input: Write a function to create a decision tree classifier for predicting the value of a stock based on a given CSV file containing stock data from the last 3 months. 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 stock_decision_tree(csv_file):
      import pandas as pd
      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 stock_decision_tree("stockdata.csv")==None
  assert stock_decision_tree("stockdata.csv")==None
output: def stock_decision_tree(csv_file):
      import pandas as pd
      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))