input: def sentiment_analysis(tweets):
      """
      Classify positive and negative sentiment in tweets. The function will clean each tweet,
      vectorize it, and then predict its sentiment. If the prediction is 1, it is classified as
      "Positive", otherwise it is classified as "Negative".
      
      >>> sentiment_analysis(['I love this product!', 'This is horrible.'])
      ['Positive', 'Negative']
      """
output:     import re
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Sample training data
      train_data = ['I love this product!', 'This is horrible.', 'Great experience!', 'Terrible service.']
      train_labels = [1, 0, 1, 0]
  
      # Clean tweets
      def clean_tweet(tweet):
          return re.sub(r'[^\w\s]', '', tweet).lower()
  
      cleaned_tweets = [clean_tweet(tweet) for tweet in tweets]
  
      # Vectorize tweets
      vectorizer = CountVectorizer()
      vectorizer.fit(train_data)
      tweet_vectors = vectorizer.transform(cleaned_tweets)
  
      # Train a Naive Bayes classifier
      classifier = MultinomialNB()
      classifier.fit(vectorizer.transform(train_data), train_labels)
  
      # Predict sentiment
      predictions = classifier.predict(tweet_vectors)
  
      # Convert predictions to labels
      sentiment_labels = ['Negative' if pred == 0 else 'Positive' for pred in predictions]
  
      return sentiment_labels