classification_disease_project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPz-uKOl8d6t"
      },
      "source": [
        "# Reading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9SqJ23wNtLrJ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "BlLLKSv_tTHV",
        "outputId": "6c893af8-f0a5-4002-b4a6-f09fad67b39d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id diagnostico  exame_1  exame_2  exame_3  exame_4  exame_5  exame_6  \\\n",
              "0    842302           M    17.99    10.38   122.80   103.78   1001.0  0.11840   \n",
              "1    842517           M    20.57    17.77   132.90   103.78   1326.0  0.08474   \n",
              "2  84300903           M    19.69    21.25   130.00   103.78   1203.0  0.10960   \n",
              "3  84348301           M    11.42    20.38    77.58   103.78    386.1  0.14250   \n",
              "4  84358402           M    20.29    14.34   135.10   103.78   1297.0  0.10030   \n",
              "5    843786           M    12.45    15.70    82.57   103.78    477.1  0.12780   \n",
              "6    844359           M    18.25    19.98   119.60   103.78   1040.0  0.09463   \n",
              "7  84458202           M    13.71    20.83    90.20   103.78    577.9  0.11890   \n",
              "8    844981           M    13.00    21.82    87.50   103.78    519.8  0.12730   \n",
              "9  84501001           M    12.46    24.04    83.97   103.78    475.9  0.11860   \n",
              "\n",
              "   exame_7  exame_8  ...  exame_24  exame_25  exame_26  exame_27  exame_28  \\\n",
              "0  0.27760  0.30010  ...    184.60    2019.0    0.1622    0.6656    0.7119   \n",
              "1  0.07864  0.08690  ...    158.80    1956.0    0.1238    0.1866    0.2416   \n",
              "2  0.15990  0.19740  ...    152.50    1709.0    0.1444    0.4245    0.4504   \n",
              "3  0.28390  0.24140  ...     98.87     567.7    0.2098    0.8663    0.6869   \n",
              "4  0.13280  0.19800  ...    152.20    1575.0    0.1374    0.2050    0.4000   \n",
              "5  0.17000  0.15780  ...    103.40     741.6    0.1791    0.5249    0.5355   \n",
              "6  0.10900  0.11270  ...    153.20    1606.0    0.1442    0.2576    0.3784   \n",
              "7  0.16450  0.09366  ...    110.60     897.0    0.1654    0.3682    0.2678   \n",
              "8  0.19320  0.18590  ...    106.20     739.3    0.1703    0.5401    0.5390   \n",
              "9  0.23960  0.22730  ...     97.65     711.4    0.1853    1.0580    1.1050   \n",
              "\n",
              "   exame_29  exame_30  exame_31  exame_32  exame_33  \n",
              "0     0.786    0.2654    0.4601   0.11890       NaN  \n",
              "1     0.786    0.1860    0.2750   0.08902       NaN  \n",
              "2     0.786    0.2430    0.3613   0.08758       NaN  \n",
              "3     0.786    0.2575    0.6638   0.17300       NaN  \n",
              "4     0.786    0.1625    0.2364   0.07678  0.854454  \n",
              "5     0.786    0.1741    0.3985   0.12440  0.804347  \n",
              "6     0.786    0.1932    0.3063   0.08368       NaN  \n",
              "7     0.786    0.1556    0.3196   0.11510       NaN  \n",
              "8     0.786    0.2060    0.4378   0.10720       NaN  \n",
              "9     0.786    0.2210    0.4366   0.20750       NaN  \n",
              "\n",
              "[10 rows x 35 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnostico</th>\n",
              "      <th>exame_1</th>\n",
              "      <th>exame_2</th>\n",
              "      <th>exame_3</th>\n",
              "      <th>exame_4</th>\n",
              "      <th>exame_5</th>\n",
              "      <th>exame_6</th>\n",
              "      <th>exame_7</th>\n",
              "      <th>exame_8</th>\n",
              "      <th>...</th>\n",
              "      <th>exame_24</th>\n",
              "      <th>exame_25</th>\n",
              "      <th>exame_26</th>\n",
              "      <th>exame_27</th>\n",
              "      <th>exame_28</th>\n",
              "      <th>exame_29</th>\n",
              "      <th>exame_30</th>\n",
              "      <th>exame_31</th>\n",
              "      <th>exame_32</th>\n",
              "      <th>exame_33</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>...</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>...</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>...</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>103.78</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>...</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>...</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0.854454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>843786</td>\n",
              "      <td>M</td>\n",
              "      <td>12.45</td>\n",
              "      <td>15.70</td>\n",
              "      <td>82.57</td>\n",
              "      <td>103.78</td>\n",
              "      <td>477.1</td>\n",
              "      <td>0.12780</td>\n",
              "      <td>0.17000</td>\n",
              "      <td>0.15780</td>\n",
              "      <td>...</td>\n",
              "      <td>103.40</td>\n",
              "      <td>741.6</td>\n",
              "      <td>0.1791</td>\n",
              "      <td>0.5249</td>\n",
              "      <td>0.5355</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.1741</td>\n",
              "      <td>0.3985</td>\n",
              "      <td>0.12440</td>\n",
              "      <td>0.804347</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>844359</td>\n",
              "      <td>M</td>\n",
              "      <td>18.25</td>\n",
              "      <td>19.98</td>\n",
              "      <td>119.60</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>0.09463</td>\n",
              "      <td>0.10900</td>\n",
              "      <td>0.11270</td>\n",
              "      <td>...</td>\n",
              "      <td>153.20</td>\n",
              "      <td>1606.0</td>\n",
              "      <td>0.1442</td>\n",
              "      <td>0.2576</td>\n",
              "      <td>0.3784</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.1932</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>0.08368</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>84458202</td>\n",
              "      <td>M</td>\n",
              "      <td>13.71</td>\n",
              "      <td>20.83</td>\n",
              "      <td>90.20</td>\n",
              "      <td>103.78</td>\n",
              "      <td>577.9</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.16450</td>\n",
              "      <td>0.09366</td>\n",
              "      <td>...</td>\n",
              "      <td>110.60</td>\n",
              "      <td>897.0</td>\n",
              "      <td>0.1654</td>\n",
              "      <td>0.3682</td>\n",
              "      <td>0.2678</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.1556</td>\n",
              "      <td>0.3196</td>\n",
              "      <td>0.11510</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>844981</td>\n",
              "      <td>M</td>\n",
              "      <td>13.00</td>\n",
              "      <td>21.82</td>\n",
              "      <td>87.50</td>\n",
              "      <td>103.78</td>\n",
              "      <td>519.8</td>\n",
              "      <td>0.12730</td>\n",
              "      <td>0.19320</td>\n",
              "      <td>0.18590</td>\n",
              "      <td>...</td>\n",
              "      <td>106.20</td>\n",
              "      <td>739.3</td>\n",
              "      <td>0.1703</td>\n",
              "      <td>0.5401</td>\n",
              "      <td>0.5390</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.4378</td>\n",
              "      <td>0.10720</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>84501001</td>\n",
              "      <td>M</td>\n",
              "      <td>12.46</td>\n",
              "      <td>24.04</td>\n",
              "      <td>83.97</td>\n",
              "      <td>103.78</td>\n",
              "      <td>475.9</td>\n",
              "      <td>0.11860</td>\n",
              "      <td>0.23960</td>\n",
              "      <td>0.22730</td>\n",
              "      <td>...</td>\n",
              "      <td>97.65</td>\n",
              "      <td>711.4</td>\n",
              "      <td>0.1853</td>\n",
              "      <td>1.0580</td>\n",
              "      <td>1.1050</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.2210</td>\n",
              "      <td>0.4366</td>\n",
              "      <td>0.20750</td>\n",
              "      <td>NaN</td>\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-64316a8d-86e0-4f41-add4-49cd7042410d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-64316a8d-86e0-4f41-add4-49cd7042410d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 927
        }
      ],
      "source": [
        "data = pd.read_csv(\"/content/exames.csv\")\n",
        "data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aY9TgV3b1tW7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "424b7d3c-e981-4368-c624-01a70eb0ab21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 35)"
            ]
          },
          "metadata": {},
          "execution_count": 928
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8MuGQhnuuLCs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f9f9335-d616-4122-b680-c19473695e0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id               0\n",
              "diagnostico      0\n",
              "exame_1          0\n",
              "exame_2          0\n",
              "exame_3          0\n",
              "exame_4          0\n",
              "exame_5          0\n",
              "exame_6          0\n",
              "exame_7          0\n",
              "exame_8          0\n",
              "exame_9          0\n",
              "exame_10         0\n",
              "exame_11         0\n",
              "exame_12         0\n",
              "exame_13         0\n",
              "exame_14         0\n",
              "exame_15         0\n",
              "exame_16         0\n",
              "exame_17         0\n",
              "exame_18         0\n",
              "exame_19         0\n",
              "exame_20         0\n",
              "exame_21         0\n",
              "exame_22         0\n",
              "exame_23         0\n",
              "exame_24         0\n",
              "exame_25         0\n",
              "exame_26         0\n",
              "exame_27         0\n",
              "exame_28         0\n",
              "exame_29         0\n",
              "exame_30         0\n",
              "exame_31         0\n",
              "exame_32         0\n",
              "exame_33       419\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 929
        }
      ],
      "source": [
        "data.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X5WGjAiouANj"
      },
      "outputs": [],
      "source": [
        "data = data.drop(columns = ['exame_33', 'id'])\n",
        "#\"id\" column doesnt contribute to the analysis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data==0].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNM2jHa9IrQE",
        "outputId": "36978599-2c5a-438c-f313-4fd45a8f49b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "diagnostico     0\n",
              "exame_1         0\n",
              "exame_2         0\n",
              "exame_3         0\n",
              "exame_4         0\n",
              "exame_5         0\n",
              "exame_6         0\n",
              "exame_7         0\n",
              "exame_8        13\n",
              "exame_9        13\n",
              "exame_10        0\n",
              "exame_11        0\n",
              "exame_12        0\n",
              "exame_13        0\n",
              "exame_14        0\n",
              "exame_15        0\n",
              "exame_16        0\n",
              "exame_17        0\n",
              "exame_18       13\n",
              "exame_19       13\n",
              "exame_20        0\n",
              "exame_21        0\n",
              "exame_22        0\n",
              "exame_23        0\n",
              "exame_24        0\n",
              "exame_25        0\n",
              "exame_26        0\n",
              "exame_27        0\n",
              "exame_28       13\n",
              "exame_29        0\n",
              "exame_30       13\n",
              "exame_31        0\n",
              "exame_32        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 931
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L44Avhe-CADg"
      },
      "outputs": [],
      "source": [
        "data['diagnostico'] = data['diagnostico'].replace({'M': 1, 'B': 0})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "kqfIAvb3uV08",
        "outputId": "8292e22a-61e1-4f82-a61a-714f88e7ed0a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   diagnostico  exame_1  exame_2  exame_3  exame_4  exame_5  exame_6  exame_7  \\\n",
              "0            1    17.99    10.38   122.80   103.78   1001.0  0.11840  0.27760   \n",
              "1            1    20.57    17.77   132.90   103.78   1326.0  0.08474  0.07864   \n",
              "2            1    19.69    21.25   130.00   103.78   1203.0  0.10960  0.15990   \n",
              "3            1    11.42    20.38    77.58   103.78    386.1  0.14250  0.28390   \n",
              "4            1    20.29    14.34   135.10   103.78   1297.0  0.10030  0.13280   \n",
              "\n",
              "   exame_8  exame_9  ...  exame_23  exame_24  exame_25  exame_26  exame_27  \\\n",
              "0   0.3001  0.14710  ...     17.33    184.60    2019.0    0.1622    0.6656   \n",
              "1   0.0869  0.07017  ...     23.41    158.80    1956.0    0.1238    0.1866   \n",
              "2   0.1974  0.12790  ...     25.53    152.50    1709.0    0.1444    0.4245   \n",
              "3   0.2414  0.10520  ...     26.50     98.87     567.7    0.2098    0.8663   \n",
              "4   0.1980  0.10430  ...     16.67    152.20    1575.0    0.1374    0.2050   \n",
              "\n",
              "   exame_28  exame_29  exame_30  exame_31  exame_32  \n",
              "0    0.7119     0.786    0.2654    0.4601   0.11890  \n",
              "1    0.2416     0.786    0.1860    0.2750   0.08902  \n",
              "2    0.4504     0.786    0.2430    0.3613   0.08758  \n",
              "3    0.6869     0.786    0.2575    0.6638   0.17300  \n",
              "4    0.4000     0.786    0.1625    0.2364   0.07678  \n",
              "\n",
              "[5 rows x 33 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-bc7afc4d-34fa-4bd5-833b-372349e843ff\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>diagnostico</th>\n",
              "      <th>exame_1</th>\n",
              "      <th>exame_2</th>\n",
              "      <th>exame_3</th>\n",
              "      <th>exame_4</th>\n",
              "      <th>exame_5</th>\n",
              "      <th>exame_6</th>\n",
              "      <th>exame_7</th>\n",
              "      <th>exame_8</th>\n",
              "      <th>exame_9</th>\n",
              "      <th>...</th>\n",
              "      <th>exame_23</th>\n",
              "      <th>exame_24</th>\n",
              "      <th>exame_25</th>\n",
              "      <th>exame_26</th>\n",
              "      <th>exame_27</th>\n",
              "      <th>exame_28</th>\n",
              "      <th>exame_29</th>\n",
              "      <th>exame_30</th>\n",
              "      <th>exame_31</th>\n",
              "      <th>exame_32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>103.78</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>103.78</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 33 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc7afc4d-34fa-4bd5-833b-372349e843ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    fill: #1967D2;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bc7afc4d-34fa-4bd5-833b-372349e843ff button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bc7afc4d-34fa-4bd5-833b-372349e843ff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 933
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBHbCkj4Ievw"
      },
      "source": [
        "# **Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### is the data balanced?"
      ],
      "metadata": {
        "id": "V_1Ikg5Hozx9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop(columns='diagnostico')\n",
        "y = data['diagnostico']"
      ],
      "metadata": {
        "id": "6O6EBSY6Fj_v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_counts = y.value_counts()\n",
        "class_labels = class_counts.index\n",
        "class_sizes = class_counts.values\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.set_palette(\"pastel\")\n",
        "plt.pie(class_sizes, labels=class_labels, autopct='%1.1f%%', startangle=140)\n",
        "plt.axis('equal')\n",
        "plt.title('Target distribution')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "aEGNml0WozXL",
        "outputId": "e7f29fae-7c2f-49cf-fa4c-80f76ca0f17a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install imbalanced-learn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPHX_GkHsUVc",
        "outputId": "1b5876a5-e09d-46c0-f55a-21a380791e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.3.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Apply SMOTE to generate synthetic samples for the minority class\n",
        "smote = SMOTE(sampling_strategy='auto', random_state=0)\n",
        "x, y = smote.fit_resample(x, y)\n",
        "\n",
        "# Create a DataFrame with the resampled data\n",
        "balanced_data = pd.DataFrame(x, columns=x.columns)\n",
        "balanced_data['binary_class'] = y\n",
        "\n",
        "# Count the samples in each class after SMOTE\n",
        "print(balanced_data['binary_class'].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iBok7TKsY6C",
        "outputId": "8e56cd80-3017-42c3-c2b9-73fc2bc30dd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    357\n",
            "0    357\n",
            "Name: binary_class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_counts = y.value_counts()\n",
        "class_labels = class_counts.index\n",
        "class_sizes = class_counts.values\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.set_palette(\"pastel\")\n",
        "plt.pie(class_sizes, labels=class_labels, autopct='%1.1f%%', startangle=140)\n",
        "plt.axis('equal')\n",
        "plt.title('Target distribution')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "snfEOB4LupMV",
        "outputId": "8833d4ad-bb1d-4133-e9aa-d250350901ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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S7UyVvcZ10u71UqzTdCKkgz8oldVLVaOlylGpLWkAeB4FEPCyfQ2pUb7Gtal5fDwduF9xrVQ5WqoeIxVUmk4DwBAKIOA1+3dLO1ZIO1dI7XtNp4FJeWVS9Vip5oTUljMAPIMCCHhBR3Oq9O1YkdqLD/ionGKpdpw0eGJq6xkArkYBBNyqa7+08/1U6WvZbjoNnKS4Vho0UaodL4VyTKcBkAEUQMBNkglp1ypp6zKpaZM8cWo1ZI7Pn1o4MvgkqWIUG1ADLkIBBNygfa+0Zam07V0p2m46DdwolCvVTkgdIi6qNp0GQD9RAAGnSiakhtXS5qXSno2m08BLigdJ9adINePYYxBwKAog4DQdzR+M9r2TOkMHYEo4X6o7WaqbIkXyTacBcAwogIBT7N0irX85tWcfkE18fqn6BKn+VKlkkOk0APqAAghkM9tOHeZd/4rUss10GuDoimpTh4drx3N4GMhiFEAgGyXi0vZ3pQ2vsFkznClSIA3/mFQ3OXU6OgBZhQIIZJNYp7TpTWnzG8zvgzuE86Rhp0lDp0qBkOk0AD5AAQSyQXd7an7flqVSImo6DZB+wRxp2LTU4eFgxHQawPMogIBJ0Y5U8dv8ppSImU4DZF4gnCqBw6al9hYEYAQFEDAh1pWa37fpdSnOiB88yB+S6qdKI85gRBAwgAIIDKRETNr0RmrUL9ZpOg1gXjBHGnlmalSQVcPAgKEAAgMhmZS2vS2tXSJ17TedBsg+uSXSmHOkmvGSZZlOA7geBRDItMZ10vv/K7XvMZ0EyH5FtdIJF0hlQ00nAVzNZzoA4Frte6U3/ii98QfKH9BXrTukVx9I/e7s3206zXH76U9/qvr6ekUiEU2bNk2vv/666UhAL4wAAukWj0rrXpA2viYlE6bTAM5lWdKQydKY86RQjuk0ffanP/1JN9xwg+69915NmzZNd999tx566CGtXr1alZWVpuMBkiiAQPrYtrT9PWnVs1I38/yAtAnlpg4LD55oOkmfTJs2Taeccop+8pOfSJKSyaSGDBmiz33uc/rqV79qOB2QwiFgIB1ad0qv3C+98yjlD0i3aIf0zmPSK/+T9YeFo9Go3nrrLV1wwQU9l/l8Pl1wwQV65ZVXDCYDeguYDgA4WqxLWvWMtGWZJAbTgYzau0V64RfS8NOkUWdn5TmGm5qalEgkVFVV1evyqqoqrVq1ylAq4GAUQOB4NayRlj/Jti7AQLKTqX00d7wvjZ8hVY02nQhwJAogcKyiHdKKRdKO5aaTAN7V2SK9+adUARx/iZRTaDqRJKm8vFx+v18NDQ29Lm9oaFB1dbWhVMDBmAMIHIsdK6Tn76H8AdmiYY205F5p2zumk0iSQqGQpkyZomeeeabnsmQyqWeeeUann366wWRAb4wAAn3RtT91uLdhjekkAD4q3p1aJLJrlXTipVI4z2icf/7nf9aNN96oqVOn6tRTT9Xdd9+t9vZ23XTTTUZzAR9GAQSOZuvb0vtPS/Eu00kAHEnDGqn5XmnCx6WaE4zFmD17tnbv3q077rhDu3bt0qRJk/TUU08dtDAEMIl9AIHDiXZI7y5k1A9wokEnSuMvloIR00mArEQBBA6laaP0Nnv6AY4WKZROmiVVDDedBMg6FEDgw5JJac3i1DYT7OsHuMPQqdIJF0p+Zj0BB1AAgQM6mqVlj0gt200nAZBuhVXSyddIeaWmkwBZgQIISNL25alVvvFu00kAZEogLJ10qVQzznQSwDgKILwtHpVW/EXa9q7pJAAGytBTpHEXSj6/6SSAMRRAeFfbHumtP0ttTaaTABhoxbXS5Kul3GLTSQAjKIDwpoa10tuPcMgX8LJgRJp4OecThidRAOEtti2te1Fa87xY5QtAkjT8dGnseZLF2VHhHRRAeEc8Kr3zaOp0UQDwYeXDpJOvloI5ppMAA4ICCG9o3yu9+WepbbfpJACyVV6pNHW2lF9uOgmQcRRAuF/jutT+fpzLF8DRBMLS5KukypGmkwAZRQGEu61/RVr1jJjvB6DPLEs64SJp2KmmkwAZQwGEO9m2tGKRtPkN00kAONXQU6TxF7E4BK5EAYT7JOKpLV5Y7AGgvypGSidflTo0DLgIBRDuEu2U3vyj1LzNdBIAblFQKZ06V4oUmE4CpA0FEO7R0SK9/nupfY/pJADcJqdYmvZ3qZXCgAtQAOEOrTulN/4odbeZTgLArcJ5qZHAwmrTSYB+owDC+Xavl96aJyWippMAcLtARDpltlRaZzoJ0C8UQDjbjuXS249KdtJ0EgBe4QtIU66RKkeZTgIcNwognGvbO9K7C1NbvgDAQLJ80sTLpUETTCcBjgsFEM60ZZn03hNig2cA5ljS+Iul+qmmgwDHjAII59n8lrT8SdMpACBlzLnSyDNNpwCOScB0AOCYbHxden+R6RQA8Dern0udPm7EGaaTAH3G+W3gHBtepfwByE6rnk09RwEOQQGEM6x7SVr5tOkUAHB4K5+WNnH+cTgDBRDZb92L0upnTacAgKNb8VRqnjKQ5SiAyG6b3kjNrwEAp1j+pLT1bdMpgCOiACJ7bX8v9d80ADjNu49L2941nQI4LAogslPjWumdx0ynAIDjZEvvPpY6WxGQhSiAyD57t6TO7cvp3QA4mW2nTlW5e4PpJMBBKIDILq27pDf+KCXjppMAQP/ZSWnpQ1LrTtNJgF4ogMge7XulN34vxbtNJwGA9IlHU//YdrSYTgL0oAAiO3Ttk157UOpuN50EANKvu016/fdStNN0EkASBRDZIN4tvf4HqbPFdBIAyJz2PdKbf5ISTHGBeRRAmGUnpaUPS/sbTScBgMxr3iq9/UhqgQhgEAUQZq1YJO1eZzoFAAycXas4rzmMowDCnI2vSZvfNJ0CAAbepjekDa+YTgEPowDCiO0tcb3fEpHtC5iOAgBmrHxG2r3edAp4FAUQA661M6kl67v1ZmykXqq6XslwvulIAGCALS17OLUFFjDAKIAYUN1xW8+u6VIskXp/Q7xSTxbPVTy/2mwwADAh1iW99efUXoHAAKIAYsAkbVtL1nVpf3fv1W97k/man3ut2kvHGEoGAAbt3825zzHgKIAYMMu2xbRz36HP79ttBzXf93E1Vp0+wKkAIAvsWimte9F0CngIBRADYmtzXCt2xo58JcvSU8nTtbpmpmyff2CCAUC2WL1YalxrOgU8ggKIjGvrTuqlDX0/v+9r8TF6teo62cHcDKYCgGxjS8sWpM4YAmQYBRAZlUjaWrKuW9HEsd1ubbxGi0rnKpFXkZlgAJCN4l3SmywKQeZRAJFRb22Nqqn90PP+jqYxWaiHc2ers2REmlMBQBZra5JWPGU6BVyOAoiM2bw3rlUN/TvpeadCmue/THsqT0lTKgBwgG3vSDuWm04BF6MAIiP2dSX18sa+z/s7Etuy9IR9ltbXzJBt8SMLwCPee1LqaDGdAi7FX1OkXSJp6/l13T2bPafLS/HxerP6GtnBnPTeMQBko3i3tOwRKXl802iAI6EAIu3e3BJVc0dmnrBWxgfr6dI5SuSWZeT+ASCrtGyT1iw2nQIuRAFEWu1ojWt1Y//m/R3NrmSxFuTPVndRfUYfBwCywvqXpaZNplPAZSiASJto3NbLGwZm64J2O6KHQleopWLygDweAJhjS+8skKIdpoPARSiASJvXN3erI2Yf/YppkpRPj+lcba45X7ZlDdjjAsCA69ovvbvQdAq4CAUQabF5b1wb9qR51UcfPR+fqLerr5YdCBt5fAAYEA1rpK3vmE4Bl6AAot86Y7Ze25SeLV+O13vxOj1XMUfJnGKjOQAgo1b+r9TVZjoFXIACiH57dVO3ujK77qNPtsVL9WjhHEULh5iOAgCZEeuSlj9pOgVcgAKIflnfFNPWZjOHfg9lfzJH88JXaV/5iaajAEBmNKyWdqwwnQIORwHEcWuPJvX65uw7YXlcfi2wLtS26nNki8UhAFxoxSJWBaNfKIA4bq9viqb9bB/p9GziZC2vuVy2P2g6CgCkV7Q9VQKB40QBxHHZ2hzX1pYsbn8fWBYfriWVc5QMF5qOAgDptWO51LDWdAo4FAUQxyyesLPy0O/hbI6X6/HiuYoV1JqOAgDptfxJKWZ2FwY4EwUQx+zdHTG1Rwduw+d0aEnmal7kGrWVnWA6CgCkT9c+adVfTaeAA1EAcUxaOpN6f1fMdIzjElNAD/su0c6qM0xHAYD02bJMat1pOgUchgKIY/Lapm4lnTX4d5Cnk9O0smaWbF/AdBQASANbWvGU6RBwGAog+mx9U0wN+5OmY6TFG/FRerlqtuxQnukoANB/zduk7e+ZTgEHoQCiT7rjtt7a4pyFH32xPl6lJ0vmKp5fZToKAPTfymekuLuep5E5FED0ybJt0aw43Vu67UkWaH7udeooHWU6CgD0T/d+ad0LplPAISiAOKqWzqTWNrqw/X2g2w5qnu9S7a6cZjoKAPTPxtek9r2mU8ABKIA4qqVbo3L4uo+jsyz9xT5Da2o+Ltvym04DAMcnmZDe/1/TKeAAFEAc0a59CW1zwBk/0uXV+Fi9Vn2t7GCu6SgAcHwa10q715tOgSxn2bbt+sEdHB/btvXk+13a0+6Olb/HotK/TxfuXyB/e5PpKEDafPPBZ/Xvf1jc67Ixg8u16t5/kiR1RWO6/deL9Mcl76k7ltCMk0fqZ5+9VFUl+Ye9T9u29Y0Hn9UvF72llvYunXFCne65dZZGDSqTJHXH4vqH/35Uj766StUl+frZrZfqgkkjem5/1/wXtWV3q358y8z0f8JellcmnX2L5GOcB4fGTwYOa9PehCfLnyQ1Jgr1cN5sdRUPNx0FSKvxdZXa+dsv97y8eOenej72xV8+pYWvr9ZDX52t5//f32vHnn266j//cMT7+6/5L+q/F76me2+bpde+/xnlRUKacccD6oqmNoz/xVNv6q11O/TK9z6tz1w8RXPvmqcD4w4bdzXrl4ve0n/ccH7mPmGvat8jbXvbdApkMQogDimRtLVsm7e3E+i0w5oXuEx7K6eYjgKkTcDvU3VJQc9LeVFqL8zW9i79+uml+sGnLtZ5E4drysha3feFK/Xyyq16ddXWQ96Xbdu6+9FX9G+zz9blp52gk4ZV64F/vko79u7XgldWSZJWbt2ty6aN0fihlbpt5jTtbm1X074OSdJnf7ZQd37yQhXmRgbmk/eatUukhHsX8KF/KIA4pDWNcbV1Mzsgafn0uD1dG2oulG3x6wLnW7tjj2pvuEvDP/VD/d1d87SlsUWS9Na6HYrFE7pg0t9GvccOqVBdRZFeOUwB3NjQrF3Nbb0O6RblRTRtzKCe20wcVq0X39+izu6YFi1dp5rSApUX5urB595RJBTQlR8bl7lP1uu69kubXjedAlmKc2HhING4rXd3eHv076NejJ+o5upinbx7oax4l+k4wHGZNmaw7v/ilRozqFw79+7Xv/9hsc76yq+1/Kf/qF3NbQoF/CrOz+l1m6rifO1qbjvk/R24vKq49xzBquJ87WpJfezvLzxZ725q0Lhbf6zywlz9+SvXqbmtU3c8+KwWf/fv9W+//av+uGS5RlSX6Defv1KDygsz8Jl72PqXpbqTpSCjrOiNAoiDLN8ZUzdHDQ6yIj5EzeVzdG7rAvk7m03HAY7ZJVNH97x90rBqTRszWEP//gf684vLlRMKZuQxgwG/fvrZS3tddtPdj+ifZp2mZRt2asErq/TOj2/Vf81/Uf/0iyc1//9en5EcnhXrlDa8Ko05x3QSZBmOaaGXrpitVQ0x0zGy1o5EiR4tuF7dRUNNRwH6rTg/R6MHlWndjr2qLslXNJ5QS1tnr+s0tLSp+jCrgA9c3tDSe4SwoaVN1cWHvs1z727Qis2N+sdLp2nxuxv18amjlBcJ6bozJ2jxexvT8FnhIJtek6KdR78ePIUCiF5WNsQU9+bC3z5rs3M0L3SFWiommo4C9EtbZ7fW72xWTWmBpoysVTDg1zPvbOj5+OptTdqyu1Wnjx1yyNsPqypRdUm+nnn7b7fZ19Gl11ZvP+RtuqIx3XbPE/r5P14mv9+nRNJWLJF6woklEkokmXecEfGotOFl0ymQZSiA6BGNM/rXVwn59ZjO15bq82TLMh0H6JMv/fopPf/eRm1qaNbLK7foyv/4g/w+S3Omn6iivIg+deHJ+udfPaXn3t2gt9bt0E13P6LTxw7RaR8qc2Nv+W898vL7kiTLsvSFy0/Xd/70vB57bZXe29SgG37wsGpLC3TF6WMPevxv//F5fXzqKE0eUSNJOmNcnR5++X29u3GXfvL4azrjhEMXTaTBpjek7nbTKZBFmAOIHqsaY4p556QfabE4MUkn1ZRoYuNCWQkWziC7bWvapzl3zdOefR2qKMrTmePq9Or3P6OKD7aC+eGnL5bPZ+nq//yTumPx1EbQt/aev7d6W5NaO7p73v+Xq89Ue1dUn/nxY2pp79KZ4+r01Lf+jyIfmVO4fFOD/vzCcr3941t7LrvmjHFa/N5GnfWVX2vMoHL9/svXZPCz97hETNrwinTCBaaTIEtwJhBIkmIJWw+/08Hij+M0xL9H01sWyNfVajoKAByaPySd/3lWBEMSh4DxgTWNccpfP2xNlGlh4RxFCwebjgIAh5aISpvfNJ0CWYICCCWStt7fxdy//mq1czUvfJX2l403HQUADm3TG5wdBJIogJC0dndcnTFmAqRDXAE94puh7dVni68ogKzT3SZtf9d0CmQBCqDHJZO2lu9k9C/dnklM1Yqay2X7M7O5LgActw2vSkz/9zwKoMdt2ptQR5QngkxYGh+hFyquVzJcYDoKAPxN+x6pYbXpFDCMAuhx7PuXWZsSFXqieK5iBTWmowDA32x4xXQCGEYB9LCmtoSa2jntR6Y1J/M0P+catZcdvDEuABjRvE3au9V0ChhEAfQwRv8GTtQOar51iRqqPmY6CgCkcHo4T6MAelRnzNamvZz2Y0BZlhYlT9OqmpmyfZyEB4BhDWukjmbTKWAIBdCj1jTGxHnXzXg9PkavVF4nO5RnOgoAr9uyzHQCGEIB9KBk0taaRjYCNWldolp/KZmreF6l6SgAvGzbO1KSo0FeRAH0oM3NCTZ+zgJNyQI9nHudOktGmo4CwKu621KHguE5FEAPYvFH9uhSSPP8s9RUearpKAC8astS0wlgAAXQY/Z2JLS7ja1fsoltWXrSPlPrai6WbfErCWCANW2Q2veaToEBxl8bj1m/m7l/2erl+Di9UX2t7GCO6SgAvGYri0G8hgLoIUnb1sY9FMBstio+SE+XzVUit8x0FABewmIQz6EAesj2loS66H9Zb1eiSI/kX6+u4mGmowDwiu52zg/sMRRAD9nQRPtzig47rHmBy9VccbLpKAC8gsUgnkIB9IjuuK2tLQzvO0nS8mmhztGm6gtYHAIg85o2SV1tplNggPBXxSM27Ylz5g+HWpI4SUurr5IdCJuOAsDVbGnn+6ZDYIBQAD1iPYd/HW1FvE7Pls9RMqfEdBQAbrZjuekEGCAUQA/Y15lUUzt7/znd9kSpHi28XtGiOtNRALhVy3apo8V0CgwACqAHrGfrF9fYn8zRQ6Er1Vp+kukoANxq5wrTCTAAKIAesIkC6CoJ+fWodYG2Vp8jW5bpOADcZgcF0AsogC7X3JHU/m5Wf7jRc4mT9W7NFbL9IdNRALjJvgaprcl0CmQYBdDltjQz+udm78SH6fmKOUpGCk1HAeAmjAK6HgXQ5bY2s/ef221JlGlh0VzFCgaZjgLALSiArkcBdLG27qT2drD61wtak7maF7la+8vGmY4CwA3a96QOBcO1KIAutoXRP0+JKaBHfBdrR/VZYtYngH5rXGs6ATKIAuhiW5n/50l/TZyi92svk+0LmI4CwMka15lOgAyiALpUV8xW434O/3rVW7GReqnqeiXD+aajAHCqlm1StNN0CmQIBdCltrbEOQzocRvilXqyeK7i+dWmowBwItuWdq83nQIZQgF0KVb/QpL2JvM1P/datZeOMR0FgBMxD9C1KIAulEza2rWPAoiUbjuo+b6Pq7HqdNNRADjN7vWpkUC4DgXQhXa3JxVn+h8+zLL0VPJ0ra6ZKdvnN50GgFPEOqWW7aZTIAMogC60s5XRPxzaa/ExerXqOtnBXNNRADgFh4FdiQLoQhz+xZGsjddoUelcJfIqTEcB4AQUQFeiALpMLGGrqZ3jvziyxmShHs6drc6SEaajAMh2+xqk7nbTKZBmFECXadyfUJL5uuiDToU0z3+Z9lSeYjoKgGy3d4vpBEgzCqDL7OTwL46BbVl6wj5L62tmyLZ4OgBwGM1bTSdAmvGM7zI793H4F8fupfh4vVl9jexgjukoALIRI4CuQwF0ka6YreYOCiCOz8r4YD1dOkeJ3DLTUQBkm327pHi36RRIIwqgi+zaz+Ff9M+uZLEW5M9Wd1G96SgAsoltS83bTKdAGlEAXWQ3BRBp0G5H9FDoCrVUTDYdBUA24TCwq1AAXYTtX5AuSfn0mM7V5przZVuW6TgAsgEF0FUogC6RtG3tZf4f0uz5+ES9XX217EDYdBQAprXskJIcaXILCqBLNHcklaD/IQPei9fpuYo5SuYUm44CwKRkPFUC4QoUQJfYw+FfZNC2eKkeLZyjaOFg01EAmMRCENegALpEUxsFEJm1P5mjeeGrta98gukoAEzZt8t0AqQJBdAlmtqZl4HMi8uvBdZF2lZ9jmyxOATwHAqga1AAXSCWsNXayQmAMXCeTZys5TWXy/YHTUcBMJDa90iJuOkUSAMKoAvsaU+K+oeBtiw+XEsq5ygZLjQdBcBAsW1pf6PpFEgDCqALsAAEpmyOl+vx4rmKFdSajgJgoHAY2BUogC7Q0kkBhDktyVzNi1yjtrITTEcBMBD2NZhOgDSgALpAKwUQhsUU0MO+S7Sz6gzTUQBkGiOArkABdIHWLgogssPTyWlaWTNLti9gOgqATNnXmJoLCEejADpcRzSpGDvAIIu8ER+ll6tmyw7lmY4CIBMSUal9r+kU6CcKoMO1sP0LstD6eJWeLJmreH6V6SgAMoGVwI5HAXQ45v8hW+1JFmh+7nXqKB1lOgqAdOtoNp0A/UQBdDjm/yGbddtBzfddqt2V00xHAZBOFEDHowA6HCOAyHa2Zekv9hlaU/Nx2ZbfdBwA6dBOAXQ6CqDDtXYxBxDO8Gp8rF6rvlZ2MNd0FAD91cEiEKejADpYd9xWV4wCCOdYE6/VorK5SuSVm44CoD8690lJtqBwMgqgg7V1c/gXztOYKNTDebPVVTzcdBQAx82WOlpMh0A/UAAdrCPK6B+cqdMOa17gMu2tnGI6CoDjxUIQR6MAOlg7BRAOlrR8etyerg01F8q2eCoCHIcC6Gg86zoYI4BwgxfjJ2pp9dWyAxHTUQAcCwqgo1EAHawjyhxAuMOK+BA9Uz5HiZwS01EA9BUF0NEogA7GIWC4yY5EiR4tuF7dRUNNRwHQF137TSdAP1AAHYxDwHCbNjtH80JXqKVioukoAI6mu810AvQDBdDBKIBwo4T8ekzna0v1ebJlmY4D4HC62yWbv0NORQF0qK6YrQS/d3CxxYlJeqfmKtn+kOkoAA7FTkrRDtMpcJwogA7VzgIQeMC78aFaXDFHyUiR6SgADoXDwI5FAXQoTgEHr9iaKNPCwjmKFg42HQXARzEC6FgUQIeKcgpGeEirnat54au0v2y86SgAPowC6FgUQIeKMQEQHhNXQI/4Zmh79dnipx/IEhRAx6IAOlQ0zp9AeNMzialaUXO5bH/QdBQAFEDHogA6FIeA4WVL4yP0QsX1SoYLTEcBvI0C6FgUQIeKcggYHrcpUaEniucqVlBjOgrgXfFu0wlwnCiADsUhYEBqTuZpfs41ai8dazoK4E2JuOkEOE4UQIeKcQgYkCRF7aDm+y5RQ9XppqMA3pOImU6A40QBdCgOAQMfYllalDxdq2pmyvYFTKcBvIMC6FgUQIeiAAIHez0+Rq9UXic7lGc6CuANSQ4BOxUF0KHiHAIGDmldolp/KZmreF6l6SiA+zEC6FgUQIdKMgAIHFZTskAP516nzpKRpqMA7kYBdCwKoEPR/4Aj61JI8/yz1FR5qukogHtRAB2LAuhQtk0FBI7Gtiw9aZ+pdTUXy7Z4ugPSjm1gHItnRIei/wF993J8nN6ovlZ2MMd0FMBdkowAOhUF0KHof8CxWRUfpKfL5iqRW2Y6CuAeSVYkOhUF0KEYAQSO3a5EkRbkz1ZX8TDTUQB3YGqFY/GdcygKIHB82u2I5gUvV3PFyaajAM5nWaYT4DhRAB2K/gccv6R8WqhztKn6AhaHAP1CAXQqnvkcihFAoP+WJE7S0uqrZAfCpqMAzsQ/UI7Fd86h6H9AeqyI1+nZ8jlK5pSYjgI4D4eAHYsC6FA+fueAtNmeKNWjhdcrWlRnOgrgLIwAOhbfOYfy850D0mp/MkcPha5Ua/lJpqMAzsEIoGNRIxwqwBAgkHYJ+fWodYG2Vp8jm8ntwNFRAB2LAuhQjAACmfNc4mS9W3OFbH/IdBQgy/HHyKn4zjlUgO8ckFHvxIfp+Yo5SkYKTUcBspePP0ZOxXfOofwcAgYybkuiTAuL5ipWMMh0FCA7sYWSY1EAHYoRQGBgtCZzNS9ytfaXjTMdBcg+FEDHokY4FItAgIETU0CP+C7Wjuoz2YMT+LBgxHQCHCcKoEOxCAQYeH9NnKr3ay+T7QuYjgJkB0YAHYsa4VAcAgbMeCs2Ui9VXa9kON90FMA8RgAdixrhUEE/h4ABUzbEK/Vk8VzF86tNRwHMYgTQsSiADhUOUAABk/Ym8zU/91q1l44xHQUwhxFAx6IAOhQFEDCv2w5qvu/jaqw63XQUwAxGAB2LAuhQkSAFEMgKlqWnkqdrdc1M2T6/6TTAwGIE0LEogA7FCCCQXV6Lj9GrVdfJDuaajgIMHEYAHYsC6FCRoOkEAD5qbbxGi0rnKpFXYToKMDAirIZ3KgqgQ+UE+dYB2agxWaiHc2ers2SE6ShA5nGubMeiRThUJCBxEBjITp0Kab5/lvZUTjUdBcgcX0AKMeXBqSiADmVZFgtBgCyWtHx6wj5b62sukm3xVAsXYvTP0XhWcrAcCiCQ9V6KT9Cb1dfIDuaYjgKkV6TAdAL0AwXQwXJDFEDACVbGB+vp0jlK5JaZjgKkTw4jgE5GAXSw/DAFEHCKXcliLcifre6ietNRgPRgBNDRKIAOVhDm2wc4Sbsd0UOhK9RSMdl0FKD/mAPoaDQIByuIMAIIOE1SPj2mc7W55nzZFr/DcDAKoKNRAB2MEUDAuZ6PT9Tb1VfL5kwKcCrmADoaDcLB8sMWewECDvZevE7PVcxRMqfYdBTg2OWWmE6AfqAAOpjfZ7ESGHC4bfFSPVo4R9HCwaajAH0XzpeCEdMp0A8UQIdjHiDgfPuTOZoXvlr7yieYjgL0TX656QToJwqgwzEPEHCHuPxaYF2kbdXTZTO5A9mOAuh4tAeHK2AvQMBVnk1M0fKay2X7g6ajAIdHAXQ8CqDDFUT4FgJusyw+XEsq5ygZZpUlshQF0PFoDw5XnMO3EHCjzfFyPV48V7GCWtNRgIPlc1pDp6M9OFxhxFKA7yLgSi3JXM2LXKO2shNMRwH+JhBmE2gXoDo4nGVZjAICLhZTQA/7LtHOqjNMRwFSGP1zBZqDC5Tm8W0E3O7p5DStrJkl2xcwHQVex/w/V6A5uEBpLt9GwAveiI/Sy1WzZYfyTEeBlxVUmk6ANKA5uAAFEPCO9fEqPVkyV/H8KtNR4FXFg0wnQBrQHFygONfHtrGAh+xJFmh+7nXqKB1lOgq8xrKkwmrTKZAGFEAXCPgsFeZQAQEv6baDmu+7VLurppmOAi/Jr5ACIdMpkAYUQJfgMDDgPbZl6S/JM7S25hLZlt90HHhBMftSugWtwSVKc3nyB7zqlfgJeq36WtnBXNNR4HbM/3MNCqBLlOfzrQS8bE28VovK5iqRxxYdyKCiGtMJkCa0Bpcoz/PJxzRAwNMaE4V6OG+2uoqHm44CN/IFpAJWn7sFBdAl/D6LUUAA6rTDmhe4THsrp5iOArcpqpZ8/J1xC76TLlKVzzxAAFLS8ulxe7o21Fwo2+JpHmnC/D9X4ZnBRSoL+HYC+JsX4ydqafXVsgMR01HgBiWDTSdAGtEYXKSywM+G0AB6WREfomfK5yiRU2I6CpyurN50AqQRBdBFgn5LJewHCOAjdiRK9GjB9eouGmo6CpyqsEoKsc2Qm9AWXKaKw8AADqHNztG80BVqqZhoOgqcqGyY6QRIM9qCy1QWsBAEwKEl5NdjOl9bqs+VzYQRHItyCqDbUABdpooCCOAoFicm652aq2T7Oacr+sDySaV1plMgzSiALhMJMg8QwNG9Gx+qxRVzlIwUmY6CbFc8SArwz4Lb0BRcaHARo4AAjm5rokwLC+coWsj2HjgCDv+6EgXQhQYVUwAB9E2rnat54au0v2y86SjIVhRAV6IAulBFvk/hgOkUAJwiroAe8c3Q9uqzZZsOg+ziD3IGEJeiALqQZVmq5TAwgGP0TGKqVtRcLtsfNB0F2aKsXvLx98SNKIAuNbiYIUAAx25pfIReqLheyXCB6SjIBlWjTSdAhlAAXaq2iNPCATg+mxIVeqJ4rmIFNaajwLRKCqBbUQBdKhywVJ7PtxfA8WlO5ml+zjVqLx1rOgpMKR4kRfJNp0CG0BBcbDCrgQH0Q9QOar7vEjVUnW46Ckzg8K+rUQBdjHmAAPrNsrQoebpW1cyU7eM5xVOqxphOgAyiALpYSa5PBWFmAgLov9fjY/RK5XWyQ3mmo2Ag5JVJBRWmUyCDKIAuN6yM/9gBpMe6RLX+UjJX8bxK01GQaTUnmE6ADKMAulw9BRBAGjUlC/Rw7nXqLBlpOgoyqZrFP25HAXS54hyfSnL5NgNIny6FNM8/S02Vp5qOgkzIKZaK2ALI7WgGHjCslNXAANLLtiw9aZ+pdTUXy7b4U+IqtZwX2gv4rfUADgMDyJSX4+P0RvW1soM5pqMgXQafZDoBBgAF0APywz5VsCk0gAxZFR+kp8vmKpFbZjoK+qt4kJRfbjoFBgCtwCPqSxkFBJA5uxJFWpA/W13Fw0xHQX8w+ucZFECPqC8LcG5gABnVbkc0L3i5mitONh0Fx8PnZ/6fh1AAPSInaKm6kG83gMxKyqeFOkebqi+QbfFvp6NUjpKYy+kZNAIPGVURNB0BgEcsSZykpdVXyw6ETUdBXw2eaDoBBhAF0EPqSvyKMBUQwABZEa/Ts+VzlMwpMR0FRxPKkyrY3NtLKIAe4vNZGsEoIIABtD1RqkcLr1e0qM50FBzJoAmSj0rgJXy3PWZ0BUOAAAbW/mSOHgpdqdZyVphmrUF8b7yGAugxBRGfalgMAmCAJeTXo9YF2lp9jmz2JMguxYOlomrTKTDAaAIeNLqSw8AAzHgucbLerblCtj9kOgoOqD/FdAIYQAH0oCElfuUE+Q8cgBnvxIfp+Yo5SkYKTUdBOF+qOcF0ChhAAfQgn2VpZDlzAQGYsyVRpoVFcxUrGGQ6ircNnZLaABqeQwH0qFGVnBkEgFmtyVzNi1yt/WXjTEfxJp9fquOsLV5FAfSo/LBPg4v5rw+AWTEF9IjvYu2sPlO26TBeU31C6hAwPIkC6GHja1gMAiA7PJ04Ve/XXibbx/SUATPsVNMJYBAF0MMqC/yqyOdHAEB2eCs2Ui9VXa8ko1KZV1wrFTP/0sv46+9xjAICyCYb4pV6sniu4vnsS5dRQ9n6xesogB43pNivwgjLQQBkj73JfM3PvVbtpWNMR3GnSKFUO950ChhGAfQ4y7I0rppRQADZpdsOar7v42qsOt10FPcZfjpbv4ACCGlEeYCNoQFkH8vSU8nTtbpmpmwKS3qE86S6yaZTIAtQACG/z9LYKlbeAchOr8XH6NWq62QHc01Hcb5hp0l+jvqAAogPjKkMKsBPQ8Y88sv/0I3T8nu9fPW6v/0XHu3u0gP/9UXdemGdPnNOlX78lblq3dNwxPu0bVsP//zb+qePj9A/nF2uO//xUu3asq7n47Fot37+jX/QzefW6F+umaQVrz/X6/ZP/vZu/fZ7t6f3EwUyZG28RotK5yqRV2E6inMFc6ShU02nQJbgTz4kSaGApdGV/FeYSYOGn6AfPbm+5+Vrv3i652O/v/srWvbiX/SP331A/3rPU2pu2qX//urfHfH+nvztD/X0n+/VJ7/yI93x68UKR/L0vc9foWh3lyRp8YLfaNOqZfr6r5/ROVfcpHvuuEm2ndpqd/eOTVr86P265pZvZO4TBtKsMVmoh3Nnq7NkhOkozjRsmhQImU6BLEEBRI8JNUEF+YnIGL8/oOKyqp6XguJySVJHW6uWPPaA5n7+uxo39RwNO2Gy/uHr92jdu69q3XuvH/K+bNvWoj/+VLNu+hedPP1S1Y2aoM988xdqadqppc8vlCTt2LRak8+eqcHDx+mCaz6j/c1N2t/SJEn6nzu/oOv+8VvKyS8cmE8eSJNOhTTfP0t7KhnJOiaBiFTPxs/4G/7co0ckaOkEVgRnzK6t6/X5mSP1pSsn6N47/l57dm2VJG1atUyJeEzjTj2357q19WNUVj1E65YfugDu3rFJrXsaNP5Dt8nNL9Lw8VN7SmPdyBO15u1XFO3q1Huv/VXF5dUqKC7Xy0/9ScFQRFPPuSyDny2QOUnLpyfss7W+5iLZFn/G+qT+FCkYNp0CWYSZ/+hlXE1QqxpiiiZMJ3GX4eNP0afvuFfVdaPVumeXFvzqu/qPmy/Sf/z+dbXuaVQgGFJeQXGv2xSWVh52HuCBy4tKKw++zd7Ux8667AZtXbdc/3r9VBUUl+m2/3hA7fua9fAvvqN/vecvmnfvv+u1p+erctAwferf7lFpZW36P3Egg16KT9De6mJNbVooK9ZpOk728odSh3+BD6EAopeQ39KEmqCWbouZjuIqEz920d/eGTVBw8dP1e2Xj9PrzzysUDgnI48ZCAR1w7/8sNdlv/zWLbroulu0efU7Wvr84/rO717RE7/9oR78/pf0uTt/n5EcQCatjA9Wc+kcnb//Ufk79piOk53qp0qhzDzPwLkYO8dBxlYF2Rcww/IKilVdN1INWzeoqKxS8VhU7ftbel1n395GFZVVHfL2By5v3dt48G1KD32blW8+r+0bV+qCa2/RqqUvaOLHZiick6dTL7hKK5e+2P9PCjBkV7JYC/Jnq7uo3nSU7BPMkUacYToFshAFEAcJ+C2dWMtcwEzq6mhT4/aNKi6vUv3YyfIHgnr/jcU9H9+5eY327NqqkRMOPWm7orZeRWVVvW7T2bZPG1a8qZEnHnybaHeXHrjrn3XTV/9bPr9fyURC8XhqlDcRj8tOcswfztZuR/RQ6Aq1VLDJcS+jzpKCEdMpkIUogDikURUB5YUYBUyXP/zo/2rV0he0e8dmrX33Vf33V+bI5/PptIuuVW5+kc6+7Ab94Uf/qpVvPq+NK5fpV9/+rEaeOK1XmfvqdZP15uLHJKVO4Tfj+tv02H3/paVLntDWdcv1i3//jIrLa3Ty9FkHPf5jv/l/OuljMzR0zERJ0qiJp+mtxY9py9rl+utDP9eok04bmC8EkEFJ+fSYztXmmvNlWzx/KbeEff9wWMwBxCH5fZYmDgrq5Y1R01Fcoblxu+75+k1qa92rguJyjZ54ur7+6+dUWJLa1HbuF+6Uz/Lpx//6CcWi3TrxtPN1w7/c3es+dm5eq862fT3vf/z/fFHdne26/7ufU0dbq0ZNPF1f+tEjCoV7/7e/bf0Kvf7XR/Tt373cc9kp512pVUtf0H/efJGqh47SZ7/1m8x98sAAez4+USdWl2jS7oWy4t2m45gz5lzO+YvDsuwDO8MCH5G0bS18r1OtXfyIAHCewYG9OqdlgXydLaajDLziWuljfy8xEorD4BAwDstnWTplKLvGA3CmbfFSPVo4R9HCwaajDLyxF1D+cEQUQBxRbVFAg4s5hADAmfYnczQvfLX2lU8wHWXgVI6SyoaaToEsRwHEUZ1SF5KPfyQBOFRcfi2wLtK26uly/YQWy5LGnm86BRyAAoijKoj4NI5TxAFwuGcTU7S89grZfhc/nw05WSqoMJ0CDkABRJ+cWBtULptDA3C4ZbHhWlI5R8lwoeko6RfKlcaee/TrAaIAoo+CfksnD2FBCADn2xwv1+PFcxUrcNn5r0+4IHXmD6APKIDos+HlAVXk8yMDwPlakrmaF7lGbWUnmI6SHqVDpcETTaeAg/DXHMfk1KEhcSAYgBvEFNDDvku0s8rh58q1fNKES0yngMNQAHFMyvL8GlXJCWQAuMfTyWlaWTNLts+hz23DT2PhB44ZBRDHbMqQkHI5TzAAF3kjPkovV82WHcozHeXY5BRLo842nQIORAHEMQv6LZ1Wz4IQAO6yPl6lJ0vmKp5fZTpK342fIbl5WxtkDAUQx2VwcUDDyzhDCAB32ZMs0Pzc69RROsp0lKOrGiNVjTadAg5FAcRxm1oXVsShU2YA4HC67aDm+y7V7qpppqMcXiAsjb/YdAo4GAUQxy0StHRqfdh0DABIO9uy9JfkGVpbc4lsKwuPdoyfIeW4cDNrDBgKIPqlvjSgupIsfHIEgDR4JX6CXq++VnYw13SUv6kaw55/6DcKIPpt2tCQQnRAAC61Ol6rRWVzlcgrNx0ldbq3E2eaTgEXoACi33JCPk2tY1UwAPdqTBTq4bzZ6ioebjbIiTOlsMO2qkFWogAiLUZWBDkUDMDVOu2w5gUu097KKWYCDDpRqh5r5rHhOhRApM3pw8JsEA3A1ZKWT4/b07Wh5kLZ1gD+CY0UsuoXaUUBRNqEA5bOHB7mXMEAXO/F+IlaWn217EBkYB7wpFlScIAeC55AAURaVRf6NaGWXekBuN+K+BA9Uz5HiZySzD7Q0KlSheG5h3AdCiDSbuKgoCry+dEC4H47EiV6tOB6dRcNzcwDFFZJJ1yYmfuGp/FXGmnnsyydNSKsIGtCAHhAm52jeaEr1FKR5r35AmHp5GskP6dcQvpRAJER+WGfTuMsIQA8IiG/HtP52lJ9rux0zYQ+6VIprzQ99wV8BAUQGTOsLKAR5fznCsA7Ficm652aK2X7+7k3av0pUs249IQCDoECiIw6dWhIxTmsCwbgHe/G67W4Yo6SkaLju4PiWub9IeMogMiooN/SOaMinCoOgKdsTZRpYeEcRQsHH9sNgxFp8tWSjydNZBYFEBlXGPHprBHsDwjAW1rtXM0LX6X9ZeP7fqOJl0u5xRnLBBxAAcSAGFQc0KTB7A8IwFviCugR3wxtrz5b9tGuPPx0qWr0QMQCKIAYOCfWhjSU8wUD8KBnElO1ouZy2f7D/CNcPlwae97AhoKnUQAxoD42PMyiEACetDQ+Qi9UXK9kuKD3B/JKpZOvkgby3MLwPH7aMKCCfkvnsigEgEdtSlToieK5ihXUpC4IhKWps6Vgjtlg8BzLtu2jTksA0m17S1zPruk++pwYAHChkBXTrMRflTd8klQ50nQceBAFEMa8vzOmN7dGTccAACNOqQvphGoWx8EMDgHDmHE1QY2t4kwhALxnTGWA8gejKIAw6pS6kOpYGQzAQwYV+XXK0H6eKg7oJwogjLIsS2eNCKsinx9FAO5XkuPT2SPD8lnshgCz+KsL4/w+S+eNjqgowhMiAPfKD1s6f0xYQT/PdTCPAoisEA5YOn9MRDlBnhgBuE9O0NKFYyLKDfFnF9mBn0RkjfywT+eNDivATyUAFwn5pQvGRFQQ4ckN2YOfRmSVsjy/po8Mi+kxANwg4JPOGx1RSS5/bpFd+IlE1hlUHNCZw8OiAwJwMp8lTR8ZVmUBOx0g+1AAkZWGlQX0seEhSiAAR7IknTE8rEHF7HWK7EQBRNYaUR7UacPYKwuA85xaH9KwMsofshcFEFltVEVQp9VTAgE4x+TBQY2p5CwfyG4UQGS90ZVBncqu+QAc4OTBQZ1Yy/MVsh/j03CEsVVBJW3pzS1R01EA4JCm1oU0jvP7wiEYAYRjjKsO6uTBPLkCyD6nDqX8wVkYAYSjTKgNyZa0bFvMdBQAkCSdVh/SaOb8wWEs27Zt0yGAY7W6IabXN0fFDy8AUyxJpw8LaWQF5Q/OQwGEY23cE9dLG7qV5CcYwAA7sM/f8HIOpMGZKIBwtO0tcS1e161E0nQSAF7hs6Qzh4dVzz5/cDAKIByvcX9Cz67pUjRhOgkAtwv6pOmjwqotovzB2SiAcIW9HQn9dXW3umL8OAPIjJygpfNGh1WWx7l94XwUQLjGvq6k/rqqS21RfqQBpFdRxNL5YyLKD7N7GtyBAghX6Ygm9czqbjV3MikQQHpU5vt07uiIwgHLdBQgbSiAcJ1YwtaSdd3a3sqkQAD9M7TErzNHhOX3Uf7gLhRAuFLStvXG5qhWN8ZNRwHgUCdUBTS1LiTLovzBfSiAcLWVu2J6cwsbRgPoO0vSlCEhjathg2e4FwUQrre9Ja4l67sV44gwgKMI+aWzRoQ1qJhtXuBuFEB4QmtnUs+u6dL+bn7cARxacY6lc0dFVBBhpS/cjwIIz+iO21qyrks797FCGEBvQ0v9+tiwsIJ+5vvBGyiA8JSkbWvZ1phW7IqZjgIgC1iSJg0O6sTakOkowICiAMKTtjbH9dKGbk4fB3gY8/3gZRRAeFZbd1JL1nWrqZ1DwoDXMN8PXkcBhKclkrbe2hrVqgb2CwS8YmR5QKcMDTHfD55GAQQkbd4b18sb2SoGcLOgXzqtPqxhZRzyBSiAwAf2dSX1/LpuNXdwSBhwm4p8n84aEVZ+mEO+gEQBBHpJJG29uYVTyAFuYUk6sTaokwYF5eOUbkAP/hVCVlmyZIlmzZql2tpaWZalBQsWDOjj+32WptWHdcGYsHJD/LEAnCw3ZOnCsRFNGhyi/AEfQQFEVmlvb9fEiRP105/+1GiO2qKALpuQo+HMFQIcqa7Er1kTclRd6DcdBchKHAJG1rIsS4888oiuuOIKozm27I3r1U3d6uKoMJD1Qn7plLqQRlQETUcBshrDG8BR1JUGVFHg16uburW1mWXCQLaqK/Fr2tCQckIc3AKOhgII9EFOMLVp7PqmmF7fHGW7GCCLRIKWpg0NaWgpf9KAvuK3BTgGI8qDqi706/XNUUYDgSwwvCy1qXM4wCIP4FhQAIFjlBfy6dxREW1rjuv1zVG1RZlGCwy03JCl0+tDnMcXOE785gDHaXBJQNWFfr27I6b3d8WUpAcCGWdJGl0Z0MlDOJUb0B8UQGSVtrY2rVu3ruf9jRs36u2331Zpaanq6uoMJju0gN/SyUNCGl4e0GubutWwn7OIAJlSme/TKUNDKstjaxegv9gGBlll8eLFOvfccw+6/MYbb9T9998/8IGO0fqmmN7aEmXLGCCNckOWpgwJcQ5fII0ogECaReO2lm6Lam1jXPxyAcfPZ0nja4KaUBPkcC+QZhRAIENaOpNaujWqbS2sFgaO1ZASv6YOCakgwp5+QCZQAIEMa9iX0Ftbo2pqZ34gcDRFOZZOqQurtoh5fkAmUQCBAbJpT1zLtkW1v5tfOeCj8kOWThoU1PDygHwWh3uBTKMAAgMokbS1pjGud3dE1c1CEUA5QUsn1gY1qiIgv4/iBwwUCiBgQDRua/nOmFY1xBTnyDA8KBxILfAYWxVUgOIHDDgKIGBQd9zWyl2pIhhlrQg8IOiTTqgOalxNUCFW9gLGUACBLBCN21rVGNPKXTEODcOVgj5pdGVQ42uCigQpfoBpFEAgi8QSqTmC7++KqTPGryacLydoaWxVQGMqgwoFKH5AtqAAAlkokbS1dndcy3fG1BHlVxTOUxixNL46taqXxR1A9qEAAlksmbS1aW9Cqxpi7CMIR6jI92l8TVBDiv2y2M4FyFoUQMAhmtpSRXDT3oSS/NYii1iSBhf7Nb4mqMoCNnAGnIACCDhMZ8zW2saYVjfGmScIo3KClkZWBDSqIqD8MKdsA5yEAgg4VDJpa3NzalRwdxuHhzFwagr9Gl0Z0JASP2ftAByKAgi4wN6OhNbvjmvjnri62EYGGRAJWhpZHtCoyoAKGO0DHI8CCLhI0ra1oyWh9U1xbW1hriD6x5JUXejXqMqA6or98rGaF3ANCiDgUt1xW5v2xrV+d5wVxDgmFfk+DSsLaGhpQDls2gy4EgUQ8IB9nUmt3xPXpj1x7e/mVx4HK8n1aViZX/WlLOgAvIACCHhMc0dSW5vj2tKc0N4ORga9rDBiqb40oGFlARXlUPoAL6EAAh7W1p3U1uaEtjTH1bg/KZ4M3M1S6vDuoGK/BhcHVJJL6QO8igIIQJLUFbO1rSU1MrhrX0JxBgddIRyQBhX5Nag4oNoiv8KcjxeAKIAADiGZtLW7Pald+xLauS+hprYkK4odpCTXp8HFfg0u8qs838cp2QAchAII4KhiCVuN+1NlcOe+pJqZO5g1LEmluT5VFvhUVeBXZYFfEVbuAjgKCiCAY9YVs7Vrf0K79yfU1J7U3o6kEnTCAeG3pPJ8nyoL/Koq8Kki36+gn8IH4NhQAAH0W9K21dKRVFP7By9tCbV22iwq6SdLUlGOpZJcn0pzU4dzy/N88rMhM4B+ogACyIhYwtbeDwphS2dSrZ1JtXYlFUuYTpadAj59UPR8vV5T9gBkAgUQwIDqiCbV2mmrteuDUtiZVGuXrc6Y+5+KLEl5IUv5EUsFYZ8KPnhdkpN6m8UaAAYKBRBAVojGbe3vTqojaqs9aqvjg5f2aLLn7USWP1tZlpQTsJQTtJQTslQQtlQQ8aVeh33KC1uM6AHIChRAAI7RFbPVEU2qM2YrmkgdZo4mbEXjvd+OJmzFErZiCcm2JVuSbdtK9rz9odcfvO2zUodh/T7rg9dSwGcd9Drol8IBS5GgpXAg9ZITTL0fCYhRPACOQAEEAADwGM4DBAAA4DEUQAAAAI+hAAIAAHgMBRAAAMBjKIAAAAAeQwEEAADwGAogAACAx1AAAQAAPIYCCAAA4DEUQAAAAI+hAAIAAHgMBRAAAMBjKIAAAAAeQwEEAADwGAogAACAx1AAAQAAPIYCCAAA4DEUQAAAAI+hAAIAAHgMBRAAAMBjKIAAAAAeQwEEAADwGAogAACAx1AAAQAAPIYCCAAA4DEUQAAAAI+hAAIAAHgMBRAAAMBjKIAAAAAeQwEEAADwmP8Pty7giEZ37sYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the context of disease classification, the goal is to use the minimum number of tests and features possible while achieving significant accuracy."
      ],
      "metadata": {
        "id": "16SfEaxgRbXj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nVOCezFpI-Ot",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "outputId": "629eac25-dd77-4548-f7d3-22dba349c570"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 939
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "matrizcorr = x.corr()\n",
        "plt.figure(figsize = (20, 15))\n",
        "sns.heatmap(matrizcorr, annot = True, fmt = \".1f\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3HM1gLq-QLK2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "059bee2f-77ef-4233-9c0f-ac680c1fc727"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[103.78]\n",
            "[0.786]\n"
          ]
        }
      ],
      "source": [
        "print(data['exame_4'].unique())\n",
        "print(data['exame_29'].unique())\n",
        "#same value for all rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JTGrD8z4Q8iK"
      },
      "outputs": [],
      "source": [
        "x = x.drop(columns = ['exame_4', 'exame_29'])\n",
        "matrizcorr = x.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9yabbb4IUtSr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9cf86ddb-9f0f-48d7-f466-c08890768ec0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              exame_1  exame_2  exame_3  exame_5  exame_6  exame_7  exame_8  \\\n",
              "exame_1       1.00000      NaN  0.99786      NaN      NaN      NaN      NaN   \n",
              "exame_2           NaN      1.0      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_3       0.99786      NaN  1.00000      NaN      NaN      NaN      NaN   \n",
              "exame_5           NaN      NaN      NaN      1.0      NaN      NaN      NaN   \n",
              "exame_6           NaN      NaN      NaN      NaN      1.0      NaN      NaN   \n",
              "exame_7           NaN      NaN      NaN      NaN      NaN      1.0      NaN   \n",
              "exame_8           NaN      NaN      NaN      NaN      NaN      NaN      1.0   \n",
              "exame_9           NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_10          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_11          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_12          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_13          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_14          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_15          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_16          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_17          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_18          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_19          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_20          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_21          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_22          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_23          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_24          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_25          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_26          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_27          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_28          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_30          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_31          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "exame_32          NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "binary_class      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
              "\n",
              "              exame_9  exame_10  exame_11  ...  exame_23  exame_24  exame_25  \\\n",
              "exame_1           NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_2           NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_3           NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_5           NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_6           NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_7           NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_8           NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_9           1.0       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_10          NaN       1.0       NaN  ...       NaN       NaN       NaN   \n",
              "exame_11          NaN       NaN       1.0  ...       NaN       NaN       NaN   \n",
              "exame_12          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_13          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_14          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_15          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_16          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_17          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_18          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_19          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_20          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_21          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_22          NaN       NaN       NaN  ...       NaN  0.993488       NaN   \n",
              "exame_23          NaN       NaN       NaN  ...       1.0       NaN       NaN   \n",
              "exame_24          NaN       NaN       NaN  ...       NaN  1.000000       NaN   \n",
              "exame_25          NaN       NaN       NaN  ...       NaN       NaN       1.0   \n",
              "exame_26          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_27          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_28          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_30          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_31          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "exame_32          NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "binary_class      NaN       NaN       NaN  ...       NaN       NaN       NaN   \n",
              "\n",
              "              exame_26  exame_27  exame_28  exame_30  exame_31  exame_32  \\\n",
              "exame_1            NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_2            NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_3            NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_5            NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_6            NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_7            NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_8            NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_9            NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_10           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_11           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_12           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_13           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_14           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_15           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_16           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_17           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_18           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_19           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_20           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_21           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_22           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_23           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_24           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_25           NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_26           1.0       NaN       NaN       NaN       NaN       NaN   \n",
              "exame_27           NaN       1.0       NaN       NaN       NaN       NaN   \n",
              "exame_28           NaN       NaN       1.0       NaN       NaN       NaN   \n",
              "exame_30           NaN       NaN       NaN       1.0       NaN       NaN   \n",
              "exame_31           NaN       NaN       NaN       NaN       1.0       NaN   \n",
              "exame_32           NaN       NaN       NaN       NaN       NaN       1.0   \n",
              "binary_class       NaN       NaN       NaN       NaN       NaN       NaN   \n",
              "\n",
              "              binary_class  \n",
              "exame_1                NaN  \n",
              "exame_2                NaN  \n",
              "exame_3                NaN  \n",
              "exame_5                NaN  \n",
              "exame_6                NaN  \n",
              "exame_7                NaN  \n",
              "exame_8                NaN  \n",
              "exame_9                NaN  \n",
              "exame_10               NaN  \n",
              "exame_11               NaN  \n",
              "exame_12               NaN  \n",
              "exame_13               NaN  \n",
              "exame_14               NaN  \n",
              "exame_15               NaN  \n",
              "exame_16               NaN  \n",
              "exame_17               NaN  \n",
              "exame_18               NaN  \n",
              "exame_19               NaN  \n",
              "exame_20               NaN  \n",
              "exame_21               NaN  \n",
              "exame_22               NaN  \n",
              "exame_23               NaN  \n",
              "exame_24               NaN  \n",
              "exame_25               NaN  \n",
              "exame_26               NaN  \n",
              "exame_27               NaN  \n",
              "exame_28               NaN  \n",
              "exame_30               NaN  \n",
              "exame_31               NaN  \n",
              "exame_32               NaN  \n",
              "binary_class           1.0  \n",
              "\n",
              "[31 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1ec9ff6d-e103-4e2b-9160-4e78f8d2cd4f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>exame_1</th>\n",
              "      <th>exame_2</th>\n",
              "      <th>exame_3</th>\n",
              "      <th>exame_5</th>\n",
              "      <th>exame_6</th>\n",
              "      <th>exame_7</th>\n",
              "      <th>exame_8</th>\n",
              "      <th>exame_9</th>\n",
              "      <th>exame_10</th>\n",
              "      <th>exame_11</th>\n",
              "      <th>...</th>\n",
              "      <th>exame_23</th>\n",
              "      <th>exame_24</th>\n",
              "      <th>exame_25</th>\n",
              "      <th>exame_26</th>\n",
              "      <th>exame_27</th>\n",
              "      <th>exame_28</th>\n",
              "      <th>exame_30</th>\n",
              "      <th>exame_31</th>\n",
              "      <th>exame_32</th>\n",
              "      <th>binary_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>exame_1</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.99786</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_3</th>\n",
              "      <td>0.99786</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_8</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_12</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_14</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_16</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_20</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.993488</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_23</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_24</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exame_32</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>binary_class</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>31 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ec9ff6d-e103-4e2b-9160-4e78f8d2cd4f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-34de7e11-b9cb-45c1-8231-13dd1d34a9cb\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-34de7e11-b9cb-45c1-8231-13dd1d34a9cb')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-34de7e11-b9cb-45c1-8231-13dd1d34a9cb button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ec9ff6d-e103-4e2b-9160-4e78f8d2cd4f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1ec9ff6d-e103-4e2b-9160-4e78f8d2cd4f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 942
        }
      ],
      "source": [
        "matrizcorr[matrizcorr>0.99]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mI4pWo9CR8yB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cf9c1de-e09d-4e56-b824-00b66ad0117e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "exame_20    1.000000\n",
              "exame_16    1.000000\n",
              "exame_13    1.000000\n",
              "exame_2     1.909461\n",
              "exame_23    1.909461\n",
              "exame_10    3.429371\n",
              "exame_11    4.110899\n",
              "exame_21    4.863323\n",
              "exame_31    5.023474\n",
              "exame_18    5.219545\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 943
        }
      ],
      "source": [
        "corr = matrizcorr[matrizcorr>0.50].sum()\n",
        "corr.sort_values().head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xlsa7JDJS-Yo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "fdb92c86-0b2f-4409-9ba5-860e3227919f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     exame_16  exame_20  exame_13    exame_2   exame_23  exame_10  exame_31  \\\n",
              "0    0.006399  0.030030  0.905300  10.380000  17.330000  0.241900  0.460100   \n",
              "1    0.005225  0.013890  0.733900  17.770000  23.410000  0.181200  0.275000   \n",
              "2    0.006150  0.022500  0.786900  21.250000  25.530000  0.206900  0.361300   \n",
              "3    0.009110  0.059630  1.156000  20.380000  26.500000  0.259700  0.663800   \n",
              "4    0.011490  0.017560  0.781300  14.340000  16.670000  0.180900  0.236400   \n",
              "..        ...       ...       ...        ...        ...       ...       ...   \n",
              "709  0.005917  0.020503  0.976907  21.087823  30.085869  0.229856  0.426725   \n",
              "710  0.008699  0.020727  1.121515  26.592507  33.700387  0.181119  0.304755   \n",
              "711  0.006461  0.023734  1.427416  18.466067  27.307162  0.211881  0.370110   \n",
              "712  0.006595  0.019826  1.265464  27.296147  35.888485  0.190505  0.299089   \n",
              "713  0.009032  0.035858  1.539750  23.037636  33.190711  0.210566  0.430530   \n",
              "\n",
              "     exame_11  exame_21  exame_26  \n",
              "0    0.078710  0.006193  0.162200  \n",
              "1    0.056670  0.003532  0.123800  \n",
              "2    0.059990  0.004571  0.144400  \n",
              "3    0.097440  0.009208  0.209800  \n",
              "4    0.058830  0.005115  0.137400  \n",
              "..        ...       ...       ...  \n",
              "709  0.072472  0.003724  0.172223  \n",
              "710  0.063009  0.004551  0.159382  \n",
              "711  0.060236  0.003742  0.141447  \n",
              "712  0.062021  0.005090  0.132457  \n",
              "713  0.063192  0.004492  0.151724  \n",
              "\n",
              "[714 rows x 10 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>exame_16</th>\n",
              "      <th>exame_20</th>\n",
              "      <th>exame_13</th>\n",
              "      <th>exame_2</th>\n",
              "      <th>exame_23</th>\n",
              "      <th>exame_10</th>\n",
              "      <th>exame_31</th>\n",
              "      <th>exame_11</th>\n",
              "      <th>exame_21</th>\n",
              "      <th>exame_26</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.030030</td>\n",
              "      <td>0.905300</td>\n",
              "      <td>10.380000</td>\n",
              "      <td>17.330000</td>\n",
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              "      <td>0.460100</td>\n",
              "      <td>0.078710</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>0.162200</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.013890</td>\n",
              "      <td>0.733900</td>\n",
              "      <td>17.770000</td>\n",
              "      <td>23.410000</td>\n",
              "      <td>0.181200</td>\n",
              "      <td>0.275000</td>\n",
              "      <td>0.056670</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>0.123800</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.022500</td>\n",
              "      <td>0.786900</td>\n",
              "      <td>21.250000</td>\n",
              "      <td>25.530000</td>\n",
              "      <td>0.206900</td>\n",
              "      <td>0.361300</td>\n",
              "      <td>0.059990</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>0.144400</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.059630</td>\n",
              "      <td>1.156000</td>\n",
              "      <td>20.380000</td>\n",
              "      <td>26.500000</td>\n",
              "      <td>0.259700</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>0.209800</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.017560</td>\n",
              "      <td>0.781300</td>\n",
              "      <td>14.340000</td>\n",
              "      <td>16.670000</td>\n",
              "      <td>0.180900</td>\n",
              "      <td>0.236400</td>\n",
              "      <td>0.058830</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>0.137400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>709</th>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.020503</td>\n",
              "      <td>0.976907</td>\n",
              "      <td>21.087823</td>\n",
              "      <td>30.085869</td>\n",
              "      <td>0.229856</td>\n",
              "      <td>0.426725</td>\n",
              "      <td>0.072472</td>\n",
              "      <td>0.003724</td>\n",
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              "      <th>710</th>\n",
              "      <td>0.008699</td>\n",
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              "      <td>1.121515</td>\n",
              "      <td>26.592507</td>\n",
              "      <td>33.700387</td>\n",
              "      <td>0.181119</td>\n",
              "      <td>0.304755</td>\n",
              "      <td>0.063009</td>\n",
              "      <td>0.004551</td>\n",
              "      <td>0.159382</td>\n",
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              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>0.006461</td>\n",
              "      <td>0.023734</td>\n",
              "      <td>1.427416</td>\n",
              "      <td>18.466067</td>\n",
              "      <td>27.307162</td>\n",
              "      <td>0.211881</td>\n",
              "      <td>0.370110</td>\n",
              "      <td>0.060236</td>\n",
              "      <td>0.003742</td>\n",
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              "      <th>712</th>\n",
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              "      <th>713</th>\n",
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              "      <td>23.037636</td>\n",
              "      <td>33.190711</td>\n",
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              "      <td>0.004492</td>\n",
              "      <td>0.151724</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>714 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8707463d-8f9a-4ad1-8e2e-c65bffd0c98b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-98bc9f6b-8283-4d94-aae6-992de82681c8\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98bc9f6b-8283-4d94-aae6-992de82681c8')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-98bc9f6b-8283-4d94-aae6-992de82681c8 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8707463d-8f9a-4ad1-8e2e-c65bffd0c98b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8707463d-8f9a-4ad1-8e2e-c65bffd0c98b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 944
        }
      ],
      "source": [
        "x = x[['exame_16','exame_20','exame_13','exame_2','exame_23','exame_10','exame_31','exame_11','exame_21','exame_26']]\n",
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MrLriULDXiTy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920
        },
        "outputId": "b70ed846-f540-4042-e7e9-5405d18f54f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 945
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 2 Axes>"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (20, 15))\n",
        "sns.heatmap(x.corr(), annot = True, fmt = \".1f\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Qbkijn5ZTlh-",
        "outputId": "1cf24587-d43d-4a3f-dd52-19e1a4954d7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     exame_16  exame_20  exame_13    exame_2   exame_23  exame_10  exame_31  \\\n",
              "0    0.006399  0.030030  0.905300  10.380000  17.330000  0.241900  0.460100   \n",
              "1    0.005225  0.013890  0.733900  17.770000  23.410000  0.181200  0.275000   \n",
              "2    0.006150  0.022500  0.786900  21.250000  25.530000  0.206900  0.361300   \n",
              "3    0.009110  0.059630  1.156000  20.380000  26.500000  0.259700  0.663800   \n",
              "4    0.011490  0.017560  0.781300  14.340000  16.670000  0.180900  0.236400   \n",
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              "712  0.006595  0.019826  1.265464  27.296147  35.888485  0.190505  0.299089   \n",
              "713  0.009032  0.035858  1.539750  23.037636  33.190711  0.210566  0.430530   \n",
              "\n",
              "     exame_11  exame_21  exame_26  \n",
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              "..        ...       ...       ...  \n",
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              "710  0.063009  0.004551  0.159382  \n",
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              "712  0.062021  0.005090  0.132457  \n",
              "713  0.063192  0.004492  0.151724  \n",
              "\n",
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              "      <th>1</th>\n",
              "      <td>0.005225</td>\n",
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              "      <td>17.770000</td>\n",
              "      <td>23.410000</td>\n",
              "      <td>0.181200</td>\n",
              "      <td>0.275000</td>\n",
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              "      <td>0.003532</td>\n",
              "      <td>0.123800</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.022500</td>\n",
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              "      <td>21.250000</td>\n",
              "      <td>25.530000</td>\n",
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              "      <td>0.059990</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>0.144400</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.059630</td>\n",
              "      <td>1.156000</td>\n",
              "      <td>20.380000</td>\n",
              "      <td>26.500000</td>\n",
              "      <td>0.259700</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>0.209800</td>\n",
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              "      <td>0.011490</td>\n",
              "      <td>0.017560</td>\n",
              "      <td>0.781300</td>\n",
              "      <td>14.340000</td>\n",
              "      <td>16.670000</td>\n",
              "      <td>0.180900</td>\n",
              "      <td>0.236400</td>\n",
              "      <td>0.058830</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>0.137400</td>\n",
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              "      <td>...</td>\n",
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              "      <th>709</th>\n",
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              "      <td>0.976907</td>\n",
              "      <td>21.087823</td>\n",
              "      <td>30.085869</td>\n",
              "      <td>0.229856</td>\n",
              "      <td>0.426725</td>\n",
              "      <td>0.072472</td>\n",
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              "      <td>26.592507</td>\n",
              "      <td>33.700387</td>\n",
              "      <td>0.181119</td>\n",
              "      <td>0.304755</td>\n",
              "      <td>0.063009</td>\n",
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              "      <th>711</th>\n",
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              "      <td>18.466067</td>\n",
              "      <td>27.307162</td>\n",
              "      <td>0.211881</td>\n",
              "      <td>0.370110</td>\n",
              "      <td>0.060236</td>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 946
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = x.drop(columns=['exame_2', 'exame_23'])\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "8EJqpxYOTPU4",
        "outputId": "c496a6a8-cf50-4bac-c934-fe012bbd56e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     exame_16  exame_20  exame_13  exame_10  exame_31  exame_11  exame_21  \\\n",
              "0    0.006399  0.030030  0.905300  0.241900  0.460100  0.078710  0.006193   \n",
              "1    0.005225  0.013890  0.733900  0.181200  0.275000  0.056670  0.003532   \n",
              "2    0.006150  0.022500  0.786900  0.206900  0.361300  0.059990  0.004571   \n",
              "3    0.009110  0.059630  1.156000  0.259700  0.663800  0.097440  0.009208   \n",
              "4    0.011490  0.017560  0.781300  0.180900  0.236400  0.058830  0.005115   \n",
              "..        ...       ...       ...       ...       ...       ...       ...   \n",
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              "712  0.006595  0.019826  1.265464  0.190505  0.299089  0.062021  0.005090   \n",
              "713  0.009032  0.035858  1.539750  0.210566  0.430530  0.063192  0.004492   \n",
              "\n",
              "     exame_26  \n",
              "0    0.162200  \n",
              "1    0.123800  \n",
              "2    0.144400  \n",
              "3    0.209800  \n",
              "4    0.137400  \n",
              "..        ...  \n",
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              "710  0.159382  \n",
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              "713  0.151724  \n",
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              "      <td>0.059630</td>\n",
              "      <td>1.156000</td>\n",
              "      <td>0.259700</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>0.209800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.017560</td>\n",
              "      <td>0.781300</td>\n",
              "      <td>0.180900</td>\n",
              "      <td>0.236400</td>\n",
              "      <td>0.058830</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>0.137400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>709</th>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.020503</td>\n",
              "      <td>0.976907</td>\n",
              "      <td>0.229856</td>\n",
              "      <td>0.426725</td>\n",
              "      <td>0.072472</td>\n",
              "      <td>0.003724</td>\n",
              "      <td>0.172223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>710</th>\n",
              "      <td>0.008699</td>\n",
              "      <td>0.020727</td>\n",
              "      <td>1.121515</td>\n",
              "      <td>0.181119</td>\n",
              "      <td>0.304755</td>\n",
              "      <td>0.063009</td>\n",
              "      <td>0.004551</td>\n",
              "      <td>0.159382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>0.006461</td>\n",
              "      <td>0.023734</td>\n",
              "      <td>1.427416</td>\n",
              "      <td>0.211881</td>\n",
              "      <td>0.370110</td>\n",
              "      <td>0.060236</td>\n",
              "      <td>0.003742</td>\n",
              "      <td>0.141447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>0.006595</td>\n",
              "      <td>0.019826</td>\n",
              "      <td>1.265464</td>\n",
              "      <td>0.190505</td>\n",
              "      <td>0.299089</td>\n",
              "      <td>0.062021</td>\n",
              "      <td>0.005090</td>\n",
              "      <td>0.132457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>713</th>\n",
              "      <td>0.009032</td>\n",
              "      <td>0.035858</td>\n",
              "      <td>1.539750</td>\n",
              "      <td>0.210566</td>\n",
              "      <td>0.430530</td>\n",
              "      <td>0.063192</td>\n",
              "      <td>0.004492</td>\n",
              "      <td>0.151724</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>714 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13883ad6-ef38-4ccf-921b-16f7cc1e3e83')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e205fdb1-3150-4c4f-8b55-13d7a2b68475')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e205fdb1-3150-4c4f-8b55-13d7a2b68475 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13883ad6-ef38-4ccf-921b-16f7cc1e3e83 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13883ad6-ef38-4ccf-921b-16f7cc1e3e83');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 947
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R9cQRoHzBCrM"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "x = MinMaxScaler().fit_transform(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0wD8x6b6cOy"
      },
      "source": [
        "# **Choosing the best model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTYUlD3oI5Df"
      },
      "source": [
        "### Baseline performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qXxQtcsu7XRV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1bef48c-4e2e-4928-ad99-66312ca2e153"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A acurácia com o modelo dummy classifier é: 44.6512%\n"
          ]
        }
      ],
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=0)\n",
        "\n",
        "dummy_stratified = DummyClassifier()\n",
        "dummy_stratified.fit(x_train, y_train)\n",
        "scoredummy = dummy_stratified.score(x_test, y_test) * 100\n",
        "print(\"A acurácia com o modelo dummy classifier é: {:.4f}%\".format(scoredummy))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBWJuIVlkh5F"
      },
      "source": [
        "### Comparing and validating classifiers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7xeOmJOhgEBd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03a6ea5d-07c4-49c7-bf33-086e236f117a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                           Classifier   Accuracy   ROC AUC\n",
            "0              RandomForestClassifier(random_state=0)  85.850939  0.911685\n",
            "1                  LogisticRegression(random_state=0)  78.141628  0.873793\n",
            "2               SVC(probability=True, random_state=0)  87.112676  0.929662\n",
            "3                              KNeighborsClassifier()  79.557903  0.867123\n",
            "4                                        GaussianNB()  71.868153  0.802303\n",
            "5                       MLPClassifier(random_state=0)  84.313380  0.911426\n",
            "6                  AdaBoostClassifier(random_state=0)  83.890845  0.903715\n",
            "7              DecisionTreeClassifier(random_state=0)  78.706964  0.744933\n",
            "8          GradientBoostingClassifier(random_state=0)  85.702269  0.916829\n",
            "9                ExtraTreesClassifier(random_state=0)  86.696009  0.924948\n",
            "10                  BaggingClassifier(random_state=0)  82.767997  0.882679\n",
            "11  VotingClassifier(estimators=[('rf', RandomFore...  86.829030  0.928828\n",
            "12  StackingClassifier(estimators=[('rf', RandomFo...  86.555164  0.913452\n",
            "13     HistGradientBoostingClassifier(random_state=0)  86.967919  0.928353\n"
          ]
        }
      ],
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier, StackingClassifier, BaggingClassifier, GradientBoostingClassifier, ExtraTreesClassifier, AdaBoostClassifier, HistGradientBoostingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "classifiers = [\n",
        "    RandomForestClassifier(random_state=0),\n",
        "    LogisticRegression(random_state=0),\n",
        "    SVC(probability=True, random_state=0),\n",
        "    KNeighborsClassifier(),\n",
        "    GaussianNB(),\n",
        "    MLPClassifier(random_state=0),\n",
        "    AdaBoostClassifier(random_state=0),\n",
        "    DecisionTreeClassifier(random_state=0),\n",
        "    GradientBoostingClassifier(random_state=0),\n",
        "    ExtraTreesClassifier(random_state=0),\n",
        "    BaggingClassifier(random_state=0),\n",
        "    VotingClassifier(estimators=[('rf', RandomForestClassifier(random_state=0)), ('svc', SVC(probability=True, random_state=0)), ('lr', LogisticRegression(random_state=0))], voting='soft'),\n",
        "    StackingClassifier(estimators=[('rf', RandomForestClassifier(random_state=0)), ('svc', SVC(probability=True, random_state=0)), ('lr', LogisticRegression(random_state=0))], final_estimator=RandomForestClassifier(random_state=0)),\n",
        "    HistGradientBoostingClassifier(random_state=0),\n",
        "]\n",
        "\n",
        "results = []\n",
        "outer_cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)\n",
        "\n",
        "for clf in classifiers:\n",
        "    inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)\n",
        "\n",
        "    # Initialize lists to store inner loop results\n",
        "    inner_results = []\n",
        "    inner_roc_aucs = []\n",
        "\n",
        "    # Perform inner loop cross-validation\n",
        "    for train_index, test_index in inner_cv.split(x, y):\n",
        "        x_train, x_test = x[train_index], x[test_index]\n",
        "        y_train, y_test = y[train_index], y[test_index]\n",
        "\n",
        "        # Fit the classifier on the inner training data\n",
        "        clf.fit(x_train, y_train)\n",
        "\n",
        "        # Evaluate the classifier on the inner test data to get accuracy and ROC AUC\n",
        "        accuracy_inner = clf.score(x_test, y_test)\n",
        "        probas = clf.predict_proba(x_test)\n",
        "        roc_auc_inner = roc_auc_score(y_test, probas[:, 1])\n",
        "\n",
        "        inner_results.append(accuracy_inner)\n",
        "        inner_roc_aucs.append(roc_auc_inner)\n",
        "\n",
        "    # Calculate the mean accuracy and ROC AUC across all inner folds\n",
        "    mean_accuracy = sum(inner_results) / len(inner_results)\n",
        "    mean_roc_auc = sum(inner_roc_aucs) / len(inner_roc_aucs)\n",
        "\n",
        "    # Evaluate the classifier using the outer loop cross-validation\n",
        "    outer_results = cross_val_score(clf, x, y, cv=outer_cv)\n",
        "    media = outer_results.mean() * 100\n",
        "\n",
        "    results.append((str(clf), media, mean_roc_auc))\n",
        "\n",
        "df = pd.DataFrame(results, columns=[\"Classifier\", \"Accuracy\", \"ROC AUC\"])\n",
        "print(df)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0YacY17jh23G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "dc9f53f6-3093-4f13-e6e5-6eb2ba1c6236"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           Classifier   Accuracy   ROC AUC\n",
              "2               SVC(probability=True, random_state=0)  87.112676  0.929662\n",
              "13     HistGradientBoostingClassifier(random_state=0)  86.967919  0.928353\n",
              "11  VotingClassifier(estimators=[('rf', RandomFore...  86.829030  0.928828\n",
              "9                ExtraTreesClassifier(random_state=0)  86.696009  0.924948\n",
              "12  StackingClassifier(estimators=[('rf', RandomFo...  86.555164  0.913452\n",
              "0              RandomForestClassifier(random_state=0)  85.850939  0.911685\n",
              "8          GradientBoostingClassifier(random_state=0)  85.702269  0.916829\n",
              "5                       MLPClassifier(random_state=0)  84.313380  0.911426\n",
              "6                  AdaBoostClassifier(random_state=0)  83.890845  0.903715\n",
              "10                  BaggingClassifier(random_state=0)  82.767997  0.882679\n",
              "3                              KNeighborsClassifier()  79.557903  0.867123\n",
              "7              DecisionTreeClassifier(random_state=0)  78.706964  0.744933\n",
              "1                  LogisticRegression(random_state=0)  78.141628  0.873793\n",
              "4                                        GaussianNB()  71.868153  0.802303"
            ],
            "text/html": [
              "\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Classifier</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SVC(probability=True, random_state=0)</td>\n",
              "      <td>87.112676</td>\n",
              "      <td>0.929662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>HistGradientBoostingClassifier(random_state=0)</td>\n",
              "      <td>86.967919</td>\n",
              "      <td>0.928353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>VotingClassifier(estimators=[('rf', RandomFore...</td>\n",
              "      <td>86.829030</td>\n",
              "      <td>0.928828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ExtraTreesClassifier(random_state=0)</td>\n",
              "      <td>86.696009</td>\n",
              "      <td>0.924948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>StackingClassifier(estimators=[('rf', RandomFo...</td>\n",
              "      <td>86.555164</td>\n",
              "      <td>0.913452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestClassifier(random_state=0)</td>\n",
              "      <td>85.850939</td>\n",
              "      <td>0.911685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GradientBoostingClassifier(random_state=0)</td>\n",
              "      <td>85.702269</td>\n",
              "      <td>0.916829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>MLPClassifier(random_state=0)</td>\n",
              "      <td>84.313380</td>\n",
              "      <td>0.911426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AdaBoostClassifier(random_state=0)</td>\n",
              "      <td>83.890845</td>\n",
              "      <td>0.903715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>BaggingClassifier(random_state=0)</td>\n",
              "      <td>82.767997</td>\n",
              "      <td>0.882679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>KNeighborsClassifier()</td>\n",
              "      <td>79.557903</td>\n",
              "      <td>0.867123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DecisionTreeClassifier(random_state=0)</td>\n",
              "      <td>78.706964</td>\n",
              "      <td>0.744933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LogisticRegression(random_state=0)</td>\n",
              "      <td>78.141628</td>\n",
              "      <td>0.873793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GaussianNB()</td>\n",
              "      <td>71.868153</td>\n",
              "      <td>0.802303</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 951
        }
      ],
      "source": [
        "df.sort_values(\"Accuracy\", ascending = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T05-KED0j2SX"
      },
      "source": [
        "The best performance model in both metrics is SVC"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_r2xvNkfSBf"
      },
      "source": [
        "# **Hyperparameters tuning**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc = (C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True,\n",
        "    probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False,\n",
        "    max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)\n",
        "#defalt SVC parameters"
      ],
      "metadata": {
        "id": "m5reyg1xxgwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Goal is to reach at least 90% of accuracy"
      ],
      "metadata": {
        "id": "Q0hKWoWtdox8"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgqZDp_HdDVw"
      },
      "source": [
        "### Using an optimization library for scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kGoWQ-SKc4A9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "105f6c49-b882-4491-bee4-849273245042"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-optimize in /usr/local/lib/python3.10/dist-packages (0.9.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.3.1)\n",
            "Requirement already satisfied: pyaml>=16.9 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (23.7.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.22.4)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.2.2)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from pyaml>=16.9->scikit-optimize) (6.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->scikit-optimize) (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install scikit-optimize"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
        "from skopt import BayesSearchCV\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "espaco_de_parametros = {\n",
        "    \"C\": (1, 10.0),            # Regularization parameter\n",
        "    \"kernel\": [\"linear\", \"rbf\"],  # Kernel type\n",
        "    \"gamma\": (1e-6, 1.0, \"log-uniform\")  # Kernel coefficient\n",
        "}\n",
        "\n",
        "outer_cv = StratifiedKFold(n_splits=20, shuffle=True, random_state=0)  # Outer cross-validation\n",
        "results = []\n",
        "\n",
        "for train_index, test_index in outer_cv.split(x, y):\n",
        "    x_train, x_test = x[train_index], x[test_index]\n",
        "    y_train, y_test = y[train_index], y[test_index]\n",
        "\n",
        "    SVC_model = SVC(probability=True, random_state=0)\n",
        "\n",
        "    busca = BayesSearchCV(\n",
        "        SVC_model,\n",
        "        espaco_de_parametros,\n",
        "        n_iter=20,\n",
        "        cv=3,  # Inner cross-validation with 3 folds\n",
        "        random_state=0\n",
        "    )\n",
        "    busca.fit(x_train, y_train)\n",
        "\n",
        "    best_model = busca.best_estimator_\n",
        "\n",
        "    # Inner cross-validation to get accuracy and roc_auc\n",
        "    inner_cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=0)\n",
        "    inner_results = cross_val_score(best_model, x_train, y_train, cv=inner_cv)\n",
        "\n",
        "    probas = best_model.fit(x_train, y_train).predict_proba(x_train)\n",
        "    roc_auc = roc_auc_score(y_train, probas[:, 1])\n",
        "\n",
        "    media = inner_results.mean() * 100\n",
        "\n",
        "    results.append((str(best_model), media, roc_auc))\n",
        "\n",
        "df = pd.DataFrame(results, columns=[\"Classifier\", \"Accuracy\", \"ROC AUC\"])\n",
        "df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "_2_0gze-5Foa",
        "outputId": "def18bbc-0526-4d4f-9ba6-38fd382986ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:449: UserWarning: The objective has been evaluated at this point before.\n",
            "  warnings.warn(\"The objective has been evaluated \"\n",
            "/usr/local/lib/python3.10/dist-packages/skopt/optimizer/optimizer.py:449: UserWarning: The objective has been evaluated at this point before.\n",
            "  warnings.warn(\"The objective has been evaluated \"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           Classifier   Accuracy   ROC AUC\n",
              "0   SVC(C=9.704181023958698, gamma=0.0346192325645...  83.923304  0.919975\n",
              "1   SVC(C=10.0, gamma=0.011612956177990073, kernel...  85.840708  0.923948\n",
              "2   SVC(C=9.704181023958698, gamma=0.0346192325645...  84.070796  0.918414\n",
              "3   SVC(C=8.543497442978808, gamma=0.8858176376886...  86.430678  0.948547\n",
              "4   SVC(C=9.704181023958698, gamma=0.0346192325645...  84.070796  0.920511\n",
              "5   SVC(C=8.543497442978808, gamma=0.8858176376886...  85.250737  0.951806\n",
              "6   SVC(C=10.0, gamma=0.02015594928790446, kernel=...  84.218289  0.922512\n",
              "7   SVC(C=10.0, gamma=1.0, probability=True, rando...  86.283186  0.947721\n",
              "8   SVC(C=9.704181023958698, gamma=0.0346192325645...  86.430678  0.920541\n",
              "9   SVC(C=10.0, gamma=1.0, probability=True, rando...  85.693215  0.948347\n",
              "10  SVC(C=8.543497442978808, gamma=0.8858176376886...  85.693215  0.948156\n",
              "11  SVC(C=8.543497442978808, gamma=0.8858176376886...  85.250737  0.945032\n",
              "12  SVC(C=10.0, gamma=1.0, probability=True, rando...  86.578171  0.949557\n",
              "13  SVC(C=10.0, gamma=1.0, probability=True, rando...  87.168142  0.951723\n",
              "14  SVC(C=8.543497442978808, gamma=0.8858176376886...  86.158434  0.947076\n",
              "15  SVC(C=8.543497442978808, gamma=0.8858176376886...  85.569114  0.945749\n",
              "16  SVC(C=8.543497442978808, gamma=0.8858176376886...  85.862150  0.946130\n",
              "17  SVC(C=8.543497442978808, gamma=0.8858176376886...  87.040141  0.951258\n",
              "18  SVC(C=8.543497442978808, gamma=0.8858176376886...  87.183086  0.949627\n",
              "19  SVC(C=8.543497442978808, gamma=0.8858176376886...  86.006394  0.947961"
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              "      <th>Classifier</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SVC(C=9.704181023958698, gamma=0.0346192325645...</td>\n",
              "      <td>83.923304</td>\n",
              "      <td>0.919975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SVC(C=10.0, gamma=0.011612956177990073, kernel...</td>\n",
              "      <td>85.840708</td>\n",
              "      <td>0.923948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SVC(C=9.704181023958698, gamma=0.0346192325645...</td>\n",
              "      <td>84.070796</td>\n",
              "      <td>0.918414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>86.430678</td>\n",
              "      <td>0.948547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SVC(C=9.704181023958698, gamma=0.0346192325645...</td>\n",
              "      <td>84.070796</td>\n",
              "      <td>0.920511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>85.250737</td>\n",
              "      <td>0.951806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SVC(C=10.0, gamma=0.02015594928790446, kernel=...</td>\n",
              "      <td>84.218289</td>\n",
              "      <td>0.922512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SVC(C=10.0, gamma=1.0, probability=True, rando...</td>\n",
              "      <td>86.283186</td>\n",
              "      <td>0.947721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>SVC(C=9.704181023958698, gamma=0.0346192325645...</td>\n",
              "      <td>86.430678</td>\n",
              "      <td>0.920541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SVC(C=10.0, gamma=1.0, probability=True, rando...</td>\n",
              "      <td>85.693215</td>\n",
              "      <td>0.948347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>85.693215</td>\n",
              "      <td>0.948156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>85.250737</td>\n",
              "      <td>0.945032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>SVC(C=10.0, gamma=1.0, probability=True, rando...</td>\n",
              "      <td>86.578171</td>\n",
              "      <td>0.949557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>SVC(C=10.0, gamma=1.0, probability=True, rando...</td>\n",
              "      <td>87.168142</td>\n",
              "      <td>0.951723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>86.158434</td>\n",
              "      <td>0.947076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>85.569114</td>\n",
              "      <td>0.945749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>85.862150</td>\n",
              "      <td>0.946130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>87.040141</td>\n",
              "      <td>0.951258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>87.183086</td>\n",
              "      <td>0.949627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
              "      <td>86.006394</td>\n",
              "      <td>0.947961</td>\n",
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              "  </tbody>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b1abbf0a-a0dc-406f-8170-95173c44e6b1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b1abbf0a-a0dc-406f-8170-95173c44e6b1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 983
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sort_values(by='Accuracy', ascending=False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uvJE8avk5hJX",
        "outputId": "9a5a2e7d-d667-4e12-a953-8ac2f3e03942"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           Classifier   Accuracy   ROC AUC\n",
              "18  SVC(C=8.543497442978808, gamma=0.8858176376886...  87.183086  0.949627\n",
              "13  SVC(C=10.0, gamma=1.0, probability=True, rando...  87.168142  0.951723\n",
              "17  SVC(C=8.543497442978808, gamma=0.8858176376886...  87.040141  0.951258\n",
              "12  SVC(C=10.0, gamma=1.0, probability=True, rando...  86.578171  0.949557\n",
              "3   SVC(C=8.543497442978808, gamma=0.8858176376886...  86.430678  0.948547"
            ],
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              "      <td>SVC(C=8.543497442978808, gamma=0.8858176376886...</td>\n",
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              "      <th>13</th>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dd61dff1-48b4-45cf-9183-ffed5e577ce9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dd61dff1-48b4-45cf-9183-ffed5e577ce9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 984
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_MF45FO4yJ6",
        "outputId": "bfc66847-7769-4a8d-8ef8-ff0453f154ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('C', 8.543497442978808),\n",
              "             ('gamma', 0.8858176376886256),\n",
              "             ('kernel', 'rbf')])"
            ]
          },
          "metadata": {},
          "execution_count": 985
        }
      ],
      "source": [
        "bestSVCparams = busca.best_params_\n",
        "bestSVCparams"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOCWtIkwIgDmIkE22ggjgln"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}