input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas using the given dataset. The function should load the data, initialize an SVM classifier with a linear kernel, fit the data, make predictions on new data, and return the predictions. If the given array is empty then return an empty list.
  
  def identify_fruits(X, y, x_test):
      if len(X) == 0 or len(y) == 0:
          return []
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      predictions = clf.predict(x_test)
      return predictions
  
  assert identify_fruits(np.array([[1,2,3], [-1,2,5], [-1,2,7], [5,5,5], [3,3,3], [5,5,7]]), np.array([0, 0, 0, 1, 0, 1]), np.array([[2,2,2], [4,4,4]])) == [0, 1]
  assert identify_fruits(np.array([]), np.array([]), np.array([])) == []
output: def identify_fruits(X, y, x_test):
      if len(X) == 0 or len(y) == 0:
          return []
      clf = SVC(kernel='linear')
      clf.fit(X, y)
      predictions = clf.predict(x_test)
      return predictions