3_EDA_blocos_de_código_essenciais_para_classificacao.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3-EDA-blocos-de-código-essenciais-para-classificacao.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNWuiEXyNiUxeKG0wFdOyMD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/willclopes/Portfolio-Data-Science/blob/master/3_EDA_blocos_de_c%C3%B3digo_essenciais_para_classificacao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sFpgSdGg3wH1"
      },
      "source": [
        "# Blocos de código essenciais para EDA - Classificação\n",
        "\n",
        "\n",
        "---\n",
        "By Willian C Lopes\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpbKMmptYZ8D"
      },
      "source": [
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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvsseET0aO2G"
      },
      "source": [
        "## Contexto\n",
        "\n",
        "Uma empresa que atua em Big Data e Data Science deseja contratar cientistas de dados entre as pessoas que obtiverem aprovação em alguns cursos ministrados pela empresa. Muitas pessoas se inscrevem para o treinamento. A empresa quer saber quais desses candidatos realmente querem trabalhar para a empresa após o treinamento ou à procura de um novo emprego, pois ajuda a reduzir o custo e o tempo, bem como a qualidade do treinamento ou planejamento dos cursos e categorização dos candidatos. Informações relacionadas à demografia, educação, experiência estão em mãos desde a inscrição e inscrição dos candidatos.\n",
        "\n",
        "O conjunto de dados foi desenvolvido para entender os fatores que levam uma pessoa a deixar o emprego atual."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zmzJCldmOl5"
      },
      "source": [
        "## Introdução\n",
        "\n",
        "A EDA ou análise exploratória de dados é uma etapa importante do aprendizado de máquina que envolve aprender sobre os dados sem gastar muito tempo ou se perder nisso. Nesse processo você se familiariza com a estrutura e as características gerais do conjunto de dados, com os recursos independentes e dependentes e suas interações. Você também terá uma ideia dos processos de limpeza de dados a serem empregados e das possíveis técnicas de modelagem.\n",
        "\n",
        "Neste artigo, exploraremos um conjunto de dados da Kaggle para uma empresa que realiza cursos de treinamento para funcionários que querem (ou não) novas oportunidades. A tarefa é prever se os trainees que cursam estão em busca de mudança de emprego e se vão deixar o cargo atual e ingressar na empresa, ou se permanecerão no cargo atual mesmo após a conclusão dos cursos. A tarefa é, portanto, uma tarefa binária, nosso atributo alvo é 0 (não procurando uma mudança de emprego) e 1 (procurando uma mudança de emprego).\n",
        "\n",
        "\n",
        "\n",
        "Você pode acompanhar baixando o conjunto de dados [aqui](https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TXUFimwPov-H"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxH62xScqj1S"
      },
      "source": [
        "## Importar as Bibliotecas e o DataSet\n",
        "\n",
        "A primeira etapa é importar as bibliotecas que usaremos em todo o projeto EDA. Em seguida, lemos o **train.csv** que baixei e salvei no meu github. Vamos a EDA no conjunto de **treino** porque ele contém a variável de destino e estamos interessados ​​em compreender os recursos individuais, bem como sua relação com o atributo de destino."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqsf9jxklsF5"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nW_srrtwlsNV"
      },
      "source": [
        "data = pd.read_csv('https://raw.githubusercontent.com/willclopes/Portfolio-Datasets/main/HR-Analytics-Job-Change-of-Data-Scientists/aug_train.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H3rYM3hEs9MA"
      },
      "source": [
        "## Exploração de conjunto de dados básicos\n",
        "\n",
        "### 1. Forma (dimensões) do DataFrame\n",
        "\n",
        "Um DataFrame tem linhas e colunas, e o atributo **.shape** do Pandas retorna uma tupla de comprimento 2, cada valor mostrando o comprimento de cada dimensão ou (linhas, colunas). \n",
        "\n",
        "Podemos ver que o conjunto de dados possui 19.158 linhas e 14 colunas. Uma dessas colunas no recurso de destino.\n",
        "\n",
        "### 2. Tipos de dados das várias colunas\n",
        "\n",
        "Logo após, exibiremos todos os tipos de dados das colunas. Isso nos ajuda a entender o tipo de dados com os quais estamos lidando, em termos de distribuições numéricas e categóricas. Isso também lançará luz sobre alguns dos gráficos e plotagens exploratórias a serem usados.\n",
        "\n",
        "#### O que procurar;\n",
        "\n",
        "* Recursos categóricos que devem ser numéricos e vice-versa."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGyCabpUlsQI",
        "outputId": "223d27c9-66ae-44f9-9e64-0b43606bf3b8"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19158, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCZdKxSMlsSp",
        "outputId": "525ed704-1df5-42e7-bb2a-d07440541bac"
      },
      "source": [
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "enrollee_id                 int64\n",
              "city                       object\n",
              "city_development_index    float64\n",
              "gender                     object\n",
              "relevent_experience        object\n",
              "enrolled_university        object\n",
              "education_level            object\n",
              "major_discipline           object\n",
              "experience                 object\n",
              "company_size               object\n",
              "company_type               object\n",
              "last_new_job               object\n",
              "training_hours              int64\n",
              "target                    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxY0bJaLtzHv"
      },
      "source": [
        "Vemos várias características categóricas (objetos). Também vemos que o nosso atributo algo é um float, representado como 1,0 para aqueles que deixaram o emprego e 0,0 para aqueles que permaneceram após o treinamento. Isso torna **target** um recurso binário.\n",
        "\n",
        "Nesse ponto, voltamos à página do Kaggle para entender as colunas e seus significados:\n",
        "\n",
        "---\n",
        "\n",
        "* enrollee_id: ID exclusivo para o candidato \n",
        "\n",
        "* cidade: código da cidade \n",
        "\n",
        "* city_ development _index: Índice de desenvolvimento da cidade (em escala)\n",
        "\n",
        "* gênero: gênero do candidato \n",
        "\n",
        "* relevent_experience: Experiência relevante do candidato \n",
        "\n",
        "* matriculado_universidade: tipo de curso universitário matriculado, se houver\n",
        "\n",
        "* nível_de_educação: nível de educação do candidato \n",
        "\n",
        "* major_discipline: disciplina principal de educação do candidato\n",
        "\n",
        "* experiência: Experiência total do candidato em anos \n",
        "\n",
        "* tamanho_da_empresa: Nº de funcionários na empresa do empregador atual \n",
        "\n",
        "* company_type: tipo de empregador atual \n",
        "\n",
        "* lastnewjob: diferença em anos entre o emprego anterior e o emprego atual \n",
        "\n",
        "* training_hours: horas de treinamento concluídas \n",
        "\n",
        "* target: 0 - Não estou procurando uma mudança de emprego, 1 - Procurando uma mudança de emprego\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LsHC_pIjDvlJ"
      },
      "source": [
        "### 3. Exibir algumas linhas\n",
        "\n",
        "Agora exibiremos uma pequena seção de nossos dados. Usamos df.head()para exibir as 5 primeiras linhas, df.tail()para exibir as 5 últimas ou df.sample()para escolher 1 linha aleatória do conjunto de dados. df.sample(5)exibe 5 linhas selecionadas aleatoriamente.\n",
        "\n",
        "#### O que procurar:\n",
        "\n",
        "* Os nomes das colunas fazem sentido? Você pode verificar as definições da coluna novamente.\n",
        "* Quais valores estão nas colunas? Eles fazem sentido?\n",
        "* Você pode ver um número significativo de valores ausentes (NaNs)\n",
        "* Quais são algumas das classes exclusivas para os recursos categóricos?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dt3yUYlTlsVH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "2ca48631-5d73-452b-8211-0a737c552820"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
              "      <th>city</th>\n",
              "      <th>city_development_index</th>\n",
              "      <th>gender</th>\n",
              "      <th>relevent_experience</th>\n",
              "      <th>enrolled_university</th>\n",
              "      <th>education_level</th>\n",
              "      <th>major_discipline</th>\n",
              "      <th>experience</th>\n",
              "      <th>company_size</th>\n",
              "      <th>company_type</th>\n",
              "      <th>last_new_job</th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8949</td>\n",
              "      <td>city_103</td>\n",
              "      <td>0.920</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29725</td>\n",
              "      <td>city_40</td>\n",
              "      <td>0.776</td>\n",
              "      <td>Male</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>15</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>&gt;4</td>\n",
              "      <td>47</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11561</td>\n",
              "      <td>city_21</td>\n",
              "      <td>0.624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>Full time course</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never</td>\n",
              "      <td>83</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33241</td>\n",
              "      <td>city_115</td>\n",
              "      <td>0.789</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No relevent experience</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Business Degree</td>\n",
              "      <td>&lt;1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>never</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>666</td>\n",
              "      <td>city_162</td>\n",
              "      <td>0.767</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Masters</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Funded Startup</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   enrollee_id      city  ...  training_hours target\n",
              "0         8949  city_103  ...              36    1.0\n",
              "1        29725   city_40  ...              47    0.0\n",
              "2        11561   city_21  ...              83    0.0\n",
              "3        33241  city_115  ...              52    1.0\n",
              "4          666  city_162  ...               8    0.0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WU-TY_6CERvZ"
      },
      "source": [
        "A tabela dá-nos uma ideia dos valores presentes nas diferentes funcionalidades e os nomes das colunas são autoexplicativos. Podemos ver alguns valores ausentes significativos nas colunas como **gender** e **company_size.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCR_IEciEh3D"
      },
      "source": [
        "## Distribuições Numéricas\n",
        "\n",
        "A distribuição de um atributo refere-se à frequência com que os valores desse atributo ocorrem. Para atributo numéricos (contínuos), os valores são agrupados em intervalos, também conhecidos como compartimentos. \n",
        "\n",
        "Usaremos **gráficos** para traçar as distribuições e ter uma ideia intuitiva dos valores em cada coluna. Também exibiremos estatísticas de resumo reais que contêm números reais das distribuições. É importante usar gráficos e estatísticas, pois eles se complementam.\n",
        "\n",
        "### 4. Histograma dos atributos numéricas\n",
        "\n",
        "Um histograma é uma forma de gráfico de barras que agrupa os números em intervalos e a altura das barras mostra quantos valores estão nesse intervalo. Usaremos a função df.hist() do Pandas para traçar um histograma de cada atributo numérico no conjunto de dados. Também passaremos a função **figsize=(8,8)** e **xrot=45** para aumentar o tamanho da grade e girar o eixo x para facilitar a leitura.\n",
        "\n",
        "#### O que procurar;\n",
        "\n",
        "* Potenciais outliers\n",
        "* Recursos numéricos que devem ser recursos categóricos (indicadores). Por exemplo, se houver apenas dois valores possíveis, como 0 e 1.\n",
        "* Limites que não fazem sentido, como um valor percentual maior que 100."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lhth-UcFlsXQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "outputId": "c982c84c-a0a6-44cf-dccb-fb6eee48eb3e"
      },
      "source": [
        "data.hist(figsize=(8,8), xrot=45)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIQCeqYcFuvX"
      },
      "source": [
        "Como esperado, vemos que **target**, nosso recurso dependente, é na verdade uma variável indicadora com apenas dois valores possíveis; 0 e 1. \n",
        "\n",
        "O **enrollee_id** é uma coluna de ID, o que significa que cada valor ocorre apenas uma vez, portanto, a distribuição igual entre todos os bins.\n",
        "\n",
        "### 5. Estatísticas de resumo dos atributos numéricos\n",
        "\n",
        "Usaremos a função **df.describe()**  para retornar uma análise de cada recurso numérico.\n",
        "\n",
        "#### O que procurar;\n",
        "\n",
        "O **count**, ou o número de valores não NaN na coluna. Se a contagem for menor do que as linhas gerais no quadro de dados, essa coluna conterá valores ausentes (NaN).\n",
        "* O valor médio, ou mean.\n",
        "* O std, ou desvio padrão dos valores na coluna\n",
        "* O minimum valor\n",
        "* O 25o valor do percentil\n",
        "* A mediana, ou 50o valor do percentil\n",
        "* O 75o do percentil\n",
        "* O maximum valor. Isso também pode indicar valores extremos (outlier).\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ez6GDMAlsaD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c660f690-c6ab-4ed5-b75d-a4ea98148c0d"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
              "      <th>city_development_index</th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19158.000000</td>\n",
              "      <td>19158.000000</td>\n",
              "      <td>19158.000000</td>\n",
              "      <td>19158.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16875.358179</td>\n",
              "      <td>0.828848</td>\n",
              "      <td>65.366896</td>\n",
              "      <td>0.249348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9616.292592</td>\n",
              "      <td>0.123362</td>\n",
              "      <td>60.058462</td>\n",
              "      <td>0.432647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.448000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8554.250000</td>\n",
              "      <td>0.740000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16982.500000</td>\n",
              "      <td>0.903000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25169.750000</td>\n",
              "      <td>0.920000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>33380.000000</td>\n",
              "      <td>0.949000</td>\n",
              "      <td>336.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        enrollee_id  city_development_index  training_hours        target\n",
              "count  19158.000000            19158.000000    19158.000000  19158.000000\n",
              "mean   16875.358179                0.828848       65.366896      0.249348\n",
              "std     9616.292592                0.123362       60.058462      0.432647\n",
              "min        1.000000                0.448000        1.000000      0.000000\n",
              "25%     8554.250000                0.740000       23.000000      0.000000\n",
              "50%    16982.500000                0.903000       47.000000      0.000000\n",
              "75%    25169.750000                0.920000       88.000000      0.000000\n",
              "max    33380.000000                0.949000      336.000000      1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vfe5r8cpGoBV"
      },
      "source": [
        "Vemos que não há valores ausentes ou outliers significativos em qualquer coluna.\n",
        "\n",
        "## Distribuição de atributos categóricos\n",
        "\n",
        "Os atributos categóricos são colunas que contêm apenas alguns valores exclusivos e, portanto, cada valor pertence a uma classe ou grupo específico.\n",
        "\n",
        "Assim como acontece com os atributos numéricos, usaremos gráficos e estatísticas resumidas para visualizar as distribuições das diferentes classes em cada coluna.\n",
        "\n",
        "### 6. Estatísticas resumidas dos recursos categóricos\n",
        "\n",
        "Usaremos o **df.describe(include=’object’)** que exibe apenas a análise das funcionalidades que possuem o tipo de dados **‘object’.**\n",
        "\n",
        "\n",
        "#### O que procurar:\n",
        "\n",
        "* O count, ou número de valores não NaN. Valores ausentes estarão presentes se a contagem for menor que o total de linhas do DataFrame.\n",
        "* O número de unique de aulas.\n",
        "* A classe mais frequente. \n",
        "* A frequência ( freq) ou o número de ocorrências da classe principal.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APLB9kEwlsb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "5ce43300-abdc-472b-9052-cde91d5977b2"
      },
      "source": [
        "data.describe(include='object')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>gender</th>\n",
              "      <th>relevent_experience</th>\n",
              "      <th>enrolled_university</th>\n",
              "      <th>education_level</th>\n",
              "      <th>major_discipline</th>\n",
              "      <th>experience</th>\n",
              "      <th>company_size</th>\n",
              "      <th>company_type</th>\n",
              "      <th>last_new_job</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19158</td>\n",
              "      <td>14650</td>\n",
              "      <td>19158</td>\n",
              "      <td>18772</td>\n",
              "      <td>18698</td>\n",
              "      <td>16345</td>\n",
              "      <td>19093</td>\n",
              "      <td>13220</td>\n",
              "      <td>13018</td>\n",
              "      <td>18735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>123</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>22</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>city_103</td>\n",
              "      <td>Male</td>\n",
              "      <td>Has relevent experience</td>\n",
              "      <td>no_enrollment</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>STEM</td>\n",
              "      <td>&gt;20</td>\n",
              "      <td>50-99</td>\n",
              "      <td>Pvt Ltd</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>4355</td>\n",
              "      <td>13221</td>\n",
              "      <td>13792</td>\n",
              "      <td>13817</td>\n",
              "      <td>11598</td>\n",
              "      <td>14492</td>\n",
              "      <td>3286</td>\n",
              "      <td>3083</td>\n",
              "      <td>9817</td>\n",
              "      <td>8040</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            city gender  ... company_type last_new_job\n",
              "count      19158  14650  ...        13018        18735\n",
              "unique       123      3  ...            6            6\n",
              "top     city_103   Male  ...      Pvt Ltd            1\n",
              "freq        4355  13221  ...         9817         8040\n",
              "\n",
              "[4 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dauQTLByHp0p"
      },
      "source": [
        "Podemos ver que muitos recursos têm valores ausentes.\n",
        "\n",
        "### 7. Trace distribuições categóricas\n",
        "\n",
        "Vamos agora representar graficamente os recursos categóricos para ver como as classes estão distribuídas em cada atributo. Usaremos o **countplot** do Seaborn, que é como um gráfico de barras em que cada barra representa uma classe e a altura é o número de valores dessa classe. Usando as estatísticas de resumo acima, vemos que alguns recursos, como **city** possui muitas classes exclusivas, podem não ser legíveis quando plotados. Plotamos apenas classe que possuem 22 ou menos classes únicas.\n",
        "\n",
        "#### O que procurar;\n",
        "\n",
        "* Classes esparsas - aquelas com poucos valores. Isso pode afetar o desempenho de um modelo.\n",
        "* Nomes ou erros de digitação inconsistentes, por exemplo, rotular 2 classes semelhantes de maneira diferente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMElbueHlsgS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fdd45bdf-5b83-4f4d-fb52-093156e38564"
      },
      "source": [
        "for col in data.select_dtypes(include='object'):\n",
        "    if data[col].nunique() <= 22:\n",
        "        sns.countplot(y=col, data=data)\n",
        "        plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oUSJ_fdKaoM"
      },
      "source": [
        "Agora podemos ver as distribuições de classes nos vários recursos categóricos.\n",
        "\n",
        "### Segmentar e agrupar pelo target \n",
        "\n",
        "Podemos segmentar nossos dados em grupos e observar as relações entre os recursos categóricos e numéricos, principalmente em relação ao atributo classe.\n",
        "\n",
        "## 8. Segmente o target por características numéricas\n",
        "\n",
        "Aqui, vamos separar os dados do target e observar como os recursos numéricos são distribuídos entre aqueles que saíram (alvo = 1) e aqueles que ficaram (alvo = 0).\n",
        "\n",
        "Usaremos o **boxplot** do Seaborn, que exibe o resumo de cinco números de um elemento numérico. Os cinco números são mínimo, primeiro quartil (Q1 ou marca de 25%), mediana (Q2), terceiro quartil (Q3 ou marca de 75%) e máximo. \n",
        "\n",
        "Confira [este](https://www.geeksforgeeks.org/how-to-show-mean-on-boxplot-using-seaborn-in-python/) artigo para mais informações sobre o box plot.\n",
        "\n",
        "#### O que procurar:\n",
        "\n",
        "* Qual classe do target é mais afetada pelos recursos numéricos\n",
        "\n",
        "Mas antes, vamos fazer uma limpeza e converter 2 colunas categóricas em numéricas.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_tmaO6BLEzw"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lGNLFHHLrib"
      },
      "source": [
        "## Limpeza imediata de dados / engenharia de recursos\n",
        "\n",
        "Vamos converter **experience** e **last_new_job** em atributos numéricos porque ambos representam os anos de experiência dos estagiários. Isso nos permitirá ver como **experience** influenciou na decisão de um estagiário de deixar ou permanecer no emprego atual. \n",
        "\n",
        "O código a seguir substitui alguns valores em uma coluna e converte a coluna inteira em um tipo de dados float ( int não funcionou porque as colunas possuem valores NaNs)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnyORK_SHmOu"
      },
      "source": [
        "data['experience_num'] = data['experience'].replace({'>20':'21', '<1': '0'}).astype(float)\n",
        "data['last_new_job_num'] = data['last_new_job'].replace({'>4':'5','never':'0'}).astype(float)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9uRQCHWLlEE"
      },
      "source": [
        "Agora vamos criar um gráfico boxplot. \n",
        "\n",
        "Primeiro salvaremos as quatro colunas numéricas de interesse em uma lista chamada **num_cols**. Em seguida, percorreremos essas colunas à medida que criamos os gráfico de cada atributo em relação ao **target.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0SOfYYh2HmRJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6a050d19-d17c-4c3a-a7c0-be7a8d87458d"
      },
      "source": [
        "num_cols = ['training_hours','city_development_index', 'experience_num', 'last_new_job_num']\n",
        "for col in num_cols:\n",
        "    sns.boxplot(y = data['target'].astype('category'), x = col, data=data)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHfeV0QpMS73"
      },
      "source": [
        "Podemos ver que **training_hours** estão igualmente distribuídos entre os que ficaram e os que saíram. Isso significa que **training_hours** pode não ser um bom preditor para o destino. \n",
        "\n",
        "No entanto, vemos que quando se trata de **experience** e **last_new_job**, uma proporção maior dos que ficaram possuem mais anos de experiência em relação aos que não ficaram e esses podem ser bons preditores."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRvDLJ2GNnZE"
      },
      "source": [
        "## Recursos categóricos do segmento pelas classes de destino\n",
        "\n",
        "Agora que vimos como o **target** se comporta com os atributos numéricos, faremos o mesmo com os atributos categóricos.\n",
        "\n",
        "### 9. Crosstab no target \n",
        "\n",
        "A função **.crosstab (col1, col2)** do Panda constrói uma \"tabela de tabulação cruzada\" que mostra a relação entre dois recursos categóricos. A tabela mostra a frequência com que os grupos de dados dos dois atributos aparecem. Você pode incluir o parâmetro **normalize=’index’** para mostrar as porcentagens nas linhas em vez dos números reais. \n",
        "\n",
        "Mais informações sobre guias cruzadas [aqui](https://pbpython.com/pandas-crosstab.html) . Vamos ver isso em ação.\n",
        "\n",
        "Vamos percorrer 3 colunas categóricas (aquelas com menos de 4 classes exclusivas) e exibir uma tabulação cruzada de seus relacionamentos com o target. A função **display()** foi usada para mostra uma tabela mais limpa do que **print()** ao executar um loop.\n",
        "\n",
        "Obs: Usamos o crosstab porque o target aqui é um atributo numérico. Se fosse categórico, usaríamos o boxplot ou o gráfico de violino.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-xlgHEJHmTY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "26df41ac-ca46-405b-c990-e972f6e2e093"
      },
      "source": [
        "for col in data.select_dtypes(include='object'):\n",
        "    if data[col].nunique() <=4:\n",
        "        display(pd.crosstab(data['target'], data[col], normalize='index'))\n",
        "        #display(pd.crosstab(data['target'], data[col]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>gender</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "      <th>Other</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>0.080980</td>\n",
              "      <td>0.90650</td>\n",
              "      <td>0.012520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>0.096222</td>\n",
              "      <td>0.88902</td>\n",
              "      <td>0.014758</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "gender    Female     Male     Other\n",
              "target                             \n",
              "0.0     0.080980  0.90650  0.012520\n",
              "1.0     0.096222  0.88902  0.014758"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>relevent_experience</th>\n",
              "      <th>Has relevent experience</th>\n",
              "      <th>No relevent experience</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>0.753147</td>\n",
              "      <td>0.246853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>0.619845</td>\n",
              "      <td>0.380155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "relevent_experience  Has relevent experience  No relevent experience\n",
              "target                                                              \n",
              "0.0                                 0.753147                0.246853\n",
              "1.0                                 0.619845                0.380155"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>enrolled_university</th>\n",
              "      <th>Full time course</th>\n",
              "      <th>Part time course</th>\n",
              "      <th>no_enrollment</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>0.164754</td>\n",
              "      <td>0.063465</td>\n",
              "      <td>0.771781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>0.307477</td>\n",
              "      <td>0.064890</td>\n",
              "      <td>0.627632</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "enrolled_university  Full time course  Part time course  no_enrollment\n",
              "target                                                                \n",
              "0.0                          0.164754          0.063465       0.771781\n",
              "1.0                          0.307477          0.064890       0.627632"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NEbbqSYROUw"
      },
      "source": [
        "Essas tabelas mostram como os atributos categóricos são divididos entre a classe target."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyYeLoGORutY"
      },
      "source": [
        "### 10. Gráficos do target x atributos categóricos\n",
        "\n",
        "Podemos traçar as relações categóricas para ter uma ideia melhor das interações. Vamos plotar um **countplot** usando a função do **Seaborn - catplot()** e o **kind=’count’** como parâmetro, pois ele seleciona o gráfico a ser plotado. Outras plotagens que você pode criar usando catplot incluem boxplot() (com kind=\"box\"), violinplot() (com kind=\"violin\"), boxenplot() (com kind=\"boxen\").\n",
        "\n",
        "\n",
        "#### O que procurar;\n",
        "\n",
        "* Atributos categóricos que mudam significativamente com as classes de destino.\n",
        "\n",
        "No código, usamos um loop para representar graficamente apenas 3 classes categóricas em relação ao target. \n",
        "\n",
        "Lembre-se de que **0** saiu enquanto o **1** permaneceu em seus empregos.\n",
        "\n",
        "Observe o parâmetro **sharey=False** no catplot que desassocia as escalas y para cada classe de destino para que possamos visualizar e comparar melhor as relações."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z24lfGpyHmWF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f5aa51c8-f9ab-47b0-d35b-ae9a398d93f2"
      },
      "source": [
        "for col in data.select_dtypes(include='object'):\n",
        "    if data[col].nunique() <= 4:\n",
        "        g = sns.catplot(x = col, kind='count', col = 'target', data=data, sharey=False)\n",
        "        g.set_xticklabels(rotation=60)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWpFDOwiUP9W"
      },
      "source": [
        "Podemos ver que as classes de gênero não mudam muito dependendo do target, mas há uma diferença significativa quando se trata do **relevant_experience** e **enrolled_university** significando que essas categorias podem ser melhores preditores."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "leIVGpTVTYHO"
      },
      "source": [
        "### 11. Agrupamento \n",
        "\n",
        "Agora que observamos como nosso target é distribuído pelos outros atributos de entrada, vamos verificar como as categorias estão relacionadas aos atributos numéricos.\n",
        "\n",
        "\n",
        "Usaremos a função **.groupby()** do DataFrame para agrupar nossos dados em categorias e observar como os atributos numéricos são distribuídos calculando uma métrica como a **média** , **mediana** e **desvio padrão** entre as classes. A métrica que usaremos é **mean**. Também forneceremos as colunas para as quais calcular as médias.\n",
        "\n",
        "Usando todos os atributos categóricos com classes menores ou iguais a 22\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rD6n2F7Hmeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "3296397c-feb2-496a-d7ca-63858b3de0dc"
      },
      "source": [
        "for col in data.select_dtypes(include='object'):\n",
        "    if data[col].nunique() <= 3:\n",
        "        display(data.groupby(col)[['training_hours', 'target', 'experience_num', 'last_new_job_num']].mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "      <th>experience_num</th>\n",
              "      <th>last_new_job_num</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>65.636511</td>\n",
              "      <td>0.263328</td>\n",
              "      <td>9.154406</td>\n",
              "      <td>1.947240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>65.297633</td>\n",
              "      <td>0.227819</td>\n",
              "      <td>10.863124</td>\n",
              "      <td>2.096328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>63.869110</td>\n",
              "      <td>0.261780</td>\n",
              "      <td>9.079787</td>\n",
              "      <td>1.780105</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        training_hours    target  experience_num  last_new_job_num\n",
              "gender                                                            \n",
              "Female       65.636511  0.263328        9.154406          1.947240\n",
              "Male         65.297633  0.227819       10.863124          2.096328\n",
              "Other        63.869110  0.261780        9.079787          1.780105"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "      <th>experience_num</th>\n",
              "      <th>last_new_job_num</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relevent_experience</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Has relevent experience</th>\n",
              "      <td>65.800174</td>\n",
              "      <td>0.214690</td>\n",
              "      <td>11.537605</td>\n",
              "      <td>2.255435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No relevent experience</th>\n",
              "      <td>64.253261</td>\n",
              "      <td>0.338427</td>\n",
              "      <td>6.402806</td>\n",
              "      <td>1.330623</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         training_hours  ...  last_new_job_num\n",
              "relevent_experience                      ...                  \n",
              "Has relevent experience       65.800174  ...          2.255435\n",
              "No relevent experience        64.253261  ...          1.330623\n",
              "\n",
              "[2 rows x 4 columns]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "      <th>experience_num</th>\n",
              "      <th>last_new_job_num</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enrolled_university</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Full time course</th>\n",
              "      <td>64.532340</td>\n",
              "      <td>0.380889</td>\n",
              "      <td>5.763447</td>\n",
              "      <td>1.209134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Part time course</th>\n",
              "      <td>67.571786</td>\n",
              "      <td>0.252087</td>\n",
              "      <td>7.980785</td>\n",
              "      <td>1.836332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_enrollment</th>\n",
              "      <td>65.362018</td>\n",
              "      <td>0.211406</td>\n",
              "      <td>11.548483</td>\n",
              "      <td>2.242733</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     training_hours    target  experience_num  last_new_job_num\n",
              "enrolled_university                                                            \n",
              "Full time course          64.532340  0.380889        5.763447          1.209134\n",
              "Part time course          67.571786  0.252087        7.980785          1.836332\n",
              "no_enrollment             65.362018  0.211406       11.548483          2.242733"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zui8fi45UcBC"
      },
      "source": [
        "## Correlação entre atributos\n",
        "\n",
        "### 12. Matriz de correlação para os vários recursos numéricos\n",
        "\n",
        "Uma matriz de [correlação](https://corporatefinanceinstitute.com/resources/knowledge/finance/correlation/) é uma tabela usada para a mostrar as relações entre atributos numéricos. Os mesmos atributos são mostrados nas linhas e colunas, e cada célula contém o coeficiente de correlação entre dois atributos. O coeficiente de correlação é uma medida da força da relação entre as duas variáveis ​​e pode assumir qualquer valor entre -1 e 1. A correlação de um atributo consigo mesmo é 1. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6soGBmf-WPjc"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgVvf6PDWUBS"
      },
      "source": [
        "Em python, para calcular as correlações é só chamar a função **.corr()** do DataFrame para retornar os coeficientes de correlação em pares de todas as variáveis ​​numéricas no quadro de dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "yjO6ejRZTEhn",
        "outputId": "b8e03b7f-cf3c-4f5c-8da7-7451f2c96d05"
      },
      "source": [
        "corr = data.corr()\n",
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>enrollee_id</th>\n",
              "      <th>city_development_index</th>\n",
              "      <th>training_hours</th>\n",
              "      <th>target</th>\n",
              "      <th>experience_num</th>\n",
              "      <th>last_new_job_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>enrollee_id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.040455</td>\n",
              "      <td>0.000998</td>\n",
              "      <td>0.049475</td>\n",
              "      <td>-0.035509</td>\n",
              "      <td>-0.030693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city_development_index</th>\n",
              "      <td>-0.040455</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001920</td>\n",
              "      <td>-0.341665</td>\n",
              "      <td>0.333967</td>\n",
              "      <td>0.188984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>training_hours</th>\n",
              "      <td>0.000998</td>\n",
              "      <td>0.001920</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021577</td>\n",
              "      <td>0.000908</td>\n",
              "      <td>-0.006276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.049475</td>\n",
              "      <td>-0.341665</td>\n",
              "      <td>-0.021577</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.176724</td>\n",
              "      <td>-0.082607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>experience_num</th>\n",
              "      <td>-0.035509</td>\n",
              "      <td>0.333967</td>\n",
              "      <td>0.000908</td>\n",
              "      <td>-0.176724</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.479033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_new_job_num</th>\n",
              "      <td>-0.030693</td>\n",
              "      <td>0.188984</td>\n",
              "      <td>-0.006276</td>\n",
              "      <td>-0.082607</td>\n",
              "      <td>0.479033</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        enrollee_id  ...  last_new_job_num\n",
              "enrollee_id                1.000000  ...         -0.030693\n",
              "city_development_index    -0.040455  ...          0.188984\n",
              "training_hours             0.000998  ...         -0.006276\n",
              "target                     0.049475  ...         -0.082607\n",
              "experience_num            -0.035509  ...          0.479033\n",
              "last_new_job_num          -0.030693  ...          1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Z2p-3dWWjTq"
      },
      "source": [
        "### 13. Mapa de calor das correlações\n",
        "\n",
        "Um [mapa de calor](https://en.wikipedia.org/wiki/Heat_map) refere-se a uma representação codificada por cores dos dados em que os valores são representados por cores. Usaremos o [mapa de calor Seaborn](https://seaborn.pydata.org/generated/seaborn.heatmap.html) para visualizar os dados acima.\n",
        "\n",
        "\n",
        "O código é: **sns.heatmap(corr, cmap=’RdBu_r’, annot=True, vmax=1, vmin=-1)** \n",
        "\n",
        "Passamos nossa matriz de correlação corr, junto com quatro outros parâmetros que são opcionais: **cmap=’RdBu_r’** que especifica a paleta de cores do mapa de calor, o vermelho-escura mostra uma forte correlação positiva e o azul-escuro mostra uma forte correlação negativa. \n",
        "\n",
        "Mais sobre as paletas de cores do Seaborn [aqui](https://medium.com/@morganjonesartist/color-guide-to-seaborn-palettes-da849406d44f). \n",
        "O **annot=True** inclui os valores dos coeficientes em cada célula enquanto o **vmax=1** e **vmin=-1** define os limites da barra de cores, já que nossos valores de correlação também estão no mesmo intervalo.\n",
        "\n",
        "#### O que procurar;\n",
        "\n",
        "* Recursos fortemente correlacionados. Estes são vermelho-escuro (forte positivo) ou azul-escuro (forte negativo).\n",
        "\n",
        "* Fortes correlações com o target. Se alguma característica tiver uma forte correlação positiva ou negativa com ele."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwMA3QrlTElS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "43381265-3f82-443c-b26d-bc1d07fe01db"
      },
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "sns.heatmap(corr, cmap='RdBu_r', annot=True, vmax=1, vmin=-1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b9+//KaUOAJuBSrmo4jlgqrHvQ8D42xwrEXge+MIovxuoq7VOAboC4UZS3w80v4vYrwH9gC+VUt8D+T89VgghxB1JS1jkSFrC+U9awgVDWsL5Ly9awmsrNXDob07HPw8VuisnY8JCCCFcglsRHBOWJPw3oJSaBbTIsnqG1vpTZ8QjhBD3wlQIe4YcJUn4b0Br/W9nxyCEECL3JAkLIYRwCW7SEhZCCCGcw1QAjyE1HuE7A3AHPtFaT86yfRrQ2lgsCZi11mWNbWnYnngIEK+17uhoPJKEhRBCuIT8HhNWSrkDs4AngZPYnoC41vgIKgBa6zfsyr+G7fn/N1w1HlucZyQJCyGEcAkF0B3dBDiqtT4OoJRaCoRiewhSdnpym+f85wVJwkIIIf4WlFL9sD2Y6IZIrXWk3XJl4ITd8kng0RzqqgbUAL62W+2llNoHpAKTtdarHY1ZkrAQQgiXYHJzbExYH9GRQOQdC96dHsByrXWa3bpqWutTSqmawNdKqYNa62OOHEQeWymEEMIluLmbHHrdhVNAVbvlKsa67PQAvrBfobU+Zfx7HIgl83jxPZGWsBBCCJdQAA/r+Baoo5SqgS359gB6ZS2klKoLlMP23P4b68oBV7TWycZ3vrcApjgakCRhIYQQLiG/P6KktU5VSg0EYrB9RGm+1vpHpdR4YJ/Weq1RtAew1O5ragEeBOYopdKx9SJPtp9Vfa/kCxxEjuQLHPKffIFDwZAvcMh/efEFDrGNmzn0Nydo3+5Cd+WkJSyEEMIlyBOzhBBCCCcxFcYuAAdJEhZCCOES3ArgsZWupuidsRBCCOEipCUshBDCJcj3CQshhBBOIklYCCGEcJKiOCYsSVjkqLB95hZgaOl6zg4hV6ZfOeLsEHJt+fFrzg4h17okrHR2CLlSrE6efltewajVxOEqimJLuOi97RBCCCFchLSEhRBCuAQ3+ZywEEII4Rz5/exoVyRJWAghhEuQx1YKIYQQTiITs4QQQghRYKQlLIQQwiXImLAQQgjhJDImLIQQQjhJUfwqw6LX9hdCCCFchLSEhRBCuAR5drQQQgjhJEXxI0qShIUQQrgEmR0thBBCOInJregl4aJ3xkIIIYSLkJawEEIIlyATs4QQQggnkTFhIYQQwkmKYhIuemcshBBCuAhpCQshhHAJRXF2tCRhIYQQLsHk7u7sEAqcJGEhhBAuoSiOCUsSFkII4RLcpDtaCMdYrVY+mDqFXXFxeHl5MWrsOOo++OAt5Y4cOcy7Y8aQnJxM8xYteHPI25hMN58bu/izRcycPo2YLV9Ttly5gjyFTJ6dN4WA4DZcspzl3YB2BXrsuLg4wqdMIT09nc6dO/PSiy9m2p6SksKIkSM5cuQIZcqUYUp4OJUrVwZg3rx5rFq9Gjc3N9555x1aNG8OwOgxY9i+fTvly5dn5YoVGXVprfnvhAlcuXKF++67j0kTJ+Lt7Z1n52K1Wtm0cBa//PANnsWL02nA21Sq8cAt5T6fNJSkv86Snp7G/XUD6PDiINzcbnZR7lofzebP5zAkciUlfcrkWXzZiTvyK+Ert5KebqVz0wa89OSjmbZH7/wfUTv34+5mokQxT0b3aEutihU4+PufvBu12Thv6N++GY//o06+xgqwY98BJs75jPT0dLq2C+LlsJBM2789+BOTIj/n519P8P7Qf9OuZZOMbe/NX8q2b/cDMKBHJzoENs33eIVN0XvbIfLVrridnDgRz/LVaxg6ciRTJk3MttyUSRMZNmoUy1ev4cSJeHbvisvYlnD6NN/s2UPFihULKuwc7V6wnA/bP1fgx01LS2PipElEzJrFqpUr2bRpE8eOHctUZtWqVfj4+LB+3Tr69OnD9BkzADh27BibYmJYuWIFERERTJw4kbS0NABCO3bk44iIW443btw4Xh80iBXLl9OmTRsWLFyYp+dzdP9ezv15ktemLyLk5Tf58pMZ2Zbr9voo+k+Zy4Cp87hy8QKH92zL2HbhjIXjB76jjK85T2PLTlp6OhOXfUXEK11YNex5Nn2vOXb6bKYyHRrXZcXQ54h+uy8vPP4I762KBaB2JV+WvNWH6Lf7EtG/C+9GbyY1LT1/401L592IhUSOH8K62eF8uW03R+NPZSpzn7kCk97sx9NBzTKtj927n8NHf2PVRxOImjaWT1duIOnK1XyNNycmdzeHXndDKdVeKaWVUkeVUkOz2f68UipRKbXfeP3LbttzSqlfjFee/GG4pySslOqvlOprF/B9jgZi1PORo/Xkd52OUkpVV0r1ukOZxkqpmbmsd6xSarBj0Tlu+7ZtPPV0MCaTiYCAhlxKusSZxMRMZc4kJnI56TIBAQ0xmUw89XQw22JjM7ZP++A9Br7+eqaWsbMc3bGXK+cuFPhxDx06RNWqValSpQqenp60b9eOWLtrBLA1NpaOIbbWzpNPPMHevXuxWq3ExsbSvl07ihUrRpXKlalatSqHDh0CoFGjRvj4+NxyvN/j42nUqBEAzZo25auvvsrT8/lpXxwNW7XFZDJRpU49rl1J4tJfZ28pV7xkKQDS09JIS70O3LwHYhZF8ETvfpnW5ZdDv5+mql9ZqviWxdPDnfYPK2IPHs1UxtureMbPV1Ouc+N2LVHMEw8jISSnpmEqgHgP/HyM++/zp2olM8U8PejQqilf7/4uU5nK/n6oGvfj5pY5nmPxp2jcoC4e7u6U9PLigRpV2bHvQL7HnJ38TsJKKXdgFvAUUA/oqZSql03RKK31Q8brE2Pf8sAY4FGgCTBGKeVwN909JWGt9Wyt9SJj8XnA4SRchFQHbpuEtdb7tNaDCiacvJVoseDvf7MFazb7k5hoyVwm0YLZ/2ZrxuzvT6LFVmZb7Fb8/Mw88IAqmIBdlMViydQTYPb3J8FiybGMh4cH3t7enD9/ngSLBX+7ff39/bFk2TerWjVrsnXrVgD+b/NmTp8+nVenAsClc2coU8EvY9mnvB+Xzp3JtuznE9/hvVeeoZhXSeo1bQXYknjp8r5UrFYrT+PKieVCEhXLls5YNpctTcKFpFvKLd3xA0+P/4Rpa7fzTpc2GesP/PYnnSctoOvkhYwMeyIjKedbvGf/oqJv+Yxlf9/yJJz96672rVvzfnZ+d4Cr15L568Il9h44wukzt75BKggmNzeHXnehCXBUa31ca50CLAVC7zK8dsBmrfU5rfVfwGag/T2dqJ27GhM2Wr2DAStwADgGJAG/AY2BxUqpq8AI4GWtdSdjvyeBV7XWnXOo9wVgGHAe+B+QbKz3A2YD9xtF/wPsBo4DD2mtzxvlfgFaAulZy2utb/Zv2spWB+YDvkAi8ILWOl4ptQC4ZpyHD/Cm1nq9Uup5oBNQCqgDvAcUA5414uygtT6nlKqF7Z2VH3DFOP+fjHovGvVWBN7WWi8HJgMPKqX2Awu11tOyuS5BwGCtdbBSaqxxXjWNf6drrWca5UYAzwEW4ATwnbH+lpiAo8Y1HKK1jlVKTQLStdYjsvvdOMO1q1dZOH8+M2fd2l0q8te4ceOYHB5O5Ny5BAUG4unp6bRY+gwPJzUlhZUfTeTXQz9wv2rAzlVL6DMi3Gkx5aTHY/+kx2P/ZMO+I8z9vz38t89TADSsXolVw57n+OmzjFy8iZb1alDc0zWn4LR4OICDPx+n1+DxlPMpzUN1azttgpSjs6OVUv2AfnarIrXWkXbLlbH9rbzhJLaWbVbPKKVaAT8Db2itT+Swb2WHAuYuWsJKqfrASKCN1vofwOs3thlJZR/QW2v9ELABqGskUYAXsCW+7OqtBIwDWmBLpPZdAjOAaVrrR4BngE+01unAGqCzsf+jwO9a64TsymdzyA+xJb2GwGLAvru3OrZ3SE8Ds5VSXsb6BkAX4BFgAnBFa/1PbMmsr1EmEnhNa90I2xsV+wxSyTi3YGzJF2AosMPo5rglAeegLrZ3YTe6QDyVUo2AHsBDQAcjxhtuiUlrnYqt1+JjpdQT2N7BjbvL49/Wsugo+vTsTp+e3fH19SUh4WYrymJJwM8v8xien58ZS8LNlpklIQE/s5mTJ0/yxx+n6NOzO52CO2CxWOjbuxdnz2TfYvo7M5vNmVqjloQE/M3mHMukpqaSlJRE2bJl8TebSbDbNyEhAbP59uOoNWrUYM7s2Sz94gvaP/UUVapUcfgc9sasZvY7/Zj9Tj+8y1XgwtmbwxIXzyVSurxvjvt6FCuGatwcvW8X5xL+4K/E08x+ux/TB/bi4rlE5gzrT9L5cw7HmBNzGW9On7+UsWw5fwn/MjlPVGv/cF22ZumuBqhZsQIli3ty9M/8vYfNFcpx+szN65Fw5hz+Fe6+p7R/j1BWfTSB+ROHYrVC9crOn49xL7TWkVrrxnavyDvvdYt1QHUjV2wG8naCRBZ387ajDbBMa30GQGud452vtbYCnwF9lFJlgWbAxhyKPwrEaq0TjW6BKLttTwAfGa3FtYCPUsrbKNPdKNPDbp+cyttrBiwxfv4MW3K8IVprna61/gVba7uusX6r1vqS1joRuIDtlwNwEKhuHKM5sMw49hxsifeG1Ua9hwH/HK7D3fhSa51s/A4sRl2PAau01le01heN8+Z2MWmtfzTOfT3wonHdHdYtrDuffxHF519E0SqoNRu/XI/VauXgwQN4e3vj6+eXqbyvnx+lvEtx8OABrFYrG79cT6vAQGrXqcOmLV+zev0GVq/fgNlsZtHiJVTwzfmP9d9V/fr1iY+P5+SpU1y/fp1NMTEEBgZmKhMUGMjadbZbcvOWLTR55BFMJhOBgYFsiokhJSWFk6dOER8fT4MGDW57vLPnbP+t09PTmTt3Lt26dXP4HJq060T/8Ej6h0dSt3ELDmz/P6xWKyd/OUzxkqUoXa5CpvIp165mjBOnp6Xxy/ff4Hvf/fjfX5MhkSv4z0dL+M9HS/Ap78crk2bjXbZ8dofNE/Xvr0h84nlOnr3A9dQ0Nn2vCWyQuSv8d8vN7t7th49zv58t6Z08eyFjItYf5y7yW8I57it/6zh8Xgp4oCa//3Gak6ctpFxPZcP2PbRu+vBd7ZuWls5fF21vOPSv8ejf4mnxcEB+hpujApiYdQqoardcxViXQWt9VmudbCx+AjS6233vRX70j3yKLVldw5a8U++hDjegqdb6mv1KpdRuoLbR0u4E/PcO5e/2eNYclpPt1qXbLadju3ZuwHmjFyA79vs7MjvDvp40bv97u1NMAdi6//NlimmLli3ZFbeTZ0I7Gh9RGpuxrU9PW7IGeHvoMMaPHUPytWSatWhB8xYtc6jRuV5aMpMHgpri7VuOSSd2s27MNHbNj87343p4eDBs6FAGDBhAeno6nUJDqV27NrMiIqhfrx5BQUF07tyZESNGEBwSgo+PD1PCbd21tWvXpu2TT9K5Sxfc3d0ZPmwY7saTiN4ZOpR9+/Zx/vx5nmzblgEDBtClc2c2bdzI0ijb7+bxxx+nU+jdDpPdnTr/fJRf9n/Dh68/i2dxL0L7D8nYNvudfvQPjyTl2lWWTh1FamoK1nQr1es/ROMnQ25Ta/7xcHdj2DNtGPDxCtv1b9qA2pV8mbUhjvpV/QkKqM3SHT+w5+d4PN3dKF3Ci3d724YHfzh+ivlb9uLp7obJZGJ4t8cp510yn+N1Z+SAvvxr5FTS09Pp0rYVdapVYeZnK2hQpwZtmj7MwZ+P89q707mYdJmt3+znw89Xsn72ZFLTUnl2iO1PaamSJZgyeAAeTnpyVQF8leG3QB2lVA1sCbQHWeboKKUqaa3/NBY7AkeMn2OAiXaTsdpiG051iMlqzZp/MjO6o1cBzbTWZ40ZYoOAJK31e0qpdcAHWuutdvusAx4GntBaH8mh3krAHqPcReBr4H9a64FKqSXAD1rrqUbZh7TW+42fp2IbY62gte5grMu2vDGu29iocy22NwWfGetDtdadjbFbM7Yu4xrANqA2tl9OY631QKPO34zlM1nq3YWtK3yZUsoENNRa/8+od73RZY9SKklr7W10I3+gtc7crMl8bYLIPCacpLV+z9h2yIi1PLAAW4+CB/A9MMf4neQUUxfgFeA1bK3hJjfG17NzPunK7W8OFzS0dHYTHV3X9CvZ/vdwaSt/cs6kHUd0Sdjg7BBypVidnGxejZEAACAASURBVN5Duy63Wk0cngZuee91h/7mmAfPuGMMSqkOwHTAHZivtZ6glBoP7NNarzXmy3QEUoFzwACt9U/Gvi8Cw42qJmitP3UkXriLlrDW+kel1ARgm1IqDfgB24SsGxZgG0e9ii1RX8U25uqXUwI26v3TSDC7sbXM9tttHgTMUkodMGLcDvQ3tkVhezfz/F2Wv+E14FOl1BCMiVl22+KBvdgmZvXXWl/LRSu6N7Zx1pGAJ7bZdv+7TfkDQJpS6n/AglyMC2eitf5eKRVlHMuC7ZrkGJNS6hS2cenHtdYnjI9uzcA2sUsIIYoErfUGbPOX7NeNtvt5GDm0cLXW88lhntO9umNL+F4Yf+B/0FrPy/PK81jWFqu4SVrC+U9awgVDWsL5Ly9awonT3nDob47fG9Oc/3CBXMrzMWGl1HfAZeCtvK5bCCHE35d8gUMeMD4Wk4lS6hugeJbVz2qtD+b18XNLa/28s46tlGoHZP3w4685fa5aCCH+zuT7hPOJ1jq7D0MXeVrrGGwz7oQQoshzK4LfJ1z03nYIIYQQLsI1n6MmhBCiyJExYSGEEMJJJAkLIYQQTiITs4QQQggnKYot4aJ3xkIIIYSLkJawEEIIl1AUW8KShIUQQrgEGRMWQgghnMTkJg/rEEIIIUQBkZawEEII11AEW8KShIUQQrgGGRMWQgghnMNUBL/AQZKwEEII11AEu6OLXttfCCGEcBHSEhZCCOEaimBLWJKwEEIIlyAP6xDCjpe7ydkh5Nr0K0ecHUKu/Kfkg84OIdfeu1y4rjEAdZ91dgS5kmwqfP/3SuRFJdISFkIIIZykCCbhotf2F0IIIVyEtISFEEK4BBkTFkIIIZylCHZHSxIWQgjhGopgEi56bX8hhBDCRUhLWAghhEuQZ0cLIYQQziITs4QQQggnKYJjwpKEhRBCuASTJGEhhBDi70sp1R6YAbgDn2itJ2fZ/ibwLyAVSARe1Fr/bmxLAw4aReO11h0djUeSsBBCCNeQz2PCSil3YBbwJHAS+FYptVZrfdiu2A9AY631FaXUAGAK0N3YdlVr/VBexiRJWAghhEsogO7oJsBRrfVxAKXUUiAUyEjCWuutduX3AH3yMyBJwkIIIVyDg0lYKdUP6Ge3KlJrHWm3XBk4Ybd8Enj0NlW+BGy0W/ZSSu3D1lU9WWu92qGAkSQshBDCVTjYHW0k3Mg7FrwLSqk+QGMg0G51Na31KaVUTeBrpdRBrfUxR45T9D6UJYQQoqg6BVS1W65irMtEKfUEMALoqLVOvrFea33K+Pc4EAv809GApCUshBDCJRTAE7O+BeoopWpgS749gF72BZRS/wTmAO211ha79eWAK1rrZKWUL9AC26Qth0gSFkII4RryeWKW1jpVKTUQiMH2EaX5WusflVLjgX1a67XAVMAbWKaUgpsfRXoQmKOUSsfWizw5y6zqe2KyWq2O1iH+pq5dvSo3Rz77T8kHnR1Crr13+YizQ8g1D5OzI8gdq6mQBQyU8PJyOOi0n3Y49DfHve5jhe7CyZiwEEII4STSHS2EEMIlmOQLHIQQQggnKYLPji56bztEnouLi6NjaCjBISHMmz//lu0pKSkMefttgkNC6N2nD6dO3fxEwLx58wgOCaFjaChxu3ZlrB89ZgxBrVvT5ZlnCmWcWmue7duXZ7p25bVBg0hKSsqz87iTZ+dNYUrCPkYdjCmwY2bHarUyJTyc0I4hdA/rxpEj2Y8lHzl8mLBuXQntGMKU8HBuzFOZPu0DunTuRPewbrz15htcunQRgD17dtO7V0/CunWld6+e7N27N1dx5cd9kFOd3+zdS/cePejyzDOMHDmS1NRUAH799Vee7duXxo88wsKFC3MVv9VqJXzyZEKCg+nWtWuO1/Xw4cN0feYZQoKDCZ88OeO6XrhwgVdeeYWQkBBeeeUVLl68mLHPt99+S1hYGF06d+alF18EIDk5md69ehHWrRtdOncmIiIiV/HmisnNsVchVDijFi4jLS2NiZMmETFrFqtWrmTTpk0cO5b5s+urVq3Cx8eH9evW0adPH6bPmAHAsWPH2BQTw8oVK4iIiGDixImkpaUBENqxIx/n4X/2go5z3LhxvD5oECuWL6dNmzYsyOUfWkfsXrCcD9s/V2DHy0nczp2ciI9n9Zq1jBw5ikkTJ2RbbtLECYwaNZrVa9ZyIj6eXXFxADzatCnRy5YTFb2MatWq8amR3MqWLcf06TOIXraccePfZfTIEXcdU37cBznVmZ6ezqhRowgPD2flihVUuu8+1q5bB4BPmTK88/bbPNe3b66v686dO4mPj2ftunWMGj2aCf/9b7blJvz3v4weM4a169YRHx9PnHFd58+fz6NNmrBu3ToebdKE+fPmAXDx4kUmTZzIjBkzWLlqFVOnTgWgWLFizP3kE6KXLSMqOppdcXEcOHAg13HfFUnCf09KqbJKqVfvYb8NSqmydygz3vhgd55QSlVXSh3Kq/ry26FDh6hatSpVqlTB09OT9u3aERsbm6nM1thYOoaEAPDkE0+wd+9erFYrsbGxtG/XjmLFilGlcmWqVq3KoUO2U2/UqBE+Pj6FNs7f4+Np1KgRAM2aNuWrr77Ks3O5k6M79nLl3IUCO15Otm2L5engYEwmEwENG5J06RKJiYmZyiQmJpJ0+TIBDRtiMpl4OjiY2Fjbo3ubNWuOh4dtxKxBQEMSEhIAqFu3Ln5mMwC1atUiOTmZlJSUu4opP+6DnOo8f/48np6eVK9WzXY+TZvy1ZYtAFQoX54GDRpknF9uxG7dSnBICCaTiYYNG3Iph+t6+fJlGhrXNTgkhK1ff52xf0hH25f/hHTsyNattuu9ceNG2jz+OJUqVQKgfIUKAJhMJkqWLAlAamoqqampFLopyC6sSCRhoCxwSxJWSt32f4DWuoPW+vwdyozWWm9xML58d6dzvVcWi4WKFStmLJv9/UmwWHIs4+Hhgbe3N+fPnyfBYsHfbl9/f38sWfYtrHHWqlkz44/b/23ezOnTp/PqVAoNS5brZvb3JzHLdUu0WPA3+2cs53Rt165ZTYsWLW9Z/9WWLdSt+yDFihW765jy+j7Iqc5y5cqRlpbGjz/+CMDmzZs5bbyRcITFYqGi/+2vmcViwT+HMmfPncPPzw8AX19fzp47B8Dvv//OxYsXeemll+jZowfrjFY72HoQwsLCaNO6NU2bNiWgYUOHzyM7VpObQ6/CqKhMzJoM1FJK7QeuA9eAv4C6wANKqdXYHmXmBcy48cBvpdRv2J4d6o3tId47gebYnrQSqrW+qpRaAKzXWi83yi8EQgBPoJvW+iellB+wBLgP2I3ta7Qaaa3P5BCvu1JqbjbHegiYDZQEjmH7nsu/lFKxwGCt9T7jSS77tNbVlVLPA12M+N2VUj2AKMAH2+9+gNZ6x71fVpGTcePGMTk8nMi5cwkKDMTT09PZIRVa8z6Zi7u7O0916JBp/bFjR5k5cwazIj52UmS3ZzKZCJ88manvvUdKSgrNmzXD3cVm/5pMpoxWbVpqKkcOHyYyMpJrycn07duXhgEBVKteHXd3d6Kjo7l48SJvvvEGR3/5hdp16uRDQK51fQpCUTnjocAx43sghwAPA69rrR8wtr+otW6ELeEOUkpVyKaOOsAsrXV94DyQ04yhM1rrh4GPgcHGujHA18a+y4H77xBvTsdaBLyjtW6I7Yulx9yhHrCda1etdSC2x7PFGNfhH8D+u9j/tsxmc6ZWniUhAX+jqzC7MqmpqSQlJVG2bFn8zWYS7PZNSEjAnGXfvFLQcdaoUYM5s2ez9IsvaP/UU1SpUiUPz8Z1RUctpWf3MHp2D8PX1zfTdbMkJGR0I9/gZzaTYLnZOsx6bdeuXcOO7Tv474SJmOweYJGQkMDgN99k/LvvUrVqVe5WftwHt6vzH//4Bws+/ZQlixfz8MMPU83oms6tpUuXEhYWRlhYGL5+fpla1Nndj2azOaP7PmuZCuXLZ3RfJyYmUr58ecDWWm7WvDklSpakXLlyNHr4YfTPP2eq18fHh0ceeSTTpLQ8ZTI59iqEikoSzmqv1vpXu+VBSqn/YfvuyKrYkmBWv2qtbySt74DqOdS9MpsyLYGlAFrrTdha4bdzy7GUUmWAslrrbcb6hUCrO9QDsFlrfc74+VvgBaXUWCBAa33pLva/rfr16xMfH8/JU6e4fv06m2JiCAwMzFQmKDAwY0LK5i1baPLII5hMJgIDA9kUE0NKSgonT50iPj6eBg0aOBqSS8R5o4svPT2duXPn0q1bt3w5L1cT1r0HX0RF80VUNEGtW/Pl+vVYrVYOHjiAt7d3RjfoDX5+fniXKsXBAwewWq18uX49gYFBAOyKi2PRgoVMmz6dEiVKZOxz6dJFXn/tNV4b9DoPPZS75+fnx31wuzpv3AcpKSl8umABXe/xPujRowfR0dFER0fTunVr1q9bh9Vq5cBtrmupUqU4YFzX9evWEdS6NQCBQUGsW7sWgHVr12asD2rdmv0//EBqaipXr17l4MGD1KxRg3PnzmXMoL527Rp79uyhRvXq93Qed+Tm5tirECoq3dFZXb7xg1IqCHgCaKa1vmJ07Xpls0+y3c9pQIlsytiXS+Per+/dHuuGVG6+ocoae8a5aq23K6VaAU8DC5RSH2itF91jjIBtzGzY0KEMGDCA9PR0OoWGUrt2bWZFRFC/Xj2CgoLo3LkzI0aMIDgkBB8fH6aEhwNQu3Zt2j75JJ27dMHd3Z3hw4bhbjzA/Z2hQ9m3bx/nz5/nybZtGTBgAF06dy40cW7auJGlUVEAPP7443QKDXXkMufKS0tm8kBQU7x9yzHpxG7WjZnGrvnRBXb8G1q2fIy4nTsJ7RiCl5cXY8eOy9jWs3sYX0TZYho6bDhjx4zmWnIyLVq0oEVL29hvePhkrqek8OqA/gAEBDRk+MiRRC2N4sSJeOZGzmFu5BwAZn08O6NFdzv5dR9kVyfAwgUL2L5jB+np6YR168ajTZoAcObMGXr26sXly5dxM5n4fPFiVq1cibe39x3P4bHHHmPnzp2EBAfj5eXFuPHjM7aFhYURHW27rsNHjGD0qFEkG9e1pXFdX3zxRd4eMoRVq1dzX6VKTDFmQdesWZPmLVoQ1q0bJpOJzl26ULtOHX7++WdGjRxJeno66enptG3bllZZ3riIe1cknh1tdC9/r7WuZiTdwVrrYGNbKPAvrXWIUqouti7a9lrr2Cxjwuu11g2MfQYD3lrrsdmMCTfWWp9RSjUG3tNaBymlZmF7CHi4UqottoeH+2U3JqyUqn6bY/0PGKi13mG0Zstord9QSn0CfKe1/lgp9R/gP3Zjwo211gONuqoBJ7XWacZDzGtrrf+T03WTZ0fnP3l2dMGQZ0fnv7x4dnTqyR8d+pvjUaV+obtwRaIlrLU+q5SKMz76cxWwn6K4CeivlDoCaGxd0nltHPCFUupZbBOzTgP30hX8HDBbKVUSOA68YKx/D4hWSvUDvrzN/kHAEKXUdSAJyP2HFIUQIr8UwYlZRaIl7GxKqeJAmvE1Ws2Aj43JUS5NWsL5T1rCBUNawvkvT1rCf2jHWsL3qUJ34YpES9gF3I+tpeoGpAAvOzkeIYQQLkCScAHQWv8CZJrGaYxTZ/cYpce11mcLJDAhhHAlRbA7WpKwkxiJ1uW7pIUQoqAU1qdeOUKSsBBCCNcgSVgIIYRwkkI4Ic1RRe9thxBCCOEipCUshBDCNUh3tBBCCOEcMjFLCCGEcJZC+iUMjih6ZyyEEEK4CGkJCyGEcA3SHS2EEEI4iSRhIYQQwkkkCQshhBDOURRnRxe9MxZCCCFchLSEhRBCuIYi2BKWJCyEEMI1FMFnR0sSFkII4RoKoCWslGoPzADcgU+01pOzbC8OLAIaAWeB7lrr34xtw4CXgDRgkNY6xtF4il7bXwghhEuymtwcet2JUsodmAU8BdQDeiql6mUp9hLwl9a6NjANCDf2rQf0AOoD7YEIoz6HSBIWQghRVDQBjmqtj2utU4ClQGiWMqHAQuPn5cDjSimTsX6p1jpZa/0rcNSozyHSHS1y5JZy2dkh5Nry49ecHUKuvHf5iLNDyLXBpR50dgi5NujPA84OIVfKl3C4gVXgSnjlQSUOdkcrpfoB/exWRWqtI+2WKwMn7JZPAo9mqSajjNY6VSl1AahgrN+TZd/KDgWMJGEhhBAuwurgxCwj4UbesaALkSQshBDCJVit+X6IU0BVu+UqxrrsypxUSnkAZbBN0LqbfXNNxoSFEEIUFd8CdZRSNZRSxbBNtFqbpcxa4Dnj567A11prq7G+h1KquFKqBlAH2OtoQJKEhRBCuIR0q9Wh151orVOBgUAMcASI1lr/qJQar5TqaBSbB1RQSh0F3gSGGvv+CEQDh4FNwL+11mmOnrPJWgDtf1E4pVw4U+hujsI2MaujquDsEHJNJmblv8I4MatimVIOP2nj0pWrDv3NKV2yRKF72oeMCQshhHAJ6YXubb/jJAkLIYRwCUWxZ1bGhIUQQggnkZawEEIIlyDd0UIIIYSTFMEcLElYCCGEa5CWsBBCCOEkMjFLCCGEEAVGWsJCCCFcQrqzA3ACScJCCCFcQhHsjZYkLIQQwjUUxYlZMiYshBBCOIm0hIUQQriEojg7WpKwEEIIlyATs4QQQggnKYINYUnCQgghXEN6EczCMjFLCCGEcBJpCQuH7dy9h/D3p5OWnk6X0BD+9dyzmbanpKQwfOy7HP5JU7ZMGaZOGE/l+yplbP/z9GlCu/fh1Zdf5Pk+vQD4fGk0K1avxWq18kynjjzbs3u+xW+1Wtm0cBa//PANnsWL02nA21Sq8cAt5T6fNJSkv86Snp7G/XUD6PDiINzc3DO271ofzebP5zAkciUlfcrkeYxTp0whLm4nXl5ejB03ngcffPCWckcOH2bMmNEkJyfTokVLhrz9NiaTienTPmD79u14enpSpUoVxo4bR+nSPuzZs5sPZ87k+vXreHp68vp/3qBJkyZ5GvvdeHbeFAKC23DJcpZ3A9oV+PGzY7VamTfzfb77Jo7ixb14bdgYaj1Q95Zyn8+NIDbmSy4nXeKLTdsz1icmnGbmxLFcTrpEeno6z74ykEZNW+RLnDPfn8o3u3ZS3MuLYaPH8UDdW+8NfeQwk8aPJSX5Go82b8mgt4ZgMpn45WfNB5MnkJKcgru7O2+8M4wH6zdg57ZY5s2JwM3khru7OwPfHEzDh/6Z5/FnOpd8rd01SUvYRSilyiqlXi2A43RSStXLq/rS0tKYMOV9Ima8z5qoxWyM2cKx479mKrNy7Xp8Spdmw8ponu3ZnWkfRWTaPnX6h7Rs1jRj+Zdjx1mxei1LFnzC8sUL2bZzF/EnTuZVyLc4un8v5/48yWvTFxHy8pt8+cmMbMt1e30U/afMZcDUeVy5eIHDe7ZlbLtwxsLxA99RxtecLzHG7dzJifh4Vq9Zy8iRo5g0cUK25SZNnMCoUaNZvWYtJ+Lj2RUXB8CjTZsSvWw5UdHLqFatGp/Onw9A2bLlmD59BtHLljNu/LuMHjkiX+K/k90LlvNh++eccuycfP/NLv44GU/E4pUMGDycOR9MzrbcI80fY8qchbesX7ZoHi1aP8EH8xbz1pgJzJkWni9xfrMrjpMn4lm8Yg2Dh43kg/BJ2Zb7IHwSQ4aPZPGKNZw8Ec83u3cBMPvDGTz3r1eYt3gpL74ygNkf2u7/hx9pwvzFUcxbvJR3Ro1h6oR38yV+e+lWx16FkSRh11EWuOskrJQyKaXu5ffXCcizJHzwxyPcX6UKVStXxtPTk6faPs7W7Tsyldm6bQcdn+4AwJNtgvjm2+8yPorwVex2Kt9Xido1a2SUP/7rbwTUr08JLy88PDxo/PBDbNm6jfzy0744GrZqi8lkokqdely7ksSlv87eUq54yVIApKelkZZ6HTBlbItZFMETvftlWpeXtm2L5engYEwmEwENG5J06RKJiYmZyiQmJpJ0+TIBDRtiMpl4OjiY2NitADRr1hwPD1vHV4OAhiQkJABQt25d/My2Nw61atUiOTmZlJSUfDmH2zm6Yy9Xzl0o8OPezt6d22jd7mlMJhOqfgCXky5x7uyZW8qp+gGUr+B7y3qTycSVK5cBuJyUlG2ZvLBzeyztOtjujfoBtnvj7JnM98bZM4lcuXyZ+gG2e6Ndh2B2brPdGybgyuUkAJKSkqjg6wdAyZIlMZls9/PVq1fz69bOxGp17FUYSXe065gM1FJK7Qe2Ag2BcoAnMFJrvUYpVR2IAb4BGgEdlFJ9gT5AInAC+E5r/Z5SqhYwC/ADrgAvA+WBjkCgUmok8IzW+pgjQVsSE6nof7P15282c+DHH3Ms4+Hhgbd3Kc5fuEDxYsWYv+hz5n40nQWff5FRvk6tmnz4cSTnz1+guFdxdsTtpv6Dt3YD5pVL585QpoJfxrJPeT8unTtD6XIVbin7+cR3OHXsJ2r/own1mrYCbEm8dHlfKlarlW8xWiwW/CtWzFg2+/uTaLHg53cz7kSLBX+zf8ayv78/FovllrrWrllN27a3dvl+tWULdes+SLFixfI4+sLp7JlEKthdzwp+Zs4lWu46mXZ/oR/j3hrIhpXRXLt6lXEfzMqXOM9YLJj9b8bpZzaTaEnMSKYAiZbEjDdbN8qcMe6NgW8OZsiggUTMmI7Vms6sTz7NKLd969fMjfiIv/46x+QPsu8hykvpRbBDWlrCrmMocExr/RAwBOistX4YaA28r5S68T60DhChta4PmIFngH8ATwGN7eqLBF7TWjcCBhv77ALWAkO01g85moAdFTF3Ps/27E7JkiUzra9Zozov9u1Nv0Fv0H/Qm9R9oA7u7q5xq/YZHs5bHy8jLfU6vx76gevJ19i5agmtw553dmh3Zd4nc3F3d+epDh0yrT927CgzZ85g+MiRTors72fHlhjaPBXMJ8u/ZGT4dKZPGEN6uut9EnbNiuUMfOMtlq/fyL//8xZT/js+Y1ur1m34bNlKJkx5n/lzPnZilH9f0hJ2TSZgolKqFbbPr1cGbrzV/V1rvcf4uQWwRmt9DbimlFoHoJTyBpoDy5RSN+osnh+Bmv38OJ1ws7WVYLHgb9c6sy9T0d9MamoqSUmXKVumDAcP/cjmr7cy7aMILl1KwuRmolixYvQK60qX0BC6hIYAMCNiNv7mvB1r3Ruzmu+/3gDAfbUUF87e7L67eC6R0uVzbu14FCuGatwcvW8X3mXL81fiaWa/3S9j3znD+vPyhFl4ly3vUIzRUUtZtXIlAPXq1yfh9OmMbZaEhEwtG7C1bhIsCRnLCQkJmO3KrF27hh3bd/DxnDkZ3Yw3yg1+803Gv/suVatWdSjmwm7Dqmg2r18NQG1Vj7N21/NsooXyfnd/H361YQ2jp84EoG6DhlxPSebihfOULefYfQGwalkU61evAkDVq48l4WaciRYLfubM/wf9zH4k2vWKJFos+Br3RsyX6xn01hAAWj/xJFMn3jr2+4+HG/HH+LGcP/8XZcuWczj+nBTWLmVHSBJ2Tb2xdSM30lpfV0r9BngZ2y7fxf5uwHmjVZ2vGtSry+8nTnLy1B/4m/3Y+H9fEf7umExlglq1ZO2XG3ioYQM2fx1Lk8aNMJlMLJx78511ROQ8SpYsQa+wrgCcPfcXFcqX48/Tp9mydRuL50fmadxN2nWiSbtOAPz8/R6+jVlNg+atOXX0CMVLlrqlKzrl2lWSr16hdLkKpKel8cv333B/3QD876/JkMgVGeWmD+xFv4kf58ns6LDuPQjr3gOAHTu2E700inbt23Po4EG8vb0zdUUD+Pn54V2qFAcPHKBBQABfrl9P9x62/XfFxbFowULmfvIJJUqUyNjn0qWLvP7aa7w26HUeyueZr4VBh85hdOgcBsC+3TvZsDKalo+35efDhyhZyjtX47q+5ooc+O5b2jwVwonffiUlJYUyeZTAOnfrTudutk8M7N65g5XLoni8bTsOHzpIKW/vTF3RABV8/ShZqhQ/HjxAvQYBxGxYzzNhtnujgp8v+7//jn82asz33+6livFG7OSJeCpXqYrJZOLnn45w/XoKZcqUzZP4c1JYJ1c5QpKw67gElDZ+LgNYjATcGqiWwz5xwByl1CRsv8tgIFJrfVEp9atSqpvWepnRld1Qa/2/LMdxmIeHB8OH2LqN09LT6BwSTO1aNflozlzqP1iX1q0eo0vHYIaNeZcOXcIo4+PDlAnj7ljvm+8M5/zFi3i4ezBiyFv4lM6zkG9R55+P8sv+b/jw9WfxLO5FaP8hGdtmv9OP/uGRpFy7ytKpo0hNTcGabqV6/Ydo/GRIvsWUVcuWjxG3cyehHUNsH1Eae/Ma9uwexhdR0QAMHTacsWNGcy05mRYtWtCiZUsAwsMncz0lhVcH9AcgIKAhw0eOJGppFCdOxDM3cg5zI+cAMOvj2ZQv73hrLTdeWjKTB4Ka4u1bjkkndrNuzDR2zY8u0BiyatS0Bd/tiWNAr862jygNHZ2x7Y2XejFt3hIAFn48kx1fxZB87Rr/6vo0TzwdSo8X+vHCv/9DxNQJrFv2BZhg0LAxmXog8krTFi3Zs2snvbqEUtzLi6GjxmZse6l3D+YtXmqL+e1hTB4/huTkZB5t3pxHm9s+LjVk+Cg+/GAqaalpFCtenMHDbEMS27/+mpgN6/Hw8KBY8eKMmTA5X+K3VxRbwqai+MBsV6WUWoJtQta3QF3AG9gHNMU25guwXmvdwG6fsUAvIAGwAJu01nOVUjWAj4FK/9/efYdHVW0NHP5NEiBC6IQANhB0oRQVFRFQih3BgoqKvTesV0UUaYoK2MAKXux4Aa8g5aJcC4iAiqJ+BMHFRUpQgVAEQkud749zkkzKpELOmWS9z5MnOfvsObMyhFmzy9kbZ3LXZFUdISJdgDeBVOCyosaF03Zujbg/jn+v2e91CKVyBDxpEwAAIABJREFUoRSc/OV3D9UqeA+q3927cZnXIZRKg0Oii6/kM03q1ip3hv51465yvee0aVqnAuZwH1jWEvYRVe1fgmpt8x0/p6rDRKQmsABY6l5rLXBeIc+xiAN4i5IxxhwoVXF2tCXhyDfBXXwjFnhXVX/yOiBjjCmLqtgxa0k4wpWw9WyMMb5XFTdwsCRsjDHGFzL9dxv1QeePFRCMMcaYKshawsYYY3zBuqONMcYYj2R6mIRFpAEwBWgOrAP6qerf+eqcgHPrZx0gExipqlPcc+8A3YDsnUhuUNVfinte6442xhjjC1nBYLm+yulR4EtVPRr40j3Oby9wnbt2/3nASyISuoxY9rr8J5QkAYO1hI0xxviExxOzLgK6uz+/C8wHBoZWUNVVIT//JSLJOEsM7yjrk1oSNsYYUymIyG3AbSFFE1S1pAvPJ6jqRvfnTeRumhPuuToC1YHQVQdHisgQ3Ja0qqYW96SWhI0xxvhCebuU3YQbNumKyBdAk0JOPZ7vOkERCRuMiDQF3geuV9Xs9vsgnORd3Y1hIDCi8CvksiRsjDHGFw72xCxVPSvcORHZLCJNVXWjm2STw9SrA/wHeDxkW1lCWtGpIvI2zj7uxbKJWcYYY3whK1i+r3KaCVzv/nw9MCN/BRGpDkwH3lPVf+c719T9HgAuBpaX5EmtJWyMMcbAs8BUEbkZWA/0AxCRk4E7VPUWt+wMoKGI3OA+LvtWpEkiEg8EgF+AO0rypJaEjTHG+ELmAWjOlpWqbgPOLKT8R+AW9+cPgA/CPL5nWZ7XkrAxxhhfsBWzjDHGGI9kVr0cbEnYGGOMP1TFlrDNjjbGGGM8Yi1hY4wxvuDlxCyvWBI2xhjjC1WxO9qSsDHGGF+wiVnGhNgXU8vrEEqt7+ZpXodQOq2v9TqCUrt34zKvQyi1cU3bex1CqQwf1cfrEErvkZfLfYmq2BK2iVnGGGOMR6wlbIwxxheybGKWMcYY4w0bEzbGGGM8YmPCxhhjjKkw1hI2xhjjC5lVsCVsSdgYY4wv2MQsY4wxxiM2McsYY4zxiE3MMsYYY0yFsZawMcYYX7CJWcYYY4xHbCtDY4wxxiOWhI0xxhiPVMUkbBOzjDHGGI9YS9gYY4wvVMWWsCVhY4wxvmBJ2BhjjPFIVUzCNiZsjDHGeMRawsYYY3yhKraELQkbY4zxBUvCxhhjjEcsCRtjjDEesSRsTBkEg0GeHzOaxQsXEhsby5DhI2h97LEF6q1csYIRw4aQuj+Vzl278o+HHyEQCPDGa6+yYP58AlEBGjRowJDhI4iPbwzA0h9/4IXnxpCRkUG9evUZ/8+JBzz+RSvXMmraPLKyglzSqS03n31qnvNTF/4fUxb+QnRUgEOqV2PIlefQsklDEtdv5Mkpn7uvAdxx3mmcefzR5Ytl0SJGjR5NVlYWl1xyCTffdFOe82lpaTw+eDArV66kbt26jB41ikMPPRSAiRMnMv2TT4iKimLgwIF06dy5yGt+v2QJL7zwAunp6Rx37LEMGzaMmJgY1q5dy5ChQ1m5ciX3DBjA9ddfX67fCZy/kYnjnmfp94uoUSOWewYNpeUxrQvU++DN15g/9z/s2Z3Cvz5bkFO+ZfMmxj09jD27U8jKyuLa2wdwUqcu5Y6rrK6dOJp2vXuSkryNJ9ud61kc+VVvcSy1z7wUAlHsW/Yte7//vNB6NY45nnoX38K290aTsWkDREVR57z+xCQcTiAqin3Ll4R9rDmwLAmbclu8aCEbkpL4eMZMlicmMuqZkbz93gcF6o16ZiSPDR5C23btuP+eAXy7eBGdu3Tlmuuu54677gZgyr8+5J8TJjDo8cGkpOxi9DPPMPaVV2nStCnbt28/4LFnZmXx9EdfMv6uy0ioV5v+z0+ie7tWtGzSMKdOr5Nb06/r8QDMT1zNc9Pn8/qdl9KqaSM+/Mc1xERHsWXnbi4f/R7d2rYkJrpsNx1kZmby9DPPMP6NN0hISKD/1VfTvVs3WrZsmVNn+vTp1KlTh9mzZvHpZ5/x0tixjBk9mt9//53P5s5l2scfk7xlC7fffjszZ8wAKPSaLVq04IknnmDChAk0P/JIXn3tNWbOmkXfSy6hTt26DHzkEebNm1eOVzavn75fzF9/JPHapGmsWrGc8S88y+g33ilQ75TOp9Orbz/uvrpvnvKP3ptIlx5ncd7Fl7Fh3RqeHHg/E6bMPGDxlda37/yb+a+8yw3vveBZDAUEAtQ+63J2TH2VzJQdNLjuYVJXJ5K5bVPeatVrUPOk7qT9tTanLFZOJBAdw/a3n4GYajS6+XH2r1xK1q4D/3+uKBketoRFpAEwBWgOrAP6qerfhdTLBBLdwyRVvdAtbwFMBhoCS4FrVTWtuOe1W5RKSERGiMhZXsfhRwvmz6dX794EAgHatW9PSkoKW7dsyVNn65Yt7Nmzh3bt2xMIBOjVuzdfu2/ycXFxOfX27dtHIBAAYO6nn9K9Z0+aNG0KQIMGDQ547MvXb+Lw+Hoc1qge1WKiOa+DMD9xdZ46cbE1cuNLS8cNj0OqV8tJuKkZmQQIlC+W5cs5/PDDOeyww6hWrRrnnXsu8+fPz1Nn3vz5XNinDwBnn3UWS5YsIRgMMn/+fM4791yqV6/OYYceyuGHH87y5cvDXnPHjh1Uq1aN5kceCcBpnTrx5RdfANCwQQPatm1LTMyB+4y+ZOHX9Dj3AgKBANKmHXt2p7B929YC9aRNOxo0bFSgPBAIsHfvHgD27N5daJ2KtPqbJezdvtPTGPKr1vRIMndsJXPnNsjKZP/KpdRo1a5AvVpdL2DP919ARkae8kC16hCIIhBTjWBmJsG0/RUVeo7MrGC5vsrpUeBLVT0a+NI9Lsw+VT3B/bowpHwU8KKqtgL+Bm4uyZNaS7gERCRaVYd4HYdfJScnk5DQJOe4ceMEkrck0yg+PrfOlmQaN07IWyc5Oef4tVdeZs5/ZhMXF8frE94EIGn9ejIyMrjj1pvZu2cvV/TvzwW9+xzY2Hfupkm92rlx1atN4vqNBepN/uZn3p+3lPTMTN68u19O+bJ1Gxn6r7ls3L6LkdecX+ZWMDivY5MmIa9jQgKJiYlh68TExBAXF8eOHTvYnJxM+/btc+olJOS+voVds379+mRmZvLrr7/Spk0bPv/8czZt3lzm2IuzbesWGob8+zeMb8z2LcklTqZX3Hgbw/8xgDnTprJ/3z6Gv/DqwQo1YkXF1SMrJbfhlpWyg2rNmuepE5NwGNG165O25ldqdTwzp3y//kyNVu2Iv/spAjHVSZk3jeD+vRUVeg6Px4QvArq7P78LzAcGluSBIhIAegL9Qx4/DHi9uMdGdBIWkWuAe4HqwPfAW8CbQEcgGlgCXAE0AkYAKUArYB5wl6pmicg5wHCgBvA7cKOq7haRdThdE2cDo0XkPGC2qv5bRE4CXgDigK3ADaq6UUTmu3H0AOoBN6vqNyISjfMp6TwgC3hTVV8Od50wv2u4a98AnKyqA9x6s4HnVHW+iOzG+SPoBWwEHgNGA0cA96uqd/15+dw14B7uGnAP77w1kY8mT+a2O+8iMzOT31au5NXxE0jdv5+bb7iOtu3ac6TbeqtIV55+IleefiJzflzJm//9jqeuOR+A9s2bMn3QDazZtI3Bkz6j63EtqFHN//+tAoEAo559ljHPPUdaWhqdTzuN6Cj/dox988Vcep7fm4uuuIbfli/jpZFDGfvOZKJ8HLP/BKjdoy875xQcKqrW9EiCwSy2vDaYQGxNGvS/n7R16rSqI4iI3AbcFlI0QVUnlPDhCSHvv5uAhDD1YkXkRyADeFZVP8Hpgt6hqtndC38Ah5bkSf3/bhGGiByLk2C7qGq6iLwGCDATeAo4BPhAVZeLSHecxHwcsB74DOjrJrbBwFmqukdEBgIP4iRsgG2q2sF9vvPc79WAl4GLVHWLiFwBjASyZ9DEqGpHEekFDAXOwvmjaA6coKoZItKgBNcpTGHXLkot4CtVfVhEpruvy9nu6/Cu+1qVyUdTJvPJ9GkAHNemDZs35447JSdvprE7sSpb4/jGJCdvzluncd46AOed34v77x3AbXfeReOEBOrWrcshhxzCIYccwgkdTuJ/q/SAJuHGdePYtCMlN64dKSTUjQtb/7wOrRn50RcFyo9q0pCaNaqxeuNW2hzRpJBHliCWxo3ZtCnkddy8mYR8r1F2nYSEBDIyMti9ezf16tUjoXFjNoc8dvPm3Nc33DWPP/543nn7bQAWL17M+vXryxR3OHOmT+Xz2Z8A0EqOY1vIv/+2Lck0iC/47x/Ol3NmMGTMOABat21Peloqu3buoF79Az9EEamydu8gqnb9nOOo2vXITNmRcxyoXoOYRk1pcNW9zvladajX93Z2TBtP7LEnk7ZmJWRlEdy7m/Q/1hDT5IgKT8KZwfK1hN2EGzbpisgXQGH/QR/Pd52giIQL5khV/VNEjgK+EpFEoMxjE5H8MfJM4CTgBxH5xT0+CieBng2cjNPqy7ZEVdeoaibwL6Ar0AknIS1yr3E9EPoOP6WQ5xWgLfC5+5jBwGEh56e535fiJF5wkuX47E9Jqrq9BNcpTGHXLkoazgcOcCYSfK2q6e7PJXl8WJdfcSWTJk9l0uSpdOvegzmzZxMMBklctoy4uLg8XdEAjeLjqVWrFonLlhEMBpkzezZndO8OQFJS7pv/11/Pp3nzFgCc0a07v/zyCxkZGezft49flyfSosVR5Qm7gDZHNCFpyw7+2LaT9IxMPvtJ6da2ZZ4665Nzu/gWrFjDEfHOG90f23aSkZkFwF/bd7Fu83aaNahT9ljatCEpKYk//vyT9PR0Pps7l27duuWp071bN2bOmgXA5198QcdTTiEQCNCtWzc+mzuXtLQ0/vjzT5KSkmjbtm2R19zmTnRLS0vj7Xfe4bLLLy9z7IXpdUk/Xpz4IS9O/JBTT+/OvLn/IRgMor8mUrNWXKnGdRs1bsKypT8AsGHdWtLS0qhbr34xj6pa0jcmEV0/nqi6DSEqmthjTyJ1de5wRjBtP1teGcTW8cPYOn4Y6X+tY8e08WRs2kDmrr+pfuQxTsVq1anWrDmZ2w/e8EQ4B3tMWFXPUtW2hXzNADaLSFMA93tymGv86X5fg9NlfSKwDagnItkN28OAP0vyO0dsSxgIAO+q6qDQQvfFiwOqAbHAHvdU/n+hoHuNz1X1qjDPsaeQsgDwq6qeFuYxqe73TIp+fYu7TkmvnUHeD1OxIT+nq2r2752V/Xi3G/6A/dt36Xo6ixcupO9FfYiNjeWJYcNzzl19ZT8mTZ4KwCODHmPE0CGkpqbSuXMXOnfpCsCr48axfv06ogJRNGnalEcfdz6UtjjqKE7r3Jmrr+hHICrARRdfQstWrQ5U2ADEREcx6NKe3Pn6x2RlZXFxp7a0atqIV+csos3hCXRv14rJ3/zMd6uSqBYdRe1DYnny6vMA+HnNn7z1xRKqRUcRCAR47PIzqR9Xs+yxxMQw6NFHufPOO51YLrqIVq1a8eprr9HmuOPo3r07l1xyCY8//ji9+/ShTp06jB41CoBWrVpxztlnc0nfvkRHR/PYoEFER0cDFHpNgHffeYcF33xDVlYW/S6/nFM7dgRg69atXNW/P3v27CEqEOCDSZOYPm1angl0pXVSpy4s/W4Rd/a/xLlF6dHcKRYP3NyfFyd+6MT0+ji++XIuqfv3c8tlF3DWBRdx5Y23cePd9/PamJHM+uhfEIB7Bw3NmcDnhZs/HMcx3TsR16g+z2z4lllDX2TxW1M9iweAYBYpX3xE/cvvgkCA/YnfkbltE7W69iJjUxKpq5eHfei+nxdQ5/xraHjTY87x8u/J2PJXRUWew+Mx4Zk4DbFn3e8z8lcQkfrAXlVNFZFGQBdgtNtyngdchjNDutDHFyYQLGfz3ysichzOL9lFVZPd6eW1cbp4JwMtgKaqOsDtjv6U3O7oT3G6LBbgtCp7qupqEakFHKqqq9wx4ZNVdav7fO8As3H+oVbgTD//1u1WPkZVf3W7tx9S1R/df6AfVbW5iNyB0xq+Mrs7Gtgd7jphft9w1+6K0+LvijMG8StwYfaYsKrGuY8fBuxW1efc45xz4ezcsy/i/jhqfPO+1yGUTrdrvY6g1NbsTPc6hFIb17R98ZV8ZPioAzsBsSIkPPJyuT8V3fDhT+V6z3mnf4cyxyAiDYGpOHNm1uPcorRdRE4G7lDVW0SkMzAep1ETBbykqhPdxx+Fk3saAD8D16hqaiFPlUfEtoRVdYWIDAb+KyJRQDpOUk5X1Q/dyVCLRaQnzgv2A/AKuROzprstwhuAf4lI9n0og4FVRTxvmohcBowTkbo4r+FLOMkvnH8CxwDLRCQdZ2LWK2W4TmEWAWtxEvpK4KdSPt4YY6o8Vd2GM6yZv/xH4Bb358VAwfu+yOme7lja543YlnBpuC3hh1S1t9exRBJrCVcAawlXCGsJH3wHoiV87Qc/lus95/1rTvZujKKMIrYlbIwxpnKxtaMrKVWdjzOLzfdE5FWcwf5QY1X1bS/iMcaYimJJ2HhOVe/2OgZjjPGCl2tHeyWS7xM2xhhjIpq1hI0xxviCdUcbY4wxHrEkbIwxxnikKiZhGxM2xhhjPGItYWOMMb5QFVvCloSNMcb4giVhY4wxxiNBS8LGGGOMN7KqYBK2iVnGGGOMR6wlbIwxxheqwq5++VkSNsYY4ws2JmyMMcZ4pCqOCVsSNsYY4wvBLK8jqHg2McsYY4zxiLWEjTHG+IJNzDLGGGM8YmPCxoSICngdQelVP/oEr0MoldRA5L3IDQ6J9jqEUhs+qo/XIZTK0IGzvA6h1N545OVyX6Mqzo62MWFjjDHGI9YSNsYY4wtVsSVsSdgYY4wvZNnELGOMMcYb1hI2xhhjPFIVk7BNzDLGGGM8Yi1hY4wxvmD3CRtjjDEesRWzjDHGGI9UxQ0cLAkbY4zxBeuONsYYY6ogEWkATAGaA+uAfqr6d746PYAXQ4paA1eq6ici8g7QDdjpnrtBVX8p7nktCRtjjPEFj29RehT4UlWfFZFH3eOBoRVUdR5wAuQk7dXAf0OqPKyq/y7Nk9otSsYYY3whmBUs11c5XQS86/78LnBxMfUvAz5V1b3leVJrCRtjjPGF8i5bKSK3AbeFFE1Q1QklfHiCqm50f94EJBRT/0rghXxlI0VkCPAl8Kiqphb3pJaEjTHGVApuwg2bdEXkC6BJIacez3edoIiE/UQgIk2BdsDckOJBOMm7uhvDQGBEcTFbEjbGGOMLB3tMWFXPCndORDaLSFNV3egm2eQiLtUPmK6q6SHXzm5Fp4rI28BDJYnJxoSNMcb4gsdjwjOB692frwdmFFH3KuBfoQVu4kZEAjjjyctL8qTWEjbGGOMLHt8n/CwwVURuBtbjtHYRkZOBO1T1Fve4OXA48HW+x08SkXggAPwC3FGSJ7UkbIwxxhe8XLZSVbcBZxZS/iNwS8jxOuDQQur1LMvzWne0McYY4xFrCRtjjPGFqrifsCVhY4wxvmBrRxtTBsFgkOdGj2bRooXExsYybPgIWh97bIF6K1esYNjQIaSmptKlS1ceeuQRAoEAr7/6Kl9/PZ+oQID6DRowbPgI4hs3Zt3atQwfOpTfflvJXQMGcO111xfy7OX3zY/LeHr8+2RlZXHZud25tV+fPOd/SPyNZyZ8wKq1G3j+0bs5t2vHnHPPvTWZr39wloe988qL6dWt0wGLKxgMMnrUKBYudF7XEU8+ybGFvK4rVqxgyBNPkJqaSteuXXlk4EACgQA7d+7kkUce4a+//qJZs2aMGTOGOnXqOL/TDz8wZswYMtLTqV+/PhPfeovU1FRuuvFG0tPTycjI4Kyzz+auu+4qV/zjnh/D94sXUiM2lkFDhnNM64Lx68oVPDNiGGmp+zm1c1fu/cfDBAIB/rdKeeHZkaSlphEdHc0DAwdxbJu2LPx6PhPHv0ZUIIro6GgGPPgQ7U84scxxhlO9xbHUPvNSCESxb9m37P3+80Lr1TjmeOpdfAvb3htNxqYNEBVFnfP6E5NwOIGoKPYtXxL2sRXp2omjade7JynJ23iy3bleh1OoYFam1yFUOBsTNuW2aOFCNiQlMX3GTB4f/ATPPD2y0HrPPD2SwU8MYfqMmWxISmLxokUAXHv99Uye+hEfTpnK6aefwZsTnHvt69Sty0MDH+Ga6647aLFnZmbx5GvvMmHEw8x6YxT/+fpbVif9madOs8YNeebB27ig+2l5yucv+YUVq9cx/ZWRTHlxGG9Pm8PuvfsOWGwLFy4kKSmJmbNm8cSQIYx86qlC64186imGDB3KzFmzSEpKYpH7ur711luc2rEjs2bN4tSOHXlr4kQAdu3axTNPP83YsWOZNn06Y8aMAaB69eq8+c9/MvWjj5gydSqLFy1i2bJlZY7/+8WL+GNDEpM+nsFDgwbzwqhnCq33wqhnePixwUz6eAZ/bEji+28XA/DGy2O5/pbbmThpMjfdfidvvDwWgA6ndOStSVOYOGkyA58YypiRT5Y5xrACAWqfdTk7PnqdbRNHEnvsSUQ3LLjGQ6B6DWqe1J20v9bmlMXKiQSiY9j+9jNse3c0NU/oQlSdBgc+xlL69p1/8/J5B+eD7IESzMos11ck8m0SFpHdZXzc/SJS80DHU4Y4ThaRccXUGSYiJbqh28++/no+vXr3JhAI0K59e1JSUti6ZUueOlu3bGHPnj20a9+eQCBAr969mT9/HgBxcXE59fbt20cgEACgQYMGtGnTlpiYg9dhs2zV7xzRLIHDmzamerUYep3Ria++XZqnzqEJ8UiLI4iKCuQp/z3pT05u25qY6GhqxsZyTIvD+ebHsiet/ObPm0fvPn0IBAK0d1/XLfle1y3u69refV179+nDvK++ynl8nwsvBKDPhRcyb57zen/66af0PPNMmjZtCkCDhg0BCAQC1Kzp/NfJyMggIyODvL9x6SxcMJ9zezl/F23atWd3SgrbtuaNf9vWLezds4c27Zz4z+3Vm4VfO3EGgL17nLeB3bt307BRPAA1a9bM+RvZt28f5QoyjGpNjyRzx1Yyd26DrEz2r1xKjVbtCtSr1fUC9nz/BWRk5CkPVKsOgSgCMdUIZmYSTNt/4IMspdXfLGHv9p3FVzQVqjJ2R98PfACUa1Ht8nKntf/oZQwVZUtyMk2a5LYSEhISSE5OplF8fE5ZcnIyCY0T8tTZkpy7IM2rr7zMnNmzqRUXx/gJb1ZM4EDytr9p0ii3lZLQqAHL9PcSPbb1UUfw6qTp3Nj3fPanprFk2UpaHVHgzoWyx5acTJOEvK9ZcnIy8flf10LqAGzbvj2nbqNGjdi2fTsA69evJyMjg5tvvpm9e/bQ/+qr6dPH6YLPzMzkqquuYkNSEldccQXt2rcvc/xbk5NpHBJbfOPGbEnekpNMAbYkbyG+ceM8dba68Q948CEevncAr419iWAwi1f/+XZOvQXzvuLN117h77+38+wLY8scYzhRcfXISsndxS4rZQfVmjXPUycm4TCia9cnbc2v1OqYe2fLfv2ZGq3aEX/3UwRiqpMybxrB/Z6+HUWMSG3Nlofvk7CIxOGsXFIfqAYMVtUZIlILmAocBkQDT+IsuN0MmCciW1W1R5hr7gbGAr2BfcBFqrrZvdH6DeAIt+r9qrpIRBKB03H2idwKPKCq74nIe8D7qlpgwEdEugMPqWpvd8urt4CjcD4c3Kaq2U2m40XkW6ARMFpVC81A7vWGuc/fFlgKXOOucboOOFlVt7o3lj+nqt1FZBjQwn3eI4AHgE7A+cCfQJ/QZde8dPeAe7h7wD28PXEiU6dM5vY7yz4WWVG6dGhH4qo19H9oBPXr1OaE1q2IivJn51IgEMhpMGZmZLByxQomTJjA/tRUrrvuOtq3a8eRzZsTHR3N1KlT2bVrFw8+8ACr//c/Wh19tCcxz/j43wx44B9063kmX33+X0Y/NYIXXn0DgDN69OSMHj35v5+W8tb413PKK06A2j36snPOBwXOVGt6JMFgFlteG0wgtiYN+t9P2jp1WtWmSMHMqpeE/fmOkdd+4BJV7QD0AJ53lwU7D/hLVY9X1bbAZ6o6DvgL6BEuAbtqAd+p6vHAAuBWt3ws8KKqngJcCvzTLV8EdAHaAGtwEjLAacDiEvwOw4GfVbU98BjwXsi59kBP91pDRKRZEdc5EaelfxxOYu1Sgudu6V7/Qpwegnmq2g7nw8cFJXh8oaZOmUz/K/rR/4p+NGrUiE2bNuWc27x5M41DWjcAjRs3ZnPy5jx14vPVATi/Vy++/PLLsoZVao0b1mfT1u25cW3dTkLD+iV+/B1XXsT0V0by1tOPEgxC80MLWxu+5CZPnky/fv3o168fjeLj2bQ572tW6Osapk7DBg1yuq+3bNlCgwZOiz8hIYHTOnfmkJo1qV+/Pid16ICuWpXnunXq1OGUU05h0eKS/Hnnmv7RFG6++kpuvvpKGjSKJzkkti3JycQ3js9TP75xfJ4ekS3JyTRy45/7n9mc0cNZ/6DHWWezcsWvBZ7v+A4n8deff7Jjx98FzpVH1u4dRNXO/TuIql2PzJQdOceB6jWIadSUBlfdS6Pbh1GtWXPq9b2dmCaHE3vsyaStWQlZWQT37ib9jzXENDmisKcx+diYsD8FgKdFZBnwBc5KJQlAInC2iIwSkdNVtTSDHWnAbPfnpUBz9+ezgFdE5BecdUTruC3xb4Az3K/XgXYicijwt6ruKcHzdQXeB1DVr4CGIlLHPTdDVfep6lZgHtAxzDUAlqjqH6qahbMsWvMi6mb71G3tJuL0GHzmlieW8PGF6nfFlXw4ZSofTplK9x49mDN7NsFgkMRly4iLi8vTFQ3QKD6eWrVqkbhsGcFgkDmzZ9OtW3cAktavz6k3f/58mjdvUdawSq3dMUex/q9N/LEpmbT0DOYs+I4enTqU6LGcAVnFAAAfb0lEQVSZmVn8vSsFAF2bhK5LokuHguOGpXHllVcydepUpk6dSo8ePZg9axbBYJBl7usan+91jXdf12Xu6zp71iy693A+f3br3p1ZM2cCMGvmzJzy7j168MvPP5ORkcG+fftITEzkqBYt2L59O7t27QJg//79fPfdd7Ro3rxU8V9y+RVMnDSZiZMmc3q37syd4/xd/Jq4jFpxcXm6ogEaNoqnZq1a/JroxD93zmy6ntHdORffiF9+csbnf/phCYcdfjgAf2xIyllZadVvK0lPT6Nu3XqlirM46RuTiK4fT1TdhhAVTeyxJ5G6OjHnfDBtP1teGcTW8cPYOn4Y6X+tY8e08WRs2kDmrr+pfuQxTsVq1anWrDmZ2zeHeSZT1fm+Oxq4GogHTlLVdLfrNVZVV4lIB6AX8JSIfKmqxW4b5UpX1ewb0jLJfR2igE6qmmcWhYgsAO7G6dJ9HLgEZ0Pnb8r+a+XIf2NcUTfKhe5NGRp3BrkfqGILe4yqZolI6O+dxQH69+/S9XQWLVzIxRf2ITY2lqHDhuec639FPz6cMhWARwc9lnOLUucuXejStSsAL48bx/r164iKiqJp06YMetzZVWzr1q1cd3V/9uzZQyAQ4F+TJjH142l5JnKVV0x0NIPvvI5bBo8hKyuLvuecwdFHHsa49z+m7dEt6NmpA4mr1nDPky+xa/ce5n3/Cy9/MI3ZbzxLRmYG1z7szFiuVfMQRj90JzHR0QcsttNPP52FCxfSp3dvYmNjGT4i98+7X79+TJ3qvK6PPf54zi1KXbp0oav7ut5000088vDDTP/kE5o1bcpodxb0UUcdRecuXeh3+eUEAgEu6duXVkcfzapVq3hi8GCysrLIysrinHPO4Yxu3cocf6cuXflu8UL6972IGrGxPPrEsJxzN199JRMnTQbggUcG8eyIoaSmpnJq586c2tnp4Hn4sSd4+YUxZGZkUr1GDR4aNBiABV99xdw5s4mJiaF6jRoMHflszkStAyaYRcoXH1H/8rsgEGB/4ndkbttEra69yNiUROrq8Gvz7/t5AXXOv4aGNz3mHC//nowtfx3Y+Mrg5g/HcUz3TsQ1qs8zG75l1tAXWfzWVK/DyiNSW7PlEfByrc6iiMhuVY0TkfuAVqp6j4j0AL7CGedMA7ar6n4R6Q3coqoXu+O3F6rq2uKu7f58GdBbVW8QkQ9xuo3HuOdOUNVf3J9XAbtU9WQRGQgMAAaoaqE7beQbEx4HbFHVJ93yF1X1RHfM9mKccdpawM84HwIK/I8NvZ57/Arwo6q+4+6R+byqfioiLwInhowJ71bV5wr5vfOcK0zK3n3+/OMoQq2NicVX8pHUQ8s+8ckrO1Mj740yMP5Rr0MolaEDZ3kdQqm9EVxX7k9Cza54vVzvOX9NufMgzJU/uCKhJTwJmOUm1x+B39zydsAYEckC0oE73fIJwGci8lcx48KFuRd41e36jsEZL87eCeN7nO5ccFrAzwALi7le9h/UMOAt97p7yd0uC2AZTjd0I+DJwhJwCQwHJorIk8D8MjzeGGM8Zy1hc8CIyKU4LXJ/3x1fBGsJH3zWEq4Y1hI++A5ES7jJpWPL9Z6z6eP7rCVsQEQuBEYCN3kdizHGGP+q1ElYRL4HauQrvlZVD1hzSUTOBUblK16rqq3LeL12uDOpQ6Sq6qlluZ4xxkSKrCrYHV2pk3BFJC5VnQvMPYDXSwROOFDXM8aYSFEVx4QrdRI2xhgTOSwJG2OMMR6xZSuNMcYYU2GsJWyMMcYXrDvaGGOM8YglYWOMMcYjVTEJ25iwMcYY4xFrCRtjjPGFYFaW1yFUOEvCxhhjfKEqdkdbEjbGGOMLloSNMcYYj1TFtaNtYpYxxhjjEWsJG2OM8YWquGylJWFjjDG+YGPCxhhjjEe8TMIicjkwDDgW6KiqP4apdx4wFogG/qmqz7rlLYDJQENgKc7e9WnFPa+NCRtjjPGFYFZmub7KaTnQF1gQroKIRAOvAucDxwFXichx7ulRwIuq2gr4G7i5JE9qSdgYY0yVp6orVVWLqdYRWK2qa9xW7mTgIhEJAD2Bf7v13gUuLsnzWne0McYYX4iAMeFDgQ0hx38Ap+J0Qe9Q1YyQ8kNLckFLwias2jUPCXgdQ6m17Oh1BKVyiNcBlMEhsV5HUAaPvOx1BKXyRoTFe6Ck/fxWud5zROQ24LaQogmqOiHk/BdAk0Ie+riqzijPc5eVJWFjjDGVgptwJxRx/qxyPsWfwOEhx4e5ZduAeiIS47aGs8uLZWPCxhhjTMn8ABwtIi1EpDpwJTBTVYPAPOAyt971QIla1paEjTHGVHkicomI/AGcBvxHROa65c1EZA6A28odAMwFVgJTVfVX9xIDgQdFZDXOGPHEkjxvIBgMHtjfxBhjjDElYi1hY4wxxiOWhI0xxhiPWBI2xhhjPGJJ2BhjjPGIJWFjKhER6SIitdyfrxGRF0TkSK/jCsdd9L7YMmMqK1uswxx0IjILCDsNX1UvrMBwSkxEngSGZy9FJyJ1gLGqeqO3kRXpdeB4ETke+AfwT+A9oJunUYX3MdAhX9m/gZM8iKVERKQ38CRwJM57aAAIqmodTwMrgrvxwAVAc0Le91X1Ba9iMg5LwqYiPOd+74uzZNwH7vFVwGZPIiqZGOB7EbkRSABeAfy+nmCGqgZF5CLgFVWdKCIl2s2lIolIa6ANUFdE+oacqgP4fWHMl3D+lhPdRRoiwSxgP5AIZHkciwlhSdgcdKr6NYCIPK+qJ4ecmiUihe7Z6QeqOshda/Z7nK3JzlDV1R6HVZwUERkEXAOcISJRQDWPYyqMAL2BekCfkPIU4FZPIiq5DcDyCErAAIepanuvgzAFWRI2FamWiBylqmsgZ+yvlscxhSUiZwDjgBFAO+BlEblZVf/yNrIiXQH0B25W1U0icgQwxuOYCnAXy58hIqep6rdex1NKjwBzRORrIDW70Oddu5+KyDmq+l+vAzF5WRI2FekBYL6IrMEZRzsSuN3bkIr0HHC5qq4AcLtNvwJaexpVGO64379UtUd2maom4YwJ+9U2EfkSSFDVtiLSHrhQVZ/yOrAijAR243SbV/c4lpL6Dpju9oykEwHj2FWFLVtpKpSI1CA3if2mqqlF1feSiESrama+soaqus2rmIrjJrS+qrrT61hKwm1NPgyMV9UT3bLlqtrW28jC83t8hRGRtcBFRNY4dpVgtyiZg05Eerrf++LM0Gzpfl2Qb1KO37QUkS9FZDmA20q70+OYirMbSBSRiSIyLvvL66CKUFNVl+Qryyi0pn/MEZFzvA6ilCJxHLtKsO5oUxG64XTj9inkXBCYVrHhlNibuK00AFVdJiIfAn7uKp2Gf1/PwmwVkZa4t7CJyGXARm9DKtadwEMikkrkdO2uwRkK+pTIGceuEiwJm4NOVYe634u8v1ZErlfVdysmqhKpqapLRCS0zNetNJ+9fiVxN84m7K1F5E9gLc7Mbt9S1dpex1AGa92v6kTOOHaVYEnY+Ml9gJ+SSMS10tyxvwJdjqp6lAfhFMudKX+Wu8pXlKqmeB1TcdxZ8wWo6oKKjqWkVHW41zGYwlkSNn4S8DqAfCKulQaE3ocdC1wONPAolmKJyIP5jgF2AktV9RdPgirewyE/xwIdgaVAT2/CKZ6IzKPwD2e+jbmqsCRs/MRXk0YisZVWyMztl0RkKTDEi3hK4GT3a5Z73BtYBtwhIh+p6mjPIgtDVfPMbRCRw3FW0fKzh0J+jgUuxedDK1WFJWHjJ75oCedvnYWUA/6ezCIioeswR+EkOD//Pz8M6KCquwFEZCjwH+AMnNal75JwIf4AjvU6iKKo6tJ8RYtEJP+sdOMBP//nNFXPIq8DcGVPvBHgFGCme9wH8Psb1/MhP2cA64B+3oRSIo0Jma2LM9s4QVX3ubOPfUdEXia31yYKOAH4ybuIiicioUMSUTgbZNT1KBwTwpKwqTAikgA8DTRT1fNF5DjgNFWdCKCqAzwN0JU9iUVEFuC00lLc42E4rTTfCl0tK0JMwtkkY4Z73Af40B0CWOFdWEUKXe88A2eVMr98gAxnKc4HhwBOzGsB323sURVZEjYV6R3gbeBx93gVMAWY6FVAxUgA0kKO09wy3xKRusBQnO5cgK+BEX5cQUtEAjh/E58CXdziO1Q1O8ld7UVcxYnA28BQVduj2acsCZuK1EhVp7q7/KCqGSKSWdyDPPQesEREprvHF+OvW6gK8xawnNwu6GtxPvj4bmUyd8vFOarajrytS18TkS7AMAruJ+zL28CyiUhnCu4n7Od1xasES8KmIu0RkYbk3nfbCed2FF9S1ZEi8hnQ1S26UVV/9jKmEmipqpeGHA8XEb/e6gPwk4icoqo/eB1IKUzE2YxkKeDnD5E5ROR9nKVifyE35iD+3tyjSrAkbCrSgziTnFqKyCIgHrjM25CK9QvOAh0xACJyhLszkV/tE5GuqroQclpt+zyOqSinAleLyHpgD7mtSj/vfbtTVT/1OohSOhk4ztaO9h9LwqbCqOpPItINZ9ZxwCnSdI/DCktE7sEZX92M03oI4LQe/Jwg7gDec8eGA8B24AZPIyrauV4HUAbzRGQMzhrdoesw+3mG9HKgCT5f8a0qsq0MTYURkZo4reEjVfVWETkaEFWd7XFohRKR1cCpft66MBwRqQOgqru8jqUkRKQxziISQM4+yL7krj6VX9DPq0+5MZ+Ac4td6AeHCz0LygDWEjYV622ccbTT3OM/gY8AXyZhnO3ffDtmXRh3v+ZLcSfghCwwMsLDsMISkQtx7m1uBiTjTHZaCbTxMq6iFHcbmA83IgFnIpnxIUvCpiK1VNUrROQqAFXd696m4lfZ27/9h8jZ/m0G7trL5F0Ew6+eBDoBX6jqiSLSA/+vz10cv21Egqp+XdR5EflWVU8rqo45OCwJm4qUJiKHkDs7uiX+ThRJ7lckbf92mKqe53UQpZCuqttEJEpEolR1noj4fR3m4vj5g2U4scVXMQeDJWFTkYYCnwGHi8gknAUabvA0oiJE6PZvi0Wknaomeh1ICe0QkThgATBJRJKB3R7HVF6RONEmEmOuFCwJmwqjqp+LyE843Y8B4D5V3epxWAWIyEuqer+IzKLw7d98N5lFRBJxYo0BbhSRNTi9DH6/5ef/gL04991ejbOecZynEZVfJLaEjUcsCZuDLt/OPpB7m8QR7n23fru14333+3OeRlE6vUtSSUTqq+rfBzuYUuihqllAFu44qogs8zakcvP7OtKFsQ8OHrFblMxBF+aWjmy+vrWjKCLycb7VqXxPRH5S1fwfiryI407gLpxVnFaHnKoNLFJV307OKm4jEr8SkSZAR5wekx9UdVPIubaqutyz4Kowawmbgy4Cd/YpKV+vFRyGX1o8H+Js3PAM8GhIeYqqbvcmpBJ7h8jaiAQRuQUYAnyF8zfwsoiMUNW3ACwBe8eSsDnoRKTIzQNUdVpFxXKARWI3ki9idnd12glc5XUsZRBpG5EAPAycmL3wjLuG+2KcDT+MhywJm4rQp4hzQZzl/4yJFBG1EYlrG5AScpzilhmPWRI2B52q3uh1DAeJX7p2SyMSY/abiNmIREQedH9cDXwvIjNwPjxcBET6BLhKwZKwqTCRtOE8gIjcp6pjiygb6EFYRRKRBoUUp4RslHFmRcZTGUXYRiS13e+/u1/ZZngQiymEzY42FUZEPsbZzSV7Sb9rgeNV1XcbzkPhM4lF5GdVPdGrmIojIuuAw4G/cRJEPWATzk5Qt6rqUs+CqyRE5G5gkqrucI/rA1ep6mveRlY8d2EUVDXSF0SpNKwlbCpSRGw4765t3R9oISIzQ07Vxtka0M8+B/6tqnMBROQcnA0d3gZew9m/15TPrar6avaBqv4tIrfivL6+JCJtce5/b+AebwWuU9VfPQ3MEOV1AKZK2SciXbMPfLzh/GKcnX1+c79nf/0D/+9/2yk7AQOo6n9x7mH9DqjhXViVSnToxiMiEo3/1xafADyoqkeq6pE4f8tvehyTwVrCpmKFbjgPTpfp9R7GUyhVXQ+sJ3fLxUiyUUQGApPd4yuAzW6iyPIurErlM2CKiIx3j293y/yslqrmLJqjqvNFpJaXARmHJWFTIdwkcK2qHh8pG8679zePAhrjjK9mr8Ncx9PAitYfZ/LbJ+7xIrcsGujnVVCVzECcxHune/w58E/vwimRNSLyBLlLsl6Ds1Wn8ZhNzDIVRkS+U9VOXsdRUiKyGuijqiu9jsWY8nAnjw0HsoeDvgGG+Wwd8SrJkrCpMCLyOnAo8BGwJ7vcrytmicgiVe3idRylISLHAA8BzQnp6YrU9bn9yJ3LMAw4Euc1zu4hicRlTI3HrDvaVKRYnFV6QhOCn1fM+lFEpuB07aZmF/r1Q4PrI+ANnO5Rvy+lGKkm4my9uBSfv8bFbMsZxJntP96duGc8YEnYVJgIXDmrDs5et+eElPn5QwNAhqq+7nUQldxOVf3U6yBKqLhtORvhrB99XMWEY/Kz7mhTYUQkHriVgl2lN3kVU2UjIsOAZGA6eVvvfr+/OWKIyLM4E92mkfc19tu+2CUiIhcC10fatpyVhbWETUWagTMh5At83o0HOeOrrwMJqtpWRNoDF6rqUx6HVpTsW74eDikLEpnbLvpV9oInJ4eUBck7zBIxVHWmiAz3Oo6qypKwqUg1VdV36y0X4U2cZDYeQFWXiciHgG+TsKq28DqGyq6S7o9tXaIesSRsKtJsEemlqnO8DqSEaqrqEhEJLcvwKpiiiEhPVf0q3N7NPp9MFlFEJAF4GmimqueLyHE4q5JN9Dg0E4EsCZuKdB8wSETSgHT8v/jFVhFpSe6+sZcBG70NKaxuwFcUvnez3yeTRZp3cNbiftw9XgVMwZk1Halsi0uPWBI2FakucDXQQlVHiMgRQFOPYyrK3Thr7rYWkT+BtTgrDfmOqg51v0faDPRI1EhVp4rIIABVzRARX89xEJEzgcWqGm6t9kgaJqpULAmbivQqzvrFPYERQArwMXCKl0GFo6prgLPcNXajVDXF65iKIyI1cHZNak7eGegjvIqpEtojIg3J7SHpBPhyT+wQ1wGvi8h2nMmRC4CF2StmuRt9GA9YEjYV6VRV7SAiP0POFnC+3X1GROrhvHk1B2Kyx4ZV9V4PwyrODJyEsJSQ22fMAfUgMBNoKSKLgHjgMm9DKpqqXg8gIs1wYn0VaIblAM/ZP4CpSOnuRg7ZLYh4/L2zzxzgOyARf8cZ6jBVPc/rICozVf1JRLoBgjOWqqqa7nFYRRKRa4DTgXbAVuAVnBax8ZglYVORxuEsItFYREbifCIf7G1IRYpV1Qe9DqKUFotIO1VN9DqQyqaIGejHiIjfZ6C/BPyOs6TpPFVd5204JputmGUqlIi0Bs7EaUF86ecdikTkAWA3MJsIWX1KRFYArXAmkaWSOwO9vaeBVQIiMlxVh4rI24WcDvp95TcRaQOcgbOT0tE4LfhrvY3KWEvYVChV/Q34zes4SigNGINzK0r2p1W/rz51vtcBVFZuAo4CPlXVqV7HUxruHt5H4Oz81BznToVIGWKp1CwJGxPeP4BWqrrV60CKIyJ1VHUXzoxzc5CoapaIPAJEVBIGFoZ8vaKqf3gcj3FZEjYmvNU4uyhFgg+B3jizooPkXXzB7633SPOFiDyEs0BH6L7Yvh2myB6OEJGaqhopf9NVgo0JGxOGiEwH2gDzyDsm7OdblMxBJiJrCykOqqpvP+iIyGk4K3rFqeoRInI8cLuq3uVxaFWetYSNCe8T9yuiiEh9nIk3sdllqrrAu4gqlwjdJOMl4Fyc+5tR1f8TkTO8DcmAJWFjwlLVd93FRFrjdOmqqqZ5HFaRROQWnDW6DwN+AToB3xKh2+z5kYjUxFmw4whVvU1EjgZEVWd7HFqRVHVDvs1IfL3UZlUR5XUAxviViPTCubdyHM7iBqtFxO+zj+/DWQZ0vbvl3onADm9DqnTexpk539k9/hMfb2/p2iAinYGgiFRzx7R9e3tgVWJJ2JjwXgB6qGp3Ve0G9ABe9Dim4uxX1f3grCPt3hImxTzGlE5LVR2NsxMY7kQnv+9CdAfOhiSH4nxoOME9Nh6z7mhjwktR1dUhx2vw/y1Af7hrXn8CfC4ifwPrPY6pskkTkUPIXX61JT5fp9u9ze5qr+MwBdnsaGPCEJHXcRY3mIrzhns5kAR8Afh9mULc9Y3rAp/5fSw7kojI2TjLrR4H/BfoAtygqvO9jKso7jrtt1Jwdy1fr/JVFVgSNiaMMMsTZvPdMoXu5hi/qmprr2Op7NytDDvhdEN/5/cFXURkMc6GDUsJmZClqh97FpQBrDvamLBU9UavYygNVc0UERWRI1Q1yet4KrluOGswB4FqOBuT+FlNVR3odRCmIGsJGxOGiBwGvIzT3QhOS+I+Py/5JyILcGZELyHvak4XehZUJSMir+FskvEvt+gK4HdV9e1EJxF5ClisqnO8jsXkZS1hY8J7G2c5yMvd42vcsrM9i6h4sTjLV2YLAKM8iqWy6gkcq6rZE7PeBX71NqRi3Qc8JiKpOLO6s3fXquNtWMaSsDHhxatq6LjwOyJyv2fRlEyMqn4dWuDO5DUHzmqcHYmyZ50f7pb5lqrWLuq8iLRRVb9/kKiULAkbE942EbmG3G7Hq4BtHsYTlojcCdwFHCUiy0JO1QYWeRNVpVUbWCkiS3DGhDsCP4pI9pKQkdj1/z7QwesgqiJLwsaEdxPOmPCLOG+2iwG/Ttb6EPgUeAZ4NKQ8xc+7+0SoIV4HcBD4fbGRSsuSsDFhqOp6ICJaNaq6E9iJ01o3B9cWVV0RWiAi3f18n3AJ2Axdj1gSNiYfEXmZIt6UbCvDKm+qiLwHjMGZCDcaOBk4zdOoTESyJGxMQT96HYDxtVNxZpwvxhkfnkTubWyRylZU84glYWPyUdV3Q49FpKa7SL8x4Nzisw84BKclvFZVs7wNqWgi8qWqnhmuTFU7eROZsSRsTBgichowEYgDjhCR44HbVfUubyMzHvsBmIHTBR0PvCEil6rq5UU/rOKJSCxQE2gkIvXJnYBVB2dHJeMx28rQmPBeAs7FvS1JVf8POMPTiIwf3Ar8D3hMVTcC9wD/521IYd2Os150a/d79tcMnD2yjcesJWxMEVR1g0ie7Xgzw9U1VcaNQBbOylkjcLa3vAh4ysugCqOqY4GxInKPqr7sdTymIGsJGxPeBhHpDARFpJqIPASs9Doo47lT3XWi9wOo6t84mzj42SYRqQ0gIoNFZJqI2OIcPmBJ2Jjw7gDuxhk7+xM4wT02VVu6u21k9trR8fj/PtsnVDVFRLoCZ+HMdXjd45gM1h1tTFECqnq110EY3xmHs3VhYxEZCVwGDPY2pGJlD6NcAExQ1f+4OysZj9lWhsaEISKrgHXAFOBjVd3hbUTGL0SkNXAmzmzjL1XV18MUIjIbpzfnbJw1ovcBS1T1eE8DM5aEjSmKiHQErgQuBlYAk1X1A2+jMqZ0RKQmcB6QqKr/E5GmQDtV/a/HoVV5loSNKQERaQS8AFytqtFex2NMWYhIY5wFRgBQ1SQPwzHYmLAxYYlIHeASnJZwS5xxwI6eBmVMGYjIhcDzQDMgGWc/5N+ANl7GZSwJG1OU/wM+AUao6rdeB2NMOTwJdAK+UNUTRaQHcI3HMRnsFiVjinKUqj6Af1dDMqak0lV1GxAlIlGqOg9n2U3jMUvCxoTXSURW4HTbISLHi8hrHsdkTFnsEJE4YAEwSUTGAns8jslgSdiYotja0aayuAjntqQHgM+A34E+nkZkABsTNqZItna0qQxUNbTV+27YiqbCWRI2Jrw8a0cD92FrR5sIIiIpFL6kZgAIqmqdCg7J5GNJ2Jjw7gDGkrt29H+xtaNNBFHV2l7HYIpmi3UYY4wxHrGWsDH5iMjLFLErjqreW4HhGGMqMZsdbUxBPwJLcZb36wD8z/06AajuYVzGmErGuqONCUNEvgO6qmqGe1wN+EZVO3kbmTGmsrCWsDHh1QdCZ4/GuWXGGHNA2JiwMeE9C/wsIvNwbuk4AxjmaUTGmErFuqONKYKINAFOdQ+/V9VNIefaqOqv3kRmjKkMLAkbU0Yi8pOqdvA6DmNM5LIxYWPKLuB1AMaYyGZJ2Jiys24kY0y5WBI2xhhjPGJJ2JiyS/M6AGNMZLOJWcaEISLTgInAp6qa5XU8xpjKx1rCxoT3GtAf+J+IPCv5NhY2xpjyspawMcUQkbrAVcDjwAbgTeADVU33NDBjTMSzlrAxRRCRhsANwC3Azzj7C3cAPvcwLGNMJWHLVhoThohMBwR4H+ijqhvdU1NE5EfvIjPGVBbWHW1MGCLSS1Xn5CuroaqpXsVkjKlcrDvamPCeKqTs2wqPwhhTaVl3tDH5uJs2HAocIiInkrs8ZR2gpmeBGWMqHUvCxhR0Ls5krMOAF0LKU4DHvAjIGFM52ZiwMWGIyKWq+rHXcRhjKi9LwsbkIyLXqOoHIvIPCtmkQVVfKORhxhhTatYdbUxBtdzvcYWcs0+txpgDxpKwMfmo6nj3x6OA+1R1B4CI1Aee9ywwY0ylY7coGRNe++wEDKCqfwMnehiPMaaSsSRsTHhRbusXABFpgPUeGWMOIHtDMSa854FvReQj9/hyYKSH8RhjKhmbHW1MEUTkOKCne/iVqq7wMh5jTOViSdgYY4zxiI0JG2OMMR6xJGyMMcZ4xJKwMcYY4xFLwsYYY4xH/h9oxeIu84eahwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSswbDwng2u8"
      },
      "source": [
        "Vemos que **experience** e **last_new_job** tem uma correlação positiva um tanto forte. Também vemos uma relação negativa ligeiramente fraca entre o **target** e **city_development_index**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f68u9JZ9hKwC"
      },
      "source": [
        "## Fechamento\n",
        "\n",
        "Isso conclui nossa Análise Exploratória de Dados para uma tarefa de classificação. Vimos alguns comandos para essa tarefa em um conjunto de dados de classificação de RH no Kaggle. \n",
        "\n",
        "Esses blocos de código, em um primeira análise, são importantes para fornecer um entendimento de nível superior do conjunto de dados e seus atributos, sem entrar muito em detalhes.\n",
        "\n",
        "Cada conjunto de dados é único e o levará a mais códigos para entender melhor os insights, esta foi apenas uma visão geral. \n",
        "\n",
        "Para garantir uma análise mais minuciosa dos dados será necessário se aprofundar mais no negócio negócio (no business) da empresa."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lk6l0eVdF4D5"
      },
      "source": [
        "## Referências\n",
        "\n",
        "* https://pandas.pydata.org/\n",
        "* https://seaborn.pydata.org/\n",
        "* https://towardsdatascience.com/\n",
        "* https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists\n",
        "* https://medium.com\n",
        "* https://medium.com/@suemnjeri"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0CwQllR2Tlc"
      },
      "source": [
        "\n",
        "# Contatos\n",
        "\n",
        "\n",
        "[![github_original_wordmark_logo_icon_146506.png](data:image/png;base64,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)](https://github.com/willclopes)\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "[![linkedin_black_logo_icon_147114.png](data:image/png;base64,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) ](https://www.linkedin.com/in/willclopes/)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "[![email.png](data:image/png;base64,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)](willclopes@gmail.com)\n",
        "\n",
        " \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MO3ekT3L2UM4"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}