Hard‑margin SVMs look clean on the whiteboard—huge margin, zero errors—but real‑world data laughs at that rigidity. Noise, overlap, and outliers wreck the ‘perfectly separable’ dream, leaving the model unsolvable. Cue slack variables: a pragmatic detour that births the soft‑margin SVM and keeps classification sane.