EmployeeTurnover-KaplanMeier-CoxPHFitter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science - Análise de Sobrevivência - Previsão de Turnover"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Complemento ao projeto de Henrique Borges (https://www.kaggle.com/henriquebas/people-analytics-turnover-python-lifelines)\n",
    "\n",
    "25 de novembro de 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Definição do problema de negócio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma empresa necessita entender os motivos da alta taxa de *turnover* e prever quanto tempo um empregado irá permanecer antes de sair de lá."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/davinwijaya/employee-turnover"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Informações gerais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O *dataset* possui diversas variáveis, quais sejam:\n",
    "\n",
    "- **stag**: Experiência do empregado. Corresponde ao tempo t de falha ou tempo de censura do estudo. Como não há um codebook do *dataset* que explique qual unidade de medida está sendo tratada, consideraremos que a unidade seja *semana*.\n",
    "- **event**: Indica se o evento ocorreu no tempo t. Caso seja 1, indica falha, ou seja, o funcionário em questão deixou a empresa no tempo t. Caso seja 0, o empregado permaneceu na empresa.\n",
    "- **gender**: Gênero do empregado.\n",
    "- **age**: Idade do empregado.\n",
    "- **industry**: Indústria de atuação do empregado.\n",
    "- **profession**: Profissão do empregado.\n",
    "- **traffic**: A partir de qual Canal de Recrutamento o empregado ingressou na empresa.\n",
    "- **coach**: Presença de um coach (sim, não ou se foi o próprio supervisor).\n",
    "- **head_gender**: Gênero do supervisor ao qual o funcionario responde.\n",
    "- **greywage**: Greywage: o empregador paga apenas uma pequena quantia de salário acima do salário mínimo. White: Salário mínimo.\n",
    "- **way**: Forma pela qual o empregado se desloca para o escritório.\n",
    "- **extraversion**: Índice de extroversão.\n",
    "- **independ**: Índice de independência.\n",
    "- **selfcontrol**: Índice de autocontrole.\n",
    "- **anxiety**: Índice de ansiedade.\n",
    "- **novator**: Índice de inexperiência.\n",
    "\n",
    "A coluna ***event*** é a variável que iremos analisar, que possui as seguintes classes:\n",
    "\n",
    "0. Empregado permaneceu na empresa (ou saiu depois do término do estudo);\n",
    "1. Empregado saiu da empresa durante o tempo de estudo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Carregamento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importa bibliotecas gerais\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# importa bibliotecas de preparação dos dados\n",
    "import pandas as pd\n",
    "\n",
    "# importa bibliotecas de visualização dos dados\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carrega os dados\n",
    "dataset = pd.read_csv('turnover.csv', encoding='iso-8859-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Análise exploratória dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Análise geral dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primeiramente, vamos dar uma olhada mais geral nos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1129 entries, 0 to 1128\n",
      "Data columns (total 16 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   stag          1129 non-null   float64\n",
      " 1   event         1129 non-null   int64  \n",
      " 2   gender        1129 non-null   object \n",
      " 3   age           1129 non-null   float64\n",
      " 4   industry      1129 non-null   object \n",
      " 5   profession    1129 non-null   object \n",
      " 6   traffic       1129 non-null   object \n",
      " 7   coach         1129 non-null   object \n",
      " 8   head_gender   1129 non-null   object \n",
      " 9   greywage      1129 non-null   object \n",
      " 10  way           1129 non-null   object \n",
      " 11  extraversion  1129 non-null   float64\n",
      " 12  independ      1129 non-null   float64\n",
      " 13  selfcontrol   1129 non-null   float64\n",
      " 14  anxiety       1129 non-null   float64\n",
      " 15  novator       1129 non-null   float64\n",
      "dtypes: float64(7), int64(1), object(8)\n",
      "memory usage: 141.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# informações gerais sobre o dataset\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stag            float64\n",
       "event             int64\n",
       "gender           object\n",
       "age             float64\n",
       "industry         object\n",
       "profession       object\n",
       "traffic          object\n",
       "coach            object\n",
       "head_gender      object\n",
       "greywage         object\n",
       "way              object\n",
       "extraversion    float64\n",
       "independ        float64\n",
       "selfcontrol     float64\n",
       "anxiety         float64\n",
       "novator         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tipos de dados de cada coluna do dataset\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    .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>stag</th>\n",
       "      <th>event</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>industry</th>\n",
       "      <th>profession</th>\n",
       "      <th>traffic</th>\n",
       "      <th>coach</th>\n",
       "      <th>head_gender</th>\n",
       "      <th>greywage</th>\n",
       "      <th>way</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>independ</th>\n",
       "      <th>selfcontrol</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>novator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.030801</td>\n",
       "      <td>1</td>\n",
       "      <td>m</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.965092</td>\n",
       "      <td>1</td>\n",
       "      <td>m</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>empjs</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.934292</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>35.0</td>\n",
       "      <td>PowerGeneration</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.934292</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>35.0</td>\n",
       "      <td>PowerGeneration</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>5.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.410678</td>\n",
       "      <td>1</td>\n",
       "      <td>m</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>youjs</td>\n",
       "      <td>yes</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.969199</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>42.0</td>\n",
       "      <td>manufacture</td>\n",
       "      <td>HR</td>\n",
       "      <td>empjs</td>\n",
       "      <td>yes</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.969199</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>42.0</td>\n",
       "      <td>manufacture</td>\n",
       "      <td>HR</td>\n",
       "      <td>empjs</td>\n",
       "      <td>yes</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120.443532</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>referal</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.607803</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>empjs</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.435318</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Consult</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>youjs</td>\n",
       "      <td>yes</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>67.351129</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>40.0</td>\n",
       "      <td>manufacture</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>car</td>\n",
       "      <td>8.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20.928131</td>\n",
       "      <td>1</td>\n",
       "      <td>m</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>referal</td>\n",
       "      <td>yes</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.928131</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Consult</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>my head</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>foot</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.936345</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>24.0</td>\n",
       "      <td>State</td>\n",
       "      <td>etc</td>\n",
       "      <td>advert</td>\n",
       "      <td>yes</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>foot</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>33.577002</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>25.0</td>\n",
       "      <td>etc</td>\n",
       "      <td>HR</td>\n",
       "      <td>KA</td>\n",
       "      <td>my head</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>foot</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          stag  event gender   age         industry  profession      traffic  \\\n",
       "0     7.030801      1      m  35.0            Banks          HR  rabrecNErab   \n",
       "1    22.965092      1      m  33.0            Banks          HR        empjs   \n",
       "2    15.934292      1      f  35.0  PowerGeneration          HR  rabrecNErab   \n",
       "3    15.934292      1      f  35.0  PowerGeneration          HR  rabrecNErab   \n",
       "4     8.410678      1      m  32.0           Retail  Commercial        youjs   \n",
       "5     8.969199      1      f  42.0      manufacture          HR        empjs   \n",
       "6     8.969199      1      f  42.0      manufacture          HR        empjs   \n",
       "7   120.443532      1      f  28.0           Retail          HR      referal   \n",
       "8     8.607803      1      f  29.0            Banks          HR        empjs   \n",
       "9     4.435318      1      f  30.0          Consult   Marketing        youjs   \n",
       "10   67.351129      1      f  40.0      manufacture          HR  rabrecNErab   \n",
       "11   20.928131      1      m  23.0            Banks          HR      referal   \n",
       "12   20.928131      1      f  22.0          Consult          HR  rabrecNErab   \n",
       "13    8.936345      1      f  24.0            State         etc       advert   \n",
       "14   33.577002      1      f  25.0              etc          HR           KA   \n",
       "\n",
       "      coach head_gender greywage   way  extraversion  independ  selfcontrol  \\\n",
       "0        no           f    white   bus           6.2       4.1          5.7   \n",
       "1        no           m    white   bus           6.2       4.1          5.7   \n",
       "2        no           m    white   bus           6.2       6.2          2.6   \n",
       "3        no           m    white   bus           5.4       7.6          4.9   \n",
       "4       yes           f    white   bus           3.0       4.1          8.0   \n",
       "5       yes           m    white   bus           6.2       6.2          4.1   \n",
       "6       yes           m    white   bus           6.2       6.2          4.1   \n",
       "7        no           m    white   bus           3.8       5.5          8.0   \n",
       "8        no           f    white   bus           8.6       6.9          2.6   \n",
       "9       yes           m    white   bus           5.4       5.5          3.3   \n",
       "10       no           m    white   car           8.6       4.1          1.8   \n",
       "11      yes           m    white   bus           3.0       6.9          4.9   \n",
       "12  my head           f    white  foot           5.4       8.4          4.9   \n",
       "13      yes           f    white  foot           7.0       6.2          4.1   \n",
       "14  my head           f    white  foot           9.4       1.2          4.1   \n",
       "\n",
       "    anxiety  novator  \n",
       "0       7.1      8.3  \n",
       "1       7.1      8.3  \n",
       "2       4.8      8.3  \n",
       "3       2.5      6.7  \n",
       "4       7.1      3.7  \n",
       "5       5.6      6.7  \n",
       "6       5.6      6.7  \n",
       "7       4.0      4.4  \n",
       "8       4.0      7.5  \n",
       "9       7.9      8.3  \n",
       "10      7.1      6.7  \n",
       "11      4.8      7.5  \n",
       "12      3.3      6.0  \n",
       "13      7.1      9.0  \n",
       "14      5.6      6.7  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostra as quinze primeiras linhas do conjunto de dados\n",
    "dataset.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>stag</th>\n",
       "      <th>event</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>industry</th>\n",
       "      <th>profession</th>\n",
       "      <th>traffic</th>\n",
       "      <th>coach</th>\n",
       "      <th>head_gender</th>\n",
       "      <th>greywage</th>\n",
       "      <th>way</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>independ</th>\n",
       "      <th>selfcontrol</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>novator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>94.784394</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>referal</td>\n",
       "      <td>yes</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>foot</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>94.784394</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>referal</td>\n",
       "      <td>yes</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>foot</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>59.170431</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>youjs</td>\n",
       "      <td>yes</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>8.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>11.006160</td>\n",
       "      <td>1</td>\n",
       "      <td>m</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Consult</td>\n",
       "      <td>HR</td>\n",
       "      <td>youjs</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>6.800821</td>\n",
       "      <td>0</td>\n",
       "      <td>m</td>\n",
       "      <td>25.0</td>\n",
       "      <td>manufacture</td>\n",
       "      <td>HR</td>\n",
       "      <td>friends</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>26.677618</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>19.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>HR</td>\n",
       "      <td>youjs</td>\n",
       "      <td>yes</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>11.564682</td>\n",
       "      <td>0</td>\n",
       "      <td>m</td>\n",
       "      <td>41.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>IT</td>\n",
       "      <td>youjs</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>foot</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>53.355236</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>27.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>HR</td>\n",
       "      <td>youjs</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>12.517454</td>\n",
       "      <td>0</td>\n",
       "      <td>m</td>\n",
       "      <td>48.0</td>\n",
       "      <td>manufacture</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>friends</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>6.669405</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>youjs</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>10.611910</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>my head</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>10.611910</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>my head</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>118.800821</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Telecom</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>KA</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>49.412731</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Consult</td>\n",
       "      <td>HR</td>\n",
       "      <td>empjs</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>grey</td>\n",
       "      <td>bus</td>\n",
       "      <td>3.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>24.837782</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>youjs</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>car</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            stag  event gender   age     industry  profession      traffic  \\\n",
       "1114   94.784394      0      f  31.0       Retail          HR      referal   \n",
       "1115   94.784394      0      f  31.0       Retail          HR      referal   \n",
       "1116   59.170431      0      f  22.0       Retail          HR        youjs   \n",
       "1117   11.006160      1      m  41.0      Consult          HR        youjs   \n",
       "1118    6.800821      0      m  25.0  manufacture          HR      friends   \n",
       "1119   26.677618      0      f  19.0           IT          HR        youjs   \n",
       "1120   11.564682      0      m  41.0           IT          IT        youjs   \n",
       "1121   53.355236      0      f  27.0           IT          HR        youjs   \n",
       "1122   12.517454      0      m  48.0  manufacture   Marketing      friends   \n",
       "1123    6.669405      0      f  27.0        Banks          HR        youjs   \n",
       "1124   10.611910      0      f  41.0        Banks          HR  rabrecNErab   \n",
       "1125   10.611910      0      f  41.0        Banks          HR  rabrecNErab   \n",
       "1126  118.800821      0      f  34.0      Telecom  Accounting           KA   \n",
       "1127   49.412731      0      f  51.0      Consult          HR        empjs   \n",
       "1128   24.837782      0      f  29.0       Retail          HR        youjs   \n",
       "\n",
       "        coach head_gender greywage   way  extraversion  independ  selfcontrol  \\\n",
       "1114      yes           m    white  foot           6.2       6.2          3.3   \n",
       "1115      yes           m    white  foot           5.4       6.2          4.9   \n",
       "1116      yes           f    white   bus           8.6       4.8          2.6   \n",
       "1117       no           m    white   bus           3.8       4.1          6.4   \n",
       "1118       no           f    white   bus           5.4       4.8          6.4   \n",
       "1119      yes           f    white   bus           7.0       2.6          6.4   \n",
       "1120       no           m    white  foot           5.4       4.1          4.9   \n",
       "1121       no           m    white   bus           3.0       5.5          6.4   \n",
       "1122       no           m    white   bus           1.4       6.9          6.4   \n",
       "1123       no           f    white   bus           9.4       5.5          3.3   \n",
       "1124  my head           m    white   bus           8.6       3.4          2.6   \n",
       "1125  my head           m    white   bus           8.6       3.4          2.6   \n",
       "1126       no           f    white   bus           4.6       5.5          7.2   \n",
       "1127       no           m     grey   bus           3.8       7.6          5.7   \n",
       "1128       no           f    white   car           9.4       1.2          4.1   \n",
       "\n",
       "      anxiety  novator  \n",
       "1114      4.0      9.0  \n",
       "1115      6.3      7.5  \n",
       "1116      7.1      8.3  \n",
       "1117      9.4      6.0  \n",
       "1118      7.1      8.3  \n",
       "1119      7.1      2.1  \n",
       "1120      7.9      7.5  \n",
       "1121      6.3      4.4  \n",
       "1122      8.7      6.0  \n",
       "1123      4.0      7.5  \n",
       "1124      4.8      8.3  \n",
       "1125      4.8      8.3  \n",
       "1126      6.3      3.7  \n",
       "1127      6.3      5.2  \n",
       "1128      5.6      6.7  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostra as quinze últimas linhas do conjunto de dados\n",
    "dataset.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stag</th>\n",
       "      <th>event</th>\n",
       "      <th>age</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>independ</th>\n",
       "      <th>selfcontrol</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>novator</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1129.000000</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>1129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.627526</td>\n",
       "      <td>0.505757</td>\n",
       "      <td>31.066965</td>\n",
       "      <td>5.592383</td>\n",
       "      <td>5.478034</td>\n",
       "      <td>5.597254</td>\n",
       "      <td>5.665633</td>\n",
       "      <td>5.879628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34.096597</td>\n",
       "      <td>0.500188</td>\n",
       "      <td>6.996147</td>\n",
       "      <td>1.851637</td>\n",
       "      <td>1.703312</td>\n",
       "      <td>1.980101</td>\n",
       "      <td>1.709176</td>\n",
       "      <td>1.904016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.394251</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.728953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>4.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.344969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.318275</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179.449692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              stag        event          age  extraversion     independ  \\\n",
       "count  1129.000000  1129.000000  1129.000000   1129.000000  1129.000000   \n",
       "mean     36.627526     0.505757    31.066965      5.592383     5.478034   \n",
       "std      34.096597     0.500188     6.996147      1.851637     1.703312   \n",
       "min       0.394251     0.000000    18.000000      1.000000     1.000000   \n",
       "25%      11.728953     0.000000    26.000000      4.600000     4.100000   \n",
       "50%      24.344969     1.000000    30.000000      5.400000     5.500000   \n",
       "75%      51.318275     1.000000    36.000000      7.000000     6.900000   \n",
       "max     179.449692     1.000000    58.000000     10.000000    10.000000   \n",
       "\n",
       "       selfcontrol      anxiety      novator  \n",
       "count  1129.000000  1129.000000  1129.000000  \n",
       "mean      5.597254     5.665633     5.879628  \n",
       "std       1.980101     1.709176     1.904016  \n",
       "min       1.000000     1.700000     1.000000  \n",
       "25%       4.100000     4.800000     4.400000  \n",
       "50%       5.700000     5.600000     6.000000  \n",
       "75%       7.200000     7.100000     7.500000  \n",
       "max      10.000000    10.000000    10.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visão geral da distribuição estatística dos dados\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifica o número de registros duplicados no dataset\n",
    "dataset.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>stag</th>\n",
       "      <th>event</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>industry</th>\n",
       "      <th>profession</th>\n",
       "      <th>traffic</th>\n",
       "      <th>coach</th>\n",
       "      <th>head_gender</th>\n",
       "      <th>greywage</th>\n",
       "      <th>way</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>independ</th>\n",
       "      <th>selfcontrol</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>novator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.969199</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>42.0</td>\n",
       "      <td>manufacture</td>\n",
       "      <td>HR</td>\n",
       "      <td>empjs</td>\n",
       "      <td>yes</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>22.735113</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>KA</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>car</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>22.735113</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>KA</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>car</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>15.572895</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>youjs</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>foot</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>78.685832</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>grey</td>\n",
       "      <td>bus</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>12.024641</td>\n",
       "      <td>1</td>\n",
       "      <td>m</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Sales</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>grey</td>\n",
       "      <td>car</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>40.312115</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>29.0</td>\n",
       "      <td>manufacture</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>my head</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>foot</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>1.117043</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Pharma</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>car</td>\n",
       "      <td>4.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>39.227926</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>friends</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>car</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>39.227926</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Retail</td>\n",
       "      <td>HR</td>\n",
       "      <td>friends</td>\n",
       "      <td>no</td>\n",
       "      <td>f</td>\n",
       "      <td>white</td>\n",
       "      <td>car</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>12.681725</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>36.0</td>\n",
       "      <td>PowerGeneration</td>\n",
       "      <td>HR</td>\n",
       "      <td>youjs</td>\n",
       "      <td>my head</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>89.363450</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>22.0</td>\n",
       "      <td>PowerGeneration</td>\n",
       "      <td>HR</td>\n",
       "      <td>recNErab</td>\n",
       "      <td>no</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>10.611910</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Banks</td>\n",
       "      <td>HR</td>\n",
       "      <td>rabrecNErab</td>\n",
       "      <td>my head</td>\n",
       "      <td>m</td>\n",
       "      <td>white</td>\n",
       "      <td>bus</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           stag  event gender   age         industry profession      traffic  \\\n",
       "6      8.969199      1      f  42.0      manufacture         HR        empjs   \n",
       "38    22.735113      1      f  34.0           Retail         HR           KA   \n",
       "39    22.735113      1      f  34.0           Retail         HR           KA   \n",
       "61    15.572895      1      f  24.0           Retail         HR        youjs   \n",
       "144   78.685832      1      f  31.0            Banks         HR  rabrecNErab   \n",
       "273   12.024641      1      m  30.0           Retail      Sales  rabrecNErab   \n",
       "345   40.312115      0      f  29.0      manufacture         HR  rabrecNErab   \n",
       "462    1.117043      1      f  54.0           Pharma         HR  rabrecNErab   \n",
       "691   39.227926      1      f  37.0           Retail         HR      friends   \n",
       "692   39.227926      1      f  37.0           Retail         HR      friends   \n",
       "766   12.681725      1      f  36.0  PowerGeneration         HR        youjs   \n",
       "1095  89.363450      1      f  22.0  PowerGeneration         HR     recNErab   \n",
       "1125  10.611910      0      f  41.0            Banks         HR  rabrecNErab   \n",
       "\n",
       "        coach head_gender greywage   way  extraversion  independ  selfcontrol  \\\n",
       "6         yes           m    white   bus           6.2       6.2          4.1   \n",
       "38         no           m    white   car           5.4       5.5          4.9   \n",
       "39         no           m    white   car           6.2       6.2          4.9   \n",
       "61         no           f    white  foot           6.2       7.6          3.3   \n",
       "144        no           f     grey   bus           3.8       4.1          7.2   \n",
       "273        no           m     grey   car           5.4       4.1          4.9   \n",
       "345   my head           m    white  foot           6.2       3.4          4.1   \n",
       "462        no           m    white   car           4.6       8.4          5.7   \n",
       "691        no           f    white   car           6.2       6.9          4.1   \n",
       "692        no           f    white   car           3.8       9.1          4.9   \n",
       "766   my head           m    white   bus           6.2       5.5          5.7   \n",
       "1095       no           m    white   bus           7.0       4.8          4.9   \n",
       "1125  my head           m    white   bus           8.6       3.4          2.6   \n",
       "\n",
       "      anxiety  novator  \n",
       "6         5.6      6.7  \n",
       "38        4.8      4.4  \n",
       "39        3.3      2.9  \n",
       "61        3.3      6.7  \n",
       "144       8.7      2.1  \n",
       "273       6.3      6.0  \n",
       "345       7.9      7.5  \n",
       "462       4.0      4.4  \n",
       "691       6.3      8.3  \n",
       "692       5.6      7.5  \n",
       "766       5.6      6.7  \n",
       "1095      6.3      7.5  \n",
       "1125      4.8      8.3  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifica quais são os registros duplicados em todas as colunas do dataset\n",
    "dataset[dataset.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificando se há algum valor faltante (missing) no dataset\n",
    "dataset.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gender' 'industry' 'profession' 'traffic' 'coach' 'head_gender'\n",
      " 'greywage' 'way'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# identificamos as variáveis categóricas do conjunto de dados\n",
    "df_objects = dataset.select_dtypes(include=['object'])\n",
    "print(df_objects.columns.values,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : ['m' 'f'] \n",
      "\n",
      "industry : ['Banks' 'PowerGeneration' 'Retail' 'manufacture' 'Consult' 'State' 'etc'\n",
      " 'Building' 'IT' ' HoReCa' 'Telecom' 'Pharma' 'Mining' 'transport'\n",
      " 'Agriculture' 'RealEstate'] \n",
      "\n",
      "profession : ['HR' 'Commercial' 'Marketing' 'etc' 'Sales' 'BusinessDevelopment'\n",
      " 'Finanñe' 'Teaching' 'manage' 'IT' 'Law' 'Consult' 'Engineer' 'PR'\n",
      " 'Accounting'] \n",
      "\n",
      "traffic : ['rabrecNErab' 'empjs' 'youjs' 'referal' 'advert' 'KA' 'recNErab'\n",
      " 'friends'] \n",
      "\n",
      "coach : ['no' 'yes' 'my head'] \n",
      "\n",
      "head_gender : ['f' 'm'] \n",
      "\n",
      "greywage : ['white' 'grey'] \n",
      "\n",
      "way : ['bus' 'car' 'foot'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# identificamos os valores únicos de cada variável categórica\n",
    "for i in df_objects.columns.values:\n",
    "    print(i,':', dataset[i].unique(),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['stag' 'event' 'age' 'extraversion' 'independ' 'selfcontrol' 'anxiety'\n",
      " 'novator'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# identificamos as variáveis numéricas do conjunto de dados\n",
    "df_numerical = dataset.select_dtypes(exclude=['object'])\n",
    "print(df_numerical.columns.values,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograma dos dados numéricos\n",
    "num_bins = 10\n",
    "histograma = dataset.hist(bins=num_bins, figsize=(20,20), color='darkred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observando as informações acima, podemos dizer que:\n",
    "- O *dataset* conta com **1.129** registros e **16** variáveis, sendo **7** do tipo *float* e **1** do tipo *int* e **8** do tipo object.\n",
    "- Existem **13** registros duplicados.\n",
    "- Não existem valores *missing*.\n",
    "- A média de idade dos empregados é de aproximadamente **31** anos.\n",
    "- A média de tempo até o abandono da empresa (ou até a censura do estudo) é de aproximadamente **37** semanas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Análise estatística dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos começar analisando a rotatividade dos empregados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    571\n",
      "0    558\n",
      "Name: event, dtype: int64 \n",
      "\n",
      "Empregados que saíram da empresa: 50.6%\n",
      "Empregados que permaneceram na empresa: 49.4%\n"
     ]
    }
   ],
   "source": [
    "# verificação da rotatividade de empregados\n",
    "event = dataset['event'].value_counts()\n",
    "percent = [round(i*100/event.sum(),1) for i in dataset['event'].value_counts()]\n",
    "print(event,'\\n')\n",
    "print('Empregados que saíram da empresa: {}%'.format(percent[0]))\n",
    "print('Empregados que permaneceram na empresa: {}%'.format(percent[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificamos que a maioria dos empregados preferem sair da empresa a permanecer (50,6%).\n",
    "\n",
    "Vamos ver, então, as médias dos valores numéricos para cada uma das opções (abandono ou permanência)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>stag</th>\n",
       "      <th>age</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>independ</th>\n",
       "      <th>selfcontrol</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>novator</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38.294819</td>\n",
       "      <td>31.411829</td>\n",
       "      <td>5.563441</td>\n",
       "      <td>5.388710</td>\n",
       "      <td>5.677419</td>\n",
       "      <td>5.774910</td>\n",
       "      <td>5.866487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.998191</td>\n",
       "      <td>30.729952</td>\n",
       "      <td>5.620665</td>\n",
       "      <td>5.565324</td>\n",
       "      <td>5.518914</td>\n",
       "      <td>5.558844</td>\n",
       "      <td>5.892469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            stag        age  extraversion  independ  selfcontrol   anxiety  \\\n",
       "event                                                                        \n",
       "0      38.294819  31.411829      5.563441  5.388710     5.677419  5.774910   \n",
       "1      34.998191  30.729952      5.620665  5.565324     5.518914  5.558844   \n",
       "\n",
       "        novator  \n",
       "event            \n",
       "0      5.866487  \n",
       "1      5.892469  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# médias dos valores númericos agrupados pela variável analisada (event)\n",
    "dataset.groupby('event').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algumas considerações gerais sobre a tabela de médias das variáveis numéricas:\n",
    "\n",
    "- Pessoas com menor tempo de emprego estão abandonando a empresa.\n",
    "- Os empregados mais extrovertidos, independentes, novatos e com menor autocontrole tendem a abandonar a empresa.\n",
    "- Pessoas mais velhas tendem a permanecer na empresa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows    = 3\n",
    "columns = 3\n",
    "c       = 1 # Inicializar contador dos gráficos\n",
    "\n",
    "# Gráficos de barras das variáveis categóricas\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "for i in df_objects.columns.values:\n",
    "\n",
    "    ax = plt.subplot(rows,columns,c)\n",
    "    pd.crosstab(dataset[i],dataset.event).plot(kind='bar',ax=ax, color=['darkred','gold'])\n",
    "    plt.title('Rotatividade por {}'.format(i))\n",
    "    plt.ylabel('Empregados')\n",
    "    plt.xlabel('{}'.format(i))\n",
    "    plt.tight_layout(pad=4.0)\n",
    "    c = c + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir dos gráficos, podemos identificar as características das pessoas que estão saindo mais em cada variável:\n",
    "\n",
    "- gender: f;\n",
    "- industry: Banks;\n",
    "- profession: Teaching;\n",
    "- traffic: referal;\n",
    "- coach: yes;\n",
    "- head_gender: m;\n",
    "- greywage: grey;\n",
    "- way: car.\n",
    "\n",
    "A seguir, uma função para verificar o percentual de abandono para uma dada variável."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>event</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>417</td>\n",
       "      <td>436</td>\n",
       "      <td>51.113716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>141</td>\n",
       "      <td>135</td>\n",
       "      <td>48.913043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "event     0    1          %\n",
       "gender                     \n",
       "f       417  436  51.113716\n",
       "m       141  135  48.913043"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# função que verifica o percentual de abandono para uma dada variável\n",
    "def percent_event(column):\n",
    "    df = pd.crosstab(column,dataset.event)\n",
    "    df['%'] = (df.loc[slice(None), 1] / df.sum(axis=1)) * 100\n",
    "    return df.sort_values(by='%', ascending=False)\n",
    "\n",
    "percent_event(dataset['gender'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui plotamos um gráfico com o estimador Kaplan Meier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importa KaplanMeierFitter da biblioteca lifelines\n",
    "from lifelines import KaplanMeierFitter\n",
    "\n",
    "# treina estimador Kaplan Meier e plota o gráfico\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(dataset.stag, dataset.event,label='Estimador Kaplan Meier')\n",
    "kmf.plot_survival_function(figsize=(15,10), color='darkred')\n",
    "plt.xlabel('Semanas')\n",
    "plt.ylabel('Probabilidade de permanência');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Transformação dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inicialmente, iremos excluir os registros duplicados.\n",
    "\n",
    "A exclusão dos registros duplicados, quando estes reflem a distribuição desses dados no mundo real, poderia distorcer a probabilidade *a priori* de cada caso, afetando, principalmente, a utilização de métodos bayesianos, para os quais a frequência importa.\n",
    "\n",
    "Se os dados de treinamento são uma amostra representativa do mundo real, então não queremos que essa distorção aconteça, porque, na verdade, estaríamos treinando um modelo para um mundo ligeiramente diferente (com probabilidades diferentes).\n",
    "\n",
    "Contudo, considerando o fato de que são todas as 16 variáveis idênticas em cada registro, sendo que algumas refletem notas em testes com identidade de inteiros e decimais (além de todas as outras variáveis coincidentes), parece muito provável que sejam dados duplicados do mesmo empregado.\n",
    "\n",
    "Em virtude disso, como forma de evitar viés indevido no resultado por eventual *oversampling*, iremos remover os registros duplicados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1116 entries, 0 to 1128\n",
      "Data columns (total 16 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   stag          1116 non-null   float64\n",
      " 1   event         1116 non-null   int64  \n",
      " 2   gender        1116 non-null   object \n",
      " 3   age           1116 non-null   float64\n",
      " 4   industry      1116 non-null   object \n",
      " 5   profession    1116 non-null   object \n",
      " 6   traffic       1116 non-null   object \n",
      " 7   coach         1116 non-null   object \n",
      " 8   head_gender   1116 non-null   object \n",
      " 9   greywage      1116 non-null   object \n",
      " 10  way           1116 non-null   object \n",
      " 11  extraversion  1116 non-null   float64\n",
      " 12  independ      1116 non-null   float64\n",
      " 13  selfcontrol   1116 non-null   float64\n",
      " 14  anxiety       1116 non-null   float64\n",
      " 15  novator       1116 non-null   float64\n",
      "dtypes: float64(7), int64(1), object(8)\n",
      "memory usage: 148.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# elimina os registros duplicados do dataset\n",
    "df1 = dataset.drop_duplicates()\n",
    "\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Foram excluídos **13** registros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Análise exploratória mais específica dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora vamos verificar a correlação entre as variáveis.\n",
    "\n",
    "Uma coisa que temos que ter em mente é que correlação não implica causalidade. Ou seja, não podemos confundir correlação com causa e efeito.\n",
    "\n",
    "É interessante, contudo, a análise das correlações entre variáveis porque são fenômenos aparentemente relacionados que acontecem ao mesmo tempo e podem suscitar perguntas a serem investigadas, mas elas não são, em si, respostas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plota heatmap de correlação entre dados númericos\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Correlações entre variáveis', y=1.05, size=15)\n",
    "sns.heatmap(df1.corr(), cmap='YlOrRd', fmt='.2f', linewidths=0.1, vmax=1.0, square=True, linecolor='white');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O método utilizado para aferir a correlação das variáveis foi o de Pearson.\n",
    "\n",
    "O coeficiente de correlação de Pearson mede o grau da correlação (e a direção dessa correlação, se positiva ou negativa) entre duas variáveis de escala métrica.\n",
    "\n",
    "Este coeficiente, normalmente representado por ρ assume apenas valores entre -1 e 1.\n",
    "\n",
    "- *p = 1* significa uma correlação perfeita positiva entre as duas variáveis;\n",
    "- *p = -1* significa uma correlação negativa perfeita entre as duas variáveis - Isto é, se uma aumenta, a outra sempre diminui;\n",
    "- *p = 0* significa que as duas variáveis não dependem linearmente uma da outra. No entanto, pode existir uma dependência não linear. Assim, o resultado *p = 0* deve ser investigado por outros meios.\n",
    "\n",
    "Podemos interpretar *p* da seguinte forma:\n",
    "\n",
    "- 0,9 para mais ou para menos indica uma correlação muito forte;\n",
    "- 0,7 a 0,9 positivo ou negativo indica uma correlação forte;\n",
    "- 0,5 a 0,7 positivo ou negativo indica uma correlação moderada;\n",
    "- 0,3 a 0,5 positivo ou negativo indica uma correlação fraca;\n",
    "- 0 a 0,3 positivo ou negativo indica uma correlação desprezível.\n",
    "\n",
    "Podemos perceber que não há uma correlação muito significativa entre a variável analisada e as demais variáveis do *dataset*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora vamos pegar todas as variáveis categóricas e transformá-las em variáveis *dummy*.\n",
    "\n",
    "Uma variável *dummy* é uma variável numérica usada na análise de regressão para representar subgrupos da amostra no estudo. Ela é freqüentemente usada para distinguir diferentes grupos de tratamento. No caso mais simples, [0,1] usaríamos uma variável *dummy* onde uma pessoa recebe um valor de \"0\" se ela está no grupo de controle ou \"1\" se ela está no grupo tratado.\n",
    "\n",
    "Aqui em nosso estudo, iremos transformar as variáveis *gender*, *industry*, *profession*, *traffic*, *coach*, *head_gender*, *greywage* e *way* em variáveis *dummy*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1116 entries, 0 to 1128\n",
      "Data columns (total 59 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   stag                            1116 non-null   float64\n",
      " 1   event                           1116 non-null   int64  \n",
      " 2   age                             1116 non-null   float64\n",
      " 3   extraversion                    1116 non-null   float64\n",
      " 4   independ                        1116 non-null   float64\n",
      " 5   selfcontrol                     1116 non-null   float64\n",
      " 6   anxiety                         1116 non-null   float64\n",
      " 7   novator                         1116 non-null   float64\n",
      " 8   gender_f                        1116 non-null   uint8  \n",
      " 9   gender_m                        1116 non-null   uint8  \n",
      " 10  industry_ HoReCa                1116 non-null   uint8  \n",
      " 11  industry_Agriculture            1116 non-null   uint8  \n",
      " 12  industry_Banks                  1116 non-null   uint8  \n",
      " 13  industry_Building               1116 non-null   uint8  \n",
      " 14  industry_Consult                1116 non-null   uint8  \n",
      " 15  industry_IT                     1116 non-null   uint8  \n",
      " 16  industry_Mining                 1116 non-null   uint8  \n",
      " 17  industry_Pharma                 1116 non-null   uint8  \n",
      " 18  industry_PowerGeneration        1116 non-null   uint8  \n",
      " 19  industry_RealEstate             1116 non-null   uint8  \n",
      " 20  industry_Retail                 1116 non-null   uint8  \n",
      " 21  industry_State                  1116 non-null   uint8  \n",
      " 22  industry_Telecom                1116 non-null   uint8  \n",
      " 23  industry_etc                    1116 non-null   uint8  \n",
      " 24  industry_manufacture            1116 non-null   uint8  \n",
      " 25  industry_transport              1116 non-null   uint8  \n",
      " 26  profession_Accounting           1116 non-null   uint8  \n",
      " 27  profession_BusinessDevelopment  1116 non-null   uint8  \n",
      " 28  profession_Commercial           1116 non-null   uint8  \n",
      " 29  profession_Consult              1116 non-null   uint8  \n",
      " 30  profession_Engineer             1116 non-null   uint8  \n",
      " 31  profession_Finanñe              1116 non-null   uint8  \n",
      " 32  profession_HR                   1116 non-null   uint8  \n",
      " 33  profession_IT                   1116 non-null   uint8  \n",
      " 34  profession_Law                  1116 non-null   uint8  \n",
      " 35  profession_Marketing            1116 non-null   uint8  \n",
      " 36  profession_PR                   1116 non-null   uint8  \n",
      " 37  profession_Sales                1116 non-null   uint8  \n",
      " 38  profession_Teaching             1116 non-null   uint8  \n",
      " 39  profession_etc                  1116 non-null   uint8  \n",
      " 40  profession_manage               1116 non-null   uint8  \n",
      " 41  traffic_KA                      1116 non-null   uint8  \n",
      " 42  traffic_advert                  1116 non-null   uint8  \n",
      " 43  traffic_empjs                   1116 non-null   uint8  \n",
      " 44  traffic_friends                 1116 non-null   uint8  \n",
      " 45  traffic_rabrecNErab             1116 non-null   uint8  \n",
      " 46  traffic_recNErab                1116 non-null   uint8  \n",
      " 47  traffic_referal                 1116 non-null   uint8  \n",
      " 48  traffic_youjs                   1116 non-null   uint8  \n",
      " 49  coach_my head                   1116 non-null   uint8  \n",
      " 50  coach_no                        1116 non-null   uint8  \n",
      " 51  coach_yes                       1116 non-null   uint8  \n",
      " 52  head_gender_f                   1116 non-null   uint8  \n",
      " 53  head_gender_m                   1116 non-null   uint8  \n",
      " 54  greywage_grey                   1116 non-null   uint8  \n",
      " 55  greywage_white                  1116 non-null   uint8  \n",
      " 56  way_bus                         1116 non-null   uint8  \n",
      " 57  way_car                         1116 non-null   uint8  \n",
      " 58  way_foot                        1116 non-null   uint8  \n",
      "dtypes: float64(7), int64(1), uint8(51)\n",
      "memory usage: 134.1 KB\n"
     ]
    }
   ],
   "source": [
    "# transforma variáveis categóricas em binárias\n",
    "df2 = pd.get_dummies(df1)\n",
    "\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, vamos comparar as classes das variáveis categóricas dentro da linha do tempo, relacionando-as com a variável *event*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# treina estimador Kaplan Meier e plota o gráfico\n",
    "kmf = KaplanMeierFitter()\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "cohort1 = df2[df2['coach_yes'] == 1]\n",
    "kmf.fit(cohort1.stag, cohort1.event,label='Estimador Kaplan Meier - empregados com coaching')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='darkred')\n",
    "\n",
    "cohort2 = df2[df2['coach_no'] == 1]\n",
    "kmf.fit(cohort2.stag, cohort2.event,label='Estimador Kaplan Meier - empregados sem coaching')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='darkgreen')\n",
    "\n",
    "cohort3 = df2[df2['coach_my head'] == 1]\n",
    "kmf.fit(cohort3.stag, cohort3.event,label='Estimador Kaplan Meier - empregados com supervisor')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='gold')\n",
    "\n",
    "# inclui comentário no gráfico\n",
    "plt.text(87,0.7,'Supervisor fazendo coaching é relevante somente',size=15,color='gray')\n",
    "plt.text(87,0.67,'até este ponto no tempo',size=15,color='gray')\n",
    "plt.axvline(x=86.5,color='gray',linestyle='--')\n",
    "\n",
    "# inclui labels nos eixos\n",
    "plt.xlabel('Semanas')\n",
    "plt.ylabel('Probabilidade de permanência');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui nós vemos que, na empresa estudada, a presença de um *coach* externo para auxiliar os empregados nunca foi muito relevante para afetar o *turnover*.\n",
    "\n",
    "Já se tivermos o supervisor do empregado como *coach* o resultado foi que os empregados permaneceram mais tempo na empresa.\n",
    "\n",
    "Contudo, essa diferença foi vista somente até a semana 86 do estudo, quando o *coaching* passou a não mais fazer diferença significativa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# treina estimador Kaplan Meier e plota o gráfico\n",
    "kmf = KaplanMeierFitter()\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "cohort1 = df2[df2['gender_f'] == 1]\n",
    "kmf.fit(cohort1.stag, cohort1.event,label='Estimador Kaplan Meier - empregados mulheres')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='darkred')\n",
    "\n",
    "cohort2 = df2[df2['gender_m'] == 1]\n",
    "kmf.fit(cohort2.stag, cohort2.event,label='Estimador Kaplan Meier - empregados homens')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='gold')\n",
    "\n",
    "# inclui labels nos eixos\n",
    "plt.xlabel('Semanas')\n",
    "plt.ylabel('Probabilidade de permanência');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir desse gráfico, percebemos que, nesta empresa, o *turnover* é maior no grupo de empregados mulheres, em relação aos empregados homens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# treina estimador Kaplan Meier e plota o gráfico\n",
    "kmf = KaplanMeierFitter()\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "cohort1 = df2[df2['head_gender_f'] == 1]\n",
    "kmf.fit(cohort1.stag, cohort1.event,label='Estimador Kaplan Meier - empregados com supervisores mulheres')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='darkred')\n",
    "\n",
    "cohort2 = df2[df2['head_gender_m'] == 1]\n",
    "kmf.fit(cohort2.stag, cohort2.event,label='Estimador Kaplan Meier - empregados com supervisores homens')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='gold')\n",
    "\n",
    "# inclui comentário no gráfico\n",
    "plt.text(87,0.7,'Supervisora mulher retém mais',size=15,color='gray')\n",
    "plt.text(87,0.67,'até este ponto no tempo',size=15,color='gray')\n",
    "plt.axvline(x=134,color='gray',linestyle='--')\n",
    "\n",
    "# inclui labels nos eixos\n",
    "plt.xlabel('Semanas')\n",
    "plt.ylabel('Probabilidade de permanência');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Já neste gráfico, percebemos que, nesta empresa, quando o supervisor do empregado é uma mulher, o abandono por parte dos empregados é menor até a semana 134."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# treina estimador Kaplan Meier e plota o gráfico\n",
    "kmf = KaplanMeierFitter()\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "cohort1 = df2[df2['greywage_grey'] == 1]\n",
    "kmf.fit(cohort1.stag, cohort1.event,label='Estimador Kaplan Meier - empregados que recebem pouco mais de um salário mínimo')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='darkred')\n",
    "\n",
    "cohort2 = df2[df2['greywage_white'] == 1]\n",
    "kmf.fit(cohort2.stag, cohort2.event,label='Estimador Kaplan Meier - empregados que recebem um salário mínimo')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='gold')\n",
    "\n",
    "# inclui labels nos eixos\n",
    "plt.xlabel('Semanas')\n",
    "plt.ylabel('Probabilidade de permanência');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Através deste gráfico, podemos afirmar que, nesta empresa, os empregados que recebem pouco mais de um salário mínimo possuem uma tendência de abandono bem maior do que aqueles que recebem um salário mínimo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# treina estimador Kaplan Meier e plota o gráfico\n",
    "kmf = KaplanMeierFitter()\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "cohort1 = df2[df2['way_bus'] == 1]\n",
    "kmf.fit(cohort1.stag, cohort1.event,label='Estimador Kaplan Meier - empregados que vão de ônibus')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='darkred')\n",
    "\n",
    "cohort2 = df2[df2['way_car'] == 1]\n",
    "kmf.fit(cohort2.stag, cohort2.event,label='Estimador Kaplan Meier - empregados que vão de carro')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='gold')\n",
    "\n",
    "cohort3 = df2[df2['way_foot'] == 1]\n",
    "kmf.fit(cohort3.stag, cohort3.event,label='Estimador Kaplan Meier - empregados que vão à pé')\n",
    "kmf.plot_survival_function(ax=ax,ci_show=False, color='darkgreen')\n",
    "\n",
    "# inclui labels nos eixos\n",
    "plt.xlabel('Semanas')\n",
    "plt.ylabel('Probabilidade de permanência');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, neste gráfico podemos afirmar que, nesta empresa, os empregados que vão à pé ao trabalho têm uma probabilidade maior de permanecer na empresa por mais tempo e aqueles que vão de ônibus possuem uma probabilidade menor.\n",
    "\n",
    "Além disso, podemos fazer essa mesma análise levando em consideração variáveis específicas, como, por exemplo, verificar somente os profissionais da área de TI dessa empresa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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>stag</th>\n",
       "      <th>event</th>\n",
       "      <th>age</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>independ</th>\n",
       "      <th>selfcontrol</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>novator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>123.334702</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>123.334702</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>13.174538</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>13.174538</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>15.540041</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>9.921971</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>79.540041</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>6.800821</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>79.802875</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>11.564682</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            stag  event   age  extraversion  independ  selfcontrol  anxiety  \\\n",
       "108   123.334702      1  22.0           4.6       4.8          6.4      7.1   \n",
       "109   123.334702      1  22.0           4.6       6.2          6.4      4.0   \n",
       "110    13.174538      1  28.0           8.6       6.2          1.0      5.6   \n",
       "111    13.174538      1  28.0           7.0       6.2          2.6      5.6   \n",
       "118    15.540041      1  38.0           6.2       4.1          1.8      8.7   \n",
       "...          ...    ...   ...           ...       ...          ...      ...   \n",
       "1097    9.921971      0  20.0           9.4       4.8          2.6      4.8   \n",
       "1105   79.540041      0  34.0           3.8       5.5          6.4      4.0   \n",
       "1112    6.800821      0  31.0           7.8       3.4          1.8      8.7   \n",
       "1113   79.802875      0  34.0           3.8       5.5          6.4      4.0   \n",
       "1120   11.564682      0  41.0           5.4       4.1          4.9      7.9   \n",
       "\n",
       "      novator  \n",
       "108       6.0  \n",
       "109       6.7  \n",
       "110       6.7  \n",
       "111       8.3  \n",
       "118       8.3  \n",
       "...       ...  \n",
       "1097      9.0  \n",
       "1105      4.4  \n",
       "1112      6.7  \n",
       "1113      4.4  \n",
       "1120      7.5  \n",
       "\n",
       "[74 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cria subset só com empregados da TI\n",
    "features_ti = dataset[dataset.profession == 'IT'][['stag','event','age','extraversion','independ','selfcontrol','anxiety','novator']].copy()\n",
    "\n",
    "features_ti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'stag'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'event'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-77.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2021-11-29 01:32:30 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extraversion</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>independ</th>\n",
       "      <td>0.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selfcontrol</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.35</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anxiety</th>\n",
       "      <td>0.26</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.09</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>novator</th>\n",
       "      <td>0.12</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>166.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>6.91 on 6 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\n",
       "covariate    &       &            &           &                 &                 &                      &                      &       &      &           \\\\\n",
       "\\midrule\n",
       "age          &  0.04 &       1.05 &      0.04 &           -0.03 &            0.12 &                 0.97 &                 1.13 &  1.15 & 0.25 &      2.01 \\\\\n",
       "extraversion &  0.07 &       1.07 &      0.19 &           -0.30 &            0.45 &                 0.74 &                 1.56 &  0.37 & 0.71 &      0.49 \\\\\n",
       "independ     &  0.16 &       1.17 &      0.16 &           -0.16 &            0.48 &                 0.85 &                 1.62 &  0.96 & 0.33 &      1.58 \\\\\n",
       "selfcontrol  & -0.02 &       0.98 &      0.16 &           -0.34 &            0.30 &                 0.71 &                 1.35 & -0.11 & 0.91 &      0.13 \\\\\n",
       "anxiety      &  0.26 &       1.30 &      0.15 &           -0.04 &            0.56 &                 0.96 &                 1.74 &  1.72 & 0.09 &      3.55 \\\\\n",
       "novator      &  0.12 &       1.13 &      0.19 &           -0.25 &            0.50 &                 0.78 &                 1.65 &  0.64 & 0.52 &      0.94 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 74 total observations, 49 right-censored observations>\n",
       "             duration col = 'stag'\n",
       "                event col = 'event'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 74\n",
       "number of events observed = 25\n",
       "   partial log-likelihood = -77.29\n",
       "         time fit was run = 2021-11-29 01:32:30 UTC\n",
       "\n",
       "---\n",
       "               coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "covariate                                                                                                            \n",
       "age            0.04       1.05       0.04            -0.03             0.12                 0.97                 1.13\n",
       "extraversion   0.07       1.07       0.19            -0.30             0.45                 0.74                 1.56\n",
       "independ       0.16       1.17       0.16            -0.16             0.48                 0.85                 1.62\n",
       "selfcontrol   -0.02       0.98       0.16            -0.34             0.30                 0.71                 1.35\n",
       "anxiety        0.26       1.30       0.15            -0.04             0.56                 0.96                 1.74\n",
       "novator        0.12       1.13       0.19            -0.25             0.50                 0.78                 1.65\n",
       "\n",
       "                 z    p   -log2(p)\n",
       "covariate                         \n",
       "age           1.15 0.25       2.01\n",
       "extraversion  0.37 0.71       0.49\n",
       "independ      0.96 0.33       1.58\n",
       "selfcontrol  -0.11 0.91       0.13\n",
       "anxiety       1.72 0.09       3.55\n",
       "novator       0.64 0.52       0.94\n",
       "---\n",
       "Concordance = 0.67\n",
       "Partial AIC = 166.58\n",
       "log-likelihood ratio test = 6.91 on 6 df\n",
       "-log2(p) of ll-ratio test = 1.60"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importa CoxPHFitter da biblioteca lifelines\n",
    "from lifelines import CoxPHFitter\n",
    "\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(features_ti, duration_col='stag', event_col='event')\n",
    "cph.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, vamos fazer um gráfico limitando nosso escopo de análise por valor da covariável.\n",
    "\n",
    "Podemos verificar somente aqueles empregados que obtiveram notas \"0\", \"5\" e \"10\" no exame de independência."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plota gráfico de valores 0, 5 e 10 da variável independ\n",
    "cph.plot_partial_effects_on_outcome(covariates='independ', values=[0,5,10], cmap='YlOrRd');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos verificar também somente os empregados que possuem as idades \"30\", \"40\" e \"50\" anos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plota gráfico de valores 30, 40 e 50 da variável age\n",
    "cph.plot_partial_effects_on_outcome(covariates='age', values=[30,40,50], cmap='YlOrRd');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Previsão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, podemos verificar, daqueles empregados da TI que permaneceram na empresa ao término do estudo, qual é o tempo estimado restante de permanência na empresa (dado pela sobrevida mediana)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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>stag</th>\n",
       "      <th>event</th>\n",
       "      <th>age</th>\n",
       "      <th>extraversion</th>\n",
       "      <th>independ</th>\n",
       "      <th>selfcontrol</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>novator</th>\n",
       "      <th>median_remaining_lifetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>73.987680</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>22.373717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>90.907598</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.373717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>52.271047</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.7</td>\n",
       "      <td>26.020534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>48.459959</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.963039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>93.897331</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>29.963039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>18.924025</td>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>35.778234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>19.121150</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>35.778234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>5.158111</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>38.537988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>11.564682</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>38.537988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1.971253</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>40.114990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>6.800821</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>44.024641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>54.965092</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>44.024641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>5.585216</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>48.459959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>8.180698</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>48.459959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>3.121150</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>50.891170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>44.484600</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>52.271047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>57.527721</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>53.979466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>38.537988</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>57.527721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>40.114990</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.9</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>57.527721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>3.482546</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>61.075975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>36.599589</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>61.075975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>39.293634</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>61.075975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>61.075975</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>61.075975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>31.507187</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>64.197125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>64.197125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>32.328542</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.7</td>\n",
       "      <td>64.197125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>5.650924</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>72.574949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>8.410678</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72.574949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>23.293634</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>72.574949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>23.293634</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>72.574949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>27.367556</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>72.574949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>28.024641</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>72.574949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>29.963039</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>72.574949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>19.318275</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>79.014374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>6.275154</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>90.907598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>9.494867</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>90.907598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>9.921971</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>90.907598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>12.320329</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.907598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>16.854209</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>90.907598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>3.121150</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>93.897331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>1.708419</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>123.334702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>16.624230</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.7</td>\n",
       "      <td>123.334702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>64.197125</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>72.574949</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>79.540041</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>79.540041</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>79.802875</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>131.613963</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>132.402464</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            stag  event   age  extraversion  independ  selfcontrol  anxiety  \\\n",
       "575    73.987680      0  33.0           7.0       6.2          3.3      6.3   \n",
       "1096   90.907598      0  32.0           7.0       4.1          4.1      5.6   \n",
       "753    52.271047      0  27.0           3.8       6.2          4.1      9.4   \n",
       "406    48.459959      0  35.0           7.0       5.5          2.6      5.6   \n",
       "927    93.897331      0  23.0           5.4       4.8          5.7      6.3   \n",
       "717    18.924025      0  43.0           6.2       4.1          5.7      7.1   \n",
       "841    19.121150      0  39.0           7.8       3.4          3.3      7.1   \n",
       "764     5.158111      0  40.0           3.8       4.1          7.2      8.7   \n",
       "1120   11.564682      0  41.0           5.4       4.1          4.9      7.9   \n",
       "544     1.971253      0  31.0           6.2       4.1          2.6      8.7   \n",
       "1112    6.800821      0  31.0           7.8       3.4          1.8      8.7   \n",
       "1011   54.965092      0  22.0           5.4       5.5          6.4      7.9   \n",
       "750     5.585216      0  37.0           6.2       6.9          4.1      4.0   \n",
       "1073    8.180698      0  30.0           3.8       5.5          2.6      9.4   \n",
       "1067    3.121150      0  32.0           9.4       6.2          4.1      4.8   \n",
       "297    44.484600      0  22.0           4.6       2.6          6.4      9.4   \n",
       "1016   57.527721      0  24.0           6.2       3.4          2.6      7.1   \n",
       "760    38.537988      0  40.0           3.8       6.2          8.7      6.3   \n",
       "733    40.114990      0  41.0           1.4       6.9          9.5      6.3   \n",
       "919     3.482546      0  37.0           4.6       6.2          4.9      6.3   \n",
       "768    36.599589      0  24.0           5.4       1.9          6.4      9.4   \n",
       "552    39.293634      0  21.0           3.8       5.5          5.7      7.9   \n",
       "751    61.075975      0  29.0           1.0       5.5         10.0      7.1   \n",
       "763    31.507187      0  29.0           3.8       2.6          6.4      7.9   \n",
       "993    32.000000      0  22.0           7.0       3.4          4.9      7.1   \n",
       "734    32.328542      0  49.0           3.0       3.4          8.7      5.6   \n",
       "754     5.650924      0  42.0           1.4       7.6          8.7      4.8   \n",
       "817     8.410678      0  24.0           3.8       4.1          4.1      8.7   \n",
       "386    23.293634      0  29.0           9.4       6.2          4.1      4.8   \n",
       "387    23.293634      0  29.0           7.0       6.2          5.7      4.8   \n",
       "339    27.367556      0  26.0           6.2       5.5          4.9      5.6   \n",
       "762    28.024641      0  34.0           6.2       1.2          7.2      6.3   \n",
       "1089   29.963039      0  32.0           3.8       7.6          4.9      4.8   \n",
       "788    19.318275      0  28.0           4.6       4.8          7.2      6.3   \n",
       "756     6.275154      0  28.0           2.2       7.6          5.7      5.6   \n",
       "1022    9.494867      0  26.0           6.2       5.5          5.7      6.3   \n",
       "1097    9.921971      0  20.0           9.4       4.8          2.6      4.8   \n",
       "826    12.320329      0  28.0           2.2       5.5          8.7      5.6   \n",
       "773    16.854209      0  28.0           4.6       4.8          5.7      6.3   \n",
       "1068    3.121150      0  32.0           7.0       6.2          5.7      4.8   \n",
       "735     1.708419      0  30.0           6.2       4.8          7.2      4.8   \n",
       "761    16.624230      0  24.0           5.4       4.8          6.4      5.6   \n",
       "352    64.197125      0  19.0           4.6       6.2          7.2      5.6   \n",
       "940    72.574949      0  26.0           4.6       4.1          6.4      4.8   \n",
       "743    79.540041      0  23.0           4.6       5.5          8.7      4.8   \n",
       "1105   79.540041      0  34.0           3.8       5.5          6.4      4.0   \n",
       "1113   79.802875      0  34.0           3.8       5.5          6.4      4.0   \n",
       "715   131.613963      0  26.0           3.0       6.9          4.9      6.3   \n",
       "1082  132.402464      0  25.0           4.6       6.9          2.6      4.8   \n",
       "\n",
       "      novator  median_remaining_lifetime  \n",
       "575       7.5                  22.373717  \n",
       "1096      6.0                  22.373717  \n",
       "753       6.7                  26.020534  \n",
       "406       9.0                  29.963039  \n",
       "927       5.2                  29.963039  \n",
       "717       8.3                  35.778234  \n",
       "841       6.7                  35.778234  \n",
       "764       6.7                  38.537988  \n",
       "1120      7.5                  38.537988  \n",
       "544       8.3                  40.114990  \n",
       "1112      6.7                  44.024641  \n",
       "1011      6.0                  44.024641  \n",
       "750       8.3                  48.459959  \n",
       "1073      3.7                  48.459959  \n",
       "1067      7.5                  50.891170  \n",
       "297       6.0                  52.271047  \n",
       "1016      5.2                  53.979466  \n",
       "760       4.4                  57.527721  \n",
       "733       1.3                  57.527721  \n",
       "919       4.4                  61.075975  \n",
       "768       6.0                  61.075975  \n",
       "552       3.7                  61.075975  \n",
       "751       2.9                  61.075975  \n",
       "763       8.3                  64.197125  \n",
       "993       6.7                  64.197125  \n",
       "734       3.7                  64.197125  \n",
       "754       5.2                  72.574949  \n",
       "817       6.0                  72.574949  \n",
       "386       7.5                  72.574949  \n",
       "387       5.2                  72.574949  \n",
       "339       6.7                  72.574949  \n",
       "762       9.0                  72.574949  \n",
       "1089      4.4                  72.574949  \n",
       "788       5.2                  79.014374  \n",
       "756       2.9                  90.907598  \n",
       "1022      5.2                  90.907598  \n",
       "1097      9.0                  90.907598  \n",
       "826       6.0                  90.907598  \n",
       "773       4.4                  90.907598  \n",
       "1068      5.2                  93.897331  \n",
       "735       3.7                 123.334702  \n",
       "761       3.7                 123.334702  \n",
       "352       6.0                        inf  \n",
       "940       4.4                        inf  \n",
       "743       4.4                        inf  \n",
       "1105      4.4                        inf  \n",
       "1113      4.4                        inf  \n",
       "715       6.0                        inf  \n",
       "1082      6.0                        inf  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtra apenas os registros censurados (empregados de TI que permaneceram na empresa)\n",
    "censored_employees = features_ti.loc[~features_ti['event'].astype(bool)]\n",
    "censored_employees_last_obs = censored_employees['stag']\n",
    "\n",
    "# prevê o tempo estimado restante de permanência para os registros censurados (empregados de TI que permaneceram na empresa)\n",
    "prediction_remaining = censored_employees.copy()\n",
    "prediction_remaining['median_remaining_lifetime'] = cph.predict_median(censored_employees, conditional_after=censored_employees_last_obs)\n",
    "prediction_remaining.sort_values(['median_remaining_lifetime','stag'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cada registro acima representa um empregado, sendo que o tempo estimado restante na empresa está demonstrado na coluna *median_remaining_lifetime*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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