input: def categorize_dataset(dataset: list) -> str:
      """ Create a machine learning algorithm in Python to categorize a dataset of records.
      The dataset is a list of dictionaries where each dictionary has a 'label' and a 'features' key.
      The 'features' key maps to a list of numerical features and the 'label' key maps to the category.
      The function trains a Support Vector Classifier (SVC) model on the given dataset and predicts
      the label for a new sample.
      
      >>> 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]}, 
      ... ]
      >>> categorize_dataset(dataset)
      'banana'
      """
      import numpy as np
      from sklearn.svm import SVC 
  
      X = []
      y = []
      for data in dataset:
          X.append(data['features'])
          y.append(data['label'])
  
      clf = SVC()
      clf.fit(X, y) 
  
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]
output:     import numpy as np
      from sklearn.svm import SVC 
  
      X = []
      y = []
      for data in dataset:
          X.append(data['features'])
          y.append(data['label'])
  
      clf = SVC()
      clf.fit(X, y) 
  
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]