input: Write a function to create a machine learning algorithm in Python that categorizes a given dataset of records using features and labels. The function should train a Support Vector Classifier (SVC) model and return the predicted label for a new set of features. If the dataset is empty, the function should return an empty string.
  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]) == "orange"
  assert categorize_records([], [1, 0, 1]) == ""
  assert categorize_records([{"label": "apple", "features": [1, 1, 0]}], [0, 1, 0]) == "apple"
  assert categorize_records([
    {"label": "apple", "features": [1, 1, 0]}, 
    {"label": "banana", "features": [0, 1, 1]}
  ], [0, 0, 1]) == "banana"
output: 
  def categorize_records(dataset, new_features):
      if not dataset:
          return ""
      features = [entry["features"] for entry in dataset]
      labels = [entry["label"] for entry in dataset]
      model = SVC()
      model.fit(features, labels)
      predicted_label = model.predict([new_features])[0]
      return predicted_label
  