[{'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:import numpy as np # linear algebra<br>import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)<br>import math<br>import IPython.display as ipd<br>import keras<br>from keras.models import *<br>from keras.callbacks import *<br>from keras.layers import *<br>from keras.preprocessing.image import random_brightness,random_rotation,random_shear,random_shift,random_zoom<br>import sklearn<br>from sklearn.preprocessing import LabelEncoder<br>from sklearn.metrics import classification_report<br>from sklearn.metrics import confusion_matrix<br>from sklearn.metrics import log_loss<br>from sklearn.model_selection import train_test_split<br>import skimage<br>import skimage.transform<br>import skimage.color<br>import imageio<br>import matplotlib.pyplot as plt<br>import seaborn as sns<br>import os<br><br>TRAIN_DIR = "../input/train/"<br>TEST_DIR = "../input/test/"<br><br>train = pd.DataFrame()<br>train[\'file\'] = os.listdir(TRAIN_DIR)<br>train[\'class\'] = train[\'file\'].apply(lambda x: x.split(\'.\')[0])<br>train[\'class_id\'] = train[\'class\'].apply(lambda x: 0 if x==\'cat\' else 1)<br>test = pd.DataFrame()<br>test[\'file\'] = os.listdir(TEST_DIR)<br>test[\'id\'] = test[\'file\'].apply(lambda x: x.split(\'.\')[0])<br>test[\'label\'] = 0.5<br><br>train.head()'}, {'role': 'assistant', 'content': 'helper_functions'}]