Ignore the missing values and proceed with analysis
- Remove all instances with missing values
Handling missing values is an important part of the EDA process. The method used to handle them depends on the nature of the data and the type of missingness (MCAR, MAR, or NMAR). Various imputation methods can be used, such as mean/median/mode imputation for MCAR or MAR data, and advanced imputation methods like regression imputation, multiple imputation, or model-based methods for NMAR data.
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