input: 
  from typing import List
  
  def sentiment_analysis(tweets: List[str]) -> List[str]:
      """
      Analyzes the sentiment of each tweet in the given list. 
      Returns a list of strings where each string is either "Positive" or "Negative" representing the sentiment of the corresponding tweet.
      >>> sentiment_analysis(['I love this product!', 'I hate Mondays.'])
      ['Positive', 'Negative']
      >>> sentiment_analysis(['Today is a good day', 'This movie 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:     # Placeholder functions for preprocessing, vectorization, and prediction
      def preprocessing(text: str) -> str:
          # Example: Remove punctuation and convert to lowercase
          return ''.join(c for c in text if c.isalnum() or c.isspace()).lower()
  
      def vectorize(text: str) -> List[float]:
          # Example: Convert text to a list of word frequencies
          words = text.split()
          word_freq = {word: words.count(word) for word in set(words)}
          return list(word_freq.values())
  
      def predict_sentiment(vector: List[float]) -> int:
          # Example: Simple rule-based prediction
          if sum(vector) > 2:  # Assuming more words indicate positive sentiment
              return 1
          else:
              return 0