**17 of the 19 analytic columns have missing values. Only `User_ID` and `User_Type` are complete.** Percentages are out of 300 rows. The table rendered from the step-4 query gives the full breakdown. | Share missing | Columns | |---|---| | Highest | `Age` 7.67% (23 rows) | | 5–6% | `Tasks_Performed` 6.0% (18), `Productivity_Score` 5.33% (16), `Accuracy_Rating` 5.0% (15), `Profession` 5.0% (15) | | 3–4.5% | `AI_Purpose` 4.33% (13), `Education_Level` 4.33% (13), `Monthly_AI_Cost` 4.0% (12), `AI_Tool` 3.67% (11), `Country` 3.33% (10), `Gender` 3.33% (10), `Monthly_Income` 3.33% (10), `Time_Saved_Hours_Per_Week` 3.33% (10), `Work_or_Study_Hours_Per_Day` 3.0% (9) | | Under 2.5% | `AI_Usage_Hours_Per_Day` 2.33% (7), `Satisfaction_Score` 2.33% (7), `Would_Recommend` 1.67% (5) | | None | `User_ID` 0%, `User_Type` 0% | - **How missing was counted:** most text columns store missing values as empty strings rather than true NULLs. A pure NULL check (step 1) showed 0% for them, and only the numeric columns `Productivity_Score`, `Accuracy_Rating` and `Satisfaction_Score` showed gaps. The percentages above count both NULLs and blank strings, which gives the real picture. Step 2 shows the blanks directly, for example 23 blank `Age` values and 10 blank `Gender` values. - **Scale:** no column is more than 7.7% missing, so the gaps are modest. Dropping every row with any missing value would still lose a large share of the 300 rows, since the gaps are spread across many columns. - **Related quirk:** there are 288 distinct `User_ID`s across 300 rows (step 0), so some IDs are duplicated. I did not examine this further. - **Not checked:** I did not check the system columns (`_ingestion_timestamp`, `_batch_id`, `_source_file`, `_source_system`) or the other sentinel values such as `$0` or `N/A`.