input: Write a function to construct a decision tree algorithm for predicting the value of a stock based on stock data provided in a CSV file. The function should read the CSV file, split the data into features and labels, train a decision tree classifier, and print the model accuracy.
  
  
  def predict_stock(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)
      return clf.score(X_test, y_test)
  
  # Test cases
  assert predict_stock("stockdata.csv") is not None
  assert isinstance(predict_stock("stockdata.csv"), float)
  
output: def predict_stock(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)
      return clf.score(X_test, y_test)