input: def customer_churn_prediction_model() -> float:
      """ This function loads a dataset of customers, splits the data into training and
      testing sets, trains a RandomForestClassifier model to predict customer churn,
      makes predictions on the test set, and returns the accuracy score of the model.
      >>> customer_churn_prediction_model()
      0.92
      """
output:     import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      data = pd.read_csv('customer_churn_dataset.csv')
  
      # Split the data into features and target
      X = data.drop('Churn', axis=1)
      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)
  
      # Train the RandomForestClassifier model
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate and return the accuracy score
      return accuracy_score(y_test, y_pred)