Multiple_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOnzQzxYJ815DFcybSLsH6n",
      "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-Learn/blob/main/Multiple_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": [
        "# **Modelos de Regressão Linear Simples e Múltipla**\n",
        "\n",
        "A regressão linear múltipla é usada para prever uma variável dependente contínua a partir de múltiplas variáveis independentes. É uma extensão da regressão linear simples para múltiplas variáveis independentes.\n",
        "\n",
        "Neste caso, além da variável ENGINESIZE, foram consideradas para efeito de análise o FUELCONSUMPTION_COMB e CYLINDERS, enquanto a variável dependente é CO2EMISSIONS.\n",
        "\n",
        "Cada variável independente contribui para prever o valor da variável dependente com um peso específico, representado pelos coeficientes 𝜃1,𝜃2,…,𝜃𝑛, no modelo.\n",
        "\n",
        "**Modelo Matemático**:\n",
        "\n",
        "  $$ \\hat{y} = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\cdots + \\theta_n x_n $$\n",
        "\n",
        "\n",
        "**Representação na forma vetorial**:\n",
        "\n",
        "  $$ \\hat{y} = \\theta^T X $$\n",
        "\n",
        "  $\\theta\\$: Vetor de coeficientes.\n",
        "\n",
        "  $X$: Vetor de recursos (variáveis independentes).\n",
        "  Funcionamento:\n",
        "\n",
        "Cada coeficiente (𝜃<sub>i</sub>) representa o impacto relativo da variável independente correspondente.\n",
        "Todavia, a adição de múltiplas variáveis preditoras, embora possa melhorar a precisão do modelo, também pode distorcê-lo, pode introduzir a problemas como o da multicolinearidade (isto é, quando as variáveis preditoras estão altamente correlacionadas). Isto pode distorcer os coeficientes e produzir *overfiting* (sobreajuste ou \"ajustamento excessivo\"), o que pode reduzir a possibilidade de generalização e até a interpretabilidade do modelo."
      ],
      "metadata": {
        "id": "_U8KGyn4B3g4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Instalando bibliotecas Python**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "QqIc9YPsDFcH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VeoRKn1R6xYL",
        "outputId": "682ec26d-b440-467d-dcb2-a1dbfcf3932e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.6.0)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.8.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.55.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
            "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (11.0.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.26.4)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.2)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.26.4)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn) (2.2.2)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.10/dist-packages (from seaborn) (3.8.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.55.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.0.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-learn\n",
        "!pip install matplotlib\n",
        "!pip install pandas\n",
        "!pip install numpy\n",
        "!pip install seaborn\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "fzWpqBhG7aqQ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Importação e Análise Exploratória dos Dados**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "11krS7esDObJ"
      }
    },
    {
      "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(\"O conjunto de dados tem {} linhas e {} colunas\".format(df.shape[0], df.shape[1]))\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "Caju79tN7i3p",
        "outputId": "959bdb3c-e1f1-48b0-f7a9-eb2a6aaec56c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O conjunto de dados tem 1067 linhas e 13 colunas\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-b2e43ea2-8ff8-420b-94eb-735800509eea\" 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-b2e43ea2-8ff8-420b-94eb-735800509eea')\"\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-b2e43ea2-8ff8-420b-94eb-735800509eea 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-b2e43ea2-8ff8-420b-94eb-735800509eea');\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-f02d304d-29f6-462a-b485-a119c4976a0b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f02d304d-29f6-462a-b485-a119c4976a0b')\"\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-f02d304d-29f6-462a-b485-a119c4976a0b 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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecionando as variáveis independentes de interesse\n",
        "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
        "cdf.head(9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "orln7jp57yIn",
        "outputId": "d24340bb-b5d6-4b83-e803-038b9c86a508"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
              "0         2.0          4                   9.9                  6.7   \n",
              "1         2.4          4                  11.2                  7.7   \n",
              "2         1.5          4                   6.0                  5.8   \n",
              "3         3.5          6                  12.7                  9.1   \n",
              "4         3.5          6                  12.1                  8.7   \n",
              "5         3.5          6                  11.9                  7.7   \n",
              "6         3.5          6                  11.8                  8.1   \n",
              "7         3.7          6                  12.8                  9.0   \n",
              "8         3.7          6                  13.4                  9.5   \n",
              "\n",
              "   FUELCONSUMPTION_COMB  CO2EMISSIONS  \n",
              "0                   8.5           196  \n",
              "1                   9.6           221  \n",
              "2                   5.9           136  \n",
              "3                  11.1           255  \n",
              "4                  10.6           244  \n",
              "5                  10.0           230  \n",
              "6                  10.1           232  \n",
              "7                  11.1           255  \n",
              "8                  11.6           267  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ac079ec0-a769-4b62-8a5d-0500861b37bb\" 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_CITY</th>\n",
              "      <th>FUELCONSUMPTION_HWY</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>9.9</td>\n",
              "      <td>6.7</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>11.2</td>\n",
              "      <td>7.7</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>6.0</td>\n",
              "      <td>5.8</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>12.7</td>\n",
              "      <td>9.1</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>12.1</td>\n",
              "      <td>8.7</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>11.9</td>\n",
              "      <td>7.7</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>11.8</td>\n",
              "      <td>8.1</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>12.8</td>\n",
              "      <td>9.0</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>13.4</td>\n",
              "      <td>9.5</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-ac079ec0-a769-4b62-8a5d-0500861b37bb')\"\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-ac079ec0-a769-4b62-8a5d-0500861b37bb 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-ac079ec0-a769-4b62-8a5d-0500861b37bb');\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-29c440c8-557c-4399-9171-47cca3300ef5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-29c440c8-557c-4399-9171-47cca3300ef5')\"\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-29c440c8-557c-4399-9171-47cca3300ef5 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_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          19.2,\n          19.8\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          14.7,\n          8.7\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotando gráficos de dispersão em pares para as variáveis selecionadas\n",
        "sns.pairplot(df, x_vars=['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_COMB'], y_vars='CO2EMISSIONS', kind='scatter', plot_kws={'color': 'green'})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "zlpX2yOtAaKW",
        "outputId": "1901181c-ac22-48b5-c5a7-5d39d1ad2b7c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 750x250 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotando gráfico de correlação entre as variável depedente, Emissão de CO2, em relação a variável idependente, Tamanho do Motor\n",
        "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='green')\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": "Fo8Ju9VW8H2b",
        "outputId": "9253468c-ed55-4cfe-856f-717d33fc650c"
      },
      "execution_count": 14,
      "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": [
        "# **Modelagem em Machine Learn**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "lryr2HDYDo3k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separa os dados nos conjuntos de treinamento (0.8) e teste (0.2), utilizando a função np.random.rand() para mascarar, selecionar linhas aleatoriamente\n",
        "msk = np.random.rand(len(df)) < 0.8\n",
        "train = cdf[msk]\n",
        "test = cdf[~msk]"
      ],
      "metadata": {
        "id": "ceLZk6tM8-lr"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Treina o modelo com base na distribuição dos dados\n",
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='green')\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": "ofK7Px7v8OvU",
        "outputId": "667a315c-1916-42a4-ff57-d9d00d316c43"
      },
      "execution_count": 16,
      "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": [
        "# Utilizando a biblioteca Scikit-learn para modelar os dados\n",
        "from sklearn import linear_model\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": "8O4GygFrCqjM",
        "outputId": "83f5e05e-a7b1-4f4e-a169-adb0df06b608"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes:  [[40.28503151]]\n",
            "Intercepto:  [122.71917194]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Modelo de Regressão Linear Simples**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "Gh1LPcq2D9PA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Modelo de regressão linear simples, traçando a linha de ajuste (de tendência) dos dados\n",
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='green')\n",
        "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
        "plt.xlabel(\"Tamanho do Motor\")\n",
        "plt.ylabel(\"Emissão de CO2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "dO5fGzIiCyRS",
        "outputId": "78129dca-41f9-4c77-d4f7-7203bb38418a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Emissão de CO2')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "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": [
        "O gráfico a seguir é produzido utilizando a função `learning_curve` da biblioteca scikit-learn, para avaliar o desempenho do modelo de regressão acima. As métricas utilizadas são:\n",
        "\n",
        "* **RMSE (Root Mean Squared Error)**: métrica de avaliação que mede a diferença entre os valores previstos e os valores reais.\n",
        "* `train_scores`: Desempenho do modelo no conjunto de treinamento.\n",
        "* `valid_scores`: Desempenho do modelo no conjunto de validação cruzada."
      ],
      "metadata": {
        "id": "0Hd8EeJBq59z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "train_sizes, train_scores, valid_scores = learning_curve(\n",
        "regr, train_x, train_y, train_sizes=np.linspace(0.01, 1.0, 40), cv=5,\n",
        "scoring=\"neg_root_mean_squared_error\")\n",
        "train_errors = -train_scores.mean(axis=1)\n",
        "valid_errors = -valid_scores.mean(axis=1)\n",
        "plt.plot(train_sizes, train_errors, \"r-+\", linewidth=2, label=\"train\")\n",
        "plt.plot(train_sizes, valid_errors, \"b-\", linewidth=3, label=\"valid\")\n",
        "plt.legend(loc=\"best\", fontsize=12)\n",
        "plt.grid(True)\n",
        "plt.xlabel(\"Tamanho do conjunto de treinamento\", fontsize=14)\n",
        "plt.ylabel(\"RMSE\", fontsize=14)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "-GyOdQiPoA32",
        "outputId": "152e5e50-05c1-4c30-ccfd-7780861f1c6c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAG1CAYAAAAFuNXgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABpY0lEQVR4nO3dd3hUVd4H8O+0TOokhCQkAZLQexAiJVJFioAKioqICsKrghQVbCgrYFlcF0FQQFQEdRcpFhALRYQgCigBFFgIPaEkhJZG2iRz3j8OM8mU9GnJfD/Pc5/MnHvn3nNPJjO/nKoQQggQERER1RFKV2eAiIiIyJ4Y3BAREVGdwuCGiIiI6hQGN0RERFSnMLghIiKiOoXBDREREdUpDG6IiIioTlG7OgOuYDAYcPHiRQQEBEChULg6O0RERFQJQghkZ2cjMjISSmXZ9TMeGdxcvHgRjRs3dnU2iIiIqBrOnTuHRo0albnfI4ObgIAAALJwdDpdjc6l1+uxZcsWDBw4EBqNxh7Zq3VYBhLLQWI5sAyMWA4Sy0GyRzlkZWWhcePGpu/xsnhkcGNsitLpdHYJbnx9faHT6Tz2TcsykFgOEsuBZWDEcpBYDpI9y6GiLiXsUExERER1CoMbIiIiqlMY3BAREVGdwuCGiIiI6hSP7FBMRERkqbi4GHq93u7n1ev1UKvVyM/PR3Fxsd3PX1uUVw4qlcquna0Z3BARkUcTQiAtLQ2ZmZkQQjjk/OHh4Th37pxHTxxbUTlotVqEhITUeBQzwOCGiIg8XGZmJjIyMhAaGgo/Pz+7ByAGgwE5OTnw9/cvd1bduq6schBCQK/XIzMzExcuXACAGgc4DG6IiMhjCSGQnp4OnU6HkJAQh1zDYDCgsLAQ3t7eHh/clFUOPj4+CAgIwPnz53HlypUaBzeeW8pEROTxiouLUVxcbJemEKoZhUKBwMBAFBQU1LjvE4MbIiLyWEVFRQAAtZoNGe7A2Km4ph2vGdwQEZHH8+SOvu7EXr8Hhqp2kpcHpKcD16/LLSYG4MLjREREzseaGzsZPlyFRo2ADh2A3r2Br75ydY6IiIg8E4MbOwkKMn9+/bpLskFEROQUMTExGDt2rKuzYRODGzsJDjZ/zuCGiIhc7ffff8fs2bORkZHh6qw4Ffvc2ElQkPmslteuuSgjREREN/3++++YM2cOxo4diyDLJoYaSkpKctt5e9wzV7VQvXrmz1lzQ0Tk4VJTgdmz5U83ZzAYkJ+fX6XXaLVau64HZU8MbuwkONi85obBDRGRh0tNBebMcVlwM3v2bLzwwgsAgCZNmkChUEChUODs2bNQKBSYPHky/vvf/6Jdu3bQarXYtGkTAGDevHm47bbbUL9+ffj4+CAuLg5f2RglY9nnZuXKlVAoFPjtt98wbdo003IW9957Ly5fvuyUezZis5SdWNb2sVmKiIhc6b777sPx48fx5ZdfYsGCBablJUJDQwEAv/zyC9auXYvJkycjJCQEMTExAICFCxfinnvuwejRo1FYWIjVq1fjgQcewPfff4+hQ4dWeN0pU6agXr16mDVrFs6ePYv33nsPkydPxpdffumwe7XE4MZO2CxFRFSH3HorkJZW9dcVF8sNAG4uIaAYMAA6tVpOUKdSya2qwsOBffuq9JLY2Fh07twZX375JYYPH24KXoySkpJw6NAhtG3b1iz9+PHj8PHxMT2fPHkyOnfujPnz51cquKlfvz62bNlimpDPYDBg0aJFyMzMdNpkiQxu7MRWs5QQACe9JCKqhdLSgJsrVNeUIiMD7vhV0KdPH6vABoBZYHP9+nUUFxejV69ela55efLJJ82CmF69emHBggVITk62CrAchcGNnVg2SxUVATk5QECAS7JDREQ1ER5evddZ1txkZEAEBUHcrLlR1KTmxs6aNGliM/3777/Hm2++iYMHD6KgoMCUXtlal6ioKLPn9W42bVy/fp3BTW1j2SwFyNobBjdERLVQFZuAbNq/H4iLg9i6FVnNm0On00HhRkOnS9fQGP3666+455570Lt3byxZsgQRERHQaDRYsWIFVq1aVanzqsoI3oQQNtMdgcGNneh0gFIJGAwladevAxYBLBERkdNUtY/L119/DW9vb2zevBlardaUvmLFCntnzaHcJ4Ss5ZRKLsFARESlREQAs2bJny7i5+cHAJWeoVilUkGhUKDY2LQG4OzZs1i/fr0Dcuc4DG7syLJpisPBiYg8WESEnMTPhcFNXFwcAODVV1/FF198gdWrV+PGjRtlHj906FDk5ubizjvvxIcffojXX38d3bp1Q/PmzZ2VZbtgcGNHXF+KiIjcSZcuXfDGG2/gr7/+wtixYzFq1KhyJ9Tr168fli9fjrS0NDz77LP48ssv8a9//Qv33nuvE3Ndc+xzY0ec64aIiNzNzJkzMXPmTLO08jr3jhs3DuPGjbNKnz17ttnzs2fPmj0fO3aszVXC+/btCyEEDAYDsrKyKp3vmmDNjR2xWYqIiMj1GNzYEZuliIiIXI/BjR2xWYqIiMj1GNzYEZuliIiIXI/BjR2x5oaIiMj1GNzYEfvcEBERuR6DGztisxQREZHrMbixI8vgJiPDfK0pIiIicjwGN3Zk2SwlBOCk+YqIiIjoJgY3dmRZcwOwaYqIiMjZGNzYkZ8foLZY0IKdiomIiJyLwY0dKRQcMUVERHXTypUroVAozNaU6tu3L/r27Vvha3fs2AGVSoVdu3Y5LoOlMLixM46YIiIici2uCm5nnMiPiIg8xZYtW1ydBZsY3NgZm6WIiMhTeHl5uToLNrFZys7YLEVERO7gq6++gkKhQEJCgtW+ZcuWQaFQ4PDhw/j7778xduxYNG3aFN7e3ggPD8e4ceNw9erVCq9hq8/N+fPnMXz4cPj5+SEsLAzPPfccCgoK7HVblcKaGztjsxQRUe1lMACV+E6v8jmzsxUoKACUNahSqF+/aq8fOnQo/P39sXbtWvTp08ds35o1a9CuXTu0b98e7777Lk6fPo3HH38c4eHhOHLkCD766CMcOXIEe/bsgUKhqPQ18/LycMcddyAlJQVTp05FZGQkvvjiC/zyyy+Vz7gdMLixMzZLERHVXlevAmFh9j6rEkBgjc+Sng6Ehlb+eB8fH9x999346quvsGjRIqhUKgBAWloaEhISMHv2bADA008/jenTp5u9tnv37hg1ahR27dqFXr16VfqaH330EY4fP461a9figQceAAA88cQT6NixY+UzbgdslrIzNksREZG7GDlyJNLT07Fjxw5T2ldffQWDwYCRI0cCkEGQUX5+Pq5cuYLu3bsDAPbv31+l6/3444+IiIjA/fffb0rz9fXFk08+WYO7qDoGN3bGZikiInIXd955JwIDA7FmzRpT2po1a3DLLbegZcuWAIBr167hmWeeQYMGDeDj44PQ0FA0adIEAJCZmVml6yUnJ6N58+ZWTVmtWrWq4Z1UDYMbO2OzFBERuQutVovhw4fj22+/RVFRES5cuIDffvvNVGsDAA8++CA+/vhjTJgwAd988w22bNmCTZs2AQAMtXT1Z/a5sTPW3BAR1V7168u+LfZkMBiQnZ2NgIAAKGvQo7h+/eq9buTIkfjss8+wbds2HD16FEIIU3Bz/fp1bNu2DXPmzMFrr71mes2JEyeqda3o6GgcPnwYQgiz2pukpKTqZb6aGNzYmWVwk5UFFBVZrzlFRETuR6msWqfdyjAYAK1WQKer2Wip6urfvz+Cg4OxZs0aHD16FF27djU1Oxk7GQshzF7z3nvvVetaQ4YMwZYtW/DVV1+ZOhTn5ubio48+qv4NVAO/cu3MslkKADIygJAQp2eFiIgIGo0G9913H1avXo0bN25g3rx5pn06nQ69e/fGO++8A71ej4YNG2LLli04c+ZMta71xBNP4IMPPsBjjz2GxMRERERE4IsvvoCvr6+9bqdS2OfGzixrbgA2TRERkWuNHDkSOTk5AGQfm9JWrVqFQYMGYfHixZgxYwY0Gg1++umnal3H19cX27Ztw8CBA/H+++/jzTffRM+ePfHOO+/U+B6qwu1qbmbPno05c+aYpbVq1QrHjh0DIIepTZ8+HatXr0ZBQQEGDRqEJUuWoEGDBq7IrhVvb7nl55ekcTg4ERG5Uv/+/a2anowaNmyIb775xird8vixY8di7NixZmmlh5gbRUVFYcOGDVbpxcXFyMrKqnyma8Ata27atWuH1NRU01Z6ifTnnnsOGzduxLp165CQkICLFy/ivvvuc2FurbFTMRERkeu4Xc0NAKjVaoSHh1ulZ2ZmYvny5Vi1ahX69esHAFixYgXatGmDPXv2mCYdslRQUGC2roUxctTr9dDr9TXKq/H1pc9Tr54aqaklvcQvXy6CXm87Yq4LbJWBJ2I5SCwHloFRbSgHvV4PIQQMBoPDhj0ba0CM1/FUlSkHg8EAIQT0er2ps3NplX0vuWVwc+LECURGRsLb2xvx8fGYO3cuoqKikJiYCL1ej/79+5uObd26NaKiorB79+4yg5u5c+daNXUBcql2e3Vy2rp1q+mxED0BlIzZ27Xrf9Dpqtc5qzYpXQaejOUgsRxYBkbuXA7Gf6ZzcnJQWFjo0GtlZ2c79Py1RXnlUFhYiLy8POzcuRNFRUVW+3Nzcyt1DbcLbrp164aVK1eiVatWSE1NxZw5c9CrVy8cPnwYaWlp8PLyQlBQkNlrGjRogLS0tDLPOWPGDEybNs30PCsrC40bN8bAgQOh0+lqlF+9Xo+tW7diwIAB0Gg0AICPP1bh6NGSYyIj22HIkDY1uo47s1UGnojlILEcWAZGtaEc8vPzce7cOfj7+8Pb29sh1xBCmOa5qcoilHVNZcohPz8fPj4+6N27t83fR2X77LhdcDN48GDT49jYWHTr1g3R0dFYu3at2foXVaHVaqHVaq3SNRqN3f7gSp/Lcth3VpYKGo119VpdY8/yrM1YDhLLgWVg5M7lUFxcDIVCAaVSWaMJ9spjbIIxXsdTVaYclEolFApFme+Zyr6P3L6Ug4KC0LJlS5w8eRLh4eEoLCxERkaG2TGXLl2y2UfHVbh4JhERkeu4fXCTk5ODU6dOISIiAnFxcdBoNNi2bZtpf1JSElJSUhAfH+/CXJrjaCkiotqlrGHS5Fz2+j24XbPU888/j7vvvhvR0dG4ePEiZs2aBZVKhVGjRiEwMBDjx4/HtGnTEBwcDJ1OhylTpiA+Pr7MzsSuwMUziYhqB2MzR25ubrW7PpD93Lhxw9QsVRNuF9ycP38eo0aNwtWrVxEaGoqePXtiz549CL252MeCBQugVCoxYsQIs0n83AmbpYiIageVSoWgoCCk31wt09fX1+6dfg0GAwoLC5Gfn+/xfW5slYMQAkVFRcjKykJWVhaCgoJsDgOvCrcLblavXl3ufm9vbyxevBiLFy92Uo6qjs1SRES1h7HPZrq9lwO/SQiBvLw8+Pj4ePxoqfLKQaVSISIiAoGBgTW+ltsFN3UBm6WIiGoPhUKBiIgIhIWFOWTCQb1ej507d6J3795uO2rMGcorB7VaDZVKZbfgj8GNA1jW3OTmAgUFgI3R6ERE5CZUKlWNm0PKOm9RURG8vb09OrhxZjl4buOfA3FlcCIiItdhcOMADG6IiIhch8GNA2g0gL+/eRpHTBERETkHgxsH4YgpIiIi12Bw4yAcMUVEROQaDG4chBP5ERERuQaDGwdhsxQREZFrMLhxEDZLERERuQaDGwdhsxQREZFrMLhxEDZLERERuQaDGwdhcENEROQaDG4chH1uiIiIXIPBjYOwzw0REZFrMLhxEFvNUkK4Ji9ERESehMGNg1g2SxUWAnl5rskLERGRJ2Fw4yC2VgZn0xQREZHjMbhxkMBA6zR2KiYiInI8BjcOolIBQUHmaQxuiIiIHI/BjQNxxBQREZHzMbhxIE7kR0RE5HwMbhyIE/kRERE5H4MbB2KzFBERkfMxuHEgNksRERE5H4MbB2KzFBERkfMxuHEgNksRERE5H4MbB2KzFBERkfMxuHEgNksRERE5H4MbB2KzFBERkfMxuHEgy+AmIwMQwiVZISIi8hgMbhzIslmquBjIznZNXoiIiDwFgxsHsqy5Adg0RURE5GgMbhwoIECuDl4aOxUTERE5FoMbB1IogKAg8zQGN0RERI7F4MbBLPvdsFmKiIjIsRjcOBgn8iMiInIuBjcOxuCGiIjIuRjcOBhnKSYiInIuBjcOxlmKiYiInIvBjYOxWYqIiMi5GNw4GJuliIiInIvBjYOxWYqIiMi5GNw4GJuliIiInIvBjYOxWYqIiMi5GNw4mGXNTUaGXB2ciIiIHIPBjYPZWhk8M9P5+SAiIvIUDG4czLJZCmDTFBERkSMxuHEwHx/Ay8s8jSOmiIiIHIfBjYMpFBwxRURE5EwMbpyAI6aIiIich8GNE3AiPyIiIudhcOMEbJYiIiJyHgY3TsBmKSIiIudhcOMEbJYiIiJyHgY3TsBmKSIiIudhcOMEDG6IiIich8GNE1j2uWGzFBERkeMwuHEC1twQERE5D4MbJ2BwQ0RE5DwMbpzAslkqJwfQ612TFyIiorqOwY0TWNbcAKy9ISIichQGN07A4IaIiMh53Dq4efvtt6FQKPDss8+a0vLz8zFp0iTUr18f/v7+GDFiBC5duuS6TFaCVgv4+pqnMbghIiJyDLcNbv78808sW7YMsbGxZunPPfccNm7ciHXr1iEhIQEXL17Efffd56JcVh5nKSYiInIOtwxucnJyMHr0aHz88ceoVyoqyMzMxPLlyzF//nz069cPcXFxWLFiBX7//Xfs2bPHhTmuGEdMEREROYfa1RmwZdKkSRg6dCj69++PN99805SemJgIvV6P/v37m9Jat26NqKgo7N69G927d7d5voKCAhQUFJieZ2VlAQD0ej30NRy2ZHx9ReepV0+F0rHklSvF0OsNNbq2u6hsGdR1LAeJ5cAyMGI5SCwHyR7lUNnXul1ws3r1auzfvx9//vmn1b60tDR4eXkhKCjILL1BgwZIS0sr85xz587FnDlzrNK3bNkCX8vOMNW0devWcvcXFHQFEGF6vnfvccTEHLfLtd1FRWXgKVgOEsuBZWDEcpBYDlJNyiE3N7dSx7lVcHPu3Dk888wz2Lp1K7y9ve123hkzZmDatGmm51lZWWjcuDEGDhwInU5Xo3Pr9Xps3boVAwYMgEajKfO4b75R4Y8/Sp6HhrbEkCHNa3Rtd1HZMqjrWA4Sy4FlYMRykFgOkj3KwdjyUhG3Cm4SExORnp6Ozp07m9KKi4uxc+dOfPDBB9i8eTMKCwuRkZFhVntz6dIlhIeHl3lerVYLrVZrla7RaOz2RqvoXCEh5s8zM1XQaFR2uba7sGd51mYsB4nlwDIwYjlILAepJuVQ2de5VXBzxx134NChQ2Zpjz/+OFq3bo2XXnoJjRs3hkajwbZt2zBixAgAQFJSElJSUhAfH++KLFcaR0sRERE5h1sFNwEBAWjfvr1Zmp+fH+rXr29KHz9+PKZNm4bg4GDodDpMmTIF8fHxZXYmdhccLUVEROQcbhXcVMaCBQugVCoxYsQIFBQUYNCgQViyZImrs1Uhy/WlGNwQERE5htsHNzt27DB77u3tjcWLF2Px4sWuyVA1sVmKiIjIOdxyEr+6iM1SREREzsHgxkksm6Xy84G8PNfkhYiIqC5jcOMkXBmciIjIORjcOInFpMoAGNwQERE5AoMbJ1GrgYAA8zQGN0RERPbH4MaJLPvdcMQUERGR/TG4cSKOmCIiInI8BjdOxOCGiIjI8RjcOBGbpYiIiByPwY0TseaGiIjI8RjcOBGDGyIiIsdjcONEbJYiIiJyPAY3TsSaGyIiIsdjcONEDG6IiIgcj8GNE7FZioiIyPEY3DiRrZobIVyTFyIiorqKwY0TWQY3RUXAjRuuyQsREVFdxeDGiSybpQD2uyEiIrI3BjdOpNMBCoV5GvvdEBER2ReDGydSKoGgIPM01twQERHZF4MbJ7NsmmJwQ0REZF9VDm5ef/117Ny50ywtPT0df//9t83j16xZg/vuu696uauDLDsVs1mKiIjIvqoc3MyePRs7duwwS1u6dCk6depk8/hjx45hw4YN1cpcXVTdifyKi4H164H584GvvgIOHgSys+2dOyIiotpP7eoMeJrqNktNmwYsWmSdHh4ONG9uewsMrHl+iYiIahsGN05WnWappCTg/fdt70tLk9uuXdb7IiOB8eOBV14BvL2rnlciIqLaiB2Knaw6zVILFlRvJuOLF4E33gA6dQL27q3664mIiGojBjdOVtXg5vJl4LPPzNMCA63nyynPsWPAbbcBL74I5OdX/nVERES1EZulnKyqi2cuWWIekGg0wJEj8jxnzgAnT5pvJ04AKSmAwWB+HoMB+Pe/gY0bgRUrgO7d7XM/RERE7qZawc3hw4exdu1as+cAsG7dOgiL9hPjPpKqUnOTlwd88IF52sMPAw0bysdt28rNUkGBHE01eTKwb5/5vmPHgB49gOnTgddfZ18cIiKqe6oV3Hz99df4+uuvTc+NAc1DDz1kdawQAoqqtKHUcVUJbj7/HLhyxTxt2rSKr6HVAt26Abt3y9qa2bOBwsKS/azFISKiuqzKwc2sWbMckQ+PYWsouMEgl2YozWCQc9qUNnAgEBtb+Wup1cCMGcDddwOPP152Lc60abIWx8en8ucmIiJyVwxunMyy5kYIICvLes2p778Hjh83T3v++epds3378mtx5s2TtTgrV7IWh4iIaj+OlnIyy+AGsN00NW+e+fPYWKB//+pf11iLs38/cOut1vuTkmQtzttvV/8aRERE7sDuwc3BgwexYMECLFiwAH/++ae9T1/r+fvLQKM0yxFTf/wB/Pqredr06VUb/l2Wdu1kLc4//wl4eZnvMxhkAPT55zW/DhERkatUObjZuXMnHnvsMezZs8dq38yZMxEXF4fnn38ezz//PLp3744pU6bYJaN1hUJRcafid981fx4ZCTz0EIDUVNmulJpaozxUVIvz5JMywCIiIqqNqhzcrFmzBuvWrUNbizHI27dvxz//+U+oVCo8+uijmDhxIkJCQrBkyRKsX7/eXvmtE8oLbs6ckQtjljZ16s1altRUYM6cGgc3RsZanNmzzdMLCoDhw+UMx0RERLVNlYOb3bt347bbboNOpzNLX7ZsGRQKBT788EOsXLkSH3zwAX777TdoNBqsXLnSXvmtE8qbyG/hQvMJ+Pz9gaeeuvnkxx/lz6Iiu+VFrQZmzQIsK9hSU4F77+WMxkREVPtUObi5ePEiOnbsaJW+fft26HQ6jB071pTWvHlzDBkyBPssxyB7uLJqbq5fBz75xHzf/43KQdDp/cCqVcA//iETZ8+WbUr799utFufdd4F+/czT/vhDBlbVWdeKiIjIVaoc3Fy/fh0+FhOipKSk4PLly+jZsyeUFhO2NG/eHFcsZ6LzcGUFN8uWATdulKQrlcAzvp8AcXHA6NElO376SabFxckX2YFGA6xdCzRtap7++edy4U4iIqLaosrBTUBAAC5cuGCWZhwVFRcXZ3W8QqGAN+f4N2OrWaqwEFi0yDz9gQeAmJdGAomJwGOPme8cOFCmm9qsaq5+fWDDBtkUVtoLLwCbN9vtMkRERA5V5eAmNjYW33//PW6UqmL49ttvoVAo0Lt3b6vjT506hcjIyJrlso6xVXPz5ZfWLUzPPw8gIgLo3FnO9Ffali2y529EhF3z1r498J//mKcZDHK01okTdr0UERGRQ1Q5uBk3bhyuXbuGPn36YNGiRZg8eTK+/PJLREVFoW/fvmbHFhcXY+fOnejQoYO98lsnWAY3165ZD//u3dtimLatyGLaNId0iBk2TC7HUFpGBnDPPUBmpt0vR0REZFdVXn7hkUcewbZt2/DZZ5/hwIEDEEJAp9Nh+fLlVv1tfvjhB1y5cgWDBg2yW4brAstmqd9/l5UwpZkttWAwAKdOycf16gEhITLY2bNHdpQZOdLueZw5E/j7b/Nh6ceOya4/GzYAKlXF58jJAbZvB37+WdZKeXvLzcen5LGt52FhwG23ccVyIiKqnmqtCr5ixQqMHz8eu3fvRv369TFo0CA0bNjQ6jitVosFCxZg2LBhNc5oXWJZc2MZ2LRqBQwdWirhwoWSMdk9egCTJgGDB8vnL70kq1rsHAkoFHKtqRMngL/+Kkn/4QcZ+Myda/0aIWRAtGmT3HbtAvT66l0/IkIuBfHII9aLihIREZWn2l8bPXv2xAsvvIBx48bZDGwAYNCgQXjmmWdQv379amewLrK1vlRp06ZZfKGXbpJq0QK4807AWBuWnCwnx3EAPz9ZSxMSYp7+9tuyjxAgm9TWrVPg/fdvQUyMGh07ynhr+/bqBzaArOkZMwaIj5cVVERUSXaayZyoNuP/xC5g2SxVWmgo8OijFoknT5Y8btFC/pw3ryQCeustID3drnk0io4Gvv7aej2sceNk4BEaCowerca2bdFITbXD4lcW/vhDXufRR2UFFhFVwM4zmRPVRlVulvq8mqsqPmY5lNmDlVdzM3my7INipnTNTfPm8mf79sATT8h5brKz5TTDS5faPa+A7Nz8/vvAxIklafn5latR0WiAnj2Bbt1k16H8fCAvT/609Tgz0zyWM/rPf4BvvpFrYk2fbqOMiDydELJjnHG5m+PHZRu3n1/Zr0lNlZ8hTz1l95GXRK5U5eBm7NixUNxcnloIYXpcFuMxDG5KlBXceHubBxAmls1SRnPmyJmLs7OBjz6SkVG7dnbNq9GECbLvzYcfVnxsTIzsEnTnncDttwMBAZW/jhBylYlp0+Rnc2m5uXKS5k8+kRVXI0bYZ6V0IrdWXgCSmQls2yYj/23bgLS0kn2jRsmfoaHyn6L27eXnR8uW8mfTpiW1PPfcw+CG6pRqdShWq9UYMmQIunfvbu/8eAQfH0Crte5IPHas/ByyYqzK8PICGjcuSW/QAHjlFVmdYTDIIVY//eSobGPhQuB//wN27jRP9/YWaNs2HY88EoKhQ1Vo0aL6QYdCITtTDxgAfPCB/Ny1nOInOVlOcNi7t8zTLbdU71pEdlOVGpCq1paUDkAaNAAOHJA99jdvlkMti4vLf/3ly3Lbvds8XaGQ5wOAb7+Vj8voP0lU64gqevDBB4W3t7dQKpWiXbt2Yt68eSI9Pb2qp3GpzMxMAUBkZmbW+FyFhYVi/fr1orCwsEqvi4gQQtZTyE2hECIpycaBxcVCeHvLg1q3tt6flydEdHTJiX76qVr3UVmZmUJMmSJE//5CPPecEJs3C5GZWb0yqIxLl4R48klZPqXLq3S5PfGEELt2CZGfb/fLV4rBIMS5c0Js3qwXb775q7h40f7lUCUXLwoxa5b86YJzV/dvolZLTJRvyMREIUQFZWBxrE0Gg3xDX78uxPffy+MHDxYiNNT2HwIghEYjRLduQowYIZ/HxwvRoYMQQUFlv8Zya95ciEmThNixQwhbea/ie8sj3ws2sBwke5RDZb+/q1xzs2bNGly/fh1ffPEFVq5ciRdeeAGvvPIK7rrrLowbNw6DBw+2mu+GrAUHm/f3u+ceWVts5fz5kmHgpZukjLy95fAlYxX0888D/ftb9wC2E53OepmImoyKqkhYmPwnd+JE4NlngYQE8/1CAB9/LDetFujaFejVS/bzue02IDDQfnkpKgJOnwaOHjXfjh2TLYOyIrQnZs6U/wR36CBbAjp0kFvbtuV3f7AbRzY1eEozRlVqV65elYvYAnLm8MOHoczJQdPERCgPHZJ/IHl5JR3MjD3jJ0+WndJyc623GzesJ+i0VSvbsqVs/73zTqBPH8DXV+bl669l1WfnzvK469dl8/aJE7IJ27L61ejkSbktXiz/2AcMKGljbtiwar//1FQolyyB1nLBOiInqNY3YL169TB16lRMnToV+/fvx/Lly7F69WqsX78e4eHhGDNmDB5//HG0sPVlTADkSO4jR+RjlQp4+eUyDrQ1UsrSyJGyfWbPHnnS5cvtuuaUO7jlFjm8/OuvZfyWnGx9TEEB8OuvcgNkrXtsbEmw06sXYGslEINBNn1lZMjvgNI/z54tCWJOnJBrgFXGpUty+/nnkjSFQnZzKB3wdOoENGvm4rl8KvoiLy6W76s9e2SHKEB+iVfUWbU2s/Ulnpkpy+HwYflz/37ZTnvtWsnrZswAAKgAVDgvu2UzUVUMHSp7+TdpUrnj69WTkX/XrkC/fiX/We3bJ3/vd90lJwo9erTkNVlZ8g/u66/l89hY+YYF5P03bizniSirDTo1Fao334S35fTrZWHnZrKnatcNWcjPzxerVq0SAwYMECqVSqhUKrF582Z7nd6u3KFZ6upVISZOFGLgQCHWri3nwA8/LKkyXrKk7ON+/73kuNBQ2X7kJM6ucs3NFeLNN4Xw9a18bbtxa9JEiL59hejUSYiYGFljX1aTl7O2gAAhevcW4tlnhfjsMyEOHRJCr69ioVy4IN9ITz4pRMOG8sQxMULceadst1u0SL5H0tNlk0dpls0kFy8K8e23Qrz0kiwsHx/bGff2FmLAACE++UT+Um6qE1Xwa9fKe3zkESEGDRKiUSPHvgk0GiHCwuTvrG1bIWJjhejcWYjbbpNvVkCI558XYs8e+Xsqr1moKk1Hlr/7S5eE+PxzIUaNEiI4uOJ8e3vLpqzBg+UH2r/+JcSaNULs3SvEli1CAGL7u+9W7r1Qmea66tyjG3Da34Sbl4tbN0uVRavVom/fvjhz5gyOHDmC1NRU5BubU8hKcDCwZEklDixrpJSl+HhZg7Nmjew8OHeu7WmE6wAfH+DVV2UH7PnzZe3IoUPWtfi2nDkjN0fw8xMoLDRAr6/E2hSlZGfLVoLSLQXe3vIf5c6db25Rl9H+1w+hnfR/Jf/VCiGHsH31lRzGdvWq+YnPnpWbJZ1Ojp4xbhqNTH/5ZSApCUhJqVzG8/OBrVvl9uyzcqbskSPlEDmj2vTf+KFDclrujRtL/u4sV5G1FBQkq950Olm1+MADQNOmKNJocCQ9He0GDIA6IEA2M+XkyLbTkyflNN/vvSfnSNBoZJViWeWzfz8QFyebno3NTOWJiJCT+FVHWJicVOrRR2WN3R9/yPdFWc1Y+fklTVll6PnKK1DNny/LqH59IDxc1iRZbleuyBdcuiSrYbXasvNZ1ebR2vQ+rAlPaTauhBoHN0VFRdiwYQM+/fRTbNmyBcXFxejSpQtee+019O/f3x559GyVaZYymjtXjnooLJTf+nl5crrgOvomb9iwZMHR69dlLf+vv8plH/74o/JNSFUVGgq0aWO9NWhQhO+//wktWw7GsWMaHD4svy8PH5a/RoOh8tfIz5f38McfpqtCjZfRZo0et7S4glvy96DjsTW4JfVH1Me18k5lLStLfmEa+4kYbd1q+/jISGS2vBVHwvrhSGYjpG7+C7mNWyHvUjZyC1XIhS9yc3yR+1+55alOIFN7C57x1yNXHwJcfxo+ywLhEygD0/I2X1/53deihdxiYsrpPladUUeWxxcUAN9/D3z2mfxZXoTcuDEwZIicbqFdO9m+GBYm9xkDkJdfBjp3htDrcfbHH9F2yJCS4NFo/34Z3PTqVblgxZEiIuQcWbbKT6WS/zStXi3L7upVGUh/9JEcqqjXAxcvyuHnlkM/S1EXFMh+RpWdhXPIEPnTz082e0VEyHIODZVbWJgMFgH5n0rLloC/f/nnrK1f+pV9j1++LD8sfvlFPq/MpK51POCrdnBz6NAhLF++HKtWrcKVK1cQEhKCKVOmYNy4cWjfvr098+jZjP9BenkBjRqVf2yTJvI/6Hfekd/sCxcCjz1WJ9+4lurVk5+Jxs/F/Hzgzz9loPPrr8Bvv1kPKbdFpZL/jAcFyXOGhgKtW5sHMWWtJqJPSUXbtavQ7J1b0P7+KNx/f8m+vDzZneHQoZtbwjUc3KdHOhpU+h6LoMGhkxocOumLL3AXgLsAAI1wDrfgIDo2vo5b+ofglh5+aPp/t0P52y55I8b/rEtvZVRf5cAP/0NbHGk2DIfbPogj2Y1x+IQ3LuwofdQI4Fw5GS0GkHtzAwCEAWmQWxWp1bKfknFqltLTtDRKT4Xy5hdWUWgEcnLk7zg7W27Gx6a0JIGiJQJIFYA4Dxz8Czh8SP5y0OnmdlNQEPwzziNqSAfEDGyJ6LZ+CG0XBkWkC/6WygtA7HHuimp5IiJKrl2/vgxuFiwoCcyEkF+mZ8/KYepHj8qg59gx4PBh5AcGQgtAkZVVuepVoxs35Garg52R8Y/M31/msUkT+V9Po0YlPxs1kv/9VIWjh/ZX5dyWQVluriznvXtlQPP778A5iz/IwYNlYNimDdC9u6xNvfVW87lGamvAV0lVDm6WLFmCTz/9FAcOHIBSqcTAgQMxfvx43HPPPVA7aISOxyq9GnizZpVbivuVV4BPPy2p4j140PX/HbqAt7f8x7hXL9nHs3Sf2Bs3SmrCjUGM8ae/fw0mBkxLQ+s1a6CfNg2IipIXOnkSOH4cPsePo/PBg+hsbPbJzIQAkIoI7EdnHEAn7Edn7EdnpCC6Spc9j8Y4j8b4/hyAFXJTowC+A5Xw8VfB27utWe2IdxPAp0kufPKuw1ufDZ9rF3DxdB6OBPXA2YybM0yeurm5WFGRnMzRckJHAPBWd4AOacjuVg95RZU5WySAOcBHxueNAAy1fWjGzZ8/3twgyy46umSLiSn13Lsh/F6ciyKvhtBflN8/qam+OHas5D70+ptbWjTEYyuA09FQ3Ay4je85hcL8sfwZAe+7ZyMgGwiAfI/6+bnRgrLG+XIaNJDNbEY3a7P2vPYaekyZApVSjeyL2cg8l4XMCznITM1F5qkryEzJRGYmkHUhG4bjJ4CISBkE5eVBkZ8HFJR0b1CgJDhSoRhqFMktpwiqE8VQnyiCGnqocQJqHDXtV6EYwGCIO+ZDhIbBEBQMEREB0TgahuAQiJBQiOD6EFpvGAyAOJ0PMScJQpMPEVMSk9nqeITkAmjmnIS3bx60beRnj1Zr/lOpBK5f1+L6dcD/dBqUc94GBg0D6pkHFVafPXkGqKGE6rvvZFD5xx9ydeKK5jYC5HeAcYTFv/8t06KjZZDTpUvJUNLKBpy1rKZHIURVQmlAqVRCo9Fg8ODBGDNmTJmLZlrq2rVrtTLoCFlZWQgMDERmZiZ0Ol2NzqXX6/Hjjz9iyJAh0FhWP9dUSop8MwIyut6wofzjU1Pltm6dHB4OyE/Cjz6SI1tK/wdmRw4tg9pCCBT9+99Qv/QSDLfeCmVamhzGXw1XEWwKdow/j6OVnTNMtZlCIQOcgAD5Jx4QULL5+8svU4NBfm8ZDCVb6efGx8XFcisqMv9pM62wCIrsLCiDdFBq1FAq5bVUKpgeGzdF7g3kHjmN9KBo5BsCkJ2tqFLFDZVQwAA1iqCB3rSZniuKoPZSQuOthkZZDOX1K1B6e0FVmA+lQQ8lDGVuCghAqYLC1xsKH28oAvyhCAoE/Pyh8PeDQqMuCbIzM6Da8TOU/e+Asn69Mn/3KhWgzL8B5f+OQNmhHYaP9kO/fvIc9viuqOz3d7WqWvR6PTZu3IiNGzdW+jXFlYk0yZytNaXKs2yZrGYsLScHePhh+XjWrOp3NCTbjAHlZ59BfXMCIOW+feW/pkEDWU107Jgcox4WJj8RAgIAnQ71VSr0V6vRPy8PyP0FUO5A9vlMHPoxBQfvfwsHizvg4HEfHDrli/x8560/oVbLJqHmzWVWfX1L+smU3rzyruLcX7+jU8eWCEg+BixaiLxb4pF34TryLmcjDz5WWy78kFe/EW7oInD2jAEn1G1wrSjQafdW2wgh/7Rzcpx9ZTWAYKBSrTx+ADqU1IJRtQkooYcX9PCytRMouLkBAKKBqozlMQDIubldLu/AIAD3Az+Xd4yRH4CuwJ9ATDuYghtnqnJwM2bMGEfkg2yp7Egpo6eekjU8gOxkMnVqyT5fX6BjR/vmj2wHlKV17ChHEBk7ijRvXjLRWlyc7BdViWbDgP37cduPcbhtRiDQOQSA/G/6xAnZ8lh6q+kC8UqlbAVt376k32y7dvIWvGx8tlrS63X48UcDhgxpCs2hG8Ci7cDyefI+z56VywZs2iSHuZX+dr56cwOAIlmDdQItcAItcBwtS34qWiJHlN+B1E9TgACvAgSocqFT5iBAlYuA4gx4ZaQD7doD4Tf7Onl7A94+Vs0BQsj+s8nJsjtDUaWavYjIkquaT6sc3KxYscIR+SBbqjJSCrDd7HTrrXKirtxcYPRo4Lvv5AzGZB+PPCL7OJXq0Ff07rtQ9+0rnzioKRCQNSnGTs7GCaoBOXjl4sWSldZLb7bS8vJkTUzbtjKQadXKgauux8TIIPypp2Sn99275bj+336zOrQ+rqE+9qJ7VwE82Rfo2Bxo1wbC2weXLgEnfkhC/v9Nhm7l+wjo0ho6XUmzjEqlBaAFUKraev9+IK4P8Hki0LmM1WttKC6W5ZmcLGOz5GTrx+UMFgIgB0yp1fKnRiM/8Ev34zD+LCstL8+xM4G7QkCA7PYRGCj7vOl05iPjLJuwSj8X+fkwJJ9DUUQUilRaFBXBtBmb0cy23ELgUhqUEQ2g0GqhUNxsOrvZx0mpBBTFRVAU66FUCChyc6FIuwhFgwZQeHsBBgGFWgmFUgmFMEBRVAjoi6CAAcgvgP56NvL9Q1AALfKLNSgwaJBfrEF+kRoC7tI5yjWUF84B+y/Lz8GQEKdd1+E9gM+cOYM5c+Zg5cqVjr5U3VPVZilbFi4E3nhD/qeclydnIv3qK/mTam7uXFNgY2jbFsr//Q+iZ8+Ka2OqOgKmCseHh8vNLZSXby8vuWTAunWyae/KFbm69bJlss9Y//7ym8ciQFTg5j12ugHgZ6BDLtDWcbegUslR4I0by1ZESwaDzLpeXxLEAHps27YJd911J7y9NXZZvb6gQI76yskpGRFW1nOg5Mvb1AfGxmOFQt6fWm3+s7zHxnu23IqLrdPU6iKcOLEPd9xxK+rXV5uCmYCAyo2PKJs3gCrMgJ96FVj2aQWdYdUwfSXuPyZrVn9MrPhv2VgLm2B9rBAyuMrJ0eP7739Gr179UfxXEjB8GLB+g/xvotSxlsThIyi6934Urf4a+hZtodebd0639djW78bW76c4+TzEwoXAM89ARDayCq6FAERWNnDlCkRGJkRyMgz79sPQqg0Mai0M+YUwFBXDUFiM4rxCGLJvwFBsMOvVUwwV2s9bA8zbLj8HXn21cr8vO3BYcJOSkoI33ngDn3/+OYqKihjcVIcxuNFqzVcDrwzjl0qTJsD69cBDD8mfBQXAvfcCX34Js7HKVHXr1slaGwDw90fx669DWdkyrepEazWZmM2VqjrUOCREBjcDBrg0QKwKpbJkuhsjvR7w8jJAra7B6DsLWq3cnPjPb43p9QI//ngJPXsKq+l+nMpFfz8KhQx4/f0Bna4QDRsCmmuFAE4DjQuBZhWcwC8YmDUS6F0PsHcFcKoKCPIDnlKVc+6AmxuA/QYgbjiwqoyALzVVzmWUnV1SIztrFnDPPLnfySOsqlVftmvXLtx+++3Q6XQIDg7GsGHDkJSUBADIzc3FtGnT0LJlSyxfvhyhoaFYZLnSIlWsuNh8GHhVGy6Nf8wREfITce1aGeAAMsQfObLi2VepbCkpwJNPljz/4AOgSxccGznSjapN6rjS73FHHE+eqyqBsCODbEe+Z+197ogI2Q3i9tvlQquA7ANqnGbd3YObxMRE9O/fHwkJCcjJyUFGRgY2btyIfv364fTp0+jatSvee+89hISEYOHChTh16hQmTZpU6fMvXboUsbGx0Ol00Ol0iI+Px0+lVsPNz8/HpEmTUL9+ffj7+2PEiBG4dOlSVW/D/Z0/XzLFbnWbpErTaGQwM3asfG4wyAn+Pvmk5uf2NMXFcnr6jAz5/KGHTJMlJo0axS/PmnDkhHVElVWVL35PCbJr2d9mlYObd955B4WFhZg7dy7S09ORnp6Ot956C6mpqejVqxeOHTuGmTNn4uTJk5gyZQq05a0PYkOjRo3w9ttvIzExEfv27UO/fv0wbNgwHLm5hPZzzz2HjRs3Yt26dUhISMDFixdx3333VfU23F9VR0pVhkolVwyfOFE+FwJ44gm5unBqqvyDM64WTGV7++2StXaio4GlS+3X9uDpausHP1FdV9WAz8WBUJX73Pz222/o168fXnrpJVPajBkz8PPPP2PHjh3497//jWnTplU7Q3fffbfZ87feegtLly7Fnj170KhRI9OSD/1uDpxfsWIF2rRpgz179qB79+7Vvq7bqepIqcpSKoHFi+VwmPnzZdrUqfJ6ixbV2am47WbvXvlHC8iy/M9/5FAPIiKS3KCPYJWDm/T0dIwePdoqPS4uDjt27LDrPDjFxcVYt24dbty4gfj4eCQmJkKv15styNm6dWtERUVh9+7dZQY3BQUFKCg1VjPr5iJDer0e+hqOrzS+vqbnsaRMSoJxMEFRTAyEvceBzp0LpVYLlXHl8Jv9ovSnTsnFlMrr/ZeaCuXHH8PwxBNARITDysDtZGVB/fDDUNyckLJ4xgwYunUzjdH1mHKoAMuBZWDEcpBYDpI9yqGyr61ycFNUVAQ/Pz+rdGNa/bJWFayCQ4cOIT4+Hvn5+fD398e3336Ltm3b4uDBg/Dy8kKQxX/KDRo0QFpa2avyzZ07F3NsTLS2ZcsW+Pr61ji/ALC1rBWVq6nrb7+ZOrD/cu4c8n780a7nBwBtixZoOXgwmpbq06R58EEYFAoU1KuHGxERyI6KQk5EBG7c3HIbNEBASgr6vvkmfq1XD5nNSrr727sMHEF77RpiNm/G2UGDUBAcXKXXdlq4EFGnTwMArrVqhV2dO0PY+L3UhnJwBpYDy8CI5SCxHKSalENubm7FB8EJ89xUR6tWrXDw4EFkZmbiq6++wpgxY5CQkFDt882YMcOsqSwrKwuNGzfGwIED7bK21NatWzFgwAC7rqukfvllAIDQanH7o486ZJpH5euvQ1UqsDGlCwGfa9fgc+0aQm72dTISSqVcPgBArzNnYOjXD/oWLbD155/tXgYOceAANOPGodm0aUCnThUff5Ni9Wqot28HAIiAAAR89x0GN2lidoyj3gu1DcuBZWDEcpBYDpI9ysHY8lKRagU3//nPf7Bnzx6ztJM3+4gMGTLE6niFQoEffvih0uf38vJC85sjhOLi4vDnn39i4cKFGDlyJAoLC5GRkWFWe3Pp0iWElzP8VqvV2uzYrNFo7PZGs+e5UFwM3KwhUDRrBk0VO2VX2tNPyzlvADlU/F//kkP5cnLkUGcbEbLCYDB1OlZ98AFUH3wAdXg4OrVuDa/iYqgHDpSTOpTmLqvJCiEn3AKgMc64VhlnzwKTJ5ueKpYuhaZlyzIPt+t7oRZjObAMjFgOEstBqkk5VPZ11QpuTp48aQpmLG3atMkqTVHDkSQGgwEFBQWIi4uDRqPBtm3bMGLECABAUlISUlJSEB8fX6NruJVz50qGgduzM7Ely6UB/vUvGYR07iwDgStX5KitkyflKCvjCCELirQ0RKWlATt2yFlne/cGhgyRW8uWMriZM8d1nZWNk0v9+98yiAPkXAzx8XKG3PvvB2Jjbb9u6VLgp58A438Lo0fLjYiI3FaVg5szZ844Ih8mM2bMwODBgxEVFYXs7GysWrUKO3bswObNmxEYGIjx48dj2rRpCA4Ohk6nw5QpUxAfH8+RUvamUAChoXK77TY5Y6xxmHhiopzAbuRIIDkZYt8+KIwrCxYWygURf/4ZmDZNzpDcpYvcZ2t+cWdYsgR4803zNL1eBms7d8rlKeLjZfA1bJjsUK1QyPt9442S1zRpIkeaERGRW6tycBMdHe2IfJikp6fjscceQ2pqKgIDAxEbG4vNmzdjwIABAIAFCxZAqVRixIgRKCgowKBBg7BkyRKH5snpHDHHTUUqmpfA1gKQL74IdO6MoowMJM6bhy5XrkC1aZNcSdDozBm5ATIYmjVLLjHtwAUlzWRnm9c4KRQyyAoIkPuMdu+W24wZctLEe+6Rq3cbqVTAf/8rF8YhIiK35nYdipcvX17ufm9vbyxevBiL6/J/0PZYMLOqajIvgZ8fLnXpAsOQIVCp1cCxY8AzzwCWPeJPnZIz+QLAc8+VzLPjKJcvy6axffvkc29vudDlc88Bv/wi+zZt2CBXSi/dcfrkSeu8PfGEXMYiNZXzABERuTnPXovdXblDs1R5yqvlUSiANm2Azz6TzVc7d5qvwWT04YfA66/b7LRsF2fPyiWcjYFNUJBsKuvdWz5XKoFu3YB//hM4fLgkoCmrZvLDD+XKv8uWOSa/RERkNwxu3JGx5sbbG2jY0LV5saUy03BHRMiOyb16yVFSAPD880C9evJxXp4MkFq1ks09BoNMt8cyEIcOAT16AMePy+eRkcCvv8q0sgKzZs1kjc7u3bJW54035MgxQHZETkyUm/FeiIjIbTG4cTelhoFXazVwdzZqlKwhmToVUN9sET1/HnjkEdlpeffukpFV1Q1udu2StTMXL8rnrVoBv/8OtG8vn1cUmEVEyJFUM2eW1NL06+eylW2JiKjq6tA3Zx3hrGHgzlS6tiQ4GFi4UNauDB1acszevTLAeeWV6l/nu+/kqC7jat1dushgx8Gd4ImIyL24XYdij+eKkVKOZquzcuvWwPffA1u2AFOmlDQhbd4sfz7wgGwWatNGNm316VNS21OacYLAwEDghRdkzRcADBwIfP219YSCVc23i1e2JSKiqmNw425cMVLKlQYOlEPES88nA8imOWPzHCD7H3XsKDv1Gre2bWXzk+W6YaNGAStXygkFa8INVrYlIqKqY3Djbtx9pJQjTJwIDB8u552ZO7ek9qa0/HzZdLV3b0maVis7C5c2dSqwYEHd6qtERERVwuDG3dTFZqmKlJ7QLyBABjfbt8sRVMZRSomJ5oEfABQUlEwQCACTJsl5dC5dYlMSEZEH47+37qb0MHDLWglPotPJUUovvACsXi3L5fp1OUz75mzVVhYvlv10OBcNEZFHY82NOyk9DLx5c89sWimvE29QkBym3bp1yVDx336TTVEffyyHahvPQUREHovBjTtJSZELOgKe0yRlqTKdeG2tS2Wch4aIiDyeB1YNuDFPGylFRETkAAxu3IknjpSqKc5FQ0REFtgs5U48caRUTXEuGiIissCaG3fCZikiIqIaY3DjTozNUj4+nj0MnIiIqAYY3LiLoiIOAyciIrIDfoO6i9LDwNkkRUREVG0MbtwFR0oRERHZBYMbd8GRUkRERHbB4MZdcKQUERGRXTC4cRdsliIiIrILBjfuwlhzw2HgRERENcLgxh1YDgNXKFybHyIiolqMwY07SEmRAQ7AJikiIqIaYnDjDjhSioiIyG4Y3LgDjpQiIiKyGwY37oAjpYiIiOyGwY07YLMUERGR3TC4cQfG4MbXF4iIcG1eiIiIajkGN65WVAScOSMfcxg4ERFRjTG4cbXkZA4DJyIisiMGN67GkVJERER2xeDG1diZmIiIyK4Y3Lgah4ETERHZFYMbV2OzFBERkV0xuHE1Y3Dj58dh4ERERHbA4MaV9Hrg7Fn5mMPAiYiI7ILBjSuVHgbOJikiIiK7YHDjShwpRUREZHcMblyJI6WIiIjsjsGNKx08WPKYzVJERER2weDGlY4eLXnMmhsiIiK7YHDjSufOyZ/e3kB4uGvzQkREVEeoXZ0Bj5OaKreiIuDCBZkWEgIcOCAfR0RwvhsiIqIaYM2Nsy1bBsTFAd26AULItPPnZVpcnNxPRERE1cbgxtmeegpITAQWLy5JGzRIpiUmyv1ERERUbWyWcjZjs9O+fSVpnTvLjYiIiGqMNTeucuZMyePISNflg4iIqI5hcOMqpYObW25xWTaIiIjqGgY3rmIMbhQKoEsX1+aFiIioDmFw4yrG4CYyEtBqXZsXIiKiOoTBjSvk5ACXL8vHTZq4Ni9ERER1DIMbVzh7tuQxgxsiIiK7YnDjCqU7EzO4ISIisisGN67A4IaIiMhhGNy4wunTJY8Z3BAREdkVgxtXYM0NERGRwzC4cQVjcKPRAA0bujYvREREdQyDG2cToiS4iY4GVCrX5oeIiKiOYXDjbFevynluADZJEREROQCDG2djfxsiIiKHYnDjbAxuiIiIHIrBjbMxuCEiInIotwtu5s6diy5duiAgIABhYWEYPnw4kpKSzI7Jz8/HpEmTUL9+ffj7+2PEiBG4dOmSi3JcRQxuiIiIHMrtgpuEhARMmjQJe/bswdatW6HX6zFw4EDcuHHDdMxzzz2HjRs3Yt26dUhISMDFixdx3333uTDXVcDghoiIyKHUrs6ApU2bNpk9X7lyJcLCwpCYmIjevXsjMzMTy5cvx6pVq9CvXz8AwIoVK9CmTRvs2bMH3bt3tzpnQUEBCgoKTM+zsrIAAHq9Hnq9vkb5Nb6+sudRnz4NBQDh54eiwECghtd3B1Utg7qK5SCxHFgGRiwHieUg2aMcKvtahRBCVPsqTnDy5Em0aNEChw4dQvv27fHLL7/gjjvuwPXr1xEUFGQ6Ljo6Gs8++yyee+45q3PMnj0bc+bMsUpftWoVfH19HZl9c8XFuHvkSCiLipAVFYXtixY579pERES1XG5uLh5++GFkZmZCp9OVeZzb1dyUZjAY8Oyzz6JHjx5o3749ACAtLQ1eXl5mgQ0ANGjQAGlpaTbPM2PGDEybNs30PCsrC40bN8bAgQPLLZzK0Ov12Lp1KwYMGACNRlP+wefOQVlUBADw79ABQ4YMqdG13UWVyqAOYzlILAeWgRHLQWI5SPYoB2PLS0XcOriZNGkSDh8+jF27dtXoPFqtFlqt1ipdo9HY7Y1WqXOdP296qGzeHMo69ia3Z3nWZiwHieXAMjBiOUgsB6km5VDZ17ldh2KjyZMn4/vvv8f27dvRqFEjU3p4eDgKCwuRkZFhdvylS5cQHh7u5FxWETsTExEROZzbBTdCCEyePBnffvstfvnlFzSxCALi4uKg0Wiwbds2U1pSUhJSUlIQHx/v7OxWDYMbIiIih3O7ZqlJkyZh1apV2LBhAwICAkz9aAIDA+Hj44PAwECMHz8e06ZNQ3BwMHQ6HaZMmYL4+HibI6XcCoMbIiIih3O74Gbp0qUAgL59+5qlr1ixAmPHjgUALFiwAEqlEiNGjEBBQQEGDRqEJUuWODmn1cDghoiIyOHcLripzMh0b29vLF68GIsXL3ZCjuzIGNyEhAD+/q7NCxERUR3ldn1u6qyCAuDCBfmYtTZEREQOw+DGWVJSAGOtFIMbIiIih2Fw4yzsb0NEROQUDG6chcENERGRUzC4cZbTp0seM7ghIiJyGAY3zlK65qZpU9flg4iIqI5jcOMsxuBGoQCiolybFyIiojqMwY2zGIObRo0ALy/X5oWIiKgOY3DjDNnZwNWr8jH72xARETkUgxtn4EgpIiIip2Fw4wwMboiIiJyGwY0zMLghIiJyGgY3zsDghoiIyGkY3DgDgxsiIiKnYXDjDMbgxssLiIx0bV6IiIjqOAY3jiZESXATHQ0oWeRERESOxG9aR7t8GbhxQz7msgtEREQOx+DG0djfhoiIyKkY3DgagxsiIiKnYnDjaAxuiIiInIrBjaMxuCEiInIqBjeOxuCGiIjIqRjcOJoxuAkIAIKDXZsXIiIiD8DgxpGKi4GUFPm4SRNAoXBtfoiIiDwAgxtHunAB0OvlYzZJEREROQWDG0difxsiIiKnY3DjSAxuiIiInI7BjSOVDm649AIREZFTMLhxpNOnSx6z5oaIiMgpGNw4Uumam5gYl2WDiIjIkzC4cSRjcBMWBvj5uTYvREREHoLBjaPk5wMXL8rHbJIiIiJyGgY3jpKcXPKYwQ0REZHTMLhxFA4DJyIicgkGN47C4IaIiMglGNw4CoMbIiIil2Bw4ygMboiIiFyCwY2jGIMbpRKIinJtXoiIiDwIgxtHMQY3jRsDGo1r80JERORBGNw4QmYmcO2afMwmKSIiIqdicOMI7G9DRETkMgxuHIHBDRERkcswuHEEBjdEREQuw+DGERjcEBERuQyDG0dgcENEROQyDG4cwRjcaLVAeLhr80JERORhGNzYmxDA2bPycUyMnMSPiIiInIbfvPaWng7k5srHbJIiIiJyOgY39sb+NkRERC7F4MbeSgc3TZu6Lh9EREQeisGNvbHmhoiIyKUY3NgbgxsiIiKXYnBjb6dPlzxmcENEROR0DG7szVhzExgI1Kvn2rwQERF5IAY39lRUBKSkyMestSEiInIJBjf2dP48UFwsHzO4ISIicgkGN/bEzsREREQux+DGnhjcEBERuRyDG3v6+++SxwxuiIiIXILBjT0dP17ymMENERGRSzC4sacLF0oex8S4LBtERESeTO3qDNR6qakIPHUKOHAASE6WaTodcOyYfBwRITciIiJyCtbc1JDy44/Rd/p0aLp1AzIzZWJWFhAXJ7dly1ybQSIiIg/jdsHNzp07cffddyMyMhIKhQLr16832y+EwGuvvYaIiAj4+Pigf//+OHHihGsyC8DwxBPY8e670P/2GzBzpkycPBlITJTbU0+5LG9ERESeyO2Cmxs3bqBjx45YvHixzf3vvPMOFi1ahA8//BB79+6Fn58fBg0ahPz8fCfn9KaICGQ2awZ06QLce69Me/xxoHNnubFJioiIyKncrs/N4MGDMXjwYJv7hBB47733MHPmTAwbNgwA8Pnnn6NBgwZYv349HnroIWdmlYiIiNyQ2wU35Tlz5gzS0tLQv39/U1pgYCC6deuG3bt3lxncFBQUoKCgwPQ8KysLAKDX66HX62uUJ+Pr9Xo9EBIC5cyZMISEADU8b21iVgYejOUgsRxYBkYsB4nlINmjHCr7WoUQQlT7Kg6mUCjw7bffYvjw4QCA33//HT169MDFixcRUaq558EHH4RCocCaNWtsnmf27NmYM2eOVfqqVavg6+vrkLwTERGRfeXm5uLhhx9GZmYmdDpdmcfVqpqb6poxYwamTZtmep6VlYXGjRtj4MCB5RZOZej1emzduhUDBgyARqOpaVZrJZaBxHKQWA4sAyOWg8RykOxRDsaWl4rUquAmPDwcAHDp0iWzmptLly7hlltuKfN1Wq0WWq3WKl2j0djtjWbPc9VWLAOJ5SCxHFgGRiwHieUg1aQcKvs6txstVZ4mTZogPDwc27ZtM6VlZWVh7969iI+Pd2HOiIiIyF24Xc1NTk4OTp48aXp+5swZHDx4EMHBwYiKisKzzz6LN998Ey1atECTJk3wj3/8A5GRkaZ+OUREROTZ3C642bdvH26//XbTc2NfmTFjxmDlypV48cUXcePGDTz55JPIyMhAz549sWnTJnh7e7sqy0RERORG3C646du3L8obwKVQKPD666/j9ddfd2KuiIiIqLaoVX1uiIiIiCrC4IaIiIjqFAY3REREVKcwuCEiIqI6xe06FDuDscNyZWc6LI9er0dubi6ysrI8dnImloHEcpBYDiwDI5aDxHKQ7FEOxu/tilaO8sjgJjs7GwDQuHFjF+eEiIiIqio7OxuBgYFl7nfrhTMdxWAw4OLFiwgICIBCoajRuYzrVJ07d67G61TVViwDieUgsRxYBkYsB4nlINmjHIQQyM7ORmRkJJTKsnvWeGTNjVKpRKNGjex6Tp1O59FvWoBlYMRykFgOLAMjloPEcpBqWg7l1dgYsUMxERER1SkMboiIiKhOYXBTQ1qtFrNmzYJWq3V1VlyGZSCxHCSWA8vAiOUgsRwkZ5aDR3YoJiIiorqLNTdERERUpzC4ISIiojqFwQ0RERHVKQxuiIiIqE5hcFMDixcvRkxMDLy9vdGtWzf88ccfrs6SXe3cuRN33303IiMjoVAosH79erP9Qgi89tpriIiIgI+PD/r3748TJ06YHXPt2jWMHj0aOp0OQUFBGD9+PHJycpx4FzUzd+5cdOnSBQEBAQgLC8Pw4cORlJRkdkx+fj4mTZqE+vXrw9/fHyNGjMClS5fMjklJScHQoUPh6+uLsLAwvPDCCygqKnLmrdTI0qVLERsba5p8Kz4+Hj/99JNpvyeUgaW3334bCoUCzz77rCnNE8ph9uzZUCgUZlvr1q1N+z2hDIwuXLiARx55BPXr14ePjw86dOiAffv2mfZ7wmdkTEyM1ftBoVBg0qRJAFz4fhBULatXrxZeXl7i008/FUeOHBFPPPGECAoKEpcuXXJ11uzmxx9/FK+++qr45ptvBADx7bffmu1/++23RWBgoFi/fr3466+/xD333COaNGki8vLyTMfceeedomPHjmLPnj3i119/Fc2bNxejRo1y8p1U36BBg8SKFSvE4cOHxcGDB8WQIUNEVFSUyMnJMR0zYcIE0bhxY7Ft2zaxb98+0b17d3HbbbeZ9hcVFYn27duL/v37iwMHDogff/xRhISEiBkzZrjilqrlu+++Ez/88IM4fvy4SEpKEq+88orQaDTi8OHDQgjPKIPS/vjjDxETEyNiY2PFM888Y0r3hHKYNWuWaNeunUhNTTVtly9fNu33hDIQQohr166J6OhoMXbsWLF3715x+vRpsXnzZnHy5EnTMZ7wGZmenm72Xti6dasAILZv3y6EcN37gcFNNXXt2lVMmjTJ9Ly4uFhERkaKuXPnujBXjmMZ3BgMBhEeHi7+/e9/m9IyMjKEVqsVX375pRBCiP/9738CgPjzzz9Nx/z0009CoVCICxcuOC3v9pSeni4AiISEBCGEvGeNRiPWrVtnOubo0aMCgNi9e7cQQgaJSqVSpKWlmY5ZunSp0Ol0oqCgwLk3YEf16tUTn3zyiceVQXZ2tmjRooXYunWr6NOnjym48ZRymDVrlujYsaPNfZ5SBkII8dJLL4mePXuWud9TPyOfeeYZ0axZM2EwGFz6fmCzVDUUFhYiMTER/fv3N6UplUr0798fu3fvdmHOnOfMmTNIS0szK4PAwEB069bNVAa7d+9GUFAQbr31VtMx/fv3h1KpxN69e52eZ3vIzMwEAAQHBwMAEhMTodfrzcqhdevWiIqKMiuHDh06oEGDBqZjBg0ahKysLBw5csSJubeP4uJirF69Gjdu3EB8fLzHlcGkSZMwdOhQs/sFPOu9cOLECURGRqJp06YYPXo0UlJSAHhWGXz33Xe49dZb8cADDyAsLAydOnXCxx9/bNrviZ+RhYWF+M9//oNx48ZBoVC49P3A4KYarly5guLiYrNfBgA0aNAAaWlpLsqVcxnvs7wySEtLQ1hYmNl+tVqN4ODgWllOBoMBzz77LHr06IH27dsDkPfo5eWFoKAgs2Mty8FWORn31RaHDh2Cv78/tFotJkyYgG+//RZt27b1qDJYvXo19u/fj7lz51rt85Ry6NatG1auXIlNmzZh6dKlOHPmDHr16oXs7GyPKQMAOH36NJYuXYoWLVpg8+bNmDhxIqZOnYrPPvsMgGd+Rq5fvx4ZGRkYO3YsANf+TXjkquBE1TFp0iQcPnwYu3btcnVWXKJVq1Y4ePAgMjMz8dVXX2HMmDFISEhwdbac5ty5c3jmmWewdetWeHt7uzo7LjN48GDT49jYWHTr1g3R0dFYu3YtfHx8XJgz5zIYDLj11lvxz3/+EwDQqVMnHD58GB9++CHGjBnj4ty5xvLlyzF48GBERka6OiusuamOkJAQqFQqqx7fly5dQnh4uIty5VzG+yyvDMLDw5Genm62v6ioCNeuXat15TR58mR8//332L59Oxo1amRKDw8PR2FhITIyMsyOtywHW+Vk3FdbeHl5oXnz5oiLi8PcuXPRsWNHLFy40GPKIDExEenp6ejcuTPUajXUajUSEhKwaNEiqNVqNGjQwCPKwVJQUBBatmyJkydPesx7AQAiIiLQtm1bs7Q2bdqYmug87TMyOTkZP//8M/7v//7PlObK9wODm2rw8vJCXFwctm3bZkozGAzYtm0b4uPjXZgz52nSpAnCw8PNyiArKwt79+41lUF8fDwyMjKQmJhoOuaXX36BwWBAt27dnJ7n6hBCYPLkyfj222/xyy+/oEmTJmb74+LioNFozMohKSkJKSkpZuVw6NAhsw+xrVu3QqfTWX041iYGgwEFBQUeUwZ33HEHDh06hIMHD5q2W2+9FaNHjzY99oRysJSTk4NTp04hIiLCY94LANCjRw+raSGOHz+O6OhoAJ7zGWm0YsUKhIWFYejQoaY0l74fqt0V2cOtXr1aaLVasXLlSvG///1PPPnkkyIoKMisx3dtl52dLQ4cOCAOHDggAIj58+eLAwcOiOTkZCGEHOYYFBQkNmzYIP7++28xbNgwm8McO3XqJPbu3St27dolWrRoUauGOU6cOFEEBgaKHTt2mA13zM3NNR0zYcIEERUVJX755Rexb98+ER8fL+Lj4037jUMdBw4cKA4ePCg2bdokQkNDa9XQ15dfflkkJCSIM2fOiL///lu8/PLLQqFQiC1btgghPKMMbCk9WkoIzyiH6dOnix07dogzZ86I3377TfTv31+EhISI9PR0IYRnlIEQcjoAtVot3nrrLXHixAnx3//+V/j6+or//Oc/pmM84TNSCDlaOCoqSrz00ktW+1z1fmBwUwPvv/++iIqKEl5eXqJr165iz549rs6SXW3fvl0AsNrGjBkjhJBDHf/xj3+IBg0aCK1WK+644w6RlJRkdo6rV6+KUaNGCX9/f6HT6cTjjz8usrOzXXA31WPr/gGIFStWmI7Jy8sTTz/9tKhXr57w9fUV9957r0hNTTU7z9mzZ8XgwYOFj4+PCAkJEdOnTxd6vd7Jd1N948aNE9HR0cLLy0uEhoaKO+64wxTYCOEZZWCLZXDjCeUwcuRIERERIby8vETDhg3FyJEjzeZ28YQyMNq4caNo37690Gq1onXr1uKjjz4y2+8Jn5FCCLF582YBwOrehHDd+0EhhBDVr/chIiIici/sc0NERER1CoMbIiIiqlMY3BAREVGdwuCGiIiI6hQGN0RERFSnMLghIiKiOoXBDREREdUpDG6IiIioTmFwQx5t7NixUCgUOHv2rKuzYkahUKBv376uzobDrVy5EgqFAitXrnR1Vpxu9uzZUCgU2LFjh6uzUmN16V6obmBw48EUCkWVNqK6whO+jD0lQK4tYmJiEBMT4+pseAy1qzNArjNr1iyrtPfeew+ZmZk29xHZ27333ovu3bsjIiLC1VmhGpg8eTIeeughREVFuTorRAAY3Hi02bNnW6WtXLkSmZmZNvcR2VtgYCACAwNdnQ2qoZCQEISEhLg6G0QmbJaiCl28eBGzZs1C9+7dERYWBq1Wi5iYGDz99NNIT0+3Ot7Yj+X06dOYN28eWrZsCR8fH7Rt2xarV68GABQWFuLVV19FTEwMvL29ERsbi59++snqXImJiZg8eTLat2+PwMBA+Pj4oEOHDnj77beh1+utjjdW/ebk5OCZZ55BZGQktFotYmNj8dVXX5V5j0IILFq0CK1bt4ZWq0V0dDTmzJkDg8FgdWxRURHmz5+Pjh07wsfHB4GBgbj99tuxcePGqhQrAOCTTz5B+/bt4e3tjcaNG+PFF19Efn5+mccnJydj/PjxaNiwIby8vNCoUSOMHz8eKSkpVbquEAIrVqxAr169EBQUBF9fX7Ro0QJPPfWU1bmqcs2+fftCoVBAr9dj9uzZiImJgVarRcuWLbFkyRKr4231uTl79iwUCgXGjh1rM++2mluqct2+fftizpw5AIDbb7/d1Oxq2WRw+PBhPPjgg6b3fJMmTfDss8/i6tWrZZSqbefOncOoUaMQHBwMf39/9OnTBzt37iz3NTt37sTdd9+NkJAQaLVatGjRAjNnzkRubm6F19uxY4epGTkhIcGsadlYzqXLfePGjejRowcCAgLMyqCwsBDz589H586d4efnh4CAAPTq1Qvfffed1TVtNfOV/j2ePHkS9957L+rVqwc/Pz/0798ff/31l9V5tm/fjnHjxqFVq1bw9/eHv78/br31Vnz00Uc279X4Xrhw4QIefvhhhISEICAgAEOHDsXp06cBAEePHsXw4cMRHByMgIAA3H///bh06ZLN8/3999946KGHEBERAS8vL0RHR2PKlClWv/Oq3Jvx2OTkZCQnJ5v9Piz/iVyxYgW6detmuvdu3bp5ZH80u6jRmuJU50RHRwvLt8WXX34p/Pz8xD333COmTp0qpk+fLvr16ycAiKZNm4qMjAyz48eMGSMAiGHDhonw8HDxxBNPiAkTJoigoCChUCjEpk2bxNChQ0WTJk3E008/LcaNGye8vb2FRqMRJ0+eNDvXU089JSIjI8VDDz0kXnjhBTFp0iTRrl07AUDcd999NvMfGRkp4uPjRevWrcXkyZPFuHHjhK+vr1AoFGLz5s028zpixAgREhIixo4dK6ZOnSqioqIEAPHKK6+YHW8wGMSwYcMEANGyZUsxffp0MWHCBFGvXj0BQMyfP7/SZf36668LAKJBgwZi8uTJ4rnnnhNRUVHirrvuEgBEnz59zI5PSkoSoaGhAoC4++67xcsvv2w6NjQ0VCQlJVXqusXFxeL+++8XAETDhg3FhAkTxIsvvigefPBBERQUJL799ttqX7NPnz6m8mzcuLF48sknxcSJE0X9+vUFAPHRRx+ZHb9ixQoBQKxYscKUdubMGQFAjBkzxmb+bZVNVa67YsUK0/FjxowRs2bNErNmzRILFiwwHfPrr78KX19foVarxUMPPSRefvll02uaNWsmLl++XKmyvnjxomjYsKEAIAYNGiRmzJghhg8fLry8vMSgQYMEALF9+3az1yxZskQoFApRr1498dhjj4nnn39e9O3bVwAQt912mygoKCj3mmfOnBGzZs0SAER0dLTp/mbNmiUOHDhgKgMAYsiQIUKtVovhw4eLF198UUyYMEEIIUR+fr7pmrfccouYMmWKmDBhgmjcuLEAIN5//32zaxqvV/pejL/HPn36iPr164vevXuLadOmmf5+6tWrJ9LS0szOM2jQINGsWTMxevRo8dJLL4mnnnrK9Jk0bdo0q3sFIGJjY0VMTIzo3r27mDZtmun92bRpU3Ho0CERFBQk7rjjDjF9+nTTPd1+++1W59qwYYPQarXCx8fH9HkzdOhQAUC0aNFCXLt2rVr3dv36dTFr1iwRGBgoAgMDzX4fpctrypQppr/JqVOniqlTp5reO1OnTi33d07WGNyQGVvBzaVLl0R2drbVsZ999pkAIN58802zdGPA0LJlS5Genm5K37t3rwAggoKCRM+ePUVOTo5p35o1awQAMWXKFLNzJScni6KiIrM0g8Egxo0bJwCIXbt22cz/sGHDzL4Efv75Z9MXjK28NmnSRFy8eNGUfvnyZREUFCQCAgLMzmO85z59+pilJycni5CQEKFWq8WpU6esysrSiRMnhFqtFg0bNhSXLl0ypWdmZopWrVrZ/AK//fbbBQCxbNkys/TFixcLAKJfv34VXlcIId5//30BQNxxxx0iNzfXbF9ubq64evVqta9pDAC6desmMjMzTenHjh0TarVatGrVyux4ewc3lb2urS9jo+LiYtGsWTMBQGzatMls3wsvvCAAiHHjxtnMmyXj+8vyb2TZsmUCgFUejhw5ItRqtejYsaO4cuWK2Wvmzp0rAIh58+ZV6tq2ysnIWO5KpVJs3brVav8rr7wiAIh//OMfwmAwmNKzsrLErbfeKry8vMSFCxdM6eUFNwDE22+/bXb+mTNnCgBi7ty5ZumnT5+2yoterxcDBgwQKpVKJCcnW90jAPHcc8+ZpU+cONH0WfPee++Z0g0GgxgyZIgAIBITE03pV65cETqdTjRs2FCcPXvW7FxffvmlACAmT55co3uLjo4W0dHRVvcnhBAJCQkCgGjTpo3ZP4vXrl0TLVu2FADEzp07bb6WbGNwQ2ZsBTdlMRgMQqfTib59+5qlGz/QP/vsM6vXNG3aVAAQCQkJZulFRUVCo9GI3r17V+raiYmJAoCYPXu2zfzb+pCMjo4WwcHBNvP66aefWh1v3Pf333+b0ow1Vnv37rU6/q233hIAxOuvv15h/ufMmSMAiHfffddq3xdffGH1xZScnCwAiLZt25p92Qghv4xbt24tAIiUlJQKr92mTRuhUqnE8ePHyz2uOtc0Bhm//PKL1fmM+7Kyskxp9g5uKnvd8oKbnTt3CgBi8ODBVvuys7NFcHCw8Pb2rrAGpaCgQHh7e4uwsDCRl5dntq+4uFi0aNHCKg9Tp04t84usuLhYhIaGiri4uHKva1SZ4Obee++1eZ169eqJZs2aWf3ehRDiu+++s6q9KS+4adKkiSguLjY7h3GfrdpXW77++msBQKxcudLqHv39/cWNGzfM0o2/Q1v38Pnnn1v9zc+fP18AEJ9//rnN63fu3FmEhITU6N7KC26M/6ytWbPGat9///vfKgXUJLFDMVXKN998g2XLlmH//v24fv06iouLTfsuXrxo8zW33HKLVVpERAROnz5ttU+lUiEsLMzqXIWFhfjggw+wevVqHDt2DDk5ORBClHvtoKAgNGnSxCq9UaNG2L17t828xsXF2TweADIyMkxpBw4cgK+vL7p27Wp1/O233w4AOHjwoM1rlGZsk+/Vq5fVPltpxnP26dPHali+UqlE7969cezYMRw8eBCNGzcu87o5OTk4evQomjdvjhYtWpSbx5pcs6LyDAgIKPfa1WWP6x44cAAAbA6jNvYB2bJlC5KSktChQ4cyz5OUlIT8/Hz069cP3t7eZvuUSiV69OiBEydOmKXv2bMHALB582Zs27bN6pwajQbHjh2r8B4qy9b7OCkpCdevX0dkZKSpb1Jply9fBoBK5+OWW26BUmnevdPW3xYAZGdnY968eVi/fj1OnTqFGzdumO239ffeokUL+Pr6mqUZR9/FxsZavXeN+0qfy1jue/fuxalTp6yukZ+fjytXruDKlStmHaercm/lKe89V5XPFSrB4IYq9O677+L5559HaGgoBg4ciEaNGsHHxweAHDpeUFBg83U6nc4qTa1Wl7vPspPw/fffj40bN6Jly5YYOXIkwsLCoNFokJGRgYULF9q8dlmjb9Rqtc0OwhXltXQgl5WVVWbwYPzQzMrKsrm/tMzMTABAWFiY1b4GDRpYpRnPaWtfVa5tvG7Dhg0rzGNNrlnZ8rQ3e1zX3mVt63dc1vmvXbsGAHjrrbcqldeaKi8PR44cwZEjR8p8rWXgUZbK/k4KCwvRt29f7N+/H506dcKjjz6K+vXrQ61W4+zZs/jss89s/r1X53MGgNlnjfGeFy9eXO693Lhxwyy4sdf7PCsrC0qlEqGhoVb7GjRoAIVCUanPFSrB4IbKVVRUhDfeeAMRERE4ePCg2Qe1EALvvPOOw679559/YuPGjRg0aBB++OEHqFQq0749e/Zg4cKFDrt2WXQ6nc0RYgCQlpZmOqYixgAsPT0d0dHRZvtsjeQwnrOsUR6VvbbxuhcuXKgwj/a6ZlUZ/xMuKiqy2mcMGBzJ3mVd1vulvN9zVlaWw2q3SrM1OacxDyNGjCh3hKG9bdiwAfv378f48ePxySefmO1bvXo1PvvsM4dd23jPhw4dQvv27R12nfKubzAYcPnyZatgOD09HUIIu/+d1XUcCk7lunLlCjIzMxEfH2/1R7dv3z7k5eU57NrG6uGhQ4eaBTYA8OuvvzrsuuXp1KkTcnNz8ccff1jtMw6DtdUcZ6ljx44AbN+HrTTjOXfu3GnWLAfIINM4tLiia/v7+6Nt27Y4c+aMVZOIo65ZVUFBQQBsB2DG6vuaMr6fbP133alTJwCwOXvxjRs3sG/fPvj4+KBVq1blXqNly5bw9vbGvn37rIb3GwwG/P7771av6datG4CSZpKaUCqV1aola9OmDXQ6Hfbt22dzugVHMf69Dxs2zGqfo//ejeVeVrO1PahUqjJ/H+W956ryuUIlGNxQucLCwuDj44P9+/ebzbFx/fp1TJkyxaHXNtZo7Nq1yyz9yJEjmDt3rkOvXZYxY8YAAGbMmGH2wX/u3DnMnz8farUao0ePrvA8Dz/8MFQqFebPn2/2n31WVhbefPNNq+OjoqJw++2348iRI/j000/N9n300Uc4evQo+vXrV25/G6NJkyahuLgYTz/9tFVwmp+fb6qit+c1q0Kn06FVq1bYtWsXTp48aUrPzs7GjBkz7HKN4OBgAPL3ZqlHjx5o1qwZfvrpJ/z8889m+958801cvXoVo0aNgpeXV7nX0Gq1ePDBB5Geno53333XbN8nn3yC48ePW73m6aefhlqtxpQpU2zOI5SRkVHpAC84OBjnz5+v1LGlqdVqTJw4EcnJyXj++edtBjiHDx8us0aqusr6e09ISMDHH39s12tZevzxxxEQEIBXX33VZlNcbm5ujQPO4OBgXLlyxeY8VsbPlTlz5pg1P2VmZpr6PRmPocphsxSVS6lU4umnn8a7776Ljh074u6770ZWVhZ++uknREdHIzIy0mHX7tq1K7p27Yq1a9ciNTUV3bt3R0pKCr777jsMHTrUqVXmRo8++ii++eYbbNiwAbGxsbjrrrtw48YNrFmzBteuXcO7776Lpk2bVnie5s2b47XXXsOsWbMQGxuLBx98EGq1Gl9//TViY2ORlJRk9ZqlS5eiZ8+eeOKJJ7Bx40a0bdsWR44cwXfffYfQ0FAsXbq0UvcwceJEJCQkYO3atWjRogXuuece6HQ6pKSkYPPmzVi+fDmGDx9u12tW1fTp0/Hkk08iPj4eDzzwAAwGA3766Sd06dLFLuc3Tt73yiuv4MiRIwgMDERQUBAmT54MpVKJlStXYtCgQRgyZAgeeOABREdHY/fu3dixYweaNWuGt99+u1LXefvtt7Ft2zbMnDkTu3btQqdOnXD06FH8+OOPGDhwILZs2WJ2fPv27bFkyRJMnDgRrVq1wpAhQ9CsWTNkZ2fj9OnTSEhIwNixY/Hhhx9WeO1+/fph7dq1GD58ODp16gSVSoV77rkHsbGxFb52zpw52L9/PxYtWoQffvgBvXv3RlhYGC5cuIBDhw7hr7/+wu7du8vsT1Qdd999N2JiYvDOO+/g8OHDaN++PZKSkvD999/j3nvvdejfe2hoKL788ks88MAD6NixI+688060bt0aBQUFOHv2LBISEnDbbbdh06ZN1b5Gv379sG/fPgwePBi9evWCl5cXevfubdqmTJmC999/H+3bt8eIESMghMDXX3+N8+fPY+rUqejdu7cd79gDuG6gFrkjW0PBCwsLxVtvvSVatGghtFqtiIqKEtOnTxfZ2dk2hzcah1CfOXPG6vzGYbllXdvyXOnp6WLcuHEiMjJSeHt7iw4dOojFixeL06dP2xwuXN5wS1vXLi+vZQ0X1uv1Yt68eaJDhw5Cq9WKgIAA0adPH7Fhwwab1y3Pxx9/LNq2bSu8vLxEo0aNxPPPPy9yc3PLHMZ79uxZ8fjjj4uIiAihVqtFRESEePzxx63m5qiIwWAQn3zyiejevbvw8/MTvr6+okWLFmLChAlWw8mrcs3yfr+2ytrWUHCjxYsXixYtWgiNRiOioqLEa6+9JgoLC8sdCl7Z6wohxMqVK02/Q9yc8K60v//+W9x///0iJCREaDQaER0dLZ555plKT+BnlJycLEaOHCmCgoKEr6+v6NWrl0hISCh3OPoff/whHnroIREZGSk0Go0ICQkRnTt3Fi+//LI4evRopa6bmpoqHnzwQRESEiKUSqVZOZdX7kZFRUVi2bJlokePHkKn05n+9u+8806xdOlSs3mqyhsKXpUh/adPnxYjRowQoaGhwtfXV3Tp0kWsXr1abN++XQAQs2bNqvAcFV27rHMJIedFGj9+vIiOjhZeXl6iXr16okOHDmLq1Knijz/+qNG9ZWdniyeeeEJEREQIlUplMw+ffvqp6NKli/D19TXdv61pKqhiCiEsGtOJiJzkww8/xMSJE7Fq1SqMGjXK1dkhojqCfW6IyGWMfWqMc4MQEdkDa26IyOm2bt2K9evXY8WKFahXrx7Onj0LjUbj6mwRUR3BmhsicroffvgB//3vfxEfH48tW7YwsCEiu2LNDREREdUprLkhIiKiOoXBDREREdUpDG6IiIioTmFwQ0RERHUKgxsiIiKqUxjcEBERUZ3C4IaIiIjqFAY3REREVKf8P9OnrPHVT02PAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* A **curva vermelha** (`train`) representa o erro médio do modelo no conjunto de treinamento para diferentes tamanhos de conjuntos de dados. Inicialmente, o erro é baixo quando há poucos dados de treinamento, pois o modelo pode se ajustar perfeitamente (*overfitting*). Conforme o tamanho do conjunto de treinamento aumenta, o erro no treinamento também aumenta, pois o modelo não consegue ajustar perfeitamente dados mais variados e complexos.\n",
        "\n",
        "* A **curva Azul** (`valid`) representa o erro médio do modelo no conjunto de validação. Inicialmente, o erro é alto porque o modelo treina com poucos dados e, portanto, generaliza mal para os dados de validação. Com o aumento do tamanho do conjunto de treinamento, o modelo melhora sua capacidade de generalização, e o erro de validação diminui até atingir um platô.\n",
        "\n",
        "O modelo inicialmente sofre de *overfitting*, que diminui conforme o conjunto de treinamento aumenta. Quando as curvas se estabilizam e estão próximas, o modelo apresenta um bom equilíbrio entre viés e variância, indicando boa generalização.\n",
        "\n",
        "Em conjuntos maiores, o modelo alcança um bom equilíbrio entre ajuste e generalização, indicado pela proximidade entre as curvas de treinamento e validação."
      ],
      "metadata": {
        "id": "UZzA9CZwr-3s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Modelo de Regressão Linear Múltipla com 2 variáveis idependentes**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "yCMmokLFC3z2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "# Definição das variáveis preditoras (X) e a variável alvo (y)\n",
        "x1 = df['ENGINESIZE']\n",
        "x2 = df['FUELCONSUMPTION_COMB']\n",
        "X = df[['ENGINESIZE', 'FUELCONSUMPTION_COMB']]\n",
        "Y = df['CO2EMISSIONS']\n",
        "\n",
        "# np.asarray() converte os dados em arrays NumPy para tornar eficiente os cálculos e train, um DataFrame Pandas contem os dados originais usados para treinar o modelo\n",
        "x = np.asanyarray(train[['ENGINESIZE','FUELCONSUMPTION_COMB']])\n",
        "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
        "regr = linear_model.LinearRegression()\n",
        "# Ajuste do modelo de regressão aos dados\n",
        "regr.fit (x, y)\n",
        "\n",
        "# Cria a malha para o gráfico de superfície\n",
        "x1_surf, x2_surf = np.meshgrid(np.linspace(x1.min(), x1.max(), 100), np.linspace(x2.min(), x2.max(), 100))\n",
        "X_surf = np.column_stack((x1_surf.ravel(), x2_surf.ravel()))\n",
        "Y_pred_surf = regr.predict(X_surf).reshape(x1_surf.shape)\n",
        "\n",
        "# Gera o gráfico de superfície 3D\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "ax.scatter(x1, x2, Y, c='green', marker='o', alpha=0.5)\n",
        "ax.plot_surface(x1_surf, x2_surf, Y_pred_surf, color='grey', alpha=0.3)\n",
        "ax.set_xlabel('Tamanho do Motor')\n",
        "ax.set_ylabel('Consumo')\n",
        "ax.set_zlabel('Emissões de CO2')\n",
        "plt.show()\n",
        "\n",
        "# Exibindo os coeficientes do modelo\n",
        "print ('Coeficientes: ', regr.coef_)\n",
        "print ('Intercepto: ',regr.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "j9gugjdhPHdg",
        "outputId": "06ac36bc-6720-4ade-8ab8-e4f9b3243f0e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes:  [[19.86043657  9.86089538]]\n",
            "Intercepto:  [76.42067005]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Regressão Linear Múltipla com 3 variáveis independentes**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "NWxG_MM5QcNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Estimação dos coeficientes do modelo de regressão linear múltipla para previsão da emissão de CO2\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Definição das variáveis preditoras (X) e a variável alvo (y)\n",
        "X_train = train[['ENGINESIZE', 'FUELCONSUMPTION_COMB', 'CYLINDERS']].values\n",
        "y_train = train['CO2EMISSIONS'].values\n",
        "\n",
        "# Ajuste do modelo de regressão aos dados\n",
        "regr = LinearRegression()\n",
        "regr.fit(X_train, y_train)\n",
        "\n",
        "# Exibindo os coeficientes e o intercepto do modelo\n",
        "print(\"Coeficientes: \", regr.coef_)\n",
        "print(\"Intercepto: \", regr.intercept_)\n",
        "\n",
        "# Fazendo previsões no conjunto de teste\n",
        "X_test = test[['ENGINESIZE', 'FUELCONSUMPTION_COMB', 'CYLINDERS']].values\n",
        "y_test = test['CO2EMISSIONS'].values\n",
        "y_pred = regr.predict(X_test)\n",
        "\n",
        "# Cálculo da soma residual dos quadrados e Variance Score (R²)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"Soma residual dos quadrados (MSE): %.2f\" % mse)\n",
        "print(\"Variance score (R²): %.2f\" % r2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMz7iToWvexD",
        "outputId": "7619e1b8-7054-4c5b-a78d-a6ac37f340c7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes:  [11.45318166  9.74391701  7.30184373]\n",
            "Intercepto:  63.641964804347424\n",
            "Soma residual dos quadrados (MSE): 491.30\n",
            "Variance score (R²): 0.85\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Métodos de Estimação dos Coeficientes**\n",
        "\n",
        "A biblioteca scikit-learn fornece a implementação direta do Método dos Mínimos Quadrados Ordinários (OLS), com o objeto `LinearRegression`. O método `.fit(X, y)` ajusta o modelo aos dados fornecidos, e o atributo `.coef_` exibe os coeficientes das variáveis preditoras, enquanto `.intercept_` retorna o intercepto do modelo.\n",
        "\n",
        "O OLS escolhe os parâmetros de uma função linear de um conjunto de variáveis explicativas minimizando a soma dos quadrados das diferenças entre a variável dependente alvo e aquelas previstas pela função linear.\n",
        "\n",
        "Em outras palavras, ele tenta minimizar a soma dos erros quadrados (SSE) ou o erro quadrático médio (MSE) entre a variável alvo (${y}$) e nossa saída prevista ($\\hat{y}$) em todas as amostras no conjunto de dados.\n",
        "\n",
        "Assim, o OLS busca encontrar os melhores parâmetros a partir dos seguintes métodos:\n",
        "\n",
        "*   Resolvendo os parâmetros do modelo analiticamente, usando equações de forma fechada; ou\n",
        "*   usando um algoritmo de otimização (Gradiente Descendente, Gradiente Descendente Estocástico, Método de Newton, etc.)."
      ],
      "metadata": {
        "id": "MiUabL8DBKnr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Preparação das variáveis preditoras (X) e a variável alvo (y) para treino\n",
        "X_train = train[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY', 'FUELCONSUMPTION_HWY']]\n",
        "y_train = train['CO2EMISSIONS']\n",
        "\n",
        "# Ajuste do modelo de regressão linear múltipla\n",
        "regr = LinearRegression()\n",
        "regr.fit(X_train, y_train)\n",
        "\n",
        "# Exibindo os coeficientes e o intercepto do modelo\n",
        "print(\"Coeficientes:\", regr.coef_)\n",
        "print(\"Intercepto:\", regr.intercept_)\n",
        "\n",
        "# Preparação dos dados de teste\n",
        "X_test = test[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY', 'FUELCONSUMPTION_HWY']]\n",
        "y_test = test['CO2EMISSIONS']\n",
        "\n",
        "# Fazendo previsões\n",
        "y_pred = regr.predict(X_test)\n",
        "\n",
        "# Calculando as métricas\n",
        "mse = mean_squared_error(y_test, y_pred)  # Erro médio quadrático\n",
        "r2 = r2_score(y_test, y_pred)  # Coeficiente de determinação\n",
        "\n",
        "# Exibindo resultados\n",
        "print(f\"Soma residual dos quadrados (MSE): {mse:.2f}\")\n",
        "print(f\"Variance score (R²): {r2:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDiFnN20vy-p",
        "outputId": "37edaa30-d8dd-4b2f-ce1b-3d9e19460035"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes: [11.7222264   6.67298461  6.72000164  2.56898267]\n",
            "Intercepto: 65.55467384849686\n",
            "Soma residual dos quadrados (MSE): 496.66\n",
            "Variance score (R²): 0.85\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Após ajustar o modelo e realizar as predições, o cálculo do Erro Quadrático Médio (MSE) e da variância explicada (R2-score) ajudam a avaliar a performance do modelo. O MSE mostra a magnitude média dos erros de previsão em termos quadráticos, enquanto a variância explicada indica quanta variabilidade o modelo conseguiu capturar em relação aos dados reais."
      ],
      "metadata": {
        "id": "zQe0AWaCF4wp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importar bibliotecas necessárias\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Dividindo os dados em treino e teste\n",
        "X = df[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY', 'FUELCONSUMPTION_HWY']]\n",
        "y = df['CO2EMISSIONS']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Criando e treinando o modelo de regressão linear\n",
        "regr = LinearRegression()\n",
        "regr.fit(X_train, y_train)\n",
        "\n",
        "# Fazendo previsões\n",
        "y_pred = regr.predict(X_test)\n",
        "\n",
        "# Convertendo y_pred para um array 2D com uma coluna\n",
        "y_pred_2d = y_pred.reshape(-1, 1)\n",
        "\n",
        "# Calculando os resíduos\n",
        "residuals = y_test - y_pred\n",
        "\n",
        "# Criando o gráfico de resíduos\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(y_pred, residuals, color='grey', alpha=0.6)\n",
        "plt.axhline(y=0, color='r', linestyle='--')\n",
        "plt.xlabel('Valores Previstos')\n",
        "plt.ylabel('Resíduos')\n",
        "plt.title('Gráfico de Resíduos')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "HB5i9rXu6yFu",
        "outputId": "9a28ac92-dc41-47ac-dcf3-9fa273744fee"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Interpretação do Gráfico de Resíduos**\n",
        "\n",
        "Os resíduos são as diferenças entre os valores reais e os valores previstos pelo modelo. O ideal é que os resíduos sejam distribuídos de forma aleatória ao redor da linha vermelha (y=0), sem padrões evidentes. Isso indicaria que o modelo não tem vieses significativos e que a relação entre as variáveis independentes e a variável dependente foi bem capturada pelo modelo.\n",
        "\n",
        "Mas se houver um padrão claro nos resíduos, como um formato de curva, tendência ou agrupamento, isso pode sugerir:\n",
        "\n",
        "1.   **Heterocedasticidade**: Quando a variabilidade dos resíduos muda ao longo dos valores previstos, indicando que o modelo pode não estar capturando adequadamente a variância dos dados.\n",
        "2.   **Falha na linearidade**: Se os resíduos formarem um padrão curvilíneo, isso pode indicar que a relação entre as variáveis preditoras e a variável alvo não é completamente linear e que um modelo mais complexo (por exemplo, regressão polinomial) pode ser necessário.\n",
        "3. **Outliers**: Resíduos muito altos (acima ou abaixo de 0) podem indicar a presença de *outliers*, isto é, \"pontos fora da curva\" que o modelo não conseguiu prever adequadamente.\n",
        "\n",
        "No gráfico acima, há alguma dispersão dos resíduos em torno da linha vermelha, com alguns grupos de pontos se destacando, podendo indicar que o modelo não está capturando bem certos padrões nos dados ou que há heterocedasticidade presente, onde a variabilidade dos resíduos muda para diferentes intervalos de valores previstos. Além disso, há alguns resíduos com valores muito altos, o que pode ser um indicativo de outliers nos dados."
      ],
      "metadata": {
        "id": "bWy67K3rHkzn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Implementação com NumPy**"
      ],
      "metadata": {
        "id": "7gNu6UzYxMR6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "# Preparar as características (X) e a variável alvo (y) para o conjunto de treinamento\n",
        "X_train = np.asanyarray(train[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY', 'FUELCONSUMPTION_HWY']])\n",
        "y_train = np.asanyarray(train[['CO2EMISSIONS']])\n",
        "\n",
        "# Criando e treinando o modelo de regressão linear\n",
        "regr = linear_model.LinearRegression()\n",
        "regr.fit(X_train, y_train)  # Treinar o modelo com os dados de treinamento\n",
        "print('Coeficientes:', regr.coef_)\n",
        "\n",
        "# Preparar as características (X) para o conjunto de teste\n",
        "X_test = np.asanyarray(test[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY', 'FUELCONSUMPTION_HWY']])\n",
        "\n",
        "# Calculando as predições no conjunto de dados de teste\n",
        "y_hat = regr.predict(X_test)  # Fazer previsões com o modelo treinado\n",
        "\n",
        "# Preparar a variável alvo (y) para o conjunto de teste\n",
        "y_test = np.asanyarray(test[['CO2EMISSIONS']])\n",
        "\n",
        "# Validando o modelo\n",
        "print(\"Erro Quadrático Médio (MSE): %.2f\" % np.mean((y_hat - y_test) ** 2))\n",
        "print('Variance Score: %.2f' % regr.score(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqTaww89xDPu",
        "outputId": "3fcef488-4b72-4690-8df4-b2e7effe265f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes: [[11.7222264   6.67298461  6.72000164  2.56898267]]\n",
            "Erro Quadrático Médio (MSE): 496.66\n",
            "Variance Score: 0.85\n"
          ]
        }
      ]
    },
    {
      "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": "mtoF5eY-zvZa"
      }
    }
  ]
}