input: Write a function to generate a Python script that can be used to compete in a Kaggle competition. The script should load necessary libraries, read in data from a CSV file, split the data into training and testing sets, define a model, fit the model, make predictions, score the predictions, and submit the predictions to Kaggle.
  assert generate_kaggle_script()=='''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)
  '''
  
  assert generate_kaggle_script().startswith('import')==True
  assert "train_data = pd.read_csv('data.csv')" in generate_kaggle_script()==True
  assert "X_train, X_test, y_train, y_test = train_test_split(" in generate_kaggle_script()==True
  assert "model = ..." in generate_kaggle_script()==True
  assert "model.fit(X_train, y_train)" in generate_kaggle_script()==True
  assert "y_pred = model.predict(X_test)" in generate_kaggle_script()==True
  assert "score = accuracy_score(y_test, y_pred)" in generate_kaggle_script()==True
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)
  