input: Write a function to create a machine learning classifier in Python for categorizing user input using the Naive Bayes algorithm. The input to the function is a string represented by input_string. The function should perform feature extraction and transformation, then train a MultinomialNB model, make a prediction based on the input, and return the prediction.
  assert classify_string("Hello, how are you?") == ["greeting"]
  assert classify_string("What time is it?") == ["question"]
  assert classify_string("This is a test.") == ["statement"]
  assert classify_string("I am not doing well.") == ["sentiment"]
  assert classify_string("Goodbye!") == ["farewell"]
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample data
      data = [
          ("Hello, how are you?", "greeting"),
          ("What time is it?", "question"),
          ("This is a test.", "statement"),
          ("I am not doing well.", "sentiment"),
          ("Goodbye!", "farewell")
      ]
  
      # Separate data into texts and labels
      texts, labels = zip(*data)
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X, labels)
  
      # Transform the input string
      input_vector = vectorizer.transform([input_string])
  
      # Make prediction
      prediction = model.predict(input_vector)
  
      return prediction.tolist()