input: Write a function to load text data, remove stopwords, vectorize the text, split the data, train a Naive Bayes classifier and evaluate its accuracy.
  assert load_and_evaluate_model(data) >= 0.7
output: def load_and_evaluate_model(data):
      import nltk
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import accuracy_score
      from nltk.corpus import stopwords
      
      nltk.download('stopwords')
      stop_words = set(stopwords.words('english'))
      
      # Remove stopwords
      cleaned_data = [' '.join([word for word in text.split() if word.lower() not in stop_words]) for text in data['text']]
      
      # Vectorize the text
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(cleaned_data)
      y = data['label']
      
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Train a Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train, y_train)
      
      # Evaluate its accuracy
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy