input: Write a function that loads the necessary libraries, reads in a dataset, splits it into predictors and response variables, trains a model, makes predictions, and scores the accuracy of the predictions. Include the necessary steps to submit the prediction to a Kaggle competition. The function should return the accuracy score of the predictions.
  
  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
      from sklearn.linear_model import LogisticRegression
  
      # 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 = LogisticRegression()
      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 (placeholder for submission code)
      # submission = pd.DataFrame({'Id': X_test.index, 'Response': y_pred})
      # submission.to_csv('submission.csv', index=False)
  
      return score
  
  assert kaggle_competition_script() == 0.85  # Example accuracy score for illustration purposes
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.linear_model import LogisticRegression
  
      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 = LogisticRegression()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      score = accuracy_score(y_test, y_pred)
      return score