input: Write a python function to classify sentences as 'conversation' or 'not conversation' using a machine learning model.
  assert classify_sentence("How are you?") == 'conversation'
  assert classify_sentence("The stock market is volatile today.") == 'not conversation'
  assert classify_sentence("What's the time now?") == 'conversation'
output: def classify_sentence(sentence):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.svm import SVC
      import numpy as np
  
      # Sample training data
      sentences = ["How are you?", "What's the time now?", "The weather is nice today.", "Good morning!", "The stock market is volatile today.", "I will have a meeting at 3 PM."]
      labels = ['conversation', 'conversation', 'not conversation', 'conversation', 'not conversation', 'not conversation']
  
      # Vectorize the sentences
      vectorizer = TfidfVectorizer()
      X_train = vectorizer.fit_transform(sentences)
  
      # Train a simple classifier
      classifier = SVC(kernel='linear')
      classifier.fit(X_train, labels)
  
      # Vectorize the input sentence
      X_input = vectorizer.transform([sentence])
  
      # Predict the label of the input sentence
      return classifier.predict(X_input)[0]