input: Write a function to develop a machine learning algorithm in Python using the Support Vector Classifier (SVC) to categorize a given dataset of records based on their features. The function should take a list of dictionaries where each dictionary contains a 'label' and 'features' key. The features are used to train the SVC model, and the function should return the predicted label for a given set of features.
  
  
  import numpy as np
  from sklearn.svm import SVC
  
  def categorize_dataset(training_data, predict_data):
      # Feature extraction
      X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['label'])
      
      # Model
      clf = SVC()
      clf.fit(X, y)
      
      # Prediction
      predicted_label = clf.predict([predict_data])
      
      return predicted_label[0]
  
  # 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]}, 
      {"label": "lemon", "features": [0, 0, 1]}, 
      {"label": "orange", "features": [1, 0, 0]}, 
      {"label": "pear", "features": [0, 1, 0]}, 
  ], [0, 0, 1]) == "lemon"
  
  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, 1, 0]) == "apple"
  
  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]}, 
  ], [0, 1, 0]) == "pear"
  
  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, 0]) == "orange"
  
output: def categorize_dataset(training_data, predict_data):
      X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['label'])
      
      clf = SVC()
      clf.fit(X, y)
      
      predicted_label = clf.predict([predict_data])
      
      return predicted_label[0]