input: Write a function to complete a Kaggle competition by loading necessary libraries, reading data, splitting it into training and testing sets, defining and fitting a model, making predictions, and scoring the predictions. If the data is loaded successfully and the model is trained, return the accuracy score. If any error occurs during the process, return "Error".
  
  assert kaggle_competition_completion() == "0.85"
  assert kaggle_competition_completion() == "Error"
output: def kaggle_competition_completion():
      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 str(accuracy)
      except:
          return "Error"