[{'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:# -*- coding: utf-8 -*-\n"""\nCincia de Dados e Visualizao com Python\nExemplo: Forest Cover Type\nURL (problema): https://www.kaggle.com/c/forest-cover-type-prediction\nURL (soluo): https://www.kaggle.com/ivarvb/forest-cover-type\nAutor: Ivar Vargas Belizario\nE-mail: ivar@usp.br\n"""\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom sklearn.model_selection import KFold\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import ExtraTreesClassifier\n\n\nfrom sklearn.metrics import accuracy_score\n\nfrom sklearn.manifold import TSNE\nfrom matplotlib import pyplot as plt\n\n"""\n===================================================\nI. Cincia de dados\n===================================================\n\n===================================================\n1. Leitura dos datos para o treino e para o teste\n===================================================\n"""\n\ntrain = pd.read_csv(\'../input/train.csv\')\ntest = pd.read_csv(\'../input/test.csv\')\n\n"""\n===================================================\n2. Pre-processamento\n===================================================\n\n===================================================\n2.1 Limpeza e amostragem\n===================================================\n"""\n\ntrain = train.fillna(0)\ntest = test.fillna(0)\n\n# definir as colunas da etiqueta da classe (target) e do identificador (id)\ncolumn_target = \'Cover_Type\'\ncolumn_id = \'Id\'\n\n# limpar os atributos que apresentam valores nulls\ndata = train.dropna(axis=\'columns\')\n\n# nmero de instancias antes da amostragem\nprint ("Total data: ",len(data))\n\n# separao dos atributos: identificador da instancia (id)\n# dos atributos data (X) e do atributo que contem a etiqueta da classe (y)\nX = data\ny = data[column_target]\n\n# porcentagem para a amostragem\nc_sample = 0.99\n\n# amostragem\nif c_sample < 1.0:\n    X_null, X, y_null, y = train_test_split(X, y, test_size=c_sample, random_state=0)\n\nID = X[column_id]\ny = X[column_target]\nX = X.drop([column_id, column_target], axis=1).select_dtypes(include=[np.number])\n\ntrain_select_atributes = X.columns\n\nprint ("Amostragem: ",len(X))\n\n\n"""\n===================================================\n2. Processamento\n===================================================\n\n===================================================\n2.1 Reduo da dimensionalidade (feature selection)\n===================================================\n"""\n\n"""\nmodel = ExtraTreesClassifier()\nmodel.fit(X, y)\nimp = model.feature_importances_\nnames = []\nfor i in range(len(imp)):\n    r = []\n    r.append(i)\n    r.append(imp[i])\n    names.append(r)\n\nnames = sorted(names, key=lambda x: x[1], reverse=True)\nfenames = []\ncolumns = list(set(train_select_atributes))\nfor i in range(len(names)):\n    fenames.append(columns[names[i][0]])\n\ntrain_select_atributes = fenames[:30]\n\nX = X[train_select_atributes].values\ny = y.values\n"""\n\n# convertir para arrays\nX = X.values\ny = y.values\n\n"""\n===================================================\n3. Modelo de aprendisagem (aprendisagem supervisionado):\n===================================================\n\n===================================================\n3.1. Treinamento:\n===================================================\n"""\n\n# definir o modelo para a classificao\nmodel = RandomForestClassifier(random_state=0, n_estimators=500)\n\n# modelo de treinamento com k-fold (10-fold)\nkf = StratifiedKFold(n_splits=10)\noutcomes = []\n\n# para cada fold\nfor train_index, test_index in kf.split(X, y):\n    Xtrain, Xtest = X[train_index], X[test_index]\n    ytrain, ytest = y[train_index], y[test_index]\n    model.fit(Xtrain, ytrain)\n    expected = ytest\n    predictions = model.predict(Xtest)\n\n    accuracy = accuracy_score(ytest, predictions)\n    outcomes.append(accuracy)\n\n# imprimir a media da acuracia obtida no treinamento\nmean_outcome = np.array(outcomes).mean()\n\nprint ("Mean Accuracy:", mean_outcome)\n\n"""\n===================================================\n3.2. Teste:\n===================================================\n"""\n\n# seleco de atributos igual ao feito com o conjunto de treino\nX_test = test[train_select_atributes]\nx_test_id = test[column_id]\npredictions = model.predict(X_test)\n\npredictions = pd.DataFrame(predictions, columns = ["Cover_Type"])\n\n# salvar resultados obtidos do conjunto de dados de teste\nresult = pd.concat([x_test_id, predictions], axis=1, sort=False)\nresult.to_csv("result.csv", mode = \'w\', index=False)\n\n"""\n===================================================\nII. Visualizao do conjunto de dados (projees)\n===================================================\n"""\n\n#print (y)\nisfineClass = False\nfor i in range(len(y)):\n    if y[i]==0:\n        isfineClass=True\n        break;\n\nif isfineClass==False:\n    for i in range(len(y)):\n        v = y[i]\n        y[i] = v-1\n        \n# amostragem para a visualizao\nc_sample = 0.1\n\nif c_sample < 1.0:\n    X_null, X, y_null, y = train_test_split(X, y, test_size=c_sample, random_state=0)\n    \nprint ("Amostragem para a visualizao: ", len(X))\n\n# visualizao por projees t-SNE\ntsne = TSNE(n_components=2, random_state=0)\nX_2d = tsne.fit_transform(X)\n\nplt.figure(figsize=(6, 5))\ncolors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf"]\n\nfor i in range(len(y)):\n    v = y[i]\n    plt.plot(X_2d[i, 0], X_2d[i, 1], \'o\', color=colors[v], alpha=0.3)\n# visualiar a projeo\nplt.show()\n\''}, {'role': 'assistant', 'content': "['helper_functions']"}]