diff --git a/dane.ipynb b/dane.ipynb
index 67eb80c..cfdb3ce 100644
--- a/dane.ipynb
+++ b/dane.ipynb
@@ -11,38 +11,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 97,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting kaggle\n",
-      "  Using cached kaggle-1.6.6.tar.gz (84 kB)\n",
-      "Requirement already satisfied: six>=1.10 in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (1.16.0)\n",
-      "Requirement already satisfied: certifi in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (2022.6.15)\n",
-      "Requirement already satisfied: python-dateutil in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (2.8.2)\n",
-      "Requirement already satisfied: requests in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (2.28.1)\n",
-      "Requirement already satisfied: tqdm in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (4.64.1)\n",
-      "Collecting python-slugify\n",
-      "  Using cached python_slugify-8.0.4-py2.py3-none-any.whl (10 kB)\n",
-      "Requirement already satisfied: urllib3 in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (1.26.11)\n",
+      "Requirement already satisfied: kaggle in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (1.6.6)\n",
       "Requirement already satisfied: bleach in c:\\users\\adrian\\miniconda3\\lib\\site-packages (from kaggle) (4.1.0)\n",
+      "Requirement already satisfied: python-slugify in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (8.0.4)\n",
+      "Requirement already satisfied: python-dateutil in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (2.8.2)\n",
+      "Requirement already satisfied: tqdm in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (4.64.1)\n",
+      "Requirement already satisfied: requests in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (2.28.1)\n",
+      "Requirement already satisfied: certifi in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (2022.6.15)\n",
+      "Requirement already satisfied: six>=1.10 in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (1.16.0)\n",
+      "Requirement already satisfied: urllib3 in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from kaggle) (1.26.11)\n",
       "Requirement already satisfied: webencodings in c:\\users\\adrian\\miniconda3\\lib\\site-packages (from bleach->kaggle) (0.5.1)\n",
       "Requirement already satisfied: packaging in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from bleach->kaggle) (22.0)\n",
-      "Collecting text-unidecode>=1.3\n",
-      "  Using cached text_unidecode-1.3-py2.py3-none-any.whl (78 kB)\n",
+      "Requirement already satisfied: text-unidecode>=1.3 in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from python-slugify->kaggle) (1.3)\n",
       "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from requests->kaggle) (2.10)\n",
       "Requirement already satisfied: charset-normalizer<3,>=2 in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from requests->kaggle) (2.1.0)\n",
-      "Requirement already satisfied: colorama in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from tqdm->kaggle) (0.4.5)\n",
-      "Building wheels for collected packages: kaggle\n",
-      "  Building wheel for kaggle (setup.py): started\n",
-      "  Building wheel for kaggle (setup.py): finished with status 'done'\n",
-      "  Created wheel for kaggle: filename=kaggle-1.6.6-py3-none-any.whl size=111961 sha256=3aa19c7655c19d77b65c2542567b6e34a57813227f1f2df6d0fd84accad6824f\n",
-      "  Stored in directory: c:\\users\\adrian\\appdata\\local\\pip\\cache\\wheels\\46\\aa\\c3\\b3e421522fb5acdd7c366a05c5fc80787615bdeed207e7f79b\n",
-      "Successfully built kaggle\n",
-      "Installing collected packages: text-unidecode, python-slugify, kaggle\n",
-      "Successfully installed kaggle-1.6.6 python-slugify-8.0.4 text-unidecode-1.3\n"
+      "Requirement already satisfied: colorama in c:\\users\\adrian\\appdata\\roaming\\python\\python39\\site-packages (from tqdm->kaggle) (0.4.5)\n"
      ]
     }
    ],
@@ -55,31 +44,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 98,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading personal-key-indicators-of-heart-disease.zip to C:\\Users\\Adrian\\Desktop\\Semestr 1 (II ST)\\ML\\zadania\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "  0%|          | 0.00/21.4M [00:00<?, ?B/s]\n",
-      "  5%|4         | 1.00M/21.4M [00:00<00:11, 1.80MB/s]\n",
-      "  9%|9         | 2.00M/21.4M [00:00<00:05, 3.51MB/s]\n",
-      " 19%|#8        | 4.00M/21.4M [00:00<00:02, 7.25MB/s]\n",
-      " 33%|###2      | 7.00M/21.4M [00:00<00:01, 12.0MB/s]\n",
-      " 51%|#####1    | 11.0M/21.4M [00:01<00:00, 17.9MB/s]\n",
-      " 65%|######5   | 14.0M/21.4M [00:01<00:00, 20.7MB/s]\n",
-      " 79%|#######9  | 17.0M/21.4M [00:01<00:00, 20.6MB/s]\n",
-      " 93%|#########3| 20.0M/21.4M [00:01<00:00, 22.7MB/s]\n",
-      "100%|##########| 21.4M/21.4M [00:01<00:00, 14.9MB/s]\n"
+      "personal-key-indicators-of-heart-disease.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
      ]
     }
    ],
@@ -92,17 +63,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "'unzip' is not recognized as an internal or external command,\n",
-      "operable program or batch file.\n"
-     ]
-    }
-   ],
+   "execution_count": 99,
+   "outputs": [],
    "source": [
     "#!unzip -o personal-key-indicators-of-heart-disease.zip #nie działa na Windowsie więc korzystam z modułu zipfile"
    ],
@@ -112,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 100,
    "outputs": [],
    "source": [
     "import zipfile\n",
@@ -125,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 101,
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -147,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 102,
    "outputs": [
     {
      "name": "stdout",
@@ -212,14 +174,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 103,
    "outputs": [
     {
      "data": {
       "text/plain": "     State     Sex GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\n0  Alabama  Female     Very good                 0.0               0.0   \n1  Alabama  Female     Excellent                 0.0               0.0   \n2  Alabama  Female     Very good                 2.0               3.0   \n3  Alabama  Female     Excellent                 0.0               0.0   \n4  Alabama  Female          Fair                 2.0               0.0   \n\n                                     LastCheckupTime PhysicalActivities  \\\n0  Within past year (anytime less than 12 months ...                 No   \n1                                                NaN                 No   \n2  Within past year (anytime less than 12 months ...                Yes   \n3  Within past year (anytime less than 12 months ...                Yes   \n4  Within past year (anytime less than 12 months ...                Yes   \n\n   SleepHours RemovedTeeth HadHeartAttack  ... HeightInMeters  \\\n0         8.0          NaN             No  ...            NaN   \n1         6.0          NaN             No  ...           1.60   \n2         5.0          NaN             No  ...           1.57   \n3         7.0          NaN             No  ...           1.65   \n4         9.0          NaN             No  ...           1.57   \n\n  WeightInKilograms    BMI AlcoholDrinkers HIVTesting FluVaxLast12  \\\n0               NaN    NaN              No         No          Yes   \n1             68.04  26.57              No         No           No   \n2             63.50  25.61              No         No           No   \n3             63.50  23.30              No         No          Yes   \n4             53.98  21.77             Yes         No           No   \n\n  PneumoVaxEver                                  TetanusLast10Tdap  \\\n0            No  Yes, received tetanus shot but not sure what type   \n1            No  No, did not receive any tetanus shot in the pa...   \n2            No                                                NaN   \n3           Yes  No, did not receive any tetanus shot in the pa...   \n4           Yes  No, did not receive any tetanus shot in the pa...   \n\n  HighRiskLastYear CovidPos  \n0               No       No  \n1               No       No  \n2               No      Yes  \n3               No       No  \n4               No       No  \n\n[5 rows x 40 columns]",
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Sex</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Alabama</td>\n      <td>Female</td>\n      <td>Very good</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>No</td>\n      <td>8.0</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes, received tetanus shot but not sure what type</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Alabama</td>\n      <td>Female</td>\n      <td>Excellent</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>6.0</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.60</td>\n      <td>68.04</td>\n      <td>26.57</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No, did not receive any tetanus shot in the pa...</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Alabama</td>\n      <td>Female</td>\n      <td>Very good</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.57</td>\n      <td>63.50</td>\n      <td>25.61</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Alabama</td>\n      <td>Female</td>\n      <td>Excellent</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.65</td>\n      <td>63.50</td>\n      <td>23.30</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No, did not receive any tetanus shot in the pa...</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Alabama</td>\n      <td>Female</td>\n      <td>Fair</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>9.0</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.57</td>\n      <td>53.98</td>\n      <td>21.77</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No, did not receive any tetanus shot in the pa...</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
      },
-     "execution_count": 39,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -233,14 +195,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 104,
    "outputs": [
     {
      "data": {
       "text/plain": "       PhysicalHealthDays  MentalHealthDays     SleepHours  HeightInMeters  \\\ncount       434205.000000     436065.000000  439679.000000   416480.000000   \nmean             4.347919          4.382649       7.022983        1.702691   \nstd              8.688912          8.387475       1.502425        0.107177   \nmin              0.000000          0.000000       1.000000        0.910000   \n25%              0.000000          0.000000       6.000000        1.630000   \n50%              0.000000          0.000000       7.000000        1.700000   \n75%              3.000000          5.000000       8.000000        1.780000   \nmax             30.000000         30.000000      24.000000        2.410000   \n\n       WeightInKilograms            BMI  \ncount      403054.000000  396326.000000  \nmean           83.074470      28.529842  \nstd            21.448173       6.554889  \nmin            22.680000      12.020000  \n25%            68.040000      24.130000  \n50%            80.740000      27.440000  \n75%            95.250000      31.750000  \nmax           292.570000      99.640000  ",
       "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>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>SleepHours</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>434205.000000</td>\n      <td>436065.000000</td>\n      <td>439679.000000</td>\n      <td>416480.000000</td>\n      <td>403054.000000</td>\n      <td>396326.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>4.347919</td>\n      <td>4.382649</td>\n      <td>7.022983</td>\n      <td>1.702691</td>\n      <td>83.074470</td>\n      <td>28.529842</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8.688912</td>\n      <td>8.387475</td>\n      <td>1.502425</td>\n      <td>0.107177</td>\n      <td>21.448173</td>\n      <td>6.554889</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.910000</td>\n      <td>22.680000</td>\n      <td>12.020000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6.000000</td>\n      <td>1.630000</td>\n      <td>68.040000</td>\n      <td>24.130000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.000000</td>\n      <td>1.700000</td>\n      <td>80.740000</td>\n      <td>27.440000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3.000000</td>\n      <td>5.000000</td>\n      <td>8.000000</td>\n      <td>1.780000</td>\n      <td>95.250000</td>\n      <td>31.750000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>24.000000</td>\n      <td>2.410000</td>\n      <td>292.570000</td>\n      <td>99.640000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 40,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,13 +234,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 105,
    "outputs": [
     {
      "data": {
       "text/plain": "<AxesSubplot:ylabel='HadHeartAttack'>"
      },
-     "execution_count": 41,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -300,13 +262,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 106,
    "outputs": [
     {
      "data": {
       "text/plain": "No     416959\nYes     25108\nName: HadHeartAttack, dtype: int64"
      },
-     "execution_count": 42,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -329,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 107,
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
@@ -344,13 +306,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 108,
    "outputs": [
     {
      "data": {
-      "text/plain": "No     333641\nYes     20042\nName: HadHeartAttack, dtype: int64"
+      "text/plain": "No     333634\nYes     20016\nName: HadHeartAttack, dtype: int64"
      },
-     "execution_count": 44,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -373,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 109,
    "outputs": [],
    "source": [
     "def oversample(dataset):\n",
@@ -391,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 110,
    "outputs": [],
    "source": [
     "train = oversample(train)"
@@ -402,20 +364,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 111,
    "outputs": [
     {
      "data": {
       "text/plain": "<AxesSubplot:ylabel='HadHeartAttack'>"
      },
-     "execution_count": 47,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -430,20 +392,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 112,
    "outputs": [
     {
      "data": {
       "text/plain": "<AxesSubplot:ylabel='HadHeartAttack'>"
      },
-     "execution_count": 48,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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\n"
+      "image/png": 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T8/WOQEQO7qc9uTC4wKIAi77CTz/9FEeOHMHs2bORn5+Pp556CnPmzEFkZCRWr16NP/3pT7bOaTdkWcH+4+dRZqzTOwoRObjMEyUoM9bqHUNzgmrhQ8JTp05h+vTpKC0thZ+fH55++mlMmjTJJTYiLViahl2HeIAmEVlv2rhkTLilk1Mvd7b4K+vYsSO++OILtG3bFp06dcKdd97pEiVjqq7H3qNFescgIieRmp7v1CUDAIaWfuDw4cOvWSQmkwmFhYUYPnw4vL29AQCCIGDz5s22S2knZFnBlvR8mGXXWClCRNorOG/CqYJydGgbAFF0zl/WW1w0ffv2dYkRy/VIkojN3DtDRDb2U1oeHr/7Jr1jaMbiOZr/ZjabYTC0uLccjqKoyC2qxFN/26Z3FCJyMv4+7vjs5TFOO6Kx+MHgv/71L8yaNav5z/v27cPgwYOxfPlymwSzR1zSTERaqKyqx8mCcqfdwGlR0SxZsgTvvPMOYmNjm98WExODMWPG4C9/+QtWrVplq3x2QxQF7Dt2Xu8YROSk9h4tguKkRWPRo7NRo0Zh4sSJV4xomixatAjr1q3DDz/8YJOA9qKsshbTXtmodwwiclLx0YH4+/8M1TuGJiwa0RQXF+Omm649cdW9e3cUFBRYFcremGUFe7OK9Y5BRE7sZEE5jNX1v/+BDsiioomKisKuXbuu+b69e/ciIiLCqlD2xiCJ2M/HZkSkIVUF0rOKnfLsM4uWid13331488030dDQgBEjRiAkJAQXL17E1q1b8cknn+CZZ56xdU5dKYqKAydK9I5BRE5uX1YxhvWK1juGzVlUNNOnT0dxcTE+++wzLF26tPntkiThoYcewowZM2yVT3eqquJkQTmqaq5/NQIRkbUyskugqqrT7Vm0ah+N0WhERkYGKioq4O/vj27duiEoKMiW+XQnywpWbM7Glz8d1zsKEbmAv//PEHRsFwjRicrGqh2Wfn5+GDJkyFVvP336NOLi4qx5abshcX6GiFpR2tFixLUNACQXL5qKigq8/fbbSEtLQ319ffMmI1VVUV1djYqKCmRlZdk0qF6qaxtwIp83aRJR69iXVYwHRifpHcOmLFp1tmDBAnz99ddo3749JEmCn58fbrrpJjQ0NKCyshKvvvqqrXPqounuGcU591ARkR06WVAOk5Mtc7aoaH755Rc8+eST+PDDDzFp0iRERETgnXfewYYNG5CYmIiTJ09aFWr48OEYPnw4TCbTVe974YUXMHXqVKtev6VEUeBjMyJqVc64zNmioqmsrERKSgqAxntpDh8+DADw8fHBzJkzsW3bNquDFRYW4q9//avVr2MNQRCw/ziLhohaV0Z2iVNd8WzRVxIUFASj0QgAiI2NRWlpKcrLywEA4eHhKC62fhd9dHQ0VqxYgV9//dXq17JUUWkVSiuc/5pVIrIvpwrK9Y5gUxYVzYABA7Bo0SIUFhYiJiYGAQEB+OabbwAAW7dutckS5zvuuAMDBgzAn//852s+QgOA8vJyvPLKKxg6dCi6deuG+++/H3v27LH6cwON8zNZORdt8lpERDei4LyJj87+8Ic/oLS0FM8//zwEQcBjjz2GhQsXol+/fli6dCnuueceq4MJgoDXX38dFRUVWLhw4VXvl2UZM2fORHp6Ot58802sWbMGCQkJePjhh5GZmWmTz3+qoMLq1yEiulGyoiK/2Kh3DJuxaHlzVFQU1q9fj5ycHADAjBkzEBoaiv3796Nbt264++67bRIuKioKzz//PObPn4/Ro0dj8ODBze/bsWMHjhw5gnXr1iEhIQEA8Morr+DQoUP4+OOP8e6771r1uUVRwKnCcqteg4jIUifyyxEd7ucUczUWfQVr165FTU0NkpL+s9Z7/PjxeOmllzB48GAsXrzYZgEnTZqEQYMGYd68eVc8QsvOzoafn19zyQCNo5DevXsjOzvbJp/7dCFHNESkjzNnK5zmdACLiubFF19Efv61b5vMysrCe++9Z1Wo//baa6/BaDTijTfeaH7bb52co6qqTa6ULr5Yjepas9WvQ0RkidOFFU5ztXOLfyLPmjULp06dAtD4w3zOnDlwd3e/6uNKS0sRExNju4QA2rZtixdeeAHz5s1DdHQ0IiMjkZiYCKPRiOzs7OZRjaqq2LdvHzp16mTV55MVBdl5PA2AiPSTc65S7wg20+Kiefzxx5uvaP7mm2/QuXNnBAcHX/ExoijC398fEyZMsG1KAPfeey82bNiAHTt2IDIyEoMHD0ZycjKeeeYZ/O///i9CQkKwfPlyZGdn46WXXrLuk6mNw1YiIr1U15pRUlaNNkHeekexWouLpmfPnujZs2fzn5944glER7fuvQmvvfYaxo8fD6DxSoIlS5Zg4cKFmDt3Lurr69G1a1csXboUPXr0sOrzSJKIvCLnWfFBRI7pRH45ggM8IYmOvSDAosmMwsJC1Ndf+yyeY8eO4bnnnsO6dessDpWamnrNt0dGRiI9Pb35z8HBwddc+mwLeU60tJCIHNPpwgr06+L4Nxa3uGjS09ObJ+D37t2LvXv34uLFqzc0bt269TcXCjiKBrOC4ovVescgIhd3+mwFJCdY3tziolm1ahW+/fbb5pvfXnnllas+pqmIbr/9dhvF08e5C1VQeGQzEenMWeaKW1w08+bNwz333ANVVfHQQw9h/vz5V63ualoMEB8fb/OgrUVWFJw55xz/conIsV0or0V1bQO8Pd30jmKVFheNn58f+vbtCwBYtmwZunTpAh8fn2t+7I4dO67Yxe9IVBUo4PwMEdmJ82U1iI10kaK5XFPhXO7ixYtYvXo1Vq5ciYKCAoe9YdMgiSgp54nNRGQfSstr0D7Cr3nawhFZvYU+LS0NX331FTZt2oSGhgbExMRgzpw5tsimmwpTnd4RiIgAAGXGOsiKCoPkYkVTWVmJNWvWYOXKlThz5gwAYMyYMZg6deoVe20cFYuGiOxFmbEWcPC1STdUNBkZGfjqq6+wceNG1NfXo3///pg2bRpefvllTJkyxSlKBgDKWTREZCfKjHUOf+ZZi4vmjjvuwIkTJxAbG4vHHnsMd911FyIjI2E0GvHyyy9rGLH1VZquvRmViKi1lRlrXadosrOzkZiYiBkzZmDIkCFXnXPmLOrqZdQ1yHrHICICAJRXOv4TlhZvOV2yZAk6deqEl156CUOGDMHjjz+OzZs3o6GhQct8ra6yiqMZIrIfzvAov8UjmoEDB2LgwIEwGo347rvvsGbNGsydOxcBAQEQBAG5ubno06ePlllbRbmJS5uJyH6UVTr+zyRB/a0bxFrg+PHjWL16NdatW4fy8nJERERg3LhxGDduHLp06WLLnK1CVVWkHSnCa5+k6R2FiKjZmoXj4WZw3DPPrCqaJmazGampqVi9ejV27NgBRVEccsOmWVawZW8ePlh1UO8oRETNls4fhZAAL71jWMz6O48BGAwGjBo1CqNGjUJJSQlWrFhhi5dtfSpQwRVnRGRnyirrHLpoLBqLJScnIzMz85rvy83Nxccff2xVKL2IouAUE29E5FxKK2pgg4dPumnxiGbJkiWorm68o0VVVaxatQrbt2+/6uMyMjLg7u5uu4StSBQFngpARHanoqreoY+haXHR1NXV4YMPPgAACIKAVatWXfUxoijCz88Ps2fPtl3CVmaqdq7l2kTk+MyyoncEq7S4aGbPnt1cIElJSVi5ciW6deumWTAiImokKyoc+MmZZXM0AwcORFVVla2z2AUHPombiJyUIjtwy8DCosnIyHDouxGux1m/LiJyXLLi2I/OLCqam2++Gd99953THT8DcERDRPZHVhx7RGPRPhoPDw989913+PHHH9GxY0d4e3tf8X5BEPDpp5/aJGBrE9k0ZCOP3tUVsZH+escgJxAe7A1H/slkUdEUFRUhJSWl+c//vb7bkdd7s2fIVkb1iYan53+W+it11VDMzvcUgLQnukmAoMDCh1C6s8kRNM5kwdI07Dp0Tu8Y5AQCfd0xZUwybu4WCV8fDygNdag6+iuMh7ahNvcIHP7aRGo1QbdMQWD/OyFINjnMpdXZPHV1dTXS09MxZMgQW790q+CIhmyl3FSPf359EP/8+iCSY4PwwOhkdOk8GH7dh8FcWQrjwVQYD22DuaxI76hk5wRBhCP/YmJR0RQWFuLll19GWloa6uuvfTaYIx6qCXCOhrSRlVOGeR/9CgAYMyAWd94ch6iBExB0872oLTgO48EtMGXtglpXrXNSskuipHcCq1hUNG+88Qb279+Pe++9F/v374eXlxd69OiBnTt3Ijs7G++//76tc7Ye9gxpbMOuHGzYlQM/bzdMGZ2EIT3aI3TcbISOfhSmY7thytyKmpxDgOrYS1rJdgQHLxqL5mj69euHJ598Eg8++CCWL1+O1NRULFmyBLIsY+bMmYiKisKCBQu0yKu5N5enY3tGod4xyMV0aheAB8cmo1tcMNzc3WA2lcN4MBWmQ9vQUMr/Hl1dyOhH4J8y0mHnaCxawlBVVYXExEQAQFxcHI4ePQoAkCQJU6ZMwe7du22XsJVxwybp4WRBBV5evBsTXlyPd7/KQKFRRED/OxH9+HuImrEQfj1HQ/T00Tsm6cTRRzQW1WNYWBguXLgAAGjfvj0qKipQUlKCNm3aIDAwEKWlpTYN2ZpE9gzpbPPePGzemwdvTwPuH5mIYT2jEDrmUYSOmomq7DQYM7ei5tQBPlpzIaKXLyA45tJmwMKiGTp0KN555x1EREQgJSUFERERWLJkCebMmYPVq1cjPDzc1jlbhaqq8PJ00zsGEQCgutaMJeuOYMm6I2gf6YepY5OREt8bkckDIVdXwpi5FcbMrWgoydc7KmnM4BsMQXTcorFojqasrAyzZs2Cj48Pli5diu+++w4vvPBC80bN+fPnY/LkyTYPqzWzrGB16gks33BM7yhEv2loShQmDOuE2Ag/iJKEuuIcGA9shunIDig1Rr3jkQai5/wTboGO+Qs8YOWGzfPnzyMsLAwAkJ6ejgMHDqBbt27o27evzQK2JrOsIDU9H++vPKB3FKLf5eku4r4Ribi1dzSC/D0AVUX1iX0wZqai+uR+QJH1jkg2EvvscogejnuVs9UnAxiNRpw/fx7R0dGQJAmS5LiTVqqqIuP4eby02HEXM5Brimrji6ljk9E7KRQeHu6Qa0wwHdoGY+Y21Bef0TseWUMyIO6FFXqnsIrFa+X27NmDt956C4cPH26+cfPf//43wsPD8cILL9gyY6sRBAGhgY77WwO5rsISE/6ybC8AYEDXCNw7IgEde41BQN/bUV+SD+OBLTAd2Q65qkLnpHSjJO8AvSNYzaLZpV27duHhhx+Gp6cnnn322ea5mcTERCxbtgyffPKJTUO2piB/T70jEFll1+Ei/PGd7bjn//2ILzYeQ6UhGMG3TkPMU/9G+KT/B5+k/oCD7sdwRZJvoN4RrGbRo7NJkyYhIiIC7777LsxmM7p27YrVq1ejS5cu+Pvf/47Nmzdj/fr1WuRtFXc9953D3/9AdLnwYG9MHZuMfp3D4OnpDqWuGsZDP8N0aBvqzp7UOx5dh1ennoic9Ge9Y1jFol9rsrKyMGfOHABXb3AcNGiQw95F0yTQzwOlFbV6xyCymeKL1Xjr830AgN7JYbhvRCISe4xEQO+xqC8923jW2uHtkI0XdU5K/83gE6h3BKtZVDR+fn4oKSm55vvOnTsHPz8/q0LpLdjfk0VDTis96zzSs87DIAJ33dIJYwbEIuyWBxA87AHUnDkE48FUVGenQTVf+8Bcal2STwBUWYbgwAutLCqaW2+9FW+//TYSEhLQuXNnAI0jm6KiIixatAi33HKLLTO2umDO05ALMCvA16kn8XXqSYQEeGLq2GQM6JqE8LjuUOprYTryC4yZW1FXcFzvqC5N8gmEI18RAFg4R1NRUYFp06YhOzsboaGhKCkpQWxsLIqKihAZGYnPP/8cwcHBWuTVnKKqWLQ6Ez/uytE7CpEuuseHYvKoRCTFBEIyGNBQVgzjwS0wHvoZcuUFveO5nLA7/wc+nQc69HlnFu+jqa+vx9q1a7F7926Ul5fDz88Pffv2xYQJE+Dl5bhLhM1mBV+nnsDnG3k6ALk2UQTGD+qI2wbHIiLYCxBE1OYdgfFgKqqO7YbaUKd3RJcQ+eAr8GrfVe8YVuFVzv+FpwMQXS3IzwMPjk3CwJsi4Ovteela6p0wZm5FbV4WHP3Rjj2LeWoxDH6O+YSoSYuLZu/evTf0wn369LEokN54OgDR9SXHBuGBMcnoEhsIg5sbzJUXGq+lztwGc3mx3vGciuDuiQ7Pfa53DKu1uGiSkpJ+866WppcQBAGqqkIQBIe9yhkALlbU4qFXN+odg8jujRsYi/E3xyEq1BuCKKE2/9h/rqWur9E7nsPziOyIqJl/1TuG1VpcNGlpaVf8WZZlzJgxA6+88go6dOhw1cc76sGaTe7/8w+oqjXrHYPIIfh5u+GBMckY0j0Svj4eUGUzqo7tgungVtTkHubdORbyvekWhN3xpEX/7IsvvogffvgB33777VU/o0tKSnDbbbdhyJAheOutt2wR9bosnqORZRldunRpPhHA2Tz/wS84eoab14huVEJMIKaMTkK3jsFwc3OD2VTWeC115lY0XDyndzyHEjzsQQT0G2/RFc6VlZW47bbbEBsbi2XLll3xROrJJ59EZmYm1q1bB39/f1tGvibHvUlHQ4qqIjZS+28+kTPKzitvvJb6hfV4b0UGCk0SAvvfhejZH6Dt9L/AL2UkRA9vvWM6BLewmMblfxbw9/fHq6++irS0NKxcubL57Rs3bsSmTZuwYMGCVikZgEVzTYrCoiGyhU1peZj75lbcP38Dvv35JOr82yF07GNo//QnCLv7j/DqmOLQVxRrzSM8FoIV359hw4bhjjvuwJtvvokLFy7AZDLh//7v/zBlyhQMGjQIp06dwqOPPoqUlBQMHjwYzzzzzBWnvuTk5ODhhx9Gr169kJKSgocffhjHj9/4Bl4+OvsN2XlleObd7XrHIHI6sZH+mDo2CSnxoXBzd4NcVXHpWuptaLjAa6mbiF6+iP2j9edGVlRU4LbbbsOgQYPg7++P7du3Y+3ataisrMQdd9yB8ePHY/LkyaipqcH777+PEydO4Pvvv4e3tzcmTJiApKQkzJo1C2azGQsXLkROTg42bdp0Qxl4VvhvaB/pD1EAeIgzkW3lnKvE/y1pXFw0tGcU7hkWj/Z9xyNwwF2oKzrdOJ9z5BcoNSadk+rLIzzOJq8TEBCAl19+GXPnzoWbmxuWL18OLy8vfPTRR4iIiMC8efOaP/add95B//79sWHDBkyYMAF5eXkYOHAgoqKi4ObmhgULFuD06dNQFAXiDTzSa3HRfPDBB1f8uWkZ84oVK5qvc24iCELz6c6OysNNQrswP+QV8w52Iq38vL8QP+8vhKe7iEkjknBrn3YIGTUTISNmoPpEeuO11KcyXPJaaveIDlAV2SZHz4wYMQJdu3ZFVFQUunfvDgA4evQoTpw4gZSUlCs+tq6uDqdOnQIAPP3001iwYAG++OIL9O3bFzfffDNuv/32GyoZ4Ab30bT4RR18Hw3QWKTvrjiALXvz9I5C5FKiwxuvpe6Z0HQttRGmzG0wZm5F/flcveO1mrC7/gif5P42O+Ns6tSpiIqKwl/+8hcAwCOPPAJFUfDSSy9d9bF+fn7N51WWlpbi559/xq5du7B9+3a4ublh7dq1CA0NbfHnbvGI5tgx1zr7S1ZUJMQEsmiIWll+sQkLljaeRDKoWyQm3pqAuN5jEdBvPOrP5zUe8Hl4O5TqSp2TassjqpOmB2nGx8dj/fr1iIyMhLu7OwCgvLwczz//PGbMmIH4+Hj84x//wKxZszBhwgRMmDABxcXFGDJkCNLS0jBu3LgWfy7O0fwGgyQiOdaxzxcicnQ7M89hZ+Y5uBtE3HtrPEb2jUHwrQ8h+NZpqD6V0Xh3zsl9gOxcm6sFD2+4BYZr+jmmTJmCFStW4Nlnn8UTTzwBAFi4cCGOHz+OhIQE+Pv7Y9u2bcjLy8MzzzwDX19frFmzBm5ubuja9cYO+Wxx0axdu/aGXviuu+66oY+3RzERfnA3iKg3c1czkZ7qzQo+33gcn288jsgQbzw4rjP6JnVDRHxvyLVVMB3e3vho7dwpvaPahFdMZ80/R3R0NJYvX46//e1vmDx5MiRJQs+ePbFs2bLmx2aLFy/GwoULMX36dNTU1CA5ORn/+te/EBMTc0Ofy+I5mqZdppf/45fvPHX0OZomz72/HcdyyvSOQUTX0KdzOCaNSEB8lD9EgwH1pYUwHtgC0+GfIZvK9Y5nsZDRj8A/ZaRFJwLYoxYXTWFhYfP/zsrKwnPPPYcnnngCY8eORVhYGMrKypCamor3338fb7zxBoYOHapZ6NYiKwo+33AMq7ac0DsKEV2HwSBiwtBOGN0/Bm0CvQABqDmTCeOBLajO3gtVbtA74g2JnvNPzR+dtSaLNmxOmDABY8eOxaOPPnrV+z799FOsWrUK33//vU0C6klVVWTnleHZ937ROwoRtVCbQE9MHdsZ/buEwcvLA0p9zaVHa9tQV5itd7zfZQgIQ8zcD/WOYVMWjctOnTqFzp2v/QwxLi4OBQUFVoWyF4IgID46CH7ebjBWO9ZvRESuqqS8Fn//cj8AoEdCG9w/MhFJ3W6Ff8/RaCgrgvHApWupjaU6J702r7juzfsUnYVFRRMbG4t169Zh0KBBV71vxYoVSEhIsDqYvRBFAT2TwvHzfucoTyJXciC7BAeySyCKwB1DOuK2AbEIH3o/gm6Zgtrcw43XUh/fY1fXUnvF9Wi8VkHQbmlza7Po0dlPP/2EP/zhD+jevTuGDRuGoKAgXLhwAT/99BNOnjyJxYsXY8CAAVrkbXWyrGDHwbN46/N9ekchIhtovJY6GYNuioCPtweUhjqYjuyAKXMbavOP6htOEBH7zDKIHl765rAxiw/VTE1NxT/+8Q8cPXoUqqpCFEWkpKTg6aefRu/evW2dU1fVtQ2Y/L8/QuHBZ0ROpUtcCKaMTkTn9o3XUjdUlDTeEJr5M8wV51s9j0dUAqKmv9Hqn1drFhdNk7q6OlRUVCAwMLB5d6kz4kVoRM5LFIFxAzrg9sEd0PbStdQ1eUcbH60d2wW1vrZVcgTefC+CBt+r6YkAerCqaEpLS1FfX9+8l0ZRFNTU1CA9PR2TJ0+2WUi9ybKCNdtOYtl659gbRES/zd/HHQ+MTsLNPSLh6+0BVW5A1dFdMGamojb3CADtnmy0nf4GPNrGO9VCAMDCojl27BieffbZ5hM+r3pRQcDRozo/67QhVVVRcN6EJ/6aqncUImpFiTFBeGBMEm6KC4LBzQ1m40UYD6bCmLkN5jLbXkstenij/R8/hWDhjZr2zKKimTlzJk6cOIGHH34YW7duhbu7O4YNG4bt27dj+/btWLZsmdPN0wDAzP/7CSXlNXrHICIdjOrXHncN7Yh2od4QJAm1hdmNpxBk/Qq1rtrq1/dO7IuIic/bIKn9sahoevXqhRdffBETJ07EihUrsG7dOixfvhwA8NRTT0EQBLz77rs2D6snVVXxz9WZ2LArR+8oRKQjX08DJo9JwtCUKPj7eACyGVXH98CYuRU1ZzIblyZbIHTc4/DrNsxpjp25nEVfUX19PWJjYwE07qm5/AqBCRMmXPN+A0enqEDfzuEsGiIXZ6o1Y/Haw1i89jDi2gbgwXFJ6BHfD75dBsNcVQ7Twa0wZm5FQ2nh779YE8kA386DnbJkAAuLpm3btsjPz0fv3r0RGxsLk8mEgoICtGvXDu7u7qioqLB1Tt1JooDu8W14mjMRNTt9tgKv/nsPAGB472jcPbQjYvrdgcCBd6Pu3GkYD26G6chOKLXXv5bau2NPp9s7czmLimbUqFH429/+Bm9vb4wePRpxcXF455138Oijj2LJkiWIjo62dU674O4moU/nCOzMPKt3FCKyM6np+UhNz4enuwGTRyVgWK92CBn9KEJGzkRV9l6YMrei+vSBa15L7dt1CFRZhiA517LmJhbN0dTV1eG5555DTU0NFi9ejF9++QVz585FfX09JEnC3//+d4waNUqLvLqSZQWHT5di3qJf9Y5CRA4gJtwPU8cmoWdCKNw93CFXG2HM3ArToW3N11ILHt6IffoTp31sBli5j6ahoQFubm4AgPz8fBw+fBhdunS54UtxHM0jr29C8UXrV5kQkesY3L0tJg6PR4dIX4iSAXXnc2E8sAWCKCH41mlOt3fmci0umhdffLHlLyoIWLBggcWh7JksK1i99SQ++5GbN4noxrkbRNw3IgEj+kQjOMATgiBCVWSnOw3gci0umuHDh1/1tnPnziE0NLR5VNP8ooKALVu22CahHSo31WH6Kxsh8+wzIrJC906heG321afgO5sWPxRMTb1yV7zZbEbXrl2xaNEidOnSxebB7Fmgrwf6dA7H7sNFekchIgfWtVMoZEWB5ISnAVzO4q/OmZ8n/h5ZVjCmf6zeMYjIgYmigNH92zt9yQBWFI0rkyQRPZPCGu8mJyKyQM/EMAT5eeodo1WwaCykqCpG9HXu1XVEpJ3R/dtDll1j8zeLxkKSKGLMgFiIrvsEkYgsFOjngb6dIyBJrvEj2Oqv0pXnaoL9PdEzKVzvGETkYG4f1EHDW23sT4tXnQ0fPvyapfL4449fc3nz5s2brU9n55oWBaRnFesdhYgchI+nAXcM6QjJhR6HtLho+vbt69Kjl2uRJBF9OocjJMATpRWtc9UrETm22wbFwcPNeTdnXotVR9BQ46hm/a85+NfaQ3pHISI75+EuYen80fD1cvv9D3YirjETpSFJEjF2YCxCAlxjmSIRWW5M/1h4ezrv4Zm/hUVjAwKA+25N0DsGEdkxN4OIibfGwxUnIFg0NiBJIkb3b482QdzASUTXNqJPDAJ83F1yrptFY0OTRnBUQ0RXk0QB941IcKklzZdj0diIJIkY0TcG4cHeekchIjtzS692CA30guiCoxmARWNbKnD/SI5qiOg/RAGYNCIRigtfK8KisSFJEjG8dwwiQ330jkJEdmJg97aIDPWB6EIbNP8bi8bGVFXF5FGJescgIjsgCMDkkUkuPZoBWDQ2J0kihqa0Q1QbX72jEJHOBnePQkyEn0uPZgAWjSY4qiEiLw8DZt3V1eVHMwCLRhOSJGJIShRiwv30jkJEOrl/ZAL8fTxcfjQDsGg0oygqZt19k94xiEgHMeF+uHNoR5bMJSwajUiSiO7xbTC4R1u9oxBRK5s9sRtcdnfmNbBoNKSoKh67uxu8PFzvED0iVzUkJQpd40Jd5vbMluB3QkOiIMDf2x0PjE7SOwoRtQIvDwMevesmKLx95QosGo2JooDxN8ehQ1t/vaMQkcamjE6En7e7yx4181tYNK1AVVXMvbcH+N8ekfNqH+GHO252rSuaW4pF0wokSURCTBBuG9RB7yhEpJEnJnYHLyy+NhZNK1FVFdNv74Iw3llD5HRu6dkOnTuEcAHAb+B3pZUIggBJFPDkfSl6RyEiG/L2NOBRngBwXSyaVmSQRPRIaINb+8ToHYWIbGTOxO7w8XLn5szrYNG0MkVVMeuurgjy89A7ChFZaWTfGAxJaccFAL+DRdPKREGAh5uEpyalcBUakQOLDvfD4xO6cQFAC7BodCBJInonh2PCLZ30jkJEFnA3iHjxoT6QRAECf2P8XSwaHU0b1xldO4boHYOIbtAjd3VFVBtfrjJrIX6XdKRCxYsP9eF8DZEDGdStLcYO6MDJ/xvAotGRJIrw8XTDCw/14X+0RA4gPNgbf7g/hUuZbxCLRmeSJCI5NhjTxiXrHYWIrkMSBTw/rTfcDCJ/MbxBLBo7IAgC7hkWj/5dI/SOQkS/4cGxyejYLhAGzsvcMH7H7ISiqvjjlF6ICPHWOwoR/ZeeiWGYODyepzJbiEVjJ0RBgJtBxJ9n9IO7gf9aiOxFkJ8Hnn2wF+dlrMCfaHbEIImIubQJjIj05+EmYf4j/eHtYeC8jBVYNHZGFAWM7Nee56ER6UwUgGcf7IUObQO4X8ZK/O7ZIVVVMWdid3TuEKx3FCKXNf32LujXJYLnmNkAi8YOCYIAUQRefnQAr4Am0sG4gbG4+5ZOPF7GRlg0dkoSRbgbRLz2+CBEhvroHYfIZfRODsdjd3Oe1JZYNHZMkkT4eBqwYPYgBPt76h2HyOklxAThhYf66B3D6bBo7JwkiQjy88DrswfCz9tN7zhETis63A+vzhoAgyRwhZmNsWgcgCSJiAzxwauzBsLLw6B3HCKn0ybQC689PhCe7hIkkT8WbY3fUQchSSI6RAVg3sy+PAKDyIb8fdzx2uyBCPBx5zJmjfC76kAkUUDXuFD8aWovDu2JbMDLw4BXZg1AeJA3S0ZD/M46GFEU0L9rJOZO7K53FCKH5uNpwKuzGrcQsGS0xe+uAxKExtMDZtzeWe8oRA4p0NcDf5l7M+KjAzkn0wo4s+zAJgyLh6ICn/5wVO8oRA6jTZAXFswehDaBXhzJtBJBVVUeSergUtPz8d6KDMg8XZboutqF+eL12YM48d/KWDROQFFUHDxRggVL01BbL+sdh8gudYwKwGuPN24RYMm0LhaNk5AVFTlnK/DS4l2oMNXrHYfIrnSJC8FLj/SHu5vIORkdsGiciCwruFBRi3mLdqKotFrvOER2oXdyOP7f9L4QRbBkdMKicTKyrKC6zoz5H+3CyYJyveMQ6WpIShT+OKUnBPBYGT2xaJyQrCgwyype/2QPMo6X6B2HSBdjBsTiiXsaT2Hmcf/6YtE4KUVRoULFu19lYOu+Ar3jELUaUQAmj07C/SMT9Y5Cl7BonJiqqhAEAUu/P4LVW0/qHYdIc/4+7njuwV7oHt+Goxg7wqJxERt35+Cjbw6hwazoHYVIE/HRgZg3ox8CfLlHxt6waFyEoqjIPVeJ15emofgiV6SRcxk7IBaP3X0TIHBlmT1i0bgQWVZQ1yDjrc/3Ye/RYr3jEFnNw13CnIndMaxXdPOjYrI/LBoXoygqRFHA16kn8NmPWVB4bA05qLahPpg3sx+i2vhy6bKdY9G4KFVVkZVzEX/9LB2lFbV6xyG6IQNuisQfp/SEmyRyPsYBsGhcmCwrqG2Q8e5XGdh16JzecYh+lygKeGhc8qWTy1WIfFTmEFg0Lq7p/6wbd+dg8beHUcdDOclOhQR44k9TeyMpNpgF42BYNASg8VDO8xersHBZOk4VVugdh6iZKABjB3bAjNs7w8BHZQ6JRUPNZEUBVOCLjcexZttJmGXuuSF9tY/ww1OTUpAQE8RVZQ6MRUNXUVUVRaXV+MfXB3HwBM9Ko9bnZhAxaUQCJt4aD6jgKMbBsWjommRFhSQK+OVAIf797WFcrOTKNGodXTuG4A+TUhAW5M1ly06CRUPXJcsKzLKCz348hu93nOZ10aQZXy83zBzfBSP7tYesKNzh70RYNNQiqqoiv9iEf3x9AEfPXNQ7DjmZwT3aYvaE7vDxMrBgnBCLhlqs6bfMLXvz8Mn3R3hlNFmtTaAXnpjYHb2Tw7kvxomxaOiGNZ2Z9ukPR7FhVw74NI1ulI+XGyYOj8edQ+IgCgIn+50ci4Ys0rTU9HRhBT75/ggOZHN1Gv0+DzcJ42+Ow723xsPT3cDJfhfBoiGryLICSRJxIr8MX248jr1ZPBWariaJAkb2a48HxiTB38edj8hcDIuGbKJp/ibnXCW+3HgMuw6fA//LIkkUcEuvdpg8KglhQV5QAZaMC2LRkE01FU7BeSO++uk4fjlQyDkcF2SQBAzvHY37RyaiTZA3J/pdHIuGNNF0701RaRW+2pSNbfvyuQfHBRgkESP6xuD+kQkI9veEqoLzMMSiIW01Fc6F8hqs2HQcm/fm8ww1JxQS4ImR/dpj3IBYBPp5sGDoCiwaahWKokIQgHJjHb775TRS0/N5rI2DEwWgZ1I4xg6MRe+k8EvzL+DBl3QVFg21KlVVmxcJHDhRgk1pudhzuAgNZo5yHEXT6GVs/1gEB3g2rzwk+i0sGtJN0w+o6toGbNtXgM1783Aiv1zvWHQNzaOXAbHonczRC90YFg3ZBbOswCCJKDxvwsY9udi2Lx9lxjq9Y7m8kABPjOwbg7EDOnD0QhZj0ZBdUVQVuPRf5P7j57EpLRdpR4q5gKAVtQn0Qu/kcPS/KRI94ttw9EJWY9GQ3Wr67bmqpgG7D59DelYxMo6fR1WtWe9oTkUUgIT2QeiTHIEBN0UiOtwPiqpCVVWepEw2waIhh9D0aE1RVBzLvYg9R4qQnlWMvCKj3tEcko+nASlJYejbOQJ9OkfA18sNsqxAFAWOXMjmWDTkcBRFhYrG400uVtYi4/h5HDxxAZknS1BawSXTv6VdmC96J4ejX9cIdI4NgSgKzQVOpCUWDTm8y39YFpVWIeP4eWSevIBjuRdxodw1i8ffxx0dowLQsV0g4qMDkdg+CCEBXlAunc7AzZTUmlg05HQuL57q2gbkFhlxurACOecqkXuuErlFlah2onmeAF93dGoXiI7tAtGpXSASYoIQEuAJoPHsOUCAxGIhHbFoyCWYzQok6T/zD6UVNThdWIEzZxuLJ+dsJQpLTHZ9HpuHm4TQQC+0DfVBx3YB6BQdiIToIAT5XyoVWQEEcAKf7A6LhlyWoqhQVLV59CPLCopKq1FaUYMyUx0qTfWoMNWhoqoelZf+XmGqQ2VVPYzV9Ta7BkEUAH8fDwT4uiPQzxOBl/7eJtALYcFeiAjxQWigF/y83Zv/GVlWIAgCH4GRQ2DREF2DrChQFEAQGhcd/PdKLEVRUVXbgEpTPcpMtaitM0MUxcZriUUBgihAEhrnQpr/Ei79ddmf/bzd4OPlds3XlxUVksgyIcfHoiEiIk3xYS4REWmKRUNERJpi0RARkaZYNEREpCkWDRERaYpFQ0REmmLREBGRplg0RESkKRYNERFpikVDRESaYtEQEZGmWDRERKQpFg0REWmKRUNERJpi0RARkaZYNEREpCkWDRERaYpFQ0REmmLREBGRplg0RESkKRYNERFpikVDRESaYtEQEZGmWDRERKQpFg0REWmKRUNERJpi0RARkaZYNEREpCkWDRERaYpFQ0REmmLREBGRplg0RESkKRYNERFpikVDRESaYtEQEZGmWDRERKQpFg0REWmKRUNERJpi0RARkaZYNEREpCkWDRERaYpFQ0REmmLREBGRplg0RESkKRYNERFpikVDRESa+v+6Mc2E2zr5MQAAAABJRU5ErkJggg==\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -458,20 +420,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 113,
    "outputs": [
     {
      "data": {
       "text/plain": "<AxesSubplot:ylabel='HadHeartAttack'>"
      },
-     "execution_count": 49,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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\n"
+      "image/png": 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\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -495,13 +457,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 114,
    "outputs": [
     {
      "data": {
       "text/plain": "<AxesSubplot:ylabel='SmokerStatus'>"
      },
-     "execution_count": 50,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -523,13 +485,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 115,
    "outputs": [
     {
      "data": {
       "text/plain": "<AxesSubplot:ylabel='ECigaretteUsage'>"
      },
-     "execution_count": 51,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -560,13 +522,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 116,
    "outputs": [
     {
      "data": {
       "text/plain": "<AxesSubplot:ylabel='CovidPos'>"
      },
-     "execution_count": 52,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -608,13 +570,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 117,
    "outputs": [
     {
      "data": {
       "text/plain": "array(['Female', 'Male'], dtype=object)"
      },
-     "execution_count": 53,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -628,13 +590,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 118,
    "outputs": [
     {
      "data": {
       "text/plain": "array(['Very good', 'Excellent', 'Fair', 'Poor', 'Good', nan],\n      dtype=object)"
      },
-     "execution_count": 54,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -648,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 119,
    "outputs": [],
    "source": [
     "health_map = {\n",
@@ -665,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 120,
    "outputs": [
     {
      "name": "stdout",
@@ -857,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 121,
    "outputs": [],
    "source": [
     "from collections import defaultdict\n",
@@ -920,14 +882,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 122,
    "outputs": [
     {
      "data": {
-      "text/plain": "               State     Sex GeneralHealth  PhysicalHealthDays  \\\n339824  South Dakota  Female          Good                 3.0   \n127927        Kansas  Female          Good                30.0   \n362523          Utah    Male     Excellent                 0.0   \n183687      Michigan    Male          Good                 0.0   \n191905      Michigan  Female     Very good                 0.0   \n\n        MentalHealthDays                                    LastCheckupTime  \\\n339824              21.0  Within past year (anytime less than 12 months ...   \n127927               0.0  Within past year (anytime less than 12 months ...   \n362523               0.0  Within past year (anytime less than 12 months ...   \n183687               7.0  Within past year (anytime less than 12 months ...   \n191905               0.0  Within past year (anytime less than 12 months ...   \n\n       PhysicalActivities  SleepHours            RemovedTeeth HadHeartAttack  \\\n339824                Yes         8.0  6 or more, but not all             No   \n127927                Yes        10.0                  1 to 5             No   \n362523                Yes         7.0                  1 to 5             No   \n183687                Yes         8.0            None of them             No   \n191905                Yes         7.0            None of them             No   \n\n        ... HeightInMeters WeightInKilograms    BMI AlcoholDrinkers  \\\n339824  ...           1.60             52.16  20.37              No   \n127927  ...           1.68             97.52  34.70              No   \n362523  ...           1.83            113.85  34.04              No   \n183687  ...           1.78             83.91  26.54             Yes   \n191905  ...           1.57             68.04  27.44             Yes   \n\n       HIVTesting FluVaxLast12 PneumoVaxEver  \\\n339824        Yes          Yes           Yes   \n127927         No          Yes           Yes   \n362523         No           No            No   \n183687         No          Yes           Yes   \n191905        Yes           No           Yes   \n\n                                        TetanusLast10Tdap HighRiskLastYear  \\\n339824                                 Yes, received Tdap               No   \n127927                                                NaN               No   \n362523                                 Yes, received Tdap               No   \n183687                                 Yes, received Tdap               No   \n191905  Yes, received tetanus shot but not sure what type               No   \n\n       CovidPos  \n339824       No  \n127927       No  \n362523      Yes  \n183687       No  \n191905       No  \n\n[5 rows x 40 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Sex</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>339824</th>\n      <td>South Dakota</td>\n      <td>Female</td>\n      <td>Good</td>\n      <td>3.0</td>\n      <td>21.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>8.0</td>\n      <td>6 or more, but not all</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.60</td>\n      <td>52.16</td>\n      <td>20.37</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes, received Tdap</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>127927</th>\n      <td>Kansas</td>\n      <td>Female</td>\n      <td>Good</td>\n      <td>30.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>10.0</td>\n      <td>1 to 5</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.68</td>\n      <td>97.52</td>\n      <td>34.70</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>362523</th>\n      <td>Utah</td>\n      <td>Male</td>\n      <td>Excellent</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>7.0</td>\n      <td>1 to 5</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.83</td>\n      <td>113.85</td>\n      <td>34.04</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes, received Tdap</td>\n      <td>No</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>183687</th>\n      <td>Michigan</td>\n      <td>Male</td>\n      <td>Good</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>8.0</td>\n      <td>None of them</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.78</td>\n      <td>83.91</td>\n      <td>26.54</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes, received Tdap</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>191905</th>\n      <td>Michigan</td>\n      <td>Female</td>\n      <td>Very good</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>7.0</td>\n      <td>None of them</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.57</td>\n      <td>68.04</td>\n      <td>27.44</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes, received tetanus shot but not sure what type</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
+      "text/plain": "            State     Sex GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\n166151   Maryland    Male          Good                 0.0               0.0   \n359753      Texas    Male          Good                 0.0               0.0   \n286723       Ohio    Male          Fair                 2.0               0.0   \n305100   Oklahoma    Male     Very good                 0.0               0.0   \n199077  Minnesota  Female     Excellent                 0.0               0.0   \n\n                                          LastCheckupTime PhysicalActivities  \\\n166151  Within past 2 years (1 year but less than 2 ye...                Yes   \n359753                                                NaN                 No   \n286723  Within past year (anytime less than 12 months ...                 No   \n305100  Within past year (anytime less than 12 months ...                Yes   \n199077  Within past year (anytime less than 12 months ...                 No   \n\n        SleepHours  RemovedTeeth HadHeartAttack  ... HeightInMeters  \\\n166151         7.0  None of them             No  ...           1.78   \n359753         8.0  None of them             No  ...           1.60   \n286723         8.0           All             No  ...           1.75   \n305100         7.0  None of them             No  ...           1.80   \n199077         8.0        1 to 5             No  ...           1.52   \n\n       WeightInKilograms    BMI AlcoholDrinkers HIVTesting FluVaxLast12  \\\n166151             90.72  28.70             Yes         No           No   \n359753             77.11  30.12              No         No           No   \n286723             80.29  26.14             NaN        NaN          NaN   \n305100             97.52  29.99             NaN        NaN          NaN   \n199077             67.13  28.90             Yes        Yes           No   \n\n       PneumoVaxEver                                  TetanusLast10Tdap  \\\n166151           Yes  No, did not receive any tetanus shot in the pa...   \n359753            No  No, did not receive any tetanus shot in the pa...   \n286723           NaN                                                NaN   \n305100           NaN                                                NaN   \n199077            No                                                NaN   \n\n       HighRiskLastYear CovidPos  \n166151               No       No  \n359753               No       No  \n286723              NaN      NaN  \n305100              NaN      NaN  \n199077               No      Yes  \n\n[5 rows x 40 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Sex</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>166151</th>\n      <td>Maryland</td>\n      <td>Male</td>\n      <td>Good</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past 2 years (1 year but less than 2 ye...</td>\n      <td>Yes</td>\n      <td>7.0</td>\n      <td>None of them</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.78</td>\n      <td>90.72</td>\n      <td>28.70</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No, did not receive any tetanus shot in the pa...</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>359753</th>\n      <td>Texas</td>\n      <td>Male</td>\n      <td>Good</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>8.0</td>\n      <td>None of them</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.60</td>\n      <td>77.11</td>\n      <td>30.12</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No, did not receive any tetanus shot in the pa...</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>286723</th>\n      <td>Ohio</td>\n      <td>Male</td>\n      <td>Fair</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>No</td>\n      <td>8.0</td>\n      <td>All</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.75</td>\n      <td>80.29</td>\n      <td>26.14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>305100</th>\n      <td>Oklahoma</td>\n      <td>Male</td>\n      <td>Very good</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>Yes</td>\n      <td>7.0</td>\n      <td>None of them</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.80</td>\n      <td>97.52</td>\n      <td>29.99</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199077</th>\n      <td>Minnesota</td>\n      <td>Female</td>\n      <td>Excellent</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>No</td>\n      <td>8.0</td>\n      <td>1 to 5</td>\n      <td>No</td>\n      <td>...</td>\n      <td>1.52</td>\n      <td>67.13</td>\n      <td>28.90</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
      },
-     "execution_count": 58,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -950,14 +912,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 123,
    "outputs": [
     {
      "data": {
-      "text/plain": "               State  Male  GeneralHealth  PhysicalHealthDays  \\\n339824  South Dakota   0.0            3.0                 3.0   \n127927        Kansas   0.0            3.0                30.0   \n362523          Utah   1.0            5.0                 0.0   \n183687      Michigan   1.0            3.0                 0.0   \n191905      Michigan   0.0            4.0                 0.0   \n\n        MentalHealthDays                                    LastCheckupTime  \\\n339824              21.0  Within past year (anytime less than 12 months ...   \n127927               0.0  Within past year (anytime less than 12 months ...   \n362523               0.0  Within past year (anytime less than 12 months ...   \n183687               7.0  Within past year (anytime less than 12 months ...   \n191905               0.0  Within past year (anytime less than 12 months ...   \n\n        PhysicalActivities  SleepHours  RemovedTeeth  HadHeartAttack  ...  \\\n339824                 1.0         8.0           2.0             0.0  ...   \n127927                 1.0        10.0           1.0             0.0  ...   \n362523                 1.0         7.0           1.0             0.0  ...   \n183687                 1.0         8.0           0.0             0.0  ...   \n191905                 1.0         7.0           0.0             0.0  ...   \n\n        HeightInMeters  WeightInKilograms    BMI  AlcoholDrinkers  HIVTesting  \\\n339824            1.60              52.16  20.37              0.0         1.0   \n127927            1.68              97.52  34.70              0.0         0.0   \n362523            1.83             113.85  34.04              0.0         0.0   \n183687            1.78              83.91  26.54              1.0         0.0   \n191905            1.57              68.04  27.44              1.0         1.0   \n\n        FluVaxLast12  PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear  \\\n339824           1.0            1.0                1.0               0.0   \n127927           1.0            1.0                NaN               0.0   \n362523           0.0            0.0                1.0               0.0   \n183687           1.0            1.0                1.0               0.0   \n191905           0.0            1.0                1.0               0.0   \n\n        CovidPos  \n339824       0.0  \n127927       0.0  \n362523       1.0  \n183687       0.0  \n191905       0.0  \n\n[5 rows x 40 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>339824</th>\n      <td>South Dakota</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>21.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>8.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.60</td>\n      <td>52.16</td>\n      <td>20.37</td>\n      <td>0.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>127927</th>\n      <td>Kansas</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>30.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.68</td>\n      <td>97.52</td>\n      <td>34.70</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>362523</th>\n      <td>Utah</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.83</td>\n      <td>113.85</td>\n      <td>34.04</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>183687</th>\n      <td>Michigan</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.78</td>\n      <td>83.91</td>\n      <td>26.54</td>\n      <td>1.0</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>191905</th>\n      <td>Michigan</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.57</td>\n      <td>68.04</td>\n      <td>27.44</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
+      "text/plain": "            State  Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\n166151   Maryland   1.0            3.0                 0.0               0.0   \n359753      Texas   1.0            3.0                 0.0               0.0   \n286723       Ohio   1.0            2.0                 2.0               0.0   \n305100   Oklahoma   1.0            4.0                 0.0               0.0   \n199077  Minnesota   0.0            5.0                 0.0               0.0   \n\n                                          LastCheckupTime  PhysicalActivities  \\\n166151  Within past 2 years (1 year but less than 2 ye...                 1.0   \n359753                                            Unknown                 0.0   \n286723  Within past year (anytime less than 12 months ...                 0.0   \n305100  Within past year (anytime less than 12 months ...                 1.0   \n199077  Within past year (anytime less than 12 months ...                 0.0   \n\n        SleepHours  RemovedTeeth  HadHeartAttack  ...  HeightInMeters  \\\n166151         7.0           0.0             0.0  ...            1.78   \n359753         8.0           0.0             0.0  ...            1.60   \n286723         8.0           3.0             0.0  ...            1.75   \n305100         7.0           0.0             0.0  ...            1.80   \n199077         8.0           1.0             0.0  ...            1.52   \n\n        WeightInKilograms    BMI  AlcoholDrinkers  HIVTesting  FluVaxLast12  \\\n166151              90.72  28.70              1.0         0.0           0.0   \n359753              77.11  30.12              0.0         0.0           0.0   \n286723              80.29  26.14              NaN         NaN           NaN   \n305100              97.52  29.99              NaN         NaN           NaN   \n199077              67.13  28.90              1.0         1.0           0.0   \n\n        PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear  CovidPos  \n166151            1.0                1.0               0.0       0.0  \n359753            0.0                1.0               0.0       0.0  \n286723            NaN                NaN               NaN       NaN  \n305100            NaN                NaN               NaN       NaN  \n199077            0.0                NaN               0.0       1.0  \n\n[5 rows x 40 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>166151</th>\n      <td>Maryland</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past 2 years (1 year but less than 2 ye...</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.78</td>\n      <td>90.72</td>\n      <td>28.70</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>359753</th>\n      <td>Texas</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Unknown</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.60</td>\n      <td>77.11</td>\n      <td>30.12</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>286723</th>\n      <td>Ohio</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.75</td>\n      <td>80.29</td>\n      <td>26.14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>305100</th>\n      <td>Oklahoma</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.80</td>\n      <td>97.52</td>\n      <td>29.99</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199077</th>\n      <td>Minnesota</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.52</td>\n      <td>67.13</td>\n      <td>28.90</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
      },
-     "execution_count": 59,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -972,57 +934,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 124,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 44513 entries, 339824 to 52161\n",
+      "Int64Index: 44513 entries, 166151 to 306609\n",
       "Data columns (total 40 columns):\n",
       " #   Column                     Non-Null Count  Dtype   \n",
       "---  ------                     --------------  -----   \n",
       " 0   State                      44513 non-null  category\n",
       " 1   Male                       44513 non-null  float64 \n",
-      " 2   GeneralHealth              44393 non-null  float64 \n",
-      " 3   PhysicalHealthDays         43469 non-null  float64 \n",
-      " 4   MentalHealthDays           43622 non-null  float64 \n",
+      " 2   GeneralHealth              44400 non-null  float64 \n",
+      " 3   PhysicalHealthDays         43445 non-null  float64 \n",
+      " 4   MentalHealthDays           43614 non-null  float64 \n",
       " 5   LastCheckupTime            44513 non-null  category\n",
-      " 6   PhysicalActivities         44408 non-null  float64 \n",
-      " 7   SleepHours                 44008 non-null  float64 \n",
-      " 8   RemovedTeeth               43413 non-null  float64 \n",
-      " 9   HadHeartAttack             44182 non-null  float64 \n",
-      " 10  HadAngina                  44074 non-null  float64 \n",
-      " 11  HadStroke                  44368 non-null  float64 \n",
-      " 12  HadAsthma                  44339 non-null  float64 \n",
-      " 13  HadSkinCancer              44184 non-null  float64 \n",
-      " 14  HadCOPD                    44299 non-null  float64 \n",
-      " 15  HadDepressiveDisorder      44218 non-null  float64 \n",
-      " 16  HadKidneyDisease           44320 non-null  float64 \n",
-      " 17  HadArthritis               44243 non-null  float64 \n",
-      " 18  HadDiabetes                44411 non-null  float64 \n",
-      " 19  DeafOrHardOfHearing        42485 non-null  float64 \n",
-      " 20  BlindOrVisionDifficulty    42387 non-null  float64 \n",
-      " 21  DifficultyConcentrating    42169 non-null  float64 \n",
-      " 22  DifficultyWalking          42172 non-null  float64 \n",
-      " 23  DifficultyDressingBathing  42182 non-null  float64 \n",
-      " 24  DifficultyErrands          41999 non-null  float64 \n",
-      " 25  SmokerStatus               41005 non-null  float64 \n",
-      " 26  ECigaretteUsage            41003 non-null  float64 \n",
-      " 27  ChestScan                  38958 non-null  float64 \n",
+      " 6   PhysicalActivities         44402 non-null  float64 \n",
+      " 7   SleepHours                 43952 non-null  float64 \n",
+      " 8   RemovedTeeth               43394 non-null  float64 \n",
+      " 9   HadHeartAttack             44227 non-null  float64 \n",
+      " 10  HadAngina                  44101 non-null  float64 \n",
+      " 11  HadStroke                  44360 non-null  float64 \n",
+      " 12  HadAsthma                  44323 non-null  float64 \n",
+      " 13  HadSkinCancer              44186 non-null  float64 \n",
+      " 14  HadCOPD                    44276 non-null  float64 \n",
+      " 15  HadDepressiveDisorder      44239 non-null  float64 \n",
+      " 16  HadKidneyDisease           44336 non-null  float64 \n",
+      " 17  HadArthritis               44254 non-null  float64 \n",
+      " 18  HadDiabetes                44386 non-null  float64 \n",
+      " 19  DeafOrHardOfHearing        42470 non-null  float64 \n",
+      " 20  BlindOrVisionDifficulty    42355 non-null  float64 \n",
+      " 21  DifficultyConcentrating    42104 non-null  float64 \n",
+      " 22  DifficultyWalking          42121 non-null  float64 \n",
+      " 23  DifficultyDressingBathing  42141 non-null  float64 \n",
+      " 24  DifficultyErrands          41986 non-null  float64 \n",
+      " 25  SmokerStatus               40996 non-null  float64 \n",
+      " 26  ECigaretteUsage            40955 non-null  float64 \n",
+      " 27  ChestScan                  38863 non-null  float64 \n",
       " 28  RaceEthnicityCategory      44513 non-null  category\n",
       " 29  AgeCategory                44513 non-null  category\n",
-      " 30  HeightInMeters             41714 non-null  float64 \n",
-      " 31  WeightInKilograms          40397 non-null  float64 \n",
-      " 32  BMI                        39724 non-null  float64 \n",
-      " 33  AlcoholDrinkers            39956 non-null  float64 \n",
-      " 34  HIVTesting                 38018 non-null  float64 \n",
-      " 35  FluVaxLast12               39886 non-null  float64 \n",
-      " 36  PneumoVaxEver              36860 non-null  float64 \n",
+      " 30  HeightInMeters             41620 non-null  float64 \n",
+      " 31  WeightInKilograms          40212 non-null  float64 \n",
+      " 32  BMI                        39520 non-null  float64 \n",
+      " 33  AlcoholDrinkers            39817 non-null  float64 \n",
+      " 34  HIVTesting                 37853 non-null  float64 \n",
+      " 35  FluVaxLast12               39779 non-null  float64 \n",
+      " 36  PneumoVaxEver              36821 non-null  float64 \n",
       " 37  TetanusLast10Tdap          36315 non-null  float64 \n",
-      " 38  HighRiskLastYear           39538 non-null  float64 \n",
-      " 39  CovidPos                   38114 non-null  float64 \n",
+      " 38  HighRiskLastYear           39453 non-null  float64 \n",
+      " 39  CovidPos                   38048 non-null  float64 \n",
       "dtypes: category(4), float64(36)\n",
       "memory usage: 12.7 MB\n"
      ]
@@ -1037,7 +999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 125,
    "outputs": [],
    "source": [
     "normalize_dataset(train)\n",
@@ -1060,14 +1022,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 126,
    "outputs": [
     {
      "data": {
-      "text/plain": "                Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\ncount  676777.000000  674433.000000       655630.000000     660417.000000   \nmean        0.538139       3.055519            6.737547          4.863972   \nstd         0.498544       1.137862           10.713287          9.115863   \nmin         0.000000       1.000000            0.000000          0.000000   \n25%         0.000000       2.000000            0.000000          0.000000   \n50%         1.000000       3.000000            0.000000          0.000000   \n75%         1.000000       4.000000           10.000000          5.000000   \nmax         1.000000       5.000000           30.000000         30.000000   \n\n       PhysicalActivities     SleepHours   RemovedTeeth  HadHeartAttack  \\\ncount       674814.000000  665609.000000  654359.000000   674355.000000   \nmean             0.690146       7.032336       0.983081        0.505244   \nstd              0.462434       1.726387       1.019679        0.499973   \nmin              0.000000       1.000000       0.000000        0.000000   \n25%              0.000000       6.000000       0.000000        0.000000   \n50%              1.000000       7.000000       1.000000        1.000000   \n75%              1.000000       8.000000       2.000000        1.000000   \nmax              1.000000      24.000000       3.000000        1.000000   \n\n           HadAngina      HadStroke  ...  HeightInMeters  WeightInKilograms  \\\ncount  657726.000000  672927.000000  ...   637313.000000      619546.000000   \nmean        0.264549       0.117193  ...        1.707193          84.657015   \nstd         0.441093       0.321650  ...        0.108002          21.753692   \nmin         0.000000       0.000000  ...        0.910000          22.680000   \n25%         0.000000       0.000000  ...        1.630000          69.400000   \n50%         0.000000       0.000000  ...        1.700000          81.650000   \n75%         1.000000       0.000000  ...        1.780000          96.160000   \nmax         1.000000       1.000000  ...        2.410000         292.570000   \n\n                 BMI  AlcoholDrinkers     HIVTesting   FluVaxLast12  \\\ncount  611024.000000    606636.000000  572562.000000  605920.000000   \nmean       28.917363         0.456819       0.325787       0.569879   \nstd         6.607455         0.498132       0.468668       0.495093   \nmin        12.050000         0.000000       0.000000       0.000000   \n25%        24.410000         0.000000       0.000000       0.000000   \n50%        27.890000         0.000000       0.000000       1.000000   \n75%        32.260000         1.000000       1.000000       1.000000   \nmax        97.650000         1.000000       1.000000       1.000000   \n\n       PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear       CovidPos  \ncount  570114.000000           554467.0     600540.000000  585150.000000  \nmean        0.527672                1.0          0.035087       0.273055  \nstd         0.499234                0.0          0.183999       0.445529  \nmin         0.000000                1.0          0.000000       0.000000  \n25%         0.000000                1.0          0.000000       0.000000  \n50%         1.000000                1.0          0.000000       0.000000  \n75%         1.000000                1.0          0.000000       1.000000  \nmax         1.000000                1.0          1.000000       1.000000  \n\n[8 rows x 36 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>HadAngina</th>\n      <th>HadStroke</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>676777.000000</td>\n      <td>674433.000000</td>\n      <td>655630.000000</td>\n      <td>660417.000000</td>\n      <td>674814.000000</td>\n      <td>665609.000000</td>\n      <td>654359.000000</td>\n      <td>674355.000000</td>\n      <td>657726.000000</td>\n      <td>672927.000000</td>\n      <td>...</td>\n      <td>637313.000000</td>\n      <td>619546.000000</td>\n      <td>611024.000000</td>\n      <td>606636.000000</td>\n      <td>572562.000000</td>\n      <td>605920.000000</td>\n      <td>570114.000000</td>\n      <td>554467.0</td>\n      <td>600540.000000</td>\n      <td>585150.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.538139</td>\n      <td>3.055519</td>\n      <td>6.737547</td>\n      <td>4.863972</td>\n      <td>0.690146</td>\n      <td>7.032336</td>\n      <td>0.983081</td>\n      <td>0.505244</td>\n      <td>0.264549</td>\n      <td>0.117193</td>\n      <td>...</td>\n      <td>1.707193</td>\n      <td>84.657015</td>\n      <td>28.917363</td>\n      <td>0.456819</td>\n      <td>0.325787</td>\n      <td>0.569879</td>\n      <td>0.527672</td>\n      <td>1.0</td>\n      <td>0.035087</td>\n      <td>0.273055</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.498544</td>\n      <td>1.137862</td>\n      <td>10.713287</td>\n      <td>9.115863</td>\n      <td>0.462434</td>\n      <td>1.726387</td>\n      <td>1.019679</td>\n      <td>0.499973</td>\n      <td>0.441093</td>\n      <td>0.321650</td>\n      <td>...</td>\n      <td>0.108002</td>\n      <td>21.753692</td>\n      <td>6.607455</td>\n      <td>0.498132</td>\n      <td>0.468668</td>\n      <td>0.495093</td>\n      <td>0.499234</td>\n      <td>0.0</td>\n      <td>0.183999</td>\n      <td>0.445529</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.910000</td>\n      <td>22.680000</td>\n      <td>12.050000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.630000</td>\n      <td>69.400000</td>\n      <td>24.410000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.700000</td>\n      <td>81.650000</td>\n      <td>27.890000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>10.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>8.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.780000</td>\n      <td>96.160000</td>\n      <td>32.260000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>1.000000</td>\n      <td>24.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>2.410000</td>\n      <td>292.570000</td>\n      <td>97.650000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 36 columns</p>\n</div>"
+      "text/plain": "                Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\ncount  676361.000000  674074.000000       655378.000000     660076.000000   \nmean        0.538461       3.055939            6.729019          4.851376   \nstd         0.498519       1.138040           10.712884          9.106112   \nmin         0.000000       1.000000            0.000000          0.000000   \n25%         0.000000       2.000000            0.000000          0.000000   \n50%         1.000000       3.000000            0.000000          0.000000   \n75%         1.000000       4.000000           10.000000          5.000000   \nmax         1.000000       5.000000           30.000000         30.000000   \n\n       PhysicalActivities     SleepHours   RemovedTeeth  HadHeartAttack  \\\ncount       674264.000000  665524.000000  653726.000000   673906.000000   \nmean             0.689958       7.038852       0.978907        0.504925   \nstd              0.462511       1.729860       1.018776        0.499976   \nmin              0.000000       1.000000       0.000000        0.000000   \n25%              0.000000       6.000000       0.000000        0.000000   \n50%              1.000000       7.000000       1.000000        1.000000   \n75%              1.000000       8.000000       2.000000        1.000000   \nmax              1.000000      24.000000       3.000000        1.000000   \n\n           HadAngina      HadStroke  ...  HeightInMeters  WeightInKilograms  \\\ncount  657289.000000  672572.000000  ...   636986.000000      619364.000000   \nmean        0.263980       0.117729  ...        1.707282          84.678030   \nstd         0.440789       0.322287  ...        0.108041          21.671753   \nmin         0.000000       0.000000  ...        0.910000          22.680000   \n25%         0.000000       0.000000  ...        1.630000          69.400000   \n50%         0.000000       0.000000  ...        1.700000          81.650000   \n75%         1.000000       0.000000  ...        1.780000          96.160000   \nmax         1.000000       1.000000  ...        2.410000         292.570000   \n\n                 BMI  AlcoholDrinkers     HIVTesting   FluVaxLast12  \\\ncount  610744.000000    607296.000000  573796.000000  606661.000000   \nmean       28.935880         0.456237       0.324913       0.571250   \nstd         6.616632         0.498082       0.468343       0.494898   \nmin        12.050000         0.000000       0.000000       0.000000   \n25%        24.410000         0.000000       0.000000       0.000000   \n50%        27.890000         0.000000       0.000000       1.000000   \n75%        32.280000         1.000000       1.000000       1.000000   \nmax        99.640000         1.000000       1.000000       1.000000   \n\n       PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear       CovidPos  \ncount  571290.000000           554109.0     601088.000000  585564.000000  \nmean        0.528798                1.0          0.035005       0.274667  \nstd         0.499170                0.0          0.183792       0.446347  \nmin         0.000000                1.0          0.000000       0.000000  \n25%         0.000000                1.0          0.000000       0.000000  \n50%         1.000000                1.0          0.000000       0.000000  \n75%         1.000000                1.0          0.000000       1.000000  \nmax         1.000000                1.0          1.000000       1.000000  \n\n[8 rows x 36 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>HadAngina</th>\n      <th>HadStroke</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>676361.000000</td>\n      <td>674074.000000</td>\n      <td>655378.000000</td>\n      <td>660076.000000</td>\n      <td>674264.000000</td>\n      <td>665524.000000</td>\n      <td>653726.000000</td>\n      <td>673906.000000</td>\n      <td>657289.000000</td>\n      <td>672572.000000</td>\n      <td>...</td>\n      <td>636986.000000</td>\n      <td>619364.000000</td>\n      <td>610744.000000</td>\n      <td>607296.000000</td>\n      <td>573796.000000</td>\n      <td>606661.000000</td>\n      <td>571290.000000</td>\n      <td>554109.0</td>\n      <td>601088.000000</td>\n      <td>585564.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.538461</td>\n      <td>3.055939</td>\n      <td>6.729019</td>\n      <td>4.851376</td>\n      <td>0.689958</td>\n      <td>7.038852</td>\n      <td>0.978907</td>\n      <td>0.504925</td>\n      <td>0.263980</td>\n      <td>0.117729</td>\n      <td>...</td>\n      <td>1.707282</td>\n      <td>84.678030</td>\n      <td>28.935880</td>\n      <td>0.456237</td>\n      <td>0.324913</td>\n      <td>0.571250</td>\n      <td>0.528798</td>\n      <td>1.0</td>\n      <td>0.035005</td>\n      <td>0.274667</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.498519</td>\n      <td>1.138040</td>\n      <td>10.712884</td>\n      <td>9.106112</td>\n      <td>0.462511</td>\n      <td>1.729860</td>\n      <td>1.018776</td>\n      <td>0.499976</td>\n      <td>0.440789</td>\n      <td>0.322287</td>\n      <td>...</td>\n      <td>0.108041</td>\n      <td>21.671753</td>\n      <td>6.616632</td>\n      <td>0.498082</td>\n      <td>0.468343</td>\n      <td>0.494898</td>\n      <td>0.499170</td>\n      <td>0.0</td>\n      <td>0.183792</td>\n      <td>0.446347</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.910000</td>\n      <td>22.680000</td>\n      <td>12.050000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.630000</td>\n      <td>69.400000</td>\n      <td>24.410000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.700000</td>\n      <td>81.650000</td>\n      <td>27.890000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>10.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>8.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.780000</td>\n      <td>96.160000</td>\n      <td>32.280000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>1.000000</td>\n      <td>24.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>2.410000</td>\n      <td>292.570000</td>\n      <td>99.640000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 36 columns</p>\n</div>"
      },
-     "execution_count": 62,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1081,14 +1043,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 127,
    "outputs": [
     {
      "data": {
-      "text/plain": "               Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\ncount  44513.000000   44393.000000        43469.000000      43622.000000   \nmean       0.471593       3.441511            4.275001          4.298221   \nstd        0.499198       1.050924            8.588663          8.299250   \nmin        0.000000       1.000000            0.000000          0.000000   \n25%        0.000000       3.000000            0.000000          0.000000   \n50%        0.000000       3.000000            0.000000          0.000000   \n75%        1.000000       4.000000            3.000000          4.000000   \nmax        1.000000       5.000000           30.000000         30.000000   \n\n       PhysicalActivities    SleepHours  RemovedTeeth  HadHeartAttack  \\\ncount        44408.000000  44008.000000  43413.000000    44182.000000   \nmean             0.760021      7.036584      0.685302        0.057241   \nstd              0.427075      1.512667      0.884912        0.232304   \nmin              0.000000      1.000000      0.000000        0.000000   \n25%              1.000000      6.000000      0.000000        0.000000   \n50%              1.000000      7.000000      0.000000        0.000000   \n75%              1.000000      8.000000      1.000000        0.000000   \nmax              1.000000     24.000000      3.000000        1.000000   \n\n          HadAngina     HadStroke  ...  HeightInMeters  WeightInKilograms  \\\ncount  44074.000000  44368.000000  ...    41714.000000       40397.000000   \nmean       0.061056      0.042869  ...        1.703194          83.021746   \nstd        0.239436      0.202563  ...        0.107438          21.551394   \nmin        0.000000      0.000000  ...        0.910000          22.680000   \n25%        0.000000      0.000000  ...        1.630000          68.040000   \n50%        0.000000      0.000000  ...        1.700000          80.740000   \n75%        0.000000      0.000000  ...        1.780000          95.250000   \nmax        1.000000      1.000000  ...        2.340000         290.300000   \n\n                BMI  AlcoholDrinkers    HIVTesting  FluVaxLast12  \\\ncount  39724.000000     39956.000000  38018.000000  39886.000000   \nmean      28.512326         0.529532      0.342233      0.527002   \nstd        6.596149         0.499133      0.474463      0.499277   \nmin       12.020000         0.000000      0.000000      0.000000   \n25%       24.030000         0.000000      0.000000      0.000000   \n50%       27.410000         1.000000      0.000000      1.000000   \n75%       31.650000         1.000000      1.000000      1.000000   \nmax       97.650000         1.000000      1.000000      1.000000   \n\n       PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear      CovidPos  \ncount   36860.000000            36315.0      39538.000000  38114.000000  \nmean        0.413592                1.0          0.043427      0.289500  \nstd         0.492484                0.0          0.203818      0.453536  \nmin         0.000000                1.0          0.000000      0.000000  \n25%         0.000000                1.0          0.000000      0.000000  \n50%         0.000000                1.0          0.000000      0.000000  \n75%         1.000000                1.0          0.000000      1.000000  \nmax         1.000000                1.0          1.000000      1.000000  \n\n[8 rows x 36 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>HadAngina</th>\n      <th>HadStroke</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>44513.000000</td>\n      <td>44393.000000</td>\n      <td>43469.000000</td>\n      <td>43622.000000</td>\n      <td>44408.000000</td>\n      <td>44008.000000</td>\n      <td>43413.000000</td>\n      <td>44182.000000</td>\n      <td>44074.000000</td>\n      <td>44368.000000</td>\n      <td>...</td>\n      <td>41714.000000</td>\n      <td>40397.000000</td>\n      <td>39724.000000</td>\n      <td>39956.000000</td>\n      <td>38018.000000</td>\n      <td>39886.000000</td>\n      <td>36860.000000</td>\n      <td>36315.0</td>\n      <td>39538.000000</td>\n      <td>38114.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.471593</td>\n      <td>3.441511</td>\n      <td>4.275001</td>\n      <td>4.298221</td>\n      <td>0.760021</td>\n      <td>7.036584</td>\n      <td>0.685302</td>\n      <td>0.057241</td>\n      <td>0.061056</td>\n      <td>0.042869</td>\n      <td>...</td>\n      <td>1.703194</td>\n      <td>83.021746</td>\n      <td>28.512326</td>\n      <td>0.529532</td>\n      <td>0.342233</td>\n      <td>0.527002</td>\n      <td>0.413592</td>\n      <td>1.0</td>\n      <td>0.043427</td>\n      <td>0.289500</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.499198</td>\n      <td>1.050924</td>\n      <td>8.588663</td>\n      <td>8.299250</td>\n      <td>0.427075</td>\n      <td>1.512667</td>\n      <td>0.884912</td>\n      <td>0.232304</td>\n      <td>0.239436</td>\n      <td>0.202563</td>\n      <td>...</td>\n      <td>0.107438</td>\n      <td>21.551394</td>\n      <td>6.596149</td>\n      <td>0.499133</td>\n      <td>0.474463</td>\n      <td>0.499277</td>\n      <td>0.492484</td>\n      <td>0.0</td>\n      <td>0.203818</td>\n      <td>0.453536</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.910000</td>\n      <td>22.680000</td>\n      <td>12.020000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>6.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.630000</td>\n      <td>68.040000</td>\n      <td>24.030000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.700000</td>\n      <td>80.740000</td>\n      <td>27.410000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>8.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.780000</td>\n      <td>95.250000</td>\n      <td>31.650000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>1.000000</td>\n      <td>24.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>2.340000</td>\n      <td>290.300000</td>\n      <td>97.650000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 36 columns</p>\n</div>"
+      "text/plain": "               Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\ncount  44513.000000   44400.000000        43445.000000      43614.000000   \nmean       0.468402       3.437815            4.266222          4.363989   \nstd        0.499006       1.047836            8.570562          8.340355   \nmin        0.000000       1.000000            0.000000          0.000000   \n25%        0.000000       3.000000            0.000000          0.000000   \n50%        0.000000       3.000000            0.000000          0.000000   \n75%        1.000000       4.000000            3.000000          5.000000   \nmax        1.000000       5.000000           30.000000         30.000000   \n\n       PhysicalActivities    SleepHours  RemovedTeeth  HadHeartAttack  \\\ncount        44402.000000  43952.000000  43394.000000    44227.000000   \nmean             0.761092      7.016746      0.683505        0.056775   \nstd              0.426421      1.490779      0.884697        0.231415   \nmin              0.000000      1.000000      0.000000        0.000000   \n25%              1.000000      6.000000      0.000000        0.000000   \n50%              1.000000      7.000000      0.000000        0.000000   \n75%              1.000000      8.000000      1.000000        0.000000   \nmax              1.000000     24.000000      3.000000        1.000000   \n\n          HadAngina     HadStroke  ...  HeightInMeters  WeightInKilograms  \\\ncount  44101.000000  44360.000000  ...    41620.000000       40212.000000   \nmean       0.059817      0.042178  ...        1.702194          83.020179   \nstd        0.237151      0.200997  ...        0.107023          21.645999   \nmin        0.000000      0.000000  ...        0.910000          22.680000   \n25%        0.000000      0.000000  ...        1.630000          68.040000   \n50%        0.000000      0.000000  ...        1.700000          80.740000   \n75%        0.000000      0.000000  ...        1.780000          95.250000   \nmax        1.000000      1.000000  ...        2.240000         290.300000   \n\n                BMI  AlcoholDrinkers    HIVTesting  FluVaxLast12  \\\ncount  39520.000000     39817.000000  37853.000000  39779.000000   \nmean      28.509078         0.531281      0.343539      0.529123   \nstd        6.571327         0.499027      0.474896      0.499157   \nmin       12.340000         0.000000      0.000000      0.000000   \n25%       24.030000         0.000000      0.000000      0.000000   \n50%       27.410000         1.000000      0.000000      1.000000   \n75%       31.660000         1.000000      1.000000      1.000000   \nmax       94.660000         1.000000      1.000000      1.000000   \n\n       PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear      CovidPos  \ncount   36821.000000            36315.0      39453.000000  38048.000000  \nmean        0.417615                1.0          0.043774      0.290896  \nstd         0.493173                0.0          0.204594      0.454181  \nmin         0.000000                1.0          0.000000      0.000000  \n25%         0.000000                1.0          0.000000      0.000000  \n50%         0.000000                1.0          0.000000      0.000000  \n75%         1.000000                1.0          0.000000      1.000000  \nmax         1.000000                1.0          1.000000      1.000000  \n\n[8 rows x 36 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>HadAngina</th>\n      <th>HadStroke</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>44513.000000</td>\n      <td>44400.000000</td>\n      <td>43445.000000</td>\n      <td>43614.000000</td>\n      <td>44402.000000</td>\n      <td>43952.000000</td>\n      <td>43394.000000</td>\n      <td>44227.000000</td>\n      <td>44101.000000</td>\n      <td>44360.000000</td>\n      <td>...</td>\n      <td>41620.000000</td>\n      <td>40212.000000</td>\n      <td>39520.000000</td>\n      <td>39817.000000</td>\n      <td>37853.000000</td>\n      <td>39779.000000</td>\n      <td>36821.000000</td>\n      <td>36315.0</td>\n      <td>39453.000000</td>\n      <td>38048.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.468402</td>\n      <td>3.437815</td>\n      <td>4.266222</td>\n      <td>4.363989</td>\n      <td>0.761092</td>\n      <td>7.016746</td>\n      <td>0.683505</td>\n      <td>0.056775</td>\n      <td>0.059817</td>\n      <td>0.042178</td>\n      <td>...</td>\n      <td>1.702194</td>\n      <td>83.020179</td>\n      <td>28.509078</td>\n      <td>0.531281</td>\n      <td>0.343539</td>\n      <td>0.529123</td>\n      <td>0.417615</td>\n      <td>1.0</td>\n      <td>0.043774</td>\n      <td>0.290896</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.499006</td>\n      <td>1.047836</td>\n      <td>8.570562</td>\n      <td>8.340355</td>\n      <td>0.426421</td>\n      <td>1.490779</td>\n      <td>0.884697</td>\n      <td>0.231415</td>\n      <td>0.237151</td>\n      <td>0.200997</td>\n      <td>...</td>\n      <td>0.107023</td>\n      <td>21.645999</td>\n      <td>6.571327</td>\n      <td>0.499027</td>\n      <td>0.474896</td>\n      <td>0.499157</td>\n      <td>0.493173</td>\n      <td>0.0</td>\n      <td>0.204594</td>\n      <td>0.454181</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.910000</td>\n      <td>22.680000</td>\n      <td>12.340000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>6.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.630000</td>\n      <td>68.040000</td>\n      <td>24.030000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.700000</td>\n      <td>80.740000</td>\n      <td>27.410000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>8.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.780000</td>\n      <td>95.250000</td>\n      <td>31.660000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>1.000000</td>\n      <td>24.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>2.240000</td>\n      <td>290.300000</td>\n      <td>94.660000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 36 columns</p>\n</div>"
      },
-     "execution_count": 63,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1102,14 +1064,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 128,
    "outputs": [
     {
      "data": {
-      "text/plain": "               Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\ncount  44514.000000   44388.000000        43458.000000      43578.000000   \nmean       0.469043       3.434554            4.355470          4.379022   \nstd        0.499046       1.051996            8.718506          8.383576   \nmin        0.000000       1.000000            0.000000          0.000000   \n25%        0.000000       3.000000            0.000000          0.000000   \n50%        0.000000       3.000000            0.000000          0.000000   \n75%        1.000000       4.000000            3.000000          5.000000   \nmax        1.000000       5.000000           30.000000         30.000000   \n\n       PhysicalActivities    SleepHours  RemovedTeeth  HadHeartAttack  \\\ncount        44401.000000  43966.000000  43360.000000    44202.000000   \nmean             0.758361      7.013010      0.684732        0.057396   \nstd              0.428081      1.491967      0.882396        0.232600   \nmin              0.000000      1.000000      0.000000        0.000000   \n25%              1.000000      6.000000      0.000000        0.000000   \n50%              1.000000      7.000000      0.000000        0.000000   \n75%              1.000000      8.000000      1.000000        0.000000   \nmax              1.000000     24.000000      3.000000        1.000000   \n\n          HadAngina     HadStroke  ...  HeightInMeters  WeightInKilograms  \\\ncount  44031.000000  44344.000000  ...    41677.000000       40327.000000   \nmean       0.058936      0.042463  ...        1.702146          82.981070   \nstd        0.235507      0.201646  ...        0.106978          21.512676   \nmin        0.000000      0.000000  ...        0.910000          22.680000   \n25%        0.000000      0.000000  ...        1.630000          68.040000   \n50%        0.000000      0.000000  ...        1.700000          79.830000   \n75%        0.000000      0.000000  ...        1.780000          95.250000   \nmax        1.000000      1.000000  ...        2.360000         263.080000   \n\n                BMI  AlcoholDrinkers    HIVTesting  FluVaxLast12  \\\ncount  39626.000000     39950.000000  38041.000000  39885.000000   \nmean      28.521370         0.527910      0.342525      0.525285   \nstd        6.622255         0.499227      0.474560      0.499367   \nmin       12.160000         0.000000      0.000000      0.000000   \n25%       24.030000         0.000000      0.000000      0.000000   \n50%       27.400000         1.000000      0.000000      1.000000   \n75%       31.750000         1.000000      1.000000      1.000000   \nmax       99.640000         1.000000      1.000000      1.000000   \n\n       PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear      CovidPos  \ncount   36926.000000            36250.0      39535.000000  38212.000000  \nmean        0.412771                1.0          0.044846      0.291872  \nstd         0.492339                0.0          0.206969      0.454630  \nmin         0.000000                1.0          0.000000      0.000000  \n25%         0.000000                1.0          0.000000      0.000000  \n50%         0.000000                1.0          0.000000      0.000000  \n75%         1.000000                1.0          0.000000      1.000000  \nmax         1.000000                1.0          1.000000      1.000000  \n\n[8 rows x 36 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>HadAngina</th>\n      <th>HadStroke</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>44514.000000</td>\n      <td>44388.000000</td>\n      <td>43458.000000</td>\n      <td>43578.000000</td>\n      <td>44401.000000</td>\n      <td>43966.000000</td>\n      <td>43360.000000</td>\n      <td>44202.000000</td>\n      <td>44031.000000</td>\n      <td>44344.000000</td>\n      <td>...</td>\n      <td>41677.000000</td>\n      <td>40327.000000</td>\n      <td>39626.000000</td>\n      <td>39950.000000</td>\n      <td>38041.000000</td>\n      <td>39885.000000</td>\n      <td>36926.000000</td>\n      <td>36250.0</td>\n      <td>39535.000000</td>\n      <td>38212.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.469043</td>\n      <td>3.434554</td>\n      <td>4.355470</td>\n      <td>4.379022</td>\n      <td>0.758361</td>\n      <td>7.013010</td>\n      <td>0.684732</td>\n      <td>0.057396</td>\n      <td>0.058936</td>\n      <td>0.042463</td>\n      <td>...</td>\n      <td>1.702146</td>\n      <td>82.981070</td>\n      <td>28.521370</td>\n      <td>0.527910</td>\n      <td>0.342525</td>\n      <td>0.525285</td>\n      <td>0.412771</td>\n      <td>1.0</td>\n      <td>0.044846</td>\n      <td>0.291872</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.499046</td>\n      <td>1.051996</td>\n      <td>8.718506</td>\n      <td>8.383576</td>\n      <td>0.428081</td>\n      <td>1.491967</td>\n      <td>0.882396</td>\n      <td>0.232600</td>\n      <td>0.235507</td>\n      <td>0.201646</td>\n      <td>...</td>\n      <td>0.106978</td>\n      <td>21.512676</td>\n      <td>6.622255</td>\n      <td>0.499227</td>\n      <td>0.474560</td>\n      <td>0.499367</td>\n      <td>0.492339</td>\n      <td>0.0</td>\n      <td>0.206969</td>\n      <td>0.454630</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.910000</td>\n      <td>22.680000</td>\n      <td>12.160000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>6.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.630000</td>\n      <td>68.040000</td>\n      <td>24.030000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.700000</td>\n      <td>79.830000</td>\n      <td>27.400000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>8.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.780000</td>\n      <td>95.250000</td>\n      <td>31.750000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>1.000000</td>\n      <td>24.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>2.360000</td>\n      <td>263.080000</td>\n      <td>99.640000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 36 columns</p>\n</div>"
+      "text/plain": "               Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\ncount  44514.000000   44388.000000        43430.000000      43655.000000   \nmean       0.470571       3.429733            4.332006          4.394823   \nstd        0.499139       1.052177            8.681714          8.423078   \nmin        0.000000       1.000000            0.000000          0.000000   \n25%        0.000000       3.000000            0.000000          0.000000   \n50%        0.000000       3.000000            0.000000          0.000000   \n75%        1.000000       4.000000            3.000000          4.500000   \nmax        1.000000       5.000000           30.000000         30.000000   \n\n       PhysicalActivities    SleepHours  RemovedTeeth  HadHeartAttack  \\\ncount        44413.000000  44011.000000  43420.000000    44190.000000   \nmean             0.759215      7.014360      0.684938        0.058407   \nstd              0.427565      1.510916      0.880082        0.234514   \nmin              0.000000      1.000000      0.000000        0.000000   \n25%              1.000000      6.000000      0.000000        0.000000   \n50%              1.000000      7.000000      0.000000        0.000000   \n75%              1.000000      8.000000      1.000000        0.000000   \nmax              1.000000     24.000000      3.000000        1.000000   \n\n          HadAngina     HadStroke  ...  HeightInMeters  WeightInKilograms  \\\ncount  44057.000000  44339.000000  ...    41618.000000       40342.000000   \nmean       0.059605      0.041747  ...        1.702956          83.157638   \nstd        0.236755      0.200012  ...        0.107780          21.489722   \nmin        0.000000      0.000000  ...        0.910000          24.950000   \n25%        0.000000      0.000000  ...        1.630000          68.040000   \n50%        0.000000      0.000000  ...        1.700000          80.740000   \n75%        0.000000      0.000000  ...        1.780000          95.250000   \nmax        1.000000      1.000000  ...        2.290000         285.000000   \n\n                BMI  AlcoholDrinkers    HIVTesting  FluVaxLast12  \\\ncount  39646.000000     39861.000000  37852.000000  39827.000000   \nmean      28.558016         0.530493      0.341118      0.524795   \nstd        6.623124         0.499076      0.474091      0.499391   \nmin       12.020000         0.000000      0.000000      0.000000   \n25%       24.130000         0.000000      0.000000      0.000000   \n50%       27.415000         1.000000      0.000000      1.000000   \n75%       31.750000         1.000000      1.000000      1.000000   \nmax       99.340000         1.000000      1.000000      1.000000   \n\n       PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear      CovidPos  \ncount   36781.000000            36224.0      39424.000000  38088.000000  \nmean        0.410810                1.0          0.042639      0.288542  \nstd         0.491988                0.0          0.202044      0.453091  \nmin         0.000000                1.0          0.000000      0.000000  \n25%         0.000000                1.0          0.000000      0.000000  \n50%         0.000000                1.0          0.000000      0.000000  \n75%         1.000000                1.0          0.000000      1.000000  \nmax         1.000000                1.0          1.000000      1.000000  \n\n[8 rows x 36 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>HadAngina</th>\n      <th>HadStroke</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>44514.000000</td>\n      <td>44388.000000</td>\n      <td>43430.000000</td>\n      <td>43655.000000</td>\n      <td>44413.000000</td>\n      <td>44011.000000</td>\n      <td>43420.000000</td>\n      <td>44190.000000</td>\n      <td>44057.000000</td>\n      <td>44339.000000</td>\n      <td>...</td>\n      <td>41618.000000</td>\n      <td>40342.000000</td>\n      <td>39646.000000</td>\n      <td>39861.000000</td>\n      <td>37852.000000</td>\n      <td>39827.000000</td>\n      <td>36781.000000</td>\n      <td>36224.0</td>\n      <td>39424.000000</td>\n      <td>38088.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.470571</td>\n      <td>3.429733</td>\n      <td>4.332006</td>\n      <td>4.394823</td>\n      <td>0.759215</td>\n      <td>7.014360</td>\n      <td>0.684938</td>\n      <td>0.058407</td>\n      <td>0.059605</td>\n      <td>0.041747</td>\n      <td>...</td>\n      <td>1.702956</td>\n      <td>83.157638</td>\n      <td>28.558016</td>\n      <td>0.530493</td>\n      <td>0.341118</td>\n      <td>0.524795</td>\n      <td>0.410810</td>\n      <td>1.0</td>\n      <td>0.042639</td>\n      <td>0.288542</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.499139</td>\n      <td>1.052177</td>\n      <td>8.681714</td>\n      <td>8.423078</td>\n      <td>0.427565</td>\n      <td>1.510916</td>\n      <td>0.880082</td>\n      <td>0.234514</td>\n      <td>0.236755</td>\n      <td>0.200012</td>\n      <td>...</td>\n      <td>0.107780</td>\n      <td>21.489722</td>\n      <td>6.623124</td>\n      <td>0.499076</td>\n      <td>0.474091</td>\n      <td>0.499391</td>\n      <td>0.491988</td>\n      <td>0.0</td>\n      <td>0.202044</td>\n      <td>0.453091</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.910000</td>\n      <td>24.950000</td>\n      <td>12.020000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>6.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.630000</td>\n      <td>68.040000</td>\n      <td>24.130000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.700000</td>\n      <td>80.740000</td>\n      <td>27.415000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>4.500000</td>\n      <td>1.000000</td>\n      <td>8.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.780000</td>\n      <td>95.250000</td>\n      <td>31.750000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>30.000000</td>\n      <td>30.000000</td>\n      <td>1.000000</td>\n      <td>24.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>2.290000</td>\n      <td>285.000000</td>\n      <td>99.340000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 36 columns</p>\n</div>"
      },
-     "execution_count": 64,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1132,12 +1094,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 129,
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 729.847x600 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1170,14 +1132,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 130,
    "outputs": [
     {
      "data": {
-      "text/plain": "   SmokerStatus  HadHeartAttack\n0           0.0        0.037883\n1           1.0        0.072598\n2           2.0        0.088887\n3           3.0        0.090192",
-      "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>SmokerStatus</th>\n      <th>HadHeartAttack</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.037883</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.072598</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.0</td>\n      <td>0.088887</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.0</td>\n      <td>0.090192</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "   SmokerStatus  HadHeartAttack\n0           0.0        0.036784\n1           1.0        0.072586\n2           2.0        0.092351\n3           3.0        0.090198",
+      "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>SmokerStatus</th>\n      <th>HadHeartAttack</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.036784</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.072586</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.0</td>\n      <td>0.092351</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.0</td>\n      <td>0.090198</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 66,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1200,14 +1162,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 131,
    "outputs": [
     {
      "data": {
-      "text/plain": "   GeneralHealth  HadHeartAttack\n0            1.0        0.228411\n1            2.0        0.129270\n2            3.0        0.056693\n3            4.0        0.027336\n4            5.0        0.014743",
-      "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>GeneralHealth</th>\n      <th>HadHeartAttack</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.228411</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>0.129270</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>0.056693</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>0.027336</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>0.014743</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "   GeneralHealth  HadHeartAttack\n0            1.0        0.228482\n1            2.0        0.125921\n2            3.0        0.058758\n3            4.0        0.028679\n4            5.0        0.014917",
+      "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>GeneralHealth</th>\n      <th>HadHeartAttack</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.228482</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>0.125921</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>0.058758</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>0.028679</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>0.014917</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 67,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1221,14 +1183,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 132,
    "outputs": [
     {
      "data": {
-      "text/plain": "SmokerStatus        0.0       1.0       2.0       3.0\nGeneralHealth                                        \n1.0            0.194640  0.310680  0.257100  0.222222\n2.0            0.090772  0.146429  0.184443  0.155059\n3.0            0.039989  0.031068  0.091645  0.060469\n4.0            0.021611  0.032070  0.035265  0.048292\n5.0            0.011078  0.012579  0.026298  0.018315",
-      "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>SmokerStatus</th>\n      <th>0.0</th>\n      <th>1.0</th>\n      <th>2.0</th>\n      <th>3.0</th>\n    </tr>\n    <tr>\n      <th>GeneralHealth</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1.0</th>\n      <td>0.194640</td>\n      <td>0.310680</td>\n      <td>0.257100</td>\n      <td>0.222222</td>\n    </tr>\n    <tr>\n      <th>2.0</th>\n      <td>0.090772</td>\n      <td>0.146429</td>\n      <td>0.184443</td>\n      <td>0.155059</td>\n    </tr>\n    <tr>\n      <th>3.0</th>\n      <td>0.039989</td>\n      <td>0.031068</td>\n      <td>0.091645</td>\n      <td>0.060469</td>\n    </tr>\n    <tr>\n      <th>4.0</th>\n      <td>0.021611</td>\n      <td>0.032070</td>\n      <td>0.035265</td>\n      <td>0.048292</td>\n    </tr>\n    <tr>\n      <th>5.0</th>\n      <td>0.011078</td>\n      <td>0.012579</td>\n      <td>0.026298</td>\n      <td>0.018315</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "SmokerStatus        0.0       1.0       2.0       3.0\nGeneralHealth                                        \n1.0            0.176024  0.258065  0.257364  0.245614\n2.0            0.085981  0.123404  0.178412  0.146520\n3.0            0.041363  0.055769  0.090743  0.063766\n4.0            0.019886  0.021918  0.048122  0.034739\n5.0            0.012267  0.028736  0.020260  0.021978",
+      "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>SmokerStatus</th>\n      <th>0.0</th>\n      <th>1.0</th>\n      <th>2.0</th>\n      <th>3.0</th>\n    </tr>\n    <tr>\n      <th>GeneralHealth</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1.0</th>\n      <td>0.176024</td>\n      <td>0.258065</td>\n      <td>0.257364</td>\n      <td>0.245614</td>\n    </tr>\n    <tr>\n      <th>2.0</th>\n      <td>0.085981</td>\n      <td>0.123404</td>\n      <td>0.178412</td>\n      <td>0.146520</td>\n    </tr>\n    <tr>\n      <th>3.0</th>\n      <td>0.041363</td>\n      <td>0.055769</td>\n      <td>0.090743</td>\n      <td>0.063766</td>\n    </tr>\n    <tr>\n      <th>4.0</th>\n      <td>0.019886</td>\n      <td>0.021918</td>\n      <td>0.048122</td>\n      <td>0.034739</td>\n    </tr>\n    <tr>\n      <th>5.0</th>\n      <td>0.012267</td>\n      <td>0.028736</td>\n      <td>0.020260</td>\n      <td>0.021978</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 68,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1251,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 133,
    "outputs": [],
    "source": [
     "from sklearn.preprocessing import MinMaxScaler\n",
@@ -1266,14 +1228,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 134,
    "outputs": [
     {
      "data": {
-      "text/plain": "               State  Male  GeneralHealth  PhysicalHealthDays  \\\n339824  South Dakota   0.0            3.0                 3.0   \n127927        Kansas   0.0            3.0                30.0   \n362523          Utah   1.0            5.0                 0.0   \n183687      Michigan   1.0            3.0                 0.0   \n191905      Michigan   0.0            4.0                 0.0   \n\n        MentalHealthDays                                    LastCheckupTime  \\\n339824              21.0  Within past year (anytime less than 12 months ...   \n127927               0.0  Within past year (anytime less than 12 months ...   \n362523               0.0  Within past year (anytime less than 12 months ...   \n183687               7.0  Within past year (anytime less than 12 months ...   \n191905               0.0  Within past year (anytime less than 12 months ...   \n\n        PhysicalActivities  SleepHours  RemovedTeeth  HadHeartAttack  ...  \\\n339824                 1.0         8.0           2.0             0.0  ...   \n127927                 1.0        10.0           1.0             0.0  ...   \n362523                 1.0         7.0           1.0             0.0  ...   \n183687                 1.0         8.0           0.0             0.0  ...   \n191905                 1.0         7.0           0.0             0.0  ...   \n\n        HeightInMeters  WeightInKilograms    BMI  AlcoholDrinkers  HIVTesting  \\\n339824            1.60              52.16  20.37              0.0         1.0   \n127927            1.68              97.52  34.70              0.0         0.0   \n362523            1.83             113.85  34.04              0.0         0.0   \n183687            1.78              83.91  26.54              1.0         0.0   \n191905            1.57              68.04  27.44              1.0         1.0   \n\n        FluVaxLast12  PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear  \\\n339824           1.0            1.0                1.0               0.0   \n127927           1.0            1.0                NaN               0.0   \n362523           0.0            0.0                1.0               0.0   \n183687           1.0            1.0                1.0               0.0   \n191905           0.0            1.0                1.0               0.0   \n\n        CovidPos  \n339824       0.0  \n127927       0.0  \n362523       1.0  \n183687       0.0  \n191905       0.0  \n\n[5 rows x 40 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>339824</th>\n      <td>South Dakota</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>21.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>8.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.60</td>\n      <td>52.16</td>\n      <td>20.37</td>\n      <td>0.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>127927</th>\n      <td>Kansas</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>30.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.68</td>\n      <td>97.52</td>\n      <td>34.70</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>362523</th>\n      <td>Utah</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.83</td>\n      <td>113.85</td>\n      <td>34.04</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>183687</th>\n      <td>Michigan</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.78</td>\n      <td>83.91</td>\n      <td>26.54</td>\n      <td>1.0</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>191905</th>\n      <td>Michigan</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.57</td>\n      <td>68.04</td>\n      <td>27.44</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
+      "text/plain": "            State  Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\n166151   Maryland   1.0            3.0                 0.0               0.0   \n359753      Texas   1.0            3.0                 0.0               0.0   \n286723       Ohio   1.0            2.0                 2.0               0.0   \n305100   Oklahoma   1.0            4.0                 0.0               0.0   \n199077  Minnesota   0.0            5.0                 0.0               0.0   \n\n                                          LastCheckupTime  PhysicalActivities  \\\n166151  Within past 2 years (1 year but less than 2 ye...                 1.0   \n359753                                            Unknown                 0.0   \n286723  Within past year (anytime less than 12 months ...                 0.0   \n305100  Within past year (anytime less than 12 months ...                 1.0   \n199077  Within past year (anytime less than 12 months ...                 0.0   \n\n        SleepHours  RemovedTeeth  HadHeartAttack  ...  HeightInMeters  \\\n166151         7.0           0.0             0.0  ...            1.78   \n359753         8.0           0.0             0.0  ...            1.60   \n286723         8.0           3.0             0.0  ...            1.75   \n305100         7.0           0.0             0.0  ...            1.80   \n199077         8.0           1.0             0.0  ...            1.52   \n\n        WeightInKilograms    BMI  AlcoholDrinkers  HIVTesting  FluVaxLast12  \\\n166151              90.72  28.70              1.0         0.0           0.0   \n359753              77.11  30.12              0.0         0.0           0.0   \n286723              80.29  26.14              NaN         NaN           NaN   \n305100              97.52  29.99              NaN         NaN           NaN   \n199077              67.13  28.90              1.0         1.0           0.0   \n\n        PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear  CovidPos  \n166151            1.0                1.0               0.0       0.0  \n359753            0.0                1.0               0.0       0.0  \n286723            NaN                NaN               NaN       NaN  \n305100            NaN                NaN               NaN       NaN  \n199077            0.0                NaN               0.0       1.0  \n\n[5 rows x 40 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>166151</th>\n      <td>Maryland</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past 2 years (1 year but less than 2 ye...</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.78</td>\n      <td>90.72</td>\n      <td>28.70</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>359753</th>\n      <td>Texas</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Unknown</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.60</td>\n      <td>77.11</td>\n      <td>30.12</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>286723</th>\n      <td>Ohio</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.75</td>\n      <td>80.29</td>\n      <td>26.14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>305100</th>\n      <td>Oklahoma</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.80</td>\n      <td>97.52</td>\n      <td>29.99</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199077</th>\n      <td>Minnesota</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.52</td>\n      <td>67.13</td>\n      <td>28.90</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
      },
-     "execution_count": 70,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1287,14 +1249,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 135,
    "outputs": [
     {
      "data": {
-      "text/plain": "               State  Male  GeneralHealth  PhysicalHealthDays  \\\n339824  South Dakota   0.0           0.50                 0.1   \n127927        Kansas   0.0           0.50                 1.0   \n362523          Utah   1.0           1.00                 0.0   \n183687      Michigan   1.0           0.50                 0.0   \n191905      Michigan   0.0           0.75                 0.0   \n\n        MentalHealthDays                                    LastCheckupTime  \\\n339824          0.700000  Within past year (anytime less than 12 months ...   \n127927          0.000000  Within past year (anytime less than 12 months ...   \n362523          0.000000  Within past year (anytime less than 12 months ...   \n183687          0.233333  Within past year (anytime less than 12 months ...   \n191905          0.000000  Within past year (anytime less than 12 months ...   \n\n        PhysicalActivities  SleepHours  RemovedTeeth  HadHeartAttack  ...  \\\n339824                 1.0    0.304348      0.666667             0.0  ...   \n127927                 1.0    0.391304      0.333333             0.0  ...   \n362523                 1.0    0.260870      0.333333             0.0  ...   \n183687                 1.0    0.304348      0.000000             0.0  ...   \n191905                 1.0    0.260870      0.000000             0.0  ...   \n\n        HeightInMeters  WeightInKilograms       BMI  AlcoholDrinkers  \\\n339824        0.482517           0.110156  0.097513              0.0   \n127927        0.538462           0.279650  0.264860              0.0   \n362523        0.643357           0.340670  0.257153              0.0   \n183687        0.608392           0.228795  0.169567              1.0   \n191905        0.461538           0.169494  0.180077              1.0   \n\n        HIVTesting  FluVaxLast12  PneumoVaxEver  TetanusLast10Tdap  \\\n339824         1.0           1.0            1.0                0.0   \n127927         0.0           1.0            1.0                NaN   \n362523         0.0           0.0            0.0                0.0   \n183687         0.0           1.0            1.0                0.0   \n191905         1.0           0.0            1.0                0.0   \n\n        HighRiskLastYear  CovidPos  \n339824               0.0       0.0  \n127927               0.0       0.0  \n362523               0.0       1.0  \n183687               0.0       0.0  \n191905               0.0       0.0  \n\n[5 rows x 40 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>339824</th>\n      <td>South Dakota</td>\n      <td>0.0</td>\n      <td>0.50</td>\n      <td>0.1</td>\n      <td>0.700000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.304348</td>\n      <td>0.666667</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.482517</td>\n      <td>0.110156</td>\n      <td>0.097513</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>127927</th>\n      <td>Kansas</td>\n      <td>0.0</td>\n      <td>0.50</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.391304</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.538462</td>\n      <td>0.279650</td>\n      <td>0.264860</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>362523</th>\n      <td>Utah</td>\n      <td>1.0</td>\n      <td>1.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.643357</td>\n      <td>0.340670</td>\n      <td>0.257153</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>183687</th>\n      <td>Michigan</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.0</td>\n      <td>0.233333</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.304348</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.608392</td>\n      <td>0.228795</td>\n      <td>0.169567</td>\n      <td>1.0</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>191905</th>\n      <td>Michigan</td>\n      <td>0.0</td>\n      <td>0.75</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.461538</td>\n      <td>0.169494</td>\n      <td>0.180077</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
+      "text/plain": "            State  Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\n166151   Maryland   1.0           0.50            0.000000               0.0   \n359753      Texas   1.0           0.50            0.000000               0.0   \n286723       Ohio   1.0           0.25            0.066667               0.0   \n305100   Oklahoma   1.0           0.75            0.000000               0.0   \n199077  Minnesota   0.0           1.00            0.000000               0.0   \n\n                                          LastCheckupTime  PhysicalActivities  \\\n166151  Within past 2 years (1 year but less than 2 ye...                 1.0   \n359753                                            Unknown                 0.0   \n286723  Within past year (anytime less than 12 months ...                 0.0   \n305100  Within past year (anytime less than 12 months ...                 1.0   \n199077  Within past year (anytime less than 12 months ...                 0.0   \n\n        SleepHours  RemovedTeeth  HadHeartAttack  ...  HeightInMeters  \\\n166151    0.260870      0.000000             0.0  ...        0.654135   \n359753    0.304348      0.000000             0.0  ...        0.518797   \n286723    0.304348      1.000000             0.0  ...        0.631579   \n305100    0.260870      0.000000             0.0  ...        0.669173   \n199077    0.304348      0.333333             0.0  ...        0.458647   \n\n        WeightInKilograms       BMI  AlcoholDrinkers  HIVTesting  \\\n166151           0.254241  0.198737              1.0         0.0   \n359753           0.203385  0.215986              0.0         0.0   \n286723           0.215268  0.167638              NaN         NaN   \n305100           0.279650  0.214407              NaN         NaN   \n199077           0.166094  0.201166              1.0         1.0   \n\n        FluVaxLast12  PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear  \\\n166151           0.0            1.0                0.0               0.0   \n359753           0.0            0.0                0.0               0.0   \n286723           NaN            NaN                NaN               NaN   \n305100           NaN            NaN                NaN               NaN   \n199077           0.0            0.0                NaN               0.0   \n\n        CovidPos  \n166151       0.0  \n359753       0.0  \n286723       NaN  \n305100       NaN  \n199077       1.0  \n\n[5 rows x 40 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>166151</th>\n      <td>Maryland</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past 2 years (1 year but less than 2 ye...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.654135</td>\n      <td>0.254241</td>\n      <td>0.198737</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>359753</th>\n      <td>Texas</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Unknown</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.518797</td>\n      <td>0.203385</td>\n      <td>0.215986</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>286723</th>\n      <td>Ohio</td>\n      <td>1.0</td>\n      <td>0.25</td>\n      <td>0.066667</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>1.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.631579</td>\n      <td>0.215268</td>\n      <td>0.167638</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>305100</th>\n      <td>Oklahoma</td>\n      <td>1.0</td>\n      <td>0.75</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.669173</td>\n      <td>0.279650</td>\n      <td>0.214407</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199077</th>\n      <td>Minnesota</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.458647</td>\n      <td>0.166094</td>\n      <td>0.201166</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
      },
-     "execution_count": 71,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1322,7 +1284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 136,
    "outputs": [
     {
      "name": "stdout",
@@ -1343,14 +1305,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 137,
    "outputs": [
     {
      "data": {
-      "text/plain": "               State  Male  GeneralHealth  PhysicalHealthDays  \\\n339824  South Dakota   0.0           0.50                 0.1   \n127927        Kansas   0.0           0.50                 1.0   \n362523          Utah   1.0           1.00                 0.0   \n183687      Michigan   1.0           0.50                 0.0   \n191905      Michigan   0.0           0.75                 0.0   \n\n        MentalHealthDays                                    LastCheckupTime  \\\n339824          0.700000  Within past year (anytime less than 12 months ...   \n127927          0.000000  Within past year (anytime less than 12 months ...   \n362523          0.000000  Within past year (anytime less than 12 months ...   \n183687          0.233333  Within past year (anytime less than 12 months ...   \n191905          0.000000  Within past year (anytime less than 12 months ...   \n\n        PhysicalActivities  SleepHours  RemovedTeeth  HadHeartAttack  ...  \\\n339824                 1.0    0.304348      0.666667             0.0  ...   \n127927                 1.0    0.391304      0.333333             0.0  ...   \n362523                 1.0    0.260870      0.333333             0.0  ...   \n183687                 1.0    0.304348      0.000000             0.0  ...   \n191905                 1.0    0.260870      0.000000             0.0  ...   \n\n        HeightInMeters  WeightInKilograms       BMI  AlcoholDrinkers  \\\n339824        0.482517           0.110156  0.097513              0.0   \n127927        0.538462           0.279650  0.264860              0.0   \n362523        0.643357           0.340670  0.257153              0.0   \n183687        0.608392           0.228795  0.169567              1.0   \n191905        0.461538           0.169494  0.180077              1.0   \n\n        HIVTesting  FluVaxLast12  PneumoVaxEver  TetanusLast10Tdap  \\\n339824         1.0           1.0            1.0                0.0   \n127927         0.0           1.0            1.0                NaN   \n362523         0.0           0.0            0.0                0.0   \n183687         0.0           1.0            1.0                0.0   \n191905         1.0           0.0            1.0                0.0   \n\n        HighRiskLastYear  CovidPos  \n339824               0.0       0.0  \n127927               0.0       0.0  \n362523               0.0       1.0  \n183687               0.0       0.0  \n191905               0.0       0.0  \n\n[5 rows x 40 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>339824</th>\n      <td>South Dakota</td>\n      <td>0.0</td>\n      <td>0.50</td>\n      <td>0.1</td>\n      <td>0.700000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.304348</td>\n      <td>0.666667</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.482517</td>\n      <td>0.110156</td>\n      <td>0.097513</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>127927</th>\n      <td>Kansas</td>\n      <td>0.0</td>\n      <td>0.50</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.391304</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.538462</td>\n      <td>0.279650</td>\n      <td>0.264860</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>362523</th>\n      <td>Utah</td>\n      <td>1.0</td>\n      <td>1.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.643357</td>\n      <td>0.340670</td>\n      <td>0.257153</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>183687</th>\n      <td>Michigan</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.0</td>\n      <td>0.233333</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.304348</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.608392</td>\n      <td>0.228795</td>\n      <td>0.169567</td>\n      <td>1.0</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>191905</th>\n      <td>Michigan</td>\n      <td>0.0</td>\n      <td>0.75</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.461538</td>\n      <td>0.169494</td>\n      <td>0.180077</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
+      "text/plain": "            State  Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\n166151   Maryland   1.0           0.50            0.000000               0.0   \n359753      Texas   1.0           0.50            0.000000               0.0   \n286723       Ohio   1.0           0.25            0.066667               0.0   \n305100   Oklahoma   1.0           0.75            0.000000               0.0   \n199077  Minnesota   0.0           1.00            0.000000               0.0   \n\n                                          LastCheckupTime  PhysicalActivities  \\\n166151  Within past 2 years (1 year but less than 2 ye...                 1.0   \n359753                                            Unknown                 0.0   \n286723  Within past year (anytime less than 12 months ...                 0.0   \n305100  Within past year (anytime less than 12 months ...                 1.0   \n199077  Within past year (anytime less than 12 months ...                 0.0   \n\n        SleepHours  RemovedTeeth  HadHeartAttack  ...  HeightInMeters  \\\n166151    0.260870      0.000000             0.0  ...        0.654135   \n359753    0.304348      0.000000             0.0  ...        0.518797   \n286723    0.304348      1.000000             0.0  ...        0.631579   \n305100    0.260870      0.000000             0.0  ...        0.669173   \n199077    0.304348      0.333333             0.0  ...        0.458647   \n\n        WeightInKilograms       BMI  AlcoholDrinkers  HIVTesting  \\\n166151           0.254241  0.198737              1.0         0.0   \n359753           0.203385  0.215986              0.0         0.0   \n286723           0.215268  0.167638              NaN         NaN   \n305100           0.279650  0.214407              NaN         NaN   \n199077           0.166094  0.201166              1.0         1.0   \n\n        FluVaxLast12  PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear  \\\n166151           0.0            1.0                0.0               0.0   \n359753           0.0            0.0                0.0               0.0   \n286723           NaN            NaN                NaN               NaN   \n305100           NaN            NaN                NaN               NaN   \n199077           0.0            0.0                NaN               0.0   \n\n        CovidPos  \n166151       0.0  \n359753       0.0  \n286723       NaN  \n305100       NaN  \n199077       1.0  \n\n[5 rows x 40 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>166151</th>\n      <td>Maryland</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past 2 years (1 year but less than 2 ye...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.654135</td>\n      <td>0.254241</td>\n      <td>0.198737</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>359753</th>\n      <td>Texas</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Unknown</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.518797</td>\n      <td>0.203385</td>\n      <td>0.215986</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>286723</th>\n      <td>Ohio</td>\n      <td>1.0</td>\n      <td>0.25</td>\n      <td>0.066667</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>1.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.631579</td>\n      <td>0.215268</td>\n      <td>0.167638</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>305100</th>\n      <td>Oklahoma</td>\n      <td>1.0</td>\n      <td>0.75</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.669173</td>\n      <td>0.279650</td>\n      <td>0.214407</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199077</th>\n      <td>Minnesota</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.458647</td>\n      <td>0.166094</td>\n      <td>0.201166</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
      },
-     "execution_count": 73,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1373,28 +1335,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Adrian\\AppData\\Local\\Temp\\ipykernel_18732\\896322512.py:4: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
-      "  test.fillna(test.median(),inplace=True)\n",
-      "C:\\Users\\Adrian\\AppData\\Local\\Temp\\ipykernel_18732\\896322512.py:5: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
-      "  train.fillna(train.median(),inplace=True)\n",
-      "C:\\Users\\Adrian\\AppData\\Local\\Temp\\ipykernel_18732\\896322512.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
-      "  valid.fillna(valid.median(),inplace=True)\n"
-     ]
-    }
-   ],
+   "execution_count": 138,
+   "outputs": [],
    "source": [
-    "#test.dropna(inplace=True)\n",
-    "#train.dropna(inplace=True)\n",
-    "#valid.dropna(inplace=True)\n",
-    "test.fillna(test.median(),inplace=True)\n",
-    "train.fillna(train.median(),inplace=True)\n",
-    "valid.fillna(valid.median(),inplace=True)"
+    "numeric_columns = train.select_dtypes(include=['number']).columns\n",
+    "test[numeric_columns] = test[numeric_columns].fillna(test[numeric_columns].median().iloc[0])\n",
+    "train[numeric_columns] = train[numeric_columns].fillna(train[numeric_columns].median().iloc[0])\n",
+    "valid[numeric_columns] = valid[numeric_columns].fillna(valid[numeric_columns].iloc[0])"
    ],
    "metadata": {
     "collapsed": false
@@ -1402,14 +1349,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 139,
    "outputs": [
     {
      "data": {
-      "text/plain": "               State  Male  GeneralHealth  PhysicalHealthDays  \\\n339824  South Dakota   0.0           0.50                 0.1   \n127927        Kansas   0.0           0.50                 1.0   \n362523          Utah   1.0           1.00                 0.0   \n183687      Michigan   1.0           0.50                 0.0   \n191905      Michigan   0.0           0.75                 0.0   \n\n        MentalHealthDays                                    LastCheckupTime  \\\n339824          0.700000  Within past year (anytime less than 12 months ...   \n127927          0.000000  Within past year (anytime less than 12 months ...   \n362523          0.000000  Within past year (anytime less than 12 months ...   \n183687          0.233333  Within past year (anytime less than 12 months ...   \n191905          0.000000  Within past year (anytime less than 12 months ...   \n\n        PhysicalActivities  SleepHours  RemovedTeeth  HadHeartAttack  ...  \\\n339824                 1.0    0.304348      0.666667             0.0  ...   \n127927                 1.0    0.391304      0.333333             0.0  ...   \n362523                 1.0    0.260870      0.333333             0.0  ...   \n183687                 1.0    0.304348      0.000000             0.0  ...   \n191905                 1.0    0.260870      0.000000             0.0  ...   \n\n        HeightInMeters  WeightInKilograms       BMI  AlcoholDrinkers  \\\n339824        0.482517           0.110156  0.097513              0.0   \n127927        0.538462           0.279650  0.264860              0.0   \n362523        0.643357           0.340670  0.257153              0.0   \n183687        0.608392           0.228795  0.169567              1.0   \n191905        0.461538           0.169494  0.180077              1.0   \n\n        HIVTesting  FluVaxLast12  PneumoVaxEver  TetanusLast10Tdap  \\\n339824         1.0           1.0            1.0                0.0   \n127927         0.0           1.0            1.0                0.0   \n362523         0.0           0.0            0.0                0.0   \n183687         0.0           1.0            1.0                0.0   \n191905         1.0           0.0            1.0                0.0   \n\n        HighRiskLastYear  CovidPos  \n339824               0.0       0.0  \n127927               0.0       0.0  \n362523               0.0       1.0  \n183687               0.0       0.0  \n191905               0.0       0.0  \n\n[5 rows x 40 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>339824</th>\n      <td>South Dakota</td>\n      <td>0.0</td>\n      <td>0.50</td>\n      <td>0.1</td>\n      <td>0.700000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.304348</td>\n      <td>0.666667</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.482517</td>\n      <td>0.110156</td>\n      <td>0.097513</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>127927</th>\n      <td>Kansas</td>\n      <td>0.0</td>\n      <td>0.50</td>\n      <td>1.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.391304</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.538462</td>\n      <td>0.279650</td>\n      <td>0.264860</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>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>362523</th>\n      <td>Utah</td>\n      <td>1.0</td>\n      <td>1.00</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.643357</td>\n      <td>0.340670</td>\n      <td>0.257153</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>183687</th>\n      <td>Michigan</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.0</td>\n      <td>0.233333</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.304348</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.608392</td>\n      <td>0.228795</td>\n      <td>0.169567</td>\n      <td>1.0</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>191905</th>\n      <td>Michigan</td>\n      <td>0.0</td>\n      <td>0.75</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.461538</td>\n      <td>0.169494</td>\n      <td>0.180077</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
+      "text/plain": "            State  Male  GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\n166151   Maryland   1.0           0.50            0.000000               0.0   \n359753      Texas   1.0           0.50            0.000000               0.0   \n286723       Ohio   1.0           0.25            0.066667               0.0   \n305100   Oklahoma   1.0           0.75            0.000000               0.0   \n199077  Minnesota   0.0           1.00            0.000000               0.0   \n\n                                          LastCheckupTime  PhysicalActivities  \\\n166151  Within past 2 years (1 year but less than 2 ye...                 1.0   \n359753                                            Unknown                 0.0   \n286723  Within past year (anytime less than 12 months ...                 0.0   \n305100  Within past year (anytime less than 12 months ...                 1.0   \n199077  Within past year (anytime less than 12 months ...                 0.0   \n\n        SleepHours  RemovedTeeth  HadHeartAttack  ...  HeightInMeters  \\\n166151    0.260870      0.000000             0.0  ...        0.654135   \n359753    0.304348      0.000000             0.0  ...        0.518797   \n286723    0.304348      1.000000             0.0  ...        0.631579   \n305100    0.260870      0.000000             0.0  ...        0.669173   \n199077    0.304348      0.333333             0.0  ...        0.458647   \n\n        WeightInKilograms       BMI  AlcoholDrinkers  HIVTesting  \\\n166151           0.254241  0.198737              1.0         0.0   \n359753           0.203385  0.215986              0.0         0.0   \n286723           0.215268  0.167638              0.0         0.0   \n305100           0.279650  0.214407              0.0         0.0   \n199077           0.166094  0.201166              1.0         1.0   \n\n        FluVaxLast12  PneumoVaxEver  TetanusLast10Tdap  HighRiskLastYear  \\\n166151           0.0            1.0                0.0               0.0   \n359753           0.0            0.0                0.0               0.0   \n286723           0.0            0.0                0.0               0.0   \n305100           0.0            0.0                0.0               0.0   \n199077           0.0            0.0                0.0               0.0   \n\n        CovidPos  \n166151       0.0  \n359753       0.0  \n286723       0.0  \n305100       0.0  \n199077       1.0  \n\n[5 rows x 40 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n      <th>Male</th>\n      <th>GeneralHealth</th>\n      <th>PhysicalHealthDays</th>\n      <th>MentalHealthDays</th>\n      <th>LastCheckupTime</th>\n      <th>PhysicalActivities</th>\n      <th>SleepHours</th>\n      <th>RemovedTeeth</th>\n      <th>HadHeartAttack</th>\n      <th>...</th>\n      <th>HeightInMeters</th>\n      <th>WeightInKilograms</th>\n      <th>BMI</th>\n      <th>AlcoholDrinkers</th>\n      <th>HIVTesting</th>\n      <th>FluVaxLast12</th>\n      <th>PneumoVaxEver</th>\n      <th>TetanusLast10Tdap</th>\n      <th>HighRiskLastYear</th>\n      <th>CovidPos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>166151</th>\n      <td>Maryland</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past 2 years (1 year but less than 2 ye...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.654135</td>\n      <td>0.254241</td>\n      <td>0.198737</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>359753</th>\n      <td>Texas</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Unknown</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.518797</td>\n      <td>0.203385</td>\n      <td>0.215986</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>286723</th>\n      <td>Ohio</td>\n      <td>1.0</td>\n      <td>0.25</td>\n      <td>0.066667</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>1.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.631579</td>\n      <td>0.215268</td>\n      <td>0.167638</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>305100</th>\n      <td>Oklahoma</td>\n      <td>1.0</td>\n      <td>0.75</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>1.0</td>\n      <td>0.260870</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.669173</td>\n      <td>0.279650</td>\n      <td>0.214407</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>199077</th>\n      <td>Minnesota</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Within past year (anytime less than 12 months ...</td>\n      <td>0.0</td>\n      <td>0.304348</td>\n      <td>0.333333</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.458647</td>\n      <td>0.166094</td>\n      <td>0.201166</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>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
      },
-     "execution_count": 75,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1433,13 +1380,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 140,
    "outputs": [
     {
      "data": {
-      "text/plain": "0.0    42796\n1.0     1717\nName: HighRiskLastYear, dtype: int64"
+      "text/plain": "0.0    42786\n1.0     1727\nName: HighRiskLastYear, dtype: int64"
      },
-     "execution_count": 76,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1453,13 +1400,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 141,
    "outputs": [
     {
      "data": {
       "text/plain": "0"
      },
-     "execution_count": 77,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1482,14 +1429,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 142,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 44513 entries, 339824 to 52161\n",
+      "Int64Index: 44513 entries, 166151 to 306609\n",
       "Data columns (total 40 columns):\n",
       " #   Column                     Non-Null Count  Dtype   \n",
       "---  ------                     --------------  -----   \n",
@@ -1547,59 +1494,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 143,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 676777 entries, 0 to 676776\n",
+      "RangeIndex: 676361 entries, 0 to 676360\n",
       "Data columns (total 40 columns):\n",
       " #   Column                     Non-Null Count   Dtype   \n",
       "---  ------                     --------------   -----   \n",
-      " 0   State                      676777 non-null  category\n",
-      " 1   Male                       676777 non-null  float64 \n",
-      " 2   GeneralHealth              676777 non-null  float64 \n",
-      " 3   PhysicalHealthDays         676777 non-null  float64 \n",
-      " 4   MentalHealthDays           676777 non-null  float64 \n",
-      " 5   LastCheckupTime            676777 non-null  category\n",
-      " 6   PhysicalActivities         676777 non-null  float64 \n",
-      " 7   SleepHours                 676777 non-null  float64 \n",
-      " 8   RemovedTeeth               676777 non-null  float64 \n",
-      " 9   HadHeartAttack             676777 non-null  float64 \n",
-      " 10  HadAngina                  676777 non-null  float64 \n",
-      " 11  HadStroke                  676777 non-null  float64 \n",
-      " 12  HadAsthma                  676777 non-null  float64 \n",
-      " 13  HadSkinCancer              676777 non-null  float64 \n",
-      " 14  HadCOPD                    676777 non-null  float64 \n",
-      " 15  HadDepressiveDisorder      676777 non-null  float64 \n",
-      " 16  HadKidneyDisease           676777 non-null  float64 \n",
-      " 17  HadArthritis               676777 non-null  float64 \n",
-      " 18  HadDiabetes                676777 non-null  float64 \n",
-      " 19  DeafOrHardOfHearing        676777 non-null  float64 \n",
-      " 20  BlindOrVisionDifficulty    676777 non-null  float64 \n",
-      " 21  DifficultyConcentrating    676777 non-null  float64 \n",
-      " 22  DifficultyWalking          676777 non-null  float64 \n",
-      " 23  DifficultyDressingBathing  676777 non-null  float64 \n",
-      " 24  DifficultyErrands          676777 non-null  float64 \n",
-      " 25  SmokerStatus               676777 non-null  float64 \n",
-      " 26  ECigaretteUsage            676777 non-null  float64 \n",
-      " 27  ChestScan                  676777 non-null  float64 \n",
-      " 28  RaceEthnicityCategory      676777 non-null  category\n",
-      " 29  AgeCategory                676777 non-null  category\n",
-      " 30  HeightInMeters             676777 non-null  float64 \n",
-      " 31  WeightInKilograms          676777 non-null  float64 \n",
-      " 32  BMI                        676777 non-null  float64 \n",
-      " 33  AlcoholDrinkers            676777 non-null  float64 \n",
-      " 34  HIVTesting                 676777 non-null  float64 \n",
-      " 35  FluVaxLast12               676777 non-null  float64 \n",
-      " 36  PneumoVaxEver              676777 non-null  float64 \n",
-      " 37  TetanusLast10Tdap          676777 non-null  float64 \n",
-      " 38  HighRiskLastYear           676777 non-null  float64 \n",
-      " 39  CovidPos                   676777 non-null  float64 \n",
+      " 0   State                      676361 non-null  category\n",
+      " 1   Male                       676361 non-null  float64 \n",
+      " 2   GeneralHealth              676361 non-null  float64 \n",
+      " 3   PhysicalHealthDays         676361 non-null  float64 \n",
+      " 4   MentalHealthDays           676361 non-null  float64 \n",
+      " 5   LastCheckupTime            676361 non-null  category\n",
+      " 6   PhysicalActivities         676361 non-null  float64 \n",
+      " 7   SleepHours                 676361 non-null  float64 \n",
+      " 8   RemovedTeeth               676361 non-null  float64 \n",
+      " 9   HadHeartAttack             676361 non-null  float64 \n",
+      " 10  HadAngina                  676361 non-null  float64 \n",
+      " 11  HadStroke                  676361 non-null  float64 \n",
+      " 12  HadAsthma                  676361 non-null  float64 \n",
+      " 13  HadSkinCancer              676361 non-null  float64 \n",
+      " 14  HadCOPD                    676361 non-null  float64 \n",
+      " 15  HadDepressiveDisorder      676361 non-null  float64 \n",
+      " 16  HadKidneyDisease           676361 non-null  float64 \n",
+      " 17  HadArthritis               676361 non-null  float64 \n",
+      " 18  HadDiabetes                676361 non-null  float64 \n",
+      " 19  DeafOrHardOfHearing        676361 non-null  float64 \n",
+      " 20  BlindOrVisionDifficulty    676361 non-null  float64 \n",
+      " 21  DifficultyConcentrating    676361 non-null  float64 \n",
+      " 22  DifficultyWalking          676361 non-null  float64 \n",
+      " 23  DifficultyDressingBathing  676361 non-null  float64 \n",
+      " 24  DifficultyErrands          676361 non-null  float64 \n",
+      " 25  SmokerStatus               676361 non-null  float64 \n",
+      " 26  ECigaretteUsage            676361 non-null  float64 \n",
+      " 27  ChestScan                  676361 non-null  float64 \n",
+      " 28  RaceEthnicityCategory      676361 non-null  category\n",
+      " 29  AgeCategory                676361 non-null  category\n",
+      " 30  HeightInMeters             676361 non-null  float64 \n",
+      " 31  WeightInKilograms          676361 non-null  float64 \n",
+      " 32  BMI                        676361 non-null  float64 \n",
+      " 33  AlcoholDrinkers            676361 non-null  float64 \n",
+      " 34  HIVTesting                 676361 non-null  float64 \n",
+      " 35  FluVaxLast12               676361 non-null  float64 \n",
+      " 36  PneumoVaxEver              676361 non-null  float64 \n",
+      " 37  TetanusLast10Tdap          676361 non-null  float64 \n",
+      " 38  HighRiskLastYear           676361 non-null  float64 \n",
+      " 39  CovidPos                   676361 non-null  float64 \n",
       "dtypes: category(4), float64(36)\n",
-      "memory usage: 188.5 MB\n"
+      "memory usage: 188.4 MB\n"
      ]
     }
    ],
@@ -1612,14 +1559,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 144,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 44514 entries, 66965 to 224311\n",
+      "Int64Index: 44514 entries, 399706 to 77011\n",
       "Data columns (total 40 columns):\n",
       " #   Column                     Non-Null Count  Dtype   \n",
       "---  ------                     --------------  -----   \n",
@@ -1659,7 +1606,7 @@
       " 33  AlcoholDrinkers            44514 non-null  float64 \n",
       " 34  HIVTesting                 44514 non-null  float64 \n",
       " 35  FluVaxLast12               44514 non-null  float64 \n",
-      " 36  PneumoVaxEver              44514 non-null  float64 \n",
+      " 36  PneumoVaxEver              36781 non-null  float64 \n",
       " 37  TetanusLast10Tdap          44514 non-null  float64 \n",
       " 38  HighRiskLastYear           44514 non-null  float64 \n",
       " 39  CovidPos                   44514 non-null  float64 \n",
@@ -1677,7 +1624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 144,
    "outputs": [],
    "source": [],
    "metadata": {