Projetos_Portfólio/Projeto_Portfólio_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Projeto_Portfólio_2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMcS9MU+/EW9u8N6+vCi5In",
      "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/bruno353/Data_Science_Projects/blob/main/Projeto_Portf%C3%B3lio_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZROvWxXeQu-O"
      },
      "source": [
        "#Projeto de estudo\n",
        
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gzv2jcSdSKhx"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nd_vCxxobS-t"
      },
      "source": [
        "df = pd.read_csv('cardio_train.csv', sep=';')"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "wqIMGVyJbWxl",
        "outputId": "4de00721-485b-4c5f-f2b3-758c855aa4a0"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 3,
      "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>id</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "      <th>cardio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>18393</td>\n",
              "      <td>2</td>\n",
              "      <td>168</td>\n",
              "      <td>62.0</td>\n",
              "      <td>110</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20228</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
              "      <td>85.0</td>\n",
              "      <td>140</td>\n",
              "      <td>90</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>18857</td>\n",
              "      <td>1</td>\n",
              "      <td>165</td>\n",
              "      <td>64.0</td>\n",
              "      <td>130</td>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>17623</td>\n",
              "      <td>2</td>\n",
              "      <td>169</td>\n",
              "      <td>82.0</td>\n",
              "      <td>150</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>17474</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
              "      <td>56.0</td>\n",
              "      <td>100</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id    age  gender  height  weight  ...  gluc  smoke  alco  active  cardio\n",
              "0   0  18393       2     168    62.0  ...     1      0     0       1       0\n",
              "1   1  20228       1     156    85.0  ...     1      0     0       1       1\n",
              "2   2  18857       1     165    64.0  ...     1      0     0       0       1\n",
              "3   3  17623       2     169    82.0  ...     1      0     0       1       1\n",
              "4   4  17474       1     156    56.0  ...     1      0     0       0       0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slK5RB3ubXYH"
      },
      "source": [
        "df.drop(['id'],axis = 1, inplace = True)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3kd2S5K5cZcI"
      },
      "source": [
        "###Visualização Gráficos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 937
        },
        "id": "Z1FyS813cPMV",
        "outputId": "d58fcac0-b312-455d-ff55-2f129b92c1ef"
      },
      "source": [
        "df.hist(figsize = (12,12))\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd301f62590>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd301f2f690>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd301ee7d10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd301ea83d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd301edda50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd301e9bf90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd301e5c690>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd301e10d10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd301da6250>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd301ddc950>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd301d4b110>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd301d00790>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "KTthh8vbcg1_",
        "outputId": "975de81e-26fb-4567-ed07-ef517e2dbb03"
      },
      "source": [
        "#gráfico de correlação\n",
        "import seaborn as sns\n",
        "plt.figure(figsize=(16,10))\n",
        "sns.heatmap(df.corr(method='pearson'), annot=True)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3019e9890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0ymGAv7c3Ln"
      },
      "source": [
        "a maior correlação entre a doença cardíaca é a idade e o colesterol."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "xLPkMr7gdqUr",
        "outputId": "46b0f97b-3903-4c53-8721-b9410cfd7cb3"
      },
      "source": [
        "df.groupby('cardio').size().plot(kind='pie', autopct='%.2f')\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2f9284350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "kGVQ0FRReXV_",
        "outputId": "f4cf1621-4f9a-4caa-c130-d8fe431a3cdc"
      },
      "source": [
        "df.groupby('cholesterol').size().plot(kind='pie', autopct='%.2f')\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2f7006850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAADnCAYAAADGrxD1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcbklEQVR4nO3deXxU9b3/8dc3kw1CWFRQMOBBqdQNcd9Qal3ae0cr9d7bn1Uucam11Uuv9rqc1lqjre1Y7WLrUpe6Va3LT+t2asWFoq32olVgqqIgjGQlLNlJSGbme/84g8QQkkkyc75zznyej8c8CGQy5x2d95zv2b5Haa0RQgRHgekAQojMklILETBSaiECRkotRMBIqYUIGCm1EAEjpRYiYKTUQgSMlFqIgJFSCxEwUmohAkZKLUTASKmFCBgptRABI6UWImCk1EIEjJRaiICRUgsRMFJqIQJGSi1EwEiphQgYKbUQASOlFiJgpNRCBIyUWoiAkVILETBSaiECptB0AJF5lu2EgCnANGCv1J/TgF2AMUBZrz/LgCKgG9ja69EFdAKNQC1Q1+tRC1THIuGEZ7+USJuSG+T5m2U704HDgINTjwOBqWT/A7sLeB+I9n7EIuH6LC9XDEJK7SOW7ShgNnAqMBc4AtjNaKgd1QJ/ST0WxyLhj42myUNS6hxn2c4U3BKfCpwETDKbaMiqgcXAS8BzsUi4xXCewJNS5yDLdvYAzgLOxl0bB0U3sAh4AnhGCp4dUuocYdnOWOBM4BzgRCBkNlHWbSv4o8CTsUi4y3CewJBSG2bZziHApcDXgFLDcUzZDDwA/DYWCX9kOozfSakNsGynADgDuAw43nCcXKJx1963AH+ORcLy5hwGKbWHLNspAy4EvgNMNxwn170PXA88LuUeGim1ByzbKQa+CfwA2N1wHL/5J3Ad7na3vFnTIKXOotQw+xzcN6WsmUdmOVAVi4SfNh0k10mps8SynTAQwT3DS2TO68AlsUg4ajpIrpJSZ5hlOxXAb4B5prMEWBy4FfhhLBJuMx0m10ipMyQ11F4I/AgoNxwnX9QDl8ci4UdMB8klUuoMSB1rvgs43HSWPPUKcG4sEq4xHSQX5EWplVL3AqcBjVrrjG3jptbOVwPXEvwzwHJdE/CtWCT8uOkgpuVLqU8A2oEHM1Xq1IUWDwNfyMTriYx5EPivfN7WzouZT7TWr+GeipgRqT3by5FC56IFwHLLdo41HcSUvFhTAyilLOD5kaypLdspAm7EPVdbZSaZyJI4cGksEr7NdBCvSanTZNnOBOApZO3sN3cCC2ORcI/pIF7Ji+H3SFm2MwP4O1JoP7oIWGTZzq6mg3hFSj0Iy3bm4BZ6X9NZxLB9AXjLsp28OLsvL0qtlPoD8CYwUylVo5S6IJ2fs2xnPu4x0Lz5lA+w6cDfLNs5znSQbMubbeqhsmznMuAXpnOIjOsAzohFwq+YDpItebGmHirLdr6PFDqoyoDnLds5zXSQbJFS92HZzrXADaZziKwqBZ6ybOc/TAfJBil1L5bt/ACoMp1DeKII+INlO2eZDpJpsk2dYtnOFcDPTOcQnusBwrFI+CXTQTJFSg1YtnM28BBylli+agdOjEXCb5sOkgl5X2rLdk7AvXtEsekswqgNwHGxSHiV6SAjldeltmzn88AbwATTWUROWAscG4uEG0wHGYm83VFm2c4k4E9IocV204HnLNspMR1kJPKy1Kkpe59FZvgUOzocd44538rLUgM3AUeZDiFy1oWW7ZxnOsRw5d02tWU7X8W9hFKIgXThbl+/azrIUOVVqS3bmQ68A4w3nUX4wlrgsFgk3GQ6yFDkzfA7tR39OFJokb7pwL2mQwxV3pQa+Ckyha8YunmW7VSaDjEUeTH8tmznKNzj0fn0ISYypwWYFYuE15kOko7Av8lTkwXeQx78riJrxuHOdeYL+fBGt5Gb1ImR+7JlOwtMh0hHoIffqdNAlwG+PkNI5IzNwMxYJLzRdJCBBHZNbdmOAu5GCi0yZxfce43ntMCWGjgLmGM6hAiciyzb2d90iIEEstSpY9I/Np1DBFIIuNl0iIEEstTAt4C9TYcQgfUvlu18yXSInQncjjLLdsqBj4GJXiyvZ1MNG5698dO/x5sbGD9nPmOPOAOA1qVP0bT4XioWPkxo9Lgdfr5p8b10fvw2WicZNf0QJpz0TZTaPgFL45PXE29uYMoFt2f/lxFD8R5wcCwSTpgO0leh6QBZcCUeFRqgaNcKppznXqmnkwlqbq9k9L7HABBv3UDn2ncJje0/TlfNB2yt/YDJ57s/3/DwlWytjlI6bRYAWz58A1U0yoPfQgzDAcB84AHTQfoK1PDbsp2JwHdNLb/rk+UUjZ9M4bhJADS9cjcTTjyPnU19phToeDc6EUcneiCZIDTanbMh2d1J61tPM+7Y/+dVfDF0V6WOsuSUoK2pLwFGm1p4xwevMXq/EwDYsurvhMp3pXjSzjftS/bcj9Jps6i5bQFoTflhp1G021QAml9/iLFHzqOgSI7I5bD9gDOAp00H6S0wa2rLdkYBF5tavk700Ll6KWWfn0Oyp4uWNx9n/PHzB/yZnqY6ejZVU3Hx/VRc8gBdnyynq/qfdK9fQ7y5ntH75u190/3ke6YD9BWYUgML8HBbuq/ONf+gePd9CJVNIN7cQLxlPXX3LqTmjvNJtG2k/v5LSbR/9rLcLR+9SfGUmRQUj6KgeBSj9j6crXUr2Vq3ku6G1dTccT4ND11Jz+Y6Gh6xDf1mYhBHWrbzRdMhegvE8Du1XWNsWxqg4/0llKWG3sUTLaYufPjT79XccT6TK3+5w97vwrETaV/+IjqZAK3ZWh2l/PAzGD3jKMoP+VcA4i3rafz/17HH2RHvfhkxVN8DXjUdYpugrKlPx+D9o5PdXXTFljF65uDD5a31q9j0wq8BGD3zOArHT6bud5dQf99CiiZOZ/QMmTrNh062bCdn7l8eiOPUlu28BJxsOofIazfGIuGc2Ebyfakt26kAPiE4ow7hTw3A1FgkHDcdJAhFOIdg/B7C3/YA/sV0CAhGGf7TdAAhUs43HQB8Pvy2bOdQ4B+mcwiREgf2jEXCjSZD+H1NLWtpkUsKgXmmQ/i21Klj03JitMg1XzEdwLelBg4GJpsOIUQfJ1m2Y+z6A/B3qXNiT6MQfZQCp5gMIKUWIvNON7lwX+79tmxnHLCRgJy7LgJnPTA5FgkbKZdf19QnI4UWuWt33JlRjPBrqU81HUCIQRxjasF+LbXMHiBynbH3qO9KnZotNKcnUxcCWVMPyRH4M7fIL/tatrOriQX7sRyHmQ4gRBoUcLSJBfux1IeYDiBEmoysgPxY6tmmAwiRJiNTHPmq1JbtFAD7mM4hRJqk1GnYEyg2HUKINH3OxEL9Vmq5k6Xwk/GW7UzyeqF+K/V00wGEGCLPh+Bpl1opNUcpdV7q64lKKRMFk1ILv/F8H1BapVZKXQtcxfb7BhUBD2Ur1ACk1MJvPL8VVLpr6q/iTtPSAaC1rgPKsxVqADLTifAbz88qS7fU3dq98FoDKKXKshdpQOMGf4oQOSVnS/24UupOYLxS6kLgZeDu7MXaqbEGlinESHhe6rQmGtBa36yUOgVoBWYCP9Rav5TVZP2TUgu/yc1SA6RKbKLIvUmphd/k5vBbKXWmUmqVUqpFKdWqlGpTSrVmO1xvqVNETW3LCzFco7xeYLpr6p8Bp2utP8hmmEFIoYUfhbxeYLo7ytYbLjRAwvDyhRgOz0ud7pr6baXUY8DTwNZt/6i1fiorqfpn/L6/QTWB1s1HTLvunTdGFx1qOkvwFLRC2NMlplvqscAWPjuLpwa8LHWPh8vKK8XE43c11p989W67LHl2TNkJKKVMZwqOpKf7niD9Q1rnZTvIYGKRsLZsJ4n/LkLJeSGSSYAbNm6eu3dP/G+/mjDuSJQqMp0rIDwfYaa797tCKfVHpVRj6vGkUqoi2+H6IWvrLChQOrnt6wtaWo/7eePGKFp7voYJqNwsNXAf8CwwJfV4LvVvXpNSZ0GIRLL330/d0nnow/Xr6wu0bjCVKUDavF5guqWeqLW+T2sdTz3ux8DVJ0CTgWUG3rbhd2+ztnbP/FNNXbIkmVxtIlOArPd6gemWepNSar5SKpR6zAc2ZTPYTnj+Hygf9FdqgD3jiSmvVNdOHJdILPM6U4DkbKnPB74GNAD1wL8DJnaeSamzIERyp3dnHJfU415dV7vfXj09b3iZKUA834RJq9Ra60+01l/RWk/UWk/SWs/TWq/Ldrh+yDZeFhTsZE29TTGUPFdTf8wxWzqXeJUpQDxfEQ14SEsp9cMBvq211j/KcJ7ByJo6Cwr77CjrjwJ11/oNc3+6y4Qlj4wdczxKyaHF9OTc8LujnwfABbjTG3lN1tRZMNDwu6/vbW6ae/WmpqVo3ZXNTAGSW2tqrfXPt32tlCoH/ht3W/pR4Oc7+7ksMjHkD7wQySGdV39WW/vRFfH4iot3nzhVKzUhW7kCIve2qZVSuyilfgyswP0QOFRrfZXWujHr6Xb0voFlBl6IRNpr6m3mdHbNeqK2oSmkdU02MgVED7DW64UOWGql1E3AW7gH0A/SWldprU0eK14DyLAvwwpV+sPv3mb29Oy9qLqueFQyafoKvly1MloZ7fZ6ocqdT3An31QqiXtVVpzUpIPbvoW7o8zzmUgs21kGHOz1coPs+IIV0d8XRw4a7s93KNV+esXklRsKCw/PRJ6a39XQtqyNwrGFfO4G9841LUtbaHy6ka31W9nnh/swanr/cw98+D8fUjCqwL0mJQQzqmYA0PBoA63LWlGFiuJJxVRcUEGoLOtXRT4crYzOz/ZC+hpsmzoX93C+j5Q6o9LZ+z2QMq3HLKqum/31KXv8dWVJ8ZyR5pkwZwK7nrQrNXdvH9mXVJQwbeE0au+vHfTnp181ncLyz761yw4sY/f/2B0VUjQ83sAGZwN7fG2PkUYdzPJsL6A/uVjawbxnOkDQhDIw/0QhFD5R1zDnlI4tIz6WXTazbIe1aOmUUkomlwz7NcsPLEeF3CtKR+8zmp7NnlxGIKVO0wrTAYJmZ6eJDscvGjfO/WZTy+tobWZSCwWxm2OsvnY1m/+yud+nNL3WRPksT+5FYaTUac8mmkPewN2+lwv5M6Qwg6UGWNjccvxe8Z63rt5t1/3x+MYPe1+9N0UTioi3xondFKNkcgllM7dHaHy2EUIw7pis3xdifbQyauRkKd+tqWOR8Cbk0FZGZWL43ddX2rcccV9D4ydK6w0Zf/EBFE1w53YoHFtI+aHldK7p/PR7Ta830ba8jakXTfVichdjF8H4rtQpr5kOECSZHH73dnjX1v2franvLNLak2O1ya1JEp2JT79uf6+dkj3d7fC2FW1sfGEje/33XhSUePK2f8WLhfTHj8NvcEv9bdMhgqJQZW+iVisen/bKutrN/1oxJdoeKkjrsFn1HdV0rOwg3h5n5WUrmTRvEoVjCql7qI5EW4LYL2OMmjYK63KLnqYeau+rxfquRbwlzrrfuCcd6oRm3NHjPt12rn+onmQ8SeymGACj9hnFnufumZ1f2vXnbL74QAY8Tp2rLNuZAgx+bEOk5azQq/8bKbrnqGwuo0upzjP2nLyirqgwq8vJEXXRymhWPzEG4svhdywSrgNWmc4RFIXDOE10qEq1HvVCTd0Rh3R15cOm0yKTC/dlqVOeNR0gKEJkZZN6BwVQ8GB94wnz2tqX4MchYvpeNLlwP5fayznHAy1bO8p25kcbN8/9blPzG2jt+XnRHkgia+phexN3aiUxQl4Mv/s6r6XtuF82bnwPrVu8XnaWvR2tjPZ/1otHfFvqWCSsgT+azhEE2ThOnY6Tt3Qe8oe69Y0FWgfpw9n4ZqFvS50iQ/AMGMrMJ5l2YHf3516oqaMkmQzCjk8NPGQ6hN9LvQTYaDqE3xUq74ffvU2JJya/Wl07aUIi8a7JHBnwerQy+onpEL4udSwSjgOPmM7hd4U5cJfgsUk97uV1tQdM7/b1VMSDrqWVUqVKqaVKqeVKqfeUUtdlOoSvS51yj+kAfmdy+N1bMRQ/U1t/zJwtnX8xnWUYOoDH0njeVuCLWuuDgdnAl5VSR2cyiO9LHYuEo8BS0zn8zNSOsv4oUHes3/CF+S2tS9Da00NtI/RYtDI66E0Ftas99dei1COjH6q+L3XKHaYD+FmhRyefDMVVm5vnXuOvqYjvTveJqVtXLQMagZe01v+bySBBKfWjmLm3VyDkyvC7r6+1tR/92/UbVimtjR73TcPyaGX07+k+WWud0FrPBiqAI5VSB2YyTCBKHYuEuxjCJ6X4rFwafvd1XGfXQU/VNrQUal1tOssAfjqcH9JaNwOLgS9nMkwgSp3yK6Bz0GeJHXh17vdwzejpmb6ourZ0dG5ORfwh8ES6T1ZKTVRKjU99PQo4BViZyUCBKXUsEl4P3GU6hx8NZzJ/r01MJCcuXlc7bWI8/rbpLH38JFoZHcqn4mRgsVJqBe6c+i9prZ/PZKDAlDrlRmSy/yErVElfzPc2WuuyRdV1s/fb2v266SwpaxjieRJa6xVa60O01rO01gdqra/PdKhAlToWCdcjx62HLFd3lPWnEAofr2s4/kvtHblwW90bo5VRM7OmDiBQpU6J4B7gF2nK5R1lO3Pzhk1zL25q/quxqYihGrjf0LIHFLhSxyLhWuBO0zn8JIQ/ht99fbu5dU5kw6ZlbD+Zw0s3mrhPVjoCV+qUKuS4ddr8NPzuK9yx5fAH6tdXF3h7F9YoObziCGSpY5FwE3C16Rx+keuHtAZz6Nbu/Z6rqe8u1nqNR4u8OBe3pbcJZKlT7gbeMR3CD0IkBh1+n/9MJ5NuauPA27ePdK95tYtZd7Qz+7ftnPr7Dura+v9wWNeS5NTfd7Dfbe3sf1s7sebPPu87L3Qx5ieDnjY9oGnxeMUr62onlCeS2b4t04PRyuhfs7yMEQlsqWORcBJYaDqHH4TSuJ7g3NlF/Hn+6M/82xXHlbDi22NY9q0xnLZvIdcv6X//5II/dnLFsSV8cMkYll5YxqSy7Z8hb9claOrKzOh/fDI5YXF1zb4VPfG0T9kcombgiiy9dsYEttQAsUj4DXJgJopcl87e7xP2KmSXUZ9doY8t2f73ju7+b272/oYE8SScso9734gxxYrRRe4zE0nNFS918bOTh383y75KNKVOTd2Rh3d2ZeOQ1zXRyqiX2+7DEuhSp1wGGLlRmV+ERnDyydWvdDH1l208HO3h+hN3LOdHm5KML1Wc+dgWDrmznSsWdZFI7Ze7dWk3X9m3kMnlmX0bFkDBfQ2Nc/+tNaNTEb+LT64GDHypY5HwRuAbpnPksoIR7Ci74aRSqi8r55yDirh16Y5HeOJJeH1dnJtPLeWtC8tY05zk/mU91LUleeL9OAuPKh5J9AFVbdo894rNzW9mYCriHuCiaGXUFwf0A19qgFgk/DxyptlOZWLv9zmzinjygx13CFeMVczeI8TeEwooLFDMm1nIO/UJ3q1PsHpzkhm/bsf6VRtbemDGr9tGnKOvBa1tx97SuPH9EU5F/INoZfStjIXKsrwodcpluOfqij6Ge/LJqk3bV1zPrIzz+d12fDsdMSVEc5dmQ4f7wfFqLMH+E0OE9y2i4fJyYpe6j9FFsPo72bkR/Be3dM5+rK5hQ0jrumH8+IvATZnOlE15U+pYJNwOLACfH5TNgnSG319/cgvH/K6DDzclqfhFG797pxv7la0ceHs7s+5oZ9GaOLd8uRRw92h/41n3KthQgeLmU0o56cEtHHRHO1rDhYcVZfX36c/+3T0zXqiuU6XJ5EdD+LEGYEG0Muqrk3N8edfLkbBs5xog41fG+JlT/L2/HlDwyRzTObzQplTraVOnrN4cCh06yFM1cGq0MvqyF7kyKW/W1L38GLmzx2f4/YyyoSjXeuzL62oP2qe7+2+DPPVGPxYa8rDUqdv1LADeM50lVxT49IKO4SqCoj/WNhx7wpbOnR3LfgO4xstMmZR3pYZPt6/PAJpMZ8kF+VZqcKcivm39hrnnNre+hta9D1WtBb6ay+d2DyYvSw0Qi4Q/Bs4CH15MnGEF6Lwr9Tb/09R8wnUbN/8DrTtxTwMN++GssYHkbakBYpHwIuC/TOcwza/XU2fKme0dR97ZsGFlgdZnRiujuTi54ZDkdakBYpHwb8nzyzTzeU2dkjy2q+tny8/952LTQTIh70sNEIuEf4LPTjDIpAKfTDyYRd+hquVR0yEyRUqdEouEryRPbwiQ52vq66lquc10iEySUn/Wt3Bv4ZNX8nHvd8oNVLVcazpEpkmpe0lNrDAfuNd0Fi+p/FtTa9wh9w9MB8kGKXUfsUg4EYuELwBuNp3FK3k2/O4BzqGq5Temg2SLlHonYpHwFYBtOocXCtD58j7oAE6nquUPpoNkU778zxyWWCR8I3AhAT9BJU+G3xuBL1LV8qLpINkmpR5ELBK+B/eU0pFcZJ/T8mBNvQ44nqqWpaaDeCHo/zMzIhYJO8ARBPQiEJXGbKI+thQ4jqqWjN4uNpdJqdMUi4RXAUcDT5rOkmkqmGtqjXtC0RyqWmpMh/FS3k2SkAmW7djADQTkQ/GfJed/MEZ17Wc6RwY1AgvyYfu5P4F4U3otFglHgJNx73zoewE7pPUycHC+Fhqk1MMWi4QXAwcBD5vOMlIBGX7Hge8DX6KqpcF0GJNk+J0Blu38G3A7MMl0luFYWVK5ulT1zDCdYwQ+Ab5OVcubpoPkgiB8QhsXi4SfBPYHHjGdZThU/3fM8YMu3DnnDpBCbydr6gyzbOd44BbgENNZ0vVRyYK1xSo+3XSOIXoSuJyqlpjpILlG1tQZFouEXwcOB74JbDAcJ006ZDrBEERxzwz7dyl0/2RNnUWW7YwDfoh7S13vZ7BP06qS/6wuUomppnMMYhPuDJ93UdUS6NN2R0pK7QHLdvYCrgQuADJ339YMWV0yv7ZQJfc0nWMntgB34U5mILO/pkFK7SHLdiYDlwMXAWWG43zq45L5DSGV3MN0jj42A7cCv6GqZaPpMH4ipTbAsp3dcG/YdxGwq+E4fFxyzvqQ0rubzpGyBvg1cA9VLR2mw/iRlNogy3ZKgDNxL+/8AoYOLa0pOWdjgdK7mVh2isa9u+StwAtUteTPfYCyQEqdIyzb+RzwDeBcPD6JZU3JOZsKlDYxYlgJPAXcT1XLKgPLDyQpdY6xbKcIOBH4Ku513JOzvcw1JWc3FyjGZ3s5Ke/iHmN+iqoW30+cn4uk1DnMsh0FHAXMwy35vtlYztqSs1uVYmw2Xht3aP0m24scG86LKKWmAg8Cu6de8y6t9S2ZChkkUmofsWxnGjCn1+MAMnAC0dqSs9uVYsxIXyelA3gHeCv1WEJVS/1IX1QpNRmYrLV+RylVDvwDmKe1fn+krx00Umofs2xnPHAs7hls+6UeM4HSobzO2pKzO5Vi1DAidAMr2F7gt4APvDg5RCn1DHCr1vqlbC/Lb6TUAWPZTgFgAZ/HHa7vjrvjbWKvPyfiHicvAFhbcna3UhT3ealmoH4nj4bUn2uoaunO7m+0I6WUBbwGHKi1bvV6+blOSp3HLNsJAcWx0rMLcLdTk6lHIldPxVRKjQGWADdorZ8ynScXSamFbyilioDngRe11r8wnSdXSamFLyilFPAAsFlrfanpPLlMSi18QSk1B3gd99LLbWecfV9r/SdzqXKTlFqIgJFJEoQIGCm1EAEjpRYiYKTUQgSMlFqIgJFSCxEwUmohAkZKLUTASKmFCBgptRABI6UWImCk1EIEjJRaiICRUgsRMFJqIQJGSi1EwEiphQgYKbUQASOlFiJgpNRCBIyUWoiAkVILETBSaiECRkotRMD8HwyoGum1EmdFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "r62hugLteaVB",
        "outputId": "cac42ebb-b79a-45c9-fdbf-8ded0a35cf08"
      },
      "source": [
        "%matplotlib inline\n",
        "plt.scatter(df.age,df.weight, color = 'green')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fd2f6ca6c10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "bPBK_ewifKFA",
        "outputId": "671f0384-0979-48c6-8b41-3774728b1484"
      },
      "source": [
        "#contagem:\n",
        "sns.countplot(df['weight'])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd301a5d150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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": "code",
      "metadata": {
        "id": "_CYXQKguxsvw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TYsguKbfq-T"
      },
      "source": [
        "##Limpeza Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "WOpgoU-ygOi3",
        "outputId": "81cf41d8-c503-4e68-ae11-5413e3e10b34"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 18,
      "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>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "      <th>cardio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19468.865814</td>\n",
              "      <td>1.349571</td>\n",
              "      <td>164.359229</td>\n",
              "      <td>74.205690</td>\n",
              "      <td>128.817286</td>\n",
              "      <td>96.630414</td>\n",
              "      <td>1.366871</td>\n",
              "      <td>1.226457</td>\n",
              "      <td>0.088129</td>\n",
              "      <td>0.053771</td>\n",
              "      <td>0.803729</td>\n",
              "      <td>0.499700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2467.251667</td>\n",
              "      <td>0.476838</td>\n",
              "      <td>8.210126</td>\n",
              "      <td>14.395757</td>\n",
              "      <td>154.011419</td>\n",
              "      <td>188.472530</td>\n",
              "      <td>0.680250</td>\n",
              "      <td>0.572270</td>\n",
              "      <td>0.283484</td>\n",
              "      <td>0.225568</td>\n",
              "      <td>0.397179</td>\n",
              "      <td>0.500003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10798.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>-150.000000</td>\n",
              "      <td>-70.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17664.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>19703.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>21327.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>23713.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>16020.000000</td>\n",
              "      <td>11000.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age        gender  ...        active        cardio\n",
              "count  70000.000000  70000.000000  ...  70000.000000  70000.000000\n",
              "mean   19468.865814      1.349571  ...      0.803729      0.499700\n",
              "std     2467.251667      0.476838  ...      0.397179      0.500003\n",
              "min    10798.000000      1.000000  ...      0.000000      0.000000\n",
              "25%    17664.000000      1.000000  ...      1.000000      0.000000\n",
              "50%    19703.000000      1.000000  ...      1.000000      0.000000\n",
              "75%    21327.000000      2.000000  ...      1.000000      1.000000\n",
              "max    23713.000000      2.000000  ...      1.000000      1.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "H2SfL4dPfg7o",
        "outputId": "eb536be7-a21c-4d15-fc95-d954c4c5a56d"
      },
      "source": [
        "#verificação de valores com Na\n",
        "total = df.isnull().sum().sort_values(ascending=False)\n",
        "percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
        "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing_data.head(20)"
      ],
      "execution_count": 17,
      "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>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cardio</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>active</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alco</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoke</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gluc</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cholesterol</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ap_lo</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ap_hi</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>height</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Total  Percent\n",
              "cardio           0      0.0\n",
              "active           0      0.0\n",
              "alco             0      0.0\n",
              "smoke            0      0.0\n",
              "gluc             0      0.0\n",
              "cholesterol      0      0.0\n",
              "ap_lo            0      0.0\n",
              "ap_hi            0      0.0\n",
              "weight           0      0.0\n",
              "height           0      0.0\n",
              "gender           0      0.0\n",
              "age              0      0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABRuxEmugnO3",
        "outputId": "3ada9afa-5707-4e05-e3f9-f263e238c118"
      },
      "source": [
        "df.value_counts()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age    gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  alco  active  cardio\n",
              "16793  1       165     68.0    120    80     1            1     0      0     1       0         2\n",
              "21778  1       160     58.0    120    80     1            1     0      0     1       0         2\n",
              "19858  1       165     68.0    120    80     1            1     0      0     1       0         2\n",
              "18955  1       165     75.0    120    80     1            1     0      0     1       1         2\n",
              "20516  1       164     66.0    120    80     1            1     0      0     0       0         2\n",
              "                                                                                              ..\n",
              "20949  1       161     85.0    120    80     1            1     0      0     1       0         1\n",
              "               160     79.0    140    85     2            1     0      0     1       1         1\n",
              "               158     91.0    140    90     3            3     0      0     0       0         1\n",
              "               150     90.0    120    80     2            2     0      0     1       0         1\n",
              "10798  2       175     92.0    100    60     1            1     0      0     1       0         1\n",
              "Length: 69976, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WxC98YLg07K",
        "outputId": "b21edd90-48c6-4be7-8ef0-6a977e3e9cea"
      },
      "source": [
        "for col in df:\n",
        "\tprint(f'{col}: {df[col].unique()}')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "age: [18393 20228 18857 ... 14925 17727 17926]\n",
            "gender: [2 1]\n",
            "height: [168 156 165 169 151 157 178 158 164 173 181 172 170 154 162 163 153 159\n",
            " 166 155 160 175 171 152 187 148 179 180 188 185 167 183 174 176 161 184\n",
            " 177 182  76 149 142 150 144 147 186 146 141 195 140 198 145 143 196 138\n",
            " 194 190 134 136 100 120 189 137 192 122 250 191 117  70  97 119 130 110\n",
            " 193  75 132  71 135  67 125 139 133  74  98 112 207  68  55  81  80  64\n",
            "  91  60 109  72 197  65 128 105 108 200 104 111 113  96 131  59  66  99\n",
            "  57]\n",
            "weight: [ 62.    85.    64.    82.    56.    67.    93.    95.    71.    68.\n",
            "  80.    60.    78.   112.    75.    52.    83.    69.    90.    45.\n",
            "  65.    59.    66.    74.   105.    73.    55.    70.    72.    63.\n",
            "  50.   107.    84.    77.    79.    76.    58.   115.    97.    53.\n",
            "  57.    49.   110.    94.    92.    87.   103.    88.    99.   100.\n",
            "  61.    48.    54.    51.    47.    91.   104.    81.    98.   108.\n",
            "  89.   101.    86.    65.5  142.    96.    44.    41.   169.   116.\n",
            " 200.    64.8  120.   117.   125.   106.   114.   113.   129.   124.\n",
            "  42.   123.   111.   102.   109.    46.   139.   150.   138.   131.\n",
            "  43.    83.6  134.   106.5  121.    40.   141.   119.   126.   177.\n",
            " 165.   122.    62.3  118.    89.5  132.   155.   130.   159.    84.6\n",
            " 140.    82.3   30.    67.9  127.    67.5   74.3  149.   180.   128.\n",
            "  60.2   64.5   58.7   90.5   37.    80.8  166.   161.    92.2   75.6\n",
            " 136.    82.9  154.    68.9  170.    84.5  135.    72.5  146.    70.5\n",
            "  74.2   84.3   38.    54.5  145.    68.5   79.5   72.8   73.2   96.5\n",
            "  53.2   39.    61.2  168.   133.    54.35  50.7   79.94  45.8  153.\n",
            "  34.    62.4  167.   156.    35.    70.3   31.    60.5   69.8   64.7\n",
            "  64.1  152.    59.5  163.    75.5   78.2  162.   121.3  178.    82.5\n",
            "  90.7   80.6   55.6   59.8   74.77  32.    70.8   57.4   73.8   56.2\n",
            "  69.5   71.5   89.1  158.    76.5   35.45  86.5   63.8   23.   144.\n",
            "  70.2   68.4  148.   164.    22.    66.5   54.9   59.2   65.3  137.\n",
            "  62.2   50.5   36.    61.5   80.5   81.1   53.6   11.    66.4   76.7\n",
            "  28.    68.3   60.6   76.8   58.8  181.    75.2   83.5   64.3  175.\n",
            "  84.9   80.7   84.7   60.1  105.5   85.5  121.8   51.5   53.9   42.2\n",
            "  74.5  114.6   73.5   71.3   33.    66.3   53.3   67.8   70.7   52.3\n",
            "  82.4   63.4   71.2   62.5   63.82  88.5   89.9   72.1  106.4  160.\n",
            "  68.2   53.67 171.    55.2   94.5  143.    62.7   53.5  109.5   10.\n",
            "  82.1   57.8   84.8   21.    29.   183.    58.5   57.6   78.5  172.\n",
            " 109.7   61.3   67.3   94.7  147.    55.4   99.9 ]\n",
            "ap_hi: [  110   140   130   150   100   120   145   170   135   125    90   180\n",
            "   160   133   190    80   122   169   126   158   200    14   123    70\n",
            "   161   147   115   137   153    11   148   105   220   119   141   165\n",
            "   164    12   124   172   902   162   906   117   134   166   210   176\n",
            "   116    10   121    16   112   159   113   118   155   142   131   157\n",
            "   136   146   138  -100   909   109    85   106   129    93     7    95\n",
            "   179   156   168   132   104   103   178   175   128   151    15   139\n",
            " 11500   127    17   108   144   102     1  1420    13   143   701   107\n",
            "   184   149   167   114   101    60  1500   181   171   202  -115   111\n",
            "   907    20   188   185   163   173   154   177  -140   174  -120 14020\n",
            "  1400   240   191   197  1620   152    96   199  -150  1130   193    99\n",
            "   196   309   401 16020  1202   806  1300   230   207   215    97  1409\n",
            " 11020    24   960 13010  1110   195  1205   187  2000]\n",
            "ap_lo: [   80    90    70   100    60    85    89   110    65    63    79  1100\n",
            "  1000   800   120    50    30   109    84  1033   150    91    40    73\n",
            "    78    75    86    87  1001    82    95    69    74    97    81  1200\n",
            "    83   119     0    93   105 10000    99    77    59  8044   140    92\n",
            "  1044   108   125   115    68    61   106   102    94    66    52   170\n",
            "    76   160    62    96   130   113    67  9100    10    88   902     8\n",
            "   112   104    71    72  1008    98  2088    20   802  8000  1022   850\n",
            "   708    57   101  9011  1011    64  1007  1177  7100    45   709  8500\n",
            "    58  1110  8099  1088   126  1077  1120     7   103  1125   180   121\n",
            "  8100   710  5700  8079  1111  1003     6  1900   809   114   801  1002\n",
            "    53   111     1   118    56   182   810     9  7099 11000  9800  8200\n",
            "  1139   107   820    55  1400   190   900   122  6800   135   700    15\n",
            "  1101   910  1140  1211   -70    54  8077   901   880   870   585    49\n",
            "   602]\n",
            "cholesterol: [1 3 2]\n",
            "gluc: [1 2 3]\n",
            "smoke: [0 1]\n",
            "alco: [0 1]\n",
            "active: [1 0]\n",
            "cardio: [0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bz4aCwV3f9KA"
      },
      "source": [
        "#verificação de outliers:\n",
        "#quero tirar valores de peso e altura em que o quantile seja menor que 0.05 e maior que 0.95\n",
        "min_thresold, max_thresold = df.weight.quantile([0.05, 0.95])\n",
        "min_thresold, max_thresold\n",
        "df = df[(df.weight<max_thresold) & (df.weight>min_thresold)]\n"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SiR72ObshDcq"
      },
      "source": [
        "min_thresold, max_thresold = df.height.quantile([0.05, 0.95])\n",
        "min_thresold, max_thresold\n",
        "df = df[(df.height<max_thresold) & (df.height>min_thresold)]\n"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8hW_3jwhIkq",
        "outputId": "7fca4786-5a61-42f3-82ae-6d0daf157692"
      },
      "source": [
        "#vendo o skew\n",
        "from scipy.stats import skew\n",
        "for col in df:\n",
        "    print(skew(df[col]))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-0.30546752584969594\n",
            "0.6533220453748914\n",
            "0.01313384846006273\n",
            "0.47739562690108134\n",
            "92.57250838192188\n",
            "32.76563800725974\n",
            "1.6526387926638288\n",
            "2.458527071646082\n",
            "2.9908569547710497\n",
            "4.045794332797786\n",
            "-1.538184811924593\n",
            "0.014989251389858052\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWyjj_zuh-yx"
      },
      "source": [
        "##Treinamento e modelagem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCPbIOYeiL0E"
      },
      "source": [
        "X = df.drop('cardio', axis = 1)\n",
        "y = df['cardio']"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTai4IIiiBdD"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0D-1lxJsie6h"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "transfer = StandardScaler()\n",
        "X_train = transfer.fit_transform(X_train)\n",
        "X_test = transfer.transform(X_test)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZJTBO-Iiri6",
        "outputId": "9f4d9e1d-0e90-4a8b-ff03-46a12ef78e92"
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV,KFold\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics  import accuracy_score,classification_report,roc_auc_score,plot_roc_curve,plot_precision_recall_curve\n",
        "\n",
        "logreg=Pipeline([('logistic',LogisticRegression())])\n",
        "decision=Pipeline([('dt',DecisionTreeClassifier())])\n",
        "randomforest=Pipeline([('rf',RandomForestClassifier())])\n",
        "naivebayes=Pipeline([('nb',GaussianNB())])\n",
        "knn=Pipeline([('knn',KNeighborsClassifier())])\n",
        "gbc=Pipeline([('gbc',GradientBoostingClassifier())])\n",
        "adaboost=Pipeline([('adaboost',AdaBoostClassifier())])\n",
        "sgdclassifier=Pipeline([('SGDclassifier',SGDClassifier())])\n",
        "svc=Pipeline([('svc',SVC())])\n",
        "mlpclass=Pipeline([('mlpc',MLPClassifier())])\n",
        "mypipeline1=[logreg,decision,randomforest,naivebayes,knn,gbc,adaboost,sgdclassifier,svc,mlpclass]\n",
        "pipelinedict1={0:'logistic',1:'dt',2:'rf',3:'nb',4:'knn',5:'gbc',6:'adaboost',7:'SGDclassifier',\n",
        "              8:'svc',9:'mlpc'}\n",
        "for i in mypipeline1:\n",
        "    i.fit(X_train,y_train)\n",
        "model_score=[]\n",
        "model_name=[]\n",
        "for i,j in enumerate(mypipeline1):\n",
        "    print(\"{} test accuracy: {}\".format(pipelinedict1[i],j.score(X_test,y_test)))\n",
        "    model_name.append(pipelinedict1[i])\n",
        "    model_score.append(j.score(X_test,y_test))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "logistic test accuracy: 0.7196800461194782\n",
            "dt test accuracy: 0.6332780860416517\n",
            "rf test accuracy: 0.7064206961158752\n",
            "nb test accuracy: 0.5892483966275132\n",
            "knn test accuracy: 0.638682712401816\n",
            "gbc test accuracy: 0.733587951286301\n",
            "adaboost test accuracy: 0.7269582762844995\n",
            "SGDclassifier test accuracy: 0.7165813936729841\n",
            "svc test accuracy: 0.7232831303595878\n",
            "mlpc test accuracy: 0.7294804352525762\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rw4j9m2mwRTF"
      },
      "source": [
        "#Conclusão\n",
        "Ao compararmos o modelo com melhor acurácia (gbc) com o “modelo
Médico” citado na introdução, percebe-se que uma acurácia muito semelhante foi
obtida (73.3% x 72%), mesmo se utilizando de “features”, “datasets” e algoritmos
diferentes.
Após a realização do treinamento e teste de “score”, analisa-se que uma
maior quantidade “features”, assim como um “dataset” maior poderiam ter sido
fatores que melhorariam a acurácia do modelo.\n",
        "\n",
       
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8uO_622ej06d"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}