Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Uninsured in America"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aren Carpenter - DS Cohort 062220\n",
    "\n",
    "I will be creating a linear regression model as an explanatory tool to explore the factors that impact the rate of uninsured in the United States. People without health insurance can receive a lower standard of care due to their inability to pay for necessary operations or even routine checkups. Previous research has identified rural counties as harder hit by this phenomenon. \n",
    "\n",
    "My model will utilize community health indicators from the CDC and demographic/financial data from the Census Bureau for each county in the United States. With this data, I hope to provide comment on how to lower the rate of uninsured."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries to Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.stats as sms\n",
    "import scipy.stats\n",
    "\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "pd.set_option('display.max_columns', 50)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load In Four Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/arencarpenter/Desktop/chsi_dataset/RISKFACTORSANDACCESSTOCARE.csv', na_values=['-1111.1', '-2222', '-2222.2'])\n",
    "df_rural_urban = pd.read_csv('/Users/arencarpenter/Desktop/PctUrbanRural_County.csv')\n",
    "df_income = pd.read_csv('/Users/arencarpenter/Desktop/2015 Median Income by County.csv')\n",
    "df_demo = pd.read_csv('/Users/arencarpenter/Desktop/demographics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>Median household income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>51281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>50254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>32964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>38678.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>45813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Sublette County</td>\n",
       "      <td>81772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3137</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Sweetwater County</td>\n",
       "      <td>69022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3138</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton County</td>\n",
       "      <td>75325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Uinta County</td>\n",
       "      <td>56569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3140</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie County</td>\n",
       "      <td>47652.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3141 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State             County  Median household income\n",
       "0     Alabama     Autauga County                  51281.0\n",
       "1     Alabama     Baldwin County                  50254.0\n",
       "2     Alabama     Barbour County                  32964.0\n",
       "3     Alabama        Bibb County                  38678.0\n",
       "4     Alabama      Blount County                  45813.0\n",
       "...       ...                ...                      ...\n",
       "3136  Wyoming    Sublette County                  81772.0\n",
       "3137  Wyoming  Sweetwater County                  69022.0\n",
       "3138  Wyoming       Teton County                  75325.0\n",
       "3139  Wyoming       Uinta County                  56569.0\n",
       "3140  Wyoming    Washakie County                  47652.0\n",
       "\n",
       "[3141 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income = df_income.iloc[:3141] # Select only the 3,141 counties in the US, not territories.\n",
    "df_income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primary Care Physician Rate and Dentist Rate are reported per 100k population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Community_Health_Center_Ind = df.Community_Health_Center_Ind - 1  # These were reported as 1 and 2, not 0 and 1. \n",
    "df.HPSA_Ind = df.HPSA_Ind - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, df_rural_urban, df_income, df_demo], axis=1) # Concatenate our datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The community health indicators dataset included confidence intervals as separate columns for each of the values. So dropped those as they don't provide additional information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['CI_Min_No_Exercise', 'CI_Max_No_Exercise', 'CI_Min_Fruit_Veg', 'CI_Max_Fruit_Veg', 'CI_Min_Obesity', \n",
    "               'CI_Max_Obesity', 'CI_Min_High_Blood_Pres', 'CI_Max_High_Blood_Pres', 'CI_Min_Smoker', 'CI_Max_Smoker',\n",
    "               'CI_Min_Diabetes', 'CI_Max_Diabetes', 'STATE', 'COUNTY', 'STATENAME', 'COUNTYNAME', 'POPDEN_URBAN',\n",
    "               'POPDEN_RURAL', 'County_FIPS_Code', 'State', 'County', 'county', 'state', 'pctprivatecoveragealone'], \n",
    "        inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.State_FIPS_Code.notnull()] # Remove US territories from the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3141, 49)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # We now have only the 3141 US counties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_FIPS_Code</th>\n",
       "      <th>CHSI_County_Name</th>\n",
       "      <th>CHSI_State_Name</th>\n",
       "      <th>CHSI_State_Abbr</th>\n",
       "      <th>Strata_ID_Number</th>\n",
       "      <th>No_Exercise</th>\n",
       "      <th>Few_Fruit_Veg</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>High_Blood_Pres</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Uninsured</th>\n",
       "      <th>Elderly_Medicare</th>\n",
       "      <th>Disabled_Medicare</th>\n",
       "      <th>Prim_Care_Phys_Rate</th>\n",
       "      <th>Dentist_Rate</th>\n",
       "      <th>Community_Health_Center_Ind</th>\n",
       "      <th>HPSA_Ind</th>\n",
       "      <th>POP_COU</th>\n",
       "      <th>POP_URBAN</th>\n",
       "      <th>POPPCT_URBAN</th>\n",
       "      <th>AREA_URBAN</th>\n",
       "      <th>AREAPCT_URBAN</th>\n",
       "      <th>POP_RURAL</th>\n",
       "      <th>POPPCT_RURAL</th>\n",
       "      <th>AREA_RURAL</th>\n",
       "      <th>AREAPCT_RURAL</th>\n",
       "      <th>Median household income</th>\n",
       "      <th>medincome</th>\n",
       "      <th>povertypercent</th>\n",
       "      <th>medianagemale</th>\n",
       "      <th>medianagefemale</th>\n",
       "      <th>percentmarried</th>\n",
       "      <th>no_hs_24</th>\n",
       "      <th>hs_24</th>\n",
       "      <th>college_24</th>\n",
       "      <th>hs_25</th>\n",
       "      <th>college_25</th>\n",
       "      <th>pctemployed</th>\n",
       "      <th>pctunemployed</th>\n",
       "      <th>pctprivatecoverage</th>\n",
       "      <th>pctempprivcoverage</th>\n",
       "      <th>pctpubliccoverage</th>\n",
       "      <th>pctpubliccoveragealone</th>\n",
       "      <th>pctwhite</th>\n",
       "      <th>pctblack</th>\n",
       "      <th>pctasian</th>\n",
       "      <th>pctotherrace</th>\n",
       "      <th>pctmarriedhouseholds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>78.6</td>\n",
       "      <td>24.5</td>\n",
       "      <td>29.1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>14.2</td>\n",
       "      <td>5690.0</td>\n",
       "      <td>4762.0</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>45.3</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54571</td>\n",
       "      <td>31650</td>\n",
       "      <td>58.00</td>\n",
       "      <td>50882539</td>\n",
       "      <td>3.30</td>\n",
       "      <td>22921</td>\n",
       "      <td>42.00</td>\n",
       "      <td>1.488700e+09</td>\n",
       "      <td>96.70</td>\n",
       "      <td>51281.0</td>\n",
       "      <td>54366.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>36.7</td>\n",
       "      <td>38.4</td>\n",
       "      <td>54.8</td>\n",
       "      <td>19.1</td>\n",
       "      <td>38.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>33.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>73.1</td>\n",
       "      <td>46.1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>77.399902</td>\n",
       "      <td>18.679488</td>\n",
       "      <td>0.967023</td>\n",
       "      <td>0.925373</td>\n",
       "      <td>56.069818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.2</td>\n",
       "      <td>76.2</td>\n",
       "      <td>23.6</td>\n",
       "      <td>30.5</td>\n",
       "      <td>24.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>19798.0</td>\n",
       "      <td>22635.0</td>\n",
       "      <td>3839.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>182265</td>\n",
       "      <td>105205</td>\n",
       "      <td>57.72</td>\n",
       "      <td>275001368</td>\n",
       "      <td>6.68</td>\n",
       "      <td>77060</td>\n",
       "      <td>42.28</td>\n",
       "      <td>3.842520e+09</td>\n",
       "      <td>93.32</td>\n",
       "      <td>50254.0</td>\n",
       "      <td>49626.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.9</td>\n",
       "      <td>43.3</td>\n",
       "      <td>53.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>33.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>28.8</td>\n",
       "      <td>19.3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>33.7</td>\n",
       "      <td>17.5</td>\n",
       "      <td>86.431496</td>\n",
       "      <td>9.601734</td>\n",
       "      <td>0.669841</td>\n",
       "      <td>0.958380</td>\n",
       "      <td>53.935010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5126.0</td>\n",
       "      <td>3288.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27457</td>\n",
       "      <td>8844</td>\n",
       "      <td>32.21</td>\n",
       "      <td>17447656</td>\n",
       "      <td>0.76</td>\n",
       "      <td>18613</td>\n",
       "      <td>67.79</td>\n",
       "      <td>2.274371e+09</td>\n",
       "      <td>99.24</td>\n",
       "      <td>32964.0</td>\n",
       "      <td>34971.0</td>\n",
       "      <td>25.4</td>\n",
       "      <td>36.6</td>\n",
       "      <td>41.9</td>\n",
       "      <td>41.5</td>\n",
       "      <td>25.1</td>\n",
       "      <td>43.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>34.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>52.7</td>\n",
       "      <td>30.3</td>\n",
       "      <td>46.6</td>\n",
       "      <td>26.5</td>\n",
       "      <td>47.363731</td>\n",
       "      <td>46.765929</td>\n",
       "      <td>0.905985</td>\n",
       "      <td>3.445715</td>\n",
       "      <td>43.276946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.1</td>\n",
       "      <td>3315.0</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22915</td>\n",
       "      <td>7252</td>\n",
       "      <td>31.65</td>\n",
       "      <td>18903236</td>\n",
       "      <td>1.17</td>\n",
       "      <td>15663</td>\n",
       "      <td>68.35</td>\n",
       "      <td>1.593578e+09</td>\n",
       "      <td>98.83</td>\n",
       "      <td>38678.0</td>\n",
       "      <td>39546.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>40.4</td>\n",
       "      <td>49.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>33.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>42.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>45.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>64.8</td>\n",
       "      <td>43.8</td>\n",
       "      <td>39.4</td>\n",
       "      <td>22.7</td>\n",
       "      <td>76.654574</td>\n",
       "      <td>21.438683</td>\n",
       "      <td>0.092904</td>\n",
       "      <td>0.030968</td>\n",
       "      <td>57.335990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Blount</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>74.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8131.0</td>\n",
       "      <td>5019.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57322</td>\n",
       "      <td>5760</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10874700</td>\n",
       "      <td>0.65</td>\n",
       "      <td>51562</td>\n",
       "      <td>89.95</td>\n",
       "      <td>1.659087e+09</td>\n",
       "      <td>99.35</td>\n",
       "      <td>45813.0</td>\n",
       "      <td>45567.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>39.6</td>\n",
       "      <td>41.9</td>\n",
       "      <td>59.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>33.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>48.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>66.2</td>\n",
       "      <td>44.7</td>\n",
       "      <td>35.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>95.097903</td>\n",
       "      <td>1.531797</td>\n",
       "      <td>0.143823</td>\n",
       "      <td>0.961705</td>\n",
       "      <td>59.439854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Bullock</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>75.0</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>2295.0</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>54.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10914</td>\n",
       "      <td>5307</td>\n",
       "      <td>48.63</td>\n",
       "      <td>12675709</td>\n",
       "      <td>0.79</td>\n",
       "      <td>5607</td>\n",
       "      <td>51.37</td>\n",
       "      <td>1.600381e+09</td>\n",
       "      <td>99.21</td>\n",
       "      <td>31938.0</td>\n",
       "      <td>26580.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>37.9</td>\n",
       "      <td>40.6</td>\n",
       "      <td>28.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>44.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>48.1</td>\n",
       "      <td>32.4</td>\n",
       "      <td>44.3</td>\n",
       "      <td>28.9</td>\n",
       "      <td>26.643566</td>\n",
       "      <td>70.706125</td>\n",
       "      <td>0.206031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.794733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>4055.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>43.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20947</td>\n",
       "      <td>6026</td>\n",
       "      <td>28.77</td>\n",
       "      <td>10503875</td>\n",
       "      <td>0.52</td>\n",
       "      <td>14921</td>\n",
       "      <td>71.23</td>\n",
       "      <td>2.001473e+09</td>\n",
       "      <td>99.48</td>\n",
       "      <td>32229.0</td>\n",
       "      <td>41123.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>40.6</td>\n",
       "      <td>48.2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>28.3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>31.8</td>\n",
       "      <td>10.7</td>\n",
       "      <td>50.8</td>\n",
       "      <td>12.3</td>\n",
       "      <td>65.0</td>\n",
       "      <td>39.3</td>\n",
       "      <td>39.7</td>\n",
       "      <td>20.2</td>\n",
       "      <td>75.110589</td>\n",
       "      <td>20.484706</td>\n",
       "      <td>0.857280</td>\n",
       "      <td>0.996159</td>\n",
       "      <td>47.191832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Calhoun</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>81.9</td>\n",
       "      <td>27.0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>25.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>16456.0</td>\n",
       "      <td>16440.0</td>\n",
       "      <td>5301.0</td>\n",
       "      <td>75.8</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118572</td>\n",
       "      <td>78617</td>\n",
       "      <td>66.30</td>\n",
       "      <td>210145581</td>\n",
       "      <td>13.39</td>\n",
       "      <td>39955</td>\n",
       "      <td>33.70</td>\n",
       "      <td>1.359044e+09</td>\n",
       "      <td>86.61</td>\n",
       "      <td>41703.0</td>\n",
       "      <td>34116.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>41.5</td>\n",
       "      <td>43.9</td>\n",
       "      <td>43.9</td>\n",
       "      <td>14.2</td>\n",
       "      <td>42.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>38.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>49.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>57.7</td>\n",
       "      <td>38.6</td>\n",
       "      <td>42.2</td>\n",
       "      <td>24.1</td>\n",
       "      <td>57.666011</td>\n",
       "      <td>40.312216</td>\n",
       "      <td>0.762933</td>\n",
       "      <td>0.035212</td>\n",
       "      <td>40.552695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Chambers</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>84.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.7</td>\n",
       "      <td>17.1</td>\n",
       "      <td>5533.0</td>\n",
       "      <td>5473.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34215</td>\n",
       "      <td>17399</td>\n",
       "      <td>50.85</td>\n",
       "      <td>54790707</td>\n",
       "      <td>3.55</td>\n",
       "      <td>16816</td>\n",
       "      <td>49.15</td>\n",
       "      <td>1.490219e+09</td>\n",
       "      <td>96.45</td>\n",
       "      <td>34177.0</td>\n",
       "      <td>38013.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>44.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>62.1</td>\n",
       "      <td>15.8</td>\n",
       "      <td>38.3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>36.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>47.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>66.3</td>\n",
       "      <td>39.9</td>\n",
       "      <td>43.3</td>\n",
       "      <td>20.3</td>\n",
       "      <td>92.921409</td>\n",
       "      <td>4.925408</td>\n",
       "      <td>0.338357</td>\n",
       "      <td>0.065365</td>\n",
       "      <td>57.173258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Cherokee</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6</td>\n",
       "      <td>13.3</td>\n",
       "      <td>3583.0</td>\n",
       "      <td>3086.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>12.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25989</td>\n",
       "      <td>3707</td>\n",
       "      <td>14.26</td>\n",
       "      <td>26419773</td>\n",
       "      <td>1.84</td>\n",
       "      <td>22282</td>\n",
       "      <td>85.74</td>\n",
       "      <td>1.407656e+09</td>\n",
       "      <td>98.16</td>\n",
       "      <td>36296.0</td>\n",
       "      <td>41450.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>37.6</td>\n",
       "      <td>39.6</td>\n",
       "      <td>54.4</td>\n",
       "      <td>13.5</td>\n",
       "      <td>41.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>39.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>52.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>41.3</td>\n",
       "      <td>35.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>84.045734</td>\n",
       "      <td>10.333417</td>\n",
       "      <td>0.287546</td>\n",
       "      <td>4.119218</td>\n",
       "      <td>54.862351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Chilton</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.3</td>\n",
       "      <td>82.8</td>\n",
       "      <td>31.2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6130.0</td>\n",
       "      <td>4527.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43643</td>\n",
       "      <td>5785</td>\n",
       "      <td>13.26</td>\n",
       "      <td>17302027</td>\n",
       "      <td>0.96</td>\n",
       "      <td>37858</td>\n",
       "      <td>86.74</td>\n",
       "      <td>1.777182e+09</td>\n",
       "      <td>99.04</td>\n",
       "      <td>41627.0</td>\n",
       "      <td>35049.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>43.8</td>\n",
       "      <td>47.1</td>\n",
       "      <td>48.3</td>\n",
       "      <td>17.4</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>40.4</td>\n",
       "      <td>13.6</td>\n",
       "      <td>56.0</td>\n",
       "      <td>34.3</td>\n",
       "      <td>44.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>55.893990</td>\n",
       "      <td>43.307204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014931</td>\n",
       "      <td>45.201794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Choctaw</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2677.0</td>\n",
       "      <td>2148.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13859</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13859</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2.365955e+09</td>\n",
       "      <td>100.00</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>36620.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>55.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>43.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>40.5</td>\n",
       "      <td>19.4</td>\n",
       "      <td>56.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>43.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>53.558037</td>\n",
       "      <td>45.257280</td>\n",
       "      <td>0.358995</td>\n",
       "      <td>0.095732</td>\n",
       "      <td>46.622324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Clarke</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>51.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.8</td>\n",
       "      <td>4593.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25833</td>\n",
       "      <td>6205</td>\n",
       "      <td>24.02</td>\n",
       "      <td>18272532</td>\n",
       "      <td>0.57</td>\n",
       "      <td>19628</td>\n",
       "      <td>75.98</td>\n",
       "      <td>3.189337e+09</td>\n",
       "      <td>99.43</td>\n",
       "      <td>32011.0</td>\n",
       "      <td>35940.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>43.2</td>\n",
       "      <td>45.1</td>\n",
       "      <td>52.3</td>\n",
       "      <td>18.9</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>36.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>47.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>60.3</td>\n",
       "      <td>34.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>21.6</td>\n",
       "      <td>81.738938</td>\n",
       "      <td>14.500997</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.418335</td>\n",
       "      <td>50.802731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Clay</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.9</td>\n",
       "      <td>12.2</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>2198.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>35.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13932</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13932</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.564252e+09</td>\n",
       "      <td>100.00</td>\n",
       "      <td>35327.0</td>\n",
       "      <td>40418.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>56.7</td>\n",
       "      <td>18.7</td>\n",
       "      <td>42.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>38.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>51.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>59.9</td>\n",
       "      <td>38.8</td>\n",
       "      <td>40.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93.974137</td>\n",
       "      <td>2.946274</td>\n",
       "      <td>0.353286</td>\n",
       "      <td>0.566591</td>\n",
       "      <td>55.869114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Cleburne</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2107.0</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14972</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14972</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.450653e+09</td>\n",
       "      <td>100.00</td>\n",
       "      <td>38056.0</td>\n",
       "      <td>46931.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>37.6</td>\n",
       "      <td>39.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>15.2</td>\n",
       "      <td>52.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>68.1</td>\n",
       "      <td>33.4</td>\n",
       "      <td>36.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>76.776590</td>\n",
       "      <td>17.351230</td>\n",
       "      <td>1.232214</td>\n",
       "      <td>0.782171</td>\n",
       "      <td>49.885177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    State_FIPS_Code CHSI_County_Name CHSI_State_Name CHSI_State_Abbr  \\\n",
       "0               1.0          Autauga         Alabama              AL   \n",
       "1               1.0          Baldwin         Alabama              AL   \n",
       "2               1.0          Barbour         Alabama              AL   \n",
       "3               1.0             Bibb         Alabama              AL   \n",
       "4               1.0           Blount         Alabama              AL   \n",
       "5               1.0          Bullock         Alabama              AL   \n",
       "6               1.0           Butler         Alabama              AL   \n",
       "7               1.0          Calhoun         Alabama              AL   \n",
       "8               1.0         Chambers         Alabama              AL   \n",
       "9               1.0         Cherokee         Alabama              AL   \n",
       "10              1.0          Chilton         Alabama              AL   \n",
       "11              1.0          Choctaw         Alabama              AL   \n",
       "12              1.0           Clarke         Alabama              AL   \n",
       "13              1.0             Clay         Alabama              AL   \n",
       "14              1.0         Cleburne         Alabama              AL   \n",
       "\n",
       "    Strata_ID_Number  No_Exercise  Few_Fruit_Veg  Obesity  High_Blood_Pres  \\\n",
       "0               29.0         27.8           78.6     24.5             29.1   \n",
       "1               16.0         27.2           76.2     23.6             30.5   \n",
       "2               51.0          NaN            NaN     25.6              NaN   \n",
       "3               42.0          NaN           86.6      NaN              NaN   \n",
       "4               28.0         33.5           74.6     24.2              NaN   \n",
       "5               75.0          NaN            NaN      NaN              NaN   \n",
       "6               76.0         24.5            NaN     22.0              NaN   \n",
       "7                6.0         29.2           81.9     27.0             33.2   \n",
       "8               50.0         34.7           84.6      NaN              NaN   \n",
       "9               64.0          NaN            NaN      NaN              NaN   \n",
       "10              32.0         30.3           82.8     31.2             26.5   \n",
       "11              66.0          NaN            NaN      NaN              NaN   \n",
       "12              51.0         31.5            NaN      NaN              NaN   \n",
       "13              63.0          NaN            NaN      NaN              NaN   \n",
       "14              41.0          NaN            NaN      NaN              NaN   \n",
       "\n",
       "    Smoker  Diabetes  Uninsured  Elderly_Medicare  Disabled_Medicare  \\\n",
       "0     26.6      14.2     5690.0            4762.0             1209.0   \n",
       "1     24.6       7.2    19798.0           22635.0             3839.0   \n",
       "2     17.7       6.6     5126.0            3288.0             1092.0   \n",
       "3      NaN      13.1     3315.0            2390.0              974.0   \n",
       "4     23.6       8.4     8131.0            5019.0             1300.0   \n",
       "5      NaN       NaN     2295.0            1433.0              504.0   \n",
       "6     27.3       9.5     4055.0            3150.0              952.0   \n",
       "7     25.5      11.2    16456.0           16440.0             5301.0   \n",
       "8     11.7      17.1     5533.0            5473.0             1497.0   \n",
       "9     23.6      13.3     3583.0            3086.0              854.0   \n",
       "10    30.0       6.0     6130.0            4527.0             1307.0   \n",
       "11    17.1      13.5     2677.0            2148.0              800.0   \n",
       "12     NaN      15.8     4593.0            3850.0             1150.0   \n",
       "13    23.9      12.2     2320.0            2198.0              610.0   \n",
       "14     NaN      10.5     2107.0            1852.0              646.0   \n",
       "\n",
       "    Prim_Care_Phys_Rate  Dentist_Rate  Community_Health_Center_Ind  HPSA_Ind  \\\n",
       "0                  45.3          22.6                          0.0       1.0   \n",
       "1                  67.0          30.8                          0.0       1.0   \n",
       "2                  45.8          24.6                          0.0       1.0   \n",
       "3                  41.8          18.6                          0.0       0.0   \n",
       "4                  16.2          10.8                          1.0       0.0   \n",
       "5                  54.3          18.1                          0.0       0.0   \n",
       "6                  43.3          19.3                          0.0       1.0   \n",
       "7                  75.8          41.9                          0.0       1.0   \n",
       "8                  53.6          22.6                          0.0       1.0   \n",
       "9                  28.5          12.2                          1.0       0.0   \n",
       "10                 31.1          19.2                          0.0       1.0   \n",
       "11                 33.8          20.3                          0.0       0.0   \n",
       "12                 55.0          29.3                          1.0       1.0   \n",
       "13                 35.8          14.3                          1.0       0.0   \n",
       "14                  0.0           6.9                          1.0       0.0   \n",
       "\n",
       "    POP_COU  POP_URBAN  POPPCT_URBAN  AREA_URBAN  AREAPCT_URBAN  POP_RURAL  \\\n",
       "0     54571      31650         58.00    50882539           3.30      22921   \n",
       "1    182265     105205         57.72   275001368           6.68      77060   \n",
       "2     27457       8844         32.21    17447656           0.76      18613   \n",
       "3     22915       7252         31.65    18903236           1.17      15663   \n",
       "4     57322       5760         10.05    10874700           0.65      51562   \n",
       "5     10914       5307         48.63    12675709           0.79       5607   \n",
       "6     20947       6026         28.77    10503875           0.52      14921   \n",
       "7    118572      78617         66.30   210145581          13.39      39955   \n",
       "8     34215      17399         50.85    54790707           3.55      16816   \n",
       "9     25989       3707         14.26    26419773           1.84      22282   \n",
       "10    43643       5785         13.26    17302027           0.96      37858   \n",
       "11    13859          0          0.00           0           0.00      13859   \n",
       "12    25833       6205         24.02    18272532           0.57      19628   \n",
       "13    13932          0          0.00           0           0.00      13932   \n",
       "14    14972          0          0.00           0           0.00      14972   \n",
       "\n",
       "    POPPCT_RURAL    AREA_RURAL  AREAPCT_RURAL  Median household income  \\\n",
       "0          42.00  1.488700e+09          96.70                  51281.0   \n",
       "1          42.28  3.842520e+09          93.32                  50254.0   \n",
       "2          67.79  2.274371e+09          99.24                  32964.0   \n",
       "3          68.35  1.593578e+09          98.83                  38678.0   \n",
       "4          89.95  1.659087e+09          99.35                  45813.0   \n",
       "5          51.37  1.600381e+09          99.21                  31938.0   \n",
       "6          71.23  2.001473e+09          99.48                  32229.0   \n",
       "7          33.70  1.359044e+09          86.61                  41703.0   \n",
       "8          49.15  1.490219e+09          96.45                  34177.0   \n",
       "9          85.74  1.407656e+09          98.16                  36296.0   \n",
       "10         86.74  1.777182e+09          99.04                  41627.0   \n",
       "11        100.00  2.365955e+09         100.00                  33536.0   \n",
       "12         75.98  3.189337e+09          99.43                  32011.0   \n",
       "13        100.00  1.564252e+09         100.00                  35327.0   \n",
       "14        100.00  1.450653e+09         100.00                  38056.0   \n",
       "\n",
       "    medincome  povertypercent  medianagemale  medianagefemale  percentmarried  \\\n",
       "0     54366.0            13.1           36.7             38.4            54.8   \n",
       "1     49626.0            13.0           40.9             43.3            53.8   \n",
       "2     34971.0            25.4           36.6             41.9            41.5   \n",
       "3     39546.0            18.1           37.8             40.4            49.0   \n",
       "4     45567.0            17.5           39.6             41.9            59.0   \n",
       "5     26580.0            35.1           37.9             40.6            28.9   \n",
       "6     41123.0            20.5           37.5             40.6            48.2   \n",
       "7     34116.0            21.3           41.5             43.9            43.9   \n",
       "8     38013.0            18.6           44.6             46.2            62.1   \n",
       "9     41450.0            18.1           37.6             39.6            54.4   \n",
       "10    35049.0            25.0           43.8             47.1            48.3   \n",
       "11    36620.0            24.9           40.0             42.3            45.5   \n",
       "12    35940.0            19.5           43.2             45.1            52.3   \n",
       "13    40418.0            17.0           41.0             41.5            56.7   \n",
       "14    46931.0            16.8           37.6             39.7            51.0   \n",
       "\n",
       "    no_hs_24  hs_24  college_24  hs_25  college_25  pctemployed  \\\n",
       "0       19.1   38.8         5.4   33.5        13.5          NaN   \n",
       "1       17.7   33.4         8.6   28.8        19.3         55.0   \n",
       "2       25.1   43.4         1.8   34.5         7.5         39.5   \n",
       "3       25.3   33.1         1.2   42.1         6.1         45.2   \n",
       "4       19.4   33.3         3.0   33.4         8.6         48.4   \n",
       "5        8.2   65.5         0.0   33.3         6.5         44.7   \n",
       "6       13.9   28.3         4.8   31.8        10.7         50.8   \n",
       "7       14.2   42.6         3.9   38.4         7.6         49.8   \n",
       "8       15.8   38.3         5.5   36.4         7.9         47.8   \n",
       "9       13.5   41.9         3.6   39.2         9.1         52.1   \n",
       "10      17.4   43.1         2.8   36.8         7.4         40.4   \n",
       "11      23.5   55.9         1.1   43.9         8.4         40.5   \n",
       "12      18.9   40.0         3.4   36.3         5.8         47.4   \n",
       "13      18.7   42.1         6.9   38.3         6.9         51.1   \n",
       "14      19.9   29.3         8.2   29.5        15.2         52.1   \n",
       "\n",
       "    pctunemployed  pctprivatecoverage  pctempprivcoverage  pctpubliccoverage  \\\n",
       "0             7.6                73.1                46.1               30.0   \n",
       "1             7.5                67.6                42.6               33.7   \n",
       "2            17.6                52.7                30.3               46.6   \n",
       "3             8.3                64.8                43.8               39.4   \n",
       "4             7.7                66.2                44.7               35.9   \n",
       "5            18.0                48.1                32.4               44.3   \n",
       "6            12.3                65.0                39.3               39.7   \n",
       "7             8.9                57.7                38.6               42.2   \n",
       "8             7.9                66.3                39.9               43.3   \n",
       "9             9.1                60.8                41.3               35.1   \n",
       "10           13.6                56.0                34.3               44.8   \n",
       "11           19.4                56.0                36.7               43.2   \n",
       "12            9.4                60.3                34.2               42.5   \n",
       "13            8.3                59.9                38.8               40.4   \n",
       "14            7.1                68.1                33.4               36.3   \n",
       "\n",
       "    pctpubliccoveragealone   pctwhite   pctblack  pctasian  pctotherrace  \\\n",
       "0                     14.3  77.399902  18.679488  0.967023      0.925373   \n",
       "1                     17.5  86.431496   9.601734  0.669841      0.958380   \n",
       "2                     26.5  47.363731  46.765929  0.905985      3.445715   \n",
       "3                     22.7  76.654574  21.438683  0.092904      0.030968   \n",
       "4                     19.0  95.097903   1.531797  0.143823      0.961705   \n",
       "5                     28.9  26.643566  70.706125  0.206031      0.000000   \n",
       "6                     20.2  75.110589  20.484706  0.857280      0.996159   \n",
       "7                     24.1  57.666011  40.312216  0.762933      0.035212   \n",
       "8                     20.3  92.921409   4.925408  0.338357      0.065365   \n",
       "9                     20.4  84.045734  10.333417  0.287546      4.119218   \n",
       "10                    24.2  55.893990  43.307204  0.000000      0.014931   \n",
       "11                    24.3  53.558037  45.257280  0.358995      0.095732   \n",
       "12                    21.6  81.738938  14.500997  0.000000      1.418335   \n",
       "13                    22.0  93.974137   2.946274  0.353286      0.566591   \n",
       "14                    18.0  76.776590  17.351230  1.232214      0.782171   \n",
       "\n",
       "    pctmarriedhouseholds  \n",
       "0              56.069818  \n",
       "1              53.935010  \n",
       "2              43.276946  \n",
       "3              57.335990  \n",
       "4              59.439854  \n",
       "5              31.794733  \n",
       "6              47.191832  \n",
       "7              40.552695  \n",
       "8              57.173258  \n",
       "9              54.862351  \n",
       "10             45.201794  \n",
       "11             46.622324  \n",
       "12             50.802731  \n",
       "13             55.869114  \n",
       "14             49.885177  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15) # Let's look at all of our columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Missing Community Indicators\n",
    "\n",
    "Using the state average for each missing community health indicator. I looked at histograms for the CHSI values and they were normally distributed, so I used the mean to impute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_state_avg(column, state):\n",
    "    state_avg = df[df.CHSI_State_Name == state][column].mean()\n",
    "    df[column] = df[column].fillna(state_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = df.CHSI_State_Name.unique()\n",
    "columns = ['No_Exercise','Few_Fruit_Veg', 'Obesity', 'High_Blood_Pres', 'Smoker', 'Diabetes', 'Elderly_Medicare', \n",
    "           'Disabled_Medicare', 'Dentist_Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute():\n",
    "    for column in columns:\n",
    "        for state in states:\n",
    "            impute_state_avg(column, state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "impute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Missing Values from Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_FIPS_Code</th>\n",
       "      <th>CHSI_County_Name</th>\n",
       "      <th>CHSI_State_Name</th>\n",
       "      <th>CHSI_State_Abbr</th>\n",
       "      <th>Strata_ID_Number</th>\n",
       "      <th>No_Exercise</th>\n",
       "      <th>Few_Fruit_Veg</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>High_Blood_Pres</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Uninsured</th>\n",
       "      <th>Elderly_Medicare</th>\n",
       "      <th>Disabled_Medicare</th>\n",
       "      <th>Prim_Care_Phys_Rate</th>\n",
       "      <th>Dentist_Rate</th>\n",
       "      <th>Community_Health_Center_Ind</th>\n",
       "      <th>HPSA_Ind</th>\n",
       "      <th>POP_COU</th>\n",
       "      <th>POP_URBAN</th>\n",
       "      <th>POPPCT_URBAN</th>\n",
       "      <th>AREA_URBAN</th>\n",
       "      <th>AREAPCT_URBAN</th>\n",
       "      <th>POP_RURAL</th>\n",
       "      <th>POPPCT_RURAL</th>\n",
       "      <th>AREA_RURAL</th>\n",
       "      <th>AREAPCT_RURAL</th>\n",
       "      <th>Median household income</th>\n",
       "      <th>medincome</th>\n",
       "      <th>povertypercent</th>\n",
       "      <th>medianagemale</th>\n",
       "      <th>medianagefemale</th>\n",
       "      <th>percentmarried</th>\n",
       "      <th>no_hs_24</th>\n",
       "      <th>hs_24</th>\n",
       "      <th>college_24</th>\n",
       "      <th>hs_25</th>\n",
       "      <th>college_25</th>\n",
       "      <th>pctemployed</th>\n",
       "      <th>pctunemployed</th>\n",
       "      <th>pctprivatecoverage</th>\n",
       "      <th>pctempprivcoverage</th>\n",
       "      <th>pctpubliccoverage</th>\n",
       "      <th>pctpubliccoveragealone</th>\n",
       "      <th>pctwhite</th>\n",
       "      <th>pctblack</th>\n",
       "      <th>pctasian</th>\n",
       "      <th>pctotherrace</th>\n",
       "      <th>pctmarriedhouseholds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>8.0</td>\n",
       "      <td>Broomfield</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>CO</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.400</td>\n",
       "      <td>80.08</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8486.253731</td>\n",
       "      <td>2246.58209</td>\n",
       "      <td>87.4</td>\n",
       "      <td>23.977612</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6499</td>\n",
       "      <td>4032</td>\n",
       "      <td>62.04</td>\n",
       "      <td>3651419</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2467</td>\n",
       "      <td>37.96</td>\n",
       "      <td>3.914641e+09</td>\n",
       "      <td>99.91</td>\n",
       "      <td>36791.0</td>\n",
       "      <td>83603.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>45.1</td>\n",
       "      <td>45.2</td>\n",
       "      <td>62.6</td>\n",
       "      <td>18.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>64.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>77.9</td>\n",
       "      <td>51.7</td>\n",
       "      <td>22.9</td>\n",
       "      <td>10.8</td>\n",
       "      <td>95.275624</td>\n",
       "      <td>0.528191</td>\n",
       "      <td>0.838399</td>\n",
       "      <td>0.301824</td>\n",
       "      <td>68.195319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>11.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>DC</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.200</td>\n",
       "      <td>68.10</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>26.7</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63403.000000</td>\n",
       "      <td>10262.00000</td>\n",
       "      <td>227.6</td>\n",
       "      <td>102.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>538479</td>\n",
       "      <td>513696</td>\n",
       "      <td>95.40</td>\n",
       "      <td>543209334</td>\n",
       "      <td>49.20</td>\n",
       "      <td>24783</td>\n",
       "      <td>4.60</td>\n",
       "      <td>5.608655e+08</td>\n",
       "      <td>50.80</td>\n",
       "      <td>65476.0</td>\n",
       "      <td>36788.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>39.1</td>\n",
       "      <td>48.7</td>\n",
       "      <td>49.8</td>\n",
       "      <td>37.6</td>\n",
       "      <td>20.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>10.1</td>\n",
       "      <td>43.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>52.8</td>\n",
       "      <td>28.7</td>\n",
       "      <td>43.3</td>\n",
       "      <td>20.9</td>\n",
       "      <td>81.991744</td>\n",
       "      <td>14.551084</td>\n",
       "      <td>0.756794</td>\n",
       "      <td>0.455796</td>\n",
       "      <td>50.507146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>15.0</td>\n",
       "      <td>Kalawao</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>37.0</td>\n",
       "      <td>29.795</td>\n",
       "      <td>80.08</td>\n",
       "      <td>26.897561</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.822917</td>\n",
       "      <td>9.651563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8486.253731</td>\n",
       "      <td>2246.58209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>185079</td>\n",
       "      <td>114766</td>\n",
       "      <td>62.01</td>\n",
       "      <td>224652200</td>\n",
       "      <td>2.15</td>\n",
       "      <td>70313</td>\n",
       "      <td>37.99</td>\n",
       "      <td>1.020890e+10</td>\n",
       "      <td>97.85</td>\n",
       "      <td>52108.0</td>\n",
       "      <td>44669.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>35.3</td>\n",
       "      <td>37.2</td>\n",
       "      <td>59.1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>33.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>59.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>50.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>37.6</td>\n",
       "      <td>22.4</td>\n",
       "      <td>86.437340</td>\n",
       "      <td>0.072212</td>\n",
       "      <td>0.597387</td>\n",
       "      <td>9.440032</td>\n",
       "      <td>59.883612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State_FIPS_Code CHSI_County_Name       CHSI_State_Name CHSI_State_Abbr  \\\n",
       "249              8.0       Broomfield              Colorado              CO   \n",
       "317             11.0       Washington  District of Columbia              DC   \n",
       "546             15.0          Kalawao                Hawaii              HI   \n",
       "\n",
       "     Strata_ID_Number  No_Exercise  Few_Fruit_Veg    Obesity  High_Blood_Pres  \\\n",
       "249              27.0       14.400          80.08  21.500000             15.9   \n",
       "317               3.0       22.200          68.10  21.300000             26.7   \n",
       "546              37.0       29.795          80.08  26.897561             31.6   \n",
       "\n",
       "        Smoker  Diabetes  Uninsured  Elderly_Medicare  Disabled_Medicare  \\\n",
       "249  17.400000  3.300000        NaN       8486.253731         2246.58209   \n",
       "317  20.400000  7.800000        NaN      63403.000000        10262.00000   \n",
       "546  23.822917  9.651563        NaN       8486.253731         2246.58209   \n",
       "\n",
       "     Prim_Care_Phys_Rate  Dentist_Rate  Community_Health_Center_Ind  HPSA_Ind  \\\n",
       "249                 87.4     23.977612                          1.0       1.0   \n",
       "317                227.6    102.800000                          0.0       1.0   \n",
       "546                  0.0      0.000000                          1.0       1.0   \n",
       "\n",
       "     POP_COU  POP_URBAN  POPPCT_URBAN  AREA_URBAN  AREAPCT_URBAN  POP_RURAL  \\\n",
       "249     6499       4032         62.04     3651419           0.09       2467   \n",
       "317   538479     513696         95.40   543209334          49.20      24783   \n",
       "546   185079     114766         62.01   224652200           2.15      70313   \n",
       "\n",
       "     POPPCT_RURAL    AREA_RURAL  AREAPCT_RURAL  Median household income  \\\n",
       "249         37.96  3.914641e+09          99.91                  36791.0   \n",
       "317          4.60  5.608655e+08          50.80                  65476.0   \n",
       "546         37.99  1.020890e+10          97.85                  52108.0   \n",
       "\n",
       "     medincome  povertypercent  medianagemale  medianagefemale  \\\n",
       "249    83603.0             5.9           45.1             45.2   \n",
       "317    36788.0            25.3           39.1             48.7   \n",
       "546    44669.0            17.1           35.3             37.2   \n",
       "\n",
       "     percentmarried  no_hs_24  hs_24  college_24  hs_25  college_25  \\\n",
       "249            62.6      18.2   32.9        11.0   27.0        22.0   \n",
       "317            49.8      37.6   20.3         1.4   35.4        10.1   \n",
       "546            59.1      23.0   44.2         4.3   33.1         8.9   \n",
       "\n",
       "     pctemployed  pctunemployed  pctprivatecoverage  pctempprivcoverage  \\\n",
       "249         64.7            6.7                77.9                51.7   \n",
       "317         43.9            9.4                52.8                28.7   \n",
       "546         59.9            3.8                50.5                27.3   \n",
       "\n",
       "     pctpubliccoverage  pctpubliccoveragealone   pctwhite   pctblack  \\\n",
       "249               22.9                    10.8  95.275624   0.528191   \n",
       "317               43.3                    20.9  81.991744  14.551084   \n",
       "546               37.6                    22.4  86.437340   0.072212   \n",
       "\n",
       "     pctasian  pctotherrace  pctmarriedhouseholds  \n",
       "249  0.838399      0.301824             68.195319  \n",
       "317  0.756794      0.455796             50.507146  \n",
       "546  0.597387      9.440032             59.883612  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Uninsured.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3141, 49)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna() # There was a subset of counties in remote areas that had very little data collected from them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2891, 49)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Correlated Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Disabled_Medicare', 'POP_URBAN', 'POPPCT_RURAL', 'AREAPCT_RURAL']\n"
     ]
    }
   ],
   "source": [
    "# Create correlation matrix\n",
    "corr_matrix = df.corr().abs()\n",
    "\n",
    "# Select upper triangle of correlation matrix\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "\n",
    "# Find features with correlation greater than 0.95\n",
    "to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]\n",
    "print(to_drop)\n",
    "# Drop features \n",
    "df.drop(to_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I had total number of uninsured, but in order to keep population from overwhelming the differences between counties, I converted to a percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Percent_Uninsured'] = round(df.Uninsured / df.POP_COU * 100, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hypothesized from the community health indicators that the presence of community health centers would affect counties differently depending on if they were rural or urban. Because there are fewer facilities in rural areas, I predicted that a community health center would have a greater affect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CHC_x_POPPCT_URBAN'] = df.Community_Health_Center_Ind * df.POPPCT_URBAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['HPSA_x_POPPCT_URBAN'] = df.HPSA_Ind * df.POPPCT_URBAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Median_Household_Income'] = df['Median household income'] # Renaming to remove spaces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of some differences in the datasets, some counties had greater than 100% uninsured rates, which are obvious outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.Percent_Uninsured <= 100] # Remove percents greater than 100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2599, 49)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA & Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by looking at the distribution of counties by the uninsured rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.hist(df.Percent_Uninsured, bins=100, density=True, color='tan')\n",
    "plt.title('Distribution of Counties by Percent Uninsured')\n",
    "plt.xlabel('Percent Uninsured')\n",
    "plt.ylabel('Percent of Counties')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most counties have less than 10% uninsured, but there is a wide range of percentages. The highest values are in counties with small, sparse populations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stat Test: Two Proportion Z-Test \n",
    "#### Do rural and urban counties have different incidence of Community Health Centers?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From research, using a 75% cutoff to define county. I.e. counties with greater than 75% of their population urban, are urban, otherwise they are rural."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rural = df[df.POPPCT_URBAN <= 75]\n",
    "df_urban = df[df.POPPCT_URBAN > 75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2091"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_rural)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "508"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_urban)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define presence of Community Health Center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rural_CHC = df_rural[df_rural.Community_Health_Center_Ind == 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1240"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_rural_CHC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_urban_CHC = df_urban[df_urban.Community_Health_Center_Ind == 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "253"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_urban_CHC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.8838732946017176, 0.00010280549625075338)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms.proportion.proportions_ztest([len(df_rural_CHC), len(df_urban_CHC)], [len(df_rural), len(df_urban)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-value = 0.0001, so can reject null hypothesis and conclude that there is a difference between rural and urban counties regarding the presence of community health centers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stat Test 2: Two Proportion Z-Test \n",
    "#### Do rural and urban counties have a different rate of underservedness?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define if the county is medically underserved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rural_HPSA = df_rural[df_rural.HPSA_Ind == 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1519"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_rural_HPSA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_urban_HPSA = df_urban[df_urban.HPSA_Ind == 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "394"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_urban_HPSA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.254041286737303, 0.024193570745392797)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms.proportion.proportions_ztest([len(df_rural_HPSA), len(df_urban_HPSA)], [len(df_rural), len(df_urban)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-value = 0.024, so can reject null hypothesis and conclude that there is a difference between rural and urban counties regarding their underservedness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the visualization..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['Urban', 'Rural']\n",
    "y = [df_urban.HPSA_Ind.mean(), df_rural.HPSA_Ind.mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.bar(x, y, color ='tan')\n",
    "plt.title('Mean Counties Underserved by County Type')\n",
    "plt.ylabel('Percent Medically Underserved')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the percentage of underserved counties is high regardless of urban/rural."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stat Test 3: Two Means T-Test \n",
    "#### Do rural and urban counties have a different rate of uninsured?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "rural = df_rural.Percent_Uninsured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "urban = df_urban.Percent_Uninsured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=11.917737293476847, pvalue=6.399598718670862e-32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(rural, urban)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a statistical difference between the uninsured rate between rural and urban counties, and that difference is large (21% vs 9% uninsured). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.163396461023442"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.157561023622048"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urban.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-value = 6.39 x 10<sup>-32</sup>, so we can reject the null hypothesis and conclude that there is a significant difference between rural and urban counties and their respective rates of uninsured."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the visualization..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['Urban', 'Rural']\n",
    "y = [urban.mean(), rural.mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.bar(x, y, color ='tan')\n",
    "plt.title('Mean Percent Uninsured by County Type')\n",
    "plt.ylabel('Percent Uninsured')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stat Test 4: Two Means T-Test \n",
    "#### Do rural and urban counties have different median household incomes?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define median household income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "rural = df_rural.Median_Household_Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "urban = df_urban.Median_Household_Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-19.708604869463954, pvalue=1.0763289819633104e-80)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(rural, urban)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is more than a \\$10,000 difference between the median household income for urban and rural counties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44728.4074605452"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56133.580708661415"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urban.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-value = 1.07 x 10<sup>-80</sup>, so we can reject the null hypothesis and conclude that there is a significant difference between rural and urban counties and their median household income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the visualization..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['Urban', 'Rural']\n",
    "y = [df_urban.Median_Household_Income.mean(), df_rural.Median_Household_Income.mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.bar(x, y, color ='tan')\n",
    "plt.title('Mean Median Household Income by County Type')\n",
    "plt.ylabel('Mean Income')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting to Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define our target variable and then drop it from the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "target = df.Percent_Uninsured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['Uninsured', 'Percent_Uninsured']) # Remove our target variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm splitting our continuous and categorical variables so that I can scale just the continuous ones later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_variables = ['No_Exercise', 'Few_Fruit_Veg', 'Obesity', 'High_Blood_Pres', 'Smoker', 'Diabetes', \n",
    "                        'Elderly_Medicare', 'Prim_Care_Phys_Rate', 'Dentist_Rate', 'POP_COU', 'POPPCT_URBAN',\n",
    "                        'AREA_URBAN', 'AREAPCT_URBAN', 'POP_RURAL', 'AREA_RURAL', 'CHC_x_POPPCT_URBAN', \n",
    "                        'HPSA_x_POPPCT_URBAN', 'Median_Household_Income', 'povertypercent', 'medianagemale', \n",
    "                        'percentmarried', 'no_hs_24', 'hs_24', 'college_24', 'hs_25', 'college_25', 'pctemployed',\n",
    "                        'pctunemployed', 'pctprivatecoverage', 'pctempprivcoverage', 'pctpubliccoverage', \n",
    "                        'pctpubliccoveragealone', 'pctwhite', 'pctblack', 'pctasian', 'pctotherrace', \n",
    "                        'pctmarriedhouseholds']\n",
    "categorical_variables = ['Community_Health_Center_Ind', 'HPSA_Ind']\n",
    "\n",
    "df_cont = df[continuous_variables]\n",
    "df_cate = df[categorical_variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = pd.concat([df_cont, df_cate], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_features, target, random_state=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape: (1949, 39)\n",
      "X_test Shape:  (650, 39)\n",
      "y_train Shape: (1949,)\n",
      "y_test Shape:  (650,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train Shape: \" + str(X_train.shape))\n",
    "print(\"X_test Shape:  \" + str(X_test.shape))\n",
    "print(\"y_train Shape: \" + str(y_train.shape))\n",
    "print(\"y_test Shape:  \" + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dummy baseline model will predict the mean uninsured rate for every county. Our model should be able to beat it if our features are able to explain more of the variance in the data than just the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dummy RMSE: 20.96888770911173\n",
      "R^2 Score:  0.0\n"
     ]
    }
   ],
   "source": [
    "dummy_regr = DummyRegressor(strategy=\"mean\")\n",
    "dummy_regr.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = dummy_regr.predict(X_train)\n",
    "dummy_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "\n",
    "print('Dummy RMSE:' , dummy_rmse)\n",
    "print('R^2 Score: ' , dummy_regr.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Model - No Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 16.370039894265265\n",
      "R^2 Score: 0.3905351160066315\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a linear regression object.\n",
    "model = linear_model.LinearRegression()\n",
    "\n",
    "# Fit the linear regression to the data.\n",
    "model = model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on our X_train data.\n",
    "y_train_pred = model.predict(X_train)\n",
    "train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "\n",
    "print('Training RMSE:' , train_rmse)\n",
    "print (\"R^2 Score:\", model.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are all of our untransformed features and their coefficients. Let's try scaling our continuous variables next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('No_Exercise', -0.13922376961822422),\n",
       " ('Few_Fruit_Veg', -0.076092135540925),\n",
       " ('Obesity', 0.131642080989579),\n",
       " ('High_Blood_Pres', -0.5850351799610971),\n",
       " ('Smoker', 0.33193040082524067),\n",
       " ('Diabetes', -0.03350060633771633),\n",
       " ('Elderly_Medicare', 0.0003948935954233043),\n",
       " ('Prim_Care_Phys_Rate', 0.04468326811322583),\n",
       " ('Dentist_Rate', 0.022233972399334872),\n",
       " ('POP_COU', -5.3053247644044314e-06),\n",
       " ('POPPCT_URBAN', -0.24398507230861707),\n",
       " ('AREA_URBAN', 6.797564414625148e-09),\n",
       " ('AREAPCT_URBAN', -0.10382043742758777),\n",
       " ('POP_RURAL', -0.00039243260384667493),\n",
       " ('AREA_RURAL', -6.578437606258139e-11),\n",
       " ('CHC_x_POPPCT_URBAN', 0.09220449128403178),\n",
       " ('HPSA_x_POPPCT_URBAN', -0.04489130473130484),\n",
       " ('Median_Household_Income', -0.00015515371827052652),\n",
       " ('povertypercent', 0.145567272271349),\n",
       " ('medianagemale', 0.17472617378764055),\n",
       " ('percentmarried', -0.07020806493189967),\n",
       " ('no_hs_24', 0.006626052762510628),\n",
       " ('hs_24', 0.025818927749259465),\n",
       " ('college_24', -0.13160217602793947),\n",
       " ('hs_25', 0.04739940514052673),\n",
       " ('college_25', 0.2730945184631993),\n",
       " ('pctemployed', -0.09196161362552334),\n",
       " ('pctunemployed', -0.20700146968134223),\n",
       " ('pctprivatecoverage', 0.037757923167902795),\n",
       " ('pctempprivcoverage', -0.1253958782569178),\n",
       " ('pctpubliccoverage', -0.32590496407142094),\n",
       " ('pctpubliccoveragealone', 0.3338123545422578),\n",
       " ('pctwhite', 0.017648671526027015),\n",
       " ('pctblack', 0.028738083489602974),\n",
       " ('pctasian', 0.18841053655574203),\n",
       " ('pctotherrace', 0.013839294252716803),\n",
       " ('pctmarriedhouseholds', 0.09437228464713104),\n",
       " ('Community_Health_Center_Ind', -10.44075377842683),\n",
       " ('HPSA_Ind', 4.953361718812637))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(zip(df_features.columns, model.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The residuals look pretty good. They seem equally spread above and below 0. There does seem to be a slight non-linearity but I hesistate to use higher degree polynomial features to keep my model's coefficients easily understandable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec4f7ef450>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KhhoLe5eJ3fs5/8Q3YyHnpJq82TxqkOsrF/IirjH7VIvoXNSRIKsWZ/ByBaQ2g1Y1NbEk2nmt49j4FLw6x3EiCB03CyItY/g1XtXUTaLIujtXVxr+arV9yzCzZiEvRjJ8CHhcVf+o5qO7gLcCt8R/P7PWYy1H0wc7rhiZy+QJsz5RtVjzoQsobq6QvhVUimhYXRJB4jgmk9FxPaIorLHDK4JDFFQ4fv/tVOYvxfvubBXTKeK4aTITGhFWixSnKoibQ0uzdRE6lYXLHPvS+0zxtMjHL83iuJm0pEGtiSXRzqMopDbM0suNpo7j2jyAanEa1ZDc2M4lWm8yYajWTG4AKI6X6+r1aEWimQeVBZP0lR9vWSnU1omxbBa6ocn/OPBzwPdE5OH4vf+EEe63i8jbgGeBf96FY7Wkmc3YcT1Cv0xp5gKxqmoEpTh4uVGiKMARN60mGfolY96pefDDcpVKoyYqjjHZEKEojpunOHWuxj5vtPs6bX7VxGaeKFrcP6CRolGx6TdSJ3PyOvSplmbxwgLiuunqJLGTJ/VtxFmsbxMFPhqFOJLvKA9g54HrOXnpzqbjqSxMpUlPvTKR1GrmXm4MvzxHtTRLRsfjrN96J6+tE2PZLHQjuuZbxI9/E25e6/47YfbCCYJKicrCFOI4eLkxNAoJg4oJIwx9IyBjC4KIoFGQRrpUizM4Xg6N0/6joGImgdAnqCwsPaDWR8REfrHh40S41yYh1SckdY4isZO4nvZROfW7MY7exsJqE7v3s+/lb0wFpLhemhMQRabezaKD2DFjiUL84mydg3Zi936cTN5MMA3XhyhII4vqQlC7aCJpLEOBQFBeIKjMN41Kst2WLJuFgc94rdXgMiMTBJUF/PKs0Uqzo2Tyo5TnLqX24mQ+Cv0ys+ef4kWv+fl0HyJxFIxGVEuzS4VVM5bdRlv8f2W0SphaESK4mdwSYZpo1qFfJUzi6bMFUygr8uunEo0Wz0IcotCvE9JJLHylON1kIixz8ZnvkBuZWNZEslpNv1Ez9zJ5XC9H6Jc5cMNblmzfypGcJLtZZ6xlWBj4KpS1GpyXzZMf30GusA00wovt7fVCMomqUSpzF3ning9w6qHPo2GIFzsoTfz7CjXlfkcj4zitIQl7LE6dSyNwwjAgqBYJq0Va/TyMj8B0yap10CZZwk2vnUYQBcs2pF5LVu1KKzg2y1bdesV1TJ85RlBewNQiOsvJI3fy5Dc+Yhu1WwaWgdfkm9lWoyhEo5Dy3CTNQjlrbeVuJo9fMiaJzMg4mZExEy9ON+zpXURcWGKyWRl+eY4nv/GRNPmrsjBDVDWlD4B0tRNGofEEtLAwqca1Z/KFOiGd2PhPHvn00i/FlGcvLHEEO16O4/ffTnHqLACZ/DhOm7DWVjkRK0m2apbs5iRRTJV586Y4VIvTqzYr2TBNy0Yz8EK+0bYa+GVTaiCOnlne+emYOuBx1Ixfmotj6jtNUlpH1ijgQYwDtjiNlxulOHW29bXREHEzaKvroEqmMI6XyS8pTTCxez+58V1U5i62HIkZxwxebhRNTEBRGE8exKUiwM3k0kmkVlg6Xg6/skAmk081/ukzx9h6xXWpj2U1AjVRGPyFqfiSxbOcRumKZSX7G/YwTTuBDQYDL+QbbatmqQ1eNk9QLS3/ZTGTgsahiQBRuHrbeX8Tx/ZrhF9qH0GyNLeAOKrI1KZ3vdySpiYJe659NSeP3MnyfgglrJZQEdAIP/TrEsaSRukaBjhutk5YVuYvmaJxXgZPMqnGX7x8qqn9vVPSMNC43v3iabtLzErNhFvj+0G1PLRhmsM+gQ0TAy/kG9PlQc2Prlpqb1fXCL8017DdsAr5leGX5hrekZq/SrU0x8jEziUCrjx3KV4hpKFMLTN8VcN0szQjWQTFQaMgnUQS239a3Cw2F4WVIl5squtG+GOiMCgCtX6c7GJ+QSvhVpy+bknkUKU4RSY/YaJ9kqs3JGGamy3PoJerll6viAZeyEO9bTW17XbqOB02B2u3WHJdNC4DL3jZxXj7RMAnVT7Dum5PcXmDlUycqogjJpchP2oE76NfrfO7JDkIteamTsIf2z1ME7v3U5y+jotP3193DpFfxgf2vPimlsLt0omjZHKj9e+r4JdmCMpziOPi5go44g5FmOZmyjPo5aplPVZEQyHka9l54PrYVGDpPoqIaWSuYciz3/2sCauMQsCJ5fnaV0WNddknYzNKpJHJY4iCOKltMZGtnZO104epePkUucLW2PmamG6ETG6Uid37l0w4YIRbFFYRd1F4m9aGml4FjSKi0hxOJs+eF9808PbszZRn0MtVy3qsiIZCyDc+MF5ujKDSaG6wdANNGpovMcFEzWV6x8XYFjX+xsYbOw9cz+lH7q5ZJThAFDcnnyc/viMV8MvV7+/kYUo0VCfOKzCnoOmxWwk3x82iYZC+b+oBiZkUReomC2hfXK7fJ4HN1I+2l6uW9VgRDbyQT2K9NajGTsV5rF29h6y6VMPyZhtxHFSV3NiOpoIuDKoISQE1Bzc3ZkoY5Ec5cMNb2mrqnT5Mywnx4/ffTml2Eg2ruJkRvFwhFW479r+CSycfNrWENClBIWRGxpZMFu0mnEFwatb6wkqzkySNaZKciX4ZZzfo5aplPVZEA58Mdf7xe4n8MhpHj5h0fyvk+w0vN1pTuK15FQw3k2fPta8GFs0rlfkpU1oiClANUY1wcwUTvhmFFKfO1iW0OV7GhMV6mbpErU6TpZq1ffT9MkG1SFBeIDsyjpsZIfRL+OX51LRU2LonPittuXpJjlctzjRNCks6iT179DME5Xk0LoPdeC79QtIMxs1kyeRGyeTHVpTANii0agXajVVLL/edMPBC3nQhkqZJT5b+wcsV0lLF5t9iiWfHy1HYtremuUhcnz4M4z649asHvzhLpTRXE0+fJwqqRhDXOH5rY+yT2kbluUmCarnlw5RkwuK4VOYvUS3NoKFvSl5oRHVhKs4GNhPXgRvewsTu/Wk7x/z4Tka27CZT2AIipoFMw8PbbMIJKkWioBJX8lRUTf+A0K/UnUu/Ubsq6ecJaS30spfvevQJHnhzjcGE1FkNvl8xNeWjsJrUjAAiHDeD4+XIjW1L49sTE83CpdO0vp+alo12s6N1CW21YZVJNm2z2ka5sZ3svfamlg9TFFbJjmyJNexJs5pIGqLEAr8yN8mT936UKKjgl+YQx4mjg1xwM6ktvjJ/iWxhG3tfvHi8Rnt2bQVUUzXTOLBr8wX60anZKuM8WWH1oz9hNfSy4U2vm+kMvJDPjm43yTGRDYXsZxrLHwOIm8HLFVIN9fyT9zH5zAMrKNFsGpgkZRLMROJTmr2IiJMK49SPIOA4Hm5mFC+bb1n6uNFmbkiSyWpaP6JUFy7j5cZIs6vFMZnCNdnCqhBUixSnz9dl7RLb6LOFLfjVUlpryc0ViEqzRqPHlOdwvBx7XnxT3dn3g3O20aYc+hX88hwiTt/6EzYbA2+u2XPtq3HcjI1372uaa+RhtUhQKaZJRkbAd3ofBRzz811SDjo2d2jox0lNcZMXVaLImIDKc5daFkQrzU4irkfoVyjNXlwaFlrjWEXVmG+SDNmmxdlCwmqRi0/fnx6LKCSKfPa+5HUcuOEtjEzsrDfh1F0yWXIF11LMrZs02pQTE5qXHx1a882gMfBCfmL3fjL58bg5t2V9iZOd1nDtQ7+Uas+adtxqj5sdSX2cURjE9YqETGELI1t24zhO8w5dsVNUo7ClPRmNCCpFs8/llIe4DEPar6AdGrW0XdcKyzT80nHJFraSH99BJpOvE5T9YgtvtCmDKTDn1Sav9ak/YbMw8OYaAL+yUFMv3rJ+JGWbV188TdwsE7v3c/qRu1cUU58dGSfwMqZWUaxF1woXTVsaNhuzxNUlm4dVmuYx8+2HoVFdjZuV0qyC5+TxI1SKU4i4ZPKL4ZeNgrKfMk4bM86T+lEJ/epP2CwMhZA3WpR1ug4cIjiOx/e+8McrMrc5XpagWjYZqXHPWaAuhn+x/WKtoDcC3ssWyI1tA1gSoxxUiiuYtJJ9m+SszrePR9Mg/BJhmQhKZ5nY6X7NOE0S16LSDBpFiOM09Sck9INfYa30+zkMvLkG4iYWlsFD1WQmr2QVFnek8kszaBSSyU9AFOJXFgj8cmobNs3DFcdLGqUsKgGqETsPXN80RjmNcnEzcXOUJo9I3EfWRNs4rZtfNqGTeOhOYqdXEl89e+EEx++/nSfu+QDH77+953Z7c6UTB/VSf0LtuPrBr7AWBuEchkKT97KFNHbZMpyI46VRN6ZNIxhBAo6XMXUexcHLjVAtzpAb20Yhu4+5C0/TuMoLq0XOP/FN9lz7avZed3OdFhb4ZbxcgSg1feiScXj50bi1YAk3kyf0S6aOTpveBaD4lQXEcdNSDM00vsbKqq2KqTVuU9i+j8njRzj76FfT70D7EgrtWImmOnn8CJlMHmdkPH0vCvymtViGoZLlIJzDUAj5wAr4oSdt4RjXxAdAnDQuPopCgspM2vUq6RTVmHSUCO3K3CTPHv0MxLVlEo3dy42iYWAyqBvLJMeVMb1MHr+8gCJ1HaRa52pIGr6ZyY2mGvdyQqCT2OnabVqVQnDc7JqE0EpLLKzEV9BPfoXVMgjnMBRC3mrxmxSNiMKIammO0C/XxWZPPvMA4ngsl1BlNO/QbCEOlfkpIo0QIAqqS7+qSlgpxpm4C3jZ0UUhn4RUtjgWOKbUcBebl9d+17ROFDMJ1TRSqS5cJje2o+47zbpttTruSjXVlfgK+tWvsBIG4RyGwiZv2dyE1WLqfPdLs4RBxZT2bZKA1ZKkjLFfJgr9eOVgVgwmPNdo6VHoE1QXcNwsmfyoydFw3Ngv1Nr5nxkZa9rgZC023drvahSZOPXSfFraIamP06xmT9Jtq91xW9XZaaWprsRXsB51WxJ65ZdYz3NYLT3X5EXkDcB7ARf4oKre0v2jdBrdYOkbklaCXUSjkHDVVTIh0bjre9tKbB7S9DVqYuMDvxxnp84vEe/GaSupgA1Kc4SV4pLGIa005fNPfLMjLVvDkKq/YMYXRwX5xRmirE8UT3aVhanYj6GIOIhnCop1oqGvVFPtxJ9Qi+Nk4vpTJnt9uVITq6WXVT1Xer4bQU+FvBgV6M+AfwScBo6IyF2q+lg3j+Nm89ZkM2ioIm4W18sZk4c4a4q3B8HLjRAGfusG5B2Nq3EMS2w2OG4mrZOTG9tOGPhEDf2EG52wzRqHQL1NN/DLhJVi2hQlzI3h5QpLhFIn9X2S58HJFIj8kmm1iIAogskSzuTH6r7TTENfTd34TvwJtYI3N7bD7DesLvud2u+uRKj22jna69oza6XX5ppXAk+r6nFVrQKfAN7UauOTJ0/y2c9+FoAgCDh06BBf+MIXACiXyxw6dIgvf/nLAMzPz3Po0CE+d+cnCKsl5hYq3HLbYR7+/hkAZuZK3HLbYb731FkALs0scMtthzn2zHkALlye55bbDvPEDy4AcG5ylltuO8xTz14E4MyFGe77ux8QRYsPUbFcZXrWPDzHz1ziltsOc/q5aQCeevYit9x2mHOTswA88YML3HLbYS5cNjbbY8+c55bbDnNpxiSKfO+ps9xy22Fm5oxwePj7Z7jltsPMLRgTw4OPneKW2w5TLJsf/gOPnuSW2w5TqRrN8NuPnOCW2w4TxL1Rv/XQcW657XA61m88+DR/+JF70tf3fOcp/uhjX09ff/m+7/Pev743ff3Fv32c933im+nrz3/zMd5/x9+mr+/6+qP8xd98O3195z1/x4fuXGyTd8dXHubDd30nff2JLz3Exz73YPr6r794lL/+4tH09cc+d4Q7vvp35Ma2khvdxse++DB3fPWR9PMP3fkAd37tWPr6L/7m29z19UfT1++/42/5/DcfI8m4fd8nv8kXv/W4sYqLw3v/+l6++p1n0u0/d+8xno7vLcDFy/OEYWcrCVXl0vQCU/G990P4+Bcf5OEnnsWvlJiZnuaW277a+rc3W+KWv/wKx54+AxpxabrIr/3W73P06FGyhS2cOXeR//wnn+Sx758g0ojT56e45bbDPHniFGFQ4dnzU/ze++/iwXs/x+yFE3zz7o/zO3/0v9r+9mDZo0MAACAASURBVC5OFyHyOXbiOW657TBTcxXy4zs59sxz/LdbP8fUlBHoRx89zu/+6R3MzM6RLWzhnnvu4dChQ8zPzzOxez/fn8zzX//8LooLc3j5UY49l+HXfuv3CQLzW/zsZz/LoUOH0ut155138ku/9Evp6zvuuIO3v/3t6euPf/zjvPPX35UK3s997bu856NfSjN2P/zhD/PO//D21Lzy33/nV3nXO98BGAH/J390C+/78F2pVv7Hf/jf+J3f+o10/+95z3v4gz/4g8Xf0ie+wEfvWvwt/+Wn7uGvPndfOqH93u/9Hu973/vSz3/3d3+XP//zP09f//Zv/zYf/OAH09fvfve7+fCHP5y+fuc738nHPvax9PU73vEOPv7xj6ev3/72t3PHHXekr3/pl36JO+9c7F536NChFcu9e+4xz/b09HTdtW9Gr4X8FcCpmten4/dSROSQiDwoIg/6/so1sOkzj9GLRKg9O8e58aVX4zjGmTY9W+TP7/g2swsrsPMOKOJm8LIFTEhIYo/uPkG1RHHqLFHSr7XuNuoKNPu4sXdQJQoDRBwc18PNjsTOV3jy5EWePT+dfuPDn/0O33roePr6rm88ysmzl83eVJmZL8dlFkBE2LF1lG0TcQExfP7lG36Ml17zfILyLIV8hv/jtS9l764J87nrMFbINRmnuY5+eZ7SzHMUp8/FNt3I1OwxNRrqvhFW4hWqCH553milJCUblv/daxTFJRfM9dHkesb3NNIotSWbqKXmGvrIlt3kJ3bxwzf9YlxnZ9eyx21GUCny5Dc+wqNffA/nHv8GoV8majjXZCVRnr9Maea51F8QBVWK0+dTDV4QxHHScg4iQnn2Yosjg+vllpoGNeor52gvEe04lXwVOxf558A/VtX/K379c8ArVfXfN9v+4MGD+uCDDzb7qCWPfvE9K6haaOkEJzNinJY1oYq9KwDXSYnoxaqPyXhEJBXCXraQdmmqlmaMOSUMFsMu+5E4EsjLFvArC0R+qcV2wsjEbqLAx8uPpuYdEaE8N9n5b18cHMclN7Y93VdSM6gbtuTlTChJ97bIL5Pe76RzVmEidUgn4wLSjN/QrxiHeBjgeFnEMeUeavtHJB23rn3tv205tsQ0VGty6nbd9o1ERI6q6sFmn/Xa8Xoa2Ffz+krgbDcP0MtJarOyROD0tC5QJ/evISFJhKte8SYmjx9ZIgxW73ztvB+BOC6ZwjZe8Iqf4tnvfo7K3OTKD6cRYbVIWC2ZYmuLO08/N3+pi9hIzjnUcGXltTXCzY7X7avRlpxEoKxU6J9/8j4uPn1/Oma/NE9p7hL74iYwk8ePoEF1sR4/EgdDRUZb93J1tv6kWXpStji5LlHoI1FAUHHIxJMBLDqCW000g+Ac7SW9FvJHgGtEZD9wBvhZ4P/s6hF6qmVucvr02moUcvqRu1FVsiPjJk5+zY53I+CdbCFtSNKK3NgOQr9MfmwHe659Dc8e/UwscFejcGj92Jdcb6VSnCKTnyColtix/yDnHrtnsTDbCuo2JTH92cK2JZ+tNgJl9sKJOgFviIiqRc4/fm/aX1c1qqsKmnTagsWa+ongnYwjetIS0rFpytjw40xjL1unlRe27+PsscNoGBL6ZfzSHMWps+z8oVex50U39r1ztJf0VMiraiAi/y/wJUwI5V+q6rE2X1sRrpclrPbxsnyQqXlwRRxwPLTDCIhekwjGclBeQfXK9gjg5caWrUJZmb9EdnQ7EPc5/aFXNdTCjycML9dhrH6bVYQqfmmGM4980WztLJZQEHFNdJlfid9rvZ+k05WGwRIBvtoIlMnjR1oqApX5SWYvnDBhmJUiii76HuJxZgvbeNFPvDXVws8++lUcN2vqEIVBvLJRUHDzBVwvRxSFqemqttGLEfClxWukEZPPPEBh654NE/D9ULys53HyqvoF4Au92n9+fAcLl0rYKpS9RTVCNMLLjZpKjf1yvbtsrgurRZxsoc0hlUpxJm3957jZ+LoYzVOjCCczgoadOenFcVbkV6rdVjVc2jSlBUti8R+/NxVAQWUBLzeGw2I8fKukp9kLJzj/+L1Ui1Ntx3322GG2XnEdpblL6JIVkhBUi5x/8j6mzxxLVxGmrEQcshmF5v183Lw98BmZ2Jm2i0yP8+hX03r2tQ3jVXXD6sj0Mj5/JQx8xmth+z76RuD0Hd2NitEoiAXKMF7vxWuVyY3GVSybbOW4OJk8RAHVhcuAUFm4jF+ex8uNGW3ZcVo7UpvQUlCKsyTbdHGskm7TKaWZ58y/2YtUS3NU5iepFmfTc/XLc3WN0JslPSVO1MrC5bbzqzgujrgUL59i34++AXEztR8ijktYLXHx6fvQMKxrgJLJ5MkWtpIZGSeTHzca/DLZpNnClqY9BBzH3bA6Mv3S2GXga9cUL59qv9GmpclT2Gd29sR8sIY9sOJJx3EbQhUFcV2jgbsZ/PK8CT1scq3y4zupzF9OC6X5pViAiENYLeKNbcfNjBBU5jtYZLQYe3JcjdCwyeeOE2dXheRGt1Mtz6NhFcfNxs3S29xfjdIib4vjN8XY/OIsmgtIiqrtfsE/qPvqEieqtq6+6Xi5dDUwsXs/XnYEN7ONKKhSLc3WFJ0zyVniunWlH0K/zN6XvK6puaPRDFLYvo/i1Nn4vCTdr5PJbVioZL8ULxt4IV+eu7TRQxgIUodi0s2oTwT9mhqwp5EoSwWhuFkjRFTJT+yiMn85brQtRrTWXQNJHac79r+CSyeOxrt3MGUOFm3d5dmLzbuQaZQmznm5AkG1GEeSOE3bGorjxc5Ip2bCqQkvbOlUTSJUFFPXfgQvN1IXZeSX5xq02g4mwfQaap3559R37+JcrkC2sJVsYSulmQvpdVSoC2VsHKeGft1qICmR4JfnmzqYg/L8Ymev+HvNHKbNzCDTZ44x/rwXMffck6afgOPiZHKI625YHZlmJSGCSpEo9Hning+kzdyTyqm9stcPvLnGxsh3Rl3EiBoBYUoBjJEZ2UJ3TDsO+YndeLmxhoYbzfctjru2yUaNE8+ttaGLQ6awhfzYNrxsAcfLxrZaJTMyQXZkwtRvqR2XmFIFu154I3tedONiExrVJM9q8ZDLXae0G2JAbmwHXn40NjlMmKQsEXLju9h1zd9P48Fr5W8yqaTn1vI4caNyhYXLp1m4fIZKcYby7EWjETuJ7qZtG+rkxneSHd1GZmQcN5PHzY6Qn9hlCq/FBJUixamzTJ95LHYkxxNRFDaswpKev6bschTWm1eSYl6t8hc0Cjsq8tVoBlGNCMrzzD33JNnCNnJjO3CzI+TGtm1oLPzSJucLcXG7DCBU5i9RiU1+vWw2MvCa/GaNk3dX2yhFnDRj0S8vEIU+ruOQG9uBX55bWeXGZJfiguOSHZkg9Mvkxrax88DrAUx4oSqCs5hxGXcNyo5soVKc7kjQO24mbvtXqtl+sStTbnwXfmWBTCaPuB5R4COuy76Xvn5JS72kb2qSfNPoxMuP7zBlh4NKQyauIKbSfAsWBdTea28CjHPTL00hjpN2sZo+c4ytV1zHpRNHTUnjeDLU+NLUH0BwsiNoUI2Fo9ZsZAqOpYqOmIgSU7tHcFyP3Nh2SjMX0u+Ja4R+sjqpzk/V3Zdd15iJTlUJqkWmTj/O5PHvpBNLFFQbB1h3/uZPzeeqTD37dyxcepZsYSvb9v09Lnz/Wy2vYLU0x8jEzmW12lozSLpySY6pEVEUsvclr9vwkMnG+Pwo9PGyo2Tyo1TmL5sJClIzX6+ajQy8kB9OJ2BrxHFxMyNxj9IdqeAqz14EcdpmeUqNo87LFaiWTL2SanGGzMiEsUe3dRrWSCJxKGzf2/KhrA0vFMcz2Zquy9YrrqN4+RTV0kycdd/MhLR4nNzY9sUHQ1wkNsMkE1UUVMjkRkGXxl3DygptJdtmvHHE9dJrmxkxjcJLM8+1vDJJJmltaCK6ra5fK4HxJe17+RtTs4NpemJ61tY1IBEh8iu4mRyhH9XdvyR6JNnWfDfR3BUvF68W0uYnurTscEPpiEsnH05DDjO5UeYvHkfEJQorpnBak+dN3IyJvkoqdtbcR780i1+abXm9GomCSlvFrdYMUhtL74jTtF5/t1lJWGStuemJez6QTk7G5GXCQ9Nw2B7Z6wdeyK+p4uDAIWTy46gqxamzxu4cVnEzI0bAJ04noYmdOnFGLT6AQaWIhtW0RoiGAW4my46rX260zNDHcdz44Zb0u0kMMqrkxnZQLc6kEQONP/Y9L7qRwtY96UOR2CGnTz9qHpAfehWXTz68GP5WN+REOEk89MUHQxyXwC+brmCqJroiSVdvosWtJOuxcVvHy+K4mdReXDch1Zh2cqPbl6wKlnO+NR6nsO35BNWyMYNolGZ7KqaJSVIGobJwCcfxcHMFgtIcpHHziqjGJiVNVyyLiUdNLnFcEtmcgqJBtU5AlmYnzequsUsWMLLleU1LJGRGxsmN7aR4+TR+eR7H9fByBaN1l+ZYVjHTiOLl05y8fBrEwcuOMLZrPxPPO0Au9gvUTthRzfMfEVIpzZHNj3XcFGWlnH/yPqO0xHb/ShR1HBZZOzmlzxCkJrVeNRsZeCFfmxgy6LQ7l0zBFMDyy3OIOGRHxgkqRUK/ZOywkSJuJv7hJ8W1HDIj5nvV0mw8GRiNLm1a3RA/3ahl+uU5U0xKHKNJ1k4mUdg2BjjRZlo5zLa/4GXMnn8qLg+QrBBMk2x1PNzY/FI7kbn5Qlq8y3E9RKRtAs9Ksh6btdYzJiAPL1sgqMwj4hpTlDhIJseeH3nNkv20q8feOKZE23NksZgZGqAKXm4be659dX05h0rRJA0l99txYxNOkI53UTMWE5kDDc7eRVSjem0yEUTS2JBbqMxfJorrBQHpBFd7n5PJNwyq7H3J6xjbeRV+cYbj3/kbM0G1EfhBZYHp048yfXqxAqmIg5sbpeovNVdG1SLVKCSTH+8oRv38k/fFCk01XmmOIELTSWH2wolUwCNxgbdqCS870tHKoXZywsmg8fjVMabTXjmJB17ID41Jvl28swiul6Myb6KJvPwoIkImP4rrZfHyo6aR8zMPxBclXgpCqnUkmmBizkiaVtcdpomW6VdLiEa4mZG6QmDiZNJIDnFMyvlyP/ZWWZXFy6d40Wt+Hmi+FE6+G/hloqCCmxnB9XKxVsiiWaJm/And0OYaNe7c2Da2XfVSY25qs992ZqLG8TlxHZfFSUFJiosRhWly0fSZY6mwINVmnbiyY8DEnmsJq/Oms5OAeAWI/PReRZIUCav3LIs4ddqkOK7Zf41ZKBlXGh2E1AnQdtmzubHtbLvyJVx8+j6g0UwnpBU2VU3WcOjXbWMcrXMt71cUVPDLceSPl0Uwv38J6xOjTM2d+8wx1UQDBaGPk1lawx/i8NEoilc/6VUgCiodmVmS39H5x+81JtHYtKZRROiX2HnVq3piYhp4Ib+2RhN9hEYoblqYKUEcB3E8Mvnx1KSRyY8vmg6oF2zZkS3pwxX45bgGyDyFbXvZ8+L6rjuJM7ITLbNRGFXjWPKkCbZqRFAtonOtnaidxA230rabjcNxMzjuoiO1cfzdzDhsPq4bO/peKzNRs/H5ftmUVsBo8YnNvbY/bPHyKfZeF++zNEPajFyjdLINq/OpZn38/tuNI7nm1jjxii8pM5wIcMnk6rTJOie0RHWKd/JfLz+aJvkktWra3efi5VN42VGioEKU1PWPV4kiLoriOA650a2U5y6hquTHd6RlqaOg2tzEF5MEENRvI1RLM5w8ehfZwlYu/eAhcxKO1MmRyC+imWzdOUHcCjFt87g4MUZRyEiHZpZkEmz000SBH+f8tP9NrZTBF/KDgMT1v2trmyTvaVLLA9AQL7+d7Vf/WEstMRHMtSSCrfHh8jJ5XC9H6JeX2IqhM2dko3BP7N3HvvS+OBpkMYUc1WXNTd1oerycGaVx/L3uCLTSMdfWZ5ksbCGolJaMLwPGFp0boVKcSm3vjf1hk30m5p3G0ru1ArWwfR8Ll0+ThDhqjbZfmZ+MM3chW9jOnh95Td21aXRCB5ViWtfHESctN1B7zE7uc7U4g5crIPlRAr+MX1qsFVRrkgPji3FczwQdJPbrTN4IcHFMWYjkGYoixHFiR3W1wRFsJrTZ80/V36Amv9lqaQY3MxL3CzarH5NVa7TudGKM8yvKc5c4fv/tHa0U1ztJygr5NWBssm1WEvGPEEAdB6LFxJjUP+q4cURcRBRWKWzdw54XNZ/RlxPMia22W/04l9OE07C/RnvZMman1bSSW45241/pw9TLYlLNrmVlYYrMyMSSejFmUn5rXdhnQuP97ESg1mrNtaa1sDqfmsla0cxU5WbyoFHduOqSfOICYx7EUUPzJtlMHGYvnGBi936cxPSoGkeM5YiCKorpFZCY5KLAN8+POGlCWzJ+LzdOUI33nUYjwfjzXkRl7iJBpWQEfm4CDX3CoGLs+ZkcXrZAea51oxF0sTroo3e/l0x+wowxrJpooyhItX/Hy5HJj3W8UuyGsrMSrJDviKXp5yNbnoeqUp6bTMPTmqKRSWAUx2wnSm5sF0ShsWc7pBmM4nhLloiNtBNs3ezHuZwmnMmNUvEr6Q9dHBc3W4hDO1sfq9t1vZcb/0oepl4Xk2p2LcVxTGGw7OJEVDu+Tuz5QbVMpTiFiIuXGzX19P0SfrWUapa1WnN6nAZtfzma1Z2vHVdQKRJUF/Cyo3UFxqIoiIMEXOP8VxOJUpy+Dr+ykJqiNAqN3b+mkQoiqe8oN/E8Zs8/Qe1KJKousOuFRhFKHKeOm2Vs1wFKM2dx4mMGlQVjvxepay4ThlVGtl1Jaep0+wsQVwFtFVkchT7VhanYlCOcf/wbZOIs4VpTYkK3lZ12WCHfEUsFeBI77eXGiKIgbuTcrA6JxPZ2E4WRHd3BnmtfbcwMaep5kg4/2tGybXm7dfeEaCtNOC0lIWAcvCZeO9Ko7Q91Pet6r+Rh6rVpp9m19HJj+OXZluamTu35mfwEQWU+jUf3cqN1DcCXOnPXpjkul+QDi2Ynv7JArrA0R+DSiaNkcqNEaXRQ4jgWMvkxcx2ikK1XvoTi5VPMPfekSaZDkTh81vFyFC+f4sANb6kL0Z2/eNz4avJ5HDJ42Xz8e9W68YXlKuWZc03LQYuYlUNmZAv7X/kzVBamqBZnqMZ/K8VpqgvTNbV3IuNXiM8j9Es8/a3/Ze5FtkB21PQxzo5uIze6lWxhG3uu/Qkun3xoXUoQWyHfijb1XYwWEqFRwI4XvIyLz3wHmiUiqZK0q/PyY+y59tXpQ3Ly6F0QBfEmnqm3vcZlWzeFaCtNWKOQTM5E9STdmBAhkxvd8CzDWlYy6fXaTtrsWpp2fDvxsvmW42t1P2snpUVhNokRlIvCjABQJdKwq5pjrZ/h2aOfIYgT0hL/gbieMW249b/lKAqJggqVoBrHz5sSzVEUGQEeh8KG5SqTzzxAdmRLauoBwRsZS30ApdlJnvzGR6jMX0KcWOEKqiYgoKbYmdb6vWJCv2xMP6lvLAlB9ciOTBBpyPNf/BNkC1uaPo+qyjP3fcKEgYpZXWgUmLaTNccLqkWCqikL0YiXGzXCv7CFytxFZsKAiT0vXKYe0OqwQr4FpibGcluYpWZYLXHpxFEcN0N2bDuVuUk0rtCnYQSYicJUOMyajkZRSBQlDiETMqaq+KU5nEyePS++qefn1wmtNOGkBK4jki5Hkz6bq6VX9vBOJ71e20lbXcu91960qvNsNikZYVgvINpVc1wLqdkmJtKIqJQ4ZV0cN1u3glhs57cYkWVKEpjnoLbOjvEfGLu/45piboppbO5l8mkiX7Xop36goDKfJhkl20Gimdc/zIkWHqVhoBqbVoMlWcvNEBF2v/CGxd6xsZnKTA6vZXTbHioL01QXpmr+TlEtTselIUzlzaCyEDvFAYTRHVd2Xau3Qr4ZSSKOxAWqaKHRawQ4aShhUCk2ZLIJIpm0ZVlYLS0KwhoBnyYY9Zk23EoTXqmDtx390Fyh13bStZjSmk2AzSYl4+BvSG5apprjWklWE5n8eF12blBewMuPsmP/K9J4fnG9dBsnO0LkV8yjlYRwitTlO0SxgxWMxuuX5yCKiAgpzV6MHb85k/EuUqeNJ99Pkv7EyyKYhKNEg0+vWTIpiuk767iZuki05ZSPdvfUyxYY3ba37polNYES4T938QfMXTgeh446PfntWyHfBMepXUaGccSj16IuTISIF8e3mwzSqFpMo05MKFf8flCJ48oTLT/OloyrE3aiDa93O7FWwqGbArEfQh174RRudoyV7q/VBFibEJXcA8fLxSUQmtv4u02ymkiyc43pzrTsS6o/1trLNQ5vVL8cr5QXM3FNGOhiNraJsDGauIlq8dM6Ncl2aZ5GivF/edkCUeinjts9L76J4vT5xXIErpf6ATRK6gEtZgy3u/a1Anil91RiRS6TG2V0+5VMn3mMbH58id+im799K+SbYFK1Z3G9bGxnl5a1P8As/bxcAdWI3Ng2otkwTUzJjW2jPBfh5QpUqsUmIYaSahbttOH11HjXosGslG7Yw7uV2dovq6iE5bKE04So+JwTM996KQG1qwk3k4vDIE0tm0YhmNjuk5IABsGLSwLX1r7JFrYwsfdaps8cSyesJCY+KRJXmb+causktUE1tunXVB+tvY5JomDoJxmqmgZFOI6Hm8nVRYe1Uz668Ztbj5h5K+SbYn4wURSy64U3mroq85PLfiOoFMmP72iadJRmlqamHCGJqtF4mdauhjasn8bbCw1mOTqxhy/3QPWDuadXtCtwtlx2cK9ZafSSmxlJG20bwRyn8x94fdNzqV0FgMn0BmIBH6TmmczIFrOKIEqj1xr3lVzHRb9ALRJ3saqvHbPcte/Wb249YuYHvmlIow2ym2gYUNi6hxe95ud5wcGfadn3E4h/rM0FdNI8wPFyZjkaO8gcLxf3fjS1Z9o1OKgWZ5b0/OxFptx696ZsbK7QOOElD1RSLbOxwUK/9NLsBUmBr1p6mTizEiZ272fvdTfj5UcJ/fKyv+EkVj+TH099UCIOjpdbNifkwA1v4drX/lsK2/aiUYhfmq9xlppnPwyqFLY9nxcc/Jk0uev4/bfzxD0f4Pj9tzN74UR6HVOTj+uyqGyZWPfGsS937bv1m2v32+8GAy/kc+M7e7ZvcZy6Err7Xv7G2MnjmEYZ6YZu2x/r3utuTrMFTQOMHCNbdnPVK97Ei//Rv+PADW9pqwGs1wO/XpNJQjth0e6BWu/xrifrIQRWy0rMFclv15hEtpOf2EV2ZAIvN7pEIDdj54Hr41WAmkCZuLQy4pLJj6XPTyuFoLB9n7mOYRD7ek1oc6YwQX58J25m6fO788D1BH6Z8twkpZkLlOcmCfxymmDWjd/cSibK1bImc42I/CHwT4Eq8Azwb1R1Ov7s3cDbgBB4u6p+aY1jbcrEnmu4uFx68hrwcmNLimflxnaaWh9qWsZ5uVFEnMV2bq3G2QXzxnplyq132jUsf33a2S03YrzrxXo4hFfDSs0VzX67gV+Og9c6K1dtotQqaSmRpJBZZX4yLZfQzodx6qHPmz4J4qZ1d6LAb/lbSQuQSJxtG7/fzd9cr31Ba9XkvwK8RFVfCjwJvBtARF4M/CxwHfAG4H9KnerbPWbPPdmFvSw1+Xi5UZy4KFEte659NV5+zDQ2Ht1m7OnrpFmtx6wP/ac9tlvB9Nt4u02t2aKTFd96sFJzRbPfrpctkMnkO95HfnxHvJB2jRbtJCHIna3qktV4ZmScTH48rY2znB8hk8mTH9/ByMQu8uM7yGTyTB4/MlC/uTVp8qr65ZqX9wNvjv//JuATqloBTojI08ArgfvWcrxmJPXVV4uTLeCIQ+CbjjxJ5pzjuE1v2ko1q26HPK5HBEi/aY/tVjD9Nt7NwGqiQhp/u7Xt8DrZx84D13PyyJ2LyUuYP15+tONVXbeyoAfpN9fN6JpfBD4Z//8KjNBPOB2/twQROQQcArjqqqtWcdiVdg1ZrBUDDhpUkfwoL3jpTwOL4WfLZb11KmgHOeqjn8IJO3mg+mm8m4FEmIYaElaKaWvGbGHrivexkqqppt3kdBqi7OYLOOKm5tJOTJq1YZ21pZ8bf1Mr7erVr7QV8iLyVWBPk49+U1U/E2/zm5gqGX+VfK3J9k2lsareCtwKcPDgwd72eYrjbAUxiU5x27Rak0enN60TDb0fknyGhUF5oDYLOw9cz6lH7ibyy6SPe9w4JrGPd7KPlfqY9vzIaxZLCaxhVdeJArbe1SJ7RVshr6qvW+5zEXkr8FPAzbqYwnYa2Fez2ZXA0go9640qYaVIbmx708SNTulUQ1/v5gCWlbHe2cPDxMTu/WRyo1SDSloTPglC6FSJWY3Jo1uruk4UsEEyySzHWqNr3gD8BvATqlrbVfcu4K9F5I+AvcA1wHfWcqzuoHU1LVY7K3eqoQ9z1MegM8imtNXS7UktCirkxnYs25WqHatZoXVjVdepAtbsWGu9juutXKw1uuZ9wDjwFRF5WET+HEBVjwG3A48BdwO/rG1bKK0PjptZc2RKpzGyg+SB32wMcwJVM9ollK2Gfk7Uasdqxj574QRP3vtRTj54J8Wpc4Cs+Dr24j60Y63RNS9c5rPfA35vLfvvjFpHant27H9Fy9Z6ndKphj4sy71hZLOZ0nrhH1oPm3WvtN6Vjn1ROM+T1Mrxy/NkRsbadnOrZSP8dINfu8ZxmzfraMH0mWMUtu5Z0wVdyQ/EOgz7k81mSuvFpNYrJSYR7OW5S6YRSWakrtNVN0xqKx17IpxNETTHFLzE+Pjc0W0dX8eNUC4GXsiLSId6vJApTKxo1m2F1dAHn2GJnOiUXk1q3VZian0lUVCN66+XFjs9dVHrXcnYE+FcX2QQ0xFqBddxI5SLgRfyjXa1ZojjmQy7uG1YMmuuZSloNfTBMLFjGAAADL1JREFUZrNN1IMyqdWZM+IiZonGnLQV3AiTWiKc0wYmGjcUWmHG+0bch4EX8uI4dZ1emm4Te/+TGtSOm+H8k/cxfebYpoqusNSzmSbqQZnUas0ZtV3WOu250CsS4eyIi5cbI6jMg8aljX/kNel1bKc4bsR9GHghnx3dTqVNgbIo9IlKizWkHTfD5DMP4GZGcPLxD8omKlmGnEGY1GrNGbVaszjuhkanNQrnwra9S4Rzp2G5630fBl7I77n21Zw88un2G8Ztv7zcKG4mZzJegwqwWD1ymKMrLJZBoNac4XjZxUYjscl1I1cf7YRzv2a4D7yQL06f72i7/MSuuqQNcVzTv7WGYY6usFgGgUaN2bQGfH3fr0Cgf8NyB17IXzpxtKPtTNf2RY920nh7vZoeWyyWzhgEs1Iz+jUsd+CFfBRUO9rOryyQYTQV6OK67LzqVRQvn+prR5TFYukd3Uy26tcIpoEX8p0mvEZRmNadrr+Za8t+tVgsg0m36xf1awTT4At5HEyHwTbbaMiBG96yHgOyWCwDQLccpf1ezXTgG3k77vLzlDguCDhudp1GZLFYBoFuNOPeiIJjK2Xghbw4y7eO1SgEjRjbdWCdRmSxWAaBblTRHIRqpgMv5L3caNtt3Eye0szZvppdLRbLxtKNUuDdWA30moEW8rMXTlBpcTHFzTKy5XmMbHke2cKWvptdLRbLxjKxez97r7sZLz+66h4Tg1BTf6Adr5PHjyAaoeKYgkG1YTZxzYuEfptdLRbLxrPWmPx+DZusZaA1+WpxxhQwEkFc09hXHDNvNRYt67fZ1WKxDD7dWA30moHW5LOFLQSVIloXKK+AII5js1ktFkvP6fcM3YHW5HceuB7xsqCKRopGEaqKmx1h5w+9qq9nV4vFYlkPBlqTn9i9n30/+gbOP34v1eIUIGRHt7Pn2lfbbFaLxWJhwIU89P9SyWKxWDaSgTbXWCwWi2V5rJC3WCyWIaYr5hoR+TXgD4FdqjoppjvHe4F/AhSBX1DV73bjWM3o9wJBFovFslGsWZMXkX3APwKerXn7J4Fr4n+HgPev9TitGIQCQRaLxbJRdMNc88fAr1Nf1f1NwEfVcD+wVUSe34VjLWEQCgRZLBbLRrEmIS8iPw2cUdVHGj66AjhV8/p0/F6zfRwSkQdF5MGLFy+ueAyDUCDIYrFYNoq2NnkR+Sqwp8lHvwn8J+D1zb7W5L2m/ZtU9VbgVoCDBw920OOpnn7tq2ixWCz9QFshr6qva/a+iPw9YD/wiPGzciXwXRF5JUZz31ez+ZXA2TWPtgmDUCDIYrFYNopVm2tU9XuqultVr1bVqzGC/cdU9TxwF/DzYrgBmFHVc90Zcj2DUCDIYrFYNopeZbx+ARM++TQmhPLf9Og4gM16tVgsllZ0TcjH2nzyfwV+uVv7tlgsFsvqsBmvFovFMsRYIW+xWCxDjBXyFovFMsQMfKnhBFu/xmKxWJYyFJq8rV9jsVgszRkKIW/r11gsFktzhkLI2/o1FovF0pyhEPLZwhY0DOres/VrLBaLZUiE/M4D1xNpSBT4qCpR4Nv6NRaLxcKQCHlbv8ZisViaMzQhlLZ+jcVisSxlKDR5i8VisTTHCnmLxWIZYqyQt1gsliHGCnmLxWIZYqyQt1gsliFGTH+P/kBELgInV/n1ncBkF4czCNhz3hzYc94crOWcX6Cqu5p90FdCfi2IyIOqenCjx7Ge2HPeHNhz3hz06pytucZisViGGCvkLRaLZYgZJiF/60YPYAOw57w5sOe8OejJOQ+NTd5isVgsSxkmTd5isVgsDVghb7FYLEPMUAh5EXmDiHxfRJ4WkXdt9Hh6gYjsE5GvicjjInJMRH4lfn+7iHxFRJ6K/27b6LF2ExFxReQhEflc/Hq/iDwQn+8nRSS70WPsNiKyVUQ+JSJPxPf7xk1wn98R/64fFZGPi0h+2O61iPyliFwQkUdr3mt6X8XwJ7FM+zsR+bHVHnfghbyIuMCfAT8JvBj4lyLy4o0dVU8IgP+oqj8C3AD8cnye7wIOq+o1wOH49TDxK8DjNa//APjj+HyngLdtyKh6y3uBu1X1WuBHMec/tPdZRK4A3g4cVNWXAC7wswzfvf4w8IaG91rd158Eron/HQLev9qDDryQB14JPK2qx1W1CnwCeNMGj6nrqOo5Vf1u/P85zIN/BeZcPxJv9hHgn23MCLuPiFwJvBH4YPxagNcCn4o3GarzBRCRCeA1wIcAVLWqqtMM8X2O8YAREfGAAnCOIbvXqnovcLnh7Vb39U3AR9VwP7BVRJ6/muMOg5C/AjhV8/p0/N7QIiJXAy8HHgCep6rnwEwEwO6NG1nXeQ/w60AUv94BTKtq0tB3GO/1AeAicFtspvqgiIwyxPdZVc8A/wN4FiPcZ4CjDP+9htb3tWtybRiEvDR5b2jjQkVkDPgb4FdVdXajx9MrROSngAuqerT27SabDtu99oAfA96vqi8HFhgi00wzYjv0m4D9wF5gFGOuaGTY7vVydO23PgxC/jSwr+b1lcDZDRpLTxGRDEbA/5Wqfjp++7lkGRf/vbBR4+syPw78tIj8AGOCey1Gs98aL+lhOO/1aeC0qj4Qv/4URugP630GeB1wQlUvqqoPfBr4+wz/vYbW97Vrcm0YhPwR4JrYE5/FOGzu2uAxdZ3YHv0h4HFV/aOaj+4C3hr//63AZ9Z7bL1AVd+tqleq6tWYe3qPqv4r4GvAm+PNhuZ8E1T1PHBKRH44futm4DGG9D7HPAvcICKF+HeenPNQ3+uYVvf1LuDn4yibG4CZxKyzYlR14P8B/wR4EngG+M2NHk+PzvEfYJZrfwc8HP/7Jxg79WHgqfjv9o0eaw/O/Sbgc/H/DwDfAZ4G7gByGz2+Hpzvy4AH43v9v4Ftw36fgd8FngAeBT4G5IbtXgMfx/gcfIym/rZW9xVjrvmzWKZ9DxN5tKrj2rIGFovFMsQMg7nGYrFYLC2wQt5isViGGCvkLRaLZYixQt5isViGGCvkLRaLZYixQt5isViGGCvkLeuKiIQi8nBcUvYOESls0Dh+td2xRWS+4fUviMj72nxnr4h8arlt1gsR+c8i8msbPQ7LxmKFvGW9Kanqy9SUlK0C/0+nX4zLSneLX8VUO+wqqnpWVd/cfsvVUZPmb7F0hBXylo3km8ALAUTkX4vId2It/y8SgS4i8yLyX0TkAeBGEbleRL4tIo/E24/HjUX+UESOxA0W/u/4uzeJyNdrGnD8VZwm/nZMIayvicjXVjNwEflw3NTh2yJyXETeHL9/ddIUItb8Py0id8dNIf57/L4bf/9REfmeiLwjfv/rInIw/v/OuG5Psp87ROSzwJfj995Zc76/WzOu3xTTQOerwA9j2fRYrcCyIcQa6U8Cd4vIjwD/AvhxVfVF5H8C/wr4KKYi4aOq+v/FtYmeAP6Fqh6Ja6+XMOnhM6p6vYjkgL8VkS/Hh3o5cB2muNPfxsf4ExH5D8A/VNXJNZzG8zHlJq7F1BppZqZ5WTyGCvB9EflTTDnZK+LVDCKytYNj3Qi8VFUvi8jrMc0kXolJf79LRF6DqVj5s/HxPOC7mJK9lk2MFfKW9WZERB6O//9NTNG1Q8ArgCOmPhUjLFbjCzGVN8FopudU9QiAxqWWY6H30kSbBrZghGAV+I6qno63exi4GvjWGsZfWwfkf6tqBDwmIs9rsf1hVZ2Jj/8Y8ALgGHAgFvifJ9bO2/AVVU0aTrw+/vdQ/HoMc77jwJ2qWoyPN3SF+iwrxwp5y3pTUtWX1b4RVx78iKq+u8n2ZVUNk01pXlNbgH+vql9q2O9NGA06IWRlv/mSiGTVdBwD2A7Uav61+25W/7txmxDwVHVKRH4U+MfALwNvAX4R0+IxMaHmG/az0HCs31fVv6jdQER+lc1Vc93SAdYmb+kHDgNvFpHdkDY3fkGT7Z4A9orI9fF247HZ50vAvxNTbx8ReZGYbkrLMYfRfJfjG8C/jvc5ghHGq7Lh1yIiOwFHVf8G+G1MvXiAH2BWNLBYYrcZXwJ+UUwDGUTkivja3Qv8jIiMiMg48E/XOlbL4GM1ecuGo6qPichvAV8WEQdTivWXgZMN21VF5F8AfxoL3RKm4cQHMWaY78argou07wd6K/BFETmnqv+wxTa/AvxF7KgVTM/Ne1d1kvVcgWnvlyhZyQrmfwC3i8jPAfe0+rKqfjn2Y9wXm7fmgX+tqt8VkU9iylCfxJjDLJscW2rYYrFYhhhrrrFYLJYhxpprLJueOAY/1/D2z6nq9zZiPBZLN7HmGovFYhlirLnGYrFYhhgr5C0Wi2WIsULe8v9vFIyCUTCMwWghPwpGwSgYBcMYAABO6AeA8cyo3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(y_train, y_train_pred,lowess=True, color=\"tan\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model - Scaling Continuous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No_Exercise</th>\n",
       "      <th>Few_Fruit_Veg</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>High_Blood_Pres</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Elderly_Medicare</th>\n",
       "      <th>Prim_Care_Phys_Rate</th>\n",
       "      <th>Dentist_Rate</th>\n",
       "      <th>POP_COU</th>\n",
       "      <th>POPPCT_URBAN</th>\n",
       "      <th>AREA_URBAN</th>\n",
       "      <th>AREAPCT_URBAN</th>\n",
       "      <th>POP_RURAL</th>\n",
       "      <th>AREA_RURAL</th>\n",
       "      <th>CHC_x_POPPCT_URBAN</th>\n",
       "      <th>HPSA_x_POPPCT_URBAN</th>\n",
       "      <th>Median_Household_Income</th>\n",
       "      <th>povertypercent</th>\n",
       "      <th>medianagemale</th>\n",
       "      <th>percentmarried</th>\n",
       "      <th>no_hs_24</th>\n",
       "      <th>hs_24</th>\n",
       "      <th>college_24</th>\n",
       "      <th>hs_25</th>\n",
       "      <th>college_25</th>\n",
       "      <th>pctemployed</th>\n",
       "      <th>pctunemployed</th>\n",
       "      <th>pctprivatecoverage</th>\n",
       "      <th>pctempprivcoverage</th>\n",
       "      <th>pctpubliccoverage</th>\n",
       "      <th>pctpubliccoveragealone</th>\n",
       "      <th>pctwhite</th>\n",
       "      <th>pctblack</th>\n",
       "      <th>pctasian</th>\n",
       "      <th>pctotherrace</th>\n",
       "      <th>pctmarriedhouseholds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.140728</td>\n",
       "      <td>-0.833895</td>\n",
       "      <td>-0.365908</td>\n",
       "      <td>0.217572</td>\n",
       "      <td>0.218053</td>\n",
       "      <td>-0.378667</td>\n",
       "      <td>0.919808</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>0.010932</td>\n",
       "      <td>0.213431</td>\n",
       "      <td>0.457721</td>\n",
       "      <td>0.760082</td>\n",
       "      <td>-0.065738</td>\n",
       "      <td>3.362688</td>\n",
       "      <td>0.179537</td>\n",
       "      <td>-0.751603</td>\n",
       "      <td>0.744785</td>\n",
       "      <td>0.262894</td>\n",
       "      <td>-0.609027</td>\n",
       "      <td>0.244370</td>\n",
       "      <td>0.295230</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>-0.173819</td>\n",
       "      <td>0.537401</td>\n",
       "      <td>-0.875305</td>\n",
       "      <td>1.135858</td>\n",
       "      <td>0.096162</td>\n",
       "      <td>-0.104687</td>\n",
       "      <td>0.298370</td>\n",
       "      <td>0.143838</td>\n",
       "      <td>-0.337689</td>\n",
       "      <td>-0.285384</td>\n",
       "      <td>0.165771</td>\n",
       "      <td>0.026891</td>\n",
       "      <td>-0.217984</td>\n",
       "      <td>-0.281841</td>\n",
       "      <td>0.425974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.319560</td>\n",
       "      <td>0.132581</td>\n",
       "      <td>0.099834</td>\n",
       "      <td>0.471333</td>\n",
       "      <td>-1.200359</td>\n",
       "      <td>-0.602950</td>\n",
       "      <td>-0.264460</td>\n",
       "      <td>-0.188297</td>\n",
       "      <td>-0.274300</td>\n",
       "      <td>-0.243278</td>\n",
       "      <td>-0.356155</td>\n",
       "      <td>-0.346360</td>\n",
       "      <td>-0.395972</td>\n",
       "      <td>-0.084651</td>\n",
       "      <td>-0.049164</td>\n",
       "      <td>-0.751603</td>\n",
       "      <td>-0.023094</td>\n",
       "      <td>-1.116039</td>\n",
       "      <td>1.316746</td>\n",
       "      <td>-0.588177</td>\n",
       "      <td>-1.499886</td>\n",
       "      <td>0.859029</td>\n",
       "      <td>0.922702</td>\n",
       "      <td>-0.973919</td>\n",
       "      <td>-0.062080</td>\n",
       "      <td>-1.078077</td>\n",
       "      <td>-1.780871</td>\n",
       "      <td>2.824671</td>\n",
       "      <td>-1.106610</td>\n",
       "      <td>-1.164279</td>\n",
       "      <td>1.313023</td>\n",
       "      <td>1.184001</td>\n",
       "      <td>-2.236444</td>\n",
       "      <td>2.570227</td>\n",
       "      <td>-0.125638</td>\n",
       "      <td>0.441741</td>\n",
       "      <td>-1.210808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319560</td>\n",
       "      <td>1.756660</td>\n",
       "      <td>0.401999</td>\n",
       "      <td>0.471333</td>\n",
       "      <td>0.058310</td>\n",
       "      <td>1.826783</td>\n",
       "      <td>-0.319429</td>\n",
       "      <td>-0.278206</td>\n",
       "      <td>-0.550331</td>\n",
       "      <td>-0.256678</td>\n",
       "      <td>-0.374022</td>\n",
       "      <td>-0.340107</td>\n",
       "      <td>-0.373101</td>\n",
       "      <td>-0.258649</td>\n",
       "      <td>-0.148453</td>\n",
       "      <td>-0.751603</td>\n",
       "      <td>-0.992650</td>\n",
       "      <td>-0.660329</td>\n",
       "      <td>0.183025</td>\n",
       "      <td>-0.355838</td>\n",
       "      <td>-0.405303</td>\n",
       "      <td>0.883842</td>\n",
       "      <td>-0.206715</td>\n",
       "      <td>-1.107271</td>\n",
       "      <td>1.022219</td>\n",
       "      <td>-1.340748</td>\n",
       "      <td>-1.090607</td>\n",
       "      <td>0.127341</td>\n",
       "      <td>0.034347</td>\n",
       "      <td>0.271459</td>\n",
       "      <td>0.391695</td>\n",
       "      <td>0.563594</td>\n",
       "      <td>-0.435396</td>\n",
       "      <td>0.836954</td>\n",
       "      <td>-0.443601</td>\n",
       "      <td>-0.551631</td>\n",
       "      <td>0.948270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.976732</td>\n",
       "      <td>-1.232442</td>\n",
       "      <td>-0.226185</td>\n",
       "      <td>0.471333</td>\n",
       "      <td>0.012486</td>\n",
       "      <td>0.069899</td>\n",
       "      <td>-0.158502</td>\n",
       "      <td>-0.853626</td>\n",
       "      <td>-0.909172</td>\n",
       "      <td>-0.155171</td>\n",
       "      <td>-1.063152</td>\n",
       "      <td>-0.374597</td>\n",
       "      <td>-0.402108</td>\n",
       "      <td>1.858757</td>\n",
       "      <td>-0.138899</td>\n",
       "      <td>-0.425729</td>\n",
       "      <td>-0.992650</td>\n",
       "      <td>-0.091290</td>\n",
       "      <td>0.089842</td>\n",
       "      <td>-0.007330</td>\n",
       "      <td>1.054141</td>\n",
       "      <td>0.151877</td>\n",
       "      <td>-0.184784</td>\n",
       "      <td>-0.707216</td>\n",
       "      <td>-0.219018</td>\n",
       "      <td>-0.871694</td>\n",
       "      <td>-0.703091</td>\n",
       "      <td>-0.046680</td>\n",
       "      <td>0.166359</td>\n",
       "      <td>0.367175</td>\n",
       "      <td>-0.056172</td>\n",
       "      <td>-0.040486</td>\n",
       "      <td>0.698655</td>\n",
       "      <td>-0.525376</td>\n",
       "      <td>-0.423689</td>\n",
       "      <td>-0.280874</td>\n",
       "      <td>1.271365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.319560</td>\n",
       "      <td>0.132581</td>\n",
       "      <td>0.401999</td>\n",
       "      <td>0.471333</td>\n",
       "      <td>0.058310</td>\n",
       "      <td>0.537740</td>\n",
       "      <td>-0.378008</td>\n",
       "      <td>0.002761</td>\n",
       "      <td>-0.573334</td>\n",
       "      <td>-0.292083</td>\n",
       "      <td>0.167712</td>\n",
       "      <td>-0.366860</td>\n",
       "      <td>-0.394298</td>\n",
       "      <td>-0.851775</td>\n",
       "      <td>-0.147460</td>\n",
       "      <td>-0.751603</td>\n",
       "      <td>-0.992650</td>\n",
       "      <td>-1.197866</td>\n",
       "      <td>2.823197</td>\n",
       "      <td>-0.336477</td>\n",
       "      <td>-3.338785</td>\n",
       "      <td>-1.237616</td>\n",
       "      <td>3.346013</td>\n",
       "      <td>-1.373975</td>\n",
       "      <td>-0.233285</td>\n",
       "      <td>-1.265699</td>\n",
       "      <td>-1.151157</td>\n",
       "      <td>2.940685</td>\n",
       "      <td>-1.540362</td>\n",
       "      <td>-0.940942</td>\n",
       "      <td>1.018710</td>\n",
       "      <td>1.575836</td>\n",
       "      <td>-3.510495</td>\n",
       "      <td>4.208577</td>\n",
       "      <td>-0.399361</td>\n",
       "      <td>-0.560640</td>\n",
       "      <td>-2.974156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No_Exercise  Few_Fruit_Veg   Obesity  High_Blood_Pres    Smoker  Diabetes  \\\n",
       "0    -0.140728      -0.833895 -0.365908         0.217572  0.218053 -0.378667   \n",
       "1     0.319560       0.132581  0.099834         0.471333 -1.200359 -0.602950   \n",
       "2     0.319560       1.756660  0.401999         0.471333  0.058310  1.826783   \n",
       "3     0.976732      -1.232442 -0.226185         0.471333  0.012486  0.069899   \n",
       "4     0.319560       0.132581  0.401999         0.471333  0.058310  0.537740   \n",
       "\n",
       "   Elderly_Medicare  Prim_Care_Phys_Rate  Dentist_Rate   POP_COU  \\\n",
       "0          0.919808             0.288223      0.010932  0.213431   \n",
       "1         -0.264460            -0.188297     -0.274300 -0.243278   \n",
       "2         -0.319429            -0.278206     -0.550331 -0.256678   \n",
       "3         -0.158502            -0.853626     -0.909172 -0.155171   \n",
       "4         -0.378008             0.002761     -0.573334 -0.292083   \n",
       "\n",
       "   POPPCT_URBAN  AREA_URBAN  AREAPCT_URBAN  POP_RURAL  AREA_RURAL  \\\n",
       "0      0.457721    0.760082      -0.065738   3.362688    0.179537   \n",
       "1     -0.356155   -0.346360      -0.395972  -0.084651   -0.049164   \n",
       "2     -0.374022   -0.340107      -0.373101  -0.258649   -0.148453   \n",
       "3     -1.063152   -0.374597      -0.402108   1.858757   -0.138899   \n",
       "4      0.167712   -0.366860      -0.394298  -0.851775   -0.147460   \n",
       "\n",
       "   CHC_x_POPPCT_URBAN  HPSA_x_POPPCT_URBAN  Median_Household_Income  \\\n",
       "0           -0.751603             0.744785                 0.262894   \n",
       "1           -0.751603            -0.023094                -1.116039   \n",
       "2           -0.751603            -0.992650                -0.660329   \n",
       "3           -0.425729            -0.992650                -0.091290   \n",
       "4           -0.751603            -0.992650                -1.197866   \n",
       "\n",
       "   povertypercent  medianagemale  percentmarried  no_hs_24     hs_24  \\\n",
       "0       -0.609027       0.244370        0.295230 -0.059028 -0.173819   \n",
       "1        1.316746      -0.588177       -1.499886  0.859029  0.922702   \n",
       "2        0.183025      -0.355838       -0.405303  0.883842 -0.206715   \n",
       "3        0.089842      -0.007330        1.054141  0.151877 -0.184784   \n",
       "4        2.823197      -0.336477       -3.338785 -1.237616  3.346013   \n",
       "\n",
       "   college_24     hs_25  college_25  pctemployed  pctunemployed  \\\n",
       "0    0.537401 -0.875305    1.135858     0.096162      -0.104687   \n",
       "1   -0.973919 -0.062080   -1.078077    -1.780871       2.824671   \n",
       "2   -1.107271  1.022219   -1.340748    -1.090607       0.127341   \n",
       "3   -0.707216 -0.219018   -0.871694    -0.703091      -0.046680   \n",
       "4   -1.373975 -0.233285   -1.265699    -1.151157       2.940685   \n",
       "\n",
       "   pctprivatecoverage  pctempprivcoverage  pctpubliccoverage  \\\n",
       "0            0.298370            0.143838          -0.337689   \n",
       "1           -1.106610           -1.164279           1.313023   \n",
       "2            0.034347            0.271459           0.391695   \n",
       "3            0.166359            0.367175          -0.056172   \n",
       "4           -1.540362           -0.940942           1.018710   \n",
       "\n",
       "   pctpubliccoveragealone  pctwhite  pctblack  pctasian  pctotherrace  \\\n",
       "0               -0.285384  0.165771  0.026891 -0.217984     -0.281841   \n",
       "1                1.184001 -2.236444  2.570227 -0.125638      0.441741   \n",
       "2                0.563594 -0.435396  0.836954 -0.443601     -0.551631   \n",
       "3               -0.040486  0.698655 -0.525376 -0.423689     -0.280874   \n",
       "4                1.575836 -3.510495  4.208577 -0.399361     -0.560640   \n",
       "\n",
       "   pctmarriedhouseholds  \n",
       "0              0.425974  \n",
       "1             -1.210808  \n",
       "2              0.948270  \n",
       "3              1.271365  \n",
       "4             -2.974156  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scaling our continuous variables:\n",
    "scaler = StandardScaler()\n",
    "scaled_df_cont = pd.DataFrame(scaler.fit_transform(df_cont), columns = continuous_variables)\n",
    "scaled_df_cont.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total = pd.concat([scaled_df_cont, df_cate], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total = df_total.fillna(0.0)\n",
    "df_total = df_total.iloc[:2599]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_total, target, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape: (1949, 39)\n",
      "X_test Shape:  (650, 39)\n",
      "y_train Shape: (1949,)\n",
      "y_test Shape:  (650,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train Shape: \" + str(X_train.shape))\n",
    "print(\"X_test Shape:  \" + str(X_test.shape))\n",
    "print(\"y_train Shape: \" + str(y_train.shape))\n",
    "print(\"y_test Shape:  \" + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 16.905852945940143\n",
      "R^2 Score: 0.3739339389923164\n"
     ]
    }
   ],
   "source": [
    "#instantiate a linear regression object\n",
    "model2 = linear_model.LinearRegression()\n",
    "\n",
    "#fit the linear regression to the data\n",
    "model2 = model2.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = model2.predict(X_train)\n",
    "\n",
    "train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "\n",
    "\n",
    "print('Training RMSE:' , train_rmse)\n",
    "print (\"R^2 Score:\", model2.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This RMSE and R2 are very similiar to the previous un-transformed model, as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are all the features and their coefficients. We will use recursive elimination to narrow this list down to significant ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('No_Exercise', -0.47521182332947964),\n",
       " ('Few_Fruit_Veg', -0.3312324466136487),\n",
       " ('Obesity', 0.4713131345856334),\n",
       " ('High_Blood_Pres', -2.1965456647826525),\n",
       " ('Smoker', 1.975156695625071),\n",
       " ('Diabetes', -0.23127420895117765),\n",
       " ('Elderly_Medicare', 6.329581337510448),\n",
       " ('Prim_Care_Phys_Rate', 3.260209346439927),\n",
       " ('Dentist_Rate', 0.37245647247199315),\n",
       " ('POP_COU', -1.363385482887224),\n",
       " ('POPPCT_URBAN', -6.208141717803918),\n",
       " ('AREA_URBAN', 1.1059051672278069),\n",
       " ('AREAPCT_URBAN', -1.9218166211196706),\n",
       " ('POP_RURAL', -6.170160622686675),\n",
       " ('AREA_RURAL', -0.3712380227106195),\n",
       " ('CHC_x_POPPCT_URBAN', -2.013712734670523),\n",
       " ('HPSA_x_POPPCT_URBAN', 0.6528580021609944),\n",
       " ('Median_Household_Income', -2.5079746583928917),\n",
       " ('povertypercent', 0.48853944980204644),\n",
       " ('medianagemale', 0.41932441712461704),\n",
       " ('percentmarried', -1.2513412558958203),\n",
       " ('no_hs_24', 0.3640091696682233),\n",
       " ('hs_24', 0.6823184636044524),\n",
       " ('college_24', -0.29060754264582217),\n",
       " ('hs_25', -0.45722482594649294),\n",
       " ('college_25', 0.7779335195671018),\n",
       " ('pctemployed', -0.4816317428966683),\n",
       " ('pctunemployed', -0.42617420596130445),\n",
       " ('pctprivatecoverage', 0.041115549986786196),\n",
       " ('pctempprivcoverage', -0.7624936757529229),\n",
       " ('pctpubliccoverage', -1.3854512382209403),\n",
       " ('pctpubliccoveragealone', 0.8092641728298726),\n",
       " ('pctwhite', -0.3951503015401599),\n",
       " ('pctblack', -0.2977294695263981),\n",
       " ('pctasian', 0.19129242037530317),\n",
       " ('pctotherrace', -0.2071522923594897),\n",
       " ('pctmarriedhouseholds', 0.7434532086862321),\n",
       " ('Community_Health_Center_Ind', -0.3078026138123013),\n",
       " ('HPSA_Ind', 0.3333799296591407))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(zip(df_features.columns, model2.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ols = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFECV(cv=None,\n",
       "      estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "                                 normalize=False),\n",
       "      min_features_to_select=1, n_jobs=None, scoring='neg_mean_squared_error',\n",
       "      step=1, verbose=0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate a recursive eliminator object\n",
    "selector = RFECV(estimator=ols, step=1, scoring='neg_mean_squared_error')\n",
    "\n",
    "# Fit recursive feature eliminator \n",
    "selector.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_rfe = X_train.columns[selector.support_]\n",
    "removed_rfe = X_train.columns[~selector.support_]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(removed_rfe)) # We eliminated 30 features from our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['High_Blood_Pres',\n",
       " 'Smoker',\n",
       " 'Elderly_Medicare',\n",
       " 'Prim_Care_Phys_Rate',\n",
       " 'POPPCT_URBAN',\n",
       " 'AREAPCT_URBAN',\n",
       " 'POP_RURAL',\n",
       " 'CHC_x_POPPCT_URBAN',\n",
       " 'Median_Household_Income']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(selected_rfe) # Here are the features we are keeping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final model with only significant features according to recursive elimination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Root Mean Squared Error: 17.01058900868868\n",
      "Testing Root Mean Squared Error: 15.686747553971113\n",
      "R^2 Score: 0.3661526325855897\n"
     ]
    }
   ],
   "source": [
    "#instantiate a linear regression object\n",
    "lm_rfe = linear_model.LinearRegression()\n",
    "\n",
    "#fit the linear regression to the data\n",
    "lm_rfe = lm_rfe.fit(X_train[selected_rfe], y_train)\n",
    "\n",
    "y_rfe = lm_rfe.predict(X_train[selected_rfe])\n",
    "trainRFE_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_rfe))\n",
    "print('Training Root Mean Squared Error:' , trainRFE_rmse)\n",
    "\n",
    "y_pred_rfe = lm_rfe.predict(X_test[selected_rfe])\n",
    "testRFE_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred_rfe))\n",
    "print('Testing Root Mean Squared Error:' , testRFE_rmse)\n",
    "\n",
    "print (\"R^2 Score:\", lm_rfe.score(X_train[selected_rfe], y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High_Blood_Pres</td>\n",
       "      <td>-2.234794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smoker</td>\n",
       "      <td>1.926092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Elderly_Medicare</td>\n",
       "      <td>6.547118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prim_Care_Phys_Rate</td>\n",
       "      <td>3.578030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POPPCT_URBAN</td>\n",
       "      <td>-5.628755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AREAPCT_URBAN</td>\n",
       "      <td>-1.927773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POP_RURAL</td>\n",
       "      <td>-5.940180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CHC_x_POPPCT_URBAN</td>\n",
       "      <td>-2.061759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Median_Household_Income</td>\n",
       "      <td>-2.437864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0         1\n",
       "0          High_Blood_Pres -2.234794\n",
       "1                   Smoker  1.926092\n",
       "2         Elderly_Medicare  6.547118\n",
       "3      Prim_Care_Phys_Rate  3.578030\n",
       "4             POPPCT_URBAN -5.628755\n",
       "5            AREAPCT_URBAN -1.927773\n",
       "6                POP_RURAL -5.940180\n",
       "7       CHC_x_POPPCT_URBAN -2.061759\n",
       "8  Median_Household_Income -2.437864"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(zip(X_train[selected_rfe], lm_rfe.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fec4dc44b90>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(y_train, y_rfe,lowess=True, color=\"tan\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we have a list of our significant features and their coefficients. We can see that the Elderly on Medicare, Smoker, and Primary Care Physician Rate all increase the uninsured rate for every increase in standard deviation from their mean. Conversely, High Blood Pressure, Urban Population and Area, and Median Household Income all decrease the uninsured rate as they increase. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}