[{'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:# There are two few reasons to use mean (aka target) encoding\n# We have 1115 stores, definetly there is correlation between store and sales\n# We could perform leave stores as it is ----> not good for known reasons\n# We could perform OneHotEncoding        ----> not goodm becaouse we will have 1115 new columns, mainly sparse\n# We can do mean encoding, eg encode stores as mean/std/other of target\n# I am going to use Customers to encode store, because we don`t have customers in test set\n# Obviusly customers can be good feature\n\ndef mean_encoding(df,column,target,func = np.mean):\n    \n    # perform target encoding on column with some function\n    \n    enc_col_name = target+'_enc_'+func.__name__\n    df_temp = (df\n               .groupby(column)[target]\n               .apply(func)\n               .reset_index()\n               .rename({target:enc_col_name},axis=1)\n              )\n    \n    df = df.merge(df_temp,how='left',on = column)\n        \n    \n    return df,df_temp\n\ndata_prep,dict_for_test = mean_encoding(data_prep,'Store','Customers',func = np.mean) "}, {'role': 'assistant', 'content': 'data_preprocessing'}]