input: 
  def categorize_records(data: list, predict_features: list) -> str:
      """
      Create a machine learning model using Support Vector Classification (SVC) to 
      categorize records based on their features. This function trains the model 
      on a given dataset and then predicts the category of a new set of features.
      
      >>> 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])
      'banana'
      """
      from sklearn.svm import SVC 
      
      X = []
      y = []
      for record in data:
          X.append(record['features'])
          y.append(record['label'])
      
      clf = SVC()
      clf.fit(X, y) 
      
      predicted_label = clf.predict([predict_features])
      return predicted_label[0]
  
output:     from sklearn.svm import SVC 
      
      X = []
      y = []
      for record in data:
          X.append(record['features'])
          y.append(record['label'])
      
      clf = SVC()
      clf.fit(X, y) 
      
      predicted_label = clf.predict([predict_features])
      return predicted_label[0]