datasetwines.ipynb

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        "<a href=\"https://colab.research.google.com/github/fernandopredes/winepredictor-back/blob/master/datasetwines.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 5,
      "metadata": {
        "id": "XMjoAT9ulh9e"
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      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split, KFold, StratifiedKFold, cross_val_score, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.pipeline import Pipeline\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"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Carregando o dataset de vinhos"
      ],
      "metadata": {
        "id": "6WItA6VbXU-1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# URL dO dataset\n",
        "url = \"https://raw.githubusercontent.com/fernandopredes/winepredictor-back/master/data/wine_dataset.csv\"\n",
        "\n",
        "# Carregar o dataset diretamente da URL\n",
        "dataset = pd.read_csv(url)\n",
        "\n",
        "# Remover a coluna 'od280/od315_of_diluted_wines'\n",
        "dataset.drop('od280/od315_of_diluted_wines', axis=1, inplace=True)\n",
        "\n",
        "# Exibindo as primeiras linhas do dataset para confirmar\n",
        "dataset.head()"
      ],
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          "height": 206
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        "outputId": "2972d060-574d-4f2d-bd89-3d28a67bcd77"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
              "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
              "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
              "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
              "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
              "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
              "\n",
              "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
              "0        3.06                  0.28             2.29             5.64  1.04   \n",
              "1        2.76                  0.26             1.28             4.38  1.05   \n",
              "2        3.24                  0.30             2.81             5.68  1.03   \n",
              "3        3.49                  0.24             2.18             7.80  0.86   \n",
              "4        2.69                  0.39             1.82             4.32  1.04   \n",
              "\n",
              "   proline  target  \n",
              "0   1065.0     0.0  \n",
              "1   1050.0     0.0  \n",
              "2   1185.0     0.0  \n",
              "3   1480.0     0.0  \n",
              "4    735.0     0.0  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
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              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
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              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>1050.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>1185.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
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              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>1480.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>735.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_size = 0.20\n",
        "seed = 7\n",
        "\n",
        "# Separação em conjuntos de treino e teste\n",
        "array = dataset.values\n",
        "X = array[:,0:12]\n",
        "y = array[:,12]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, shuffle=True, random_state=seed, stratify=y)\n"
      ],
      "metadata": {
        "id": "jjcnVXj8mECF"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(7)\n",
        "models = []\n",
        "models.append(('KNN', KNeighborsClassifier()))\n",
        "models.append(('CART', DecisionTreeClassifier()))\n",
        "models.append(('NB', GaussianNB()))\n",
        "models.append(('SVM', SVC()))\n",
        "\n",
        "results = []\n",
        "names = []\n",
        "\n",
        "for name, model in models:\n",
        "    kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
        "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='accuracy')\n",
        "    results.append(cv_results)\n",
        "    names.append(name)\n",
        "    print(f\"{name}: {cv_results.mean():.3f} ({cv_results.std():.3f})\")\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.boxplot(results)\n",
        "plt.title('Comparação de Modelos')\n",
        "plt.xticks([1, 2, 3, 4], names)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "piT_GA5MmK9g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "70ba5c15-c842-4b16-fb5f-c6f3658ed575"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN: 0.647 (0.116)\n",
            "CART: 0.923 (0.067)\n",
            "NB: 0.972 (0.047)\n",
            "SVM: 0.690 (0.073)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipelines = []\n",
        "\n",
        "# Algoritmos\n",
        "knn = ('KNN', KNeighborsClassifier())\n",
        "cart = ('CART', DecisionTreeClassifier())\n",
        "naive_bayes = ('NB', GaussianNB())\n",
        "svm = ('SVM', SVC())\n",
        "\n",
        "# Transformações\n",
        "standard_scaler = ('StandardScaler', StandardScaler())\n",
        "min_max_scaler = ('MinMaxScaler', MinMaxScaler())\n",
        "\n",
        "# Pipelines\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",
        "\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",
        "\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",
        "\n",
        "results = []\n",
        "names = []\n",
        "\n",
        "for name, pipeline in pipelines:\n",
        "    kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
        "    cv_results = cross_val_score(pipeline, X_train, y_train, cv=kfold, scoring='accuracy')\n",
        "    results.append(cv_results)\n",
        "    names.append(name)\n",
        "    print(f\"{name}: {cv_results.mean():.3f} ({cv_results.std():.3f})\")\n",
        "\n",
        "plt.figure(figsize=(15, 7))\n",
        "plt.boxplot(results)\n",
        "plt.title('Comparação de Modelos com Pipelines')\n",
        "plt.xticks(list(range(1, len(names) + 1)), names, rotation=45)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "a7h5f6jOmRyk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        },
        "outputId": "1a601744-948d-41ae-a259-5439b6e548a0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN-orig: 0.647 (0.116)\n",
            "CART-orig: 0.909 (0.064)\n",
            "NB-orig: 0.972 (0.047)\n",
            "SVM-orig: 0.690 (0.073)\n",
            "KNN-padr: 0.951 (0.054)\n",
            "CART-padr: 0.916 (0.062)\n",
            "NB-padr: 0.972 (0.047)\n",
            "SVM-padr: 0.979 (0.045)\n",
            "KNN-norm: 0.965 (0.046)\n",
            "CART-norm: 0.901 (0.057)\n",
            "NB-norm: 0.972 (0.047)\n",
            "SVM-norm: 0.986 (0.028)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Parâmetros para o KNN / Melhores hiperparâmetros encontrados"
      ],
      "metadata": {
        "id": "909ZyL-9W8qz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "param_grid = {\n",
        "    'n_neighbors': np.arange(1, 31),\n",
        "    'metric': ['euclidean', 'manhattan', 'minkowski'],\n",
        "    'weights': ['uniform', 'distance']\n",
        "}\n",
        "\n",
        "grid = GridSearchCV(KNeighborsClassifier(), param_grid=param_grid, scoring='accuracy', cv=kfold)\n",
        "grid_result = grid.fit(X_train, y_train)\n",
        "\n",
        "print(f\"Melhor: {grid_result.best_score_:.3f} usando {grid_result.best_params_}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6LjSnsFPmWhY",
        "outputId": "e62fa79e-60e2-49dd-a7cc-2ba24d0e53a4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Melhor: 0.839 usando {'metric': 'manhattan', 'n_neighbors': 1, 'weights': 'uniform'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelo final / Predição e avaliação"
      ],
      "metadata": {
        "id": "ucw8g8sdWhWi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "best_knn = KNeighborsClassifier(**grid_result.best_params_)\n",
        "best_knn.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "predictions = best_knn.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "print(f\"Acurácia no conjunto de testes: {accuracy:.3f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3BqRVg-pTuI",
        "outputId": "5a33d163-394e-43f6-c54a-e350fb80bfee"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia no conjunto de testes: 0.778\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_knn.fit(X, y)\n"
      ],
      "metadata": {
        "id": "Xe0AS17DpYni",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "83f755d4-a979-4fb0-cf60-b51a369de05b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='manhattan', n_neighbors=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(metric=&#x27;manhattan&#x27;, n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(metric=&#x27;manhattan&#x27;, n_neighbors=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dados fictícios"
      ],
      "metadata": {
        "id": "EY0UBkaWWWFz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "new_data = [\n",
        "    [12.00, 1.00, 2.40, 10.0, 100, 2.65, 1, 1, 0.20, 1.38, 3.05, 1100],\n",
        "    [12.0, 1.50, 2.30, 10.0, 85, 2.20, 2.10, 0.30, 1.10, 3.80, 0.90, 950],\n",
        "    [13.5, 2.00, 2.50, 12.5, 110, 3.00, 3.10, 0.25, 1.50, 5.00, 1.10, 1150]\n",
        "]\n"
      ],
      "metadata": {
        "id": "UOE2vOUqpeTv"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_new_data = best_knn.predict(new_data)\n",
        "print(predictions_new_data)\n"
      ],
      "metadata": {
        "id": "dSOEl7sRphQ_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86121c96-9c84-41a6-8479-0458dc0c7c67"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Análise de Resultados do Modelo"
      ],
      "metadata": {
        "id": "SE4GLnrVripG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "O Naive Bayes mostrou alta precisão e consistência, com acurácia média de 0.972 e baixo desvio padrão. CART seguiu com bom desempenho, embora mais variável. SVM e KNN tiveram menor acurácia e maior variabilidade, sugerindo maior sensibilidade às características dos dados.\n"
      ],
      "metadata": {
        "id": "0fwT8Pw2rkc-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Análise de Resultados do Modelo com Pipelines"
      ],
      "metadata": {
        "id": "8STY25RqtmaT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Impacto das Transformações de Dados:\n",
        "Transformações como padronização e normalização revelaram a sensibilidade do KNN e SVM à escala dos dados. O KNN teve melhora expressiva de desempenho, e o SVM alcançou a maior acurácia pós-normalização, destacando-se pela confiabilidade.\n",
        "\n",
        "Análise de Desempenho dos Modelos com Transformação de Dados:\n",
        "A análise evidencia que modelos baseados em distância, como KNN e SVM, se beneficiam significativamente do pré-processamento adequado, enquanto CART e Naive Bayes são menos afetados pela escala dos dados."
      ],
      "metadata": {
        "id": "2Rodr7cftsL0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Conclusões Finais\n"
      ],
      "metadata": {
        "id": "YKvWOvM2t3gc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Os resultados sublinham a importância do pré-processamento no sucesso dos modelos de machine learning. Naive Bayes provou ser robusto, enquanto CART apresentou uma ligeira queda na acurácia com dados normalizados, indicando a necessidade de ajustes específicos."
      ],
      "metadata": {
        "id": "N-9_YScGt9k0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Reflexões Finais\n"
      ],
      "metadata": {
        "id": "1H6XT-ixt-97"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este trabalho destaca a importância da integração das boas práticas de engenharia de software com a análise de dados, onde uma metodologia criteriosa e a otimização de hiperparâmetros são cruciais para desenvolver soluções eficazes de machine learning."
      ],
      "metadata": {
        "id": "yj2TW3HPuAer"
      }
    }
  ]
}