input: 
  def sentiment_analysis(tweets):
      """
      Given a list of tweets, this function classifies each tweet as either "Positive" or "Negative" by performing the following steps:
      1. Preprocesses each tweet.
      2. Vectorizes the preprocessed tweet.
      3. Predicts the sentiment using a predefined model.
      4. Appends the result to a list and returns it.
  
      Example:
      >>> sentiment_analysis(["I love this product!", "This is terrible"])
      ['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
  