model_generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing, tree\n",
    "from sklearn.utils import shuffle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "_diseases = [\"stroke\", \"heart_attack\", \"diabetes\", \"cancer\", \"kidney_disease\", \"ang_or_chd\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dataframes containing data for people who did and didn't develop x diseases\n",
    "#Probably don't merge data frames (could be done at a later stage but probably doesn't work in our timeframe, requires too much data cleaning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a model to predict based off all of the features in the dataset for each disease\n",
    "#Proabably use a neural network? Some kind of ensemble learning method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create models to impute missing input features based on the features that the user does provide\n",
    "#Develop a confidence interval for the missing values, create an input vector with each quartile (?) from the confidence interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Take in user input\n",
    "#Predict likelihood of development of each disease\n",
    "#Use each input vector if we had to create a confidence interval imputation for missing data\n",
    "#Create a confidence interval for likelhood of developing disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determine which of the features contributed the most (?) to the likelihood of the more likely diseases\n",
    "#Make recommendations for how to reduce the likelhiood of the diseases based on these features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>BPHIGH4</th>\n",
       "      <th>BPMEDS</th>\n",
       "      <th>SEX</th>\n",
       "      <th>TOLDHI2</th>\n",
       "      <th>CVDINFR4</th>\n",
       "      <th>CVDSTRK3</th>\n",
       "      <th>DIABETE3</th>\n",
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       "      <th>SMOKE100</th>\n",
       "      <th>SMOKDAY2</th>\n",
       "      <th>DRNK3GE5</th>\n",
       "      <th>_AGE80</th>\n",
       "      <th>_FRUTSUM</th>\n",
       "      <th>_VEGESUM</th>\n",
       "      <th>_PA150R2</th>\n",
       "      <th>_RACE</th>\n",
       "      <th>CHCOCNCR</th>\n",
       "      <th>CHCKIDNY</th>\n",
       "      <th>CVDCRHD4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>165.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BPHIGH4  BPMEDS  SEX  TOLDHI2  CVDINFR4  CVDSTRK3  DIABETE3  WEIGHT2  \\\n",
       "0      1.0     1.0  2.0      1.0       2.0       2.0       3.0    280.0   \n",
       "1      3.0     NaN  2.0      2.0       2.0       2.0       3.0    165.0   \n",
       "2      3.0     NaN  2.0      1.0       7.0       1.0       3.0    158.0   \n",
       "3      1.0     1.0  2.0      1.0       2.0       2.0       3.0    180.0   \n",
       "4      3.0     NaN  2.0      2.0       2.0       2.0       3.0    142.0   \n",
       "\n",
       "   HEIGHT3  SMOKE100  SMOKDAY2  DRNK3GE5  _AGE80  _FRUTSUM  _VEGESUM  \\\n",
       "0    510.0       1.0       3.0       NaN    63.0      50.0     217.0   \n",
       "1    508.0       1.0       1.0       NaN    52.0      24.0      78.0   \n",
       "2    511.0       NaN       NaN       NaN    71.0       NaN       NaN   \n",
       "3    507.0       2.0       NaN       NaN    63.0     100.0      20.0   \n",
       "4    504.0       2.0       NaN       NaN    61.0       NaN     200.0   \n",
       "\n",
       "   _PA150R2  _RACE  CHCOCNCR  CHCKIDNY  CVDCRHD4  \n",
       "0       3.0    1.0       2.0       2.0       2.0  \n",
       "1       1.0    1.0       2.0       2.0       2.0  \n",
       "2       9.0    1.0       1.0       2.0       2.0  \n",
       "3       3.0    1.0       1.0       2.0       2.0  \n",
       "4       3.0    1.0       2.0       2.0       2.0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"2015.csv\")\n",
    "df = df[[\"BPHIGH4\", \"BPMEDS\", \"SEX\", \"TOLDHI2\", \"CVDINFR4\", \"CVDSTRK3\", \"DIABETE3\", \"WEIGHT2\", \"HEIGHT3\", \"SMOKE100\", \"SMOKDAY2\", \"DRNK3GE5\", \"_AGE80\", \"_FRUTSUM\", \"_VEGESUM\", \"_PA150R2\", \"_RACE\", \"CHCOCNCR\", \"CHCKIDNY\", \"CVDCRHD4\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"HEIGHT3\"] != 7777.0]\n",
    "df = df[df[\"HEIGHT3\"] != 9999.0]\n",
    "df = df[df[\"WEIGHT2\"] != 7777.0]\n",
    "df = df[df[\"WEIGHT2\"] != 9999.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>BPHIGH4</th>\n",
       "      <th>BPMEDS</th>\n",
       "      <th>SEX</th>\n",
       "      <th>TOLDHI2</th>\n",
       "      <th>CVDINFR4</th>\n",
       "      <th>CVDSTRK3</th>\n",
       "      <th>DIABETE3</th>\n",
       "      <th>WEIGHT2</th>\n",
       "      <th>HEIGHT3</th>\n",
       "      <th>SMOKE100</th>\n",
       "      <th>SMOKDAY2</th>\n",
       "      <th>DRNK3GE5</th>\n",
       "      <th>_AGE80</th>\n",
       "      <th>_FRUTSUM</th>\n",
       "      <th>_VEGESUM</th>\n",
       "      <th>_PA150R2</th>\n",
       "      <th>_RACEGR3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>412999.000000</td>\n",
       "      <td>166838.000000</td>\n",
       "      <td>413000.000000</td>\n",
       "      <td>358034.000000</td>\n",
       "      <td>413000.000000</td>\n",
       "      <td>413000.000000</td>\n",
       "      <td>412993.000000</td>\n",
       "      <td>407685.000000</td>\n",
       "      <td>407369.000000</td>\n",
       "      <td>400846.000000</td>\n",
       "      <td>175276.000000</td>\n",
       "      <td>201379.000000</td>\n",
       "      <td>413000.000000</td>\n",
       "      <td>3.755700e+05</td>\n",
       "      <td>3.689370e+05</td>\n",
       "      <td>413000.000000</td>\n",
       "      <td>413000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.206155</td>\n",
       "      <td>1.173641</td>\n",
       "      <td>1.560460</td>\n",
       "      <td>1.623659</td>\n",
       "      <td>1.965128</td>\n",
       "      <td>1.971663</td>\n",
       "      <td>2.756938</td>\n",
       "      <td>196.126544</td>\n",
       "      <td>555.154678</td>\n",
       "      <td>1.598976</td>\n",
       "      <td>2.432849</td>\n",
       "      <td>67.468837</td>\n",
       "      <td>55.433254</td>\n",
       "      <td>1.361374e+02</td>\n",
       "      <td>1.943727e+02</td>\n",
       "      <td>2.546293</td>\n",
       "      <td>1.652012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.027882</td>\n",
       "      <td>0.445738</td>\n",
       "      <td>0.496332</td>\n",
       "      <td>0.718881</td>\n",
       "      <td>0.426887</td>\n",
       "      <td>0.334988</td>\n",
       "      <td>0.714990</td>\n",
       "      <td>397.807382</td>\n",
       "      <td>562.901366</td>\n",
       "      <td>0.701376</td>\n",
       "      <td>0.881476</td>\n",
       "      <td>36.053693</td>\n",
       "      <td>17.132764</td>\n",
       "      <td>1.375521e+02</td>\n",
       "      <td>1.549042e+02</td>\n",
       "      <td>2.409574</td>\n",
       "      <td>1.472622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.397605e-79</td>\n",
       "      <td>5.397605e-79</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>5.700000e+01</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>507.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.680000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>2.430000e+02</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9501.000000</td>\n",
       "      <td>9503.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>1.992900e+04</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BPHIGH4         BPMEDS            SEX        TOLDHI2  \\\n",
       "count  412999.000000  166838.000000  413000.000000  358034.000000   \n",
       "mean        2.206155       1.173641       1.560460       1.623659   \n",
       "std         1.027882       0.445738       0.496332       0.718881   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000       1.000000       1.000000   \n",
       "50%         3.000000       1.000000       2.000000       2.000000   \n",
       "75%         3.000000       1.000000       2.000000       2.000000   \n",
       "max         9.000000       9.000000       2.000000       9.000000   \n",
       "\n",
       "            CVDINFR4       CVDSTRK3       DIABETE3        WEIGHT2  \\\n",
       "count  413000.000000  413000.000000  412993.000000  407685.000000   \n",
       "mean        1.965128       1.971663       2.756938     196.126544   \n",
       "std         0.426887       0.334988       0.714990     397.807382   \n",
       "min         1.000000       1.000000       1.000000      50.000000   \n",
       "25%         2.000000       2.000000       3.000000     145.000000   \n",
       "50%         2.000000       2.000000       3.000000     172.000000   \n",
       "75%         2.000000       2.000000       3.000000     200.000000   \n",
       "max         9.000000       9.000000       9.000000    9501.000000   \n",
       "\n",
       "             HEIGHT3       SMOKE100       SMOKDAY2       DRNK3GE5  \\\n",
       "count  407369.000000  400846.000000  175276.000000  201379.000000   \n",
       "mean      555.154678       1.598976       2.432849      67.468837   \n",
       "std       562.901366       0.701376       0.881476      36.053693   \n",
       "min       204.000000       1.000000       1.000000       1.000000   \n",
       "25%       504.000000       1.000000       2.000000      77.000000   \n",
       "50%       507.000000       2.000000       3.000000      88.000000   \n",
       "75%       510.000000       2.000000       3.000000      88.000000   \n",
       "max      9503.000000       9.000000       9.000000      99.000000   \n",
       "\n",
       "              _AGE80      _FRUTSUM      _VEGESUM       _PA150R2       _RACEGR3  \n",
       "count  413000.000000  3.755700e+05  3.689370e+05  413000.000000  413000.000000  \n",
       "mean       55.433254  1.361374e+02  1.943727e+02       2.546293       1.652012  \n",
       "std        17.132764  1.375521e+02  1.549042e+02       2.409574       1.472622  \n",
       "min        18.000000  5.397605e-79  5.397605e-79       1.000000       1.000000  \n",
       "25%        43.000000  5.700000e+01  1.100000e+02       1.000000       1.000000  \n",
       "50%        58.000000  1.000000e+02  1.680000e+02       2.000000       1.000000  \n",
       "75%        69.000000  2.000000e+02  2.430000e+02       3.000000       1.000000  \n",
       "max        80.000000  1.500000e+04  1.992900e+04       9.000000       9.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_height(height):\n",
    "    if height <= 711:\n",
    "        feet = height // 100\n",
    "        inches = height % 100 + 12 * feet\n",
    "    else:\n",
    "        meters = height // 100\n",
    "        centimeters = height % 100 + 12 * meters\n",
    "        inches = centimeters / 2.54\n",
    "    return inches\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_weight(weight):\n",
    "    if weight <= 999:\n",
    "        lbs = weight\n",
    "    else:\n",
    "        kilos = weight - 9000\n",
    "        lbs = kilos / 2.2\n",
    "    return lbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>BPHIGH4</th>\n",
       "      <th>BPMEDS</th>\n",
       "      <th>SEX</th>\n",
       "      <th>TOLDHI2</th>\n",
       "      <th>CVDINFR4</th>\n",
       "      <th>CVDSTRK3</th>\n",
       "      <th>DIABETE3</th>\n",
       "      <th>SMOKE100</th>\n",
       "      <th>SMOKDAY2</th>\n",
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       "      <th>_AGE80</th>\n",
       "      <th>_FRUTSUM</th>\n",
       "      <th>_VEGESUM</th>\n",
       "      <th>_PA150R2</th>\n",
       "      <th>_RACEGR3</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>217.0</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>280.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>158.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>180.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BPHIGH4  BPMEDS  SEX  TOLDHI2  CVDINFR4  CVDSTRK3  DIABETE3  SMOKE100  \\\n",
       "0      1.0     1.0  2.0      1.0       2.0       2.0       3.0       1.0   \n",
       "1      3.0     NaN  2.0      2.0       2.0       2.0       3.0       1.0   \n",
       "2      3.0     NaN  2.0      1.0       7.0       1.0       3.0       NaN   \n",
       "3      1.0     1.0  2.0      1.0       2.0       2.0       3.0       2.0   \n",
       "4      3.0     NaN  2.0      2.0       2.0       2.0       3.0       2.0   \n",
       "\n",
       "   SMOKDAY2  DRNK3GE5  _AGE80  _FRUTSUM  _VEGESUM  _PA150R2  _RACEGR3  height  \\\n",
       "0       3.0       NaN    63.0      50.0     217.0       3.0       1.0    70.0   \n",
       "1       1.0       NaN    52.0      24.0      78.0       1.0       1.0    68.0   \n",
       "2       NaN       NaN    71.0       NaN       NaN       9.0       1.0    71.0   \n",
       "3       NaN       NaN    63.0     100.0      20.0       3.0       1.0    67.0   \n",
       "4       NaN       NaN    61.0       NaN     200.0       3.0       1.0    64.0   \n",
       "\n",
       "   weight  \n",
       "0   280.0  \n",
       "1   165.0  \n",
       "2   158.0  \n",
       "3   180.0  \n",
       "4   142.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"height\"] = df[\"HEIGHT3\"].apply(fix_height)\n",
    "df[\"weight\"] = df[\"WEIGHT2\"].apply(fix_weight)\n",
    "df.drop([\"HEIGHT3\", \"WEIGHT2\"], axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
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       "      <td>52.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>25.140621</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>28.250932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>26.576299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>23.937772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   high_bp  bp_meds  sex  high_chol  heart_attack  stroke  diabetes  \\\n",
       "0      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "1      3.0      2.0  2.0        2.0           0.0     0.0       0.0   \n",
       "3      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "5      1.0      1.0  2.0        2.0           0.0     0.0       0.0   \n",
       "6      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "\n",
       "   smoke_100  smoke_freq  drinking   age  daily_fruit  daily_veg  weekly_phys  \\\n",
       "0        1.0         3.0       0.0  63.0         0.50       2.17          3.0   \n",
       "1        1.0         1.0       0.0  52.0         0.24       0.78          1.0   \n",
       "3        2.0         3.0       0.0  63.0         1.00       0.20          3.0   \n",
       "5        2.0         3.0       0.0  73.0         2.00       6.00          1.0   \n",
       "6        2.0         3.0       0.0  70.0         1.29       1.14          1.0   \n",
       "\n",
       "   race  height  weight        BMI  \n",
       "0   1.0    70.0   280.0  40.259821  \n",
       "1   1.0    68.0   165.0  25.140621  \n",
       "3   1.0    67.0   180.0  28.250932  \n",
       "5   1.0    62.0   145.0  26.576299  \n",
       "6   4.0    66.0   148.0  23.937772  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename({\"_AGE80\": \"age\", \"CVDINFR4\": \"heart_attack\", \"CVDSTRK3\": \"stroke\", \"BPHIGH4\": \"high_bp\", \"BPMEDS\": \"bp_meds\", \"TOLDHI2\": \"high_chol\", \"_RACE\": \"race\"}, axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>28.250932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>26.576299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>23.937772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   high_bp  bp_meds  sex  high_chol  heart_attack  stroke  diabetes  \\\n",
       "0      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "1      3.0      2.0  2.0        2.0           0.0     0.0       0.0   \n",
       "3      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "5      1.0      1.0  2.0        2.0           0.0     0.0       0.0   \n",
       "6      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "\n",
       "   smoke_100  smoke_freq  drinking   age  daily_fruit  daily_veg  weekly_phys  \\\n",
       "0        1.0         3.0       0.0  63.0         0.50       2.17          3.0   \n",
       "1        1.0         1.0       0.0  52.0         0.24       0.78          1.0   \n",
       "3        2.0         3.0       0.0  63.0         1.00       0.20          3.0   \n",
       "5        2.0         3.0       0.0  73.0         2.00       6.00          1.0   \n",
       "6        2.0         3.0       0.0  70.0         1.29       1.14          1.0   \n",
       "\n",
       "   race  height  weight        BMI  \n",
       "0   1.0    70.0   280.0  40.259821  \n",
       "1   1.0    68.0   165.0  25.140621  \n",
       "3   1.0    67.0   180.0  28.250932  \n",
       "5   1.0    62.0   145.0  26.576299  \n",
       "6   4.0    66.0   148.0  23.937772  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename({\"DIABETE3\": 'diabetes', \"SEX\": \"sex\", \"SMOKE100\": \"smoke_100\", \"SMOKDAY2\": \"smoke_freq\", \"DRNK3GE5\": 'drinking', \"_FRUTSUM\": \"daily_fruit\", \"_VEGESUM\": \"daily_veg\", \"_PA150R2\": \"weekly_phys\"}, inplace = True, axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 296927 entries, 0 to 441455\n",
      "Data columns (total 18 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   high_bp       296927 non-null  float64\n",
      " 1   bp_meds       296927 non-null  float64\n",
      " 2   sex           296927 non-null  float64\n",
      " 3   high_chol     296927 non-null  float64\n",
      " 4   heart_attack  296927 non-null  float64\n",
      " 5   stroke        296927 non-null  float64\n",
      " 6   diabetes      296927 non-null  float64\n",
      " 7   smoke_100     296927 non-null  float64\n",
      " 8   smoke_freq    296927 non-null  float64\n",
      " 9   drinking      296927 non-null  float64\n",
      " 10  age           296927 non-null  float64\n",
      " 11  daily_fruit   296927 non-null  float64\n",
      " 12  daily_veg     296927 non-null  float64\n",
      " 13  weekly_phys   296927 non-null  float64\n",
      " 14  race          296927 non-null  float64\n",
      " 15  height        296927 non-null  float64\n",
      " 16  weight        296927 non-null  float64\n",
      " 17  BMI           296927 non-null  float64\n",
      "dtypes: float64(18)\n",
      "memory usage: 43.0 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>smoke_100</th>\n",
       "      <th>smoke_freq</th>\n",
       "      <th>drinking</th>\n",
       "      <th>age</th>\n",
       "      <th>daily_fruit</th>\n",
       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
       "      <th>race</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>2.969270e+05</td>\n",
       "      <td>2.969270e+05</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.134733</td>\n",
       "      <td>1.629138</td>\n",
       "      <td>1.574360</td>\n",
       "      <td>1.573501</td>\n",
       "      <td>0.062931</td>\n",
       "      <td>0.042812</td>\n",
       "      <td>0.168186</td>\n",
       "      <td>1.559673</td>\n",
       "      <td>2.773345</td>\n",
       "      <td>0.473514</td>\n",
       "      <td>57.593041</td>\n",
       "      <td>1.374188e+00</td>\n",
       "      <td>1.972965e+00</td>\n",
       "      <td>1.719487</td>\n",
       "      <td>1.552947</td>\n",
       "      <td>66.767347</td>\n",
       "      <td>179.994532</td>\n",
       "      <td>28.333837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.003671</td>\n",
       "      <td>0.483036</td>\n",
       "      <td>0.494441</td>\n",
       "      <td>0.494569</td>\n",
       "      <td>0.242840</td>\n",
       "      <td>0.202433</td>\n",
       "      <td>0.374032</td>\n",
       "      <td>0.496427</td>\n",
       "      <td>0.602512</td>\n",
       "      <td>2.407998</td>\n",
       "      <td>15.381330</td>\n",
       "      <td>1.130886e+00</td>\n",
       "      <td>1.326744e+00</td>\n",
       "      <td>0.860221</td>\n",
       "      <td>1.354358</td>\n",
       "      <td>4.097215</td>\n",
       "      <td>47.575121</td>\n",
       "      <td>6.640913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.397605e-81</td>\n",
       "      <td>5.397605e-81</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>15.454545</td>\n",
       "      <td>3.012282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>5.700000e-01</td>\n",
       "      <td>1.140000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>24.081192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.030000e+00</td>\n",
       "      <td>1.710000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>27.319164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.490000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>31.258831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>1.950000e+01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>182.329022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high_bp        bp_meds            sex      high_chol  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        2.134733       1.629138       1.574360       1.573501   \n",
       "std         1.003671       0.483036       0.494441       0.494569   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000       1.000000       1.000000   \n",
       "50%         3.000000       2.000000       2.000000       2.000000   \n",
       "75%         3.000000       2.000000       2.000000       2.000000   \n",
       "max         4.000000       2.000000       2.000000       2.000000   \n",
       "\n",
       "        heart_attack         stroke       diabetes      smoke_100  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        0.062931       0.042812       0.168186       1.559673   \n",
       "std         0.242840       0.202433       0.374032       0.496427   \n",
       "min         0.000000       0.000000       0.000000       1.000000   \n",
       "25%         0.000000       0.000000       0.000000       1.000000   \n",
       "50%         0.000000       0.000000       0.000000       2.000000   \n",
       "75%         0.000000       0.000000       0.000000       2.000000   \n",
       "max         1.000000       1.000000       1.000000       2.000000   \n",
       "\n",
       "          smoke_freq       drinking            age   daily_fruit  \\\n",
       "count  296927.000000  296927.000000  296927.000000  2.969270e+05   \n",
       "mean        2.773345       0.473514      57.593041  1.374188e+00   \n",
       "std         0.602512       2.407998      15.381330  1.130886e+00   \n",
       "min         1.000000       0.000000      18.000000  5.397605e-81   \n",
       "25%         3.000000       0.000000      47.000000  5.700000e-01   \n",
       "50%         3.000000       0.000000      60.000000  1.030000e+00   \n",
       "75%         3.000000       0.000000      69.000000  2.000000e+00   \n",
       "max         3.000000      30.000000      80.000000  1.900000e+01   \n",
       "\n",
       "          daily_veg    weekly_phys           race         height  \\\n",
       "count  2.969270e+05  296927.000000  296927.000000  296927.000000   \n",
       "mean   1.972965e+00       1.719487       1.552947      66.767347   \n",
       "std    1.326744e+00       0.860221       1.354358       4.097215   \n",
       "min    5.397605e-81       1.000000       1.000000      32.000000   \n",
       "25%    1.140000e+00       1.000000       1.000000      64.000000   \n",
       "50%    1.710000e+00       1.000000       1.000000      66.000000   \n",
       "75%    2.490000e+00       3.000000       1.000000      70.000000   \n",
       "max    1.950000e+01       3.000000       9.000000      91.000000   \n",
       "\n",
       "              weight            BMI  \n",
       "count  296927.000000  296927.000000  \n",
       "mean      179.994532      28.333837  \n",
       "std        47.575121       6.640913  \n",
       "min        15.454545       3.012282  \n",
       "25%       148.000000      24.081192  \n",
       "50%       175.000000      27.319164  \n",
       "75%       200.000000      31.258831  \n",
       "max       700.000000     182.329022  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
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       "      <th>smoke_freq</th>\n",
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       "      <th>age</th>\n",
       "      <th>daily_fruit</th>\n",
       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
       "      <th>race</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
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       "      <td>2.969270e+05</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.134733</td>\n",
       "      <td>1.629138</td>\n",
       "      <td>1.574360</td>\n",
       "      <td>1.573501</td>\n",
       "      <td>0.062931</td>\n",
       "      <td>0.042812</td>\n",
       "      <td>0.168186</td>\n",
       "      <td>1.559673</td>\n",
       "      <td>2.773345</td>\n",
       "      <td>0.473514</td>\n",
       "      <td>57.593041</td>\n",
       "      <td>1.374188e+00</td>\n",
       "      <td>1.972965e+00</td>\n",
       "      <td>1.719487</td>\n",
       "      <td>1.552947</td>\n",
       "      <td>66.767347</td>\n",
       "      <td>179.994532</td>\n",
       "      <td>28.333837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.003671</td>\n",
       "      <td>0.483036</td>\n",
       "      <td>0.494441</td>\n",
       "      <td>0.494569</td>\n",
       "      <td>0.242840</td>\n",
       "      <td>0.202433</td>\n",
       "      <td>0.374032</td>\n",
       "      <td>0.496427</td>\n",
       "      <td>0.602512</td>\n",
       "      <td>2.407998</td>\n",
       "      <td>15.381330</td>\n",
       "      <td>1.130886e+00</td>\n",
       "      <td>1.326744e+00</td>\n",
       "      <td>0.860221</td>\n",
       "      <td>1.354358</td>\n",
       "      <td>4.097215</td>\n",
       "      <td>47.575121</td>\n",
       "      <td>6.640913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.397605e-81</td>\n",
       "      <td>5.397605e-81</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>15.454545</td>\n",
       "      <td>3.012282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>5.700000e-01</td>\n",
       "      <td>1.140000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>24.081192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.030000e+00</td>\n",
       "      <td>1.710000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>27.319164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.490000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>31.258831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>1.950000e+01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>182.329022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high_bp        bp_meds            sex      high_chol  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        2.134733       1.629138       1.574360       1.573501   \n",
       "std         1.003671       0.483036       0.494441       0.494569   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000       1.000000       1.000000   \n",
       "50%         3.000000       2.000000       2.000000       2.000000   \n",
       "75%         3.000000       2.000000       2.000000       2.000000   \n",
       "max         4.000000       2.000000       2.000000       2.000000   \n",
       "\n",
       "        heart_attack         stroke       diabetes      smoke_100  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        0.062931       0.042812       0.168186       1.559673   \n",
       "std         0.242840       0.202433       0.374032       0.496427   \n",
       "min         0.000000       0.000000       0.000000       1.000000   \n",
       "25%         0.000000       0.000000       0.000000       1.000000   \n",
       "50%         0.000000       0.000000       0.000000       2.000000   \n",
       "75%         0.000000       0.000000       0.000000       2.000000   \n",
       "max         1.000000       1.000000       1.000000       2.000000   \n",
       "\n",
       "          smoke_freq       drinking            age   daily_fruit  \\\n",
       "count  296927.000000  296927.000000  296927.000000  2.969270e+05   \n",
       "mean        2.773345       0.473514      57.593041  1.374188e+00   \n",
       "std         0.602512       2.407998      15.381330  1.130886e+00   \n",
       "min         1.000000       0.000000      18.000000  5.397605e-81   \n",
       "25%         3.000000       0.000000      47.000000  5.700000e-01   \n",
       "50%         3.000000       0.000000      60.000000  1.030000e+00   \n",
       "75%         3.000000       0.000000      69.000000  2.000000e+00   \n",
       "max         3.000000      30.000000      80.000000  1.900000e+01   \n",
       "\n",
       "          daily_veg    weekly_phys           race         height  \\\n",
       "count  2.969270e+05  296927.000000  296927.000000  296927.000000   \n",
       "mean   1.972965e+00       1.719487       1.552947      66.767347   \n",
       "std    1.326744e+00       0.860221       1.354358       4.097215   \n",
       "min    5.397605e-81       1.000000       1.000000      32.000000   \n",
       "25%    1.140000e+00       1.000000       1.000000      64.000000   \n",
       "50%    1.710000e+00       1.000000       1.000000      66.000000   \n",
       "75%    2.490000e+00       3.000000       1.000000      70.000000   \n",
       "max    1.950000e+01       3.000000       9.000000      91.000000   \n",
       "\n",
       "              weight            BMI  \n",
       "count  296927.000000  296927.000000  \n",
       "mean      179.994532      28.333837  \n",
       "std        47.575121       6.640913  \n",
       "min        15.454545       3.012282  \n",
       "25%       148.000000      24.081192  \n",
       "50%       175.000000      27.319164  \n",
       "75%       200.000000      31.258831  \n",
       "max       700.000000     182.329022  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df[\"height\"] <= 96]\n",
    "df = df[df[\"weight\"] <= 998]\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 296927 entries, 0 to 441455\n",
      "Data columns (total 18 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   high_bp       296927 non-null  float64\n",
      " 1   bp_meds       296927 non-null  float64\n",
      " 2   sex           296927 non-null  float64\n",
      " 3   high_chol     296927 non-null  float64\n",
      " 4   heart_attack  296927 non-null  float64\n",
      " 5   stroke        296927 non-null  float64\n",
      " 6   diabetes      296927 non-null  float64\n",
      " 7   smoke_100     296927 non-null  float64\n",
      " 8   smoke_freq    296927 non-null  float64\n",
      " 9   drinking      296927 non-null  float64\n",
      " 10  age           296927 non-null  float64\n",
      " 11  daily_fruit   296927 non-null  float64\n",
      " 12  daily_veg     296927 non-null  float64\n",
      " 13  weekly_phys   296927 non-null  float64\n",
      " 14  race          296927 non-null  float64\n",
      " 15  height        296927 non-null  float64\n",
      " 16  weight        296927 non-null  float64\n",
      " 17  BMI           296927 non-null  float64\n",
      "dtypes: float64(18)\n",
      "memory usage: 43.0 MB\n"
     ]
    }
   ],
   "source": [
    "df[\"smoke_freq\"].fillna(3, inplace = True)\n",
    "df[\"bp_meds\"].fillna(2, inplace = True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 296927 entries, 0 to 441455\n",
      "Data columns (total 18 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   high_bp       296927 non-null  float64\n",
      " 1   bp_meds       296927 non-null  float64\n",
      " 2   sex           296927 non-null  float64\n",
      " 3   high_chol     296927 non-null  float64\n",
      " 4   heart_attack  296927 non-null  float64\n",
      " 5   stroke        296927 non-null  float64\n",
      " 6   diabetes      296927 non-null  float64\n",
      " 7   smoke_100     296927 non-null  float64\n",
      " 8   smoke_freq    296927 non-null  float64\n",
      " 9   drinking      296927 non-null  float64\n",
      " 10  age           296927 non-null  float64\n",
      " 11  daily_fruit   296927 non-null  float64\n",
      " 12  daily_veg     296927 non-null  float64\n",
      " 13  weekly_phys   296927 non-null  float64\n",
      " 14  race          296927 non-null  float64\n",
      " 15  height        296927 non-null  float64\n",
      " 16  weight        296927 non-null  float64\n",
      " 17  BMI           296927 non-null  float64\n",
      "dtypes: float64(18)\n",
      "memory usage: 43.0 MB\n"
     ]
    }
   ],
   "source": [
    "df[\"drinking\"].fillna(88, inplace = True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>smoke_100</th>\n",
       "      <th>smoke_freq</th>\n",
       "      <th>drinking</th>\n",
       "      <th>age</th>\n",
       "      <th>daily_fruit</th>\n",
       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
       "      <th>race</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>40.259821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>25.140621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>28.250932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>26.576299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>23.937772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   high_bp  bp_meds  sex  high_chol  heart_attack  stroke  diabetes  \\\n",
       "0      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "1      3.0      2.0  2.0        2.0           0.0     0.0       0.0   \n",
       "3      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "5      1.0      1.0  2.0        2.0           0.0     0.0       0.0   \n",
       "6      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "\n",
       "   smoke_100  smoke_freq  drinking   age  daily_fruit  daily_veg  weekly_phys  \\\n",
       "0        1.0         3.0       0.0  63.0         0.50       2.17          3.0   \n",
       "1        1.0         1.0       0.0  52.0         0.24       0.78          1.0   \n",
       "3        2.0         3.0       0.0  63.0         1.00       0.20          3.0   \n",
       "5        2.0         3.0       0.0  73.0         2.00       6.00          1.0   \n",
       "6        2.0         3.0       0.0  70.0         1.29       1.14          1.0   \n",
       "\n",
       "   race  height  weight        BMI  \n",
       "0   1.0    70.0   280.0  40.259821  \n",
       "1   1.0    68.0   165.0  25.140621  \n",
       "3   1.0    67.0   180.0  28.250932  \n",
       "5   1.0    62.0   145.0  26.576299  \n",
       "6   4.0    66.0   148.0  23.937772  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"BMI\"] = df[\"weight\"] / 2.2 / (df[\"height\"] * 0.0254) ** 2\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>smoke_100</th>\n",
       "      <th>smoke_freq</th>\n",
       "      <th>drinking</th>\n",
       "      <th>age</th>\n",
       "      <th>daily_fruit</th>\n",
       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
       "      <th>race</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>2.969270e+05</td>\n",
       "      <td>2.969270e+05</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.134733</td>\n",
       "      <td>1.629138</td>\n",
       "      <td>1.574360</td>\n",
       "      <td>1.573501</td>\n",
       "      <td>0.062931</td>\n",
       "      <td>0.042812</td>\n",
       "      <td>0.168186</td>\n",
       "      <td>1.559673</td>\n",
       "      <td>2.773345</td>\n",
       "      <td>0.473514</td>\n",
       "      <td>57.593041</td>\n",
       "      <td>1.374188e-02</td>\n",
       "      <td>1.972965e-02</td>\n",
       "      <td>1.719487</td>\n",
       "      <td>1.552947</td>\n",
       "      <td>66.767347</td>\n",
       "      <td>179.994532</td>\n",
       "      <td>28.333837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.003671</td>\n",
       "      <td>0.483036</td>\n",
       "      <td>0.494441</td>\n",
       "      <td>0.494569</td>\n",
       "      <td>0.242840</td>\n",
       "      <td>0.202433</td>\n",
       "      <td>0.374032</td>\n",
       "      <td>0.496427</td>\n",
       "      <td>0.602512</td>\n",
       "      <td>2.407998</td>\n",
       "      <td>15.381330</td>\n",
       "      <td>1.130886e-02</td>\n",
       "      <td>1.326744e-02</td>\n",
       "      <td>0.860221</td>\n",
       "      <td>1.354358</td>\n",
       "      <td>4.097215</td>\n",
       "      <td>47.575121</td>\n",
       "      <td>6.640913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.397605e-83</td>\n",
       "      <td>5.397605e-83</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>15.454545</td>\n",
       "      <td>3.012282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>5.700000e-03</td>\n",
       "      <td>1.140000e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>24.081192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.030000e-02</td>\n",
       "      <td>1.710000e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>27.319164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>2.490000e-02</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>31.258831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.900000e-01</td>\n",
       "      <td>1.950000e-01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>182.329022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high_bp        bp_meds            sex      high_chol  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        2.134733       1.629138       1.574360       1.573501   \n",
       "std         1.003671       0.483036       0.494441       0.494569   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000       1.000000       1.000000   \n",
       "50%         3.000000       2.000000       2.000000       2.000000   \n",
       "75%         3.000000       2.000000       2.000000       2.000000   \n",
       "max         4.000000       2.000000       2.000000       2.000000   \n",
       "\n",
       "        heart_attack         stroke       diabetes      smoke_100  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        0.062931       0.042812       0.168186       1.559673   \n",
       "std         0.242840       0.202433       0.374032       0.496427   \n",
       "min         0.000000       0.000000       0.000000       1.000000   \n",
       "25%         0.000000       0.000000       0.000000       1.000000   \n",
       "50%         0.000000       0.000000       0.000000       2.000000   \n",
       "75%         0.000000       0.000000       0.000000       2.000000   \n",
       "max         1.000000       1.000000       1.000000       2.000000   \n",
       "\n",
       "          smoke_freq       drinking            age   daily_fruit  \\\n",
       "count  296927.000000  296927.000000  296927.000000  2.969270e+05   \n",
       "mean        2.773345       0.473514      57.593041  1.374188e-02   \n",
       "std         0.602512       2.407998      15.381330  1.130886e-02   \n",
       "min         1.000000       0.000000      18.000000  5.397605e-83   \n",
       "25%         3.000000       0.000000      47.000000  5.700000e-03   \n",
       "50%         3.000000       0.000000      60.000000  1.030000e-02   \n",
       "75%         3.000000       0.000000      69.000000  2.000000e-02   \n",
       "max         3.000000      30.000000      80.000000  1.900000e-01   \n",
       "\n",
       "          daily_veg    weekly_phys           race         height  \\\n",
       "count  2.969270e+05  296927.000000  296927.000000  296927.000000   \n",
       "mean   1.972965e-02       1.719487       1.552947      66.767347   \n",
       "std    1.326744e-02       0.860221       1.354358       4.097215   \n",
       "min    5.397605e-83       1.000000       1.000000      32.000000   \n",
       "25%    1.140000e-02       1.000000       1.000000      64.000000   \n",
       "50%    1.710000e-02       1.000000       1.000000      66.000000   \n",
       "75%    2.490000e-02       3.000000       1.000000      70.000000   \n",
       "max    1.950000e-01       3.000000       9.000000      91.000000   \n",
       "\n",
       "              weight            BMI  \n",
       "count  296927.000000  296927.000000  \n",
       "mean      179.994532      28.333837  \n",
       "std        47.575121       6.640913  \n",
       "min        15.454545       3.012282  \n",
       "25%       148.000000      24.081192  \n",
       "50%       175.000000      27.319164  \n",
       "75%       200.000000      31.258831  \n",
       "max       700.000000     182.329022  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"drinking\"].replace({88: 0}, inplace = True)\n",
    "df = df[df[\"high_bp\"] // 6.0 < 1]\n",
    "df = df[df[\"high_chol\"] // 6.0 < 1]\n",
    "df = df[df[\"smoke_100\"] // 6.0 < 1]\n",
    "df = df[df[\"smoke_freq\"] // 6.0 < 1]\n",
    "df = df[df[\"heart_attack\"] // 6.0 < 1]\n",
    "df = df[df[\"stroke\"] // 6.0 < 1]\n",
    "df = df[df[\"diabetes\"] // 6.0 < 1]\n",
    "df = df[df[\"weekly_phys\"] // 6.0 < 1]\n",
    "df[\"daily_veg\"] = df[\"daily_veg\"] / 100\n",
    "df[\"daily_fruit\"] = df[\"daily_fruit\"] / 100\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>smoke_100</th>\n",
       "      <th>smoke_freq</th>\n",
       "      <th>drinking</th>\n",
       "      <th>age</th>\n",
       "      <th>daily_fruit</th>\n",
       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
       "      <th>race</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>2.969270e+05</td>\n",
       "      <td>2.969270e+05</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.134733</td>\n",
       "      <td>1.629138</td>\n",
       "      <td>1.574360</td>\n",
       "      <td>1.573501</td>\n",
       "      <td>0.062931</td>\n",
       "      <td>0.042812</td>\n",
       "      <td>0.168186</td>\n",
       "      <td>1.559673</td>\n",
       "      <td>2.773345</td>\n",
       "      <td>0.473514</td>\n",
       "      <td>57.593041</td>\n",
       "      <td>1.374188e-02</td>\n",
       "      <td>1.972965e-02</td>\n",
       "      <td>1.719487</td>\n",
       "      <td>1.552947</td>\n",
       "      <td>66.767347</td>\n",
       "      <td>179.994532</td>\n",
       "      <td>28.333837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.003671</td>\n",
       "      <td>0.483036</td>\n",
       "      <td>0.494441</td>\n",
       "      <td>0.494569</td>\n",
       "      <td>0.242840</td>\n",
       "      <td>0.202433</td>\n",
       "      <td>0.374032</td>\n",
       "      <td>0.496427</td>\n",
       "      <td>0.602512</td>\n",
       "      <td>2.407998</td>\n",
       "      <td>15.381330</td>\n",
       "      <td>1.130886e-02</td>\n",
       "      <td>1.326744e-02</td>\n",
       "      <td>0.860221</td>\n",
       "      <td>1.354358</td>\n",
       "      <td>4.097215</td>\n",
       "      <td>47.575121</td>\n",
       "      <td>6.640913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.397605e-83</td>\n",
       "      <td>5.397605e-83</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>15.454545</td>\n",
       "      <td>3.012282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>5.700000e-03</td>\n",
       "      <td>1.140000e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>24.081192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.030000e-02</td>\n",
       "      <td>1.710000e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>27.319164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>2.490000e-02</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>31.258831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.900000e-01</td>\n",
       "      <td>1.950000e-01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>182.329022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high_bp        bp_meds            sex      high_chol  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        2.134733       1.629138       1.574360       1.573501   \n",
       "std         1.003671       0.483036       0.494441       0.494569   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000       1.000000       1.000000   \n",
       "50%         3.000000       2.000000       2.000000       2.000000   \n",
       "75%         3.000000       2.000000       2.000000       2.000000   \n",
       "max         4.000000       2.000000       2.000000       2.000000   \n",
       "\n",
       "        heart_attack         stroke       diabetes      smoke_100  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        0.062931       0.042812       0.168186       1.559673   \n",
       "std         0.242840       0.202433       0.374032       0.496427   \n",
       "min         0.000000       0.000000       0.000000       1.000000   \n",
       "25%         0.000000       0.000000       0.000000       1.000000   \n",
       "50%         0.000000       0.000000       0.000000       2.000000   \n",
       "75%         0.000000       0.000000       0.000000       2.000000   \n",
       "max         1.000000       1.000000       1.000000       2.000000   \n",
       "\n",
       "          smoke_freq       drinking            age   daily_fruit  \\\n",
       "count  296927.000000  296927.000000  296927.000000  2.969270e+05   \n",
       "mean        2.773345       0.473514      57.593041  1.374188e-02   \n",
       "std         0.602512       2.407998      15.381330  1.130886e-02   \n",
       "min         1.000000       0.000000      18.000000  5.397605e-83   \n",
       "25%         3.000000       0.000000      47.000000  5.700000e-03   \n",
       "50%         3.000000       0.000000      60.000000  1.030000e-02   \n",
       "75%         3.000000       0.000000      69.000000  2.000000e-02   \n",
       "max         3.000000      30.000000      80.000000  1.900000e-01   \n",
       "\n",
       "          daily_veg    weekly_phys           race         height  \\\n",
       "count  2.969270e+05  296927.000000  296927.000000  296927.000000   \n",
       "mean   1.972965e-02       1.719487       1.552947      66.767347   \n",
       "std    1.326744e-02       0.860221       1.354358       4.097215   \n",
       "min    5.397605e-83       1.000000       1.000000      32.000000   \n",
       "25%    1.140000e-02       1.000000       1.000000      64.000000   \n",
       "50%    1.710000e-02       1.000000       1.000000      66.000000   \n",
       "75%    2.490000e-02       3.000000       1.000000      70.000000   \n",
       "max    1.950000e-01       3.000000       9.000000      91.000000   \n",
       "\n",
       "              weight            BMI  \n",
       "count  296927.000000  296927.000000  \n",
       "mean      179.994532      28.333837  \n",
       "std        47.575121       6.640913  \n",
       "min        15.454545       3.012282  \n",
       "25%       148.000000      24.081192  \n",
       "50%       175.000000      27.319164  \n",
       "75%       200.000000      31.258831  \n",
       "max       700.000000     182.329022  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df[\"daily_veg\"]  < 20]\n",
    "df = df[df[\"daily_fruit\"] < 20]\n",
    "df = df[df[\"bp_meds\"] // 6.0 < 1]\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"drinking\"] <= 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>smoke_100</th>\n",
       "      <th>smoke_freq</th>\n",
       "      <th>drinking</th>\n",
       "      <th>age</th>\n",
       "      <th>daily_fruit</th>\n",
       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
       "      <th>race</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>2.969270e+05</td>\n",
       "      <td>2.969270e+05</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.134733</td>\n",
       "      <td>1.629138</td>\n",
       "      <td>1.574360</td>\n",
       "      <td>1.573501</td>\n",
       "      <td>0.062931</td>\n",
       "      <td>0.042812</td>\n",
       "      <td>0.168186</td>\n",
       "      <td>1.559673</td>\n",
       "      <td>2.773345</td>\n",
       "      <td>0.473514</td>\n",
       "      <td>57.593041</td>\n",
       "      <td>1.374188e-02</td>\n",
       "      <td>1.972965e-02</td>\n",
       "      <td>1.719487</td>\n",
       "      <td>1.552947</td>\n",
       "      <td>66.767347</td>\n",
       "      <td>179.994532</td>\n",
       "      <td>28.333837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.003671</td>\n",
       "      <td>0.483036</td>\n",
       "      <td>0.494441</td>\n",
       "      <td>0.494569</td>\n",
       "      <td>0.242840</td>\n",
       "      <td>0.202433</td>\n",
       "      <td>0.374032</td>\n",
       "      <td>0.496427</td>\n",
       "      <td>0.602512</td>\n",
       "      <td>2.407998</td>\n",
       "      <td>15.381330</td>\n",
       "      <td>1.130886e-02</td>\n",
       "      <td>1.326744e-02</td>\n",
       "      <td>0.860221</td>\n",
       "      <td>1.354358</td>\n",
       "      <td>4.097215</td>\n",
       "      <td>47.575121</td>\n",
       "      <td>6.640913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.397605e-83</td>\n",
       "      <td>5.397605e-83</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>15.454545</td>\n",
       "      <td>3.012282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>5.700000e-03</td>\n",
       "      <td>1.140000e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>24.081192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.030000e-02</td>\n",
       "      <td>1.710000e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>27.319164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>2.490000e-02</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>31.258831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.900000e-01</td>\n",
       "      <td>1.950000e-01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>182.329022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high_bp        bp_meds            sex      high_chol  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        2.134733       1.629138       1.574360       1.573501   \n",
       "std         1.003671       0.483036       0.494441       0.494569   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000       1.000000       1.000000   \n",
       "50%         3.000000       2.000000       2.000000       2.000000   \n",
       "75%         3.000000       2.000000       2.000000       2.000000   \n",
       "max         4.000000       2.000000       2.000000       2.000000   \n",
       "\n",
       "        heart_attack         stroke       diabetes      smoke_100  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        0.062931       0.042812       0.168186       1.559673   \n",
       "std         0.242840       0.202433       0.374032       0.496427   \n",
       "min         0.000000       0.000000       0.000000       1.000000   \n",
       "25%         0.000000       0.000000       0.000000       1.000000   \n",
       "50%         0.000000       0.000000       0.000000       2.000000   \n",
       "75%         0.000000       0.000000       0.000000       2.000000   \n",
       "max         1.000000       1.000000       1.000000       2.000000   \n",
       "\n",
       "          smoke_freq       drinking            age   daily_fruit  \\\n",
       "count  296927.000000  296927.000000  296927.000000  2.969270e+05   \n",
       "mean        2.773345       0.473514      57.593041  1.374188e-02   \n",
       "std         0.602512       2.407998      15.381330  1.130886e-02   \n",
       "min         1.000000       0.000000      18.000000  5.397605e-83   \n",
       "25%         3.000000       0.000000      47.000000  5.700000e-03   \n",
       "50%         3.000000       0.000000      60.000000  1.030000e-02   \n",
       "75%         3.000000       0.000000      69.000000  2.000000e-02   \n",
       "max         3.000000      30.000000      80.000000  1.900000e-01   \n",
       "\n",
       "          daily_veg    weekly_phys           race         height  \\\n",
       "count  2.969270e+05  296927.000000  296927.000000  296927.000000   \n",
       "mean   1.972965e-02       1.719487       1.552947      66.767347   \n",
       "std    1.326744e-02       0.860221       1.354358       4.097215   \n",
       "min    5.397605e-83       1.000000       1.000000      32.000000   \n",
       "25%    1.140000e-02       1.000000       1.000000      64.000000   \n",
       "50%    1.710000e-02       1.000000       1.000000      66.000000   \n",
       "75%    2.490000e-02       3.000000       1.000000      70.000000   \n",
       "max    1.950000e-01       3.000000       9.000000      91.000000   \n",
       "\n",
       "              weight            BMI  \n",
       "count  296927.000000  296927.000000  \n",
       "mean      179.994532      28.333837  \n",
       "std        47.575121       6.640913  \n",
       "min        15.454545       3.012282  \n",
       "25%       148.000000      24.081192  \n",
       "50%       175.000000      27.319164  \n",
       "75%       200.000000      31.258831  \n",
       "max       700.000000     182.329022  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"BMI\"] < 200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
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       "      <th>smoke_freq</th>\n",
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       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
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       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
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       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
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       "      <td>2.969270e+05</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "      <td>296927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.134733</td>\n",
       "      <td>1.629138</td>\n",
       "      <td>1.574360</td>\n",
       "      <td>1.573501</td>\n",
       "      <td>0.062931</td>\n",
       "      <td>0.042812</td>\n",
       "      <td>0.168186</td>\n",
       "      <td>1.559673</td>\n",
       "      <td>2.773345</td>\n",
       "      <td>0.473514</td>\n",
       "      <td>57.593041</td>\n",
       "      <td>1.374188e-02</td>\n",
       "      <td>1.972965e-02</td>\n",
       "      <td>1.719487</td>\n",
       "      <td>1.552947</td>\n",
       "      <td>66.767347</td>\n",
       "      <td>179.994532</td>\n",
       "      <td>28.333837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.003671</td>\n",
       "      <td>0.483036</td>\n",
       "      <td>0.494441</td>\n",
       "      <td>0.494569</td>\n",
       "      <td>0.242840</td>\n",
       "      <td>0.202433</td>\n",
       "      <td>0.374032</td>\n",
       "      <td>0.496427</td>\n",
       "      <td>0.602512</td>\n",
       "      <td>2.407998</td>\n",
       "      <td>15.381330</td>\n",
       "      <td>1.130886e-02</td>\n",
       "      <td>1.326744e-02</td>\n",
       "      <td>0.860221</td>\n",
       "      <td>1.354358</td>\n",
       "      <td>4.097215</td>\n",
       "      <td>47.575121</td>\n",
       "      <td>6.640913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.397605e-83</td>\n",
       "      <td>5.397605e-83</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>15.454545</td>\n",
       "      <td>3.012282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>5.700000e-03</td>\n",
       "      <td>1.140000e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>24.081192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.030000e-02</td>\n",
       "      <td>1.710000e-02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>27.319164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>2.490000e-02</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>31.258831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.900000e-01</td>\n",
       "      <td>1.950000e-01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>182.329022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high_bp        bp_meds            sex      high_chol  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        2.134733       1.629138       1.574360       1.573501   \n",
       "std         1.003671       0.483036       0.494441       0.494569   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000       1.000000       1.000000   \n",
       "50%         3.000000       2.000000       2.000000       2.000000   \n",
       "75%         3.000000       2.000000       2.000000       2.000000   \n",
       "max         4.000000       2.000000       2.000000       2.000000   \n",
       "\n",
       "        heart_attack         stroke       diabetes      smoke_100  \\\n",
       "count  296927.000000  296927.000000  296927.000000  296927.000000   \n",
       "mean        0.062931       0.042812       0.168186       1.559673   \n",
       "std         0.242840       0.202433       0.374032       0.496427   \n",
       "min         0.000000       0.000000       0.000000       1.000000   \n",
       "25%         0.000000       0.000000       0.000000       1.000000   \n",
       "50%         0.000000       0.000000       0.000000       2.000000   \n",
       "75%         0.000000       0.000000       0.000000       2.000000   \n",
       "max         1.000000       1.000000       1.000000       2.000000   \n",
       "\n",
       "          smoke_freq       drinking            age   daily_fruit  \\\n",
       "count  296927.000000  296927.000000  296927.000000  2.969270e+05   \n",
       "mean        2.773345       0.473514      57.593041  1.374188e-02   \n",
       "std         0.602512       2.407998      15.381330  1.130886e-02   \n",
       "min         1.000000       0.000000      18.000000  5.397605e-83   \n",
       "25%         3.000000       0.000000      47.000000  5.700000e-03   \n",
       "50%         3.000000       0.000000      60.000000  1.030000e-02   \n",
       "75%         3.000000       0.000000      69.000000  2.000000e-02   \n",
       "max         3.000000      30.000000      80.000000  1.900000e-01   \n",
       "\n",
       "          daily_veg    weekly_phys           race         height  \\\n",
       "count  2.969270e+05  296927.000000  296927.000000  296927.000000   \n",
       "mean   1.972965e-02       1.719487       1.552947      66.767347   \n",
       "std    1.326744e-02       0.860221       1.354358       4.097215   \n",
       "min    5.397605e-83       1.000000       1.000000      32.000000   \n",
       "25%    1.140000e-02       1.000000       1.000000      64.000000   \n",
       "50%    1.710000e-02       1.000000       1.000000      66.000000   \n",
       "75%    2.490000e-02       3.000000       1.000000      70.000000   \n",
       "max    1.950000e-01       3.000000       9.000000      91.000000   \n",
       "\n",
       "              weight            BMI  \n",
       "count  296927.000000  296927.000000  \n",
       "mean      179.994532      28.333837  \n",
       "std        47.575121       6.640913  \n",
       "min        15.454545       3.012282  \n",
       "25%       148.000000      24.081192  \n",
       "50%       175.000000      27.319164  \n",
       "75%       200.000000      31.258831  \n",
       "max       700.000000     182.329022  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>smoke_100</th>\n",
       "      <th>smoke_freq</th>\n",
       "      <th>drinking</th>\n",
       "      <th>age</th>\n",
       "      <th>daily_fruit</th>\n",
       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
       "      <th>race</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0217</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>40.259821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>25.140621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>28.250932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>26.576299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>23.937772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   high_bp  bp_meds  sex  high_chol  heart_attack  stroke  diabetes  \\\n",
       "0      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "1      3.0      2.0  2.0        2.0           0.0     0.0       0.0   \n",
       "3      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "5      1.0      1.0  2.0        2.0           0.0     0.0       0.0   \n",
       "6      1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "\n",
       "   smoke_100  smoke_freq  drinking   age  daily_fruit  daily_veg  weekly_phys  \\\n",
       "0        1.0         3.0       0.0  63.0       0.0050     0.0217          3.0   \n",
       "1        1.0         1.0       0.0  52.0       0.0024     0.0078          1.0   \n",
       "3        2.0         3.0       0.0  63.0       0.0100     0.0020          3.0   \n",
       "5        2.0         3.0       0.0  73.0       0.0200     0.0600          1.0   \n",
       "6        2.0         3.0       0.0  70.0       0.0129     0.0114          1.0   \n",
       "\n",
       "   race  height  weight        BMI  \n",
       "0   1.0    70.0   280.0  40.259821  \n",
       "1   1.0    68.0   165.0  25.140621  \n",
       "3   1.0    67.0   180.0  28.250932  \n",
       "5   1.0    62.0   145.0  26.576299  \n",
       "6   4.0    66.0   148.0  23.937772  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"stroke\"].replace({2: 0}, inplace = True)\n",
    "df[\"heart_attack\"].replace({2: 0}, inplace = True)\n",
    "df[\"diabetes\"].replace({2: 1, 4: 1, 3: 0}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_age50to70 = df[(df[\"age\"] >= 50) & (df[\"age\"] <= 70)]\n",
    "df_age50to70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "928/928 [==============================] - 1s 656us/step - loss: 0.6302 - acc: 0.9384\n",
      "Epoch 2/20\n",
      "928/928 [==============================] - 1s 606us/step - loss: 0.3590 - acc: 0.9481\n",
      "Epoch 3/20\n",
      "928/928 [==============================] - 1s 599us/step - loss: 0.2682 - acc: 0.9534\n",
      "Epoch 4/20\n",
      "928/928 [==============================] - 1s 609us/step - loss: 0.2284 - acc: 0.9548\n",
      "Epoch 5/20\n",
      "928/928 [==============================] - 1s 591us/step - loss: 0.2020 - acc: 0.9563\n",
      "Epoch 6/20\n",
      "928/928 [==============================] - 1s 589us/step - loss: 0.1911 - acc: 0.9566\n",
      "Epoch 7/20\n",
      "928/928 [==============================] - 1s 596us/step - loss: 0.1833 - acc: 0.9568\n",
      "Epoch 8/20\n",
      "928/928 [==============================] - 1s 597us/step - loss: 0.1826 - acc: 0.9568\n",
      "Epoch 9/20\n",
      "928/928 [==============================] - 1s 594us/step - loss: 0.1783 - acc: 0.9568\n",
      "Epoch 10/20\n",
      "928/928 [==============================] - 1s 588us/step - loss: 0.1755 - acc: 0.9569\n",
      "Epoch 11/20\n",
      "928/928 [==============================] - 1s 586us/step - loss: 0.1738 - acc: 0.9569\n",
      "Epoch 12/20\n",
      "928/928 [==============================] - 1s 588us/step - loss: 0.1731 - acc: 0.9570\n",
      "Epoch 13/20\n",
      "928/928 [==============================] - 1s 593us/step - loss: 0.1708 - acc: 0.9570\n",
      "Epoch 14/20\n",
      "928/928 [==============================] - 1s 586us/step - loss: 0.1703 - acc: 0.9570\n",
      "Epoch 15/20\n",
      "928/928 [==============================] - 1s 583us/step - loss: 0.1695 - acc: 0.9570\n",
      "Epoch 16/20\n",
      "928/928 [==============================] - 1s 580us/step - loss: 0.1697 - acc: 0.9570\n",
      "Epoch 17/20\n",
      "928/928 [==============================] - 1s 582us/step - loss: 0.1673 - acc: 0.9570\n",
      "Epoch 18/20\n",
      "928/928 [==============================] - 1s 582us/step - loss: 0.1675 - acc: 0.9570\n",
      "Epoch 19/20\n",
      "928/928 [==============================] - 1s 588us/step - loss: 0.1669 - acc: 0.9570\n",
      "Epoch 20/20\n",
      "928/928 [==============================] - 1s 585us/step - loss: 0.1671 - acc: 0.9570\n",
      "1856/1856 [==============================] - 1s 402us/step\n",
      "Epoch 1/20\n",
      "696/696 [==============================] - 0s 606us/step - loss: 0.1666 - acc: 0.9569\n",
      "Epoch 2/20\n",
      "696/696 [==============================] - 0s 585us/step - loss: 0.1672 - acc: 0.9569\n",
      "Epoch 3/20\n",
      "696/696 [==============================] - 0s 595us/step - loss: 0.1666 - acc: 0.9569\n",
      "Epoch 4/20\n",
      "696/696 [==============================] - 0s 609us/step - loss: 0.1671 - acc: 0.9569\n",
      "Epoch 5/20\n",
      "696/696 [==============================] - 0s 591us/step - loss: 0.1666 - acc: 0.9569\n",
      "Epoch 6/20\n",
      "696/696 [==============================] - 0s 591us/step - loss: 0.1663 - acc: 0.9569\n",
      "Epoch 7/20\n",
      "696/696 [==============================] - 0s 608us/step - loss: 0.1662 - acc: 0.9569\n",
      "Epoch 8/20\n",
      "696/696 [==============================] - 0s 587us/step - loss: 0.1662 - acc: 0.9569\n",
      "Epoch 9/20\n",
      "696/696 [==============================] - 0s 594us/step - loss: 0.1658 - acc: 0.9569\n",
      "Epoch 10/20\n",
      "696/696 [==============================] - 0s 624us/step - loss: 0.1654 - acc: 0.9569\n",
      "Epoch 11/20\n",
      "696/696 [==============================] - 0s 583us/step - loss: 0.1654 - acc: 0.9569\n",
      "Epoch 12/20\n",
      "696/696 [==============================] - 0s 594us/step - loss: 0.1649 - acc: 0.9569\n",
      "Epoch 13/20\n",
      "696/696 [==============================] - 0s 609us/step - loss: 0.1653 - acc: 0.9569\n",
      "Epoch 14/20\n",
      "696/696 [==============================] - 0s 582us/step - loss: 0.1648 - acc: 0.9569\n",
      "Epoch 15/20\n",
      "696/696 [==============================] - 0s 593us/step - loss: 0.1650 - acc: 0.9569\n",
      "Epoch 16/20\n",
      "696/696 [==============================] - 0s 610us/step - loss: 0.1647 - acc: 0.9569\n",
      "Epoch 17/20\n",
      "696/696 [==============================] - 0s 591us/step - loss: 0.1646 - acc: 0.9569\n",
      "Epoch 18/20\n",
      "696/696 [==============================] - 0s 600us/step - loss: 0.1645 - acc: 0.9569\n",
      "Epoch 19/20\n",
      "696/696 [==============================] - 0s 602us/step - loss: 0.1641 - acc: 0.9569\n",
      "Epoch 20/20\n",
      "696/696 [==============================] - 0s 577us/step - loss: 0.1646 - acc: 0.9569\n",
      "1856/1856 [==============================] - 1s 404us/step\n",
      "Epoch 1/20\n",
      "464/464 [==============================] - 0s 605us/step - loss: 0.1647 - acc: 0.9569\n",
      "Epoch 2/20\n",
      "464/464 [==============================] - 0s 593us/step - loss: 0.1645 - acc: 0.9569\n",
      "Epoch 3/20\n",
      "464/464 [==============================] - 0s 591us/step - loss: 0.1647 - acc: 0.9569\n",
      "Epoch 4/20\n",
      "464/464 [==============================] - 0s 603us/step - loss: 0.1647 - acc: 0.9569\n",
      "Epoch 5/20\n",
      "464/464 [==============================] - 0s 604us/step - loss: 0.1643 - acc: 0.9569\n",
      "Epoch 6/20\n",
      "464/464 [==============================] - 0s 594us/step - loss: 0.1650 - acc: 0.9569\n",
      "Epoch 7/20\n",
      "464/464 [==============================] - 0s 589us/step - loss: 0.1641 - acc: 0.9569\n",
      "Epoch 8/20\n",
      "464/464 [==============================] - 0s 588us/step - loss: 0.1647 - acc: 0.9569\n",
      "Epoch 9/20\n",
      "464/464 [==============================] - 0s 581us/step - loss: 0.1646 - acc: 0.9569\n",
      "Epoch 10/20\n",
      "464/464 [==============================] - 0s 598us/step - loss: 0.1641 - acc: 0.9569\n",
      "Epoch 11/20\n",
      "464/464 [==============================] - 0s 620us/step - loss: 0.1641 - acc: 0.9569\n",
      "Epoch 12/20\n",
      "464/464 [==============================] - 0s 588us/step - loss: 0.1646 - acc: 0.9569\n",
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      "Epoch 20/20\n",
      "464/464 [==============================] - 0s 595us/step - loss: 0.1640 - acc: 0.9569\n",
      "1856/1856 [==============================] - 1s 405us/step\n",
      "Epoch 1/20\n",
      "232/232 [==============================] - 0s 634us/step - loss: 0.1649 - acc: 0.9566\n",
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      "232/232 [==============================] - 0s 579us/step - loss: 0.1649 - acc: 0.9566\n",
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      "232/232 [==============================] - 0s 608us/step - loss: 0.1643 - acc: 0.9566\n",
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      "232/232 [==============================] - 0s 582us/step - loss: 0.1646 - acc: 0.9566\n",
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      "232/232 [==============================] - 0s 707us/step - loss: 0.1644 - acc: 0.9566\n",
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      "232/232 [==============================] - 0s 614us/step - loss: 0.1645 - acc: 0.9566\n",
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      "Epoch 15/20\n",
      "232/232 [==============================] - 0s 617us/step - loss: 0.1647 - acc: 0.9566\n",
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      "232/232 [==============================] - 0s 575us/step - loss: 0.1644 - acc: 0.9566\n",
      "Epoch 19/20\n",
      "232/232 [==============================] - 0s 610us/step - loss: 0.1645 - acc: 0.9566\n",
      "Epoch 20/20\n",
      "232/232 [==============================] - 0s 593us/step - loss: 0.1644 - acc: 0.9566\n",
      "1856/1856 [==============================] - 1s 409us/step\n",
      "Epoch 1/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0684 - acc: 1.0000\n",
      "Epoch 2/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0163 - acc: 1.0000\n",
      "Epoch 3/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0114 - acc: 1.0000\n",
      "Epoch 4/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0089 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0073 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0062 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0054 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0048 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0043 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0039 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0036 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0033 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0031 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0029 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0027 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0026 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0024 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0023 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0022 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0021 - acc: 1.0000\n",
      "1856/1856 [==============================] - 1s 411us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.95842384, 0.95743033, 0.95692515, 0.95722826, 0.9566052 ])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_model(1, [32, 32, 32], [1, 1, 1, 1], \"stroke\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "928/928 [==============================] - 1s 605us/step - loss: 1.0488 - acc: 0.9020\n",
      "Epoch 2/20\n",
      "928/928 [==============================] - 1s 594us/step - loss: 0.3548 - acc: 0.9268\n",
      "Epoch 3/20\n",
      "928/928 [==============================] - 1s 587us/step - loss: 0.2641 - acc: 0.9345\n",
      "Epoch 4/20\n",
      "928/928 [==============================] - 1s 592us/step - loss: 0.2425 - acc: 0.9358\n",
      "Epoch 5/20\n",
      "928/928 [==============================] - 1s 623us/step - loss: 0.2326 - acc: 0.9362\n",
      "Epoch 6/20\n",
      "928/928 [==============================] - 1s 627us/step - loss: 0.2244 - acc: 0.9369\n",
      "Epoch 7/20\n",
      "928/928 [==============================] - 1s 622us/step - loss: 0.2206 - acc: 0.9371\n",
      "Epoch 8/20\n",
      "928/928 [==============================] - 1s 597us/step - loss: 0.2179 - acc: 0.9373\n",
      "Epoch 9/20\n",
      "928/928 [==============================] - 1s 604us/step - loss: 0.2173 - acc: 0.9372\n",
      "Epoch 10/20\n",
      "928/928 [==============================] - 1s 622us/step - loss: 0.2138 - acc: 0.9374\n",
      "Epoch 11/20\n",
      "928/928 [==============================] - 1s 611us/step - loss: 0.2139 - acc: 0.9373\n",
      "Epoch 12/20\n",
      "928/928 [==============================] - 1s 595us/step - loss: 0.2117 - acc: 0.9374\n",
      "Epoch 13/20\n",
      "928/928 [==============================] - 1s 695us/step - loss: 0.2112 - acc: 0.9375\n",
      "Epoch 14/20\n",
      "928/928 [==============================] - 1s 671us/step - loss: 0.2112 - acc: 0.9374\n",
      "Epoch 15/20\n",
      "928/928 [==============================] - 1s 693us/step - loss: 0.2109 - acc: 0.9373\n",
      "Epoch 16/20\n",
      "928/928 [==============================] - 1s 640us/step - loss: 0.2103 - acc: 0.9374\n",
      "Epoch 17/20\n",
      "928/928 [==============================] - 1s 645us/step - loss: 0.2091 - acc: 0.9374\n",
      "Epoch 18/20\n",
      "928/928 [==============================] - 1s 603us/step - loss: 0.2090 - acc: 0.9374\n",
      "Epoch 19/20\n",
      "928/928 [==============================] - 1s 603us/step - loss: 0.2085 - acc: 0.9375\n",
      "Epoch 20/20\n",
      "928/928 [==============================] - 1s 643us/step - loss: 0.2082 - acc: 0.9375\n",
      "1856/1856 [==============================] - 1s 427us/step\n",
      "Epoch 1/20\n",
      "696/696 [==============================] - 0s 649us/step - loss: 0.2071 - acc: 0.9377\n",
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      "696/696 [==============================] - 0s 605us/step - loss: 0.2071 - acc: 0.9377\n",
      "Epoch 3/20\n",
      "696/696 [==============================] - 0s 683us/step - loss: 0.2068 - acc: 0.9377\n",
      "Epoch 4/20\n",
      "696/696 [==============================] - 0s 597us/step - loss: 0.2064 - acc: 0.9378\n",
      "Epoch 5/20\n",
      "696/696 [==============================] - 0s 606us/step - loss: 0.2063 - acc: 0.9378\n",
      "Epoch 6/20\n",
      "696/696 [==============================] - 0s 675us/step - loss: 0.2062 - acc: 0.9377\n",
      "Epoch 7/20\n",
      "696/696 [==============================] - 0s 714us/step - loss: 0.2058 - acc: 0.9377\n",
      "Epoch 8/20\n",
      "696/696 [==============================] - 0s 617us/step - loss: 0.2064 - acc: 0.9377\n",
      "Epoch 9/20\n",
      "696/696 [==============================] - 0s 688us/step - loss: 0.2056 - acc: 0.9378\n",
      "Epoch 10/20\n",
      "696/696 [==============================] - 0s 594us/step - loss: 0.2053 - acc: 0.9377\n",
      "Epoch 11/20\n",
      "696/696 [==============================] - 0s 620us/step - loss: 0.2056 - acc: 0.9378\n",
      "Epoch 12/20\n",
      "696/696 [==============================] - 0s 644us/step - loss: 0.2051 - acc: 0.9377\n",
      "Epoch 13/20\n",
      "696/696 [==============================] - 0s 671us/step - loss: 0.2053 - acc: 0.9377\n",
      "Epoch 14/20\n",
      "696/696 [==============================] - 0s 710us/step - loss: 0.2051 - acc: 0.9378\n",
      "Epoch 15/20\n",
      "696/696 [==============================] - 0s 606us/step - loss: 0.2048 - acc: 0.9378\n",
      "Epoch 16/20\n",
      "696/696 [==============================] - 0s 611us/step - loss: 0.2051 - acc: 0.9378\n",
      "Epoch 17/20\n",
      "696/696 [==============================] - 0s 608us/step - loss: 0.2049 - acc: 0.9377\n",
      "Epoch 18/20\n",
      "696/696 [==============================] - 0s 589us/step - loss: 0.2049 - acc: 0.9377\n",
      "Epoch 19/20\n",
      "696/696 [==============================] - 0s 604us/step - loss: 0.2044 - acc: 0.9378\n",
      "Epoch 20/20\n",
      "696/696 [==============================] - 0s 600us/step - loss: 0.2047 - acc: 0.9378\n",
      "1856/1856 [==============================] - 1s 410us/step\n",
      "Epoch 1/20\n",
      "464/464 [==============================] - 0s 679us/step - loss: 0.2029 - acc: 0.9383\n",
      "Epoch 2/20\n",
      "464/464 [==============================] - 0s 600us/step - loss: 0.2028 - acc: 0.9383\n",
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      "464/464 [==============================] - 0s 604us/step - loss: 0.2034 - acc: 0.9383\n",
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      "464/464 [==============================] - 0s 598us/step - loss: 0.2037 - acc: 0.9383\n",
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      "464/464 [==============================] - 0s 598us/step - loss: 0.2035 - acc: 0.9382\n",
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      "464/464 [==============================] - 0s 608us/step - loss: 0.2027 - acc: 0.9383\n",
      "Epoch 7/20\n",
      "464/464 [==============================] - 0s 614us/step - loss: 0.2031 - acc: 0.9383\n",
      "Epoch 8/20\n",
      "464/464 [==============================] - 0s 615us/step - loss: 0.2023 - acc: 0.9382\n",
      "Epoch 9/20\n",
      "464/464 [==============================] - 0s 613us/step - loss: 0.2033 - acc: 0.9383\n",
      "Epoch 10/20\n",
      "464/464 [==============================] - 0s 613us/step - loss: 0.2021 - acc: 0.9383\n",
      "Epoch 11/20\n",
      "464/464 [==============================] - 0s 603us/step - loss: 0.2025 - acc: 0.9383\n",
      "Epoch 12/20\n",
      "464/464 [==============================] - 0s 599us/step - loss: 0.2032 - acc: 0.9382\n",
      "Epoch 13/20\n",
      "464/464 [==============================] - 0s 603us/step - loss: 0.2027 - acc: 0.9383\n",
      "Epoch 14/20\n",
      "464/464 [==============================] - 0s 603us/step - loss: 0.2024 - acc: 0.9383\n",
      "Epoch 15/20\n",
      "464/464 [==============================] - 0s 612us/step - loss: 0.2024 - acc: 0.9382\n",
      "Epoch 16/20\n",
      "464/464 [==============================] - 0s 609us/step - loss: 0.2026 - acc: 0.9383\n",
      "Epoch 17/20\n",
      "464/464 [==============================] - 0s 612us/step - loss: 0.2028 - acc: 0.9383\n",
      "Epoch 18/20\n",
      "464/464 [==============================] - 0s 602us/step - loss: 0.2024 - acc: 0.9382\n",
      "Epoch 19/20\n",
      "464/464 [==============================] - 0s 608us/step - loss: 0.2026 - acc: 0.9382\n",
      "Epoch 20/20\n",
      "464/464 [==============================] - 0s 599us/step - loss: 0.2027 - acc: 0.9382\n",
      "1856/1856 [==============================] - 1s 410us/step\n",
      "Epoch 1/20\n",
      "232/232 [==============================] - 0s 639us/step - loss: 0.2036 - acc: 0.9375\n",
      "Epoch 2/20\n",
      "232/232 [==============================] - 0s 606us/step - loss: 0.2042 - acc: 0.9375\n",
      "Epoch 3/20\n",
      "232/232 [==============================] - 0s 608us/step - loss: 0.2040 - acc: 0.9375\n",
      "Epoch 4/20\n",
      "232/232 [==============================] - 0s 616us/step - loss: 0.2034 - acc: 0.9375\n",
      "Epoch 5/20\n",
      "232/232 [==============================] - 0s 590us/step - loss: 0.2031 - acc: 0.9374\n",
      "Epoch 6/20\n",
      "232/232 [==============================] - 0s 639us/step - loss: 0.2041 - acc: 0.9374\n",
      "Epoch 7/20\n",
      "232/232 [==============================] - 0s 573us/step - loss: 0.2039 - acc: 0.9375\n",
      "Epoch 8/20\n",
      "232/232 [==============================] - 0s 630us/step - loss: 0.2039 - acc: 0.9374\n",
      "Epoch 9/20\n",
      "232/232 [==============================] - 0s 590us/step - loss: 0.2038 - acc: 0.9375\n",
      "Epoch 10/20\n",
      "232/232 [==============================] - 0s 631us/step - loss: 0.2032 - acc: 0.9376\n",
      "Epoch 11/20\n",
      "232/232 [==============================] - 0s 617us/step - loss: 0.2038 - acc: 0.9375\n",
      "Epoch 12/20\n",
      "232/232 [==============================] - 0s 593us/step - loss: 0.2034 - acc: 0.9375\n",
      "Epoch 13/20\n",
      "232/232 [==============================] - 0s 635us/step - loss: 0.2032 - acc: 0.9375\n",
      "Epoch 14/20\n",
      "232/232 [==============================] - 0s 572us/step - loss: 0.2033 - acc: 0.9375\n",
      "Epoch 15/20\n",
      "232/232 [==============================] - 0s 622us/step - loss: 0.2035 - acc: 0.9374\n",
      "Epoch 16/20\n",
      "232/232 [==============================] - 0s 591us/step - loss: 0.2031 - acc: 0.9375\n",
      "Epoch 17/20\n",
      "232/232 [==============================] - 0s 616us/step - loss: 0.2027 - acc: 0.9375\n",
      "Epoch 18/20\n",
      "232/232 [==============================] - 0s 623us/step - loss: 0.2041 - acc: 0.9374\n",
      "Epoch 19/20\n",
      "232/232 [==============================] - 0s 593us/step - loss: 0.2029 - acc: 0.9375\n",
      "Epoch 20/20\n",
      "232/232 [==============================] - 0s 630us/step - loss: 0.2036 - acc: 0.9376\n",
      "1856/1856 [==============================] - 1s 411us/step\n",
      "Epoch 1/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0938 - acc: 1.0000\n",
      "Epoch 2/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0170 - acc: 1.0000\n",
      "Epoch 3/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0125 - acc: 1.0000\n",
      "Epoch 4/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0100 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0083 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0072 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0063 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0057 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0051 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0047 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0043 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0040 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0037 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0035 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0033 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0031 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0029 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0028 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0027 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0026 - acc: 1.0000\n",
      "1856/1856 [==============================] - 1s 412us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.93735792, 0.93713901, 0.93703797, 0.93921024, 0.93762735])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_model(1, [32, 32, 32], [1, 1, 1, 1], \"heart_attack\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "928/928 [==============================] - 1s 628us/step - loss: 1.6952 - acc: 0.7731\n",
      "Epoch 2/20\n",
      "928/928 [==============================] - 1s 625us/step - loss: 0.4401 - acc: 0.8250\n",
      "Epoch 3/20\n",
      "928/928 [==============================] - 1s 592us/step - loss: 0.4160 - acc: 0.8292\n",
      "Epoch 4/20\n",
      "928/928 [==============================] - 1s 628us/step - loss: 0.4093 - acc: 0.8307\n",
      "Epoch 5/20\n",
      "928/928 [==============================] - 1s 646us/step - loss: 0.4053 - acc: 0.8307\n",
      "Epoch 6/20\n",
      "928/928 [==============================] - 1s 608us/step - loss: 0.4028 - acc: 0.8306\n",
      "Epoch 7/20\n",
      "928/928 [==============================] - 1s 588us/step - loss: 0.4011 - acc: 0.8308\n",
      "Epoch 8/20\n",
      "928/928 [==============================] - 1s 587us/step - loss: 0.4003 - acc: 0.8310\n",
      "Epoch 9/20\n",
      "928/928 [==============================] - 1s 585us/step - loss: 0.3997 - acc: 0.8311\n",
      "Epoch 10/20\n",
      "928/928 [==============================] - 1s 632us/step - loss: 0.3982 - acc: 0.8315\n",
      "Epoch 11/20\n",
      "928/928 [==============================] - 1s 597us/step - loss: 0.3982 - acc: 0.8311\n",
      "Epoch 12/20\n",
      "928/928 [==============================] - 1s 608us/step - loss: 0.3971 - acc: 0.8314\n",
      "Epoch 13/20\n",
      "928/928 [==============================] - 1s 626us/step - loss: 0.3962 - acc: 0.8323\n",
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      "928/928 [==============================] - 1s 599us/step - loss: 0.3947 - acc: 0.8321\n",
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      "928/928 [==============================] - 1s 619us/step - loss: 0.3941 - acc: 0.8323\n",
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      "1856/1856 [==============================] - 1s 409us/step\n",
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      "1856/1856 [==============================] - 1s 403us/step\n",
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      "232/232 [==============================] - 0s 695us/step - loss: 0.3923 - acc: 0.8318\n",
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      "232/232 [==============================] - 0s 637us/step - loss: 0.3919 - acc: 0.8324\n",
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      "232/232 [==============================] - 0s 626us/step - loss: 0.3915 - acc: 0.8321\n",
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      "232/232 [==============================] - 0s 625us/step - loss: 0.3913 - acc: 0.8324\n",
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      "232/232 [==============================] - 0s 586us/step - loss: 0.3926 - acc: 0.8314\n",
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      "232/232 [==============================] - 0s 621us/step - loss: 0.3918 - acc: 0.8324\n",
      "Epoch 18/20\n",
      "232/232 [==============================] - 0s 568us/step - loss: 0.3918 - acc: 0.8324\n",
      "Epoch 19/20\n",
      "232/232 [==============================] - 0s 625us/step - loss: 0.3919 - acc: 0.8323\n",
      "Epoch 20/20\n",
      "232/232 [==============================] - 0s 576us/step - loss: 0.3915 - acc: 0.8329\n",
      "1856/1856 [==============================] - 1s 403us/step\n",
      "Epoch 1/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.6987 - acc: 0.5000\n",
      "Epoch 2/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.5592 - acc: 0.5000\n",
      "Epoch 3/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1859 - acc: 1.0000\n",
      "Epoch 4/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1488 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1250 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1077 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0946 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0843 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0759 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0690 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0633 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0584 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0542 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0506 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0474 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0446 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0421 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0399 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0379 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0360 - acc: 1.0000\n",
      "1856/1856 [==============================] - 1s 424us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.83276922, 0.83287025, 0.83015913, 0.83578345, 0.70371306])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_model(1, [32, 32, 32], [1, 1, 1, 1], \"diabetes\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    246988\n",
       "1.0     42095\n",
       "4.0      5546\n",
       "2.0      2298\n",
       "Name: diabetes, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"diabetes\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(hidden_layers, nodes, biases, disease, folds = 5):\n",
    "    # assert hidden_layers == len(nodes) - 1 or hidden_layers == 0\n",
    "    # assert hidden_layers == len(biases) - 2 or hidden_layers == 0\n",
    "    ##Create func_model from params\n",
    "    tf.random.set_seed(42)\n",
    "    func_model = Sequential()\n",
    "    ##Input layer\n",
    "    func_model.add(Dense(nodes[0], use_bias = biases[0], input_dim = 15))\n",
    "    ##Hidden layers\n",
    "    if hidden_layers != 0:\n",
    "        for i in range(hidden_layers - 2):\n",
    "            func_model.add(Dense(nodes[i + 1], use_bias = biases[i + 1]))\n",
    "    ##Output layers\n",
    "    func_model.add(Dense(1, use_bias=biases[hidden_layers + 1], activation='sigmoid'))\n",
    "    func_model.compile(loss=\"binary_crossentropy\", optimizer=tf.keras.optimizers.SGD(\n",
    "    learning_rate=0.001, momentum=0.0, nesterov=False, name=\"SGD\"), metrics=['acc'])\n",
    "    accuracy = np.ones(folds)\n",
    "    #Splits for cross validation\n",
    "    shuffled = df.sample(len(df.index), replace=True)\n",
    "    fold_size = len(shuffled.index) // folds\n",
    "    ##Cross-validation\n",
    "    for i in range(folds):\n",
    "        test_fold = shuffled.iloc[i * fold_size:(i + 1) * fold_size]\n",
    "        rest = shuffled.iloc[(i + 1) * fold_size:]\n",
    "        test_y = test_fold[disease]\n",
    "        rest_y = rest[disease]\n",
    "        vec = DictVectorizer()\n",
    "        train_dict = rest.drop(_diseases, axis = 1).to_dict('records')\n",
    "        train_vectorized = vec.fit_transform(train_dict).toarray()\n",
    "        test_dict = test_fold.drop(_diseases, axis = 1).to_dict('records')\n",
    "        test_vectorized = vec.fit_transform(test_dict).toarray()\n",
    "        func_model.fit(train_vectorized, rest_y, epochs=20, batch_size=256, verbose = 1)\n",
    "        test_pred = func_model.predict(test_vectorized)\n",
    "        test_pred = test_pred.reshape(-1)\n",
    "        accuracy[i] = np.count_nonzero(np.round(test_y) == np.round(test_pred)) / len(test_pred)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
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      "Epoch 3/20\n",
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      "Epoch 7/20\n",
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      "Epoch 8/20\n",
      "460/460 [==============================] - 0s 574us/step - loss: 0.2114 - acc: 0.9578\n",
      "Epoch 9/20\n",
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      "Epoch 10/20\n",
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      "Epoch 11/20\n",
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      "Epoch 12/20\n",
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      "Epoch 13/20\n",
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      "Epoch 14/20\n",
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      "Epoch 19/20\n",
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      "Epoch 17/20\n",
      "230/230 [==============================] - 0s 627us/step - loss: 0.1687 - acc: 0.9580\n",
      "Epoch 18/20\n",
      "230/230 [==============================] - 0s 598us/step - loss: 0.1673 - acc: 0.9580\n",
      "Epoch 19/20\n",
      "230/230 [==============================] - 0s 593us/step - loss: 0.1674 - acc: 0.9580\n",
      "Epoch 20/20\n",
      "230/230 [==============================] - 0s 623us/step - loss: 0.1684 - acc: 0.9580\n",
      "919/919 [==============================] - 0s 410us/step\n",
      "Epoch 1/20\n",
      "115/115 [==============================] - 0s 705us/step - loss: 0.1678 - acc: 0.9580\n",
      "Epoch 2/20\n",
      "115/115 [==============================] - 0s 621us/step - loss: 0.1653 - acc: 0.9580\n",
      "Epoch 3/20\n",
      "115/115 [==============================] - 0s 583us/step - loss: 0.1662 - acc: 0.9580\n",
      "Epoch 4/20\n",
      "115/115 [==============================] - 0s 627us/step - loss: 0.1658 - acc: 0.9580\n",
      "Epoch 5/20\n",
      "115/115 [==============================] - 0s 658us/step - loss: 0.1677 - acc: 0.9580\n",
      "Epoch 6/20\n",
      "115/115 [==============================] - 0s 600us/step - loss: 0.1669 - acc: 0.9580\n",
      "Epoch 7/20\n",
      "115/115 [==============================] - 0s 642us/step - loss: 0.1681 - acc: 0.9580\n",
      "Epoch 8/20\n",
      "115/115 [==============================] - 0s 688us/step - loss: 0.1654 - acc: 0.9580\n",
      "Epoch 9/20\n",
      "115/115 [==============================] - 0s 594us/step - loss: 0.1683 - acc: 0.9580\n",
      "Epoch 10/20\n",
      "115/115 [==============================] - 0s 575us/step - loss: 0.1673 - acc: 0.9580\n",
      "Epoch 11/20\n",
      "115/115 [==============================] - 0s 673us/step - loss: 0.1668 - acc: 0.9579\n",
      "Epoch 12/20\n",
      "115/115 [==============================] - 0s 619us/step - loss: 0.1670 - acc: 0.9579\n",
      "Epoch 13/20\n",
      "115/115 [==============================] - 0s 577us/step - loss: 0.1664 - acc: 0.9580\n",
      "Epoch 14/20\n",
      "115/115 [==============================] - 0s 605us/step - loss: 0.1662 - acc: 0.9580\n",
      "Epoch 15/20\n",
      "115/115 [==============================] - 0s 662us/step - loss: 0.1669 - acc: 0.9580\n",
      "Epoch 16/20\n",
      "115/115 [==============================] - 0s 581us/step - loss: 0.1662 - acc: 0.9580\n",
      "Epoch 17/20\n",
      "115/115 [==============================] - 0s 575us/step - loss: 0.1665 - acc: 0.9580\n",
      "Epoch 18/20\n",
      "115/115 [==============================] - 0s 608us/step - loss: 0.1663 - acc: 0.9580\n",
      "Epoch 19/20\n",
      "115/115 [==============================] - 0s 653us/step - loss: 0.1669 - acc: 0.9580\n",
      "Epoch 20/20\n",
      "115/115 [==============================] - 0s 594us/step - loss: 0.1665 - acc: 0.9580\n",
      "919/919 [==============================] - 0s 417us/step\n",
      "Epoch 1/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0339 - acc: 1.0000\n",
      "Epoch 2/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0117 - acc: 1.0000\n",
      "Epoch 3/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0081 - acc: 1.0000\n",
      "Epoch 4/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0063 - acc: 1.0000\n",
      "Epoch 5/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0052 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0045 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0039 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0035 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0031 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0028 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0026 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0024 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0023 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0021 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0020 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0019 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0018 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0017 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0016 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0015 - acc: 1.0000\n",
      "919/919 [==============================] - 0s 417us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.95723035, 0.95944199, 0.95903368, 0.95811501, 0.9579789 ])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# old people\n",
    "test_model_age_filtered(1, [32, 32, 32], [1, 1, 1, 1], \"stroke\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model_age_filtered(hidden_layers, nodes, biases, disease, folds = 5):\n",
    "    # assert hidden_layers == len(nodes) - 1 or hidden_layers == 0\n",
    "    # assert hidden_layers == len(biases) - 2 or hidden_layers == 0\n",
    "    ##Create func_model from params\n",
    "    tf.random.set_seed(42)\n",
    "    func_model = Sequential()\n",
    "    ##Input layer\n",
    "    func_model.add(Dense(nodes[0], use_bias = biases[0], input_dim = 15))\n",
    "    ##Hidden layers\n",
    "    if hidden_layers != 0:\n",
    "        for i in range(hidden_layers - 2):\n",
    "            func_model.add(Dense(nodes[i + 1], use_bias = biases[i + 1]))\n",
    "    ##Output layers\n",
    "    func_model.add(Dense(1, use_bias=biases[hidden_layers + 1], activation='sigmoid'))\n",
    "    func_model.compile(loss=\"binary_crossentropy\", optimizer=tf.keras.optimizers.SGD(\n",
    "    learning_rate=0.001, momentum=0.0, nesterov=False, name=\"SGD\"), metrics=['acc'])\n",
    "    accuracy = np.ones(folds)\n",
    "    #Splits for cross validation\n",
    "    shuffled = df_age50to70.sample(len(df_age50to70.index), replace=True)\n",
    "    fold_size = len(shuffled.index) // folds\n",
    "    ##Cross-validation\n",
    "    for i in range(folds):\n",
    "        test_fold = shuffled.iloc[i * fold_size:(i + 1) * fold_size]\n",
    "        rest = shuffled.iloc[(i + 1) * fold_size:]\n",
    "        test_y = test_fold[disease]\n",
    "        rest_y = rest[disease]\n",
    "        vec = DictVectorizer()\n",
    "        train_dict = rest.drop(_diseases, axis = 1).to_dict('records')\n",
    "        train_vectorized = vec.fit_transform(train_dict).toarray()\n",
    "        test_dict = test_fold.drop(_diseases, axis = 1).to_dict('records')\n",
    "        test_vectorized = vec.fit_transform(test_dict).toarray()\n",
    "        func_model.fit(train_vectorized, rest_y, epochs=20, batch_size=256, verbose = 1)\n",
    "        test_pred = func_model.predict(test_vectorized)\n",
    "        test_pred = test_pred.reshape(-1)\n",
    "        accuracy[i] = np.count_nonzero(np.round(test_y) == np.round(test_pred)) / len(test_pred)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_diabetes_model(hidden_layers, nodes, biases, disease = 'diabetes', folds = 5):\n",
    "    # assert hidden_layers == len(nodes) - 1 or hidden_layers == 0\n",
    "    # assert hidden_layers == len(biases) - 2 or hidden_layers == 0\n",
    "    ##Create func_model from params\n",
    "    tf.random.set_seed(42)\n",
    "    func_model = Sequential()\n",
    "    ##Input layer\n",
    "    func_model.add(Dense(nodes[0], use_bias = biases[0], input_dim = 15))\n",
    "    ##Hidden layers\n",
    "    if hidden_layers != 0:\n",
    "        for i in range(hidden_layers - 2):\n",
    "            func_model.add(Dense(nodes[i + 1], use_bias = biases[i + 1]))\n",
    "    ##Output layers\n",
    "    func_model.add(Dense(3, use_bias=biases[hidden_layers + 1], activation='sigmoid'))\n",
    "    func_model.compile(loss=\"binary_crossentropy\", optimizer=tf.keras.optimizers.SGD(\n",
    "    learning_rate=0.001, momentum=0.0, nesterov=False, name=\"SGD\"), metrics=['acc'])\n",
    "    accuracy = np.ones(folds)\n",
    "    #Splits for cross validation\n",
    "    shuffled = df.sample(len(df.index), replace=True)\n",
    "    shuffled[\"diabetes\"].replace({1: 'Yes', 2: 'Kinda', 3: 'No', 4: 'Kinda'}, inplace = True)\n",
    "    fold_size = len(shuffled.index) // folds\n",
    "    ##Cross-validation\n",
    "    for i in range(folds):\n",
    "        test_fold = shuffled.iloc[i * fold_size:(i + 1) * fold_size]\n",
    "        rest = shuffled.iloc[(i + 1) * fold_size:]\n",
    "        test_y = test_fold[[disease]]\n",
    "        rest_y = rest[[disease]]\n",
    "        vec = DictVectorizer()\n",
    "        test_y_dict = test_y.to_dict('records')\n",
    "        test_y_vec = vec.fit_transform(test_y_dict).toarray()\n",
    "        rest_y_dict = test_y.to_dict('records')\n",
    "        rest_y_vec = vec.fit_transform(rest_y_dict).toarray()\n",
    "        train_dict = rest.drop(_diseases, axis = 1).to_dict('records')\n",
    "        train_vectorized = vec.fit_transform(train_dict).toarray()\n",
    "        test_dict = test_fold.drop(_diseases, axis = 1).to_dict('records')\n",
    "        test_vectorized = vec.fit_transform(test_dict).toarray()\n",
    "        func_model.fit(train_vectorized, rest_y, epochs=20, batch_size=256, verbose = 1)\n",
    "        test_pred = func_model.predict(test_vectorized)\n",
    "        test_pred = test_pred.reshape(-1)\n",
    "        accuracy[i] = np.count_nonzero(np.round(test_y) == np.round(test_pred)) / len(test_pred)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 890, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 948, in compute_loss\n        return self.compiled_loss(\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/losses.py\", line 139, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/losses.py\", line 243, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/losses.py\", line 1930, in binary_crossentropy\n        backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/backend.py\", line 5283, in binary_crossentropy\n        return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)\n\n    ValueError: `logits` and `labels` must have the same shape, received ((None, 3) vs (None, 1)).\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/willrathgeb/CalHacks2022/notebook.ipynb Cell 36\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/willrathgeb/CalHacks2022/notebook.ipynb#Y130sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m test_diabetes_model(\u001b[39m2\u001b[39;49m, [\u001b[39m32\u001b[39;49m, \u001b[39m32\u001b[39;49m, \u001b[39m32\u001b[39;49m], [\u001b[39m1\u001b[39;49m, \u001b[39m1\u001b[39;49m, \u001b[39m1\u001b[39;49m, \u001b[39m1\u001b[39;49m])\n",
      "\u001b[1;32m/Users/willrathgeb/CalHacks2022/notebook.ipynb Cell 36\u001b[0m in \u001b[0;36mtest_diabetes_model\u001b[0;34m(hidden_layers, nodes, biases, disease, folds)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/willrathgeb/CalHacks2022/notebook.ipynb#Y130sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m test_dict \u001b[39m=\u001b[39m test_fold\u001b[39m.\u001b[39mdrop(_diseases, axis \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mto_dict(\u001b[39m'\u001b[39m\u001b[39mrecords\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/willrathgeb/CalHacks2022/notebook.ipynb#Y130sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m test_vectorized \u001b[39m=\u001b[39m vec\u001b[39m.\u001b[39mfit_transform(test_dict)\u001b[39m.\u001b[39mtoarray()\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/willrathgeb/CalHacks2022/notebook.ipynb#Y130sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m func_model\u001b[39m.\u001b[39;49mfit(train_vectorized, rest_y, epochs\u001b[39m=\u001b[39;49m\u001b[39m20\u001b[39;49m, batch_size\u001b[39m=\u001b[39;49m\u001b[39m256\u001b[39;49m, verbose \u001b[39m=\u001b[39;49m \u001b[39m1\u001b[39;49m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/willrathgeb/CalHacks2022/notebook.ipynb#Y130sZmlsZQ%3D%3D?line=37'>38</a>\u001b[0m test_pred \u001b[39m=\u001b[39m func_model\u001b[39m.\u001b[39mpredict(test_vectorized)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/willrathgeb/CalHacks2022/notebook.ipynb#Y130sZmlsZQ%3D%3D?line=38'>39</a>\u001b[0m test_pred \u001b[39m=\u001b[39m test_pred\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/utils/traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:  \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m     66\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[0;32m---> 67\u001b[0m   \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[1;32m     68\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m     69\u001b[0m   \u001b[39mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m/var/folders/m3/f23mtyd11999h6hwmgcqzz9c0000gn/T/__autograph_generated_filedbc6672q.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     do_return \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m     retval_ \u001b[39m=\u001b[39m ag__\u001b[39m.\u001b[39mconverted_call(ag__\u001b[39m.\u001b[39mld(step_function), (ag__\u001b[39m.\u001b[39mld(\u001b[39mself\u001b[39m), ag__\u001b[39m.\u001b[39mld(iterator)), \u001b[39mNone\u001b[39;00m, fscope)\n\u001b[1;32m     16\u001b[0m \u001b[39mexcept\u001b[39;00m:\n\u001b[1;32m     17\u001b[0m     do_return \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 890, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/training.py\", line 948, in compute_loss\n        return self.compiled_loss(\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/engine/compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/losses.py\", line 139, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/losses.py\", line 243, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/losses.py\", line 1930, in binary_crossentropy\n        backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),\n    File \"/Users/willrathgeb/opt/anaconda3/envs/deep_learning/lib/python3.10/site-packages/keras/backend.py\", line 5283, in binary_crossentropy\n        return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)\n\n    ValueError: `logits` and `labels` must have the same shape, received ((None, 3) vs (None, 1)).\n"
     ]
    }
   ],
   "source": [
    "test_diabetes_model(2, [32, 32, 32], [1, 1, 1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>high_bp</th>\n",
       "      <th>bp_meds</th>\n",
       "      <th>sex</th>\n",
       "      <th>high_chol</th>\n",
       "      <th>heart_attack</th>\n",
       "      <th>stroke</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>smoke_100</th>\n",
       "      <th>smoke_freq</th>\n",
       "      <th>drinking</th>\n",
       "      <th>age</th>\n",
       "      <th>daily_fruit</th>\n",
       "      <th>daily_veg</th>\n",
       "      <th>weekly_phys</th>\n",
       "      <th>race</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>40.259821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>25.140621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>28.250932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>23.937772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.14</td>\n",
       "      <td>3.14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>25.268890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441441</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.71</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>31.258831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441442</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.85</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>23.452547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441445</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.07</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>27.555611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441454</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>23.200710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441455</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>24.743451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146954 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        high_bp  bp_meds  sex  high_chol  heart_attack  stroke  diabetes  \\\n",
       "0           1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "1           3.0      2.0  2.0        2.0           0.0     0.0       0.0   \n",
       "3           1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "6           1.0      1.0  2.0        1.0           0.0     0.0       0.0   \n",
       "9           1.0      1.0  1.0        1.0           0.0     0.0       0.0   \n",
       "...         ...      ...  ...        ...           ...     ...       ...   \n",
       "441441      1.0      1.0  1.0        2.0           0.0     0.0       0.0   \n",
       "441442      1.0      1.0  1.0        1.0           1.0     1.0       0.0   \n",
       "441445      3.0      2.0  1.0        2.0           0.0     0.0       0.0   \n",
       "441454      1.0      1.0  1.0        2.0           0.0     0.0       0.0   \n",
       "441455      1.0      1.0  2.0        1.0           1.0     0.0       1.0   \n",
       "\n",
       "        smoke_100  smoke_freq  drinking   age  daily_fruit  daily_veg  \\\n",
       "0             1.0         3.0       0.0  63.0         0.50       2.17   \n",
       "1             1.0         1.0       0.0  52.0         0.24       0.78   \n",
       "3             2.0         3.0       0.0  63.0         1.00       0.20   \n",
       "6             2.0         3.0       0.0  70.0         1.29       1.14   \n",
       "9             1.0         3.0       0.0  68.0         1.14       3.14   \n",
       "...           ...         ...       ...   ...          ...        ...   \n",
       "441441        2.0         3.0       0.0  63.0         0.14       1.71   \n",
       "441442        2.0         3.0       0.0  59.0         0.29       0.85   \n",
       "441445        2.0         3.0       0.0  68.0         0.60       1.07   \n",
       "441454        2.0         3.0       0.0  52.0         2.00       1.35   \n",
       "441455        2.0         3.0       0.0  63.0         2.00       0.71   \n",
       "\n",
       "        weekly_phys  race  height  weight        BMI  \n",
       "0               3.0   1.0    70.0   280.0  40.259821  \n",
       "1               1.0   1.0    68.0   165.0  25.140621  \n",
       "3               3.0   1.0    67.0   180.0  28.250932  \n",
       "6               1.0   4.0    66.0   148.0  23.937772  \n",
       "9               1.0   1.0    67.0   161.0  25.268890  \n",
       "...             ...   ...     ...     ...        ...  \n",
       "441441          3.0   5.0    72.0   230.0  31.258831  \n",
       "441442          3.0   5.0    66.0   145.0  23.452547  \n",
       "441445          3.0   5.0    75.0   220.0  27.555611  \n",
       "441454          3.0   5.0    71.0   166.0  23.200710  \n",
       "441455          2.0   5.0    62.0   135.0  24.743451  \n",
       "\n",
       "[146954 rows x 18 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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