input: Write a function to create a Python script that competes in a Kaggle competition. The function should load necessary libraries, read data from a CSV file, split the data into predictors and response variables, train a model, make predictions, and calculate the accuracy score.
  
  
  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
  
  # Test cases
  assert kaggle_competition_script() # Assuming the function to be tested with real data
  
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