Python_para_ML_12.ipynb

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    "colab": {
      "name": "Python para ML - 12.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyM0c94F98ngYhmY0QtKUT/z",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ricardorocha86/Python-para-Machine-Learning/blob/master/Python_para_ML_12.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7utdAe5d2sLN",
        "colab_type": "text"
      },
      "source": [
        "![texto alternativo](https://raw.githubusercontent.com/ricardorocha86/Python-para-Machine-Learning/master/Imagens/capa%20-%20python%20para%20ml%2012.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-b5mjxUoSzou"
      },
      "source": [
        "# **Python para Machine Learning** - Parte 12"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5ci8BYxxrLn",
        "colab_type": "text"
      },
      "source": [
        "Nesse notebook vamos ver:\n",
        "- Métricas para Classificação Binária\n",
        "- Exemplo Final: Modelagem de Churn\n",
        "- Exercícios\n",
        "- Links Úteis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCPmNK__4ryO",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Z4Go_M720Y4",
        "colab_type": "text"
      },
      "source": [
        "## **Métricas para Classificação Binária**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwhT9yFV4vuS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwzWV5roWp-R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url1 = 'https://raw.githubusercontent.com/ricardorocha86/Datasets/master/Titanic/train.csv' \n",
        "titanic = pd.read_csv(url1) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUeh-zd34wLk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "teste1 = titanic['Sex'].map({'male': 0, 'female': 1})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6xlGADLpmKd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lH-Zb7bYvgV8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mc = confusion_matrix(titanic['Survived'], teste1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khCtOQ6EyFKG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f2303307-c550-46fa-9864-53af4065decd"
      },
      "source": [
        "mc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[468,  81],\n",
              "       [109, 233]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tC8k2TWXtaCu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "64745897-3c4b-4209-b03a-5961f08a38b0"
      },
      "source": [
        "vn, fp, fn, vp = mc.flatten()\n",
        "\n",
        "print('VP: {}'.format(vp))\n",
        "print('VN: {}'.format(vn))\n",
        "print('FP: {}'.format(fp))\n",
        "print('FN: {}'.format(fn))\n",
        "print('\\nTOTAL: {}'.format(fn+fp+vn+vp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "VP: 233\n",
            "VN: 468\n",
            "FP: 81\n",
            "FN: 109\n",
            "\n",
            "TOTAL: 891\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7qIlvRhxi54",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def MetricasClassBinaria(observado, predito, custo = None):\n",
        "    mc = confusion_matrix(observado, predito)\n",
        "    vn, fp, fn, vp = mc.flatten()\n",
        "\n",
        "    pre = observado.mean()\n",
        "    acc = (vn + vp)/(vn + fp + fn + vp) \n",
        "    sen = vp/(vp + fn)\n",
        "    esp = vn/(vn + fp)\n",
        "    vpp = vp/(vp + fp)\n",
        "    vpn = vn/(vn + fn) \n",
        "    mcc = (vp*vn - fp*fn)/np.sqrt((vp + fp)*(vp + fn)*(vn + fp)*(vn + fn))\n",
        "    f1s = 2*vpp*sen/(vpp + sen) \n",
        "    efi = (sen + esp)/2\n",
        "\n",
        "    print('  Prevalência de Positivos: {:.4f}'.format(pre))\n",
        "    print('                  Acurácia: {:.4f}'.format(acc))\n",
        "    print('             Sensibilidade: {:.4f}'.format(sen))\n",
        "    print('            Especificidade: {:.4f}'.format(esp))\n",
        "    print('  Valor Preditivo Positivo: {:.4f}'.format(vpp))\n",
        "    print('  Valor Preditivo Negativo: {:.4f}'.format(vpn))\n",
        "    print('Coef. de Corr. de Matthews: {:.4f}'.format(mcc))\n",
        "    print('                  F1-Score: {:.4f}'.format(f1s))\n",
        "    print('                Eficiência: {:.4f}'.format(efi))\n",
        " \n",
        "\n",
        "    metricas = {'prevalência': pre, 'acurácia': acc, 'sensibilidade': sen, \n",
        "            'especificidade': esp, 'vpp': vpp, 'vpn': vpn, 'mcc': mcc,\n",
        "            'f1-score': f1s, 'eficiência': efi}\n",
        "\n",
        "    if custo is not None:\n",
        "        cost = (mc * np.array(custo).reshape(2,2)).sum()\n",
        "        print('\\n                     Custo: {}'.format(cost))\n",
        "        metricas['custo'] = cost\n",
        "\n",
        "    return metricas"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1jm1adY1PLI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "22a7da29-8851-4fbf-d408-8d545ca9d4d4"
      },
      "source": [
        "metricas = MetricasClassBinaria(titanic['Survived'], teste1, [1, -1, 20, 2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Prevalência de Positivos: 0.3838\n",
            "                  Acurácia: 0.7868\n",
            "             Sensibilidade: 0.6813\n",
            "            Especificidade: 0.8525\n",
            "  Valor Preditivo Positivo: 0.7420\n",
            "  Valor Preditivo Negativo: 0.8111\n",
            "Coef. de Corr. de Matthews: 0.5434\n",
            "                  F1-Score: 0.7104\n",
            "                Eficiência: 0.7669\n",
            "\n",
            "                     Custo: 3033\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iw1pM8ho4CQw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "metricas"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrFVEL8E6BCm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kt3NH9w2QbrO",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmJAvGN0QceR",
        "colab_type": "text"
      },
      "source": [
        "## **Exemplo Final: Modelagem de Churn**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Az78uyadQhjI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "c0d27b59-a62e-4256-d14a-f24be7e37e49"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/ricardorocha86/Datasets/master/Churn/churn.csv'\n",
        "churn = pd.read_csv(url)\n",
        "churn.head()"
      ],
      "execution_count": null,
      "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",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RowNumber</th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>15634602</td>\n",
              "      <td>Hargrave</td>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>15647311</td>\n",
              "      <td>Hill</td>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>15619304</td>\n",
              "      <td>Onio</td>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>15701354</td>\n",
              "      <td>Boni</td>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>15737888</td>\n",
              "      <td>Mitchell</td>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   RowNumber  CustomerId   Surname  ...  IsActiveMember EstimatedSalary Exited\n",
              "0          1    15634602  Hargrave  ...               1       101348.88      1\n",
              "1          2    15647311      Hill  ...               1       112542.58      0\n",
              "2          3    15619304      Onio  ...               0       113931.57      1\n",
              "3          4    15701354      Boni  ...               0        93826.63      0\n",
              "4          5    15737888  Mitchell  ...               1        79084.10      0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SfHWbEzRZk6",
        "colab_type": "text"
      },
      "source": [
        "### Análise Exploratória de Dados Básica"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9x-i9ofRgoJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "70302a6b-f0fb-48ee-ff5d-69913991a889"
      },
      "source": [
        "churn.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   RowNumber        10000 non-null  int64  \n",
            " 1   CustomerId       10000 non-null  int64  \n",
            " 2   Surname          10000 non-null  object \n",
            " 3   CreditScore      10000 non-null  int64  \n",
            " 4   Geography        10000 non-null  object \n",
            " 5   Gender           10000 non-null  object \n",
            " 6   Age              10000 non-null  int64  \n",
            " 7   Tenure           10000 non-null  int64  \n",
            " 8   Balance          10000 non-null  float64\n",
            " 9   NumOfProducts    10000 non-null  int64  \n",
            " 10  HasCrCard        10000 non-null  int64  \n",
            " 11  IsActiveMember   10000 non-null  int64  \n",
            " 12  EstimatedSalary  10000 non-null  float64\n",
            " 13  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(3)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9CL4Tj7RlqB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "02686d1e-d344-4c28-af3b-597c617685aa"
      },
      "source": [
        "churn.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber          0\n",
              "CustomerId         0\n",
              "Surname            0\n",
              "CreditScore        0\n",
              "Geography          0\n",
              "Gender             0\n",
              "Age                0\n",
              "Tenure             0\n",
              "Balance            0\n",
              "NumOfProducts      0\n",
              "HasCrCard          0\n",
              "IsActiveMember     0\n",
              "EstimatedSalary    0\n",
              "Exited             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyhZJNFBHPAf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "2c1f22b3-3235-4249-bc01-c06889df8294"
      },
      "source": [
        "!pip install sweetviz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sweetviz in /usr/local/lib/python3.6/dist-packages (1.0b5)\n",
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            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.11.1->sweetviz) (1.1.1)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_BLjf0eHOCm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sweetviz "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHzabtjKJzBg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "b84b002d-9324-4b8f-b6bc-2293fc030942"
      },
      "source": [
        "eda = sweetviz.compare_intra(churn, churn['Exited'] == 0, ['Cliente', 'Ex-Cliente'])\n",
        "eda.show_html()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ":FEATURES DONE:                    |█████████████████████| [100%]   00:06  -> (00:00 left)\n",
            ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:00  -> (00:00 left)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Creating Associations graph... DONE!\n",
            "Report SWEETVIZ_REPORT.html was generated! NOTEBOOK/COLAB USERS: no browser will pop up, the report is saved in your notebook/colab files.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yX9QZY5iRowi",
        "colab_type": "text"
      },
      "source": [
        "### Pré-processamento dos Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xPOK_S9mSfr6",
        "colab_type": "text"
      },
      "source": [
        "#### 1. Novas Variáveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cmyw0RxfUm4g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def aux1(x):\n",
        "    if x == 0:\n",
        "        return 'zero'\n",
        "    elif x < 100000:\n",
        "        return '0-100k'\n",
        "    else:\n",
        "        return '100k+'\n",
        "\n",
        "churn['Saldo'] = churn['Balance'].apply(aux1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EHU-YyMVZP-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def aux2(x):\n",
        "    if x < 3:\n",
        "        return '0-2'\n",
        "    elif x < 8:\n",
        "        return '3-7'\n",
        "    else:\n",
        "        return '8+'\n",
        "\n",
        "churn['Tempo'] = churn['Tenure'].apply(aux2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "O8dgtPQZXBiA",
        "colab": {}
      },
      "source": [
        "def aux3(x):\n",
        "    if x == 1:\n",
        "        return '1'\n",
        "    elif x == 2:\n",
        "        return '2'\n",
        "    else:\n",
        "        return '3+'\n",
        "\n",
        "churn['Produtos'] = churn['NumOfProducts'].apply(aux3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TK4cof6XWjpW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "outputId": "d4840254-7beb-4fcd-e959-1bc1ace904bb"
      },
      "source": [
        "churn.head()"
      ],
      "execution_count": null,
      "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>RowNumber</th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "      <th>Saldo</th>\n",
              "      <th>Tempo</th>\n",
              "      <th>Produtos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>15634602</td>\n",
              "      <td>Hargrave</td>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "      <td>zero</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>15647311</td>\n",
              "      <td>Hill</td>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "      <td>0-100k</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>15619304</td>\n",
              "      <td>Onio</td>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "      <td>100k+</td>\n",
              "      <td>8+</td>\n",
              "      <td>3+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>15701354</td>\n",
              "      <td>Boni</td>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "      <td>zero</td>\n",
              "      <td>0-2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>15737888</td>\n",
              "      <td>Mitchell</td>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "      <td>100k+</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   RowNumber  CustomerId   Surname  CreditScore  ... Exited   Saldo  Tempo  Produtos\n",
              "0          1    15634602  Hargrave          619  ...      1    zero    0-2         1\n",
              "1          2    15647311      Hill          608  ...      0  0-100k    0-2         1\n",
              "2          3    15619304      Onio          502  ...      1   100k+     8+        3+\n",
              "3          4    15701354      Boni          699  ...      0    zero    0-2         2\n",
              "4          5    15737888  Mitchell          850  ...      0   100k+    0-2         1\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrkKYUgRSZ6g",
        "colab_type": "text"
      },
      "source": [
        "#### 2. Dropando Variáveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zU0VcXWSe1p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "churn.drop(['RowNumber', 'CustomerId', 'Surname', 'Balance', 'Tenure', 'NumOfProducts'], axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqw6pDvjSd8J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "8ed51ada-fe10-49ba-abcd-40f1f62f192a"
      },
      "source": [
        "churn.head()"
      ],
      "execution_count": null,
      "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>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "      <th>Saldo</th>\n",
              "      <th>Tempo</th>\n",
              "      <th>Produtos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "      <td>zero</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "      <td>0-100k</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "      <td>100k+</td>\n",
              "      <td>8+</td>\n",
              "      <td>3+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "      <td>zero</td>\n",
              "      <td>0-2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "      <td>100k+</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CreditScore Geography  Gender  Age  ...  Exited   Saldo  Tempo  Produtos\n",
              "0          619    France  Female   42  ...       1    zero    0-2         1\n",
              "1          608     Spain  Female   41  ...       0  0-100k    0-2         1\n",
              "2          502    France  Female   42  ...       1   100k+     8+        3+\n",
              "3          699    France  Female   39  ...       0    zero    0-2         2\n",
              "4          850     Spain  Female   43  ...       0   100k+    0-2         1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Ph-cI3VQSfr5"
      },
      "source": [
        "#### 3. Dummificações"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYJ830t8ShXE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "churn = pd.get_dummies(churn, columns = ['Geography', 'Gender', 'Saldo', 'Tempo', 'Produtos'], drop_first = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDUODYnyShZ4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "outputId": "6bfcd353-7165-4eeb-a9d1-874926c75079"
      },
      "source": [
        "churn.head()"
      ],
      "execution_count": null,
      "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>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "      <th>Geography_Germany</th>\n",
              "      <th>Geography_Spain</th>\n",
              "      <th>Gender_Male</th>\n",
              "      <th>Saldo_100k+</th>\n",
              "      <th>Saldo_zero</th>\n",
              "      <th>Tempo_3-7</th>\n",
              "      <th>Tempo_8+</th>\n",
              "      <th>Produtos_2</th>\n",
              "      <th>Produtos_3+</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>619</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CreditScore  Age  HasCrCard  ...  Tempo_8+  Produtos_2  Produtos_3+\n",
              "0          619   42          1  ...         0           0            0\n",
              "1          608   41          0  ...         0           0            0\n",
              "2          502   42          1  ...         1           0            1\n",
              "3          699   39          0  ...         0           1            0\n",
              "4          850   43          1  ...         0           0            0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vzM911q_Sf-Q"
      },
      "source": [
        "#### 4. Padronizações"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYTMt8Pa5FVr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nl9NAL9c77_k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "aux0 = sc.fit_transform(churn[['CreditScore', 'Age', 'EstimatedSalary']])\n",
        "aux1 = pd.DataFrame(aux0, columns = ['Escore', 'Idade', 'Salario'])\n",
        "churn = pd.concat([churn, aux1], axis = 1)\n",
        "churn.drop(['CreditScore', 'Age', 'EstimatedSalary'], axis = 1, inplace = True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHWIRZ6CX7tm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "outputId": "cfb74332-a674-4584-f644-094e5bab0101"
      },
      "source": [
        "churn.head()"
      ],
      "execution_count": null,
      "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>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>Exited</th>\n",
              "      <th>Geography_Germany</th>\n",
              "      <th>Geography_Spain</th>\n",
              "      <th>Gender_Male</th>\n",
              "      <th>Saldo_100k+</th>\n",
              "      <th>Saldo_zero</th>\n",
              "      <th>Tempo_3-7</th>\n",
              "      <th>Tempo_8+</th>\n",
              "      <th>Produtos_2</th>\n",
              "      <th>Produtos_3+</th>\n",
              "      <th>Escore</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Salario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.326221</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>0.021886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.440036</td>\n",
              "      <td>0.198164</td>\n",
              "      <td>0.216534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.536794</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>0.240687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.501521</td>\n",
              "      <td>0.007457</td>\n",
              "      <td>-0.108918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.063884</td>\n",
              "      <td>0.388871</td>\n",
              "      <td>-0.365276</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   HasCrCard  IsActiveMember  Exited  ...    Escore     Idade   Salario\n",
              "0          1               1       1  ... -0.326221  0.293517  0.021886\n",
              "1          0               1       0  ... -0.440036  0.198164  0.216534\n",
              "2          1               0       1  ... -1.536794  0.293517  0.240687\n",
              "3          0               0       0  ...  0.501521  0.007457 -0.108918\n",
              "4          1               1       0  ...  2.063884  0.388871 -0.365276\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UoAeQ6-eYGL0",
        "colab_type": "text"
      },
      "source": [
        "## **Modelagem**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkyFUMP1YMH2",
        "colab_type": "text"
      },
      "source": [
        "Nesse caso, temos **dois objetivos**:\n",
        "\n",
        "- Selecionar um bom modelo que se ajuste a esses dados\n",
        "- Estimar o erro de generalização do modelo\n",
        "\n",
        "Para isso, vamos dividir os dados em **treinamento (90%)** e **validação (10%)**. Como temos 10000 observações no conjunto de dados, a divisão pode ser mais desbalanceada, de forma que dessa maneira ainda sobra 1000 elementos no conjunto de validação.\n",
        "\n",
        "Vamos utilizar **validação cruzada no conjunto de dados de treinamento** para encontrar os melhores parâmetros para os modelos propostos e, por fim, verificaremos o **erro de generalização dos modelos na amostra final de validação**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5zaNTFWZZ-D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Fazendo a Divisão em Teste e Treinamento\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "Xtreino, Xvalid, Ytreino, Yvalid = train_test_split(churn.drop('Exited', axis = 1), \n",
        "                                                    churn['Exited'], \n",
        "                                                    test_size = 1000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3qBFvVIZyOB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Configuração da Validação Cruzada\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "kf = KFold(n_splits = 20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqIIvcgFZznJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importando os Modelos\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1LlrINDexHB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Definindo a métrica de interesse \n",
        "metrica = 'f1'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zyz9DnNvJO38",
        "colab_type": "text"
      },
      "source": [
        "### Regressão Logística "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNG_hF-rJSmu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "938d6d43-71ef-4e67-cadb-75b9d196e62a"
      },
      "source": [
        "# Verificando a Performance da Regressão Logística\n",
        "\n",
        "sens_reglog = cross_val_score(LogisticRegression(), Xtreino, Ytreino, cv = kf, scoring = metrica)\n",
        " \n",
        "print('F1-Score: {:.2f}%'.format(100*sens_reglog.mean()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1-Score: 49.45%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pq-NEx6_bwrr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "c4e10577-b412-45c6-a0ec-399deb7035e0"
      },
      "source": [
        "reglog.fit(Xtreino, Ytreino)\n",
        "pred_reglog = reglog.predict(Xvalid)\n",
        "\n",
        "met_reglog = MetricasClassBinaria(Yvalid, pred_reglog)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Prevalência de Positivos: 0.2010\n",
            "                  Acurácia: 0.8370\n",
            "             Sensibilidade: 0.3731\n",
            "            Especificidade: 0.9537\n",
            "  Valor Preditivo Positivo: 0.6696\n",
            "  Valor Preditivo Negativo: 0.8581\n",
            "Coef. de Corr. de Matthews: 0.4153\n",
            "                  F1-Score: 0.4792\n",
            "                Eficiência: 0.6634\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHQoNHN1I-jM",
        "colab_type": "text"
      },
      "source": [
        "### K Vizinhos Mais Próximos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dtlCS4AAt8p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "outputId": "c533ab8e-3643-46db-f0a1-f2aa36869535"
      },
      "source": [
        "# Encontrando o melhor valor de K através de Validação Cruzada K-FOLD \n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "valores = np.arange(1, 51, 2)\n",
        "parametros = {'n_neighbors': valores}\n",
        "\n",
        "clf = GridSearchCV(KNeighborsClassifier(), param_grid = parametros, cv = kf, scoring = metrica)\n",
        "clf.fit(Xtreino, Ytreino)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=KFold(n_splits=20, random_state=None, shuffle=False),\n",
              "             error_score=nan,\n",
              "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
              "                                            metric='minkowski',\n",
              "                                            metric_params=None, n_jobs=None,\n",
              "                                            n_neighbors=5, p=2,\n",
              "                                            weights='uniform'),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'n_neighbors': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33,\n",
              "       35, 37, 39, 41, 43, 45, 47, 49])},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='f1', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpx_mspcBTFU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "dffdf143-2924-4c2d-98e8-0a55a7708a74"
      },
      "source": [
        "# melhor escolha de K\n",
        "print(clf.cv_results_['mean_test_score'])\n",
        "print(clf.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.47383495 0.48164783 0.4992857  0.48532457 0.49308602 0.48417992\n",
            " 0.4663896  0.46587732 0.46049176 0.46399325 0.46248402 0.45161951\n",
            " 0.44983225 0.44730907 0.44811583 0.44952059 0.4459441  0.44226384\n",
            " 0.4421292  0.43908712 0.43649845 0.43593595 0.43013927 0.42377427\n",
            " 0.42617382]\n",
            "{'n_neighbors': 5}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6NpAG4H8GDF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "02b97979-5f4e-4bcb-ccd3-33b865a849a2"
      },
      "source": [
        "# verificando a performance do KNN para um dado valor de K\n",
        "\n",
        "knn5 = KNeighborsClassifier(n_neighbors = 5)\n",
        "knn5.fit(Xtreino, Ytreino)\n",
        "pred_knn5 = knn5.predict(Xvalid)\n",
        "\n",
        "met_knn5 = MetricasClassBinaria(Yvalid, pred_knn5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Prevalência de Positivos: 0.2010\n",
            "                  Acurácia: 0.8380\n",
            "             Sensibilidade: 0.3831\n",
            "            Especificidade: 0.9524\n",
            "  Valor Preditivo Positivo: 0.6696\n",
            "  Valor Preditivo Negativo: 0.8599\n",
            "Coef. de Corr. de Matthews: 0.4215\n",
            "                  F1-Score: 0.4873\n",
            "                Eficiência: 0.6678\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Ob3dA8W9fuj5"
      },
      "source": [
        "### Árvore de Decisão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IpR_G6CGJ1Rp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "e7d9f208-bd9b-4025-f4ea-36f3e74f7a88"
      },
      "source": [
        "# Encontrando o melhor valor do 'max_depth' através de Validação Cruzada K-FOLD\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "valores = np.arange(2, 15, 1)\n",
        "parametros = {'max_depth': valores}\n",
        "\n",
        "clf = GridSearchCV(DecisionTreeClassifier(), param_grid = parametros, cv = kf, scoring = metrica)\n",
        "clf.fit(Xtreino, Ytreino)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=KFold(n_splits=20, random_state=None, shuffle=False),\n",
              "             error_score=nan,\n",
              "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features=None,\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              presort='deprecated',\n",
              "                                              random_state=None,\n",
              "                                              splitter='best'),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='f1', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "r_6x0H0hf_XZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "ebd58aeb-0e8d-4395-9e16-650c9a993b25"
      },
      "source": [
        "# melhor escolha de profundidade\n",
        "print(clf.cv_results_['mean_test_score'])\n",
        "print(clf.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.53124138 0.49368762 0.51994294 0.54468641 0.53986816 0.55455309\n",
            " 0.55842828 0.5555886  0.54140483 0.54075041 0.53904522 0.52662225\n",
            " 0.51967165]\n",
            "{'max_depth': 8}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "G3TFF5iyf_Xo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "ac7b80b0-8b1e-433f-9e45-6c5dde140677"
      },
      "source": [
        "# verificando a performance do KNN para um dado valor de K\n",
        "\n",
        "arvore = DecisionTreeClassifier(max_depth = 8)\n",
        "arvore.fit(Xtreino, Ytreino)\n",
        "pred_arvore = arvore.predict(Xvalid)\n",
        "\n",
        "met_arvore = MetricasClassBinaria(Yvalid, pred_arvore)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Prevalência de Positivos: 0.2010\n",
            "                  Acurácia: 0.8520\n",
            "             Sensibilidade: 0.4627\n",
            "            Especificidade: 0.9499\n",
            "  Valor Preditivo Positivo: 0.6992\n",
            "  Valor Preditivo Negativo: 0.8754\n",
            "Coef. de Corr. de Matthews: 0.4870\n",
            "                  F1-Score: 0.5569\n",
            "                Eficiência: 0.7063\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XXBFlpeMMsSv"
      },
      "source": [
        "### Random Forests"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QiV4ex62MsTA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "bb0e66a2-3b9b-4c21-968b-237d99677bf6"
      },
      "source": [
        "# Encontrando o melhor valor do 'max_depth' através de Validação Cruzada K-FOLD\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "valores = np.arange(12, 23, 1)\n",
        "parametros = {'max_depth': valores}\n",
        "\n",
        "clf = GridSearchCV(RandomForestClassifier(), param_grid = parametros, cv = kf, scoring = metrica)\n",
        "clf.fit(Xtreino, Ytreino)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=KFold(n_splits=20, random_state=None, shuffle=False),\n",
              "             error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                              class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=None,\n",
              "                                              oob_score=False,\n",
              "                                              random_state=None, verbose=0,\n",
              "                                              warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20])},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='f1', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ix5-Nlxng0WP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "98f0775f-7183-4b62-b108-911c42d73ec3"
      },
      "source": [
        "# melhor escolha de profundidade\n",
        "print(clf.cv_results_['mean_test_score'])\n",
        "print(clf.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.09971342 0.36534886 0.49602502 0.5489173  0.56415922 0.5817941\n",
            " 0.5811164  0.58550045 0.57766715 0.58064815]\n",
            "{'max_depth': 16}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hzoK-jF9g3uT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "cc3479eb-5411-48f9-c969-2385ddb47890"
      },
      "source": [
        "# verificando a performance do KNN para um dado valor de K\n",
        "\n",
        "rf = RandomForestClassifier(max_depth = 16)\n",
        "rf.fit(Xtreino, Ytreino)\n",
        "pred_rf = rf.predict(Xvalid)\n",
        "\n",
        "met_rf = MetricasClassBinaria(Yvalid, pred_rf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Prevalência de Positivos: 0.2010\n",
            "                  Acurácia: 0.8630\n",
            "             Sensibilidade: 0.4527\n",
            "            Especificidade: 0.9662\n",
            "  Valor Preditivo Positivo: 0.7712\n",
            "  Valor Preditivo Negativo: 0.8753\n",
            "Coef. de Corr. de Matthews: 0.5204\n",
            "                  F1-Score: 0.5705\n",
            "                Eficiência: 0.7095\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Np82CcjiOru",
        "colab_type": "text"
      },
      "source": [
        "### Síntese dos Resultados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nG16DfQiQy4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Regressão Logística\n",
        "reglog = LogisticRegression()\n",
        "reglog.fit(Xtreino, Ytreino)\n",
        "pred_reglog = reglog.predict(Xvalid)\n",
        "met_reglog = MetricasClassBinaria(Yvalid, pred_reglog, custo = [0, 1, 10, 1])\n",
        "\n",
        "#KNNs\n",
        "knn3 = KNeighborsClassifier(n_neighbors = 3)\n",
        "knn3.fit(Xtreino, Ytreino)\n",
        "pred_knn3 = knn3.predict(Xvalid)\n",
        "met_knn3 = MetricasClassBinaria(Yvalid, pred_knn3, custo = [0, 1, 10, 1])\n",
        "\n",
        "knn5 = KNeighborsClassifier(n_neighbors = 5)\n",
        "knn5.fit(Xtreino, Ytreino)\n",
        "pred_knn5 = knn5.predict(Xvalid)\n",
        "met_knn5 = MetricasClassBinaria(Yvalid, pred_knn5, custo = [0, 1, 10, 1])\n",
        "\n",
        "knn7 = KNeighborsClassifier(n_neighbors = 7)\n",
        "knn7.fit(Xtreino, Ytreino)\n",
        "pred_knn7 = knn7.predict(Xvalid)\n",
        "met_knn7 = MetricasClassBinaria(Yvalid, pred_knn7, custo = [0, 1, 10, 1])\n",
        "\n",
        "knn9 = KNeighborsClassifier(n_neighbors = 9)\n",
        "knn9.fit(Xtreino, Ytreino)\n",
        "pred_knn9 = knn9.predict(Xvalid)\n",
        "met_knn9 = MetricasClassBinaria(Yvalid, pred_knn9, custo = [0, 1, 10, 1])\n",
        "\n",
        "#Arvores\n",
        "arvore4 = DecisionTreeClassifier(max_depth = 4)\n",
        "arvore4.fit(Xtreino, Ytreino)\n",
        "pred_arvore4 = arvore4.predict(Xvalid)\n",
        "met_arvore4 = MetricasClassBinaria(Yvalid, pred_arvore4, custo = [0, 1, 10, 1])\n",
        "\n",
        "arvore8 = DecisionTreeClassifier(max_depth = 8)\n",
        "arvore8.fit(Xtreino, Ytreino)\n",
        "pred_arvore8 = arvore8.predict(Xvalid)\n",
        "met_arvore8 = MetricasClassBinaria(Yvalid, pred_arvore8, custo = [0, 1, 10, 1])\n",
        "\n",
        "arvore12 = DecisionTreeClassifier(max_depth = 12)\n",
        "arvore12.fit(Xtreino, Ytreino)\n",
        "pred_arvore12 = arvore12.predict(Xvalid)\n",
        "met_arvore12 = MetricasClassBinaria(Yvalid, pred_arvore12, custo = [0, 1, 10, 1])\n",
        "\n",
        "#Florestas\n",
        "rf8 = RandomForestClassifier(max_depth = 8)\n",
        "rf8.fit(Xtreino, Ytreino)\n",
        "pred_rf8 = rf8.predict(Xvalid)\n",
        "met_rf8 = MetricasClassBinaria(Yvalid, pred_rf8, custo = [0, 1, 10, 1])\n",
        "\n",
        "rf16 = RandomForestClassifier(max_depth = 16)\n",
        "rf16.fit(Xtreino, Ytreino)\n",
        "pred_rf16 = rf16.predict(Xvalid)\n",
        "met_rf16 = MetricasClassBinaria(Yvalid, pred_rf16, custo = [0, 1, 10, 1])\n",
        "\n",
        "rf24 = RandomForestClassifier(max_depth = 24)\n",
        "rf24.fit(Xtreino, Ytreino)\n",
        "pred_rf24 = rf24.predict(Xvalid)\n",
        "met_rf24 = MetricasClassBinaria(Yvalid, pred_rf24, custo = [0, 1, 10, 1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLh1cExMj9xB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modelos = ['Reg. Log.', '3-NN', '5-NN', '7-NN', '9-NN', \n",
        "           'Árvore (4)', 'Árvore (8)', 'Árvore (12)',\n",
        "           'Random Forest (8)', 'Random Forest (16)', 'Random Forest (24)']\n",
        "\n",
        "preditos = [met_reglog, met_knn3, met_knn5, met_knn7, met_knn9, met_arvore4,\n",
        "            met_arvore8, met_arvore12, met_rf8, met_rf16, met_rf24]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpU24LAYjFnI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "resultados = pd.DataFrame()\n",
        "\n",
        "for i in range(len(modelos)):\n",
        "    aux = pd.DataFrame.from_dict(preditos[i], orient = 'index', columns = [modelos[i]])\n",
        "    resultados = pd.concat([resultados, aux], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcxaJm8dkw8w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "67dea8c9-c6db-4598-b83d-454a31c8dd72"
      },
      "source": [
        "resultados.T.round(4)"
      ],
      "execution_count": null,
      "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>prevalência</th>\n",
              "      <th>acurácia</th>\n",
              "      <th>sensibilidade</th>\n",
              "      <th>especificidade</th>\n",
              "      <th>vpp</th>\n",
              "      <th>vpn</th>\n",
              "      <th>mcc</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>eficiência</th>\n",
              "      <th>custo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Reg. Log.</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.849</td>\n",
              "      <td>0.4021</td>\n",
              "      <td>0.9566</td>\n",
              "      <td>0.6903</td>\n",
              "      <td>0.8692</td>\n",
              "      <td>0.4479</td>\n",
              "      <td>0.5081</td>\n",
              "      <td>0.6793</td>\n",
              "      <td>1273.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-NN</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.833</td>\n",
              "      <td>0.4381</td>\n",
              "      <td>0.9280</td>\n",
              "      <td>0.5944</td>\n",
              "      <td>0.8728</td>\n",
              "      <td>0.4136</td>\n",
              "      <td>0.5045</td>\n",
              "      <td>0.6831</td>\n",
              "      <td>1233.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5-NN</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.831</td>\n",
              "      <td>0.4021</td>\n",
              "      <td>0.9342</td>\n",
              "      <td>0.5954</td>\n",
              "      <td>0.8665</td>\n",
              "      <td>0.3941</td>\n",
              "      <td>0.4800</td>\n",
              "      <td>0.6682</td>\n",
              "      <td>1291.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7-NN</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.847</td>\n",
              "      <td>0.4124</td>\n",
              "      <td>0.9516</td>\n",
              "      <td>0.6723</td>\n",
              "      <td>0.8706</td>\n",
              "      <td>0.4445</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6820</td>\n",
              "      <td>1259.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9-NN</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.856</td>\n",
              "      <td>0.3918</td>\n",
              "      <td>0.9677</td>\n",
              "      <td>0.7451</td>\n",
              "      <td>0.8686</td>\n",
              "      <td>0.4697</td>\n",
              "      <td>0.5135</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>1282.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Árvore (4)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.863</td>\n",
              "      <td>0.4124</td>\n",
              "      <td>0.9715</td>\n",
              "      <td>0.7767</td>\n",
              "      <td>0.8729</td>\n",
              "      <td>0.4993</td>\n",
              "      <td>0.5387</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1243.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Árvore (8)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.853</td>\n",
              "      <td>0.4742</td>\n",
              "      <td>0.9442</td>\n",
              "      <td>0.6715</td>\n",
              "      <td>0.8818</td>\n",
              "      <td>0.4812</td>\n",
              "      <td>0.5559</td>\n",
              "      <td>0.7092</td>\n",
              "      <td>1157.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Árvore (12)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.5155</td>\n",
              "      <td>0.9045</td>\n",
              "      <td>0.5650</td>\n",
              "      <td>0.8858</td>\n",
              "      <td>0.4351</td>\n",
              "      <td>0.5391</td>\n",
              "      <td>0.7100</td>\n",
              "      <td>1117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest (8)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.852</td>\n",
              "      <td>0.4794</td>\n",
              "      <td>0.9417</td>\n",
              "      <td>0.6643</td>\n",
              "      <td>0.8826</td>\n",
              "      <td>0.4799</td>\n",
              "      <td>0.5569</td>\n",
              "      <td>0.7105</td>\n",
              "      <td>1150.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest (16)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.5155</td>\n",
              "      <td>0.8734</td>\n",
              "      <td>0.4950</td>\n",
              "      <td>0.8822</td>\n",
              "      <td>0.3830</td>\n",
              "      <td>0.5051</td>\n",
              "      <td>0.6945</td>\n",
              "      <td>1142.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest (24)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.5052</td>\n",
              "      <td>0.8759</td>\n",
              "      <td>0.4949</td>\n",
              "      <td>0.8803</td>\n",
              "      <td>0.3782</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>1158.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    prevalência  acurácia  ...  eficiência   custo\n",
              "Reg. Log.                 0.194     0.849  ...      0.6793  1273.0\n",
              "3-NN                      0.194     0.833  ...      0.6831  1233.0\n",
              "5-NN                      0.194     0.831  ...      0.6682  1291.0\n",
              "7-NN                      0.194     0.847  ...      0.6820  1259.0\n",
              "9-NN                      0.194     0.856  ...      0.6797  1282.0\n",
              "Árvore (4)                0.194     0.863  ...      0.6919  1243.0\n",
              "Árvore (8)                0.194     0.853  ...      0.7092  1157.0\n",
              "Árvore (12)               0.194     0.829  ...      0.7100  1117.0\n",
              "Random Forest (8)         0.194     0.852  ...      0.7105  1150.0\n",
              "Random Forest (16)        0.194     0.804  ...      0.6945  1142.0\n",
              "Random Forest (24)        0.194     0.804  ...      0.6905  1158.0\n",
              "\n",
              "[11 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCEOh8vJk5vC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab.data_table import DataTable"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9mwNUWfm7Hz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "3454d79a-f7f4-4a0a-9188-984a246d592b"
      },
      "source": [
        "DataTable(resultados.T.round(4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[\"Reg. Log.\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.849,\n            'f': \"0.849\",\n        },\n{\n            'v': 0.4021,\n            'f': \"0.4021\",\n        },\n{\n            'v': 0.9566,\n            'f': \"0.9566\",\n        },\n{\n            'v': 0.6903,\n            'f': \"0.6903\",\n        },\n{\n            'v': 0.8692,\n            'f': \"0.8692\",\n        },\n{\n            'v': 0.4479,\n            'f': \"0.4479\",\n        },\n{\n            'v': 0.5081,\n            'f': \"0.5081\",\n        },\n{\n            'v': 0.6793,\n            'f': \"0.6793\",\n        },\n{\n            'v': 1273.0,\n            'f': \"1273.0\",\n        }],\n [\"3-NN\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.833,\n            'f': \"0.833\",\n        },\n{\n            'v': 0.4381,\n            'f': \"0.4381\",\n        },\n{\n            'v': 0.928,\n            'f': \"0.928\",\n        },\n{\n            'v': 0.5944,\n            'f': \"0.5944\",\n        },\n{\n            'v': 0.8728,\n            'f': \"0.8728\",\n        },\n{\n            'v': 0.4136,\n            'f': \"0.4136\",\n        },\n{\n            'v': 0.5045,\n            'f': \"0.5045\",\n        },\n{\n            'v': 0.6831,\n            'f': \"0.6831\",\n        },\n{\n            'v': 1233.0,\n            'f': \"1233.0\",\n        }],\n [\"5-NN\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.831,\n            'f': \"0.831\",\n        },\n{\n            'v': 0.4021,\n            'f': \"0.4021\",\n        },\n{\n            'v': 0.9342,\n            'f': \"0.9342\",\n        },\n{\n            'v': 0.5954,\n            'f': \"0.5954\",\n        },\n{\n            'v': 0.8665,\n            'f': \"0.8665\",\n        },\n{\n            'v': 0.3941,\n            'f': \"0.3941\",\n        },\n{\n            'v': 0.48,\n            'f': \"0.48\",\n        },\n{\n            'v': 0.6682,\n            'f': \"0.6682\",\n        },\n{\n            'v': 1291.0,\n            'f': \"1291.0\",\n        }],\n [\"7-NN\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.847,\n            'f': \"0.847\",\n        },\n{\n            'v': 0.4124,\n            'f': \"0.4124\",\n        },\n{\n            'v': 0.9516,\n            'f': \"0.9516\",\n        },\n{\n            'v': 0.6723,\n            'f': \"0.6723\",\n        },\n{\n            'v': 0.8706,\n            'f': \"0.8706\",\n        },\n{\n            'v': 0.4445,\n            'f': \"0.4445\",\n        },\n{\n            'v': 0.5112,\n            'f': \"0.5112\",\n        },\n{\n            'v': 0.682,\n            'f': \"0.682\",\n        },\n{\n            'v': 1259.0,\n            'f': \"1259.0\",\n        }],\n [\"9-NN\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.856,\n            'f': \"0.856\",\n        },\n{\n            'v': 0.3918,\n            'f': \"0.3918\",\n        },\n{\n            'v': 0.9677,\n            'f': \"0.9677\",\n        },\n{\n            'v': 0.7451,\n            'f': \"0.7451\",\n        },\n{\n            'v': 0.8686,\n            'f': \"0.8686\",\n        },\n{\n            'v': 0.4697,\n            'f': \"0.4697\",\n        },\n{\n            'v': 0.5135,\n            'f': \"0.5135\",\n        },\n{\n            'v': 0.6797,\n            'f': \"0.6797\",\n        },\n{\n            'v': 1282.0,\n            'f': \"1282.0\",\n        }],\n [\"\\u00c1rvore (4)\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.863,\n            'f': \"0.863\",\n        },\n{\n            'v': 0.4124,\n            'f': \"0.4124\",\n        },\n{\n            'v': 0.9715,\n            'f': \"0.9715\",\n        },\n{\n            'v': 0.7767,\n            'f': \"0.7767\",\n        },\n{\n            'v': 0.8729,\n            'f': \"0.8729\",\n        },\n{\n            'v': 0.4993,\n            'f': \"0.4993\",\n        },\n{\n            'v': 0.5387,\n            'f': \"0.5387\",\n        },\n{\n            'v': 0.6919,\n            'f': \"0.6919\",\n        },\n{\n            'v': 1243.0,\n            'f': \"1243.0\",\n        }],\n [\"\\u00c1rvore (8)\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.853,\n            'f': \"0.853\",\n        },\n{\n            'v': 0.4742,\n            'f': \"0.4742\",\n        },\n{\n            'v': 0.9442,\n            'f': \"0.9442\",\n        },\n{\n            'v': 0.6715,\n            'f': \"0.6715\",\n        },\n{\n            'v': 0.8818,\n            'f': \"0.8818\",\n        },\n{\n            'v': 0.4812,\n            'f': \"0.4812\",\n        },\n{\n            'v': 0.5559,\n            'f': \"0.5559\",\n        },\n{\n            'v': 0.7092,\n            'f': \"0.7092\",\n        },\n{\n            'v': 1157.0,\n            'f': \"1157.0\",\n        }],\n [\"\\u00c1rvore (12)\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.829,\n            'f': \"0.829\",\n        },\n{\n            'v': 0.5155,\n            'f': \"0.5155\",\n        },\n{\n            'v': 0.9045,\n            'f': \"0.9045\",\n        },\n{\n            'v': 0.565,\n            'f': \"0.565\",\n        },\n{\n            'v': 0.8858,\n            'f': \"0.8858\",\n        },\n{\n            'v': 0.4351,\n            'f': \"0.4351\",\n        },\n{\n            'v': 0.5391,\n            'f': \"0.5391\",\n        },\n{\n            'v': 0.71,\n            'f': \"0.71\",\n        },\n{\n            'v': 1117.0,\n            'f': \"1117.0\",\n        }],\n [\"Random Forest (8)\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.852,\n            'f': \"0.852\",\n        },\n{\n            'v': 0.4794,\n            'f': \"0.4794\",\n        },\n{\n            'v': 0.9417,\n            'f': \"0.9417\",\n        },\n{\n            'v': 0.6643,\n            'f': \"0.6643\",\n        },\n{\n            'v': 0.8826,\n            'f': \"0.8826\",\n        },\n{\n            'v': 0.4799,\n            'f': \"0.4799\",\n        },\n{\n            'v': 0.5569,\n            'f': \"0.5569\",\n        },\n{\n            'v': 0.7105,\n            'f': \"0.7105\",\n        },\n{\n            'v': 1150.0,\n            'f': \"1150.0\",\n        }],\n [\"Random Forest (16)\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.804,\n            'f': \"0.804\",\n        },\n{\n            'v': 0.5155,\n            'f': \"0.5155\",\n        },\n{\n            'v': 0.8734,\n            'f': \"0.8734\",\n        },\n{\n            'v': 0.495,\n            'f': \"0.495\",\n        },\n{\n            'v': 0.8822,\n            'f': \"0.8822\",\n        },\n{\n            'v': 0.383,\n            'f': \"0.383\",\n        },\n{\n            'v': 0.5051,\n            'f': \"0.5051\",\n        },\n{\n            'v': 0.6945,\n            'f': \"0.6945\",\n        },\n{\n            'v': 1142.0,\n            'f': \"1142.0\",\n        }],\n [\"Random Forest (24)\",\n{\n            'v': 0.194,\n            'f': \"0.194\",\n        },\n{\n            'v': 0.804,\n            'f': \"0.804\",\n        },\n{\n            'v': 0.5052,\n            'f': \"0.5052\",\n        },\n{\n            'v': 0.8759,\n            'f': \"0.8759\",\n        },\n{\n            'v': 0.4949,\n            'f': \"0.4949\",\n        },\n{\n            'v': 0.8803,\n            'f': \"0.8803\",\n        },\n{\n            'v': 0.3782,\n            'f': \"0.3782\",\n        },\n{\n            'v': 0.5,\n            'f': \"0.5\",\n        },\n{\n            'v': 0.6905,\n            'f': \"0.6905\",\n        },\n{\n            'v': 1158.0,\n            'f': \"1158.0\",\n        }]],\n        columns: [[\"string\", \"index\"], [\"number\", \"preval\\u00eancia\"], [\"number\", \"acur\\u00e1cia\"], [\"number\", \"sensibilidade\"], [\"number\", \"especificidade\"], [\"number\", \"vpp\"], [\"number\", \"vpn\"], [\"number\", \"mcc\"], [\"number\", \"f1-score\"], [\"number\", \"efici\\u00eancia\"], [\"number\", \"custo\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "text/plain": [
              "<google.colab.data_table.DataTable object>"
            ],
            "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>prevalência</th>\n",
              "      <th>acurácia</th>\n",
              "      <th>sensibilidade</th>\n",
              "      <th>especificidade</th>\n",
              "      <th>vpp</th>\n",
              "      <th>vpn</th>\n",
              "      <th>mcc</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>eficiência</th>\n",
              "      <th>custo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Reg. Log.</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.849</td>\n",
              "      <td>0.4021</td>\n",
              "      <td>0.9566</td>\n",
              "      <td>0.6903</td>\n",
              "      <td>0.8692</td>\n",
              "      <td>0.4479</td>\n",
              "      <td>0.5081</td>\n",
              "      <td>0.6793</td>\n",
              "      <td>1273.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-NN</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.833</td>\n",
              "      <td>0.4381</td>\n",
              "      <td>0.9280</td>\n",
              "      <td>0.5944</td>\n",
              "      <td>0.8728</td>\n",
              "      <td>0.4136</td>\n",
              "      <td>0.5045</td>\n",
              "      <td>0.6831</td>\n",
              "      <td>1233.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5-NN</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.831</td>\n",
              "      <td>0.4021</td>\n",
              "      <td>0.9342</td>\n",
              "      <td>0.5954</td>\n",
              "      <td>0.8665</td>\n",
              "      <td>0.3941</td>\n",
              "      <td>0.4800</td>\n",
              "      <td>0.6682</td>\n",
              "      <td>1291.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7-NN</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.847</td>\n",
              "      <td>0.4124</td>\n",
              "      <td>0.9516</td>\n",
              "      <td>0.6723</td>\n",
              "      <td>0.8706</td>\n",
              "      <td>0.4445</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6820</td>\n",
              "      <td>1259.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9-NN</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.856</td>\n",
              "      <td>0.3918</td>\n",
              "      <td>0.9677</td>\n",
              "      <td>0.7451</td>\n",
              "      <td>0.8686</td>\n",
              "      <td>0.4697</td>\n",
              "      <td>0.5135</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>1282.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Árvore (4)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.863</td>\n",
              "      <td>0.4124</td>\n",
              "      <td>0.9715</td>\n",
              "      <td>0.7767</td>\n",
              "      <td>0.8729</td>\n",
              "      <td>0.4993</td>\n",
              "      <td>0.5387</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1243.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Árvore (8)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.853</td>\n",
              "      <td>0.4742</td>\n",
              "      <td>0.9442</td>\n",
              "      <td>0.6715</td>\n",
              "      <td>0.8818</td>\n",
              "      <td>0.4812</td>\n",
              "      <td>0.5559</td>\n",
              "      <td>0.7092</td>\n",
              "      <td>1157.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Árvore (12)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.5155</td>\n",
              "      <td>0.9045</td>\n",
              "      <td>0.5650</td>\n",
              "      <td>0.8858</td>\n",
              "      <td>0.4351</td>\n",
              "      <td>0.5391</td>\n",
              "      <td>0.7100</td>\n",
              "      <td>1117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest (8)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.852</td>\n",
              "      <td>0.4794</td>\n",
              "      <td>0.9417</td>\n",
              "      <td>0.6643</td>\n",
              "      <td>0.8826</td>\n",
              "      <td>0.4799</td>\n",
              "      <td>0.5569</td>\n",
              "      <td>0.7105</td>\n",
              "      <td>1150.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest (16)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.5155</td>\n",
              "      <td>0.8734</td>\n",
              "      <td>0.4950</td>\n",
              "      <td>0.8822</td>\n",
              "      <td>0.3830</td>\n",
              "      <td>0.5051</td>\n",
              "      <td>0.6945</td>\n",
              "      <td>1142.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest (24)</th>\n",
              "      <td>0.194</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.5052</td>\n",
              "      <td>0.8759</td>\n",
              "      <td>0.4949</td>\n",
              "      <td>0.8803</td>\n",
              "      <td>0.3782</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>1158.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 286
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6aATBoI5khr0",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJTzEY5bq9HX",
        "colab_type": "text"
      },
      "source": [
        "### Quais as variáveis que são mais relevantes nesse problema?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouOK3nAmDn09",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "62c2eab2-5284-4c2f-d11a-add06e773d57"
      },
      "source": [
        "nomes = list(Xtreino.columns)\n",
        "x = np.arange(len(nomes))\n",
        "y = arvore12.feature_importances_\n",
        " \n",
        "plt.style.use('ggplot')\n",
        "data = pd.DataFrame({'Importância': y}, index = nomes) \n",
        "data.sort_values(by = 'Importância').plot(kind = 'barh', colormap = 'Dark2')\n",
        "plt.title('Gráfico de importância das variáveis')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZLsPBMXkkgfJ",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qx104lLyrBrf",
        "colab_type": "text"
      },
      "source": [
        "### Enfim, qual estratégia tomar agora?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kltqJ3AjPAN8",
        "colab_type": "text"
      },
      "source": [
        "De acordo com nossos resultados preliminares, temos que o modelo baseado em Árvore de Decisão, com profundidade igual 12, retorna o menor custo na amostra de validação. \n",
        "\n",
        "Vamos utilizar o método predict_proba para prever as probabilidades de cada indivíduo na amostra de validação entrar em churn e classificá-los em faixas.\n",
        "\n",
        "Podemos compor as faixas da seguinte maneira:\n",
        "\n",
        "- 0% - 25% de probabilidade de Churn: Ações de Marketing Leves\n",
        "- 25% - 50% de probabilidade de Churn: Ações de Marketing Intermediárias\n",
        "- 50% - 75% de probabilidade de Churn: Ações de Marketing Fortes\n",
        "- 75% - 100% de probabilidade de Churn: Ações de Marketing Agressivas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtIMkhurQCnM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "probs = pd.DataFrame(arvore12.predict_proba(Xvalid))\n",
        "\n",
        "def aux(x):\n",
        "    if x < 0.25:\n",
        "        return 'Leve'\n",
        "    elif x < 0.5:\n",
        "        return 'Intermediário'\n",
        "    elif x < 0.75:\n",
        "        return 'Forte'\n",
        "    else:\n",
        "        return 'Agressivo'\n",
        "\n",
        "probs['Campanha'] = probs[1].apply(aux)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWvFX4uV9MZg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "probs.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpPqh2GqRptC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "cde7aa67-ad1c-40c1-d3e8-b9f9cb752acc"
      },
      "source": [
        "probs['Campanha'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Leve             761\n",
              "Agressivo        127\n",
              "Intermediário     90\n",
              "Forte             22\n",
              "Name: Campanha, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqX1oLPkQpzd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "784f5bfc-0796-460d-bc17-13d3118262e8"
      },
      "source": [
        "probs['Campanha'].value_counts().sort_values(ascending = True).plot(kind = 'barh')\n",
        "plt.title('Quantidade de clientes em cada ação a ser tomada')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPrsaN6Lkejk",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHnoQ9N7R8Dd",
        "colab_type": "text"
      },
      "source": [
        "## **Exercício Final**\n",
        "\n",
        "Faça um projeto com suas ideias e adicione ao seu portfólio!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-nBEEw-Ojy2E"
      },
      "source": [
        "## **Links Úteis**\n",
        "\n",
        "- [Conjunto de dados de Churn no kaggle](https://www.kaggle.com/shubh0799/churn-modelling)"
      ]
    }
  ]
}