input: Write a function to complete a task that involves writing a Python script to compete in a Kaggle competition. The script should load necessary libraries, read data, split the data, define and fit a model, make predictions, and score the predictions. 
  
  assert complete_kaggle_task()==None
output: def complete_kaggle_task():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Load data
      data = pd.read_csv('data.csv')
  
      # Split data
      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)
  
      # Define and fit model
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Score predictions
      score = accuracy_score(y_test, y_pred)
      return None