input: Write a python function to train and evaluate a RandomForestClassifier on a given dataset with 'dep_delay' and 'arr_delay' as features and 'arrival_airport' as the target, then return the accuracy.
  assert train_and_evaluate_model(data) > 0.8
output: def train_and_evaluate_model(data):
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Split the data into features and target
      X = data[['dep_delay', 'arr_delay']]
      y = data['arrival_airport']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Initialize and train the RandomForestClassifier
      clf = RandomForestClassifier(random_state=42)
      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