ML01_Simple_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "ADz_6sv0oXS5",
        "wB22N1bNfRTX"
      ],
      "authorship_tag": "ABX9TyPjghW60johuqXlus0FYSex",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SampMark/Machine-Learning/blob/main/ML01_Simple_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Modelo de Regressão Linear Simples**\n",
        "\n",
        "A análise de regressão é um método interessante que pode ser aplicado utilizando-se a biblioteca Python, Scikit-learn, de modelagem em *machine learn* para prever valores contínuos.\n",
        "\n",
        "\n",
        "### **Definição Matemática**\n",
        "\n",
        "A **Regressão Linear** é um método supervisionado que modela a relação entre variáveis explicativas (independentes) e uma variável alvo contínua (dependente) por meio de uma combinação linear. Formalmente, no caso de uma única variável explicativa, o modelo assume a forma de uma **reta** $y = \\beta_0 + \\beta_1 x$, onde $\\beta_0$ é o intercepto e $\\beta_1$ o coeficiente angular (inclinação). O intercepto representa o valor previsto de $y$ quando $x = 0$, enquanto o coeficiente $\\beta_1$ indica a variação esperada em $y$ a cada incremento unitário em $x$.\n",
        "\n",
        "Numa regressão linear múltipla (com $d$ features), o modelo estende-se para $y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\cdots + \\beta_d x_d$, podendo ser escrito de forma vetorial: $y = \\beta_0 + \\mathbf{x}^\\top \\boldsymbol{\\beta}$. Aqui, $\\mathbf{x} = (x_1,\\dots,x_d)$ é o vetor de features do exemplo e $\\boldsymbol{\\beta} = (\\beta_1,\\dots,\\beta_d)$ são os pesos ou coeficientes associados a cada feature (costuma-se incluir $\\beta_0$ anexando um $x_0=1$ à feature vector para simplificar a notação).\n",
        "\n",
        "**Interpretação dos coeficientes**: cada $\\beta_j$ representa o impacto (aproximado) da respectiva variável $x_j$ sobre a resposta $y$, assumindo as demais variáveis constantes. Por exemplo, se $\\beta_2 = 5.0$ em um modelo de preço de casas, isso sugeriria que, ao aumentar em uma unidade o atributo $x_2$ (por exemplo, metragem do imóvel), o preço previsto aumenta em média 5 unidades monetárias (desde que os demais atributos permaneçam iguais).\n",
        "\n",
        "A regressão linear busca **estimar os coeficientes** $\\beta_j$ a partir de dados de treino, de modo que a reta (ou hiperplano, em dimensões maiores) resultante seja a que melhor se **ajusta aos dados observados**. “Ajustar” aqui significa minimizar os erros de predição.\n",
        "\n",
        "Convencionalmente, utiliza-se a **metodologia dos mínimos quadrados ordinários** (OLS, *ordinary least squares*), em que a qualidade do ajuste é medida pela soma dos **resíduos ao quadrado** – isto é, a diferença entre o valor real $y_i$ e o valor predito $\\hat{y}_i$ elevado ao quadrado e somado para todos os $n$ pontos de treino. A função objetivo (função de custo) da regressão linear OLS pode ser expressa como:\n",
        "\n",
        "$$ J(\\boldsymbol{\\beta}) = \\frac{1}{n}\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2, $$\n",
        "\n",
        "onde $\\hat{y}_i = \\beta_0 + \\sum_{j=1}^{d} \\beta_j x_{ij}$ é a predição do modelo para o $i$-ésimo exemplo. Essa métrica é conhecida como **Erro Quadrático Médio (EQM)** e quantifica o desvio médio quadrático entre predições e valores reais.\n",
        "\n",
        "Minimizar o EQM leva a um compromisso ótimo entre todos os erros: penaliza severamente erros grandes (devido ao quadrado) e de forma mais branda erros pequenos – diferenças grandes entre predito e real resultam em perdas muito maiores, incentivando o modelo a não deixar outliers com erro alto. Essa escolha de função de custo é a mais comum pela conveniência matemática (derivada simples) e eficácia prática em muitos cenários.\n",
        "\n",
        "A regressão linear simples é a base para modelos mais complexos, pois utiliza outras métricas de avaliação como MAE, MSE, RMSE e 𝑅2, cruciais para validar a acurácia do modelo.\n",
        "\n",
        "---\n",
        "\n",
        "### **Objetivo deste laboratório**\n",
        "\n",
        "A análise de dados que se segue objetiva prever a emissão de CO2 de veículos (CO2EMISSIONS) com base em características como o tamanho do motor (ENGINESIZE) e o consumo de combustível (FUELCONSUMPTION_COMB).\n",
        "\n",
        "Tipo de Modelo: prevê a emissão de CO2 usando apenas uma variável preditora por vez (ENGINESIZE ou FUELCONSUMPTION_COMB).\n",
        "\n",
        "**Etapas**:\n",
        "\n",
        "**1) Carregamento e Preparação dos Dados:**\n",
        "Os dados são carregados de um arquivo CSV (hospedado no GitHub).\n",
        "Foram divididos em conjuntos de treinamento (80%) e teste (20%).\n",
        "\n",
        "**2) Treinamento do Modelo:**\n",
        "Usando a biblioteca scikit-learn o modelo criado e treinado usando os dados de treinamento para encontrar a reta que melhor representa a relação entre a variável preditora e a emissão de CO2.\n",
        "\n",
        "**3) Avaliação do Modelo:**\n",
        "O modelo é avaliado usando os dados de teste.\n",
        "Métricas como Erro Absoluto Médio (MAE), Erro Quadrático Médio (MSE) e R-quadrado são calculadas para medir o desempenho do modelo.\n",
        "\n",
        "**4) Visualização:**\n",
        "Gráficos são gerados para visualizar a relação entre as variáveis e a linha de regressão.\n",
        "\n",
        "**Resumo:**\n",
        "O código imprime os coeficientes da reta de regressão (coeficiente angular e intercepto). As métricas de avaliação (MAE, MSE e R-quadrado) indicam o quão bem o modelo se ajusta aos dados. Os gráficos ajudam na visualização da relação entre as variáveis e o modelo."
      ],
      "metadata": {
        "id": "T_0_m49T3hen"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Importando os pacotes necessários**"
      ],
      "metadata": {
        "id": "48Vkj0x7ErJh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVF6QTbTnVgg",
        "outputId": "0124c311-fbf5-4ec8-8406-d3f1b96ea77d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "from sklearn import linear_model"
      ],
      "metadata": {
        "id": "fq8BwMuwntQO"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Análise Exploratória de Dados**\n",
        "\n",
        "O conjunto de dado analisado fornece classificações de consumo de combustível específicas do modelo e emissões estimadas de dióxido de carbono para novos veículos leves para venda no varejo no Canadá.\n",
        "\n",
        "Download dos dados: [**Classificações de consumo de combustível**](https://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)"
      ],
      "metadata": {
        "id": "61oe7JK_E2jk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importando e explorando os dados\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/SampMark/files/refs/heads/main/FuelConsumptionCo2.csv\")\n",
        "print(f\"Número de linhas: {df.shape[0]}\")\n",
        "print(f\"Número de colunas: {df.shape[1]}\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "CgNUbiqsoOTd",
        "outputId": "9434eb4a-0001-4697-8e98-592db8853aaf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de linhas: 1067\n",
            "Número de colunas: 13\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
              "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
              "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
              "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
              "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
              "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
              "\n",
              "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
              "0          AS5        Z                   9.9                  6.7   \n",
              "1           M6        Z                  11.2                  7.7   \n",
              "2          AV7        Z                   6.0                  5.8   \n",
              "3          AS6        Z                  12.7                  9.1   \n",
              "4          AS6        Z                  12.1                  8.7   \n",
              "\n",
              "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
              "0                   8.5                        33           196  \n",
              "1                   9.6                        29           221  \n",
              "2                   5.9                        48           136  \n",
              "3                  11.1                        25           255  \n",
              "4                  10.6                        27           244  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8b203e5e-0d46-4ae9-b7b0-8e8621408526\" 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>MODELYEAR</th>\n",
              "      <th>MAKE</th>\n",
              "      <th>MODEL</th>\n",
              "      <th>VEHICLECLASS</th>\n",
              "      <th>ENGINESIZE</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>TRANSMISSION</th>\n",
              "      <th>FUELTYPE</th>\n",
              "      <th>FUELCONSUMPTION_CITY</th>\n",
              "      <th>FUELCONSUMPTION_HWY</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
              "      <th>CO2EMISSIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>AS5</td>\n",
              "      <td>Z</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.5</td>\n",
              "      <td>33</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4</td>\n",
              "      <td>M6</td>\n",
              "      <td>Z</td>\n",
              "      <td>11.2</td>\n",
              "      <td>7.7</td>\n",
              "      <td>9.6</td>\n",
              "      <td>29</td>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX HYBRID</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
              "      <td>AV7</td>\n",
              "      <td>Z</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>5.9</td>\n",
              "      <td>48</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>MDX 4WD</td>\n",
              "      <td>SUV - SMALL</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>AS6</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>25</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>RDX AWD</td>\n",
              "      <td>SUV - SMALL</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>AS6</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>10.6</td>\n",
              "      <td>27</td>\n",
              "      <td>244</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-8b203e5e-0d46-4ae9-b7b0-8e8621408526')\"\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-8b203e5e-0d46-4ae9-b7b0-8e8621408526 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-8b203e5e-0d46-4ae9-b7b0-8e8621408526');\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-798a8ee8-a10a-442d-aa19-60761c34dcea\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-798a8ee8-a10a-442d-aa19-60761c34dcea')\"\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-798a8ee8-a10a-442d-aa19-60761c34dcea 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": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1067,\n  \"fields\": [\n    {\n      \"column\": \"MODELYEAR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2014,\n        \"max\": 2014,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAKE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 39,\n        \"samples\": [\n          \"SMART\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MODEL\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 663,\n        \"samples\": [\n          \"XJL SUPERCHARGED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VEHICLECLASS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"COMPACT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENGINESIZE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.415895051424066,\n        \"min\": 1.0,\n        \"max\": 8.4,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          3.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CYLINDERS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 12,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TRANSMISSION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"AS5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELTYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_CITY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.1012533170683385,\n        \"min\": 4.6,\n        \"max\": 30.2,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          21.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_HWY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7945104498854083,\n        \"min\": 4.9,\n        \"max\": 20.5,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          5.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_COMB\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.485594849634841,\n        \"min\": 4.7,\n        \"max\": 25.8,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          6.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_COMB_MPG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 11,\n        \"max\": 60,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CO2EMISSIONS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63,\n        \"min\": 108,\n        \"max\": 488,\n        \"num_unique_values\": 159,\n        \"samples\": [\n          366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "source": [
        "# @title FUELCONSUMPTION_CITY vs FUELCONSUMPTION_HWY\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "df.plot(kind='scatter', x='FUELCONSUMPTION_CITY', y='FUELCONSUMPTION_HWY', s=32, alpha=.8)\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "9VSLzWlmsAe3",
        "outputId": "88fd1095-d6cf-41fc-857a-e2ba92fa8881"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Resumo dos dados\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "ZGyS3k16sCo3",
        "outputId": "30a06cd5-495e-4964-c758-31fbc4901f16"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
              "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
              "mean      2014.0     3.346298     5.794752             13.296532   \n",
              "std          0.0     1.415895     1.797447              4.101253   \n",
              "min       2014.0     1.000000     3.000000              4.600000   \n",
              "25%       2014.0     2.000000     4.000000             10.250000   \n",
              "50%       2014.0     3.400000     6.000000             12.600000   \n",
              "75%       2014.0     4.300000     8.000000             15.550000   \n",
              "max       2014.0     8.400000    12.000000             30.200000   \n",
              "\n",
              "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
              "count          1067.000000           1067.000000               1067.000000   \n",
              "mean              9.474602             11.580881                 26.441425   \n",
              "std               2.794510              3.485595                  7.468702   \n",
              "min               4.900000              4.700000                 11.000000   \n",
              "25%               7.500000              9.000000                 21.000000   \n",
              "50%               8.800000             10.900000                 26.000000   \n",
              "75%              10.850000             13.350000                 31.000000   \n",
              "max              20.500000             25.800000                 60.000000   \n",
              "\n",
              "       CO2EMISSIONS  \n",
              "count   1067.000000  \n",
              "mean     256.228679  \n",
              "std       63.372304  \n",
              "min      108.000000  \n",
              "25%      207.000000  \n",
              "50%      251.000000  \n",
              "75%      294.000000  \n",
              "max      488.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02132daa-e83f-40e9-bf37-a36b87350382\" 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>MODELYEAR</th>\n",
              "      <th>ENGINESIZE</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>FUELCONSUMPTION_CITY</th>\n",
              "      <th>FUELCONSUMPTION_HWY</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
              "      <th>CO2EMISSIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1067.0</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "      <td>1067.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>3.346298</td>\n",
              "      <td>5.794752</td>\n",
              "      <td>13.296532</td>\n",
              "      <td>9.474602</td>\n",
              "      <td>11.580881</td>\n",
              "      <td>26.441425</td>\n",
              "      <td>256.228679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.415895</td>\n",
              "      <td>1.797447</td>\n",
              "      <td>4.101253</td>\n",
              "      <td>2.794510</td>\n",
              "      <td>3.485595</td>\n",
              "      <td>7.468702</td>\n",
              "      <td>63.372304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>108.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>207.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>8.800000</td>\n",
              "      <td>10.900000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>251.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>15.550000</td>\n",
              "      <td>10.850000</td>\n",
              "      <td>13.350000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>294.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>30.200000</td>\n",
              "      <td>20.500000</td>\n",
              "      <td>25.800000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>488.000000</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-02132daa-e83f-40e9-bf37-a36b87350382')\"\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-02132daa-e83f-40e9-bf37-a36b87350382 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-02132daa-e83f-40e9-bf37-a36b87350382');\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-8fd73b13-63e5-43af-89b7-1f7107747023\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fd73b13-63e5-43af-89b7-1f7107747023')\"\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-8fd73b13-63e5-43af-89b7-1f7107747023 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\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"MODELYEAR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 742.2999850849674,\n        \"min\": 0.0,\n        \"max\": 2014.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1067.0,\n          2014.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENGINESIZE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 376.04336222834814,\n        \"min\": 1.0,\n        \"max\": 1067.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.346298031865042,\n          3.4,\n          1067.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CYLINDERS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 375.2046584534259,\n        \"min\": 1.7974472750409691,\n        \"max\": 1067.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.794751640112465,\n          6.0,\n          1067.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_CITY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 372.753722580423,\n        \"min\": 4.1012533170683385,\n        \"max\": 1067.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13.296532333645736,\n          12.6,\n          1067.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_HWY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 374.0044761078589,\n        \"min\": 2.7945104498854083,\n        \"max\": 1067.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.474601686972822,\n          8.8,\n          1067.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_COMB\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 373.32274511507654,\n        \"min\": 3.485594849634841,\n        \"max\": 1067.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.580880974695408,\n          10.9,\n          1067.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_COMB_MPG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 368.34737387553685,\n        \"min\": 7.468701989863615,\n        \"max\": 1067.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          26.441424554826618,\n          26.0,\n          1067.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CO2EMISSIONS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 319.7089899224837,\n        \"min\": 63.37230444280006,\n        \"max\": 1067.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          256.2286785379569,\n          251.0,\n          1067.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Seleção das colunas de interesse\n",
        "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
        "cdf.head(9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "OqbhiER1sRFH",
        "outputId": "0365a581-78fa-4064-a674-c1cff144182a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
              "0         2.0          4                   8.5           196\n",
              "1         2.4          4                   9.6           221\n",
              "2         1.5          4                   5.9           136\n",
              "3         3.5          6                  11.1           255\n",
              "4         3.5          6                  10.6           244\n",
              "5         3.5          6                  10.0           230\n",
              "6         3.5          6                  10.1           232\n",
              "7         3.7          6                  11.1           255\n",
              "8         3.7          6                  11.6           267"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-335227eb-eaec-437a-ba55-08171c52c988\" 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>ENGINESIZE</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>CO2EMISSIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.4</td>\n",
              "      <td>4</td>\n",
              "      <td>9.6</td>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
              "      <td>5.9</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>11.1</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>10.6</td>\n",
              "      <td>244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>10.1</td>\n",
              "      <td>232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3.7</td>\n",
              "      <td>6</td>\n",
              "      <td>11.1</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3.7</td>\n",
              "      <td>6</td>\n",
              "      <td>11.6</td>\n",
              "      <td>267</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-335227eb-eaec-437a-ba55-08171c52c988')\"\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-335227eb-eaec-437a-ba55-08171c52c988 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-335227eb-eaec-437a-ba55-08171c52c988');\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-a866a14f-3403-42da-b475-0427c1d11778\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a866a14f-3403-42da-b475-0427c1d11778')\"\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-a866a14f-3403-42da-b475-0427c1d11778 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": "cdf",
              "summary": "{\n  \"name\": \"cdf\",\n  \"rows\": 1067,\n  \"fields\": [\n    {\n      \"column\": \"ENGINESIZE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.415895051424066,\n        \"min\": 1.0,\n        \"max\": 8.4,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          3.4,\n          6.4,\n          4.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CYLINDERS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 12,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FUELCONSUMPTION_COMB\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.485594849634841,\n        \"min\": 4.7,\n        \"max\": 25.8,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          6.3,\n          16.1,\n          7.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CO2EMISSIONS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63,\n        \"min\": 108,\n        \"max\": 488,\n        \"num_unique_values\": 159,\n        \"samples\": [\n          366,\n          108,\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotagem das variáveis selecionadas\n",
        "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
        "viz.hist()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "lGpYnTMEscUf",
        "outputId": "081eba09-6203-4c9d-b5ed-a15d126a051b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlação entre variáveis selecionadas em relação à emissão de Co2\n",
        "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Consumo de Combustível\")\n",
        "plt.ylabel(\"Emissão de CO2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "t26VqmDbtD_T",
        "outputId": "3d54a53d-245c-42d1-8e36-fab37798747e"
      },
      "execution_count": 10,
      "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": [
        "# @title Correlação entre Emissão de CO2 (variável depedente) e Tamanho do Motor (variável idependente)\n",
        "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='green')\n",
        "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Tamanho do motor\")\n",
        "plt.ylabel(\"Emissão de CO2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "w5y8gOAetP9z",
        "outputId": "225f5d70-36d0-4aa3-8c4f-ac4944e4366f"
      },
      "execution_count": 11,
      "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": [
        "# @title Correlação entre CILINDRADAS e Emissão CO2\n",
        "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
        "plt.xlabel(\"Cilindros\")\n",
        "plt.ylabel(\"Emissão de CO2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "J5DsDM4JuGQu",
        "outputId": "42473265-c407-4a2e-b25b-f42eca4e9371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Modelando os conjuntos de dados de treinamento e teste**\n",
        "\n",
        "A divisão Treino/Teste consiste em separar o conjunto de dados em dois subconjuntos mutuamente exclusivos: um para treinamento e outro para teste.\n",
        "\n",
        "O modelo é treinado utilizando o conjunto de treinamento e posteriormente avaliado com o conjunto de teste. Essa abordagem permite obter uma avaliação mais precisa da performance do modelo em novos dados, pois o conjunto de teste é independente e não foi utilizado durante o treinamento.\n",
        "\n",
        "Ao utilizar dados nunca vistos (conjunto de teste), podemos medir de maneira confiável a capacidade de generalização do modelo. Como o resultado de cada ponto de dados no conjunto de teste é conhecido, este método se mostra satisfatório para avaliar o modelo. Como esses dados não foram incluídos no treinamento, o modelo não possui informações prévias sobre eles, proporcionando assim uma avaliação fora da amostra genuína.\n",
        "\n",
        "O conjunto de dados será dividido de forma que:\n",
        "\n",
        "* 80% dos dados sejam utilizados para treinamento.\n",
        "* 20% dos dados sejam reservados para teste.\n",
        "\n",
        "Para realizar uma divisão confiável, uma máscara será criada para selecionar aleatoriamente linhas, utilizando-se a função `np.random.rand()` da biblioteca NumPy. Essa função permite uma seleção aleatória eficiente, garantindo que os dados sejam distribuídos de forma adequada entre os dois subconjuntos."
      ],
      "metadata": {
        "id": "rveFrmnzFUJr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cria um conjunto de dados de treinamento (0.8) e teste (0.2), a função np.random.rand() seleciona linhas aleatórias\n",
        "msk = np.random.rand(len(df)) < 0.8\n",
        "train = cdf[msk]\n",
        "test = cdf[~msk]"
      ],
      "metadata": {
        "id": "R0hrvlfVvRgO"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Modelo de Regressão Simples**\n",
        "\n",
        "A Regressão Linear ajusta um modelo linear com coeficientes B = (B1, ..., Bn) para minimizar a \"soma dos quadrados dos resíduos\" entre o valor real *y* no conjunto de dados e o valor previsto *ŷ* usando aproximação linear.\n",
        "\n",
        "A regressão linear encontra a melhor linha reta (modelo linear) que representa a relação entre os dados, minimizando a diferença entre os valores reais e os valores previstos pela linha. Essa diferença é chamada de \"resíduo\", e a regressão linear busca minimizar a soma dos quadrados desses resíduos."
      ],
      "metadata": {
        "id": "rQshxldZGVws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Treina o modelo com base na distribuição dos dados\n",
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Tamanho do Motor\")\n",
        "plt.ylabel(\"Emissões de CO2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "VbVzfhiYwDGF",
        "outputId": "19a5170f-209b-4907-9d72-7955a1dad482"
      },
      "execution_count": 15,
      "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": [
        "# Modelo de regressão linear usando o Scikit-learn\n",
        "regr = linear_model.LinearRegression()\n",
        "# Definição da variável preditora (X) e a variável alvo (y)\n",
        "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
        "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
        "# Ajuste do modelo de regressão aos dados\n",
        "regr.fit(train_x, train_y)\n",
        "# Exibindo os coeficientes do modelo\n",
        "print ('Coeficientes: ', regr.coef_)\n",
        "print ('Intercepto: ',regr.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qM0OdlW3spH2",
        "outputId": "eb863ad3-c160-4426-91e1-231515dbbdf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes:  [[39.00738216]]\n",
            "Intercepto:  [125.08438849]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Encontrando a \"Melhor Reta\"**\n",
        "\n",
        "A \"melhor reta\" é aquela que minimiza a distância entre os pontos de dados e a reta de regressão.\n",
        "\n",
        "Matematicamente, a regressão linear simples modela a relação entre uma variável dependente (Y) e uma independente (X) através de uma equação linear: Y = θ<sub>0</sub> + θ<sub>1</sub>X, onde θ<sub>0</sub> é o intercepto e θ<sub>1</sub> é a inclinação da reta. O objetivo é encontrar os valores ótimos para θ<sub>0</sub> e θ<sub>1</sub> que minimizem o erro entre os valores previstos e os reais.\n",
        "\n",
        "Matematicamente, isso se traduz em minimizar o Erro Quadrático Médio (MSE).  Existem métodos matemáticos e de otimização para encontrar essa reta, incluindo a minimização do MSE através do cálculo dos coeficientes θ<sub>0</sub> e θ<sub>1</sub>.\n",
        "\n",
        "Numa regressão linear simples, o coeficiente e o intercepto são os parâmetros da linha de ajuste. O Scikit-learn percorre os dados e estima os parâmetros diretamente."
      ],
      "metadata": {
        "id": "Jg1L5vvpt6Sj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Regressão linear com linha de ajuste (de tendência)\n",
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
        "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r', label=\"Linha de Regressão\")\n",
        "plt.grid()\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.xlabel(\"Tamanho do Motor\")\n",
        "plt.ylabel(\"Emissão de CO2\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "7kUOyOZbuYbS",
        "outputId": "7bd07ff2-09c3-4606-b20e-ddbd47d40123"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Métricas de Avaliação em Modelos de Regressão**\n",
        "\n",
        "As métricas de avaliação medem o desempenho do modelo, comparando os valores previstos com os valores reais.\n",
        "\n",
        "1. **Mean Absolute Error (MAE)**\n",
        "\n",
        "  O Erro Médio Absoluto (MAE) é a média das diferenças absolutas entre os valores reais (𝑦<sub>𝑗</sub>) e os valores previstos (ŷ<sub>𝑗</sub>).\n",
        "\n",
        "  Fórmula:\n",
        "\n",
        "  $$ MAE = \\frac{1}{n} \\sum_{j=1}^{n} |y_j - \\hat{y}_j| $$\n",
        "\n",
        "  Indica o quão próximos os valores previstos estão dos valores reais em média. Sua escala é na mesma unidade dos dados de entrada. É robusto contra grandes erros individuais (outliers), pois não eleva o erro ao quadrado.\n",
        "\n",
        "\n",
        "2. **Mean Squared Error (MSE)**\n",
        "\n",
        "  O Erro Quadrático Médio (MSE) calcula a média dos quadrados das diferenças entre os valores reais e os valores previstos.\n",
        "\n",
        "  Fórmula:\n",
        "\n",
        "  $$ MSE = \\frac{1}{n} \\sum_{i=1}^n (y_i - \\hat{y}_i)^2 $$\n",
        "\n",
        "  Penaliza erros maiores devido ao quadrado da diferença, tornando-o mais sensível a *outliers*.\n",
        "Sua unidade está ao quadrado em relação aos dados de entrada. É muito usado em otimização de modelos pois é diferenciável e pode ser usado com gradientes.\n",
        "\n",
        "3. **Relative Squared Error (RSE)**\n",
        "\n",
        "  O Erro Quadrático Relativo é uma extensão do RAE, mas utiliza o quadrado das diferenças.\n",
        "\n",
        "  Fórmula:\n",
        "\n",
        "  $$ RSE = \\frac{\\sum_{j=1}^n (y_j - \\bar{y})^2}{\\sum_{j=1}^n (y_j - \\hat{y}_j)^2} $$\n",
        "\n",
        "  Mede quão próximo o modelo está da variância dos valores reais, fornecendo uma comparação direta com a variabilidade da média dos dados.\n",
        "\n",
        "4. **Coeficiente de Determinação** ($R^2$)\n",
        "\n",
        "  É uma métrica que indica a proporção da variância nos valores reais explicada pelo modelo.\n",
        "  \n",
        "  Fórmula: $$ R^2 = 1 - RSE $$\n",
        "\n",
        "  Mede a proporção da variância explicada pelo modelo, quanto maior o $R^2$, melhor o ajuste do modelo. Intervalo: $R^2 \\in (-\\infty, 1]$\n",
        "\n",
        "  * $R^2 = 1$: O modelo explica perfeitamente a variância.\n",
        "  * $R^2 = 0$: O modelo não explica nenhuma variância além da média.\n",
        "  * $R^2 < 0$: O modelo é pior que uma previsão simples (usando a média).\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ADz_6sv0oXS5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
        "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
        "test_y_ = regr.predict(test_x)\n",
        "\n",
        "print(\"Erro Absoluto Médio (MAE): %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
        "print(\"Erro Quadrático Médio (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
        "print(\"R-quadrado: %.2f\" % r2_score(test_y , test_y_) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_hVfqzQvYK7",
        "outputId": "fb87105f-bec3-4c24-dd68-5f3eb0312426"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erro Absoluto Médio (MAE): 21.84\n",
            "Erro Quadrático Médio (MSE): 806.16\n",
            "R-quadrado: 0.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Treina o modelo de regressão linear usando o train_x e o train_y\n",
        "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
        "test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
        "regr = linear_model.LinearRegression()\n",
        "regr.fit(train_x, train_y)\n",
        "# Encontra as previsões usando a função de previsão do modelo e os dados test_x\n",
        "predictions = regr.predict(test_x)\n",
        "# Calcula o valor do erro médio absoluto usando as funções np.mean e np.absolute\n",
        "print(\"Erro Absoluto Médio (MAE): %.2f\" % np.mean(np.absolute(predictions - test_y)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyib7UkLx8up",
        "outputId": "346dd19c-e88d-42b7-efb2-1d1c7bc5ca33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erro Absoluto Médio (MAE): 18.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Treina o modelo de regressão usando a variável/feature FUELCONSUMPTION_COMB e apresenta as métricas de avaliação\n",
        "\n",
        "train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\n",
        "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
        "regr = linear_model.LinearRegression()\n",
        "regr.fit (train_x, train_y)\n",
        "print ('Coeficientes: ', regr.coef_)\n",
        "print ('Intercepto: ',regr.intercept_)\n",
        "\n",
        "plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS,  color='blue')\n",
        "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
        "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
        "plt.ylabel(\"Emission\")\n",
        "\n",
        "test_x = np.asanyarray(test[['FUELCONSUMPTION_COMB']])\n",
        "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
        "test_y_ = regr.predict(test_x)\n",
        "\n",
        "print(\"Erro Absoluto Médio (MAE): %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
        "print(\"Erro Quadrático Médio (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
        "print(\"R-quadrado: %.2f\" % r2_score(test_y , test_y_) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "G_02ykTswKiF",
        "outputId": "5f91aa8e-aebf-47ca-e36c-7228625fb590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes:  [[16.39678774]]\n",
            "Intercepto:  [66.01182832]\n",
            "Erro Absoluto Médio (MAE): 18.99\n",
            "Erro Quadrático Médio (MSE): 748.25\n",
            "R-quadrado: 0.80\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABnkUlEQVR4nO3de1xUZeIG8GcYLgoKCsZN8Ya3TMxSM+pnWZqo5CWw1TS1zSwVS7zhurmZtqut5q3SbLtobampYaVbKpqiJZmpJJlLylqigpgKCCrIcH5/vM0whxlg5jAzZy7P9/OZD8w755x5hxHn4b1qJEmSQEREROSmvNSuABEREZE9MewQERGRW2PYISIiIrfGsENERERujWGHiIiI3BrDDhEREbk1hh0iIiJya95qV8AZVFZW4sKFC2jcuDE0Go3a1SEiIiILSJKEa9euITIyEl5eNbffMOwAuHDhAqKiotSuBhERESmQm5uLFi1a1Pg4ww6Axo0bAxA/rMDAQJVrQ0RERJYoLi5GVFSU4XO8Jgw7gKHrKjAwkGGHiIjIxdQ1BIUDlImIiMitqRp2Xn75ZWg0GtmtU6dOhsdv3ryJpKQkhISEoFGjRkhMTMTFixdl1zh79izi4+Ph7++P0NBQzJo1CxUVFY5+KUREROSkVO/GuuOOO7B7927DfW/vqipNmzYN//nPf7B582YEBQVhypQpSEhIwLfffgsA0Ol0iI+PR3h4OA4ePIi8vDyMHTsWPj4+WLhwocNfCxERETkf1cOOt7c3wsPDTcqLiorw3nvvYf369Xj44YcBAGvXrsXtt9+O7777Dvfeey927dqFn3/+Gbt370ZYWBi6deuGV155BbNnz8bLL78MX19fs89ZVlaGsrIyw/3i4mL7vDgiIiJSnepjdk6dOoXIyEi0bdsWo0ePxtmzZwEAR44cwa1bt9CvXz/DsZ06dULLli2RkZEBAMjIyEBMTAzCwsIMx8TFxaG4uBgnTpyo8TkXLVqEoKAgw43TzomIiNyXqmGnV69eWLduHXbs2IG33noLZ86cQe/evXHt2jXk5+fD19cXTZo0kZ0TFhaG/Px8AEB+fr4s6Ogf1z9Wkzlz5qCoqMhwy83Nte0LIyIiIqehajfWwIEDDd937doVvXr1QqtWrbBp0yY0bNjQbs/r5+cHPz8/u12fiIiInIfq3VjGmjRpgg4dOuD06dMIDw9HeXk5CgsLZcdcvHjRMMYnPDzcZHaW/r65cUBERETkeZwq7JSUlCAnJwcRERHo3r07fHx8sGfPHsPj2dnZOHv2LGJjYwEAsbGxyMrKQkFBgeGYtLQ0BAYGonPnzg6vPxERETkfVbuxZs6cicGDB6NVq1a4cOEC5s2bB61WiyeeeAJBQUEYP348pk+fjuDgYAQGBuL5559HbGws7r33XgBA//790blzZ4wZMwaLFy9Gfn4+5s6di6SkJHZTEREREQCVw865c+fwxBNP4PLly7jtttvwf//3f/juu+9w2223AQCWL18OLy8vJCYmoqysDHFxcVi9erXhfK1Wi+3bt2PSpEmIjY1FQEAAxo0bhwULFqj1koiIyAnodMCBA0BeHhARAfTuDWi1ateK1KKRJElSuxJqKy4uRlBQEIqKirg3FhGRi0tNBaZOBc6dqypr0QJYuRJISFCvXmR7ln5+O9WYHSIiovpITQWGD5cHHQA4f16Up6aqUy+PVlkJ3LypahUYdoiIyC3odKJFx1x/hb4sOVkcRw4yd67oP2zRAjDaucDRVN8ugoiIyBYOHDBt0TEmSUBurjiuTx+HVcszFRQAxov+Xr6sXl3Alh0iInITeXm2PY4UWrxYHnQA8UNXcZY0W3aIiMgtRETY9jiy0tWrQHCwvGzWLBF+VMaWHSIicgu9e4uhIRqN+cc1GiAqShxHNrZqlWnQ+e03pwg6AMMOERG5Ca1WTC8HTAOP/v6KFVxvx6auXRM/3ClTqsqee04MkGrZUr16VcOwQ0REbiMhAdiyBWjeXF7eooUo5zo7NvTBB0D1tW1++QVYs0ad+tSCY3aIiMitJCQAQ4dyBWW7uXEDaNIEKC+vKnviCWD9etWqVBeGHSIicjtaLaeX28XmzcCf/iQvy8oCunRRpz4WYtghIiKi2pWXi77AS5eqygYNArZvr3lEuBPhmB0iIiKq2ZdfijVyjIPO4cPAf/7jEkEHYMsOERERmaPTAZ06AadPV5Xddx/wzTcuE3L02LJDREREcvv2Ad7e8qCzfz/w7bcuF3QAtuwQERGRXmUl0KsX8MMPVWV33AH8+KNLT2djyw4REREB330nAo1x0NmxA/jpJ5cOOgBbdoiIiDybJAGPPALs2VNV1qIF8L//AT4+6tXLhtiyQ0RE5Kl+/BHw8pIHna1bgdxctwk6AFt2iIiIPFNiIpCaWnU/MBAoKBDTzN0MW3aIiIg8yX//K2ZUGQedjz4CiorcMugAbNkhIiLyHH/+M7BunbyspAQICFClOo7Clh0iIiJ3d+aMaM0xDjpvvy0GJ7t50AHYskNEROTekpOBlSvlZYWFQFCQGrVRBVt2iIiI3NH586I1xzjoLF0qWnM8KOgADDtERETu56WXxFo5xn7/HZg+XZ36qIxhh4iIyF1cuiRac155paps/nzRmhMSol69VMYxO0RERO5g8WJg9mx52YULQESEOvVxIgw7RERErqywEGjaVF42Ywbw2muqVMcZsRuLiIjIVb31lmnQ+fVXBp1q2LJDRETkakpKgMaN5WUTJgD/+pc69XFybNkhIiJyJR9+aBp0srMZdGrBlh0iIiJXcPOm6LK6ebOqbMQIYONG9erkItiyQ0RE5Ow+/RRo2FAedI4fZ9CxkNOEnVdffRUajQbJycmGsj59+kCj0chuEydOlJ139uxZxMfHw9/fH6GhoZg1axYqKiocXHsiIiI7KC8HwsKA4cOrygYMACorgZgY9erlYpyiG+vw4cN4++230bVrV5PHJkyYgAULFhju+/v7G77X6XSIj49HeHg4Dh48iLy8PIwdOxY+Pj5YuHChQ+pORERkF199BQwaJC/7/nugZ0916uPCVG/ZKSkpwejRo/HOO++gafXpcxDhJjw83HALDAw0PLZr1y78/PPP+Oijj9CtWzcMHDgQr7zyClatWoXy8vIan7OsrAzFxcWyGxERkVPQ6YCOHeVBJzZWtOYw6CiiethJSkpCfHw8+vXrZ/bxjz/+GM2aNUOXLl0wZ84cXL9+3fBYRkYGYmJiEBYWZiiLi4tDcXExTpw4UeNzLlq0CEFBQYZbVFSU7V4QERGRUvv2Ad7ewC+/VJWlpwMHD4ptIEgRVbuxNm7ciKNHj+Lw4cNmHx81ahRatWqFyMhIHD9+HLNnz0Z2djZSU1MBAPn5+bKgA8BwPz8/v8bnnTNnDqYbbYZWXFzMwENEROqprBStN99/X1XWubMYhKzVqlcvN6Fa2MnNzcXUqVORlpaGBg0amD3m2WefNXwfExODiIgI9O3bFzk5OYiOjlb83H5+fvDz81N8PhERkc0cOgTce6+8bMcOIC5Onfq4IdW6sY4cOYKCggLcfffd8Pb2hre3N9LT0/H666/D29sbOp3O5JxevXoBAE6fPg0ACA8Px8WLF2XH6O+Hh4fb+RUQERHVgyQBjzwiDzrNm4sZWAw6NqVa2Onbty+ysrKQmZlpuPXo0QOjR49GZmYmtGaa7TIzMwEAEX/s4BobG4usrCwUFBQYjklLS0NgYCA6d+7skNdBRERktePHAS8vYPfuqrLUVODcOcDHR716uSnVurEaN26MLl26yMoCAgIQEhKCLl26ICcnB+vXr8egQYMQEhKC48ePY9q0aXjggQcMU9T79++Pzp07Y8yYMVi8eDHy8/Mxd+5cJCUlsZuKiIic0/DhYpFAvUaNgEuXgBqGdFD9qT4bqya+vr7YvXs3+vfvj06dOmHGjBlITEzEtm3bDMdotVps374dWq0WsbGxePLJJzF27FjZujxEREROITtbzKgyDjoffQRcu8agY2caSZIktSuhtuLiYgQFBaGoqEi2jg8RkSfT6YADB4C8PCAiAujdmxODFBs/Hnj/fXlZSQkQEKBOfdyEpZ/fTtuyQ0RE6klNBVq3Bh56CBg1Snxt3VqUkxV+/VW05hgHnTVrxOBkBh2HYdghIiKZTz4BEhPFWFlj58+L4SYMPBaaPh1o00ZeVlgIPPecKtXxZAw7RERkMGsWMHKk+cf0gx6Sk0UXF9XgwgXRmrN8eVXZkiXiBxgUpF69PBjDDhERAQBmzABee632YyQJyM0VY3nIjHnzxFo5xi5dAmbOVKc+BIBhh4iIIHpcli2z/Pi8PPvVxSX9/rtozTGeDTxvnkiHzZqpVy8CoPLeWEREpL6UFHmPiyX+WNuVANEcNmuWvOz8eSAyUp36kAmGHSIiD1Zebl2LDgBERYlp6B6vqAho0kReNn06sHSpKtWhmrEbi4jIg61ebf1g4xUruN4O1qwxDTpnzjDoOCm27BARebCcHOuO37gRSEiwT11cQmmp2N7B2DPPAO+8o059yCJs2SEi8mDR0ZYfO3MmMGKE/eri9P79b9Ogk53NoOMCGHaIiDzY5MmWdUlNny6WivFIN28C/v7A2LFVZY8/LmZadeigXr3IYgw7REQezNdXBJnaTJvmwUNRUlOBhg2BGzeqyn78Edi0Sb06kdU4ZoeIyMMtXiy+LlsmH6ys1YogpH/co9y6BbRsCeTnV5X17w/s2CHW0yGXwl3PwV3Pich9lZeLGVc5OWJ8zuTJojWnvse6tR07gIED5WWHDgH33KNOfahGln5+M+yAYYeI3FNKCltrrKLTAV26AP/9b1XZvfcC334LeHHUhzOy9POb7x4RkRuaNk0MKK6+ho5OJ8pTUtSpl9NKTwe8veVBZ98+ICODQccN8B0kInIzgweLhf9qs2yZ6LbyeJIkWm/69Kkqu/12oKICePBB1apFtsWwQ0TkRoYNA7Zvr/s4nU6Mz/Fo338vWm0OHaoq++or4OefuUS0m+FsLCIiN3HjBvD555Yfb+3qyW5DkoABA4Bdu6rKIiKA334DfHzUqxfZDVt2iIhc3I0bwJQp1q9vZ83qyW7j+HHRmmMcdD79FLhwgUHHjbFlh4jIhQ0bZl1rjp5WK6aWe5QRI+SLAfr7A5cvAw0aqFcnN6fTAQcOAHl5ovGsd291egjZskNE5KKUBh1ATD/3mDV0fvlFLARoHHQ+/FBs6smgYzepqUDr1sBDDwGjRomvrVuLckdj2CEickHWjs8xlpzsQevsTJgAdOwoL7t2DRgzRp36eIgtW4DERODcOXn5+fPA8OGODzwMO0RELmjWLGXnPfoosHy5bevilH79VbTmvPtuVdnq1WJwcvWdy8mmNm8GRo40/5h+GePkZNM1oOyJYYeIyMWUlwO7d1t/3tChwLZttq+P05kxA2jTRl529SowaZI69fEgqanAn/5Ue5CRJCA3V4zlcRSGHSIiF3HjBnDXXYCfH5Cdbdk5LVoASUnA9evAZ5/ZtXrqy8sTrTnLllWVLV4sPl2bNFGtWp5CpwOmTrX8+Lw8+9WlOs7GIiJyAUoHI//yC9Cwoc2r43xefhmYP19edukS0KyZKtXxRAcOmI7RqU1EhP3qUh3DDhGRk1MadIYO9YCg8/vvwG23ycteesk0+JDdWdNSExUlpqE7CsMOEZETUzrrauhQD+i2WroUmDlTXnb+PBAZqU59PJw1LTUrVjh2vR2GHSIiJ2btrKsuXcSWT27dolNUZDoGJznZQ6aZOa/evcUYsfPnq2ZdVafVAhs3AgkJjq0bBygTETkpnU6+R6Ulxo9386Dz9tumQefMGQYdJ6DVAitXiu81GvPHbNgg1tlxNIYdIiIno9MBCxYAoaHADz9Yfp5bbwFRWio+QSdOrCp7+mnRhNC6tWrVIrmEBLGgYPPm8vKoKLEF2eOPq1MvdmMRETmR1FTg2WfFlk3WctstID76yHTF4//+13RlZHIKCQlizJgz7Iml5zQtO6+++io0Gg2Sk5MNZTdv3kRSUhJCQkLQqFEjJCYm4uLFi7Lzzp49i/j4ePj7+yM0NBSzZs1CRUWFg2tPRFR/qamiiV9J0Jk1yw23gLh5U6x2bBx0EhNFaw6DjlPTaoE+fYAnnhBf1Qw6gJOEncOHD+Ptt99G165dZeXTpk3Dtm3bsHnzZqSnp+PChQtIMBrVpNPpEB8fj/Lychw8eBAffPAB1q1bh5deesnRL4GIqF70C7LVNLCzJl26AGVlbhh0tm4Vg49KS6vKMjNFHwmRlVQPOyUlJRg9ejTeeecdNG3a1FBeVFSE9957D8uWLcPDDz+M7t27Y+3atTh48CC+++47AMCuXbvw888/46OPPkK3bt0wcOBAvPLKK1i1ahXKy8trfM6ysjIUFxfLbkREarJ2QbZHHhGrImdluVnX1a1bYsCH8XSdRx4BKiuBO+9Ur17k0lQPO0lJSYiPj0e/fv1k5UeOHMGtW7dk5Z06dULLli2RkZEBAMjIyEBMTAzCwsIMx8TFxaG4uBgnTpyo8TkXLVqEoKAgwy0qKsrGr4qIyDrWLp3/17+64ayrnTtFcrtwoarsu++AXbtqnt5DZAFVByhv3LgRR48exeHDh00ey8/Ph6+vL5pUm2IYFhaG/Px8wzHGQUf/uP6xmsyZMwfTp0833C8uLmbgISJVWbMgm6NXn7U7nQ6IiQFOnqwqu+ceICMD8FL9b3JyA6qFndzcXEydOhVpaWlo0KCBQ5/bz88Pfn5+Dn1OIiJj5eXA6tVATg4QHQ0891zdC7LpOXr1Wbs6cAB44AF52b59wIMPqlIdck+qReYjR46goKAAd999N7y9veHt7Y309HS8/vrr8Pb2RlhYGMrLy1FYWCg77+LFiwgPDwcAhIeHm8zO0t/XH0NE5GxSUgB/f2DaNODNN8XXxo2B7t3F4zX12ISEiLVKHL36rF1IEhAbKw86HToAFRUMOmRzqoWdvn37IisrC5mZmYZbjx49MHr0aMP3Pj4+2LNnj+Gc7OxsnD17FrGxsQCA2NhYZGVloaCgwHBMWloaAgMD0blzZ4e/JiKiuqSkAEuWiJ4bYzqd2ANryBDTBdlCQsS+lhcvuknQ+eEH0T31x2QTAMCXXwLZ2W7UZEXORLVurMaNG6NLly6ysoCAAISEhBjKx48fj+nTpyM4OBiBgYF4/vnnERsbi3vvvRcA0L9/f3Tu3BljxozB4sWLkZ+fj7lz5yIpKYndVETkVMrLgddfF0GnNtu3A9euiW0inGVBNpuRJGDQIGDHjqqysDAgNxfw8VGvXuT2nHoF5eXLl8PLywuJiYkoKytDXFwcVq9ebXhcq9Vi+/btmDRpEmJjYxEQEIBx48ZhwYIFKtaaiEguJQVYtsy0NcccnU5s/2S0vqp7yMoCqq2lhi1bxCKBRHamkSRrl7ByP8XFxQgKCkJRURECAwPVrg4RuRF9t5U1pkwB3njDPvVRxciRwCefVN1v0AC4elV8JaoHSz+/OaePiMhOystFi461oqNtXxdVnDolRlsbB50PPgBu3GDQIYdy6m4sIiJX9txzlnVdGXObncufew7417/kZdeuib2uiByMLTtERHaQmgqsW2f9eS6/c/lvv4nWHOOgs2qVGJzMoEMqYdghIrKhGzdEy8yoUdadp9W6wc7lM2cCrVvLy65edZOmKnJl7MYiIrKRYcPEWjnW0GhEwHnhBRdu0cnLAyIj5WX//KcYnU3kBBh2iIjqKTsb6NRJ2bkzZ4qby1qwAJg3T15WUADcdps69SEyg2GHiKge6rMZ94gRLtxtdfky0KyZvGzuXOCVV9SpD1EtGHaIiBSqT9Bp3hz4+GPb1cWhli8XI6mNnTtnus8FkZNg2CEiUiA7W9l5+oD0+usuuAVEURHQpIm8bOpUsQ07kRPjbCwiIgVuv13ZeS1aiF0SXG5Dz3/9yzTo/O9/DDrkEtiyQ0RkpZQUsWyMtXbsAPr1c7EWndJS0/Vx/vxn4P331akPkQJs2SEislB5udjnytq9rgBg6FAgLs7Fgs769aZB5+RJBh1yOWzZISKygDU7l1c3dCjw2Wc2r5L9lJWJqePXrlWVJSQAn36qXp2I6oFhh4ioDkp2Lte7fh1o2NC29bGrzz4DHntMXnbsGNCtmxq1IbIJhh0ioloo3bkcUDauRzW3bgFt24op5Hr9+gG7dtVvjj2RE+CYHSKiWijZuRwQPUEuY9cusVeFcdDJyADS0hh0yC2wZYeIqAYbNijbuXzWLBfZ50qnA+68EzhxoqqsRw/g0CHAi38L24NOBxw4ILYTi4gAevd2sUHrLophh4iomvJy4O675RnAElqtWFjYJbaA+OYb8UlrbO9eoE8fVarjCVJTxRqMxg1oLVoAK1e64LpLLobRnYjISEoK4OdnXdDR71x+/boLBB1JAu6/Xx502rcHKioYdOwoNRUYPlwedADg/HlRnpqqTr08BcMOEdEflM66mjnTRbqufvhBdE8dPFhV9p//AL/8wr4UO9LpRIuOuQHr+rLkZGVjw8gyDDtERABKSoDXXrP+PJfYuVySgEGDgJ49q8pCQ8Uo6kGD1KuXhzhwwLRFx5gkAbm54jiyD4YdIvJ4KSlAYKD1U8VdYufyn34SrTlffVVVtnkzcPGiCzRFuZ6SErFMUdeu4mtJiRiMbAlLjyPrcYAyEXm0qVPFDuRKOP3O5U88AWzcWHXfzw+4etXFVjl0HffcAxw+XHU/Kwto3Bjo1Mmy8yMi7FMvYtghIg/Wowdw5Iiyc9evd+IZNKdOAR06yMvWrQPGjVOlOp6gZ08xJMqc//4X8PERY8DNtR5qNGJWVvXJcWQ7DDtE5JHuugvIzFR27pAhotHEKU2cCLz9trzs2jXTDT3JZtavrzno6N26Jb5qNPLAo1+zccUKJ28ldHEcs0NEHicsTHnQGToU+Pxzm1bHNs6eFZ+cxkHnzTfFJyuDjt2kpgKjR1t2bM+eYpyXsRYtgC1bnLiV0E2wZYeIPEp4OFBQYP153bqJGdtOOdzF3Jz5K1eApk3VqY+H0E8pt9TNm8Cvv3IFZTUw7BCRxxg4UExCsoZGAxQXO2njSH6+6ajWV18FZs9Wpz4epq4p5dVFR4tgw7UbHY9hh4g8wqBBwI4d1p83c6aTBp1XXgFeekledvGiWD+HbKq8HFi9GsjJEYFl8mQxa9/aqeL//rd96kd1Y9ghIrcXHQ3873/Wn/fCC064YODly0CzZvKyF18E/v53derj5lJSgGXL5Ksbz5wp9kCzZj3Gnj2dNDR7CIYdInJr7dopCzrdu4sNGp3KihXAtGnysnPnTEe9kk3UtH2ITifKKyvFAOPz52tfkLJHD+D77+1XT6obZ2MRkdv6979F14O1unWreyqxQxUXi8FDxkHn+efFJyyDjl2Ul4sWndqsWFG1xYh+Cnl1H38sX2iQ1MGwQ0RuafNmYOxY688LDQWOHbN9fRR75x0gKEhelpOjfNlnqpNOJ7qp6tqYU6cT43a2bDHNnFFRwKefAqNG2a+eZDlVw85bb72Frl27IjAwEIGBgYiNjcVXRvu39OnTBxqNRnabOHGi7Bpnz55FfHw8/P39ERoailmzZqGiosLRL4WInMiWLcoW/QsLs362lt1cvy6aC559tqps3DjRmtO2rXr1cnOpqUDr1sCqVZYdn5Mj1sj59Vdg716xwODevcCZM1w7x5moOmanRYsWePXVV9G+fXtIkoQPPvgAQ4cOxbFjx3DHHXcAACZMmIAFCxYYzvH39zd8r9PpEB8fj/DwcBw8eBB5eXkYO3YsfHx8sHDhQoe/HiJS15UrogsqN9f6cwcMkO+VqaoNG0ybBH7+Gbj9dnXq4yFSU4Hhw63bEDY6WnzllHLnppEka/f5ta/g4GAsWbIE48ePR58+fdCtWzesWLHC7LFfffUVHn30UVy4cAFhYWEAgDVr1mD27Nm4dOkSfC3c0be4uBhBQUEoKipCYGCgrV4KETlQeLjyVpmBA4Evv7RtfRQpKxP9aMXFVWWPPSY+hcmmdDr54n733SeCizXr5mi1ogGOm8erx9LPb6cZs6PT6bBx40aUlpYiNjbWUP7xxx+jWbNm6NKlC+bMmYPr168bHsvIyEBMTIwh6ABAXFwciouLceLEiRqfq6ysDMXFxbIbEbmu+gSdtm2dJOh8/jnQoIE86Bw7xqBjB/quqoceEg1oDz0kZlVZE3QAMa6HQcc1qD71PCsrC7Gxsbh58yYaNWqErVu3onPnzgCAUaNGoVWrVoiMjMTx48cxe/ZsZGdnI/WPX/78/HxZ0AFguJ+fn1/jcy5atAjz58+30ysiIkeaMkV50ImOBk6ftm19rFZRIRKXcd/bww8Du3fXPMWHFDPXQwgAly5Zfg2tVgQdp1uDiWqketjp2LEjMjMzUVRUhC1btmDcuHFIT09H586d8azRwLyYmBhERESgb9++yMnJQbS+o1SBOXPmYPr06Yb7xcXFiIqKqtfrICLHS0mxfCBpdR9+CIwZY9v6WC0tDejfX16WkQHce6869XFzQ4cCX3xRv2skJYkp6WzRcS2qhx1fX1+0a9cOANC9e3ccPnwYK1euxNvGO/f+oVevXgCA06dPIzo6GuHh4fi+2kpNF//4Ey88PLzG5/Tz84Ofn5+tXgIRqcCSdVDM0WrFX/ePP277OlmsslKMpM7Kqiq7+26xIIuX04wucCtDhgDbtik/X6MRXV0rV3LjTlfkdL9VlZWVKCsrM/tYZmYmACDij43vYmNjkZWVhQKjLYzT0tIQGBho6AojIvdz5QrQsmXd66CYs3GjykHn22/Fp6Vx0NmzBzhyhEHHTqZNq3/QAcQiggw6rknVlp05c+Zg4MCBaNmyJa5du4b169dj37592LlzJ3JycrB+/XoMGjQIISEhOH78OKZNm4YHHngAXbt2BQD0798fnTt3xpgxY7B48WLk5+dj7ty5SEpKYssNkZtSOhi5eXOxDp9qa59IEvDAA8A331SVRUcD//0v4K16I7vbSkkRIcUazZoBv/9edb9FC3ENrpvjulT9DSsoKMDYsWORl5eHoKAgdO3aFTt37sQjjzyC3Nxc7N69GytWrEBpaSmioqKQmJiIuXPnGs7XarXYvn07Jk2ahNjYWAQEBGDcuHGydXmIyH3cdpv8Q8hSTZsCv/2m4l/lR46IDZKMbd8OxMerUx8PoaSrMypKDFo/eLBqWnrv3mzRcXVOt86OGrjODpHzCwwErl2z/rywMKCWyZn2JUnA4MHAf/5TVdasmdg5kiNc7c7cvql1+fRTtuC4EpdbZ4eIqCZBQcqCTlKSikHnxAkxBsc46HzyiZjjzKDjENZuArt+PYOOu2JHMRE5tW7d5OvsWUL1dVBGjxafnHo+PkBREdCwoUoV8kzWrFAyZIiy/dTINbAbC+zGInJWPXsCP/xg3TlhYcDZsyo1npw+DbRvLy97/33gz39WoTLur/qWD9XH1pSXA/7+dc/aGzy4/uvvkDrYjUVELu3jj60POoDYL1OVoDN5smnQKS5m0LETc1s+tG4t313D11e08NUmOZlBxxMw7BCR00lNBZ580vrzwsKA4GDb16dWubliIZa33qoqe+MNMTi5cWMHV8a96XTAvn1i0HFiouleVufPi13LjQPP4sXArFmms6m0WlG+fLndq01OgN1YYDcWkTPR6cRf6NZuytismXX7G9nE7NmmA4OuXBFz3cmmUlOBqVPr/nehX+n4zBnTLq3Vq8Wg5eho0RDHceKuz9LPbw5QJiKnoP8wSk+3Pug0buzgoHPxoljd0NjChcCcOQ6shOdITRUtNpb8aS5JorHtwAGgT5+qcl9f0WVFnolhh4hUl5IiFn9Tsv1DYKCY6OQw//gHYLS4KQARfkJDHVgJz6HTiRYda/sg8vLsUx9yTRyzQ0SqmjYNWLJEWdC5804HBp0rV0QfiXHQmTNHfAoz6NjNgQPWt/QBYnYWkR5bdohINfXZibpHD7FJuEOsXGnaB5KbKwaHkF1Z20KjH7PTu7d96kOuiS07RKSKRx9VHnQ++shBQae4WHx6GgedKVNEaw6DjkNY00LD3cmpJgw7RORwPXrId1GwVFSU2Lto9Gjb18nEe++JfSqM5eSIaeXkML17i1ypDzK1adEC2LKFWz6QKcXdWIWFhfj+++9RUFCAyspK2WNjx46td8WIyD317Ck2AbfGk08C48c7aPfp69eBRo3kI2LHjAE+/NDOT+xZLJ0KrtWKXsThw0XgMX5b9PeTk4GhQ7k7OdVM0To727Ztw+jRo1FSUoLAwEBojCK3RqPBlStXbFpJe+M6O0SOMXUq8Prr1p2j1Yr84ZA1UTZuNN0g6cQJoHNnBzy55zA3+66u/czMrbMTFSW6rNiS47ks/fxWFHY6dOiAQYMGYeHChfD3969XRZ0Bww6R/SkJOoBY5dbuG3qWlYl1cwoLq8qGDgW2brWs/4QsNnMmsHRpzY/X9n7XtRcWeR67hp2AgABkZWWhbdu29aqks2DYIbIvJRt6AqJ7wu7L+X/xhQg2xo4eBe66y85P7Hk++QQYObL2Yxzakkcuz64bgcbFxeEHJf9zEZHHueceZUFn8GA7B52KCqBNG3nQ6dMHqKxk0LGDzZvrDjqAaL1Zvdr+9SHPomiAcnx8PGbNmoWff/4ZMTEx8PHxkT0+ZMgQm1SOiFxbSYmyKeLx8XbeiXr3buCRR+RlBw8CsbF2fFLPtWWL6VCo2uTk2K8u5JkUhZ0JEyYAABYsWGDymEajgU7JUqhE5FaKioAmTaw/r3t3YPt2m1dH0LfaHD9eVXbXXaLpyYsrcdiSfrbVrl3AV19Zd250tH3qRJ5LUdipPtWciMhYu3bK/jq366rIBw8C998vL9u9G+jb105P6Lnqs9eZViumoRPZEv+UISKbKS8HmjVTFnReeMFOQUeSgAcflAedtm2BW7cYdOwgJUX5XmeAmH7Owclka4rDTnp6OgYPHox27dqhXbt2GDJkCA4cOGDLuhGRC0lJARo2BC5ftv7cF14QC8fZ3NGjontq//6qsi++EGnMm1sD2lp5uWjRUWrGDAcsM0AeSVHY+eijj9CvXz/4+/vjhRdewAsvvICGDRuib9++WL9+va3rSEROTv/XvJIe7h497BB0JElM5+revaosOFispzN4sI2fjPRWr1beorNxI/Daa7atD5GeonV2br/9djz77LOYNm2arHzZsmV45513cPLkSZtV0BG4zg6RcuXlQIMG8mX8LeXlpfzDsUY//wzccYe8bONGYMQIGz8RVff888Cbb1p3jlYLbNgAPP64fepE7s2u6+z873//w2Azfx0NGTIEZ86cUXJJInJRkZHKgg4A2Py/izFj5EHH21usUMeg4xBKZlFt3MigQ/anKOxERUVhz549JuW7d+9GVFRUvStFRK6hdWtlY3QAkUNatrRRRXJyxLYOH31UVfbee2IQcsOGNnoSqsvkyZZv36DfwX74cPvWiQhQOPV8xowZeOGFF5CZmYn77rsPAPDtt99i3bp1WGmXUYZE5EyuXAFCQpSf7+0tcohNJCWZLrlbXAw0bmyjJyBL+fqK2VRLltR8zKOPioHI3NeKHElR2Jk0aRLCw8OxdOlSbNq0CYAYx/PJJ59gaPU9ZojIrYSHAxcvKjtXowF+/dVGLTq5uaYXev11MXCEVKOfTWXtruZE9qRogLK74QBlIsuEhgKXLll/XvPmwIkTQFCQjSoyZw7w6qvyssuXxYwrcgr6FZRzcsRYnsmTuX4O2Z6ln99caIKILNKmjbKgExkJnDtno0pcvCialoz94x/AX/9qoycgW/H1FbvWEzkDi8NOcHAwfvnlFzRr1gxNmzaFRqOp8dgrV67YpHJE5BzatRPdT0r8/LONKrFwIfDii/Ky/HwgLMxGT0BE7srisLN8+XI0/mPA3/Lly2sNO0TkPoqKlO9CHR1tg64rc6Oh//IXYNGiel6YiDwFx+yAY3aIauPjA1RUWH9eq1bKW4MM3nhD7CVh7OxZMW+ZiDyeXRcVPHr0KLKysgz3P//8cwwbNgx//etfUV5ebvF13nrrLXTt2hWBgYEIDAxEbGwsvvrqK8PjN2/eRFJSEkJCQtCoUSMkJibiYrVpIGfPnkV8fDz8/f0RGhqKWbNmoULJ/8xEZCIsTFnQ8fWtZ9C5dk1M3TIOOpMni9ULGXSIyEqKws5zzz2HX375BYBYTXnEiBHw9/fH5s2bkZKSYvF1WrRogVdffRVHjhzBDz/8gIcffhhDhw7FiRMnAADTpk3Dtm3bsHnzZqSnp+PChQtISEgwnK/T6RAfH4/y8nIcPHgQH3zwAdatW4eXXnpJycsiIohVjRs2FFmjoMD68/39xRZUir3/PlD9L7TTp4FVq+pxUSLyaJICgYGB0unTpyVJkqRXX31V6t+/vyRJkvTNN99ILVq0UHJJg6ZNm0rvvvuuVFhYKPn4+EibN282PHby5EkJgJSRkSFJkiR9+eWXkpeXl5Sfn2845q233pICAwOlsrIyi5+zqKhIAiAVFRXVq+5Ers7LS5JE84myW15ePZ78+nVJ0mrlF3zySZu9NiJyP5Z+fitq2ZEkCZV/bG+8e/duDBo0CIDYRuL3339XFLp0Oh02btyI0tJSxMbG4siRI7h16xb69etnOKZTp05o2bIlMjIyAAAZGRmIiYlBmNFsjLi4OBQXFxtah8wpKytDcXGx7Ebk6bRaZbuWA2LbCEkynRVusU2bRJOQ8Sp0J04A//63wgsSEVVRFHZ69OiBv//97/j3v/+N9PR0xMfHAwDOnDkjCx6WyMrKQqNGjeDn54eJEydi69at6Ny5M/Lz8+Hr64smTZrIjg8LC0N+fj4AID8/3+T59Pf1x5izaNEiBAUFGW7cz4scQacD9u0TOzzv22eH3b7r4cwZ5UHHx6ceG3qWl4uZVsYbdQ4ZIirTubPCi5I5+fkijDZoIL7W8l8kkdtRFHZWrFiBo0ePYsqUKXjxxRfRrl07AMCWLVsMe2VZqmPHjsjMzMShQ4cwadIkjBs3Dj/bbGEO8+bMmYOioiLDLTc3167PR5SaKlo/HnoIGDVKfG3dWpQ7g7ZtlZ+r+ENz+3bAz09MLdc7cgT4/HMxYIhsJiAAiIgQazKWlYmvERGinMgTKFpBuWvXrrLZWHpLliyB1sqd3Xx9fQ1hqXv37jh8+DBWrlyJESNGoLy8HIWFhbLWnYsXLyL8j7by8PBwfP/997Lr6WdrhdfSnu7n5wc/Pz+r6kmkVGqq2Nm5+iIP58+L8i1bAKNx9w5Xn1wRFqZgh4aKCqB9e/l0rQceEM1dDDk2FxAAXL9u/rHr18XjpaWOrRORoylq2cnNzcU5o/Xfv//+eyQnJ+PDDz+Ej49PvSpUWVmJsrIydO/eHT4+PtizZ4/hsezsbJw9exaxsbEAgNjYWGRlZaHAaMpIWloaAgMD0ZlN4OQEdDpg6lTToANUlSUnq9OllZ9fv2wRGqqgVWfPHtHvZRx0vvkGSE9n0LGD/Pyag47e9evs0iIPoGT08//93/9JH374oSRJkpSXlycFBgZKsbGxUrNmzaT58+dbfJ2//OUvUnp6unTmzBnp+PHj0l/+8hdJo9FIu3btkiRJkiZOnCi1bNlS+vrrr6UffvhBio2NlWJjYw3nV1RUSF26dJH69+8vZWZmSjt27JBuu+02ac6cOVa9Hs7GInvZu9eyWUx79zq2Xv7+9Zt11a2blU+o04mTql9Ep7PL6yMhLMyy9zMsTO2aEilj6ee3om6sn376Cffccw8AYNOmTejSpQu+/fZb7Nq1CxMnTrR4nZuCggKMHTsWeXl5CAoKQteuXbFz50488sgjAMS2FF5eXkhMTERZWRni4uKwevVqw/larRbbt2/HpEmTEBsbi4CAAIwbNw4LFixQ8rKIbC4vz7bH2UKDBvVbB6dnT6Ba73HtMjKA6mP50tIAo5mWVD817TBeWGjZ+ZYeR+SqFIWdW7duGca87N69G0OGDAEgpobnWfG/9nvvvVfr4w0aNMCqVauwqpbFxFq1aoUvv/zS4uckcqSICNseV1933aU86AwbJmaCN2pk4QmSJEZip6dXlbVuDZw6BXgr+q+HzEhJAZYtk3eFzpwJTJ8ONGkiBiPXpdqkVyK3o2jMzh133IE1a9bgwIEDSEtLw4ABAwAAFy5cQEj1DfuIPFjv3kCLFjUPR9FoxO4HvXvbvy733ANkZio798QJYOtWK4LOsWOAl5c86HzxhZijzqBjMykpwJIlpmO+dDpRPmyYZddR+u+CyFUoCjv//Oc/8fbbb6NPnz544okncOeddwIAvvjiC0P3FhGJhfpWrhTfVw88+vsrVojj7KmkBDh8WPn5Fo/3lyRg6FDg7rurypo2BW7eBAYPVl4BkrlxA5g4UQSa2rz7rlirsTb+/vVYDJLIRSje9Vyn06G4uBhNmzY1lP3666+GDTldCXc9J3tLTRWzsowmMSIqSgQdR0w7V7pzOWB+JplZJ0+apqING4CRI5U9MZk1bJhYishSy5cDL75oflaWvz+nnZNrs/TzW3F7slarlQUdAGjdurXSyxG5tYQE0eBx4IAYjBwRIbqu7N2iA4iuJ7sHnXHjgA8/rLqv0YjmpLqaFcgq1gYdQAxaLi0V08u7dRODkZs0EV1XbNEhT2Fx2Ln77ruxZ88eNG3aFHfddRc0tayJcfToUZtUjsjZ1TQLxhytFujTxzH1On8eiIkBrl5Vfg2Lgs7//ideuLF33wXGj1f+xGTWjRvWBx2g6u3hFhHkySwOO0OHDjXMwBpm6ag3IjdW2yyYxYvVq5efnwhhSh0/LoJSnZ5/HnjzTXlZURHArmCbMQ7Tx45Zf75WKwI4kadTPGbHnXDMDllLPwumJrNmqRN46hN0unWz8AP13Dkx4MjYihViUBLZjLkwbS21/h0SOYqln9/1DjslJSWorLZdsqsFBoYdskZ5uRiKUtuHkFYrBoTW1KVlD+fPi2nuSvj4WBiSXnwRWLhQXnb5soINsqg2M2cCS5cqP1+rVb+FkcgRLP38VjT1/MyZM4iPj0dAQACCgoLQtGlTNG3aFE2aNDEZtEzkbp57ru6/tnU60f3gSEqDDiCCUq0KCsSgY+Og8/e/i4E9DDo2ceMGMGUK0LVr/YLOq6+KoM2gQ1RF0WysJ598EpIk4f3330dYWFitg5WJ3ElqKrBunWXH5uTYtSoyDRsqPzcoCLjttloOePVVYM4ceVl+vtjynGxCySwrc4YOBWbPrv91iNyNorDz448/4siRI+jYsaOt60PktPQ7mFuq+iQle6nPGjoBAbXsi3T1qmmrzezZIvyQzdgy6Hz2Wf2vQ+SOFIWdnj17Ijc3l2GHPMqBA/JFAWvjqFkw3t7KB7B6eYmlcMx6800x28rY2bOmA5OpXpROJweA++8XYbV9ezFYvj6te0TuTlHYeffddzFx4kScP38eXbp0gY+Pj+zxrl272qRyRM7Emp3Jp0+3/+Dk0FDlQcfbG7h1y8wDly8DzZrJyyZNcvwAJDem01UtLrlpk7JraLXA1187dgA8kStTFHYuXbqEnJwc/PnPfzaUaTQaSJIEjUYDXX3mShI5KUt3Jh8xwv6DQ0NCgCtXrD+vUSOxqWfLlmYeTE6u2shL79QpoF07JVUkM8xtG6KEI8I0kTtRFHaefvpp3HXXXdiwYQMHKJPH0O9gfv58zasLN28OfPyxfeuh1QLVVnuwSF5eDdsDXLtmuhBgUFAtg3lIic2bgT/9qf7XmTGDM62IrKUo7Pz222/44osv0I5/8ZEH0e9gPny4mIVtHHj0ef/11+2735XSoFPjztYvvwzMny8v+/pr4KGHlFSPzLhxA3jsMWDnzvpf65NPbBOYiDyNonV2Hn74Yfz444+2rguR00tIALZsES04xlq0EOX23MG8ZUtlQadBAzM7W9+4IRJa9aBTWcmgY0PDhomgWd+gExUFfPopgw6RUopadgYPHoxp06YhKysLMTExJgOUhwwZYpPKEanBeACpud3J1djBvFUrIDfX+vO8vUWukVmxApg2TV62bRvw6KNKq0dmDB0KfPFF/a7RvTvw2mv2//dF5O4UbRfh5VVzg5ArDlDmdhGkZ24AaYsWovvKnq02NdHpRMuAkv2utNpq6+/cumV+VGtlZVU/HNnEhg3AqFHWnzdsmFj9mNPJiSxj1+0iKisra7y5WtAh0ktNFeNxqs+UOX9elKemOr4+3t7Kgs5tt1ULOu+/bxp01q8XA48YdOqtpESMy+naFbjnHmVBJypKdIXu3CmWOWLQIbIdq8LOoEGDUFRUZLj/6quvotBoxsbly5fRuXNnm1WOyFH0qyOba+fUlyUn128HamukpgKJicrODQ4WW1kBEBXWaIDx4+UHVVQATzxRrzqScM89QOPGYvXirCzg8GFl11mxgl1VRPZiVdjZuXMnysrKDPcXLlyIK0aLfVRUVCA7O9t2tSNykLpWR5YkMWbmwAH71+XsWeVBx8tLrAsIQMx19q42LG/NGvFi+KlqE/fcozzc6Gm1YnFBNbpJiTyFVQOUqw/vUTDch8gpWbo6sjWrKCvh56es2woQQUengwgz5sbVlZVxJTobKimpf9ABgI0bRTcpEdmPojE7RO7G0tWRLT1OCY1GedCJivoj6OzYYRp0/vlPEYAYdGxqzJj6nd+ggZhOzqBDZH9WtexoNBqT1ZK5ejK5g7pWR9ZoxOO9e9vn+evza9SyJfDbrxKgMfO3S2mpmM5FNnHjBjBrlthFoz6tOvfdB+zfz95EIkexuhvrqaeegp+fHwDg5s2bmDhxIgICAgBANp6HyJVYsjqyvQaQ1ifoNGgA/PbRAcDrAfkDc+YACxfWr2IkM2yY8h3Kja1fz7HhRI5m1To7xht/1mbt2rWKK6QGrrNDeubW2YmKEkHHHgNI6xN0fH2BMv+mpntYFRaKva2o3vQLTM6cCRw5Ur9r2fPfEZGnsvTzW9Gigu6GYYeM1bWCsq3UJ+jchaM4iu7ywokTgbfeql+lyMAWO5R36gS89JJjVtkm8kSWfn4r2i6CyJ1ptUCfPvZ9jvoEnWx0QAeckhcWFIiVBMkmPvkEGDmyftfo2RP4/nvb1IeI6oezsYgcTGnQ6YSTkKCRB50RI8QAIwYdm5k5U1nQadYMiIkRY3uuXWPQIXImbNkhciClQedb3If7kCEvzM0VU8TIZlJSgKVLlZ07YoTY5oGInA/DDpGDKAk6rfArfkUbeWG/fkBamm0qRYYxWrm5yoMOIDbuJCLnxLBD5ABKgs7nGIIh2CYvPHUKaNfONpXycDduiJlRe/eKxaXrY+hQbtxJ5MxUHbOzaNEi9OzZE40bN0ZoaCiGDRtmsrdWnz59DIsZ6m8TJ06UHXP27FnEx8fD398foaGhmDVrFipkWz4TqcfaoBOOPEjQyINOt25ibA6Djk0MGybWWtyxwzZB57PPbFErIrIXVcNOeno6kpKS8N133yEtLQ23bt1C//79UVpaKjtuwoQJyMvLM9wWL15seEyn0yE+Ph7l5eU4ePAgPvjgA6xbtw4vvfSSo18OkYk/1tu02Fo8hTxEyguPHweOHbNdpTycLRYHvPNOICkJuH6dQYfIFTjVOjuXLl1CaGgo0tPT8cADYkXYPn36oFu3blixYoXZc7766is8+uijuHDhAsLCwgAAa9aswezZs3Hp0iX4WrAfENfZIVvLz7duH62muIIrCJEXNm9ev0VeyMSNG/XfPWPjRjEYmYjUZ+nnt1NNPS8qKgIABAcHy8o//vhjNGvWDF26dMGcOXNw/fp1w2MZGRmIiYkxBB0AiIuLQ3FxMU6cOGH2ecrKylBcXCy7EdlKQIB1QWcpppsGne++Y9Cxg1mz6nf+jBkMOkSuyGkGKFdWViI5ORn3338/unTpYigfNWoUWrVqhcjISBw/fhyzZ89GdnY2UlNTAQD5+fmyoAPAcD8/P9/scy1atAjz58+30yshT9awIXDzpmXHNsI1XEO1v0R8fes/iIRqdOpU3ceYo9UC06cDRj3oRORCnCbsJCUl4aeffsI333wjK3/22WcN38fExCAiIgJ9+/ZFTk4OoqOjFT3XnDlzMH36dMP94uJiREVFKas40R+sGYj8NyzAAsyTF+7ZAzz8sG0rRTLt2wO7dll2bFAQMHq0OGfyZJFDicg1OUXYmTJlCrZv3479+/ejRR2LpPXq1QsAcPr0aURHRyM8PBzfV1uq9OLFiwCA8PBws9fw8/Mz7NxOZAuWBh0/3MRNmJmjXFlZvz0kyCJLlgCrVll27Pvvc9NOIneh6pgdSZIwZcoUbN26FV9//TXatGlT5zmZmZkAgIg/BkXExsYiKysLBQUFhmPS0tIQGBiIzp0726XeRMYszSgvYKVJ0BmML8SUcgYdh2jYUEwVr02DBsCnnzLoELkTVWdjTZ48GevXr8fnn3+Ojh07GsqDgoLQsGFD5OTkYP369Rg0aBBCQkJw/PhxTJs2DS1atEB6ejoAMfW8W7duiIyMxOLFi5Gfn48xY8bgmWeewcKFCy2qB2djkVKWZBRv3MItmPaBNGqoQ8l1p5oj4DFqmn5+//1Aejp3JydyFZZ+fqsadjQ1fFKsXbsWTz31FHJzc/Hkk0/ip59+QmlpKaKiovDYY49h7ty5shf122+/YdKkSdi3bx8CAgIwbtw4vPrqq/D2tqyXjmGHlGjcGCgpqf2YcViHdfizrGwUPsaXQaNQWGi/ulHdbtwQs7NOnRLjcpYs4SrIRK7GJcKOs2DYIWvVFXQ0qEQlTJsHvHELeQXe3KS8nsrLgdWrgZwcIDqaA4iJPJVLrrND5Aq02tqDTgI+NQk6E/EWGjaQUCEx6NRXSopYGHDaNLHL+LRp4n5Kito1IyJn5RSzsYhcRe1jdCRIZv5+8MNNlMMP0g27VctjpKSY311cp6sq51o4RFQdW3aILFRb0OmPnSZB5y9YBA0kEXQ8vrO4/srLgWXLaj9m2TJxHBGRMbbsEFmgtqAjwfTBAJTgOsQuoAw6trF6tWjBqY1OJ45LTnZIlYjIRbBlh6gO2dnmy+/DtyZBZxH+Ag0kBh07yMmx7XFE5DnYskNOS6cDDhwA8vLExpq9ezt2/ZOffwZiYsTixtX9jhCE4IqsrAmuoghNAACNGgHXrjmgkm6opvfd0t1hFO4iQ0RujC075JRSU4HWrYGHHgJGjRJfW7cW5Y6g0QB33GEadLrhGCRoZEFnDZ6DBhKDTj2VlwN//rPYk8rc+z55ct1hV6sVxxERGWPYIaeTmgoMHw6cOycvP39elNs78NQ0PuckOuEY7paVheIiJmGN4b6XF4OOEikpYkG/deuA0lL5Y/r3fft2sfN4baZP53o7RGSKYYecik4HTJ1qfqyLviw5ue6Bqko1amRa1hH/hQQNOqFq8M4mPA4NJFxCqOxYe9XLnemnk5vrLgTk7/uiRWLV4+otPFqtKOe0cyIyhysogysoO5MFC4B58+o+bu9eoE8f2z53kyZAUZG8bD96oze+kZVF4SzOIcrkfP4mWa+8XCwIaGlI1L/vXEGZiADLP785QJmcRmqqZUEHEINXbenSJXnQaYnf8Btay47Zg4fRD3vMns+go4wl08mN6d93X19OLyciyzHskFPQd19ZKiLCts8fatQbtRXDMAzyLbHb4xecRnuz5xYU2LYu7s54tlV6unXn2vp9JyLPwLBDTuEf/zAdkFyTqCgxHdlW9AOSw5CPfMg/TX9EV3TDjzWeGxQE7nVlofJyYOJEYPPmuneLN8fW7zsReQ4OUCbVWdN9BQArVthmvZ2srKqg8x6eNgk6XfFjnUGnsLD+9fAE+tlWa9cqCzqA7d53IvI8bNkhVVnbfTV/PpCQUP/n1YecpriCKwiRPXYBEWiOCzWe27o18P33bNGxVE2bd1qqUSPggw9s874TkWdiyw6p6sABy7uvWrQAXnyx/s+pDzqvYYZJ0LkXGTUGnYAAMRD5zBkGHUtZsnlnTTQaYMQI0XrGoENE9cGWHVKVNbOqVq6sfzdGVpbYpLMEjWXlFdDCBxW1nqu0+8WTWTvbaswY0T3I6eREZEsMO6QqS2fX2Kr7alPXV1CCl2RlfbEbX6NvrecdO1b/5/ZE1m7K+fTTtl8/iYiIYYdU1bu36J46f77mtWps0n118ybQsCFeqVasQSWAGvaHMNKtWz2f30NZsyknZ1sRkb1wzA6pSqsV3VOA6Z5UGo241bf76tqiN8RUICND8Rk0kGBJ0OGCgcpZsnknIN5nzrYiInth2CGH0emAffuADRvEV/1YjoQEYMsWoHlz+fEtWohyxd1Xt24BGg0a//UFWbEXdPgCQ+s8vVEjBp368vWte/POxo3r+T4TEdWBYYccIjVVTNl+6CFg1CjxtXXrqh3MExKAX38Vex+tXy++njmj/AOwZNUHJqNbn8S/oYEEyYJ/9kFB3L3cVhYvNr95p3621dWrDDpEZF/cCBTcCNTeUlOB4cNNW0n03VY2/au+stJsX4g3bkFn4RC1ggJOLbcHbt5JRLZm6ec3ww4YduxJpxMtODWtpaPRiO6qM2dsMF4jNRVITJQVTcYqvIXJFl+CQYeIyHVw13NyCnUtGihJQG6uOE7xlGNJArxMu6b8cBPl8LP4MtzniojIPXHMDtmVpYsGWrO4oMyuXSZBZw4WQgPJqqDj7899roiI3BVbdsiuLF000NLjZKrPVYdYHfk6Aqy+VGmpgucnIiKXwJYdsiv9ooFmcgkAUW71YnIHD5pc8J9IgQaSoqDDUWtERO6NLTtkV/pFA4cPF/nEOFjo84pVi8nddhvw+++yoia4iiI0sbpuJ04AnTtbfRoREbkYtuyQ3dlk0cDMTJGOjILOvzABGkhWBR0vLzHjSpIYdIiIPAVbdsghEhKAoUPFrKu8PDFGp3dvC1t0OncGTp6UFYXiIi4h1Op6WLMDNxERuQeGHXIYrdbK6eXnz4vmH2OJifBK3aJonA13Lici8kzsxiLnNG+eadD57Tf4fqEs6ADcuZyIyFOpGnYWLVqEnj17onHjxggNDcWwYcOQnZ0tO+bmzZtISkpCSEgIGjVqhMTERFy8eFF2zNmzZxEfHw9/f3+EhoZi1qxZqKiocORLIVu5dEmMzVmwoKqsXz9AkqBp1RK3bim7LGdcERF5LlXDTnp6OpKSkvDdd98hLS0Nt27dQv/+/VFqtOjJtGnTsG3bNmzevBnp6em4cOECEoxGtOp0OsTHx6O8vBwHDx7EBx98gHXr1uGll15S4yV5jPJyMYvq+efF1/JyG1z0tdeA0GrjcC5cANLSapy6bgkGHSIiDyc5kYKCAgmAlJ6eLkmSJBUWFko+Pj7S5s2bDcecPHlSAiBlZGRIkiRJX375peTl5SXl5+cbjnnrrbekwMBAqayszKLnLSoqkgBIRUVFNnw17mvWLEnSaiVJxAhx02pFuSJXr8ovBkjS9OnSsWOmxdbeiIjIfVn6+e1UY3aKiooAAMHBwQCAI0eO4NatW+jXr5/hmE6dOqFly5bIyMgAAGRkZCAmJgZhYWGGY+Li4lBcXIwTJ06YfZ6ysjIUFxfLbmSZlBRgyRLTWU06nShPSbHygm+9BTRtKi87cwaaZUtx113K63nihGe36Oh0wL59wIYN4itnoRGRJ3OasFNZWYnk5GTcf//96NKlCwAgPz8fvr6+aNKkiezYsLAw5OfnG44xDjr6x/WPmbNo0SIEBQUZblFRUTZ+Ne6pvBxYurT2Y5Yts7BLq7RUjM2ZbLQj+TPPiLE5bVrXp5rIy/PsNXRSU8VO8w89BIwaJb62bi3KiYg8kdOEnaSkJPz000/YuHGj3Z9rzpw5KCoqMtxyc3Pt/pzuYMAAoLKy9mN0OmD16jou9O9/A40aycuys4F33kFmZn1qKDb0DA+v3zVcWWqqWK26+k7z58+LcgYeIvJEThF2pkyZgu3bt2Pv3r1oYTTdODw8HOXl5Sisth31xYsXEf7HJ1p4eLjJ7Cz9/fAaPvX8/PwQGBgou1HNdDpg5Ehg717Ljs/JqeGBmzdFGhk7tqrs8cdFf1OHDgBQr64rHx/P3tBTpwOmTjXffacvS05mlxYReR5Vw44kSZgyZQq2bt2Kr7/+Gm3atJE93r17d/j4+GDPnj2GsuzsbJw9exaxsbEAgNjYWGRlZaGgoMBwTFpaGgIDA9HZk/sybCQ1FWjZEvjkE8vPiY6u4UINGwI3blSV/fgjsGmT4W59ZlwBNpoR5sIOHDBt0TEmSUBurjiOiMiTqLqCclJSEtavX4/PP/8cjRs3NoyxCQoKQsOGDREUFITx48dj+vTpCA4ORmBgIJ5//nnExsbi3nvvBQD0798fnTt3xpgxY7B48WLk5+dj7ty5SEpKgp+fn5ovz+Xpu0SsGeir1cqH4eDWLZGWjMdPxcUBX31lSDfmFkq2licPRtbLy7PtcURE7kLVsPPWW28BAPpU20Ng7dq1eOqppwAAy5cvh5eXFxITE1FWVoa4uDisNhoUotVqsX37dkyaNAmxsbEICAjAuHHjsMB4UTqyWm1dIrWZPh3w9f3jzo4dwMCB8gMOHQLuucdw18+vfi0yvr5AWZny891JRIRtjyMichcaSeLfxMXFxQgKCkJRURHH7/xh3z4xi8caDz0EfP01RFLq0gX473+rHrz3XuDbb8W243+ob9Dx9/fsMTrV6XRi1tX58+ZDqkYjWtDOnLFwA1YiIidn6ee3UwxQJudy4wbwj39Yd45WKxpykJ4OeHvLg86+fUBGhizonD+vPOiEhYmuGAYdOa0WWLlSfF99/JP+/ooVDDpE5HkYdkhm2DDRYrJ7t3XnTZ8mwfeBe+Xbmt9+O1BRATz4oMnxLVsqq99vv4nhP548vbw2CQnAli1A8+by8hYtRLnRTitERB5D1TE75FyGDQM+/9y6c7RaYPkT3+P513rJH/jqK7Ewjxm33173ej3meHsrD0meJCEBGDpUzLrKyxNjdHr3ZosOEXkuhh0CILqurA06T42T8N75AfD6aFdVYUSEaH7x8TF7Tn2mlyvd8dwTabXyRjYiIk/GbiwCAMyaZfmxUVFA2tLjWPuBF7x2GwWdTz8Vu5TbIehwGD0RESnFlh1CebnlY3R69AAOtf4TvGZsrir09wcuXwYaNKjxvGrbm1mFQYeIiOqDLTseLiVFZJXs7LqPbY9fcPgHDby2GAWdDz8U06JqCDpHjogWnT82tLfKDz8w6BARUf2xZceDpaQAS5ZYduw7eAbP4D154bVrpht6GqlPt1UdlyYiIrIYW3Y8VHk5sGxZ3ce1wq+QoJEHndWrRZOLnYJOz54MOkREZDts2fFQq1fXvfv1a5iBGaiWiK5erXMATn2mOHfqBHz/vfLziYiIqmPLjofKyan5sQhcgASNPOgsWSJac+oIOr6+ytbQ0Tt5Uvm5RERE5jDseKjoaPPl8/AyLqDa8ruXLgEzZ9Z5TV/f+q2Fw8HIRERkDww7HmryZHl3Uwh+hwQNXsZ8Q9krmpdQXiYBzZrVeT1/f+VBJyiIQYeIiOyHYcdD+foC06eL76djKX7HbbLHI3Ee12bOh69v3dfq2lWswKxEWBhQWKjsXCIiIksw7HiwxS8WQYIGS1HVRbUcyfDWSnhyViQWL677Gt27A1lZyp7/8mWxqac70enEJu8bNoivdQ0CJyIi++NsLE+1Zg0waZKsaN7YM2h6V2tcnwyLWnQefRQ4elTZ07tjt1VqKjB1KnDuXFVZixbAypXcbZyISE0MO56mtNR0EZvx44F33zUarVO3QYPExubW8vJyz9aO1FRg+HDTEHf+vCjfsoWBh4hILezG8iQffWQadP77X+Ddd626THS0sqDj4+OeQUenEy065lqr9GXJye752omIXAHDjie4eRMICADGjKkqS0wUn8QdO1p1qbZtgf/9z/oq+PiIVZvd0YED8q6r6iQJyM0VxxERkeOxG8vdbd1q2n/y449iCpWV7r4bOHPG+io0bAhcv279ec6svFysQp2TY/kmp3l59q0TERGZx7Djrm7dAlq3Bi5cqCp75BFg506rN64qKREDbZXsXB4TAxw/bv15ziwlRewrZm23VESEfepDRES1YzeWO9q5U0ynMg46330H7NplddC55x6gcWNlQefuu90n6OinlA8eLHbOsCboaDRAVBTQu7fdqkdERLVgy4470elEU4rxBlP33ANkZIhpUFa6+27g2DFlVYmPB7ZvV3auszE3pdxS+my5YkX9NkglIiLl2LLjLvbvB7y95UFn3z7g0CFFQadNG+VBZ+BA9wo6w4crCzqA6P7jtHMiInWxZcfVSRJw332im0qvY0fgxAnFTQlt2gC//qqsOm3bAl9+qexcZ1PblPLaDBsG/OlPYoxO795s0SEiUhvDjis7fFh0Uxn78kvRtKJQfYJOmzZidpK7qGtKeU0efBB44gnb14eIiJRh2HFFkiQCzc6dVWXh4cDZs2JBG4XatVMedO66S/nWEc5KyVRxrVbsKE9ERM6DY3ZcTVaWGINjHHS2bBGfzPUIOkVFyltlevZ0v6ADKJsqPn26ZfuKERGR47Blx5WMGAFs2lR1v0ED4OpV8bWe7r3X+nOCgkQ3T/UdKNxF795igPH583WP29FqRdCxZKd4IiJyLLbsuIJTp8QcZuOg88EHwI0bNgk6qaliiyxr3HUXUFjovkEHEAFm5UrxffXlifT3Bw4Eli8XK0Qz6BAROSeGHWf37LNAhw7ysmvXgLFj633p8nKxEvC4cdad17q1e3ZbmZOQIHoJmzeXl7doAXz6qRgPnpzMrisiImfGbixn9dtvIlUYW7XKZqNflW550Lq1sv2xXFlCAjB0qJidlZfHKeVERK6GYccZzZwJLF0qL7t6FWjSxCaXT0kRWx5YyxODjp5WC/Tpo3YtiIhICVW7sfbv34/BgwcjMjISGo0Gn332mezxp556ChqNRnYbMGCA7JgrV65g9OjRCAwMRJMmTTB+/HiUlJQ48FXYUF6eGAxiHHT++U8xOtZGQUffdWWtyEjPDTpEROTaVA07paWluPPOO7Fq1aoajxkwYADy8vIMtw0bNsgeHz16NE6cOIG0tDRs374d+/fvx7PPPmvvqtve/PkiURgrKBDNMDa0erX1XVeRkWIJHyIiIlekajfWwIEDMbCO1X79/PwQHh5u9rGTJ09ix44dOHz4MHr06AEAeOONNzBo0CC89tpriKweHpzR5ctAs2bysrlzgVdesflTlZeLwbaW0s84euMNjk8hIiLX5fSzsfbt24fQ0FB07NgRkyZNwuXLlw2PZWRkoEmTJoagAwD9+vWDl5cXDh06VOM1y8rKUFxcLLupYtky06Bz7pxNg86NG8CUKWK8TYMGwLffWn4uN7EkIiJ34NQDlAcMGICEhAS0adMGOTk5+Otf/4qBAwciIyMDWq0W+fn5CA0NlZ3j7e2N4OBg5Ofn13jdRYsWYf78+faufs2KikzH4CQniwVbbGjYMODzz60/T6MRCzQ//DBbdIiIyPU5ddgZOXKk4fuYmBh07doV0dHR2LdvH/r27av4unPmzMH06dMN94uLixEVFVWvulrsX/8CnntOXva//4ldNG1oyBBg2zZl586cCTzyiE2rQ0REpBqnDjvVtW3bFs2aNcPp06fRt29fhIeHo6CgQHZMRUUFrly5UuM4H0CMA/Lz87N3deVKS02XG/7zn4H337f5U02bpizocMsDIiJyR04/ZsfYuXPncPnyZUT8sUNjbGwsCgsLceTIEcMxX3/9NSorK9GrVy+1qmlq/XrToHPypF2CTkoKsGKF9ef93/9xywMiInJPqrbslJSU4PTp04b7Z86cQWZmJoKDgxEcHIz58+cjMTER4eHhyMnJQUpKCtq1a4e4uDgAwO23344BAwZgwoQJWLNmDW7duoUpU6Zg5MiRzjETq6xMDEA2XvcnIUHsM2AHStfQAYDERG55QERE7kkjSXXt52w/+/btw0MPPWRSPm7cOLz11lsYNmwYjh07hsLCQkRGRqJ///545ZVXEBYWZjj2ypUrmDJlCrZt2wYvLy8kJibi9ddfRyMrdqgsLi5GUFAQioqKEBgYaJPXBgB44AGxx4DesWNAt262u76RGzfEOBtrZlvpabWiVYdhh4iIXImln9+qtuz06dMHtWWtnTt31nmN4OBgrF+/3pbVsp277hJhp29fIC3NdOtsG1E660pv+nQGHSIicl+qtuw4C7u17Oh/tHYKOYDYoPKLL5SdywHJRETkylyiZcft2THkAMCGDcqCTuvWwNSpYgN1tugQEZG7Y9hxUampwKhR1p9nh7ULiYiInJpLTT0nQacTLTPW0GqBWbMYdIiIyPOwZccFHTggttCy1P33A19/zS4rIiLyTGzZcTE6HbBnj3XnpKUx6BARkediy44LSU0V3VfWtOoMHQo0bGi/OhERETk7hh0XkZoKDB9eNZvdEkOGAJ99ZrcqERERuQR2Y7kA/YBka4LO+vX1W2iQiIjIXbBlx4mVlwOrVwPp6ZZ3XUVFiY1AExLsWjUiIiKXwbDjpFJSxKaeOp3l58ydC7z8sphmTkRERALDjhOaORNYutT68/r2ZdAhIiKqjmHHyWzaZH3Q0WiAFi2A3r3tUyciIiJXxgHKTiQ1FRgxwrpz9NtvrVjBVh0iIiJzGHachJItIADRorNlCwckExER1YTdWE7C2i0gnnwSGD9edF2xRYeIiKhmDDtOIi/P8mO1WuC997gFBBERkSXYjeUkIiIsP3b6dAYdIiIiSzHsOInevcX4G/2A45rMmAEsXuyYOhEREbkDhh0nodUCK1eK72sKPJ98Arz2muPqRERE5A4YdpxIQoKYWdW8ubw8Kgr49FPgT39Sp15ERESujAOUnUxCAjB0qJidlZcnxvJwxhUREZFyDDtOSKsF+vRRuxZERETugWHHzvQ7l+fkANHRwOTJnElFRETkSAw7dmRu5/KZM8XUcc6oIiIicgyGHTupaedynQ5YskR8z8BDRERkf5yNZQeW7Fy+bJno4iIiIiL7YtixMUt3LtfpxFgeIiIisi+GHRuydufynBz71YWIiIgEhh0bsnbn8uho+9WFiIiIBIYdG7J25/LJk+1XFyIiIhIYdmyIO5cTERE5H4YdG+LO5URERM5H1bCzf/9+DB48GJGRkdBoNPjss89kj0uShJdeegkRERFo2LAh+vXrh1OnTsmOuXLlCkaPHo3AwEA0adIE48ePR0lJiQNfRRXuXE5EROR8VA07paWluPPOO7Fq1Sqzjy9evBivv/461qxZg0OHDiEgIABxcXG4efOm4ZjRo0fjxIkTSEtLw/bt27F//348++yzjnoJJrhzORERkXPRSJIkqV0JANBoNNi6dSuGDRsGQLTqREZGYsaMGZg5cyYAoKioCGFhYVi3bh1GjhyJkydPonPnzjh8+DB69OgBANixYwcGDRqEc+fOITIy0qLnLi4uRlBQEIqKihAYGGiT16PTcedyIiIie7L089tpx+ycOXMG+fn56Nevn6EsKCgIvXr1QkZGBgAgIyMDTZo0MQQdAOjXrx+8vLxw6NChGq9dVlaG4uJi2c3W9DuXP/GE+MqgQ0REpA6nDTv5+fkAgLCwMFl5WFiY4bH8/HyEhobKHvf29kZwcLDhGHMWLVqEoKAgwy0qKsrGtSciIiJn4bRhx57mzJmDoqIiwy03N1ftKhEREZGdOG3YCQ8PBwBcvHhRVn7x4kXDY+Hh4SgoKJA9XlFRgStXrhiOMcfPzw+BgYGyGxEREbknpw07bdq0QXh4OPbs2WMoKy4uxqFDhxAbGwsAiI2NRWFhIY4cOWI45uuvv0ZlZSV69erl8DoTERGR8/FW88lLSkpw+vRpw/0zZ84gMzMTwcHBaNmyJZKTk/H3v/8d7du3R5s2bfC3v/0NkZGRhhlbt99+OwYMGIAJEyZgzZo1uHXrFqZMmYKRI0daPBOLiIiI3JuqYeeHH37AQw89ZLg/ffp0AMC4ceOwbt06pKSkoLS0FM8++ywKCwvxf//3f9ixYwcaNGhgOOfjjz/GlClT0LdvX3h5eSExMRGvv/66w18LEREROSenWWdHTfZYZ4eIiIjsy+XX2SEiIiKyBYYdIiIicmuqjtlxFvqePHuspExERET2of/crmtEDsMOgGvXrgEAV1ImIiJyQdeuXUNQUFCNj3OAMoDKykpcuHABjRs3hkajcchzFhcXIyoqCrm5uRwU7cT4PrkOvleug++V63D290qSJFy7dg2RkZHw8qp5ZA5bdgB4eXmhRYsWqjw3V3B2DXyfXAffK9fB98p1OPN7VVuLjh4HKBMREZFbY9ghIiIit8awoxI/Pz/MmzcPfn5+aleFasH3yXXwvXIdfK9ch7u8VxygTERERG6NLTtERETk1hh2iIiIyK0x7BAREZFbY9ghIiIit8aw40Avv/wyNBqN7NapUye1q0UA9u/fj8GDByMyMhIajQafffaZ7HFJkvDSSy8hIiICDRs2RL9+/XDq1Cl1Kuvh6nqvnnrqKZPfswEDBqhTWQ+2aNEi9OzZE40bN0ZoaCiGDRuG7Oxs2TE3b95EUlISQkJC0KhRIyQmJuLixYsq1dhzWfJe9enTx+T3auLEiSrV2HoMOw52xx13IC8vz3D75ptv1K4SASgtLcWdd96JVatWmX188eLFeP3117FmzRocOnQIAQEBiIuLw82bNx1cU6rrvQKAAQMGyH7PNmzY4MAaEgCkp6cjKSkJ3333HdLS0nDr1i30798fpaWlhmOmTZuGbdu2YfPmzUhPT8eFCxeQkJCgYq09kyXvFQBMmDBB9nu1ePFilWqsgEQOM2/ePOnOO+9UuxpUBwDS1q1bDfcrKyul8PBwacmSJYaywsJCyc/PT9qwYYMKNSS96u+VJEnSuHHjpKFDh6pSH6pZQUGBBEBKT0+XJEn8Dvn4+EibN282HHPy5EkJgJSRkaFWNUkyfa8kSZIefPBBaerUqepVqp7YsuNgp06dQmRkJNq2bYvRo0fj7NmzaleJ6nDmzBnk5+ejX79+hrKgoCD06tULGRkZKtaMarJv3z6EhoaiY8eOmDRpEi5fvqx2lTxeUVERACA4OBgAcOTIEdy6dUv2e9WpUye0bNmSv1cqq/5e6X388cdo1qwZunTpgjlz5uD69etqVE8RbgTqQL169cK6devQsWNH5OXlYf78+ejduzd++uknNG7cWO3qUQ3y8/MBAGFhYbLysLAww2PkPAYMGICEhAS0adMGOTk5+Otf/4qBAwciIyMDWq1W7ep5pMrKSiQnJ+P+++9Hly5dAIjfK19fXzRp0kR2LH+v1GXuvQKAUaNGoVWrVoiMjMTx48cxe/ZsZGdnIzU1VcXaWo5hx4EGDhxo+L5r167o1asXWrVqhU2bNmH8+PEq1ozIfYwcOdLwfUxMDLp27Yro6Gjs27cPffv2VbFmnispKQk//fQTxyi6gJreq2effdbwfUxMDCIiItC3b1/k5OQgOjra0dW0GruxVNSkSRN06NABp0+fVrsqVIvw8HAAMJklcvHiRcNj5Lzatm2LZs2a8fdMJVOmTMH27duxd+9etGjRwlAeHh6O8vJyFBYWyo7n75V6anqvzOnVqxcAuMzvFcOOikpKSpCTk4OIiAi1q0K1aNOmDcLDw7Fnzx5DWXFxMQ4dOoTY2FgVa0aWOHfuHC5fvszfMweTJAlTpkzB1q1b8fXXX6NNmzayx7t37w4fHx/Z71V2djbOnj3L3ysHq+u9MiczMxMAXOb3it1YDjRz5kwMHjwYrVq1woULFzBv3jxotVo88cQTalfN45WUlMj+Qjlz5gwyMzMRHByMli1bIjk5GX//+9/Rvn17tGnTBn/7298QGRmJYcOGqVdpD1XbexUcHIz58+cjMTER4eHhyMnJQUpKCtq1a4e4uDgVa+15kpKSsH79enz++edo3LixYRxOUFAQGjZsiKCgIIwfPx7Tp09HcHAwAgMD8fzzzyM2Nhb33nuvyrX3LHW9Vzk5OVi/fj0GDRqEkJAQHD9+HNOmTcMDDzyArl27qlx7C6k9HcyTjBgxQoqIiJB8fX2l5s2bSyNGjJBOnz6tdrVIkqS9e/dKAExu48aNkyRJTD//29/+JoWFhUl+fn5S3759pezsbHUr7aFqe6+uX78u9e/fX7rtttskHx8fqVWrVtKECROk/Px8tavtccy9RwCktWvXGo65ceOGNHnyZKlp06aSv7+/9Nhjj0l5eXnqVdpD1fVenT17VnrggQek4OBgyc/PT2rXrp00a9YsqaioSN2KW0EjSZLkyHBFRERE5Egcs0NERERujWGHiIiI3BrDDhEREbk1hh0iIiJyaww7RERE5NYYdoiIiMitMewQERGRW2PYISIiIrfGsENERERujWGHyMGeeuopaDQak9vp06fRp08fJCcnm5yzbt06NGnSxHD/5ZdfNnuNTp06GY6p6VrG9u7da9jvxt/fH507d8aMGTNw/vx5wzE6nQ7Lly9HTEwMGjRogKZNm2LgwIH49ttvTeqo0WgwYMAAWXlhYSE0Gg327dtnKEtPT8fDDz+M4OBg+Pv7o3379hg3bhzKy8vNvl5jGo0Gn332GQDg119/hUajgVarldUZAPLy8uDt7Q2NRoNff/1Vdrz+FhISgv79++PYsWMmj5m7rVu3Dvv27YNGo5Ht1m2Pn1Fd7PHe3X777SbPs3nzZmg0GrRu3drkeP2tUaNG6N69O1JTUy2uP5EjMewQqWDAgAHIy8uT3SzZadjYHXfcYXKNb775xuLz3377bfTr1w/h4eH49NNP8fPPP2PNmjUoKirC0qVLAYjdkEeOHIkFCxZg6tSpOHnyJPbt24eoqCj06dPHEDr0vL29sXv3buzdu7fG5/35558xYMAA9OjRA/v370dWVhbeeOMN+Pr6QqfTWfUz0GvevDk+/PBDWdkHH3yA5s2bmz1+9+7dyMvLw86dO1FSUoKBAweicePGsp/ljBkzTH7GI0aMMLmWPX5GdbHHexcQEICCggJkZGTIyt977z20bNnSpA6BgYGGn8uxY8cQFxeHP/3pT8jOzlb8uojsRtWduYg80Lhx46ShQ4eafezBBx+Upk6dalK+du1aKSgoyHB/3rx50p133lnr89R0LUmSpNzcXMnX11dKTk42+/jVq1clSZKkjRs3SgCkL774wuSYhIQEKSQkRCopKZHVccKECdI999wjuxYAae/evZIkSdLy5cul1q1b11r36q/XGABp69atkiRJ0pkzZyQA0ty5c6X27dvLjuvQoYP0t7/9TQIgnTlzRnb8sWPHDMd9++23EgBpx44dsvNr+hnrNyK158+oNvZ876ZMmSI988wzsufy8/OT/vKXv0itWrUylJt7f3Q6neTj4yNt2rSpztdA5Ghs2SHyQJs3b0Z5eTlSUlLMPq7vQlq/fj06dOiAwYMHmxwzY8YMXL58GWlpabLyl19+GVlZWdiyZYvZa4eHhyMvLw/79++v34swMmTIEFy9etXQsvXNN9/g6tWrZutdXcOGDQHA0IVmLXv8jGpjz/fu6aefxqZNm3D9+nUAortqwIABCAsLq7VOOp0OH3zwAQDg7rvvtvYlEdkdww6RCrZv345GjRoZbo8//rjV18jKypJdo1GjRpg4caJF5546dQqBgYGIiIio9bhffvnF7DgOAIbyX375RVYeGRmJqVOn4sUXX0RFRYXJeY8//jieeOIJPPjgg4iIiMBjjz2GN998E8XFxRbV3RwfHx88+eSTeP/99wEA77//Pp588kn4+PjUel5hYSFeeeUVNGrUCPfcc4+i57bHz6g29nzv7rrrLrRt2xZbtmyBJElYt24dnn76abPXKCoqMvy78/X1xaRJk/Cvf/0L0dHRVr0eIkdg2CFSwUMPPYTMzEzD7fXXX7f6Gh07dpRdIzMzEwsWLLDoXEmSoNFoLD7WWrNnz8alS5cM4cOYVqvF2rVrce7cOSxevBjNmzfHwoULDeNjlHr66aexefNm5OfnY/PmzTV+SAPAfffdh0aNGqFp06b48ccf8cknn9TZelEbW/+M6noue753Tz/9NNauXYv09HSUlpZi0KBBZo9r3Lix4d/dsWPHsHDhQkycOBHbtm2z+jmJ7I1hh0gFAQEBaNeuneGm/ys9MDAQRUVFJscXFhYiKChIVubr6yu7Rrt27RAaGmrR83fo0AFFRUV1hosOHTrg5MmTZh/Tl3fo0MHksSZNmmDOnDmYP3++oUukuubNm2PMmDF48803ceLECdy8eRNr1qwBIH4OpaWlqKyslJ2jnwFV/WcBADExMejUqROeeOIJ3H777ejSpUuNr+uTTz7Bjz/+iKtXryInJ6fGD3RL2PNnVNPz2fO9Gz16NL777ju8/PLLGDNmDLy9vc1ew8vLy/DvrmvXrpg+fTr69OmDf/7znxa/FiJHYdghciIdO3bE0aNHTcqPHj1q9oNJqeHDh8PX1xeLFy82+7g+VIwcORKnTp0y+9f60qVLERISgkceecTsNZ5//nl4eXlh5cqVddanadOmiIiIQGlpKQDxc6ioqEBmZqbsOP3PpqafxdNPP419+/bV2qoDAFFRUYiOjq5xers1HPUz0rP3exccHIwhQ4YgPT29zp9jdVqtFjdu3LDqHCJHMB/ZiUgVkyZNwptvvokXXngBzzzzDPz8/PCf//wHGzZsMPnQqqioQH5+vqxMo9HIumMuXbpkEhgiIiIQFRWF5cuXY8qUKSguLsbYsWPRunVrnDt3Dh9++CEaNWqEpUuXYuTIkdi8eTPGjRuHJUuWoG/fviguLsaqVavwxRdfYPPmzQgICDD7Who0aID58+cjKSlJVv72228jMzMTjz32GKKjo3Hz5k18+OGHOHHiBN544w0AYlp9//798fTTT2Pp0qVo27YtsrOzkZycjBEjRtQ4pXzChAl4/PHHbRJiLGWPn1FtHPHerVu3DqtXr0ZISEiN9ZAkyfDv78aNG0hLS8POnTvx0ksvWfxaiBxGxZlgRB6ptqnnkiRJ33//vfTII49It912mxQUFCT16tXLMNVab968eRIAk5ufn5/hmAcffNDsMa+88orhmLS0NCkuLk5q2rSp1KBBA6lTp07SzJkzpQsXLhiOuXXrlrRkyRLpjjvukHx9faXAwEApLi5O+uabb2R1MjcduaKiQurcubNsWvXRo0elJ598UmrTpo3k5+cnhYSESA888IDJFOmrV69KL7zwghQdHS01bNhQat++vZSSkiJdu3bNcIy5qeTGjh07VufU85pYOvXcHj8jS9j7vTO2fPlyk6nn1f/ddejQQfrHP/4hVVRUWPwaiBxFI0kKRrARERERuQiO2SEiIiK3xrBDROREFi5caLJ+kv42cOBAtatH5JLYjUVE5ESuXLmCK1eumH2sYcOGNQ7OJqKaMewQERGRW2M3FhEREbk1hh0iIiJyaww7RERE5NYYdoiIiMitMewQERGRW2PYISIiIrfGsENERERu7f8BY/uTTkXymNMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Referências**\n",
        "\n",
        "GÉRON, A. **Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems**. Canada: O’Reilly, 2023."
      ],
      "metadata": {
        "id": "wB22N1bNfRTX"
      }
    }
  ]
}