Qualidade_Vinho_Tinto_CLASSIFICACAO.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOpn+8tv+upCnFv4zUxY9Pu",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/salimNabbout/MFP/blob/main/Qualidade_Vinho_Tinto_CLASSIFICACAO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**POS GRADUAÇÃO EM CIÊNCIAS DE DADOS -  PUC RIO**\n",
        "\n",
        "##**MVP SPRINT 1** \n",
        "\n",
        "##**PROJETO - CLASSIFICAÇÃO DE VINHOS DE ALTA QUALIDADE - NORTE DE PORTUGAL**\n",
        "\n",
        "##**ALUNO: SALIM JORGE NABBOUT**"
      ],
      "metadata": {
        "id": "0pvbRc5CDqc4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**1. JUSTIFICATIVA DO PROJETO**\n",
        "\n",
        "Esta análise foi solicitada por um grande distribuidor de vinhos, localizado no estado do Rio de janeiro. Esta empresa solicitou uma análise minuciosa dos dados físico-químico de vinhos produzidos ao norte de Portugal, tidos como uns dos melhores vinhos do mundo.\n",
        "\n",
        "O Objetivo é importar vinhos não somente de Portugal, mas também do Brasil que atendesse as proximidades das características físico-químico dos vinhos analisados. A partir das análises de dados, referenciado pelos vinhos portugueses, cria se uma modelagem para o problema com objetivo de uma melhor tomadas de decisão na hora de avaliar as características dos vinhos a serem adquiridos para venda.\n",
        "\n",
        "A primeira etapa foi entender o problema. Identificamos que para este distribuidor não interessava vinhos com baixo valor agregado, o que está diretamente ligado a Melhor Qualidade desta bebida.\n",
        "\n",
        "A segunda etapa foi obter os dados para análise exploratória e o Pré-Processamento. Através da Câmara de Comercio de Vinhos do Norte de Portugal - CCVNP obtivemos o \"dataset\" dos 1599 diferentes tipos de vinhos fabricados nesta região.\n",
        "\n",
        "Identificamos que as tarefas associadas a este dataset são: CLASSIFICAÇÃO E REGRESSÃO. O que nos leva a concluir que se trata de um APRENDIZADO SUPERVISIONADO\n",
        "\n",
        "Junto ao engenheiro de alimentos e ao sommelier do distribuidor, levantamos algumas premissas relacionadas a características físico-químicas que nos remetem a uma qualidade melhor de um vinho. Dentre as principais características, declarada pelo especialista está o pH do vinho, entretanto somente esta informação não nos remete a selecionar o de melhor qualidade. Deverá ser analisado outras características fundamentais para que a Ciências de Dados possa entregar uma modelagem mais assertiva aos interessados.\n"
      ],
      "metadata": {
        "id": "zw7T7gdIgh0f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "________________________________________________________________________________"
      ],
      "metadata": {
        "id": "-7dkoJvKqZQk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2. ANALISE EXPLORATÓRIA DE DADOS**"
      ],
      "metadata": {
        "id": "Glr9MUeR5Jhu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.1 Etapa de Importação das Bibliotecas**"
      ],
      "metadata": {
        "id": "x43zeXaUnspY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " 2.1.1 Etapa de Importação das Bibliotecas"
      ],
      "metadata": {
        "id": "N6SBwBcis8y6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-op5aakkn_UN"
      },
      "outputs": [],
      "source": [
        "# Configuração para não exibir os warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Imports necessários\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import missingno as ms # para tratamento de missings\n",
        "\n",
        "# transformação Numerica\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# transformação categórica\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Exibição de Dados no Jupyter Notebook\n",
        "\n",
        "from IPython.display import display\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split # para particionar em bases de treino e teste (holdout)\n",
        "from sklearn.model_selection import KFold # para preparar os folds da validação cruzada\n",
        "from sklearn.model_selection import cross_val_score # para executar a validação cruzada\n",
        "from sklearn.metrics import accuracy_score # para a exibição da acurácia do modelo\n",
        "from sklearn.neighbors import KNeighborsClassifier # algoritmo KNN\n",
        "from sklearn.tree import DecisionTreeClassifier # algoritmo Árvore de Classificação\n",
        "from sklearn.naive_bayes import GaussianNB # algoritmo Naive Bayes\n",
        "from sklearn.svm import SVC # algoritmo SVM\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.2 Caminho de Leitura do Datasets**"
      ],
      "metadata": {
        "id": "RoDErMTGn4ha"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.2.1 Importando dados da URL para o Dataframe"
      ],
      "metadata": {
        "id": "n48d7jYJtS1C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.2.1.1 URL a importar"
      ],
      "metadata": {
        "id": "-cMNoR65thdf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Qldvinhos= pd.read_csv(r'https://raw.githubusercontent.com/salimNabbout/Qualidade_Vinhos_Norte_Portugal/main/content/winequality-red.csv')"
      ],
      "metadata": {
        "id": "7BXx_aGwoco7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.3 Definição dos Atributos do Datasets**"
      ],
      "metadata": {
        "id": "20Uuc30joScm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.3.1 Labels do Atributos do Dataset"
      ],
      "metadata": {
        "id": "i6gXlVSEtq9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels_atributos = ['volatile acidity', 'citric acid', 'residual sugar', 'chlorides',\n",
        "       'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH',\n",
        "       'sulphates', 'alcohol', 'quality', ]"
      ],
      "metadata": {
        "id": "n04eztKfpR1-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.4 Verificando que o Qldvinhos é um Dataframe**"
      ],
      "metadata": {
        "id": "qTEn9ZlF1A_9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(Qldvinhos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntWG2o5-pYNz",
        "outputId": "ea85a36f-650c-40f0-de25-94066e8e7e46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.4.1 Exibindo as Cinco Primeiras Linhas do Dataset**\n",
        "\n",
        "***Observa se nas duas primeiras linhas do atributo citric acidy ( Acido Citrico) estão zeradas.***"
      ],
      "metadata": {
        "id": "mLolkNUsxwl5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Qldvinhos.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OXyim8Guptjp",
        "outputId": "eaaa2c68-ed38-4a92-875c-5deffe71be27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7fc42ab2-c65c-47f0-9d92-46f445516ffa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fc42ab2-c65c-47f0-9d92-46f445516ffa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7fc42ab2-c65c-47f0-9d92-46f445516ffa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7fc42ab2-c65c-47f0-9d92-46f445516ffa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.4.2 Exibindo as Cinco Ultimas Linhas do Dataset Original**"
      ],
      "metadata": {
        "id": "iEOsqni2zVGl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Qldvinhos.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FxsZE3k_p7JB",
        "outputId": "bcb3be31-f5d8-4733-f98b-d5812fad0a40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1596            6.3             0.510         0.13             2.3      0.076   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol  quality  \n",
              "1594     10.5        5  \n",
              "1595     11.2        6  \n",
              "1596     11.0        6  \n",
              "1597     10.2        5  \n",
              "1598     11.0        6  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c01559db-dd28-4aba-8e30-4d53d623d806\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c01559db-dd28-4aba-8e30-4d53d623d806')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c01559db-dd28-4aba-8e30-4d53d623d806 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c01559db-dd28-4aba-8e30-4d53d623d806');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.4.3 Exibindo o nome dos Atributos.\n",
        "\n",
        "Os atributos são do tipo MULTIVARIADA e com seus valores em REAL"
      ],
      "metadata": {
        "id": "U4IG2gWqzhrO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Qldvinhos.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bg53jTNiqQNy",
        "outputId": "de08059f-5925-466d-89b4-ba289e854b73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
              "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
              "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.5 Definição dos Atributos do Dataset**\n",
        "\n",
        "2.5.1 os valores referencias foram fornecidos pelo especialista em vinhos\n",
        "\n",
        "\n",
        "\n",
        "*   **Fixed Acidity** = a acidez fixa refere-se, basicamente, aos ácidos que não são capazes de se volatilizar, provenientes da fermentação. \n",
        "\n",
        "Valor referencia - 5.8 - 8.9 g/l\n",
        "\n",
        "*   **Volatile Acidity** = acidez e aroma muito desagradável, provocado pela presença de teor elevado de ácido acético e de acetato de etila no vinho, compostos produzidos essencialmente por bactérias acéticas.\n",
        "\n",
        "Valor referencia - 0.009 - 1.210 g/l\n",
        "\n",
        "*   **Citric Acid** = O ácido cítrico está presente nas uvas em menor quantidade, sendo que nos vinhos apresenta pouca ou nenhuma presença. \n",
        "\n",
        "Valor referencia - 0.000 - 0.098 g/l\n",
        "\n",
        "*  **Residual sugar **= o açúcar residual é proveniente da própria uva – pode ser frutose ou glicose – e resta ao final do processo de fermentação.\n",
        "\n",
        "Valor referencia - 2.00 - 2.500 g/l\n",
        "\n",
        "*   **chlorides** = Uma adição de sais no vinho. A sua principal função é de antioxidante, mas também possui funções de desinfetante porque é bactericida (em particular) e fungicida, melhorador de aroma e afinador de cor.\n",
        "\n",
        "Valor referencia - 0.009 - 0.098 mg/l\n",
        "\n",
        "*   **Free Sulfur Dioxide** = Os sulfitos (SO2) são sais do ácido sulfuroso que são liberados quando o ácido se dissocia.\n",
        "\n",
        "Valor referencia - 8.5 - 19.3 mg/l\n",
        "\n",
        "*   **total sulfur dioxide** = O dióxido de enxofre (SO2) é um composto muito importante utilizado na conservação de vinhos, devido às suas propriedades antioxidantes.\n",
        "\n",
        "Valor referencia - 25.7 - 58.2 mg/l\n",
        "\n",
        "*  **density** =  Está relacionada principalmente ao seu teor alcoólico e de açúcares residuais.\n",
        "\n",
        "Valor referencia - 0.095 - 1.000 (Amostra de FT)\n",
        "\n",
        "*  **pH** = Teor de acidez do vinho, que varia entre o vinho branco e o vinho tinto.\n",
        "\n",
        "Valor referencia - 3.2 - 3.6\n",
        "\n",
        "*   **sulphates**= No vinho, o sulfito é utilizado como conservante, devido às suas propriedades antioxidantes e antibacterianas, evitando que os vinhos degradem de forma mais rápida.\n",
        "Valor referencia - 0.310 - 1.980 ppm\n",
        "\n",
        "*  **alcohol ** = É o resultado da fermentação dos açúcares da uva (glicose e frutose), que acontece graças a presença de leveduras no mosto. \n",
        "\n",
        "Valor referencia - 9.5 - 10.2 GL\n",
        "\n",
        "*   **quality** = As pontuações , onde varia de 0 a 10, onde 0 são vinhos de pessima qualidade e 10 de excelente qualidade\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "npxKPRtI7uDB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.6 Dimensões do Dataset**\n",
        "\n",
        "O dataset possui 1599 linhas, 12 atributos e 4898 instâncias"
      ],
      "metadata": {
        "id": "xi52PqA8WPoJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Qldvinhos.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7hT1TH0Wc5t",
        "outputId": "0553dfeb-7b7c-47e7-9cb4-ecb4b4a85e6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2.7 Exibindo a Quantidade de Linhas do Dataset**"
      ],
      "metadata": {
        "id": "WtU7FmqSYbpS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Qldvinhos.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TJRJX_vqr4W",
        "outputId": "d2ae7011-2e21-4f28-92b2-58ce34315a29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           1599\n",
              "volatile acidity        1599\n",
              "citric acid             1599\n",
              "residual sugar          1599\n",
              "chlorides               1599\n",
              "free sulfur dioxide     1599\n",
              "total sulfur dioxide    1599\n",
              "density                 1599\n",
              "pH                      1599\n",
              "sulphates               1599\n",
              "alcohol                 1599\n",
              "quality                 1599\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**3. SUMÁRIO ESTATISTICO**"
      ],
      "metadata": {
        "id": "TeUlxotSVpEN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisando o Sumário estatistico, observamos que as variáveis : volatile acidity, citric acid, chlorides, density, pH, sulphate, apresentam baixo desvio padrão (std). Eliminaremos estas variáves da analise de dados, pois praticamente não há muita dispersão nos dados destes atributos. \n",
        "\n",
        "Estes dados não serão eliminados do dataframe. Eles não serão analisados individualmente como os outros atributos.\n",
        "\n"
      ],
      "metadata": {
        "id": "TmLCMOMIYmTE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Qldvinhos.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "3h8hXOi_q69V",
        "outputId": "9899c1f7-b63d-45c3-86c8-04f4347cd1d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
              "mean        8.319637          0.527821     0.270976        2.538806   \n",
              "std         1.741096          0.179060     0.194801        1.409928   \n",
              "min         4.600000          0.120000     0.000000        0.900000   \n",
              "25%         7.100000          0.390000     0.090000        1.900000   \n",
              "50%         7.900000          0.520000     0.260000        2.200000   \n",
              "75%         9.200000          0.640000     0.420000        2.600000   \n",
              "max        15.900000          1.580000     1.000000       15.500000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
              "mean      0.087467            15.874922             46.467792     0.996747   \n",
              "std       0.047065            10.460157             32.895324     0.001887   \n",
              "min       0.012000             1.000000              6.000000     0.990070   \n",
              "25%       0.070000             7.000000             22.000000     0.995600   \n",
              "50%       0.079000            14.000000             38.000000     0.996750   \n",
              "75%       0.090000            21.000000             62.000000     0.997835   \n",
              "max       0.611000            72.000000            289.000000     1.003690   \n",
              "\n",
              "                pH    sulphates      alcohol      quality  \n",
              "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
              "mean      3.311113     0.658149    10.422983     5.636023  \n",
              "std       0.154386     0.169507     1.065668     0.807569  \n",
              "min       2.740000     0.330000     8.400000     3.000000  \n",
              "25%       3.210000     0.550000     9.500000     5.000000  \n",
              "50%       3.310000     0.620000    10.200000     6.000000  \n",
              "75%       3.400000     0.730000    11.100000     6.000000  \n",
              "max       4.010000     2.000000    14.900000     8.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc2bcf84-b69b-438f-890c-1d1d6fbe6eba\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.319637</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>5.636023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.741096</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>0.807569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996750</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc2bcf84-b69b-438f-890c-1d1d6fbe6eba')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cc2bcf84-b69b-438f-890c-1d1d6fbe6eba button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cc2bcf84-b69b-438f-890c-1d1d6fbe6eba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4. PRÉ-PROCESSAMENTO**"
      ],
      "metadata": {
        "id": "d01jMq2acf02"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.1 Carregamento o Dataset ORIGINAL**"
      ],
      "metadata": {
        "id": "gwn6rkZ1J_-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Informa a URL de importação do dataset\n",
        "url = (r'https://raw.githubusercontent.com/salimNabbout/Qualidade_Vinhos_Norte_Portugal/main/content/winequality-red.csv')\n",
        "\n",
        "# Lê o arquivo \n",
        "dataset = pd.read_csv(url, delimiter=',')\n",
        "\n",
        "# Exibindo as primeiras linhas\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yloGR4irJSz8",
        "outputId": "3ba2605e-fcff-469d-971f-001bdbacf29e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-de4aae8c-e488-4175-9b75-b166cc457a92\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4aae8c-e488-4175-9b75-b166cc457a92')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-de4aae8c-e488-4175-9b75-b166cc457a92 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-de4aae8c-e488-4175-9b75-b166cc457a92');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.2 Tratamento de MISSING**\n",
        "\n",
        "Verificando os Valores faltantes no Datasets\n"
      ],
      "metadata": {
        "id": "yxlc0uWckrfv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TawkGRh-j2fr",
        "outputId": "3e94a771-37a9-4b8e-b124-2d0eeedcecad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nesta etapa será salvo um novo arquivo, chamado de Dataset em substituição ao original \"Qldvinhos\" para executar o tratamento de MISSING.\n",
        "\n",
        "Observamos na coluna do atributo citric acidity, que existem valores nulos. Este atrubuto deverá sofrer um tratamento na sequencia do pré processamento"
      ],
      "metadata": {
        "id": "A80_IXi6c36k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# recuperando os nomes das colunas\n",
        "col = list(dataset.columns)\n",
        "\n",
        "# o novo dataset irá conter todas as colunas do dataset original\n",
        "datasetSemMissings = dataset[col[:]]\n",
        "\n",
        "# substituindo os zeros por NaN\n",
        "datasetSemMissings.replace(0, np.nan, inplace=True)\n",
        "\n",
        "# exibindo visualização matricial da nulidade do dataset\n",
        "ms.matrix(datasetSemMissings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        },
        "id": "HW5JyzNBKMzf",
        "outputId": "eab11586-ce75-4553-db01-0c43557faa73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.3 Reverificando o Dataset Apos as Substituições dos Zeros por NaN**"
      ],
      "metadata": {
        "id": "HXHZvwLD_6rC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ucl8RGLoLxfB",
        "outputId": "2ffb8a92-090a-4291-e27b-5619d1dd7d53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity             0\n",
              "volatile acidity          0\n",
              "citric acid             132\n",
              "residual sugar            0\n",
              "chlorides                 0\n",
              "free sulfur dioxide       0\n",
              "total sulfur dioxide      0\n",
              "density                   0\n",
              "pH                        0\n",
              "sulphates                 0\n",
              "alcohol                   0\n",
              "quality                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.4 Tratando O DATASET**"
      ],
      "metadata": {
        "id": "XkI-MKvxYGlm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Existem 132 valores zerados na coluna do \"citric acid\", como este valor representa apenas 8,2% do total dos valores desta coluna, iremos substituir esses valores faltantes pela mediana da coluna."
      ],
      "metadata": {
        "id": "DuBclNOXRh5Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Substituindo os NaN de \"citric acid\" pela mediana do atributo\n",
        "\n",
        "Exibindo a Matriz de nulidade do dataset apos tratamento"
      ],
      "metadata": {
        "id": "c8zDEJmkDKlI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings['citric acid'].fillna(datasetSemMissings['citric acid'].median(), inplace=True)\n",
        "\n",
        "ms.matrix(datasetSemMissings)"
      ],
      "metadata": {
        "id": "HCZIav3PS7QL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        },
        "outputId": "49a0c6bf-da06-4b4c-b513-ca7d0bc5445a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A figura acima , mostra o Dataset tratado preservando as caracteristicas de linhas e colunas do dataset original. Entretanto os zeros na coluna do atributo \"citric acid\" foram substituidos pela mediana desta coluna (0,28), que se observava nas linhas 0 e 1 do dataset original em comparação ao dataset antes do tratamento\n",
        "\n"
      ],
      "metadata": {
        "id": "BPjkVyODX2ZO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.5 Tabelas Comparativas - Antes e depois do Tratamento**\n",
        "\n",
        "As tabelas abaixo servem de comparação entre o dataset original( Qldvinhos ) e o dataset apos o tratamento (datasetSemMissing ).\n",
        "\n",
        " Observamos que apos o TRATAMENTO, os valores zerados nas duas primeiras linhas do dataframe foram SUBSTITUIDAS PELA MEDIANA DO ATRIBUTO."
      ],
      "metadata": {
        "id": "AFNyhmH8bDpa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Qldvinhos.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XL0UR9qIavT8",
        "outputId": "f89684a2-1ba8-4a30-e82d-e374548e680a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf7fa980-d6c3-478d-9e6e-a51447cdf226\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf7fa980-d6c3-478d-9e6e-a51447cdf226')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cf7fa980-d6c3-478d-9e6e-a51447cdf226 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cf7fa980-d6c3-478d-9e6e-a51447cdf226');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hCf1LR2LXQbk",
        "outputId": "2d0a234e-e5c7-4e29-9402-11a723001588"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.28             1.9      0.076   \n",
              "1            7.8              0.88         0.28             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.28             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a161b6fc-fc8f-4bef-8ceb-e4ccdbbea51a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a161b6fc-fc8f-4bef-8ceb-e4ccdbbea51a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a161b6fc-fc8f-4bef-8ceb-e4ccdbbea51a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a161b6fc-fc8f-4bef-8ceb-e4ccdbbea51a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As duas tabelas abaixo, servem apenas de CONFIRMAÇÃO do tratamento realizado.\n",
        "\n",
        "A PRIMEIRA tabela mostra que nenhuma linha desceu por NÃO apresentar MISSING em TODAS linhas ou colunas.\n",
        "\n",
        "\n",
        "A SEGUNDA tabela constata que nenhuma linha desceu por NÃO apresentar MISSING em ALGUMAS linhas ou colunas.\n",
        "\n"
      ],
      "metadata": {
        "id": "v65XcKlKZLC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings=datasetSemMissings.dropna(how='all')\n",
        "dataset.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w3Fcg6W-bNd7",
        "outputId": "4e2450d0-30c9-4dca-cfa8-39fccd28b306"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1596            6.3             0.510         0.13             2.3      0.076   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol  quality  \n",
              "1594     10.5        5  \n",
              "1595     11.2        6  \n",
              "1596     11.0        6  \n",
              "1597     10.2        5  \n",
              "1598     11.0        6  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0687a864-4af7-4232-8ac5-2dd8fecf1b9e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0687a864-4af7-4232-8ac5-2dd8fecf1b9e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0687a864-4af7-4232-8ac5-2dd8fecf1b9e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0687a864-4af7-4232-8ac5-2dd8fecf1b9e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings=datasetSemMissings.dropna(how='any')\n",
        "dataset.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hxsPO-yYdzWw",
        "outputId": "ab8fce0c-b2fa-4f48-936b-17d3411248c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1596            6.3             0.510         0.13             2.3      0.076   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol  quality  \n",
              "1594     10.5        5  \n",
              "1595     11.2        6  \n",
              "1596     11.0        6  \n",
              "1597     10.2        5  \n",
              "1598     11.0        6  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7c420e6-63f4-4eac-abc3-fc3b97c63063\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7c420e6-63f4-4eac-abc3-fc3b97c63063')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f7c420e6-63f4-4eac-abc3-fc3b97c63063 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f7c420e6-63f4-4eac-abc3-fc3b97c63063');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.6 Vereificando os NULLS do Dataset Tratado**"
      ],
      "metadata": {
        "id": "ouDolvI7b0ee"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B8QldclEbvX3",
        "outputId": "54d909ca-ee36-4925-cbd7-2c8cbcb3082e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.7 TRANSFORMAÇÃO NUMERICA**"
      ],
      "metadata": {
        "id": "j3Sqn7S7UJY3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.7.1 Etapa de Normalização dos Dados**"
      ],
      "metadata": {
        "id": "anlz3UjSWCSX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Separandop os componentes do dataset em X (imput) e y (output)\n",
        "x= Qldvinhos.values[:,0:8]\n",
        "y= Qldvinhos.values[:,8]\n",
        "\n",
        "# Normalizando os dados\n",
        "rescaledX=MinMaxScaler().fit_transform(x)\n",
        "print('qldvinhos:\\n\\n', datasetSemMissings.values)\n",
        "print('datasetSemMissing:\\n\\n', rescaledX)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jyasx1QhU4i",
        "outputId": "424c8f77-4492-4fa8-8e9b-39bac12da353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "qldvinhos:\n",
            "\n",
            " [[ 7.4    0.7    0.28  ...  0.56   9.4    5.   ]\n",
            " [ 7.8    0.88   0.28  ...  0.68   9.8    5.   ]\n",
            " [ 7.8    0.76   0.04  ...  0.65   9.8    5.   ]\n",
            " ...\n",
            " [ 6.3    0.51   0.13  ...  0.75  11.     6.   ]\n",
            " [ 5.9    0.645  0.12  ...  0.71  10.2    5.   ]\n",
            " [ 6.     0.31   0.47  ...  0.66  11.     6.   ]]\n",
            "datasetSemMissing:\n",
            "\n",
            " [[0.24778761 0.39726027 0.         ... 0.14084507 0.09893993 0.56754772]\n",
            " [0.28318584 0.52054795 0.         ... 0.33802817 0.2155477  0.49412628]\n",
            " [0.28318584 0.43835616 0.04       ... 0.1971831  0.16961131 0.50881057]\n",
            " ...\n",
            " [0.15044248 0.26712329 0.13       ... 0.3943662  0.12014134 0.41629956]\n",
            " [0.11504425 0.35958904 0.12       ... 0.43661972 0.13427562 0.39647577]\n",
            " [0.12389381 0.13013699 0.47       ... 0.23943662 0.12720848 0.3979442 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.7.2 Etapa de Padronização dos Dados**"
      ],
      "metadata": {
        "id": "YVnB1aiBoT5X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rescaledX=StandardScaler().fit_transform(x)\n",
        "print('qldvinhos:\\n\\n', datasetSemMissings.values)\n",
        "print('datasetSemMissing:\\n\\n', rescaledX)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nncf7DFvm-0K",
        "outputId": "0126d9b9-5acd-43f4-8352-0017e48c7e7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "qldvinhos:\n",
            "\n",
            " [[ 7.4    0.7    0.28  ...  0.56   9.4    5.   ]\n",
            " [ 7.8    0.88   0.28  ...  0.68   9.8    5.   ]\n",
            " [ 7.8    0.76   0.04  ...  0.65   9.8    5.   ]\n",
            " ...\n",
            " [ 6.3    0.51   0.13  ...  0.75  11.     6.   ]\n",
            " [ 5.9    0.645  0.12  ...  0.71  10.2    5.   ]\n",
            " [ 6.     0.31   0.47  ...  0.66  11.     6.   ]]\n",
            "datasetSemMissing:\n",
            "\n",
            " [[-0.52835961  0.96187667 -1.39147228 ... -0.46619252 -0.37913269\n",
            "   0.55827446]\n",
            " [-0.29854743  1.96744245 -1.39147228 ...  0.87263823  0.62436323\n",
            "   0.02826077]\n",
            " [-0.29854743  1.29706527 -1.18607043 ... -0.08366945  0.22904665\n",
            "   0.13426351]\n",
            " ...\n",
            " [-1.1603431  -0.09955388 -0.72391627 ...  1.2551613  -0.19667889\n",
            "  -0.53355375]\n",
            " [-1.39015528  0.65462046 -0.77526673 ...  1.54205361 -0.07504302\n",
            "  -0.67665745]\n",
            " [-1.33270223 -1.21684919  1.02199944 ...  0.20322286 -0.13586095\n",
            "  -0.66605717]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8. ANALISE ESTATISTICA**"
      ],
      "metadata": {
        "id": "WqmbbgdKptIY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.1 Sumário Estatistico do Datasets Tratado**\n",
        "\n",
        "Assim como no sumario estatistico antes do tratamento, manteremos os atributos com pequenos desvio padrão fora das analises individuais deles.\n",
        "\t\t"
      ],
      "metadata": {
        "id": "S8ujUayAsygP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "sJXtOXI77r4L",
        "outputId": "55497c54-32e7-4c01-faca-6ad2ad99f19f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    1599.000000       1599.000000  1467.000000     1599.000000   \n",
              "mean        8.319637          0.527821     0.295358        2.538806   \n",
              "std         1.741096          0.179060     0.184819        1.409928   \n",
              "min         4.600000          0.120000     0.010000        0.900000   \n",
              "25%         7.100000          0.390000     0.140000        1.900000   \n",
              "50%         7.900000          0.520000     0.280000        2.200000   \n",
              "75%         9.200000          0.640000     0.440000        2.600000   \n",
              "max        15.900000          1.580000     1.000000       15.500000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
              "mean      0.087467            15.874922             46.467792     0.996747   \n",
              "std       0.047065            10.460157             32.895324     0.001887   \n",
              "min       0.012000             1.000000              6.000000     0.990070   \n",
              "25%       0.070000             7.000000             22.000000     0.995600   \n",
              "50%       0.079000            14.000000             38.000000     0.996750   \n",
              "75%       0.090000            21.000000             62.000000     0.997835   \n",
              "max       0.611000            72.000000            289.000000     1.003690   \n",
              "\n",
              "                pH    sulphates      alcohol      quality  \n",
              "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
              "mean      3.311113     0.658149    10.422983     5.636023  \n",
              "std       0.154386     0.169507     1.065668     0.807569  \n",
              "min       2.740000     0.330000     8.400000     3.000000  \n",
              "25%       3.210000     0.550000     9.500000     5.000000  \n",
              "50%       3.310000     0.620000    10.200000     6.000000  \n",
              "75%       3.400000     0.730000    11.100000     6.000000  \n",
              "max       4.010000     2.000000    14.900000     8.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-46eb32d9-677d-498c-a56c-6cf9577123e7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1467.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.319637</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.295358</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>5.636023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.741096</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.184819</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>0.807569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.280000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996750</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46eb32d9-677d-498c-a56c-6cf9577123e7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-46eb32d9-677d-498c-a56c-6cf9577123e7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-46eb32d9-677d-498c-a56c-6cf9577123e7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.2 Verificando Ausencia de Valores no Dataset Tratado por Atributo**\n",
        "\n",
        "Observamos que NÃO existem valores não nulos "
      ],
      "metadata": {
        "id": "cv7ZDYa6XC-g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "twFMK6SzW5i4",
        "outputId": "746423ec-d795-4046-c744-9558e290c472"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1599 entries, 0 to 1598\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         1599 non-null   float64\n",
            " 1   volatile acidity      1599 non-null   float64\n",
            " 2   citric acid           1599 non-null   float64\n",
            " 3   residual sugar        1599 non-null   float64\n",
            " 4   chlorides             1599 non-null   float64\n",
            " 5   free sulfur dioxide   1599 non-null   float64\n",
            " 6   total sulfur dioxide  1599 non-null   float64\n",
            " 7   density               1599 non-null   float64\n",
            " 8   pH                    1599 non-null   float64\n",
            " 9   sulphates             1599 non-null   float64\n",
            " 10  alcohol               1599 non-null   float64\n",
            " 11  quality               1599 non-null   int64  \n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 150.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.3 CALCULO ESTATISTICO POR ATRIBUTO** \n",
        "\n",
        "Os atributos mencionados, no sumário estatistico , que possuem baixo desvio padrão estam fora das analises individuais deles.\n",
        "\n",
        "Como estes atributos possuem pequenas variações dentro do daframe. Iremos desconsidera los"
      ],
      "metadata": {
        "id": "kR7W52un5tQI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.3.1 Analise Estatistica do Atributo Qualidade**\n"
      ],
      "metadata": {
        "id": "EQTdhKLIg_YX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculo da Variancia\n",
        "variancia = datasetSemMissings.loc[:,'quality'].var(ddof=0)\n",
        "# Calculo do desvio padrão\n",
        "desviopd= datasetSemMissings.loc[:,'quality'].std(ddof=0)\n",
        "# Calculo da media\n",
        "media= datasetSemMissings.loc[:,'quality']. mean()\n",
        "# calculo da Mediana\n",
        "median= datasetSemMissings.loc[:,'quality']. median()\n",
        "# Calculo da Moda\n",
        "mode= datasetSemMissings.loc[:,'quality']. mode()\n",
        "# calculo da Maxima\n",
        "max= datasetSemMissings.loc[:,'quality']. max()\n",
        "# calculo do Minimo\n",
        "min= datasetSemMissings.loc[:,'quality']. min()\n",
        "\n",
        "print(\"variancia quality = \" + str(variancia))\n",
        "print(\"desviopd quality = \" + str(desviopd))\n",
        "print(\"media quality = \" + str(media))\n",
        "print(\"median quality = \" + str(median))\n",
        "print(\"mode quality = \" + str(mode))\n",
        "print(\"max quality = \" + str(max))\n",
        "print(\"min quality = \" + str(min))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNc56Sq9Rc-4",
        "outputId": "026e3efb-d476-4ce4-f6ec-886a16d003ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "variancia quality = 0.6517605398308277\n",
            "desviopd quality = 0.8073168769639513\n",
            "media quality = 5.6360225140712945\n",
            "median quality = 6.0\n",
            "mode quality = 0    5\n",
            "Name: quality, dtype: int64\n",
            "max quality = 8\n",
            "min quality = 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=datasetSemMissings['quality'])"
      ],
      "metadata": {
        "id": "SZ-NJ6ktULP3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A qualidade é o atributo que nos aponta os vinhos de melhores peculiaridades. Na analise estatistica deste atributo, observamos a maior frequencia os vinhos pontuados por 5, mostrado pela MODA . Podemos classificar estes vinhos como de qualidade media, o que tambem é provado no calculo estatistico da qualidade. Onde se aponta a media no valor de 5.6. \n",
        "\n",
        "Tendo como objetivo , selecionar os vinhos com caracteristicas dentro de valores fornecidos pelo especialista do cliente. Classificamos os vinhos com qualidade 6 e 7 como os vinhos a serem selecionados.\n"
      ],
      "metadata": {
        "id": "ZDe7n1dWgXpp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(datasetSemMissings['quality'], bins=6);"
      ],
      "metadata": {
        "id": "K3CZ3ybKUlKF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conforme claculo estatistico desta variável, confirmamos que os vinhos pontuados por 5 aparecem em maior frequencia em nossos dados(Moda). Entretanto, como a meta são vinhos de maior qualidade, confirma se a escolha dos vinhos pontuados por 6 e 7. \n",
        "\n",
        "Observa se a existência de uma quantidade proxima a 837 tipos diferentes de vinhos na soma destas categoria de qualidade.\n",
        "\n",
        "Esta etapa é o principal filtro para a escolha dos fabricantes que deverão ser futuros parceiros da referida distribuidora. Entretanto as proximas analises, os outros atributos, deverá estar contida, conforme valores referências de cada atributo, nestes 837 tipos de vinhos, conforme tabela abaixo.\n",
        "\n",
        "Serão avaliados 52,34% do total de diferentes  tipos de vinhos do dataframe."
      ],
      "metadata": {
        "id": "kc47qJ1ThWuX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfqualidade = datasetSemMissings[(datasetSemMissings['quality']>=6) & (datasetSemMissings['quality']<=7)] \n",
        "dfqualidade['quality']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5KeyLlPjdoS",
        "outputId": "412a6879-acce-4d75-be00-8f77068fd92e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3       6\n",
              "7       7\n",
              "8       7\n",
              "16      7\n",
              "19      6\n",
              "       ..\n",
              "1592    6\n",
              "1593    6\n",
              "1595    6\n",
              "1596    6\n",
              "1598    6\n",
              "Name: quality, Length: 837, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.3.2 Analise Estatística do Atributo - fixed acidity**\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "vhItQjMSA4uv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculo da Variancia\n",
        "variancia = datasetSemMissings.loc[:,'fixed acidity'].var(ddof=0)\n",
        "# Calculo do desvio padrão\n",
        "desviopd= datasetSemMissings.loc[:,'fixed acidity'].std(ddof=0)\n",
        "# Calculo da media\n",
        "media= datasetSemMissings.loc[:,'fixed acidity']. mean()\n",
        "# calculo da Mediana\n",
        "median= datasetSemMissings.loc[:,'fixed acidity']. median()\n",
        "# Calculo da Moda\n",
        "mode= datasetSemMissings.loc[:,'fixed acidity']. mode()\n",
        "# calculo da Maxima\n",
        "max= datasetSemMissings.loc[:,'fixed acidity']. max()\n",
        "# calculo do Minimo\n",
        "min= datasetSemMissings.loc[:,'fixed acidity']. min()\n",
        "\n",
        "print(\"variancia fixed acidity = \" + str(variancia))\n",
        "print(\"desviopd fixed acidity = \" + str(desviopd))\n",
        "print(\"media fixed acidity = \" + str(media))\n",
        "print(\"median fixed acidity = \" + str(median))\n",
        "print(\"mode fixed acidity = \" + str(mode))\n",
        "print(\"max fixed acidity = \" + str(max))\n",
        "print(\"min fixed acidity = \" + str(min))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ifBp66AD22r",
        "outputId": "035a81ad-1f73-41a2-e5ec-15c5c1976911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "variancia fixed acidity = 3.0295205688671114\n",
            "desviopd fixed acidity = 1.7405518001102729\n",
            "media fixed acidity = 8.31963727329581\n",
            "median fixed acidity = 7.9\n",
            "mode fixed acidity = 0    7.2\n",
            "Name: fixed acidity, dtype: float64\n",
            "max fixed acidity = 15.9\n",
            "min fixed acidity = 4.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=datasetSemMissings['fixed acidity'])"
      ],
      "metadata": {
        "id": "BdOyK2f9Mabp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "No grafico acima percebemos a dispersão dos dados com sua simetria a direita. O valor maximo distribuito no dataframe deste atributo é 15,9 g/l. Entretanto podemos observar que valores acima de aproximadamente 12 g/l são considerados outliers.\n",
        "\n",
        "Os valores aceitáveis deste atributo, fornecido pelo especialista, são de 5.8 g/l a 8.9 g/l. Esta faixa de valores correspondem a pelo menos 50% dos diferentes tipos de vinhos deste dataframe, pois Q2 (Mediana) tem seu valor em  7.9 g/l."
      ],
      "metadata": {
        "id": "b1lCZ0CusJK9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(datasetSemMissings['fixed acidity'], bins=7);"
      ],
      "metadata": {
        "id": "QS7PSWU6HV8Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "o histograma mostra que os valores de acidez fixa desejado pelo cliente estão presente em 1087 diferentes tipos desta bebida, conforme os dados apontam na tabela abaixo.\n",
        "\n",
        "1087 diferentes tipos de vinhos se limitam dentro dos valores referencias."
      ],
      "metadata": {
        "id": "-pL8WG6-w-2v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfacidezfixa= datasetSemMissings[(datasetSemMissings['fixed acidity']>=5.8) & (datasetSemMissings['fixed acidity']<=8.9)]\n",
        "dfacidezfixa['fixed acidity']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePa0WgezjVgW",
        "outputId": "7f1f8dcc-9877-43d8-f808-1d4e287b6837"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       7.4\n",
              "1       7.8\n",
              "2       7.8\n",
              "4       7.4\n",
              "5       7.4\n",
              "       ... \n",
              "1594    6.2\n",
              "1595    5.9\n",
              "1596    6.3\n",
              "1597    5.9\n",
              "1598    6.0\n",
              "Name: fixed acidity, Length: 1087, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.3.3 Analise Estatística do Atributo - Residual Sugar** \n",
        "\n"
      ],
      "metadata": {
        "id": "VCiXyr7Ne0-d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculo da Variancia\n",
        "variancia = datasetSemMissings.loc[:,'residual sugar'].var(ddof=0)\n",
        "# Calculo do desvio padrão\n",
        "desviopd= datasetSemMissings.loc[:,'residual sugar'].std(ddof=0)\n",
        "# Calculo da media\n",
        "media= datasetSemMissings.loc[:,'residual sugar']. mean()\n",
        "# calculo da Mediana\n",
        "median= datasetSemMissings.loc[:,'residual sugar']. median()\n",
        "# Calculo da Moda\n",
        "mode= datasetSemMissings.loc[:,'residual sugar']. mode()\n",
        "# calculo da Maxima\n",
        "max= datasetSemMissings.loc[:,'residual sugar']. max()\n",
        "# calculo do Minimo\n",
        "min= datasetSemMissings.loc[:,'residual sugar']. min()\n",
        "\n",
        "print(\"variancia residual sugar = \" + str(variancia))\n",
        "print(\"desviopd residual sugar = \" + str(desviopd))\n",
        "print(\"media residual sugar = \" + str(media))\n",
        "print(\"median residual sugar = \" + str(median))\n",
        "print(\"mode residual sugar = \" + str(mode))\n",
        "print(\"max residual sugar = \" + str(max))\n",
        "print(\"min residual sugar = \" + str(min))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ct_UxdrcUhbx",
        "outputId": "97738293-e386-48c4-b2c0-bc4193772cee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "variancia residual sugar = 1.9866539202698996\n",
            "desviopd residual sugar = 1.4094871124880495\n",
            "media residual sugar = 2.53880550343965\n",
            "median residual sugar = 2.2\n",
            "mode residual sugar = 0    2.0\n",
            "Name: residual sugar, dtype: float64\n",
            "max residual sugar = 15.5\n",
            "min residual sugar = 0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=datasetSemMissings['residual sugar'])"
      ],
      "metadata": {
        "id": "1cnIWaolevHR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observando o grafico de caixa, identificamos os limites minimo  e máximo de aproximadamente 0,9 g/l e 3.8 g/l. O valor máximo dentro do dataframe é 15,5 g/l . Entretanto este valor é considerado outliers, assim como quaisquer valor acima de aproximadamente 3.8 g/l. \n",
        "\n",
        "Os valores referencia são entre 2.0 a 3.7 g/l. trata se de um gráfico com uma assimetria a direta. \n",
        "\n",
        "A MODA = 2.0 , MEDIA= 2.5 e a MEDIANA = 2.2, existe a possibilidade de uma grande quantidade de diferentes tipos de vinhos estarem dentro dos valores referencias. "
      ],
      "metadata": {
        "id": "U7CdQUDWzl90"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(datasetSemMissings['residual sugar'], bins=5);"
      ],
      "metadata": {
        "id": "kU09akD1gQkK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisando o histograma observamos que os valores referencias estam presentes em grande quantidade neste atributo.\n",
        "\n",
        "Averiguando o df abaixo, identificamos 984 diferentes tipos de vinhos que obedecem aos valores referencias."
      ],
      "metadata": {
        "id": "keVGk24n2Csx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfacucarresidual= datasetSemMissings[(datasetSemMissings['residual sugar']>=2.0) & (datasetSemMissings['residual sugar']<=3.7)]\n",
        "dfacucarresidual['residual sugar']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Txe0YVEmynH7",
        "outputId": "21da203f-6a06-4ae1-f69d-e7e2214bb6ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       2.6\n",
              "2       2.3\n",
              "8       2.0\n",
              "21      2.3\n",
              "23      2.3\n",
              "       ... \n",
              "1594    2.0\n",
              "1595    2.2\n",
              "1596    2.3\n",
              "1597    2.0\n",
              "1598    3.6\n",
              "Name: residual sugar, Length: 984, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.3.4 Analise Estatistica do Atibuto - free sulfur dioxide**\n"
      ],
      "metadata": {
        "id": "wHpGd7S9sCM0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculo da Variancia\n",
        "variancia = datasetSemMissings.loc[:,'free sulfur dioxide'].var(ddof=0)\n",
        "# Calculo do desvio padrão\n",
        "desviopd= datasetSemMissings.loc[:,'free sulfur dioxide'].std(ddof=0)\n",
        "# Calculo da media\n",
        "media= datasetSemMissings.loc[:,'free sulfur dioxide']. mean()\n",
        "# calculo da Mediana\n",
        "median= datasetSemMissings.loc[:,'free sulfur dioxide']. median()\n",
        "# Calculo da Moda\n",
        "mode= datasetSemMissings.loc[:,'free sulfur dioxide']. mode()\n",
        "# calculo da Maxima\n",
        "max= datasetSemMissings.loc[:,'free sulfur dioxide']. max()\n",
        "# calculo do Minimo\n",
        "min= datasetSemMissings.loc[:,'free sulfur dioxide']. min()\n",
        "\n",
        "print(\"variancia free sulfur dioxide = \" + str(variancia))\n",
        "print(\"desviopd free sulfur dioxide = \" + str(desviopd))\n",
        "print(\"media free sulfur dioxide = \" + str(media))\n",
        "print(\"median free sulfur dioxide = \" + str(median))\n",
        "print(\"mode free sulfur dioxide = \" + str(mode))\n",
        "print(\"max free sulfur dioxide = \" + str(max))\n",
        "print(\"min free sulfur dioxide = \" + str(min))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "habpda6ksQRW",
        "outputId": "62b91c1e-4a4d-48f9-c1bc-a0d18ac8ecb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "variancia free sulfur dioxide = 109.34645676374501\n",
            "desviopd free sulfur dioxide = 10.45688561493072\n",
            "media free sulfur dioxide = 15.874921826141339\n",
            "median free sulfur dioxide = 14.0\n",
            "mode free sulfur dioxide = 0    6.0\n",
            "Name: free sulfur dioxide, dtype: float64\n",
            "max free sulfur dioxide = 72.0\n",
            "min free sulfur dioxide = 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=datasetSemMissings['free sulfur dioxide'])"
      ],
      "metadata": {
        "id": "KXzlQ0_ftlDL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grafico com Assimetrica a direita, observamos que o valor maximo é 40 mg/l e que os valores acima são considerados dispersões. A MODA = 6, valor abaixo do minimo do valor referencia, que é 8.5 mg/l. \n",
        "\n",
        "Levando em consideração que Q2 = 14.000 mg/l e  Q3 = 21.000 mg/l. Existem grandes possibilidades de se possuir uma quantidade razoável de diferentes tipos de vinhos para serem classificados"
      ],
      "metadata": {
        "id": "Xrcu1M_y1VNf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(datasetSemMissings['free sulfur dioxide'], bins=14);"
      ],
      "metadata": {
        "id": "7QX6X2_HtmLT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "8.5 mg/l a 19.3 mg/l são os valores referencias fornecidos pelo especialista. Analisando o histograma observamos a presença destes valores na segunda e terceira barra do histograma. O somatório destas barras resulta nos diferentes tipos de vinhos com seus padrões de referencia aceitáveis.\n",
        "\n",
        "O df abaixo, nos permite conhecer com precisão o quantitativo disponivel no df que atendem a os valores referencias deste atributo. No caso deste atributo, possem 666 diferentes tipos de vinhos."
      ],
      "metadata": {
        "id": "pd8bvkHv2ZHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfdioxidolivre= datasetSemMissings[(datasetSemMissings['free sulfur dioxide']>=8.5) & (datasetSemMissings['free sulfur dioxide']<=19.3)]\n",
        "dfdioxidolivre['free sulfur dioxide']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ga4a-lvZwU3q",
        "outputId": "a78d2199-cd15-4c44-a530-912ae77086a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       11.0\n",
              "2       15.0\n",
              "3       17.0\n",
              "4       11.0\n",
              "5       13.0\n",
              "        ... \n",
              "1580     9.0\n",
              "1582    13.0\n",
              "1587    18.0\n",
              "1591    16.0\n",
              "1598    18.0\n",
              "Name: free sulfur dioxide, Length: 666, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.3.5 Analise Estatística do Atibuto - total sulfur dioxide**"
      ],
      "metadata": {
        "id": "Uj2Mx0F7ve0y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculo da Variancia\n",
        "variancia = datasetSemMissings.loc[:,'total sulfur dioxide'].var(ddof=0)\n",
        "# Calculo do desvio padrão\n",
        "desviopd= datasetSemMissings.loc[:,'total sulfur dioxide'].std(ddof=0)\n",
        "# Calculo da media\n",
        "media= datasetSemMissings.loc[:,'total sulfur dioxide']. mean()\n",
        "# calculo da Mediana\n",
        "median= datasetSemMissings.loc[:,'total sulfur dioxide']. median()\n",
        "# Calculo da Moda\n",
        "mode= datasetSemMissings.loc[:,'total sulfur dioxide']. mode()\n",
        "# calculo da Maxima\n",
        "max= datasetSemMissings.loc[:,'total sulfur dioxide']. max()\n",
        "# calculo do Minimo\n",
        "min= datasetSemMissings.loc[:,'total sulfur dioxide']. min()\n",
        "\n",
        "print(\"variancia total sulfur dioxide = \" + str(variancia))\n",
        "print(\"desviopd total sulfur dioxide = \" + str(desviopd))\n",
        "print(\"media total sulfur dioxide = \" + str(media))\n",
        "print(\"median total sulfur dioxide = \" + str(median))\n",
        "print(\"mode total sulfur dioxide = \" + str(mode))\n",
        "print(\"max total sulfur dioxide = \" + str(max))\n",
        "print(\"min total sulfur dioxide = \" + str(min))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_PktdIcvv68",
        "outputId": "1edf29ad-6c7f-41fe-86a0-f2c59f94a8fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "variancia total sulfur dioxide = 1081.42563558916\n",
            "desviopd total sulfur dioxide = 32.88503665178374\n",
            "media total sulfur dioxide = 46.46779237023139\n",
            "median total sulfur dioxide = 38.0\n",
            "mode total sulfur dioxide = 0    28.0\n",
            "Name: total sulfur dioxide, dtype: float64\n",
            "max total sulfur dioxide = 289.0\n",
            "min total sulfur dioxide = 6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=datasetSemMissings['total sulfur dioxide'])"
      ],
      "metadata": {
        "id": "Xcjc9FbQwfqD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observamos uma alta dispersão dos dados deste atributo. A maxima aponta para 125 mg/l. Este valor atende a maxima do valor referencia que é  98.2 mg/l do dioxido de enxofre total. A MEDIANA = 38.000 mg/l e a MODA = 28.000 mg/l. Estes valores estatisticos, apontam para uma pequena quantidade de vinhos, que atendem a os valores referencias, que são 55.7 mg/l a 98.2 mg/l.  A MODA < que o valor minimo referencia, confirma um indicio de pequena quantidade de vinhos dentro do padrao de referencia.\n",
        "\n",
        "Um valor baixo de opção nos restringe em todos os outros atributos, ainda que estes atendam aos valores referências"
      ],
      "metadata": {
        "id": "O6ZQH5eT755X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(datasetSemMissings['total sulfur dioxide'], bins=10);"
      ],
      "metadata": {
        "id": "Ec0BTkM6xOtg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Investigando o histograma acima observando a terceira coluna do grafico, que nos remetem ao valor maximo referencia, 98.2 mg/l. Esta coluna possue um pouco mais de 200  unidades de vinhos nos valores referências.\n",
        "\n",
        "Observando o df abaixo, podemos quantificar com precisão quantos tipos de vinhos o df possue , atendendo aos valores referências. são exatos 337 unidades."
      ],
      "metadata": {
        "id": "IDflMb_r9mLZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfdioxidototal= datasetSemMissings[(datasetSemMissings['total sulfur dioxide']>=55.7) & (datasetSemMissings['total sulfur dioxide']<=98.2)]\n",
        "dfdioxidototal['total sulfur dioxide']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "so5Q2YndOn7m",
        "outputId": "94e52bed-f3d1-4153-946e-c4aadc7ae2ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       67.0\n",
              "3       60.0\n",
              "6       59.0\n",
              "10      65.0\n",
              "12      59.0\n",
              "        ... \n",
              "1539    60.0\n",
              "1574    92.0\n",
              "1583    98.0\n",
              "1586    60.0\n",
              "1589    79.0\n",
              "Name: total sulfur dioxide, Length: 337, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**8.3.6 Analise Estatística do Atibuto - alcohol**"
      ],
      "metadata": {
        "id": "dJrvkzsj5dac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculo da Variancia\n",
        "variancia = datasetSemMissings.loc[:,'alcohol'].var(ddof=0)\n",
        "# Calculo do desvio padrão\n",
        "desviopd= datasetSemMissings.loc[:,'alcohol'].std(ddof=0)\n",
        "# Calculo da media\n",
        "media= datasetSemMissings.loc[:,'alcohol']. mean()\n",
        "# calculo da Mediana\n",
        "median= datasetSemMissings.loc[:,'alcohol']. median()\n",
        "# Calculo da Moda\n",
        "mode= datasetSemMissings.loc[:,'alcohol']. mode()\n",
        "# calculo da Maxima\n",
        "max= datasetSemMissings.loc[:,'alcohol']. max()\n",
        "# calculo do Minimo\n",
        "min= datasetSemMissings.loc[:,'alcohol']. min()\n",
        "\n",
        "print(\"variancia alcohol = \" + str(variancia))\n",
        "print(\"desviopd alcohol = \" + str(desviopd))\n",
        "print(\"media alcohol = \" + str(media))\n",
        "print(\"median alcohol = \" + str(median))\n",
        "print(\"mode alcohol = \" + str(mode))\n",
        "print(\"max alcohol = \" + str(max))\n",
        "print(\"min alcohol = \" + str(min))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1tz0dAe45lM_",
        "outputId": "a81598f2-d8e5-42a9-f97b-b52c38a4b916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "variancia alcohol = 1.1349371714889036\n",
            "desviopd alcohol = 1.0653343003437483\n",
            "media alcohol = 10.422983114446529\n",
            "median alcohol = 10.2\n",
            "mode alcohol = 0    9.5\n",
            "Name: alcohol, dtype: float64\n",
            "max alcohol = 14.9\n",
            "min alcohol = 8.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=datasetSemMissings['alcohol'])"
      ],
      "metadata": {
        "id": "IvU42Zkv6Rz-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "O grafico de caixa, Assimetrico a direita, com desvio padrão pequeno, 1.065, e pequena amplitude. \n",
        "A MODA deste atibuto é = 9.5 GL, o mesmo valor da minima dos valores de referencias. A MODA tendo o mesmo valor do valor minimo de referencia, nos permitem acreditar que teremos um valor razoável de diferentes tipos de vinhos dentro desta faixa de referencia."
      ],
      "metadata": {
        "id": "Zwx7QWgDBGt0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(datasetSemMissings['alcohol'], bins=10);"
      ],
      "metadata": {
        "id": "8VqTJPDV6cIS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Explorando o histograma acima, observamos no minimo 500 diferentes tipos de vinhos dentro da faixa que contem os valores referências que podem estar disponiveis, favorecidos pelo fato de a MODA = ao valor minimo referencia.\n",
        "\n",
        "Observando a tabela abaixo constata se que 545 tipos diferentes de vinhos estão disponiveis dentro dos valores referencias."
      ],
      "metadata": {
        "id": "aU9K12IAG8OM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfalcool= datasetSemMissings[(datasetSemMissings['alcohol']>=9.50) & (datasetSemMissings['alcohol']<=10.20)]\n",
        "dfalcool['alcohol']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YaV_v-I1PIKz",
        "outputId": "86fb8b7d-e83b-4359-90ee-6541e2ce0f0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1        9.8\n",
              "2        9.8\n",
              "3        9.8\n",
              "7       10.0\n",
              "8        9.5\n",
              "        ... \n",
              "1567    10.1\n",
              "1572     9.5\n",
              "1583     9.8\n",
              "1593     9.5\n",
              "1597    10.2\n",
              "Name: alcohol, Length: 545, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**9. RENOMEANDO O DATASET**\n",
        "\n",
        "De datasetSemMissing para dataframe final (dffinal)"
      ],
      "metadata": {
        "id": "FxW_OhkSHcO3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dffinal=datasetSemMissings\n",
        "dffinal.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "txVZPMq05YsD",
        "outputId": "1d3d8c27-8858-4508-828d-405078b9226e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70          NaN             1.9      0.076   \n",
              "1            7.8              0.88          NaN             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70          NaN             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bcd102e5-d09f-4623-b1dc-d2b7abcb7857\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bcd102e5-d09f-4623-b1dc-d2b7abcb7857')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bcd102e5-d09f-4623-b1dc-d2b7abcb7857 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bcd102e5-d09f-4623-b1dc-d2b7abcb7857');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**10. Definição dos Vinhos Selecionados** \n",
        "\n",
        "\n",
        "TABELA FINAL COM A SELEÇÃO DOS VINHOS DE ALTA QUALIDADE\n",
        "\n",
        "Esta tabela define os diferentes tipos de vinhos selecionados para o cliente.\n",
        "\n",
        "Estes vinhos respeitam as caracteristicas fisica-quimica fornecida pelo especialista do distribuidor.\n",
        "\n",
        "Dos 1599 tipos diferentes de vinhos analisados, somente 17 tipos atendem aos pré requisitos do especialista do  distribuidor."
      ],
      "metadata": {
        "id": "thRGS5VTIELM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dffinal[(dffinal['quality']>=6) &\n",
        "                    (dffinal['quality']<=7) &\n",
        "                    (dffinal['fixed acidity']>=5.8) &\n",
        "                    (dffinal['fixed acidity']<=8.9) &\n",
        "                    (dffinal['residual sugar']>=2) & \n",
        "                    (dffinal['residual sugar']<=3.7) &\n",
        "                    (dffinal['free sulfur dioxide']>=8.5) & \n",
        "                    (dffinal['free sulfur dioxide']<=19.3) & \n",
        "                    (dffinal['total sulfur dioxide']>=55.7) & \n",
        "                    (dffinal['total sulfur dioxide']<=98.2) &\n",
        "                    (dffinal['alcohol']>=9.5) &\n",
        "                    (dffinal['alcohol']<=10.2)] "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "w8AIjsh1GQT8",
        "outputId": "57a40654-a8c3-4708-eeb4-01e76a7d7fd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "108             8.0             0.330         0.53             2.5      0.091   \n",
              "211             8.0             0.725         0.24             2.8      0.083   \n",
              "232             8.5             0.370         0.20             2.8      0.090   \n",
              "790             7.4             0.370         0.43             2.6      0.082   \n",
              "792             7.1             0.610         0.02             2.5      0.081   \n",
              "894             7.2             0.630         0.03             2.2      0.080   \n",
              "1139            8.2             0.510         0.24             2.0      0.079   \n",
              "1216            7.9             0.570         0.31             2.0      0.079   \n",
              "1275            8.0             0.715         0.22             2.3      0.075   \n",
              "1278            8.0             0.715         0.22             2.3      0.075   \n",
              "1283            8.7             0.630         0.28             2.7      0.096   \n",
              "1315            7.5             0.630         0.27             2.0      0.083   \n",
              "1318            7.5             0.630         0.27             2.0      0.083   \n",
              "1329            7.4             0.600         0.26             2.1      0.083   \n",
              "1330            7.4             0.600         0.26             2.1      0.083   \n",
              "1368            8.0             0.770         0.32             2.1      0.079   \n",
              "1431            7.6             0.430         0.31             2.1      0.069   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "108                  18.0                  80.0  0.99760  3.37       0.80   \n",
              "211                  10.0                  62.0  0.99685  3.35       0.56   \n",
              "232                  18.0                  58.0  0.99800  3.34       0.70   \n",
              "790                  18.0                  82.0  0.99708  3.33       0.68   \n",
              "792                  17.0                  87.0  0.99745  3.48       0.60   \n",
              "894                  17.0                  88.0  0.99745  3.53       0.58   \n",
              "1139                 16.0                  86.0  0.99764  3.34       0.64   \n",
              "1216                 10.0                  79.0  0.99677  3.29       0.69   \n",
              "1275                 13.0                  81.0  0.99688  3.24       0.54   \n",
              "1278                 13.0                  81.0  0.99688  3.24       0.54   \n",
              "1283                 17.0                  69.0  0.99734  3.26       0.63   \n",
              "1315                 17.0                  91.0  0.99616  3.26       0.58   \n",
              "1318                 17.0                  91.0  0.99616  3.26       0.58   \n",
              "1329                 17.0                  91.0  0.99616  3.29       0.56   \n",
              "1330                 17.0                  91.0  0.99616  3.29       0.56   \n",
              "1368                 16.0                  74.0  0.99656  3.27       0.50   \n",
              "1431                 13.0                  74.0  0.99580  3.26       0.54   \n",
              "\n",
              "      alcohol  quality  \n",
              "108       9.6        6  \n",
              "211      10.0        6  \n",
              "232       9.6        6  \n",
              "790       9.7        6  \n",
              "792       9.7        6  \n",
              "894       9.8        6  \n",
              "1139      9.5        6  \n",
              "1216      9.5        6  \n",
              "1275      9.5        6  \n",
              "1278      9.5        6  \n",
              "1283     10.2        6  \n",
              "1315      9.8        6  \n",
              "1318      9.8        6  \n",
              "1329      9.8        6  \n",
              "1330      9.8        6  \n",
              "1368      9.8        6  \n",
              "1431      9.9        6  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f2ef5706-359b-4a10-83d2-e7aa1aef3e88\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.330</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.091</td>\n",
              "      <td>18.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.99760</td>\n",
              "      <td>3.37</td>\n",
              "      <td>0.80</td>\n",
              "      <td>9.6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.725</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0.083</td>\n",
              "      <td>10.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0.99685</td>\n",
              "      <td>3.35</td>\n",
              "      <td>0.56</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>8.5</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.20</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0.090</td>\n",
              "      <td>18.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0.99800</td>\n",
              "      <td>3.34</td>\n",
              "      <td>0.70</td>\n",
              "      <td>9.6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>790</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.082</td>\n",
              "      <td>18.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.99708</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.7</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>792</th>\n",
              "      <td>7.1</td>\n",
              "      <td>0.610</td>\n",
              "      <td>0.02</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.081</td>\n",
              "      <td>17.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.99745</td>\n",
              "      <td>3.48</td>\n",
              "      <td>0.60</td>\n",
              "      <td>9.7</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.080</td>\n",
              "      <td>17.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.99745</td>\n",
              "      <td>3.53</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>8.2</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.079</td>\n",
              "      <td>16.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.99764</td>\n",
              "      <td>3.34</td>\n",
              "      <td>0.64</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1216</th>\n",
              "      <td>7.9</td>\n",
              "      <td>0.570</td>\n",
              "      <td>0.31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.079</td>\n",
              "      <td>10.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>0.99677</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0.69</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1275</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.715</td>\n",
              "      <td>0.22</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.075</td>\n",
              "      <td>13.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0.99688</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.54</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1278</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.715</td>\n",
              "      <td>0.22</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.075</td>\n",
              "      <td>13.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0.99688</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.54</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1283</th>\n",
              "      <td>8.7</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.7</td>\n",
              "      <td>0.096</td>\n",
              "      <td>17.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.99734</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.63</td>\n",
              "      <td>10.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1315</th>\n",
              "      <td>7.5</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.27</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.083</td>\n",
              "      <td>17.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.99616</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1318</th>\n",
              "      <td>7.5</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.27</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.083</td>\n",
              "      <td>17.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.99616</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1329</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.26</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.083</td>\n",
              "      <td>17.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.99616</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.26</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.083</td>\n",
              "      <td>17.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.99616</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.770</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.079</td>\n",
              "      <td>16.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.99656</td>\n",
              "      <td>3.27</td>\n",
              "      <td>0.50</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1431</th>\n",
              "      <td>7.6</td>\n",
              "      <td>0.430</td>\n",
              "      <td>0.31</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.069</td>\n",
              "      <td>13.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.99580</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.54</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2ef5706-359b-4a10-83d2-e7aa1aef3e88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f2ef5706-359b-4a10-83d2-e7aa1aef3e88 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f2ef5706-359b-4a10-83d2-e7aa1aef3e88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**11. VISUALIZAÇÕES**\n",
        "\n",
        "Utilizando-se da visualização dos graficos, junstificamos nessa etapa ao cliente final os reais motivos pelo qual desconsideramos as variáveis com baixo desvio padrão na analise da Ciencia de Dados\n",
        "\n",
        "Os graficos abaixo, referem-se as informações genericas complementares e que ajudam aos cientistas de dados quantificarem aos especialistas parametros por eles muitas vezes não entendidos e que são pertinentes dentro das areas afins."
      ],
      "metadata": {
        "id": "9IBOiJ_YNoPY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Durante o entendimento do problema levantado pelos especialistas, percebemos uma grande preocupação entre o pH e a densidade do vinho. Como exemplo demonstramos essa relação entre os 1599  tipos diferentes de vinhos, descritos no dataframe, entre pares destas variaveis.\n"
      ],
      "metadata": {
        "id": "lVZKTZ6x56_g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "O pH e a densidade são duas variáveis com pequeno desvio padrão. Isto é mostrado pela alta densidade de dados, dentro de uma area do gráfico. \n"
      ],
      "metadata": {
        "id": "pv2w-rZNR632"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='pH', y='density', data=dffinal)"
      ],
      "metadata": {
        "id": "A5T72L3j7JGw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observando o grafico abaixo, inversamente entre o que foi apresentado anteriormenteos, os atributos dioxido de enxofre livers e dioxido de enxofre total possuem altissimo desvio padrão em ambas as variáveis. \n",
        "\n",
        "Observa se uma tendencia de dispersão linear positiva e a formação de pontos de outliers entre essas variáveis.\n",
        "\n",
        " "
      ],
      "metadata": {
        "id": "3BqfCcDk8dXs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='free sulfur dioxide', y='total sulfur dioxide', data=dffinal)"
      ],
      "metadata": {
        "id": "6Q-B3BuP8UEf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**12. Considerações Finais**"
      ],
      "metadata": {
        "id": "noA2_i9Uopjj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A ciência de dados permite mostrar para efeito de analise, independente da especialidade , as relações entre os pares de  atributos de um dataframe. No caso do cliente, o distribuidor de vinhos e seus especialistas, foi disponibilizado toda a relação entre  os pares de atributos apresentados nesta analise.\n",
        "\n",
        "Mostra-se toda a relação fisica-quimica  entre todas as variáveis disponivel neste dataframe. Estes graficos irão orientar os especialistas de uma forma mais assertiva quando da escolha da aquisição de vinhos para vendas em seus estabelecimentos.\n",
        "\n",
        "Um dos facilitadores para analise, ainda que nao sejam cientistas de dados, são os histogramas na diagonal dos conjuntos de dados. Eles permitem verificar a frequencia de cada metrica da composição quimica, de cada par de atributo analisado. Facilitando o entendimento dos especialistas."
      ],
      "metadata": {
        "id": "Xp1dqXQcHUn6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(dffinal);"
      ],
      "metadata": {
        "id": "yhFD5RjHaPyT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CLASSIFICAÇÃO**"
      ],
      "metadata": {
        "id": "Ckdm2wIONIPt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Preparação Treino e Teste**\n"
      ],
      "metadata": {
        "id": "tZRm7eEyKRtu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparação dos dados\n",
        "\n",
        "# Separação em bases de treino e teste (holdout)\n",
        "array =Qldvinhos.values\n",
        "X = array[:,0:11] # atributos - Total de 12 ( de 0 a 11)\n",
        "y = array[:,11] # classe (quality) - somente quality\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=7) # faz a divisão\n",
        "\n",
        "# Criando os folds para a validação cruzada\n",
        "num_particoes = 10 # número de folds da validação cruzada\n",
        "kfold = KFold(n_splits=num_particoes, shuffle=True, random_state=7) # faz o particionamento em 10 folds"
      ],
      "metadata": {
        "id": "pvSOUzgcKizC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Modelagem**"
      ],
      "metadata": {
        "id": "ykKr6NjyLCw2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Modelagem\n",
        "\n",
        "# Definindo uma seed global para esta célula de código\n",
        "np.random.seed(7) \n",
        "\n",
        "# Listas para armazenar os modelos, os resultados e os nomes dos modelos\n",
        "models = []\n",
        "results = []\n",
        "names = []\n",
        "\n",
        "# Preparando os modelos e adicionando-os em uma lista\n",
        "models.append(('KNN', KNeighborsClassifier()))\n",
        "models.append(('CART', DecisionTreeClassifier()))\n",
        "models.append(('NB', GaussianNB()))\n",
        "models.append(('SVM', SVC()))\n",
        "\n",
        "# Avaliando um modelo por vez\n",
        "for name, model in models:\n",
        "  cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='accuracy')\n",
        "  results.append(cv_results)\n",
        "  names.append(name)\n",
        "  msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std()) # média e desvio padrão dos 10 resultados da validação cruzada\n",
        "  print(msg)\n",
        "\n",
        "# Boxplot de comparação dos modelos\n",
        "fig = plt.figure() \n",
        "fig.suptitle('Comparação da Acurácia dos Modelos') \n",
        "ax = fig.add_subplot(111) \n",
        "plt.boxplot(results) \n",
        "ax.set_xticklabels(names) \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "z8xLZXBtLBRq",
        "outputId": "5fe5877a-987b-43f3-f130-ab8fcff024a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN: 0.499656 (0.040829)\n",
            "CART: 0.611436 (0.066729)\n",
            "NB: 0.541831 (0.035975)\n",
            "SVM: 0.499606 (0.051481)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando um modelo com todo o conjunto de treino\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Fazendo as predições com o conjunto de teste\n",
        "predictions = model.predict(X_test) \n",
        "\n",
        "# Estimando a acurácia no conjunto de teste\n",
        "print(accuracy_score(y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icRj7hiWLTna",
        "outputId": "97d77f1a-e3fd-4de5-8b43-3e2746d4faad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.684375\n"
          ]
        }
      ]
    }
  ]
}