input: Write a function that creates a machine learning model using the Support Vector Classifier (SVC) from sklearn to categorize a dataset of records. The function should take a list of dictionaries, where each dictionary contains a 'label' and 'features' list as input, and train a model on this dataset. Then, given a new feature list, it should return the predicted label. 
  
  def create_and_predict_ml_model(training_data, predict_data):
      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]
  
  assert create_and_predict_ml_model(
      [
          {"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"
output: def create_and_predict_ml_model(training_data, predict_data):
      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]