input: Write a script in Python to compete in a Kaggle competition. The script should load necessary libraries, read in the data, split the data into training and testing sets, define a model, fit the model, make predictions, score the prediction, and submit the prediction 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# ..."
  assert kaggle_competition_script() is not None
output: def kaggle_competition_script():
      return "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# ..."