input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas using support vector machines (SVM) with a linear kernel. The function should load the provided data, initialize the classifier, fit the data, and predict the class for the given test data. Finally, the function should print the predictions for the test data.
  
  def identify_fruits(X, y, x_test):
      import numpy as np
      from sklearn.svm import SVC
      
      # Initialize classifier
      clf = SVC(kernel='linear')
      
      # Fit data
      clf.fit(X, y)
      
      # Test classifier
      prediction = clf.predict(x_test)
      print(prediction)
  
  # Test cases
  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])
  x_test = np.array([[2,2,2], [4,4,4]])
  
  identify_fruits(X, y, x_test)
output: def identify_fruits(X, y, x_test):
      import numpy as np
      from sklearn.svm import SVC
      
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      prediction = clf.predict(x_test)
      print(prediction)