input: Write a function to implement a decision tree algorithm for predicting the value of a stock based on historical data provided in a CSV file. The function should load the CSV file, split the data into training and testing sets, train a decision tree classifier, and then print the model's accuracy on the test set. Ensure that the model splits the data with 80% for training and 20% for testing.
  
  
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  import pandas as pd
  
  def predict_stock_value(file_path):
      # Load data
      data = pd.read_csv(file_path)
      
      # 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_value("stockdata.csv") == print('Model accuracy: ', ...)
  
output: def predict_stock_value(file_path):
      data = pd.read_csv(file_path)
      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))