Support Vector Machines for Classification

Explain the core intuition of Support Vector Machines (SVMs): maximizing the margin between classes using a hyperplane and support vectors.

Explain the 'kernel trick' and how it enables SVMs to solve non-linearly separable problems using kernels like RBF and polynomial.

Identify the strengths and weaknesses of SVMs, such as their effectiveness in high-dimensional spaces and sensitivity to feature scaling.

Apply an SVM classifier to a dataset, experiment with different kernels, and evaluate its performance.