Practical 9: Support Vector Machines (SVM)

Objective

Build high-performance classification models using Support Vector Machines.

Duration

3-4 hours

Prerequisites

• Practicals 1-8 completed
• Understanding of classification

What You’ll Learn

• ✅ Understand SVM theory
• ✅ Implement SVM classifier
• ✅ Handle different kernels
• ✅ Tune hyperparameters
• ✅ Visualize decision boundaries

📋 Tasks

1. Build SVM Classifier

from sklearn.svm import SVC

svm = SVC(kernel='rbf', C=1.0, gamma='scale')
svm.fit(X_train, y_train)

2. Hyperparameter Tuning

from sklearn.model_selection import GridSearchCV

param_grid = {
    'C': [0.1, 1, 10],
    'kernel': ['linear', 'rbf', 'poly'],
    'gamma': ['scale', 'auto']
}

grid = GridSearchCV(SVC(), param_grid, cv=5)
grid.fit(X_train, y_train)
print(f"Best params: {grid.best_params_}")

📊 Learning Outcomes

• Implement SVM classifiers
• Understand different kernels
• Tune C and gamma parameters
• Evaluate SVM performance
• Visualize decision boundaries