input: Write a function to create a classification model using scikit-learn that predicts customer churn based on a dataset. The function should load the dataset, select features, select targets, split the data into training and testing sets, train the model, and then measure the accuracy of the predictions. If the dataset is empty, return a message indicating that the dataset is empty.
  
  assert customer_churn_model(pd.DataFrame({'churn': [True, False, True], 'feature1': [1, 2, 3]})) == 1.0
  assert customer_churn_model(pd.DataFrame({'churn': [False, False, False], 'feature1': [1, 2, 3]})) == 1.0
  assert customer_churn_model(pd.DataFrame()) == "The dataset is empty"
output: def customer_churn_model(df):
      if df.empty:
          return "The dataset is empty"
      
      X = df.drop('churn', axis=1)
      y = df['churn']
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = LogisticRegression()
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy