input: 
  def predict_customer_churn(data: pd.DataFrame) -> float:
      """Given a dataset of customers, create a classification model using RandomForestClassifier
      from scikit-learn to determine if a customer is likely to churn. The model should be trained
      and evaluated on a test set, returning the accuracy score.
      
      >>> predict_customer_churn(pd.DataFrame({
      ...     'feature1': [1, 2, 3, 1, 2, 3],
      ...     'feature2': [4, 5, 6, 4, 5, 6],
      ...     'churn': [0, 1, 0, 0, 1, 1]
      ... }))
      1.0
      """
      # Select features
      X = data.drop(['churn'], axis=1)
  
      # Select targets
      y = data['churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Create the model
      model = RandomForestClassifier()
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions and measure accuracy
      return model.score(X_test, y_test)
  
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier