input: from typing import List, Dict
  
  def categorize_dataset(training_data: List[Dict[str, any]]) -> str:
      """Create a machine learning algorithm in Python to categorize a dataset of records.
      >>> 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]}])
      'banana'
      """
output:     from sklearn.neighbors import KNeighborsClassifier
      from sklearn.preprocessing import StandardScaler
      import numpy as np
  
      labels = [item['label'] for item in training_data]
      features = [item['features'] for item in training_data]
  
      scaler = StandardScaler()
      scaled_features = scaler.fit_transform(features)
  
      knn = KNeighborsClassifier(n_neighbors=1)
      knn.fit(scaled_features, labels)
  
      new_data = np.array([[0, 1, 1]])
      scaled_new_data = scaler.transform(new_data)
  
      prediction = knn.predict(scaled_new_data)
      return prediction[0]