input: from typing import List, Dict
  import numpy as np
  from sklearn.svm import SVC
  
  def categorize_data(training_data: List[Dict[str, any]], predict_data: List[float]) -> str:
      """Create a machine learning model to categorize a dataset of records. Given a list of training data and a set of features for prediction, this function uses an SVM classifier to predict the category of the input features.
      >>> categorize_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]}], [1, 0, 1])
      '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]