dataset_25.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "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/dataset_25.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Importando o conjunto de dados\n",
        "url = 'https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv'\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# Exibindo as primeiras linhas do conjunto de dados\n",
        "print(df.head())\n",
        "\n",
        "# Informações básicas sobre o conjunto de dados\n",
        "print(df.info())\n",
        "\n",
        "# Estatísticas descritivas\n",
        "print(df.describe())\n",
        "\n",
        "# Configuração dos subplots para histogramas\n",
        "fig, eixos = plt.subplots(nrows=3, ncols=3, figsize=(15, 10))\n",
        "\n",
        "# Lista de colunas\n",
        "colunas = df.columns\n",
        "\n",
        "# Loop para criar histogramas\n",
        "for i, ax in enumerate(eixos.flatten()):\n",
        "    if i < len(colunas):\n",
        "        ax.hist(df[colunas[i]], bins=15, edgecolor='k')\n",
        "        ax.set_title(colunas[i])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Matriz de correlação\n",
        "matriz_corr = df.corr()\n",
        "\n",
        "# Heatmap da matriz de correlação\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "cax = ax.matshow(matriz_corr, cmap='coolwarm')\n",
        "\n",
        "# Adicionando barras de cores e rótulos\n",
        "plt.colorbar(cax)\n",
        "ticks = np.arange(0, len(df.columns), 1)\n",
        "plt.xticks(ticks, df.columns, rotation=90)\n",
        "plt.yticks(ticks, df.columns)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# Análise da relação entre IMC e Resultado (Outcome)\n",
        "fig, ax = plt.subplots()\n",
        "df.boxplot(column='BMI', by='Outcome', ax=ax)\n",
        "ax.set_title('IMC vs Resultado')\n",
        "ax.set_ylabel('IMC')\n",
        "plt.suptitle('')  # Remove o título automático do matplotlib\n",
        "plt.show()\n",
        "\n",
        "# Análise da relação entre Idade e Resultado (Outcome)\n",
        "fig, ax = plt.subplots()\n",
        "df.boxplot(column='Age', by='Outcome', ax=ax)\n",
        "ax.set_title('Idade vs Resultado')\n",
        "ax.set_ylabel('Idade')\n",
        "plt.suptitle('')  # Remove o título automático do matplotlib\n",
        "plt.show()\n",
        "\n",
        "# Seleção de variáveis independentes (X) e dependente (y)\n",
        "X = df.drop('Outcome', axis=1)\n",
        "y = df['Outcome']\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 linear\n",
        "modelo = LinearRegression()\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",
        "mse = mean_squared_error(y_teste, y_pred)\n",
        "r2 = r2_score(y_teste, y_pred)\n",
        "\n",
        "print(f'Erro Quadrático Médio: {mse}')\n",
        "print(f'R-quadrado: {r2}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "p_7-siM6OskK",
        "outputId": "611b57d8-bbaa-42af-eb2c-bc89f4a1909d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
            "0            6      148             72             35        0  33.6   \n",
            "1            1       85             66             29        0  26.6   \n",
            "2            8      183             64              0        0  23.3   \n",
            "3            1       89             66             23       94  28.1   \n",
            "4            0      137             40             35      168  43.1   \n",
            "\n",
            "   DiabetesPedigreeFunction  Age  Outcome  \n",
            "0                     0.627   50        1  \n",
            "1                     0.351   31        0  \n",
            "2                     0.672   32        1  \n",
            "3                     0.167   21        0  \n",
            "4                     2.288   33        1  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n",
            "None\n",
            "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
            "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
            "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
            "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
            "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
            "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
            "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
            "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
            "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
            "\n",
            "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
            "count  768.000000                768.000000  768.000000  768.000000  \n",
            "mean    31.992578                  0.471876   33.240885    0.348958  \n",
            "std      7.884160                  0.331329   11.760232    0.476951  \n",
            "min      0.000000                  0.078000   21.000000    0.000000  \n",
            "25%     27.300000                  0.243750   24.000000    0.000000  \n",
            "50%     32.000000                  0.372500   29.000000    0.000000  \n",
            "75%     36.600000                  0.626250   41.000000    1.000000  \n",
            "max     67.100000                  2.420000   81.000000    1.000000  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 9 Axes>"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erro Quadrático Médio: 0.17104527280850104\n",
            "R-quadrado: 0.25500281176741757\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bKiPAkjiWOXr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}