input: Write a function to compete in a Kaggle competition using Python. The function should load data, split it into training and testing sets, define a model, make predictions, and calculate the accuracy score. Finally, it should simulate submitting predictions to Kaggle. If the data is successfully loaded and the model makes predictions, return True; otherwise, return False.
  
  assert kaggle_competition(...)==True
  assert kaggle_competition(None)==False
output: def kaggle_competition(data):
      if data is None:
          return False
      try:
          from sklearn.model_selection import train_test_split
          from sklearn.ensemble import RandomForestClassifier
          from sklearn.metrics import accuracy_score
          import pandas as pd
          
          df = pd.DataFrame(data)
          X = df.drop('target', axis=1)
          y = df['target']
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          model = RandomForestClassifier()
          model.fit(X_train, y_train)
          
          y_pred = model.predict(X_test)
          accuracy = accuracy_score(y_test, y_pred)
          
          return True
      except:
          return False