01-Multiclass-Problems.ipynb

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        "<a href=\"https://colab.research.google.com/github/devdebdeb/ML-Classificacao/blob/main/Problemas_Multiclasse.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "O9eI8l8cio0C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
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        "id": "6DNHg_U3b8Lw"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "##Análise exploratória"
      ],
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      "source": [
        "###Carregando os dados"
      ],
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      }
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      "cell_type": "code",
      "source": [
        "students = pd.read_csv('https://raw.githubusercontent.com/alura-cursos/classificacao_multiclasse/main/Dados/dados_estudantes.csv')"
      ],
      "metadata": {
        "id": "fzgeHc5ItXHh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "students.head()"
      ],
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              "  Estado civil Migração       Sexo  Idade na matrícula Estrangeiro  \\\n",
              "0     Solteiro      Sim  Masculino                  20         Não   \n",
              "1     Solteiro      Sim  Masculino                  19         Não   \n",
              "2     Solteiro      Sim  Masculino                  19         Não   \n",
              "3     Solteiro      Sim   Feminino                  20         Não   \n",
              "4       Casado      Não   Feminino                  45         Não   \n",
              "\n",
              "  Necessidades educacionais especiais Devedor Taxas de matrícula em dia  \\\n",
              "0                                 Não     Não                       Sim   \n",
              "1                                 Não     Não                       Não   \n",
              "2                                 Não     Não                       Não   \n",
              "3                                 Não     Não                       Sim   \n",
              "4                                 Não     Não                       Sim   \n",
              "\n",
              "  Bolsista  Taxa de desemprego  ...  disciplinas 1º semestre (aprovadas)  \\\n",
              "0      Não                10.8  ...                                    0   \n",
              "1      Não                13.9  ...                                    6   \n",
              "2      Não                10.8  ...                                    0   \n",
              "3      Não                 9.4  ...                                    6   \n",
              "4      Não                13.9  ...                                    5   \n",
              "\n",
              "   disciplinas 1º semestre (notas) disciplinas 1º semestre (sem avaliações)  \\\n",
              "0                         0.000000                                        0   \n",
              "1                        14.000000                                        0   \n",
              "2                         0.000000                                        0   \n",
              "3                        13.428571                                        0   \n",
              "4                        12.333333                                        0   \n",
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              "  disciplinas 2º semestre (creditadas) disciplinas 2º semestre (matriculadas)  \\\n",
              "0                                    0                                      0   \n",
              "1                                    0                                      6   \n",
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              "3                                    0                                      6   \n",
              "4                                    0                                      6   \n",
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              "   disciplinas 2º semestre (avaliações)  disciplinas 2º semestre (aprovadas)  \\\n",
              "0                                     0                                    0   \n",
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              "   disciplinas 2º semestre (notas)  disciplinas 2º semestre (sem avaliações)  \\\n",
              "0                         0.000000                                         0   \n",
              "1                        13.666667                                         0   \n",
              "2                         0.000000                                         0   \n",
              "3                        12.400000                                         0   \n",
              "4                        13.000000                                         0   \n",
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              "       Target  \n",
              "0  Desistente  \n",
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              "4    Graduado  \n",
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        "###Entendendo os dados"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4424 entries, 0 to 4423\n",
            "Data columns (total 28 columns):\n",
            " #   Column                                    Non-Null Count  Dtype  \n",
            "---  ------                                    --------------  -----  \n",
            " 0   Estado civil                              4424 non-null   object \n",
            " 1   Migração                                  4424 non-null   object \n",
            " 2   Sexo                                      4424 non-null   object \n",
            " 3   Idade na matrícula                        4424 non-null   int64  \n",
            " 4   Estrangeiro                               4424 non-null   object \n",
            " 5   Necessidades educacionais especiais       4424 non-null   object \n",
            " 6   Devedor                                   4424 non-null   object \n",
            " 7   Taxas de matrícula em dia                 4424 non-null   object \n",
            " 8   Bolsista                                  4424 non-null   object \n",
            " 9   Taxa de desemprego                        4424 non-null   float64\n",
            " 10  Taxa de inflação                          4424 non-null   float64\n",
            " 11  PIB                                       4424 non-null   float64\n",
            " 12  Curso                                     4424 non-null   object \n",
            " 13  Período                                   4424 non-null   object \n",
            " 14  Qualificação prévia                       4424 non-null   object \n",
            " 15  disciplinas 1º semestre (creditadas)      4424 non-null   int64  \n",
            " 16  disciplinas 1º semestre (matriculadas)    4424 non-null   int64  \n",
            " 17  disciplinas 1º semestre (avaliações)      4424 non-null   int64  \n",
            " 18  disciplinas 1º semestre (aprovadas)       4424 non-null   int64  \n",
            " 19  disciplinas 1º semestre (notas)           4424 non-null   float64\n",
            " 20  disciplinas 1º semestre (sem avaliações)  4424 non-null   int64  \n",
            " 21  disciplinas 2º semestre (creditadas)      4424 non-null   int64  \n",
            " 22  disciplinas 2º semestre (matriculadas)    4424 non-null   int64  \n",
            " 23  disciplinas 2º semestre (avaliações)      4424 non-null   int64  \n",
            " 24  disciplinas 2º semestre (aprovadas)       4424 non-null   int64  \n",
            " 25  disciplinas 2º semestre (notas)           4424 non-null   float64\n",
            " 26  disciplinas 2º semestre (sem avaliações)  4424 non-null   int64  \n",
            " 27  Target                                    4424 non-null   object \n",
            "dtypes: float64(5), int64(11), object(12)\n",
            "memory usage: 967.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "students['Target'].unique()\n",
        "#Três valores que queremos prever"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-c7lqHgUt95f",
        "outputId": "9fe54048-0098-4b04-cdba-a6e690ba69e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Desistente', 'Graduado', 'Matriculado'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "Y9IOeyHH99qY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Análise inicial dos dados"
      ],
      "metadata": {
        "id": "N9kf-YFzvFqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "students.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lu5nBUPtu17J",
        "outputId": "9d2c8e71-f574-4e2f-dd8e-b60c13664023"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Idade na matrícula  Taxa de desemprego  Taxa de inflação          PIB  \\\n",
              "count         4424.000000         4424.000000       4424.000000  4424.000000   \n",
              "mean            23.265145           11.566139          1.228029     0.001969   \n",
              "std              7.587816            2.663850          1.382711     2.269935   \n",
              "min             17.000000            7.600000         -0.800000    -4.060000   \n",
              "25%             19.000000            9.400000          0.300000    -1.700000   \n",
              "50%             20.000000           11.100000          1.400000     0.320000   \n",
              "75%             25.000000           13.900000          2.600000     1.790000   \n",
              "max             70.000000           16.200000          3.700000     3.510000   \n",
              "\n",
              "       disciplinas 1º semestre (creditadas)  \\\n",
              "count                           4424.000000   \n",
              "mean                               0.709991   \n",
              "std                                2.360507   \n",
              "min                                0.000000   \n",
              "25%                                0.000000   \n",
              "50%                                0.000000   \n",
              "75%                                0.000000   \n",
              "max                               20.000000   \n",
              "\n",
              "       disciplinas 1º semestre (matriculadas)  \\\n",
              "count                             4424.000000   \n",
              "mean                                 6.270570   \n",
              "std                                  2.480178   \n",
              "min                                  0.000000   \n",
              "25%                                  5.000000   \n",
              "50%                                  6.000000   \n",
              "75%                                  7.000000   \n",
              "max                                 26.000000   \n",
              "\n",
              "       disciplinas 1º semestre (avaliações)  \\\n",
              "count                           4424.000000   \n",
              "mean                               8.299051   \n",
              "std                                4.179106   \n",
              "min                                0.000000   \n",
              "25%                                6.000000   \n",
              "50%                                8.000000   \n",
              "75%                               10.000000   \n",
              "max                               45.000000   \n",
              "\n",
              "       disciplinas 1º semestre (aprovadas)  disciplinas 1º semestre (notas)  \\\n",
              "count                          4424.000000                      4424.000000   \n",
              "mean                              4.706600                        10.640822   \n",
              "std                               3.094238                         4.843663   \n",
              "min                               0.000000                         0.000000   \n",
              "25%                               3.000000                        11.000000   \n",
              "50%                               5.000000                        12.285714   \n",
              "75%                               6.000000                        13.400000   \n",
              "max                              26.000000                        18.875000   \n",
              "\n",
              "       disciplinas 1º semestre (sem avaliações)  \\\n",
              "count                               4424.000000   \n",
              "mean                                   0.137658   \n",
              "std                                    0.690880   \n",
              "min                                    0.000000   \n",
              "25%                                    0.000000   \n",
              "50%                                    0.000000   \n",
              "75%                                    0.000000   \n",
              "max                                   12.000000   \n",
              "\n",
              "       disciplinas 2º semestre (creditadas)  \\\n",
              "count                           4424.000000   \n",
              "mean                               0.541817   \n",
              "std                                1.918546   \n",
              "min                                0.000000   \n",
              "25%                                0.000000   \n",
              "50%                                0.000000   \n",
              "75%                                0.000000   \n",
              "max                               19.000000   \n",
              "\n",
              "       disciplinas 2º semestre (matriculadas)  \\\n",
              "count                             4424.000000   \n",
              "mean                                 6.232143   \n",
              "std                                  2.195951   \n",
              "min                                  0.000000   \n",
              "25%                                  5.000000   \n",
              "50%                                  6.000000   \n",
              "75%                                  7.000000   \n",
              "max                                 23.000000   \n",
              "\n",
              "       disciplinas 2º semestre (avaliações)  \\\n",
              "count                           4424.000000   \n",
              "mean                               8.063291   \n",
              "std                                3.947951   \n",
              "min                                0.000000   \n",
              "25%                                6.000000   \n",
              "50%                                8.000000   \n",
              "75%                               10.000000   \n",
              "max                               33.000000   \n",
              "\n",
              "       disciplinas 2º semestre (aprovadas)  disciplinas 2º semestre (notas)  \\\n",
              "count                          4424.000000                      4424.000000   \n",
              "mean                              4.435805                        10.230206   \n",
              "std                               3.014764                         5.210808   \n",
              "min                               0.000000                         0.000000   \n",
              "25%                               2.000000                        10.750000   \n",
              "50%                               5.000000                        12.200000   \n",
              "75%                               6.000000                        13.333333   \n",
              "max                              20.000000                        18.571429   \n",
              "\n",
              "       disciplinas 2º semestre (sem avaliações)  \n",
              "count                               4424.000000  \n",
              "mean                                   0.150316  \n",
              "std                                    0.753774  \n",
              "min                                    0.000000  \n",
              "25%                                    0.000000  \n",
              "50%                                    0.000000  \n",
              "75%                                    0.000000  \n",
              "max                                   12.000000  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Idade na matrícula</th>\n",
              "      <th>Taxa de desemprego</th>\n",
              "      <th>Taxa de inflação</th>\n",
              "      <th>PIB</th>\n",
              "      <th>disciplinas 1º semestre (creditadas)</th>\n",
              "      <th>disciplinas 1º semestre (matriculadas)</th>\n",
              "      <th>disciplinas 1º semestre (avaliações)</th>\n",
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              "      <th>disciplinas 2º semestre (matriculadas)</th>\n",
              "      <th>disciplinas 2º semestre (avaliações)</th>\n",
              "      <th>disciplinas 2º semestre (aprovadas)</th>\n",
              "      <th>disciplinas 2º semestre (notas)</th>\n",
              "      <th>disciplinas 2º semestre (sem avaliações)</th>\n",
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4424.000000</td>\n",
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              "      <td>4424.000000</td>\n",
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              "      <td>4424.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23.265145</td>\n",
              "      <td>11.566139</td>\n",
              "      <td>1.228029</td>\n",
              "      <td>0.001969</td>\n",
              "      <td>0.709991</td>\n",
              "      <td>6.270570</td>\n",
              "      <td>8.299051</td>\n",
              "      <td>4.706600</td>\n",
              "      <td>10.640822</td>\n",
              "      <td>0.137658</td>\n",
              "      <td>0.541817</td>\n",
              "      <td>6.232143</td>\n",
              "      <td>8.063291</td>\n",
              "      <td>4.435805</td>\n",
              "      <td>10.230206</td>\n",
              "      <td>0.150316</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.587816</td>\n",
              "      <td>2.663850</td>\n",
              "      <td>1.382711</td>\n",
              "      <td>2.269935</td>\n",
              "      <td>2.360507</td>\n",
              "      <td>2.480178</td>\n",
              "      <td>4.179106</td>\n",
              "      <td>3.094238</td>\n",
              "      <td>4.843663</td>\n",
              "      <td>0.690880</td>\n",
              "      <td>1.918546</td>\n",
              "      <td>2.195951</td>\n",
              "      <td>3.947951</td>\n",
              "      <td>3.014764</td>\n",
              "      <td>5.210808</td>\n",
              "      <td>0.753774</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>7.600000</td>\n",
              "      <td>-0.800000</td>\n",
              "      <td>-4.060000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>-1.700000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>10.750000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>12.285714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
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              "      <td>12.200000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>13.900000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>1.790000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>10.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>26.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>18.875000</td>\n",
              "      <td>12.000000</td>\n",
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              "      <td>33.000000</td>\n",
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              "      <td>18.571429</td>\n",
              "      <td>12.000000</td>\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b0a639f4-7ba3-41fc-9a5f-5c175075820a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"students\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Idade na matr\\u00edcula\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1555.0477556027724,\n        \"min\": 7.587815615029815,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          23.265144665461122,\n          20.0,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Taxa de desemprego\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1560.4672615284294,\n        \"min\": 2.6638504843026354,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.56613924050633,\n          11.1,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Taxa de infla\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1563.62526797698,\n        \"min\": -0.8,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.228028933092224,\n          1.4,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PIB\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1564.014326687478,\n        \"min\": -4.06,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.001968806509945778,\n          0.32,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 1\\u00ba semestre (creditadas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1562.970047001889,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7099909584086799,\n          20.0,\n          2.3605066191181976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 1\\u00ba semestre (matriculadas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1561.4757357838098,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.2705696202531644,\n          6.0,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 1\\u00ba semestre (avalia\\u00e7\\u00f5es)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1560.067336557855,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.299050632911392,\n          8.0,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 1\\u00ba semestre (aprovadas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1561.726534694024,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.706600361663653,\n          5.0,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 1\\u00ba semestre (notas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1560.5421006201755,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.640821575154185,\n          12.285714285714286,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 1\\u00ba semestre (sem avalia\\u00e7\\u00f5es)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1563.4777882910946,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.13765822784810128,\n          12.0,\n          0.6908801837297979\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 2\\u00ba semestre (creditadas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1563.0499600803462,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5418173598553345,\n          19.0,\n          1.9185461437101994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 2\\u00ba semestre (matriculadas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1561.6389649622445,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.232142857142857,\n          6.0,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 2\\u00ba semestre (avalia\\u00e7\\u00f5es)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1560.6661512818223,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.063291139240507,\n          8.0,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 2\\u00ba semestre (aprovadas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1562.0890273342366,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.435804701627487,\n          5.0,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 2\\u00ba semestre (notas)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1560.5794744747732,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.230205722716985,\n          12.2,\n          4424.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disciplinas 2\\u00ba semestre (sem avalia\\u00e7\\u00f5es)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1563.4739639755792,\n        \"min\": 0.0,\n        \"max\": 4424.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.15031645569620253,\n          12.0,\n          0.7537740685130098\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colunas_categoricas = ['Estado civil', 'Migração', 'Sexo', 'Estrangeiro', 'Necessidades educacionais especiais', 'Devedor',  'Taxas de matrícula em dia', 'Bolsista', 'Curso', 'Período', 'Qualificação prévia', 'Target']\n",
        "\n",
        "students[colunas_categoricas].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GcptxTb9wrAA",
        "outputId": "90cb1e49-da8f-4ae6-df20-d347f1def21d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Estado civil Migração      Sexo Estrangeiro  \\\n",
              "count          4424     4424      4424        4424   \n",
              "unique            6        2         2           2   \n",
              "top        Solteiro      Sim  Feminino         Não   \n",
              "freq           3919     2426      2868        4314   \n",
              "\n",
              "       Necessidades educacionais especiais Devedor Taxas de matrícula em dia  \\\n",
              "count                                 4424    4424                      4424   \n",
              "unique                                   2       2                         2   \n",
              "top                                    Não     Não                       Sim   \n",
              "freq                                  4373    3921                      3896   \n",
              "\n",
              "       Bolsista       Curso Período  Qualificação prévia    Target  \n",
              "count      4424        4424    4424                 4424      4424  \n",
              "unique        2          17       2                   17         3  \n",
              "top         Não  Enfermagem  Diurno  Educação secundária  Graduado  \n",
              "freq       3325         766    3941                 3717      2209  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Estado civil</th>\n",
              "      <th>Migração</th>\n",
              "      <th>Sexo</th>\n",
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              "      <td>Graduado</td>\n",
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              "      <td>3919</td>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"students[colunas_categoricas]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Estado civil\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          \"3919\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Migra\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"2426\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"2868\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estrangeiro\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"4314\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Necessidades educacionais especiais\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"4373\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Devedor\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"3921\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Taxas de matr\\u00edcula em dia\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"3896\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bolsista\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"3325\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          17,\n          \"766\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Per\\u00edodo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"3941\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Qualifica\\u00e7\\u00e3o pr\\u00e9via\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          17,\n          \"3717\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"2209\",\n          \"4424\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Dados demograficos"
      ],
      "metadata": {
        "id": "USVXP745xt51"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "students['Estado civil'].value_counts(normalize=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "VJ5G97ZcxwTw",
        "outputId": "4afa5309-32bf-4295-be50-6e24f0f3965a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Estado civil\n",
              "Solteiro               88.584991\n",
              "Casado                  8.566908\n",
              "Divorciado              2.056962\n",
              "União Estável           0.565099\n",
              "Legalmente Separado     0.135624\n",
              "Viúvo                   0.090416\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estado civil</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Solteiro</th>\n",
              "      <td>88.584991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Casado</th>\n",
              "      <td>8.566908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Divorciado</th>\n",
              "      <td>2.056962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>União Estável</th>\n",
              "      <td>0.565099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Legalmente Separado</th>\n",
              "      <td>0.135624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Viúvo</th>\n",
              "      <td>0.090416</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "OqLTstcWyB9F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(students['Idade na matrícula'], bins=20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "W74vmhFwyIJ_",
        "outputId": "1d66be76-a751-448d-f277-82ee91f3d76f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "color_dict = {'Desistente': '#e34c42', 'Graduado': '#4dc471', 'Matriculado': '#3b71db'}\n",
        "sns.set_palette(list(color_dict.values()))"
      ],
      "metadata": {
        "id": "xLrC5tmQyxSA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(data=students, x='Idade na matrícula', hue='Target', kind='kde', fill=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xngEv4uJzJw5",
        "outputId": "cef37c64-4bd3-4c2b-ba01-f2624968b3a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 632x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "students['Estrangeiro'].value_counts(normalize=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IyMeguIgzrzD",
        "outputId": "2a7683cc-b906-4845-b240-49a4145e45dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Estrangeiro\n",
              "Não    97.513562\n",
              "Sim     2.486438\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estrangeiro</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Não</th>\n",
              "      <td>97.513562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sim</th>\n",
              "      <td>2.486438</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "students['Sexo'].value_counts(normalize=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHBDStLAz3Te",
        "outputId": "5c9ed3f4-b167-497a-90b2-5095c88b2bd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sexo\n",
              "Feminino     64.82821\n",
              "Masculino    35.17179\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sexo</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Feminino</th>\n",
              "      <td>64.82821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Masculino</th>\n",
              "      <td>35.17179</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Sexo', hue='Target', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GxoVs6G70Cx7",
        "outputId": "cc734d21-32d7-4b80-9a40-b291c3925daf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Dados socioeconômicos e macroeconômicos"
      ],
      "metadata": {
        "id": "BlXmyiEq0bhu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Devedor', hue='Target', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-R0B2ZoV0dfq",
        "outputId": "0c44a8b1-1556-4b43-9208-1d441f286357"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Taxas de matrícula em dia', hue='Target', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y12_XPdw06fk",
        "outputId": "57dea554-11c9-4d1d-cc61-9e684e79370f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Bolsista', hue='Target', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfBf-omx1EUK",
        "outputId": "cb7f6fc8-d407-485a-8334-bb88a009fce9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Dados no momento da matrícula"
      ],
      "metadata": {
        "id": "eGHNTqO82qCy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "lvPN3NZp2tee"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contagem = students.groupby(['Curso', 'Target']).size().reset_index(name='Contagem')\n",
        "\n",
        "contagem['Porcentagem'] = contagem.groupby('Curso')['Contagem'].transform(lambda x: (x/x.sum())*100)\n",
        "\n",
        "fig = px.bar(contagem, y='Curso', x='Porcentagem', color='Target', orientation='h', color_discrete_map={'Desistente': '#e34c42', 'Graduado': '#4dc471', 'Matriculado': '#3b71db'})\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHgtZrDS22-k",
        "outputId": "c10edaf2-dc5d-4408-de25-022bb6744f5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "###Dados acadêmicos"
      ],
      "metadata": {
        "id": "qjFjVoEy5Afv"
      }
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    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Target', y='disciplinas 1º semestre (notas)', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suBaI5RJ5C7Y",
        "outputId": "f8ac221d-3293-4056-e3f3-a730ac51a449"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Target', y='disciplinas 2º semestre (notas)', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oOe5mLdo5tjA",
        "outputId": "318fced0-4686-420c-da62-f8dc625a7aaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "students['Target'].value_counts(normalize=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8KOdr9g58FH",
        "outputId": "3c911eb8-7694-4837-a5f3-7af6fda56a16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Target\n",
              "Graduado       49.932188\n",
              "Desistente     32.120253\n",
              "Matriculado    17.947559\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Graduado</th>\n",
              "      <td>49.932188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Desistente</th>\n",
              "      <td>32.120253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Matriculado</th>\n",
              "      <td>17.947559</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Migração', hue='Target', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hU71dcR86pD_",
        "outputId": "8b97a1c3-3519-49bd-a8fe-bf3683d0a7e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JyZKkI0eOKDk5WceOHVN+fr7+9re/ae/evVq1apUKCgqUlpamtLQ05eXlSZLq16+vTp06afDgwdqzZ4/i4+M1YsQI9e3bV0FBQZKkJ598Ui4uLho0aJAOHjyo999/X3PnztWoUaPsNW0AAEpV//791bNnzxI/TmxsrPHFp7LGrpfM9u7dq/bt2xvvL4WUyMhITZo0SevWrZMkNWvWzGa7r776Sg8++KAkadWqVRoxYoQ6duwoJycn9e7dW/PmzTPG+vj4aOPGjRo+fLhatmypO+64Q9HR0XzlHgBuQ6X96J+bfcROWlqa8bDUX3/9VT4+PqpVq5aeeuopRUZGysPDo5grxbXYNRA9+OCDutqj1K7nMWuVKlXSu+++e9UxTZo00c6dO2+4PgAAitvPP/+stm3bytfXV6+99poaN24sV1dXHThwQEuWLNGdd95Z5CbE0sVlJOXLl7dDxebg0GuIAAC43QwbNkzlypXT3r171adPH9WvX181a9ZUjx499Pnnn6t79+6SJIvFokWLFumRRx6Rp6enXn31VRUUFGjQoEEKCQmRu7u76tatq7lz59rsv6CgQKNGjZKvr68qV66scePGFTnBUKNGDc2ZM8emrVmzZpo0aZLxfvbs2cbjs4KDgzVs2LAij8aKjY1VtWrV5OHhoV69eunMmTNF5rto0SLdddddcnFxUd26dbVy5cpb+O2VHAIRAACl5MyZM8YyDk9Pz8uOsVgsxs+TJk1Sr169dODAAQ0cOFCFhYWqWrWqPvzwQx06dEjR0dF65ZVX9MEHHxjbvP7664qNjdXSpUv1n//8R2fPntUnn3xyw7U6OTlp3rx5OnjwoJYvX66tW7dq3LhxRv/u3bs1aNAgjRgxQsnJyWrfvr2mTZtms49PPvlEL7zwgkaPHq3vvvtOzzzzjAYMGKCvvvrqhuspaQ7/tXsAAG4XP/74o6xWq+rWrWvTfscddygnJ0eSNHz4cM2YMUPSxS8GDRgwwGbs5MmTjZ9DQkKUkJCgDz74QH369JEkzZkzR1FRUXr00UclSYsXL9aXX355w7WOHDnS+LlGjRqaNm2ann32WS1cuFCSNHfuXHXq1MkISXXq1NGuXbsUFxdnbDdr1iz1799fw4YNk3RxrfDXX3+tWbNm2awhdgScIQIAwM727Nmj5ORkNWzYULm5uUb7nx9NdcmCBQvUsmVL+fn5ycvLS0uWLNGxY8ckXXzE1cmTJ41bz0gXn+Bwuf1cy+bNm9WxY0fdeeedqlChgp5++mmdOXNG58+flySlpKTYHEe6eKubP0tJSVHbtm1t2tq2bauUlJQbrqekEYgAACgltWrVksViUWpqqk17zZo1VatWLbm7u9u0//Wy2urVqzVmzBgNGjRIGzduVHJysgYMGGDcjuZ6OTk5FVlX9Of79R09elTdunVTkyZN9PHHHyspKUkLFiyQpBs+VllBIAIAoJRUrlxZDz30kN58802dO3fuhrePj49XmzZtNGzYMDVv3ly1atXSTz/9ZPT7+PioSpUqxgPOpYtPcPjzDYwlyc/PTydPnjTeZ2Vl6ciRI8b7pKQkFRYW6vXXX9e9996rOnXq6MSJEzb7qF+/vs1xJOnrr78uMiY+Pr7IHBo0aHCDMy95rCECAKAULVy4UG3btlWrVq00adIkNWnSRE5OTkpMTNT333+vli1bXnHb2rVra8WKFfryyy8VEhKilStXKjExUSEhIcaYF154QdOnT1ft2rVVr149zZ49WxkZGTb76dChg2JjY9W9e3f5+voqOjpazs7ORn+tWrWUn5+v+fPnq3v37oqPj9fixYtt9vH888+rbdu2mjVrlnr06KEvv/zSZv2QJI0dO1Z9+vRR8+bNFRYWps8++0xr1qzR5s2bb+E3WDI4QwQAQCm666679M033ygsLExRUVFq2rSpWrVqpfnz52vMmDGaOnXqFbd95pln9Oijj+rxxx9X69atdebMGWPB8iWjR4/W008/rcjISIWGhqpChQrq1auXzZioqCg98MAD6tatm7p27aqePXvqrrvuMvqbNm2q2bNna8aMGWrUqJFWrVqlmJgYm33ce++9euuttzR37lw1bdpUGzdu1Pjx423G9OzZU3PnztWsWbPUsGFD/etf/9KyZcuMmys7Eov1eu5+aHJZWVny8fFRZmamzcNlUTJK+06zjupm74AL3O5ycnJ05MgRhYSEyM3Nzd7lwM6u9u/hRv5+c4YIAACYHoEIAACYHoEIAACYHoEIAACYHoEIAACYHoEIAACYHoEIAACYHoEIAACYHoEIAACYHoEIAAATqFGjhubMmVNs++vfv7969ux5S/s4evSoLBaLkpOTi6WmW8HDXQEAt42eUV9fe1AxWhtz7w2N79+/v5YvX65nnnmmyMNShw8froULFyoyMlKxsbHX3NfRo0cVEhKib775Rs2aNbvm+MTERHl6et5QvWbCGSIAAEpRcHCwVq9erT/++MNoy8nJ0bvvvqtq1aoV+/Hy8vIkSX5+fvLw8Cj2/d8uCEQAAJSiFi1aKDg4WGvWrDHa1qxZo2rVqql58+ZGW1xcnO677z75+vqqcuXK6tatm3766SejPyQkRJLUvHlzWSwW4wnyly5lvfrqqwoKClLdunUlFb1klpGRoWeeeUYBAQFyc3NTo0aNtH79eknSpEmTipx1mjNnjmrUqHHFeV2rXknas2ePmjdvLjc3N7Vq1UrffPNNkf1s375d99xzj1xdXVWlShW9/PLLunDhwpV/ocWEQAQAQCkbOHCgli1bZrxfunSpBgwYYDPm3LlzGjVqlPbu3astW7bIyclJvXr1UmFhoaSL4UKSNm/erJMnT9oErC1btig1NVWbNm0yQs6fFRYWqnPnzoqPj9c777yjQ4cOafr06XJ2dr7pOV2r3uzsbHXr1k0NGjRQUlKSJk2apDFjxtjs47///a+6dOmiu+++W/v379eiRYv09ttva9q0aTdd1/ViDREAAKXsqaeeUlRUlH755RdJUnx8vFavXq1t27YZY3r37m2zzdKlS+Xn56dDhw6pUaNG8vPzkyRVrlxZgYGBNmM9PT3173//Wy4uLpc9/ubNm7Vnzx6lpKSoTp06kqSaNWve0pyuVe+7776rwsJCvf3223Jzc1PDhg3166+/aujQocY2CxcuVHBwsN58801ZLBbVq1dPJ06c0EsvvaTo6Gg5OZXceRzOEAEAUMr8/PzUtWtXxcbGatmyZeratavuuOMOmzGHDx/WE088oZo1a8rb29u4XHXs2LFr7r9x48ZXDEOSlJycrKpVqxphqDhcq96UlBQ1adJEbm5uxjahoaE2+0hJSVFoaKgsFovR1rZtW2VnZ+vXX38ttlovhzNEAADYwcCBAzVixAhJ0oIFC4r0d+/eXdWrV9dbb72loKAgFRYWqlGjRsYi6au51rfJ3N3dr9rv5OQkq9Vq05afn3/VbW6lXkfAGSIAAOygU6dOysvLU35+vsLDw236zpw5o9TUVI0fP14dO3ZU/fr19dtvv9mMuXQGqKCg4IaP3aRJE/3666/64YcfLtvv5+entLQ0m1B0tXsFXU+99evX17fffqucnByj7euvvy4yJiEhwea48fHxqlChgqpWrXojU7xhBCIAAOzA2dlZKSkpOnToUJHFzBUrVlTlypW1ZMkS/fjjj9q6datGjRplM8bf31/u7u6Ki4vTqVOnlJmZed3HfuCBB9SuXTv17t1bmzZt0pEjR7RhwwbFxcVJkh588EGdPn1aM2fO1E8//aQFCxZow4YNV9zf9dT75JNPymKxaPDgwTp06JC++OILzZo1y2bMsGHDdPz4cT333HP6/vvv9emnn2rixIkaNWpUia4fkrhkBgC4jdzojRLtzdvb+7LtTk5OWr16tZ5//nk1atRIdevW1bx584yv1ktSuXLlNG/ePE2ZMkXR0dG6//77bRZlX8vHH3+sMWPG6IknntC5c+dUq1YtTZ8+XdLFMzULFy7Ua6+9pqlTp6p3794aM2aMlixZctP1enl56bPPPtOzzz6r5s2bq0GDBpoxY4bNYuw777xTX3zxhcaOHaumTZuqUqVKGjRokMaPH3/d87pZFutfLxKiiKysLPn4+CgzM/OK/3hRfJ7+4mV7l+AQVnaZbu8SAIeUk5OjI0eOKCQkxGaBLszpav8ebuTvN5fMAACA6RGIAACA6RGIAACA6RGIAACA6RGIAABlEt8JglR8/w4IRACAMqV8+fKSpPPnz9u5EjiCS3fCvpUH00rchwgAUMY4OzvL19dX6enpkiQPDw+bZ1/BPAoLC3X69Gl5eHioXLlbizQEIgBAmXPp6e6XQhHMy8nJSdWqVbvlUEwgAgCUORaLRVWqVJG/v/81HzqK25uLi0uxPNaDQAQAKLOcnZ1vee0IILGoGgAAgEAEAABg10C0Y8cOde/eXUFBQbJYLFq7dq1Nv9VqVXR0tKpUqSJ3d3eFhYXp8OHDNmPOnj2riIgIeXt7y9fXV4MGDVJ2drbNmG+//Vb333+/3NzcFBwcrJkzZ5b01AAAQBli10B07tw5NW3aVAsWLLhs/8yZMzVv3jwtXrxYu3fvlqenp8LDw5WTk2OMiYiI0MGDB7Vp0yatX79eO3bs0JAhQ4z+rKwsPfzww6pevbqSkpL0z3/+U5MmTdKSJUtKfH4AAKBssOui6s6dO6tz586X7bNarZozZ47Gjx+vHj16SJJWrFihgIAArV27Vn379lVKSori4uKUmJioVq1aSZLmz5+vLl26aNasWQoKCtKqVauUl5enpUuXysXFRQ0bNlRycrJmz55tE5wAAIB5OewaoiNHjigtLU1hYWFGm4+Pj1q3bq2EhARJUkJCgnx9fY0wJElhYWFycnLS7t27jTHt2rWTi4uLMSY8PFypqan67bffLnvs3NxcZWVl2bwAAMDty2EDUVpamiQpICDApj0gIMDoS0tLk7+/v01/uXLlVKlSJZsxl9vHn4/xVzExMfLx8TFewcHBtz4hAADgsBw2ENlTVFSUMjMzjdfx48ftXRIAAChBDhuILt2W/dSpUzbtp06dMvoCAwOL3Lb9woULOnv2rM2Yy+3jz8f4K1dXV3l7e9u8AADA7cthA1FISIgCAwO1ZcsWoy0rK0u7d+9WaGioJCk0NFQZGRlKSkoyxmzdulWFhYVq3bq1MWbHjh02t3bftGmT6tatq4oVK5bSbAAAgCOzayDKzs5WcnKykpOTJV1cSJ2cnKxjx47JYrFo5MiRmjZtmtatW6cDBw6oX79+CgoKUs+ePSVJ9evXV6dOnTR48GDt2bNH8fHxGjFihPr27augoCBJ0pNPPikXFxcNGjRIBw8e1Pvvv6+5c+dq1KhRdpo1AABwNHb92v3evXvVvn174/2lkBIZGanY2FiNGzdO586d05AhQ5SRkaH77rtPcXFxcnNzM7ZZtWqVRowYoY4dO8rJyUm9e/fWvHnzjH4fHx9t3LhRw4cPV8uWLXXHHXcoOjqar9wDAACDxWq1Wu1dhKPLysqSj4+PMjMzWU9UCp7+4mV7l+AQVnaZbu8SAKBMu5G/3w67hggAAKC0EIgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDpEYgAAIDplbN3AQAA83j6i5ftXYJDWNllur1LwF9whggAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJieQweigoICTZgwQSEhIXJ3d9ddd92lqVOnymq1GmOsVquio6NVpUoVubu7KywsTIcPH7bZz9mzZxURESFvb2/5+vpq0KBBys7OLu3pAAAAB+XQgWjGjBlatGiR3nzzTaWkpGjGjBmaOXOm5s+fb4yZOXOm5s2bp8WLF2v37t3y9PRUeHi4cnJyjDERERE6ePCgNm3apPXr12vHjh0aMmSIPaYEAAAcUDl7F3A1u3btUo8ePdS1a1dJUo0aNfTee+9pz549ki6eHZozZ47Gjx+vHj16SJJWrFihgIAArV27Vn379lVKSori4uKUmJioVq1aSZLmz5+vLl26aNasWQoKCrLP5AAAgMNw6DNEbdq00ZYtW/TDDz9Ikvbv36///Oc/6ty5syTpyJEjSktLU1hYmLGNj4+PWrdurYSEBElSQkKCfH19jTAkSWFhYXJyctLu3bsve9zc3FxlZWXZvAAAwO3Loc8Qvfzyy8rKylK9evXk7OysgoICvfrqq4qIiJAkpaWlSZICAgJstgsICDD60tLS5O/vb9Nfrlw5VapUyRjzVzExMZo8eXJxTwcAADgohz5D9MEHH2jVqlV69913tW/fPi1fvlyzZs3S8uXLS/S4UVFRyszMNF7Hjx8v0eMBAAD7cugzRGPHjtXLL7+svn37SpIaN26sX375RTExMYqMjFRgYKAk6dSpU6pSpYqx3alTp9SsWTNJUmBgoNLT0232e+HCBZ09e9bY/q9cXV3l6upaAjMCAACOyKHPEJ0/f15OTrYlOjs7q7CwUJIUEhKiwMBAbdmyxejPysrS7t27FRoaKkkKDQ1VRkaGkpKSjDFbt25VYWGhWrduXQqzAAAAjs6hzxB1795dr776qqpVq6aGDRvqm2++0ezZszVw4EBJksVi0ciRIzVt2jTVrl1bISEhmjBhgoKCgtSzZ09JUv369dWpUycNHjxYixcvVn5+vkaMGKG+ffvyDTMAACDJwQPR/PnzNWHCBA0bNkzp6ekKCgrSM888o+joaGPMuHHjdO7cOQ0ZMkQZGRm67777FBcXJzc3N2PMqlWrNGLECHXs2FFOTk7q3bu35s2bZ48pAQAAB2Sx/vm2z7isrKws+fj4KDMzU97e3vYu57b39Bcv27sEh7Cyy3R7lwAUOz7fF/H5Lh038vfbodcQAQAAlAYCEQAAMD0CEQAAMD0CEQAAML2bCkQdOnRQRkZGkfasrCx16NDhVmsCAAAoVTcViLZt26a8vLwi7Tk5Odq5c+ctFwUAAFCabug+RN9++63x86FDh2wejlpQUKC4uDjdeeedxVcdANwmkiMft3cJjuHxEHtXAFzWDQWiZs2ayWKxyGKxXPbSmLu7u+bPn19sxQEAAJSGGwpER44ckdVqVc2aNbVnzx75+fkZfS4uLvL395ezs3OxFwkAAFCSbigQVa9eXZKMh6sCAADcDm76WWaHDx/WV199pfT09CIB6c/PGgMAAHB0NxWI3nrrLQ0dOlR33HGHAgMDZbFYjD6LxUIgAgAAZcpNBaJp06bp1Vdf1UsvvVTc9QAAAJS6m7oP0W+//abHHnusuGsBAACwi5sKRI899pg2btxY3LUAAADYxU1dMqtVq5YmTJigr7/+Wo0bN1b58uVt+p9//vliKQ4AAKA03FQgWrJkiby8vLR9+3Zt377dps9isRCIAABAmXJTgejIkSPFXQcAAIDd3NQaIgAAgNvJTZ0hGjhw4FX7ly5delPFAAAA2MNNBaLffvvN5n1+fr6+++47ZWRkXPahrwAAAI7spgLRJ598UqStsLBQQ4cO1V133XXLRQEAAJSmYltD5OTkpFGjRumNN94orl0CAACUimJdVP3TTz/pwoULxblLAACAEndTl8xGjRpl895qterkyZP6/PPPFRkZWSyFAQAAlJabCkTffPONzXsnJyf5+fnp9ddfv+Y30AAAABzNTQWir776qrjrAAAAsJubCkSXnD59WqmpqZKkunXrys/Pr1iKAgAAKE03FYjOnTun5557TitWrFBhYaEkydnZWf369dP8+fPl4eFRrEWaRXLk4/YuwTE8HmLvCgAAJnNT3zIbNWqUtm/frs8++0wZGRnKyMjQp59+qu3bt2v06NHFXSMAAECJuqkzRB9//LE++ugjPfjgg0Zbly5d5O7urj59+mjRokXFVR8AAECJu6kzROfPn1dAQECRdn9/f50/f/6WiwIAAChNNxWIQkNDNXHiROXk5Bhtf/zxhyZPnqzQ0NBiKw4AAKA03NQlszlz5qhTp06qWrWqmjZtKknav3+/XF1dtXHjxmItEAAAoKTdVCBq3LixDh8+rFWrVun777+XJD3xxBOKiIiQu7t7sRYIAABQ0m4qEMXExCggIECDBw+2aV+6dKlOnz6tl156qViKAwAAKA03tYboX//6l+rVq1ekvWHDhlq8ePEtFwUAAFCabioQpaWlqUqVKkXa/fz8dPLkyVsuCgAAoDTdVCAKDg5WfHx8kfb4+HgFBQXdclEAAACl6abWEA0ePFgjR45Ufn6+OnToIEnasmWLxo0bx52qAQBAmXNTgWjs2LE6c+aMhg0bpry8PEmSm5ubXnrpJUVFRRVrgQAAACXtpgKRxWLRjBkzNGHCBKWkpMjd3V21a9eWq6trcdcHAABQ4m4qEF3i5eWlu+++u7hqAQAAsIubWlRdmv773//qqaeeUuXKleXu7q7GjRtr7969Rr/ValV0dLSqVKkid3d3hYWF6fDhwzb7OHv2rCIiIuTt7S1fX18NGjRI2dnZpT0VAADgoBw6EP32229q27atypcvrw0bNujQoUN6/fXXVbFiRWPMzJkzNW/ePC1evFi7d++Wp6enwsPDbZ6zFhERoYMHD2rTpk1av369duzYoSFDhthjSgAAwAHd0iWzkjZjxgwFBwdr2bJlRltISIjxs9Vq1Zw5czR+/Hj16NFDkrRixQoFBARo7dq16tu3r1JSUhQXF6fExES1atVKkjR//nx16dJFs2bNuuxtAnJzc5Wbm2u8z8rKKqkpAgAAB+DQZ4jWrVunVq1a6bHHHpO/v7+aN2+ut956y+g/cuSI0tLSFBYWZrT5+PiodevWSkhIkCQlJCTI19fXCEOSFBYWJicnJ+3evfuyx42JiZGPj4/xCg4OLqEZAgAAR+DQgejnn3/WokWLVLt2bX355ZcaOnSonn/+eS1fvlzSxTtmS1JAQIDNdgEBAUZfWlqa/P39bfrLlSunSpUqGWP+KioqSpmZmcbr+PHjxT01AADgQBz6kllhYaFatWql1157TZLUvHlzfffdd1q8eLEiIyNL7Liurq7cQgAAABNx6DNEVapUUYMGDWza6tevr2PHjkmSAgMDJUmnTp2yGXPq1CmjLzAwUOnp6Tb9Fy5c0NmzZ40xAADA3Bw6ELVt21apqak2bT/88IOqV68u6eIC68DAQG3ZssXoz8rK0u7duxUaGipJCg0NVUZGhpKSkowxW7duVWFhoVq3bl0KswAAAI7OoS+Zvfjii2rTpo1ee+019enTR3v27NGSJUu0ZMkSSRfvmD1y5EhNmzZNtWvXVkhIiCZMmKCgoCD17NlT0sUzSp06ddLgwYO1ePFi5efna8SIEerbty8PogUAAJIcPBDdfffd+uSTTxQVFaUpU6YoJCREc+bMUUREhDFm3LhxOnfunIYMGaKMjAzdd999iouLk5ubmzFm1apVGjFihDp27CgnJyf17t1b8+bNs8eUAACAA3LoQCRJ3bp1U7du3a7Yb7FYNGXKFE2ZMuWKYypVqqR33323JMoDAAC3AYdeQwQAAFAaCEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0ytm7AACX1zPqa3uX4DDWxtxr7xIA3OY4QwQAAEyPQAQAAEyPQAQAAEyPQAQAAEyvTAWi6dOny2KxaOTIkUZbTk6Ohg8frsqVK8vLy0u9e/fWqVOnbLY7duyYunbtKg8PD/n7+2vs2LG6cOFCKVcPAAAcVZkJRImJifrXv/6lJk2a2LS/+OKL+uyzz/Thhx9q+/btOnHihB599FGjv6CgQF27dlVeXp527dql5cuXKzY2VtHR0aU9BQAA4KDKRCDKzs5WRESE3nrrLVWsWNFoz8zM1Ntvv63Zs2erQ4cOatmypZYtW6Zdu3bp668vfmV548aNOnTokN555x01a9ZMnTt31tSpU7VgwQLl5eVd9ni5ubnKysqyeQEAgNtXmQhEw4cPV9euXRUWFmbTnpSUpPz8fJv2evXqqVq1akpISJAkJSQkqHHjxgoICDDGhIeHKysrSwcPHrzs8WJiYuTj42O8goODS2BWAADAUTh8IFq9erX27dunmJiYIn1paWlycXGRr6+vTXtAQIDS0tKMMX8OQ5f6L/VdTlRUlDIzM43X8ePHi2EmAADAUTn0naqPHz+uF154QZs2bZKbm1upHdfV1VWurq6ldjwAAGBfDn2GKCkpSenp6WrRooXKlSuncuXKafv27Zo3b57KlSungIAA5eXlKSMjw2a7U6dOKTAwUJIUGBhY5Ftnl95fGgMAAMzNoQNRx44ddeDAASUnJxuvVq1aKSIiwvi5fPny2rJli7FNamqqjh07ptDQUElSaGioDhw4oPT0dGPMpk2b5O3trQYNGpT6nAAAgONx6EtmFSpUUKNGjWzaPD09VblyZaN90KBBGjVqlCpVqiRvb28999xzCg0N1b33XnwY5MMPP6wGDRro6aef1syZM5WWlqbx48dr+PDhXBYDAACSHDwQXY833nhDTk5O6t27t3JzcxUeHq6FCxca/c7Ozlq/fr2GDh2q0NBQeXp6KjIyUlOmTLFj1QAAwJGUuUC0bds2m/dubm5asGCBFixYcMVtqlevri+++KKEKwMAAGWVQ68hAgAAKA0EIgAAYHoEIgAAYHplbg0RAABlXc+or+1dgsNYG3OvvUuQxBkiAAAAAhEAAACBCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmB6BCAAAmJ5DB6KYmBjdfffdqlChgvz9/dWzZ0+lpqbajMnJydHw4cNVuXJleXl5qXfv3jp16pTNmGPHjqlr167y8PCQv7+/xo4dqwsXLpTmVAAAgANz6EC0fft2DR8+XF9//bU2bdqk/Px8Pfzwwzp37pwx5sUXX9Rnn32mDz/8UNu3b9eJEyf06KOPGv0FBQXq2rWr8vLytGvXLi1fvlyxsbGKjo62x5QAAIADKmfvAq4mLi7O5n1sbKz8/f2VlJSkdu3aKTMzU2+//bbeffdddejQQZK0bNky1a9fX19//bXuvfdebdy4UYcOHdLmzZsVEBCgZs2aaerUqXrppZc0adIkubi42GNqAADAgTj0GaK/yszMlCRVqlRJkpSUlKT8/HyFhYUZY+rVq6dq1aopISFBkpSQkKDGjRsrICDAGBMeHq6srCwdPHjwssfJzc1VVlaWzQsAANy+ykwgKiws1MiRI9W2bVs1atRIkpSWliYXFxf5+vrajA0ICFBaWpox5s9h6FL/pb7LiYmJkY+Pj/EKDg4u5tkAAABHUmYC0fDhw/Xdd99p9erVJX6sqKgoZWZmGq/jx4+X+DEBAID9OPQaoktGjBih9evXa8eOHapatarRHhgYqLy8PGVkZNicJTp16pQCAwONMXv27LHZ36VvoV0a81eurq5ydXUt5lkAAABH5dBniKxWq0aMGKFPPvlEW7duVUhIiE1/y5YtVb58eW3ZssVoS01N1bFjxxQaGipJCg0N1YEDB5Senm6M2bRpk7y9vdWgQYPSmQgAAHBoDn2GaPjw4Xr33Xf16aefqkKFCsaaHx8fH7m7u8vHx0eDBg3SqFGjVKlSJXl7e+u5555TaGio7r33XknSww8/rAYNGujpp5/WzJkzlZaWpvHjx2v48OGcBQIAAJIcPBAtWrRIkvTggw/atC9btkz9+/eXJL3xxhtycnJS7969lZubq/DwcC1cuNAY6+zsrPXr12vo0KEKDQ2Vp6enIiMjNWXKlNKaBgAAcHAOHYisVus1x7i5uWnBggVasGDBFcdUr15dX3zxRXGWBgAAbiMOvYYIAACgNBCIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6ZkqEC1YsEA1atSQm5ubWrdurT179ti7JAAA4ABME4jef/99jRo1ShMnTtS+ffvUtGlThYeHKz093d6lAQAAOzNNIJo9e7YGDx6sAQMGqEGDBlq8eLE8PDy0dOlSe5cGAADsrJy9CygNeXl5SkpKUlRUlNHm5OSksLAwJSQkFBmfm5ur3Nxc431mZqYkKSsrq0TrzM7LL9H9lxV553OvPcgE8nPP2bsEh1HSn73SwOf7Ij7fF/H5/v9K8vN9ad9Wq/WaY00RiP73v/+poKBAAQEBNu0BAQH6/vvvi4yPiYnR5MmTi7QHBweXWI34k9X2LsBRzLF3AQ7D5w17V4Biw+f7/8yxdwEOozQ+37///rt8fHyuOsYUgehGRUVFadSoUcb7wsJCnT17VpUrV5bFYrFjZSgNWVlZCg4O1vHjx+Xt7W3vcgAUIz7f5mK1WvX7778rKCjommNNEYjuuOMOOTs769SpUzbtp06dUmBgYJHxrq6ucnV1tWnz9fUtyRLhgLy9vfkPJnCb4vNtHtc6M3SJKRZVu7i4qGXLltqyZYvRVlhYqC1btig0NNSOlQEAAEdgijNEkjRq1ChFRkaqVatWuueeezRnzhydO3dOAwYMsHdpAADAzkwTiB5//HGdPn1a0dHRSktLU7NmzRQXF1dkoTXg6uqqiRMnFrlsCqDs4/ONK7FYr+e7aAAAALcxU6whAgAAuBoCEQAAMD0CEQAAMD0CEfB/LBaL1q5da+8yAJSQNWvWyNfXVxMmTNCmTZs0fPhwe5cEB0IggmmcPn1aQ4cOVbVq1eTq6qrAwECFh4crPj5eknTy5El17tzZzlUCuFH9+/eXxWLR9OnTbdrXrl1r83SBNWvWaOXKlTpx4oSGDh2qyMjI0i4VDsw0X7sHevfurby8PC1fvlw1a9bUqVOntGXLFp05c0aSLnvXcgBlg5ubm2bMmKFnnnlGFStWvOyYd955R5LUvXv30iwNZQRniGAKGRkZ2rlzp2bMmKH27durevXquueeexQVFaVHHnlEku0ls6NHj8piseiDDz7Q/fffL3d3d91999364YcflJiYqFatWsnLy0udO3fW6dOn7TgzAJIUFhamwMBAxcTEXLb/zJkzeuKJJ3TnnXfKw8NDjRs31nvvvWczJjc3V88//7z8/f3l5uam++67T4mJiaVRPhwAgQim4OXlJS8vL61du1a5ubnXvd3EiRM1fvx47du3T+XKldOTTz6pcePGae7cudq5c6d+/PFHRUdHl2DlAK6Hs7OzXnvtNc2fP1+//vprkf6cnBy1bNlSn3/+ub777jsNGTJETz/9tPbs2WOMGTdunD7++GMtX75c+/btU61atRQeHq6zZ8+W5lRgL1bAJD766CNrxYoVrW5ubtY2bdpYo6KirPv37zf6JVk/+eQTq9VqtR45csQqyfrvf//b6H/vvfeskqxbtmwx2mJiYqx169YttTkAKCoyMtLao0cPq9Vqtd57773WgQMHWq1Wq/WTTz6xXu3PXNeuXa2jR4+2Wq1Wa3Z2trV8+fLWVatWGf15eXnWoKAg68yZM0uueDgMzhDBNHr37q0TJ05o3bp16tSpk7Zt26YWLVooNjb2its0adLE+PnSY14aN25s05aenl5iNQO4MTNmzNDy5cuVkpJi015QUKCpU6eqcePGqlSpkry8vPTll1/q2LFjkqSffvpJ+fn5atu2rbFN+fLldc899xTZF25PBCKYipubmx566CFNmDBBu3btUv/+/TVx4sQrji9fvrzx86Vvq/y1rbCwsOQKBnBD2rVrp/DwcEVFRdm0//Of/9TcuXP10ksv6auvvlJycrLCw8OVl5dnp0rhaAhEMLUGDRro3Llz9i4DQDGaPn26PvvsMyUkJBht8fHx6tGjh5566ik1bdpUNWvW1A8//GD033XXXXJxcTFuwyFJ+fn5SkxMVIMGDUq1ftgHX7uHKZw5c0aPPfaYBg4cqCZNmqhChQrau3evZs6cqR49eti7PADFqHHjxoqIiNC8efOMttq1a+ujjz7Srl27VLFiRc2ePVunTp0ywo6np6eGDh2qsWPHqlKlSqpWrZpmzpyp8+fPa9CgQfaaCkoRgQim4OXlpdatW+uNN94w1goEBwdr8ODBeuWVV+xdHoBiNmXKFL3//vvG+/Hjx+vnn39WeHi4PDw8NGTIEPXs2VOZmZnGmOnTp6uwsFBPP/20fv/9d7Vq1UpffvnlFe9rhNuLxWq1Wu1dBAAAgD2xhggAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQgAAJgegQhAmfTggw9q5MiR9i5DkpSRkaF69eqpbdu2OnHihOrXr2/vkgDcIAIRAIfRv39/WSwWPfvss0X6hg8fLovFov79+0uS1qxZo6lTp5ZyhZe3a9cuPfjggxoyZIgeeOABPfroo/YuCcAN4tEdABxG//79tXXrVmVlZenkyZNyd3eXJOXk5KhKlSry9vZW+/btFRsbWyzHy8/PV/ny5YtlXwDKNs4QAXAoLVq0UHBwsNasWWO0rVmzRtWqVVPz5s2Ntr9eMjt58qS6du0qd3d3hYSE6N1331WNGjU0Z84cY4zFYtGiRYv0yCOPyNPTU6+++qoKCgo0aNAghYSEyN3dXXXr1tXcuXOL1LV06VI1bNhQrq6uqlKlikaMGGH0zZ49W40bN5anp6eCg4M1bNgwZWdn22z/8ccfG9vXqFFDr7/+ejH8tgAUFwIRAIczcOBALVu2zHi/dOlSDRgw4Krb9OvXTydOnNC2bdv08ccfa8mSJUpPTy8ybtKkSerVq5cOHDiggQMHqrCwUFWrVtWHH36oQ4cOKTo6Wq+88oo++OADY5tFixZp+PDhGjJkiA4cOKB169apVq1aRr+Tk5PmzZungwcPavny5dq6davGjRtn9CclJalPnz7q27evDhw4oEmTJmnChAnFdqYLQDGwAoCDiIyMtPbo0cOanp5udXV1tR49etR69OhRq5ubm/X06dPWHj16WCMjI61Wq9X6wAMPWF944QWr1Wq1pqSkWCVZExMTjX0dPnzYKsn6xhtvGG2SrCNHjrxmHcOHD7f27t3beB8UFGT9xz/+cd3z+PDDD62VK1c23j/55JPWhx56yGbM2LFjrQ0aNLjufQIoWeXsnMcAoAg/Pz917dpVsbGxslqt6tq1q+64444rjk9NTVW5cuXUokULo61WrVqqWLFikbGtWrUq0rZgwQItXbpUx44d0x9//KG8vDw1a9ZMkpSenq4TJ06oY8eOVzz+5s2bFRMTo++//15ZWVm6cOGCcnJydP78eXl4eCglJUU9evSw2aZt27aaM2eOCgoK5OzsfK1fCYASxiUzAA5p4MCBio2N1fLlyzVw4MBi26+np6fN+9WrV2vMmDEaNGiQNm7cqOTkZA0YMEB5eXmSZCzsvpKjR4+qW7duatKkiT7++GMlJSVpwYIFkmTsA4Dj4wwRAIfUqVMn5eXlyWKxKDw8/Kpj69atqwsXLuibb75Ry5YtJUk//vijfvvtt2seJz4+Xm3atNGwYcOMtp9++sn4uUKFCqpRo4a2bNmi9u3bF9k+KSlJhYWFev311+XkdPH/Y/55/ZEk1a9fX/Hx8UWOW6dOHc4OAQ6CQATAITk7OyslJcX4+Wrq1aunsLAwDRkyRIsWLVL58uU1evRoubu7y2KxXHXb2rVra8WKFfryyy8VEhKilStXKjExUSEhIcaYSZMm6dlnn5W/v786d+6s33//XfHx8XruuedUq1Yt5efna/78+erevbvi4+O1ePFim2OMHj1ad999t6ZOnarHH39cCQkJevPNN7Vw4cKb/O0AKG5cMgPgsLy9veXt7X1dY1esWKGAgAC1a9dOvXr10uDBg1WhQgW5ublddbtnnnlGjz76qB5//HG1bt1aZ86csTlbJEmRkZGaM2eOFi5cqDp16qhjx446fPiwJKlp06aaPXu2ZsyYoUaNGmnVqlWKiYmx2b5Fixb64IMPtHr1ajVq1EjR0dGaMmWKcZNJAPbHjRkB3JZ+/fVXBQcHa/PmzVddEH2j3nvvPR06dMhh7pINoHhwhgjAbWHr1q1at26djhw5ol27dqlv376qUaOG2rVrV2zHOHjwoKxWq9atW1ds+wTgGFhDBOC2kJ+fr1deeUU///yzKlSooDZt2mjVqlXF+miOHj166MSJExo/fnyx7ROAY+CSGQAAMD0umQEAANMjEAEAANMjEAEAANMjEAEAANMjEAEAANMjEAEAANMjEAEAANMjEAEAANP7f9OLD7sxGClvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "students.groupby('Target')['Necessidades educacionais especiais'].value_counts(normalize=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24_kC3Ex7blI",
        "outputId": "4d912759-1fa2-4b65-b9f3-0135263bac9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Target       Necessidades educacionais especiais\n",
              "Desistente   Não                                    98.803659\n",
              "             Sim                                     1.196341\n",
              "Graduado     Não                                    98.958805\n",
              "             Sim                                     1.041195\n",
              "Matriculado  Não                                    98.614610\n",
              "             Sim                                     1.385390\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Target</th>\n",
              "      <th>Necessidades educacionais especiais</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Desistente</th>\n",
              "      <th>Não</th>\n",
              "      <td>98.803659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sim</th>\n",
              "      <td>1.196341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Graduado</th>\n",
              "      <th>Não</th>\n",
              "      <td>98.958805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sim</th>\n",
              "      <td>1.041195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Matriculado</th>\n",
              "      <th>Não</th>\n",
              "      <td>98.614610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sim</th>\n",
              "      <td>1.385390</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Target', y='Taxa de desemprego', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pUTiCFu8T8R",
        "outputId": "0758162e-3497-4382-cd7e-9f552a4b9920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Target', y='Taxa de inflação', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwGJ2hbu8_ig",
        "outputId": "060905ee-77cd-41c5-a09a-20fe0f3e84c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Target', y='PIB', data=students)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5E5Kty89YDW",
        "outputId": "f9c3e067-c79d-454d-b17e-adf31deafe56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "students['Qualificação prévia'].value_counts(normalize=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlaOBQlh9mzy",
        "outputId": "8256c409-4c9c-40ee-8c9e-b657993a01f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Qualificação prévia\n",
              "Educação secundária                                         84.018987\n",
              "Curso de especialização tecnológica                          4.950271\n",
              "Educação básica 3º ciclo (9º/10º/11º ano) ou equivalente     3.661844\n",
              "Educação superior - licenciatura                             2.848101\n",
              "Outro - 11º ano de escolaridade                              1.017179\n",
              "Educação superior - licenciatura (1º ciclo)                  0.904159\n",
              "Curso técnico superior profissional                          0.813743\n",
              "Educação superior - bacharelado                              0.519892\n",
              "Frequência de educação superior                              0.361664\n",
              "12º ano de escolaridade - não concluído                      0.248644\n",
              "Educação superior - mestrado                                 0.180832\n",
              "Educação básica 2º ciclo (6º/7º/8º ano) ou equivalente       0.158228\n",
              "Educação superior - mestrado (2º ciclo)                      0.135624\n",
              "11º ano de escolaridade - não concluído                      0.090416\n",
              "10º ano de escolaridade - não concluído                      0.045208\n",
              "Educação superior - doutorado                                0.022604\n",
              "10º ano de escolaridade                                      0.022604\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Qualificação prévia</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Educação secundária</th>\n",
              "      <td>84.018987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Curso de especialização tecnológica</th>\n",
              "      <td>4.950271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Educação básica 3º ciclo (9º/10º/11º ano) ou equivalente</th>\n",
              "      <td>3.661844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Educação superior - licenciatura</th>\n",
              "      <td>2.848101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outro - 11º ano de escolaridade</th>\n",
              "      <td>1.017179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Educação superior - licenciatura (1º ciclo)</th>\n",
              "      <td>0.904159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Curso técnico superior profissional</th>\n",
              "      <td>0.813743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Educação superior - bacharelado</th>\n",
              "      <td>0.519892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Frequência de educação superior</th>\n",
              "      <td>0.361664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12º ano de escolaridade - não concluído</th>\n",
              "      <td>0.248644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Educação superior - mestrado</th>\n",
              "      <td>0.180832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Educação básica 2º ciclo (6º/7º/8º ano) ou equivalente</th>\n",
              "      <td>0.158228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Educação superior - mestrado (2º ciclo)</th>\n",
              "      <td>0.135624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11º ano de escolaridade - não concluído</th>\n",
              "      <td>0.090416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10º ano de escolaridade - não concluído</th>\n",
              "      <td>0.045208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Educação superior - doutorado</th>\n",
              "      <td>0.022604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10º ano de escolaridade</th>\n",
              "      <td>0.022604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "ihNwNejr92sW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Preparação do modelo"
      ],
      "metadata": {
        "id": "WCEpysb5SZM0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Preparando os dados para o modelo"
      ],
      "metadata": {
        "id": "8WEZZTlN6Mvu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "metadata": {
        "id": "W6ojyusZoj7B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colunas_categoricas = ['Migração', 'Sexo','Estrangeiro', 'Necessidades educacionais especiais', 'Devedor',\n",
        "                     'Taxas de matrícula em dia', 'Bolsista', 'Período','Estado civil', 'Curso', 'Qualificação prévia']"
      ],
      "metadata": {
        "id": "cZleeZWQovQb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = OneHotEncoder(drop='if_binary')\n",
        "\n",
        "students_categorico = students[colunas_categoricas]\n",
        "\n",
        "students_encoded = pd.DataFrame(encoder.fit_transform(students_categorico).toarray(),\n",
        "                          columns=encoder.get_feature_names_out(colunas_categoricas))\n",
        "\n",
        "students_final = pd.concat([students.drop(colunas_categoricas, axis=1), students_encoded], axis=1)"
      ],
      "metadata": {
        "id": "Ra3KroTDo4Hq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "students_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DEAPudVqiKM",
        "outputId": "c812a5ee-c460-40a9-f84c-1549ca2daef4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Idade na matrícula  Taxa de desemprego  Taxa de inflação   PIB  \\\n",
              "0                     20                10.8               1.4  1.74   \n",
              "1                     19                13.9              -0.3  0.79   \n",
              "2                     19                10.8               1.4  1.74   \n",
              "3                     20                 9.4              -0.8 -3.12   \n",
              "4                     45                13.9              -0.3  0.79   \n",
              "...                  ...                 ...               ...   ...   \n",
              "4419                  19                15.5               2.8 -4.06   \n",
              "4420                  18                11.1               0.6  2.02   \n",
              "4421                  30                13.9              -0.3  0.79   \n",
              "4422                  20                 9.4              -0.8 -3.12   \n",
              "4423                  22                12.7               3.7 -1.70   \n",
              "\n",
              "      disciplinas 1º semestre (creditadas)  \\\n",
              "0                                        0   \n",
              "1                                        0   \n",
              "2                                        0   \n",
              "3                                        0   \n",
              "4                                        0   \n",
              "...                                    ...   \n",
              "4419                                     0   \n",
              "4420                                     0   \n",
              "4421                                     0   \n",
              "4422                                     0   \n",
              "4423                                     0   \n",
              "\n",
              "      disciplinas 1º semestre (matriculadas)  \\\n",
              "0                                          0   \n",
              "1                                          6   \n",
              "2                                          6   \n",
              "3                                          6   \n",
              "4                                          6   \n",
              "...                                      ...   \n",
              "4419                                       6   \n",
              "4420                                       6   \n",
              "4421                                       7   \n",
              "4422                                       5   \n",
              "4423                                       6   \n",
              "\n",
              "      disciplinas 1º semestre (avaliações)  \\\n",
              "0                                        0   \n",
              "1                                        6   \n",
              "2                                        0   \n",
              "3                                        8   \n",
              "4                                        9   \n",
              "...                                    ...   \n",
              "4419                                     7   \n",
              "4420                                     6   \n",
              "4421                                     8   \n",
              "4422                                     5   \n",
              "4423                                     8   \n",
              "\n",
              "      disciplinas 1º semestre (aprovadas)  disciplinas 1º semestre (notas)  \\\n",
              "0                                       0                         0.000000   \n",
              "1                                       6                        14.000000   \n",
              "2                                       0                         0.000000   \n",
              "3                                       6                        13.428571   \n",
              "4                                       5                        12.333333   \n",
              "...                                   ...                              ...   \n",
              "4419                                    5                        13.600000   \n",
              "4420                                    6                        12.000000   \n",
              "4421                                    7                        14.912500   \n",
              "4422                                    5                        13.800000   \n",
              "4423                                    6                        11.666667   \n",
              "\n",
              "      disciplinas 1º semestre (sem avaliações)  ...  \\\n",
              "0                                            0  ...   \n",
              "1                                            0  ...   \n",
              "2                                            0  ...   \n",
              "3                                            0  ...   \n",
              "4                                            0  ...   \n",
              "...                                        ...  ...   \n",
              "4419                                         0  ...   \n",
              "4420                                         0  ...   \n",
              "4421                                         0  ...   \n",
              "4422                                         0  ...   \n",
              "4423                                         0  ...   \n",
              "\n",
              "      Qualificação prévia_Educação básica 3º ciclo (9º/10º/11º ano) ou equivalente  \\\n",
              "0                                                   0.0                              \n",
              "1                                                   0.0                              \n",
              "2                                                   0.0                              \n",
              "3                                                   0.0                              \n",
              "4                                                   0.0                              \n",
              "...                                                 ...                              \n",
              "4419                                                0.0                              \n",
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              "\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1957a90b-e286-4d04-8bff-a0bb667d6d12 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_124c71d1-e7f9-4e43-9b64-ced6577546d0\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('students_final')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_124c71d1-e7f9-4e43-9b64-ced6577546d0 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('students_final');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "students_final"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Separando os dados"
      ],
      "metadata": {
        "id": "TPiqcefIq-IO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#features\n",
        "X = students_final.drop('Target', axis=1)\n",
        "#target\n",
        "y = students_final['Target']"
      ],
      "metadata": {
        "id": "ykUHZJ-JrBXv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X,X_test,y,y_test = train_test_split(X, y, test_size=0.15, stratify=y,random_state=0)\n",
        "X_train,X_val,y_train,y_val = train_test_split(X, y, stratify=y, random_state=0)"
      ],
      "metadata": {
        "id": "G9Kcvn6wrcbi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Modelo"
      ],
      "metadata": {
        "id": "DNnpFEMeSSFV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "v23XBWOPVOVE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestClassifier(random_state=0)\n",
        "rf_model.fit(X_train, y_train)\n",
        "y_pred = rf_model.predict(X_val)\n",
        "print(f'Training accuracy: {rf_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {rf_model.score(X_val, y_val)}')\n",
        "#Overfitting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bdr9CgiWA_X",
        "outputId": "284dd9ff-08a9-4b48-beae-e6d1e96be4b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.9975177304964539\n",
            "Validation accuracy 0.7680851063829788\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestClassifier(random_state=0, max_depth=10)\n",
        "rf_model.fit(X_train, y_train)\n",
        "y_pred = rf_model.predict(X_val)\n",
        "print(f'Training accuracy: {rf_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {rf_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6sad0CrMWOE5",
        "outputId": "98b3491b-89aa-4f24-e8a0-be4617cbc541"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.898936170212766\n",
            "Validation accuracy 0.7734042553191489\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "7N014_IKD-BV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Análise do modelo"
      ],
      "metadata": {
        "id": "OIOM4r3F1xCU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Conf matrix"
      ],
      "metadata": {
        "id": "QjkITDGo2FQT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "FLdaFC1G100N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Matriz de confusão = linhas: val, colunas:pred\n",
        "conf_matrix = confusion_matrix(y_val, y_pred)"
      ],
      "metadata": {
        "id": "Co6z7__4151y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visualizacao = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=rf_model.classes_)\n",
        "visualizacao.plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPcDqWmY2Obj",
        "outputId": "b3439dcb-cb86-4177-d50f-e3343df0843f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_val, y_pred, normalize='true', cmap='Blues');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYQUIirK3f6c",
        "outputId": "d240a321-fa92-499f-9963-df00e65bccb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Classification Report"
      ],
      "metadata": {
        "id": "srCxBDsR5gvV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "xmClKMkb5iTS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "  Taxa de acerto na classificação de positivos\n",
        "\n",
        "Precision: VP/ (VP+FP)\n",
        "\n",
        "  Proporção dos positivos que foram classificados corretamente versus incorretamente\n",
        "  \n",
        "  (alto recall é bom: raramente chama um positivo de negativo)\n",
        "\n",
        "Recall: VP/ (VP+FN)\n",
        "\n"
      ],
      "metadata": {
        "id": "-epGmrRv8vxl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "report = classification_report(y_val, y_pred)\n",
        "print(f'Relatório de Classificação:\\n', report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Cwp-xDs5mao",
        "outputId": "303d4327-03e6-4017-c07b-6581a4cca400"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relatório de Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "  Desistente       0.82      0.77      0.79       302\n",
            "    Graduado       0.77      0.96      0.86       469\n",
            " Matriculado       0.61      0.26      0.37       169\n",
            "\n",
            "    accuracy                           0.77       940\n",
            "   macro avg       0.73      0.66      0.67       940\n",
            "weighted avg       0.76      0.77      0.75       940\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Balanceamento - Oversampling\n",
        "\n",
        "    Inflar a quantidade de amostras na classe minoritária até todas as classes terem a mesma quantidade que a classe majoritária"
      ],
      "metadata": {
        "id": "VzDJ_IxYC_Jb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spglaQxc6lRO",
        "outputId": "b493e941-dd68-4de7-e4db-185db6b4960a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Target\n",
              "Graduado       1408\n",
              "Desistente      906\n",
              "Matriculado     506\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Graduado</th>\n",
              "      <td>1408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Desistente</th>\n",
              "      <td>906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Matriculado</th>\n",
              "      <td>506</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ],
      "metadata": {
        "id": "DqWIyhIYDX7_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "oversample = SMOTE(random_state=0)\n",
        "X_balanced, y_balanced = oversample.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "dmGavcQ5DjRe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_balanced.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9My30XSEBRR",
        "outputId": "0c9f77d0-dbdb-482d-d353-f226fb5db72e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Target\n",
              "Desistente     1408\n",
              "Graduado       1408\n",
              "Matriculado    1408\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Desistente</th>\n",
              "      <td>1408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Graduado</th>\n",
              "      <td>1408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Matriculado</th>\n",
              "      <td>1408</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "balanced_model = RandomForestClassifier(random_state=0, max_depth=10)\n",
        "balanced_model.fit(X_balanced, y_balanced)\n",
        "y_pred = balanced_model.predict(X_val)"
      ],
      "metadata": {
        "id": "4F_kwHBZEKgi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_val, y_pred, normalize='true', cmap='Blues');\n",
        "print(classification_report(y_val, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00nYMl-_EmYC",
        "outputId": "852e8d78-a073-495a-96c4-e613370fcdde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "  Desistente       0.85      0.72      0.78       302\n",
            "    Graduado       0.81      0.87      0.84       469\n",
            " Matriculado       0.48      0.52      0.50       169\n",
            "\n",
            "    accuracy                           0.76       940\n",
            "   macro avg       0.72      0.70      0.71       940\n",
            "weighted avg       0.77      0.76      0.76       940\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Desafio - Oversampling"
      ],
      "metadata": {
        "id": "GFcq_KM5676o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "G9djPGrQ7EM4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_model = GradientBoostingClassifier()\n",
        "gbc_model.fit(X_train,y_train)\n",
        "y_pred = gbc_model.predict(X_val)\n",
        "\n",
        "print(f'Training accuracy: {gbc_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {gbc_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NLc3rcbA65-O",
        "outputId": "6df13d5d-404e-4645-9bad-917cacd1e757"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.8411347517730496\n",
            "Validation accuracy 0.7702127659574468\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ],
      "metadata": {
        "id": "JFqXYxdi7IjL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "oversample = SMOTE(random_state=0)\n",
        "X_balanced, y_balanced = oversample.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "9anw8epR7VgF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_balanced = GradientBoostingClassifier()\n",
        "gbc_balanced.fit(X_balanced, y_balanced)\n",
        "y_pred = gbc_balanced.predict(X_val)\n",
        "\n",
        "print(f'Training accuracy: {gbc_model.score(X_balanced, y_balanced)}')\n",
        "print(f'Validation accuracy {gbc_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "guqNK95M7jen",
        "outputId": "e04f002a-11e9-46a3-e12c-70f74c63d84f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.775094696969697\n",
            "Validation accuracy 0.7702127659574468\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "MOEXVxEb8E_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(gbc_balanced, X_val, y_val, normalize='true', cmap='Blues');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ryqIpQX-8I6l",
        "outputId": "822809a5-6a30-4a81-fac2-dff3fec34c9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "srOe9rbW8jxf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "report = classification_report(y_val, y_pred)\n",
        "print(f'Relatório de Classificação:\\n', report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q7hjtIG8nAQ",
        "outputId": "df7928c9-b401-4855-ad1b-ddaed3954d12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relatório de Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "  Desistente       0.83      0.74      0.78       302\n",
            "    Graduado       0.82      0.89      0.85       469\n",
            " Matriculado       0.50      0.47      0.48       169\n",
            "\n",
            "    accuracy                           0.77       940\n",
            "   macro avg       0.71      0.70      0.71       940\n",
            "weighted avg       0.76      0.77      0.76       940\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Antes do oversampling:\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "bPsKjTCE9Ihm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "BQEBD_1W9nUl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Pipeline para validação"
      ],
      "metadata": {
        "id": "vxmS9txX-EcE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.pipeline import Pipeline as imbpipeline"
      ],
      "metadata": {
        "id": "n_oVKkWx-Gry"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(max_depth=10)\n",
        "pipeline = imbpipeline([('oversample', SMOTE()), ('Random Forest', model)])"
      ],
      "metadata": {
        "id": "G8v-L4Ro_CxY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_validate, StratifiedKFold"
      ],
      "metadata": {
        "id": "1B1xoQxN_gsa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)\n",
        "cv_results = cross_validate(pipeline, X, y, cv=skf,scoring='recall_weighted')\n",
        "cv_results['test_score']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WjgcI5CW_vsd",
        "outputId": "d8fd7eee-5400-4e8a-81f5-b6c8f49990f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.76462766, 0.73537234, 0.74601064, 0.77659574, 0.75132979])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Intervalo conf"
      ],
      "metadata": {
        "id": "4zjvlzWWlixw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean = cv_results['test_score'].mean()\n",
        "std = cv_results['test_score'].std()\n",
        "print(f'Intervalo de confiança: [{mean-2*std}], [{min(mean+2*std, 1.0)}]')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VarY3SVAllIQ",
        "outputId": "b0e0992a-65ee-45ea-aff4-a2e291d0a4a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intervalo de confiança: [0.725965495352885], [0.7836089727322214]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Teste do modelo"
      ],
      "metadata": {
        "id": "Rt6yu2whng1n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "oversample = SMOTE(random_state=0)\n",
        "X_balanced, y_balanced = oversample.fit_resample(X, y)\n",
        "final_model = RandomForestClassifier(random_state=0, max_depth=10)\n",
        "final_model.fit(X_balanced, y_balanced)\n",
        "y_pred = final_model.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, y_pred))\n",
        "ConfusionMatrixDisplay.from_predictions(y_test, y_pred, normalize='true', cmap='Blues');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MSdxGK_njy9",
        "outputId": "c8458dc2-b3a6-4f6a-9ec8-d187d2078c3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "  Desistente       0.88      0.75      0.81       213\n",
            "    Graduado       0.82      0.85      0.84       332\n",
            " Matriculado       0.50      0.59      0.54       119\n",
            "\n",
            "    accuracy                           0.77       664\n",
            "   macro avg       0.73      0.73      0.73       664\n",
            "weighted avg       0.78      0.77      0.77       664\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Pxsl_dkoc9S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Teste de diferentes algorítmos"
      ],
      "metadata": {
        "id": "Z5-yy9IJD0ZT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##tree.DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "AKC94yUOh4Xe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "oia9-F4Yh8oP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt_model = DecisionTreeClassifier(random_state=0)\n",
        "dt_model.fit(X_train,y_train)\n",
        "y_pred = dt_model.predict(X_val)\n",
        "print(f'Training accuracy: {dt_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {dt_model.score(X_val, y_val)}')\n",
        "#Still overfitting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STAExibvh_P7",
        "outputId": "870e7c9e-f316-4e04-de6a-461266c103a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.9975177304964539\n",
            "Validation accuracy 0.6882978723404255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_model = DecisionTreeClassifier(random_state=0, max_depth=10)\n",
        "dt_model.fit(X_train,y_train)\n",
        "y_pred = dt_model.predict(X_val)\n",
        "print(f'Training accuracy: {dt_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {dt_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxecYjX1iUrw",
        "outputId": "98aba9dc-745b-467f-d602-f740b6fbde5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.8776595744680851\n",
            "Validation accuracy 0.7457446808510638\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##tree.ExtraTreeClassifier"
      ],
      "metadata": {
        "id": "lV9TIJ8Jibj2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import ExtraTreeClassifier"
      ],
      "metadata": {
        "id": "iDmza7QGiezo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "et_model = ExtraTreeClassifier(random_state=0)\n",
        "et_model.fit(X_train,y_train)\n",
        "y_pred = et_model.predict(X_val)\n",
        "print(f'Training accuracy: {dt_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {dt_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-FpoJV8FiiSW",
        "outputId": "5a21148c-4248-4902-ca97-8134d3e4b66c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.8776595744680851\n",
            "Validation accuracy 0.7457446808510638\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "et_model = ExtraTreeClassifier(random_state=0, max_depth=10)\n",
        "et_model.fit(X_train,y_train)\n",
        "y_pred=et_model.predict(X_val)\n",
        "print(f'Training accuracy: {dt_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {dt_model.score(X_val, y_val)}')\n",
        "#Não mudou nada"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cfJJQ3-jBqZ",
        "outputId": "a16d11f1-a50c-4900-e1ac-0bf0dd5324de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.8776595744680851\n",
            "Validation accuracy 0.7457446808510638\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##neighbors.KNeighborsClassifier"
      ],
      "metadata": {
        "id": "YcoZox69jKWY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "SZGEkvRAjO3u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knc_model = KNeighborsClassifier(n_neighbors=8)\n",
        "knc_model.fit(X_train,y_train)\n",
        "y_pred = knc_model.predict(X_val)\n",
        "print(f'Training accuracy: {knc_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {knc_model.score(X_val, y_val)}')\n",
        "#Less overfitting but poor no acurracy improvement overall"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EoTAfiFvjPjS",
        "outputId": "e8d57481-e9e2-4d74-d258-2727824c41a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.7656028368794326\n",
            "Validation accuracy 0.7297872340425532\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##linear_model.LogisticRegression (setting multi_class=\"multinomial\")"
      ],
      "metadata": {
        "id": "bzYhss28kV_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "ZSqq4PyRkfCm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model = LogisticRegression(multi_class=\"multinomial\")\n",
        "lr_model.fit(X_train,y_train)\n",
        "y_pred = lr_model.predict(X_val)\n",
        "print(f'Training accuracy: {lr_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {lr_model.score(X_val, y_val)}')\n",
        "#Almost no overfitting and slight acuraccy improvement"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8d_trELkyIg",
        "outputId": "b59b1009-54fd-41b8-c040-8cf20f13a414"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.7808510638297872\n",
            "Validation accuracy 0.7734042553191489\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:\n",
            "\n",
            "lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pesquisei o output anterior no chatgpt para achar o que era\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_valid_scaled = scaler.transform(X_val)\n",
        "\n",
        "lr_model = LogisticRegression(max_iter=200, multi_class=\"multinomial\")\n",
        "lr_model.fit(X_train_scaled, y_train)\n",
        "\n",
        "print(f'Training accuracy: {lr_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {lr_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5BXb-3TpmJU2",
        "outputId": "053ba29c-154f-47bf-d0d4-a123032e4c96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.7404255319148936\n",
            "Validation accuracy 0.7468085106382979\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning:\n",
            "\n",
            "X has feature names, but LogisticRegression was fitted without feature names\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning:\n",
            "\n",
            "X has feature names, but LogisticRegression was fitted without feature names\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##linear_model.RidgeClassifier"
      ],
      "metadata": {
        "id": "bzML2CL1ljzK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import RidgeClassifier"
      ],
      "metadata": {
        "id": "jXbDVWU8lmmL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rc_model = RidgeClassifier()\n",
        "rc_model.fit(X_train,y_train)\n",
        "y_pred = rc_model.predict(X_val)\n",
        "print(f'Training accuracy: {rc_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {rc_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "574JWYXDlrkx",
        "outputId": "8f142714-7d19-4fab-d0f4-785aec9f95d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.7737588652482269\n",
            "Validation accuracy 0.7510638297872341\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##ensemble.GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "T6AiwOuA_3Uk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "XgCgVu6d_7Gi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_model = GradientBoostingClassifier()\n",
        "gbc_model.fit(X_train,y_train)\n",
        "y_pred = gbc_model.predict(X_val)\n",
        "\n",
        "print(f'Training accuracy: {gbc_model.score(X_train, y_train)}')\n",
        "print(f'Validation accuracy {gbc_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYQbG0JWAADc",
        "outputId": "9967d898-ef97-4824-f85d-90fc0b4140b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.8411347517730496\n",
            "Validation accuracy 0.7691489361702127\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Relatório de Classificação:\\n', classification_report(y_val, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmLQJu0N81_O",
        "outputId": "73c74488-0379-4f94-c9e9-ad880cb52873"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relatório de Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "  Desistente       0.82      0.75      0.79       302\n",
            "    Graduado       0.79      0.93      0.85       469\n",
            " Matriculado       0.53      0.36      0.43       169\n",
            "\n",
            "    accuracy                           0.77       940\n",
            "   macro avg       0.71      0.68      0.69       940\n",
            "weighted avg       0.75      0.77      0.76       940\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Automatizar a busca pelos melhores valores dos parâmetros:"
      ],
      "metadata": {
        "id": "jDrKSQMPB632"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200],\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'max_depth': [3, 4, 5],\n",
        "    'subsample': [0.7, 0.8, 0.9]\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(GradientBoostingClassifier(), param_grid, cv=3)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Melhores hiperparâmetros\n",
        "print(grid_search.best_params_)\n"
      ],
      "metadata": {
        "id": "2dxpX5fxCB16",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11f05a18-218a-47c2-9367-68268ebb1172"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 200, 'subsample': 0.7}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Patologias de pele\n"
      ],
      "metadata": {
        "id": "gD8TRvAvvRQc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/alura-cursos/classificacao_multiclasse/main/Dados/dermatology_database_1.csv')"
      ],
      "metadata": {
        "id": "lRkdKpiQvTlo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "0cHi8Ximvcba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf492d22-c098-47ce-ecc1-14a02deea0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 366 entries, 0 to 365\n",
            "Data columns (total 35 columns):\n",
            " #   Column                               Non-Null Count  Dtype \n",
            "---  ------                               --------------  ----- \n",
            " 0   erythema                             366 non-null    int64 \n",
            " 1   scaling                              366 non-null    int64 \n",
            " 2   definite_borders                     366 non-null    int64 \n",
            " 3   itching                              366 non-null    int64 \n",
            " 4   koebner_phenomenon                   366 non-null    int64 \n",
            " 5   polygonal_papules                    366 non-null    int64 \n",
            " 6   follicular_papules                   366 non-null    int64 \n",
            " 7   oral_mucosal_involvement             366 non-null    int64 \n",
            " 8   knee_and_elbow_involvement           366 non-null    int64 \n",
            " 9   scalp_involvement                    366 non-null    int64 \n",
            " 10  family_history                       366 non-null    int64 \n",
            " 11  melanin_incontinence                 366 non-null    int64 \n",
            " 12  eosinophils_infiltrate               366 non-null    int64 \n",
            " 13  PNL_infiltrate                       366 non-null    int64 \n",
            " 14  fibrosis_papillary_dermis            366 non-null    int64 \n",
            " 15  exocytosis                           366 non-null    int64 \n",
            " 16  acanthosis                           366 non-null    int64 \n",
            " 17  hyperkeratosis                       366 non-null    int64 \n",
            " 18  parakeratosis                        366 non-null    int64 \n",
            " 19  clubbing_rete_ridges                 366 non-null    int64 \n",
            " 20  elongation_rete_ridges               366 non-null    int64 \n",
            " 21  thinning_suprapapillary_epidermis    366 non-null    int64 \n",
            " 22  spongiform_pustule                   366 non-null    int64 \n",
            " 23  munro_microabcess                    366 non-null    int64 \n",
            " 24  focal_hypergranulosis                366 non-null    int64 \n",
            " 25  disappearance_granular_layer         366 non-null    int64 \n",
            " 26  vacuolisation_damage_basal_layer     366 non-null    int64 \n",
            " 27  spongiosis                           366 non-null    int64 \n",
            " 28  saw_tooth_appearance_retes           366 non-null    int64 \n",
            " 29  follicular_horn_plug                 366 non-null    int64 \n",
            " 30  perifollicular_parakeratosis         366 non-null    int64 \n",
            " 31  inflammatory_mononuclear_infiltrate  366 non-null    int64 \n",
            " 32  band_like_infiltrate                 366 non-null    int64 \n",
            " 33  age                                  366 non-null    object\n",
            " 34  class                                366 non-null    int64 \n",
            "dtypes: int64(34), object(1)\n",
            "memory usage: 100.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "VTcn4xSZvroK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40115e42-09a5-46d5-aa20-72e744f1d903"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         erythema     scaling  definite_borders     itching  \\\n",
              "count  366.000000  366.000000        366.000000  366.000000   \n",
              "mean     2.068306    1.795082          1.549180    1.366120   \n",
              "std      0.664753    0.701527          0.907525    1.138299   \n",
              "min      0.000000    0.000000          0.000000    0.000000   \n",
              "25%      2.000000    1.000000          1.000000    0.000000   \n",
              "50%      2.000000    2.000000          2.000000    1.000000   \n",
              "75%      2.000000    2.000000          2.000000    2.000000   \n",
              "max      3.000000    3.000000          3.000000    3.000000   \n",
              "\n",
              "       koebner_phenomenon  polygonal_papules  follicular_papules  \\\n",
              "count          366.000000         366.000000          366.000000   \n",
              "mean             0.633880           0.448087            0.166667   \n",
              "std              0.908016           0.957327            0.570588   \n",
              "min              0.000000           0.000000            0.000000   \n",
              "25%              0.000000           0.000000            0.000000   \n",
              "50%              0.000000           0.000000            0.000000   \n",
              "75%              1.000000           0.000000            0.000000   \n",
              "max              3.000000           3.000000            3.000000   \n",
              "\n",
              "       oral_mucosal_involvement  knee_and_elbow_involvement  \\\n",
              "count                366.000000                  366.000000   \n",
              "mean                   0.377049                    0.614754   \n",
              "std                    0.834147                    0.982979   \n",
              "min                    0.000000                    0.000000   \n",
              "25%                    0.000000                    0.000000   \n",
              "50%                    0.000000                    0.000000   \n",
              "75%                    0.000000                    1.000000   \n",
              "max                    3.000000                    3.000000   \n",
              "\n",
              "       scalp_involvement  ...  focal_hypergranulosis  \\\n",
              "count         366.000000  ...             366.000000   \n",
              "mean            0.519126  ...               0.393443   \n",
              "std             0.905639  ...               0.849406   \n",
              "min             0.000000  ...               0.000000   \n",
              "25%             0.000000  ...               0.000000   \n",
              "50%             0.000000  ...               0.000000   \n",
              "75%             1.000000  ...               0.000000   \n",
              "max             3.000000  ...               3.000000   \n",
              "\n",
              "       disappearance_granular_layer  vacuolisation_damage_basal_layer  \\\n",
              "count                    366.000000                        366.000000   \n",
              "mean                       0.464481                          0.456284   \n",
              "std                        0.864899                          0.954873   \n",
              "min                        0.000000                          0.000000   \n",
              "25%                        0.000000                          0.000000   \n",
              "50%                        0.000000                          0.000000   \n",
              "75%                        1.000000                          0.000000   \n",
              "max                        3.000000                          3.000000   \n",
              "\n",
              "       spongiosis  saw_tooth_appearance_retes  follicular_horn_plug  \\\n",
              "count  366.000000                  366.000000            366.000000   \n",
              "mean     0.953552                    0.453552              0.103825   \n",
              "std      1.130172                    0.954744              0.450433   \n",
              "min      0.000000                    0.000000              0.000000   \n",
              "25%      0.000000                    0.000000              0.000000   \n",
              "50%      0.000000                    0.000000              0.000000   \n",
              "75%      2.000000                    0.000000              0.000000   \n",
              "max      3.000000                    3.000000              3.000000   \n",
              "\n",
              "       perifollicular_parakeratosis  inflammatory_mononuclear_infiltrate  \\\n",
              "count                    366.000000                           366.000000   \n",
              "mean                       0.114754                             1.866120   \n",
              "std                        0.488723                             0.726108   \n",
              "min                        0.000000                             0.000000   \n",
              "25%                        0.000000                             1.000000   \n",
              "50%                        0.000000                             2.000000   \n",
              "75%                        0.000000                             2.000000   \n",
              "max                        3.000000                             3.000000   \n",
              "\n",
              "       band_like_infiltrate       class  \n",
              "count            366.000000  366.000000  \n",
              "mean               0.554645    2.803279  \n",
              "std                1.105908    1.597803  \n",
              "min                0.000000    1.000000  \n",
              "25%                0.000000    1.000000  \n",
              "50%                0.000000    3.000000  \n",
              "75%                0.000000    4.000000  \n",
              "max                3.000000    6.000000  \n",
              "\n",
              "[8 rows x 34 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32b71064-2996-47a6-b290-7bad4cb813d3\" 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>erythema</th>\n",
              "      <th>scaling</th>\n",
              "      <th>definite_borders</th>\n",
              "      <th>itching</th>\n",
              "      <th>koebner_phenomenon</th>\n",
              "      <th>polygonal_papules</th>\n",
              "      <th>follicular_papules</th>\n",
              "      <th>oral_mucosal_involvement</th>\n",
              "      <th>knee_and_elbow_involvement</th>\n",
              "      <th>scalp_involvement</th>\n",
              "      <th>...</th>\n",
              "      <th>focal_hypergranulosis</th>\n",
              "      <th>disappearance_granular_layer</th>\n",
              "      <th>vacuolisation_damage_basal_layer</th>\n",
              "      <th>spongiosis</th>\n",
              "      <th>saw_tooth_appearance_retes</th>\n",
              "      <th>follicular_horn_plug</th>\n",
              "      <th>perifollicular_parakeratosis</th>\n",
              "      <th>inflammatory_mononuclear_infiltrate</th>\n",
              "      <th>band_like_infiltrate</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.068306</td>\n",
              "      <td>1.795082</td>\n",
              "      <td>1.549180</td>\n",
              "      <td>1.366120</td>\n",
              "      <td>0.633880</td>\n",
              "      <td>0.448087</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.377049</td>\n",
              "      <td>0.614754</td>\n",
              "      <td>0.519126</td>\n",
              "      <td>...</td>\n",
              "      <td>0.393443</td>\n",
              "      <td>0.464481</td>\n",
              "      <td>0.456284</td>\n",
              "      <td>0.953552</td>\n",
              "      <td>0.453552</td>\n",
              "      <td>0.103825</td>\n",
              "      <td>0.114754</td>\n",
              "      <td>1.866120</td>\n",
              "      <td>0.554645</td>\n",
              "      <td>2.803279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.664753</td>\n",
              "      <td>0.701527</td>\n",
              "      <td>0.907525</td>\n",
              "      <td>1.138299</td>\n",
              "      <td>0.908016</td>\n",
              "      <td>0.957327</td>\n",
              "      <td>0.570588</td>\n",
              "      <td>0.834147</td>\n",
              "      <td>0.982979</td>\n",
              "      <td>0.905639</td>\n",
              "      <td>...</td>\n",
              "      <td>0.849406</td>\n",
              "      <td>0.864899</td>\n",
              "      <td>0.954873</td>\n",
              "      <td>1.130172</td>\n",
              "      <td>0.954744</td>\n",
              "      <td>0.450433</td>\n",
              "      <td>0.488723</td>\n",
              "      <td>0.726108</td>\n",
              "      <td>1.105908</td>\n",
              "      <td>1.597803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 34 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-32b71064-2996-47a6-b290-7bad4cb813d3');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5deec2fb-88b9-4f5f-b9e6-febe91066f44')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "</svg>\n",
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              "\n",
              "<style>\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5deec2fb-88b9-4f5f-b9e6-febe91066f44 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Vinhos"
      ],
      "metadata": {
        "id": "Ui3FeTO31d77"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df  = pd.read_csv('https://raw.githubusercontent.com/alura-cursos/classificacao_multiclasse/main/Dados/df_vinho.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "id": "ibuZTl3m1fiq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0730838b-ad2b-4971-8e77-5959c234de13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   acidez fixa  acidez volátil  ácido cítrico  açúcar residual  cloretos  \\\n",
              "0          7.4            0.70           0.00              1.9     0.076   \n",
              "1          7.8            0.88           0.00              2.6     0.098   \n",
              "2          7.8            0.76           0.04              2.3     0.092   \n",
              "3         11.2            0.28           0.56              1.9     0.075   \n",
              "4          7.4            0.66           0.00              1.8     0.075   \n",
              "\n",
              "   dióxido de enxofre livre   dióxido de enxofre total  densidade    pH  \\\n",
              "0                       11.0                      34.0     0.9978  3.51   \n",
              "1                       25.0                      67.0     0.9968  3.20   \n",
              "2                       15.0                      54.0     0.9970  3.26   \n",
              "3                       17.0                      60.0     0.9980  3.16   \n",
              "4                       13.0                      40.0     0.9978  3.51   \n",
              "\n",
              "   sulfatos  álcool qualidade  \n",
              "0      0.56     9.4   mediano  \n",
              "1      0.68     9.8   mediano  \n",
              "2      0.65     9.8   mediano  \n",
              "3      0.58     9.8   mediano  \n",
              "4      0.56     9.4   mediano  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-915a77e6-40b6-412e-88c1-7d6c50ee1bb6\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>acidez fixa</th>\n",
              "      <th>acidez volátil</th>\n",
              "      <th>ácido cítrico</th>\n",
              "      <th>açúcar residual</th>\n",
              "      <th>cloretos</th>\n",
              "      <th>dióxido de enxofre livre</th>\n",
              "      <th>dióxido de enxofre total</th>\n",
              "      <th>densidade</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulfatos</th>\n",
              "      <th>álcool</th>\n",
              "      <th>qualidade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>mediano</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>mediano</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>mediano</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>mediano</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.075</td>\n",
              "      <td>13.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>mediano</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-915a77e6-40b6-412e-88c1-7d6c50ee1bb6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-915a77e6-40b6-412e-88c1-7d6c50ee1bb6 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-915a77e6-40b6-412e-88c1-7d6c50ee1bb6');\n",
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              "          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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c450c58a-50aa-45bc-9bf5-a78611edbb9f')\"\n",
              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
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              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c450c58a-50aa-45bc-9bf5-a78611edbb9f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1359,\n  \"fields\": [\n    {\n      \"column\": \"acidez fixa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7369898075324683,\n        \"min\": 4.6,\n        \"max\": 15.9,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          5.3,\n          12.7,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acidez vol\\u00e1til\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1830313176190719,\n        \"min\": 0.12,\n        \"max\": 1.58,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          1.025,\n          0.4,\n          0.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u00e1cido c\\u00edtrico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1955365445504638,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.37,\n          0.0,\n          0.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"a\\u00e7\\u00facar residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3523137577104225,\n        \"min\": 0.9,\n        \"max\": 15.5,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          11.0,\n          3.0,\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cloretos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04937686244348607,\n        \"min\": 0.012,\n        \"max\": 0.611,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.096,\n          0.3429999999999999,\n          0.159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"di\\u00f3xido de enxofre livre \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.447270259048693,\n        \"min\": 1.0,\n        \"max\": 72.0,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          11.0,\n          9.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"di\\u00f3xido de enxofre total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.40894570661652,\n        \"min\": 6.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          68.0,\n          35.0,\n          101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"densidade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018689171325591413,\n        \"min\": 0.99007,\n        \"max\": 1.00369,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          0.99974,\n          1.0001,\n          0.99471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15503631128729617,\n        \"min\": 2.74,\n        \"max\": 4.01,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          3.07,\n          3.0,\n          3.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulfatos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17066689057420686,\n        \"min\": 0.33,\n        \"max\": 2.0,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          1.07,\n          1.04,\n          1.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u00e1lcool\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0820654499402542,\n        \"min\": 8.4,\n        \"max\": 14.9,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          8.5,\n          9.95,\n          9.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualidade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"mediano\",\n          \"bom\",\n          \"ruim\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qN1JYTjr0mY",
        "outputId": "3adf619f-078b-4b5a-a559-f447811315fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1359 entries, 0 to 1358\n",
            "Data columns (total 12 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   acidez fixa                1359 non-null   float64\n",
            " 1   acidez volátil             1359 non-null   float64\n",
            " 2   ácido cítrico              1359 non-null   float64\n",
            " 3   açúcar residual            1359 non-null   float64\n",
            " 4   cloretos                   1359 non-null   float64\n",
            " 5   dióxido de enxofre livre   1359 non-null   float64\n",
            " 6   dióxido de enxofre total   1359 non-null   float64\n",
            " 7   densidade                  1359 non-null   float64\n",
            " 8   pH                         1359 non-null   float64\n",
            " 9   sulfatos                   1359 non-null   float64\n",
            " 10  álcool                     1359 non-null   float64\n",
            " 11  qualidade                  1359 non-null   object \n",
            "dtypes: float64(11), object(1)\n",
            "memory usage: 127.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('qualidade', axis=1)\n",
        "y = df['qualidade']"
      ],
      "metadata": {
        "id": "93O-lbLqsGtS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X, X_test, y, y_test = train_test_split(X, y, test_size=0.15,stratify=y, random_state=0)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, stratify=y, random_state=0)"
      ],
      "metadata": {
        "id": "33ZJL7YWseI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "ex_model = RandomForestClassifier(random_state=0, max_depth=4)\n",
        "ex_model.fit(X_train, y_train)\n",
        "y_pred = ex_model.predict(X_val)\n",
        "print(f'Acurácia de treino: {ex_model.score(X_train, y_train)}')\n",
        "print(f'Acurácia de validação: {ex_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j84Jg92YtLcJ",
        "outputId": "93811362-27b8-49cd-864c-30d2ab4e4869"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia de treino: 0.8545034642032333\n",
            "Acurácia de validação: 0.8477508650519031\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(ex_model, X_val, y_val, normalize='true', cmap='Blues');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "m2u42qB3ttMO",
        "outputId": "0327c237-3e5e-43c7-8a82-36934bb60641"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "report = classification_report(y_val, y_pred)\n",
        "print(\"Relatório de Classificação:\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTXhJLlwvt7e",
        "outputId": "47baab39-5e4d-446b-9259-ff1f32393b74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relatório de Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         bom       1.00      0.23      0.38        39\n",
            "     mediano       0.84      1.00      0.91       236\n",
            "        ruim       0.00      0.00      0.00        14\n",
            "\n",
            "    accuracy                           0.85       289\n",
            "   macro avg       0.61      0.41      0.43       289\n",
            "weighted avg       0.82      0.85      0.80       289\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()\n",
        "#Considerar o balanceamento dos dados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "KQt9AUhwyS1n",
        "outputId": "3e9f26cf-6000-4df4-a35d-103d3df8f83f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "qualidade\n",
              "mediano    709\n",
              "bom        117\n",
              "ruim        40\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qualidade</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mediano</th>\n",
              "      <td>709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bom</th>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ruim</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oversample = SMOTE(random_state=0)\n",
        "X_balanced, y_balanced = oversample.fit_resample(X_train, y_train)\n",
        "wine_model = RandomForestClassifier(random_state=0, max_depth=4)\n",
        "wine_model.fit(X_balanced, y_balanced)\n",
        "y_pred = wine_model.predict(X_val)\n",
        "print(classification_report(y_val, y_pred))\n",
        "ConfusionMatrixDisplay.from_predictions(y_val, y_pred, normalize='true', cmap='Blues');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "Q0ryJs6s0TU0",
        "outputId": "7eee90e3-bf1f-4204-b3ff-14477dcf44e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         bom       0.36      0.77      0.49        39\n",
            "     mediano       0.92      0.56      0.69       236\n",
            "        ruim       0.13      0.57      0.21        14\n",
            "\n",
            "    accuracy                           0.59       289\n",
            "   macro avg       0.47      0.63      0.47       289\n",
            "weighted avg       0.80      0.59      0.64       289\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.pipeline import Pipeline as imbpipeline\n",
        "modelo = RandomForestClassifier(max_depth = 4)\n",
        "pipeline = imbpipeline([('oversample', SMOTE()), ('floresta', modelo)])"
      ],
      "metadata": {
        "id": "tbCnGkVY1C8L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_validate, StratifiedKFold\n",
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)\n",
        "cv_resultados = cross_validate(pipeline, X, y, cv=skf, scoring='recall_weighted')\n",
        "cv_resultados['test_score']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OylCIxDo1FHi",
        "outputId": "951e6d75-cd1e-4ffb-e075-938d050be232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.60606061, 0.65367965, 0.6017316 , 0.65367965, 0.63203463])"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "media = cv_resultados['test_score'].mean()\n",
        "desvio_padrao = cv_resultados['test_score'].std()\n",
        "print(f'Intervalo de confiança: [{media-2*desvio_padrao}, {min(media+2*desvio_padrao, 1.0)}]')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHbEDAGp1KHA",
        "outputId": "c2ad3e7d-e5c6-42ef-8f00-522dd131cac7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intervalo de confiança: [0.5847498254525926, 0.6741246334218666]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oversample = SMOTE(random_state=0)\n",
        "X_balanced, y_balanced = oversample.fit_resample(X, y)\n",
        "modelo = RandomForestClassifier(random_state=0, max_depth=4)\n",
        "modelo.fit(X_balanced, y_balanced)\n",
        "y_pred = modelo.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, y_pred))\n",
        "ConfusionMatrixDisplay.from_predictions(y_test, y_pred, normalize='true', cmap='Blues');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VqcR0dS1MNu",
        "outputId": "41e24d8a-56e1-4e52-fcb6-7bc2b717136f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         bom       0.45      0.89      0.60        28\n",
            "     mediano       0.96      0.69      0.81       167\n",
            "        ruim       0.18      0.56      0.27         9\n",
            "\n",
            "    accuracy                           0.72       204\n",
            "   macro avg       0.53      0.71      0.56       204\n",
            "weighted avg       0.86      0.72      0.75       204\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Iris de flores"
      ],
      "metadata": {
        "id": "ZJorjTDW2ch9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iris = pd.read_csv('https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv')\n",
        "iris.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ejTGQJWD2iDl",
        "outputId": "d673b107-ebfc-4a28-9da5-0e80919c6870"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e3370e6e-1426-42f1-9798-50cbd2b661a0\" 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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3370e6e-1426-42f1-9798-50cbd2b661a0')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-e3370e6e-1426-42f1-9798-50cbd2b661a0 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-e3370e6e-1426-42f1-9798-50cbd2b661a0');\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",
              "\n",
              "\n",
              "<div id=\"df-5a207eee-462a-4e0a-b027-c20b82855716\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a207eee-462a-4e0a-b027-c20b82855716')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5a207eee-462a-4e0a-b027-c20b82855716 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "iris",
              "summary": "{\n  \"name\": \"iris\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522626,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008411,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tb37pvvb3ZYt",
        "outputId": "cce711cb-3eb0-4e6d-ff96-aa2dab10c3fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal_length  150 non-null    float64\n",
            " 1   sepal_width   150 non-null    float64\n",
            " 2   petal_length  150 non-null    float64\n",
            " 3   petal_width   150 non-null    float64\n",
            " 4   species       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = iris.drop('species', axis=1)\n",
        "y = iris['species']"
      ],
      "metadata": {
        "id": "TgyjKeqW3ced"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X, X_test, y, y_test = train_test_split(X, y, test_size=0.15,stratify=y, random_state=0)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, stratify=y, random_state=0)"
      ],
      "metadata": {
        "id": "ssjDhbnu3jS4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "ex_model = RandomForestClassifier(random_state=0, max_depth=10)\n",
        "ex_model.fit(X_train, y_train)\n",
        "y_pred = ex_model.predict(X_val)\n",
        "print(f'Acurácia de treino: {ex_model.score(X_train, y_train)}')\n",
        "print(f'Acurácia de validação: {ex_model.score(X_val, y_val)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rS2vrcLP6GO4",
        "outputId": "757c14d6-fdce-4b5d-c8bc-5e1af063c84c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia de treino: 1.0\n",
            "Acurácia de validação: 0.96875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(ex_model, X_val, y_val, normalize='true', cmap='Blues');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "xYTKfhC-6JZb",
        "outputId": "123cfb12-9f34-4f33-9cf6-48dacaa35141"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "report = classification_report(y_val, y_pred)\n",
        "print(\"Relatório de Classificação:\\n\", report)"
      ],
      "metadata": {
        "id": "OaZ_T3qh6Oe9",
        "outputId": "4864b380-41a1-4407-9a4b-abd10ee516c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relatório de Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        11\n",
            "  versicolor       1.00      0.90      0.95        10\n",
            "   virginica       0.92      1.00      0.96        11\n",
            "\n",
            "    accuracy                           0.97        32\n",
            "   macro avg       0.97      0.97      0.97        32\n",
            "weighted avg       0.97      0.97      0.97        32\n",
            "\n"
          ]
        }
      ]
    }
  ]
}