Machine_Learning_Qualidade_Vinhos.ipynb

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      "source": [
        "##**POS GRADUAÇÃO EM CIÊNCIAS DE DADOS -  PUC RIO**\n",
        "\n",
        "##**MVP SPRINT 2**\n",
        "\n",
        "##**PROJETO - Machine Learning - Classificação Qualitativa - Vinhos do NORTE DE PORTUGAL**\n",
        "\n",
        "##**ALUNO: SALIM JORGE NABBOUT**"
      ],
      "metadata": {
        "id": "0pvbRc5CDqc4"
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    },
    {
      "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",
        "A primeira fase de nosso trabalhos foi identificar, dentro das caracteristicas quimicas, os melhores vinhos. A seleção das caracteristicas qualitativa dos diferentes vinhos foi determinada pela avaliação de grandes Somelies, de diferentes nacionalidades.\n",
        "\n",
        "Ao final de nosso trabalho, dos 1599 diferentes tipos de vinhos, identificamos aproximadamente 17 vinhos que apresentavam a qualidade acima de 7, em uma escala de 1 a 10.\n",
        "\n",
        "\n",
        "A segunda etapa de nosso trabalho será desenvolver um modelo em **Machine Learning,** que seja capaz atravez de um leitor de um portal de RFID ler as caracteristicas quimica dos vinhos pretendidos por essa distribuidora de forma automatica e enviando uma resposta , atraves de uma interface em dispositivo mobile.\n",
        "\n",
        "A interface mobile, será desenvolvida por outra equipe. A saida de nosso modela será disponibilizado em um Banco de dados que fará a comunicação com os dispositivos pré selecionados em MQTT. Configurando a terceira etapa deste projeto.\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"
      ],
      "metadata": {
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      "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": 1,
      "metadata": {
        "id": "-op5aakkn_UN"
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      "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 mean_squared_error # métrica de avaliação MSE\n",
        "from sklearn.linear_model import LinearRegression # algoritmo Regressão Linear\n",
        "from sklearn.linear_model import Ridge # algoritmo Regularização Ridge\n",
        "from sklearn.linear_model import Lasso # algoritmo Regularização Lasso\n",
        "from sklearn.neighbors import KNeighborsRegressor # algoritmo KNN\n",
        "from sklearn.tree import DecisionTreeRegressor # algoritmo Árvore de Regressão\n",
        "from sklearn.svm import SVR # algoritmo SVM\n",
        "\n",
        "# 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",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "from sklearn.ensemble import ExtraTreesClassifier # ExtraTrees, para a Importância de Atributos\n",
        "from sklearn.feature_selection import SelectKBest # para a Seleção Univariada\n",
        "from sklearn.feature_selection import f_classif # para o teste ANOVA da Seleção Univariada\n",
        "from sklearn.feature_selection import RFE # para a Eliminação Recursiva de Atributos\n",
        "from sklearn.linear_model import LogisticRegression # Regressão Logística, para a Eliminação Recursiva de Atributos\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\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": 2,
      "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": 3,
      "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": "372fb349-9085-4a7a-f175-4ad93ef9d338"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "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": 267
        },
        "id": "OXyim8Guptjp",
        "outputId": "2868fb79-23df-407b-c3df-785cb2ca6f2b"
      },
      "execution_count": 5,
      "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  "
            ],
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              "    <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",
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              "      <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",
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              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
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              "      <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",
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              "      <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",
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              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
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              "      <td>17.0</td>\n",
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              "      <td>0.9980</td>\n",
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              "      <td>9.8</td>\n",
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              "      <td>0.9978</td>\n",
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              "    domScope.querySelector('#df-99704a75-487a-42e7-a66c-e4a33b8828bc button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-093f3a1d-335a-4468-b06f-fb98f91dfed1 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-093f3a1d-335a-4468-b06f-fb98f91dfed1');\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": 5
        }
      ]
    },
    {
      "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": 267
        },
        "id": "FxsZE3k_p7JB",
        "outputId": "e2a2a299-d148-4eb7-ea6b-a3a8016d020b"
      },
      "execution_count": 6,
      "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  "
            ],
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              "\n",
              "\n",
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              "  <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",
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              "  </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-1a947895-8e3c-4be1-b612-2e6a70a21fea')\"\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",
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              "      </button>\n",
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              "    <div id=\"df-1e7d03d9-eeb9-4a7e-913f-5b64d91ca0fb\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1e7d03d9-eeb9-4a7e-913f-5b64d91ca0fb')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1e7d03d9-eeb9-4a7e-913f-5b64d91ca0fb button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1a947895-8e3c-4be1-b612-2e6a70a21fea 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-1a947895-8e3c-4be1-b612-2e6a70a21fea');\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": 6
        }
      ]
    },
    {
      "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": "9e80a126-053e-47eb-81d1-254f6bea16e4"
      },
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "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": "3a28cdc5-ff28-4ac0-b5de-c9fb2710a1e6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "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": "440770f4-47f1-45dd-ddb7-7be8cd7eb818"
      },
      "execution_count": 9,
      "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": 9
        }
      ]
    },
    {
      "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": 399
        },
        "id": "3h8hXOi_q69V",
        "outputId": "cca17548-e257-464c-9703-339ddf378fad"
      },
      "execution_count": 10,
      "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  "
            ],
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              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
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              "      <th>residual sugar</th>\n",
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              "  <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",
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              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
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              "      <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",
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              "      <td>46.467792</td>\n",
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              "      <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",
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              "  </tbody>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-19294120-b906-4e5f-8fd6-8f46a0bad240');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "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": 267
        },
        "id": "yloGR4irJSz8",
        "outputId": "1d421cfc-744c-442d-c30d-4f0cae5386f1"
      },
      "execution_count": 11,
      "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  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
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              "      <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-d12082b8-f872-4c69-8c4a-817c13f8d172')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-0e066e6b-bfff-4e3c-9f35-959897501a83\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e066e6b-bfff-4e3c-9f35-959897501a83')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-0e066e6b-bfff-4e3c-9f35-959897501a83 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d12082b8-f872-4c69-8c4a-817c13f8d172 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-d12082b8-f872-4c69-8c4a-817c13f8d172');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**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": "53bfee34-aafa-4e38-e753-4b0b58797fdd"
      },
      "execution_count": 12,
      "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": 12
        }
      ]
    },
    {
      "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": 562
        },
        "id": "HW5JyzNBKMzf",
        "outputId": "4a0d0840-93c0-4b8a-b89d-aebc7485e4c9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "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": "750613f6-2b22-4ff3-934f-baf14a3da051"
      },
      "execution_count": 14,
      "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": 14
        }
      ]
    },
    {
      "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",
        "outputId": "05f4c4a8-f2b9-495b-8116-2505d8f9977c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4.5 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": "90384ba5-59cd-4369-a343-b0b823b2a79e"
      },
      "execution_count": 16,
      "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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4.4 Carga do Dataset Tratado\n",
        "\n",
        "Elaboração do Modelo Machine learning - Dataset Tratado"
      ],
      "metadata": {
        "id": "ERxyCgReFDc9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasetSemMissings.head()"
      ],
      "metadata": {
        "id": "qhjiyvYSibKs",
        "outputId": "442d2e14-a28f-4e21-ba03-3d0fbacd20c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        }
      },
      "execution_count": 17,
      "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  "
            ],
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              "      <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",
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              "  </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",
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              "              title=\"Suggest charts.\"\n",
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              "<style>\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f94831ee-edca-4b84-a7be-1bddf93a7d97 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-f94831ee-edca-4b84-a7be-1bddf93a7d97');\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": [
        "# 5.0 Preparação dos dados\n",
        "\n",
        "Separação Treino e Teste"
      ],
      "metadata": {
        "id": "ehzsay_7E5Ic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparação dos dados\n",
        "\n",
        "# Separação em bases de treino e teste (holdout)\n",
        "array = datasetSemMissings.values\n",
        "X = array[:,0:11] # atributos\n",
        "y = array[:,11] # classe (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",
        "# 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": "GxMfY0MHienh"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6.0 Modelagem"
      ],
      "metadata": {
        "id": "keNdNHDAJEsR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 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(('LR', LinearRegression()))\n",
        "models.append(('Ridge', Ridge()))\n",
        "models.append(('Lasso', Lasso()))\n",
        "models.append(('KNN', KNeighborsRegressor()))\n",
        "models.append(('CART', DecisionTreeRegressor()))\n",
        "models.append(('SVM', SVR()))\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='neg_mean_squared_error')\n",
        "  results.append(cv_results)\n",
        "  names.append(name)\n",
        "  # imprime MSE, desvio padrão do MSE e RMSE dos 10 resultados da validação cruzada\n",
        "  msg = \"%s: MSE %0.2f (%0.2f) - RMSE %0.2f\" % (name, abs(cv_results.mean()), cv_results.std(), np.sqrt(abs(cv_results.mean())))\n",
        "  print(msg)\n",
        "\n",
        "# Comparação dos Modelos - Boxplot\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Comparação do MSE dos Modelos')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.boxplot(results)\n",
        "ax.set_xticklabels(names)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mACeJBspikqb",
        "outputId": "580a2392-194d-43ad-8d41-c4a78c47dc56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR: MSE 0.44 (0.07) - RMSE 0.66\n",
            "Ridge: MSE 0.44 (0.07) - RMSE 0.66\n",
            "Lasso: MSE 0.65 (0.11) - RMSE 0.81\n",
            "KNN: MSE 0.60 (0.11) - RMSE 0.77\n",
            "CART: MSE 0.65 (0.11) - RMSE 0.81\n",
            "SVM: MSE 0.57 (0.11) - RMSE 0.75\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6.1Criando o Modelo - BoxPlot\n",
        "\n",
        "Criando modelo a partir dos resultados declarados nos Boxplot. Os  resultados mostram LR ou LR com Ridge com os melhores resultados ( MSE 0.44/RMSE 0.66 )"
      ],
      "metadata": {
        "id": "Mc4QlsTNH-6b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando um modelo com todo o conjunto de treino\n",
        "model = LinearRegression()\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 o MSE e o RMSE no conjunto de teste\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "print(\"MSE %0.2f\" % mse)\n",
        "print(\"RMSE %0.2f\" % np.sqrt(abs(mse)))"
      ],
      "metadata": {
        "id": "_ZapR46al5zY",
        "outputId": "32370114-95a2-4f46-a9b8-aecf1dc95079",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE 0.38\n",
            "RMSE 0.61\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7.0 Preparação dos dados - Treino e Teste"
      ],
      "metadata": {
        "id": "VIY82PDxeIzS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparação dos dados\n",
        "\n",
        "# Separação em bases de treino e teste (holdout)\n",
        "array = dataset.values\n",
        "X = array[:,0:11] # atributos\n",
        "y = array[:,11] # quality(target)"
      ],
      "metadata": {
        "id": "yjBxn_1Oduyc"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8.0 Seleção dos Atributos"
      ],
      "metadata": {
        "id": "kWa5JJqkFcla"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# SelectKBest\n",
        "\n",
        "# Seleção de atributos com SelectKBest\n",
        "best_var = SelectKBest(score_func=f_classif, k=4)\n",
        "\n",
        "# Executa a função de pontuação em (X, y) e obtém os atributos selecionados\n",
        "fit = best_var.fit(X, y)\n",
        "\n",
        "# Reduz X para os atributos selecionados\n",
        "features = fit.transform(X)\n",
        "\n",
        "# Resultados\n",
        "print('\\nNúmero original de atributos:', X.shape[1])\n",
        "print('\\nNúmero reduzido de atributos:', features.shape[1])\n",
        "\n",
        "# Exibe os atributos orginais\n",
        "print(\"\\nAtributos Originais:\", dataset.columns[0:13])\n",
        "\n",
        "# Exibe as pontuações de cada atributos e os 4 escolhidas (com as pontuações mais altas)\n",
        "np.set_printoptions(precision=3) # 3 casas decimais\n",
        "print(\"\\nScores dos Atributos Originais:\", fit.scores_)\n",
        "print(\"\\nAtributos Selecionados:\", best_var.get_feature_names_out(input_features=dataset.columns[0:11]))"
      ],
      "metadata": {
        "id": "Wx6nHy5xeTBv",
        "outputId": "fc410dc1-7ffc-4235-afcd-828fb7da8c6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Número original de atributos: 11\n",
            "\n",
            "Número reduzido de atributos: 4\n",
            "\n",
            "Atributos Originais: 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')\n",
            "\n",
            "Scores dos Atributos Originais: [  6.283  60.914  19.691   1.053   6.036   4.754  25.479  13.396   4.342\n",
            "  22.273 115.855]\n",
            "\n",
            "Atributos Selecionados: ['volatile acidity' 'total sulfur dioxide' 'sulphates' 'alcohol']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 9.0 Eliminação Recursiva dos Atributos"
      ],
      "metadata": {
        "id": "6kN67pfnb74n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Criação do modelo\n",
        "modelo = LogisticRegression(max_iter=200)\n",
        "\n",
        "# Eliminação Recursiva de Atributos\n",
        "rfe = RFE(modelo, n_features_to_select=4)\n",
        "fit = rfe.fit(X, y)\n",
        "\n",
        "# Print dos resultados\n",
        "print(\"Atributos Originais:\", dataset.columns[0:13])\n",
        "\n",
        "# Exibe os atributos selecionados (marcados como True em \"Atributos Selecionados\"\n",
        "# e com valor 1 em \"Ranking dos Atributos\")\n",
        "print(\"\\nAtributos Selecionados: %s\" % fit.support_)\n",
        "print(\"\\nRanking de atributos: %s\" % fit.ranking_)\n",
        "print(\"\\nQtd de melhores Atributos: %d\" % fit.n_features_)\n",
        "print(\"\\nNomes dos Atributos Selecionados: %s\" % fit.get_feature_names_out(input_features=dataset.columns[0:11]))"
      ],
      "metadata": {
        "id": "Dft7I4mUfVzY",
        "outputId": "2fecac71-8363-4c94-993e-8f802a78ab22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Atributos Originais: 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')\n",
            "\n",
            "Atributos Selecionados: [False  True False False  True False False False  True  True False]\n",
            "\n",
            "Ranking de atributos: [5 1 4 6 1 7 8 2 1 1 3]\n",
            "\n",
            "Qtd de melhores Atributos: 4\n",
            "\n",
            "Nomes dos Atributos Selecionados: ['volatile acidity' 'chlorides' 'pH' 'sulphates']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importância de Atributos com ExtraTrees\n",
        "\n",
        "# Criação do modelo para seleção de atributos\n",
        "modelo = ExtraTreesClassifier(n_estimators=100)\n",
        "modelo.fit(X,y)\n",
        "\n",
        "# Exibe os atributos orginais\n",
        "print(\"\\nAtributos Originais:\", dataset.columns[0:11])\n",
        "\n",
        "# Exibe a pontuação de importância para cada atributo (quanto maior a pontuação, mais importante é o atributo).\n",
        "print(modelo.feature_importances_)"
      ],
      "metadata": {
        "id": "-tWksPjPf0C_",
        "outputId": "45a55118-9241-4816-f78c-c595da659431",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Atributos Originais: Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
            "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
            "       'pH', 'sulphates', 'alcohol'],\n",
            "      dtype='object')\n",
            "[0.078 0.098 0.08  0.081 0.076 0.075 0.1   0.087 0.077 0.101 0.148]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 10.0 Separação em conjunto de TREINO e conjunto de TESTE com holdout\n",
        "\n",
        "O programa rodou sem a validação Cruzada e não foi observado nenhum tipo de comportamento diferente no resultado final do aprendizado.\n",
        "\n",
        "Como não houve alteração , pelo menos nessa aplicação, foi habilitado o comendo da Validação Cruzada e o programa roda com esta função"
      ],
      "metadata": {
        "id": "TIk4USSmgdje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_size = 0.20 # tamanho do conjunto de teste\n",
        "seed = 7 # semente aleatória\n",
        "\n",
        "# Separação em conjuntos de treino e teste\n",
        "array = dataset.values\n",
        "X = array[:,0:11]\n",
        "y = array[:,11]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "    test_size=test_size, shuffle=True, random_state=seed, stratify=y) # holdout com estratificação\n",
        "\n",
        "# Parâmetros e partições da validação cruzada\n",
        "scoring = 'accuracy'\n",
        "num_particoes = 10\n",
        "kfold = StratifiedKFold(n_splits=num_particoes, shuffle=True, random_state=seed) # validação cruzada com estratificação"
      ],
      "metadata": {
        "id": "L4YVqXxPglVm"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 11.0 Modelagem e Inferência"
      ],
      "metadata": {
        "id": "hkwsxygag4hI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 11.1 Criação e avaliação de modelos: linha base"
      ],
      "metadata": {
        "id": "fXpAcCtDg8bA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(7) # definindo uma semente global\n",
        "\n",
        "# Lista que armazenará os modelos\n",
        "models = []\n",
        "\n",
        "# Criando os modelos e adicionando-os na lista de modelos\n",
        "models.append(('LR', LogisticRegression(max_iter=200)))\n",
        "models.append(('KNN', KNeighborsClassifier()))\n",
        "models.append(('CART', DecisionTreeClassifier()))\n",
        "models.append(('NB', GaussianNB()))\n",
        "models.append(('SVM', SVC()))\n",
        "\n",
        "# Definindo os parâmetros do classificador base para o BaggingClassifier\n",
        "base = DecisionTreeClassifier()\n",
        "num_trees = 100\n",
        "max_features = 3\n",
        "\n",
        "# Criando os modelos para o VotingClassifier\n",
        "bases = []\n",
        "model1 = LogisticRegression(max_iter=200)\n",
        "bases.append(('logistic', model1))\n",
        "model2 = DecisionTreeClassifier()\n",
        "bases.append(('cart', model2))\n",
        "model3 = SVC()\n",
        "bases.append(('svm', model3))\n",
        "\n",
        "# Criando os ensembles e adicionando-os na lista de modelos\n",
        "models.append(('Bagging', BaggingClassifier(base_estimator=base, n_estimators=num_trees)))\n",
        "models.append(('RF', RandomForestClassifier(n_estimators=num_trees, max_features=max_features)))\n",
        "models.append(('ET', ExtraTreesClassifier(n_estimators=num_trees, max_features=max_features)))\n",
        "models.append(('Ada', AdaBoostClassifier(n_estimators=num_trees)))\n",
        "models.append(('GB', GradientBoostingClassifier(n_estimators=num_trees)))\n",
        "models.append(('Voting', VotingClassifier(bases)))\n",
        "\n",
        "# Listas para armazenar os resultados\n",
        "results = []\n",
        "names = []\n",
        "\n",
        "# Avaliação dos modelos\n",
        "for name, model in models:\n",
        "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n",
        "    results.append(cv_results)\n",
        "    names.append(name)\n",
        "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
        "    print(msg)\n",
        "\n",
        "# Boxplot de comparação dos modelos\n",
        "fig = plt.figure(figsize=(15,10))\n",
        "fig.suptitle('Comparação dos Modelos')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.boxplot(results)\n",
        "ax.set_xticklabels(names)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "hZ8XqWAihEqP",
        "outputId": "3995bfe1-9d45-495f-b38c-a46dd2bb635a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR: 0.581730 (0.035900)\n",
            "KNN: 0.502768 (0.043741)\n",
            "CART: 0.589505 (0.028259)\n",
            "NB: 0.559787 (0.037451)\n",
            "SVM: 0.494131 (0.042979)\n",
            "Bagging: 0.671629 (0.021935)\n",
            "RF: 0.679454 (0.027095)\n",
            "ET: 0.675535 (0.019397)\n",
            "Ada: 0.495755 (0.089910)\n",
            "GB: 0.635642 (0.027686)\n",
            "Voting: 0.591086 (0.034054)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 11.2 Avaliando Modelos:\n",
        "\n",
        "Dados Originais - Padronizados - Normalizados\n"
      ],
      "metadata": {
        "id": "RP2OhWvjhb13"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definindo a Semente Global para este Bloco\n",
        "np.random.seed(7)\n",
        "\n",
        "# Listas para armazenar os armazenar os pipelines e os resultados para todas as visões do dataset\n",
        "pipelines = []\n",
        "results = []\n",
        "names = []\n",
        "\n",
        "\n",
        "# Criando os elementos do pipeline\n",
        "\n",
        "# Algoritmos que serão utilizados\n",
        "reg_log = ('LR', LogisticRegression(max_iter=200))\n",
        "knn = ('KNN', KNeighborsClassifier())\n",
        "cart = ('CART', DecisionTreeClassifier())\n",
        "naive_bayes = ('NB', GaussianNB())\n",
        "svm = ('SVM', SVC())\n",
        "bagging = ('Bag', BaggingClassifier(base_estimator=base, n_estimators=num_trees))\n",
        "random_forest = ('RF', RandomForestClassifier(n_estimators=num_trees, max_features=max_features))\n",
        "extra_trees = ('ET', ExtraTreesClassifier(n_estimators=num_trees, max_features=max_features))\n",
        "adaboost = ('Ada', AdaBoostClassifier(n_estimators=num_trees))\n",
        "gradient_boosting = ('GB', GradientBoostingClassifier(n_estimators=num_trees))\n",
        "voting = ('Voting', VotingClassifier(bases))\n",
        "\n",
        "# Transformações que serão utilizadas\n",
        "standard_scaler = ('StandardScaler', StandardScaler())\n",
        "min_max_scaler = ('MinMaxScaler', MinMaxScaler())\n",
        "\n",
        "\n",
        "# Montando os pipelines\n",
        "\n",
        "# Dataset original\n",
        "pipelines.append(('LR-orig', Pipeline([reg_log])))\n",
        "pipelines.append(('KNN-orig', Pipeline([knn])))\n",
        "pipelines.append(('CART-orig', Pipeline([cart])))\n",
        "pipelines.append(('NB-orig', Pipeline([naive_bayes])))\n",
        "pipelines.append(('SVM-orig', Pipeline([svm])))\n",
        "pipelines.append(('Bag-orig', Pipeline([bagging])))\n",
        "pipelines.append(('RF-orig', Pipeline([random_forest])))\n",
        "pipelines.append(('ET-orig', Pipeline([extra_trees])))\n",
        "pipelines.append(('Ada-orig', Pipeline([adaboost])))\n",
        "pipelines.append(('GB-orig', Pipeline([gradient_boosting])))\n",
        "pipelines.append(('Vot-orig', Pipeline([voting])))\n",
        "\n",
        "# Dataset Padronizado\n",
        "pipelines.append(('LR-padr', Pipeline([standard_scaler, reg_log])))\n",
        "pipelines.append(('KNN-padr', Pipeline([standard_scaler, knn])))\n",
        "pipelines.append(('CART-padr', Pipeline([standard_scaler, cart])))\n",
        "pipelines.append(('NB-padr', Pipeline([standard_scaler, naive_bayes])))\n",
        "pipelines.append(('SVM-padr', Pipeline([standard_scaler, svm])))\n",
        "pipelines.append(('Bag-padr', Pipeline([standard_scaler, bagging])))\n",
        "pipelines.append(('RF-padr', Pipeline([standard_scaler, random_forest])))\n",
        "pipelines.append(('ET-padr', Pipeline([standard_scaler, extra_trees])))\n",
        "pipelines.append(('Ada-padr', Pipeline([standard_scaler, adaboost])))\n",
        "pipelines.append(('GB-padr', Pipeline([standard_scaler, gradient_boosting])))\n",
        "pipelines.append(('Vot-padr', Pipeline([standard_scaler, voting])))\n",
        "\n",
        "# Dataset Normalizado\n",
        "pipelines.append(('LR-norm', Pipeline([min_max_scaler, reg_log])))\n",
        "pipelines.append(('KNN-norm', Pipeline([min_max_scaler, knn])))\n",
        "pipelines.append(('CART-norm', Pipeline([min_max_scaler, cart])))\n",
        "pipelines.append(('NB-norm', Pipeline([min_max_scaler, naive_bayes])))\n",
        "pipelines.append(('SVM-norm', Pipeline([min_max_scaler, svm])))\n",
        "pipelines.append(('Bag-norm', Pipeline([min_max_scaler, bagging])))\n",
        "pipelines.append(('RF-norm', Pipeline([min_max_scaler, random_forest])))\n",
        "pipelines.append(('ET-norm', Pipeline([min_max_scaler, extra_trees])))\n",
        "pipelines.append(('Ada-norm', Pipeline([min_max_scaler, adaboost])))\n",
        "pipelines.append(('GB-norm', Pipeline([min_max_scaler, gradient_boosting])))\n",
        "pipelines.append(('Vot-norm', Pipeline([min_max_scaler, voting])))\n",
        "\n",
        "# Executando os pipelines\n",
        "for name, model in pipelines:\n",
        "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n",
        "    results.append(cv_results)\n",
        "    names.append(name)\n",
        "    msg = \"%s: %.3f (%.3f)\" % (name, cv_results.mean(), cv_results.std()) # formatando para 3 casas decimais\n",
        "    print(msg)\n",
        "\n",
        "# Boxplot de comparação dos modelos\n",
        "fig = plt.figure(figsize=(25,6))\n",
        "fig.suptitle('Comparação dos Modelos - Dataset orginal, padronizado e normalizado')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.boxplot(results)\n",
        "ax.set_xticklabels(names, rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kLI6YtIGhoFi",
        "outputId": "03a91abe-d02d-4d1e-afa5-1abf67d6f388",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 941
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR-orig: 0.582 (0.036)\n",
            "KNN-orig: 0.503 (0.044)\n",
            "CART-orig: 0.590 (0.028)\n",
            "NB-orig: 0.560 (0.037)\n",
            "SVM-orig: 0.494 (0.043)\n",
            "Bag-orig: 0.672 (0.022)\n",
            "RF-orig: 0.679 (0.027)\n",
            "ET-orig: 0.676 (0.019)\n",
            "Ada-orig: 0.496 (0.090)\n",
            "GB-orig: 0.636 (0.028)\n",
            "Vot-orig: 0.591 (0.034)\n",
            "LR-padr: 0.584 (0.041)\n",
            "KNN-padr: 0.586 (0.031)\n",
            "CART-padr: 0.593 (0.025)\n",
            "NB-padr: 0.557 (0.042)\n",
            "SVM-padr: 0.617 (0.032)\n",
            "Bag-padr: 0.682 (0.024)\n",
            "RF-padr: 0.685 (0.033)\n",
            "ET-padr: 0.683 (0.026)\n",
            "Ada-padr: 0.496 (0.090)\n",
            "GB-padr: 0.635 (0.028)\n",
            "Vot-padr: 0.608 (0.036)\n",
            "LR-norm: 0.580 (0.036)\n",
            "KNN-norm: 0.578 (0.033)\n",
            "CART-norm: 0.583 (0.030)\n",
            "NB-norm: 0.557 (0.042)\n",
            "SVM-norm: 0.613 (0.033)\n",
            "Bag-norm: 0.689 (0.030)\n",
            "RF-norm: 0.680 (0.032)\n",
            "ET-norm: 0.673 (0.028)\n",
            "Ada-norm: 0.495 (0.089)\n",
            "GB-norm: 0.640 (0.025)\n",
            "Vot-norm: 0.604 (0.029)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x600 with 1 Axes>"
            ],
            "image/png": 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16e+0CwEAAHJHKE5eqa2tjYqKim4do7q6Wi9cYK/J5LrU2fzTrknkkt+rQF/imkR/p10IAACQO0Jx8ko6nY7q6uoO12cyJ3M6ne6p6vWKpqamWL16daxfvz5GjRoVU6dOjcLCwlxXCwaszq5LmfZog1zp7PzN5HdqyzEA9gZtffo77UIAAIDcEYqTV1KpVEZPzufrnMwrV66MxYsXR319feuysrKyuPzyy2PWrFm5qxgMYHu6Lpl/mr4sk9+r+fo7Feh7Bnpbn/5PuxAAACB3CnJdAWDvWLlyZcyePTuOPPLIePjhh2Pr1q3x8MMPx5FHHhmzZ8+OlStX5rqKAAAAAAAA0OuE4pAHmpqaYvHixTFjxoxYtWpVTJo0KYYOHRqTJk2KVatWxYwZM+KCCy6IpqamXFcVAAAAAAAAetWAHj69oaEhamtr212X6XxeqVSqJ6sIGVm9enXU19fHLbfcEgUFbZ91KSgoiCVLlsTkyZNj9erVMW3atNxUkj3q7JoU8do8ma//uz395brU2WvN5HVG9J/XCvQP2oX0Z85f+jPtQgDYe7QL84+2EtDX9OffNQM6FK+trY2Kioou719dXW2uOvqE9evXR0TEhAkT2l3fsrxlO/qmTK9JlZWVHa7rL9elTF5rZ68zov+8VqB/0C6kP3P+0p9pFwLA3qNdmH+0lYC+pj//rhnQoXg6nY7q6up219XU1ERlZWUsX748ysvLO9wf+oJRo0ZFRMTatWtj0qRJu61fu3Ztm+3omzq7JkVk/pRVf9DZa83kdbYcA2Bv0S6kP3P+0p9pFwLA3qNdmH+0lYC+pj//rhnQoXgqldrj0wjl5eWeoqLPmzp1apSVlcWyZcti1apVbYZQb25ujqqqqhgzZkxMnTo1h7VkTzK5Jk2ZMqWXatOz9vRa8+V1Av2HdiH9mfOX/ky7MKKpqSlWr14d69evj1GjRsXUqVOjsLAw19UCoB/SLsw/2kpAX9Off9cM6FAc8kVhYWFcfvnlMXv27Jg5c2YsWbIkJkyYEGvXro2qqqq4/fbbY8WKFW6sAGRo0KuvxDEjC6Jk07qI5wv2vMNeULJpXRwzsiAGvfpKr5QHAOTeypUrY/HixVFfX9+6rKysLC6//PKYNWtW7ioGAAC0ysW9wgj3C/c2oTjkiVmzZsWKFSti8eLFMXny5NblY8aMiRUrVrihApCF4m3PxpqFQyMeXBjxYO+UWR4RaxYOjZptz0bE5D1tDgD0cytXrozZs2fHjBkz4pZbbml9sHnZsmUxe/Zs/48DAIA+Ihf3CiPcL9zbhOKQR2bNmhVnnnmmofcAuumVoYfGxO9ti5tvvjnKe2mem5ra2pgzZ05cd9qhvVIeAJA7TU1NsXjx4pgxY0abKbAmTZoUq1atipkzZ8YFF1wQZ555pv/PAQBAjuXiXmGE+4V7m1Ac8kxhYWFMmzYt19UA6NeSfYrj0Q3N0bj/+IjRR/dKmY0bmuPRDc2R7FPcK+VBvmtoaIja2tp21zU2NkZ9fX2UlZVFSUlJh8dIp9ORSqV6qorQoc7O35qamjZ/d8T527etXr066uvr45ZbbmkNxFsUFBTEkiVLYvLkybF69Wr/v6PX1NXVxdatW7PeL9PrUkdKS0tj3LhxXdoXAKA35OJeYYT7hXubUBwAAMg7tbW1UVFR0a1jVFdXx8SJE/dSjSBzmZy/lZWVna53/vZt69evj4iICRMmtLu+ZXnLduROLuaPzMXckXV1dTF+/PhuHWNP16XOrFu3TjAOAECPEooDAAB5J51OR3V1dbvrampqorKyMpYvXx7l5eWdHoPel4uein2tl2Jn5282Ix3Qd40aNSoiItauXRuTJk3abf3atWvbbEfu5GL+yFzMHdly3d3T78b2ZHpdak/L7+SuXPeB/KddCMDeJBQHAADyTiqV2mMv2fLycj1p+5hc9lTsS70U93T+TpkypRdrQ0+YOnVqlJWVxbJly9rMKR4R0dzcHFVVVTFmzJiYOnVqDmtJRG7mj8zl3JFd/d3ougTsbdqFAOxtQnEAAAD6hFz0VNRLkVwoLCyMyy+/PGbPnh0zZ86MJUuWxIQJE2Lt2rVRVVUVt99+e6xYsSIKCwtzXdUBLxfzR5o7EkC7EIC9TygOAABAn6KnIgPBrFmzYsWKFbF48eKYPPlfQ2SPGTMmVqxYEbNmzcph7QCgb9AuBGBvEYoDAMAA0dDQELW1tR2uz6RHRTqdjlQq1VNVBBhQZs2aFWeeeWasXr061q9fH6NGjYqpU6fqIQ6wF3XWBs60R7E2MAD0f0JxAAAYIGpra6OioqJbx6iurjYPN8BeVFhYGNOmTct1NQDyljYwABAhFAcAgAEjnU5HdXV1h+tb5tDrbN6+dDrdU9UDAIC9rrM2cCbt35ZjADBwNTQ0RETEmjVrst4301FJ2lNTU5N1eXRMKA4AAANEKpXKqIdLV+ftAwCAviaTNrD2LwCdaZmGY8GCBTkpv7S0NCfl5huhOAAAAAAAAEA7Zs6cGRGvjRySSqWy2jfTUUk6UlpaGuPGjct6P3YnFAcAGODq6upi69atWe3TMnxTV4dx0qAHAAAAoD8YPnx4zJ8/v1vHMCpJ7gnFAQB20dV5gvrjHEF1dXUxfvz4Lu9fWVnZ5X3XrVsnGAcAAAAAepxQHABgF7mcJ6i35whq6SGe7RBO3X0AoLKyMuve6UD+G/TqK3HMyIIo2bQu4vmCXimzZNO6OGZkQQx69ZVeKQ/oXwbKw5K5uP5GuAbnUkNDQ+v/e3aV6fnblSFkATrS2XUpk9Hq+ss1qbPXGZFfrxX6GqE4AMAuujpPUH+eI6grQzhNmTKlh2oDDFTF256NNQuHRjy4MOLB3imzPCLWLBwaNduejYjJvVMo0G8MlIclc3H9jXANzqXa2tqoqKjo1jGqq6sNA0uP8bDkwJPJdamz0er6yzUp0+tvPrxW6GuE4gAAu+juPEHmCOqbujJ3ekT/nD89F6/VPPHsDa8MPTQmfm9b3HzzzVGeTvdKmTW1tTFnzpy47rRDe6U8oH8ZKA9L5uL6G+EanEvpdDqqq6vbXZfp+ZvuxXNlTwZSW3+g8LDkwNPZdSmTESz60jWpM529zoj8eq3Q1wjFAQDIe92dOz2i/8yfnsvXmot54rtyA9TNz74r2ac4Ht3QHI37j48YfXSvlNm4oTke3dAcyT7FvVIe0L8MlIclc3H9jXANzqVUKrXHc7O/nL8Dqa0/kHhYcuDZ03UpX0ary+T6my+vlf4rXx82y/tQPF8/OAAAMtfVudMj+t/86bl4rbmaJ767N0Dd/AQA6P8GUlt/IPGwJEBu5PPDZnkdiufzBwcAQPa62tulPz6lPRBea1dvgLr5CQCQfwZC+xcAelo+P2yW16F4Pn9wAADAa7pyA9TNTwAAAID25ePDZnkdirfIxw8OAAAAANh7Ghoaora2tsP1mXSiSafTkUqleqqKAAB00YAIxck/uZgr3jzxAAAAAPmrtrY2KioqunWM6urqLnXO6SlduYfWnftnEe6hAQB9k1B8gGtqaorVq1fH+vXrY9SoUTF16tQoLCzMdbU6lcu54s0TDwAAAJCf0ul0VFdXd7i+ZcrEzqZqTKfTPVW9rHX3HlpX759FuIcGAPQ9QvEBbOXKlbF48eKor69vXVZWVhaXX355zJo1K3cV24NczBVvnngAAACA/JZKpTLq5d3VqRp7W1fvoXX1/lmEe2gAQN8lFB+gVq5cGbNnz44ZM2bELbfcEhMmTIi1a9fGsmXLYvbs2bFixYo+HYxHmCseAAAAAPakK/fQ3D8DAPJNQVd2uvrqq6OsrCyKi4vj+OOPj0ceeaTT7Tdt2hSf+tSnYtSoUVFUVBTjx4+PO++8s0sVpvuamppi8eLFMWPGjFi1alVMmjQphg4dGpMmTYpVq1bFjBkz4oILLoimpqZcVxUAAAAAAACgW7LuKX7rrbfGokWL4pprronjjz8+rrzyyjjllFPiiSeeiAMPPHC37Xfu3Bnvete74sADD4wVK1bEQQcdFM8880zsv//+e6P+dMHq1aujvr4+brnlligoaPtcREFBQSxZsiQmT54cq1evjmnTpuWmkuxRQ0ND1NbWtrsu02Gu0ul0pFKpnqoiAABkpaGhISIi1qxZk/W+3ZkqCQAAALqqs7ym5f+cnf3fU1bTO7IOxa+44opYsGBBzJs3LyIirrnmmrjjjjvi+uuvjy9+8Yu7bX/99dfHP/7xj/jd734X++67b0S8Nm91Z3bs2BE7duxo/XnLli3ZVpNOrF+/PiIiJkyY0O76luUt29E31dbWRkVFRbeOUV1d3S/mwAIAYGBouYmwYMGCXi+7tLS018sEAACg/8skr6msrOxwXV/Kaga9+kocM7IgSjati3i+SwOOd0nJpnVxzMiCGPTqKz1WRlah+M6dO6O6ujqWLFnSuqygoCCmT58eDz/8cLv7/OIXv4gTTjghPvWpT8XPf/7zOOCAA+IjH/lIfOELX4jCwsJ296mqqoqlS5dmUzWyMGrUqIiIWLt2bUyaNGm39WvXrm2zHX1TOp2O6urqdtfV1NREZWVlLF++PMrLyzs9BgAA9BUzZ86MiK49JZ9pG7g9paWlMW7cuKz2AQAAgIjO85pMRjXrS1lN8bZnY83CoREPLox4sPfKLY+INQuHRs22ZyNico+UkVUo/tJLL0VTU1OMGDGizfIRI0Z0OCzAU089Fffdd1/MmTMn7rzzznjyySfj3HPPjX/+859x0UUXtbvPkiVLYtGiRa0/b9myJQ455JBsqkonpk6dGmVlZbFs2bJYtWpVmyHUm5ubo6qqKsaMGRNTp07NYS3Zk1Qqtccnh8rLy/vM00UAALAnw4cPj/nz53frGNrAAAAA9KY95TVTpkzpxdp0zytDD42J39sWN998c5T3YlhfU1sbc+bMietOO7THysh6+PRsNTc3x4EHHhjXXnttFBYWRkVFRTz33HNx2WWXdRiKFxUVRVFRUU9XbcA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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 12.0 Otimização dos hiperparâmetros\n",
        "\n",
        "\n",
        "Random Forest Classifier foi o algoritmo escolhido por apresentar os melhores resultados entre os mofdelos.\n",
        "\n",
        "Foi alterado o numero de arvores do classificador, de 50, 100 e 150 para 100, 150 e 200. Observou se uma mudança na saida final do resultado referente a qualidade de vinho"
      ],
      "metadata": {
        "id": "CaXIGyNyiZDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definindo a Semente Global\n",
        "np.random.seed(7)\n",
        "\n",
        "pipelines = []\n",
        "\n",
        "# Definindo os componentes do pipeline\n",
        "rf = ('RF', RandomForestClassifier())\n",
        "standard_scaler = ('StandardScaler', StandardScaler())\n",
        "min_max_scaler = ('MinMaxScaler', MinMaxScaler())\n",
        "\n",
        "pipelines.append(('RF-orig', Pipeline(steps=[rf])))\n",
        "pipelines.append(('RF-padr', Pipeline(steps=[standard_scaler, rf])))\n",
        "pipelines.append(('RF-norm', Pipeline(steps=[min_max_scaler, rf])))\n",
        "\n",
        "param_grid = {\n",
        "    'RF__n_estimators': [50, 100, 150],  # Definindo o numero de arvores\n",
        "    'RF__criterion': ['gini', 'entropy']  # Criterio de Qualidade\n",
        "}\n",
        "\n",
        "# Preparando e executando o GridSearchCV\n",
        "for name, model in pipelines:\n",
        "    grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\n",
        "    grid.fit(X_train, y_train)\n",
        "    # Print da Melhor Modelo\n",
        "    print(\"Without missing treatment: %s - Best score: %f using %s\" % (name, grid.best_score_, grid.best_params_))\n"
      ],
      "metadata": {
        "id": "sqwO9LgtpzJM",
        "outputId": "0db73e8a-3a3e-4c47-f8ad-add764308e88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Without missing treatment: RF-orig - Best score: 0.689678 using {'RF__criterion': 'entropy', 'RF__n_estimators': 100}\n",
            "Without missing treatment: RF-padr - Best score: 0.688060 using {'RF__criterion': 'gini', 'RF__n_estimators': 150}\n",
            "Without missing treatment: RF-norm - Best score: 0.683372 using {'RF__criterion': 'entropy', 'RF__n_estimators': 50}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 13.0 Finalização do Modelo\n",
        "\n"
      ],
      "metadata": {
        "id": "X1l4rwHait0f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparando o Modelo\n",
        "scaler = StandardScaler().fit(X_train)  # ajustando o scaler com os dados de treinamento\n",
        "rescaledX = scaler.transform(X_train)   #aplicando padronização aos dados de treinamento\n",
        "model = RandomForestClassifier()       # Inicializar o classificador Random Forest\n",
        "model.fit(rescaledX, y_train)          # Ajuste do modelo nos dados de treinamento\n",
        "\n",
        "# Estimativa de Acurracia de Teste\n",
        "rescaledTestX = scaler.transform(X_test)   # Aplicando Padronização nos dados de testes\n",
        "predictions = model.predict(rescaledTestX)   # Predicção dos registros de Testes\n",
        "print(accuracy_score(y_test, predictions))   # Mostrando o Score da Acurracia\n"
      ],
      "metadata": {
        "id": "kbJpP9V1ivTQ",
        "outputId": "55607df5-3af6-4cd4-b1a6-9003d3d57597",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.709375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparação do modelo com TODO o dataset\n",
        "scaler = StandardScaler().fit(X) # ajuste do scaler com TODO o dataset\n",
        "rescaledX = scaler.transform(X) # aplicação da padronização com TODO o dataset\n",
        "model.fit(rescaledX, y)"
      ],
      "metadata": {
        "id": "JBjOHtQui5Zz",
        "outputId": "a0f93e0b-9c1c-4e38-a08b-6e8dab395c6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 14.0 Simulando a aplicação do modelo em dados não vistos"
      ],
      "metadata": {
        "id": "ZyNxqoMJi-hD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Novos dados - não sabemos a classe!\n",
        "data = {'fixed acidity':  [3.6, 11.0, 7.4],\n",
        "        'volatile acidity': [0.16, 0.40, 0.53],\n",
        "        'citric acid': [0.25, 0.30, 0.61],\n",
        "        'residual sugar': [3.1, 2.0, 1.1],\n",
        "        'chlorides': [0.059, 0.060, 0.039],\n",
        "        'free sulfur dioxide': [11.0, 10.0, 15.0],\n",
        "        'total sulfur dioxide': [64.0, 70.0, 50.0],\n",
        "        'density': [0.9900, 0.9900, 0.9858],\n",
        "        'pH' : [3.96, 3.20, 2.70],\n",
        "        'sulphates' : [0.74, 0.50, 0.34],\n",
        "        'alcohol': [12.1, 5.0, 6.9 ]\n",
        "        }\n",
        "\n",
        "atributos = ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol']\n",
        "entrada = pd.DataFrame(data, columns=atributos)\n",
        "\n",
        "array_entrada = entrada.values\n",
        "X_entrada = array_entrada[:,0:11].astype(float)\n",
        "\n",
        "# Padronização nos dados de entrada usando o scaler utilizado em X\n",
        "rescaledEntradaX = scaler.transform(X_entrada)\n",
        "print(rescaledEntradaX)"
      ],
      "metadata": {
        "id": "DUtqoBnFdbEF",
        "outputId": "3b2f3f62-a3b6-47d7-c3d0-4d9bae4c400c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-2.712 -2.055 -0.108  0.398 -0.605 -0.466  0.533 -3.576  4.204  0.483\n",
            "   1.574]\n",
            " [ 1.54  -0.714  0.149 -0.382 -0.584 -0.562  0.716 -3.576 -0.72  -0.933\n",
            "  -5.09 ]\n",
            " [-0.528  0.012  1.741 -1.021 -1.03  -0.084  0.107 -5.802 -3.96  -1.877\n",
            "  -3.307]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 15.0 Resultado Final do Aprendizado\n",
        "\n",
        "O desempenho do modelo foi mensurado com a mudanças nos valores dos dados não visto e comparando com o resultado original. Valores de entradas foram alterados e uma resposta satisfatoria, dentro  dos parametros estabelecidos, de saida foi observada\n",
        "\n",
        "\n",
        "Alteração no hiperparametros, nos estimadores, mudaram o resultado final da identificação da qualidade do vinho\n"
      ],
      "metadata": {
        "id": "hzBEcXGg2RBU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predição de classes dos dados de entrada\n",
        "saidas = model.predict(rescaledEntradaX)\n",
        "print(saidas)"
      ],
      "metadata": {
        "id": "hMSzhQdBjaoQ",
        "outputId": "6545ba59-1672-459b-8df2-521f880ab181",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6. 5. 5.]\n"
          ]
        }
      ]
    }
  ]
}