TestEDA.ipynb

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   "file_extension": ".py",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    id  gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0   67  Female  17.0           0.0            0.0           No        Private   \n",
       "1  210    Male  81.0           0.0            0.0          Yes  Self-employed   \n",
       "2  242    Male   4.0           NaN            0.0           No       children   \n",
       "3  711    Male  81.0           0.0            NaN          Yes        Private   \n",
       "4  724    Male  17.0           0.0            0.0           No        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status blood  height  \n",
       "0          Urban              92.97   NaN  formerly smoked    AB     NaN  \n",
       "1          Rural              91.54  31.4     never smoked     A    1.60  \n",
       "2          Urban              98.56  17.5          Unknown     O     NaN  \n",
       "3          Rural              92.96  22.2     never smoked    AB     NaN  \n",
       "4          Rural              81.77  44.7     never smoked     O    1.34  "
      ],
      "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>gender</th>\n      <th>age</th>\n      <th>hypertension</th>\n      <th>heart_disease</th>\n      <th>ever_married</th>\n      <th>work_type</th>\n      <th>Residence_type</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>smoking_status</th>\n      <th>blood</th>\n      <th>height</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>67</td>\n      <td>Female</td>\n      <td>17.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>92.97</td>\n      <td>NaN</td>\n      <td>formerly smoked</td>\n      <td>AB</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>210</td>\n      <td>Male</td>\n      <td>81.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Self-employed</td>\n      <td>Rural</td>\n      <td>91.54</td>\n      <td>31.4</td>\n      <td>never smoked</td>\n      <td>A</td>\n      <td>1.60</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>242</td>\n      <td>Male</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>children</td>\n      <td>Urban</td>\n      <td>98.56</td>\n      <td>17.5</td>\n      <td>Unknown</td>\n      <td>O</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>711</td>\n      <td>Male</td>\n      <td>81.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>92.96</td>\n      <td>22.2</td>\n      <td>never smoked</td>\n      <td>AB</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>724</td>\n      <td>Male</td>\n      <td>17.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>81.77</td>\n      <td>44.7</td>\n      <td>never smoked</td>\n      <td>O</td>\n      <td>1.34</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "raw_df = pd.read_csv('./Dataset/public_test.csv')\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0       67  Female  17.0           0.0            0.0           No   \n",
       "1      210    Male  81.0           0.0            0.0          Yes   \n",
       "2      242    Male   4.0           NaN            0.0           No   \n",
       "3      711    Male  81.0           0.0            NaN          Yes   \n",
       "4      724    Male  17.0           0.0            0.0           No   \n",
       "..     ...     ...   ...           ...            ...          ...   \n",
       "495  72393  Female  23.0           0.0            NaN          Yes   \n",
       "496  72491    Male  53.0           0.0            0.0          Yes   \n",
       "497  72562  Female  57.0           0.0            NaN          Yes   \n",
       "498  72792  Female  53.0           NaN            0.0          Yes   \n",
       "499  72940  Female   2.0           0.0            NaN           No   \n",
       "\n",
       "         work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0          Private          Urban              92.97   NaN  formerly smoked   \n",
       "1    Self-employed          Rural              91.54  31.4     never smoked   \n",
       "2         children          Urban              98.56  17.5          Unknown   \n",
       "3          Private          Rural              92.96  22.2     never smoked   \n",
       "4          Private          Rural              81.77  44.7     never smoked   \n",
       "..             ...            ...                ...   ...              ...   \n",
       "495       Govt_job          Rural              84.93  24.2     never smoked   \n",
       "496        Private          Urban              74.66  29.2           smokes   \n",
       "497        Private          Rural              64.37  32.8     never smoked   \n",
       "498        Private          Rural              77.94   NaN     never smoked   \n",
       "499       children          Urban             102.92  17.6          Unknown   \n",
       "\n",
       "    blood  height  \n",
       "0      AB     NaN  \n",
       "1       A    1.60  \n",
       "2       O     NaN  \n",
       "3      AB     NaN  \n",
       "4       O    1.34  \n",
       "..    ...     ...  \n",
       "495     A    1.82  \n",
       "496     O    1.66  \n",
       "497    AB    1.56  \n",
       "498    AB    1.56  \n",
       "499    AB     NaN  \n",
       "\n",
       "[500 rows x 13 columns]"
      ],
      "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>gender</th>\n      <th>age</th>\n      <th>hypertension</th>\n      <th>heart_disease</th>\n      <th>ever_married</th>\n      <th>work_type</th>\n      <th>Residence_type</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>smoking_status</th>\n      <th>blood</th>\n      <th>height</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>67</td>\n      <td>Female</td>\n      <td>17.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>92.97</td>\n      <td>NaN</td>\n      <td>formerly smoked</td>\n      <td>AB</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>210</td>\n      <td>Male</td>\n      <td>81.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Self-employed</td>\n      <td>Rural</td>\n      <td>91.54</td>\n      <td>31.4</td>\n      <td>never smoked</td>\n      <td>A</td>\n      <td>1.60</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>242</td>\n      <td>Male</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>children</td>\n      <td>Urban</td>\n      <td>98.56</td>\n      <td>17.5</td>\n      <td>Unknown</td>\n      <td>O</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>711</td>\n      <td>Male</td>\n      <td>81.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>92.96</td>\n      <td>22.2</td>\n      <td>never smoked</td>\n      <td>AB</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>724</td>\n      <td>Male</td>\n      <td>17.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>No</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>81.77</td>\n      <td>44.7</td>\n      <td>never smoked</td>\n      <td>O</td>\n      <td>1.34</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>72393</td>\n      <td>Female</td>\n      <td>23.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Yes</td>\n      <td>Govt_job</td>\n      <td>Rural</td>\n      <td>84.93</td>\n      <td>24.2</td>\n      <td>never smoked</td>\n      <td>A</td>\n      <td>1.82</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>72491</td>\n      <td>Male</td>\n      <td>53.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>74.66</td>\n      <td>29.2</td>\n      <td>smokes</td>\n      <td>O</td>\n      <td>1.66</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>72562</td>\n      <td>Female</td>\n      <td>57.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>64.37</td>\n      <td>32.8</td>\n      <td>never smoked</td>\n      <td>AB</td>\n      <td>1.56</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>72792</td>\n      <td>Female</td>\n      <td>53.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>77.94</td>\n      <td>NaN</td>\n      <td>never smoked</td>\n      <td>AB</td>\n      <td>1.56</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>72940</td>\n      <td>Female</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>children</td>\n      <td>Urban</td>\n      <td>102.92</td>\n      <td>17.6</td>\n      <td>Unknown</td>\n      <td>AB</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 13 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "df = raw_df.copy()\n",
    "df"
   ]
  },
  {
   "source": [
    "## Analyze data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   count          mean           std    min        25%  \\\n",
       "id                 500.0  37196.254000  20978.931494  67.00  18386.500   \n",
       "age                489.0     42.885399     22.546490   0.24     26.000   \n",
       "hypertension       455.0      0.112088      0.315822   0.00      0.000   \n",
       "heart_disease      366.0      0.057377      0.232880   0.00      0.000   \n",
       "avg_glucose_level  494.0    106.475020     47.208236  55.35     76.675   \n",
       "bmi                457.0     29.070460      8.277457  14.00     23.500   \n",
       "height             376.0      1.620718      0.154750   0.93      1.520   \n",
       "\n",
       "                         50%         75%       max  \n",
       "id                 37545.500  55267.2500  72940.00  \n",
       "age                   43.000     60.0000     82.00  \n",
       "hypertension           0.000      0.0000      1.00  \n",
       "heart_disease          0.000      0.0000      1.00  \n",
       "avg_glucose_level     89.415    115.4075    263.32  \n",
       "bmi                   27.900     33.2000     92.00  \n",
       "height                 1.650      1.7400      1.85  "
      ],
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>id</th>\n      <td>500.0</td>\n      <td>37196.254000</td>\n      <td>20978.931494</td>\n      <td>67.00</td>\n      <td>18386.500</td>\n      <td>37545.500</td>\n      <td>55267.2500</td>\n      <td>72940.00</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>489.0</td>\n      <td>42.885399</td>\n      <td>22.546490</td>\n      <td>0.24</td>\n      <td>26.000</td>\n      <td>43.000</td>\n      <td>60.0000</td>\n      <td>82.00</td>\n    </tr>\n    <tr>\n      <th>hypertension</th>\n      <td>455.0</td>\n      <td>0.112088</td>\n      <td>0.315822</td>\n      <td>0.00</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.0000</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>heart_disease</th>\n      <td>366.0</td>\n      <td>0.057377</td>\n      <td>0.232880</td>\n      <td>0.00</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.0000</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>494.0</td>\n      <td>106.475020</td>\n      <td>47.208236</td>\n      <td>55.35</td>\n      <td>76.675</td>\n      <td>89.415</td>\n      <td>115.4075</td>\n      <td>263.32</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>457.0</td>\n      <td>29.070460</td>\n      <td>8.277457</td>\n      <td>14.00</td>\n      <td>23.500</td>\n      <td>27.900</td>\n      <td>33.2000</td>\n      <td>92.00</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>376.0</td>\n      <td>1.620718</td>\n      <td>0.154750</td>\n      <td>0.93</td>\n      <td>1.520</td>\n      <td>1.650</td>\n      <td>1.7400</td>\n      <td>1.85</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_missing_data_table(data):\n",
    "    total = data.isnull().sum().sort_values(ascending=False)\n",
    "    percent = (data.isnull().sum()/data.isnull().count()).sort_values(ascending=False)\n",
    "    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "    return missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   Total  Percent\n",
       "heart_disease        134    0.268\n",
       "height               124    0.248\n",
       "hypertension          45    0.090\n",
       "bmi                   43    0.086\n",
       "age                   11    0.022\n",
       "avg_glucose_level      6    0.012\n",
       "blood                  0    0.000\n",
       "smoking_status         0    0.000\n",
       "Residence_type         0    0.000\n",
       "work_type              0    0.000\n",
       "ever_married           0    0.000\n",
       "gender                 0    0.000\n",
       "id                     0    0.000"
      ],
      "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>heart_disease</th>\n      <td>134</td>\n      <td>0.268</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>124</td>\n      <td>0.248</td>\n    </tr>\n    <tr>\n      <th>hypertension</th>\n      <td>45</td>\n      <td>0.090</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>43</td>\n      <td>0.086</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>11</td>\n      <td>0.022</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>6</td>\n      <td>0.012</td>\n    </tr>\n    <tr>\n      <th>blood</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>smoking_status</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>Residence_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>work_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>gender</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "source": [
    "### Fill age with mean"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'] = df['age'].fillna(np.mean(df['age']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fill bmi with the bmi for that age_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3    129\n",
       "4    111\n",
       "5     94\n",
       "2     86\n",
       "1     80\n",
       "Name: age_bin, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "labels = [1,2,3,4,5]\n",
    "df[\"age_bin\"] = pd.cut(df[\"age\"], 5, labels=labels)\n",
    "df['age_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "age_bin\n",
       "1    21.190909\n",
       "2    28.552632\n",
       "3    32.482645\n",
       "4    30.884000\n",
       "5    29.695181\n",
       "Name: bmi, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "gb_bmi = df.groupby(\"age_bin\")[\"bmi\"].mean()\n",
    "gb_bmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_bmi(cols):\n",
    "    bmi = cols[0]\n",
    "    age_bin = cols[1]\n",
    "    \n",
    "    if pd.isnull(bmi):\n",
    "        if age_bin == 1:\n",
    "            return 21.190909\n",
    "        elif age_bin == 2:\n",
    "            return 28.552632\n",
    "        elif age_bin == 3:\n",
    "            return 32.456890\n",
    "        elif age_bin == 4:\n",
    "            return 30.884000\n",
    "        elif age_bin == 5:\n",
    "            return 29.695181\n",
    "    else:\n",
    "        return bmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   Total  Percent\n",
       "heart_disease        134    0.268\n",
       "height               124    0.248\n",
       "hypertension          45    0.090\n",
       "bmi                   43    0.086\n",
       "avg_glucose_level      6    0.012\n",
       "age_bin                0    0.000\n",
       "blood                  0    0.000\n",
       "smoking_status         0    0.000\n",
       "Residence_type         0    0.000\n",
       "work_type              0    0.000\n",
       "ever_married           0    0.000\n",
       "age                    0    0.000\n",
       "gender                 0    0.000\n",
       "id                     0    0.000"
      ],
      "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>heart_disease</th>\n      <td>134</td>\n      <td>0.268</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>124</td>\n      <td>0.248</td>\n    </tr>\n    <tr>\n      <th>hypertension</th>\n      <td>45</td>\n      <td>0.090</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>43</td>\n      <td>0.086</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>6</td>\n      <td>0.012</td>\n    </tr>\n    <tr>\n      <th>age_bin</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>blood</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>smoking_status</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>Residence_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>work_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>gender</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0      28.552632\n",
       "1      31.400000\n",
       "2      17.500000\n",
       "3      22.200000\n",
       "4      44.700000\n",
       "         ...    \n",
       "495    24.200000\n",
       "496    29.200000\n",
       "497    32.800000\n",
       "498    30.884000\n",
       "499    17.600000\n",
       "Name: bmi, Length: 500, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "source": [
    "df[\"bmi\"] = df[[\"bmi\",\"age_bin\"]].apply(impute_bmi, axis=1)\n",
    "df['bmi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   Total  Percent\n",
       "heart_disease        134    0.268\n",
       "height               124    0.248\n",
       "hypertension          45    0.090\n",
       "avg_glucose_level      6    0.012\n",
       "age_bin                0    0.000\n",
       "blood                  0    0.000\n",
       "smoking_status         0    0.000\n",
       "bmi                    0    0.000\n",
       "Residence_type         0    0.000\n",
       "work_type              0    0.000\n",
       "ever_married           0    0.000\n",
       "age                    0    0.000\n",
       "gender                 0    0.000\n",
       "id                     0    0.000"
      ],
      "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>heart_disease</th>\n      <td>134</td>\n      <td>0.268</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>124</td>\n      <td>0.248</td>\n    </tr>\n    <tr>\n      <th>hypertension</th>\n      <td>45</td>\n      <td>0.090</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>6</td>\n      <td>0.012</td>\n    </tr>\n    <tr>\n      <th>age_bin</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>blood</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>smoking_status</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>Residence_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>work_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>gender</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   Total  Percent\n",
       "heart_disease        134    0.268\n",
       "height               124    0.248\n",
       "hypertension          45    0.090\n",
       "avg_glucose_level      6    0.012\n",
       "blood                  0    0.000\n",
       "smoking_status         0    0.000\n",
       "bmi                    0    0.000\n",
       "Residence_type         0    0.000\n",
       "work_type              0    0.000\n",
       "ever_married           0    0.000\n",
       "age                    0    0.000\n",
       "gender                 0    0.000\n",
       "id                     0    0.000"
      ],
      "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>heart_disease</th>\n      <td>134</td>\n      <td>0.268</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>124</td>\n      <td>0.248</td>\n    </tr>\n    <tr>\n      <th>hypertension</th>\n      <td>45</td>\n      <td>0.090</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>6</td>\n      <td>0.012</td>\n    </tr>\n    <tr>\n      <th>blood</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>smoking_status</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>Residence_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>work_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>gender</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "source": [
    "df = df.drop(['age_bin'],axis=1)\n",
    "draw_missing_data_table(df)"
   ]
  },
  {
   "source": [
    "### Drop height"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['height'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   Total  Percent\n",
       "heart_disease        134    0.268\n",
       "hypertension          45    0.090\n",
       "avg_glucose_level      6    0.012\n",
       "blood                  0    0.000\n",
       "smoking_status         0    0.000\n",
       "bmi                    0    0.000\n",
       "Residence_type         0    0.000\n",
       "work_type              0    0.000\n",
       "ever_married           0    0.000\n",
       "age                    0    0.000\n",
       "gender                 0    0.000\n",
       "id                     0    0.000"
      ],
      "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>heart_disease</th>\n      <td>134</td>\n      <td>0.268</td>\n    </tr>\n    <tr>\n      <th>hypertension</th>\n      <td>45</td>\n      <td>0.090</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>6</td>\n      <td>0.012</td>\n    </tr>\n    <tr>\n      <th>blood</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>smoking_status</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>Residence_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>work_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>gender</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 54
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "source": [
    "### Fill avg_glucose_level with mean"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['avg_glucose_level'] = df['avg_glucose_level'].fillna(np.mean(df['avg_glucose_level']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   Total  Percent\n",
       "heart_disease        134    0.268\n",
       "hypertension          45    0.090\n",
       "blood                  0    0.000\n",
       "smoking_status         0    0.000\n",
       "bmi                    0    0.000\n",
       "avg_glucose_level      0    0.000\n",
       "Residence_type         0    0.000\n",
       "work_type              0    0.000\n",
       "ever_married           0    0.000\n",
       "age                    0    0.000\n",
       "gender                 0    0.000\n",
       "id                     0    0.000"
      ],
      "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>heart_disease</th>\n      <td>134</td>\n      <td>0.268</td>\n    </tr>\n    <tr>\n      <th>hypertension</th>\n      <td>45</td>\n      <td>0.090</td>\n    </tr>\n    <tr>\n      <th>blood</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>smoking_status</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>Residence_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>work_type</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>gender</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Value counts: \n\n46079    1\n8553     1\n21830    1\n37192    1\n51532    1\n        ..\n12482    1\n28367    1\n66258    1\n724      1\n56322    1\nName: id, Length: 500, dtype: int64\n\n\nFemale    305\nMale      175\nOther      20\nName: gender, dtype: int64\n\n\n54.00    13\n43.00    12\n37.00    12\n66.00    11\n40.00    11\n         ..\n1.48      1\n1.72      1\n9.00      1\n0.72      1\n1.80      1\nName: age, Length: 87, dtype: int64\n\n\n0.0    404\n1.0     51\nName: hypertension, dtype: int64\n\n\n0.0    345\n1.0     21\nName: heart_disease, dtype: int64\n\n\nYes    327\nNo     173\nName: ever_married, dtype: int64\n\n\nPrivate          274\nSelf-employed     88\nchildren          71\nGovt_job          65\nNever_worked       2\nName: work_type, dtype: int64\n\n\nUrban      245\nRural      232\nUnknown     23\nName: Residence_type, dtype: int64\n\n\n106.47502    6\n112.02000    2\n90.11000     2\n91.89000     2\n81.51000     2\n            ..\n64.62000     1\n56.90000     1\n110.20000    1\n93.72000     1\n83.75000     1\nName: avg_glucose_level, Length: 481, dtype: int64\n\n\n29.695181    11\n30.884000    11\n28.552632    10\n32.456890     8\n23.500000     8\n             ..\n17.300000     1\n38.900000     1\n40.400000     1\n57.200000     1\n17.500000     1\nName: bmi, Length: 240, dtype: int64\n\n\nnever smoked       199\nUnknown            143\nformerly smoked     82\nsmokes              76\nName: smoking_status, dtype: int64\n\n\nAB    217\nO     200\nA      44\nB      39\nName: blood, dtype: int64\n\n\n"
     ]
    }
   ],
   "source": [
    "unknown_feature = df.columns\n",
    "print('Value counts: \\n')\n",
    "for i in unknown_feature:\n",
    "    print(df[i].value_counts())\n",
    "    print('\\n')"
   ]
  },
  {
   "source": [
    "### Fill hypertension and heart_disease with the value of the most value_count : 0"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['hypertension'] = df['hypertension'].fillna(0.0)\n",
    "df['hypertension'] = df['hypertension'].astype(int)\n",
    "df['heart_disease'] = df['heart_disease'].fillna(0)\n",
    "df['heart_disease'] = df['heart_disease'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   Total  Percent\n",
       "blood                  0      0.0\n",
       "smoking_status         0      0.0\n",
       "bmi                    0      0.0\n",
       "avg_glucose_level      0      0.0\n",
       "Residence_type         0      0.0\n",
       "work_type              0      0.0\n",
       "ever_married           0      0.0\n",
       "heart_disease          0      0.0\n",
       "hypertension           0      0.0\n",
       "age                    0      0.0\n",
       "gender                 0      0.0\n",
       "id                     0      0.0"
      ],
      "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>blood</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Residence_type</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>heart_disease</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>hypertension</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    <tr>\n      <th>gender</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 59
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "source": [
    "## One-hot encode: 'gender', 'blood', 'smoking_status', 'Residence_type', 'work_type'\n",
    "## Categorical: 'ever_married'"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "id= [   67   210   242   711   724   751   841   917  1119  1192  1552  1847\n",
      "  1953  2267  2304  2424  2456  2580  2647  2692  2818  3013  3318  3512\n",
      "  3700  4148  4929  5170  5294  5647  5654  5714  5987  6202  6390  6422\n",
      "  6472  6574  6599  6605  6625  6805  7047  7054  7538  7653  7841  7871\n",
      "  7924  8022  8045  8122  8543  8553  8623  8920  8964  9189  9394 10018\n",
      " 10056 10135 10166 10321 10696 10870 10950 11312 11325 11382 11632 11762\n",
      " 11816 11872 11891 11908 11933 12345 12367 12482 12594 12668 12834 12857\n",
      " 12900 12963 13031 13172 13237 13286 13540 13598 13768 13846 13902 14444\n",
      " 14553 14658 14789 14892 14943 14996 15062 15422 15464 15978 16024 16263\n",
      " 16320 16377 16446 16590 16617 16627 16629 16817 17098 17174 17238 17308\n",
      " 17478 17569 18178 18283 18352 18398 18578 18696 18833 19498 19814 19907\n",
      " 20237 20387 20426 20439 20979 21067 21110 21346 21366 21407 21677 21830\n",
      " 22151 22282 22330 22515 22614 22880 23194 23210 23238 23339 23439 23449\n",
      " 23568 23989 24106 24163 24246 24262 24421 24585 24669 24736 24783 24854\n",
      " 24955 24972 25051 25404 25446 25476 25636 25643 25718 26025 26062 26594\n",
      " 26884 26935 27119 27416 27480 27596 27789 27859 28011 28367 28382 28513\n",
      " 28873 29267 29276 29281 29764 30002 30650 30683 30734 31092 31131 31153\n",
      " 31426 31563 31642 31697 31703 31708 31795 31911 32126 32503 32523 32571\n",
      " 32610 32638 32669 32726 33102 33439 33560 33657 33886 33924 34026 34045\n",
      " 34130 34184 34285 34415 34416 34567 34900 35095 35210 35217 35941 35999\n",
      " 36045 36087 36162 36210 36633 36710 36922 37192 37289 37440 37651 37728\n",
      " 37866 38074 38207 38287 38397 38617 38661 38980 39616 39796 39940 39977\n",
      " 40163 40447 40705 40887 40980 40998 41241 41263 41481 41654 41715 41942\n",
      " 42400 42760 42806 42899 43035 43054 43138 43364 43433 43487 43507 43656\n",
      " 44047 44288 44662 44799 44992 44993 45252 45279 45325 45372 45395 45563\n",
      " 45817 45965 46048 46079 46454 46503 46517 46647 46670 46703 46987 47057\n",
      " 47113 47144 47356 47410 47456 47668 47799 47802 47886 48721 48824 49053\n",
      " 49190 49261 49277 49509 49605 49762 49930 49955 50009 50070 50141 50210\n",
      " 50410 50412 50931 51329 51374 51408 51476 51512 51532 51651 51823 51856\n",
      " 52092 52172 52247 52361 52368 52447 52461 52619 52681 52960 53004 53105\n",
      " 53219 53426 53440 53636 53802 53821 53954 53976 53998 54117 54127 54400\n",
      " 54869 54871 55244 55337 55361 55681 55824 56139 56254 56277 56322 56339\n",
      " 56450 56547 56567 56575 56594 56600 56692 56939 56961 56979 57080 57322\n",
      " 57343 57645 57870 58313 58477 58978 59027 59058 59126 59272 59405 59437\n",
      " 59522 59691 59908 60455 60602 60635 60675 60934 60964 60981 61238 61384\n",
      " 61477 61667 61684 61830 61987 61997 62167 62296 62395 62715 62738 62798\n",
      " 62861 63026 63043 63312 63333 63404 63453 63478 63773 63804 63915 64155\n",
      " 64416 64435 64670 64864 64931 65103 65351 65636 65729 65966 66006 66258\n",
      " 66306 66690 66786 66945 67356 67711 67921 67981 68020 68034 68059 68371\n",
      " 68789 68853 69010 69059 69222 69528 69551 69647 69732 69768 70318 70428\n",
      " 70615 70670 71057 71143 71151 71279 71515 71551 71622 71777 71900 71966\n",
      " 72081 72184 72215 72393 72491 72562 72792 72940]\n",
      "gender= ['Female' 'Male' 'Other']\n",
      "age= [17.         81.          4.          5.         34.         32.\n",
      " 47.         31.         73.         20.          0.72       51.\n",
      " 60.         66.         55.         80.         79.         18.\n",
      " 42.88539877 37.          8.         42.         11.         49.\n",
      " 78.         12.         48.         35.         64.         52.\n",
      " 54.         57.         33.         50.         40.         67.\n",
      " 16.         74.         53.         58.          3.         19.\n",
      " 22.          2.         76.         46.         69.         68.\n",
      " 28.         45.         29.         61.         59.         38.\n",
      " 43.          7.         62.         71.         36.         63.\n",
      " 15.          9.         72.         44.         41.         82.\n",
      " 39.         30.         77.         24.          1.48       14.\n",
      " 56.         21.         25.          1.8        75.         13.\n",
      "  6.         10.         27.         26.         23.          1.72\n",
      " 65.         70.          0.24      ]\n",
      "hypertension= [0 1]\n",
      "heart_disease= [0 1]\n",
      "ever_married= ['No' 'Yes']\n",
      "work_type= ['Private' 'Self-employed' 'children' 'Govt_job' 'Never_worked']\n",
      "Residence_type= ['Urban' 'Rural' 'Unknown']\n",
      "avg_glucose_level= [ 92.97        91.54        98.56        92.96        81.77\n",
      "  75.1         83.75        85.18       101.81        70.66\n",
      " 101.58        79.53       112.19        82.31        95.19\n",
      "  80.67       100.2         70.28        80.35        73.87\n",
      " 230.74        83.7        101.12       106.47502024 232.29\n",
      "  71.18        78.48        67.97        92.74        99.01\n",
      "  94.77        98.9         89.42        87.          73.99\n",
      " 108.51       101.76       103.29        85.66       235.06\n",
      "  70.43       107.74        69.72       112.83        94.75\n",
      "  83.16        91.68        86.78       101.46        82.95\n",
      "  94.12       105.28       195.74        78.79        76.35\n",
      "  94.37        80.27        92.24        56.33        98.02\n",
      " 112.02        77.91        73.94        81.32       232.64\n",
      " 112.75       208.99       111.08        98.07        96.02\n",
      " 207.28        71.12        69.24       106.54        83.55\n",
      " 169.67        73.18        94.39        77.82       105.9\n",
      " 195.43       115.52        73.57        80.08        67.56\n",
      "  91.16       203.76       112.37        81.         115.68\n",
      " 227.23        77.2         88.          74.8         90.71\n",
      "  76.63        77.1        117.63        81.58        79.62\n",
      "  85.98        82.46        80.57       221.24        80.48\n",
      "  80.54        77.92       102.76        89.06        76.12\n",
      "  81.76       192.5        110.38       112.08       130.54\n",
      " 116.06        78.9         85.         221.79       101.66\n",
      "  93.52        86.06        81.38       108.32       108.96\n",
      " 121.66       168.68        84.43        99.44        71.77\n",
      "  97.05       104.9        206.09       103.68        90.11\n",
      " 110.1         93.3         70.13       103.72       117.03\n",
      "  75.13        82.63        77.99       116.62        82.94\n",
      " 118.55        82.62       125.43        74.43        77.95\n",
      "  95.23       194.75        75.          90.44       153.24\n",
      "  86.4         84.68       116.04        77.76        70.91\n",
      " 113.08       115.07       231.56        94.27        87.91\n",
      "  79.42       102.          91.32        93.72        79.98\n",
      "  83.84       201.96       119.9        100.8        103.22\n",
      "  78.04        92.75       103.78       213.43       104.16\n",
      "  86.92        86.19       115.71        73.          86.95\n",
      " 197.36        69.47        73.81        88.98        74.24\n",
      " 229.94        72.76       194.37        96.62        83.59\n",
      " 216.19       199.2         94.24        88.56        76.78\n",
      " 189.82       216.07       101.45        85.9         76.42\n",
      "  85.82        84.03        73.24        98.44        91.89\n",
      "  76.57       217.74        85.27        94.89       124.78\n",
      "  76.08        69.2         86.21        95.87        87.12\n",
      "  80.94       207.84        87.15       116.44        76.52\n",
      "  92.59        79.57        74.34        74.02        85.08\n",
      " 104.02       112.96       234.5        167.16        86.85\n",
      " 119.4        103.65       111.24       112.7         73.08\n",
      "  62.21        62.68        72.99       203.87       208.39\n",
      "  72.17        68.99       193.61       131.42        76.64\n",
      " 106.53       111.48        73.98        56.64       107.33\n",
      "  99.72        60.73        64.62        87.4        222.52\n",
      "  82.42        66.16       135.82        72.04        58.01\n",
      "  68.43        75.06        64.45       110.2         74.\n",
      " 247.87        94.92        57.46       200.66       145.18\n",
      " 102.16        55.79        59.62        63.74        63.49\n",
      " 253.93        80.2        207.37        95.24        66.3\n",
      " 126.57       141.37        83.03        61.13        76.09\n",
      "  57.79       142.63        60.64       203.27        78.8\n",
      "  63.57       106.8        196.58       228.5        197.06\n",
      " 223.83        57.52        76.47       110.42        88.62\n",
      " 111.76        58.89       125.63       191.94       256.74\n",
      "  56.94       190.67       120.22       120.56       112.55\n",
      " 106.52        70.87        78.5         74.39       123.89\n",
      " 236.04        78.98        81.51       261.67        91.3\n",
      " 113.41        96.47        57.92        68.01       138.44\n",
      "  77.94        78.85        57.4         81.36       114.46\n",
      " 123.08        56.9         77.72        85.77        89.68\n",
      "  78.11        60.32       131.81       205.78       254.95\n",
      "  98.14       228.26        63.9         63.98        58.42\n",
      " 190.14        88.79       125.32       100.47        69.45\n",
      "  58.66       103.5        106.76       128.61       116.98\n",
      " 119.13        65.29       126.12       202.06       140.1\n",
      " 129.66       108.82        83.8         88.97        65.58\n",
      "  65.36        57.56        60.7         69.94        77.77\n",
      "  84.04       248.24        92.98        70.56        61.42\n",
      "  59.11       102.97       134.65       132.46        89.41\n",
      "  60.67        81.24       239.07       108.63        68.66\n",
      " 126.09        79.22       150.74       221.89       229.73\n",
      "  86.07       121.15        76.19        68.68        99.96\n",
      " 221.08        57.38       105.72       130.07       122.23\n",
      " 117.77        68.07       144.14       151.26        78.05\n",
      " 101.06       102.03       115.98       108.38        78.43\n",
      "  70.38       100.54        67.29        79.33        61.8\n",
      "  80.55       110.33        87.71        76.34       146.1\n",
      "  55.93        87.39       200.25        62.66        76.03\n",
      "  71.79        60.17       131.05       141.84        82.26\n",
      " 123.98        89.14        86.67       195.71        82.57\n",
      "  69.46        94.14        85.33        80.8         88.85\n",
      "  55.35       151.16       111.84        83.73       103.08\n",
      " 134.76        62.44       149.8         83.2         86.3\n",
      "  57.09        56.48       182.99        63.42        66.69\n",
      "  88.06        76.98       179.14        57.96        70.19\n",
      " 179.67        82.64       263.32       200.91        85.07\n",
      " 144.33        77.16        77.43        81.73        67.41\n",
      "  89.73       212.92        84.93        74.66        64.37\n",
      " 102.92      ]\n",
      "bmi= [28.552632 31.4      17.5      22.2      44.7      20.7      37.\n",
      " 28.4      27.2      35.9      20.1      31.9      24.3      33.5\n",
      " 28.5      29.695181 28.7      33.7      30.2      34.2      40.8\n",
      " 23.9      16.1      23.8      25.5      22.7      35.5      24.1\n",
      " 19.       16.3      33.3      20.6      39.9      20.8      30.884\n",
      " 39.5      18.2      27.9      20.2      22.4      25.9      21.4\n",
      " 21.8      23.1      32.7      22.6      38.1      29.4      20.4\n",
      " 29.1      39.1      24.8      27.6      45.2      25.1      23.2\n",
      " 24.       34.9      27.3      16.7      28.3      27.5      28.6\n",
      " 28.9      33.9      19.6      38.       33.8      27.1      40.\n",
      " 23.4      30.6      50.6      45.8      17.3      55.9      36.2\n",
      " 21.6      28.       25.3      28.2      24.2      17.7      18.5\n",
      " 24.9      20.3      34.8      16.8      32.45689  29.       27.4\n",
      " 23.       16.       30.       35.4      31.5      34.1      14.2\n",
      " 32.4      27.7      26.9      45.7      25.       23.6      30.9\n",
      " 17.8      40.3      31.1      17.9      32.       29.3      38.4\n",
      " 32.2      35.2      21.190909 25.7      38.2      32.6      34.7\n",
      " 18.1      41.8      36.9      14.       40.4      20.9      41.4\n",
      " 35.       30.1      37.6      39.3      20.5      34.5      36.7\n",
      " 21.5      22.       26.2      18.9      19.8      23.5      18.8\n",
      " 27.       18.6      30.3      26.6      45.3      28.8      43.4\n",
      " 34.6      27.8      25.8      23.3      35.6      19.3      38.9\n",
      " 24.5      14.8      31.8      14.6      26.1      25.4      43.7\n",
      " 18.3      22.9      17.       38.8      29.6      18.4      46.4\n",
      " 26.4      36.       36.8      19.5      30.4      29.9      24.7\n",
      " 22.3      35.1      21.7      42.6      36.3      21.3      50.8\n",
      " 29.5      40.2      47.5      26.       32.9      29.7      41.9\n",
      " 31.6      32.1      55.7      31.       31.3      43.       25.6\n",
      " 43.2      48.8      92.       38.7      14.1      41.7      46.\n",
      " 45.4      26.8      32.8      36.5      24.4      41.3      32.3\n",
      " 19.1      42.4      37.3      32.5      25.2      57.2      33.1\n",
      " 30.5      29.8      24.6      15.       34.       22.8      57.9\n",
      " 37.9      33.2      46.1      41.5      37.2      47.6      21.2\n",
      " 19.4      35.3      64.4      30.7      21.9      29.2      26.3\n",
      " 16.2      17.6     ]\n",
      "smoking_status= ['formerly smoked' 'never smoked' 'Unknown' 'smokes']\n",
      "blood= ['AB' 'A' 'O' 'B']\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(str(i) + '= ' + str(df[i].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "marriage_mapping = {'No': 0, 'Yes': 1}\n",
    "df['ever_married'] = df['ever_married'].map(marriage_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    id  gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0   67  Female  17.0             0              0             0   \n",
       "1  210    Male  81.0             0              0             1   \n",
       "2  242    Male   4.0             0              0             0   \n",
       "3  711    Male  81.0             0              0             1   \n",
       "4  724    Male  17.0             0              0             0   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level        bmi  \\\n",
       "0        Private          Urban              92.97  28.552632   \n",
       "1  Self-employed          Rural              91.54  31.400000   \n",
       "2       children          Urban              98.56  17.500000   \n",
       "3        Private          Rural              92.96  22.200000   \n",
       "4        Private          Rural              81.77  44.700000   \n",
       "\n",
       "    smoking_status blood  \n",
       "0  formerly smoked    AB  \n",
       "1     never smoked     A  \n",
       "2          Unknown     O  \n",
       "3     never smoked    AB  \n",
       "4     never smoked     O  "
      ],
      "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>gender</th>\n      <th>age</th>\n      <th>hypertension</th>\n      <th>heart_disease</th>\n      <th>ever_married</th>\n      <th>work_type</th>\n      <th>Residence_type</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>smoking_status</th>\n      <th>blood</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>67</td>\n      <td>Female</td>\n      <td>17.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>92.97</td>\n      <td>28.552632</td>\n      <td>formerly smoked</td>\n      <td>AB</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>210</td>\n      <td>Male</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Self-employed</td>\n      <td>Rural</td>\n      <td>91.54</td>\n      <td>31.400000</td>\n      <td>never smoked</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>242</td>\n      <td>Male</td>\n      <td>4.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>children</td>\n      <td>Urban</td>\n      <td>98.56</td>\n      <td>17.500000</td>\n      <td>Unknown</td>\n      <td>O</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>711</td>\n      <td>Male</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>92.96</td>\n      <td>22.200000</td>\n      <td>never smoked</td>\n      <td>AB</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>724</td>\n      <td>Male</td>\n      <td>17.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>81.77</td>\n      <td>44.700000</td>\n      <td>never smoked</td>\n      <td>O</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "oh = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features_oh = ['gender', 'blood', 'smoking_status', 'Residence_type', 'work_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        id  gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0       67  Female  17.0             0              0             0   \n",
       "1      210    Male  81.0             0              0             1   \n",
       "2      242    Male   4.0             0              0             0   \n",
       "3      711    Male  81.0             0              0             1   \n",
       "4      724    Male  17.0             0              0             0   \n",
       "..     ...     ...   ...           ...            ...           ...   \n",
       "495  72393  Female  23.0             0              0             1   \n",
       "496  72491    Male  53.0             0              0             1   \n",
       "497  72562  Female  57.0             0              0             1   \n",
       "498  72792  Female  53.0             0              0             1   \n",
       "499  72940  Female   2.0             0              0             0   \n",
       "\n",
       "         work_type Residence_type  avg_glucose_level        bmi  ...  \\\n",
       "0          Private          Urban              92.97  28.552632  ...   \n",
       "1    Self-employed          Rural              91.54  31.400000  ...   \n",
       "2         children          Urban              98.56  17.500000  ...   \n",
       "3          Private          Rural              92.96  22.200000  ...   \n",
       "4          Private          Rural              81.77  44.700000  ...   \n",
       "..             ...            ...                ...        ...  ...   \n",
       "495       Govt_job          Rural              84.93  24.200000  ...   \n",
       "496        Private          Urban              74.66  29.200000  ...   \n",
       "497        Private          Rural              64.37  32.800000  ...   \n",
       "498        Private          Rural              77.94  30.884000  ...   \n",
       "499       children          Urban             102.92  17.600000  ...   \n",
       "\n",
       "    smoking_status_never smoked smoking_status_smokes  Residence_type_Rural  \\\n",
       "0                             0                     0                     0   \n",
       "1                             1                     0                     1   \n",
       "2                             0                     0                     0   \n",
       "3                             1                     0                     1   \n",
       "4                             1                     0                     1   \n",
       "..                          ...                   ...                   ...   \n",
       "495                           1                     0                     1   \n",
       "496                           0                     1                     0   \n",
       "497                           1                     0                     1   \n",
       "498                           1                     0                     1   \n",
       "499                           0                     0                     0   \n",
       "\n",
       "     Residence_type_Unknown  Residence_type_Urban  work_type_Govt_job  \\\n",
       "0                         0                     1                   0   \n",
       "1                         0                     0                   0   \n",
       "2                         0                     1                   0   \n",
       "3                         0                     0                   0   \n",
       "4                         0                     0                   0   \n",
       "..                      ...                   ...                 ...   \n",
       "495                       0                     0                   1   \n",
       "496                       0                     1                   0   \n",
       "497                       0                     0                   0   \n",
       "498                       0                     0                   0   \n",
       "499                       0                     1                   0   \n",
       "\n",
       "     work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                         0                  1                        0   \n",
       "1                         0                  0                        1   \n",
       "2                         0                  0                        0   \n",
       "3                         0                  1                        0   \n",
       "4                         0                  1                        0   \n",
       "..                      ...                ...                      ...   \n",
       "495                       0                  0                        0   \n",
       "496                       0                  1                        0   \n",
       "497                       0                  1                        0   \n",
       "498                       0                  1                        0   \n",
       "499                       0                  0                        0   \n",
       "\n",
       "     work_type_children  \n",
       "0                     0  \n",
       "1                     0  \n",
       "2                     1  \n",
       "3                     0  \n",
       "4                     0  \n",
       "..                  ...  \n",
       "495                   0  \n",
       "496                   0  \n",
       "497                   0  \n",
       "498                   0  \n",
       "499                   1  \n",
       "\n",
       "[500 rows x 31 columns]"
      ],
      "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>gender</th>\n      <th>age</th>\n      <th>hypertension</th>\n      <th>heart_disease</th>\n      <th>ever_married</th>\n      <th>work_type</th>\n      <th>Residence_type</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>...</th>\n      <th>smoking_status_never smoked</th>\n      <th>smoking_status_smokes</th>\n      <th>Residence_type_Rural</th>\n      <th>Residence_type_Unknown</th>\n      <th>Residence_type_Urban</th>\n      <th>work_type_Govt_job</th>\n      <th>work_type_Never_worked</th>\n      <th>work_type_Private</th>\n      <th>work_type_Self-employed</th>\n      <th>work_type_children</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>67</td>\n      <td>Female</td>\n      <td>17.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>92.97</td>\n      <td>28.552632</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>210</td>\n      <td>Male</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Self-employed</td>\n      <td>Rural</td>\n      <td>91.54</td>\n      <td>31.400000</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>242</td>\n      <td>Male</td>\n      <td>4.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>children</td>\n      <td>Urban</td>\n      <td>98.56</td>\n      <td>17.500000</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</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>711</td>\n      <td>Male</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>92.96</td>\n      <td>22.200000</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>724</td>\n      <td>Male</td>\n      <td>17.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>81.77</td>\n      <td>44.700000</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>72393</td>\n      <td>Female</td>\n      <td>23.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Govt_job</td>\n      <td>Rural</td>\n      <td>84.93</td>\n      <td>24.200000</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>72491</td>\n      <td>Male</td>\n      <td>53.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>74.66</td>\n      <td>29.200000</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>72562</td>\n      <td>Female</td>\n      <td>57.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>64.37</td>\n      <td>32.800000</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>72792</td>\n      <td>Female</td>\n      <td>53.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>77.94</td>\n      <td>30.884000</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>72940</td>\n      <td>Female</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>children</td>\n      <td>Urban</td>\n      <td>102.92</td>\n      <td>17.600000</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 31 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 65
    }
   ],
   "source": [
    "for i in categorical_features_oh:\n",
    "    df[i] = pd.Categorical(df[i])\n",
    "    dfDummies = pd.get_dummies(df[i], prefix= i)\n",
    "    df = pd.concat([df, dfDummies], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        id   age  hypertension  heart_disease  ever_married  \\\n",
       "0       67  17.0             0              0             0   \n",
       "1      210  81.0             0              0             1   \n",
       "2      242   4.0             0              0             0   \n",
       "3      711  81.0             0              0             1   \n",
       "4      724  17.0             0              0             0   \n",
       "..     ...   ...           ...            ...           ...   \n",
       "495  72393  23.0             0              0             1   \n",
       "496  72491  53.0             0              0             1   \n",
       "497  72562  57.0             0              0             1   \n",
       "498  72792  53.0             0              0             1   \n",
       "499  72940   2.0             0              0             0   \n",
       "\n",
       "     avg_glucose_level        bmi  gender_Female  gender_Male  gender_Other  \\\n",
       "0                92.97  28.552632              1            0             0   \n",
       "1                91.54  31.400000              0            1             0   \n",
       "2                98.56  17.500000              0            1             0   \n",
       "3                92.96  22.200000              0            1             0   \n",
       "4                81.77  44.700000              0            1             0   \n",
       "..                 ...        ...            ...          ...           ...   \n",
       "495              84.93  24.200000              1            0             0   \n",
       "496              74.66  29.200000              0            1             0   \n",
       "497              64.37  32.800000              1            0             0   \n",
       "498              77.94  30.884000              1            0             0   \n",
       "499             102.92  17.600000              1            0             0   \n",
       "\n",
       "     ...  smoking_status_never smoked  smoking_status_smokes  \\\n",
       "0    ...                            0                      0   \n",
       "1    ...                            1                      0   \n",
       "2    ...                            0                      0   \n",
       "3    ...                            1                      0   \n",
       "4    ...                            1                      0   \n",
       "..   ...                          ...                    ...   \n",
       "495  ...                            1                      0   \n",
       "496  ...                            0                      1   \n",
       "497  ...                            1                      0   \n",
       "498  ...                            1                      0   \n",
       "499  ...                            0                      0   \n",
       "\n",
       "     Residence_type_Rural  Residence_type_Unknown  Residence_type_Urban  \\\n",
       "0                       0                       0                     1   \n",
       "1                       1                       0                     0   \n",
       "2                       0                       0                     1   \n",
       "3                       1                       0                     0   \n",
       "4                       1                       0                     0   \n",
       "..                    ...                     ...                   ...   \n",
       "495                     1                       0                     0   \n",
       "496                     0                       0                     1   \n",
       "497                     1                       0                     0   \n",
       "498                     1                       0                     0   \n",
       "499                     0                       0                     1   \n",
       "\n",
       "     work_type_Govt_job  work_type_Never_worked  work_type_Private  \\\n",
       "0                     0                       0                  1   \n",
       "1                     0                       0                  0   \n",
       "2                     0                       0                  0   \n",
       "3                     0                       0                  1   \n",
       "4                     0                       0                  1   \n",
       "..                  ...                     ...                ...   \n",
       "495                   1                       0                  0   \n",
       "496                   0                       0                  1   \n",
       "497                   0                       0                  1   \n",
       "498                   0                       0                  1   \n",
       "499                   0                       0                  0   \n",
       "\n",
       "     work_type_Self-employed  work_type_children  \n",
       "0                          0                   0  \n",
       "1                          1                   0  \n",
       "2                          0                   1  \n",
       "3                          0                   0  \n",
       "4                          0                   0  \n",
       "..                       ...                 ...  \n",
       "495                        0                   0  \n",
       "496                        0                   0  \n",
       "497                        0                   0  \n",
       "498                        0                   0  \n",
       "499                        0                   1  \n",
       "\n",
       "[500 rows x 26 columns]"
      ],
      "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>hypertension</th>\n      <th>heart_disease</th>\n      <th>ever_married</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>gender_Female</th>\n      <th>gender_Male</th>\n      <th>gender_Other</th>\n      <th>...</th>\n      <th>smoking_status_never smoked</th>\n      <th>smoking_status_smokes</th>\n      <th>Residence_type_Rural</th>\n      <th>Residence_type_Unknown</th>\n      <th>Residence_type_Urban</th>\n      <th>work_type_Govt_job</th>\n      <th>work_type_Never_worked</th>\n      <th>work_type_Private</th>\n      <th>work_type_Self-employed</th>\n      <th>work_type_children</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>67</td>\n      <td>17.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>92.97</td>\n      <td>28.552632</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>210</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>91.54</td>\n      <td>31.400000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>242</td>\n      <td>4.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>98.56</td>\n      <td>17.500000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</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>711</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>92.96</td>\n      <td>22.200000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>724</td>\n      <td>17.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>81.77</td>\n      <td>44.700000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>72393</td>\n      <td>23.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>84.93</td>\n      <td>24.200000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>72491</td>\n      <td>53.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>74.66</td>\n      <td>29.200000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>72562</td>\n      <td>57.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>64.37</td>\n      <td>32.800000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>72792</td>\n      <td>53.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>77.94</td>\n      <td>30.884000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>72940</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>102.92</td>\n      <td>17.600000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 26 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 66
    }
   ],
   "source": [
    "for i in categorical_features_oh:\n",
    "    df.drop(columns=i, axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                Total  Percent\n",
       "work_type_children                  0      0.0\n",
       "work_type_Self-employed             0      0.0\n",
       "age                                 0      0.0\n",
       "hypertension                        0      0.0\n",
       "heart_disease                       0      0.0\n",
       "ever_married                        0      0.0\n",
       "avg_glucose_level                   0      0.0\n",
       "bmi                                 0      0.0\n",
       "gender_Female                       0      0.0\n",
       "gender_Male                         0      0.0\n",
       "gender_Other                        0      0.0\n",
       "blood_A                             0      0.0\n",
       "blood_AB                            0      0.0\n",
       "blood_B                             0      0.0\n",
       "blood_O                             0      0.0\n",
       "smoking_status_Unknown              0      0.0\n",
       "smoking_status_formerly smoked      0      0.0\n",
       "smoking_status_never smoked         0      0.0\n",
       "smoking_status_smokes               0      0.0\n",
       "Residence_type_Rural                0      0.0\n",
       "Residence_type_Unknown              0      0.0\n",
       "Residence_type_Urban                0      0.0\n",
       "work_type_Govt_job                  0      0.0\n",
       "work_type_Never_worked              0      0.0\n",
       "work_type_Private                   0      0.0\n",
       "id                                  0      0.0"
      ],
      "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>work_type_children</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_Self-employed</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    <tr>\n      <th>hypertension</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>heart_disease</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>gender_Female</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>gender_Male</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>gender_Other</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>blood_A</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>blood_AB</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>blood_B</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>blood_O</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status_Unknown</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status_formerly smoked</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status_never smoked</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status_smokes</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Residence_type_Rural</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Residence_type_Unknown</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Residence_type_Urban</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_Govt_job</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_Never_worked</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_Private</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 67
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "source": [
    "### Scaling data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "std = StandardScaler()\n",
    "columns = ['avg_glucose_level','bmi','age']\n",
    "scaled = std.fit_transform(df[columns])\n",
    "scaled = pd.DataFrame(scaled,columns=columns)\n",
    "df=df.drop(columns=columns,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        id  hypertension  heart_disease  ever_married  gender_Female  \\\n",
       "0       67             0              0             0              1   \n",
       "1      210             0              0             1              0   \n",
       "2      242             0              0             0              0   \n",
       "3      711             0              0             1              0   \n",
       "4      724             0              0             0              0   \n",
       "..     ...           ...            ...           ...            ...   \n",
       "495  72393             0              0             1              1   \n",
       "496  72491             0              0             1              0   \n",
       "497  72562             0              0             1              1   \n",
       "498  72792             0              0             1              1   \n",
       "499  72940             0              0             0              1   \n",
       "\n",
       "     gender_Male  gender_Other  blood_A  blood_AB  blood_B  ...  \\\n",
       "0              0             0        0         1        0  ...   \n",
       "1              1             0        1         0        0  ...   \n",
       "2              1             0        0         0        0  ...   \n",
       "3              1             0        0         1        0  ...   \n",
       "4              1             0        0         0        0  ...   \n",
       "..           ...           ...      ...       ...      ...  ...   \n",
       "495            0             0        1         0        0  ...   \n",
       "496            1             0        0         0        0  ...   \n",
       "497            0             0        0         1        0  ...   \n",
       "498            0             0        0         1        0  ...   \n",
       "499            0             0        0         1        0  ...   \n",
       "\n",
       "     Residence_type_Unknown  Residence_type_Urban  work_type_Govt_job  \\\n",
       "0                         0                     1                   0   \n",
       "1                         0                     0                   0   \n",
       "2                         0                     1                   0   \n",
       "3                         0                     0                   0   \n",
       "4                         0                     0                   0   \n",
       "..                      ...                   ...                 ...   \n",
       "495                       0                     0                   1   \n",
       "496                       0                     1                   0   \n",
       "497                       0                     0                   0   \n",
       "498                       0                     0                   0   \n",
       "499                       0                     1                   0   \n",
       "\n",
       "     work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                         0                  1                        0   \n",
       "1                         0                  0                        1   \n",
       "2                         0                  0                        0   \n",
       "3                         0                  1                        0   \n",
       "4                         0                  1                        0   \n",
       "..                      ...                ...                      ...   \n",
       "495                       0                  0                        0   \n",
       "496                       0                  1                        0   \n",
       "497                       0                  1                        0   \n",
       "498                       0                  1                        0   \n",
       "499                       0                  0                        0   \n",
       "\n",
       "     work_type_children  avg_glucose_level       bmi       age  \n",
       "0                     0          -0.288097 -0.071492 -1.162120  \n",
       "1                     0          -0.318603  0.286883  1.711148  \n",
       "2                     1          -0.168848 -1.462595 -1.745753  \n",
       "3                     0          -0.288311 -0.871045  1.711148  \n",
       "4                     0          -0.527022  1.960843 -1.162120  \n",
       "..                  ...                ...       ...       ...  \n",
       "495                   0          -0.459611 -0.619321 -0.892751  \n",
       "496                   0          -0.678697  0.009987  0.454093  \n",
       "497                   0          -0.898210  0.463089  0.633672  \n",
       "498                   0          -0.608726  0.221938  0.454093  \n",
       "499                   1          -0.075838 -1.450009 -1.835542  \n",
       "\n",
       "[500 rows x 26 columns]"
      ],
      "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>hypertension</th>\n      <th>heart_disease</th>\n      <th>ever_married</th>\n      <th>gender_Female</th>\n      <th>gender_Male</th>\n      <th>gender_Other</th>\n      <th>blood_A</th>\n      <th>blood_AB</th>\n      <th>blood_B</th>\n      <th>...</th>\n      <th>Residence_type_Unknown</th>\n      <th>Residence_type_Urban</th>\n      <th>work_type_Govt_job</th>\n      <th>work_type_Never_worked</th>\n      <th>work_type_Private</th>\n      <th>work_type_Self-employed</th>\n      <th>work_type_children</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>67</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.288097</td>\n      <td>-0.071492</td>\n      <td>-1.162120</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>210</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-0.318603</td>\n      <td>0.286883</td>\n      <td>1.711148</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>242</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.168848</td>\n      <td>-1.462595</td>\n      <td>-1.745753</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>711</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.288311</td>\n      <td>-0.871045</td>\n      <td>1.711148</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>724</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.527022</td>\n      <td>1.960843</td>\n      <td>-1.162120</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>72393</td>\n      <td>0</td>\n      <td>0</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      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.459611</td>\n      <td>-0.619321</td>\n      <td>-0.892751</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>72491</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.678697</td>\n      <td>0.009987</td>\n      <td>0.454093</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>72562</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.898210</td>\n      <td>0.463089</td>\n      <td>0.633672</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>72792</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.608726</td>\n      <td>0.221938</td>\n      <td>0.454093</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>72940</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.075838</td>\n      <td>-1.450009</td>\n      <td>-1.835542</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 26 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "source": [
    "df = df.reset_index(drop=True)\n",
    "df = df.merge(scaled, left_index=True, right_index=True, how = \"left\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                Total  Percent\n",
       "age                                 0      0.0\n",
       "bmi                                 0      0.0\n",
       "hypertension                        0      0.0\n",
       "heart_disease                       0      0.0\n",
       "ever_married                        0      0.0\n",
       "gender_Female                       0      0.0\n",
       "gender_Male                         0      0.0\n",
       "gender_Other                        0      0.0\n",
       "blood_A                             0      0.0\n",
       "blood_AB                            0      0.0\n",
       "blood_B                             0      0.0\n",
       "blood_O                             0      0.0\n",
       "smoking_status_Unknown              0      0.0\n",
       "smoking_status_formerly smoked      0      0.0\n",
       "smoking_status_never smoked         0      0.0\n",
       "smoking_status_smokes               0      0.0\n",
       "Residence_type_Rural                0      0.0\n",
       "Residence_type_Unknown              0      0.0\n",
       "Residence_type_Urban                0      0.0\n",
       "work_type_Govt_job                  0      0.0\n",
       "work_type_Never_worked              0      0.0\n",
       "work_type_Private                   0      0.0\n",
       "work_type_Self-employed             0      0.0\n",
       "work_type_children                  0      0.0\n",
       "avg_glucose_level                   0      0.0\n",
       "id                                  0      0.0"
      ],
      "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>age</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>bmi</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>hypertension</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>heart_disease</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>ever_married</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>gender_Female</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>gender_Male</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>gender_Other</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>blood_A</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>blood_AB</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>blood_B</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>blood_O</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status_Unknown</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status_formerly smoked</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status_never smoked</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>smoking_status_smokes</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Residence_type_Rural</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Residence_type_Unknown</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Residence_type_Urban</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_Govt_job</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_Never_worked</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_Private</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_Self-employed</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>work_type_children</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>avg_glucose_level</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>id</th>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "source": [
    "### Reorder the dataframe"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    id  hypertension  heart_disease  ever_married  gender_Female  gender_Male  \\\n",
       "0   67             0              0             0              1            0   \n",
       "1  210             0              0             1              0            1   \n",
       "2  242             0              0             0              0            1   \n",
       "3  711             0              0             1              0            1   \n",
       "4  724             0              0             0              0            1   \n",
       "\n",
       "   gender_Other  blood_A  blood_AB  blood_B  ...  Residence_type_Unknown  \\\n",
       "0             0        0         1        0  ...                       0   \n",
       "1             0        1         0        0  ...                       0   \n",
       "2             0        0         0        0  ...                       0   \n",
       "3             0        0         1        0  ...                       0   \n",
       "4             0        0         0        0  ...                       0   \n",
       "\n",
       "   Residence_type_Urban  work_type_Govt_job  work_type_Never_worked  \\\n",
       "0                     1                   0                       0   \n",
       "1                     0                   0                       0   \n",
       "2                     1                   0                       0   \n",
       "3                     0                   0                       0   \n",
       "4                     0                   0                       0   \n",
       "\n",
       "   work_type_Private  work_type_Self-employed  work_type_children  \\\n",
       "0                  1                        0                   0   \n",
       "1                  0                        1                   0   \n",
       "2                  0                        0                   1   \n",
       "3                  1                        0                   0   \n",
       "4                  1                        0                   0   \n",
       "\n",
       "   avg_glucose_level       bmi       age  \n",
       "0          -0.288097 -0.071492 -1.162120  \n",
       "1          -0.318603  0.286883  1.711148  \n",
       "2          -0.168848 -1.462595 -1.745753  \n",
       "3          -0.288311 -0.871045  1.711148  \n",
       "4          -0.527022  1.960843 -1.162120  \n",
       "\n",
       "[5 rows x 26 columns]"
      ],
      "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>hypertension</th>\n      <th>heart_disease</th>\n      <th>ever_married</th>\n      <th>gender_Female</th>\n      <th>gender_Male</th>\n      <th>gender_Other</th>\n      <th>blood_A</th>\n      <th>blood_AB</th>\n      <th>blood_B</th>\n      <th>...</th>\n      <th>Residence_type_Unknown</th>\n      <th>Residence_type_Urban</th>\n      <th>work_type_Govt_job</th>\n      <th>work_type_Never_worked</th>\n      <th>work_type_Private</th>\n      <th>work_type_Self-employed</th>\n      <th>work_type_children</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>67</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.288097</td>\n      <td>-0.071492</td>\n      <td>-1.162120</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>210</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-0.318603</td>\n      <td>0.286883</td>\n      <td>1.711148</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>242</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.168848</td>\n      <td>-1.462595</td>\n      <td>-1.745753</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>711</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.288311</td>\n      <td>-0.871045</td>\n      <td>1.711148</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>724</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.527022</td>\n      <td>1.960843</td>\n      <td>-1.162120</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['id', 'hypertension', 'heart_disease', 'ever_married', 'gender_Female',\n",
       "       'gender_Male', 'gender_Other', 'blood_A', 'blood_AB', 'blood_B',\n",
       "       'blood_O', 'smoking_status_Unknown', 'smoking_status_formerly smoked',\n",
       "       'smoking_status_never smoked', 'smoking_status_smokes',\n",
       "       'Residence_type_Rural', 'Residence_type_Unknown',\n",
       "       'Residence_type_Urban', 'work_type_Govt_job', 'work_type_Never_worked',\n",
       "       'work_type_Private', 'work_type_Self-employed', 'work_type_children',\n",
       "       'avg_glucose_level', 'bmi', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 72
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Dataset/processed_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}