input: Write a function to create a decision tree model for predicting stock values using stock data from a CSV file. The function should load the data, split it into training and testing sets, train a decision tree classifier, and print the model's accuracy.
  
  
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  
  def predict_stock_value(csv_file):
      # Load the data
      data = pd.read_csv(csv_file)
      
      # Split the data into features and target
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      
      # Initialize and train the Decision Tree Classifier
      clf = DecisionTreeClassifier(criterion='entropy', random_state=0)
      clf.fit(X_train, y_train)
      
      # Predict and print the model accuracy
      y_pred = clf.predict(X_test)
      accuracy = clf.score(X_test, y_test)
      print('Model accuracy: ', accuracy)
      
      return accuracy
  
  # Test cases
  assert predict_stock_value("stockdata.csv") > 0
  assert predict_stock_value("another_stockdata.csv") >= 0
  
output: def predict_stock_value(csv_file):
      data = pd.read_csv(csv_file)
      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)
      y_pred = clf.predict(X_test)
      accuracy = clf.score(X_test, y_test)
      print('Model accuracy: ', accuracy)
      return accuracy