input: Write a function to construct a decision tree algorithm for predicting the value of a stock based on a csv file containing stock data from the last 3 months. The function should load the data, split it into training and testing sets, create and train a DecisionTreeClassifier, and print the model's accuracy.
  assert construct_decision_tree("stockdata.csv") == "Model accuracy: 0.85"
output: def construct_decision_tree(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      data = pd.read_csv(csv_file)
      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)
      return f"Model accuracy: {accuracy:.2f}"