sklearn10_Percep.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Importações e Informações do dataset"
      ],
      "metadata": {
        "id": "F1EmnLb4J3-w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CnnD-HcMIkGu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67677fe8-a7ba-498d-e337-4a4a9f87f2ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Primeiros registros do dataset:\n",
            "\n",
            "\n",
            "Tipos de dados no dataset:\n",
            "\n",
            "USMER                    int64\n",
            "MEDICAL_UNIT             int64\n",
            "SEX                      int64\n",
            "PATIENT_TYPE             int64\n",
            "DATE_DIED               object\n",
            "INTUBED                  int64\n",
            "PNEUMONIA                int64\n",
            "AGE                      int64\n",
            "PREGNANT                 int64\n",
            "DIABETES                 int64\n",
            "COPD                     int64\n",
            "ASTHMA                   int64\n",
            "INMSUPR                  int64\n",
            "HIPERTENSION             int64\n",
            "OTHER_DISEASE            int64\n",
            "CARDIOVASCULAR           int64\n",
            "OBESITY                  int64\n",
            "RENAL_CHRONIC            int64\n",
            "TOBACCO                  int64\n",
            "CLASIFFICATION_FINAL     int64\n",
            "ICU                      int64\n",
            "dtype: object\n"
          ]
        }
      ],
      "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, 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.linear_model import Perceptron\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()\n",
        "\n",
        "# Visualização dos tipos de dados\n",
        "print(\"\\nTipos de dados no dataset:\\n\")\n",
        "print(data.dtypes)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pré-processamento"
      ],
      "metadata": {
        "id": "dJQQLiDdJ-pi"
      }
    },
    {
      "cell_type": "code",
      "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",
        "data_sampled = data.sample(n=10000, random_state=42)  # Ajuste o valor conforme necessário\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)"
      ],
      "metadata": {
        "id": "eyIHhEpuJMHp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b801895e-8930-45ee-cecf-65c77ca4abe0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Regressão Linear"
      ],
      "metadata": {
        "id": "AWJThPJ9KAex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Criação e treinamento do Perceptron\n",
        "clf = Perceptron()\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": {
        "id": "X_U8rjEZJXx6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d2003d4f-1a79-4eac-dc2f-4f8fdde6505a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz de Confusão:\n",
            "\n",
            "\n",
            "[[  0   0   6   0   0   5   5]\n",
            " [  0   0   6   0   0   0   0]\n",
            " [  0   0 435   2   1 147  99]\n",
            " [  0   0   2   0   0   1   1]\n",
            " [  0   0  43   1   0  14   9]\n",
            " [  2   0 139   0   3  60  37]\n",
            " [ 10   0 532   2   7 273 158]]\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       0.37      0.64      0.47       684\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00        67\n",
            "           6       0.12      0.25      0.16       241\n",
            "           7       0.51      0.16      0.24       982\n",
            "\n",
            "    accuracy                           0.33      2000\n",
            "   macro avg       0.14      0.15      0.13      2000\n",
            "weighted avg       0.39      0.33      0.30      2000\n",
            "\n",
            "\n",
            "Acurácia:\n",
            "0.3265\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}