input: Write a python function to classify emails as either spam or ham using a machine learning model with Scikit-Learn.
  assert classify_email("Free money now!!!") == "spam"
  assert classify_email("Meeting at 10 AM tomorrow") == "ham"
  assert classify_email("Congratulations, you've won a prize!") == "spam"
output: def classify_email(email_text):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample training data
      emails = [
          "Free money now!!!",
          "Congratulations, you've won a prize!",
          "Meeting at 10 AM tomorrow",
          "Let's schedule a meeting",
          "Earn $1000 per day",
          "Hello, how are you?",
          "Important project discussion"
      ]
  
      labels = ["spam", "spam", "ham", "ham", "spam", "ham", "ham"]
  
      # Vectorize the email text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(emails)
  
      # Train a Multinomial Naive Bayes classifier
      model = MultinomialNB()
      model.fit(X, labels)
  
      # Vectorize the input email
      email_vector = vectorizer.transform([email_text])
  
      # Predict the label
      prediction = model.predict(email_vector)
  
      return prediction[0]