input: Write a function to create a Python script that will compete in a Kaggle competition. The script should load necessary libraries, read in the data, split the data into training and testing sets, define and fit a model, make predictions on the test set, score the predictions, and potentially submit the predictions to Kaggle.
  assert kaggle_competition_script() == "import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\ntrain_data = pd.read_csv('data.csv')\nX = train_data.drop('response', axis=1)\ny = train_data['response']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\nmodel = ...\nmodel.fit(X_train, y_train)\ny_pred = model.predict(X_test)\nscore = accuracy_score(y_test, y_pred)\n# Submit the prediction to Kaggle\n# ...\n"
output: 
  import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  
  train_data = pd.read_csv('data.csv')
  X = train_data.drop('response', axis=1)
  y = train_data['response']
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
  model = ...
  model.fit(X_train, y_train)
  y_pred = model.predict(X_test)
  score = accuracy_score(y_test, y_pred)
  # Submit the prediction to Kaggle
  # ...
  