input: Write a function to develop a machine learning algorithm in Python using a Support Vector Machine (SVM) to classify apples and bananas based on given data. The function should take no input and return the predictions for a test dataset.
  
  
  def classify_apples_from_bananas():
      import numpy as np
      from sklearn.svm import SVC
  
      # 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
  
  assert np.array_equal(classify_apples_from_bananas(), np.array([0, 1]))
  
output: def classify_apples_from_bananas():
      import numpy as np
      from sklearn.svm import SVC
  
      # 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