input: def analyze_customer_churn(data_path: str) -> float:
      """
      Analyze a dataset of customers and create a classification model using RandomForestClassifier
      from scikit-learn to determine customer churn. Train the model on 80% of the data and evaluate
      its accuracy on the remaining 20%.
  
      Args:
      data_path (str): Path to the customer data CSV file.
  
      Returns:
      float: The accuracy score of the model on the test set.
  
      Example:
      >>> analyze_customer_churn("customer_data.csv")
      0.92
      """
      import pandas as pd
      import numpy as np
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      # Load the data set
      data = pd.read_csv(data_path)
  
      # 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
      return model.score(X_test, y_test)
output:     # Make predictions and measure accuracy
      return model.score(X_test, y_test)