SVM

from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.svm import SVC

from sklearn.metrics import accuracy_score, confusion_matrix

# Step 1: Load the Iris dataset

iris = datasets.load_iris()

X = iris.data

y = iris.target

# Step 2: Select only two classes ('setosa' and 'versicolor')

X = X[y != 2]   # Remove 'virginica' class

y = y[y != 2]   # Keep only classes 0 and 1

# Step 3: Split the dataset

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Step 4: Train the SVC model

model = SVC(kernel='linear')

model.fit(X_train, y_train)

# Step 5: Make predictions and evaluate

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

conf_matrix = confusion_matrix(y_test, y_pred)

print("Accuracy:", accuracy)

print("Confusion Matrix:\n", conf_matrix)