sklearn2_SVM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGxZfiARUz10"
      },
      "source": [
        "## Importações e Informações do dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "ur-a2NCrFyri",
        "outputId": "8e9e498f-2c9b-4897-f34b-1e79e29c32a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Primeiros registros do dataset:\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-3c7ec03f-d9f2-45e4-b0cf-6232d556e372\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USMER</th>\n",
              "      <th>MEDICAL_UNIT</th>\n",
              "      <th>SEX</th>\n",
              "      <th>PATIENT_TYPE</th>\n",
              "      <th>DATE_DIED</th>\n",
              "      <th>INTUBED</th>\n",
              "      <th>PNEUMONIA</th>\n",
              "      <th>AGE</th>\n",
              "      <th>PREGNANT</th>\n",
              "      <th>DIABETES</th>\n",
              "      <th>...</th>\n",
              "      <th>ASTHMA</th>\n",
              "      <th>INMSUPR</th>\n",
              "      <th>HIPERTENSION</th>\n",
              "      <th>OTHER_DISEASE</th>\n",
              "      <th>CARDIOVASCULAR</th>\n",
              "      <th>OBESITY</th>\n",
              "      <th>RENAL_CHRONIC</th>\n",
              "      <th>TOBACCO</th>\n",
              "      <th>CLASIFFICATION_FINAL</th>\n",
              "      <th>ICU</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>03/05/2020</td>\n",
              "      <td>97</td>\n",
              "      <td>1</td>\n",
              "      <td>65</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>03/06/2020</td>\n",
              "      <td>97</td>\n",
              "      <td>1</td>\n",
              "      <td>72</td>\n",
              "      <td>97</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>09/06/2020</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>55</td>\n",
              "      <td>97</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12/06/2020</td>\n",
              "      <td>97</td>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>21/06/2020</td>\n",
              "      <td>97</td>\n",
              "      <td>2</td>\n",
              "      <td>68</td>\n",
              "      <td>97</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c7ec03f-d9f2-45e4-b0cf-6232d556e372')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3c7ec03f-d9f2-45e4-b0cf-6232d556e372 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-3c7ec03f-d9f2-45e4-b0cf-6232d556e372');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ecdefd74-1925-443c-be4b-edd485831b42\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ecdefd74-1925-443c-be4b-edd485831b42')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ecdefd74-1925-443c-be4b-edd485831b42 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE   DATE_DIED  INTUBED  PNEUMONIA  \\\n",
              "0      2             1    1             1  03/05/2020       97          1   \n",
              "1      2             1    2             1  03/06/2020       97          1   \n",
              "2      2             1    2             2  09/06/2020        1          2   \n",
              "3      2             1    1             1  12/06/2020       97          2   \n",
              "4      2             1    2             1  21/06/2020       97          2   \n",
              "\n",
              "   AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
              "0   65         2         2  ...       2        2             1              2   \n",
              "1   72        97         2  ...       2        2             1              2   \n",
              "2   55        97         1  ...       2        2             2              2   \n",
              "3   53         2         2  ...       2        2             2              2   \n",
              "4   68        97         1  ...       2        2             1              2   \n",
              "\n",
              "   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL  ICU  \n",
              "0               2        2              2        2                     3   97  \n",
              "1               2        1              1        2                     5   97  \n",
              "2               2        2              2        2                     3    2  \n",
              "3               2        2              2        2                     7   97  \n",
              "4               2        2              2        2                     3   97  \n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Importações necessárias\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import SelectKBest, chi2, f_classif\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "# Carregamento dos dados\n",
        "file_path = '/content/drive/MyDrive/covid_data.csv'\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Visualização dos dados\n",
        "print(\"Primeiros registros do dataset:\\n\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "laJUn4KxVFhz"
      },
      "source": [
        "## Pré-processamento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z142RgPBVJja",
        "outputId": "2e8e1bac-fd2c-4b38-bd95-b01f0b308efe"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 19  20  21  22  24  26  29  30  31  32  34  36  38  42  43  44  45  46\n",
            "  47  48  53  54  55  56  57  59  63  64  65  66  67  68  69  70  71  75\n",
            "  76  77  78  79  80  81  82  83  85  89  90  91  92  93  94  95  96  97\n",
            "  98 104 105 106 108 109 110 111 116 117 118 119 120 121 122 124 128 129\n",
            " 130 131 132 133 134 135 136 137 141 142 143 144 145 146 147 148 149 150\n",
            " 154 155 157 158 161 166 167 168 169 170 172 173 177 178 179 180 181 182\n",
            " 183 184 185 187 191 192 193 194 195 196 197 198 200 204 205 206 207 209\n",
            " 210 211 213 217 218 219 220 222 223 224 225 227 231 233 234 235 236 237\n",
            " 239 241 245 246 247 248 250 251 252 253 254 256 260 261 262 263 264 265\n",
            " 266 267 268 270 274 275 276 277 279 281 286 287 288 289 290 291 292 293\n",
            " 295 299 300 301 302 303 304 305 306 307 308 313 314 315 316 317 318 319\n",
            " 320 321 322 324 328 329 330 331 332 333 334 335 336 338 343 344 345 346\n",
            " 347 348 349 350 351 352 354 357 358 359 360 361 362 363 364 365 366 367\n",
            " 368 372 373 374 375 376 377 378 379 385 386 387 388 389 390 391 392 398\n",
            " 399 400 402 404 408 409 410 411 412 416 417 418] are constant.\n",
            "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
            "  f = msb / msw\n"
          ]
        }
      ],
      "source": [
        "# Tratamento de valores faltantes (se necessário)\n",
        "data = data.dropna()\n",
        "\n",
        "# Transformação de atributos categóricos para numéricos\n",
        "categorical_columns = data.select_dtypes(include=['object']).columns\n",
        "data = pd.get_dummies(data, columns=categorical_columns)\n",
        "\n",
        "# Amostragem do dataset para evitar estouro de memória\n",
        "# Ajuste o valor conforme necessário\n",
        "data_sampled = data.sample(n=10000, random_state=42)\n",
        "\n",
        "# Separação entre atributos (X) e rótulos (y)\n",
        "X = data_sampled.drop(columns=['CLASIFFICATION_FINAL'])\n",
        "y = data_sampled['CLASIFFICATION_FINAL']\n",
        "\n",
        "# Divisão do conjunto de dados em treinamento e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Normalização dos dados\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Seleção de atributos\n",
        "select_k_best = SelectKBest(f_classif, k=50)  # Mantém 50 atributos mais relevantes\n",
        "X_train = select_k_best.fit_transform(X_train, y_train)\n",
        "X_test = select_k_best.transform(X_test)\n",
        "\n",
        "# Redução da dimensionalidade usando PCA\n",
        "pca = PCA(n_components=0.95)  # Mantém 95% da variância explicada\n",
        "X_train = pca.fit_transform(X_train)\n",
        "X_test = pca.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-zbSZHqVQii"
      },
      "source": [
        "## Support Vector Machine (SVM)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OhrK7LbgVQ-y",
        "outputId": "d6aa2293-1341-468c-df4c-25e8ac834e48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matriz de Confusão:\n",
            "\n",
            "\n",
            "[[  0   0   3   0   0   1  12]\n",
            " [  0   0   6   0   0   0   0]\n",
            " [  0   0 197   0   0   0 487]\n",
            " [  0   0   0   0   0   0   4]\n",
            " [  0   0  14   0   0   1  52]\n",
            " [  0   0  35   0   0  15 191]\n",
            " [  0   0 106   0   0   4 872]]\n",
            "\n",
            "Relatório de Classificação:\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        16\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.55      0.29      0.38       684\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00        67\n",
            "           6       0.71      0.06      0.11       241\n",
            "           7       0.54      0.89      0.67       982\n",
            "\n",
            "    accuracy                           0.54      2000\n",
            "   macro avg       0.26      0.18      0.17      2000\n",
            "weighted avg       0.54      0.54      0.47      2000\n",
            "\n",
            "\n",
            "Acurácia:\n",
            "0.542\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 700x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Criação e treinamento do classificador SVM\n",
        "clf = SVC()\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predição e avaliação do modelo\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Avaliação do modelo\n",
        "print(\"Matriz de Confusão:\\n\\n\")\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(conf_matrix)\n",
        "\n",
        "print(\"\\nRelatório de Classificação:\\n\\n\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(\"\\nAcurácia:\")\n",
        "print(accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Visualização da matriz de confusão\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
        "plt.title('Matriz de Confusão')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}