input: 
  def sentiment_analysis(tweets):
      """Classifies tweets as positive or negative sentiment based on a prediction model.
      >>> sentiment_analysis(["I love this product!", "This is terrible."])
      ["Positive", "Negative"]
      >>> sentiment_analysis(["Great job!", "I hate Mondays."])
      ["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:     # Define the preprocessing function
      def preprocessing(tweet):
          # Remove punctuation and convert to lowercase
          tweet = ''.join(c for c in tweet if c.isalnum() or c.isspace()).lower()
          return tweet
  
      # Define the vectorize function
      def vectorize(tweet):
          # Dummy vectorization (replace with actual vectorization logic)
          return [len(word) for word in tweet.split()]
  
      # Define the predict_sentiment function
      def predict_sentiment(vector):
          # Dummy prediction (replace with actual prediction logic)
          return 1 if sum(vector) > 10 else 0
  
      # Call the sentiment_analysis function with the provided tweets
      return sentiment_analysis(tweets)