03. Logica_Nebulosa/2020_2_AP_03_LógicaNebulosa.ipynb

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      "source": [
        "**Instituto de Informática - UFG**\n",
        "\n",
        "Disciplina: Inteligência Artificial\n",
        "\n",
        "Prof. Cedric Luiz de Carvalho\n",
        "\n",
        "\n",
        "Grupo (A)\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Aluna(o): Gabriel Viana Dantas - Engenheiro de Conhecimento\n",
        "\n",
        "---\n",
        "\n",
        "Aluna(o): Thiago Emanuell Vieira Moura - Gerente\n",
        "\n",
        "---\n",
        "\n",
        "Aluna(o): Wendel Marques de Jesus Souza - Analista de Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_bZhd8FMiSa"
      },
      "source": [
        "#Especificação do problema : Lógica nebulosa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTE0x5IrTiwN"
      },
      "source": [
        "O setor de aluguéis de imóveis movimenta milhões por ano. Com a pandemia do coronavírus, esse setor tem sido forçado a se reinventar, uma vez que o contato direto (presencial) entre os interessados em alugar um imóvel e os corretores tem sido prejudicado. Você e sua equipe foram contratados por uma empresa do setor para projetar e implementar um sistema de recomendação que faça sugestões para os clientes de imóveis que possam ser do seu interesse. \n",
        "\n",
        "Utilizando a base de dados [brazilian_houses_to_rent](https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent), projete e implemente um sistema de recomendação, baseado em lógica nebulosa, que identifique os imóveis mais adequados às necessidades de um determinado cliente.\n",
        "\n",
        "Nesse projeto, devem ser identificadas as variáveis de entrada nebulosas, as quais devem ter, no mínimo, três faixas valores. Também deve ser definida uma variável de saída que indique a probabilidade do cliente se interessar por um dado imóvel.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtKqcll18SvD"
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      "source": [
        "**Recomendações:**\n",
        "\n",
        "O primeiro passo para resolver um problema é entendê-lo muito bem, considerar o contexto e fazer as abstrações necessárias. A seguir, para não \"reinventar a roda\" é preciso identificar como a comunidade resolveu o problema em questão, ou problemas parecidos, cuja solução possa ser mapeada para situação em análise. Desta forma, deve ser feito um estudo, o mais amplo possível sobre esse assunto. O próximo passo é o grupo se reunir e instanciar o modelo em estudo para o caso específico do problema a ser resolvido. Em paralelo, uma análise rigorosa deve ser feita na base de dados a fim de prepará-la para seu uso no modelo. Finalmente, com o modelo bem compreendido e instanciado e os dados devidamente preparados, a implementação do modelo pode ser feita."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q__nJy9T8XQZ"
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      "source": [
        "Observação: O trabalho deve ser feito EM GRUPO, mas a avaliação será feita individualmente. Para isso, o grupo deverá fazer um planejamento/cronograma de trabalho."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wb63RNsd8aKF"
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      "source": [
        "**Produtos a serem entregues:**\n",
        "\n",
        "Relatório técnico, no formato especificado (gerente). Deve indicar o grupo, com a designação dos respectivos papeis.\n",
        "Notebook com a AED (analista de dados). A base de dados original não deve ser alterada. Todo o processamento e seleção dos dados a serem utilizados deve ser feita na aplicação.\n",
        "Notebook com a implementação (engenheiro de conhecimento)\n",
        "Video de apresentação do trabalho (gerente) - no grupo com 4 alunos, essa tarefa deve ser feita pelo quarto aluno, e não pelo gerente."
      ]
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Objetivos deste artefato"
      ]
    },
    {
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      "metadata": {
        "id": "SfGRAAi2Sbnd"
      },
      "source": [
        "O objetivo geral é a obtenção de *insights* sobre o conjunto de dados.  Para tanto, serão utilizadas técnicas de visualização de dados e alguns cálculos estátisticos básicos. Essa etapa é chamada de análise exploratória de dados (AED). Por meio dela, é possível também encontrar problemas, padrões e relacionamentos que poderão apoiar a etapa de pré-processamento, que antecede a implementação. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7iiFNQiZi7qF"
      },
      "source": [
        "# Descrição geral da base de dados"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "M3_4Gtn-i-my"
      },
      "source": [
        "### A base de dados"
      ]
    },
    {
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      "metadata": {
        "id": "tLbsrfL1v-CY"
      },
      "source": [
        "A base [brazilian_houses_to_rent](https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent) é composta por dados sobre imóveis para alugar no Brasil. Os dados foram coletados da plataforma [Quinto Andar](https://www.quintoandar.com.br/) via Web Crawler."
      ]
    },
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      "metadata": {
        "id": "7ng6R8GTxRM8"
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      "source": [
        "#### Headers\n",
        "\n",
        "* **city**: Cidade onde o imóvel está localizado;\n",
        "* **area**: Área do imóvel (em m²);\n",
        "* **rooms**: quantidade de quartos;\n",
        "* **bathroom**: quantidade de banheiros;\n",
        "* **parking spaces**: Quantidade de vagas de estacionamento;\n",
        "* **floor**: informa o andar do imóvel;\n",
        "* **animal**: informa se aceita animal;\n",
        "* **furniture**: informa se o apartamento já é mobilíado;\n",
        "\n",
        "* **hoa**: Valor do condomínio (em R$);\n",
        "\n",
        "* **rent amount**: valor do aluguel (em R$);\n",
        "\n",
        "* **property tax**: IPTU (em R$);\n",
        "\n",
        "* **fire insurance**: valor do Seguro Incendio (em R$)\n",
        "\n",
        "* **total**: custo total por mês (em R$)\n",
        "\n",
        "</br></br></br>\n",
        "\n",
        "Cobertura temporal: 13/03/2020 a 10/04/2020\n",
        "\n",
        "Nome do arquivo (base de dados): houses_to_rent_v2.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ao9brJj5T136"
      },
      "source": [
        "# Passos preliminares"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78jz3gvIn6Tp"
      },
      "source": [
        "###Bibliotecas utilizadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebUQXRyyTwDU"
      },
      "source": [
        "#biblitoecas utilizadas na etada AED\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore') #evita que warnings sejam mostrados\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from scipy import stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z30w6iF1n-Jy"
      },
      "source": [
        "### Leitura dos dados"
      ]
    },
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      "source": [
        "# Carrega a base de dados para o ambiente virtual\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f6580b0c-f524-4a59-8ea2-2d86980aac17\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-f6580b0c-f524-4a59-8ea2-2d86980aac17\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving houses_to_rent_v2.csv to houses_to_rent_v2.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WGBT3NPUBIh"
      },
      "source": [
        "#Leitura dos dados\n",
        "df = pd.read_csv('/content/houses_to_rent_v2.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "eu2KFmeCJN6O",
        "outputId": "7a68c8b6-0cbc-4160-ccdb-cf8447031b1d"
      },
      "source": [
        "#Exemplo de uma linha do conjunto de dados \n",
        "df.head(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>70</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>2065</td>\n",
              "      <td>3300</td>\n",
              "      <td>211</td>\n",
              "      <td>42</td>\n",
              "      <td>5618</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        city  area  rooms  ...  property tax (R$)  fire insurance (R$) total (R$)\n",
              "0  São Paulo    70      2  ...                211                   42       5618\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixNi1L46TtBT"
      },
      "source": [
        "# Análise exploratória dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvDJ7aaTUBu5"
      },
      "source": [
        "### Informações básicas sobre o DataFrame\n",
        "Dimensionalidade e descrição estatística"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "CDeeAfgXU2cc",
        "outputId": "e57ff8aa-93e4-461d-fa56-6b1891769da5"
      },
      "source": [
        "#Verificação de algumas linhas \n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>70</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>2065</td>\n",
              "      <td>3300</td>\n",
              "      <td>211</td>\n",
              "      <td>42</td>\n",
              "      <td>5618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>320</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1200</td>\n",
              "      <td>4960</td>\n",
              "      <td>1750</td>\n",
              "      <td>63</td>\n",
              "      <td>7973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Porto Alegre</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1000</td>\n",
              "      <td>2800</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>3841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Porto Alegre</td>\n",
              "      <td>51</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>270</td>\n",
              "      <td>1112</td>\n",
              "      <td>22</td>\n",
              "      <td>17</td>\n",
              "      <td>1421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>800</td>\n",
              "      <td>25</td>\n",
              "      <td>11</td>\n",
              "      <td>836</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           city  area  ...  fire insurance (R$)  total (R$)\n",
              "0     São Paulo    70  ...                   42        5618\n",
              "1     São Paulo   320  ...                   63        7973\n",
              "2  Porto Alegre    80  ...                   41        3841\n",
              "3  Porto Alegre    51  ...                   17        1421\n",
              "4     São Paulo    25  ...                   11         836\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HY3AHP8SUTzR",
        "outputId": "58808f6b-acd4-4269-b725-90e40a185f23"
      },
      "source": [
        "#Verificação da dimensionalidade do DataFrame\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10692, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vRBEbwPUdqw",
        "outputId": "1a3d0071-6a3e-4e51-f509-10ede1380f4f"
      },
      "source": [
        "#Informações dos atributos\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10692 entries, 0 to 10691\n",
            "Data columns (total 13 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   city                 10692 non-null  object\n",
            " 1   area                 10692 non-null  int64 \n",
            " 2   rooms                10692 non-null  int64 \n",
            " 3   bathroom             10692 non-null  int64 \n",
            " 4   parking spaces       10692 non-null  int64 \n",
            " 5   floor                10692 non-null  object\n",
            " 6   animal               10692 non-null  object\n",
            " 7   furniture            10692 non-null  object\n",
            " 8   hoa (R$)             10692 non-null  int64 \n",
            " 9   rent amount (R$)     10692 non-null  int64 \n",
            " 10  property tax (R$)    10692 non-null  int64 \n",
            " 11  fire insurance (R$)  10692 non-null  int64 \n",
            " 12  total (R$)           10692 non-null  int64 \n",
            "dtypes: int64(9), object(4)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "BZf6o92ohsN-",
        "outputId": "50f266ec-9c23-4c56-9836-df9370c75379"
      },
      "source": [
        "#Descrição estatística de cada recurso\n",
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10692.000000</td>\n",
              "      <td>10692.000000</td>\n",
              "      <td>10692.000000</td>\n",
              "      <td>10692.000000</td>\n",
              "      <td>1.069200e+04</td>\n",
              "      <td>10692.000000</td>\n",
              "      <td>10692.000000</td>\n",
              "      <td>10692.000000</td>\n",
              "      <td>1.069200e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>149.217920</td>\n",
              "      <td>2.506079</td>\n",
              "      <td>2.236813</td>\n",
              "      <td>1.609147</td>\n",
              "      <td>1.174022e+03</td>\n",
              "      <td>3896.247194</td>\n",
              "      <td>366.704358</td>\n",
              "      <td>53.300879</td>\n",
              "      <td>5.490487e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>537.016942</td>\n",
              "      <td>1.171266</td>\n",
              "      <td>1.407198</td>\n",
              "      <td>1.589521</td>\n",
              "      <td>1.559231e+04</td>\n",
              "      <td>3408.545518</td>\n",
              "      <td>3107.832321</td>\n",
              "      <td>47.768031</td>\n",
              "      <td>1.648473e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.990000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.700000e+02</td>\n",
              "      <td>1530.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>2.061750e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.600000e+02</td>\n",
              "      <td>2661.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>3.581500e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>182.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.237500e+03</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>375.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>6.768000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>46335.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.117000e+06</td>\n",
              "      <td>45000.000000</td>\n",
              "      <td>313700.000000</td>\n",
              "      <td>677.000000</td>\n",
              "      <td>1.120000e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               area         rooms  ...  fire insurance (R$)    total (R$)\n",
              "count  10692.000000  10692.000000  ...         10692.000000  1.069200e+04\n",
              "mean     149.217920      2.506079  ...            53.300879  5.490487e+03\n",
              "std      537.016942      1.171266  ...            47.768031  1.648473e+04\n",
              "min       11.000000      1.000000  ...             3.000000  4.990000e+02\n",
              "25%       56.000000      2.000000  ...            21.000000  2.061750e+03\n",
              "50%       90.000000      2.000000  ...            36.000000  3.581500e+03\n",
              "75%      182.000000      3.000000  ...            68.000000  6.768000e+03\n",
              "max    46335.000000     13.000000  ...           677.000000  1.120000e+06\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3dYmfasrCui"
      },
      "source": [
        "**Observações:**\n",
        "\n",
        "* Os valores máximos para cada coluna (dos atributos númericos) estão longe da mediana da coluna e da média que significa que eles são valores discrepantes em potencial (outliers).\n",
        "\n",
        "* Há tipos *int e object.*\n",
        "\n",
        "* Possui 10692 linhas (imóveis para alugar) e 13 colunas (atributos).\n",
        "\n",
        "* Média dos aluguéis: R$ 3896,25"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ueNal_8vVLWI"
      },
      "source": [
        "### Valores faltantes e linhas duplicadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGGxjtjIVPSr",
        "outputId": "66cc5093-bdcc-4315-b447-30e756e0dc3b"
      },
      "source": [
        "#Verificação de valores missing\n",
        "for coluna in df.columns:\n",
        "    print(\"Qtd. de valores faltantes em {}: {}\".format(coluna, df[coluna].isna().sum()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Qtd. de valores faltantes em city: 0\n",
            "Qtd. de valores faltantes em area: 0\n",
            "Qtd. de valores faltantes em rooms: 0\n",
            "Qtd. de valores faltantes em bathroom: 0\n",
            "Qtd. de valores faltantes em parking spaces: 0\n",
            "Qtd. de valores faltantes em floor: 0\n",
            "Qtd. de valores faltantes em animal: 0\n",
            "Qtd. de valores faltantes em furniture: 0\n",
            "Qtd. de valores faltantes em hoa (R$): 0\n",
            "Qtd. de valores faltantes em rent amount (R$): 0\n",
            "Qtd. de valores faltantes em property tax (R$): 0\n",
            "Qtd. de valores faltantes em fire insurance (R$): 0\n",
            "Qtd. de valores faltantes em total (R$): 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "qTpYhxR9WPP7",
        "outputId": "5e3411f0-4536-4b34-dd8f-3659acc865d3"
      },
      "source": [
        "#Verifica se tem duplicates\n",
        "df[df.duplicated()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>Porto Alegre</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>not acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>400</td>\n",
              "      <td>2200</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>2633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>920</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>not acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>970</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1060</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1430</td>\n",
              "      <td>4206</td>\n",
              "      <td>364</td>\n",
              "      <td>54</td>\n",
              "      <td>6054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1064</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>602</td>\n",
              "      <td>1800</td>\n",
              "      <td>130</td>\n",
              "      <td>23</td>\n",
              "      <td>2555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1108</th>\n",
              "      <td>Campinas</td>\n",
              "      <td>184</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>-</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>3240</td>\n",
              "      <td>220</td>\n",
              "      <td>49</td>\n",
              "      <td>3509</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>10560</th>\n",
              "      <td>Porto Alegre</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>not acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>1300</td>\n",
              "      <td>1200</td>\n",
              "      <td>68</td>\n",
              "      <td>18</td>\n",
              "      <td>2586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10569</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>1100</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>1115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10623</th>\n",
              "      <td>Porto Alegre</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>not acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>400</td>\n",
              "      <td>2200</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>2633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10640</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1405</td>\n",
              "      <td>3500</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>4951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10641</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>58</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>400</td>\n",
              "      <td>999</td>\n",
              "      <td>99</td>\n",
              "      <td>14</td>\n",
              "      <td>1512</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>358 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 city  area  ...  fire insurance (R$)  total (R$)\n",
              "244      Porto Alegre    47  ...                   33        2633\n",
              "920    Belo Horizonte    40  ...                   13         983\n",
              "1060        São Paulo   136  ...                   54        6054\n",
              "1064        São Paulo    20  ...                   23        2555\n",
              "1108         Campinas   184  ...                   49        3509\n",
              "...               ...   ...  ...                  ...         ...\n",
              "10560    Porto Alegre    40  ...                   18        2586\n",
              "10569  Belo Horizonte    20  ...                   15        1115\n",
              "10623    Porto Alegre    47  ...                   33        2633\n",
              "10640       São Paulo    27  ...                   45        4951\n",
              "10641  Belo Horizonte    58  ...                   14        1512\n",
              "\n",
              "[358 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WO5Ou20fVc8c"
      },
      "source": [
        "**Resultados:**\n",
        "* Como base nos resultados, é possível afirmar que a base de dados não possui valores faltantes, mas que possui linhas duplicadas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPkpKTNoXlNJ"
      },
      "source": [
        "### Análise dos atributos\n",
        "\n",
        "O processo de análise foi guiado pelos seguintes questionamentos:\n",
        "* Como os dados estão distribuídos?\n",
        "* A base tem outliers?\n",
        "* Qual é a correlação entre cada atributo?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVVvgYXLSLxa"
      },
      "source": [
        "#### Como os dados estão distribuídos?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7GZGhmSdN_L9",
        "outputId": "b3298634-6652-453f-c08d-85030238bdbc"
      },
      "source": [
        "#Verificando a distribuição dos dados\n",
        "plt.style.use('seaborn-whitegrid')\n",
        "df.hist(bins=18,figsize=(20,20))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1440 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 874
        },
        "id": "fagURzurOx93",
        "outputId": "8c699470-ff0d-4ec6-85b1-b0d8a3339571"
      },
      "source": [
        "discrete=['city','rooms','bathroom','parking spaces','animal']\n",
        "fig,axis=plt.subplots(3,2,figsize=(15,15))\n",
        "k=0\n",
        "for discrete in discrete:\n",
        "    plt.style.use('seaborn-whitegrid')\n",
        "    data=df[discrete].value_counts().to_dict()\n",
        "    axis=axis.flatten()\n",
        "    sns.countplot(x=df[discrete],hue=df[discrete],ax=axis[k])\n",
        "    k=k+1\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 874
        },
        "id": "lYeGFrNyO_QD",
        "outputId": "a6d6fe26-b85f-4e08-95bf-70a46ad9adfa"
      },
      "source": [
        "df2 = df\n",
        "#Subsitui \"-\" por zero em floor\n",
        "df2.floor.replace(to_replace={\"-\": 0}, inplace=True)\n",
        "discrete=['floor', 'furniture']\n",
        "fig,axis=plt.subplots(2,1,figsize=(15,15))\n",
        "k=0\n",
        "for discrete in discrete:\n",
        "    plt.style.use('seaborn-whitegrid')\n",
        "    data=df2[discrete].value_counts().to_dict()\n",
        "    axis=axis.flatten()\n",
        "    if k == 0:\n",
        "      sns.countplot(df2.floor,order=df2.floor.value_counts().keys(),ax=axis[k])\n",
        "    else:\n",
        "      sns.countplot(x=df[discrete],hue=df[discrete],ax=axis[k])\n",
        "    k=k+1\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2E1sjx9SW6f",
        "outputId": "7d5ecfc4-eea0-4fde-e119-86e9a05a05a1"
      },
      "source": [
        "print('furniture:')\n",
        "print(df.furniture.value_counts())\n",
        "\n",
        "print('\\n\\nanimal:')\n",
        "print(df.animal.value_counts())\n",
        "\n",
        "print('\\n\\ncity:')\n",
        "print(df.city.value_counts())\n",
        "\n",
        "print('\\n\\nbathroom:')\n",
        "print(df.bathroom.value_counts())\n",
        "\n",
        "print('\\nfloor:')\n",
        "print(df2.floor.value_counts())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "furniture:\n",
            "not furnished    8086\n",
            "furnished        2606\n",
            "Name: furniture, dtype: int64\n",
            "\n",
            "\n",
            "animal:\n",
            "acept        8316\n",
            "not acept    2376\n",
            "Name: animal, dtype: int64\n",
            "\n",
            "\n",
            "city:\n",
            "São Paulo         5887\n",
            "Rio de Janeiro    1501\n",
            "Belo Horizonte    1258\n",
            "Porto Alegre      1193\n",
            "Campinas           853\n",
            "Name: city, dtype: int64\n",
            "\n",
            "\n",
            "bathroom:\n",
            "1     4301\n",
            "2     2910\n",
            "3     1474\n",
            "4     1111\n",
            "5      578\n",
            "6      215\n",
            "7       85\n",
            "8       11\n",
            "9        4\n",
            "10       3\n",
            "Name: bathroom, dtype: int64\n",
            "\n",
            "floor:\n",
            "0      2461\n",
            "1      1081\n",
            "2       985\n",
            "3       931\n",
            "4       748\n",
            "5       600\n",
            "6       539\n",
            "7       497\n",
            "8       490\n",
            "9       369\n",
            "10      357\n",
            "11      303\n",
            "12      257\n",
            "13      200\n",
            "14      170\n",
            "15      147\n",
            "16      109\n",
            "17       96\n",
            "18       75\n",
            "19       53\n",
            "20       44\n",
            "21       42\n",
            "23       25\n",
            "25       25\n",
            "22       24\n",
            "26       20\n",
            "24       19\n",
            "27        8\n",
            "28        6\n",
            "29        5\n",
            "32        2\n",
            "46        1\n",
            "35        1\n",
            "51        1\n",
            "301       1\n",
            "Name: floor, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "d57nUXRIpsEs",
        "outputId": "2fbc2d09-37e4-49b0-97c3-7df61b6a5773"
      },
      "source": [
        "#Verificando a taxa HOA por cidade\n",
        "for col in df2.columns:\n",
        "    if 'float' in str(df2[col].dtype) or 'int' in str(df2[col].dtype) and col not in ['bathroom', 'rooms', 'parking spaces']:\n",
        "        #pandas.DataFrame.std Retorna o desvio padrão da amostra sobre o eixo solicitado\n",
        "        df2 = df2[ df2[col] < df2[col].mean()+df2[col].std()*4 ]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(13, 7))\n",
        "sns.boxplot(data=df2, x='city', y='hoa (R$)', ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc555d17450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "5M4QwNXrs0ae",
        "outputId": "53e3bf4b-b950-4033-9dc9-7b148b015ec5"
      },
      "source": [
        "#Verificando a porcentagem de casas que permitem animais de estimação em cada cidade\n",
        "g = sns.FacetGrid(df, col=\"city\")\n",
        "g.map(sns.countplot, 'animal', order=['acept', 'not acept'])\n",
        "\n",
        "print('\\n\\n')\n",
        "for city in df.city.unique():\n",
        "    city_data = df[df.city == city]\n",
        "    print('Em {}, {} dos imóveis permitem animais.'.format(city, str(city_data.animal.value_counts()['acept']/city_data.shape[0])[:4]))\n",
        "print('\\n\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "Em São Paulo, 0.76 dos imóveis permitem animais.\n",
            "Em Porto Alegre, 0.84 dos imóveis permitem animais.\n",
            "Em Rio de Janeiro, 0.80 dos imóveis permitem animais.\n",
            "Em Campinas, 0.81 dos imóveis permitem animais.\n",
            "Em Belo Horizonte, 0.74 dos imóveis permitem animais.\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1080x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "SOrU4bomtrrl",
        "outputId": "ac075667-7ad1-4080-b158-980d72a38b26"
      },
      "source": [
        "#Verificando a frequência de cada quantidade quarto por cidade\n",
        "\n",
        "g = sns.FacetGrid(df2, col='city')\n",
        "g.map(sns.countplot, 'rooms')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fc555e32510>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1080x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGfC2oymvJDy"
      },
      "source": [
        "*   Em São Paulo casas com cômodos de 1 a 4 são as mais comuns\n",
        "*   Em Porto Alegre casas com cômodos de 1 a 3 são as mais comuns\n",
        "*   Em Rio de Janeiro casas com cômodos de 1 a 3 são as mais comuns\n",
        "*   Em Campinas as casas com cômodos de 1 a 3 são as mais comuns\n",
        "*   Em Belo Horizonte as casas com cômodos de 2 a 4 são as mais comuns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "LSpByMXgwLE9",
        "outputId": "9549c9a9-27ed-4b2e-9f84-b1d0c40a1a33"
      },
      "source": [
        "#Verificando a média de aluguel por cidade\n",
        "df.groupby('city')['rent amount (R$)'].mean().reset_index().sort_values('rent amount (R$)',ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>4652.793783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>3664.127981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>3232.904064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Campinas</td>\n",
              "      <td>2364.290739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Porto Alegre</td>\n",
              "      <td>2337.699916</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             city  rent amount (R$)\n",
              "4       São Paulo       4652.793783\n",
              "0  Belo Horizonte       3664.127981\n",
              "3  Rio de Janeiro       3232.904064\n",
              "1        Campinas       2364.290739\n",
              "2    Porto Alegre       2337.699916"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlbCefaNQ9m_"
      },
      "source": [
        "**Observações gerais**\n",
        "\n",
        "**De acordo com o resultados acima, podemos afirmar que a maioria dos imóveis possuem as seguintes caracteristicas:**\n",
        "* Localização: São Paulo;\n",
        "* Quantidade de quartos: 1 a 2;\n",
        "* Quantidade de banheiros: 1 a 2;\n",
        "* Vagas na garagem: 0 a 1;\n",
        "* Andar: 1 e 2;\n",
        "* Aceitam animais;\n",
        "* Não possuem mobília.\n",
        "\n",
        "**Sobre \"floor\":**\n",
        "* Aparentemente, quanto mais alto o número do andar, menos frequente ele é.\n",
        "\n",
        "* Alguns campos da coluna \"floor\" possuem valor \"-\". \n",
        "\n",
        "**Valor do aluguel:**\n",
        "* Nessa base de dados, São Paulo é a cidade com o maior valor de aluguel.\n",
        "\n",
        "**Taxa de condomínio por cidade:**\n",
        "* Os imóveis das cidades de São Paulo e Rio de Janeiro tendem a possuir as maiores taxas de condomínio.\n",
        "* Em Porto Alegre paga-se taxas menores.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_GlVhiamXTru"
      },
      "source": [
        "#### A base tem outliers?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "kbl6KfG_6Z--",
        "outputId": "2932ca63-184d-4c23-a435-9764aec7e55e"
      },
      "source": [
        "#Verificando outliers em total\n",
        "total = df['total (R$)']\n",
        "plt.boxplot(total, total.min(), 'o')\n",
        "plt.title('Outlier Univariado')\n",
        "plt.xlabel('Distribuição dos valores')\n",
        "plt.ylabel('Total (R$)')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Total (R$)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "95fwTJ9v6jYj",
        "outputId": "50f58464-983d-457f-88a9-2fccf8240567"
      },
      "source": [
        "#exibição dos imóveis fora da curva\n",
        "df.loc[total>100000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>155</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1117000</td>\n",
              "      <td>2790</td>\n",
              "      <td>64</td>\n",
              "      <td>38</td>\n",
              "      <td>1120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2859</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>285</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>200000</td>\n",
              "      <td>20000</td>\n",
              "      <td>1834</td>\n",
              "      <td>254</td>\n",
              "      <td>222100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6230</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>340</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>220000</td>\n",
              "      <td>12000</td>\n",
              "      <td>1000</td>\n",
              "      <td>153</td>\n",
              "      <td>233200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6645</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>690</td>\n",
              "      <td>2500</td>\n",
              "      <td>313700</td>\n",
              "      <td>32</td>\n",
              "      <td>316900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6979</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>155</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1117000</td>\n",
              "      <td>2790</td>\n",
              "      <td>64</td>\n",
              "      <td>38</td>\n",
              "      <td>1120000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                city  area  ...  fire insurance (R$)  total (R$)\n",
              "255   Belo Horizonte   155  ...                   38     1120000\n",
              "2859       São Paulo   285  ...                  254      222100\n",
              "6230       São Paulo   340  ...                  153      233200\n",
              "6645       São Paulo    42  ...                   32      316900\n",
              "6979  Belo Horizonte   155  ...                   38     1120000\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "GIizykzNjgSd",
        "outputId": "94fbf40c-8f8b-4139-b618-6dd42bb9a5ee"
      },
      "source": [
        "#Verificando outliers em hoa\n",
        "valor_condominio = df['hoa (R$)']\n",
        "plt.boxplot(valor_condominio, valor_condominio.min(), 'o')\n",
        "plt.title('Outlier Univariado')\n",
        "plt.xlabel('Distribuição dos valores')\n",
        "plt.ylabel('Valor do condomínio (R$)')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Valor do condomínio (R$)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "H8SQmvZ25xLu",
        "outputId": "47d3fb25-5c17-4e62-dd08-405f06a0b4ed"
      },
      "source": [
        "#exibição dos imóveis fora da curva\n",
        "df.loc[valor_condominio>100000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>155</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1117000</td>\n",
              "      <td>2790</td>\n",
              "      <td>64</td>\n",
              "      <td>38</td>\n",
              "      <td>1120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2859</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>285</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>200000</td>\n",
              "      <td>20000</td>\n",
              "      <td>1834</td>\n",
              "      <td>254</td>\n",
              "      <td>222100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6230</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>340</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>220000</td>\n",
              "      <td>12000</td>\n",
              "      <td>1000</td>\n",
              "      <td>153</td>\n",
              "      <td>233200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6979</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>155</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1117000</td>\n",
              "      <td>2790</td>\n",
              "      <td>64</td>\n",
              "      <td>38</td>\n",
              "      <td>1120000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                city  area  ...  fire insurance (R$)  total (R$)\n",
              "255   Belo Horizonte   155  ...                   38     1120000\n",
              "2859       São Paulo   285  ...                  254      222100\n",
              "6230       São Paulo   340  ...                  153      233200\n",
              "6979  Belo Horizonte   155  ...                   38     1120000\n",
              "\n",
              "[4 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "Y9fBnZuFeo43",
        "outputId": "a9236628-f1b1-4421-ec6d-d4b91058666c"
      },
      "source": [
        "#Verificando outliers em area\n",
        "plt.figure(figsize=(6,5))\n",
        "plt.title('Outlier Univariado')\n",
        "plt.ylabel('Distribuição das areas')\n",
        "sns.boxplot(x=df.area)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc555fa79d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "Uof15T0OmrCL",
        "outputId": "d90d2739-740c-473e-fd92-91dda9ed6a82"
      },
      "source": [
        "#exibição dos imóveis fora da curva\n",
        "df.loc[df.area>10000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2397</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>46335</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>960</td>\n",
              "      <td>8500</td>\n",
              "      <td>646</td>\n",
              "      <td>114</td>\n",
              "      <td>10220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5915</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>24606</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>2254</td>\n",
              "      <td>8100</td>\n",
              "      <td>7859</td>\n",
              "      <td>103</td>\n",
              "      <td>18320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9241</th>\n",
              "      <td>Campinas</td>\n",
              "      <td>12732</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>700</td>\n",
              "      <td>1600</td>\n",
              "      <td>96</td>\n",
              "      <td>21</td>\n",
              "      <td>2417</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                city   area  ...  fire insurance (R$)  total (R$)\n",
              "2397  Belo Horizonte  46335  ...                  114       10220\n",
              "5915       São Paulo  24606  ...                  103       18320\n",
              "9241        Campinas  12732  ...                   21        2417\n",
              "\n",
              "[3 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "vvyivX94e_L3",
        "outputId": "14a7440e-abe5-424f-94f6-594ea81f3e6d"
      },
      "source": [
        "valor_aluguel = df['rent amount (R$)']\n",
        "plt.boxplot(valor_aluguel, valor_aluguel.min(), 'o')\n",
        "plt.title('Outlier Univariado')\n",
        "plt.xlabel('Distribuição dos valores')\n",
        "plt.ylabel('Valor do aluguel (R$)')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Valor do aluguel (R$)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "0geLv5vLnDQi",
        "outputId": "81ef6895-aa8f-4594-fbf6-ad58b36f3535"
      },
      "source": [
        "#Exibição dos imóveis fora da curva\n",
        "df.loc[df['rent amount (R$)']>20000]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2182</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>700</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>45000</td>\n",
              "      <td>8750</td>\n",
              "      <td>677</td>\n",
              "      <td>54430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2619</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>80</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>875</td>\n",
              "      <td>24000</td>\n",
              "      <td>0</td>\n",
              "      <td>305</td>\n",
              "      <td>25180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6947</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>486</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>25000</td>\n",
              "      <td>2200</td>\n",
              "      <td>376</td>\n",
              "      <td>27580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7748</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>350</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>30000</td>\n",
              "      <td>560</td>\n",
              "      <td>451</td>\n",
              "      <td>31010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           city  area  ...  fire insurance (R$)  total (R$)\n",
              "2182  São Paulo   700  ...                  677       54430\n",
              "2619  São Paulo    80  ...                  305       25180\n",
              "6947  São Paulo   486  ...                  376       27580\n",
              "7748  São Paulo   350  ...                  451       31010\n",
              "\n",
              "[4 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "tOUc56lNXRMJ",
        "outputId": "450357a0-98c6-4ca4-fff2-a44d30b0bd94"
      },
      "source": [
        "fig = plt.figure(figsize=(15,5))\n",
        "area = df['area']\n",
        "valor = df['rent amount (R$)']\n",
        "plt.xlabel('Área')\n",
        "plt.ylabel('Valor do aluguel (R$)')\n",
        "plt.title('Outlier multivariado')\n",
        "plt.plot(area, valor, \"r+\",)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc55374b750>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "FN2NJAlanTSb",
        "outputId": "3ad76ec3-6db3-48c6-e961-e4352f5b60e8"
      },
      "source": [
        "df.loc[(df['area'] > 10000) | (df['total (R$)'] > 220000)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>155</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1117000</td>\n",
              "      <td>2790</td>\n",
              "      <td>64</td>\n",
              "      <td>38</td>\n",
              "      <td>1120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2397</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>46335</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>960</td>\n",
              "      <td>8500</td>\n",
              "      <td>646</td>\n",
              "      <td>114</td>\n",
              "      <td>10220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2859</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>285</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>200000</td>\n",
              "      <td>20000</td>\n",
              "      <td>1834</td>\n",
              "      <td>254</td>\n",
              "      <td>222100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5915</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>24606</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>2254</td>\n",
              "      <td>8100</td>\n",
              "      <td>7859</td>\n",
              "      <td>103</td>\n",
              "      <td>18320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6230</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>340</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>220000</td>\n",
              "      <td>12000</td>\n",
              "      <td>1000</td>\n",
              "      <td>153</td>\n",
              "      <td>233200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6645</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>690</td>\n",
              "      <td>2500</td>\n",
              "      <td>313700</td>\n",
              "      <td>32</td>\n",
              "      <td>316900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6979</th>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>155</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>not acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>1117000</td>\n",
              "      <td>2790</td>\n",
              "      <td>64</td>\n",
              "      <td>38</td>\n",
              "      <td>1120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9241</th>\n",
              "      <td>Campinas</td>\n",
              "      <td>12732</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>700</td>\n",
              "      <td>1600</td>\n",
              "      <td>96</td>\n",
              "      <td>21</td>\n",
              "      <td>2417</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                city   area  ...  fire insurance (R$)  total (R$)\n",
              "255   Belo Horizonte    155  ...                   38     1120000\n",
              "2397  Belo Horizonte  46335  ...                  114       10220\n",
              "2859       São Paulo    285  ...                  254      222100\n",
              "5915       São Paulo  24606  ...                  103       18320\n",
              "6230       São Paulo    340  ...                  153      233200\n",
              "6645       São Paulo     42  ...                   32      316900\n",
              "6979  Belo Horizonte    155  ...                   38     1120000\n",
              "9241        Campinas  12732  ...                   21        2417\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfkhmhD_pJwA",
        "outputId": "e0f6f45d-64ab-4996-a1b6-a098b503d3ff"
      },
      "source": [
        "df.loc[255]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "city                   Belo Horizonte\n",
              "area                              155\n",
              "rooms                               1\n",
              "bathroom                            4\n",
              "parking spaces                      0\n",
              "floor                               4\n",
              "animal                      not acept\n",
              "furniture               not furnished\n",
              "hoa (R$)                      1117000\n",
              "rent amount (R$)                 2790\n",
              "property tax (R$)                  64\n",
              "fire insurance (R$)                38\n",
              "total (R$)                    1120000\n",
              "Name: 255, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Y211RNOpAqF"
      },
      "source": [
        "Este é uma exemplo de outlier, em que:\n",
        "* hoa (R$) =                     1117000\n",
        "\n",
        "* total (R$) = 1220000\n",
        "\n",
        "* rent amount (R$)      =           2790"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "id": "x9GFpgj3YVAO",
        "outputId": "1667fa88-e580-4602-f35f-2002656ff93f"
      },
      "source": [
        "plt.figure(figsize=(12,12))\n",
        "sns.set(style='white')\n",
        "sns.scatterplot(x=df['area'], y=df['total (R$)'])\n",
        "sns.regplot(x=df['area'], y=df['total (R$)'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc553d28c10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_qroOBsVLZk"
      },
      "source": [
        "Resultados: \n",
        "* A base de dados possui outliers. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "945AAsvBhzkt"
      },
      "source": [
        "#### Qual é a correlação entre cada atributo?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCnNVaC8h5dW",
        "outputId": "7e3f050a-53a4-4cfb-8f1d-0c502297b774"
      },
      "source": [
        "correlacao = df.corr()\n",
        "plt.figure(figsize =(10,10))\n",
        "sns.heatmap(correlacao,annot =True)\n",
        "plt.show()\n",
        "print('\\n\\n\\n')\n",
        "print(correlacao['rent amount (R$)'].sort_values(ascending=False))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "rent amount (R$)       1.000000\n",
            "fire insurance (R$)    0.987343\n",
            "bathroom               0.668504\n",
            "parking spaces         0.578361\n",
            "rooms                  0.541758\n",
            "total (R$)             0.264490\n",
            "area                   0.180742\n",
            "property tax (R$)      0.107884\n",
            "hoa (R$)               0.036490\n",
            "Name: rent amount (R$), dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lt3eCDzOkf6y"
      },
      "source": [
        "O mapa de calor sugere que o valor do aluguel tem baixa correlação linear com o valor do condomínio, em quanto que com \"fire insurance\" apresenta uma correlação muito forte"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7l07uAfvWrC1"
      },
      "source": [
        "#Possíveis problemas encontrados na base de dados\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gz6ryiuq8bUR"
      },
      "source": [
        "**Floor**\n",
        "* Alguns campos da coluna \"floor\" possuem valor \"-\". Pela análises realizadas, vamos considerar que esses valores representam o térreo. Uma solução, é subistituir \"-\" por \"0\".\n",
        "\n",
        "**Outliers**:\n",
        "* É razoável o fato de que exista outliers na base de dado, pricipalmente se levarmos em consideração a forma de extração. Entretanto, faz-se necessário remover alguns desses dados. \n",
        "\n",
        "**Duplicates:**\n",
        "* A base de dados possui dados duplicados. É necessário remover.\n",
        "\n",
        "**Valores discrepantes:**\n",
        "* É possível que a base possua valores discrepantes, que não condizem com a realidade. Podem ter sugido por causa de erros na extração dos dados ou erro de digitação (dos usuários do Quinto Andar). Uma possível solução é a remoção de alguns outliers, mas isso não garante que todos os valores disrepencates serão removidos. Em relação aos valores discrepantes que sobrarem, nada podemos fazer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpmMeqJ5m6wB"
      },
      "source": [
        "# Sugestão de variáveis para serem utilizadas na implementação\n",
        "As células abaixo foram repassadas tanto para o Engenheiro de Conhecimento quanto para o Gerente."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nso1IL5F9BjN"
      },
      "source": [
        "## Limpeza dos dados (Resolução de problemas)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6inI9azqnrgR"
      },
      "source": [
        "#Leitura dos dados\n",
        "df = pd.read_csv('/content/houses_to_rent_v2.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ikddXCJV9F2h"
      },
      "source": [
        "#### Floor\n",
        "\n",
        "Alguns campos da coluna \"floor\" possuem valor \"-\". Pelas análises realizadas, vamos considerar que esses valores representam o térreo. Assim, uma solução, é subistituir \"-\" por \"0\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "IEnDZ9Gd3f7o",
        "outputId": "1f1be322-0193-43b1-97c0-2b281d9b6036"
      },
      "source": [
        "#Solução: substituição de '-' por '0'\n",
        "df.floor.replace(to_replace={\"-\": '0'}, inplace=True)\n",
        "df.loc[df['floor']=='0']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>city</th>\n",
              "      <th>area</th>\n",
              "      <th>rooms</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>parking spaces</th>\n",
              "      <th>floor</th>\n",
              "      <th>animal</th>\n",
              "      <th>furniture</th>\n",
              "      <th>hoa (R$)</th>\n",
              "      <th>rent amount (R$)</th>\n",
              "      <th>property tax (R$)</th>\n",
              "      <th>fire insurance (R$)</th>\n",
              "      <th>total (R$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>376</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>8000</td>\n",
              "      <td>834</td>\n",
              "      <td>121</td>\n",
              "      <td>8955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Campinas</td>\n",
              "      <td>330</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>680</td>\n",
              "      <td>8000</td>\n",
              "      <td>328</td>\n",
              "      <td>121</td>\n",
              "      <td>9129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>600</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>12000</td>\n",
              "      <td>9500</td>\n",
              "      <td>181</td>\n",
              "      <td>21680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>100</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>3800</td>\n",
              "      <td>118</td>\n",
              "      <td>58</td>\n",
              "      <td>3976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>1100</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>1117</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>10659</th>\n",
              "      <td>Campinas</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>3500</td>\n",
              "      <td>186</td>\n",
              "      <td>53</td>\n",
              "      <td>3739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10661</th>\n",
              "      <td>Campinas</td>\n",
              "      <td>250</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>2200</td>\n",
              "      <td>602</td>\n",
              "      <td>34</td>\n",
              "      <td>2836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10668</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>870</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10674</th>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>135</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>3300</td>\n",
              "      <td>115</td>\n",
              "      <td>51</td>\n",
              "      <td>3466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10691</th>\n",
              "      <td>São Paulo</td>\n",
              "      <td>80</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>acept</td>\n",
              "      <td>not furnished</td>\n",
              "      <td>0</td>\n",
              "      <td>1400</td>\n",
              "      <td>165</td>\n",
              "      <td>22</td>\n",
              "      <td>1587</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2461 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 city  area  ...  fire insurance (R$)  total (R$)\n",
              "5           São Paulo   376  ...                  121        8955\n",
              "15           Campinas   330  ...                  121        9129\n",
              "19          São Paulo   600  ...                  181       21680\n",
              "20          São Paulo   100  ...                   58        3976\n",
              "23          São Paulo    35  ...                   14        1117\n",
              "...               ...   ...  ...                  ...         ...\n",
              "10659        Campinas   150  ...                   53        3739\n",
              "10661        Campinas   250  ...                   34        2836\n",
              "10668       São Paulo    24  ...                   14         884\n",
              "10674  Rio de Janeiro   135  ...                   51        3466\n",
              "10691       São Paulo    80  ...                   22        1587\n",
              "\n",
              "[2461 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LH_Jcw6m9WfY"
      },
      "source": [
        "#### Duplicates:\n",
        "\n",
        "A base de dados possui dados duplicados. É necessário remover."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0ZeHfRS9gI8",
        "outputId": "f0834c17-dba2-4e68-eac0-74329e3bc197"
      },
      "source": [
        "#Estrutura original\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10692, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctVuLcT4O3p3"
      },
      "source": [
        "#Remoção de duplicates\n",
        "df.drop_duplicates(keep= 'first', inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECcPmmv6PADG",
        "outputId": "f146c9f3-66c9-4f2f-e95e-041e67e580f2"
      },
      "source": [
        "#Estrutura após a remoção de linhas duplicadas\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10334, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agui3uPs9puB"
      },
      "source": [
        "#### Outliers:\n",
        "\n",
        "É razoável o fato de que exista outliers na base de dados, principalmente se levarmos em consideração a forma de extração. Entretanto, faz-se necessário remover alguns desses dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aHKllKl9scj",
        "outputId": "6b726e0a-d694-4338-b751-7954d63e82e5"
      },
      "source": [
        "#Remoção de outliers em \"total (R$)\", \"hoa (R$)\", \"rent amount (R$)\" e \"area\"\n",
        "\n",
        "row_remove = []\n",
        "row_remove.append(list(df.loc[df['total (R$)']>100000].index))\n",
        "row_remove.append(list(df.loc[df['hoa (R$)']>100000].index))\n",
        "row_remove.append(list(df.loc[df['rent amount (R$)']>20000].index))\n",
        "row_remove.append(list(df.loc[df['area']>10000].index))\n",
        "row_remove"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[255, 2859, 6230, 6645],\n",
              " [255, 2859, 6230],\n",
              " [2182, 2619, 6947, 7748],\n",
              " [2397, 5915, 9241]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfOI7uVhDHyR",
        "outputId": "1e3af22d-9b9f-40ea-a562-09f71e8599d8"
      },
      "source": [
        "#Estrutura original\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10334, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JyljIl-1CKlG"
      },
      "source": [
        "lista_indices = []\n",
        "for indices in row_remove:\n",
        "  for index in indices:\n",
        "    lista_indices.append(index)\n",
        "df.drop(df.index[lista_indices], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWDvgQMXDHOh",
        "outputId": "23743e1f-1a77-4faf-d511-9cddb474f1ad"
      },
      "source": [
        "#Estrutura após exclusão\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10323, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7RfGvd0DwFf"
      },
      "source": [
        "##Seleção de variáveis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNqW0oOWhQgg"
      },
      "source": [
        "Em um recente estudo, o QuintoAndar verificou o comportamento dos usuários ao buscar imóveis cadastrados na plataforma de acordo com os filtros usados. \n",
        "\n",
        "Do total das buscas realizadas em 2020:\n",
        "* 23% possuía o filtro \"Pode ter animais de estimação\";\n",
        "* 14% possuía o filtro \"Mobiliado\";\n",
        "* 10% possuía o filtro \"Não mobiliado\";\n",
        "\n",
        "Além disso, principalmente com base na análise exploratória dos dados, é possível afimar que o valor do aluguel, bem como o número de quartos são fatores relevantes.\n",
        "\n",
        "Sendo assim, como variáveis de entrada, é recomendado uso da combinação dos valores das colunas \"animal\", \"furniture\", \"rent amount (R$)\" e \"rooms\".\n",
        "<br/>\n",
        "<br/><br/>\n",
        "**Referências:**\n",
        "* [Conheça os filtros oferecidos pelo QuintoAndar pra você buscar o seu imóvel ideal](https://meulugar.quintoandar.com.br/buscar-imoveis-no-quintoandar-conheca-os-principais-filtros-de-pesquisa/)\n",
        "* [QuintoAndar traça comportamento de usuários da plataforma por meio de seus filtros de busca](https://meulugar.quintoandar.com.br/quintoandar-identifica-comportamento-de-usuarios-ao-buscar-imovel/)\n",
        "* [5 dicas para escolher o imóvel certo para alugar](https://financaspessoais.organizze.com.br/5-dicas-para-escolher-o-imovel-certo-para-alugar/)\n"
      ]
    }
  ]
}