Projeto 01_Classificacao_de_credito.ipynb

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   "cell_type": "markdown",
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   "source": [
    "<img src=\"https://raw.githubusercontent.com/rhatiro/Curso_EBAC-Profissao_Cientista_de_Dados/main/ebac-course-utils/media/logo/newebac_logo_black_half.png\" alt=\"ebac-logo\">\n",
    "\n",
    "---\n",
    "\n",
    "# **Profissão: Cientista de Dados**\n",
    "### **Módulo 02** | Projeto 01 - Concessão de cartões de crédito \n",
    "\n",
    "Aluno [Lays Félix](https://www.linkedin.com/in/laysfelixbusiness)<br>\n",
    "Data: 03 de setembro de 2023\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\"> </span>\n",
    "<span style=\"color:blue\"> Este notebook é semelhante ao visto em vídeo, mas contém células azuis como esta, que trazem instruções para a sua atividade.</span>\n",
    "\n",
    "<span style=\"color:blue\">Após realizar as tarefas indicadas, você vai fazer o upload do seu arquivo no GitHub e enviar o link para a EBAC, ou alternativamente, fazer o upload do arquivo na plataforma da EBAC. Recomendamos o github, pois assim você já vai montando o seu portfólio.</span>"
   ]
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    "## Etapa 1 CRISP - DM: Entendimento do negócio\n",
    "\n",
    "Como primeira etapa do CRISP-DM, vamos entender do que se trata o negócio, e quais os objetivos. \n",
    "\n",
    "Este é um problema de concessão de cartões de crédito, publicado no [Kaggle](https://www.kaggle.com/), uma plataforma que promove desafios de ciência de dados, oferecendo prêmios em dinheiro para os melhores colocados. O link original está [aqui](https://www.kaggle.com/rikdifos/credit-card-approval-prediction).  \n",
    "  \n",
    "Essa é uma base de proponentes de cartão de crédito, nosso objetivo é construir um modelo preditivo para identificar o risco de inadimplência (tipicamente definida pela ocorrência de um atraso maior ou igual a 90 em um horizonte de 12 meses) através de variáveis que podem ser observadas na data da avaliação do crédito (tipicamente quando o cliente solicita o cartão).\n",
    "\n",
    "Atividades do CRISP-DM:\n",
    "\n",
    "- Objetivos do negócio\n",
    "Note que o objetivo aqui é que o modelo sirva o mutuário (o cliente) para que avalie suas próprias decisões, e não a instituição de crédito.\n",
    "- Objetivos da modelagem\n",
    "O objetivo está bem definido: desenvolver o melhor modelo preditivo de modo a auxiliar o mutuário a tomar suas próprias decisões referentes a crédito.\n",
    "  \n",
    "Nessa etapa também se avalia a situação da empresa/segmento/assunto de modo a se entender o tamanho do público, relevância, problemas presentes e todos os detalhes do processo gerador do fenômeno em questão, e portanto dos dados.\n",
    "\n",
    "Também é nessa etapa que se constrói um planejamento do projeto."
   ]
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    "## Etapa 2 Crisp-DM: Entendimento dos dados\n",
    "A segunda etapa é o entendimento dos dados. Foram fornecidas 15 variáveis mais a variável resposta (em negrito na tabela). O significado de cada uma dessas variáveis se encontra na tabela.\n",
    "\n",
    "#### Dicionário de dados\n",
    "\n",
    "Os dados estão dispostos em uma tabela com uma linha para cada cliente, e uma coluna para cada variável armazenando as características desses clientes. Colocamos uma cópia o dicionário de dados (explicação dessas variáveis) abaixo neste notebook:\n",
    "\n",
    "| Variable Name            | Description                                         | Tipo  |\n",
    "| ------------------------ |:---------------------------------------------------:| -----:|\n",
    "| sexo| M = 'Masculino'; F = 'Feminino' |M/F|\n",
    "| posse_de_veiculo| Y = 'possui'; N = 'não possui' |Y/N|\n",
    "| posse_de_imovel| Y = 'possui'; N = 'não possui' |Y/N|\n",
    "| qtd_filhos| Quantidade de filhos |inteiro|\n",
    "| tipo_renda|Tipo de renda (ex: assaliariado, autônomo etc) | texto |\n",
    "| educacao| Nível de educação (ex: secundário, superior etc) |texto|\n",
    "| estado_civil | Estado civil (ex: solteiro, casado etc)| texto |\n",
    "| tipo_residencia | tipo de residência (ex: casa/apartamento, com os pais etc) | texto |\n",
    "| idade | idade em anos |inteiro|\n",
    "| tempo de emprego | tempo de emprego em anos |inteiro|\n",
    "| possui_celular | Indica se possui celular (1 = sim, 0 = não) |binária|\n",
    "| possui_fone_comercial | Indica se possui telefone comercial (1 = sim, 0 = não) |binária|\n",
    "| possui_fone | Indica se possui telefone (1 = sim, 0 = não) |binária|\n",
    "| possui_email | Indica se possui e-mail (1 = sim, 0 = não) |binária|\n",
    "| qt_pessoas_residencia | quantidade de pessoas na residência |inteiro|\n",
    "| **mau** | indicadora de mau pagador (True = mau, False = bom) |binária|\n",
    "\n",
    "\n",
    "\n"
   ]
  },
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   "metadata": {},
   "source": [
    "#### Carregando os pacotes\n",
    "É considerado uma boa prática carregar os pacotes que serão utilizados como a primeira coisa do programa."
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Carregando os dados\n",
    "O comando pd.read_csv é um comando da biblioteca pandas (pd.) e carrega os dados do arquivo csv indicado para um objeto *dataframe* do pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de linhas e colunas da tabela: (16650, 16)\n"
     ]
    },
    {
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       "      <td>House / apartment</td>\n",
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       "      <td>2.106849</td>\n",
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       "      <td>Working</td>\n",
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       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
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       "      <td>Single / not married</td>\n",
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       "      <td>F</td>\n",
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       "      <td>Working</td>\n",
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       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>30.049315</td>\n",
       "      <td>4.438356</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
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       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>34.764384</td>\n",
       "      <td>3.186301</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>32.443836</td>\n",
       "      <td>5.523288</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>32.443836</td>\n",
       "      <td>5.523288</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
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       "      <td>1</td>\n",
       "      <td>Commercial associate</td>\n",
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       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>32.443836</td>\n",
       "      <td>5.523288</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>56.169863</td>\n",
       "      <td>12.191781</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>56.169863</td>\n",
       "      <td>12.191781</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>56.169863</td>\n",
       "      <td>12.191781</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>56.169863</td>\n",
       "      <td>12.191781</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>56.169863</td>\n",
       "      <td>12.191781</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>43.180822</td>\n",
       "      <td>8.693151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sexo posse_de_veiculo posse_de_imovel  qtd_filhos            tipo_renda  \\\n",
       "0     M                Y               Y           0               Working   \n",
       "1     F                N               Y           0  Commercial associate   \n",
       "2     F                N               Y           0  Commercial associate   \n",
       "3     M                Y               Y           0               Working   \n",
       "4     F                Y               N           0               Working   \n",
       "5     F                Y               N           0               Working   \n",
       "6     F                N               Y           0               Working   \n",
       "7     F                N               Y           0               Working   \n",
       "8     F                N               Y           1               Working   \n",
       "9     F                N               Y           1               Working   \n",
       "10    M                Y               Y           3               Working   \n",
       "11    M                N               Y           1  Commercial associate   \n",
       "12    M                N               Y           1  Commercial associate   \n",
       "13    M                N               Y           1  Commercial associate   \n",
       "14    M                Y               Y           0  Commercial associate   \n",
       "15    M                Y               Y           0  Commercial associate   \n",
       "16    M                Y               Y           0  Commercial associate   \n",
       "17    M                Y               Y           0  Commercial associate   \n",
       "18    M                Y               Y           0  Commercial associate   \n",
       "19    F                Y               Y           2               Working   \n",
       "\n",
       "                         educacao          estado_civil    tipo_residencia  \\\n",
       "0   Secondary / secondary special               Married  House / apartment   \n",
       "1   Secondary / secondary special  Single / not married  House / apartment   \n",
       "2   Secondary / secondary special  Single / not married  House / apartment   \n",
       "3                Higher education               Married  House / apartment   \n",
       "4               Incomplete higher               Married  House / apartment   \n",
       "5               Incomplete higher               Married  House / apartment   \n",
       "6   Secondary / secondary special               Married  House / apartment   \n",
       "7   Secondary / secondary special               Married  House / apartment   \n",
       "8   Secondary / secondary special  Single / not married  House / apartment   \n",
       "9   Secondary / secondary special  Single / not married  House / apartment   \n",
       "10  Secondary / secondary special               Married  House / apartment   \n",
       "11               Higher education               Married  House / apartment   \n",
       "12               Higher education               Married  House / apartment   \n",
       "13               Higher education               Married  House / apartment   \n",
       "14  Secondary / secondary special               Married  House / apartment   \n",
       "15  Secondary / secondary special               Married  House / apartment   \n",
       "16  Secondary / secondary special               Married  House / apartment   \n",
       "17  Secondary / secondary special               Married  House / apartment   \n",
       "18  Secondary / secondary special               Married  House / apartment   \n",
       "19  Secondary / secondary special               Married  House / apartment   \n",
       "\n",
       "        idade  tempo_emprego  possui_celular  possui_fone_comercial  \\\n",
       "0   58.832877       3.106849               1                      0   \n",
       "1   52.356164       8.358904               1                      0   \n",
       "2   52.356164       8.358904               1                      0   \n",
       "3   46.224658       2.106849               1                      1   \n",
       "4   29.230137       3.021918               1                      0   \n",
       "5   29.230137       3.021918               1                      0   \n",
       "6   27.482192       4.024658               1                      0   \n",
       "7   27.482192       4.024658               1                      0   \n",
       "8   30.049315       4.438356               1                      0   \n",
       "9   30.049315       4.438356               1                      0   \n",
       "10  34.764384       3.186301               1                      0   \n",
       "11  32.443836       5.523288               1                      0   \n",
       "12  32.443836       5.523288               1                      0   \n",
       "13  32.443836       5.523288               1                      0   \n",
       "14  56.169863      12.191781               1                      0   \n",
       "15  56.169863      12.191781               1                      0   \n",
       "16  56.169863      12.191781               1                      0   \n",
       "17  56.169863      12.191781               1                      0   \n",
       "18  56.169863      12.191781               1                      0   \n",
       "19  43.180822       8.693151               1                      0   \n",
       "\n",
       "    possui_fone  possui_email  qt_pessoas_residencia    mau  \n",
       "0             0             0                    2.0  False  \n",
       "1             1             1                    1.0  False  \n",
       "2             1             1                    1.0  False  \n",
       "3             1             1                    2.0  False  \n",
       "4             0             0                    2.0  False  \n",
       "5             0             0                    2.0  False  \n",
       "6             1             0                    2.0  False  \n",
       "7             1             0                    2.0  False  \n",
       "8             0             0                    2.0  False  \n",
       "9             0             0                    2.0  False  \n",
       "10            0             0                    5.0  False  \n",
       "11            0             0                    3.0  False  \n",
       "12            0             0                    3.0  False  \n",
       "13            0             0                    3.0  False  \n",
       "14            1             0                    2.0  False  \n",
       "15            1             0                    2.0  False  \n",
       "16            1             0                    2.0  False  \n",
       "17            1             0                    2.0  False  \n",
       "18            1             0                    2.0  False  \n",
       "19            0             0                    4.0  False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observe que demo01.csv está na mesma pasta que este notebook\n",
    "# do contrário, seria necessário indicar a pasta no nome do arquivo\n",
    "# df = pd.read_csv('demo01.csv')\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/laysfelix/dataFrame/48792d277681eea168475dce269c5531ef6218d8/demo01.csv')\n",
    "print (\"Número de linhas e colunas da tabela: {}\".format(df.shape))\n",
    "\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Entendimento dos dados - Univariada\n",
    "Nesta etapa tipicamente avaliamos a distribuição de todas as variáveis. Nesta demonstração vamos ver a variável resposta e dois exemplos de univariada apenas. Mas sinta-se à vontade para tentar observar outras variáveis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    16260\n",
      "True       390\n",
      "Name: mau, dtype: int64\n",
      "\n",
      "Taxa de inadimplentes:\n",
      "0.023423423423423424\n"
     ]
    }
   ],
   "source": [
    "print(df['mau'].value_counts())\n",
    "print(\"\\nTaxa de inadimplentes:\")\n",
    "print(df['mau'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'mau'\n",
    "grafico_barras = df[var].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:blue\">Tarefa 01 - gráfico de barras</span>\n",
    "<span style=\"color:blue\"> Com base no código da célula anterior, construa um gráfico de barras para pelo menos duas outras variáveis. \n",
    "**Dica:** Não tente usar as variáveis ```tempo_emprego``` e ```idade``` pois o gráfico de barras dessa forma como construímos não é adequado para elas. </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'posse_de_imovel'\n",
    "grafico_barras = df[var].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'posse_de_veiculo'\n",
    "grafico_barras = df[var].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "var = \"tempo_emprego\"\n",
    "\n",
    "sns.displot(df, x = var, bins = 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alterando valores de -1000 pra -2, para visualizar melhor no gráfico\n",
    "var = \"tempo_emprego\"\n",
    "df.loc[df[var]<0,var] = -2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "var = \"tempo_emprego\"\n",
    "\n",
    "sns.displot(df, x = var, bins = 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:blue\">Tarefa 02 - Histograma </span>\n",
    "<span style=\"color:blue\"> Com base no código da célula anterior, construa o histograma da variavel ```idade```.  </span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "var = \"idade\"\n",
    "\n",
    "sns.displot(df, x = var, bins = 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entendimento dos dados - Bivariadas\n",
    "\n",
    "Entender a alteração da inadimplência indicada pela variável resposta (```AtrasoRelevante2anos```) e as variáveis explicativas (demais). Para isto, vamos calcular a taxa de inadimplentes (qtd inadimplentes / total) para diferentes grupos definidos pelas variáveis explicativas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WrVtltkdERMDb27tCQpFu6dPcFQ6Wpkh9/BQHr6aLHYeIiKhSaFwkffjhh/j0009x9uxZSCQS3Lt3D5s2bcLnn3+O8ePHayMjiUxmYoShz5pLrg5PBCdEEhFRVWCs6Qu++OILZGdn44033kBBQQHat28PMzMzfP755/j444+1kZF0wHttPbDy1G3EpjxGTNIj+Hvaix2JiIhIq165T1J+fj6uXbsGhUKBBg0awMrKqqKz6bSq0Cfp/5u68zK2nktBSEMn/PSev9hxiIiINKbVPkkjR45Ebm4uLCws4O/vj1atWsHKygpPnjzByJEjXzk06b5R7bwAAIev3UdS1hOR0xAREWmXxkXShg0b1C5k+/TpU/zyyy8VEop0Ux0na3SoVx2CAKzlUiVERGTgyl0k5eTkIDs7G4IgIDc3Fzk5OcrHo0ePsH//ftSoUUObWUkHjG5XOoNx+/m7yM5nc0kiIjJc5R64Xa1aNUgkEkgkEtStW7fMfolEgu+++65Cw5HuCfRxQH1na1xPz8Wm6CSM7+AjdiQiIiKtKHeRdOLECQiCgI4dO2Lnzp2wt/97dpOpqSk8PDzg4uKilZCkOySS0uaSn/8Wiw2RdzC6nTdMjV9pCUAiIiKdVu4iKTg4GACQmJgINzc3SKX8YayqejZ1wfyD13E/pxB/XL6Hvi1qiR2JiIiowmncJ8nDwwOPHz9GdHQ0MjIyoFCoLlMxbNiwCgtHusnUWIrhAZ5YcOgGwsIT0ae5KyQSidixiIiIKpTGRdLevXsxZMgQPHnyBNbW1io/jhKJhEVSFTGktTuWH7+Fa2k5iLqdhQAfR7EjERERVSiN75l99tlnyl5Jjx8/xqNHj5SPhw8faiMj6aBqFqZ4x6/0NlsY2wEQEZEB0rhISk1NRWhoKCwsLCokwIoVK+Dl5QWZTAY/Pz+Eh4e/9PhTp07Bz88PMpkM3t7eWLVqlcr+1atXIygoCHZ2drCzs0OnTp0QHR2t9nsMHToUDg4OsLCwQLNmzRATE1Mh36mqGNnOCxIJcPx6Bm5l5Iodh4iIqEJpXCSFhITg/PnzFfLh27Ztw4QJE/DVV1/h4sWLCAoKQteuXZGcnKz2+MTERHTr1g1BQUG4ePEivvzyS4SGhmLnzp3KY06ePIlBgwbhxIkTiIqKgru7O7p06YLU1FTlMY8ePUJgYCBMTExw4MABXLt2DT/88AOqVatWId+rqvBytEQnXycAwJqIO+KGISIiqmAar922Zs0azJw5EyNGjEDjxo1hYmKisr9nz57lfq/WrVujRYsWWLlypXKbr68vevfujblz55Y5fsqUKdizZw/i4uKU28aOHYvY2FhERUWp/Qy5XA47OzssX75cOV5q6tSpOH369L9etXqZqrh2mzrRiQ/x7k9RMDOWInJqRzhYmYkdiYiI6IU0+f3WeOD2mDFjAAAzZ84ss08ikUAul5frfYqKihATE4OpU6eqbO/SpQsiIyPVviYqKgpdunRR2RYSEoI1a9aguLi4TMEGlC7EW1xcrNLXac+ePQgJCUH//v1x6tQpuLq6Yvz48crvRuXX0tMOTWrZ4vLdbGw8k4xPO9UROxIREVGF0Ph2m0KheOGjvAUSAGRmZkIul8PJyUllu5OTE9LT09W+Jj09Xe3xJSUlyMzMVPuaqVOnwtXVFZ06dVJuS0hIwMqVK1GnTh0cOnQIY8eORWho6EvXnissLFRZiiUnJ6e8X9WgPW8uCQC/nrmDguLynwNERES67LU6QhYUFLx2gP/fX0cQhJf23FF3vLrtADB//nxs2bIFu3btgkwmU25XKBRo0aIF5syZg+bNm+PDDz/EmDFjVG77/X9z586Fra2t8uHm5lau71cVdG3kDBdbGTLzivD7pdR/fwEREZEe0LhIksvlmDVrFlxdXWFlZYWEhAQAwNdff401a9aU+30cHR1hZGRU5qpRRkZGmatFzzk7O6s93tjYGA4ODirbFy5ciDlz5uDw4cNo0qSJyr6aNWuiQYMGKtt8fX1fOGAcAKZNm4bs7GzlIyUl5V+/Y1VhYiTFiEAvAEBYeCI0HOZGRESkkzQukmbPno3169dj/vz5MDU1VW5v3LgxwsLCyv0+pqam8PPzw5EjR1S2HzlyBAEBAWpf07Zt2zLHHz58GP7+/irjkRYsWIBZs2bh4MGD8Pf3L/M+gYGBuHHjhsq2+Ph4eHh4vDCvmZkZbGxsVB70twGt3GBlZoybGXk4Ff9A7DhERESvT9BQ7dq1haNHjwqCIAhWVlbC7du3BUEQhLi4OKFatWoavdfWrVsFExMTYc2aNcK1a9eECRMmCJaWlsKdO3cEQRCEqVOnCu+9957y+ISEBMHCwkKYOHGicO3aNWHNmjWCiYmJsGPHDuUx8+bNE0xNTYUdO3YIaWlpykdubq7ymOjoaMHY2FiYPXu2cPPmTWHTpk2ChYWFsHHjxnJnz87OFgAI2dnZGn1nQzZz71XBY8ofwpDVZ8SOQkREpJYmv98aF0kymUxZxPyzSLp69apgaWmp6dsJP/74o+Dh4SGYmpoKLVq0EE6dOqXcN3z4cCE4OFjl+JMnTwrNmzcXTE1NBU9PT2HlypUq+z08PAQAZR4zZsxQOW7v3r1Co0aNBDMzM6F+/frCzz//rFFuFkllJWc9Ebym/iF4TPlDuJrK/y5ERKR7NPn91rhPkr+/PyZMmIChQ4fC2toasbGx8Pb2xnfffYejR4++Vu8hfcI+Sep9tPkC9l1OQ78WtfDDu03FjkNERKRCq32SZsyYgffeew+pqalQKBTYtWsXbty4gV9++QV//PHHK4cmwzAmyBv7LqdhT2wqvnirHpxsZP/+IiIiIh2k8cDtHj16YNu2bdi/fz8kEgm++eYbxMXFYe/evejcubM2MpIeaeZWDf4ediiWC/gl6o7YcYiIiF6ZxrfbqBRvt73Ywb/SMXZjDGzNTRA1rSMsTDW+YElERKQVmvx+v1YzSSJ1OjdwgoeDBbKfFmNnzF2x4xAREb2Scv0V387O7qVdsP/p4cOHrxWI9J+RVIKRgV6Ysecq1kQkYnBrDxhJy3f+EBER6YpyFUmLFy/WcgwyNO/41cIPh2/gTlY+jsXdR5eGzmJHIiIi0ki5iqThw4drOwcZGEszYwxp44GVJ28jLDyRRRIREemdVxpRK5fLsXv3bsTFxUEikcDX1xe9evWCsTEH6NLfhrf1xOo/ExB95yFiUx6jqVs1sSMRERGVm8ZVzV9//YVevXohPT0d9erVA1C67ln16tWxZ88eNG7cuMJDkn5ytpWhZ1MX7LqYirCIRCwb1FzsSEREROWm8ey20aNHo2HDhrh79y4uXLiACxcuICUlBU2aNMEHH3ygjYykx0YFeQEA9l9JQ+rjpyKnISIiKj+Ni6TY2FjMnTsXdnZ2ym12dnaYPXs2Ll26VJHZyAA0dLFFQG0HyBUC1p9OFDsOERFRuWlcJNWrVw/3798vsz0jIwM+Pj4VEooMy+hnV5O2Rqcgt6BY5DRERETlo3GRNGfOHISGhmLHjh24e/cu7t69ix07dmDChAmYN28ecnJylA8iAOhQtwZqV7dEbmEJtp1LETsOERFRuWi8LIlU+ndd9bzB5PO3+OdziUQCuVxeUTl1Dpcl0czms8n4cvcVuFYzx6nJHWBsxGbvRERU+TT5/dZ4dtuJEydeORhVXX1buGLh4RtIffwUB6+m4+0mLmJHIiIieimNi6Tg4GBt5CADJzMxwtA2Hlh67CZWhyeie+Oa5V7qhoiISAyv1P2xoKAAly9fRkZGBhQKhcq+nj17VkgwMjzvtfHAqlO3EZvyGDFJj+DvaS92JCIiohfSuEg6ePAghg0bhszMzDL7DH0cEr2e6tZm6NPMFdvOpyAsPJFFEhER6TSNR89+/PHH6N+/P9LS0qBQKFQeLJDo3zxvLnnoWjqSsp6InIaIiOjFNC6SMjIyMGnSJDg5OWkjDxm4uk7WCK5bHYIArDt9R+w4REREL6RxkfTOO+/g5MmTWohCVcWYIG8AwPbzKcjOZ3NJIiLSTRqPSVq+fDn69++P8PBwNG7cGCYmJir7Q0NDKywcGaZAHwfUd7bG9fRcbI5OxrgOtcWOREREVIbGzSTDwsIwduxYmJubw8HBQWUat0QiQUJCQoWH1EVsJvl6dsTcxee/xcLJxgzhX3SEqTGbSxIRkfZp8vut8S/T9OnTMXPmTGRnZ+POnTtITExUPqpKgUSvr0fTmqhubYb7OYXYd+We2HGIiIjK0LhIKioqwoABA1SWJyHSlJmxEd4P8AQArP4zERpe0CQiItI6jSud4cOHY9u2bdrIQlXM4FbukJlIcS0tB1G3s8SOQ0REpELjgdtyuRzz58/HoUOH0KRJkzIDtxctWlRh4ciw2Vmaor+fG349k4SwiEQE+DiKHYmIiEhJ4yLpypUraN68OQDgr7/+UtnHtbhIUyPbeWHj2SQcv56BWxm58KlhLXYkIiIiAK9QJJ04cUIbOaiK8nK0RCdfJxy5dh9rIu5gbt/GYkciIiIC8Apjkogq2vPmkrsu3EVWXqHIaYiIiEqV60pS3759sX79etjY2KBv374vPXbXrl0VEoyqjpaedmhSyxaX72Zj45lkfNqpjtiRiIiIynclydbWVjneyNbW9qUPIk1JJBKMfnY16dczd1BQzIWSiYhIfBp33KZS7LhdsYrlCgTPP4F72QWY168xBrR0FzsSEREZIK123CbSBhMjKUYEegEAwsLZXJKIiMRXrjFJzZs3L/f0/gsXLrxWIKq6BrRyw5JjN3EzIw+n4h+gQ70aYkciIqIqrFxXknr37o1evXqhV69eCAkJwe3bt2FmZoYOHTqgQ4cOkMlkuH37NkJCQrSdlwyYjcwEA1q6ASi9mkRERCQmjcckjR49GjVr1sSsWbNUts+YMQMpKSlYu3ZthQbUVRyTpB0pD/MRvOAEFAJw4NMg+Nbkf1siIqo4Wh2T9Ntvv2HYsGFltg8dOhQ7d+7U9O2IVLjZW6Br45oAeDWJiIjEpXGRZG5ujoiIiDLbIyIiIJPJKiQUVW2j25UO4N4Tm4qMnAKR0xARUVWl8bIkEyZMwLhx4xATE4M2bdoAAM6cOYO1a9fim2++qfCAVPU0d7eDv4cdzic9woaoO5gcUl/sSEREVAVpXCRNnToV3t7eWLJkCTZv3gwA8PX1xfr16/Huu+9WeECqmkYHeeF80iNsOpuMj97wgYWpxqcqERHRa2EzyVfEgdvaJVcIeGPhSSQ/zMesXg3xXltPsSMREZEBYDNJ0ntGUglGBnoCANZEJEKhYC1PRESVS+MiSS6XY+HChWjVqhWcnZ1hb2+v8iCqKP393WAjM8adrHwcjbsvdhwiIqpiNC6SvvvuOyxatAjvvvsusrOzMWnSJPTt2xdSqRTffvutFiJSVWVpZozBrT0AAGERbAdARESVS+MiadOmTVi9ejU+//xzGBsbY9CgQQgLC8M333yDM2fOaCMjVWHvB3jCWCpBdOJDXL77WOw4RERUhWhcJKWnp6Nx48YAACsrK2RnZwMA3n77bezbt69i01GV52wrQ4+mLgDYXJKIiCqXxkVSrVq1kJaWBgDw8fHB4cOHAQDnzp2DmZlZxaYjAjDqWXPJfVfSkPr4qchpiIioqtC4SOrTpw+OHTsGAPj000/x9ddfo06dOhg2bBhGjhxZ4QGJGrnaoq23A+QKARsi74gdh4iIqojX7pN05swZREZGwsfHBz179qyoXDqPfZIq1/Hr9zFy/XlYmxkjclpHWMtMxI5ERER6SJPf79duY9ymTRvl8iRE2tKhbg14V7dEwoMn2H7+rvIWHBERkba8UpEUHx+PkydPIiMjAwqFQmUf128jbZBKJRjdzhtf7r6CtRGJGN7WA8ZG7IVKRETao3GRtHr1aowbNw6Ojo5wdnaGRCJR7pNIJCySSGv6tnDFwsM3kPr4KQ5eTcfbTVzEjkRERAZM4yLp+++/x+zZszFlyhRt5CF6IZmJEYa28cDSYzexOjwR3RvXVCnSiYiIKpLG9ysePXqE/v37ayML0b96r40HTI2liE15jJikR2LHISIiA6ZxkdS/f39lbySiylbd2gx9mrkCYHNJIiLSLo1vt/n4+ODrr7/GmTNn0LhxY5iYqE7FDg0NrbBwROqMCvLCtvMpOHQtHUlZT+DhYCl2JCIiMkAa90ny8nrx1GuJRIKEhITXDqUP2CdJXMPXRuNU/AO8H+CJb3s2FDsOERHpCa32SUpM5C0OEt+YIG+cin+A7edTMLFTXdhasLkkERFVLDaaIb0U6OOA+s7WyC+SY3N0sthxiIjIAJXrStKkSZMwa9YsWFpaYtKkSS89dtGiRRUSjOhlJBIJRgd54/PfYrE+MhGj2nnB1Jg1PxERVZxy/apcvHgRxcXFyn9/0ePSpUsaB1ixYgW8vLwgk8ng5+eH8PDwlx5/6tQp+Pn5QSaTwdvbG6tWrVLZv3r1agQFBcHOzg52dnbo1KkToqOjX/h+c+fOhUQiwYQJEzTOTuLq0bQmqlub4X5OIfZduSd2HCIiMjDlupJ04sQJtf/+urZt24YJEyZgxYoVCAwMxE8//YSuXbvi2rVrcHd3L3N8YmIiunXrhjFjxmDjxo04ffo0xo8fj+rVq6Nfv34AgJMnT2LQoEEICAiATCbD/Pnz0aVLF1y9ehWurq4q73fu3Dn8/PPPaNKkSYV9J6o8ZsZGeD/AEwsO3cDqPxPRu5krm0sSEVGF0Xh2W0Vq3bo1WrRogZUrVyq3+fr6onfv3pg7d26Z46dMmYI9e/YgLi5OuW3s2LGIjY1FVFSU2s+Qy+Wws7PD8uXLMWzYMOX2vLw8tGjRAitWrMD333+PZs2aYfHixeXOztltuuHRkyK0/c8xFBQrsHlMawTUdhQ7EhER6TBNfr9FG8RRVFSEmJgYdOnSRWV7ly5dEBkZqfY1UVFRZY4PCQnB+fPnlbcD/7/8/HwUFxfD3t5eZftHH32E7t27o1OnTuXKW1hYiJycHJUHic/O0hT9/dwAsLkkERFVLNGKpMzMTMjlcjg5Oalsd3JyQnp6utrXpKenqz2+pKQEmZmZal8zdepUuLq6qhRDW7duxYULF9RerXqRuXPnwtbWVvlwc3Mr92tJu0a284JEAhy/noFbGXlixyEiIgMh+nSg/z+GRBCEl44rUXe8uu0AMH/+fGzZsgW7du2CTCYDAKSkpODTTz/Fxo0bldvKY9q0acjOzlY+UlJSyv1a0i4vR0t08i0tntdE8GoSERFVDNGKJEdHRxgZGZW5apSRkVHmatFzzs7Oao83NjaGg4ODyvaFCxdizpw5OHz4sMrA7JiYGGRkZMDPzw/GxsYwNjbGqVOnsHTpUhgbG0Mul6v9bDMzM9jY2Kg8SHeMblfaCX7XhbvIyisUOQ0RERmCVyqSfv31VwQGBsLFxQVJSUkAgMWLF+P3338v93uYmprCz88PR44cUdl+5MgRBAQEqH1N27Ztyxx/+PBh+Pv7q6wht2DBAsyaNQsHDx6Ev7+/yvFvvvkmrly5gkuXLikf/v7+GDJkCC5dugQjI6NyfwfSHa287NGkli0KSxTYeIbNJYmI6PVpXCStXLkSkyZNQrdu3fD48WPllZdq1appNDsMKG1SGRYWhrVr1yIuLg4TJ05EcnIyxo4dC6D0Ftc/Z6SNHTsWSUlJmDRpEuLi4rB27VqsWbMGn3/+ufKY+fPnY/r06Vi7di08PT2Rnp6O9PR05OWVjlWxtrZGo0aNVB6WlpZwcHBAo0aNNP3PQTpCIpFg1LOrSb+euYOCYvVXBImIiMpL4yJp2bJlWL16Nb766iuVqy7+/v64cuWKRu81YMAALF68GDNnzkSzZs3w559/Yv/+/fDw8AAApKWlITn576sCXl5e2L9/P06ePIlmzZph1qxZWLp0qbJHElDanLKoqAjvvPMOatasqXwsXLhQ069KeqZb45pwsZUhM68Iv19KFTsOERHpOY37JJmbm+P69evw8PCAtbU1YmNj4e3tjZs3b6JJkyZ4+vSptrLqFPZJ0k0//3kbc/ZfR50aVjg8sT2bSxIRkQqt9kny8vJSu/zIgQMH0KBBA03fjqhCDWzlDktTI9zMyMOp+AdixyEiIj1WrmVJ/mny5Mn46KOPUFBQAEEQEB0djS1btmDu3LkICwvTRkaicrORmWBAS3esPZ2INRGJ6FCvhtiRiIhIT2lcJI0YMQIlJSX44osvkJ+fj8GDB8PV1RVLlizBwIEDtZGRSCMjAj2xPjIR4TczEZeWA9+avB1KRESae6212zIzM6FQKFCjRtX72zrHJOm2jzZdwL4raXjHrxYW9m8qdhwiItIRlbZ2m6OjY5UskEj3jQ4qbQfw+6VUZOQUiJyGiIj0UblutzVv3rzcs4QuXLjwWoGIKkJzdzv4edghJukRfolKwuch9cSOREREeqZcV5J69+6NXr16oVevXggJCcHt27dhZmaGDh06oEOHDpDJZLh9+zZCQkK0nZeo3MY8u5q08WwS8otKRE5DRET6plxXkmbMmKH899GjRyM0NBSzZs0qcwwXfSVd0rmBM9ztLZD8MB87Y+7ivbaeYkciIiI9ovGYpN9++01lqZDnhg4dip07d1ZIKKKKYCSVYGSgJwBgTUQiFIpXnqNARERVkMZFkrm5OSIiIspsj4iIgEwmq5BQRBWlv78bbGTGuJOVj6Nx98WOQ0REekTjPkkTJkzAuHHjEBMTgzZt2gAAzpw5g7Vr1+Kbb76p8IBEr8PSzBiDW3tg1anbCItIRJeGzmJHIiIiPaFxkTR16lR4e3tjyZIl2Lx5MwDA19cX69evx7vvvlvhAYle1/sBnggLT0B04kNcvvsYTWpVEzsSERHpgddqJlmVsZmkfpm47RJ2X0xFz6YuWDqoudhxiIhIJJXWTJJIX4xqV9oOYN+VNKQ+fipyGiIi0gcskqhKaORqi7beDpArBGyIvCN2HCIi0gMskqjKGNO+9GrSlrPJyC0oFjkNERHpOhZJVGV0qFsD3tUtkVtYgu3n74odh4iIdNxrFUmCIIDjvklfSKUSjG7nDQBYG5GIErlC5ERERKTLXqlI+uWXX9C4cWOYm5vD3NwcTZo0wa+//lrR2YgqXN8WrrC3NEXq46c4dJXNJYmI6MU0LpIWLVqEcePGoVu3bti+fTu2bduGt956C2PHjsV///tfbWQkqjAyEyMMbeMBAFgdnsAroURE9EIa90ny8vLCd999V2b9tg0bNuDbb79FYmJihQbUVeyTpL8e5BYicN5xFJUosHNcW/h52IsdiYiIKolW+ySlpaUhICCgzPaAgACkpaVp+nZEla66tRn6NHMFAKz+s2oU9UREpDmNiyQfHx9s3769zPZt27ahTp06FRKKSNtGBZW2Azh0LR1JWU9ETkNERLpI47XbvvvuOwwYMAB//vknAgMDIZFIEBERgWPHjqktnoh0UV0nawTXrY5T8Q+w7vQdfNuzodiRiIhIx2h8Jalfv344e/YsHB0d8b///Q+7du2Co6MjoqOj0adPH21kJNKK0c+uJm0/n4LsfDaXJCLSNWK3atH4ShIA+Pn5YePGjRWdhahStfNxRH1na1xPz8Xm6GSM61Bb7EhERFXS4/wixN/PQ/z9XNy8n4v4+3m4mZGLXs1c8fXbDUTL9UpF0nNPnz5FcbHq38A504v0hUQiwah2Xpi84zLWRyZiVDsvmBqzCT0RkbZkPy1WFkHx93NxM6P03x/kFqo9Pv5+biUnVKVxkZSfn48vvvgC27dvR1ZWVpn9crm8QoIRVYaezVww/9AN3M8pxL4r99CneS2xIxER6b2cgmLcvJ+nclUo/n4u7ueoL4YAwLWaOeo4WaGukzXq1Cj9p08Nq0pMXVa5iiRfX1/0798fM2fOxOTJk3HixAmsWLECw4YNw48//ojU1FT89NNP+M9//qPtvEQVyszYCMPbemDh4XiEhSeidzNXSCQSsWMREemFvMIS3Lyfi5vPrgzFZ5QWRmnZBS98TU1bGeo4WaPus0KojpMV6jhZw8rstW5uaUW5mkmmpKTgzTffRHx8PNzd3fHLL7+gQ4cOsLGxwYULF+Dj44Nff/0VW7Zswf79+ysjt+jYTNJwPHpShLb/OYaCYgU2j2mNgNqOYkciItIpTwpLcDOjtBC6lfF87FAeUh8/feFrnGzMnl0VskbdZ4VQHScr2MhMKjF5WZr8fperbBswYAC+/vprAMDDhw/h5VU6K8jGxgYPHz4EALRr1w7jxo17ndxEorCzNMU7frWw8Uwy1oQnskgioiorv6jkWRH0/FZZ6e2ylxVDNazNlLfG6jr9XRDZmotbDFWEchVJDx8+RFRUFN577z14e3vjzp078PDwQIMGDbB9+3a0atUKe/fuRbVq1bQcl0g7RgZ6YdPZZBy7noFbGXmi3wcnItKmgmK58oqQsiDKyMXdR0/xovtLjlZmqOv09y2y52OHqlmYVm74SlSuIuns2bOIjIwEAIwYMQKxsbEIDg7GtGnT0L17dyxbtgwlJSVYtGiRVsMSaYt3dSu8Wd8JR+PuY+3pRMzp01jsSEREr62gWI7bD/L+HjP0bBB18sP8FxZDDpamfxdB/xg7ZGdpuMXQi2i8wO3/l5ycjPPnz6N27dpo2rRpReXSeRyTZHjOJmRhwM9nYGYsReTUjnCwMhM7EhFRuRSWyJHw4IlyrFDp9Po8JGU9geIFv/J2FialRZDyqlDpvxv6n30VPibpZdzd3eHu7v66b0MkulZe9mhSyxaX72Zj45lkfNqJaxESkW4pKlEgMfOJStPF+IxcJGXlQ/6CasjW3EQ5TujvGWXWcLQy5Wzef6FxkRQaGgofHx+EhoaqbF++fDlu3bqFxYsXV1Q2okr1vLnkp1sv4dczd/BhsDdkJkZixyKiKqhYrsCdzCdlmi7eyXyCkhcUQ9Yy478HTtewVv57dWszFkOvSOMiaefOndizZ0+Z7YGBgZgxYwZsbW2xZ88evPvuu5g2bVqFhCSqLN0a18S8A9dxL7sAv19KxYCWvEpKRNpTLFcgKesfxdCzfya+rBgyM4aPkxXq1vh7AHVdJ2s42bAYqmgaF0lZWVmwtbUts93a2hqPHj2Cr68v6tWrhw8++IBFEukdEyMp3g/0xJz91xEWnoh3/d34hw4RvbYSuQJJD/NVl+S4n4eEzDwUy9UXQ5amRqpjhp7NJqtpK+OfS5VE4yLJx8cHBw8exMcff6yy/cCBA6hfvz4GDhyIa9euwdnZucJCElWmga3cseToTdzMyMOp+AfoUK+G2JGISE/IFQKSH+arjhm6n4uEzCcoKlG/or2FqRHq1LBSFkR1nl0ZcmExJDqNi6RJkybh448/xoMHD9CxY0cAwLFjx/DDDz8oxyM1aNAAt27dqtCgRJXFRmaCAS3dsfZ0ItZEJLJIIqIyFAoBKY/yy6xcf/tBHgpfUAzJTKSoo3KLrHTskGs1c0ilLIZ0kcZF0siRI1FYWIjZs2dj1qxZAABPT0+sXLkSw4YNq/CARGIYEeiJ9ZGJCL+Zibi0HPjWZJsHoqpIoRCQ+vhpmaaLtzLyUFCsvhgyM5Yqu0/XeTZ2qK6TNWrZsRjSNxoVSSUlJdi0aRP69OmDcePG4cGDBzA3N4eVFbsTk2Fxs7dA10Y1se9KGtZEJGJh/6rTA4yoKnpeDN3MeD54Ok/570+L5WpfY2osRe3qVv/oM1T6Tzd7CxixGDIIGjeTtLCwQFxcHDw8PLSVSS+wmaThu5j8CH1WRMLESILTUzqiho1M7EhE9JoEQcC97AKVW2Q3ny3a+qToBcWQkRTe1S2VfYaejx3ycLBkMaSHtNpMsnXr1rh48WKVL5LI8DV3t4Ofhx1ikh7hl6gkfB5ST+xIRFROgiAgPaegzEKttzLykFdYovY1JkYSeDtaqY4ZcrKGh70FjI2klfwNSBdoXCSNHz8en332Ge7evQs/Pz9YWlqq7G/SpEmFhSMS25ggL8QkPcLGs0kY/0ZtWJi+dpN6IqpAgiAgI7dQdczQsyU5cgvUF0PGUgm8HC1VFmp9fmXIhMUQ/YPGt9uk0rInkEQigSAIkEgkkMvVX640NLzdVjXIFQLeWHgSyQ/zMat3I7zXhldQicQgCAIe5BWqLtT6rCDKeUExZCSVwNPB4u+FWp8VRJ4OljA1ZjFUVWn1dltiYuIrByPSN0ZSCUYGeuLbvdewNiIRQ1q5c3YKkZZl5hWqLtT6bH2yx/nFao+XSgBPB0vVleudrODlaAkzYy4tRK9O4yKJY5Goqunv74ZFR+KRmPkEx65noHMDJ7EjERmEh0+KyjRdvJmRh4dPitQeL5EAHvYWZVau965uyXUWSSteaYDF7du3sXjxYsTFxUEikcDX1xeffvopateuXdH5iERnaWaMwa09sOrUbawOT2CRRKShR8+KofiMv2+R3crIQ2bei4shNzuLf3SfLm266FPDisUQVSqNi6RDhw6hZ8+eaNasGQIDAyEIAiIjI9GwYUPs3bsXnTt31kZOIlEND/BAWHgCohMf4vLdx2hSq5rYkYh0TnZ+MeIzclVulcXfz0NmXuELX+Nmb/5soVbVYsjclMUQiU/jgdvNmzdHSEgI/vOf/6hsnzp1Kg4fPowLFy5UaEBdxYHbVc/EbZew+2IqejZ1wdJBzcWOQ6QT9l1Ow9ZzybiRnouM3BcXQ67VzFUWaq3rZAWfGlacMUqVTpPfb42LJJlMhitXrqBOnToq2+Pj49GkSRMUFBRonlgPsUiqev5KzcbbyyJgJJUg/Is34FLNXOxIRKLJflqMr//3F/bE3lPZ7mIrK7NQq08NK1iZsRgi3aDV2W3Vq1fHpUuXyhRJly5dQo0aXAiUDFcjV1u09XZAVEIW1kfewZfdfMWORCSKyNuZ+Hx7LO5lF8BIKsGH7b3RqYET6tSwgrXMROx4RBVG4yJpzJgx+OCDD5CQkICAgABIJBJERERg3rx5+Oyzz7SRkUhnjA7yQlRCFracTUbom3X4t2OqUgpL5Fh46AbCIhIhCICngwX+O6AZmrvbiR2NSCs0/hP+66+/hrW1NX744QdMmzYNAODi4oJvv/0WoaGhFR6QSJe8Ua8GvKtbIuHBE2w7l4JR7bzEjkRUKa6n52DC1ku4np4LABjUyh3Tu/vCkn9RIAOm8Zikf8rNLf2fxdrausIC6QuOSaq6Np1Nwle7/0ItO3Oc/LwD13Qig6ZQCFh7OhHzD95AkVwBB0tT/KdfE7bCIL2lye/3K//pnpGRgUuXLiE2NhYPHjx41bch0jv9WtSCnYUJ7j56ikNX74sdh0hr0rKfYuias/h+XxyK5Aq8Wb8GDk5ozwKJqgyNi6ScnBy89957cHFxQXBwMNq3bw8XFxcMHToU2dnZ2shIpFNkJkbKNdzCIhJETkOkHXti7yHkv38i8nYWzE2MMLtPI4QN90d1azOxoxFVGo2LpNGjR+Ps2bPYt28fHj9+jOzsbPzxxx84f/48xowZo42MRDrnvbaeMDWS4mLyY8QkPRQ7DlGFyX5ajAlbLyJ0y0XkFJSgaS1b7AtthyGtPSCRcN1Cqlo0HnG3b98+HDp0CO3atVNuCwkJwerVq/HWW29VaDgiXVXd2gy9m7tg+/m7CAtPhJ+HvdiRiF5b1O0sfLb9Eu5lF0AqAT7uWAefdPSBCcfdURWl8Znv4OAAW1vbMtttbW1hZ6f5NNAVK1bAy8sLMpkMfn5+CA8Pf+nxp06dgp+fH2QyGby9vbFq1SqV/atXr0ZQUBDs7OxgZ2eHTp06ITo6WuWYuXPnomXLlrC2tkaNGjXQu3dv3LhxQ+PsVLWNDvIGABy6mo7krHyR0xC9usISOebuj8PgsDO4l10ADwcL7BgXgEmd67JAoipN47N/+vTpmDRpEtLS0pTb0tPTMXnyZHz99dcavde2bdswYcIEfPXVV7h48SKCgoLQtWtXJCcnqz0+MTER3bp1Q1BQEC5evIgvv/wSoaGh2Llzp/KYkydPYtCgQThx4gSioqLg7u6OLl26IDU1VXnMqVOn8NFHH+HMmTM4cuQISkpK0KVLFzx58kTD/xpUldV1skZw3epQCMDa04lixyF6JTfSc9H7x0j89GcCBAEY2NIN+0OD0IK9j4hebe22W7duobCwEO7u7gCA5ORkmJmZlenC/W/ruLVu3RotWrTAypUrldt8fX3Ru3dvzJ07t8zxU6ZMwZ49exAXF6fcNnbsWMTGxiIqKkrtZ8jlctjZ2WH58uUYNmyY2mMePHiAGjVq4NSpU2jfvv1LMz/HFgAEAOE3H+C9NdGwMDVC1NQ3YWvBbsOkHxQKAesi72DewesoKlHA3tIU/+nbGF0aOosdjUirtLosSe/evV81l4qioiLExMRg6tSpKtu7dOmCyMhIta+JiopCly5dVLaFhIRgzZo1KC4uholJ2R+o/Px8FBcXw97+xWNGns/Ke9kxROq083FEfWdrXE/PxeboZIzrUFvsSET/Ki37KT7/LRanb2UBAN6oVx3z3mmCGtYykZMR6RaNi6QZM2ZUyAdnZmZCLpfDyUm134aTkxPS09PVviY9PV3t8SUlJcjMzETNmjXLvGbq1KlwdXVFp06d1L6nIAiYNGkS2rVrh0aNGr0wb2FhIQoL/17hOicn54XHUtUhkUgwqp0XJu+4jPWRiRjVzgumxhzDQbrrj8v38NXuv5D9tBgyEymmd2+AIa3dOXONSI1X7icfExODuLg4SCQSNGjQAM2bN3+l9/n//2MKgvDS/1nVHa9uOwDMnz8fW7ZswcmTJyGTqf8b0scff4zLly8jIiLipTnnzp2L77777qXHUNXUs5kL5h+6gfs5hdh35R76NK8ldiSiMnIKijHj96vYfbF0fGaTWrb474BmqF3dSuRkRLpL47/yZmRkoGPHjmjZsiVCQ0Px8ccfw8/PD2+++aZGnbcdHR1hZGRU5qpRRkZGmatFzzk7O6s93tjYGA4ODirbFy5ciDlz5uDw4cNo0qSJ2vf75JNPsGfPHpw4cQK1ar38h23atGnIzs5WPlJSUv7tK1IVYWZshOFtnzWXDE/Ea6z0Q6QVZxOy0HVxOHZfTIVUAoR29MHOcQEskIj+hcZF0ieffIKcnBxcvXoVDx8+xKNHj/DXX38hJydHowVuTU1N4efnhyNHjqhsP3LkCAICAtS+pm3btmWOP3z4MPz9/VXGIy1YsACzZs3CwYMH4e/vX+Z9BEHAxx9/jF27duH48ePw8vr3RUrNzMxgY2Oj8iB6bkhrD8hMpLh6LwdRCVlixyEC8Gxq/4E4DFx9BqmPn8Ld3gK/jW2LSV3qcWo/UXkIGrKxsRGio6PLbD979qxga2ur0Xtt3bpVMDExEdasWSNcu3ZNmDBhgmBpaSncuXNHEARBmDp1qvDee+8pj09ISBAsLCyEiRMnCteuXRPWrFkjmJiYCDt27FAeM2/ePMHU1FTYsWOHkJaWpnzk5uYqjxk3bpxga2srnDx5UuWY/Pz8cmfPzs4WAAjZ2dkafWcyXF/tvix4TPlDGLmu7P8fRJXtRnqO0HXxn4LHlD8Ejyl/CJN/uyTkFhSLHYtIdJr8fms8JkmhUKidRWZiYgKFQqHRew0YMABZWVmYOXMm0tLS0KhRI+zfvx8eHqW3LtLS0lR6Jnl5eWH//v2YOHEifvzxR7i4uGDp0qXo16+f8pgVK1agqKgI77zzjspnzZgxA99++y0AKFsOdOjQQeWYdevW4f3339foOxA9NzLQC5vOJuPY9QzcysiDTw3eyqDKp1AI2BB1B3MPlE7tt7MwwX/6NUEIp/YTaUzjPkm9evXC48ePsWXLFri4uAAAUlNTMWTIENjZ2WH37t1aCapr2CeJ1Bm94TyOxt3H4NbumNOnsdhxqIpJzy7A5B2xCL+ZCQAIrlsdC95pgho2nNpP9Jwmv98a35Revnw5cnNz4enpidq1a8PHxwdeXl7Izc3FsmXLXjk0kSEYE1Q6vm1nzF1k5RX+y9FEFWf/lTSELP4T4TczITORYlavhlg/oiULJKLXoPHtNjc3N1y4cAFHjhzB9evXIQgCGjRo8MI+RERVSSsvezR2tcWV1GxsOpuM0Dfr/PuLiF5DbkExZuy5il0XSqf2N3YtndrP271Er0+j220lJSWQyWS4dOnSSxsvVgW83UYv8vulVHy69RIcrUwRMaUjZCZGYkciAxWd+BCTtl/C3UdPIZUA4zv4IPTNOmxoSvQSWrvdZmxsDA8PD8jl8tcKSGTIujWuiZq2MmTmFWHPpXtixyEDVFSiwLyD1zHg5yjcffQUbvbm2P5hW3weUo8FElEF0vj/punTp2PatGl4+PChNvIQ6T0TIylGBHoCAMIiEthckirUzfu56LPiNFaevA1BAPr71cL+0CD4e3LtSaKKpvGYpKVLl+LWrVtwcXGBh4cHLC0tVfZfuHChwsIR6asBLd2x5OhNxN/Pw583MxFct7rYkUjPCYKADZGlU/sLn03tn9u3Md5qVHbNSiKqGBoXSb169eJCiET/wtbcBANaumPt6USEhSewSKLXcj+nAJN3XMaf8aVLP7WvWx0LObWfSOs07pNEpThwm/5NysN8BC84AYUAHJwQhPrOPE9IcweupGHa7it4nF8MM2Mpvuzmi2FtPfiXVaJXpJWB2/n5+fjoo4/g6uqKGjVqYPDgwcjMzHztsESGys3eAl2f3QoJC08UOQ3pm9yCYnz+WyzGbbqAx/nFaORqg32h7TA8wJMFElElKXeRNGPGDKxfvx7du3fHwIEDceTIEYwbN06b2Yj03qhnzSV/v5SKjJwCkdOQvjh35yG6LgnHjpi7kEiA8R1qY9e4QPjUsBY7GlGVUu4xSbt27cKaNWswcOBAAMDQoUMRGBgIuVwOIyP2gSFSp4W7Hfw87BCT9Ai/RCXh85B6YkciHVZUosCSY/FYefI2FAJQy84c/x3QDC05c41IFOW+kpSSkoKgoCDl81atWsHY2Bj37rEPDNHLjG5XejVp49kkPC1ijzFS71ZGHvquPI0fT5QWSP1a1MKBT4NYIBGJqNxXkuRyOUxNTVVfbGyMkpKSCg9FZEi6NHSGm705Uh4+xY4Ld/FeGw+xI5EOEQQBv55Jwpz9cSgoVqCahQnm9mmMro05tZ9IbOUukgRBwPvvvw8zMzPltoKCAowdO1alV9KuXbsqNiGRnjOSSjAy0Avf7b2GtRGJGNLKHVIpB94SkPFsav+pZ1P7g+o4YmH/pnDi1H4inVDuImn48OFltg0dOrRCwxAZqnf93bDoSDwSM5/g2PUMdG7gJHYkEtnBv9IxbddlPHo2tX9a1/oY1taTBTSRDil3kbRu3Tpt5iAyaJZmxhjc2h0/nUpAWHgCi6QqLK+wBN/tuYrfYu4CABrUtMGSgc1Qx4kz14h0DVdCJKok7wd4wlgqwdnEh7hyN1vsOCSCmKSH6LYkHL89m9o/rkNt/O+jQBZIRDqKRRJRJalpa44eTV0AAKvDE0ROQ5WpWK7AwkM30H9VFJIf5sO1mjm2jmmDKW/Vh6kx/xgm0lX8v5OoEo161g5g35U03Hv8VOQ0VBluP8hDv5WRWH7iFhQC0LeFKw5MCEJrbwexoxHRv2CRRFSJGrnaoq23A+QKAesj74gdh7To+dT+7kvDcfluNmzNTfDj4BZY9G4z2MhMxI5HROXAIomoko1+tlTJlrPJyCtknzFDlJFbgJHrz+Hr//2FgmIFguo44tCE9ujehL2PiPQJiySiSvZGvRrwrm6J3MISbDuXInYcqmCHrqbjrcXhOHHjAUyNpfjm7QbYMKIVnG3Z+4hI37BIIqpkUqlEOTZp3elElMgVIieiipBXWIIpOy7jw19j8PBJERrUtMEfn7TDyHZe7H1EpKdYJBGJoF+LWrCzMMHdR09x6Op9sePQa4pJeoRuS8Kx7XwKJBJgbHBt7P4oAHU5tZ9Ir7FIIhKBzMRIuYZbWATbAeirYrkCiw7fQP9Vkcqp/VvGtMHUrvVhZmwkdjwiek0skohE8l5bT5gaSXEx+TFikh6KHYc0lPAgD++sjMTS46VT+/s0L53a34ZT+4kMBoskIpFUtzZD7+alzSXDwhNFTkPlJQgCNp5JQvelEYi9mw0bmTGWDWqO/w7g1H4iQ1PutduIqOKNDvLG9vN3cehqOpKz8uHuYCF2JHqJB7mFmLLzMo5fzwAABPo4YGH/pqhpay5yMiLSBl5JIhJRXSdrtK9bHQoBWHuaV5N02ZFr9/HW4j9x/HoGTI2lmN7dF7+ObM0CiciAsUgiEtmYZ80lt59PQXZ+schp6P97UliCabsuY8wv55H1pAj1na2x9+N2GB3kzan9RAaORRKRyNr5OKK+szXyi+TYci5Z7Dj0DxeSH6H70nBsiS6d2v9he2/8/nEg6jlzaj9RVcAiiUhkEsnfzSXXn76DohI2lxRbsVyB/x6JR/9VUbiTlQ8XWxk2j26Dad18ObWfqAphkUSkA3o2c0F1azOk5xRg/5U0seNUaYmZT/DOqigsOXYTcoWAXs1ccGBCe7Stzan9RFUNiyQiHWBmbIThbUubS64OT4AgCCInqnoEQcDms8notiQcsSmPYSMzxtJBzbFkYHPYmnNqP1FVxCKJSEcMae0BmYkUV+/l4EwCm0tWpsy8QozecB5f7r6Cp8VyBNR2wMEJ7dGzqYvY0YhIRCySiHSEnaUp3vGrBQAIC+dSJZXl6LX7CPnvnzh2PQOmRqVT+zeOag2XapzaT1TVsUgi0iEjA70gkQDHrmfg9oM8seMYtPyiEkzbdQWj/zG1f88ngZzaT0RKLJKIdIh3dSu8Wd8JALAmgs0lteVSymN0XxqBLdGlLRfGBHnhfx8For6zjcjJiEiXsEgi0jGjnzWX3BlzFw+fFImcxrCUyBVYfDQe/VZGIjHzCWrayrB5dGt81b0BZCac2k9EqlgkEemY1l72aOxqi8ISBTaeSRI7jsG482xq/+KjpVP7ezZ1wcFP2yPAx1HsaESko1gkEekYiUSivJr0S9QdFBTLRU6k3wRBwJboZHRbGo5LKY9hLTPGkoHNsHRQc9hacGo/Eb0YiyQiHdStcU3UtJUhM68Iey7dEzuO3srMK8SYX2IwbdcV5BfJ0cbbHgcntEevZq5iRyMiPcAiiUgHmRhJ8X6AJwAgLILNJV/F8ev38dbiP3E07j5MjaT4qpsvNo9uA1dO7SeicmKRRKSjBrZyh6WpEeLv5+HPm5lix9Eb+UUl+Gr3FYxcfx6ZeUWo52SN3z8OxJj2nNpPRJphkUSko2zNTTCgpTsANpcsr9hnU/s3nS2d2j+6nRd+/zgQvjU5tZ+INMciiUiHjQj0hFQChN/MxPX0HLHj6KwSuQJLj91E32dT+51tZNg0ujWmv82p/UT06lgkEekwN3sLdG1UEwAQFs7mkuokZT1B/5+isOhIPOQKAW83qYlDE9ojkFP7ieg1sUgi0nGjnrUD+P1SKjJyCkROozsEQcC2c8nouiQcF5Mfw9rMGIsHNMMyTu0nogrCIolIx7Vwt4Ofhx2K5QJ+iWJzSQDIyivEh7/GYMrO0qn9rb3scWBCEHo3d4VEwsHZRFQxWCQR6YHR7UqvJm08m4SnRVW7ueSJ6xkIWRyOw9fuw8RIgmld62PzmDaoZWchdjQiMjDGYgcgon/XpaEz3OzNkfLwKXZcuIv32niIHanSPS2SY87+OPz6bKmWuk5W+O+AZmjoYityMiIyVLySRKQHjKQSjAwsvZq0NiIRCkXVai55+e5jdF8WriyQRgZ6Yc/H7VggEZFWsUgi0hPv+rvBWmaMxMwnOHY9Q+w4laJErsDy4zfRd0UkEh48gZONGTaOao1venBqPxFpH4skIj1haWaMwa2rTnPJ5Kx8DPj5DBYejkeJQkD3Z1P729Xh1H4iqhwskoj0yPsBnjCWSnA28SGu3M0WO45WCIKA7edS0HXJn4hJegRrM2P8d0BTLB/UHNUsTMWOR0RVCIskIj1S09Ycbzd51lwywvCuJj18UoSxG2Pwxc7LeFIkR6tnU/v7NK/Fqf1EVOlYJBHpmdFB3gCAPy6n4d7jpyKnqTgnbmQgZPGfOHS1dGr/lLfqYwun9hORiFgkEemZRq62aONtD7lCwIbIO2LHeW1Pi+T45ve/MGLdOTzILUSdGlbYPT4Q4zrUhpGUV4+ISDwskoj00JhnV5M2Rycjr7BE5DSv7srdbLy9LFzZSfz9AE/s/aQdGrlyaj8RiY/NJIn00Bv1asC7uiUSHjzB9nMpGPmsI7e+kCsErDp1G/89UjpzzcnGDAv7N0VQnepiRyMiUuKVJCI9JJVKMOpZYbT2dCJK5AqRE5VfysN8DPgpCgsO3UCJQkC3xs44+Gl7FkhEpHNEL5JWrFgBLy8vyGQy+Pn5ITw8/KXHnzp1Cn5+fpDJZPD29saqVatU9q9evRpBQUGws7ODnZ0dOnXqhOjo6Nf+XCJd07d5LdhZmODuo6c4fO2+2HH+lSAI+O18CrouCcf5pEewMjPGD/2b4sfBLWBnyan9RKR7RC2Stm3bhgkTJuCrr77CxYsXERQUhK5duyI5OVnt8YmJiejWrRuCgoJw8eJFfPnllwgNDcXOnTuVx5w8eRKDBg3CiRMnEBUVBXd3d3Tp0gWpqamv/LlEusjc1Ei5httqHW8u+ehJEcZtvIDJOy4jr7AELT3tcODTIPTz49R+ItJdEkEQRFsEqnXr1mjRogVWrlyp3Obr64vevXtj7ty5ZY6fMmUK9uzZg7i4OOW2sWPHIjY2FlFRUWo/Qy6Xw87ODsuXL8ewYcNe6XPVycnJga2tLbKzs2FjY1Ou1xBVtIzcArT7zwkUyRXYOS4Afh52Ykcq41T8A0z+LRYZuYUwMZJgYue6+LA9Z64RkTg0+f0W7UpSUVERYmJi0KVLF5XtXbp0QWRkpNrXREVFlTk+JCQE58+fR3FxsdrX5Ofno7i4GPb29q/8uQBQWFiInJwclQeR2GpYy9C7uQsA3VuqpKBYjm/3XMXwtdHIyC2Ez7Op/eM7+LBAIiK9IFqRlJmZCblcDicnJ5XtTk5OSE9PV/ua9PR0tceXlJQgMzNT7WumTp0KV1dXdOrU6ZU/FwDmzp0LW1tb5cPNze1fvyNRZRjVrrQdwKGr6UjOyhc5Tam/UrPx9rIIrH/Wx+n9AE/8wan9RKRnRB+4/f/HIwiC8NIxCuqOV7cdAObPn48tW7Zg165dkMlkr/W506ZNQ3Z2tvKRkpLywmOJKlM9Z2u0r1sdCqF0ppuY5AoBP564hT4rTuNWRh5qWJthw8hW+LZnQ8hMjETNRkSkKdH6JDk6OsLIyKjM1ZuMjIwyV3mec3Z2Vnu8sbExHBwcVLYvXLgQc+bMwdGjR9GkSZPX+lwAMDMzg5mZWbm+G1FlG93OC3/GP8D28ymY2LkubM1NKj1DysN8TNp+CefuPAIAvNXQGXP7NubMNSLSW6JdSTI1NYWfnx+OHDmisv3IkSMICAhQ+5q2bduWOf7w4cPw9/eHicnfPwoLFizArFmzcPDgQfj7+7/25xLpuqA6jqjnZI38Ijm2RFfuLE1BELAz5i66LgnHuTulU/sXvNMEK4dyaj8R6TdRb7dNmjQJYWFhWLt2LeLi4jBx4kQkJydj7NixAEpvcT2fkQaUzmRLSkrCpEmTEBcXh7Vr12LNmjX4/PPPlcfMnz8f06dPx9q1a+Hp6Yn09HSkp6cjLy+v3J9LpG8kEglGBZU2l1x/+g6KSiqnueSjJ0X4aPMFfPZbLPIKS+DvUTq1v7+/G6f2E5HeE3VZkgEDBiArKwszZ85EWloaGjVqhP3798PDo7T3S1pamkrvIi8vL+zfvx8TJ07Ejz/+CBcXFyxduhT9+vVTHrNixQoUFRXhnXfeUfmsGTNm4Ntvvy3X5xLpo17NXLDg0A2k5xRg/5U09G7uqtXP+zP+AT5/NrXfWFo6tX9sMKf2E5HhELVPkj5jnyTSRcuP38TCw/Fo6GKDPz5pp5WrOQXFcvznwHXlzLXa1S2xeEBzNK7FmWtEpPv0ok8SEVW8Ia09IDOR4uq9HJxJeFjh7/9XajZ6/GNq/7C2HvjjkyAWSERkkFgkERkQO0tTvONXC0DFNpeUKwSsPHkbfVacxs2MPFS3NsP6ES0xs1cjmJtyaj8RGSYWSUQGZmSgFyQS4Nj1DNx+kPfvL/gXdx/lY9DqM5h38DqK5QJCGjrh0IT26FCvRgWkJSLSXSySiAyMd3UrvFm/tOfXmohXby4pCAJ2XbiLrovDEZ34EJamRpj/ThOsGuoHe07tJ6IqgEUSkQEa/awdwM6Yu3j4pEjj1z/OL8LHmy9i0vZY5BaWwM/DDgc+bY93ObWfiKoQFklEBqi1lz0au9qisESBjWeSNHptxM1MhCz+E/uupMFYKsHnXepi2wdt4O5goaW0RES6iUUSkQGSSCTKq0m/RN1BQbH8X19TUCzHzL3XMHTNWdzPKYS3oyV2jQ/Axx3rwNiIf1QQUdXDP/mIDFS3xjVR01aGzLwi7Ll076XHXruXg57LI5QL5A5t444/QtuhSa1qlZCUiEg3sUgiMlAmRlK8H+AJAAiLSIC6vrFyhYCfTt1Grx8jEH8/D45WZlj3fkt837sxLExFbchPRCQ6FklEBmxgK3dYmhoh/n4e/ryZqbIv9fFTDF59BnMPlE7t79zACYcmBOGN+pzaT0QEsEgiMmi25iZ4t6UbgL+bSwqCgP9dTMVbi//E2cSHsDA1wrx+jfHze35wsDITMy4RkU7h9XQiAzcy0AsbIu8g/GYmziZk4dczSfjjchoAoLl7NSwe0AweDpYipyQi0j0skogMnJu9Bd5q5Iz9V9IxcPUZCAJgJJXg0zfrYHyH2py5RkT0AvzTkagKGB3kDQAQBMDL0RI7xwUg9E1O7SciehleSSKqAlq422F6d1/kFpTgw2BvzlwjIioH/klJVEU8v5pERETlw2vtRERERGqwSCIiIiJSg0USERERkRoskoiIiIjUYJFEREREpAaLJCIiIiI1WCQRERERqcEiiYiIiEgNFklEREREarBIIiIiIlKDRRIRERGRGiySiIiIiNRgkURERESkhrHYAfSVIAgAgJycHJGTEBERUXk9/91+/jv+MiySXlFubi4AwM3NTeQkREREpKnc3FzY2tq+9BiJUJ5SispQKBS4d+8erK2tIZFIKvS9c3Jy4ObmhpSUFNjY2FToe1PVxfOKtIHnFWmLts4tQRCQm5sLFxcXSKUvH3XEK0mvSCqVolatWlr9DBsbG/6hQxWO5xVpA88r0hZtnFv/dgXpOQ7cJiIiIlKDRRIRERGRGiySdJCZmRlmzJgBMzMzsaOQAeF5RdrA84q0RRfOLQ7cJiIiIlKDV5KIiIiI1GCRRERERKQGiyQiIiIiNVgkEREREanBIomIiIhIDXbcFsmrLIzLbrb0b3hekTbwvCJtuXz5ssavadCgAYyNK6d8YQsAkUilUo3WfJNIJIiPj4e3t7cWU5G+43lF2sDzirTl+blV3lJEKpVW6rnFK0ki2rFjB+zt7f/1OEEQ0K1bt0pIRIaA5xVpA88r0pazZ8+ievXq/3qcIAho1KhRJST6G4skkXh4eKB9+/ZwcHAo1/He3t4wMTHRcirSdzyvSBt4XpG2BAcHw8fHB9WqVSvX8e3bt4e5ubl2Q/0Db7cRERERqcHZbURERERqsEjSQd7e3rh586bYMUiPZWVl4cSJE3j48CEAIDMzE/PmzcPMmTMRFxcncjoyRPfv38fMmTPFjkEGKCUlBSNHjhTls3m7TURLly5Vu33SpEn44osv4OzsDAAIDQ2tzFik56Kjo9GlSxfk5OSgWrVqOHLkCPr37w9jY2MIgoDU1FRERESgRYsWYkclAxIbG4sWLVpALpeLHYUMjJjnFoskEUmlUri6upbp95CUlAQXFxeYmJhAIpEgISFBpISkjzp37gxPT08sWrQIP/30E5YsWYK33noLq1evBgCMHj0aWVlZ2L17t8hJSZ/8Wz+b69evY9CgQSySSGN79ux56f6EhAR89tlnLJKqmg8//BDR0dHYvHkzfH19ldtNTEwQGxuLBg0aiJiO9JW9vT1Onz4NX19fFBcXQyaTISoqCq1atQIAXLx4ET169MDdu3dFTkr65GX9bJ5vl0gkLJJIY+XplSTWucUxSSL66aefMGPGDISEhGD58uVixyEDUVRUpJwia2JiAgsLCzg6Oir3Ozg4ICsrS6x4pKccHBywevVqJCYmlnkkJCTgjz/+EDsi6amaNWti586dUCgUah8XLlwQLRuLJJH17t0bUVFR2L17N7p27Yr09HSxI5Gec3NzU7lFu3XrVtSsWVP5PC0tTaVoIioPPz8/3Lt3Dx4eHmofrq6u5e6aTPRPfn5+Ly2ENOnIXdHYTFIHuLq64ujRo/jPf/6D5s2b8w8aei0DBw5ERkaG8nn37t1V9u/Zs0d5642ovD788EM8efLkhfvd3d2xbt26SkxEhmLy5MkvPbd8fHxw4sSJSkz0N45J0jExMTGIiIjAsGHDYGdnJ3YcMkD5+fkwMjKCmZmZ2FGIiHQaiyQiIiIiNTgmSUSffPIJwsPDxY5BBigtLQ3ffPMNOnbsCF9fXzRq1Ag9evTAmjVrOPuItELMhn+k/2JjYzFs2DB4e3vD3NwcVlZWaNy4Mb7++mvk5OSIlotXkkT0fNpj7dq1MWrUKAwfPlzZQJLoVZ0/fx6dOnWCl5cXzM3NcfbsWQwZMgRFRUU4dOgQfH19cejQIVhbW4sdlQwIm0nSqzp06BD69OmDkJAQmJub4/fff8fIkSNhaWmJnTt3QhAEREREiPL7yCJJRFKpFEeOHMHevXuxadMmZGdno2vXrhgzZgy6desGqZQX+khz7dq1Q+fOnTFjxgwAwMaNG7F8+XKcOXMGjx49QseOHdG+fXssWbJE5KSkT3S54R/pt+bNm+PDDz/E2LFjAQBHjhxBaGgo4uLiUFxcjK5du8LNzU2UiQEskkQklUqRnp6OGjVqoLi4GLt378batWtx9OhRODk54f3338eIESPg4+MjdlTSIxYWFvjrr7/g7e0NAFAoFJDJZEhJSYGTkxOOHDmC999/H6mpqSInJX2iyw3/SL+Zm5sjLi4Onp6eAABBEGBmZoakpCTUrFkT4eHh6Nevn8qs3crCSxU6wsTEBO+++y4OHjyIhIQEjBkzBps2bUK9evXEjkZ6pkaNGkhLS1M+v3//PkpKSmBjYwMAqFOnjnLhW6Ly0uWGf6TfXF1dcePGDeXz27dvQ6FQwMHBAQBQq1Yt5OXliZKNRZIOcnd3x7fffovExEQcPHhQ7DikZ3r37o2xY8fi4MGDOHHiBIYMGYLg4GBlF+4bN27A1dVV5JSkb3S54R/pt2HDhmH06NFYtWoV1q1bhz59+qBnz54wNTUFAFy6dAleXl6iZGMzSRF5eHjAyMjohfslEgk6d+5ciYnIEHz//fdIS0tDjx49IJfL0bZtW2zcuFG5XyKRYO7cuSImJH2kyw3/SL99+eWXePLkCWbNmoXCwkKEhISojJl0dXXFypUrRcnGMUlEBqqgoAAlJSWwsrISOwoRkV7i7TYdxdqVXpdMJlMpkBITE1FSUiJiIjJEPK/IkLFIElFhYSE+++wzBAcHY8GCBQBKb5VYWVnBysoKgwcPFrWJFhmWevXq4ebNm2LHIAPD84q07csvvxStUSnHJIlo2rRp2LZtGwYNGoR169YhKSkJe/fuxU8//QSpVIpvvvkG06dPx9KlS8WOSnqkb9++arfL5XKEhoYqm0ju2rWrMmORnuN5RWJJTU1FSkqKKJ/NIklEO3bswIYNG9CpUyeMHz8ederUwa5du9CrVy8AgKOjI8aMGcMiiTTyv//9D+3bt1c7G8TKygq2trYipCJ9x/OKxLJhwwbRPpsDt0VkYWGB69evw93dHQBgamqKixcvomHDhgCAO3fuoGHDhi+dUUL0/23duhWTJ0/GzJkzMWLECOV2ExMTxMbGokGDBiKmI33F84qqIo5JEpG7uzuioqIAAOfOnYNEIkF0dLRy/9mzZ9nPhjQ2cOBAREREYO3atejXrx8ePXokdiQyADyvSJv27t2LGTNmKH8Tjx8/jm7duuGtt97Czz//LFouFkkiGjt2LN5//3107twZISEh+OGHH/Dll19iypQpmDZtGj788EO89957YsckPeTh4YFTp06hUaNGaNq0KQ4dOgSJRCJ2LNJzPK9IG1atWoW+ffti3759eOutt7Bp0yb07t0brq6u8PT0xIQJE8Rba1IgUW3cuFH4+OOPha1btwqCIAgnTpwQgoKCBD8/P+Hbb78V5HK5yAlJ30VERAheXl6CVCoVrl69KnYcMhA8r6ii+Pr6Cj///LMgCIJw/PhxQSaTCT/++KNy/7p16wRfX19RsnFMElEVkJeXh9u3b8PX11fZ6p/odfG8ooqgbnzuhQsX0KhRIwDijs/l7TaiKsDKygpNmzblDxlVKJ5XVBEcHByQlJQEALh37x5KSkqQnJys3J+UlAR7e3tRsrEFgA778ssvkZ6ejrVr14odhQwIzyvSBp5X9Kp69eqFUaNGYfjw4dizZw+GDRuGzz77DFKpFBKJBJMnT0aXLl1EycYiSYeJ2UCLDBfPK9IGnlf0qubNm4fCwkJs3boV7dq1w9KlS7FkyRL06tULxcXFCA4OFm1Rbo5JIiIiIp1TUFCA4uJiZTd3MbBIIiIiIlKDA7d1kLe3NxeMpNeSlZWFEydO4OHDhwCAzMxMzJs3DzNnzkRcXJzI6Uhf8bwibbh48SISExOVzzdu3IjAwEC4ubmhXbt22Lp1q2jZeCVJRC9ak23SpEn44osv4OzsDAAIDQ2tzFik56Kjo9GlSxfk5OSgWrVqOHLkCPr37w9jY2MIgoDU1FRERESgRYsWYkclPcLzirSlRYsW+OGHH/DGG28gLCwMoaGhGDNmDHx9fXHjxg2EhYVhyZIlGDlyZKVnY5EkIqlUCldXVxgbq46fT0pKgouLC0xMTCCRSJCQkCBSQtJHnTt3hqenJxYtWoSffvoJS5YswVtvvYXVq1cDAEaPHo2srCzs3r1b5KSkT3hekbZYWloiLi4O7u7uaNGiBcaOHYsPPvhAuX/z5s2YPXs2rl69WunZWCSJ6MMPP0R0dDQ2b94MX19f5XYuGEmvw97eHqdPn4avry+Ki4shk8kQFRWFVq1aASi9tN2jRw/cvXtX5KSkT3hekbY4Ojri0KFD8PPzg5OTEw4fPoymTZsq99++fRuNGzdGfn5+pWfjmCQR/fTTT5gxYwZCQkKwfPlyseOQgSgqKoK5uTmA0oLbwsICjo6Oyv0ODg7IysoSKx7pKZ5XpC1du3bFypUrAQDBwcHYsWOHyv7t27fDx8dHjGjskyS23r17o2XLlhg2bBj27duHdevWiR2J9JybmxsSEhLg6ekJANi6dStq1qyp3J+Wlqby40ZUHjyvSFvmzZuHwMBABAcHw9/fHz/88ANOnjypHJN05swZ0W7j8kqSDnB1dcXRo0fRvn17NG/eHLwDSq9j4MCByMjIUD7v3r278goAAOzZs0d5i4SovHhekba4uLjg4sWLaNu2LQ4ePAhBEBAdHY3Dhw+jVq1aOH36NLp16yZKNo5J0jExMTGIiIjAsGHDYGdnJ3YcMkD5+fkwMjKCmZmZ2FHIgPC8IkPEIomoihAEARKJROwYRER6g2OSRBYXF4czZ86gbdu2qF+/Pq5fv44lS5agsLAQQ4cORceOHcWOSAbCzMwMsbGxKjMpiV7Vo0ePsGHDBty8eRM1a9bE8OHD4ebmJnYsogrFK0kiOnjwIHr16gUrKyvk5+dj9+7dGDZsGJo2bQpBEHDq1CkcOnSIhRJpZNKkSWq3L1myBEOHDoWDgwMAYNGiRZUZi/Sci4sLrly5AgcHByQmJiIgIAAA0LhxY8TFxSE3NxdnzpxB/fr1RU5KVHFYJIkoICAAHTt2xPfff4+tW7di/PjxGDduHGbPng0A+Oqrr3Du3DkcPnxY5KSkT6RSKZo2bYpq1aqpbD916hT8/f1haWkJiUSC48ePixOQ9JJUKkV6ejpq1KiBQYMGIT09Hfv27YOFhQUKCwvxzjvvQCaT4bfffhM7KlGFYZEkIltbW8TExMDHxwcKhQJmZmY4e/assq3/X3/9hU6dOiE9PV3kpKRP5s6di9WrVyMsLEzlKiSblNLr+GeR5O3tXeb8Onv2LN555x2kpKSImJKoYrEFgI6QSqWQyWQqf/u3trZGdna2eKFIL02bNg3btm3DuHHj8Pnnn6O4uFjsSGQgng/8LywshJOTk8o+JycnPHjwQIxYVAX8+eefovweskgSkaenJ27duqV8HhUVBXd3d+XzlJQUlWZtROXVsmVLxMTE4MGDB/D398eVK1c4s41e25tvvokWLVogJycH8fHxKvuSk5PZTJK0pkOHDvD29sYPP/xQqZ/L2W0iGjduHORyufJ5o0aNVPYfOHCAg7bplVlZWWHDhg3YunUrOnfurHKuEWlqxowZKs8tLCxUnu/duxdBQUGVGYmqkMTERCQmJuLQoUOV+rkck0RUBdy9excxMTHo1KkTLC0txY5DRKQXWCQRERERqcExSURERKSzYmNjYWRkJMpns0giIiIinSbWTS8O3CYiIiLR9O3b96X7s7OzRZudyyKJiIiIRLN371507ty5TO+t58ScmcsiScf9+eefaNq0KWxtbcWOQgaE5xVpA88rehW+vr7o168fRo0apXb/pUuX8Mcff1RyqlIck6TjxGqgRYaN5xVpA88rehV+fn64cOHCC/ebmZmpNFquTLySpOPEaqBFho3nFWkDzyt6FatWrXrpLTVfX18kJiZWYqK/sU8SERERkRq83aYjkpOTcfbsWZw/fx6ZmZlixyEDtH79ei6YTBVOEAQoFAqxY5ABKykpQXJysiifzSJJZCtWrICHhwe8vLwQEBCA1q1bw8nJCe3atUNMTIzY8ciAfPDBB7h3757YMUhPlZSUYPr06QgODlau47ZgwQJYWVnB3Nwcw4cPR1FRkcgpyRBdvXoVXl5eonw2xySJaOHChVi0aBGmTJkCmUyGJUuWYNCgQWjZsiU2b96M9u3b49SpU/D39xc7KukRe3t7tdtLSkrQtm1bSKWlfzd6+PBhZcYiPffdd98hLCwMQ4YMwY4dO5CRkYF9+/bh559/hkKhwJdffonFixfjiy++EDsqUYXhmCQReXl5YcWKFejatSsAID4+HgEBAUhPT4exsTE+/fRTxMXF4fDhwyInJX1ibW2N4OBg9O/fX7lNEASMHj0aM2fOhKurKwBg+PDhYkUkPVS7dm0sWbIEb7/9Nm7duoV69eph8+bNGDBgAADgt99+w8yZM3HlyhWRk5K+adGixUv3P336FPHx8aL0S2KRJCJLS0tcvXoVnp6eAEp/yExNTZGcnIyaNWsiNjYW7dq1Q25urrhBSa/cunULgwcPhq+vL3788UdYWVkBAExMTBAbG4sGDRqInJD0kbm5OeLj4+Hm5qZ8fvHiRdSvXx9A6cy2pk2bIicnR8yYpIdkMhkGDhz4wltqaWlpWL16tShFEm+3iahu3bo4cuQIxowZAwA4ceIETE1N4ezsDKD0xBGrFTvpLx8fH0RGRuKrr75Cs2bNsGHDBgQGBoodi/Scra0tHj9+rCySWrRoAWtra+X+wsJC/nlFr6RRo0Zo3bo1xo0bp3b/pUuXsHr16kpOVYpFkoimTZuGoUOH4ujRo5DJZNi1axdCQ0OVf9CcPHkSjRo1Ejkl6SNjY2PMmzcPISEhGDx4MIYMGcIfMHotDRo0wIULF9C4cWMAwOnTp1X2X7lyBXXq1BEjGum5du3a4caNGy/cb21tjfbt21dior/xdpvIDhw4gI0bN6KwsBAhISHKq0oAkJWVBQBwcHAQKx4ZgKysLIwZMwYnTpzAmTNnUK9ePbEjkR6Kj4+HiYnJC2+JbN68GcbGxnj33XcrORmR9rBIIiIiIlKDfZKIDAybkZI2PXnyBH/++Se2bduGHTt2ICYmBvy7NmnDiBEjRO/txitJIluxYgV27doFe3t7jB07Fh07dlTuy8zMRKtWrZCQkCBiQtI3RkZG6NChA0aNGoV+/frBzMxM7EhkAORyOaZNm4Yff/wRBQUFAKAsjtzd3bFs2TL06NFDzIikpy5fvqx2u7+/P7Zv3w5vb28AQJMmTSozFgAWSaJaunQppk2bhhEjRiA7Oxu//fYbZsyYgWnTpgEA7t+/DxcXF1GmPZL+kkqlCAkJwfHjx2FpaYkhQ4Zg1KhRaNasmdjRSI9NnToVe/bswdy5cyGTyTB79my8/fbb6NmzJzZv3oz58+djz5496NKli9hRSc9IpVJIJBK1VySfb5dIJOyTVNU0bNgQX331FQYPHgwAiIqKQu/evfHhhx9i5syZLJLolUilUqSnp0MqlWLDhg1Yt24d4uLi0KxZM4wePRqDBw+Gra2t2DFJz7i6umLr1q0ICgoCAKSmpqJ+/frIzMyEmZkZZs2ahQMHDiAyMlLkpKRvmjVrhlq1amHhwoUwNzcHUHqVsk6dOjhw4IBy1qSHh0elZ+OYJBElJiYiICBA+bxt27Y4fvw4fv75Z+XVJKJX5ejoiM8++wx//fUXIiIi0KxZM0yZMgUuLi4YNmyY2PFIz+Tm5iq7tQNAzZo1UVBQgEePHgEA+vXrh9jYWLHikR6Ljo6Gj48P+vXrh4cPH8LDw0PZZNnFxQUeHh6iFEgAiyRROTo6IiUlRWVbw4YNcfz4caxbtw6TJ08WKRnpM3X9kNq2bYs1a9YgLS0NS5cuxe3bt0VIRvqscePG2LJli/L59u3bYWVlpWx+q1AoOP6NXompqSkWL16MhQsXomfPnpg7dy4UCoXYsQDwdpuoBg8ejBo1amDx4sVl9l29ehVvvPEGsrKyeLuNNPL8dluNGjXEjkIG5NixY+jevTuaNm0KmUyGyMhILFiwABMmTABQumD3gQMHcOzYMXGDkl67f/8+RowYgdzcXJw5c0b0pZRYJIno8uXLiImJwYgRI9Tuv3r1Knbs2IEZM2ZUcjLSZxs2bMDAgQP5t3qqcJcvX8a2bduUzW87d+4sdiQyUEuXLsWJEyewbNky1KpVS7QcLJKIiIiI1ODabTrg5s2biIyMRHp6OiQSCZycnBAQEMB1kOiVZWRk4OrVq/Dz84ONjQ3u37+PDRs2QKFQoHv37sr1t4he14gRIzB79my4uLiIHYUMVFpaGoqLi+Hu7l7pn80rSSLKzs7GsGHDsHfvXtja2qJGjRoQBAEPHjxATk4OevTogV9++QU2NjZiRyU9cvLkSbz99tvIz8+Hs7MzDh48iO7du8Pc3BxSqRR37txhPxvSmC43/CPD5uvri/j4ePZJqmqGDRuGS5cuYfXq1WjdurXKvrNnz+KDDz5As2bNsGHDBpESkj5q164dmjVrhv/85z9YtWoVlixZgl69emH58uUAgMmTJyMyMrLMKu5EL6PLDf/IsJ07dw75+fkIDg6u9M9mkSSiatWq4dChQ2UKpOfOnDmDt956C48fP67cYKTXbG1tceHCBdSuXRslJSUwNzfHuXPnlB23b968iZYtW/K8Io3ocsM/Im1hnySRqetpU559RC9iamqqXFurqKgICoVC+RwAnj59ChMTE7HikZ7S5YZ/ZJgEQRC9XxKLJBH16NEDY8aMwfnz58vsO3/+PMaOHYuePXuKkIz0WWBgIKZOnYrTp09j4sSJaNGiBb7//ns8efIE+fn5mDVrFvz9/cWOSXpGlxv+kX4rKSnB9OnTERwcrGx5s2DBAlhZWcHc3BzDhw9HUVGRKNlYJIlo2bJlcHFxQatWrWBvb4/69evD19cX9vb2aN26NWrWrImlS5eKHZP0zIIFC3D9+nUEBQXh9OnT+P3332FkZIRq1arB1tYWp06dwuzZs8WOSXqqa9euOH/+PMLDw0UZI0KG57vvvkNYWBj8/f2xY8cOjBs3DsuWLcPPP/+MsLAwHD9+XG3T5crAMUk64Pr164iKikJ6ejoAwNnZGW3btkX9+vVFTkb6LCsrCw4ODsrnx44dw9OnT9G2bVuV7USvSlca/pF+q127NpYsWYK3334bt27dQr169bB582YMGDAAAPDbb79h5syZuHLlSqVnY5FEREREojE3N0d8fDzc3NyUzy9evKi8UJCYmIimTZsiJyen0rPxdpsOS0tLQ3JystgxyMDwvCJt4HlFr8rW1lZltm2LFi1gbW2tfF5YWCjaRCYWSTqsY8eO8PLyEjsGGRieV6QNPK/oVTVo0AAXLlxQPj99+jRcXV2Vz69cuSLaChRclkSH/fLLL8jPzxc7BhkYnlekDTyv6FWtWrXqpW1JiouL8cUXX1Rior9xTBIRERGRGrzdRlRFrF+/HtnZ2WLHIAOjCw3/yDA9ePAAxcXFomZgkSSyffv2YfTo0fjiiy9w/fp1lX2PHj1Cx44dRUpGhuaDDz7AvXv3xI5BekqXG/6Rfvv5559RWFgIoLTonjNnDuzs7ODs7Ixq1aph0qRJohXiLJJEtHnzZvTq1Qvp6emIiopC8+bNsWnTJuX+oqIinDp1SsSEpI/s7e3VPkpKStC2bVvlcyJN6HLDP9Jv48aNU17l/vnnnzFnzhx8/fXXCA8Px7x587B27VqsWLFClGwcuC2ihQsX4r///S8++eQTAMCOHTswYsQIFBQUYNSoUSKnI31VXFyM4OBg9O/fX7lNEATlFct/zhohKq/NmzcjLCwMb7/9NsaNG1em4Z9MJsPMmTNFG2BL+uufQ6PXrFmDWbNmYeLEiQCAgIAAyGQyLFu2DB9//HGlZ+PAbRFZWVnhypUrKtNmT548iZ49e2L+/Pno06cPXFxcIJfLRUxJ+ubWrVsYPHgwfH198eOPP8LKygoAYGJigtjYWDRo0EDkhKSPdLnhH+k3qVSK+/fvo3r16qhevTqOHTuGJk2aKPcnJCSgadOmyM3Nrfxslf6JpGRjY4P79++rbOvQoQP27t2LyZMnY9myZSIlI33m4+ODyMhIODs7o1mzZjh9+rTYkcgA6HLDP9J/Bw8exJ49e2Bubo6nT5+q7Hv69CmkUnHKFd5uE1GrVq1w4MABtGnTRmV7cHAw9u7di7ffflukZKTvjI2NMW/ePISEhGDw4MEYMmQIf8DotTxv+Ne4cWMAKFN8i9nwj/Tf8OHDlf9+7NgxtG7dWvk8KioKtWvXFiMWiyQxTZw4EZGRkWr3dejQAX/88Qc2bNhQyanIkHTs2BEXLlzAmDFjYGlpCSMjI7EjkZ7S5YZ/pN/+beaas7Mz5s6dW0lpVHFMEhEREZEaHJNEVEXcv3+fC5BShfvuu++QmZkpdgzSU5r+mZSamqqlJOqxSBLZihUr0KlTJ7z77rs4fvy4yr7MzEx4e3uLlIz0VW5uLoYOHQoPDw9lg7+PPvoINWvWhJeXF4KDgzkDiTSWk5NT5pGdnY3Zs2cjISFBuY1IEy1btsSYMWMQHR39wmOys7OxevVqNGrUCLt27arEdLzdJqqlS5di2rRpGDFiBLKzs/Hbb79hxowZmDZtGoDSv/mzBQBp6pNPPsHRo0cxfvx47Nq1C7a2trh9+zZWrVoFhUKB8ePHo2fPnpg9e7bYUUmPvGg8myAIkEgkyn/yzyvSxMOHDzFnzhysXbsWJiYm8Pf3h4uLC2QyGR49eoRr167h6tWr8Pf3x/Tp09G1a9dKzcciSUQNGzbEV199hcGDBwMoHcHfu3dvfPjhh5g5cyaLJHol7u7u2LBhA9544w3cu3cPtWrVwu+//44ePXoAAPbv349JkyaVWQaH6GVq1aqFZs2a4bPPPlNOxxYEAZ06dUJYWJiy31twcLCYMUlPFRQUYP/+/QgPD8edO3fw9OlTODo6onnz5ggJCUGjRo1EycUiSUQWFha4du0aPD09lduuXr2KN998EyNGjMCECRNYJJHGZDIZbt68qWz6Z2lpiYsXL6Ju3boAgKSkJDRo0ABPnjwRMybpmYcPH2LUqFHIzs7Gr7/+quzczialZMg4JklEjo6OSElJUdnWsGFDHD9+HOvWrcPkyZNFSkb6zMHBAQ8ePFA+79WrF6pVq6Z8npeXBzMzMxGSkT6zt7fH7t270b9/f7Rq1QpbtmwROxKR1rFIElG7du2wc+fOMtsbNGiAY8eO4eDBgyKkIn3XpEkTnDt3Tvl88+bNqFGjhvL5uXPn4OvrK0Y0MgDjxo3DkSNHMG/ePOVQASJDxWaSIpo6dSpiYmLU7mvYsCFOnDiBHTt2VHIq0nebNm16aQt/JycnDtqm19KgQQNER0dj6tSpaNSoEczNzcWORKQVHJNEREREpAZvt4lE1xtokX7ieUXawPOKqioWSSLR9QZapJ94XpE28LyiqopjkkQSFxeHOXPm4K233vrXBloLFiyo9AZapJ94XpE28LyiqopjkkSmqw20SL/xvCJt4HlFVQ2LJCIiIiI1OCaJiIiISA0WSURERERqsEgiIiIiUoNFEhEREZEaLJKIyKB06NABEyZMeOF+T09PLF68+LU+Y/369SqLBhORYWKfJCIyKLt27YKJiYnYMYjIALBIIiKDYm9vL3YEIjIQvN1GRAbln7fbMjIy0KNHD5ibm8PLywubNm0qc/yiRYvQuHFjWFpaws3NDePHj0deXp7KMevXr4e7uzssLCzQp08fZGVllXmfvXv3ws/PDzKZDN7e3vjuu+9QUlKile9IRJWDRRIRGaz3338fd+7cwfHjx7Fjxw6sWLECGRkZKsdIpVIsXboUf/31FzZs2IDjx4/jiy++UO4/e/YsRo4cifHjx+PSpUt444038P3336u8x6FDhzB06FCEhobi2rVr+Omnn7B+/XrMnj27Ur4nEWkHO24TkUHp0KEDmjVrhvHjx6NevXo4c+YMWrduDQC4fv06fH198d///veFg7t/++03jBs3DpmZmQCAwYMH49GjRzhw4IDymIEDB+LgwYN4/PgxAKB9+/bo2rUrpk2bpjxm48aN+OKLL3Dv3j3tfFEi0jqOSSIigxQXFwdjY2P4+/srt9WvX7/MrLQTJ05gzpw5uHbtGnJyclBSUoKCggI8efIElpaWiIuLQ58+fVRe07ZtWxw8eFD5PCYmBufOnVO5ciSXy1FQUID8/HxYWFho50sSkVaxSCIig/T8IrlEInnhMUlJSejWrRvGjh2LWbNmwd7eHhERERg1ahSKi4tV3udlFAoFvvvuO/Tt27fMPplM9orfgIjExiKJiAySr68vSkpKcP78ebRq1QoAcOPGDeUtMgA4f/48SkpK8MMPP0AqLR2iuX37dpX3adCgAc6cOaOy7f8/b9GiBW7cuAEfHx8tfBMiEguLJCIySPXq1cNbb72FMWPG4Oeff4axsTEmTJgAc3Nz5TG1a9dGSUkJli1bhh49euD06dNYtWqVyvuEhoYiICAA8+fPR+/evXH48GGVW20A8M033+Dtt9+Gm5sb+vfvD6lUisuXL+PKlStlBnkTkf7g7DYiMljr1q2Dm5sbgoOD0bdvX3zwwQeoUaOGcn+zZs2waNEizJs3D40aNcKmTZswd+5clfdo06YNwsLCsGzZMjRr1gyHDx/G9OnTVY4JCQnBH3/8gSNHjqBly5Zo06YNFi1aBA8Pj0r5nkSkHZzdRkRERKQGryQRERERqcEiiYiIiEgNFklEREREarBIIiIiIlKDRRIRERGRGiySiIiIiNRgkURERESkBoskIiIiIjVYJBERERGpwSKJiIiISA0WSURERERqsEgiIiIiUuP/AJqoRHSqGI32AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'idade'\n",
    "cat_srs, bins = pd.qcut(df[var], 4, retbins=True)\n",
    "g = df.groupby(cat_srs)\n",
    "biv = g['mau'].mean()\n",
    "\n",
    "ax = biv.plot.line()\n",
    "ax.set_ylabel(\"Proporção de inadimplentes\")\n",
    "ticks = plt.xticks(range(len(biv.index.values)), biv.index.values, rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:blue\">Tarefa 03 - Bivariada </span>\n",
    "<span style=\"color:blue\"> Com base no código da célula anterior, construa uma análise bivariada para a variável  ```tempo_emprego```.  Em seguida, insira uma célula de markdown e conclua se a variável parece discriminar risco de crédito. </span>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'tempo_emprego'\n",
    "cat_srs, bins = pd.qcut(df[var], 4, retbins=True)\n",
    "g = df.groupby(cat_srs)\n",
    "biv = g['qtd_filhos'].mean()\n",
    "\n",
    "ax = biv.plot.line()\n",
    "ax.set_ylabel(\"Proporção de inadimplentes\")\n",
    "ticks = plt.xticks(range(len(biv.index.values)), biv.index.values, rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos no gráfico acima que quanto maior a quantidade de filhos maior é o risco de inadimplência. E o tempo de emprego só reduz a inadimplência após a estabilidade de 5 anos de emprego."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etapa 3 Crisp-DM: Preparação dos dados\n",
    "Nessa etapa realizamos tipicamente as seguintes operações com os dados:\n",
    "- seleção\n",
    "Neste caso, os dados já estão pré-selecionados\n",
    "- limpeza\n",
    "Precisaremos identificar e tratar dados faltantes\n",
    "- construção\n",
    "Neste primeiro exercício não faremos construção de novas variáveis\n",
    "- integração\n",
    "Temos apenas uma fonte de dados, não é necessário agregação\n",
    "- formatação\n",
    "Os dados já se encontram em formatos úteis\n",
    "\n",
    "Os dados já estão pré-selecionados, construídos e integrados, mas há dados faltantes que serão eliminados na próxima célula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tipo</th>\n",
       "      <th>n_categorias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sexo</th>\n",
       "      <td>object</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posse_de_veiculo</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>posse_de_imovel</th>\n",
       "      <td>object</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qtd_filhos</th>\n",
       "      <td>int64</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tipo_renda</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
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       "      <th>educacao</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estado_civil</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tipo_residencia</th>\n",
       "      <td>object</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idade</th>\n",
       "      <td>float64</td>\n",
       "      <td>5298</td>\n",
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       "    <tr>\n",
       "      <th>tempo_emprego</th>\n",
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       "    </tr>\n",
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       "      <th>possui_celular</th>\n",
       "      <td>int64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>possui_fone_comercial</th>\n",
       "      <td>int64</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>possui_fone</th>\n",
       "      <td>int64</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>possui_email</th>\n",
       "      <td>int64</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qt_pessoas_residencia</th>\n",
       "      <td>float64</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mau</th>\n",
       "      <td>bool</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          tipo  n_categorias\n",
       "sexo                    object             2\n",
       "posse_de_veiculo        object             2\n",
       "posse_de_imovel         object             2\n",
       "qtd_filhos               int64             8\n",
       "tipo_renda              object             5\n",
       "educacao                object             5\n",
       "estado_civil            object             5\n",
       "tipo_residencia         object             6\n",
       "idade                  float64          5298\n",
       "tempo_emprego          float64          3005\n",
       "possui_celular           int64             1\n",
       "possui_fone_comercial    int64             2\n",
       "possui_fone              int64             2\n",
       "possui_email             int64             2\n",
       "qt_pessoas_residencia  float64             9\n",
       "mau                       bool             2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = pd.DataFrame(df.dtypes, columns = ['tipo'])\n",
    "\n",
    "metadata['n_categorias'] = 0\n",
    "\n",
    "for var in metadata.index:\n",
    "    metadata.loc[var,'n_categorias'] = len(df.groupby([var]).size())\n",
    "    \n",
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def convert_dummy(df, feature,rank=0):\n",
    "    pos = pd.get_dummies(df[feature], prefix=feature)\n",
    "    mode = df[feature].value_counts().index[rank]\n",
    "    biggest = feature + '_' + str(mode)\n",
    "    pos.drop([biggest],axis=1,inplace=True)\n",
    "    df.drop([feature],axis=1,inplace=True)\n",
    "    df=df.join(pos)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for var in metadata[metadata['tipo'] == 'object'].index:\n",
    "    df = convert_dummy(df, var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "<p>16650 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       qtd_filhos      idade  tempo_emprego  possui_celular  \\\n",
       "0               0  58.832877       3.106849               1   \n",
       "1               0  52.356164       8.358904               1   \n",
       "2               0  52.356164       8.358904               1   \n",
       "3               0  46.224658       2.106849               1   \n",
       "4               0  29.230137       3.021918               1   \n",
       "...           ...        ...            ...             ...   \n",
       "16645           0  54.109589       9.884932               1   \n",
       "16646           0  43.389041       7.380822               1   \n",
       "16647           0  30.005479       9.800000               1   \n",
       "16648           0  30.005479       9.800000               1   \n",
       "16649           0  33.936986       3.630137               1   \n",
       "\n",
       "       possui_fone_comercial  possui_fone  possui_email  \\\n",
       "0                          0            0             0   \n",
       "1                          0            1             1   \n",
       "2                          0            1             1   \n",
       "3                          1            1             1   \n",
       "4                          0            0             0   \n",
       "...                      ...          ...           ...   \n",
       "16645                      0            0             0   \n",
       "16646                      1            1             0   \n",
       "16647                      1            0             0   \n",
       "16648                      1            0             0   \n",
       "16649                      0            1             1   \n",
       "\n",
       "       qt_pessoas_residencia    mau  sexo_M  ...  educacao_Lower secondary  \\\n",
       "0                        2.0  False       1  ...                         0   \n",
       "1                        1.0  False       0  ...                         0   \n",
       "2                        1.0  False       0  ...                         0   \n",
       "3                        2.0  False       1  ...                         0   \n",
       "4                        2.0  False       0  ...                         0   \n",
       "...                      ...    ...     ...  ...                       ...   \n",
       "16645                    2.0   True       0  ...                         0   \n",
       "16646                    2.0   True       0  ...                         0   \n",
       "16647                    2.0   True       1  ...                         0   \n",
       "16648                    2.0   True       1  ...                         0   \n",
       "16649                    2.0   True       0  ...                         0   \n",
       "\n",
       "       estado_civil_Civil marriage  estado_civil_Separated  \\\n",
       "0                                0                       0   \n",
       "1                                0                       0   \n",
       "2                                0                       0   \n",
       "3                                0                       0   \n",
       "4                                0                       0   \n",
       "...                            ...                     ...   \n",
       "16645                            1                       0   \n",
       "16646                            0                       0   \n",
       "16647                            0                       0   \n",
       "16648                            0                       0   \n",
       "16649                            0                       0   \n",
       "\n",
       "       estado_civil_Single / not married  estado_civil_Widow  \\\n",
       "0                                      0                   0   \n",
       "1                                      1                   0   \n",
       "2                                      1                   0   \n",
       "3                                      0                   0   \n",
       "4                                      0                   0   \n",
       "...                                  ...                 ...   \n",
       "16645                                  0                   0   \n",
       "16646                                  0                   0   \n",
       "16647                                  0                   0   \n",
       "16648                                  0                   0   \n",
       "16649                                  0                   0   \n",
       "\n",
       "       tipo_residencia_Co-op apartment  tipo_residencia_Municipal apartment  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "...                                ...                                  ...   \n",
       "16645                                0                                    0   \n",
       "16646                                0                                    0   \n",
       "16647                                0                                    0   \n",
       "16648                                0                                    0   \n",
       "16649                                0                                    0   \n",
       "\n",
       "       tipo_residencia_Office apartment  tipo_residencia_Rented apartment  \\\n",
       "0                                     0                                 0   \n",
       "1                                     0                                 0   \n",
       "2                                     0                                 0   \n",
       "3                                     0                                 0   \n",
       "4                                     0                                 0   \n",
       "...                                 ...                               ...   \n",
       "16645                                 0                                 0   \n",
       "16646                                 0                                 0   \n",
       "16647                                 0                                 0   \n",
       "16648                                 0                                 0   \n",
       "16649                                 0                                 0   \n",
       "\n",
       "       tipo_residencia_With parents  \n",
       "0                                 0  \n",
       "1                                 0  \n",
       "2                                 0  \n",
       "3                                 0  \n",
       "4                                 0  \n",
       "...                             ...  \n",
       "16645                             0  \n",
       "16646                             0  \n",
       "16647                             0  \n",
       "16648                             0  \n",
       "16649                             0  \n",
       "\n",
       "[16650 rows x 29 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etapa 4 Crisp-DM: Modelagem\n",
    "Nessa etapa que realizaremos a construção do modelo. Os passos típicos são:\n",
    "- Selecionar a técnica de modelagem\n",
    "Utilizaremos a técnica de floresta aleatória (**random forest**), pois é uma técnica bastante versátil e robusta que captura bem padrões complexos nos dados, relativamente fácil de se usar e que costuma produzir excelentes resultados para uma classificação como estas. Vamos ver esse algoritmo em detalhes mais adiante no curso, mas pense nele por enquanto como uma regra complexa baseada nas variáveis explicativas que classifica o indivíduo como inadimplente ou não. Mais adiante no curso vamos extrair mais dessa técnica.\n",
    "- Desenho do teste\n",
    "Antes de rodar o modelo precisamos construir um desenho do teste que será realizado. Para desenvolver um modelo como este, é considerado uma boa prática dividir a base em duas, uma chamada ```treinamento```, onde o algoritmo 'aprende', e outra chamada ```teste```, onde o algoritmo é avaliado. Essa prática fornece uma métrica de avaliação mais fidedigna do algoritmo, falaremos mais detalhes em lições futuras.\n",
    "- Avaliação do modelo\n",
    "Faremos a avaliação do nosso modelo através do percentual de acerto, avaliando a classificação do modelo (inadimplente e não inadimplente) e comparando com o estado real armazenado na variável resposta (```AtrasoRelevante2anos```). Esse percentual de acerto é frequentemente chamado de acurácia (**obs:** nunca usar assertividade... a**ss**ertivo não é aquele que a**c**erta, e sim \"*adj.: em que o locutor declara algo, positivo ou negativo, do qual assume inteiramente a validade; declarativo*.\" a**C**ertivo está errado ;)\n",
    "#### Dividindo a base em treino e teste\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Tirando a v. resposta da base de treinamento\n",
    "x = df.drop(\"mau\",axis = 1)\n",
    "y = df[\"mau\"]\n",
    "\n",
    "# Tirando ID da base de treinamento e teste\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>qtd_filhos</th>\n",
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       "<p>12487 rows × 28 columns</p>\n",
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      ],
      "text/plain": [
       "       qtd_filhos      idade  tempo_emprego  possui_celular  \\\n",
       "7488            0  27.876712       1.569863               1   \n",
       "2887            2  31.682192       8.065753               1   \n",
       "6849            0  57.183562      -2.000000               1   \n",
       "6070            1  44.800000      10.334247               1   \n",
       "6125            0  29.512329       1.495890               1   \n",
       "...           ...        ...            ...             ...   \n",
       "9547            2  35.986301      10.019178               1   \n",
       "8624            0  46.435616       7.361644               1   \n",
       "11510           0  27.621918       4.767123               1   \n",
       "3526            0  29.257534       3.273973               1   \n",
       "4759            1  38.564384       5.769863               1   \n",
       "\n",
       "       possui_fone_comercial  possui_fone  possui_email  \\\n",
       "7488                       0            0             0   \n",
       "2887                       0            0             1   \n",
       "6849                       0            0             0   \n",
       "6070                       0            0             0   \n",
       "6125                       0            0             0   \n",
       "...                      ...          ...           ...   \n",
       "9547                       1            1             0   \n",
       "8624                       0            0             0   \n",
       "11510                      1            1             0   \n",
       "3526                       0            0             0   \n",
       "4759                       0            0             0   \n",
       "\n",
       "       qt_pessoas_residencia  sexo_M  posse_de_veiculo_Y  ...  \\\n",
       "7488                     1.0       0                   1  ...   \n",
       "2887                     4.0       1                   1  ...   \n",
       "6849                     2.0       1                   0  ...   \n",
       "6070                     2.0       0                   0  ...   \n",
       "6125                     2.0       1                   1  ...   \n",
       "...                      ...     ...                 ...  ...   \n",
       "9547                     4.0       0                   1  ...   \n",
       "8624                     2.0       0                   1  ...   \n",
       "11510                    2.0       0                   1  ...   \n",
       "3526                     1.0       0                   1  ...   \n",
       "4759                     2.0       0                   1  ...   \n",
       "\n",
       "       educacao_Lower secondary  estado_civil_Civil marriage  \\\n",
       "7488                          0                            0   \n",
       "2887                          0                            0   \n",
       "6849                          0                            0   \n",
       "6070                          0                            0   \n",
       "6125                          0                            0   \n",
       "...                         ...                          ...   \n",
       "9547                          0                            0   \n",
       "8624                          0                            0   \n",
       "11510                         0                            0   \n",
       "3526                          0                            0   \n",
       "4759                          0                            0   \n",
       "\n",
       "       estado_civil_Separated  estado_civil_Single / not married  \\\n",
       "7488                        0                                  1   \n",
       "2887                        0                                  0   \n",
       "6849                        0                                  0   \n",
       "6070                        1                                  0   \n",
       "6125                        0                                  0   \n",
       "...                       ...                                ...   \n",
       "9547                        0                                  0   \n",
       "8624                        0                                  0   \n",
       "11510                       0                                  0   \n",
       "3526                        0                                  1   \n",
       "4759                        0                                  1   \n",
       "\n",
       "       estado_civil_Widow  tipo_residencia_Co-op apartment  \\\n",
       "7488                    0                                0   \n",
       "2887                    0                                0   \n",
       "6849                    0                                0   \n",
       "6070                    0                                0   \n",
       "6125                    0                                0   \n",
       "...                   ...                              ...   \n",
       "9547                    0                                0   \n",
       "8624                    0                                0   \n",
       "11510                   0                                0   \n",
       "3526                    0                                0   \n",
       "4759                    0                                0   \n",
       "\n",
       "       tipo_residencia_Municipal apartment  tipo_residencia_Office apartment  \\\n",
       "7488                                     0                                 0   \n",
       "2887                                     0                                 0   \n",
       "6849                                     0                                 0   \n",
       "6070                                     0                                 0   \n",
       "6125                                     0                                 0   \n",
       "...                                    ...                               ...   \n",
       "9547                                     0                                 0   \n",
       "8624                                     0                                 0   \n",
       "11510                                    0                                 0   \n",
       "3526                                     0                                 0   \n",
       "4759                                     0                                 0   \n",
       "\n",
       "       tipo_residencia_Rented apartment  tipo_residencia_With parents  \n",
       "7488                                  0                             0  \n",
       "2887                                  0                             0  \n",
       "6849                                  0                             0  \n",
       "6070                                  0                             0  \n",
       "6125                                  0                             0  \n",
       "...                                 ...                           ...  \n",
       "9547                                  0                             0  \n",
       "8624                                  0                             0  \n",
       "11510                                 0                             0  \n",
       "3526                                  0                             0  \n",
       "4759                                  0                             0  \n",
       "\n",
       "[12487 rows x 28 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rodando o modelo\n",
    "A função RandomForestClassifier gera a estrutura da floresta aleatória, e o parâmetro ```n_estimator``` define o número de árvores na floresta. Normalmente a acurácia do modelo tende a aumentar com o número de árvores, pelo menos até um certo limite - e aumenta também o recurso computacional demandado. Você pode alterar esse parâmetro e verificar se a acurácia do seu modelo melhora - não recomendamos valores muito altos. Vá alterando aos poucos e percebendo como o tempo aumenta com os seus recursos. Não é necessário ir muito além de umas 100 árvores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Treinar uma Random Forest com 5 árvores\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=3)\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 97.57%\n"
     ]
    }
   ],
   "source": [
    "# Calculando a acuracia\n",
    "\n",
    "y_pred = clf.predict(x_test)\n",
    "acc = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Acurácia: {0:.2f}%'.format(acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.017060687301974166\n",
      "0.48333333333333334\n"
     ]
    },
    {
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "mau    False  True \n",
       "row_0              \n",
       "False   4033     70\n",
       "True      31     29"
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "# Matriz de confusão com erro KeyError File ~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802, in Index.get_loc(self, key, method, tolerance)\n",
    "\n",
    "#tab = pd.crosstab(index = y_pred, columns = y_test)\n",
    "#print(tab[1][0]/(tab[1][0] + tab[0][0]))\n",
    "#print(tab[1][1]/(tab[1][1] + tab[0][1]))\n",
    "#tab\n",
    "\n",
    "# Matriz de confusão corrigida\n",
    "\n",
    "tab = pd.crosstab(index=y_pred, columns=y_test)\n",
    "print(tab.iloc[0, 1] / (tab.iloc[0, 1] + tab.iloc[0, 0]))\n",
    "print(tab.iloc[1, 1] / (tab.iloc[1, 1] + tab.iloc[1, 0]))\n",
    "tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:blue\">Tarefa 04 - Bivariada </span>\n",
    "<span style=\"color:blue\"> Para essa tarefa, crie três células extras, copie nelas o código das três células anteriores (na mesma ordem) e altere o parâmetro ```n_estimators=``` da função ```RandomForestClassifier``` e insira uma quantidade maior que 3 nesse parâmetro. Rode as três células anteriores para calcular a acurácia do modelo e veja se você consegue uma acurácia melhor. </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=15)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Treinar uma Random Forest com 5 árvores\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=15)\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 97.72%\n"
     ]
    }
   ],
   "source": [
    "# Calculando a acuracia\n",
    "\n",
    "y_pred = clf.predict(x_test)\n",
    "acc = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Acurácia: {0:.2f}%'.format(acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.016565164433617538\n",
      "0.5344827586206896\n"
     ]
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       "mau    False  True \n",
       "row_0              \n",
       "False   4037     68\n",
       "True      27     31"
      ]
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matriz de confusão\n",
    "\n",
    "tab = pd.crosstab(index=y_pred, columns=y_test)\n",
    "print(tab.iloc[0, 1] / (tab.iloc[0, 1] + tab.iloc[0, 0]))\n",
    "print(tab.iloc[1, 1] / (tab.iloc[1, 1] + tab.iloc[1, 0]))\n",
    "tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etapa 5 Crisp-DM: Avaliação dos resultados\n",
    "A etapa final do CRISP. Neste casp, a nossa avaliação termina com a acurácia. Mas em problemas futuros aprofundaremos mais - a ideia seria avaliar o impacto do uso do modelo no negócio, ou seja, o quanto o resultado financeiro melhora em detrimento da utilização do modelo.\n",
    "\n",
    "Como um exemplo simples, considere que um cliente bom pagador deixa (em média) 5 '*dinheiros*' de lucro, e um mau pagador deixa (em média) 100 '*dinheiros*' de prejuízo.\n",
    "\n",
    "de acordo com a matriz de confusão:\n",
    "\n",
    "| Decisão   | lucro dos bons    | lucro dos maus | total  |\n",
    "| --------- |:-----------------:|:--------------:| ------:|\n",
    "| Aprovador | 4042 x 5          | 72 x (-100)    | 13.010 |\n",
    "| Reprovar  |  27 x 5           | 22 x (-100)    | -2.065 |\n",
    "\n",
    "Estariamos evitando, portanto, um prejuízo de -2.145 '*dinheiros*' - o que na prática significa um aumento no lucro.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etapa 6 Crisp-DM: Implantação\n",
    "Nessa etapa colocamos em uso o modelo desenvolvido, normalmente implementando o modelo desenvolvido em um motor de crédito que toma as decisões com algum nível de automação - tipicamente aprovando automaticamente clientes muito bons, negando automaticamente clientes muito ruins, e enviando os intermediários para análise manual."
   ]
  }
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