[{'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:# We will visualize all the attributes using Violin Plot - a combination of box and density plots\n\n#names of all the attributes \ncols = dataset.columns\n\n#number of attributes (exclude target)\nsize = len(cols)-1\n\n#x-axis has target attribute to distinguish between classes\nx = cols[size]\n\n#y-axis shows values of an attribute\ny = cols[0:size]\n\n#Plot violin for all attributes\nfor i in range(0,size):\n    sns.violinplot(data=dataset,x=x,y=y[i])  \n    plt.show()\n\n#Elevation is has a separate distribution for most classes. Highly correlated with the target and hence an important attribute\n#Aspect contains a couple of normal distribution for several classes\n#Horizontal distance to road and hydrology have similar distribution\n#Hillshade 9am and 12pm display left skew\n#Hillshade 3pm is normal\n#Lots of 0s in vertical distance to hydrology\n#Wilderness_Area3 gives no class distinction. As values are not present, others gives some scope to distinguish\n#Soil_Type, 1,5,8,9,12,14,18-22, 25-30 and 35-40 offer class distinction as values are not present for many classes'}, {'role': 'assistant', 'content': 'result_visualization'}]