input: Write a python function to load the iris dataset, split it into training and test sets, fit a Decision Tree classifier, and calculate the accuracy score.
  assert round(load_and_evaluate_model('iris.csv'), 2) == 0.97
output: def load_and_evaluate_model(filename):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      df = pd.read_csv(filename)
      
      # Split the dataset into features and target variable
      X = df.drop('species', axis=1)
      y = df['species']
      
      # Split the dataset 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)
      
      # Fit the Decision Tree classifier
      clf = DecisionTreeClassifier(random_state=42)
      clf.fit(X_train, y_train)
      
      # Predict the test set results
      y_pred = clf.predict(X_test)
      
      # Calculate the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy