input: Write a function to create a machine learning classifier in Python for categorizing user input using the Multinomial Naive Bayes algorithm. The function should accept an input string, convert it to numeric features, train a model using the data and target, and return the prediction made by the model based on the input.
  
  
  from sklearn.naive_bayes import MultinomialNB 
  
  # Assuming an input string is passed to the function as a parameter
  
  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("This is a positive review")==['positive']
  assert classify_string("This product is bad")==['negative']
  assert classify_string("I love this movie")==['positive']
  assert classify_string("The service was poor")==['negative']
  assert classify_string("")==['neutral']
output: 
  def classify_string(input_string):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
  
    data = ["This is a positive review", "This product is bad", "I love this movie", "The service was poor", ""]
    target = ['positive', 'negative', 'positive', 'negative', 'neutral']
  
    vectorizer = CountVectorizer()
    features = vectorizer.fit_transform(data)
  
    model = MultinomialNB()
    model.fit(features, target)
  
    input_features = vectorizer.transform([input_string])
    prediction = model.predict(input_features)
  
    return prediction
  