input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function should load the given data, initialize a classifier, fit the data, and then test the classifier with new data points. The function should return the predictions made by the classifier.
  
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.svm import SVC
  
  def identify_apples_and_bananas():
      # 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)
      return prediction
  
  # Test cases
  assert np.array_equal(identify_apples_and_bananas(), np.array([0, 1]))
  
output: def identify_apples_and_bananas():
      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)
      return prediction