Previsão_de_Diabetes.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/juliosaopedro/Meus_Projetos/blob/master/Previs%C3%A3o_de_Diabetes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwJnMIeUslqj"
      },
      "source": [
        "## Objetivo\n",
        "Analisar conjunto de dados para criar modelo para previsão de resultado para diabetes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hnFR3jStg3y"
      },
      "source": [
        "### O que é diabetes?\n",
        "De acordo com o NIH, \"O diabetes é uma doença que ocorre quando a glicose no sangue, também chamada de açúcar no sangue, está muito alta. A glicose no sangue é sua principal fonte de energia e vem dos alimentos que você ingere. A insulina, um hormônio produzido pelo pâncreas, ajuda a glicose dos alimentos a entrar nas células para ser usada como energia. Às vezes, seu corpo não produz insulina suficiente, ou nenhuma, ou não usa bem a insulina. A glicose fica no sangue e não chega às células.\n",
        "\n",
        "Com o tempo, o excesso de glicose no sangue pode causar problemas de saúde. Embora o diabetes não tenha cura, você pode tomar medidas para controlá-lo e permanecer saudável.\n",
        "\n",
        "Às vezes, as pessoas chamam o diabetes de \"um toque de açúcar\" ou \"diabetes limítrofe\". Esses termos sugerem que alguém não tem realmente diabetes ou tem um caso menos sério, mas todo caso de diabetes é sério.\n",
        "\n",
        "Quais são os diferentes tipos de diabetes? Os tipos mais comuns de diabetes são diabetes tipo 1, tipo 2 e diabetes gestacional.\n",
        "\n",
        "**Diabetes tipo 1** Se você tem diabetes tipo 1, seu corpo não produz insulina. Seu sistema imunológico ataca e destrói as células do pâncreas que produzem insulina. O diabetes tipo 1 geralmente é diagnosticado em crianças e adultos jovens, embora possa aparecer em qualquer idade. Pessoas com diabetes tipo 1 precisam tomar insulina todos os dias para se manterem vivas.\n",
        "\n",
        "**Diabetes tipo 2** Se você tem diabetes tipo 2, seu corpo não produz ou usa insulina bem. Você pode desenvolver diabetes tipo 2 em qualquer idade, mesmo durante a infância. No entanto, esse tipo de diabetes ocorre com mais frequência em pessoas de meia-idade e mais velhas. O tipo 2 é o tipo mais comum de diabetes.\n",
        "\n",
        "**Diabetes gestacional** O diabetes gestacional se desenvolve em algumas mulheres durante a gravidez. Na maioria das vezes, esse tipo de diabetes desaparece após o nascimento do bebê. No entanto, se você teve diabetes gestacional, tem uma chance maior de desenvolver diabetes tipo 2 mais tarde na vida. Às vezes, o diabetes diagnosticado durante a gravidez é, na verdade, diabetes tipo 2.\n",
        "\n",
        "**Outros tipos de diabetes** Os tipos menos comuns incluem o diabetes monogênico, que é uma forma hereditária de diabetes, e o diabetes relacionado à fibrose cística. \" \n",
        "Mais sobre o texto originalÉ necessário fornecer o texto original para ver mais informações sobre a tradução\n",
        "Enviar feedback\n",
        "Painéis laterais"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TB2UYfC8jLq1"
      },
      "source": [
        "# Instalando a biblioteca pycaret\n",
        "!pip install pycaret"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dwb5C8fI0yBk"
      },
      "source": [
        "# Importando as bibliotecas dos modelos de regressao\n",
        "from pycaret.regression import *"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luY1Rk0r-Wm4"
      },
      "source": [
        "# Importando a biblioteca pandas\n",
        "import pandas as pd\n",
        "import plotly.graph_objs as go\n",
        "import plotly.offline as py"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qHis2cJg-Vf"
      },
      "source": [
        "# Importando nossos dados\n",
        "df = pd.read_csv('/content/test_diabetes.csv', sep=',')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPLhCfpJl5T3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cd0b40b6-fad5-454c-a894-498ae12cc338"
      },
      "source": [
        "# Nossos dados\n",
        "df.head()"
      ],
      "execution_count": 6,
      "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>148.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0          NaN    148.0           72.0           35.0      0.0  33.6   \n",
              "1          1.0     85.0           66.0           29.0      0.0  26.6   \n",
              "2          8.0    183.0           64.0            0.0      0.0  23.3   \n",
              "3          1.0     89.0           66.0           23.0     94.0  28.1   \n",
              "4          0.0      NaN           40.0           35.0    168.0  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction   Age  Outcome  \n",
              "0                     0.627  50.0        1  \n",
              "1                     0.351  31.0        0  \n",
              "2                     0.672  32.0        1  \n",
              "3                     0.167  21.0        0  \n",
              "4                     2.288   NaN        1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4IiKF4LiuE25"
      },
      "source": [
        "## Qual é a taxa de distribuição?\n",
        "\n",
        "O gráfico acima mostra que os dados estão desequilibrados. O número de não diabéticos é 268 o número de pacientes diabéticos é 500"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0QvEiOXbuWiW",
        "outputId": "50059fb8-6c5e-4715-d807-a47c9e51a1c5"
      },
      "source": [
        "# 2 datasets\n",
        "D = df[(df['Outcome'] != 0)]\n",
        "H = df[(df['Outcome'] == 0)]\n",
        "\n",
        "#------------COUNT-----------------------\n",
        "def target_count():\n",
        "    trace = go.Bar( x = df['Outcome'].value_counts().values.tolist(), \n",
        "                    y = ['healthy','diabetic' ], \n",
        "                    orientation = 'h', \n",
        "                    text=df['Outcome'].value_counts().values.tolist(), \n",
        "                    textfont=dict(size=15),\n",
        "                    textposition = 'auto',\n",
        "                    opacity = 0.8,marker=dict(\n",
        "                    color=['lightskyblue', 'gold'],\n",
        "                    line=dict(color='#000000',width=1.5)))\n",
        "\n",
        "    layout = dict(title =  'Count of Outcome variable')\n",
        "\n",
        "    fig = dict(data = [trace], layout=layout)\n",
        "    py.iplot(fig)\n",
        "\n",
        "#------------PERCENTAGE-------------------\n",
        "def target_percent():\n",
        "    trace = go.Pie(labels = ['healthy','diabetic'], values = df['Outcome'].value_counts(), \n",
        "                   textfont=dict(size=15), opacity = 0.8,\n",
        "                   marker=dict(colors=['lightskyblue', 'gold'], \n",
        "                               line=dict(color='#000000', width=1.5)))\n",
        "\n",
        "\n",
        "    layout = dict(title =  'Distribution of Outcome variable')\n",
        "\n",
        "    fig = dict(data = [trace], layout=layout)\n",
        "    py.iplot(fig)\n",
        "\n",
        "target_count()\n",
        "target_percent()"
      ],
      "execution_count": 7,
      "outputs": [
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          "output_type": "display_data",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Distribution of Outcome variable\"}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('278f1442-1c6c-40b2-8a5e-da103c500cfc');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DISVHRN8hMC1"
      },
      "source": [
        "### Análise Exploratória de Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U08M_rXxjhR1"
      },
      "source": [
        "# Tratando os dadas para eliminar os valores nulos\n",
        "df['Glucose'].fillna(df['Glucose'].mean(), inplace = True)\n",
        "df['BloodPressure'].fillna(df['BloodPressure'].mean(), inplace = True)\n",
        "df['SkinThickness'].fillna(df['SkinThickness'].median(), inplace = True)\n",
        "df['Insulin'].fillna(df['Insulin'].median(), inplace = True)\n",
        "df['DiabetesPedigreeFunction'].fillna(df['DiabetesPedigreeFunction'].median(), inplace = True)\n",
        "df['BMI'].fillna(df['BMI'].median(), inplace = True)\n",
        "df['Age'].fillna(df['Age'].median(), inplace = True)\n",
        "df['Pregnancies'].fillna(df['Pregnancies'].median(), inplace = True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhSkzVSggwGM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5449e22-56e9-446a-d6b0-07269efd4e95"
      },
      "source": [
        "# Verificando os valores nulo\n",
        "print(\"\\nValores Nulos:\")\n",
        "print((df.isnull().sum() / df.shape[0]).sort_values(ascending=False))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Valores Nulos:\n",
            "Outcome                     0.0\n",
            "Age                         0.0\n",
            "DiabetesPedigreeFunction    0.0\n",
            "BMI                         0.0\n",
            "Insulin                     0.0\n",
            "SkinThickness               0.0\n",
            "BloodPressure               0.0\n",
            "Glucose                     0.0\n",
            "Pregnancies                 0.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPYrt0_qfkOG"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.figure_factory as ff"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSTt7SAVfY1B"
      },
      "source": [
        "def dist_box(data):\n",
        "    # função plota um gráfico combinado para análise univariada de variável contínua para verificar a disposição, \n",
        "    # a tendência central, a dispersão e outliers  \n",
        "    Name=data.name.upper()\n",
        "    fig,(ax_box,ax_dis)  =plt.subplots(2,1,gridspec_kw = {\"height_ratios\": (.25, .75)},figsize=(8, 5))\n",
        "    mean=data.mean()\n",
        "    median=data.median()\n",
        "    mode=data.mode().tolist()[0]\n",
        "    fig.suptitle(\"DISPOSIÇÃO DOS DADOS POR \"+ Name  , fontsize=18, fontweight='bold')\n",
        "    sns.boxplot(x=data,showmeans=True, orient='h',color=\"violet\",ax=ax_box)\n",
        "    ax_box.set(xlabel='')\n",
        "    sns.distplot(data,kde=False,color='blue',ax=ax_dis)\n",
        "    ax_dis.axvline(mean, color='r', linestyle='--',linewidth=2)\n",
        "    ax_dis.axvline(median, color='g', linestyle='-',linewidth=2)\n",
        "    ax_dis.axvline(mode, color='y', linestyle='-',linewidth=2)\n",
        "    plt.legend({'Média':mean,'Mediana':median,'Moda':mode})"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSsbVrJNfctC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d8de9b4c-4e36-46dc-9a9e-ee9d1aba5ad7"
      },
      "source": [
        "# Plotar o gráfico para cada coluna para verificar a disposição dos dados\n",
        "list_col=  df.select_dtypes([np.number]).columns\n",
        "for i in range(len(list_col)):\n",
        "    dist_box(df[list_col[i]])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pez2C8KsnXbwL/a2oYeNxvk7UzRt9CR9O+Oed+kU2inCoNZeGtNeHk+Gs0gxOJJw1lTI/S8ffiWb2PXdfT4m2u0BHxJ6vofUwy3xau8L3MbOto0Q07nO03SfbbEd0KO630QMzG0A4Ky7Vw7G3me0YjTErmJntAcQP58f36VNp/YLdCmjM8n6cbGYXR/t4Pg6i7fucWmem93JJhuXjhxnSl8HdG4j2wSghOIZwQkYqARpF69iUfKXOkPuIcG2sNzNsb9Hve8xlhM/hvxGuN7cTYcD3f9F2zGgxjkh73eH3WVpiD3ClmWX6Vb1rVH+mswRx92djdf6Z1n9qAwnbnGkIxrvx5crkr/mso8jP522EswSd0Pv2VvTDKJeit93Mtqft+OF8/n/Fv8t7E86SfhbA3e8L1VotbYcMZXM64X/JeMIYyq0J3/lvEDoOsqmPOkLy9UYn2yqfto0ndP0zzI8nxzm/k919nZn9B6FD5xBar+CQroFwAgiEI1C7uXum78NyKcn3cZobgR8QrjGZK4HNqBS/IC4hHJpJuaOjBcxsq0zjyaLBoenH9bPF+K67P+vuf3L3hgwJXGpsU/wf0/fSx31FAym/EpsUv7TDF9PHAEQDdB8Hns8jxrgXCGenpvzEzLL1TAyNvcz3EExLj12x210oMzuctmMT746N64jXXwNMzlDFj2PP1xKdeGFmfc3s39ILu/ty2p7qDUXu01GCcwOtvyI30jZBz/f92JUwljGfde5E2/3+PVpPOnmOtl8SF1jrySKp5ccTLk+TEt+Hx5hZ73h5d9/g7vcQTslPKaTd6qPP4HPu/nqWhLXg9z0Td2929z/QdpzYyWaWPua006JkLD4m51Xy+6d+Cvm3X777T/ovg74ZS20GSvT5fDv2ff5yHglc0aIOiF/Q9nB1h/+/CONM4ycd/NzMtis0jujzeC/hjOSU8/IdR1hBr8Se947vA9F32hdi8/M5ueNm4O/R8wOzlJlF+E6GsC9dn/59GK2/p5llugRUsYr6Ps4k+vGTOrkr23Zn1emeuOh4dQ/gXwjjIOK/XB8h+hXWga0I41oWm9lsQkL0LqFn68xYuTXk9+bnch7hF1Eq5j+ZWfr10lJfmG/T9oN0BnCsmf0+quNvhF+KB9H2H3R8YGxG7v6xmX2b8Gu6B+GSDC9Fg1FfIrTJUML1YhqB8RZO7vh8rJpzCVcbj/sy4fALhO7xVMJRzHbnax8z+5jQpTyG0EuV2qcW0zo2EXd/3MweofXMw+vMbDDwOOHX7ETaHja+LJaY9wXqzGwhYbxMA2E8xADCL5iU17OdJZbDjtE+nbpO3GRar9UE4Szit6Hl2nupEzU2Ec4oTT9McBytPdMTCddDS/eJaJ3bEgawn0X4FQZh/zo7dWa2u28ys/No/SIYBcw1s/8i9H59HrggVvdLhFP7U34MfNbCQPRnCO9Ld0ICHz9busN9uBBFvu+53EY4pLdH9PpSwtCGztg9eh92JHx5fovWQzQbgW+ljXfKJp6Y3Ub4wRa3K629Kkeb2U7u3u6wXWwsUB/aXxct2/fgJyzzGKKl7v73DNMLsY+ZZRpk/1LUe1/Oz2cund32baLyWxN62uPXiYNwSaTnMyzXhru/b2bfp3U87AjgZTP7NeHOPlDYGdbXEq7btz3hEhkXEK5VmsmI6EdHuuey/JjaK0tbveodX78wmzrC/5PUSXcPmtlMwnCN+MlfTtsT8TKKvuumkiORdvfFZnYZrT1WRwP/a2Y3EX6Ubke4EsREwgWcc53s0Wkl+D7O5nbCD9O8TuhMD6qja8FMJPM1XdIfvyHPOzYQ/onkU+c30+qri83r8Do2seWOI3T95lrXG7S/ns9v8ojxf4jdainbNsfm533HBsKOmpr2YpZti1+b6iNgYLHbnaMdB+f5vmW7Y8MOhKQm17Ifk3bNOsKFOTta5yZgbJ7bcXue9Z2XttxNsfmPZan7K7Ey64CdMuy72R657tjwHdpegy/T48/ArmnLPZvHeu/Ks91q05YbnOdyBb3v0bKLYmWmps07M235ffOIZVEHcTiduGMDIfmLx7BrhuW2Ihx2TpX7Vme/W3N8D2Z7/CKPfb+uyM/554r5fOZ6b3O8f53adsp7x4ZJhEO/+Wz7NrHl4vMmptUZvx7oh0TXiiP/Ow/slGU92R7js7RtXv9fgUPzaIMLMywXnz8mNn0r2l7b0sl8x4Yfk8cdG/L4Dsi439Hx//FCv4/j+9S8tHknZaijLNeJc1rvv/kiYbDq5939ZM9zTJWHQ2xHEsZLPBs16oeEnX0p4Wy6Q9395gLiy7S++wlj666MYl5D2AHeo/U+a/t6++v5TCX8EnqQcCh0JSFRWkXoufgBcITnP+4Jd3+YkG1fROgZSf0y/ZCQ/FwJXGjtrw2X6QxDojhSV+5us0wR290ZzYT2WEg40eJrhAtmvpVe0MMhki8TxmM9QHivNxK2/Q3CgOgD3P3itEUXE35x/YJwmDh1780NhH3nN9E6sx6G64DTej/RZwnJ817u3tI7GZ1gE7+ExP1Z6nqS1rEiPUm7xlzMx4Qvv7dpvXfqnu6ecZyku/+a0FM4jXBW91pa77f4KOFL5/Pe/q4F3yaMMX2UMD5xFWEfXkHYB86gdOPJMirife/IbbReH84IvXGd9TGhLd8iJJrnEN779EuNZHNa7PlzGdofD715D8UmTeygztT7U0foGT4tZ+nKK/fns5Sc1vtfP0+IeR93/4HnvqRH+4rcZxC+y39C2O5/Et67Dwj79QOEnrVBnvnSOplcR+uJR73YzM9Sdfc/Eu46cyPh+2U9rf/H7wcOc/erstfQrr6PyT0WMFXuPwh31vkFYcz8GsJ3yhLC5ybeU1pyRXwf53IPHY+fa8eiDFAqyMyeIgwK/2alYxEREZFkKOWp0VK4ewnXvSt4cKyIiIhsWYq+xIgUzsxOIHS7H0x4L/K9+4OIiIhs4ZTEVdbZhLNb3gd+5KW5h6WIiIhsATQmTkRERCSBNCZOREREJIGUxImIiIgkkJI4ERERkQRSEiciIiKSQEriRERERBJISZyIiIhIAimJExEREUkgJXEiIiIiCaQkTkRERCSBlMSJiIiIJJCSOBEREZEEUhInIiIikkBK4kREREQSSEmciIiISAJ1r3QAAv379/fBgwdXOgwREZEu0dDQ8J6711Q6jqRTErcZGDx4MPX19ZUOQ0REpEuY2eJKx1ANdDhVREREJIGUxImIiIgkkJI4ERERkQTSmDgpidmzZ7N06dKS1tnU1ARATc3mO/Z14MCBHHvssZUOQ0REtkBK4qQkli5dypK3llCzdekSrnUb1gGwfsP6ktVZSk0bmyodgoiIbMGUxEnJ1Gxdw4m7nliy+u79x70AJa2zlFLxiYiIVILGxImIiIgkkJI4ERERkQRSEiciIiKSQEriRERERBJISZyIiIhIAimJExEREUkgJXEiIiIiCaQkTkRERCSBlMSJiIiIJJCSOBEREZEEUhJXZWbPns3s2bMrHYZISWm/FhFpT/dOrTJLly6tdAgiJaf9WkSkPfXEiYiIiCSQkjgRERGRBFISJyIiIpJASuJEREREEkhJnIiIiEgCKYkTERERSSAlcSIiIiIJpCRORBKhubmZ6667jquvvpqrr76aa665hquvvprrrruONWvWsHr1aq677jquueaalmlAy/SrrrqK888/v+Wac6tXr2batGkt5VJS5eN1ADQ2NnLBBRe0u2ZdtnoaGxs5//zzueqqq9rVlW3Z1OvGxsaMdRYrW6ydkakd8qm3FOsuR13l1lWx5lpPsTEkqb23NEripGqttJVcsN0FrLSVlQ5FSmDlypUsXryYpUuXsnTpUhobG1m6dCmLFy/mscce4/HHH2fx4sU0Nja2TANapi9btowNGzZw5513tkx/6623WsqlpMrH6wCYOXMm69evb1k+Xj5TPTNnzmTDhg0sW7asXV3Zlk29njlzZsY6i5Ut1s7I1A751FuKdZejrnLrqlhzrafYGJLU3lsaJXFStWb1nMUr3V7hnp73VDoUKVJzc3POXoB58+bx/PPPt5n2/PPP09jY2G768uXLef3113nhhRdwd1544YU2PWHx8s8//zxr1qyhsbGR5cuXtywf783LVE+8fHpdKenLLl26tOX18uXL29VZrGyxdkamdsin3lKsuxx1lVtXxZprPcXGkKT23hLptltVpqmpiQ0bNnD99dd36XobGxvpvmnz2Z1W2kqe3PpJ3Jy5W89lwvoJ9PW+JV3Hqk2raG5s7vK23hItWbIk5/yPPvoo47SZM2dmnHfbbbfx8ccfA/Dxxx/z2GOPccIJJ/D444/T3Nzcpo7HHnuMv/3tb22Wv/POO7nooot4/PHHM9Yzc+bMdutsbm5umQ+0W/bOO+9seZ0Sr7NY2WLtjPTtuvPOO9lzzz07rLcU6y5HXeXWVbHmWk+xMSSpvbdE6omrEDObbGb1Zlbf1NRU6XCqzqyes/iY6IuHj9Ubl3CZErGOxHu00q1bt66lzo8++oiGhgaAlr/xOhoaGtr1qqVeNzQ0ZKwnvXxKvP70ZZcvX95uO+N1FitbrJ2RqR3yqbcU6y5HXeXWVbHmWk+xMSSpvbdEm0/XyRbG3acD0wFGjBjR/r9MgWpqagA4++yzS1VlXq6//nrWN67v0nVmk+qFa7bQo9JszWXpjdupx070HNSzy9t6S3TJJZd0+jCOmbHzzjvzzjvvtEvkevXqxcaNG/noo4/o1q0bw4cPB2D48OH86U9/alPH8OHD+dvf/tYmgRkwYEBL+Xnz5rWrZ8CAARkTudT8TMvW1NTQ1NTUJpGL11msbLF2Rvp2DRgwgD333LPDekux7nLUVW5dFWuu9RQbQ5Lae0uknjipOvFeuBT1xiVb3765k+9u3brRvXv3dtNOOeUUunXr1q786aefzlZbha+/rbbaijFjxgBw5JFHtqmnW7dujBkzhlNOOaXN8qeeempL+Uz1pJcH6N69e8v8TMueeuqpLa9T4nUWK1usnZGpHfKptxTrLkdd5dZVseZaT7ExJKm9t0RK4qTqvNbttZZeuJRma2Zht4UVikiK1b17d3bYYYes80ePHs2oUaPaTBs1ahSDBg1qN33AgAHsvffejBw5EjNj5MiRLXXvuOOObcqPGjWKHXbYgUGDBrX0vg0YMICBAwe2lM9UT7x8el0p6csOHDiw5fWAAQPa1VmsbLF2RqZ2yKfeUqy7HHWVW1fFmms9xcaQpPbeEulwqlSd69fqRINq1LdvX/r06dNy4oGZ4e4tPVzuTmNjY8thn3jvWmNjIxs3bmTlypVtetGWL1/ermchVR5oM++UU07h+uuvb1k+Xj5TPaeccgrTpk2jb9++bL311ll7qOLLpl4fe+yxzJ49u+S9Htli7YxM7ZBPvaVYdznqKreuijXXeoqNIUntvaWxTIN+pWuNGDHC6+vrS1JX6kzJSo2JO3HXE0tW573/uBegpHWW0r3/uFdj4rpIpfZrESkPM2tw9xGVjiPpdDhVREREJIGUxImIiIgkkJI4ERERkQRSEiciIiKSQEriRERERBJISZyIiIhIAuk6cVUmdRFSkWqi/VpEpD0lcVXm2GOPrXQIIiWn/VpEpD0dThURERFJICVxIiIiIgmkJE5EREQkgZTEiYiIiCSQkjgRERGRBFISJyIiIpJASuJEREREEkhJnIiIiEgCKYkTERERSSAlcSIiIiIJpNtuSck0bWzi3n/cW7L63t3wLkBJ6yylpo1N7MZulQ5DRES2UEripCTKcYPyXk29AOhZ07PkdZfCbuymG7OLiEjFKImTktANykVERLqWxsSJiIiIJJCSOBEREZEEUhInIiIikkBK4kREREQSyNy90jFs8cysCVicYVZ/4L0uDmdLobYtH7Vteahdy0dtWz7Z2vZf3L2mq4OpNkriNmNmVu/uIyodRzVS25aP2rY81K7lo7YtH7VteelwqoiIiEgCKYkTERERSSAlcZu36ZUOoIqpbctHbVseatfyUduWj9q2jDQmTkRERCSB1BMnIiIikkBK4jYDZjbGzF43szfN7MIM87cxs3uj+c+b2eCujzKZ8mjbiWbWZGYvR49vVCLOpDGzGcXf1Q8AACAASURBVGb2rpktyDLfzGxa1O7zzWz/ro4xifJo11ozWx3bXy/t6hiTysx2M7OnzOxVM3vFzL6XoYz2207Ks12135ZJ90oHsKUzs27Ar4EvAY3An81sjru/Git2BvBPd/+kmU0Afgqc2PXRJkuebQtwr7t/t8sDTLbbgV8Bd2aZ/2Vgr+gxCrgh+iu53U7udgV4xt2P6ppwqkoz8AN3f9HMtgcazGxu2veB9tvOy6ddQfttWagnrvJGAm+6+1vuvhG4Bzg6rczRwB3R8/uAw8zMujDGpMqnbaUA7v40sDJHkaOBOz2YB+xkZrt0TXTJlUe7SoHcfZm7vxg9fx9YCAxMK6b9tpPybFcpEyVxlTcQWBJ73Uj7D0BLGXdvBlYD/bokumTLp20BjosOndxnZrt1TWhVL9+2l8470Mz+YmaPmtnQSgeTRNGQlP2A59Nmab8tQo52Be23ZaEkTrZ0vwMGu/swYC6tPZ4im6MXCbcr2he4HniwwvEkjpltB9wPfN/d11Q6nmrRQbtqvy0TJXGVtxSI9/4MiqZlLGNm3YEdgRVdEl2yddi27r7C3TdEL28BhndRbNUun/1aOsnd17j72uj5I0APM+tf4bASw8x6EBKNu9x9doYi2m8L0FG7ar8tHyVxlfdnYC8z28PMtgYmAHPSyswBToueHw/80XWBv3x02LZp413GEcZzSPHmAKdGZ/uNBla7+7JKB5V0ZjYgNR7WzEYSvsP1gy4PUbvdCix09+uyFNN+20n5tKv22/LR2akV5u7NZvZd4HGgGzDD3V8xs8uBenefQ/iAzDSzNwmDnidULuLkyLNtzzGzcYQzrFYCEysWcIKY2SygFuhvZo3AFKAHgLvfCDwCjAXeBD4ETq9MpMmSR7seD5xlZs3AOmCCftDl7QvAKcBfzezlaNrFwO6g/bYI+bSr9tsy0R0bRERERBJIh1NFREREEkhJnIiIiEgCKYkTERERSSAlcSIiIiIJpCROREREJIGUxImIiIgkkJI4ERERkQRSEiciIiKSQLpjw2agf//+Pnjw4EqHsdl5//0GALbfvsDbmTaE5Rmu26GKiGxOGhoa3nP3mkrHkXS6Y8NmYMSIEV5fX1/pMDY7dXUGQG1tgftouFUfaB8XEdmsmFmDu4+odBxJp8OpIiIiIgmkJE5EREQkgZTEiYiIiCSQTmyQ6rX//pWOQESqxKZNm2hsbGT9+vWVDiVRevbsyaBBg+jRo0elQ6lKSuI6YGYzgKOAd939s2nzfgBcC9S4+3tmZsAvgbHAh8BEd3+xq2OWSOrsVBGRIjU2NrL99tszePBgLHXSlOTk7qxYsYLGxkb22GOPSodTlXQ4tWO3A2PSJ5rZbsARwNuxyV8G9ooek4EbuiA+EREps/Xr19OvXz8lcJ1gZvTr10+9l2WkJK4D7v40sDLDrJ8D5wPx61ccDdzpwTxgJzPbpQvCFBGRMlMC13lqs/LS4dQCmNnRwFJ3/0vaDjoQWBJ73RhNW5ahjsmE3jp233338gVbRtOnd36ZyZNLH0dWuk6ciGyhbrrpJk444QT69OlT6VCkjNQT10lm1hu4GLi0mHrcfbq7j3D3ETU1umi1iIjkZmacfPLJLa+bm5upqanhqKOOalPu8ssvp0+fPlkTuNraWlIXmB87diyrVq0qX9BSVuqJ67w9gT2AVC/cIOBFMxsJLAV2i5UdFE0TEREpyrbbbsuCBQtYt24dvXr1Yu7cuQwcOLBduUsvzb+P4ZFHHilliNLF1BPXSe7+V3f/hLsPdvfBhEOm+7v7cmAOcKoFo4HV7t7uUKqIiEghxo4dy8MPPwzArFmzOOmkk1rmffDBB0yaNImRI0ey33778dBDDwGwbt06JkyYwJAhQzjmmGNYt25dyzKDBw/mvffeA2D8+PEMHz6coUOHMr2Q8TLS5ZTEdcDMZgHPAXubWaOZnZGj+CPAW8CbwM3At7sgRBER6Wpm2R/xBGj69NxlO2nChAncc889rF+/nvnz5zNq1KiWeVdccQWHHnooL7zwAk899RQ//OEP+eCDD7jhhhvo3bs3Cxcu5LLLLqMhy+WXZsyYQUNDA/X19UybNo0VK1Z0Oj7pWjqc2gF3P6mD+YNjzx34TrljEhGRLdOwYcNYtGgRs2bNYuzYsW3mPfHEE8yZM4drr70WCJdFefvtt3n66ac555xzWpYfNmxYxrqnTZvGAw88AMCSJUt444036NevXxm3RoqlJE5ERKSz8j3rffLkkp+WP27cOM477zzq6ura9Ja5O/fffz977713p+usq6vjySef5LnnnqN3797U1tbq+m4JoMOpUr1uuik8RESqyKRJk5gyZQr77LNPm+lHHnkk119/PR4lmC+99BIAhxxyCHfffTcACxYsYP78+e3qXL16NX369KF379689tprzJs3r8xbIaWgJE6qVxl+AYuIVNqgQYNaDo/GXXLJJWzatIlhw4YxdOhQLrnkEgDOOuss1q5dy5AhQ7j00ksZPnx4u2XHjBlDc3MzQ4YM4cILL2T06NFl3w4png6nioiIJMDatWvbTautraW2thaAXr16cVOGow+9evXinnvuyVjnokWLWp4/+uijJYlTuo564qR6TZ9e2G0lREREEkA9cVK9zjwz/NUhVRERqULqiRMRERFJICVxIiIiIgmkJE5EREQkgZTEiYiIiCSQkjgREZEEMDNOPvnkltfNzc3U1NRw1FFHdaqe2tpa6uvrARg7diyrVq0qaZzSdXR2qoiISAJsu+22LFiwgHXr1tGrVy/mzp3LwIEDi6rzkUceKVF0UgnqiZPq5Z7//Q1FRBJg7NixPPzwwwDMmjWLk046qWXeBx98wKRJkxg5ciT77bcfDz30EADr1q1jwoQJDBkyhGOOOYZ169a1LDN48GDee+89AMaPH8/w4cMZOnQo02PX2Nxuu+340Y9+xL777svo0aN55513APjd737HqFGj2G+//Tj88MNbpkvXUU+ciIhIJ9hlVpZ6fUrHPzonTJjA5ZdfzlFHHcX8+fOZNGkSzzzzDABXXHEFhx56KDNmzGDVqlWMHDmSww8/nJtuuonevXuzcOFC5s+fz/7775+x7hkzZtC3b1/WrVvHAQccwHHHHUe/fv344IMPGD16NFdccQXnn38+N998Mz/+8Y856KCDmDdvHmbGLbfcwtVXX83PfvazkraJ5KYkTkREJCGGDRvGokWLmDVrFmPHjm0z74knnmDOnDlce+21AKxfv563336bp59+uuVeq8OGDWPYsGEZ6542bRoPPPAAAEuWLOGNN96gX79+bL311i3j7oYPH87cuXMBaGxs5MQTT2TZsmVs3LiRPfbYoyzbLNkpiZPqlbrJc0NDZeMQkaqST49ZOY0bN47zzjuPuro6VqxY0TLd3bn//vvZe++9O11nXV0dTz75JM899xy9e/emtraW9evXA9CjRw/MQu9jt27daG5uBuDss8/m3HPPZdy4cdTV1TF16tTiN046RWPiOmBmM8zsXTNbEJt2jZm9ZmbzzewBM9spNu8iM3vTzF43syMrE7UA8OKL4SEiUkUmTZrElClT2GeffdpMP/LII7n++uvxaCzwSy+9BMAhhxzC3XffDcCCBQuYP39+uzpXr15Nnz596N27N6+99hrz5s3rMI7Vq1e3nFhxxx13FLVNUhglcR27HRiTNm0u8Fl3Hwb8H3ARgJl9BpgADI2W+S8z69Z1oYqISLUbNGhQy+HRuEsuuYRNmzYxbNgwhg4dyiWXXALAWWedxdq1axkyZAiXXnopw1NHKWLGjBlDc3MzQ4YM4cILL2T06NEdxjF16lS+9rWvMXz4cPr371/8hkmnmevsvQ6Z2WDg9+7+2QzzjgGOd/evm9lFAO7+n9G8x4Gp7v5crvpHjBjhqWv2JEns5KW8deZe9HV1ofu+trbAfTTq/tcZqiJSrIULFzJkyJBKh5FImdrOzBrcfUSFQqoa6okr3iTg0ej5QGBJbF5jNE1ERESkpJTEFcHMfgQ0A3cVsOxkM6s3s/qmpqbSByciIiJVTUlcgcxsInAU8HVvPSa9FNgtVmxQNK0dd5/u7iPcfURNTU1ZYxUREZHqo0uMFMDMxgDnA//m7h/GZs0B7jaz64Bdgb2AFyoQ4marM+PoPvWpIlf2zW8WWYGIiMjmS0lcB8xsFlAL9DezRmAK4WzUbYC50bVz5rn7t9z9FTP7LfAq4TDrd9z9o8pELgWdeSEiIpIQSuI64O4nZZh8a47yVwBXlC8iEREREY2Jk2rW0KC7NYhI1TAzTj755JbXzc3N1NTUtNwSK1/xm95LsqknTqrXiOgSRLpOnIhUgW233ZYFCxawbt06evXqxdy5c1vumCBbJvXEiYiIJMTYsWN5+OGHAZg1axYnndQ64mflypWMHz+eYcOGMXr06Jbba61YsYIjjjiCoUOH8o1vfIP4Rf7Hjx/P8OHDGTp0KNM1jjhx1BMnIiLSCam7yZRaPnenmTBhApdffjlHHXUU8+fPZ9KkSTzzzDMATJkyhf32248HH3yQP/7xj5x66qm8/PLLXHbZZRx00EFceumlPPzww9x6a+uw7hkzZtC3b1/WrVvHAQccwHHHHUe/fv3Ksn1SekriREREEmLYsGEsWrSIWbNmMXbs2Dbznn32We6//34ADj30UFasWMGaNWt4+umnmT17NgBf+cpX6NOnT8sy06ZN44EHHgBgyZIlvPHGG0riEkRJnIiISCcUfD/nEhk3bhznnXcedXV1rFixouB66urqePLJJ3nuuefo3bs3tbW1rF+/voSRSrlpTJyIiEiCTJo0iSlTprDPPvu0mX7wwQdz113hLpB1dXX079+fHXbYgUMOOYS7774bgEcffZR//vOfAKxevZo+ffrQu3dvXnvtNebNm9e1GyJFU0+ciIhIggwaNIhzzjmn3fSpU6cyadIkhg0bRu/evbnjjjuAMFbupJNOYujQoXz+859n9913B2DMmDHceOONDBkyhL333pvRo0d36XZI8ZTESfWqr690BCIiJbN27dp202pra6mtrQWgb9++PPjgg+3K9OvXjyeeeCJjnY8++mhJY5SupSROqtfw4ZWOQEREpGw0Jk5EREQkgZTESfWaPDk8RERKwHX3l05Tm5WXkjipXjffHB4iIkXq2bMnK1asUFLSCe7OihUr6NmzZ6VDqVoaEyciItKBQYMG0djYSFNTU6VDSZSePXsyaNCgSodRtZTEiYiIdKBHjx7ssccelQ5DpA0dThURERFJICVxHTCzGWb2rpktiE3ra2ZzzeyN6G+faLqZ2TQze9PM5pvZ/pWLXERERKqZkriO3Q6MSZt2IfAHd98L+EP0GuDLwF7RYzJwQxfFKCIiIlsYJXEdcPengZVpk48G7oie3wGMj02/04N5wE5mtkvXRCrt7L9/eIiIiFQhndhQmJ3dfVn0fDmwc/R8ILAkVq4xmrYM6XoNDZWOQEREpGzUE1ckDxcN6vSFg8xsspnVm1m9TlkXERGRzlISV5h3UodJo7/vRtOXArvFyg2KprXj7tPdfYS7j6ipqSlrsCIiIlJ9lMQVZg5wWvT8NOCh2PRTo7NURwOrY4ddpauZhYeIiEgV0pi4DpjZLKAW6G9mjcAU4Crgt2Z2BrAYOCEq/ggwFngT+BA4vcsDFhERkS2CkrgOuPtJWWYdlqGsA98pb0QiIiIiSuIkMn16pSMQERGRztCYOBEREZEEUhInIiIikkBK4kREREQSSGPipHrddFOlIxARESkbJXFSvSZPrnQEIiIiZaPDqSIiIiIJpCROqtf06bp2ioiIVC0dTpXqdeaZ4a8Oq4qISBVST5yIiIhIAimJExEREUkgJXEiIiIiCaQkTkRERCSBlMSJiIiIJJCSOBEREZEE0iVGpHq5VzoCERGRslFPXBHM7N/N7BUzW2Bms8ysp5ntYWbPm9mbZnavmW1d6ThFRESk+iiJK5CZDQTOAUa4+2eBbsAE4KfAz939k8A/gTMqF6WIiIhUKyVxxekO9DKz7kBvYBlwKHBfNP8OYHyFYpPhw8NDRESkCmlMXIHcfamZXQu8DawDngAagFXu3hwVawQGVihEefHFSkcgIiJSNuqJK5CZ9QGOBvYAdgW2BcZ0YvnJZlZvZvVNTU1lilJERESqlZK4wh0O/N3dm9x9EzAb+AKwU3R4FWAQsDTTwu4+3d1HuPuImpqarolYREREqoaSuMK9DYw2s95mZsBhwKvAU8DxUZnTgIcqFJ+IiIhUMSVxBXL35wknMLwI/JXQltOBC4BzzexNoB9wa8WCFBERkaqlExuK4O5TgClpk98CRlYgHBEREdmCKImT6vXNb1Y6AhERkbJREifVa/r0SkcgIiJSNhoTJyIiIpJASuKkejU0hIeIiEgV0uFUqV4jRoS/7pWNQ0REpAzUEyciIiKSQEriRERERBJISZyIiIhIAimJExEREUkgJXEiIiIiCaQkTkRERCSBdIkRqV719ZWOQEREpGyUxEn1Gj680hGIiIiUjZI42ewVcgvUyZNLH4eIiMjmRGPipHpNnqxsTkREqpaSOKleN98cHiIiIlVISVwRzGwnM7vPzF4zs4VmdqCZ9TWzuWb2RvS3T6XjFBERkeqjJK44vwQec/dPA/sCC4ELgT+4+17AH6LXIiIiIiWlJK5AZrYjcAhwK4C7b3T3VcDRwB1RsTuA8ZWJUERERKqZkrjC7QE0AbeZ2UtmdouZbQvs7O7LojLLgZ0rFqGIiIhULSVxhesO7A/c4O77AR+QdujU3R3wTAub2WQzqzez+qamprIHKyIiItVFSVzhGoFGd38+en0fIal7x8x2AYj+vptpYXef7u4j3H1ETU1NlwS8xdl///AQERGpQkriCuTuy4ElZrZ3NOkw4FVgDnBaNO004KEKhCcADQ3hISIiUoV0x4binA3cZWZbA28BpxMS49+a2RnAYuCECsYnIiIiVUpJXBHc/WVgRIZZh3V1LHGF3KZKREREkkWHU6V6mYWHiIhIFVISJyIiIpJASuJEREREEkhJnIiIiEgC6cQGkUihJ4RMnlzaOERERPKhnjgRERGRBFISJyIiIpJAOpwq1eummyodgYiISNkoiZPqpcFqIiJSxXQ4VURERCSB1BMnVWn6dPj00+F009cOUY+ciIhUHyVxUrUOuetMQEmciIhUJx1OFREREUkgJXEiIiIiCaQkTkRERCSBlMSJiIiIJJCSOBEREZEEUhJXJDPrZmYvmdnvo9d7mNnzZvammd1rZltXOkYRERGpPrrESPG+BywEdohe/xT4ubvfY2Y3AmcAN1QquC3Z9Ju80iFkNX1655fRDShERCROPXFFMLNBwFeAW6LXBhwK3BcVuQMYX5noREREpJopiSvOL4DzgY+j1/2AVe7eHL1uBAZmWtDMJptZvZnVNzU1lT9SERERqSpK4gpkZkcB77p7QyHLu/t0dx/h7iNqampKHJ0AHHPFcI65YnilwxARESkLjYkr3BeAcWY2FuhJGBP3S2AnM+se9cYNApZWMMYtWs3bL1Y6BBERkbJRElcgd78IuAjAzGqB89z962b238DxwD3AacBDFQtSukQhJymIiIgUS4dTS+8C4Fwze5MwRu7WCscjIiIiVUg9cSXg7nVAXfT8LWBkJeMRERGR6qeeOBEREZEEUhInIiIikkA6nCpVa+FB36x0CCIiImWjJE6q1jOn6LRRERGpXjqcKiIiIpJASuKkavVf3ED/xQXdUENERGSzp8OpUrWOvXIEANNv8gpHIiIiUnrqiRMRERFJICVxIiIiIgmkJE5EREQkgZTEiYiIiCSQkjgRERGRBFISJyIiIpJAusSIVK3ZF9dXOoSSml7ADSgmTy59HCIisnlQEidV671/GV7pEERERMpGh1NFREREEkhJXIHMbDcze8rMXjWzV8zse9H0vmY218zeiP72qXSsW6qDZ07m4Jk6nigiItVJSVzhmoEfuPtngNHAd8zsM8CFwB/cfS/gD9FrqYAhz97MkGdvrnQYIiIiZaExcQVy92XAsuj5+2a2EBgIHA3URsXuAOqACyoQoohOhhARqWLqiSsBMxsM7Ac8D+wcJXgAy4GdKxSWiIiIVDElcUUys+2A+4Hvu/ua+Dx3d8CzLDfZzOrNrL6pqakLIhUREZFqoiSuCGbWg5DA3eXus6PJ75jZLtH8XYB3My3r7tPdfYS7j6ipqemagEVERKRqKIkrkJkZcCuw0N2vi82aA5wWPT8NeKirYxMREZHqpxMbCvcF4BTgr2b2cjTtYuAq4LdmdgawGDihQvFt8Zp237/SISSSToYQEUkGJXEFcvdnAcsy+7CujEUye+BHDZUOQUREpGx0OFVEREQkgZTEiYiIiCSQkjipWpPPNCafme2It4iISLIpiRMRERFJICVxIiIiIgmkJE5EREQkgZTEiYiIiCSQkjgRERGRBNLFfkUkMXQ3CRGRVkripGo9/fWbKh2CiIhI2SiJk6r12iHqghERkeqlMXEiIiIiCaSeOKlan346DKBSj9zmqZDxbV21Ho2jE5EkUBInVeuQu84ElMR1ha5KyEREpJUOp4qIiIgkkHriRETSdGXPog7dikihlMSVgZmNAX4JdANucferKhySiGymNucxe5tzbCKiJK7kzKwb8GvgS0Aj8Gczm+Pur1Y2MhERqTZKtLdsGhNXeiOBN939LXffCNwDHF3hmERERKTKqCeu9AYCS2KvG4FRFYpFRASovku6bM49UJtzbLD5xyf5M3evdAxVxcyOB8a4+zei16cAo9z9u2nlJgOpj8XewOsZqusPvFfGcLdkatvyUduWh9q1fNS25ZOtbf/F3Wu6Ophqo5640lsK7BZ7PSia1oa7Twdy/h4ys3p3H1Ha8ATUtuWkti0PtWv5qG3LR21bXhoTV3p/BvYysz3MbGtgAjCnwjGJiIhIlVFPXIm5e7OZfRd4nHCJkRnu/kqFwxIREZEqoySuDNz9EeCRElSlmxmVj9q2fNS25aF2LR+1bfmobctIJzaIiIiIJJDGxImIiIgkkJK4zYCZjTGz183sTTO7MMP8bczs3mj+82Y2uOujTKY82naimTWZ2cvR4xuViDNpzGyGmb1rZguyzDczmxa1+3wz27+rY0yiPNq11sxWx/bXS7s6xqQys93M7Ckze9XMXjGz72Uoo/22k/JsV+23ZaIxcRWW5226zgD+6e6fNLMJwE+BE7s+2mTpxC3Q7k2/jp906HbgV8CdWeZ/GdgreowCbkAXvc7H7eRuV4Bn3P2orgmnqjQDP3D3F81se6DBzOamfR9ov+28fNoVtN+WhXriKi+f23QdDdwRPb8POMzMrAtjTCrdAq1M3P1pYGWOIkcDd3owD9jJzHbpmuiSK492lQK5+zJ3fzF6/j6wkHCHnTjtt52UZ7tKmSiJq7xMt+lK/wC0lHH3ZmA10K9Loku2fNoW4Ljo0Ml9ZrZbhvnSefm2vXTegWb2FzN71MyGVjqYJIqGpOwHPJ82S/ttEXK0K2i/LQslcbKl+x0w2N2HAXNp7fEU2Ry9SLhd0b7A9cCDFY4nccxsO+B+4PvuvqbS8VSLDtpV+22ZKImrvHxu09VSxsy6AzsCK7okumTrsG3dfYW7b4he3gIM76LYql1et5+TznH3Ne6+Nnr+CNDDzPpXOKzEMLMehETjLnefnaGI9tsCdNSu2m/LR0lc5eVzm645wGnR8+OBP7ou8JePDts2bbzLOMJ4DineHODU6Gy/0cBqd19W6aCSzswGpMbDmtlIwne4ftDlIWq3W4GF7n5dlmLabzspn3bVfls+Oju1wrLdpsvMLgfq3X0O4QMy08zeJAx6nlC5iJMjz7Y9x8zGEc6wWglMrFjACWJms4BaoL+ZNQJTgB4A7n4j4Y4lY4E3gQ+B0ysTabLk0a7HA2eZWTOwDpigH3R5+wJwCvBXM3s5mnYxsDtovy1CPu2q/bZMdMcGERERkQTS4VQRERGRBFISJyIiIpJASuJEREREEkhJnIiIiEgCKYkTERERSSAlcSJbMDP7yMxeNrNXolvi/MDMtormjTCzaR0sP9HMftXJdV5cRLy3m9nfo5hfNLMDO7FsS6xm9i0zO7XQOPJc32AzWxfFmnpsXcL6J5rZrrHXt5jZZ0pVv4hs/nSdOJEt2zp3/xyAmX0CuBvYAZji7vVAfRnWeTFwZRHL/9Dd7zOzI4CbgGGdrSC6dlXezKx7dN/izvpbqn3LYCKwAPgHgLt/o0zrEZHNlHriRAQAd38XmAx8N7pifa2Z/R7CVdbN7Dkze8nM/tfM9o4tupuZ1ZnZG2Y2JTXRzE42sxeiHqibzKybmV0F9Iqm3ZWjXLeo122Bmf3VzP49Q8hPA5/MVkc0/XQz+z8ze4FwUdJUbFPN7Lzo+QFmNj9a9hozWxBNn2hmc8zsj8AfzGxbM5sRreclMzs6KtctWu7PUT1n5mpnM1sbe368md0ePb/dzKZF7fuWmR0fK3dB1A5/MbOronkjgLuiuHtF78GIqPxJUfkFZvbT+LrN7IqonnlmtnOuWEVk86YkTkRauPtbhLtbfCJt1mvAwe6+H3ApbXvSRgLHEXrEvhYdhh0CnAh8IeqJ+gj4urtfSNT75+5fz1YO+Bww0N0/6+77ALdlCPerhKvEZ6zDwi3VLiMkbwcB2Q413gacGVs2bn/geHf/N+BHhFvejQS+CFxjZtsCZxBuz3QAcADwTTPbI1p+z9ih1F9nWX/cLlGsRwFXAZjZl4GjgVHRDcSvdvf7CL2kX4/acl2qgugQ60+BQwnteICZjY9mbwvMi+p5GvhmHjGJyGZKh1NFJB87AneY2V6AE90KKjLX3VcAmNlsQhLSDAwH/mzhlom9gHcz1HtYlnK/A/7VzK4HHgaeiC1zjZn9GGgiJFDZ6hgF1Ll7UxTbvcCn4is3s52AsxhH5gAAAohJREFU7d39uWjS3YQEKr5tK6PnRwDjUj14QE/CrYWOAIbFes52BPYC/o/OH0590N0/Bl6N9ZIdDtzm7h8CxOLJ5gDabvddwCHAg8BG4PdRuQbgS52ITUQ2M0riRKSFmf0roTfqXWBIbNZPgKfc/RgzGwzUxeal37vPAQPucPeLOlpltnJmti9wJPAt4ARgUjTrh1FPVKrcFzPVEet9KsYHabEe5+6vp63HgLPd/fG06YOz1Blvr55p8zakra/UNsXuWfkR+h8gkmg6nCoiAJhZDXAj8KsMN6feEVgaPZ+YNu9LZtbXzHoB44E/AX8Ajo9OliCa/y9R+U1mlurJy1jOzPoDW7n7/cCPCYc1s8m2rueBfzOzftH6vpa+oLuvAt43s1HRpAk51vM4cHaUtGFm+8Wmn5XaJjP7VHSYNZt3zGyIhbOAj8lRLmUucLqZ9U5tXzT9fWD7DOVfIGx3/2hs4EnA/+SxHhFJGP0KE9my9TKzlwmHR5uBmcB1GcpdTTic+mPC4c24F4D7gUHAb6KzWonKPhElK5uA7wCLgenAfDN7MRoXl6ncOuC2aBpA1h49d381Ux3uPs/MpgLPAauAl7NUcQZws5l9TEh2Vmcp9xPgF1HsWwF/Jxx6vQUYDLwYJXhNhGQ2mwsJhzSbCOPatstRFnd/zMw+B9Sb2UbgEcIZvrcDN5rZOuDAWPllZnYh8BShN+9hd38o1zpEJJms/Q9uEZEth5lt5+5ro+cXAru4+/cqHJaISIfUEyciW7qvmNlFhO/DxbQ/XCwisllST5yIiIhIAunEBhEREZEEUhInIiIikkBK4kREREQSSEmciIiISAIpiRMRERFJICVxIiIiIgn0/wHccXp5Gm1rUAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MswLYssS6W27o7Mpfx9HxP+Klhr9RdKENkn87VHrfy6v83MYXzTeyyvm61e7pvM9kpjm7aNyxRfN/sEIc8zPTn1pmmh9mppldNK74Qkblhue6sc18qNrvs2Yc6h5Aow2sm5jfJjkb8gWSL+CpwIermbdo3CdJzqj8C8lVwF5Pv3jaSc5w/ViJ5c3KLO/iLtbjnSSJro3k5JK3SM4m/RPJ1Xg2LjHPe0hO1riGZM99abphvkTy95YTgQ2rrXNmmiHAqSRnky9Lp32d5LjiuSR7MuuR7MkWlvXtMstaj+SklZJfON2pdyefYakvmbfSz+NRkt6SQyi6AEbRMkSyhzQjbes30ro/TnJFrzEl5hlI0ivwE5I9u2dJkt7KdN25DBjdhXpcXGKd/mcaz1+A/6Qo6ZDsQb2UmefLZZa9IR0vg/qVEutukPyIei2tyx0ke2olLziTWfYOJHt5D5H84HqT5BK0N5F0sQ8sMc+uJHvUN5H8BeyldB1+MV0HjgbWq/JzG19Uh5HVzNfddk/neybzfmcXjduApJu/MP7qTt5/XFHsO5SZ7sNF0xVfpncH4EfAfSTHuVel7f0IyV8Yjyb9MdbJNuPEXDT47lKWO5LuBJ6MiC/XOxYzs77mk78sj64i+V/2pvUOxMysr/nkL8sNSYeSHOPdk2TdrPYqYmZmTcOJ2fLkeJKzbF8FTo+euWaxmVlD8TFmMzOzHPExZjMzsxxxYjYzM8sRJ2YzM7MccWI2MzPLESdmMzOzHHFiNjMzyxEnZjMzsxxxYjYzM8sRJ2YzM7MccWI2MzPLESdmMzOzHHFiNjMzyxEnZjMzsxxxYjYzM8uRXNyPediwYTFy5Mh6h2FmZtZn2traXoyIluLyXCTmkSNHMmfOnHqHYWZm1mckPVuq3F3ZZmZmOeLEbGZmliNOzGZmZjmSi2PMVtqMGTNYsGBBxemWLFkCQEvLOucQ1Gz48OEcdNBBPb5cMzMrzYk5xxYsWMDzTz9PywadJ9wVb6wAYOUbK3v0/Ze8uaRHl2dmZpU5MedcywYtHPbOwzqd5qp/XAVQcbquKizXzMz6jo8xm5mZ5YgTs5mZWY44MZuZmeWIE7OZmVmOODGbmZnliBOzmZlZjjgxm5mZ5YgTs5mZWY44MZuZmeWIE7OZmVmOODHnwIwZM5gxY0a9w2hK/mzNrNH4Wtk5UM0dpKx7/NmaWaPxHrOZmVmOODGbmZnliBOzmZlZjjgxm5mZ5YgTs5mZWY44MZuZmeWIE7OZmVmONF1ibm9v55RTTvH/V62D+fPnc8IJJ3DDDTfwjW98g9tvv51TTjmFxx57jPPPP5/58+d3eP3KK68AsHz58g6vu6unlmPNrxHWlbzH2NPx9XV9my4xX3bZZaxcuZJLL7203qHkzjIt45RNT2GZltU7lD538cUXExHMnDkTgD/+8Y+sXLmSX//61zz99NNcfPHFHV7fcsstANx6660dXndXTy3Hml8jrCt5j7Gn4+vr+jZVYm5vb2fRokUALFq0yHvNRa4YdAWPDHiEKwddWe9Q+tTrr7/OihUrSo5bsWIFEbFmfOH1vffeS3t7O/fee++a1939tbx8+fIeWY41v0ZYV/IeY0/HV4/6NtUlOS+77LIOry+99FJOO+20OkVTvSVLlvDGG29wwQUXdChvb29n/bd6pomWaRm3b3A7oWDmBjOZtHISQ2JIp/O8/NbLrGpftU5cjaS9vZ2VK1d2eb63336byy67jLfffnvN61tuuYVDDz20y8u69dZbe2Q51vwaYV3Je4w9HV896lu3PWZJUyTNkTRnyZIlPbLMwt5yudf92RWDruBt0pWLt/vdXnNXrV69mkWLFrF69eo1r9va2rq1rLa2th5ZjjW/RlhX8h5jT8dXj/rWbY85IqYB0wBaW1ujJ5a59dZbd0jGW2+9dU8stte1tLQAcPzxx3cov+CCC1jZ3vW9vWKFveVVWgXAKq2qaq95y4FbMmjEoHXiaiQXXHABTz31FBFdW8UGDBhAS0sLS5YsYfXq1QwYMIAxY8Z0K4YxY8Ywe/bsmpdjza8R1pW8x9jT8dWjvk11jPnwww/v8PqII46oUyT5kt1bLuhPe83d+YG23nrrcfjhh7PeeuuteT1hwoRuvf++++7bI8ux5tcI60reY+zp+OpR36ZKzCNGjFjzJbz11lszfPjwOkeUD/MHzF+zt1ywSquYN2BenSLqW5tssgkbbbRRyXEbbbQRktaML7weO3YsI0aMYOzYsWteb7755t16/y222KJHlmPNrxHWlbzH2NPx1aO+TZWYIdlrHjRokPeWMy547QJufPnGdYYLXmvck7q66qijjkISn/jEJwD4zGc+w6BBg/jiF7/I9ttvz1FHHdXhdeFX8b777tvhdXf11HKs+TXCupL3GHs6vr6ur7p67K03tLa2xpw5c+odRt0Uznoud4z5sHce1un8V/3jKoCK03XVVf+4qimOMcO6n62ZWb1JaouI1uLypttjNjMza2ROzGZmZjnixGxmZpYjTsxmZmY54sRsZmaWI07MZmZmOdJUN7FoVL4QSu/xZ2tmjcaJOQcOOuigeofQtPzZmlmjcVe2mZlZjjgxm5mZ5YgTs5mZWY44MZuZmeWIE7OZmVmOODGbmZnliBOzmZlZjjgxm5mZ5YgTs5mZWY44MZuZmeWIL8mZc0veXMJV/7iq02kWv7EYoOJ03Xnvbdm2R5dpZmadc2LOsWpvwLDRko0AGNQyqEfff1u29U0gzMz6mBNzjvkGDGZm/Y+PMZuZmeWIE7OZmVmOODGbmZnliBOzmZlZjigi6h0DkpYAz/bS4ocBL/bSsvuS65E/zVKXZqkHNE9dmqUe0Dx16Y16vCsiWooLc5GYe5OkORHRWu84auV65E+z1KVZ6gHNU5dmqQc0T136sh7uyjYzM8sRJ2YzM7Mc6Q+JeVq9A+ghrkf+NEtdmqUe0Dx1aZZ6QPPUpc/q0fTHmM3MzBpJf9hjNjMzaxhNm5glTZD0mKQnJZ1a73iqJWlbSXdKelTSI5K+kZafLWmBpAfSYb96x1oNSc9IeiiNeU5aNkTSTElPpI+D6x1nZyTtmPncH5D0iqQTGqVNJE2XtFjSw5mykm2gxPnpdjNX0uj6Rd5RmXr8UNL8NNZrJG2Zlo+UtCLTNr+sX+TrKlOXsuuTpNPSNnlM0r71iXpdZepxVaYOz0h6IC3Pe5uU++7t+20lIppuAAYATwHbAxsADwI71zuuKmPfBhidPt8MeBzYGTgbOKne8XWjPs8Aw4rKfgCcmj4/Ffh+vePsQn0GAIuAdzVKmwB7AaOBhyu1AbAfcDMgYBxwT73jr1CPTwLrp8+/n6nHyOx0eRvK1KXk+pRu/w8CGwKj0u+2AfWuQ7l6FI3/EXBmg7RJue/ePt9WmnWPeSzwZEQ8HRFvAlcCE+scU1UiYmFE3Jc+fxWYBzTbvRcnApekzy8BDqxjLF31ceCpiOitC+L0uIi4C1hWVFyuDSYCl0ZiNrClpG36JtLOlapHRNwWEavSl7OBEX0eWDeUaZNyJgJXRsQbEfF34EmS77i666wekgQcClzRp0F1UyffvX2+rTRrYh4OPJ953U4DJjdJI4HdgHvSoq+lXSbT8979mxHAbZLaJE1Jy7aKiIXp80XAVvUJrVsm0fGLphHbBMq3QSNvO0eT7MEUjJJ0v6Q/S9qzXkF1Uan1qVHbZE/ghYh4IlPWEG1S9N3b59tKsybmhidpU+Bq4ISIeAX4BfBu4EPAQpIuokawR0SMBj4FHCdpr+zISPqEGuKvAZI2AA4Afp8WNWqbdNBIbVCOpNOBVcDladFCYLuI2A04EfitpM3rFV+VmmJ9yphMxx+xDdEmJb571+irbaVZE/MCYNvM6xFpWUOQNJBkxbg8ImYARMQLEbE6It4GLiQnXVmVRMSC9HExcA1J3C8UunzSx8X1i7BLPgXcFxEvQOO2SapcGzTctiPpKGB/4PPpFydpt+/S9HkbyXHZ99YtyCp0sj41YpusDxwEXFUoa4Q2KfXdSx22lWZNzH8DdpA0Kt3LmQRcX+eYqpIel7kImBcRP86UZ49dfBZ4uHjevJG0iaTNCs9JTtR5mKQtjkwnOxK4rj4RdlmHPYBGbJOMcm1wPXBEesbpOGB5phsvdyRNAE4GDoiIf2bKWyQNSJ9vD+wAPF2fKKvTyfp0PTBJ0oaSRpHU5d6+jq+L9gHmR0R7oSDvbVLuu5d6bCv1PhOutwaSM+YeJ/lVdnq94+lC3HuQdJXMBR5Ih/2Ay4CH0vLrgW3qHWsVddme5GzSB4FHCu0ADAXuAJ4AbgeG1DvWKuqyCbAU2CJT1hBtQvJjYiHwFslxsGPKtQHJGaY/S7ebh4DWesdfoR5PkhznK2wrv0ynPThd5x4A7gM+U+/4q6hL2fUJOD1tk8eAT9U7/s7qkZZfDHylaNq8t0m5794+31Z85S8zM7McadaubDMzs4bkxGxmZpYjTsxmZmY54sRsZmaWI07MZmZmOeLEbGZmliNOzGZmZjnixGxmZpYj69c7AIBhw4bFyJEj6x1Gw3r11TYANttsTOcTtiXTMabCdGZm1uva2tpejIiW4vJcXPmrtbU15syZU+8wGtasWQJg/PgKbalkOnLQ5mZm/Z2ktohoLS53V7aZmVmOODGbmZnliBOzmZlZjuTi5C/rI6NH1zsCM2tgb731Fu3t7axcubLeoTSUQYMGMWLECAYOHFjV9BUTs6TpwP7A4oh4f6b8eOA4YDVwY0ScnJafRnJv0dXA1yPi1i7XwnpH4axsM7NuaG9vZ7PNNmPkyJGocDKpdSoiWLp0Ke3t7YwaNaqqearpyr4YmJAtkPQxYCLwwYjYBTgvLd8ZmATsks7zc0kDqq6BmZnl1sqVKxk6dKiTchdIYujQoV3qZaiYmCPiLmBZUfFXge9FxBvpNIvT8onAlRHxRkT8HXgSGFt1NGZmlmtOyl3X1c+su8eY3wvsKelcYCVwUkT8DRgOzM5M156WrUPSFGAKwHbbbdfNMBrXtGmdj58ypRfe1P9jNrN+ZOrUqRx66KEMHjy43qF0SXfPyl4fGAKMA74J/E5d/EkQEdMiojUiWlta1rnwiZmZ2Tok8YUvfGHN61WrVtHS0sL+++/fYbrvfOc7DB48uGxSHj9+PIULW+233368/PLLvRd0F3V3j7kdmBHJZcPulfQ2MAxYAGybmW5EWmZmZlazTTbZhIcffpgVK1aw0UYbMXPmTIYPX7dj9swzz6x6mTfddFNPhliz7u4xXwt8DEDSe4ENgBeB64FJkjaUNArYAbi3JwI1MzODZA/3xhtvBOCKK65g8uTJa8a9/vrrHH300YwdO5bddtuN6667DoAVK1YwadIkdtppJz772c+yYsWKNfOMHDmSF198EYADDzyQMWPGsMsuuzCt0jHHXlIxMUu6AvgrsKOkdknHANOB7SU9DFwJHBmJR4DfAY8CtwDHRcTq3gvfzMzqRio/ZJPatGmdT9tFkyZN4sorr2TlypXMnTuX3Xfffc24c889l7333pt7772XO++8k29+85u8/vrr/OIXv2DjjTdm3rx5nHPOObSV+fvo9OnTaWtrY86cOZx//vksXbq0y/HVqmJXdkRMLjPqC6UKI+Jc4NxagjIzMytn11135ZlnnuGKK65gv/326zDutttu4/rrr+e8884Dkr94Pffcc9x11118/etfXzP/rrvuWnLZ559/Ptdccw0Azz//PE888QRDhw7txdqsy1f+MjOz7qn2Hx5TpvT4X00OOOAATjrpJGbNmtVhrzYiuPrqq9lxxx27vMxZs2Zx++2389e//pWNN96Y8ePH1+UqZ75Wdn8ydWoymJk1uKOPPpqzzjqLD3zgAx3K9913Xy644AIKtzS+//77Adhrr7347W9/C8DDDz/M3Llz11nm8uXLGTx4MBtvvDHz589n9uzZ60zTF5yY+5Ne+NVqZlYPI0aMWNM1nXXGGWfw1ltvseuuu7LLLrtwxhlnAPDVr36V1157jZ122okzzzyTMWPGrDPvhAkTWLVqFTvttBOnnnoq48aN6/V6lKLIwcUmWltbo/B/sv6i1guMZOd/73uTkycef3xtWzr/mllPm2PWWB8AABY/SURBVDdvHjvttFO9w2hIpT47SW0R0Vo8rfeY+5Np0yr/IjAzs7ryyV/9ybHHJo/enTYzyy3vMZuZmeWIE7OZmVmOODGbmZnliBOzmZlZjjgxm5lZw6j2to+VNONtH83MzPpctbd97Ipmue2jNaKI6q9ta2aWU/W47eOmm27K6aefzgc/+EHGjRvHCy+8AMAf//hHdt99d3bbbTf22WefNeW1qLjHLGk6sD+wOCLeXzTu34HzgJaIeFGSgJ8C+wH/BI6KiPtqjrIf8nVAzCzPdE7Xb9dYjTir8s7DpEmT+M53vsP+++/P3LlzOfroo/nLX/4CrL3t4/Tp03n55ZcZO3Ys++yzD1OnTl1z28e5c+cyevToksuePn06Q4YMYcWKFfzLv/wLBx98MEOHDuX1119n3LhxnHvuuZx88slceOGFfPvb32aPPfZg9uzZSOJXv/oVP/jBD/jRj35U02dQTVf2xcD/AJdmCyVtC3wSeC5T/Clgh3TYHfhF+mhmZtYj6nHbxw022GDNcewxY8Ywc+ZMANrb2znssMNYuHAhb775JqNGjaq5ftXcj/kuSSNLjPoJcDJwXaZsInBpJBfgni1pS0nbRMTCmiO12hUu2l7mBuFmZtWqZs+2N/X1bR8HDhxI0ikMAwYMYNWqVQAcf/zxnHjiiRxwwAHMmjWLs88+u+a6desYs6SJwIKIeLBo1HDg+czr9rSs1DKmSJojac6SJUu6E4Z11X33JYOZWYPLy20fly9fvubks0suuaSmOhV0OTFL2hj4FnBmLW8cEdMiojUiWltaWmpZlJmZ9TN5ue3j2Wefzec+9znGjBnDsGHDaq8YVd72Me3KviEi3i/pA8AdJCd3AYwA/gGMBc4BZkXEFel8jwHjK3Vl+7aPtan6to9pN4zPzDaz7vBtH7uvV2/7GBEPRcQ7ImJkRIwk6a4eHRGLgOuBI5QYByz38WUzM7PqVUzMkq4A/grsKKld0jGdTH4T8DTwJHAh8K89EqWZmVk/Uc1Z2ZMrjB+ZeR7AcbWHZWZm1j/5kpz9yZe/XO8IzMysAifm/sSXEzMzyz1fK9vMzCxHnJj7k7Y2X/XLzBpaT932MXvjirxxV3Z/0pr+Xc7/YzazBtUbt33MG+8xm5lZQ+nsto/Lli3jwAMPZNddd2XcuHFrLr25dOlSPvnJT7LLLrvwpS99iezFtcrd6rFevMdsZmZdNmtW79z2cfz42m77eNZZZ7Hbbrtx7bXX8qc//YkjjjiCBx54gHPOOYc99tiDM888kxtvvJGLLrpozfLK3eqxXpyYzcysoXR228e7776bq6++GoC9996bpUuX8sorr3DXXXcxY8YMAD796U8zePDgNfOUu9VjvTgxm5lZl1WzZ9ubyt32sas6u9VjvfgYs5mZNZxyt33cc889ufzyy4Ek6Q4bNozNN9+8w20fb775Zl566SWge7d67G3eYzYzs4ZT7raPZ599NkcffTS77rorG2+88Zp7JJ911llMnjyZXXbZhY985CNst912QHKrx1/+8pfstNNO7LjjjlXd6rG3OTH3J/3s1ppm1nxee+21dcrGjx/P+PHjARgyZAjXXnvtOtMMHTqU2267reQyb7755h6NsVZOzP1JiRuDm5lZvlRz28fpkhZLejhT9kNJ8yXNlXSNpC0z406T9KSkxyTt21uBm5mZNaNqTv66GJhQVDYTeH9E7Ao8DpwGIGlnYBKwSzrPzyUN6LForTZTpiSDmVk3ha8c2GVd/cwqJuaIuAtYVlR2W0SsSl/OBkakzycCV0bEGxHxd+BJYGyXIrLec+GFyWBm1g2DBg1i6dKlTs5dEBEsXbqUQYMGVT1PTxxjPhq4Kn0+nCRRF7SnZeuQNAWYAqw5O87MzPJrxIgRtLe3s2TJknqH0lAGDRrEiBEjKk+YqikxSzodWAVc3tV5I2IaMA2gtbXVP7/MzHJu4MCBjBo1qt5hNL1uJ2ZJRwH7Ax+Ptf0aC4BtM5ONSMvMzMysCt268pekCcDJwAER8c/MqOuBSZI2lDQK2AG4t/YwzczM+oeKe8ySrgDGA8MktQNnkZyFvSEwUxLA7Ij4SkQ8Iul3wKMkXdzHRcTq3grezMys2VRMzBExuUTxRSXKCtOfC5xbS1DWS0aPrncEZmZWga/81Z+0tdU7AjMzq8B3lzIzM8sRJ2YzM7MccWLuT6RkMDOz3HJiNjMzyxEnZjMzsxxxYjYzM8sRJ2YzM7MccWI2MzPLESdmMzOzHPGVv/qTqVPrHYGZmVXgxNyfTJlS7wjMzKwCd2WbmZnlSMXELGm6pMWSHs6UDZE0U9IT6ePgtFySzpf0pKS5knw7ozyZNi0ZzMwst6rZY74YmFBUdipwR0TsANyRvgb4FLBDOkwBftEzYVqPOPbYZDAzs9yqmJgj4i5gWVHxROCS9PklwIGZ8ksjMRvYUtI2PRWsmZlZs+vuMeatImJh+nwRsFX6fDjwfGa69rRsHZKmSJojac6SJUu6GYaZmVlzqfnkr4gIILox37SIaI2I1paWllrDMDMzawrdTcwvFLqo08fFafkCYNvMdCPSMjMzM6tCdxPz9cCR6fMjgesy5UekZ2ePA5ZnurzNzMysgooXGJF0BTAeGCapHTgL+B7wO0nHAM8Ch6aT3wTsBzwJ/BP4Yi/EbGZm1rQqJuaImFxm1MdLTBvAcbUGZb0kunwqgJmZ9TFf+cvMzCxHnJjNzMxyxIm5PxkzJhnMzCy3fHep/uS+++odgZmZVeA9ZjMzsxxxYjYzM8sRJ2YzM7MccWI2MzPLESdmMzOzHPFZ2f3Jl79c7wjMzKwCJ+b+ZNq0ekdgZmYVuCvbzMwsR5yY+5O2tmQwM7PcqqkrW9K/AV8CAniI5DaP2wBXAkOBNuDwiHizxjitJ7S2Jo++y5SZWW51e49Z0nDg60BrRLwfGABMAr4P/CQi3gO8BBzTE4GamZn1B7V2Za8PbCRpfWBjYCGwN/CHdPwlwIE1voeZmVm/0e3EHBELgPOA50gS8nKSruuXI2JVOlk7MLzWIM3MzPqLWrqyBwMTgVHAO4FNgAldmH+KpDmS5ixZsqS7YZiZmTWVWrqy9wH+HhFLIuItYAbwUWDLtGsbYASwoNTMETEtIlojorWlpaWGMMzMzJpHLYn5OWCcpI0lCfg48ChwJ3BIOs2RwHW1hWhmZtZ/dPvvUhFxj6Q/APcBq4D7gWnAjcCVkv4zLbuoJwK1HjBnTr0jMDOzCmr6H3NEnAWcVVT8NDC2luVaLxkzpt4RmJlZBb7yl5mZWY44MfcnU6Ykg5mZ5ZYTc39y4YXJYGZmueXEbGZmliNOzGZmZjnixGxmZpYjTsxmZmY54sRsZmaWIzVdYMQazOjR9Y7AzMwqcGLuT9ra6h2BmZlV4K5sMzOzHHFiNjMzyxEn5v5ESgYzM8utmhKzpC0l/UHSfEnzJH1Y0hBJMyU9kT4O7qlgzczMml2tJ3/9FLglIg6RtAGwMfAt4I6I+J6kU4FTgVNqfB+rg2nTOh/v+2GYmfW8bu8xS9oC2Au4CCAi3oyIl4GJwCXpZJcAB9YapJmZWX9RS1f2KGAJ8GtJ90v6laRNgK0iYmE6zSJgq1qDNDMz6y9q6cpeHxgNHB8R90j6KUm39RoREZKi1MySpgBTALbbbrsawrDuqtRVbWZmfa+WPeZ2oD0i7klf/4EkUb8gaRuA9HFxqZkjYlpEtEZEa0tLSw1hmJmZNY9u7zFHxCJJz0vaMSIeAz4OPJoORwLfSx+v65FIrXZTp9Y7AjMzq6DWs7KPBy5Pz8h+GvgiyV747yQdAzwLHFrje1hP8WnUZma5V1NijogHgNYSoz5ey3LNzMz6K1/5qz+ZNs1nfJmZ5ZzvLtWfHHts8ugubTOz3PIes5mZWY54j7lJleqxntLJODMzywfvMZuZmeWIE7OZmVmOODGbmZnliBOzmZlZjvjkr35k2tSS9xMxM7Mc8R6zmZlZjjgxm5mZ5YgTcz/y2XPH8Nlzx9Q7DDMz64SPMfcjLc/dV+8QzMysgpr3mCUNkHS/pBvS16Mk3SPpSUlXpbeENDMzsyr0RFf2N4B5mdffB34SEe8BXgKO6YH3MDMz6xdqSsySRgCfBn6VvhawN/CHdJJLgANreQ8zM7P+pNY95v8GTgbeTl8PBV6OiFXp63ZgeI3vYWZm1m90OzFL2h9YHBFt3Zx/iqQ5kuYsWbKku2GYmZk1lVrOyv4ocICk/YBBwObAT4EtJa2f7jWPABaUmjkipgHTAFpbW31Jqj4wb48v9+jyKt0+csqUzsebmdm6up2YI+I04DQASeOBkyLi85J+DxwCXAkcCVzXA3FaD/jL4b4Rs5lZ3vXGBUZOAU6U9CTJMeeLeuE9zMzMmlKPXGAkImYBs9LnTwNje2K51rOGPZucDvDiu3z1LzOzvPKVv/qRg77bCvguU2ZmeeZrZZuZmeWIE7OZmVmOODGbmZnliBOzmZlZjjgxm5mZ5YgTs5mZWY7471L9yIxvzal3CGZmVoETcz/iC4uYmeWfu7LNzMxyxIm5H9nzsinseZlv+WRmlmfuyu5Hdrr7QqDv7jLl20KamXWd95jNzMxypNuJWdK2ku6U9KikRyR9Iy0fImmmpCfSx8E9F66ZmVlzq2WPeRXw7xGxMzAOOE7SzsCpwB0RsQNwR/razMzMqtDtY8wRsRBYmD5/VdI8YDgwERifTnYJyX2aT6kpSmtKPgZtZrauHjnGLGkksBtwD7BVmrQBFgFb9cR7mJmZ9Qc1n5UtaVPgauCEiHhF0ppxERGSosx8U4ApANttt12tYVgVlmw3ut4hmJlZBTUlZkkDSZLy5RExIy1+QdI2EbFQ0jbA4lLzRsQ0YBpAa2tryeRtPeua09vqHYKZmVVQy1nZAi4C5kXEjzOjrgeOTJ8fCVzX/fDMzMz6l1r2mD8KHA48JOmBtOxbwPeA30k6BngWOLS2EM3MzPqPWs7KvhtQmdEf7+5yrfdMOTZprmlTG+PIgc/aNrP+yJfktIblxG1mzciX5DQzM8sRJ2YzM7MccWI2MzPLESdmMzOzHHFiNjMzyxGfld2P3PX5qfUOoU/5rG0za0ROzP3I/L2ciczM8s5d2WZmZjnixNyPvO+uabzvrgr9u2ZmVlfuyu5H9rr8WMBd2mZmeeY9ZjMzsxzxHrP1W5XO2gafuW1mfa/X9pglTZD0mKQnJZ3aW+9jZmbWTHolMUsaAPwM+BSwMzBZ0s698V5mZmbNpLe6sscCT0bE0wCSrgQmAo/20vt14C5KMzNrVL2VmIcDz2detwO799J7mfWaWq8e5quPWTXyvp7kPT5orm1VEdHzC5UOASZExJfS14cDu0fE1zLTTAEKVd0ReKzHA0kMA17spWX3Jdcjf5qlLs1SD2ieujRLPaB56tIb9XhXRLQUF/bWHvMCYNvM6xFp2RoRMQ3o9atdSJoTEa29/T69zfXIn2apS7PUA5qnLs1SD2ieuvRlPXrrrOy/ATtIGiVpA2AScH0vvZeZmVnT6JU95ohYJelrwK3AAGB6RDzSG+9lZmbWTHrtAiMRcRNwU28tvwua5eLQrkf+NEtdmqUe0Dx1aZZ6QPPUpc/q0Ssnf5mZmVn3+FrZZmZmOdK0iblRLwkqaVtJd0p6VNIjkr6Rlp8taYGkB9Jhv3rHWg1Jz0h6KI15Tlo2RNJMSU+kj4PrHWdnJO2Y+dwfkPSKpBMapU0kTZe0WNLDmbKSbaDE+el2M1fS6PpF3lGZevxQ0vw01mskbZmWj5S0ItM2v6xf5OsqU5ey65Ok09I2eUzSvvWJel1l6nFVpg7PSHogLc97m5T77u37bSUimm4gOeHsKWB7YAPgQWDnesdVZezbAKPT55sBj5Nc1vRs4KR6x9eN+jwDDCsq+wFwavr8VOD79Y6zC/UZACwC3tUobQLsBYwGHq7UBsB+wM2AgHHAPfWOv0I9Pgmsnz7/fqYeI7PT5W0oU5eS61O6/T8IbAiMSr/bBtS7DuXqUTT+R8CZDdIm5b57+3xbadY95jWXBI2IN4HCJUFzLyIWRsR96fNXgXkkV1JrJhOBS9LnlwAH1jGWrvo48FREPFvvQKoVEXcBy4qKy7XBRODSSMwGtpS0Td9E2rlS9YiI2yJiVfpyNsk1E3KvTJuUMxG4MiLeiIi/A0+SfMfVXWf1kCTgUOCKPg2qmzr57u3zbaVZE3OpS4I2XHKTNBLYDbgnLfpa2mUyPe/dvxkB3CapTcnV3gC2ioiF6fNFwFb1Ca1bJtHxi6YR2wTKt0EjbztHk+zBFIySdL+kP0vas15BdVGp9alR22RP4IWIeCJT1hBtUvTd2+fbSrMm5oYnaVPgauCEiHgF+AXwbuBDwEKSLqJGsEdEjCa509hxkvbKjoykT6gh/hqg5GI5BwC/T4satU06aKQ2KEfS6cAq4PK0aCGwXUTsBpwI/FbS5vWKr0pNsT5lTKbjj9iGaJMS371r9NW20qyJueIlQfNM0kCSFePyiJgBEBEvRMTqiHgbuJCcdGVVEhEL0sfFwDUkcb9Q6PJJHxfXL8Iu+RRwX0S8AI3bJqlybdBw246ko4D9gc+nX5yk3b5L0+dtJMdl31u3IKvQyfrUiG2yPnAQcFWhrBHapNR3L3XYVpo1MTfsJUHT4zIXAfMi4seZ8uyxi88CDxfPmzeSNpG0WeE5yYk6D5O0xZHpZEcC19Unwi7rsAfQiG2SUa4NrgeOSM84HQcsz3Tj5Y6kCcDJwAER8c9MeYuS+8IjaXtgB+Dp+kRZnU7Wp+uBSZI2lDSKpC739nV8XbQPMD8i2gsFeW+Tct+91GNbqfeZcL01kJwx9zjJr7LT6x1PF+Leg6SrZC7wQDrsB1wGPJSWXw9sU+9Yq6jL9iRnkz4IPFJoB2AocAfwBHA7MKTesVZRl02ApcAWmbKGaBOSHxMLgbdIjoMdU64NSM4w/Vm63TwEtNY7/gr1eJLkOF9hW/llOu3B6Tr3AHAf8Jl6x19FXcquT8DpaZs8Bnyq3vF3Vo+0/GLgK0XT5r1Nyn339vm24it/mZmZ5UizdmWbmZk1JCdmMzOzHHFiNjMzyxEnZjMzsxxxYjYzM8sRJ2azJifpQEkh6X31jsXMKnNiNmt+k4G700czyzknZrMmll73dw+SC1hMSsvWk/RzJfcxninpJkmHpOPGpDcYaJN0a17uLGXWnzgxmzW3icAtEfE4sFTSGJJrGI8kudfs4cCHYc11gi8ADomIMcB04Nx6BG3Wn61f7wDMrFdNBn6aPr8yfb0+8PtIbpawSNKd6fgdgfcDM5PLBjOA5HKLZtaHnJjNmpSkIcDewAckBUmiDZK7fJWcBXgkIj7cRyGaWQnuyjZrXocAl0XEuyJiZERsC/wdWAYcnB5r3goYn07/GNAiaU3XtqRd6hG4WX/mxGzWvCaz7t7x1cDWJHcCehT4DcmdfpZHxJskyfz7kh4kubvOR/ouXDMDfHcps/5I0qYR8ZqkoST39v1oRCyqd1xm5mPMZv3VDZK2BDYA/sNJ2Sw/vMdsZmaWIz7GbGZmliNOzGZmZjnixGxmZpYjTsxmZmY54sRsZmaWI07MZmZmOfL/ARc+EJ5Q8iP7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vn9Q94-NEvpK"
      },
      "source": [
        "def median_target(var):   \n",
        "    temp = df[df[var].notnull()]\n",
        "    temp = temp[[var, 'Outcome']].groupby(['Outcome'])[[var]].median().reset_index()\n",
        "    return temp"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0rdu65-BJvt"
      },
      "source": [
        "# Analisando nossos dados com relação à incidência de diabetes\n",
        "def plot_distribution(data_select, size_bin) :  \n",
        "    # 2 datasets\n",
        "\n",
        "    D = df[(df['Outcome'] != 0)]\n",
        "    H = df[(df['Outcome'] == 0)]\n",
        "\n",
        "    tmp1 = D[data_select]\n",
        "    tmp2 = H[data_select]\n",
        "\n",
        "    hist_data = [tmp1, tmp2]\n",
        "    \n",
        "    group_labels = ['diabetic', 'healthy']\n",
        "    colors = ['lightskyblue', 'gold']\n",
        "\n",
        "    fig = ff.create_distplot(hist_data, group_labels, colors = colors, show_hist = True, bin_size = size_bin, curve_type='kde')\n",
        "    \n",
        "    fig['layout'].update(title = data_select)\n",
        "\n",
        "    py.iplot(fig, filename = 'Density plot')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ae75tQdiG0p0"
      },
      "source": [
        "### Analisando a Glicose\n",
        "- Glicose : Concentração de glicose plasmática a 2 horas em um teste oral de tolerância à glicose"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "x0xQYy1mGS5u",
        "outputId": "070a1e3f-7855-4552-e4a6-69b0351515dd"
      },
      "source": [
        "plot_distribution('Glucose', 0)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "code",
      "metadata": {
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      "source": [
        "median_target('Glucose')"
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      "execution_count": 16,
      "outputs": [
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          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Outcome</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>109.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>138.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Outcome  Glucose\n",
              "0        0    109.0\n",
              "1        1    138.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Lai0PY-GjbR"
      },
      "source": [
        "#### Valor de 109 para Glicose indica pessoa saudável e 138 para diabético"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FwumQiAZHslu"
      },
      "source": [
        "### BMI - Indice de massa corporal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "L6tq-_vzH2H8",
        "outputId": "1b41a696-0756-4460-cbb9-84cac00b60b7"
      },
      "source": [
        "plot_distribution('BMI', 0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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        "outputId": "698cd821-2c7c-4758-e984-1224f0048db5"
      },
      "source": [
        "median_target('BMI')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Outcome</th>\n",
              "      <th>BMI</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>30.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>34.00</td>\n",
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              "   Outcome    BMI\n",
              "0        0  30.75\n",
              "1        1  34.00"
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          "metadata": {
            "tags": []
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          "execution_count": 50
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      ]
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      "cell_type": "markdown",
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        "id": "U-ez6NZrIqP8"
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      "source": [
        "#### Indice de massa corporal menor que 30.75 saudável, maior que 34 diabético"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hAIV4vTJAFg"
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      "source": [
        "### Idade"
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          "height": 542
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        "id": "18xayrn1IKa8",
        "outputId": "d7f030f0-2827-4386-c6f1-0be5f379a66b"
      },
      "source": [
        "plot_distribution('Age', 0)"
      ],
      "execution_count": null,
      "outputs": [
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      "source": [
        "median_target('Age')"
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Outcome</th>\n",
              "      <th>Age</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
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              "   Outcome   Age\n",
              "0        0  27.0\n",
              "1        1  35.0"
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      "source": [
        "### Número de Gravidezes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "cAWVYrncIX_0",
        "outputId": "bf1c79cf-5576-4a74-de67-d3de7a323ccf"
      },
      "source": [
        "plot_distribution('Pregnancies', 0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Outcome</th>\n",
              "      <th>Pregnancies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Outcome  Pregnancies\n",
              "0        0          2.5\n",
              "1        1          4.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JDuRWqjRJhAI"
      },
      "source": [
        "### Salvando nosso conjunto de dados tratado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTSr-N-BmFrx"
      },
      "source": [
        "# Salvando nosso conjunto de dados tratado\n",
        "df.to_csv('test_diabetes_clean.csv')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAwp6YZM0UmW"
      },
      "source": [
        "# Utilizando a biblioteca\n",
        "from pycaret.datasets import get_data"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3oZ016Bs0iGX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d481cb2f-9825-473b-e88b-34d61ce9a58e"
      },
      "source": [
        "# Importando nosso dataset tratado\n",
        "dados = get_data(\"test_diabetes_clean\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>66.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>66.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>121.152055</td>\n",
              "      <td>40.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Pregnancies     Glucose  BloodPressure  SkinThickness  Insulin  \\\n",
              "0           0          3.0  148.000000           72.0           35.0      0.0   \n",
              "1           1          1.0   85.000000           66.0           29.0      0.0   \n",
              "2           2          8.0  183.000000           64.0            0.0      0.0   \n",
              "3           3          1.0   89.000000           66.0           23.0     94.0   \n",
              "4           4          0.0  121.152055           40.0           35.0    168.0   \n",
              "\n",
              "    BMI  DiabetesPedigreeFunction   Age  Outcome  \n",
              "0  33.6                     0.627  50.0        1  \n",
              "1  26.6                     0.351  31.0        0  \n",
              "2  23.3                     0.672  32.0        1  \n",
              "3  28.1                     0.167  21.0        0  \n",
              "4  43.1                     2.288  29.0        1  "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sy8b3J3qpWDr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1731a4ef-bb5e-4874-bb8b-37ff2d481839"
      },
      "source": [
        "# Verificando os tipos de nossos dados\n",
        "dados.info()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 10 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Unnamed: 0                768 non-null    int64  \n",
            " 1   Pregnancies               768 non-null    float64\n",
            " 2   Glucose                   768 non-null    float64\n",
            " 3   BloodPressure             768 non-null    float64\n",
            " 4   SkinThickness             768 non-null    float64\n",
            " 5   Insulin                   768 non-null    float64\n",
            " 6   BMI                       768 non-null    float64\n",
            " 7   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 8   Age                       768 non-null    float64\n",
            " 9   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(8), int64(2)\n",
            "memory usage: 60.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFO3Tok9ub5n"
      },
      "source": [
        "# Copia dos dados\n",
        "dados_cp = dados[['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']].copy()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-MTX308ud04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2cd34b01-487c-4b5c-ffb8-ca127a60d8d9"
      },
      "source": [
        "# Verificando os dados copiados\n",
        "dados_cp.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>66.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.0</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>66.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>121.152055</td>\n",
              "      <td>40.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies     Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0          3.0  148.000000           72.0           35.0      0.0  33.6   \n",
              "1          1.0   85.000000           66.0           29.0      0.0  26.6   \n",
              "2          8.0  183.000000           64.0            0.0      0.0  23.3   \n",
              "3          1.0   89.000000           66.0           23.0     94.0  28.1   \n",
              "4          0.0  121.152055           40.0           35.0    168.0  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction   Age  Outcome  \n",
              "0                     0.627  50.0        1  \n",
              "1                     0.351  31.0        0  \n",
              "2                     0.672  32.0        1  \n",
              "3                     0.167  21.0        0  \n",
              "4                     2.288  29.0        1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9TScOF21Gwr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "77651f3089c34513afc6f2c2cb4204e4",
            "b9d8d5d4fc154b9c96c82e13c29d94f2",
            "be679ef6479f4c73b113b41a940e587e",
            "21eb92586c32469b81623b7f252109c4",
            "bb52cfc105f941649cd66c7fce994322",
            "84ed499a91944eeba8f23a85dc5549fd"
          ]
        },
        "outputId": "bb0112e0-e1d6-4033-d3f7-9865b589e379"
      },
      "source": [
        "# Configurações de pre-processamento e validação dos modelos que serão utilizados\n",
        "#     data : nossos dados\n",
        "#     target : variável de avaliação \n",
        "#     normalize : padronização dos dados\n",
        "#     combine_rare_levels : combinar os valores raros em um grupo apenas\n",
        "#     remove_outliers : remover ou não os outliers\n",
        "\n",
        "regressao = setup ( data = dados_cp,\n",
        "                   target = 'Outcome',\n",
        "                   normalize = True,\n",
        "                   combine_rare_levels = True,\n",
        "                   remove_outliers = True,\n",
        "                   session_id = 1\n",
        " )\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>Outcome</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(768, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(510, 8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(231, 8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>USI</td>\n",
              "      <td>34e5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>zscore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Transform Target</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Transform Target Method</td>\n",
              "      <td>box-cox</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id                 1\n",
              "1                                   Target           Outcome\n",
              "2                            Original Data          (768, 9)\n",
              "3                           Missing Values             False\n",
              "4                         Numeric Features                 8\n",
              "5                     Categorical Features                 0\n",
              "6                         Ordinal Features             False\n",
              "7                High Cardinality Features             False\n",
              "8                  High Cardinality Method              None\n",
              "9                    Transformed Train Set          (510, 8)\n",
              "10                    Transformed Test Set          (231, 8)\n",
              "11                      Shuffle Train-Test              True\n",
              "12                     Stratify Train-Test             False\n",
              "13                          Fold Generator             KFold\n",
              "14                             Fold Number                10\n",
              "15                                CPU Jobs                -1\n",
              "16                                 Use GPU             False\n",
              "17                          Log Experiment             False\n",
              "18                         Experiment Name  reg-default-name\n",
              "19                                     USI              34e5\n",
              "20                         Imputation Type            simple\n",
              "21          Iterative Imputation Iteration              None\n",
              "22                         Numeric Imputer              mean\n",
              "23      Iterative Imputation Numeric Model              None\n",
              "24                     Categorical Imputer          constant\n",
              "25  Iterative Imputation Categorical Model              None\n",
              "26           Unknown Categoricals Handling    least_frequent\n",
              "27                               Normalize              True\n",
              "28                        Normalize Method            zscore\n",
              "29                          Transformation             False\n",
              "30                   Transformation Method              None\n",
              "31                                     PCA             False\n",
              "32                              PCA Method              None\n",
              "33                          PCA Components              None\n",
              "34                     Ignore Low Variance             False\n",
              "35                     Combine Rare Levels              True\n",
              "36                    Rare Level Threshold               0.1\n",
              "37                         Numeric Binning             False\n",
              "38                         Remove Outliers              True\n",
              "39                      Outliers Threshold              0.05\n",
              "40                Remove Multicollinearity             False\n",
              "41             Multicollinearity Threshold              None\n",
              "42                              Clustering             False\n",
              "43                    Clustering Iteration              None\n",
              "44                     Polynomial Features             False\n",
              "45                       Polynomial Degree              None\n",
              "46                    Trignometry Features             False\n",
              "47                    Polynomial Threshold              None\n",
              "48                          Group Features             False\n",
              "49                       Feature Selection             False\n",
              "50                Feature Selection Method           classic\n",
              "51            Features Selection Threshold              None\n",
              "52                     Feature Interaction             False\n",
              "53                           Feature Ratio             False\n",
              "54                   Interaction Threshold              None\n",
              "55                        Transform Target             False\n",
              "56                 Transform Target Method           box-cox"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqACXafb3hVa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "b9801e8b-02f0-42f5-f5b0-e1616a1776dc"
      },
      "source": [
        "# Listando os modelos disponiveis\n",
        "models()"
      ],
      "execution_count": 24,
      "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>Name</th>\n",
              "      <th>Reference</th>\n",
              "      <th>Turbo</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>sklearn.linear_model._base.LinearRegression</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lasso</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>sklearn.linear_model._coordinate_descent.Lasso</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>sklearn.linear_model._ridge.Ridge</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>en</th>\n",
              "      <td>Elastic Net</td>\n",
              "      <td>sklearn.linear_model._coordinate_descent.Elast...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lar</th>\n",
              "      <td>Least Angle Regression</td>\n",
              "      <td>sklearn.linear_model._least_angle.Lars</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>llar</th>\n",
              "      <td>Lasso Least Angle Regression</td>\n",
              "      <td>sklearn.linear_model._least_angle.LassoLars</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>omp</th>\n",
              "      <td>Orthogonal Matching Pursuit</td>\n",
              "      <td>sklearn.linear_model._omp.OrthogonalMatchingPu...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>br</th>\n",
              "      <td>Bayesian Ridge</td>\n",
              "      <td>sklearn.linear_model._bayes.BayesianRidge</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ard</th>\n",
              "      <td>Automatic Relevance Determination</td>\n",
              "      <td>sklearn.linear_model._bayes.ARDRegression</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>par</th>\n",
              "      <td>Passive Aggressive Regressor</td>\n",
              "      <td>sklearn.linear_model._passive_aggressive.Passi...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ransac</th>\n",
              "      <td>Random Sample Consensus</td>\n",
              "      <td>sklearn.linear_model._ransac.RANSACRegressor</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tr</th>\n",
              "      <td>TheilSen Regressor</td>\n",
              "      <td>sklearn.linear_model._theil_sen.TheilSenRegressor</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>huber</th>\n",
              "      <td>Huber Regressor</td>\n",
              "      <td>sklearn.linear_model._huber.HuberRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kr</th>\n",
              "      <td>Kernel Ridge</td>\n",
              "      <td>sklearn.kernel_ridge.KernelRidge</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>Support Vector Regression</td>\n",
              "      <td>sklearn.svm._classes.SVR</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Regressor</td>\n",
              "      <td>sklearn.neighbors._regression.KNeighborsRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>sklearn.tree._classes.DecisionTreeRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>sklearn.ensemble._forest.RandomForestRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Regressor</td>\n",
              "      <td>sklearn.ensemble._forest.ExtraTreesRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>AdaBoost Regressor</td>\n",
              "      <td>sklearn.ensemble._weight_boosting.AdaBoostRegr...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbr</th>\n",
              "      <td>Gradient Boosting Regressor</td>\n",
              "      <td>sklearn.ensemble._gb.GradientBoostingRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mlp</th>\n",
              "      <td>MLP Regressor</td>\n",
              "      <td>sklearn.neural_network._multilayer_perceptron....</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>lightgbm.sklearn.LGBMRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       Name  ...  Turbo\n",
              "ID                                           ...       \n",
              "lr                        Linear Regression  ...   True\n",
              "lasso                      Lasso Regression  ...   True\n",
              "ridge                      Ridge Regression  ...   True\n",
              "en                              Elastic Net  ...   True\n",
              "lar                  Least Angle Regression  ...   True\n",
              "llar           Lasso Least Angle Regression  ...   True\n",
              "omp             Orthogonal Matching Pursuit  ...   True\n",
              "br                           Bayesian Ridge  ...   True\n",
              "ard       Automatic Relevance Determination  ...  False\n",
              "par            Passive Aggressive Regressor  ...   True\n",
              "ransac              Random Sample Consensus  ...  False\n",
              "tr                       TheilSen Regressor  ...  False\n",
              "huber                       Huber Regressor  ...   True\n",
              "kr                             Kernel Ridge  ...  False\n",
              "svm               Support Vector Regression  ...  False\n",
              "knn                   K Neighbors Regressor  ...   True\n",
              "dt                  Decision Tree Regressor  ...   True\n",
              "rf                  Random Forest Regressor  ...   True\n",
              "et                    Extra Trees Regressor  ...   True\n",
              "ada                      AdaBoost Regressor  ...   True\n",
              "gbr             Gradient Boosting Regressor  ...   True\n",
              "mlp                           MLP Regressor  ...  False\n",
              "lightgbm    Light Gradient Boosting Machine  ...   True\n",
              "\n",
              "[23 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkH0V8uJ3ifd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "1eb2f828829f4c4b93391b411fa0e37d",
            "e2d1fc8386db4bb8869ced0c33c728ba",
            "cf3f01c1dc2542bd8b4ef8457a575db8"
          ]
        },
        "outputId": "89199ec7-f677-4448-e3e6-91564f33d69a"
      },
      "source": [
        "# Criando nosso modelo Bayesian Ridge\n",
        "bayesian = create_model('br', fold=10)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "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>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.3901</td>\n",
              "      <td>0.2081</td>\n",
              "      <td>0.4562</td>\n",
              "      <td>-0.1567</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.6374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.3817</td>\n",
              "      <td>0.1926</td>\n",
              "      <td>0.4388</td>\n",
              "      <td>0.2294</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.3092</td>\n",
              "      <td>0.1413</td>\n",
              "      <td>0.3760</td>\n",
              "      <td>0.2558</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.3655</td>\n",
              "      <td>0.1802</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.2110</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.3246</td>\n",
              "      <td>0.1540</td>\n",
              "      <td>0.3924</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.3029</td>\n",
              "      <td>0.1268</td>\n",
              "      <td>0.3560</td>\n",
              "      <td>0.3894</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.3634</td>\n",
              "      <td>0.1763</td>\n",
              "      <td>0.4199</td>\n",
              "      <td>0.2458</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.3874</td>\n",
              "      <td>0.2073</td>\n",
              "      <td>0.4553</td>\n",
              "      <td>0.0921</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.3321</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>0.3988</td>\n",
              "      <td>0.2843</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.3498</td>\n",
              "      <td>0.1608</td>\n",
              "      <td>0.4009</td>\n",
              "      <td>0.3123</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.3507</td>\n",
              "      <td>0.1706</td>\n",
              "      <td>0.4119</td>\n",
              "      <td>0.2008</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0305</td>\n",
              "      <td>0.0258</td>\n",
              "      <td>0.0314</td>\n",
              "      <td>0.1427</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0595</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         MAE     MSE    RMSE      R2  RMSLE    MAPE\n",
              "0     0.3901  0.2081  0.4562 -0.1567   -0.0  0.6374\n",
              "1     0.3817  0.1926  0.4388  0.2294   -0.0  0.5051\n",
              "2     0.3092  0.1413  0.3760  0.2558   -0.0  0.5391\n",
              "3     0.3655  0.1802  0.4245  0.2110   -0.0  0.5566\n",
              "4     0.3246  0.1540  0.3924  0.1444   -0.0  0.4486\n",
              "5     0.3029  0.1268  0.3560  0.3894   -0.0  0.4197\n",
              "6     0.3634  0.1763  0.4199  0.2458   -0.0  0.5326\n",
              "7     0.3874  0.2073  0.4553  0.0921   -0.0  0.5425\n",
              "8     0.3321  0.1591  0.3988  0.2843   -0.0  0.5133\n",
              "9     0.3498  0.1608  0.4009  0.3123   -0.0  0.4561\n",
              "Mean  0.3507  0.1706  0.4119  0.2008    0.0  0.5151\n",
              "SD    0.0305  0.0258  0.0314  0.1427    0.0  0.0595"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGjIG9if34Et",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "3ce6732d79304ce3812ef30bd23a9c4c",
            "8e5460ced4874a7baf2f8cccdec5e5c1",
            "13356e20fcfa44f0aca5c3f79a90df71"
          ]
        },
        "outputId": "64518daa-985e-4c21-e4b7-06563c96f81a"
      },
      "source": [
        "# Otimizando o nosso modelo\n",
        "bayesian_tunada = tune_model(bayesian, optimize='RMSE', n_iter=20)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "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>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.3900</td>\n",
              "      <td>0.2080</td>\n",
              "      <td>0.4561</td>\n",
              "      <td>-0.1561</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.6374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.3819</td>\n",
              "      <td>0.1926</td>\n",
              "      <td>0.4389</td>\n",
              "      <td>0.2291</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.3093</td>\n",
              "      <td>0.1413</td>\n",
              "      <td>0.3759</td>\n",
              "      <td>0.2558</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.3656</td>\n",
              "      <td>0.1802</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.2109</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.3248</td>\n",
              "      <td>0.1540</td>\n",
              "      <td>0.3924</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.3030</td>\n",
              "      <td>0.1268</td>\n",
              "      <td>0.3560</td>\n",
              "      <td>0.3894</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.3635</td>\n",
              "      <td>0.1763</td>\n",
              "      <td>0.4199</td>\n",
              "      <td>0.2457</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.3874</td>\n",
              "      <td>0.2073</td>\n",
              "      <td>0.4553</td>\n",
              "      <td>0.0924</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.3321</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.3988</td>\n",
              "      <td>0.2843</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.3499</td>\n",
              "      <td>0.1608</td>\n",
              "      <td>0.4010</td>\n",
              "      <td>0.3122</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.3507</td>\n",
              "      <td>0.1706</td>\n",
              "      <td>0.4119</td>\n",
              "      <td>0.2008</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0304</td>\n",
              "      <td>0.0258</td>\n",
              "      <td>0.0314</td>\n",
              "      <td>0.1425</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0594</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         MAE     MSE    RMSE      R2  RMSLE    MAPE\n",
              "0     0.3900  0.2080  0.4561 -0.1561   -0.0  0.6374\n",
              "1     0.3819  0.1926  0.4389  0.2291   -0.0  0.5054\n",
              "2     0.3093  0.1413  0.3759  0.2558   -0.0  0.5393\n",
              "3     0.3656  0.1802  0.4245  0.2109   -0.0  0.5569\n",
              "4     0.3248  0.1540  0.3924  0.1444   -0.0  0.4491\n",
              "5     0.3030  0.1268  0.3560  0.3894   -0.0  0.4202\n",
              "6     0.3635  0.1763  0.4199  0.2457   -0.0  0.5329\n",
              "7     0.3874  0.2073  0.4553  0.0924   -0.0  0.5427\n",
              "8     0.3321  0.1590  0.3988  0.2843   -0.0  0.5135\n",
              "9     0.3499  0.1608  0.4010  0.3122   -0.0  0.4565\n",
              "Mean  0.3507  0.1706  0.4119  0.2008    0.0  0.5154\n",
              "SD    0.0304  0.0258  0.0314  0.1425    0.0  0.0594"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6s6h7CY5B7I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "a6a5ea80-fd37-4dcb-bafd-1820cfbc9dc0"
      },
      "source": [
        "# Importancia das variáveis de acordo com a Bayesian Ridge (bayesian_tunada)\n",
        "plot_model(bayesian_tunada, plot = 'feature')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HQ63qVNJDQbXU8o5KXJIHAIANeBAErql69eqKjIzUtGnT9PTTT6tSpUoaP368goODC3pqAAAAQJ4jNCFHunXrpm7duhX0NAAAAIB8x+V5AAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNRUS7du20YsWKq+7r3LmzVq1aZcs4q1evVkhISLb7Bw0apFmzZl2zn7Fjx+qpp56yZU4AAABAXipW0BNAzqSnp2v+/PnauHGjTp06JYfDIX9/fz355JNq1KiR5Xs//fTTHI3xwgsvaN26dZKkzMxMXbx4UR4eHs79S5YsuWYfOWmDoscYo21H/9CJc8mqcksptbzjNjkcjoKeFgAAgC0ITYVEZGSkdu/erTlz5sjHx0cpKSl6++23NWjQIG3cuNGWMSZPnqzJkydLurSiNGPGDH3zzTcubWJjY20ZC0XHmn0xGrMhSkdOn3duq1OhrKZ2C1avgBoFODMAAAB7cHleIfHNN9/ovvvuk6+vr9zd3VWmTBlFRERo8uTJLqtBkpSWlqZ+/fpp7Nixklwv3Rs7dqxeeuklTZkyRU2aNFGzZs20aNGiXM/ns88+U/v27RUQEKBnn31W6enpkqQBAwZo+vTpznZLlixR27ZtFRwcrMGDB+vYsWNX7e9//ud/1K1bNyUmJmru3LmKiIjQokWLFBISosaNGzvDnCRduHBBkyZNUps2bdSgQQMNGDBAhw8fdu5fuHCh2rZtq7vvvludO3d2WT2LjIxUaGioGjRooO7du2vbtm25Pnb8vzX7YtRn2VaXwCRJR06fV59lW7VmX0wBzQwAAMA+rDQVErVr19aaNWsUGhoqPz8/5/bu3btnaTt+/Hh5eHjopZdeumpfH374ocaOHatvvvlG69ev14svvqgePXrotttuy9FckpKStHv3bm3YsEHR0dHq06ePOnTooE6dOrm0+/zzz7Vo0SK9+eabuuOOOzRp0iSNHj1aK1eudGm3Zs0abdiwQatWrVKZMmUkSVFRUQoMDNSWLVu0e/duDRw4UN27d1dgYKCmT5+un3/+We+99568vLw0Z84cjRgxQh9//LF++OEHvfXWW3r//fdVuXJlffPNNxo5cqRCQ0O1fft2bd++XevXr5eXl5fWrl2rMWPG6KuvvlLx4sVzdOz57aw8dDIlQyVNekFPJQtjjP69frcyjbnq/kxjNPbDKPX0r86legAAoFAjNBUSL774op5++mn17NlTVatWVcOGDdW6dWt16tTJZaVp8eLF2rdvn1asWJFtEKhWrZp69eolSbr33nv13HPPKTo6OsehKTU1VSNHjlSpUqV011136Y477tBvv/2Wpd0HH3yg++67T/Xq1ZMkPfXUU9q5c6cyMzOdbaKiohQZGamlS5fq9ttvd253d3dXeHi43Nzc1Lx5c3l7e+vIkSPy9/fX6tWrNWvWLFWqVEmSNGrUKL3zzjvau3evzp8/Lzc3N5UsWVIOh0OhoaHavXu33NzcdO7cORUrVkyenp5yd3fX/fffr169esnNLecLrsYYJScn57j9jUhJSdFOt8raGZMqKTVfxsyN307F6/f4RMs2h/86r88PxCqkVsV8mtXVpaSkuPwXN4Z62ot62ot62oda2ot62iu7ehpj8uSXtYSmQqJKlSpauXKlDh8+rO3bt2vXrl164YUXNHv2bL3zzjuSpK1bt+rLL7/U4sWLVbZs2Wz7qlatmvPvnp6eki5d8pZT5cuXV+nSpZ2vS5YsqbS0tCztYmNj1bRpU+frChUqqEuXLs7XcXFxGjFihPr27euyenb5eK8MM56enrpw4YJOnz6tpKQkDRs2zOULIjMzU3FxcWrXrp3uuusutWvXTs2bN1erVq3Uo0cPlSpVSvfdd5/WrVunVq1aKSQkRG3atNF9992Xq9CUnp6uAwcO5Lj9DXOrmX9j5dL5lJydM7sPHpZ3yp95PJuciY6OLugpFCnU017U017U0z7U0l7U015Xq+ffb12xA6GpkPHx8ZGPj4/+9a9/6c8//9SDDz6oZcuWSZJ++OEHtW7dWq+++qqaNm0qd3f3q/aRm5BwNTlN7w6HQyabS7ckae/everWrZuWL1+usLAwl5Wm7OZYsmRJSdLKlSvl7+9/1TYLFizQwYMH9cUXX2j58uVasmSJVq9erXLlyun9999XVFSUtmzZojlz5mjFihVavny5ihXL2ZdC8eLF5ePjk6O2NyolJUVNok+ocuXKKlGiRL6MmRu1VFbvfX3tdg3r+cjvH7DSFB0drVq1ajl/UYDrRz3tRT3tRT3tQy3tRT3tlV09r7zP3U6EpkLg5MmTWrBggUaPHu2850eSbr31VtWrV8+5LDly5Ej17NlT3bt314IFCzR8+PCCmrIkqXr16i6X7cXHx2vt2rUaMGCAJKlDhw6aOnWqnnjiCf3nP//RkiVLrhnIypYtq3LlyunQoUMuoenYsWOqVq2a0tPTlZqaqnr16qlevXoKDw/Xvffeq+3bt6tNmzbKzMxUcHCwgoODFRERoZCQEB08eDDbAPZ3DodDpUqVuo5qXB8vpalmuVL5OmZO1fYuo3Ef78nyEIgr+VQsqw5+/5x7mjw9Pf+RtSysqKe9qKe9qKd9qKW9qKe9/l7PvPrMwdPzCgFvb29t375dzzzzjI4eParMzEylpKToww8/1I4dO9SuXTtJl1ZnSpcurSlTpmjBggX6+eefC3Te999/vzZu3Kg9e/YoLS1N8+bN0yeffOK81+ryStiECRP0yy+/aPny5Tnqt2/fvpo/f76OHDmi9PR0LV26VA888IBSUlK0ZMkSPfbYYzp58qQk6ciRIzp79qxq1Kihl19+WWPGjFF8fLyMMdq/f78yMzNVpUqVvClAEedwODS1W7Dcsvnm5OZwKLJr8D8mMAEAAFwvVpoKAQ8PD7399tuaO3euBg8erPj4eLm5ucnPz08zZsxQy5YtXdo3adJEYWFhevbZZ7V69eoCmrXUvn17PfXUUxo+fLiSk5MVFBSkGTNmZGnn7e2tiRMnavTo0QoJCblmv8OGDdO5c+fUr18/paeny8/PT4sWLZKnp6ceffRRnThxQj179tSFCxdUuXJljR49Wn5+fvr3v/+t8ePHq3Pnzrp48aJq1qypGTNmyNvbOy8O/6bQK6CG3n+klcZ+GKXDf/3/ipNPxbKK7Mq/0wQAAIoGh7G66QSA0759+yRJAQEB+TJecnKyDhw4ID8/v3/8Mr4xRtuO/qG4cymq4uWp0Nq3/aNWmApTLQsD6mkv6mkv6mkfamkv6mmv7OqZV5/XWGkCcMMcDoda1alU0NMAAADIE9zTBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQBAAAAAAWCE0AAAAAYIHQhCKlXbt2WrFihSRp0KBBmjVrVgHPCAAAAIVdsYKeAG4eAwYM0N13363Ro0fny3hLlizJl3GKKmOMth39QyfOJavKLaXU8o7b5HA4CnpaAAAA+Y7QBCCLNftiNGZDlI6cPu/cVqdCWU3tFqxeATUKcGYAAAD5j8vzkO+OHTsmX19fffPNN+rZs6caNGigvn376tixY5Kkv/76S8OHD1fTpk0VHBysgQMHKjY2VpI0duxYPfXUU86+UlNT5evrq++++y7LOAMGDND06dMlSXPnzlVERIQWLVqkkJAQNW7cWJMnT86Hoy181uyLUZ9lW10CkyQdOX1efZZt1Zp9MQU0MwAAgIJBaEKBeeutt/T666/ryy+/VHJyst544w1J0uzZs+Xl5aWtW7fq66+/Vo0aNTR16tQbHi8qKkoXL17Uli1bNGfOHL399tvau3fvDfebl87KQydTMnQiKT1f/hxPTNO/1+9WpjFXnU+mMRr7YZRMNvsBAACKIi7PQ4EJCwtTpUqVJEmhoaHat2+fJOncuXMqV66cPDw85HA4NGHCBLm53Xi+d3d3V3h4uNzc3NS8eXN5e3vryJEjCgwMzHEfxhglJyff8FxyIiUlRTvdKmtnTKqk1HwZ87dT8fo9PtGyzeG/zuvzA7EKqVUxX+Zkh5SUFJf/4sZQT3tRT3tRT/tQS3tRT3tlV09jTJ7cg01oQoGpVq2a8++enp5KTb0UDIYMGaKIiAht27ZNoaGh6tKli5o3b37D41WpUsUlfHl6eurChQu56iM9PV0HDhy44bnkmFvN/BtL0vmUnNVj98HD8k75M49nY7/o6OiCnkKRQj3tRT3tRT3tQy3tRT3tdbV6enh42D4OoQkFJrvfAgQEBGjz5s3atm2bvvzyS40YMUJ9+vTRmDFjsrTNyMjI8Xh2rFYVL15cPj4+N9xPTqSkpKhJ9AlVrlxZJUqUyJcxa6ms3vv62u0a1vORXyFbaYqOjlatWrXk6elZ0NMp9KinvainvainfailvainvbKr5+HDh/NkPEIT/nESEhLk5eWl9u3bq3379urWrZvCw8M1ZswYeXh4KCEhwdk2JiZ/H0rgcDhUqlSpfBvPS2mqWa5Uvo1Z27uMxn28J8tDIK7kU7GsOvhVL5SPH/f09MzX/39FHfW0F/W0F/W0D7W0F/W019/rmVefT3gQBP5x+vbtq0WLFik1NVXp6enas2ePata8dJlarVq1tGfPHp08eVLnz5/XkiVL5O7uXsAzLjocDoemdguWWzbfcNwcDkV2DS6UgQkAAOB6EZrwjzNr1ixt2bJFzZo1U4sWLbRjxw7no8MfeOAB1a9fX/fcc4/uv/9+de3aVSVLlizgGRctvQJq6P1HWsmnYlmX7T4Vy+r9R1rx7zQBAICbjsPw7GAgRy4/3S8gICBfxktOTtaBAwfk5+dXIMv4xhhtO/qH4s6lqIqXp0Jr31ZoV5gKupZFDfW0F/W0F/W0D7W0F/W0V3b1zKvPa9zTBOCqHA6HWtWpVNDTAAAAKHBcngcAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFghNAAAAAGCB0AQAAAAAFhzGGFPQkwAKg6ioKBlj5OHhkS/jGWOUnp6u4sWLy+Fw5MuYRRW1tBf1tBf1tBf1tA+1tBf1tFd29UxLS5PD4VBwcLCt4xWztTegCMvvb3AOhyPfAlpRRy3tRT3tRT3tRT3tQy3tRT3tlV09HQ5HnnxmY6UJAAAAACxwTxMAAAAAWCA0AQAAAIAFQhMAAAAAWCA0AQAAAIAFQhMAAAAAWCA0AQAAAIAFQhMAAAAAWCA0AQAAAIAFQhMAAAAAWCA0AXno+PHjGjp0qJo2baq2bdtq2rRpyszMvGrbt956S507d1ZwcLDCwsL0008/OfelpqZq3LhxatWqlZo2baonnnhCZ86cua5xCjO76nnhwgW9/PLLatWqlRo1aqRHH31Uv/zyi3N/u3bt5O/vr4CAAOefxx9/PM+PLz/ZVcsBAwaofv36LrXq3r37dY1TmNlVzyvrePmPr6+vdu7cKUny9fXNcm6+9NJL+XKM+Sk39UxKStLo0aPl6+urI0eOuOxLSEjQqFGj1KJFC4WGhur555/XhQsXnPsPHDig/v37q2HDhurUqZOWLFmSp8dVUOyq55kzZzRmzBiFhISoadOmGjFihOLi4pz7b4bz065aXuvnDOdmVtnV8/jx49l+7zx+/LiOHTsmX1/fLPsXL16cu8kaAHmmV69e5oUXXjDnzp0zv/32m+nUqZNZsmRJlnZffPGFadSokfnxxx9NSkqKef31101ISIhJSkoyxhgzZcoU07t3b3PixAlz5swZM2LECBMeHp7rcQo7u+o5adIk06tXL3P8+HGTlJRknnvuOdOxY0fn+9u2bWu+/fbbfDuugmBXLfv3728++OCDGx6nsLOrnn/3/fffm7Zt25qUlBRjjDF169Y1sbGxeXos/wQ5refJkydNp06dzLPPPmvq1q1rDh8+7LJ/xIgRZujQoeb06dPm5MmT5qGHHjIvvfSSMcaYlJQU07JlSzN37lyTlJRkfvrpJ9OkSRPz6aef5ssx5ie76hkeHm4GDRpkTp8+bc6cOWOGDh1qHnnkEef+m+H8tKuWVj9nODdzX8+/W79+vXnwwQdNZmamiY2NNXXr1r3huRKagDyyd+9e4+fnZxISEpzb3n33XdO5c+csbYcOHWpeeeUV5+uMjAwTEhJiPvzwQ5Oenm4aNmxoPv/8c+f+w4cPG19fX3Py5MlcjVOY2VVPY4yZOXOm2bFjh3P/oUOHTN26dc3JkyeNMUU/NNlZS6vQxLmZ+3pe6eLFi6Z79+7mo48+cm67GT6U5qaeBw4cMJ999pnzQ9GVH6T+/PNPU69ePXPgwAHntq+++so0aNDApKWlmY8//tg0a9bMXLx40bl/2rRpZtCgQXl0ZAXDrnpmZmaacePGmUOHDjm3bd682fj7+5vMzExjTNE/P+2qpTHWP2c4N3NfzyudP3/etGzZ0uzZs8cYY2wLTVyeB+SR/fv3q2rVqvLy8nJuq1+/vn777TclJiZmaXvXXXc5X7u5ucnPz0/79u1TTEyMzp8/r/r16zv316lTRyVLltT+/ftzNU5hZlc9Jempp55Ss2bNnPvj4uJUokQJlStXzrntrbfeUocOHRQUFKQnnnhCp0+fzqtDy3d21lKSPvroI917770KCgrSwIEDFRMTk+txCjO763nZ2rVr5eHhoS5durhsnzFjhtq0aaNGjRrpxRdfVFJSks1HVLByU8969eqpQ4cOV+3nwIEDcnd3l6+vr0s/ycnJOnr0qPbv3y9fX1+5u7s79991110ul0sWBXbV0+FwaOLEiapbt65zW1xcnG699VY5HA7ntqJ8ftpVy8uy+znDuXl99bxsyZIlCgoKUmBgoMv2Z599VqGhoWrWrJlmzJih9PT0XM2X0ATkkYSEBN1yyy0u2y5/Y7jyfqTLba/8pnG57ZkzZ5SQkCBJWfq65ZZbnPtzOk5hZlc9/+7s2bN6+eWXNWjQIJUoUUKS5Ofnp8DAQK1bt04fffSREhIS9OSTT9p5OAXKzlrWqVNHd955p95991198cUX8vb21pAhQ5SWlsa5qes/NzMzM7Vw4UKFh4e7bG/QoIFatGihTZs26b333tOPP/6oiRMn2nUo/wh2nTcJCQkqU6aMywf6K/u52jjlypVTQkJCkbrvLq++Do8dO6bZs2crIiLCua2on5921tLq5wzn5vWfm4mJiVq+fLnL/WEeHh4KCgpSx44dtWXLFi1cuFDr16/Xa6+9lqu+i13XjADkiDHGtrZW+3MzTmFmZz0l6Y8//tCQIUPk5+enkSNHOrfPmzfP+ffSpUtr/PjxuvfeexUTE6MaNWrkbtL/UHbVcsKECS6vJ02apKZNm2r37t25Hqcws/vc/Oqrr5Senq727du7bH/vvfecf69Tp45Gjx6tiIgITZ48WR4hdgUSAAAO7UlEQVQeHjmf8D+cXefN9fRzZcgqKuz+Ojxy5IgGDx6sXr166cEHH3RuvxnOT7tqafVzJjucm9e2du1a3XnnnfLz83Nuu+2227Ry5Urn68DAQIWHh+v111/P1S9EWWkC8oi3t7dzleiyhIQEORwOeXt7u2wvX778Vdt6e3s72/59/9mzZ1WhQoVcjVOY2VXPy2JiYtS3b181bNhQM2fOdLkM4u+qVq0q6VLIKgrsruWVypQpIy8vL506dYpz8wbq+cknn6ht27bX/JBUrVo1ZWRkFKnLR+06b7y9vZWYmKiMjAyXfiQ5v3debSWwXLlycnMrOh+P7P463Lt3r/r166ewsDCNHTvWsm1ROz/z8nvalT9nODevv56ffPKJ2rVrd812VatW1V9//ZWr0FZ0Kg/8w/j7+ysuLk7x8fHObfv27ZOPj49Kly6dpe3+/fudrzMyMvTzzz/r7rvvVvXq1eXl5eWy/5dfflFaWpr8/f1zNU5hZlc9JSk+Pl6DBg1S7969NX78eJfAdPz4cY0fP15paWnObZcfbVq9evU8Obb8ZlctExMTNWHCBJ06dcq5Pz4+XvHx8apevTrn5nWcm9Kl37xu2bJFISEhLu/9+eefFRkZ6bLtyJEj8vDw0G233WbnIRUou84bPz8/GWN08OBBl35uueUW1a5dW/7+/jp06JAuXrzosv/K/xdFgZ1fh9HR0Ro6dKjGjBmT5dLRm+H8tKuW1/o5w7l5fT8jzpw5o927d2f53rljxw7Nnz/fZdvRo0dVtWrVXK3eEZqAPHLXXXcpICBAM2bMUGJioo4cOaI333xTYWFhkqR77rlH33//vSQpLCxMa9eu1Y8//qiUlBTNnz9fHh4eatOmjdzd3dWnTx8tWLBAcXFxOnPmjGbOnKmOHTuqYsWK1xynqLCrnpI0c+ZM3X333RoxYkSWcSpUqKDNmzcrMjJSycnJOnXqlKZMmaK2bduqUqVK+Xa8ecmuWpYpU0Z79uzR5MmTlZCQoLNnz2rixIny9fVVUFAQ5+Z1nJvSpXtFzp49q2rVqrmMU6FCBb333ntauHCh0tLS9Ntvv2n27Nl66KGHLFdKC5vc1NOKt7e3OnfurFmzZik+Pl4nT57UvHnz9MADD6hYsWJq3bq1ypQpo/nz5yslJUV79uzRf//735v6/LyWSZMmqU+fPurdu3eWfTfD+WlXLa/1c4ZzM/fnpiQdOnRImZmZWb53li1bVvPmzdO6deuUnp6uffv2afHixbmv5w0/fw9AtuLi4syQIUNMYGCgadGihZkzZ47Lo1m/+uorZ9vly5eb1q1bG39/fxMWFubyWNfU1FQzYcIE07hxYxMUFGSefvppc+7cuRyNU5TYVc969eqZ+vXrG39/f5c/a9asMcYYc/DgQTNw4EDTsGFD07BhQzN27Fhz9uzZ/D3YPGZXLY8fP26GDx9umjRpYho0aGAiIiKcj26/1jhFiV31NMaYqKgoU7duXXP69Oks4+zcudM89NBDpkGDBqZJkyZmypQp5sKFC3l7cAUgp/WcN2+e8ff3N/Xr1zd169Z1fl3PmzfPGGPMuXPnzFNPPWUaNGhgGjdubCZOnGhSU1Od4xw6dMj07dvX+Pv7mzZt2pjly5fn/8HmAzvqeeLECZdtV/7ZuXOnMebmOD/tOjev9XOGczN39TTGmA0bNpgGDRpcdZxNmzaZ7t27m8DAQBMSEmIWLFhgMjIycjVXhzE3yV26AAAAAHAduDwPAAAAACwQmgAAAADAAqEJAAAAACwQmgAAAADAAqEJAAAAACwQmgAAAADAAqEJAAAAACwQmgAAsMnatWsVEBCgtLS0HLWfO3euQkJCLNv4+vpqxYoVdkwPAHCdCE0AgJvK4MGDFRYWlu3+cePGqW3btsrIyMh13z179tS+ffvk4eFxI1O0VU6CWUH5/vvvtX379oKeBgBcE6EJAHBT6d+/v6KionTw4MEs+xITE7VhwwaFhYXJ3d29AGZ3c1m2bBmhCUChQGgCANxUWrdurRo1aujdd9/Nsm/dunXKzMxUnz59FB0drccff1wNGzZUUFCQevfura+//trZdu7cuerRo4fmzp2r4OBgffLJJ1q9erV8fX2VmpoqSdfs47KPP/5YnTp1UlBQkPr27atDhw5lO//33ntP3bt3V1BQkEJCQjRp0iSlpKTk+PjHjh2riIgILVmyRCEhIQoKCtLkyZN18uRJPfroowoKCtI999yjXbt2Od/j6+urZcuWadiwYQoKClLjxo01Y8YMZWZmOtt89tln6t27t4KDg9W0aVONHj1a8fHxkqRjx47J19dX77//vtq1a6dhw4bpwQcf1KZNm7RkyRLnJY3JycmaMGGCmjdvrsDAQHXo0EFLly51jvHdd9/J19dXe/fuVb9+/RQUFKR27dpp7dq1zjYXL17U7Nmz1aZNGwUFBemhhx7Sd99959wfFxenJ554QqGhobr77rv1wAMPENwAXBOhCQBwU3Fzc9PDDz+sDRs2KDEx0WXfypUr1bVrV5UrV04jR45U8eLFtXXrVn333XcKDQ3VyJEjdebMGWf7kydP6uzZs9q+fbs6d+6cZayc9HHu3Dlt2rRJK1eu1NatW1WhQgU99thjunjxYpb+PvjgA02bNk3/+c9/tHv3br399tvatWuXxo0bl6saREVFKTMzU1u2bNH48eP19ttva9SoUXruuef03XffqXr16poyZYrLexYtWqSHH35Yu3bt0syZM7V06VJ98MEHkqSdO3dq5MiR+te//qVvv/1WH3zwgY4ePapRo0Zlmf9bb72lefPmadWqVapataoGDRrkvKRxxowZ+vrrr7VmzRrt2bNHL7zwgqZMmaJt27a59DNr1iy98sor2rVrlzp27KgXX3xRCQkJki6F2fXr1+uNN97Qrl271KlTJ4WHh+v48eNKS0vTwIEDVaJECW3YsEE7d+5U165dNXToUB05ciRXNQRwcyE0AQBuOvfff78kuaxQ7Nq1S7/88osGDBgg6VKAmjp1qkqXLi0PDw/17NlTycnJ+uWXX5zvOXv2rIYPH66SJUvK4XBkGScnfaSlpemZZ56Rt7e3ypYtq2HDhunUqVPas2dPlv7efvttPfDAA2revLnc3Nx0xx13aPjw4froo49y/PAJSSpWrJgGDx4sDw8PZ9hr0aKF7rzzTnl4eKhNmzY6fPiwy3vatm2rkJAQFStWTC1btlRISIg+/fRTSdI777yj5s2bq2fPnvLw8FC1atU0bNgwfffddzpx4oSzjy5duqhatWpXrZUkjRkzRqtXr9btt98uh8OhNm3a6NZbb9WPP/7o0u7hhx9WrVq1VKxYMXXt2lVpaWn6/fffZYzRypUr1b9/f/n4+KhYsWIaOHCgXnrpJbm7u2vr1q2KiYnRuHHjVL58eZUoUUIDBw5UrVq19OGHH+a4fgBuPsUKegIAAOS3smXLqmfPns4P2JK0YsUKNW7cWPXq1ZMk7d27V/PmzdOhQ4dcLn+7fOmdJN1yyy0qX758tuPktI8qVao4X9esWVPSpcvI/u7o0aP69ddftXz5cpftxhjFxcU533stlStXdgYXT09PSXKZg6enp8scJcnHx8fldbVq1fTtt99Kkn7//Xc1a9bsqu1jYmJUrVo1SVL16tUt53Xq1ClNmzZN33//vc6fPy/pUqj8+1yuPM5SpUpJki5cuKAzZ84oISHBZRx3d3d169ZNkrR+/XplZmaqRYsWLv0ZY3T8+HHLuQG4uRGaAAA3pf79++vdd9/Vzp07VadOHW3atEkzZsyQdCkEDB06VA899JDmzJkjb29vxcTEqGPHji59FC9ePNv+c9qHm9vVL/ooUaJElm0lS5bU0KFDNWTIkNwe7jXHzG4el13taYKXg9ffQ40k5/1OV64qWdUrMzNTQ4YMUcWKFbVixQrVqFFDDodDrVu3znbcv7v88I4r77W6UsmSJVWqVCn98MMP2c4DAK6Gy/MAADelOnXqKCQkRKtXr9b69et16623qkOHDpKkn376SWlpaYqIiJC3t7ckZblE7Fpy2kdCQoL+/PNP5+ujR49KurQa9He1a9fW/v37XbadPXtWZ8+ezdXcrkd0dLTL65iYGOfqVK1atbI8vOLXX3917suJ06dPKzo6Wg8//LBq1qwph8OhuLg4nTp1Ksdz9PLyUvny5bPcn7Rs2TL98ssvql27tpKTk7Psj42NlTEmx+MAuPkQmgAAN63+/fvrs88+0+rVq10eM16jRg1Jlx5wkJaWpq1bt+qTTz6RdPXL5q4mp32UKFFC06dP19mzZ3Xu3DnNmzdPtWrVUv369bP0OXDgQG3atEnr1q1TWlqaTp48qSeffFJPP/309RchhzZv3qwdO3YoPT1dW7du1Y4dO9SlSxdJUlhYmL799lutXbtW6enp+v333zVv3jy1bdtWlSpVyrZPT09PxcTE6Pz58/Ly8lLZsmUVFRWlixcv6tChQ5o4caKqV6+e45pLUr9+/bR8+XL99NNPunjxolasWKGZM2fK09NTISEhqlu3riZMmKATJ07o4sWL2rhxo7p06aKoqKgbrhGAoovL8wAAN602bdrI29tbv//+ux588EHn9oCAAI0YMUITJ07UCy+8oBYtWmjy5Mny9PTU5MmTc9R3Tvu49dZb1bJlS/Xu3Vvx8fGqV6+eXnvttategtalSxfFx8frtdde0/PPP6/SpUurQ4cOeuaZZ268GNfw8MMP65133tGwYcNUvHhxDRkyRD169JB06THuU6ZM0ZtvvqmJEyeqfPnyat++fZan5/1dv379NH36dLVt21Zr1qxRZGSkIiMj9d///ld169bVuHHjtGfPHk2bNk3PPPOMHnjggWvOc8SIEXI4HHr88ceVlJQkHx8fvf766877nObPn6/IyEh1795dqampqlOnjl599VU1bNjwxosEoMhyGNajAQCABV9fX02YMEFhYWEFPRUAKBBcngcAAAAAFghNAAAAAGCBy/MAAAAAwAIrTQAAAABggdAEAAAAABYITQAAAABggdAEAAAAABYITQAAAABggdAEAAAAABYITQAAAABggdAEAAAAABYITQAAAABg4f8A4ErY5ZNsoB4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egmX7o345lnN"
      },
      "source": [
        "### Avaliando os modelos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545,
          "referenced_widgets": [
            "493c689b6b6a430c9385ed235b932a54",
            "46d018f901404008bb049883e4609c0b",
            "c3c81233036f44d092e92f08a524ffda"
          ]
        },
        "id": "IKEiY-WR5n9A",
        "outputId": "3b76b493-772d-4495-a605-ef57c61b7293"
      },
      "source": [
        "# Comparando os modelos e ordenando por R2\n",
        "modelos = compare_models(sort = 'R2', n_select = 5)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>br</th>\n",
              "      <td>Bayesian Ridge</td>\n",
              "      <td>0.3507</td>\n",
              "      <td>0.1706</td>\n",
              "      <td>0.4119</td>\n",
              "      <td>0.2008</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5151</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>0.3487</td>\n",
              "      <td>0.1710</td>\n",
              "      <td>0.4123</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5077</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>0.3487</td>\n",
              "      <td>0.1710</td>\n",
              "      <td>0.4123</td>\n",
              "      <td>0.1986</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5073</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lar</th>\n",
              "      <td>Least Angle Regression</td>\n",
              "      <td>0.3487</td>\n",
              "      <td>0.1710</td>\n",
              "      <td>0.4123</td>\n",
              "      <td>0.1986</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5073</td>\n",
              "      <td>0.019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>0.3234</td>\n",
              "      <td>0.1725</td>\n",
              "      <td>0.4142</td>\n",
              "      <td>0.1951</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4823</td>\n",
              "      <td>0.492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Regressor</td>\n",
              "      <td>0.3311</td>\n",
              "      <td>0.1736</td>\n",
              "      <td>0.4157</td>\n",
              "      <td>0.1884</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4996</td>\n",
              "      <td>0.415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>AdaBoost Regressor</td>\n",
              "      <td>0.3682</td>\n",
              "      <td>0.1741</td>\n",
              "      <td>0.4165</td>\n",
              "      <td>0.1826</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4984</td>\n",
              "      <td>0.052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbr</th>\n",
              "      <td>Gradient Boosting Regressor</td>\n",
              "      <td>0.3277</td>\n",
              "      <td>0.1757</td>\n",
              "      <td>0.4174</td>\n",
              "      <td>0.1802</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4807</td>\n",
              "      <td>0.097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>huber</th>\n",
              "      <td>Huber Regressor</td>\n",
              "      <td>0.3399</td>\n",
              "      <td>0.1766</td>\n",
              "      <td>0.4187</td>\n",
              "      <td>0.1746</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5200</td>\n",
              "      <td>0.019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>omp</th>\n",
              "      <td>Orthogonal Matching Pursuit</td>\n",
              "      <td>0.3675</td>\n",
              "      <td>0.1814</td>\n",
              "      <td>0.4246</td>\n",
              "      <td>0.1564</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5489</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lasso</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>0.4430</td>\n",
              "      <td>0.2222</td>\n",
              "      <td>0.4706</td>\n",
              "      <td>-0.0318</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6706</td>\n",
              "      <td>0.017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>en</th>\n",
              "      <td>Elastic Net</td>\n",
              "      <td>0.4430</td>\n",
              "      <td>0.2222</td>\n",
              "      <td>0.4706</td>\n",
              "      <td>-0.0318</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6706</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>llar</th>\n",
              "      <td>Lasso Least Angle Regression</td>\n",
              "      <td>0.4430</td>\n",
              "      <td>0.2222</td>\n",
              "      <td>0.4706</td>\n",
              "      <td>-0.0318</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6706</td>\n",
              "      <td>0.017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>0.3412</td>\n",
              "      <td>0.3412</td>\n",
              "      <td>0.5821</td>\n",
              "      <td>-0.6177</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5509</td>\n",
              "      <td>0.295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>par</th>\n",
              "      <td>Passive Aggressive Regressor</td>\n",
              "      <td>0.5094</td>\n",
              "      <td>0.4208</td>\n",
              "      <td>0.6446</td>\n",
              "      <td>-0.9631</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6506</td>\n",
              "      <td>0.017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Model     MAE     MSE    RMSE      R2  \\\n",
              "br                         Bayesian Ridge  0.3507  0.1706  0.4119  0.2008   \n",
              "ridge                    Ridge Regression  0.3487  0.1710  0.4123  0.1987   \n",
              "lr                      Linear Regression  0.3487  0.1710  0.4123  0.1986   \n",
              "lar                Least Angle Regression  0.3487  0.1710  0.4123  0.1986   \n",
              "rf                Random Forest Regressor  0.3234  0.1725  0.4142  0.1951   \n",
              "et                  Extra Trees Regressor  0.3311  0.1736  0.4157  0.1884   \n",
              "ada                    AdaBoost Regressor  0.3682  0.1741  0.4165  0.1826   \n",
              "gbr           Gradient Boosting Regressor  0.3277  0.1757  0.4174  0.1802   \n",
              "huber                     Huber Regressor  0.3399  0.1766  0.4187  0.1746   \n",
              "omp           Orthogonal Matching Pursuit  0.3675  0.1814  0.4246  0.1564   \n",
              "lightgbm  Light Gradient Boosting Machine  0.0000  0.0000  0.0000  0.0000   \n",
              "lasso                    Lasso Regression  0.4430  0.2222  0.4706 -0.0318   \n",
              "en                            Elastic Net  0.4430  0.2222  0.4706 -0.0318   \n",
              "llar         Lasso Least Angle Regression  0.4430  0.2222  0.4706 -0.0318   \n",
              "dt                Decision Tree Regressor  0.3412  0.3412  0.5821 -0.6177   \n",
              "par          Passive Aggressive Regressor  0.5094  0.4208  0.6446 -0.9631   \n",
              "\n",
              "          RMSLE    MAPE  TT (Sec)  \n",
              "br          0.0  0.5151     0.015  \n",
              "ridge       0.0  0.5077     0.016  \n",
              "lr          0.0  0.5073     0.016  \n",
              "lar         0.0  0.5073     0.019  \n",
              "rf          0.0  0.4823     0.492  \n",
              "et          0.0  0.4996     0.415  \n",
              "ada         0.0  0.4984     0.052  \n",
              "gbr         0.0  0.4807     0.097  \n",
              "huber       0.0  0.5200     0.019  \n",
              "omp         0.0  0.5489     0.016  \n",
              "lightgbm    0.0  0.0000     0.012  \n",
              "lasso       0.0  0.6706     0.017  \n",
              "en          0.0  0.6706     0.016  \n",
              "llar        0.0  0.6706     0.017  \n",
              "dt          0.0  0.5509     0.295  \n",
              "par         0.0  0.6506     0.017  "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roDLENUsbATx",
        "outputId": "5d46bb26-b045-4814-ec6d-0a750f36b18a"
      },
      "source": [
        "# Nossos 5 primeiros modelos selecionados\n",
        "modelos"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,\n",
              "               compute_score=False, copy_X=True, fit_intercept=True,\n",
              "               lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,\n",
              "               normalize=False, tol=0.001, verbose=False),\n",
              " Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "       normalize=False, random_state=1, solver='auto', tol=0.001),\n",
              " LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False),\n",
              " Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,\n",
              "      jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\n",
              "      random_state=1, verbose=False),\n",
              " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                       max_samples=None, min_impurity_decrease=0.0,\n",
              "                       min_impurity_split=None, min_samples_leaf=1,\n",
              "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                       n_estimators=100, n_jobs=-1, oob_score=False,\n",
              "                       random_state=1, verbose=0, warm_start=False)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "96b73a174630421989277cbadb3056b3",
            "fcb3c8f4d55641c09afb1892777db34a",
            "24a6c49fc23743859732c55d73db334e",
            "ffc61ced21bb4c658d0e116227f56b7d",
            "4bbc1757b6164b6e8fc1fb895de8b9ff",
            "06a05bf7635549c897499be131f2134f",
            "6b280fbd81064730ac7333e43185b985",
            "74ff09eebc294cf6be1164e9ed02f8f0",
            "62f925e79e144070825cbb8ef7d312f9",
            "6498d7b3992a41289c5e311b3726d81b",
            "ee7cca3abcd9495ab1d809910b9856c3",
            "26ab3108f95d4811b8c87c728a319615",
            "dd67e14c095940feac2925efde2d3c52",
            "a061f1535d3848ba87fd85865a91acdb",
            "2a63cf7514244de9b08e88e90aec93ea"
          ]
        },
        "id": "GBv-bRoca8X-",
        "outputId": "78f52c8a-4813-450f-f43c-cd86e45051ba"
      },
      "source": [
        "# Otimizando nosso modelo\n",
        "modelos_tunados = [tune_model(i, optimize='RMSE') for i in modelos]"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "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>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.3643</td>\n",
              "      <td>0.2002</td>\n",
              "      <td>0.4474</td>\n",
              "      <td>-0.1126</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.6077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1854</td>\n",
              "      <td>0.4306</td>\n",
              "      <td>0.2581</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.2987</td>\n",
              "      <td>0.1465</td>\n",
              "      <td>0.3827</td>\n",
              "      <td>0.2287</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.3367</td>\n",
              "      <td>0.1753</td>\n",
              "      <td>0.4187</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.3126</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.4027</td>\n",
              "      <td>0.0988</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.2553</td>\n",
              "      <td>0.1188</td>\n",
              "      <td>0.3447</td>\n",
              "      <td>0.4276</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.3965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.3455</td>\n",
              "      <td>0.1930</td>\n",
              "      <td>0.4394</td>\n",
              "      <td>0.1742</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.3298</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>0.4233</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.3065</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>0.3989</td>\n",
              "      <td>0.2840</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.3341</td>\n",
              "      <td>0.1707</td>\n",
              "      <td>0.4132</td>\n",
              "      <td>0.2696</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.3224</td>\n",
              "      <td>0.1690</td>\n",
              "      <td>0.4102</td>\n",
              "      <td>0.2076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0290</td>\n",
              "      <td>0.0227</td>\n",
              "      <td>0.0285</td>\n",
              "      <td>0.1331</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         MAE     MSE    RMSE      R2  RMSLE    MAPE\n",
              "0     0.3643  0.2002  0.4474 -0.1126   -0.0  0.6077\n",
              "1     0.3403  0.1854  0.4306  0.2581   -0.0  0.4687\n",
              "2     0.2987  0.1465  0.3827  0.2287   -0.0  0.5178\n",
              "3     0.3367  0.1753  0.4187  0.2324   -0.0  0.5178\n",
              "4     0.3126  0.1622  0.4027  0.0988   -0.0  0.4732\n",
              "5     0.2553  0.1188  0.3447  0.4276   -0.0  0.3965\n",
              "6     0.3455  0.1930  0.4394  0.1742   -0.0  0.5266\n",
              "7     0.3298  0.1792  0.4233  0.2155   -0.0  0.4655\n",
              "8     0.3065  0.1591  0.3989  0.2840   -0.0  0.5120\n",
              "9     0.3341  0.1707  0.4132  0.2696   -0.0  0.4841\n",
              "Mean  0.3224  0.1690  0.4102  0.2076    0.0  0.4970\n",
              "SD    0.0290  0.0227  0.0285  0.1331    0.0  0.0518"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "60a7e4d8cc7848c0b4b26f0f9e410645",
            "655ffd8da5664b13bfa50af20841acff",
            "d551f53c0e28459f8e8653cb6f8e29a1"
          ]
        },
        "id": "OnE33QhJbKIj",
        "outputId": "ce1f8e5b-1842-4308-ae02-1935b03e02ba"
      },
      "source": [
        "# Combinando os modelos\n",
        "modelo_combinado = blend_models(modelos_tunados)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "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>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.3842</td>\n",
              "      <td>0.2051</td>\n",
              "      <td>0.4528</td>\n",
              "      <td>-0.1396</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.6320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.3709</td>\n",
              "      <td>0.1869</td>\n",
              "      <td>0.4323</td>\n",
              "      <td>0.2521</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.3053</td>\n",
              "      <td>0.1407</td>\n",
              "      <td>0.3752</td>\n",
              "      <td>0.2590</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.3582</td>\n",
              "      <td>0.1767</td>\n",
              "      <td>0.4203</td>\n",
              "      <td>0.2264</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.3195</td>\n",
              "      <td>0.1526</td>\n",
              "      <td>0.3906</td>\n",
              "      <td>0.1519</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.2913</td>\n",
              "      <td>0.1195</td>\n",
              "      <td>0.3457</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.3573</td>\n",
              "      <td>0.1755</td>\n",
              "      <td>0.4190</td>\n",
              "      <td>0.2491</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.3750</td>\n",
              "      <td>0.1997</td>\n",
              "      <td>0.4468</td>\n",
              "      <td>0.1258</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.3255</td>\n",
              "      <td>0.1565</td>\n",
              "      <td>0.3956</td>\n",
              "      <td>0.2959</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.5091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.3449</td>\n",
              "      <td>0.1584</td>\n",
              "      <td>0.3979</td>\n",
              "      <td>0.3226</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.3432</td>\n",
              "      <td>0.1671</td>\n",
              "      <td>0.4076</td>\n",
              "      <td>0.2167</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0298</td>\n",
              "      <td>0.0253</td>\n",
              "      <td>0.0315</td>\n",
              "      <td>0.1429</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0590</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         MAE     MSE    RMSE      R2  RMSLE    MAPE\n",
              "0     0.3842  0.2051  0.4528 -0.1396   -0.0  0.6320\n",
              "1     0.3709  0.1869  0.4323  0.2521   -0.0  0.4929\n",
              "2     0.3053  0.1407  0.3752  0.2590   -0.0  0.5323\n",
              "3     0.3582  0.1767  0.4203  0.2264   -0.0  0.5459\n",
              "4     0.3195  0.1526  0.3906  0.1519   -0.0  0.4465\n",
              "5     0.2913  0.1195  0.3457  0.4245   -0.0  0.4075\n",
              "6     0.3573  0.1755  0.4190  0.2491   -0.0  0.5279\n",
              "7     0.3750  0.1997  0.4468  0.1258   -0.0  0.5247\n",
              "8     0.3255  0.1565  0.3956  0.2959   -0.0  0.5091\n",
              "9     0.3449  0.1584  0.3979  0.3226   -0.0  0.4562\n",
              "Mean  0.3432  0.1671  0.4076  0.2167    0.0  0.5075\n",
              "SD    0.0298  0.0253  0.0315  0.1429    0.0  0.0590"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "jrtdqq-n7MNT",
        "outputId": "94497d86-22a6-413d-b6ba-b9156a84af79"
      },
      "source": [
        "# Verificando a performance nos dados de validação\n",
        "pred = predict_model(modelo_combinado)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Voting Regressor</td>\n",
              "      <td>0.3244</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.346</td>\n",
              "      <td>0.2706</td>\n",
              "      <td>0.4596</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Model     MAE     MSE  RMSE     R2   RMSLE    MAPE\n",
              "0  Voting Regressor  0.3244  0.1521  0.39  0.346  0.2706  0.4596"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYb-nInh7awP"
      },
      "source": [
        "# Aplicando o modelo final em todos os dados disponíveis\n",
        "modelo_final = finalize_model(modelo_combinado)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkKqZ2H27mvv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a17e4ffa-a112-48d2-b5b9-842863f38270"
      },
      "source": [
        "# Salvando meu modelo\n",
        "save_model(modelo_final, 'meu_modelo_final')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Succesfully Saved\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=None,\n",
              "          steps=[('dtypes',\n",
              "                  DataTypes_Auto_infer(categorical_features=[],\n",
              "                                       display_types=True, features_todrop=[],\n",
              "                                       id_columns=[], ml_usecase='regression',\n",
              "                                       numerical_features=[], target='Outcome',\n",
              "                                       time_features=[])),\n",
              "                 ('imputer',\n",
              "                  Simple_Imputer(categorical_strategy='not_available',\n",
              "                                 fill_value_categorical=None,\n",
              "                                 fill_value_numerical=None,\n",
              "                                 numeric_strategy...\n",
              "                                                                     ccp_alpha=0.0,\n",
              "                                                                     criterion='mse',\n",
              "                                                                     max_depth=10,\n",
              "                                                                     max_features='log2',\n",
              "                                                                     max_leaf_nodes=None,\n",
              "                                                                     max_samples=None,\n",
              "                                                                     min_impurity_decrease=0.0001,\n",
              "                                                                     min_impurity_split=None,\n",
              "                                                                     min_samples_leaf=5,\n",
              "                                                                     min_samples_split=7,\n",
              "                                                                     min_weight_fraction_leaf=0.0,\n",
              "                                                                     n_estimators=70,\n",
              "                                                                     n_jobs=-1,\n",
              "                                                                     oob_score=False,\n",
              "                                                                     random_state=1,\n",
              "                                                                     verbose=0,\n",
              "                                                                     warm_start=False))],\n",
              "                                  n_jobs=-1, verbose=False, weights=None)]],\n",
              "          verbose=False), 'meu_modelo_final.pkl')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsEUC3JR8ZTP"
      },
      "source": [
        "# Deploy Básico com aplicação de uma rentabilidade 80% sobre o valor de custo\n",
        "\n",
        "\n",
        "def Diabetes(Pregnancies,\t\n",
        "                 Glucose,\n",
        "                 BloodPressure,\n",
        "                 SkinThickness,\n",
        "                 Insulin,\n",
        "                 BMI,\n",
        "                 DiabetesPedigreeFunction,\n",
        "                 Age):\n",
        "  dados0 = {'Pregnancies':[Pregnancies],\n",
        "            'Glucose':[Glucose],\n",
        "            'BloodPressure':[BloodPressure],\n",
        "            'SkinThickness':[SkinThickness],\n",
        "            'Insulin':[Insulin],\n",
        "            'BMI':[BMI],\n",
        "            'DiabetesPedigreeFunction':[DiabetesPedigreeFunction],\n",
        "            'Age':[Age]\n",
        "\n",
        "  }\n",
        "  dados = pd.DataFrame(dados0)\n",
        "\n",
        "  modelo = load_model('/content/meu_modelo_final')\n",
        "  \n",
        "  pred = float(predict_model(modelo, data = dados)['Label'].round(2))\n",
        "  \n",
        "  valor = round(1.8 * pred,2)\n",
        "\n",
        "  print ('');\n",
        "\n",
        "  print(pred)  \n",
        "  \n",
        "  if pred > 0.5:\n",
        "     print('Chance de Diabetes')\n",
        "\n",
        "  else:\n",
        "    print('Pouca chance de Diabetes')\n",
        "  "
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IOtZ5xF93DG",
        "outputId": "782dc480-ce00-4c9d-d9a1-06b118b1e1d2"
      },
      "source": [
        "# Aplicando nosso modelo\n",
        "Diabetes(Pregnancies='3.0',\tGlucose='148.000000',\tBloodPressure='72.0',\tSkinThickness='35.0', Insulin='0.0', BMI='33.6', DiabetesPedigreeFunction='0.627', Age='50.0')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n",
            "\n",
            "0.59\n",
            "Chance de Diabetes\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}