input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function should load the data, initialize and fit a classifier, and test the classifier with given test data. If the test data is classified as apples (class 0) and bananas (class 1), the function should print the predictions.
  
  import numpy as np
  import matplotlib.plot as plt
  from sklearn.svm import SVC
  
  def apple_banana_classifier():
      # Load data
      X = np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]])
      y = np.array([0, 0, 0, 1, 0, 1])
  
      # Initialize classifier
      clf = SVC(kernel='linear')
  
      # Fit data
      clf.fit(X, y)
  
      # Test classifier
      x_test = np.array([[2,2,2], [4,4,4]])
  
      prediction = clf.predict(x_test)
      print(prediction)
  
  assert apple_banana_classifier() == [0, 0]
  assert apple_banana_classifier() == [0, 1]
output: def apple_banana_classifier():
      X = np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]])
      y = np.array([0, 0, 0, 1, 0, 1])
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      x_test = np.array([[2,2,2], [4,4,4]])
      prediction = clf.predict(x_test)
      print(prediction)