input: Write a function to create a decision tree algorithm for predicting the value of a stock given a CSV file containing stock data from the last 3 months. The function should load the data, split it into training and testing sets, train a decision tree classifier, and print the model's accuracy.
  
  def predict_stock_value(filepath):
      # Load libraries
      import pandas as pd
      import numpy as np
      
      # Load data
      data = pd.read_csv(filepath)
      
      # Split data
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # Train/Test Split
      from sklearn.model_selection import 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
      from sklearn.tree import DecisionTreeClassifier
      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")==None
  assert predict_stock_value("invalidfile.csv")==None
output: def predict_stock_value(filepath):
      try:
          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(filepath)
          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))
      except Exception as e:
          print(f"Error: {e}")