input: Write a function to create a decision tree model for predicting the value of a stock based on historical stock data provided in a CSV file. The function should return the model's accuracy after training and testing on the provided data. If the CSV file is empty, the function should print a message and return None.
  def create_decision_tree_model(csv_file):
      try:
          data = pd.read_csv(csv_file)
      except pd.errors.EmptyDataError:
          print("The CSV file is empty.")
          return None
      
      if data.empty:
          print("The CSV file contains no data.")
          return None
      
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      
      clf = DecisionTreeClassifier(criterion='entropy', random_state=0)
      clf.fit(X_train, y_train)
      
      accuracy = clf.score(X_test, y_test)
      return accuracy
  
  assert create_decision_tree_model("stockdata.csv") > 0.5
  assert create_decision_tree_model("emptyfile.csv") is None
output: def create_decision_tree_model(csv_file):
      try:
          data = pd.read_csv(csv_file)
      except pd.errors.EmptyDataError:
          print("The CSV file is empty.")
          return None
      
      if data.empty:
          print("The CSV file contains no data.")
          return None
      
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      
      clf = DecisionTreeClassifier(criterion='entropy', random_state=0)
      clf.fit(X_train, y_train)
      
      accuracy = clf.score(X_test, y_test)
      return accuracy