input: Write a function to create a Python script that prepares and trains a model for a Kaggle competition. The script should load data, split it into training and testing sets, fit a model, make predictions, and score the model's accuracy. If the script runs successfully, it should return the accuracy score. If the data cannot be read or the model cannot be fit, it should return False.
  assert kaggle_competition_script() == 0.95
  assert kaggle_competition_script() == False
output: def kaggle_competition_script():
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.ensemble import RandomForestClassifier
          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 = RandomForestClassifier()
          model.fit(X_train, y_train)
          
          y_pred = model.predict(X_test)
          accuracy = accuracy_score(y_test, y_pred)
          
          return accuracy
      except:
          return False