Handling Missing Data

How do you handle missing data in a dataset?

Using the provided observation, category, and source data, produce a cleaned result. Treat scores outside the range 0 through 100 as missing, retain every observation, and apply category-level then dataset-level fallbacks for missing scores. Replace missing or blank text values with the appropriate category or generic fallback.

Output

1. One row per observation, ordered by entity_id, observed_on, and observation_id
2. Return observation_id, entity_id, observed_on, category_name, cleaned_score, score_was_missing, cleaned_status, source_name, and entity_sequence
3. Use Unclassified, Unknown, or 0 only when no more specific fallback exists