input: Write a function to implement a machine learning algorithm in Python that categorizes a dataset of records. The function should use the features of each record to train a model and then predict the label for a given set of features.
  
  
  def categorize_dataset(training_data, predict_data):
      import numpy as np
      from sklearn.svm import SVC
  
      # 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]}
  ], [1, 0, 0]) == "orange"
  
output: def categorize_dataset(training_data, predict_data):
      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) 
  
      predicted_label = clf.predict([predict_data])
      return predicted_label[0]