# ML Program 9 - Random Forest Classifier (Iris)
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

data = load_iris()
X, y = data.data, data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Confusion matrix: \n{confusion_matrix(y_test, y_pred)}")
print(f"Classification report: \n{classification_report(y_test, y_pred)}")
