input: 
  def sentiment_analysis(tweets):
      """Given a list of tweets, classify each tweet as either Positive or Negative.
      
      The function does the following:
      1. Preprocesses each tweet.
      2. Vectorizes the clean tweet.
      3. Predicts the sentiment using a vectorized model.
      4. Appends the classification ('Positive' or 'Negative') to the result list.
      
      Example:
      >>> sentiment_analysis(["I love this!", "I hate that!"])
      ['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:     # 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