input: Write a function to automate the process of participating in a Kaggle competition by loading the necessary libraries, reading the data, splitting it into training and testing sets, defining and fitting a model, making predictions, scoring the predictions, and submitting them to Kaggle. The function should be flexible enough to work with any dataset and model.
  
  assert kaggle_competition_automation("data.csv", model=..., scoring_method=accuracy_score)=={"score": 0.85}
  assert kaggle_competition_automation("another_data.csv", model=..., scoring_method=accuracy_score)=={"score": 0.78}
  assert kaggle_competition_automation("empty_data.csv", model=..., scoring_method=accuracy_score)=={"score": None}
output: def kaggle_competition_automation(data_file, model, scoring_method):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      try:
          data = pd.read_csv(data_file)
          if data.empty:
              return {"score": None}
          
          X = data.iloc[:, :-1]
          y = data.iloc[:, -1]
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          model.fit(X_train, y_train)
          y_pred = model.predict(X_test)
          score = scoring_method(y_test, y_pred)
          
          return {"score": score}
      except Exception as e:
          return {"score": None}