input: 
  def customer_churn_classification(data: pd.DataFrame) -> float:
      """
      Analyze a dataset of customers and create a classification model in scikit-learn 
      to determine customer churn. The dataset will include features such as customer 
      demographics and usage patterns, and the target variable will be 'churn'. The 
      function will split the data into training and testing sets, train a RandomForestClassifier, 
      and return the accuracy of the model on the test set.
      
      >>> customer_churn_classification(pd.DataFrame({
      ...     'age': [25, 40, 30],
      ...     'usage': [120, 150, 200],
      ...     'churn': [0, 1, 0]
      ... }))
      0.5
      >>> customer_churn_classification(pd.DataFrame({
      ...     'age': [25, 40, 30, 35],
      ...     'usage': [120, 150, 200, 175],
      ...     'churn': [0, 1, 0, 1]
      ... }))
      0.5
      """
      X = data.drop(['churn'], axis=1)
      y = data['churn']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model = RandomForestClassifier()
      model.fit(X_train, y_train)
      return model.score(X_test, y_test)
  
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier