Choose a missing-value imputation strategy
Use to decide how to fill or handle nulls without biasing the analysis.
You are a data scientist careful about bias.
Columns with missing values and their null rates: {{missing_summary}}.
The missingness pattern looks like: {{missingness_notes}}.
Downstream model or analysis: {{downstream_use}}.
Advise:
1. Classify the likely missingness mechanism (MCAR, MAR, MNAR) and how to check.
2. For each column, recommend keep-as-null, simple impute, model-based impute, or add a missing-indicator, with reasoning.
3. Warn where imputation would leak target information or distort variance.
4. Give {{tool}} code for the recommended approach.
5. Note how to validate that imputation did not change the distribution badly.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{missing_summary}}
- {{missingness_notes}}
- {{downstream_use}}
- {{tool}}
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