RegressãoLinear_Sklearn.ipynb

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        "<a href=\"https://colab.research.google.com/github/vaguiarfinance/AprendizadoDNC/blob/main/Regress%C3%A3oLinear_Sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics"
      ]
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        "# Vamos adicionar o dataset e exibi-lo para que termos o primeiro contato\n",
        "imoveis = pd.read_excel(\"Cópia de imoveis.xlsx\")\n",
        "imoveis.head()"
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              "     area  preco\n",
              "0  359.87  25000\n",
              "1  353.12  45000\n",
              "2  341.89  45000\n",
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      "source": [
        "# Analisando os dados com o describe\n",
        "imoveis.describe()"
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            "text/plain": [
              "              area          preco\n",
              "count    54.000000      54.000000\n",
              "mean    457.483519   41296.296296\n",
              "std     261.527053   16488.566227\n",
              "min     293.220000   20000.000000\n",
              "25%     355.087500   30000.000000\n",
              "50%     380.360000   40000.000000\n",
              "75%     470.087500   50000.000000\n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Observando se o dataset possui dados nulos\n",
        "imoveis.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCgIBjckohbv",
        "outputId": "8add81b2-ccb7-4bb4-e783-f14b545b1a35"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 54 entries, 0 to 53\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   area    54 non-null     float64\n",
            " 1   preco   54 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 992.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotando um grafico de histograma da biblioteca seaborn, cujo o dataset é imoveis e o ; é para retirar o link gerado que normalmente fica em cima do gráfico\n",
        "sns.histplot(imoveis, x=\"area\", kde=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "478SvqBbou1E",
        "outputId": "2842979e-7b8a-42f2-8437-ae7ad1cb81ab"
      },
      "execution_count": 9,
      "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": [
        "# Criamos o gráfico com o eixo X sendo o Preço\n",
        "sns.histplot(imoveis, x=\"preco\", kde=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "JGdx0MxgqK64",
        "outputId": "8908aa07-0176-4e81-90e4-8b08de76a22e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Observando a correlação\n",
        "imoveis.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "gOjy1IYbqdlq",
        "outputId": "0e7fdef5-70ed-4336-dd71-177e973950a2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           area     preco\n",
              "area   1.000000  0.511373\n",
              "preco  0.511373  1.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-a9103c97-016f-45a6-b54d-854accb0b98b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>area</th>\n",
              "      <th>preco</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>area</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.511373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>preco</th>\n",
              "      <td>0.511373</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9103c97-016f-45a6-b54d-854accb0b98b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-4d85f92b-5ec1-4ef3-8fe3-af511ac5e19d\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d85f92b-5ec1-4ef3-8fe3-af511ac5e19d')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4d85f92b-5ec1-4ef3-8fe3-af511ac5e19d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9103c97-016f-45a6-b54d-854accb0b98b 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-a9103c97-016f-45a6-b54d-854accb0b98b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "51% há pouca correlação, uma correlação boa é acima de 70%, mas tbm não podemos considerar uma hipotese nula, visto que teve 51%."
      ],
      "metadata": {
        "id": "menHmQQoq9m-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(data=imoveis, x=\"area\", y=\"preco\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ZPqb_yZWrMsA",
        "outputId": "7f27daea-6d40-4248-90bd-80068c445817"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Observamos que a area esta na casa das centenas, enquanto que a média do preço esta na casa dos milhares\n",
        "imoveis.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "V2fM3l6ssuVW",
        "outputId": "c37f58f1-4c22-40a6-f23a-fd721c797e6e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              area          preco\n",
              "count    54.000000      54.000000\n",
              "mean    457.483519   41296.296296\n",
              "std     261.527053   16488.566227\n",
              "min     293.220000   20000.000000\n",
              "25%     355.087500   30000.000000\n",
              "50%     380.360000   40000.000000\n",
              "75%     470.087500   50000.000000\n",
              "max    2057.570000  120000.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-cf0c3009-87e4-4756-85d7-b99d0ae33169\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "      <th>preco</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>457.483519</td>\n",
              "      <td>41296.296296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>261.527053</td>\n",
              "      <td>16488.566227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>293.220000</td>\n",
              "      <td>20000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>355.087500</td>\n",
              "      <td>30000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>380.360000</td>\n",
              "      <td>40000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>470.087500</td>\n",
              "      <td>50000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2057.570000</td>\n",
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              "    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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-64750a58-bed4-4109-8978-07c9bbab2656 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cf0c3009-87e4-4756-85d7-b99d0ae33169 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-cf0c3009-87e4-4756-85d7-b99d0ae33169');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos transformar os dados em medida de normalização - (0 até 1) Escala logarítmica\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Vamos Transformar o dados, porém eu quero que mantenha o index e as colunas\n",
        "imovel_norm = pd.DataFrame(scaler.fit_transform(imoveis), index=imoveis.index, columns=imoveis.columns)"
      ],
      "metadata": {
        "id": "IA7PIOLgswfr"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos ver como  ficou\n",
        "imovel_norm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "s-LOzmo5u5OY",
        "outputId": "c941763a-ff2c-4e65-c89b-be7fdbfa674d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       area  preco\n",
              "0  0.037776   0.05\n",
              "1  0.033950   0.25\n",
              "2  0.027585   0.25\n",
              "3  0.080919   0.00\n",
              "4  0.027517   0.05"
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              "\n",
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "      <th>preco</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.037776</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.033950</td>\n",
              "      <td>0.25</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.027585</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.080919</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.027517</td>\n",
              "      <td>0.05</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10a524cf-0f54-4f07-ab5e-ec24e07b9f94')\"\n",
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              "      </button>\n",
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              "<style>\n",
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              "    border: none;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3d3a70d8-1890-4f93-b456-8168e085b74e button.colab-df-quickchart');\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-10a524cf-0f54-4f07-ab5e-ec24e07b9f94 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-10a524cf-0f54-4f07-ab5e-ec24e07b9f94');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Podemos notar que a coisas fica mais proxima da normal agora\n",
        "imovel_norm.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "aOH0wqMlvLxn",
        "outputId": "ee6336d1-5eaf-4770-ff58-4b6480adb073"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            area      preco\n",
              "count  54.000000  54.000000\n",
              "mean    0.093101   0.212963\n",
              "std     0.148229   0.164886\n",
              "min     0.000000   0.000000\n",
              "25%     0.035065   0.100000\n",
              "50%     0.049389   0.200000\n",
              "75%     0.100245   0.300000\n",
              "max     1.000000   1.000000"
            ],
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              "\n",
              "\n",
              "  <div id=\"df-91c1ca23-8152-4e5c-856c-a9a936ff7914\">\n",
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              "      <th></th>\n",
              "      <th>area</th>\n",
              "      <th>preco</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.093101</td>\n",
              "      <td>0.212963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.148229</td>\n",
              "      <td>0.164886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.035065</td>\n",
              "      <td>0.100000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.049389</td>\n",
              "      <td>0.200000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>0.100245</td>\n",
              "      <td>0.300000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c95ec2c1-8b1d-47a6-a6af-ff7714de0fd5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91c1ca23-8152-4e5c-856c-a9a936ff7914 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-91c1ca23-8152-4e5c-856c-a9a936ff7914');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Observe a escala, como alterou em relação ao gráfico gerado anteriormente\n",
        "\n",
        "sns.regplot(data=imovel_norm, x=\"area\", y=\"preco\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "NLYixsRIvqJV",
        "outputId": "dd106245-20e9-4d01-e671-4b6306671164"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='area', ylabel='preco'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "WSFGoX8gwLl7"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#modelo MRLS\n",
        "function = 'preco~area-1'\n",
        "model = smf.ols(formula=function, data=imovel_norm).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElBmtcLVwUw3",
        "outputId": "4f8d3af3-00af-4d53-de25-1d1725956113"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 OLS Regression Results                                \n",
            "=======================================================================================\n",
            "Dep. Variable:                  preco   R-squared (uncentered):                   0.473\n",
            "Model:                            OLS   Adj. R-squared (uncentered):              0.463\n",
            "Method:                 Least Squares   F-statistic:                              47.56\n",
            "Date:                Thu, 03 Aug 2023   Prob (F-statistic):                    6.62e-09\n",
            "Time:                        19:04:30   Log-Likelihood:                          11.695\n",
            "No. Observations:                  54   AIC:                                     -21.39\n",
            "Df Residuals:                      53   BIC:                                     -19.40\n",
            "Df Model:                           1                                                  \n",
            "Covariance Type:            nonrobust                                                  \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "area           1.0616      0.154      6.896      0.000       0.753       1.370\n",
            "==============================================================================\n",
            "Omnibus:                       20.978   Durbin-Watson:                   1.315\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               65.006\n",
            "Skew:                          -0.894   Prob(JB):                     7.66e-15\n",
            "Kurtosis:                       8.069   Cond. No.                         1.00\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
            "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = imovel_norm[[\"area\"]]\n",
        "\n",
        "y= imovel_norm[[\"preco\"]]"
      ],
      "metadata": {
        "id": "XKKBs0_Ayxyz"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos criar as areas de Teste e de Treino, sendo 20% Teste e 80% Treino, com 42 amostras aleatórias\n",
        "\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x,y, test_size=0.20, random_state=42)\n",
        "\n",
        "#Criando o algotimo de Machine Learning\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(x_treino, y_treino)\n",
        "LinearRegression()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "JUED1eFTzK-c",
        "outputId": "bd451e51-2a59-4fb8-e97b-52a9b674bb23"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r_sq = lr.score(x_treino, y_treino)\n",
        "print(r_sq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgFIYE_A0SeX",
        "outputId": "264d5987-69d2-4925-9795-8ce02de0fffa"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2754399686961232\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos ver a MAE (Erro Médio Absoluto) calculado pelo almoritmo\n",
        "\n",
        "y_pred_treino = lr.predict(x_treino)\n",
        "\n",
        "print(\"MAE:\", metrics.mean_absolute_error(y_treino, y_pred_treino))\n",
        "print(\"MSE:\", metrics.mean_squared_error(y_treino, y_pred_treino))\n",
        "print(\"RMSE:\", np.sqrt(metrics.mean_squared_error(y_treino, y_pred_treino)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "leyKkKn309z0",
        "outputId": "e765518c-cbb2-4495-cf92-ab95970d164f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.1068423059800295\n",
            "MSE: 0.019987119738590717\n",
            "RMSE: 0.1413758103021543\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Construindo o algoritmo de teste\n",
        "lr.fit(x_teste, y_teste)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "WaGJACa221YO",
        "outputId": "7f3bc2aa-d37d-4eca-fda4-acfd301a7722"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_teste = lr.predict(x_teste)\n",
        "print(\"MAE:\", metrics.mean_absolute_error(y_teste, y_pred_teste))\n",
        "print(\"MSE:\", metrics.mean_squared_error(y_teste, y_pred_teste))\n",
        "print(\"RMSE:\", np.sqrt(metrics.mean_squared_error(y_teste, y_pred_teste)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_gQkGK028hk",
        "outputId": "7b0739c0-2fc5-4a28-ed61-6207eab13c6d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.10852020952335328\n",
            "MSE: 0.01746864564020875\n",
            "RMSE: 0.13216900408268478\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Os valores ficaram bem parecidos para Treino e Teste\n",
        "\n",
        "É muito importante entender as métricas de sucesso de uma algoritmo"
      ],
      "metadata": {
        "id": "1aquQZUA39iV"
      }
    }
  ]
}