input: 
  def sentiment_analysis(tweets):
      """Create a Python algorithm to classify positive and negative sentiment in tweets.
      
      The algorithm processes each tweet by cleaning it, converting it into a vector
      representation, and then predicting its sentiment. If the predicted sentiment
      is 1, the tweet is classified as "Positive". If the predicted sentiment is 0, 
      the tweet is classified as "Negative". The algorithm returns a list of 
      sentiment classifications for each tweet.
      
      Example:
      >>> sentiment_analysis(["I love this product!", "This 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:     # Define the preprocessing function
      def preprocessing(tweet):
          # Remove special characters, convert to lowercase, etc.
          import re
          tweet = re.sub(r'\W', ' ', tweet)
          tweet = tweet.lower()
          return tweet
  
      # Define the vectorize function
      def vectorize(tweet):
          # Convert the tweet into a vector representation
          from sklearn.feature_extraction.text import CountVectorizer
          vectorizer = CountVectorizer()
          vectorizer.fit([tweet])
          return vectorizer.transform([tweet]).toarray()[0]
  
      # Define the predict_sentiment function
      def predict_sentiment(tweet_vector):
          # Predict the sentiment using a pre-trained model
          from sklearn.linear_model import LogisticRegression
          model = LogisticRegression()
          model.fit([tweet_vector], [1])  # Dummy fit for demonstration
          return model.predict([tweet_vector])[0]