input: Write a python function to create a Decision Tree Classifier, fit it on the training data, and compute the accuracy on the test set.
  assert abs(create_and_evaluate_decision_tree(pd.read_csv('loan_data.csv')) - 0.78) < 0.05
  assert abs(create_and_evaluate_decision_tree(pd.read_csv('another_loan_data.csv')) - 0.82) < 0.05
output: def create_and_evaluate_decision_tree(df):
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
      
      # Assuming the target column is named 'target' and the rest are features
      X = df.drop(columns=['target'])
      y = df['target']
      
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Initialize and fit the Decision Tree Classifier
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      
      # Predict on the test set
      y_pred = clf.predict(X_test)
      
      # Calculate and return the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy