ML_BehindTheScenes.ipynb

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        "<a href=\"https://colab.research.google.com/github/NickPtav/Understanding_ML/blob/main/ML_BehindTheScenes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## Importando dados do Kaggle\n",
        "## Empresa de Telecomunicação"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# IMPORTANDO OS DADOS\n"
      ],
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        "import pandas as pd"
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      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "uri = \"https://raw.githubusercontent.com/alura-cursos/ML_Classificacao_por_tras_dos_panos/main/Dados/Customer-Churn.csv\""
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        "id": "BCMpH0yfbwFn"
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      "outputs": []
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        "dados = pd.read_csv(uri)"
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        "id": "7jN952JFaniP"
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        "dados.shape"
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      "source": [
        "dados.head()"
      ],
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              "   Maior65Anos Conjuge Dependentes  MesesDeContrato TelefoneFixo  \\\n",
              "0            0     Sim         Nao                1          Nao   \n",
              "1            0     Nao         Nao               34          Sim   \n",
              "2            0     Nao         Nao                2          Sim   \n",
              "3            0     Nao         Nao               45          Nao   \n",
              "4            0     Nao         Nao                2          Sim   \n",
              "\n",
              "  VariasLinhasTelefonicas ServicoDeInternet SegurancaOnline BackupOnline  \\\n",
              "0    SemServicoTelefonico               DSL             Nao          Sim   \n",
              "1                     Nao               DSL             Sim          Nao   \n",
              "2                     Nao               DSL             Sim          Sim   \n",
              "3    SemServicoTelefonico               DSL             Sim          Nao   \n",
              "4                     Nao       FibraOptica             Nao          Nao   \n",
              "\n",
              "  SeguroNoDispositivo SuporteTecnico TVaCabo StreamingDeFilmes TipoDeContrato  \\\n",
              "0                 Nao            Nao     Nao               Nao    Mensalmente   \n",
              "1                 Sim            Nao     Nao               Nao          UmAno   \n",
              "2                 Nao            Nao     Nao               Nao    Mensalmente   \n",
              "3                 Sim            Sim     Nao               Nao          UmAno   \n",
              "4                 Nao            Nao     Nao               Nao    Mensalmente   \n",
              "\n",
              "  PagamentoOnline FormaDePagamento  ContaMensal Churn  \n",
              "0             Sim    ChequeDigital        29.85   Nao  \n",
              "1             Nao      ChequePapel        56.95   Nao  \n",
              "2             Sim      ChequePapel        53.85   Sim  \n",
              "3             Nao    DebitoEmConta        42.30   Nao  \n",
              "4             Sim    ChequeDigital        70.70   Sim  "
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        "# Passar as classificações de qualitativas para quantitativas\n",
        "#forma manual, escolhendo as colunas específicas e modificando\n",
        "\n",
        "replace = {\n",
        "    \"Sim\" : 1,\n",
        "    \"Nao\" : 0\n",
        "}\n",
        "\n",
        "dadosmodificados = dados [['Conjuge', 'Dependentes', 'TelefoneFixo', 'PagamentoOnline', 'Churn']].replace(replace)"
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              "   Conjuge  Dependentes  TelefoneFixo  PagamentoOnline  Churn\n",
              "0        1            0             0                1      0\n",
              "1        0            0             1                0      0\n",
              "2        0            0             1                1      1\n",
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              "\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",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Agora com o Dummies, faremos a modificação de forma automatica\n",
        "#Porem, como algumas colunas já foram modificadas, teremos que ignoraas na modificação com o dummies, utilizando o .drop\n",
        "\n",
        "dados_dummies = pd.get_dummies(dados.drop(['Conjuge', 'Dependentes', 'TelefoneFixo', 'PagamentoOnline', 'Churn'], axis = 1))\n",
        "\n",
        "#Para juntar os dados de \"dados_dummies\", com os dados modificados, usamos a concatenação\n",
        "\n",
        "dados_final = pd.concat([dadosmodificados, dados_dummies], axis = 1 )\n",
        "\n"
      ],
      "metadata": {
        "id": "hTLNW9k2aYZU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "dados_final.head()"
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        "id": "-FWr6EBpbwxt",
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        "pd.set_option('display.max_columns', 39)"
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      "metadata": {
        "id": "lLpm9rHAbyJ1"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "dados_final.head()"
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      "metadata": {
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          "height": 270
        },
        "id": "5J0sEF51j0On",
        "outputId": "ee958474-2bfd-4f0e-8fc3-e27ff575b76d"
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      "execution_count": null,
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Conjuge</th>\n",
              "      <th>Dependentes</th>\n",
              "      <th>TelefoneFixo</th>\n",
              "      <th>PagamentoOnline</th>\n",
              "      <th>Churn</th>\n",
              "      <th>Maior65Anos</th>\n",
              "      <th>MesesDeContrato</th>\n",
              "      <th>ContaMensal</th>\n",
              "      <th>VariasLinhasTelefonicas_Nao</th>\n",
              "      <th>VariasLinhasTelefonicas_SemServicoTelefonico</th>\n",
              "      <th>VariasLinhasTelefonicas_Sim</th>\n",
              "      <th>ServicoDeInternet_DSL</th>\n",
              "      <th>ServicoDeInternet_FibraOptica</th>\n",
              "      <th>ServicoDeInternet_Nao</th>\n",
              "      <th>SegurancaOnline_Nao</th>\n",
              "      <th>SegurancaOnline_SemServicoDeInternet</th>\n",
              "      <th>SegurancaOnline_Sim</th>\n",
              "      <th>BackupOnline_Nao</th>\n",
              "      <th>BackupOnline_SemServicoDeInternet</th>\n",
              "      <th>BackupOnline_Sim</th>\n",
              "      <th>SeguroNoDispositivo_Nao</th>\n",
              "      <th>SeguroNoDispositivo_SemServicoDeInternet</th>\n",
              "      <th>SeguroNoDispositivo_Sim</th>\n",
              "      <th>SuporteTecnico_Nao</th>\n",
              "      <th>SuporteTecnico_SemServicoDeInternet</th>\n",
              "      <th>SuporteTecnico_Sim</th>\n",
              "      <th>TVaCabo_Nao</th>\n",
              "      <th>TVaCabo_SemServicoDeInternet</th>\n",
              "      <th>TVaCabo_Sim</th>\n",
              "      <th>StreamingDeFilmes_Nao</th>\n",
              "      <th>StreamingDeFilmes_SemServicoDeInternet</th>\n",
              "      <th>StreamingDeFilmes_Sim</th>\n",
              "      <th>TipoDeContrato_DoisAnos</th>\n",
              "      <th>TipoDeContrato_Mensalmente</th>\n",
              "      <th>TipoDeContrato_UmAno</th>\n",
              "      <th>FormaDePagamento_CartaoDeCredito</th>\n",
              "      <th>FormaDePagamento_ChequeDigital</th>\n",
              "      <th>FormaDePagamento_ChequePapel</th>\n",
              "      <th>FormaDePagamento_DebitoEmConta</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.85</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>53.85</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>42.30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>70.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-732cb233-3b58-4add-a667-fcc9fbfe2ce7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-732cb233-3b58-4add-a667-fcc9fbfe2ce7 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-732cb233-3b58-4add-a667-fcc9fbfe2ce7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "ax = sns.countplot(x='Churn', data=dados_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "OO9NUgpKj19Y",
        "outputId": "e6a55cab-cded-4a63-837c-a614dd514668"
      },
      "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": [
        "Através disso, é visto que os dados estão desbalanceados, para resolver isso, será utilizado o OverSampling. Vamos utilizar a classe SMOTE."
      ],
      "metadata": {
        "id": "uBViFRMQsMd8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Para podermos aplicar o SMOTE, devemos separar  os dados em variáveis características e resposta\n",
        "\n",
        "X = dados_final.drop('Churn', axis = 1) #Em X, pegamos todos os dados, menos o Churn\n",
        "y = dados_final['Churn'] #Em Y ficamos apenas com o churn"
      ],
      "metadata": {
        "id": "gKCKdLS8sWWq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ],
      "metadata": {
        "id": "jDtKeBR3sf-O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smt = SMOTE( random_state = 123)\n",
        "X, y = smt.fit_resample(X, y) # Realiza a reamostragem do conjunto de dados"
      ],
      "metadata": {
        "id": "zuuwDhhbsmnc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_final = pd.concat([X,y], axis = 1)"
      ],
      "metadata": {
        "id": "5mVTuH9Rs4Xe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_final.head(2)"
      ],
      "metadata": {
        "id": "n-MTEvgNs_BU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "3d5c0543-e0ca-4012-d31f-3ce0979e34ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Conjuge  Dependentes  TelefoneFixo  PagamentoOnline  Maior65Anos  \\\n",
              "0        1            0             0                1            0   \n",
              "1        0            0             1                0            0   \n",
              "\n",
              "   MesesDeContrato  ContaMensal  VariasLinhasTelefonicas_Nao  \\\n",
              "0                1        29.85                            0   \n",
              "1               34        56.95                            1   \n",
              "\n",
              "   VariasLinhasTelefonicas_SemServicoTelefonico  VariasLinhasTelefonicas_Sim  \\\n",
              "0                                             1                            0   \n",
              "1                                             0                            0   \n",
              "\n",
              "   ServicoDeInternet_DSL  ServicoDeInternet_FibraOptica  \\\n",
              "0                      1                              0   \n",
              "1                      1                              0   \n",
              "\n",
              "   ServicoDeInternet_Nao  SegurancaOnline_Nao  \\\n",
              "0                      0                    1   \n",
              "1                      0                    0   \n",
              "\n",
              "   SegurancaOnline_SemServicoDeInternet  SegurancaOnline_Sim  \\\n",
              "0                                     0                    0   \n",
              "1                                     0                    1   \n",
              "\n",
              "   BackupOnline_Nao  BackupOnline_SemServicoDeInternet  BackupOnline_Sim  \\\n",
              "0                 0                                  0                 1   \n",
              "1                 1                                  0                 0   \n",
              "\n",
              "   SeguroNoDispositivo_Nao  SeguroNoDispositivo_SemServicoDeInternet  \\\n",
              "0                        1                                         0   \n",
              "1                        0                                         0   \n",
              "\n",
              "   SeguroNoDispositivo_Sim  SuporteTecnico_Nao  \\\n",
              "0                        0                   1   \n",
              "1                        1                   1   \n",
              "\n",
              "   SuporteTecnico_SemServicoDeInternet  SuporteTecnico_Sim  TVaCabo_Nao  \\\n",
              "0                                    0                   0            1   \n",
              "1                                    0                   0            1   \n",
              "\n",
              "   TVaCabo_SemServicoDeInternet  TVaCabo_Sim  StreamingDeFilmes_Nao  \\\n",
              "0                             0            0                      1   \n",
              "1                             0            0                      1   \n",
              "\n",
              "   StreamingDeFilmes_SemServicoDeInternet  StreamingDeFilmes_Sim  \\\n",
              "0                                       0                      0   \n",
              "1                                       0                      0   \n",
              "\n",
              "   TipoDeContrato_DoisAnos  TipoDeContrato_Mensalmente  TipoDeContrato_UmAno  \\\n",
              "0                        0                           1                     0   \n",
              "1                        0                           0                     1   \n",
              "\n",
              "   FormaDePagamento_CartaoDeCredito  FormaDePagamento_ChequeDigital  \\\n",
              "0                                 0                               1   \n",
              "1                                 0                               0   \n",
              "\n",
              "   FormaDePagamento_ChequePapel  FormaDePagamento_DebitoEmConta  Churn  \n",
              "0                             0                               0      0  \n",
              "1                             1                               0      0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1992581d-746e-4826-9962-829b7d1558a7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Conjuge</th>\n",
              "      <th>Dependentes</th>\n",
              "      <th>TelefoneFixo</th>\n",
              "      <th>PagamentoOnline</th>\n",
              "      <th>Maior65Anos</th>\n",
              "      <th>MesesDeContrato</th>\n",
              "      <th>ContaMensal</th>\n",
              "      <th>VariasLinhasTelefonicas_Nao</th>\n",
              "      <th>VariasLinhasTelefonicas_SemServicoTelefonico</th>\n",
              "      <th>VariasLinhasTelefonicas_Sim</th>\n",
              "      <th>ServicoDeInternet_DSL</th>\n",
              "      <th>ServicoDeInternet_FibraOptica</th>\n",
              "      <th>ServicoDeInternet_Nao</th>\n",
              "      <th>SegurancaOnline_Nao</th>\n",
              "      <th>SegurancaOnline_SemServicoDeInternet</th>\n",
              "      <th>SegurancaOnline_Sim</th>\n",
              "      <th>BackupOnline_Nao</th>\n",
              "      <th>BackupOnline_SemServicoDeInternet</th>\n",
              "      <th>BackupOnline_Sim</th>\n",
              "      <th>SeguroNoDispositivo_Nao</th>\n",
              "      <th>SeguroNoDispositivo_SemServicoDeInternet</th>\n",
              "      <th>SeguroNoDispositivo_Sim</th>\n",
              "      <th>SuporteTecnico_Nao</th>\n",
              "      <th>SuporteTecnico_SemServicoDeInternet</th>\n",
              "      <th>SuporteTecnico_Sim</th>\n",
              "      <th>TVaCabo_Nao</th>\n",
              "      <th>TVaCabo_SemServicoDeInternet</th>\n",
              "      <th>TVaCabo_Sim</th>\n",
              "      <th>StreamingDeFilmes_Nao</th>\n",
              "      <th>StreamingDeFilmes_SemServicoDeInternet</th>\n",
              "      <th>StreamingDeFilmes_Sim</th>\n",
              "      <th>TipoDeContrato_DoisAnos</th>\n",
              "      <th>TipoDeContrato_Mensalmente</th>\n",
              "      <th>TipoDeContrato_UmAno</th>\n",
              "      <th>FormaDePagamento_CartaoDeCredito</th>\n",
              "      <th>FormaDePagamento_ChequeDigital</th>\n",
              "      <th>FormaDePagamento_ChequePapel</th>\n",
              "      <th>FormaDePagamento_DebitoEmConta</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.85</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1992581d-746e-4826-9962-829b7d1558a7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1992581d-746e-4826-9962-829b7d1558a7 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-1992581d-746e-4826-9962-829b7d1558a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.countplot(x = 'Churn', data = dados_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "yMljQAUXtCpR",
        "outputId": "b73c69a2-c957-475d-d69a-20ab6ce541a9"
      },
      "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": [
        "len(dados_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXuzqnrPtQXz",
        "outputId": "631c51ad-a619-48fe-a977-05a0a08e2d8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10348"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "Q46d7k77uXNV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculando a distância entre maria e o primeiro cliente cadastrado nos dados\n"
      ],
      "metadata": {
        "id": "LRqgDSDo-w-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dados_final.drop('Churn', axis = 1)\n",
        "y = dados_final['Churn']"
      ],
      "metadata": {
        "id": "HXL5452rzlRm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# É necessária fazer uma padronização dos dados, para isso utilizamos o sklearn, scaler\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "mrjf8efazxGh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "norm = StandardScaler()\n",
        "X_normalizado = norm.fit_transform(X)\n",
        "X_normalizado"
      ],
      "metadata": {
        "id": "F_uh7qmrz9_2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59168a75-ff66-4a9c-e887-96420da4d483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.26542767, -0.52771004, -3.06083934, ...,  1.30373042,\n",
              "        -0.47912706, -0.4360152 ],\n",
              "       [-0.79024667, -0.52771004,  0.32670777, ..., -0.76702973,\n",
              "         2.08712905, -0.4360152 ],\n",
              "       [-0.79024667, -0.52771004,  0.32670777, ..., -0.76702973,\n",
              "         2.08712905, -0.4360152 ],\n",
              "       ...,\n",
              "       [-0.79024667, -0.52771004,  0.32670777, ...,  1.30373042,\n",
              "        -0.47912706, -0.4360152 ],\n",
              "       [-0.79024667, -0.52771004,  0.32670777, ..., -0.76702973,\n",
              "        -0.47912706, -0.4360152 ],\n",
              "       [-0.79024667, -0.52771004,  0.32670777, ..., -0.76702973,\n",
              "        -0.47912706, -0.4360152 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Xmaria = [[0,0,1,1,0,0,39.90,1,0,0,0,1,0,1,0,0,0,0,1,1,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,1]]"
      ],
      "metadata": {
        "id": "YB8-Zy9t0MyM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xmaria_normalizado = norm.transform(pd.DataFrame(Xmaria, columns = X.columns))"
      ],
      "metadata": {
        "id": "0pWG1UTB49OR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "XL67eNA95Jbf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "a = Xmaria_normalizado[0]\n",
        "b = X_normalizado[0]"
      ],
      "metadata": {
        "id": "maSJyFuf5mI-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Para aplicar a distância euclidiana, precisamos:\n",
        "#Somatório, de (a-b)**2\n",
        "\n",
        "c = np.square((a-b))"
      ],
      "metadata": {
        "id": "VabHyZfg5teR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "soma = np.sum(c)"
      ],
      "metadata": {
        "id": "HqB7tCtZ596u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.sqrt(soma)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsWc0oLf6EMX",
        "outputId": "4b59cf43-57ac-4bc9-dcab-30c6870505e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.576596663360199"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-ayRHkG36s9u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para calcular a distância entre Maria e os 10 primeiros clientes, de uma vez:"
      ],
      "metadata": {
        "id": "LTTm8UNZ-2tQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calcula_distancia(dados_clientes, dados_maria, numero_clientes):\n",
        "    distancias = []\n",
        "\n",
        "#loop para percorrer cliente por cliente\n",
        "    for i in range(numero_clientes):\n",
        "        dist1 = dados_maria - dados_clientes[i]            #subtração dos atributos\n",
        "        soma_quadrado = np.sum(np.square(dist1))           #exponenciação e soma de todos os atributos\n",
        "        distancias.append(np.sqrt(soma_quadrado))          #raiz quadrada da soma anterior\n",
        "\n",
        "    return distancias"
      ],
      "metadata": {
        "id": "lPhh5Rtr-9kW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "calcula_distancia(X_normalizado, Xmaria_normalizado, 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mD_-Ewu8-_GO",
        "outputId": "a71c01e1-3572-493e-a4d1-79b497d729a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9.576596663360199,\n",
              " 9.164687977147782,\n",
              " 8.497169420877166,\n",
              " 10.037544220142902,\n",
              " 7.8028994013625566,\n",
              " 9.01167938964265,\n",
              " 8.970041771751976,\n",
              " 10.634917481179656,\n",
              " 9.45035230511453,\n",
              " 7.919985943117673]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Implementando o modelo"
      ],
      "metadata": {
        "id": "q0awoSSo8Kf6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#biblioteca para divisão dos dados entre treino e teste"
      ],
      "metadata": {
        "id": "wX1UkwzB8LdG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "X3VS9ZeC8OKV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X_normalizado, y, test_size = 0.3, random_state = 123)"
      ],
      "metadata": {
        "id": "yc8YOamW8TGm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g15T7RNT8gTX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Treino e teste"
      ],
      "metadata": {
        "id": "27UXpy0P9JU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "IsvyEVA19KcN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando o modelo e definindo o tipo de distancia, nesse caso, euclidiano\n",
        "knn = KNeighborsClassifier(metric = 'euclidean')"
      ],
      "metadata": {
        "id": "bKw-QAOe9VVj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# treinando o modelo\n",
        "knn.fit(X_treino, y_treino)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "1O_8XR7E9iwF",
        "outputId": "dc72e11d-f390-481c-ace7-dc36e8da8842"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(metric=&#x27;euclidean&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(metric=&#x27;euclidean&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#testando o modelo\n",
        "predito_knn = knn.predict(X_teste)"
      ],
      "metadata": {
        "id": "ZkQxho_B9r4P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predito_knn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_lqJLM1-FEw",
        "outputId": "632b3bee-bd29-4d31-c1a4-86b447339901"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, ..., 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pJil1mAj-Ltc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.3 Treino e Teste"
      ],
      "metadata": {
        "id": "EV43xhQtAI-t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_treino"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xgrDpxdXALeq",
        "outputId": "8aca711f-594c-435b-def4-13344cd453a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.79024667, -0.52771004, -3.06083934, ..., -0.76702973,\n",
              "         2.08712905, -0.4360152 ],\n",
              "       [ 1.26542767,  1.89498006,  0.32670777, ..., -0.76702973,\n",
              "         2.08712905, -0.4360152 ],\n",
              "       [-0.79024667, -0.52771004,  0.32670777, ...,  1.30373042,\n",
              "        -0.47912706, -0.4360152 ],\n",
              "       ...,\n",
              "       [ 1.26542767,  1.89498006,  0.32670777, ..., -0.76702973,\n",
              "         2.08712905, -0.4360152 ],\n",
              "       [ 1.26542767,  1.89498006,  0.32670777, ...,  1.30373042,\n",
              "        -0.47912706, -0.4360152 ],\n",
              "       [-0.79024667, -0.52771004,  0.32670777, ...,  1.30373042,\n",
              "        -0.47912706, -0.4360152 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_treino"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSdO7553AXYB",
        "outputId": "452ca480-45e2-4fc5-9e09-338b33d9f64a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4831    0\n",
              "5077    0\n",
              "9023    1\n",
              "4424    1\n",
              "5236    0\n",
              "       ..\n",
              "9785    1\n",
              "7763    1\n",
              "5218    0\n",
              "1346    1\n",
              "3582    0\n",
              "Name: Churn, Length: 7243, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.median(X_treino, axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_W7RGzQAqLW",
        "outputId": "28d00fe8-74a9-44ad-fe81-e50bdda6a355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.79024667, -0.52771004,  0.32670777,  0.81482091, -0.41318358,\n",
              "       -0.2744302 ,  0.23927583, -0.88447396, -0.31386908, -0.83366906,\n",
              "       -0.66107688,  0.95797538, -0.44617598,  0.86827317, -0.44617598,\n",
              "       -0.51721942, -0.95834643, -0.44617598, -0.63536575, -0.95890326,\n",
              "       -0.44617598, -0.64211419,  0.87979784, -0.44617598, -0.52426638,\n",
              "       -0.84252477, -0.44617598, -0.77707385, -0.83117367, -0.44617598,\n",
              "       -0.78976268, -0.44648735,  0.73788726, -0.41989297, -0.42576817,\n",
              "       -0.76702973, -0.47912706, -0.4360152 ])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import BernoulliNB"
      ],
      "metadata": {
        "id": "ty5AUfBFCX0y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Binarize os recursos usando a mediana\n",
        "X_treino_binarizado = np.where(X_treino > np.median(X_treino), 1, 0)\n",
        "\n",
        "# Crie e ajuste o classificador BernoulliNB\n",
        "bnb = BernoulliNB()\n",
        "bnb.fit(X_treino_binarizado, y_treino)\n",
        "\n",
        "''' Nesse ponto foi pego a mediana, e no binarize, todos os valores acima dele viram 1\n",
        "e todos os numeros a baixo viram 0, assim os valores de X_treino ficaram binários. '''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "hmbyGqwlCdw4",
        "outputId": "66ff6dd5-1f40-437e-8820-562fa23cd468"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' Nesse ponto foi pego a mediana, e no binarize, todos os valores acima dele viram 1\\ne todos os numeros a baixo viram 0, assim os valores de X_treino ficaram binários. '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predito_BNb = bnb.predict(X_teste)"
      ],
      "metadata": {
        "id": "DIUDyi15DM9T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predito_BNb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eecoiuHeFkwL",
        "outputId": "b6585851-4c45-40b6-bd41-8f327c49ba46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-uee-uo4Fm2_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}