input: Write a function to classify positive and negative sentiment in tweets. The function should preprocess each tweet, vectorize it, and then predict its sentiment using a predefined model. If the prediction is 1, the sentiment is classified as "Positive"; if the prediction is 0, the sentiment is classified as "Negative". Return a list of classifications for each tweet in the input list.
  
  def sentiment_analysis(tweets):
      # 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
  
  assert sentiment_analysis(["I love this product!", "I hate this product!"]) == ["Positive", "Negative"]
  assert sentiment_analysis(["This is amazing!", "Terrible service."]) == ["Positive", "Negative"]
  assert sentiment_analysis(["Good experience", "Bad experience"]) == ["Positive", "Negative"]
  assert sentiment_analysis([]) == []
  assert sentiment_analysis(["neutral", "mixed feelings"]) == ["Negative", "Negative"]
output: def sentiment_analysis(tweets):
      results = [] 
      for tweet in tweets:
          tweet = preprocessing(tweet)
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
      return results