**Overall, the data is clean. There are no impossible values, no format problems and no mixed units. The only real issues are one duplicate row and a handful of statistical outliers that look like genuine measurements.** **What the checks found (step-0 profile)** - **Missing values:** none. All 150 rows have a value in every measurement column and in `species`. - **Impossible values:** none. Sepal length runs from 4.3 to 7.9, sepal width from 2.0 to 4.4, petal length from 1.0 to 6.9, and petal width from 0.1 to 2.5. All are positive and plausible as centimetre measurements for iris flowers, with no zeros, negatives or sentinel values such as -1 or 999. - **Inconsistent formats:** none. - `species` has exactly 3 distinct values, and my format-issue check found 0 problems (casing, whitespace or variant spellings). - The lineage columns are uniform: 1 batch, 1 source file and 1 source system. - **Mixed units:** no sign of it. Each column stays within one narrow range, and a mix of units such as cm and mm would show up as a cluster of values about 10× larger or smaller. **What the checks did flag (step-1)** - **Duplicates:** there is 1 extra fully duplicated row (an exact repeat of another specimen). This is the one genuine data-hygiene problem. It slightly over-weights one specimen, so it is worth removing for benchmarking. It may also be a legitimate repeat measurement, since iris specimens can share identical values. - **Statistical outliers:** sepal length has 1, sepal width 5, petal length 5 and petal width 2. I did not record the outlier rule in the evidence, so treat these counts as a screening signal rather than a verdict. Their values sit inside the plausible ranges above. They most likely reflect natural between-species variation, for example the small-petal setosa group and the large-petal virginica group. Checking outliers within each species would be the fair test, because the pooled data is multi-modal. **Recommendation:** drop or confirm the one duplicate row and keep the outliers. Nothing in this dataset needs unit conversion, format cleanup or null handling before modelling.