input: from typing import Tuple
  
  def analyze_customer_churn(data_path: str) -> float:
      """Analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn.
      The function loads the data, splits it into train and test sets, trains a RandomForestClassifier, and returns
      the accuracy of the model on the test set.
      Args:
          data_path (str): Path to the customer data CSV file.
      Returns:
          float: Accuracy of the model on the test set.
      >>> analyze_customer_churn("customer_data.csv")
      0.87
      """
      import pandas as pd
      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:     import pandas as pd
      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)