Likewise with the "predictions" of the authors likes/dislikes. Testing how the model will perform on an independent data-set (or at least cross validation [1]) would be much more interesting.
The other thing I wondered about the predictions: she apparently rated all of the dresses, and the top/bottom matched the ratings. Fair enough. But what about the residuals, the missclassified ones - the ones where the logistic regression predicts a high or low score and her rating was actually the opposite? That might be interesting to look at.
One problem seems to be that it concluded she'd dislike anything the exact opposite color from her favorite shade of red. A common flaw in linear models.
The blog post seems to be getting modified at this moment. When I first saw it, it didn't have anything about the misclassifications, but that has been added now.
I've only glanced at her code, but it looks like[1] the predictions are from held-out data.
EDIT: All of the data was used in forming the PCA basis, but that isn't (necessarily) an error, depending on the use-case. And the logistic regression model was evaluated on held-out data.