input: 
  from typing import List
  
  def sentiment_analysis(tweets: List[str]) -> List[str]:
      """
      Classify each tweet in the list as either 'Positive' or 'Negative' sentiment.
      >>> sentiment_analysis(['I love this product', 'I hate this product'])
      ['Positive', 'Negative']
      >>> sentiment_analysis(['A wonderful experience', 'Terrible customer service'])
      ['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: 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 occurrence of each word
          words = tweet.split()
          vector = [words.count(word) for word in set(words)]
          return vector
  
      # Define the predict_sentiment function
      def predict_sentiment(vector: List[int]) -> int:
          # Simple prediction: if the sum of the vector is positive, classify as 'Positive'
          if sum(vector) > 0:
              return 1
          else:
              return 0