A Python script that demonstrates Support Vector Machine (SVM) classification on the breast cancer dataset using scikit-learn. The script visualizes the decision boundary of the SVM classifier using the first two features of the dataset.

• Python 3.10 or above
• scikit-learn
• matplotlib

• Loads the breast cancer dataset from scikit-learn
• Implements SVM classification with RBF kernel
• Visualizes the decision boundary and data points
• Uses only the first two features for 2D visualization

Simply run the script to see the visualization. The plot will show:

• Data points colored by their class (malignant/benign)
• Decision boundary colored using a spectral colormap
• Feature names on the x and y axes

• SVM kernel: RBF (Radial Basis Function)
• Gamma: 0.5 (kernel coefficient)
• C: 1.0 (regularization parameter)

The script generates a plot showing how the SVM classifier separates the two classes of breast cancer data (malignant and benign) based on the first two features. The decision boundary is displayed with a spectral colormap, and data points are scattered with black edges for better visibility.