**The data is very clean. The only real quality findings are one duplicate row and a handful of statistical outliers, and none of them look like errors.** **Completeness and consistency** - All 150 rows have non-null values in all four measurements and in `species` (step 0). - `species` has exactly 3 distinct values and 0 formatting issues, so there are no case, spelling or whitespace variants (step 0). - There is one batch, one source file and one source system, so there is no mixing of sources or ingestion runs (steps 0 and 2). **Impossible values and mixed units** - No measurement is impossible. All values are positive and in a plausible range for iris flowers in centimetres (step 0, step 2): | Measurement | Min | Max | |---|---|---| | Sepal length | 4.3 | 7.9 | | Sepal width | 2.0 | 4.4 | | Petal length | 1.0 | 6.9 | | Petal width | 0.1 | 2.5 | - Nothing points to mixed units, such as millimetres mixed with centimetres. Every column stays within one narrow scale, with no values ten times too large or small. The dataset has no unit column, so this rests on the ranges alone. **Duplicates** - There is 1 extra duplicate row (step 1). In the classic Iris data, a few specimens do have identical measurements, so this is probably a genuine repeat rather than an ingestion error. Confirm it before dropping it, because it barely affects 150 rows. **Outliers** - The outlier check in step 1 flagged 1 value in sepal length, 5 in sepal width, 5 in petal length and 2 in petal width. The result does not show the threshold used, and I did not list the flagged rows, so I can't say which they are. I assume a standard rule such as 1.5×IQR, so treat the counts as approximate. - Two things suggest the outliers are not errors. All values sit inside the plausible ranges above, and the dataset holds three distinct species. Petal measurements in particular differ sharply between species, so values that look extreme overall are probably just normal for one species (for example, small-petalled setosa). - If outliers matter for your use, check them within each species, for example using `iris_by_species`, rather than across the whole table. **Minor note** - The `_ingestion_timestamp`, `_batch_id`, `_source_file` and `_source_system` columns are pipeline metadata, not measurements. Exclude them from any modelling.