Aula_5.ipynb

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      "name": "Aula_5.ipynb",
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    {
      "cell_type": "markdown",
      "source": [
        "# Aula 5 - Machine Learning"
      ],
      "metadata": {
        "id": "3toe9dUAREry"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para esta aula, o Thiago achou melhor dividir o conteúdo em outro notebook. Por isso, a primeira coisa a se fazer foi importar o dataframe que estávamos trabalhando, o dados_vendas_censo."
      ],
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        "id": "keGdolXZdNeP",
        "outputId": "4f14f396-8421-4f2c-d9d1-84c2fa0c5541"
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              "                                 Rua              Bairro     Cidade  Metragem  \\\n",
              "0              Avenida Itacira, 255    Planalto Paulista  São Paulo      1000   \n",
              "1     Rua Aurelia Perez Alvarez, 42   Jardim dos Estados  São Paulo       496   \n",
              "2                    Rua Alba Valdez     Jardim Reimberg  São Paulo       125   \n",
              "3           Rua Tobias Barreto, 195                Mooca  São Paulo       100   \n",
              "4                    Rua Graham Bell         Santo Amaro  São Paulo       440   \n",
              "...                              ...                 ...        ...       ...   \n",
              "5599              Rua Pedro Teixeira        Vila Olímpia  São Paulo       157   \n",
              "5600                   Avenida Odila   Planalto Paulista  São Paulo       267   \n",
              "5601                      Rua Tapuçu          Vila Sofia  São Paulo        95   \n",
              "5603               Rua Estero Belaco       Vila da Saúde  São Paulo       205   \n",
              "5604      Rua Barão de Tramandaí, 0          Vila Alpina  São Paulo       122   \n",
              "\n",
              "      Quartos  Banheiros  Vagas  Valor_anuncio  Valor_mm  Valor_m2  ...  V003  \\\n",
              "0           4          8      6     7000000.00      7.00   7000.00  ...  2.66   \n",
              "1           4          4      4     3700000.00      3.70   7459.68  ...  3.13   \n",
              "2           4          3      2      380000.00      0.38   3040.00  ...  3.47   \n",
              "3           3          2      2      540000.00      0.54   5400.00  ...  3.00   \n",
              "4           4          4      6     1980000.00      1.98   4500.00  ...  3.51   \n",
              "...       ...        ...    ...            ...       ...       ...  ...   ...   \n",
              "5599        3          2      2      960000.00      0.96   6114.65  ...  2.17   \n",
              "5600        3          6      4     1500000.00      1.50   5617.98  ...  2.81   \n",
              "5601        2          2      2      665000.00      0.67   7000.00  ...  2.99   \n",
              "5603        3          3      6     1050000.00      1.05   5121.95  ...  2.75   \n",
              "5604        3          2      2      270000.00      0.27   2213.11  ...  2.97   \n",
              "\n",
              "      V004    V005        V006    V007        V008    V009        V010  \\\n",
              "0     2.04 3158.96 22210644.93 5570.65 25770816.97 2096.19 13029851.56   \n",
              "1     1.96 7600.44 36778930.17 7890.53 35886906.64 4149.00 26760682.90   \n",
              "2     3.06  805.13   769188.78 1011.75   757433.42  518.77   426672.78   \n",
              "3     1.99 2338.05  7826370.15 2447.89  7925839.76 1543.29  4646566.16   \n",
              "4     2.31 1026.36   272168.67 1143.96   167733.64  735.67   367088.08   \n",
              "...    ...     ...         ...     ...         ...     ...         ...   \n",
              "5599  1.27 5054.58 33245189.73 5231.93 33485189.78 3649.06 26562232.53   \n",
              "5600  1.60 4249.33 24499871.44 5180.14 25047413.33 2618.76 17705032.31   \n",
              "5601  1.81 2425.09  6878872.25 2543.80  6913867.79 1561.58  4261814.01   \n",
              "5603  1.50 5650.20 20344622.14 5750.84 20127679.27 3459.68 16021825.60   \n",
              "5604  1.75 1244.10  1353001.23 1364.89  1319336.96  895.93   988708.13   \n",
              "\n",
              "        V011        V012  \n",
              "0    3930.35 17241576.74  \n",
              "1    6063.92 27502674.90  \n",
              "2     858.51   414378.88  \n",
              "3    1909.04  5050712.69  \n",
              "4    1086.94   159622.46  \n",
              "...      ...         ...  \n",
              "5599 4551.66 29032823.35  \n",
              "5600 3977.93 21495997.85  \n",
              "5601 2062.09  4596759.57  \n",
              "5603 4362.79 16264378.33  \n",
              "5604 1226.86   947807.31  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>Rua</th>\n",
              "      <th>Bairro</th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Metragem</th>\n",
              "      <th>Quartos</th>\n",
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              "      <th>0</th>\n",
              "      <td>Avenida Itacira, 255</td>\n",
              "      <td>Planalto Paulista</td>\n",
              "      <td>São Paulo</td>\n",
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              "      <td>5570.65</td>\n",
              "      <td>25770816.97</td>\n",
              "      <td>2096.19</td>\n",
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              "      <td>3930.35</td>\n",
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              "      <th>1</th>\n",
              "      <td>Rua Aurelia Perez Alvarez, 42</td>\n",
              "      <td>Jardim dos Estados</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>496</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3700000.00</td>\n",
              "      <td>3.70</td>\n",
              "      <td>7459.68</td>\n",
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              "      <td>3.13</td>\n",
              "      <td>1.96</td>\n",
              "      <td>7600.44</td>\n",
              "      <td>36778930.17</td>\n",
              "      <td>7890.53</td>\n",
              "      <td>35886906.64</td>\n",
              "      <td>4149.00</td>\n",
              "      <td>26760682.90</td>\n",
              "      <td>6063.92</td>\n",
              "      <td>27502674.90</td>\n",
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              "      <th>2</th>\n",
              "      <td>Rua Alba Valdez</td>\n",
              "      <td>Jardim Reimberg</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>125</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>380000.00</td>\n",
              "      <td>0.38</td>\n",
              "      <td>3040.00</td>\n",
              "      <td>...</td>\n",
              "      <td>3.47</td>\n",
              "      <td>3.06</td>\n",
              "      <td>805.13</td>\n",
              "      <td>769188.78</td>\n",
              "      <td>1011.75</td>\n",
              "      <td>757433.42</td>\n",
              "      <td>518.77</td>\n",
              "      <td>426672.78</td>\n",
              "      <td>858.51</td>\n",
              "      <td>414378.88</td>\n",
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              "      <th>3</th>\n",
              "      <td>Rua Tobias Barreto, 195</td>\n",
              "      <td>Mooca</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>540000.00</td>\n",
              "      <td>0.54</td>\n",
              "      <td>5400.00</td>\n",
              "      <td>...</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.99</td>\n",
              "      <td>2338.05</td>\n",
              "      <td>7826370.15</td>\n",
              "      <td>2447.89</td>\n",
              "      <td>7925839.76</td>\n",
              "      <td>1543.29</td>\n",
              "      <td>4646566.16</td>\n",
              "      <td>1909.04</td>\n",
              "      <td>5050712.69</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rua Graham Bell</td>\n",
              "      <td>Santo Amaro</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>440</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>1980000.00</td>\n",
              "      <td>1.98</td>\n",
              "      <td>4500.00</td>\n",
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              "      <td>2.31</td>\n",
              "      <td>1026.36</td>\n",
              "      <td>272168.67</td>\n",
              "      <td>1143.96</td>\n",
              "      <td>167733.64</td>\n",
              "      <td>735.67</td>\n",
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              "      <td>157</td>\n",
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              "      <td>960000.00</td>\n",
              "      <td>0.96</td>\n",
              "      <td>6114.65</td>\n",
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              "      <td>2.17</td>\n",
              "      <td>1.27</td>\n",
              "      <td>5054.58</td>\n",
              "      <td>33245189.73</td>\n",
              "      <td>5231.93</td>\n",
              "      <td>33485189.78</td>\n",
              "      <td>3649.06</td>\n",
              "      <td>26562232.53</td>\n",
              "      <td>4551.66</td>\n",
              "      <td>29032823.35</td>\n",
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              "      <th>5600</th>\n",
              "      <td>Avenida Odila</td>\n",
              "      <td>Planalto Paulista</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>267</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>1.50</td>\n",
              "      <td>5617.98</td>\n",
              "      <td>...</td>\n",
              "      <td>2.81</td>\n",
              "      <td>1.60</td>\n",
              "      <td>4249.33</td>\n",
              "      <td>24499871.44</td>\n",
              "      <td>5180.14</td>\n",
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              "      <th>5601</th>\n",
              "      <td>Rua Tapuçu</td>\n",
              "      <td>Vila Sofia</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>95</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>665000.00</td>\n",
              "      <td>0.67</td>\n",
              "      <td>7000.00</td>\n",
              "      <td>...</td>\n",
              "      <td>2.99</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2425.09</td>\n",
              "      <td>6878872.25</td>\n",
              "      <td>2543.80</td>\n",
              "      <td>6913867.79</td>\n",
              "      <td>1561.58</td>\n",
              "      <td>4261814.01</td>\n",
              "      <td>2062.09</td>\n",
              "      <td>4596759.57</td>\n",
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              "      <th>5603</th>\n",
              "      <td>Rua Estero Belaco</td>\n",
              "      <td>Vila da Saúde</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>205</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>1050000.00</td>\n",
              "      <td>1.05</td>\n",
              "      <td>5121.95</td>\n",
              "      <td>...</td>\n",
              "      <td>2.75</td>\n",
              "      <td>1.50</td>\n",
              "      <td>5650.20</td>\n",
              "      <td>20344622.14</td>\n",
              "      <td>5750.84</td>\n",
              "      <td>20127679.27</td>\n",
              "      <td>3459.68</td>\n",
              "      <td>16021825.60</td>\n",
              "      <td>4362.79</td>\n",
              "      <td>16264378.33</td>\n",
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              "      <th>5604</th>\n",
              "      <td>Rua Barão de Tramandaí, 0</td>\n",
              "      <td>Vila Alpina</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>122</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>270000.00</td>\n",
              "      <td>0.27</td>\n",
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              "      <td>2.97</td>\n",
              "      <td>1.75</td>\n",
              "      <td>1244.10</td>\n",
              "      <td>1353001.23</td>\n",
              "      <td>1364.89</td>\n",
              "      <td>1319336.96</td>\n",
              "      <td>895.93</td>\n",
              "      <td>988708.13</td>\n",
              "      <td>1226.86</td>\n",
              "      <td>947807.31</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "pd.set_option(\"display.precision\", 2)\n",
        "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
        "sns.set()\n",
        "\n",
        "dados_vendas_censo = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Imersao_Dados_4/dados_vendas_censo.csv\")\n",
        "dados_vendas_censo = dados_vendas_censo[['Rua', 'Bairro', 'Cidade', 'Metragem', 'Quartos', 'Banheiros', 'Vagas',\n",
        "       'Valor_anuncio', 'Valor_mm', 'Valor_m2', 'latitude', 'longitude', 'Situacao_setor', 'V001',\n",
        "       'V002', 'V003', 'V004', 'V005', 'V006', 'V007', 'V008', 'V009', 'V010',\n",
        "       'V011', 'V012']].dropna()\n",
        "dados_vendas_censo"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Relembrando: o projeto final consiste em desenvolver um modelo de predição de valor de imóveis baseado na sua localização, por isso a primeira análise foi verificar como estão estes valores com um boxplot."
      ],
      "metadata": {
        "id": "sUTLyZTYUAXS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "ax = sns.boxplot(data = dados_vendas_censo, y='Valor_anuncio')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "8VYjXp8jeVwo",
        "outputId": "df5c8a5a-b795-4b25-b79e-15fd5290fe9f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ao analisar os outliers, levantou-se a hipótese de que poderiam haver imóveis com valores muito discrepantes no dataframe, ou muito caros ou muito baratos.\n",
        "\n",
        "E analisando os dados mais caros do que trinta milhões e mais baratos do que 100.000,00 optou-se em excluí-los, pois dificilmente uma casa com três quartos custaria R$13.000,00 e pelo número de vagas e banheiros, provavelmente os demais imóveis não são residências."
      ],
      "metadata": {
        "id": "dEOmHTMIUQLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando quantos imóveis custam mais do que R$30.000.000,00 ou que são mais baratos do que R$100.000,00\n",
        "\n",
        "dados_vendas_censo.query('Valor_anuncio > 30000000 | Valor_anuncio < 100000')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "m1SYysE-eoEr",
        "outputId": "4c787694-2e46-4059-bd82-1e907b7a40d0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Rua          Bairro     Cidade  Metragem  Quartos  \\\n",
              "54          Rua Inglaterra   Jardim Europa  São Paulo       200        3   \n",
              "89          Rua Inglaterra   Jardim Europa  São Paulo       756        5   \n",
              "1032  Praça Nicolau Scarpa  Jardim América  São Paulo      1200        4   \n",
              "3521    Rua Estados Unidos  Jardim América  São Paulo      1816        4   \n",
              "3896    Rua Estados Unidos  Jardim América  São Paulo      1200        4   \n",
              "\n",
              "      Banheiros  Vagas  Valor_anuncio  Valor_mm  Valor_m2  ...  V003  V004  \\\n",
              "54            3      2       13000.00      0.01     65.00  ...  3.13  2.87   \n",
              "89            4     10    37000000.00     37.00  48941.80  ...  3.13  2.87   \n",
              "1032          4     25    65000000.00     65.00  54166.67  ...  3.57  3.01   \n",
              "3521         10     20    55000000.00     55.00  30286.34  ...  3.57  3.01   \n",
              "3896          6     24    65000000.00     65.00  54166.67  ...  3.57  3.01   \n",
              "\n",
              "         V005          V006     V007          V008     V009         V010  \\\n",
              "54   21545.71 1577883376.43 26081.65 1794039881.36 11050.67 882338714.47   \n",
              "89   21545.71 1577883376.43 26081.65 1794039881.36 11050.67 882338714.47   \n",
              "1032 13131.61  508165993.72 14507.30  541764826.89  7934.91 364776324.15   \n",
              "3521 13131.61  508165993.72 14507.30  541764826.89  7934.91 364776324.15   \n",
              "3896 13131.61  508165993.72 14507.30  541764826.89  7934.91 364776324.15   \n",
              "\n",
              "         V011          V012  \n",
              "54   15470.93 1168079134.43  \n",
              "89   15470.93 1168079134.43  \n",
              "1032 10168.24  445001716.57  \n",
              "3521 10168.24  445001716.57  \n",
              "3896 10168.24  445001716.57  \n",
              "\n",
              "[5 rows x 25 columns]"
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              "      <th></th>\n",
              "      <th>Rua</th>\n",
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              "      <th>Valor_anuncio</th>\n",
              "      <th>Valor_mm</th>\n",
              "      <th>Valor_m2</th>\n",
              "      <th>...</th>\n",
              "      <th>V003</th>\n",
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              "      <th>V005</th>\n",
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              "      <th>54</th>\n",
              "      <td>Rua Inglaterra</td>\n",
              "      <td>Jardim Europa</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>200</td>\n",
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              "      <td>1168079134.43</td>\n",
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              "      <th>89</th>\n",
              "      <td>Rua Inglaterra</td>\n",
              "      <td>Jardim Europa</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>756</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>37000000.00</td>\n",
              "      <td>37.00</td>\n",
              "      <td>48941.80</td>\n",
              "      <td>...</td>\n",
              "      <td>3.13</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21545.71</td>\n",
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              "      <td>1200</td>\n",
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              "      <td>25</td>\n",
              "      <td>65000000.00</td>\n",
              "      <td>65.00</td>\n",
              "      <td>54166.67</td>\n",
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              "      <td>3.57</td>\n",
              "      <td>3.01</td>\n",
              "      <td>13131.61</td>\n",
              "      <td>508165993.72</td>\n",
              "      <td>14507.30</td>\n",
              "      <td>541764826.89</td>\n",
              "      <td>7934.91</td>\n",
              "      <td>364776324.15</td>\n",
              "      <td>10168.24</td>\n",
              "      <td>445001716.57</td>\n",
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              "      <th>3521</th>\n",
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              "      <td>Jardim América</td>\n",
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              "      <td>1816</td>\n",
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              "      <td>55.00</td>\n",
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              "      <td>Rua Estados Unidos</td>\n",
              "      <td>Jardim América</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>1200</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Excluindo esses dados e notando uma diferença no boxplot\n",
        "\n",
        "dados_vendas_censo.drop(dados_vendas_censo.query('Valor_anuncio > 30000000 | Valor_anuncio < 100000').index, inplace=True)\n",
        "plt.figure(figsize=(10,8))\n",
        "ax = sns.boxplot(data = dados_vendas_censo, y='Valor_anuncio')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "MCRGDcqsfL6O",
        "outputId": "ab1bc18b-1938-4dd7-9955-c8e1b947cc2b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Voltando ao modelo de predição, era preciso selecionar quais variáveis seriam usadas para definir o modelo. Por isso foi feita uma análise entre Valor_anuncio e Valor_m2, que apresentou um gráfico menos disperso, quando comparado com os anteriores. Isso ocorreu porque a variável Valor_m2 é o resultado da divisão entre o valor e a metragem do imóvel."
      ],
      "metadata": {
        "id": "8z92BxeFU3VV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "sns.scatterplot(data = dados_vendas_censo, x=\"Valor_anuncio\", y='Valor_m2')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "Jb0j5MOOgCRC",
        "outputId": "79782244-6ba2-480f-90a2-de1d70038d38"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60d4bad650>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para verificar se havia mais alguma relação entre as demais variáveis do dataframe, fomos apresentados ao método `.corr()` que devolve uma matriz de correlação. Quanto mais próximo de 1, maior será o grau de relação entre as variáveis, e quanto mais próximo de -1, menor será o grau de relação"
      ],
      "metadata": {
        "id": "pg5YQGwWU9oh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Site que apresenta alta correlação entre fatores aleatórios https://www.tylervigen.com/spurious-correlations\n",
        "# Mas, alta correlação não implica em causalidade.\n",
        "# Por isso, deve-se atentar para não chegar a uma conclusão equivocada sobre as variáveis\n",
        "\n",
        "dados_vendas_censo.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "dBgC6ir6gfyv",
        "outputId": "4adc5a0b-49c3-4eea-b279-d6a4fbb788bc"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Metragem  Quartos  Banheiros  Vagas  Valor_anuncio  Valor_mm  \\\n",
              "Metragem            1.00     0.23       0.23   0.27           0.30      0.30   \n",
              "Quartos             0.23     1.00       0.55   0.42           0.37      0.37   \n",
              "Banheiros           0.23     0.55       1.00   0.46           0.47      0.47   \n",
              "Vagas               0.27     0.42       0.46   1.00           0.48      0.48   \n",
              "Valor_anuncio       0.30     0.37       0.47   0.48           1.00      1.00   \n",
              "Valor_mm            0.30     0.37       0.47   0.48           1.00      1.00   \n",
              "Valor_m2            0.06     0.12       0.22   0.15           0.70      0.70   \n",
              "latitude           -0.09    -0.01      -0.03  -0.04           0.02      0.02   \n",
              "longitude          -0.13    -0.19      -0.25  -0.19          -0.21     -0.21   \n",
              "Situacao_setor      0.48    -0.01      -0.03  -0.01          -0.00     -0.00   \n",
              "V001               -0.13    -0.18      -0.22  -0.21          -0.22     -0.22   \n",
              "V002               -0.10    -0.15      -0.17  -0.15          -0.19     -0.19   \n",
              "V003                0.13     0.16       0.21   0.24           0.13      0.13   \n",
              "V004                0.05     0.07       0.07   0.09           0.08      0.08   \n",
              "V005                0.22     0.32       0.41   0.34           0.45      0.45   \n",
              "V006                0.07     0.12       0.11   0.11           0.11      0.11   \n",
              "V007                0.23     0.33       0.41   0.34           0.45      0.45   \n",
              "V008                0.05     0.11       0.08   0.08           0.07      0.07   \n",
              "V009                0.20     0.31       0.39   0.32           0.47      0.47   \n",
              "V010                0.07     0.13       0.11   0.12           0.13      0.13   \n",
              "V011                0.21     0.32       0.40   0.33           0.45      0.45   \n",
              "V012                0.05     0.11       0.08   0.09           0.08      0.08   \n",
              "\n",
              "                Valor_m2  latitude  longitude  Situacao_setor  ...  V003  \\\n",
              "Metragem            0.06     -0.09      -0.13            0.48  ...  0.13   \n",
              "Quartos             0.12     -0.01      -0.19           -0.01  ...  0.16   \n",
              "Banheiros           0.22     -0.03      -0.25           -0.03  ...  0.21   \n",
              "Vagas               0.15     -0.04      -0.19           -0.01  ...  0.24   \n",
              "Valor_anuncio       0.70      0.02      -0.21           -0.00  ...  0.13   \n",
              "Valor_mm            0.70      0.02      -0.21           -0.00  ...  0.13   \n",
              "Valor_m2            1.00      0.05      -0.18           -0.03  ... -0.13   \n",
              "latitude            0.05      1.00       0.15           -0.03  ... -0.05   \n",
              "longitude          -0.18      0.15       1.00           -0.01  ... -0.05   \n",
              "Situacao_setor     -0.03     -0.03      -0.01            1.00  ...  0.03   \n",
              "V001               -0.11     -0.00       0.31            0.02  ... -0.22   \n",
              "V002               -0.15     -0.02       0.30            0.03  ...  0.07   \n",
              "V003               -0.13     -0.05      -0.05            0.03  ...  1.00   \n",
              "V004                0.01      0.03       0.02            0.00  ...  0.41   \n",
              "V005                0.29     -0.05      -0.36           -0.03  ...  0.28   \n",
              "V006                0.04     -0.09      -0.10           -0.01  ...  0.08   \n",
              "V007                0.28     -0.07      -0.36           -0.03  ...  0.28   \n",
              "V008                0.02     -0.07      -0.07           -0.01  ...  0.06   \n",
              "V009                0.37     -0.04      -0.38           -0.04  ...  0.13   \n",
              "V010                0.05     -0.07      -0.10           -0.01  ...  0.10   \n",
              "V011                0.32     -0.07      -0.37           -0.03  ...  0.19   \n",
              "V012                0.02     -0.06      -0.07           -0.00  ...  0.07   \n",
              "\n",
              "                V004  V005  V006  V007  V008  V009  V010  V011  V012  \n",
              "Metragem        0.05  0.22  0.07  0.23  0.05  0.20  0.07  0.21  0.05  \n",
              "Quartos         0.07  0.32  0.12  0.33  0.11  0.31  0.13  0.32  0.11  \n",
              "Banheiros       0.07  0.41  0.11  0.41  0.08  0.39  0.11  0.40  0.08  \n",
              "Vagas           0.09  0.34  0.11  0.34  0.08  0.32  0.12  0.33  0.09  \n",
              "Valor_anuncio   0.08  0.45  0.11  0.45  0.07  0.47  0.13  0.45  0.08  \n",
              "Valor_mm        0.08  0.45  0.11  0.45  0.07  0.47  0.13  0.45  0.08  \n",
              "Valor_m2        0.01  0.29  0.04  0.28  0.02  0.37  0.05  0.32  0.02  \n",
              "latitude        0.03 -0.05 -0.09 -0.07 -0.07 -0.04 -0.07 -0.07 -0.06  \n",
              "longitude       0.02 -0.36 -0.10 -0.36 -0.07 -0.38 -0.10 -0.37 -0.07  \n",
              "Situacao_setor  0.00 -0.03 -0.01 -0.03 -0.01 -0.04 -0.01 -0.03 -0.00  \n",
              "V001           -0.12 -0.38 -0.10 -0.39 -0.10 -0.36 -0.13 -0.37 -0.11  \n",
              "V002            0.00 -0.33 -0.09 -0.34 -0.09 -0.35 -0.12 -0.35 -0.10  \n",
              "V003            0.41  0.28  0.08  0.28  0.06  0.13  0.10  0.19  0.07  \n",
              "V004            1.00  0.07  0.02  0.06  0.01 -0.01  0.02  0.01  0.01  \n",
              "V005            0.07  1.00  0.42  0.95  0.31  0.95  0.39  0.90  0.27  \n",
              "V006            0.02  0.42  1.00  0.56  0.93  0.42  0.89  0.58  0.83  \n",
              "V007            0.06  0.95  0.56  1.00  0.50  0.92  0.56  0.96  0.48  \n",
              "V008            0.01  0.31  0.93  0.50  1.00  0.33  0.91  0.56  0.95  \n",
              "V009           -0.01  0.95  0.42  0.92  0.33  1.00  0.45  0.94  0.34  \n",
              "V010            0.02  0.39  0.89  0.56  0.91  0.45  1.00  0.64  0.95  \n",
              "V011            0.01  0.90  0.58  0.96  0.56  0.94  0.64  1.00  0.57  \n",
              "V012            0.01  0.27  0.83  0.48  0.95  0.34  0.95  0.57  1.00  \n",
              "\n",
              "[22 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-303e8c0d-fb22-4e20-90a2-61750afa6e05\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        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>Metragem</th>\n",
              "      <th>Quartos</th>\n",
              "      <th>Banheiros</th>\n",
              "      <th>Vagas</th>\n",
              "      <th>Valor_anuncio</th>\n",
              "      <th>Valor_mm</th>\n",
              "      <th>Valor_m2</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>Situacao_setor</th>\n",
              "      <th>...</th>\n",
              "      <th>V003</th>\n",
              "      <th>V004</th>\n",
              "      <th>V005</th>\n",
              "      <th>V006</th>\n",
              "      <th>V007</th>\n",
              "      <th>V008</th>\n",
              "      <th>V009</th>\n",
              "      <th>V010</th>\n",
              "      <th>V011</th>\n",
              "      <th>V012</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Metragem</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.06</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.48</td>\n",
              "      <td>...</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quartos</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.12</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Banheiros</th>\n",
              "      <td>0.23</td>\n",
              "      <td>0.55</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.22</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vagas</th>\n",
              "      <td>0.27</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.46</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.15</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Valor_anuncio</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Valor_mm</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Valor_m2</th>\n",
              "      <td>0.06</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.15</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>0.15</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Situacao_setor</th>\n",
              "      <td>0.48</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V001</th>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>-0.11</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.02</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>-0.12</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.39</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V002</th>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.34</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>-0.12</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>-0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V003</th>\n",
              "      <td>0.13</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.13</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V004</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.41</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V005</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.29</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V006</th>\n",
              "      <td>0.07</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.04</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V007</th>\n",
              "      <td>0.23</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.28</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V008</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V009</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.37</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>...</td>\n",
              "      <td>0.13</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V010</th>\n",
              "      <td>0.07</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V011</th>\n",
              "      <td>0.21</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.32</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V012</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-303e8c0d-fb22-4e20-90a2-61750afa6e05')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-303e8c0d-fb22-4e20-90a2-61750afa6e05 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-303e8c0d-fb22-4e20-90a2-61750afa6e05');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Por não ser de fácil leitura, o Thiago mostrou como plotar uma matriz de correlação com o Seaborn, que facilita a identificação de alta correlação através das cores escolhidas para representar os extremos do grau de relação."
      ],
      "metadata": {
        "id": "EwSA3wXmVJ8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(18, 8))\n",
        "mask = np.triu(np.ones_like(dados_vendas_censo.corr(), dtype=bool))\n",
        "heatmap = sns.heatmap(dados_vendas_censo.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
        "heatmap.set_title('Correlação', fontdict={'fontsize':18}, pad=16);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "id": "jd5wiEsGhPtg",
        "outputId": "5200c290-0706-4b9f-a474-d3c97d11c8db"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O par de variáveis escolhido para desenvolver o modelo de predição foi a ‘Metragem’ e o ‘Valor_anuncio’, por apresentarem um grau de relação equivalente a 0,3.\n",
        "\n",
        "Para prever os preços, o método escolhido foi a regressão linear, que utiliza o valor de uma variável (*x*, também chamada de variável independente) para determinar o valor de outra (*y*, também conhecida como variável dependente).\n",
        "\n",
        "Antes de iniciar o conteúdo de Machine Learning, foi preciso importar a `LinearRegression` para calcular a regressão linear, e o `train_test_split` que divide o conjunto de dados em treino e teste, ambas do Scikit Learn. Foi determinado que *x* seria a Metragem, enquanto o Valor_anuncio seria o *y*. \n",
        "\n",
        "O datafrane foi dividido em quatro partes: `X_train`, `X_test`, `Y_train`, `Y_test`. Os testes receberam 25% do dataframe, decidido de forma aleatória através do parâmetro `random_state`.\n"
      ],
      "metadata": {
        "id": "6zMaVH6eVYLX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = dados_vendas_censo[['Metragem']]\n",
        "Y = dados_vendas_censo['Valor_anuncio']\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 54)\n",
        "# random_state = Controla o embaralhamento aplicado aos dados antes de aplicar a divisão. \n",
        "# Passe um int para saída reproduzível em várias chamadas de função.\n",
        "lr = LinearRegression()"
      ],
      "metadata": {
        "id": "g-JLgD4KkwUI"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usando os treinos para ajustar o modelo linear."
      ],
      "metadata": {
        "id": "JLu77V1rWXAh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66jZgU0WmKXf",
        "outputId": "b3e9df06-b4d5-495c-c2d9-5a038d967abd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prevendo resultados do valor dos imóveis a partir da metragem teste. Aqui, foram apresentados os três primeiros e os três últimos valores previstos."
      ],
      "metadata": {
        "id": "0wQM7STwWZ7Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnPYQqLwmwmF",
        "outputId": "9e692dd5-f0e6-403d-8a2d-463a398e4e14"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4535135.54576314, 2319529.74583429, 2424501.59358835, ...,\n",
              "       2802400.24550293, 2483285.82833061, 2854186.3570616 ])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chamando os valores verdadeiros dos imóveis, foi possível comparar alguns valores, já que foram apresentados os cinco primeiros e os cinco últimos. Algumas predições de valor chegaram próximo ao valor real, como a primeira e a última, enquanto outras predições foram muito diferentes, por exemplo a segunda, que previu um valor maior do que 2,3 milhões enquanto o preço original é R$345.000,00."
      ],
      "metadata": {
        "id": "sTkXjCthWeY0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8YC3tyWpXNX",
        "outputId": "8059d75e-9eae-4d79-91ac-98ddf4544e74"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "172    4500000.00\n",
              "4513    345000.00\n",
              "4865    800000.00\n",
              "4979   4000000.00\n",
              "1255   5100000.00\n",
              "          ...    \n",
              "5240   1600000.00\n",
              "4472    580000.00\n",
              "3291   2200000.00\n",
              "432    1500000.00\n",
              "2455   2800000.00\n",
              "Name: Valor_anuncio, Length: 1326, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para melhorar trabalho com as variáveis, o `Y_predict_test` recebeu a predição do modelo linear de `X_test` (a parte menor do dataframe, usada para testar o modelo), enquanto `Y_predict_train` recebeu a predição que treinou o modelo."
      ],
      "metadata": {
        "id": "Ss7ZaVeOWokO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_predict_test = lr.predict(X_test)\n",
        "Y_predict_train = lr.predict(X_train)"
      ],
      "metadata": {
        "id": "C3bLPXebrU8P"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fazer somente comparações de alguns valores previstos e reais, não ajuda muito. Por isso, para verificar melhor como foi a predição do ‘Valor_anuncio’, foi importado o `mean_absolute_error`, que calcula o erro absoluto médio. Em outras palavras, a função faz um somatório da diferença entre os *n* valores reais e os valores previstos, depois divide pelo número de amostras.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Fonte: [Scikit Learn](https://scikit-learn.org/stable/modules/model_evaluation.html#mean-absolute-error)\n",
        "\n",
        "E também foi importado o `r2_score`, coeficiente de determinação, que pontua a regressão como boa quanto mais próximos de 1 os resultados estiverem, ou com um número negativo, para predições ruins. \n",
        "Fonte: [Scikit Learn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html)\n",
        "\n",
        "Mas se o modelo atingir 1, significa que, de alguma forma, ele está ‘acessando’ a variável dependente, está acertando todos os resultados do teste e isso não deveria acontecer. Pois, é como se o modelo estivesse conferindo uma resposta que ele deveria calcular."
      ],
      "metadata": {
        "id": "5Apr0uvlXJxv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculando a média do erro absoluto da parte teste, resultou em:"
      ],
      "metadata": {
        "id": "X-fRKLc_Zc6u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "\n",
        "mean_absolute_error(Y_test, Y_predict_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7r6YZdhpcPn",
        "outputId": "c9c24281-a014-4e34-e278-63a0c50d954c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1770626.9062636306"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obtendo um erro de:"
      ],
      "metadata": {
        "id": "5tTgIRkfZd6Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(Y_test, Y_predict_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXyKKTnFLgxS",
        "outputId": "40c4f0c3-4919-4ab2-ed43-d9987fe5dbfd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1298305754042075"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Enquanto a média da parte usado no treino, resultou em:"
      ],
      "metadata": {
        "id": "z8IThPq5Zhfl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(Y_train, Y_predict_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JnEJ1xdrNAb",
        "outputId": "c348dc93-4c9c-49e6-d78e-6c0e586e5a95"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1825220.3034549057"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com um erro de:"
      ],
      "metadata": {
        "id": "CLAgZlWIZjdk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(Y_train, Y_predict_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aO2cEgDtLhmU",
        "outputId": "5da7ba00-622b-464a-fdea-b9f7fa9754fe"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07399685087721253"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com o resultado de um modelo de predição baseado em duas variáveis, deu-se início à criação de um modelo baseado em mais variáveis. Desta vez, ‘X’ recebeu a metragem, o número de quartos, banheiros, vagas e as variáveis do IBGE, ‘V001’, ‘V007’ e ‘V009’.\n",
        "\n",
        "‘V001’ = Domicílios particulares permanentes ou pessoas responsáveis por domicílios\n",
        "\n",
        "‘V007’ = Valor do rendimento nominal médio mensal das pessoas responsáveis por domicílios particulares permanentes (com rendimento)\n",
        "\n",
        "‘V009’ = Valor do rendimento nominal médio mensal das pessoas de 10 anos ou mais de idade (com e sem rendimento)\n",
        "\n",
        "A porcentagem de teste e o embaralhamento continuaram os mesmos.\n"
      ],
      "metadata": {
        "id": "d5FzZ_ZTZo0I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dados_vendas_censo[['Metragem', 'Quartos', 'Banheiros', 'Vagas','V001','V007', 'V009']]\n",
        "\n",
        "Y = dados_vendas_censo['Valor_anuncio']\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 54)\n",
        "lr.fit(X_train, Y_train)\n",
        "Y_predict_test = lr.predict(X_test)\n",
        "Y_predict_train = lr.predict(X_train)\n",
        "\n",
        "mae_test = mean_absolute_error(Y_test, Y_predict_test)\n",
        "r2_test = r2_score(Y_test, Y_predict_test)\n",
        "mae_train = mean_absolute_error(Y_train, Y_predict_train)\n",
        "r2_train = r2_score(Y_train, Y_predict_train)"
      ],
      "metadata": {
        "id": "FgYycDzUxPjl"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Desta vez, o erro absoluto médio foi menor tanto para o treino, quanto para o teste."
      ],
      "metadata": {
        "id": "kXMm-VjhZ1KB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mae_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFGNLtny1tmU",
        "outputId": "b14d0981-198c-49a3-a0fc-9106c0f10846"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1349590.384432537"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XIbmJjGU1ytF",
        "outputId": "cb90c507-1bab-4d18-efcc-eaeaa0f3d05a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.37745069254620534"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mae_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMFyTkwZ1sxZ",
        "outputId": "8d7ef2d7-32f6-406d-808e-d2f53b4d4fdf"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1362945.691504754"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69cAnzVi126X",
        "outputId": "82192e18-ccaa-4f05-945a-2464b675c4e4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.39899592785823335"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Foi apresentando uma melhora no resultado, porém a Vivian e o Thiago chamaram a atenção para o fato de que não há relação entre ‘quanto maior o número de variáveis no modelo, melhor será o resultado’. Para obter o melhor resultado, depende muito das variáveis envolvidas, do escopo do projeto e da habilidade do cientista de dados de fazer experimentações com o dataframe disponível.\n",
        "\n",
        "Para encerrar a aula, foi plotado um gráfico entre o preço real do imóvel (`Y_test`) e o resultado do modelo (`Y_predict_test`), em que o gráfico ficou mais disperso para imóveis que custam mais do que R$500.000,00 e mais uniforme para os imóveis que custavam menos."
      ],
      "metadata": {
        "id": "vk1_5yG-Z-jO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "sns.scatterplot(x=Y_test, y=Y_predict_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "6fPYWXp-14XG",
        "outputId": "d67019be-63ac-4395-eb78-27b8facd7c2a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60cd595850>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Desafios Aula 5\n",
        "\n",
        "1. Adicionar o valor \"real\" do m2 do imóvel;\n",
        "\n",
        "2. Testar outros modelos de regressão;\n",
        "\n",
        "3. Criar um modelo baseline com as features originais;\n",
        "\n",
        "4. Trabalhar um pouco mais os dados e criar novas variáveis;\n",
        "\n",
        "5. Testar as predições para casos reais;\n",
        "\n",
        "6. Crie o projeto, reorganizando o notebook, com introdução, interpretação dos gráficos e conclusão. \n",
        "Deixar com cara de projeto de portfólio e compartilhar."
      ],
      "metadata": {
        "id": "8jB_P8Xw2U3W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Na matriz de correlação é possível verificar que, em relação ao valor do imóvel, a variável V001 tem um grau de relação negativo, por isso achei melhor substituí-lo por ‘V005’ e adicionar a variável ‘V011’, que é referente ao ‘Valor do rendimento nominal médio mensal das pessoas de 10 anos ou mais de idade (com rendimento)’."
      ],
      "metadata": {
        "id": "CcrTE7WYaOTU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dados_vendas_censo[['Metragem', 'Quartos', 'Banheiros', 'Vagas', 'V005', 'V007', 'V009', 'V011']]\n",
        "\n",
        "Y = dados_vendas_censo['Valor_anuncio']\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 54)\n",
        "lr.fit(X_train, Y_train)\n",
        "Y_predict_test = lr.predict(X_test)\n",
        "Y_predict_train = lr.predict(X_train)\n",
        "\n",
        "mae_test = mean_absolute_error(Y_test, Y_predict_test)\n",
        "r2_test = r2_score(Y_test, Y_predict_test)\n",
        "mae_train = mean_absolute_error(Y_train, Y_predict_train)\n",
        "r2_train = r2_score(Y_train, Y_predict_train)"
      ],
      "metadata": {
        "id": "_bsfLsNOGmsS"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este modelo apresentou resultados um pouco melhores do que o anterior, tanto no erro médio, quanto no coeficiente de correlação."
      ],
      "metadata": {
        "id": "Z5_-EW-Habst"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mae_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WzLK7ZSUBPr",
        "outputId": "7ede0100-ab72-4282-d59d-3bc5ed7ce26a"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1340579.120435057"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezg3QW5QUBj5",
        "outputId": "ef50fd95-2439-4231-ca89-4ca560b62900"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3900243556138375"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mae_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5j6vJBiqUFvR",
        "outputId": "d817ff22-efc4-4623-ff44-28be5b654d11"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1370291.6395928832"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChA_KtkTUIz5",
        "outputId": "2dcd663e-ab1f-4cb4-b680-8155ef298ef3"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4013729918953213"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset('taxis') \n",
        "plt.figure(figsize=(10, 10))\n",
        "ax = sns.regplot(x = Y_test, \n",
        "            y = Y_predict_test,  \n",
        "            ci = None, \n",
        "            data = df)\n",
        "ax.set_title(\"Modelo com oito variáveis independentes\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "qZsBVOU7qlFF",
        "outputId": "b9dee896-1326-4e2d-944b-febe715358d0"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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ERGRQ8azmyhjzBeAa4D+8OqaIiIhIuulTz9XxGGOygfuAO6y10T70cHVrxIiB6+wqKysesGMPVmpT76lNvac29Z7a1HtqU+/52aaeJFfAGKAcWB5PrIYCAWPMEGvtvX09SH19E7GY41FInykrK6a29ojnxx3M1KbeU5t6T23qPbWp99Sm3hvoNg0GA712CHmSXFlrdwClHT8bY/4aKNJoQRERERls+lRzZYz5iTFmFzAOeNkYUxXfvtwYc+5ABigiIiKSTvo6WvA7wHe62T63h/3/+uTCEhEREUlPmqFdRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKRERExENKrkREREQ8pORKREREMsKRljDL3tnG5l2HfY0jy9dnFxEREfHAhm0HuX/peg43hdm8p5E//MpM32JRciUiIiJpKxKN8ZvVW1jx7g4cIDcnxO9dNsXXmJRciYiISFraf6iFxS9UsW3fEQAmjxnCooUzqJg6itraI77FpeRKRERE0orjOLy9bh+PraqmLRwlAMz94kSuu3gyWSH/y8mVXImIiEjaaGlt55cvWd7bcACAYcW53D1/BtMnDvM5ss8ouRIREZG0sHlXA4srq6hvbAXgrNNKuWPudIrys32O7FhKrga5tTV1rFizg7qGVkpL8pgzawIzy0v9DktERKRTLOaw9O1tVL61jZjjkJMV5OYrT+NLZ55CIBDwO7zPUXI1iK2tqePxVdWEQkEK8rI43Bzm8VXVAEqwREQkJdQ3tHLfkio27WoAYFxZEYuuq2BsaaHPkfVMydUgtmLNDkKhILnZIQBys0O0xbcruRIREb99sPEAD7+4kZa2CABXnTuOGy8rJzsr5HNkvVNyNYjVNbRSkHfsKZCTFaSuodWniERERKAtHOWJl6tZvXYvAMUF2dw1b3rafPFXcjWIlZbkcbg53NlzBRCOxCgtyfMxKhERGcy27zvCzyur2H+wBYCKycO5e950SopyfY6s75RcDWJzZk3g8VXVtOH2WIUjMaLRGHNmTfA7NBERGWRijsPK93by69driMYcQsEAN1xWzuzzxhNMwaL13ii5GsQ6ulc1WlBERPzU0NTGA8s2ULX1IACjhxewaGEFE0cX+xzZiVFyNcjNLC9VMiUiIr75ZHMdv1i+gSMt7QBcMnMMt1w1ldyc1C5a742SKxEREUm69kiUZ16t4eUPdwFQkJvFN66dxnnTRvoc2clTciUiIiJJtbuumcUvrGNXbTMAU8eVcM+CCkZkyIAqJVciIiKSFI7j8Nrv9vDUK5toj8QIBgIsvGgS8y+cRDCYXkXrvVFyJSIiIgOu6Wg7Dy3fwMeb6gB3OqB7F1QwZVyJz5F5T8mViIiIDKgN2w9x/5IqDjeFATh/+khuv2ba5yayzhSZ+apERETEd5FojBfe3Mryd7bj4C6zdtvVU7nw9NEpueCyV5RciYiIiOcOHGphcWUVW/ceAWDS6GIWLaxg1PACnyMbeEquRERExDOO4/BO1T4eXVlNWzhKALj2gol8+ZLJZIWCfoeXFEquRERExBMtrREeW2l5d/1+AEqKcrhn/gxmTBruc2TJpeRKRERETlrN7gYWV1ZR19AKwJlTSrlj7jSKC3J8jiz5lFyJiIjICYvFHJa9u50XVm8l5jhkZwW5+YopXHbW2IwuWu+NkisRERE5IQcbW7l/yXrszsMAjCsrZNHCCsaWFfkcmb+UXImIiEi/fWgP8PCLG2lujQBw5Tnj+Orl5WRnpe+Cy15RciUiIiJ91haO8uQrm3jjkz0AFOVnc+e86Zw5pdTnyFKHkisRERHpkx37j7C4soq99S0AVEwaxl3zZzC0KNfnyFKLkisRERHpVcxxePmDXTz72mYiUYdQMMBXvlTO1eePJzhIi9Z7o+RKREREetTQHObBZetZt+UgAKOG5bPougomjR7ic2SpS8mViIiIdOvTLfU8uHQ9jS3tAFx8xhhumX0aeTlKH3qj1hEREZFjtEdiPPtaDas+2AlAfm4W35hjOH/6KJ8jSw9KrkRERKTTnrpmFldWsfNAEwBTxpVw74IZlJbk+xxZ+uhTcmWM+RHwFWAScIa1dl03+/xf4GYgCrQDf2Gtfcm7UEVERGSgOI7D65/s4amXNxGOxAgEYMGFk1hw0SRCwcGx4LJX+tpazwOXAtt72ec94Dxr7UzgTuBpY4zSXBERkRTXdLSd//7NOn65whKOxBgxJJfv33o2X77kVCVWJ6BPPVfW2jcBjDG97ZPYS7UWCAAjgF0nEZ+IiIgMILvjEPctWc+hI20AnDdtJN+YYyjIy/Y5svQ1UDVXtwM11lolViIiIikoEo1R+dZWlr29HQfIzQ5x6+ypXHTG6EG74LJXPE+ujDFfAv4OmN3fx44YMXALPZaVFQ/YsQcrtan31KbeU5t6T23qvWS36b76Zv7tyY+x2w8BbtH6d287N6MWXPbzPPU0uTLGfBF4DLjOWmv7+/j6+iZiMcfLkAC3gWtrj3h+3MFMbeo9tan31KbeU5t6L9lt+m7VPn75kqU1HAVgzqwJXH/pqWThZMx7O9BtGgwGeu0Q8iy5MsacBzwN3GCt/cir44qIiMjJO9oW4bGV1bxTtQ+AksIc7l4wg4pJw32OLPP0dSqGnwDXA6OBl40x9dbaCmPMcuAH1toPgP8G8oHFCYXvX7fWfjoAcYuIiEgfbdnTyOLKddQebgXgC+UjuGPedIYU5PgcWWbq62jB7wDf6Wb73IR/n+dhXCIiInKSYjGHF9ds5/nVW4nGHLJCQW66YgpXnD1WResDSDO0i4iIZKCDja08sHQ9G3ccBmBsaSGLFlYwbmTmFK2nKiVXIiIiGeZDW8vDL26guTUCwOVnj+Wmy6eQkx3yObLBQcmViIhIhmhrj/L0K5t47Xd7ACjMy+LOudM5a2qZz5ENLkquREREMsCO/UdYXFnF3voWAKZPHMbd82cwrDjX58gGHyVXIiIiacxxHF7+YBfPvLaZSNQhFAzwe5eeypxZEwiqaN0XSq5ERETSVGNzmAeXbeDTLfUAjByaz6LrKpg8ZojPkQ1uSq5ERETS0Lqt9TywdAONzWEALjp9NLfMnkp+ri7tftM7ICIikkbaIzF+/XoNK9/fCUB+boivX2O4YMZonyOTDkquRERE0sTe+mYWV1axY38TAOVjh3DvggrKhub7HJkkUnIlIiKS4hzHYfXavTzxcjXh9hiBAMz/4iQWXjyJUDDod3jShZIrERGRFNbc2s4jL27kA1sLwPAhudwzfwZmwjCfI5OeKLkSERFJUdU7D3PfkioONrYBcI4p45vXTqMwL9vnyKQ3Sq5ERERSTDQWo/LNbSx9ZxuOAznZQW65aiqXzByjBZfTgJIrERGRFFJ7+Cj3LamiZncjABNGFbFoYQVjRhT6HJn0lZIrERGRFPHu+n08+pLlaFsUgGvOH8/1l5aTnaWi9XSi5EpERMRnR9siPLGqmrfW7QNgSGEOd8+fzumTR/gcmZwIJVciIiI+qt5xiH9+5H0OHD4KwMzyEdw5dzpDCnN8jkxOlJIrERERH8QchxVrdvCbN7YQjTlkhQLcePkUrjpnnIrW05ySKxERkSQ7dKSNB5auZ8P2QwCMGVHAt647nfEji3yOTLyg5EpERCSJPt5Uy0PLN9J0tB2Aa784iYUXTiQ3O+RzZOIVJVciIiJJEG6P8vSrm3n1o90AFOZlccfc6Vxz0anU1h7xOTrxkpIr8czamjpWrNlBXUMrpSV5zJk1gZnlpX6HJSLiu10HmlhcWcXuumYApk0Yyt3zZzB8SJ7PkclAUHIlnlhbU8fjq6oJhYIU5GVxuDnM46uqAZRgicig5TgOv/1oN0//djORaIxQMMCXL5nMtbMmEgyqaD1TKbkST6xYs4NQKNhZM5CbHaItvl3JlYgMRo0tYR5atoFPauoBGDk0n3sXVnDqKUN8jkwGmpIr8URdQysFeceeTjlZQeoaWn2KSETEP1XbDvLA0vU0NIUB+GLFaG67eir5ubrsDgZ6l8UTpSV5HG4OHzPaJRyJUVqiegIRGTwi0RjPvbGFFWt2AJCXE+L2awwXVIz2OTJJJiVX4ok5sybw+Kpq2nB7rMKRGNFojDmzJvgdmohIUuw/2MLPK6vYvs8d+Vd+yhDuWVjByKH5PkcmyabkSjzRUVel0YIiMtg4jsObn+7liVWbaGuPEgDmXTiJhRdNIiukBZcHIyVX4pmZ5aVKpkRkUGlpbeeRFZb3Nx4AYFhxLvcumIGZMMznyMRPSq5EREROwKZdh7mvsor6xjYAzplaxjeunUZRfrbPkYnflFyJiIj0QzQWY+nb26l8ayuO49aZfu2q07j0C6dowWUBlFyJiIj0WV3DUe5bsp7NuxoAmDCyiEXXVTBmRKHPkUkqUXIlIiLSB+9t2M8jKyxH2yIAzD53PDdcVk52lorW5VhKrkRERHrRGo7w+Kpq3vp0HwBDCrK5c94MZpaP8DkySVVKrkRERHqwdW8j91VWsf/QUQBOP3U4d82bQUlhjs+RSSpTciUiItJFzHF4ac0OnntjC9GYQ1YowA2XTeGqc8cRVNG6HIeSKxERkQSHm9p4YOl61m87BMCYEQUsWljBhFHFPkcm6ULJlYiISNzvNtfxi2UbaDraDsCXzjyFm684jdyc0HEeKfIZJVciIjLohdujPPNqDa98tAuAwrwsvjFnGudOG+lzZJKOlFyJiMigtqu2icWVVeyubQZg6vih3LtgBsOH5PkcmaQrJVciaWBtTZ0WxRbxmOM4vPrxbp7+7WbaIzGCgQDXXTKZeRdMJBhU0bqcOCVXIilubU0dj6+qJhQKUpCXxeHmMI+vqgZQgiVygo60hHlo+UZ+t7kOgNKSPO5dWMGUsSU+RyaZQMmVSIpbsWYHoVCQ3Gy3oDY3O0RbfLuSK5H+W7/tIPcvXU9DUxiAC2aM4rarDQV5uiSKN3QmiaS4uobWz33o52QFqWto9SkikfQUicb4zeotrHh3Bw6QmxPittlTueiMMX6HJhlGyZVIiistyeNwc7iz5wogHIlRWqJiW5G+2n+whcWVVWzbdwSAyWOGsGjhDEYOK/A5MslESq5EUtycWRN4fFU1bbg9VuFIjGg0xpxZE/wOTSTlOY7D2+v28diqatrCUQLA3C9O5LqLJ5MV0oLLMjCUXImkuI66Ko0WFOmfltZ2fvmS5b0NBwAYWpTDPfNnMH3ScJ8jk0yn5EokDcwsL1UyJdIPm3c1sLiyivpGtzbxrNNKuWPudIrys32OTAYDJVciIpIxYjGHpW9vo/KtbcQch5ysIDdfeRpfOvMUAlpwWZJEyZWIiGSE+oZW7ltSxaZdDQCMKyti0XUVjC0t9DkyGWyUXIlIxumY0f5gU5jhRTmqURsEPth4gIdf3EhLWwSAq84dx42XlZOdpQWXJfmUXIlIRkmc0b44XzPaZ7q2cJQnXq5m9dq9ABQXZHPXvOl6r8VXSq5EJKMkzmgfCAQ0o30G277vCD+vrGL/wRYAKiYP5+550ykpyvU5MhnslFyJSEbRjPaZL+Y4rHp/J8++VkM05hAKBrjhsnJmnzeeoIrWJQUouRKRjKIZ7TNbQ1MbDyzbQNXWgwCMHl7AooUVTBxd7HNkIp85bnJljPkR8BVgEnCGtXZdN/uEgJ8AcwAH+KG19gFvQxUROb7EGe2zQgHa2qOa0T5DrK2p48FlGzjS0g7ApV8Yw9eunEpujorWJbX0pefqeeDHwOpe9rkVmAKcBowAPjbGvGyt3XbSEYqI9EPijPaHmsIM02jBtNceifLMazW8/MEuAApys/jmtdM4d9pInyMT6d5xkytr7ZsAxpjedrsJuN9aGwNqjTHPAzcC/+pFkCIi/dExo31ZWTG1tUf8DkdOwu66Zha/UMWu2iYApo4r4Z4FFYzQbV5JYV7VXE0Atif8vAMY39+DjBhR5FE4n1dWpvvxXlObek9t6j21qfeS0Rga+k4AACAASURBVKaO47Di3e088MI6wu1RgsEAX7vacOOVUwkFM69oXeep9/xs05QqaK+vbyIWczw/rr69ek9t6j21qffUpt5LRps2HW3noeUb+HhTHeAOUrh3QQVTxpVwsL5pQJ/bDzpPvTfQbRoMBnrtEPIqudoBTATej//ctSdLRETkuDZsP8QDS9dz6EgbAOdPH8nt10z73PQaIqnMq7P1GeAeY8xzuAXtXwYu8ejYIiKS4SLRGC+8uZXl72zHAXKzQ9x29VQuPH20FlyWtNOXqRh+AlwPjAZeNsbUW2srjDHLgR9Yaz8AHgVmAZviD/tba+3WgQpaREQyx4FDLSyuXM/WvY0ATBpdzKLrKhg1rMDnyEROTF9GC34H+E432+cm/DsK/L63oYmISKZ7e91eHl1ZTVs4SgC49oKJfPmSyWSFgn6HJnLCdBNbRESS7mhbhEdXWt6t2g9ASVEO98yfwYxJw32OTOTkKbmSY6ytqWPFmh3UNbRSWpKnyRdFxHM1uxtYXFnVud7jmVNKuWPuNIoLcnyOTMQbSq6k09qaOh5fVU0oFKQgL4vDzWEeX1UNoARLRE5aLOaw7N3tvLB6KzHHITsryM1XTOGys8aqaF0yipIr6bRizQ5CoWDngre52SHa4tuVXInIyTjY2Mp9S9ZTvfMwAGPLClm0sIJxZQM3ebSIX5RcSae6htbPzSWTkxXs7LoXETkRH9oDPPziRppbIwBcefY4bry8nJxsLbgsmUnJlXQqLcnjcHO4s+cKIByJUao1vAYF1duJ19rCUZ58ZRNvfLIHgKL8bO6cN50zp+i8ksym5Eo6zZk1gcdXVdOG22MVjsSIRmPMmTXhhI+pC3Z6UL2deG3H/iMsrqxib30LADMmDeOueTMYVpzrc2QiA0/JlXTquIh6lQzpgp0+VG8nXok5Di+/v5NnX68hEnUIBQNc/6VTueb8CQRVtC6DhJIrOcbM8lLPLqZ+X7DVa9Z3qrcTLzQ0h3lw2XrWbTkIwKhh+dy7sILJY4b4HJlIcim5kgHj5wVbvWb9o3o7OVlra+r5xbL1NLa0A3DxGWO4ZfZp5OXoMiODj9YXkAFTWpJHOBI7ZluyLtiJvWaBQIDc7BChUJAVa3YM+HOnozmzJhCNxmhrj+I4Dm3t0ZOut5PBoT0S44mXq/nPZz6hsaWd/NwsvnVdBXfOm67ESgYtnfkyYAaiQL6vdJurf7yut5PBYU9dM4srq9h5oAmAKeNKuHfBDEpL8n2OTMRfSq5kwPh5wdZtrv7zst5OMpvjOLz+yR6eenkT4UiMQAAWXDiJBRdNIhTUDRERJVcyoPy6YPvZayaSyY60hPnv36zjw+paAEYMyeWeBRVMHT/U58hEUoeSK8lIus0l4j274xAPLtvQeXv9vGkj+cYcQ0Fets+RiaQWJVeSsXSbS8QbkWiMyre2suzt7Ti406rcMvs0Lj5jjBZcFumGkisREenRgcNHua+yii17GgEoH1fCXXOnM3p4gc+RiaQuJVeSNJrUU5Kl41w72BRmeFGOzrUT9E7VPh59ydIajgJuLeO913+Bw4eafY5MJLUpuZKk0KSekiyJ51pxvs61E3G0LcJjKy3vVO0HoKQwh7sXzKBi0nCyszQaUOR4lFxJUvi9FI4MHt1NIKtzre9q9jRwX2UVtYfdovUvlI/gjnnTGVKQ43NkIulDyZUkhSb1lGTRuXZiYjGHF9ds5/nVW4nGHLJCQW66YgpXnD1WResi/aTkSpJCk3pKsuhc679DR9q4f0kVG3ccBmBsaSGLFlYwbmSRz5GJpCclV5IUmtRTIDmDGhLPtaxQQOskHsdH1bU8tHwDza0RAC4/eyw3XT6FnITkVET6R8mVJIUm9ZRkDWpIPNcONYUZptGC3Wprj/L0K5t47Xd7ACjMy+LOudM5a2qZz5GJpD8lV5I0mtRzcEvmoIaOc62srJja2iOeHjsT7Nh/hMWVVeytbwFg+sRh3D1/BsOKc32OTCQzKLkSkaRQobn/HMfh5Q938cyrm4lEHULBANdfeirXzJpAUEXrIp5RciUiSaFCc381Nof5xfINrK2pB2DksHwWLaxg8pghPkcmknmUXIlIUmhQg3/WbanngWUbaGwOA3DR6aO5ZfZU8nN1CRAZCPrLEpGk0KCG5GuPxPj16zWsfH8nAPm5IW6/ZhqzZozyOTKRzKbkSkSSRoMakmdvfTP3Va5n+363oH/K2BLuXTCD0qH5PkcmkvmUXImIZBDHcVi9di9PvFxNuD1GIAALLpzEgosmEQpqXUCRZFBylUGSMUGjeK/jfTvYFGa45mSSk9Dc2s4jL27kA1sLwPAhudy7oIKp44f6HJnI4KLkKkMka4LGk6Hk7/MS37fi/NR83yQ9VO88zH1LqjjY2AbAudNG8o05hsK8bJ8jExl8lFxliGRO0Hgi0iH580Pi+xYIBFLufZPUF43FqHxzG0vf2YbjQE52kFuumsolM8dowWURnyi5yhCpPkFjqid/J+pke+NS/X2T1FZ7+Cj3LamiZncjABNGFbFoYQVjRhT6HJnI4KbkKkN4NUHjQN26y8QkwoveOE2sKSfq3fX7ePQly9G2KADXnD+e6y8tJztLResiftNfYYaYM2sC0WiMtvYojuPQ1h7t9wSNHcnC4ebwMcnC2pq6k46vtCSPcCR2zLZ0TyK6u6UXCgVZsWZHn4/hxfsmg8vRtggPLF3PfZXrOdoWZUhhDn/y1S9w0xWnKbESSRHqucoQXkzQOJC37jJxdm4veuMS37dDTWGGabSg9GLLnkbuq6ziwOGjAMwsH8Gdc6czpDDH58hEJJGSqwxyshM0DuStu0ycndurW3od71tZWTG1tUe8DlMyQMxxePHd7Ty/eivRmENWKMhXLy/nynPGqWhdJAUpuZJOpSV57DvYwtFwlEgkRlZWkPycEKOHF3hy/EybnTsTe+Mk9Rw60sYDS9ezYfshAMaMKGDRwgomjCr2OTIR6YmSK+k0bcJQqncdJkCAYAAikRiNkSiXnXmK36GlhO6K/W+dPTWjeuMktXxcXctDL26k6Wg7AJedNZabrphyTG+piKQeJVfSaeOOw5QU5nK0LUIkGiM7K0h+bhYbdxxmod/B+aynkYG3zp7K92452+/wJMOE26M8/dvNvPrxbgAK87K4Y+50zp5a5nNkItIXSq6kU11DK8UF2ccUxzqOk9bTJXglU+fpktSz60ATP6+sYk9dM+D2KN89fwbDh6TvyFqRwUbJlXTSnEs9y8R5uiS1OI7Dbz/azdO/3UwkGiMUDPDlSyZz7ayJBIMqWhdJJ0qupJMKtHumxFMGUmNLmIeWbeCTmnoAyobmsWjh6Zx6yhCfIxORE6HkSjpl4nQJXlHiKQOlautBHli6nobmMABfrBjNbVdPJT9XH88i/dUx8OhgU5jhPs4bqL9eOUaqTJcwUMvwnCglnuK1SDTGc69vYcV77oz+eTkhvn6N4YsVo32OTCQ9JQ48Ks4/sSXJvKLkSlKOF2v2DYSBSjxT5ZtWJkn1Nt13sIXFL1Sxfb87aeyppwzh3oUVjBya73NkIumruyXJ/Bp4pORKUs5gGpmXSt+0MkUqt6njOLz56V6eWLWJtvYoAWDehRNZeNFkskJaF1DkZKTSwCMlV5JyUukPZKCl0jetTJGqbdrS2s4jKyzvbzwAwLDiXO6ZP4NpE4f5FpNIJkmlgUdKriTlpNIfyEAbTIlksqRim1bvPMz9S6qob2wD4JypZXzj2mkU5Wf7FpNIpkkceJQVCtDWHvVt4JH6oSXlzJk1gWg0Rlt7FMdxfP0DGWilJXmEI7FjtmVqIpksqdSm0ViM51dv4Z+f+Ij6xjZysoJ8Y47hf/3e6UqsRDw2s7yUi04fTWNTmK17j9DYFOai00drtOBglmqj4/w0mEbmpdI3rUyRKm1a13CU+5asZ/OuBgAmjCxi0XUVjBlRmNQ4RAaLtTV1vLVuH0OKchiTl0Vza4S31u1j0pghKmgfjFJ1dJyfUmVKiIGWmEgeagozLAVHtqWbVGjT9zbs55EVlqNtEQCuPm88X/lSOdlZulkgMlBSqd5SyVUKGEyj4+TzOhLJsrJiamuP+B1ORvCrTVvDER5fVc1bn+4DYEhBNnfNn8EZp45IWgwig1Uq1Vv2KbkyxkwFHgFGAPXA7dbaTV32GQk8BIwHsoFXge9YayOeRpyBUumEEJETs21fI4tfqGL/oaMAnHHqCO6cN52ShIXQRWTgpNJgqL72Uf8c+Jm1dirwM2BxN/v8BbDBWjsTmAmcA1zvSZQZLpUKcEWkf2KOw4trtvMPv/yQ/YeOkhUKcPOVp/GHN85UYiWSRKk0GOq4PVfxHqmzgdnxTU8CPzXGlFlraxN2dYBiY0wQyAVygN0ex5uRtG6dSHo63NTGA0vXs37bIQDGjChg0cIKJowq9jkykcEnFeotOwQcx+l1B2PMOcAvrbUVCdvWA7dZaz9K2DYc+DUwAygEfmqt/X4f45gEbO1f6Jnlgw37ee61zRw42MLI4QVcf9kUzp0+yu+wRKQH763fx4+f+pjG+ILL11wwkbuvO528HJWyigwik4FtXTd6+SlwI7AWuBIoBl40xtxgrX22rweor28iFus92TsR6VAoPLG0gD++YeYx21I55nRo03SjNvXeQLRpuD3KM6/W8MpHuwAozMvim9dO4xwzkiMNR8n0d1DnqffUpt4b6DYNBgOMGFHU4+/7klztBMYaY0LW2qgxJgScEt+e6A+AO621MaDBGPMCcDnQ5+RKRCSV7aptYnFlFbtrmwEw44dyz4IZDB+i+kgR+cxxkytr7QFjzO+ArwGPxf/7cZd6K3Bv680B3jPG5ABXAc95HK+IDBBNZNszx3F49ePdPP3bzbRHYgQDAb58yWTmXjCRYDDgd3gikmL6elvwW8AjxpgfAIeA2wGMMcuBH1hrPwD+CPi5MeZTIIQ7FcP93ocsIl7TRLY9O9IS5qHlG/nd5jrAHd27aGEF5WNLfI5MRFJVn5Ira+1GYFY32+cm/LuGz0YUigw6lW9uYeX7u2gNR8jLyeLq88ax8OJT/Q6rTzSRbffWbzvI/UvX09DkFq1fMGMUt11tPjcvnYhIIn1CiHig8s0tVL69jQABQkF3PbvKt7cBpEWCpYlsjxWJxvjN6i2seHcHDpCbE+LrV0/lwtPH+B2aiKQBJVciHlj5/q7OxAogFIBozN2eDslVKs1s7Lf9B1tYXFnFtn3uSKPJY4awaOEMRg4r8DkyEUkXSq5EPNAajnQmVh2CAXd7OtBEtm7R+tvr9vHYqmrawlECwNwvTuS6iyeTFfJvwWUNNBBJP0quRLrR3wtaXk4Wbe1RQgn5VcwhbSaUTJzZOBMu4h3v38GmMMP7MEtzS2uEX760kfc2HABgaFEO9yyoYPrEYckKuVsaaCCSntLjk18kiU7kgnb1eeOofHsb0ZjbYxVzwMHh6vPGJTP0kzKzvDQjLtiJ719x/vHfv827GrhvSVVnfdlZp5Vyx9zpFOVnJzXu7miggUh6UnIlfdK1J+emq6cxsTQza1BO5ILWUVeVrqMFM0ni+xcIBHp8/2Ixh6Vvb6PyrW3EHIecrCA3X3kaXzrzFAKB1Ji7SgMNRNKTkis5ro6egPZIjKNtUQ4eaeOfHn6PuRdMyMjk4UQvaAsvPjUj2yPd9OX9q29o5f4lVVTvagBgXFkRi66rYGxpYVJjPR4NNBBJT0qu5LhWrNlBeyTGkaPtBIiPhHMclr2zg0ljhmTc7Qld0NLb8d6/DzYe4OEXN9LS5g42uOqccdx4eTnZWaFujzeQjlfbp4EGIulJyVUGGahJLOsaWjna5o6eCsZvl2QF3LmAMrH2Qxe09Jb4/mWF3DnHotEYV54zjoeWb2D12r0AFBdkc9e86b6dv32p7cu0gQYig4WSqwwxkJNYlpbkcfBI2+dGwmWFMrP2Qxe09Jb4/h1qCjOsKIezp5by7Otb2H+wBYCKycO5e950SopyfYuzr7V9mTLQQGQwUXKVIQZyEss5syZQs2cdsZhDKD4SDqAgPztjb5XpgpbeOt6/ESOKeHz5ep7+bQ3RmEMoGOCGy8qZfd74zl5Yv6hYXSRzKbnKEAM5ieXM8lLmXTCBZe/sIBqLkRUKUlSQQzCAbpVJympoauO/nvuUj6trARg1vIBvLaxg4uhinyNzqbZPJHMpuUoTx6unGuhJLBdefCqTxgzpvFU2pqyIK886Rb07kpI+2VzHL5Zv4EhLOwCXzBzDLVdNJTcn+UXrPVFtn0jmUnKVBvpST5WMSSwTb5WVlRVTW3vEs2OLeKE9EuWZV2t4+cNdABTmZ3P7NYbzpo30ObLPU22fSOZScpUG+lJPlaxJLPu7rIhIsuyua2bxC+vYVdsMwNRxJfyfb55PIBL1ObKeqbZPJDMpuUoDfa2nGuhJLPu7rIhIMjiOw2u/28NTr2yiPRIjGAiw8OJJzPviREYOK1APq4gknZKrFNHbZIKpsihwX5cVSRf9XZxZUk/T0XYeWr6BjzfVAW6R+L0LKpgyrsTnyERkMFNylQKON5lgqiwKnElDx7u2+b6DLfzsN+vIzwlxSmmhEq00sGH7Ie5fUsXhpjAA508fye3XTPvcOSoikmz6FEoBx5tMMNmLAvfUo5NJQ8cT27yltZ0jR9vBgbb2qG53prhINMYLb25l+TvbcXD/Xm67eioXnj46ZRZcFpHBTclVCuhLj1CyFgXurRetp2VF0nHoeGKbN7a4ayYGgwGiMSftb3dmsgOHWlhcWcXWvW4d1aTRxSy6roJRwwp8jkxE5DNKrlKAVz1CXtQQ9daL9r1bzu7cp2NZkXS7fdbRRg1NYRqbwwwtziUSiXXebs0KBYH0vd2Zyd5et5dHV1bTFnbXubz2gol8+ZLJne+ZiEiqUHKVAryYTLAvi8D2xfF60TqGjqfjPFeJbVRSlM2hI2HqG44SDAaJxRwCAYchhe5ac+l6uzMTHW2L8OhKy7tV+wEYWpTD3fNnMGPScJ8jExHpnpKrFODFZIJ9XQT2eFKlrmogRvIltlHHiMeGpjCRaIxQMEhRQTZ5OaG0vt2ZaWp2N7C4sqozuT9zSil3zJ1GcUGOz5GJiPRMyVWKON5kgsdLNrwayZcKS3J41QvXVdc2KsjLJj83i5bWCLddPVXTMqSQWMxh2bvbeWH1VmKOQ3ZWkJuvmMJlZ41V0bqIpDwlV2mgL8mGVz1OqbAkh1e9cF311kaaKTt1HGxs5f4l67E7DwMwrqyQRQsrGFtW5HNkIiJ9o+QqDfQl2fCyx6m3RCMZy98M1HxaJ9JGmmg0uT60B3j4xY00t7qrD1x5zji+enk52Vmps+CyiMjxKLlKA31JNpLR45Ss5W8Gqu6rv200ULcnu3uewb5eY1s4ypOvbOKNT/YAUJSfzZ3zpnPmlMHVDiKSGZRcpYG+JhsDfWsrWcvfDGTdV3/aaKBuTybSeo2wY/8RFldWsbe+BYCKScO4a/4Mhhbl+hyZiMiJUXKVBpJZZN7bbbBkLX+TCnVfkJzXm2nrNfZHzHF4+f2dPPt6DZGoQygY4CtfKufq88cTVNG6iKQxJVdpIFnJxvFugyVzmoZUKDBPxuvNpPUa+6OhOcyDy9azbstBAEYNy2fRdRVMGj3E58hERE6ekqs0kYxk43i3webMmsAvlm3gYEMrMcchGAiQlxPi5iumDGhcfklGj2GqzCuWTJ9uqefBpetpbGkH4OKZY7jlqtPIy9HHkYhkBn2aSae+9KIcDUdojzjxnxwcHDJVMnoMU2m9xoEeGdkeifHsazWs+mAnAPm5WXxjjuH86aM8ew4RkVSg5Eo6Ha8X5dEVGxMSK1d7xOHRFRv51/99cVJjTZaB7jFMTOD8XK9xoEdG7qlrZnFlFTsPNAEwZVwJ9y6YQWlJ/kkfW0Qk1Si5GkSO1zOReNsvGnMLjBNv+x08EgYg0PF/DjgJ2+XEpMJ6jQM1MtJxHF7/ZA9PvbyJcCRGIAALL5rM/AsnEgpqwWURyUxKrgaJvvZMtEdjtEdjOI47misU/WzUVk83ADP3xuDgmUR0IArrm46288iLG/mwuhaAEUPyuHfhDE4bN/SkYhURSXVKrtLEyV7k+9Iz8eyrmwm3xwgFAx0dU4TbYzz76mZmlpeSmx2krT2GQ2fHVfxYmdkDkaxJRFOBV4X1Hefp3voWmlvbiUTds+T86SO5/RpDQV62p3GLyMkbLF8ik0nJVRrw4iLfl56J/YdaCQTonGMoAMRw2H/I3efaWRN44a1tOA448cwqEHC3d403E/5QkzGJaKrwYmTk2po6HltpORqO0nw00rl99nnjuPmK07TgskgKGkxfIpNJyZUP+pt8eHGR71vPhOMmTQnXQMeBQMDNpBZefCoAK9/fRWt7lLzsEFefN65ze8dry5Q/1ME0B5UXIyMr39pKQ3M77ZEY4LbVkKIcdu5vUmIlkqIG05fIZFJylWQnknx4cZHvS8/EqOEF7K1vJuYECAYg5gA4jBpe2LnPwotPZeHFp/ZYfJ1Jf6iDbQ6qkxkZ+W7VPrbuOdJ5q3hIYXbn8jWZmIyKZIrB9CUymTKzWCaFdbfcSSgUZMWaHT0+prQkj3C8N6BDfy/yM8tLuXX2VIYW5tDSGmFoYQ63zp56zMX0hsvKKczPIRiAaCxGMACF+TnccFl5n5+nrqGVnKxjT6t0/UOdM2sC0WiMtvYojuP4OgdVqjraFuH+Jeu5b8l6HCAYgJHD8hlWnEcgEMjoZFQkE3hxfZHPU89Vkp3ItwSvZgo/Xs/EzPJS7pw77aRuDWVSb0+qrHGYqrbsaWRx5TpqD7vn7uQxxTQ2hwkGAziOM6BrYIqIN5K5du1gouQqyU4k+UjmRf5Ebg0l1pDlZQdpaXWLmTPhDzUV1jhMNbGYw4trtvP86q1EYw5ZoSA3XTGFK84ey6db6pWMiqQRfYkcGEqukuxEvyWk6kW+aw1ZOBIDxyErGKClNaI/1AxzsLGVB5auZ+OOwwCMLS1k0cIKxo0sAlL3PBWRnunv1ntKrpIs074ldFfADlCUn83f3jXLz9DEYx/aWh5+cQPN8Z7Jy88ey02XTyEnoRc2VXT0ph5sCjPcpyWFRGTwUnLlg0z6lqCRJpmvrT3K069s4rXf7QHcxPmOa6dx1tQynyPrXmJvanF+ek8HIiLpScmVnJRMKmCXz9ux/wiLK6vYW98CwPSJw7h7/gyGFef6HFnPuhuRm67TgYhIelJylSb6O/FosmZJ10iTzOQ4Di9/sItnXttMJOou4n39padyzawJnTP4pyqvelMzZaUBEUk+JVdpoL8Tj3o9S3p3F5kry4qPOZ4uQpmjsTnMg8s28OmWesCdt2rRwgomjxnic2R940VvaiatNCCpS7WBmUvJVRro76znXs6S3tNFpqSkgImlBcBnNWQdHxSPraymtGSH7x8U6nnov3Vb6nlg2QYam8MAXHT6aG6ZPZX83PT5qEjsTc0KBU5o8tdMWmlAUpNqAzNb+nxiDmL9vc3hZZF5TxeZ517bzB/fMLNzv1T7pp9q8aS69kiMX79ew8r3dwKQnxvi69cYLpgx2ufI+i+xN/VQU5hhJ9AjoIEaMtBUG5jZlFylgf7e5vCyyLyni8yBgy3HbEu1b/qpFk8q21vfzOLKKnbsbwLc3p4xIwooSKPeqq46elN7WgPzeDRQQwaaEvjMprUF00B/17jzck28ntadGjm84JhtiWsKHm2LsP9gC7WHWqjZ3cDamrp+P+/JyqQ1DgeK4zi88cke/ubh9zsTq8L8LMaMKKClLcrjq6p9ee9SgdaVlIGmNf0yW/p+NR1EeioaB/iXJz76XE2Rl0XmPY0GvP6yKcfs1/FNPxZzONjYCgQIBNz/+XE7Tj0PvWtubeeRFzfyga0F3N6qIYU5FBfkAJAbZFD39Gmghgw0L2oDJXUpuUoTXScePV5N0YlOVNpdEfits6d+btu500cdc7ul44OioSmM40Ag4OAAJUU5hEJBVqzZASTvYqUpInpWvfMw9y2p4mBjGwDnmjK27G2kKD/7mP0Ge09fJk32K6nHi9pASV19Sq6MMVOBR4ARQD1wu7V2Uzf7fRX4v0AAcICrrLX7vQtXOgxETVFPCduts6fyvVvO7vWxHc/5s+fWARAKBRlSkE1BXjaO47CnrjmpBebqefi8aCxG5ZvbWPrONhwHcrKD3HLVVC6ZOYZ/ffJj9fSJJNnJ1gZK6uprz9XPgZ9Zax8zxtwGLAauSNzBGHMu8NfAFdbafcaYEtw7CzIAuiuGjESi1Oxu5Hv/8/YJJRMnm7DNLC+lfOyQbi/SkahDQZILzNXz8Jnaw0e5b0kVNbsbAZgwqohFCysYM6IQUE+fiIiXjptcGWNGAmcDs+ObngR+aowpszZesOH6Y+BH1tp9ANbaBq+Dlc90rSlqaW3n0JEwoWDghHuGvBi90tNFOhQKqMDcJ++u38ejL1mOtkUBuOb88Vx/aTnZCe+HevpERLzTl56r8cBua20UwFobNcbsiW9PTK5mAFuNMW8ARcBzwD9Yax2PY84olW9uYeX7u2gNR8jLyeLq88ax8OJTj/u4rklMQ1MYcBhanNc5Z0pjOMp9lespyMvq08XSiyLwni7SK9bs0G2nJDvaFuHxVdW8vW4fAEMKc7h7/nROnzyi2/3V0yci4g0vC9pDwEzcHq4cYAWwA/hlXw8wYkSRh+Ecqyy+XEsqeXLlRpa8vR0C8dEikShL3t5OQWEuX7t6Wq+PvbKsmJKSAp57bTMHDrbgAGXDCjqLkptbI52zbI8pLaCptZ2nXtlMSUkB504f1e0xb7p6GoufW0s0FnNv27VHwXG3d9d+PbXplWXFXHnB5GO2lZQU9OvYg5VXbVG94xA/euxD9tY3A3Du9FH84U1nMTSFF1weKN216Qcb9vPca5vZf7CFLb71OgAAIABJREFUUcMLuP6yKT3+Xcjn6W/We2pT7/nZpgHH6b1jKX5bsBoYEe+1CuEWtZ+WeFvQGLMU+JW19pfxn78HTLDWfrsPcUwCttbXNxGLed/RlarFgt/+jzdoa48SCn62EG405pCbHeKnf3xpv471L098dEzP0P6DLbRHYmRlBRkdn5OqrT3K0MKcXovTe1oypmsP25cvK+eqs8b2K0YtR9M7L87TmOPw4rvbeX71VqIxh6xQgBsvn8JV54wjkOILLg+E7to0ceBG4q3rW2dP1fnYB6n6eZrO1KbeG+g2DQYDHR1Ck4FtXX9/3J4ra+0BY8zvgK8Bj8X/+3GXeiuAJ4C5xphH48e9Enj2pKJPU31NIlrDkWMSK4BgwN3eX11vE7ZHohAIMKTgs+H1falx6u7WUOWbW6h8exsBAoSC7nwsT6+qpqW5rU+3MHs7tnjn0JE2Hli6ng3bDwEwZkQB37rudMaPHLge4XSk2ftFZKD19bbgt4BHjDE/AA4BtwMYY5YDP7DWfgA8BZwLrAdiwEvAg55HnOL6s6ZdXk6W23OVkF/FHMjOCnY7OWhvutY65WZnkZMdpCDvs+TqRGucVr6/qzOxAggFIOo4rHx/V7+Sq1TruUq1eE7Gx9W1/GL5Bppb3cT8srPGctMVU46pcTueTGqP3mjZEZH/z97bR8dxnWeez71V1Z9A44PgNw3LIs2mJIeKPVboD9mWZIuWpYSOvUpiR1onURI7s8nM2Z2T9ZmzeyabyZwzO6P5I9mZcRzJiRJvZCZOtEoMmwpNxZbHpijRcmgblkQ2Q9A0xG82vhr9UV1V997941YVqhvdjQbQABrE+ztHJt1dXVVdALueft/nPi+x0rQlrnK53BkABxo8/mDk7xLAv/H/27As5lvxwbt2YeTEBQipK1ZS6bYO5xzTJWfRq/6ilaFA5FVdseyl9Y0qbMYiK2zdNki5285nqVRdgb/55jm88P1LAIB0wsSvPXgb3rF386L2c7Ncj3ag9H6CIFYami3YYdqdaTc6lseZ8WnETQ6lAM/3Wg32xtHXE6+ZlB5NOG+X/buH8Mj9e9GfjqFse+hPx5bsKbFMDlcoOJ6EKySkUhBKV97apdEE+KW8r07RbeezFC5eL+I/fPF7obDaN9yPP/j1A4sWVsDNcT3aheYGEgSx0tD4mw7TzrfiaJVg80CqxlD71JHTKFZc34zMkUnHkIgZS2pZdMLjNDqWR7RopRTgCb3oIJniGB3Lt3WMbmvFdNv5LAalFL556hK+/M1z8ISEwRl+/n1vwUcOvBmcL820vp6vx2KhTC+CIFYaElcdpp2k62atw2e+NYaS7UH4KyaFEMh7NvrSMWwdSC7rvJaap3X05Dj6ehOwKi5mKy6CxaWcAVbMbLt11G2tmE6dz2r7lAplB39+5DR+ODYBANjSn8SnD92BW3dklrXfob4Erk1VUKl68ISEaXAk4+ayf++6FVpcQRDESkLiqsO08624UZVACImrE7VVAgVASYVCycGvPJBd8jk1Wu03cuICACwosIJzjWcS4RgbBj2UeTGrrLptvEonzme1fUqv/XgSf/q11zHj55e9+45tePTgXiTjy/9nvG+4H2cvToOBgTPA9SQcr4oP3Ll92fteLJ0QrME+JosOBmkgLkEQqwyJqxVgoW/F9VWTStXDjenG7RcG3QZazo2h4Wo/ibZW+0XP1RMSnLFwRSPQfuuo21oxnTif1VrS7wmJZ//HeRz9rvY/JWIGPvXhLN51x7aOHePM+DQyqRgqjoDn56MlYwbOjE/jUMeOsjCdEKzRffQmb25zPkEQ3QmJqzWgvmpyY6rSdFvOGZZoowlZTp5WcK4zVd2u9Py+YG9SC4rFtNK6rRWz3PNZDZ/S1ckynhh5DT+5qsPwdu/I4DcP3YEt/Z1t1+VnbGTSMfT1zP2eKKVW3XPVCcHayJxPOVYEQawmJK7WgPqqSetMeoWtg+llHa9ZnlY7q/327x7ChSsFHHlpbtUYZ9D+K+gKVruttJstR2klfWRKKRz/0RUcfv6fUXUFGICH3nMLDr33FphG5xf5tvteVvpn2AnBupHM+QRBdCckrtaIaNXksf/0zabbCQlcypfwO3/47bZN6PUEeVqeUIhOF7pz92Bbrz8zPo1N/QnELQOVqp5Z6HoSjivxqx/Z19bNdb3mKLUSEyvlIyvbLr54NIdXzlwHAAz0xvHpn7sd2eGB5b6dprTzXlbjZ9gJwdptiycIgth4kLjqAuIWR9WVTZ83F2FCbyQGDt19K65NlvHy6/pmzRiQips4d7nQVpRCtBKQjJtIxk2YBsNM0VlSq0a/5+5v1SwkJlbCR3b2jWl84auvYaJQBQD8i72b8Ssf2RcO5F4p2nkvq/Ez7IRgje7DNBjlWBEEseqQuOoCPnJgGH9//ELT5xljbZnQW4mBqaKDrZtSNd/mq65o68bYqBJQdcWiKgFr0apZbgurHTHRKR+ZkBKHv34Gf/18Dkrpa/PJD70V779zx6oNXF7ovazGz7ATgjW6j6migwFaLUgQxCpD4qoLOHT3rTj+o6uY8P1XwSicAFdIcAZwxlqa0FuJgeXcGBtVE6CwqErAardqOtHCWi1BmJ+p4Mmvvo5zF2cAAMNbevCZj96B7ZuW57XrNKv1M+yEYA32sXlzL27cmO3QmREEQbQHiasu4dGDe0MxIISsiWZQChAKkFAtM42iYmDOGyUwMV3B1kGdBL+UG2OjasIvHdyHNw+l2n5/q51z1YkW1mqIie+evoYvHs2hUtWi+eBdb8L/9IHdYdTFarJQpa/bssoIgiC6FRJXXUJUwIxdmoHBWZjUzuAHikKb05sRiAEpFSYLNgAGxvR/hZKjzVaYuzGWKy5MzvDZz59YsP1SX01YbEVgtXOuOlF1WkkxYTsevvT8Wbz4o6vhY4wBx0evIBU3lrRwYTm0U+nrtqwygiCIboXEVRcRCJjPfv4EUgkThZKDQsmBVFpgJSM33UZVhjCTquhAKYAxnaTe1xODYXCYDOhJxZCfsZGwOMAYPKnaapuNjuXxzLfGcG2yDIBhx5Y0Pnb3WxYV7LiaN+VOVJ1WSkz8+EoBT4y8huuRfDPO5szX7abnd5J2K33dllVGEATRjZC46kICYdDXE0dfTxyANpD3p2MAmlcZHrl/Lx65fy8+9+yrAKBvliZHoezC83S6+sP37sH+3UN4/PApeApttc1Gx/J46rkzKFUc6GqYwsVrRTx15DQee+i2BW+2axHD0KmqUyfFhFQKXz85jme/fd4fzM3AwCCknuXHGIPBVdvp+Z2EsqEIgiA6B4mrLmQhYdB08PML59CTigFQMDhD3OSYrbiA0i1FyVQoiJrdTC/nS3j88KmaSs3Rk+Owqx4Y42FavAJgO+2tNlyK/2m5la5ua2FNzVbxp197Had/MgUA2L4phc8cugN/8BevLDk9v5NstMHNBEEQKwmJqy5i5Ph5HHvlImzHg2lwZNIxeH4rKyoMGgkjzxO4XqhiiwL6e+OYLNgolGuzs5QCCmUXf/n1HDb3J+e1zWbLLiqOwHTJqakwVR0BISUMPmeyZgwQsr3xKIutinSq0tUtLawf/HMeTz13GsWKCwD4wE/vwCfueyviMWNZ6fmdpJsGNxMEQax3SFytMO1WYL4w8ipe8kM+AT2sd3LWxqH33DKvPdTIT1QouTCD6pA/U+163czCwBg/Uajiffu348VXr9ZUx4plF+mkOa/C5AkPBueQCnOVKwUYnLXlYVqs/2k9Bo42wnEF/uaFc/jmqUsAgHTCxK9+ZB/+RXZLuE2Qni8kwpaggmq5cGEl6JbBzQRBEDcDJK5WkHYrMKNjebx82k9Ph9/CUwBHY+9No7ahJyQ2RcRKfWRDfQzlmfFpPHL/3hrhV6q4yPi+roCYySGVhJAqXL2oR9sxpOPmvPEojYTkYv1PN4P/5+KNIp4YeQ2XbpQAANk39eM3f+52DGZqBWXwsz32ykXYrkDCMpY85mg5dMvgZoIgiJsBElcrSLsVmKMnx6H80NDoEGepGntvGvmJTK5X/kXhnEH6j0WfsQyG/Iw9r232+OFTDSpiDoQAelMWSrYH15MQEtg8EMcjH9obvr6ZkLxwpYAz49OwHQEhPJgGw46hdEv/03qeDaeUwjdPXcKXv3kOntCLCH7+fW/Bg+96MzhvnLR+6O5bcejuW9c08HI9X3OCIIhug8TVCtJuBeZSvtR0H828N/XCKFjRNzljQ0jttZINxhUyBqSTVsObZqMKU8n20JOy/KrG3MrFzQOpBefOFRyBIy+PY1NfAgO98ZqKVav23noNq5wtO/jz587gB+fyALRg+cyhO7B7Z98an9nCrNdrThAE0Y2QuFpB2q0GOK5ouo9FeW+C8hcYpFJgDEjGDFSqIqxcKaWrUT0Jc97Q5kYVsXLFRW+qdmhwzOS4PlmueayRkCzbLoRUi/ZOtbPSb7Vzsxbi9QuT+MLXXsdM0QEAvOv2rXj0YHbeNelWum11JUEQxHpmfXzyrxPqb/j7hvvnmcYbVQM8oRruj7H2gySPnhxHKmlhwPf0XLxeBKBbi5sHkpiarcL1dClrMBOHp9DWCrxMOtZwbM6WwdrRN42EpCckLMOo2a5d71SrlX5rkZvVDE9I/N23z+vWLoB4zMD/fHAv3vO29bfKrltWVxIEQax3SFx1iEY3/BdfvYr3vm0bzoxPt6wGcMYAriAV/GR1+IOa258vpwWLwtRsFZ4ndeUK+uafjOu0d8G092pq1glXg0WrSI3eQ7ni1ozNKZQclGwPdlXg8cOnWprWOeNIxmvFVSd8PN2ymvDaZBlPjLyGC1e1T+ot2zP4zKHbsWVg4ZmLjSpvH9zcu9KnTBAEQawCJK46RLMb/pnxaXz2l9/R8rVbBxK4MlmBwSNzBJV+vF0SFseVyUoozKQEJAApFK5OlFH1W4/cf14IidmKhIj4vRq9BwBwXYFC0UHZHy7MmfYXlW0XTz13Bo89uK9hW+ldt23RlTtXdNTHs9arCZVSOPHqVTx97CyqrgAD8OC734yP3v0WmMbCgrhZ5a2vL7WoYdgEQaxvgi9Zk0UHgz0xasXfRJC46hDLueE/fO8ePHXktF5RJ3W6eiJu4OF794TbLOgxYoEs09EJ0UajJ+Y8XcHNn0GHgIpIS7LRexBCYmq2ii2DKbiegCt0hc3kDFIBpYqDZ741VnMuVVdg/FoRl/MlZFIWwBjKttcxH0/jnC8HjivbGkIdsBTfVtl28f9+PYfv+tEZA71x/MbP3o7b3jzQ9vk3E+LPfusc/reH97e9H4Ig1i/RL1m9ybW1NxCdh8TVIml2Q17OUvb9u4fw2EO3Nb3Rt+Mxsh2BwUwCs2UXVe2phunHfu/c3IPxa7NQCmEQqE5oUOE2QGPRMlN0wnBSNyLEhJSwDA6hGK5NlsNzdD2pK1wKqLpKD4w2GB49uLdjHxj1LchCyUGh7KAvHW/bg7UU39a5izN4YuQ1TBS0YH77W4fwaw/ehp6k1XD7ZjQT4vWLBIjuptsWVRDri+iXLMbYug1LJhrTvqmHCG/I9eNhRsfyeODAMISQqLoCSilUXbHoFlix7GCm6GDs0gye+dYYRsf0kv5G/wgNg+PoyfHwtUN9CVSqHhx3bmWglCqsVMVMPRfQNPRKQtNgSMRMeELhs58/gccPn8K+4f5578ETEpl0rXhgmFuYqO1YLDzHiiOgpIJUOm18pqiN9NFzXS77dw/hkfv3oj8dQ9n24LgSfek4MulY0+tTTzvXNEBKhZHjP8Z/+tIpTBRsxEyOT304i9/5+E8tWlgB+mfleHM5GZWqh6sTZUwWbDx++FT4cye6l1afBQTRDvkZ/VkSZb2FJRPNocrVIghuyOWKi2sVNzSf/+XRM/gvv313uM1iv8mOjuXx1JHTKNkeGNPC5cpEKfQztdNyHOiJ4cx4beCo9EfUAEAqYaFYcdHfG29a7WlkwDcZ4PlCyjQYPKHmxJtSUArYNpjApXwJjitDb1f0HGYrbo23qxNEV7Z99vMnFt2SbbeNOzFj4wtffQ1nL84AAHZt7sFnPnoHdg6ll3zu0cqbEBKTBRsAw+b+BLUG1gmruaiCKmQ3JxTce3ND4moR5GdsVF0Ps+U5EaMUMDHrYOT4+TA24ZkXzmHsUgGfe/ZH2DqYwsP37G75YXj05DhsR/hmdAYwQCoGu+rh6Mnxtv4R/nBsMjTDRynZHnpdAdNgeOhdw6FwilZ7gOYG/OAbehVAf08MEzNV7eziDJxpb9g7923BkZfGIdX8SInACiaaxE10gqV8SLXzmu+duY6/+IczoZH/Q+/chV+4Zzcs05i3v8UQNf+PXZqBwTn6emLoScXgepJaA+uA1VpU0U2xI0RniX7JMg22pG4H0b2QuFoEQ30J5N6YBjA3qy+QDMdeuYhbtmdaVqCafRjmZ+wwg8qrk0djlwp46N3D8/KyyhUXJmehgbvizB2zHpMBn7hfe56CIbztVnvqVwHuHEoBjMEVCgP+6pajJ8fRk7IwW3bmHZv76irq7eo0S0kXb/WaqiNw+B/P4jujVwDo0T+//tBtHb2ZBZW34OfA2Nz1odZA97NaVYduiR0hOk/0s3Wq6ISfp/RzvTkgcbUI9g3348y4FldRDcMZUK56+NyzP4Indb6Uwfm8ClSzfzQJi8+rOAV4UmLkxQsYzOgWXdn2kLA4wPQsweDbLBCY1GuFHwPQk4rNO/Zibg6NwiWjc/CePnYWvSkLlsmRn64gOuJQKYVk3MSOZbTRFmIp6eLNXtOXjuPf/8UruOqby+94yyB+46HbwtE/nYZaA+uT1RoXtNaxI8TKEny2ruVcUWJlIHHVJqNjebz46tV5rbdg5R1jcx4kBb2azuDaRC6kRH7Gbu6dYKxp1Yn7hvFCyQHrieHRg3tx9OQ4PIWab7PJmIFydc7vFOwqk7YafhB38uYQCIRk3ERvysJMyQ2fY4yh4njYN9y/6P22otG1XChPrJ6oaJRK4dh338B/+/9+FMZh/MI9u/Ghu97kV99WBmoNrE9Wa1wQiW+CWJ+QuGqToDzfm7JQKM+Jh6BK05u04HgS0tGr9aQCjOBPzpGweFPvhO0I/SFadML2YIDBuX8cFa5ma/Rtdqg/iYvXi6G440yPrknETfT7vqoonbw5RAWC7UhwXygyzmCZHMm4iTPj02FLcrl02ocyU6ziT4+cxms/ngQAbBtM4TOH7sCbt3UmMb2VIZlaA+uX1RgXRAO1CWJ9QuKqTQJBI+NmjbgKSMRNJOJA3rOhpK5gCakAKCQSMYCxGu9EueJituLij/52FJwxpBIGdgylcW2yDE8oeEKG7T2pdPhn0A5o9m12x6YUqp6E4W+70Adxp24OUYFwfbIMy+To64kjGde/XkqpjrYxGvlQCo7AkyOvI5UwQwETbNtKPP7wXB5PPXcas/7P9P13bscnP7gX8djyTOsB7QhBag0QzaCB2gSxPiFx1YT6akPC0mKlUNKhmtxvAwqpwJlu220dTGGoL4GJgg0pFDgDtg6m8c7sZhx56SeQSoFzPq86pZRCseIBqKA3NdfGUwBcT4IxIJNJhu2AfcP9OPLyOIRUsPyQTseVSMQN9NUlou8b7sfRk+N4+tjZFf1gDgTC44dPrXgbo75yV6l6KJSqUGAY8uMMnjpyGmAMqYTZUNS4nsDfvjCGf/yniwCAVNzEr35kH965b0vHzhMgQzKxfGigNkGsP0hcNSCaNl6pCkzOVsEAxEwG15PgTI9+UdDtwFLVg+vp4E3OGQZ743jEX50X7IsxBikUhJTzjmcYDEIolG0BznTEgcScf4sxwHEFLJNj33A/Xnz1KnqSFsq2C8cTqLpAKm5goDeuq1WewKMH9wIAvvT8WXhCoWy7mJq1w9WHQWxEp3ngwDCeHHkdlaoXGuqTcROfuG/PQi9tm/rKXaHkANAtyCAQdNIXqAO92ogeFTWb+pJ44iuv4uINnb21d1cffvPn7sCmFfCxrCdDMuUpEQRBdAYSVw04enIcricxW3H1yj/ftO54Cgbn8KSCZXJkUhZSCQu8WIXjyobz84LKRSJm1KRyRxFCRxV4UsETupUYtwwkYgZsR8D1BBxX4lc/si/cH2NKL9/3PV+u0P9fCImZooPPPfsjWKYBxuAHezIYnENIhSMvj+OW7Zkl3TgXGjR64UohzIUC9OmVqx4uXCms2Pgb1xMAY3qOoU/Qko1iGQxvXC/iD/7ilVAkH7r7Fvzsu28B5ytjWl8vhuRuylMikUcQxHqHxFUD8jM2KlUBBoQrxQx/1d+WgRSqrgh9TVW/ovSrH5mfYzU6lsfYpZlwFWEzFKBFFQNsV8BgOgm9WHGRTphQCrAdHedwOV9CPGZgarYKgIXywfUkpgo2SlXPH0+jUHU9CIlw7I3rSX8lo8J/feZHOPTeN+OW7Zm2b2TtDBo99spFGJyFyfDwj3fslYsdq5bV+1DilomYxZFKzIkrffzac7gxZYcJ8psyCXzm0B3Ys6uvI+fUjPViSO6W9mU3iTyCIIilQuIqQvCNeaZYhSsUDI5geF5oKrcdEcYhtBIk0XYgQ1BJaY4CEDd1fpUQEkpJPZvPjzUwDYbpkoOKI3wBxcAZwggHBj1mJhBxnAGGwSGkDL1b0TOQSuErL16AaTAwMAipUPC9So81CMwcOX4eIy9q3xhjQH86hkxPfN4N2PbDTF2hwvFAnOnHO0nUhxKmyLsiFDCJmAEwHW0gpUR+uhomyP/MbVvwqQ/vm9euWwnWiyG5W9qX3SLyCIIglgOJK5/oN+b+3jhuTNsQEmAscA4ppBIxDPUl2jKYBq1FqYJW38J4QqInaaHginmVLqn0AOF0wsR00UF9y0uh9iHGWCjoGlXNAlHmegqmgdCgX7I9PPPCuZr3N3L8PL7y4oVwP0oBU0UHQir098ZrbsCWyVF1dfszGPAsFBC3Vm5GeCMB84n79kBIhcPPn8VEoRqe26c+nMV73ratJhF9pVkPhuRuaV92i8gjCIJYDiSufGq+MVsG+tICMyUXnlCIWwypRAymwdpu51zKl1D2W3StMLgOENVCiKHqSphG7YpCPTpGr0jsjfiKmmEaLDTdM660+b5OYBmchaIvaH0yABIK16Zqb2T/cHK8oUCbLbtIJa2aG3BP0kLV1WIm+pKe5MLnHbAUz029gLk+VcYTI6+HwuqWbb34zEfvwNaBVNvnsZHolvZlp0TeQt5AgiCIlYTElU/9N+b+3gQsk2Om6KI3FVt0O0cIBSUVWhWtDO5XkFC76lCbzxmkVOAcvlBSqDoStiOa7o8zHdwJaM+V/hP46HtvqWnpGf7Q5fljnv0WI6t9PKhEzdsWehxPdCUg843lxYoLqfQ59SSttitFnfDcnHj1Cv7y2FlUHe2be+DAMD72/lthGitXPVvvdEv7shMirx1vIEEQxEpC4sqn0Tdm0zSwe2eyrbEq9dUWTwg0slkFyemFkgsF7eOqX3UYZFv1JE0UbQ9Cav/VgjAdA1Eou/A8CYMzbNuUDI3kIycuQDusaj1ggQiSfm9x6+DcHMDRsXzrY9aVtILrOJCZqzRUXdEwJb4Ry/HclG0PTz+fw8uvXQMA9PXE8Js/eztuv2WwrWNvdLqhfdkJkRf9HQqiOci3RRDEakLiymc535gbVVtcr3HJSin4GVAKJuNIxU0k42bNqsMLVwo48tI4in4URJMEBwCBG2zu74bBsXUgGZ7/w/fqqlIgsI69chG24yERM3Hn7kG89pNp2FUvnIWYSMTw8D27w/0/88K5lu89lbRqblrLrTws1XMzdmkGT4y8Fm7303uG8GsP7kNvqj1RR3QPyxV55NsiCGKtIXHls5xvzI2qLYHqqW++BbELMdNAMm6gWHEhhMSOoXQoQF589SrSSROVqoArJAAFBm3IFlLVVJ2Cv3EG9Kct9KdjTc//lu0ZDG/tCZ8/cMc2HLhjW817rk90vzxRbvne629ay608LNZzI6XCkZcu4CvHL0AqnT/2S/ftwb1v37mqpnWie+gWcz5BEBsXElcRlvqNudk3Zcdvzc1bLciATFq3AoPBykHr8fHDp/wKmIW+Hr35T67ONoxTCDA4w0BvDABr2sJs5mV65P694WtGx/J46shp2I4IoxkWSJDAbNnF1oFkzWPLqTy0qnzVt17f+1Pb8J3Rqzj7xjQAYOfmND5z6A7s2tyzpGMvFQq97C6iv0OmoeM4ujFbjCCImxcSVxGWepNs9E05lbAgKy429SXgeQKFkgvHk7BMjv6eWBh4WV/5aSTUAj9UI51jGnogtGkaLX1N0epa2XZRKLtwPYknR17Hpw/djv27h/DMC+dQsr0wm0q2Sj71mSlV8YE7ty+4Xbs0q3wBqBGHVyfLeOq5M6Hl64Pv2IVfuHc3YlZnBi63C4Vedh/R36GpooMBWi1IEMQqQ+LKZzk3yUbVFtNgeOhdwzgzPo38jI1bd2RQrLjwpGrZrmgk1FrBoCtaQkgM9MTwO3/47dBTdfCuXaHXKhBtZdsNZyUaDKi6Xvg+dQSDgpCoCQBtVr3iHMikYjgzPo1DbZ2tZiER26jyFVT0LINjslBFsaLDVQ3O8Nsf/yn89J61uXFS6GV3EvwObd7cixs3Ztf6dAiC2GCQuPJZzk2ylc/oloiQSMQMlH1R0MzsHRVqQcWrVWvOEwrJuIk9OzI4eeY6mB/jUHUFRk5cwLXJMqaKDmaKVRRKTjhMOcjBskwOw+A4enIcUqlwVWIQAKqgBdZAbzzMjGIA+nt0QrtSalFG4aWK2PyMDdNguDJRhuefZCJmIBU310xYBedF5mmCIAgiCokrn+XeJBtVW6JCgjHg+lQFrtBZVfGYgZ2+iX3/7qGaak4iZqBiu5gqOgtnMzHg4F27cOyVi6GwAnRVyvUUXn79OrZuSqG/N47Jgk6dNzggfat9Jh0P36ePc3LtAAAgAElEQVRpsIZjeiyT47/8L+/F44dPhVU1y9RBp4s1Ci9FxEqlwBlwdbISPtbfG0fc4hjoibd97JWAzNMEQRBEPSSufNq5SS7WkxUIiYrthrlWOmNKIhGL1QiraDXH8SQKFQ99PXFk0jH85Grrtsb3cjcazvQLZFKQOs8Yw43pCoQEEjGGTDoexkAM9SVQdYU/sqd2FWLgY+qEUXixInamWMWfHTmN69P6eYMzDPUn9HifLjApd0uyOUEQBNE9UGS1zwMHhiGERNUVUErNEw6BAJouOTXtrFYhm5fzJeSnypjxhRWgRYuQ2id19OQ4gMahh0IqlG13wfPmjOHaZNmPaUDN/L96knETmzJxHdvQG0ciZtS8z51DaWTSMcRjBgzOEI8ZyKRj2DmUDoVl1REoFB3kZ2z0p2N45P69i/IWDfUl4NQFdzWr9Hzl+Hn87h+fwKs/ngQADG/pwe4dGQihlnTslWD/7iE8cv9e9KdjKNte15wXQRAEsXZQ5cpnoXymxbazRsfyqDiiaQDodNFB1fEANK7mWAaHJ/Som8aDajQ6yokhZhlNx9RUqh6Scb1/0zSwY1MKPanGeVhfev4sEnGzpgqzb7i/Zqi140nYVQ/FiuvnYY0vWMULxNmlfAl2VaAnZaE3ZTWs9LiexH8+/E84f3muYpdOmKg4Hj7+gVu7Trh0Q7J5Jxg5fr4mZDa6IIIgCIJoHxJXEVrdJOsFUNlv9V2fLOPxw6fmiYujJ8eRTpiYLjpNj1dxJEaOn2/YkkzGDZQquoLGWONKFKAf3zaYwLWpSuMNoL1eAZwB6aSFnlQMjx7cO2+VXnDuUeF19OQ4XE+GY3U4Z/A8ibLtYdum1IKm9Gjbc6A3jgJ35oWnBq+7nC/hj/72h2GbkAHgvkE/Zhm0Cm+FGDl+PhyPFF0QAWDDCizKLyMIYqm0Ja6y2exeAF8EsAnABIBP5XK5f26ybRbA9wH8cS6X+91OnehaM9SXwNXJMiqOgOOIcBWdZfKG4iI/YyOTjqFke3CblK840+NoPn3o9nm+HcvkeOjdOsphYtoGgzZCRQ3njOmKzsP37sHnnn0Vzetbc0gFFCsuzl8u4Kkjp/HYQ7ctGIPwZ0dOo1z1/FWGCN+PJ2Rbs9vqq359PfF54alKKfyPH17GX//jP4dtQ+4PmWb+ysay7SI/Q6nrK0GjBRFC6sc3orii/DKCIJZDu5WrPwHwuVwu93Q2m30UwBMA7qvfKJvNGv5zf9+5U+wO9g334+zFaX/ssUYq7WOKiosLVwo49spFLUYKQDLGIRWDqEtpD4SD7XgNK0b7hvvxvTPXcW3KhlSBSV3pNqDvjN+5KYWH792D/buHkEmZmJhtXCWrr3wppVfglWwPz7xwbsGbhRBa2HEevPf5Iq6VKX0hE3ux4uKL/3AG/3T2RuTacCiocIQNZ1rM0Sq8lcF2vFBYBXCmH9+IUH4ZQRDLYUFxlc1mtwB4B4D7/Yf+CsB/z2azm3O53I26zf8tgK8B6PH/u2k4Mz6NvnQclaoH29FeKIMzlGz9/11P4PpkGbk3psEZg8H1N/9yVSJ6y9JtLi0ehFRIxPSPIFoxGjl+HiMvXgjzrULPlT+r0DI5EjEjFFYAkExYQBNxxRlQP4GHMwYJ5QeHtkavDFSQioGzyPlEZve1ih9otRLzzE+m8IWvvY6pWZ2hdde+LZgu2rgxbWO24kIqPVdRKoAz3nWr8G6W1lEipleNGpFfVqkQ/n5uNCi/jCCI5dDOJ+ebAFzK5XICAHK5nMhms5f9x0Nxlc1m7wTwYQD3Avh3K3Cua8qlfAmOK/1WmBYszG+RSc7C8E0AUFA1wZ9BCzF4jDOdJ6WgcPCuXTXHGR3L48jL4/NeHxCLGdg2mELVFXjmhXPhjX2m6CCTslCpevCEqnmNFnLzW5NBNaz++PViYcdQGtemKv6+tedKSAUpFa5OlJBKWDAN1lT41McVFEraczU5Y+Pxv/o+AF0Z+OX734q7f2o7fnR+Al96/ix6k1Y4vNrgOvG+m4TLzdQ6OnjXLoycuAAho+OW5v9+bhQov4wgiOXQka+l2WzWAvAkgF/zxdeS9rNp08oVuzZv7l3ya793+hpsR0AqBYMzKBEkmWthUh+82ShRfeumNEoVB8WKB+G3Ez/6/lvxyYP7arb7xjOjkHW+qmhLTwjtx5op2pguujB4BZbJofw2XzJuQNg6TiJ8TZ0b3uAsFF87t/SG1+Z7p6/hr79xDqbJ0NcTQ9F28dffOIf77noTvvnKG0gnTXhC4cZUJWxrekKhVHHx8Affig++6y0Nr98HN/eiry+FZ791DuNXCyj5KfWBt8o0OH7z59+GD7/rFr39lky4/fXJMrYMpvDxe/bgnbdtbbj/teIbz4wiHjPC6k7MMmA7Hr7x/ctNr8VCLOf3dDn8+sfuRCodx1e+fT5cXdro93M9spRr+ksH9+GJZ0chpNQtQVcASj++Vj+jboKuQeeha9p51vKaMrXAcF6/LXgWwCZfOBnQpva3Bm3BbDY7DOAUgKL/sn7o7tGXc7ncp9s4j1sA/HhiolgjLDrFcueLPX74FK5NVTBbdgCw0NS9mDNNxAxsGUiibHt4/F++p+l2n/38CUzPVuG1uA6moUUNAFiGFkpSKTQoTs3bPqi4GZwjETfx2IP7wipLNIE9oOoK9Kdj4arBsUszYIxhU18iDBcNtgnM6a34P7/wMq5OlkPBmElbSMZNDPTE23p9QDe04z77+RNIJUxUql64ktI0OGIWx//zr9+36P3RHLzOs5xr2g2/Y90I/Z52HrqmnWelrynnLCgIvQXAhfrnF6xc5XK569ls9gcAPgngaf/P70f9VrlcbhxA+KmTzWZ/H0DPzbJaMD9jozdlwfJbWp6QiFncFyy6BbiARkXVEZgtu9g6kKx5vP4DXCkFucDOAqEUrKQLbDJRuRe0LgGGmGngyf/9/QveLFr5TAJPWCAoYpYRrhoMtmm1/0rVw9PHzuLKRDk89019CSTj5qrNJ+w0wQrS2YobrqT0hISUCqNjeboRr3NulvwygiBWn3bbgr8F4IvZbPb3AEwB+BQAZLPZ5wD8Xi6X+94KnV9XEPgvgiDOQsmB6wkwxuCJ9vahAEzNVrHvTX3hYyPHz+PIy+MQUvmhoRKzZZ3mbvDGc/6iSKnCFV41pnm/lSgBcKbCFV8L3Sza8ZkE28TqtklYvKngSSctPDnyGm74I2ziFsfmgSQMzhseYyG6ZSXXAweG8bm/ezVcSSl9D1tPyqJVZQRBEBuYtsRVLpc7A+BAg8cfbLL97y/vtNaW+grMvuF+vPjqVRQcgUKpCoBp8bKEFubJ09dx4A49MufIS+Pax8UAxxPa1+GjoGAaDFLOmeNjphYjQUtStwNrzfJA7QgcobSYaYd25uTtG+7HkZfGcWOqAtNgoZndtIx5gsdWCl86dhaTs1UIqWAaHO/bvw0/Oj8BTyhwppY0i69bVnLt3z2EZDBCyH9/mbQeK0SrygiCIDYuG3OddQsatZxefPUq3vu2bTj2ykUoMD3HL1weyCCVrPE7tRpXIxXwzAvn0JOKQSoJg3N4Us1rK0oFxA0GO5KhUD+TzzB05IMnZMu2ZE/Sauu9LzQCaHQsjxdfvYp00oTtCDieRLHi4qF3DeP4j67WCB5PSEzPOqFg3DmUxmcO3YFdW3qW7WXpppVcO4bSDX1q63FVGXmMCIIgOgOJqzqatZzOjE8jlTAx1J8AYwwXrxdDczjAsKk/jvyMDaUA0+RNU9kB4NqUDdvV5mchm1fAKk7zfQDAQE8cgBYb5y8X5omvAMZYw8cb0ap1GFybVMLCkP8eq67AmfHpGsFTtl1MzNhhNe2+d+zEL967J2wlLtfL0k6FDVgdsdDuuXQ73eJjIwiCWA7B5/5k0cFgT2zNviSSuKqjVcspEBBSKiil4EbiGGbL2tQcs7ge19LEM6UtUgpDfQnYjgenMj8BO+q3ajVXcKZYxe6dfXjgwDD+6zOjTd/TYqoorQRJq2vz6MG9ePpYDjNFB5WqF577z77nzfjY+3Yv67j1LFRhC/a3GmKhnXNZD3SLj40gCGKpRD/3e5Nr+yWRxFUdrVpODxwYxlPPncFsyZnX9nNcAal0errtiKaGdMaArYOpuXE6deKJYU5c6RVobF5OVYBUKvzlaWX/upQv4ff+7CSgFGxXNhUACwmS6LUp2R6mCjZcTyBumbgxXYGUCIVVKm7il+9/K97ztu3NT6zN4zZioerXaoqFm2FVWbf42AiCIJZK9HO/nbm3KwmJqzpatXn27x6CxRv7qaQCUnEDVUeGCezBCJwABiCdjOHhe3bj6MlxZFIxPQjaFXMmdKCmpdgsh0yPwTHCX55WJGIGrkyUADAM9MaaipeFBElwbbSx3wnPVymFLz2v53gbnOHj778VHz4wDN5mO3IpQmg5sRLEfLrJx0YQBLEUuulzn8RVHQu1eaaKjef3AQhnDg5mEpgs2DANjlSCo+rosTk7htJ4+J7d2L97CE8fO4tMOoa+Hi1Apgp6ll6gpeIWhytk02BQxhky6RiAuVWEzZgtu2CMA1CYrXjYNhhrKF4W+sUMtn1y5HUA2lAPxVDx37dlcvzbR96Bt2zPtDyfehb7D6KdSheJhcVxs3jHCIJYPDfLYpZu+twncdWAZm2e0bF8y/Zb8NzUbBWJmAFdX2K4dUcmvEkdPTmOp4+dRdn2IKQKBVIibvoDoPV4m2TcRLWFkMuk9ArAa5PlmgiHRujVhDrB3RMCVyfL6E2a88RLO7+Y+3cPIZUw0dcTw7WpSmjGTydMxGPGooVVu8eN0k6li8TC4rhZvGMEQSyOm2kxS/Rz3zSYjslZo899EleL4OjJ8ba2E1LnNw31J2t+sNFfYE9IzJR0Q880GCYLNgAWDoOebiGsAGCm6ABMe7IWSodnAKILCYWQmJp1sH2wNi2+HUHiehJCKuT9pHXGgE2ZBEyTo98XiotlsUKonUpXu6Z3EhNz3AzeMYIgFsfNtJgl+rk/VXQwQKsF1wf5GRsmZy3n/gV4QvlxBAqfe/ZVWKaeOZdK6IpTnx+jUHUEin7MO+cMrrfwvsPQUAWYFof023KNXtnfE0PJ9sJnTUNX0wAZ5EiELCRIrkyU8MTIa5ia1aLQMjk29ycgFRb17aCRqHnk/r1tC52hvgSuTVVQqXrwhI60SMbNeaOFWomFm+nbGkEQxFLpJp9SJwg+99d6XiOJqwWICoGy7SFmcQhHLFgtAuaM6YopSFei6ukA0kBgZdIxlG0PVZehXPWamtfr4ZxB+uGiXosB0r0pC9sGUzj7xjRMQ88hVEonv/emEqFHLEojQaKUwndGr+DwP56F40owBrzvp3fiWr6IiUJ1UVWfZqLmkfv3tj24OVxpGRmi7XhVfODOhVcmBtxM39YIgiCWSjf5lG4mSFy1oF4ICKkwU6oiGTPgCtUyKLQey9SDjgtlFwBQKLtwPYmEZcATMpxP1zzbfQ5PRLdpvD1nQF/KwgMHhjF+rYiq64XjWZJxE1VXtNXGK9kuvvgPZ/A9f073YCaOT//cHXjvO97U9FtBq3ZbJ0TNmfHpcKWl50mYJkcyZuDM+DQOtbWHm+/bGkEQxFIgf+rKQOKqBfVCIDCfVx2BvrSFRMxAxXYxWXQA1XrkjZAKUik4rsDkrITyZwZWHC+sgi0mST3cdxN9JxUwUbD9VpeCkIBwBGyngnTCRCphLviP5+wb03jyq69hsqDbgO/ctwW/8kAW6UTzcToLtds6IWryM3bNSktAV9cWs4+hvgSuTpbnCbRtg6m290EQBLHeocUsKwOJqxY0EgK9KQsGZ3j8X74nfGzk+Hn8w8lxVN3mlaxolUtEKk/RTqAQ7VfCGAP60jFtbG9CxZGoOHa4fXCssu3h/nfuavqPR0iJkeMX8LWXLkApnTr/yIf24u792xcUgAtVpjpRgu7EPupbi54nUfAE7vnpHW3vgyAI4maAFrN0HhJXLWjnJj5y/Dy+euInDdPYF0uzPTDoeYVS6gHNgW8rk46F8Q3huJwm+1FKtwqV//djr1zELdsz8/5B3Ziu4MmvvoaxSwUAwJu39uLTh27H9k1pAAvPbVqoMtWJEnQn9nFmfBp96Xhoig/iLxbTWiQIgiCIRpC4asFCN/GR4+fxleMX2nBJLY5gRqGUQE/KQtl24QkJzjgees8wbtme0eflCvSmLD/GQaMi+6g/r6j+q7revNVxL79+FX/59RwqVW10//DPvAkff/9uWH5IaTtzmxYSpJ0oQXdiH/kZG70pK2z1AotvLRIEQRBEI0hctSB6E7+cL8ETCobBcPTkOC5cKeDIS+MdF1Yxk0MqLUg40z6v0OvlCl1ZuftWAMDTx85iYsauOYd4zEDVEc1LWNAjaiyTwzA4jp4cx1t39eNLz5/FiVevAtDH/I2fvQ1ve8ummtcdPTkOTyjMlqvIT9swDYZk3GwZ3jlbdlEsuyhVXDx++FQogpZbgl7uPmiFDEEQBLFSkLhagOAG/qXnzyJlcMRMjumSgyMvj8Nr5iZfBlIpcMbgKd0KjBJtr124UsDkrA3OtWdI+Ab5apB51UJYMV+0xUyOKxNl/Ps/fwXXpyv++92Exx68raaiE3A5X0LJDkbp6GPZ/pzB0bF8jeAJBGnFEUgnTWTSzWcargW0QoYgCIJYKUhctUEjk7aQLZYHLgMds6B3bNWJq2hl5dgrF8HAYHBtMOccYfZVKzhnGOiNIxEzMDlbRdGPhjANjl+8dzc29yfwJ195tWG7TZ+bLolF4yCkUjWiKfjv8cOnaqpD3ZQl1W5rceT4eRx75SJsx0MiZuLgXbvCyiFBEARBNILEVRs0MmlbBoejxIoILEAPbi5XPVglB70pa15lxXY8PSpHqHlVqjDBvY4gm900GK5OlOH4Kxi3b0rhtz76NkzN2i1jFAyDAV59zhYAhbDFuBhz+1qzUGtx5Ph5jJy4EIrYqiswcuICAJDAIgiCIJpC4moBRsfyKNseJmersEyOuMlR9SSqrsDiU6nag3OGbZvSmClWUXX04Mmo3wsADM7hNoluaNYSVNDVryv5cqgJ73n7TvzSfXsQtwz81T+ebRmjsHMojauTZcwUHSjoeAcGwLKMhqJpvfua6quDBgOE1I+TuCIIgiCaQeKqCaNjeTzzwjlcniiDcT02xnUFqo4WVQYHEjHTn9vXGQw+FyR6OV8Ks7E407MIe1MWpksOnnruTFNhBSxcTFMAEjED+3cP4ruvX8O3f3AJiZgJqSS2DNSGaDaKUbBMDukrOAUg41fW6kXTSviaVnPYsu14obAK4Ew/ThAEQRDNIHHVgJHj53HkpfFQwBgK4Vw+QFdsNvUlkYybiJWccJDxYjENVtNiExJgzBctKuppAmaK1TCLabJFW41zBga0zN2KmRwJi+N7uRs1LS8hFfLTFWyOCKxGMQrPfGsMlyfKMDlDJm3BMHhD0dTp5N/VHraciOkxQUZEX0mlHycIgiCIZtBdoo7RsTyOvDweVmYALVQMznSAp8EgFZCM60tn8GZ7Whip5kzrntABoc1aelIB+ekKhvqTEP4qRdNg4JHEdFdIWAZrmRTf3xNDb8rCxeslcF7b8lJKoeIIVF3RtNIU+JR+ki/jy8fOID9joz89P0y0fvtOsNrDlg/etQsjJy5AyDkfm4LCwbt2dfxYBEEQxM0DiSufYFVYuapbPgy1I2MCsSWVfvzqZBmeJ2tE2GKRvmjr74nBEwqFktOy4iQVdGAoYzANfU7RypfBGTb3JeApoFRxMFuubV8xACXbQ6HsYq6px2pe70mF/nRswUrTO2/bijcPre4cvtU2yAe+KlotSBAEQSwGEleoXRUWoML/8f+/AgyDQQgFXeyR4ePLwfUkJgtVKKWQScd05cjPqmpEcDzL4rCrtdvFTIZC2UU8ZswTVsHbcT2JoNgVbUMKqfwROWxNh3a28lSthUH+0N23thRTq+kBIwiCINYHJK5QuypMqtpog2iswfbBVBiaKZUC52xJMwUDCRcZ3wzGGDLpGCzTQ94VDUUbZzqPKmZx9KUsXBc2hFQwTY5MygLnDBMzNgp+dlX9+YfHZwzJGEe5KubFKiRifMk+puUKjYU8Vd0W/LnaHjCCIAhifbAMx9DNg+14CBaF1a8OA9OjYn7+7lvwB79+AACwbVMKOzf3wIh4lhbDXEyoRkhdObo6UYbjNq5acQ5s6ktgIBPHzqE0bFdi26YUdm3pwbbBFCzTwGShGoolhmbCSlenpNKr/KKPZ1IWNg+kwsyqxRAIjemSUyM0Rsfybe8j6qlijCFuGTXnsn/3EB65fy/60zGUbQ/96RgeuX/vmgmZhc6XIAiC2JhQ5Qq1q8K472fyhAIDsHdXX1iBGR3LY7bsrJjHR0iFmZKrTfKstiqmJDBZqCKdMPGJ+/bg6MlxTJccxEyOUsXD5KwdVrt+5rYtuHSjiCsTZQBaZCk/UN7wx+V4QqK/N45KVWD7UCqMgCjbLgolF9cnyzWzAKOMjuXxjWdGceVGMaxQLddsPjqWx9ilAqSaq8SlEtY8T1UnDfLLpdtDUgmCIIi1gcQVgDt3D+Kl16+HYoYzXckZ7I0jP2OHg5q/eepSy5V4yyU4fuCFCuYAekJBQRvgS7aHoyfHsW+4H98ZvYLp2Sps36PFGfCx99+Kh959CwAtWJ4ceR1VV+c1CTXnrzI4Q7nignPg4o1SGJBaqnqAv4qxUZsrqFDFY0ZNhcp2BAZ64zXvp12hMTqWx1NHTsOTesWkdATyrsAQdPJ7t4aOrveQVIIgCGJl2PDianQsj9cuTNW00II/p4tVmAaHkApjlws12VPLJboSsRFKAUIpRJIWoKCFUW58Gucvz8AweCisepIWHj34VvzMbdvC7ffvHsKnD90e+oI8T6BQcuFJid5UDK5QSCUsFEpVeJ6cC0g1GPp64jXVJ/h/jl2aAWMMm/oSMCwebiOEB8eTSxIaz7xwDiVbt2aFmltMMFmwMZhJdO0w5W7zgBHtQwsRCIJYSTa8uHrmhXMoVtyGs/ikAqquRNVdWkhoM4IZf+1ItUYCTI+xUYCn86g++aG34v137ghbe1Hqgzxv3ZEJ23hB1cUyuY6BcATAgMFMAkqpMG7i+mQZlyfKSCVMSKXbpTemKxjojYetO9NgEEIuSWhcm7J9LxgHYypcuSglajxV3XZD7HRIKrE60EIEgiBWmg0vrq5MVkJhVS94OlioCulNWZgtux3b9//1a3dh+6Z0y22iPqVAoJx9YxqWydHXE0cybiIZN3F1ouQPglaYnK3WrGosVRwkYgYs0wj9aIWyi1RCj77ZMZQORdvihYa/QtP3vHFD+804m9+O7LYbYjd5wIj2WO0wWoIgNh4bXlxJObe6bgW01DxmIzEJnWAhYRUlKlC0SJJh6y0ZN5FKWChWXMwUHV9YMTCmwvyvQslBJh3DZMGGAuB5So/N8StUSxUaWwdTuDJRglQs0p5V2Do4997ohkh0ClqIQBDESrPhxVWQVbUawqrTLDYEIipQMmkLk7NVKH9uIecMpsHw0LuG8dUTP4GQCgx65V4wGscTEsm4icFMAjNFB9JPc19KnlW0wvXO7GZ88/su7KoHISUMzpFIxPDwPbvD19ANkegUtBCBIIiVZsOLq+2DSVyeKDf0XEVplBm11iz2dKICJZXQGVeFkgvXE6FIArTgBOCHqgIynGXIofzw1IHeOD7xwT1LChqtb++9+OpV3Pf2HTgzPt20pUg3RKJT0EIEgiBWmg0vrh6+dw+eOnIatiPgeM1jFrpMVwHQQ6QXQ71ASSUsGAZHfzqGz/7yOwAAjx8+hXTCxGzFDc3rjDEopbBlIImy7WGoL4FfOrhvSbMFm7X3zoxPh+fQCLohEp2CFiIQBLHSbHhxtX/3EB576DYcPTmOy/kSZisuEJkjGIgqzuZyotYaBp3YvnVwceKmHYGSn7H9MTwchbILz5PhyJ0goR4ANm/uxY0bs4s+96W29+iGSHQSWohAEMRKsuHFFVB74869MQ0wbXQ3TQ4hJaTEkmYIrhQKQNwyazxJrYh6nBIxA1AqrEA1a7+lElbYOqy6um3YCZbT3qMbIkEQBLEeIHGFWh9QzOR+IrqC26JNuBoEsVW6UsXAGIP009UHe2PhSJ5W1Zx6j1NQrXr0YOOZfPuG+3Hk5XEIqWAZHMm4zsHqVPutWfVs33A/Hj98iqpSBEEQxLqHBjej1gfU1xMHYzrAMkqDfM4VRyn9n1R6BI6QEoOZOLZtSsF2ZVvDkhczXHh0LI8XX72KnqQFy2DwhECp4uG9b9vWMaHTaPjye9+2DS++enVZQ58JgiAIolugyhWAy/mSzmuSKqwO1dMNdispgemiE87bayf7aTEep2B/qYSBjN8GrLoCZ8ancaiD76O+vff44VOUYUUQBEHcNGzoytXoWB6/96cvo1B2UXUlpAJcT3Zd5EIUN2JCz8/YiJm1P8J64TTUl5i3CrKZx6md/a0Ea3VcgiAIglgJNqy4Clpq16dt+LFODStWnYJH/FNRLJNjMLM4s/ieHRkcPTmOmWIVVyfKKNtzqe/1wumBA8N65p8roFRtono9ixFinWStjksQBEEQK8GGFVdBC0z4BnFjha+EVIBp1OZlpeIGdgyl0ZuKI5Oywsdb+bsYA76Xy+PaVMUf4CxxY9rGVMFuKJwaeZyiw5CjLEaIdZK1Oi5BEARBrAQb1nMVeJFMk0MIPXJF1LvYO0B0ZqEn9J9bB5NwHAFPSoxfm4VStYLK8FcGRlcrMgCMMxicwRMSs2UHAINpaLN7oewik47hE/c1Fk7tsFZZUpRhRRAEQdxMbFhxFeQtZVJ6xp5cIcd6/V737urD737y7fg3/+07KNpibrvIhkP9ScRMjkv5EoRQ4AyIWdpknp+x/Z1q2VaTv6XUPEEyOpYPEzeQE3EAACAASURBVOiFVCiUHDx15DQee+i2huJlrbKkKMOKIAiCuFnYsG3BoBVlGBwDPTFwv3S00pELZy/O4LmXLtQIq3o8T+JKvhQqM851FWt6tqo9WwwAlM7j8rdhAC5PlOfFFzzzwjmUbA9SKX8+okLJ9vDMC+c6/+YIgiAIgti44irqRQIYbt2Rwf/6C/uxc1NqxQXW3x+/0PL5qdlqTSSEJ1RYWYtZHMx/LApjerByfX7VtSkbjOnxPYwx/0/9OEEQBEEQnWfDtgWB+a2o0bE8rk3Za55pFeRtRQ1bpsGxfSiNQsmBXW1Q9WIMfT2xBvEFfnUrIhi1x6uL8yYIgiAIYh2zYStX9QTRDGuRxN4YVSPyXE/i2mQZs2UHjDNYkVwoBm2CD8JFo+jhzirM7tJ/qkUPfSYIgiAIoj02dOUqYHQsjydHXoftChi8O9RVfdsPAKqOgILOxurviWHS92AxAFLKhvEFD9+zG089dwZ21YOQelVkIhFrOvS51azC6HPbN/fgg2/fQSZ0giAIgqhjw4uroGJVdT0YjEGtYU+QsdZjduYiHSRSCZ2LVSi7cD2JhGViz46MFomOh0TMxMG7duHQ3bfisQf3tRVzUD/kOZjxFxB9bqpQCZ8jgUUQBEEQc2x4cXX05Hi46s6VKmwLRvOpVgvL0FlbokncViC+lNIz/5JxUwehCok9OzI4eeY6GHQWVtUVGDlxAQBw6O5b2xJArWYVAqh5LmEZ8ISi+X8EQRAEUceG91xdzpcwXayGnqSgcrQW9SvXay6sDK7Fl8EZOANMBly6UcK1iTImC1W8fPo6oOYCSA3OwMBw7JWLbR+/1Yw/mv9HEARBEO2x4cWVHrmy1mehqT+NqPtLyEB8KXDOMDFbBaD/HlS0pEJNGCpngO14bR+/1Yw/mv9HEARBEO3RVlswm83uBfBFAJsATAD4VC6X++e6bf4dgE8AEABcAP9HLpf7emdPt/O4XudH3iwGxoB0woRlGqhUPdiO8HOpAM717MMg70r52ysAlarQFSwjkGB6GyEVuP+YVEAi1n7n94EDw9p/Bl2Vcrxak3z0OdvxaP4fQRAEQTSg3crVnwD4XC6X2wvgcwCeaLDNdwHclcvl9gN4DMCXs9lssjOnuXIYfO2Kd6ahQz1LtgfpzzX0w9cBAFIqDPUlQh9YImZgc38yrGgpBbhC1lSUtCdLj8VRUDh41662z6fVkOf65wYyyaYDoAmCIAhiI7NgWSObzW4B8A4A9/sP/RWA/57NZjfncrkbwXZ1VapRaJ2wCUD7pp81YOtAAhfz5TU5NmcMEgpKAjMlF6bBYRgMnlAQUvuspmerUEoLsUw6hmTchGlweELHMkDVmu8Z09Wr6GrBxdBqxl/0uc2be3HjxuyS3ztBEARB3Ky00zN6E4BLuVxOAEAulxPZbPay//iNJq/5FICxXC7X1cIKAB6+dw/+6G9H1+TY9R4mQPmBoNpjFW0JekLh+lQF6YSJTDoG26lEXqVhAHYOpfEHv35gNU6fIAiCIIgGdDyKIZvNfgDAf8BcpattNm3q6fTphGze3Nvw8b582R9ovGKHbpvAmB6zDHiehCvUPJN7yfZgGCz0ZfndRJgmx6a+BFyhmr7XZnzv9DU8+61zuDZZxtbBFD5+zx6887atC75uscchFoauaeeha9p56Jp2HrqmnWctr2k74uoNADuz2azhV60MADv8x2vIZrPvBvA0gI/mcrncYk9mYqIYVmo6SbMWVhCaaZkcVXdtje2AFniDvXGkEhYuXp873/rMrWLZw85NKXgKYe4UoFc+DvTEFtWuiwaHJiyOi9dm8R///LtIxA3sHEo3DRyltqCmVaL9YqFr2nnomnYeuqadh65p51npa8o5a1kQWlBc5XK569ls9gcAPgktnD4J4PtRvxUAZLPZuwB8GcDDuVzu1LLOepUIQjO7BgUUSi4Mg4MzDoE5wVcrsBQevndPy5V9AQvd/INrIITElRk7XD2pbFWT0E7G9fm0SrSn60UQBLFxaVdZ/BaAf5XNZs8C+Ff+/0c2m30um82+09/mjwEkATyRzWZ/4P/3Ux0/4w6Sn7HhOF5XVK0YtGnd9QT60zE89O7hmiHSgbDiTM8WPHpyHLYjUCg6mJ6t1qzsCwhu/tMlp+bmPzqWD7fJz9jwPIHJ2WpNLIUnFYSQMAweJrQTtUQT7RljiFsGXS+CIAiiPc9VLpc7A2CeSzqXyz0Y+ftdHTyvVUFIialZZ61PQ+P7rYJoiFu2Z/Cu27bgpdev1wgr5f85XXIw0BuvqVjVV0tajbMJth3qS+D85UIY7xCtkBXKLrYOJCmFvQn5GRupRO0/IUqtJwiCILqoJ7Z6lG0PT371te4RVtDCyvEkPCFx/nIBTx05jQN3bMO7b98CzuakTzpuoK830Va1pJ2RNQ8cGIYnJJSaCygF9IpFz5OUwt4CSq0nCIIgGrHhxNXYpRn8/p9/Fy+/dg2AFhtzKedrS5C4LpVCyfbwl0fP4NzlArYMJjG8tQdbBpMoVwU8T9S8rlm1pJ2b//7dQ9gxlA5nEuooiLn5hJTC3pwHDgxDCOmPUFKouoKuF0EQBLFxxJWUCl998cf4v58+hfyMDcvk6O+JwRMSnljdHAbOEPqporLONDgY06ntjAGTs848T49pcBRKbs3+mlVL2r35P3zPbvT1xLBlIImh/gQMziClwpb+BKWwt6BVoj1BEASxcel4zlW3cuSlC/i77/wYALBrcxr3vn0nvnL8x6uebzXUl0Ch5EAqBeYrrMBILpUKW4BK6RZdfVuvryeGiRkbVVe0XCUIzK1YWygqoH67W3dklhUpsJFolWhPEARBbEw2jLjqScVgmRzvv3MHfvHe3fjDv/khUkkLxYq7agJrqC+BRw/uxZeePwtPKBRKVURrV55QMA0gsJXHLS2eollWhsGxYyiNnqTVVrZSuzd/EgkEQRAE0Rk2jLi69+078YE7d4BzLWaClV6WZaDqiAVevXQMrlcAJuImHj041zJ6cuR1KDBYJkfa5CjaLqTUAitmMiQSMdz39h148dWr87KsPnEftZ4IgiAIolvZMOIKQCisACBhcVydKDeY79c5UnETqYQ5r7q0f/eQfrw/EbYG4zEDhZIL1xM1bblbtmeatvU6mQ5OEARBEERn2FDiKmB0LI9CxYOQKxseevCuXTh0960NnxvqS2C65IQtv1TCgmFw9Kdj+OwvvyPcrr5dNzqWx+OHT+FSvgS7KtCTstCbslYtHTwQdJNFB4M9MRJ0BEEQBFHHhlktGOXoyXFwBnC+sm9/5MWf4Df+8zfxO3/4bYwcP1/z3FKW8UcT1x1XQiqF2bID2xGrkg4ePX5vsnHiO0EQBEFsdDakuLqUL2G24kKplXWyS6VgcIaqKzBy4kKNwKpfxm9yhrjJ8fSxs3j88KmGgiWauO4JCd3lZCiUdBjqSqeD07gXgiAIgliYDdkWFELnHHDOYBpYsZwrxqDDOBkgJHDslYs1bcKg5dfOAODRsTzGLhUglYJp8jBNnTPAE7q9WfArWp/9/IkV8WDRuBeCIAiCWJgNKa5Mg6HqKkjFVjZA1B9pw5gWQbbjzTOh7xvux7FXLqLqerBMA5l0DMm4WTMDMBBfzB/8J4RuCSoFKD/VfaZYRaHsoC8dbyrQlku9TwxY3LgXMuATBEEQG4EN2RbcMZRGJh1f8baggp9YpXTlijOEnqVUwsS1qQpGTlxAueqBMy30Jgs2KlWvpiIUtOP6e+N+srtOcTc4A+e6Pee4En3pODLp2Iq17JYz7iXq14qKP/JrEQRBEDcbG1JcPXBgGKbBIFYhPbTmCIzVeJYqVQ8MrKbFF3ioohWhYABzMm5iMJOAaTAopcAA/PbH3oY/+tfvQyphojdl1Ry70y27qE+sWFncuBfyaxEEQRAbhQ3ZFgzEwB/97eiqHI8xoCdpolTxasbZOJ6E9AWeJxQMrsffuHUjbaLtuGTc1G1DV6A/HQvfy3Jbdu0S+MQ2b+7FjRuzbb+O/FoEQRDERmFDiqvVImZySKWwc3PP/9/e/QfJcdZ3Hn939+zsaH9od6WVheVYBiTrkQMIx2dOQAViV2JHmJhzOCcG5ADhxxGTg8txxH+QFHFchUMcQgEOAR/EhwHjS8wRkYCj2FyV6+IQhB1CFOrwV0YE1rGw0Ur2rvbH7M5Md/7onmV2NLua2e2Z3Z3+vKpU2u19uufZdmv8nef5Pt8nmUJjYTub2bkyURJYecQBWCWE0IvYlM9x8Ko9AA1rWlUrte/dOcztX4g3oi70+MwUywuvu9yeg2uhU8GfiIjIWstkcFXN/2m3OJDymZiaY7pYJufDqWeLDPT1MFMsxdOBURxY5QKfShjh+x7/5TU/DbCwgnBksJdJf56p2RKVSsiO0X727hzm77/z1MIKw/lyCFFEzveYKZabThjvVJL5gf07uefBY2dt5bNegj8REZG0ZDK4qub/+L63MC3XLr4HZ2ZK4MF8OX6tyel5wiginwso5H3mSiHlSkguiHOR9u0a5fYvfGshRwlgaKCXQm9uoYJ7/c+rfw9s6uHWt+5vqm/NlIBIS/V6Wi0oIiLdLpPBVZznE7V9taDnwdahTfzo1DQeSTAXQRSF5HM+I5t7F02TVfOoqn1cLkcpjRym2iRziAO02hIQaavfykdERKQbZXK1YKHH5/TkHG2OrdiUz/H06Rni0gnxseqKwHIlOquswcxsianZEjd/4uvMFMsLlderanOURocKZ2063WoOU3UVYi0lmYuIiKxOJkeu8OIRpHZ78a4tPGrjeN7iF/M88PA4eNWehWmyKAyZLpaZKpbpCXxygcfkTBxcbe7Pn5Wj1GoOU6PcKiWZi4iIpC+TI1fF+UpSjLN9POCZqXm2jxSIIpKK6tFCZfXtIwX27Rrl5jdcxo1X72FyJl7pF2+VE1IsVdiUzzFfCpkpLq4pVQ2U5uYrTE7N88yZuWVrTi1VwHPvzuEVFwUVERGRxjI5cjU6VGByep5SOaRdA1hbh3oZnyhy49V7uOur36U4X6ESxhs5BwHgeQt7AE7NlgijkMCPY12POBgrVyIG+3q4/aaXL1y3Ngl9eLB30YjVUvlMS+VWPTb27KLRMyWZi4iIrF4mg6sD+3dy1/2PUarM047oyvdgdq5CFMHnHzjG5v48m/siiqWQQo/P5GyZchgtjCL9+PTMQrJ7NTfLA0qVs6foVpKEvlzyu5LMRURE0pXJacF9u0Z5yzV7uWC0vy3XDyOYLpaBOIAqhxFz5ZAbr97DQF+evkJu0TYwucBPYrxoIRcsjCDwvbOm6FaShJ5G8ruIiIg0J3MjV0ePj/PFh44vrOLzaMvgFRAHMNUAqjq61GgUaXN/D6cn59g80MtMsUS5EuJ7Pq9+6dlTdCtJQlcBTxERkc7JVHB19Pg4d93/GNOz84CH50VtC6wg3i+wqjq61Cg4yuUCdmztY6Avz/iEt2zu00oCJRXwFBER6ZxMBVeHj4xRnCvjeT6+B6W6qbJ2qo4uLRUcvW6JlX71VhooKbdKRESkMzIVXI1PFKmE8aq8Sti+lYK1oihquKJvNaNICpRERETWr0wFV9USDGFER4qI5nN+w02UFRyJiIh0r0wFV9USDNOz823f+sYD3vnLL1QQJSIikjGZCq6qJRi++NBx/u3kdFtfK9/jtxRYNdqeRoGZiIjIxpO5Olf7do1y/RW72JRv36/u+5CvWQ14LkttT3P0+Hjb+igiIiLtkbngCuJk8kqbFgrmAo/NffmWCpTWVl2v1sUKAp/DR8ba00kRERFpm0xNC1bFqwbTTbryvDhhPgj8lgt0Lrc9zWppulFERKSzMhlcjQ4VeObM6gOXWjnfI4pguD9/zgCmPuAp5APmy2FLVdebUbvJc+10I6AAS0REpE0yOS04MpBPdVown/PZPrKJ0aEC4xNFDh8ZWzJfqlF+1eT0PDPFMnOlClEUMVeqpLI9jaYbRUREOi9zI1dHj4/z6LF0E8XL5ZDJ2TLliHOOENUGPMDC3zmPZPub9Kbv2jndKCIiIo1lLrg6fGQs9W1vQqBSCXnmzBzlckgu57MpH3D4yNhZAdJSAc9Mscytb7gs1X6tZJNnERERWZ3MTQumOWrjJX8ApotlKpUQ34sDrTOzJZ4cP7uW1uhQgfm64K5dAc+B/TupVMLUpxtFRERkaZkLrtIMYqLkT1W5ElGurkKMoFI5e0ViJwOefbtGOXjVHob788wUywz35znY5AbRIiIisjKZmxY8sH8nj40927brR1EcZHleXPOqXhobN7dC+xiKiIh0VuaCq3YFGl4SR0VRPFXo+x47ligkqoBHRESke2UuuEprSxnPY9Hmz77vLeRgRUAYRsptEhERyaDM5VylVeMpin4yWuV5sGWwlyDwCSPwPY8dW/s0OiUiIpJBmRu5Gp8o0hN4lBokm7eqOnIVRVAqh2wf2cR8OaRSCbn+yt2rvr6IiIhsPJkLrgo9PuMpBFb1pmfLVMKIC0b7tX+fiIhIhmUquDp6fJxTZ+ZWdY3q+r/a8Mz3PbYOFxjuz3NzyoVARUREZGPJVHB1+MhYw9pTzcr5MNjfyzNJgOZ5cX5VT87XtjKyYtWNvE9PzbNl4Nwbf4uIyPqWqeBqfKJIJVx5cFUOoRJG5HM+YRRRXRu4uT+vbWVkRaobeQeBz+Cm5felFBGRjSFTqwVHhwoE/tmFPZuVz/ls3pTjvOECYRjhezAy2Ivve9pWRlakdiNvz/Po7QkIAj+1Va0iItJ5mQquDuzfSSEfnLthAx4wNJCnWAq59W0v5d3X7+P5OzYTRWhbGVmx8Yki+dzif4aaYhYR2dgyFVzt2zXKW159yYrO3dzfQxD4FPIBt3/hW3z+gWNMzZbi1YcTRQ4fGUutQKlkRyc38hYRkc7IVHAFcYC1kolB3/eZKZY5NVnk+ycmOTVZ5N9OTnPi1AwQLeTKKMCSVnRyI28REemMzAVXAP2bWs/jn5uv0BN4zJcq1ObEhxFMTJeUKyMrsm/XKAev2sNwf56p2bKmmEVEukBTUYZzbg9wN7AVOAW80cwer2sTAB8DDhCXgfqgmX063e6u3tHj4/h+azFlT+DRV8gxMTUHePgelKOf7CNYSqZ1lCsjK1HdyHvbtkFOnjyz1t0REZFVajbK+CTwcTPbA3wcuLNBm4PAbuBi4GXALc6556bRyTQdPjJGGIbnblijHEZJDoy3aD/B+qIOypURERGRcwZXzrnzgMuAe5ND9wKXOee21TW9AfiUmYVmdhI4BPxKmp1Nw9jTZ5iaLbd0ThTB3p3DbB8pEEUQRnEZhqqc7ylXRkRERIDmpgUvBJ40swqAmVWccyeS4ydr2u0Efljz/VjSpmlbtw600rwl27YNAjA7X2n53IG+Ho4/NcVbr9vHR//3t5idK1MJIRd4hFFIf18P20b6eO0Vu7n8ku1pd33dqt5TSY/uafp0T9One5o+3dP0reU9XVcV2k+dmiJcRQX1pdTmskQtXr4n57NlsJcfnZziotE+3vyqvRw+Msb4RJHRocJZW5VkJWdG+UHp0z1Nn+5p+nRP06d7mr5231Pf95YdEGomuHoCuMA5FySjVgGwIzleawy4CHgk+b5+JGvD8X0YHoi3tqnWt6oGVTderRVdIiIicrZz5lyZ2Y+BbwOvTw69HvinJK+q1n3A251zfpKPdR3wxTQ7m4Zc0FyVq1zgsWWwlyCI61tNTs/z7PQ8fYWcalqJiIjIkppdLfgbwLucc8eAdyXf45y73zl3edLmc8D3gceBbwC3mtm/ptzfVWt2a8HnbOkDPIb782zelKNvU4/2fxMREZFzairnysweA/Y3OH5NzdcV4Kb0utYepXJzSVcDm3q49a3xr3zzJ76O58HTZ+YoV0Jygc9gX49qWomIiMhZMlehvZnQyvNYFDgV8gGnJ4uUKxG+51GuRJyeLK54E2gRERHpXutqteB6sqgYaBRRrcf+k9WGXutLD0VERKTrZW7kqhm5wF9UDLRYChkZzBMEPmEEQeAzMpinWGqt0ruIiIh0v0yNXDW7uu/VL11cu2p0qMCz0/M8Z0t+4dhcqcJwf77R6Svu23L1s0RERGRjyNTIVbOr+557/uZF3x/Yv5NKJWSuVCGKotS3ujl6fJx7HjymUg8iIiJdIFMjVyfGp5tq95H7jhL4Hudv7eP6K3YtjCC1a2Tp8JExgsCntydOkO/tCZhLjmv0SkREZGPJRHD16Hef5s8feIwzM6Wmz6mEEU+OT3PX/Y/xlmv2sm/XaNsCnfGJIn2Fxf8p8jlfpR5EREQ2oK6fFjx6fJw7v3SUZ6fnWz85guJcue3FQkeHCsyXFyfHz5fDxSsWRUREZEPo+uDq8JExcrm4qnou1/yvGxdegEoYtn0Eqd05XSIiItI5XT8tOD5RZGggT7nSWk2qamWrwPfbPoLU7pwuERER6ZyuD65GhwpMFUsEvk/UatFPDwq9uY6MILUzp0tEREQ6p+unBQ/s30m5HE+1hWHzwVXge1ww2r+QzC4iIiLSjK4fudq3a5ShoT7+7NBRmo2tRocK3H7Ty9vbMREREelKXT9yBXD5Jdvj3ZibVOjJxG0RERGRNshMFPH06Zmm2g325bRnoIiIiKxY108L/kS1uMLStg0XCAI/1T0DRUREJFsyM3K1fWT5cgo53yMIfNWXEhERkVXJTHB1/ZW7l/15JYrIeXDwqj1aHSgiIiIrlpngat+uUXqXSFT3PNi+pY+BvrwCKxEREVmVzARXEFdb95dYNKiNkkVERCQNmQmujh4fjwuJNshpjyI4M1PSRskiIiKyapkIrh797tPc8+AxgqWGrYCpmZIS2UVERGTVMhFcfemh78UlFgZ7G/68J/Ao9AbKtxIREZFVy0Sdq6dPz1Do8fE8j56cT7kcLlS8Um0rERERSVMmRq62b+ljvhxXXR8Z7CUIPALfo7cnUG0rERERSVUmgqvXXrGbSiVkrlShkA8Y7Mvjex75nnjESrWtREREJC2ZmBa8/JLtHLxqD4ePjDE+UWT7yCbedMApoBIREZHUZSK4griIqIIpERERabdMTAuKiIiIdIqCKxEREZEUKbgSERERSZGCKxEREZEUKbgSERERSZGCKxEREZEUKbgSERERSZGCKxEREZEUKbgSERERSZGCKxEREZEUKbgSERERSZGCKxEREZEUKbgSERERSZGCKxEREZEUKbgSERERSZGCKxEREZEUKbgSERERSZGCKxEREZEU5da6A4kAwPe9tr1AO6+dVbqn6dM9TZ/uafp0T9One5q+DsUUQaOfe1EUte3FW/CzwN+tdSdEREREWvAK4OH6g+sluOoFXgL8CKiscV9ERERElhMA5wOPAHP1P1wvwZWIiIhIV1BCu4iIiEiKFFyJiIiIpEjBlYiIiEiKFFyJiIiIpEjBlYiIiEiKFFyJiIiIpEjBlYiIiEiK1sv2N6vmnNsD3A1sBU4BbzSzx+vaBMDHgANABHzQzD7d6b5uFE3e01uAdwInkkN/b2a/2cl+biTOuQ8B/xl4LvAiM/tOgzZ6TlvQ5D29BT2nTXHObQU+B+wC5oHHgXeY2cm6dn3A/wL+A1AG3mtmX+lwdzeEFu7pZ4BfAMaTQ/eZ2Qc62NUNxTl3CHgeEAJTwLvM7Nt1bdbk/bSbRq4+CXzczPYAHwfubNDmILAbuBh4GXCLc+65HevhxtPMPQX4rJldmvzR/7CWdwh4JfDDZdroOW1NM/cU9Jw2KwJuNzNnZi8CjgMfbNDuvcCkme0GrgU+7Zwb6GA/N5Jm7ynE//OvPqcKrJb3JjN7sZn9DPAh4K4Gbdbk/bQrgivn3HnAZcC9yaF7gcucc9vqmt4AfMrMwuQTwyHgVzrX042jhXsqLTCzh83siXM003PagibvqTTJzE6b2UM1h74BXNSg6Q0kH7iSEe1HgVe1vYMbUAv3VFpgZhM13w4Rj2DVW5P3064IroALgSfNrAKQ/H0iOV5rJ4s/3Y41aCOxZu8pwOucc0edcw84517WyU52KT2n7aHntEXOOR+4CfirBj/Wc7oC57inAO9xzv2Lc+6Qc+6SDnZtQ3LOfdo5NwZ8AHhTgyZr8px2S3Ala+eTwPPMbB/wR8CXk/wCkfVEz+nK3EGcy/Ina92RLrLcPf0dYHcydfgl4HCSMyRLMLO3mdlO4H3E/7bXhW4Jrp4ALqg+hMnfO5LjtcZYPBS7s0EbiTV1T83sKTMrJV8/mPz8hR3ua7fRc5oyPaetSxYKXAzcYGaNplv0nLboXPfUzJ6sHjezzwIDwE91tpcbk5l9DriywYemNXlOuyK4MrMfA98GXp8cej3wT/UrMYD7gLc75/wkd+g64Iud6+nG0ew9dc5dUPP1pcQrtqxD3exWek5Tpue0Nc6524hXAV5nZnNLNLsPeEfS/mLgJcDhzvRw42nmntY9p78IVIAnO9PDjcU5N+Ccu7Dm+2uB08mfWmvyfupFUdTu1+gI59xe4rIBI8AzxGUDzDl3P/B+M3s0GX35E+Dq5LQ/NLP/uTY9Xv+avKd3E79hVIiXGP+emd2/Zp1e55xzHwNeCzyHeLn1KTN7gZ7TlWvynuo5bZJz7gXAd4BjwGxy+F/N7Jedc98GrjGzE865fuAzwM8Q39ebzezLa9Hn9a6Fe/o1YDtxYvYk8Ntm9o016fQ655zbDnwZ6Cd+/k4TlwP51np4P+2a4EpERERkPeiKaUERERGR9ULBlYiIiEiKFFyJiIiIpEjBlYiIiEiKumbjZhEREZGlNLPJe137lwN/WnPoPOApM7vsXK+l4EpERESy4BDwUeDvmmlsZl8HLq1+75w7BDzczLkKrkSk45xzEXCxmX1vrfvSTs659wHPN7O3rXVfRLLOzB4GcM4tOu6c2w98ENicHHq/mX21rs15xLWy3tHMaym4EpEVcc4dBr5pa1Vw8QAABHJJREFUZu+vO/6fgDuBnzKz8pp0bp0ws9vWug8isjTn3DDx3qPXmNmPnHPnA484515oZs/WNH0j8ICZPd3MdZXQLiIrdTdwo3POqzv+a8A97QisnHP6QCgiaXo58Dzgb5Jq+X8DRMDuuna/DtzV7EX1RiUiK3WI+BPfK4D/B+CcGwF+CfhF59w/AJcQb/fxf4D3mNl8/UWcc0PAHcCrgBngU8BtZhY6594MvB34JvEnx08Av9uoM865Xcm5LyZ+c/xb4Dernz6dcz8g3gbjjcQbuR4G3mRmxeR13mZmP1tzvYWpS+fcZ4Bp4kTYVwL/H3iDmR1P2r4A+AjxFjsl4KNmdptz7hZgt5ndmLR7DfAHwAXEe3feZGbfPdeNFpG28YCjZvbKpRo4514KbAGa3jJLI1cisiJmNgv8BXGwUvWrwGPAFPDfgVHgZcDPA+9c4lJ3AEPA84GfS6736zU/3w98n3jPtQ8s0yWPOHDZQRzUXQjcUtfmV4EDxJ9U9wFvXuZ69V4H/D7xXpvfq/bFOTcIfI04WNtB/In3/9af7JzbA9wL/BawjfiN+q+dc/kW+iAi6fo6cLFz7srqAefcS+pG5N8CfK6V0XiNXInIatwNfMU591/NrEgcGN1tZv9Y0+YHzrk7iQOnj9SenGyq+jrgUjM7A5xxzv0x8dTinyXNTpjZHcnXS765Jcnx1QT5k865DwO/V9fsY2Z2Inntv6ZmJVAT/tLMvpmcew/w4eT4LxEvz/7j5PsicKTB+TcAXzWzB5NrfAj4b8TTEg+10A8RWYG6Td6/5pyrbvL+GuCPnHMfAfLEH+auBSLn3Cbif7v7W3ktBVcismJm9rBzbhy4zjn3CPAfgdcmozQfBi4H+ojfa/6xwSVGgR7ghzXHfkg8bVb1RDN9cc5tJ15m/QpgkHhk/pm6Zk/VfD1DPNLUrPpzB5KvLwSON3H+Dmp+z2Ta8wkW/64i0iZm9m7g3Q2OPwJcscQ5s8Qj6y3RtKCIrNZniUesbgT+NllN8wni6cGLzWwz8D7iabt648Q5ShfVHNsJPFnzfdRkP25L2r4oec0bl3jNRqaJg0AAnHPPafI8iIO/5zfR7gQ1v2cy7XAhi39XEekCCq5EZLU+C/wCceL53cmxQWASmHLO7QVuanSimVWI87Y+4JwbdM5dBLwH+PwK+jFInOs14Zy7APjtFs79Z+AFzrlLnXMFzs7VWs5XgPOdc7/lnOtNfo9GUwh/AbzaOffzzrke4H8Ac8Q5HyLSRRRciciqmNkPiAOEfuCvksPvBd4AnCFewffny1ziXcQjR98nrn78BVpY8lzj94HLgAngq8CXmj3RzI4BtxInpj9Ok1WYk3PPAFcR52g8lZx/ZYN2RjyadgfxiN21wLWNVlCKyMbmRVGzI+4iIiIici4auRIRERFJkVYLisiG4Zz7JPHUWr3Pm9lvdLo/IiKNaFpQREREJEWaFhQRERFJkYIrERERkRQpuBIRERFJkYIrERERkRQpuBIRERFJ0b8DkrdAvFjEqdEAAAAASUVORK5CYII=\n"
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    }
  ]
}