input: Write a function to create and evaluate a decision tree model for predicting stock values using a given csv file containing stock data. The function should load the data, split it into training and testing sets, train the decision tree model, and print the model's accuracy.
  
  def predict_stock_decision_tree(csv_file):
      # Load libraries
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      
      # Load data
      data = pd.read_csv(csv_file)
      
      # Split data
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # Train/Test Split
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      
      # Decision Tree Model
      clf = DecisionTreeClassifier(criterion='entropy', random_state=0)
      clf.fit(X_train, y_train)
      
      # Model Accuracy
      y_pred = clf.predict(X_test)
      print('Model accuracy: ', clf.score(X_test, y_test))
  
  assert predict_stock_decision_tree("stockdata.csv")==None
output: def predict_stock_decision_tree(csv_file):
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      
      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)
      print('Model accuracy: ', clf.score(X_test, y_test))