input: Write a function to complete a Python script for a Kaggle competition. The function should load necessary libraries, read in the data, split the data into predictors and response variables, split into training and testing sets, define a model, fit the model, make predictions, score the prediction, and submit the prediction to Kaggle.
  
  def kaggle_competition_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
      # ...
      
      return score
  
  assert kaggle_competition_script() == 0.95
  assert kaggle_competition_script() == 0.88
  assert kaggle_competition_script() == 0.92
  assert kaggle_competition_script() == 0.89
  assert kaggle_competition_script() == 0.91
output: def kaggle_competition_script():
      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
      
      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 = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      
      score = accuracy_score(y_test, y_pred)
      
      return score