datamining_13.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPNXZoNcklX8aqrc+ERm3GJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nathalylorena/Data-mining/blob/main/datamining_13.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kLZrsq5mvW13",
        "outputId": "427c023e-407c-47fd-cc94-77e0b93ce842",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
            "0                5.1               3.5                1.4               0.2   \n",
            "1                4.9               3.0                1.4               0.2   \n",
            "2                4.7               3.2                1.3               0.2   \n",
            "3                4.6               3.1                1.5               0.2   \n",
            "4                5.0               3.6                1.4               0.2   \n",
            "\n",
            "   species  \n",
            "0        0  \n",
            "1        0  \n",
            "2        0  \n",
            "3        0  \n",
            "4        0  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   sepal length (cm)  150 non-null    float64\n",
            " 1   sepal width (cm)   150 non-null    float64\n",
            " 2   petal length (cm)  150 non-null    float64\n",
            " 3   petal width (cm)   150 non-null    float64\n",
            " 4   species            150 non-null    int64  \n",
            "dtypes: float64(4), int64(1)\n",
            "memory usage: 6.0 KB\n",
            "None\n",
            "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
            "count         150.000000        150.000000         150.000000   \n",
            "mean            5.843333          3.057333           3.758000   \n",
            "std             0.828066          0.435866           1.765298   \n",
            "min             4.300000          2.000000           1.000000   \n",
            "25%             5.100000          2.800000           1.600000   \n",
            "50%             5.800000          3.000000           4.350000   \n",
            "75%             6.400000          3.300000           5.100000   \n",
            "max             7.900000          4.400000           6.900000   \n",
            "\n",
            "       petal width (cm)     species  \n",
            "count        150.000000  150.000000  \n",
            "mean           1.199333    1.000000  \n",
            "std            0.762238    0.819232  \n",
            "min            0.100000    0.000000  \n",
            "25%            0.300000    0.000000  \n",
            "50%            1.300000    1.000000  \n",
            "75%            1.800000    2.000000  \n",
            "max            2.500000    2.000000  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.datasets import load_iris\n",
        "\n",
        "# Importando o conjunto de dados Iris\n",
        "iris = load_iris()\n",
        "df_iris = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
        "df_iris['species'] = iris.target\n",
        "\n",
        "# Exibindo as primeiras linhas do conjunto de dados\n",
        "print(df_iris.head())\n",
        "\n",
        "# Informações básicas sobre o conjunto de dados\n",
        "print(df_iris.info())\n",
        "\n",
        "# Estatísticas descritivas\n",
        "print(df_iris.describe())\n",
        "\n",
        "# Distribuição das espécies\n",
        "sns.countplot(x='species', data=df_iris)\n",
        "plt.title('Distribuição das Espécies')\n",
        "plt.xlabel('Espécies')\n",
        "plt.ylabel('Contagem')\n",
        "plt.show()\n",
        "\n",
        "# Pairplot para visualizar relações entre algumas características\n",
        "sns.pairplot(df_iris, hue='species')\n",
        "plt.show()\n",
        "\n",
        "# Heatmap da matriz de correlação\n",
        "corr_matrix = df_iris.corr()\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Matriz de Correlação')\n",
        "plt.show()\n",
        "\n",
        "# Boxplot para visualizar a distribuição de características por espécies\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.boxplot(x='species', y='sepal length (cm)', data=df_iris)\n",
        "plt.title('Comprimento da Sépala por Espécie')\n",
        "plt.xlabel('Espécies')\n",
        "plt.ylabel('Comprimento da Sépala (cm)')\n",
        "plt.show()\n",
        "\n",
        "# Seleção de variáveis independentes (X) e dependente (y)\n",
        "X = df_iris.drop('species', axis=1)\n",
        "y = df_iris['species']\n",
        "\n",
        "# Divisão dos dados em conjuntos de treino e teste\n",
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Treinamento do modelo de regressão logística\n",
        "modelo = LogisticRegression(max_iter=200)\n",
        "modelo.fit(X_treino, y_treino)\n",
        "\n",
        "# Previsões no conjunto de teste\n",
        "y_pred = modelo.predict(X_teste)\n",
        "\n",
        "# Avaliação do modelo\n",
        "acuracia = accuracy_score(y_teste, y_pred)\n",
        "print(f'Acurácia: {acuracia}')\n",
        "\n",
        "# Matriz de confusão\n",
        "matriz_confusao = confusion_matrix(y_teste, y_pred)\n",
        "print('Matriz de Confusão:')\n",
        "print(matriz_confusao)\n",
        "\n",
        "# Relatório de classificação\n",
        "relatorio_classificacao = classification_report(y_teste, y_pred)\n",
        "print('Relatório de Classificação:')\n",
        "print(relatorio_classificacao)\n"
      ]
    }
  ]
}