Florest_TDCSP.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Florest_TDCSP.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "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"
    }
  },
  "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_TDCSP.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",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "6c9749fd-b5ed-4be6-95aa-c0e8ea9b7489"
      },
      "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-17594a14-b164-4618-9f70-21fd5dbb1001\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-17594a14-b164-4618-9f70-21fd5dbb1001\">\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 exempçlo 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": "82b66d9a-16b6-4355-efdf-05ead82c14f8",
        "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": "zLu8KgCVdo3E",
        "colab_type": "code",
        "outputId": "5a75c5f5-b466-4282-9ab8-4aafa01282c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "#verificar as colunas utlizadas\n",
        "dados.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45211 entries, 0 to 45210\n",
            "Data columns (total 17 columns):\n",
            "age          45211 non-null int64\n",
            "job          45211 non-null object\n",
            "marital      45211 non-null object\n",
            "education    45211 non-null object\n",
            "default      45211 non-null object\n",
            "balance      45211 non-null int64\n",
            "housing      45211 non-null object\n",
            "loan         45211 non-null object\n",
            "contact      45211 non-null object\n",
            "day          45211 non-null int64\n",
            "month        45211 non-null object\n",
            "duration     45211 non-null int64\n",
            "campaign     45211 non-null int64\n",
            "pdays        45211 non-null int64\n",
            "previous     45211 non-null int64\n",
            "poutcome     45211 non-null object\n",
            "y            45211 non-null object\n",
            "dtypes: int64(7), object(10)\n",
            "memory usage: 5.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEctmVI4do3J",
        "colab_type": "code",
        "outputId": "478d0de5-b585-41d5-b92b-09985a49efd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Nº de linhas e colunas\n",
        "dados.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45211, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LMxWOcqdo3N",
        "colab_type": "text"
      },
      "source": [
        "![logos](https://psucd8.files.wordpress.com/2011/10/outlierscatterplot_10001.gif)"
      ]
    },
    {
      "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": "6ea0f125-ec60-4d94-afaa-93700be1530f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "numerical_columns"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRDI9J6bdo3Y",
        "colab_type": "code",
        "outputId": "b798d2f5-4d37-446a-e9ee-941e05eee66e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "dados[numerical_columns].describe().loc[['min', 'max', 'mean', '50%'],:]"
      ],
      "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>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": 9
        }
      ]
    },
    {
      "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": "68c16755-edc0-4610-8f86-5d521af81926",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "dados[dados['balance'] == dados['balance'].min ()]"
      ],
      "execution_count": 10,
      "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": 10
        }
      ]
    },
    {
      "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": "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": "a4610d43-2ef6-4912-a8e5-607a0863ec6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#1ª segmentação \n",
        "dados = dados.head(30000)\n",
        "dados.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30000, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkgiWktHdo3s",
        "colab_type": "code",
        "outputId": "9a13c278-2aaa-4764-d888-14a995f355ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#2ª segmentação\n",
        "df = dados.tail(25211)\n",
        "df.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25211, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "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": "g-lL-4aVdo30",
        "colab_type": "text"
      },
      "source": [
        "Como podemos perceber os dados estão distribuidos de varias maneiras, temos variaveis categoricas, representadas como objetos e variaveis numericas. Varios dados são desconhecidos, porém estão salvos como 'unknown'."
      ]
    },
    {
      "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": "e0e70b41-02b9-44d4-c0c1-3eb3c4c6bb9e",
        "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": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAADQCAYAAADs+BIxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlCS4EREREdFT/gbf6A0ujY0zfwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 701.25x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bxg3K0undo39",
        "colab_type": "code",
        "outputId": "db2260ad-4a63-4680-9f03-c8110c399de6",
        "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": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAADQCAYAAADs+BIxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/D+NDWxK7AD18QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 701.25x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKI2lhrido4C",
        "colab_type": "code",
        "outputId": "2b86d09e-fd77-453e-fb80-b766d7130705",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(dados['y'])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60c4aaf358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySvXo0QKdo4F",
        "colab_type": "code",
        "outputId": "c4c1b1ea-5cb8-46d3-b20b-e82f31d5ac72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(dados['marital'])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60c4bd19e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5v8DeduR7A0D/C0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcxIDqaWdo4I",
        "colab_type": "code",
        "outputId": "801053b4-a535-4e2e-d24f-cc05d4b94565",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.countplot(dados['education'])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60c4b3eef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzMyycaiYmVk2/x/p0qEAk519GAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": "6c26a7b4-6da1-4945-ec88-d74f57a9ccdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "education.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4508     secondary\n",
              "1478     secondary\n",
              "27842    secondary\n",
              "21422      primary\n",
              "6986      tertiary\n",
              "Name: education, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfDQ9t5_do4V",
        "colab_type": "code",
        "outputId": "ab8e5821-2aec-46e3-9028-0eed20436913",
        "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": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4508     1\n",
              "1478     1\n",
              "27842    1\n",
              "21422    0\n",
              "6986     2\n",
              "dtype: int8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLLBrW_Ddo4Y",
        "colab_type": "code",
        "outputId": "77e55529-879f-4d7f-903b-8cd0cf388dde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "education.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4508     secondary\n",
              "1478     secondary\n",
              "27842    secondary\n",
              "21422      primary\n",
              "6986      tertiary\n",
              "Name: education, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NxejzYRdo4c",
        "colab_type": "code",
        "outputId": "555b000a-a7c2-4788-fc37-67f0e3090672",
        "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": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 0, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "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": "fe81ba31-2e97-4f8a-8b9b-63c0b156ec57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": 21,
      "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>4508</th>\n",
              "      <td>35</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1163</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>284</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>1478</th>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>230</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27842</th>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>111</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>21422</th>\n",
              "      <td>58</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1241</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>194</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6986</th>\n",
              "      <td>24</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1385</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>1277</td>\n",
              "      <td>2</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",
              "4508    35    2        1          1  ...     -1         0         3  0\n",
              "1478    32    0        2          1  ...     -1         0         3  0\n",
              "27842   55    1        1          1  ...     -1         0         3  0\n",
              "21422   58    5        1          0  ...     -1         0         3  1\n",
              "6986    24    7        2          2  ...     -1         0         3  0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "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": "165b0e21-aa35-4944-ea44-ded85a216844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "rf = RandomForestClassifier(max_depth=4)\n",
        "rf.fit(xTreino,yTreino)"
      ],
      "execution_count": 29,
      "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": 29
        }
      ]
    },
    {
      "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": "63c07ef6-0e1c-45be-e724-d1fb0482da9e",
        "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": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKkQWuDEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCVHgIPsdo5G",
        "colab_type": "code",
        "outputId": "670e204c-ed63-49d8-8347-c4fb3b3cf323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print (\"\\nAcurácia de classificação = \"+str(100*roc_auc)+\"%\\n\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Acurácia de classificação = 87.10748525239417%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihDdbIUSdo5J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": "052bb941-c9e5-4311-c989-caa96991c1a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25211, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPmNlWjbdo5U",
        "colab_type": "code",
        "outputId": "f10f093c-c3e7-40b1-a8ca-f234e28aca6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 33,
      "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>4562</th>\n",
              "      <td>33</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>89</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>20</td>\n",
              "      <td>may</td>\n",
              "      <td>341</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>8341</th>\n",
              "      <td>42</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>130</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>2</td>\n",
              "      <td>jun</td>\n",
              "      <td>180</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1563</th>\n",
              "      <td>41</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>639</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>8</td>\n",
              "      <td>may</td>\n",
              "      <td>1187</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37234</th>\n",
              "      <td>45</td>\n",
              "      <td>management</td>\n",
              "      <td>divorced</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>2813</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>13</td>\n",
              "      <td>may</td>\n",
              "      <td>43</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>42340</th>\n",
              "      <td>53</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>163</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>cellular</td>\n",
              "      <td>24</td>\n",
              "      <td>nov</td>\n",
              "      <td>108</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",
              "4562    33     services   married    unknown  ...    -1         0  unknown  no\n",
              "8341    42  blue-collar   married  secondary  ...    -1         0  unknown  no\n",
              "1563    41     services   married  secondary  ...    -1         0  unknown  no\n",
              "37234   45   management  divorced   tertiary  ...    -1         0  unknown  no\n",
              "42340   53   technician   married  secondary  ...    -1         0  unknown  no\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "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": "fa302d28-50a7-4df1-8e66-486acca2fdf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "education.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4562       unknown\n",
              "8341     secondary\n",
              "1563     secondary\n",
              "37234     tertiary\n",
              "42340    secondary\n",
              "Name: education, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O76rtTJgdo5g",
        "colab_type": "code",
        "outputId": "c6386bbb-f92c-4408-aa3d-e5193a2c060a",
        "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": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 1, 1, 2, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "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": "d52381a4-7a9e-44a9-8163-c41e3c1e7a10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 38,
      "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>4562</th>\n",
              "      <td>33</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>341</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>8341</th>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>180</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>1563</th>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>639</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1187</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37234</th>\n",
              "      <td>45</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2813</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>43</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>42340</th>\n",
              "      <td>53</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>108</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",
              "4562    33    7        1          3  ...     -1         0         3  0\n",
              "8341    42    1        1          1  ...     -1         0         3  0\n",
              "1563    41    7        1          1  ...     -1         0         3  0\n",
              "37234   45    4        0          2  ...     -1         0         3  0\n",
              "42340   53    9        1          1  ...     -1         0         3  0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y59GvPVsdo5u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xx = df.drop('y',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bM8UArofdo5w",
        "colab_type": "code",
        "outputId": "95ca3fb7-49f6-48f2-f7eb-e9ed84a72017",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "xx.head()"
      ],
      "execution_count": 40,
      "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>4562</th>\n",
              "      <td>33</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>341</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8341</th>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>180</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1563</th>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>639</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1187</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37234</th>\n",
              "      <td>45</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2813</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42340</th>\n",
              "      <td>53</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>108</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",
              "4562    33    7        1          3  ...         1     -1         0         3\n",
              "8341    42    1        1          1  ...         3     -1         0         3\n",
              "1563    41    7        1          1  ...         2     -1         0         3\n",
              "37234   45    4        0          2  ...         1     -1         0         3\n",
              "42340   53    9        1          1  ...         1     -1         0         3\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHb23yv8do5y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Predizendo dados \n",
        "preditos = rf.predict(xx)\n",
        "perc_resp = rf.predict_proba(xx)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4zrNN6Bdo51",
        "colab_type": "code",
        "outputId": "0d4d9f50-53c8-44ce-c340-af0818e3bc67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "preditos"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 1], dtype=int8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "838fz_Cpdo53",
        "colab_type": "code",
        "outputId": "f929009f-ac70-4d14-a48c-57b7bbd41ff1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "perc_resp[:,0] #todas as linhas da 1ª coluna"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.95388693, 0.96298637, 0.60895   , ..., 0.89172665, 0.85589321,\n",
              "       0.48707293])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8oHE4UNGdo56",
        "colab_type": "code",
        "outputId": "097a6c19-f40d-43f3-fd47-b2ed19e97448",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "perc_resp[:,1] #todas as linhas da 2ª coluna"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04611307, 0.03701363, 0.39105   , ..., 0.10827335, 0.14410679,\n",
              "       0.51292707])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBzkOx7ddo59",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xx['resp']=preditos"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ja20rGJ7do5_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xx['perc_resp1'] =perc_resp[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KteitdOfdo6A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xx['perc_resp2'] = perc_resp[:,1] "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8d6eDEJ3do6B",
        "colab_type": "code",
        "outputId": "62a2ba15-00a5-4ed3-93de-b17b6efca961",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "xx.head()"
      ],
      "execution_count": 48,
      "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>resp</th>\n",
              "      <th>perc_resp1</th>\n",
              "      <th>perc_resp2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4562</th>\n",
              "      <td>33</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>341</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.953887</td>\n",
              "      <td>0.046113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8341</th>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>180</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.962986</td>\n",
              "      <td>0.037014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1563</th>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>639</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1187</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.608950</td>\n",
              "      <td>0.391050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37234</th>\n",
              "      <td>45</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2813</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.952607</td>\n",
              "      <td>0.047393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42340</th>\n",
              "      <td>53</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.956256</td>\n",
              "      <td>0.043744</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  job  marital  education  ...  poutcome  resp  perc_resp1  perc_resp2\n",
              "4562    33    7        1          3  ...         3     0    0.953887    0.046113\n",
              "8341    42    1        1          1  ...         3     0    0.962986    0.037014\n",
              "1563    41    7        1          1  ...         3     0    0.608950    0.391050\n",
              "37234   45    4        0          2  ...         3     0    0.952607    0.047393\n",
              "42340   53    9        1          1  ...         3     0    0.956256    0.043744\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mBuDYBUdo6E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}