[{'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:merge.loc[merge[\'latitude\'] < 1, \'latitude\'] = merge[\'latitude\'].mode()[0]<br>merge.loc[merge[\'longitude\']>-1, \'longitude\'] = merge[\'longitude\'].mode()[0]<br>scaler = sklearn.preprocessing.MinMaxScaler(feature_range=(0,1))<br><br>merge[\'latitude\'] = scaler.fit_transform(np.array(merge[\'latitude\']).reshape(-1,1))<br>scaler = sklearn.preprocessing.MinMaxScaler(feature_range=(0,1))<br>merge[\'longitude\'] = scaler.fit_transform(np.array(merge[\'longitude\']).reshape(-1,1))<br><br>merge["pos"] = merge.longitude.round(3).astype(str) + \'_\' + merge.latitude.round(3).astype(str)<br>pos_vc = merge[\'pos\'].value_counts()<br>d_pos_vc = pos_vc.to_dict()<br>merge[\'density\'] = merge["pos"].apply(lambda x: d_pos_vc.get(x, pos_vc.min()))<br><br>features_to_use = np.concatenate([features_to_use, [\'latitude\', \'longitude\', \'density\']])<br>features_to_use = np.unique(features_to_use)<br>features_to_use'}, {'role': 'assistant', 'content': 'data_preprocessing'}]