input: 
  def analyze_customer_churn(data: pd.DataFrame) -> float:
      """Analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn.
      The function takes a pandas DataFrame as input and returns the accuracy of the model as a float.
      >>> data = pd.DataFrame({
      ...     'feature1': [1, 2, 3, 4, 5],
      ...     'feature2': [5, 4, 3, 2, 1],
      ...     'churn': [0, 0, 1, 1, 0]
      ... })
      >>> analyze_customer_churn(data)
      1.0
      """
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      # 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:     from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
  
      # 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)