[{'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:#Gender classification using universal data set\n\n#import libaries\nimport numpy as np\nimport pandas as pd\n#ML Packages\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction import DictVectorizer\n#load teh data set\ndata_set = pd.read_csv("../input/names-dataset/names_dataset.csv")\nxfeatures  = data_set["name"]\n\n#feature extraction\ncv = CountVectorizer()\nX = cv.fit_transform(xfeatures)\n\n\ndata_set.sex.replace({\'F\':0,\'M\':1},inplace=True)\n\n#features\nX = X\n#label\ndata_set.drop_duplicates(keep="first", inplace=True)\ny  =data_set.sex\nfrom collections import  Counter\nprint("ty",Counter(y))\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n\n\n# Naive Bayes Classifier\nfrom sklearn.naive_bayes import MultinomialNB\nclf = MultinomialNB()\nclf.fit(X, y)\n\ndef Predict(data):\n    test_name = [data]\n    vector = cv.transform(test_name).toarray()\n    result = clf.predict(vector)[0]\n    return result\n\n\''}, {'role': 'assistant', 'content': "['modelling']"}]