input: 
  from typing import List
  
  def sentiment_analysis(tweets: List[str]) -> List[str]:
      """ Classify each tweet in the input list as either "Positive" or "Negative" sentiment.
      >>> sentiment_analysis(["I love this movie!", "This is the worst!", "I feel neutral."])
      ["Positive", "Negative", "Negative"]
      >>> sentiment_analysis(["Great day!", "Horrible experience...", "Just okay."])
      ["Positive", "Negative", "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: str) -> str:
          # 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: str) -> List[int]:
          # Simple vectorization: count the occurrences of each word
          words = tweet.split()
          word_count = {}
          for word in words:
              if word in word_count:
                  word_count[word] += 1
              else:
                  word_count[word] = 1
          return list(word_count.values())
  
      # Define the predict_sentiment function
      def predict_sentiment(tweet_vector: List[int]) -> int:
          # Simple rule-based sentiment prediction
          positive_words = {"love", "great", "happy", "good", "excellent"}
          negative_words = {"worst", "horrible", "bad", "terrible", "awful"}
          
          positive_count = sum(tweet_vector[i] for i, word in enumerate(tweet_vector) if word in positive_words)
          negative_count = sum(tweet_vector[i] for i, word in enumerate(tweet_vector) if word in negative_words)
          
          return 1 if positive_count > negative_count else 0