Support Vector Classifiers (SVCs) struggle when data isn’t linearly separable. The real-world isn’t clean, and straight-line boundaries fail. That’s where constrained optimization and the kernel trick step in—transforming SVC into full-blown SVMs capable of tackling nonlinear patterns with elegance and efficiency.