input: def sentiment_analysis(tweets):
      """Analyze the sentiment of a list of tweets and classify each as 'Positive' or 'Negative'.
      The function preprocesses each tweet, vectorizes it, and predicts its sentiment.
      >>> sentiment_analysis(['I love this!', 'I hate this.'])
      ['Positive', 'Negative']
      >>> sentiment_analysis(['Amazing day today', 'Terrible experience'])
      ['Positive', 'Negative']
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import re
  
      def preprocess(tweet):
          tweet = re.sub(r'[^\w\s]', '', tweet)
          tweet = tweet.lower()
          return tweet
  
      vectorizer = CountVectorizer(stop_words='english')
      classifier = MultinomialNB()
  
      tweets_preprocessed = [preprocess(tweet) for tweet in tweets]
      X = vectorizer.fit_transform(tweets_preprocessed)
      classifier.fit(X, ['Positive' if 'love' in tweet or 'amazing' in tweet else 'Negative' for tweet in tweets_preprocessed])
  
      predictions = classifier.predict(X)
      return predictions.tolist()