SVM Hyperparameters

Optimize the hyperparameters of the scikit-learn Support Vector Machine (SVC). A custom training dataset can be used, and the features are standardized before training.

The searched hyperparameters are:

1. Regularization strength (C).
2. Kernel coefficient (gamma).

The kernel (rbf, poly, or sigmoid) is chosen on the form rather than searched. At least one hyperparameter must be selected for optimization.

The objective is to find the best set of hyperparameters that increases the classification accuracy.