Eden: Adams

# Load user data user_data = pd.read_csv('user_data.csv')

# Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(user_data.drop('preference', axis=1), user_data['preference'], test_size=0.2, random_state=42) eden adams

# Make predictions on the test set y_pred = model.predict(X_test) # Load user data user_data = pd

# Evaluate the model accuracy = model.score(X_test, y_test) print(f'Model Accuracy: {accuracy:.2f}') This code snippet demonstrates a basic approach to training a model for predicting user preferences based on their data. The actual implementation would require more complex data processing and model tuning. y_test = train_test_split(user_data.drop('preference'

# Train a random forest classifier model = RandomForestClassifier(n_estimators=100) model.fit(X_train, y_train)