input: 
  import pandas as pd
  import numpy as np
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  
  def classify_customer_churn_model(filepath: str) -> float:
      """ Load a dataset of customers from a CSV file, create a classification model 
      using scikit-learn's RandomForestClassifier to determine customer churn, 
      split the data into training and testing sets, train the model, make 
      predictions, and return the accuracy of the model.
      >>> classify_customer_churn_model("customer_data.csv")
      0.85
      """
      # Load the data set
      data = pd.read_csv(filepath)
  
      # 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
      predictions = model.predict(X_test)
      return model.score(X_test, y_test)
  
output:     # Make predictions and measure accuracy
      predictions = model.predict(X_test)
      return model.score(X_test, y_test)