input: Write a function to create a machine learning algorithm in Python that categorizes a dataset of records based on their features. The function should use the Support Vector Classifier (SVC) from the sklearn library. The dataset is represented as a list of dictionaries where each dictionary has a 'label' and a 'features' list. The function should train the model on the provided dataset and then predict the label for a given input feature vector. If the model is trained successfully and a prediction is made, the function should return the predicted label as a string. If the dataset is empty or any other error occurs, the function should return None.
  
  
  from sklearn.svm import SVC 
  
  def categorize_dataset(dataset, predict_data):
      if not dataset:
          return None
  
      X = []
      y = []
      for data in dataset:
          X.append(data['features'])
          y.append(data['label'])
  
      try:
          clf = SVC()
          clf.fit(X, y) 
          predicted_label = clf.predict([predict_data])
          return predicted_label[0]
      except:
          return None
  
  # Test cases
  assert categorize_dataset([
    {"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_dataset([
    {"label": "apple", "features": [1, 1, 0]}, 
    {"label": "banana", "features": [0, 1, 1]}
  ], [0, 0, 1]) == None
  
  assert categorize_dataset([], [1, 0, 1]) == None
  assert categorize_dataset([{"label": "apple", "features": [1, 1, 0]}], [1, 0, 0]) == "apple"
  
output: from sklearn.svm import SVC 
  
  def categorize_dataset(dataset, predict_data):
      if not dataset:
          return None
  
      X = []
      y = []
      for data in dataset:
          X.append(data['features'])
          y.append(data['label'])
  
      try:
          clf = SVC()
          clf.fit(X, y) 
          predicted_label = clf.predict([predict_data])
          return predicted_label[0]
      except:
          return None