input: 
  def sentiment_analysis(tweets):
      """
      Classify a list of tweets as either positive or negative sentiment based on a pre-trained model.
      The function cleans each tweet, vectorizes it, and then predicts the sentiment using a predefined 
      prediction function. The result for each tweet is appended to the results list, which is returned 
      at the end.
      
      >>> sentiment_analysis(['I love this!', 'I hate this.'])
      ['Positive', 'Negative']
      >>> sentiment_analysis(['Great day!', 'Terrible weather.'])
      ['Positive', 'Negative']
      >>> sentiment_analysis(['This is awesome!', 'I feel so bad.'])
      ['Positive', 'Negative']
      """
      results = []
      for tweet in tweets:
          tweet = preprocessing(tweet)
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
      return results
  
output:     results = []
      for tweet in tweets:
          tweet = preprocessing(tweet)
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
      return results