input: from typing import List, Dict
  from sklearn.svm import SVC
  
  def categorize_dataset(training_data: List[Dict[str, str or List[int]]], predict_data: List[int]) -> str:
      """
      Create a machine learning algorithm to categorize a dataset of records using an SVM classifier.
      The function takes training data where each record has a label and corresponding features, and a list of prediction data.
      It returns the predicted label for the given prediction data.
  
      >>> training_data = [
      ...     {"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]}, 
      ... ]
      >>> predict_data = [1, 0, 1]
      >>> categorize_dataset(training_data, predict_data)
      'banana'
      """
      
      X = [data['features'] for data in training_data]
      y = [data['label'] for data in training_data]
  
      clf = SVC()
      clf.fit(X, y)
  
      predicted_label = clf.predict([predict_data])
      return predicted_label[0]
output:     X = [data['features'] for data in training_data]
      y = [data['label'] for data in training_data]
  
      clf = SVC()
      clf.fit(X, y)
  
      predicted_label = clf.predict([predict_data])
      return predicted_label[0]