[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# copy_df = df.copy()\n# copy_test_df = test_df.copy()\n# numeric_features = df.dtypes[df.dtypes != "object"].index\n# skewed_features = df[numeric_features].apply(lambda x: skew(x))\n# skewed_features = skewed_features[skewed_features > 0.5].index\n# df[skewed_features] = np.log1p(df[skewed_features])\n# test_df[skewed_features.drop(\'revenue\')] = np.log1p(test_df[skewed_features.drop(\'revenue\')])\n# Above handles skewed features using log transformation\n# Below uses multiple imputation for P1-P37, since they are actually categorical\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import IterativeImputer\n\nimp_train = IterativeImputer(max_iter=30, missing_values=0, sample_posterior=True, min_value=1, random_state=37)\nimp_test = IterativeImputer(max_iter=30, missing_values=0, sample_posterior=True, min_value=1, random_state=23)\n\np_data = [\'P\'+str(i) for i in range(1,38)]\ndf[p_data] = np.round(imp_train.fit_transform(df[p_data]))\ntest_df[p_data] = np.round(imp_test.fit_transform(test_df[p_data]))\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]