input: Write a function to analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn. The function should load the dataset, select features and targets, split the data into training and testing sets, create and train a RandomForestClassifier, and finally make predictions and measure accuracy. If the dataset file is not found, the function should return an error message.
  
  
  def customer_churn_model():
      try:
          # Load the data set
          data = pd.read_csv("customer_data.csv")
          
          # 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)
      
      except FileNotFoundError:
          return "Error: customer_data.csv file not found."
  
  # Test cases
  assert customer_churn_model() != None
  assert type(customer_churn_model()) == float
  
output: 
  def customer_churn_model():
      try:
          data = pd.read_csv("customer_data.csv")
          X = data.drop(['churn'], axis=1)
          y = data['churn']
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          model = RandomForestClassifier()
          model.fit(X_train, y_train)
          predictions = model.predict(X_test)
          return model.score(X_test, y_test)
      except FileNotFoundError:
          return "Error: customer_data.csv file not found."
  