input: def analyze_customer_data(data: pd.DataFrame) -> float:
      """
      Analyze a dataset of customers and create a classification model in scikit-learn 
      to determine customer churn. The function loads the data, selects features and 
      targets, splits the data into training and testing sets, creates a RandomForestClassifier, 
      trains the model, makes predictions, and returns the accuracy of the model.
      >>> data = pd.DataFrame({
      ...     'feature1': [0, 1, 0, 1], 
      ...     'feature2': [1, 0, 1, 0], 
      ...     'churn': [0, 1, 1, 0]
      ... })
      >>> analyze_customer_data(data)
      1.0
      """
      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)
      predictions = model.predict(X_test)
      return model.score(X_test, y_test)
output:     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)
      predictions = model.predict(X_test)
      return model.score(X_test, y_test)