Data Science, Data Analysis, Machine Learning, Statistics, Data Visualization.
= train_test_split(X, y, test_size=0.2, random_state=42) # Create an SVM classifier svm_classifier = SVC(kernel='linear', C=1.0) # Train the classifier on the training data svm_classifier.fit(X_train, y_train) # Make predictions on the test data y_pred = svm_classifier.predict(X_test) # Evaluate