input: Write a function that creates a machine learning classifier in Python for categorizing user input using the Naive Bayes algorithm. The function should take a string input, extract features from it, train a Naive Bayes classifier, and return the prediction.
  
  def classify_string(input_string):
      from sklearn.naive_bayes import MultinomialNB
      
      # Feature extraction and transformation from string to numeric
      features = . . .
      
      # Create an instance of the classifier 
      model = MultinomialNB()
      
      # Train the classifier on the data
      model.fit(data, target)
      
      # Make a prediction based on the input
      prediction = model.predict([features])
      
      # Return the prediction
      return prediction
  
  assert classify_string("Hello, how are you?") == ...
  assert classify_string("I need help with my order") == ...
  assert classify_string("Can you recommend a product?") == ...
  assert classify_string("Is there a sale today?") == ...
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      
      # Sample data for training
      data = ["Hello, how are you?", "I need help with my order", "Can you recommend a product?", "Is there a sale today?"]
      target = [0, 1, 2, 3]
      
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      
      # Train the classifier on the data
      model.fit(data, target)
      
      # Make a prediction based on the input
      prediction = model.predict([input_string])
      
      # Return the prediction
      return prediction[0]