Clusterização_dos_dados.ipynb

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    "colab": {
      "name": "Clusterização dos dados.ipynb",
      "provenance": [],
      "mount_file_id": "1cDsiKvg6d8Vjbuej4_TxiukP148GYztI",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/viniciusfranca019/group-project-codenation/blob/main/Clusteriza%C3%A7%C3%A3o_dos_dados.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Código de clusterização implementado conforme modelo disponivel no link abaixo:\n",
        "\n",
        "[Find and Visualize clusters with K-Means](https://app.datacamp.com/workspace/w/2315d3ec-481c-4599-bc71-d2f32a961cbb/edit)"
      ],
      "metadata": {
        "id": "t0ITwjXaokVZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Clusterização dos dados do mercado"
      ],
      "metadata": {
        "id": "dUzYzPNbdwMv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "X89aeS5FcdO7"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "sns.set_style('darkgrid')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Drive_projeto_Codenation/OHE_df.csv').drop(columns=['Unnamed: 0'])\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "3l_ovic0d7kZ",
        "outputId": "fd19ec93-fa24-477b-8924-1c07af8937cb"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1f9bcabc9d3173c1fe769899e4fac14b053037b953a1e4...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>210000.0</td>\n",
              "      <td>210000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 95 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8607daa-9672-4b05-85df-8e1394fb3b80')\"\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",
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              "    <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-c8607daa-9672-4b05-85df-8e1394fb3b80 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-c8607daa-9672-4b05-85df-8e1394fb3b80');\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",
              "  "
            ],
            "text/plain": [
              "                                                  id  ...  qt_filiais\n",
              "0  a6984c3ae395090e3bee8ad63c3758b110de096d5d8195...  ...           0\n",
              "1  6178f41ade1365e44bc2c46654c2c8c0eaae27dcb476c4...  ...           0\n",
              "2  4a7e5069a397f12fdd7fd57111d6dc5d3ba558958efc02...  ...           0\n",
              "3  3348900fe63216a439d2e5238c79ddd46ede454df7b9d8...  ...           0\n",
              "4  1f9bcabc9d3173c1fe769899e4fac14b053037b953a1e4...  ...           0\n",
              "\n",
              "[5 rows x 95 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# g = sns.PairGrid(df)\n",
        "# g.map(sns.scatterplot);\n",
        "keep_ids_slice = list(df['id'])\n",
        "columns_array = df.columns\n",
        "df_without_id = df.set_index('id')"
      ],
      "metadata": {
        "id": "Ja7v2MdneFQT"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_without_id.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "2bZqs9lZtu9q",
        "outputId": "af05ff7f-dca7-4907-eb7f-f489f65f514c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sg_uf_AC</th>\n",
              "      <th>sg_uf_AM</th>\n",
              "      <th>sg_uf_MA</th>\n",
              "      <th>sg_uf_PI</th>\n",
              "      <th>sg_uf_RN</th>\n",
              "      <th>sg_uf_RO</th>\n",
              "      <th>natureza_juridica_macro_ADMINISTRACAO PUBLICA</th>\n",
              "      <th>natureza_juridica_macro_CARGO POLITICO</th>\n",
              "      <th>natureza_juridica_macro_ENTIDADES EMPRESARIAIS</th>\n",
              "      <th>natureza_juridica_macro_ENTIDADES SEM FINS LUCRATIVOS</th>\n",
              "      <th>natureza_juridica_macro_INSTITUICOES EXTRATERRITORIAIS</th>\n",
              "      <th>natureza_juridica_macro_OUTROS</th>\n",
              "      <th>natureza_juridica_macro_PESSOAS FISICAS</th>\n",
              "      <th>fl_rm_NAO</th>\n",
              "      <th>fl_rm_SIM</th>\n",
              "      <th>nm_segmento_ADMINISTRACAO PUBLICA DEFESA E SEGURIDADE SOCIAL</th>\n",
              "      <th>nm_segmento_AGRICULTURA PECUARIA PRODUCAO FLORESTAL PESCA E AQUICULTURA</th>\n",
              "      <th>nm_segmento_AGUA ESGOTO ATIVIDADES DE GESTAO DE RESIDUOS E DESCONTAMINACAO</th>\n",
              "      <th>nm_segmento_ALOJAMENTO E ALIMENTACAO</th>\n",
              "      <th>nm_segmento_ARTES CULTURA ESPORTE E RECREACAO</th>\n",
              "      <th>nm_segmento_ATIVIDADES ADMINISTRATIVAS E SERVICOS COMPLEMENTARES</th>\n",
              "      <th>nm_segmento_ATIVIDADES FINANCEIRAS DE SEGUROS E SERVICOS RELACIONADOS</th>\n",
              "      <th>nm_segmento_ATIVIDADES IMOBILIARIAS</th>\n",
              "      <th>nm_segmento_ATIVIDADES PROFISSIONAIS CIENTIFICAS E TECNICAS</th>\n",
              "      <th>nm_segmento_COMERCIO; REPARACAO DE VEICULOS AUTOMOTORES E MOTOCICLETAS</th>\n",
              "      <th>nm_segmento_CONSTRUCAO</th>\n",
              "      <th>nm_segmento_EDUCACAO</th>\n",
              "      <th>nm_segmento_ELETRICIDADE E GAS</th>\n",
              "      <th>nm_segmento_INDUSTRIAS DE TRANSFORMACAO</th>\n",
              "      <th>nm_segmento_INDUSTRIAS EXTRATIVAS</th>\n",
              "      <th>nm_segmento_INFORMACAO E COMUNICACAO</th>\n",
              "      <th>nm_segmento_ORGANISMOS INTERNACIONAIS E OUTRAS INSTITUICOES EXTRATERRITORIAIS</th>\n",
              "      <th>nm_segmento_OUTRAS ATIVIDADES DE SERVICOS</th>\n",
              "      <th>nm_segmento_SAUDE HUMANA E SERVICOS SOCIAIS</th>\n",
              "      <th>nm_segmento_SERVICOS DOMESTICOS</th>\n",
              "      <th>nm_segmento_TRANSPORTE ARMAZENAGEM E CORREIO</th>\n",
              "      <th>fl_spa_False</th>\n",
              "      <th>fl_spa_True</th>\n",
              "      <th>fl_antt_False</th>\n",
              "      <th>fl_antt_True</th>\n",
              "      <th>...</th>\n",
              "      <th>sg_uf_matriz_AP</th>\n",
              "      <th>sg_uf_matriz_BA</th>\n",
              "      <th>sg_uf_matriz_CE</th>\n",
              "      <th>sg_uf_matriz_DF</th>\n",
              "      <th>sg_uf_matriz_ES</th>\n",
              "      <th>sg_uf_matriz_GO</th>\n",
              "      <th>sg_uf_matriz_MA</th>\n",
              "      <th>sg_uf_matriz_MG</th>\n",
              "      <th>sg_uf_matriz_MS</th>\n",
              "      <th>sg_uf_matriz_MT</th>\n",
              "      <th>sg_uf_matriz_PA</th>\n",
              "      <th>sg_uf_matriz_PB</th>\n",
              "      <th>sg_uf_matriz_PE</th>\n",
              "      <th>sg_uf_matriz_PI</th>\n",
              "      <th>sg_uf_matriz_PR</th>\n",
              "      <th>sg_uf_matriz_RJ</th>\n",
              "      <th>sg_uf_matriz_RN</th>\n",
              "      <th>sg_uf_matriz_RO</th>\n",
              "      <th>sg_uf_matriz_RR</th>\n",
              "      <th>sg_uf_matriz_RS</th>\n",
              "      <th>sg_uf_matriz_SC</th>\n",
              "      <th>sg_uf_matriz_SE</th>\n",
              "      <th>sg_uf_matriz_SP</th>\n",
              "      <th>sg_uf_matriz_TO</th>\n",
              "      <th>de_saude_tributaria_AMARELO</th>\n",
              "      <th>de_saude_tributaria_AZUL</th>\n",
              "      <th>de_saude_tributaria_CINZA</th>\n",
              "      <th>de_saude_tributaria_LARANJA</th>\n",
              "      <th>de_saude_tributaria_VERDE</th>\n",
              "      <th>de_saude_tributaria_VERMELHO</th>\n",
              "      <th>de_nivel_atividade_ALTA</th>\n",
              "      <th>de_nivel_atividade_BAIXA</th>\n",
              "      <th>de_nivel_atividade_MEDIA</th>\n",
              "      <th>de_nivel_atividade_MUITO BAIXA</th>\n",
              "      <th>idade_empresa_anos</th>\n",
              "      <th>vl_total_veiculos_pesados_grupo</th>\n",
              "      <th>vl_total_veiculos_leves_grupo</th>\n",
              "      <th>vl_faturamento_estimado_aux</th>\n",
              "      <th>vl_faturamento_estimado_grupo_aux</th>\n",
              "      <th>qt_filiais</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.00000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>432292.000000</td>\n",
              "      <td>4.322920e+05</td>\n",
              "      <td>4.322920e+05</td>\n",
              "      <td>432292.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.044296</td>\n",
              "      <td>0.176751</td>\n",
              "      <td>0.277995</td>\n",
              "      <td>0.166415</td>\n",
              "      <td>0.212854</td>\n",
              "      <td>0.121689</td>\n",
              "      <td>0.006274</td>\n",
              "      <td>0.000028</td>\n",
              "      <td>0.222977</td>\n",
              "      <td>0.084406</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.683742</td>\n",
              "      <td>0.002568</td>\n",
              "      <td>0.515390</td>\n",
              "      <td>0.484610</td>\n",
              "      <td>0.005947</td>\n",
              "      <td>0.007169</td>\n",
              "      <td>0.002720</td>\n",
              "      <td>0.064160</td>\n",
              "      <td>0.012054</td>\n",
              "      <td>0.045997</td>\n",
              "      <td>0.007331</td>\n",
              "      <td>0.005915</td>\n",
              "      <td>0.036496</td>\n",
              "      <td>0.463360</td>\n",
              "      <td>0.054838</td>\n",
              "      <td>0.024335</td>\n",
              "      <td>0.001353</td>\n",
              "      <td>0.069495</td>\n",
              "      <td>0.002043</td>\n",
              "      <td>0.011737</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.128286</td>\n",
              "      <td>0.022129</td>\n",
              "      <td>0.001312</td>\n",
              "      <td>0.033302</td>\n",
              "      <td>0.999352</td>\n",
              "      <td>0.000648</td>\n",
              "      <td>0.992424</td>\n",
              "      <td>0.007576</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.000544</td>\n",
              "      <td>0.002103</td>\n",
              "      <td>0.002822</td>\n",
              "      <td>0.000224</td>\n",
              "      <td>0.000444</td>\n",
              "      <td>0.272836</td>\n",
              "      <td>0.000703</td>\n",
              "      <td>0.000116</td>\n",
              "      <td>0.000581</td>\n",
              "      <td>0.000937</td>\n",
              "      <td>0.000474</td>\n",
              "      <td>0.001032</td>\n",
              "      <td>0.163743</td>\n",
              "      <td>0.000682</td>\n",
              "      <td>0.001515</td>\n",
              "      <td>0.208556</td>\n",
              "      <td>0.118982</td>\n",
              "      <td>0.000120</td>\n",
              "      <td>0.000294</td>\n",
              "      <td>0.000280</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.006442</td>\n",
              "      <td>0.000150</td>\n",
              "      <td>0.209537</td>\n",
              "      <td>0.243227</td>\n",
              "      <td>0.116488</td>\n",
              "      <td>0.083534</td>\n",
              "      <td>0.334249</td>\n",
              "      <td>0.012966</td>\n",
              "      <td>0.34194</td>\n",
              "      <td>0.166103</td>\n",
              "      <td>0.487427</td>\n",
              "      <td>0.004529</td>\n",
              "      <td>10.449355</td>\n",
              "      <td>3.808886</td>\n",
              "      <td>51.682513</td>\n",
              "      <td>8.056713e+05</td>\n",
              "      <td>3.373478e+08</td>\n",
              "      <td>30.374303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.205753</td>\n",
              "      <td>0.381458</td>\n",
              "      <td>0.448011</td>\n",
              "      <td>0.372453</td>\n",
              "      <td>0.409326</td>\n",
              "      <td>0.326926</td>\n",
              "      <td>0.078957</td>\n",
              "      <td>0.005269</td>\n",
              "      <td>0.416243</td>\n",
              "      <td>0.277996</td>\n",
              "      <td>0.002634</td>\n",
              "      <td>0.465016</td>\n",
              "      <td>0.050608</td>\n",
              "      <td>0.499764</td>\n",
              "      <td>0.499764</td>\n",
              "      <td>0.076890</td>\n",
              "      <td>0.084365</td>\n",
              "      <td>0.052086</td>\n",
              "      <td>0.245039</td>\n",
              "      <td>0.109129</td>\n",
              "      <td>0.209478</td>\n",
              "      <td>0.085305</td>\n",
              "      <td>0.076681</td>\n",
              "      <td>0.187521</td>\n",
              "      <td>0.498656</td>\n",
              "      <td>0.227664</td>\n",
              "      <td>0.154088</td>\n",
              "      <td>0.036762</td>\n",
              "      <td>0.254294</td>\n",
              "      <td>0.045149</td>\n",
              "      <td>0.107702</td>\n",
              "      <td>0.004563</td>\n",
              "      <td>0.334408</td>\n",
              "      <td>0.147102</td>\n",
              "      <td>0.036192</td>\n",
              "      <td>0.179423</td>\n",
              "      <td>0.025442</td>\n",
              "      <td>0.025442</td>\n",
              "      <td>0.086709</td>\n",
              "      <td>0.086709</td>\n",
              "      <td>...</td>\n",
              "      <td>0.005890</td>\n",
              "      <td>0.023309</td>\n",
              "      <td>0.045808</td>\n",
              "      <td>0.053049</td>\n",
              "      <td>0.014978</td>\n",
              "      <td>0.021070</td>\n",
              "      <td>0.445418</td>\n",
              "      <td>0.026509</td>\n",
              "      <td>0.010754</td>\n",
              "      <td>0.024089</td>\n",
              "      <td>0.030594</td>\n",
              "      <td>0.021771</td>\n",
              "      <td>0.032104</td>\n",
              "      <td>0.370043</td>\n",
              "      <td>0.026114</td>\n",
              "      <td>0.038896</td>\n",
              "      <td>0.406277</td>\n",
              "      <td>0.323768</td>\n",
              "      <td>0.010967</td>\n",
              "      <td>0.017138</td>\n",
              "      <td>0.016728</td>\n",
              "      <td>0.006453</td>\n",
              "      <td>0.080006</td>\n",
              "      <td>0.012261</td>\n",
              "      <td>0.406978</td>\n",
              "      <td>0.429031</td>\n",
              "      <td>0.320810</td>\n",
              "      <td>0.276688</td>\n",
              "      <td>0.471728</td>\n",
              "      <td>0.113127</td>\n",
              "      <td>0.47436</td>\n",
              "      <td>0.372174</td>\n",
              "      <td>0.499842</td>\n",
              "      <td>0.067148</td>\n",
              "      <td>9.489797</td>\n",
              "      <td>74.816391</td>\n",
              "      <td>1244.711539</td>\n",
              "      <td>3.108901e+07</td>\n",
              "      <td>7.126433e+09</td>\n",
              "      <td>482.115549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.246575</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.104703e+04</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.315068</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.648512e+05</td>\n",
              "      <td>1.854576e+05</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.178082</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.100000e+05</td>\n",
              "      <td>2.100000e+05</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.147945</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.100000e+05</td>\n",
              "      <td>2.100000e+05</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>106.432877</td>\n",
              "      <td>9782.000000</td>\n",
              "      <td>122090.000000</td>\n",
              "      <td>1.454662e+10</td>\n",
              "      <td>2.227618e+11</td>\n",
              "      <td>9647.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 85 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cff62542-2180-404e-88d0-d77c3fefe6fa')\"\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-cff62542-2180-404e-88d0-d77c3fefe6fa 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-cff62542-2180-404e-88d0-d77c3fefe6fa');\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",
              "  "
            ],
            "text/plain": [
              "            sg_uf_AC  ...     qt_filiais\n",
              "count  432292.000000  ...  432292.000000\n",
              "mean        0.044296  ...      30.374303\n",
              "std         0.205753  ...     482.115549\n",
              "min         0.000000  ...       0.000000\n",
              "25%         0.000000  ...       0.000000\n",
              "50%         0.000000  ...       0.000000\n",
              "75%         0.000000  ...       0.000000\n",
              "max         1.000000  ...    9647.000000\n",
              "\n",
              "[8 rows x 85 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_without_id.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MhblOLIuDi2",
        "outputId": "177ccbbc-6bdf-4bee-9cb4-d42cb36d982d"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(432292, 94)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "df_without_id = scaler.fit_transform(df_without_id)"
      ],
      "metadata": {
        "id": "Gi_oPGZYl1Ka"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normalized_data = pd.DataFrame(df_without_id)"
      ],
      "metadata": {
        "id": "Md2ylUOFsgse"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(normalized_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "955HgJ8Mx8us",
        "outputId": "24e9965c-a5d0-4d3e-bbc5-ed85ed86821c"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yellowbrick"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjxJVQQ5l4Sj",
        "outputId": "4bacfbba-daeb-4672-a85c-09719ce5ec82"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: yellowbrick in /usr/local/lib/python3.7/dist-packages (1.4)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from yellowbrick) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from yellowbrick) (1.21.5)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from yellowbrick) (1.4.1)\n",
            "Requirement already satisfied: cycler>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from yellowbrick) (0.11.0)\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from yellowbrick) (1.0.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (3.0.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0.0->yellowbrick) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0.0->yellowbrick) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.cluster import KElbowVisualizer\n",
        "\n",
        "model = KMeans()\n",
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "visualizer = KElbowVisualizer(model)\n",
        "visualizer.fit(normalized_data)    \n",
        "\n",
        "\n",
        "visualizer.show()"
      ],
      "metadata": {
        "id": "zInPw_9aeLLT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "outputId": "b0107397-71b4-4c50-eac6-093c6d0d6ed7"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WloYdO3agW7duAIC77roL7777LtLT0yGEwMmTJ1FcXIxBgwYhPT0dGzduhNlsxpYtW3DmzBncdtttte7ntttuQ3p6OtavXw+TyQSTyYSkpKTLBv/ciq+//ho5OTkoKSnBihUrMGLECADAqFGjsHbtWqSkpMBoNOKtt95C165d0bRpUwDVPzLXr1+PNm3aQKPRIC4uDmvWrEHTpk0REBBQp33f6HNxLaGhofj888+xe/duLFq0qMZyT09PrF69Gq+//nqNZV27doVWq8XHH38MvV4Pi8WC1NRUJCUlAQB0Oh20Wi20Wi3S0tLwzTff1DnX1q1bkZOTAwDw9fWFJEm1jvTu27cv+vbti8cffxzJyckwm82oqKjAN998g++++67G+pGRkTh06BCysrJQXl6Ojz76yLasoKAA27ZtQ2VlJTQaDTw9PW37DAwMRG5urm3QjkKhwF133YVFixbZ3su5ubnYvXt3ndt4LQkJCVi7di3S0tJQVVWFDz74oF62S9QYOGUP45V0Oh0SExMxe/Zs232XjgoEqnsX4+Pj7TK/HTmXmTNnQqlUQqFQoG3btpg+fTruueeeWtc9d+4cFi5ciKKiIvj4+GDy5Mm2Hx0PP/wwXnvtNSxZsgSPPvooZsyYgSVLlmDhwoXIzc1FZGQkVqxYAY1Gc9UsV27jygmG33rrLSxYsAADBgyAj48PnnzySfTt2/eG2+zj44MdO3bgnXfeQVVVFfz9/TF8+HA8+OCDAIDp06fDaDTigQceQHFxMVq3bo33338fYWFhWLFiBRYtWoSXX34ZLVq0wIoVK65aZHl5eWHlypVYvHgxFi9eDCEEOnTogOeee+6q2V599dXLiqZWrVph7dq1ta47atQoPPDAA8jLy8PQoUPx6KOPAqguYGbPno0nn3wSZWVliI6Oto10B4Do6GgYDAZbb2Lbtm2veY5ebfz9/W/oubieiIgIrF69GlOnToWbmxsmTZp02fIuXbrU+jilUokVK1bgjTfewNChQ2E0GtGqVSvbiOa5c+di/vz5WLlyJSIjIzFixAj8/vvvdcp0/PhxLFq0CBUVFQgMDMQLL7yAZs2a1brusmXLsGLFCjz99NPIz8+Hv7+/rYi8Ur9+/TBixAiMGTMG/v7+eOihh7Bjxw4AgNVqxeeff465c+dCkiRERkbi5ZdfBgD07t0bbdu2Rf/+/SFJEg4cOIA5c+bg/fffx913343i4mKEhoZi8uTJl/X03qxBgwZh2rRpuO+++yBJEh577DGsX7/+mp9hIlchCfH3fApO5sKFC5g5cyY2bdqEiooKJCQkYM+ePVddf9y4cXjppZdqTGFBRERUm7S0NIwaNQrHjx/nfI7k8hrFIWkvLy80bdoUW7duBQDbIbWL0tLSbL0OREREV/PLL7/AaDSitLQUS5YsweDBg1ksEsFJexifeeYZHDx4EMXFxQgMDLRNYPvyyy8jPz8fZrMZI0aMwBNPPAGgetJYg8Fw2cSxREREV5oxYwaOHTsGpVKJ2NhYLFiwwDa4isiVOWXBSEREREQNp1EckiYiIiIi+2HBSERERETX5JRn8paVVcFisV5/xZvk769FcbHObtt3dK7cflduO+Da7WfbXbPtgOu2f/bsxyBJwDvvuO58k/Z+7ZVKBXx8POy2/YbklAWjxWKF2Wy/ghGA3bfv6Fy5/a7cdsC128+2uy5XbH9iYiIUCskl234pV29/XfGQNBERERFdEwtGIiIiIromFoxEREREdE1OeQ4jERER3ZqNG39GUJAXqqo4HTNdH3sYiYiIXJBWq4WXl5fcMchJsIeRiIjIBWVknIdOp4VWGyh3FHICLBiJiIhc0P33T4ZCIWHbtj1yRyEnwEPSRERERHRNLBiJiIiI6JpYMBIRERHRNdntHEaDwYB7770XRqMRFosF8fHxmDVr1mXrZGZm4vnnn0dRURH8/PywZMkShIWF2SsSEREREd0Eu/UwajQarF69Ghs2bMD69euxe/duHDt27LJ13njjDYwbNw4bN27EY489hjfffNNecYiIiIjoJtmtYJQkCVqtFgBgNpthNpshSdJl66SlpaF3794AgN69e2P79u32ikNERESXWLDgNSxdulTuGOQk7DqtjsViwfjx43H+/HlMmTIF3bp1u2x5x44d8fPPP+P+++/HL7/8Ap1Oh+LiYvj7+19zu/7+WnvGBgAEB3vbfR+OzJXb78ptB1y7/Wy763LF9k+cOEbuCA7BFV/7myEJIex+TaCysjI8/vjjmD9/Ptq3b2+7Pzc3FwsXLsSFCxcQExODn3/+GZs2bYKPj881t1dcrIPZbLVb3uBgb+Tnl9tt+47Oldvvym0HXLv9bLtrth1w7fa7ctsB+7dfpVI0SCdXQ2iQibt9fHzQq1cv7N69+7KCMTQ0FO+99x4AQKfT4eeff75usUhERES3bvLkCVCrlfjii2/ljkJOwG7nMBYVFaGsrAwAoNfrsW/fPrRu3brGOlZrdU/hxx9/jAkTJtgrDhEREV0iNzcHWVlZcscgJ2G3Hsa8vDzMmzcPFosFQggkJCRg8ODBePfddxEVFYWhQ4fi4MGDeOuttyBJEmJiYrBgwQJ7xamzJbtToNW64bEera+/MhEREZELsFvB2LFjR6xfv77G/bNnz7b9nZCQgISEBHtFuGFLdqfgzb2nAAA6nQFzBkTKnIiIiIhIfg1yDqMzuLRYBGD7m0UjERFR41KgS0dmeTIsORVQwgtNvKMQpG0pdyyHxoIRNYvFi1g0EhERNS4FunScLtwDANBolKg0lthus2i8OpcvGK9WLF7EopGIiBqjkSPHwNNTI3eMBneh9A8YLBVQQAEN/jcHY2Z5MgvGa3D5gpGIiMgVPfvsXJeah1FvKkNWeQrydGcgIKCUVNCK/xWMVaZSGdM5PpcvGC/2HF6tl3F2n/bsXSQiInJSlcZiXChLQlFVBoQA1Ep3KCQ1NEoP4JIrFnuofeUL6QRcvmAErl005lToIYSocR1sIiIiZ/bmm2/A01ODRx99Wu4o9e7S722DpRKFlRnw0gQg3KcThLDiTOG+Go9p4h3V0DGdCgvGv11ZNM7u0x6/ns3D/x0/j+7hfpjOeRmJiKgR2bx5AxQKqVEVjFZhQYHuLLIrTqJj0GC4qbTwc49AVOgweGmCbUWkBEX1KGno4Knx5ijpOmDBeImLRePFibvvi26JYZ//ivnbjqNTsC96NQuUOSERERFdyWw1ILfiNHLKT8Jo0UMhKVBuyIebSgtJkuDtFnLZ+kHalgjStnSpczhvld0uDeis5gyIxIL4bgCAJj6e+HhsLIQAHlx/ENnlVTKnIyIioouEEDhXfARHs9bifMkxWIQZET6dEB0+jj2G9YwF43X0axGMBUM6I19nwIPrDsJgtsgdiYiIyKVZRfV3sSRJMFh0UEoatPDrgR4R49HCrwc0Kk+ZEzY+PCRdBw/FtMEfOSX4/s8LeHHbcSxJ6C53JCIiIpcihECpIRtZZScgQUJkyFAAQCv/OCgVGigk9oHZEwvGOpAkCUsSuuNUfjm+PJaObmF+mNq9pdyxiIiIblpoaBjUaqXcMa7LKqworExHdvkJ6IwlAABf91BYhQUKSQm10l3mhK6BBWMdeapV+Gx8HOI//xXP/5KEyGAf9GwSIHcsIiKim/LNN987/KCPckM+ThfuhsFcCUkCgjxbINynE7w0HITa0Nh/ewOa+2mxYmwszFYrZqw7iLwKvdyRiIiIGhWjpRJWYQUAuKt8YBUWhHl3QPfwsWgXNIDFokxYMN6gQa1C8MJtnZFTocdD6w/BaLHKHYmIiOiG7dq1E7/88ovcMWwqTaVIK9yPxKz1KKo8BwBQK93QI2I8WvnHwl3lfZ0tkD3xkPRNeCyuLf7ILsGGk5lYsP04/jWsm9yRiIiIbsgrr7wIhULCtm17ZMsghEC5IQ9Z5SdQXJUJAPBQe0Mh/e/cykv/JvmwYLwJkiTh7RHRSC0sx6qjZ9EtzA/3dG0hdywiIiKncqZoLwp06QAAb7cgRHh3hr9HU16O1wHxkPRN0mpUWDU+Dr5uasz96Q8cyy6WOxIREZFDs1jNKDcU2G77uIUiwLMpokLjERWagADPZiwWHRQLxlvQyt8LH4yJgdFSPQgmX2eQOxIREZHDMVn0yCj9A4nZ63AyfzssVhMAINSrHToE3QZvt2CZE9L1sGC8RUPbhGLuwEhkllXhkR8OwWzlIBgiIiIA0JvKcLboII5mrcOF0uMQQiDMuwMEhNzR6AbxHMZ6MKtPeyTllGBLajYW7vwTrwztInckIiIiWVWZyvBHzgYIAbiptAj3jkSItg2UCrXc0egmsGCsBwpJwrKRPXC68Dd8dCgN3cL8ML5zM7ljERERXdXq1d8gIEBbb9sTQqBEnwl3lQ881NX/QrRt4eMWhkDP5pB46T6nxlevnni5qbFqfBy83VR4dusxJOeWyB2JiIjoqpo1a46WLVve8naswoK8ijNIytmEk/m/IqvsT9uy1gG9EaRtyWKxEeArWI/aBnrjvVE9UWW2YPragyiqMsodiYiIqFY6nQ4VFRU3/Xiz1YjMsmQkZq1HWtHvqDKXIVjbCuHeHesxJTkKFoz1LL5dOJ7t1wEZpZWYyUEwRETkoEaPHoZ+/frd9OMzSo/hfMkxWIQJET6RiA4fh7aB/eCp8a/HlOQoeA6jHTzbvyOSckrwS1ou/rUrBfMHd5Y7EhER0S3RGYtRVHUeTX26QpIkhHl1hJtSixCvdlApNHLHIztjD6MdKCQJ743uidb+Wrx/4DR+SMmUOxIREdENqx7IkoWUvO1IytmMC6XHUWGsnnjbQ+2DCJ/OLBZdBAtGO/F112DVhF7QalR4astRpOSVyh2JiIioToSwokB3FsdztyAlbwdK9NnwdQ9Fx+DB8NIEyR2PZMCC0Y46BPlg2cgeqDJVD4Ip0XMQDBEROT6rsOJs8SFUmooR6NkcXcKGo1PIHfD3aMJL97konsNoZyM7RGBWn/ZYtj8Vj204gi8n9oZSwQ8bERHJp0CXjmFTu8A3yAN/5GxCqLYtjJYqeKh9EKxtA6VChbaB/eCh9oG7ylvuuOQAWDA2gLkDInE8pwQ7/srF0j0pmDuwk9yRiIjIRRXo0nG6cA86d28PpUpCUeU55JafgrvKBwGezRCsbQMA8PdoInNSciQ8JN0AlAoJH4yJQQs/T7y9LxVbU7PkjkRERC4qszwZgICXnzvUnlYYLVWQJCXcVJ6IDL5d7njkoNjD2ED8PTRYNb4XRn75G57cdBRb7vNG+yB28xMRUcOqMpXCZDXAaKmESqmGu0ILlcINAoBSwbKgoZmtJvyY9BEsVjMErGgR2AXRLe5Aub4Iu05+A4O5EoFeTTCg/d21vj5JGTtxOvcwJElCr9Zj0MS/vV1ysoexAXUK8cXbw6NRYTTjgbUHUKY3yR2JiIhcjIfaF2qFG4rzy5F9rhAqhTsACR5qX7mjuSSlpEJ8l4cwtsdTGNN9NjKLU5FXdh5H0reiU5P+mBAzBxqVB07nHq7x2JLKXJzN/wPjejyNOzo/gN/T1sMq7HPBEBaMDWxcp6Z4NK4tzhRV4MlNR2AVQu5IRETkAkr1OcgoPYYm3lEAJBQXlF92acDq+6mhSZIEtdINQPV1ua3CAkkCskvS0DKo+jVpG9ID5wv/rPHY84Un0Cq4G5QKFbzdA+DtHoiC8gy75GTBKIMXbuuEAS2C8dOZHLyz75TccYiIqJHLrTiNlPztyCo7Aa0mAO0C+6O0oBLCKuCp8UO7wP4I0raUO6bLsgorfkh8F/898Boi/NrB2z0QGpUHFJISAKB180WlsazG4yqNZdC6+dluX229+sCTFWSgUiiwYmwM4j//FUt2n0RUqB+GtQ2TOxYRETUyQlhxruQIsstPQa3UoH3gIHiofeCh9sHP/zkOhULCo2Pfljumy1NICoyNng2DuQo7U75EaWW+3JFqYA+jTAI93fDZ+F5wUynw+MbDSCuquP6DiIiI6shsNeJk/k5kl5+Cp9oXUaHD4eMeKncsugY3lQfCfFsjv/wcjOYqWIUFAKAzlMJT41NjfU+ND3SGEtvtq61XH1gwyqhrmB+WJHRHucGM6WsPoMLAQW6LDrEAACAASURBVDBERFQ/8irOoESfDX+PCESFJnACbgelN1XAYK4CAJgtJmSVnIGvZwjCfNsgvSAZAHAm7yiaB9acw7lZQCeczf8DFqsZ5foilFUVIsi7mV1y8pC0zO6Kao4/skvw6ZG/MHtLIj4dF8vLLhER0S0L946ESqFBsLZNrd8rvXv3hbu7WoZkdKlKYzn2pH4LIQQEBFoGdUGzgEj4eYZg18lvkHjuZwRoI9AuNBZA9UCXwooLiG4xDP7aULQM7or1R9+CJCnQu81YKCT79AVKQjjfMN3iYh3MZvsMGweA4GBv5OeX2237VzJZrLjrv3vxe0YhXhjUCU/2sc8cSnXV0O13JK7cdsC128+2u2bbgcbV/tyK0zBbDWjiU7cRz42p7TfD3u1XqRTw99fabfsNiYekHYBaqcAn42IR4e2BRbtOYOdfuXJHIiIiJyKEQHrxYfxVdADZ5SkwWw1yR6JGhgWjgwjWumPl+DholAo8uuEwzpXo5I5EREROwGw14lTBTmSXn/x7cEsCVAq36z7u889X4sMPP2yAhNQYsGB0INHh/vjXsG4o0Zsw/fsD0BnNckciIiIHpjeX48/cn1BclQV/jwh0Do2v8+CWL774DCtWrLBzQmosWDA6mCndWuD+6FY4kV+GZ7cmwglPMSUiogaSU34KlaZShHt3RIegwVApNHJHokaKo6Qd0MLbu+BEXinWp2Sie7g/Zsa1lTsSERE5oOZ+PeDjFooAT/tMpUJ0EXsYHZBGqcCnd8Yh1Msdr+5Mxp50x5vxnYiIGt7FwS055dWXlVVIChaL1CBYMDqoUC93fDouFkpJwsM/HEJGaaXckYiISEYWq8k2uCVXdxpWYb/p5YiuxILRgcU2DcTrd3RFUZURD6w9gCqTRe5IREQkA725HMm5P6K4Kgt+7uHoHDLslidoVqlUUKs5cTfVDQtGBzete0tM6doCx3NL8c+fjnEQDBGRiynT5yI590dUmkoR5t0BHYPrZ3DLzz/vwuHDh+shIbkCFowOTpIkLBrWFdHh/liTnIGVR/6SOxIRETWg3IpUmK1GtA6IQyv/WEh2uvQb0bXwXecE3FVKrLwzDkGebliwPRn7zxfIHYmIiOzo0qNJrQP6oHPIHQj1qt/LxiYnH8exY8fqdZvUeLFgdBIRPh74ZFwsJAl4aP0hZJVVyR2JiIjsoHpwy68o0J0FACgVKni7hdT7fmbNmon777+/3rdLjRMLRifSp3kQXh4ShYJKA2asOwi9mYNgiIgaE7254u/BLZkoqEzneevkMFgwOpkZPVvjrqhmSMwuxvM/J/E/EyKiRqLMkIfk3K22wS0dggZBkiS5YxEBYMHodCRJwr/ju6NrqC++TjqHL4+lyx2JiIhuUZ4uDSl52zi4hRwW341OyEOtxMrxvRDgocELvyTh0IVCuSMREdFNEkKgQPcXFJIKkcFD6n1wC1F9YMHopJr5euLjsbGwCIEZ6w4it0IvdyQiIroBF08pkiQJ7YMGIio0Ab7u4TKnIqodC0Yn1r9lMF4aHIU8nQEPrjsIo4WXiSIicgZ6cwWO525BcVUmAEClcIOH2qdBM7z11nJ8+umnDbpPcl4sGJ3cI7FtcGenpjiUWYT5247LHYeIiK6j/O/BLTpjMcoNubLl6N69B2JjY2XbPzkXFoxOTpIkvDm8OzoF+2B14ll8/Ue63JGIiOgq8nVpOPH34JZW/rFo7tdD7khEdcKCsRHwVKuwakIv+LmrMe/nJBzNKpY7EhERXUIIgXMlR3GmcD8UkhKRwUMQ5t1B1kwjR96OPn36yJqBnIfdCkaDwYCJEydizJgxGDlyJJYtW1ZjnaysLEybNg3jxo3D6NGjsWvXLnvFafRa+GmxYmwszFYrZqw7gHwdB8EQETkKq7CgTJ8DD7U3okKHO8TglqqqKlRWVsodg5yE3QpGjUaD1atXY8OGDVi/fj12795d45qVH374IYYPH47169fj7bffxiuvvGKvOC7htlYheG5gJ2SX6/HQ+kMwcRAMEZGsrKL6ilxKhQodgm9DVGhCgw9uIaoPdisYJUmCVqsFAJjNZpjN5hoz1kuShIqKCgBAeXk5QkLq/1qZruaJ3u0wqkMEfs8oxCs7kuWOQ0TkssoN+TiW/QPKDfkAAI3SEyqFm8ypiG6OJOx4bTmLxYLx48fj/PnzmDJlCubMmXPZ8ry8PMyYMQOlpaWoqqrCqlWrEBUVZa84LqPCYELfZVvxZ04pPp/cD9NiWssdiYjIpWQWpyI58zdACHRuMhBNA+Q9X7E23bp1AwD88ccfMichZ2DXgvGisrIyPP7445g/fz7at//fDParVq2CEAIPPPAAEhMT8cILL2DTpk1QKK7d8VlcrIPZbL/DrcHB3sjPL7fb9hvCX0UVSFj9K4wWKzZMHYiuYX51fmxjaP/NcuW2A67dfrbdNdsO1G/7hRDIKD2GzLI/oVKo0S5oAPzcI+pl2/VtyJB+UCgkbNu2R+4osrH3e1+lUsDfX2u37TekBhkl7ePjg169emH37t2X3f/dd99h+PDhAIDo6GgYDAYUF3OEb31oHeCF90fHwGC2YvraAyisNMgdiYioUbNYTUgt3IXMsj/hrvJGVGiCwxaLADB58jTMmDFD7hjkJOxWMBYVFaGsrAwAoNfrsW/fPrRuffmh0fDwcOzfvx8AkJaWBoPBgICAAHtFcjl3tA3DnAEdkVlWhZk/HIbZykEwRET2IiBQZSqDr3souoQlwEPtK3eka3rooZmYNWuW3DHISajsteG8vDzMmzcPFosFQggkJCRg8ODBePfddxEVFYWhQ4di3rx5ePHFF/H5559DkiQsXry4xsAYujVP9e2ApJwS/Hg6B6//egILhvAcUSKi+mSxmqBUqKFSaNAp5HaoFO5QSJzmmBqXBjmHsb7xHMYbU24wYfjqXThTVIEVY2IwrlPTa67f2Np/I1y57YBrt59td822A7fW/gLdWaSXHEan4NvhqfGv52T29dJLz8PDQ43nnnPdKe14DmPd8SeQC/B2U2PVhF7w0qjw9JZEnMgrlTsSEZFTE0LgfEkiThfuhRBWmKzOd574nj27sH37drljkJNgwegi2gV6Y/moHqgyWzB97QEUVxnljkRE5JSqB7f89vfgFi9EhSbA1z1M7lhEdsWC0YUMbx+Bp/u2x7mSSjy24TAsVqc7G4GISFYGsw5/5v2MosoM+LqHIip0uMMPbiGqDywYXcw/+kdiaJtQ7Dybhzd2p8gdh4jIqUiSAmarAaFebdExeCjUSl65hVwDC0YXo1RI+GB0T7Ty12LZ/lRsOpkpdyQiIodnslSfo6hReqBL6Ai08u/FkdDkUvhud0G+7hp8Nr4XPNVKzNp8FCfzy+SORETkkKoHtxzDHzkboDdXj6ZVK90bxRRw7dq1R2RkpNwxyEmwYHRRkcE+eGdkD1SaLHhg7QGU6jkIhojoUhar+e/BLclQSmoI0bgufvDRR6vw3//+V+4Y5CRYMLqwMR2b4Ine7fBXsQ5PbDwCqxBYsjsFr/zEC9ETkWurHtzyE4oqM+DjHsLBLeTy7HalF3IOzw3shOTcUvySlosJX+/B/oxCAIBOZ8CcATxUQUSuR2cswsn8HTBa9Aj1aouW/nGN8nzFjRvXw8fHA4MGxcsdhZxA4/sE0A1RKiR8MCYGPm4qW7EIAG/uPYUlHEVNRC5IqVBDAGjp37NRD255++0lWLhwodwxyEmwh5Gw8nAaygzmGve/ufcUALCnkYgaPSEETJYqaFSecFd5Izp8LJQKtdyxiBwGC0YXt2R3iq0wrA2LRiJqrAp06cgsT4Y5pxyVeh1UCjf0bDIRGqUHi0WiK7BgJCIil1OgS8fpwj0QwgK9pRQmixEqhQZFlRkI824vdzwih9M4T8ygOpszIBLP9utw1eXP9uvA3kUianQyy5MBCOhMRbBYTdAoPeCp9keuLlXuaEQOiT2MZCsIrzw07aVR4sGYNnJEIiKyqypTKUxWPazCAg+NFmrJC4CEKlOp3NGIHBJ7GAlAzZ7G/s2DUGG0YM6PxyCEkDEZEVH981D7wmipBAC4q70BSLb7XcW6dVuwa9cuuWOQk2API9lc7GnUat3wSPdWGP/1Hmw6lYU1yRm4u0tzmdMREdWfJt5R0BkLYbYaoVQoYYHFdr+r8PX1hZ+fN/Lzy+WOQk6APYx0mTkDIrEgvhuUCgnLR/WAl0aF539JwrkSndzRiIjqTZC2JdoHDoKvezgkKOCp8UO7wP4I0raUO1qDycnJRmZmptwxyEmwYKSrau6nxb+GdUWF0YwnNx2BxcpD00Tk/AxmHYoqMxDo2QLdwkYhvsuD6BY2yqWKRQCYMmUiRowYIXcMchIsGOmaJnZuhjEdm+DghSIs/52jB4nI+eVUnMKpgl0orEyXOwqR02DBSNckSRLeiO+GcG93LN1zEonZxXJHIiK6aRarGXkVp6FWuiHAk+dmE9UVC0a6Ln8PDZaN7AmzVeCJjUegM9a8jCARkTPI16XBbDUh1Ks9FJJS7jhEToMFI9XJgJbBmBnXFmlFFXhlZ7LccYiIbpgQAjkVp6CQFAj14tVciG4EC0aqs+cGRiIy2AdfJKbj5zM5cschIrohpfpsVJnKEOjZAhqlh9xxiJwKC0aqMzeVEh+M7gk3pQJPbzmKfJ1e7khERHVmFRa4q7QI9+blTgHg+ecX4F//+pfcMchJsGCkGxIZ4osXbuuEwkojnt6SyKvAEJHTCPBshu7h46DVBMgdxSHcfvswTqtDdcaCkW7YgzFtMLBlMLal5eKLY+lyxyEiuq6LP24lSZI5CZFzYsFIN0whSVg2sgf83dV4eXsyzhTyslJE5LjMVgOOZf+AnPJTckdxKNOmTcKYMWPkjkFOggUj3ZQwbw/8O6E7qswWPLbxCEwWq9yRiIhqlVeRBr25AlZhkTuKQ8nMvIBz587JHYOcBAtGummjOzbBpC7NkZRTgqV7Tsodh4ioBiGsyKk4CaWkRIhXG7njEDktFox0S167vQua+3pi2f5U/J5RIHccIqLLFFVdgMFciWBtG6gUbnLHIXJaLBjplni7qfHe6J6QJOCJjUdQpjfJHYmIyCanPAUAEObdQeYkRM6NBSPdsrimgZjdpz0ulFXhhW1JcschIgIAVBqLUWbIh59HBDzUvnLHIXJqKrkDUOPwTL+O2Hk2D2uSM3B7mzCMjWwidyQicnEeaj90CrkdSoVa7igOKT5+ODw8NHLHICfBHkaqF2qlAu+PjoGHWom5Px1DVlmV3JGIyMVJkgRf9zB4aQLljuKQ5s59EQsXLpQ7BjkJFoxUb9oEeOHVoV1Qojdh1uYjsPIqMEQkk5KqTOjNnCOWqL6wYKR6NbVbCwxrG4Y95wrw8aE0ueMQkQuyCgvOFO3D8ZytsArOEXs1y5a9xWtJU53xHEaqV5Ik4c3h0Ri8cgcW7TqBgS2D0SmEJ5sTUcMprEyHyWJAhE8nKCT2i1zN+vXfQ6GQ8OCDT8gdxaXpDCXYnfotqowVkCSgfWgcOjXpj19Pfo3SqnwAgNFcBY3KA2OjZ9d4/JpDi6FWukGSFFBICozu/qRdcrJgpHoXrHXDOyOjMXXN73hs4xH8eP8guKuUcsciIhcghEB2+UlIEhDm1V7uOETXJUkKxLYaiUCvJjCZDdh4bDki/Nvhto5TbOsc+msT1Cr3q24jocvDcFdr7ZqTP73ILm5vE4b/16MVTuaXYdGvJ+SOQ0QuotyQB52xGAEezeCm8pI7DtF1eWp8EOhVPbOIWuUGX89gVBrKbMuFEDhbcBytg7vLFREAC0ayo5cGd0bbAC98fDgNu87myR2HiFxAdkX1ZUrDvCNlTkJ048r1RSjSZSHIu5ntvtyys/DQeMHHI6jWx0iQ8HPySmxMXI5TOQfslo0FI9mNp1qF98fEQKWQMHvzURRVGeWORESNmBACKkkDb7cgeGuC5Y5DdENMFgN+TfkKca1GQ3PJ4eez+X+gVVC3qz5ueNeZGBM9C7d3no6TWfuRU/qXXfKxYCS76hbmh38OiEROhR5zfjwGwal2iMhOJElCm8A+6BwSD0mS5I7j8AICAhEczMLaEVitFuxM+Q9ah3RHi6Co/90vLDhX+CdaBV+9YNS6VQ8s9dB4oXlgZxSUX7BLRhaMZHeP92qH3k0DsflUFr5NzpA7DhE1Qpf+GGWxWDfffbcB27ZtkzuGyxNCYO/p7+DrGYLOTQZctiyr5Ax8PYJtReGVTBYjTGaD7e+sktPw04baJSdHSZPdKRUSlo3qgaGrduL5X5LQu1kgWvjZdzQXEbmW3IpUFFaeQ6uAXvDkdaPJieSVnUNafiL8PcPwQ+K7AICeLeLRNKBj9eHoK3oXKw1l2Hvme9zReTr0pnLsOPElAEDAilbB3dHUv4NdckrCCY8RFhfrYDbbbzLW4GBv5Oe77hUC7NX+75Iz8MSmI4htEoB19/aHSuF4Hdx87V23/Wy787ZdCIE/cjbAYNahR8SdUCs9bujxzt7+m7Vv3x74+nqgc+eeckeRjb1fe5VKAX//xtFB4njf2NRoTejcFGMjm+BQZhGW7z8tdxwiaiRK9FmoMpUj0LPlDReLruzFF+di9uyaE0ET1YYFIzUYSZLwRnw3RHh7YOmekziaVSx3JCJqBHLKq6fSCffuKHMSosaLBSM1KD93DZaN7AGrEHhi42HojGa5IxGRE6s0laJEnw0ftxBoNQFyxyFqtFgwUoPr3zIYM+Pa4q9iHV7ZkSx3HCJyYrkVqQDYu0hkbywYSRbzBkaiU7APvjiWjp9OZ8sdh4icVDPfbmgdEAd/j6ZyRyFq1FgwkizcVEq8PyYGbkoFnt6SiLwKvdyRiMgJqRQahHq1hyTx64zInvgJI9lEBvvgxds6o6jKiKe3JvIqMERUZ0JYka/7C1ZhkTuK0/rkk9VYs2aN3DHISbBgJFnNiGmNQS2DsT0tF6sT0+WOQ0ROoqgqA2cK9yGj5JjcUZxWmzZt0b59e7ljkJNgwUiyUkgS3h3ZA/7uaryyIxmnC11v8lwiunHZf0+lE+LVTuYkzstoNMJoNModg5wEC0aSXZi3B5YOj0aV2YLHNxyG0WK/q/gQkfOrMBai3JAPf48IeKh95I7jtBISBiM2NlbuGOQkWDCSQxjZIQL3dGmOpNxSLN1zUu44ROTALk7UHcapdIgaDAtGchiv3d4FLfw8sXx/Kn7PKJA7DhE5IKOlEoWV5+Cp9oWvW7jccYhcBgtGchhebmq8N6onJAl4YuMRlOlNckciIgdjsujhofZBmHcHSJIkdxwil8GCkRxKbNNAPNW3Ay6UVeH5X5LkjkNEDkarCUCX0JEI0XKwC1FDYsFIDufpvh0QHe6P7/7MwPqUC3LHISIHcXGuVkmS2LtI1MBU9tqwwWDAvffeC6PRCIvFgvj4eMyaNeuydRYtWoQDBw4AAPR6PQoLC3H48GF7RSInoVYq8P7onhi6aifm/vgHYpsEoImPp9yxiEhGQgicyPsFWk0AWvj1ZMFYD2bOfAJeXm5yxyAnYbeCUaPRYPXq1dBqtTCZTJgyZQoGDhyI7t2729Z5/vnnbX9/+eWXOHHihL3ikJNpHeCFhUO74B8/HsOsTUexZnI/KPgFQeSyyg15KDPkQa10Z7FYT+6+ezKCg72Rn8/5b+n67HZIWpIkaLVaAIDZbIbZbL7mh3zz5s0YNWqUveKQE7q3WwsktAvD3vMF+OjgGbnjEJGMsiuqp9IJ946UOQmRa5KEHS/ga7FYMH78eJw/fx5TpkzBnDlzal0vMzMTkyZNwq5du6BUKu0Vh5xQfoUe3ZZuRHGlEb8/NRzdIgLkjkREDazSWIbfTv0XPh7B6NNmHHsY68mMGTMAACtXrpQ5CTkDux2SBgClUokffvgBZWVlePzxx5GamlrrdSs3b96M+Pj4OheLxcU6mM32uxqIq3fRO1r730qIxr1r9mPy6t/w4/23wUNtvx8Vjtb2hubK7WfbHbft6cVHYDRa4O/dBgUFFfW+fUdvv70cPHgICoXkkm2/yN6vvUqlgL+/1m7bb0gNMkrax8cHvXr1wu7du2tdvmXLFowcObIhopATGtomFNN7tMKpgnIs2vWn3HGIqAFZrGbk6c5Ao/RAgGcLueMQuSy7FYxFRUUoKysDUD0Cet++fWjdunWN9dLS0lBWVobo6Gh7RaFGYP7gzmgX6IVPDv+FX8/myR2HiBqIUqFC55BhaBXQCwqJM8ERycVun768vDzcd999GD16NCZOnIi+ffti8ODBePfdd7F9+3bbelu2bMGIESN4Tgpdk6dahfdHx0ClkDB781EUVRnljkREDUSrCUCAR1O5YxC5NLudw9ixY0esX7++xv2zZ8++7PaTTz5prwjUyHQN88PcAZF4fdcJzNmaiE/vjOMPDaJGTGcsAlBdMBKRvNi/T07lsV7t0LtZIDanZuO/x8/LHYeI7OhcyVEk5WxBlalM7iiNUs+esejTp4/cMchJsGAkp6JUSFg+qie83VR4cdtxpBfr5I5ERHZQaSpBqT4HPu4h8FD7yB2nUVqy5B2sWLFC7hjkJFgwktNp5uuJxcO6QWc044lNR2C22m+KJSKSR0753xN1e3GibiJHwIKRnNL4Tk0xLrIJDmcWYdn+VLnjEFE9MlkMyNedhbvKC/4c7GI3X321Gp9++qncMchJsGAkpyRJEhbHd0MTHw+8uecUjmYVyx2JiOpJnu40rMKCMO8OHNhmRytXfozly5fLHYOcBAtGclp+7hq8O7IHrELg8Y2HoTOa5Y5ERPVEo/RAsLaN3DGI6G8sGMmp9W8RjEd7tcXZYh0WbD8udxwiqgdNfKLQI+JOqBQauaMQ0d9YMJLTmzsgEp1DfPCfP87hx9RsueMQ0S0QQgAAJF7Vhcih8BNJTs9NpcT7o2PgplTgma2JyKvQyx2JiG5ChaEASbmbUaLPkjsKEV2BBSM1Ch2DfTB/cGcUVRnx1JZEWy8FETmP7IqTqDSWyB2DiGrBgpEajQd6tsbgViHY8VcuVh09K3ccIroBRkslCivPwVPtC1+3cLnjuIQdO/bijz/+kDsGOQkWjNRoKCQJb4+IRoCHBq/uTEZqQbnckYiojnLKUyGEQJh3R06lQ+SAWDBSoxLm7YElCd2hN1vx+MbDMFp4FRgiR2cVFuTpTkOl0CDIs5XccVzGqVMnceLECbljkJNgwUiNzsgOEZjctTmO55Ziye4UueMQ0XUUVqbDZDEg1KsdlAqV3HFcxqOPzsDkyZPljkFOggUjNUoLh3ZBCz9PvPf7aew/XyB3HCK6hkDPlmgb2AehXu3ljkJEV8GCkRolLzc13h8dA4Uk4YlNR1CqN8odiYiuQiEpEaxtAzeVVu4oRHQVLBip0YppEoCn+rZHZlkVnv8lSe44RFSLosrzMFv5g47I0bFgpEbtqb4d0CPCH9//eQHrTlyQOw4RXUJvLkdq4W9ILdgldxQiug4WjNSoqZUKvDeqJzzVSsz96RgulFbKHYmI/pZTfgpCACHatnJHIaLrYMFIjV7rAC8svL0LygxmzNp8FFZeBYZIdharCXm6M9AoPRDg2ULuOC7pjTfewocffih3DHISLBjJJUzp2gIJ7cKw73wBPjx4Ru44RC4vT5cGi9WMUK/2UEj8KpJDbGwv9O3bV+4Y5CT4KSWXIEkS3hwejRCtGxbvOoHkXF6vlkguQgjklJ+CQlIg1Kud3HGIqA5YMJLLCPR0wzsje8BkFXhs4xFUmSxyRyJySWarAW4qTwRpW0GtdJc7jssaO3Y4Bg4cKHcMchIsGMmlDGkdigd6tEJqQTle//VPueMQuSS10h2dQu5AK/9eckdxaeXlZSgtLZU7BjkJFozkcl4c3BntAr3x6ZG/sPOvXLnjELkUccmgM567SOQ8+Gkll+OpVuGD0T2hVkiYvfkoCisNckcichnpJYdwunAPJ+smcjIsGMkldQnzw9yBnZCnM+AfW49d1utBRPZhsuiRV5GGCkMBlJJa7jhEdANUcgcgksujcW2xPS0HW09n45uk88gsq4RW64bHerSWOxpRo5SnOwOrsCDMuyMkSZI7DhHdABaM5LKUCgnLR/XEkM924J8/HYPZWt3LqNMZMGdApMzpiBoXq7Aip/wUlAoVQrRt5I5DACZOnASt1k3uGOQkWDCSS2vq64n+LYKxJTXbdt+be08BAItGonpUVHkeRksVwrw7QKng4WhH8NhjsxAc7I38/HK5o5AT4DmM5NKW7E65rFi86M29p7Bkd4oMiYgap5yKk5AkINyrg9xRiOgmsIeRXNaS3Sm23sTasKeRqP60DeyHUn0O3NU+ckehv7322gJ4eGjw7LMvyB2FnAB7GImIyO7cVd68DKCD2bFjG7Zu3Sp3DHISLBjJZc0ZEIln+1398NiAFkHsXSS6RUZLJUr12Zy6isjJsWAkl3a1otFLo8LucwVYtOsEv+iIbkFO+SmcyNuOwspzckcholvAgpFc3pVF47P9OmDnjCFo5a/Fsv2peP6XJFhZNBLdMIvVjDzdaaiVGvh7NJU7DhHdAg56IcL/BrZcOnH3D/cOwKT/24dVR8+i3GDGOyOjoVLwNxZRXRVUnoXJYkQTn85QKvh1Q+TM+Akm+tucAZGXzUkW4uWOtVP64941+/HdnxmoNJnx4ZgYuKmUMiclcnxCCOSUn4QkSQjjVDoOqVWr1lCr+f8Z1Q27S4iuwd9Dg28n9UW/5kHYkpqN+777HTqjWe5YRA6vzJCDSlMpAj2aQ6PylDsO1WLlyi/x/fffyx2DnAQLRqLr8HJT46u7+2BY2zDsSs/HPf+3D6V6o9yxiByayWqAWumOMG/ONEDUGLBgJKoDd5USK++Mw52dmuJQZhEmfL0X+TqD3LGIHFaQZ0v0iBgPb7cguaPQVfz442b88MMPcscgJ8GCkaiOTLfKogAAIABJREFU1EoF3hvVE9O6t0RyXinu/Go3ssqq5I5F5HAuTkWlkPgV48j+/e9FeOmll+SOQU6Cg16IboBSIeHf8d3gpVHhw4NnMOY/v2HN5H5o5e8ldzQih2CxmpCc+yPCvSMR4tVW7jhEDk9nKMHu1G9RZayAJAHtQ+PQqUl/JJ77BadzD8FNrQUA9GwRj6YBHWs8/kLxKRz8ayOEEGgXGouuzW6zS04WjEQ3SJIkvDS4M3zc1HhjdwrG/mc3/u+efogM5jVyifJ0Z1BpKoXJqpc7CpFTkCQFYluNRKBXE5jMBmw8thwR/tWX0ewU0R9RTQde9bFWYcWBtB8wLGoGPDW+2HTsPTQPjISfZ2i95+TxAqKbIEkSnu7XAa/d3gV5OgPGf7UbidnFcsciklX1VDqnoJCUCNGyd5GoLjw1Pgj0agIAUKvc4OsZjEpDWZ0eW1CeAW/3QHi7B0KpUOH/s3fn4VGV5//H32e2ZDKZTPaFLCQkYV/CIjsIqIAIgtSKotba1n5brUtrcWt/ta222lrr+vXb2mqL1g2piiLKIiA7sq8BEiAQQvY9M8lMZub8/gBTKcsEyORkZu7XdXHpLOfM5zg4c895nnM/WQmDOF693y85pWAU4jL8YFg2z08bTL2zlRvfWc/6Y5VaRxJCM7UtJbS4m4i3ZGHUh2sdR4iA09hSQ439JPHWdADySzewaPvzrDv0Pk6346znO1wNWMJsbbctYTYcrvYVmxdLCkYhLtPNA7vz15lX4HJ7uPX9jSwvLNM6khCaKGvMByBFGnULcdFaPU5W57/F8KwZmAzh9E4ZybeGPcT1g+8jwhTFliOfappPCkYhOsCM3qnMv3EkCgp3frCZj/JPaB1JiE7lcNVR31KOLTyJCFOM1nFEOyxY8BHLly/XOoYAvF4Pq/L/RY/EPLrH9wfAbLKiU3Qoio7c5Cuoajr7eyXCFIXdWd922+6sJ8Lkn/n0UjAK0UEm9UjinTmjMBv1/HjRVt7aVaR1JCE6jdloo1fCBNJtg7SOItopPj6BxMRErWOEPFVVWV+wEFtEIv1Sx7Xd/82h5ePV+855IUu8NY2G5moaW2rweN0crdxFemxfv+SUq6SF6EAj0+NZeMtYbnlvAw9+tpMmp5v/GS6T/0XwUxSFWHOa1jHERaiqqkRVm1EUs9ZRQlpFwzEOV+4gJiKZRTteAE610DlSuYsa+0kUFCLDYxiVcwMADmcD6wv/zTX97kSn6BmZfT3L976OipecpGHEWDr+CmkARf26w2oAqa2143Z7/bb/hAQrlZWNftt/VxfKx99Rx36wqoE5726grKmFB8f04udje6MoSgck9C957+XYL0VDSznhRismfWCuGR2q7/2kSWPQ6RRWrFindRTN+Pu9Nxh0xMRY/Lb/ziRD0kL4Qa/4KBbdNo4MWwTPrj/I41/sJQB/mwnhk1f1UlC9jt1li/Gq/vshL4TQlhSMQvhJ92gLi24bR894K69uPcyDn+3E45WiUQSXGscxXJ5m4iN6yFKAQgQx+b9bCD9KsZr5cO5YBiZH8/buY/z44624PHIWRgSP0sYDKAokW6WVjhDBTApGIfwsLiKMhTePYWRaHB8fKOG7/96Mo9WtdSwhLlujs5ImVzUx4WmEG6xaxxFC+JEUjEJ0gqhwI2/PGcXEHomsPFLO3AUbaXS2ah1LiMtS1ngAgGRrb42TCCH8TQpGITpJhNHA/G+NZHqvbmwqrubGd9ZT0+zSOpYQl0RVVVRULKYYosL808ZD+NdDDz3Gb3/7W61jiAAhBaMQncik1/GXmcO4eUAGu8rquOGttZQ3tWgdS4iLpigKPePH0z9pakC0jBJnmzr1OmbOnKl1DBEgpGAUopMZdDr+PG0wPxjag4NVjVz/rzUcq7NrHUuIS6JT9FpHEEJ0AikYhdCATlF44uoB/HR0L47VOZj11loOVYVe42ARmCqaCjlQuYrm1gbfTxZd1ve/fzvf+ta3tI4hAoQUjEJoRFEUHh7fh8cn9qO0sYVZb61ld1md1rGEuCBVVSltzKeu5SR6nawuG8iOHj1CYWGh1jFEgPBbweh0Ornxxhu5/vrrue6663jxxRfP+bwlS5Ywbdo0rrvuOh588EF/xRGiy/rxiFyemZpHbbOLb72zjs3F1VpHEuK8GpxlOFrriYvoHrBLAQohLp7ffh6aTCbmz5+PxWKhtbWVuXPnMn78ePLy8tqeU1RUxKuvvso777yDzWajulq+KEVouj0vk0iTgXsXb+Pm9zbwj2+NYEJWotaxhDhL6elWOimR0kpHiFDitzOMiqJgsZxacNvtduN2u8+6km7BggXceuut2Gw2AOLi4vwVR4gu74a+abw+ewReVeU7Czfx6cGTWkcS4gzNrQ3UNpdgDYsnMixe6zhCiIvU0FzNwbLN7Di2jN3FqzhRcwC3t309gRVVVf22uK3H42H27NkcP36cuXPnMm/evDMev/vuu8nMzGT79u14vV5+8pOfMH78eH/FESIgrCosY9brq3C4PPx9zijuuCJb60hCAJB/cj3HqvcxKP0qUqLl72WgGzRoEAC7du3SOInwt4qG42wr+gyn206CNQOz0YrH20pdcyV1jnJyEocyIG0CBr3xvPvw64xlvV7PokWLaGho4J577uHQoUP07Nmz7XGPx8OxY8d48803KSsr47bbbuOTTz4hKirqgvutrbXjdvtvPd6EBCuVlaF7xWooH39XOPb+NgsL5oxm7oKNfO/dDZRWN/H9oT065bW7wvFrRY7d97HbdLmkROjRueKD6r9VqL73EyZchdlsCslj/5q/33uDQUdMjMVv+2+v/JPrGdFjBrGR3c56zO1xcbhyB0crd5KbfMV599Epl7hFRUUxYsQI1q5de0bBmJSUxKBBgzAajaSnp5OZmUlRUREDBw7sjFhCdFlDusXy4dyx3PTeBn6xfDdNzlbuG9VTGiQLTRn1ZrpF9dM6huggv/zlb0K2WA41V/a+5byPGfQmeiWP8LkPv81hrKmpoaHhVI+ulpYWNmzYQI8eZ54lufrqq/nqq6/anl9UVER6erq/IgkRUPok2lh06zhSo8w8tSafJ1fvx48zSIQ4L1VVqXEUo6r+G9kRQvhfWf0RWj1OAA6VbWFD4Yc0ttS0a1ufBaOqqrz//vs888wzAJw4cYLt27f73HFFRQXf+c53mDFjBjfeeCOjR49m4sSJvPDCC3zxxRcAjBs3jujoaKZNm8Ydd9zBQw89RExMTLuCCxEKesRG8vFt48iOjeR/Nxfw8LJdeKVoFJ2stvkEB6u+5Hj9Dq2jiA70yisv8qc//UnrGKITbT78MQadiVp7OftK1hIZFs36goXt2tbnkPRTTz1FdXU1+/btY968eVgsFn7/+9+zcOGFX6B379589NFHZ91///33t/27oig8+uijPProo+0KK0QoSo2K4KNbx3Hze+t5Y0cRTU43L1w3BKNe+u6LzlHalA9AgkUudAkmCxe+h06ncMcd/6N1FNFJFEWHoiiU1B6kd8oI+nQbQ1HV7nZt6/MbZ/PmzfzpT38iPDwcgJiYGJxO5+UlFkJclARLGP+eO5ZhqbF8sP8EP/jwK1rcHq1jiRBgd9XQ0FKBLTyZCGO01nGEEJdBVb1UNh7nWPVekm05p+9r36iVz4IxLCzsjIn2Xq/MYRFCC9HhJt6bM5rxmQksLSzj9vc3YXe5tY4lglxZ40EAUqx9NE4ihLhcg7tfw4bCD0mwZhBjSaK+uRKruX09sH0OSffs2ZOPP/4YVVU5ceIEr776KkOHDr3s0EKIi2cxGXjjxpH8aNEWPi8o46Z31/PWTaOIDjdpHU0EoVZPC1WOo5iNVqLDz27HIYQILBlx/ciI+0+nA5s5gUl9bm/Xtj4LxkceeYSnn36ayspKbrrpJiZNmsQjjzxy6WmFEJcl3KDnb7OG89Ml21m47wTfensd784ZTYIlXOtoIsg43U2Y9BEkRfaSlk5CBLCtR5dc8PFhWdN87uOCBaPH4+H111/nySefvLhkQgi/Mup1vDh9KBaTkfk7jjLrrXW8N2c0abYIraOJIBIZFk9eykxU5Mr8YGS1RmEwyMVzocCgPzUK1dhcTVnDUbqfPst4rHofybb2LQxxwb8per2eNWvWXGZMIYQ/6BSFpycP5N6RuRyuaWLmW2s5UtOkdSwRZBRFQadIURGMFi36TL7jQ0RextXkZVyN3VXPjLx7Gd5jBsN7zGBG3r3YnXXt2ofPT4EJEybw2muvUV1dTXNzc9sfIYT2FEXhFxP68Ysr+1LS0MzMt9ayv6Je61giwKmqSn7lSorrd0qzeCGCSLOriXDjf5YqDDdaaHa170SDzzmML7/8MkBb42449SWVn59/sTmFEH5y76ieRIYZeHTZbm54ax1v3zSKoamxWscSAarJVUld80l0p3u2ieC0ZctmoqMjyM0doHUU0UmiI5JYX7CQ3KRTa0YXlm8jOiKxXdv6LBgPHDhweemEEJ3iziE9iDQZeODTHXz73fW88a2RjM1M0DqWCECljac+95Mje2ucRPjTww//DJ1OYcWKdVpHEZ1kTO632FX8BZsPfwxAcnR2uy54gXYUjAC1tbXs2rULgLy8PKKjpXmrEF3Rt/tnYDEa+NHHW7n1/Y38bdYVTM5N0TqWCCBOt52a5mIspmiiwpK0jiOE6EAmQzhXZF13Sdv6nMO4du1arr32WubPn8/8+fOZNm0a69evv6QXE0L437Re3XjzxpHodAp3fvAVH+4/oXUkEUDKmg6iqirJkb1lOFqIIOP2tnKobAvbij5n69ElbX/aw+cZxueee4633nqL7OxTa4gePnyYefPmMWbMmMtLLYTwmyuzEnlvzmhue38jd3+8lSaXm9vzMrWOJbo4j9dNRVMhRn0Y8ZYsreMIITrY6vy38KoeEqzpKLp2DTK38flst9vdViwCZGdn43bLcmRCdHXD0+L49y1juXnBBuZ9vpMGZyv3jMjVOpbownSKnl7xV+LyNqNT9FrHEUJ0sMaWam4Y+uAlbetzSDo2NpYPPvig7faHH35IbKxcfSlEIBiQHM1Ht46jm9XME6v28Yc1+6VNijgvRVGICk8iPiJT6yhCCD+whsfS6nZe0rY+zzD+9re/5ec//zm//vWvAejTp88ZLXaEEF1bbpyVj24dy03vbuC5DYdocLp54uoB6GR+mvgGR2sdCjrMxiito4hO8n//9xqxsRbfTxRBw2gI55NdL5Ea3RP9N4akL3tpQICMjAwWLFiA3W4HwGKRv1xCBJqMaAuLbhvHnPc28Nq2IzS53Dx7bR4GnazgIU45Xred2uaT5KVcL0VjiOjVqzcJCVYqKxu1jiI6ic2cgM18ae3WfBaMH330ERMnTsRmswFQV1fHmjVruP766y/pBYUQ2kiKDOeDuWOZu2AD7+05TpPLzSszhhJmODVX7Zm1+VgsYdw9pH3riorgYXfWU9t8EmtYghSLQgSxvIyrL3lbnwXj66+/zqxZs9puR0dH8/rrr0vBKEQAijWbWHjzGG5fuIlPD57kuy43r80ezv9uKuDZ9QcBsNudzBvXR+OkojNU2YsoadxLw7ETtLS2kBQpF0WFkkmTxkjj7hDj9rjYVbySk3WFAKRG5zIwfSIGvcnntpc0HuXxeC5lMyFEFxAZZuTtm0ZzdXYSq45WMO7VL9qKRYBn1x/kmbWy9Gewq7IXUVC9Drurhha3A1ApbzpElb1I62hCCD/ZfORjHK4GhveYzvAe03G4Gth0eFG7tvVZMCYkJLBs2bK220uXLiUuLu7S0wohNGc26nl99gh6xVspaWw+63EpGoNfSeNeQMXuqgFVxaSPAJTT9wshglFV4wnG9byJpKhMkqIyGZN7I9VNJe3a1ueQ9GOPPcbdd9/ddmW0Xq/nlVdeubzEQgjNvbDhIAerzj/Z/euzjjI8HXw8XjfNrfWAglEXhl6vw6BEAJy+XwgRjFRUWj0ujKeHoN3eVlTa12rNZ8GYnZ3NkiVLOHr0KABZWVno9dLQVQghAo3T3cTx+p20tDZgNthwtNYRZrBiMulxuU5NNTIbbRqnFEL4S3biYJbseoWshEEAHK3cRU7ikHZt63NI+ujRo7jdbnJycigtLeW1116jvl5+gQoR6OaN68ODY3qd9/EHx/SSs4tBwuNt5XjdDnaWftw2R/F8F7ikWvt3YjIhRGcakDaBoVnX4nQ7cLodDM26lv5pV7ZrW59nGB944AEWLlxIcXExjz/+OGPGjOHhhx/mL3/5y2UHF0Jo6+uC8JsXvQDcMThTisUgoKpeKuyFFNfvotXjJMwQQbotj/iILBRFwaALo6RxLx7sRJispFr7E2/J1Dq26CTf//4PiYwM1zqG6GRpMb1Iizn/yYLz8Vkw6nQ6jEYjX375Jbfccgt33XUXM2fOvKSQQoiu51xF4/aTtbg8Xkx6aewdyLyqhxP1e/CqbtJtg0ix9jljdYd4Sybxlkxp3hyibr31DnnvQ8yS3f/HVX3vIMxwas6ys9XByvw3uHbgj3xu67NgdDqdVFVVsWrVKh544AEAWYtWiCDzddFosYRxqLSOd/cc57n1B3l4vJxlDDSO1jqcbjsx5lT0OiO58eMIN1gx6c1aRxNCaKzV42orFgHCjBG0etq3trTP0wd33HEHU6dOJSIiggEDBlBcXIzVar30tEKILmneuD48PmUQT1w9gLQoMy9uPMT2k7VaxxLt1Opp5kjNZnaXLeZwzQY83lYAosISpVgU5zRv3gP86Ee+zyyJIKKquD2utputHide1duuTX2eYZwzZw5z5sxpu92tWzf+8Y9/XEJKIUQgsIYZeeG6IXzrnfXct3gby+6cQITR50eF0IhX9VDamE9Jw148XjdmYxTdo4eiU+Q9Exe2bdsWdDpF6xiiE2UlDGLZ3tfolTISgIOlm+iRMLhd2170J4per5e2OkIEuTHdE/jhsGxe3XqY36/ez5PXDNQ6kjgHp9vOvoplON12jPowMmIGkxiZi06RuadCiLMNTJ9IhCmK4pr9APRMHk5O0tB2bSs/QYUQ5/TolX1ZdbScv287wpTcFMZlJmgdSZymqiqKomDSRxBuiCQuojupUf0x6HyvByuECG05SUPbXSR+k/wMFUKck9mo56XpQ9ErCg8s2U5DS6vWkUJei7uRQ1VrKKrbAoCiKPRJuJru0UOkWBRC+FTfXMmS3f/Hwi1/AKC6qYQdx5a3a1spGIUQ55WXEsNPx/SipKGZX67YrXWckOX2ujhWt51dpZ9Q7TiO3VWLenqiuqLIHDQhRPtsKlzEwPRJGA2n+m/GWlI4Vr2nXdv6HJLevn07zzzzDMXFxXg8nrahkI0bN15eaiFEQLh/VE+WFZaxYG8xU3NTmNarm9aRQoaqeilvKuBEw+62xtsZtiHERXSXQlFctkGDBhMWJjPTQonL00JaTC+2F30OgKLo0Cntuy7F59+UX/ziF9x9993k5eWh08kJSSFCjVGv4+XpQ7nmH6uYt3QnV6TFkWAJ0zpWSGh2N1JUtwWdYiAjOo/kyN5nNN4W4nI899zL0rg7xCiKgtfrAU794LQ761HaOdjs85MnPDycGTNmXFZAIURg6xlv5RcT+vKrL/Yy7/Md/GP2CDnD5ScOVy0oOiKMNiKMNnJix2ALT8YovRSFEJepd/JIVua/idNtZ8ex5Ryu2MGQzMnt2tZnWTl+/Hi+/PLLyw4phAhsPxiWzeiMeD4vODU8LTqWy9PM4ZpN7C7/lKM1m9tW1Iq3ZEmxKPxiwYJ3mD9/vtYxRCfKSRrKgLQJZMXn4fG2Mq7nt+mRkNeubX2eYXzvvff461//isViwWQyyRxGIUKUTlF4ftpgJr2+il+u2M2YjHjSbBG+NxQX5PG6KW3M52TjXjxeDxFGG6lR/eQMrvC7v/zlZXQ6hWnTZmsdRXSiJFsmSbZMPF43Tndzu7fzWTD++9//vqxgQojgkRFt4YmrB/DTJTt4YMl2Ftw8Bp0UNpesyVXNoaovcbodGPVhdI8dRqIlG0Uabwsh/GD1gbcZnTMbnaLn4x0v0OK2MzBtIv3Txvvc1uenUmpqKklJSTgcDhwOB0lJSaSmpnZIcCFE4Ll5QAaTc5JZd6yK17cd0TpOQAs3RKKqKqlR/RicMoukyFwpFoUQftPQXInJEM6J2gMkR2dz0xWPcrhie7u29XmGcc+ePdx3331tw9Fut5uXXnqJfv36XXZwIUTgURSFZ6/N48q/r+TJ1fu4MiuR3Dir1rECQktrA8fqdxBrTifB0gODLozB3Wa1u62FEEJcDu/p/q1l9UdIi+mFQW+Cdo4S+fwp+7vf/Y7f//73LF26lGXLlvG73/2OJ5544vISCyECWoIlnD9OGUSL28u9i7fh9nq1jtSlub1Oimq3sqtsMTWOYmqbT7Q9JsWiEKKzREcksnzf6xTX5JMSnYPb0/4VvHwWjM3NzYwaNart9qhRo2hubv8kSSFEcJreO5Ub+6Wxs7SOFzce0jpOl+RVvZQ2HmDHyUWUNh7ApDfTM34cuXHjtI4mhAhBY3NvomfyCKYO+CFGvQmn28HQzKnt2tZnwWg2m9m8eXPb7a+++gqzWVo8CCHgd9cMJMUazp/XH2RXWZ3WcbqcGscximq3ouKle/RgBqVcL6u0iC7j889XsWXLFq1jiE5k0BvpHtcPa3gsAJYwG2kxvdq3ra8nPPbYY9x///2YTKcWtm9tbeXFF1+8jLhCiGBhCzfx/LQhzHlvA/cu3say704g3BDaQ6x2Vw1hhkgMOhNxEZm0uJtIiszFqA/XOpoQZzCZTKe/251aRxF+tmTX/9Gn22gy4vqdtVpUQ3MVB0o3EmWOp3fKqPPsoR0F48CBA1m2bBlHjx4FICsrC6PReJnRhRDB4sqsRO4cksU/th/l6TX5/HpSf60jacLlcVBct5NKxxG6WfuSET0ERVFIsw3QOpoQ53T4cCG1tRZiYlK0jiL8bEKfW9l1fCWbDn9MlDkOszESj+qm3lGFyRDOgLQryUoYdMF9nLdgdLlcmEymtvmK6enpALjdbtxutwxLCyHa/HJCP748WsFfvypkck4yozPitY7UaU413t7PyYZ9eFQPEaZobOHyBSy6vrvuugOdTmHFinVaRxF+FmGKYlTOLIb3mE5V4wnsrnoMOgMxlmSs4XHt2sd5C8Y5c+bw4YcfMnjw4DPm23y90kt+fv7lH4EQIihYTAZemj6UGf9aw/2fbmfV9yYSGRb8IxG1zSUcqdmEy9OMUR9Od9swEi05MkdRCNEl6XUGkmyZl7TteQvGDz/8EIADBw5c0o6FEKFlaGos947syQsbD/H4yr08e+1grSP5nUFnwuN1kWbrTzdrP/S64C+ShRAdy+6sY+2hBTS7mlAU6Jk0nL6pY9lydAnFNfnoFT3W8FjG9Pw2YYazR3ff3/I0Rn0YiqJDp+iYkXevX3K2qw9je+4TQogHx/amX2IUb+06xvLCMq3jdLjm1gYOVn2Jo7UeAGtYAkNSZ5Nuy5NiUQhxSRRFxxVZ13HD0J9x3cB7OFC6iTpHOd2ic5g15AFmDnmAKHMCe4pXn3cfUwf8kJmD7/dbsQjtKBi3bt161n1yGb4Q4lxMeh0vTx+KSa/jZ5/toNoRHFdfnmq8vYVdZZ9Q4yimyv6fJRENujANkwkhAl2EKYq4yFNLLhsNYdgiEnA4G0iN6dnW2D/Bmo7DVa9lzPMPSX/22Wd89tlnlJSUcP/997fd39TURHi4tIcQQpxbn0QbD43rw5Or9/Hw0l38bdYVATunz6t6KW86yIn6Pbi9LsINkXSPHkKMOV3raEKIINTYUkON/STx1jM/YwrKt573KmYFhWV7X0NBoWfKcHolj7jga5ysK6TeUUGfbqNpdjXi8rRgMyf4zHbegjErK4sJEyawZ88eJkyY0HZ/ZGTkGSu/CCHEf/vx8ByWFZay+OBJPtx/gtn9ArPAOlG/i5KGfRh0RjJjhpIU2VOW8hNB48kn/4DNJh1PuopWj5PV+W8xPGsGJsN/TsztKl6JTtHRIyHvnNtdO/BHWMJsNLuaWLb379jMCSTbepzzubuLV1NSewCHq5E+3UbjVb2sL1jItIE/9pnvvAVj7969yc3NZf/+/dxwww0+dySEEF/T6xRevG4ok15fyaPLdjMqI54Ua9f8YqqyF1HSuBdPWRN6IomPyKKbtS+KopBs7Y1X9ZAa1V8ab4ugM3r0WBISrFRWNmodJeR5vR5W5f+LHol5dI//Ty/bgvKtnKg5wJT+PzjvSI0lzAaA2RRJRlw/qhpPnLdgPFq5kxl597J418tt27a62zd16IJzGPV6PTt37mzXjoQQ4psyYyz8elJ/6p2t/HTJDlRV1TrSWarsRRRUr8PhqsPjdVPtOMbe8s8oqj01T9ukN5MZM0yKRSGE36iqyvqChdgiEumX+p915k/UHmTviTVc1fc7GPSmc27b6nG1FXytHhcn6wqItiSd97X0eiM63aWNkvhc6WXChAm89tprzJo1i4iIiLb7pXG3EMKX2/My+ayglFVHKpi/o4jvDsnSOtIZShr3AtDqcdDU2oTX60WvGKluPkYWwzVOJ4R/3Xjj9RiNet5550Oto4S0ioZjHK7cQUxEMot2vADA0O5T2HzkEzxeN0v3vgZAgjWD0Tk34HA2sL7w31zT705aWhtZuf9NAFS8ZCXkXXBtaIspmvL6IkBBVb3sLl51wQLzmxTVx8/+3r17n72Rxo27a2vtuN1ev+0/1E/Rh/Lxh/Kxg3+Ov6yxmQmvrcTp8bLyexPJions0P1fjk3Fb+FVPTQ5K9HpdJh0kRj14SiKjpHpt2odr9PI3/vQPP5Jk8aE/Eov/n7vDQYdMTEWv+3/Yjlcjaw7tICy+iOAQpItk/E9b8Zs8v257PMMozTuFkJcjmSrmacnD+JHH2/l3sXbWXQLswvbAAAgAElEQVTrOPS6rnHVtNloo665BBWVcGMkesxt9wshRLCJMFmZ3P/7uD0uVFSM+va3BfPZhxGgtraW1atXs3r1aurq6i45qBAiNM3qm8bMPqlsLanhlc0FWsdpk2rtj4qKougIM0accb8QQgSjhuZqyhuKKK8/yomaA5yoad+JQZ9nGNeuXcu8efPo06cPAI899hjPPPMMY8aMubzEQoiQ8tTkQWwqruaPa/O5KjuJvonan8WLt2TSP3EqJQ178CgOTCYLqdb+xFsytY4mhBAdbuvRJRRWbMdmjkdR/nPOMC327OmH/81nwfjcc8/x1ltvkZ2dDcDhw4eZN2+eFIxCiIsSazbx52sHc+v7G/nJJ9v47I4rCTNo39Mw3pJJvCUzZOexCSFCx7Hqfdw47KHzXnV9IT6HpN1ud1uxCJCdnY3b7b7oFxJCiKuyk7g9L5P9lQ38aZ2286ObWxvYU7aEuuYSTXMIoZVZs77FzTffrHUM0Ykiw6L911YnNjaWDz74gNmzZwPw4YcfEhsb63PHTqeTW2+9FZfLhcfjYcqUKdx3331nPOeDDz7gj3/8I0lJpy7pvu222/j2t799KcfRIf67ga8MTQnR8X49qT9riir4380FTM5J5oq0OE1ylDcdpMlVg1tt1eT1hdDafff9TM6sh5hhWdfxxf75dIvORa/7TwnYO8X3Cn4+C8bf/OY3zJs3j1//+tcA9OnTh2eeecbnjk0mE/Pnz8disdDa2srcuXMZP348eXlnLm0zbdo0fvWrX/ncn7993cAXwGTS43DVtd2WolGIjmMxGXjxuqHMemst9y7ezhffm4jF5POjqEN5vK1U2A9j0puJNWd06msLIYRW9pxYjcPVSE3TyTPmMJLie1ufn9Ldu3dnwYIF2O12ACyW9vUTUhSl7blutxu3233eZW26gq8b+AK4PS48Xg96nYmSxr1SMArRwUakx/HjETm8srmQ367axx+mDOrU16+0H8HjddPN1hed0q5mEUIEnT/84UnMZhP33feQ1lFEJ6luKmH20J9fUj3ms2C85ZZbeOedd84oFL++zxePx8Ps2bM5fvw4c+fOZdCgs78Uli1bxpYtW8jKyuLRRx8lJcV3meuPJpiesiZMJj1e1UudoxoFBVtYIh7sJCRYO/z1urpQPOavhfKxQ+cd/zM3DGfNsSrm7zjKnGE9mNK7W6e8rqqqHKg9QliYkb7dB5/RTieU3/tQPnYIzeNfvvxzAJ544gmNk2grlN57mzket9d1Uf0Xv+azYGxpaTnjtsfjob6+vl071+v1LFq0iIaGBu655x4OHTpEz5492x6fOHEi06dPx2Qy8e677/Lwww/zxhtv+NyvP1Z60ROJw3Wqx6TZaMXurKfRUUtcRGbIze8I5TktoXzs0PnH//y1eVw7/0u+9856Vv9gEtHhF3/l3sWqbymjtrGaeEsmDXUe4NTxhvJ7H8rHDqF7/F6vik6nhOSxfy3UVnox6sP5ZOdLpEb3PGMO47CsaT63Pe9YzN///ndGjhxJQUEBo0aNavszbNgwhg0bdlEBo6KiGDFiBGvXrj3j/piYGEymU18Q3/72t9m3b99F7bcjfbNRr9loQa8z0eptIdLk+wIfIcSl6Z8UzYNje1PW1MJjy3Z3ymtGmuLJjh1JN2vfTnk9IYToKmwRCfRIyCPMGIFBb2r70x7nPcM4Z84cpk6dyhNPPHHGRSmRkZHYbL4b7tbU1GAwGIiKiqKlpYUNGzZw1113nfGciooKEhMTAVi5cuUZ7Xs629fzFEsa9+LBTqw5HUdrLTXNxaS57YQZus4vBCGCyU9G5rKssIwP9p9gas8Uru+d6tfX0+sMJEbm+PU1hBCiK8rLuPqStz1vwWi1WrFarfz1r39tu6+6upqjR4+edaXzuVRUVPDII4/g8XhQVZWpU6cyceJEXnjhBfr3789VV13Fm2++ycqVK9Hr9dhsNp566qlLPpCO8N8NfMubCjhSs5kT9bvJjvN9ybkQ4uIZdDpemj6Uq19fxSNLdzEiLY6kyHC/vFajs5IIYzR6ndEv+xdCiK6oqGo3mfEDOVC68ZyPd0hbnblz5/LXv/4VVVWZNWsWUVFRjB8/nocffviC2/Xu3ZuPPvrorPvvv//+tn9/8MEHefDBB32G1EqiJQdQiY/ooXUUIYJadmwkv5zYj18s382Dn+3gzRtHdnhXBY/XzYHKVRh0JvJSZnbprg1CdIbU1DRMndzSSmij1l5OZjxUNZ449xM6oq2Ow+HAarWyaNEiZsyYwc9//nNmzpzps2AMBoqikBT5n4t0VFWVLxkh/OTOIVksLShlxeFy3t59jFsHZXbo/qsdRbi9LpIic+X/YyGAN998L2Qv+Ak1g7tfA8DwHjMwGc4cwXG5W861yVl8NiBzuVwAbN68mTFjxqDT6dDrtV//tbOVNxWwv2I5XrVjr84WQpyiUxSemzaYqDADv/piL8fq7B22b1VVKWs6iKJwxo9AIYQIJZ/vebVd952Lz4Jx+PDhTJs2jW3btjF8+HAaGhrQ6UKv0W2Ts4oGZwUn6jvnSk4hQlFqVAS/u2Ygdpeb+xdvx+NVO2S/Ta5K7K5aYszpcgGbEKetWLGMJUuWaB1DdAKv6sHtcaGi4va04va4cHtcOFwNuL3tWx7V55D0448/zoEDB0hPT8doNNLY2MiTTz552eEDTWbMMBqcZZxs3EuMORVrWILWkYQISjf2S+fzQ6V8eqiUV7cU8uMRuZe9z7LGgwAkR/a67H0JESx+//vfoNMprFixTusows92F69i5/EvUIB/bfxP5xujPoz+qePatY/zFowulwuTyURLSwuZmZkANDc3YzabycrKuqzggUivM5IdO5r9lcsprF7PwOTr5EpLIfxAURT+MDWPzSdqeHpNPhN7JNE7IeqS96eqKq3eFiJM0USFJXVgUiGECAx5GVeTl3E1mw4vYmT2zEvax3nHlufMmQPA4MGDGTJkyFn/DEVR4UmkWPvS4m7iWN12reMIEbTiI8J49to8nB4v9y7ehstz6XOHFUWhb+I19EucLBe7CCFC2qUWi3CBM4wffvghAAcOHLjknQejdNsg6lpO4vI48KpedErozecUojNMyU3h5gEZvLvnOM+tP8jD4/tc1v4MOv8vOyiEEMHqvAVjc3PzBTc0m80dHiYQ6BQ9fROuwaAzydkKIfzsiasHsO5YJS9uPMQ1OckM6RZzUdtXO45R11xCqm0A4Qarn1IKIUTwO2/BOHjw4AsWRPn5+X4JFAiM+rC2f29yVWMxxkrxKIQfWMOMPH/dEG58Zz33Ld7GsjsnEGFsf6Ph0sZ8mlxVpNoG+DGlEEIEhjpHBdERiT7vO5fzfvJ+PRT9yiuvYDKZmDNnDqqq8v7779Pa2r5LsINdaeMBimq3khM3mgSLrAYjhD+M7Z7AD4dl8+rWw/x+9X6evGZgu7ZrclXT6KwixpwqZxeFOIe3315IbKy0mQolaw6+y/WD7/N537n4nIC3fPlyfvCDH2C1WomKiuL73/8+y5Ytu/S0QSTGnIZeZ+Bo7Vc43U1axxEiaD16ZV9y4yL5+7YjrCuqbNc20kpHiAtLTk4hNTVV6xiiE7S02qlzlOPxuqlzVFDnKKfOUU5FwzHcXle79uGzYGxpaeHYsWNtt48fP+5zfmOoCDdEkhk9DI/XTWHNBlS1Y5oMCyHOZDbqeWn6UPSKwv1LttPQcuFRjlZPC9WOIsxGK7bwdiySKkQIqq+vp66uTusYohMcqdjBin3/pLGlmhX7/sGKff9kxb5/svHwR/RPvbJd+/A5GeinP/0pN910E/379wdg//79PPHEE5eXPIgkWLKpbT5BTfMJShvz6RbVV+tIQgSlvJQYHhjdk2fXH+SXK3bz4vSh531uhb0Qr+olKbKXzC8W4jxuuGGaNO4OEX1Tx9I3dSy7i1cxMH3iJe3DZ8E4efJkhg4dyq5duwDIy8sjNjb2kl4sGCmKQo/YkTSWfUJx/U6izalEGG1axxIiKD0wuhfLD5ezYG8x1/ZM4dqe3c75vFhzBq2eFplbLIQQgMfrRq8z0LfbGNyes4egDXrfbcfadblhXFwckyZNuviEIcKoD6dH7CianJUyuV4IPzLqdbw0fQiT/7Gan3++k2GpcSRYws56ntkYRWbMMA0SCiFE1/Pprle4fvB9/Gvj4yjANyfQKcAdY5/yuY/296cQFxRrTiPWnKZ1DCGCXq/4KB67si+Pr9zLQ5/v5PXZw88Ydra7aogwxshQtBBCnHZl71sA+G47CsPzkWVKOpiqqpxs2Eejs0LrKEIErbuuyGZUehyfFZTy/t7itvsdrlp2ly3haO1XGqYTQoiuZc2BdwFYuudvl7wPKRg7mKO1luP1Oyis3oDHK/0qhfAHnaLwwnVDiDQZ+MWK3ZyodwBQ1nQIgOjwc89tFEKIUOT2tlJUtYcmZy0nag6c9ac9ZEi6g1lMsXSz9qOkYR9FddvIjh2pdSQhglJGtIXfXjWAn322gweWbOftm4ZRZT9CmMFCjEwPEcKnn/50HlFRobnMb6gZmjmFg2Vf0dzaxN6SNWc9nhbb2+c+pGD0gzTbIOpaSqhoKiRG5jYK4Te3DMzg84JSlhWWsWDPRrJjPaRF9pT5i0K0w4wZs0hIsFJZ2ah1FOFnGXH9yIjrx1dHFjO8x/RL2ocMSfuBTtGREzsWnaLjSM1GWj0tWkcSIigpisKz1+YRazZSWL2PFrdKgiVH61hCCNElXWqxCFIw+k2EKZqM6MG0epxUOYq0jiNE0EqwhPPHKbkYdF6WF+pQFKPWkYQICP/zP3dy8803ax1DBAgZkvaj5MjemI02mYAvhJ9N792TpQV1LD5YTIKlgJ+OkfWjhfCloOAQOp1M3xDtI2cY/UhRlDOKRY/XrWEaIYLbk9fkEW228Oz6A+wuk/VxhRCiI0nB2Am8qoeC6nUcqFyJqqq+NxBCtNvJhn2UNuZjDdPz/LQhuL0qP1m8jRa3R+toQggRNKRg7AQKOryqhwZnBaWN+7WOI0TQ8HhbOdGwh5MN+wGFK7MSuXNIFoeqGvnDmnyt4wkhRNCQgrETKIpCj5gRGPXhFNfvwuGq1TqSEEGh0n4Ej9dNUmQuOuXUx9kvJ/SjR4yFv3xVyMbjVRonFEKI4CAFYycx6sPJjh2FV/VSULMeryrDZUJcDlVVKW86hE7RkRiZ23a/xWTgpelDURS479PtNDllxSUhzmXs2Cu56qqrtI4hAoQUjJ0oxpxKUmQODlcdxfW7tI4jREBrcJbhaK0nNiIDk/7M1SqGpsZy78ieFNc7eHzlXo0SCtG1/fa3v+fPf/6z1jFEgJCCsZN1jx5KhNGGURemdRQhAlpZ40EAkiPP3ULnwbG96ZcYxVu7jrG8sKwzowkhRNCRgrGT6XVGBiRfR7eoflpHESKgJVh6kBSZS6Qp/pyPm/Q6Xp4+FJNex88+20G1w9nJCYXo2v72t7/w4osvah1DBAgpGDXw9eR8r+qlxnFc4zRCBKbYiAx6xI644LrRfRJtPDSuD5V2J48s2yVtrYT4hnfeeZPXXntN6xgiQEjBqKGjNZs5WLWGGkex1lGECBhe1XNR67P/eHgOw9Ni+eTAST7KL/FjMiGECF5SMGooJaovOkXHkdpNF/UFKEQoq7IfZfvJD6h2HGvX8/U6hRevG0qEUc8jS3dR2tjs54RCCBF8pGDUUITRRkb0YFo9Tg7XbJThMiF8UFWVsqaDqKjnnbt4LpkxFn49qT/1zlZ+tmSH/L8mhBAXSQpGjSVH9sYWnkRtcwmV9sNaxxGiS2t0VWJ31RJrTiPMYLmobW/Py2Rij0RWHa3gjZ1F/gkohBBBSgpGjSmKQnbsaAw6I0V1W2n1yJWcQpxPeVsrnd4Xva2iKDx37WCiw438euVejtY2dXQ8IQKK2WwmIiJC6xgiQEjB2AWEGSz0iB1Fbtw4jHrpzyjEubg8DqqbjxNhisYalnhJ+0i2mnl68iCaWz3ct3g7Hq8MTYvQ9emnK9i4caPWMUSAkIKxi4iLyCDGnKp1DCG6rPqWMkAlObLXBVvp+DKrbxoz+6SypaSGV74q6LiAQggRxAxaBxBnavU0c7T2K1KjBmAxxWodR4guI8HSA2tYAkad2feTfXhq8iA2Hq/ij2vyuapHEp8ePInFEsbdQ3p0QFIhAsPOnduJjo4gM/Pip3iI0CMFYxdjd9VS7SimubWBAcnT0Cl6rSMJ0WWEG6wdsp9Ys4k/TxvMbe9v4qZ311PlcAFgtzuZN65Ph7yGEF3dz352LzqdwooV67SOIgKADEl3MdHmbiRH9sTRWk9x3U6t4wjRJRyv2356SLrjXJ2dzMAkW1uxCPDs+oM8sza/Q19HCCGCgRSMXVBG9BDMRiulTfkd/iUpRKBpclVT0rCf0saOLeSeWZvP7vL6s+6XolEIIc4mBWMXpNcZyI4dAygcrtmA2+vyuY0Qwaqs8QAAydZeHbbPZ9bm8+z6g+d9XIpGIYQ4kxSMXZQ1LJ7UqP64PU7srmqt4wihiVZPC9WOY5iNVmxhKZ362v/eV8w/th+hpMHRqa8rhBBdkVz00oWlRg0gISKLcGOU1lGE0ESFvRCv6iXpMlvp/LevL2w531nGhIgwiuocPLpsN48u282AJBtTclOYkpNM/yRbh2YRQohAIAVjF6ZTdG3Fosfrxqt6pLG3CBmq6qW86RB6nYFES3aH7/98ReODY3oxb1wfTtQ7WFZYxrLCMtYfq2RPeT1/WneAblYzk3OTmZKTzOiMeMIM0slABKYXX/wLMTGy0otoHykYA4DL08z+iuWEG6z0ip8gZzdESPCqXhJOF4p6ndEvr/HfRePXxSJAmi2C7w3twfeG9qDR2crKIxUsKyxlRWE5/9x+lH9uP4rFZGBiViJTcpO5KjuZWLPJLzmF8If+/QeQkGClsrJR6ygiAEjBGACMunBM+ghqm0uosBeSFJmrdSQh/E6vM5BuG+T31/m6QLxQ425rmJGZfVKZ2SeVVo+Xr05Us6ywjM8LSll88CSLD55ErygMT4s9NXSdm0xWTKTfswshRGdRVFUNuMVUa2vtuN1ev+2/K/7icrrt7C5bjIqXgcnTO6yB8bl0xePvLKF87NB1jt/tdaFXjJ16Nv1Sjl1VVQ5VN7KsoIylhWVsK6nh6w/U3DgrU04PXQ/pFote13VHBrrK+66VUD3+yZOvRK/X8dlnq7SOohl/v/cGg46YGIvf9t+Z5AxjgAgzWMiKGU5B9XoKq9fTL3EyiiIXuYvgdKpRdyl9Eq/264+jy6UoCr3io+gVH8W9o3pSaW9heWE5SwtKWVNUycubCnh5UwFxESauyU5mSm4y4zMTsZjko1doz+124/V23R8yomuRT60AEm/Jorb5BFWOY5xs3E9qVH+tIwnR4dxeJ1X2oxj14YTpA2tYN8ESztxB3Zk7qDuOVjfriipZevrCmXf3HOfdPccJ0+sYl5nAlNwUJuckkxQZrnVsIYTwSQrGAJMVOxynx4HVlKB1FCH8otJ+BI/qIS2yZ0Bf4BVhNDA5N4XJuSl4VZWdpbUsPT10veJwOSsOlzMPGJwS0zZ03TshKqCPWQgRvKRgDDAGXdjp4Wj5UhHBR1VVyhoPolP0JEbmaB2nw+gUhSHdYhnSLZZHr+zLsTr76XmPpWw8Xs2O0lqeXpNPui2CKTnJTMlNYWR6HEa9TDsRItjZnXWsPbSAZlcTigI9k4bTN3UszlYHqw++TVNLLZHhMUzoPZcww9ltkArLt7GreCUAg9InkZM01C85pWAMQF8Xi063nbqWk3LVtAgadS0ltLibSIzMxqAL3p6j3aMt3HVFNnddkU1di4uVh8tZWljGyiPl/H3bEf6+7QhRYQYm9Uhiam4KE3skYguXlj1CBCNF0XFF1nXERabS6nbyyc6X6BaTS2H5NlJsOQzsP4HdxavZU/wlw7KuPWNbZ6uDnce/YEbeT0CBT3a8THpcn3MWlpdLCsYAderqzDU0OasJN1ixhSdrHUmIy1btOAZAcmRvjZN0nuhwE7P7pTO7Xzouj5dNxVWnhq4LSvkov4SP8ksw6BRGpcczJTeZa3KS6R4dHFddCm195zvfIzIyeH+YBYoIUxQRplOLdBgNYdgiEnA4Gzhes5+pA34IQE7SED7f8+pZBWNJ3SG6xeQQZjxVIHaLyaGk9hA9EvI6PKcUjAFKURSyoq9gb8VSDtdsYGDydAw6OQMhAlt27GgSLTlYTDFaR9GESa9jfGYi4zMTefLqAeRXNpye91jK2mOVrD1WyS9X7KFPQhRTcpOZnJNCXko0OpmiIi7Bd7/7/ZBtKdRVNbbUUGM/Sbw1nWZXU1shaTZaaXY1nfV8h7MBi8nWdttisuFwNvglmxSMASwyLJ60qAEU1++mqHYLOXFjtI4kxGVRFIWo8CStY3QJiqLQN9FG30QbPx3Ti7LGZpYfPtWyZ21RJc9vOMTzGw6RFBnO5JxkJuckM7Z7AmajLFUoRCBq9ThZnf8Ww7NmYDKc2T1BURS0/lkoBWOA6xbVn9rmEirtR4kxpxEX0V3rSEJcNI+3ldLGfBIjczDpZW3bc0m2mrk9L5Pb8zKxu9x8ebSCpYVlLC8s482dRby5swizUc+EzEQm5yZzdXYyCRbfw43PrM2/4Co3Ing99tg8wsON/OpXv9c6Ssjzej2syv8XPRLz6B5/qmWe2RSJw9VAhCkKh6uBcNPZbcYiwqIoqz/SdtvuqifZ5p//l6VgDHA6RUdO3Bj2lH3K8bodxJrTpaG3CDiV9iMU1+8GIM02UOM0XZ/FZGBar25M69UNj1dl28kalhaUsaywlM8KTv1RgGGpsUzOTWZKTgq5cZFndVd4Zm1+2zradruzbZlEERo2bdqArguvQBQqVFVlfcFCbBGJ9Esd13Z/emxfCsu3MzB9AoXl28mI7XvWtqnRPdletBSn2wHAydoChnaf6pecUjAGAbMxipy4sVhMsVIsioCjqiplTQfRKTq54v8S6HUKw9PiGJ4Wx/+b2I/DNU0sKyhlaWEZX52oZktJDb9bvZ+sGAuTc5KZmpvCFWmxPLf+YFuxCLT9uxSNQnSuioZjHK7cQUxEMot2vADA0O5TGJB2JV8eeJuC8i1Ehp1qqwNQ1XiCg2WbGJN7I2HGCAalT2Lxzv8FYFDGVW0XwHQ0v60l7XQ6ufXWW3G5XHg8HqZMmcJ99913zucuXbqU++67j4ULFzJgwACf+w7FtaQvhqqql9WnMdCP/3KE8rGDNsdf31LK/oovSLBkaToPNxjf+5pmFysKTzULX3WkHEerB4BwvY4Wz7k/Qx8c0yvkisZgfO/bY9KkMeh0CitWrNM6imZkLen289sZRpPJxPz587FYLLS2tjJ37lzGjx9PXt6Zl3o3NTXxxhtvMGjQIH9FCSkNLeUcrf2KnvFXYjZGaR1HCJ9KGw8AkBTZS+MkwSfWbOKmARncNCCDFreHDcer+MOafHaV1Z13GznTKIQ4F7+NXyqKgsVyqqp2u9243e5znvV64YUXuOuuuwgLk15QHcHlbcbRWk9hzXpU1X9nYYXoCC3uRupaSog0xWENi9c6TlALN+iZ1COJq7PlKnQhxMXz6xxGj8fD7NmzOX78OHPnzj3rLOK+ffsoKytjwoQJvPbaa+3eb2ec3k1IsPr9NfwhgQG4lApK6w/TyGGyE4Zc2n4C9Pg7QigfO3Tu8dudHlJd2STbskiI1v6/eyi893+cPRyLJYzfLtt9zse7x1j4ycR+JATJMFp7hcJ7/98GDz414heKx/5NoX787eXXglGv17No0SIaGhq45557OHToED179gTA6/Xy9NNP89RTT130fmUO44Ulhg2i1FPM/hNfoWuNJdIUd1HbB/rxX45QPnbQ4vj1pEeMglY0/+8eSu/93UN6YLc7z7joBSA71sLhGjuDnvmYP07JY1bfNI0Sdq5Qeu+/6dln/zdkj/1rMoex/TrlktqoqChGjBjB2rVr2+6z2+0cOnSI73znO0yaNImdO3fy4x//mD179nRGpKBm0IWREzcaVVUprF6Px+vWOpIQZ/GqHq0jhLR54/rw4Jj/zBt9cEwv1t11Nc9em0erV+VHH2/lnk+20tDSqmFKIURX4bczjDU1NRgMBqKiomhpaWHDhg3cddddbY9brVY2b97cdvv222/noYceatdV0sI3W3gKydZe1LeU0uptRq+TU+6i61BVlb3lnxNmsNAz7srLuqpfXLqvL2z5ZuPuWwdlMio9nns+2ca/951gU3E1L08fyqgMmWMabD744H2s1nCuuWaG1lFEAPBbwVhRUcEjjzyCx+NBVVWmTp3KxIkTeeGFF+jfvz9XXXWVv15anJZhGwy2weh10m5TdC2NrkrsrlrCDVYpFjU2b1yfs4blesRG8vFt43huw0Ge33CQ2W+v4ycjc5k3rg8mvfR6DRYvv/w8Op0iBaNoF79VEr179+ajjz466/7777//nM9/8803/RUlZH2zUGxubcCoD8Ogk6vRhfbKTrfSSZZWOl2WUa/joXF9mNQjiXs+2cpLmwr48mgF/3v9MHLjZMRCiFAjPxVDQJOrmj1ln3K0dovWUYTA5XZQ01xMhCkaa1ii1nGED8NSY/nizoncPCCD3eX1TP7Hav65/Sh+WvNBCNFFScEYAizGGMymaKrsRVQ5irSOI0Jcub0AVVVJjuwlw9EBIjLMyPPXDeFvs64gzKDjkWW7uH3hJirtLVpHE0J0EikYQ4Ci6MiJHYNe0XO05itcpxcpF6KzqapKpf0wBp2R+IgsreOIizSjdyqrvj+J8ZkJrDhczoTXVrKsoFTrWEKITiAFY4gwG6PoHjMUt9fF4ZqNMpwkNKEoCv2TriU3fpxcjBWgUqxm3p0zmt9e1Z8mp5vv/HszD32+E7tL2ncJEcykYAwhiZZcYqQrEEwAACAASURBVMzdqGsppcJeoHUcEaJMejPR4d20jiEug05R+OEVOXx+x5X0SYjijZ1FTP7nanaW1modTVyETz5Zxvr167WOIQKEFIwhRFEUesSOJMGSRaw5Xes4IsQ4XHVUO47LGudBpE+ijc/uuJL/uSKbwzVNTH9zDS9sOIjHKyMYgcBisRAZGal1DBEgpGAMMSZ9BDlxYzDqzVpHESHmZOM+DlWtocFZrnUU0YHCDXp+c9UAFtw8mviIMJ5ak8/st9dxvM6udTThQ3HxcYqKirSOIQKEFIwhrLa5hNLT/fCE8KdWTwvVjmOYjVFEhSVrHUf4wfjMRFZ9fxLTe3Vj84lqrvrHKhbuLZb50l3YHXfcwsyZM7WOIQKEFIwhyqt6OFq7mWN122hyVmkdRwS58qYCvKqX5Mie0koniMWYTfxt1hW8cN0QvCr8ZPE2fvTxVupaXFpHE0JcJikYQ5RO0ZMdOxpVVSms2YDHK1c4Cv/wql7Kmw6h1xlIsGRrHUf4maIozBmQwcrvTeSK1FgW5Zcw6bVVrCuq1DqaEOIySMEYwmzhyaRYe9Pc2sDx+h1axxFBqra5GJenmQRLNnqdUes4opN0j7bw4a1jeWhcb8qbWvj2u+v5zcq9ON0eraMJIS6BFIwhLiN6MBFGG2WNB6lrOal1HBGUFMINVpIje2odRHQyg07Hz8b05pPbx5MZY+H/vipk2htfcqCyQetoQoiLJAVjiNMpenLixqAoCkdqvmJX2WKW7vkbu8oWU2Uv0jqeCAJxERnkpVyP2WjTOorQyJBuMay4cyK3DerOvooGps5fzd+3HpYLYoQIILLUgsBiiqVbZF9KGvcCCiaTHoerjoLqdQDEWzI1zScCl6qqKIoiF7oILCYDf7p2MFdnJ/Ozz3bwyxV7+OJwOc9fN4SkyHCt44Wkxx9/EptNWqyJ9pEzjAKAWmcJcOpL/f+3d9/hUVX5H8ffd1rKpHcINRBa6IgovQqKiKugK7I2XHbXggIquGJZLFgWy7r7s60FVyyrKFhXmiDFFZASQu9CSG+TZDL9/v6IZEFCCJCZO+X7eh4fk5s7935OJsx859xzzzn5U39tESnEuXN57GzJWyxTN4lTjOnQjFVThjMsI4XvDhUy7M2VfL1HhsNoYciQYYwaNUrrGCJASMEoAKhxVgDgcFdTUVMIqKdsF+JcFVYdwO6qlpVdxGlSosJ5f+KlPDmqO1ani9s+28CMr7fIetRC+DEpGAVA3fgyj+rG43HjdNecsl2Ic6Gqau1UOoqelCiZSkecTlEUpvTJ4NtbhtI1JZb3s48w4q3v2Hy8VOtoIeOGG67l8ssv1zqGCBBSMAoA0qO7AhBmMIOiYHdXA2rddiHORbktF5urikRzWwy6MK3jCD/WMSmGr24azJ39MjlSXs24f61h/trduDzSM+1tBQX5HD8uwwFE40jBKIDaG1syEwdiNiUSbjADCinmdnLDizgv+ZV7AGgW1VHjJCIQhBn0PDwsi09uGEBadDjPrd3N+PfWcLhM1qMWwl9IwSjqJJnb0CPtSi7v/geiw5KpdBTLtBfinNlclZTb8ogJSyHSFK91HBFABrROZsVtw7i6czo/HS9jxNvf8UH2EXkdEsIPSMEoThNhiiY5MoMapwWLvUDrOCLAhOmjyEoZRau4XlpHEQEoLtzEq+P78o9xfdApMP3rLdz+2QZKa2Q9aiG0JPMwinqlx3YjNSqTqLAkraOIAKMoCjHhqVrHEAHu2qyWXNwikbu/+Imv9uax6XgZfxvbmyFtU7SOJkRIkh5GUa9wQ5QUi+KcWWwF1Dhl2TfRNFrGRrJo0kAeGtKFEqud6z9azyPLt2OT9aibxNixV3HttddqHUMECCkYRYOsjjJ+Lt8iY4jEWamqysGyH8nO/wqXRy4fiqah1yncfWkHvr5pCO0Tonh90wHGLFjNzkKZI/ZCzZw5i0ceeUTrGCJASMEoGnTUkk2uZQcV9jytowg/V2HPo8ZpITGyFQadSes4Ish0T4tj6a1DuaV3W3YXWRizYDWvbtiPRz7MCuETUjCKBrWI6QZAboUsESgadmIqnTSZSkd4SaTRwNOX9eBfEy4hJszIYytzuP7D9Ry31GgdLSDNn/8Mc+fO1TqGCBBSMIoGmU0JxEekY7EXYrHJHdOifrVT6eQSZUqUsa/C60a1T+O7KcMZ1S6VNUeKGP7WSj7fnat1rIDz1Vefs2jRIq1jiAAhBaM4q/SY2tVeci3SyyjqV1C1F1WFtGjpXRS+kWwO490Jl/Ds6B7YXR6mLt7ItC9/otLu1DqaEEFJCkZxVtFhycSGp1Fuy6PSXqx1HOGHVFUlzBBJYmRrraOIEKIoCjf1asuyW4fSIy2Of+ccZcRb37HhWInW0YQIOlIwikZpEdONtOiOhOkjtY4i/FCb+Ivo2exqdIpe6ygiBLVPjObL3w3m3v4dOGaxcvXCNTzz/U6cblmPWoimIgWjaJSY8FTaxvfFZJCCUdRPp8jLidCOUa9j9uAufDppIM2jI3hh/V7Gvfc9B0qrtI4mRFCQV3hxTlRVpdpRqnUM4ScstgJ2FHxLpb1Q6yhCAHBJyyRW3jaciV1bsjWvnJFvf8e/th6WuWTrkZqaRvPmzbWOIQKEFIzinBws/YHtBV9jdcqkuQLyq/ZgsRfJm7HwKzHhRl6+sg+vje+LSafj/v9s5ZZFP1JstWsdza988MEivvnmG61jiAAhBaM4J/ERLVFVOC53TIc8h8tKac1RIk1xRIfJ+r7C/4zvnM53U4YzsHUS3+7PZ9ibK1lx4NTpwZ5bs4u/fLtNo4RCBA4pGMU5iY9oQaQpjmLrIWyuSq3jCA0VVO9FVVXSojqiKIrWcYSoV/OYCP792wE8OiyLCpuTGz/+gQeXbsPqdPHcml3MX7eHuUuzeW7NLq2j+tzq1d+xbNkyrWOIAGHQOoAILIqikB7TlX3Fa8m15NAu4VKtIwkNeFQ3BVX7MOhMJEW21TqOEA3SKQp/6pfJ4DYp/OmLTby9+RBLduVSWvO/Nc/nr6tdqej+QZ21iulzf/nLHHQ6heXL12odRQQA6WEU5ywxojURxhiKqg9id1VrHUdooMR6BKfbTkpUO/Q6+dwpAkNWaizf3jyU3s3iTikWT5i/bk9I9jQK0RhSMIpzdqKX0aALw+ayaB1HaCAhoiVt4i8iVdaNFgHm7//dy+a88jP+XIpGIeonXQPivCRFtiEhopX0LoUovc5Is+hOWscQQgjhI9LDKM6LoujqikWZUiW0WGwFuD0urWMIcV7uH9SZmQPO3DM+c0DHkBrHKERjScEoLkhR9QG25X+O023TOorwAafbxq6iFewqWq51FCHO25mKxqFtk6VYFOIM5HqiuCAuj5MaZyV5lbtpFddT6zjCywqq9uFRPXJntAh4JwrDE3dHh+l1/HS8jKJqO8nmMC2j+cyCBR+QkGDWOoYIEFIwiguSYm5PrmU7+VW7aR7TBYPOpHUk4SUe1UNB1V70OgPJ5gyt4whxwU4UjWZzGOEq/HlZNvNW7+T5K3ppnMw3WrZsRXJyNEVFMqeuODu5JC0uiF5noHl0F9weF/mVe7SOI7yorOYoDncNyeZ26HVGreMI0STuH9SZR0f34KZebeicHMMH2UfYklemdSyfqK6upqqqSusYIkBIwSguWGpUBww6E3mVu3B7nFrHEV5y4gNBmkylI4KQQafjiZHdUIE5y7LxhMDNfOPGXcaAAQO0jiEChBSM4oKdmGLF5XFQbD2sdRzhBW6PE7fqJC68GRHGGK3jCOEVA1onc1WndH46XsYnOUe1jiOEX5GCUTSJtOhOdEwaQoq5vdZRhBfodUa6pV5Bh6TBWkcRwqseGZZFhEHPE6t2UGmXKyZCnCAFo2gSBp2JhMiWKIqidRThJYqiyNhFEfRaxEZy96WZFFbbeWG9jMsW4gQpGEWTcritHK3Yhkf1aB1FNJH8yj0cKt2A012jdRQhfOJPF2fSMjaSNzYeYH+J3EEsBEjBKJpYnmUXxyq2U1R9QOsoogmoqkpe5U6Kqg+gKPJyIUJDhFHPX4Z3xelReXjFdlnNSghkHkbRxJrFdCa/ag/HLTtIMbeTIiPAldlysbmqSY1qj0EXGpMZCwFweYdmDG6TzHcHC1l2oIDL2qdpHanJ3XXXvURHh2sdQwQIeTcXTcqkjyTF3B6bq0rumA4CBTKVjghRiqLw+Mhu6BWFR5Zvx+5yax2pyV1zzUQmTZqkdQwRIKRgFE2ueUwXFEUh15Ijl3ICWI2zgnJbHjFhKUSa4rWOI4TPdUyKYUqfDA6XV/P6RhlmI0KbFIyiyYUZokiOzKDGaaG05met44jzlF+1F4C0aOldFKFr5sCOJEaaeGH9HvIqg+vGr7vumspNN92kdQwRIKRgFF7RPCaLhMiWRBhkkudAlWpuT7PoTiREtNQ6ihCaiQ038dCQLlidbp5YtUPrOE1q584dbNu2TesYIkBIwSi8IsIYQ8ekIXIpM4BFmuJpE3+R3LgkQt5vu7emR1oci3YcY8OxEq3jCKEJeScQXmd1VshYxgCiqirVjlKtYwjhN3SKwpOjugPw0LJs3B55PROhRwpG4VW5lh1sy/uCCnue1lFEI1XY88jO/5qjFXKpSogTLkpPYGLXlmwvqOD97CNaxxHC52QeRuFVceHN+Jkt5FbkEBfeXOs4ohHyf5lKJz48XeMkQviXOUOz+HpvHvNW72Rcp+bEhZu0jiSCwNq9H3OsbDfhxiiu7j0dgFW736eipggAh6sGkyGC8b3uOe2xH298GqM+DEXRoVN0jOt5t9dySsEovMpsSiA+Ip2ymlwstgJiwlO1jiQaYHVYKLflEhWWSFRYktZxhPArqVHhzBjQkce/28Fza3bXXaYOVJdc0p/wcFkfXmvtU/vQuXl/1uz9d922oZ3+Nz/mxoNfYjSceYL1Md2mEm40ezUjyCVp4QPpMV0ByLXkaJxEnM3PJTtRVZmoW4gz+f1F7WiXEMU7mw+xq7BC6zgX5KmnnuPll1/WOkbIS4vNwGSIqPdnqqpyqHg7Gck9fZzqdFIwCq+LDksmNjyNclselfZireOIM3B7XBwr3Y1RH0ZiZGut4wjhl0x6HXNHdMOtqsxZLutMC+8qsBwiwhRFTET9V3wUFJbmvMkXW15mT/6PXs3itUvSdrudG2+8EYfDgdvtZvTo0UybNu2UfT744APef/99dDodkZGRPP7447Rv395bkYSG0mO6UmUvosZZTrRc6vQ7xdWHOVy+iXLbUSKNCZRaj5JkbqN1LCH80oh2qYxql8qyAwV8uec44zoF5njfd955k6ioMCZMmKx1FHEGh4q20Tapxxl/fnn3P2IOi6XGUcXSnH8SG5FMWmyGV7J4rYfRZDKxYMECPv/8cxYvXsyaNWvYunXrKfuMGzeOL774giVLlnD77bczb948b8URGosJS6VX82tIiZIPBP6muPow+0rW4nTbiItMBRT2layluPqw1tGE8FtzR3bDpNfx2MocrE6X1nHOy7vvvsWrr76qdQxxBh7VzZGSHbRNPnPBaA6LBSDCFEWrxCyKK495LY/XCkZFUTCbawdhulwuXC4XiqKcsk9UVFTd1zU1Naf9XAQPRVEw6sMA5BKOn8mtzAFqnxNFUVB+eVmo3S6EqE/b+Cj+0LcduZYa/vHffVrHEUHoePl+YiOS64rCX3O6HThd9rqvj5fvI87svRtLvXqXtNvt5pprruHnn39m0qRJ9OhxepW8cOFC3n77bZxOJwsWLGjUcePjvX83UHJytNfP4c+81f6Sqlz25P1I95bDiAr3z1VgQu25tx8vo9pZ8suLkh6TSQ+Am+qQ+12EWntPFspth/Nr/xNX9WHRzmP848f93DG0C20Sos7+ID+i09V20shzr237V+/+gPyKg9hc1fx7w1P0bDWKDml9ay9H/6p30Wq3sG7/IkZl3YrNWcnKnf8CQMVD2+SetIj33g2LiuqD7h6LxcKdd97Jww8/TIcOHerd54svvmDt2rU888wzZz1eWVk1LpenqWPWSU6Opqio0mvH93febH+p9Sh7ileTbG5L+8QBXjnHhQi1597lcbDm8Bs43DVEGuOJDI/E4XADEGmKo0falRon9J1Qe+5PFspthwtr/6IdR7nzi58Y27E5b/7m4iZO5l3Dhw9Ap1NYvnyt1lE04+2/fYNB55NOLl/wyV3SMTEx9OvXjzVr1pxxn7Fjx7J8+XJfxBEaio9oQaQpjmLrIWyu0H2D8geqqrK/ZB16xUSY3oxBF3bKz9Oju2qUTIjAcU2XFvRNT+CrPcdZc7hI6zhCeI3XCsbS0lIsFgsANpuN9evXk5Fx6p07hw8frvt61apVtG4tU3kEO0VRSI/piqrWLhsotHO8cgdlNbkkmduSlTqGSFMcCjoiTXFkJg6Uu6SFaATll3WmFWDO8mycbu9d/WpqBoMBo1Em7haN47UxjIWFhcyePRu3242qqowZM4Zhw4bx0ksv0bVrV0aMGMF7773HDz/8gMFgICYmplGXo0XgS4xozTHjNoqrD9IiphthhuDorg8kFbY8jlZsJcwQSWbiQIz6cJLNbUP+0qQQ56N7WhyTe7bhX1sPs2DLIW6/qJ3WkRpl6dLV8m9eNJpPxjA2NRnD6F2+aH9R9QH2l/xA85gutI7r7dVznYtQee7trmr2l6ylVVyfU+bFDJX210faHppth6Zpf7HVzoDXlqOisv4Po0iKDDv7g/yAPPcyhrGxZKUXoYnEyLa0S7iElrFnnl9KeE+YwUyXlMtkEnUhmkhSZBj3D+qExe7i6dU7tY7TKDk520+bH1mIM5GCUWhCp+hIiWqPTtFrHSWkHK3YRqW9dmC+zHsqRNO6pXdbOiZFs3DbEbbll2sd56ymTfsjN998s9YxRICQglFoyu1xkVe5C6fbpnWUoFdUfZBjFds5XLZRJk8XwgsMOh1PjuyOCjy0LFv+nYmgIgWj0FRR9QEOl/1EXuVuraMEtWpHGYdKf8SgM5KZOFB6F4XwkoFtkrmyY3M25ZayaIf3lmkTwtekYBSaSja3w6gPI79qNy6PQ+s4QcnlcbC3eDVu1U27xAGEG2O0jiREUHt0eFfCDToeX7WDKrtT6zhCNAkpGIWm9DoDzaO74Pa4yK/co3WcoHNicm6bq4r0mCwSIlpoHUmIoNcyNpK7LulAQZWNF3/Yq3UcIZqEFIxCc6lRHTDoTORV7sLtkU/jTcnlsWFzVRIbnkbL2J5axxEiZNzZL5MWMRG8tmE/B0urtI4jxAWTglFoTq8z0iy6Ey6Pg4KqfVrHCSpGfQTdUi8nM3GQjFsUwocijHoeG94Vp0fl4RXbtY5Tr+eff5l//vOfWscQAUIKRuEX0qI7ERueRqQpTusoQcHuqsbqrABqC3KjPjAmERYimIzt2JyBrZNYcaCAZfvztY5zmp49e9O3b1+tY4gAIQWj8AsGnYkuKSOJC2+udZSA51Hd7C3+npyCr7G5QncFByG0pigKj4/sjl5ReGTFduwut9aRhDhvUjAKv+NwWfGo8sJ6vg6XbaLKUUJCRCvC9FFaxxEipHVOjuHW3m05VFbN65sOaB3nFGPHjuTSSy/VOoYIEFIwCr9SYj3ClrzFFFUf1DpKQCqqPkBB1T4iTXG0je8n4xaF8AP3DepEQoSJF9btIb+yRus4dWpqarBarVrHEAFCCkbhV6LDkgE4btmBqno0ThNYqh2lHCzdgEFnpGPSEPQ6g9aRhBBAXLiJPw/pgtXp5olVO7SOI8R5kYJR+BWTPpIUc3tsriqKrYe1jhNQDpdtwnNicm5DtNZxhBAnuaF7a7qnxvLJjmNsOFaidRwhzpkUjMLvNI/pgqIo5FpyZC3Wc9AhaRDtEi+VybmF8EN6ncITo7oDMGdZNm6PvLaJwCIFo/A7YYYokiMzqHFaKK35Wes4fs/tcQG1cy6mmNtpnEYIcSYXt0hkQlYLsgsq+CD7iNZxhDgnUjAKv5Qek4WiKFQ7SrWO4tfKbcfZkvcZFTb/m+NNCHG6OUOziDTqeWr1TsptDk2z3HDD75gyZYqmGUTgkIJR+KVwYwy9m/+GVnG9tI7it+yuKvaXrMXtcaJX5AYXIQJBWnQE0wd0pLTGwV/X7NY0y+9//0emTZumaQYROKRgFH7LpI/UOoLfOjE5t9PtoE38RUSFJWkdSQjRSFMvakfbeDNvbz7EriKL1nGEaBQpGIVfszrL2V20knLbca2j+JXDZRupcpSSbM4gxZypdRwhxDkIM+iZO6IbblXl4eXZmt3c98gjf2bGjBmanFsEHikYhV9TVQ9lNcfJrcjROorfKKo+SEHVfsymONrGXyyTcwsRgEa1T2NEu1TWHinmqz3afCBeu3Y1K1as0OTcIvBIwSj8mtmUQHxEOhZ7IRZbgdZx/EJ0WDKx4al0kMm5hQhoc0d0w6hTeHRlDlanS+s4QjRICkbh99JjugKQa5FeRoBwQzRdUkbJ5NxCBLh2CVFM7dueXEsN//fjfq3jCNEgKRiF36vtUUuj3JZHpb1Y6ziaUFWVg6U/UuWQFSKECCbT+3cgxRzG3/+7l6MVsq6z8F9SMIqA0CKmGwB5laG5DmuuZTsFVfs4VpGtdRQhRBOKCjPy8LAsbC4Pf1kpV1GE/5KCUQSE6LAUMhIupm18P62j+Fx5zXGOWbIJM0TSLuFSreMIIZrYtVktuSg9gS/3HGft4SKfnTczswOdO3f22flEYJOCUQQERVFIjeqAUR+udRSfsruq2FeyFgUdHRIHh1z7hQgFOkXhyVHdUYCHlmfj8nh8ct7XXnubDz/80CfnEoFPCkYRUFRVpdh6mBpn8E92e2JybpfHQZv4vjI5txBBrEdaHDf2aM2e4kre2XxI6zhCnEYKRhFQKmzH2Ve8llzLdq2jeJ3DbcXlcZBiziDF3F7rOEIIL5s9pAsxYQaeW7ObYqvd6+f74ovFfPLJJ14/jwgOUjCKgBIb3pxIUxzF1kPYXJVax/GqcEM03dKuoI1Mzi1ESEiKDOOBQZ2psDt55vtdXj/fCy88x+OPP+7184jgIAWjCCiKotAiphuqCrmW4Lxj2uooq7vkbtCZZHJuIULIzb3a0jEpmve2HiY7v1zrOELUkYJRBJyEiFZEGGMorj6I3VWtdZwm5fLY2VO8mpyCb3C6bVrHEUL4mFGv44mR3VGBh5Zpt860EL8mBaMIOIqikB7TFY/q4XgQzcuoqir7S9Zhc1WRFt1J7ogWIkQNapPM2I7N2Zhbyqc7j2kdRwhACkYRoBIj2xAbnorZmKB1lCaTa9lOWc1x4sKb0SKmu9ZxhBAaenRYFuEGHXO/20GV3al1HCGkYBSBSafo6JIyipSo4Lh7uLwm95fJuc20TxwoN7kIEeJaxZm5s18mBVU2Xvphr9ZxhJCCUQQ+t8eJ2xO4n8BVVeVI+ebaybmTBmPUh2kdSQjhB+68JJP0mAhe23iAg6VVTX78zz77mtWrVzf5cUVwkoJRBLQqezGbj38W0HdMK4pC55QRZCYNJsqUqHUcIYSfiDQaeGx4VxxuD4+uaPq5Z2NjY4mLi2vy44rgJAWjCGgRxjgURSG/ajcuj0PrOOfsRGaTPpKEiBYapxFC+JsrOzZnQKsklh0oYPmB/CY9dn5+Hrm5uU16TBG8pGAUAU2vM9A8ugtuj4v8yt1axzknhVX72Zr3OZX2Iq2jCCH8lKIoPDGyG3pF4ZHl23G4m26d6UmTJnDFFVc02fFEcJOCUQS81KgOGHQm8ip3B8xYxmpHKYfKNqKqbpk+RwjRoM4psdzSuy0Hy6p5Y9MBreOIECUFowh4ep2RZtGdcHkcFFT5/92ELo+dvcXf41HdtE8cSLghWutIQgg/d/+gTiREmHh+3R4KqmRSf+F7UjCKoJAW3Qm9zkClw78v7548OXeL2G7ER6RrHUkIEQDiwk08OKQL1Q4XT6wK3Jv8ROCSglEEBYPORPe0K+mQOETrKA06XrlDJucWQpyXSd1b0y01lo9zjrIpt1TrOCLESMEogka4IcrvJ7xOiGhFfES6TM4thDhnep3Ck6NqP2j+eVk2HllnWviQQesAQjQlh9vKsYpsokxJfrkKTIQxhk7Jw7SOIYQIUBe3SOTarBYs2nGMD7OPMKlHm/M+1p///CixsRFNF04ENelhFEGnqPoguZYcPGrTTT9xITyqm73F31PtKNM6ihAiCMwZmkWkUc+Tq3dSYTv/+WdHjrxMptURjSYFowgqJn0kKVHtsbmqKLEe1joOAIfKNlJi/Tkg7uAWQvi/ZtERTO/fkRKrg/lr92gdR4QIKRhF0GkenYWiKORaclA1HuNTWLWfwqr9mE3xtI7ro2kWIUTwmNq3HW3izLz500H2FFvO6xi/+931XHXVVU2cTAQrKRhF0AkzmEk2Z1DjtFBa87NmOaocJRwq24BBZ6JD0mD0OhkyLIRoGmEGPXNHdsOtqsxZtv28Phzn5h7jyJEjXkgngpEUjCIopUdnoSho1svo8tjZV/w9Kh7aJw6QybmFEE1uVLtUhmeksuZIEd/szdM6jghyUjCKoBRujKFtfD8yEwdpMn2Nw12Dikp6jEzOLYTwDkVRmDuiK0adwqMrc6hxurWOJIKYFIwiaKVGZRJhjNHk3JHGOLqnjZXJuYUQXtU+MZrf923H0Qorr2zYp3UcEcSkYBRBz2IvpMpe7Jtz2QqwuSoBMOjCZHJuIYTXTe/fkRRzGC//sI9jFVat44ggJQWjCGo1Tgs7CpZyuPwnr49ltLkq2VO8mh0FS3F7XF49lxBCnBAdZmTO0CxqXG7mftf4daZHj75c7pIWjSYFowhqEcYY4iPSqbQXUWkv9Np5TkzO7fI4aBHbXe6IFkL41ISuLenTPJ7Pd+ey7khRox4za9YcHn/8cS8nE8FCCkYR9NJjugJwzLLda+c4OT1RLgAAHyJJREFUVLaBakcZKeZ2pEZleu08QghRH52i8MSo7ijAnOXbcXn8Y6UrETykYBRBLzosmdjwNCps+VTaG/fJ+1wUVO2jsOoAZlM8beL7NvnxhRCiMXo1i+eG7q3ZVWTh3S2Hz7r/3/72PPPmzfN+MBEUpGAUIaFFTDegdl7GpuRR3RyryJbJuYUQfuHBIV2IDjPw7JpdlFjtDe67ePEiPvzwQx8lE4FOCkYREqLDUoiPSMdsSmjSm190ip6uqWPomDREJucWQmgu2RzG/QM7UW5z8syaXVrHEUFECkYREhRFoVPyMFrG9miSqW5UVcXprv30HmYwExOeesHHFEKIpnBr7wwyE6P515bDbM8v1zqOCBJSMIqQo6oeXJ6GL9WczTFLNtn5X2J1lDVRKiGEaBpGvY4nRnZDBR5anq3J8qgi+HhtwJXdbufGG2/E4XDgdrsZPXo006ZNO2Wft99+m48//hi9Xk9CQgJPPfUU6emyjJrwHrurmp2Fy4kKSyQzceB5HaOsJpdcy3bC9GZMhsgmTiiEEBduSNsUrujQjK/35vHZzmNck9VS60giwHmtYDSZTCxYsACz2YzT6WTSpEkMHjyYnj171u3TuXNnFi1aREREBO+//z7PPfccL774orciCYFJH4lOp6fEepiWMd0JP8elA22uSvaXrENBR4ekIRh0YV5KKoQQF+bR4V1ZcaCAud/tYHRmM8ymU9/yExISMRr1GqUTJ6zd+zHHynYTbozi6t7TAdhyZBn7CjYSZjQD0Kf1aFokdDrtscfK9rDh4Beoqkpmal+6txzqtZxeuyStKApmc21DXS4XLpfrtLFjl1xyCREREQD07NmT/Px8b8URAqj9u0yP6YqqQm5l41dEAHB7XHWTc7eNvxizKcFLKYUQ4sK1jjNzR79M8qtsvPTD3tN+/sknn7N8+XINkomTtU/tw6is207b3qX5QMb3uofxve6pt1j0qB5+PLCEUVm3cnXv6Rwq2kq5tcBrOb06B4jb7eaaa67h559/ZtKkSfTo0eOM+37yyScMHjy4UceNjzc3VcQzSk4O7Tteg7n9SWoWxbadVDh+Jiq2PxGmqFN+fqa278hdixMLGalZZLXo5Yuomgjm5/5spO2hK1jbP3dcbz7ZeZRXN+znzqGdaZ90+lWVYG17Y2nd/rTYDCptpef8uOLKo0SHJxIdnghA2+Qe/Fyyk7hI79yE6dWCUa/Xs2TJEiwWC3feeSd79+6lQ4cOp+23ZMkScnJyeO+99xp13LKyalwu781in5wcTVFRpdeO7+9Cof2JYZ0oq1rP9sM/0jb+4rrtDbU9SmlNjMFCsql70P5+QuG5PxNpe2i2HYK//XOGZPGHJRu5++MfeXfCJXXb169fS2xsBFlZfTRMpy1vP/cGg+68O7l25a3nQOFmEqPS6ZsxlrBfjZm3OiyYw2LrvjeHxVJUefSC8jbEJ3dJx8TE0K9fP9asWXPaz9avX8+rr77KK6+8gslk8kUcIUiMbEO4IQqLrQBVbdyHj0hjHJlJg9ApMuZHCBE4rurUnEtbJrJ0fz4rDvzvkuWcObO45557NEwmzqRTs0u49qIHuKrXNCJNMWw8+JXWkbxXMJaWlmKxWACw2WysX7+ejIyMU/bZuXMnjzzyCK+88gqJiYneiiLEaXSKjs7Jw+meNhZFOfM/A6fbzq7CFVidFT5MJ4QQTUdRFJ4c1R2dAo+s2I7DLetMAzy3Zhd/+Xab1jHqFWGKRqfoUBQdmWl9Ka46dto+kaYYqu3/e2+qtlcQaTq3GznPhdcuSRcWFjJ79mzcbjeqqjJmzBiGDRvGSy+9RNeuXRkxYgTPPvssVqu17hNOs2bNePXVV8/7nB6PB4/HfcHZHQ4HLpfzgo8TqEKl/QYlArfbDfzvb+bktquqyt7S76mw5WE2JtI8OkujpL4TKs89gE6nR6eTqWhFaOiSEsvNvdry9uZD/HPTAe7ol6l1JE09t2YX89ftAaC62s79gzprnOhUVoelrvj7uWRHveMSk6JbYKkpodJWSqQphkNF2xjc8QavZVLUAJzRs74xjA6HHVX1oNdfeA1sMOi8OkbS34VS+90eJ/lVu9EpBppFdz6l7bmW7eRW5hAX3pzMhMFNskKMvwup597tQlF0mEy1UyMF+zi2hoRy2yF02l9W46D/a8tweFTWTx3JDVeNQKdTWL58rdbRfOrkYvGEmQM6eqVobMwYxtW7PyC/4iA2VzURxih6thpFfsVBSquPo6AQFR7Ppe1/Q6QpBqvdwrr9ixiVdSsAx0p3s+Hgl6h4aJ96ET1aDm/yNtS1xWtH9iFVVfF43ISHN80kygaDDgiNN836hFL7FY9CUc0BPKqbZrGdMBiMgIeymlyOV+0kwhhNh+RBGHShMb42lJ57g8GIzWZFVdWQ+DAgRHyEidlDujDr2208uWoHFTZHyP3t11csAnXbtOhpHNLp9F7BDml96903MiymrlgEaJHQqd4pd7whKApGj8fdJD2LIvTodQaaR3fhSPkW8iv30CaxR93k3DpFJucOdnq9Xl4/REiZ3KMN7245xL9zjhJtdwG1RZS/XZI9F063h2qnC6vDTbXTRbXDhdXp/tX/XfxnXx5rjxSf8ThaFo2BICheJaWHQFyI1KgOHCrbyJ7iVRyzbMOoi8DpttE+sb9Mzh3kFEUn6+yKkKLXKXRNjWVHoYXqUX8EfFcoOdwerL8UdNUOd93XVqf7l4LvfwVetdON1eGq3X5i2y8/t/5qm9Mj/4Z9ISgKRiEuRFlNLk6PDZfHjsNdjaqCooBOkX8eQojg8tyaXXy0vXauPk/c/26kOLlodLg9pxRoJ4q5E0VcbaHnPqWYO2XbL/vWnNj2y8+borCLMOiJMOoxmwykRUcQadRjNhowm/REGg2YTQYijad+ffL/P9+Vy4fbf6732N4axxgs5B1RhLzcyhxM+kjsripcHidGnQroyK3MIcncRut4QgjRJE4bv+euvSTNL0My5q/bw/Pr9tAU/XURRn1dMdcsJgLzrwq3SOOpX9cVfPVtM9VujzAa0Osu7Gri8IxU0mMifHbTSzCRglGEvBpnBQo6wg3RuDz2U7YLIUSwiv70SQAqJz5aty0tOpzMxGjMRgORJv0v//9VT90Zeu/MvxSBTVHYedOJwvBE0SjFYuNIwShCXoQxFqujHJPeTLjBzImrJhHG2IYfKIQQAeTXhdKvhVLhdKKdZnMYd/TOOMveAny0NKBo2IQJ49i48cfTvg4k11wzlr17d2sd47ykR3c9p+1CCBGo7h/UmZkDOp62PZSKxRPuH9SZR0f30DpGwJAeRtGgCRPGMWvWHPr27XfGfSwWCyUlxbRu3bbJznv48CGef/4Z9uzZRVxcPHfccQ9DhgxrsuOf7MQ4xdzKHGwuC5HGGNKju2o2fnHu3If56acN1NTYSEhI5MYbb2LcuKsBWLToI77++ksOHtzPyJGjeeihx055bF7ecebPf5qcnO2YTCaGDh3OtGkzMRgMZz32qFGDTjmW3W7nN7+ZwPTpDzTq2A09Zw2d99fOdp677prKzp056PW1a3onJSXzwQefNvrxQoS6E4Xh6x/Xfh+KxaI4d/IKKi7YwYP7SU9vQVhY08xX6HK5mD17JldffQ0vvPAPtm7dzKxZ02nbdiGtWrVuknP8WpK5DUnmNo1e6cTtdtcVLE1t8uRbmD37YUwmE0eOHObuu/9AZmZHOnXqTFJSMjffPIUNG37Abref9tj5858mPj6BJUv+Q1VVJdOn38lnn33CxIm/Peuxly1bU9d+q9XK+PGjGTZsZKOOfbbnrKHznmsbAKZPf+CMBWdjHi9EqLt/UGc+DDOgKIoUi6JR5JK0jxQXF/HQQ/dz5ZUjmTjxKj7++MMz7rt7904mT57ImDHDeOqpv5xSGBw+fIi77prKmDFDmTz5OtauXQ3AV199zgMPTK/b77e//Q1z5syq+/6aa8ayb1/941b+9a93uO668YwaNZjJkyeyatVKAB5//GEKCvKZNWsGo0YNYuHCBfU+/sCBfWRktAPAZrPx2GMP8ec/34/Vam3kb+dUP/98mJKSIq6//kb0ej19+vSlW7cefPvt140+Rm7uMe6//x7Gjh3BZZcN4d5776j72bJl/2Hy5OsYMWIA1103ns2bN6GqKu+99w5XX30FY8YM5eGHZ1NVVVX3mC+/XMy9997BvHlzGTNmGB99tBCAzz//jMmTJzJ69BBmzpxGWVnpebX5ZBkZ7TCZaleWUZTa/3JzaxeeHzJkOIMHDyUmpv7xlXl5xxk+fCRhYWEkJibRr19/Dh060Khjn2z16pXExSXQo0evRh37bM9ZY8/bmDaczYU+XohQERtuIi4iNFaxEhcuqHsYhw8fUO/2KVOmcuONNwNw//338tNPG0/bp0ePXrzwwt8B+Pe/P+DVV/9+2j4rV65rVA6Px8MDD0xn0KAhPPbYUxQWFnDvvXfSqlVr+vW79LT9ly79hvnzXyYiIoJZs6azYMGbTJ16By6Xi1mzpjN27FW88MI/yM7eyuzZM3nzzXfp2bM3L7/8PB6Ph9LSEpxOJzt2bAdq35itVivt2tW/2Hx6egv+7//+SUJCIt99t5zHHpvDhx8u5uGHH2fbtq1nvSR94MB+MjLac/x4Lg89dD+DBg3l1lt/f8pk6g88cC/Z2VvrfXz37j159tkXG/wdqqrKwYONf9N/4olHGTlyNE8//Twul4s9e3YB8MEH7/HVV5/z8MNzyczswKFDB4mMjOSNN14hO3sr//znAiIionj00Qd5++03uPvu2iJ8//597NiRw4QJ1zNr1hxcLhfvvvsWq1at5Omnnyc1NY3585/m9ddfYdashy643X/969N8880X2O12OnToyKWX1v+3/GsTJ97A8uVL6dXrIiorLfz3v+u4/fY/nfOxv/nmS8aMueKU57Axxz7Zr5+zxrapMed57bW/8+qrL9OqVWt+//s76N37ovPOKUSo+uMf7yIqSlayEo0T1AWjv9i1ayfl5WXceuvvgdoC7aqrrmbFiqX1FozXXnsdqalpANx002288MJzTJ16Bzt2bKempobJk29Bp9PRp09f+vcfyLJl3zJlyh+IjDSzb99ejh49wsUXX8r+/Xs4cuQwOTnZ9OjRE52u/g7l4cP/d9lxxIjLeO+9d9i1K4dBg4Y2qn0HDuxHURSmTfsj99wzs97Hna0gPFmrVm2Ii0vg/fff5frrb2Tz5k1s3br5lKLgbI4fP4bH48Hj8RAWFkb37j0pKyvj7bff4B//eIPMzA4AtGvXntLSEhYt+oiFCz8hKSkZl8vD0KEj+PLLJXXH279/HzfcMJmBA4cAUF1dxbvvvsVbby2kRYuWAFx55Xjmz3/6vNt9svvum8306feTk7OdLVs21fXOnU3Pnr35/PPPGD16CG63m8svv5LBg4ee07Hz8/PYunUzs2c/3OhjN+Y5a2ybztaGP/1pGm3btsVgMLJixVJmzZrBO++8T3p6i0b/DoQQcN11N5CcHE1RUaXWUUQACOqCsTE9gM89d/ob+q/HsV133Q1cd93pi4M3Vn5+HiUlxYwZM7Rum9vtoUePnvXun5KSVvd1amoziotr174sLi4iJSX1lMIvLa0ZxcVFQO0b5ZYtmzh27Bi9evUmOjqKLVt+YseO7fTs2eeM+b755ks++uh98vOPA1BTU0NFRXmj2naiF+n48Vyuv35So4vMhhgMBubN+ysvvvgcCxe+S6dOnRk+fBRGo7HRx3jkkSd49923eOedNxg4cAh33nkPmzb9SLt27eqKxRO2bdtCRkZ7kpKS67ZVVJSTmJhY9/3+/fuYOXN23febNm3A6XQyderNddtUVSUz8/S7D8+XXq+nR4+eLF36daPG4Hk8HmbOvJurrvoNr776FjU1VubNm8srr/yNO+64p9HH/s9/vqJ79540b57e6GM39jk7W5sa04asrP/dvX755VeybNm3/PDDWiZM+O05/Q6EEEI0XlAXjP4iNTWVZs2a8+GHnzVq/8LC/LqvCwrySUpKAmrvBi0sLMDj8dQVjQUF+bRs2QqoLRjXrfuevLzj3HTTrURFRbN06Tfs2LGda6+9rt5z5efn8eyzT/Lii6/QtWs39Ho9t946iRPL655tje7jx3MBePHF/+Oee/7ERRddTKdOXU7bb+bMaWRnb6n3GN2792L+/L+dsq19+0z+/vfX677/4x9vY8yYsQ1mOVmfPn3p06cvZWWl3HffPXz99RcYjSaioqJP27e8vJyoqKhTtq1Zs5phw0YAtb8jt9tF69Zt6n5usVgYNGgoTzzxTIM5zrXd9XG73Wcc73cyi8VCQUE+1157PSaTCZPJxBVXXMUbb/zfGYul+o79n/98zeTJN5+yrTHHPpfn7ExtOp82KIpS9/d6Po8XIlRNn34XYWEGnn76/K6EiNAiN734QOfOWURGRvLee+9gt9twu90cPLifXbt21Lv/p59+TGFhARZLBe+++xYjRlwGQJcuXQkLC2fhwndxuVxs3ryJdevWMHLkaKC2YNy8+SfsdjspKan06NGTH3/8gYqKijP2fNXU1KAoCvHxcUDtzTMnjzuLj0+oKwrrc+DAftq3b0+7du154IHam11O9IiebP78v7Fs2Zp6/6uvaNq/fx92ux2bzcb77/+LkpJirrhiXN3Pn3zyMZ588rF6M61evZKjR39GVVWsViuVlRYyMzvSoUNHsrO3sm/fXlRV5ejRnzl8+BCdO3chJ2d73VjPf/7zVcrKShk7dvwvWfaSkdH+lJ7djh07sWXLJvbsqZ17srq6ijVrVqGqpy6qda7tLisrZfnyb7Farbjdbn788QeWL/+Wiy7qC9TeQW6323+53O7GbrfjctUu7xUXF0ezZul89tknuFwuKisr+eabL+vGrp7t2ADZ2dsoLi48ZZhCY47d0HPWmPM29jyVlZX8+OMPde1euvQbtm3bXDe0ozE5hRC1tm3bwqZNm7SOIQKE9DD6gF6v59lnX+Tll19g4sTxOByOXwbr1z8Qf9SoMcyYcRfFxUUMHDiEm2+eAoDRaOSZZ57n+eef4b333iY5OYU5c/5S1/PVqlVrIiMj6u5sNZujaN48nbi4+DNOAdO2bQbXX38jf/jDbeh0CmPGjKV79/9NZPq7393CCy88xyuv/I2bbprCpEm/O+XxBw/ur3szHjx4KAcP7ufBB2fy97+/fkHT7Hz77dd88cVi3G4X3bv34oUX/nHKmLfCwoK6QvrXsrO38vzzz2K1VpOUlMzkybfQp09tcXLzzVOYNWs6lZUW0tKaMWfOXDp16sJNN93GHXdMwW63c9FF/XjppVcJDw8HaguhX1/G7tq1O7fc8nvmzHmA8vIyoqKiGTBgUBNckldYvHgRf/3rPDwelbS0NKZNm1k3dnLBgjd5++03Tvo9fcOtt/6eKVP+AMBTTz3LSy/NZ+HCBej1Onr37su0aTMadWyAr7/+giFDhhEZaT4tWcPHPvNzVl199vPOnDmNHj16ctNNtzV4HpfLxRtvvMKRI4fR63W0atWGefP+esp0S2fLKYQQ4twp6q+7RAJAWVn1KWMMXS4nAAZD48e4NaSxc/EFK39vv9Pp5JZbbmDBgg+bfDJmf2+7t4Va+09+7Qjlwf+h3HYI3fYPHz4AnU5h+fK1WkfRjLefe4NBR3z86R/AA5H0MIqAYzQaWbjwE61jCCGEECFDxjAKIYQQQogGSQ+jEEIIEYL69OlLeHjTDOUSwS8oCkZFUfB4QmfclRCiaaiqB53OO2uCC+HvnnvuxZAdvynOXVBcktbp9LjdLq1jCCECjNvtloJRCCEaIWh6GHU6PXZ7DXp9UzQptO4UPV0otz+U2w6h1H6324VOpz/r5PRCBKuFCxcQFRXO+PHXax1FBICg6GEEMJnCMBqbZhH12NjIJjlOoArl9ody2yG02m80hmEyNc1rhhCB6M03X+fll1/WOoYIEEHRw3iCTqc7ZTWO82UymZpsTsdAFMrtD+W2g7RfCCFE/YKmh1EIIYQQQniHFIxCCCGEEKJBUjAKIYQQQogGBeQYRr3e+3WuwRDatXQotz+U2w6h3X5pe+gKxfb36tULRQnNtp/Mm+33Rb3iK4qqqqrWIYQQQgghhP8KntJXCCGEEEJ4hRSMQgghhBCiQVIwCiGEEEKIBknBKIQQQgghGiQFoxBCCCGEaJAUjEIIIYQQokFSMAohhBBCiAYF5MTd3pKXl8cDDzxASUkJiqJw3XXXcfPNN2sdyyfsdjs33ngjDocDt9vN6NGjmTZtmtaxfM7tdnPttdeSmprKa6+9pnUcnxk+fDhmsxmdToder+fTTz/VOpJPWSwW5syZw969e1EUhaeeeopevXppHcvrDh48yPTp0+u+P3r0KNOmTeOWW27RLpQPvfPOO3z88ccoikKHDh2YN28eYWFhWsfymQULFvDxxx+jqioTJ04M+uf9wQcfZNWqVSQmJvLll18CUF5ezvTp08nNzSU9PZ0XX3yR2NhYjZP6KVXUKSgoUHNyclRVVdXKykr1sssuU/ft26dxKt/weDxqVVWVqqqq6nA41AkTJqhbtmzROJXvvfXWW+qMGTPUqVOnah3Fp4YNG6aWlJRoHUMzDzzwgPrvf/9bVVVVtdvtakVFhcaJfM/lcqn9+/dXjx07pnUUn8jPz1eHDRum1tTUqKqqqtOmTVMXLVqkcSrf2bNnjzp27FjVarWqTqdTvfnmm9XDhw9rHcurNmzYoObk5Khjx46t2/bMM8+or732mqqqqvraa6+pzz77rFbx/J5ckj5JSkoKWVlZAERFRZGRkUFBQYHGqXxDURTMZjMALpcLl8uFoigap/Kt/Px8Vq1axYQJE7SOInyosrKSjRs31j3vJpOJmJgYjVP53g8//EDLli1JT0/XOorPuN1ubDYbLpcLm81GSkqK1pF85sCBA3Tv3p2IiAgMBgN9+/Zl6dKlWsfyqr59+57We7hixQquvvpqAK6++mqWL1+uRbSAIAXjGRw7doxdu3bRo0cPraP4jNvtZvz48fTv35/+/fuHVNsBnnrqKe6//350utD8ZzFlyhSuueYaPvroI62j+NSxY8dISEjgwQcf5Oqrr+ahhx7CarVqHcvnvvrqK6688kqtY/hMamoqt912G8OGDWPgwIFERUUxcOBArWP5TIcOHfjpp58oKyujpqaG77//nvz8fK1j+VxJSUndB4Xk5GRKSko0TuS/QvOd8Syqq6uZNm0af/7zn4mKitI6js/o9XqWLFnC6tWryc7OZu/evVpH8pnvvvuOhIQEunbtqnUUTXzwwQd89tlnvPHGGyxcuJCNGzdqHclnXC4XO3fu5IYbbmDx4sVERETw+uuvax3LpxwOBytXrmTMmDFaR/GZiooKVqxYwYoVK1izZg01NTUsWbJE61g+065dO26//XamTJnC7bffTqdOnUL2w/IJiqKE3JW1cxHafx31cDqdTJs2jXHjxnHZZZdpHUcTMTEx9OvXjzVr1mgdxWc2b97MypUrGT58ODNmzOC///0v9913n9axfCY1NRWAxMRERo0aRXZ2tsaJfCctLY20tLS6HvUxY8awc+dOjVP51vfff09WVhZJSUlaR/GZ9evX06JFCxISEjAajVx22WVs2bJF61g+NXHiRD799FMWLlxIbGwsbdq00TqSzyUmJlJYWAhAYWEhCQkJGifyX1IwnkRVVR566CEyMjK49dZbtY7jU6WlpVgsFgBsNhvr168nIyND41S+M3PmTL7//ntWrlzJ888/zyWXXMJf//pXrWP5hNVqpaqqqu7rdevWkZmZqXEq30lOTiYtLY2DBw8CtWP52rVrp3Eq3/rqq68YO3as1jF8qnnz5mzbto2amhpUVQ3J5/3E5dfjx4+zdOlSxo0bp3Ei3xs+fDiLFy8GYPHixYwYMULjRP5LptU5yU8//cSSJUvo0KED48ePB2DGjBkMGTJE42TeV1hYyOzZs3G73aiqypgxYxg2bJjWsYQPlJSUcOeddwK141ivvPJKBg8erHEq33r44Ye57777cDqdtGzZknnz5mkdyWesVivr169n7ty5WkfxqR49ejB69Gh+85vfYDAY6Ny5M9dff73WsXzq7rvvpry8HIPBwKOPPhr0N3vNmDGDDRs2UFZWxuDBg7n77ruZOnUq9957L5988gnNmzfnxRdf1Dqm31JUVVW1DiGEEEIIIfyXXJIWQgghhBANkoJRCCGEEEI0SApGIYQQQgjRICkYhRBCCCFEg6RgFEIIIYQQDZKCUQgR0jp27Eh1dbXWMYQQwq9JwSiEEEIIIRokBaMQQgAej4ennnqKGTNm4HA4tI4jhBB+RQpGIUTIs9vt3Hvvvej1eubPn4/JZNI6khBC+BVZ6UUIEdI6duxIVlYWY8eOZcqUKVrHEUIIvyQ9jEKIkNevXz/WrFlDTU2N1lGEEMIvScEohAh5d911F/3792fKlClUVVVpHUcIIfyOFIxCCAFMnTqVMWPGcMstt1BeXq51HCGE8CsyhlEIIYQQQjRIehiFEEIIIUSDpGAUQgghhBANkoJRCCGEEEI0SApGIYQQQgjRICkYhRBCCCFEg6RgFEIIIYQQDZKCUQghhBBCNOj/ATJSQtgzvCzDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f64ae8246d0>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters = 9)\n",
        "kmeans = kmeans.fit(df_without_id)\n",
        "pred_y = kmeans.predict(df_without_id)\n",
        "\n",
        "print(kmeans, pred_y)"
      ],
      "metadata": {
        "id": "9SL9mx6BeMG6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a237d88-eeaa-4c90-f97c-2753d621fb67"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KMeans(n_clusters=9) [3 5 1 ... 8 6 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dir(kmeans)"
      ],
      "metadata": {
        "id": "0IgMYon52ljg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f5dc7b1-da35-4ffe-a0b0-bf64afe75da5"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getstate__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__setstate__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_algorithm',\n",
              " '_check_feature_names',\n",
              " '_check_mkl_vcomp',\n",
              " '_check_n_features',\n",
              " '_check_params',\n",
              " '_check_test_data',\n",
              " '_estimator_type',\n",
              " '_get_param_names',\n",
              " '_get_tags',\n",
              " '_init_centroids',\n",
              " '_more_tags',\n",
              " '_n_init',\n",
              " '_n_threads',\n",
              " '_repr_html_',\n",
              " '_repr_html_inner',\n",
              " '_repr_mimebundle_',\n",
              " '_tol',\n",
              " '_transform',\n",
              " '_validate_center_shape',\n",
              " '_validate_data',\n",
              " 'algorithm',\n",
              " 'cluster_centers_',\n",
              " 'copy_x',\n",
              " 'fit',\n",
              " 'fit_predict',\n",
              " 'fit_transform',\n",
              " 'get_params',\n",
              " 'inertia_',\n",
              " 'init',\n",
              " 'labels_',\n",
              " 'max_iter',\n",
              " 'n_clusters',\n",
              " 'n_features_in_',\n",
              " 'n_init',\n",
              " 'n_iter_',\n",
              " 'predict',\n",
              " 'random_state',\n",
              " 'score',\n",
              " 'set_params',\n",
              " 'tol',\n",
              " 'transform',\n",
              " 'verbose']"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "market_cluster_labels = kmeans.labels_\n",
        "market_cluster_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5OuCfaVb0fzK",
        "outputId": "7a7a7c26-6213-44ef-c3a7-443995d00eae"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 5, 1, ..., 8, 6, 6], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_without_id.shape)\n",
        "print(market_cluster_labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqPIoJx60nMb",
        "outputId": "133820bb-d164-466a-87f2-e604c7a3ae2d"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(432292, 94)\n",
            "(432292,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(market_cluster_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssgGX-6Z0rVw",
        "outputId": "a9c9b0f6-2e1c-46da-d963-33bf02eff1df"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "companies_and_clusters = pd.DataFrame({ 'company_ids': keep_ids_slice, 'cluster_labels': market_cluster_labels })"
      ],
      "metadata": {
        "id": "ovdsPoh90u0-"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "companies_and_clusters['cluster_labels'] = companies_and_clusters['cluster_labels'].map(lambda cluster: f\"empresa do grupo {int(cluster) + 1}\")"
      ],
      "metadata": {
        "id": "DGZPGZPR1Zhq"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "companies_and_clusters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8oXUEWJq1rhT",
        "outputId": "e90b6821-64d5-4d2c-9b4b-d712b0d485a1"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-85266650-d9a5-4910-9b8e-f8a2e14608fc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>company_ids</th>\n",
              "      <th>cluster_labels</th>\n",
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              "      <th>0</th>\n",
              "      <td>a6984c3ae395090e3bee8ad63c3758b110de096d5d8195...</td>\n",
              "      <td>empresa do grupo 4</td>\n",
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              "      <td>6178f41ade1365e44bc2c46654c2c8c0eaae27dcb476c4...</td>\n",
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              "      <th>432288</th>\n",
              "      <td>656f496e7e2fad708f0022d8ad97d2cb9344e839a5f3cb...</td>\n",
              "      <td>empresa do grupo 6</td>\n",
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              "    <tr>\n",
              "      <th>432289</th>\n",
              "      <td>9b933a5cf2149d1338f00fb459d34a16b9b0f2e8d3e8e1...</td>\n",
              "      <td>empresa do grupo 9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>432290</th>\n",
              "      <td>84db0f17467cf4f02439e01ade57150b6ac59d9dae53ed...</td>\n",
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              "      <th>432291</th>\n",
              "      <td>3d43e934e150b86be1e67524f5ba1018b27da9ef25566d...</td>\n",
              "      <td>empresa do grupo 7</td>\n",
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              "<p>432292 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ],
            "text/plain": [
              "                                              company_ids      cluster_labels\n",
              "0       a6984c3ae395090e3bee8ad63c3758b110de096d5d8195...  empresa do grupo 4\n",
              "1       6178f41ade1365e44bc2c46654c2c8c0eaae27dcb476c4...  empresa do grupo 6\n",
              "2       4a7e5069a397f12fdd7fd57111d6dc5d3ba558958efc02...  empresa do grupo 2\n",
              "3       3348900fe63216a439d2e5238c79ddd46ede454df7b9d8...  empresa do grupo 2\n",
              "4       1f9bcabc9d3173c1fe769899e4fac14b053037b953a1e4...  empresa do grupo 4\n",
              "...                                                   ...                 ...\n",
              "432287  469ee5fc984d0bb60aa2f40cda4d003d7c9b963d149ed3...  empresa do grupo 4\n",
              "432288  656f496e7e2fad708f0022d8ad97d2cb9344e839a5f3cb...  empresa do grupo 6\n",
              "432289  9b933a5cf2149d1338f00fb459d34a16b9b0f2e8d3e8e1...  empresa do grupo 9\n",
              "432290  84db0f17467cf4f02439e01ade57150b6ac59d9dae53ed...  empresa do grupo 7\n",
              "432291  3d43e934e150b86be1e67524f5ba1018b27da9ef25566d...  empresa do grupo 7\n",
              "\n",
              "[432292 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "companies_and_clusters.to_csv('normalized_data_clusters.csv')"
      ],
      "metadata": {
        "id": "SuDcJJq12Bzk"
      },
      "execution_count": 108,
      "outputs": []
    }
  ]
}