input: 
  def sentiment_analysis(tweets):
      """Classify each tweet in the list as either "Positive" or "Negative" based on sentiment.
      >>> sentiment_analysis(["I love this movie!", "This is the worst day ever."])
      ['Positive', 'Negative']
      >>> sentiment_analysis(["Happy weekend!", "Absolutely terrible experience."])
      ['Positive', 'Negative']
      """
  
      # Initialize a list to store the result 
      results = [] 
  
      # Iterate through each tweet
      for tweet in tweets:
          # Clean the tweet 
          tweet = preprocessing(tweet)
  
          # Vectorize the tweet and obtain the prediction 
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
  
          # Append the prediction to the result list
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
  
      return results
  
output:     # Placeholder functions for preprocessing, vectorization, and prediction
      def preprocessing(tweet):
          # Example: Remove punctuation and convert to lowercase
          import string
          return tweet.translate(str.maketrans('', '', string.punctuation)).lower()
  
      def vectorize(tweet):
          # Example: Simple bag of words vectorization
          from sklearn.feature_extraction.text import CountVectorizer
          vectorizer = CountVectorizer()
          return vectorizer.fit_transform([tweet]).toarray()[0]
  
      def predict_sentiment(tweet_vector):
          # Example: Simple rule-based prediction
          if sum(tweet_vector) > 0:  # Assuming positive words are present
              return 1
          else:
              return 0