Great post with apt EDA.

I was wondering that the metric 'accuracy' doesn’t make sense as the data was imbalanced with lot of clean comments (all 0s for labels). And accuracy will definitely be high as model can just predict lot of 0s. AUC ROC sounds apt in this case. Please let me know your rationale on choosing accuracy as metric.