Regressao_Venda_Imovel.ipynb

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  "metadata": {
    "colab": {
      "name": "Regressao_Venda_Imovel.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/silviolima07/Regression_Apartment_Sales/blob/master/Regressao_Venda_Imovel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEm9-7qI3nMm"
      },
      "source": [
        "# Aplicando algoritmos de Regressão\n",
        "### - Objetivo:\n",
        "####  Para prever o valor de venda de apartamentos\n",
        "### - Processo:\n",
        "####  Dados obtidos através de scrap de 200 páginas da internet\n",
        "####  Foram coletados 4k anúncios e após removidos os duplicados restaram 3k\n",
        "####  O score R2 foi aplicado para avaliar se os modelos.\n",
        "####  Valores obtidos ficaram entre 0.85 e 0.95.\n",
        "####  Banheiro e quarto são os items menos importantes na previsão."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Referencia:\n",
        "- https://www.analyticsvidhya.com/blog/2023/02/how-to-save-and-load-machine-learning-models-in-python-using-joblib-library/#:~:text=Saving%20the%20Model%20Using%20Joblib,-Saving%20our%20trained&text=%23%20save%20the%20model%20to%20a,will%20be%20saved%20as%20'regression_model."
      ],
      "metadata": {
        "id": "eFBQcO9QOSMq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kM5qpFQqcxeY",
        "outputId": "f5f94d9f-7d94-4f33-acf0-4bdd738cee3d"
      },
      "execution_count": 44,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_Q1YykVCRkp"
      },
      "source": [
        "![alt text](https://drive.google.com/uc?id=1Dh8jYdyTpT6IlGYzDXmpiK2hpt6Z0Wdw)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaX5JbRoHci2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea4ad967-1cb2-4699-ce2d-b4eb9d735d26"
      },
      "source": [
        "# Isto foi necessário que os modelos gerados tivessem a mesma versão no momento da leitura\n",
        "# no servidor do Heroku que emitia um aviso de as versões eram diferentes e o pode ocorrer\n",
        "# comportamentos imprevisiveis do modelo devido a essa diferença\n",
        "#\n",
        "!pip install scikit-learn==1.2.2 #0.23.1"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn==1.2.2 in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2) (3.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57tm0P0E5kdp"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from IPython.core.pylabtools import figsize\n",
        "from IPython.display import display, HTML\n",
        "import matplotlib.pyplot as plt\n",
        "pd.set_option('display.max_columns', 200)\n",
        "pd.set_option('display.max_rows', 400)\n",
        "import seaborn as sns\n",
        "sns.set(color_codes=True)\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "import xgboost as xgb\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn import metrics\n",
        "import seaborn as sns\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from scipy import stats\n",
        "import pickle\n",
        "from sklearn import preprocessing\n",
        "import re\n",
        "import joblib"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGCVr3U4L_OS"
      },
      "source": [
        "# Ajustes\n",
        "\n",
        "nbairros = 30 # número de bairros a serem considerados no modelo"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ad86KKpXU8Fb"
      },
      "source": [
        "def count_encode(X, categorical_features, normalize=False):\n",
        "    print('Count encoding: {}'.format(categorical_features))\n",
        "    X_ = pd.DataFrame()\n",
        "    for cat_feature in categorical_features:\n",
        "        X_[cat_feature] = X[cat_feature].astype(\n",
        "            'object').map(X[cat_feature].value_counts())\n",
        "        if normalize:\n",
        "            X_[cat_feature] = X_[cat_feature] / np.max(X_[cat_feature])\n",
        "    X_ = X_.add_suffix('_count_encoded')\n",
        "    if normalize:\n",
        "        X_ = X_.astype(np.float32)\n",
        "        X_ = X_.add_suffix('_normalized')\n",
        "    else:\n",
        "        X_ = X_.astype(np.uint32)\n",
        "    return X_"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHZ-Nejo6NAC"
      },
      "source": [
        "df = pd.read_csv(\"/content/imovelweb_venda_utf8.csv\",encoding = \"Latin-1\")"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WIPwVzvP7Q0J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2f40ba0-b679-41be-ade9-20a3a42884a4"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jx6nm4n_0U96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4b4a576-b119-4914-ec29-2cc1aa950880"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'price', 'area_total', 'area_util', 'quarto', 'banheiro',\n",
              "       'vaga', 'bairro'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oy_seutb1jPx"
      },
      "source": [
        "df = df.drop(columns=['Unnamed: 0'])"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbnO7kHA3VfO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d241eb22-98b3-4cd5-9651-91ef945a8bbc"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['price', 'area_total', 'area_util', 'quarto', 'banheiro', 'vaga',\n",
              "       'bairro'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqkbLJ9W8Sae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "c1837b1e-90ce-457a-e19a-f54632fdad42"
      },
      "source": [
        "# Remover linhas duplicadas\n",
        "#\n",
        "df = df.drop_duplicates(subset=[\"price\", \"area_total\",\"area_util\", \"quarto\", \"banheiro\", \"vaga\", \"bairro\"])\n",
        "df"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             price         area_total         area_util     quarto  \\\n",
              "0     R$ 2.450.000  489 m² área total  217 m² área útil  4 Quartos   \n",
              "1     R$ 2.250.000  115 m² área total  115 m² área útil   1 Quarto   \n",
              "2       R$ 770.000   41 m² área total   41 m² área útil  2 Quartos   \n",
              "3       R$ 320.000   33 m² área total   33 m² área útil   1 Quarto   \n",
              "4     R$ 2.070.000  173 m² área total  173 m² área útil  4 Quartos   \n",
              "...            ...                ...               ...        ...   \n",
              "3995  R$ 1.678.000  255 m² área total  160 m² área útil  3 Quartos   \n",
              "3996    R$ 260.000   50 m² área total   50 m² área útil  2 Quartos   \n",
              "3997  R$ 1.430.000  104 m² área total  104 m² área útil  3 Quartos   \n",
              "3998    R$ 315.000   80 m² área total   29 m² área útil   1 Quarto   \n",
              "3999  R$ 1.489.999  190 m² área total  162 m² área útil  3 Quartos   \n",
              "\n",
              "         banheiro     vaga                                bairro  \n",
              "0     6 Banheiros  5 Vagas                     <span> Campo Belo  \n",
              "1      1 Banheiro   1 Vaga                        <span> Jardins  \n",
              "2      1 Banheiro   1 Vaga            <span> Vila Nova Conceição  \n",
              "3      1 Banheiro   1 Vaga                          <span> Moema  \n",
              "4     6 Banheiros  4 Vagas                       <span> Perdizes  \n",
              "...           ...      ...                                   ...  \n",
              "3995  5 Banheiros  3 Vagas                      <span> Aclimação  \n",
              "3996   1 Banheiro   1 Vaga                    <span> Vila Marari  \n",
              "3997  3 Banheiros  3 Vagas          <span> Chácara Santo Antônio  \n",
              "3998   1 Banheiro   1 Vaga                     <span> Bela Vista  \n",
              "3999  4 Banheiros  2 Vagas  <span> Parque Industrial Tomas Edson  \n",
              "\n",
              "[3213 rows x 7 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>area_total</th>\n",
              "      <th>area_util</th>\n",
              "      <th>quarto</th>\n",
              "      <th>banheiro</th>\n",
              "      <th>vaga</th>\n",
              "      <th>bairro</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>R$ 2.450.000</td>\n",
              "      <td>489 m² área total</td>\n",
              "      <td>217 m² área útil</td>\n",
              "      <td>4 Quartos</td>\n",
              "      <td>6 Banheiros</td>\n",
              "      <td>5 Vagas</td>\n",
              "      <td>&lt;span&gt; Campo Belo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>R$ 2.250.000</td>\n",
              "      <td>115 m² área total</td>\n",
              "      <td>115 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Jardins</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>R$ 770.000</td>\n",
              "      <td>41 m² área total</td>\n",
              "      <td>41 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Nova Conceição</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>R$ 320.000</td>\n",
              "      <td>33 m² área total</td>\n",
              "      <td>33 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Moema</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>R$ 2.070.000</td>\n",
              "      <td>173 m² área total</td>\n",
              "      <td>173 m² área útil</td>\n",
              "      <td>4 Quartos</td>\n",
              "      <td>6 Banheiros</td>\n",
              "      <td>4 Vagas</td>\n",
              "      <td>&lt;span&gt; Perdizes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3995</th>\n",
              "      <td>R$ 1.678.000</td>\n",
              "      <td>255 m² área total</td>\n",
              "      <td>160 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>5 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Aclimação</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3996</th>\n",
              "      <td>R$ 260.000</td>\n",
              "      <td>50 m² área total</td>\n",
              "      <td>50 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Marari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3997</th>\n",
              "      <td>R$ 1.430.000</td>\n",
              "      <td>104 m² área total</td>\n",
              "      <td>104 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>3 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Chácara Santo Antônio</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3998</th>\n",
              "      <td>R$ 315.000</td>\n",
              "      <td>80 m² área total</td>\n",
              "      <td>29 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Bela Vista</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3999</th>\n",
              "      <td>R$ 1.489.999</td>\n",
              "      <td>190 m² área total</td>\n",
              "      <td>162 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>4 Banheiros</td>\n",
              "      <td>2 Vagas</td>\n",
              "      <td>&lt;span&gt; Parque Industrial Tomas Edson</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3213 rows × 7 columns</p>\n",
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              "<div id=\"df-d1e8e5bc-16c9-4fa6-8328-378185fe8743\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d1e8e5bc-16c9-4fa6-8328-378185fe8743')\"\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",
              "    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 charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d1e8e5bc-16c9-4fa6-8328-378185fe8743 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-O2xW7ggFbIb"
      },
      "source": [
        "## Remover caracteres de price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJ6THfYmFWLq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "490c2dc4-78e2-488a-f515-2a4f2ca78d78"
      },
      "source": [
        "temp = df.price.str.replace('R\\$', '')\n",
        "df['price_clean'] = temp\n",
        "#\n",
        "temp = df.price_clean.str.replace('.', '')\n",
        "df['price_clean'] = temp\n",
        "df.price_clean = df.price_clean.astype('int64')\n",
        "df"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             price         area_total         area_util     quarto  \\\n",
              "0     R$ 2.450.000  489 m² área total  217 m² área útil  4 Quartos   \n",
              "1     R$ 2.250.000  115 m² área total  115 m² área útil   1 Quarto   \n",
              "2       R$ 770.000   41 m² área total   41 m² área útil  2 Quartos   \n",
              "3       R$ 320.000   33 m² área total   33 m² área útil   1 Quarto   \n",
              "4     R$ 2.070.000  173 m² área total  173 m² área útil  4 Quartos   \n",
              "...            ...                ...               ...        ...   \n",
              "3995  R$ 1.678.000  255 m² área total  160 m² área útil  3 Quartos   \n",
              "3996    R$ 260.000   50 m² área total   50 m² área útil  2 Quartos   \n",
              "3997  R$ 1.430.000  104 m² área total  104 m² área útil  3 Quartos   \n",
              "3998    R$ 315.000   80 m² área total   29 m² área útil   1 Quarto   \n",
              "3999  R$ 1.489.999  190 m² área total  162 m² área útil  3 Quartos   \n",
              "\n",
              "         banheiro     vaga                                bairro  price_clean  \n",
              "0     6 Banheiros  5 Vagas                     <span> Campo Belo      2450000  \n",
              "1      1 Banheiro   1 Vaga                        <span> Jardins      2250000  \n",
              "2      1 Banheiro   1 Vaga            <span> Vila Nova Conceição       770000  \n",
              "3      1 Banheiro   1 Vaga                          <span> Moema       320000  \n",
              "4     6 Banheiros  4 Vagas                       <span> Perdizes      2070000  \n",
              "...           ...      ...                                   ...          ...  \n",
              "3995  5 Banheiros  3 Vagas                      <span> Aclimação      1678000  \n",
              "3996   1 Banheiro   1 Vaga                    <span> Vila Marari       260000  \n",
              "3997  3 Banheiros  3 Vagas          <span> Chácara Santo Antônio      1430000  \n",
              "3998   1 Banheiro   1 Vaga                     <span> Bela Vista       315000  \n",
              "3999  4 Banheiros  2 Vagas  <span> Parque Industrial Tomas Edson      1489999  \n",
              "\n",
              "[3213 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a76a85f7-55b2-4e16-a34c-8c80755ca33f\" 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",
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              "\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>price</th>\n",
              "      <th>area_total</th>\n",
              "      <th>area_util</th>\n",
              "      <th>quarto</th>\n",
              "      <th>banheiro</th>\n",
              "      <th>vaga</th>\n",
              "      <th>bairro</th>\n",
              "      <th>price_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>R$ 2.450.000</td>\n",
              "      <td>489 m² área total</td>\n",
              "      <td>217 m² área útil</td>\n",
              "      <td>4 Quartos</td>\n",
              "      <td>6 Banheiros</td>\n",
              "      <td>5 Vagas</td>\n",
              "      <td>&lt;span&gt; Campo Belo</td>\n",
              "      <td>2450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>R$ 2.250.000</td>\n",
              "      <td>115 m² área total</td>\n",
              "      <td>115 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Jardins</td>\n",
              "      <td>2250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>R$ 770.000</td>\n",
              "      <td>41 m² área total</td>\n",
              "      <td>41 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Nova Conceição</td>\n",
              "      <td>770000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>R$ 320.000</td>\n",
              "      <td>33 m² área total</td>\n",
              "      <td>33 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Moema</td>\n",
              "      <td>320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>R$ 2.070.000</td>\n",
              "      <td>173 m² área total</td>\n",
              "      <td>173 m² área útil</td>\n",
              "      <td>4 Quartos</td>\n",
              "      <td>6 Banheiros</td>\n",
              "      <td>4 Vagas</td>\n",
              "      <td>&lt;span&gt; Perdizes</td>\n",
              "      <td>2070000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3995</th>\n",
              "      <td>R$ 1.678.000</td>\n",
              "      <td>255 m² área total</td>\n",
              "      <td>160 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>5 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Aclimação</td>\n",
              "      <td>1678000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3996</th>\n",
              "      <td>R$ 260.000</td>\n",
              "      <td>50 m² área total</td>\n",
              "      <td>50 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Marari</td>\n",
              "      <td>260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3997</th>\n",
              "      <td>R$ 1.430.000</td>\n",
              "      <td>104 m² área total</td>\n",
              "      <td>104 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>3 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Chácara Santo Antônio</td>\n",
              "      <td>1430000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3998</th>\n",
              "      <td>R$ 315.000</td>\n",
              "      <td>80 m² área total</td>\n",
              "      <td>29 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Bela Vista</td>\n",
              "      <td>315000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3999</th>\n",
              "      <td>R$ 1.489.999</td>\n",
              "      <td>190 m² área total</td>\n",
              "      <td>162 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>4 Banheiros</td>\n",
              "      <td>2 Vagas</td>\n",
              "      <td>&lt;span&gt; Parque Industrial Tomas Edson</td>\n",
              "      <td>1489999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3213 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a76a85f7-55b2-4e16-a34c-8c80755ca33f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a76a85f7-55b2-4e16-a34c-8c80755ca33f 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-a76a85f7-55b2-4e16-a34c-8c80755ca33f');\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-711dec52-ee2a-4c2e-94ad-e89f68a78fab\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-711dec52-ee2a-4c2e-94ad-e89f68a78fab')\"\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",
              "    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 charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-711dec52-ee2a-4c2e-94ad-e89f68a78fab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V99lBvcgFHO-"
      },
      "source": [
        "## Remover caracteres em area_total"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjdjYBP88xGH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "outputId": "fd97e0cc-980e-4015-e9d3-fb594b3f4a00"
      },
      "source": [
        "temp = df.area_total.replace(to_replace=r'[a-zá²]*', value='', regex=True)\n",
        "df['area_total_clean'] = temp\n",
        "#\n",
        "df.area_total_clean = df.area_total_clean.astype('int64')\n",
        "df"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             price         area_total         area_util     quarto  \\\n",
              "0     R$ 2.450.000  489 m² área total  217 m² área útil  4 Quartos   \n",
              "1     R$ 2.250.000  115 m² área total  115 m² área útil   1 Quarto   \n",
              "2       R$ 770.000   41 m² área total   41 m² área útil  2 Quartos   \n",
              "3       R$ 320.000   33 m² área total   33 m² área útil   1 Quarto   \n",
              "4     R$ 2.070.000  173 m² área total  173 m² área útil  4 Quartos   \n",
              "...            ...                ...               ...        ...   \n",
              "3995  R$ 1.678.000  255 m² área total  160 m² área útil  3 Quartos   \n",
              "3996    R$ 260.000   50 m² área total   50 m² área útil  2 Quartos   \n",
              "3997  R$ 1.430.000  104 m² área total  104 m² área útil  3 Quartos   \n",
              "3998    R$ 315.000   80 m² área total   29 m² área útil   1 Quarto   \n",
              "3999  R$ 1.489.999  190 m² área total  162 m² área útil  3 Quartos   \n",
              "\n",
              "         banheiro     vaga                                bairro  price_clean  \\\n",
              "0     6 Banheiros  5 Vagas                     <span> Campo Belo      2450000   \n",
              "1      1 Banheiro   1 Vaga                        <span> Jardins      2250000   \n",
              "2      1 Banheiro   1 Vaga            <span> Vila Nova Conceição       770000   \n",
              "3      1 Banheiro   1 Vaga                          <span> Moema       320000   \n",
              "4     6 Banheiros  4 Vagas                       <span> Perdizes      2070000   \n",
              "...           ...      ...                                   ...          ...   \n",
              "3995  5 Banheiros  3 Vagas                      <span> Aclimação      1678000   \n",
              "3996   1 Banheiro   1 Vaga                    <span> Vila Marari       260000   \n",
              "3997  3 Banheiros  3 Vagas          <span> Chácara Santo Antônio      1430000   \n",
              "3998   1 Banheiro   1 Vaga                     <span> Bela Vista       315000   \n",
              "3999  4 Banheiros  2 Vagas  <span> Parque Industrial Tomas Edson      1489999   \n",
              "\n",
              "      area_total_clean  \n",
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          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZBfQ_N-EaBF"
      },
      "source": [
        "## Remover caracteres em area_util"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GeRAR_sEoxx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "2a33ea6e-59c8-47f5-ebb6-28370bcf6d91"
      },
      "source": [
        "temp = df.area_util.replace(to_replace=r'[a-záú²]*', value='', regex=True)\n",
        "df['area_util_clean'] = temp\n",
        "#\n",
        "df.area_util_clean = df.area_util_clean.astype('int64')\n",
        "df\n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             price         area_total         area_util     quarto  \\\n",
              "0     R$ 2.450.000  489 m² área total  217 m² área útil  4 Quartos   \n",
              "1     R$ 2.250.000  115 m² área total  115 m² área útil   1 Quarto   \n",
              "2       R$ 770.000   41 m² área total   41 m² área útil  2 Quartos   \n",
              "3       R$ 320.000   33 m² área total   33 m² área útil   1 Quarto   \n",
              "4     R$ 2.070.000  173 m² área total  173 m² área útil  4 Quartos   \n",
              "...            ...                ...               ...        ...   \n",
              "3995  R$ 1.678.000  255 m² área total  160 m² área útil  3 Quartos   \n",
              "3996    R$ 260.000   50 m² área total   50 m² área útil  2 Quartos   \n",
              "3997  R$ 1.430.000  104 m² área total  104 m² área útil  3 Quartos   \n",
              "3998    R$ 315.000   80 m² área total   29 m² área útil   1 Quarto   \n",
              "3999  R$ 1.489.999  190 m² área total  162 m² área útil  3 Quartos   \n",
              "\n",
              "         banheiro     vaga                                bairro  price_clean  \\\n",
              "0     6 Banheiros  5 Vagas                     <span> Campo Belo      2450000   \n",
              "1      1 Banheiro   1 Vaga                        <span> Jardins      2250000   \n",
              "2      1 Banheiro   1 Vaga            <span> Vila Nova Conceição       770000   \n",
              "3      1 Banheiro   1 Vaga                          <span> Moema       320000   \n",
              "4     6 Banheiros  4 Vagas                       <span> Perdizes      2070000   \n",
              "...           ...      ...                                   ...          ...   \n",
              "3995  5 Banheiros  3 Vagas                      <span> Aclimação      1678000   \n",
              "3996   1 Banheiro   1 Vaga                    <span> Vila Marari       260000   \n",
              "3997  3 Banheiros  3 Vagas          <span> Chácara Santo Antônio      1430000   \n",
              "3998   1 Banheiro   1 Vaga                     <span> Bela Vista       315000   \n",
              "3999  4 Banheiros  2 Vagas  <span> Parque Industrial Tomas Edson      1489999   \n",
              "\n",
              "      area_total_clean  area_util_clean  \n",
              "0                  489              217  \n",
              "1                  115              115  \n",
              "2                   41               41  \n",
              "3                   33               33  \n",
              "4                  173              173  \n",
              "...                ...              ...  \n",
              "3995               255              160  \n",
              "3996                50               50  \n",
              "3997               104              104  \n",
              "3998                80               29  \n",
              "3999               190              162  \n",
              "\n",
              "[3213 rows x 10 columns]"
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              "      <th>3</th>\n",
              "      <td>R$ 320.000</td>\n",
              "      <td>33 m² área total</td>\n",
              "      <td>33 m² área útil</td>\n",
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              "      <th>4</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3995</th>\n",
              "      <td>R$ 1.678.000</td>\n",
              "      <td>255 m² área total</td>\n",
              "      <td>160 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>5 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Aclimação</td>\n",
              "      <td>1678000</td>\n",
              "      <td>255</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3996</th>\n",
              "      <td>R$ 260.000</td>\n",
              "      <td>50 m² área total</td>\n",
              "      <td>50 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Marari</td>\n",
              "      <td>260000</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3997</th>\n",
              "      <td>R$ 1.430.000</td>\n",
              "      <td>104 m² área total</td>\n",
              "      <td>104 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>3 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Chácara Santo Antônio</td>\n",
              "      <td>1430000</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3998</th>\n",
              "      <td>R$ 315.000</td>\n",
              "      <td>80 m² área total</td>\n",
              "      <td>29 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Bela Vista</td>\n",
              "      <td>315000</td>\n",
              "      <td>80</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3999</th>\n",
              "      <td>R$ 1.489.999</td>\n",
              "      <td>190 m² área total</td>\n",
              "      <td>162 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>4 Banheiros</td>\n",
              "      <td>2 Vagas</td>\n",
              "      <td>&lt;span&gt; Parque Industrial Tomas Edson</td>\n",
              "      <td>1489999</td>\n",
              "      <td>190</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3213 rows × 10 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nddhGBpfSaVQ"
      },
      "source": [
        "## Remover caracteres em quarto / banheiro / vaga"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kte2_zprSaVS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "outputId": "0a31a4e4-db32-42b7-9669-9f69b012ab92"
      },
      "source": [
        "temp = df.quarto.replace(to_replace=r'[A-Za-z]*', value='', regex=True)\n",
        "df['quarto_clean'] = temp\n",
        "#\n",
        "temp = df.banheiro.replace(to_replace=r'[A-Za-z]*', value='', regex=True)\n",
        "df['banheiro_clean'] = temp\n",
        "#\n",
        "temp = df.vaga.replace(to_replace=r'[A-Za-z]*', value='', regex=True)\n",
        "df['vaga_clean'] = temp\n",
        "#\n",
        "df.quarto_clean = df.quarto_clean.astype('int64')\n",
        "df.banheiro_clean = df.banheiro_clean.astype('int64')\n",
        "df.vaga_clean = df.vaga_clean.astype('int64')\n",
        "df\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             price         area_total         area_util     quarto  \\\n",
              "0     R$ 2.450.000  489 m² área total  217 m² área útil  4 Quartos   \n",
              "1     R$ 2.250.000  115 m² área total  115 m² área útil   1 Quarto   \n",
              "2       R$ 770.000   41 m² área total   41 m² área útil  2 Quartos   \n",
              "3       R$ 320.000   33 m² área total   33 m² área útil   1 Quarto   \n",
              "4     R$ 2.070.000  173 m² área total  173 m² área útil  4 Quartos   \n",
              "...            ...                ...               ...        ...   \n",
              "3995  R$ 1.678.000  255 m² área total  160 m² área útil  3 Quartos   \n",
              "3996    R$ 260.000   50 m² área total   50 m² área útil  2 Quartos   \n",
              "3997  R$ 1.430.000  104 m² área total  104 m² área útil  3 Quartos   \n",
              "3998    R$ 315.000   80 m² área total   29 m² área útil   1 Quarto   \n",
              "3999  R$ 1.489.999  190 m² área total  162 m² área útil  3 Quartos   \n",
              "\n",
              "         banheiro     vaga                                bairro  price_clean  \\\n",
              "0     6 Banheiros  5 Vagas                     <span> Campo Belo      2450000   \n",
              "1      1 Banheiro   1 Vaga                        <span> Jardins      2250000   \n",
              "2      1 Banheiro   1 Vaga            <span> Vila Nova Conceição       770000   \n",
              "3      1 Banheiro   1 Vaga                          <span> Moema       320000   \n",
              "4     6 Banheiros  4 Vagas                       <span> Perdizes      2070000   \n",
              "...           ...      ...                                   ...          ...   \n",
              "3995  5 Banheiros  3 Vagas                      <span> Aclimação      1678000   \n",
              "3996   1 Banheiro   1 Vaga                    <span> Vila Marari       260000   \n",
              "3997  3 Banheiros  3 Vagas          <span> Chácara Santo Antônio      1430000   \n",
              "3998   1 Banheiro   1 Vaga                     <span> Bela Vista       315000   \n",
              "3999  4 Banheiros  2 Vagas  <span> Parque Industrial Tomas Edson      1489999   \n",
              "\n",
              "      area_total_clean  area_util_clean  quarto_clean  banheiro_clean  \\\n",
              "0                  489              217             4               6   \n",
              "1                  115              115             1               1   \n",
              "2                   41               41             2               1   \n",
              "3                   33               33             1               1   \n",
              "4                  173              173             4               6   \n",
              "...                ...              ...           ...             ...   \n",
              "3995               255              160             3               5   \n",
              "3996                50               50             2               1   \n",
              "3997               104              104             3               3   \n",
              "3998                80               29             1               1   \n",
              "3999               190              162             3               4   \n",
              "\n",
              "      vaga_clean  \n",
              "0              5  \n",
              "1              1  \n",
              "2              1  \n",
              "3              1  \n",
              "4              4  \n",
              "...          ...  \n",
              "3995           3  \n",
              "3996           1  \n",
              "3997           3  \n",
              "3998           1  \n",
              "3999           2  \n",
              "\n",
              "[3213 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>area_total</th>\n",
              "      <th>area_util</th>\n",
              "      <th>quarto</th>\n",
              "      <th>banheiro</th>\n",
              "      <th>vaga</th>\n",
              "      <th>bairro</th>\n",
              "      <th>price_clean</th>\n",
              "      <th>area_total_clean</th>\n",
              "      <th>area_util_clean</th>\n",
              "      <th>quarto_clean</th>\n",
              "      <th>banheiro_clean</th>\n",
              "      <th>vaga_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>R$ 2.450.000</td>\n",
              "      <td>489 m² área total</td>\n",
              "      <td>217 m² área útil</td>\n",
              "      <td>4 Quartos</td>\n",
              "      <td>6 Banheiros</td>\n",
              "      <td>5 Vagas</td>\n",
              "      <td>&lt;span&gt; Campo Belo</td>\n",
              "      <td>2450000</td>\n",
              "      <td>489</td>\n",
              "      <td>217</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>R$ 2.250.000</td>\n",
              "      <td>115 m² área total</td>\n",
              "      <td>115 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Jardins</td>\n",
              "      <td>2250000</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>R$ 770.000</td>\n",
              "      <td>41 m² área total</td>\n",
              "      <td>41 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Nova Conceição</td>\n",
              "      <td>770000</td>\n",
              "      <td>41</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>R$ 320.000</td>\n",
              "      <td>33 m² área total</td>\n",
              "      <td>33 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Moema</td>\n",
              "      <td>320000</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>R$ 2.070.000</td>\n",
              "      <td>173 m² área total</td>\n",
              "      <td>173 m² área útil</td>\n",
              "      <td>4 Quartos</td>\n",
              "      <td>6 Banheiros</td>\n",
              "      <td>4 Vagas</td>\n",
              "      <td>&lt;span&gt; Perdizes</td>\n",
              "      <td>2070000</td>\n",
              "      <td>173</td>\n",
              "      <td>173</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3995</th>\n",
              "      <td>R$ 1.678.000</td>\n",
              "      <td>255 m² área total</td>\n",
              "      <td>160 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>5 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Aclimação</td>\n",
              "      <td>1678000</td>\n",
              "      <td>255</td>\n",
              "      <td>160</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3996</th>\n",
              "      <td>R$ 260.000</td>\n",
              "      <td>50 m² área total</td>\n",
              "      <td>50 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Marari</td>\n",
              "      <td>260000</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3997</th>\n",
              "      <td>R$ 1.430.000</td>\n",
              "      <td>104 m² área total</td>\n",
              "      <td>104 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>3 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Chácara Santo Antônio</td>\n",
              "      <td>1430000</td>\n",
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              "      <td>104</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3998</th>\n",
              "      <td>R$ 315.000</td>\n",
              "      <td>80 m² área total</td>\n",
              "      <td>29 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Bela Vista</td>\n",
              "      <td>315000</td>\n",
              "      <td>80</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3999</th>\n",
              "      <td>R$ 1.489.999</td>\n",
              "      <td>190 m² área total</td>\n",
              "      <td>162 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>4 Banheiros</td>\n",
              "      <td>2 Vagas</td>\n",
              "      <td>&lt;span&gt; Parque Industrial Tomas Edson</td>\n",
              "      <td>1489999</td>\n",
              "      <td>190</td>\n",
              "      <td>162</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3213 rows × 13 columns</p>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "                                                    [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",
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              "\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoFHSHdSlQ0B"
      },
      "source": [
        "## Remover caracteres em bairro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nMWe_Z8lIko",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "outputId": "fbd93308-de12-4b11-cad1-ca58c105fd20"
      },
      "source": [
        "temp = df.bairro.replace(to_replace=r'<span>', value='', regex=True)\n",
        "df['bairro_clean'] = temp\n",
        "#\n",
        "df\n"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             price         area_total         area_util     quarto  \\\n",
              "0     R$ 2.450.000  489 m² área total  217 m² área útil  4 Quartos   \n",
              "1     R$ 2.250.000  115 m² área total  115 m² área útil   1 Quarto   \n",
              "2       R$ 770.000   41 m² área total   41 m² área útil  2 Quartos   \n",
              "3       R$ 320.000   33 m² área total   33 m² área útil   1 Quarto   \n",
              "4     R$ 2.070.000  173 m² área total  173 m² área útil  4 Quartos   \n",
              "...            ...                ...               ...        ...   \n",
              "3995  R$ 1.678.000  255 m² área total  160 m² área útil  3 Quartos   \n",
              "3996    R$ 260.000   50 m² área total   50 m² área útil  2 Quartos   \n",
              "3997  R$ 1.430.000  104 m² área total  104 m² área útil  3 Quartos   \n",
              "3998    R$ 315.000   80 m² área total   29 m² área útil   1 Quarto   \n",
              "3999  R$ 1.489.999  190 m² área total  162 m² área útil  3 Quartos   \n",
              "\n",
              "         banheiro     vaga                                bairro  price_clean  \\\n",
              "0     6 Banheiros  5 Vagas                     <span> Campo Belo      2450000   \n",
              "1      1 Banheiro   1 Vaga                        <span> Jardins      2250000   \n",
              "2      1 Banheiro   1 Vaga            <span> Vila Nova Conceição       770000   \n",
              "3      1 Banheiro   1 Vaga                          <span> Moema       320000   \n",
              "4     6 Banheiros  4 Vagas                       <span> Perdizes      2070000   \n",
              "...           ...      ...                                   ...          ...   \n",
              "3995  5 Banheiros  3 Vagas                      <span> Aclimação      1678000   \n",
              "3996   1 Banheiro   1 Vaga                    <span> Vila Marari       260000   \n",
              "3997  3 Banheiros  3 Vagas          <span> Chácara Santo Antônio      1430000   \n",
              "3998   1 Banheiro   1 Vaga                     <span> Bela Vista       315000   \n",
              "3999  4 Banheiros  2 Vagas  <span> Parque Industrial Tomas Edson      1489999   \n",
              "\n",
              "      area_total_clean  area_util_clean  quarto_clean  banheiro_clean  \\\n",
              "0                  489              217             4               6   \n",
              "1                  115              115             1               1   \n",
              "2                   41               41             2               1   \n",
              "3                   33               33             1               1   \n",
              "4                  173              173             4               6   \n",
              "...                ...              ...           ...             ...   \n",
              "3995               255              160             3               5   \n",
              "3996                50               50             2               1   \n",
              "3997               104              104             3               3   \n",
              "3998                80               29             1               1   \n",
              "3999               190              162             3               4   \n",
              "\n",
              "      vaga_clean                    bairro_clean  \n",
              "0              5                      Campo Belo  \n",
              "1              1                         Jardins  \n",
              "2              1             Vila Nova Conceição  \n",
              "3              1                           Moema  \n",
              "4              4                        Perdizes  \n",
              "...          ...                             ...  \n",
              "3995           3                       Aclimação  \n",
              "3996           1                     Vila Marari  \n",
              "3997           3           Chácara Santo Antônio  \n",
              "3998           1                      Bela Vista  \n",
              "3999           2   Parque Industrial Tomas Edson  \n",
              "\n",
              "[3213 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>area_total</th>\n",
              "      <th>area_util</th>\n",
              "      <th>quarto</th>\n",
              "      <th>banheiro</th>\n",
              "      <th>vaga</th>\n",
              "      <th>bairro</th>\n",
              "      <th>price_clean</th>\n",
              "      <th>area_total_clean</th>\n",
              "      <th>area_util_clean</th>\n",
              "      <th>quarto_clean</th>\n",
              "      <th>banheiro_clean</th>\n",
              "      <th>vaga_clean</th>\n",
              "      <th>bairro_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>R$ 2.450.000</td>\n",
              "      <td>489 m² área total</td>\n",
              "      <td>217 m² área útil</td>\n",
              "      <td>4 Quartos</td>\n",
              "      <td>6 Banheiros</td>\n",
              "      <td>5 Vagas</td>\n",
              "      <td>&lt;span&gt; Campo Belo</td>\n",
              "      <td>2450000</td>\n",
              "      <td>489</td>\n",
              "      <td>217</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>Campo Belo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>R$ 2.250.000</td>\n",
              "      <td>115 m² área total</td>\n",
              "      <td>115 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Jardins</td>\n",
              "      <td>2250000</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Jardins</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>R$ 770.000</td>\n",
              "      <td>41 m² área total</td>\n",
              "      <td>41 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Nova Conceição</td>\n",
              "      <td>770000</td>\n",
              "      <td>41</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Vila Nova Conceição</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>R$ 320.000</td>\n",
              "      <td>33 m² área total</td>\n",
              "      <td>33 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Moema</td>\n",
              "      <td>320000</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Moema</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>R$ 2.070.000</td>\n",
              "      <td>173 m² área total</td>\n",
              "      <td>173 m² área útil</td>\n",
              "      <td>4 Quartos</td>\n",
              "      <td>6 Banheiros</td>\n",
              "      <td>4 Vagas</td>\n",
              "      <td>&lt;span&gt; Perdizes</td>\n",
              "      <td>2070000</td>\n",
              "      <td>173</td>\n",
              "      <td>173</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>Perdizes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3995</th>\n",
              "      <td>R$ 1.678.000</td>\n",
              "      <td>255 m² área total</td>\n",
              "      <td>160 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>5 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Aclimação</td>\n",
              "      <td>1678000</td>\n",
              "      <td>255</td>\n",
              "      <td>160</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>Aclimação</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3996</th>\n",
              "      <td>R$ 260.000</td>\n",
              "      <td>50 m² área total</td>\n",
              "      <td>50 m² área útil</td>\n",
              "      <td>2 Quartos</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Vila Marari</td>\n",
              "      <td>260000</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Vila Marari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3997</th>\n",
              "      <td>R$ 1.430.000</td>\n",
              "      <td>104 m² área total</td>\n",
              "      <td>104 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>3 Banheiros</td>\n",
              "      <td>3 Vagas</td>\n",
              "      <td>&lt;span&gt; Chácara Santo Antônio</td>\n",
              "      <td>1430000</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Chácara Santo Antônio</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3998</th>\n",
              "      <td>R$ 315.000</td>\n",
              "      <td>80 m² área total</td>\n",
              "      <td>29 m² área útil</td>\n",
              "      <td>1 Quarto</td>\n",
              "      <td>1 Banheiro</td>\n",
              "      <td>1 Vaga</td>\n",
              "      <td>&lt;span&gt; Bela Vista</td>\n",
              "      <td>315000</td>\n",
              "      <td>80</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bela Vista</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3999</th>\n",
              "      <td>R$ 1.489.999</td>\n",
              "      <td>190 m² área total</td>\n",
              "      <td>162 m² área útil</td>\n",
              "      <td>3 Quartos</td>\n",
              "      <td>4 Banheiros</td>\n",
              "      <td>2 Vagas</td>\n",
              "      <td>&lt;span&gt; Parque Industrial Tomas Edson</td>\n",
              "      <td>1489999</td>\n",
              "      <td>190</td>\n",
              "      <td>162</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>Parque Industrial Tomas Edson</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3213 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-db8f34e6-3dd1-49da-a86e-79a59acfaf36 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-db8f34e6-3dd1-49da-a86e-79a59acfaf36');\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-61f8c616-d13c-495e-b36e-430963658a3d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-61f8c616-d13c-495e-b36e-430963658a3d')\"\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",
              "    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 charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-61f8c616-d13c-495e-b36e-430963658a3d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkmOuJKbms1w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "650509bc-128f-43db-d181-cfc8e6017aa8"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['price', 'area_total', 'area_util', 'quarto', 'banheiro', 'vaga',\n",
              "       'bairro', 'price_clean', 'area_total_clean', 'area_util_clean',\n",
              "       'quarto_clean', 'banheiro_clean', 'vaga_clean', 'bairro_clean'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9sncrfqSaVi"
      },
      "source": [
        "df2 = df.drop([ 'price', 'area_total', 'area_util', 'banheiro', 'vaga','quarto', 'bairro'], axis=1)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fseLNwZHghc"
      },
      "source": [
        "df2.rename(columns={'price_clean':'price', 'area_total_clean':'area_total', 'area_util_clean':'area_util', 'quarto_clean':'quarto', 'banheiro_clean':'banheiro', 'vaga_clean':'vaga', 'bairro_clean':'bairro'}, inplace=True)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXFm_ep3nBAS"
      },
      "source": [
        "df2['bairro'] = df2['bairro'].str.lstrip()"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xIrzEgkWdpu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "80f5a94b-e9f0-442d-c010-8d0258b37fe9"
      },
      "source": [
        "df2.describe()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              price    area_total    area_util       quarto     banheiro  \\\n",
              "count  3.213000e+03   3213.000000  3213.000000  3213.000000  3213.000000   \n",
              "mean   2.837114e+06    203.926237    99.003735     2.220355     2.276066   \n",
              "std    6.137541e+07   1505.895119    96.099030     1.049519     1.529304   \n",
              "min    3.820000e+02     12.000000    12.000000     0.000000     1.000000   \n",
              "25%    3.800000e+05     59.000000    47.000000     1.000000     1.000000   \n",
              "50%    6.500000e+05     85.000000    70.000000     2.000000     2.000000   \n",
              "75%    1.200000e+06    144.000000   120.000000     3.000000     3.000000   \n",
              "max    2.960000e+09  40000.000000  1980.000000     7.000000    11.000000   \n",
              "\n",
              "             vaga  \n",
              "count  3213.00000  \n",
              "mean      1.81357  \n",
              "std       1.15155  \n",
              "min       1.00000  \n",
              "25%       1.00000  \n",
              "50%       1.00000  \n",
              "75%       2.00000  \n",
              "max      14.00000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d49a73bf-bdb2-4b5c-a51c-218b88944082\" 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>price</th>\n",
              "      <th>area_total</th>\n",
              "      <th>area_util</th>\n",
              "      <th>quarto</th>\n",
              "      <th>banheiro</th>\n",
              "      <th>vaga</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.213000e+03</td>\n",
              "      <td>3213.000000</td>\n",
              "      <td>3213.000000</td>\n",
              "      <td>3213.000000</td>\n",
              "      <td>3213.000000</td>\n",
              "      <td>3213.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.837114e+06</td>\n",
              "      <td>203.926237</td>\n",
              "      <td>99.003735</td>\n",
              "      <td>2.220355</td>\n",
              "      <td>2.276066</td>\n",
              "      <td>1.81357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.137541e+07</td>\n",
              "      <td>1505.895119</td>\n",
              "      <td>96.099030</td>\n",
              "      <td>1.049519</td>\n",
              "      <td>1.529304</td>\n",
              "      <td>1.15155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.820000e+02</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.800000e+05</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.500000e+05</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.200000e+06</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.960000e+09</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>1980.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>14.00000</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-d49a73bf-bdb2-4b5c-a51c-218b88944082')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      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",
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              "\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-d49a73bf-bdb2-4b5c-a51c-218b88944082 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-d49a73bf-bdb2-4b5c-a51c-218b88944082');\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-b53cdca9-d4b7-4598-8ba6-87fdd30aba06\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b53cdca9-d4b7-4598-8ba6-87fdd30aba06')\"\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",
              "    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 charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b53cdca9-d4b7-4598-8ba6-87fdd30aba06 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTNU2sAOpJjV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8efadb11-f967-4a9c-f937-446dfdd33f9d"
      },
      "source": [
        "# Número de bairros que possuem anúncios de apartamentos a venda\n",
        "df2.bairro.nunique()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "299"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NmMXNRW7_hO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d21ab7a1-95d9-4857-aa79-8981ca48192f"
      },
      "source": [
        "# Número de anúncios por bairro\n",
        "# nbairros vale 30, apenas para filtrar os 30 maiores valores\n",
        "df2.bairro.value_counts()[:nbairros]"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Moema                    161\n",
              "Jardins                  148\n",
              "Perdizes                 137\n",
              "Itaim Bibi               124\n",
              "Vila Olímpia             115\n",
              "Brooklin                 112\n",
              "Jardim Paulista           96\n",
              "Pinheiros                 82\n",
              "Vila Mariana              77\n",
              "Vila Nova Conceição       75\n",
              "Vila Mascote              70\n",
              "Jardim Marajoara          65\n",
              "Ipiranga                  64\n",
              "Vila Romana               61\n",
              "Bela Vista                59\n",
              "Vila Leopoldina           59\n",
              "Morumbi                   59\n",
              "Campo Belo                58\n",
              "Santana                   53\n",
              "Chácara Santo Antônio     50\n",
              "Saúde                     48\n",
              "Cerqueira César           48\n",
              "Pompéia                   41\n",
              "Tatuapé                   41\n",
              "Paraíso                   40\n",
              "Jabaquara                 38\n",
              "Higien                    36\n",
              "Barra Funda               35\n",
              "Consolação                34\n",
              "Vila Guarani              33\n",
              "Name: bairro, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFgDTnftTy52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e748943-0dbf-45d8-ae72-c9720fae28f3"
      },
      "source": [
        "# Contar quantas vezes cada bairro aparece no dataset e criar uma coluna com esse valor\n",
        "temp = count_encode(df2,['bairro']);\n"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count encoding: ['bairro']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjsCmeRDVh4X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ee0fbedf-f3ac-465e-8482-a13eb5b6b4d0"
      },
      "source": [
        "df2['anuncios'] = temp\n",
        "df2"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        price  area_total  area_util  quarto  banheiro  vaga  \\\n",
              "0     2450000         489        217       4         6     5   \n",
              "1     2250000         115        115       1         1     1   \n",
              "2      770000          41         41       2         1     1   \n",
              "3      320000          33         33       1         1     1   \n",
              "4     2070000         173        173       4         6     4   \n",
              "...       ...         ...        ...     ...       ...   ...   \n",
              "3995  1678000         255        160       3         5     3   \n",
              "3996   260000          50         50       2         1     1   \n",
              "3997  1430000         104        104       3         3     3   \n",
              "3998   315000          80         29       1         1     1   \n",
              "3999  1489999         190        162       3         4     2   \n",
              "\n",
              "                             bairro  anuncios  \n",
              "0                        Campo Belo        58  \n",
              "1                           Jardins       148  \n",
              "2               Vila Nova Conceição        75  \n",
              "3                             Moema       161  \n",
              "4                          Perdizes       137  \n",
              "...                             ...       ...  \n",
              "3995                      Aclimação        28  \n",
              "3996                    Vila Marari         3  \n",
              "3997          Chácara Santo Antônio        50  \n",
              "3998                     Bela Vista        59  \n",
              "3999  Parque Industrial Tomas Edson         1  \n",
              "\n",
              "[3213 rows x 8 columns]"
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            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TN4wapKhUapk"
      },
      "source": [
        "# Filtrar e selecionar os bairros com  50 ou mais anuncios\n",
        "df3 = df2.loc[df2['anuncios'] >= 50]\n",
        "df3 = df3.sort_values(by='anuncios', ascending=False)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8U3Nl5sv4V3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "905616f0-01b4-4b29-ab6f-ea148a3dc1ca"
      },
      "source": [
        "df3.head()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        price  area_total  area_util  quarto  banheiro  vaga bairro  anuncios\n",
              "3196  1500000         110        110       3         4     2  Moema       161\n",
              "1230  1530000         127        127       3         3     3  Moema       161\n",
              "1623  2495000         141        141       2         5     3  Moema       161\n",
              "3465  1000000         200        143       2         4     5  Moema       161\n",
              "1543   340000          65         33       1         1     1  Moema       161"
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              "      fill: #174EA6;\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-62e6c565-1b42-4c77-ab01-2f127d213783 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-62e6c565-1b42-4c77-ab01-2f127d213783');\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcF7tno1UhwV"
      },
      "source": [
        "listaBairro = list(df3.bairro.unique())"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFwQpzXsXyQt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4c4b5f7-16ba-4258-c2b4-9f75a9a914ab"
      },
      "source": [
        "listaBairro"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Moema',\n",
              " 'Jardins',\n",
              " 'Perdizes',\n",
              " 'Itaim Bibi',\n",
              " 'Vila Olímpia',\n",
              " 'Brooklin',\n",
              " 'Jardim Paulista',\n",
              " 'Pinheiros',\n",
              " 'Vila Mariana',\n",
              " 'Vila Nova Conceição',\n",
              " 'Vila Mascote',\n",
              " 'Jardim Marajoara',\n",
              " 'Ipiranga',\n",
              " 'Vila Romana',\n",
              " 'Morumbi',\n",
              " 'Bela Vista',\n",
              " 'Vila Leopoldina',\n",
              " 'Campo Belo',\n",
              " 'Santana',\n",
              " 'Chácara Santo Antônio']"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S6UP6rfZYAj"
      },
      "source": [
        "# Função pra criar o dataset com os dados de cada bairro na listaBairro\n",
        "#\n",
        "def filtraBairro(df, bairro):\n",
        "  temp = df.loc[df.bairro == bairro]\n",
        "  temp = temp.drop(['bairro', 'anuncios'], axis=1)\n",
        "  return temp\n"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlvK-JnsXGOn"
      },
      "source": [
        "# Checar outliers\n",
        "def plot_outliers(df,var):\n",
        "  sns.boxplot(x=df[var]);"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StYVeWHx3LYA"
      },
      "source": [
        "# Remover outliers do conjunto de dados do bairro\n",
        "def remover_outliers(df):\n",
        "    z = np.abs(stats.zscore(df))\n",
        "    temp  = df[(z < 2.5).all(axis=1)]\n",
        "    return temp\n"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9QOikhofkun"
      },
      "source": [
        "def criar_modelo(bairro,df, nome_alg, algoritmo):\n",
        "  y = np.log1p(df.price) # Caso o target seja um valor monetário é recomendado\n",
        "                         # aplicar essa transformação nos dados\n",
        "  X =  df.drop(['price'], axis=1)\n",
        "  #\n",
        "  #\n",
        "  # Break off validation set from training data\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7,\n",
        "                                                      test_size=0.3,random_state=42)\n",
        "  # Treinar o modelo\n",
        "  reg = algoritmo\n",
        "  reg.fit(X_train, y_train)\n",
        "  #\n",
        "  # Testar com dados de teste\n",
        "  pred = reg.predict(X_test)\n",
        "  #\n",
        "  # Avaliar R2 score\n",
        "  r2 = r2_score(y_test, pred)\n",
        "  print(\"Bairro:\", bairro, \"Modelo:\", nome_alg, \"R2 score:\", round(r2,2))\n",
        "  #\n",
        "  if bairro == 'Moema':\n",
        "      r2 = round(r2,2)\n",
        "      filename = nome_alg+'-Moema-'+str(r2)+'.joblib'\n",
        "      #pickle.dump(reg, open(filename, 'wb'))\n",
        "      # joblib\n",
        "      with open(filename, 'wb') as f:\n",
        "        joblib.dump(reg, f)\n",
        "        print(\"Created\")\n",
        "\n",
        "  if bairro == 'Itaim Bibi':\n",
        "      r2 = round(r2,2)\n",
        "      filename = nome_alg+'-Itaim_Bibi-'+str(r2)+'.joblib'\n",
        "      #pickle.dump(reg, open(filename, 'wb'))\n",
        "\n",
        "       # joblib\n",
        "      with open(filename, 'wb') as f:\n",
        "        joblib.dump(reg, f)\n",
        "        print(\"Created\")\n",
        "\n",
        "  if bairro == 'Vila Mariana':\n",
        "      r2 = round(r2,2)\n",
        "      filename = nome_alg+'-Vila_Mariana-'+str(r2)+'.joblib'\n",
        "      #pickle.dump(reg, open(filename, 'wb'))\n",
        "\n",
        "       # joblib\n",
        "      with open(filename, 'wb') as f:\n",
        "        joblib.dump(reg, f)\n",
        "        print(\"Created\")\n",
        "  print(\"Salvando modelo\")\n",
        "  return (reg)\n",
        "\n"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Krj9RIvxaSL"
      },
      "source": [
        "## Três bairros foram selecionados para serem treinados e avaliados\n",
        "####  - Moema com 161 anúncios\n",
        "####  - Itaim Bibi com 124\n",
        "####  - Vila Mariana com 77"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfTWgGo35YcL"
      },
      "source": [
        "## Algoritmos utilizados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "he1p4YdA5WjI"
      },
      "source": [
        "# Algoritmos a serem usados\n",
        "nome_alg = ['RandomForestRegressor', 'DecisionTreeRegressor', 'KNeighborsRegressor', 'ExtraTreesRegressor']\n",
        "lista_reg = [RandomForestRegressor(n_estimators=250),DecisionTreeRegressor(), KNeighborsRegressor(), ExtraTreesRegressor(n_estimators=150)]"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLiExlp2xyOS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a482b0a-b67d-4a95-81c6-4e07d53859a4"
      },
      "source": [
        "df3.columns"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['price', 'area_total', 'area_util', 'quarto', 'banheiro', 'vaga',\n",
              "       'bairro', 'anuncios'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ses4yP11Kyd6"
      },
      "source": [
        "## Qual a importância das variáveis na previsão do valor de venda?\n",
        "### Foram considerados apenas os items area (total e útil), número de quartos, banheiros e vagas.\n",
        "### Pois os modelos foram treinados com os dados de cada bairro."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wp8DD1-GOpH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "680e8edf-0025-454a-acee-b5a7186a1cb7"
      },
      "source": [
        "# Descobrir a importância das variáveis usando o algoritmo RandomForestRegressor\n",
        "y = np.log1p(df3.price)\n",
        "X =  df3.drop(['price'], axis=1)\n",
        "X =  X.drop(['bairro'], axis=1)\n",
        "X =  X.drop(['anuncios'], axis=1)\n",
        "X\n",
        "#\n",
        "# Break off validation set from training data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3,random_state=42)\n",
        "#\n",
        "# Treinar o modelo\n",
        "reg = lista_reg[0]\n",
        "reg.fit(X_train, y_train)\n",
        "#\n",
        "feature_importances = pd.DataFrame(reg.feature_importances_,index =\n",
        "                                   X_train.columns,columns=['importance']).sort_values('importance', ascending=False)\n",
        "feature_importances"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            importance\n",
              "area_util     0.898617\n",
              "area_total    0.048039\n",
              "vaga          0.019207\n",
              "banheiro      0.017346\n",
              "quarto        0.016791"
            ],
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47d61889-166e-4470-ac1d-3ecabab034bd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfTuOd3d6G-L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "f7534c11-ee64-4b39-c3c0-68549e56282f"
      },
      "source": [
        "importances = list(zip(reg.feature_importances_, X_train.columns))\n",
        "importances.sort(reverse=True)\n",
        "pd.DataFrame(importances, index=[x for (_,x) in importances]).plot(kind = 'bar');"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAO0upSQ6gAG"
      },
      "source": [
        "## Treinar e avaliar modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yi9MZQv4xQdZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "043d2188-ad75-4cf8-f2a3-f31e0035f9fc"
      },
      "source": [
        "# Selecionar os bairros cujos modelos seráo criados.\n",
        "# Foram escolhidos os bairros: Moema, Itaim Bibi e Vila Mariana\n",
        "# Recomendo fazer um bairro de cada vez\n",
        "#\n",
        "lista_bairros = ['Moema', 'Itaim Bibi', 'Vila Mariana']\n",
        "#lista_bairros = ['Moema'] # Treinamento e avaliação sera feita com este bairro\n",
        "size = len(lista_reg)\n",
        "modelos_treinados = []\n",
        "for b in range(len(lista_bairros)):\n",
        "  bairro = lista_bairros[b]\n",
        "  print(\"Treinando, avaliando e salvando modelos\\n\")\n",
        "  df4 = filtraBairro(df3, bairro)\n",
        "  df5 = remover_outliers(df4)\n",
        "  for i in range(size):\n",
        "      reg = criar_modelo(bairro,df5, nome_alg[i],lista_reg[i])\n",
        "      modelos_treinados.append(reg)\n",
        "      print(\"done\")\n",
        "  print(\"\\nCriando os regressores para download, de refresh em Arquivos caso não apareça\")\n",
        "  print(\"Remova antes caso rode novamente\\n\")\n",
        "\n",
        ""
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treinando, avaliando e salvando modelos\n",
            "\n",
            "Bairro: Moema Modelo: RandomForestRegressor R2 score: 0.93\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Moema Modelo: DecisionTreeRegressor R2 score: 0.93\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Moema Modelo: KNeighborsRegressor R2 score: 0.77\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Moema Modelo: ExtraTreesRegressor R2 score: 0.93\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "\n",
            "Criando os regressores para download, de refresh em Arquivos caso não apareça\n",
            "Remova antes caso rode novamente\n",
            "\n",
            "Treinando, avaliando e salvando modelos\n",
            "\n",
            "Bairro: Itaim Bibi Modelo: RandomForestRegressor R2 score: 0.9\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Itaim Bibi Modelo: DecisionTreeRegressor R2 score: 0.84\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Itaim Bibi Modelo: KNeighborsRegressor R2 score: 0.89\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Itaim Bibi Modelo: ExtraTreesRegressor R2 score: 0.83\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "\n",
            "Criando os regressores para download, de refresh em Arquivos caso não apareça\n",
            "Remova antes caso rode novamente\n",
            "\n",
            "Treinando, avaliando e salvando modelos\n",
            "\n",
            "Bairro: Vila Mariana Modelo: RandomForestRegressor R2 score: 0.85\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Vila Mariana Modelo: DecisionTreeRegressor R2 score: 0.85\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Vila Mariana Modelo: KNeighborsRegressor R2 score: 0.87\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "Bairro: Vila Mariana Modelo: ExtraTreesRegressor R2 score: 0.81\n",
            "Created\n",
            "Salvando modelo\n",
            "done\n",
            "\n",
            "Criando os regressores para download, de refresh em Arquivos caso não apareça\n",
            "Remova antes caso rode novamente\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NoXhl1E3ZJwY"
      },
      "source": [
        "## Teste do modelo com dados conhecidos\n",
        "## Ou com dados obtidos de anúncios na internet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HggWGmGGuT1Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78c5a9e5-e86c-4856-e0ed-ba66995e88e4"
      },
      "source": [
        "# Escolha um dos modelos treinados\n",
        "modelos_treinados"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[RandomForestRegressor(n_estimators=250),\n",
              " DecisionTreeRegressor(),\n",
              " KNeighborsRegressor(),\n",
              " ExtraTreesRegressor(n_estimators=150),\n",
              " RandomForestRegressor(n_estimators=250),\n",
              " DecisionTreeRegressor(),\n",
              " KNeighborsRegressor(),\n",
              " ExtraTreesRegressor(n_estimators=150),\n",
              " RandomForestRegressor(n_estimators=250),\n",
              " DecisionTreeRegressor(),\n",
              " KNeighborsRegressor(),\n",
              " ExtraTreesRegressor(n_estimators=150)]"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuO2co8QOBMa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb5435c0-ec2b-4e1f-e877-0e3c2a68ed1e"
      },
      "source": [
        "#\n",
        "reg = modelos_treinados[0]\n",
        "dados = np.array([90,55,1,1,1]) # O modelo foi treinado com 5 variaveis (colunas)\n",
        "dados = dados.reshape(1,5)        # Esse reshape cria uma matriz com 1 linha e 5 colunas\n",
        "pred_teste = reg.predict(dados)   # Dessa forma o modelo consegue usar as informações.\n",
        "#\n",
        "# np.array([65,33,1,1,1]) = 340000        0 Moema\n",
        "# np.array([110,110,3,4,2]) = 1500000     0\n",
        "# np.array([65,38,1,1,1]) = 410000        3 Itaim Bibi\n",
        "# np.array([57, 57,2,2,2]) = 650000       8 Vila Mariana\n",
        "# np.array([23, 23,1,1,1]) = 250000       8\n",
        "\n",
        "\n",
        "resultado = np.round(np.expm1(pred_teste),0) # No treinamento a variavel price foi transformada\n",
        "print (resultado)                            # Aplicando-se o np.log, portanto na validação com testes reais\n",
        "                                             # É necessário retornar ao valor real com np.expm1"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[531108.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip freeze > teste.txt"
      ],
      "metadata": {
        "id": "3zncCvfifTl3"
      },
      "execution_count": 87,
      "outputs": []
    }
  ]
}