Risk_credit.ipynb

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      "name": "Risk_credit.ipynb",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/paulo-santos-ds/Machine-Learning/blob/main/Risk_credit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "## O Dataset a seguir tem como objetivo utilizar a renda, idade e a dividada que cada individuo possui junto ao banco, esses dados serão analisados na previsão futura visando observar se as pessoas com estas caracteristicas ira pagar ou não o emprestimo."
      ],
      "metadata": {
        "id": "tgdTpOSGQzmI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Instalação de Bilioteca"
      ],
      "metadata": {
        "id": "L7k0MBVMs5eG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 200,
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      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.8.1)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly) (8.0.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install plotly --upgrade\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Utilizando as bibliotecas"
      ],
      "metadata": {
        "id": "WDZyZyOEtDzs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "TV6l9rkDsYPV"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importando base de dados"
      ],
      "metadata": {
        "id": "fA_R97-vtSmU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fonte: https://www.kaggle.com/datasets/laotse/credit-risk-dataset"
      ],
      "metadata": {
        "id": "-ap5bV_NtYPt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importandos os dados"
      ],
      "metadata": {
        "id": "R19O8K28t-qd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit = pd.read_csv('/content/credit_risk_dataset.csv')\n",
        "base_credit.head(10)"
      ],
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      "execution_count": 202,
      "outputs": [
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              "   person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "0          22          59000                  RENT              123.0   \n",
              "1          21           9600                   OWN                5.0   \n",
              "2          25           9600              MORTGAGE                1.0   \n",
              "3          23          65500                  RENT                4.0   \n",
              "4          24          54400                  RENT                8.0   \n",
              "5          21           9900                   OWN                2.0   \n",
              "6          26          77100                  RENT                8.0   \n",
              "7          24          78956                  RENT                5.0   \n",
              "8          24          83000                  RENT                8.0   \n",
              "9          21          10000                   OWN                6.0   \n",
              "\n",
              "  loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "0    PERSONAL          D      35000          16.02            1   \n",
              "1   EDUCATION          B       1000          11.14            0   \n",
              "2     MEDICAL          C       5500          12.87            1   \n",
              "3     MEDICAL          C      35000          15.23            1   \n",
              "4     MEDICAL          C      35000          14.27            1   \n",
              "5     VENTURE          A       2500           7.14            1   \n",
              "6   EDUCATION          B      35000          12.42            1   \n",
              "7     MEDICAL          B      35000          11.11            1   \n",
              "8    PERSONAL          A      35000           8.90            1   \n",
              "9     VENTURE          D       1600          14.74            1   \n",
              "\n",
              "   loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \n",
              "0                 0.59                         Y                           3  \n",
              "1                 0.10                         N                           2  \n",
              "2                 0.57                         N                           3  \n",
              "3                 0.53                         N                           2  \n",
              "4                 0.55                         Y                           4  \n",
              "5                 0.25                         N                           2  \n",
              "6                 0.45                         N                           3  \n",
              "7                 0.44                         N                           4  \n",
              "8                 0.42                         N                           2  \n",
              "9                 0.16                         N                           3  "
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    },
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      "source": [
        "Tratamento de valores Inconsistentes, observamos que não ha valor invalido"
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        "base_credit.loc[base_credit['person_age'] < 0]"
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              "Columns: [person_age, person_income, person_home_ownership, person_emp_length, loan_intent, loan_grade, loan_amnt, loan_int_rate, loan_status, loan_percent_income, cb_person_default_on_file, cb_person_cred_hist_length]\n",
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              "          const element = document.querySelector('#df-3a3f01e8-faf1-4236-8ad4-3b8a577efe4f');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit[base_credit['person_age'] < 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "id": "lh6lvZPssVNl",
        "outputId": "6da0088e-839f-4435-a47c-744e228af237"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [person_age, person_income, person_home_ownership, person_emp_length, loan_intent, loan_grade, loan_amnt, loan_int_rate, loan_status, loan_percent_income, cb_person_default_on_file, cb_person_cred_hist_length]\n",
              "Index: []"
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              "      <th>cb_person_cred_hist_length</th>\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-24d791d7-3bae-4f0b-b154-d2d13cde2418 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-24d791d7-3bae-4f0b-b154-d2d13cde2418');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tratamento de valores ausentes,  todos os dados estão corretos"
      ],
      "metadata": {
        "id": "R6uKixSftR-g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "XlkRnVp3tcxz",
        "outputId": "473681a7-36a3-4079-8cd6-3525737dd1b4"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       person_age  person_income  person_home_ownership  person_emp_length  \\\n",
              "0           False          False                  False              False   \n",
              "1           False          False                  False              False   \n",
              "2           False          False                  False              False   \n",
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              "\n",
              "       loan_intent  loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "0            False       False      False          False        False   \n",
              "1            False       False      False          False        False   \n",
              "2            False       False      False          False        False   \n",
              "3            False       False      False          False        False   \n",
              "4            False       False      False          False        False   \n",
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              "\n",
              "       loan_percent_income  cb_person_default_on_file  \\\n",
              "0                    False                      False   \n",
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              "2                    False                      False   \n",
              "3                    False                      False   \n",
              "4                    False                      False   \n",
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              "       cb_person_cred_hist_length  \n",
              "0                           False  \n",
              "1                           False  \n",
              "2                           False  \n",
              "3                           False  \n",
              "4                           False  \n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5e065158-8c9b-4c1c-b993-20c233e69f26');\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": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.isnull().sum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGpmZXEwv6UQ",
        "outputId": "aecf585c-b92a-404d-b733-820dd9716e34"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame._add_numeric_operations.<locals>.sum of        person_age  person_income  person_home_ownership  person_emp_length  \\\n",
              "0           False          False                  False              False   \n",
              "1           False          False                  False              False   \n",
              "2           False          False                  False              False   \n",
              "3           False          False                  False              False   \n",
              "4           False          False                  False              False   \n",
              "...           ...            ...                    ...                ...   \n",
              "32576       False          False                  False              False   \n",
              "32577       False          False                  False              False   \n",
              "32578       False          False                  False              False   \n",
              "32579       False          False                  False              False   \n",
              "32580       False          False                  False              False   \n",
              "\n",
              "       loan_intent  loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "0            False       False      False          False        False   \n",
              "1            False       False      False          False        False   \n",
              "2            False       False      False          False        False   \n",
              "3            False       False      False          False        False   \n",
              "4            False       False      False          False        False   \n",
              "...            ...         ...        ...            ...          ...   \n",
              "32576        False       False      False          False        False   \n",
              "32577        False       False      False          False        False   \n",
              "32578        False       False      False          False        False   \n",
              "32579        False       False      False          False        False   \n",
              "32580        False       False      False          False        False   \n",
              "\n",
              "       loan_percent_income  cb_person_default_on_file  \\\n",
              "0                    False                      False   \n",
              "1                    False                      False   \n",
              "2                    False                      False   \n",
              "3                    False                      False   \n",
              "4                    False                      False   \n",
              "...                    ...                        ...   \n",
              "32576                False                      False   \n",
              "32577                False                      False   \n",
              "32578                False                      False   \n",
              "32579                False                      False   \n",
              "32580                False                      False   \n",
              "\n",
              "       cb_person_cred_hist_length  \n",
              "0                           False  \n",
              "1                           False  \n",
              "2                           False  \n",
              "3                           False  \n",
              "4                           False  \n",
              "...                           ...  \n",
              "32576                       False  \n",
              "32577                       False  \n",
              "32578                       False  \n",
              "32579                       False  \n",
              "32580                       False  \n",
              "\n",
              "[32581 rows x 12 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "emvDjmAywTL0"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizando os  Ultimos Registros da Base de dados"
      ],
      "metadata": {
        "id": "RnfWvlHVSoTp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "wkwBIHc_Shdx",
        "outputId": "396c3552-4621-4439-bb48-26068758963c"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "32576          57          53000              MORTGAGE                1.0   \n",
              "32577          54         120000              MORTGAGE                4.0   \n",
              "32578          65          76000                  RENT                3.0   \n",
              "32579          56         150000              MORTGAGE                5.0   \n",
              "32580          66          42000                  RENT                2.0   \n",
              "\n",
              "           loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "32576         PERSONAL          C       5800          13.16            0   \n",
              "32577         PERSONAL          A      17625           7.49            0   \n",
              "32578  HOMEIMPROVEMENT          B      35000          10.99            1   \n",
              "32579         PERSONAL          B      15000          11.48            0   \n",
              "32580          MEDICAL          B       6475           9.99            0   \n",
              "\n",
              "       loan_percent_income cb_person_default_on_file  \\\n",
              "32576                 0.11                         N   \n",
              "32577                 0.15                         N   \n",
              "32578                 0.46                         N   \n",
              "32579                 0.10                         N   \n",
              "32580                 0.15                         N   \n",
              "\n",
              "       cb_person_cred_hist_length  \n",
              "32576                          30  \n",
              "32577                          19  \n",
              "32578                          28  \n",
              "32579                          26  \n",
              "32580                          30  "
            ],
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              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizando as estatisticas dos dados"
      ],
      "metadata": {
        "id": "eFZozR8FS8_H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "apYb1EaPSwwY",
        "outputId": "98157b1e-4f1e-4b78-a0c3-13670fc2c856"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         person_age  person_income  person_emp_length     loan_amnt  \\\n",
              "count  32581.000000   3.258100e+04       31686.000000  32581.000000   \n",
              "mean      27.734600   6.607485e+04           4.789686   9589.371106   \n",
              "std        6.348078   6.198312e+04           4.142630   6322.086646   \n",
              "min       20.000000   4.000000e+03           0.000000    500.000000   \n",
              "25%       23.000000   3.850000e+04           2.000000   5000.000000   \n",
              "50%       26.000000   5.500000e+04           4.000000   8000.000000   \n",
              "75%       30.000000   7.920000e+04           7.000000  12200.000000   \n",
              "max      144.000000   6.000000e+06         123.000000  35000.000000   \n",
              "\n",
              "       loan_int_rate   loan_status  loan_percent_income  \\\n",
              "count   29465.000000  32581.000000         32581.000000   \n",
              "mean       11.011695      0.218164             0.170203   \n",
              "std         3.240459      0.413006             0.106782   \n",
              "min         5.420000      0.000000             0.000000   \n",
              "25%         7.900000      0.000000             0.090000   \n",
              "50%        10.990000      0.000000             0.150000   \n",
              "75%        13.470000      0.000000             0.230000   \n",
              "max        23.220000      1.000000             0.830000   \n",
              "\n",
              "       cb_person_cred_hist_length  \n",
              "count                32581.000000  \n",
              "mean                     5.804211  \n",
              "std                      4.055001  \n",
              "min                      2.000000  \n",
              "25%                      3.000000  \n",
              "50%                      4.000000  \n",
              "75%                      8.000000  \n",
              "max                     30.000000  "
            ],
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              "      <th></th>\n",
              "      <th>person_age</th>\n",
              "      <th>person_income</th>\n",
              "      <th>person_emp_length</th>\n",
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              "      <th>count</th>\n",
              "      <td>32581.000000</td>\n",
              "      <td>3.258100e+04</td>\n",
              "      <td>31686.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>27.734600</td>\n",
              "      <td>6.607485e+04</td>\n",
              "      <td>4.789686</td>\n",
              "      <td>9589.371106</td>\n",
              "      <td>11.011695</td>\n",
              "      <td>0.218164</td>\n",
              "      <td>0.170203</td>\n",
              "      <td>5.804211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.348078</td>\n",
              "      <td>6.198312e+04</td>\n",
              "      <td>4.142630</td>\n",
              "      <td>6322.086646</td>\n",
              "      <td>3.240459</td>\n",
              "      <td>0.413006</td>\n",
              "      <td>0.106782</td>\n",
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              "      <th>min</th>\n",
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              "      <td>500.000000</td>\n",
              "      <td>5.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>3.850000e+04</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>7.900000</td>\n",
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              "      <td>0.090000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>5.500000e+04</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>10.990000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>7.920000e+04</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12200.000000</td>\n",
              "      <td>13.470000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.230000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>144.000000</td>\n",
              "      <td>6.000000e+06</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>35000.000000</td>\n",
              "      <td>23.220000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.830000</td>\n",
              "      <td>30.000000</td>\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Filtrando o salario anual mais alto "
      ],
      "metadata": {
        "id": "mfI4OsSOTeqz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit[base_credit['person_income'] > 3000000]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "ZBCYc4lHTi4C",
        "outputId": "a789ad45-23d1-4143-a39f-ec736c362723"
      },
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "32297         144        6000000              MORTGAGE               12.0   \n",
              "\n",
              "      loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "32297    PERSONAL          C       5000          12.73            0   \n",
              "\n",
              "       loan_percent_income cb_person_default_on_file  \\\n",
              "32297                  0.0                         N   \n",
              "\n",
              "       cb_person_cred_hist_length  \n",
              "32297                          25  "
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            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Filtrando pessoa com menor divida"
      ],
      "metadata": {
        "id": "QIyJYTDcW5SQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_credit[base_credit['loan_amnt'] <= 550]"
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        "outputId": "3b46678c-0e1c-40da-8467-9181a8710d15"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       person_age  person_income person_home_ownership  person_emp_length  \\\n",
              "871            26          19500                  RENT                0.0   \n",
              "1355           22          26000              MORTGAGE                3.0   \n",
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              "\n",
              "             loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "871              MEDICAL          C        500          11.41            0   \n",
              "1355            PERSONAL          B        500          10.46            0   \n",
              "8607     HOMEIMPROVEMENT          B        500           9.76            0   \n",
              "15951    HOMEIMPROVEMENT          B        500          10.71            0   \n",
              "18502  DEBTCONSOLIDATION          A        500            NaN            0   \n",
              "\n",
              "       loan_percent_income cb_person_default_on_file  \\\n",
              "871                   0.03                         N   \n",
              "1355                  0.02                         N   \n",
              "8607                  0.01                         N   \n",
              "15951                 0.06                         N   \n",
              "18502                 0.03                         N   \n",
              "\n",
              "       cb_person_cred_hist_length  \n",
              "871                             2  \n",
              "1355                            4  \n",
              "8607                            2  \n",
              "15951                           3  \n",
              "18502                           6  "
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              "      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-7f95b72a-9a73-498c-8785-7fd74ab6e0b6 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-7f95b72a-9a73-498c-8785-7fd74ab6e0b6');\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": 210
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Contagem de registro por classe"
      ],
      "metadata": {
        "id": "fWQwg2EvaHx5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(base_credit['loan_status'], return_counts = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XVGbxRXbaMKJ",
        "outputId": "6807b476-4211-46b4-d6ca-5fe81e66ed46"
      },
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), array([25473,  7108]))"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Grafico de pessoas que pagam versos não pagam "
      ],
      "metadata": {
        "id": "14W_KfMEb0Do"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = base_credit['loan_status']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "_l9HgvoPb-h2",
        "outputId": "a623d620-fca6-4352-b9b0-6d06db850f64"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Histograma com intervalo de idade"
      ],
      "metadata": {
        "id": "Ej4BZHpucVsN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(x = base_credit['person_age']);\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "IB-dCXAtcceW",
        "outputId": "5c6c0302-22bb-40c7-a273-d38a4ad83e21"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histograma de pessoas com maior renda"
      ],
      "metadata": {
        "id": "wTUV98j-dKXH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(x = base_credit['person_income']);\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "ExpLB1Rhc1sA",
        "outputId": "5ddbad64-b665-49a7-b69d-ffec5a57b15b"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEFCAYAAAABjYvXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASiElEQVR4nO3df4xdZZ3H8feHFpTFH0UZSdM2W6JVU91YcIK4GuNChIJmi4kayK40prEmFoNZd1fwH/xFon8oGxIkqdK17Kq1C7I0brU2SKJuFujwQ6BFZLZAaAN0pEVkzcrCfveP+zR7U2c6t/PrzjDvV3Jzz/me5zz3OSHM555znnuaqkKSNL8d1+8BSJL6zzCQJBkGkiTDQJKEYSBJAhb2ewATdcopp9Ty5cv7PQxJmlPuuuuu31TVwJH1ORsGy5cvZ2hoqN/DkKQ5Jcljo9W9TCRJMgwkSYaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJObwL5AnY/nl/9aXz330K+/vy+dK0ng8M5AkGQaSJMNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJoocwSPLyJHcm+WWS3Um+0OqnJbkjyXCS7yc5odVf1taH2/blXX1d0eoPJTmvq7661YaTXD71hylJOppezgz+AJxdVW8DVgGrk5wFfBW4uqreABwC1rX264BDrX51a0eSlcBFwFuA1cA3kixIsgC4FjgfWAlc3NpKkmbIuGFQHc+11ePbq4CzgRtbfTNwYVte09Zp289JklbfUlV/qKpHgGHgzPYarqq9VfU8sKW1lSTNkJ7uGbRv8PcCB4CdwH8Cz1TVC63JPmBJW14CPA7Qtv8WeG13/Yh9xqqPNo71SYaSDI2MjPQydElSD3oKg6p6sapWAUvpfJN/87SOauxxbKyqwaoaHBgY6McQJOkl6ZhmE1XVM8BtwDuBRUkO/0tpS4H9bXk/sAygbX818HR3/Yh9xqpLkmZIL7OJBpIsassnAu8DHqQTCh9qzdYCt7TlbW2dtv2nVVWtflGbbXQasAK4E9gFrGizk06gc5N521QcnCSpN738G8iLgc1t1s9xwNaq+mGSPcCWJF8G7gGub+2vB/4pyTBwkM4fd6pqd5KtwB7gBWBDVb0IkORSYAewANhUVbun7AglSeMaNwyq6j7g9FHqe+ncPziy/t/Ah8fo6yrgqlHq24HtPYxXkjQN/AWyJMkwkCQZBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEmihzBIsizJbUn2JNmd5LJW/3yS/Unuba8Luva5IslwkoeSnNdVX91qw0ku76qfluSOVv9+khOm+kAlSWPr5czgBeAzVbUSOAvYkGRl23Z1Va1qr+0AbdtFwFuA1cA3kixIsgC4FjgfWAlc3NXPV1tfbwAOAeum6PgkST0YNwyq6omqurst/w54EFhylF3WAFuq6g9V9QgwDJzZXsNVtbeqnge2AGuSBDgbuLHtvxm4cKIHJEk6dsd0zyDJcuB04I5WujTJfUk2JTm51ZYAj3fttq/Vxqq/Fnimql44oj7a569PMpRkaGRk5FiGLkk6ip7DIMkrgJuAT1fVs8B1wOuBVcATwNemZYRdqmpjVQ1W1eDAwMB0f5wkzRsLe2mU5Hg6QfCdqvoBQFU91bX9m8AP2+p+YFnX7ktbjTHqTwOLkixsZwfd7SVJM6CX2UQBrgcerKqvd9UXdzX7IPBAW94GXJTkZUlOA1YAdwK7gBVt5tAJdG4yb6uqAm4DPtT2XwvcMrnDkiQdi17ODN4FfBS4P8m9rfY5OrOBVgEFPAp8AqCqdifZCuyhMxNpQ1W9CJDkUmAHsADYVFW7W3+fBbYk+TJwD53wkSTNkHHDoKp+AWSUTduPss9VwFWj1LePtl9V7aUz20iS1Af+AlmSZBhIkgwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJoocwSLIsyW1J9iTZneSyVn9Nkp1JHm7vJ7d6klyTZDjJfUnO6OprbWv/cJK1XfW3J7m/7XNNkkzHwUqSRtfLmcELwGeqaiVwFrAhyUrgcuDWqloB3NrWAc4HVrTXeuA66IQHcCXwDuBM4MrDAdLafLxrv9WTPzRJUq/GDYOqeqKq7m7LvwMeBJYAa4DNrdlm4MK2vAa4oTpuBxYlWQycB+ysqoNVdQjYCaxu215VVbdXVQE3dPUlSZoBx3TPIMly4HTgDuDUqnqibXoSOLUtLwEe79ptX6sdrb5vlLokaYb0HAZJXgHcBHy6qp7t3ta+0dcUj220MaxPMpRkaGRkZLo/TpLmjZ7CIMnxdILgO1X1g1Z+ql3iob0faPX9wLKu3Ze22tHqS0ep/5Gq2lhVg1U1ODAw0MvQJUk96GU2UYDrgQer6utdm7YBh2cErQVu6apf0mYVnQX8tl1O2gGcm+TkduP4XGBH2/ZskrPaZ13S1ZckaQYs7KHNu4CPAvcnubfVPgd8BdiaZB3wGPCRtm07cAEwDPwe+BhAVR1M8iVgV2v3xao62JY/CXwbOBH4UXtJkmbIuGFQVb8Axpr3f84o7QvYMEZfm4BNo9SHgLeONxZJ0vTwF8iSJMNAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJIkewiDJpiQHkjzQVft8kv1J7m2vC7q2XZFkOMlDSc7rqq9uteEkl3fVT0tyR6t/P8kJU3mAkqTx9XJm8G1g9Sj1q6tqVXttB0iyErgIeEvb5xtJFiRZAFwLnA+sBC5ubQG+2vp6A3AIWDeZA5IkHbtxw6CqfgYc7LG/NcCWqvpDVT0CDANnttdwVe2tqueBLcCaJAHOBm5s+28GLjzGY5AkTdJk7hlcmuS+dhnp5FZbAjze1WZfq41Vfy3wTFW9cER9VEnWJxlKMjQyMjKJoUuSuk00DK4DXg+sAp4AvjZlIzqKqtpYVYNVNTgwMDATHylJ88LCiexUVU8dXk7yTeCHbXU/sKyr6dJWY4z608CiJAvb2UF3e0nSDJnQmUGSxV2rHwQOzzTaBlyU5GVJTgNWAHcCu4AVbebQCXRuMm+rqgJuAz7U9l8L3DKRMUmSJm7cM4Mk3wPeC5ySZB9wJfDeJKuAAh4FPgFQVbuTbAX2AC8AG6rqxdbPpcAOYAGwqap2t4/4LLAlyZeBe4Drp+zoJEk9GTcMquriUcpj/sGuqquAq0apbwe2j1LfS2e2kSSpT/wFsiTJMJAkGQaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJoocwSLIpyYEkD3TVXpNkZ5KH2/vJrZ4k1yQZTnJfkjO69lnb2j+cZG1X/e1J7m/7XJMkU32QkqSj6+XM4NvA6iNqlwO3VtUK4Na2DnA+sKK91gPXQSc8gCuBdwBnAlceDpDW5uNd+x35WZKkaTZuGFTVz4CDR5TXAJvb8mbgwq76DdVxO7AoyWLgPGBnVR2sqkPATmB12/aqqrq9qgq4oasvSdIMmeg9g1Or6om2/CRwalteAjze1W5fqx2tvm+U+qiSrE8ylGRoZGRkgkOXJB1p0jeQ2zf6moKx9PJZG6tqsKoGBwYGZuIjJWlemGgYPNUu8dDeD7T6fmBZV7ulrXa0+tJR6pKkGTTRMNgGHJ4RtBa4pat+SZtVdBbw23Y5aQdwbpKT243jc4EdbduzSc5qs4gu6epLkjRDFo7XIMn3gPcCpyTZR2dW0FeArUnWAY8BH2nNtwMXAMPA74GPAVTVwSRfAna1dl+sqsM3pT9JZ8bSicCP2kuSNIPGDYOquniMTeeM0raADWP0swnYNEp9CHjreOOQJE0ff4EsSTIMJEmGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJKYZBgkeTTJ/UnuTTLUaq9JsjPJw+395FZPkmuSDCe5L8kZXf2sbe0fTrJ2cockSTpWU3Fm8BdVtaqqBtv65cCtVbUCuLWtA5wPrGiv9cB10AkP4ErgHcCZwJWHA0SSNDOm4zLRGmBzW94MXNhVv6E6bgcWJVkMnAfsrKqDVXUI2AmsnoZxSZLGMNkwKOAnSe5Ksr7VTq2qJ9ryk8CpbXkJ8HjXvvtabaz6H0myPslQkqGRkZFJDl2SdNjCSe7/7qran+R1wM4kv+reWFWVpCb5Gd39bQQ2AgwODk5Zv5I0303qzKCq9rf3A8DNdK75P9Uu/9DeD7Tm+4FlXbsvbbWx6pKkGTLhMEhyUpJXHl4GzgUeALYBh2cErQVuacvbgEvarKKzgN+2y0k7gHOTnNxuHJ/bapKkGTKZy0SnAjcnOdzPd6vqx0l2AVuTrAMeAz7S2m8HLgCGgd8DHwOoqoNJvgTsau2+WFUHJzEuSdIxmnAYVNVe4G2j1J8GzhmlXsCGMfraBGya6FgkSZPjL5AlSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKzKAySrE7yUJLhJJf3ezySNJ/MijBIsgC4FjgfWAlcnGRlf0clSfPHrAgD4ExguKr2VtXzwBZgTZ/HJEnzxsJ+D6BZAjzetb4PeMeRjZKsB9a31eeSPDTBzzsF+M0E952wfHXKu+zLcUwTj2V28lhmn8kex5+OVpwtYdCTqtoIbJxsP0mGqmpwCobUVy+V4wCPZbbyWGaf6TqO2XKZaD+wrGt9aatJkmbAbAmDXcCKJKclOQG4CNjW5zFJ0rwxKy4TVdULSS4FdgALgE1VtXsaP3LSl5pmiZfKcYDHMlt5LLPPtBxHqmo6+pUkzSGz5TKRJKmPDANJ0vwKg5fKIy+SbEpyIMkD/R7LZCVZluS2JHuS7E5yWb/HNFFJXp7kziS/bMfyhX6PaTKSLEhyT5If9nssk5Hk0ST3J7k3yVC/xzMZSRYluTHJr5I8mOSdU9b3fLln0B558WvgfXR+1LYLuLiq9vR1YBOQ5D3Ac8ANVfXWfo9nMpIsBhZX1d1JXgncBVw4R/+7BDipqp5LcjzwC+Cyqrq9z0ObkCR/AwwCr6qqD/R7PBOV5FFgsKrm/A/OkmwGfl5V32ozL/+kqp6Zir7n05nBS+aRF1X1M+Bgv8cxFarqiaq6uy3/DniQzi/S55zqeK6tHt9ec/LbVpKlwPuBb/V7LOpI8mrgPcD1AFX1/FQFAcyvMBjtkRdz8o/OS1WS5cDpwB39HcnEtUsr9wIHgJ1VNVeP5R+Avwf+t98DmQIF/CTJXe2RNnPVacAI8I/t8t23kpw0VZ3PpzDQLJbkFcBNwKer6tl+j2eiqurFqlpF51f0ZyaZc5fxknwAOFBVd/V7LFPk3VV1Bp2nIm9ol1nnooXAGcB1VXU68F/AlN37nE9h4CMvZql2ff0m4DtV9YN+j2cqtNP324DV/R7LBLwL+Mt2rX0LcHaSf+7vkCauqva39wPAzXQuGc9F+4B9XWebN9IJhykxn8LAR17MQu2m6/XAg1X19X6PZzKSDCRZ1JZPpDNZ4Vf9HdWxq6orqmppVS2n8//JT6vqr/s8rAlJclKbmEC7pHIuMCdn4VXVk8DjSd7USucAUzbRYlY8jmIm9OGRF9MmyfeA9wKnJNkHXFlV1/d3VBP2LuCjwP3tWjvA56pqex/HNFGLgc1t5tpxwNaqmtPTMl8CTgVu7nznYCHw3ar6cX+HNCmfAr7TvtDuBT42VR3Pm6mlkqSxzafLRJKkMRgGkiTDQJJkGEiSMAwkaU441gdUJvlI1wMgvztue2cTSdLsdywPqEyyAtgKnF1Vh5K8rv3obkyeGUjSHDDaAyqTvD7Jj9tzl36e5M1t08eBa6vqUNv3qEEAhoEkzWUbgU9V1duBvwW+0epvBN6Y5N+T3J5k3MeizJtfIEvSS0l7uOOfA//SfmEN8LL2vhBYQedJBUuBnyX5s6M98towkKS56TjgmfaU3CPtA+6oqv8BHknyazrhsOtonUmS5pj2qPdHknwYOg99TPK2tvlf6ZwVkOQUOpeN9h6tP8NAkuaA9oDK/wDelGRfknXAXwHrkvwS2M3//+uNO4Cnk+yh8yj1v6uqp4/av1NLJUmeGUiSDANJkmEgScIwkCRhGEiSMAwkSRgGkiTg/wBp76QmG2C1YQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histograma considerando a divida das pessoas"
      ],
      "metadata": {
        "id": "48KpSxnMd7EL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(x = base_credit['loan_amnt']);\n",
        "plt.show();\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YTJZLnyWd_xF",
        "outputId": "47579182-a9b4-4753-a163-d304f5729242"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Grafico de dispersão em amarelo são os inadinplentes"
      ],
      "metadata": {
        "id": "xq_9p6Ecn2lP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grafico = px.scatter_matrix(base_credit, dimensions = ['person_age', 'person_income','loan_amnt'], color = 'loan_status' )\n",
        "grafico.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "FwuyVnBknXhN",
        "outputId": "0f0c46bb-c5ed-4a8d-e9c5-4fcd6ab47c2d"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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                       {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('223a97e9-23f5-4624-ba8b-7262c17287c2');\n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
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              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Divisão de previsores e classe"
      ],
      "metadata": {
        "id": "9hJZf57FrQLe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_credit = base_credit.iloc[:, 1:4].values"
      ],
      "metadata": {
        "id": "2q_zsvErw8Nj"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_credit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bdJ4E5DrPKD",
        "outputId": "c935aebc-9be0-48f6-f2bc-985a74c920e8"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[59000, 'RENT', 123.0],\n",
              "       [9600, 'OWN', 5.0],\n",
              "       [9600, 'MORTGAGE', 1.0],\n",
              "       ...,\n",
              "       [76000, 'RENT', 3.0],\n",
              "       [150000, 'MORTGAGE', 5.0],\n",
              "       [42000, 'RENT', 2.0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tipo de variaveis"
      ],
      "metadata": {
        "id": "oizxJBppx94G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(x_credit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YF-VP5GXyA21",
        "outputId": "4e828fec-bb75-4160-88f3-9ecc43acef26"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_credit = base_credit.iloc[:, 4].values\n",
        "y_credit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0gOMXZmyFrR",
        "outputId": "b8c06c41-b018-4e25-c1e6-3f7bd2e1ce95"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['PERSONAL', 'EDUCATION', 'MEDICAL', ..., 'HOMEIMPROVEMENT',\n",
              "       'PERSONAL', 'MEDICAL'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tipo de Variaveis"
      ],
      "metadata": {
        "id": "szY0U08TyVj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(y_credit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtOoEjNcyaac",
        "outputId": "52db0f9a-44ba-4c84-c999-1175dd6719f3"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Escalonamento dos valores"
      ],
      "metadata": {
        "id": "A5ikOd9iyrkk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_credit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6FjfurGyqRs",
        "outputId": "ac61a41f-b168-4a34-c45a-d6006e5443b8"
      },
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[59000, 'RENT', 123.0],\n",
              "       [9600, 'OWN', 5.0],\n",
              "       [9600, 'MORTGAGE', 1.0],\n",
              "       ...,\n",
              "       [76000, 'RENT', 3.0],\n",
              "       [150000, 'MORTGAGE', 5.0],\n",
              "       [42000, 'RENT', 2.0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pessoa com menor renda"
      ],
      "metadata": {
        "id": "Q__eIlEJzMiK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_credit[:,0].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHFBfIQ2zBJF",
        "outputId": "1056db86-0b36-4b65-b504-8fdbfc72ccf6"
      },
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4000"
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pessoa com maior renda"
      ],
      "metadata": {
        "id": "JQ5WHN09zQN1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_credit[:,0].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uDXZr3RzX2R",
        "outputId": "e6bf11d1-982a-472a-abc3-405c0025c4c5"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6000000"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pessoa mais nova"
      ],
      "metadata": {
        "id": "zxL_ehsWzqq_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_credit[:,0].min(), x_credit[:,2].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIyhsV_Jze8i",
        "outputId": "5e45c454-7980-484b-daa9-04b21f7519ee"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4000, 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pessoa mais velha"
      ],
      "metadata": {
        "id": "vQGsgPabzunU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_credit[:, 0].max(), x_credit[:,2].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1u1G71eGzxcU",
        "outputId": "a123fcbe-3c60-4fd0-c255-5a463eb2355a"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6000000, 38.0)"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    }
  ]
}