Bases_doenças_no_coração.ipynb

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    "colab": {
      "name": "Bases_doenças_no_coração.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN5BOYPbkMZgW9Rr2R/qedE",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OseiasBeu/Data-Science/blob/master/Bases_doen%C3%A7as_no_cora%C3%A7%C3%A3o.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jvd5zxMGadyD"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "rK7f-Npgcx_o",
        "outputId": "7ab9f6a2-5299-4870-a258-cb84a0eb191b"
      },
      "source": [
        "dados = pd.read_csv('https://raw.githubusercontent.com/OseiasBeu/Data-Science/master/DataSets/framingham.csv')\n",
        "dados.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>male</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>currentSmoker</th>\n",
              "      <th>cigsPerDay</th>\n",
              "      <th>BPMeds</th>\n",
              "      <th>prevalentStroke</th>\n",
              "      <th>prevalentHyp</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>totChol</th>\n",
              "      <th>sysBP</th>\n",
              "      <th>diaBP</th>\n",
              "      <th>BMI</th>\n",
              "      <th>heartRate</th>\n",
              "      <th>glucose</th>\n",
              "      <th>TenYearCHD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>106.0</td>\n",
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              "      <td>26.97</td>\n",
              "      <td>80.0</td>\n",
              "      <td>77.0</td>\n",
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              "      <td>250.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>28.73</td>\n",
              "      <td>95.0</td>\n",
              "      <td>76.0</td>\n",
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              "      <td>127.5</td>\n",
              "      <td>80.0</td>\n",
              "      <td>25.34</td>\n",
              "      <td>75.0</td>\n",
              "      <td>70.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>61</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>225.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>28.58</td>\n",
              "      <td>65.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>285.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>23.10</td>\n",
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            "text/plain": [
              "   male  age  education  currentSmoker  ...    BMI  heartRate  glucose  TenYearCHD\n",
              "0     1   39        4.0              0  ...  26.97       80.0     77.0           0\n",
              "1     0   46        2.0              0  ...  28.73       95.0     76.0           0\n",
              "2     1   48        1.0              1  ...  25.34       75.0     70.0           0\n",
              "3     0   61        3.0              1  ...  28.58       65.0    103.0           1\n",
              "4     0   46        3.0              1  ...  23.10       85.0     85.0           0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vp6HNru3dBcw"
      },
      "source": [
        "#Informações do Dataset:\n",
        "* male: 1 = homem, 0 = mulher  > variável categórica\n",
        "* age: idade (variável contínua)\n",
        "* educação: nível educacional (variável categórica)\n",
        "* currentSmoker: 1 = fumante, 0 = não fumante (variável categórica)\n",
        "* cigsPerDay: quantidade de cigarros por dia (variável contínua)\n",
        "* BPMeds: 1= paciente toma medicamentos para pressão, 0=não toma medicamentos(variável categórica)\n",
        "* prevalentStroke: 1 = paciente teve derrame, 0 = não teve (variável categórica)\n",
        "* prevalentHyp: 1= hipertenso, 0 = não hipertenso (variável categórica)\n",
        "* diabetes: 1= tem diabetes, 0 = não tem (variável categórica)\n",
        "* totChol: colesterol total (variável contínua)\n",
        "* sysBP: pressão sanguínea diastólica (variável contínua)\n",
        "* diaBP: pressão sanguínea diastólica (variável contínua)\n",
        "* BMI: Indice de massa corporal (variável contínua)\n",
        "* heartRate: batimento cardíaco (variável contínua)\n",
        "* glucose: glicose (variável contínua)\n",
        "* TenYearCHD: 1 = terá problema, 0 = não terá (variável categórica)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "320tF7QlfvMG"
      },
      "source": [
        "# Fazendo Analise exploratória:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nF6wzTXmf2nx"
      },
      "source": [
        "* Observando a ausência de valores nas variáveis. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_b4EtVe1fU-Z",
        "outputId": "2b44fc9e-20d0-41d4-c9c7-00afd1f7067e"
      },
      "source": [
        "dados.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4240 entries, 0 to 4239\n",
            "Data columns (total 16 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   male             4240 non-null   int64  \n",
            " 1   age              4240 non-null   int64  \n",
            " 2   education        4135 non-null   float64\n",
            " 3   currentSmoker    4240 non-null   int64  \n",
            " 4   cigsPerDay       4211 non-null   float64\n",
            " 5   BPMeds           4187 non-null   float64\n",
            " 6   prevalentStroke  4240 non-null   int64  \n",
            " 7   prevalentHyp     4240 non-null   int64  \n",
            " 8   diabetes         4240 non-null   int64  \n",
            " 9   totChol          4190 non-null   float64\n",
            " 10  sysBP            4240 non-null   float64\n",
            " 11  diaBP            4240 non-null   float64\n",
            " 12  BMI              4221 non-null   float64\n",
            " 13  heartRate        4239 non-null   float64\n",
            " 14  glucose          3852 non-null   float64\n",
            " 15  TenYearCHD       4240 non-null   int64  \n",
            "dtypes: float64(9), int64(7)\n",
            "memory usage: 530.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f96rTrwhg3vC"
      },
      "source": [
        "* Verificando a quantidade de valores do tipo NaN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92UEw5dOftl5",
        "outputId": "8bbb3886-af77-4bf5-9b81-f5608feba4c6"
      },
      "source": [
        "dados.isna().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male                 0\n",
              "age                  0\n",
              "education          105\n",
              "currentSmoker        0\n",
              "cigsPerDay          29\n",
              "BPMeds              53\n",
              "prevalentStroke      0\n",
              "prevalentHyp         0\n",
              "diabetes             0\n",
              "totChol             50\n",
              "sysBP                0\n",
              "diaBP                0\n",
              "BMI                 19\n",
              "heartRate            1\n",
              "glucose            388\n",
              "TenYearCHD           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01FRSpd3gOVu"
      },
      "source": [
        "A glicose é a variável com maior quantidade de valores ausêntes. Em relação a quantidade total de valores, podemos remover todos estes casos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWxbbT9IgZRX"
      },
      "source": [
        "dados = dados.dropna()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdPijASTgs1p",
        "outputId": "f2291da9-0941-45dc-8629-157e756b44d0"
      },
      "source": [
        "dados.isna().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male               0\n",
              "age                0\n",
              "education          0\n",
              "currentSmoker      0\n",
              "cigsPerDay         0\n",
              "BPMeds             0\n",
              "prevalentStroke    0\n",
              "prevalentHyp       0\n",
              "diabetes           0\n",
              "totChol            0\n",
              "sysBP              0\n",
              "diaBP              0\n",
              "BMI                0\n",
              "heartRate          0\n",
              "glucose            0\n",
              "TenYearCHD         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhgffPa6hAkt"
      },
      "source": [
        "* Verificando a distribuição dos dados:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9THjtyd5hDeH",
        "outputId": "4719d2b8-fe90-4416-8075-0cd4b57f4fe9"
      },
      "source": [
        "fig = plt.figure(figsize = (15,20))\n",
        "ax = fig.gca()\n",
        "dados.hist(ax=ax)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f071cbb3510>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f071a975b10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f071ab32110>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f071a8d8ad0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f071a894190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f071a8c2810>,\n",
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              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f071a83e7d0>,\n",
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              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f071a6da5d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f071a70fc50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f071a6d1310>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f071a687990>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x1440 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zFEudxWhaBV"
      },
      "source": [
        "* Verificando a correlação entre as variáveis:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYOxqxg4heFg"
      },
      "source": [
        "correlacao = dados.corr()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "wRPhbfp6hjqB",
        "outputId": "75a14972-8c6c-4805-a518-354831b4f10d"
      },
      "source": [
        "sns.heatmap(correlacao)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f07184678d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iEZr1ZYh6rz"
      },
      "source": [
        "O grau de escolaridade da pessoa não é um fator importante uma vez que depende do entendimento médico."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCQ1Tz_IhnRx"
      },
      "source": [
        "dados = dados.drop('education',axis=1)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AdYJv1wiLDa"
      },
      "source": [
        "* Normalizando os dados de algumas colunas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YC4ro3V1iOno"
      },
      "source": [
        "colunas = ['age','cigsPerDay','totChol','sysBP','diaBP','BMI','heartRate','glucose']"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgO8vFTKi9Np"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_aJPMymjA0p"
      },
      "source": [
        "normalizador = MinMaxScaler(feature_range=(0,1))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAnwlzbkjFzJ"
      },
      "source": [
        "dados[colunas]= normalizador.fit_transform(dados[colunas])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "CuLeOCFsjLNp",
        "outputId": "7dafb37d-ee08-46e3-fb2f-ba04335a98b8"
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>male</th>\n",
              "      <th>age</th>\n",
              "      <th>currentSmoker</th>\n",
              "      <th>cigsPerDay</th>\n",
              "      <th>BPMeds</th>\n",
              "      <th>prevalentStroke</th>\n",
              "      <th>prevalentHyp</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>totChol</th>\n",
              "      <th>sysBP</th>\n",
              "      <th>diaBP</th>\n",
              "      <th>BMI</th>\n",
              "      <th>heartRate</th>\n",
              "      <th>glucose</th>\n",
              "      <th>TenYearCHD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.184211</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.168378</td>\n",
              "      <td>0.106383</td>\n",
              "      <td>0.232804</td>\n",
              "      <td>0.277024</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.104520</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.368421</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.281314</td>\n",
              "      <td>0.177305</td>\n",
              "      <td>0.349206</td>\n",
              "      <td>0.319680</td>\n",
              "      <td>0.515152</td>\n",
              "      <td>0.101695</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>1</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.271047</td>\n",
              "      <td>0.208038</td>\n",
              "      <td>0.338624</td>\n",
              "      <td>0.237518</td>\n",
              "      <td>0.313131</td>\n",
              "      <td>0.084746</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.763158</td>\n",
              "      <td>1</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.229979</td>\n",
              "      <td>0.314421</td>\n",
              "      <td>0.497354</td>\n",
              "      <td>0.316045</td>\n",
              "      <td>0.212121</td>\n",
              "      <td>0.177966</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.368421</td>\n",
              "      <td>1</td>\n",
              "      <td>0.328571</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.353183</td>\n",
              "      <td>0.219858</td>\n",
              "      <td>0.380952</td>\n",
              "      <td>0.183228</td>\n",
              "      <td>0.414141</td>\n",
              "      <td>0.127119</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   male       age  currentSmoker  ...  heartRate   glucose  TenYearCHD\n",
              "0     1  0.184211              0  ...   0.363636  0.104520           0\n",
              "1     0  0.368421              0  ...   0.515152  0.101695           0\n",
              "2     1  0.421053              1  ...   0.313131  0.084746           0\n",
              "3     0  0.763158              1  ...   0.212121  0.177966           1\n",
              "4     0  0.368421              1  ...   0.414141  0.127119           0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNA7cgf1jfKY"
      },
      "source": [
        "* Verificando se a amostra está balanceada:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "d7NSh9YQjjFS",
        "outputId": "cd4b7be0-d13e-4cdc-c0c3-9ef0b8ec7583"
      },
      "source": [
        "sns.countplot(x='TenYearCHD',data=dados)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f070a781610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GpopjAGBj3c_"
      },
      "source": [
        "Este gráfico nos dia que existem muito menos casos de pessoas que poderão ter alguma doença no coração. Esté fato nos diz que a amostra está totalmente desbalanceada."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDwZvr-qkkN0"
      },
      "source": [
        "* Contagem de cada classe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOIjVXWUkB5Z"
      },
      "source": [
        "contagem = dados['TenYearCHD'].value_counts()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_f5i98VEks1q",
        "outputId": "bdb07368-ac4b-48bc-9d95-4a2b1b21908d"
      },
      "source": [
        "print('Classe 0 = ', contagem[0])\n",
        "print('Classe 1 = ', contagem[1])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classe 0 =  3101\n",
            "Classe 1 =  557\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atPyBQpOk_sI"
      },
      "source": [
        "Para balancear a amostra vou selecionar aleatóriamente 557 casos da classe 0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vr5Fhdek1xK"
      },
      "source": [
        "dados_1 = dados[dados['TenYearCHD']==1]\n",
        "dados_0 = dados[dados['TenYearCHD']==0]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0f97dxGlQUq"
      },
      "source": [
        "dados_0_novo = dados_0.sample(n=contagem[1],random_state=42)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0JCPXFwlgjZ"
      },
      "source": [
        "* Concatenando ambos os casos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDoEFw4vljgq"
      },
      "source": [
        "dados = pd.concat([dados_0_novo,dados_1])"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzYSh9dFl8Zc"
      },
      "source": [
        "* Verificando novamente a contagem de cada caso:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVw--vtEmCJ5",
        "outputId": "b8b2b09f-1e3d-4040-f99e-a96ab210e0ef"
      },
      "source": [
        "contagem = dados['TenYearCHD'].value_counts()\n",
        "print('Classe 0 = ', contagem[0])\n",
        "print('Classe 1 = ', contagem[1])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classe 0 =  557\n",
            "Classe 1 =  557\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xStCBTlNmOyX"
      },
      "source": [
        "* Determinando as variáveis X e Y:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hob2e2WUmSPx"
      },
      "source": [
        "X = dados.drop('TenYearCHD', axis=1).values\n",
        "Y = dados['TenYearCHD'].values"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIhdnviamuly"
      },
      "source": [
        "* Separando em amostra de treino e teste:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xS0Z_CZGmfMJ"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "io1YJLxxm-sD"
      },
      "source": [
        "X_treino, X_teste, Y_treino, Y_teste = train_test_split(X,Y,test_size=0.25,random_state=0)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A4wuN_ornZZF"
      },
      "source": [
        "* Criando modelo de regressão logística:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpIC4yZ1nYOC"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eC3jzH8GnkQD"
      },
      "source": [
        "modelo = LogisticRegression()"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpCRGcWxnmX6",
        "outputId": "12ab944d-78d8-437e-d5d4-f10769ee9147"
      },
      "source": [
        "modelo.fit(X_treino,Y_treino)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7G2bHvH0n4iI"
      },
      "source": [
        "* Realizando previsão na amostra de teste:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEIGp640nq2D"
      },
      "source": [
        "Y_previsto = modelo.predict(X_teste)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJfPJFltoEXD"
      },
      "source": [
        "* Criando uma matriz de confusão:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4GY6q28oDQa"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GigjQrEyogOD",
        "outputId": "6d2447dc-b8ce-4a3c-d7c8-64552f4c26ee"
      },
      "source": [
        "cm = confusion_matrix(Y_teste, Y_previsto)\n",
        "cm"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 84,  37],\n",
              "       [ 51, 107]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "xBCRfcBPolnS",
        "outputId": "9a61e4d8-919b-4f93-c3e2-804012978b16"
      },
      "source": [
        "sns.heatmap(cm, annot=True, fmt=\"d\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0707fd1490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uj5rQOvCpA9o"
      },
      "source": [
        "* Calculando as métricas do modelo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkC_fgCYozFr"
      },
      "source": [
        "from sklearn.metrics import classification_report,f1_score,precision_score, recall_score,accuracy_score, average_precision_score"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGmGRepLphka"
      },
      "source": [
        "* Relatório de classificação:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WOzn0VYpdKT",
        "outputId": "d020c9ad-e7d5-4963-b15b-a140e628abbe"
      },
      "source": [
        "cr = classification_report(Y_teste,Y_previsto,labels=[0,1])\n",
        "print(cr)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.62      0.69      0.66       121\n",
            "           1       0.74      0.68      0.71       158\n",
            "\n",
            "    accuracy                           0.68       279\n",
            "   macro avg       0.68      0.69      0.68       279\n",
            "weighted avg       0.69      0.68      0.69       279\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzmDjvtGps46"
      },
      "source": [
        "F1-Score:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BcJtFU_dprwb",
        "outputId": "0d41bdee-c621-45f9-f8fd-2c683f1a0c9b"
      },
      "source": [
        "f1 = f1_score(Y_teste,Y_previsto)\n",
        "print(\"F1 Score = {:0.2f}%\".format(f1*100))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score = 70.86%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVJwvTMcp9d5"
      },
      "source": [
        "* Precision Score:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYq7JvFMp8Yi",
        "outputId": "c511178b-f071-4ba4-d97a-e0483a806381"
      },
      "source": [
        "precisao = precision_score(Y_teste,Y_previsto)\n",
        "print('Precision Score = {:0.2f}% '.format(precisao*100))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision Score = 74.31% \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pU5MUy0hqm_3"
      },
      "source": [
        "Avarage Precision Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IooF3jvpqljT",
        "outputId": "312487a1-2d6b-436c-96e7-2b04dac6356d"
      },
      "source": [
        "avg_precision = average_precision_score(Y_teste, Y_previsto)\n",
        "print('Averaged Precision Score = {:0.2f}%'.format(avg_precision*100))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Averaged Precision Score = 68.60%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cok23wGXrUJb"
      },
      "source": [
        "* Recall score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTTRBXoqrGBT",
        "outputId": "3e9e23d1-bfab-4fae-ba2b-527b58ab99d1"
      },
      "source": [
        "rec = recall_score(Y_teste, Y_previsto)\n",
        "print(\"Recal Score = {:0.2f}%\".format(rec*100))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Recal Score = 67.72%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnnbSmFHrzTH"
      },
      "source": [
        "* Accuracy Score:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vyLoqutrw1j",
        "outputId": "c43fda2d-f7f7-4a3b-a2fe-25f40331e8af"
      },
      "source": [
        "acc = accuracy_score(Y_teste,Y_previsto)\n",
        "print(\"Accuracy Score = {:0.2f}%\".format(acc*100))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score = 68.46%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvmUDInNskno"
      },
      "source": [
        "o modelo atual não apresentou uma boa precisão indicando que este não seja o melhor modelo para esta situação devendo ser modificado. (e.g KNN, SVM, Decision Tree, Random Forest) ou que os parâmetros do modelo de regressão logistíca precisam ser ajustados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ht6o0hCXs-2U"
      },
      "source": [
        "# Curva ROC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWoo6ohPsja-"
      },
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjWidRaotHGc",
        "outputId": "a97a3c77-7644-40fd-fce5-7bc4d3233fdb"
      },
      "source": [
        "roc_score = roc_auc_score(Y_teste, Y_previsto)\n",
        "print(\"ROC score = {:0.2f}%\".format(roc_score*100))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ROC score = 68.57%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EJp6xW1tgwE"
      },
      "source": [
        "roc_fpr, roc_tpr, _ = roc_curve(Y_teste,Y_previsto)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "tZJVRPlPtqGk",
        "outputId": "50e3ed60-afa4-4614-a311-133b0ea735ac"
      },
      "source": [
        "plt.plot(roc_fpr, roc_tpr, linestyle='--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'True Positive Rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UkwcL7Ibu_Fr"
      },
      "source": [
        "# Curva Precision-Recall"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ilIGie2us6s"
      },
      "source": [
        "from sklearn.metrics import precision_recall_curve,auc"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHrBmARTvHM8"
      },
      "source": [
        "lr_precision, lr_recall, _ = precision_recall_curve(Y_teste,Y_previsto)\n",
        "lr_auc = auc(lr_recall,lr_precision)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDGDZZZ-vYzU",
        "outputId": "408ccce4-3b8c-4993-a2aa-8b77fb977e1e"
      },
      "source": [
        "print(\"AUC score = {:0.2f}%\".format(lr_auc*100))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC score = 80.15%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "7NMVFGEGvloc",
        "outputId": "3b757e56-cdf4-4b6a-8bce-ad7dbbe843df"
      },
      "source": [
        "plt.plot(lr_recall,lr_precision)\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Precision')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}