input: Write a function that creates a machine learning classifier in Python for categorizing user input using the Multinomial Naive Bayes algorithm. The function should take an input string, extract features, train the model on provided data, and return a prediction based on the input.
  
  
  def classify_string(input_string, data, target):
      # 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 world", data, target)==[0]
  assert classify_string("how are you", data, target)==[1]
  assert classify_string("machine learning", data, target)==[2]
  assert classify_string("data science", data, target)==[3]
  assert classify_string("python programming", data, target)==[4]
output: def classify_string(input_string, data, target):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      vectorizer = CountVectorizer()
      data_features = vectorizer.fit_transform(data)
      input_features = vectorizer.transform([input_string])
  
      model = MultinomialNB()
      model.fit(data_features, target)
  
      prediction = model.predict(input_features)
      return prediction