[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\nle_count = 0\n\n# Iterate through the columns\n# for col in application_full_data:\nfor i in range(len(nominal_list)):    \n    \n#     if application_full_data[col].dtype == 'object':\n        # If 2 or fewer unique categories\n        if len(list(full_data[nominal_list[i]].unique())) <= 2:\n            # full_data on the full_dataing data\n            le.fit(full_data[nominal_list[i]])\n            # Transform both full_dataing and testing data\n            full_data[nominal_list[i]] = le.transform(full_data[nominal_list[i]])\n            \n            # Keep track of how many columns were label encoded\n            le_count += 1\n            \nprint('%d columns were label encoded.' % le_count)"}, {'role': 'assistant', 'content': 'data_preprocessing'}]