input: Write a function to categorize a dataset of records using a machine learning algorithm in Python. The function should take a list of dictionaries where each dictionary contains 'label' and 'features' as keys. The function should return the predicted label for a given list of features. If the input is an empty list, the function should return None.
  
  def categorize_records(training_data, predict_data):
      if not training_data:
          return None
      
      import numpy as np
      from sklearn.svm import SVC 
  
      X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['label'])
  
      clf = SVC()
      clf.fit(X, y) 
  
      if not predict_data:
          return None
      
      predicted_label = clf.predict([predict_data])
      return predicted_label[0]
  
  assert categorize_records([
      {"label": "apple", "features": [1, 1, 0]}, 
      {"label": "banana", "features": [0, 1, 1]}, 
      {"label": "lemon", "features": [0, 0, 1]}, 
      {"label": "orange", "features": [1, 0, 0]}, 
      {"label": "pear", "features": [0, 1, 0]}, 
  ], [1, 0, 1]) == "banana"
  
  assert categorize_records([], [1, 0, 1]) == None
  
  assert categorize_records([
      {"label": "apple", "features": [1, 1, 0]}, 
      {"label": "banana", "features": [0, 1, 1]}, 
      {"label": "lemon", "features": [0, 0, 1]}, 
      {"label": "orange", "features": [1, 0, 0]}, 
      {"label": "pear", "features": [0, 1, 0]}, 
  ], []) == None
output: def categorize_records(training_data, predict_data):
      if not training_data:
          return None
      
      import numpy as np
      from sklearn.svm import SVC 
  
      X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['label'])
  
      clf = SVC()
      clf.fit(X, y) 
  
      if not predict_data:
          return None
      
      predicted_label = clf.predict([predict_data])
      return predicted_label[0]