Diabetes Dataset 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "003b3bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "01f700d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Datasets\\diabetes.xlsx')\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aaa5e5d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Registration Number</th>\n",
       "      <th>Patient Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Address</th>\n",
       "      <th>Contact</th>\n",
       "      <th>Date of Admission</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI (kg/m2)</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Systolic BP</th>\n",
       "      <th>Diastolic BP</th>\n",
       "      <th>Blood Sugar Before Meal</th>\n",
       "      <th>Blood Sugar After Meal</th>\n",
       "      <th>Urine Color Before Meal</th>\n",
       "      <th>Urine Color After Meal</th>\n",
       "      <th>Drug History</th>\n",
       "      <th>Weight Loss</th>\n",
       "      <th>Thirst</th>\n",
       "      <th>Hunger</th>\n",
       "      <th>Relatives</th>\n",
       "      <th>Physical Activity</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>H/O Tabacco Chewing</th>\n",
       "      <th>Headache</th>\n",
       "      <th>Burning Extremities</th>\n",
       "      <th>Weakness</th>\n",
       "      <th>Symptoms</th>\n",
       "      <th>Duration of Symptoms</th>\n",
       "      <th>Diabetes Mellitus</th>\n",
       "      <th>Typical</th>\n",
       "      <th>Non Typical</th>\n",
       "      <th>Unnamed: 37</th>\n",
       "      <th>SHORT FORMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.10.18</td>\n",
       "      <td>10:30:00</td>\n",
       "      <td>88832</td>\n",
       "      <td>Md. Kamrul Islam</td>\n",
       "      <td>m</td>\n",
       "      <td>47</td>\n",
       "      <td>ge</td>\n",
       "      <td>Port Abashik, Khalishpur</td>\n",
       "      <td>1951434844</td>\n",
       "      <td>29.09.18</td>\n",
       "      <td>170</td>\n",
       "      <td>90</td>\n",
       "      <td>31.1</td>\n",
       "      <td>71</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>9.5</td>\n",
       "      <td>15.4</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>?</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>n</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>?</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.10.18</td>\n",
       "      <td>10:00:00</td>\n",
       "      <td>88838</td>\n",
       "      <td>Ahmed Mottalib</td>\n",
       "      <td>m</td>\n",
       "      <td>68</td>\n",
       "      <td>?</td>\n",
       "      <td>101, Nirala Praktia Area, Khulna</td>\n",
       "      <td>1714479914</td>\n",
       "      <td>29.9.18</td>\n",
       "      <td>162</td>\n",
       "      <td>92</td>\n",
       "      <td>35.1</td>\n",
       "      <td>68</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>g</td>\n",
       "      <td>g</td>\n",
       "      <td>?</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
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       "      <td>y</td>\n",
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       "      <td>?</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M= Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.10.18</td>\n",
       "      <td>10:35:00</td>\n",
       "      <td>88806</td>\n",
       "      <td>Amena Begum</td>\n",
       "      <td>f</td>\n",
       "      <td>66</td>\n",
       "      <td>retired</td>\n",
       "      <td>jogipol</td>\n",
       "      <td>1715568572</td>\n",
       "      <td>25.9.18</td>\n",
       "      <td>146</td>\n",
       "      <td>67</td>\n",
       "      <td>31.4</td>\n",
       "      <td>76</td>\n",
       "      <td>120-80</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>g</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
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       "      <td>?</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F= Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date      Time Registration Number      Patient Name Sex Age  \\\n",
       "0  24.10.18  10:30:00               88832  Md. Kamrul Islam   m  47   \n",
       "1  24.10.18  10:00:00               88838    Ahmed Mottalib   m  68   \n",
       "2  24.10.18  10:35:00               88806       Amena Begum   f  66   \n",
       "\n",
       "  Profession                           Address     Contact Date of Admission  \\\n",
       "0         ge          Port Abashik, Khalishpur  1951434844          29.09.18   \n",
       "1          ?  101, Nirala Praktia Area, Khulna  1714479914           29.9.18   \n",
       "2    retired                           jogipol  1715568572           25.9.18   \n",
       "\n",
       "  Height Weight BMI (kg/m2) Heart Rate Blood Pressure Systolic BP  \\\n",
       "0    170     90        31.1         71         130-80         130   \n",
       "1    162     92        35.1         68         130-80         130   \n",
       "2    146     67        31.4         76         120-80         120   \n",
       "\n",
       "  Diastolic BP Blood Sugar Before Meal Blood Sugar After Meal  \\\n",
       "0           80                     9.5                   15.4   \n",
       "1           80                      13                     16   \n",
       "2           80                     7.6                   10.9   \n",
       "\n",
       "  Urine Color Before Meal Urine Color After Meal Drug History Weight Loss  \\\n",
       "0                       G                      G            ?           Y   \n",
       "1                       g                      g            ?           n   \n",
       "2                       g                      y            ?           y   \n",
       "\n",
       "  Thirst Hunger Relatives Physical Activity Smoking H/O Tabacco Chewing  \\\n",
       "0      Y      Y         n                 Y       N                   N   \n",
       "1      n      n         y                 y       ?                   n   \n",
       "2      y      y         ?                 y       n                   n   \n",
       "\n",
       "  Headache Burning Extremities Weakness Symptoms Duration of Symptoms  \\\n",
       "0        Y                   Y        Y        Y                    ?   \n",
       "1        y                   y        n        y                    ?   \n",
       "2        y                   y        y        y                    ?   \n",
       "\n",
       "  Diabetes Mellitus Typical Non Typical  Unnamed: 37 SHORT FORMS  \n",
       "0                 Y       Y           Y          NaN         Sex  \n",
       "1                 ?       y           ?          NaN     M= Male  \n",
       "2                 ?       y           y          NaN   F= Female  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "030e3155",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Time', 'Registration Number', 'Patient Name', 'Sex', 'Age',\n",
       "       'Profession', 'Address', 'Contact', 'Date of Admission', 'Height',\n",
       "       'Weight', 'BMI (kg/m2)', 'Heart Rate', 'Blood Pressure', 'Systolic BP',\n",
       "       'Diastolic BP', 'Blood Sugar Before Meal', 'Blood Sugar After Meal',\n",
       "       'Urine Color Before Meal', 'Urine Color After Meal', 'Drug History',\n",
       "       'Weight Loss', 'Thirst', 'Hunger', 'Relatives', 'Physical Activity',\n",
       "       'Smoking', 'H/O Tabacco Chewing', 'Headache', 'Burning Extremities',\n",
       "       'Weakness', 'Symptoms', 'Duration of Symptoms', 'Diabetes Mellitus',\n",
       "       'Typical', 'Non Typical', 'Unnamed: 37', 'SHORT FORMS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74a8a9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "unnecessary_cols=['Date', 'Time', 'Registration Number', 'Patient Name','Address', 'Contact', 'Date of Admission',\n",
    "                  'Unnamed: 37','SHORT FORMS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6b0a678",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(unnecessary_cols, axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2804a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(how='all', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1bdb5232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(340, 30)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a211e2db",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Blood Pressure</th>\n",
       "      <th>Systolic BP</th>\n",
       "      <th>Diastolic BP</th>\n",
       "      <th>Blood Sugar Before Meal</th>\n",
       "      <th>Blood Sugar After Meal</th>\n",
       "      <th>Urine Color Before Meal</th>\n",
       "      <th>Urine Color After Meal</th>\n",
       "      <th>Drug History</th>\n",
       "      <th>Weight Loss</th>\n",
       "      <th>Thirst</th>\n",
       "      <th>Hunger</th>\n",
       "      <th>Relatives</th>\n",
       "      <th>Physical Activity</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>H/O Tabacco Chewing</th>\n",
       "      <th>Headache</th>\n",
       "      <th>Burning Extremities</th>\n",
       "      <th>Weakness</th>\n",
       "      <th>Symptoms</th>\n",
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       "      <th>Diabetes Mellitus</th>\n",
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       "      <th>346</th>\n",
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       "      <td>152</td>\n",
       "      <td>79</td>\n",
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       "      <td>76</td>\n",
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       "      <th>347</th>\n",
       "      <td>m</td>\n",
       "      <td>46</td>\n",
       "      <td>Misc</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>24.913495</td>\n",
       "      <td>70</td>\n",
       "      <td>110-80</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>7</td>\n",
       "      <td>18.2</td>\n",
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       "      <td>y</td>\n",
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       "      <th>348</th>\n",
       "      <td>f</td>\n",
       "      <td>58</td>\n",
       "      <td>Far</td>\n",
       "      <td>150</td>\n",
       "      <td>57</td>\n",
       "      <td>25.333333</td>\n",
       "      <td>64</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>12.1</td>\n",
       "      <td>13.8</td>\n",
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       "      <th>349</th>\n",
       "      <td>f</td>\n",
       "      <td>70</td>\n",
       "      <td>HW</td>\n",
       "      <td>146</td>\n",
       "      <td>43</td>\n",
       "      <td>20.17264</td>\n",
       "      <td>80</td>\n",
       "      <td>120-80</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>19.3</td>\n",
       "      <td>27.6</td>\n",
       "      <td>?</td>\n",
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       "      <td>?</td>\n",
       "      <td>150</td>\n",
       "      <td>61</td>\n",
       "      <td>27.111111</td>\n",
       "      <td>80</td>\n",
       "      <td>130-85</td>\n",
       "      <td>130</td>\n",
       "      <td>85</td>\n",
       "      <td>19.3</td>\n",
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      ],
      "text/plain": [
       "    Sex Age Profession Height Weight BMI (kg/m2) Heart Rate Blood Pressure  \\\n",
       "346   f  70         HW    152     79   34.193213         76         180-80   \n",
       "347   m  46       Misc    170     72   24.913495         70         110-80   \n",
       "348   f  58        Far    150     57   25.333333         64         130-80   \n",
       "349   f  70         HW    146     43    20.17264         80         120-80   \n",
       "350   f  30          ?    150     61   27.111111         80         130-85   \n",
       "\n",
       "    Systolic BP Diastolic BP Blood Sugar Before Meal Blood Sugar After Meal  \\\n",
       "346         180           80                       7                    9.3   \n",
       "347         110           80                       7                   18.2   \n",
       "348         130           80                    12.1                   13.8   \n",
       "349         120           80                    19.3                   27.6   \n",
       "350         130           85                    19.3                   22.5   \n",
       "\n",
       "    Urine Color Before Meal Urine Color After Meal Drug History Weight Loss  \\\n",
       "346                       b                      g            y           y   \n",
       "347                       b                     lg            y           y   \n",
       "348                       y                      g            y           y   \n",
       "349                       ?                      ?            y           y   \n",
       "350                       ?                      ?            ?           y   \n",
       "\n",
       "    Thirst Hunger Relatives Physical Activity Smoking H/O Tabacco Chewing  \\\n",
       "346      y      y         y                 ?       ?                   ?   \n",
       "347      y      y         y                 ?       n                   n   \n",
       "348      y    NaN         n                 ?       ?                   ?   \n",
       "349      y      y         y                 ?       n                   n   \n",
       "350      y      y         y                 ?       ?                   ?   \n",
       "\n",
       "    Headache Burning Extremities Weakness Symptoms Duration of Symptoms  \\\n",
       "346        ?                   y        y        y                    ?   \n",
       "347        y                   y        y        y                    ?   \n",
       "348        y                   y        y        y                    ?   \n",
       "349        ?                   ?        y        y                    ?   \n",
       "350        ?                   ?        y        y                    ?   \n",
       "\n",
       "    Diabetes Mellitus Typical Non Typical  \n",
       "346                 ?       y           n  \n",
       "347                 ?       y           y  \n",
       "348                 y       y           n  \n",
       "349                 ?       n           y  \n",
       "350                 ?       n           y  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c963b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace('?', np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f9eb6b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "original_label = ['m','f','Y', 'y', 'N', 'n', 'YES', 'NO','yes','no','F','M']\n",
    "correct_labels = ['Male','Female','Yes', 'Yes', 'No', 'No', 'Yes', 'No', 'Yes', 'No','Female','Male']\n",
    "df.replace(original_label, correct_labels, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6aa9ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI (kg/m2)</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Systolic BP</th>\n",
       "      <th>Diastolic BP</th>\n",
       "      <th>Blood Sugar Before Meal</th>\n",
       "      <th>Blood Sugar After Meal</th>\n",
       "      <th>Urine Color Before Meal</th>\n",
       "      <th>Urine Color After Meal</th>\n",
       "      <th>Drug History</th>\n",
       "      <th>Weight Loss</th>\n",
       "      <th>Thirst</th>\n",
       "      <th>Hunger</th>\n",
       "      <th>Relatives</th>\n",
       "      <th>Physical Activity</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>H/O Tabacco Chewing</th>\n",
       "      <th>Headache</th>\n",
       "      <th>Burning Extremities</th>\n",
       "      <th>Weakness</th>\n",
       "      <th>Symptoms</th>\n",
       "      <th>Duration of Symptoms</th>\n",
       "      <th>Diabetes Mellitus</th>\n",
       "      <th>Typical</th>\n",
       "      <th>Non Typical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>ge</td>\n",
       "      <td>170.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>71</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>15.4</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>68</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16</td>\n",
       "      <td>g</td>\n",
       "      <td>g</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>retired</td>\n",
       "      <td>146.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>76</td>\n",
       "      <td>120-80</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>g</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>retired</td>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>80</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>o</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>68</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>10</td>\n",
       "      <td>B</td>\n",
       "      <td>LG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex   Age Profession  Height  Weight  BMI (kg/m2) Heart Rate  \\\n",
       "0    Male  47.0         ge   170.0    90.0         31.1         71   \n",
       "1    Male  68.0        NaN   162.0    92.0         35.1         68   \n",
       "2  Female  66.0    retired   146.0    67.0         31.4         76   \n",
       "3  Female  60.0    retired   156.0    49.0         20.1         80   \n",
       "4  Female  66.0        NaN   152.0    60.0         26.0         68   \n",
       "\n",
       "  Blood Pressure  Systolic BP  Diastolic BP  Blood Sugar Before Meal  \\\n",
       "0         130-80        130.0          80.0                      9.5   \n",
       "1         130-80        130.0          80.0                     13.0   \n",
       "2         120-80        120.0          80.0                      7.6   \n",
       "3         130-80        130.0          80.0                     18.5   \n",
       "4         130-80        130.0          80.0                      6.4   \n",
       "\n",
       "  Blood Sugar After Meal Urine Color Before Meal Urine Color After Meal  \\\n",
       "0                   15.4                       G                      G   \n",
       "1                     16                       g                      g   \n",
       "2                   10.9                       g                    Yes   \n",
       "3                   26.1                     Yes                      o   \n",
       "4                     10                       B                     LG   \n",
       "\n",
       "  Drug History Weight Loss Thirst Hunger Relatives Physical Activity Smoking  \\\n",
       "0          NaN         Yes    Yes    Yes        No               Yes      No   \n",
       "1          NaN          No     No     No       Yes               Yes     NaN   \n",
       "2          NaN         Yes    Yes    Yes       NaN               Yes      No   \n",
       "3          NaN         NaN    NaN    NaN       NaN               Yes     NaN   \n",
       "4          NaN          No    Yes    Yes       NaN               Yes      No   \n",
       "\n",
       "  H/O Tabacco Chewing Headache Burning Extremities Weakness Symptoms  \\\n",
       "0                  No      Yes                 Yes      Yes      Yes   \n",
       "1                  No      Yes                 Yes       No      Yes   \n",
       "2                  No      Yes                 Yes      Yes      Yes   \n",
       "3                 NaN      NaN                 NaN      NaN      Yes   \n",
       "4                  No      Yes                 Yes      Yes      Yes   \n",
       "\n",
       "   Duration of Symptoms Diabetes Mellitus Typical Non Typical  \n",
       "0                   NaN               Yes     Yes         Yes  \n",
       "1                   NaN               NaN     Yes         NaN  \n",
       "2                   NaN               NaN     Yes         Yes  \n",
       "3                  30.0               NaN      No         Yes  \n",
       "4                1460.0               NaN      No         Yes  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c22f0b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex                          1\n",
       "Age                         11\n",
       "Profession                 107\n",
       "Height                      14\n",
       "Weight                       3\n",
       "BMI (kg/m2)                 15\n",
       "Heart Rate                  40\n",
       "Blood Pressure               6\n",
       "Systolic BP                  6\n",
       "Diastolic BP                 6\n",
       "Blood Sugar Before Meal     23\n",
       "Blood Sugar After Meal      13\n",
       "Urine Color Before Meal    184\n",
       "Urine Color After Meal     180\n",
       "Drug History               278\n",
       "Weight Loss                143\n",
       "Thirst                     119\n",
       "Hunger                     150\n",
       "Relatives                  120\n",
       "Physical Activity          207\n",
       "Smoking                    185\n",
       "H/O Tabacco Chewing        184\n",
       "Headache                   200\n",
       "Burning Extremities        204\n",
       "Weakness                   161\n",
       "Symptoms                   109\n",
       "Duration of Symptoms       248\n",
       "Diabetes Mellitus          307\n",
       "Typical                    165\n",
       "Non Typical                168\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6cba6419",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_rows = df.shape[0]\n",
    "nan_percentage = df.isnull().sum()/total_rows*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77c03d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex                         0.294118\n",
       "Age                         3.235294\n",
       "Profession                 31.470588\n",
       "Height                      4.117647\n",
       "Weight                      0.882353\n",
       "BMI (kg/m2)                 4.411765\n",
       "Heart Rate                 11.764706\n",
       "Blood Pressure              1.764706\n",
       "Systolic BP                 1.764706\n",
       "Diastolic BP                1.764706\n",
       "Blood Sugar Before Meal     6.764706\n",
       "Blood Sugar After Meal      3.823529\n",
       "Urine Color Before Meal    54.117647\n",
       "Urine Color After Meal     52.941176\n",
       "Drug History               81.764706\n",
       "Weight Loss                42.058824\n",
       "Thirst                     35.000000\n",
       "Hunger                     44.117647\n",
       "Relatives                  35.294118\n",
       "Physical Activity          60.882353\n",
       "Smoking                    54.411765\n",
       "H/O Tabacco Chewing        54.117647\n",
       "Headache                   58.823529\n",
       "Burning Extremities        60.000000\n",
       "Weakness                   47.352941\n",
       "Symptoms                   32.058824\n",
       "Duration of Symptoms       72.941176\n",
       "Diabetes Mellitus          90.294118\n",
       "Typical                    48.529412\n",
       "Non Typical                49.411765\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e3f12259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex',\n",
       " 'Age',\n",
       " 'Height',\n",
       " 'Weight',\n",
       " 'BMI (kg/m2)',\n",
       " 'Heart Rate',\n",
       " 'Blood Pressure',\n",
       " 'Systolic BP',\n",
       " 'Diastolic BP',\n",
       " 'Blood Sugar Before Meal',\n",
       " 'Blood Sugar After Meal']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the column names with more than 35% missing values\n",
    "initial_selected_fatures = nan_percentage[nan_percentage < 15].index.tolist()\n",
    "initial_selected_fatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e822b6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI (kg/m2)</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Systolic BP</th>\n",
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       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>71</td>\n",
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       "      <td>130.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>68</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>76</td>\n",
       "      <td>120-80</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>80</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>68</td>\n",
       "      <td>130-80</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex   Age  Height  Weight  BMI (kg/m2) Heart Rate Blood Pressure  \\\n",
       "0    Male  47.0   170.0    90.0         31.1         71         130-80   \n",
       "1    Male  68.0   162.0    92.0         35.1         68         130-80   \n",
       "2  Female  66.0   146.0    67.0         31.4         76         120-80   \n",
       "3  Female  60.0   156.0    49.0         20.1         80         130-80   \n",
       "4  Female  66.0   152.0    60.0         26.0         68         130-80   \n",
       "\n",
       "   Systolic BP  Diastolic BP  Blood Sugar Before Meal Blood Sugar After Meal  \\\n",
       "0        130.0          80.0                      9.5                   15.4   \n",
       "1        130.0          80.0                     13.0                     16   \n",
       "2        120.0          80.0                      7.6                   10.9   \n",
       "3        130.0          80.0                     18.5                   26.1   \n",
       "4        130.0          80.0                      6.4                     10   \n",
       "\n",
       "  Outcome  \n",
       "0     Yes  \n",
       "1     Yes  \n",
       "2     Yes  \n",
       "3     Yes  \n",
       "4     Yes  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1 = df[initial_selected_fatures].copy()\n",
    "diabetes1['Outcome'] = 'Yes'\n",
    "diabetes1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85d7a5fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Weight'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Weight', x='Sex',hue = 'Sex', width=0.4, data=diabetes1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "199c4cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Weight'].fillna(diabetes1['Weight'].median(), inplace = True)\n",
    "diabetes1['Weight'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9dcb9ea4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Height'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=diabetes1, y='Height', x='Sex',hue = 'Sex',  width=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "273f4ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Height'].fillna(diabetes1['Height'].median(), inplace = True)\n",
    "diabetes1['Height'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d61cc7d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Systolic BP'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Lc+ePVtr167V+vXr7ULRdXv37tWXX36pjIwMeXp6SpLmzZundevW6aOPPtLEiROd+F8CAFBdEXZMrnv37lq0aJFt2cfHR61atdK+ffvsZnKKi4uVl5enq1evqlatWpKkVq1a2dqDg4MlSS1btrRbl5eXp5ycHFmtVuXm5mrmzJnauHGjzp8/r6KiIl27du2WMzvJycnKzc1VnTp17NZfu3bN7vAYAABlQdgxOR8fnxuuvMrNzdWsWbM0aNCgG/p7eXnZfq5Ro4btZ4vFcst1JSUlkqRnnnlGW7du1bx583TvvffK29tbQ4YMUUFBwU1ry83NVb169bRz584b2vz9/Uu3gQAA/ALCTjUUERGh48ePO/3y83379mn06NEaOHCgpJ/CzOnTp29bR1pamjw8PNSwYUOn1gIAwHWEnWpoxowZ6tevn8LDwzVkyBC5ubkpOTlZ33zzjX7/+9/f8biNGzfWmjVrFBsbK4vFounTp9tmfW6mZ8+eioyM1MMPP6w33nhDTZo00blz57Rx40YNHDhQbdu2veNaAAC4jquxyqDYkIpKyv9V7PjtdW4rOjpaGzZs0JYtW9SuXTt17NhRCxYsUIMGDco07vz583XXXXepU6dOio2NVXR0tCIiIm7Z32Kx6J///Ke6dOmiMWPGqEmTJho2bJjOnDljO0cIAICyshh3cqc6k8nJyZGfn5+ys7NltVrt2vLy8nTq1Ck1atTIdj5LVbmDshnd7PcBAKiebvf9/XMcxroD7u7u+vvqNXd0R+M7ZbFYqn3QAQDgThB27hDBAwCAqoFzdgAAgKkRdgAAgKkRdkqJ87grB34PAABHEXZ+wfU7Bl+9etXFlUD69+/h53dyBgDgdjhB+Re4u7vL399fGRkZkqRatWrZHpOAimMYhq5evaqMjAz5+/tzgjgAlxk1apRSU1MVHh6uDz/80NXloBRcGnZ2796tN998U0lJSTp//rzWrl2rhx9+2Naem5urF154QevWrdOlS5fUqFEjPfnkk3r88cdtffLy8vT0009r1apVys/PV3R0tN59912n3pQuJCREkmyBB67j7+9v+30AQEU7ceKE7eHGqampOnHihBo3buziqvBLXBp2rly5otatW2vs2LE3fShlQkKCtm/frv/5n/9Rw4YNtWXLFv3ud79TaGio+vfvL0maOnWqNm7cqNWrV8vPz0/x8fEaNGiQ9u3b57Q6LRaL6tWrp6CgIBUWFjptXDimRo0azOgAcKknnnjihuXPP//cRdWgtFwadmJiYhQTE3PL9v379ysuLk7dunWTJE2cOFHvvfeevvzyS/Xv31/Z2dn64IMPtHLlSvXo0UOStHTpUjVr1kxffPGFOnbs6NR63d3d+bIFgGpq8eLFKioqsltXVFSkxYsX2x1xQOVTqU9Q7tSpk9avX68ffvhBhmFox44d+v7779WrVy9JUlJSkgoLC9WzZ0/be5o2barw8HAdOHDgluPm5+crJyfH7gUAwK0UFhZq1apVN21btWoVs/6VXKUOOwsXLlTz5s1Vv3591axZU71799Y777yjLl26SJLS0tJUs2ZN+fv7270vODhYaWlptxx3zpw58vPzs73CwsLKczMAAFXcwoULy9QO16r0YeeLL77Q+vXrlZSUpP/+7//WpEmTynx8dNq0acrOzra9zp4966SKAQBmNHny5DK1w7Uq7aXn165d04svvqi1a9eqb9++kqRWrVrpyJEjmjdvnnr27KmQkBAVFBQoKyvLbnYnPT39tlfseHp6ytPTs7w3AQBgEjVq1NCwYcNueihr+PDh3Purkqu0MzuFhYUqLCyUm5t9ie7u7iopKZEkPfDAA6pRo4a2bdtmaz9+/LhSU1MVGRlZofUCAMzt8ccfl4eH/RyBh4eHJk6c6KKKUFoundnJzc3VyZMnbcunTp3SkSNHFBAQoPDwcHXt2lXPPvusvL291aBBA+3atUsffvih5s+fL0ny8/PTuHHjlJCQoICAAFmtVk2ePFmRkZFOvxILAIBFixZpwoQJdsuo/FwadhITE9W9e3fbckJCgiQpLi5Oy5Yt06pVqzRt2jSNGDFCmZmZatCggV577TW7S/wWLFggNzc3DR482O6mggAAOFvjxo0VHh5uu4MyNxSsGiwGT1ZUTk6O/Pz8lJ2dLavV6upyAABAKZT2+7vSnrMDAADgDIQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgah6uLgAAAFcwDEN5eXkOvyc/P1+S5OnpKYvFUur3enl5OdQfzkPYAQBUS3l5eYqJiamwz9u0aZO8vb0r7PPwbxzGAgAApsbMDgCgWvLy8tKmTZscek9eXp4GDhwoSVq7dq28vLwc+jy4BmEHAFAtWSyWMh1W8vLy4rBUFcFhLAAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGouDTu7d+9WbGysQkNDZbFYtG7duhv6HDt2TP3795efn598fHzUrl07paam2trz8vI0adIk1alTR76+vho8eLDS09MrcCsAAEBl5tKwc+XKFbVu3VrvvPPOTdtTUlLUuXNnNW3aVDt37tTXX3+t6dOn292rYOrUqfr000+1evVq7dq1S+fOndOgQYMqahMAAEAl59L77MTExNz2Vt0vvfSS+vTpozfeeMO27p577rH9nJ2drQ8++EArV65Ujx49JElLly5Vs2bN9MUXX6hjx47lVzx+kaPPneGZMwCA8lBpbypYUlKijRs36rnnnlN0dLQOHz6sRo0aadq0aXr44YclSUlJSSosLFTPnj1t72vatKnCw8N14MCBW4ad/Px825eqJOXk5JTrtlRXFfncGZ45AwC4lUp7gnJGRoZyc3P1hz/8Qb1799aWLVs0cOBADRo0SLt27ZIkpaWlqWbNmvL397d7b3BwsNLS0m459pw5c+Tn52d7hYWFleemAAAAF6rUMzuSNGDAAE2dOlWSdP/992v//v1avHixunbtesdjT5s2TQkJCbblnJwcAk85cPS5MzxzBgBQHipt2Klbt648PDzUvHlzu/XNmjXT3r17JUkhISEqKChQVlaW3exOenq6QkJCbjm2p6enPD09y6Vu/FtZnjvDM2cAAM5SaQ9j1axZU+3atdPx48ft1n///fdq0KCBJOmBBx5QjRo1tG3bNlv78ePHlZqaqsjIyAqtFwAAVE4undnJzc3VyZMnbcunTp3SkSNHFBAQoPDwcD377LN69NFH1aVLF3Xv3l2bN2/Wp59+qp07d0qS/Pz8NG7cOCUkJCggIEBWq1WTJ09WZGQkV2IBAABJLg47iYmJ6t69u235+nk0cXFxWrZsmQYOHKjFixdrzpw5evLJJ3Xffffp448/VufOnW3vWbBggdzc3DR48GDl5+crOjpa7777boVvCwAAqJwshmEYri7C1XJycuTn56fs7GxZrVZXl1NtXbt2zXapOpeSA6iM2E9VLqX9/q605+wAAAA4A2EHAACYGmEHAACYmkMnKOfk5OjgwYMqKChQ+/btFRgYWF51AQAAOEWpw86RI0fUp08fpaenyzAM1a5dW//4xz8UHR1dnvUBAACUSakPYz3//PNq1KiR9u7dq6SkJEVFRSk+Pr48awMAACizUs/sJCUlacuWLYqIiJAkLVmyRAEBAcrJyeFybQAAUGmVemYnMzNT9evXty37+/vLx8dHly5dKpfCAAAAnMGhE5S/++47paWl2ZYNw9CxY8d0+fJl27pWrVo5rzoAAIAycijsREVF6T9vuNyvXz9ZLBYZhiGLxaLi4mKnFggAAFAWpQ47p06dKs86AAAAykWpw06DBg3Ksw4AAIByUaannl+5ckV///vfde3aNfXq1UuNGzd2Vl0AAABOUeqrsVJTU9W1a1fVrl1bDz30kFJTUxUREaHx48dr8uTJuv/++7V79+7yrBUAAMBhpQ47zzzzjAoKCrR48WLVqlVL0dHRaty4sc6fP6/09HTFxMRo5syZ5VgqAACA40p9GGv37t1av3692rdvr5iYGNWtW1dLlixRcHCwJGn69OmKiooqt0IBAADuRKlndjIyMmwnKQcEBKhWrVq2oCNJISEh+vHHH51fIQAAQBmUOuxIksViuenPAAAAlZVDV2PNmDFDtWrVkiQVFBTotddek5+fnyTp6tWrzq8OAACgjEoddrp06aLjx4/bljt16qT//d//vaEPAABAZVLqsLNz585yLAMAAKB8OHTODgAAQFVD2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKbmcNhZunSpVq9efcP61atXa/ny5U4pCgAAwFkcDjtz5sxR3bp1b1gfFBSk119/3SlFAQAAOIvDYSc1NVWNGjW6YX2DBg2UmprqlKIAAACcxeGwExQUpK+//vqG9cnJyapTp45TigIAAHAWh8POY489pieffFI7duxQcXGxiouLtX37dk2ZMkXDhg0rjxoBAADumEMPApWk2bNn6/Tp04qKipKHx09vLykp0ahRozhnBwAAVDoOh52aNWvq73//u2bPnq3k5GR5e3urZcuWatCgQXnUBwAAUCYOh53rmjRpoiZNmjizFgAAAKcrVdhJSEjQ7Nmz5ePjo4SEhNv2nT9/vlMKAwAAcIZShZ3Dhw+rsLDQ9vOtWCwW51QFAADgJKUKOzt27LjpzwAAAJUdz8YCAACmVqqZnUGDBpV6wDVr1txxMQAAAM5WqrDj5+dX3nUAAACUi1KFnaVLl5Z3HQAAAOXiju+zc+HCBR0/flySdN999ykwMNBpRQEAADiLwycoX7lyRWPHjlW9evXUpUsXdenSRaGhoRo3bpyuXr1aHjUCAADcMYfDTkJCgnbt2qVPP/1UWVlZysrK0ieffKJdu3bp6aefLo8aAQAA7pjDh7E+/vhjffTRR+rWrZttXZ8+feTt7a1HHnlEixYtcmZ9AAAAZeLwzM7Vq1cVHBx8w/qgoCAOYwEAgErH4bATGRmpV155RXl5ebZ1165d06xZsxQZGenU4gAAAMrK4cNYb731lnr37q369eurdevWkqTk5GR5eXnps88+c3qBAAAAZeFw2GnZsqVOnDihFStW6F//+pck6bHHHtOIESPk7e3t9AIBAADKwuGws3v3bnXq1EkTJkywW19UVKTdu3erS5cuTisOAACgrBw+Z6d79+7KzMy8YX12dra6d+/ulKIAAACcxeGwYxiGLBbLDesvXbokHx8fh8bavXu3YmNjFRoaKovFonXr1t2y7+OPPy6LxaK33nrLbn1mZqZGjBghq9Uqf39/jRs3Trm5uQ7VAQAAzKvUh7GuP/ncYrFo9OjR8vT0tLUVFxfr66+/VqdOnRz68CtXrqh169YaO3bsbZ+svnbtWn3xxRcKDQ29oW3EiBE6f/68tm7dqsLCQo0ZM0YTJ07UypUrHaoFv8wwDLur8Jzt52OX5+dIkpeX101DO4Cqq7z3URL7qaqq1GHn+pPPDcNQ7dq17U5Grlmzpjp27HjDeTy/JCYmRjExMbft88MPP2jy5Mn67LPP1LdvX7u2Y8eOafPmzTp06JDatm0rSVq4cKH69OmjefPm3TQcSVJ+fr7y8/Ntyzk5OQ7VXV3l5eX94u/LWQYOHFiu42/atIkT6gGTqch9lMR+qiopddi5/uTzhg0b6plnnnH4kNWdKCkp0ciRI/Xss8/qV7/61Q3tBw4ckL+/vy3oSFLPnj3l5uamgwcP3vIf4pw5czRr1qxyqxsAAFQeDl+N9dxzz8kwDNvymTNntHbtWjVv3ly9evVyanFz586Vh4eHnnzyyZu2p6WlKSgoyG6dh4eHAgIClJaWdstxp02bpoSEBNtyTk6OwsLCnFN0NZF7/2My3Bz+53N7hiGVFP30s5uH5OTpW0tJkXyP/M2pYwKonP7UOVOe7sYvd3SQYUgFJT/9XNPN6bsp5RdbFL83wLmDwvGwM2DAAA0aNEiPP/64srKy1L59e9WsWVMXL17U/Pnz9cQTTzilsKSkJP3xj3/UV1995fRjlp6ennbnHMFxhpuH5F6jHEauWQ5j/sT5uz0AlZWnuyFP9/IZ26t8hv3/2FOVB4evxvrqq6/04IMPSpI++ugjhYSE6MyZM/rwww/19ttvO62wPXv2KCMjQ+Hh4fLw8JCHh4fOnDmjp59+Wg0bNpQkhYSEKCMjw+59RUVFyszMVEhIiNNqAQAAVZfDMztXr15V7dq1JUlbtmzRoEGD5Obmpo4dO+rMmTNOK2zkyJHq2bOn3bro6GiNHDlSY8aMkfTTc7qysrKUlJSkBx54QJK0fft2lZSUqEOHDk6rBQAAVF0Oh517771X69at08CBA/XZZ59p6tSpkqSMjAxZrVaHxsrNzdXJkydty6dOndKRI0cUEBCg8PBw1alTx65/jRo1FBISovvuu0+S1KxZM/Xu3VsTJkzQ4sWLVVhYqPj4eA0bNuyWV2IBAIDqxeHDWDNmzNAzzzyjhg0bqkOHDrYnnW/ZskVt2rRxaKzExES1adPG9r6EhAS1adNGM2bMKPUYK1asUNOmTRUVFaU+ffqoc+fO+vOf/+xQHQAAwLwcntkZMmSIOnfurPPnz9ueei5JUVFRDt9zoFu3bnZXdv2S06dP37AuICCAGwgCAIBbcnhmZ+nSpfLz81ObNm3k5vbvt7dv315NmzZ1anEAAABl5XDYeeGFFxQcHKxx48Zp//795VETAACA0zgcdn744QctX75cFy9eVLdu3dS0aVPNnTv3tjfxAwAAcBWHw46Hh4cGDhyoTz75RGfPntWECRO0YsUKhYeHq3///vrkk09UUlJSHrUCAAA4zOGw83PBwcHq3LmzIiMj5ebmpqNHjyouLk733HOPdu7c6aQSAQAA7twdhZ309HTNmzdPv/rVr9StWzfl5ORow4YNOnXqlH744Qc98sgjiouLc3atAAAADnM47MTGxiosLEzLli3ThAkT9MMPP+hvf/ub7W7HPj4+evrpp3X27FmnFwsAAOAoh++zExQUpF27dtluJngzgYGBOnXqVJkKAwAAcAaHw84HH3zwi30sFosaNGhwRwUBAAA4U6kPYx04cEAbNmywW/fhhx+qUaNGCgoK0sSJE5Wfn+/0AgEAAMqi1GHn1Vdf1bfffmtbPnr0qMaNG6eePXvqhRde0Keffqo5c+aUS5EAAAB3qtRh58iRI4qKirItr1q1Sh06dND777+vhIQEvf322/rHP/5RLkUCAADcqVKHnR9//FHBwcG25V27dikmJsa23K5dO67AAgAAlU6pw05wcLDtCquCggJ99dVX6tixo6398uXLqlGjhvMrBAAAKINSh50+ffrohRde0J49ezRt2jTVqlVLDz74oK3966+/1j333FMuRQIAANypUl96Pnv2bA0aNEhdu3aVr6+vli9frpo1a9ralyxZol69epVLkQAAAHeq1GGnbt262r17t7Kzs+Xr6yt3d3e79tWrV8vX19fpBQIAAJSFwzcV9PPzu+n6gICAMhcDAADgbGV66jkAAEBlR9gBAACmRtgBAACm5vA5O6i+DMP490JxoesKuVM/q9luWwCYws//rvOLXVhIGfy8bvZTzkPYQan9/EGvtZNXubCSssvPz1etWrVcXQYAJ/r5Pip+bx0XVuIc7Kech8NYAADA1JjZQal5enrafr7cepjkXsUeD1JcaJuR+vm2ADCHn/9d/6nzJXm636ZzJZVf/O9ZKfZTzkPYQalZLJZ/L7jXqHph52fstgWAKfz879rTXVUy7Pwc+ynn4TAWAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNZeGnd27dys2NlahoaGyWCxat26dra2wsFDPP/+8WrZsKR8fH4WGhmrUqFE6d+6c3RiZmZkaMWKErFar/P39NW7cOOXm5lbwlgAAgMrKw5UffuXKFbVu3Vpjx47VoEGD7NquXr2qr776StOnT1fr1q31448/asqUKerfv78SExNt/UaMGKHz589r69atKiws1JgxYzRx4kStXLmyojenWrGUFMlw9qCGIZUU/fSzm4dksTh1eMv1sQGYXn6xRXL+XkqGIRWU/PRzTTen76b+f91wNpeGnZiYGMXExNy0zc/PT1u3brVb96c//Unt27dXamqqwsPDdezYMW3evFmHDh1S27ZtJUkLFy5Unz59NG/ePIWGht507Pz8fOXn59uWc3JynLRF1Yfvkb+5ugQAuKX4vQGuLgGVSJU6Zyc7O1sWi0X+/v6SpAMHDsjf398WdCSpZ8+ecnNz08GDB285zpw5c+Tn52d7hYWFlXfpAADARVw6s+OIvLw8Pf/883rsscdktVolSWlpaQoKCrLr5+HhoYCAAKWlpd1yrGnTpikhIcG2nJOTQ+ApBS8vL23atKncxs/Ly9PAgQMlSWvXrpWXl1e5fVZ5jg3ANcp7HyWxn6qqqkTYKSws1COPPCLDMLRo0aIyj+fp6SlPT08nVFa9WCwWeXt7V8hneXl5VdhnATCHitxHSeynqpJKH3auB50zZ85o+/bttlkdSQoJCVFGRoZd/6KiImVmZiokJKSiSwUAAJVQpT5n53rQOXHihD7//HPVqVPHrj0yMlJZWVlKSkqyrdu+fbtKSkrUoUOHii4XAABUQi6d2cnNzdXJkydty6dOndKRI0cUEBCgevXqaciQIfrqq6+0YcMGFRcX287DCQgIUM2aNdWsWTP17t1bEyZM0OLFi1VYWKj4+HgNGzbslldiAQCA6sWlYScxMVHdu3e3LV8/aTguLk4zZ87U+vXrJUn333+/3ft27Nihbt26SZJWrFih+Ph4RUVFyc3NTYMHD9bbb79dIfUDAIDKz6Vhp1u3bjKMW9/06XZt1wUEBHADQQAAcEuV+pwdAACAsiLsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU3Np2Nm9e7diY2MVGhoqi8WidevW2bUbhqEZM2aoXr168vb2Vs+ePXXixAm7PpmZmRoxYoSsVqv8/f01btw45ebmVuBWAACAysylYefKlStq3bq13nnnnZu2v/HGG3r77be1ePFiHTx4UD4+PoqOjlZeXp6tz4gRI/Ttt99q69at2rBhg3bv3q2JEydW1CYAAIBKzsOVHx4TE6OYmJibthmGobfeeksvv/yyBgwYIEn68MMPFRwcrHXr1mnYsGE6duyYNm/erEOHDqlt27aSpIULF6pPnz6aN2+eQkNDbzp2fn6+8vPzbcs5OTlO3jIAAFBZVNpzdk6dOqW0tDT17NnTts7Pz08dOnTQgQMHJEkHDhyQv7+/LehIUs+ePeXm5qaDBw/ecuw5c+bIz8/P9goLCyu/DQEAAC5VacNOWlqaJCk4ONhufXBwsK0tLS1NQUFBdu0eHh4KCAiw9bmZadOmKTs72/Y6e/ask6sHAACVhUsPY7mKp6enPD09XV0GAACoAJV2ZickJESSlJ6ebrc+PT3d1hYSEqKMjAy79qKiImVmZtr6AACA6q3Shp1GjRopJCRE27Zts63LycnRwYMHFRkZKUmKjIxUVlaWkpKSbH22b9+ukpISdejQocJrBgAAlY9LD2Pl5ubq5MmTtuVTp07pyJEjCggIUHh4uJ566in9/ve/V+PGjdWoUSNNnz5doaGhevjhhyVJzZo1U+/evTVhwgQtXrxYhYWFio+P17Bhw255JRYAAKheXBp2EhMT1b17d9tyQkKCJCkuLk7Lli3Tc889pytXrmjixInKyspS586dtXnzZnl5ednes2LFCsXHxysqKkpubm4aPHiw3n777QrfFgAAUDm5NOx069ZNhmHcst1isejVV1/Vq6++ess+AQEBWrlyZXmUBwAATKDSnrMDAADgDIQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgah6uLgDmZRiG8vLySt3/530deZ8keXl5yWKxOPQeANWbo/soif1UVWUxDMNwdRGulpOTIz8/P2VnZ8tqtbq6HNO4du2aYmJiKuSzNm3aJG9v7wr5LADmUJH7KIn9VHko7fc3h7EAAICpMbMjZnbKi6NTxIZhKD8/X5Lk6enp0HQv08MAHHUnh7HYT1Uupf3+5pwdlBuLxeLwlG2tWrXKqRoAsHcn+yiJ/VRVxGEsAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgajz1XJJhGJJ+elQ8AACoGq5/b1//Hr8Vwo6ky5cvS5LCwsJcXAkAAHDU5cuX5efnd8t2i/FLcagaKCkp0blz51S7dm1ZLBZXl4NylpOTo7CwMJ09e1ZWq9XV5QBwIv6+qxfDMHT58mWFhobKze3WZ+YwsyPJzc1N9evXd3UZqGBWq5WdIWBS/H1XH7eb0bmOE5QBAICpEXYAAICpEXZQ7Xh6euqVV16Rp6enq0sB4GT8feNmOEEZAACYGjM7AADA1Ag7AADA1Ag7AADA1Ag7gKTTp0/LYrHoyJEjri4FgAs0bNhQb731lqvLQDkh7KDKGj16tCwWix5//PEb2iZNmiSLxaLRo0dXfGEAbuv63+5/vk6ePOnq0mBShB1UaWFhYVq1apWuXbtmW5eXl6eVK1cqPDzchZUBuJ3evXvr/Pnzdq9GjRq5uiyYFGEHVVpERITCwsK0Zs0a27o1a9YoPDxcbdq0sa3bvHmzOnfuLH9/f9WpU0f9+vVTSkrKbcf+5ptvFBMTI19fXwUHB2vkyJG6ePFiuW0LUJ14enoqJCTE7uXu7q5PPvlEERER8vLy0t13361Zs2apqKjI9j6LxaL33ntP/fr1U61atdSsWTMdOHBAJ0+eVLdu3eTj46NOnTrZ/X2npKRowIABCg4Olq+vr9q1a6fPP//8tvVlZWVp/PjxCgwMlNVqVY8ePZScnFxu/z1Qvgg7qPLGjh2rpUuX2paXLFmiMWPG2PW5cuWKEhISlJiYqG3btsnNzU0DBw5USUnJTcfMyspSjx491KZNGyUmJmrz5s1KT0/XI488Uq7bAlRne/bs0ahRozRlyhR99913eu+997Rs2TK99tprdv1mz56tUaNG6ciRI2ratKmGDx+u3/72t5o2bZoSExNlGIbi4+Nt/XNzc9WnTx9t27ZNhw8fVu/evRUbG6vU1NRb1jJ06FBlZGRo06ZNSkpKUkREhKKiopSZmVlu249yZABVVFxcnDFgwAAjIyPD8PT0NE6fPm2cPn3a8PLyMi5cuGAMGDDAiIuLu+l7L1y4YEgyjh49ahiGYZw6dcqQZBw+fNgwDMOYPXu20atXL7v3nD171pBkHD9+vDw3CzC9uLg4w93d3fDx8bG9hgwZYkRFRRmvv/66Xd+//vWvRr169WzLkoyXX37ZtnzgwAFDkvHBBx/Y1v3tb38zvLy8blvDr371K2PhwoW25QYNGhgLFiwwDMMw9uzZY1itViMvL8/uPffcc4/x3nvvOby9cD2eeo4qLzAwUH379tWyZctkGIb69u2runXr2vU5ceKEZsyYoYMHD+rixYu2GZ3U1FS1aNHihjGTk5O1Y8cO+fr63tCWkpKiJk2alM/GANVE9+7dtWjRItuyj4+PWrVqpX379tnN5BQXFysvL09Xr15VrVq1JEmtWrWytQcHB0uSWrZsabcuLy9POTk5slqtys3N1cyZM7Vx40adP39eRUVFunbt2i1ndpKTk5Wbm6s6derYrb927dovHv5G5UTYgSmMHTvWNm39zjvv3NAeGxurBg0a6P3331doaKhKSkrUokULFRQU3HS83NxcxcbGau7cuTe01atXz7nFA9WQj4+P7r33Xrt1ubm5mjVrlgYNGnRDfy8vL9vPNWrUsP1ssVhuue76/9Q888wz2rp1q+bNm6d7771X3t7eGjJkyG3//uvVq6edO3fe0Obv71+6DUSlQtiBKfTu3VsFBQWyWCyKjo62a7t06ZKOHz+u999/Xw8++KAkae/evbcdLyIiQh9//LEaNmwoDw/+TICKEBERoePHj98Qgspq3759Gj16tAYOHCjppzBz+vTp29aRlpYmDw8PNWzY0Km1wDU4QRmm4O7urmPHjum7776Tu7u7Xdtdd92lOnXq6M9//rNOnjyp7du3KyEh4bbjTZo0SZmZmXrsscd06NAhpaSk6LPPPtOYMWNUXFxcnpsCVFszZszQhx9+qFmzZunbb7/VsWPHtGrVKr388stlGrdx48Zas2aNjhw5ouTkZA0fPvyWFydIUs+ePRUZGamHH35YW7Zs0enTp7V//3699NJLSkxMLFMtcA3CDkzDarXKarXesN7NzU2rVq1SUlKSWrRooalTp+rNN9+87VihoaHat2+fiouL1atXL7Vs2VJPPfWU/P395ebGnw1QHqKjo7VhwwZt2bJF7dq1U8eOHbVgwQI1aNCgTOPOnz9fd911lzp16qTY2FhFR0crIiLilv0tFov++c9/qkuXLhozZoyaNGmiYcOG6cyZM7ZzhFC1WAzDMFxdBAAAQHnhf1EBAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAVEkXLlzQE088ofDwcHl6eiokJETR0dHat2+fq0sDUMnwOGcAVdLgwYNVUFCg5cuX6+6771Z6erq2bdumS5cuubo0AJUMMzsAqpysrCzt2bNHc+fOVffu3dWgQQO1b99e06ZNU//+/W19xo8fr8DAQFmtVvXo0UPJycmSfpoVCgkJ0euvv24bc//+/apZs6a2bdvmkm0CUH4IOwCqHF9fX/n6+mrdunXKz8+/aZ+hQ4cqIyNDmzZtUlJSkiIiIhQVFaXMzEwFBgZqyZIlmjlzphITE3X58mWNHDlS8fHxioqKquCtAVDeeOo5gCrp448/1oQJE3Tt2jVFRESoa9euGjZsmFq1aqW9e/eqb9++ysjIkKenp+099957r5577jlNnDhRkjRp0iR9/vnnatu2rY4ePapDhw7Z9QdgDoQdAFVWXl6e9uzZoy+++EKbNm3Sl19+qb/85S+6cuWKnnzySXl7e9v1v3btmp555hnNnTvXttyiRQudPXtWSUlJatmypSs2A0A5I+wAMI3x48dr69at+t3vfqeFCxdq586dN/Tx9/dX3bp1JUnffPON2rVrp8LCQq1du1axsbEVXDGAisDVWABMo3nz5lq3bp0iIiKUlpYmDw8PNWzY8KZ9CwoK9Jvf/EaPPvqo7rvvPo0fP15Hjx5VUFBQxRYNoNwxswOgyrl06ZKGDh2qsWPHqlWrVqpdu7YSExM1efJk9e3bV3/5y1/UpUsXXb58WW+88YaaNGmic+fOaePGjRo4cKDatm2rZ599Vh999JGSk5Pl6+urrl27ys/PTxs2bHD15gFwMsIOgConPz9fM2fO1JYtW5SSkqLCwkKFhYVp6NChevHFF+Xt7a3Lly/rpZde0scff2y71LxLly6aM2eOUlJS9NBDD2nHjh3q3LmzJOn06dNq3bq1/vCHP+iJJ55w8RYCcCbCDgAAMDXuswMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEzt/wGjKQ13vgN9XQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=diabetes1, y='Systolic BP', x='Sex',hue = 'Sex',  width=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "44f167a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values in the 'Sex' column with the mode value\n",
    "diabetes1['Sex'].fillna(diabetes1['Sex'].mode()[0], inplace=True)\n",
    "diabetes1['Sex'].isnull().sum()\n",
    "#diabetes1['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "86122dc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Diastolic BP'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=diabetes1, y='Diastolic BP', x='Sex',hue = 'Sex',  width=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b80adffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 340 entries, 0 to 350\n",
      "Series name: Diastolic BP\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "334 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 5.3 KB\n"
     ]
    }
   ],
   "source": [
    "diabetes1['Diastolic BP'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9b949441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    334.000000\n",
       "mean      81.730539\n",
       "std        7.631480\n",
       "min       40.000000\n",
       "25%       80.000000\n",
       "50%       80.000000\n",
       "75%       80.000000\n",
       "max      110.000000\n",
       "Name: Diastolic BP, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Diastolic BP'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "89909f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(diabetes1['Diastolic BP']<70).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e84f156a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(diabetes1['Diastolic BP']>100).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "741d16d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    334.000000\n",
       "mean     129.919162\n",
       "std       14.453905\n",
       "min       90.000000\n",
       "25%      120.000000\n",
       "50%      130.000000\n",
       "75%      140.000000\n",
       "max      200.000000\n",
       "Name: Systolic BP, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Systolic BP'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "33e818eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     300\n",
       "unique     18\n",
       "top        80\n",
       "freq      163\n",
       "Name: Heart Rate, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Heart Rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9d0504fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Heart Rate'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "25aed852",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes1['Heart Rate'] = pd.to_numeric(df['Heart Rate'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b8eb0e8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Heart Rate'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5245a569",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Heart Rate'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=diabetes1, y='Heart Rate', x='Sex',hue = 'Sex',  width=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "94754643",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    299.000000\n",
       "mean      74.836120\n",
       "std        6.732913\n",
       "min       56.000000\n",
       "25%       68.000000\n",
       "50%       80.000000\n",
       "75%       80.000000\n",
       "max       90.000000\n",
       "Name: Heart Rate, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Heart Rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "17bf3461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0    163\n",
       "68.0     42\n",
       "64.0     35\n",
       "76.0     20\n",
       "70.0     14\n",
       "60.0      7\n",
       "71.0      4\n",
       "66.0      3\n",
       "82.0      2\n",
       "78.0      2\n",
       "72.0      1\n",
       "90.0      1\n",
       "56.0      1\n",
       "84.0      1\n",
       "65.0      1\n",
       "77.0      1\n",
       "74.0      1\n",
       "Name: Heart Rate, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Heart Rate'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86d209d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values in the 'Heart Rate' column with the mean value\n",
    "diabetes1['Heart Rate'].fillna(diabetes1['Heart Rate'].mean(), inplace=True)\n",
    "diabetes1['Heart Rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0c9055e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Systolic BP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "da36c23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Diastolic BP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e8fa0f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI (kg/m2)</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Systolic BP</th>\n",
       "      <th>Diastolic BP</th>\n",
       "      <th>Blood Sugar Before Meal</th>\n",
       "      <th>Blood Sugar After Meal</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sex   Age  Height  Weight  BMI (kg/m2)  Heart Rate Blood Pressure  \\\n",
       "84     Male  60.0   162.0    57.0         21.7    74.83612            NaN   \n",
       "85   Female  35.0   160.0    67.0         26.2    74.83612            NaN   \n",
       "174  Female  48.0   156.0    65.0         23.9    74.83612            NaN   \n",
       "\n",
       "     Systolic BP  Diastolic BP  Blood Sugar Before Meal  \\\n",
       "84           NaN           NaN                      NaN   \n",
       "85           NaN           NaN                      NaN   \n",
       "174          NaN           NaN                      NaN   \n",
       "\n",
       "    Blood Sugar After Meal Outcome  \n",
       "84                     NaN     Yes  \n",
       "85                     NaN     Yes  \n",
       "174                    NaN     Yes  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1[diabetes1['Diastolic BP'].isnull() & diabetes1['Systolic BP'].isnull() & diabetes1['Blood Sugar Before Meal'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c98d8e57",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# So we can delete these columns, bcoz most of the important values are missing\n",
    "# Define the conditions for selecting rows\n",
    "conditions = (diabetes1['Diastolic BP'].isnull()) & \\\n",
    "             (diabetes1['Systolic BP'].isnull()) & \\\n",
    "             (diabetes1['Blood Sugar Before Meal'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "28b8caaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([84, 85, 174], dtype='int64')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the indexes of the rows that satisfy the conditions\n",
    "rows_to_delete = diabetes1[conditions].index\n",
    "rows_to_delete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "90e4d044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(337, 12)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1.drop(index=rows_to_delete, inplace= True)\n",
    "diabetes1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b311e1af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex                         0\n",
       "Age                        11\n",
       "Height                      0\n",
       "Weight                      0\n",
       "BMI (kg/m2)                15\n",
       "Heart Rate                  0\n",
       "Blood Pressure              3\n",
       "Systolic BP                 3\n",
       "Diastolic BP                3\n",
       "Blood Sugar Before Meal    20\n",
       "Blood Sugar After Meal     10\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "45a2736b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.73053892215569"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Diastolic BP'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b523c6fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Diastolic BP'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c1eb06f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values in the 'Diastolic BP' column with the median value\n",
    "diabetes1['Diastolic BP'].fillna(diabetes1['Diastolic BP'].median(), inplace=True)\n",
    "diabetes1['Diastolic BP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6959bc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129.9191616766467"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Systolic BP'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "41568986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Systolic BP'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4989fd56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values in the 'Diastolic BP' column with the median value\n",
    "diabetes1['Diastolic BP'].fillna(diabetes1['Diastolic BP'].median(), inplace=True)\n",
    "diabetes1['Diastolic BP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ac361b1e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Blood Pressure'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[56], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdiabetes1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mBlood Pressure\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:5399\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5251\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m   5252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]\u001b[39;00m\n\u001b[0;32m   5253\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5260\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5261\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5262\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5263\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5264\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5397\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5398\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5399\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   5400\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5401\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5402\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5403\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5404\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5405\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5406\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5407\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4505\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4503\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4504\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4505\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4508\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4546\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4544\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4545\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4546\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4547\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4549\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4550\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6934\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   6933\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 6934\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6935\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   6936\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['Blood Pressure'] not found in axis\""
     ]
    }
   ],
   "source": [
    "diabetes1.drop('Blood Pressure', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c0bd5ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes1.drop('BMI (kg/m2)', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7cbbbd0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sex', 'Age', 'Height', 'Weight', 'Heart Rate', 'Systolic BP',\n",
       "       'Diastolic BP', 'Blood Sugar Before Meal', 'Blood Sugar After Meal',\n",
       "       'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6f16d3c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Blood Sugar Before Meal'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = diabetes1, y='Blood Sugar Before Meal', x='Sex',hue = 'Sex',  width=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e9f34603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    317.000000\n",
       "mean      12.402934\n",
       "std        4.954084\n",
       "min        4.200000\n",
       "25%        8.700000\n",
       "50%       11.400000\n",
       "75%       15.700000\n",
       "max       45.500000\n",
       "Name: Blood Sugar Before Meal, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Blood Sugar Before Meal'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9a377399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.2"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Blood Sugar Before Meal'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "119f0185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Blood Sugar Before Meal'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3d706055",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values in the 'Diastolic BP' column with the median value\n",
    "diabetes1['Blood Sugar Before Meal'].fillna(diabetes1['Blood Sugar Before Meal'].median(), inplace=True)\n",
    "diabetes1['Blood Sugar Before Meal'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4751efcc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1['Blood Sugar After Meal'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "381651db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the problematic value '19+.9' with 19.9\n",
    "diabetes1['Blood Sugar After Meal'].replace('19+.9', 19.9, inplace=True)\n",
    "\n",
    "# Convert the 'Blood Sugar After Meal' column to numeric dtype\n",
    "diabetes1['Blood Sugar After Meal'] = pd.to_numeric(diabetes1['Blood Sugar After Meal'], errors='coerce')\n",
    "\n",
    "# Fill missing values in the 'Blood Sugar After Meal' column with the median value\n",
    "diabetes1['Blood Sugar After Meal'].fillna(diabetes1['Blood Sugar After Meal'].median(), inplace=True)\n",
    "\n",
    "diabetes1['Blood Sugar After Meal'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a1baad86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Age'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = diabetes1, y='Age', x='Sex',hue = 'Sex',  width=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "221a4f2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values in the 'Age' column with the median value\n",
    "diabetes1['Age'].fillna(diabetes1['Age'].median(), inplace=True)\n",
    "\n",
    "diabetes1['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "96f67a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Systolic BP'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = diabetes1, y='Systolic BP', x='Sex',hue = 'Sex',  width=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6e416be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing values in the 'Systolic BP' column with the median value\n",
    "diabetes1['Systolic BP'].fillna(diabetes1['Systolic BP'].median(), inplace=True)\n",
    "\n",
    "diabetes1['Systolic BP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ee1f499e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex                        0\n",
       "Age                        0\n",
       "Height                     0\n",
       "Weight                     0\n",
       "Heart Rate                 0\n",
       "Systolic BP                0\n",
       "Diastolic BP               0\n",
       "Blood Sugar Before Meal    0\n",
       "Blood Sugar After Meal     0\n",
       "Outcome                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "487fd9d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Systolic BP</th>\n",
       "      <th>Diastolic BP</th>\n",
       "      <th>Blood Sugar Before Meal</th>\n",
       "      <th>Blood Sugar After Meal</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>15.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.60</td>\n",
       "      <td>10.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>26.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>56.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.20</td>\n",
       "      <td>18.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>20.90</td>\n",
       "      <td>23.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.90</td>\n",
       "      <td>15.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>14.11</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>15.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.60</td>\n",
       "      <td>14.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.20</td>\n",
       "      <td>9.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>12.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>13.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Female</td>\n",
       "      <td>53.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.20</td>\n",
       "      <td>20.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.30</td>\n",
       "      <td>19.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.30</td>\n",
       "      <td>17.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>56.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.50</td>\n",
       "      <td>26.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>12.90</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>12.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.50</td>\n",
       "      <td>19.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>16.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16.40</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>19.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>110.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>28.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>20.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.30</td>\n",
       "      <td>15.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.30</td>\n",
       "      <td>24.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>43.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.80</td>\n",
       "      <td>25.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>23.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.40</td>\n",
       "      <td>14.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Female</td>\n",
       "      <td>68.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.10</td>\n",
       "      <td>25.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.60</td>\n",
       "      <td>12.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>25.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.10</td>\n",
       "      <td>12.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16.90</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Female</td>\n",
       "      <td>43.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.20</td>\n",
       "      <td>28.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Male</td>\n",
       "      <td>72.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>20.90</td>\n",
       "      <td>25.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4.70</td>\n",
       "      <td>10.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>13.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>11.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.70</td>\n",
       "      <td>25.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.70</td>\n",
       "      <td>7.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>18.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.90</td>\n",
       "      <td>21.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>25.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>9.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>13.90</td>\n",
       "      <td>19.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>14.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.90</td>\n",
       "      <td>18.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Female</td>\n",
       "      <td>65.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>45.50</td>\n",
       "      <td>45.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>15.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>136.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.60</td>\n",
       "      <td>14.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>8.70</td>\n",
       "      <td>20.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>128.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16.50</td>\n",
       "      <td>28.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>10.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>15.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>12.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.20</td>\n",
       "      <td>16.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>15.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.40</td>\n",
       "      <td>19.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.20</td>\n",
       "      <td>18.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Male</td>\n",
       "      <td>78.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.80</td>\n",
       "      <td>14.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>19.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.40</td>\n",
       "      <td>24.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>16.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16.10</td>\n",
       "      <td>21.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>24.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.30</td>\n",
       "      <td>20.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>13.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>13.30</td>\n",
       "      <td>16.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.70</td>\n",
       "      <td>21.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>23.80</td>\n",
       "      <td>27.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.30</td>\n",
       "      <td>12.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>24.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.90</td>\n",
       "      <td>23.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>11.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>15.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>26.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>14.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>23.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.10</td>\n",
       "      <td>18.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>14.60</td>\n",
       "      <td>15.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>5.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>23.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Female</td>\n",
       "      <td>46.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>17.20</td>\n",
       "      <td>20.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>20.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>15.70</td>\n",
       "      <td>20.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Female</td>\n",
       "      <td>54.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.20</td>\n",
       "      <td>22.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.90</td>\n",
       "      <td>22.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Male</td>\n",
       "      <td>56.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>21.10</td>\n",
       "      <td>25.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>20.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>12.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Male</td>\n",
       "      <td>57.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>14.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>11.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Female</td>\n",
       "      <td>67.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.20</td>\n",
       "      <td>17.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.10</td>\n",
       "      <td>28.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.40</td>\n",
       "      <td>20.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.90</td>\n",
       "      <td>22.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>11.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.10</td>\n",
       "      <td>22.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Male</td>\n",
       "      <td>75.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.20</td>\n",
       "      <td>14.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>18.20</td>\n",
       "      <td>21.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>14.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>16.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>14.10</td>\n",
       "      <td>21.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.10</td>\n",
       "      <td>19.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>21.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>15.20</td>\n",
       "      <td>25.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>13.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.40</td>\n",
       "      <td>15.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.70</td>\n",
       "      <td>19.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>21.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>24.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.20</td>\n",
       "      <td>23.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>6.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.10</td>\n",
       "      <td>6.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>13.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.90</td>\n",
       "      <td>16.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>20.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>9.40</td>\n",
       "      <td>11.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>14.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>21.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>8.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>22.10</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>13.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.30</td>\n",
       "      <td>10.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>12.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>10.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.70</td>\n",
       "      <td>10.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Female</td>\n",
       "      <td>51.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>24.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16.10</td>\n",
       "      <td>25.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>16.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.40</td>\n",
       "      <td>24.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>16.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.40</td>\n",
       "      <td>23.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.30</td>\n",
       "      <td>20.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>13.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>13.30</td>\n",
       "      <td>10.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.70</td>\n",
       "      <td>21.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>84.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>21.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>23.80</td>\n",
       "      <td>27.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.40</td>\n",
       "      <td>12.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>15.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>12.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>20.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Male</td>\n",
       "      <td>57.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>14.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>11.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.40</td>\n",
       "      <td>11.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>13.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>16.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>18.20</td>\n",
       "      <td>21.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>14.60</td>\n",
       "      <td>22.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>13.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.30</td>\n",
       "      <td>16.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>7.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>16.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>19.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.90</td>\n",
       "      <td>23.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Female</td>\n",
       "      <td>54.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Male</td>\n",
       "      <td>61.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.70</td>\n",
       "      <td>19.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>115.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.40</td>\n",
       "      <td>11.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Male</td>\n",
       "      <td>57.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.40</td>\n",
       "      <td>15.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.70</td>\n",
       "      <td>19.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>21.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>28.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.20</td>\n",
       "      <td>23.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>15.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>10.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16.50</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>8.70</td>\n",
       "      <td>20.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.22</td>\n",
       "      <td>25.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.90</td>\n",
       "      <td>23.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Female</td>\n",
       "      <td>54.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>19.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>28.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>20.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Female</td>\n",
       "      <td>68.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>12.10</td>\n",
       "      <td>25.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>11.90</td>\n",
       "      <td>18.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.20</td>\n",
       "      <td>21.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Female</td>\n",
       "      <td>65.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>14.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.30</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Female</td>\n",
       "      <td>65.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.40</td>\n",
       "      <td>17.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>Male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>20.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.60</td>\n",
       "      <td>13.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.40</td>\n",
       "      <td>14.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Female</td>\n",
       "      <td>65.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16.40</td>\n",
       "      <td>21.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.20</td>\n",
       "      <td>25.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.10</td>\n",
       "      <td>18.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Male</td>\n",
       "      <td>72.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.10</td>\n",
       "      <td>16.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.80</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>14.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.01</td>\n",
       "      <td>14.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>164.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.80</td>\n",
       "      <td>14.01</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>19.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.40</td>\n",
       "      <td>17.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.90</td>\n",
       "      <td>23.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.60</td>\n",
       "      <td>20.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.90</td>\n",
       "      <td>23.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Female</td>\n",
       "      <td>51.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.60</td>\n",
       "      <td>20.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>11.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>20.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.60</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>Female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.80</td>\n",
       "      <td>12.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>16.70</td>\n",
       "      <td>25.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.40</td>\n",
       "      <td>10.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.20</td>\n",
       "      <td>25.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.70</td>\n",
       "      <td>25.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>74.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>18.70</td>\n",
       "      <td>21.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.60</td>\n",
       "      <td>16.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Male</td>\n",
       "      <td>61.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>12.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>14.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>10.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>14.90</td>\n",
       "      <td>24.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Male</td>\n",
       "      <td>52.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.80</td>\n",
       "      <td>14.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>82.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.90</td>\n",
       "      <td>12.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>Male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>17.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.20</td>\n",
       "      <td>12.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>Male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.70</td>\n",
       "      <td>16.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>Female</td>\n",
       "      <td>43.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.10</td>\n",
       "      <td>7.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>Female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.90</td>\n",
       "      <td>20.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>Male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.60</td>\n",
       "      <td>17.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.60</td>\n",
       "      <td>18.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>Male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>160.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.20</td>\n",
       "      <td>25.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>Male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>78.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>13.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>15.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Female</td>\n",
       "      <td>65.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>135.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.40</td>\n",
       "      <td>22.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>12.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>16.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>82.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>13.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>17.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>Female</td>\n",
       "      <td>65.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.10</td>\n",
       "      <td>23.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Female</td>\n",
       "      <td>68.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>78.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>22.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>19.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>18.70</td>\n",
       "      <td>24.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>24.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.70</td>\n",
       "      <td>11.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>Male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>15.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>20.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>14.50</td>\n",
       "      <td>21.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.30</td>\n",
       "      <td>21.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.70</td>\n",
       "      <td>8.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>23.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>18.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.20</td>\n",
       "      <td>23.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.30</td>\n",
       "      <td>17.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>15.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>11.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.60</td>\n",
       "      <td>24.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.80</td>\n",
       "      <td>8.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>Female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.60</td>\n",
       "      <td>7.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.90</td>\n",
       "      <td>14.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Female</td>\n",
       "      <td>43.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>14.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>16.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>12.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>12.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.80</td>\n",
       "      <td>20.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>16.10</td>\n",
       "      <td>21.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>16.30</td>\n",
       "      <td>22.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>19.70</td>\n",
       "      <td>23.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>25.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>18.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>16.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>21.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>18.70</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.20</td>\n",
       "      <td>20.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>11.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>21.70</td>\n",
       "      <td>24.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>Male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.40</td>\n",
       "      <td>17.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>12.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.80</td>\n",
       "      <td>11.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>16.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>8.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>Female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>16.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Male</td>\n",
       "      <td>52.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>17.10</td>\n",
       "      <td>25.10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>Female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.30</td>\n",
       "      <td>11.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>Male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.60</td>\n",
       "      <td>15.40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.80</td>\n",
       "      <td>12.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>Female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.10</td>\n",
       "      <td>16.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>Male</td>\n",
       "      <td>56.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.83612</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>11.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>Female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.60</td>\n",
       "      <td>9.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.10</td>\n",
       "      <td>14.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>20.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>18.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>15.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>25.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>Female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.90</td>\n",
       "      <td>18.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Male</td>\n",
       "      <td>52.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.60</td>\n",
       "      <td>13.90</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>76.00000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>18.20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.10</td>\n",
       "      <td>13.80</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.30</td>\n",
       "      <td>27.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>130.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>19.30</td>\n",
       "      <td>22.50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sex   Age  Height  Weight  Heart Rate  Systolic BP  Diastolic BP  \\\n",
       "0      Male  47.0   170.0    90.0    71.00000        130.0          80.0   \n",
       "1      Male  68.0   162.0    92.0    68.00000        130.0          80.0   \n",
       "2    Female  66.0   146.0    67.0    76.00000        120.0          80.0   \n",
       "3    Female  60.0   156.0    49.0    80.00000        130.0          80.0   \n",
       "4    Female  66.0   152.0    60.0    68.00000        130.0          80.0   \n",
       "5      Male  56.0   160.0    54.0    80.00000        120.0          80.0   \n",
       "6    Female  63.0   150.0    57.0    80.00000        100.0          90.0   \n",
       "7    Female  50.0   154.0    62.0    80.00000        140.0          80.0   \n",
       "8    Female  55.0   152.0    63.0    60.00000        140.0          80.0   \n",
       "9    Female  50.0   156.0    60.0    74.83612        120.0          80.0   \n",
       "10   Female  55.0   154.0    60.0    74.83612        130.0          80.0   \n",
       "11   Female  58.0   158.0    56.0    80.00000        130.0          80.0   \n",
       "12     Male  47.0   164.0    56.0    76.00000        100.0          70.0   \n",
       "13   Female  40.0   156.0    68.0    80.00000        130.0          80.0   \n",
       "14   Female  53.0   138.0    40.0    70.00000        120.0          80.0   \n",
       "15     Male  55.0   164.0    62.0    68.00000        130.0          80.0   \n",
       "16   Female  61.0   150.0    69.0    68.00000        130.0          80.0   \n",
       "17     Male  45.0   158.0    46.0    56.00000        130.0          80.0   \n",
       "18   Female  40.0   162.0    59.0    74.83612        140.0          90.0   \n",
       "19   Female  28.0   152.0    61.0    68.00000        130.0          80.0   \n",
       "20   Female  45.0   154.0    54.0    68.00000        120.0          80.0   \n",
       "21   Female  48.0   150.0    55.0    80.00000        140.0          90.0   \n",
       "22     Male  62.0   164.0    65.0    80.00000        140.0          80.0   \n",
       "23   Female  40.0   152.0    64.0    80.00000        120.0          80.0   \n",
       "24     Male  45.0   162.0    72.0    68.00000        130.0          80.0   \n",
       "25     Male  55.0   162.0    68.0    74.83612        110.0          75.0   \n",
       "26     Male  40.0   176.0    82.0    76.00000        110.0          80.0   \n",
       "27     Male  48.0   170.0    68.0    80.00000        120.0          80.0   \n",
       "28   Female  60.0   142.0    57.0    80.00000        160.0         100.0   \n",
       "29   Female  43.0   146.0    69.0    80.00000        100.0          80.0   \n",
       "30     Male  34.0   170.0    66.0    74.83612        120.0          80.0   \n",
       "31     Male  65.0   166.0    72.0    80.00000        130.0          80.0   \n",
       "32     Male  37.0   162.0    65.0    68.00000        130.0          80.0   \n",
       "33   Female  68.0   150.0    55.0    76.00000        130.0          80.0   \n",
       "34     Male  34.0   164.0    88.0    80.00000        140.0          90.0   \n",
       "35   Female  48.0   148.0    56.0    68.00000        140.0         100.0   \n",
       "36   Female  40.0   144.0    53.0    80.00000        130.0          80.0   \n",
       "37   Female  60.0   148.0    55.0    80.00000        130.0          80.0   \n",
       "38     Male  37.0   166.0    62.0    68.00000        125.0          80.0   \n",
       "39   Female  43.0   150.0    79.0    80.00000        130.0          80.0   \n",
       "40     Male  72.0   156.0    60.0    80.00000        120.0          80.0   \n",
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       "283  Female  65.0   154.0    61.0    80.00000        140.0          80.0   \n",
       "284  Female  68.0   166.0    53.0    78.00000        120.0          68.0   \n",
       "285  Female  47.0   160.0    63.0    80.00000        120.0          80.0   \n",
       "286  Female  45.0   152.0    51.0    76.00000        120.0          80.0   \n",
       "287  Female  38.0   160.0    58.0    80.00000        120.0          90.0   \n",
       "288  Female  60.0   152.0    45.0    76.00000        120.0          80.0   \n",
       "289  Female  45.0   160.0    76.0    80.00000        120.0          80.0   \n",
       "290    Male  26.0   142.0   105.0    64.00000        125.0          80.0   \n",
       "291  Female  80.0   144.0    44.0    76.00000        120.0          80.0   \n",
       "292  Female  37.0   150.0    53.0    80.00000        140.0          90.0   \n",
       "293  Female  30.0   156.0    60.0    76.00000        120.0          80.0   \n",
       "294    Male  50.0   150.0    72.0    76.00000        140.0          80.0   \n",
       "295  Female  37.0   160.0    55.0    76.00000        120.0          66.0   \n",
       "296  Female  66.0   154.0    51.0    70.00000        110.0          60.0   \n",
       "297    Male  65.0   162.0    64.0    80.00000        140.0          90.0   \n",
       "298  Female  80.0   152.0    58.0    76.00000        120.0          80.0   \n",
       "299    Male  25.0   170.0    67.0    68.00000        120.0          80.0   \n",
       "300    Male  49.0   156.0    57.0    80.00000        130.0          80.0   \n",
       "301  Female  40.0   150.0    56.0    80.00000        130.0          90.0   \n",
       "302    Male  35.0   150.0    54.0    66.00000        120.0          80.0   \n",
       "303  Female  45.0   146.0    50.0    80.00000        120.0          80.0   \n",
       "304    Male  49.0   158.0    53.0    80.00000        120.0          80.0   \n",
       "305  Female  33.0   146.0    66.0    80.00000         90.0          60.0   \n",
       "306    Male  48.0   158.0    59.0    80.00000        140.0          90.0   \n",
       "307  Female  43.0   156.0    60.0    80.00000        120.0          80.0   \n",
       "308  Female  36.0   148.0    54.0    80.00000        140.0          90.0   \n",
       "309  Female  50.0   156.0    59.0    68.00000        130.0          80.0   \n",
       "310    Male  65.0   170.0    69.0    68.00000        125.0          80.0   \n",
       "311  Female  70.0   148.0    59.0    80.00000        160.0          85.0   \n",
       "312    Male  48.0   166.0    63.0    68.00000        120.0          80.0   \n",
       "313    Male  36.0   170.0    71.0    80.00000        140.0          90.0   \n",
       "314  Female  25.0   144.0    51.0    80.00000        130.0          90.0   \n",
       "315    Male  39.0   164.0    62.0    80.00000        100.0          65.0   \n",
       "316    Male  60.0   168.0    71.0    71.00000        140.0          80.0   \n",
       "317    Male  73.0   162.0    75.0    80.00000        140.0          80.0   \n",
       "318    Male  34.0   160.0    55.0    70.00000        120.0          80.0   \n",
       "319  Female  55.0   150.0    63.0    64.00000        130.0          60.0   \n",
       "320  Female  50.0   150.0    50.0    80.00000        140.0          85.0   \n",
       "321    Male  36.0   170.0    75.0    70.00000        120.0          80.0   \n",
       "322    Male  67.0   166.0    67.0    70.00000        130.0          80.0   \n",
       "323    Male  29.0   158.0    57.0    64.00000        120.0          80.0   \n",
       "324    Male  34.0   166.0    63.0    80.00000        140.0          90.0   \n",
       "325  Female  36.0   148.0    48.0    66.00000        120.0          80.0   \n",
       "326    Male  50.0   166.0    57.0    80.00000        140.0          90.0   \n",
       "327  Female  42.0   167.0    57.0    80.00000        120.0          80.0   \n",
       "328    Male  55.0   166.0    72.0    80.00000        130.0          80.0   \n",
       "329  Female  50.0   150.0    63.0    70.00000        130.0          80.0   \n",
       "330  Female  28.0   152.0    52.0    80.00000        120.0          80.0   \n",
       "331    Male  52.0   170.0    78.0    80.00000        150.0          90.0   \n",
       "332    Male  55.0   152.0    54.0    80.00000        130.0          80.0   \n",
       "333  Female  32.0   154.0    56.0    70.00000        110.0          80.0   \n",
       "334    Male  50.0   156.0    59.0    80.00000        140.0          80.0   \n",
       "335    Male  48.0   168.0    82.0    80.00000        140.0          90.0   \n",
       "336  Female  33.0   152.0    55.0    74.83612        130.0          80.0   \n",
       "337    Male  56.0   158.0    64.0    74.83612        110.0          80.0   \n",
       "338  Female  50.0   144.0    59.0    76.00000        120.0          80.0   \n",
       "339  Female  45.0   158.0    61.0    80.00000        160.0         100.0   \n",
       "340    Male  47.0   170.0    63.0    80.00000        120.0          80.0   \n",
       "341    Male  73.0   162.0    75.0    80.00000        150.0          80.0   \n",
       "342    Male  26.0   182.0   105.0    64.00000        125.0          80.0   \n",
       "343    Male  60.0   168.0    71.0    71.00000        140.0          80.0   \n",
       "344  Female  42.0   152.0    52.0    80.00000        120.0          80.0   \n",
       "345    Male  52.0   174.0    78.0    64.00000        130.0          80.0   \n",
       "346  Female  70.0   152.0    79.0    76.00000        180.0          80.0   \n",
       "347    Male  46.0   170.0    72.0    70.00000        110.0          80.0   \n",
       "348  Female  58.0   150.0    57.0    64.00000        130.0          80.0   \n",
       "349  Female  70.0   146.0    43.0    80.00000        120.0          80.0   \n",
       "350  Female  30.0   150.0    61.0    80.00000        130.0          85.0   \n",
       "\n",
       "     Blood Sugar Before Meal  Blood Sugar After Meal Outcome  \n",
       "0                       9.50                   15.40     Yes  \n",
       "1                      13.00                   16.00     Yes  \n",
       "2                       7.60                   10.90     Yes  \n",
       "3                      18.50                   26.10     Yes  \n",
       "4                       6.40                   10.00     Yes  \n",
       "5                       8.20                   18.70     Yes  \n",
       "6                      20.90                   23.20     Yes  \n",
       "7                      10.90                   15.50     Yes  \n",
       "8                       8.80                   14.11     Yes  \n",
       "9                       9.90                   15.50     Yes  \n",
       "10                     10.60                   14.20     Yes  \n",
       "11                      7.20                    9.80     Yes  \n",
       "12                     11.40                   12.30     Yes  \n",
       "13                      5.20                   13.00     Yes  \n",
       "14                     12.20                   20.60     Yes  \n",
       "15                     12.30                   19.60     Yes  \n",
       "16                     11.30                   17.90     Yes  \n",
       "17                     19.50                   26.20     Yes  \n",
       "18                     12.90                   19.20     Yes  \n",
       "19                      9.10                   12.60     Yes  \n",
       "20                     17.50                   19.90     Yes  \n",
       "21                      8.80                   16.20     Yes  \n",
       "22                     13.50                   22.40     Yes  \n",
       "23                     16.40                   22.00     Yes  \n",
       "24                     13.00                   19.50     Yes  \n",
       "25                     15.00                   28.40     Yes  \n",
       "26                     10.80                   20.60     Yes  \n",
       "27                     10.30                   15.50     Yes  \n",
       "28                     20.30                   24.90     Yes  \n",
       "29                     11.40                   17.00     Yes  \n",
       "30                     18.80                   25.60     Yes  \n",
       "31                     15.00                   23.20     Yes  \n",
       "32                      9.40                   14.40     Yes  \n",
       "33                     12.10                   25.80     Yes  \n",
       "34                     10.00                   16.00     Yes  \n",
       "35                      7.60                   12.70     Yes  \n",
       "36                     17.00                   25.90     Yes  \n",
       "37                      5.10                   12.10     Yes  \n",
       "38                     16.90                   16.00     Yes  \n",
       "39                     20.20                   28.10     Yes  \n",
       "40                     12.50                   19.20     Yes  \n",
       "41                     10.50                   17.00     Yes  \n",
       "42                     20.90                   25.60     Yes  \n",
       "43                      4.70                   10.60     Yes  \n",
       "44                      6.40                   13.50     Yes  \n",
       "45                      6.40                   11.20     Yes  \n",
       "46                     18.70                   25.70     Yes  \n",
       "47                      9.00                   17.50     Yes  \n",
       "48                      9.00                    4.40     Yes  \n",
       "49                      5.70                    7.90     Yes  \n",
       "50                     10.80                   18.20     Yes  \n",
       "51                     17.90                   21.10     Yes  \n",
       "52                     13.50                   25.50     Yes  \n",
       "53                      5.20                    9.10     Yes  \n",
       "54                     10.00                   10.20     Yes  \n",
       "55                     13.90                   19.90     Yes  \n",
       "56                     10.80                   14.80     Yes  \n",
       "57                      8.90                   18.50     Yes  \n",
       "58                     45.50                   45.80     Yes  \n",
       "59                     11.40                   15.10     Yes  \n",
       "60                      8.60                   14.20     Yes  \n",
       "61                     18.40                   23.00     Yes  \n",
       "62                      8.70                   20.40     Yes  \n",
       "63                     16.50                   28.10     Yes  \n",
       "64                      6.40                   10.30     Yes  \n",
       "65                      7.50                   15.60     Yes  \n",
       "66                      9.90                   12.00     Yes  \n",
       "67                     11.20                   16.90     Yes  \n",
       "68                      8.00                   15.80     Yes  \n",
       "69                     15.40                   19.40     Yes  \n",
       "70                     13.20                   18.20     Yes  \n",
       "71                      7.80                   14.60     Yes  \n",
       "72                     14.00                   19.50     Yes  \n",
       "73                     20.40                   24.10     Yes  \n",
       "74                     12.00                   16.70     Yes  \n",
       "75                     16.10                   21.80     Yes  \n",
       "76                     17.00                   24.30     Yes  \n",
       "77                     14.30                   20.00     Yes  \n",
       "78                      5.40                   13.90     Yes  \n",
       "79                     13.30                   16.20     Yes  \n",
       "80                     15.70                   21.30     Yes  \n",
       "81                     11.40                   17.00     Yes  \n",
       "82                     23.80                   27.10     Yes  \n",
       "83                      7.30                   12.00     Yes  \n",
       "86                     12.50                   24.00     Yes  \n",
       "87                     14.90                   23.10     Yes  \n",
       "88                      9.00                   11.20     Yes  \n",
       "89                      9.50                   15.40     Yes  \n",
       "90                     22.00                   26.30     Yes  \n",
       "91                      9.10                   14.90     Yes  \n",
       "92                      8.80                   16.00     Yes  \n",
       "93                     20.00                   23.90     Yes  \n",
       "94                     14.10                   18.00     Yes  \n",
       "95                     10.50                   11.80     Yes  \n",
       "96                     14.60                   15.30     Yes  \n",
       "97                     11.40                    5.30     Yes  \n",
       "98                     20.00                   23.40     Yes  \n",
       "99                     17.20                   20.90     Yes  \n",
       "100                    12.00                   20.90     Yes  \n",
       "101                    15.70                   20.50     Yes  \n",
       "102                    10.00                   16.20     Yes  \n",
       "103                    12.60                   17.00     Yes  \n",
       "104                    10.20                   22.20     Yes  \n",
       "105                    11.90                   22.80     Yes  \n",
       "106                    21.10                   25.50     Yes  \n",
       "107                    18.00                   20.40     Yes  \n",
       "108                     5.00                   12.10     Yes  \n",
       "109                     8.80                   14.30     Yes  \n",
       "110                     8.70                    8.40     Yes  \n",
       "111                    11.40                   11.40     Yes  \n",
       "112                    11.20                   17.60     Yes  \n",
       "113                    20.10                   28.80     Yes  \n",
       "119                     9.40                   20.30     Yes  \n",
       "120                    13.40                   14.10     Yes  \n",
       "121                    14.90                   22.80     Yes  \n",
       "122                     9.10                   11.60     Yes  \n",
       "123                    20.10                   22.10     Yes  \n",
       "124                    11.20                   14.10     Yes  \n",
       "125                    18.20                   21.80     Yes  \n",
       "126                    11.40                   14.00     Yes  \n",
       "127                    13.00                   16.80     Yes  \n",
       "128                    14.10                   21.40     Yes  \n",
       "129                    11.10                   19.10     Yes  \n",
       "130                    18.00                   21.20     Yes  \n",
       "131                    15.20                   25.20     Yes  \n",
       "132                     8.10                   13.10     Yes  \n",
       "133                    11.40                   16.00     Yes  \n",
       "134                     9.40                   15.50     Yes  \n",
       "135                    13.70                   19.80     Yes  \n",
       "136                    16.00                   21.80     Yes  \n",
       "137                    20.00                   24.40     Yes  \n",
       "138                    20.20                   23.70     Yes  \n",
       "139                     5.40                    6.60     Yes  \n",
       "140                     5.10                    6.70     Yes  \n",
       "141                     8.10                   13.20     Yes  \n",
       "142                    13.90                   16.80     Yes  \n",
       "143                    13.80                   20.80     Yes  \n",
       "144                     9.10                   11.80     Yes  \n",
       "145                     9.40                   11.50     Yes  \n",
       "146                     7.70                   14.50     Yes  \n",
       "147                     8.80                   13.40     Yes  \n",
       "148                    11.40                   21.90     Yes  \n",
       "149                    11.40                   11.80     Yes  \n",
       "150                    11.40                    8.30     Yes  \n",
       "151                    22.10                   17.00     Yes  \n",
       "152                    11.00                   12.70     Yes  \n",
       "153                    11.40                   13.40     Yes  \n",
       "154                     7.30                   10.40     Yes  \n",
       "155                    11.40                   12.20     Yes  \n",
       "156                    11.40                   10.60     Yes  \n",
       "157                     5.70                   10.60     Yes  \n",
       "158                    17.00                   24.30     Yes  \n",
       "159                    16.10                   25.80     Yes  \n",
       "160                    15.00                   16.70     Yes  \n",
       "161                    20.40                   24.10     Yes  \n",
       "162                    14.00                   16.50     Yes  \n",
       "163                    18.40                   23.10     Yes  \n",
       "164                    14.30                   20.00     Yes  \n",
       "165                     5.40                   13.20     Yes  \n",
       "166                    13.30                   10.20     Yes  \n",
       "167                    15.70                   21.30     Yes  \n",
       "168                    12.40                   21.90     Yes  \n",
       "169                    23.80                   27.10     Yes  \n",
       "170                     7.40                   12.00     Yes  \n",
       "171                    11.50                   15.50     Yes  \n",
       "172                     9.60                   12.40     Yes  \n",
       "173                    18.00                   20.40     Yes  \n",
       "175                     8.80                   14.30     Yes  \n",
       "176                     9.00                   17.50     Yes  \n",
       "177                    11.40                   11.60     Yes  \n",
       "178                     8.40                   11.70     Yes  \n",
       "179                     7.70                   13.20     Yes  \n",
       "180                     8.80                   13.40     Yes  \n",
       "181                    13.50                   16.80     Yes  \n",
       "182                    18.20                   21.40     Yes  \n",
       "183                    14.60                   22.80     Yes  \n",
       "184                     6.70                   13.20     Yes  \n",
       "185                    10.30                   16.10     Yes  \n",
       "186                     4.20                    7.20     Yes  \n",
       "187                     8.10                   16.50     Yes  \n",
       "188                    13.00                   19.50     Yes  \n",
       "189                    14.90                   23.50     Yes  \n",
       "190                    10.00                   16.20     Yes  \n",
       "191                    12.60                   17.00     Yes  \n",
       "192                     9.70                   19.50     Yes  \n",
       "193                     7.40                   11.40     Yes  \n",
       "194                     9.10                   11.80     Yes  \n",
       "195                     9.40                   15.50     Yes  \n",
       "196                    13.70                   19.80     Yes  \n",
       "197                    16.00                   21.80     Yes  \n",
       "198                    20.00                   28.80     Yes  \n",
       "199                    20.20                   23.70     Yes  \n",
       "200                     7.50                   15.60     Yes  \n",
       "201                     9.90                   17.00     Yes  \n",
       "202                     6.40                   10.50     Yes  \n",
       "203                    16.50                   17.00     Yes  \n",
       "204                     8.70                   20.40     Yes  \n",
       "205                    17.22                   25.90     Yes  \n",
       "206                    14.90                   23.10     Yes  \n",
       "207                    10.00                   16.20     Yes  \n",
       "208                    11.40                   17.00     Yes  \n",
       "209                    13.00                   19.50     Yes  \n",
       "210                    12.60                   17.00     Yes  \n",
       "211                    15.00                   28.60     Yes  \n",
       "212                    10.80                   20.60     Yes  \n",
       "213                    12.10                   25.80     Yes  \n",
       "214                    10.00                   16.00     Yes  \n",
       "215                    11.90                   18.50     Yes  \n",
       "216                    17.20                   21.30     Yes  \n",
       "217                     8.10                   10.00     Yes  \n",
       "218                     9.00                   14.50     Yes  \n",
       "219                    14.30                   22.40     Yes  \n",
       "220                     9.40                   17.30     Yes  \n",
       "221                     8.50                   20.90     Yes  \n",
       "222                    10.60                   13.00     Yes  \n",
       "223                     7.40                   14.00     Yes  \n",
       "224                    16.40                   21.70     Yes  \n",
       "225                    20.20                   25.70     Yes  \n",
       "226                    15.10                   18.40     Yes  \n",
       "227                    11.40                   17.00     Yes  \n",
       "228                    11.10                   16.90     Yes  \n",
       "229                    20.80                   11.80     Yes  \n",
       "230                     7.70                   14.50     Yes  \n",
       "231                    15.00                   20.00     Yes  \n",
       "232                    11.01                   14.30     Yes  \n",
       "239                    14.80                   14.01     Yes  \n",
       "240                    11.40                   19.50     Yes  \n",
       "241                     8.40                   17.90     Yes  \n",
       "242                    15.90                   23.50     Yes  \n",
       "243                    18.60                   20.80     Yes  \n",
       "244                    17.90                   23.00     Yes  \n",
       "245                    18.60                   20.20     Yes  \n",
       "246                     7.10                   11.00     Yes  \n",
       "247                    15.50                   20.00     Yes  \n",
       "248                    14.60                   22.00     Yes  \n",
       "249                     7.80                   12.50     Yes  \n",
       "250                    11.40                   17.00     Yes  \n",
       "251                    16.70                   25.10     Yes  \n",
       "252                     8.40                   10.10     Yes  \n",
       "253                    11.20                   25.70     Yes  \n",
       "254                    11.40                   17.00     Yes  \n",
       "255                    22.70                   25.10     Yes  \n",
       "256                    18.70                   21.00     Yes  \n",
       "257                    10.60                   16.90     Yes  \n",
       "258                    17.00                   24.00     Yes  \n",
       "259                     7.10                   12.40     Yes  \n",
       "260                     7.90                   14.60     Yes  \n",
       "261                     8.10                   10.10     Yes  \n",
       "262                    14.90                   24.50     Yes  \n",
       "263                     7.80                   14.00     Yes  \n",
       "264                     5.90                   12.80     Yes  \n",
       "265                    15.50                   17.20     Yes  \n",
       "266                     7.20                   12.50     Yes  \n",
       "267                     8.70                   16.20     Yes  \n",
       "268                     6.10                    7.20     Yes  \n",
       "269                    11.00                   16.40     Yes  \n",
       "270                    14.00                   21.00     Yes  \n",
       "271                    10.90                   20.50     Yes  \n",
       "272                    10.60                   17.30     Yes  \n",
       "273                    13.60                   18.90     Yes  \n",
       "274                    14.20                   25.80     Yes  \n",
       "275                     6.00                   13.10     Yes  \n",
       "276                    10.50                   17.00     Yes  \n",
       "277                     7.00                   15.10     Yes  \n",
       "278                    17.40                   22.10     Yes  \n",
       "279                     5.40                   12.70     Yes  \n",
       "280                     9.10                   16.90     Yes  \n",
       "281                     8.10                   13.60     Yes  \n",
       "282                    13.80                   17.30     Yes  \n",
       "283                    15.10                   23.20     Yes  \n",
       "284                    12.50                   22.20     Yes  \n",
       "285                     9.10                   19.90     Yes  \n",
       "286                     7.00                   10.00     Yes  \n",
       "287                    18.70                   24.30     Yes  \n",
       "288                    18.50                   24.10     Yes  \n",
       "289                     8.70                   11.40     Yes  \n",
       "290                     9.90                   15.20     Yes  \n",
       "291                    17.00                   20.50     Yes  \n",
       "292                    14.50                   21.50     Yes  \n",
       "293                    12.30                   21.50     Yes  \n",
       "294                     5.70                    8.20     Yes  \n",
       "295                    11.40                   17.00     Yes  \n",
       "296                    20.00                   23.50     Yes  \n",
       "297                     7.90                   18.70     Yes  \n",
       "298                    19.20                   23.00     Yes  \n",
       "299                    13.30                   17.60     Yes  \n",
       "300                     6.00                   10.20     Yes  \n",
       "301                    12.60                   15.10     Yes  \n",
       "302                     6.00                   11.50     Yes  \n",
       "303                    19.60                   24.60     Yes  \n",
       "304                     5.80                    8.60     Yes  \n",
       "305                     5.60                    7.80     Yes  \n",
       "306                     5.90                   14.80     Yes  \n",
       "307                    11.00                   14.50     Yes  \n",
       "308                     9.50                   16.80     Yes  \n",
       "309                    19.00                   17.00     Yes  \n",
       "310                     8.80                   12.60     Yes  \n",
       "311                     9.50                   12.40     Yes  \n",
       "312                    11.80                   20.00     Yes  \n",
       "313                    16.10                   21.10     Yes  \n",
       "314                    16.30                   22.80     Yes  \n",
       "315                    19.70                   23.80     Yes  \n",
       "316                    22.20                   25.80     Yes  \n",
       "317                    10.80                   18.00     Yes  \n",
       "318                     8.80                   16.50     Yes  \n",
       "319                    17.00                   21.80     Yes  \n",
       "320                    13.80                   18.70     Yes  \n",
       "321                    15.20                   20.20     Yes  \n",
       "322                     7.70                   11.40     Yes  \n",
       "323                    21.70                   24.40     Yes  \n",
       "324                     9.40                   17.90     Yes  \n",
       "325                     7.90                   12.20     Yes  \n",
       "326                    16.00                   17.00     Yes  \n",
       "327                     6.80                   11.10     Yes  \n",
       "328                    15.50                   16.00     Yes  \n",
       "329                     6.20                    8.20     Yes  \n",
       "330                    10.50                   16.80     Yes  \n",
       "331                    17.10                   25.10     Yes  \n",
       "332                    12.00                   17.00     Yes  \n",
       "333                     5.30                   11.00     Yes  \n",
       "334                     6.60                   15.40     Yes  \n",
       "335                     5.80                   12.60     Yes  \n",
       "336                    11.10                   16.80     Yes  \n",
       "337                    11.40                   11.20     Yes  \n",
       "338                     7.60                    9.90     Yes  \n",
       "339                    12.10                   14.20     Yes  \n",
       "340                    18.00                   20.80     Yes  \n",
       "341                    10.80                   18.00     Yes  \n",
       "342                     9.90                   15.20     Yes  \n",
       "343                    22.20                   25.80     Yes  \n",
       "344                    12.90                   18.20     Yes  \n",
       "345                     7.60                   13.90     Yes  \n",
       "346                     7.00                    9.30     Yes  \n",
       "347                     7.00                   18.20     Yes  \n",
       "348                    12.10                   13.80     Yes  \n",
       "349                    19.30                   27.60     Yes  \n",
       "350                    19.30                   22.50     Yes  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ae4a9c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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