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 from a CSV file, select features and targets, split the data into training and testing sets, create a RandomForestClassifier model, train the model, and print its accuracy on the test set. If the dataset is not available or empty, the function should return "No data available".
  
  def analyze_customer_churn(file_path):
      try:
          # Load the data set
          data = pd.read_csv(file_path)
          
          if data.empty:
              return "No data available"
          
          # 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
          accuracy = model.score(X_test, y_test)
          return accuracy
      except Exception as e:
          return str(e)
  
  assert analyze_customer_churn("customer_data.csv") >= 0
  assert analyze_customer_churn("nonexistent_file.csv") == "No such file or directory: 'nonexistent_file.csv'"
  assert analyze_customer_churn("empty_file.csv") == "No data available"
output: 
  def analyze_customer_churn(file_path):
      try:
          data = pd.read_csv(file_path)
          
          if data.empty:
              return "No data available"
          
          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)
          
          accuracy = model.score(X_test, y_test)
          return accuracy
      except Exception as e:
          return str(e)
  