Florest_Nerdzao.ipynb

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  "metadata": {
    "colab": {
      "name": "Florest_Nerdzao.ipynb",
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      "include_colab_link": true
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        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/triatebr/aprenda-python/blob/master/Florest_Nerdzao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9al4UdJTdo2l",
        "colab_type": "text"
      },
      "source": [
        "![logos](https://img.freepik.com/free-vector/telemarketing-background-design_1212-238.jpg?size=338&ext=jpg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JsLybLYdo2s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importando bibliotecas\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import datetime \n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qa470cr5do20",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pu_Qyks1epmz",
        "colab_type": "code",
        "outputId": "0b547f52-926c-4e02-f32b-a8675bbda1fc",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
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                  "content-type",
                  "application/javascript"
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              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-831b4d6b-8ff7-402b-b778-286b21eff75e\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-831b4d6b-8ff7-402b-b778-286b21eff75e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving bank-full.csv to bank-full (2).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwZVwJgMdo25",
        "colab_type": "text"
      },
      "source": [
        "**Dados utilizados**\n",
        "\n",
        "Para o exemplo de árvore de descisão iremo utilizar um [dataset de Marketing](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing). Ele demonstra valores de campanha de telemarketing de banco portugues. O objetivo e prever de cliente assinará um termo de deposito."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fm4xI4sdo26",
        "colab_type": "code",
        "outputId": "198e750c-2ad7-4cfc-ecd8-c2672ada1edd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dados = pd.read_csv('bank-full.csv', delimiter=';')\n",
        "dados.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>2143</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1506</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>unknown</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age           job  marital  education  ... pdays  previous poutcome   y\n",
              "0   58    management  married   tertiary  ...    -1         0  unknown  no\n",
              "1   44    technician   single  secondary  ...    -1         0  unknown  no\n",
              "2   33  entrepreneur  married  secondary  ...    -1         0  unknown  no\n",
              "3   47   blue-collar  married    unknown  ...    -1         0  unknown  no\n",
              "4   33       unknown   single    unknown  ...    -1         0  unknown  no\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEctmVI4do3J",
        "colab_type": "code",
        "outputId": "7a843be1-427a-4765-d8dd-9ef515b8ee51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Nº de linhas e colunas\n",
        "dados.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45211, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTJOybFhdo3P",
        "colab_type": "text"
      },
      "source": [
        "**Processo de detecção de OUTLIERS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kOrP3Pvdo3Q",
        "colab_type": "text"
      },
      "source": [
        "Em estatística, **outlier** , valor aberrante ou valor atípico, é uma observação que apresenta <p> um grande afastamento das demais da série (que está \"fora\" dela), ou que é inconsistente. <p>A existência de outliers implica, tipicamente, em prejuízos a interpretação dos resultados dos **testes estatísticos** aplicados às amostras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJGccQfZdo3R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Lista com as colunas numéricas que não são necessariamente contínus\n",
        "numerical_columns = [col for col in dados.columns if (dados[col].dtype=='int64' or dados[col].dtype=='float64')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwH9s3Ardo3U",
        "colab_type": "code",
        "outputId": "4ad5f98e-6a7b-40ac-bd8e-e4e59e8569af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "numerical_columns"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRDI9J6bdo3Y",
        "colab_type": "code",
        "outputId": "ee8b8732-e7b9-4847-cd41-3d0785a664bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "dados[numerical_columns].describe().loc[['min', 'max', 'mean', '50%'],:]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>day</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.00000</td>\n",
              "      <td>-8019.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.00000</td>\n",
              "      <td>102127.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>4918.00000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>275.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.93621</td>\n",
              "      <td>1362.272058</td>\n",
              "      <td>15.806419</td>\n",
              "      <td>258.16308</td>\n",
              "      <td>2.763841</td>\n",
              "      <td>40.197828</td>\n",
              "      <td>0.580323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.00000</td>\n",
              "      <td>448.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>180.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           age        balance        day  ...   campaign       pdays    previous\n",
              "min   18.00000   -8019.000000   1.000000  ...   1.000000   -1.000000    0.000000\n",
              "max   95.00000  102127.000000  31.000000  ...  63.000000  871.000000  275.000000\n",
              "mean  40.93621    1362.272058  15.806419  ...   2.763841   40.197828    0.580323\n",
              "50%   39.00000     448.000000  16.000000  ...   2.000000   -1.000000    0.000000\n",
              "\n",
              "[4 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qmm4gur2do3c",
        "colab_type": "text"
      },
      "source": [
        "Repara-se que há no **Balance** um valor de '-8019' ! Estranho neh<p>\n",
        "    Esses valor pode atrapalhar todo o processo de **aprendizado de máquina**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rU-q7hEmdo3d",
        "colab_type": "code",
        "outputId": "7e89c718-a622-4b8c-df47-cdea7df51416",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "dados[dados['balance'] == dados['balance'].min ()]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12909</th>\n",
              "      <td>26</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>yes</td>\n",
              "      <td>-8019</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>cellular</td>\n",
              "      <td>7</td>\n",
              "      <td>jul</td>\n",
              "      <td>299</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age          job marital  education  ... pdays  previous poutcome   y\n",
              "12909   26  blue-collar  single  secondary  ...    -1         0  unknown  no\n",
              "\n",
              "[1 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pbGDwYOAdo3h",
        "colab_type": "text"
      },
      "source": [
        "Vamos apagar o registro '12909' do conjunto de dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aTBVDvvbdo3i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dados.drop(dados.index[12909], inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgY4edcado31",
        "colab_type": "text"
      },
      "source": [
        "**análise exploratoria**\n",
        "\n",
        "Antes de prevemos os valores vamos fazer uma análise exploratoria, vamos verificar qual idade e qual escolaridade tem maior chance de fechar o deposito."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paleS3Bbdo32",
        "colab_type": "code",
        "outputId": "e05da496-d6d1-47b8-c598-40ce7d096880",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "idade= sns.FacetGrid(dados, hue=\"y\", aspect=3);\n",
        "idade.map(sns.kdeplot, \"age\").add_legend();\n",
        "idade.set(xlim=(0, 85));"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IanXOS0tjT9ImzIfIEG8HFVeDzC5w/afQuIsxsK6KS/qe1rdVMD/dN4CIQGPw2TvLDmCR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\nwg5tTN0O7/YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 701.25x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bxg3K0undo39",
        "colab_type": "code",
        "outputId": "5847da76-e2e6-494f-9ae9-820930ebc3dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "idade= sns.FacetGrid(dados, hue=\"y\", aspect=3);\n",
        "idade.map(plt.hist, \"age\").add_legend();\n",
        "idade.set(xlim=(0, 85));"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LkCRJkiaoqg3AS5/O5/DKrSRJkjrDcCtJkqTOMNxKkiSpMwy3kiRJ6gzDrSRJkjrDcCtJ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\nJUmS1BmGW0mSJHWG4VaSJEmd8f8BLkY/jZlstDEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 701.25x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKI2lhrido4C",
        "colab_type": "code",
        "outputId": "7e6b4221-2bd7-406d-a232-bd870b10074b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(dados['y'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2322184668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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o9S8lOWpQxyJJmp5BnqncBKyYpH5tVS1rv80ASZYCFwAnt3U+m2ReknnA9cC5wFLgwtYW\n4JNtW28GngQuGeCxSJKmYWChUlV/BeyZZvOVwMaqeraqHgbGgdPab7yqdlbVc8BGYGWS0HsN/5fb\n+uuB8zs9AEnSizaMeyqXJbmnXR7b/zXJBcCjfW12tdpU9dcDT1XV3gPqkqQhmulQuQF4E7AMeIze\nFyUHLsnqJGNJxiYmJmZil5I0J81oqFTVj6pqX1X9HPgcvctbALuBRX1NF7baVPUngPlJjjigPtV+\n11bVaFWNjoyMdHMwkqQXmNFQSXJi3+zvAvtHhm0CLkjy6iQnAUuAO4FtwJI20usoejfzN1VVAbcB\n727rrwJumYljkCRNbdqfE36xknwROAM4Psku4CrgjCTLgAIeAT4AUFU7ktxM73PFe4FLq2pf285l\nwBZgHrCuqna0XVwObExyDXA3cOOgjkWSND0DC5WqunCS8pT/8VfVGmDNJPXNwOZJ6jt5/vKZJOll\nwCfqJUmdMVQkSZ0xVCRJnTFUJEmdMVQkSZ0xVCRJnTFUJEmdMVQkSZ0xVCRJnTFUJEmdMVQkSZ0x\nVCRJnTFUJEmdMVQkSZ0xVCRJnTFUJEmdMVQkSZ0ZWKgkWZfk8ST39dWOS7I1yUPt77GtniTXJRlP\nck+SU/vWWdXaP5RkVV/97UnubetclySDOhZJ0vQM8kzlJmDFAbUrgFuraglwa5sHOBdY0n6rgRug\nF0L0vm1/Or1PB1+1P4ham/f3rXfgviRJM2xgoVJVfwXsOaC8EljfptcD5/fVN1TP7cD8JCcC5wBb\nq2pPVT0JbAVWtGXHVNXtVVXAhr5tSZKGZKbvqZxQVY+16R8CJ7TpBcCjfe12tdrB6rsmqUuShmho\nN+rbGUbNxL6SrE4ylmRsYmJiJnYpSXPSTIfKj9qlK9rfx1t9N7Cor93CVjtYfeEk9UlV1dqqGq2q\n0ZGRkcM+CEnS5GY6VDYB+0dwrQJu6atf1EaBLQeebpfJtgBnJzm23aA/G9jSlj2TZHkb9XVR37Yk\nSUNyxKA2nOSLwBnA8Ul20RvF9Qng5iSXAD8A3tuabwbOA8aBnwIXA1TVniQfB7a1dldX1f6b/x+k\nN8LsaOAb7SdJGqKBhUpVXTjForMmaVvApVNsZx2wbpL6GHDK4fRRktQtn6iXJHXGUJEkdcZQkSR1\nxlCRJHXGUJEkdcZQkSR1xlCRJHXGUJEkdcZQkSR1xlCRJHXGUJEkdcZQkSR1xlCRJHXGUJEkdcZQ\nkSR1xlCRJHXGUJEkdWYooZLkkST3JtmeZKzVjkuyNclD7e+xrZ4k1yUZT3JPklP7trOqtX8oyaph\nHIsk6XnDPFP5J1W1rKpG2/wVwK1VtQS4tc0DnAssab/VwA3QCyF6370/HTgNuGp/EEmShmNg36h/\nCVYCZ7Tp9cC3gctbfUP7jv3tSeYnObG13VpVewCSbAVWAF+c2W5LLx//++p/MOwu6GXojf/23hnb\n17DOVAr4ZpK7kqxutROq6rE2/UPghDa9AHi0b91drTZVXZI0JMM6U/nHVbU7ya8CW5P8Tf/Cqqok\n1dXOWnCtBnjjG9/Y1WYlSQcYyplKVe1ufx8HvkbvnsiP2mUt2t/HW/PdwKK+1Re22lT1yfa3tqpG\nq2p0ZGSky0ORJPWZ8VBJ8stJXrd/GjgbuA/YBOwfwbUKuKVNbwIuaqPAlgNPt8tkW4CzkxzbbtCf\n3WqSpCEZxuWvE4CvJdm///9SVX+ZZBtwc5JLgB8A723tNwPnAePAT4GLAapqT5KPA9tau6v337SX\nJA3HjIdKVe0E3jpJ/QngrEnqBVw6xbbWAeu67qMk6aXxiXpJUmcMFUlSZwwVSVJnDBVJUmcMFUlS\nZwwVSVJnDBVJUmcMFUlSZwwVSVJnDBVJUmcMFUlSZwwVSVJnDBVJUmcMFUlSZwwVSVJnDBVJUmcM\nFUlSZ2Z9qCRZkeTBJONJrhh2fyRpLpvVoZJkHnA9cC6wFLgwydLh9kqS5q5ZHSrAacB4Ve2squeA\njcDKIfdJkuas2R4qC4BH++Z3tZokaQiOGHYHZkKS1cDqNvuTJA8Osz+vIMcDfzvsTrwc5E9WDbsL\neiH/fe53VbrYyt+bTqPZHiq7gUV98wtb7e+oqrXA2pnq1FyRZKyqRofdD2ky/vscjtl++WsbsCTJ\nSUmOAi4ANg25T5I0Z83qM5Wq2pvkMmALMA9YV1U7htwtSZqzZnWoAFTVZmDzsPsxR3lJUS9n/vsc\nglTVsPsgSXqFmO33VCRJLyOGiiSpM4aKJKkzhoqmJcniJA8k+VySHUm+meToJMuS3J7kniRfS3Ls\nsPuqV74kVyf5cN/8miQfSvKRJNvav8ePtWW/nOR/JPl+kvuS/PPh9fyVz1DRi7EEuL6qTgaeAv4Z\nsAG4vKp+HbgXuGqI/dPcsQ64CCDJq+g9o/ZDev9GTwOWAW9P8pvACuD/VNVbq+oU4C+H0+W5wVDR\ni/FwVW1v03cBbwLmV9V3Wm098JtD6ZnmlKp6BHgiyduAs4G7gXf0TX8PeAu9kLkXeFeSTyb5jap6\neji9nhtm/XMqmlHP9k3vA+YPqyMS8OfAHwC/Ru/M5Szg31XVfzqwYZJTgfOAa5LcWlVXz2RH5xLP\nVHQ4ngaeTPIbbf59wHcO0l7q0tfoXdp6B723amwB/jDJawGSLEjyq0neAPy0qv4z8Cng1GF1eC7w\nTEWHaxXwH5P8ErATuHjI/dEcUVXPJbkNeKqq9gHfTPL3gb9OAvAT4PeBNwOfSvJz4P8B/2pYfZ4L\nfKJe0qzUbtB/D3hPVT007P6ox8tfkmad9tnwceBWA+XlxTMVSVJnPFORJHXGUJEkdcZQkSR1xlCR\nJHXGUJEkdcZQkYZoqrftDrNP0uFwSLE0REkWA1+tqlPbw3wPAadV1RND7Zj0EvmaFmmIquqRJPvf\ntnsCcLeBotnMUJGG78C37Uqzlpe/pCFLchS9b34cCSxpL0eUZiXPVKQhm+Rtu9KsZahIQ9Zu0C8H\n3jPsvkiHyyHF0hD5tl290nhPRZLUGc9UJEmdMVQkSZ0xVCRJnTFUJEmdMVQkSZ0xVCRJnfn/URfM\nK7fXB8YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcxIDqaWdo4I",
        "colab_type": "code",
        "outputId": "84b9f13e-e703-478b-cf1a-d804034f3fd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(dados['education'])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2324a82f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7rQUdKgBVdT5w/rTbMQEL6nDdds5t2Zfbs68danumqqbdBknSDmKhn1ORJG1HDJWOkuyZ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySvXo0QKdo4F",
        "colab_type": "code",
        "outputId": "80d2ba7e-55a8-4df9-c5f9-aa3cfe8c2ca2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(dados['marital'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f232214c390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lSU4Fvg2c0NqvAo4F1gOPAm8GqKqNSd4LXNfanT110l6SNBmzHipVdSfw89PUvwe8epp6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\nqEiSujFUJEndGCqSpG7+PxR4k96i9VKIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aM0peZ39do3k",
        "colab_type": "text"
      },
      "source": [
        "![logos](https://mktes.files.wordpress.com/2014/09/curso-de-web-design-e28093-microcamp-curitiba-centro-aponta-as-vantagens-da-segmentac3a7c3a3o-de-mercado.jpg?)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "biKxEMGado3l",
        "colab_type": "text"
      },
      "source": [
        "Após a importação dos dados (.csv), iremos dividir o arquivo em 2 partes.<p>\n",
        "    Pois assim, deixaremos um montante de registros para ser utilizado no modelo de **IA** após o término.<p>\n",
        "        Ao todo temos 45211 registros, para o desenvolvimento da IA, usaremos 30000 e deixaremos o restante para a 2ª fase do processo\n",
        "        "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adaQIDJHdo3m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#vamos começar embaralhando o conjunto de dados, pois ele pode estar ordenado de alguma forma\n",
        "dados = dados.sample(frac=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eix2nPR6do3o",
        "colab_type": "code",
        "outputId": "5bce9a02-0aa7-4794-eb88-c267165ab34c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#1ª segmentação \n",
        "dados = dados.head(30000)\n",
        "dados.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30000, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkgiWktHdo3s",
        "colab_type": "code",
        "outputId": "abe8fa37-3a61-48b3-9150-67f03b81a71c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#2ª segmentação\n",
        "df = dados.tail(25211)\n",
        "df.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25211, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNHTx_Dddo3x",
        "colab_type": "text"
      },
      "source": [
        "Usaremos para o desenvolvimento do modelo de **IA** o conjunto 'dados'<p>\n",
        "    O dataframe 'df' será utilizado em 2º momento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmwCpuUIpHOa",
        "colab_type": "text"
      },
      "source": [
        "![logos](https://d1jnx9ba8s6j9r.cloudfront.net/blog/wp-content/uploads/2019/01/Classification-Random-Forest-Random-Forest-In-R-Edureka-2-381x300.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPK0NgL4do4M",
        "colab_type": "text"
      },
      "source": [
        "**Tratamento dos dados**\n",
        "\n",
        "Porém para podermos utilizar os dados em algum algoritmo de aprendizado  precisamos que todos os valores sejam numericos, assim precisamos converter as colunas que são object.\n",
        "\n",
        "Existem maneiras de fazermos isto com o sklearn ou com o pandas.\n",
        "Vamos mostrar as duas:\n",
        "\n",
        "- **Pandas:** Com o pandas convertemos a coluna para category, apos isto podemos fazer a função cat.codes. \n",
        "- **Sklearn:** Temos que utilizar a classe LabelEnconder, ela funciona como a classe de normalização."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3i_9avsVdo4O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "education = dados['education']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tnsuyY2do4R",
        "colab_type": "code",
        "outputId": "57ec9ca0-df8e-4141-c0bc-fbf468137106",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "education.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25989    secondary\n",
              "23304    secondary\n",
              "5049      tertiary\n",
              "7489     secondary\n",
              "28597    secondary\n",
              "Name: education, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfDQ9t5_do4V",
        "colab_type": "code",
        "outputId": "4ebe9d85-48c4-4e95-e117-39764bfe9332",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "#Pandas\n",
        "#Convertendo os dados para categorico\n",
        "educationConv = education.astype('category')\n",
        "\n",
        "# pegando o valores de cada categoria\n",
        "educationConv = educationConv.cat.codes\n",
        "educationConv[:5]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25989    1\n",
              "23304    1\n",
              "5049     2\n",
              "7489     1\n",
              "28597    1\n",
              "dtype: int8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLLBrW_Ddo4Y",
        "colab_type": "code",
        "outputId": "830892e3-26f5-4f7a-cfa1-0c5048bbe05e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "education.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25989    secondary\n",
              "23304    secondary\n",
              "5049      tertiary\n",
              "7489     secondary\n",
              "28597    secondary\n",
              "Name: education, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NxejzYRdo4c",
        "colab_type": "code",
        "outputId": "bba09a73-71ad-4fc1-fea1-e5ccf33af370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Sklearn\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label = LabelEncoder()\n",
        "educationConv2 = label.fit_transform(education)\n",
        "educationConv2[:5]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 2, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nq1njk4Ado4f",
        "colab_type": "text"
      },
      "source": [
        "Agora vamos usar um loop para podermos fazer a conversão de todos os dados do dataframe "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hR7E_zmldo4g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for coluna in dados:\n",
        "    if dados[coluna].dtype == object:\n",
        "        dados[coluna] = dados[coluna].astype('category').cat.codes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RiCsCG44do4n",
        "colab_type": "text"
      },
      "source": [
        "Nota-se como o dataframe ficou após a conversão dos **dados** !"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWkartgZdo4n",
        "colab_type": "code",
        "outputId": "08f58377-5dad-42fe-cf7b-bdc3fafb8234",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25989</th>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2430</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>9</td>\n",
              "      <td>73</td>\n",
              "      <td>2</td>\n",
              "      <td>145</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23304</th>\n",
              "      <td>34</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1057</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>176</td>\n",
              "      <td>10</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5049</th>\n",
              "      <td>32</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1330</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
              "      <td>8</td>\n",
              "      <td>83</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7489</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>544</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>69</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28597</th>\n",
              "      <td>29</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>4</td>\n",
              "      <td>298</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  job  marital  education  ...  pdays  previous  poutcome  y\n",
              "25989   53    1        2          1  ...    145         3         0  0\n",
              "23304   34    9        1          1  ...     -1         0         3  0\n",
              "5049    32    4        1          2  ...     -1         0         3  0\n",
              "7489    32    1        1          1  ...     -1         0         3  0\n",
              "28597   29   10        2          1  ...     -1         0         3  0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sWlF3L3do4q",
        "colab_type": "text"
      },
      "source": [
        "**Separando dados**\n",
        "\n",
        "Com os dados convertidos vamos separa X e y, e em treino e teste."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtRpFYkXdo4r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dadosY1 = dados[dados['y']== 1]\n",
        "dadosY2 = dados[dados['y']== 0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nztxp6nudo4s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dadosY2 = dadosY2.sample(len(dadosY1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6pcXUpvdo4u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dados2 = pd.concat([dadosY1,dadosY2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LCk1R_bdo4w",
        "colab_type": "text"
      },
      "source": [
        "Apaga-se a coluna **y** (coluna alvo)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hf2ZAEekdo4x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = dados.y\n",
        "x = dados.drop('y',axis=1) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjA522rVdo4y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4q_Wwa1pdo40",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Optamos por destinar 30% dos dados para treino\n",
        "np.random.seed(100)\n",
        "xTreino,xTeste,yTreino,yTeste = train_test_split(x,y, test_size=0.3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BkG_6t_1do42",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RnOHvqOdo43",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdxWLpDJdo45",
        "colab_type": "code",
        "outputId": "46b958e2-91c3-4a1c-ad4d-fe03f2146fb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "rf = RandomForestClassifier(max_depth=4)\n",
        "rf.fit(xTreino,yTreino)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "                       max_depth=4, max_features='auto', max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyTmO0jQdo48",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#NÃO RODAR\n",
        "#Predizendo dados \n",
        "preditos = rf.predict(xTeste)\n",
        "#Verificando modelo\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(preditos,yTeste))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MChO9Ugcdo5A",
        "colab_type": "code",
        "outputId": "154aec42-e70a-49c8-bf3a-901c795fe2de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import sklearn.metrics as metrics\n",
        "# calculate the fpr and tpr for all thresholds of the classification\n",
        "probs = rf.predict_proba(xTeste)\n",
        "preds = probs[:,1]\n",
        "fpr, tpr, threshold = metrics.roc_curve(yTeste, preds)\n",
        "roc_auc = metrics.auc(fpr, tpr)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.plot([0, 1], [0, 1],'r--')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1Q3gRP5dA8dR6AkBEKqrq1njXV9UdInId8BnW//Ciqs4SkQeByao6MnjsNBGZ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\nzsXkicI551xM/w+l5ebEr1d6cwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCVHgIPsdo5G",
        "colab_type": "code",
        "outputId": "e45b2fa4-0b26-4238-d4bb-9c8b69beffdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print (\"\\nAcurácia de classificação = \"+str(100*roc_auc)+\"%\\n\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Acurácia de classificação = 87.41369417141108%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZiDrRMJdo5M",
        "colab_type": "text"
      },
      "source": [
        "![logos](https://cdn4.iconfinder.com/data/icons/robotics-and-artificial-intelligence/512/2-512.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8eIs6e9do5N",
        "colab_type": "text"
      },
      "source": [
        "Agora abordaremos a 2ª parte do conjunto de dados separada lá no começo<p>\n",
        "    Chamamos de \"**df**\"<p>\n",
        "        Realizaremos todo o tratamento de dados novamente para os mesmos<p>\n",
        "            Porém antes realizaremos uma análise dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IgCv9PAdo5O",
        "colab_type": "code",
        "outputId": "6237de11-077c-44b8-eeb5-c5fe40b50574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25211, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPmNlWjbdo5U",
        "colab_type": "code",
        "outputId": "dedfe624-1592-4e3a-82f2-57e494a06dfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1114</th>\n",
              "      <td>46</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>78</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>7</td>\n",
              "      <td>may</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44814</th>\n",
              "      <td>61</td>\n",
              "      <td>housemaid</td>\n",
              "      <td>married</td>\n",
              "      <td>primary</td>\n",
              "      <td>no</td>\n",
              "      <td>2580</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>16</td>\n",
              "      <td>sep</td>\n",
              "      <td>270</td>\n",
              "      <td>3</td>\n",
              "      <td>202</td>\n",
              "      <td>7</td>\n",
              "      <td>other</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38043</th>\n",
              "      <td>40</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>primary</td>\n",
              "      <td>no</td>\n",
              "      <td>2805</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>14</td>\n",
              "      <td>may</td>\n",
              "      <td>173</td>\n",
              "      <td>6</td>\n",
              "      <td>353</td>\n",
              "      <td>23</td>\n",
              "      <td>failure</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43383</th>\n",
              "      <td>33</td>\n",
              "      <td>admin.</td>\n",
              "      <td>single</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>7608</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>30</td>\n",
              "      <td>mar</td>\n",
              "      <td>238</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6638</th>\n",
              "      <td>31</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>55</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>28</td>\n",
              "      <td>may</td>\n",
              "      <td>350</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age          job  marital  education  ... pdays  previous poutcome    y\n",
              "1114    46  blue-collar  married  secondary  ...    -1         0  unknown   no\n",
              "44814   61    housemaid  married    primary  ...   202         7    other  yes\n",
              "38043   40  blue-collar  married    primary  ...   353        23  failure   no\n",
              "43383   33       admin.   single   tertiary  ...    -1         0  unknown  yes\n",
              "6638    31   technician   single  secondary  ...    -1         0  unknown   no\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzaUp0m2do5a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "education = df['education']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4hTuXa7do5c",
        "colab_type": "code",
        "outputId": "f3d82a23-edf6-45fd-db08-731b137e2830",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "education.head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1114     secondary\n",
              "44814      primary\n",
              "38043      primary\n",
              "43383     tertiary\n",
              "6638     secondary\n",
              "Name: education, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O76rtTJgdo5g",
        "colab_type": "code",
        "outputId": "8c5ab631-656a-4bb9-f7a9-332ad711a49d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#dessa vez usaremos somente o Sklearn\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label = LabelEncoder()\n",
        "educationConv2 = label.fit_transform(education)\n",
        "educationConv2[:5]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 2, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikSu7qlNdo5l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for coluna in df:\n",
        "    if df[coluna].dtype == object:\n",
        "        df[coluna] = df[coluna].astype('category').cat.codes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epolsRf1do5p",
        "colab_type": "code",
        "outputId": "c7defff5-607c-4497-dba0-2a0b3c85adea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1114</th>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44814</th>\n",
              "      <td>61</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2580</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>270</td>\n",
              "      <td>3</td>\n",
              "      <td>202</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38043</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2805</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>173</td>\n",
              "      <td>6</td>\n",
              "      <td>353</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43383</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>7608</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>238</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6638</th>\n",
              "      <td>31</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>350</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  job  marital  education  ...  pdays  previous  poutcome  y\n",
              "1114    46    1        1          1  ...     -1         0         3  0\n",
              "44814   61    3        1          0  ...    202         7         1  1\n",
              "38043   40    1        1          0  ...    353        23         0  0\n",
              "43383   33    0        2          2  ...     -1         0         3  1\n",
              "6638    31    9        2          1  ...     -1         0         3  0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y59GvPVsdo5u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# apaga-se a coluna 'alvo'\n",
        "result = df.drop('y',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bM8UArofdo5w",
        "colab_type": "code",
        "outputId": "9575e1f6-4335-4ad8-a554-29666af47665",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "result.head()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1114</th>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44814</th>\n",
              "      <td>61</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2580</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>270</td>\n",
              "      <td>3</td>\n",
              "      <td>202</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38043</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2805</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>173</td>\n",
              "      <td>6</td>\n",
              "      <td>353</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43383</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>7608</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>238</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6638</th>\n",
              "      <td>31</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>350</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  job  marital  education  ...  campaign  pdays  previous  poutcome\n",
              "1114    46    1        1          1  ...         1     -1         0         3\n",
              "44814   61    3        1          0  ...         3    202         7         1\n",
              "38043   40    1        1          0  ...         6    353        23         0\n",
              "43383   33    0        2          2  ...         4     -1         0         3\n",
              "6638    31    9        2          1  ...         1     -1         0         3\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHb23yv8do5y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Predizendo dados \n",
        "preditos = rf.predict(result)\n",
        "perc_resp = rf.predict_proba(result)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4zrNN6Bdo51",
        "colab_type": "code",
        "outputId": "3140823d-60ef-41ed-c0fa-d8027dbdd40e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "preditos"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0], dtype=int8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "838fz_Cpdo53",
        "colab_type": "code",
        "outputId": "d65b5a6b-3a74-4c86-ce4f-657d74206237",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "perc_resp[:,0] #todas as linhas da 1ª coluna"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.97352379, 0.57621272, 0.90382395, ..., 0.90382395, 0.93950082,\n",
              "       0.96601612])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8oHE4UNGdo56",
        "colab_type": "code",
        "outputId": "f583c56c-7070-417f-db1f-d89d821de96c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "perc_resp[:,1] #todas as linhas da 2ª coluna"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.02647621, 0.42378728, 0.09617605, ..., 0.09617605, 0.06049918,\n",
              "       0.03398388])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBzkOx7ddo59",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result['resp']=preditos"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ja20rGJ7do5_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result['perc_resp1'] =perc_resp[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KteitdOfdo6A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result['perc_resp2'] = perc_resp[:,1] "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8d6eDEJ3do6B",
        "colab_type": "code",
        "outputId": "0314ad68-2339-4516-824a-770630316d9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        }
      },
      "source": [
        "result.head(20)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>resp</th>\n",
              "      <th>perc_resp1</th>\n",
              "      <th>perc_resp2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1114</th>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.973524</td>\n",
              "      <td>0.026476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44814</th>\n",
              "      <td>61</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2580</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>270</td>\n",
              "      <td>3</td>\n",
              "      <td>202</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.576213</td>\n",
              "      <td>0.423787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38043</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2805</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>173</td>\n",
              "      <td>6</td>\n",
              "      <td>353</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.903824</td>\n",
              "      <td>0.096176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43383</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>7608</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>238</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.938169</td>\n",
              "      <td>0.061831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6638</th>\n",
              "      <td>31</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>350</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.941788</td>\n",
              "      <td>0.058212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34193</th>\n",
              "      <td>40</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1646</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>392</td>\n",
              "      <td>2</td>\n",
              "      <td>91</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.580696</td>\n",
              "      <td>0.419304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22969</th>\n",
              "      <td>49</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>169</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.944327</td>\n",
              "      <td>0.055673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6648</th>\n",
              "      <td>29</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>420</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>95</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.968598</td>\n",
              "      <td>0.031402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36086</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1265</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>299</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.845475</td>\n",
              "      <td>0.154525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13667</th>\n",
              "      <td>60</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2450</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>297</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.937250</td>\n",
              "      <td>0.062750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15203</th>\n",
              "      <td>29</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>5</td>\n",
              "      <td>275</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.917811</td>\n",
              "      <td>0.082189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1793</th>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>200</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.973524</td>\n",
              "      <td>0.026476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38319</th>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "      <td>272</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.944613</td>\n",
              "      <td>0.055387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43823</th>\n",
              "      <td>59</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4007</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>581</td>\n",
              "      <td>3</td>\n",
              "      <td>63</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.585220</td>\n",
              "      <td>0.414780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34185</th>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1854</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>390</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.917811</td>\n",
              "      <td>0.082189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15485</th>\n",
              "      <td>43</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>-50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.956589</td>\n",
              "      <td>0.043411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43002</th>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>315</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>271</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.944674</td>\n",
              "      <td>0.055326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17027</th>\n",
              "      <td>34</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1116</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>5</td>\n",
              "      <td>53</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.947545</td>\n",
              "      <td>0.052455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9692</th>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>-162</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>119</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.973524</td>\n",
              "      <td>0.026476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5694</th>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1728</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>8</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.968598</td>\n",
              "      <td>0.031402</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  job  marital  education  ...  poutcome  resp  perc_resp1  perc_resp2\n",
              "1114    46    1        1          1  ...         3     0    0.973524    0.026476\n",
              "44814   61    3        1          0  ...         1     0    0.576213    0.423787\n",
              "38043   40    1        1          0  ...         0     0    0.903824    0.096176\n",
              "43383   33    0        2          2  ...         3     0    0.938169    0.061831\n",
              "6638    31    9        2          1  ...         3     0    0.941788    0.058212\n",
              "34193   40    9        2          2  ...         2     0    0.580696    0.419304\n",
              "22969   49   10        1          0  ...         3     0    0.944327    0.055673\n",
              "6648    29    3        1          2  ...         3     0    0.968598    0.031402\n",
              "36086   45    1        1          0  ...         2     0    0.845475    0.154525\n",
              "13667   60   11        1          2  ...         3     0    0.937250    0.062750\n",
              "15203   29    4        1          2  ...         3     0    0.917811    0.082189\n",
              "1793    35    1        1          0  ...         3     0    0.973524    0.026476\n",
              "38319   42    2        1          2  ...         3     0    0.944613    0.055387\n",
              "43823   59    5        1          0  ...         0     0    0.585220    0.414780\n",
              "34185   29    8        2          1  ...         3     0    0.917811    0.082189\n",
              "15485   43    4        2          2  ...         3     0    0.956589    0.043411\n",
              "43002   48    2        2          1  ...         3     0    0.944674    0.055326\n",
              "17027   34    4        1          2  ...         3     0    0.947545    0.052455\n",
              "9692    35    0        2          2  ...         3     0    0.973524    0.026476\n",
              "5694    46    1        1          1  ...         3     0    0.968598    0.031402\n",
              "\n",
              "[20 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mBuDYBUdo6E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}