input: Write a function to create a simple machine learning script for a Kaggle competition. The function should read data from a CSV file, split it into training and testing sets, define and fit a model, make predictions on the test set, score the prediction using accuracy, and optionally submit the prediction to Kaggle. The function should return the accuracy score.
  
  assert kaggle_competition_script("data.csv")==0.95
  assert kaggle_competition_script("another_dataset.csv")==0.88
  assert kaggle_competition_script("empty_data.csv")==None
output: def kaggle_competition_script(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      try:
          data = pd.read_csv(file_path)
          if data.empty:
              return None
          
          X = data.drop('target', axis=1)
          y = data['target']
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 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)
          
          return accuracy
      except Exception as e:
          return None