input: def sentiment_analysis(tweets):
      """ Returns the sentiment classification (either 'Positive' or 'Negative') for
      a list of tweets. The method involves preprocessing, vectorizing, and predicting
      sentiment for each tweet.
      
      >>> sentiment_analysis(['I love this product!', 'This sucks, worst experience ever.'])
      ['Positive', 'Negative']
      >>> sentiment_analysis(['Great job team!', 'The weather is not good today.'])
      ['Positive', 'Negative']
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import re
  
      # Preprocessing function
      def preprocess(text):
          text = re.sub(r'[^\w\s]', '', text)
          text = text.lower()
          return text
  
      # Preprocess tweets
      preprocessed_tweets = [preprocess(tweet) for tweet in tweets]
  
      # Vectorize tweets
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(preprocessed_tweets)
  
      # Train a Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X, ['Positive' if 'love' in tweet or 'great' in tweet else 'Negative' for tweet in preprocessed_tweets])
  
      # Predict sentiment for each tweet
      sentiment_labels = clf.predict(X)
  
      return sentiment_labels