input: from typing import List
  
  
  def sentiment_analysis(tweets: List[str]) -> List[str]:
      """
      Analyze the sentiment of a list of tweets and classify each tweet as 'Positive' or 'Negative'.
      The function preprocesses each tweet, vectorizes it, and then predicts its sentiment.
      
      >>> sentiment_analysis(["I love cats", "Cats are dreadful"])
      ['Positive', 'Negative']
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Sample training data
      train_data = ["I love cats", "Cats are dreadful"]
      train_labels = ["Positive", "Negative"]
  
      # Create a pipeline for text classification
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      model.fit(train_data, train_labels)
  
      # Predict sentiment for each tweet
      predictions = model.predict(tweets)
      return predictions