notebook.ipynb

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      "toc_visible": true,
      "collapsed_sections": [
        "-DadogEucPdr",
        "7g-jPehMRvUL",
        "wCxNwYbaqb6p",
        "UHEigX5QqjJO"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lucioew28/TCC/blob/main/notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1 Imports e Setup Inicial"
      ],
      "metadata": {
        "id": "-DadogEucPdr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#imports\n",
        "import pandas as pd\n",
        "import re\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "Zx09VJ2GPKjE"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "PgqW8dFSrOsY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80f3538c-6bf0-44b8-d044-b811ae6306f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# connect to google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/drive/My Drive/TCC/properties_list.csv'\n",
        "\n",
        "df = pd.read_csv(file_path, sep=';', encoding='ISO-8859-1',)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lW5Bk-lnej--",
        "outputId": "5d8386ec-72af-4c83-a068-84c4ab9ee6e7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Cidade                   Bairro       Preço  \\\n",
              "0                  ARACRUZ               GUAXINDIBA   160.000,54   \n",
              "1             BAIXO GUANDU                   CENTRO   150.000,00   \n",
              "2  CACHOEIRO DE ITAPEMIRIM   SAO FRANCISCO DE ASSIS    84.000,00   \n",
              "3  CACHOEIRO DE ITAPEMIRIM                SAO LUCAS    79.800,00   \n",
              "4  CACHOEIRO DE ITAPEMIRIM   SAO FRANCISCO DE ASSIS   152.460,00   \n",
              "\n",
              "  Valor de avaliação  Desconto  \\\n",
              "0         145.000,00       0.0   \n",
              "1         115.000,00       0.0   \n",
              "2         140.000,00      40.0   \n",
              "3         133.000,00      40.0   \n",
              "4         152.460,00       0.0   \n",
              "\n",
              "                                           Descrição  \\\n",
              "0  Casa, 0.00 de área total, 96.00 de área privat...   \n",
              "1  Casa, 0.00 de área total, 61.57 de área privat...   \n",
              "2  Apartamento, 0.00 de área total, 49.49 de área...   \n",
              "3  Apartamento, 0.00 de área total, 45.30 de área...   \n",
              "4  Apartamento, 0.00 de área total, 49.49 de área...   \n",
              "\n",
              "         Modalidade de venda  \n",
              "0  Leilão SFI - Edital Único  \n",
              "1  Leilão SFI - Edital Único  \n",
              "2           Licitação Aberta  \n",
              "3           Licitação Aberta  \n",
              "4  Leilão SFI - Edital Único  "
            ],
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              "      <th></th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Bairro</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Valor de avaliação</th>\n",
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              "      <th>0</th>\n",
              "      <td>ARACRUZ</td>\n",
              "      <td>GUAXINDIBA</td>\n",
              "      <td>160.000,54</td>\n",
              "      <td>145.000,00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Casa, 0.00 de área total, 96.00 de área privat...</td>\n",
              "      <td>Leilão SFI - Edital Único</td>\n",
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              "      <th>1</th>\n",
              "      <td>BAIXO GUANDU</td>\n",
              "      <td>CENTRO</td>\n",
              "      <td>150.000,00</td>\n",
              "      <td>115.000,00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Casa, 0.00 de área total, 61.57 de área privat...</td>\n",
              "      <td>Leilão SFI - Edital Único</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CACHOEIRO DE ITAPEMIRIM</td>\n",
              "      <td>SAO FRANCISCO DE ASSIS</td>\n",
              "      <td>84.000,00</td>\n",
              "      <td>140.000,00</td>\n",
              "      <td>40.0</td>\n",
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              "      <td>Licitação Aberta</td>\n",
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              "      <th>3</th>\n",
              "      <td>CACHOEIRO DE ITAPEMIRIM</td>\n",
              "      <td>SAO LUCAS</td>\n",
              "      <td>79.800,00</td>\n",
              "      <td>133.000,00</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Apartamento, 0.00 de área total, 45.30 de área...</td>\n",
              "      <td>Licitação Aberta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CACHOEIRO DE ITAPEMIRIM</td>\n",
              "      <td>SAO FRANCISCO DE ASSIS</td>\n",
              "      <td>152.460,00</td>\n",
              "      <td>152.460,00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Apartamento, 0.00 de área total, 49.49 de área...</td>\n",
              "      <td>Leilão SFI - Edital Único</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 217,\n  \"fields\": [\n    {\n      \"column\": \"Cidade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"ARACRUZ \",\n          \"MARATAIZES \",\n          \"GUACUI \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bairro\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 81,\n        \"samples\": [\n          \"CASCATA \",\n          \"GUAXINDIBA \",\n          \"SETOR II \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pre\\u00e7o\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 169,\n        \"samples\": [\n          \"159.629,86\",\n          \"99.201,80\",\n          \"114.777,78\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valor de avalia\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 108,\n        \"samples\": [\n          \"155.000,00\",\n          \"161.000,00\",\n          \"152.460,00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desconto\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.689709216067484,\n        \"min\": 0.0,\n        \"max\": 45.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          45.0,\n          40.31,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Descri\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 151,\n        \"samples\": [\n          \"Apartamento, 0.00 de \\u00e1rea total, 32.64 de \\u00e1rea privativa, 0.00 de \\u00e1rea do terreno,  1 qto(s), a.serv, WC, WC Emp, 1 sala(s), 1 lavabo(s), cozinha, 1 vaga(s) de garagem.\",\n          \"Casa, 0.00 de \\u00e1rea total, 51.74 de \\u00e1rea privativa, 144.00 de \\u00e1rea do terreno,  2 qto(s), a.serv, WC, sacada, cozinha.\",\n          \"Apartamento, 0.00 de \\u00e1rea total, 45.28 de \\u00e1rea privativa, 0.00 de \\u00e1rea do terreno,  2 qto(s), WC, 1 sala(s), cozinha.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Modalidade de venda\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Licita\\u00e7\\u00e3o Aberta\",\n          \"Venda Online\",\n          \"Leil\\u00e3o SFI - Edital \\u00danico\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2 Coleta e Preparação dos Dados\n"
      ],
      "metadata": {
        "id": "wR34OEusisxW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 Limpeza dos Dados"
      ],
      "metadata": {
        "id": "WjQk271tmeRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Removendo as colunas indesejadas\n",
        "# df = df.drop(columns=[\"Preço\", \"Desconto\", \"Bairro\"])\n",
        "\n",
        "# Convertendo colunas \"Preço\" e \"Valor de avaliação\" para valores numéricos\n",
        "df['Preço'] = df['Preço'].str.replace('.', '', regex=False).str.replace(',', '.', regex=False)\n",
        "df['Valor de avaliação'] = df['Valor de avaliação'].str.replace('.', '', regex=False).str.replace(',', '.', regex=False)\n",
        "\n",
        "# Convertendo strings para números\n",
        "df['Preço'] = pd.to_numeric(df['Preço'], errors='coerce')\n",
        "df['Valor de avaliação'] = pd.to_numeric(df['Valor de avaliação'], errors='coerce')\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "bpRJLLf4i9um",
        "outputId": "4bc93429-efb9-4f58-b773-509b4315ce1f"
      },
      "execution_count": 22,
      "outputs": [
        {
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              "                     Cidade                   Bairro      Preço  \\\n",
              "0                  ARACRUZ               GUAXINDIBA   160000.54   \n",
              "1             BAIXO GUANDU                   CENTRO   150000.00   \n",
              "2  CACHOEIRO DE ITAPEMIRIM   SAO FRANCISCO DE ASSIS    84000.00   \n",
              "3  CACHOEIRO DE ITAPEMIRIM                SAO LUCAS    79800.00   \n",
              "4  CACHOEIRO DE ITAPEMIRIM   SAO FRANCISCO DE ASSIS   152460.00   \n",
              "\n",
              "   Valor de avaliação  Desconto  \\\n",
              "0            145000.0       0.0   \n",
              "1            115000.0       0.0   \n",
              "2            140000.0      40.0   \n",
              "3            133000.0      40.0   \n",
              "4            152460.0       0.0   \n",
              "\n",
              "                                           Descrição  \\\n",
              "0  Casa, 0.00 de área total, 96.00 de área privat...   \n",
              "1  Casa, 0.00 de área total, 61.57 de área privat...   \n",
              "2  Apartamento, 0.00 de área total, 49.49 de área...   \n",
              "3  Apartamento, 0.00 de área total, 45.30 de área...   \n",
              "4  Apartamento, 0.00 de área total, 49.49 de área...   \n",
              "\n",
              "         Modalidade de venda  \n",
              "0  Leilão SFI - Edital Único  \n",
              "1  Leilão SFI - Edital Único  \n",
              "2           Licitação Aberta  \n",
              "3           Licitação Aberta  \n",
              "4  Leilão SFI - Edital Único  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 217,\n  \"fields\": [\n    {\n      \"column\": \"Cidade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"ARACRUZ \",\n          \"MARATAIZES \",\n          \"GUACUI \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bairro\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 81,\n        \"samples\": [\n          \"CASCATA \",\n          \"GUAXINDIBA \",\n          \"SETOR II \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pre\\u00e7o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131659.53201536223,\n        \"min\": 42502.5,\n        \"max\": 900000.0,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          159629.86,\n          99201.8,\n          114777.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valor de avalia\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 151128.66505811937,\n        \"min\": 70800.0,\n        \"max\": 920000.0,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          155000.0,\n          161000.0,\n          152460.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desconto\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.689709216067484,\n        \"min\": 0.0,\n        \"max\": 45.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          45.0,\n          40.31,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Descri\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 151,\n        \"samples\": [\n          \"Apartamento, 0.00 de \\u00e1rea total, 32.64 de \\u00e1rea privativa, 0.00 de \\u00e1rea do terreno,  1 qto(s), a.serv, WC, WC Emp, 1 sala(s), 1 lavabo(s), cozinha, 1 vaga(s) de garagem.\",\n          \"Casa, 0.00 de \\u00e1rea total, 51.74 de \\u00e1rea privativa, 144.00 de \\u00e1rea do terreno,  2 qto(s), a.serv, WC, sacada, cozinha.\",\n          \"Apartamento, 0.00 de \\u00e1rea total, 45.28 de \\u00e1rea privativa, 0.00 de \\u00e1rea do terreno,  2 qto(s), WC, 1 sala(s), cozinha.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Modalidade de venda\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Licita\\u00e7\\u00e3o Aberta\",\n          \"Venda Online\",\n          \"Leil\\u00e3o SFI - Edital \\u00danico\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2  Transformação de Variáveis Categóricas\n",
        "\n",
        "Optamos pelo uso do **Label Encoding** porque as categorias não possuem uma ordem intrínseca."
      ],
      "metadata": {
        "id": "p0I5PfNAp2sn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando o objeto LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Aplicando Label Encoding nas colunas categóricas\n",
        "df['Cidade'] = label_encoder.fit_transform(df['Cidade'])\n",
        "df['Bairro'] = label_encoder.fit_transform(df['Bairro'])\n",
        "df['Modalidade de venda'] = label_encoder.fit_transform(df['Modalidade de venda'])\n",
        "\n",
        "# Visualizando o DataFrame com as colunas categóricas codificadas\n",
        "df.head()"
      ],
      "metadata": {
        "id": "CBvHNX4JqaX6",
        "outputId": "f9418ab2-ff27-477a-eb8e-5f52c37e2caf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "execution_count": 23,
      "outputs": [
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              "   Cidade  Bairro      Preço  Valor de avaliação  Desconto  \\\n",
              "0       1      24  160000.54            145000.0       0.0   \n",
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              "4       3      64  152460.00            152460.0       0.0   \n",
              "\n",
              "                                           Descrição  Modalidade de venda  \n",
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              "1  Casa, 0.00 de área total, 61.57 de área privat...                    0  \n",
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              "3  Apartamento, 0.00 de área total, 45.30 de área...                    1  \n",
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              "      <td>Apartamento, 0.00 de área total, 49.49 de área...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>67</td>\n",
              "      <td>79800.00</td>\n",
              "      <td>133000.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Apartamento, 0.00 de área total, 45.30 de área...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>64</td>\n",
              "      <td>152460.00</td>\n",
              "      <td>152460.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Apartamento, 0.00 de área total, 49.49 de área...</td>\n",
              "      <td>0</td>\n",
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              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-df8e50de-9598-4d5f-9181-cec2bf2890b6 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-df8e50de-9598-4d5f-9181-cec2bf2890b6');\n",
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              "          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",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "  }\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",
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              "  }\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",
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              "  }\n",
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              "\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 217,\n  \"fields\": [\n    {\n      \"column\": \"Cidade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1,\n          11,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bairro\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 0,\n        \"max\": 80,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          11,\n          24,\n          73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pre\\u00e7o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131659.53201536223,\n        \"min\": 42502.5,\n        \"max\": 900000.0,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          159629.86,\n          99201.8,\n          114777.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valor de avalia\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 151128.66505811937,\n        \"min\": 70800.0,\n        \"max\": 920000.0,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          155000.0,\n          161000.0,\n          152460.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desconto\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.689709216067484,\n        \"min\": 0.0,\n        \"max\": 45.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          45.0,\n          40.31,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Descri\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 151,\n        \"samples\": [\n          \"Apartamento, 0.00 de \\u00e1rea total, 32.64 de \\u00e1rea privativa, 0.00 de \\u00e1rea do terreno,  1 qto(s), a.serv, WC, WC Emp, 1 sala(s), 1 lavabo(s), cozinha, 1 vaga(s) de garagem.\",\n          \"Casa, 0.00 de \\u00e1rea total, 51.74 de \\u00e1rea privativa, 144.00 de \\u00e1rea do terreno,  2 qto(s), a.serv, WC, sacada, cozinha.\",\n          \"Apartamento, 0.00 de \\u00e1rea total, 45.28 de \\u00e1rea privativa, 0.00 de \\u00e1rea do terreno,  2 qto(s), WC, 1 sala(s), cozinha.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Modalidade de venda\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 Feature Engineering (Engenharia de Atributos)\n",
        "\n",
        "\n",
        "A coluna \"**Descrição**\" contém informações valiosas sobre as características dos imóveis, como área total e área privativa, que podem influenciar o preço e o valor de avaliação. Realizar Feature Engineering nessa coluna permitirá extrair dados estruturados a partir do texto livre, transformando informações importantes em variáveis numéricas ou categóricas."
      ],
      "metadata": {
        "id": "7g-jPehMRvUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_features(description):\n",
        "    # Inicializando um dicionário para armazenar as novas features com valores padrão\n",
        "    features = {\n",
        "        'Tipo de Imóvel': '',\n",
        "        'Área Total': 0.0,\n",
        "        'Área Privativa': 0.0,\n",
        "        'Área do Terreno': 0.0,\n",
        "        'Número de Quartos': 0,\n",
        "        'Número de Salas': 0,\n",
        "        'Número de Vagas de Garagem': 0\n",
        "    }\n",
        "\n",
        "    # Extraindo o tipo de imóvel\n",
        "    if 'Casa' in description:\n",
        "        features['Tipo de Imóvel'] = 'Casa'\n",
        "    elif 'Apartamento' in description:\n",
        "        features['Tipo de Imóvel'] = 'Apartamento'\n",
        "    elif 'Terreno' in description:\n",
        "        features['Tipo de Imóvel'] = 'Terreno'\n",
        "    elif 'Loja' in description:\n",
        "        features['Tipo de Imóvel'] = 'Loja'\n",
        "    elif 'Sala' in description:\n",
        "        features['Tipo de Imóvel'] = 'Sala'\n",
        "    elif 'Prédio' in description:\n",
        "        features['Tipo de Imóvel'] = 'Prédio'\n",
        "\n",
        "    # Extraindo a área total\n",
        "    total_area = re.search(r'(\\d+\\.\\d+|\\d+)(?=\\s*de área total)', description)\n",
        "    if total_area:\n",
        "        features['Área Total'] = float(total_area.group(0))\n",
        "\n",
        "    # Extraindo a área privativa\n",
        "    priv_area = re.search(r'(\\d+\\.\\d+|\\d+)(?=\\s*de área privativa)', description)\n",
        "    if priv_area:\n",
        "        features['Área Privativa'] = float(priv_area.group(0))\n",
        "\n",
        "    # Extraindo a área do terreno\n",
        "    land_area = re.search(r'(\\d+\\.\\d+|\\d+)(?=\\s*de área do terreno)', description)\n",
        "    if land_area:\n",
        "        features['Área do Terreno'] = float(land_area.group(0))\n",
        "\n",
        "    # Extraindo o número de quartos\n",
        "    rooms = re.search(r'(\\d+)\\s*qto\\(s\\)', description)\n",
        "    if rooms:\n",
        "        features['Número de Quartos'] = int(rooms.group(1))\n",
        "\n",
        "    # Extraindo o número de salas\n",
        "    living_rooms = re.search(r'(\\d+)\\s*sala\\(s\\)', description)\n",
        "    if living_rooms:\n",
        "        features['Número de Salas'] = int(living_rooms.group(1))\n",
        "\n",
        "    # Extraindo o número de vagas de garagem\n",
        "    garage_spots = re.search(r'(\\d+)\\s*vaga\\(s\\)', description)\n",
        "    if garage_spots:\n",
        "        features['Número de Vagas de Garagem'] = int(garage_spots.group(1))\n",
        "\n",
        "    return features\n",
        "\n",
        "# Aplicando a função em todas as descrições e convertendo em um DataFrame\n",
        "features_df = df['Descrição'].apply(extract_features).apply(pd.Series)\n",
        "\n",
        "# Concatenando as novas features ao DataFrame original\n",
        "df = pd.concat([df, features_df], axis=1)\n",
        "\n",
        "# Excluindo a coluna \"Descrição\"\n",
        "df.drop(columns=['Descrição'], inplace=True)\n",
        "\n",
        "# Aplicando Label Encoding na coluna 'Tipo de Imóvel'\n",
        "df['Tipo de Imóvel'] = label_encoder.fit_transform(df['Tipo de Imóvel'])\n",
        "\n",
        "# Visualizando o DataFrame com as novas features\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Hr6mN2t5SnQO",
        "outputId": "a26d2dc7-0fb8-4dcb-f75e-5ee0f27386cd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cidade  Bairro      Preço  Valor de avaliação  Desconto  \\\n",
              "0       1      24  160000.54            145000.0       0.0   \n",
              "1       2      12  150000.00            115000.0       0.0   \n",
              "2       3      64   84000.00            140000.0      40.0   \n",
              "3       3      67   79800.00            133000.0      40.0   \n",
              "4       3      64  152460.00            152460.0       0.0   \n",
              "\n",
              "   Modalidade de venda  Tipo de Imóvel  Área Total  Área Privativa  \\\n",
              "0                    0               1         0.0           96.00   \n",
              "1                    0               1         0.0           61.57   \n",
              "2                    1               0         0.0           49.49   \n",
              "3                    1               0         0.0           45.30   \n",
              "4                    0               0         0.0           49.49   \n",
              "\n",
              "   Área do Terreno  Número de Quartos  Número de Salas  \\\n",
              "0            300.0                  2                1   \n",
              "1            153.4                  2                1   \n",
              "2              0.0                  2                1   \n",
              "3              0.0                  2                1   \n",
              "4              0.0                  2                1   \n",
              "\n",
              "   Número de Vagas de Garagem  \n",
              "0                           1  \n",
              "1                           0  \n",
              "2                           1  \n",
              "3                           1  \n",
              "4                           1  "
            ],
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              "      <th></th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Bairro</th>\n",
              "      <th>Preço</th>\n",
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              "      <th>Tipo de Imóvel</th>\n",
              "      <th>Área Total</th>\n",
              "      <th>Área Privativa</th>\n",
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              "      <th>0</th>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>64</td>\n",
              "      <td>84000.00</td>\n",
              "      <td>140000.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>67</td>\n",
              "      <td>79800.00</td>\n",
              "      <td>133000.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>64</td>\n",
              "      <td>152460.00</td>\n",
              "      <td>152460.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a30a690-e43e-4023-98cc-d8dd48c54964')\"\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-4a30a690-e43e-4023-98cc-d8dd48c54964 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-4a30a690-e43e-4023-98cc-d8dd48c54964');\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-073fa168-40d3-4d33-9d93-ee72378b0062\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-073fa168-40d3-4d33-9d93-ee72378b0062')\"\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-073fa168-40d3-4d33-9d93-ee72378b0062 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\": 217,\n  \"fields\": [\n    {\n      \"column\": \"Cidade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1,\n          11,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bairro\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 0,\n        \"max\": 80,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          11,\n          24,\n          73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pre\\u00e7o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131659.53201536223,\n        \"min\": 42502.5,\n        \"max\": 900000.0,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          159629.86,\n          99201.8,\n          114777.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valor de avalia\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 151128.66505811937,\n        \"min\": 70800.0,\n        \"max\": 920000.0,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          155000.0,\n          161000.0,\n          152460.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desconto\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.689709216067484,\n        \"min\": 0.0,\n        \"max\": 45.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          45.0,\n          40.31,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Modalidade de venda\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tipo de Im\\u00f3vel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u00c1rea Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.06406565983963,\n        \"min\": 0.0,\n        \"max\": 218.58,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          53.25,\n          218.58,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u00c1rea Privativa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.175465612615014,\n        \"min\": 0.0,\n        \"max\": 266.34,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          51.38,\n          46.03,\n          50.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u00c1rea do Terreno\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.7520846022926,\n        \"min\": 0.0,\n        \"max\": 886.13,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          882.0,\n          292.74,\n          113.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N\\u00famero de Quartos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N\\u00famero de Salas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N\\u00famero de Vagas de Garagem\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3 Análise Exploratória de Dados (EDA)\n",
        "\n",
        "O objetivo é obter insights iniciais sobre os dados e identificar relações entre as variáveis, ajudando na escolha das melhores features para o modelo de regressão linear."
      ],
      "metadata": {
        "id": "n1ndwb5sXrtW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 Visualização dos Dados\n",
        "\n",
        "A visualização dos dados permite identificar padrões, outliers e a distribuição das variáveis, além de entender a relação entre as variáveis independentes e a variável dependente \"Valor de avaliação\"."
      ],
      "metadata": {
        "id": "yOkmYn9op1Bv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Histograma"
      ],
      "metadata": {
        "id": "wCxNwYbaqb6p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# # Histograma para o Preço\n",
        "plt.figure(figsize=(8, 6))\n",
        "df['Preço'].plot(kind='hist', bins=50, color='skyblue')\n",
        "plt.title('Distribuição de Preços')\n",
        "plt.xlabel('Preço')\n",
        "plt.ylabel('Frequência')\n",
        "plt.show()\n",
        "\n",
        "# Histograma para o Valor de Avaliação\n",
        "plt.figure(figsize=(8, 6))\n",
        "df['Valor de avaliação'].plot(kind='hist', bins=50, color='lightgreen')\n",
        "plt.title('Distribuição de Valor de Avaliação')\n",
        "plt.xlabel('Valor de Avaliação')\n",
        "plt.ylabel('Frequência')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k7iIkqubfMch",
        "outputId": "62d9ca32-4bef-4d4b-f76f-8e00be441aa1"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "### Scatter Plot"
      ],
      "metadata": {
        "id": "UHEigX5QqjJO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scatter Plot\n",
        "# Scatter plot entre Cidade e Valor de Avaliação\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(df['Cidade'], df['Valor de avaliação'], color='purple')\n",
        "plt.title('Relação entre Cidade e Valor de Avaliação')\n",
        "plt.xlabel('Cidade')\n",
        "plt.ylabel('Valor de Avaliação')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "b1KpYvq8gvqF",
        "outputId": "1e5f9ce7-03bd-4cf4-905c-8c408e47f2d8"
      },
      "execution_count": 26,
      "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": [
        "### Heatmap de Correlação"
      ],
      "metadata": {
        "id": "FI43Pbjyqpfv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mapa de calor de correlação\n",
        "plt.figure(figsize=(10, 8))\n",
        "correlation_matrix = df.corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "plt.title('Mapa de Calor de Correlação')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "id": "vbF04q5Mg9wG",
        "outputId": "c81f28c4-583d-4d08-dea0-cf27004133f9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 Correlação\n",
        "\n",
        "Avalie a correlação entre as variáveis independentes e a variável dependente \"Valor de avaliação\" para identificar quais features podem ser mais relevantes para o modelo."
      ],
      "metadata": {
        "id": "IuS99XTc6kOc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlação entre Cidade e Valor de Avaliação\n",
        "correlacao_cidade_valor = df['Cidade'].corr(df['Valor de avaliação'])\n",
        "print(f\"Correlação entre Cidade e Valor de Avaliação: {correlacao_cidade_valor}\")\n",
        "\n",
        "# Correlação entre Área Privativa e Valor de Avaliação\n",
        "correlacao_area_privativa_valor = df['Área Privativa'].corr(df['Valor de avaliação'])\n",
        "print(f\"Correlação entre Área Privativa e Valor de Avaliação: {correlacao_area_privativa_valor}\")\n",
        "\n",
        "# Correlação entre Área do Terreno e Valor de Avaliação\n",
        "correlacao_area_terreno_valor = df['Área do Terreno'].corr(df['Valor de avaliação'])\n",
        "print(f\"Correlação entre Área do Terreno e Valor de Avaliação: {correlacao_area_terreno_valor}\")\n",
        "\n",
        "# Correlação entre Tipo de Imóvel e Valor de Avaliação\n",
        "correlacao_tipo_imovel_valor = df['Tipo de Imóvel'].corr(df['Valor de avaliação'])\n",
        "print(f\"Correlação entre Tipo de Imóvel e Valor de Avaliação: {correlacao_tipo_imovel_valor}\")\n",
        "\n",
        "# Correlação entre Número de Quartos e Valor de Avaliação\n",
        "correlacao_num_quartos_valor = df['Número de Quartos'].corr(df['Valor de avaliação'])\n",
        "print(f\"Correlação entre Número de Quartos e Valor de Avaliação: {correlacao_num_quartos_valor}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71IZg5vN7aj-",
        "outputId": "6c43c12f-1e29-46ff-8003-42ee22ecee84"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlação entre Cidade e Valor de Avaliação: 0.09667827596273404\n",
            "Correlação entre Área Privativa e Valor de Avaliação: 0.7311104647577815\n",
            "Correlação entre Área do Terreno e Valor de Avaliação: 0.24847912647986786\n",
            "Correlação entre Tipo de Imóvel e Valor de Avaliação: 0.0671033862275154\n",
            "Correlação entre Número de Quartos e Valor de Avaliação: 0.2950262740189995\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4 Divisão dos Dados"
      ],
      "metadata": {
        "id": "RhZTYoJwLpkT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definindo X e y\n",
        "X = df.drop('Valor de avaliação', axis=1)  # Variáveis independentes\n",
        "y = df['Valor de avaliação']  # Variável dependente\n",
        "\n",
        "# Dividindo os dados em conjuntos de treinamento e teste (80/20)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "wO_fVTRzLzfW"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5 Construção do Modelo"
      ],
      "metadata": {
        "id": "nVmwFLhOSD3u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instanciar o modelo\n",
        "modelo = LinearRegression()\n",
        "\n",
        "# Treinar o modelo\n",
        "modelo.fit(X_train, y_train)\n",
        "\n",
        "# Visualizar os coeficientes do modelo\n",
        "print(\"Coeficientes do modelo:\")\n",
        "print(modelo.coef_)\n",
        "\n",
        "print(\"\\nIntercepto do modelo:\")\n",
        "print(modelo.intercept_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Y_7l3BmSYmC",
        "outputId": "8cb245ce-9ae8-47b5-ad09-fc0c436b6736"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes do modelo:\n",
            "[-3.82383889e+01  5.45788231e+01  1.15377985e+00  1.59446142e+03\n",
            "  2.78027558e+04 -1.52076652e+04  3.94011428e+02  1.07137545e+02\n",
            "  6.73078104e+01 -5.32671205e+03  4.89029322e+03 -2.02063432e+03]\n",
            "\n",
            "Intercepto do modelo:\n",
            "-36652.72391622572\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6 Avaliação do Modelo"
      ],
      "metadata": {
        "id": "y3JgyySJTal3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fazer previsões no conjunto de teste\n",
        "y_pred = modelo.predict(X_test)\n",
        "\n",
        "# Calcular MSE e R²\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"Erro Quadrático Médio (MSE):\", mse)\n",
        "print(\"Coeficiente de Determinação (R²):\", r2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MiAB-kcPTdkL",
        "outputId": "b80b1d14-ef5a-49d9-a2bf-b362c49d2100"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erro Quadrático Médio (MSE): 768551233.8198005\n",
            "Coeficiente de Determinação (R²): 0.9656463149920793\n"
          ]
        }
      ]
    }
  ]
}