MLV1_GYMBUDDY_CL.ipynb

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        "<a href=\"https://colab.research.google.com/github/henriquebap/Machine_Learning_HB/blob/main/GYMBUDDY-IA/MLV1_GYMBUDDY_CL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Introducao ao Modelo de Recomendação de Treino e Dieta\n",
        "Neste projeto, desenvolvemos um modelo de Machine Learning para um aplicativo de academia que recomenda treinos e dietas personalizados com base nas informações dos usuários. A seguir, apresentamos o passo a passo detalhado do desenvolvimento e implementação do modelo.\n",
        "\n",
        "---\n",
        "## 1. Coleta e Pré-processamento dos Dados\n",
        "- Coleta dos Dados:\n",
        "  - Esta Primeira versao do modelo esta usando dados gerados por inteligencias artificiais [ChatGPT](https://chatgpt.com/?oai-dm=1) [Gemini](https://gemini.google.com/app) e [Copilot](https://copilot.microsoft.com/)\n",
        "  - Contem Informacoes sobre Nome, Idade, Sexo, Altura, Ativa ou nao na academia, Peso, Tipo de Corpo, Condicao de Deficiencia\n",
        "  - Calculamos o IMC e geramos se esta em um condicao sobrepeso ou nao.\n",
        "- Pré-processamento dos Dados:\n",
        "  - Limpeza e tratamento de valores ausentes\n",
        "  - Transformação de variáveis categóricas, como tipo de corpo, usando codificação numérica.\n",
        "  - Normalização de variáveis numéricas, como altura, peso e IMC, para garantir que todas as características tenham a mesma escala.\n",
        "---\n",
        "## 2. Engenharia de Recursos e Criação do Alvo\n",
        "- Criação do Alvo:\n",
        "  - Definição de objetivos de treino com base no IMC e na condição de deficiência dos usuários:\n",
        "    - Treino de Força para IMC entre 18,5 e 24,9.\n",
        "    - Treino de Perda de Peso para usuários com sobrepeso.\n",
        "    - Treino Especial para Deficientes para usuários com condição de deficiência.\n",
        "    - Treino Ganho de Massa para pessoas com o IMC abaixo de 18,5\n",
        "- Engenharia de Recursos:\n",
        "  - Criação de novos recursos derivados, como índice de massa corporal (IMC) e classificação de sobrepeso.\n",
        "---\n",
        "## 3. Modelagem de Machine Learning\n",
        "- Seleção de Algoritmo:\n",
        "  - Foram treinamos 3 algoritmos de Machine Learning e 1 Rede Neural Artificial\n",
        "    - RandomForestClassifier\n",
        "    - DecisionTreeClassifier\n",
        "    - SVC (Support Vector Machines)\n",
        "  - Rede Neural\n",
        "    - Keras\n",
        "- Treinamento do Modelo:\n",
        "  - Divisão dos dados em conjuntos de treinamento e teste.\n",
        "  - Treinamento do modelo usando o conjunto de treinamento para prever os objetivos de treino dos usuários.\n",
        "- Avaliação do Modelo:\n",
        "  - Avaliação da precisão do modelo usando métricas como acurácia, precisão, recall e F1-score.\n",
        "---\n",
        "## 4. Implementação do Modelo\n",
        "- Previsão de Novos Dados:\n",
        "  - Utilização do modelo treinado para prever os objetivos de treino de novos dados.\n",
        "### Integração com o ChatGPT:\n",
        "  - Para integrar o modelo com a API do ChatGPT da OpenAI, é necessário utilizar a chave da API fornecida pelo OpenAI. A chave da API é essencial para autenticar suas solicitações à API e obter respostas do modelo.\n",
        "  - Geração de mensagens de recomendação de treino e dieta com base nas previsões do modelo.\n",
        "  - Envio das mensagens para a API do ChatGPT para interação com os usuários finais.\n",
        "### Chave da **API** do OpenAI:\n",
        "- Para utilizar a API do OpenAI, você precisará de uma chave de API válida. Você pode obter sua chave da API ao se inscrever no OpenAI e criar um projeto de integração na plataforma. Para mais informações, visite o site do OpenAI em [OpenAI Platform.](https://openai.com/)\n",
        "  \n",
        "---\n",
        "## 5. Considerações Finais e Próximos Passos\n",
        "- Considerações Finais:\n",
        "  - Analisar e Refletir sobre possiveis melhorias e limitacoes encontradas no modelo durante o desenvolvimento\n",
        "- Próximos Passos:\n",
        "  - Expansão do modelo para incluir mais variáveis e dados.\n",
        "  - Desenvolver a API com o GPT e os inputs e outputs\n",
        "  - Desenvolver o alvo e os seus parametros para a classificacao\n",
        "  - Melhoria da interface do usuário e da experiência do usuário final.\n",
        "---\n",
        "## Contato\n",
        "LinkedIn: [Henrique Baptista](https://www.linkedin.com/in/henrique-baptista777/)\n",
        "\n",
        "GitHub: [henriquebap](https://github.com/henriquebap)\n",
        "\n",
        "Email: henriquebaptista2003@gmail.com\n",
        "\n",
        "**Para mais informações ou para entrar em contato, sinta-se à vontade para usar os links acima.**"
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      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
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      }
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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "import requests\n",
        "import json\n",
        "import time"
      ]
    },
    {
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      "source": [
        "# Dataset Coleta de dados"
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      "source": [
        "dataframe = pd.read_csv(\"/content/datagpt2.csv\")"
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "dataframe.head()"
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              "       Nome  Idade Sexo  Treina  Altura  Peso Tipo de Corpo  Deficiente\n",
              "0     Maria     28    F    True    1.65    55     Mesomorfo       False\n",
              "1      João     35    M    True    1.80    85     Endomorfo       False\n",
              "2       Ana     22    F    True    1.70    60     Ectomorfo        True\n",
              "3     Pedro     30    M   False    1.75    70     Mesomorfo       False\n",
              "4  Carolina     40    F    True    1.60    70     Endomorfo       False"
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            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w6Bg_iJ63Xok",
        "outputId": "00088c91-d25c-4b67-d8c1-fda392625f9d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Nome  Idade Sexo  Treina  Altura  Peso Tipo de Corpo  Deficiente\n",
              "379   Otávio     42    M    True    1.81    79     Mesomorfo       False\n",
              "380    Paula     21    F    True    1.66    58     Ectomorfo       False\n",
              "381  Ricardo     36    M   False    1.79    76     Endomorfo        True\n",
              "382    Sofia     24    F    True    1.70    62     Mesomorfo       False\n",
              "383    Tiago     29    M    True    1.83    81     Endomorfo       False"
            ],
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              "      <td>Otávio</td>\n",
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              "      <td>Paula</td>\n",
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              "    <tr>\n",
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              "      <td>Tiago</td>\n",
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              "      <td>True</td>\n",
              "      <td>1.83</td>\n",
              "      <td>81</td>\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Nome\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Paula\",\n          \"Tiago\",\n          \"Ricardo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 21,\n        \"max\": 42,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21,\n          29,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treina\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07395944834840244,\n        \"min\": 1.66,\n        \"max\": 1.83,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.66,\n          1.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Peso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 58,\n        \"max\": 81,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          58,\n          81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo de Corpo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Mesomorfo\",\n          \"Ectomorfo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deficiente\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BhFN2gI5e0U",
        "outputId": "84e7623e-7c31-4fb3-d23f-a74b9d528870"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(384, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eEFxRCk06XE6",
        "outputId": "2c07dc21-3ac7-4867-87d6-4b6b2b491fce"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 384 entries, 0 to 383\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Nome           384 non-null    object \n",
            " 1   Idade          384 non-null    int64  \n",
            " 2   Sexo           384 non-null    object \n",
            " 3   Treina         384 non-null    bool   \n",
            " 4   Altura         384 non-null    float64\n",
            " 5   Peso           384 non-null    int64  \n",
            " 6   Tipo de Corpo  384 non-null    object \n",
            " 7   Deficiente     384 non-null    bool   \n",
            "dtypes: bool(2), float64(1), int64(2), object(3)\n",
            "memory usage: 18.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FImBKt06a0-",
        "outputId": "5718b8c6-9fb8-4d02-c1b2-5474a2fa5a48"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Nome', 'Idade', 'Sexo', 'Treina', 'Altura', 'Peso',\n",
              "       'Tipo de Corpo', 'Deficiente'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QW7fCKQG6fhX",
        "outputId": "d1c8614f-c291-4789-91bb-009d6f1845c3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Nome              object\n",
              "Idade              int64\n",
              "Sexo              object\n",
              "Treina              bool\n",
              "Altura           float64\n",
              "Peso               int64\n",
              "Tipo de Corpo     object\n",
              "Deficiente          bool\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBUE2iOS6l23",
        "outputId": "23a314b2-7e0b-466a-82b7-0f1ab94bc867"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Nome             0\n",
              "Idade            0\n",
              "Sexo             0\n",
              "Treina           0\n",
              "Altura           0\n",
              "Peso             0\n",
              "Tipo de Corpo    0\n",
              "Deficiente       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Engenharia de Recurso\n",
        "## Criando Variavel IMC e Sobrepeso de acordo com os dados Peso e Altura\n",
        "Fonte [Tabela IMC](https://www.programasaudefacil.com.br/calculadora-de-imc)"
      ],
      "metadata": {
        "id": "rog-Pc_tFYbQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe['imc'] = dataframe['Peso'] / (dataframe['Altura'] ** 2)\n",
        "\n",
        "dataframe['sobrepeso'] = dataframe['imc'] > 30\n",
        "dataframe['sobrepeso'] = dataframe['sobrepeso']"
      ],
      "metadata": {
        "id": "3K54gE8y8XdI"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dZ7Yaait883a",
        "outputId": "a488866c-1dfd-4910-e3f8-98b96902158d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Nome  Idade Sexo  Treina  Altura  Peso Tipo de Corpo  Deficiente  \\\n",
              "0     Maria     28    F    True    1.65    55     Mesomorfo       False   \n",
              "1      João     35    M    True    1.80    85     Endomorfo       False   \n",
              "2       Ana     22    F    True    1.70    60     Ectomorfo        True   \n",
              "3     Pedro     30    M   False    1.75    70     Mesomorfo       False   \n",
              "4  Carolina     40    F    True    1.60    70     Endomorfo       False   \n",
              "\n",
              "         imc  sobrepeso  \n",
              "0  20.202020      False  \n",
              "1  26.234568      False  \n",
              "2  20.761246      False  \n",
              "3  22.857143      False  \n",
              "4  27.343750      False  "
            ],
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              "      <td>1.65</td>\n",
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              "      <td>False</td>\n",
              "      <td>20.202020</td>\n",
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              "      <th>1</th>\n",
              "      <td>João</td>\n",
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              "    <tr>\n",
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              "      <td>Ana</td>\n",
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              "      <td>1.70</td>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Pedro</td>\n",
              "      <td>30</td>\n",
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              "      <td>False</td>\n",
              "      <td>1.75</td>\n",
              "      <td>70</td>\n",
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              "      <td>22.857143</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Carolina</td>\n",
              "      <td>40</td>\n",
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              "      <td>True</td>\n",
              "      <td>1.60</td>\n",
              "      <td>70</td>\n",
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              "            'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 384,\n  \"fields\": [\n    {\n      \"column\": \"Nome\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 86,\n        \"samples\": [\n          \"Sergio\",\n          \"Maria\",\n          \"Giovanni\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 18,\n        \"max\": 45,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          29,\n          37,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treina\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07570566181791216,\n        \"min\": 1.55,\n        \"max\": 1.92,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          1.57,\n          1.81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Peso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 48,\n        \"max\": 95,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          53,\n          64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo de Corpo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Mesomorfo\",\n          \"Endomorfo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deficiente\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4151531818051364,\n        \"min\": 18.818924310286427,\n        \"max\": 29.065926806711587,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          21.773842357381334,\n          24.898143956541425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sobrepeso\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analise dos Dados"
      ],
      "metadata": {
        "id": "KprMWZgCBNjX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Histograma da idade\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(dataframe['Idade'], bins=20, color='skyblue', edgecolor='black')\n",
        "plt.title('Distribuição da Idade')\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Frequência')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "VTtpTew1BRUP",
        "outputId": "3f1e4cda-3184-424f-daf9-dcfeac4be715"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Histograma da altura\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(dataframe['Altura'], bins=20, color='lightgreen', edgecolor='black')\n",
        "plt.title('Distribuição da Altura')\n",
        "plt.xlabel('Altura (m)')\n",
        "plt.ylabel('Frequência')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TfbLSzoDBaPo",
        "outputId": "7b261c6d-3223-42f5-d43d-26c375b50b49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Histograma do peso\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(dataframe['Peso'], bins=20, color='salmon', edgecolor='black')\n",
        "plt.title('Distribuição do Peso')\n",
        "plt.xlabel('Peso (kg)')\n",
        "plt.ylabel('Frequência')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kISI-8NxBe0H",
        "outputId": "a35f66cc-3e2e-41a2-fbc1-a07d08588978",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico de dispersão entre altura e peso\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(dataframe['Altura'], dataframe['Peso'], color='orange', alpha=0.5)\n",
        "plt.title('Dispersão entre Altura e Peso')\n",
        "plt.xlabel('Altura (m)')\n",
        "plt.ylabel('Peso (kg)')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "E-grd29ABiko",
        "outputId": "dd3a66c4-7d1d-4b1e-c135-7dffdd23306c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot do IMC por tipo de corpo\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.boxplot([dataframe[dataframe['Tipo de Corpo'] == 'Mesomorfo']['imc'],\n",
        "             dataframe[dataframe['Tipo de Corpo'] == 'Endomorfo']['imc'],\n",
        "             dataframe[dataframe['Tipo de Corpo'] == 'Ectomorfo']['imc']],\n",
        "            labels=['Mesomorfo', 'Endomorfo', 'Ectomorfo'])\n",
        "plt.title('Boxplot do IMC por Tipo de Corpo')\n",
        "plt.xlabel('Tipo de Corpo')\n",
        "plt.ylabel('IMC')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dr5rQNH7BqrE",
        "outputId": "4350d3ad-a57d-481d-ce93-0e0e4734ae26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico de barras da distribuição de tipos de corpo\n",
        "plt.figure(figsize=(8, 6))\n",
        "dataframe['Tipo de Corpo'].value_counts().plot(kind='bar', color='lightblue')\n",
        "plt.title('Distribuição de Tipos de Corpo')\n",
        "plt.xlabel('Tipo de Corpo')\n",
        "plt.ylabel('Frequência')\n",
        "plt.grid(axis='y')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "miWbCE8jBwKk",
        "outputId": "d1587fa9-8b3d-4da7-e117-9b9ef3053160",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico de pizza da distribuição de sexo\n",
        "plt.figure(figsize=(8, 6))\n",
        "dataframe['Sexo'].value_counts().plot(kind='pie', autopct='%1.1f%%', colors=['skyblue', 'lightgreen'])\n",
        "plt.title('Distribuição de Sexo')\n",
        "plt.ylabel('')\n",
        "plt.legend(['Feminino', 'Masculino'], loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "d4H7udwjBzKo",
        "outputId": "3d03a0e8-a86d-416d-dce5-74169b67784c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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CMYiJiIgEYhATEREJxCAmIiISiEFMREQkEIOYiIhIIAYxERGRQAxiIiIigRjEREREAnGvaSKiFuL1euF2u0WXQQGg0+n858tuLgYxEVGQKYqC48ePo7y8XHQpFEBxcXFITk6GJEnNehwGMRFRkJ0J4datW8NsNjf7g5vEUhQF1dXVKC4uBgCkpKQ06/EYxEREQeT1ev0hnJCQILocChCTyQQAKC4uRuvWrZvVTc3JWkREQXRmTNhsNguuhALtzHva3HF/BjERUQtgd3TkCdR7yiAmIiISiEFMREQh4emnn0bv3r0bdZ+MjAzMmjUrKPW0FElRFEV0EUREkcrhcCAvLw+ZmZkwGo3+4y/+eqJF63i0T2Kj7zNp0iTMnTv3vOP79+9Hp06dAlFWDVarFU6ns1GT2kpKSmCxWISMwdf13jYWZ00TEVGdrr32WsyePbvGsaSkpKA8V1RUFKKiohp1n2DV0pLYNU1ERHUyGAxITk6u8SPLMpYuXYq+ffvCaDSiQ4cOmDlzJjwej/9+kiTh3XffxejRo2E2m9G1a1ds3LgRBw4cwJAhQ2CxWDBgwADk5ub673Nu1/SkSZMwbtw4/POf/0RKSgoSEhJw77331pilfG7XtCRJ+Pe//43rr78eZrMZnTt3xhdffFHjNX377be49NJLYTAYkJKSgkcffbRG7S2NQUxERI3y/fff449//CMeeOAB7Nq1C++++y7mzJmD559/vsbtnn32Wfzxj3/E1q1b0aVLF9x2222455578Nhjj2Hz5s1QFAXTp0+v97nWrVuH3NxcrFu3DnPnzsWcOXMwZ86ceu8zc+ZM3Hzzzdi+fTtGjhyJ22+/HaWlpQCAo0ePYuTIkejXrx+2bduGt99+G//3f/+H5557rln/Js3BICYiojotW7bM32UcFRWFm266CTNnzsSjjz6KiRMnokOHDrj66qvx7LPP4t13361x38mTJ+Pmm29GVlYWHnnkERw6dAi33347hg8fjq5du+KBBx7A+vXr633++Ph4vPHGG+jSpQtGjx6NUaNGYe3atfXeZ9KkSZgwYQI6deqEF154AVarFT///DMA4K233kK7du38jzlu3DjMnDkTL7/8Mnw+X7P+rZqKY8RERFSnK6+8Em+//bb/ssViQc+ePbFhw4YaLWCv1wuHw4Hq6mr/xKmePXv6r2/Tpg0A4KKLLqpxzOFwoLKyEjExMbU+f/fu3WvsWpWSkoIdO3bUW/PZz2uxWBATE+PfjnL37t24/PLLa6wBzsnJgdVqRUFBAdq3b1/vYwcDg5iIiOpksVjOmyFttVoxc+ZM3HDDDefd/uzZwzqdzv/3M8FX27H6WqJn3/7MfS7Ucm3KfURiEBMRUaP07dsXe/fuDcoSpmDr2rUrFi9eDEVR/F8ENmzYgOjoaKSlpQmpiWPERCFg0qRJkCTpvJ8DBw6ILo3oPE8++SQ+/PBDzJw5Ezt37sTu3bsxf/58PPHEE6JLu6Bp06bhyJEjuO+++7Bnzx4sXboUTz31FGbMmAGNRkwkMoiJQsS1116LY8eO1fjJzMwUXRbReYYPH45ly5Zh9erV6NevH/r3749XX30V6enpoku7oLZt22L58uX4+eef0atXL/zpT3/CnXfeKfRLBHfWIgoBkyZNQnl5OT7//HPRpVCABWr3JQo9gXpv2SImIiISiEFMFCJqW69JRJGPs6aJQkRt6zWJKPIxiIlCRG3rNYko8rFrmoiISCAGMRERkUAMYiIiIoE4RkwUAi50WjciilxsERMREQnEICYiorA3adIkjBs3zn95yJAhePDBB4XV0xjsmiYiEuC1stda9PkeiH+g0feZNGkS5s6di3vuuQfvvPNOjevuvfdevPXWW5g4cWJIDq0sWbLkvNMhhiq2iImIqE7t2rXD/PnzYbfb/cccDgc++eQTtG/fXmBl9WvVqhWio6NFl9EgDGIiIqpT37590a5dOyxZssR/bMmSJWjfvj369OnjP7Zy5UoMHDgQcXFxSEhIwOjRo5Gbm+u/3uVyYfr06UhJSYHRaER6ejr+/ve/+68vLy/HPffcgzZt2sBoNKJHjx5YtmwZAODpp59G7969a9Q1a9YsZGRk1Fn3uV3TGRkZeOGFFzBlyhRER0ejffv2eO+992rcZ8eOHRg6dChMJhMSEhIwdepUWK3WxvxzNQmDmIiI6jVlyhTMnj3bf/mDDz7A5MmTa9zGZrNhxowZ2Lx5M9auXQuNRoPrr78ePp8PAPD666/jiy++wIIFC7B37158/PHH/iD1+XwYMWIENmzYgI8++gi7du3Ciy++CFmWA/o6Xn75ZVxyySX49ddfMW3aNPz5z3/G3r17/fUPHz4c8fHx2LRpExYuXIg1a9Zg+vTpAa2hNhwjJiKiet1xxx147LHHkJ+fDwDYsGED5s+fj/Xr1/tvM378+Br3+eCDD5CUlIRdu3ahR48eOHz4MDp37oyBAwdCkqQa5y5es2YNfv75Z+zevRtZWVkAgA4dOgT8dYwcORLTpk0DADzyyCN49dVXsW7dOmRnZ+OTTz6Bw+HAhx9+6N/n/Y033sCYMWPwv//7v2jTpk3A6zmDLWIiIqpXUlISRo0ahTlz5mD27NkYNWoUEhMTa9xm//79mDBhAjp06ICYmBh/a/fw4cMATk382rp1K7Kzs3H//fdj9erV/vtu3boVaWlp/hAOlp49e/r/LkkSkpOTUVxcDADYvXs3evXqVeNkKzk5OfD5fP5Wc7CwRUxERBc0ZcoUfzftm2++ed71Y8aMQXp6Ot5//32kpqbC5/OhR48ecLlcAE6NNefl5WHFihVYs2YNbr75Zlx11VVYtGgRTCZTvc+t0WigKEqNY263u9Gv4dxZ1JIk+bvORWKLmIiILujaa6+Fy+WC2+3G8OHDa1x38uRJ7N27F0888QSGDRuGrl27oqys7LzHiImJwS233IL3338fn376KRYvXozS0lL07NkTBQUF2LdvX63PnZSUhOPHj9cI461btwb09XXt2hXbtm2DzWbzH9uwYQM0Gg2ys7MD+lznYhATEdEFybKM3bt3Y9euXedNooqPj0dCQgLee+89HDhwAN988w1mzJhR4zavvPIK5s2bhz179mDfvn1YuHAhkpOTERcXh8GDB2PQoEEYP348vv76a3/LeeXKlQBOzYAuKSnBSy+9hNzcXLz55ptYsWJFQF/f7bffDqPRiIkTJ+K3337DunXrcN999+EPf/hDUMeHAXZNExEJ0ZQNNkSLiYmp9bhGo8H8+fNx//33o0ePHsjOzsbrr7+OIUOG+G8THR2Nl156Cfv374csy+jXrx+WL18OjeZUe3Dx4sV4+OGHMWHCBNhsNnTq1AkvvvgigFOt1bfeegsvvPACnn32WYwfPx4PP/zwecuPmsNsNmPVqlV44IEH0K9fP5jNZowfPx6vvPJKwJ6jLpJybsc7EREFjMPhQF5eHjIzM2E0GkWXQwEUqPeWLWIigbw+BTaPDza3DzbPqb87PD64fApcXgVuH+DyKfCc+VEAj0+BD4AEQCOd+lOSAAnSWX8/dZ1B1sAoSzDU+Pn9mFGWYNFqYNRylIpIFAYxUZD4FAUVLh/KnF6UOr0od3phdftg8/hQ7VZg9fjg9IZGh5ReIyFKp0GMXoNo3ekfvQYxOhnReg3i9DL0siS6TKKIxCAmaiar24diu8cfuGWnfypcPvhCI2cvyOVTUHq6/rrE6DRINMpIMMpINGpP/ymzNU3UTAxiokYod3pRZPegqNqD46f/tHnCJG2bqdLtQ6Xbh4NVNddvWrQSEoxatDbJSDXrkGrRIs4Q2K0JiSIZg5ioDnaPD0esbhy1eXC82oMiuweOEOlKDiU2jwKb1Y3DVjcABwDArJWQYtYi1aI79adZq/qWM+fFRp5AvacMYqLTqj0+HLa6ccTqxuEqN0ocdXfTUv2qPQpyK93Irfy99dzKICPNokV6tA4Z0XpYdOoI5jO7OVVXV19wBykKL9XV1QDO37GrsRjEpFoOjw+Hqk615A5b3TjB4A2qM2PQ20udAIAko4yMaB0yY/RoF6WDThOZk8FkWUZcXJx/T2Oz2QxJiszXqhaKoqC6uhrFxcWIi4tr9lmiuI6YVKXU4cX+CicOVLpw1OqB+F1mCQBkCWhr0SEjWocOMXokmyOrjaAoCo4fP47y8nLRpVAAxcXFITk5udlfrBjEFNF8ioICmwcHKlw4UOGqd1YwhY5YvQZZsXpkxxnQ1qKNmBak1+tt0skKKPTodLqAnS+ZQUwRx6soyKt0Y3eZE7mVLk6wCnNRWg06x+mRHatH+2gdNBESykRnMIgpYhRY3dhV5sTucifsKllSpDZGWULnWD26xhuQwVCmCMEgprB20uHBzlIndpU5Ue7iiK+aROk06B5vQI9WBiSZImtMmdSFQUxhx+HxYUepEztLnThu94guh0JAskmLngkGdGtlgFFWx7IoihwMYgobx6s92FJix+5yJ9xs/FIttBKQHWdArwQj2kc3b20nUUthEFNI8/gU7C5z4tcTDhRWs/VLDZdklHFJkgndWxmgjdA1yhQZGMQUksqdXvx6woHtpQ5OvKJmMWkl9Ekwom+SCVEq2c2LwguDmELKUZsbPxXZcaDCBf5iUiDJEtAlzoB+rU0Rt2EIhTcGMQmnKKf2Jf6pqBoFNnY/U/ClWbS4tLUJnWP1EbNZCIUvBjEJoygK9pS78OPxap5ggYRobZKRk2xGFgOZBGIQU4vzKQp2ljqxscjOLScpJDCQSSQGMbUYRVGwu8yF747ZuPkGhSQGMonAIKYWcajShXWFNhTZ2QKm0JdklDEwxYzsOIPoUkgFGMQUVMerPVhfaMOhKp5xhsJPmkWLYW0tSLFwcxAKHgYxBUW504tvC23YXe4SXQpRs3WPN2Bwqhkx+sCc9o7obAxiCiiHx4fvj1fj1xMO+PibRRFEKwGXtjahfxsz9DLHjylwGMQUEIqiYEepE+sLbajmTlgUwSxaCYNSLOiZYOCELgoIBjE1W7Hdg9VHrNyMg1SltUnG8HZRaMvxY2omBjE1mdPrw/fHqvFLiYPbUZIqSQD6JBoxONUMA0+/SE3EIKYm2VXqxDdHbbB6uB6YKFqnwdVpFmRxuRM1AYOYGqXc6cWKw1bkW7kciehcnWP1uCbNgmjOrqZGYBBTg/16wo51R6vh4nRoojoZNBIGpZrRN9HIyVzUIAxiuqBK16lWcB435SBqsLYWLUanRyPewNYx1Y9BTPXaftKBtUdtcHr5a0LUWHqNhGFpFvRKMIouhUIYg5hqZXX7sPKwFQcquTMWUXNlxeoxon0UTFrOrKbzMYjpPHvKnFh1xAo7W8FEAROl1WBkehQ6xOhFl0IhhkFMfh6fgm+O2rDlhEN0KUQR6+IkI65MtUCr4UQuOoVBTACAMqcXn+dV8jSFRC0g0ShjbEY0Wpu0okuhEMAgJuwpc2LFYSucXJZE1GJ0GuDadlHo3ooTudSOQaxiXp+CteyKJhKqb6IRw9IskLnmWLUYxCpV7vTi80NVOF7NEzUQiZZm0WJcZgyidJxVrUYMYhXKq3Th80NVXBtMFEIsWgnXZcagfRTP5qQ2DGKV2Vxix9oCG8+WRBSCNACGtLXg0tYm0aVQC2IQq4RPUfB1gQ2/cjyYKOR1izdgZPsoLnFSCQaxCjg8PnyWV8UzJhGFkTSLFuM7xHA3LhVgEEe4kw4PFh2sRJmT5w0mCjetDDJu6hjDE0dEOAZxBDtU6cJnnJRFFNZMWgk3dohBWwsncUUqBnGE2n7SgZWHrWA7mCj8aSVgdHo0usQbRJdCQcAgjkA/F9vxzVGb6DKIKMCGpJrRv41ZdBkUYAziCPNtoQ0bi+yiyyCiIOmbaMTVaRZI3IkrYjCII4SiKFh1xIatJ7k8iSjSdYs3YHR6FDQM44jAII4AXp+CL/OrsKfcJboUImohnWL1GJcRzbXGEYBBHOZcXgWf5VUir4prhInUJj1Kh/EdYqCXGcbhjEEcxhxeHxYcqEQhT9xApFppFi1u6hgDg8yNP8IV37kw5fT6sDCXIUykdgU2DxbkVsLh5WLFcMUgDkMur4KFuZU4amMIExFw1ObB/AOVcHgYxuGIQRxm3D4Fiw5WooAhTERnOV7twae5lXBxJ72wwyAOIx6fgsUHK3GYJ28golocqz61t7zHxzAOJwziMOHxKVhysBKHODuaiOpx2OrGZ3mV8HIebthgEIcBr3JqidJBhjARNUBupRvLDlWBi2LCA4M4xCmKgi8PVSG3kiFMRA23u9yFlUesosugBmAQh7g1R23cMYuImmTbSSdPABMGGMQhbOPxavxSwr2jiajpfi62Y8PxatFlUD0YxCFqx0kHvj3G/3mIqPm+P1aN30r5pT5UMYhD0KEqF1ZwbIeIAmjFYSuOcOljSGIQh5gSuwef5VWBywCJKJC8CrDkYCXKnF7RpdA5GMQhxOo+tX+0kzvjEFEQ2E9vj8utMEMLgzhEnNk1q9LN/0GIKHhKnV58llfFDT9CCIM4RKw6YsUxnkmJiFpAvtWNVZyHEjIYxCFgS4kdO0qdossgIhXZftKJn4q4MiMUaEUXoHZHrG6s4YL7iLXmnZew9r1/1DiWlNEJM5ZsBAD8vPhDbF25GIV7tsNps+LJbw/AFB1b72P+tHA2/rtwDsqOHQYAtO7QBcOmPoTsnKv8t1n28t+w5cv50JvMGH7f39Bn5I3+63Z8vRRbli3AxNc+DtTLpDD1bWE1kk1aZMToRZeiagxigapcXnyeV8kZ0hGuTccuuPPtRf7LGvn3/+1cjmpkDRiKrAFDsepfzzXo8WJbp2L4/U8gsX0HKAqw5cv5+M9f/oj75n2DNh27YPe3q7Bt5WJMeWshThw+iMUzH0DW5VfCEp8AR1UlVr35Au56e3HAXyeFHwXAF/lVmJQdhxi9LLoc1WIQC+L1Kfgsrwo2D1M40mlkGdGJbWq9buDtfwIAHNy8ocGP13Xw8BqXh09/HP9dNAeHd2xGm45dUJy3Dx0uzkFat95I69YbX/3zCZQWHoYlPgErXpuJ/jdORlxKWtNfEEWUao+CpYeqcFvnWMiSJLocVeIYsSCrC6wo5OQsVThxOA8vXNMDL425BPMf/xPKjxUE7LF9Xi+2rfoMLns12vfsBwBIyeqOgl1bYa8sx9Fd2+B22pHYLhOHfv0JR/dsx4AJdwfs+SkyHLV5sI5DZMKwRSzAthMObDvJyVlq0O6ivrhp5utITO+EqhNFWPveP/HunWPw4MLvYbBENflxj+/fhbcnjYDH5YTeZMEdL89Bmw7ZAICsAUPRZ+RNeOOOq6EzmnDTzDegM5nx+d//Bzc9/S/8tHA2Nn76b1jiWuH6J15Bm45dAvVyKYxtLnEgzaJDl3iD6FJUR1J4wsoWddLhwZy95eByYXWyV1Xgf0f1wagZz6DfuDv8xw9u3oD3p45r0GQtAPC4XSg/VgCntQo71n6BzZ99jLv/vdQfxuda8+4/4KiqwMVjJ+CDe2/CAwu+w57vVmPjp/+H+z5ZG7DXR+FNr5EwKTsOrYwcL25J7JpuQV7fqbEYhrB6maJjkdi+I04eyWvW42h1eiS274C23Xrh2vv+huSs7vjxk/dqvW1x3n5sXb4QV097FAd/2YDMvpcjKj4RPa+5zj9bmwgAXD4Fn+VVws0ZpC2KQdyC1hfaUGznPq9q5qy2orTgUJ2Tt5pK8fngcZ8/3KEoCj5//iGMmvEsDOYoKF4fvJ5TcxPO/Onz8XeSflfi8GJNAb+ctSQGcQs5WOnCJp5bWHWWv/oUDv6yAWWFh5G/7Wd89NAkaDQyel17AwCg6kQRCvfuwMkjBwGcGvst3LsD1RVl/sf49z034Mf5//ZfXvmvZ5H3y48oKzyM4/t3nb68Ab1H3IhzbfrsI1jiE/wzrdN7X4rcTd/j8PbN+OGjd9C6Q3aDusJJXbaddGJ/BeextBRO1moBNrcPX+VXiS6DBKgoKsT8x+5BdUUZLPEJyOh9Gf48dwWi4hMBAP9dNLfGhh/v3TUWAHDj06/j4rETAAAnCw6hurzUfxtb6QkseHI6qk4UwRgVg+TO3TD5zQXo3H9IjeeuOlmMdf/3Kv48Z7n/WLsefXHFHdMw54HbEBWfiJueeSNYL53C3MrDVrTtqoNZy/ZasHGyVgtYmFuB3EqeB5SIwktWrB43dIgRXUbE41edINtcYmcIE1FY2lfhwo6THFILNgZxEJU5vVjPRfJEFMbWFNhQ4eKEvmBiEAeJoihYcdgK7mBJROHM6VPwVb4VHMUMHgZxkGw76cRhK7ukiSj8Hba6sZmrPoKGQRwEVrcP6wrZJU1EkeO7YzaUO9lFHQwM4iBYfcQKp5fdOEQUOdw+4Gtu9BEUDOIA21PuxL4Kl+gyiIgCLrfSjb3l3Ogj0BjEAeTw+PD1EX5jJKLItabABqeXG+YHEoM4gL4ptMHGadJEFMGq3D58f6xadBkRhUEcIIU2N7bzHMNEpAK/lDhwvNojuoyIwSAOAEVRsKaAs6SJSB0UAKuOcG1xoDCIA2BnmROF/HZIRCpyrNqDX09wbXEgMIibyeVVsL6Q4yVEpD7fH6uGgxO3mo1B3Ew/FVXD6uYvIhGpj92r4KfjdtFlhD0GcTOUO734uZi/hESkXptL7DwpRDMxiJthXaGNJ3UgIlXzKMB3HJ5rFgZxEx22urG3nDtoERHtLHNyOVMzMIib6Fue1IGIyG8dz73eZAziJsitcOGojd/+iIjOyLe6kct99puEQdwE3x/neAgR0bnWFdrg4yYfjcYgbqR95RwLISKqzQmHF7vLuNVvYzGIG0FRFPzA1jARUZ02Ftm59WUjMYgbYW+5C8V2rpcjIqrLCYcXezlW3CgM4gZia5iIqGF+5GdlozCIG2hXmRMnHGwNExFdSLHdi/0VHCtuKAZxAyiKgo1F3MqSiKihfuQe1A3GIG6Ag5VutoaJiBrhWLUHeZUcK24IBnED8MQORESNt4FjxQ3CIL6AomoP8q1u0WUQEYWdApsHR/j5eUEM4gtga5iIqOk2l/Az9EIYxPWocnmxu5wz/4iImmpfuYvnK74ABnE9filxwMcNYoiImkzBqc9SqhuDuA4ur4KtJ/nLQ0TUXNtOOuDyslVTFwZxHXaUOuDgLw4RUbM5vQp28WQQdWIQ12HrCbaGiYgCZcsJTtqqC4O4FoU2N0q4gQcRUcAU270o4FKmWjGIa7GNY8NERAG3hT2NtWIQn8PlVbC7jNuyEREF2r5yJxwen+gyQg6D+By7y51wcc0SEVHAeRRgTzkbOudiEJ9jO7uliYiC5rdSfsaei0F8lhMOD47aPKLLICKKWAU2D8qcnAx7NgbxWbZxIgERUdCxVVwTg/g0n6JgJxecExEF3W+lTigK5+KcwSA+Lb/KjWoPfzGIiIKtwuVDAYcB/RjEp+1ma5iIqMWwe/p3DGIAXp+CfRWcUk9E1FL2lLng4VJRAAxiAEBelZsneCAiakFOn4JDVdzyEmAQAwD2lLNbmoiope2v4GcvwCCGT1FwgN3SREQt7kCFi7OnwSBGPruliYiEsHkUbqIEBjH2ct9TIiJh9rNHkkGcW8lfAiIiURjEKg/iYrsHVW6ekouISJRSpxcnHOrunlZ1EOexNUxEJNx+lQ8RqjuIuYaNiEi4AypvFKk2iN0+BUesDGIiItEKbR44veodJlRtEB+ucoOrloiIxFMAHLGqd5xYtUGcV6XurhAiolCSr+LPZPUGcSW7pYmIQkW+iocKVRnEFS4vTjq9ossgIqLTiu1e2D3qHCdWZRAf5mxpIqKQo9ZWsSqDmHubEhGFHrU2klQaxOp8s4mIQlk+g1gdnF4fTjg4PkxEFGpOOr2wqXDbYdUFcaHNAy4fJiIKTYXV6msVqy6IOT5MRBS6jler7zNadUFcyPFhIqKQxSCOcIqi4KgK32QionBxTIWf0aoK4pMOL5zcYJqIKGRVexRUuNQ1oVZVQazGb1pEROFGbd3TqgriEi5bIiIKeWprNKkqiE/Y1fXmEhGFI7aIIxhbxEREoY9BHKEcXh+qVLhjCxFRuHF4FVXtsKWaID5hZ2uYiChcqOlUtaoJ4hKHuro6iIjCWamKhhLVE8RsERMRhY1StogjD8+4REQUPk6qqBdTNUGspjeViCjcsUUcYTw+BTYPt7YkIgoXFU4fvIo6PrdVEcSVLvVMgyciigQ+AOUqaRWrIojVtoE4EVEkOKmSuT2qCOJKFS0MJyKKFGr57FZFELNFTEQUfqwM4sjBMWIiovDDII4gbBETEYUfBnEEYYuYiCj8MIgjhKIoPOsSEVEYYhBHCKdXgU8da8KJiCKKw6vAo4IP8IgPYoc38t9EIqJIpYZWccQHsd0b+W8iEVGkUsPQYsQHsYN7TBMRhS27h0Ec9tg1TUQUvlwcIw5/avg2RUQUqVwqaExFfBCzRUxEFL7YIo4AbBETEYUvtogjAFvEREThiy3iCKCGxeBERJGKLeIIwI5pIqLwxRZxBFDBlykioojFFnEE8CmR/yYSEUUqtogjgAreQyKiiKWGj/CID2IvW8RERGFLDR/hER/EangTiYgilRo+wiM+iFUwzk9EFLHU8BGuFV1AsHGyFoUbg9YNo94Jg84Fnc4FrdYJjeyEpHFA0TjglRzwSA644IBTscMDj+iSiYLGILcGMEF0GUEV8UFMJJYCk84No84Fvd4JndYJrdYFjewANE5/sLolO1xwwKHY4YMPLgCu+h+WSBUkSKJLCLqID2KtJvLfRGo5Enww6V0w6l3Q61zQaZ2QT7dYoXHAdyZY4YATdjgVBxQocABw1PfADFaiWjGII4AsRf6bSE0nS16Y9G4Y9M7fg1V2QJKdgMYJn+ZUN7D7dKg64YACwH76p1YMVaKAYRBHAG3ET0ejs+lkD4x6Fww6F/S6U13BGtkJST7VDeyTzgTrqfFVF1zwAqg+/VMrBiuRMJIKGlORH8QqeBMjmUF2+4NVp3NCq3VClp2A7ICicZ6euGSvMXHJA8ADwFbXgzJYicKGDjrRJQRd5Acxx4hDiALj6YlLBp3zrBnB505ccsAFOycuERH0kl50CUEX8UEsM4eDptaJS/LpiUuyAz6NE17JDjfOLLVxwAcfnACc9T0wg5WITtNJbBGHPbaIG44Tl4go1LBFHAG0Ks5hTlwionDHFnEE0EVQi5gTl4hIbThZKwIYQ3b9UuMmLjkVB7zwcuISEakKu6YjgLGFZmtJ8MGod8Ok/70bmBOXiIiah13TEcDUxEFizemJS8azJi5p5JpbGXrO2srQpTihQOHEJSKiAGKLOAKY5FNd01qN51Sw6lzQ6RzQal3+GcGK7Dw9ccleY+KSD5y4REQkkkVjEV1C0EV8EFuMNkRnLIAHbngAWOu7MYOViCikWKTID+JQnckUMGbZBA/cossgIqImMGvMoksIuogPYoNkgAxZdBlERNRIBskArRTxHbeRH8SAOr5RERFFGjV0SwMqCWK1vJlERJFELY0oVQRxjCZGdAlERNRIapgxDagliGUGMRFRuFFLb6YqgjhWEyu6BCIiaqRoTbToElqEKoKYXdNEROEnTo4TXUKLUEUQs0VMRBR+4jRxoktoEaoI4mhNNCREzukQiYginQYa1fRmqiKINZJGNWMNRESRIEYTA42kiohSRxADHCcmIgonahkfBlQUxGoZayAiigRq+sxWTRAnyAmiSyAiogZiEEegRDlRdAlERNRA8XK86BJaDIOYiIhCjpp6MVUTxEaNEVFSlOgyiIjoAsySWTX7TAMqCmKArWIionDQWm4tuoQWpa4g1jKIiYhCXWstgzhisUVMRBT6kuQk0SW0KAYxERGFFHZNR7B4TTx00Ikug4iI6mCUjKo7h7yqglgjaZCsTRZdBhER1UFt3dKAyoIYAFK0KaJLICKiOqitWxpQYRCnalNFl0BERHVQ42e06oI4WZvMcxMTEYUgCRLaatuKLqPFqS6IDZJBVVunERGFi0Q5EQaNQXQZLU51QQyos+uDiCjUqbE1DKg0iDlhi4go9DCIVSRVZouYiCjUMIhVJEaOQawmVnQZRER0WoImASaNSXQZQqgyiAEgQ5chugQiIjotVafenkrVBnG6Ll10CUREdFp7bXvRJQij2iBO06ZBhiy6DCIi1ZMho72OQaw6OknHZUxERCEgVZsKvaQXXYYwqg1igOPEREShIFOXKboEoVQdxBwnJiISj0GsYglyAqKkKNFlEBGpVrwmHnFynOgyhFJ1EANApl7d38SIiETiECGDGJ11nUWXQESkWmrvlgYYxGirbQuTpM7dXIiIRDJIBq5eAYMYGkmDTrpOossgIlKdjrqOkCXu56D6IAaAznp2TxMRtbRsfbboEkICgxjsniYiamlmyYw0bZroMkICgxjsniYiammd9Z2hkRhBAIPYj93TREQth93Sv2MQn9ZW2xZmySy6DCKiiBejiUGKNkV0GSGDQXyaRtKgq76r6DKIiCJelj5LdAkhhUF8lu6G7qJLICKKeFk6BvHZGMRniZfjkSKzu4SIKFhay62RpE0SXUZIYRCfg61iIqLguchwkegSQg6D+BxZ+izood4TVBMRBYseeo4P14JBfA6dpONSJiKiIMjWZ0MvsaFzLgZxLdg9TUQUeOyWrh2DuBYp2hQkaBJEl0FEFDHayG04SasODOI69DL2El0CEVHEYGu4bgziOnTVd4VRMooug4go7OklTtKqD4O4DlpJy29wREQB0F3fHTpJJ7qMkMUgrkcvQy/I4EmriYiaSgMN+hj7iC4jpDGI62HRWLiUiYioGbL0WYjWRIsuI6QxiC+gr6Gv6BKIiMIWP0MvjEF8AUnaJKRp00SXQUQUdtK16Vyy1AAM4gboY+D4BhFRY11svFh0CWGBQdwAmbpMJMqJossgIgobreXWaKdrJ7qMsMAgbgBJknCZ8TLRZRARhY2+Ro4NNxSDuIE66jqyVUxE1ADxmnh01nHFSUMxiBuIrWIioobpb+oPjcR4aSj+SzUCW8VERPVLlBPZGm4kBnEjsFVMRFS/y42XQ5Ik0WWEFQZxI3XUdUSSzHVxRETnSpaT0UHfQXQZYYdB3EhsFRMR1a6/qb/oEsISg7gJOuo7IlWbKroMIqKQ0VbbFum6dNFlhCUGcRNdYbpCdAlERCHjctPloksIWwziJkrWJiNbny26DCIi4TrqOqKttq3oMsIWg7gZckw50EIrugwiImFkyBhkGiS6jLDGIG6GaE00T3hNRKrW19gXMXKM6DLCGoO4mS4xXgKzZBZdBhFRi4uSotDP2E90GWGPQdxMeknPSQpEpEo5phzoJJ3oMsIegzgAuum7cZMPIlKVFDkFXQxdRJcRERjEAaCRNBhqHgoJ3NaNiNRhsHmw6BIiBoM4QJK1yehp6Cm6DCKioOuu74422jaiy4gYDOIAGmAagCgpSnQZRERBY5bM3NAowBjEAaSX9OyuIaKINsQ8BAaNQXQZEYVBHGCd9J3QQcezjxBR5Omo64jOep5rONAYxEEwxDwEOnBKPxFFDoNkwJXmK0WXEZEYxEEQrYnm2mIiiigDTQNh0VhElxGRGMRB0tvQm5ugE1FESNOmoYehh+gyIhaDOEgkScI15mugh150KURETaaFFsPMw0SXEdEYxEEUI8dwFjURhbWBpoGIk+NElxHReA6/IOtm6IaD7oPIdeeKLoXCwJpZa7DsmWUYdM8g3PD3GwAAlUWV+OKpL7B3/V44rU607tQaV8+4Gr3G9qrzcWb2momyI2XnHR9450Dc+I8bAQCfPf4ZNs3bBL1Zj9FPjcYlN13iv93Wz7di06ebcPe8uwP8CimcZOoy0ctY9+8ZBQaDuAUMMw/DscpjqFaqRZdCIezwlsP4cc6PSO2eWuP4x3/+GPZKO+76+C5YEizYsmgL5kyZg4e+eQhpPdNqfayH1j4En9fnv3xs9zG8fcPb6HXdqQ/V31b+hi2Lt+BPi/+EkoMlmH/ffHQZ2gVRCVGwV9rx1fNfYdpn04L3YinkmSUzrjJfJboMVWDXdAswaUy4ysJfaKqb0+rEf+75D26ZdQtMcaYa1+VtysMVd1+B9IvTkZiRiGsevgamWBOObD1S5+NFJUYhpk2M/2fnqp1IzExEp5xOAICifUXolNMJ7fu0x8XjL4Yh2oDS/FIAwBdPfYGcyTmIT4sP3gumkHeN5RqYNTzFa0tgELeQTF0meug565Bqt+h/FqHb1d2QPST7vOsy+2Xi189+ha3MBp/Phy2Lt8Dj9KDTwE4NemyPy4NfFv6Cy26/DJJ06sQkqd1TcWTrEVSXV+PI1iNw291I7JCIgz8dRMH2Agy6Z1BAXx+Flz6GPkjXpYsuQzXYNd2CBpkH4ZjnGE76ToouhULIlsVbULCtADPWzqj1+omzJ2LulLl4vOPj0Gg10Jv0mPLhFCR1aNipN3d8tQP2CjsunXCp/1jXYV1x8U0X45Vhr0Bn1OH2t26H3qzHwocW4rY3b8OGDzbgu/e/Q1SrKNz86s1I6ZoSkNdKoS9RTsQA0wDRZagKg7gF6SQdRkWNwvzK+XDBJbocCgFlBWVY8tclmLZkGnTG2ndjW/HCCtgr7Jj22TRYEizY8dUOzJkyB/cvvx+p3VJrvc/ZfvroJ3S9qitiU2JrHB/x6AiMeHSE//LK/12JrMFZkHUyVr+8Go/88Ah2rtqJj6d9jIfXPdy8F0phQQstRlhGQCsxGloSu6ZbWLwcz/Fi8juy7QisJVb8c8g/MSNpBmYkzUDuhlx8/973mJE0AyfyTuD797/HhH9NQNbgLLTt0RbXPnIt2vdpjx/+/cMFH7/0SCn2fbsP/f/Qv97bFe0rwuaFmzHyryOx/4f96Hh5R0QlRqH3uN4o2FYAR5UjUC+ZQtiV5ivRSm4lugzV4dceATrrO6O3pze2OreKLoUEyxqUhUd+eKTGsU/u+wRtOrfBsPuHwWU/1XMiaaQat5E0EhSfcsHH/+/H/0V0UjS6XdOtztsoioIFMxZg3HPjYIgyQPEq8Hq8AOD/0+fz1Xl/igwXGS5CN0PdvycUPGwRCzLQNBApMsfd1M4YbURKt5QaP3qzHuZ4M1K6paBN5zZI7JCIBTMWIP+XfJzIO4F1b6zDvvX7cNGoi/yP8+a4N/H9+9/XeGyfz4efP/kZ/W7tB1kr11nDTx/+hKiEKPS49tRkwszLMrH/u/04tOkQvn3rWyRnJ8Mcy9mzkSxFTsFgEzcfEoUtYkFkScaIqBGYVzkPdsUuuhwKUbJOxj2f3oMvZ36J9297Hy6bC4mZibjtrdvQ7erfWy8n8k7AetJa47771u9DWUEZLrv9sjofv6q4CqtfWY0HVz7oP5Z+cTqG3DsE7936HqISo3D7W7cH/HVR6DBLZoyMGglZqvvLGgWXpCjKhfu3KGjy3flYal0KBXwbiKhlaaDBDdE38AQ1grFrWrB0XTpyTDmiyyAiFbrCdAVDOAQwiEPAxcaL0V3fXXQZRKQiXfRd0NvYW3QZBAZxyLjSfCXStLXvG0xEFEgpcgpPbRhCGMQhQpZkjLKMQryG+/sSUfDEamIxJmoMN+0IIQziEGLUGDE2aiyMklF0KUQUgUySCeOixsGkMV34xtRiGMQhJk6OwyjLKMjgUgIiChwZMsZEjUGcHCe6FDoHgzgEpenSMNQ8VHQZRBRBhluGI0XLTYRCEYM4RHUzdOOyJiIKiCtMV6CzvrPoMqgODOIQdonxElxsuFh0GUQUxnoaeqKvsa/oMqgeDOIQN9A8ED30PUSXQURhKFufjSGmIaLLoAtgEIeBoeahyNJliS6DiMJIB10HXGO+BpIkXfjGJBSDOAxIkoRrLNcgXZsuuhQiCgPttO0wwjICGokf8eGA71KYkCUZo6JGIVWbKroUIgphqdpUbtgRZhjEYUQn6TA2aiyS5WTRpRBRCEqRU3Bd1HXQSTrRpVAjMIjDjEEyYFz0OKTIXA9IRL9rI7fBddHXQS/pRZdCjcTzEYcpl+LCF9YvcNRzVHQpRCRYspyM66Kug1HD7XHDEYM4jLkVN760fokjniOiSyEiQdK0aRgTNYYt4TDGIA5zHsWDZdZlyPfkiy6FiFpYpi4TIy0jOTErzDGII4BX8WK5bTkOug+KLoWIWkiWLgvXWK6BLPEEMeGOQRwhvIoXq22rsc+9T3QpRBRk3fXdMcw8jJt1RAgGcQRRFAXf2b/DVudW0aUQUZD0MfTBIPMg0WVQADGII9Bmx2ZssG8QXQYRBdjlxstxqelS0WVQgDGII9Ru526sqV4DH3yiSyGiZpIh42rL1cjWZ4suhYKAQRzBjriP4CvbV3AqTtGlEFETmSQTRkeN5va2EYxBHOFOek9iqXUpqnxVokshokaK08ThuqjrECfHiS6FgohBrAI2nw3LrMtw3HtcdClE1EBttW0x2jKau2WpAINYJbyKF+uq12Gna6foUojoArrou+Aq81VcI6wSDGKV2e7cjm+rv+UkLqIQJEFCjikHFxsvFl0KtSAGsQod9RzFcutyVCvVokshotPMkhkjLCOQpksTXQq1MAaxSll9ViyzLkORt0h0KUSqlyKnYGTUSERpokSXQgIwiFXMo3iwrnoddrl2iS6FSLV6G3pjoGkgx4NVjEFM2OXchfXV6+GGW3QpRKqhgw7DLMO4SQcxiOmUMm8ZVtpWothbLLoUoojXStMKI6JGIFFOFF0KhQAGMfl5FS822DfgV+evokshili9DL0w0DSQ5xAmPwYxnSffnY/VttWcVU0UQBbJgqstVyNdly66FAoxDGKqlc1nw9e2r5HvyRddClHY66jriGHmYTBpTKJLoRDEIKY6KYqC7c7t2GDfwIlcRE2ggw6DzIPQw9BDdCkUwhjEdEGV3kqsqV6DI54jokshChspcgqusVzDEzbQBTGIqcF2Onfie/v3PK0iUT300CPHnIOL9BdBkiTR5VAYYBBTo1h9VqyrXoeD7oOiSyEKOR11HTHEPIQ7ZFGjMIipSfa69uLb6m9hV+yiSyESziJZMMQ8BJ30nUSXQmGIQUxNZvfZsdG+Eb+5foMC/hqROl1kuAg5phwYJIPoUihMMYip2Uo8JVhvX49CT6HoUohaTGu5NYaYhyBFmyK6FApzDGIKmD3OPfjB/gNsik10KURBY5EsGGAagK76rpyMRQHBIKaAciku/Gz/GVudW+GFV3Q5RAGjhRZ9jX1xifES6CSd6HIogjCIKSjKvGXYYN+AXHeu6FKImi1Ll4Uccw5iNDGiS6EIxCCmoDruOY4f7T9yMxAKSylyCq4wX8FxYAoqBjG1iMPuw/jR/iOKvEWiSyG6oDZyG/Q39UeGLkN0KaQCDGJqUbmuXGy0b8RJ30nRpRCdJ0lOQn9jf3TQdxBdCqkIg5hanKIo2OPag02OTSjzlYkuhwgJmgT0N/VHR11HzoSmFscgJmEURUGuOxebHZvZZU1CJGgScKnpUnTWdWYAkzAMYgoJR9xH8IvjF57/mFpEe2179DX2RbouXXQpRAxiCi0lnhJsdmzGfvd+bptJASVDRpY+C30MfZCkTRJdDpEfg5hCUoW3Atud27HLtQsOxSG6HApjBsmAiwwXoZehF8+KRCGJQUwhzaN4sN+1H9ud23Hce1x0ORRGWsut0d3QHV31XbkTFoU0BjGFjRJPCbY7t2Ovay/ccIsuh0KQHnpk67PRw9ADrbWtRZdD1CAMYgo7TsWJPc492OXahWJvsehyKASkyCnobuiOLH0WW78UdhjEFNZKvaXY69qLva69qPBViC6HWlCUFIXO+s7obuiOBDlBdDlETcYgpohxzHMMe1x7sN+1H3bFLrocCgKzZEZnfWdk6bOQIqdw7S9FBAYxRRyf4kO+Jx/7XfuR587jrOswZ5JM6KjriCx9FtK0aQxfijgMYopoPsWHY55jyHPn4aD7ILfUDBOxmlhk6DKQqctEO207aCSN6JKIgoZBTKpS5i3zh3Khp5CbhoQIGTLaatsiQ5eBDF0G4uV40SURtRgGMamWw+fAUc9RFHgKUOApwAnvCdElqUqsJhbtte2RoctAO107znYm1WIQE51m99l/D2Z3AU/VGEASJCTKiWirbYtUbSpStamwaCyiyyIKCQxiojrYfXYUegpR7C1GkacIxd5izsZuID30SNImIVWbirbatkjWJsMgGUSXRRSSGMREjVDpq0SxpxhF3iIUe4pR7C1W/azsKCkKSdokJMlJSJQTkSQnIVYTy9nNRA3EICZqJpvPhjJvGcp8ZTX+rPRVRsxkMA00iNHEIFYTi1g5FnGaOCTICUiSk2DSmESXRxTWGMREQeJVvKjwVaDMWwarzwqbYoPVZz31d58NVsUKl+ISXSaAU2coMktmmDVmmCUzojXRiJVjEas5FbrRmmguISIKEgYxkUBuxQ2rz4pqXzWcihMuuOBSzv9xKk54FA+Uc/87/b/vmcsSJGglLWTI0EpaaCUtdNBBL+mhk079aZAM/sA986csyYL/JYjUi0FMREQkEPuaiIiIBGIQExERCcQgJiIiEohBTEREJBCDmIiISCAGMRERkUAMYiIiIoEYxERERAIxiImIiARiEBMREQnEICYiIhKIQUykEpMmTYIkSfjTn/503nX33nsvJEnCpEmTWr4wIpVjEBOpSLt27TB//nzY7Xb/MYfDgU8++QTt27cXWBmRejGIiVSkb9++aNeuHZYsWeI/tmTJErRv3x59+vQRWBmRejGIiVRmypQpmD17tv/yBx98gMmTJwusiEjdGMREKnPHHXfghx9+QH5+PvLz87FhwwbccccdossiUi2t6AKIqGUlJSVh1KhRmDNnDhRFwahRo5CYmCi6LCLVYhATqdCUKVMwffp0AMCbb74puBoidWMQE6nQtddeC5fLBUmSMHz4cNHlEKkag5hIhWRZxu7du/1/JyJxGMREKhUTEyO6BCICICmKoogugoiISK24fImIiEggBjEREZFADGIiIiKBGMREREQCMYiJiIgEYhATEREJxCAmIiISiEFMREQkEIOYiIhIIAYxERGRQAxiIiIigRjEREREAjGIiYiIBGIQExERCcQgJiIiEohBTEREJBCDmIiISCAGMRERkUAMYiIiIoEYxERERAIxiImIiARiEBMREQnEICYiIhKIQUxERCQQg5iIiEggBjEREZFADGIiIiKBGMREREQCMYiJiIgEYhATEREJxCAmIiISiEFMREQkEIOYiIhIoP8Pn+2D5H6EZWoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe['Deficiente'].value_counts().plot(kind='bar')\n",
        "plt.title('Distribuição de Frequência por Deficiência')\n",
        "plt.xlabel('Deficiência')\n",
        "plt.ylabel('Frequência')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "0eGL_L78UkKC",
        "outputId": "c4ef056d-dd2e-4df3-acf6-275684d45405",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=dataframe['Nome'], y=dataframe['imc'], palette='viridis')\n",
        "plt.title('IMC por Pessoa')\n",
        "plt.xlabel('Pessoa')\n",
        "plt.ylabel('IMC')\n",
        "plt.ylim(15, 30)\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "u_2LAMtyVIu0",
        "outputId": "10c4ee45-be66-4479-da18-d6960ea2ce4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-aa43f366e1d6>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=dataframe['Nome'], y=dataframe['imc'], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "sns.countplot(x=dataframe['Tipo de Corpo'], hue=dataframe['Sexo'], palette='pastel')\n",
        "plt.title('Tipo de Corpo por Gênero')\n",
        "plt.xlabel('Tipo de Corpo')\n",
        "plt.ylabel('Contagem')\n",
        "plt.grid(True)\n",
        "plt.legend(title='Sexo', loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3AtZkeMNVcXc",
        "outputId": "e74655d1-6ae1-4a4a-cfb4-0f6a2c44b539",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "sns.scatterplot(x=dataframe['Idade'], y=dataframe['imc'], s=100, color='blue', alpha=0.8)\n",
        "plt.title('IMC vs Idade')\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('IMC')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3ImdwfZqV9iY",
        "outputId": "d3e1bd44-92ee-4375-c532-b6bfd0a96c38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "sns.scatterplot(x=dataframe['Peso'], y=dataframe['imc'], s=100, color='green', alpha=0.8)\n",
        "plt.title('IMC vs Peso')\n",
        "plt.xlabel('Peso (kg)')\n",
        "plt.ylabel('IMC')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "czSlwnTpWF_-",
        "outputId": "3853b7c1-fa4c-4f91-bd6a-08eb1e7e57c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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hhGgQJLgKIYQQQogGQYKrEEIIIYRoECS4CiGEEEKIBkGqCgghhBCiSfH4PXj8HnToUFCwmWzYTLa6Pi2hggRXIYQQQjQJbp+brPws1h9cT/rJdAr8BUSYIki8KpHhXYYTZ4/DYXbU9WmKSkhwFUIIIUSjl1eYx/I9y1mduZqgEgzfnkMOx13HSTuQxtjuY5nYayJR1qg6PFNRGVnjKoQQQohGze1zs3zPclIzUkuF1osFlSCpGaks27MMt89dy2co1JLgKoQQQohG7XT+aVZnrlY1dnXmarIuZNXwGYnqkuAqhBBCiEbL4/eQdjCtwpnWSwWVIGkH0/D4PTV8ZqI66jS4Pv300yQmJmK324mNjeX+++/n0KFDpcZkZWWRkpJCXFwcERER9OnTh3Xr1tXRGQshhBCiIfH4PaSfTNf0mJ0/7JTgWk/VaXD99NNPmTp1Ktu3b+eDDz7A7/dz1113UVBQEB7zwAMPcOjQId555x0yMjIYPnw4o0eP5quvvqrDMxdCCCFEQ6BDR4G/oOqBFykplSXqnzqtKrBx48ZS/79s2TJiY2PZtWsXAwcOBODzzz9n6dKlJCUlAfD73/+eRYsWsWvXLnr37l3r5yyEEEKIhkNBIcIUQQ45qh9jM9lQUGrwrER11atyWC6XC4Do6Ojwbf3792fNmjUMHTqU5s2b8+abb+L1ern11lvLPYbP58Pn84X/3+0u3hno9/vx+/01d/K1pOQ1NIbXIsqSz7dxk8+3cZPPt35qRjP6xvfltPu06sf0a92PZjQr9VnK51uz1L6vOkVR6sVXilAoxL333sv58+fZtm1b+Pbz588zZswY/vOf/2A0GrHZbLz11lvcdddd5R7nT3/6E48//niZ21etWoXNJl0xhBBCCCHqG4/Hw/jx43G5XDgcFTeBqDfB9eGHH+b9999n27ZttGnTJnz79OnT2blzJ0899RQxMTFs2LCBRYsWsXXrVhISEsocp7wZ17Zt25KTk1PpG9FQ+P1+PvjgA+68805MJlNdn464wuTzbdzk823c5POtfbmFuWRfyGZ15mo2H92My+fCaXYyqMMgxnYfS2xkLC2sLcgvyuf1Pa/z5v43qzzm6K6jeaDXA9ib2Usdf+t3W/lNq9/w7JlnubndzaWOX135RfmcyT/Du4ffZffp3eFOXn3i+3BPp3toZW+FvZm92sevrkJ/IZ7ARS1xjTasJmuNPqfb7SYmJqbK4FovlgpMmzaN9957jy1btpQKrd9++y2LFy8mMzOTbt26AdCzZ0+2bt3KSy+9xMsvv1zmWGazGbPZXOZ2k8nUqP4gaWyvR5Qmn2/jJp9v4yafb+3ILshmya4lzN82H3+o9GXm9DPpPLvjWebeNJdpSdOIjYjlp31+iqJXynTOKmHQGRjbfSw/7fVToqxR4eMv+GwBgVAAq94KreCr7K/4POtzntv5HHMGzAkfX6u8wjyW7yvbyQvgiOsI6w6tq/VOXnXZElft75k6Da6KojB9+nTS0tL45JNPaN++fan7PZ7iUhR6feniBwaDgVAoVGvnKYQQQoj6I8eTw+Kdi3liyxMVjvGH/OH7H+n7CDG2GCb1mcTQTkNJO5gWLnllM9lIapPEsC7DiIssDmYlx5+3dV6Fxw+EAuH7S46v1sWdvCpS0skLYFKfSTUWGEs0lJa4dRpcp06dyqpVq3j77bex2+1kZRV3qnA6nVitVrp06ULHjh2ZPHkyzz77LC1atGDDhg188MEHvPfee3V56kIIIYSoI1n5WczfNl/V2Pnb5jO622hibDE4zA4cZgfTk6bj6X3RpXCTDZvpf/tgsvKzWPDZAlXHX/DZgvDx1dLayWtop6E1GlzrY5CuSJ3WcV26dCkul4tbb72V+Pj48K81a9YAxdPG//73v2nZsiX33HMPPXr04PXXX2f58uXcfffddXnqQgghhKgD5wrPsWLfijLLAyriD/lZuW8l5wrPhW+zmWzE2GJoYWtBjC2mVGg9X3ieFftWEAgFVB0/EAqwct9KzheeVzW+Pnbyakgtces0uCqKUu6viRMnhsdcd911rFu3jjNnzlBQUMDevXtJSUmpu5MWQgghRJ3xBX1sPLKx6oEX2XhkI76gr+qBgDfoZdO3mzQdf9O3m/AGvarG1rdOXvUxSFemToOrEEIIIYQWep0el8+l6TEunwu9Tl3k0ev0uLwaj+9Vf/z61smrvgXpqkhwFUIIIUSDEVJCOM1OTY9xmp2EFHWbukNKCKdF4/Et6o9f0slLi5rs5FXfgnRVJLgKIYQQosEwG8wM6ThE02OGdByC2VC2VGZ5LAYLg68drOn4g68djMVgUTXWZrKReFWipuMntUkqtQ73SqpvQboqElyFEEII0WBEWaNI6ZGCSa+u7qdJb2JCjwmqSzg1tzYnpUcKRr26wktGvZEJPSbQ3Npc1XibycawLsMw6Ayqxht0BoZ1GVZjwbW+BemqSHAVQgghRIMSZ49j7k1zVY2de9Nc4iLjNB9/zoA5qsbOGTCnzPE9fg85nhxyPbnkeHLKrAeNt8cztvtYVccf232s5vPXor4F6arUi85ZQgghhBBqxdhimJY0DaDczllQPNNa0jlLS43VS49f0jnrUka9Mdw5q+T4ajtPOcwOJvaaCFBlJ6+JvSbWeM3UkiBdWR3XEjUdpKsiwVUIIYQQDU5sRCyP9H2E0d1Gs3LfSjYe2YjL58JpdjKk4xAm9JhAXGSc5tBa0fE/OfoJAN1ju3Nrh1vLHF9r56koa5TqTl41rb4F6cpIcBVCCCFEgxRjiyHGFsOcAXOY2W8mep2ekBLCbDBfkbakJcefO2Au026cxq5Pd7F29FoizZGl1rRWt/OU2k5etaE+BenKSHAVQoha4PF7cBe6AcgtzMWBo87WiAnR2FyJkFqZ5tbmRBiLd963imiFyVR6Y9jltnCti6BanvoUpCsim7OEEKIGuX1uDucc5oUdLzDz/ZkAzHx/Ji/seIHDOYdx+9x1e4JCiMvS0DpPqVFZS9y6JjOuQghRQy5d89ZM1wwscMJ9giOuI2XWvAkhGp5qd57q7alXgbChkBlXIYSoAReveatoJqZkzduyPctk5lWIBqqhdZ5q6CS4CiFEDdC65i3rQlYNn5EQoiY0tM5TDZ0EVyGEuMIa45o3IUT5GlrnqYZOgqsQQlxh1V7zJsFViFpRVWerS50rPMeZgjMAnCk4w7nCc+H7GlrnqYZONmcJIcQVJmvehLg8Lq8Lb8CLTqdDURQsRgtOi/Oyj6u2s1WJHE8OWReyWLF3BR9/+zGPxT/G8NXDue3a20jpmRJuQNCQOk81dBJchRDiCitZ85ZDjurHyJo3IYorcZzOP83KjJVsProZt8+Nw+xgUIdBTEiYQLw9nmhrdLWPraWzVXZBNot3Lg63lLXqrRAPmWczST+TzqLti8ItZWMjYhtM56mGToKrEEJcYSVr3o67jqt+jKx5E03d2YKzLElfwsLtCwmEAqXu2392P0vSlzCr3yymJE6hZURLTcfW2tkqpWcKi3cu5oktT1Q43h/yh+9/pO8jxNhiGkTnqYZOgqsQQlxhJWve0g6o26Ala95EU5dXmMeS9CU88/kzFY4JhALh+6f3na5p5lVLlY+vs78mKz+LF3e8qGr8/G3zGd1tNDG2mAbReaqhk81ZQghRA0rWvKkha95EU3cq/xQLty9UNXbh9oWczj+t+thaq3zcfM3NrNi3gkhzpKrx/pCflftWltmwVV87TzV0ElyFEKIGOMwOJvaaSHJCcoW7jQ06A8kJybLmTTRpLq+L1IzUMssDKhIIBUjNSMXldakar7XKR+eYzmw8slFT2Nx4ZCO+oE/1eFF9slRACCFqSJQ1qtSat69++AqAqx1X07tNb1nzJgTgDXjZfHSzpsdsPrqZGX1n4KTqSgNaq3xYjBZcPhdWk1X1Y1w+F3qdzAXWBgmuQghRgy5e8+ZOcLPj4x08/+PncVgdcvlQCECn02lueez2udHp1JWP01rlwxvw4jQ78Qa9qs/HaXYSUkKqx4vqk68HQghRC2wmGy2sLQBoYW0hoVWI/1IURfNVB4fZgaKoKx+ntbPVoZxDDOk4RFNDkCEdh2A2mFWPF9UnM65CCCGEqDMWo4VBHQax/+x+1Y8Z1GEQFqNF1diLq3yElBAJrRK49Zpb6RTTCYvRgjfg5XDOYT45/gkZZzLYenwrM/vN5NVdrwIQYYogyhpFVLMoADpGd+Rc0TnOFZ6jwF+ASW9iQo8JRFmjtL94oZkEVyGEEELUKI/fE+4Od2l5KKfFSXJCMkvSl6jaoGXUG0lOSC7VSet84Xm8QS96nZ6QEsJisNDc2jx8f7w9nl/0+QVtnG0wG8ys3b+W+Z/NDzc4uK3dbaT0SMEX9PGD6wfi7HHM6DeDFftWYNAZOOc9x/eF30Mb+N71PUaTkdb21gSVICk9UqQqSC2S4CqEEEI0cVpbrJ4rPIcv6AsHRbPBXO6Mo9oWq63trZnVb1aldVxLzOo3i3h7PFC6Jeumbzfh8rpwWpwMvnZwqZasDrODe7vcy+Kdi1nw2YJSAfn0hdMcyj3E37/6O3MGzGFa0jRibDFMSZxCSAmV7pwF+II+zvvP4/K6mHvTXKYkTiHGFqP1LRfVJMFVCCGEaKK0tli9OChuPLIRl8+F0+xkSMchpYJiybHVtliNtkYzJXEKQLmds6B4prWkc1a0NTrckvXSIIobMrIzeH7H8+Eg2szQjJe/fJnVmatp17wd573nyfflE1JC6HV67GY7zS3NWZ25GqPeyJTEKaz9ei392/Zn04RNrDuwjm3HtgHQKboTN7W/iRHXj8AX9LH267WMTRhb7Va0QhsJrkIIIUQTpLXFaklQLJmBvNi+7H0s2r6IuTfNZVrSNGxGm6YWq5P6TKJlREum953OyK4jSc1ILROkkxOSw0E6x5PD4p2Lmbd1XoXHD4QCzNs6D6PeyM97/5xFXyyiKFSE1WjFbrYTbY1Gj54QIbwBL7meXAoDhaSfTCerSxZ/3vJnCooKuKfTPQzuOJhxXceRszeHl4a+xJHzR/jbrr/x7uF30ev13NLuFgmutUSCqxBCCNHEaG2xOjVpKot3LuaJLU9UON4f8vPElifQoWNq0lTezHxT1bmszlzN0E5DcZgdRFujibZGM2fAHGb0nVHh0oWs/CwWfLZA1fFf3/s6QzoOoW+bvmw9sZXCQCGFgcIKx9/b+V5W7FuB1Wglvyiftw+/zduH38aqt/La9a9x3+r7KAz97/GhUIjUjFTmDJhT6fIKcWVIOSwhhBCiidHSYvVC0QWy8rN45rOq158CPPvFs5y5cIahnYaqGh9UgqQdTCtVfsppcdIqshWxEbG0imxVZiPWin0rVHfaMulNrMpYxYjrR6ga3z22OxuPbFRdtQCKGyJ4A+rrvorqk+AqhBBCNCFaW6yO6T6GFftWoKCubmowFGTlvpWqgyvAzh92qq6b6g162fTtJtXH1ul0fHTsI7rFdlM13mqy4vK5VDc4AG0NEcTlkeAqhBBCNCFaW6zGRsSy8chGdKgLZjqdjk3fbqKFrYXq5ygplaWGXqfH5XWpPraiKBT4C1Q3/Sj0F+I0O1U3OABtDRHE5ZHgKoQQQjQhWlusGvVGTTOQiqLg8rkw6tVvo7GZbKpndENKSNNa0sJAIXGRcapndDOzMxnScYimS/9aGiKIyyPBVQghhGhCtLZYDYQCmmYgQ0qIKEuU6qUIAEltklTPiFoMFgZfO1j1sc95zzH42sEcOHsAAwZm95/Ntge3cWDqAb6Z/g0Hph5g24PbmN1/NgYMvHPoHVJ6pFS6geti5TVEEDVHqgoIIYQQTYjWFqvZBdkM6TiEg7kHVY0PKkEGXzuYvMI8dOhIaJXALdfcQueYzuEWq4dyDvHp8U/JOJOBXqdnWJdhpYJrZQ0Omlubk9Ijhed3PE8gFKCtoy0Os6PUjLCiKLh9br53f48v4GNU11EcP3+crx7+igu+C6zMWMnGbzaGy20NuW4IExImMKHnBD478Rlx9jgevvFhzQ0RRM2T4CqEEEI0IRe3WA2GgiRdlcT9Xe6nZ1xPbEYbnoCHvVl72XBwAztP7mRN5hp+2vOn/HXHX9Gh40dtfsSIriPoE98Hm8mGx+9h9+ndrNu/ji9++AKDzsCEHhPI8+TxxO1PlG6x6nXjsDi4vd3tpVus/rdlqtoGB3H2OP5v4P/x5v43CYQCZBdk4/a5CYaCGPQGHGYHLWwt6B7bnVFdRxEXGUcLW4sK69Bmns3kr9v/Gq5DW9I5C9Q3RBC1Q4KrEEII0ch4/J7whicFBZvJVmpGs7W9NY/f8jhXN78aezM7aQfTWPrlUvJ9+djNdm5rdxuP3vQo+UX5HD9/nDh7HPNun8fVzqtxWpysP7CeV3a9Umr8H2/9Iy6vixOuE7SKbEXLiJblBsVTF05xMOcgf9v9t3BQdJgdmhocxEbE8osbfoEv6Cs7PlS8rjWvMI+5N83llzf8kpASCnfZ0uv0GPVGgqH/dfMy6A3odfpwbdhH+j5SpiHClmNbAOgS04WB7QeWaoggao9OaeTb4NxuN06nE5fLhcOhfk1PfeX3+/n3v//N3XffjclkquvTEVeYfL6Nm3y+jVtNfr5VBdESbp+brPws1h9cT/rJdAr8BUSYIki8KpHhXYYTZ48Lr2+tsGXqfxn1xnDL1NiI2EqDJRTXSy0JlmaDmee3P8+KfSvQ6/TFLVaL8sMzovZmxS1WQ0qIlB4pTE2aygs7Xqi0wUGJPw78I9P6TmP5nuXhIJ12MI2Pj31MflE+9mZ2bmt/G8O6DMPldREIBegZ15NeL/fCH/Kj1+kx6IqDakmDg5ASIqgECSkhTHoTuyfvpnts9/BzurwuLngv8OWnX3LjLTcSaYmUNa1XmNq8JjOuQgghRC2riSCaV5jH8j3LWZ25mqDyv9nEHHI47jpO2oE0xnYfy8ReEzHoDaTuS+WdQ+/QMbojeYV5ZS61R1ujeefQO0RZokjpmcIrX77C24fe5roW15HnycPlcxFUghh0BpxmJ9G2aN4+9DY2k42UHinM3zafolARNpONKEsUUdao8JrVQn8hpy+cxuP3sPPUTkbkj2DxzsWq3juXz0VWfhaPffQYRcEi+rbpy6iuoxjddTQRzSIoKCrgq6yv+OMnf2THDzv465C/snb/WppbmnPWc5aQEiKkhCo8vj/kZ+W+lcwZMCe8rtZpcWIzFH8+sRGx8sWzDklwFUIIIWpJTQbR5XuWk5qRWuFzB5UgqRmpxEfG0zOuJ6szV2PUGzHqjcRFxhEbERsO0iWzklDckvXOa+/kg6MfkJmdSevI1sRExBAbGRs+dkgJcb7wPKcunGJVxipua3cbN7S+gS9++CIc0ityf+f7WbFvBXaznXPec1W+h8k9ksMNERQUtv+wne0/bK9wfI9WPfjdh78jxhbDWc/ZKo8PsPHIRmb2m6lqrKhdElyFEEKIWlAbQVSNQCjA2v1rS52DQWcIB9VLFQWLWLlvJcO6DOOz7z/j1IVTnLpwqtLnWJWxipFdR/LFD19UeT7dY7uzeOdirEarqvPX2hDBZrKRdSGLiGYRqsZD8ayuXicVQ+sj+VSEEEKIGub2ucNB9OLAeLGSILrpyCZOuk9eVhCtzHUtruODox9Uern8YiFCbDyykV5xvVSN1+l0fPzdx6rHW0wWTUFRa0MEj9+DzWTT1JLVaXaqfn9E7ZLgKoQQQtSw0/mn600QtRgt4bWeaujQ4fK5VM9YKoqCx+8hwqRuvNfv1RQUtTZE+CrrK25rf5umlqxDOg7BbDCrHi9qjwRXIYQQogZ5/B7SDqbVmyDqDXhVd6kCUFBwmp0UFBWoGh8IBYiLjKPAr258SYtVtZ2qShoilLSI1ev0NDM0w2wwYzFaMBvMNDM0C8/grt2/lvEJ41UdG4qrI0zoMSG8MQuKqwpkF2SHn9/ldak+nriyJLgKIYQQNcjj95B+Ml31+JoOoodzD3PLNbeoHq9Hz5COQ9iTtUfV+NzCXAZ3HEzGmQx06Lit3W0svXspWx/cype//JKtD25l6d1Lua3dbejQseHQBlJ6pJDvywcgxhZDlxZdSIhNoEdsDxJiE+jSogsxthgAUvelktIjBb1Oj9lgxqQ3EVJCFAWL8AV8FAWLwmWtzAYzX576kqsdVzOm2xhV5z/3prnhhgh5hXl8nf018z+bz+i1owEYvXY08z+bz9fZX5NXmKf6fRRXhmzOEkIIIWqQDp3q2Uf4XxC9uEB+ZUqC6EfHPlI1/uPvPuahGx9i24ltqsJxM0MzJvSYwJR/FXeSqqrFqsvrYuT1I9mXtY+3x76N1WRl3f51LNy+MNyw4I72d/C7m3/Hr/r9isO5h4mzxzGz30ze/PpNFEUhtzAXl89FKBRCr9fjNDuJtcXSKqIVUZYo4uxxzB4wm6e2PlVuDdpAKECAAEa9kd/d/DssJkvFDQv+6+I6tDG2GM4WnGVJ+pJw5yyr3got4VDOIfZk72FJ+pJw56yWES1Vvffi8klwFUIIIWqQgkKEKYIcclSNr+kguj97P20dbRnXfVy4akEwFCRE6H/lsNBj0BdXGRjbfSxxkXEMvnYwLp9LVYvVVhGtuK3DbeU3LLgAh3IPleqcFWOL4aEbHyIQCpTbCcsb8IY7YT2U+BBmvZnJN0wmFAox/7P5FTZQmDtgLpNvmIxepyc2IpZH+j7C6G6jWblvZZmWshN6TAi3lM0rzGNJ+hKe+fyZCt/HQCgQvn963+nSQauWSHAVQgghapDNZCPxqkSOu46rGn9xEEWB+7rcx9BOQ2lhbYFBbyAYCpJbmMu/Dv+Ltw++XW4QrcyY7mOIbBbJxF4TCSpBVuxbwZkLZ4qD6H8bCjjMDlpFtiKlRwoTe00kyhqlqcWqQW9g8Y7FrM5cTfuo9pwrPFemc1aUNSpcS3ZK4hRW7ltJz1Y9+df4f7H+4PoynbCGdxmO2+fmjYw3GNV1FD/d8FNe/snLjOo2ipX7VrLpyKZwEB3ccTATekwgwhTBT9N+yqIhi2huaU6MLYYYWwxzBsxhZr+Z4YYIZoO51JrWU/mnWLh9oarPa+H2hYzsOlKCay2R4CqEEELUIJvJxrAuw0g7kIaiKKqD6Kx+s0hqk8SFogsVBrNxCePY8f2OcBAFytSJLWHQGcJ1Yu1mOzmeHG5rdxs9W/WsMChGW6MJKkFyPDms2LuChNgE3hv/XqUtVtdkrmH49cNZ+MVC/CE/VqMVezM7Lawtwi1WvQEvuZ5cCgOFLE1fyvDrh7PpyCY+PPYhN119E8O7DGdMtzFEmCIo8BewL2sf87bOY9uJbbRv3p4ftf0RIUJ0WdyFqYlTmdBzAtOSpmHUG8Mzwv/Y/Q9eSn+JECFW7lvJ3AFzaW5tDlAqpF7K5XWRmpFa7ixueQKhAKkZqcwZMEfawNYCCa5CCCHEZfL4PbgL3UDx5iQHjlIbpuLt8fyq369UB1FHMwe3d7idxTsXs+CzBaVC1Pd8T+bZTP6646/MGTCHaUnTsJvtAEzqM4mhnYaSdjCNnT/sDNcwTWqTxLAuw4iLLO7MlePJ4YUdL/DklicrbJn6f5/8Hzt+2ME/7vsHia0TefTDRwmEAlW2WO0W242kq5LoHd+bnSd3UhgorLRigMVoYeW+lYy4fgSbj21m64mtbD2xtcLxRoORVRmrGNN1DJ989wkvpr/Ii+kvVvr5bPp2k+pOWN6Al81HN6saW2Lz0c3M6DsDJxJca5oEVyGEEKKaLm7huufkHsZZxjHz/Zn0uqpXqRauDrO2IOpX/CxNX8qznz8bbr8aUkIoKOjQodfp0ev0PPv5sxh0hvAay5Lnmp40HU9vT3jNqs1kKxWks/KzmL9tvqqWqVBcUqqZoRn+kL/K8Tp0rMpYxbDOw9h5cmeV76HFZGHjkY0sGbpExTtefPyPjn3E6G6jVY2H4llUtQ0OdDodbp9b9bGh+OdAS4MDUX0SXIUQQohquLSFazNdM7DACfcJjriOlGrhqtPpVAdRi9HCT3v+lEXbF6HT6dChQ6cvG4pKglJ5aywvDaoXO1d4jhX7VpS7s748XWK68OqXrxJljVJVHUFHceesYdcPU3X8kgYHVpO6lq8KCgX+AtXjAZwW9Q0OFEXBYXaoPjaAw+zQ1OBAVJ/UcRVCCCE00tLCddmeZRQUFYSDqF6nx6g3hovmNzM0w6g3otfp0el0rM5czQ/uH+gd3zt8LJ1OV+ZXiZI1lmqL4vuCPjYe2aj6tVqMFs4UnFFdK1ZBW+eskgYHhX51DQgK/YXERcapHg8w+NrBWAwWVWMtRguDOgxSfWyAQR0GYTGqO35d8Pg95HhyyPXkkuPJweP31PUpVZsEVyGEEEIjLS1cV2Wu0hRESx4zrLO6GUsoXmPpDXhVjdXr9Lh86js/ldSVVXup3R/0a+qc5fV7GdJxCJlnMlWNP+89z+BrB7M/e7+q8Ua9kQk9JoQ3ZlXFaXGSnJCMUa/uorRRbyQ5Iblebsxy+9wczjnMCzteYPK7k0len8zkdyfzwo4XOJxzWPOSiPpAgqsQQgihgdYWrkXBIt7IfINhXVReOtfp+PjYx/SI66H6nLSssQwpIZxm9SHrwNkD3N7hdtWX2nMKcxjccTCZ2eqCqDfgZUKPCaw7sE71+FFdR6HXq4swcwbMCXfCUqu1vTWz+s1SNXZWv1nE2+M1Hb825BXm8dru10hJS2Ht/rUcdx0nx5PDcddx1u5fS0paCq/tfo1zhefq+lQ1keAqhBBCaKC1hWvJZqKerXqqGq8o2i61g7Y1lmaDmSEdh4T/P8IUQRtHGzq16ESXmC50atGJNo424edfu38t47uPxx/0qxpf0jnLalS3BvWhGx8iLjKOfm36qRo/e8BsYiNi+Umnn/DYzY9VODNq1Bt57ObHwg0OtIi2RjMlcQqz+8+u9Piz+89mSuKUelfDVetSloY081qnm7Oefvpp1q9fz8GDB7FarfTv358FCxbQuXPnUuO++OILHnvsMXbs2IHBYKBXr15s2rQJq1X9wmwhhBDiStDawrVkzafaNaKBUEDTpXYou8bS5XXhDXjDdVMtRkv4UnaUNYqUHim8lP4SrSJbYdAZOOc9x9mCs4SUEHqdHrvZTmt7a4JKkC3Ht9DW2ZYHez3Isr3Lqhyf0iOF2IhY7u18Lz+4fwi3TL2UUW8Mt0yNscUwNWkqCkqZqgsXjy+putDC1gKgVCesTd9uwuV14bQ4GXzt4FKdsKqjZURLpvedzsiuI0nNSGXLsS1A8Wa1ge0HkpyQTLw9vt6FVtC2lGV15mqGdhqqeUNaXanT4Prpp58ydepUEhMTCQQC/O53v+Ouu+5i//79REQUf3P74osvGDJkCI8++igvvvgiRqORvXv3qr5EIIQQQlxJWlu46nV64u3xqjfEuHwuxiWMIzM7Ex06kq5K4v4u99Mzric2ow1PwMPerL1sOLiBnSd3YtAbwmss8wrzOJ1/mpUZK9l8dDNunxuH2cGgDoOYkDAhHLTi7HH8YeAfWPjFQrI92WXOwefxkePJIdYWy+8H/h6b0cbkGyfjD/nLds7673iX18Xcm+YyJXFKOFheHPwuPZ9Lg9+lLVnVBNGSTlhzB8wt1QnLYrCoXtNamWhrNNHWaOYMmMPUG6by5adfsmbkGiItkfVyTStoX8oSVIKkHUxjetJ01V+u6lKdBteNG0vvaly2bBmxsbHs2rWLgQMHAvCrX/2KRx55hLlz54bHXTojK4QQl/L4PXj8FdexFKK6tLZwNegMDL52MBnZGarGFxQVMPL6kew7s4/Xh72OvZmdtINpLP1yKfm+fOxmO7e1u41Hb3qU/KJ8jp8/Trw9nrMFZ1mSvqTcGc79Z/ezJH1JeIbTarRyX+f7yLqQxeL0xRXOcI5PGM99ne8DHbxz8B36t+3PpgmbWHdgHR8e/TDcOeuODncw4voR+II+3jn4DqO7j8ZhdpQKfjP6zih3Bvhi1Q2iVyKkVsZpcWIzFP/5ERsRi8lkqtHnuxxal7IAxc0qequ/KlCX6lUdV5ereJdjdHTxt6/s7Gx27NhBcnIy/fv359tvv6VLly7MmzePm266qdxj+Hw+fD5f+P/d7uJ1G36/H79fXc26+qzkNTSG1yLKks/38uUX5XMm/wzvHn6X3ad3U+AvIMIUQZ/4PtzT6R5a2Vthb2avk3OTz7dxMGHi3o738q+D/yo1q9VM16zUv0sYdAaGdRrGjh92YNVXvcRtRt8ZtLS25Oa2N/Pqrld5fvvzpYLlec95Xj/3Oqv2rmJmv5n88oZfogQVXk5/mRd3vIgJEyZ9+cHqxe0vYsTIAz0f4Pcf/J5JN0zino738Paht/n0u0+5UHSByGaR3NLuFu7rfB++oI/lu5czLmEcy79aTkgJ0bVlV266+ibGdR2H1WilMFDIkbwjrNq7iv1n96PX6RnQZgDW6P+9VpvBFg5+JSr7fRBhjCDCWHqNb13/vmkov3+DgSBF/qIyP4eV8fv9BAPBOn1tap9bp9STirmhUIh7772X8+fPs23bNgC2b9/Oj370I6Kjo3n22Wfp1asXr7/+OkuWLCEzM5PrrruuzHH+9Kc/8fjjj5e5fdWqVdhs9f+bhBBCCCFEU+PxeBg/fjwulwuHo+L1tvUmuD788MO8//77bNu2jTZt2gDw+eefM2DAAB599FGeeuqp8NgePXowdOhQnn766TLHKW/GtW3btuTk5FT6RjQUfr+fDz74gDvvvLNeX6oQ1SOfb/XlF+Xz+p7XeXP/m1WOHd11NA/0eqDWZ17l821cznvPk7ovlXUH1oU7Z/2q5a9YdHYRRUoRBp2BEdePILlHMs0tzQE45z3HmfwzvLn/TT7+7uNSl/5Hdx1NK3sroixRfJP7DZPfmxye0Q0pIUJKKLz0paTTFhSXz3r2zmf50yd/YtfpXVWedxtHGwa1H0SPVj14edfLVY5/7q7n+M0HvyEQDGDQG1S9N1c7rub5Hz9PC2sLVeMbgoby+7fQX8jLX77MhkMbVD9mWJdhTL5hcrgbmdvnLrO5r6Y3b7ndbmJiYqoMrvViqcC0adN477332LJlSzi0AsTHF9dF69q1a6nx119/PSdOnCj3WGazGbPZXOZ2k8lUr3/QtGpsr0eUJp+vdmddZ3lj/xuqNiS8sf8N7u5yN9ERdbMbWD7fxqGlqSU/u/Fn3N3lbtIOpvHVD18BEGePo3eb3gzrMoy4yLhSf+HHmmKJtcdydfTVTOs3rdw1nx6/h3eOvENh6JLOUBeVaQ0Rgv9OO/mDftYcWMPQLkPZdnJbleftV/x8cPwDRnQfQZFSVOV4m9lGjjcHq9GKUVEXG1x+FyZj4/w5r++/f00mE/d1vY91h9ap+vPQoDNwX9f7cNgcqjf31dR5q1GnwVVRFKZPn05aWhqffPIJ7du3L3V/u3btaN26NYcOHSp1++HDh/nxj39cm6cqhKjHGvsuWlF/OcwOHGYH05Om405ws+PjHTz/4+dxWB2V/mw5LU6clL8rvbp1YtU2OAgRoqCoIDy7VhVvwIvD7AjXcVXDZrKhUC8u6DZJ8fZ4xnYfS2pGapVjx3YfS1xknKbNfS0jWtbUqVepToPr1KlTWbVqFW+//TZ2u52srCwAnE4nVqsVnU7Hb3/7W/74xz/Ss2dPevXqxfLlyzl48CBr166ty1MXQtQjjX0XbVPQ0KtA2Ew2TBTPGLWwtrisGbmarhPrDXhpbW9Nob+w6sHA4dzD3NnhTjYe2Vj14P9KapPUoD6/xsZhdjCx10SguE5reV/qDToDY7uPZWKviYSUEEvSl/DM589UeMxAKBC+f3rf6XVWv7ZOg+vSpUsBuPXWW0vd/s9//pOJEycCMHPmTLxeL7/61a/Iy8ujZ8+efPDBB1x77bW1fLZCiPpK61/0QDgkibrl9rnJys9i/cH1pJ9MD1eBSLwqkeFdhhNnj2swhdGvlJquE5vvy2dIryF8m/etqvGffvcpM380kw+PfkhICZHQKoHb2t1GpxadsBgteANeDuce5uPvPibjTAZ6nZ5hXYZJcK1jUdYoJvWZxNBOQ0k7mFb8Zf2/X3CS2iSVWsqSmZ3Jwu0LVR134faFjOw6smkGV7X7wubOnVuqjqsQQlxM61/0IJcy64O8wjyW71leZkYoh+J+6mkH0sIzQlHWqDo809p1OXVi1TQsCIQCjOw6kr1Ze1Udv1tsN1rbW/OLPr+grbMtZqOZ9QfW89wXz4XruA68ZiAP9HwAX8DH967viYuMu5y3QFwhFy9l8fQu/4qGy+siNSO13Fq+5QmEAqRmpDJnwJw6acJQLzZnCSHE5dD6Fz2UvZTZ0C9VNzQX91KvSEkvdYBJfSY1mZlXm8nGsC7DSDugbt22QWcoDqKn96puWNAqshWDOw7m9IXTqi4lR1mjuK/LfeWugcwii2/yvmH53uXhNZBN5bNqKCr788wb8LL56GZNx9t8dDMz+s6ocJ12TZLgKoRo8KrzF33JpUy5VF03GnMv9StB6+aa1vbWRFujWbxzMQs+W1AqWJ6+cJrDuYd57avXmDNgDtOSpoUv86q9lOz2uVm5byUbDm2graMt573nKfAXEFJC6HV6IkwRNLc0Z8OhDTjMjib1RaOh0+l0uH1uTY9x+9zodHWz1EqCqxCiUajOLlq5VF03GmMVCJfXxQXvBQCyC7Ivu5e91s01OnSk7kvlnUPv0DG6I3mFebh9boKhIAa9Idx+9Z1D7xBliQoHSzWXkuF/XzQMOgMGo4HYiFhCSih8/8V1ZZviF42GTFEUzZ+Vw+xQvdzzSpPgKoRoFKrzF71cqq4bjakKxMV1L7cc28LslrMZvXY0A9sPvOy6l1o21xzKOcTqzNUY9UaMeiNxkXHERsSWalhg0BU3DygvWFZ2Kbm8LxoXB9VLNYQvGuJ/LEYLgzoMYv/Z/aofM6jDICxGSw2eVcUkuAohGo3q/EWvhswgXVl1UQXC5XWV6QR0uRtLLq17adVboSUcyjnEnuw9V6TupZoZ0fKCpUFnCAfVS2kNlo3pi4Yoy2lxkpyQzJL0Jao2aBn1RpITkutkYxZIcBVCNDLV/Yu+MjKDdGXVZhWImuoElFeYV6t1L6uaEa3JYCnl5hq/1vbWzOo3q9Kf5xKz+s0i3h5fC2dVPgmuQohGqS7/oheVuxJVINSoyU5Ap/JP1Zu6lzUdLKXcXOMXbY1mSuIUgHJ/v0DxTGvJ75e6quEKUP4CFSGEaMRkBqlulVSBqOhS9qUurgKh1sUzohVd/iyZEV2SvoS8wjzVx65u3UuX16X6ObQoCZZaaAmWJV80tJDOWQ1Py4iWTO87nc9/9jkz+s6gW8tutHW0pVvLbszoO4PPf/Y50/tOr9N2ryDBVQjRBNX0X/SiaiVVINQoqQKhhdYZ0dP5p1Ufu7p1L70Br6bHqFXTwbI2vmiI+iHaGk232G7MGTCHd8e9y5YHt/DuuHeZM2AO3WK71elMawkJrkKIJkdmkOpeSRWI5ITkCgORQWcgOSGZib0matoYV9MzovWt7mVtBMua/qIh6henxUmryFbERsTSKrJVnW3EKo8EVyFEkyMzSPVDSRWIFcNXMKrbKK5xXkNLW0uucV7DqG6jWDF8BZP6TNJcQ7emZ0TrY93Lmg6WNflFQwgtZHOWEKJJqk7DAnHlqS2Ar0VNz4jWx7qXWusYVydYaik3J0RNkeAqhGiSauMveqHe5QTVS9X0jGh5dS8VRQk/vuS/S4JwbdW9rI1gWRNfNITQQoKrEKLJkhmkxqk2ZkRL6l4u+GwBCgqBUAC9Urz6zhf04Vf8GPVGdOhqte5lbQVLCaqirkhwFUI0aTKD1PhcPCMaDAW5+eqbGX79cLrHdsdqslLoLyQzO5P1B9az9cRWDHqD5hnRaGs0Dyc+TFAJMn/bfAKhACa9CYCQEiIQCqBDx9yb5vJw4sO1vhtbfn5FYyXBVQghkL/oG5vW9tb8+dY/0zG6IzaTjbX71/LcF8+R78vHbrZzR/s7mD1gNtOSpvFN3jeaZ0TzCvN45+A7pPRIYcT1I0jNSOWTo58A0D22O7d2uJXkhGRsJhvvHHyH4V2H14tSQg2Fx+8J106WL5LiYhJchRBCNDrR1mjGJYxj8c7FzN82H3/I/787L8Ch3EP8bfffmHvTXKYlTdMcKk/ln+LXH/yaUCjEA70e4IEeD/BQ74fI+CyD1SNWc9Z7lld3v8rre15Hr9fzo7Y/kuCqgtvnJis/i/UH15N+Mp0CfwERpggSr0pkeJfhxNll6U5TJ8FVCCFEo5NXmMfLX77M6szVtI9qz7nCc+QX5RMMBTHoDdib2YmyRrE6czVGvVFTeL20TuyyPctYtmcZVr2V165/jaS/J1EYKgyPD4VCpGakMmfAnHpVD7O+ySvMY/me5WU2S+aQw3HXcdIOpIU3S2otkSYaD6njKoQQotE5lX+K5754jpP5J8n15BJhiqCdsx0dozvSztmOCFMEuZ5cTuaf5LkvnmvQnbMaA7fPzfI9y0nNSC23wgdAUAmSmpHKsj3LNJc7E42HBFchhBCNyqUzooWBQrI92Zxwn+C46zgn3CfI9mRTGCieFW3onbMag9P5p1mduVrV2NWZq8m6kFXDZyTqKwmuQgghGpWm2DmrIfP4PaQdTKtwpvVSQSVI2sE0PH5PDZ+ZqI8kuAohhGhUaqtzVgmr0UqsLZY2jjYAtHG0IdYWi9VoDY+p6c5ZDZnH7yH9ZLqmx5TUXBZNjwRXIYQQjcqVmBH1+D3keHLI9eSS48kpFZJK6sRajVausl9FC1sLCvwFnHCdAOCE6wQF/gJa2Fpwlf0qrEZrrXTOaqh06CjwF2h6TEmprNri8XvILcwFILcwV0JzHZKqAkIIIRqV6nbOshqtqssxtba3Znb/2Ty3/TnOFpxFQcGqL55h9Qa8FBYVkuPJoWVES2b3n11rnbMaIgWFCFMEOeSofozNZEOh5pdeXPzzsOfkHsZZxjHz/Zn0uqqXlOeqIxJchRBCNCoXd84q2aBVGaPeyIQeE0AHr+1+TVU5Jr1Ozz2d7+H0hdMsTl9c7vMY9AbGdx/PPZ3vQa+TC5wVsZlsJF6VyHHXcXToSGiVwG3tbqNTi05YjBa8AS+Hcw/z8Xcfk3EmAwWFpDZJpRoSuLwuvAEvOp0ORVGwGC2XPcN9aXmuZrpmYIET7hMccR2R8lx1RIKrEEKIBudc4Tl8QR96nZ6QEsJsMJcKD63trZnVbxbPfP5Mlcea1W8WsRGxvLb7NVIzUiscV1KOKT4ynh6tejBu3TieuuMp7u18L+sOrGPbsW0AdIruxE3tb2LE9SPw+D2MXTuWVSNW0dzS/LJfd0NVWbC0mWwM6zKML77/gimJUzAbzaw/sL6401lRPvZmdgZeM5AHej6AL+DjpZ0vMazLMGwmG3mFeZzOP83KjJVsProZt8+Nw+xgUIdBTEiYQLw9vlqNHy4uz1WRkp8HgEl9JsnMay2R4CqEEKLByPHkkHUhixV7V7DxyEZcPhdOs5MhHYeQ0jOFuMg4YmwxRFujmZI4BYCF2xeWOyNq1BuZ1W8WUxOn4vK6VJdjCoQCvHXgLU7mn2TM2jHcdHVxSB3bdSy5e3NZPHQxB3IPsOCzBWw7sQ0Fpck2IFAbLFvbW7PgzgW8tvs1Xt39aqnPK4ssvsn7huV7l/PLPr/kmbueIT4ynrMFZ1mSvqTcz3f/2f0sSV/CrH6zmJI4hZYRLTWdt9byXEM7DZXgWkskuAohhGgQsguyy2/hCuzL3sei7YvCLVxjI2JpGdGS6X2nM7LrSFIzUssEp+SEZOLt8ViNVv7+1d9Vl2Pq2KIjS79cit1spzBQyNYTW9l6Ymu4c9b9q+8v1TkListtzeg7AydNJ7hqCZZmo5n3Dr/H6q9XV7i8IxAKsPrr1bSKbMWDvR5kSfqSSmfUA6FA+P7pfaernnmtbnmu6UnTSy1fEDVDgqsQok54/J7wzmAFBZvJJn/oiwrleHJYvHMxT2x5osIx/pA/fP8jfR8Jz7xGW6OZM2AOM/rOKPdSdY4nR1M5JovRQlZBlqbyVk2tAUFeYZ7qYNnG0Yb+bfvz1tdvEWOLwWl2ct57ngJ/ASElhF6nJ8IUQXNLc0wGE299/RZDOg5h64mtqs5l4faFjOw6UlNwrVZ5rt4e+TOsFkhwFULUKrW7toW4WFZ+FvO3zVc1dv62+YzuNpoYW0z4NqfFWeFsp9ZyTN6AlwhTBMGQuhk5aHoNCE7ln2Lh9oWqxhYGCnlr/1sElSAGnQGD0UBsRCwhJRQeo9fpwxvcikJFrNy3kns738sXP3xR5fFLOqOpXarREMpzNWWyzVEIUWvyCvN4bfdrpKSlsHb/Wo67jpPjKd6xvXb/WlLSUnht92ucKzxX16cq6pFzhedYsW9FmeUBFfGH/Kzct1L1z1FJOSa1Ducc5vb2txMiVPXg/2pKDQgubblblYRWCfzn2/+UujSv1+kx6o3hXxdXZQgpITZ9u4merXqqPidNndE0/jxA7ZXnEhJchRC15OJduhWtHSvZpbtszzLNnY9E4+UL+th4ZKOmx2w8shFf0KdqbEk5JrU+Of4J47qPUz3jatQbm1QDAq0td21GG6cvnC41w1oZHTpcXpemy/Jalmpo/XkAypTnEjVHgqsQolZo3aWbdSGrhs9INBR6nR6Xz6XpMS6fS3Xt1JJyTAadQdX4/dn7aetsS0qPFFXjZ/Wb1aQaEGhtuesJeLAZbaovtSsoOC1OTd2rtCzV0PrzYNAZwuW5RM2T4CqEqHHV3aV7OW0VK2vZKRqWkBLCadY2W+k0O1XP4AHE2+MZ232sqrFjuo/B3szOQzc+xOz+szHqy98uYtQbmd1/NlMSp1SrlmhDpbXl7t6svdze/nbVl9r1Oj2Drx3M3jN7VT+H1qUaWn4exnYfS1xknOpji8sjm7OEEDWuNnfpyuavxsdsMDOk4xD2Ze9T/ZghHYdgNphVj3eYHUzsNRGgTOesEgadIdwpyW62YzfbS5Xb2nJsCwBdYrowsP3AcLmtphRaQXvL3Q0HN/DUHU/x+Q+fq5oVbaZvxoQeE5i5caaq41dnqYbWnwf5M6X2SHAVQtS42tqle2mLxhLlteyUFo0NR5Q1ipQeKSzavgh/yE+EKYIoaxQ2ky3cOcvj93Cu8BwF/gJMehMTekzQ/BlHWaOY1GcSQzsNJe1gWvGXJ3/xl6ekNkkM6zKMuMjSX3wuLrc19YapfPnpl6wZuYZIS2STWdN6Ka0td3ef3s3VjqsZ3318pZ2qSpTMcN589c2qqgpUd6nGpT8PX/3wFQBXO66md5ve5f48iJonwVUIUeNKdunmkKP6MZfu0q2q7qu0aGzc4uxx/O7m37Fi3woMOgPnvOc4W3A2XOfTbrbT2t6aoBIkpUdKtS/dOswOHGYH05Om4+mtvs6w0+LEZii+PzYiFpPJVK3nbyy0tNz9Vb9fYWtm0zTDGWWNUt0Z7XKWalz88+BOcLPj4x08/+PncVgdsqa1jkhwFULUuJJdusddx1U/JqlNEhGmCNWX/qVFY+MWY4thSuIUQkqo3M5ZPo8Pl9fF3JvmMiVxSqkartUhDTEuj5aWu1MSp9Dc0hxA04y32s5oV2Kphs1kw0Txl5EW1hZN/otJXZLgKoSocSW7dNMOqNugZdAZGNV1FIWBQtWX/qVFY+Pm9rl55+A79G/bn00TNrHuwDo+PPoh+UX52JvZuaPDHYy4fgS+oI93Dr7D6O6jG9UXE5fXhTfgLbfzV31VnWCpdcZbTWc00bhIcBVC1IqSXbpq17A1tzRXfen/7uvuZvsP2zWdj7RobFhO55/mb7v/RkgJ0T22O7e2u5XkhGSsJiuF/kIO5x7m9b2vk5mdiV6nZ2C7gY0iuOYV5nE6/zQrM1aWCX4TEibU+81f1Q2WWme8K+uMJhoXCa5CiFqhZZfupD6TNF369wa8uLza6nxKi8aG49JyahnZGWRkZ1Q4vrHMqJ8tOMuS9CXlXmrff3Y/S9KXhC+1t4xoWUdnqY4ES3GlSHAVQlwRVW2eAvW7to16o6ZL/96Al6ASDG/UUUNaNDYctVlOrb7IK8xjSfqSSjc3BUKB8P3T+06v1zOvQlwpElyFEJdFa91UNWvYcjw5moLK4dzD3Nj6Rj469pHq4CotGhuO2iqnVp+cyj/Fwu0LVY1duH0hI7uOlOAqmgQJrkKIarucuqmVrWHTGlQ+/u5jJvaayJbjW1SNlxaNDcuVKKfWkLi8LlIzUlXVQIXimdfUjFTmDJgjG5JEoyctX4UQ1XJx3dSKLumXbJ5atmeZpt7lJUFFrYwzGaAgLRobqZJyalo05Bl1b8DL5qObNT1m89HNeAPeGjojIeoPCa5CiGrRWjc160KW6mNrDSoKCplnM5nYayLJCckYdIZyxxl0BpITkqVFYz10rvAcWReyyC7IJutCFucKz4XvKymnVtHneqmGPqOu0+k0fdGD4i+SOl3DXRohhFqyVEAIodmlu7yronWX98V1X0NKiIRWCdxyzS10jumMxWjBG/ByKOcQnx7/lIwzGeh1egZ1GESMLUZzy05Rt3I8OWRdyGLF3hVsPLIRl8+F0+xkSMchpPQs7oAVY4vRXE6tIc+oK4qi+WfUYXagKOo7zQnRUElwFUJoVhu7vOPt8fyizy9o42yD2WBm7f61PL3tafJ9+djNdu5ofwcpPVLwBX384PohHFSq27Kzpnn8HtyFxbNouYW5OJCWkdkF2SzeubjcTlj7svexaPsi5t40l2lJ04iNiNXUErQhfzmxGC0M6jCI/Wf3q37MoA6DsBqtmjdLCtHQSHAVQmhWG7u8HWYH93a5t/xgcwEO5R7ib7v/Fg42l/5lXNdBtcTFQWLPyT2Ms4xj5vsz6XVVryYdJHI8OSzeuZgntjxR4Rh/yB++/5G+jzSqGfXKZkSdFifJCcksSV+iaoOWUW9kYs+JBJQAy7+q3mZJIRoKCa5CCM2uxC7vc4Xn8AV96HV6QkoIs8Fc6i/THE8OS9KXkJqRSrvm7TjvPY/b5w7XanWYHTS3NCc1IxW9Ts+0pGmX3Z/+Sru06kIzXTOwwAn3CY64jjTpIJGVn8X8bfNVjZ2/bT6ju40mxhZTb2fU1VI7I9ra3ppZ/WZVWse1xKx+s4i2RavuNAcwqc+kBhHwhbiUBFchhGYlm6eOu46rfkzJLm+1axqz8rN4autT+EN+IkwRRFmjuMZ6TTjoevweTuWfosBfwFNbn2Jk15H1KrheXHWhIk01SJwrPMeKfSvKLA+oiD/kZ+W+lcwZMCcc8Gs6qNbEGlEt5eOirdFMSZwCUG7nLCieaZ3VbxaP9H2E7IJsTZslh3Ya2mR+3kTjIsFVCKHZxZun1GzQKtnl7Q14eWHHC1WuaZzZd2apYFPgL6h0aUJ5waauaa260JSChC/oY+ORjZoes/HIRmb2m1kzJ3SRmlojWp0vMi0jWjK973RGdh1JakYqm49uxu1z4zA7GNRhEMkJycTb4zEbzTW6WVKI+kSCqxCiWrTu8o62RvPX7X9VtaZx8g2T2XxMWx3L2go2atR01YWGTq/T4/K5ND3G5XOp7opWXZfTUKMq1f0iE22NJtoazZwBc5jRdwY6nQ5FUbAYLeFmA1o7zUHDb4krmi6p4yqEqBaH2aGpbmpuQS5Pb3ta1bFL6lhqCSq1EWzUqnbVBb+nhs6ofgkpIZxmbR2enGYnISVUQ2dUsw01qvtF5uKfB6fFSavIVsRGxNIqslWpDllNsSWuaLpkxlUIUW1R1ihVu7xDSoh/7PmH6jWNgVCASFMkRr2RomCRqsfUdLDRQoJE5cwGM0M6DmFf9j7VjxnScQhmg7nGzqkml3bUdPm4ptYSVzRtmqYnPvroI7p27YrbXfabpsvlolu3bmzduvWKnZwQov5zmB10atGJ6UnTeeWeV1g5fCWv3PMK05Om06lFJxxmB96AV9OaxuyCbO689k5NQa6mg40WWlvWQtMKElHWKFJ6pGDSm1SNN+lNTOgxocbWL1+JGdHK1PQXmabWElc0bZqC6/PPP88vfvELHI6y3zKdTieTJ09m4cKFV+zkhBANh81kI8YWQwtbC2JsMaX+UtS6pvH1va+TnJBMM0MzVeNrOthoJUECzheeL9XC9Xzh+VL3x9njmHvTXFXHmnvT3BrthFXTSztq+otMU2uJK5o2TcF17969DBkypML777rrLnbt2qX6eE8//TSJiYnY7XZiY2O5//77OXToULljFUXhxz/+MTqdjg0bNmg5bSFEHdO6pvGlnS/hMDv41Y9+pWp8TQcbrZpykMjx5JCZncnT255myMoh9P1bX4asHMLT254mMzuTHE/x5ewYWwzTkqbxh4F/qHDm1aQ38YeBf6jxGr2NYUa0ZLOkGg29Ja5o2jQF1zNnzmAyVXxpx2g0cvbsWdXH+/TTT5k6dSrbt2/ngw8+wO/3c9ddd1FQUPYPkOeffx6drmms/xKisSlZ06hWiBDvH3mfqYlT60WwqY6mGCSyC7J5YccL3PDqDTz7xbNkZGdwwn2CjOwMnv3iWW549QZe2PEC2QXZAMRGxPJI30fYPXk3cwbMoWernrRr3o6erXoyZ8Acdk/ezSN9HyE2IrZGz7sxzIhq3SzZVEqvicZH0+asq666iszMTDp27Fju/fv27SM+Pl718TZuLL3mbdmyZcTGxrJr1y4GDhwYvn3Pnj0899xzfPnll5qOL4SoH0rWNC7avkjVBi2T3sTAawaGg83obqNZuW9lmYYFE3pMCDcsqG9KggRQprxSCYPOEC6v1NCDREkL13lb51U4JhAKhO8vaeFa8mvOgDnM7Dezwk5qNelyGmqopbV8XHW+yKjdLNnQf9ZE06YpuN5999384Q9/YMiQIVgsllL3FRYW8sc//pGf/OQn1T4Zl6t4DVx0dHT4No/Hw/jx43nppZeIi6v6N7LP58Pn84X/v2Qjmd/vx+9Xt6O5Pit5DY3htYiyGvPnG2ON4XcDfsczn1XdwnL2gNnEWGLw+/04TU6cUU5+3ffXTLtxWqlg09zSHKi/71ekMZKf9vgpQzoM4b3D77H31F4AOjg60LN1T37S6Se0imxFpDGy1l9Dob8QT+CizlBGG1aTtdrHO3XuFC988QJWfdXHeOGLFxjZeSRO0/+Wj0QaI4k0RpYaV1vviQkT93a8l38d/Jfqhhr3drwXE6ZS51jZ71+r3sqE7hPQhXSsO7Cuwi8yI64fQXL3ZKx6a7Vev1Vvpb2jPQ/3fpifJvy03M+3vv5+qe8a85/P9YHa91WnKIrqbaxnzpyhT58+GAwGpk2bRufOnQE4ePAgL730EsFgkN27d9OqVSvNJxwKhbj33ns5f/4827ZtC98+efJkgsEgf//734tPWKcjLS2N+++/v9zj/OlPf+Lxxx8vc/uqVauw2Rr++jEhhBBCiMamZKLS5XKVWwSghKbgCnD8+HEefvhhNm3aRMlDdTodgwcP5qWXXqJ9+/bVOuGHH36Y999/n23bttGmTRsA3nnnHX7961/z1VdfERkZGX6uyoJreTOubdu2JScnp9I3oqHw+/188MEH3HnnnZWuNxYNU0P8fPOL8jmTf4Z3D7/L7tO7wy0y+8T34Z5O99DK3gp7M3t4fG5hLtkXslnz9Ro2H90cvvQ/qMMgxnQbQ2xkLC2sLerwFdWcuv58z3nPsWrfqqpn/Hokh2ez1ThTcIYRa0bw9dmvVT+me2x31o5eS6sI7RMdWpzznuNM/hnW7F/Dx999TL4vH7vZzm3tbmNM1zG0srciylK8JOG89zyp+1Kr/f5o+Xyv9Iy3qHl1/fu3sXO73cTExFQZXDU3ILjmmmv497//zblz5zhy5AiKonDdddcRFVX9tUjTpk3jvffeY8uWLeHQCsV1Y7/99luaN29eavyIESO4+eab+eSTT8ocy2w2YzaXreVoMpka1Q9aY3s9orSG8vnmFeaxfF/ZFpkAR1xHWHdoXZkWmXGmOOIccbSNasv0H02vkzWNda0uPl+3z01qZiqpX1eyxlKBlV+vRNErTOozSfVayGamZmQXZlMYKlR9PtmF2TQzNavR9+FswVmW7lrKwu0LCYQCpe7bk72HF798kVn9ZjElcQotI1rS0tSSn934M+7ucvdlrRFV8/maTCYcNPzJlKaoofz53NCofU+r3TkrKiqKxERt5T0upSgK06dPJy0tjU8++aTMbO3cuXP5+c9/Xuq2hIQEFi1axD333HNZzy2EuDwXt8isSEmLTKBMEGoqIbW+qMnOUCElVNyCVH0XVJyW0p3OPH5PuMSUgoLNZLus8mB5hXksSV/CM59XvKY6EAqE75/edzrR1mgcZgcOs4PpSdPx9L5y5yOEuDI0Bdef/exnqsb94x//UDVu6tSprFq1irfffhu73U5WVhZQ3MzAarUSFxdX7oasq6++utpLEoQQV0ZNBiFxZVW3M9T0pOmqwprFYGHwtYPJyM4I31beKrSLSxoOvnYwVqMVt89NVn4W6w+uJ/1kenipSeJViQzvMpw4e/V2wZ/KP8XC7eoa4izcvpCRXUcSbf3fxmAJqkLUT5rquC5btoyPP/6Y8+fPc+7cuQp/qbV06VJcLhe33nor8fHx4V9r1qzR/EKEELWnpltkiiurpjtDNbc2J6VHCka9EUVRwqFVuegfIHyfUW/kwV4PElSCvLb7NVLSUli7fy3HXcfJ8eRw3HWctfvXkpKWwmu7X+Ncofq/VwBcXhepGalllgdUJBAKkJqRisurvrubEKJuaJpxffjhh3njjTc4duwYDz74IBMmTChVukorjfvCqv0YIcSVVe0g1Nsjs1h14Ep0hqrqUn6cPY45A+bw5JYnyy3MX3KbDh1zB8ylha3FZS01qYw34GXz0c2qxpbYfHQzM/rOwIn6Dm9CiNqnacb1pZde4vTp08yePZt3332Xtm3bMnr06FIVBoQQjV9Nt8gUV9bldIZy+9wczjnMCzteYPK7k0len8zkdyfzwo4XOJxzGLeveGGr1WBlWuI0fj/w95V2Ovv9wN8zo+8McgpyNC01ybqQpfrcdTpd+LzUcvvc0p1RiAZA8+Yss9nMuHHjGDduHMePH2fZsmVMmTKFQCDA119/HS5bJYRovEqCUA45qh+jpUWmuLKq2xlKr9Pz2u7XylSNyKH4cn7agbRw1Yi8wjyWpi/lNwN+U2mnsxhbDF9lfcXn339eY2tuFUXRvC7WYXbIBIwQDYCmGdcyD9br0el0KIpCMKjuDyAhRMNXEoS00NoiU1w5NpONYV2GVdjD/lIGnYH7Ot/Hf478h9SM1AoDZsml/H/u+Sf5Rfl8evxT7lt1H+kn05mSOIX3k99nx8938H7y+0xJnEL6yXTuW3Ufkc0i+eDoB6WqClRFy5pbi9HCoA6DVB8bYFCHQViMlqoHCiHqlObg6vP5eOONN7jzzjvp1KkTGRkZLF68mBMnTshsqxBNRHWC0LAuwyS41qF4ezxju49VNXZs97E4zU6e3/68qvGrMlZxoegC3WO7EyTIS+kvMXzNcO554x7uTr2be964h+FrhvNS+ksECWIxWjjrOaspuGpZauK0OElOSMaoV3dR0ag3kpyQXFzSSwhRr2kKrlOmTCE+Pp758+fzk5/8hO+//5633nqLu+++G73+siZvhRANjNYgFBdZtrSdqD0Os4OJvSaSnJBc4RcOg85AckIyKT1TSDuYRkBRvyt//YH13N7+dlXjvQGv5i8xWpeatLa3Zla/WarGzuo3i3h7vKbzEULUDU1rXF9++WWuvvpqOnTowKeffsqnn35a7rj169dfkZMTQtRfJUEIKLdzFhQHoZI1kFLDte5FWaOY1GcSQzsNrbQzVCAU4MOjH2o69rYT2xjdbbSqsYdzD3PLNbfw0bGPVB9f61KTaGs0UxKnAJTbOQuKZ1pLOmddXMNVCFF/aQquDzzwgOy6FEKEqQ1CElrrDzWdoXI9uZqqRpS07bUararGf/zdxzx040NsO7FN1XKB6i41aRnRkul9pzOy60hSM1LZfHQzbp8bh9nBoA6DSE5IJt4eL6FViAZEU3BdtmxZDZ2GEKKhkhaZDVNln4/WqhF6nZ74yHgKA4Wqxu/P3k9bR1vGdR9XaR3XEpez1CTaGk20NZo5A+Ywo++M8IZii9Eia1qFaIA0Bdfhw4dXOUan07Fu3bpqn5AQomGSoNp4VKd8Vv+2/VG7BHVM9zFENous1aUmTouzRpsLuLwuLngvAJBdkE2kJVKCsRA1QFNwdTrlN6EQQjR2JVUj0g6oa+tr0BkY0XUEcZFxJCckqwqidrMdoMEvNckrzON0/mlWZqxky7EtzG45m9FrRzOw/UAmJEyQpQhCXGGagus///nPmjoPIYQQ9UhJ1Qgtl/IdZofmINqQl5qcLTjLkvQl4c1fVr0VWsKhnEPsyd7DkvQl4c1fLSNa1vXpCtEoaO6cJYQQovGrbtWI6gbRhhBUL5ZXmMeS9CU88/kzFY4JhALh+6f3nS4zr0JcARJchRCiEThfeB5v0Bve4W8xWGhubX5Zx7ycqhENLYhqdSr/FAu3L1Q1duH2hYzsOlKCqxBXgARXIYRowHI8OWRdyGLF3hVs+nYTLq8Lp8XJ4GsHk9IzhbjIOGJsMdU+fkO+lF9TXF4XqRmp5daGLU8gFCA1I5U5A+bIhi0hLpMEVyGEaKCyC7JZvHMxCz5bUDpEuSEjO4PndzzPnAFzmJY0jdiI2Mt6rqYcVC/lDXjZfHSzpsdsPrqZGX1nVLuygcfvCbe9bepfHETTJsFVCCEaoBxPDot3Lmbe1nkVjgmEAuH7H+n7yGXNvGrl8rrwBryNsm6qTqfD7XNreozb565WAx+3z01WfhbrD64n/WQ6Bf4CIkwRJF6VyPAuw4mz1++qC0JcaRJchRCiAcrKz2LBZwtUjV3w2QJGdxtdK8H14vJQl3aqaizloRRF0RwWHWYHiqKy0O1/5RXmsXzP8jKb43LI4bjrOGkH0sKb46KsUZqOLURDpa/rExBCCKHN+cLzrNi3QtMay5X7VnK+8HyNntfZgrO8uONF+v+jPy/seIH9Z/fzg/sH9p/dzws7XqD/P/rz4o4XOVtwtkbPo6ZZjBYGdRik6TGDOgzCYrSoHu/2uVm+ZzmpGakV1tINKkFSM1JZtmeZ5hlgIRoqCa5CCNHAeINeNn27SdNjNn27CW/QW0NnVLo8VEWBuqQ81JL0JeQV5tXYudQ0p8VJckIyRr26i5ZGvZHkhGRNSyVO559mdeZqVWNXZ64m60KW6mML0ZBJcBVC1AmP30OOJ4dcTy45nhw8fk9dn1KDodfpcXldmh7j8rrQ62ruj3yt5aFO55+usXOpDa3trZnVb5aqsbP6zSLeHq/62B6/h7SD6rqWQfHMa9rBNPk9JJoEWeMqhKhVstnk8oWUUPHsnYarw06Lk5ASqpHzaYrloaKt0UxJnAIQ7px1KaPeGO6cpWVdr8fvIf1kuqbz2fnDTjy9PVJpQDR6ElyFELVGNptcGRaDhcHXDiYjO0P1YwZfOxiLQf0aSy3qojxUfdAyoiXT+05nZNeRpGaksuXYFgC6xHRhYPuBJCckV2szmg4dBf4CTY8pKZUlRGMnSwWEELVCNptcOc2tzUnpkaJpjeWEHhMuu5NWRWqzPFR9E22NpltsN+YMmMOakWsAWDNyDXMGzKFbbLdqVVBQUIgwRWh6jM1kQ0Fb1QIhGiIJrkKIWiGbTa6sOHsccwbMUTV2zoA5xEXG1di51FZ5qPrMaXGGmzzERsRe1hIIm8lG4lWJmh6T1CZJlgmIJkGCqxCixslmkysvxhbDtKRpPHbzYxXOvBr1Rh67+TGmJU2r0RqutVEeqimxmWwM6zIMg86garxBZ2BYl2ESXEWTIMFVCFGuK7nrv9qbTRpRcPX4PeQW5gKQW5h7RV5bbEQsj/R9hF2/3MVv+/+WHq16cI3zGnq06sFv+/+WXb/cxSN9H7nsdq9VqY3yUE1NvD2esd3Hqho7tvvYGp1RF6I+kc1ZQohSamLXf1PebHLx+7nn5B7GWcYx8/2Z9Lqq1xWpohBjiyHGFsPcAXOZ2W8mep2ekBLCYrDU2JrW8pSUh3rm82eqHKu1PFRT5DA7mNhrIkCZzYwlDDpDeDOjVOIQTYUEVyFEWE3t+i/ZbJJDjurHNIbNJpe+n810zcACJ9wnOOI6ckWrKNRmSC1PTZaHaqqirFFM6jOJoZ2GknYwLXwVwmaykdQmiWFdhhEXKeXjRNMiwVWIJsLj94RnMRUUbCZbqTVxF+/6r0jJrn+ASX0mqf4Ls2SzyXHXcdXn29A3m9Tk+1lfXVoeavPRzbh9bhxmB4M6DKp2eaimzGF24DA7mJ40HU/vin//CtFUSHAVopFTe+lf667/oZ2Gagquw7oMI+2Aug1ajWGzSU2+n/VZtDWaaGs0cwbMYUbfGeh0OhRFwWK0XNE1rVV9EWtsGvvrE0ItCa5CNGJaLv1XZ9f/9KTpqv8yLdlsUtkMZImGvtmkulUUtLyf9Z3T4qyR5gLSeU2Ipk2CqxCNlJZL1Xdfdzfbf9iu6fiXtpg8V3gOX9AX3hxkNphLrdtsSptNpGVnzZDOa0IICa5CNFJaLlV7A15cXpem45dcps3x5JB1IYsVe1ew8chGXD4XTrOTIR2HkNIzhbjIuHAN0aay2aQpV1GoKU1xzbAQoiwJrkI0QlovVXsDXoJKkJASQq9TV97ZZrLhD/lZkr6E+dvm4w/5S92/L3sfi7YvYu5Nc5mWNC1cS7QpbDZpqlUUalJTXTMshChNGhAI0QhpvVR9OPcwN7a+kZASUv2YG1vfyKGcQzyx5YkyobWEP+TniS1PsHjnYnI8pUOczWQjxhZDC1sLYmwxjSa0grTsvNKk85oQooQEVyEaIa2Xqj/+7mPu73K/6s5HBp2Bezrfw+8/+r2q8fO3zSfrQpbq82nopGXnlSWd14QQJSS4CtEIlVyqVivjTAYoqG4xOarbKLx+L599/5mq8f6Qn5X7VnKu8Jzqc2ropGXnlSNrhoUQJSS4CtEIab1UraCQeTaTib0mkpyQXOFMoUFnIDkhmQd6PsBjHz2maU3mxiMb8QV9qsc3dCVVFNS8nw29ikJN0/pFDGTNsBCNlWzOEqIRqk7B/0EdBhFji1G1678oWMSh3EOazsnlc5Xa+NUUCshfWkXhqx++AuBqx9X0btO70VRRqGlNsfOaEKJ8ElyFaKSqW/Bfza7/rAtZOM3aisuXjG9qBeQvfj/dCW52fLyD53/8PA6rQ4KVSk2x85oQonwSXIVopC634H9lM6Bmg5khHYewL3uf6vMZ230sOnS8tvu1JllA3mayYcIEQAtrC0wmUx2fUcPSlDqvCSEqJsFViAZKzaX2mir4H2WNIqVHCou2L6qwFNbFmumbkdIjhZX7VkoBeVEtTanzmhCiYhJchWhgtF5qr6mC/3H2OObeNJcntjxR5djFdy/G7XNLAXlxWZpK5zUhRMUkuArRgFxOr/YrvfkpxhbDtKRpAOV2zgIw6U3MvWkuw64fxt93/11zAfnpSdNlnaIopSl0XhNCVEyCqxD1hMfvwV3oBiC3MBcHpTfv1HavdjVLEWIjYnmk7yOM7jaalftWsvHIRlw+F06zkyEdhzChx4TwWsNqFZDv7ZEwIsolQVWIpkmCqxB17OJL/3tO7mGcZRwz359Jr6t6lbr0X1u92rUuRYixxRBji2HOgDnM7DcTvU5PSAlhNpjDs765nlwpIC+EEOKySXAVog5deum/ma4ZWOCE+wRHXEfCl/5Teqbw4bEPa/xS++UsRaisCkBJAfkcclSfixSQF0IIcSkJrkLUES2X/oNKkKTWSeHL9mpceqnd5XXhDXjR6XQoioLFaMFp+V8t1ppciiAF5IUQQlwJElxFrWkKnZK00Hrpv+9Vfeke252M7AxVj/H4PZj0JvIK8zidf5qVGSvZfHQzbp8bh9nBoA6DmJAwgXh7PNHW6BpditAQCshXFeyFEELUPQmuosY1tU5Janj8HtIOqgtxAIFQgA0HN3B7+9tVB9fecb3xBX0sTV/Kwu0LCYQCpe7ff3Y/S9KXMKvfLKYkTmHHyR01uhShvhaQVxvsa5sEaSGEKEuCq6hRl7NmsjHz+D2adtnrdXq+PPUlo7qNCt8WUkKElFCpMXqdvvi/0TMlaQpL0pfwl8//UuFxA6EAz3z+DAoKE3tNZPGOxYQIVTj+YpcuRahqRr0+FpA/W3CWJelLVAX7lhEta/x8oP4GaSGEqA8kuIoaU9vlmxoSHTpNu+z1Oj0GvQGr0UpQCeIP+jnvPU+Bv4CQEkKv0xNhiqC5pTkmg4kR14/gfOF5Fm1fpOr4i7YvYvj1w7m3871sOLRB1WNKQqqWGfX6VEA+rzCPJelLeObzZyocUxLsAab3nX5ZgdHldXHBewGA7IJsIi2RZWZQ62OQFkKI+kSCq6gxtVW+qSGqzi57p8VJM0Mzcjw55BXmlbm/KFjEOe85oq3RjEkYw6u7Xi0TfiriD/pJ3ZfKuIRxqoOrzWQjEArw+t7XNc2o15cC8qfyT7Fw+0JVYxduX8jIriOrFVwvnkHdcmwLs1vOZvTa0QxsP7DUDGptB2khhGiI9HV9AqJx0rqGs2TNpMfvqeEzqx9KdtlfLKgEw0EzEAqUee+SWifhMDsY220sRn353zmNeiNju43FZrTx4dEPNZ3T5qObSwWhkBIiEAqEf128LAHgxtY3cuzcsXDVg/KUzKgv27MMt89d6j6byUaMLYYWthbE2GJqfSNWakaq6mAfCAVIzUjF5XVpep6zBWd5cceL9P9Hf17Y8QKHcg4BcCjnEC/seIH+/+jPizte5HzheU7nn9YUpE/nn9Z0LkII0RhIcBU1QusaTiB82bgpKNllb9AZCIQCFAYKybqQxdFzRwE4eu4oWReyKAwUEggFMOgM3Nv5Xp7f/jyDOgzi7bFv8/M+P6dTi07ER8bTqUUnft7n57w99m0GdRiEgsJ533nV56PT6bhQdAGT3kRQCeINeMkuyOa46zjHzh/juOs42QXZeANegkowfD5/2/03VcdfnbmarAtZ1XmraoQ34GXz0c2aHrP56Ga8Aa/q8RfPoFYUkEtmUDPPZrLuwLoaD9JCCNHQyVIBUSO0ruGEptcpKd4ez6huo1j4xULOFJwBwKq3AsXB6lzRObILsmkV0YpZP5qFgsIn333CJ999QvfY7tze/nbGdR+H1WilMFDIN7nfsHzPcjKzM1k/Zj32ZnYURUGnU/eeOi1OQkpI1VKEGX1noKCw78w+VceubkOEmqLT6crMAFfF7XOrfi9B21IEg97AF99/Ef4s1dh8dDMz+s7AiVQaEEI0HRJcRY2QTklVCykhftzxx3zv/p6Xv3y53Nk2o97IqG6jGHrd0PDsmoJCRnZGpWWxcgtzubPDnRzMOQiA1WjF3syOxWQJryn1+r3kF+WHg9KgDoNoZmjG2G5jeXV3+etjS5Yi/OS6n2ie7dPaEKEmKYqieT21w+xAUdT9fGpdimA1WjmZfxJ7M7vq4Ko1SAshRGMgwVXUCOmUVHUw+8H9A2PWjuGJ257gJ9f9hA0HN/D58c8BuC76Ovpf05/7u9xPflE+MzfO5LGBj6luQPCvw/9ifMJ4XvvqNaKt0eh0Os4VnuP0hdPhKgQOs4MWthYoikJeYR7jE8azJnMNgzoMCu/633J8C/m+fOxmOwOvGciwLsPwBXz8+j+/Zu5Nc2u0IUJNshgtDOowiP1n96t+zKAOg7AYLarGal2KUOgvRIdO9fFBW5AWQojGok6D69NPP8369es5ePAgVquV/v37s2DBAjp37gxAXl4ef/zjH/nPf/7DiRMnaNmyJffffz9PPPEETqdcHqvPGkKnpJqiJpgZdUZSM1L57vx3PJD2AIlXJTKsyzBGdRmFK9PFosGL2Jezj6e2PUX6yXTaOtuSdiCN29rdpioovn3wbX7a86fM7j+b57Y/R3ZBdpkxZz1nOes5S2xELLP7z6aFtQWv730dBaXKpQj+kD/cEOHr7K+5r8t9DO00lBbWFhj0BoKhILmFufzr8L94++DbhAhpbohQk+WenBYnyQnJLElfompW1Kg3kpyQrHpGWOtShMzsTG5pd4umsKslSAshRGNRp8H1008/ZerUqSQmJhIIBPjd737HXXfdxf79+4mIiODUqVOcOnWKZ599lq5du3L8+HEeeughTp06xdq1a+vy1IUK9bVTUk1SW4dz8o2T2frdVqD40v/OkzvZeXInVr2V165/jZFvjaQw9L9Lxt6Al52ndjK62+jwbZU1IAAw6U3c0/keTl84zeL0xRVe+h/ffTz3dL6HUCgUPp+qliKUNER4sPeDrB61mgtFF1i5byWbjmzC5XPhNDsZ3HEwE3pMYFzCOJ785ElNDRGg5ss9tba3Zla/WZWWnyoxq98s4u3xqo+tdSnC+gPrefTmR/n4u49VjdcapIUQorGo0+C6cePGUv+/bNkyYmNj2bVrFwMHDqR79+6sW7cufP+1117LvHnzmDBhAoFAAKNRVjrUZ/WxU1JN0lKHc3zCeJoZm6k+dr4vH4PDgM1kU92A4KznLKPWjuIvd/6Fezvfy7oD6/jw6IfkF+Vjb2bnjg53MOL6ERT4Cxj91mhSR6SqbkCg1+np0aoHLW0tWbxzcZmd89/zPZlnM/nrjr8yu/9snh38LKfyT6luiHA5dVPVirZGMyVxSvj5Kgr2JTPAWs5F61KErSe28lSzpxifMJ4ntzxZ5XitQVoIIRqLepX8XK7izR7R0RX/BeFyuXA4HBWGVp/Ph8/nC/+/2118uc7v9+P3+6/g2daNktfQUF5LpDGSn/b4KUM6DOG9w++x69SucKekG1rfwE86/YRWka2INEY2mNdUkR/O/cCSnUvClQEqc8p1CofJQZwlDlfR/zY5WfSWUv8OC0GUKQqDYuBC4QXOec8BYPjvPyjgLfKSVZRFlCWKkdePZNmeZRzPO86YNWO4+eqbGX79cMZ2HRu+9L8/ez8Ltixg64mtNDM0Y/Xe1YzoOoIPDn/AvEHz6NiiI4FQAL1OT0gJYdQbOZJ7hMc2P0aQIDOTZvJy+sss+nwRJkyY9KZyX+uizxcxodsE3tj3RqXjLrV632pm9psZ/kLj9rnLrBm+3C87zZs15+EbHmZ4p+G8uf9NPv7u4/Ca3tva3cborqNpZW9F82bNNf182gw2xl0/jn/s+gfB0P++sFX0+Rr0Bix6Cw8mPEjAH2Bx+uJSj7t43LTEafy898+xG+0N/vdMY9PQ/nwW2sjnW7PUvq86pZ6s7g+FQtx7772cP3+ebdu2lTsmJyeHG264gQkTJjBv3rxyx/zpT3/i8ccfL3P7qlWrsNka/vpJIYQQQojGxuPxMH78+PAEZUXqTXB9+OGHef/999m2bRtt2rQpc7/b7ebOO+8kOjqad955B5Op/Fmb8mZc27ZtS05OTqVvREPh9/v54IMPuPPOOyt8D0Ttyy7IZvTa0eHOSFW5Mf5GZg+YzQs7X2DyDZMxG8xsOLiB7Se289hVjzHv5Dz6Xd2P+7vcjy/o48WdLzL/jvmsyljFre1uxWw08+7hd/n8+8/DM4T92/bnnk734Av46B7bnSGpQ/ju/HdlOl6VR6/T84s+v+C3A37Lq7teZeEXlVw6/9EsHrrxIZakL2FJ+hIURUFBIRgKElJCKCjo0KHX6THoDejQsWfyHgavHMyZgjOqSzgltU7i7/f9ndd2v1b1DGSfnxNji1F13NqU48nh77v/Hj5/i97CS51fYuqhqXhD3krPvyZmmEXNkj+fGzf5fGuW2+0mJiamyuBaL5YKTJs2jffee48tW7aUG1rz8/MZMmQIdrudtLS0Sn9gzGYzZrO5zO0mk6lR/aA1ttfT0JlMJnK8OaU2VFVm28lt/L7Z73n6zqd5dder4c1TVr0VroLM3EzSz6az9KulTEucxsIhCzHoDXx4/EM+PP5heNf/6ITRpXb9v57xOpnZmawbsw6T0USRUoQ/VPXlFwMG5gycw5L0JTyx9YmKB4bg8a2PMzphNHuy95R6vQoKF/ePCBIkoBSH35AhhMlkojBUiF6nJ+mqJO7vcj8943piM9rwBDzszdrLhoMb2HlyJwC/vfm3rMxYyfwv5ld6PvO/mE9IF6rxzVzVEe+MZ2q/qQzvNpzUjFS2HNsCQLvodgxsP5DkhOQKy3+1MLWo7dMVV4j8+dy4yedbM9S+p3UaXBVFYfr06aSlpfHJJ5/Qvn37MmPcbjeDBw/GbDbzzjvvYLFI+RdR/2jdRa6nuALAe4ffY1XGqkpbgq7KWEVcZBwTe01Ej54gwaobEHhyufPaOzmQcwADBp4a9BSjuo7CYrSE16x6A17e2v8Wv9v8O/5y1184W3CWBZ8tUHX+BUUFuH1umpubh1vLVjaTmpWfxeCOg/Ee9PL0oKexN7OTdjCNpV8uLbWm9NGbHiW/KJ8NBzZgM9n4+1d/V3U+tbGZq7qirdFEW6OZM2AOU2+YypeffsmakWuItERKVQAhhNCoToPr1KlTWbVqFW+//TZ2u52srOJe5k6nE6vVitvt5q677sLj8bBy5Urcbnd4s1XLli0xGAx1efpChF28i1yHrsoZxWHXD8OgM/DKrleINBcHmPPe8+HF6WaDmUhLJM0tzQkqQZ7c+iRDOw3locSHeCn9pSrP59/f/JvkhGR+cP/Ac3c9R64nl5e/fJn3j7yP2+vGYXHw444/JqVHCik9UkCB53c8T1GwSNXr9fg9+IP+4vP+b3CtzMp9K3n4xof5cccfs+HQhjKdwk5fOM3h3MO89tVrPHTjQ/zhlj/w3jfvldt6tjyBUIDUjFTmDJhTb8Og0+LEZiheZx8bESszNkIIUQ11GlyXLl0KwK233lrq9n/+859MnDiR3bt3s2PHDgA6duxYasyxY8do165dbZymEFUqKWj/7qF3+fNtf65yRlFRFDYe2UjWhSwK/AVEmCKIskYRZyuuZdvW2ZZzRec4lX+KAn8BOnSs3LeSKTdOURVc3z74Nr/o/QsWDV7ES+kv8fS2p0vP6rohIzuDhdsX8uhNjzItaRqZ2ZmqX+9XWV9xQ+sb+OS7T1SNX75nOb8f+HtWf72axTsXVzguEAqweOdirrJfRWLrRLwBr+pz2nx0MzP6zsBJ/QyuQgghLl+dLxWozK233iotDUWD0cbRhjUj1/Dq7lernFGcO2AuaQfSKPAXAFDgL6DAX0CuPhfawJG8I2XWy248spEZfWdgxszrI14nyhpFUagofOm/mb4Z5wrP8cC6BwgSRK/X88LOF3hyy5PF60/LEQgFeHLLk0Q2i2Rmv5m8f+T9CsdebO3+tTx+6+N89v1nqt6bvm37kuPJ4d/f/JtWEa04U3CmwrGtIlrx5tdvktg6kcSrEsNrXqvi9rlVb/wSQgjRMOmrHiKEUEOv0/P+kfd56+u3Kl2z+tbXb5Gakcq4hHHoUBe0FBTcPjc2k423k9+mMFjIgs8WMHjFYJJeTWLwisEs+GwBhcFC3k5+m9WjVpN1IYv524o3NlX0PCW3r9i3AovRwu3tb1d1Pp9//zmBUICx3caqGv+Hm//AhoMbgOLL5F1iuhAbEYvFaMGkN2ExWkrd7g/5efvQ2wzrMkzV8aG44YV80RVCiMatXlQVEKIxOJ1/mre+fovYiFiirFHkFebh9rkJhoIY9AYcZgfR1mhMehNvZL7Bc3c9R982fdn+w3ZVx5/UaxK+gI/F6YuZv21+6WoBbtiXvY9F2xcx96a5TE+azuqvV4fH6P77T3kUFPJ9+fzrm38xuttoPjr2Ef3a9GNk15H0ie+DzWTD4/ew+/Ru1u5fy/YftqOgsPv0bn7e5+d4/J4qO091junMX3f8NXybUW8kLjKO2IhYdOhQUIrLZ+mK160HlSC7Tu/SFFwHdRiExSibN4UQojGT4CrEFeDxe0g7mEZQCaoLZqEgG49sZFTXUez4YUc4KPaO7Y07082/xv+Lr7K/CgdFAwZ+eeMveXHnizyxpeJyVf6Qnye2PEGkKZK+V/UNP3dVl//zCvPYm7WXUV1HsWbkGpwWJ+sPrOeVXa+UWqP7p1v/hMvrYu7mudzT+R5aRrRket/pjOw6ktSMVDYf3Yzb58ZhdjCow6BwuSdFUcLLIkoYdIbw+3Epg86AUW8kolmEqvffqDeSnJBcbzdmCSGEuDIkuApxBXj8HtJPppe6rdJgpjeQmZ3JYzc/ViooLtu9jCfaPMGMjTPof03/cFC0Gq2cuXAmfOm/KiszVtKvTT9ub387Hx77sMrx+UX5RJgiaG1vzfK9y1n65dIK1+g+fOPDvDPuHeLt8UDpck8z+s4oVTC/JEjmeHKIMEWQQ46q8wdwmp1YjVW3zwWY1W9W+HyEEEI0XrLGVTRZHr+HHE8OuZ5ccjw5ePyeah9Lh67MjGJVmlua0655O7ae2MrQVUN5ZdcrHMk7AhRvznpl1ysMXTWUrSe20q9tP1IzUlU1EwAoChax/uB6xnQbgx490xKnkf7zdI7PPM7JWSc5PvM46T9PZ1riNPTo0aHjFzf8gtR9qazdv7bSNbpr96/l/SPvc+kkrtPipFVkK2IjYmkV2arU7KfNZCPxqkRN78+P2v6IuMg4ZvefjVFf/ndso97I7P6zmZI4pV7WcBVCCHFlyYyraHLcPjdZ+VmsP7ie9JPp4XJUiVclMrzLcOLscZpbayoommYUdej4RR/1QXFK4hR2n96t+nwKA4Wkn0znwV4Pcmj6IQqKCliZsZKN32wMX8ofct0Qft7n58zoN4PnPn8Op8XJG1+/8b81up48XD4XQSWIQWfAaXYSbSteo/vX7X9lSMchRNvUhUWbycawLsNIO1C8nKIqBp2BYV2G0cLWQtVSBAmtQgjRNEhwFU1KXmEey/csZ3Xm6lIBKoccjruOk3YgjbHdxzKx10SirFGqj1syo3jcdRwdOhJaJXDLNbfQOaYzFqMFb8DLoZxDfHr8UzLOZJDQKgGL0cI/9vyDlhEtw0GxyF/cAMBqtOI0FQdFo96IL+DDE/AQYYpQNbN7rvAcfa/qS1xkHIt3Lmb+Z/PLhOPMs5k8v/155g6Yy//d8n+8+fWbZF/I5mT+Sa6NvrZ49jTyf2t0S2aVD5w9gILCyn0rmTNgjur3Kd4ez9juY0nNSK1y7NjuY4mLLK5pq2YpghBCiKZBgqtoMtw+N8v3LK80OAWVYPj+SX0mqZ55LZlR/OL7L3g48WHMBjNr96/l6W1Phzc33dH+DlJ6pOAL+rhQdIGNRzZywnUCj9/D1c6raRnREou+eFf8tdHX4g15cfvcnHCdoDBQSDAYJMoapSq4Xii6wNTEqSzbs4wntz5Z4bhAKMCTW5/E3sxO3zZ9OZV/CgUlvGShMhuPbGRmv5mq3h8oLlc1sddEgDJfHEoYdIbwF4dL33unxSnNBYQQoomT4CqajNP5p1mduVrV2NWZqxnaaaimJQOt7a2ZP2g+r+56lcXpi0vPcF6AQ7mHeHX3q0xLnMZv+v+G1756LRxCj7uOA2DVWyEOMrMzSzUgOJRziMQ2ieQeyVV1LkM6DsFitPD33X/HqDdWuBQBiteJrsxYSb+2/bir411sPLJR1XO4fC70Om3L5KOsUUzqM4mhnYaSdjCNnT/sxOP3YDPZSGqTxLAuw4iL1L5UQwghRNMgwVU0CReXq1IjqARJO5jG9KTp2EzF/eVdXhfegLfCS9W+oI93Dr3DqoxVla5ZXZWximlJ0/AH/eHL8FX59zf/ZkriFD469pGq8x/dbTQbj2zkTMGZ4uoGBgNBJUgw9L/Xb9D/r+pBUbCI9QfWM6rrKNXB1Wl2ElJCqsZezGF24DA7mJ40HU9vT/g9sJls4fdaCCGEKI8EV9EklFeuqio7f9hJ0Q1FeANeTuefZmXGyjKbgyYkTAhvDsrKz+LPW/5MXGQcTouT897zuH1uQkoIvU6Pw+yguaU5QSXIyfyTmI1mrrJfxQ/5P1R5LiUbtCYkTGDuh3OrHN+vTT/eyHgjPKOrQ4dBb8BoMIIOUCBEiEAogIISfn/GdBuj+v0Z0nEIZoNZ9fhLSVAVQgihlQRX0SRUp1zVNc5rKAoWsTR9abmdofaf3c+S9CXM6jeLhxMf5tPjn+Lxezh67igRpgiirFFcY70GvU5PSAnh8Xs4lX+KAn8BX576ku4tu/O963tO55/mqUFPMarrKEyY2LVlF4enHcaPn7f2v8XvNv+OECEyszP5We+fUeAv4OltT1fYqerRmx4l2hrN0fNHw7crKJUuFzjvPc81za9RHSRNehMTekzQtIFNCCGEuFwSXEWToLVclR49j/R7hCXpS/jL53+pcFwgFOCZz59BQSGlRwp69IQIUeAvqDQor92/lnm3zyMQCrBpwiZyPbm8/OXLfPTtR/w+/vcMWzOM26+9nZQeKaT0SOEPH/2BW9rdQtr+NB5JeoTR3Uazct9KNh7ZiMvnwml2MqTjECb0mEBcRBwBJUCESV3XKSBcEqyZoZmq8XNvmhve9S+EEELUFmlAIJoErQXw7+tyH26fm0XbF6kav2j7Ii74L/CzPj9TNX77D9uxN7Mze8Bsln65lN6v9uaZz5/h67NfA/D12a955vNn6P1qb5Z+uZR5d8zDbDDz0L8fovWzrTnlPsVv+v+GjRM2suPnO9g4YSO/6f8bTrlP0frZ1nj8HgZfOxhF+d/6WUVRyvy6WJ/4PsRGxPKHgX/ApDeVe94mvYk/DPwD05KmEWOLUfVahRBCiCtFZlxFk6C1AP7QTkMr3WR1KX/Qz6p9q/hZr5/xz93/5Jk7n2F41+GYDebwUgFf0Mf6/euZ/cFsANo42/BS+ks8ueXJCjdoBUIBntxSXM5qSuIU9Ojx42dw6uBKz+fNr99kQo8JPL/jefzB/3Xbuvh5dOjC4dVkMDE+YTwtbC14pG8VM7qRcRJahRBC1AkJrqLJ0FIAv6WtJZuPbtZ0/A+OfsCv+v2K73/9PbkFuSxNX8r7R97H7XXjsDj4cccfk9IjhXE9xvHFiS84W3CWBZ8tAKiwuoAOHQALPlvA6G6jefKOJ3n0w0erPJfff/h7ftrrp8wZMKfCYFxymw4dcwbMCV/6j7HFEGOLYc6AOczsNzMcvM0Gs6xpFUIIUackuIowj9+Dx994yxNpKYBvMpjIL8pXfWydTkeXmC7odXoWpy8uu3nKDRnZGSzcvpBHb3qU6UnTeW33a/iD/nBXqpJ/gFL/raDgD/pZuW8lk2+YrCq4Bgnyl21/Ye7NxRUI5m+bjz/kLzPOpDcx96a5TEuchtVgLXWfhFQhhBD1jQRXgdvnJis/i/UH15N+Mj28USfxqkSGdxlOnL3xFIRXWwC/0F+o6TXr0TPv9nm89OVLPLHliQrHBUIBntjyBJHNIklqkxS+vWT28+J/XzpLuvHIRmb0naHufHR6zEYzF3wXmJI4pdJL/zG2GJ757Bl+eeMv6WTupPo1CyGEELVNgmsTl1eYx/I9y8vMQOaQw3HXcdIOpIVbcDaWGTg1BfAVRWFQh0HsP7sfHTqSrkri/s730zOuJzaTDY/fw96svWw4tIGdJ3fyiz6/IL8on+c+f07VOaTuS6XvVX25rd1tfPSduqYCbp9bdacqi9HC+ITxrMpYxbuH3uWhxIeYkjiFGX1nYNAbCIaC+EN+Pjz6IS+nv0yQIC0jW5ZquCCEEELUNxJcmzC3z83yPcsrXfMZVILh+yf1mdRoZl6h8gL4TouT5IRk3j30Lo/f+jgOs4N1B9bx0pcvke/Lx262c1u723j0pkdx+9xc1+I6VmWsojBQWOoS/6VK7isKFZF2MI0x3cbw0XcflVoiUPLvi4+joOC0qO9U9Zv+v8FutvPOoXcIEeKl9Jd4Kf2lSh+z84edeHp7JLgKIYSot6QcVhN2Ov80qzNXqxq7OnM1WReyaviM6pc2jjasGbmGLce3cPequ3ll1ysczj3M6QunOZx7mFd2vcLdq+5my/EttIpoRcaZDEJKKBw0deX8U3Jfob+Q9FPpdG/VvUxAvfS/S+4ffO1gzAazqnJVUxOn8vwXzxNCfUvWkvXNQgghRH0lM65NlMfvIe2gutJQUDzzmnYwrUldSg6Ggvz7m3/z5v43KyyLFQgFeHP/m7R1tuXhGx/m/SPvl1mvWp5z3nO0dbb939KECsaWBOBmhmZM6DGBaFu0qnJVRr2R793fa3q9NpOt0nMWQggh6poE1yaqpDe9Fo3tUnJVVRRO559m3tZ5tHG0IdoSTW5hLm6fm6ASxKAz4DA7aGFtgU6n443MN3jurufo26Yv23/YXuVzu31uIk2RmA3mKsOigqK5XJXH7yHxqkSOu46rfj+S2iQ1ms9WCCFE4yTBtYnSoau0JWl5GsulZDVVFIKhICv2rcDlc+E66yoOi9YYYiNiw0HXH/ST7ckmx5NDj1Y92HhkI6O6jlIVXAF6x/cOd6oqUz7rv4x6I4/e9ChTEqdQUFRQqvB/ZZvltDZcMOgMDOsyTIKrEEKIek2CaxOloBBhiiCHHNWPaQyXktVWURiXMK5UA4IcTw45norfq3xfPpnZmfym/29UnYdJX9ypKjM7k4dvfDh86f/jbz8GICE2gduuvY0JPSbQwtqCR95/hI7RHZkzYA5Oi1PVc2hpuDC2+9jwjK4QQghRX8nmrCbKZrKReFWipsdc7qVkj99DjieHXE8uOZ4cPH5PtY9VHRdXUahoFrKkikJBUQEWo0X1sY+dP0ZRsIir7FepGj/3prm0imjFvavupfOLndl4ZCOTb5jMutHrAFg3eh2Tb5jMxiMb6fxiZ/71zb/YfHQz3oBX9TmVNFxITkjGoDOUO8agM5CckMzEXhMbVcUIIYQQjZPMuDZRtXkpubYaHKhZs6q2isJZz1nsZjsRpgjVSyq8AS92s50/DPxD1Z2qkqax6ItFBCheHvDHT/7IHz/5I1a9ldeuf41er/SiMFRY6rFunxudTttSDbUNFyS0CiGEaAgkuDZhtXEpuTYaHKgJxia9SVMVhUM5h+jZqidfn/2aAn8BLW0tibHFYNQb0el0KIpCIBQgx5PDWc9ZAPq37Y/VYFW96/+DYx9oep0OswNF0b5UQ03DBSGEEKIhkODahJVcSgbKBMsSBp0hHCy1zsrVRoMDtcF4Qo8JHDt3TPVxPz3+KRN7TeT9I+/T3NKcUChEbmEuLp+LUCiEXq/HaXbS0taSVpGt+MH9AxN6TMBpLV5/WtWuf5fXVW5nrh4te+DKdLF21Fr2nd0X7sylUNzJS8vyhUtJUBVCCNHQSXBt4mryUrLWBgdDOw3V9DxagnFQCTK2+1h2n96taoNZxpkM9Do9D/R4gIXbF3L6wunSA0LFSwPOFJwhPjKe3930O+Ij40sNqWwGubzOXOsPrOfvu/7OE22e4JGNjzDgmgHhzlz/98n/kZyQrHpjVnmqWkohhBBC1HcSXEW1LyVXFoSuRIOD84Xn8Qa94RlLi8FCc2vz8GO0BuN+bfrRPbY7GdkZqh5j1BkZ2mkoJ9wnWPrl0grLVY3sOpK7r7tb8/rTks5cr+56lSVfLiEQCmDVW6ENHMk7QkZOBn//6u9MuXEKb458k6sc6jZ+Xaq21hgLIYQQNU2CqwhTOwOnJggVBYuq3eDA4/eQdSGLFXtXsOnbTbi8LpwWJ4OvHUxKzxTiIuOwmWyagnFICfHvb/7N7e1vVxVcE1oloNPp+MW7v+BX/X7FPZ3uYf3B9Xx87GPyi/KxN7NzW/vbGN5lOG6fm/tX30/a2DSirdHhY1Q1w1lyTm/tf4tgqIIqB6Egb+1/izaONkzqM0nVa71YbawxFkIIIWqLBFehidoglNwjuVoNDkJKiMU7F7PgswWlZzjdkJGdwfM7nmfOgDlMSZyiKRjrdXoyzmRwb+d7VY2/rd1tbDyykT1Zexj11ij6t+3PyK4jGdNtTLjSwJ6sPTz+6eN8/v3nKCis3LeSR296FJ1Op2qG81T+KZ774jlaRrQkyhpFricXv7+4EoHVaMVhctDC1gK9Ts9zXzzH3dfdXSoYV6U21hgLIYQQtUmCq1BNSxC6+7q7NW8kMhvNnC04y7yt8yocEwgFmLd1Hik9UnD73JqO7wv6aGlrqWps4lWJrNy3Mhy+P/v+Mz77/rNKH7M3ay++gI/UjFRVm8XeO/we+UX55Bfl09zcnGhrNBGREQB0iOpAQbCA84XnOe87D0BqRqqmBgQ1vcZYCCGEqG3SgECopiUIpZ9M58bWN2o6/o2tb+SDo+pKRJ3KP0UzYzNNx7c3sxPZLFJVQf5oazTfnvtW9bF16JiaNJXX972uqsHB8j3L6R3XO9xC97zvPMddxzl2vrjywbHzxzjuOh4OrYCmBgTVXWNc200hhBBCCC0kuApVtAahj777iLuuvQu9Tt2PmA4dd3a4k7f2v6Vq/O7Tu+nesnuFa0PLk9QmCbvZzqQ+k1gxfAWjuo3iGuc1tLS15BrnNYzqNooVw1cwqc8kQkqI/2/v3qOiKvc+gH9nhmEYhmG4iQN4SfQohkjeuEi1rDhGdbqJ2gVNS9c6JaCIFZy3LMsytSN10rBV+ZIppll6TPNuiZdU0FLxTsFSExBJGe7jMLPfP3yZIkD3AMPMpu9nLf9w72dvHudhr7498zz7p1FqrNdqlBr08OyB/r79EeIXgv6+/dHDs4e1TVSPKGiUGnxx4gtRfVl9cjVcFa42VS+zpQBBram2bWuMGVyJiMiJcakAiWJrEMq/nI96Uz3Gh44XNUv7ROgTuFZ/DYd+PSTq/l+f/hpz75mLnUU7RbX/c+WvW71FwWwxI65fHM78dgY9PHtAIVPgWv01XKm5AotggVwmh1alRaA2EGbBjPGh47H1562wWCxQyFuezf0jk8WEDWc34PGQx5F7KVfUv8GWAgQyyNq0xrhxBpiIiMgZccaVRLE1CAkQ8OmPn2JS+CRRX80/c8czmJszV9Q7VgHg4K8HUWOqwVODnhLVvqXKX+5Kd/i5+8HX3Rd+7n5Ndvx7q70xMXwi+nn3Q7WxGgVXC1BeWw6j2QiTxQSj2Yjy2nIUXC1AtbEakUGROHHlhKjQCtzYLHak5AjCu4eLag/ApgIEAoQmM8ZiuCvdRX/+REREjsAZVxKlMQiVo1z0NTWmGijkClEFDuob6m0Oxh/mfYjljy2HQqawS+UvvYce8bfHY+6euTdtV1ZbBj93P1xvuC763gqZAi5yF2hcxYVLF7mLTQUI3JXuGBE0AucN50X3KaJHBAsSEBGRU2NwJVHaGoTcXNzgrnS/5VfzFosF9/e9X3RxAAAI7x4OlULV5spft3rPqqHegLh+cagyVmFJ3pJWCxAkjUiCykUFN6UbIG7vFABAp9JB7aIW1TY1KhUB2oBbN/x/7kp3PB7yONafFrcu+c9LKYiIiJwRg6sDSakEZ0cEoZv9+7zUXpg4eCLeP/R+iwHxz1zkLpgweIJ1BtKWyl9iCigo5UqsPbUWe8/vRXJkMh4Z8Ai+Pv01dhXushYguC/4PsQPjIfRbETur7kI8w/DpcpLojekRfeMht5Dj5dHvoyMgxmtBuPUqFRMGzHNpne4AkCANgBPDnrypq8va9TSUgoiIiJnw+DqAFItwWnvIKTX6pEWk3bT97g2SotJa3HN6q2Cv9gCChMGT0DRtSJcrrmM2d/NxiD/QRh12ygkhCVArVSjzlSHc7+dw+fHPseJshMI6x6GZ+94Ftt/2S7q39oY7H3dfZEcmYyxt49Fdn429hTtAQCE+IXg7j53IyEsAQHaAJtDK3AjzE++YzIA2GUpBRERUWdjcO1kUi7Bae8g5Ofuh6SIJABoXjnr/7nIXZAWk4akiCT4ufvZdH9bCiiYBTOeHPQkfiz5EQIE5Jfl33QZQ/7lfDRYGvDkoCdFvUXhj8HeR+0DH7UP0mLSkDgsEYdzDmPN2DXwcPMQvaa1Nd5q7zYvpSAiInI2DK6dyNlLcBrqDahvqIdMJoMgCHBzcWsWnOwdhPw1/pgeOR3jQ8dj5fGV2PbLNhjqDdC56XB/3/sxYfAE6D30NodWwPZKUlE9ojDIf5CodbcCBKw+sRpv3ftWmzeL6dx0cFfcmDH21/hDqVSK6uuteKo8bVpKQURE5KwYXDuRs5bgvFp3FSVVJViZvxI7C3ei0lgJT5UnYoNjMSFsQrOvqu0dhPzc/eDn7of0mHSkRKVALpPDIljgpnCDl9qrTfe0tYCCRbBgc8Fm3NvnXtEbxoJ9guHh6uG0M5wMqkREJHUMrp2krSU4kyOS7Ro2rtRcQWZeZoubg05dOYXMvEzr5qBumm5Nzts7CLU1pLbE1gIKcpkc+Zfz8ciAR0S1b1yzqlaqoYaaM5xERER2wODaSdpcgnNIrd3CztW6q8jMy8TCHxa22qbB0mA9nxyZ3KZNQs6gLZWkjGYjurl3u3VDtF7ggEGViIio47ByVidxxhKcxVXFyDiYIaptxsEMlFSV2K0vLblWdw2l1aUoqylDaXUprtVda/O92lJJSuuqhYerh6jKX9yVT0REZH+cce0kbak81d4SnDfbbGWoNyA7P1vUO1OBGzOv2fnZSItJa/dO91spry1HaXUpVhxbga0/b4XBaIBOpUNcvzhMDJ/Yps1ZbS2goFVpnXbNKhER0V8Ng2sn6cwSnGI2WwmCgJ2FO226787CnZgROQM62C+4ltWUYUnuEszfNx8mi6nJueNlx/HewfeQfmc6kiKS4K/xF33f9hZQ4JpVIiIix+NSgU7SGJxa+8r5z9pagvNKzRUsPrQYI/93JD449AFOXTmFXyt/xakrp/DBoQ8w8n9HYvGhxTALZvTx6mPTvSuNlZDJ7Ld0oby2HEtyl2DunrnNQmsjk8WEuXvmYknuEpTXip+9Bn4voCBGa2tW/dz94OvuCz93P4ZWIiKiTsbg2onaG5xu5Y+brVpbAtC42SozLxPzY+dDbsOvgKfKE4LQ9qULt1JaVYr5++aLajt/33yUVpfadP/GAgpcs0pERCRNDK6dyN7ByZbNVu8dfA8112vwzB3PiL5/bHAs3FzcbOqTWNfqrmHF8RWtzrT+mcliwsrjK23esNVYQGHFmBUYFzoOvXW90c29G3rremNc6DisGLMCU4ZOabFqWa2pFuW15fit9jeU15aj1lRr088mIiKi9uEa107WnspTFXUVqDZWAwAu11yGh8rD+q7Ttmy2+iL/C0waPAmfHf3slu1d5C5ICEuw28Yso9mIrT9vtemarT9vRUpUis0/y9YCCpXGSpRWlWLdmXXIu5SHGlMNNEoNRgSNwJiQMdBruTmLiIioMzC4OoCtwemPu+x3F+7G/+j/B/Fr4jEqeJR1l73ZYrZ5s9WOwh2YFjFNVNvUqFQEaANsur8t5DI5DEaDTdcYjAbIZW3/0kDM5qqrdVex/OjyZiVcy1GO84bzWH96vbWEa0uztERERNRxGFw70M1eP9USMcGpcZf9gv0L0GBpgFquBvTAySsncfjyYbx/6H2kxaQhKSIJlcZKm/pbdb0KrgpXvDzy5RYrZwE3ZlobK2fZs/iARbBAp7JtNlen0sEiWOzUoxszrcuPLkd2fnarbcyC2Xp+ytApnHklIiKyIwbXDiDm9VNtCX2Nu+zf3vt2q20aLA14e+/b+Oewf0LrqrXp/o2brZIjkzH29rHIzs9u1v+EsIQ2998WKoUKcf3icLzsuOhr4vrFQaVQ2a1PJVUlWH1itai2q0+sxkP9H2JwJSIisiOHbs565513MGLECGi1Wvj7++Oxxx7D2bNnm7Spr69HYmIifH194eHhgfj4eFy+fNlBPW5O7OunrtRcsfnepVWlWLB/gai2FysvIjY41qb7N2628lH7INQ/FGkxadj41EbseXYPNj61EWkxaQj1D+2UMq/eam9MHDwRSrlSVHulXIkJgyfY7ev5WlMt1p8R985X4MbM6/oz67lhi4iIyI4cGlxzcnKQmJiIgwcPYseOHTCZTBg9ejRqan4vjTpz5kxs3LgRa9euRU5ODoqLizFmzBgH9vp3tr5+6mrdVdH3rqirwIrjK0Rvtvrs6Gd4OuxpuMjFTaK3tNlK56ZDd4/u8Nf4o7tHd7tXyPozvVaP9DvTRbVNvzPd5teF2aLWVIu8S3k2XdO40Y6IiIjsw6HBdevWrZg8eTJCQ0MRHh6Ozz77DBcuXMCRI0cAAAaDAcuWLUNGRgbuvfdeDBs2DFlZWfjhhx9w8OBBR3YdgG2vn8o4mIGSqhLR964312PbL9tEt1/24zJoXDWYGTVTVHt7b7ZqCz93PyRFJGH23bNbnXlVypWYffdsJEUk2Vz21RYyyFBjqrl1wz+oNd3YaEdERET24VRrXA2GG7vKfXxufDV95MgRmEwmxMb+/hV4SEgIevXqhQMHDiAqKqrZPYxGI4xGo/XvlZU3NiyZTCaYTOLeESpGpbESq4+vhhJK0V9vrz6+GilRKaLWQVrMFtQb629sxvqDxr//+TgAvLL9FWTEZUAhKLAkbwnMluZfcyvkCiSNSMLUIVOhddF26GfSEbxdvTFt2DSMHTAWa06uwc7CnTAYDdCpdIgNjsUToU/A38Mf3q7edu27qcEEL6UXKmXiN7zplDqYGtr3e9Z4rbONC3UMjm/XxvHt2ji+9iX2c5UJ9iyFZAOLxYJHHnkEFRUV2LdvHwBg1apVePbZZ5sEUQCIiIjAPffcgwULmq//nDNnDt54441mx1etWgV3d5boJCIiInI2tbW1ePrpp2EwGODp2foEn9PMuCYmJuLEiRPW0NpW//rXv5Cammr9e2VlJXr27InRo0ff9IOw1ZXaK4hbGYfiqmLR1wRpg7BlwhZ0c+92y7aGegMyDmTgg9wPmhxXy9X4NPRTTD05FXWWuibnZkTOwMyomda1qZXGymav5+Kud/EKfivAPzf9U9QGLYVMgY8f/hj9fPq162eaTCbs2LEDf//736FUipvJJ+ng+HZtHN+ujeNrX43fkN+KUwTXpKQkbNq0CXv27EGPHj2sx/V6Pa5fv46Kigp4eXlZj1++fBl6fcsbc1QqFVSq5q9IUiqVHfqLplAooHJVNQuPN6NyVUGhUIjqh5/SD0+HP41FuYta3KBVZ6lr8rNd5C54Kvwp+Gl/X/fpq/QV3Tdqrod3D4wJHXPT97g2ShiUgCCvoA77Hevo31dyLhzfro3j27VxfO1D7Gfq0M1ZgiAgKSkJ69evx3fffYc+ffo0OT9s2DAolUrs2rXLeuzs2bO4cOECoqOjO7u7Tbi5uLX59VNi6bV6pMWkiWqbFpNm1132f0WeKk9MvmMyEsISoJApWmyjkCmQEJaAyXdM5mw2ERGRnTl0xjUxMRGrVq3Chg0boNVqUVpaCgDQ6XRQq9XQ6XSYMmUKUlNT4ePjA09PTyQnJyM6OrrFjVmdSeemQ0JYAjLzMkW9sqql10/dSuMuewDWylkt3bexcpY9d9n/VXmrvTFl6BQ81P8hrD+z3vrKK3elOyJ6RODxkMeh99AztBIREXUChwbXpUuXAgBGjRrV5HhWVhYmT54MAHjvvfcgl8sRHx8Po9GI+++/H5mZmZ3c05YFagORGpWKhT8svGXbtr5+yl/jj+mR0zE+dDxWHl+J3YW7AQCD/AdhVPAoTBg8AXoPPUOrHXmqPOGp8kRyRDJqh9x45ZUAQVTJXiIiIuo4Dg2uYl5o4Obmhg8//BAffvhhJ/TINj5qH0wbMQ3Ajfe0tjYjmhqVimkjprW5ApWfux/83P2QHpOOpOFJOJJzBF+N/woeKg94qb3a808gGzCoEhEROZZTbM6Ssm6abkiOTMbY28ciOz8bOwt3otJYCU+VJ2KDY5EQloAAbUCHlE31UntB46IBAHTXdOficCIiIvpLYXDtAD5qH/iofZAWk4YZkTOavH6qs8umEhEREXVVDK4dSOemgw4MqkRERET24NDXYRERERERicXgSkRERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOAqIYZ6A8pqygAAZTVlMNQbHNwjIiIios7D4CoBV+uu4mTZSczfPx/jvxoPABj/1XjM3z8fJ8tO4mrdVQf3kIiIiMj+XBzdAbq5KzVXkJmXiYyDGWiwNEAtVwPdgLPlZ3G07Cgy8zKRGpWKaSOmoZumm6O7S0RERGQ3DK5O7GrdVWTmZWLhDwtbbdNgabCeT45Mho/ap7O6R0RERNSpuFTAiRVXFSPjYIaothkHM1BSVWLnHhERERE5DoOrkzLUG5Cdn40GS4Oo9g2WBmTnZ3PDFhEREXVZDK5Oqr6hHjsLd9p0zc7CnahvqLdTj4iIiIgci8HVSclkMlQaK226ptJYCZlMZqceERERETkWg6uTEgQBnipPm67xVHlCEAQ79YiIiIjIsRhcnZSbixtig2NtuiY2OBZuLm526hERERGRYzG4Oimdmw4JYQlwkYt7Y5mL3AUJYQnQuens3DMiIiIix2BwdWKB2kCkRqWKapsalYoAbYCde0RERETkOCxA4MR81D6YNmIaAFgrZ/2Zi9zFWjmLxQeIiIioK2NwdXLdNN2QHJmMsbePRXZ+NvYU7QEAhPiF4O4+dyMhLAEB2gCGViIiIuryGFwlwEftAx+1D9Ji0pA4LBGHcw5jzdg18HDz4JpWIiIi+stgcJUQnZsO7gp3AIC/xh9KpdLBPSIiIiLqPNycRURERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOBKRERERJLA4EpEREREksDgSkRERESSwOBKRERERJLQ5QsQCIIAAKisrHRwTzqGyWRCbW0tKisrWYCgC+L4dm0c366N49u1cXztqzGnNea21nT54FpVVQUA6Nmzp4N7QkREREQ3U1VVBZ2u9XL2MuFW0VbiLBYLiouLodVqIZPJHN2ddqusrETPnj1x8eJFeHp6Oro71ME4vl0bx7dr4/h2bRxf+xIEAVVVVQgMDIRc3vpK1i4/4yqXy9GjRw9Hd6PDeXp68sHpwji+XRvHt2vj+HZtHF/7udlMayNuziIiIiIiSWBwJSIiIiJJYHCVGJVKhddffx0qlcrRXSE74Ph2bRzfro3j27VxfJ1Dl9+cRURERERdA2dciYiIiEgSGFyJiIiISBIYXImIiIhIEhhciYiIiEgSGFyd0Jw5cyCTyZr8CQkJsZ6vr69HYmIifH194eHhgfj4eFy+fNmBPSZbXbp0CRMmTICvry/UajXCwsJw+PBh63lBEPDaa68hICAAarUasbGxKCgocGCPSazbbrut2fMrk8mQmJgIgM+v1JnNZsyePRt9+vSBWq1G3759MXfu3Cb11fn8SltVVRVSUlLQu3dvqNVqjBw5Enl5edbzHF/HYnB1UqGhoSgpKbH+2bdvn/XczJkzsXHjRqxduxY5OTkoLi7GmDFjHNhbssW1a9cQExMDpVKJLVu24NSpU1i0aBG8vb2tbRYuXIgPPvgAH330EQ4dOgSNRoP7778f9fX1Duw5iZGXl9fk2d2xYwcAYNy4cQD4/ErdggULsHTpUixZsgSnT5/GggULsHDhQixevNjahs+vtE2dOhU7duzAihUrkJ+fj9GjRyM2NhaXLl0CwPF1OIGczuuvvy6Eh4e3eK6iokJQKpXC2rVrrcdOnz4tABAOHDjQST2k9khLSxPuvPPOVs9bLBZBr9cL7777rvVYRUWFoFKphC+++KIzukgdaMaMGULfvn0Fi8XC57cLeOihh4TnnnuuybExY8YICQkJgiDw+ZW62tpaQaFQCJs2bWpyfOjQocIrr7zC8XUCnHF1UgUFBQgMDERwcDASEhJw4cIFAMCRI0dgMpkQGxtrbRsSEoJevXrhwIEDjuou2eCbb77B8OHDMW7cOPj7+2PIkCH45JNPrOeLiopQWlraZIx1Oh0iIyM5xhJz/fp1rFy5Es899xxkMhmf3y5g5MiR2LVrF86dOwcAOHbsGPbt24cHHngAAJ9fqWtoaIDZbIabm1uT42q1Gvv27eP4OgEGVycUGRmJzz77DFu3bsXSpUtRVFSEu+66C1VVVSgtLYWrqyu8vLyaXNO9e3eUlpY6psNkk8LCQixduhR/+9vfsG3bNrzwwguYPn06li9fDgDWcezevXuT6zjG0vPf//4XFRUVmDx5MgDw+e0C0tPT8eSTTyIkJARKpRJDhgxBSkoKEhISAPD5lTqtVovo6GjMnTsXxcXFMJvNWLlyJQ4cOICSkhKOrxNwcXQHqLnG/3MHgMGDByMyMhK9e/fGl19+CbVa7cCeUUewWCwYPnw45s2bBwAYMmQITpw4gY8++giTJk1ycO+oIy1btgwPPPAAAgMDHd0V6iBffvklsrOzsWrVKoSGhuLo0aNISUlBYGAgn98uYsWKFXjuuecQFBQEhUKBoUOH4qmnnsKRI0cc3TUCZ1wlwcvLC/3798fPP/8MvV6P69evo6Kiokmby5cvQ6/XO6aDZJOAgADcfvvtTY4NHDjQuhykcRz/vNOcYywt58+fx86dOzF16lTrMT6/0vfSSy9ZZ13DwsIwceJEzJw5E++88w4APr9dQd++fZGTk4Pq6mpcvHgRubm5MJlMCA4O5vg6AQZXCaiursYvv/yCgIAADBs2DEqlErt27bKeP3v2LC5cuIDo6GgH9pLEiomJwdmzZ5scO3fuHHr37g0A6NOnD/R6fZMxrqysxKFDhzjGEpKVlQV/f3889NBD1mN8fqWvtrYWcnnT/3QqFApYLBYAfH67Eo1Gg4CAAFy7dg3btm3Do48+yvF1Bo7eHUbNzZo1S9i9e7dQVFQk7N+/X4iNjRX8/PyEsrIyQRAE4fnnnxd69eolfPfdd8Lhw4eF6OhoITo62sG9JrFyc3MFFxcX4e233xYKCgqE7Oxswd3dXVi5cqW1zfz58wUvLy9hw4YNwvHjx4VHH31U6NOnj1BXV+fAnpNYZrNZ6NWrl5CWltbsHJ9faZs0aZIQFBQkbNq0SSgqKhLWrVsn+Pn5CS+//LK1DZ9fadu6dauwZcsWobCwUNi+fbsQHh4uREZGCtevXxcEgePraAyuTuiJJ54QAgICBFdXVyEoKEh44oknhJ9//tl6vq6uTpg2bZrg7e0tuLu7C48//rhQUlLiwB6TrTZu3CgMGjRIUKlUQkhIiPDxxx83OW+xWITZs2cL3bt3F1QqlXDfffcJZ8+edVBvyVbbtm0TALQ4Znx+pa2yslKYMWOG0KtXL8HNzU0IDg4WXnnlFcFoNFrb8PmVtjVr1gjBwcGCq6uroNfrhcTERKGiosJ6nuPrWDJB+EO5DyIiIiIiJ8U1rkREREQkCQyuRERERCQJDK5EREREJAkMrkREREQkCQyuRERERCQJDK5EREREJAkMrkREREQkCQyuRERERCQJDK5ERF3MxIkTMW/ePOvfb7vtNrz//vvtumdUVBS+/vrrdvaMiKh9GFyJiNpo8uTJkMlkkMlkcHV1Rb9+/fDmm2+ioaHBYX06duwYNm/ejOnTp3fofV999VWkp6fDYrF06H2JiGzB4EpE1A5xcXEoKSlBQUEBZs2ahTlz5uDdd991WH8WL16McePGwcPDo0Pv+8ADD6Cqqgpbtmzp0PsSEdmCwZWIqB1UKhX0ej169+6NF154AbGxsfjmm28AAEajES+++CKCgoKg0WgQGRmJ3bt3W689f/48Hn74YXh7e0Oj0SA0NBSbN2+2ns/JyUFERARUKhUCAgKQnp5+09lcs9mMr776Cg8//PBN+/zpp5/Cy8sLu3btAgBUVVUhISEBGo0GAQEBeO+99zBq1CikpKRYr1EoFHjwwQexevXqNnxKREQdg8GViKgDqdVqXL9+HQCQlJSEAwcOYPXq1Th+/DjGjRuHuLg4FBQUAAASExNhNBqxZ88e5OfnY8GCBdaZ0kuXLuHBBx/EiBEjcOzYMSxduhTLli3DW2+91erPPn78OAwGA4YPH95qm4ULFyI9PR3bt2/HfffdBwBITU3F/v378c0332DHjh3Yu3cvfvzxx2bXRkREYO/evW3+bIiI2svF0R0gIuoKBEHArl27sG3bNiQnJ+PChQvIysrChQsXEBgYCAB48cUXsXXrVmRlZWHevHm4cOEC4uPjERYWBgAIDg623i8zMxM9e/bEkiVLIJPJEBISguLiYqSlpeG1116DXN583uH8+fNQKBTw9/dvsY9paWlYsWIFcnJyEBoaCuDGbOvy5cuxatUqa5DNysqy9vmPAgMDcfHiRVgslhZ/PhGRvTG4EhG1w6ZNm+Dh4QGTyQSLxYKnn34ac+bMwe7du2E2m9G/f/8m7Y1GI3x9fQEA06dPxwsvvIDt27cjNjYW8fHxGDx4MADg9OnTiI6Ohkwms14bExOD6upq/Prrr+jVq1ezvtTV1UGlUjW5ptGiRYtQU1ODw4cPNwnIhYWFMJlMiIiIsB7T6XQYMGBAs3uo1WpYLBYYjUao1WobPykiovbj/zITEbXDPffcg6NHj6KgoAB1dXVYvnw5NBoNqquroVAocOTIERw9etT65/Tp0/jPf/4DAJg6dSoKCwsxceJE5OfnY/jw4Vi8eHGb++Ln54fa2lrrUoU/uuuuu2A2m/Hll1+2+f5Xr16FRqNhaCUih2FwJSJqB41Gg379+qFXr15wcfn9S6whQ4bAbDajrKwM/fr1a/JHr9db2/Xs2RPPP/881q1bh1mzZuGTTz4BAAwcOBAHDhyAIAjWtvv374dWq0WPHj1a7Msdd9wBADh16lSzcxEREdiyZQvmzZuHf//739bjwcHBUCqVyMvLsx4zGAw4d+5cs3ucOHECQ4YMEfnJEBF1PAZXIiI76N+/PxISEvDMM89g3bp1KCoqQm5uLt555x18++23AICUlBRs27YNRUVF+PHHH/H9999j4MCBAIBp06bh4sWLSE5OxpkzZ7Bhwwa8/vrrSE1NbXV9abdu3TB06FDs27evxfMjR47E5s2b8cYbb1gLEmi1WkyaNAkvvfQSvv/+e5w8eRJTpkyBXC5vtuRg7969GD16dAd9QkREtmNwJSKyk6ysLDzzzDOYNWsWBgwYgMceewx5eXnW9almsxmJiYkYOHAg4uLi0L9/f2RmZgIAgoKCsHnzZuTm5iI8PBzPP/88pkyZgldfffWmP3Pq1KnIzs5u9fydd96Jb7/9Fq+++qp1WUJGRgaio6Pxj3/8A7GxsYiJicHAgQPh5uZmve7SpUv44Ycf8Oyzz7b3YyEiajOZ8MfvoYiISNLq6uowYMAArFmzBtHR0W26R01NDYKCgrBo0SJMmTIFwI03Ely7dg0ff/xxR3aXiMgmfKsAEVEXolar8fnnn6O8vFz0NT/99BPOnDmDiIgIGAwGvPnmmwCARx991NrG398fqampHd5fIiJbcMaViOgv7qeffsLUqVNx9uxZuLq6YtiwYcjIyLC+X5aIyFkwuBIRERGRJHBzFhERERFJAoMrEREREUkCgysRERERSQKDKxERERFJAoMrEREREUkCgysRERERSQKDKxERERFJAoMrEREREUnC/wFqBkPnUjMhJAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico de dispersão interativo com zoom e seleção de dados\n",
        "fig = px.scatter(dataframe, x='Sexo', y='imc', color='Tipo de Corpo', size='Idade', hover_name='Nome', title='Comparação de IMC por Sexo')\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "Kt00GuUAWzvw",
        "outputId": "ca7d4d5f-eb29-4e86-9f1a-733091b16d53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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de Corpo\"},\"tracegroupgap\":0,\"itemsizing\":\"constant\"},\"title\":{\"text\":\"Compara\\u00e7\\u00e3o de IMC por Sexo\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "sns.violinplot(x=dataframe['Treina'], y=dataframe['imc'], palette='dark')\n",
        "plt.title('IMC por Treino')\n",
        "plt.xlabel('Treina?')\n",
        "plt.ylabel('IMC')\n",
        "plt.grid(True)\n",
        "plt.xticks([0, 1], ['Não', 'Sim'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6P3QC7iUWZhz",
        "outputId": "48408fa5-9172-4c4a-dd18-d1dbf838b5b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-edae9ef5a74e>:2: FutureWarning:\n",
            "\n",
            "\n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "sns.boxplot(x=dataframe['Tipo de Corpo'], y=dataframe['Altura'], palette='Set1')\n",
        "plt.title('Altura por Tipo de Corpo')\n",
        "plt.xlabel('Tipo de Corpo')\n",
        "plt.ylabel('Altura (m)')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "KX3diZMoWl43",
        "outputId": "a4c79ece-3efe-4ee6-d379-dc21f7cb13b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-3ab57a9375aa>:2: FutureWarning:\n",
            "\n",
            "\n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analise dos dados\n",
        "- O IMC tem muita relacao sobre a altura e peso.\n",
        "- Conseguimos analise que pessoas que nao tem rotina de Treino tem a tendencia de ter um IMC maior\n",
        "- O Sexo feminino tem tendencias maiores em Ectomorfo do que Sexo Masculino\n",
        "- Mesomorfo e o tipo de corpo mais comum entre as pessoas\n",
        "- Pessoas Endomorfas tem a tendencia de ter um indice de IMC maior\n",
        "#### Conclusao\n",
        "- O conjunto de dados e feito por dados gerados por IA ou aleatorios, existe um padrao, mas nao ha significado exato ou pesos corretos.\n",
        "- Nesta ultima versao do dataset conseguimos criar dados com a minima coerencia e simulando um pouco o mundo real.\n",
        "- Conjunto de dados muito pequeno para um treinamento real"
      ],
      "metadata": {
        "id": "PlCyqy5IDKhC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tratamento dos dados"
      ],
      "metadata": {
        "id": "G1U983pXZPSZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.dtypes"
      ],
      "metadata": {
        "id": "xQITgVzVl2eP",
        "outputId": "312b3e07-fcb7-40e5-b600-72f834bed3e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Nome              object\n",
              "Idade              int64\n",
              "Sexo              object\n",
              "Treina              bool\n",
              "Altura           float64\n",
              "Peso               int64\n",
              "Tipo de Corpo     object\n",
              "Deficiente          bool\n",
              "imc              float64\n",
              "sobrepeso           bool\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para o treinamento e necessario transformar as variaveis categorias em numericas"
      ],
      "metadata": {
        "id": "qwP0fhvTC8ua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe['Sexo'] = dataframe['Sexo'].replace({'F': 0, 'M': 1})\n",
        "dataframe['Treina'] = dataframe['Treina'].astype(int)\n",
        "dataframe['Deficiente'] = dataframe['Deficiente'].astype(int)\n",
        "dataframe['Tipo de Corpo'] = dataframe['Tipo de Corpo'].map({'Mesomorfo': 0, 'Ectomorfo': 1, 'Endomorfo': 2})\n"
      ],
      "metadata": {
        "id": "a4YoafYkZarf"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.tail()"
      ],
      "metadata": {
        "id": "nC370Av_ml14",
        "outputId": "cdaad629-2ca1-4940-cd3c-d3b8a716c7ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Nome  Idade  Sexo  Treina  Altura  Peso  Tipo de Corpo  Deficiente  \\\n",
              "379   Otávio     42     1       1    1.81    79              0           0   \n",
              "380    Paula     21     0       1    1.66    58              1           0   \n",
              "381  Ricardo     36     1       0    1.79    76              2           1   \n",
              "382    Sofia     24     0       1    1.70    62              0           0   \n",
              "383    Tiago     29     1       1    1.83    81              2           0   \n",
              "\n",
              "           imc  sobrepeso  \n",
              "379  24.114038      False  \n",
              "380  21.048048      False  \n",
              "381  23.719609      False  \n",
              "382  21.453287      False  \n",
              "383  24.187046      False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2a16f548-c261-4767-897f-eb31038c46f4\" 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",
              "        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>Nome</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Treina</th>\n",
              "      <th>Altura</th>\n",
              "      <th>Peso</th>\n",
              "      <th>Tipo de Corpo</th>\n",
              "      <th>Deficiente</th>\n",
              "      <th>imc</th>\n",
              "      <th>sobrepeso</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>379</th>\n",
              "      <td>Otávio</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.81</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24.114038</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>Paula</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.66</td>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21.048048</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>Ricardo</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.79</td>\n",
              "      <td>76</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>23.719609</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>Sofia</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.70</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21.453287</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>Tiago</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.83</td>\n",
              "      <td>81</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24.187046</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a16f548-c261-4767-897f-eb31038c46f4')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2a16f548-c261-4767-897f-eb31038c46f4 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-2a16f548-c261-4767-897f-eb31038c46f4');\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",
              "\n",
              "\n",
              "<div id=\"df-f9d733a2-decc-4432-9c8c-f0d1eb975d67\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9d733a2-decc-4432-9c8c-f0d1eb975d67')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f9d733a2-decc-4432-9c8c-f0d1eb975d67 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Nome\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Paula\",\n          \"Tiago\",\n          \"Ricardo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 21,\n        \"max\": 42,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21,\n          29,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treina\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07395944834840244,\n        \"min\": 1.66,\n        \"max\": 1.83,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.66,\n          1.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Peso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 58,\n        \"max\": 81,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          58,\n          81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo de Corpo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deficiente\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5268222615210645,\n        \"min\": 21.04804761213529,\n        \"max\": 24.187046492878256,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21.04804761213529,\n          24.187046492878256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sobrepeso\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tirando nome do conjunto de dados"
      ],
      "metadata": {
        "id": "Ocp1tacQCy5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Removendo a coluna 'Nome'\n",
        "df_sn = dataframe.drop(columns=['Nome'], inplace=True)"
      ],
      "metadata": {
        "id": "RAZwwG8MmU_N"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizando o plt Heatmap para verificar a relacao entre os dados"
      ],
      "metadata": {
        "id": "793ZvhHmC1dY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(dataframe.corr(), annot=True, cmap='coolwarm', linewidths=.5)\n",
        "plt.title('Mapa de Calor - Correlação entre Variáveis')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "bLiad8CQZesf",
        "outputId": "41fe475c-51e8-452f-d9ba-82c1c55094e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Depois de uma analise sobre o objetivo final e o conjunto de dados, foi decidido criar uma variavel alvo com 5 valores possiveis"
      ],
      "metadata": {
        "id": "WfIxF9o9Ce7E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe['Objetivo de Treino'] = 'Treino Manutencao'\n",
        "\n",
        "dataframe['Objetivo de Treino'] = np.where(dataframe['Deficiente'] == 1, 'Treino Especial', dataframe['Objetivo de Treino'])\n",
        "\n",
        "# Definir Treino Perda de Peso para clientes com sobrepeso\n",
        "dataframe['Objetivo de Treino'] = np.where(dataframe['sobrepeso'] == 1, 'Treino Perda de Peso', dataframe['Objetivo de Treino'])\n",
        "\n",
        "# Definir Treino Ganho de Massa para clientes com IMC < 18.5\n",
        "dataframe['Objetivo de Treino'] = np.where(dataframe['imc'] < 18.5, 'Treino Ganho de Massa', dataframe['Objetivo de Treino'])\n",
        "\n",
        "# Definir Treino de Força para clientes com IMC entre 18.5 e 24.9\n",
        "dataframe['Objetivo de Treino'] = np.where((dataframe['imc'] >= 18.5) & (dataframe['imc'] <= 24.9) & (dataframe['Deficiente'] == 0), 'Treino de Forca', dataframe['Objetivo de Treino'])"
      ],
      "metadata": {
        "id": "KLAR57bgql9f"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.tail()"
      ],
      "metadata": {
        "id": "RynIa8wCr2jr",
        "outputId": "935e385e-f5b0-4602-a7d1-01c13077da63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Idade  Sexo  Treina  Altura  Peso  Tipo de Corpo  Deficiente        imc  \\\n",
              "379     42     1       1    1.81    79              0           0  24.114038   \n",
              "380     21     0       1    1.66    58              1           0  21.048048   \n",
              "381     36     1       0    1.79    76              2           1  23.719609   \n",
              "382     24     0       1    1.70    62              0           0  21.453287   \n",
              "383     29     1       1    1.83    81              2           0  24.187046   \n",
              "\n",
              "     sobrepeso Objetivo de Treino  \n",
              "379      False    Treino de Forca  \n",
              "380      False    Treino de Forca  \n",
              "381      False    Treino Especial  \n",
              "382      False    Treino de Forca  \n",
              "383      False    Treino de Forca  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f4b04c7-9819-4b03-84fe-fbeec07a838b\" 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",
              "        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>Idade</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Treina</th>\n",
              "      <th>Altura</th>\n",
              "      <th>Peso</th>\n",
              "      <th>Tipo de Corpo</th>\n",
              "      <th>Deficiente</th>\n",
              "      <th>imc</th>\n",
              "      <th>sobrepeso</th>\n",
              "      <th>Objetivo de Treino</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>379</th>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.81</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24.114038</td>\n",
              "      <td>False</td>\n",
              "      <td>Treino de Forca</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.66</td>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21.048048</td>\n",
              "      <td>False</td>\n",
              "      <td>Treino de Forca</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.79</td>\n",
              "      <td>76</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>23.719609</td>\n",
              "      <td>False</td>\n",
              "      <td>Treino Especial</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.70</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21.453287</td>\n",
              "      <td>False</td>\n",
              "      <td>Treino de Forca</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.83</td>\n",
              "      <td>81</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24.187046</td>\n",
              "      <td>False</td>\n",
              "      <td>Treino de Forca</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f4b04c7-9819-4b03-84fe-fbeec07a838b')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-3f4b04c7-9819-4b03-84fe-fbeec07a838b 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-3f4b04c7-9819-4b03-84fe-fbeec07a838b');\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",
              "\n",
              "\n",
              "<div id=\"df-ba88a470-cb51-4850-b4ac-6ac117affbf7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba88a470-cb51-4850-b4ac-6ac117affbf7')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ba88a470-cb51-4850-b4ac-6ac117affbf7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 21,\n        \"max\": 42,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21,\n          29,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treina\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07395944834840244,\n        \"min\": 1.66,\n        \"max\": 1.83,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.66,\n          1.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Peso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 58,\n        \"max\": 81,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          58,\n          81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo de Corpo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deficiente\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5268222615210645,\n        \"min\": 21.04804761213529,\n        \"max\": 24.187046492878256,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21.04804761213529,\n          24.187046492878256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sobrepeso\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Objetivo de Treino\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Treino Especial\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Treinamento do Modelo"
      ],
      "metadata": {
        "id": "MUUUKQbuBSME"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definindo Variavel alvo do modelo"
      ],
      "metadata": {
        "id": "y1Dfh6ylCZ_D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataframe[['Idade', 'Sexo', 'Treina', 'Altura', 'Peso', 'Tipo de Corpo', 'Deficiente', 'imc', 'sobrepeso']]\n",
        "y = dataframe['Objetivo de Treino']"
      ],
      "metadata": {
        "id": "SYZbFGsvs3TE"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividir os dados em treino e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "KgcZw2dds9bV"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instanciar e treinar o modelo\n",
        "model_dt = DecisionTreeClassifier(random_state=42)\n",
        "model_dt.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "SkpMyelns_mz",
        "outputId": "2b4eb9ee-8325-4ed8-e725-b4141f56bd56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fazer previsões\n",
        "y_pred_dt = model_dt.predict(X_test)"
      ],
      "metadata": {
        "id": "VT_cQaQXtA_H"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Avaliar precisão\n",
        "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
        "print(f'Acurácia Decision Tree Classifier: {accuracy_dt:.2f}')"
      ],
      "metadata": {
        "id": "N4JQ30bftDj2",
        "outputId": "d913e308-e122-43db-cb73-fef143170be2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia Decision Tree Classifier: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exibir relatório de classificação\n",
        "print(classification_report(y_test, y_pred_dt))"
      ],
      "metadata": {
        "id": "WMlw1LJVtEYK",
        "outputId": "918f7669-52a6-4af4-f24d-5a412ea5992b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   precision    recall  f1-score   support\n",
            "\n",
            "  Treino Especial       1.00      1.00      1.00         5\n",
            "Treino Manutencao       1.00      1.00      1.00        29\n",
            "  Treino de Forca       1.00      1.00      1.00        43\n",
            "\n",
            "         accuracy                           1.00        77\n",
            "        macro avg       1.00      1.00      1.00        77\n",
            "     weighted avg       1.00      1.00      1.00        77\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Forest"
      ],
      "metadata": {
        "id": "iP2oCXhBtJV5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_rf = RandomForestClassifier(random_state=42)\n",
        "model_rf.fit(X_train, y_train)\n",
        "\n",
        "# Fazer previsões\n",
        "y_pred_rf = model_rf.predict(X_test)"
      ],
      "metadata": {
        "id": "E7fXM5SPtNFK"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Avaliar precisão\n",
        "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
        "print(f'Acurácia Random Forest Classifier: {accuracy_rf:.2f}')"
      ],
      "metadata": {
        "id": "CqsdC1cjtPY0",
        "outputId": "ab391d6c-dde1-4f75-beac-eb7ed25897d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia Random Forest Classifier: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exibir relatório de classificação\n",
        "print(classification_report(y_test, y_pred_rf))"
      ],
      "metadata": {
        "id": "-HRCnMOMtRbi",
        "outputId": "fc67ed75-f57d-4d48-c85b-a01110fd6df6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   precision    recall  f1-score   support\n",
            "\n",
            "  Treino Especial       1.00      1.00      1.00         5\n",
            "Treino Manutencao       1.00      1.00      1.00        29\n",
            "  Treino de Forca       1.00      1.00      1.00        43\n",
            "\n",
            "         accuracy                           1.00        77\n",
            "        macro avg       1.00      1.00      1.00        77\n",
            "     weighted avg       1.00      1.00      1.00        77\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "id": "ncmDWWfzu95L",
        "outputId": "66a30556-da6a-4cc8-d0dd-0faf110de02d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "268    Treino Manutencao\n",
              "250    Treino Manutencao\n",
              "355      Treino de Forca\n",
              "332      Treino de Forca\n",
              "56     Treino Manutencao\n",
              "             ...        \n",
              "104    Treino Manutencao\n",
              "167    Treino Manutencao\n",
              "63       Treino de Forca\n",
              "233    Treino Manutencao\n",
              "84     Treino Manutencao\n",
              "Name: Objetivo de Treino, Length: 77, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_rf"
      ],
      "metadata": {
        "id": "NLLDZ1GavHYv",
        "outputId": "06c90424-92cf-4588-cf29-17f2dc452a88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Treino Manutencao', 'Treino Manutencao', 'Treino de Forca',\n",
              "       'Treino de Forca', 'Treino Manutencao', 'Treino de Forca',\n",
              "       'Treino de Forca', 'Treino Manutencao', 'Treino Especial',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino de Forca',\n",
              "       'Treino Manutencao', 'Treino de Forca', 'Treino Manutencao',\n",
              "       'Treino Manutencao', 'Treino Manutencao', 'Treino Especial',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino Manutencao',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino de Forca',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino de Forca',\n",
              "       'Treino Especial', 'Treino de Forca', 'Treino Manutencao',\n",
              "       'Treino de Forca', 'Treino Manutencao', 'Treino de Forca',\n",
              "       'Treino Manutencao', 'Treino de Forca', 'Treino de Forca',\n",
              "       'Treino Manutencao', 'Treino de Forca', 'Treino Manutencao',\n",
              "       'Treino Manutencao', 'Treino de Forca', 'Treino de Forca',\n",
              "       'Treino Manutencao', 'Treino Manutencao', 'Treino de Forca',\n",
              "       'Treino Manutencao', 'Treino de Forca', 'Treino de Forca',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino Manutencao',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino de Forca',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino Manutencao',\n",
              "       'Treino de Forca', 'Treino Especial', 'Treino de Forca',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino Especial',\n",
              "       'Treino Manutencao', 'Treino Manutencao', 'Treino Manutencao',\n",
              "       'Treino Manutencao', 'Treino Manutencao', 'Treino de Forca',\n",
              "       'Treino de Forca', 'Treino de Forca', 'Treino de Forca',\n",
              "       'Treino Manutencao', 'Treino Manutencao', 'Treino de Forca',\n",
              "       'Treino Manutencao', 'Treino Manutencao'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Primeira Tentativa de mapping"
      ],
      "metadata": {
        "id": "7r2q3Jc-CSYw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Mapear os valores numéricos para suas categorias originais\n",
        "# class_mapping = {0: 'Treino Especial', 1: 'Treino Manutenção', 2: 'Treino de Forca'}\n",
        "# y_test_mapped = y_test.reset_index(drop=True).map(class_mapping)\n",
        "# y_pred_mapped = pd.Series(y_pred_rf).map(class_mapping)\n",
        "\n",
        "# # Criar DataFrame com resultados mapeados\n",
        "# results_df = pd.DataFrame({'Real': y_test_mapped, 'Previsão': y_pred_mapped})"
      ],
      "metadata": {
        "id": "pctFxnDhuOuS"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### SVM"
      ],
      "metadata": {
        "id": "qliSw3mstT-T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pipeline com pré-processamento e modelo SVM\n",
        "pipeline_svm = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('svm', SVC(kernel='rbf', random_state=42))\n",
        "])\n",
        "\n",
        "# Treinar o modelo\n",
        "pipeline_svm.fit(X_train, y_train)\n",
        "\n",
        "# Fazer previsões\n",
        "y_pred_svm = pipeline_svm.predict(X_test)\n"
      ],
      "metadata": {
        "id": "g3lvkrBAtWn9"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Avaliar precisão\n",
        "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
        "print(f'Acurácia SVM Classifier: {accuracy_svm:.2f}')"
      ],
      "metadata": {
        "id": "mKbFqfMPtYmY",
        "outputId": "408807c6-f812-45c9-9b27-cb1240de6431",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia SVM Classifier: 0.97\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exibir relatório de classificação\n",
        "print(classification_report(y_test, y_pred_svm))"
      ],
      "metadata": {
        "id": "vUlEWzrZtaCW",
        "outputId": "e3a34de7-da7c-496b-da85-2dd1d5307dac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   precision    recall  f1-score   support\n",
            "\n",
            "  Treino Especial       1.00      1.00      1.00         5\n",
            "Treino Manutencao       1.00      0.93      0.96        29\n",
            "  Treino de Forca       0.96      1.00      0.98        43\n",
            "\n",
            "         accuracy                           0.97        77\n",
            "        macro avg       0.99      0.98      0.98        77\n",
            "     weighted avg       0.98      0.97      0.97        77\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### NEURAL NETWORK W KERAS"
      ],
      "metadata": {
        "id": "nAKft5V0tdpA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Codificar variável alvo\n",
        "encoder = LabelEncoder()\n",
        "y = encoder.fit_transform(dataframe['Objetivo de Treino'])"
      ],
      "metadata": {
        "id": "lc_XOyvPthhK"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividir os dados em treino e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "4xBG8k1FtjaO"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizar os dados\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "vpmLeuTwtoTs"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Construir o modelo\n",
        "model_nn = Sequential([\n",
        "    Dense(64, input_shape=(X_train.shape[1],), activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(len(encoder.classes_), activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "yfJD8uyNtqfe"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compilar o modelo\n",
        "model_nn.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "DXwoMxywtvV0"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Treinando o modelo nesta sua versao com 100 epocas"
      ],
      "metadata": {
        "id": "mqB9q_1bB9_y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Treinar o modelo\n",
        "history = model_nn.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test), verbose=0)"
      ],
      "metadata": {
        "id": "ryxOsp-btxYH"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Avaliar o modelo\n",
        "loss, accuracy_nn = model_nn.evaluate(X_test, y_test, verbose=0)\n",
        "print(f'Acurácia Neural Network (Keras): {accuracy_nn:.2f}')"
      ],
      "metadata": {
        "id": "fgwtIgY-t2XE",
        "outputId": "0ca9b596-e40d-4e13-b6ef-40ff0ec9d8df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia Neural Network (Keras): 0.97\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exibir relatório de classificação\n",
        "y_pred_nn = model_nn.predict(X_test)\n",
        "y_pred_nn_classes = np.argmax(y_pred_nn, axis=1)\n",
        "print(classification_report(y_test, y_pred_nn_classes, target_names=encoder.classes_))"
      ],
      "metadata": {
        "id": "kyr4uwPHt4s-",
        "outputId": "4ea117e9-041b-4d6a-eca7-c6fd8bc6a8a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 5ms/step\n",
            "                   precision    recall  f1-score   support\n",
            "\n",
            "  Treino Especial       1.00      1.00      1.00         5\n",
            "Treino Manutencao       0.97      0.97      0.97        29\n",
            "  Treino de Forca       0.98      0.98      0.98        43\n",
            "\n",
            "         accuracy                           0.97        77\n",
            "        macro avg       0.98      0.98      0.98        77\n",
            "     weighted avg       0.97      0.97      0.97        77\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Teste do Modelo com novos dados e simulacao"
      ],
      "metadata": {
        "id": "FNX40P4dxWuR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.head()"
      ],
      "metadata": {
        "id": "Mpplg8i5xb0C",
        "outputId": "3a0f86e6-24c3-45f8-d232-f99f067c8929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Idade  Sexo  Treina  Altura  Peso  Tipo de Corpo  Deficiente        imc  \\\n",
              "0     28     0       1    1.65    55              0           0  20.202020   \n",
              "1     35     1       1    1.80    85              2           0  26.234568   \n",
              "2     22     0       1    1.70    60              1           1  20.761246   \n",
              "3     30     1       0    1.75    70              0           0  22.857143   \n",
              "4     40     0       1    1.60    70              2           0  27.343750   \n",
              "\n",
              "   sobrepeso Objetivo de Treino  \n",
              "0      False    Treino de Forca  \n",
              "1      False  Treino Manutencao  \n",
              "2      False    Treino Especial  \n",
              "3      False    Treino de Forca  \n",
              "4      False  Treino Manutencao  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Idade</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Treina</th>\n",
              "      <th>Altura</th>\n",
              "      <th>Peso</th>\n",
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              "      <th>Deficiente</th>\n",
              "      <th>imc</th>\n",
              "      <th>sobrepeso</th>\n",
              "      <th>Objetivo de Treino</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>False</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0</td>\n",
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              "      <td>1.60</td>\n",
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              "\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-92c8b1fb-fc2e-4070-87c5-4e75b6a716b9 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-92c8b1fb-fc2e-4070-87c5-4e75b6a716b9');\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",
              "\n",
              "\n",
              "<div id=\"df-f863d9d9-10eb-4833-91c3-74d99a4441e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f863d9d9-10eb-4833-91c3-74d99a4441e6')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f863d9d9-10eb-4833-91c3-74d99a4441e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 384,\n  \"fields\": [\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 18,\n        \"max\": 45,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          29,\n          37,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treina\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07570566181791216,\n        \"min\": 1.55,\n        \"max\": 1.92,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          1.57,\n          1.81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Peso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 48,\n        \"max\": 95,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          53,\n          64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo de Corpo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deficiente\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4151531818051364,\n        \"min\": 18.818924310286427,\n        \"max\": 29.065926806711587,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          21.773842357381334,\n          24.898143956541425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sobrepeso\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Objetivo de Treino\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Treino de Forca\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Criando novos dados para testar o modelo e simular o uso da API com o GPT"
      ],
      "metadata": {
        "id": "bWKUkFtZBqqO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "novos_dados = {\n",
        "    'Idade': [30,25,40],\n",
        "    'Sexo': [1,0,1],\n",
        "    'Treina': [1,0,0],\n",
        "    'Altura': [1.75,1.60,1.65],\n",
        "    'Peso': [65,80,77],\n",
        "    'Tipo de Corpo': [0,2,1],\n",
        "    'Deficiente': [0,0,1]\n",
        "}\n",
        "\n",
        "df_novos_dados = pd.DataFrame(novos_dados)"
      ],
      "metadata": {
        "id": "T3cvYJZcxmP-"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_novos_dados"
      ],
      "metadata": {
        "id": "HqND5VqOyYPR",
        "outputId": "77b1ab95-a6ca-492b-b5e8-f4a49a7de489",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Idade  Sexo  Treina  Altura  Peso  Tipo de Corpo  Deficiente\n",
              "0     30     1       1    1.75    65              0           0\n",
              "1     25     0       0    1.60    80              2           0\n",
              "2     40     1       0    1.65    77              1           1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a4402d9-c39e-4688-bb26-589e7f84521a\" 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",
              "        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>Idade</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Treina</th>\n",
              "      <th>Altura</th>\n",
              "      <th>Peso</th>\n",
              "      <th>Tipo de Corpo</th>\n",
              "      <th>Deficiente</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.75</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.60</td>\n",
              "      <td>80</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.65</td>\n",
              "      <td>77</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a4402d9-c39e-4688-bb26-589e7f84521a')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5a4402d9-c39e-4688-bb26-589e7f84521a 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-5a4402d9-c39e-4688-bb26-589e7f84521a');\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",
              "\n",
              "\n",
              "<div id=\"df-0ceabc09-8ae6-4174-82e1-d1605940068e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ceabc09-8ae6-4174-82e1-d1605940068e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0fc864f3-a0aa-490f-98f3-39696a597ac5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_novos_dados')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_novos_dados');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_novos_dados",
              "summary": "{\n  \"name\": \"df_novos_dados\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 25,\n        \"max\": 40,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          30,\n          25,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treina\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0763762615825973,\n        \"min\": 1.6,\n        \"max\": 1.75,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.75,\n          1.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Peso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 65,\n        \"max\": 80,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          65,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo de Corpo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deficiente\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculando o IMC e definindo sobrepeso"
      ],
      "metadata": {
        "id": "ViCRjGHWAzmr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_novos_dados['imc'] = df_novos_dados['Peso'] / (df_novos_dados['Altura'] ** 2)\n",
        "\n",
        "df_novos_dados['sobrepeso'] = df_novos_dados['imc'] > 30\n",
        "df_novos_dados['sobrepeso'] = df_novos_dados['sobrepeso'] #Posso passar um map para mudar o valor padrao booleano, mas vou deixar True e False"
      ],
      "metadata": {
        "id": "1_t6_yUIypfw"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criacao de Target"
      ],
      "metadata": {
        "id": "wI_1iYzvAx5o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_novos_dados['Objetivo de Treino'] = 'Treino Manutencao'\n",
        "\n",
        "df_novos_dados['Objetivo de Treino'] = np.where(df_novos_dados['Deficiente'] == 1, 'Treino Especial', df_novos_dados['Objetivo de Treino'])\n",
        "\n",
        "# Definir Treino Perda de Peso para clientes com sobrepeso\n",
        "df_novos_dados['Objetivo de Treino'] = np.where(df_novos_dados['sobrepeso'] == 1, 'Treino Perda de Peso', df_novos_dados['Objetivo de Treino'])\n",
        "\n",
        "# Definir Treino Ganho de Massa para clientes com IMC < 18.5\n",
        "df_novos_dados['Objetivo de Treino'] = np.where(df_novos_dados['imc'] < 18.5, 'Treino Ganho de Massa', df_novos_dados['Objetivo de Treino'])\n",
        "\n",
        "# Definir Treino de Força para clientes com IMC entre 18.5 e 24.9\n",
        "df_novos_dados['Objetivo de Treino'] = np.where((df_novos_dados['imc'] >= 18.5) & (df_novos_dados['imc'] <= 24.9) & (df_novos_dados['Deficiente'] == 0), 'Treino de Forca', df_novos_dados['Objetivo de Treino'])"
      ],
      "metadata": {
        "id": "X5d-P1cPzBAi"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_novos_dados"
      ],
      "metadata": {
        "id": "G80prYl-zEc0",
        "outputId": "66770715-5963-4923-87b5-163dc847ebb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Idade  Sexo  Treina  Altura  Peso  Tipo de Corpo  Deficiente        imc  \\\n",
              "0     30     1       1    1.75    65              0           0  21.224490   \n",
              "1     25     0       0    1.60    80              2           0  31.250000   \n",
              "2     40     1       0    1.65    77              1           1  28.282828   \n",
              "\n",
              "   sobrepeso    Objetivo de Treino  \n",
              "0      False       Treino de Forca  \n",
              "1       True  Treino Perda de Peso  \n",
              "2      False       Treino Especial  "
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_novos_dados",
              "summary": "{\n  \"name\": \"df_novos_dados\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 25,\n        \"max\": 40,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          30,\n          25,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sexo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treina\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0763762615825973,\n        \"min\": 1.6,\n        \"max\": 1.75,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.75,\n          1.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Peso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 65,\n        \"max\": 80,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          65,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo de Corpo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deficiente\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.150001699791344,\n        \"min\": 21.224489795918366,\n        \"max\": 31.249999999999993,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          21.224489795918366,\n          31.249999999999993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sobrepeso\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Objetivo de Treino\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Treino de Forca\",\n          \"Treino Perda de Peso\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "s78hTckk1Q1g"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_novos_dados = df_novos_dados[X_train.columns] # alinhando as colunas dos novos valores"
      ],
      "metadata": {
        "id": "Rj9AqNfv0Tuv"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "RandomForestClassifier escolhido porque e o modelo menor. nao ha necessidade de utilizar modelos maiores ou desenvolvidos para a quantidade de dados"
      ],
      "metadata": {
        "id": "plgCQLgkAiqR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_rf = RandomForestClassifier(random_state=42)\n",
        "model_rf.fit(X_train, y_train)\n",
        "\n",
        "# Fazer previsões usando os valores novos\n",
        "y_pred_novos = model_rf.predict(df_novos_dados)"
      ],
      "metadata": {
        "id": "F17yoZvP10j4"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mapear os valores numéricos para suas categorias originais\n",
        "class_mapping = {0: 'Treino Especial', 1: 'Treino Perda de Peso', 2: 'Treino de Forca'}\n",
        "y_pred_novos_mapped = pd.Series(y_pred_novos).map(class_mapping)"
      ],
      "metadata": {
        "id": "7hSOw1Og0X1R"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_novos_mapped # Ver o predict do modelo"
      ],
      "metadata": {
        "id": "iz_WF6x62Bd_",
        "outputId": "eaa0991a-53ce-4d97-e41b-a9b012b6fbf8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Treino de Forca\n",
              "1    Treino Perda de Peso\n",
              "2         Treino Especial\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Geração da mensagem de saída\n",
        "output_messages = []\n",
        "for pred in y_pred_novos_mapped: # Somente essas 3 classificacoes estao mapeadas por enquanto.\n",
        "    if pred == 'Treino de Forca':\n",
        "        output_message = \"Crie um plano de treino de força de 4 semanas para um indivíduo saudável que treina regularmente\"   #Input Para o GPT\n",
        "    elif pred == 'Treino Perda de Peso':\n",
        "        output_message = \"Desenvolva um programa de exercícios de 6 semanas focado na perda de peso com um estilo de vida sedentário\"\n",
        "    elif pred == 'Treino Especial':\n",
        "        output_message = \"Elabore um plano de treino seguro e eficaz de 8 semanas para uma pessoa com deficiência nas pernas que deseja melhorar a força do tronco e dos braços\"\n",
        "    output_messages.append(output_message) # Salva"
      ],
      "metadata": {
        "id": "hqPrMwf20aka"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## API COM O GPT"
      ],
      "metadata": {
        "id": "H2C8x5M63J_f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Integração com o ChatGPT\n",
        "def enviar_para_chatgpt(message):\n",
        "    url = 'https://api.openai.com/v1/chat/completions'\n",
        "    headers = {\n",
        "        'Content-Type': 'application/json',\n",
        "        'Authorization': 'Bearer ChaveAPIAQUI'  # Substitua ChaveAPIAQUI pela API Key do OpenAI\n",
        "    }\n",
        "    data = {\n",
        "        'model': 'gpt-3.5-turbo',  # modelo escolhido\n",
        "        'messages': [\n",
        "            {'role': 'user', 'content': message}\n",
        "        ]\n",
        "    }\n",
        "    response = requests.post(url, headers=headers, json=data)\n",
        "    if response.status_code == 200:\n",
        "        return response.json()['choices'][0]['message']['content']\n",
        "    elif response.status_code == 429:\n",
        "        return \"Erro 429 - Excedeu o limite de solicitações.\"\n",
        "    else:\n",
        "        return f\"Erro ao enviar mensagem para o ChatGPT: {response.status_code}\"\n"
      ],
      "metadata": {
        "id": "2BNXBIZk3OJ9"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Enviar mensagens para o ChatGPT e obter respostas\n",
        "for message in output_messages:\n",
        "    input_message = f\"Classificação: {message}\"\n",
        "    response = enviar_para_chatgpt(input_message)\n",
        "    print(f\"Input: {input_message}\")\n",
        "    if \"Erro\" in response:\n",
        "        print(f\"Erro ao enviar mensagem para o ChatGPT: {response}\")\n",
        "    else:\n",
        "        print(f\"Output: {response}\")\n",
        "    print(\"\\n\")\n",
        "    time.sleep(2)  # Aguarda 2 segundos entre cada solicitação para evitar erro de limite de solicitacao"
      ],
      "metadata": {
        "id": "KYSbQFVo3eAV",
        "outputId": "4e9fe8b9-0fa7-41cf-d408-0a5e527c060b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input: Classificação: Crie um plano de treino de força de 4 semanas para um indivíduo saudável que treina regularmente\n",
            "Output: Sem conhecer os objetivos específicos do indivíduo, como ganho de massa muscular, aumento de força, definição muscular ou resistência, é difícil criar um plano de treino de força personalizado. No entanto, irei apresentar um exemplo genérico de um plano de treino de força de 4 semanas para um indivíduo saudável que treina regularmente:\n",
            "\n",
            "Semana 1:\n",
            "- Dia 1: Agachamento com barra - 4 séries de 10 repetições\n",
            "           Supino reto - 4 séries de 10 repetições\n",
            "           Barra fixa - 3 séries de 10 repetições\n",
            "- Dia 2: Descanso ou cardio leve (caminhada, bicicleta, etc.)\n",
            "- Dia 3: Levantamento terra - 4 séries de 8 repetições\n",
            "           Desenvolvimento com halteres - 4 séries de 10 repetições\n",
            "           Remada curvada com barra - 3 séries de 12 repetições\n",
            "- Dia 4: Descanso ou cardio leve\n",
            "- Dia 5: Afundo com halteres - 3 séries de 12 repetições por perna\n",
            "           Elevação de quadril - 4 séries de 10 repetições\n",
            "           Rosca direta - 3 séries de 12 repetições\n",
            "- Dia 6 e 7: Descanso\n",
            "\n",
            "Semana 2:\n",
            "- Repetir o mesmo plano de treino da semana 1, mas aumentar o peso ou intensidade em todas as séries\n",
            "\n",
            "Semanas 3 e 4:\n",
            "- Aumentar a intensidade e variar os exercícios para evitar a adaptação do corpo. Introduzir novos exercícios como lunges, agachamento frontal, flexão lateral com halteres, entre outros.\n",
            "\n",
            "Este plano de treino é apenas um exemplo genérico e deve ser personalizado de acordo com as necessidades e objetivos do indivíduo. Lembre-se de sempre consultar um profissional de educação física antes de iniciar qualquer tipo de treinamento de força.\n",
            "\n",
            "\n",
            "Input: Classificação: Desenvolva um programa de exercícios de 6 semanas focado na perda de peso com um estilo de vida sedentário\n",
            "Output: Sempre consulte um profissional de saúde antes de iniciar qualquer programa de exercícios:\n",
            "\n",
            "Semana 1-2:\n",
            "- Segunda-feira: 20 minutos de caminhada leve\n",
            "- Quarta-feira: 15 minutos de exercícios de alongamento\n",
            "- Sexta-feira: 20 minutos de dança aeróbica em casa\n",
            "\n",
            "Semana 3-4:\n",
            "- Segunda-feira: 30 minutos de caminhada em ritmo moderado\n",
            "- Quarta-feira: 20 minutos de exercícios de resistência com halteres leves\n",
            "- Sexta-feira: 30 minutos de dança aeróbica em casa\n",
            "\n",
            "Semana 5-6:\n",
            "- Segunda-feira: 40 minutos de caminhada em ritmo moderado\n",
            "- Quarta-feira: 30 minutos de exercícios de resistência com halteres leves\n",
            "- Sexta-feira: 40 minutos de dança aeróbica em casa\n",
            "\n",
            "Além dos exercícios, também é importante manter uma alimentação saudável com a ingestão adequada de proteínas, carboidratos e gorduras saudáveis, além de evitar alimentos processados e ricos em açúcares. Lembre-se de se manter hidratado bebendo bastante água ao longo do dia e descansar adequadamente para permitir a recuperação muscular.\n",
            "\n",
            "\n",
            "Input: Classificação: Elabore um plano de treino seguro e eficaz de 8 semanas para uma pessoa com deficiência nas pernas que deseja melhorar a força do tronco e dos braços\n",
            "Output: Sem saber a gravidade da deficiência nas pernas da pessoa, é crucial consultar um fisioterapeuta antes de começar qualquer programa de treino. A seguir está um plano de treino genérico de 8 semanas que pode ser ajustado de acordo com as necessidades individuais:\n",
            "\n",
            "Semana 1-2:\n",
            "- Dia 1: 3 séries de flexões de joelhos (ou flexões de parede) - 10 repetições\n",
            "- Dia 2: 3 séries de elevações de braços com ou sem pesos leves - 10 repetições\n",
            "- Dia 3: Descanso\n",
            "- Dia 4: Repetir o dia 1\n",
            "- Dia 5: Repetir o dia 2\n",
            "- Dia 6: Descanso\n",
            "- Dia 7: Caminhada leve por 20-30 minutos\n",
            "\n",
            "Semana 3-4:\n",
            "- Dia 1: 4 séries de flexões de joelhos - 12 repetições\n",
            "- Dia 2: 4 séries de elevações de braços - 12 repetições\n",
            "- Dia 3: Descanso\n",
            "- Dia 4: Repetir o dia 1\n",
            "- Dia 5: Repetir o dia 2\n",
            "- Dia 6: Descanso\n",
            "- Dia 7: Caminhada leve por 30-45 minutos\n",
            "\n",
            "Semana 5-6:\n",
            "- Dia 1: 4 séries de flexões de joelhos - 15 repetições\n",
            "- Dia 2: 4 séries de elevações de braços com pesos moderados - 15 repetições\n",
            "- Dia 3: Descanso\n",
            "- Dia 4: Repetir o dia 1\n",
            "- Dia 5: Repetir o dia 2\n",
            "- Dia 6: Descanso\n",
            "- Dia 7: Caminhada leve por 45-60 minutos\n",
            "\n",
            "Semana 7-8:\n",
            "- Dia 1: 4 séries de flexões de joelhos - 15 repetições\n",
            "- Dia 2: 4 séries de elevações de braços com pesos mais pesados - 15 repetições\n",
            "- Dia 3: Descanso\n",
            "- Dia 4: Repetir o dia 1\n",
            "- Dia 5: Repetir o dia 2\n",
            "- Dia 6: Descanso\n",
            "- Dia 7: Caminhada leve por 60 minutos\n",
            "\n",
            "Lembre-se de sempre aquecer antes do treino e alongar após o treino. Monitore a intensidade do treino e ajuste conforme necessário. É importante ouvir o corpo e respeitar os limites da pessoa com deficiência nas pernas durante o treinamento. Em todos os casos, o acompanhamento de um profissional de saúde qualificado é fundamental.\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
  ]
}