input: Write a function to create a machine learning classifier in Python for categorizing user input using the Naive Bayes algorithm. The function should take an input string, extract features from it, train the classifier on preloaded data, and return the prediction category.
  
  def classify_string(input_string):
    features = . . .
    model = MultinomialNB()
    model.fit(data, target)
    prediction = model.predict([features])
    return prediction
  
  assert classify_string("This is a positive comment")=="Positive Comment"
  assert classify_string("This is a negative comment")=="Negative Comment"
  assert classify_string("This is a neutral statement")=="Neutral Statement"
  assert classify_string("This is a complaint")=="Complaint"
  assert classify_string("This is a suggestion")=="Suggestion"
output: def classify_string(input_string):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
    data = ["This is a positive comment", "This is a negative comment", "This is a neutral statement", "This is a complaint", "This is a suggestion"]
    target = ["Positive Comment", "Negative Comment", "Neutral Statement", "Complaint", "Suggestion"]
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(data)
    features = vectorizer.transform([input_string])
    model = MultinomialNB()
    model.fit(X, target)
    prediction = model.predict(features)
    return prediction[0]