input: Write a function to create a Python script for a Kaggle competition. The function should load necessary libraries, read a CSV file, split the data into training and testing sets, define and fit a model, make predictions, and calculate the accuracy score.
  assert kaggle_script() == "Script generated successfully."
  assert kaggle_script() != "Script generation failed."
  assert kaggle_script() is not None
  assert kaggle_script().startswith("# Load the necessary libraries")
  assert kaggle_script().endswith("# Submit the prediction to Kaggle\n# ...")
output: def kaggle_script():
      script = """# Load the necessary libraries
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import accuracy_score
  
  # Read the CSV file
  data = pd.read_csv('data.csv')
  
  # Split the data into training and testing sets
  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)
  
  # Define and fit the model
  model = RandomForestClassifier()
  model.fit(X_train, y_train)
  
  # Make predictions
  y_pred = model.predict(X_test)
  
  # Calculate the accuracy score
  accuracy = accuracy_score(y_test, y_pred)
  print(f'Accuracy: {accuracy}')
  
  # Submit the prediction to Kaggle
  # ...
  """
      return script