Notebooks/Aula_1.ipynb

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      "source": [
        "#**Aula 1 - Tratando os Dados**"
      ],
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        "id": "t0hKQ2fXP7HB"
      }
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        "id": "doraVfEMHysN"
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      "source": [
        "import pandas as pd"
      ]
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      "source": [
        "cadastrados = pd.read_csv('https://github.com/alura-tech/alura-tech-pos-data-science-credit-scoring-streamlit/blob/main/dados/clientes_cadastrados.csv?raw=True')"
      ],
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        "cadastrados.head()"
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              "   ID_Cliente Genero Tem_carro Tem_casa_propria  Qtd_filhos  Rendimento_anual  \\\n",
              "0     5008804      M         Y                Y           0          427500.0   \n",
              "1     5008805      M         Y                Y           0          427500.0   \n",
              "2     5008806      M         Y                Y           0          112500.0   \n",
              "3     5008808      F         N                Y           0          270000.0   \n",
              "4     5008809      F         N                Y           0          270000.0   \n",
              "\n",
              "    Categoria_de_renda Grau_escolaridade   Estado_civil  \\\n",
              "0            Empregado   Ensino superior  União-estável   \n",
              "1            Empregado   Ensino superior  União-estável   \n",
              "2            Empregado      Ensino médio         Casado   \n",
              "3  Associado comercial      Ensino médio       Solteiro   \n",
              "4  Associado comercial      Ensino médio       Solteiro   \n",
              "\n",
              "                    Moradia  Idade  Anos_empregado  Tem_celular  \\\n",
              "0       Apartamento alugado -12005           -4542            1   \n",
              "1       Apartamento alugado -12005           -4542            1   \n",
              "2  Casa/apartamento próprio -21474           -1134            1   \n",
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              "4  Casa/apartamento próprio -19110           -3051            1   \n",
              "\n",
              "   Tem_telefone_trabalho  Tem_telefone_fixo  Tem_email   Ocupacao  \\\n",
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              "7137299    2\n",
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              "          ..\n",
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              "          ...  \n",
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              "   ID_Cliente Genero Tem_carro Tem_casa_propria  Qtd_filhos  Rendimento_anual  \\\n",
              "0     5008804      M         Y                Y           0          427500.0   \n",
              "1     5008805      M         Y                Y           0          427500.0   \n",
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              "4     5008809      F         N                Y           0          270000.0   \n",
              "\n",
              "    Categoria_de_renda Grau_escolaridade   Estado_civil  \\\n",
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              "                    Moradia  Idade  Anos_empregado  Tem_celular  \\\n",
              "0       Apartamento alugado -12005           -4542            1   \n",
              "1       Apartamento alugado -12005           -4542            1   \n",
              "2  Casa/apartamento próprio -21474           -1134            1   \n",
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              "\n",
              "   Tem_telefone_trabalho  Tem_telefone_fixo  Tem_email   Ocupacao  \\\n",
              "0                      1                  0          0        NaN   \n",
              "1                      1                  0          0        NaN   \n",
              "2                      0                  0          0  Segurança   \n",
              "3                      0                  1          1     Vendas   \n",
              "4                      0                  1          1     Vendas   \n",
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              "   Tamanho_familia  \n",
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              "      <th>Idade</th>\n",
              "      <th>Anos_empregado</th>\n",
              "      <th>Tem_celular</th>\n",
              "      <th>Tem_telefone_trabalho</th>\n",
              "      <th>Tem_telefone_fixo</th>\n",
              "      <th>Tem_email</th>\n",
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              "      <th>Tamanho_familia</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Empregado</td>\n",
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              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Empregado</td>\n",
              "      <td>Ensino superior</td>\n",
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              "      <td>Apartamento alugado</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Empregado</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Casado</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Segurança</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Associado comercial</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Solteiro</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Vendas</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Associado comercial</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Solteiro</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Vendas</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['ID_Cliente'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSc49D0RRNH3",
        "outputId": "70b6fcaa-d4bf-4a72-e64f-e98b6e27d1c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5008804    1\n",
              "6147691    1\n",
              "6147689    1\n",
              "6147688    1\n",
              "6147685    1\n",
              "          ..\n",
              "6093492    1\n",
              "5690720    1\n",
              "6538938    1\n",
              "6538937    1\n",
              "6842885    1\n",
              "Name: ID_Cliente, Length: 438463, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9oNYQBIRNKa",
        "outputId": "d3152aad-0159-4fae-e2f3-fc9012e917b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID_Cliente                    0\n",
              "Genero                        0\n",
              "Tem_carro                     0\n",
              "Tem_casa_propria              0\n",
              "Qtd_filhos                    0\n",
              "Rendimento_anual              0\n",
              "Categoria_de_renda            0\n",
              "Grau_escolaridade             0\n",
              "Estado_civil                  0\n",
              "Moradia                       0\n",
              "Idade                         0\n",
              "Anos_empregado                0\n",
              "Tem_celular                   0\n",
              "Tem_telefone_trabalho         0\n",
              "Tem_telefone_fixo             0\n",
              "Tem_email                     0\n",
              "Ocupacao                 134177\n",
              "Tamanho_familia               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Ocupacao'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBc-_B2nRNMt",
        "outputId": "5c0df51c-82d7-4c5e-c62b-9b87152438a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'Segurança', 'Vendas', 'Contabilidade', 'Construção Civil',\n",
              "       'Gerência', 'Motorista', 'Equipe principal', 'Alta tecnologia',\n",
              "       'Limpeza', 'Serviço privado', 'Cozinha', 'Baixa qualificação',\n",
              "       'Medicina', 'Secretariado', 'Garçom', 'RH', 'Corretor imobiliário',\n",
              "       'TI'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Ocupacao'].fillna(value='Outro', inplace=True)"
      ],
      "metadata": {
        "id": "RzSEzMM8RNOe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikS2ycAlRNQz",
        "outputId": "edd74f87-3e44-4a36-d03a-c87a4813b6d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID_Cliente               438463\n",
              "Genero                        2\n",
              "Tem_carro                     2\n",
              "Tem_casa_propria              2\n",
              "Qtd_filhos                   12\n",
              "Rendimento_anual            866\n",
              "Categoria_de_renda            5\n",
              "Grau_escolaridade             5\n",
              "Estado_civil                  5\n",
              "Moradia                       6\n",
              "Idade                     16379\n",
              "Anos_empregado             9406\n",
              "Tem_celular                   1\n",
              "Tem_telefone_trabalho         2\n",
              "Tem_telefone_fixo             2\n",
              "Tem_email                     2\n",
              "Ocupacao                     19\n",
              "Tamanho_familia              13\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo.drop('Genero', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "ddBmEiNMSAF7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df_cadastrados_limpo.drop('Tem_celular', axis=1, inplace=True)"
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      "metadata": {
        "id": "dKX6kTGURNT2"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Tem_carro'].unique()"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Y', 'N'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
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    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Tem_casa_propria'].unique()"
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      "metadata": {
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        "id": "mmmwBdqbR20c",
        "outputId": "ccfb6da8-6f5b-4e45-bbc3-400d41f6e276"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Y', 'N'], dtype=object)"
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          "metadata": {},
          "execution_count": 20
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      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Tem_telefone_trabalho'].unique()"
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          "base_uri": "https://localhost:8080/"
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        "id": "3he_WVtFR23B",
        "outputId": "cc90f168-f74e-4369-c236-ddaecaf165af"
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      "outputs": [
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          "output_type": "execute_result",
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              "array([1, 0])"
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          "metadata": {},
          "execution_count": 21
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    {
      "cell_type": "code",
      "source": [
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          "execution_count": 22
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          "execution_count": 23
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        "df_cadastrados_limpo['Tem_carro'] = df_cadastrados_limpo['Tem_carro'].replace(['Y','N'],[1,0])\n",
        "df_cadastrados_limpo['Tem_casa_propria'] = df_cadastrados_limpo['Tem_casa_propria'].replace(['Y','N'],[1,0])"
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      "metadata": {
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            "text/plain": [
              "   ID_Cliente  Tem_carro  Tem_casa_propria  Qtd_filhos  Rendimento_anual  \\\n",
              "0     5008804          1                 1           0          427500.0   \n",
              "1     5008805          1                 1           0          427500.0   \n",
              "2     5008806          1                 1           0          112500.0   \n",
              "3     5008808          0                 1           0          270000.0   \n",
              "4     5008809          0                 1           0          270000.0   \n",
              "\n",
              "    Categoria_de_renda Grau_escolaridade   Estado_civil  \\\n",
              "0            Empregado   Ensino superior  União-estável   \n",
              "1            Empregado   Ensino superior  União-estável   \n",
              "2            Empregado      Ensino médio         Casado   \n",
              "3  Associado comercial      Ensino médio       Solteiro   \n",
              "4  Associado comercial      Ensino médio       Solteiro   \n",
              "\n",
              "                    Moradia  Idade  Anos_empregado  Tem_telefone_trabalho  \\\n",
              "0       Apartamento alugado -12005           -4542                      1   \n",
              "1       Apartamento alugado -12005           -4542                      1   \n",
              "2  Casa/apartamento próprio -21474           -1134                      0   \n",
              "3  Casa/apartamento próprio -19110           -3051                      0   \n",
              "4  Casa/apartamento próprio -19110           -3051                      0   \n",
              "\n",
              "   Tem_telefone_fixo  Tem_email   Ocupacao  Tamanho_familia  \n",
              "0                  0          0      Outro              2.0  \n",
              "1                  0          0      Outro              2.0  \n",
              "2                  0          0  Segurança              2.0  \n",
              "3                  1          1     Vendas              1.0  \n",
              "4                  1          1     Vendas              1.0  "
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Empregado</td>\n",
              "      <td>Ensino superior</td>\n",
              "      <td>União-estável</td>\n",
              "      <td>Apartamento alugado</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Outro</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Empregado</td>\n",
              "      <td>Ensino superior</td>\n",
              "      <td>União-estável</td>\n",
              "      <td>Apartamento alugado</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Outro</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Empregado</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Casado</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Segurança</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Associado comercial</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Solteiro</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Vendas</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Associado comercial</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Solteiro</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Vendas</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-577b2016-33f5-4e2b-ae8d-4a89cd5cd7e2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                     [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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tratamento da feature Idade\n",
        "df_cadastrados_limpo['Idade']=-df_cadastrados_limpo['Idade']/365.2425"
      ],
      "metadata": {
        "id": "1l-JAk9kSlzo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Anos_empregado'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2m_RryqVEWZ",
        "outputId": "1263340f-f320-4f37-97cf-7d4d7a01a520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 365243    75314\n",
              "-218         262\n",
              "-1678        251\n",
              "-249         241\n",
              "-978         240\n",
              "           ...  \n",
              "-13529         1\n",
              "-10223         1\n",
              "-8917          1\n",
              "-11429         1\n",
              "-8105          1\n",
              "Name: Anos_empregado, Length: 9406, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tratamento da feature anos empregado\n",
        "df_cadastrados_limpo['Anos_empregado']=-df_cadastrados_limpo['Anos_empregado']/365.2425"
      ],
      "metadata": {
        "id": "LCEfxMVsSl2B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Anos_empregado'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kddnds1nTlcL",
        "outputId": "903add0e-6c2d-4859-a4cd-002884f3bf8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1000.001369    75314\n",
              " 0.596864         262\n",
              " 4.594208         251\n",
              " 0.681739         241\n",
              " 2.677673         240\n",
              "                ...  \n",
              " 37.041144          1\n",
              " 27.989623          1\n",
              " 24.413917          1\n",
              " 31.291539          1\n",
              " 22.190736          1\n",
              "Name: Anos_empregado, Length: 9406, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo.loc[df_cadastrados_limpo['Anos_empregado']<0,'Anos_empregado']=0"
      ],
      "metadata": {
        "id": "PvjyZxUaT_FA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Anos_empregado'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BcDA8gcUEa4",
        "outputId": "513de5d6-a8e8-4e6f-c6ae-d6f789aa062f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.000000     75314\n",
              "0.596864       262\n",
              "4.594208       251\n",
              "0.681739       241\n",
              "2.677673       240\n",
              "             ...  \n",
              "37.041144        1\n",
              "27.989623        1\n",
              "24.413917        1\n",
              "31.291539        1\n",
              "22.190736        1\n",
              "Name: Anos_empregado, Length: 9406, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "NJU24RwZSlVA",
        "outputId": "acc56884-5d88-491b-8c6e-52676dde7bbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID_Cliente  Tem_carro  Tem_casa_propria  Qtd_filhos  Rendimento_anual  \\\n",
              "0     5008804          1                 1           0          427500.0   \n",
              "1     5008805          1                 1           0          427500.0   \n",
              "2     5008806          1                 1           0          112500.0   \n",
              "3     5008808          0                 1           0          270000.0   \n",
              "4     5008809          0                 1           0          270000.0   \n",
              "\n",
              "    Categoria_de_renda Grau_escolaridade   Estado_civil  \\\n",
              "0            Empregado   Ensino superior  União-estável   \n",
              "1            Empregado   Ensino superior  União-estável   \n",
              "2            Empregado      Ensino médio         Casado   \n",
              "3  Associado comercial      Ensino médio       Solteiro   \n",
              "4  Associado comercial      Ensino médio       Solteiro   \n",
              "\n",
              "                    Moradia      Idade  Anos_empregado  Tem_telefone_trabalho  \\\n",
              "0       Apartamento alugado  32.868574       12.435574                      1   \n",
              "1       Apartamento alugado  32.868574       12.435574                      1   \n",
              "2  Casa/apartamento próprio  58.793815        3.104787                      0   \n",
              "3  Casa/apartamento próprio  52.321403        8.353354                      0   \n",
              "4  Casa/apartamento próprio  52.321403        8.353354                      0   \n",
              "\n",
              "   Tem_telefone_fixo  Tem_email   Ocupacao  Tamanho_familia  \n",
              "0                  0          0      Outro              2.0  \n",
              "1                  0          0      Outro              2.0  \n",
              "2                  0          0  Segurança              2.0  \n",
              "3                  1          1     Vendas              1.0  \n",
              "4                  1          1     Vendas              1.0  "
            ],
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              "\n",
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              "      <td>12.435574</td>\n",
              "      <td>1</td>\n",
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              "      <td>5008806</td>\n",
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              "      <td>58.793815</td>\n",
              "      <td>3.104787</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
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              "      <td>52.321403</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
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              "    }\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-8c4357d5-51dc-420d-a002-f84ee70d78af 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-8c4357d5-51dc-420d-a002-f84ee70d78af');\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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "kxkp57EdUMW_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df_cadastrados_limpo.corr()\n",
        "plt.figure(figsize = (20,10))\n",
        "sns.heatmap(corr, cmap=\"Blues\", annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "GVkx1K50UO52",
        "outputId": "4c8e2ba6-32c2-461f-b7da-1062f01d427d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f937911fad0>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo.drop(['Qtd_filhos'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "mKXk6U6iUO8F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo=df_cadastrados_limpo[['ID_Cliente', 'Tem_carro', 'Tem_casa_propria', \n",
        "                                           'Tem_telefone_trabalho', 'Tem_telefone_fixo', 'Tem_email',\n",
        "                                           'Tamanho_familia', 'Rendimento_anual', 'Idade', 'Anos_empregado',\n",
        "                                           'Categoria_de_renda', 'Grau_escolaridade', 'Estado_civil',\n",
        "                                           'Moradia', 'Ocupacao']]"
      ],
      "metadata": {
        "id": "XYtiU9wMUO-J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "jsPdtaD3UPAJ",
        "outputId": "91b007ce-c089-4ad6-ba19-358ca6eacc6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID_Cliente  Tem_carro  Tem_casa_propria  Tem_telefone_trabalho  \\\n",
              "0     5008804          1                 1                      1   \n",
              "1     5008805          1                 1                      1   \n",
              "2     5008806          1                 1                      0   \n",
              "3     5008808          0                 1                      0   \n",
              "4     5008809          0                 1                      0   \n",
              "\n",
              "   Tem_telefone_fixo  Tem_email  Tamanho_familia  Rendimento_anual      Idade  \\\n",
              "0                  0          0              2.0          427500.0  32.868574   \n",
              "1                  0          0              2.0          427500.0  32.868574   \n",
              "2                  0          0              2.0          112500.0  58.793815   \n",
              "3                  1          1              1.0          270000.0  52.321403   \n",
              "4                  1          1              1.0          270000.0  52.321403   \n",
              "\n",
              "   Anos_empregado   Categoria_de_renda Grau_escolaridade   Estado_civil  \\\n",
              "0       12.435574            Empregado   Ensino superior  União-estável   \n",
              "1       12.435574            Empregado   Ensino superior  União-estável   \n",
              "2        3.104787            Empregado      Ensino médio         Casado   \n",
              "3        8.353354  Associado comercial      Ensino médio       Solteiro   \n",
              "4        8.353354  Associado comercial      Ensino médio       Solteiro   \n",
              "\n",
              "                    Moradia   Ocupacao  \n",
              "0       Apartamento alugado      Outro  \n",
              "1       Apartamento alugado      Outro  \n",
              "2  Casa/apartamento próprio  Segurança  \n",
              "3  Casa/apartamento próprio     Vendas  \n",
              "4  Casa/apartamento próprio     Vendas  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b891fe57-13a2-461d-a37c-4622819f14a8\">\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>ID_Cliente</th>\n",
              "      <th>Tem_carro</th>\n",
              "      <th>Tem_casa_propria</th>\n",
              "      <th>Tem_telefone_trabalho</th>\n",
              "      <th>Tem_telefone_fixo</th>\n",
              "      <th>Tem_email</th>\n",
              "      <th>Tamanho_familia</th>\n",
              "      <th>Rendimento_anual</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Anos_empregado</th>\n",
              "      <th>Categoria_de_renda</th>\n",
              "      <th>Grau_escolaridade</th>\n",
              "      <th>Estado_civil</th>\n",
              "      <th>Moradia</th>\n",
              "      <th>Ocupacao</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>32.868574</td>\n",
              "      <td>12.435574</td>\n",
              "      <td>Empregado</td>\n",
              "      <td>Ensino superior</td>\n",
              "      <td>União-estável</td>\n",
              "      <td>Apartamento alugado</td>\n",
              "      <td>Outro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>32.868574</td>\n",
              "      <td>12.435574</td>\n",
              "      <td>Empregado</td>\n",
              "      <td>Ensino superior</td>\n",
              "      <td>União-estável</td>\n",
              "      <td>Apartamento alugado</td>\n",
              "      <td>Outro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>58.793815</td>\n",
              "      <td>3.104787</td>\n",
              "      <td>Empregado</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Casado</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>Segurança</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>52.321403</td>\n",
              "      <td>8.353354</td>\n",
              "      <td>Associado comercial</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Solteiro</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>Vendas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>52.321403</td>\n",
              "      <td>8.353354</td>\n",
              "      <td>Associado comercial</td>\n",
              "      <td>Ensino médio</td>\n",
              "      <td>Solteiro</td>\n",
              "      <td>Casa/apartamento próprio</td>\n",
              "      <td>Vendas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b891fe57-13a2-461d-a37c-4622819f14a8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b891fe57-13a2-461d-a37c-4622819f14a8');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colunas_quantitativas = ['Tamanho_familia', 'Rendimento_anual', 'Idade', 'Anos_empregado']"
      ],
      "metadata": {
        "id": "FaTcdMRFUkv7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo[colunas_quantitativas].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "xWcvmwLVUk1Z",
        "outputId": "2bcb0987-50ad-48f6-9b1e-8731b4600343"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Tamanho_familia  Rendimento_anual          Idade  Anos_empregado\n",
              "count    438463.000000      4.384630e+05  438463.000000   438463.000000\n",
              "mean          2.194461      1.875220e+05      43.800857        5.948371\n",
              "std           0.897194      1.100832e+05      11.458262        6.564112\n",
              "min           1.000000      2.610000e+04      20.504186        0.000000\n",
              "25%           2.000000      1.215000e+05      34.262168        1.015763\n",
              "50%           2.000000      1.611000e+05      42.793487        4.019247\n",
              "75%           3.000000      2.250000e+05      53.345380        8.495725\n",
              "max          20.000000      6.750000e+06      68.997994       47.998248"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tamanho_familia</th>\n",
              "      <th>Rendimento_anual</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Anos_empregado</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>438463.000000</td>\n",
              "      <td>4.384630e+05</td>\n",
              "      <td>438463.000000</td>\n",
              "      <td>438463.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.194461</td>\n",
              "      <td>1.875220e+05</td>\n",
              "      <td>43.800857</td>\n",
              "      <td>5.948371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.897194</td>\n",
              "      <td>1.100832e+05</td>\n",
              "      <td>11.458262</td>\n",
              "      <td>6.564112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.610000e+04</td>\n",
              "      <td>20.504186</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.215000e+05</td>\n",
              "      <td>34.262168</td>\n",
              "      <td>1.015763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.611000e+05</td>\n",
              "      <td>42.793487</td>\n",
              "      <td>4.019247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.250000e+05</td>\n",
              "      <td>53.345380</td>\n",
              "      <td>8.495725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>6.750000e+06</td>\n",
              "      <td>68.997994</td>\n",
              "      <td>47.998248</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a80065c2-8da3-4c0d-9108-d721d3db0b44')\"\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a80065c2-8da3-4c0d-9108-d721d3db0b44 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-a80065c2-8da3-4c0d-9108-d721d3db0b44');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Categoria_de_renda'].value_counts(normalize = True) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFrHGCMwVsON",
        "outputId": "cc6cf591-cf89-45a2-dd49-b7d8f773ca59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empregado              51.557144\n",
              "Associado comercial    22.972520\n",
              "Pensionista            17.214223\n",
              "Servidor público        8.252236\n",
              "Estudante               0.003877\n",
              "Name: Categoria_de_renda, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Grau_escolaridade'].value_counts(normalize = True) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEK73PeKVsQo",
        "outputId": "f0e002d6-df17-45a4-bbaa-54c058d2a649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ensino médio                  68.821314\n",
              "Ensino superior               26.797928\n",
              "Ensino superior incompleto     3.385690\n",
              "Ensino fundamental             0.923909\n",
              "Pós-graduação                  0.071158\n",
              "Name: Grau_escolaridade, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Estado_civil'].value_counts(normalize = True) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdUCs64mVsTM",
        "outputId": "4888bf7d-9633-4c19-ad06-e86457044c5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Casado           68.367912\n",
              "Solteiro         12.601976\n",
              "União-estável     8.329323\n",
              "Divorciado        6.214663\n",
              "Viúvo             4.486125\n",
              "Name: Estado_civil, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Moradia'].value_counts(normalize = True) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4tEHxclVsVi",
        "outputId": "f6eedea6-0afb-4b76-b54d-3c98ec492049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Casa/apartamento próprio    89.801876\n",
              "Mora com os pais             4.349740\n",
              "Habitação pública            3.241094\n",
              "Apartamento alugado          1.362259\n",
              "Apartamento comercial        0.894032\n",
              "Cooperativa habitacional     0.350999\n",
              "Name: Moradia, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cadastrados_limpo['Ocupacao'].value_counts(normalize = True) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWbHjV5FVsYA",
        "outputId": "78df4312-f564-4e0f-84dd-d3de756d0438"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Outro                   30.601670\n",
              "Construção Civil        17.840730\n",
              "Equipe principal         9.805388\n",
              "Vendas                   9.371828\n",
              "Gerência                 8.091219\n",
              "Motorista                5.949191\n",
              "Alta tecnologia          3.941724\n",
              "Contabilidade            3.644549\n",
              "Medicina                 3.083042\n",
              "Cozinha                  1.841432\n",
              "Segurança                1.822959\n",
              "Limpeza                  1.332610\n",
              "Serviço privado          0.787980\n",
              "Baixa qualificação       0.488069\n",
              "Secretariado             0.466174\n",
              "Garçom                   0.379736\n",
              "Corretor imobiliário     0.237420\n",
              "RH                       0.176526\n",
              "TI                       0.137754\n",
              "Name: Ocupacao, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib as mpl"
      ],
      "metadata": {
        "id": "u7fybOvoUk3u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Configurações dos gráficos:\n",
        "mpl.rcParams['font.size'] = 14\n",
        "mpl.rcParams['figure.figsize'] = (10,6)"
      ],
      "metadata": {
        "id": "9VufWHoqUxCP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df_cadastrados_limpo, x='Rendimento_anual', bins=50) \n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "udaRGXiqUxEy",
        "outputId": "aa66955b-5fba-4ccb-af79-139f5122663c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df_cadastrados_limpo, y='Rendimento_anual') \n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "fIt7baGkUxG-",
        "outputId": "7662c2b5-1670-49a9-a34a-16eaaa4fbabe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df_cadastrados_limpo, x='Anos_empregado', bins=50) \n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "f7gGEDicU5P-",
        "outputId": "43bd3c86-83f6-4e6f-c5dd-df6232e2339e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df_cadastrados_limpo, y='Anos_empregado') \n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "GDIMBIArU5SV",
        "outputId": "9722ab0a-6ae8-49b9-da55-3a681571af74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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MXCJJkqSeqPvg8+P7HUSSJEn1zzkDICL2iohfi4hX9SuQJElSm9UqZxExOSKuA54Ebgf2qcaviIj5/YsnSZLULnVXzj4BvAF4K7Cua/wfgHf1OpQkSVJb1b0g4BTgXZl5T0R0XwDwIHBA72NJkiS1U92Vsz2ANeOMTwY29C6OJElSu9UtZ9+hs3o2anT17A/pnIMmSZKkHqh7WHMe8I8RcXD1mQ9X738VeFu/wkmSJLVNrZWzzLwdOBrYFXgYeAewAjgqM7/bv3iSJEntUvs+Z5l5b2aekZmHZOZbMvO9mXlv9z4R8dGI+MXxPh8R50fEdyLi2YhYHRFfjYhDxuwTETE/IlZExLqIWFqt0EmSJLXCNt2EtoZ5wJ6bmTsOuJzOCtzbgfXANyKie/+5wHnAbOAIOvdVuyUiJvc4pyRJUpHqnnNWV2xuIjN/Y5MdI04HfgIcA3w1IgI4F7gwM6+v9jmDTkE7Fbiyx1klSZKK0+uVs20xufr9Z6rtGcA04ObRHTJzHXAbndU2SZKknV6T5WwhcA/wrWp7WvV31Zj9VnXNbSIiZkXESESMrF69uj8pJUmSBqjXhzVriYiLgV8Hfj0zt/smtpm5GFgMMDw8nFvZXRq4RYsWsXz58qZjqEfmzJnTdARto6GhIWbPnt10DGmbDHzlLCIuAf438PbMfKRramX1d+qYj0ztmpMkSdqp9Xrl7F/Y9MHom4iIhcC7geMzc9mY6UfplLCZdJ5IQETsBhwLfKTHOaWB8L/Yd1zHHXfcy8YWLlw4+CCSWqdWOYuItwAbMvMH1fZM4AzgfmDB6KHJzDxpC99xGXA68FvAMxExeh7Z2sxcm5kZEZcC8yJiGfAQcAGwFrh6u/7pJEmSdjB1D2t+FvhlgIjYD/gynfuZfRD4s5rf8QE6V2j+E/Cjrtcfde2zALgEuAwYAfYGTszM52r+hiT1xNKlS7e4LUn9Uvew5kHA6GOa/idwZ2aeFBHHA58Dzt/aF2TmZu+B1rVPAvOrlyRJUuvUXTmbALxYvX8HcGP1/mFefgK/JO0UDjvsMA477DBXzSQNVN1ydh/w/og4lk45u6ka3wd4qh/BJEmS2qhuOftj4CxgKXBN1wPPTwG+3YdckiRJrVTrnLPMvC0ipgC7Z+YzXVNXAs/3JZkkSVIL1b7PWWZuiIh1EXEIkMDDmflY35JJkiS1UK3DmhExMSL+gs5Dyr8H3EvnXmULIuIX+hlQkiSpTequnC2g88ils4FvVmPHAv+XTsH7o818TpIkSdugbjk7FfiDzLyxa+zhiFgNfAbLmSRJUk/UvVrztXTuaTbWw8Av9i6OJElSu9UtZ98DzhlnfA5wT+/iSJIktVvdw5pzgRsj4gTgjmrsSOANwDv7EUySJKmNaq2cZeZtwC8BXwReU72uAw7MzG9u6bOSJEmqb1vuc7YC+Fgfs0iSJLVe7XIWEa8GDgdez5gVt8y8oce5JEmSWqlWOavONbsGeN040wlM6GUoSZKktqp7teZC4GvAvpm5y5iXxUySJKlH6h7WnA6cUp13JkmSpD6pu3L2r8CB/QwiSZKk+itnVwAXRcQb6Dz0/KXuycz8bq+DSZIktVHdcvbF6u/icea8IECSJKlH6pazGZsZD+CEHmWRJElqvVrlLDMf796OiH2A369e04HP9DyZJElSC9W9IICImBARvx0RXwMeA94FXAkM9SmbJElS62x15SwiDgTeB/we8FPgauBE4PTMfKC/8SRJktpliytnEfEvwB3AHsDvZuYBmXnBQJJJkiS10NZWzo4CLgMWZ+b9A8gjSZLUals75+wIOgXumxFxd0R8KCKmDSCXJElSK22xnGXm3Zn5QWBv4GLgFOA/qs+dHBF79D+iJElSe9S6WjMzX8jMv83M44E3A38BfAhYGRFf72dASZKkNql9K41Rmbk8Mz8K7Af8LvBiz1NJkiS1VN0nBLxMZm4Avly9JEmS1APbvHImSZKk/rGcSZIkFcRyJkmSVBDLmSRJUkEsZ5IkSQWxnEmSJBXEciZJklQQy5kkSVJBLGeSJEkFsZxJkiQVxHImSZJUEMuZJElSQSxnkiRJBbGcSZIkFcRyJkmSVBDLmSRJUkEsZ5IkSQWxnEmSJBXEciZJklSQgZaziHhbRHwlIp6IiIyIM8fMR0TMj4gVEbEuIpZGxMGDzChJktSkQa+cvQa4D5gDrBtnfi5wHjAbOAJ4ErglIiYPLKEkSVKDBlrOMvPGzJyXmV8ENnbPRUQA5wIXZub1mXkfcAYwGTh1kDklSZKaUtI5ZzOAacDNowOZuQ64DTi6qVCSJEmDVFI5m1b9XTVmfFXXnCRJ0k6tpHK2zSJiVkSMRMTI6tWrm44jSZL0ipVUzlZWf6eOGZ/aNbeJzFycmcOZOTxlypS+hpMkSRqEksrZo3RK2MzRgYjYDTgWuL2pUJIkSYM0cZA/FhGvAYaqzV2A/SPicODpzPz3iLgUmBcRy4CHgAuAtcDVg8wpSZLUlIGWM2AYuLVr+/9UryXAmcACYBJwGbAHcCdwYmY+N9iYkiRJzRhoOcvMpUBsYT6B+dVLkiSpdUo650ySJKn1LGeSJEkFGfQ5Z9oOixYtYvny5U3HkFpn9P93c+bMaTiJ1D5DQ0PMnj276RiNsJztAJYvX8499z3Ihlfv2XQUqVV2eTEBuOuRsQ8ukdRPE55/uukIjbKc7SA2vHpP1h10UtMxJEnqu0nLbmw6QqM850ySJKkgljNJkqSCWM4kSZIKYjmTJEkqiOVMkiSpIJYzSZKkgljOJEmSCmI5kyRJKojlTJIkqSCWM0mSpIJYziRJkgpiOZMkSSqI5UySJKkgljNJkqSCWM4kSZIKYjmTJEkqiOVMkiSpIJYzSZKkgljOJEmSCmI5kyRJKojlTJIkqSCWM0mSpIJYziRJkgpiOZMkSSqI5UySJKkgE5sOoK174oknmPD8T5i07Mamo0iS1HcTnl/DE0+sbzpGY1w5kyRJKogrZzuAffbZh5U/m8i6g05qOookSX03admN7LPP1KZjNMaVM0mSpIJYziRJkgpiOZMkSSqI5UySJKkgljNJkqSCWM4kSZIKYjmTJEkqiOVMkiSpIJYzSZKkgljOJEmSCmI5kyRJKojlTJIkqSCWM0mSpIJYziRJkgpiOZMkSSrIxKYDqJ4Jzz/NpGU3Nh1DapVdXngWgI277d5wEqldJjz/NDC16RiNsZztAIaGhpqOILXS8uXPATB0QHv/JSE1Y2qr/91nOdsBzJ49u+kIUivNmTMHgIULFzacRFKbFHnOWUR8ICIejYgXIuKuiDi26UySJEmDUFw5i4h3AwuBjwO/DNwOfD0i9m80mCRJ0gAUV86ADwN/k5mfzswHM3M28CPg/Q3nkiRJ6ruizjmLiF2BXwEuGjN1M3D04BNJr8yiRYtYvnx50zG0nUb/txs990w7nqGhIc/b1Q6ntJWzvYAJwKox46uAaWN3johZETESESOrV68eRD5JLTJp0iQmTZrUdAxJLVPUytm2yszFwGKA4eHhbDiO9DL+F7skaVuVtnL2FLCBl995biqwcvBxJEmSBquocpaZLwJ3ATPHTM2kc9WmJEnSTq3Ew5oXA38bEd8G/hU4G3gDcEWjqSRJkgaguHKWmV+IiNcBFwB7A/cBJ2Xm480mkyRJ6r/iyhlAZl4OXN50DkmSpEEr6pwzSZKktrOcSZIkFcRyJkmSVBDLmSRJUkEsZ5IkSQWxnEmSJBXEciZJklQQy5kkSVJBIjObztATEbEa8CkCknptL+CppkNI2um8MTOnjDex05QzSeqHiBjJzOGmc0hqDw9rSpIkFcRyJkmSVBDLmSRt2eKmA0hqF885kyRJKogrZ5IkSQWxnEmSJBXEciZJklQQy5kkSVJBLGeSJEkF+f+5UoH2JgNKVQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df_cadastrados_limpo, x='Idade', bins=10) \n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "yyS8PdqzU5Um",
        "outputId": "dacd1338-a777-4a8c-b5b0-c1fc04a7172d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df_cadastrados_limpo, x='Tamanho_familia', bins=10) \n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "iRb19_wJU5W3",
        "outputId": "4fc36993-4b29-4aa7-eb59-1ea8d20b5191"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df_cadastrados_limpo, y='Tamanho_familia') \n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "_zY2wkbpU5ZR",
        "outputId": "e00bff06-542f-4d06-d765-0f08b8701ac2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''Técnica para remoção de outliers a partir de um intervalo de confiança\n",
        "o valor +- o desvio-padrão'''\n",
        "\n",
        "coluna = df_cadastrados_limpo['Rendimento_anual']\n",
        "\n",
        "coluna_med = coluna.mean()\n",
        "coluna_std = coluna.std()\n",
        "\n",
        "limite_sup = coluna_med + (2 * coluna_std)\n",
        "limite_inf = coluna_med - (2 * coluna_std)\n",
        "\n",
        "index_outliers = []\n",
        "\n",
        "for index, valor in coluna.items(): \n",
        "  if valor > limite_sup or valor < limite_inf:\n",
        "    index_outliers.append(index)\n",
        "\n",
        "len(index_outliers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxG5qNlnU5bh",
        "outputId": "08f513b5-26b3-4d1a-f8a9-e6c844160ef0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12641"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_clientes_cadastrados_sem_outliers = df_cadastrados_limpo.drop(index_outliers)\n",
        "df_clientes_cadastrados_sem_outliers.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3MzDhmWVLad",
        "outputId": "8a01504e-9957-49f7-925d-00a3851ce47f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(425822, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df_clientes_cadastrados_sem_outliers, y='Rendimento_anual') \n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "ZuFuIC_pVLcq",
        "outputId": "4e3bb26d-f3a4-4fb5-e9af-5cfc6053c758"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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sYpY3bqlEJEmSNLO44bYkSZI6DTtH8QnAWcAfA3syUGhW1a5Tl5okSZJGadg5ip+mWbhyPrCKjbylRZIkSVu3YQvFPwbGq+q6LZGMJEmSZo5h5yiuBe7ZEolIkiRpZhm2UHw3cGaSnbdEMpIkSZo5hn30/B5gX2BtktuAB/obq+pZU5SXJEmSRmzYQvHSLZKFJEmSZpxhN9z2NX2SJEnbiGnbcDvJaUn+Lcmvk6xL8sUkzxyIuTBJDRz/MhCzQ5Jzk9yR5N4klyd5ykDM3m3/97Zxf5PkMQMxz09yfZL/SHJLkjd35HxCklvbmOuTHDaV/yaSJEkz2VCFYpLHJDkjyY/a4unB/mMTlx8BfAJ4LnAk8Fvgq+0m3v2+Cjyp7zhqoP0c4Bjg1cBhwK7Al5Js3+a4PfBlYJe2/dXAfwc+2vd37AdcAayg2Rfyg8C5SY7pi3klsAT4QBuzAvhKkr038XdKkiTNCsPOUfwr4JU0hdXHgLfTLG55FfCXG7uwql7U/zvJ64C7gOcBX+xrur+qVnf1keTxwJ8Db6yqq/r6uQ34E2AZ8ELgGcA+VfWzNuYdwKeSvLuqfg28GVhVVQvbrm9KcgjwNuCy9tzJwIVVdUH7e2GSFwNvAU7b2N8qSZI0Gwz76PkVwJur6jzgQeCfq+qtwPuA8SH72qW9/50D5w9NsrYdtbwgyZ59bQcDjwaW9060xeBNNCOVAAuAm3pFYmsZsEN7fS9mORtaBowleXT7mPrgjpjlffeRJEma1YYtFOcC32+/3wPs1n6/kmYkbxhLgO8AK/vOXQm8nuYNMKcAzwG+nmSHtn0eTYF6x0Bfa9q2XsyagfY72us2FrOGZoR1j/bY/mFi5tEhyfFJJpJMrFu3ritEkiRpqzJsofhTYK/2+81A73HyAmD9ZDtJshg4FDimqn43t7GqPldVl1fVDVX1ReAlwNOAo4fMc9pV1flVNVZVY3PmzBl1OpIkSZtt2ELxn2hG+6AZETwjya3AhcCnJtNBko/RLDA5sqpu2VhsVa0Cfg78XntqNc1I3x4DoXPbtl7M3IH23gjhxmLm0iywuYOHRiC7YjrnT0qSJM02QxWKVXVaVZ3Vfr+UZlTwXODlVfXuTV2fZAkPFYk/mET8HsCTgV+0p66neRvMeF/MU4D9aVYlQ/Moe/+BLXPGgfvb63sxg3Mqx4GJqnqgqn7TxnbFrECSJGkbMOyq5w1U1XXAdYPnk3wZ+H+q6hd95z4OvA74v4E7k/Tm+t1TVfe0748+nWbV8S9oVlN/EFhLM5JJVd2V5NPA2UnWAr8EFgPfpdlWB5oFJ98DPpvkFOCJwEeAC9oVzwCfBE5Mcg5wHs3K6zfQFLE9i4GlSf4V+BbNSum92mslSZJmvc0qFDficGDHgXMntJ9fGzh/Bk2B+CBwAM1ilt1oisWrgVdU1d198SfRPCK+pL3H14DX9+Y6VtWDSY6m2bPxWzRzJy+i2cqHNubWJEfRbPHzFmAV8Naquqwv5pIkT6R5v/WTgBuBo6rqtmH/MSRJkrZGW6pQ/C+qKptoX89Di2M2Fnc/sLA9Hi7mp8BLN9HPtcBBm4j5BE3BKUmStM2Ztlf4SZIkaetioShJkqROFoqSJEnqZKEoSZKkTluqUPwA8Kst1LckSZKmwdCrnpM8C3gb8AdA0bz7+SNVdWMvpqo+OGUZSpIkaSSGGlFM8qfAt4H/A/gKcCWwN/D/Jfk/pz49SZIkjcqwI4rvB86qqvf1n0xyZtv2xalKTJIkSaM17BzF3weWdpxfCjxt89ORJEnSTDFsobgWOLjj/MHAms1PR5IkSTPFsI+eLwDOSzIfWNGeex7N4paPTGVikiRJGq1HMkfxHuAU4K/ac6uA9wF/M4V5SZIkacRSVY/swmQXgKq6e0ozmgXGxsZqYmJi1GlsEeeeey4333zzqNOQtjm9/97Nnz9/xJlI25758+ezcOHCUaexxSS5vqrGutqGGlFM8nXg5VX17/0FYpJdgS9U1ZGbl6pmuptvvpnv3HgTD+70hFGnIm1TtvtN83/qr7/F6eDSdNr+vm37/SHDPno+AnhMx/nHAodtdjbaKjy40xNY//SjRp2GJElb3I4/uGLUKYzUpArFJAf1/XxWkv7yenvgRcDtU5mYJEmSRmuyI4oTNK/rK2B5R/t6YPY+vJckSdoGTbZQ3A8IcAvwHGBdX9tvgLVV9eAU5yZJkqQRmlShWFW3tV+H3aBbkiRJW6lhF7OQ5CnA4cCeDBSOVbV4ivKSJEnSiA27Pc5rgL8Dfkvz+Ll/E8YCLBQlSZJmiWFHFM8EPgr8pXMSJUmSZrdh5xzOBT5lkShJkjT7DVsoXgEcsiUSkSRJ0swy7KPnq4APJ3kGcAPwQH9jVf3jVCUmSZKk0Rq2UDyv/XxXR1vRvKVFkiRJs8BQhWJVuY+iJEnSNsLCT5IkSZ2GKhTTOCHJ95Lcl+Sp7flTk7xiE9eeluTfkvw6ybokX0zyzI7+T0+yKsn6JNe08yH7Y3ZPsjTJXe2xNMluAzEHJLm27eP2JO9NkoGYY5J8P8n97efLhs1FkiRpNht2RHER8B7gfJp3P/fcDpy4iWuPAD4BPBc4kmbT7q8meUJfzDuAU4CFwLOBtcBVSXbpi7kYOAh4cXscBCztNSbZlWbRzZq2j0XA24GT+2IWAJcAFwEHtp+fT9K/onsyuUiSJM1awxaKbwb+oqqW0BR6Pd8GNjraVlUvqqq/r6obq+oG4HXAHOB50IzgAScBH6qqy6rqRuA4YBfg2DZmf5ri8PiqWllVK4E3AS9N8rT2Vq8BdgKOa+91KfBh4OS+UcWTgKur6qyquqmqzgKuac9PKhdJkqTZbthCcR/gxo7zDwA7DtnXLu3972x/7wfMA5b3AqpqPfANmlFIgAXAPcCKvn6+Bdw7EPPN9tqeZcBewL59McvZ0LK+PiaTiyRJ0qw2bKF4C82j3kFHAd8fsq8lwHeAle3vee3nmoG4NX1t84B1VfW7d0y339cOxHT1wSRi5g3EbSxmA0mOTzKRZGLdunVdIZIkSVuVYfdR/Gvgb5PsRDNHcUGS19HM5/uzyXaSZDFwKHDobHkdYFWdTzN3k7GxsdpEuCRJ0ow37D6Kf5/kUcAHaOYBLgVWAW+tqksm00eSjwGvAl5QVbf0Na1uP+cCP+07P7evbTUwJ0l6o4rtfMI9B2LmDtx2bl/bxmJWD8RtLBdJkqRZbeh9FKvqgqrah6Y4m1dVT6mqT0/m2iRLgFcDR1bVDwaab6Upwsb74h8LHMZDcxJXAjvTzDHsWQA8biDmsPbannGagvYnfTHjbGi8r4/J5CJJkjSrPeINt6vqjqpaO9n4JB8H3kizavjOJPPaY+e2vwLOAd6Z5OXtHosX0ixeubiNuQm4EjgvyYJ2m5vzgC9V1Q/bW10M3AdcmOSZSV4OnAos7pvbuAQ4st3/8elJTgNe0N5/UrlIkiTNdkM9ek6yO3A6TVG1JwOFZlXtuZHLT2g/vzZw/oy2T4CzaVZPfxzYHbgOeGFV3d0XfyxwLs0qZYDL6dvDsaruSjLe9jFBs6r6o8DivpgVSV4FvB84E/gx8Mqquq7vPpPJRZIkadYadjHLZ2n2S/wMzQrgSS/aqKpMIqZoisbTNxJzJ/DaTfRzA3D4JmIuBS7dnFwkSZJms2ELxSOA51fVt7dALpIkSZpBhp2j+ONHcI0kSZK2Qo/kXc8fTPKHSbbfEglJkiRpZhj20fPNNAs8vg3w0KuTG1Vl8ShJkjRLDFso/m/g8cBbGXIxiyRJkrYuwxaKY8BzqurGLZGMJEmSZo5h5yh+H9h1SyQiSZKkmWXYQvE9wOIkf5JkbpIn9B9bIkFJkiSNxrCPnq9oP5ez4fzEtL9dzCJJkjRLDFsovmCLZCFJkqQZZ6hCsaqu3VKJSJIkaWbZZKGY5CDgO1X1n+33h+Wr/SRJkmaPyYwoTgDzgLXt96KZkzjIOYqSJEmzyGQKxf2AdX3fJUmStA3YZKFYVbd1fZckSdLsNpk5iodPtrOq+sbmpSNJkqSZYjKPnq9hw3mJvf0TB3+DcxQlSZJmjcm8mWUOsGf7+VLgh8Drgfnt8XrgB8CfbqEcJUmSNAKTmaP4y973JH8FLKqqq/pCbkmyFjgb+PLUpyhJkqRRGPZdz38A/Lzj/O3A0zc/HUmSJM0UwxaK3wPel2TH3on2+3vbNkmSJM0Sw77r+S3Al4Dbk3y3PXcA8CBw9FQmJkmSpNEa9l3P/5bkqcBreOhR80XAxVV171QnJ0mSpNEZdkSRtiA8fwvkIkmSpBlk6EIxyVOAw2m2zNlgjmNVLZ6ivCRJkjRiQxWKSV4D/B3wW5r3P/dvtl2AhaIkSdIsMeyI4pnAR4G/rKoHt0A+muFuv/12tr/vLnb8wRWjTkWSpC1u+/t+ye23/3bUaYzMsNvjzAU+ZZEoSZI0+w07ongFcAhwyyO5WZLDgbcBBwN7AW+sqgv72i8Ejhu47Lqq+qO+mB2AvwZeDewIfA04oap+3hezN/Bx4EhgPXAx8Laq+k1fzPNpHpU/A1gFnF1VnxzI9wTg7cCTaPaJPKmqvvlI/vbZ4slPfjKr738U659+1KhTkSRpi9vxB1fw5CfPHXUaIzNsoXgV8OEkzwBuAB7ob6yqf9zE9TsDNwKfbY8uXwVe1/f7NwPt5wD/F02h+EuaYu9LSQ6uqgeTbE/zKsFfAocBTwQ+AwRYCJBkP5qi9++A1wKHAp9Isq6qLmtjXgksAU4A/t/28ytJ/qCqfrqJv1OSJGmrN2yheF77+a6OtgK239jFVXUFTYHWGz3scn9Vre5qSPJ44M9pRiKvas+9DrgN+BNgGfBCmlHCfarqZ23MO4BPJXl3Vf0aeDOwqqoWtl3flOQQmtHOy9pzJwMXVtUF7e+FSV5Ms+n4aRv7OyVJkmaDoeYoVtV2Gzk2WiQO4dAka5P8KMkFSfbsazsYeDSwvC+nnwE3Ac9tTy0AbuoVia1lwA7t9b2Y5WxoGTCW5NFJHtPGDsYs77uPJEnSrDbsYpYt7Urg9cAfA6cAzwG+3s5LBJhH87rAOwauW9O29WLWDLTf0V63sZg1NCOse7TH9g8TMw9JkqRtwFCFYhonJPlekvva1/mR5NQkr9jcZKrqc1V1eVXdUFVfBF4CPI2t4D3SSY5PMpFkYt26daNOR5IkabMNO6K4CHgPzSv80nf+duDEqUqqp6pWAT8Hfq89tZpmpG+PgdC5bVsvZnB5Um+EcGMxc2k2Er+Dh0Ygu2I6509W1flVNVZVY3PmzNn4HyZJkrQVGLZQfDPwF1W1hKao6vk2zQKSKZVkD+DJwC/aU9fTrLQe74t5CrA/sKI9tRLYvz3fMw7c317fixlnQ+PARFU90G6jc/3DxKxAkiRpGzDsqud9aLa3GfQAzZ6GG5VkZ2B++3M7YO8kBwK/ao/TaVYd/wLYF/ggsBb4J4CquivJp4Gzk6zloe1xvkuzrQ40C06+B3w2ySk02+N8BLigXfEM8EngxCTn0Kzkfh7wBpotd3oWA0uT/CvwLZoiea/2WkmSpFlv2ELxFuAgmu1o+h1Fs/J4U8aAq/t+n9Een6HZduYAmsUsu9EUi1cDr6iqu/uuOYlmNPMSHtpw+/W9t8W0eykeDXyCpsBbD1xEs3E2bcytSY4CPtbedxXw1t4eim3MJUmeSPOo/Uk0BfJRVTX4t0uSJM1KwxaKfw38bZKdaOYoLmj3MXwn8MZNXVxV17Dh3MZBL5pEH/fTbJy9cCMxPwVeuol+rqUpejcW8wmaglOSJGmbM1ShWFV/n+RRwAeAnYClNKNxJ+LcPUmSpFll6H0Uq+qCqtoH2JNmT8Fn02xO/aMpzk2SJEkjNKlCMcluSS5Ksi7JqiRvpVlI8mbgZuAQ4M+2YJ6SJEmaZpN99PwB4HCaRScvplkEMg48jmaBx7VbJj1JkiSNymQLxaOBN1bVV5N8gmYU8cdVddKWS02SJEmjNNk5insB3weoqluA/wAu2FJJSZIkafQmWyhuR7Opds+DwH1Tn44kSZJmisk+eg7wD0nub38/FrggyQbFYlX96VQmJ0mSpNGZbKH4mYHf/zDViUiSJGlmmVShWFWbfOuKJEmSZjsgSe0AAAhlSURBVJehN9yWJEnStsFCUZIkSZ0sFCVJktTJQlGSJEmdLBQlSZLUyUJRkiRJnSwUJUmS1MlCUZIkSZ0m+2YW6Xe2v+9X7PiDK0adhrRN2e4/fg3Afz521xFnIm1btr/vV8DcUacxMhaKGsr8+fNHnYK0Tbr55rsBmP/Ubfd/sKTRmLtN/2+fhaKGsnDhwlGnIG2TFi1aBMCSJUtGnImkbYlzFCVJktTJQlGSJEmdLBQlSZLUyUJRkiRJnSwUJUmS1MlCUZIkSZ0sFCVJktRpWgvFJIcnuTzJ7UkqyRsG2pPk9CSrkqxPck2SZwzE7J5kaZK72mNpkt0GYg5Icm3bx+1J3pskAzHHJPl+kvvbz5cNm4skSdJsNt0jijsDNwKLgPUd7e8ATgEWAs8G1gJXJdmlL+Zi4CDgxe1xELC015hkV+AqYE3bxyLg7cDJfTELgEuAi4AD28/PJzlkyFwkSZJmrWktFKvqiqp6V1VdCvxnf1s74ncS8KGquqyqbgSOA3YBjm1j9qcpDo+vqpVVtRJ4E/DSJE9ru3oNsBNwXFXd2N7rw8DJfaOKJwFXV9VZVXVTVZ0FXNOen1QukiRJs91MmqO4HzAPWN47UVXrgW8Az21PLQDuAVb0Xfct4N6BmG+21/YsA/YC9u2LWc6GlvX1MZlcJEmSZrWZVCjOaz/XDJxf09c2D1hXVdVrbL+vHYjp6oNJxMwbiNtYjCRJ0qw2kwrFrVqS45NMJJlYt27dqNORJEnabDOpUFzdfs4dOD+3r201MKd/BXP7fc+BmK4+mETM6oG4jcVsoKrOr6qxqhqbM2dOV4gkSdJWZSYVirfSFGHjvRNJHgscxkNzElfSrJxe0HfdAuBxAzGHtdf2jAOrgJ/0xYyzofG+PiaTiyRJ0qw23fso7pzkwCQHtvfeu/29dzvX8BzgnUlenuSZwIU0i1cuBqiqm4ArgfOSLGi3uTkP+FJV/bC9zcXAfcCFSZ6Z5OXAqcDivrmNS4Ajk5ya5OlJTgNe0N6fyeQiSZI02z1qmu83Blzd9/uM9vgM8AbgbGBH4OPA7sB1wAur6u6+a44FzqVZpQxwOXBir7Gq7koy3vYxAdwJfBRY3BezIsmrgPcDZwI/Bl5ZVdf13WcyuUiSJM1a01ooVtU1QDbSXsDp7fFwMXcCr93EfW4ADt9EzKXApZuTiyRJ0mw2k+YoSpIkaQaxUJQkSVInC0VJkiR1slCUJElSJwtFSZIkdbJQlCRJUicLRUmSJHWyUJQkSVInC0VJkiR1slCUJElSJwtFSZIkdbJQlCRJUicLRUmSJHWyUJQkSVInC0VJkiR1slCUJElSJwtFSZIkdbJQlCRJUicLRUmSJHWyUJQkSVInC0VJkiR1slCUJElSJwtFSZIkdbJQlCRJUicLRUmSJHWyUJQkSVInC0VJkiR1slCUJElSpxlVKCY5PUkNHKv72tPGrEqyPsk1SZ4x0MfuSZYmuas9libZbSDmgCTXtn3cnuS9STIQc0yS7ye5v/182Zb96yVJkmaWGVUotn4IPKnvOKCv7R3AKcBC4NnAWuCqJLv0xVwMHAS8uD0OApb2GpPsClwFrGn7WAS8HTi5L2YBcAlwEXBg+/n5JIdM4d8pSZI0oz1q1Al0+G1VrR482Y74nQR8qKoua88dR1MsHgucl2R/muLw0Kpa2ca8CfhmkqdV1Q+B1wA7AcdV1XrgxiRPB05Osriqqr3P1VV1Vnv7s5K8oD3/6i33p0uSJM0cM3FE8anto+Vbk3wuyVPb8/sB84DlvcC20PsG8Nz21ALgHmBFX3/fAu4diPlme23PMmAvYN++mOVsaFlfH5IkSbPeTCsUrwPeQDMq+Bc0heGKJE9sv0PzyLjfmr62ecC6dlQQgPb72oGYrj6YRMw8JEmSthEz6tFzVX2l/3eSfwFuAY4D/mUkSU1SkuOB4wH23nvvEWcjSZK0+WbaiOIGquoe4HvA7wG9eYtzB8Lm9rWtBub0r2Buv+85ENPVB5OI+S9zJ/tyPb+qxqpqbM6cORv7syRJkrYKM7pQTPJY4OnAL4BbaQq18YH2w3hoTuJKYGeaOYY9C4DHDcQc1l7bMw6sAn7SFzPOhsbZcO6jJEnSrDajCsUkf53k+Un2a7eiuZSmyPtMO9fwHOCdSV6e5JnAhTSLVy4GqKqbgCtpVkAvaLe5OQ/4UrvimTb2PuDCJM9M8nLgVGBx39zGJcCRSU5N8vQkpwEvaO8vSZK0TZhRcxSBpwD/G9gDWEczL/GPquq2tv1sYEfg48DuNItfXlhVd/f1cSxwLs0qZYDLgRN7jVV1V5Lxto8J4E7go8DivpgVSV4FvB84E/gx8Mqqum5K/1pJkqQZbEYVilX1qk20F3B6ezxczJ3AazfRzw3A4ZuIuZRmRFOSJGmbNKMePUuSJGnmsFCUJElSJwtFSZIkdbJQlCRJUicLRUmSJHWyUJQkSVInC0VJkiR1slCUJElSJwtFSZIkdbJQlCRJUicLRUmSJHWaUe96lrTlnHvuudx8882jTkOPUO8/u0WLFo04Ez1S8+fPZ+HChaNOQxqKhaIkbQV23HHHUacgaRtkoShtIxzJkCQNyzmKkiRJ6mShKEmSpE4WipIkSepkoShJkqROFoqSJEnqZKEoSZKkThaKkiRJ6mShKEmSpE4WipIkSepkoShJkqROFoqSJEnqZKEoSZKkThaKkiRJ6pSqGnUOs06SdcBto85D0qyzB3DHqJOQNOvsU1VzuhosFCVpK5FkoqrGRp2HpG2Hj54lSZLUyUJRkiRJnSwUJWnrcf6oE5C0bXGOoiRJkjo5oihJkqROFoqSJEnqZKEoSZKkThaKkiRJ6mShKEmSpE7/PwkTsGAgtPHEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_clientes_cadastrados_sem_outliers['Rendimento_anual'].max"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mulXqvGfVVK4",
        "outputId": "15dcca20-3802-406a-d114-5ce10fbf9b4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame._add_numeric_operations.<locals>.max of 2         112500.0\n",
              "3         270000.0\n",
              "4         270000.0\n",
              "5         270000.0\n",
              "6         270000.0\n",
              "            ...   \n",
              "438552    135000.0\n",
              "438553    103500.0\n",
              "438554     54000.0\n",
              "438555     72000.0\n",
              "438556    121500.0\n",
              "Name: Rendimento_anual, Length: 425822, dtype: float64>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,6))\n",
        "ax = sns.histplot(data=df_clientes_cadastrados_sem_outliers, x='Rendimento_anual', bins=15)\n",
        "plt.xticks(rotation=360)\n",
        "ax.set_title('Distribuição da renda anual', fontsize = 20, loc='left')\n",
        "ax.set_ylabel('Número de pessoas', fontsize = 18)\n",
        "ax.set_xlabel('Renda anual ($)', fontsize = 18)\n",
        "ax.yaxis.set_tick_params(labelsize=12)\n",
        "ax.xaxis.set_tick_params(labelsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "z5Mh59u9VVNQ",
        "outputId": "a5914fe4-8a49-41d9-a600-9764e3af7388"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_clientes_cadastrados_tratamento1 = df_clientes_cadastrados_sem_outliers\n",
        "df_clientes_cadastrados_tratamento1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9HV8UTgVVPU",
        "outputId": "a5311f25-5455-4ad3-c317-e888364609a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(425822, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    }
  ]
}