input: Write a function named `construct_stock_prediction_tree` that reads a CSV file containing stock data from the last 3 months and constructs a decision tree algorithm for predicting the stock's value. The function should load the data, split it into training and testing sets, train a decision tree classifier, and print the model's accuracy.
  assert construct_stock_prediction_tree("test_stockdata_1.csv") == "Model accuracy: 0.85"
  assert construct_stock_prediction_tree("test_stockdata_2.csv") == "Model accuracy: 0.78"
  assert construct_stock_prediction_tree("test_stockdata_3.csv") == "Model accuracy: 0.92"
  assert construct_stock_prediction_tree("test_stockdata_4.csv") == "Model accuracy: 0.67"
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.metrics import accuracy_score
  
  def construct_stock_prediction_tree(filename):
      data = pd.read_csv(filename)
      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}"