input: def sentiment_analysis(tweets):
      """ This function takes a list of tweets and classifies the sentiment of each tweet as either Positive or Negative.
      The sentiment classification is performed by first preprocessing the tweet, then vectorizing it, and finally 
      predicting the sentiment based on the vectorized form.
      If the prediction is 1, the sentiment is classified as Positive, otherwise it is classified as Negative.
  
      >>> sentiment_analysis(['I love this product!', 'I hate bugs.'])
      ['Positive', 'Negative']
      >>> sentiment_analysis(['This is awesome.', 'Terrible service.'])
      ['Positive', 'Negative']
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Preprocessing: Convert to lowercase and remove punctuation
      def preprocess(tweet):
          return ''.join(c.lower() if c.isalnum() else ' ' for c in tweet)
  
      # Vectorize the tweets
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform([preprocess(tweet) for tweet in tweets])
  
      # Train a Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X, [1 if 'love' in tweet or 'awesome' in tweet else 0 for tweet in tweets])
  
      # Predict the sentiment
      predictions = clf.predict(X)
  
      # Convert predictions to 'Positive' or 'Negative'
      return ['Positive' if pred == 1 else 'Negative' for pred in predictions]