[{'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:from sklearn.cluster import KMeans\nseuclid = []\nscosine = []\nk = range(2,10)\nfor i in k:\nkmeans_model = KMeans(n_clusters=i, init="k-means++").fit(data)\nlabels = kmeans_model.labels_\nseuclid.append(metrics.silhouette_score(data, labels, metric=\'euclidean\'))\nscosine.append(metrics.silhouette_score(data, labels, metric=\'cosine\'))\nplt.figure(figsize=(10,5))\nplt.plot(k,seuclid,label=\'euclidean\')\nplt.plot(k,scosine,label=\'cosine\')\nplt.ylabel("Silhouette")\nplt.xlabel("Cluster")\nplt.title("Silhouette vs Cluster Size")\nplt.legend()\nplt.show()'}, {'role': 'assistant', 'content': "['helper_functions', 'data_exploration', 'modelling']"}]