input: Write a function named `kaggle_competition` that takes no input and simulates the process of competing in a Kaggle competition by loading data, splitting it, training a model, making predictions, and scoring the model. Ensure the function prints the accuracy score from the prediction. If the accuracy score is above 0.80, the function should return "SUCCESS", otherwise "FAILURE".
  assert kaggle_competition()=="SUCCESS"
  assert kaggle_competition()=="FAILURE"
output: 
  def kaggle_competition():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      
      data = load_iris()
      X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=42)
      
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      print(accuracy)
      
      if accuracy > 0.80:
          return "SUCCESS"
      else:
          return "FAILURE"
  