input: Write a function to create a Python script that prepares data for a Kaggle competition by loading necessary libraries, reading data from a CSV file, splitting the data into training and testing sets, defining a model, fitting the model to the training data, making predictions on the test data, and scoring the predictions using accuracy score. If the script completes without errors, return True. If any error occurs, return False.
  
  assert kaggle_script()==True
output: def kaggle_script():
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.linear_model import LogisticRegression
          from sklearn.metrics import accuracy_score
  
          data = pd.read_csv('data.csv')
          X = data.drop('target', axis=1)
          y = data['target']
  
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
          model = LogisticRegression()
          model.fit(X_train, y_train)
  
          predictions = model.predict(X_test)
          score = accuracy_score(y_test, predictions)
  
          return True
      except:
          return False