diagnostico_diabetes.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ql5KifrgbOb6"
   },
   "source": [
    "# Resumo\n",
    "Este projeto irá explorar brevemente os dados de um Hospital com possíveis pacientes diabéticos, assim sendo necessário criar modelos preditivos para auxiliar no diagnóstico dos futuros pacientes baseado no histórico dos pacientes já registrados \n",
    "\n",
    " ![](https://blogpilates.com.br/wp-content/uploads/2020/04/pilates-para-diabetes-capa.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Oe-greVwbOb7"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "BpzWrYZGbOb7",
    "outputId": "0dc8d6dc-4dec-4478-afaa-4a7a60dd6b9b"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
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       "      <td>29</td>\n",
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       "      <td>31</td>\n",
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       "      <th>2</th>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0      148             72             35        0  33.6   \n",
       "1       85             66             29        0  26.6   \n",
       "2      183             64              0        0  23.3   \n",
       "3       89             66             23       94  28.1   \n",
       "4      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"diabetes.csv\").iloc[:,1:]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "76QLw5VQbOb9"
   },
   "source": [
    "## Traduzindo colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "bnYCRQ5UbOb9"
   },
   "outputs": [],
   "source": [
    "df.columns = ['Gestacao', 'Glicose', 'Pressao arterial',\n",
    "       'Expessura da pele', 'Insulina', 'IMC', 'Idade', 'Tem diabetes?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "CYbcLz_cbOb9",
    "outputId": "45791674-1ba8-4e5e-e82f-6b75e50cb5c3"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gestacao</th>\n",
       "      <th>Glicose</th>\n",
       "      <th>Pressao arterial</th>\n",
       "      <th>Expessura da pele</th>\n",
       "      <th>Insulina</th>\n",
       "      <th>IMC</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Tem diabetes?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gestacao  Glicose  Pressao arterial  Expessura da pele  Insulina    IMC  \\\n",
       "0       148       72                35                  0      33.6  0.627   \n",
       "1        85       66                29                  0      26.6  0.351   \n",
       "2       183       64                 0                  0      23.3  0.672   \n",
       "3        89       66                23                 94      28.1  0.167   \n",
       "4       137       40                35                168      43.1  2.288   \n",
       "\n",
       "   Idade  Tem diabetes?  \n",
       "0     50              1  \n",
       "1     31              0  \n",
       "2     32              1  \n",
       "3     21              0  \n",
       "4     33              1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DXbcCk08bOb-"
   },
   "source": [
    "## Informações adicionais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ECr8WRWYbOb-",
    "outputId": "96e53230-4a03-4223-a671-d59c1ffbbd19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Gestacao           768 non-null    int64  \n",
      " 1   Glicose            768 non-null    int64  \n",
      " 2   Pressao arterial   768 non-null    int64  \n",
      " 3   Expessura da pele  768 non-null    int64  \n",
      " 4   Insulina           768 non-null    float64\n",
      " 5   IMC                768 non-null    float64\n",
      " 6   Idade              768 non-null    int64  \n",
      " 7   Tem diabetes?      768 non-null    int64  \n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 48.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6nBxtNdJbOb-"
   },
   "source": [
    "## Verificando dados nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fo0jMHKTbOb-",
    "outputId": "da44f40f-efb1-4aa1-ab0d-7b68b653b824"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gestacao             0\n",
       "Glicose              0\n",
       "Pressao arterial     0\n",
       "Expessura da pele    0\n",
       "Insulina             0\n",
       "IMC                  0\n",
       "Idade                0\n",
       "Tem diabetes?        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mkmZbeEQbOb-"
   },
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uklg1x10uHqN"
   },
   "source": [
    "## Distribuição das idades dos diagnosticados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "id": "LRuZUGKriUja",
    "outputId": "bf1c321c-0545-4817-ac65-8066c11a50f1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(dpi=120)\n",
    "sns.set_palette(\"coolwarm\")\n",
    "sns.histplot(data=df, x='Idade', bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qj9Tgk_Qvojb"
   },
   "source": [
    "Obs: Notamos que a maioria dos pacientes tem entre 22 e 30 anos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lROX7aYpbOb-"
   },
   "source": [
    "\n",
    "## Distribuição de pessoas com e sem diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "drASE8wrbOb-",
    "outputId": "26b1241f-6418-4731-fda4-c71138234fa8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=120)\n",
    "sns.set_palette(\"inferno\")\n",
    "sns.countplot(x='Tem diabetes?', data=df)\n",
    "plt.ylabel(\"Quantidade\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nzjtUhi2bOb_"
   },
   "source": [
    "## Visualizando distribuição dos diagnosticados baseados em seu IMC e em sua glicose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "id": "OhKJJshbbOb_",
    "outputId": "80d30cf6-e4ce-46b9-9aa3-3d3f749b5068",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=130)\n",
    "sns.set_palette(\"Set1\")\n",
    "sns.scatterplot(y='Glicose', x='IMC', hue='Tem diabetes?', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oC_K3X7abOb_"
   },
   "source": [
    "Obs: É evidente que a maioria dos pacientes estão acima do peso, e os diagnosticados com diabetes são os que mais tem o percentual de glicose alto\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CqmVK2GHbOb_"
   },
   "source": [
    "## Distribuição da pressão arterial dos pacientes baseados em sua idade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "LuW8h2rQbOb_",
    "outputId": "a722dbe3-01c3-470d-af4c-93d0ff33c8cb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=120)\n",
    "sns.set_palette(\"PuBuGn_r\")\n",
    "sns.scatterplot(y='Idade', x='Pressao arterial', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IwNYl0xibOb_"
   },
   "source": [
    "Obs: Vemos que a idade nao tem uma correlação muito forte com a pressão arterial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "90j5M1cUbOb_"
   },
   "source": [
    "***\n",
    "## Agrupando média de glicose de quem tem e de quem nao tem diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "uqAu8MIWbOb_",
    "outputId": "8aa1eb18-a12e-4105-c84c-5a5c28d5121c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Glicose</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tem diabetes?</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.824627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.184000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Glicose\n",
       "Tem diabetes?           \n",
       "1              70.824627\n",
       "0              68.184000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Glicose\",\"Tem diabetes?\"]].groupby(\"Tem diabetes?\").mean().sort_values(by=\"Glicose\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pJmZPMWEbOb_"
   },
   "source": [
    "## Média de insulina de quem tem e de quem nao tem diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "uOy5zziSbOb_",
    "outputId": "5d547de6-ad8f-4701-c0d4-b7537914e5cb"
   },
   "outputs": [
    {
     "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>Insulina</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tem diabetes?</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.142537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.304200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Insulina\n",
       "Tem diabetes?           \n",
       "1              35.142537\n",
       "0              30.304200"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Insulina\",\"Tem diabetes?\"]].groupby(\"Tem diabetes?\").mean().sort_values(by=\"Insulina\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yBI4U0BdbOcA"
   },
   "source": [
    "## Média de idade das pessoas diagnosticadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "h2ygVbFhbOcA",
    "outputId": "6d8ce8f0-93a5-4a06-c9f6-04786d6334da"
   },
   "outputs": [
    {
     "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>Idade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tem diabetes?</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.067164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.190000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Idade\n",
       "Tem diabetes?           \n",
       "1              37.067164\n",
       "0              31.190000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Idade\",\"Tem diabetes?\"]].groupby(\"Tem diabetes?\").mean().sort_values(by=\"Idade\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y1lpx8SsbOcA"
   },
   "source": [
    "## Expessura da pele dos diagnosticados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "luekYCTRbOcA",
    "outputId": "1e59b945-8f8e-4785-958e-f131d183862b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Expessura da pele</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tem diabetes?</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.335821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.792000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Expessura da pele\n",
       "Tem diabetes?                   \n",
       "1                     100.335821\n",
       "0                      68.792000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Expessura da pele\",\"Tem diabetes?\"]].groupby(\"Tem diabetes?\").mean().sort_values(by=\"Expessura da pele\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mxaF1xjvbOcA"
   },
   "source": [
    "## Pressão arterial dos diagnosticados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "0oOdVxPQbOcA",
    "outputId": "2247baa7-a1bb-40a4-9550-411aa9984500"
   },
   "outputs": [
    {
     "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>Pressao arterial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tem diabetes?</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.164179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.664000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pressao arterial\n",
       "Tem diabetes?                  \n",
       "1                     22.164179\n",
       "0                     19.664000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Pressao arterial\",\"Tem diabetes?\"]].groupby(\"Tem diabetes?\").mean().sort_values(by=\"Pressao arterial\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hbpLgTUibOcA"
   },
   "source": [
    "## Índice de massa corporal(IMC) dos diagnosticados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "tfAAWVRSbOcA",
    "outputId": "8b1c177e-d12c-4eb4-a953-46ec40a707cb"
   },
   "outputs": [
    {
     "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>IMC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tem diabetes?</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.550500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    IMC\n",
       "Tem diabetes?          \n",
       "1              0.550500\n",
       "0              0.429734"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"IMC\",\"Tem diabetes?\"]].groupby(\"Tem diabetes?\").mean().sort_values(by=\"IMC\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ytYCd42ubOcB"
   },
   "source": [
    "## Visualizando correlações entre as variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "i1CTTFD8bOcB",
    "outputId": "56e115c0-1904-4c7b-cf6e-9f32b63af8ac"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 660x440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=110)\n",
    "sns.heatmap(df.corr(), annot=True, cmap=\"Blues\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wOylcsKRbOcB"
   },
   "source": [
    "***\n",
    "# Preparando dados para início dos modelos\n",
    "Modelos escolhidos: Adaboost, GradientBoosting, Random Forest, SVC e Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "id": "ZowrIOi9bOcB"
   },
   "outputs": [],
   "source": [
    "X = df.drop(\"Tem diabetes?\", axis=1)\n",
    "y = df['Tem diabetes?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "id": "YMtAA7GubOcB"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, recall_score, f1_score, precision_score, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6x-riZAAbOcB"
   },
   "source": [
    "## Instanciando modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "id": "4aK3Nzm9bOcB"
   },
   "outputs": [],
   "source": [
    "adamodel = AdaBoostClassifier()\n",
    "gradmodel = GradientBoostingClassifier()\n",
    "logmodel = LogisticRegression()\n",
    "rfmodel = RandomForestClassifier()\n",
    "svcmodel = SVC()\n",
    "extmodel = ExtraTreesClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dBZoQD7HbOcB"
   },
   "source": [
    "## Treinando Modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "id": "4XByG_ZIbOcB"
   },
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.30, random_state=46)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nvU7TaEEbOcB",
    "outputId": "9d97d668-e754-4078-be31-7e273bc49857"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adamodel.fit(Xtrain, ytrain)\n",
    "gradmodel.fit(Xtrain, ytrain)\n",
    "logmodel.fit(Xtrain, ytrain)\n",
    "rfmodel.fit(Xtrain, ytrain)\n",
    "svcmodel.fit(Xtrain, ytrain)\n",
    "extmodel.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jTl1s7HMbOcB"
   },
   "source": [
    "## Armazenando predições"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "id": "Un26VHawbOcB"
   },
   "outputs": [],
   "source": [
    "adapred = adamodel.predict(Xtest)\n",
    "gradpred = gradmodel.predict(Xtest)\n",
    "logpred = logmodel.predict(Xtest)\n",
    "rfpred = rfmodel.predict(Xtest)\n",
    "svcpred = svcmodel.predict(Xtest)\n",
    "extpred = extmodel.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rfHZL7ePbOcB"
   },
   "source": [
    "## Armazenando Métricas gerais(acurácia, revocação, precisão e F1-score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.array([adapred, gradpred, logpred, rfpred, svcpred, extpred])\n",
    "precision = []\n",
    "recall = []\n",
    "f1 = []\n",
    "accuracy = []\n",
    "for i in scores:\n",
    "    accuracy.append(accuracy_score(ytest, i)*100)\n",
    "    precision.append(precision_score(ytest, i)*100)\n",
    "    recall.append(recall_score(ytest, i)*100)\n",
    "    f1.append(f1_score(ytest, i)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelos = \"Adaboost GradientBoosting LogisticRegression RandomForest SVC ExtraTree\".split()\n",
    "metricasGerais = pd.DataFrame({\n",
    "        \"Modelo\":modelos,\n",
    "        \"Acurácia\":accuracy,\n",
    "        \"Precisão\":precision,\n",
    "        \"Revocação\":recall,\n",
    "        \"F1\":f1\n",
    "             }).sort_values(by=\"Revocação\",ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exibindo ranking de modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "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>Modelo</th>\n",
       "      <th>Acurácia</th>\n",
       "      <th>Precisão</th>\n",
       "      <th>Revocação</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>74.458874</td>\n",
       "      <td>61.538462</td>\n",
       "      <td>70.00</td>\n",
       "      <td>65.497076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>73.593074</td>\n",
       "      <td>60.674157</td>\n",
       "      <td>67.50</td>\n",
       "      <td>63.905325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adaboost</td>\n",
       "      <td>70.129870</td>\n",
       "      <td>56.179775</td>\n",
       "      <td>62.50</td>\n",
       "      <td>59.171598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>77.056277</td>\n",
       "      <td>68.493151</td>\n",
       "      <td>62.50</td>\n",
       "      <td>65.359477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ExtraTree</td>\n",
       "      <td>74.891775</td>\n",
       "      <td>64.473684</td>\n",
       "      <td>61.25</td>\n",
       "      <td>62.820513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>77.922078</td>\n",
       "      <td>74.576271</td>\n",
       "      <td>55.00</td>\n",
       "      <td>63.309353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Modelo   Acurácia   Precisão  Revocação         F1\n",
       "1    GradientBoosting  74.458874  61.538462      70.00  65.497076\n",
       "3        RandomForest  73.593074  60.674157      67.50  63.905325\n",
       "0            Adaboost  70.129870  56.179775      62.50  59.171598\n",
       "2  LogisticRegression  77.056277  68.493151      62.50  65.359477\n",
       "5           ExtraTree  74.891775  64.473684      61.25  62.820513\n",
       "4                 SVC  77.922078  74.576271      55.00  63.309353"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metricasGerais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G5m3H9vMxvrD"
   },
   "source": [
    "Obs:Neste caso, **priorizando a revocação**, Gradient Boosting obteve o melhor resultado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bFbU06a5bOcC"
   },
   "source": [
    "## Melhorando performance do modelo com melhor desempenho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecionando parametros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'n_estimators':[100,200,300,400,500],\n",
    "    'min_samples_leaf':[1,2,3],\n",
    "    'max_depth':[1,2,3]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(GradientBoostingClassifier(random_state=46), param_grid=params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Executando treino do modelo escolhido com todos os parametros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=GradientBoostingClassifier(random_state=46),\n",
       "             param_grid={'max_depth': [1, 2, 3], 'min_samples_leaf': [1, 2, 3],\n",
       "                         'n_estimators': [100, 200, 300, 400, 500]})"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Melhores parametros encontrados durante o treino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'min_samples_leaf': 3, 'n_estimators': 100}"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Armazenando as novas predições"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridpred = grid.predict(Xtest)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passando o novo resultado das métricas para um novo dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "modeloFinal = pd.DataFrame({\n",
    "    \"Modelo melhorado\":\"Gradient Boosting\",\n",
    "    \"Acurácia\":[accuracy_score(ytest, gridpred)*100],\n",
    "    \"Precisão\":[precision_score(ytest, gridpred)*100],\n",
    "    \"Revocacão\":[recall_score(ytest, gridpred)*100],\n",
    "    \"F1\":[f1_score(ytest, gridpred)*100]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo antes do Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "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>Modelo</th>\n",
       "      <th>Acurácia</th>\n",
       "      <th>Precisão</th>\n",
       "      <th>Revocação</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>74.458874</td>\n",
       "      <td>61.538462</td>\n",
       "      <td>70.0</td>\n",
       "      <td>65.497076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Modelo   Acurácia   Precisão  Revocação         F1\n",
       "1  GradientBoosting  74.458874  61.538462       70.0  65.497076"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metricasGerais[metricasGerais[\"Modelo\"] == \"GradientBoosting\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo após Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "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>Modelo melhorado</th>\n",
       "      <th>Acurácia</th>\n",
       "      <th>Precisão</th>\n",
       "      <th>Revocacão</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>88.311688</td>\n",
       "      <td>81.927711</td>\n",
       "      <td>85.0</td>\n",
       "      <td>83.435583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Modelo melhorado   Acurácia   Precisão  Revocacão         F1\n",
       "0  Gradient Boosting  88.311688  81.927711       85.0  83.435583"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeloFinal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_DExyXqHbOcC"
   },
   "source": [
    "# Conclusão\n",
    "Apesar do modelo SVC ter apresentado uma melhor acurácia e precisão, por se tratar de uma classificação de doença, a prioridade é minimizar ao máximo os falsos negativos(ter a doença, e nao acusar), e o modelo Gradient Boosting obteve o melhor score de revocação, sendo a melhor opção para este caso"
   ]
  }
 ],
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