input: 
  def predict_customer_churn(data: pd.DataFrame) -> float:
      """ Analyze a dataset of customers and create a classification model in scikit-learn 
      to determine customer churn. The function will split the data into training and 
      testing sets, train a RandomForestClassifier, and return the accuracy of the model's
      predictions on the test set.
      >>> data = pd.DataFrame({
      ...     'feature1': [1, 2, 3, 4],
      ...     'feature2': [4, 3, 2, 1],
      ...     'churn': [0, 1, 0, 1]
      ... })
      >>> predict_customer_churn(data)
      0.5
      """
      
      # 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
      predictions = model.predict(X_test)
      return model.score(X_test, y_test)
  
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier