input: 
  def categorize_data(dataset):
      """
      Create a machine learning algorithm using SVC to categorize a dataset of records.
      The dataset contains the label and features for each record.
      The algorithm will be used to predict the label for a new set of features.
  
      >>> 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]}, 
      ... ]
      >>> categorize_data(dataset)
      'banana'
      """
      import numpy as np
      from sklearn.svm import SVC 
  
      # Feature extraction 
      X = []
      y = []
      for data in dataset:
          X.append(data['features'])
          y.append(data['label'])
  
      # Model 
      clf = SVC()
      clf.fit(X, y) 
  
      # Prediction 
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]
  
output:     import numpy as np
      from sklearn.svm import SVC 
  
      # Feature extraction 
      X = []
      y = []
      for data in dataset:
          X.append(data['features'])
          y.append(data['label'])
  
      # Model 
      clf = SVC()
      clf.fit(X, y) 
  
      # Prediction 
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]