रैंडम फॉरेस्ट मॉडल बनाना

# Perform a 75% training and 25% test data split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=____, random_state=0)

# Fit the random forest model to the training data
rf = RandomForestClassifier(random_state=0)
rf.____(____, ____)

# Calculate the accuracy
acc = accuracy_score(____, ____)

# Print the importances per feature
print(dict(zip(X.columns, rf.____.round(2))))

# Print accuracy
print(f"{acc:.1%} accuracy on test set.")