sklearn7_GNBayes.ipynb

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      "source": [
        "## Importações e Informações do dataset"
      ]
    },
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          "text": [
            "Primeiros registros do dataset:\n",
            "\n"
          ]
        },
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              "      <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",
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              "      <td>03/06/2020</td>\n",
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              "      <td>97</td>\n",
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              "      <td>09/06/2020</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>12/06/2020</td>\n",
              "      <td>97</td>\n",
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              "      <td>21/06/2020</td>\n",
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            "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.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.naive_bayes import GaussianNB\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": "dJQQLiDdJ-pi"
      },
      "source": [
        "## Pré-processamento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "eyIHhEpuJMHp"
      },
      "outputs": [],
      "source": [
        "# Tratamento de valores faltantes (se necessário)\n",
        "data = data.dropna()\n",
        "\n",
        "# Remoção de atributos qualitativos\n",
        "quantitative_columns = data.select_dtypes(include=[np.number]).columns\n",
        "data = data[quantitative_columns]\n",
        "\n",
        "# Amostragem do dataset para evitar estouro de memória\n",
        "# Ajuste o valor conforme necessário\n",
        "#  data_sampled = data.sample(n=2000, random_state=42)\n",
        "# Tava perdendo acurácia\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",
        "# 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": "AWJThPJ9KAex"
      },
      "source": [
        "## Gaussian Naive Bayes (padrão)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "X_U8rjEZJXx6",
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matriz de Confusão:\n",
            "\n",
            "\n",
            "[[  0   0   0   0   0   0   3]\n",
            " [  0   0   0   0   0   0   0]\n",
            " [  6  11   2  22   2   1 114]\n",
            " [  0   0   0   0   0   0   0]\n",
            " [  1   0   0   2   1   0   8]\n",
            " [  1   0   0   4   5   0  35]\n",
            " [ 11   6   1  24   3   0 137]]\n",
            "\n",
            "Relatório de Classificação:\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00         3\n",
            "           2       0.00      0.00      0.00         0\n",
            "           3       0.67      0.01      0.02       158\n",
            "           4       0.00      0.00      0.00         0\n",
            "           5       0.09      0.08      0.09        12\n",
            "           6       0.00      0.00      0.00        45\n",
            "           7       0.46      0.75      0.57       182\n",
            "\n",
            "    accuracy                           0.35       400\n",
            "   macro avg       0.17      0.12      0.10       400\n",
            "weighted avg       0.48      0.35      0.27       400\n",
            "\n",
            "\n",
            "Acurácia:\n",
            "0.35\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true 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: Recall and F-score are ill-defined and being set to 0.0 in labels with no true 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: Recall and F-score are ill-defined and being set to 0.0 in labels with no true 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 Gaussian Naive Bayes\n",
        "clf = GaussianNB()\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",
      "version": "3.11.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}