input: def analyze_customer_churn(data: pd.DataFrame) -> float:
      """ Create a classification model in scikit-learn to determine customer churn.
      The function loads the dataset, selects features and targets, splits the data 
      into training and testing sets, trains a RandomForestClassifier, and returns 
      the accuracy of the model on the test set.
      >>> data = pd.read_csv("customer_data.csv")
      >>> accuracy = analyze_customer_churn(data)
      """
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Select features and target
      features = data.drop(columns=['churn'])
      target = data['churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      # Train a RandomForestClassifier
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate and return the accuracy
      return accuracy_score(y_test, y_pred)