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

# 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)

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

# Make predictions on the test set y_pred = model.predict(X_test)

Eden - Adams

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

# 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

# Load user data user_data = pd.read_csv('user_data.csv') # Train a random forest classifier model =

# Make predictions on the test set y_pred = model.predict(X_test) y_test = train_test_split(user_data.drop('preference'