input: Write a function to develop a machine learning algorithm in Python to identify apples from bananas. The function should return a list of predictions for a given test set. If the input test set is empty, the function should return an empty list.
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.svm import SVC
  
  def identify_fruits(X_train, y_train, X_test):
      # Initialize classifier
      clf = SVC(kernel='linear')
      
      # Fit data
      clf.fit(X_train, y_train)
      
      # If X_test is empty, return an empty list
      if len(X_test) == 0:
          return []
      
      # Predict and return the predictions
      return clf.predict(X_test).tolist()
  
  # Test cases
  assert identify_fruits(np.array([[1,2,3], [-1,2,5]]), np.array([0, 0]), np.array([[2,2,2]])) == [0]
  assert identify_fruits(np.array([[1,2,3], [5,5,5]]), np.array([0, 1]), np.array([[4,4,4]])) == [1]
  assert identify_fruits(np.array([[1,2,3], [-1,2,5]]), np.array([0, 0]), np.array([])) == []
  assert identify_fruits(np.array([[1,2,3], [5,5,5]]), np.array([0, 1]), np.array([[2,2,2], [4,4,4]])) == [0, 1]
  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]
output: def identify_fruits(X_train, y_train, X_test):
      clf = SVC(kernel='linear')
      clf.fit(X_train, y_train)
      if len(X_test) == 0:
          return []
      return clf.predict(X_test).tolist()