input: Write a function to train a Decision Tree Classifier on a given dataset and return the accuracy score.
  assert train_decision_tree(dataset) > 0.50
  assert isinstance(train_decision_tree(dataset), float)
  assert 0 <= train_decision_tree(dataset) <= 1
output: def train_decision_tree(dataset):
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      X = dataset.drop('target', axis=1)
      y = dataset['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)
      
      y_pred = clf.predict(X_test)
      return accuracy_score(y_test, y_pred)