sklearn1_Tree.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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            "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": 15,
          "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.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, 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": "H5TQzMwBQ3Tl"
      },
      "source": [
        "## Pré-processamento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "NRh_yoBhO7id"
      },
      "outputs": [],
      "source": [
        "# Tratamento de valores faltantes (se necessário)\n",
        "# Remover linhas com valores faltantes, mas posso optar por outras estratégias dependendo da análise\n",
        "data = data.dropna()\n",
        "\n",
        "# Transformação de atributos categóricos para numéricos\n",
        "# Identificação das colunas categóricas\n",
        "categorical_columns = data.select_dtypes(include=['object']).columns\n",
        "\n",
        "# Transformação usando get_dummies\n",
        "data = pd.get_dummies(data, columns=categorical_columns)\n",
        "\n",
        "# Separação entre atributos (X) e rótulos (y)\n",
        "X = data.drop(columns=['CLASIFFICATION_FINAL'])\n",
        "y = data['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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryUzKgcyQyJK"
      },
      "source": [
        "## Árvore de Decisão CART"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "id": "ITySqgozQx8q",
        "outputId": "15578308-770b-45d5-9f75-098bd114a98f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matriz de Confusão:\n",
            "\n",
            "\n",
            "[[   73     7   431     2    12   155  1001]\n",
            " [    4    25   241     0     6    46    48]\n",
            " [  337   228 26624    58   410  2744 45839]\n",
            " [    2     0   131     1     1     8   477]\n",
            " [   23     3  1260     2    42   215  3738]\n",
            " [  148    46  6291    11   140  3969 15091]\n",
            " [  192    54 17095    46   481  3093 78864]]\n",
            "\n",
            "Relatório de Classificação:\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.09      0.04      0.06      1681\n",
            "           2       0.07      0.07      0.07       370\n",
            "           3       0.51      0.35      0.41     76240\n",
            "           4       0.01      0.00      0.00       620\n",
            "           5       0.04      0.01      0.01      5283\n",
            "           6       0.39      0.15      0.22     25696\n",
            "           7       0.54      0.79      0.64     99825\n",
            "\n",
            "    accuracy                           0.52    209715\n",
            "   macro avg       0.24      0.20      0.20    209715\n",
            "weighted avg       0.49      0.52      0.49    209715\n",
            "\n",
            "\n",
            "Acurácia:\n",
            "0.5226044870419378\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 Árvore de Decisão\n",
        "clf = DecisionTreeClassifier()\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
}