Rankeamento_Modelo_FIIS.ipynb

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  "metadata": {
    "colab": {
      "name": "Rankeamento_Modelo_FIIS.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMQdhIWlsMj6Qx9gXhWov4f",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TailUFPB/fundos-imobiliarios/blob/main/Rankeamento_Modelo_FIIS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kX-eGrTqu4A9"
      },
      "source": [
        "# Rankeamento de fundos imobiliários e modelo de classificação\n",
        "\n",
        "Análise produzida pelo setor de Dados da Technology and Artificial Intelligence League (TAIL)\n",
        "\n",
        "Autores: André Hugo, Daniel Cabral, Epitácio Neto, Igor Simões, Joás Filho\n",
        "\n",
        "Líder: Humberto Navarro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukvb0wL9qgHA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a265ead3-dc12-41eb-bbe3-248b56165015"
      },
      "source": [
        "#Imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "!pip install pycaret\n",
        "from pycaret.classification import *\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import preprocessing\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
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            "Installing collected packages: threadpoolctl, tangled-up-in-unicode, smmap, scipy, multimethod, websocket-client, visions, tqdm, scikit-learn, requests, python-editor, Mako, imagehash, gitdb, querystring-parser, PyYAML, pynndescent, pydantic, prometheus-flask-exporter, phik, htmlmin, gunicorn, gitpython, funcy, docker, databricks-cli, alembic, yellowbrick, umap-learn, scikit-plot, pyod, pyLDAvis, pandas-profiling, mlxtend, mlflow, lightgbm, kmodes, imbalanced-learn, Boruta, pycaret\n",
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            "    Found existing installation: scipy 1.4.1\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed Boruta-0.3 Mako-1.1.4 PyYAML-5.4.1 alembic-1.4.1 databricks-cli-0.14.3 docker-5.0.0 funcy-1.16 gitdb-4.0.7 gitpython-3.1.18 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.2.1 imbalanced-learn-0.7.0 kmodes-0.11.0 lightgbm-3.2.1 mlflow-1.19.0 mlxtend-0.18.0 multimethod-1.4 pandas-profiling-3.0.0 phik-0.12.0 prometheus-flask-exporter-0.18.2 pyLDAvis-3.2.2 pycaret-2.3.3 pydantic-1.8.2 pynndescent-0.5.4 pyod-0.9.0 python-editor-1.0.4 querystring-parser-1.2.4 requests-2.26.0 scikit-learn-0.23.2 scikit-plot-0.3.7 scipy-1.5.4 smmap-4.0.0 tangled-up-in-unicode-0.1.0 threadpoolctl-2.2.0 tqdm-4.61.2 umap-learn-0.5.1 visions-0.7.1 websocket-client-1.1.0 yellowbrick-1.3.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aIy20cepmFM8"
      },
      "source": [
        "## Rankeamento dos fundos:\n",
        "\n",
        "O objetivo da primeira parte desse notebook é rankear os fundos imobiliários no Brasil, a fim de descobrir quais são os melhores.\n",
        "\n",
        "Para fazermos o rankeamento dos fundos, a fim de descobrir quais são os melhores no mercado brasileiro, escolhemos a estratégia de atribuir uma nota para cada um deles, que levará em consideração tanto os atributos atuais de cada um, como sua performance ao longo dos anos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vPEPxiMOm81X"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Primeiramente, vamos pegar o CSV com atributos atuais dos fundos, que ficará na variável df, e os CSVs com atributos históricos, que ficarão na lista dfs_old:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYc8tWGCrFd0"
      },
      "source": [
        "#Recebendo o CSV dos fundos\n",
        "\n",
        "url = 'https://raw.githubusercontent.com/TailUFPB/fundos-imobiliarios/main/CSV_Modelo.csv'\n",
        "data = pd.read_csv(url)\n",
        "df = pd.DataFrame(data)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6wHJK_8z7MZN"
      },
      "source": [
        "datas = ['202012', '202009', '202003', '201902', '201812', '201809', '201806', '201803', '201712']\n",
        "dfs_old = []\n",
        "\n",
        "for i in datas:\n",
        "  url = 'https://raw.githubusercontent.com/TailUFPB/fundos-imobiliarios/main/CSV_Antigos_PreProc/' + i + '.csv'\n",
        "  data = pd.read_csv(url)\n",
        "  dfs_old.append(pd.DataFrame(data))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1UnHj2AytiE"
      },
      "source": [
        "pd.set_option(\"display.max_rows\", None)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xzzil78naRW"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Agora, vamos definir as funções que iremos utilizar para obter a nota de cada fundo:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrLrxY_jI-cy"
      },
      "source": [
        "Na função *NotaInicial*, iremos usar alguns atributos do fundo para dar uma nota inicial a ele, e faremos isso subtraindo o valor de alguns campos de cada fundo com a média de todos no dataset, e multiplicando o valor por um peso dado a cada atributo baseado na sua importância para a avaliação do fundo, ou seja, se o peso do campo for positivo, quanto maior for o valor do atributo, melhor nota obterá, e caso seja negativo, o contrário. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efW3AH3Pr5jB"
      },
      "source": [
        "#Dando notas para colunas que não tem delimitador definido (Quanto mais alto ou mais baixo, melhor)\n",
        "def NotaInicial(df):\n",
        "  pesos = {'Variação Preço': 0.6, 'Rentab.Período': 0.3, 'PatrimônioLíq.': 0.15,'Liquidez Diária': 0.7,'Rentab.Acumulada': 0.75, 'VPA': 0.25,'QuantidadeAtivos': 0.45, 'VacânciaFísica': -0.65}\n",
        "  colunas = ['Liquidez Diária' , 'Variação Preço','Rentab.Período','Rentab.Acumulada', 'PatrimônioLíq.', 'VPA','QuantidadeAtivos', 'VacânciaFísica']\n",
        "\n",
        "  i = 0\n",
        "  notas = []\n",
        "\n",
        "  while i < df.shape[0]:\n",
        "\n",
        "    nota_row = 0\n",
        "    for j in colunas:\n",
        "      if j in df.columns:\n",
        "        nota_row += pesos[j] * (df.loc[i, j] - df[j].mean())\n",
        "    \n",
        "    notas.append(nota_row)\n",
        "\n",
        "    i += 1\n",
        "\n",
        "  df['Notas'] = notas\n",
        "\n",
        "  return df"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nwspWbKTKEaZ"
      },
      "source": [
        "A função *PVPA* funciona de forma parecida, porém ao invés de comparar o atributo com a média do dataset, compara com um valor ideal, multiplica pelo peso e se subtrai da nota total, ou seja, quanto mais perto do valor ideal, melhor para o fundo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GsGiI_KQMVwP"
      },
      "source": [
        "#Dando notas para colunas que tem delimitador definido (Quanto mais perto do valor ideal, melhor)\n",
        "def PVPA(df):\n",
        "  pesos2 = {'P/VPA': 0.7}\n",
        "  ideal_value = {'P/VPA': 0.67346938}\n",
        "  colunas = ['P/VPA']\n",
        "\n",
        "  for i in range(df.shape[0]):\n",
        "\n",
        "    for j in colunas:\n",
        "      df.loc[i, 'Notas'] -= pesos2[j] * abs(df.loc[i, j] - ideal_value[j])\n",
        "\n",
        "  return df"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSui8JqdKPIk"
      },
      "source": [
        "Para obter a nota do fundo no momento atual dele, a última função que vamos usar é a *DividendAnalysis*, que dividirá o dataset em quartis baseados na sua performance nos campos relacionados a Dividend Yield, e multiplicando o valor dado a cada quartil por um peso de cada atributo, (de quanto mais tempo for a média do yield, maior o peso) será obtido um resultado, que será somado à nota do campo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7io6yirFh7U"
      },
      "source": [
        "def DividendAnalysis(df):\n",
        "  d = df[\"DividendYield\"].describe()\n",
        "  d_3 = df[\"DY (3M)Média\"].describe()\n",
        "  d_6 = df[\"DY (6M)Média\"].describe()\n",
        "  d_12 = df[\"DY (12M)Média\"].describe()\n",
        "\n",
        "  for column in [\"DividendYield\", \"DY (3M)Média\", \"DY (6M)Média\", \"DY (12M)Média\"]:\n",
        "\n",
        "    if column == \"DividendYield\":\n",
        "      x = 1/22\n",
        "    elif column == \"DY (3M)Média\":\n",
        "      x = 3/22\n",
        "    elif column == \"DY (6M)Média\":\n",
        "      x = 6/22\n",
        "    else:\n",
        "      x = 12/22\n",
        "\n",
        "    for i in range(df.shape[0]):\n",
        "      if df[column].iloc[i] < df[column].describe()[\"25%\"]:\n",
        "        df.loc[i, 'Notas'] += 0*x\n",
        "      elif df[column].iloc[i] >= df[column].describe()[\"25%\"] and df[column].iloc[i] < df[column].describe()[\"50%\"]:\n",
        "        df.loc[i, 'Notas'] += 0.4*x\n",
        "      elif df[column].iloc[i] >= df[column].describe()[\"50%\"] and df[column].iloc[i] < df[column].describe()[\"75%\"]:\n",
        "        df.loc[i, 'Notas'] += 0.8*x\n",
        "      elif df[column].iloc[i] >= df[column].describe()[\"75%\"]:\n",
        "        df.loc[i, 'Notas'] += 1.25*x\n",
        "  \n",
        "  return df"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vh6hLomTLlXt"
      },
      "source": [
        "Por fim, para obtermos uma análise mais completa, vamos levar em consideração também a performance histórica de cada fundo, somando-se à nota original do fundo a que ele obteve no passado, após mutiplicá-la por um peso, que é menor quanto mais distante estiver do momento atual."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RVreFwkvXH_"
      },
      "source": [
        "def AdaptarNota(df, old_dfs):\n",
        "  pesos_data = {'202012': 1/3, '202009': 1/6, '202003': 1/12, '201912': 1/15, '201902': 1/24, '201812': 1/26, '201809': 1/29, '201806': 1/32, '201803': 1/35, '201712': 1/38}\n",
        "\n",
        "  for index, row in df.iterrows():\n",
        "    \n",
        "    df_fundo = old_dfs.loc[old_dfs['Códigodo fundo'] == df.loc[index, 'Códigodo fundo'], ['Notas', 'Ano/Mês']]\n",
        "\n",
        "    if df_fundo.empty:\n",
        "      continue\n",
        "  \n",
        "    for index_old, row_old in df_fundo.iterrows():\n",
        "      df.loc[index, 'Notas'] += df_fundo.loc[index_old, 'Notas'] * pesos_data[str(df_fundo.loc[index_old, 'Ano/Mês'])]\n",
        "\n",
        "  return df"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Xfqtmo8NiEz"
      },
      "source": [
        "Com as funções definidas, podemos começar nossa análise aplicando as três funções iniciais ao nosso dataset:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PW27MTphttkb"
      },
      "source": [
        "df = NotaInicial(df)\n",
        "df = PVPA(df)\n",
        "df = DividendAnalysis(df)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62dCr5_0Np32"
      },
      "source": [
        "E também aos datasets do passado:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "by8ySKACThbI"
      },
      "source": [
        "for i in range(len(dfs_old)):\n",
        "  dfs_old[i].drop('Unnamed: 0', axis='columns', inplace=True)\n",
        "  dfs_old[i] = NotaInicial(dfs_old[i])\n",
        "  dfs_old[i] = PVPA(dfs_old[i])\n",
        "  dfs_old[i] = DividendAnalysis(dfs_old[i])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gmE3Y5wHNuYj"
      },
      "source": [
        "Depois, concatenamos todos os datasets do passado em um só, e chamamos a função *AdaptarNota*, assim, obtendo a nota final de cada fundo imobiliário:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94JlLTGjADax"
      },
      "source": [
        "dfs_old = pd.concat(dfs_old)\n",
        "dfs_old.reset_index(drop=True, inplace=True)\n",
        "df = AdaptarNota(df, dfs_old)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wupewXNOCeR"
      },
      "source": [
        "E ficamos com o seguinte rankeamento dos fundos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlKhNneJnjUG"
      },
      "source": [
        "df_sort = df.sort_values('Notas', ascending=False)\n",
        "df_sort.reset_index(drop=True, inplace=True)\n",
        "df_sort = df_sort[['Códigodo fundo', 'Setor', 'Preço Atual', 'Notas']]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LOlrwiheBZNn",
        "outputId": "4dc9a40f-92bf-444a-8d12-8f4d742b1bd6"
      },
      "source": [
        "df_sort"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Códigodo fundo</th>\n",
              "      <th>Setor</th>\n",
              "      <th>Preço Atual</th>\n",
              "      <th>Notas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BBPO11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>114.10</td>\n",
              "      <td>2.642532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>IRDM11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>131.39</td>\n",
              "      <td>2.419633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RECR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>101.55</td>\n",
              "      <td>2.412386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CPTS11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>99.60</td>\n",
              "      <td>2.358630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HABT11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>124.66</td>\n",
              "      <td>2.161607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>TGAR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>137.13</td>\n",
              "      <td>2.157821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BBRC11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>116.00</td>\n",
              "      <td>2.116562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>BNFS11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>123.60</td>\n",
              "      <td>2.109137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>BARI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>106.70</td>\n",
              "      <td>2.048882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>BCRI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>112.25</td>\n",
              "      <td>2.020643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>VRTA11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>111.37</td>\n",
              "      <td>1.992557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>CVBI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>104.81</td>\n",
              "      <td>1.979118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>PORD11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>101.68</td>\n",
              "      <td>1.977127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>MFII11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>113.90</td>\n",
              "      <td>1.940412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>ARCT11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>108.14</td>\n",
              "      <td>1.930780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>XPCI11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>100.05</td>\n",
              "      <td>1.929163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>XPSF11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>97.50</td>\n",
              "      <td>1.915921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>TRXF11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>105.35</td>\n",
              "      <td>1.905679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>KNHY11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>111.05</td>\n",
              "      <td>1.901487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>FEXC11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>97.85</td>\n",
              "      <td>1.884188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MXRF11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>10.43</td>\n",
              "      <td>1.883941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>RBED11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>146.63</td>\n",
              "      <td>1.881106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>HGRU11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>121.94</td>\n",
              "      <td>1.866567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>VGIP11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>106.83</td>\n",
              "      <td>1.805712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>TORD11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>10.54</td>\n",
              "      <td>1.801683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>VINO11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>60.30</td>\n",
              "      <td>1.775076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>RBRY11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>104.49</td>\n",
              "      <td>1.759620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>RBRF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>89.16</td>\n",
              "      <td>1.753204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>RBRL11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>106.70</td>\n",
              "      <td>1.727285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>RBFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>68.00</td>\n",
              "      <td>1.702280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>HGCR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>105.31</td>\n",
              "      <td>1.686025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>FIIP11B</td>\n",
              "      <td>Logística</td>\n",
              "      <td>194.01</td>\n",
              "      <td>1.680511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>NEWL11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>105.50</td>\n",
              "      <td>1.670228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>OUFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>83.00</td>\n",
              "      <td>1.669231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>MGFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>83.00</td>\n",
              "      <td>1.660374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>VCJR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>102.40</td>\n",
              "      <td>1.624349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>GTWR11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>106.34</td>\n",
              "      <td>1.616851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>BTLG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>112.88</td>\n",
              "      <td>1.615202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>RBRR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>99.90</td>\n",
              "      <td>1.601796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>BPFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>78.55</td>\n",
              "      <td>1.590255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>HFOF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>98.41</td>\n",
              "      <td>1.587658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>RVBI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>97.36</td>\n",
              "      <td>1.584051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>BCFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>85.71</td>\n",
              "      <td>1.578156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>HCTR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>138.76</td>\n",
              "      <td>1.519675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>VSLH11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>10.33</td>\n",
              "      <td>1.498805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>CPFF11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>83.03</td>\n",
              "      <td>1.493901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>MCCI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>102.40</td>\n",
              "      <td>1.406297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>PLCR11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>93.55</td>\n",
              "      <td>1.404150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>VTLT11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>108.70</td>\n",
              "      <td>1.403720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>MBRF11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>875.10</td>\n",
              "      <td>1.380575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>ARRI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>102.46</td>\n",
              "      <td>1.364986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>CBOP11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>72.50</td>\n",
              "      <td>1.363384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>NSLU11</td>\n",
              "      <td>Hospital</td>\n",
              "      <td>244.10</td>\n",
              "      <td>1.361613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>RECT11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>81.74</td>\n",
              "      <td>1.354957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>HGLG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>169.90</td>\n",
              "      <td>1.340320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>BRCR11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>83.37</td>\n",
              "      <td>1.325750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>FATN11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>102.32</td>\n",
              "      <td>1.307537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>RFOF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>82.21</td>\n",
              "      <td>1.305622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>OUJP11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>101.50</td>\n",
              "      <td>1.304873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>RBRP11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>85.50</td>\n",
              "      <td>1.296495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>RBIV11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>83.48</td>\n",
              "      <td>1.276447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>XPLG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>114.10</td>\n",
              "      <td>1.260764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>BTCR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>93.90</td>\n",
              "      <td>1.257688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>VILG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>116.10</td>\n",
              "      <td>1.239170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>BRCO11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>110.00</td>\n",
              "      <td>1.200012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>GGRC11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>120.00</td>\n",
              "      <td>1.195472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>FIIB11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>500.61</td>\n",
              "      <td>1.172741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>OULG11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>69.67</td>\n",
              "      <td>1.168108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>VIFI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>87.01</td>\n",
              "      <td>1.152488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>EDFO11B</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>251.33</td>\n",
              "      <td>1.122426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>XPIN11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>105.07</td>\n",
              "      <td>1.108692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>KNSC11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>101.57</td>\n",
              "      <td>1.072938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>CEOC11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>68.53</td>\n",
              "      <td>1.063505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>QAGR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>53.74</td>\n",
              "      <td>1.052995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>FCFL11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>112.35</td>\n",
              "      <td>1.051298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>MGHT11</td>\n",
              "      <td>Hotel</td>\n",
              "      <td>98.37</td>\n",
              "      <td>1.044015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>GCFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>95.31</td>\n",
              "      <td>1.039062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>HSAF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>101.95</td>\n",
              "      <td>1.020853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>GALG11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>109.98</td>\n",
              "      <td>0.988239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>OURE11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>88.13</td>\n",
              "      <td>0.980395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>LGCP11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>93.40</td>\n",
              "      <td>0.978501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>VGIR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>91.63</td>\n",
              "      <td>0.956022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>SDIL11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>94.75</td>\n",
              "      <td>0.955982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>KNRI11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>141.54</td>\n",
              "      <td>0.951795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>CXCE11B</td>\n",
              "      <td>Outros</td>\n",
              "      <td>82.00</td>\n",
              "      <td>0.949283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>HGFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>85.05</td>\n",
              "      <td>0.947771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>EURO11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>208.80</td>\n",
              "      <td>0.935154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>AFCR11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>110.30</td>\n",
              "      <td>0.931158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>JSRE11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>90.05</td>\n",
              "      <td>0.930070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>HLOG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>113.00</td>\n",
              "      <td>0.927874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>MORE11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>87.96</td>\n",
              "      <td>0.923059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>SARE11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>100.80</td>\n",
              "      <td>0.912413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>JPPA11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>99.60</td>\n",
              "      <td>0.907460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>MGCR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>99.30</td>\n",
              "      <td>0.897630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>NCHB11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>94.86</td>\n",
              "      <td>0.871783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>BCIA11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>96.26</td>\n",
              "      <td>0.842022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>CXRI11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>92.15</td>\n",
              "      <td>0.840551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>MFAI11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>98.70</td>\n",
              "      <td>0.834181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>QIFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>101.18</td>\n",
              "      <td>0.818099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>ALZR11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>128.00</td>\n",
              "      <td>0.781677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>FAED11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>150.00</td>\n",
              "      <td>0.778135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>XPML11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>107.49</td>\n",
              "      <td>0.754637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>RZTR11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>101.20</td>\n",
              "      <td>0.752259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>HGPO11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>222.27</td>\n",
              "      <td>0.751308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>FLRP11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>1312.00</td>\n",
              "      <td>0.749990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>XPPR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>62.80</td>\n",
              "      <td>0.729167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>LVBI11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>105.29</td>\n",
              "      <td>0.710998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>PLRI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>45.84</td>\n",
              "      <td>0.692669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>HSML11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>90.00</td>\n",
              "      <td>0.678874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>PVBI11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>95.30</td>\n",
              "      <td>0.673206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>HOSI11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>80.50</td>\n",
              "      <td>0.644497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>VISC11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>108.55</td>\n",
              "      <td>0.633613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>URPR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>125.77</td>\n",
              "      <td>0.629316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>MALL11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>97.69</td>\n",
              "      <td>0.628442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>PATL11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>88.06</td>\n",
              "      <td>0.615917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>KFOF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>93.31</td>\n",
              "      <td>0.615047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>KNCR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>91.92</td>\n",
              "      <td>0.610912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>CRFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>73.68</td>\n",
              "      <td>0.610707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>HREC11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>101.48</td>\n",
              "      <td>0.606712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>HBRH11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>94.00</td>\n",
              "      <td>0.595855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>BMLC11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>90.97</td>\n",
              "      <td>0.588569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>RCRB11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>151.98</td>\n",
              "      <td>0.567273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>IBFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>68.79</td>\n",
              "      <td>0.562905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>BLMG11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>101.75</td>\n",
              "      <td>0.556727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>BLCP11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>98.28</td>\n",
              "      <td>0.550102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>RELG11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>97.07</td>\n",
              "      <td>0.544756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>AIEC11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>86.27</td>\n",
              "      <td>0.527587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>LUGG11</td>\n",
              "      <td>Residencial</td>\n",
              "      <td>95.00</td>\n",
              "      <td>0.476564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>HUSC11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>131.00</td>\n",
              "      <td>0.468695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>HGIC11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>106.38</td>\n",
              "      <td>0.467878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>RNGO11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0.465561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>VOTS11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>82.97</td>\n",
              "      <td>0.462210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>ABCP11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>70.84</td>\n",
              "      <td>0.448962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>RDPD11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>84.00</td>\n",
              "      <td>0.418001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>BZLI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>14.70</td>\n",
              "      <td>0.414999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>LASC11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>94.76</td>\n",
              "      <td>0.401312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>ONEF11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>140.65</td>\n",
              "      <td>0.397624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>THRA11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>137.99</td>\n",
              "      <td>0.397313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>EVBI11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>101.99</td>\n",
              "      <td>0.395940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>JRDM11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>68.49</td>\n",
              "      <td>0.385901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>HGBS11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>199.78</td>\n",
              "      <td>0.384267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>TEPP11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>78.00</td>\n",
              "      <td>0.381703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>PQDP11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>2853.00</td>\n",
              "      <td>0.376121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>IFIE11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>93.99</td>\n",
              "      <td>0.371534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>VGHF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>9.56</td>\n",
              "      <td>0.350820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>SHPH11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>828.50</td>\n",
              "      <td>0.323615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>RRCI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>86.04</td>\n",
              "      <td>0.315680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>VLOL11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>98.99</td>\n",
              "      <td>0.314338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>CXCO11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>102.40</td>\n",
              "      <td>0.312499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>HAAA11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>101.00</td>\n",
              "      <td>0.310465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>HGRE11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>126.60</td>\n",
              "      <td>0.306092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>HSLG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>105.49</td>\n",
              "      <td>0.303210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>BTAL11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>101.56</td>\n",
              "      <td>0.285051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>RZAK11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>94.55</td>\n",
              "      <td>0.283000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>BREV11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>106.51</td>\n",
              "      <td>0.270125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>HPDP11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>82.48</td>\n",
              "      <td>0.243620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>BICR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>94.91</td>\n",
              "      <td>0.238912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>SADI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>86.50</td>\n",
              "      <td>0.218763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>BBFO11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>87.98</td>\n",
              "      <td>0.216298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>PLOG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>94.32</td>\n",
              "      <td>0.204267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>PQAG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>63.39</td>\n",
              "      <td>0.192990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>IFID11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>94.42</td>\n",
              "      <td>0.136467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>ATSA11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>107.97</td>\n",
              "      <td>0.123495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>RNDP11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>686.00</td>\n",
              "      <td>0.122635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>DEVA11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>108.50</td>\n",
              "      <td>0.119110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>RBDS11</td>\n",
              "      <td>Residencial</td>\n",
              "      <td>20.85</td>\n",
              "      <td>0.108807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>JFLL11</td>\n",
              "      <td>Residencial</td>\n",
              "      <td>94.02</td>\n",
              "      <td>0.091783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>BLMR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>82.50</td>\n",
              "      <td>0.090272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>SEQR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>80.49</td>\n",
              "      <td>0.050392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>PRSV11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>142.00</td>\n",
              "      <td>0.010427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>RECX11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>100.00</td>\n",
              "      <td>-0.005694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>NVHO11</td>\n",
              "      <td>Hospital</td>\n",
              "      <td>14.02</td>\n",
              "      <td>-0.012551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>PATC11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>67.76</td>\n",
              "      <td>-0.021317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>VSHO11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>77.14</td>\n",
              "      <td>-0.047702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>FVPQ11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>136.53</td>\n",
              "      <td>-0.073589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>RBRS11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>74.80</td>\n",
              "      <td>-0.120152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>RBCO11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>57.50</td>\n",
              "      <td>-0.131428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>KISU11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>115.68</td>\n",
              "      <td>-0.132223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>FPAB11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>258.80</td>\n",
              "      <td>-0.149568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>FIGS11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>59.66</td>\n",
              "      <td>-0.172179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>WPLZ11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>66.69</td>\n",
              "      <td>-0.236836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>DRIT11B</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>116.00</td>\n",
              "      <td>-0.310561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>SCPF11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>9.12</td>\n",
              "      <td>-0.376116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>RBVO11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>12.14</td>\n",
              "      <td>-0.524521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>XTED11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>7.13</td>\n",
              "      <td>-0.571691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>RBGS11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>37.93</td>\n",
              "      <td>-0.666681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>WTSP11B</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>45.50</td>\n",
              "      <td>-0.712700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>FIVN11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>2.97</td>\n",
              "      <td>-0.767051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>NEWU11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>36.48</td>\n",
              "      <td>-0.790015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>TRNT11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>149.00</td>\n",
              "      <td>-0.875775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>CNES11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>42.61</td>\n",
              "      <td>-1.016564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>EDGA11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>26.77</td>\n",
              "      <td>-1.040911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>ALMI11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>1160.00</td>\n",
              "      <td>-1.103355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>XPHT11</td>\n",
              "      <td>Hotel</td>\n",
              "      <td>87.90</td>\n",
              "      <td>-1.470117</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Códigodo fundo                Setor  Preço Atual     Notas\n",
              "0           BBPO11   Lajes Corporativas       114.10  2.642532\n",
              "1           IRDM11  Títulos e Val. Mob.       131.39  2.419633\n",
              "2           RECR11  Títulos e Val. Mob.       101.55  2.412386\n",
              "3           CPTS11  Títulos e Val. Mob.        99.60  2.358630\n",
              "4           HABT11  Títulos e Val. Mob.       124.66  2.161607\n",
              "5           TGAR11               Outros       137.13  2.157821\n",
              "6           BBRC11               Outros       116.00  2.116562\n",
              "7           BNFS11               Outros       123.60  2.109137\n",
              "8           BARI11  Títulos e Val. Mob.       106.70  2.048882\n",
              "9           BCRI11  Títulos e Val. Mob.       112.25  2.020643\n",
              "10          VRTA11  Títulos e Val. Mob.       111.37  1.992557\n",
              "11          CVBI11  Títulos e Val. Mob.       104.81  1.979118\n",
              "12          PORD11  Títulos e Val. Mob.       101.68  1.977127\n",
              "13          MFII11              Híbrido       113.90  1.940412\n",
              "14          ARCT11              Híbrido       108.14  1.930780\n",
              "15          XPCI11               Outros       100.05  1.929163\n",
              "16          XPSF11               Outros        97.50  1.915921\n",
              "17          TRXF11               Outros       105.35  1.905679\n",
              "18          KNHY11  Títulos e Val. Mob.       111.05  1.901487\n",
              "19          FEXC11  Títulos e Val. Mob.        97.85  1.884188\n",
              "20          MXRF11              Híbrido        10.43  1.883941\n",
              "21          RBED11               Outros       146.63  1.881106\n",
              "22          HGRU11              Híbrido       121.94  1.866567\n",
              "23          VGIP11               Outros       106.83  1.805712\n",
              "24          TORD11               Outros        10.54  1.801683\n",
              "25          VINO11   Lajes Corporativas        60.30  1.775076\n",
              "26          RBRY11  Títulos e Val. Mob.       104.49  1.759620\n",
              "27          RBRF11  Títulos e Val. Mob.        89.16  1.753204\n",
              "28          RBRL11            Logística       106.70  1.727285\n",
              "29          RBFF11  Títulos e Val. Mob.        68.00  1.702280\n",
              "30          HGCR11  Títulos e Val. Mob.       105.31  1.686025\n",
              "31         FIIP11B            Logística       194.01  1.680511\n",
              "32          NEWL11              Híbrido       105.50  1.670228\n",
              "33          OUFF11  Títulos e Val. Mob.        83.00  1.669231\n",
              "34          MGFF11  Títulos e Val. Mob.        83.00  1.660374\n",
              "35          VCJR11  Títulos e Val. Mob.       102.40  1.624349\n",
              "36          GTWR11   Lajes Corporativas       106.34  1.616851\n",
              "37          BTLG11            Logística       112.88  1.615202\n",
              "38          RBRR11  Títulos e Val. Mob.        99.90  1.601796\n",
              "39          BPFF11  Títulos e Val. Mob.        78.55  1.590255\n",
              "40          HFOF11  Títulos e Val. Mob.        98.41  1.587658\n",
              "41          RVBI11  Títulos e Val. Mob.        97.36  1.584051\n",
              "42          BCFF11  Títulos e Val. Mob.        85.71  1.578156\n",
              "43          HCTR11               Outros       138.76  1.519675\n",
              "44          VSLH11               Outros        10.33  1.498805\n",
              "45          CPFF11              Híbrido        83.03  1.493901\n",
              "46          MCCI11  Títulos e Val. Mob.       102.40  1.406297\n",
              "47          PLCR11              Híbrido        93.55  1.404150\n",
              "48          VTLT11            Logística       108.70  1.403720\n",
              "49          MBRF11               Outros       875.10  1.380575\n",
              "50          ARRI11  Títulos e Val. Mob.       102.46  1.364986\n",
              "51          CBOP11   Lajes Corporativas        72.50  1.363384\n",
              "52          NSLU11             Hospital       244.10  1.361613\n",
              "53          RECT11              Híbrido        81.74  1.354957\n",
              "54          HGLG11            Logística       169.90  1.340320\n",
              "55          BRCR11              Híbrido        83.37  1.325750\n",
              "56          FATN11              Híbrido       102.32  1.307537\n",
              "57          RFOF11  Títulos e Val. Mob.        82.21  1.305622\n",
              "58          OUJP11  Títulos e Val. Mob.       101.50  1.304873\n",
              "59          RBRP11               Outros        85.50  1.296495\n",
              "60          RBIV11  Títulos e Val. Mob.        83.48  1.276447\n",
              "61          XPLG11            Logística       114.10  1.260764\n",
              "62          BTCR11  Títulos e Val. Mob.        93.90  1.257688\n",
              "63          VILG11            Logística       116.10  1.239170\n",
              "64          BRCO11            Logística       110.00  1.200012\n",
              "65          GGRC11            Logística       120.00  1.195472\n",
              "66          FIIB11              Híbrido       500.61  1.172741\n",
              "67          OULG11               Outros        69.67  1.168108\n",
              "68          VIFI11  Títulos e Val. Mob.        87.01  1.152488\n",
              "69         EDFO11B   Lajes Corporativas       251.33  1.122426\n",
              "70          XPIN11               Outros       105.07  1.108692\n",
              "71          KNSC11  Títulos e Val. Mob.       101.57  1.072938\n",
              "72          CEOC11   Lajes Corporativas        68.53  1.063505\n",
              "73          QAGR11               Outros        53.74  1.052995\n",
              "74          FCFL11               Outros       112.35  1.051298\n",
              "75          MGHT11                Hotel        98.37  1.044015\n",
              "76          GCFF11  Títulos e Val. Mob.        95.31  1.039062\n",
              "77          HSAF11  Títulos e Val. Mob.       101.95  1.020853\n",
              "78          GALG11              Híbrido       109.98  0.988239\n",
              "79          OURE11  Títulos e Val. Mob.        88.13  0.980395\n",
              "80          LGCP11            Logística        93.40  0.978501\n",
              "81          VGIR11  Títulos e Val. Mob.        91.63  0.956022\n",
              "82          SDIL11            Logística        94.75  0.955982\n",
              "83          KNRI11              Híbrido       141.54  0.951795\n",
              "84         CXCE11B               Outros        82.00  0.949283\n",
              "85          HGFF11  Títulos e Val. Mob.        85.05  0.947771\n",
              "86          EURO11            Logística       208.80  0.935154\n",
              "87          AFCR11              Híbrido       110.30  0.931158\n",
              "88          JSRE11              Híbrido        90.05  0.930070\n",
              "89          HLOG11            Logística       113.00  0.927874\n",
              "90          MORE11  Títulos e Val. Mob.        87.96  0.923059\n",
              "91          SARE11              Híbrido       100.80  0.912413\n",
              "92          JPPA11  Títulos e Val. Mob.        99.60  0.907460\n",
              "93          MGCR11  Títulos e Val. Mob.        99.30  0.897630\n",
              "94          NCHB11  Títulos e Val. Mob.        94.86  0.871783\n",
              "95          BCIA11  Títulos e Val. Mob.        96.26  0.842022\n",
              "96          CXRI11              Híbrido        92.15  0.840551\n",
              "97          MFAI11              Híbrido        98.70  0.834181\n",
              "98          QIFF11  Títulos e Val. Mob.       101.18  0.818099\n",
              "99          ALZR11            Logística       128.00  0.781677\n",
              "100         FAED11               Outros       150.00  0.778135\n",
              "101         XPML11            Shoppings       107.49  0.754637\n",
              "102         RZTR11              Híbrido       101.20  0.752259\n",
              "103         HGPO11   Lajes Corporativas       222.27  0.751308\n",
              "104         FLRP11            Shoppings      1312.00  0.749990\n",
              "105         XPPR11               Outros        62.80  0.729167\n",
              "106         LVBI11            Logística       105.29  0.710998\n",
              "107         PLRI11  Títulos e Val. Mob.        45.84  0.692669\n",
              "108         HSML11            Shoppings        90.00  0.678874\n",
              "109         PVBI11   Lajes Corporativas        95.30  0.673206\n",
              "110         HOSI11              Híbrido        80.50  0.644497\n",
              "111         VISC11            Shoppings       108.55  0.633613\n",
              "112         URPR11               Outros       125.77  0.629316\n",
              "113         MALL11            Shoppings        97.69  0.628442\n",
              "114         PATL11              Híbrido        88.06  0.615917\n",
              "115         KFOF11  Títulos e Val. Mob.        93.31  0.615047\n",
              "116         KNCR11  Títulos e Val. Mob.        91.92  0.610912\n",
              "117         CRFF11  Títulos e Val. Mob.        73.68  0.610707\n",
              "118         HREC11  Títulos e Val. Mob.       101.48  0.606712\n",
              "119         HBRH11              Híbrido        94.00  0.595855\n",
              "120         BMLC11   Lajes Corporativas        90.97  0.588569\n",
              "121         RCRB11   Lajes Corporativas       151.98  0.567273\n",
              "122         IBFF11  Títulos e Val. Mob.        68.79  0.562905\n",
              "123         BLMG11               Outros       101.75  0.556727\n",
              "124         BLCP11               Outros        98.28  0.550102\n",
              "125         RELG11              Híbrido        97.07  0.544756\n",
              "126         AIEC11   Lajes Corporativas        86.27  0.527587\n",
              "127         LUGG11          Residencial        95.00  0.476564\n",
              "128         HUSC11               Outros       131.00  0.468695\n",
              "129         HGIC11  Títulos e Val. Mob.       106.38  0.467878\n",
              "130         RNGO11   Lajes Corporativas        62.00  0.465561\n",
              "131         VOTS11  Títulos e Val. Mob.        82.97  0.462210\n",
              "132         ABCP11            Shoppings        70.84  0.448962\n",
              "133         RDPD11  Títulos e Val. Mob.        84.00  0.418001\n",
              "134         BZLI11  Títulos e Val. Mob.        14.70  0.414999\n",
              "135         LASC11            Shoppings        94.76  0.401312\n",
              "136         ONEF11   Lajes Corporativas       140.65  0.397624\n",
              "137         THRA11   Lajes Corporativas       137.99  0.397313\n",
              "138         EVBI11              Híbrido       101.99  0.395940\n",
              "139         JRDM11            Shoppings        68.49  0.385901\n",
              "140         HGBS11            Shoppings       199.78  0.384267\n",
              "141         TEPP11   Lajes Corporativas        78.00  0.381703\n",
              "142         PQDP11            Shoppings      2853.00  0.376121\n",
              "143         IFIE11  Títulos e Val. Mob.        93.99  0.371534\n",
              "144         VGHF11  Títulos e Val. Mob.         9.56  0.350820\n",
              "145         SHPH11            Shoppings       828.50  0.323615\n",
              "146         RRCI11  Títulos e Val. Mob.        86.04  0.315680\n",
              "147         VLOL11   Lajes Corporativas        98.99  0.314338\n",
              "148         CXCO11   Lajes Corporativas       102.40  0.312499\n",
              "149         HAAA11   Lajes Corporativas       101.00  0.310465\n",
              "150         HGRE11   Lajes Corporativas       126.60  0.306092\n",
              "151         HSLG11            Logística       105.49  0.303210\n",
              "152         BTAL11               Outros       101.56  0.285051\n",
              "153         RZAK11  Títulos e Val. Mob.        94.55  0.283000\n",
              "154         BREV11              Híbrido       106.51  0.270125\n",
              "155         HPDP11            Shoppings        82.48  0.243620\n",
              "156         BICR11  Títulos e Val. Mob.        94.91  0.238912\n",
              "157         SADI11  Títulos e Val. Mob.        86.50  0.218763\n",
              "158         BBFO11  Títulos e Val. Mob.        87.98  0.216298\n",
              "159         PLOG11            Logística        94.32  0.204267\n",
              "160         PQAG11            Logística        63.39  0.192990\n",
              "161         IFID11  Títulos e Val. Mob.        94.42  0.136467\n",
              "162         ATSA11            Shoppings       107.97  0.123495\n",
              "163         RNDP11  Títulos e Val. Mob.       686.00  0.122635\n",
              "164         DEVA11  Títulos e Val. Mob.       108.50  0.119110\n",
              "165         RBDS11          Residencial        20.85  0.108807\n",
              "166         JFLL11          Residencial        94.02  0.091783\n",
              "167         BLMR11  Títulos e Val. Mob.        82.50  0.090272\n",
              "168         SEQR11               Outros        80.49  0.050392\n",
              "169         PRSV11   Lajes Corporativas       142.00  0.010427\n",
              "170         RECX11               Outros       100.00 -0.005694\n",
              "171         NVHO11             Hospital        14.02 -0.012551\n",
              "172         PATC11   Lajes Corporativas        67.76 -0.021317\n",
              "173         VSHO11            Shoppings        77.14 -0.047702\n",
              "174         FVPQ11            Shoppings       136.53 -0.073589\n",
              "175         RBRS11              Híbrido        74.80 -0.120152\n",
              "176         RBCO11   Lajes Corporativas        57.50 -0.131428\n",
              "177         KISU11  Títulos e Val. Mob.       115.68 -0.132223\n",
              "178         FPAB11   Lajes Corporativas       258.80 -0.149568\n",
              "179         FIGS11            Shoppings        59.66 -0.172179\n",
              "180         WPLZ11            Shoppings        66.69 -0.236836\n",
              "181        DRIT11B   Lajes Corporativas       116.00 -0.310561\n",
              "182         SCPF11            Shoppings         9.12 -0.376116\n",
              "183         RBVO11               Outros        12.14 -0.524521\n",
              "184         XTED11   Lajes Corporativas         7.13 -0.571691\n",
              "185         RBGS11            Shoppings        37.93 -0.666681\n",
              "186        WTSP11B              Híbrido        45.50 -0.712700\n",
              "187         FIVN11            Shoppings         2.97 -0.767051\n",
              "188         NEWU11   Lajes Corporativas        36.48 -0.790015\n",
              "189         TRNT11   Lajes Corporativas       149.00 -0.875775\n",
              "190         CNES11   Lajes Corporativas        42.61 -1.016564\n",
              "191         EDGA11   Lajes Corporativas        26.77 -1.040911\n",
              "192         ALMI11   Lajes Corporativas      1160.00 -1.103355\n",
              "193         XPHT11                Hotel        87.90 -1.470117"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzsA28CmpwAv"
      },
      "source": [
        "## Modelo de classificação de fundos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOhXOvqAON83"
      },
      "source": [
        "Agora, podemos também fazer um modelo de Machine Learning que, com os dados de um fundo em mãos, possa classificá-lo em: bom, médio ou ruim.\n",
        "\n",
        "Para fazer isso, primeiro vamos utilizar os datasets que já temos para treinar o modelo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXVIjvUv6hgH"
      },
      "source": [
        "dfs_modelo = []\n",
        "url = 'https://raw.githubusercontent.com/TailUFPB/fundos-imobiliarios/main/CSV_Modelo.csv'\n",
        "data = pd.read_csv(url)\n",
        "dfs_modelo.append(pd.DataFrame(data))\n",
        "datas = ['202012', '202009', '202003', '201902']\n",
        "\n",
        "for i in datas:\n",
        "  url = 'https://raw.githubusercontent.com/TailUFPB/fundos-imobiliarios/main/CSV_Antigos_PreProc/' + i + '.csv'\n",
        "  data = pd.read_csv(url)\n",
        "  dfs_modelo.append(pd.DataFrame(data))\n",
        "\n",
        "dfs_modelo[0].drop(['Unnamed: 0', 'Ano/Mês','Setor', 'Preço Atual', 'Dividendo', 'DY Ano'], axis='columns', inplace=True)\n",
        "for i in range(1,len(dfs_modelo)):\n",
        "  #Retirando colunas desnecessárias\n",
        "  dfs_modelo[i].drop(['Unnamed: 0', 'Ano/Mês','Unnamed: 0.1', 'Preço Atual', 'Dividendo', 'DY Ano'], axis='columns', inplace=True)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XeibpF7wPp7W"
      },
      "source": [
        "Vamos dar uma nota para todos os fundos desse nosso dataset, utilizando as funções do começo do notebook:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OACRPXq7wiF"
      },
      "source": [
        "for i in range(len(dfs_modelo)):\n",
        "  dfs_modelo[i] = NotaInicial(dfs_modelo[i])\n",
        "  dfs_modelo[i] = PVPA(dfs_modelo[i])\n",
        "  dfs_modelo[i] = DividendAnalysis(dfs_modelo[i])\n",
        "\n",
        "dfs_modelo = pd.concat(dfs_modelo)\n",
        "\n",
        "dfs_modelo = dfs_modelo.sort_values('Notas', ascending=False)\n",
        "dfs_modelo.reset_index(drop=True, inplace=True)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wD76G-AhP79y"
      },
      "source": [
        "Levando em consideração a nota obtida de cada modelo, podemos separá-los nas 3 categorias em que queremos classificar:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMaBZRa0_GXn"
      },
      "source": [
        "dfs_modelo['Label'] = ''\n",
        "for index, row in dfs_modelo.iterrows():\n",
        "  if dfs_modelo.loc[index, 'Notas'] <= 0:\n",
        "    dfs_modelo.loc[index,'Label'] = 'Ruim'\n",
        "  elif dfs_modelo.loc[index, 'Notas'] <= 0.85:\n",
        "    dfs_modelo.loc[index,'Label'] = 'Médio'\n",
        "  else:\n",
        "    dfs_modelo.loc[index,'Label'] = 'Bom'\n",
        "\n",
        "dfs_modelo.drop(['Notas'], axis='columns', inplace=True)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RyjZcDQgQPx7"
      },
      "source": [
        "Agora, podemos utilizar a biblioteca Pycaret para aplicar vários modelos de ML nos nossos dados e descobrir qual o que melhor consegue classificá-los:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUh_qecK7jN9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c711b4f7-4224-4c5f-93ed-92b1de7b9460"
      },
      "source": [
        "grid = setup(data=dfs_modelo.iloc[:,1:], target='Label', html=False, silent=True, verbose=False)\n",
        "\n",
        "model = compare_models()\n",
        "plot_model(model, plot ='feature_all')"
      ],
      "execution_count": 38,
      "outputs": [
        {
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              "                  Model  Accuracy     AUC  Recall   Prec.      F1   Kappa  \\\n",
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              "                      Model  Accuracy     AUC  Recall   Prec.      F1   Kappa  \\\n",
              "knn  K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590  0.8508  0.7348   \n",
              "lr      Logistic Regression    0.8183  0.9271  0.7487  0.8385  0.8094  0.6581   \n",
              "\n",
              "        MCC  TT (Sec)  \n",
              "knn  0.7384     0.117  \n",
              "lr   0.6775     0.037  "
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              "                      Model  Accuracy     AUC  Recall   Prec.      F1   Kappa  \\\n",
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              "lr      Logistic Regression    0.8183  0.9271  0.7487  0.8385  0.8094  0.6581   \n",
              "nb              Naive Bayes    0.8057  0.9061  0.8185  0.8213  0.8073  0.6741   \n",
              "\n",
              "        MCC  TT (Sec)  \n",
              "knn  0.7384     0.117  \n",
              "lr   0.6775     0.037  \n",
              "nb   0.6801     0.016  "
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "      <th>TT (Sec)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.8532</td>\n",
              "      <td>0.9160</td>\n",
              "      <td>0.8133</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>0.8508</td>\n",
              "      <td>0.7348</td>\n",
              "      <td>0.7384</td>\n",
              "      <td>0.117</td>\n",
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              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>0.9271</td>\n",
              "      <td>0.7487</td>\n",
              "      <td>0.8385</td>\n",
              "      <td>0.8094</td>\n",
              "      <td>0.6581</td>\n",
              "      <td>0.6775</td>\n",
              "      <td>0.037</td>\n",
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              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.8090</td>\n",
              "      <td>0.8217</td>\n",
              "      <td>0.7840</td>\n",
              "      <td>0.8127</td>\n",
              "      <td>0.8058</td>\n",
              "      <td>0.6626</td>\n",
              "      <td>0.6675</td>\n",
              "      <td>0.019</td>\n",
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              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.8057</td>\n",
              "      <td>0.9061</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
              "      <td>0.6741</td>\n",
              "      <td>0.6801</td>\n",
              "      <td>0.016</td>\n",
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              "                        Model  Accuracy     AUC  Recall   Prec.      F1  \\\n",
              "knn    K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590  0.8508   \n",
              "lr        Logistic Regression    0.8183  0.9271  0.7487  0.8385  0.8094   \n",
              "dt   Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127  0.8058   \n",
              "nb                Naive Bayes    0.8057  0.9061  0.8185  0.8213  0.8073   \n",
              "\n",
              "      Kappa     MCC  TT (Sec)  \n",
              "knn  0.7348  0.7384     0.117  \n",
              "lr   0.6581  0.6775     0.037  \n",
              "dt   0.6626  0.6675     0.019  \n",
              "nb   0.6741  0.6801     0.016  "
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              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
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              "      <td>0.8087</td>\n",
              "      <td>0.7957</td>\n",
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              "                        Model  Accuracy     AUC  Recall   Prec.      F1  \\\n",
              "knn    K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590  0.8508   \n",
              "lr        Logistic Regression    0.8183  0.9271  0.7487  0.8385  0.8094   \n",
              "dt   Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127  0.8058   \n",
              "nb                Naive Bayes    0.8057  0.9061  0.8185  0.8213  0.8073   \n",
              "svm       SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087  0.7957   \n",
              "\n",
              "      Kappa     MCC  TT (Sec)  \n",
              "knn  0.7348  0.7384     0.117  \n",
              "lr   0.6581  0.6775     0.037  \n",
              "dt   0.6626  0.6675     0.019  \n",
              "nb   0.6741  0.6801     0.016  \n",
              "svm  0.6438  0.6508     0.064  "
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              "      <td>0.7884</td>\n",
              "      <td>0.7429</td>\n",
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              "                          Model  Accuracy     AUC  Recall   Prec.      F1  \\\n",
              "knn      K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590  0.8508   \n",
              "lr          Logistic Regression    0.8183  0.9271  0.7487  0.8385  0.8094   \n",
              "dt     Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127  0.8058   \n",
              "nb                  Naive Bayes    0.8057  0.9061  0.8185  0.8213  0.8073   \n",
              "svm         SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087  0.7957   \n",
              "ridge          Ridge Classifier    0.7650  0.0000  0.6496  0.7884  0.7429   \n",
              "\n",
              "        Kappa     MCC  TT (Sec)  \n",
              "knn    0.7348  0.7384     0.117  \n",
              "lr     0.6581  0.6775     0.037  \n",
              "dt     0.6626  0.6675     0.019  \n",
              "nb     0.6741  0.6801     0.016  \n",
              "svm    0.6438  0.6508     0.064  \n",
              "ridge  0.5409  0.5728     0.013  "
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              "      <td>0.8811</td>\n",
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              "                          Model  Accuracy     AUC  Recall   Prec.      F1  \\\n",
              "rf     Random Forest Classifier    0.8732  0.9533  0.8265  0.8811  0.8697   \n",
              "knn      K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590  0.8508   \n",
              "lr          Logistic Regression    0.8183  0.9271  0.7487  0.8385  0.8094   \n",
              "dt     Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127  0.8058   \n",
              "nb                  Naive Bayes    0.8057  0.9061  0.8185  0.8213  0.8073   \n",
              "svm         SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087  0.7957   \n",
              "ridge          Ridge Classifier    0.7650  0.0000  0.6496  0.7884  0.7429   \n",
              "\n",
              "        Kappa     MCC  TT (Sec)  \n",
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              "knn    0.7348  0.7384     0.117  \n",
              "lr     0.6581  0.6775     0.037  \n",
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              "ridge  0.5409  0.5728     0.013  "
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              "      <td>0.7384</td>\n",
              "      <td>0.117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>0.9271</td>\n",
              "      <td>0.7487</td>\n",
              "      <td>0.8385</td>\n",
              "      <td>0.8094</td>\n",
              "      <td>0.6581</td>\n",
              "      <td>0.6775</td>\n",
              "      <td>0.037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.8090</td>\n",
              "      <td>0.8217</td>\n",
              "      <td>0.7840</td>\n",
              "      <td>0.8127</td>\n",
              "      <td>0.8058</td>\n",
              "      <td>0.6626</td>\n",
              "      <td>0.6675</td>\n",
              "      <td>0.019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.8057</td>\n",
              "      <td>0.9061</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
              "      <td>0.6741</td>\n",
              "      <td>0.6801</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7999</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7593</td>\n",
              "      <td>0.8087</td>\n",
              "      <td>0.7957</td>\n",
              "      <td>0.6438</td>\n",
              "      <td>0.6508</td>\n",
              "      <td>0.064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.7963</td>\n",
              "      <td>0.8957</td>\n",
              "      <td>0.8072</td>\n",
              "      <td>0.8123</td>\n",
              "      <td>0.7978</td>\n",
              "      <td>0.6561</td>\n",
              "      <td>0.6625</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.7650</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.6496</td>\n",
              "      <td>0.7884</td>\n",
              "      <td>0.7429</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.5728</td>\n",
              "      <td>0.013</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "                                 Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "rf            Random Forest Classifier    0.8732  0.9533  0.8265  0.8811   \n",
              "knn             K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590   \n",
              "lr                 Logistic Regression    0.8183  0.9271  0.7487  0.8385   \n",
              "dt            Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127   \n",
              "nb                         Naive Bayes    0.8057  0.9061  0.8185  0.8213   \n",
              "svm                SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087   \n",
              "qda    Quadratic Discriminant Analysis    0.7963  0.8957  0.8072  0.8123   \n",
              "ridge                 Ridge Classifier    0.7650  0.0000  0.6496  0.7884   \n",
              "\n",
              "           F1   Kappa     MCC  TT (Sec)  \n",
              "rf     0.8697  0.7692  0.7770     0.513  \n",
              "knn    0.8508  0.7348  0.7384     0.117  \n",
              "lr     0.8094  0.6581  0.6775     0.037  \n",
              "dt     0.8058  0.6626  0.6675     0.019  \n",
              "nb     0.8073  0.6741  0.6801     0.016  \n",
              "svm    0.7957  0.6438  0.6508     0.064  \n",
              "qda    0.7978  0.6561  0.6625     0.016  \n",
              "ridge  0.7429  0.5409  0.5728     0.013  "
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
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              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.8732</td>\n",
              "      <td>0.9533</td>\n",
              "      <td>0.8265</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>0.8697</td>\n",
              "      <td>0.7692</td>\n",
              "      <td>0.7770</td>\n",
              "      <td>0.513</td>\n",
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              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
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              "      <td>0.8590</td>\n",
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              "      <td>0.7384</td>\n",
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              "      <th>lr</th>\n",
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              "      <th>dt</th>\n",
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              "      <td>0.8090</td>\n",
              "      <td>0.8217</td>\n",
              "      <td>0.7840</td>\n",
              "      <td>0.8127</td>\n",
              "      <td>0.8058</td>\n",
              "      <td>0.6626</td>\n",
              "      <td>0.6675</td>\n",
              "      <td>0.019</td>\n",
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              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.8057</td>\n",
              "      <td>0.9061</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
              "      <td>0.6741</td>\n",
              "      <td>0.6801</td>\n",
              "      <td>0.016</td>\n",
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              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.8053</td>\n",
              "      <td>0.8527</td>\n",
              "      <td>0.7173</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>0.7931</td>\n",
              "      <td>0.6286</td>\n",
              "      <td>0.6535</td>\n",
              "      <td>0.115</td>\n",
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              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7999</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7593</td>\n",
              "      <td>0.8087</td>\n",
              "      <td>0.7957</td>\n",
              "      <td>0.6438</td>\n",
              "      <td>0.6508</td>\n",
              "      <td>0.064</td>\n",
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              "      <td>0.8957</td>\n",
              "      <td>0.8072</td>\n",
              "      <td>0.8123</td>\n",
              "      <td>0.7978</td>\n",
              "      <td>0.6561</td>\n",
              "      <td>0.6625</td>\n",
              "      <td>0.016</td>\n",
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              "      <th>ridge</th>\n",
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              "      <td>0.7650</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.6496</td>\n",
              "      <td>0.7884</td>\n",
              "      <td>0.7429</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.5728</td>\n",
              "      <td>0.013</td>\n",
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              "                                 Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "rf            Random Forest Classifier    0.8732  0.9533  0.8265  0.8811   \n",
              "knn             K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590   \n",
              "lr                 Logistic Regression    0.8183  0.9271  0.7487  0.8385   \n",
              "dt            Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127   \n",
              "nb                         Naive Bayes    0.8057  0.9061  0.8185  0.8213   \n",
              "ada               Ada Boost Classifier    0.8053  0.8527  0.7173  0.8281   \n",
              "svm                SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087   \n",
              "qda    Quadratic Discriminant Analysis    0.7963  0.8957  0.8072  0.8123   \n",
              "ridge                 Ridge Classifier    0.7650  0.0000  0.6496  0.7884   \n",
              "\n",
              "           F1   Kappa     MCC  TT (Sec)  \n",
              "rf     0.8697  0.7692  0.7770     0.513  \n",
              "knn    0.8508  0.7348  0.7384     0.117  \n",
              "lr     0.8094  0.6581  0.6775     0.037  \n",
              "dt     0.8058  0.6626  0.6675     0.019  \n",
              "nb     0.8073  0.6741  0.6801     0.016  \n",
              "ada    0.7931  0.6286  0.6535     0.115  \n",
              "svm    0.7957  0.6438  0.6508     0.064  \n",
              "qda    0.7978  0.6561  0.6625     0.016  \n",
              "ridge  0.7429  0.5409  0.5728     0.013  "
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              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.8732</td>\n",
              "      <td>0.9533</td>\n",
              "      <td>0.8265</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>0.8697</td>\n",
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              "      <td>0.7770</td>\n",
              "      <td>0.513</td>\n",
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              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.8677</td>\n",
              "      <td>0.9478</td>\n",
              "      <td>0.8310</td>\n",
              "      <td>0.8724</td>\n",
              "      <td>0.8647</td>\n",
              "      <td>0.7620</td>\n",
              "      <td>0.7685</td>\n",
              "      <td>0.514</td>\n",
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              "      <td>0.8385</td>\n",
              "      <td>0.8094</td>\n",
              "      <td>0.6581</td>\n",
              "      <td>0.6775</td>\n",
              "      <td>0.037</td>\n",
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              "      <th>dt</th>\n",
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              "      <td>0.8217</td>\n",
              "      <td>0.7840</td>\n",
              "      <td>0.8127</td>\n",
              "      <td>0.8058</td>\n",
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              "      <td>0.9061</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
              "      <td>0.6741</td>\n",
              "      <td>0.6801</td>\n",
              "      <td>0.016</td>\n",
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              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.8053</td>\n",
              "      <td>0.8527</td>\n",
              "      <td>0.7173</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>0.7931</td>\n",
              "      <td>0.6286</td>\n",
              "      <td>0.6535</td>\n",
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              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7999</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7593</td>\n",
              "      <td>0.8087</td>\n",
              "      <td>0.7957</td>\n",
              "      <td>0.6438</td>\n",
              "      <td>0.6508</td>\n",
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              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
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              "      <td>0.8957</td>\n",
              "      <td>0.8072</td>\n",
              "      <td>0.8123</td>\n",
              "      <td>0.7978</td>\n",
              "      <td>0.6561</td>\n",
              "      <td>0.6625</td>\n",
              "      <td>0.016</td>\n",
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              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.7650</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.6496</td>\n",
              "      <td>0.7884</td>\n",
              "      <td>0.7429</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.5728</td>\n",
              "      <td>0.013</td>\n",
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            "text/plain": [
              "                                 Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "rf            Random Forest Classifier    0.8732  0.9533  0.8265  0.8811   \n",
              "gbc       Gradient Boosting Classifier    0.8677  0.9478  0.8310  0.8724   \n",
              "knn             K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590   \n",
              "lr                 Logistic Regression    0.8183  0.9271  0.7487  0.8385   \n",
              "dt            Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127   \n",
              "nb                         Naive Bayes    0.8057  0.9061  0.8185  0.8213   \n",
              "ada               Ada Boost Classifier    0.8053  0.8527  0.7173  0.8281   \n",
              "svm                SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087   \n",
              "qda    Quadratic Discriminant Analysis    0.7963  0.8957  0.8072  0.8123   \n",
              "ridge                 Ridge Classifier    0.7650  0.0000  0.6496  0.7884   \n",
              "\n",
              "           F1   Kappa     MCC  TT (Sec)  \n",
              "rf     0.8697  0.7692  0.7770     0.513  \n",
              "gbc    0.8647  0.7620  0.7685     0.514  \n",
              "knn    0.8508  0.7348  0.7384     0.117  \n",
              "lr     0.8094  0.6581  0.6775     0.037  \n",
              "dt     0.8058  0.6626  0.6675     0.019  \n",
              "nb     0.8073  0.6741  0.6801     0.016  \n",
              "ada    0.7931  0.6286  0.6535     0.115  \n",
              "svm    0.7957  0.6438  0.6508     0.064  \n",
              "qda    0.7978  0.6561  0.6625     0.016  \n",
              "ridge  0.7429  0.5409  0.5728     0.013  "
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              "      <td>0.7692</td>\n",
              "      <td>0.7770</td>\n",
              "      <td>0.513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.8677</td>\n",
              "      <td>0.9478</td>\n",
              "      <td>0.8310</td>\n",
              "      <td>0.8724</td>\n",
              "      <td>0.8647</td>\n",
              "      <td>0.7620</td>\n",
              "      <td>0.7685</td>\n",
              "      <td>0.514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.8532</td>\n",
              "      <td>0.9160</td>\n",
              "      <td>0.8133</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>0.8508</td>\n",
              "      <td>0.7348</td>\n",
              "      <td>0.7384</td>\n",
              "      <td>0.117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.8239</td>\n",
              "      <td>0.9348</td>\n",
              "      <td>0.7694</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>0.8151</td>\n",
              "      <td>0.6723</td>\n",
              "      <td>0.6902</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>0.9271</td>\n",
              "      <td>0.7487</td>\n",
              "      <td>0.8385</td>\n",
              "      <td>0.8094</td>\n",
              "      <td>0.6581</td>\n",
              "      <td>0.6775</td>\n",
              "      <td>0.037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.8090</td>\n",
              "      <td>0.8217</td>\n",
              "      <td>0.7840</td>\n",
              "      <td>0.8127</td>\n",
              "      <td>0.8058</td>\n",
              "      <td>0.6626</td>\n",
              "      <td>0.6675</td>\n",
              "      <td>0.019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.8057</td>\n",
              "      <td>0.9061</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
              "      <td>0.6741</td>\n",
              "      <td>0.6801</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.8053</td>\n",
              "      <td>0.8527</td>\n",
              "      <td>0.7173</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>0.7931</td>\n",
              "      <td>0.6286</td>\n",
              "      <td>0.6535</td>\n",
              "      <td>0.115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7999</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7593</td>\n",
              "      <td>0.8087</td>\n",
              "      <td>0.7957</td>\n",
              "      <td>0.6438</td>\n",
              "      <td>0.6508</td>\n",
              "      <td>0.064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.7963</td>\n",
              "      <td>0.8957</td>\n",
              "      <td>0.8072</td>\n",
              "      <td>0.8123</td>\n",
              "      <td>0.7978</td>\n",
              "      <td>0.6561</td>\n",
              "      <td>0.6625</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.7650</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.6496</td>\n",
              "      <td>0.7884</td>\n",
              "      <td>0.7429</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.5728</td>\n",
              "      <td>0.013</td>\n",
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            "text/plain": [
              "                                 Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "rf            Random Forest Classifier    0.8732  0.9533  0.8265  0.8811   \n",
              "gbc       Gradient Boosting Classifier    0.8677  0.9478  0.8310  0.8724   \n",
              "knn             K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590   \n",
              "lda       Linear Discriminant Analysis    0.8239  0.9348  0.7694  0.8400   \n",
              "lr                 Logistic Regression    0.8183  0.9271  0.7487  0.8385   \n",
              "dt            Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127   \n",
              "nb                         Naive Bayes    0.8057  0.9061  0.8185  0.8213   \n",
              "ada               Ada Boost Classifier    0.8053  0.8527  0.7173  0.8281   \n",
              "svm                SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087   \n",
              "qda    Quadratic Discriminant Analysis    0.7963  0.8957  0.8072  0.8123   \n",
              "ridge                 Ridge Classifier    0.7650  0.0000  0.6496  0.7884   \n",
              "\n",
              "           F1   Kappa     MCC  TT (Sec)  \n",
              "rf     0.8697  0.7692  0.7770     0.513  \n",
              "gbc    0.8647  0.7620  0.7685     0.514  \n",
              "knn    0.8508  0.7348  0.7384     0.117  \n",
              "lda    0.8151  0.6723  0.6902     0.016  \n",
              "lr     0.8094  0.6581  0.6775     0.037  \n",
              "dt     0.8058  0.6626  0.6675     0.019  \n",
              "nb     0.8073  0.6741  0.6801     0.016  \n",
              "ada    0.7931  0.6286  0.6535     0.115  \n",
              "svm    0.7957  0.6438  0.6508     0.064  \n",
              "qda    0.7978  0.6561  0.6625     0.016  \n",
              "ridge  0.7429  0.5409  0.5728     0.013  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.8807</td>\n",
              "      <td>0.9524</td>\n",
              "      <td>0.8380</td>\n",
              "      <td>0.8885</td>\n",
              "      <td>0.8767</td>\n",
              "      <td>0.7842</td>\n",
              "      <td>0.7913</td>\n",
              "      <td>0.462</td>\n",
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              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.8732</td>\n",
              "      <td>0.9533</td>\n",
              "      <td>0.8265</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>0.8697</td>\n",
              "      <td>0.7692</td>\n",
              "      <td>0.7770</td>\n",
              "      <td>0.513</td>\n",
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              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.8677</td>\n",
              "      <td>0.9478</td>\n",
              "      <td>0.8310</td>\n",
              "      <td>0.8724</td>\n",
              "      <td>0.8647</td>\n",
              "      <td>0.7620</td>\n",
              "      <td>0.7685</td>\n",
              "      <td>0.514</td>\n",
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              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.8532</td>\n",
              "      <td>0.9160</td>\n",
              "      <td>0.8133</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>0.8508</td>\n",
              "      <td>0.7348</td>\n",
              "      <td>0.7384</td>\n",
              "      <td>0.117</td>\n",
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              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.8239</td>\n",
              "      <td>0.9348</td>\n",
              "      <td>0.7694</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>0.8151</td>\n",
              "      <td>0.6723</td>\n",
              "      <td>0.6902</td>\n",
              "      <td>0.016</td>\n",
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              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>0.9271</td>\n",
              "      <td>0.7487</td>\n",
              "      <td>0.8385</td>\n",
              "      <td>0.8094</td>\n",
              "      <td>0.6581</td>\n",
              "      <td>0.6775</td>\n",
              "      <td>0.037</td>\n",
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              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.8090</td>\n",
              "      <td>0.8217</td>\n",
              "      <td>0.7840</td>\n",
              "      <td>0.8127</td>\n",
              "      <td>0.8058</td>\n",
              "      <td>0.6626</td>\n",
              "      <td>0.6675</td>\n",
              "      <td>0.019</td>\n",
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              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.8057</td>\n",
              "      <td>0.9061</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
              "      <td>0.6741</td>\n",
              "      <td>0.6801</td>\n",
              "      <td>0.016</td>\n",
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              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.8053</td>\n",
              "      <td>0.8527</td>\n",
              "      <td>0.7173</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>0.7931</td>\n",
              "      <td>0.6286</td>\n",
              "      <td>0.6535</td>\n",
              "      <td>0.115</td>\n",
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              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7999</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7593</td>\n",
              "      <td>0.8087</td>\n",
              "      <td>0.7957</td>\n",
              "      <td>0.6438</td>\n",
              "      <td>0.6508</td>\n",
              "      <td>0.064</td>\n",
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              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.7963</td>\n",
              "      <td>0.8957</td>\n",
              "      <td>0.8072</td>\n",
              "      <td>0.8123</td>\n",
              "      <td>0.7978</td>\n",
              "      <td>0.6561</td>\n",
              "      <td>0.6625</td>\n",
              "      <td>0.016</td>\n",
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              "      <td>Ridge Classifier</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>0.6496</td>\n",
              "      <td>0.7884</td>\n",
              "      <td>0.7429</td>\n",
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              "                                 Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "et              Extra Trees Classifier    0.8807  0.9524  0.8380  0.8885   \n",
              "rf            Random Forest Classifier    0.8732  0.9533  0.8265  0.8811   \n",
              "gbc       Gradient Boosting Classifier    0.8677  0.9478  0.8310  0.8724   \n",
              "knn             K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590   \n",
              "lda       Linear Discriminant Analysis    0.8239  0.9348  0.7694  0.8400   \n",
              "lr                 Logistic Regression    0.8183  0.9271  0.7487  0.8385   \n",
              "dt            Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127   \n",
              "nb                         Naive Bayes    0.8057  0.9061  0.8185  0.8213   \n",
              "ada               Ada Boost Classifier    0.8053  0.8527  0.7173  0.8281   \n",
              "svm                SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087   \n",
              "qda    Quadratic Discriminant Analysis    0.7963  0.8957  0.8072  0.8123   \n",
              "ridge                 Ridge Classifier    0.7650  0.0000  0.6496  0.7884   \n",
              "\n",
              "           F1   Kappa     MCC  TT (Sec)  \n",
              "et     0.8767  0.7842  0.7913     0.462  \n",
              "rf     0.8697  0.7692  0.7770     0.513  \n",
              "gbc    0.8647  0.7620  0.7685     0.514  \n",
              "knn    0.8508  0.7348  0.7384     0.117  \n",
              "lda    0.8151  0.6723  0.6902     0.016  \n",
              "lr     0.8094  0.6581  0.6775     0.037  \n",
              "dt     0.8058  0.6626  0.6675     0.019  \n",
              "nb     0.8073  0.6741  0.6801     0.016  \n",
              "ada    0.7931  0.6286  0.6535     0.115  \n",
              "svm    0.7957  0.6438  0.6508     0.064  \n",
              "qda    0.7978  0.6561  0.6625     0.016  \n",
              "ridge  0.7429  0.5409  0.5728     0.013  "
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.8807</td>\n",
              "      <td>0.9524</td>\n",
              "      <td>0.8380</td>\n",
              "      <td>0.8885</td>\n",
              "      <td>0.8767</td>\n",
              "      <td>0.7842</td>\n",
              "      <td>0.7913</td>\n",
              "      <td>0.462</td>\n",
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              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.8788</td>\n",
              "      <td>0.9556</td>\n",
              "      <td>0.8506</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>0.8769</td>\n",
              "      <td>0.7831</td>\n",
              "      <td>0.7864</td>\n",
              "      <td>0.181</td>\n",
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              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.8732</td>\n",
              "      <td>0.9533</td>\n",
              "      <td>0.8265</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>0.8697</td>\n",
              "      <td>0.7692</td>\n",
              "      <td>0.7770</td>\n",
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              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.8677</td>\n",
              "      <td>0.9478</td>\n",
              "      <td>0.8310</td>\n",
              "      <td>0.8724</td>\n",
              "      <td>0.8647</td>\n",
              "      <td>0.7620</td>\n",
              "      <td>0.7685</td>\n",
              "      <td>0.514</td>\n",
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              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.8532</td>\n",
              "      <td>0.9160</td>\n",
              "      <td>0.8133</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>0.8508</td>\n",
              "      <td>0.7348</td>\n",
              "      <td>0.7384</td>\n",
              "      <td>0.117</td>\n",
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              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.8239</td>\n",
              "      <td>0.9348</td>\n",
              "      <td>0.7694</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>0.8151</td>\n",
              "      <td>0.6723</td>\n",
              "      <td>0.6902</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>0.9271</td>\n",
              "      <td>0.7487</td>\n",
              "      <td>0.8385</td>\n",
              "      <td>0.8094</td>\n",
              "      <td>0.6581</td>\n",
              "      <td>0.6775</td>\n",
              "      <td>0.037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.8090</td>\n",
              "      <td>0.8217</td>\n",
              "      <td>0.7840</td>\n",
              "      <td>0.8127</td>\n",
              "      <td>0.8058</td>\n",
              "      <td>0.6626</td>\n",
              "      <td>0.6675</td>\n",
              "      <td>0.019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.8057</td>\n",
              "      <td>0.9061</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
              "      <td>0.6741</td>\n",
              "      <td>0.6801</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.8053</td>\n",
              "      <td>0.8527</td>\n",
              "      <td>0.7173</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>0.7931</td>\n",
              "      <td>0.6286</td>\n",
              "      <td>0.6535</td>\n",
              "      <td>0.115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7999</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7593</td>\n",
              "      <td>0.8087</td>\n",
              "      <td>0.7957</td>\n",
              "      <td>0.6438</td>\n",
              "      <td>0.6508</td>\n",
              "      <td>0.064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.7963</td>\n",
              "      <td>0.8957</td>\n",
              "      <td>0.8072</td>\n",
              "      <td>0.8123</td>\n",
              "      <td>0.7978</td>\n",
              "      <td>0.6561</td>\n",
              "      <td>0.6625</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.7650</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.6496</td>\n",
              "      <td>0.7884</td>\n",
              "      <td>0.7429</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.5728</td>\n",
              "      <td>0.013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "et                 Extra Trees Classifier    0.8807  0.9524  0.8380  0.8885   \n",
              "lightgbm  Light Gradient Boosting Machine    0.8788  0.9556  0.8506  0.8811   \n",
              "rf               Random Forest Classifier    0.8732  0.9533  0.8265  0.8811   \n",
              "gbc          Gradient Boosting Classifier    0.8677  0.9478  0.8310  0.8724   \n",
              "knn                K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590   \n",
              "lda          Linear Discriminant Analysis    0.8239  0.9348  0.7694  0.8400   \n",
              "lr                    Logistic Regression    0.8183  0.9271  0.7487  0.8385   \n",
              "dt               Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127   \n",
              "nb                            Naive Bayes    0.8057  0.9061  0.8185  0.8213   \n",
              "ada                  Ada Boost Classifier    0.8053  0.8527  0.7173  0.8281   \n",
              "svm                   SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087   \n",
              "qda       Quadratic Discriminant Analysis    0.7963  0.8957  0.8072  0.8123   \n",
              "ridge                    Ridge Classifier    0.7650  0.0000  0.6496  0.7884   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "et        0.8767  0.7842  0.7913     0.462  \n",
              "lightgbm  0.8769  0.7831  0.7864     0.181  \n",
              "rf        0.8697  0.7692  0.7770     0.513  \n",
              "gbc       0.8647  0.7620  0.7685     0.514  \n",
              "knn       0.8508  0.7348  0.7384     0.117  \n",
              "lda       0.8151  0.6723  0.6902     0.016  \n",
              "lr        0.8094  0.6581  0.6775     0.037  \n",
              "dt        0.8058  0.6626  0.6675     0.019  \n",
              "nb        0.8073  0.6741  0.6801     0.016  \n",
              "ada       0.7931  0.6286  0.6535     0.115  \n",
              "svm       0.7957  0.6438  0.6508     0.064  \n",
              "qda       0.7978  0.6561  0.6625     0.016  \n",
              "ridge     0.7429  0.5409  0.5728     0.013  "
            ]
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.8807</td>\n",
              "      <td>0.9524</td>\n",
              "      <td>0.8380</td>\n",
              "      <td>0.8885</td>\n",
              "      <td>0.8767</td>\n",
              "      <td>0.7842</td>\n",
              "      <td>0.7913</td>\n",
              "      <td>0.462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.8788</td>\n",
              "      <td>0.9556</td>\n",
              "      <td>0.8506</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>0.8769</td>\n",
              "      <td>0.7831</td>\n",
              "      <td>0.7864</td>\n",
              "      <td>0.181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.8732</td>\n",
              "      <td>0.9533</td>\n",
              "      <td>0.8265</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>0.8697</td>\n",
              "      <td>0.7692</td>\n",
              "      <td>0.7770</td>\n",
              "      <td>0.513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.8677</td>\n",
              "      <td>0.9478</td>\n",
              "      <td>0.8310</td>\n",
              "      <td>0.8724</td>\n",
              "      <td>0.8647</td>\n",
              "      <td>0.7620</td>\n",
              "      <td>0.7685</td>\n",
              "      <td>0.514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.8532</td>\n",
              "      <td>0.9160</td>\n",
              "      <td>0.8133</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>0.8508</td>\n",
              "      <td>0.7348</td>\n",
              "      <td>0.7384</td>\n",
              "      <td>0.117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.8239</td>\n",
              "      <td>0.9348</td>\n",
              "      <td>0.7694</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>0.8151</td>\n",
              "      <td>0.6723</td>\n",
              "      <td>0.6902</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>0.9271</td>\n",
              "      <td>0.7487</td>\n",
              "      <td>0.8385</td>\n",
              "      <td>0.8094</td>\n",
              "      <td>0.6581</td>\n",
              "      <td>0.6775</td>\n",
              "      <td>0.037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.8090</td>\n",
              "      <td>0.8217</td>\n",
              "      <td>0.7840</td>\n",
              "      <td>0.8127</td>\n",
              "      <td>0.8058</td>\n",
              "      <td>0.6626</td>\n",
              "      <td>0.6675</td>\n",
              "      <td>0.019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.8057</td>\n",
              "      <td>0.9061</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.8213</td>\n",
              "      <td>0.8073</td>\n",
              "      <td>0.6741</td>\n",
              "      <td>0.6801</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.8053</td>\n",
              "      <td>0.8527</td>\n",
              "      <td>0.7173</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>0.7931</td>\n",
              "      <td>0.6286</td>\n",
              "      <td>0.6535</td>\n",
              "      <td>0.115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7999</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.7593</td>\n",
              "      <td>0.8087</td>\n",
              "      <td>0.7957</td>\n",
              "      <td>0.6438</td>\n",
              "      <td>0.6508</td>\n",
              "      <td>0.064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.7963</td>\n",
              "      <td>0.8957</td>\n",
              "      <td>0.8072</td>\n",
              "      <td>0.8123</td>\n",
              "      <td>0.7978</td>\n",
              "      <td>0.6561</td>\n",
              "      <td>0.6625</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.7650</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.6496</td>\n",
              "      <td>0.7884</td>\n",
              "      <td>0.7429</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.5728</td>\n",
              "      <td>0.013</td>\n",
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              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "et                 Extra Trees Classifier    0.8807  0.9524  0.8380  0.8885   \n",
              "lightgbm  Light Gradient Boosting Machine    0.8788  0.9556  0.8506  0.8811   \n",
              "rf               Random Forest Classifier    0.8732  0.9533  0.8265  0.8811   \n",
              "gbc          Gradient Boosting Classifier    0.8677  0.9478  0.8310  0.8724   \n",
              "knn                K Neighbors Classifier    0.8532  0.9160  0.8133  0.8590   \n",
              "lda          Linear Discriminant Analysis    0.8239  0.9348  0.7694  0.8400   \n",
              "lr                    Logistic Regression    0.8183  0.9271  0.7487  0.8385   \n",
              "dt               Decision Tree Classifier    0.8090  0.8217  0.7840  0.8127   \n",
              "nb                            Naive Bayes    0.8057  0.9061  0.8185  0.8213   \n",
              "ada                  Ada Boost Classifier    0.8053  0.8527  0.7173  0.8281   \n",
              "svm                   SVM - Linear Kernel    0.7999  0.0000  0.7593  0.8087   \n",
              "qda       Quadratic Discriminant Analysis    0.7963  0.8957  0.8072  0.8123   \n",
              "ridge                    Ridge Classifier    0.7650  0.0000  0.6496  0.7884   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "et        0.8767  0.7842  0.7913     0.462  \n",
              "lightgbm  0.8769  0.7831  0.7864     0.181  \n",
              "rf        0.8697  0.7692  0.7770     0.513  \n",
              "gbc       0.8647  0.7620  0.7685     0.514  \n",
              "knn       0.8508  0.7348  0.7384     0.117  \n",
              "lda       0.8151  0.6723  0.6902     0.016  \n",
              "lr        0.8094  0.6581  0.6775     0.037  \n",
              "dt        0.8058  0.6626  0.6675     0.019  \n",
              "nb        0.8073  0.6741  0.6801     0.016  \n",
              "ada       0.7931  0.6286  0.6535     0.115  \n",
              "svm       0.7957  0.6438  0.6508     0.064  \n",
              "qda       0.7978  0.6561  0.6625     0.016  \n",
              "ridge     0.7429  0.5409  0.5728     0.013  "
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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "blddvVaRTHRJ"
      },
      "source": [
        "Após uma série de testes, concluímos que o melhor modelo é o ExtraTreesClassifier, que ficou com uma acurácia média de 87.5%,então vamos usá-lo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iymZXsBZ9kUv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "3d9b2b09-6d72-4c40-ef08-41fb96fb9165"
      },
      "source": [
        "best_model = tune_model(ExtraTreesClassifier(), n_iter=200)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 200 candidates, totalling 2000 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   17.8s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   47.4s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  1.9min\n",
            "[Parallel(n_jobs=-1)]: Done 808 tasks      | elapsed:  3.3min\n",
            "[Parallel(n_jobs=-1)]: Done 1278 tasks      | elapsed:  5.3min\n",
            "[Parallel(n_jobs=-1)]: Done 1828 tasks      | elapsed:  7.3min\n",
            "[Parallel(n_jobs=-1)]: Done 2000 out of 2000 | elapsed:  8.0min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.9636</td>\n",
              "      <td>0.9975</td>\n",
              "      <td>0.9792</td>\n",
              "      <td>0.9679</td>\n",
              "      <td>0.9642</td>\n",
              "      <td>0.9370</td>\n",
              "      <td>0.9393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.7818</td>\n",
              "      <td>0.8890</td>\n",
              "      <td>0.7771</td>\n",
              "      <td>0.7913</td>\n",
              "      <td>0.7839</td>\n",
              "      <td>0.6205</td>\n",
              "      <td>0.6230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.9455</td>\n",
              "      <td>0.9666</td>\n",
              "      <td>0.9257</td>\n",
              "      <td>0.9466</td>\n",
              "      <td>0.9456</td>\n",
              "      <td>0.9027</td>\n",
              "      <td>0.9032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8545</td>\n",
              "      <td>0.9545</td>\n",
              "      <td>0.8385</td>\n",
              "      <td>0.8557</td>\n",
              "      <td>0.8544</td>\n",
              "      <td>0.7489</td>\n",
              "      <td>0.7493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8909</td>\n",
              "      <td>0.9177</td>\n",
              "      <td>0.8452</td>\n",
              "      <td>0.8976</td>\n",
              "      <td>0.8880</td>\n",
              "      <td>0.8068</td>\n",
              "      <td>0.8106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.9630</td>\n",
              "      <td>0.9920</td>\n",
              "      <td>0.9654</td>\n",
              "      <td>0.9630</td>\n",
              "      <td>0.9630</td>\n",
              "      <td>0.9356</td>\n",
              "      <td>0.9356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8333</td>\n",
              "      <td>0.9147</td>\n",
              "      <td>0.8245</td>\n",
              "      <td>0.8346</td>\n",
              "      <td>0.8336</td>\n",
              "      <td>0.7133</td>\n",
              "      <td>0.7137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8704</td>\n",
              "      <td>0.9464</td>\n",
              "      <td>0.8593</td>\n",
              "      <td>0.8727</td>\n",
              "      <td>0.8709</td>\n",
              "      <td>0.7717</td>\n",
              "      <td>0.7723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.7963</td>\n",
              "      <td>0.9189</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.8152</td>\n",
              "      <td>0.7915</td>\n",
              "      <td>0.6338</td>\n",
              "      <td>0.6393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8889</td>\n",
              "      <td>0.9531</td>\n",
              "      <td>0.8392</td>\n",
              "      <td>0.8966</td>\n",
              "      <td>0.8870</td>\n",
              "      <td>0.7960</td>\n",
              "      <td>0.8033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8788</td>\n",
              "      <td>0.9450</td>\n",
              "      <td>0.8589</td>\n",
              "      <td>0.8841</td>\n",
              "      <td>0.8782</td>\n",
              "      <td>0.7866</td>\n",
              "      <td>0.7890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0616</td>\n",
              "      <td>0.0333</td>\n",
              "      <td>0.0738</td>\n",
              "      <td>0.0585</td>\n",
              "      <td>0.0619</td>\n",
              "      <td>0.1081</td>\n",
              "      <td>0.1074</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "0       0.9636  0.9975  0.9792  0.9679  0.9642  0.9370  0.9393\n",
              "1       0.7818  0.8890  0.7771  0.7913  0.7839  0.6205  0.6230\n",
              "2       0.9455  0.9666  0.9257  0.9466  0.9456  0.9027  0.9032\n",
              "3       0.8545  0.9545  0.8385  0.8557  0.8544  0.7489  0.7493\n",
              "4       0.8909  0.9177  0.8452  0.8976  0.8880  0.8068  0.8106\n",
              "5       0.9630  0.9920  0.9654  0.9630  0.9630  0.9356  0.9356\n",
              "6       0.8333  0.9147  0.8245  0.8346  0.8336  0.7133  0.7137\n",
              "7       0.8704  0.9464  0.8593  0.8727  0.8709  0.7717  0.7723\n",
              "8       0.7963  0.9189  0.7351  0.8152  0.7915  0.6338  0.6393\n",
              "9       0.8889  0.9531  0.8392  0.8966  0.8870  0.7960  0.8033\n",
              "Mean    0.8788  0.9450  0.8589  0.8841  0.8782  0.7866  0.7890\n",
              "SD      0.0616  0.0333  0.0738  0.0585  0.0619  0.1081  0.1074"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WP1PAC6aVfQX"
      },
      "source": [
        "Agora, podemos testar nosso modelo em funcionamento usando dados dos fundos em uma data que não tínhamos visto antes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvgBtB5xGoPB"
      },
      "source": [
        "df_teste = pd.read_csv('https://raw.githubusercontent.com/TailUFPB/fundos-imobiliarios/main/CSV_Modelo.csv')\n",
        "df_teste.drop(['Unnamed: 0','Setor', 'Preço Atual','Rentab.Período', 'DY Ano', 'Dividendo', 'Ano/Mês'], axis='columns', inplace=True)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8wMY7S2WUfD"
      },
      "source": [
        "Usando as notas obtidas no nosso rankeamento, vamos selecionar alguns fundos bons, médios e ruins, para vermos como nosso modelo os classifica:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJP9BvAVHdW3"
      },
      "source": [
        "#Fundos bons:\n",
        "df_irdm = df_teste[df_teste[\"Códigodo fundo\"] == 'IRDM11']\n",
        "df_bbpo = df_teste[df_teste[\"Códigodo fundo\"] == 'BBPO11']\n",
        "\n",
        "#Fundos médios:\n",
        "df_alzr = df_teste[df_teste[\"Códigodo fundo\"] == 'ALZR11']\n",
        "df_qiff = df_teste[df_teste[\"Códigodo fundo\"] == 'QIFF11']\n",
        "\n",
        "#Fundos ruins:\n",
        "df_xpht = df_teste[df_teste[\"Códigodo fundo\"] == 'XPHT11']\n",
        "df_edga = df_teste[df_teste[\"Códigodo fundo\"] == 'EDGA11']"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPmt21xsWzdE",
        "outputId": "9615151c-7d86-4a59-8cce-df451f2a27aa"
      },
      "source": [
        "print('Fundo IRDM11: ',best_model.predict(df_irdm.iloc[:,1:]))\n",
        "print('Fundo BBPO11: ',best_model.predict(df_bbpo.iloc[:,1:]))\n",
        "print('Fundo ALZR11: ',best_model.predict(df_alzr.iloc[:,1:]))\n",
        "print('Fundo QIFF11: ',best_model.predict(df_qiff.iloc[:,1:]))\n",
        "print('Fundo XPHT11: ',best_model.predict(df_xpht.iloc[:,1:]))\n",
        "print('Fundo EDGA11: ',best_model.predict(df_edga.iloc[:,1:]))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fundo IRDM11:  [0]\n",
            "Fundo BBPO11:  [0]\n",
            "Fundo ALZR11:  [1]\n",
            "Fundo QIFF11:  [1]\n",
            "Fundo XPHT11:  [2]\n",
            "Fundo EDGA11:  [2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0fIP--ZXrnQ"
      },
      "source": [
        "Como a label 0 corresponde a bom, a label 1 corresponde a médio, e a label 2 corresponde a ruim, podemos ver que nosso modelo conseguiu classificar esses exemplos corretamente!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_GoecMfZgeFH"
      },
      "source": [
        "## Clusterização\n",
        "\n",
        "Outra coisa que podemos fazer para achar os melhores fundos é aplicar um algoritmo de clusterização nos nossos dados, e ver se eles são agrupados em conjunto.\n",
        "\n",
        "O algoritmo KMeans agrupa os dados tentando separar amostras em n grupos de variância igual, minimizando um critério conhecido como inércia ou soma dos quadrados dentro do cluster. Ele se adapta bem a um grande número de amostras e tem sido usado em uma grande variedade de áreas de aplicação em muitos campos diferentes.\n",
        "\n",
        "\n",
        "![kameans-cluster.png](data:image/png;base64,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)\n",
        "\n",
        "[Saiba Mais](https://scikit-learn.org/stable/modules/clustering.html#k-means)\n",
        "\n",
        "[Guia de Uso](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hSe4pjZ-jtLK"
      },
      "source": [
        "Para descobrir o número K de núcleos que iremos utilizar, podemos usar a técnica bastante usada para clusterização que é a \"Soma do erro Quadrático\". Ela nos ajuda a ver quando o erro começa a ter menor variação e nos ajuda a descobrir os melhores \"K\" para nosso modelo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "YO_I_d9okHcu",
        "outputId": "9fa2c87f-49e0-4390-a178-175ff0681690"
      },
      "source": [
        "sse =[]\n",
        "for k in range(1, 20):\n",
        "  kmeans = KMeans(n_clusters=k, random_state=0).fit(df_teste.iloc[:,1:])\n",
        "  sse.append(kmeans.inertia_)\n",
        "plt.xlabel(\"K\")\n",
        "plt.ylabel(\"Sum of Square Error\")\n",
        "plt.plot(range(1,20), sse)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4b3976d2d0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iU_QCg-zlwhc"
      },
      "source": [
        "Podemos perceber que a descida é menos ingrime a cada iteração, e que a partir da sétima, a diferença entre dois núcleos vizinhos não é tão grande. E levando em consideração que não é bom para nossa análise ter um número muito grande de núcleos, pois isso dificultaria a visualização e separação entre apenas 3 categorias, vamos adotar 7 como o numéro de núcleos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "io6K6DWTtVK0"
      },
      "source": [
        "kmeans = KMeans(n_clusters=7, random_state=0, max_iter=500).fit(df_teste.iloc[:,1:])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rM72XOb9oucQ"
      },
      "source": [
        "Com os clusters separados, vamos aplicar nosso modelo nos modelos separados por cluster:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UlaNn0_FuQTd",
        "outputId": "8b123e02-2392-4dec-92d0-3f8e0a5b29ac"
      },
      "source": [
        "df_teste[\"cluster\"] = pd.DataFrame(kmeans.labels_)\n",
        "\n",
        "clusters = []\n",
        "for i in range(0, 7):\n",
        "  clusters.append(df_teste[df_teste[\"cluster\"] == i])\n",
        "  clusters[i].reset_index(drop=True, inplace=True)\n",
        "\n",
        "for i in clusters:\n",
        "  for j in range(i.shape[0]):\n",
        "    fundo = i.iloc[j:j+1,1:-1]\n",
        "    print(i.loc[j,'Códigodo fundo'],': ', best_model.predict(fundo))\n",
        "  print(\"-------------------------------------------------------------------------------\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ABCP11 :  [1]\n",
            "ALZR11 :  [1]\n",
            "BBRC11 :  [0]\n",
            "BCIA11 :  [1]\n",
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            "BTCR11 :  [0]\n",
            "CBOP11 :  [0]\n",
            "CEOC11 :  [1]\n",
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            "CXRI11 :  [1]\n",
            "EDFO11B :  [1]\n",
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            "FATN11 :  [0]\n",
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            "FIIP11B :  [0]\n",
            "FVPQ11 :  [1]\n",
            "GGRC11 :  [1]\n",
            "GTWR11 :  [0]\n",
            "HGFF11 :  [1]\n",
            "HGPO11 :  [1]\n",
            "HLOG11 :  [1]\n",
            "HOSI11 :  [1]\n",
            "HUSC11 :  [1]\n",
            "IBFF11 :  [1]\n",
            "KFOF11 :  [1]\n",
            "LASC11 :  [1]\n",
            "LGCP11 :  [1]\n",
            "LUGG11 :  [1]\n",
            "MALL11 :  [1]\n",
            "MCCI11 :  [0]\n",
            "MFII11 :  [0]\n",
            "MGFF11 :  [0]\n",
            "MGHT11 :  [1]\n",
            "MORE11 :  [1]\n",
            "NCHB11 :  [1]\n",
            "NSLU11 :  [0]\n",
            "NVHO11 :  [1]\n",
            "OUFF11 :  [0]\n",
            "OUJP11 :  [0]\n",
            "OULG11 :  [1]\n",
            "OURE11 :  [0]\n",
            "PLRI11 :  [1]\n",
            "QAGR11 :  [0]\n",
            "RBED11 :  [0]\n",
            "RBFF11 :  [0]\n",
            "RBIV11 :  [0]\n",
            "RBRF11 :  [0]\n",
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            "RCRB11 :  [1]\n",
            "RDPD11 :  [1]\n",
            "RECT11 :  [1]\n",
            "RFOF11 :  [0]\n",
            "RNDP11 :  [1]\n",
            "RNGO11 :  [1]\n",
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            "SDIL11 :  [1]\n",
            "TEPP11 :  [1]\n",
            "VCJR11 :  [0]\n",
            "VGIR11 :  [1]\n",
            "VIFI11 :  [0]\n",
            "VINO11 :  [0]\n",
            "VLOL11 :  [1]\n",
            "VOTS11 :  [1]\n",
            "VTLT11 :  [0]\n",
            "XPIN11 :  [1]\n",
            "XPPR11 :  [1]\n",
            "-------------------------------------------------------------------------------\n",
            "ALMI11 :  [2]\n",
            "CNES11 :  [2]\n",
            "DRIT11B :  [1]\n",
            "EDGA11 :  [2]\n",
            "FIGS11 :  [2]\n",
            "FIVN11 :  [2]\n",
            "FPAB11 :  [2]\n",
            "NEWU11 :  [2]\n",
            "PATC11 :  [2]\n",
            "PRSV11 :  [2]\n",
            "RBCO11 :  [2]\n",
            "RBGS11 :  [2]\n",
            "RBVO11 :  [2]\n",
            "SCPF11 :  [2]\n",
            "TRNT11 :  [2]\n",
            "WPLZ11 :  [2]\n",
            "WTSP11B :  [2]\n",
            "XPHT11 :  [2]\n",
            "XTED11 :  [2]\n",
            "-------------------------------------------------------------------------------\n",
            "BRCO11 :  [0]\n",
            "CPTS11 :  [0]\n",
            "HGCR11 :  [0]\n",
            "MXRF11 :  [0]\n",
            "RBRL11 :  [0]\n",
            "TRXF11 :  [0]\n",
            "VSLH11 :  [1]\n",
            "-------------------------------------------------------------------------------\n",
            "AFCR11 :  [1]\n",
            "AIEC11 :  [1]\n",
            "BLCP11 :  [1]\n",
            "GALG11 :  [0]\n",
            "GCFF11 :  [0]\n",
            "HREC11 :  [1]\n",
            "HSAF11 :  [0]\n",
            "JPPA11 :  [1]\n",
            "KNSC11 :  [0]\n",
            "MFAI11 :  [1]\n",
            "MGCR11 :  [1]\n",
            "PATL11 :  [1]\n",
            "QIFF11 :  [1]\n",
            "RZTR11 :  [1]\n",
            "URPR11 :  [1]\n",
            "-------------------------------------------------------------------------------\n",
            "BCFF11 :  [0]\n",
            "BRCR11 :  [1]\n",
            "BTLG11 :  [0]\n",
            "HFOF11 :  [0]\n",
            "HGBS11 :  [1]\n",
            "HGLG11 :  [1]\n",
            "HGRE11 :  [2]\n",
            "HGRU11 :  [0]\n",
            "HSML11 :  [1]\n",
            "JSRE11 :  [1]\n",
            "KNCR11 :  [1]\n",
            "KNRI11 :  [1]\n",
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            "VILG11 :  [1]\n",
            "VISC11 :  [1]\n",
            "XPLG11 :  [1]\n",
            "XPML11 :  [1]\n",
            "-------------------------------------------------------------------------------\n",
            "ATSA11 :  [1]\n",
            "BBFO11 :  [1]\n",
            "BICR11 :  [1]\n",
            "BLMG11 :  [1]\n",
            "BLMR11 :  [1]\n",
            "BREV11 :  [1]\n",
            "BTAL11 :  [1]\n",
            "BZLI11 :  [1]\n",
            "CXCO11 :  [1]\n",
            "DEVA11 :  [1]\n",
            "EVBI11 :  [1]\n",
            "FLRP11 :  [1]\n",
            "HAAA11 :  [1]\n",
            "HBRH11 :  [1]\n",
            "HGIC11 :  [1]\n",
            "HPDP11 :  [1]\n",
            "HSLG11 :  [1]\n",
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            "IFIE11 :  [1]\n",
            "JFLL11 :  [1]\n",
            "JRDM11 :  [1]\n",
            "KISU11 :  [2]\n",
            "MBRF11 :  [1]\n",
            "ONEF11 :  [1]\n",
            "PLOG11 :  [1]\n",
            "PQAG11 :  [1]\n",
            "PQDP11 :  [1]\n",
            "PVBI11 :  [1]\n",
            "RBDS11 :  [1]\n",
            "RBRS11 :  [2]\n",
            "RECX11 :  [1]\n",
            "RELG11 :  [1]\n",
            "RRCI11 :  [1]\n",
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            "SADI11 :  [1]\n",
            "SEQR11 :  [1]\n",
            "SHPH11 :  [1]\n",
            "THRA11 :  [1]\n",
            "VGHF11 :  [1]\n",
            "VSHO11 :  [2]\n",
            "-------------------------------------------------------------------------------\n",
            "ARCT11 :  [0]\n",
            "ARRI11 :  [0]\n",
            "BARI11 :  [0]\n",
            "BBPO11 :  [0]\n",
            "BCRI11 :  [0]\n",
            "BNFS11 :  [0]\n",
            "CPFF11 :  [0]\n",
            "CVBI11 :  [0]\n",
            "CXCE11B :  [0]\n",
            "FEXC11 :  [0]\n",
            "HABT11 :  [0]\n",
            "HCTR11 :  [0]\n",
            "IRDM11 :  [0]\n",
            "KNHY11 :  [0]\n",
            "NEWL11 :  [0]\n",
            "PLCR11 :  [0]\n",
            "PORD11 :  [0]\n",
            "RECR11 :  [0]\n",
            "TGAR11 :  [0]\n",
            "TORD11 :  [0]\n",
            "VGIP11 :  [0]\n",
            "VRTA11 :  [0]\n",
            "XPCI11 :  [0]\n",
            "XPSF11 :  [0]\n",
            "-------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5mk2FGRPprHg"
      },
      "source": [
        "Vendo os resultados, é perceptível que a clusterização conseguiu cumprir seu papel de separar nossos dados de acordo com a qualidade dos fundos, visto que no último cluster, todos foram classificados como bons pelo nosso modelo, ou seja, podemos afirmar que esses são os que possuem os melhores atributos, e por isso, foram agrupados em um único cluster."
      ]
    }
  ]
}