Median Imputation and Missingness Indicators

You are given a dataset with missing values. What steps would you take to handle this?

Using the supplied PostgreSQL tables, write a query that returns a cleaned but auditable application-level dataset. Use explicit treatments for missing categorical and numeric values, while retaining indicators for values that remain unknown.

Output

1. One row per application, including applications without a matching candidate.
2. Columns: application_id, candidate_id, candidate_name, location, years_experience, status, salary_expectation, applied_on, and applied_on_missing.
3. Replace missing names, locations, and statuses with Unknown, replace missing salaries with the non-null median salary, retain unknown experience and dates as NULL, and sort by application_id ascending.

Output details

• Define the salary median as the discrete 50th percentile over non-NULL salary values, without rounding.
• Set applied_on_missing to True exactly when applied_on is NULL, otherwise False.