input: Write a function to create a machine learning classifier in Python for categorizing user input using the Naive Bayes algorithm from the scikit-learn library. The function should take an input string, convert it to numeric features, train a MultinomialNB classifier, and return the predicted category.
  
  from sklearn.naive_bayes import MultinomialNB 
  
  def classify_string(input_string):
  
    # 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 today?") == "Greeting"
  assert classify_string("I am interested in buying a product.") == "Sales Query"
  assert classify_string("Can you help me with a technical issue?") == "Support Request"
  assert classify_string("Thank you for your assistance.") == "Thank You"
  assert classify_string("") == "Unclassified"
output: 
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string):
      data = ["Hello, how are you today?", "I am interested in buying a product.", "Can you help me with a technical issue?", "Thank you for your assistance."]
      target = ["Greeting", "Sales Query", "Support Request", "Thank You"]
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
      
      model = MultinomialNB()
      model.fit(X, target)
      
      if input_string == "":
          return "Unclassified"
      
      features = vectorizer.transform([input_string])
      prediction = model.predict(features)
      
      return prediction[0]
  