input: def categorize_dataset(training_data: list, predict_data: list) -> str:
      """
      Create a machine learning algorithm to categorize a dataset of records. 
      The function will use a Support Vector Classifier to train on the dataset 
      and predict the label for the given predict_data.
  
      >>> 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'
      """
      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]
output:     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]