input: def classify_customer_churn() -> float:
      """
      Analyzes a dataset of customers and creates a classification model using scikit-learn's RandomForestClassifier to determine customer churn.
      The dataset is loaded from "customer_data.csv". The features and targets are selected, and the data is split into training and testing sets.
      The model is trained and used to make predictions, and its accuracy is measured and returned.
      
      Example:
      >>> classify_customer_churn()
      0.85
      """
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_data.csv")
  
      # 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)
  
      # Initialize the RandomForestClassifier
      model = RandomForestClassifier(random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Measure the accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy