input: Write a function to construct a decision tree algorithm for predicting the value of a stock using a CSV file containing stock data from the last 3 months. If the CSV file is empty, return "No data available". The function should print the model accuracy after training.
  assert decision_tree_stock_prediction("stockdata_nonempty.csv")==None
  assert decision_tree_stock_prediction("stockdata_empty.csv")=="No data available"
output: def decision_tree_stock_prediction(csv_file):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      try:
          data = pd.read_csv(csv_file)
          if data.empty:
              return "No data available"
          
          X = data.drop('target', axis=1)
          y = data['target']
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          model = DecisionTreeClassifier()
          model.fit(X_train, y_train)
          
          y_pred = model.predict(X_test)
          accuracy = accuracy_score(y_test, y_pred)
          print(f"Model accuracy: {accuracy:.2f}")
          
      except Exception as e:
          return "No data available"