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# Import necessary libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
# Load the Iris dataset
data = load_iris()
X = data.data # Features
y = data.target # Target labels
# Split the data into training and testing sets (70% train, 30% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Initialize the Gaussian Naïve Bayes classifier
nb_classifier = GaussianNB()
# Train the model
nb_classifier.fit(X_train, y_train)
# Make predictions on the test set
y_pred = nb_classifier.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")
# Classification Report
print("\nClassification Report:")
print(classification_report(y_test, y_pred, target_names=data.target_names))
# Confusion Matrix
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))
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