**Yes, there is one exact duplicate and several near-duplicates.** **Exact duplicate** - Exactly one measurement combination appears more than once: sepal length 5.8, sepal width 2.7, petal length 5.1, petal width 1.9, species *virginica*. It occurs 2 times (`copies` = 2). - Two checks returned this same single group. No other row is repeated across all four measurements plus species. - Because the dataset has 150 rows, this duplicate is 1 surplus row, under 1% of the data. **Near-duplicates** - The near-duplicate comparison returned 7 matched pairs. Six are *setosa* pairs whose measurements differ by only 0.1 cm in at least one column. - The differences are always small. Examples are petal width 0.1 vs 0.2 for the 4.9 / 3.1 / 1.5 flower, and sepal length 5.0 vs 5.1 for the 5.0 / 3.4 / 1.5 / 0.2 flower. - The seventh matched pair is the exact *virginica* duplicate, whose two sides are identical. - All the near-duplicates are in *setosa*. This is expected, because setosa flowers are small and tightly clustered, and measurements are recorded to only one decimal place. The near matches probably reflect this rounding rather than data-entry errors. **Takeaway** - The single exact duplicate is worth removing or at least flagging if you are deduplicating, because it can slightly bias training or evaluation, for example if the same row lands in both the train and test sets. - The setosa near-duplicates are plausible distinct specimens. I would keep them unless you have an external reason to think they are repeats. - I checked the near-duplicates only within the 0.1 cm tolerance shown in the results.