input: Write a function to generate a Python script that can be used to compete in a Kaggle competition. The function should include loading necessary libraries, reading input data, splitting the data into train and test sets, defining and fitting a model, making predictions, scoring the model, and submitting the predictions to Kaggle.
  assert kaggle_script()== '''
  # Load the necessary libraries
  import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  
  # Read in the data
  train_data = pd.read_csv("data.csv")
  
  # Split the data into X (predictors) and y (response)
  X = train_data.drop('response', axis=1)
  y = train_data['response']
  
  # Split into training and testing sets
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
  
  # Define the model and fit it
  model = ...
  model.fit(X_train, y_train)
  
  # Make predictions on the test set
  y_pred = model.predict(X_test)
  
  # Score the prediction
  score = accuracy_score(y_test, y_pred)
  
  # Submit the prediction to Kaggle
  # ...
  '''
  assert kaggle_script()!=''
output: 
  # Load the necessary libraries
  import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from sklearn.ensemble import RandomForestClassifier
  
  # Read in the data
  train_data = pd.read_csv("data.csv")
  
  # Split the data into X (predictors) and y (response)
  X = train_data.drop('response', axis=1)
  y = train_data['response']
  
  # Split into training and testing sets
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
  
  # Define the model and fit it
  model = RandomForestClassifier(random_state=42)
  model.fit(X_train, y_train)
  
  # Make predictions on the test set
  y_pred = model.predict(X_test)
  
  # Score the prediction
  score = accuracy_score(y_test, y_pred)
  
  # Submit the prediction to Kaggle
  # ...
  