input: Write a function to load a CSV file containing stock data from the last 3 months, split the data into features and target, train a decision tree classifier on the training data, and print the model's accuracy on the test data.
  assert stock_prediction(*load_data_and_split('stockdata.csv')) == print('Model accuracy: ', clf.score(X_test, y_test))
output: def load_data_and_split(filename):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
  
      data = pd.read_csv(filename)
      X = data.drop('target', axis=1)
      y = data['target']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      accuracy = clf.score(X_test, y_test)
      print('Model accuracy: ', accuracy)
      return X_train, X_test, y_train, y_test, clf