input: Write a function to create a machine learning classifier in Python for categorizing user input. The classifier should use a Naive Bayes model from the sklearn library. The input to the function is a string, and the output is the predicted category for that string.
  
  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?") == "greeting"
  assert classify_string("What is the weather today?") == "weather"
  assert classify_string("I need help with my homework.") == "help"
  assert classify_string("I have a question about your services.") == "inquiry"
  assert classify_string("") == "unknown"
output: def classify_string(input_string):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
    
    data = ["Hello, how are you?", "What is the weather today?", "I need help with my homework.", "I have a question about your services."]
    target = ["greeting", "weather", "help", "inquiry"]
    
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(data)
    
    model = MultinomialNB()
    model.fit(X, target)
    
    features = vectorizer.transform([input_string])
    prediction = model.predict(features)
    
    if input_string == "":
      return "unknown"
    
    return prediction[0]