Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of EPA and Census Data\n",
    "\n",
    "## Methods\n",
    " - Logistic regression of carcinogens and socioeconomic data\n",
    " - All chemicals and socioeconomic data\n",
    "     - Logistic regression\n",
    "     - Linear regression\n",
    "     - Decision tree\n",
    "     - Random Forest\n",
    " - Harris county releases and socioeconomic data\n",
    "     - Linear regression\n",
    "     - Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.metrics import accuracy_score, roc_curve, auc\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Unnamed: 0</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Total Releases Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>77511</td>\n",
       "      <td>4.881777e+06</td>\n",
       "      <td>47393</td>\n",
       "      <td>12.514506</td>\n",
       "      <td>55.833140</td>\n",
       "      <td>2.321018</td>\n",
       "      <td>0.160361</td>\n",
       "      <td>1.042348</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>2.795772</td>\n",
       "      <td>39.896187</td>\n",
       "      <td>3.990041</td>\n",
       "      <td>16.333636</td>\n",
       "      <td>29.972781</td>\n",
       "      <td>32.215728</td>\n",
       "      <td>49.059988</td>\n",
       "      <td>64.382926</td>\n",
       "      <td>76.952293</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>77979</td>\n",
       "      <td>3.529298e+06</td>\n",
       "      <td>17599</td>\n",
       "      <td>13.267799</td>\n",
       "      <td>36.678220</td>\n",
       "      <td>2.977442</td>\n",
       "      <td>0.068186</td>\n",
       "      <td>6.273084</td>\n",
       "      <td>0.039775</td>\n",
       "      <td>1.948974</td>\n",
       "      <td>54.207625</td>\n",
       "      <td>3.812717</td>\n",
       "      <td>19.904540</td>\n",
       "      <td>34.876982</td>\n",
       "      <td>37.587363</td>\n",
       "      <td>55.719075</td>\n",
       "      <td>68.617535</td>\n",
       "      <td>79.442014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>77536</td>\n",
       "      <td>3.511945e+06</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77530</td>\n",
       "      <td>1.386491e+06</td>\n",
       "      <td>33387</td>\n",
       "      <td>16.922754</td>\n",
       "      <td>18.770779</td>\n",
       "      <td>12.441968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240782</td>\n",
       "      <td>67.472369</td>\n",
       "      <td>9.488723</td>\n",
       "      <td>25.755534</td>\n",
       "      <td>39.997604</td>\n",
       "      <td>42.121185</td>\n",
       "      <td>61.014766</td>\n",
       "      <td>80.252793</td>\n",
       "      <td>87.507114</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>78380</td>\n",
       "      <td>1.007567e+06</td>\n",
       "      <td>24147</td>\n",
       "      <td>23.597134</td>\n",
       "      <td>20.072887</td>\n",
       "      <td>0.401706</td>\n",
       "      <td>0.583923</td>\n",
       "      <td>0.062120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.199031</td>\n",
       "      <td>78.924918</td>\n",
       "      <td>10.456786</td>\n",
       "      <td>27.303599</td>\n",
       "      <td>38.191080</td>\n",
       "      <td>41.528968</td>\n",
       "      <td>58.392347</td>\n",
       "      <td>72.311260</td>\n",
       "      <td>83.451360</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>456</td>\n",
       "      <td>77061</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>26253</td>\n",
       "      <td>20.024378</td>\n",
       "      <td>8.246677</td>\n",
       "      <td>23.364949</td>\n",
       "      <td>0.239973</td>\n",
       "      <td>5.450806</td>\n",
       "      <td>0.087609</td>\n",
       "      <td>22.271740</td>\n",
       "      <td>63.501314</td>\n",
       "      <td>10.261684</td>\n",
       "      <td>28.221537</td>\n",
       "      <td>46.352798</td>\n",
       "      <td>51.902640</td>\n",
       "      <td>69.165429</td>\n",
       "      <td>77.793014</td>\n",
       "      <td>88.511789</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>457</td>\n",
       "      <td>78735</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>17814</td>\n",
       "      <td>7.488492</td>\n",
       "      <td>66.402829</td>\n",
       "      <td>2.907825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.919951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.263052</td>\n",
       "      <td>20.051645</td>\n",
       "      <td>2.133154</td>\n",
       "      <td>8.914337</td>\n",
       "      <td>13.596048</td>\n",
       "      <td>14.808578</td>\n",
       "      <td>22.123049</td>\n",
       "      <td>31.548221</td>\n",
       "      <td>39.609296</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>458</td>\n",
       "      <td>76155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5813</td>\n",
       "      <td>8.360571</td>\n",
       "      <td>23.636676</td>\n",
       "      <td>34.302426</td>\n",
       "      <td>0.928952</td>\n",
       "      <td>16.377086</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.629795</td>\n",
       "      <td>20.729400</td>\n",
       "      <td>3.285739</td>\n",
       "      <td>8.773439</td>\n",
       "      <td>23.550662</td>\n",
       "      <td>26.131085</td>\n",
       "      <td>45.776707</td>\n",
       "      <td>66.093239</td>\n",
       "      <td>77.722346</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>459</td>\n",
       "      <td>77048</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>18296</td>\n",
       "      <td>24.289462</td>\n",
       "      <td>2.273721</td>\n",
       "      <td>66.812418</td>\n",
       "      <td>0.054657</td>\n",
       "      <td>1.688894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.373196</td>\n",
       "      <td>29.509182</td>\n",
       "      <td>13.325317</td>\n",
       "      <td>35.608876</td>\n",
       "      <td>48.387626</td>\n",
       "      <td>50.448185</td>\n",
       "      <td>60.980542</td>\n",
       "      <td>73.026891</td>\n",
       "      <td>84.400962</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>460</td>\n",
       "      <td>78254</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>58690</td>\n",
       "      <td>6.563299</td>\n",
       "      <td>34.985517</td>\n",
       "      <td>6.121997</td>\n",
       "      <td>0.771852</td>\n",
       "      <td>3.066962</td>\n",
       "      <td>0.342477</td>\n",
       "      <td>6.776282</td>\n",
       "      <td>53.988754</td>\n",
       "      <td>2.901687</td>\n",
       "      <td>8.955529</td>\n",
       "      <td>14.944624</td>\n",
       "      <td>18.032033</td>\n",
       "      <td>36.282161</td>\n",
       "      <td>52.704038</td>\n",
       "      <td>67.403306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0    ZIP  Total Releases  Total Population  \\\n",
       "0             0  77511    4.881777e+06             47393   \n",
       "1             1  77979    3.529298e+06             17599   \n",
       "2             2  77536    3.511945e+06             31865   \n",
       "3             3  77530    1.386491e+06             33387   \n",
       "4             4  78380    1.007567e+06             24147   \n",
       "..          ...    ...             ...               ...   \n",
       "456         456  77061    0.000000e+00             26253   \n",
       "457         457  78735    0.000000e+00             17814   \n",
       "458         458  76155    0.000000e+00              5813   \n",
       "459         459  77048    0.000000e+00             18296   \n",
       "460         460  78254    0.000000e+00             58690   \n",
       "\n",
       "     Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0                      12.514506      55.833140   \n",
       "1                      13.267799      36.678220   \n",
       "2                       7.453319      58.104503   \n",
       "3                      16.922754      18.770779   \n",
       "4                      23.597134      20.072887   \n",
       "..                           ...            ...   \n",
       "456                    20.024378       8.246677   \n",
       "457                     7.488492      66.402829   \n",
       "458                     8.360571      23.636676   \n",
       "459                    24.289462       2.273721   \n",
       "460                     6.563299      34.985517   \n",
       "\n",
       "     Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                          2.321018                 0.160361       1.042348   \n",
       "1                          2.977442                 0.068186       6.273084   \n",
       "2                          1.449867                 0.922642       1.653852   \n",
       "3                         12.441968                 0.000000       0.185701   \n",
       "4                          0.401706                 0.583923       0.062120   \n",
       "..                              ...                      ...            ...   \n",
       "456                       23.364949                 0.239973       5.450806   \n",
       "457                        2.907825                 0.000000       8.919951   \n",
       "458                       34.302426                 0.928952      16.377086   \n",
       "459                       66.812418                 0.054657       1.688894   \n",
       "460                        6.121997                 0.771852       3.066962   \n",
       "\n",
       "     Percent_Native_Hawaiian_PI  Percent_Other_Race  Percent_Hispanic_Latino  \\\n",
       "0                      0.027430            2.795772                39.896187   \n",
       "1                      0.039775            1.948974                54.207625   \n",
       "2                      0.000000            2.815001                37.363879   \n",
       "3                      0.000000            3.240782                67.472369   \n",
       "4                      0.000000            2.199031                78.924918   \n",
       "..                          ...                 ...                      ...   \n",
       "456                    0.087609           22.271740                63.501314   \n",
       "457                    0.000000            1.263052                20.051645   \n",
       "458                    0.000000            3.629795                20.729400   \n",
       "459                    0.000000            7.373196                29.509182   \n",
       "460                    0.342477            6.776282                53.988754   \n",
       "\n",
       "     Percent_50_PL  Percent_125_PL  Percent_185_PL  Percent_200_PL  \\\n",
       "0         3.990041       16.333636       29.972781       32.215728   \n",
       "1         3.812717       19.904540       34.876982       37.587363   \n",
       "2         3.276322        8.771379       18.732151       20.878707   \n",
       "3         9.488723       25.755534       39.997604       42.121185   \n",
       "4        10.456786       27.303599       38.191080       41.528968   \n",
       "..             ...             ...             ...             ...   \n",
       "456      10.261684       28.221537       46.352798       51.902640   \n",
       "457       2.133154        8.914337       13.596048       14.808578   \n",
       "458       3.285739        8.773439       23.550662       26.131085   \n",
       "459      13.325317       35.608876       48.387626       50.448185   \n",
       "460       2.901687        8.955529       14.944624       18.032033   \n",
       "\n",
       "     Percent_300_PL  Percent_400_PL  Percent_500_PL  Total Releases Binned  \n",
       "0         49.059988       64.382926       76.952293                      4  \n",
       "1         55.719075       68.617535       79.442014                      4  \n",
       "2         36.908834       54.815628       67.174015                      4  \n",
       "3         61.014766       80.252793       87.507114                      4  \n",
       "4         58.392347       72.311260       83.451360                      4  \n",
       "..              ...             ...             ...                    ...  \n",
       "456       69.165429       77.793014       88.511789                      1  \n",
       "457       22.123049       31.548221       39.609296                      1  \n",
       "458       45.776707       66.093239       77.722346                      1  \n",
       "459       60.980542       73.026891       84.400962                      1  \n",
       "460       36.282161       52.704038       67.403306                      1  \n",
       "\n",
       "[461 rows x 20 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Cleaned Data/capstone_cleaned_3.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>ZIP</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Total Releases Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77511</td>\n",
       "      <td>4.881777e+06</td>\n",
       "      <td>47393</td>\n",
       "      <td>12.514506</td>\n",
       "      <td>55.833140</td>\n",
       "      <td>2.321018</td>\n",
       "      <td>0.160361</td>\n",
       "      <td>1.042348</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>2.795772</td>\n",
       "      <td>39.896187</td>\n",
       "      <td>3.990041</td>\n",
       "      <td>16.333636</td>\n",
       "      <td>29.972781</td>\n",
       "      <td>32.215728</td>\n",
       "      <td>49.059988</td>\n",
       "      <td>64.382926</td>\n",
       "      <td>76.952293</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77979</td>\n",
       "      <td>3.529298e+06</td>\n",
       "      <td>17599</td>\n",
       "      <td>13.267799</td>\n",
       "      <td>36.678220</td>\n",
       "      <td>2.977442</td>\n",
       "      <td>0.068186</td>\n",
       "      <td>6.273084</td>\n",
       "      <td>0.039775</td>\n",
       "      <td>1.948974</td>\n",
       "      <td>54.207625</td>\n",
       "      <td>3.812717</td>\n",
       "      <td>19.904540</td>\n",
       "      <td>34.876982</td>\n",
       "      <td>37.587363</td>\n",
       "      <td>55.719075</td>\n",
       "      <td>68.617535</td>\n",
       "      <td>79.442014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77536</td>\n",
       "      <td>3.511945e+06</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77530</td>\n",
       "      <td>1.386491e+06</td>\n",
       "      <td>33387</td>\n",
       "      <td>16.922754</td>\n",
       "      <td>18.770779</td>\n",
       "      <td>12.441968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240782</td>\n",
       "      <td>67.472369</td>\n",
       "      <td>9.488723</td>\n",
       "      <td>25.755534</td>\n",
       "      <td>39.997604</td>\n",
       "      <td>42.121185</td>\n",
       "      <td>61.014766</td>\n",
       "      <td>80.252793</td>\n",
       "      <td>87.507114</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78380</td>\n",
       "      <td>1.007567e+06</td>\n",
       "      <td>24147</td>\n",
       "      <td>23.597134</td>\n",
       "      <td>20.072887</td>\n",
       "      <td>0.401706</td>\n",
       "      <td>0.583923</td>\n",
       "      <td>0.062120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.199031</td>\n",
       "      <td>78.924918</td>\n",
       "      <td>10.456786</td>\n",
       "      <td>27.303599</td>\n",
       "      <td>38.191080</td>\n",
       "      <td>41.528968</td>\n",
       "      <td>58.392347</td>\n",
       "      <td>72.311260</td>\n",
       "      <td>83.451360</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>77061</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>26253</td>\n",
       "      <td>20.024378</td>\n",
       "      <td>8.246677</td>\n",
       "      <td>23.364949</td>\n",
       "      <td>0.239973</td>\n",
       "      <td>5.450806</td>\n",
       "      <td>0.087609</td>\n",
       "      <td>22.271740</td>\n",
       "      <td>63.501314</td>\n",
       "      <td>10.261684</td>\n",
       "      <td>28.221537</td>\n",
       "      <td>46.352798</td>\n",
       "      <td>51.902640</td>\n",
       "      <td>69.165429</td>\n",
       "      <td>77.793014</td>\n",
       "      <td>88.511789</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>78735</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>17814</td>\n",
       "      <td>7.488492</td>\n",
       "      <td>66.402829</td>\n",
       "      <td>2.907825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.919951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.263052</td>\n",
       "      <td>20.051645</td>\n",
       "      <td>2.133154</td>\n",
       "      <td>8.914337</td>\n",
       "      <td>13.596048</td>\n",
       "      <td>14.808578</td>\n",
       "      <td>22.123049</td>\n",
       "      <td>31.548221</td>\n",
       "      <td>39.609296</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>76155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5813</td>\n",
       "      <td>8.360571</td>\n",
       "      <td>23.636676</td>\n",
       "      <td>34.302426</td>\n",
       "      <td>0.928952</td>\n",
       "      <td>16.377086</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.629795</td>\n",
       "      <td>20.729400</td>\n",
       "      <td>3.285739</td>\n",
       "      <td>8.773439</td>\n",
       "      <td>23.550662</td>\n",
       "      <td>26.131085</td>\n",
       "      <td>45.776707</td>\n",
       "      <td>66.093239</td>\n",
       "      <td>77.722346</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>77048</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>18296</td>\n",
       "      <td>24.289462</td>\n",
       "      <td>2.273721</td>\n",
       "      <td>66.812418</td>\n",
       "      <td>0.054657</td>\n",
       "      <td>1.688894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.373196</td>\n",
       "      <td>29.509182</td>\n",
       "      <td>13.325317</td>\n",
       "      <td>35.608876</td>\n",
       "      <td>48.387626</td>\n",
       "      <td>50.448185</td>\n",
       "      <td>60.980542</td>\n",
       "      <td>73.026891</td>\n",
       "      <td>84.400962</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>78254</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>58690</td>\n",
       "      <td>6.563299</td>\n",
       "      <td>34.985517</td>\n",
       "      <td>6.121997</td>\n",
       "      <td>0.771852</td>\n",
       "      <td>3.066962</td>\n",
       "      <td>0.342477</td>\n",
       "      <td>6.776282</td>\n",
       "      <td>53.988754</td>\n",
       "      <td>2.901687</td>\n",
       "      <td>8.955529</td>\n",
       "      <td>14.944624</td>\n",
       "      <td>18.032033</td>\n",
       "      <td>36.282161</td>\n",
       "      <td>52.704038</td>\n",
       "      <td>67.403306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ZIP  Total Releases  Total Population  Percent_Below_Poverty_Level  \\\n",
       "0    77511    4.881777e+06             47393                    12.514506   \n",
       "1    77979    3.529298e+06             17599                    13.267799   \n",
       "2    77536    3.511945e+06             31865                     7.453319   \n",
       "3    77530    1.386491e+06             33387                    16.922754   \n",
       "4    78380    1.007567e+06             24147                    23.597134   \n",
       "..     ...             ...               ...                          ...   \n",
       "456  77061    0.000000e+00             26253                    20.024378   \n",
       "457  78735    0.000000e+00             17814                     7.488492   \n",
       "458  76155    0.000000e+00              5813                     8.360571   \n",
       "459  77048    0.000000e+00             18296                    24.289462   \n",
       "460  78254    0.000000e+00             58690                     6.563299   \n",
       "\n",
       "     Percent_White  Percent_Black_African_American  Percent_American_Indian  \\\n",
       "0        55.833140                        2.321018                 0.160361   \n",
       "1        36.678220                        2.977442                 0.068186   \n",
       "2        58.104503                        1.449867                 0.922642   \n",
       "3        18.770779                       12.441968                 0.000000   \n",
       "4        20.072887                        0.401706                 0.583923   \n",
       "..             ...                             ...                      ...   \n",
       "456       8.246677                       23.364949                 0.239973   \n",
       "457      66.402829                        2.907825                 0.000000   \n",
       "458      23.636676                       34.302426                 0.928952   \n",
       "459       2.273721                       66.812418                 0.054657   \n",
       "460      34.985517                        6.121997                 0.771852   \n",
       "\n",
       "     Percent_Asian  Percent_Native_Hawaiian_PI  Percent_Other_Race  \\\n",
       "0         1.042348                    0.027430            2.795772   \n",
       "1         6.273084                    0.039775            1.948974   \n",
       "2         1.653852                    0.000000            2.815001   \n",
       "3         0.185701                    0.000000            3.240782   \n",
       "4         0.062120                    0.000000            2.199031   \n",
       "..             ...                         ...                 ...   \n",
       "456       5.450806                    0.087609           22.271740   \n",
       "457       8.919951                    0.000000            1.263052   \n",
       "458      16.377086                    0.000000            3.629795   \n",
       "459       1.688894                    0.000000            7.373196   \n",
       "460       3.066962                    0.342477            6.776282   \n",
       "\n",
       "     Percent_Hispanic_Latino  Percent_50_PL  Percent_125_PL  Percent_185_PL  \\\n",
       "0                  39.896187       3.990041       16.333636       29.972781   \n",
       "1                  54.207625       3.812717       19.904540       34.876982   \n",
       "2                  37.363879       3.276322        8.771379       18.732151   \n",
       "3                  67.472369       9.488723       25.755534       39.997604   \n",
       "4                  78.924918      10.456786       27.303599       38.191080   \n",
       "..                       ...            ...             ...             ...   \n",
       "456                63.501314      10.261684       28.221537       46.352798   \n",
       "457                20.051645       2.133154        8.914337       13.596048   \n",
       "458                20.729400       3.285739        8.773439       23.550662   \n",
       "459                29.509182      13.325317       35.608876       48.387626   \n",
       "460                53.988754       2.901687        8.955529       14.944624   \n",
       "\n",
       "     Percent_200_PL  Percent_300_PL  Percent_400_PL  Percent_500_PL  \\\n",
       "0         32.215728       49.059988       64.382926       76.952293   \n",
       "1         37.587363       55.719075       68.617535       79.442014   \n",
       "2         20.878707       36.908834       54.815628       67.174015   \n",
       "3         42.121185       61.014766       80.252793       87.507114   \n",
       "4         41.528968       58.392347       72.311260       83.451360   \n",
       "..              ...             ...             ...             ...   \n",
       "456       51.902640       69.165429       77.793014       88.511789   \n",
       "457       14.808578       22.123049       31.548221       39.609296   \n",
       "458       26.131085       45.776707       66.093239       77.722346   \n",
       "459       50.448185       60.980542       73.026891       84.400962   \n",
       "460       18.032033       36.282161       52.704038       67.403306   \n",
       "\n",
       "     Total Releases Binned  \n",
       "0                        4  \n",
       "1                        4  \n",
       "2                        4  \n",
       "3                        4  \n",
       "4                        4  \n",
       "..                     ...  \n",
       "456                      1  \n",
       "457                      1  \n",
       "458                      1  \n",
       "459                      1  \n",
       "460                      1  \n",
       "\n",
       "[461 rows x 19 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns='Unnamed: 0', axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>ZIP</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Total Releases Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>79916</td>\n",
       "      <td>96.0</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ZIP  Total Releases  Total Population  Percent_Below_Poverty_Level  \\\n",
       "257  79916            96.0                 0                          NaN   \n",
       "\n",
       "     Percent_White  Percent_Black_African_American  Percent_American_Indian  \\\n",
       "257            NaN                             NaN                      NaN   \n",
       "\n",
       "     Percent_Asian  Percent_Native_Hawaiian_PI  Percent_Other_Race  \\\n",
       "257            NaN                         NaN                 NaN   \n",
       "\n",
       "     Percent_Hispanic_Latino  Percent_50_PL  Percent_125_PL  Percent_185_PL  \\\n",
       "257                      NaN            NaN             NaN             NaN   \n",
       "\n",
       "     Percent_200_PL  Percent_300_PL  Percent_400_PL  Percent_500_PL  \\\n",
       "257             NaN             NaN             NaN             NaN   \n",
       "\n",
       "     Total Releases Binned  \n",
       "257                      2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for missing values\n",
    "#Population of zero\n",
    "\n",
    "null_data = df[df.isnull().any(axis=1)]\n",
    "null_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove that row\n",
    "df = df.drop(257)"
   ]
  },
  {
   "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",
       "    .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>ZIP</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Total Releases Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ZIP, Total Releases, Total Population, Percent_Below_Poverty_Level, Percent_White, Percent_Black_African_American, Percent_American_Indian, Percent_Asian, Percent_Native_Hawaiian_PI, Percent_Other_Race, Percent_Hispanic_Latino, Percent_50_PL, Percent_125_PL, Percent_185_PL, Percent_200_PL, Percent_300_PL, Percent_400_PL, Percent_500_PL, Total Releases Binned]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_data = df[df.isnull().any(axis=1)]\n",
    "null_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Just Carcinogens- Including all Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    4\n",
       "2    4\n",
       "3    4\n",
       "4    4\n",
       "Name: Total Releases Binned, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Total Releases Binned']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47393</td>\n",
       "      <td>12.514506</td>\n",
       "      <td>55.833140</td>\n",
       "      <td>2.321018</td>\n",
       "      <td>0.160361</td>\n",
       "      <td>1.042348</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>2.795772</td>\n",
       "      <td>39.896187</td>\n",
       "      <td>3.990041</td>\n",
       "      <td>16.333636</td>\n",
       "      <td>29.972781</td>\n",
       "      <td>32.215728</td>\n",
       "      <td>49.059988</td>\n",
       "      <td>64.382926</td>\n",
       "      <td>76.952293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17599</td>\n",
       "      <td>13.267799</td>\n",
       "      <td>36.678220</td>\n",
       "      <td>2.977442</td>\n",
       "      <td>0.068186</td>\n",
       "      <td>6.273084</td>\n",
       "      <td>0.039775</td>\n",
       "      <td>1.948974</td>\n",
       "      <td>54.207625</td>\n",
       "      <td>3.812717</td>\n",
       "      <td>19.904540</td>\n",
       "      <td>34.876982</td>\n",
       "      <td>37.587363</td>\n",
       "      <td>55.719075</td>\n",
       "      <td>68.617535</td>\n",
       "      <td>79.442014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33387</td>\n",
       "      <td>16.922754</td>\n",
       "      <td>18.770779</td>\n",
       "      <td>12.441968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240782</td>\n",
       "      <td>67.472369</td>\n",
       "      <td>9.488723</td>\n",
       "      <td>25.755534</td>\n",
       "      <td>39.997604</td>\n",
       "      <td>42.121185</td>\n",
       "      <td>61.014766</td>\n",
       "      <td>80.252793</td>\n",
       "      <td>87.507114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24147</td>\n",
       "      <td>23.597134</td>\n",
       "      <td>20.072887</td>\n",
       "      <td>0.401706</td>\n",
       "      <td>0.583923</td>\n",
       "      <td>0.062120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.199031</td>\n",
       "      <td>78.924918</td>\n",
       "      <td>10.456786</td>\n",
       "      <td>27.303599</td>\n",
       "      <td>38.191080</td>\n",
       "      <td>41.528968</td>\n",
       "      <td>58.392347</td>\n",
       "      <td>72.311260</td>\n",
       "      <td>83.451360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0             47393                    12.514506      55.833140   \n",
       "1             17599                    13.267799      36.678220   \n",
       "2             31865                     7.453319      58.104503   \n",
       "3             33387                    16.922754      18.770779   \n",
       "4             24147                    23.597134      20.072887   \n",
       "\n",
       "   Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                        2.321018                 0.160361       1.042348   \n",
       "1                        2.977442                 0.068186       6.273084   \n",
       "2                        1.449867                 0.922642       1.653852   \n",
       "3                       12.441968                 0.000000       0.185701   \n",
       "4                        0.401706                 0.583923       0.062120   \n",
       "\n",
       "   Percent_Native_Hawaiian_PI  Percent_Other_Race  Percent_Hispanic_Latino  \\\n",
       "0                    0.027430            2.795772                39.896187   \n",
       "1                    0.039775            1.948974                54.207625   \n",
       "2                    0.000000            2.815001                37.363879   \n",
       "3                    0.000000            3.240782                67.472369   \n",
       "4                    0.000000            2.199031                78.924918   \n",
       "\n",
       "   Percent_50_PL  Percent_125_PL  Percent_185_PL  Percent_200_PL  \\\n",
       "0       3.990041       16.333636       29.972781       32.215728   \n",
       "1       3.812717       19.904540       34.876982       37.587363   \n",
       "2       3.276322        8.771379       18.732151       20.878707   \n",
       "3       9.488723       25.755534       39.997604       42.121185   \n",
       "4      10.456786       27.303599       38.191080       41.528968   \n",
       "\n",
       "   Percent_300_PL  Percent_400_PL  Percent_500_PL  \n",
       "0       49.059988       64.382926       76.952293  \n",
       "1       55.719075       68.617535       79.442014  \n",
       "2       36.908834       54.815628       67.174015  \n",
       "3       61.014766       80.252793       87.507114  \n",
       "4       58.392347       72.311260       83.451360  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns=['ZIP', 'Total Releases', 'Total Releases Binned'], axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fef5e505080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef60e35be0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61843400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61870da0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fef618a4780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef618db160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61901b00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61937518>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fef61937550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef6199f860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef619d8240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61a03be0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fef61a3d5c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61a70f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61aa4940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61ae1320>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total Population                  0\n",
       "Percent_Below_Poverty_Level       0\n",
       "Percent_White                     0\n",
       "Percent_Black_African_American    0\n",
       "Percent_American_Indian           0\n",
       "Percent_Asian                     0\n",
       "Percent_Native_Hawaiian_PI        0\n",
       "Percent_Other_Race                0\n",
       "Percent_Hispanic_Latino           0\n",
       "Percent_50_PL                     0\n",
       "Percent_125_PL                    0\n",
       "Percent_185_PL                    0\n",
       "Percent_200_PL                    0\n",
       "Percent_300_PL                    0\n",
       "Percent_400_PL                    0\n",
       "Percent_500_PL                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 460 entries, 0 to 460\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   Total Population                460 non-null    int64  \n",
      " 1   Percent_Below_Poverty_Level     460 non-null    float64\n",
      " 2   Percent_White                   460 non-null    float64\n",
      " 3   Percent_Black_African_American  460 non-null    float64\n",
      " 4   Percent_American_Indian         460 non-null    float64\n",
      " 5   Percent_Asian                   460 non-null    float64\n",
      " 6   Percent_Native_Hawaiian_PI      460 non-null    float64\n",
      " 7   Percent_Other_Race              460 non-null    float64\n",
      " 8   Percent_Hispanic_Latino         460 non-null    float64\n",
      " 9   Percent_50_PL                   460 non-null    float64\n",
      " 10  Percent_125_PL                  460 non-null    float64\n",
      " 11  Percent_185_PL                  460 non-null    float64\n",
      " 12  Percent_200_PL                  460 non-null    float64\n",
      " 13  Percent_300_PL                  460 non-null    float64\n",
      " 14  Percent_400_PL                  460 non-null    float64\n",
      " 15  Percent_500_PL                  460 non-null    float64\n",
      "dtypes: float64(15), int64(1)\n",
      "memory usage: 61.1 KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.401741</td>\n",
       "      <td>0.190438</td>\n",
       "      <td>0.601237</td>\n",
       "      <td>0.030074</td>\n",
       "      <td>0.020796</td>\n",
       "      <td>0.020539</td>\n",
       "      <td>0.005441</td>\n",
       "      <td>0.069090</td>\n",
       "      <td>0.407563</td>\n",
       "      <td>0.092401</td>\n",
       "      <td>0.216687</td>\n",
       "      <td>0.349609</td>\n",
       "      <td>0.368712</td>\n",
       "      <td>0.488945</td>\n",
       "      <td>0.632559</td>\n",
       "      <td>0.724385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.148996</td>\n",
       "      <td>0.201901</td>\n",
       "      <td>0.390529</td>\n",
       "      <td>0.038579</td>\n",
       "      <td>0.008842</td>\n",
       "      <td>0.123606</td>\n",
       "      <td>0.007889</td>\n",
       "      <td>0.048164</td>\n",
       "      <td>0.553763</td>\n",
       "      <td>0.088294</td>\n",
       "      <td>0.264060</td>\n",
       "      <td>0.406813</td>\n",
       "      <td>0.430191</td>\n",
       "      <td>0.568583</td>\n",
       "      <td>0.682650</td>\n",
       "      <td>0.754158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.270016</td>\n",
       "      <td>0.113420</td>\n",
       "      <td>0.626222</td>\n",
       "      <td>0.018786</td>\n",
       "      <td>0.119651</td>\n",
       "      <td>0.032588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069565</td>\n",
       "      <td>0.381694</td>\n",
       "      <td>0.075873</td>\n",
       "      <td>0.116364</td>\n",
       "      <td>0.218496</td>\n",
       "      <td>0.238959</td>\n",
       "      <td>0.343626</td>\n",
       "      <td>0.519388</td>\n",
       "      <td>0.607452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.282927</td>\n",
       "      <td>0.257520</td>\n",
       "      <td>0.193544</td>\n",
       "      <td>0.161212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003659</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080087</td>\n",
       "      <td>0.689270</td>\n",
       "      <td>0.219739</td>\n",
       "      <td>0.341681</td>\n",
       "      <td>0.466541</td>\n",
       "      <td>0.482081</td>\n",
       "      <td>0.631915</td>\n",
       "      <td>0.820283</td>\n",
       "      <td>0.850604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.204544</td>\n",
       "      <td>0.359087</td>\n",
       "      <td>0.207868</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>0.075725</td>\n",
       "      <td>0.001224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054343</td>\n",
       "      <td>0.806265</td>\n",
       "      <td>0.242157</td>\n",
       "      <td>0.362219</td>\n",
       "      <td>0.445469</td>\n",
       "      <td>0.475303</td>\n",
       "      <td>0.600553</td>\n",
       "      <td>0.726343</td>\n",
       "      <td>0.802104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0          0.401741                     0.190438       0.601237   \n",
       "1          0.148996                     0.201901       0.390529   \n",
       "2          0.270016                     0.113420       0.626222   \n",
       "3          0.282927                     0.257520       0.193544   \n",
       "4          0.204544                     0.359087       0.207868   \n",
       "\n",
       "   Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                        0.030074                 0.020796       0.020539   \n",
       "1                        0.038579                 0.008842       0.123606   \n",
       "2                        0.018786                 0.119651       0.032588   \n",
       "3                        0.161212                 0.000000       0.003659   \n",
       "4                        0.005205                 0.075725       0.001224   \n",
       "\n",
       "   Percent_Native_Hawaiian_PI  Percent_Other_Race  Percent_Hispanic_Latino  \\\n",
       "0                    0.005441            0.069090                 0.407563   \n",
       "1                    0.007889            0.048164                 0.553763   \n",
       "2                    0.000000            0.069565                 0.381694   \n",
       "3                    0.000000            0.080087                 0.689270   \n",
       "4                    0.000000            0.054343                 0.806265   \n",
       "\n",
       "   Percent_50_PL  Percent_125_PL  Percent_185_PL  Percent_200_PL  \\\n",
       "0       0.092401        0.216687        0.349609        0.368712   \n",
       "1       0.088294        0.264060        0.406813        0.430191   \n",
       "2       0.075873        0.116364        0.218496        0.238959   \n",
       "3       0.219739        0.341681        0.466541        0.482081   \n",
       "4       0.242157        0.362219        0.445469        0.475303   \n",
       "\n",
       "   Percent_300_PL  Percent_400_PL  Percent_500_PL  \n",
       "0        0.488945        0.632559        0.724385  \n",
       "1        0.568583        0.682650        0.754158  \n",
       "2        0.343626        0.519388        0.607452  \n",
       "3        0.631915        0.820283        0.850604  \n",
       "4        0.600553        0.726343        0.802104  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalize the data using z score\n",
    "X_norm = X.apply(lambda x : (x - x.min()) /(x.max() - x.min()), axis=0)\n",
    "X_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fef61dd9c88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef60e35710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef60f08550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef60f36ef0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fef60f6e8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef60faa2b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef60fd7c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef61011668>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fef610116a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef610799b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef610b5390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef62060d30>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fef62099710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef620d60f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef62102a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fef6213f470>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAFXCAYAAADztXfAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd1gU5/rw8e8CIgL2qNEoxI69HitqICqgFIldQxIhJiaWoEcjGsUSYwO7sURzkhxU7IolMYnYYm+xJmp+FixBJCIqRcru8/7hyxwRpElbvD/X5XW5u+zMPfM8M3vPU2Z0SimFEEIIIUQ2mBR0AEIIIYQwPpJACCGEECLbJIEQQgghRLZJAiGEEEKIbJMEQgghhBDZJgmEEEIIIbLNrKADAJg2bRonTpwA4OrVq7zxxhtYWFgAsG7dOu3/z3v48CGfffYZ33//fYbL37BhA3v37mXJkiWp3j98+DBDhgyhevXq6HQ6lFKYmZkxYsQIOnXq9PIb9px58+YRFxfHF1988cK/UUoxaNAgFi5cSKlSpfDx8eGLL76gRo0aGS779u3bdOnShTp16qRa1nvvvUevXr1ybRuyy9vbm8DAQMqVK/fCv3k+doPBgIWFBX5+frRo0SLD5S9atIgHDx7g7++fq3E/y9HRkWLFimFhYYFOpyMpKYn27dvj5+eHiUne5uD79u3j7NmzfPbZZzn6vp+fH7Vr18bHxyeXIyuY9QghCo9C0QIxYcIEQkJCCAkJoWLFigQGBmqvX5Q8AERHR3PhwoWXWnf16tUJCQlh69athISEMGXKFHx9fYmOjn6p5eaUXq/nyJEj2utvv/020+QhhYWFhbbfQkJCWLFiBbNmzeLSpUt5FW6mDh06lKW/ezb27du3M2jQIMaNG5fH0WVdSp3cunUrW7Zs4ezZs6xZsybP13v+/HkePnyY5+vJDQ8fPqRevXp4eHho/9zd3dm4cWOBxuXt7U1UVFSGf+Pj48MPP/ygvb5+/Tp169Zl7ty52nv379+nYcOGPH78GEdHR86fP59mOefPn2fEiBEA7N69Gzs7O6PcH7dv305Vlm5ubvTu3ZtTp04BcOzYMVxdXXMcQ926dTONIT1RUVE0btyYSZMmpXpfr9fzySef4OTkxKpVq9J8z8PDg0ePHuU43py6fPkydevW5ZtvvsnzdX3xxRccPnw4z9fzrELRApGZ48ePExAQQEJCAsWKFWPkyJHY29szbtw4YmNj8fDwICQkhPXr17NhwwaSkpJ4+PAhQ4YMoW/fvtlaV8OGDSlWrBjh4eFYWloyY8YMjh8/jomJCU2bNsXPzw8rKys6duxIjx49OHjwIDExMfj4+NC3b18OHz7MrFmzCAkJAUjzOsXu3btZuXIliYmJREVF0bNnT4YPH679aA4cOJCVK1fSu3dvli9fTr169VizZg1r1qzBxMSEChUq4O/vj62tLaNHj6ZYsWI8efKEzp07U79+fWbNmkWlSpWwtbXlxo0bnD9/nuDgYAwGA2XKlGHixInUrFkTPz8/oqOjuXXrFm+99Raffvop06ZN4/Tp05iamtK5c2dGjhxJUlISgYGBnDhxAr1eT/369ZkwYQLW1tY4Ojri6enJkSNHCA8Px8PDA19fX21b3n//fb755hsqV66c5XKIjo6mQoUK2us9e/awdOlSkpKSsLCwYOzYsTRr1izVd/766y+mTp1KdHQ0Op0Ob29vevTogYeHB35+frRt25YdO3Ywbtw4Tpw4gYWFBV988QUNGjRgwIABWY7N3NycFi1acO3aNa0sFy9ejMFgwMrKinHjxtGgQQMcHR35+uuvadiwIQC+vr60atWKAQMGsHTpUn755RcMBgNvvPEGkyZNolKlSnh5eVG6dGmuXbtGt27dWLt2LXq9npIlS3Lu3DlcXFzo06cPAEuWLCE6Oprx48dnOfZnnT59msDAQOLj4zExMWHYsGE4ODjQr18/Bg0ahJOTEwABAQEAjBkzhg0bNqRbj1KkJIIpIiIicHV1pWHDhtjZ2eUozpeVlSS2Y8eOHDt2jPfffx+AvXv34uDgQGhoKKNGjQLg6NGjNG/enJIlS75wOY0aNWLhwoUAhIWFodPpjHJ/QNqy/PHHHxk3bhy//PJLXoWWqY0bN/L222+zY8cORo4cSZkyZYCn+/XgwYOcOXMGU1PTNN97/vybX9asWYObmxurV6/G29sbM7O8+8n96quv8mzZL1LoE4ioqCh8fX1Zvnw5jRo14vLly7z33nts2bKFGTNm0LNnT0JCQoiJiWHTpk2sWLGCMmXKcPLkST755JNsJxA//fQT5ubm1KhRg6+//poHDx4QEhKCTqdj/PjxzJkzR2suf/ToEZs3b+bvv//G09Mz0+b2FAaDge+++46AgACqVatGeHg4jo6OvPfee8yYMYNt27axevVqSpUqpX3n4MGD/PDDDwQHB1OuXDk2bNjAsGHD2L59O/D0x7N48eL8+OOP9OrVi59//hlbW1tu3rxJuXLlCAoKYvXq1ZQoUYKDBw8ybNgwfvrpJwCePHnCzp07AZgxYwYJCQn8+OOP6PV6vL29OX78OCdOnMDU1JTNmzej0+mYO3cugYGBTJ48GYC4uDjWrFlDREQEXbp0oWfPnsyYMYPNmzfzww8/ZNiFkRKDh4eHtl8jIyP5+uuvAbhx4wbz5s3jv//9L2XLluWvv/5i0KBBqU5kycnJfPLJJ3z++ed07dqViIgIevfuja2tLV26dOHAgQO0bduW3377jdKlS3Py5Enat2/P/v378fX1zVK5pYiIiGDv3r34+vpy9epVJk2axNq1a6lWrRpHjhzh008/ZdeuXfTs2ZPNmzfTsGFDHj58yJEjR/jyyy/ZunUrV65cYcOGDZiZmbFu3TomTJjAihUrAChVqhQ//vgj8LQb6sGDB4wcOZLdu3ezbNky+vTpg8FgYOPGjaxcuTJbsad4+PAh48aN49tvv6Vq1apERETQp08f6tatS+/evdm8eTNOTk7o9Xq2bdtGUFAQx48fZ+vWrS+sR+kxliS2Y8eOfP311xgMBkxMTNi7dy8jR45k1KhR3Lx5ExsbG44cOcJbb72lfWfdunVMmjSJqKgoPDw8GDlyJMeOHePLL79kxYoVfPfddxgMBsaNG8eMGTO0JDgxMZHPPvuMrl27cujQoUK5P9LzfFKf4vr160ydOpXY2FgiIyOxs7Nj/vz5FC9enLNnzzJt2jTi4+MpVqwYn3/+OW3bttW+GxkZyaBBg+jfvz8DBw7McP0Gg0Hb53Fxcaxfv56PPvqImJgYPvzwQ5KTk3nnnXdYtGgR3bp14+233+bSpUsEBgbSq1cvjhw5Qrly5Vi+fDlbtmzBzMwMW1tbZs6ciampKZMnTyYsLIzo6GisrKwIDAykRo0aeHl50bRpU06fPk14eDht27blyy+/zLT7MiYmhu3bt7NhwwYuXbrEzz//TPfu3YGnXa83b94kIiKCyMhIGjRoQOvWrdm6dSu3b99mzJgxWgtPVi42+vfvzy+//MLAgQNxdnZm7969zJ8/H4PBgKWlJVOmTMHOzo5ly5YRGhrKkydPiI+PZ+zYsXTp0oVFixZx584dIiMjuXPnDpUqVSIgIICKFStmuI2FPoH4/fffqVGjBo0aNQKeNn01adKE48ePp7oCtba2ZunSpezdu5cbN27w559/EhcXl+nyr1+/rv1wJSUl8cYbb7BkyRKKFy/OgQMHGDt2rJY1Dhw4ULsaAXj33XcBqFKlCu3bt+fQoUPUrl0703WamJiwfPly9u3bR0hICP/3f/+HUoonT55gZWWV7nd+++03unfvrv0Q9+7dm+nTpxMeHg5Aq1atuHjxIr179yY8PJyAgABq1KhBQEAA+/btIywsjH79+mnLe/TokdZN82zic/jwYcaNG4epqSmmpqZac2BAQACPHz/WmsiSkpIoX7689r23334bePqDUb58eR4+fEi1atUy3Rcpnr/aOXz4MEOHDmXbtm0cOnSIe/fu8cEHH2if63Q6bt68qb2+ceMGCQkJdO3aVYuja9eu/Pbbbzg5OTFq1Cg+//xzTp48yQcffMChQ4ewsrLCxsYm3ZPi80aPHo2FhQUGg4FixYrRu3dvnJycWL16NW3atNG2tW3btpQrV44LFy7Qs2dPevXqhZ+fHzt27MDR0ZGSJUuyd+9ezp8/T8+ePYGnJ8b4+HhtXS1btkw3BgcHB7766isuXbpEREQEVatWzXL31vPOnDlDZGQkQ4cO1d7T6XRcvnyZbt26MXv2bCIjI/njjz948803efPNN1m/fn2G9Sg9v//+u1EksdWrV6dUqVJcvnyZKlWqcP36dZo2bUrHjh3Zs2cPH3zwAUeOHGHQoEHad4oXL87mzZuJjIzE0dEx1X6pXLkygwYNIjAwkBkzZmhJ8JgxYxg9ejQff/wx/v7+7N+/n/Llyxe6/ZESw4uS+metX79ea+lLSkrinXfeYd++fTg6OjJ06FCmTZvGW2+9xYULFxg3bpx2nEdERGj7wt3dPcNY4Ok58MmTJ7Rr147Y2FhmzJiBt7c31tbWfPPNN7i5uWnLTkpKwsHBgQULFqRaRmhoKJs3b2b9+vWULl2aGTNmsGrVKq38161bB4C/vz+rV69m4sSJANy8eZOgoCDi4uJwcXHh+PHjtGnTJsN4Q0JCePPNN6lZsyY9evTg+++/1xIIgFOnThESEkKxYsXo2LEjr732GqtXr2b37t0EBATg6uqarYuNlAuqf/75hzFjxvDf//6X+vXr88svvxAYGMiUKVM4fPgwQUFBWFhYsHPnThYuXEiXLl0AOHnyJFu3bsXa2pohQ4awdu1arTvuRQp9AmEwGNDpdGneS05OTvXenTt3GDBgAP369aNly5Z07dqVgwcPZrr8lDEQ6dHr9anWrZRKtd5nm8oMBgOmpqbaYMwUSUlJaZYbExODp6cnTk5OtGjRgp49e/Lrr7+S0WNJ0ovl2XjMzc21H+HRo0fTsGFD7Qf3yJEjeHh4MGbMGC3We/fuUbp0aQAsLS215ZqZmaVaT3h4uPbDOX78eG1waWxsLAkJCdrfFS9eXPv/8/sgJ9q1a4eNjQ3nz5/HYDDQtm1b5s+fnyquihUr8uuvv6a7f1L2UXJyMnXr1iUpKYnQ0FDefPNNHBwcGDlyJGZmZlozfWYCAwO1JPZZ6dXPlPW+8cYb1K9fn3379rF582atq8FgMPDhhx9q3SaJiYmpxjk8Wx7PMjU1pW/fvmzcuJF79+6l+sHKLr1eT82aNdmwYYP2XkREBOXKlaNYsWI4OTmxY8cOfv/9d3r37q3FnVE9gtQ/Onq9nrJlyxpNEpvSjVG+fHnatWuHiYkJDg4OrF69ms6dO6PT6VJ116RcIVaoUIHXXnuN+/fvp1lmyj578OABkZGRjBgxgvLlyxMQEIBer2fgwIHasVPY9kdGSf2zxowZw6FDh1ixYgU3btzg3r17xMXFceXKFUxMTLRWm4YNG2otpgCDBw/m9ddfx83NLUvxBAcH4+bmhpmZGW+//TaTJk1i165dLxyLkV4ifuTIEZydnbU6++w4q2rVqhEUFERYWFiaC1QHBwdMTEywtrbG1tY2S+OS1q5dq3U3uru7M3fuXH7//Xdtue3atdO6wypWrEiHDh0AsLGx0epBTi42Tp8+Te3atalfvz4AXbt21S6sZs+ezfbt2wkLC+Ps2bPExsZq32vVqhXW1tYA1K9fP0vbWCgGUWakWbNmXLlyRRuwdPnyZU6fPk3r1q0xNTVFr9ejlOL8+fNUqFCBIUOGYG9vz969ezEYDC+17g4dOhAcHExycjIGg4HVq1fTrl077fOtW7cCTwccHTlyhA4dOlC2bFnu3LlDVFQUSintKuJZ169fJz4+ns8++wwHBweOHDlCcnIyer1eS0KeT5A6dOjAjh07ePDgAfB0ZkmFChWoWrVqptthb2/Pzp07uXfvHvD0QEzp631e27Zt2bJlCwaDgcTEREaMGMGJEyewt7dn9erVJCYmYjAYmDhxYqoBZi9iamqaZluy4vr169y5c4d69erRtm1bDh06xNWrVwHYv38/7u7uPHnyRPv7GjVqYGZmpmXhERER/Pzzz1p5de7cmTlz5tC+fXtq1qypNS+mHFg51bZtWw4ePMitW7cAtCbjJk2aANCnTx9WrFhBfHy89qNgb2/Pxo0biYmJAWDBggV8/vnn6S7/+f3Xu3dvdu/ezcWLF7Urh5xo2rQpYWFh2uynP//8EycnJyIiIrS4t2zZwunTp7UkKyv16NnBsDt27CAoKIhOnTppP6Qpn23ZsoVNmzZlOYl98OCBlsSmLGPDhg2prjBfNont2LEjJ0+eZN++fdqPXtu2bfnzzz/TdF+kxJnZ+kxMTAgJCWHw4ME4OTlx5swZQkND8fDwoG/fvuzcubPQ7o/nPZvUP2vUqFGsX7+eN954gw8++IAGDRqglNLOZc+6cuWKVp+nTp2KiYkJ3333XabrvnPnDvv372fnzp04Ojri7OxMcnJyhjPw0kvEn4/p0aNH3L59mzVr1vDFF19gYWGBm5sbrq6uqfbXs4P5s7IvT548yV9//cXKlSu11qlixYqlitfc3DzVd9IbH5FysZFSxps2bSI4ODhb26iU4tKlS1y8eJG+ffsSExND+/bt+fDDD1N9L7vbCEaQQLz22mvMnz+fyZMn4+bmxueff87s2bOpVq0alSpVol69eri6utK+fXvKlSuHs7MzLi4uREZGUrp06VTN3Nk1bNgwSpcujYeHBy4uLuh0ulQZ682bN/H09OTjjz9m0qRJ2NraYmdnR8+ePenZsyd9+vTBxsYmzXLr16+Pvb09Li4uuLi48Ntvv1GjRg1u3ryJTqeja9eu9O/fX/vBBOjUqRMDBgzAy8uL7t27s337dpYtW5bmAE2Pvb09gwcPxtvbGzc3N3bs2MHixYvT/e6wYcMoVqwYHh4e9OjRg06dOtG1a1c+/fRT3njjDTw9PenWrRtKKfz8/DJdt7OzM15eXly5ciXDv0u5ck35N2LECKZOnUr16tWpVasWU6dOZdSoUbi7u7NgwQKWLl2aqrunWLFiLFmyhP/+97+4ubkxaNAghg4dqjUzdunShWvXrmkJRbt27ahQoUK2+oDTU6tWLSZNmsSwYcNwdXVlzpw5LFu2TLuycHR05M6dO9pVPDxNAt566y369OlD9+7duXz5MjNnzkx3+W3atOHgwYN8+eWXAJQvX56GDRvi6upKsWLFshTjvHnzaNasmfZv1KhRlCtXjoULFzJ79mzc3d214yolIW3YsCGmpqY4OztrP0TZqUfPM4YktnXr1vz5558cP35cuxq0sLCgQYMGrFq1KttTu5/tI38+CS5VqhRr167VEs/CuD+e92xS/6yDBw8ydOhQunXrBsDZs2fR6/XUqFEDnU6nDdq8ePEi77//vnZh17RpU2bOnMnSpUszPT+sW7eOFi1a8Ntvv7Fnzx727NnD5s2b+eOPPzh9+nSWt6Fdu3b8+uuvWvK+aNEivv/+ew4ePIinpye9e/emevXq7NmzB71en+XlPi84OBgPDw/279+vxbts2TJ+/fVX/v777ywvJzsXGymaNGnC1atX+euvv4Cn3TZjxozhxIkTNGzYkEGDBtGqVStCQ0NfahsBUCJHOnTooP7444+CDkO8Yu7fv68cHBzU33//XdChpHHr1i3VtGnTF36+atUq1b17d+Xq6qr69eunrly5opRSauzYsWrlypXa38XGxqrx48crV1dX5eLiohYtWqSUUio+Pl5NnjxZdevWTTk5OSlfX1/1+PFjpZRSDg4O6ty5c9oynn09cuRI1bVrV3X58uVMt8Hb21t5eXmlibtJkyYqISEh3eU/+/ro0aOqe/fuSimljh49qurWrauGDh2qlFLqxx9/VO7u7srNzU15enqqr776qtDuj1u3bik7Ozvl7u6u/XN1dVU7d+7Uti1lO1etWqUcHR217fj4449VYGCgUkqpc+fOqX79+il3d3fVs2dPdeLECaWUUnXq1FH3799XSin1n//8R7m7u6fav89KSEhQbdu2VXv37k3z2ejRo9Xw4cPT1L1nl//865UrVypXV1fl6uqq7bMTJ04oFxcX7f0JEyaoPn36KKWUevfdd9VPP/2kLev518+7f/++atiwYbr7t3///mrmzJlq4cKFasqUKdr73bt3V0ePHlVKKXX58mXVqlUrpZRSer1eLViwQLm4uKhu3bopHx8fdffu3UzjOnDggPL09FTu7u5qwIAB6q+//lKRkZGqf//+ytnZWXXt2lXNmDFDNWnSRD1+/DhNPM+/fhGdUi/ZrvWK6tixoza9UmTO19eX69evp/vZvHnzcjwYMDesXLkyVd/ss3x8fLI0wCs/rF+/nrlz5zJ8+HBtxPrRo0eZMWNGun/funXrHE/xFEKIzEgCIYR4JRTmJLYgFLb9MWDAgFSD+p61evVqbYBfYRATE/PCaadWVlb5cpO5wkASCCGEEEJkW6EfRCmEEEKIwkcSCCGEEEJkW4HeSCoy8nGq12XLWvLgQeZ3jyxIxhAjpI2zQoUX378/txljuYJxxFmQ5QrGWbbGECPIMZsTxhBnQR+zealQtUCYmaV9CEphYwwxQuGKszDFkhFjiLOwxVjY4kmPMcQIhSvOwhRLRowhTmOIMacKVQIhhBBCCONQqJ6F4fbvjB+5+h8/x3yKROQ375l7Mvxcyl7kVGbnFZD69TzZZyIrpAVCCCGEENlWqFoghBBCpC8pKYnx48dz584dEhMT+eSTT6hVqxZ+fn7odDpq167NpEmTMDExYfHixezbtw8zMzPGjx9P48aNCzp8UQRJAiGEyLFXpduxMHSxbdu2jTJlyhAQEMCDBw/w9PTEzs4OX19fWrdujb+/P6GhoVSpUoXjx4+zYcMGwsPDGT58OJs2bcrz+MSrx6gSiMwOYig6JywhRP7JyrmloDk7O2uPVoenT9W8ePEirVq1Ap4+n+fQoUNUr14de3t7dDodVapUQa/XExUVRbly5QoqdFFEGVUCIYQQr6qUx9fHxMQwYsQIfH19mTVrlvY4dSsrKx4/fkxMTAxlypRJ9b3Hjx9nmECULWuZ7emGheV+BoUljowYQ4w5IQmEEK+IHj16ULLk0xNZ1apV6du3L1999RWmpqbY29szbNiwAo5QZCY8PJyhQ4cyYMAA3NzcCAgI0D6LjY2lVKlSWFtbp3ooVWxsrFbuL5KTmzE9f/OpglChQslCEUdGno+xKCUTkkAI8QpISEgAICgoSHvPw8ODRYsWUa1aNT766CMuXrxIgwYNCipEkYl//vkHb29v/P39adu2LQD169fn2LFjtG7dmgMHDtCmTRtsbGwICAjAx8eHu3fvYjAYpPtC5AlJIIR4BVy6dIn4+Hi8vb1JTk5m+PDhJCYmYmNjA4C9vT1HjhyRBKIQW7ZsGY8ePWLJkiUsWbIEgC+++IJp06Yxd+5catSogZOTE6amprRs2ZK+fftiMBjw9/cv4MhFUSUJhBCvAAsLC3x8fOjduzc3btxg8ODBlCpVSvvcysqKW7duZbqc7PaV50dzbVZuepTXcms7M1rOhAkTmDBhQpr3V61alea94cOHM3z48FyJSYgXyVICcfbsWQIDAwkKCiIsLEzmHYt8Vxim0Rmz6tWrY2tri06no3r16pQsWZLo6Gjt85T+88xkt6+8sPdP55bc2M6i3FcuiqZM70S5YsUKJkyYoPWhzpgxA19fX9asWYNSitDQUC5evKjNO547dy5TpkzJ88CFEFm3ceNGZs6cCUBERATx8fFYWlpy8+ZNlFIcPHiQli1bFnCUQghjkmkLhI2NDYsWLeLzzz8HkHnHQhihXr16MW7cOPr3749Op2P69OmYmJgwevRo9Ho99vb2NGnSpKDDFEIYkUwTCCcnJ27fvq29VkrlyrxjyNnc48xk1tS9fY7HS6/DWJoWjSVOkffMzc2ZM2dOmvfXr19fANH8j9wcTgjjle1BlCYm/+v1eJl5x5Czuccv62X7Ko1h3jFIf6oQ+UnG6IhXUbafxpky7xjgwIEDtGzZkubNm3Pw4EEMBgN///23zDs2ImfPnsXLywuAsLAw+vfvz4ABA5g0aRIGgwGAxYsX06tXL/r168e5c+cKMlwhhBCFRLZbIMaOHcvEiRNl3nERsGLFCrZt20aJEiWA/w2QlQfzCCGEyEyWEoiqVatqfaXVq1eXecdFhAyQFUIIkVNyI6lXWF4NkE1vcGxej8HIjxv5FBbGEKMQouiTBEJocmuA7PODY/Nj4Gle3MinMJLBsUKIwiLbgyhF0SUDZIUQQmSVtEAIjQyQFUIIkVWSQLziZICsEEKInJAEQghRqGXlbpVCiPwnYyCEEEIIkW2SQAghhBAi2ySBEEIIIUS2yRgIUSTIUx0LJxm/IETRJS0QQghhROQBeKKwkARCCCGMxIoVK5gwYQIJCQnA/x6At2bNGpRShIaGcvHiRe0BeHPnzmXKlCkFHLUoqqQLQwghjERhegBeZt1T0mVY9EkCIcQrICkpifHjx3Pnzh0SExP55JNPeP311xkyZAhvvvkmAP3796dbt24FG6jIUH4+AO9l5ddzWozheTDGEGNO5DiB6NGjh/ZQpapVq9K3b1+++uorTE1Nsbe3Z9iwYbkWpDBubv8OKegQXnnbtm2jTJkyBAQE8ODBAzw9PRk6dCiDBg3C29u7oMMTOZRXD8DLDfnxYDp5AF7BylECkdL/FhQUpL3n4eHBokWLqFatGh999BEXL16kQYMGuROlEOKlODs74+TkpL02NTXlwoULXL9+ndDQUGxtbRk/fjzW1tYFGKXIrpQH4LVu3ZoDBw7Qpk0bbGxsCAgIwMfHh7t378oD8ESeyVECcenSJeLj4/H29iY5OZnhw4eTmJiIjY0NAPb29hw5ckQSCCEKCSsrKwBiYmIYMWIEvr6+JCYm0rt3bxo2bMjSpUv5+uuvGTt2bIbLyYum7ldBVq86s3t1Kg/AEwUpRwmEhYUFPj4+9O7dmxs3bjB48GBKlSqlfW5lZcWtW7cyXU5BnIxyo/nIWJqgjCVOkT/Cw8MZOnQoAwYMwKEOJYEAACAASURBVM3NjUePHmnHbZcuXfjyyy8zXUZeNHW/CrLSzJ7Vpm55AJ4oLHKUQFSvXh1bW1t0Oh3Vq1enZMmSREdHa5+n9MVlpiBORpn1x2c2ctgY+tygaPe7iez7559/8Pb2xt/fn7Zt2wLg4+PDxIkTady4sbQYCiGyLUcJxMaNG7ly5QqTJ08mIiKC+Ph4LC0tuXnzJtWqVePgwYMyiFKIQmTZsmU8evSIJUuWsGTJEgD8/PyYPn06xYoV47XXXstSC4TImazckXP7HI98iESI3JOjBKJXr16MGzeO/v37o9PpmD59OiYmJowePRq9Xo+9vT1NmjTJ7ViFEDk0YcIEJkyYkOb9tWvXFkA0QoiiIEcJhLm5OXPmzEnzfkq/nBBCCCGKNrmVtRBCCCGyTe5EKYQQItfJra6LPmmBEEIIIUS2SQIhhBBCiGyTLoznZNbsJlOthBBCCGmBEEIIIUQOSAuEeGVI65IQQuQeaYEQQgghRLZJAiGEEEKIbJMEQgghhBDZJgmEEEIIIbJNBlFmU2aPAwe5w5oQQmRG7lRp/CSBEOL/yyw5lBOaEEL8j3RhCCGEECLbpAUiD0jTnBBCvJzMzqMg924paLmaQBgMBiZPnszly5cxNzdn2rRp2Nra5uYqRAGQci26pGyLJilXkR9yNYHYvXs3iYmJrFu3jjNnzjBz5kyWLl2am6sQBUDK9ami2LIkZVs0vSrlmpVB7RnJyjGblZaQjBTlVpJcTSBOnTpFhw4dAGjatCkXLlzIzcUXGS9bIbMiNyutlGvWZKVcMzth5ffttqVsiyYp16zJj3NxUZarCURMTAzW1tbaa1NTU5KTkzEzS381FSqUTPW6KGdqBeH5/ZtTUq75Jyv7KrfKFaRsCxs5Zoum3DxmC5NcnYVhbW1NbGys9tpgMLywwgrjIeVadEnZFk1SriI/5GoC0bx5cw4cOADAmTNnqFOnTm4uXhQQKdeiS8q2aJJyFflBp5RSubWwlJG/V65cQSnF9OnTqVmzZm4tXhQQKdeiS8q2aJJyFfkhVxMIIYQQQrwaMu3CuH37NvXq1cPDw0P75+7uzsaNG/Mjvhfy9vYmKioqS3+blJSEvb09H374YR5HBcHBwXzzzTd5vp5n+fn58e2332brO+fPn8fR8emMgPyO+VWsU+fPn2fEiBEvEx5g3Pvu9u3b1K1blw0bNqR6/9tvv8XPzy/TdSxevJjdu3cDsGDBArZu3ZrzgF9g8+bNfPzxx2ne9/LyYteuXbm+vhQRERH069cPyN3j8fn60rBhQxo2bEinTp3o0aMH3bt3Z/r06RgMBiDvtzNFdo614OBg3N3d6datG927d2fMmDH8/fff2ucbNmxg9erVACxatIipU6fmScwAx44do3HjxqmOv86dOzNkyBAePHiQZ+strLI0qsbCwoKQkP/Nt42IiMDV1ZWGDRtiZ2eXZ8Fl5NChQ1n+219//RU7OzsuXLjA1atX87Qpr3///nm27LxSEDG/anWqUaNGLFy48GVDBIx735mYmDBr1ixatGhBjRo1srWOY8eOUatWLQA+++yzbMdYmFWqVIm1a9cCuX88PltfHB0d8ff3Z8yYMSxfvpwaNWrg5eXFmjVrePfdd3N1vRnJan2ZNWsWly5dYvny5VSuXBmDwcC2bdvo27cvGzZs4PXXX+fUqVPUrl07jyP+Hxsbm1THn16vZ/jw4fznP//h3//+d77FURjkaFhupUqVsLW15caNG5w/f57g4GAMBgNlypRh4sSJ1KxZEz8/P6Kjo7l16xZvvfUWn376KdOmTePUqVM8ePAACwsLbG1tmTx5MuvXr+fEiRPo9XosLS2Jj4+nePHi3Lp1iwEDBnDkyBHCw8Px8PDA19eXcePGAfD+++/zzTffULly5QzjDQ4Oplu3btjY2PDDDz9oGeqxY8eYO3culStX5vr165QoUYKPPvqIoKAgrl+/TsmSJbG0tMTc3Jzu3buzadMmkpKSsLCwoFKlSty6dYsHDx4QExNDhQoVqF+/Pra2tjx48AB/f3+uX7+Ov78/UVFRmJiY8Mknn9CtWzf27t3L8uXLSUxMJCoqih49euDr68uxY8eYN28e1apV46+//iI5OZkpU6bQokWLDLcvKiqK4OBgfHx88PLyomnTppw+fZpr166hlMLGxoZevXqRnJzMDz/8gLW1dapBVYsWLdJifpnYMrv73fr161m7di0GgwG9Xp9rder06dOYmprSuXNnRo4cSVJSEoGBgVqdql+/PhMmTMDa2hoHBwfKli3LjRs3SEpKolevXkyaNEmrUy4uLlSsWJEyZcoAsGTJEkqWTDsF60V1KjY2lnHjxhEWFoaJiQkNGjRg6tSpnDhxgi+//JIdO3Zw/fp1pk6dSmxsLJGRkdjZ2TF//nyKFy9Oo0aN+Oijjzh06BC3b9/G0tKSX375JdW69Xo9PXv2xMzMjJ49e9KnT5883Xfjx48nMDCQzZs3U6FCBcqVK0dUVJR2PKZcOTs4OODv70/Pnj1fWFctLCwYNGgQo0ePZu3atZibm6f6/EX7ZuPGjVy4cIHZs2djampKaGgotWvXxtramr1797Js2TIMBgOjRo1i9+7dNG3alI8//pjvvvuO6Oho9Ho9ZcuW5dGjR1hZWWVYtpnZuHEj69atIykpiYcPHzJ48GD69u1L+/btWbduHba2tixfvpy1a9eyd+9eAD744AMGDRpE6dKlCQgIIDo6mps3b+Lm5sb06dO5ffs2bm5uzJkzB39/f5RSfPbZZ1SoUCFXzxUA5cuX1+rLuXPn+PPPPzl//jwLFizg9ddfB9Dqy4ULFzAYDFSuXJmSJUsSHh6e5WPN0dERT0/PHJ+/7969y9q1a9mzZw/z5s1LdV5xcnJi+fLltGvXjp07d2IwGLQyqVq1Kl5eXkRGRvLaa68xd+5cKlasSEREBFOnTiU8PJykpCS6d+/OkCFDuH37NgMHDqRmzZrcuXOHoKAgKlasmOX6EBMTQ1RUFM2bN+fs2bNMnjwZS0tLEhMTiYyMpF27dnTu3Jmvv/6a2NhY4uPjKVWqFJaWlkyZMgU7OztOnz5NYGAg8fHxmJiYMGzYMBwcHLIcQ4FRmbh165Zq2rRpqvdOnz6t/vWvf6ljx46pAQMGqLi4OKWUUr/99ptydnZWSik1duxY9f7772vfmT59uho5cqT66aef1JgxY9TAgQPVqlWrlJOTk5o5c6YyGAzq3r17qk2bNmrixInq0aNHqn79+uqrr75SSil19+5d1ahRI3Xz5k2llFJ16tRR9+/fzyx89ddff6kGDRqoqKgodfbsWdW4cWMVFRWllFLq6NGjql69eurixYtKKaV8fHxU3759VUJCgtq0aZOqW7euunv3rvrxxx9Vs2bNtO9duXJF1a9fX92+fVstXLhQOTk5qaSkJKWUUgsXLlRTpkxRSinVo0cPtWrVKqWUUn///bd6++231aNHj9S7776rrl+/rm1XvXr11P3797V4/vjjD6WUUt9++60aOHBghtv3zTffqJYtW6pOnToppZR699131YgRI9STJ0+Uo6Ojat++vfrtt9+Ui4uLat26tbp3755SSqmJEycqBweHVDEbDIaXiu3nn39WY8eOVUop9fvvv6shQ4Zon927d0+5urqqhIQEdenSJVW3bl2VkJCgff4ydSo5OVklJCSogQMHqqNHj6pFixZpdUoppebMmaMmTZqklFKqTZs2qlu3bkoppUJDQ5WdnV2qOtWrV69M61VGdWrLli3K29tbKaVUcnKy+uKLL9SNGzfU0aNHVffu3ZVSSs2cOVNt3bpVKaVUYmKicnV1Vbt27dJiCAoKUt98843q3Lmzqlu3rnry5Im27uvXr6u6deuq6OholZCQoN555x21Z8+ePN133t7eauzYscrBwUGNGjVKDRkyRDsez5w5o1xdXVWdOnXUzZs3tTJOT8q5RK/Xq4EDB6qZM2cqpZRauXKlVm8y2jfvvvuu+umnn7TtWblypXr8+LFq2bKlunfvnvr5559V9+7d1dy5c9XJkydVs2bN1IULF5RSSj169Eg1btxY7d+/P8Oy3bRpk2revLlyd3dP9a9p06bqp59+UjExMapPnz5aef/+++/a+dHPz08FBQUppZQaOHCgat++vbp27Zp69OiRat26tUpISFAjR45UEyZMUK6uruqdd95RrVu3VufPn1e3bt1STZo0UZ07d1azZ89W/v7+ytPTU/Xt2/elzhXPn78dHBzUhg0btPrSpUsX1axZM7Vr1y71/fffKzs7O/XTTz+psWPHqv79+2vlOXnyZNWiRQsVFxeX5WPNwcFBK+OcnL937dql3nnnnXTPK6GhocrNzU0p9fSYXrhwoVLq6bnM0dFRW/Ynn3yiFi9erJRSysvLS4WGhiqllHry5Iny8vJSO3fuVLdu3VJ16tRRJ06cyDAepZ7+ZjRq1Ei5u7urbt26qTZt2qgePXqo5cuXq6VLlypXV1fVqlUrdfToUaWUUjExMap169aqY8eO6urVq6pFixbKxcVFq68+Pj4qOjpade3aVd26dUvbVx07dlR37tzJNJ6ClqUWiCdPnuDh8fTGIimZfEBAAPv27SMsLEy7AgF49OgR0dHRAKmy4cOHDzNu3Dj2799Pp06d6N69OwDTp09nz549HD58mJiYGJKTk7WrfzMzM61puFKlSpQvX56HDx9SrVq1LCdIwcHB2lVn2bJlqVq1KuvXr9f6OatWrUr9+vWBp01TJUuWxNzcnMuXL2NpacnDhw+JiooiLi6ODz74QFuuXq9n/Pjx3Lhxg8qVK6eZYx0dHc2lS5fo3bs3AJUrV9b6b5ctW8a+ffvYsWMHV69eRSlFfHw8AFWqVKFevXoA1K9fny1btmS4fTY2NrRu3TrVneYcHBy4fv06b775JgkJCcTGxmrbX6FCBQD69u3LwYMHUy1Lp9O9VGwZ3f3u3LlzNGvWDHNzc6ysrFBK4erqSokSJV66TpmammJqasqqVasACAgI4PHjxxw+fBh4Ol6hfPnyACQkJODs7AygjQF5tk7dunULf39//vnnH3r16kWvXr3S7POM6lSLFi2YN28eXl5etGvXjvfffx9bW1vu3r2rfX/MmDEcOnSIFStWcOPGDe7du0dcXJz2+dtvv825c+f45ptvcHZ2Ji4ujuLFiwMQFhaGUor33nsPeHqVFhAQkKf7LiYmhnfeeYfjx4/Tv39/Ro4cqR2Pp0+fplmzZly5cgUrKytsbGy4dOkSjRs3TrPfUpiYmBAQEECPHj2wt7dP9Vlm++Z51tbWdOnShW3btnH37l3u3btHr169tHo/fvx47W8TExOZM2cOS5YseWHZArRs2ZLly5enes/LywsAKysrli1bxv79+7lx4waXLl3S4uvSpQtr166lR48eREZG4urqyuHDhyldujQdOnTA3NycmTNnsmDBAtq1a0dISAgJCQnExcVRpkwZrbXQwsKC+Ph4WrZsSf369bl48eJLnSuePX+nXIVXqFCBESNGEB0dTcmSJVmyZAnwtBUx5f4RlSpVolatWpibm3Py5Elq1arFX3/9RePGjbN0rMHTupyyrJycv5OTk9M9r3h4eKDT6bR4f/75Zw4dOkTZsmVp37495cqVA8DOzk47f584cYKHDx+yYMECAOLi4rS6amZmRtOmTbMU07NdGJs2bWLevHm4uLjwxx9/sGjRIkaPHs3jx49ZtmwZ165dIz4+nqpVq/J///d/1K5dm0aNGnHy5ElcXFzo2rUr+/fvJzIykqFDh2rr0Ol0XL58mSpVqmR5XxWEHI2BSHHkyBE8PDwYM2YM8LTy3bt3j9KlSwNgaWn5vxWZmaHT6bQ7pIWHh2NhYQE8bS5zcHAgJCSEixcvMmTIEODpTkxOTtaWodPpUNmYNBIXF0dISAjm5ubaj0VMTAyrVq3C29sbIE0TakoiEBMTo71nMBgoXrw4mzZtwszMjJiYGL7++muGDh3K8uXL2bhxI5cuXUrV/5yyHJ1Op7137do1Xn/9dTw9PencuTMtW7akZ8+e7N69W9uulH2S1e11cnJix44dqd6zsLAgJiaGkiVLkpiYiFIKc3PzVDeWMTU1TXd/vUxsGd39LiWeZz+bPHky7dq1S7WMnNSpFCl1ymAwMH78eDp16gQ87VZISEgAQClFqVKlUm3Hs3Wsd+/efPrpp+j1et5777004woyq1PVqlXj119/5dixYxw9epRBgwYxdepUrdkcYNSoUej1elxcXHjrrbcIDw9PtS+LFy+Ok5MTt2/f1mJ+dv2mpqba8bhgwQKqVKlCp06d8mzfTZo0SSvX4sWLa+Wq0+mIjY1NVa5WVlapjp0XqVy5MlOmTGHs2LH06NEjy/smPX369GHixIlUqFCBKlWqUK1aNS5fvoyJiUmqY3bp0qUMHjwYMzOzdMs2K+7evUvfvn3p06cPLVq0wNnZWeumaN++PRMmTGD//v20bt2adu3aERwcTIkSJejWrRsA7777LnXr1qV+/fpUqlSJJ0+eaNunlEq1L83NzZk9ezY9evR4qXPF82MgFixYQKNGjZg5cyaHDh1i9OjRdOrUCYPBQOPGjbVkVafTafGYmZlRokQJrWyzcqwB2rIyizE9TZs2JSwsjFq1aqU5rxw5coRmzZoRFxdHjRo1cHZ25t1338XZ2Zl//etfadZpMBhQSrF27VpKlCgBPO36LV68OA8ePMDc3DxHN9rq2bMnZ8+e5bPPPmP9+vXcvXuXq1evsn//fjp06ICLiwvHjx+nRIkSmJqaotPptGNEKcXly5fR6/XUrFkz1eDiiIgILQkqzF7qRlL29vbs3LmTe/fuAU+vzN5///10/7Zt27Zs2bIFKysrHj58yIgRIzhx4gTm5uYEBweTmJiIpaUle/fuZe7cucDTA+rZk96zUk5iGdm+fTtlypTht99+Y8+ePezZs4fdu3cTFxeX6UjjZyts27ZtSUhIICwsDIDjx4+zYcMGdDod5ubmVKpUiUuXLqX5foMGDbSR4uHh4fTv358rV64QExODr68vjo6OHDt2jMTERG0UdG55/k50r732GlevXtWuhNO7WgkLC3up2DK6+93znwHp9j/npE4ZDAYSExO1OmVvb8/q1au12CdOnKjVKZ1Op13BwdM6lpJMmZqa8s4771CiRAmsra1p06ZNmnLNrE6tWbOGcePGYW9vz5gxY7C3t+ePP/5ItYyDBw8ydOhQ7Ufl7NmzacaEvIilpWWqk/CzP+B5te8uXbr0wnK1tLQkNjZWOx6fTygy4uzsTMeOHfnhhx+ytG9edMynXDlevnxZ+/GoXr06ADt37gSetghu3LiRq1evvrBss+LChQuUK1eOTz/9FHt7ey150Ov1FC9enH/9618sXryY9u3b06pVK86cOcPJkyfp0KEDjx494vz584wePZoOHTqQlJTEzZs3teMrJSFLcffuXRISEvLsXGFvb8+dO3e0GQ3BwcEkJSVpx8OzFx1t27bl2rVrWFlZZflYy0hWzt+VKlXCy8uLkydPcufOHe392NhYdu/ezeDBgylRogR16tRBp9NhbW1N1apV050NYW1tTdOmTfnuu++Apy1z/fv3JzQ0NGs7KwOjR48mPDyc1atXExMTQ1xcHKNHj6Zr167cvXuXiIgI4uPjadKkCVevXuX27duULFmS0NBQxowZoyVKJ06cAODPP//EycmJiIiIl44tr73UvU3t7e0ZPHgw3t7eWgEuXrw41ZVNimHDhvHVV1/xyy+/sHnzZj744AMqVqxI06ZNeeONN/D09CQxMZEHDx4wcuRIHj9+THJy8gsfQevs7IyXlxeLFi164V3WgoODGTRoUKqr7VKlSuHl5cX333/P559//sJta968OWvWrAGeXmHWrVuXUaNGoZRCr9dTpkwZLCws0Ov1REZG0qBBgzTLmDNnDlOmTCEoKAidTsdXX31F48aNeeutt3BxccHc3Jw6depQq1YtwsLC0rSGvIyaNWsSFhZG+fLlSU5O5urVqwwfPpz3338fKyurdJuY69at+1KxNW/enL1799KtW7c0d79r3Lgx8+fP15qWDQZDuuWWkzrl4eGBXq+nW7dudO3alY4dOzJr1iw8PT3R6/XUq1dPmyZYvHhxfv/9d+DpHfqKFSuWat1ubm5s3LiRmjVrcvr0aTw9PVOtM7M6FRQUxPHjx+nWrRslSpSgcuXKeHl5pfqxGjlyJEOHDsXS0hJra2v+9a9/cfPmzUz3L4CtrS1KKaKjo7G0tOTkyZP4+Pjk6b778MMPtbsaXrlyJVW51a5dmy1bttClSxcGDhyIXq/P1l0PJ0yYwKlTp7K0bxwdHZk7dy5JSUlpltO7d2/mz5/Pw4cPAfjjjz9o0qQJGzduZOXKldrA7KZNm5KUlJRu2WZF+/bt2bhxI87Ozuh0Olq1akW5cuUICwujRo0adOnShV9++YU2bdpgYWGBnZ0dpUuXpnjx4hQvXpyPPvoIT09PihUrxv3792nevDlhYWFUq1YNnU5HWFgYderUQa/Xc/XqVTp27Jhn5wp7e3tcXFyYM2cOa9as0epMSn1J6XZNSEjggw8+YN26dYwfPx6lVJaOtYxk5fwN8O9//5vHjx+zYMEC1qxZw+PHjylevDjBwcG88cYbXL16lRMnTnD8+HH0ej3h4eEvHEgaGBjIl19+iZubG4mJibi6uuLu7q619OVUqVKlGD16NDNmzKBZs2ZUrFgRT09PLC0tqVSpEs2aNeP69euYmZkxc+ZMfH19+eOPPyhTpgzz5s2jXLlyLFy4kNmzZ5OQkIBSitmzZ1O1atWXiis/5PuNpNK7Q9qBAwewsbHh7bffZv369axbtw6lFB9//DFOTk75GV6W41yxYgW7du2iWLFieHh4FOj0zdu3bzNq1CjWr1/P9u3biYuLo2/fvuzZs4evv/4apRQ9e/Zk4MCBeRqHMZStlGv2GUO5ZiVOKdu0jKFspVwLL6O/E6Wvry/Xr19P97N58+Zle655YbRy5Uq2b9+e7mc+Pj64u7vnc0RF26tQp/JKYd93165dY+TIkel+Vr16debPn5/PEeUuYztXFLb6EhMT88IfdysrK61VWjxl9AmEEEIIIfJfrj6NUxif+/fv06lTJ65evUpYWBj9+/dnwIABTJo0SRustXjxYnr16kW/fv04d+5cAUcshBCiMJAE4hWWlJSEv7+/Nh1sxowZ+Pr6smbNGpRShIaGcvHiRW3Wydy5c5kyZUoBRy2EEKIweKlZGC8rMvJxqtdly1ry4MGLbxpTGBhDjJA2zgoV0k6tmzVrFv369dMe3HPx4kVatWoFQMeOHTl06BDVq1fH3t4enU5HlSpV0Ov1REVFZThH2RjLFYwjzqyUa14yxrI1hhihYMvWGMsVjCPOgj5m81KBJhDPMzNLe3OjwsYYYoTM49y8eTPlypWjQ4cOWgKhlNKmcFlZWfH48WNiYmK050I8+35GCUTZspZp1m8sB40xxFmYYjSG48EYYoTCFWdhiiUjxhCnMcSYU4UqgRD5Z9OmTeh0Oo4cOcKff/7J2LFjUz1eNzY2llKlSqW5AVRWbhT0/BVBhQol01zhFEbGEOfzMRamZEII8WopVAmE27/T3i77Wf/xc8ynSIq+1atXa//38vJi8uTJBAQEcOzYMVq3bs2BAwdo06YNNjY2BAQE4OPjw927dzEYDNm+xWpm5QpStsZKjtmiSY5ZkRWFKoEQBWvs2LHarWhr1KiBk5MTpqamtGzZkr59+2IwGPD39y/oMIUQQhQCkkAIgoKCtP+nPGXvWcOHD2f48OH5GZJ4Sffv3+edd97hP//5D2ZmZvj5+aHT6ahduzaTJk3CxMSExYsXs2/fPszMzBg/fnyGT9AUQojnyTROIYoYmZ4rhMgPkkAIUcSkTM+tWLEikHZ67uHDhzl16lS603OFECKrpAtDiCIkL6fnQvpTdDNSWGaJFJY4MmMscQoBkkAIUaTk5fRcSDtFNzOFYVqsMUzPBZmiK4yPdGEIUYSsXr2aVatWERQURL169Zg1axYdO3bk2LFjABw4cICWLVvSvHlzDh48iMFg4O+//87R9FwhxKtNWiCEKOJkeq4QIi9IAiFEESXTc4uWpKQkxo8fz507d0hMTOSTTz6hVq1aMkVXFBhJIIQQwghs27aNMmXKEBAQwIMHD/D09MTOzg5fX19at26Nv78/oaGhVKlSRZuiGx4ezvDhw9m0aVNBhy+KIEkghBDCCDg7O+Pk5KS9NjU1zbUn6AqRE5JACCGEEbCysgIgJiaGESNG4Ovry6xZs/LsCbqZKSyzRApLHBkxhhhzQhIIIYQwEuHh4QwdOpQBAwbg5uZGQECA9lluPkE3KwrD1FhjmKJblKfnyjROIYQwAv/88w/e3t6MGTOGXr16AVC/fn2ZoisKjLRACCGEEVi2bBmPHj1iyZIlLFmyBIAvvviCadOmyRRdUSAkgRBCCCMwYcIEJkyYkOZ9maIrCop0YQghhBAi2ySBEEIIIUS2SQIhhBBCiGyTBEIIIYQQ2SYJhBBCCCGyTRIIIYQQQmRbhtM45elvQgghhEhPhgmEPP1NCCGEEOnJMIGQp78VXdK6JIQQ4mVkmEDk5dPfIPtPgCssDyEpLHFkJqM4pXVJCCHEy8j0VtZ59fQ3yP4T4ArDU9eM4elvkPkT4KR1SQghxMvIMIFIefqbv78/bdu2Bf739LfWrVtz4MAB2rRpg42NDQEBAfj4+HD37l15+psRyMvWpey2LEHhadUpLHFkxBhiFEIUfRkmEPL0t6Itr1qXstuyBNK6lFWZtSzJ2BYhRH7JMIGQp78VXdK6VDTJ2BYhRH6Rx3m/oqR1qWiSsS1CiPwiCcQrSlqXiiaZOZW+whJHZrIS59mzZwkMDCQoKIiwsLAC657ynrknw8//4+eY6+sU41W8EQAAIABJREFUhYskEEIUMTJzKjVjGNsCmY9vAVixYgXbtm2jRIkSAMyYMUO6p0SBkWdhCFGEpIxtGTNmDL169QL+N7YF4MCBA7Rs2ZLmzZtz8OBBDAYDf//9t4xtMRI2NjYsWrRIe/1899Thw4c5depUut1TQuQ2aYEQogiRsS1Fm5OTE7dv39ZeK6UKbOp1ZvKr28gYuqeMIcackARCiCJExra8WkxM/teInN9TrzOTH91GxtA9lZWuKWMlXRhCCGGkpHtKFCRpgRBCCCM1duxYJk6cKN1TokBIAiGEEEakatWqrF+/HoDq1atL95QoMNKFIYQQQohskwRCCCGEENkmCYQQQgghsk0SCCGEEEJkmyQQQgghhMg2SSCEEEIIkW1GNY0zs6e/gTwBTgghhMgP0gIhhBBCiGyTBEIIIYQQ2WZUXRhCCCGMQ2ZdztLdbPykBUIIIYQQ2SYtEKJQkKsVIYQwLtICIYQQQohskwRCCCGEENkmXRhCCCHynXRbGr9cTSAMBgOTJ0/m8uXLmJubM23aNGxtbXNzFZmSSpn7CkO5irwhZVs0SbmK/JCrCcTu3btJTExk3bp1nDlzhpkzZ7J06dLcXIUoAFKuRZeUbdFUFMo1K3ce3j7HIx8iES+SqwnEqVOn6NChAwBNmzblwoULubl4UUAKQ7lm5WTysl7Fk1Fel21ulJu0GmZfYThm84Pbv0PyfB2Z1b/M6nhRPq/kagIRExODtbW19trU1JTk5GTMzNJfTYUKJVO9NpYd/XzchVVuxfmqlCsYR9nmZoyvStkaQ7mCHLPGKCv7yljqX3bl6iwMa2trYmNjtdcGg+GFFVYYDynXokvKtmiSchX5IVcTiObNm3PgwAEAzpw5Q506dXJz8aKASLkWXVK2RZOUq8gPOqWUyq2FpYz8vXLlCkoppk+fTs2aNXNr8aKASLkWXVK2RZOUq8gPuZpACCGEEOLVkK0ujNu3b1OvXj08PDy0f+7u7mzcuDGv4ssSb29voqKiMv27unXrpvm7zZs38/HHHwOwYMECtm7dmicxpiciIoJ+/frl+PteXl7s2rUrW985d+4c/v7+AJw/f54RI0bkeP1CCCFeXdkeVWNhYUFIyP+mzkRERODq6krDhg2xs7PL1eCy6tChQ7mynM8++yxXlpNVlSpVYu3atfm6zv/7v/8jIiICgEaNGrFw4cJ8We/t27fp0qVLqr5YpRTvvfcevXr1ypcY0uPt7U1gYCDlypXL8O9mzpzJrl27KF26NADVq1dn/vz5ACxfvpwtW7ag1+txd3dn2LBh6HS6Fy4rs31x7NgxvvzyS3bs2JELW5j7jL0s4Wm8fn5+1KlTB5//196dhzV15f8DfwcQRdCKVlFULDouVaugdtRvsdYNFKGIoOCCTqVqx4JfrBtaBawoWFymxdapy3xbcalUccC9Wuu4TkWkbh2lWkEcUayKsihLcn5/+MstAQSCIckN79fz+DwmN9z7yf3kJJ+ce3JOYCAA4NmzZ1iyZAkuXboEIQR69OiB8PBwNGjQANeuXYO/vz8cHBykfaxZswbt27d/4TESEhKwbNkytGnTBgqFAkIIWFlZYf78+XB2dkZsbCwePXokFfTGwFRzq1Qq8cknnyA5ORkAMHDgQMybNw8KhQJHjx5FaGgoWrVqJe1j69atGr9iKSs2NhZbt26FnZ0dFAoFlEolmjVrhvDwcDg6OiI0NBQdO3aUjm+qXnpYrp2dHdq1a4f09HRcunQJ27dvh0qlQpMmTbB48WJ06NABoaGhyMnJQWZmJt555x3MmDEDkZGROH/+PMzNzTF06FDMmjULxcXFWLlyJZKTk6FUKtG1a1csWrQINjY2GDx4MLy9vXHmzBlkZWXBy8sLISEhWLBgAQBg8uTJWL9+vcaLQFulk/7555/j8OHDqFevHmxtbREVFYUWLVqga9eumDp1Kk6cOIGCggJ89NFHcHV1RUFBASIiIpCRkYGcnBxYW1tj5cqVaN++PQICAuDk5ITz588jKysL/fv3x9KlS3Hnzh14enoiNTUVJSUliImJwbFjx2Bubg5nZ2eEh4fD0tJS6+ehUqmwfPlyXLhwAfn5+RBCIDIyEvb29vj888+Rm5uLBQsWYNSoUdIHVWhoKGxsbHDt2jXcvXsXnTt3xooVK2BtbY1z587h008/xdOnT1GvXj2EhITg7bff1jouORefqampWL16NXr16qVx/7/+9S8cOHAACQkJMDc3R2BgIDp06AB3d/dK91fZuZADOefyxo0bWLJkCS5evKjxQblu3ToolUokJSVBCIG5c+fiq6++wv/+7/8iNTUVHh4eWLp0qVYx9enTB1999ZV0++jRowgODsaxY8e02o8+mWJuExMTcfPmTezZswcqlQr+/v44ePAgRowYgdTUVEyZMgUffPCBVjG5u7trFH9xcXGYPXs2EhIStNqPnL10AZGamopbt26hadOmiIuLw9atW2FlZYWTJ08iKCgIBw4cAPC8ut+3bx9UKhW8vLyQnZ2Njh07IiIiAhERETh79iySk5Nx/fp1AM9fxE+fPsXKlSsREREBACgoKMC2bdtw7949DBs2DD4+PoiKikJCQgK++eabalWnkydPhpnZH1duHj9+jM6dO2s8JisrC9988w2GDx+O69evIyMjA0eOHMH48eOhVCphZWWFXr164fTp0wgJCUGPHj3g5+eHxo0bY8eOHQCAsLAwbN26FYsXLwYA3Lp1C3FxcSgoKMCIESNw9uxZtGnTRjrmtm3bcOXKFSQmJsLS0hIfffQR9u/fj1GjRlX5nC5cuICVK1ciLi5Oup2dnY3p06dj3bp1ePDgAZYsWYKkpCTMnDkThw4dQlRUFH766SeN/Vy+fBmbN2+GQqHA2LFjcfDgQQwePBgzZ87EunXr0LNnT/z666+YOHEidu7cibZt22r8fVXT55aUlGD06NGwsLDAX//6VwwaNEjvxeedO3dga2sLS0tL3L59W3pNqItP9b6tra0BAF9++SXq16+PX375BRs3bkRmZiZee+01LFiwAPb29jh8+DA8PDzQsGFDAMDo0aORlJRUZQFRVulC3NbWtsK8qh09ehRffPEFLCws4OPjg7Fjx2p1LG2VzWtQUJDG9vj4eHz77bcoLi7G3r17DfpFYvjw4WjdujVsbGzKvf7Ux7p//z5sbGwwZMgQjefx5ptvonXr1tL7w+uvvy69H6WmpiIzMxPe3t4wNzfHtGnT4OrqqvW57N+/P+7fv48zZ84gISEBgwYN0tjO3JbPbWhoKABg0KBB6Ny5M2JiYirMq7W1NdLT0zFz5kzY29trPA+lUomnT5+iqKgIKpUKxcXFqF+/PoDnubWwsMD+/fthY2ODWbNm4c0339T6XPbv3x8xMTEICAhA69atNbbpO6/6onUB8ezZM3h5PZ84Q6lUwtbWVvrmnJGRoXFN/8mTJ8jJyQEA9O7dG8DzKVbv3r2Lzz77DA0bNsSqVauwZcsWAEBUVBSuX7+O9u3bo6SkBEePHkWPHj2k/akbvJ2dHZo1a4bHjx+X+xCrStlCIyEhAYcOHdJ4jJ2dHezs7HDo0CGMGTMG9vb2OHHiBMaPHw8AmDhxIqZOnYotW7YgKCgIkydPhpubGzp16oS4uDhkZGTg7NmzcHZ2lvY5aNAgmJmZwcbGBu3atcPjx481CojTp0/Dy8sLDRo0AACpe7wqR44cwbVr12BlZSXd5+zsDGtra0yaNAnu7u5ITU1FRkYG7t+/X+m+BgwYIPV4dOrUCY8fP8bFixfh4OCAnj17AgA6duyIXr164ezZs+XOfWXT5z58+BDFxcX49ttvUVhYiPHjx8Pa2lqr4hN4/hopLCzE/v37oVQqMWXKFKn4NDc3R0JCAhQKBVavXl1h8RkfH4/w8HB8//33ePDgAfz8/DReE1euXMHGjRs1XiOZmZno168fQkJC0LFjR2zatAkzZszA7t27pR4ltZYtW0qXiLShLsR79uyJW7du4eHDh1i0aJFGXgGguLgYUVFR2LlzJ6ysrDBu3DgMGjQIzZs31/qY1VU2r6Vfm/fv30dcXBwWL16MDz/8EAcOHECLFi30ksuKvki89dZbWLNmTYXTN5fNrfqDSc3FxUX6/3//+1988803Uo+DlZUVRo4cCX9/f6Snp2PixIlo1aoV3njjjWqfRyEEduzYgVdffRWffvoplEqlxnbmtuLcDh48GLt378aPP/6IW7duVZlX4PlMnKWNHj0aBw8exNtvv42SkhK4uLhg8ODnM0w2adIEHh4ecHNzQ0pKCj788EMkJiaiZcuW1T6PJSUlCAsLg4WFBQoLCzW2GSKv+vLSYyDUzpw5Ay8vL8ydOxfA88o2Oztbumas/oaWkpKCRo0aQaFQSFOsZmVloUGDBsjLy0P//v2xYcMGAMD06dMREBAgHUNdMQKQrinWBjMzM7i4uMDW1hZmZmaIj4+Xvq2qj52RkYGwsDBcvXoVP/30Ex48eID4+HhMmDABnp6eaNKkicbfqAuDF8VedpKX33//HSqVCi1atKg01ldffRVBQUGYN2+edN+xY8cQHh6Opk2bwt3dHa+//jpiY2Nx7ty5SvdVUYxKpbLc9XwhBEpKSsr9fWXT5169ehUAMGbMGADP36CjoqK0Kj6B54XWggULYG5uDnNzc6n4jImJQW5uLk6fPg3geaNt1qyZ9Hfq4vPGjRt45ZVX8PjxYzg5OWnEr1KppLz+/vvv8PX1ha+vL9q2bSu9JgEgMDAQX375JW7fvg0hhMb5EUJo9HC9yIsK8VatWuHWrVuwtLREbGysRl7V8Ts4OEjtqnfv3jh37hxGjBhR5TFrqmxe09LSpPjz8vLw5MkTxMbGYuXKlVi2bBlu3rypl1xW9EWiX79+UpylX38V5fZFLl++jKCgIEycOFHqIVB/wAGQLlH9+OOPVRYQ586dg5eXFxQKBYqKitC+fXt88MEHGDBgACZNmqTxWOa24tyWLgaqk9eKcrt27Vo0bdoUp06dQmFhIWbMmIF//OMfmDJlCtauXSs9rk+fPnB2dsapU6fg4+NT6bnbv3+/FFtxcTFsbW2xceNGREdHazzOEHnVF51NTebi4oJFixZh8uTJaNGiBbZv347NmzeX+5VAXl4eunXrht27d6Nv374wMzNDcHAwpk2bhj/96U+4fv06ioqKYGFhgV9//RXffPONxjeDiqinadWVq1evYvfu3YiMjISbmxteffVVRERESMf47rvvMHHiRPTr1w9nz55FSkoKrl+/Dm9vb4wZMwZPnjzBkiVLtPrddf/+/bF37154eXnBwsICERER6N27N957771K/87Jyalc8XHq1Ck4OTlBoVCge/fu2LBhA8zMzJCXlwdLS0utzpWTkxN+++03XLx4ET169MCvv/6K5ORkzJ8/v9xjK5s+Nz8/H+bm5lLxOW/ePIwaNQr/8z//U+3iE3heaJX+wFYXnyqVCgsXLsTAgQMBAPn5+RrfBNTFZ15eHszMzDQKOPU3wYKCAkycOBHvvfcelEolJk2aJI1JuHr1qsblJCEE6tWrh1atWiE7O1u6Pzs7u1rfXF5UiKvZ2NhUOHNgXl4eGjX6Y1pca2tr5OXlVXm8l1E2r2ZmZqhfvz4SExORmJiItLQ0KXcrVqxAv379pG+ytZlLoHwxrr70BGi+/irKrZ2dXbnnum/fPixZsgSLFy+Gp6cngOevj/Xr1yMgIEA6D0KIas3sWHYMhFrpLxdqzG3FuS17DqrKa0XjiA4fPoxFixbB0tISlpaW8Pb2xqFDh+Dr64tt27Zh+vTpUrzVzW3ZMRCA8eRVX3Q2E6WLiwumTp2KKVOmwNPTE3v37sXatWvLfXtVX+uqV68evLy8cP/+fbzzzjtwdXWVuvC9vb3h7u6OkpISvP/++1Uee/jw4QgICEBaWppOnkuXLl3Qvn17REZGYvTo0di1a5fGG/rly5dx8OBBLFmyBH/729/w1ltvwdnZGTt27ICnpycmTJiAbt264datW9U+pr+/P7p164bRo0fD09MTzZs31+h9eZF58+Zh5MiRuHTpEpydnRETEwN/f39cu3YN//rXv+Dt7Y22bdvi8ePHsLa2hpOTEzIzM8td63yRpk2b4rPPPsPSpUvh6emJ2bNnIyoqCo6OjuUeW9n0uaXf2IHnbxzqRuXi4oJ9+/ZJH8Tbt2/H5MmTK4ynf//+2L17N1QqFYqKijBz5kwkJyfDxcUFW7dula5xLl68GKtXr64wRpVKpXGf+gPIysoKkyZNgpWVFWxsbNCvXz9cvXoVZmZmWLZsGTIzMwE8H6/SuXNntGzZEkOGDEFSUhIKCgpQVFSEhIQEDB06tFrntibKnuPS51Ffxyzd61J2W+PGjZGcnKyXXJalUCiQm5sr3S79+qsot48fP9b4+6NHjyIyMhKbNm2Sigfg+QfW0aNHER8fD+B579n3338PNze3KmPSBnP74hjNzMykLz5V5VXd21la165dpUstxcXFOHr0KHr27Alra2ts3boV33//PQDgl19+wcWLF6VeGV0wRF71RujZwYMHxfz584UQQqSmporAwEBpW3Z2tvDw8BDPnj0TT548EW5ubuLZs2f6DrHSODt16iRSUlKEp6enKCkpEUVFRcLPz0+kpaUZJE4hhMjMzBRjxozRuK+oqEgMGzZMPHr0SBQWFgpvb29x9+7dWo2jstxeuHBBdO7c+YW53bJlixg5cqTw8PAQ/v7+0vmcP3++2Lhxo/S4/Px8sXDhQuHh4SFGjBghYmNjhRBCPH36VERERAh3d3fh5uYmQkJCRG5urhBCiEGDBomLFy9KMTo5OYmLFy+K1NRU8dZbbwlXV1dx7do1cf369Rfm9Z///KcYOXKkGD58uPjLX/4i/vvf/0oxrVu3Tri7u4thw4aJ6OhooVKpKj1PmZmZwsnJ6YXb//3vf4suXbqIHj16iC5duggnJyfh5OQknjx5YhR5nTBhghR/RW3266+/1ksuy9729/cXzs7O4tq1a+VefxXl9q9//atGPK6urqJv377i3Xfflf5FREQIIYRIT08XkydPlmLdt29fledt165dYtq0aRVuy8zMFAMGDBDdunWT8tuzZ0/xxhtvMLf/n/r2wYMHxeDBg4Wrq6tITEysMq9paWnl4nn48KGYNWuWcHNzE+7u7mLp0qWisLBQCCHExYsXxdixY8XIkSPFu+++K86cOVPlefv888/FkiVLyt2vfi+eP3++lNuePXuKzp07iw8//FBvedUXvc9EWdEUq8ePH4eDgwOGDBmC+Ph47NixA0IITJ8+XasqPyQkBDdv3qxwW1W/2a5unNHR0Thz5gx27dqFgwcPSj0p48aNq/a+qyspKQmbNm2qcJunp6fUO3P79m189NFHiI+Px549e1BQUAA/Pz9p5K8QAj4+PpgwYYLOYyytNnOrrxg3bNhQ63mtLuZVt3Eyt+XJIbfMq/HiVNZE0G3xWRv7o+rT17nPy8t74QeBtbU1tm3bppPj0B/0ldvffvsNs2bNqnBb6Unk6joWEERERKQ1nS7nTURERHWDzn7GWRP37+dq3La1bYhHjwoMFE31yCFGoHyczZvrb9SvHPMKyCNOQ+YVkGdu5RAjwDZbE3KI09BttjYZVQ+EhYW5oUOokhxiBIwrTmOKpTJyiNPYYjS2eCoihxgB44rTmGKpjBzilEOMNWVUBQQREVXuwoUL0hwxGRkZGDduHMaPH4/w8HBpjpO1a9fC19cX/v7+uHjxoiHDJRNm0EsY2poSfbTKx/wjdLAeIiFdqyq3zKtpYpvWzoYNG5CUlCStkRIVFYWQkBD07dsXYWFh+OGHH2Bvb4+zZ8/iu+++Q1ZWFoKDg7Fr1y4DR6696rw29qzy0kMk9CLsgSAikgkHBwfExsZKt69cuYI///nPAIC3334bp0+fRkpKClxcXKBQKGBvbw+lUomHDx8aKmQyYbLqgSAiqsvc3Nw01lsQpaaetra2Rm5uLvLy8tCkSRPpMer7S69WWZatbcNy1+rlMthPDnHKIcaaYAFBRCRTpVd+zc/PR+PGjWu09kLZXzI0b96o3C8zjJWxx1n2XJpSMWFyBQSvpRNRXdG1a1f89NNP6Nu3L44fP45+/frBwcEBMTExCAwMxN27d6FSqSrtfTCU6oxxIONmcgUEEVFdMX/+fGlFy/bt28PNzQ3m5ubo06cP/Pz8oFKpyi05TaQrLCCIiGSkTZs20tLijo6O2LJlS7nHBAcHIzg4WN+hUR3DX2EQERGR1tgDQbXOc3aioUMgIj3jGAfTxwKCiGoNP0SITBcvYRCZIE53TES1jT0QRCbG1KY75k+ziYwTeyCITAynOyYifWAPBJGJqa3pjgHjnPK4Osc3dIzVJZc4iQAWEEQmT1fTHQPGOeVxVcc3hhirw5SnPCbTxEsYRCZOPd0xABw/fhx9+vRBr169cPLkSahUKty5c8dopzsmIuPFHggiE8fpjomoNrCAIDJBnO6YiGpbnSsg+JMwIiKil8cxEERERKQ1FhBERESktTp3CYOIiExDVQv18ZJ07WIPBBEREWmtxj0Qo0aNkiaeadOmDfz8/LBs2TKYm5vDxcUFQUFBOguSiIiIjEuNCojCwkIAQFxcnHSfl5cXYmNj0bZtW0ybNg1XrlxBt27ddBMl1ZoLFy5g5cqViIuLQ0ZGBkJDQ6FQKNCxY0eEh4fDzMwMa9euxbFjx2BhYYGFCxeiR48ehg6biIgMrEYFxNWrV/H06VNMmTIFJSUlCA4ORlFRERwcHAAALi4uOHPmjCwLiKp+5rlnlZeeIql9prZqIxER6U+NCogGDRogMDAQY8aMQXp6OqZOnYrGjRtL262trZGZmVnlfoxxYZ7qkEOMQNVxqldtnDdvHoDyqzaeOnUKjo6OFa7ayGmPiYjqthoVEI6OjmjXrh0UCgUcHR3RqFEj5OTkSNvVC/ZUxRgX5qkOOcRYnYV5amvVxooKw5elr6JNDsWhHGIk/eKYNDKEGhUQO3fuRFpaGiIiInDv3j08ffoUDRs2xK1bt9C2bVucPHmyRi/Yqn6SQ7VLV6s2li0MdUEfRZscCliu2EhlcUwaGUqNfsbp6+uL3NxcjBs3DrNmzcLy5csRGRmJOXPmwNfXF127dkXPnj11HSvVMq7aSCQ/pcekTZo0CcnJydKYNIVCIY1JI9K1GvVAWFpaYtWqVeXuVy/eQ/LEVRuJ5EcXY9LkOh6tKsbyHIwlDl3jTJRaqs5lFjnNfsZVG4nkTRdj0uQ6Hq0qxvAcTPmyI2eiJCKSsZ07dyI6OhoAyo1JE0Lg5MmT6NOnj4GjJFPEHgiSBS7D/vI4Ut80+fr6YsGCBRg3bhwUCgWWL18OMzMzzJkzB0qlEi4uLhyTRrWCBQRRHcCR+qaLY9LIUFhAENUBpjx7LBlGVb2CZPpYQBDVAaY8e2x1jm/oGKtLLnESASwgiOoEU549tqpfRu1Z5WXwGKvDlEfrk2nirzCI6gCO1CciXWMPBFEdwJH6VBfx11u1iwVELeCLlowNR+oTka6xgCCiGuMCeER1F8dAEBERkdbYA2EAvMRBRERyxx4IIiIi0hoLCCIiItIaCwgiIiLSGsdAGCFdzDG/Z5WXDiIhkr/q/FKE446ItMceCCIiItIaCwgiIiLSGi9hEBGRBk4QRtXBAoJMQnXGjfA6NxGR7rCAICKiOolfPF4Ox0AQERGR1lhAEBERkdZ0eglDpVIhIiIC165dg6WlJSIjI9GuXTtdHoIMgHk1XcytaWJeSR902gNx5MgRFBUVYceOHZg9ezaio6N1uXsyEObVdDG3pol5JX3QaQ9ESkoKBgwYAABwcnLC5cuXdbl7MhBTyWtVA6bq4uydppLbl2VqK+Qyr6QPOi0g8vLyYGNjI902NzdHSUkJLCwqPkzz5o00btfFN/DaVPb81lRdyquuzllt0mWMdSm3csA2a5rk8L5SEzq9hGFjY4P8/HzptkqleuELluSDeTVdzK1pYl5JH3RaQPTq1QvHjx8HAPz888/o1KmTLndPBsK8mi7m1jQxr6QPCiGE0NXO1CN/09LSIITA8uXL0aFDB13tngyEeTVdzK1pYl5JH3RaQBAREVHdwImkiIiISGtGMarm9u3bGDZsmMZ1OiEEJk2aBF9fX4PFNWXKFKxcuRJNmzat9HGJiYnYtGkTFAoFrKys8PHHH+ONN94AAHz11VfYvXs3lEol3n33XQQFBUGhUODhw4eYN28e7ty5AzMzM3zyySfo1atXpceJjY3F1q1bYWdnB4VCAaVSiWbNmiE8PByOjo4IDQ1Fx44dERgYqLNz8DLkntctW7Zg+/btUCgUaNu2LSIjI9GsWTMolUpER0fjxIkTUCqVmDJlCsaNGwcASE9Px8cff4xHjx6hYcOGWLFiRZVdx6GhoTh16hSaNm0KhUKBkpISjeMFBARgwoQJGD58uM7OwcuSe27Vjhw5grlz5yI1NVW6j21WvnmNjo7GwYMH8corrwAAHB0d8be//Q1A3c5rrRFGIDMzUzg5OWncd/fuXdGnTx/xn//8x0BRCdGpUyfx4MGDSh9z48YN8dZbb4l79+4JIYQ4duyYGDhwoPR/Ly8vkZ+fL549eyYmTJgg9u3bJ4QQYubMmWLdunVCCCF++eUX4eLiIgoKCio91ueffy6WLFmicd/mzZuFt7e3EEKI+fPni40bN2r9PGuLnPN66dIlMWjQIPHkyRMhhBDR0dFi8eLFQgghtmzZIt5//31RXFwscnJyhJubm7hw4YIQQggfHx+RlJQkhHie/5EjRwqVSlXpsSrKW1RUlAgODhZCCDFx4kRx4MAB7Z9oLZJzbtVu3rwphg4dqvE82GblndexY8eKlJSUcvfX9bzWFr33QFQ0xaq5ubm0PT4+Ht9++y0sLCxga2uL9PR0XLp0Cdu3b4dKpUJ0p7tKAAATiUlEQVSTJk2wePFidOjQAaGhocjJyUFmZibeeecdzJgxA5GRkTh//jzMzc0xdOhQzJo1C8XFxVi5ciWSk5OhVCrRtWtXLFq0CDY2Nhg8eDC8vb1x5swZZGVlwcvLCyEhIQgNDQUADBo0CJ07d0ZMTIzGVLDq46jjtLKyAgB0794dv//+O4qKinD48GF4eHigYcOGAIDRo0cjKSkJrq6uOHbsGMLDwwEAr7/+Ol577TWcOHECrq6uWp3P/v37IyYmBgEBAWjdurXGtqNHj+KLL76AhYUFfHx8MHbsWO0TpoWyuQ0KCtLYrs5tcXEx9u7da9R5tba2RqtWrQAAhYWFuHfvHtq0aQPg+bfWsWPHwsLCAq+88gpGjhyJpKQk2NnZ4bfffsPIkSMBAAMHDsSSJUvwyy+/oFu3blqdy/79+2Pp0qUICAgot83QeZV7m7W2toZSqcSzZ88QGhqKOXPmSI9hm5Vvm7WyssKFCxfw97//HVlZWXjttdewYMEC2Nvb17m86oveC4jSU6z+/PPPiI6OxscffwwAuH//PuLi4rBr1y4kJyfj/fffR6NGjRAXF4etW7fCysoKJ0+eRFBQEA4cOAAAePbsGfbt2wcAiIqKQmFhIfbv3y91LZ89exbJyckwNzdHQkICFAoFVq9ejZUrVyIiIgIAUFBQgG3btuHevXsYNmwYfHx8MHjwYOzevRs//vgjbt26hejoaKxbt056HleuXMHGjRs1utSEEIiKisLgwYNhaWmJrKws9O/fX9resmVL3Lt3D48ePYJKpdL4Wzs7O9y9e1erc1lSUoKwsDBYWFigsLBQY1txcTGioqKwc+dOWFlZYdy4cRg0aBCaN2+u1TG0UTa36q5D4I/cLl68GB9++CEOHDiAFi1aGH1ejxw5go8//hiWlpaYOXMmACArK0sqLoDneb127RqysrLQokULmJn9MbRInVdtCohnz55h1apVyM3NRdOmTVG/fn1pmzHk1RTa7Ny5c9G3b1907txZ47myzcq3zebn5+OTTz7BnDlz0LFjR2zatAkzZszA7t2761xe9UXvBcSLplh99uwZ/P39kZeXh9GjR8PW1hY9evTAP//5T2RkZMDf31/ax5MnT5CTkwMA6N27t3T/6dOnsWDBApibm8Pc3BxbtmwBAMTExCA3NxenT58G8DyhzZo1k/5uyJAhAJ6/cJo1a4bHjx8jJSVF2l52KliVSoWMjAyEhYXh999/h6+vL9zd3REaGoq7d+9i48aNAJ4XFAqFQvo7IQTMzMygUqk07ldvK/2t7kX2798vxVZcXAxbW1ts3Lix3Fz3N27cgIODg3QtsHfv3jh37hxGjBhR5TFqqmxu09LS8OzZM3h5eSEvLw9PnjxBbGwsVq5ciWXLluHmzZtGn1dfX18MHToU8fHxCAwMxOHDh2slr19//TWSkpIAAEqlEnZ2dlixYoX0zUjNGPIq9zb7n//8By1atEBMTAxu376t8VzZZuXfZtXjNwIDA/Hll1/i9u3bdS6v+qL3AqKiKVaVSiUaNGiAmTNnIi0tDXPnzgUAzJs3T3oxq+9TqVTIzs6WkqHukgIACwsLjRdDVlYWGjRoAJVKhYULF2LgwIEAgPz8fI0qsfQ3PIVCASEE8vLyNOIuPRVsQUEBJk6ciPfeew9KpRL+/v7YsGEDunbtis2bN6NBgwYAgFatWiE7O1vaR3Z2Nlq2bIlmzZpBCIGcnBw0adJE2mZnZ1fl+XN3d0dYWJjGfWXfBNXnuVGjP6ZPtba2LvecdK1sbs3MzFC/fn0kJiYiMTFRI7crVqxAv379pG88xpbX9PR0hISEoHv37ujSpQt8fHwQHh6Ox48fvzCv9vb2uH//vsablXpbVf7yl7+UG3BlrHmVe5udMGECrly5guHDh8PMzEyKd/369WyzMm6zV69exezZs6U2CzwvBurVq1fn8qovev8ZZ0VTrKqrvbLb8vPz8eabb2Lfvn1S8rdv347JkydXuO/+/ftj9+7dUKlUKCoqwsyZM5GcnAwXFxds3boVRUVFUKlUWLx4MVavXl1lnGZmZigpKZHiVE8Fa2VlhUmTJknjHu7evYs//elPWLNmjVQ8AM+r6aSkJBQUFKCoqAgJCQkYOnQoLCws8M477yA+Ph4AcPXqVdy4cQN9+/bV6lxWFX/Zc1n6RVwbyh6z9Adp2W2NGzdGcnKy0eY1Pz8f2dnZ0jeMPXv2oGPHjrC1tcWQIUOwa9culJSU4MmTJ9i3bx+GDh2Kli1bwsHBAfv37wcAnDhxAmZmZjqdBdAY8ir3NpuQkICJEyfigw8+wPr169GgQQMkJibCzs6ObVbGbdba2hr379+Xeje2bduGzp07o2XLlnUur/qi9wKisilWe/TogZSUFBQWFiI3Nxc3btzA2LFjMXXqVEyZMgWenp7Yu3cv1q5dW67bCQCCgoJQr149eHl5YdSoURg4cCBcXV0xY8YMtG7dGt7e3nB3d4cQQhqYU1mc9vb2CAgIQFJSkkac6enpGD9+PJRKJTZv3izF6uXlJf179OgRBg8eDFdXV4wZMwYeHh7o3r07Ro0aBQAIDw/H+fPn4eHhgblz5+LTTz/VyYtqzZo1cHZ2hr+/P06cOIGgoCAUFRXh3LlzcHZ2fun9V6Zsbh0dHaVtZXObk5ODadOmGW1ee/bsiaZNm+Lrr7+Gl5cX9u3bhy+++AIAMG7cOLRt2xZeXl7SZY4///nPAIDVq1fj22+/hYeHB9asWYPPPvtMY0xETc2bN08jr5GRkQbLq9zbbHFxMc6fP1/huBS2Wfm2WUdHR7Rq1Qrbt2/HiBEjcOTIEak4qWt51Re9z0RZ0RSrx48fh4ODA4YMGYL4+Hjs2LEDQghMnz4dbm5u+gyv2nFu2LABBw8elBqJeh4AQ7h9+zY++ugjxMfHY8+ePSgoKICfn5808lcIAR8fH0yYMKFW45BDbplX7ckhr9WJk7ktTw65ZV6NF6eyroaQkBDcvHmzwm1r1qxB+/btdXKc3377DbNmzapwW+kJUUg39JXXf//734iKiqpwW9++fbFw4UKdHIf+wDZrmphX48ICgoiIiLTGtTCIiIhIaywgiIiISGsGXUzr/v1cjdu2tg3x6FGBgaKpHjnECJSPs3lz/f1sSI55BeQRpyHzCsgzt3KIEWCbrQk5xGnoNlubjKoHwsKi6tm/DE0OMQLGFacxxVIZOcRpbDEaWzwVkUOMgHHFaUyxVEYOccohxpoyqgKCiIiI5MGglzCM0ZToo5Vu37PKS0+RkLGp6rXxj9DBeoqEdK2q3FaFuZen6uSduX2xavVAXLhwQVpWOCMjA+PGjcP48eMRHh4OlUoFAFi7di18fX3h7++Pixcv1l7ERFQltlkiqm1V9kBs2LABSUlJ0roPUVFRCAkJQd++fREWFoYffvgB9vb2OHv2LL777jtkZWUhODgYu3btqvXgiag8tlki3WHP44tVWUA4ODggNjYW8+bNA/B87XX1vP9vv/02Tp06BUdHR7i4uEChUMDe3h5KpRIPHz7UWGOdyNh5zk40dAg6wTZLRPpQZQHh5uamsURp6dXarK2tkZubi7y8PGkp1NL3V/VmZGvbsNwIVTn8xEUOMQLyiZN0i222vNqOUVf7l8O5JFLTehBl6ZUF8/Pz0bhx4xovV1r297vNmzcq93tkYySHGMueS74x1V11vc3qI0Zd7J9tluRG6wKia9eu+Omnn9C3b18cP34c/fr1g4ODA2JiYhAYGIi7d+9CpVIZbVfoy462JpIbubdZIjJOWhcQ8+fPx+LFi7F69Wq0b98ebm5uMDc3R58+feDn5weVSoWwsLDaiJWIaoBtlohqQ7UKiDZt2iA+Ph7A86VMt2zZUu4xwcHBCA4O1m10RDpUl3qf2GaJqLZxJkoiIiLSGgsIIiIZ4SRhZCxYQBARycSGDRuwaNEiFBYWAvhjkrBt27ZBCIEffvgBV65ckSYJW716NZYsWWLgqMlUsYAgIpIJ9SRhamUnCTt9+jRSUlIqnCSMSNe4mBYRkUzU1iRhcp0gDDB8nNU5vqFjrC0sIIiIZEpXk4TJcYIwwDjirOr4pjxBGC9hEBHJlHqSMAA4fvw4+vTpg169euHkyZNQqVS4c+cOJwmjWsMeCC1VZ8Glurw6GxHpDycJI0NiAUFE9JL0ueQzJwkjY8ECgkhHqjPTJXuniMhUcAwEERERaY0FBBEREWmNBQQRERFpzeTGQNSlFReJiIgMhT0QREREpDWT64Gglzdq1Chp5ro2bdrAz88Py5Ytg7m5OVxcXBAUFGTgCImIqsYe6drFAoI0qFf5i4uLk+7z8vJCbGws2rZti2nTpuHKlSvo1q2boUIkIiIjwEsYpOHq1at4+vQppkyZgkmTJiE5ORlFRUVwcHCAQqGAi4sLzpw5Y+gwiYjIwNgDQRoaNGiAwMBAjBkzBunp6Zg6dSoaN24sbbe2tkZmZmal+5Dzyn61TRfngeeSiIxBjQsIXic3TY6OjmjXrh0UCgUcHR3RqFEj5OTkSNvVK/5VRq4r++nDy56Hl1nZj22WiHSpRgUEr5Obrp07dyItLQ0RERG4d+8enj59ioYNG+LWrVto27YtTp48yQ8aGWKbJSJdq1EBUfo6eUlJCYKDg6Xr5ACk6+R8M5IfX19fLFiwAOPGjYNCocDy5cthZmaGOXPmQKlUwsXFBT179jR0mKQltlki0rUaFRC6uE4OmO61cmN5DjWJw9LSEqtWrSp3v3r1P5KnutxmPWcnGjqEap8jYz+XRKXVqIDQxXVywHSvlRvDc3iZa+VkethmDas654jjW0hualRA8Do5kbywzZoujm8xrKomq9qzyktPkehfjQoIXicnkhe2WdPF8S1kKDUqIHidvHJVVaT/CB2sp0iInmObNazqTKlc02+qdX3uFjnEKYcYa4ITSRHpEYtL0rW6PHeLXOI01fFonMqaiEjGdu7ciejoaAAoN75FCIGTJ0+iT58+Bo6STJGseiC4shq9CF8bVFdxfAsZiqwKCCIi0sTxLWQoLCBIFtjDQERkXFhAEJGssbgkMgwOoiQiIiKtsQeCiIhkyRjWOanL2ANBREREWmMBQURERFrjJQwyChwIR0QkLywgiMiosbgkMk4sIAyA6yEQEZHcsYAgIoNh7wKRfHEQJREREWmNPRBERKR37H2SP6MqIDgpiGliXomITI9RFRBEZFr4LZPIdLGAIDIiVX3g7lnlpadIiF4Oi0fTp9MCQqVSISIiAteuXYOlpSUiIyPRrl07XR6CDIB5NV3MrWliXkkfdPorjCNHjqCoqAg7duzA7NmzER0drcvdk4Ewr6aLuTVNzCvpg057IFJSUjBgwAAAgJOTEy5fvqzL3dcZuuj602VXN/Nqul42txwga5yYV9IHnRYQeXl5sLGxkW6bm5ujpKQEFhYVH6Z580Yat3l9V7fKnt+aYl6Ni67yCjC3xoZt1jTpss0aE51ewrCxsUF+fr50W6VSvfAFS/LBvJou5tY0Ma+kDzotIHr16oXjx48DAH7++Wd06tRJl7snA2FeTRdza5qYV9IHhRBC6Gpn6pG/aWlpEEJg+fLl6NChg652TwbCvJou5tY0Ma+kDzotIIiIiKhu4GJaREREpDUWEERERKQ1vRcQKpUKYWFh8PPzQ0BAADIyMjS2x8fHY/To0Rg7dix+/PFHfYcnqSrOyMhIjB49GgEBAQgICEBubq6BIgUuXLiAgICAcvcfPXoUPj4+8PPzQ3x8fK3HIYfcMq/ak0NeAea2JuSQW+bViAk9O3TokJg/f74QQojU1FTxwQcfSNuys7OFh4eHKCwsFE+ePJH+bwiVxSmEEP7+/uLBgweGCE3D+vXrhYeHhxgzZozG/UVFRWLo0KEiJydHFBYWitGjR4vs7OxajUUOuWVetSeHvFYVpxDMbUXkkFvm1XjpvQeishnSLl68CGdnZ1haWqJRo0ZwcHDA1atX9R1ilXGqVCpkZGQgLCwM/v7+2Llzp0FiBAAHBwfExsaWu//GjRtwcHDAK6+8AktLS/Tu3Rvnzp2r1VjkkFvmVXtyyGtVcTK3FZNDbplX46X3mUUqmyEtLy8PjRr9MWOXtbU18vLy9B0igMrjLCgowMSJE/Hee+9BqVRi0qRJ6N69O7p06aL3ON3c3HD79u1y9xviXMoht8yr9uSQV4C5rQk55JZ5NV5674GobIa0stvy8/M1Trw+VRanlZUVJk2aBCsrK9jY2KBfv34G+9b1IoY4l3LILfP68sc0xrxWFAtzq/0xjTG3zKvx0nsBUdkMaT169EBKSgoKCwuRm5uLGzduGGwGtcriTE9Px/jx46FUKlFcXIzz58+jW7duBonzRTp06ICMjAzk5OSgqKgI586dg7Ozc60eUw65ZV61J4e8VhUnc1sxOeSWeTVeer+EMWzYMJw6dQr+/v7SDGn/93//BwcHBwwZMgQBAQEYP348hBCYNWsW6tevr+8QqxWnp6cnxo4di3r16sHLywsdO3Y0SJxl7dmzBwUFBfDz80NoaCgCAwMhhICPjw/s7Oxq9dhyyC3zqj055LU6cTK35ckht8yr8eJMlERERKQ1TiRFREREWmMBQURERFpjAUFERERaYwFBREREWmMBQURERFpjAUFERERaYwFBREREWmMBQURERFr7f99Hf/y8YgqsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_norm.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_norm, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidcuervo/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000000000000.0, class_weight=None, dual=False,\n",
       "                   fit_intercept=False, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# Instantiate the model\n",
    "logreg = LogisticRegression(fit_intercept=False, C=1e12, solver='liblinear')\n",
    "\n",
    "# Fit the model\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2782608695652174"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the score\n",
    "logreg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Just Carcinogens - Using Selected Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features in the green area: ['Total Population', 'Percent_125_PL']\n",
      "features in the blue area: ['Percent_Asian']\n"
     ]
    }
   ],
   "source": [
    "#Using Boruta for feature selection\n",
    "\n",
    "from boruta import BorutaPy\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "###initialize Boruta\n",
    "forest = RandomForestRegressor(\n",
    "   n_jobs = -1, \n",
    "   max_depth = 5\n",
    ")\n",
    "boruta = BorutaPy(\n",
    "   estimator = forest, \n",
    "   n_estimators = 'auto',\n",
    "   max_iter = 100 # number of trials to perform\n",
    ")\n",
    "### fit Boruta (it accepts np.array, not pd.DataFrame)\n",
    "boruta.fit(np.array(X), np.array(y))\n",
    "### print results\n",
    "green_area = X.columns[boruta.support_].to_list()\n",
    "blue_area = X.columns[boruta.support_weak_].to_list()\n",
    "print('features in the green area:', green_area)\n",
    "print('features in the blue area:', blue_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Total Population</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47393</td>\n",
       "      <td>16.333636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17599</td>\n",
       "      <td>19.904540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31865</td>\n",
       "      <td>8.771379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33387</td>\n",
       "      <td>25.755534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24147</td>\n",
       "      <td>27.303599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Percent_125_PL\n",
       "0             47393       16.333636\n",
       "1             17599       19.904540\n",
       "2             31865        8.771379\n",
       "3             33387       25.755534\n",
       "4             24147       27.303599"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = df[green_area]\n",
    "X2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Total Population</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.401741</td>\n",
       "      <td>0.216687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.148996</td>\n",
       "      <td>0.264060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.270016</td>\n",
       "      <td>0.116364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.282927</td>\n",
       "      <td>0.341681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.204544</td>\n",
       "      <td>0.362219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Percent_125_PL\n",
       "0          0.401741        0.216687\n",
       "1          0.148996        0.264060\n",
       "2          0.270016        0.116364\n",
       "3          0.282927        0.341681\n",
       "4          0.204544        0.362219"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalize the data using z score\n",
    "X2_norm = X2.apply(lambda x : (x - x.min()) /(x.max() - x.min()), axis=0)\n",
    "X2_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X2_norm, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidcuervo/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000000000000.0, class_weight=None, dual=False,\n",
       "                   fit_intercept=False, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "logreg2 = LogisticRegression(fit_intercept=False, C=1e12, solver='liblinear')\n",
    "\n",
    "# Fit the model\n",
    "logreg2.fit(X2_train, y2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33043478260869563"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the score\n",
    "logreg2.score(X2_test, y2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression with All Chemicals, Not Just Carcinogens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Unnamed: 0</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>101. TOTAL RELEASES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>77511</td>\n",
       "      <td>1.979512e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>77979</td>\n",
       "      <td>1.059076e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77590</td>\n",
       "      <td>8.548026e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>623</td>\n",
       "      <td>78629</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>624</td>\n",
       "      <td>77076</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>625</td>\n",
       "      <td>79935</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>626</td>\n",
       "      <td>75647</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>627</td>\n",
       "      <td>76012</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>628 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0    ZIP  101. TOTAL RELEASES\n",
       "0             0  77511         1.979512e+07\n",
       "1             1  77536         1.593846e+07\n",
       "2             2  77979         1.059076e+07\n",
       "3             3  77590         8.548026e+06\n",
       "4             4  77571         7.254232e+06\n",
       "..          ...    ...                  ...\n",
       "623         623  78629         0.000000e+00\n",
       "624         624  77076         0.000000e+00\n",
       "625         625  79935         0.000000e+00\n",
       "626         626  75647         0.000000e+00\n",
       "627         627  76012         0.000000e+00\n",
       "\n",
       "[628 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chem = pd.read_csv('Cleaned Data/all_chem_per_zipcode.csv')\n",
    "df_chem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>ZIP</th>\n",
       "      <th>101. TOTAL RELEASES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77511</td>\n",
       "      <td>1.979512e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77979</td>\n",
       "      <td>1.059076e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77590</td>\n",
       "      <td>8.548026e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>78629</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>77076</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>79935</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>75647</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>76012</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>628 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ZIP  101. TOTAL RELEASES\n",
       "0    77511         1.979512e+07\n",
       "1    77536         1.593846e+07\n",
       "2    77979         1.059076e+07\n",
       "3    77590         8.548026e+06\n",
       "4    77571         7.254232e+06\n",
       "..     ...                  ...\n",
       "623  78629         0.000000e+00\n",
       "624  77076         0.000000e+00\n",
       "625  79935         0.000000e+00\n",
       "626  75647         0.000000e+00\n",
       "627  76012         0.000000e+00\n",
       "\n",
       "[628 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chem = df_chem.drop(columns='Unnamed: 0', axis = 1)\n",
    "df_chem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>ZIP</th>\n",
       "      <th>All Chem Total Releases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77511</td>\n",
       "      <td>1.979512e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77979</td>\n",
       "      <td>1.059076e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77590</td>\n",
       "      <td>8.548026e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ZIP  All Chem Total Releases\n",
       "0  77511             1.979512e+07\n",
       "1  77536             1.593846e+07\n",
       "2  77979             1.059076e+07\n",
       "3  77590             8.548026e+06\n",
       "4  77571             7.254232e+06"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename Total Releases\n",
    "df_chem = df_chem.rename(columns={'101. TOTAL RELEASES':'All Chem Total Releases'})\n",
    "df_chem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ZIP                        0\n",
       "All Chem Total Releases    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chem.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>ZIP</th>\n",
       "      <th>All Chem Total Releases</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Total Releases Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77511</td>\n",
       "      <td>1.979512e+07</td>\n",
       "      <td>4.881777e+06</td>\n",
       "      <td>47393</td>\n",
       "      <td>12.514506</td>\n",
       "      <td>55.833140</td>\n",
       "      <td>2.321018</td>\n",
       "      <td>0.160361</td>\n",
       "      <td>1.042348</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>2.795772</td>\n",
       "      <td>39.896187</td>\n",
       "      <td>3.990041</td>\n",
       "      <td>16.333636</td>\n",
       "      <td>29.972781</td>\n",
       "      <td>32.215728</td>\n",
       "      <td>49.059988</td>\n",
       "      <td>64.382926</td>\n",
       "      <td>76.952293</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "      <td>3.511945e+06</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77979</td>\n",
       "      <td>1.059076e+07</td>\n",
       "      <td>3.529298e+06</td>\n",
       "      <td>17599</td>\n",
       "      <td>13.267799</td>\n",
       "      <td>36.678220</td>\n",
       "      <td>2.977442</td>\n",
       "      <td>0.068186</td>\n",
       "      <td>6.273084</td>\n",
       "      <td>0.039775</td>\n",
       "      <td>1.948974</td>\n",
       "      <td>54.207625</td>\n",
       "      <td>3.812717</td>\n",
       "      <td>19.904540</td>\n",
       "      <td>34.876982</td>\n",
       "      <td>37.587363</td>\n",
       "      <td>55.719075</td>\n",
       "      <td>68.617535</td>\n",
       "      <td>79.442014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77590</td>\n",
       "      <td>8.548026e+06</td>\n",
       "      <td>3.205528e+05</td>\n",
       "      <td>31321</td>\n",
       "      <td>22.808978</td>\n",
       "      <td>41.524856</td>\n",
       "      <td>20.149421</td>\n",
       "      <td>0.472526</td>\n",
       "      <td>0.341624</td>\n",
       "      <td>0.303311</td>\n",
       "      <td>2.353054</td>\n",
       "      <td>33.878229</td>\n",
       "      <td>6.075796</td>\n",
       "      <td>29.472239</td>\n",
       "      <td>42.125092</td>\n",
       "      <td>45.902110</td>\n",
       "      <td>62.517161</td>\n",
       "      <td>75.019955</td>\n",
       "      <td>82.471824</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "      <td>4.287650e+05</td>\n",
       "      <td>37298</td>\n",
       "      <td>9.528661</td>\n",
       "      <td>56.657193</td>\n",
       "      <td>5.946700</td>\n",
       "      <td>0.423615</td>\n",
       "      <td>1.177007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.480133</td>\n",
       "      <td>33.830232</td>\n",
       "      <td>3.394284</td>\n",
       "      <td>12.839831</td>\n",
       "      <td>21.097646</td>\n",
       "      <td>24.274760</td>\n",
       "      <td>42.163119</td>\n",
       "      <td>57.633117</td>\n",
       "      <td>67.126924</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ZIP  All Chem Total Releases  Total Releases  Total Population  \\\n",
       "0  77511             1.979512e+07    4.881777e+06             47393   \n",
       "1  77536             1.593846e+07    3.511945e+06             31865   \n",
       "2  77979             1.059076e+07    3.529298e+06             17599   \n",
       "3  77590             8.548026e+06    3.205528e+05             31321   \n",
       "4  77571             7.254232e+06    4.287650e+05             37298   \n",
       "\n",
       "   Percent_Below_Poverty_Level  Percent_White  Percent_Black_African_American  \\\n",
       "0                    12.514506      55.833140                        2.321018   \n",
       "1                     7.453319      58.104503                        1.449867   \n",
       "2                    13.267799      36.678220                        2.977442   \n",
       "3                    22.808978      41.524856                       20.149421   \n",
       "4                     9.528661      56.657193                        5.946700   \n",
       "\n",
       "   Percent_American_Indian  Percent_Asian  Percent_Native_Hawaiian_PI  \\\n",
       "0                 0.160361       1.042348                    0.027430   \n",
       "1                 0.922642       1.653852                    0.000000   \n",
       "2                 0.068186       6.273084                    0.039775   \n",
       "3                 0.472526       0.341624                    0.303311   \n",
       "4                 0.423615       1.177007                    0.000000   \n",
       "\n",
       "   Percent_Other_Race  Percent_Hispanic_Latino  Percent_50_PL  Percent_125_PL  \\\n",
       "0            2.795772                39.896187       3.990041       16.333636   \n",
       "1            2.815001                37.363879       3.276322        8.771379   \n",
       "2            1.948974                54.207625       3.812717       19.904540   \n",
       "3            2.353054                33.878229       6.075796       29.472239   \n",
       "4            4.480133                33.830232       3.394284       12.839831   \n",
       "\n",
       "   Percent_185_PL  Percent_200_PL  Percent_300_PL  Percent_400_PL  \\\n",
       "0       29.972781       32.215728       49.059988       64.382926   \n",
       "1       18.732151       20.878707       36.908834       54.815628   \n",
       "2       34.876982       37.587363       55.719075       68.617535   \n",
       "3       42.125092       45.902110       62.517161       75.019955   \n",
       "4       21.097646       24.274760       42.163119       57.633117   \n",
       "\n",
       "   Percent_500_PL  Total Releases Binned  \n",
       "0       76.952293                      4  \n",
       "1       67.174015                      4  \n",
       "2       79.442014                      4  \n",
       "3       82.471824                      4  \n",
       "4       67.126924                      4  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge with previous dataframe\n",
    "df_chem_merge = pd.merge(df_chem, df, on=[\"ZIP\"])\n",
    "df_chem_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "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>ZIP</th>\n",
       "      <th>All Chem Total Releases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>628.000000</td>\n",
       "      <td>6.280000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>77145.173567</td>\n",
       "      <td>3.077756e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1404.611699</td>\n",
       "      <td>1.364865e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>75001.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>75967.000000</td>\n",
       "      <td>1.150250e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>77075.500000</td>\n",
       "      <td>4.657725e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78250.250000</td>\n",
       "      <td>5.477388e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79936.000000</td>\n",
       "      <td>1.979512e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ZIP  All Chem Total Releases\n",
       "count    628.000000             6.280000e+02\n",
       "mean   77145.173567             3.077756e+05\n",
       "std     1404.611699             1.364865e+06\n",
       "min    75001.000000             0.000000e+00\n",
       "25%    75967.000000             1.150250e+01\n",
       "50%    77075.500000             4.657725e+03\n",
       "75%    78250.250000             5.477388e+04\n",
       "max    79936.000000             1.979512e+07"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chem.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>ZIP</th>\n",
       "      <th>All Chem Total Releases</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>...</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Total Releases Binned</th>\n",
       "      <th>All Chem Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77511</td>\n",
       "      <td>1.979512e+07</td>\n",
       "      <td>4.881777e+06</td>\n",
       "      <td>47393</td>\n",
       "      <td>12.514506</td>\n",
       "      <td>55.833140</td>\n",
       "      <td>2.321018</td>\n",
       "      <td>0.160361</td>\n",
       "      <td>1.042348</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>...</td>\n",
       "      <td>39.896187</td>\n",
       "      <td>3.990041</td>\n",
       "      <td>16.333636</td>\n",
       "      <td>29.972781</td>\n",
       "      <td>32.215728</td>\n",
       "      <td>49.059988</td>\n",
       "      <td>64.382926</td>\n",
       "      <td>76.952293</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "      <td>3.511945e+06</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77979</td>\n",
       "      <td>1.059076e+07</td>\n",
       "      <td>3.529298e+06</td>\n",
       "      <td>17599</td>\n",
       "      <td>13.267799</td>\n",
       "      <td>36.678220</td>\n",
       "      <td>2.977442</td>\n",
       "      <td>0.068186</td>\n",
       "      <td>6.273084</td>\n",
       "      <td>0.039775</td>\n",
       "      <td>...</td>\n",
       "      <td>54.207625</td>\n",
       "      <td>3.812717</td>\n",
       "      <td>19.904540</td>\n",
       "      <td>34.876982</td>\n",
       "      <td>37.587363</td>\n",
       "      <td>55.719075</td>\n",
       "      <td>68.617535</td>\n",
       "      <td>79.442014</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77590</td>\n",
       "      <td>8.548026e+06</td>\n",
       "      <td>3.205528e+05</td>\n",
       "      <td>31321</td>\n",
       "      <td>22.808978</td>\n",
       "      <td>41.524856</td>\n",
       "      <td>20.149421</td>\n",
       "      <td>0.472526</td>\n",
       "      <td>0.341624</td>\n",
       "      <td>0.303311</td>\n",
       "      <td>...</td>\n",
       "      <td>33.878229</td>\n",
       "      <td>6.075796</td>\n",
       "      <td>29.472239</td>\n",
       "      <td>42.125092</td>\n",
       "      <td>45.902110</td>\n",
       "      <td>62.517161</td>\n",
       "      <td>75.019955</td>\n",
       "      <td>82.471824</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "      <td>4.287650e+05</td>\n",
       "      <td>37298</td>\n",
       "      <td>9.528661</td>\n",
       "      <td>56.657193</td>\n",
       "      <td>5.946700</td>\n",
       "      <td>0.423615</td>\n",
       "      <td>1.177007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>33.830232</td>\n",
       "      <td>3.394284</td>\n",
       "      <td>12.839831</td>\n",
       "      <td>21.097646</td>\n",
       "      <td>24.274760</td>\n",
       "      <td>42.163119</td>\n",
       "      <td>57.633117</td>\n",
       "      <td>67.126924</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>77092</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>38312</td>\n",
       "      <td>31.483608</td>\n",
       "      <td>20.594070</td>\n",
       "      <td>11.134892</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>1.524327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>65.483399</td>\n",
       "      <td>11.998852</td>\n",
       "      <td>38.959073</td>\n",
       "      <td>56.368762</td>\n",
       "      <td>60.774692</td>\n",
       "      <td>72.794425</td>\n",
       "      <td>80.802360</td>\n",
       "      <td>87.069326</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>78501</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>62366</td>\n",
       "      <td>26.144053</td>\n",
       "      <td>10.201071</td>\n",
       "      <td>0.622134</td>\n",
       "      <td>0.367187</td>\n",
       "      <td>1.959401</td>\n",
       "      <td>0.094603</td>\n",
       "      <td>...</td>\n",
       "      <td>87.344066</td>\n",
       "      <td>11.235288</td>\n",
       "      <td>34.238207</td>\n",
       "      <td>49.012282</td>\n",
       "      <td>52.745085</td>\n",
       "      <td>67.604143</td>\n",
       "      <td>79.233877</td>\n",
       "      <td>84.966167</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>77077</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>59449</td>\n",
       "      <td>10.905146</td>\n",
       "      <td>39.746674</td>\n",
       "      <td>21.640398</td>\n",
       "      <td>0.348198</td>\n",
       "      <td>12.881630</td>\n",
       "      <td>0.117748</td>\n",
       "      <td>...</td>\n",
       "      <td>22.883480</td>\n",
       "      <td>4.899998</td>\n",
       "      <td>14.937173</td>\n",
       "      <td>23.934801</td>\n",
       "      <td>25.416744</td>\n",
       "      <td>39.053643</td>\n",
       "      <td>51.376810</td>\n",
       "      <td>61.242409</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>77515</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>29106</td>\n",
       "      <td>11.530269</td>\n",
       "      <td>56.345771</td>\n",
       "      <td>7.950251</td>\n",
       "      <td>0.666529</td>\n",
       "      <td>2.123274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>31.618910</td>\n",
       "      <td>6.294235</td>\n",
       "      <td>15.611901</td>\n",
       "      <td>24.019103</td>\n",
       "      <td>26.671477</td>\n",
       "      <td>46.031746</td>\n",
       "      <td>62.155569</td>\n",
       "      <td>73.026180</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>75647</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>13321</td>\n",
       "      <td>17.821485</td>\n",
       "      <td>74.634036</td>\n",
       "      <td>11.042715</td>\n",
       "      <td>0.405375</td>\n",
       "      <td>0.960889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.563847</td>\n",
       "      <td>7.056527</td>\n",
       "      <td>22.580887</td>\n",
       "      <td>37.226935</td>\n",
       "      <td>39.869379</td>\n",
       "      <td>54.868253</td>\n",
       "      <td>69.146460</td>\n",
       "      <td>77.561745</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ZIP  All Chem Total Releases  Total Releases  Total Population  \\\n",
       "0    77511             1.979512e+07    4.881777e+06             47393   \n",
       "1    77536             1.593846e+07    3.511945e+06             31865   \n",
       "2    77979             1.059076e+07    3.529298e+06             17599   \n",
       "3    77590             8.548026e+06    3.205528e+05             31321   \n",
       "4    77571             7.254232e+06    4.287650e+05             37298   \n",
       "..     ...                      ...             ...               ...   \n",
       "455  77092             0.000000e+00    0.000000e+00             38312   \n",
       "456  78501             0.000000e+00    0.000000e+00             62366   \n",
       "457  77077             0.000000e+00    0.000000e+00             59449   \n",
       "458  77515             0.000000e+00    0.000000e+00             29106   \n",
       "459  75647             0.000000e+00    0.000000e+00             13321   \n",
       "\n",
       "     Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0                      12.514506      55.833140   \n",
       "1                       7.453319      58.104503   \n",
       "2                      13.267799      36.678220   \n",
       "3                      22.808978      41.524856   \n",
       "4                       9.528661      56.657193   \n",
       "..                           ...            ...   \n",
       "455                    31.483608      20.594070   \n",
       "456                    26.144053      10.201071   \n",
       "457                    10.905146      39.746674   \n",
       "458                    11.530269      56.345771   \n",
       "459                    17.821485      74.634036   \n",
       "\n",
       "     Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                          2.321018                 0.160361       1.042348   \n",
       "1                          1.449867                 0.922642       1.653852   \n",
       "2                          2.977442                 0.068186       6.273084   \n",
       "3                         20.149421                 0.472526       0.341624   \n",
       "4                          5.946700                 0.423615       1.177007   \n",
       "..                              ...                      ...            ...   \n",
       "455                       11.134892                 0.044373       1.524327   \n",
       "456                        0.622134                 0.367187       1.959401   \n",
       "457                       21.640398                 0.348198      12.881630   \n",
       "458                        7.950251                 0.666529       2.123274   \n",
       "459                       11.042715                 0.405375       0.960889   \n",
       "\n",
       "     Percent_Native_Hawaiian_PI  ...  Percent_Hispanic_Latino  Percent_50_PL  \\\n",
       "0                      0.027430  ...                39.896187       3.990041   \n",
       "1                      0.000000  ...                37.363879       3.276322   \n",
       "2                      0.039775  ...                54.207625       3.812717   \n",
       "3                      0.303311  ...                33.878229       6.075796   \n",
       "4                      0.000000  ...                33.830232       3.394284   \n",
       "..                          ...  ...                      ...            ...   \n",
       "455                    0.000000  ...                65.483399      11.998852   \n",
       "456                    0.094603  ...                87.344066      11.235288   \n",
       "457                    0.117748  ...                22.883480       4.899998   \n",
       "458                    0.000000  ...                31.618910       6.294235   \n",
       "459                    0.000000  ...                 9.563847       7.056527   \n",
       "\n",
       "     Percent_125_PL  Percent_185_PL  Percent_200_PL  Percent_300_PL  \\\n",
       "0         16.333636       29.972781       32.215728       49.059988   \n",
       "1          8.771379       18.732151       20.878707       36.908834   \n",
       "2         19.904540       34.876982       37.587363       55.719075   \n",
       "3         29.472239       42.125092       45.902110       62.517161   \n",
       "4         12.839831       21.097646       24.274760       42.163119   \n",
       "..              ...             ...             ...             ...   \n",
       "455       38.959073       56.368762       60.774692       72.794425   \n",
       "456       34.238207       49.012282       52.745085       67.604143   \n",
       "457       14.937173       23.934801       25.416744       39.053643   \n",
       "458       15.611901       24.019103       26.671477       46.031746   \n",
       "459       22.580887       37.226935       39.869379       54.868253   \n",
       "\n",
       "     Percent_400_PL  Percent_500_PL  Total Releases Binned  All Chem Binned  \n",
       "0         64.382926       76.952293                      4                4  \n",
       "1         54.815628       67.174015                      4                4  \n",
       "2         68.617535       79.442014                      4                4  \n",
       "3         75.019955       82.471824                      4                4  \n",
       "4         57.633117       67.126924                      4                4  \n",
       "..              ...             ...                    ...              ...  \n",
       "455       80.802360       87.069326                      1              NaN  \n",
       "456       79.233877       84.966167                      1              NaN  \n",
       "457       51.376810       61.242409                      1              NaN  \n",
       "458       62.155569       73.026180                      1              NaN  \n",
       "459       69.146460       77.561745                      1              NaN  \n",
       "\n",
       "[460 rows x 21 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bin All Chemicals Column\n",
    "bins = [0, 12, 5000, 55000, 20000000]\n",
    "labels = [1,2,3,4]\n",
    "df_chem_merge['All Chem Binned'] = pd.cut(df_chem_merge['All Chem Total Releases'], bins=bins, labels=labels)\n",
    "df_chem_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>ZIP</th>\n",
       "      <th>All Chem Total Releases</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>...</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Total Releases Binned</th>\n",
       "      <th>All Chem Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77511</td>\n",
       "      <td>1.979512e+07</td>\n",
       "      <td>4.881777e+06</td>\n",
       "      <td>47393</td>\n",
       "      <td>12.514506</td>\n",
       "      <td>55.833140</td>\n",
       "      <td>2.321018</td>\n",
       "      <td>0.160361</td>\n",
       "      <td>1.042348</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>...</td>\n",
       "      <td>39.896187</td>\n",
       "      <td>3.990041</td>\n",
       "      <td>16.333636</td>\n",
       "      <td>29.972781</td>\n",
       "      <td>32.215728</td>\n",
       "      <td>49.059988</td>\n",
       "      <td>64.382926</td>\n",
       "      <td>76.952293</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "      <td>3.511945e+06</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77979</td>\n",
       "      <td>1.059076e+07</td>\n",
       "      <td>3.529298e+06</td>\n",
       "      <td>17599</td>\n",
       "      <td>13.267799</td>\n",
       "      <td>36.678220</td>\n",
       "      <td>2.977442</td>\n",
       "      <td>0.068186</td>\n",
       "      <td>6.273084</td>\n",
       "      <td>0.039775</td>\n",
       "      <td>...</td>\n",
       "      <td>54.207625</td>\n",
       "      <td>3.812717</td>\n",
       "      <td>19.904540</td>\n",
       "      <td>34.876982</td>\n",
       "      <td>37.587363</td>\n",
       "      <td>55.719075</td>\n",
       "      <td>68.617535</td>\n",
       "      <td>79.442014</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77590</td>\n",
       "      <td>8.548026e+06</td>\n",
       "      <td>3.205528e+05</td>\n",
       "      <td>31321</td>\n",
       "      <td>22.808978</td>\n",
       "      <td>41.524856</td>\n",
       "      <td>20.149421</td>\n",
       "      <td>0.472526</td>\n",
       "      <td>0.341624</td>\n",
       "      <td>0.303311</td>\n",
       "      <td>...</td>\n",
       "      <td>33.878229</td>\n",
       "      <td>6.075796</td>\n",
       "      <td>29.472239</td>\n",
       "      <td>42.125092</td>\n",
       "      <td>45.902110</td>\n",
       "      <td>62.517161</td>\n",
       "      <td>75.019955</td>\n",
       "      <td>82.471824</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "      <td>4.287650e+05</td>\n",
       "      <td>37298</td>\n",
       "      <td>9.528661</td>\n",
       "      <td>56.657193</td>\n",
       "      <td>5.946700</td>\n",
       "      <td>0.423615</td>\n",
       "      <td>1.177007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>33.830232</td>\n",
       "      <td>3.394284</td>\n",
       "      <td>12.839831</td>\n",
       "      <td>21.097646</td>\n",
       "      <td>24.274760</td>\n",
       "      <td>42.163119</td>\n",
       "      <td>57.633117</td>\n",
       "      <td>67.126924</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>77092</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>38312</td>\n",
       "      <td>31.483608</td>\n",
       "      <td>20.594070</td>\n",
       "      <td>11.134892</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>1.524327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>65.483399</td>\n",
       "      <td>11.998852</td>\n",
       "      <td>38.959073</td>\n",
       "      <td>56.368762</td>\n",
       "      <td>60.774692</td>\n",
       "      <td>72.794425</td>\n",
       "      <td>80.802360</td>\n",
       "      <td>87.069326</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>78501</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>62366</td>\n",
       "      <td>26.144053</td>\n",
       "      <td>10.201071</td>\n",
       "      <td>0.622134</td>\n",
       "      <td>0.367187</td>\n",
       "      <td>1.959401</td>\n",
       "      <td>0.094603</td>\n",
       "      <td>...</td>\n",
       "      <td>87.344066</td>\n",
       "      <td>11.235288</td>\n",
       "      <td>34.238207</td>\n",
       "      <td>49.012282</td>\n",
       "      <td>52.745085</td>\n",
       "      <td>67.604143</td>\n",
       "      <td>79.233877</td>\n",
       "      <td>84.966167</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>77077</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>59449</td>\n",
       "      <td>10.905146</td>\n",
       "      <td>39.746674</td>\n",
       "      <td>21.640398</td>\n",
       "      <td>0.348198</td>\n",
       "      <td>12.881630</td>\n",
       "      <td>0.117748</td>\n",
       "      <td>...</td>\n",
       "      <td>22.883480</td>\n",
       "      <td>4.899998</td>\n",
       "      <td>14.937173</td>\n",
       "      <td>23.934801</td>\n",
       "      <td>25.416744</td>\n",
       "      <td>39.053643</td>\n",
       "      <td>51.376810</td>\n",
       "      <td>61.242409</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>77515</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>29106</td>\n",
       "      <td>11.530269</td>\n",
       "      <td>56.345771</td>\n",
       "      <td>7.950251</td>\n",
       "      <td>0.666529</td>\n",
       "      <td>2.123274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>31.618910</td>\n",
       "      <td>6.294235</td>\n",
       "      <td>15.611901</td>\n",
       "      <td>24.019103</td>\n",
       "      <td>26.671477</td>\n",
       "      <td>46.031746</td>\n",
       "      <td>62.155569</td>\n",
       "      <td>73.026180</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>75647</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>13321</td>\n",
       "      <td>17.821485</td>\n",
       "      <td>74.634036</td>\n",
       "      <td>11.042715</td>\n",
       "      <td>0.405375</td>\n",
       "      <td>0.960889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.563847</td>\n",
       "      <td>7.056527</td>\n",
       "      <td>22.580887</td>\n",
       "      <td>37.226935</td>\n",
       "      <td>39.869379</td>\n",
       "      <td>54.868253</td>\n",
       "      <td>69.146460</td>\n",
       "      <td>77.561745</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ZIP  All Chem Total Releases  Total Releases  Total Population  \\\n",
       "0    77511             1.979512e+07    4.881777e+06             47393   \n",
       "1    77536             1.593846e+07    3.511945e+06             31865   \n",
       "2    77979             1.059076e+07    3.529298e+06             17599   \n",
       "3    77590             8.548026e+06    3.205528e+05             31321   \n",
       "4    77571             7.254232e+06    4.287650e+05             37298   \n",
       "..     ...                      ...             ...               ...   \n",
       "455  77092             0.000000e+00    0.000000e+00             38312   \n",
       "456  78501             0.000000e+00    0.000000e+00             62366   \n",
       "457  77077             0.000000e+00    0.000000e+00             59449   \n",
       "458  77515             0.000000e+00    0.000000e+00             29106   \n",
       "459  75647             0.000000e+00    0.000000e+00             13321   \n",
       "\n",
       "     Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0                      12.514506      55.833140   \n",
       "1                       7.453319      58.104503   \n",
       "2                      13.267799      36.678220   \n",
       "3                      22.808978      41.524856   \n",
       "4                       9.528661      56.657193   \n",
       "..                           ...            ...   \n",
       "455                    31.483608      20.594070   \n",
       "456                    26.144053      10.201071   \n",
       "457                    10.905146      39.746674   \n",
       "458                    11.530269      56.345771   \n",
       "459                    17.821485      74.634036   \n",
       "\n",
       "     Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                          2.321018                 0.160361       1.042348   \n",
       "1                          1.449867                 0.922642       1.653852   \n",
       "2                          2.977442                 0.068186       6.273084   \n",
       "3                         20.149421                 0.472526       0.341624   \n",
       "4                          5.946700                 0.423615       1.177007   \n",
       "..                              ...                      ...            ...   \n",
       "455                       11.134892                 0.044373       1.524327   \n",
       "456                        0.622134                 0.367187       1.959401   \n",
       "457                       21.640398                 0.348198      12.881630   \n",
       "458                        7.950251                 0.666529       2.123274   \n",
       "459                       11.042715                 0.405375       0.960889   \n",
       "\n",
       "     Percent_Native_Hawaiian_PI  ...  Percent_Hispanic_Latino  Percent_50_PL  \\\n",
       "0                      0.027430  ...                39.896187       3.990041   \n",
       "1                      0.000000  ...                37.363879       3.276322   \n",
       "2                      0.039775  ...                54.207625       3.812717   \n",
       "3                      0.303311  ...                33.878229       6.075796   \n",
       "4                      0.000000  ...                33.830232       3.394284   \n",
       "..                          ...  ...                      ...            ...   \n",
       "455                    0.000000  ...                65.483399      11.998852   \n",
       "456                    0.094603  ...                87.344066      11.235288   \n",
       "457                    0.117748  ...                22.883480       4.899998   \n",
       "458                    0.000000  ...                31.618910       6.294235   \n",
       "459                    0.000000  ...                 9.563847       7.056527   \n",
       "\n",
       "     Percent_125_PL  Percent_185_PL  Percent_200_PL  Percent_300_PL  \\\n",
       "0         16.333636       29.972781       32.215728       49.059988   \n",
       "1          8.771379       18.732151       20.878707       36.908834   \n",
       "2         19.904540       34.876982       37.587363       55.719075   \n",
       "3         29.472239       42.125092       45.902110       62.517161   \n",
       "4         12.839831       21.097646       24.274760       42.163119   \n",
       "..              ...             ...             ...             ...   \n",
       "455       38.959073       56.368762       60.774692       72.794425   \n",
       "456       34.238207       49.012282       52.745085       67.604143   \n",
       "457       14.937173       23.934801       25.416744       39.053643   \n",
       "458       15.611901       24.019103       26.671477       46.031746   \n",
       "459       22.580887       37.226935       39.869379       54.868253   \n",
       "\n",
       "     Percent_400_PL  Percent_500_PL  Total Releases Binned  All Chem Binned  \n",
       "0         64.382926       76.952293                      4                4  \n",
       "1         54.815628       67.174015                      4                4  \n",
       "2         68.617535       79.442014                      4                4  \n",
       "3         75.019955       82.471824                      4                4  \n",
       "4         57.633117       67.126924                      4                4  \n",
       "..              ...             ...                    ...              ...  \n",
       "455       80.802360       87.069326                      1                1  \n",
       "456       79.233877       84.966167                      1                1  \n",
       "457       51.376810       61.242409                      1                1  \n",
       "458       62.155569       73.026180                      1                1  \n",
       "459       69.146460       77.561745                      1                1  \n",
       "\n",
       "[460 rows x 21 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chem_merge['All Chem Binned'].fillna(1, inplace=True)\n",
    "df_chem_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      4\n",
       "1      4\n",
       "2      4\n",
       "3      4\n",
       "4      4\n",
       "      ..\n",
       "455    1\n",
       "456    1\n",
       "457    1\n",
       "458    1\n",
       "459    1\n",
       "Name: All Chem Binned, Length: 460, dtype: category\n",
       "Categories (4, int64): [1 < 2 < 3 < 4]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make X and y\n",
    "y4 = df_chem_merge['All Chem Binned']\n",
    "y4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47393</td>\n",
       "      <td>12.514506</td>\n",
       "      <td>55.833140</td>\n",
       "      <td>2.321018</td>\n",
       "      <td>0.160361</td>\n",
       "      <td>1.042348</td>\n",
       "      <td>0.027430</td>\n",
       "      <td>2.795772</td>\n",
       "      <td>39.896187</td>\n",
       "      <td>3.990041</td>\n",
       "      <td>16.333636</td>\n",
       "      <td>29.972781</td>\n",
       "      <td>32.215728</td>\n",
       "      <td>49.059988</td>\n",
       "      <td>64.382926</td>\n",
       "      <td>76.952293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17599</td>\n",
       "      <td>13.267799</td>\n",
       "      <td>36.678220</td>\n",
       "      <td>2.977442</td>\n",
       "      <td>0.068186</td>\n",
       "      <td>6.273084</td>\n",
       "      <td>0.039775</td>\n",
       "      <td>1.948974</td>\n",
       "      <td>54.207625</td>\n",
       "      <td>3.812717</td>\n",
       "      <td>19.904540</td>\n",
       "      <td>34.876982</td>\n",
       "      <td>37.587363</td>\n",
       "      <td>55.719075</td>\n",
       "      <td>68.617535</td>\n",
       "      <td>79.442014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31321</td>\n",
       "      <td>22.808978</td>\n",
       "      <td>41.524856</td>\n",
       "      <td>20.149421</td>\n",
       "      <td>0.472526</td>\n",
       "      <td>0.341624</td>\n",
       "      <td>0.303311</td>\n",
       "      <td>2.353054</td>\n",
       "      <td>33.878229</td>\n",
       "      <td>6.075796</td>\n",
       "      <td>29.472239</td>\n",
       "      <td>42.125092</td>\n",
       "      <td>45.902110</td>\n",
       "      <td>62.517161</td>\n",
       "      <td>75.019955</td>\n",
       "      <td>82.471824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37298</td>\n",
       "      <td>9.528661</td>\n",
       "      <td>56.657193</td>\n",
       "      <td>5.946700</td>\n",
       "      <td>0.423615</td>\n",
       "      <td>1.177007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.480133</td>\n",
       "      <td>33.830232</td>\n",
       "      <td>3.394284</td>\n",
       "      <td>12.839831</td>\n",
       "      <td>21.097646</td>\n",
       "      <td>24.274760</td>\n",
       "      <td>42.163119</td>\n",
       "      <td>57.633117</td>\n",
       "      <td>67.126924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>38312</td>\n",
       "      <td>31.483608</td>\n",
       "      <td>20.594070</td>\n",
       "      <td>11.134892</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>1.524327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.241595</td>\n",
       "      <td>65.483399</td>\n",
       "      <td>11.998852</td>\n",
       "      <td>38.959073</td>\n",
       "      <td>56.368762</td>\n",
       "      <td>60.774692</td>\n",
       "      <td>72.794425</td>\n",
       "      <td>80.802360</td>\n",
       "      <td>87.069326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>62366</td>\n",
       "      <td>26.144053</td>\n",
       "      <td>10.201071</td>\n",
       "      <td>0.622134</td>\n",
       "      <td>0.367187</td>\n",
       "      <td>1.959401</td>\n",
       "      <td>0.094603</td>\n",
       "      <td>23.666741</td>\n",
       "      <td>87.344066</td>\n",
       "      <td>11.235288</td>\n",
       "      <td>34.238207</td>\n",
       "      <td>49.012282</td>\n",
       "      <td>52.745085</td>\n",
       "      <td>67.604143</td>\n",
       "      <td>79.233877</td>\n",
       "      <td>84.966167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>59449</td>\n",
       "      <td>10.905146</td>\n",
       "      <td>39.746674</td>\n",
       "      <td>21.640398</td>\n",
       "      <td>0.348198</td>\n",
       "      <td>12.881630</td>\n",
       "      <td>0.117748</td>\n",
       "      <td>3.196017</td>\n",
       "      <td>22.883480</td>\n",
       "      <td>4.899998</td>\n",
       "      <td>14.937173</td>\n",
       "      <td>23.934801</td>\n",
       "      <td>25.416744</td>\n",
       "      <td>39.053643</td>\n",
       "      <td>51.376810</td>\n",
       "      <td>61.242409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>29106</td>\n",
       "      <td>11.530269</td>\n",
       "      <td>56.345771</td>\n",
       "      <td>7.950251</td>\n",
       "      <td>0.666529</td>\n",
       "      <td>2.123274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.380128</td>\n",
       "      <td>31.618910</td>\n",
       "      <td>6.294235</td>\n",
       "      <td>15.611901</td>\n",
       "      <td>24.019103</td>\n",
       "      <td>26.671477</td>\n",
       "      <td>46.031746</td>\n",
       "      <td>62.155569</td>\n",
       "      <td>73.026180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>13321</td>\n",
       "      <td>17.821485</td>\n",
       "      <td>74.634036</td>\n",
       "      <td>11.042715</td>\n",
       "      <td>0.405375</td>\n",
       "      <td>0.960889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.337812</td>\n",
       "      <td>9.563847</td>\n",
       "      <td>7.056527</td>\n",
       "      <td>22.580887</td>\n",
       "      <td>37.226935</td>\n",
       "      <td>39.869379</td>\n",
       "      <td>54.868253</td>\n",
       "      <td>69.146460</td>\n",
       "      <td>77.561745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Total Population  Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0               47393                    12.514506      55.833140   \n",
       "1               31865                     7.453319      58.104503   \n",
       "2               17599                    13.267799      36.678220   \n",
       "3               31321                    22.808978      41.524856   \n",
       "4               37298                     9.528661      56.657193   \n",
       "..                ...                          ...            ...   \n",
       "455             38312                    31.483608      20.594070   \n",
       "456             62366                    26.144053      10.201071   \n",
       "457             59449                    10.905146      39.746674   \n",
       "458             29106                    11.530269      56.345771   \n",
       "459             13321                    17.821485      74.634036   \n",
       "\n",
       "     Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                          2.321018                 0.160361       1.042348   \n",
       "1                          1.449867                 0.922642       1.653852   \n",
       "2                          2.977442                 0.068186       6.273084   \n",
       "3                         20.149421                 0.472526       0.341624   \n",
       "4                          5.946700                 0.423615       1.177007   \n",
       "..                              ...                      ...            ...   \n",
       "455                       11.134892                 0.044373       1.524327   \n",
       "456                        0.622134                 0.367187       1.959401   \n",
       "457                       21.640398                 0.348198      12.881630   \n",
       "458                        7.950251                 0.666529       2.123274   \n",
       "459                       11.042715                 0.405375       0.960889   \n",
       "\n",
       "     Percent_Native_Hawaiian_PI  Percent_Other_Race  Percent_Hispanic_Latino  \\\n",
       "0                      0.027430            2.795772                39.896187   \n",
       "1                      0.000000            2.815001                37.363879   \n",
       "2                      0.039775            1.948974                54.207625   \n",
       "3                      0.303311            2.353054                33.878229   \n",
       "4                      0.000000            4.480133                33.830232   \n",
       "..                          ...                 ...                      ...   \n",
       "455                    0.000000           37.241595                65.483399   \n",
       "456                    0.094603           23.666741                87.344066   \n",
       "457                    0.117748            3.196017                22.883480   \n",
       "458                    0.000000            6.380128                31.618910   \n",
       "459                    0.000000            0.337812                 9.563847   \n",
       "\n",
       "     Percent_50_PL  Percent_125_PL  Percent_185_PL  Percent_200_PL  \\\n",
       "0         3.990041       16.333636       29.972781       32.215728   \n",
       "1         3.276322        8.771379       18.732151       20.878707   \n",
       "2         3.812717       19.904540       34.876982       37.587363   \n",
       "3         6.075796       29.472239       42.125092       45.902110   \n",
       "4         3.394284       12.839831       21.097646       24.274760   \n",
       "..             ...             ...             ...             ...   \n",
       "455      11.998852       38.959073       56.368762       60.774692   \n",
       "456      11.235288       34.238207       49.012282       52.745085   \n",
       "457       4.899998       14.937173       23.934801       25.416744   \n",
       "458       6.294235       15.611901       24.019103       26.671477   \n",
       "459       7.056527       22.580887       37.226935       39.869379   \n",
       "\n",
       "     Percent_300_PL  Percent_400_PL  Percent_500_PL  \n",
       "0         49.059988       64.382926       76.952293  \n",
       "1         36.908834       54.815628       67.174015  \n",
       "2         55.719075       68.617535       79.442014  \n",
       "3         62.517161       75.019955       82.471824  \n",
       "4         42.163119       57.633117       67.126924  \n",
       "..              ...             ...             ...  \n",
       "455       72.794425       80.802360       87.069326  \n",
       "456       67.604143       79.233877       84.966167  \n",
       "457       39.053643       51.376810       61.242409  \n",
       "458       46.031746       62.155569       73.026180  \n",
       "459       54.868253       69.146460       77.561745  \n",
       "\n",
       "[460 rows x 16 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X4 = df_chem_merge.drop(columns=['ZIP', 'Total Releases', 'Total Releases Binned', 'All Chem Total Releases', 'All Chem Binned'], axis=1)\n",
    "X4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.401741</td>\n",
       "      <td>0.190438</td>\n",
       "      <td>0.601237</td>\n",
       "      <td>0.030074</td>\n",
       "      <td>0.020796</td>\n",
       "      <td>0.020539</td>\n",
       "      <td>0.005441</td>\n",
       "      <td>0.069090</td>\n",
       "      <td>0.407563</td>\n",
       "      <td>0.092401</td>\n",
       "      <td>0.216687</td>\n",
       "      <td>0.349609</td>\n",
       "      <td>0.368712</td>\n",
       "      <td>0.488945</td>\n",
       "      <td>0.632559</td>\n",
       "      <td>0.724385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.270016</td>\n",
       "      <td>0.113420</td>\n",
       "      <td>0.626222</td>\n",
       "      <td>0.018786</td>\n",
       "      <td>0.119651</td>\n",
       "      <td>0.032588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069565</td>\n",
       "      <td>0.381694</td>\n",
       "      <td>0.075873</td>\n",
       "      <td>0.116364</td>\n",
       "      <td>0.218496</td>\n",
       "      <td>0.238959</td>\n",
       "      <td>0.343626</td>\n",
       "      <td>0.519388</td>\n",
       "      <td>0.607452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.148996</td>\n",
       "      <td>0.201901</td>\n",
       "      <td>0.390529</td>\n",
       "      <td>0.038579</td>\n",
       "      <td>0.008842</td>\n",
       "      <td>0.123606</td>\n",
       "      <td>0.007889</td>\n",
       "      <td>0.048164</td>\n",
       "      <td>0.553763</td>\n",
       "      <td>0.088294</td>\n",
       "      <td>0.264060</td>\n",
       "      <td>0.406813</td>\n",
       "      <td>0.430191</td>\n",
       "      <td>0.568583</td>\n",
       "      <td>0.682650</td>\n",
       "      <td>0.754158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.265401</td>\n",
       "      <td>0.347093</td>\n",
       "      <td>0.443843</td>\n",
       "      <td>0.261078</td>\n",
       "      <td>0.061278</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.060162</td>\n",
       "      <td>0.058149</td>\n",
       "      <td>0.346086</td>\n",
       "      <td>0.140703</td>\n",
       "      <td>0.390988</td>\n",
       "      <td>0.491356</td>\n",
       "      <td>0.525354</td>\n",
       "      <td>0.649883</td>\n",
       "      <td>0.758384</td>\n",
       "      <td>0.790390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.316104</td>\n",
       "      <td>0.145001</td>\n",
       "      <td>0.610302</td>\n",
       "      <td>0.077052</td>\n",
       "      <td>0.054935</td>\n",
       "      <td>0.023192</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110715</td>\n",
       "      <td>0.345596</td>\n",
       "      <td>0.078604</td>\n",
       "      <td>0.170337</td>\n",
       "      <td>0.246087</td>\n",
       "      <td>0.277827</td>\n",
       "      <td>0.406463</td>\n",
       "      <td>0.552716</td>\n",
       "      <td>0.606889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0          0.401741                     0.190438       0.601237   \n",
       "1          0.270016                     0.113420       0.626222   \n",
       "2          0.148996                     0.201901       0.390529   \n",
       "3          0.265401                     0.347093       0.443843   \n",
       "4          0.316104                     0.145001       0.610302   \n",
       "\n",
       "   Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                        0.030074                 0.020796       0.020539   \n",
       "1                        0.018786                 0.119651       0.032588   \n",
       "2                        0.038579                 0.008842       0.123606   \n",
       "3                        0.261078                 0.061278       0.006731   \n",
       "4                        0.077052                 0.054935       0.023192   \n",
       "\n",
       "   Percent_Native_Hawaiian_PI  Percent_Other_Race  Percent_Hispanic_Latino  \\\n",
       "0                    0.005441            0.069090                 0.407563   \n",
       "1                    0.000000            0.069565                 0.381694   \n",
       "2                    0.007889            0.048164                 0.553763   \n",
       "3                    0.060162            0.058149                 0.346086   \n",
       "4                    0.000000            0.110715                 0.345596   \n",
       "\n",
       "   Percent_50_PL  Percent_125_PL  Percent_185_PL  Percent_200_PL  \\\n",
       "0       0.092401        0.216687        0.349609        0.368712   \n",
       "1       0.075873        0.116364        0.218496        0.238959   \n",
       "2       0.088294        0.264060        0.406813        0.430191   \n",
       "3       0.140703        0.390988        0.491356        0.525354   \n",
       "4       0.078604        0.170337        0.246087        0.277827   \n",
       "\n",
       "   Percent_300_PL  Percent_400_PL  Percent_500_PL  \n",
       "0        0.488945        0.632559        0.724385  \n",
       "1        0.343626        0.519388        0.607452  \n",
       "2        0.568583        0.682650        0.754158  \n",
       "3        0.649883        0.758384        0.790390  \n",
       "4        0.406463        0.552716        0.606889  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalize the data using z score\n",
    "X4_norm = X4.apply(lambda x : (x - x.min()) /(x.max() - x.min()), axis=0)\n",
    "X4_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "X4_train, X4_test, y4_train, y4_test = train_test_split(X4_norm, y4, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidcuervo/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000000000000.0, class_weight=None, dual=False,\n",
       "                   fit_intercept=False, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "logreg4 = LogisticRegression(fit_intercept=False, C=1e12, solver='liblinear')\n",
    "\n",
    "# Fit the model\n",
    "logreg4.fit(X4_train, y4_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30434782608695654"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the score\n",
    "logreg4.score(X4_test, y4_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression with All Chemicals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.979512e+07\n",
       "1    1.593846e+07\n",
       "2    1.059076e+07\n",
       "3    8.548026e+06\n",
       "4    7.254232e+06\n",
       "Name: All Chem Total Releases, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make new y\n",
    "y_all_chem = df_chem_merge['All Chem Total Releases']\n",
    "y_all_chem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fef5c14e320>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_all_chem.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X3 = df_chem_merge.drop(columns=['ZIP', 'Total Releases', 'Total Releases Binned', 'All Chem Total Releases', 'All Chem Binned'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.401741</td>\n",
       "      <td>0.190438</td>\n",
       "      <td>0.601237</td>\n",
       "      <td>0.030074</td>\n",
       "      <td>0.020796</td>\n",
       "      <td>0.020539</td>\n",
       "      <td>0.005441</td>\n",
       "      <td>0.069090</td>\n",
       "      <td>0.407563</td>\n",
       "      <td>0.092401</td>\n",
       "      <td>0.216687</td>\n",
       "      <td>0.349609</td>\n",
       "      <td>0.368712</td>\n",
       "      <td>0.488945</td>\n",
       "      <td>0.632559</td>\n",
       "      <td>0.724385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.270016</td>\n",
       "      <td>0.113420</td>\n",
       "      <td>0.626222</td>\n",
       "      <td>0.018786</td>\n",
       "      <td>0.119651</td>\n",
       "      <td>0.032588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069565</td>\n",
       "      <td>0.381694</td>\n",
       "      <td>0.075873</td>\n",
       "      <td>0.116364</td>\n",
       "      <td>0.218496</td>\n",
       "      <td>0.238959</td>\n",
       "      <td>0.343626</td>\n",
       "      <td>0.519388</td>\n",
       "      <td>0.607452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.148996</td>\n",
       "      <td>0.201901</td>\n",
       "      <td>0.390529</td>\n",
       "      <td>0.038579</td>\n",
       "      <td>0.008842</td>\n",
       "      <td>0.123606</td>\n",
       "      <td>0.007889</td>\n",
       "      <td>0.048164</td>\n",
       "      <td>0.553763</td>\n",
       "      <td>0.088294</td>\n",
       "      <td>0.264060</td>\n",
       "      <td>0.406813</td>\n",
       "      <td>0.430191</td>\n",
       "      <td>0.568583</td>\n",
       "      <td>0.682650</td>\n",
       "      <td>0.754158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.265401</td>\n",
       "      <td>0.347093</td>\n",
       "      <td>0.443843</td>\n",
       "      <td>0.261078</td>\n",
       "      <td>0.061278</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.060162</td>\n",
       "      <td>0.058149</td>\n",
       "      <td>0.346086</td>\n",
       "      <td>0.140703</td>\n",
       "      <td>0.390988</td>\n",
       "      <td>0.491356</td>\n",
       "      <td>0.525354</td>\n",
       "      <td>0.649883</td>\n",
       "      <td>0.758384</td>\n",
       "      <td>0.790390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.316104</td>\n",
       "      <td>0.145001</td>\n",
       "      <td>0.610302</td>\n",
       "      <td>0.077052</td>\n",
       "      <td>0.054935</td>\n",
       "      <td>0.023192</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110715</td>\n",
       "      <td>0.345596</td>\n",
       "      <td>0.078604</td>\n",
       "      <td>0.170337</td>\n",
       "      <td>0.246087</td>\n",
       "      <td>0.277827</td>\n",
       "      <td>0.406463</td>\n",
       "      <td>0.552716</td>\n",
       "      <td>0.606889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0          0.401741                     0.190438       0.601237   \n",
       "1          0.270016                     0.113420       0.626222   \n",
       "2          0.148996                     0.201901       0.390529   \n",
       "3          0.265401                     0.347093       0.443843   \n",
       "4          0.316104                     0.145001       0.610302   \n",
       "\n",
       "   Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                        0.030074                 0.020796       0.020539   \n",
       "1                        0.018786                 0.119651       0.032588   \n",
       "2                        0.038579                 0.008842       0.123606   \n",
       "3                        0.261078                 0.061278       0.006731   \n",
       "4                        0.077052                 0.054935       0.023192   \n",
       "\n",
       "   Percent_Native_Hawaiian_PI  Percent_Other_Race  Percent_Hispanic_Latino  \\\n",
       "0                    0.005441            0.069090                 0.407563   \n",
       "1                    0.000000            0.069565                 0.381694   \n",
       "2                    0.007889            0.048164                 0.553763   \n",
       "3                    0.060162            0.058149                 0.346086   \n",
       "4                    0.000000            0.110715                 0.345596   \n",
       "\n",
       "   Percent_50_PL  Percent_125_PL  Percent_185_PL  Percent_200_PL  \\\n",
       "0       0.092401        0.216687        0.349609        0.368712   \n",
       "1       0.075873        0.116364        0.218496        0.238959   \n",
       "2       0.088294        0.264060        0.406813        0.430191   \n",
       "3       0.140703        0.390988        0.491356        0.525354   \n",
       "4       0.078604        0.170337        0.246087        0.277827   \n",
       "\n",
       "   Percent_300_PL  Percent_400_PL  Percent_500_PL  \n",
       "0        0.488945        0.632559        0.724385  \n",
       "1        0.343626        0.519388        0.607452  \n",
       "2        0.568583        0.682650        0.754158  \n",
       "3        0.649883        0.758384        0.790390  \n",
       "4        0.406463        0.552716        0.606889  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalize the data using z score\n",
    "X3_norm = X3.apply(lambda x : (x - x.min()) /(x.max() - x.min()), axis=0)\n",
    "X3_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>All Chem Total Releases</td> <th>  R-squared:         </th> <td>   0.024</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                      <td>OLS</td>           <th>  Adj. R-squared:    </th> <td>  -0.011</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>Least Squares</td>      <th>  F-statistic:       </th> <td>  0.6799</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                <td>Wed, 28 Apr 2021</td>     <th>  Prob (F-statistic):</th>  <td> 0.815</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                    <td>17:44:09</td>         <th>  Log-Likelihood:    </th> <td> -7212.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>         <td>   460</td>          <th>  AIC:               </th> <td>1.446e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>             <td>   443</td>          <th>  BIC:               </th> <td>1.453e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                 <td>    16</td>          <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>nonrobust</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                          <td>-2.109e+06</td> <td> 5.84e+06</td> <td>   -0.361</td> <td> 0.718</td> <td>-1.36e+07</td> <td> 9.37e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Population</th>               <td> 1.754e+05</td> <td> 5.34e+05</td> <td>    0.328</td> <td> 0.743</td> <td>-8.74e+05</td> <td> 1.22e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Below_Poverty_Level</th>    <td> 1.823e+06</td> <td> 2.52e+06</td> <td>    0.722</td> <td> 0.471</td> <td>-3.14e+06</td> <td> 6.78e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_White</th>                  <td> 2.294e+06</td> <td> 5.44e+06</td> <td>    0.422</td> <td> 0.673</td> <td>-8.39e+06</td> <td>  1.3e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Black_African_American</th> <td> 1.639e+06</td> <td> 4.51e+06</td> <td>    0.363</td> <td> 0.717</td> <td>-7.23e+06</td> <td> 1.05e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_American_Indian</th>        <td>-8.087e+05</td> <td> 8.73e+05</td> <td>   -0.926</td> <td> 0.355</td> <td>-2.52e+06</td> <td> 9.07e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Asian</th>                  <td> 2.018e+05</td> <td> 3.31e+06</td> <td>    0.061</td> <td> 0.951</td> <td> -6.3e+06</td> <td> 6.71e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Native_Hawaiian_PI</th>     <td> 6.886e+04</td> <td> 1.15e+06</td> <td>    0.060</td> <td> 0.952</td> <td>-2.18e+06</td> <td> 2.32e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Other_Race</th>             <td> -9.31e+05</td> <td> 5.08e+05</td> <td>   -1.832</td> <td> 0.068</td> <td>-1.93e+06</td> <td> 6.77e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Hispanic_Latino</th>        <td> 2.603e+06</td> <td> 5.67e+06</td> <td>    0.459</td> <td> 0.647</td> <td>-8.55e+06</td> <td> 1.38e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_50_PL</th>                  <td>-1.079e+06</td> <td> 1.18e+06</td> <td>   -0.915</td> <td> 0.361</td> <td> -3.4e+06</td> <td> 1.24e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_125_PL</th>                 <td>-1.258e+06</td> <td> 3.25e+06</td> <td>   -0.388</td> <td> 0.698</td> <td>-7.64e+06</td> <td> 5.12e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_185_PL</th>                 <td> 3.411e+06</td> <td> 3.99e+06</td> <td>    0.855</td> <td> 0.393</td> <td>-4.43e+06</td> <td> 1.13e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_200_PL</th>                 <td>-2.528e+06</td> <td> 3.88e+06</td> <td>   -0.652</td> <td> 0.515</td> <td>-1.01e+07</td> <td> 5.09e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_300_PL</th>                 <td>-2.341e+06</td> <td> 2.08e+06</td> <td>   -1.127</td> <td> 0.260</td> <td>-6.42e+06</td> <td> 1.74e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_400_PL</th>                 <td> 1.656e+06</td> <td> 2.11e+06</td> <td>    0.784</td> <td> 0.433</td> <td>-2.49e+06</td> <td> 5.81e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_500_PL</th>                 <td> 3.686e+05</td> <td>  1.5e+06</td> <td>    0.246</td> <td> 0.806</td> <td>-2.58e+06</td> <td> 3.31e+06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>659.123</td> <th>  Durbin-Watson:     </th>  <td>   0.093</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>105739.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 7.548</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>75.725</td>  <th>  Cond. No.          </th>  <td>    278.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               OLS Regression Results                              \n",
       "===================================================================================\n",
       "Dep. Variable:     All Chem Total Releases   R-squared:                       0.024\n",
       "Model:                                 OLS   Adj. R-squared:                 -0.011\n",
       "Method:                      Least Squares   F-statistic:                    0.6799\n",
       "Date:                     Wed, 28 Apr 2021   Prob (F-statistic):              0.815\n",
       "Time:                             17:44:09   Log-Likelihood:                -7212.0\n",
       "No. Observations:                      460   AIC:                         1.446e+04\n",
       "Df Residuals:                          443   BIC:                         1.453e+04\n",
       "Df Model:                               16                                         \n",
       "Covariance Type:                 nonrobust                                         \n",
       "==================================================================================================\n",
       "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------\n",
       "const                          -2.109e+06   5.84e+06     -0.361      0.718   -1.36e+07    9.37e+06\n",
       "Total Population                1.754e+05   5.34e+05      0.328      0.743   -8.74e+05    1.22e+06\n",
       "Percent_Below_Poverty_Level     1.823e+06   2.52e+06      0.722      0.471   -3.14e+06    6.78e+06\n",
       "Percent_White                   2.294e+06   5.44e+06      0.422      0.673   -8.39e+06     1.3e+07\n",
       "Percent_Black_African_American  1.639e+06   4.51e+06      0.363      0.717   -7.23e+06    1.05e+07\n",
       "Percent_American_Indian        -8.087e+05   8.73e+05     -0.926      0.355   -2.52e+06    9.07e+05\n",
       "Percent_Asian                   2.018e+05   3.31e+06      0.061      0.951    -6.3e+06    6.71e+06\n",
       "Percent_Native_Hawaiian_PI      6.886e+04   1.15e+06      0.060      0.952   -2.18e+06    2.32e+06\n",
       "Percent_Other_Race              -9.31e+05   5.08e+05     -1.832      0.068   -1.93e+06    6.77e+04\n",
       "Percent_Hispanic_Latino         2.603e+06   5.67e+06      0.459      0.647   -8.55e+06    1.38e+07\n",
       "Percent_50_PL                  -1.079e+06   1.18e+06     -0.915      0.361    -3.4e+06    1.24e+06\n",
       "Percent_125_PL                 -1.258e+06   3.25e+06     -0.388      0.698   -7.64e+06    5.12e+06\n",
       "Percent_185_PL                  3.411e+06   3.99e+06      0.855      0.393   -4.43e+06    1.13e+07\n",
       "Percent_200_PL                 -2.528e+06   3.88e+06     -0.652      0.515   -1.01e+07    5.09e+06\n",
       "Percent_300_PL                 -2.341e+06   2.08e+06     -1.127      0.260   -6.42e+06    1.74e+06\n",
       "Percent_400_PL                  1.656e+06   2.11e+06      0.784      0.433   -2.49e+06    5.81e+06\n",
       "Percent_500_PL                  3.686e+05    1.5e+06      0.246      0.806   -2.58e+06    3.31e+06\n",
       "==============================================================================\n",
       "Omnibus:                      659.123   Durbin-Watson:                   0.093\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           105739.186\n",
       "Skew:                           7.548   Prob(JB):                         0.00\n",
       "Kurtosis:                      75.725   Cond. No.                         278.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create model\n",
    "X_int = sm.add_constant(X3_norm)\n",
    "model = sm.OLS(y_all_chem,X_int).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=10, splitter='best')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a DT classifier\n",
    "classifier = DecisionTreeClassifier(random_state=10)  \n",
    "classifier.fit(X4_train, y4_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions for test data\n",
    "y4_pred = classifier.predict(X4_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is :30.434782608695656\n",
      "\n",
      "Confusion Matrix\n",
      "----------------\n"
     ]
    },
    {
     "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>Predicted</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>36</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted   1   2   3   4  All\n",
       "True                          \n",
       "1           6   3   6   4   19\n",
       "2           5   8   9   8   30\n",
       "3           5   9   6   9   29\n",
       "4          11   3   8  15   37\n",
       "All        27  23  29  36  115"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate accuracy \n",
    "acc = accuracy_score(y4_test,y4_pred) * 100\n",
    "print('Accuracy is :{0}'.format(acc))\n",
    "\n",
    "# Create and print a confusion matrix \n",
    "print('\\nConfusion Matrix')\n",
    "print('----------------')\n",
    "pd.crosstab(y4_test, y4_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate and fit a RandomForestClassifier using green columns\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier\n",
    "forest = RandomForestClassifier(n_estimators=100, max_depth= 5)\n",
    "forest.fit(X4_train, y4_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7942028985507247"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test accuracy \n",
    "forest.score(X4_train, y4_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2608695652173913"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.score(X4_test, y4_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X4_train = X4_train.rename(columns={'Percent_50_PL':'Percent Population Living Below 50 Percent of Poverty Line',\n",
    "                                    'Percent_125_PL':'Percent Population Living Below 125 Percent of Poverty Line',\n",
    "                                    'Percent_200_PL':'Percent Population Living Below 200 Percent of Poverty Line',\n",
    "                                    'Percent_300_PL':'Percent Population Living Below 300 Percent of Poverty Line',\n",
    "                                    'Percent_400_PL':'Percent Population Living Below 400 Percent of Poverty Line',\n",
    "                                    'Percent_500_PL':'Percent Population Living Below 500 Percent of Povety Line'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Feature Importance': array([0.09980951, 0.06718589, 0.06008891, 0.0459882 , 0.10626543,\n",
       "        0.07158156, 0.03447773, 0.05739608, 0.0548835 , 0.05492775,\n",
       "        0.07955611, 0.05821112, 0.05970302, 0.04283579, 0.04852353,\n",
       "        0.05856588]),\n",
       " 'Features': Index(['Total Population', 'Percent_Below_Poverty_Level', 'Percent_White',\n",
       "        'Percent_Black_African_American', 'Percent_American_Indian',\n",
       "        'Percent_Asian', 'Percent_Native_Hawaiian_PI', 'Percent_Other_Race',\n",
       "        'Percent_Hispanic_Latino',\n",
       "        'Percent Population Living Below 50 Percent of Poverty Line',\n",
       "        'Percent Population Living Below 125 Percent of Poverty Line',\n",
       "        'Percent_185_PL',\n",
       "        'Percent Population Living Below 200 Percent of Poverty Line',\n",
       "        'Percent Population Living Below 300 Percent of Poverty Line',\n",
       "        'Percent Population Living Below 400 Percent of Poverty Line',\n",
       "        'Percent Population Living Below 500 Percent of Povety Line'],\n",
       "       dtype='object')}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feature_barh = {'Feature Importance': forest.feature_importances_,\n",
    "                   'Features': X4_train.columns}\n",
    "df_feature_barh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Feature Importance</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.099810</td>\n",
       "      <td>Total Population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.067186</td>\n",
       "      <td>Percent_Below_Poverty_Level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.060089</td>\n",
       "      <td>Percent_White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.045988</td>\n",
       "      <td>Percent_Black_African_American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.106265</td>\n",
       "      <td>Percent_American_Indian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.071582</td>\n",
       "      <td>Percent_Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.034478</td>\n",
       "      <td>Percent_Native_Hawaiian_PI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.057396</td>\n",
       "      <td>Percent_Other_Race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.054883</td>\n",
       "      <td>Percent_Hispanic_Latino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.054928</td>\n",
       "      <td>Percent Population Living Below 50 Percent of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.079556</td>\n",
       "      <td>Percent Population Living Below 125 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.058211</td>\n",
       "      <td>Percent_185_PL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.059703</td>\n",
       "      <td>Percent Population Living Below 200 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.042836</td>\n",
       "      <td>Percent Population Living Below 300 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.048524</td>\n",
       "      <td>Percent Population Living Below 400 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.058566</td>\n",
       "      <td>Percent Population Living Below 500 Percent of...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature Importance                                           Features\n",
       "0             0.099810                                   Total Population\n",
       "1             0.067186                        Percent_Below_Poverty_Level\n",
       "2             0.060089                                      Percent_White\n",
       "3             0.045988                     Percent_Black_African_American\n",
       "4             0.106265                            Percent_American_Indian\n",
       "5             0.071582                                      Percent_Asian\n",
       "6             0.034478                         Percent_Native_Hawaiian_PI\n",
       "7             0.057396                                 Percent_Other_Race\n",
       "8             0.054883                            Percent_Hispanic_Latino\n",
       "9             0.054928  Percent Population Living Below 50 Percent of ...\n",
       "10            0.079556  Percent Population Living Below 125 Percent of...\n",
       "11            0.058211                                     Percent_185_PL\n",
       "12            0.059703  Percent Population Living Below 200 Percent of...\n",
       "13            0.042836  Percent Population Living Below 300 Percent of...\n",
       "14            0.048524  Percent Population Living Below 400 Percent of...\n",
       "15            0.058566  Percent Population Living Below 500 Percent of..."
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feature_barh = pd.DataFrame(df_feature_barh)\n",
    "df_feature_barh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>Feature Importance</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.034478</td>\n",
       "      <td>Percent_Native_Hawaiian_PI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.042836</td>\n",
       "      <td>Percent Population Living Below 300 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.045988</td>\n",
       "      <td>Percent_Black_African_American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.048524</td>\n",
       "      <td>Percent Population Living Below 400 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.054883</td>\n",
       "      <td>Percent_Hispanic_Latino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.054928</td>\n",
       "      <td>Percent Population Living Below 50 Percent of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.057396</td>\n",
       "      <td>Percent_Other_Race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.058211</td>\n",
       "      <td>Percent_185_PL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.058566</td>\n",
       "      <td>Percent Population Living Below 500 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.059703</td>\n",
       "      <td>Percent Population Living Below 200 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.060089</td>\n",
       "      <td>Percent_White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.067186</td>\n",
       "      <td>Percent_Below_Poverty_Level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.071582</td>\n",
       "      <td>Percent_Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.079556</td>\n",
       "      <td>Percent Population Living Below 125 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.099810</td>\n",
       "      <td>Total Population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.106265</td>\n",
       "      <td>Percent_American_Indian</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature Importance                                           Features\n",
       "6             0.034478                         Percent_Native_Hawaiian_PI\n",
       "13            0.042836  Percent Population Living Below 300 Percent of...\n",
       "3             0.045988                     Percent_Black_African_American\n",
       "14            0.048524  Percent Population Living Below 400 Percent of...\n",
       "8             0.054883                            Percent_Hispanic_Latino\n",
       "9             0.054928  Percent Population Living Below 50 Percent of ...\n",
       "7             0.057396                                 Percent_Other_Race\n",
       "11            0.058211                                     Percent_185_PL\n",
       "15            0.058566  Percent Population Living Below 500 Percent of...\n",
       "12            0.059703  Percent Population Living Below 200 Percent of...\n",
       "2             0.060089                                      Percent_White\n",
       "1             0.067186                        Percent_Below_Poverty_Level\n",
       "5             0.071582                                      Percent_Asian\n",
       "10            0.079556  Percent Population Living Below 125 Percent of...\n",
       "0             0.099810                                   Total Population\n",
       "4             0.106265                            Percent_American_Indian"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feature_barh = df_feature_barh.sort_values(by=['Feature Importance'])\n",
    "df_feature_barh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.barh('Features', 'Feature Importance', data=df_feature_barh, align='center')\n",
    "plt.xlabel('Feature importance')\n",
    "plt.ylabel('Feature')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "From [Towards Data Science article](https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters currently in use:\n",
      "\n",
      "{'bootstrap': True,\n",
      " 'class_weight': None,\n",
      " 'criterion': 'gini',\n",
      " 'max_depth': None,\n",
      " 'max_features': 'auto',\n",
      " 'max_leaf_nodes': None,\n",
      " 'min_impurity_decrease': 0.0,\n",
      " 'min_impurity_split': None,\n",
      " 'min_samples_leaf': 1,\n",
      " 'min_samples_split': 2,\n",
      " 'min_weight_fraction_leaf': 0.0,\n",
      " 'n_estimators': 'warn',\n",
      " 'n_jobs': None,\n",
      " 'oob_score': False,\n",
      " 'random_state': 42,\n",
      " 'verbose': 0,\n",
      " 'warm_start': False}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestClassifier(random_state = 42)\n",
    "from pprint import pprint\n",
    "# Look at parameters used by our current forest\n",
    "print('Parameters currently in use:\\n')\n",
    "pprint(rf.get_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': [True, False],\n",
      " 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],\n",
      " 'max_features': ['auto', 'sqrt'],\n",
      " 'min_samples_leaf': [1, 2, 4],\n",
      " 'min_samples_split': [2, 5, 10],\n",
      " 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "pprint(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   28.7s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  4.1min finished\n",
      "/Users/davidcuervo/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/model_selection/_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    n_estimators='warn',\n",
       "                                                    n_jobs=None,\n",
       "                                                    oob_sc...\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [200, 400, 600, 800,\n",
       "                                                         1000, 1200, 1400, 1600,\n",
       "                                                         1800, 2000]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestClassifier()\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "# Fit the random search model\n",
    "rf_random.fit(X4_train, y4_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 4,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 50,\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'max_depth': [60, 70, 80, 90],\n",
    "    'max_features': [3, 4],\n",
    "    'min_samples_leaf': [3, 4, 5],\n",
    "    'min_samples_split': [8, 10, 12],\n",
    "    'n_estimators': [100, 200, 300, 1000]\n",
    "}\n",
    "# Create a based model\n",
    "rf = RandomForestClassifier()\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = -1, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 288 candidates, totalling 864 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    8.7s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   33.1s\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  2.5min\n",
      "[Parallel(n_jobs=-1)]: Done 864 out of 864 | elapsed:  3.4min finished\n",
      "/Users/davidcuervo/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/model_selection/_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'max_depth': 80,\n",
       " 'max_features': 4,\n",
       " 'min_samples_leaf': 5,\n",
       " 'min_samples_split': 8,\n",
       " 'n_estimators': 200}"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search to the data\n",
    "grid_search.fit(X4_train, y4_train)\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "y4_test = y4_test.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "Average Error: 1.0174 degrees.\n",
      "Accuracy = 45.22%.\n"
     ]
    }
   ],
   "source": [
    "def evaluate(model, test_features, test_labels):\n",
    "    predictions = model.predict(test_features)\n",
    "    errors = abs(predictions - test_labels)\n",
    "    mape = 100 * np.mean(errors / test_labels)\n",
    "    accuracy = 100 - mape\n",
    "    print('Model Performance')\n",
    "    print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))\n",
    "    print('Accuracy = {:0.2f}%.'.format(accuracy))\n",
    "    \n",
    "best_grid = grid_search.best_estimator_\n",
    "grid_accuracy = evaluate(best_grid, X4_test, y4_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=80, max_features=4, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=5, min_samples_split=8,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Feature Importance': array([0.08015201, 0.0694919 , 0.0536213 , 0.05626972, 0.10905719,\n",
       "        0.07331679, 0.03750883, 0.05689608, 0.05769293, 0.06009624,\n",
       "        0.06973088, 0.05618397, 0.05562831, 0.04910572, 0.05437693,\n",
       "        0.06087121]),\n",
       " 'Features': Index(['Total Population', 'Percent_Below_Poverty_Level', 'Percent_White',\n",
       "        'Percent_Black_African_American', 'Percent_American_Indian',\n",
       "        'Percent_Asian', 'Percent_Native_Hawaiian_PI', 'Percent_Other_Race',\n",
       "        'Percent_Hispanic_Latino',\n",
       "        'Percent Population Living Below 50 Percent of Poverty Line',\n",
       "        'Percent Population Living Below 125 Percent of Poverty Line',\n",
       "        'Percent_185_PL',\n",
       "        'Percent Population Living Below 200 Percent of Poverty Line',\n",
       "        'Percent Population Living Below 300 Percent of Poverty Line',\n",
       "        'Percent Population Living Below 400 Percent of Poverty Line',\n",
       "        'Percent Population Living Below 500 Percent of Povety Line'],\n",
       "       dtype='object')}"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bestgrid_barh = {'Feature Importance': best_grid.feature_importances_,\n",
    "                   'Features': X4_train.columns}\n",
    "df_bestgrid_barh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>Feature Importance</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.080152</td>\n",
       "      <td>Total Population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.069492</td>\n",
       "      <td>Percent_Below_Poverty_Level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.053621</td>\n",
       "      <td>Percent_White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.056270</td>\n",
       "      <td>Percent_Black_African_American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.109057</td>\n",
       "      <td>Percent_American_Indian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.073317</td>\n",
       "      <td>Percent_Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.037509</td>\n",
       "      <td>Percent_Native_Hawaiian_PI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.056896</td>\n",
       "      <td>Percent_Other_Race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.057693</td>\n",
       "      <td>Percent_Hispanic_Latino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.060096</td>\n",
       "      <td>Percent Population Living Below 50 Percent of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.069731</td>\n",
       "      <td>Percent Population Living Below 125 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.056184</td>\n",
       "      <td>Percent_185_PL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.055628</td>\n",
       "      <td>Percent Population Living Below 200 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.049106</td>\n",
       "      <td>Percent Population Living Below 300 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.054377</td>\n",
       "      <td>Percent Population Living Below 400 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.060871</td>\n",
       "      <td>Percent Population Living Below 500 Percent of...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature Importance                                           Features\n",
       "0             0.080152                                   Total Population\n",
       "1             0.069492                        Percent_Below_Poverty_Level\n",
       "2             0.053621                                      Percent_White\n",
       "3             0.056270                     Percent_Black_African_American\n",
       "4             0.109057                            Percent_American_Indian\n",
       "5             0.073317                                      Percent_Asian\n",
       "6             0.037509                         Percent_Native_Hawaiian_PI\n",
       "7             0.056896                                 Percent_Other_Race\n",
       "8             0.057693                            Percent_Hispanic_Latino\n",
       "9             0.060096  Percent Population Living Below 50 Percent of ...\n",
       "10            0.069731  Percent Population Living Below 125 Percent of...\n",
       "11            0.056184                                     Percent_185_PL\n",
       "12            0.055628  Percent Population Living Below 200 Percent of...\n",
       "13            0.049106  Percent Population Living Below 300 Percent of...\n",
       "14            0.054377  Percent Population Living Below 400 Percent of...\n",
       "15            0.060871  Percent Population Living Below 500 Percent of..."
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bestgrid_barh = pd.DataFrame(df_bestgrid_barh)\n",
    "df_bestgrid_barh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>Feature Importance</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.037509</td>\n",
       "      <td>Percent_Native_Hawaiian_PI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.049106</td>\n",
       "      <td>Percent Population Living Below 300 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.053621</td>\n",
       "      <td>Percent_White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.054377</td>\n",
       "      <td>Percent Population Living Below 400 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.055628</td>\n",
       "      <td>Percent Population Living Below 200 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.056184</td>\n",
       "      <td>Percent_185_PL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.056270</td>\n",
       "      <td>Percent_Black_African_American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.056896</td>\n",
       "      <td>Percent_Other_Race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.057693</td>\n",
       "      <td>Percent_Hispanic_Latino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.060096</td>\n",
       "      <td>Percent Population Living Below 50 Percent of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.060871</td>\n",
       "      <td>Percent Population Living Below 500 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.069492</td>\n",
       "      <td>Percent_Below_Poverty_Level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.069731</td>\n",
       "      <td>Percent Population Living Below 125 Percent of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.073317</td>\n",
       "      <td>Percent_Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.080152</td>\n",
       "      <td>Total Population</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.109057</td>\n",
       "      <td>Percent_American_Indian</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature Importance                                           Features\n",
       "6             0.037509                         Percent_Native_Hawaiian_PI\n",
       "13            0.049106  Percent Population Living Below 300 Percent of...\n",
       "2             0.053621                                      Percent_White\n",
       "14            0.054377  Percent Population Living Below 400 Percent of...\n",
       "12            0.055628  Percent Population Living Below 200 Percent of...\n",
       "11            0.056184                                     Percent_185_PL\n",
       "3             0.056270                     Percent_Black_African_American\n",
       "7             0.056896                                 Percent_Other_Race\n",
       "8             0.057693                            Percent_Hispanic_Latino\n",
       "9             0.060096  Percent Population Living Below 50 Percent of ...\n",
       "15            0.060871  Percent Population Living Below 500 Percent of...\n",
       "1             0.069492                        Percent_Below_Poverty_Level\n",
       "10            0.069731  Percent Population Living Below 125 Percent of...\n",
       "5             0.073317                                      Percent_Asian\n",
       "0             0.080152                                   Total Population\n",
       "4             0.109057                            Percent_American_Indian"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bestgrid_barh = df_bestgrid_barh.sort_values(by=['Feature Importance'])\n",
    "df_bestgrid_barh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.barh('Features', 'Feature Importance', data=df_bestgrid_barh, align='center')\n",
    "plt.xlabel('Feature importance')\n",
    "plt.ylabel('Feature')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_chem_merge = df_chem_merge.rename(columns={'All Chem Binned':'Total Toxic Releases Categories'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFrCAYAAAD4qAEDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzde1yO9/8H8NfdGbdT1GwIa+6MUJnEhEoYkhwqhzIzhzmPYWhpITlsTMzhu60NQ+a8mCWZthxmbbF8idmQGEmodL4/vz/6dX3dOt2l2+3i9Xw8PB5dh/tzva/rut2v+3OdboUQQoCIiIhkw0DfBRAREVHlMLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3lV04sQJ2NjYoFOnTsjLy9N3ORpcXV3h5+en7zJK2L17N2xsbLB79+5qa/PxdfXz84Orq2u1ta+NsLAw2NjYaPxr1aoV7Ozs4Onpia+//hpqtfqp1lSaJ9k2mZmZuHv3rjRcvM7Xr1+vrvKeGRWt26lTp0rs77L+Vbdt27bBxsYGCQkJT9TO5cuXS6339ddfh6OjI4YNG4aDBw9Wqe0VK1bAxsYGqampT1Qjlc9I3wXIVWRkJGrWrIl79+4hJiYGffr00XdJknnz5qFGjRr6LkMvJkyYgOzsbL0t+9VXXwUACCGQnZ2NI0eOYMmSJUhOTsZHH32kl7qeVGJiIt577z2sWLECnTp1AgC4u7vDysoK5ubmeq7u6bO2tsayZcs0xs2ePRuvvvoqJkyYoNNld+7cGcuWLYOVlVW1tOfk5IRBgwZJw2q1GsnJydi6dSvef/99mJiYoGfPntWyLKpeDO8qyMvLQ1RUFDw9PREZGYk9e/Y8U+H9Iv9ne/PNN/W27C5dukjhVszHxwfDhg3D1q1bMW7cOLz00kt6qq7qLl68iNu3b2uMa9WqFVq1aqWnivSrYcOG8PT01Bg3e/bsUsdXt+bNm6N58+bV1l6zZs1KrXnAgAHo378/1q5d+0J/njzLeNi8Co4dO4YHDx6gU6dO6Nq1K37++WceIqJSGRgYoE+fPlCr1Thz5oy+yyHSSvPmzWFvb4/z588/c6cFqQjDuwq+//57KBQKdOzYEe7u7igsLMS+ffs05nF1dUVwcDC+++479O7dG+3atcPgwYNx9uxZpKamYtq0abC3t4ezszNWrlxZ4pzo0aNH4evri/bt26Njx46YMmUK/vnnH415bGxssGrVKkyYMAG2trbo27cvCgoKSj3nfebMGYwdOxYdO3ZEp06dMG7cOCQlJUnThRDYtm0bhgwZAnt7e7Rt2xZ9+vTBxo0b8egTdF1dXREYGIh9+/ahX79+aNu2LXr16oVvv/22StuyMu0dPHgQnp6eaNeuHfr374+TJ0+WmKe087qHDh3CyJEj0aFDB9ja2sLV1RXLli3T+FDy8/PDmDFjEBsbi0GDBqFt27bo0aMHwsLCnvh8tUKhAAAUFBRI45KSkjBx4kS88cYbaNeuHby9vREdHV1iXd5++23ExMSgb9++aNeuHQYOHIgff/yxwnUub/yjKto2YWFhmDt3LgDA399faq+088Lp6ekICgqCs7MzbG1t0bt3b2zcuBGFhYXSPGFhYWjbti2uXLmC8ePHw97eHh07dsScOXOQnp5e4bY8d+4cpkyZgi5duqBNmzbo3LkzZs6ciX///bdKy7h27RqmTJki/b9YuXIldPHE6LS0NHz00Ufo2rUrbG1t8dZbb+Grr76S3luZmZno0aMHOnXqpHFtwYkTJ9CqVStpH5R2zjs3NxcrV65Ez5490a5dO/Tp0wfh4eFP/L6tWbMmAGi0c/fuXSxYsEBaj379+mn1f1/b1509exaTJk1C586d0aZNG7z55puYNWtWiSM/mzZtQv/+/dG+fXt06tQJU6dOxd9//60xT3Z2NlasWAFXV1fY2trC3d0da9eu1fh/CAAHDhzAoEGDYG9vjzfeeANjxoyRxRdtHjavpMzMTPz000+ws7NDw4YN0b17d5iYmGDPnj149913NeY9cuQIoqKiMGrUKAghsG7dOkyZMgW1a9dGy5Yt8eGHHyIqKgrr169H8+bN4eXlBaDowq558+ahc+fOmDVrFu7fv49t27bB29sbO3bsQIsWLaRlfPPNN2jfvj0CAgKQk5MDI6OSu/S3337D22+/DUtLS4wZMwZmZmbYtGkT/P39sWvXLjRp0gSrVq3C+vXr4eXlBW9vb2RlZWHv3r345JNPYGFhIdUGAD///LP0od+wYUNEREQgODgYTZo0Qffu3Su9TbVpb/fu3Zg7dy7s7e0xa9YsXL16FRMmTIBarUbjxo3LbPu7775DQEAAXF1d8cEHHyA/Px+HDx/Gl19+iZo1a2Ly5MnSvBcvXsT06dPh4+MDHx8fREZGYs2aNTA3N8eIESMqvV7Fir9ktGnTBkDRB5S/vz+USiVGjx6NWrVqYd++fZg0aRICAwM1lnX58mVMnToVgwcPhq+vL/bu3YupU6dixYoV8PDwqHJNgHbbxt3dHampqYiIiMCECRPQtm3bUtu6f/8+fH19kZKSAl9fX7Ro0QJxcXH45JNP8N///herVq2S5lWr1fD398cbb7yBOXPm4M8//8TOnTuRk5ODzz77rMx6k5KSMHz4cDRr1gzjxo1DjRo18Pvvv2Pfvn24ffs2Nm/eXKll3LlzB76+vsjPz8eoUaNgZmaGrVu34sGDB0+0XR939+5deHt74/bt2xg+fDisrKwQGxuLpUuX4sKFC1i2bBmUSiWCg4MxduxYLF++HEuWLEFWVhbmz5+Pl19+GfPnzy+1bSEExo0bh5MnT8LLywvt27dHfHw8QkNDkZ6ejhkzZlSp5szMTPz+++949dVXYWZmJo0bPnw40tPTMWzYMFhYWOD48eMIDg5GcnIyPvzwwzLb0uZ1iYmJGDFiBFq2bIkJEybA1NQUv/32G77//nukp6fjiy++AFD0vl28eDGGDBmCUaNGITU1Fd988w38/f0RFRWFmjVroqCgAO+++y4SExPh6+uL5s2b48yZMwgLC0NSUhJWr14NAIiLi8MHH3wANzc3+Pj4IDMzE1u2bMGoUaPwww8/4OWXX67S9nsqBFXKzp07hUqlEl9++aU0bty4cUKlUokzZ85I41xcXISNjY24cOGCNG7p0qVCpVKJ6dOnS+OysrJEmzZtxIwZM4QQQmRkZAgHBwfx/vvvayz39u3bomPHjmLixInSOJVKJTp06CDu37+vMa+Li4sYOXKkNDxkyBDx5ptvirt370rj/v77b9GqVSuxdOlSkZeXV+oyMzIyhK2trRg/fnyJ9Tp//rxGbTY2NtI6lGXXrl1CpVKJXbt2Vaq9goIC0blzZzF48GCRl5dXor1H13XkyJHCxcVFGu7Tp4/w8fERarVaGpefny+6desm+vfvr/E6lUoljhw5Io3LyckRHTt2FD4+PuWu1+rVq4VKpRKHDx8WaWlpIi0tTdy5c0f8+eefIjg4WKhUKjFp0iRp/qFDhwo7Oztx8+ZNjWV5eXmJdu3aibS0NI2awsPDpfmys7OFu7u76Nq1qygsLCx1ncvaFlXdNsXb+eTJkyXWOTk5WQghxPLly6Vt8KigoCChUqnETz/9pPG6JUuWaMw3ZswY0bp1a/Hw4cOyNrMIDAwU7du3F+np6Rrj33//faFSqaTx2i4jNDRU2NjYiMTERGmeO3fuCCcnJ41108bj78NHLVq0SKhUKnHs2DGN8XPnzhUqlUqcOHFCGvfhhx8KGxsb8dtvv4kFCxYIGxsbjelbt24VKpVK/PHHH0IIIaKiooRKpRJfffWVRttTpkwR7dq1E5mZmaXW9NdffwmVSiXmzJkjvWfT0tLErVu3xOnTp4Wfn59QqVTi4MGD0muWL18u2rZtK/766y+NtkJCQoSNjY00vvi9cPv27Uq97sMPPxQODg4iIyNDY76JEycKGxsbkZWVJYQQws/PT3h5eWnMExUVJfr16yd9Bhdvp0ffs0II8c033wiVSiViY2OlZXbq1EljnrNnz4revXtrfBY8i3jYvJK+//57AEVX2xYr/nvPnj0a81pZWWncLlLcY370tTVr1kSDBg2kc+ZxcXHIzMxEz549cffuXemfoaEhnJyc8Msvv2gc9mnfvj3q1KlTZr1paWn4888/4eHhgfr162vUsmvXLowdOxbGxsbSN+FHpaenQ6lU4uHDhxrjW7RooXGxkoWFBRo2bIg7d+6UWUd5Kmrv3LlzSEtLw6BBg2BsbCzN5+npibp165bb9v79+7Fx40bp0DVQtE3q1KlTYr1q1KiBHj16SMOmpqZo0aKF1utVfLivc+fO6NKlCwYPHoxt27ahf//+CA0NBVDU2ztz5gw8PT3RqFEjjWWNGTMGOTk5OH78uDS+du3aGD58uDRsZmaGYcOG4fbt20hMTNSqrrJUZttUJCYmBtbW1iUubpo4cSKAoqNQj3rrrbc0hl9//XUUFBTg3r17ZS4jKCgIMTExqFevnjQuMzMTpqamAFCi5oqWERsbi7Zt20pHRACgQYMG6NevX7nrWlkxMTFo3bo1unXrpjG+eNs8erpk7ty5aNiwIWbNmoXt27fD398fTk5OZbZ99OhRGBsbY9iwYRrj58+fjz179ki95rLs2bNHes927twZzs7OGDFiBNLS0rB69WqNbRgVFYXWrVujfv36Gp9N7u7uEELg2LFjpS5D29ctXrwYhw8fhlKplF6bkZEBMzMz6e4NAGjUqBGSkpKwbt063LhxA0DRZ2pkZCTatWsnLbNRo0Zo2bKlxjJdXFygUCjw008/SW2lp6cjNDRUOi3Ztm1bHDp06KnfclpZPGxeCbdv38avv/6K5s2bQ6FQSOf7WrVqBYVCgQMHDmDu3LkwMTEBUPRB8ChDQ0MAKHF7jaGhoXSe7dq1awCA999/v8w67t69C0tLy1LbelxKSgqEEGjWrFmJaa1bt5b+NjY2xk8//YQjR47gn3/+wdWrV3H//n0AKHEOsLRlmpiYVPkcW0XtpaSkAECJ22MMDQ1LXa9HGRsb4/Tp04iMjMTff/+Na9euIS0tDQBKHG6vV68eDAw0v89WZr3mzJkjfQlRKBSoVasWrK2tUatWLWme4nV59NRHMWtrawCQPpCAonUufj8VK17nlJQU6cOqKiqzbSpy/fp1ODs7lxhvYWGBOnXqSOtd7PF9XryOj54ff5xCoUB6ejo2bNiApKQkXLt2DTdu3JDen4/vp4qWkZKSAjc3txLLKb7drzoIIXDjxg20b9++xLQmTZrAzMxMY3/XqVMH8+fPx/Tp02FhYYGZM2eW235KSgosLS1LhLS2dzW4uLjA399fqvOLL75AdnY2goOD0aFDB431uH79Oq5evYrOnTuX2taj61GV1xkYGCAtLQ3r1q3DxYsXkZycXOr+nTp1Kv7880+sWrUKq1atgkqlgqurK4YOHYomTZoAKPoc/ffffytc5ttvv40TJ04gPDwc4eHhsLKygouLC4YMGQKVSqXNJtQbhnclHDx4EIWFhbhy5Uqp/+nv37+P6Oho9O3bFwBKPf8MQKOn87jiN+jChQulN+LjHu1tFn8hqKi9x0PpUUIIzJo1C5GRkejQoQPs7e3h4+ODjh07YtSoUSXmL6+tqqioveLtlZubW2JaRcH6ySefYOPGjWjdurX00BR7e3ssXLgQN2/erFQdFWnTpk2JW8Ue9/gXoUcVr8ujRxce/fvx+Sra9+UFIVC5bVORitbr8fUo7/9AWX766SdMnDgRlpaWcHJyQrdu3WBra4tffvkFGzZsKDF/RctQKBSlvqfKW5fKKq8tIQSEECW2TXx8PICiozS///57mQEEFG3bJ3nfWlpaokuXLtKwm5sbhgwZgjFjxmDTpk3Sl0MhBNRqNZycnDB+/PhS2yrtC0NlXhcVFYVp06bh5ZdfRqdOndCjRw+0bdsW0dHRCA8Pl+Zv0qQJIiMjceLECcTExODnn3/G+vXrER4ejm+++Qb29vZQq9Vo2bIl5s2bV+oyi49C1q1bF9u3b0d8fDyio6Pxyy+/4JtvvsHmzZuxatUq9O7dW4utqB8M70oovso8NDRU49AOAFy4cAFhYWHYs2ePFN5VUdzjMTc31/hPBRQ92UmtVpfoiZWn+IKLq1evlpi2fPly1K1bF/b29oiMjMTEiRMxbdo0aXrxIcamTZtWZVWqTfHyr1y5ojFeCIGUlBS0bNmy1NelpKRg48aN8PT0LPFQjaoe4n9Sxfv38StjAUiH7R49nH79+nUIITSCqHg7FPfADQwMSr2dp7x1rO5t07hx41LXKTU1FZmZmdVy4c/ChQvRrFkz7Nq1S7oSGvjfqazKatKkSYn3FAAkJydXtcQSDAwM8PLLL5e6bVJSUpCbm6uxbeLj4/Htt99i2LBhOHHiBAICAvD9999rrO+jXn75Zfzxxx/Iy8vT+FxISEjAli1bMHny5ErdF25ubo7ly5dj5MiRmDFjBvbv34+aNWtK65GdnV3ic+nu3bs4ffp0qUeTKvO64OBgtGzZEjt27NA4krBjxw7pbyEEkpKSYGhoiK5du6Jr164Aii4KHT16NLZs2QJ7e3s0btwYV65cQefOnTX+7+Tm5uLIkSN45ZVXABT9P3z48CE6dOiADh06YM6cObhw4QJGjhyJ8PDwZzq8ec5bS1euXEFiYiIcHR0xcOBA9OzZU+Pf+PHjYWFhgbi4ONy6davKy+nSpQtMTU3xxRdfID8/Xxp/69YtTJw4EStWrKhUr+Wll15Cq1atcODAAWRmZkrjk5OTsWnTJty5c0c6B/jaa69pvHbHjh3Izs4ucWvF09a6dWs0btwY27Zt03h62oEDB8q9vaj4sP/j63Xs2DFcuXJFL+tlYWEBW1tb7N+/X+P2pry8PISHh8PExETjQTN37tzBDz/8IA1nZ2dj27ZtaN68uXQ9RcOGDZGWlqbxvktMTCz1C1uxymyb4p5deUc5XFxc8Pfff5e43W3jxo0AoHEtQVXdu3cPr7zyikaQ3bx5E1FRUQAqPtLwuF69euHSpUuIjY2VxmVkZJS47fNJubi44Pz58xrLAUpum9zcXMyfPx+Wlpb44IMPsGDBAly/fh2ffvppmW13794d+fn52Llzp8b4b7/9FocOHULDhg0rXW+HDh3g7++P5ORkjbsEXF1dcebMGZw4cUJj/rCwMEydOrXUL0Lavk4IgQcPHkinEoolJycjJiYGQNH+VSgUmDhxIubOnavxfrS1tYWRkZF0NMrV1RWpqakltsvmzZvx/vvv4/Tp0wCABQsWYPLkyRqfK6+99hqUSmWFR7b0jT1vLRV/ux8yZEip042NjTF48GCsX7/+if7zm5ubY8aMGViyZAl8fHwwYMAAFBQUYOvWrcjNzcWcOXMq3ebcuXPx7rvvYvDgwRg6dCgMDAywZcsW1KlTB2PHjoVCoYBSqcSSJUtw48YN1KlTB6dOncLBgwdhamqKrKysKq9PdVAoFPjoo48wadIk+Pj4YPDgwbh16xa+/fZbjYuXHvfaa6/hlVdewfr165Gbm4tGjRrh7Nmz2LNnj17XKyAgAKNGjcKQIUMwbNgw1KpVC/v378e5c+cQEBCgcQGisbEx5s6di3PnzsHS0hK7du3CrVu3sH79emme/v37IzIyEmPHjsWwYcOQlpaGzZs3o3nz5hpfAB9VmW1TfO5427ZtuHPnTqm3qI0fPx5RUVGYPn06hg0bhubNm+PkyZOIiopCr169qnQL4eO6deuGgwcPIjAwEG3btsX169elL5gAKr0/R48ejf3792PKlCkYNWoUzM3NERERUe33eU+cOBFHjhzBpEmTMGLECDRt2hS//PILYmJi0L9/f+mCtNWrV+Off/7BmjVroFQq0aVLF/Tr1w9btmxBnz598MYbb5Rou0+fPti+fTsWLlyICxcuoHXr1tJ1DDNmzChxhFBb06ZNQ1RUFDZv3oz+/fujXbt20nqMHz8ew4YNw6uvvopff/0VkZGR6NmzZ5kX1mnzOoVCga5duyImJgbBwcF4/fXXce3aNezYsUM6qlS8f8eMGYPg4GCMGTMGPXv2hFqtxp49e6BWq6UL94YPH479+/cjMDAQZ8+eha2tLf773//iu+++Q/v27TFgwAAARe+BiRMnYuTIkfD09ISRkRGioqJw8+bNKn3WPk0Mby1FRkaidu3a6NWrV5nzeHt7Y+PGjSWuOq+st99+Gy+99BLCw8OxcuVKmJmZoU2bNli+fLnGRSTacnJywjfffIPVq1dj7dq1MDU1RceOHTFr1ixYWFgAKOoFrFixAp9//jlMTEzQokULfPrppzh79qzUQ6/Kt/jq4uLigg0bNiAsLAyffvopXnrpJSxevLjcB0SYmJhg48aNCA0NxaZNmyCEgJWVFebNm4eCggIsXrwYiYmJsLW1fYprAtjb22Pbtm1YvXq19KCOVq1alfooSktLS8ybNw9Lly5Famoq2rRpg/DwcHTs2FGax8XFBYGBgdi0aRMWL16MFi1aICgoCKdPn5auqn1cZbZN586d8dZbb+Ho0aM4efJkqf8H6tWrh4iICKxatQoHDx7EgwcP0LRpU8yePRtvv/12tWy3oKAg1KxZEzExMdi3bx8aNWqEgQMHwt3dHcOGDcPJkyc1LsKsiFKpxNatW7F8+XJERESgsLAQffv2RcuWLbFo0aJqqRkounC1eNvs378fGRkZaNasGebOnQt/f38ARUdKwsPD4eLionE3yocffohjx45h/vz5pXYKDA0NsWHDBqxZswYHDx7E3r170axZMyxcuBBDhw6tcs01a9ZEYGAgJkyYgICAAOzevVtaj88++wwHDhzA/fv38corr2DKlClSJ6C89a/odYsWLcKyZcvw448/Yvfu3WjUqBG8vb3h7OwMPz8/nDx5EtbW1hgxYgRMTEywdetWfPLJJwCKet5fffUV7O3tARTdlbF582aEhYXh8OHD2LNnDywtLeHn54eJEydKdyi4urpizZo1+OKLLxAWFob8/HzY2Nhg9erVz/QhcwBQiOr+mklE1cLPzw8pKSnSYUMiomI8501ERCQzDG8iIiKZYXgTERHJDM95ExERyYzOet5qtRqBgYHw8fGBn59fiXtOv/zySwwaNAiDBw/G4cOHdVUGERHRc0dnt4pFR0cjLy8PERERSEhIQGhoKNatWwcAePDgATZv3oyoqChkZ2dLt3uUJzU1Q1elEhERPXMsLGqXOU1nPe/4+Hjphwrs7Ow0fgGpRo0aeOWVV5CdnY3s7OwqPeeYiIjoRaWznndmZqbG030MDQ1RUFAg/VjHyy+/jH79+qGwsLDMB9Y/qn79mjAyerYfV0dERPQ06Cy8lUqlxuMK1Wq1FNyxsbG4ffu29Bu/Y8aMgYODQ7k/b5ieXrnfFyYiIpIzvRw2d3BwkB7En5CQoPHbqHXr1oWZmRlMTExgamqK2rVr48GDB7oqhYiI6Lmis563u7s74uLi4OvrCyEEQkJCpB87d3Nzw/Hjx+Ht7Q0DAwM4ODho/JISERERlU0293nzanMiInqR6OWwOREREekGw5uIiEhmGN5EREQyw/AmIiKSGYY3ERGRzDC8iYiIZIbhTUQvrK++2ghf34H46quN+i6FqFIY3kRPgB/+8pWTk43Dh38AABw+fAg5Odl6rohIewxvoirih7+85efno/gZVUKokZ+fr+eKiLTH8CaqIn74E5G+MLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimTHSVcNqtRpBQUFISkqCiYkJFi1ahGbNmgEAzp8/j5CQEGnehIQErF27Ft26ddNVOURERM8NnYV3dHQ08vLyEBERgYSEBISGhmLdunUAgNdffx2bN28GAPzwww+wtLRkcBMREWlJZ+EdHx8PZ2dnAICdnR0SExNLzPPw4UOEhYVhy5YtuiqDiIjouaOz8M7MzIRSqZSGDQ0NUVBQACOj/y1y586d6NOnD8zNzStsr379mjAyMtRJrURVYWKi1hhu0ECJunVr66kaqizuP5IznYW3UqlEVlaWNKxWqzWCGwC+//57rF69Wqv20tMfVmt9RE8qIyNTYzgtLRN5ebwGVC64/+hZZ2FR9pdJnb1THRwcEBsbC6DogjSVSqUxPSMjA3l5eXj55Zd1VQIREdFzSWc9b3d3d8TFxcHX1xdCCISEhCA8PBxWVlZwc3PDP//8g8aNG+tq8URERM8tnYW3gYEBgoODNcZZW1tLf7dr1w6ff/65rhZPRET03OIJHiIiIplheBMREckMw5uIiEhmGN5EREQyw/AmIiKSGYY3ERGRzDC8iYiIZIbhTUREJDMMbyIiIplheBMREckMw5uIiEhmGN5EREQyw/AmIiKSGYY3ERGRzDC8iYiIZIbhTUREJDMMbyIiIplheBMREckMw5uIiEhmGN5EREQyw/AmIiKSGYY3ERGRzDC8iYiIZMZI3wUQ0fPj9Myp+i5Ba9mFhRrDf3w0FzUMDfVUTeV0/GS1vksgPWPPm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkRmdXm6vVagQFBSEpKQkmJiZYtGgRmjVrJk0/duwY1q5dCwBo3bo1FixYAIVCoatyiIiInhs663lHR0cjLy8PERERmDlzJkJDQ6VpmZmZWL58OdavX48dO3agcePGSE9P11UpREREzxWdhXd8fDycnZ0BAHZ2dkhMTJSm/fHHH1CpVFi6dCmGDx+Ohg0bwtzcXFelEBHRc+irrzbC13cgvvpqo75Leep0dtg8MzMTSqVSGjY0NERBQQGMjIyQnp6OU6dOYe/evahZsyZGjBgBOzs7tGjRosz26tevCSMjeTxAgV4MJiZqjeEGDZSoW7e2nqqhF4mFBd9n2dnZOHz4BwBAdPQhTJ06ETVq1NBzVU+PzsJbqVQiKytLGlar1TAyKlpcvXr10LZtW1hYWAAA3njjDZw/f77c8E5Pf/wYGPsAACAASURBVKirUomqJCMjU2M4LS0TeXm8BpR0LzU1Q98l6F1GxgMIIQAU5cvNm3dRu3YdPVdVvcr7kqaz8HZwcMDRo0fRt29fJCQkQKVSSdNsbW1x8eJF3L17F3Xq1MGZM2fg7e2tq1JIZmZFBui7BK0U5hZoDC+ICoGhqTyeOLy8/yJ9l0BET0BnnzTu7u6Ii4uDr68vhBAICQlBeHg4rKys4ObmhpkzZ+Ldd98FAPTp00cj3ImIiKhsOgtvAwMDBAcHa4yztraW/u7Xrx/69eunq8UTERE9t3iCjoiISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwzvZ8CL/HxeIiKqPK3v8y4oKMAvv/yCe/fuaYwfOHBgtRf1IsnJ+d/zeQ8fPoThw/1gZvbiPJ+XiIgqT+vwnjlzJm7cuAFra2uN391meD+Z/Px86fm8QqiRn5/P8CYionJpHd5JSUk4dOiQLmshIiIiLWh9ztva2hq3b9/WZS1ERESkBa173jk5OdIPiJiYmEjjN23apJPCiIiIqHRah/f48eN1WQcRERFpSevD5o6OjlAqlTAwMIBCoYBarca1a9d0WRsRERGVQuued0BAAH799Vfcv38fr776Ki5cuAAHBwcMGTJEl/URERHRY7TueR8/fhwHDhxA7969sXDhQmzatAk5OTm6rI2IiIhKoXV4W1pawtjYGNbW1khKSkLbtm2RkZGhy9qIiIioFFofNn/ppZewYcMGdO7cGcuXLwcA5OXl6awwIiIiKp3WPe/FixejSZMmaNeuHXr16oXIyEgEBQXpsDQiIiIqTYU979TUVFhYWODBgwewt7fHjRs34ObmBjc3t6dRHxERET2mwvAOCAjAhg0bMHLkSCgUCuk53ACgUChw5MgRnRZIREREmioM7w0bNgAAYmJidF4MERERVazC8J47d26505csWVJtxRAREVHFKrxgzdHREY6OjsjKysLt27fh5OSErl274sGDBxqH0ImIiOjpqLDn7eXlBQDYunUrIiIiYGBQlPdvvfUWvL29dVsdERERlaD1rWIZGRm4d++eNHznzh08fPhQJ0URERFR2bR+SMuECRMwYMAAODg4QAiBhIQEBAQE6LI2IiIiKoXW4T1w4EB06dIFf/zxBxQKBYKCgtCgQQNd1kZERESl0Dq8Hzx4gMOHD+PevXsQQuDixYsAgMmTJ+usOCIiXTFUKKS/FY8NEz3rtA7vadOmoXbt2mjZsiUUWrzJ1Wo1goKCkJSUBBMTEyxatAjNmjWTpi9atAi///47atWqBQD4/PPPUbt27SqsAhFR5ZkYGMCulhIJWZloX0sJEwOtLwF6bv1n1SF9l6C1/HzNX7XcvD4GxsZmeqpGe2On96mWdrQO7zt37iA8PFzrhqOjo5GXl4eIiAgkJCQgNDQU69atk6afO3cOX3zxBczNzStXMRFRNXGrZw63evwMIvnR+qvm66+/jgsXLmjdcHx8PJydnQEAdnZ2SExMlKap1WpcvXoVgYGB8PX1xc6dOytRMhER0YtN6573pUuX4OXlhQYNGsDU1BRCiHKfbZ6ZmQmlUikNGxoaoqCgAEZGRnj48CFGjhyJ0aNHo7CwEP7+/rC1tUWrVq3KXH79+jVhZGRYiVWTBxMTtcZwgwZK1K3L0wekWxYWfI/JGfeffFXXvtM6vNesWVOphpVKJbKysqRhtVoNI6OixdWoUQP+/v6oUaMGAMDJyQkXLlwoN7zT05/Pe8ozMjI1htPSMpGXx3NvpFupqRn6LoGeAPeffFVm35UX9BWmxI0bN3Djxg0oFIpS/5XFwcEBsbGxAICEhASoVCpp2pUrVzB8+HAUFhYiPz8fv//+O9q0aaP1ChEREb3IKux5l/ZToMXKO2zu7u6OuLg4+Pr6QgiBkJAQhIeHw8rKCm5ubvDw8IC3tzeMjY3h6emJli1bPvnaEBERvQAqDG9tfgr03LlzJXrOBgYGCA4O1hhnbW0t/T127FiMHTtW2zqJiIjo/1XLyVU+JpWIiOjpqZbw5k+DEhERPT3VEt7aPHGNiIiIqgfvSSIiIpIZhjcREZHM8Jw3ERGRzGj9hDWg6BGp9+/f1wjrjh07IiwsrNoLIyIiotJpHd4ff/wxjh49iqZNm0rjFAoFNm3apDHuWTBt+X59l6A1dYHmz9rNW3MIBkbP/s/aAcBnswbouwQioheS1uEdFxeHQ4cOwcxMHsFCRET0vNL6nHfTpk15bpuIiOgZoHXPu27duujXrx/s7e1hYmIijV+yZIlOCiMiIqLSaR3ezs7OcHZ21mUtRLKiMHjk4USKx4aJiHRI6/D28vLCvXv3kJ2dDSEECgsLcf36dV3WRvRMMzA2hFJljsyLd6FsaQ4DY0N9l0RELwitwzssLAxff/01CgoKUL9+fdy6dQu2trb47rvvdFkf0TOtvuMrqO/4ir7LIKIXjNYXrO3ZswfHjh1D3759sWnTJqxbtw7169fXZW1ERERUCq3D29LSEkqlEi1btsSFCxfQo0cP3Lx5U5e1ERERUSm0PmyuVCqxd+9etGnTBlu2bIGlpSVycnIqfiERERFVK6173osXL0Z6ejo6deqExo0bY8GCBZg+fbouayMiIqJSaN3zbtiwIaysrAAA48aNg7W1Nfr27auzwoiIiKh0Wve8AwICEBUVJQ3/+uuvWLBggU6KIiIiorJp3fNOTEzE999/DwAwNzfH8uXL4eHhobPCiIiIqHRa97zVajVu374tDaelpcHAoFp+DpyIiIgqQeue94QJE+Dl5YUOHToAAM6cOYP58+frrDAiIiIqndbh7eHhAUdHRyQkJMDIyAgBAQGwtLTUZW1ERERUigrDOyIiAj4+PlizZo3G+PPnzwMAJk+erJvKiIiIqFQVnrTmb3gTERE9Wyrsefv6+gIAUlJS+NvdREREzwCtLxe/ePEisrKydFkLERGRVhQGj/4Er+Kx4eef1hesGRgYwMXFBS1atICpqak0ftOmTTopjIiIqCxGhsZo8lIbXL91Dk1eag0jQ2N9l/RUaR3es2bNqlTDarUaQUFBSEpKgomJCRYtWoRmzZqVmGfcuHFwc3PDsGHDKtU+ERG92GxaOMOmhbO+y9ALrQ+bOzo6wtDQEJcvX4adnR0UCgUcHR3LnD86Ohp5eXmIiIjAzJkzERoaWmKeVatW4f79+1WrnIiI6AWldXh/8803WLVqFb7++mtkZWUhMDAQX375ZZnzx8fHw9m56BuRnZ0dEhMTNaYfOnQICoUC3bp1q2LpRERELyatD5vv2bMHO3bsgLe3N+rXr4+dO3di6NChGDNmTKnzZ2ZmQqlUSsOGhoYoKCiAkZERLl68iMjISKxevRpr167Vavn169eEkdGLdUHCs87Cora+S6Aq4r6TN+4/+aqufVepC9ZMTEykYVNTUxgalh2mSqVS4+p0tVoNI6Oixe3duxe3bt3CqFGjkJKSAmNjYzRu3LjcXnh6+kNtS6WnJDU1Q98lUBVx38kb9598VWbflRf0Woe3o6Mjli5diuzsbERHRyMiIgJOTk5lzu/g4ICjR4+ib9++SEhIgEqlkqbNnj1b+jssLAwNGzbk4XMiIiItaR3es2fPxo4dO2BjY4O9e/eie/fu0gNcSuPu7o64uDj4+vpCCIGQkBCEh4fDysoKbm5u1VI8ERHRi6hSh8379++v0UO+ffs2XnnllTLnDw4O1hhnbW1dYr4pU6ZoWwIRERGhEuG9dOlS7NixA/Xq1QNQ9MxzhUKBI0eO6Kw4IiIiKknr8D5y5AhiY2NRq1YtXdZDREREFdD6Pm8bGxvk5eXpshYiIiLSgtY9b09PT/Tq1QsqlUrjFjE+2/wJKTQfrq85TEREVJLW4b1y5UrMnz+/zAvUqGoMDI1Rw+J1ZKeeRw2LVjB4wR6uT0RElad1eNeuXRsDBw7UZS0vrDpWnVHHqrO+yyAiIpnQOrxbt26NKVOmoFu3bjA2/l/vkIFORET0dGkd3tnZ2VAqlfj99981xjO8iYiIni6tw3vJkiUaw1lZWYiMjKz2goiIiKh8Wt8qVuzChQsICgpCt27dsGPHDl3UREREROXQquedm5uLAwcOYPv27UhKSoKBgQE2bNgAR0dHXddHREREj6mw571o0SK4ubnh8OHDGDlyJOLi4lC/fn0GNxERkZ5U2PM+dOgQ2rVrh169esHFxQVKpRIKheJp1EZERESlqLDnfezYMQwZMgTR0dHo3r07Jk2ahOzsbD4qlYiISE8qDG9DQ0O4urpizZo1iI6ORseOHWFhYQFnZ2csW7bsadRIREREj6jU1ebm5uZ4++23sW/fPnz55ZfIzc0FABw9elQnxREREVFJWt/n/ThbW1vY2toCAFavXg0XF5dqK4qIiIjKVun7vEsjhKiOZoiIiEgL1RLevPqciIjo6amW8CYiIqKnh+FNREQkMzznTUREJDNah/eUKVNKjBs1ahQAICIiovoqIiIionJVeKvY5MmTcf78edy+fRtubm7S+MLCQjRq1AgAYGpqqrsKiYiISEOF4R0aGop79+5h8eLFCAgI+N8LjYzQoEEDnRZHREREJVUY3kqlEkqlEuvWrcOlS5dw//596Rz3tWvX0LFjR50XSURERP+j9RPWgoODERMTg6ZNm0rjFAoFNm3apJPCiIiIqHRah/cvv/yCQ4cOwczMTJf1EBERUQW0vtq8adOmvCWMiIjoGaB1z7tu3bro168f7O3tYWJiIo1fsmRJqfOr1WoEBQUhKSkJJiYmWLRoEZo1ayZN//bbb7F7924oFApMmjSJP2xCRESkJa3D29nZGc7Ozlo3HB0djby8PERERCAhIQGhoaFYt24dAODu3bvYunUr9u7di9zcXPTr1w89evTgM9KJiIi0oHV4e3l54fr16/jrr7/QtWtX3Lx5U+PitcfFx8dLYW9nZ4fExERpmrm5Ofbt2wcjIyOkpKSgTp06DG4iIiItaR3eBw8exLp165CTk4Pt27fD19cXs2fPhqenZ6nzZ2ZmQqlUSsOGhoYoKCiAkVHRIo2MjLBlyxaEhYXBz8+vwuXXr18TRkaG2pZLT4GFRW19l0BVxH0nb9x/8lVd+07r8P7Pf/6Dbdu2YeTIkWjQoAH27NmD0aNHlxneSqUSWVlZ0rBarZaCu9jIkSPh7e2NsWPH4uTJk3Bycipz+enpD7UtlZ6S1NQMfZdAVcR9J2/cf/JVmX1XXtBrfbW5gYGBRk/a0tISBgZlv9zBwQGxsbEAgISEBKhUKmna33//jcmTJ0MIAWNjY5iYmJTbFhEREf2P1j3vli1bYsuWLSgoKMD58+exdetWtGrVqsz53d3dERcXB19fXwghEBISgvDwcFhZWcHNzQ2tWrWCj48PFAoFnJ2d4ejoWC0rRERE9LzTOrwDAwOxbt06mJqaYv78+ejUqRPmzJlT5vwGBgYIDg7WGGdtbS39PXnyZEyePLkKJRMREb3YtA5vU1NT2NnZYebMmbh79y5iYmJQq1YtXdZGREREpdD6RHNAQACioqKk4VOnTmHBggU6KYqIiIjKpnXPOzExEd9//z2Aovu0ly9fDg8PD50VRkRERKXTuuetVqtx+/ZtaTgtLY1XiBMREemB1j3vCRMmwMvLCx06dAAAnDlzBvPnz9dZYURERFS6St0qtnv3biQkJMDIyAgBAQGwtLTUZW1ERERUCq3D+/3338cPP/yA3r1767IeIiIiqoDW4f3aa69hzZo1aN++PczMzKTxHTt21ElhREREVDqtw/vevXs4deoUTp06JY1TKBTYtGmTTgojIiKi0mkd3ps3b9ZlHURERKQlre/1SklJwejRo9GrVy+kpqbC398f169f12VtREREVAqtwzswMBBjxoxBzZo10bBhQ/Tv37/cZ5sTERGRbmgd3unp6ejatSuAonPd3t7eyMzM1FlhREREVDqtw9vMzAz//vsvFAoFAOC3336DiYmJzgojIiKi0ml9wdrcuXMxfvx4XLt2DZ6enrh//z4+++wzXdZGREREpagwvG/duoVly5bh0qVLaNu2LT766CMolUq8+uqr7HkTERHpQYWHzefNmwdLS0vMmDEDarUau3btQqtWrRjcREREeqJVz/vLL78EALz55psYOHCgzosiIiKislXY8zY2Ntb4+9FhIiIievoq/YPcxVebExERkX5UeNj80qVLcHNzk4Zv3boFNzc3CCGgUChw5MgRnRZIREREmioM7x9//PFp1EFERERaqjC8Gzdu/DTqICIiIi1V+pw3ERER6RfDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkRuufBK0stVqNoKAgJCUlwcTEBIsWLUKzZs2k6V9//TUOHDgAAOjevTsmT56sq1KIiIieKzrreUdHRyMvLw8RERGYOXMmQkNDpWnJycnYv38/tm/fjoiICPzyyy+4cOGCrkohIiJ6ruis5x0fHw9nZ2cAgJ2dHRITE6VpjRo1whdffAFDQ0MAQEFBAUxNTXVVChER0XNFZz3vzMxMKJVKadjQ0BAFBQUAin6dzNzcHEIILF26FK1bt0aLFi10VQoREdFzRWc9b6VSiaysLGlYrVbDyOh/i8vNzcW8efNQq1YtLFiwoML26tevCSMjQ53USlVjYVFb3yVQFXHfyRv3n3xV177TWXg7ODjg6NGj6Nu3LxISEqBSqaRpQghMnDgRnTp1wrhx47RqLz39oa5KpSpKTc3QdwlURdx38sb9J1+V2XflBb3Owtvd3R1xcXHw9fWFEAIhISEIDw+HlZUV1Go1fv31V+Tl5eHnn38GAMyYMQP29va6KoeIiOi5obPwNjAwQHBwsMY4a2tr6e8///xTV4smIiJ6rvEhLURERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimdFZeKvVagQGBsLHxwd+fn64evVqiXnu3r2LXr16ITc3V1dlEBERPXd0Ft7R0dHIy8tDREQEZs6cidDQUI3pP//8M9555x3cuXNHVyUQERE9l3QW3vHx8XB2dgYA2NnZITExUXPBBgYIDw9HvXr1dFUCERHRc8lIVw1nZmZCqVRKw4aGhigoKICRUdEi33zzzUq1V79+TRgZGVZrjfRkLCxq67sEqiLuO3nj/pOv6tp3OgtvpVKJrKwsaVitVkvBXRXp6Q+royyqRqmpGfougaqI+07euP/kqzL7rryg19lhcwcHB8TGxgIAEhISoFKpdLUoIiKiF4rOet7u7u6Ii4uDr68vhBAICQlBeHg4rKys4ObmpqvFEhERPfd0Ft4GBgYIDg7WGGdtbV1ivpiYGF2VQERE9FziQ1qIiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLyJiIhkhuFNREQkMwxvIiIimWF4ExERyQzDm4iISGZ0Ft5qtRqBgYHw8fGBn58frl69qjF9x44dGDRoELy9vXH06FFdlUFERPTcMdJVw9HR0cjLy0NERAQSEhIQGhqKdevWAQBSU1OxefNm7Nq1C7m5uRg+fDjefPNNmJiY6KocIiKi54bOet7x8fFwdnYGANjZ2SExMVGadvbsWdjb28PExAS1a9eGlZUVLly4oKtSiIiInis663lnZmZCqVRKw4aGhigoKICRkREyMzNRu3ZtaVqtWrWQmZlZbnsWFrXLnf6orctGVL5gemZ8PfozfZdAVdR3U7i+S6AnMG/xUH2XQFrSWc9bqVQiKytLGlar1TAyMip1WlZWlkaYExERUdl0Ft4ODg6IjY0FACQkJEClUknT2rVrh/j4eOTm5iIjIwOXL1/WmE5ERERlUwghhC4aVqvVCAoKwsWLFyGEQEhICGJjY2FlZQU3Nzfs2LEDEREREEJg/Pjx6N27ty7KICIieu7oLLyJiIhIN/iQFiIiIplheBMREckMw/sZcObMGfj5+em7DKqk/Px8zJo1C8OHD8eQIUNw5MgRfZdElVBYWIi5c+fC19cXI0aMwLVr1/RdElVSWloaunfvjsuXL+u7lKdOZ/d5k3b+85//YP/+/ahRo4a+S6FK2r9/P+rVq4fly5cjPT0dXl5ecHNz03dZpKXixzJv374dp06dwpIlS6SnQNKzLz8/H4GBgTAzM9N3KXrBnreeWVlZISwsTN9lUBX06dMH06ZNk4YNDQ31WA1VVs+ePbFw4UIAwI0bN9CwYUM9V0SVsXTpUvj6+sLS0lLfpegFw1vPevfuLT28huSlVq1aUCqVyMzMxNSpUzF9+nR9l0SVZGRkhDlz5mDhwoW8XVVGdu/eDXNzc+kR3C8ihjfRE7h58yb8/f3h6ekJDw8PfZdDVbB06VL8+OOP+Oijj/Dw4UN9l0Na2LVrF44fPw4/Pz+cP38ec+bMQWpqqr7LeqrY5SOqojt37uCdd95BYGAgOnfurO9yqJL27t2LW7duYfz48ahRowYUCgVPfcjEt99+K/3t5+eHoKAgWFhY6LGip489b6IqWr9+PR48eIDPP/8cfn5+8PPzQ05Ojr7LIi316tUL//3vfzFixAiMGTMG8+bNg6mpqb7LItIKn7BGREQkM+x5ExERyQzDm4iISGYY3kRERDLD8CYiIpIZhjcREZHMMLzphfDxxx/D09MTffv2ha2tLTw9PeHp6Yldu3aV+ZqrV68iICCg3HavXr0Kd3d3jXFpaWlS+2+++Sa6du0qDT948KBSdf/+++/4+OOPtZr38uXLaNu2rbQsDw8PuLq6Vvi87suXL6NPnz6VqkuXLl26hHHjxsHDwwMeHh6YPXs27t+/X+5r/vnnHwQGBuq0ruvXr2PSpEk6XQaRtviQFnohLFiwAEDRB7C/vz/27dtX4WtSUlJw/fr1Si+rQYMGUvsrV66EqakpJk6cWOl2AMDBwQEODg5az9+4cWONdbtx4wZ69eqF/v37o2nTplWq4Wm6ceMGRo8ejSVLlsDZ2RlCCKxevRrTp09HeHh4ma9LTk5GSkqKTmtr0qQJ1q5dq9NlEGmLPW964WVlZWHGjBno378/PDw8sH//fgDAokWLcObMGSxatAj5+fmYN28evL294erqismTJyM3N7dKy/vtt98wePBgeHh44J133kFycjIePHiA7t2747fffoMQAn5+fti5cydiY2MxZswYAMDZs2el1/n7+2v1OMhbt27B0NAQNWvWBACsWbMGXl5e8PDwwMqVK0udf8KECRg0aBCGDh2KX3/9FUDRF5l33nkHQ4cORY8ePfD5558DABITEzF06FAMGjQII0aMkL7sHDlyBIMHD8bAgQMxbdo0PHjwAEIILFq0CAMGDMDAgQOxYcOGEsvfvHkzunfvLj2zWqFQYOLEiRg8eDDUanWZdSxevBh//PEHQkJCyl3PL7/8Er169cKQIUMwc+ZMbNy4EQBw+PBhDBgwAB4eHpg6dSru3r0LAOjWrRumTZuGt956C3/88Yd0hKKs7XTs2DF4eXlh0KBBePfddys8YkBUZYLoBZKcnCxcXFw0xoWEhIiQkBAhhBB37twRLi4u4tKlSyIuLk6MGjVKCCHEiRMnxMKFC4UQQhQWFgpfX19x+PBhceXKFdGzZ88yl/fpp5+KtWvXSsPZ2dmiW7du4vz580IIIfbs2SOGDRsmhBDip59+Ev369RPh4eHivffeE0IIcezYMfHOO+8IIYTo2bOniIuLE0IIER4eLj799FONZf3111/C1tZWDBgwQPTq1Us4OjqKcePGiePHjwshhDh8+LCYOXOmKCwsFAUFBWLKlCnihx9+EH/99Zfo3bu3EEKI9957T/z8889CCCFu3Lgh3NzcRE5Ojvj8889FZGSkEEKI9PR00b59e5GRkSHef/99ceTIESGEEBEREeLAgQPi1q1bYuDAgSIjI0MIIcTXX38tPv74Y3H58mXh5eUlhBAiIyNDTJs2TeTl5Wmsw6hRo8SWLVvK3J5l1fHodiprPc+cOSP69u0rMjMzxcOHD8XAgQPFhg0bxM2bN4Wzs7O4efOmEEKINWvWiA8++EAIIUSXLl2k5Wmznby9vcWFCxekWk+ePFnmuhA9CR42pxfeyZMnsWLFCgBFh7xdXFxw6tQptGjRQprHyckJ5ubm+Pbbb/H3338jOTm5Sj9icenSJTRq1AitWrUCAHh6euLjjz9GTk4OunfvjkOHDmHdunX44YcfNF7377//Ijs7G126dAEAvP3226W2X3zYvLCwEAsXLsS///4LJycnAMDx48cRHx8PLy8vAEBOTg5atmyJli1bamyLlJQULF++HEDRbyZfv34dEyZMwIkTJ/DFF1/g0qVLKCgoQE5ODnr06IGPPvoIR48eRY8ePeDi4oKoqCjcuHEDI0aMAAAUFhbC0tISTZo0QUFBAUaMGIEePXpg9uzZMDY21qjfwMCg3EeUllXHo8pazytXrqBnz56oVasWAOCtt94CACQkJOCNN95Ao0aNAAA+Pj4YOHCg1F67du1K1FHWdnJ1dcX48ePRs2dPuLm5oVOnTmWuC9GTYHjTC0+tVmsMCyFQWFioMe7w4cNYu3Yt/P39MWjQIKSmpkJU4cnCpb2msLAQz0lyEQAABF9JREFUQgio1WpcuXIFxsbGuHr1KszNzaV5jIyMoFAopOGcnBykpqaWeR7b0NAQ8+bNg4eHBzZv3gx/f38UFhbi3XfflUL1/v37MDY2xs2bN6Xa1Go1tm3bJh1mv3XrFiwsLBAcHIy0tDT07dsXvXv3xk8//QQhBAYMGICOHTsiJiYG//nPf3D8+HF06NABTk5O+Oyzz6Ras7OzYWJigt27d+PUqVOIjY2Ft7c3tm/fjiZNmkh129raIjExEUOGDJHGFRQUYMqUKViyZAk+++yzUut4fHuWtp5btmwpsa+Bive/mZlZiellbafx48fD3d0dx44dQ0hICLy8vPDOO++Uuo+IngTPedMLz8nJCTt37gQA3L17FzExMXB0dIShoaH0IR4XF4d+/fph0KBBUCqVOH36dKlBUJGWLVvi5s2buHDhAgBg3759sLGxQY0aNfD111/D0tISq1atwrx58zR6lA0bNkStWrVw+vRpAMB3332H9evXl7ssExMTzJo1C2FhYUhPT4eTkxP27NmD7Oxs5OXlYfz48Th69Kg0v0KhgKOjI7Zt2wYAOH/+PDw9PZGXl4fjx49j/Pjx6NOnD86fP4979+5BrVbjvffew6VLlzBixAhMnjwZ586dg729PX799VckJycDAFatWoWVK1ciISEB77zzDpycnPDhhx+iadOmuHr1qkbNw4cPx48//oi4uDgARcG6evVqZGdno169emXWYWRkJO2rstazS5cuOHr0KB4+fIjc3Nz/a+/+XVIL4ziOvxu0IxyDCEHh0FYIQUOBS5u0+aPljEIUIZJQFASBnLJAiFDP4uBkc1PR2l+gu4tRSw3h4BpBxxouCJd783LbDn5e83MOz/k+w+d8Hx54uL+/B34dCux0Ory+vgJwfX09tmMeV6eNjQ2GwyFbW1vkcjm63e7YNRL5KXXeMvH29vYol8tkMhk8z6NYLBKPxxkMBgwGA46Pj9nc3OTo6Ii7uzsCgQCrq6u8vLz810lwgFAoRK1Wo1Qq8f7+zuzsLNVqlaenJ1qtFjc3N0QiEVZWVnBdl7W1tdGz9Xqd8/Nz3t7emJubG23ZjrO+vs7V1RWNRgPHcej1eti2jed5JJNJUqkUj4+Po/FnZ2c4jsPt7S1TU1O4rothGBQKBfb395mensayLOLxOM/Pz+zu7nJ6eorrugSDQU5OTojFYpTLZYrFIsPhEMuyuLy8JBwOs7i4SDqdxjAMEonEH1epRqNRms0m1WqVi4sLPM9jeXl51MV/N4+FhQX6/T6lUolKpfLX7wSwbRvbtjFNk5mZGQzDIBqN4jgO+Xyej48P5ufnqVQqY+v6XZ0ODw85ODggEAhgmuY/3yPyU7pVTEQmwsPDA+12m1wux+fnJzs7O2xvb//2gyTiF9o2F5GJYFkWnU6HdDpNNptlaWlJwS2+pc5bRETEZ9R5i4iI+IzCW0RExGcU3iIiIj6j8BYREfEZhbeIiIjPKLxFRER85gsh0/DyJ+n0YAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=\"Total Toxic Releases Categories\", y=\"Percent_American_Indian\", data=df_chem_merge).set_title('American Indian Population and Toxic Releases', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=\"Total Toxic Releases Categories\", y=\"Percent_Asian\", data=df_chem_merge).set_title(\"Asian Population and Toxic Releases\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"Total Toxic Releases Categories\", y=\"Total Population\", data=df_chem_merge).set_title(\"Population by ZIP Code and Toxic Releases\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"Total Toxic Releases Categories\", y=\"Percent_125_PL\", data=df_chem_merge).set_title('Population Living Under 125 Percent of the Poverty Line and Toxic Releases', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAFrCAYAAABMlIr9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd1gU5/428HtZBESiWPBobFFDsQUrSAQUFDERJCbGjiXGht1EAVFQ1GDXiLEkefVEFFsswRIbFqIilqgkRnNMjhEEK02aArvz/sGPOa7AMuAWYe/PdXld7rAz853dmd17n+eZGZkgCAKIiIjIYBnpuwAiIiLSL4YBIiIiA8cwQEREZOAYBoiIiAwcwwAREZGBYxggIiIycBoLAwEBAbC1tVX516pVK3Ts2BGffvop9u/fr6lVvRZ3d3f4+vpWaN6UlBTk5OSIj4u2WZf27dsHW1tb7Nu3T9Lz4uLiJC87Li5O0rI1zd3dvdi+0759e3h7e2Pz5s1QKBQVWq6trS0CAgI0XG35JCYmqjx+nf1PqspyLJbm1eOsosLDwyUdA0XPu3///muv801T1rYVHfNS/mnajh07YGtri+vXr7/Wcv7+++8S623VqhUcHBwwZMgQHDlypELLXrFiBWxtbfHkyZPXqrEyMNb0AgMDA1G7dm0AgCAIyMrKQlRUFAICApCWlobPPvtM06vUibNnz+LLL7/E/v37YW5uDgAYNGgQnJyc9FxZybp06YJly5ahZcuWkudp2bIlli1bho4dO2qxspLVrl0bgYGBAAr3m+zsbFy5cgVLly7FkydP4O/vr/OaXtfevXuxYMECxMfH62X9lfFYLOk40zYPDw80bdoUderU0cn63iRFx/zLZs+ejRYtWmDChAlaXbeTkxOWLVuGpk2bamR5Xbt2xccffyw+ViqVSExMRGRkJGbMmAETExP06tVLI+uqijQeBnr16oXGjRurTBswYAA+/PBDfPPNNxg+fDhMTEw0vVqti4+Px7Nnz1SmdejQAR06dNBTReo1adIETZo0Kdc89erVg4+Pj5YqUs/c3LzYuocNGwZBEBAREYHx48fD0tJSL7VV1OXLl/HixQu9rb8yHoslHWfaZmdnBzs7O52u801R0jE/e/ZsnXwWvPPOO3jnnXc0trxmzZqVWHO/fv3g5eWFb775hmFADZ2MGTAzM4O7uzuysrJw584dXaySqghHR0fk5+cjISFB36VUCTwWydC888476NChA27duoW8vDx9l/PG0tkAQplMBgAq/b9XrlzBqFGjxF/YI0aMwOXLl1Xmc3d3R1BQEPbs2YOePXuiffv2GDx4MC5evFjseSX1xZbVRysIAnbs2IEBAwagQ4cOaNeuHfr06YNvv/0WRVdqDggIwLp16wAAPXv2FJdX0piBpKQkzJo1C127dkW7du3Qr18/7N69W+U5AQEB6NOnD+Lj4zF8+HDY29vj/fffx6JFi/D8+XO1r6NUL48ZePz4MVq1aoVFixYVe154eDjs7OyQlJRUbMxA0ePz589jwYIFcHJygr29PUaOHInbt2+rLCc/Px9r1qxBjx49YG9vj+HDh+P27dto3bo1wsPDK7wdDx8+hFwux9tvv60y/fTp0xg8eDDs7e3RpUsXTJkyBXfv3i1zeSdPnsTgwYPx3nvvoXPnzpgwYYK4LUqlEo6OjvDz81OZZ8mSJbC1tcXx48fFaUXPnT9/fonr8fX1FfvmSxq7cPDgQfTt2xdt27aFp6cnduzYUWwZ165dw+jRo8Xj47PPPtNIl0NFjsWQkBC0bt0aqampKsvKzc1F+/btxS4eqXW7u7tj7ty5mDNnDtq1awdXV1eMGzeu2HG2c+dO2Nra4uzZs8W249NPP8WAAQNe+/V4tV89PDwc7dq1wz///IPx48ejQ4cO6NKlC/z9/ZGWlqYyb0ZGBhYuXAgXFxe0bdsWH3zwAX744QdIucr7zZs3MWXKFLz//vto06YNnJyc8MUXX+Dhw4cqtUmtJSEhAVOmTEGXLl3g6OiI1atXS6qjvFJSUjBv3jw4OzuL27x582YolUoAQFZWFnr06AFHR0eV/SU2NhZ2dnbivlLSmIEXL15g9erV6NWrF9577z306dMHW7ZsEZddUUVdTi8vJzU1FSEhIeJ29O3bF9u3by9zWVLni4+Px6RJk+Dk5IQ2bdqgW7dumDVrFh4/fqzyvK1bt8LLywv29vZwdHTE1KlT8d///lflObm5uVixYgXc3d3Rtm1beHh44JtvvkFBQYHK8w4fPoyPP/4YHTp0QOfOnTFmzBjcuHFD0mukkzCgVCpx6dIlmJiYiH3Y0dHR8PX1xYMHDzBx4kRMnDgRDx48wKhRoxAdHa0y/4ULFxAaGgpPT09MmzYNqamp+Pzzz3Hp0qXXrm3NmjWYP38+3n33XQQGBmLmzJkwNTXFypUrceDAAQCFYwM8PDwAFPbDltaXlpiYiAEDBiA6OhoDBw7E7NmzUatWLcybN69Yv1xqairGjBmDFi1aICgoCB07dkRERATWrl372tv0qvr168PR0RHHjh0rdlAdOXIEHTt2RKNGjUqdf+7cufjjjz/g5+eHsWPH4saNGxg7dqzKjvjll19iw4YN6Nq1K2bPng0zMzOMGDFC8kGsVCqRmpoq/ktKSsKBAwewdetWDB48GPXq1ROfu2/fPkycOBHVq1fHrFmzMGrUKFy7dg0DBw5UGwi2b9+OSZMmIT8/HzNnzsSoUaMQHx+PIUOGID4+HkZGRnB2dsbly5dV6i7az65cuSJO++2335Ceno4ePXqUuK4JEyagc+fOAIBly5Zh0KBBKvMuWrQIffr0QWBgIExMTDB//nycPHlSfM758+fh6+uLzMxMTJs2DRMnTkRycjKGDRumUkd5VfRY9Pb2hkKhwLFjx1SWd/r0aeTm5qJfv37lrvvw4cO4ffs2goKCMHDgQEycOLHYcdanTx9Uq1YNP//8s8q8iYmJiI+Ph5eXV4Vfi7JepxEjRqBGjRrw9/dH7969ceDAAZXwl5OTg+HDh+Onn35C//79MWfOHFhbW+Orr75CaGio2uX/+eefGDp0KO7du4dx48YhODgYrq6uOHz4MGbNmlXuWp4+fSr+SBo5ciTGjh2LgwcPYuvWrZp8WZCamoqBAwfiwIED6Nu3LwIDA9G0aVMsXbpUDLwWFhYIDQ1Feno6li9fDgDIzs5GUFAQGjZsiKCgoBKXLQgCxo0bh40bN6Jz584IDAxE27ZtsWTJEqxZs6bCNWdlZeHXX39FixYtYGZmJk4bOnQojh49igEDBiAwMBDvvPMOQkNDsWTJErXLkjLf77//jmHDhuHBgweYMGEC5s2bBycnJxw8eBBz5swRn7dnzx4sXrwY9vb2mDt3LkaOHIm4uDiMGDFCHERbUFCAzz//HBEREfDw8EBQUBA6deqE8PBwzJw5U1zW+fPn8eWXX+Ltt99GQEAAJk6ciP/+978YOXIkHjx4UPYLJWiIv7+/YGNjI9y8eVNISUkRUlJShMePHwvXrl0Tpk2bJtjY2AhfffWVIAiCkJ+fL7i6ugrdu3cXMjMzxWVkZGQILi4ugouLi5CXlycIgiC4ubkJNjY2wokTJ8TnpaSkCJ07dxYGDhwoTnNzcxOGDx9erK5Xp7/8OC8vT+jYsaMwY8YMlXkyMzOFtm3bCuPHjxenrV27VrCxsRESExOLbXOR6dOnC3Z2dsLvv/8uTlMoFML48eMFW1tb4T//+Y/KfFu3blVZ7wcffCA4OzuX+hoLgiDs3btXsLGxEfbu3SvpeRcvXhQEQRB+/PFHwcbGRrh8+bL4nFu3bgk2NjZCZGSkIAiCcPHiRZVlFz3+5JNPhIKCAnG+TZs2CTY2NsK5c+cEQRCEy5cvCzY2NsKqVavE5yiVSmHSpEmCjY2NsHbtWrW1Fr3HJf3z9PQUUlJSxOdmZmaW+J49fvxY6NKli+Dn5ydOs7GxEfz9/QVBEITU1FTB3t5eGDBggPDixQvxOYmJieJ0QRCE/fv3CzY2NkJ8fLwgCIX7pJ2dneDi4iL0799fnC88PFxo166dkJubW+p2vbp/FG2rra2tyj5y//59wdbWVpg1a5YgCIX7TM+ePYXBgwervO7Z2dmCh4eH4OPjo/b11MaxqFQqBTc3N2HEiBEq65o0aZLQrVs3QaFQlKtuNzc3wc7OTrh3757K8ko6zsaPHy907txZ5X3buHGjYGdnJzx8+LDU16FoWUXHQFnPK1pn0eOwsDCV540ZM0Zo3bq1kJOTIz6vTZs2wu3bt1Wet3LlSsHGxka4detWqesMDg4W7O3thbS0NJXpM2bMEGxsbMTpUmtZsmRJsf3q6dOnQteuXYu9nmWxsbEp8bNUEARh0aJFgo2NjXD27FmV6YGBgYKNjY0QGxsrTgsICBBsbW2FK1euCCEhIYKtra3K3yMjIwUbGxvh2rVrgiAIwvHjxwUbGxth8+bNKsueMmWK8N577wlZWVkl1vTXX3+Jx3rR/p6SkiI8evRIuHz5suDr6yvY2NgIR44cEedZvny50K5dO+Gvv/5SWdZXX30l2NraitOXL18u2NjYCI8fPy7XfAEBAULHjh1VjilBEAQ/Pz/B1tZWyM7OFgRBEHx9fVU+V4peh759+wo3btxQeZ1e3Y9/+OEHwcbGRoiJiRHX6ejoqPKc+Ph4wdPTU4iOji7xtXuZxlsG+vfvDycnJzg5OcHZ2RmDBg0Sf3l88cUXAIA//vgDDx8+xLBhw2BhYSHOW7NmTQwfPhyPHj3C77//Lk5v0aKFysCPOnXqwMfHBzdu3EBKSkqFa61WrZrY6vCytLQ0WFhYlOv0JoVCgTNnzsDZ2Rlt2rQRpxsZGWHChAkQBAGnTp1SmeeDDz5QeWxnZ/da26OOp6cnTE1NVX5hHTlyBNWqVUOfPn3Uztu7d2/I5XLxcatWrQBAPN3mxIkTAIDRo0eLz5HJZBg7dqzk+urVq4ctW7aI/9avXy+2An388cdi0+n58+eRlZWFXr16qbQkyOVydO3aFefOnSvWdAYUNlHm5uZi9OjRKoPmGjdujH79+iE+Ph6PHz+Gi4sLZDKZ2A116dIlGBkZid0eWVlZAIBz586ha9eu4i+N8njnnXdU9pFGjRqhTp06ePr0KYDC4yMxMRG9evVCRkaGuI3Pnz+Hm5sbbt26pdKUXBpNHosymQxeXl64fPmyuI9mZWUhJiYGXl5eMDIyKnfdTZs2lTSS3NvbG8+ePcP58+fFaYcPH0aXLl3wr3/9q8z5K+rV47NVq1YoKChAeno6AOD48eOwsbGBlZWVyr5Y9Fl1+vTpUpc9f/58nDp1SmVQbFZWFkxNTQGg2GdPWbXExMSgXbt2KvtV3bp10bdv3/JutlqnTp1C69at4erqqjK9qGvt5datwMBA1KtXD7NmzcLOnTsxYsQIdO3atdRlnz59GtWqVcOQIUNUpgcFBWH//v1lHmv79+8X93cnJye4uLhg2LBhSElJwdq1a1Vew+PHj6N169aoXbu2ynvn4eEBQRBK7JYqz3yLFy/GiRMnVI6pzMxMmJmZQRAE5ObmAgAaNGiAP//8Exs2bEBycjKAwrNbDh06hPfee09cZ4MGDWBtba2yTjc3N8hkMpw5c0ZcVlpaGpYsWSK2kLZr1w5Hjx6Fu7u72tcO0MLZBMuXLxebdI2MjFCzZk20bNlS3MkBiH1zzZs3LzZ/ixYtAADJycniSP1333232POaNWsGQRCQlJSEunXrVrjeatWq4cyZM4iOjsbdu3dx7949ZGRkAEC5+tvS0tKQk5NT4jYVNccmJSWpTH/1VCYTE5MKn1NfFgsLC/To0QPHjh1DUFAQjIyM8PPPP8PZ2Vk8/aw0JdUJ/K//7d69e7C0tCw22r/ovZTC1NQU77//vsq0nj17okOHDhg1ahQ2bNiABQsWiAMJZ8yYUeqyUlNTUb9+fZVpRftcSTUVvT/Jyclo37492rRpg4sXL2Ls2LGIi4tD69at4ezsjJUrV+LXX39F+/btER8fX2pzZ1lK2l/NzMyQn58PAOI2Llu2rFj3UpEHDx6gQYMGatej6WPR29sbmzZtwvHjxzFkyBCcPHkSL168gLe3d4Xqlnrcuru7w9zcHEePHoWbmxv+/vtv/PnnnyWOgdGk0vb7omM0ISEBz58/L/X0YnVNszKZDGlpadi0aRP+/PNPJCQkIDk5WfzMebV7raxakpKS0LNnz2LrKc8xWBZBEJCcnAx7e/tif2vcuDHMzMzELzSgMFAGBQVh+vTpsLKyEgNoaZKSklC/fv1iX/pSA5+bmxtGjBgh1vn9998jNzcXoaGh6NSpk8p23L9/H/fu3Sv1vXt5Oyoyn5GREVJSUrBhwwb85z//QWJiYonv79SpU/Hbb79hzZo1WLNmDWxsbODu7o5PP/1UPBMoISEBDx8+LHOdo0aNQmxsrPiDqmnTpnBzc8OAAQNgY2NT5uun8TDQsWPHYqczvUrdl2zR36pVqyZOe/n/RYoOgpd/sZZE3ZerIAiYNWsWDh06hE6dOqFDhw4YNGgQunTpgpEjR6pdbml1l6TojX/1NC4jI91eANLb2xvHjh3D1atXYWZmhoSEBEyfPr3M+cqqMz8/v8T36OUvnYpycnJCrVq1cO3aNQD/ey0XLlxY6n5Wq1atcq3j1X3O1dUV//73v5Gfn4+4uDg4OzvDzs4ONWvWxNWrV5GTkwOFQoHu3btXaJvKej2LtnHatGlo3759ic+R8iGv6WPR2toatra2+PnnnzFkyBD8/PPPaN68ufhrtLx1l3XsFqlevTp69eqF6Oho5OXliS1avXv3ljR/RRUNtCyNQqFAp06dMHny5BL//mogfdmZM2fg5+eH+vXro2vXrnB1dUXbtm1x7tw5bNq0qdy1yGSyEk9jLc8PmrKUta8IglDsc+Dq1asACsc0/Prrr2qvy6JUKl/rM7F+/foqPyh69uyJAQMGYMyYMdi6dav4S1sQBCiVSnTt2hXjx48vcVklBZDyzHf8+HFMmzYNDRs2hKOjI3r06IF27drh5MmT2LJli/j8xo0b49ChQ4iNjcWpU6fwyy+/YOPGjdiyZQt++OEHdOjQAUqlEtbW1ipjDV5W9GOuVq1a2LlzJ65evYqTJ0/i3Llz+OGHHxAREYE1a9bA09NT7eun8TAgRdFgtVdHTAIQmzde/tVT0mll9+7dg1wuFz/sjIyMip02UlBQgLS0tFKbIq9cuYJDhw7Bz88P06ZNU5kvPT29XOfp16lTB+bm5pK3SR+6d++OmjVr4uTJk5DL5TA3N5fUfFSWJk2a4MKFC8jKylJpFvvnn39ee9lA4UFY9CFRtO/UqVOnWEtCXFwclEpliefOv7zPvXpOedF7VvT+dO/eHevXr8cvv/yC//znP5g5cyaMjIzQqVMnsZn83XffLfOLtqKKajU3Ny+2jfHx8cjIyKhQ94S6dUndb729vbF69WokJibi/PnzmDhxok7q9vLyQlRUFC5fvozo6Gi4uLiUO/RpWqNGjZCdnV1sWzMyMhAbG4tmzZqVOu/ChQvRrFkz7N27V+XiSgcPHqxQLY0bNy7xeHv1Cpivw8jICA0bNixxX0lKSsKLFy/QsGFDcdrVq1exfft2DBkyBLGxsZg7dy4OHjxY6sWkGjZsiGvXriEvL0/lGL5+/Tq2bduGyZMnl+u6BHXq1MHy5csxfPhwzJw5E1FRUTA3Nxe3Izc3t9h7l5qaisuXL5fYUlae+UJDQ2FtbY3du3er7PMvn1kmCAL+/PNPyOVyODs7w9nZGQBw8eJFjB49Gtu2bUOHDh3QqFEj/PPPP3ByclIJhS9evEB0dLR4ptV///tf5OTkoFOnTujUqRP8/f1x+/ZtDB8+HFu2bCkzDOjl3gRt2rSBlZUVduzYIfbBAoV9ZpGRkbCyskLbtm3F6b/99pvK6SdPnz5FVFQUunbtKn4g1KtXD3fv3lU5Ne/UqVNqL/pS1N/2ajfE7t27kZubq9L3XPRlVFo6lsvlcHFxwfnz53Hz5k1xuiAI+O677yCTyUodea4rJiYm8PT0FLtFPDw8UL169dderoeHB5RKJSIjI1WmSzlNpyxnz57Fs2fP4ODgAAB4//33YWpqiu+//15sVgeAR48ewc/PDytWrCjxV1TRfFu2bFEJjQ8fPsTBgwfx3nvvic3W7733HiwtLbF+/XrIZDKxidHBwQHx8fGIiYmR1CpQtM+U97Sotm3bwsrKChEREcjOzhanZ2VlYfr06QgMDJT8q7os5T0Wvby8oFQqsXjxYuTn54tdBJqqu7TjrFu3bqhTpw727NmD27dva+0sgvJwd3fH7du3xT7bIhs2bMC0adPUXschPT0db7/9tsoX44MHD8TTV8vbXdi7d2/cuXMHMTEx4rTMzEz89NNP5VpOWYrGfry8HgD49ttvAUD8jHvx4gWCgoJQv359fPnllwgJCcH9+/exatWqUpfdvXt35Ofn48cff1SZvn37dhw9elTljCKpOnXqhBEjRiAxMVHljAR3d3fcuHEDsbGxKs8PDw/H1KlTS/0hI2U+QRDw7NkzseukSGJiojhuTKFQQCaTwc/PD4GBgSqfEW3btoWxsbF4rLi7u+PJkyfFXpeIiAjMmDFDPAU4JCQEkydPFscjAIXfbRYWFpI+L/TSMlCtWjXMmzcP06dPxyeffCKeK/zjjz/i8ePHWLt2rUpzkYmJCcaOHYuRI0fCzMwMkZGRUCqVmD17tvgcLy8vLFy4EJ9//jn69euHe/fuYffu3WpPmevQoQMsLCwQFhaG5ORk1KxZE3FxcThy5AhMTU1VPtCK+uy+//57uLq6ltg/9+WXXyIuLg6+vr7w9fWFlZUVTpw4ISa9ksY+VNT+/ftLvKZ3q1atig3AeZm3tzf27NkDoPCUQU3o1q0b3NzcsHLlSty9exft2rXDhQsX8MsvvwAou4kTKBww9fIHl1KpxO3bt7Fnzx7Url0bY8aMAVD4PsycORNhYWEYNGgQ+vXrh4KCAkRGRuLFixelXra4du3a4nxDhgyBt7c3srOzsWPHDiiVSpXXougUw0OHDqFNmzZ46623ABSGgfz8fDx69EhSsCvaZ9auXQtHR0fJl65++fj4+OOPMWDAAJiammLPnj1ITk7GihUrYGysmUO3vMdiw4YN0aVLF5w+fRrt27dXaXXTRN2lHWfGxsb44IMPsH379nK3aG3ZsgWHDx8uNt3JyanYwLzyGD9+PI4fP47Jkydj8ODBsLa2xtWrV/HTTz/B1dW12CC7l7m6uuLIkSMIDg5Gu3btcP/+ffFHCACVzx4pRo8ejaioKEyZMgUjR45EnTp1sGvXLo1fZ8DPzw/R0dGYNGkShg0bhiZNmuDcuXM4deoUvLy8xAGCa9euxd27d7Fu3TpYWFjg/fffR9++fbFt2zb06dNHPO32ZX369MHOnTuxcOFC8Rolly9fxqFDhzBz5kyVVsfymDZtGo4fP46IiAh4eXnhvffeE7dj/PjxGDJkCFq0aIFLly7h0KFD6NWrV6kDHaXMJ5PJ4OzsjFOnTiE0NBStWrVCQkICdu/eLf4QKXp/x4wZg9DQUIwZMwa9evWCUqnE/v37oVQqxc/xoUOHIioqCsHBwYiPj0fbtm3xxx9/YM+ePbC3txdP6x09ejT8/PwwfPhw+Pj4wNjYGMePH8eDBw8kXc5dL2EAKBzdvnnzZqxfvx7ffPMNjI2NYW9vj8WLFxfbUdq3b4++ffti/fr1yMzMROfOnfHFF1+oNPcOHToU6enp+PHHH7Fw4ULY2dlh3bp12Lx5c6lnBdSrVw/ffvstVqxYgfXr18PExATNmzfHqlWrEB8fj61bt+Lp06eoV68e+vbti+PHj2Pfvn24dOlSiWGgadOm2L17N9asWYOdO3fi+fPnaNmyJRYvXqyRi6O87NKlSyVeZ6Fnz55qw0CXLl3QoEED5OfnF2vqeh2rV6/G6tWrcfjwYRw6dAgdOnTAqlWr4OfnJ+mSt2lpaSrhrlq1arCyskLv3r3h5+en0oc3atQo/Otf/8KWLVuwevVqmJmZoU2bNli+fLnKQKFXjRo1CvXr18fmzZuxatUqVK9eHQ4ODpg8eXKxi0e5uLjg0KFDKvtiq1atxGAg5f4NQ4YMwcWLF/H999/jt99+K9d9LIqOjw0bNmD9+vUwMjKCtbU1NmzYADc3N8nLKc+6pByLQGGgvHTpUom/zl+3bnXHmbe3N7Zv3w53d/dytWiVNqrf1NT0tcKApaUldu3ahbVr1+Lo0aPYtWsX3n77bfj5+WHcuHFq+7/nz58Pc3NznDp1Cj/99BMaNGiAjz76CB4eHuJ+07p1a8m1WFhYIDIyEsuXL8euXbugUCjw4YcfwtraWqMDLevWrYtdu3ZhzZo1iIqKQmZmJpo1a4bAwECMGDECQOE59lu2bIGbm5t43Qig8GJrZ8+eRVBQUIktFnK5HJs2bcK6detw5MgRHDhwAM2aNcPChQvx6aefVrhmc3NzBAcHY8KECZg7dy727dsnbsfXX3+Nw4cPIyMjA2+//TamTJmCsWPHlvoDRup8ixYtwrJly3Ds2DHs27cPDRo0wMCBA+Hi4gJfX19cvHgRLVu2xLBhw2BiYoLIyEisXLkSQGHLwObNm8UB9GZmZoiIiEB4eDhOnDiB/fv3o379+vD19YWfn584Nsvd3R3r1q3D999/j/DwcOTn58PW1hZr164ts4sAAGSCpqOjhrm7u6NRo0aIiIjQdylUiszMTJiYmBQbMPj777/jk08+0UoYIsNz48YNDBw4EN9++22FB28SUcn0MmaAqpbjx4+jffv2+PXXX1WmFzXNFo3iJXodO3fuRP369cWBVkSkOXrrJqCqw83NDW+99RZmzJiBYcOGwdLSEtevX8e+ffvQr18/See4EpVm7ty5SExMxMWLFxEQEKCxwZNE9D8MA/Ta6tSpgx07diA8PBxbt27Fs2fP0KhRI8ycOVMc+EdUUSkpKYiPj8egQYPEfmki0qw3fswAERERaRfHDBARERm4N7qb4MmTTH2XQEREpFNWVm/pfJ1sGSAiIjJwDANEREQGjmGAiIjIwDEMEBERGTiGAYa276UAACAASURBVCIiIgPHMEBERGTgGAaIiIgMHMMAERGRgWMYICKiSiM9PQ1LloQiIyNd36VUKQwDRERUaRw8uB937vyJqKh9+i6lSmEYICKiSiE9PQ3nzp2FIAg4dy6GrQMaxDBARESVwsGD+6FUFt5oV6lUsnVAgxgGiCqA/ZZEuhcbex4KRQEAQKEoQGzseT1XVHUwDBBVAPstiXTPyakb5PLCm+3K5cZwcuqm54qqDoYBonJivyWRfnh794eRkQwAYGRkhH79PtZzRVUHwwBRObHfkkg/LC1rw9m5O2QyGZydXVGrlqW+S6oyGAaIyon9lkT64+3dH9bWtmwV0DCGAaJyYr8lkf5YWtZGQEAwWwU0jGGAqJzYb0lEVQ3DAFE5sd+SiKoaY30XQFQZeXv3R1LSfbYKEFGVIBMEQdB3EaV58iRT3yUQERHplJXVWzpfJ7sJiIiIDBzDABERkYFjGCAiIjJwDANEREQGjmGAiIjIwDEMEBERGTiGASIiIgPHMEBERGTgGAaIiIgMHMMAERGRgWMYICIiMnBavVHRpk2bcOrUKeTn52PIkCFwcHBAQEAAZDIZrK2tERISAiMj5hEiIiJ90to3cVxcHK5du4YdO3YgIiICDx8+RFhYGKZPn47IyEgIgoDo6GhtrZ6IiIgk0loYOHfuHGxsbDBp0iRMmDABPXr0wM2bN+Hg4AAAcHV1xYULF7S1eiIiIpJIa90EaWlpSE5OxsaNG3H//n1MnDgRgiBAJpMBAGrUqIHMTPW3KK5d2xzGxnJtlUhERETQYhiwtLREixYtYGJighYtWsDU1BQPHz4U/56dnY2aNWuqXUZaWo62yiMiInojWVm9pfN1aq2boFOnTvjll18gCAIePXqE3NxcODk5IS4uDgAQExODzp07a2v1REREJJFMEARBWwtftmwZ4uLiIAgCZsyYgcaNG2PevHnIz89HixYtsGjRIsjlpXcDPHmivhuBiIioqtFHy4BWw8DrYhggIiJDU6W6CYiIiKhyYBggIiIycAwDREREBo5hgIiIyMAxDBARERk4hgEiIiIDxzBARERk4BgGiIiIDBzDABERkYFjGCAiIjJwDANEREQGjmGAiIjIwDEMEBERGTiGASIiIgPHMEBERGTgGAaIiIgMHMMAERGRgWMYICIiMnAMA0RERAaOYYCIiMjAMQwQEREZOIYBIiIiA8cwQEREZOAYBoiIiAwcwwAREZGBYxggIiIycAwDREREBo5hgIiIyMAxDBARERk4hgEiIiIDxzBARERk4BgGiIiIDBzDABERkYEz1ubCP/roI7z11lsAgMaNG2PQoEFYvHgx5HI5nJ2dMXnyZG2unoiIiCTQWhh48eIFACAiIkKc5uPjg/DwcDRp0gTjxo3DzZs30aZNG22VQERERBKoDQOBgYFqZw4LCyv1b7dv30Zubi4+++wzFBQUYMqUKcjLy0PTpk0BAM7OzoiNjWUYICIi0jO1YcDBwaHCCzYzM8OYMWPw6aef4p9//sHYsWNRs2ZN8e81atRAYmKi2mXUrm0OY2N5hWsgIiKisqkNA/379xf/f//+ffz1119wdnbGgwcP0KRJE7ULbt68OZo1awaZTIbmzZvjrbfeQnp6uvj37OxslXBQkrS0HCnbQEREVGVYWb2l83VKOpvgyJEjmDhxIhYvXoyMjAwMHjwYP/30k9p5fvzxRyxZsgQA8OjRI+Tm5sLc3BwJCQkQBAHnzp1D586dX38LiIiI6LXIBEEQynpS//79ERERgeHDh+PAgQN4/PgxRo8ejcOHD5c6T15eHgIDA5GcnAyZTIYvv/wSRkZG+Oqrr6BQKODs7IwZM2aoXe+TJ5nl3yIiIqJKTB8tA5LOJjAyMoKFhYX4uH79+jAyUt+oYGJigpUrVxabvnv37nKWSESkOenpadi4MRwTJ05FrVqW+i6H6I0gqZvA2toa27ZtQ0FBAW7duoV58+bBzs5O27UREWncwYP7cefOn4iK2qfvUojeGJLCQHBwMB49egRTU1PMmTMHFhYWCAkJ0XZtREQalZ6ehnPnzv7fuKUYZGSklz0TkQGQNGbghx9+gJeXF+rWrauLmkQcM0BEmhQRsRkxMWegUBRALjeGq2sP+Pp+pu+yiFS8sWcTPHz4EJ9++ik+//xzREVFITc3V9t1ERFpXGzseSgUBQAAhaIAsbHn9VwR0ZtBUhjw9/fHqVOnMGHCBFy/fh0fffQRZs+ere3aiIg0ysmpG+TywnHTcrkxnJy66bkiojeD5LsWCoKA/Px85OfnQyaToVq1atqsi4hI47y9+8PISAag8Cypfv0+1nNFRG8GSacWLlq0CCdOnECrVq3Qr18/zJ07F6amptqujYhIoywta8PZuTvOnImGs7MrTy0k+j+SwkCzZs2wf/9+1KlTR9v1EBFplbd3fyQl3WerANFLJJ1NkJeXh82bN+Pu3buYN28e/v3vf2PcuHEwMTHRanE8m4CIiAzNG3s2QWhoKHJycnDz5k3I5XIkJCRgzpw52q6NiIiIdEBSGLh58yZmzpwJY2NjVK9eHUuXLsXt27e1XRsRERHpgKQwIJPJkJeXB5mscBRuWlqa+H8iIiKq3CQNIBwxYgRGjx6NJ0+eYPHixTh58iT8/Py0XRsRERHpgKQBhADw119/IS4uDgqFAg4ODjq5UREHEBIRkaHRxwBCyWHgVePGjcO3336r6XpUMAwQEZGheWPPJijJlStXNFkHERER6UmFwwARERFVDQwDREREBk7t2QTu7u4lnkIoCAKeP3+utaKIiIhId9QOIExKSlI7c6NGjXDz5k20adNG44UBHEBIRESGp1KdTVCkf//+2L9/v6bqUcEwQEREL0tPT8PGjeGYOHFqlb3rZKU6m6DIa2YJIiIiyQ4e3I87d/5EVNQ+fZdSpbx2GOBliYmISBfS09Nw7txZCIKAc+dikJGRru+SqgyeTUBERJXCwYP7oVQWtkYrlUq2DmgQwwAREVUKsbHnoVAUAAAUigLExp7Xc0VVB8cMEBFRpeDk1A1yeeEZ8XK5MZycuum5oqpDUhgYO3Ysfv75Z+Tl5RX7W3h4uMaLMgTp6WlYsiSUfV5ERBJ5e/eHkVHhODUjIyP06/exniuqOiSHgV9++QV9+vTBggULEB8fL/6tSZMmWiuuKuOIWCKi8rG0rA1n5+6QyWRwdnatsqcW6oOkMODg4ICvvvoKR44cgb29PaZOnQovLy/8+9//LrG1gNTjiFgioorp3t0dZmZm6NGjp75LqVIkjxmIi4tDaGgoVq9eDRcXFwQFBSElJQUTJ07UZn1VEkfEEhFVzNmzp/D8+XOcOROt71KqFElhwM3NDevWrYODgwOOHTuGhQsXwsnJCTNmzEBqaqq2a6xyOCKWiKj82KqqPZLCwKZNmxAREYGPPvoIZmZmAIDr16/DyMhIa5cirso4IpaIqPzYqqo9asPA1atXcfnyZUyZMgVXrlzB5cuXcfnyZcTGxsLf319XNVY5HBFLRFR+bFXVHrW3ML5w4QIuXbqEx48f4+uvv/7fTMbGGDRokNaLq6qKRsSeORPNEbFERBI5OXVDTMwZKBQFbFXVMLVhYMqUKQCAAwcO4KOPPir3wlNSUvDxxx9j8+bNMDY2RkBAAGQyGaytrRESEgIjI8O9AKK3d38kJd1nq0AlZQh3TiN603h798e5c2ehULBVVdMkfRt/99135V5wfn4+goODxTEGYWFhmD59OiIjIyEIAqKjDXskqKVlbQQEBPOLpJLidSKIdI/XGdAeSWGgSZMmCAwMxM6dO3HgwAHxnzpLly7F4MGDUb9+fQDAzZs34eDgAABwdXXFhQsXXrN0Iv3giGYi/fH27g9ra1u2CmiY2m6CIrVr1wYA3LhxQ2V6aV0H+/btQ506deDi4oJvv/0WQOE9DIpud1yjRg1kZmZKWK85jI3lUkok0pk9eyLEe3IIghInThzi9TaIdMTK6i2sXLlc32VUOZLCQFhYGAAgIyMDtWrVKvP5e/fuhUwmQ2xsLG7dugV/f3+V6xFkZ2ejZs2aZS4nLS1HSnlEOnX69BkUFBSOaC4oKMCpU6cxYMBwPVdFRFWFldVbOl+npG6C27dvo0+fPvDx8cGjR4/g4eGBmzdvlvr87du3Y9u2bYiIiECrVq2wdOlSuLq6Ii4uDgAQExODzp07a2YLiHSM14kgoqpGUhhYuHAhvvnmG1haWuJf//oX5s+fj5CQkHKtyN/fH+Hh4Rg0aBDy8/Ph6elZoYKJ9I3XiSCiqkZSN0Fubi5atmwpPu7WrRuWLl0qaQURERHi/7dt21bO8ojePLxOBBFVNZLCgKWlJW7fvi0OAIyKipI0doCoquJ1IoioKpEJRcOi1UhISIC/vz9+++03mJmZoVmzZlixYgWaN2+u1eKePCn7jAMiIqKqRB8DCCW1DOTk5GDHjh3IycmBUqmEhYWFtusiIiIiHZEUBubMmYP8/Hx4e3vD29ubYYCIiKgKkdRNAAD//PMPDh8+jKNHj8LS0hI+Pj4YMGCAVotjNwERERmaN/Y6AwDwzjvvYPTo0Rg3bhyys7PFKwsSERHpSnp6GpYsCeVlwDVMUhg4ceIEpk6dig8++ABXr17F3Llzcfz4cW3XRkREpII3CdMOSWMGoqKi4OPjg5UrV6JatWraromIiKiYV28S1q/fx7zOh4ZIahkIDw9HtWrVsGrVKixevBgnT57Udl1EREQqDh7cD6WycJibUqlk64AGSQoD33//PdatW4eGDRuicePG2LhxIzZs2KDt2oiIiESxseehUBTeJEyhKEBs7Hk9V1R1SOom+Omnn7Bnzx6YmZkBAAYOHIiPP/6Yt20lIiKdcXLqhpiY01AoFJDL5bxJmAZJCgOCIIhBAABMTU1hbCxpViKiCtm9ezsuX47T+HKzs7MBADVq1ND4sgGgSxdHDBw4TCvLNnTe3v1x9uwpAIXfS7wcuOZI+kbv2rUrpkyZgv79+wMADhw4AEdHR60WRkSkDXl5LwBoLwyQdhVdGkfaFXJIKkkXHRIEATt27MDFixchCAK6du2KQYMGab11gBcdIiJNmzVrKgBg+fK1eq6EyisiYjPOnj0NpVIBIyM5und3g6/vZ/ouS+PeyHsT/Pe//8Xff/+NHj16YOjQobqoiYiIqJjY2PNQKhUAAKVSgdjY81UyDOiD2jCwfft2rFixAi1atEBiYiIWLlwIT09PXdX2RqiM/ZbssySiqqhwAOEZKBQFkMuNOYBQg9SGgcjISJw8eRJ169bF7du3ERISYnBhQFvYb6l92gpyAMMcUVm0cfwplUqVUwuvX/8V8fHXNbZ8Qz721IaBatWqoW7dugAAOzs75OTk6KSoN8nAgcO0snOw37JyY5gj0j0jo/9dGsfU1FTlMb0etWFAJpOpPpmnE1Iloq0gBzDMEZVFW8ffF19MRmbmMyxZspqXItYgtd/u6enpOHDgQKmPP/roI+1VRkRE9AojIyPUqmXJIKBhasNA165dERcXV+pjhgEiIqLKT20YCAsLK3MB4eHhmDJlisYKIiIiIt167dEXp06d0kQdREREpCevHQYkXMCQiIiI3mCvHQZePeOAiIiIKheepElERGTgGAaIiIgMnKQwkJqaWurfWrZsqbFiiIiISPckXVJw+PDhqFmzJrp37w43NzfY2dmJf1uxYoXWiiMiIiLtkxQGjhw5gvv37yMmJgZff/01/vnnHzg6OmL+/PlaLo+IiIi0TVI3gVKpRFpaGnJzcyEIAgoKCtR2HRAREVHlIalloEuXLqhevTqGDh2K6dOnq3QTEBERUeUmKQx8/fXXuHjxIn755RecP38enTt3hoODA7p166bt+oiIiEjLJIUBZ2dnODs749mzZzhx4gQ2bdqErVu34tq1a6XOo1AoMHfuXNy9exdyuRxhYWEQBAEBAQGQyWSwtrZGSEgI70dNRESkZ5LCwIoVKxAbG4usrCy4uLhg3rx5cHR0VDvP6dOnAQA7d+5EXFycGAamT58OR0dHBAcHIzo6Gh4eHq+/FURERFRhksJA3bp1sWLFCjRv3lzygnv16oUePXoAAJKTk1GvXj2cOXMGDg4OAABXV1ecP3+eYYCIiEjPJIWBfv36ITQ0FBcvXoRCoYCjoyMWLFiAevXqqV+4sTH8/f1x4sQJrF27FqdPnxbvZVCjRg1kZmaqnb92bXMYG8slbkrlIpcXdo9YWb2l50qoIvj+VV587yo3vn/aISkMhISEoEOHDli8eDGUSiV27dqFoKAgbNq0qcx5ly5dii+//BIDBw7EixcvxOnZ2dmoWbOm2nnT0nKklFcpKRRKAMCTJ+oDEb2Z+P5VXnzvKjdDeP/0EXQkjd5LTEzEmDFjYGFhgZo1a2Ls2LFITk5WO8+BAwfEsFC9enXIZDK0bdsWcXFxAICYmBh07tz5NcsnIiKi1yWpZUAmk+HBgwdo2LAhgMIxAMbG6mft3bs3AgMDMWzYMBQUFGDOnDlo2bIl5s2bh1WrVqFFixbw9PR8/S0gIiKi1yIpDEybNg2DBg2Cvb09BEHAjRs3sHDhQrXzmJub4+uvvy42fdu2bRWrlIiIiLRCUhhwc3ODvb094uPjoVQqsWDBAtStW1fbtREREZEOqA0D69atK3H6H3/8AQCYPHmy5isiIiIineLl/4iIiAyc2paBl3/55+TkICEhATY2Nnj+/DnMzc21XhwRERFpn6SWgdjYWPj4+MDPzw8pKSlwc3PDuXPntF0bERER6YCkMLBq1SpERkaiZs2asLKywvbt27Fs2TJt10ZEREQ6ICkMKJVKWFlZiY/fffddrRVEREREuiXp1MIGDRqI9xV49uwZtm/fjrffflvbtREREZEOSGoZCA0NxcGDB/HgwQP06tULt27dQmhoqLZrIyIiIh2QfAvjVatWabsWIiIi0gO1LQN5eXnYsWMHTpw4gaysLHz++efo2LEjfH19cffuXV3VSERERFqkNgwsXLgQFy5cwK5duzB8+HA0b94ckZGRcHd3R0hIiK5qJCIiIi1S201w7do1HDp0CHl5eXB1dUVQUBAAwM7ODlFRUTopkIiIiLRLbctA0W2KTUxM0KBBA50URERERLqlNgzIZLIS/09ERERVh9puglu3bqFVq1YAAEEQVP7PcEBERFQ1qA0Dt2/fLnMBp0+fhpubm8YKIiIiIt167VsYr127VhN1EBERkZ68dhgQBEETdRAREZGevHYY4NgBIiKiyu21wwARERFVbgwDREREBo5jBoiIiAycpDAwZcqUYtNGjhwJANi1a5dmKyIiIiKdUnudgcmTJ+PWrVt4/PgxevbsKU5XKBTi5YlNTU21WyERERFpldowsGTJEqSnp2Px4sWYO3fu/2YyNkbdunW1XhwRERFpn9owYGFhAQsLC2zYsAF37txBRkaGOEYgISEBXbp00UmRREREpD1qw0CR0NBQnDp1Ck2aNBGnyWQybN26VWuFERERkW5ICgPnzp3D0aNHYWZmpu16iIiISMcknU3QpEkTnkJIRERURUlqGahVqxb69u2LDh06wMTERJweFhamtcKIiIhINySFARcXF7i4uGi7FiIiItIDSWGgf//+uH//Pv766y84OzvjwYMHKoMJichwffXVfKSlpeq7DMmKap01a6qeKymf2rXrYM6c+foug6ooSWHgyJEj2LBhA54/f46dO3di8ODBmD17Nnx8fEqdJz8/H3PmzEFSUhLy8vIwceJEvPvuuwgICIBMJoO1tTVCQkJgZMTbIxBVZmlpqUhNeQqLSnIsy5VKAEBeJQowWf9Xs6ZVtiAHMMxpi6Qw8N1332HHjh0YPnw46tati/3792P06NFqw0BUVBQsLS2xfPlypKWloX///rCzs8P06dPh6OiI4OBgREdHw8PDQ2MbQ5UTP5B0Q5sfRhZGRhheq45Wlk3AtgztHB9paalISUmBaTVzrSxfG2SQAwCynuXquRLpXuTn6LuEMkkKA0ZGRrCwsBAf169fv8xf9H369IGnp6f4WC6X4+bNm3BwcAAAuLq64vz58wwDVPiBlPoURtUl7Y5vBKVR4dk1abnpeq5EGmVugb5LoDeUaTVzdGz1ib7LqNJ+vbVX3yWUSdKnr7W1NbZt24aCggLcunULkZGRsLOzUztPjRo1AABZWVmYOnUqpk+fjqVLl0Imk4l/z8zMVLuM2rXNYWwsl1JipSOXF4YpK6u39FyJ/snlRjCqbozafZrqu5QqK+1oAuRyI63sb0X7MmmXNt4/vne6o63jT1MkhYHg4GBs2LABpqamCAoKgqOjI/z9/cuc78GDB5g0aRKGDh0Kb29vLF++XPxbdnY2atasqXb+tLQ3v2mlohSKwj7AJ0/UByJDUPRakHYpFEqt7G98/3RDG+8f3zvdKc/7p4/QICkMmJqaon379vjiiy+QmpqKU6dOib/8S/P06VN89tlnCA4OhpOTEwCgdevWiIuLg6OjI2JiYtC1a9fX34L/U9n6nStjnzPw5g+CISKi8pMUBubOnQulUinexjguLg7x8fEIDQ0tdZ6NGzfi2bNnWL9+PdavXw8ACAoKwqJFi7Bq1Sq0aNFCZUzB6yoaCCOrVl1jy9Qm4f8u/pj6rPK0fgj5lWfADhERSScpDPz+++84ePAgAKBOnTpYvnw5vL291c4zd+5cldseF9m2bVsFypRGVq06LN7tp7XlG7qsv6L0XQIREWmBpNEjSqUSjx8/Fh+npKTw+gBERERVhKSWgQkTJqB///7o1KkTAODGjRsICgrSamFERESkG5JPLdy3bx+uX78OY2NjzJ07F/Xr19d2bURERKQDksLAjBkz8PPPP2t0wB8RERG9GSSFgXfffRfr1q2Dvb09zMzMxOldunTRWmFERESkG5LCQHp6OuLi4hAXFydOk8lk2Lp1q9YKIyIiIt2QFAYiIiK0XQcRERHpiaTzA5OSkjB69Gj07t0bT548wYgRI3D//n1t10ZEREQ6ICkMBAcHY8yYMTA3N0e9evXg5eUl6d4ERERE9OaTFAbS0tLg7OwMoHCswMCBA5GVlaXVwoiIiEg3JIUBMzMzPHz4ULz98JUrV2BiYqLVwoiIiEg3JA0gDAwMxPjx45GQkAAfHx9kZGTg66+/1nZtRFQJZGdn44VSiW0ZleeuoZVNllIJ0+xsfZdBVZjaMPDo0SMsW7YMd+7cQbt27TBv3jxYWFigRYsWbBkgIiKqItSGgTlz5sDGxgbe3t44duwY9u7di7CwMF3VRkSVQI0aNVAt7wWG16qj71KqrG0ZqTCpUUPfZVAVVmbLwP/7f/8PANCtWzd89NFHOimKiIiIdEftAMJq1aqp/P/lx0RERFQ1SBpAWKTobAIiIqr8srOz8SL/OX69tVffpVRpL/JzIMtW6rsMtdSGgTt37qBnz57i40ePHqFnz54QBAEymQzR0dFaL5CIiIi0S20YOHbsmK7qICIiHatRowYEhRE6tvpE36VUab/e2osaNarruwy11IaBRo0a6aoOIiIi0hNJVyAkIiKiqothgIiIyMAxDBARERk4hgEiIiIDxzBARERk4BgGiIiIDFy5rkBIpA3Z2dlQvihA2tEEfZdSZSlzC5Ct5C1wiahkVSYMZGdnQ8h/jqy/ovRdSpUl5OciO1vQdxlERKRhVSYMUOVVo0YN5Bnlo3afpvoupcpKO5qAGtV5C1wiKlmVCQM1atTAC4UMFu/203cpVVbWX1GoUcNc32UQEZGGcQAhERGRgasyLQNERFR+L/JzKtUtjAsUeQAAY7mJniuR7kV+DixQiW9UREREVVft2nX0XUK5paXlAgAsar7ZX64vs0D1N/61ZhggoteWpVRiW0aqvsuQ5LlSCQAwM6o8vaRZSiW08VUyZ858LSxVu2bNmgoAWL58rZ4rqVq0GgZu3LiBFStWICIiAvfu3UNAQABkMhmsra0REhICo0p0MBJRyd70Xzyvyk4rDC0mlajuOqh8rzNVLloLA9999x2ioqJQvXphU05YWBimT58OR0dHBAcHIzo6Gh4eHtpaPRHpSGX7dclflkTFae2nedOmTREeHi4+vnnzJhwcHAAArq6uuHDhgrZWTUREROWgtZYBT09P3L9/X3wsCAJkMhmAwmsCZGZmlrmM2rXNYWwsl7Q+uZxdDroglxvByuotjS+TtE8b711lVLS/8bWonPj+aYfOBhC+PD4gOzsbNWvWLHOetLQcyctXKJQVqovKR6FQ4smTsoNceZdJ2qeN964yKtrf+FpUTobw/ukj6OjsJ1nr1q0RFxcHAIiJiUHnzp11tWoiIiJSQ2dhwN/fH+Hh4Rg0aBDy8/Ph6empq1UTERGRGlrtJmjcuDF2794NAGjevDm2bdumzdURERFRBXDkFhERkYFjGCAiIjJwvBwxvRGUuQVIO5qg7zIkU+YpAABGJtJOfdU3ZW4B3vD7pBCRHjEMkN5Vxsuspj0vvKRt7eqWeq5EouqV83UmIt1gGCC9q2yXswV4SVsiqlo4ZoCIiMjAVamWASE/F1l/Rem7DEkERR4AQCY30XMl0gn5uQDM9V0GERFpWJUJA5WtPzQt7TkAoHbNyvTlal7pXmciIipblQkDla3fmX3ORET0puCYASIiIgPHMEBERGTgGAaIiIgMHMMAERGRgWMYICIiMnAMA0RERAaOYYCIiMjAMQwQEREZOIYBIiIiA8cwQEREZOAYBoiIiAwcwwAREZGBYxggIiIycAwDREREBo5hgIiIyMAxDBARERk4Y30X8KbbvXs7Ll+O0/hy09JSAQCzZk3V+LK7dHHEwIHDNL5cIiKqmhgG9MTExFTfJRAREQFgGCjTwIHD+CubiIiqNI4ZICIiMnAMA0RERAaOYYCIiMjAMQwQEREZOA4gpCpLW6eFAjw1lIiqFp2GAaVSifnz5+PPP/+EiYkJFi1acnAP+QAADXxJREFUhGbNmumyBCKN4KmhRFSV6DQMnDx5Enl5edi1axeuX7+OJUuWYMOGDbosgQwITwslIpJGp2Hg6tWrcHFxAQC0b98ev//+uy5XT0SVSGW8+ifAbp4ilfH9M+T3TqdhICsrCxYWFuJjuVyOgoICGBuXXEbt2uYwNpbrqjwieoNUr24CuVzzY5zNzMwAQCvLBgrrtrJ6SyvLrkwq4/tnyO+dTBAEQVcrCwsLg729PT788EMAgKurK2JiYkp9/pMnmboqjYiI6I2gj0Ci01MLO3bsKH75X79+HTY2NrpcPREREZVAp90EHh4eOH/+PAYPHgxBEPDVV1/pcvVERERUAp12E5QXuwmIiMjQVPluAiIiInrzMAwQEREZOIYBIiIiA8cwQEREZOAYBoiIiAwcwwAREZGBYxggIiIycAwDREREBo5hgIiIyMC90VcgJCIiIu1jywAREZGBYxggIiIycAwDREREBo5hgIiIyMAxDBARERk4hgEiIiIDxzCgJzdu3ICvr6++y6Byys/Px6xZszB06FAMGDAA0dHR+i6JykGhUCAwMBCDBw/GsGHDkJCQoO+SqJxSUlLQvXt3/P333/oupUox1ncBhui7775DVFQUqlevru9SqJyioqJgaWmJ5cuXIy0tDf3790fPnj31XRZJdPr0aQDAzp07ERcXh7CwMGzYsEHPVZFU+fn5CA4OhpmZmb5LqXLYMqAHTZs2RXh4uL7LoAro06cPpk2bJj6Wy+V6rIbKq1evXli4cCEAIDk5GfXq1dNzRVQeS5cuxeDBg1G/fn19l1LlMAzogaenJ4yN2ShTGdWoUQMWFhbIysrC1KlTMX36dH2XROVkbGwMf39/LFy4EJ6envouhyTat28f6tSpAxcXF32XUiUxDBCV04MHDzBixAj4+PjA29tb3+VQBSxduhTHjh3DvHnzkJOTo+9ySIK9e/fiwoUL8PX1xa1bt+Dv748nT57ou6wqgz9Picrh6dOn+OyzzxAcHAwnJyd9l0PldODAATx69Ajjx49H9erVIZPJ2NVTSWzfvl38v6+vL+bPnw8rKys9VlS1sGWAqBw2btyIZ8+eYf369fD19YWvry+eP3+u77JIot69e+OPP/7AsGHDMGbMGMyZMwempqb6LotI73jXQiIiIgPHlgEiIiIDxzBARERk4BgGiIiIDBzDABERkYFjGCAiIjJwDANUZS1YsAA+Pj748MMP0bZtW/j4+MDHxwd79+4tdZ579+5h7ty5apd77949eHh4qExLSUkRl9+tWzc4OzuLj589e1auun/99VcsWLBA0nP//vtvtGvXTlyXt7c33N3dy7ze/t9//40+ffqUqy5tunPnDsaNGwdvb294e3tj9uzZyMjIUDvP3bt3ERwcrNW67t+/j0mTJml1HURvAl50iKqskJAQAIUf6CNGjMBPP/1U5jxJSUm4f/9+uddVt25dcfmrV6+Gqakp/Pz8yr0cAOjYsSM6duwo+fmNGjVS2bbk5GT07t0bXl5eaNKkSYVq0KXk5GSMHj0aYWFhcHFxgSAIWLt2LaZPn44tW7aUOl9iYiKSkpK0Wlvjxo3xzTffaHUdRG8CtgyQQcrOzsbMmTPh5eUFb29vREVFAQAWLVqEGzduYNGiRcjPz8ecOXMwcOBAuLu7Y/LkyXjx4kWF1nflyhV88skn8Pb2xmeffYbE/9/evcZEcbUBHP8jLmKlTbVqaLI16QeUpEZTL7ihqYCuWkWW3XVRKxR0SwS0SrzGYKiiBZuKrBe8tSomaqypkdZotN0oouFqjZdoMF01mLUCWvGCcVeys8/7wbyb8spi334ycn7fZvbMnOc858M8e2Yy43bz5MkT4uLi+P333xERvvjiCw4fPszZs2f58ssvAbhy5UrguPT09H/0+tWWlhZCQ0N56623ACgtLcVisZCUlITD4ei0fXZ2NlarlZSUFOrr64EXhZHdbiclJYX4+Hi2bdsGwNWrV0lJScFqtZKamhoonk6dOsW0adMwm83k5uby5MkTRIRvvvkGk8mE2Wxm586dL/W/b98+4uLiAu+cDwkJYd68eUybNg2/3x80jsLCQi5evEhRUVGX49y9ezcTJ07EZrOxZMkSvv/+ewCcTicmk4mkpCQWLlxIa2srAGPHjiU3N5fJkydz8eLFwApKsDxVVlZisViwWq1kZma+ckVDUV5LoihvOLfbLQkJCR32FRUVSVFRkYiI/PXXX5KQkCAul0uqqqokIyNDRERqampk7dq1IiKiaZrMnDlTnE6nNDY2itFoDNpfSUmJbN26NbDt8Xhk7Nix0tDQICIi5eXl8vnnn4uIyJkzZyQxMVHKysokJydHREQqKyvFbreLiIjRaJSqqioRESkrK5OSkpIOfd24cUOGDh0qJpNJJk6cKDExMTJ37lyprq4WERGn0ylLliwRTdPE5/PJggUL5MSJE3Ljxg2ZNGmSiIjk5OTIuXPnRETk7t27Mn78ePF6vbJt2zY5duyYiIg8fPhQhg8fLm1tbbJo0SI5deqUiIgcOnRIjh8/Li0tLWI2m6WtrU1ERPbu3SsFBQVy8+ZNsVgsIiLS1tYmubm50t7e3mEMGRkZsn///qD5DBbH3/MUbJyXL1+WKVOmyNOnT+XZs2diNptl586d0tTUJJ9++qk0NTWJiEhpaaksXbpURERiY2MD/f2TPE2fPl2uX78eiLW2tjboWBTldaVuEyjdUm1tLcXFxcCLJf6EhATq6ur48MMPA20MBgP9+vXjwIED3Lp1C7fb/a8+auNyuYiMjCQ6OhqA5ORkCgoK8Hq9xMXFcfLkSbZv386JEyc6HNfc3IzH4yE2NhaA2bNnd3r+/94m0DSNtWvX0tzcjMFgAKC6upoLFy5gsVgA8Hq9REVFERUV1SEXf/75J+vXrwdefDP+zp07ZGdnU1NTw65du3C5XPh8PrxeL/Hx8eTn51NRUUF8fDwJCQn89ttv3L17l9TUVAA0TWPgwIHo9Xp8Ph+pqanEx8ezfPlydDpdh/h79OjR5SuBg8Xxd8HG2djYiNFopE+fPgBMnjwZgEuXLjFq1CgiIyMBmDFjBmazOXC+YcOGvRRHsDyNGzeOrKwsjEYj48ePZ8yYMUHHoiivK1UMKN2S3+/vsC0iaJrWYZ/T6WTr1q2kp6djtVq5f/8+8i/e3t3ZMZqmISL4/X4aGxvR6XTcvn2bfv36Bdr07NmTkJCQwLbX6+X+/ftBnwMIDQ0lLy+PpKQk9u3bR3p6OpqmkZmZGbhIP378GJ1OR1NTUyA2v9/PwYMHA7cVWlpaGDBgAGvWrOHBgwdMmTKFSZMmcebMGUQEk8nE6NGjOX36ND/88APV1dWMHDkSg8HApk2bArF6PB7CwsI4cuQIdXV1nD17lunTp/Pjjz+i1+sDcQ8dOpSrV69is9kC+3w+HwsWLGDdunVs2rSp0zj+N5+djXP//v0vzTW8ev7Dw8Nf+j1YnrKyspgwYQKVlZUUFRVhsViw2+2dzpGivK7UMwNKt2QwGDh8+DAAra2tnD59mpiYGEJDQwMXhaqqKhITE7FarURERHD+/PlOLyyvEhUVRVNTE9evXwfgl19+YciQIfTu3Zu9e/cycOBANm7cSF5eXod/vP3796dPnz6cP38egJ9++okdO3Z02VdYWBjLli1jy5YtPHz4EIPBQHl5OR6Ph/b2drKysqioqAi0DwkJISYmhoMHDwLQ0NBAcnIy7e3tVFdXk5WVxWeffUZDQwOPHj3C7/eTk5ODy+UiNTWVr776imvXrvHxxx9TX1+P2+0GYOPGjTgcDi5duoTdbsdgMLBixQo++OADbt++3SHmWbNm8euvv1JVVQW8uFBv3rwZj8fDu+++GzSOnj17BuYq2DhjY2OpqKjg2bNnPH/+HKfTCbx4SLO+vp7m5mYADh061OU/+q7ylJycjN/vZ86cOaSlpXHt2rUu50hRXkdqZUDplhYuXMjq1atJSkpC0zTmz59PdHQ0ra2ttLa2smLFCjIyMli2bBlHjx5Fp9MxcuRI7ty583896Q/Qu3dvNmzYwMqVK3n+/Dl9+/aluLiYW7dusWfPHsrLyxkwYAAjRozA4XDwySefBI4tKSlhzZo1eDwe3nvvvcASdVeMRiNlZWWUlpaSn5/PH3/8gc1mQ9M0xo0bR2JiIjdv3gy0LygoID8/n59//pmQkBAcDgfh4eFkZ2eTm5tLr1690Ov1REdH43a7mTdvHqtWrcLhcBAWFsbXX3/N+++/z+rVq5k/fz5+vx+9Xs93333H22+/zeDBg5k6dSrh4eHExMS89OnnyMhIduzYQXFxMd9++y2apjFs2LDAKkOwOKKiorh37x4rV66ksLCw03EC2Gw2bDYbERERvPPOO4SHhxMZGUl+fj5z587F5/MxaNAgCgsLu8xrsDwtXryYRYsWodPpiIiIeOV5FOV1pL5aqCjKG8vlclFXV0daWhoiQmZmJna7vUPBpSiKuk2gKMobTK/XU19fz9SpUzGZTHz00UeqEFCUTqiVAUVRFEXp5tTKgKIoiqJ0c6oYUBRFUZRuThUDiqIoitLNqWJAURRFUbo5VQwoiqIoSjenigFFURRF6eb+A2voL1KDIGw9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"Total Toxic Releases Categories\", y=\"Percent_Below_Poverty_Level\", data=df_chem_merge).set_title('Population Living Below the Poverty Line and Toxic Releases', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "y4_pred_forest = best_grid.predict(X4_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y4_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y4_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329    2\n",
       "153    3\n",
       "135    4\n",
       "78     4\n",
       "452    1\n",
       "      ..\n",
       "8      4\n",
       "377    1\n",
       "5      4\n",
       "134    4\n",
       "213    3\n",
       "Name: All Chem Binned, Length: 115, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y4_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 4, 3, 4, 4, 1, 3, 4, 2, 2, 3, 2, 4, 4, 3, 2, 2, 4, 2, 3, 4,\n",
       "       1, 3, 2, 4, 3, 3, 4, 4, 3, 4, 3, 3, 4, 2, 3, 2, 3, 1, 3, 4, 3, 2,\n",
       "       4, 1, 4, 4, 3, 4, 3, 4, 4, 1, 4, 4, 4, 2, 1, 2, 3, 4, 4, 2, 4, 3,\n",
       "       3, 3, 2, 2, 4, 1, 4, 3, 3, 4, 4, 4, 3, 3, 3, 4, 3, 3, 2, 3, 3, 4,\n",
       "       4, 3, 4, 3, 4, 2, 3, 4, 2, 4, 3, 3, 4, 2, 2, 3, 2, 2, 4, 4, 1, 1,\n",
       "       3, 2, 3, 2, 2])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y4_pred_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.44      0.21      0.29        19\n",
      "           2       0.22      0.20      0.21        30\n",
      "           3       0.26      0.34      0.30        29\n",
      "           4       0.39      0.43      0.41        37\n",
      "\n",
      "    accuracy                           0.31       115\n",
      "   macro avg       0.33      0.30      0.30       115\n",
      "weighted avg       0.32      0.31      0.31       115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y4_test, y4_pred_forest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  4  6  5]\n",
      " [ 3  6 10 11]\n",
      " [ 2  8 10  9]\n",
      " [ 0  9 12 16]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y4_test, y4_pred_forest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Model Only Looking at Harris County"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>Unnamed: 0</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "      <td>37298</td>\n",
       "      <td>9.528661</td>\n",
       "      <td>56.657193</td>\n",
       "      <td>5.946700</td>\n",
       "      <td>0.423615</td>\n",
       "      <td>1.177007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.480133</td>\n",
       "      <td>33.830232</td>\n",
       "      <td>3.394284</td>\n",
       "      <td>12.839831</td>\n",
       "      <td>21.097646</td>\n",
       "      <td>24.274760</td>\n",
       "      <td>42.163119</td>\n",
       "      <td>57.633117</td>\n",
       "      <td>67.126924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>77530</td>\n",
       "      <td>4.999060e+06</td>\n",
       "      <td>33387</td>\n",
       "      <td>16.922754</td>\n",
       "      <td>18.770779</td>\n",
       "      <td>12.441968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240782</td>\n",
       "      <td>67.472369</td>\n",
       "      <td>9.488723</td>\n",
       "      <td>25.755534</td>\n",
       "      <td>39.997604</td>\n",
       "      <td>42.121185</td>\n",
       "      <td>61.014766</td>\n",
       "      <td>80.252793</td>\n",
       "      <td>87.507114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>77520</td>\n",
       "      <td>4.044139e+06</td>\n",
       "      <td>35114</td>\n",
       "      <td>18.118130</td>\n",
       "      <td>29.455488</td>\n",
       "      <td>12.946403</td>\n",
       "      <td>0.065501</td>\n",
       "      <td>0.734750</td>\n",
       "      <td>0.082588</td>\n",
       "      <td>10.050122</td>\n",
       "      <td>54.978071</td>\n",
       "      <td>6.407701</td>\n",
       "      <td>23.950561</td>\n",
       "      <td>36.765962</td>\n",
       "      <td>39.366065</td>\n",
       "      <td>62.055021</td>\n",
       "      <td>75.226405</td>\n",
       "      <td>82.995386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>77507</td>\n",
       "      <td>3.543322e+06</td>\n",
       "      <td>312</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>68.269231</td>\n",
       "      <td>14.423077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.487179</td>\n",
       "      <td>17.307692</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>32.051282</td>\n",
       "      <td>39.423077</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>60.256410</td>\n",
       "      <td>75.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>77076</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>35866</td>\n",
       "      <td>30.898344</td>\n",
       "      <td>4.826298</td>\n",
       "      <td>6.220376</td>\n",
       "      <td>0.225841</td>\n",
       "      <td>0.471198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.172029</td>\n",
       "      <td>88.431941</td>\n",
       "      <td>11.841298</td>\n",
       "      <td>37.021134</td>\n",
       "      <td>55.863492</td>\n",
       "      <td>61.453744</td>\n",
       "      <td>82.495957</td>\n",
       "      <td>92.129036</td>\n",
       "      <td>96.807561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>77077</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>59449</td>\n",
       "      <td>10.905146</td>\n",
       "      <td>39.746674</td>\n",
       "      <td>21.640398</td>\n",
       "      <td>0.348198</td>\n",
       "      <td>12.881630</td>\n",
       "      <td>0.117748</td>\n",
       "      <td>3.196017</td>\n",
       "      <td>22.883480</td>\n",
       "      <td>4.899998</td>\n",
       "      <td>14.937173</td>\n",
       "      <td>23.934801</td>\n",
       "      <td>25.416744</td>\n",
       "      <td>39.053643</td>\n",
       "      <td>51.376810</td>\n",
       "      <td>61.242409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>73</td>\n",
       "      <td>77043</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>24608</td>\n",
       "      <td>13.666287</td>\n",
       "      <td>43.006339</td>\n",
       "      <td>4.396944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.797952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.689207</td>\n",
       "      <td>43.042913</td>\n",
       "      <td>6.351593</td>\n",
       "      <td>19.497724</td>\n",
       "      <td>30.234070</td>\n",
       "      <td>33.521619</td>\n",
       "      <td>48.390767</td>\n",
       "      <td>63.255852</td>\n",
       "      <td>70.042263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>77033</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>30438</td>\n",
       "      <td>27.919049</td>\n",
       "      <td>1.176161</td>\n",
       "      <td>66.499113</td>\n",
       "      <td>0.111702</td>\n",
       "      <td>0.298968</td>\n",
       "      <td>0.147842</td>\n",
       "      <td>2.881267</td>\n",
       "      <td>31.693935</td>\n",
       "      <td>10.125501</td>\n",
       "      <td>38.264669</td>\n",
       "      <td>55.900519</td>\n",
       "      <td>59.800250</td>\n",
       "      <td>77.735068</td>\n",
       "      <td>88.931599</td>\n",
       "      <td>94.056771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>77092</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>38312</td>\n",
       "      <td>31.483608</td>\n",
       "      <td>20.594070</td>\n",
       "      <td>11.134892</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>1.524327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.241595</td>\n",
       "      <td>65.483399</td>\n",
       "      <td>11.998852</td>\n",
       "      <td>38.959073</td>\n",
       "      <td>56.368762</td>\n",
       "      <td>60.774692</td>\n",
       "      <td>72.794425</td>\n",
       "      <td>80.802360</td>\n",
       "      <td>87.069326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0    ZIP  Total Releases  Total Population  \\\n",
       "0            0  77536    1.593846e+07             31865   \n",
       "1            1  77571    7.254232e+06             37298   \n",
       "2            2  77530    4.999060e+06             33387   \n",
       "3            3  77520    4.044139e+06             35114   \n",
       "4            4  77507    3.543322e+06               312   \n",
       "..         ...    ...             ...               ...   \n",
       "71          71  77076    0.000000e+00             35866   \n",
       "72          72  77077    0.000000e+00             59449   \n",
       "73          73  77043    0.000000e+00             24608   \n",
       "74          74  77033    0.000000e+00             30438   \n",
       "75          75  77092    0.000000e+00             38312   \n",
       "\n",
       "    Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0                      7.453319      58.104503   \n",
       "1                      9.528661      56.657193   \n",
       "2                     16.922754      18.770779   \n",
       "3                     18.118130      29.455488   \n",
       "4                     19.230769      68.269231   \n",
       "..                          ...            ...   \n",
       "71                    30.898344       4.826298   \n",
       "72                    10.905146      39.746674   \n",
       "73                    13.666287      43.006339   \n",
       "74                    27.919049       1.176161   \n",
       "75                    31.483608      20.594070   \n",
       "\n",
       "    Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                         1.449867                 0.922642       1.653852   \n",
       "1                         5.946700                 0.423615       1.177007   \n",
       "2                        12.441968                 0.000000       0.185701   \n",
       "3                        12.946403                 0.065501       0.734750   \n",
       "4                        14.423077                 0.000000       0.000000   \n",
       "..                             ...                      ...            ...   \n",
       "71                        6.220376                 0.225841       0.471198   \n",
       "72                       21.640398                 0.348198      12.881630   \n",
       "73                        4.396944                 0.000000       8.797952   \n",
       "74                       66.499113                 0.111702       0.298968   \n",
       "75                       11.134892                 0.044373       1.524327   \n",
       "\n",
       "    Percent_Native_Hawaiian_PI  Percent_Other_Race  Percent_Hispanic_Latino  \\\n",
       "0                     0.000000            2.815001                37.363879   \n",
       "1                     0.000000            4.480133                33.830232   \n",
       "2                     0.000000            3.240782                67.472369   \n",
       "3                     0.082588           10.050122                54.978071   \n",
       "4                     0.000000            4.487179                17.307692   \n",
       "..                         ...                 ...                      ...   \n",
       "71                    0.000000            5.172029                88.431941   \n",
       "72                    0.117748            3.196017                22.883480   \n",
       "73                    0.000000            5.689207                43.042913   \n",
       "74                    0.147842            2.881267                31.693935   \n",
       "75                    0.000000           37.241595                65.483399   \n",
       "\n",
       "    Percent_50_PL  Percent_125_PL  Percent_185_PL  Percent_200_PL  \\\n",
       "0        3.276322        8.771379       18.732151       20.878707   \n",
       "1        3.394284       12.839831       21.097646       24.274760   \n",
       "2        9.488723       25.755534       39.997604       42.121185   \n",
       "3        6.407701       23.950561       36.765962       39.366065   \n",
       "4       19.230769       19.230769       32.051282       39.423077   \n",
       "..            ...             ...             ...             ...   \n",
       "71      11.841298       37.021134       55.863492       61.453744   \n",
       "72       4.899998       14.937173       23.934801       25.416744   \n",
       "73       6.351593       19.497724       30.234070       33.521619   \n",
       "74      10.125501       38.264669       55.900519       59.800250   \n",
       "75      11.998852       38.959073       56.368762       60.774692   \n",
       "\n",
       "    Percent_300_PL  Percent_400_PL  Percent_500_PL  \n",
       "0        36.908834       54.815628       67.174015  \n",
       "1        42.163119       57.633117       67.126924  \n",
       "2        61.014766       80.252793       87.507114  \n",
       "3        62.055021       75.226405       82.995386  \n",
       "4        45.833333       60.256410       75.961538  \n",
       "..             ...             ...             ...  \n",
       "71       82.495957       92.129036       96.807561  \n",
       "72       39.053643       51.376810       61.242409  \n",
       "73       48.390767       63.255852       70.042263  \n",
       "74       77.735068       88.931599       94.056771  \n",
       "75       72.794425       80.802360       87.069326  \n",
       "\n",
       "[76 rows x 19 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_harris = pd.read_csv('Cleaned Data/harris_county_df.csv')\n",
    "df_harris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>ZIP</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "      <td>37298</td>\n",
       "      <td>9.528661</td>\n",
       "      <td>56.657193</td>\n",
       "      <td>5.946700</td>\n",
       "      <td>0.423615</td>\n",
       "      <td>1.177007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.480133</td>\n",
       "      <td>33.830232</td>\n",
       "      <td>3.394284</td>\n",
       "      <td>12.839831</td>\n",
       "      <td>21.097646</td>\n",
       "      <td>24.274760</td>\n",
       "      <td>42.163119</td>\n",
       "      <td>57.633117</td>\n",
       "      <td>67.126924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77530</td>\n",
       "      <td>4.999060e+06</td>\n",
       "      <td>33387</td>\n",
       "      <td>16.922754</td>\n",
       "      <td>18.770779</td>\n",
       "      <td>12.441968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240782</td>\n",
       "      <td>67.472369</td>\n",
       "      <td>9.488723</td>\n",
       "      <td>25.755534</td>\n",
       "      <td>39.997604</td>\n",
       "      <td>42.121185</td>\n",
       "      <td>61.014766</td>\n",
       "      <td>80.252793</td>\n",
       "      <td>87.507114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77520</td>\n",
       "      <td>4.044139e+06</td>\n",
       "      <td>35114</td>\n",
       "      <td>18.118130</td>\n",
       "      <td>29.455488</td>\n",
       "      <td>12.946403</td>\n",
       "      <td>0.065501</td>\n",
       "      <td>0.734750</td>\n",
       "      <td>0.082588</td>\n",
       "      <td>10.050122</td>\n",
       "      <td>54.978071</td>\n",
       "      <td>6.407701</td>\n",
       "      <td>23.950561</td>\n",
       "      <td>36.765962</td>\n",
       "      <td>39.366065</td>\n",
       "      <td>62.055021</td>\n",
       "      <td>75.226405</td>\n",
       "      <td>82.995386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77507</td>\n",
       "      <td>3.543322e+06</td>\n",
       "      <td>312</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>68.269231</td>\n",
       "      <td>14.423077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.487179</td>\n",
       "      <td>17.307692</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>32.051282</td>\n",
       "      <td>39.423077</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>60.256410</td>\n",
       "      <td>75.961538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ZIP  Total Releases  Total Population  Percent_Below_Poverty_Level  \\\n",
       "0  77536    1.593846e+07             31865                     7.453319   \n",
       "1  77571    7.254232e+06             37298                     9.528661   \n",
       "2  77530    4.999060e+06             33387                    16.922754   \n",
       "3  77520    4.044139e+06             35114                    18.118130   \n",
       "4  77507    3.543322e+06               312                    19.230769   \n",
       "\n",
       "   Percent_White  Percent_Black_African_American  Percent_American_Indian  \\\n",
       "0      58.104503                        1.449867                 0.922642   \n",
       "1      56.657193                        5.946700                 0.423615   \n",
       "2      18.770779                       12.441968                 0.000000   \n",
       "3      29.455488                       12.946403                 0.065501   \n",
       "4      68.269231                       14.423077                 0.000000   \n",
       "\n",
       "   Percent_Asian  Percent_Native_Hawaiian_PI  Percent_Other_Race  \\\n",
       "0       1.653852                    0.000000            2.815001   \n",
       "1       1.177007                    0.000000            4.480133   \n",
       "2       0.185701                    0.000000            3.240782   \n",
       "3       0.734750                    0.082588           10.050122   \n",
       "4       0.000000                    0.000000            4.487179   \n",
       "\n",
       "   Percent_Hispanic_Latino  Percent_50_PL  Percent_125_PL  Percent_185_PL  \\\n",
       "0                37.363879       3.276322        8.771379       18.732151   \n",
       "1                33.830232       3.394284       12.839831       21.097646   \n",
       "2                67.472369       9.488723       25.755534       39.997604   \n",
       "3                54.978071       6.407701       23.950561       36.765962   \n",
       "4                17.307692      19.230769       19.230769       32.051282   \n",
       "\n",
       "   Percent_200_PL  Percent_300_PL  Percent_400_PL  Percent_500_PL  \n",
       "0       20.878707       36.908834       54.815628       67.174015  \n",
       "1       24.274760       42.163119       57.633117       67.126924  \n",
       "2       42.121185       61.014766       80.252793       87.507114  \n",
       "3       39.366065       62.055021       75.226405       82.995386  \n",
       "4       39.423077       45.833333       60.256410       75.961538  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_harris = df_harris.drop(columns='Unnamed: 0', axis = 1)\n",
    "df_harris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Harris County "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.593846e+07\n",
       "1    7.254232e+06\n",
       "2    4.999060e+06\n",
       "3    4.044139e+06\n",
       "4    3.543322e+06\n",
       "Name: Total Releases, dtype: float64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_harris = df_harris['Total Releases']\n",
    "y_harris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_harris = df_harris.drop(columns=['ZIP', 'Total Releases'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.268296</td>\n",
       "      <td>0.181509</td>\n",
       "      <td>0.626222</td>\n",
       "      <td>0.009625</td>\n",
       "      <td>0.388505</td>\n",
       "      <td>0.066701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059827</td>\n",
       "      <td>0.397510</td>\n",
       "      <td>0.111534</td>\n",
       "      <td>0.184508</td>\n",
       "      <td>0.229771</td>\n",
       "      <td>0.241730</td>\n",
       "      <td>0.340962</td>\n",
       "      <td>0.476344</td>\n",
       "      <td>0.560309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.314493</td>\n",
       "      <td>0.232050</td>\n",
       "      <td>0.610302</td>\n",
       "      <td>0.068435</td>\n",
       "      <td>0.178375</td>\n",
       "      <td>0.047469</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.095217</td>\n",
       "      <td>0.359916</td>\n",
       "      <td>0.115549</td>\n",
       "      <td>0.270088</td>\n",
       "      <td>0.267314</td>\n",
       "      <td>0.292387</td>\n",
       "      <td>0.407082</td>\n",
       "      <td>0.513171</td>\n",
       "      <td>0.559644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.281238</td>\n",
       "      <td>0.412116</td>\n",
       "      <td>0.193544</td>\n",
       "      <td>0.153381</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068877</td>\n",
       "      <td>0.717831</td>\n",
       "      <td>0.323018</td>\n",
       "      <td>0.541772</td>\n",
       "      <td>0.567275</td>\n",
       "      <td>0.558596</td>\n",
       "      <td>0.644312</td>\n",
       "      <td>0.808827</td>\n",
       "      <td>0.847362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.295923</td>\n",
       "      <td>0.441227</td>\n",
       "      <td>0.311078</td>\n",
       "      <td>0.159978</td>\n",
       "      <td>0.027581</td>\n",
       "      <td>0.029633</td>\n",
       "      <td>0.181142</td>\n",
       "      <td>0.213596</td>\n",
       "      <td>0.584906</td>\n",
       "      <td>0.218133</td>\n",
       "      <td>0.503804</td>\n",
       "      <td>0.515986</td>\n",
       "      <td>0.517499</td>\n",
       "      <td>0.657402</td>\n",
       "      <td>0.743129</td>\n",
       "      <td>0.783667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468323</td>\n",
       "      <td>0.738036</td>\n",
       "      <td>0.179290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.095366</td>\n",
       "      <td>0.184135</td>\n",
       "      <td>0.654661</td>\n",
       "      <td>0.404523</td>\n",
       "      <td>0.441159</td>\n",
       "      <td>0.518349</td>\n",
       "      <td>0.453268</td>\n",
       "      <td>0.547459</td>\n",
       "      <td>0.684367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Percent_Below_Poverty_Level  Percent_White  \\\n",
       "0          0.268296                     0.181509       0.626222   \n",
       "1          0.314493                     0.232050       0.610302   \n",
       "2          0.281238                     0.412116       0.193544   \n",
       "3          0.295923                     0.441227       0.311078   \n",
       "4          0.000000                     0.468323       0.738036   \n",
       "\n",
       "   Percent_Black_African_American  Percent_American_Indian  Percent_Asian  \\\n",
       "0                        0.009625                 0.388505       0.066701   \n",
       "1                        0.068435                 0.178375       0.047469   \n",
       "2                        0.153381                 0.000000       0.007489   \n",
       "3                        0.159978                 0.027581       0.029633   \n",
       "4                        0.179290                 0.000000       0.000000   \n",
       "\n",
       "   Percent_Native_Hawaiian_PI  Percent_Other_Race  Percent_Hispanic_Latino  \\\n",
       "0                    0.000000            0.059827                 0.397510   \n",
       "1                    0.000000            0.095217                 0.359916   \n",
       "2                    0.000000            0.068877                 0.717831   \n",
       "3                    0.181142            0.213596                 0.584906   \n",
       "4                    0.000000            0.095366                 0.184135   \n",
       "\n",
       "   Percent_50_PL  Percent_125_PL  Percent_185_PL  Percent_200_PL  \\\n",
       "0       0.111534        0.184508        0.229771        0.241730   \n",
       "1       0.115549        0.270088        0.267314        0.292387   \n",
       "2       0.323018        0.541772        0.567275        0.558596   \n",
       "3       0.218133        0.503804        0.515986        0.517499   \n",
       "4       0.654661        0.404523        0.441159        0.518349   \n",
       "\n",
       "   Percent_300_PL  Percent_400_PL  Percent_500_PL  \n",
       "0        0.340962        0.476344        0.560309  \n",
       "1        0.407082        0.513171        0.559644  \n",
       "2        0.644312        0.808827        0.847362  \n",
       "3        0.657402        0.743129        0.783667  \n",
       "4        0.453268        0.547459        0.684367  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalize the data using z score\n",
    "X_harris_norm = X_harris.apply(lambda x : (x - x.min()) /(x.max() - x.min()), axis=0)\n",
    "X_harris_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features in the green area: []\n",
      "features in the blue area: []\n"
     ]
    }
   ],
   "source": [
    "### fit Boruta (it accepts np.array, not pd.DataFrame)\n",
    "boruta.fit(np.array(X_harris_norm), np.array(y_harris))\n",
    "### print results\n",
    "green_area = X_harris_norm.columns[boruta.support_].to_list()\n",
    "blue_area = X_harris_norm.columns[boruta.support_weak_].to_list()\n",
    "print('features in the green area:', green_area)\n",
    "print('features in the blue area:', blue_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>Total Releases</td>  <th>  R-squared:         </th> <td>   0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.242</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:28:50</td>     <th>  Log-Likelihood:    </th> <td> -1204.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    76</td>      <th>  AIC:               </th> <td>   2443.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    59</td>      <th>  BIC:               </th> <td>   2482.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    16</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                          <td>-1.001e+07</td> <td> 4.02e+07</td> <td>   -0.249</td> <td> 0.804</td> <td>-9.05e+07</td> <td> 7.05e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Population</th>               <td> 9.274e+05</td> <td> 1.74e+06</td> <td>    0.534</td> <td> 0.595</td> <td>-2.55e+06</td> <td>  4.4e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Below_Poverty_Level</th>    <td> 6.793e+06</td> <td> 6.84e+06</td> <td>    0.993</td> <td> 0.325</td> <td> -6.9e+06</td> <td> 2.05e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_White</th>                  <td> 1.174e+07</td> <td> 3.81e+07</td> <td>    0.308</td> <td> 0.759</td> <td>-6.45e+07</td> <td>  8.8e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Black_African_American</th> <td> 6.015e+06</td> <td> 3.12e+07</td> <td>    0.193</td> <td> 0.848</td> <td>-5.64e+07</td> <td> 6.84e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_American_Indian</th>        <td> 1.558e+06</td> <td> 1.71e+06</td> <td>    0.910</td> <td> 0.367</td> <td>-1.87e+06</td> <td> 4.99e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Asian</th>                  <td> 1.126e+05</td> <td> 1.08e+07</td> <td>    0.010</td> <td> 0.992</td> <td>-2.15e+07</td> <td> 2.17e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Native_Hawaiian_PI</th>     <td>-9.038e+05</td> <td> 1.32e+06</td> <td>   -0.685</td> <td> 0.496</td> <td>-3.54e+06</td> <td> 1.74e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Other_Race</th>             <td>-7.048e+05</td> <td> 1.37e+06</td> <td>   -0.516</td> <td> 0.608</td> <td>-3.44e+06</td> <td> 2.03e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_Hispanic_Latino</th>        <td> 8.119e+06</td> <td> 3.81e+07</td> <td>    0.213</td> <td> 0.832</td> <td>-6.81e+07</td> <td> 8.43e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_50_PL</th>                  <td>-2.996e+04</td> <td> 3.13e+06</td> <td>   -0.010</td> <td> 0.992</td> <td>-6.29e+06</td> <td> 6.23e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_125_PL</th>                 <td>  -4.3e+06</td> <td> 7.84e+06</td> <td>   -0.549</td> <td> 0.585</td> <td>   -2e+07</td> <td> 1.14e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_185_PL</th>                 <td> 3.512e+06</td> <td> 1.34e+07</td> <td>    0.261</td> <td> 0.795</td> <td>-2.34e+07</td> <td> 3.04e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_200_PL</th>                 <td> -5.76e+06</td> <td> 1.35e+07</td> <td>   -0.427</td> <td> 0.671</td> <td>-3.27e+07</td> <td> 2.12e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_300_PL</th>                 <td> -1.93e+07</td> <td> 1.14e+07</td> <td>   -1.690</td> <td> 0.096</td> <td>-4.21e+07</td> <td> 3.55e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_400_PL</th>                 <td> 2.934e+07</td> <td> 1.46e+07</td> <td>    2.005</td> <td> 0.050</td> <td> 6.42e+04</td> <td> 5.86e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent_500_PL</th>                 <td>-8.594e+06</td> <td> 9.41e+06</td> <td>   -0.914</td> <td> 0.365</td> <td>-2.74e+07</td> <td> 1.02e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>96.188</td> <th>  Durbin-Watson:     </th> <td>   0.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>1713.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.827</td> <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>24.964</td> <th>  Cond. No.          </th> <td>    676.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         Total Releases   R-squared:                       0.257\n",
       "Model:                            OLS   Adj. R-squared:                  0.056\n",
       "Method:                 Least Squares   F-statistic:                     1.277\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):              0.242\n",
       "Time:                        13:28:50   Log-Likelihood:                -1204.3\n",
       "No. Observations:                  76   AIC:                             2443.\n",
       "Df Residuals:                      59   BIC:                             2482.\n",
       "Df Model:                          16                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================================\n",
       "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------\n",
       "const                          -1.001e+07   4.02e+07     -0.249      0.804   -9.05e+07    7.05e+07\n",
       "Total Population                9.274e+05   1.74e+06      0.534      0.595   -2.55e+06     4.4e+06\n",
       "Percent_Below_Poverty_Level     6.793e+06   6.84e+06      0.993      0.325    -6.9e+06    2.05e+07\n",
       "Percent_White                   1.174e+07   3.81e+07      0.308      0.759   -6.45e+07     8.8e+07\n",
       "Percent_Black_African_American  6.015e+06   3.12e+07      0.193      0.848   -5.64e+07    6.84e+07\n",
       "Percent_American_Indian         1.558e+06   1.71e+06      0.910      0.367   -1.87e+06    4.99e+06\n",
       "Percent_Asian                   1.126e+05   1.08e+07      0.010      0.992   -2.15e+07    2.17e+07\n",
       "Percent_Native_Hawaiian_PI     -9.038e+05   1.32e+06     -0.685      0.496   -3.54e+06    1.74e+06\n",
       "Percent_Other_Race             -7.048e+05   1.37e+06     -0.516      0.608   -3.44e+06    2.03e+06\n",
       "Percent_Hispanic_Latino         8.119e+06   3.81e+07      0.213      0.832   -6.81e+07    8.43e+07\n",
       "Percent_50_PL                  -2.996e+04   3.13e+06     -0.010      0.992   -6.29e+06    6.23e+06\n",
       "Percent_125_PL                   -4.3e+06   7.84e+06     -0.549      0.585      -2e+07    1.14e+07\n",
       "Percent_185_PL                  3.512e+06   1.34e+07      0.261      0.795   -2.34e+07    3.04e+07\n",
       "Percent_200_PL                  -5.76e+06   1.35e+07     -0.427      0.671   -3.27e+07    2.12e+07\n",
       "Percent_300_PL                  -1.93e+07   1.14e+07     -1.690      0.096   -4.21e+07    3.55e+06\n",
       "Percent_400_PL                  2.934e+07   1.46e+07      2.005      0.050    6.42e+04    5.86e+07\n",
       "Percent_500_PL                 -8.594e+06   9.41e+06     -0.914      0.365   -2.74e+07    1.02e+07\n",
       "==============================================================================\n",
       "Omnibus:                       96.188   Durbin-Watson:                   0.652\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1713.142\n",
       "Skew:                           3.827   Prob(JB):                         0.00\n",
       "Kurtosis:                      24.964   Cond. No.                         676.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create model\n",
    "X_harris_int = sm.add_constant(X_harris_norm)\n",
    "model = sm.OLS(y_harris,X_harris_int).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Harris County"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7.600000e+01\n",
       "mean     6.137768e+05\n",
       "std      2.152041e+06\n",
       "min      0.000000e+00\n",
       "25%      3.250000e+01\n",
       "50%      6.436300e+03\n",
       "75%      1.206785e+05\n",
       "max      1.593846e+07\n",
       "Name: Total Releases, dtype: float64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_harris['Total Releases'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>ZIP</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Harris Releases Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "      <td>37298</td>\n",
       "      <td>9.528661</td>\n",
       "      <td>56.657193</td>\n",
       "      <td>5.946700</td>\n",
       "      <td>0.423615</td>\n",
       "      <td>1.177007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.480133</td>\n",
       "      <td>33.830232</td>\n",
       "      <td>3.394284</td>\n",
       "      <td>12.839831</td>\n",
       "      <td>21.097646</td>\n",
       "      <td>24.274760</td>\n",
       "      <td>42.163119</td>\n",
       "      <td>57.633117</td>\n",
       "      <td>67.126924</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77530</td>\n",
       "      <td>4.999060e+06</td>\n",
       "      <td>33387</td>\n",
       "      <td>16.922754</td>\n",
       "      <td>18.770779</td>\n",
       "      <td>12.441968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240782</td>\n",
       "      <td>67.472369</td>\n",
       "      <td>9.488723</td>\n",
       "      <td>25.755534</td>\n",
       "      <td>39.997604</td>\n",
       "      <td>42.121185</td>\n",
       "      <td>61.014766</td>\n",
       "      <td>80.252793</td>\n",
       "      <td>87.507114</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77520</td>\n",
       "      <td>4.044139e+06</td>\n",
       "      <td>35114</td>\n",
       "      <td>18.118130</td>\n",
       "      <td>29.455488</td>\n",
       "      <td>12.946403</td>\n",
       "      <td>0.065501</td>\n",
       "      <td>0.734750</td>\n",
       "      <td>0.082588</td>\n",
       "      <td>10.050122</td>\n",
       "      <td>54.978071</td>\n",
       "      <td>6.407701</td>\n",
       "      <td>23.950561</td>\n",
       "      <td>36.765962</td>\n",
       "      <td>39.366065</td>\n",
       "      <td>62.055021</td>\n",
       "      <td>75.226405</td>\n",
       "      <td>82.995386</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77507</td>\n",
       "      <td>3.543322e+06</td>\n",
       "      <td>312</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>68.269231</td>\n",
       "      <td>14.423077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.487179</td>\n",
       "      <td>17.307692</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>32.051282</td>\n",
       "      <td>39.423077</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>60.256410</td>\n",
       "      <td>75.961538</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>77076</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>35866</td>\n",
       "      <td>30.898344</td>\n",
       "      <td>4.826298</td>\n",
       "      <td>6.220376</td>\n",
       "      <td>0.225841</td>\n",
       "      <td>0.471198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.172029</td>\n",
       "      <td>88.431941</td>\n",
       "      <td>11.841298</td>\n",
       "      <td>37.021134</td>\n",
       "      <td>55.863492</td>\n",
       "      <td>61.453744</td>\n",
       "      <td>82.495957</td>\n",
       "      <td>92.129036</td>\n",
       "      <td>96.807561</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>77077</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>59449</td>\n",
       "      <td>10.905146</td>\n",
       "      <td>39.746674</td>\n",
       "      <td>21.640398</td>\n",
       "      <td>0.348198</td>\n",
       "      <td>12.881630</td>\n",
       "      <td>0.117748</td>\n",
       "      <td>3.196017</td>\n",
       "      <td>22.883480</td>\n",
       "      <td>4.899998</td>\n",
       "      <td>14.937173</td>\n",
       "      <td>23.934801</td>\n",
       "      <td>25.416744</td>\n",
       "      <td>39.053643</td>\n",
       "      <td>51.376810</td>\n",
       "      <td>61.242409</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>77043</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>24608</td>\n",
       "      <td>13.666287</td>\n",
       "      <td>43.006339</td>\n",
       "      <td>4.396944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.797952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.689207</td>\n",
       "      <td>43.042913</td>\n",
       "      <td>6.351593</td>\n",
       "      <td>19.497724</td>\n",
       "      <td>30.234070</td>\n",
       "      <td>33.521619</td>\n",
       "      <td>48.390767</td>\n",
       "      <td>63.255852</td>\n",
       "      <td>70.042263</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>77033</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>30438</td>\n",
       "      <td>27.919049</td>\n",
       "      <td>1.176161</td>\n",
       "      <td>66.499113</td>\n",
       "      <td>0.111702</td>\n",
       "      <td>0.298968</td>\n",
       "      <td>0.147842</td>\n",
       "      <td>2.881267</td>\n",
       "      <td>31.693935</td>\n",
       "      <td>10.125501</td>\n",
       "      <td>38.264669</td>\n",
       "      <td>55.900519</td>\n",
       "      <td>59.800250</td>\n",
       "      <td>77.735068</td>\n",
       "      <td>88.931599</td>\n",
       "      <td>94.056771</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>77092</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>38312</td>\n",
       "      <td>31.483608</td>\n",
       "      <td>20.594070</td>\n",
       "      <td>11.134892</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>1.524327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.241595</td>\n",
       "      <td>65.483399</td>\n",
       "      <td>11.998852</td>\n",
       "      <td>38.959073</td>\n",
       "      <td>56.368762</td>\n",
       "      <td>60.774692</td>\n",
       "      <td>72.794425</td>\n",
       "      <td>80.802360</td>\n",
       "      <td>87.069326</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ZIP  Total Releases  Total Population  Percent_Below_Poverty_Level  \\\n",
       "0   77536    1.593846e+07             31865                     7.453319   \n",
       "1   77571    7.254232e+06             37298                     9.528661   \n",
       "2   77530    4.999060e+06             33387                    16.922754   \n",
       "3   77520    4.044139e+06             35114                    18.118130   \n",
       "4   77507    3.543322e+06               312                    19.230769   \n",
       "..    ...             ...               ...                          ...   \n",
       "71  77076    0.000000e+00             35866                    30.898344   \n",
       "72  77077    0.000000e+00             59449                    10.905146   \n",
       "73  77043    0.000000e+00             24608                    13.666287   \n",
       "74  77033    0.000000e+00             30438                    27.919049   \n",
       "75  77092    0.000000e+00             38312                    31.483608   \n",
       "\n",
       "    Percent_White  Percent_Black_African_American  Percent_American_Indian  \\\n",
       "0       58.104503                        1.449867                 0.922642   \n",
       "1       56.657193                        5.946700                 0.423615   \n",
       "2       18.770779                       12.441968                 0.000000   \n",
       "3       29.455488                       12.946403                 0.065501   \n",
       "4       68.269231                       14.423077                 0.000000   \n",
       "..            ...                             ...                      ...   \n",
       "71       4.826298                        6.220376                 0.225841   \n",
       "72      39.746674                       21.640398                 0.348198   \n",
       "73      43.006339                        4.396944                 0.000000   \n",
       "74       1.176161                       66.499113                 0.111702   \n",
       "75      20.594070                       11.134892                 0.044373   \n",
       "\n",
       "    Percent_Asian  Percent_Native_Hawaiian_PI  Percent_Other_Race  \\\n",
       "0        1.653852                    0.000000            2.815001   \n",
       "1        1.177007                    0.000000            4.480133   \n",
       "2        0.185701                    0.000000            3.240782   \n",
       "3        0.734750                    0.082588           10.050122   \n",
       "4        0.000000                    0.000000            4.487179   \n",
       "..            ...                         ...                 ...   \n",
       "71       0.471198                    0.000000            5.172029   \n",
       "72      12.881630                    0.117748            3.196017   \n",
       "73       8.797952                    0.000000            5.689207   \n",
       "74       0.298968                    0.147842            2.881267   \n",
       "75       1.524327                    0.000000           37.241595   \n",
       "\n",
       "    Percent_Hispanic_Latino  Percent_50_PL  Percent_125_PL  Percent_185_PL  \\\n",
       "0                 37.363879       3.276322        8.771379       18.732151   \n",
       "1                 33.830232       3.394284       12.839831       21.097646   \n",
       "2                 67.472369       9.488723       25.755534       39.997604   \n",
       "3                 54.978071       6.407701       23.950561       36.765962   \n",
       "4                 17.307692      19.230769       19.230769       32.051282   \n",
       "..                      ...            ...             ...             ...   \n",
       "71                88.431941      11.841298       37.021134       55.863492   \n",
       "72                22.883480       4.899998       14.937173       23.934801   \n",
       "73                43.042913       6.351593       19.497724       30.234070   \n",
       "74                31.693935      10.125501       38.264669       55.900519   \n",
       "75                65.483399      11.998852       38.959073       56.368762   \n",
       "\n",
       "    Percent_200_PL  Percent_300_PL  Percent_400_PL  Percent_500_PL  \\\n",
       "0        20.878707       36.908834       54.815628       67.174015   \n",
       "1        24.274760       42.163119       57.633117       67.126924   \n",
       "2        42.121185       61.014766       80.252793       87.507114   \n",
       "3        39.366065       62.055021       75.226405       82.995386   \n",
       "4        39.423077       45.833333       60.256410       75.961538   \n",
       "..             ...             ...             ...             ...   \n",
       "71       61.453744       82.495957       92.129036       96.807561   \n",
       "72       25.416744       39.053643       51.376810       61.242409   \n",
       "73       33.521619       48.390767       63.255852       70.042263   \n",
       "74       59.800250       77.735068       88.931599       94.056771   \n",
       "75       60.774692       72.794425       80.802360       87.069326   \n",
       "\n",
       "   Harris Releases Binned  \n",
       "0                       4  \n",
       "1                       4  \n",
       "2                       4  \n",
       "3                       4  \n",
       "4                       4  \n",
       "..                    ...  \n",
       "71                    NaN  \n",
       "72                    NaN  \n",
       "73                    NaN  \n",
       "74                    NaN  \n",
       "75                    NaN  \n",
       "\n",
       "[76 rows x 19 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bin Harris Releases\n",
    "bins_harris = [0, 35, 7000, 125000, 20000000]\n",
    "labels = [1,2,3,4]\n",
    "df_harris['Harris Releases Binned'] = pd.cut(df_harris['Total Releases'], bins=bins, labels=labels)\n",
    "df_harris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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>ZIP</th>\n",
       "      <th>Total Releases</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Percent_Below_Poverty_Level</th>\n",
       "      <th>Percent_White</th>\n",
       "      <th>Percent_Black_African_American</th>\n",
       "      <th>Percent_American_Indian</th>\n",
       "      <th>Percent_Asian</th>\n",
       "      <th>Percent_Native_Hawaiian_PI</th>\n",
       "      <th>Percent_Other_Race</th>\n",
       "      <th>Percent_Hispanic_Latino</th>\n",
       "      <th>Percent_50_PL</th>\n",
       "      <th>Percent_125_PL</th>\n",
       "      <th>Percent_185_PL</th>\n",
       "      <th>Percent_200_PL</th>\n",
       "      <th>Percent_300_PL</th>\n",
       "      <th>Percent_400_PL</th>\n",
       "      <th>Percent_500_PL</th>\n",
       "      <th>Harris Releases Binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77536</td>\n",
       "      <td>1.593846e+07</td>\n",
       "      <td>31865</td>\n",
       "      <td>7.453319</td>\n",
       "      <td>58.104503</td>\n",
       "      <td>1.449867</td>\n",
       "      <td>0.922642</td>\n",
       "      <td>1.653852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.815001</td>\n",
       "      <td>37.363879</td>\n",
       "      <td>3.276322</td>\n",
       "      <td>8.771379</td>\n",
       "      <td>18.732151</td>\n",
       "      <td>20.878707</td>\n",
       "      <td>36.908834</td>\n",
       "      <td>54.815628</td>\n",
       "      <td>67.174015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77571</td>\n",
       "      <td>7.254232e+06</td>\n",
       "      <td>37298</td>\n",
       "      <td>9.528661</td>\n",
       "      <td>56.657193</td>\n",
       "      <td>5.946700</td>\n",
       "      <td>0.423615</td>\n",
       "      <td>1.177007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.480133</td>\n",
       "      <td>33.830232</td>\n",
       "      <td>3.394284</td>\n",
       "      <td>12.839831</td>\n",
       "      <td>21.097646</td>\n",
       "      <td>24.274760</td>\n",
       "      <td>42.163119</td>\n",
       "      <td>57.633117</td>\n",
       "      <td>67.126924</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77530</td>\n",
       "      <td>4.999060e+06</td>\n",
       "      <td>33387</td>\n",
       "      <td>16.922754</td>\n",
       "      <td>18.770779</td>\n",
       "      <td>12.441968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240782</td>\n",
       "      <td>67.472369</td>\n",
       "      <td>9.488723</td>\n",
       "      <td>25.755534</td>\n",
       "      <td>39.997604</td>\n",
       "      <td>42.121185</td>\n",
       "      <td>61.014766</td>\n",
       "      <td>80.252793</td>\n",
       "      <td>87.507114</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77520</td>\n",
       "      <td>4.044139e+06</td>\n",
       "      <td>35114</td>\n",
       "      <td>18.118130</td>\n",
       "      <td>29.455488</td>\n",
       "      <td>12.946403</td>\n",
       "      <td>0.065501</td>\n",
       "      <td>0.734750</td>\n",
       "      <td>0.082588</td>\n",
       "      <td>10.050122</td>\n",
       "      <td>54.978071</td>\n",
       "      <td>6.407701</td>\n",
       "      <td>23.950561</td>\n",
       "      <td>36.765962</td>\n",
       "      <td>39.366065</td>\n",
       "      <td>62.055021</td>\n",
       "      <td>75.226405</td>\n",
       "      <td>82.995386</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77507</td>\n",
       "      <td>3.543322e+06</td>\n",
       "      <td>312</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>68.269231</td>\n",
       "      <td>14.423077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.487179</td>\n",
       "      <td>17.307692</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>32.051282</td>\n",
       "      <td>39.423077</td>\n",
       "      <td>45.833333</td>\n",
       "      <td>60.256410</td>\n",
       "      <td>75.961538</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>77076</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>35866</td>\n",
       "      <td>30.898344</td>\n",
       "      <td>4.826298</td>\n",
       "      <td>6.220376</td>\n",
       "      <td>0.225841</td>\n",
       "      <td>0.471198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.172029</td>\n",
       "      <td>88.431941</td>\n",
       "      <td>11.841298</td>\n",
       "      <td>37.021134</td>\n",
       "      <td>55.863492</td>\n",
       "      <td>61.453744</td>\n",
       "      <td>82.495957</td>\n",
       "      <td>92.129036</td>\n",
       "      <td>96.807561</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>77077</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>59449</td>\n",
       "      <td>10.905146</td>\n",
       "      <td>39.746674</td>\n",
       "      <td>21.640398</td>\n",
       "      <td>0.348198</td>\n",
       "      <td>12.881630</td>\n",
       "      <td>0.117748</td>\n",
       "      <td>3.196017</td>\n",
       "      <td>22.883480</td>\n",
       "      <td>4.899998</td>\n",
       "      <td>14.937173</td>\n",
       "      <td>23.934801</td>\n",
       "      <td>25.416744</td>\n",
       "      <td>39.053643</td>\n",
       "      <td>51.376810</td>\n",
       "      <td>61.242409</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>77043</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>24608</td>\n",
       "      <td>13.666287</td>\n",
       "      <td>43.006339</td>\n",
       "      <td>4.396944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.797952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.689207</td>\n",
       "      <td>43.042913</td>\n",
       "      <td>6.351593</td>\n",
       "      <td>19.497724</td>\n",
       "      <td>30.234070</td>\n",
       "      <td>33.521619</td>\n",
       "      <td>48.390767</td>\n",
       "      <td>63.255852</td>\n",
       "      <td>70.042263</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>77033</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>30438</td>\n",
       "      <td>27.919049</td>\n",
       "      <td>1.176161</td>\n",
       "      <td>66.499113</td>\n",
       "      <td>0.111702</td>\n",
       "      <td>0.298968</td>\n",
       "      <td>0.147842</td>\n",
       "      <td>2.881267</td>\n",
       "      <td>31.693935</td>\n",
       "      <td>10.125501</td>\n",
       "      <td>38.264669</td>\n",
       "      <td>55.900519</td>\n",
       "      <td>59.800250</td>\n",
       "      <td>77.735068</td>\n",
       "      <td>88.931599</td>\n",
       "      <td>94.056771</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>77092</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>38312</td>\n",
       "      <td>31.483608</td>\n",
       "      <td>20.594070</td>\n",
       "      <td>11.134892</td>\n",
       "      <td>0.044373</td>\n",
       "      <td>1.524327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.241595</td>\n",
       "      <td>65.483399</td>\n",
       "      <td>11.998852</td>\n",
       "      <td>38.959073</td>\n",
       "      <td>56.368762</td>\n",
       "      <td>60.774692</td>\n",
       "      <td>72.794425</td>\n",
       "      <td>80.802360</td>\n",
       "      <td>87.069326</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ZIP  Total Releases  Total Population  Percent_Below_Poverty_Level  \\\n",
       "0   77536    1.593846e+07             31865                     7.453319   \n",
       "1   77571    7.254232e+06             37298                     9.528661   \n",
       "2   77530    4.999060e+06             33387                    16.922754   \n",
       "3   77520    4.044139e+06             35114                    18.118130   \n",
       "4   77507    3.543322e+06               312                    19.230769   \n",
       "..    ...             ...               ...                          ...   \n",
       "71  77076    0.000000e+00             35866                    30.898344   \n",
       "72  77077    0.000000e+00             59449                    10.905146   \n",
       "73  77043    0.000000e+00             24608                    13.666287   \n",
       "74  77033    0.000000e+00             30438                    27.919049   \n",
       "75  77092    0.000000e+00             38312                    31.483608   \n",
       "\n",
       "    Percent_White  Percent_Black_African_American  Percent_American_Indian  \\\n",
       "0       58.104503                        1.449867                 0.922642   \n",
       "1       56.657193                        5.946700                 0.423615   \n",
       "2       18.770779                       12.441968                 0.000000   \n",
       "3       29.455488                       12.946403                 0.065501   \n",
       "4       68.269231                       14.423077                 0.000000   \n",
       "..            ...                             ...                      ...   \n",
       "71       4.826298                        6.220376                 0.225841   \n",
       "72      39.746674                       21.640398                 0.348198   \n",
       "73      43.006339                        4.396944                 0.000000   \n",
       "74       1.176161                       66.499113                 0.111702   \n",
       "75      20.594070                       11.134892                 0.044373   \n",
       "\n",
       "    Percent_Asian  Percent_Native_Hawaiian_PI  Percent_Other_Race  \\\n",
       "0        1.653852                    0.000000            2.815001   \n",
       "1        1.177007                    0.000000            4.480133   \n",
       "2        0.185701                    0.000000            3.240782   \n",
       "3        0.734750                    0.082588           10.050122   \n",
       "4        0.000000                    0.000000            4.487179   \n",
       "..            ...                         ...                 ...   \n",
       "71       0.471198                    0.000000            5.172029   \n",
       "72      12.881630                    0.117748            3.196017   \n",
       "73       8.797952                    0.000000            5.689207   \n",
       "74       0.298968                    0.147842            2.881267   \n",
       "75       1.524327                    0.000000           37.241595   \n",
       "\n",
       "    Percent_Hispanic_Latino  Percent_50_PL  Percent_125_PL  Percent_185_PL  \\\n",
       "0                 37.363879       3.276322        8.771379       18.732151   \n",
       "1                 33.830232       3.394284       12.839831       21.097646   \n",
       "2                 67.472369       9.488723       25.755534       39.997604   \n",
       "3                 54.978071       6.407701       23.950561       36.765962   \n",
       "4                 17.307692      19.230769       19.230769       32.051282   \n",
       "..                      ...            ...             ...             ...   \n",
       "71                88.431941      11.841298       37.021134       55.863492   \n",
       "72                22.883480       4.899998       14.937173       23.934801   \n",
       "73                43.042913       6.351593       19.497724       30.234070   \n",
       "74                31.693935      10.125501       38.264669       55.900519   \n",
       "75                65.483399      11.998852       38.959073       56.368762   \n",
       "\n",
       "    Percent_200_PL  Percent_300_PL  Percent_400_PL  Percent_500_PL  \\\n",
       "0        20.878707       36.908834       54.815628       67.174015   \n",
       "1        24.274760       42.163119       57.633117       67.126924   \n",
       "2        42.121185       61.014766       80.252793       87.507114   \n",
       "3        39.366065       62.055021       75.226405       82.995386   \n",
       "4        39.423077       45.833333       60.256410       75.961538   \n",
       "..             ...             ...             ...             ...   \n",
       "71       61.453744       82.495957       92.129036       96.807561   \n",
       "72       25.416744       39.053643       51.376810       61.242409   \n",
       "73       33.521619       48.390767       63.255852       70.042263   \n",
       "74       59.800250       77.735068       88.931599       94.056771   \n",
       "75       60.774692       72.794425       80.802360       87.069326   \n",
       "\n",
       "   Harris Releases Binned  \n",
       "0                       4  \n",
       "1                       4  \n",
       "2                       4  \n",
       "3                       4  \n",
       "4                       4  \n",
       "..                    ...  \n",
       "71                      1  \n",
       "72                      1  \n",
       "73                      1  \n",
       "74                      1  \n",
       "75                      1  \n",
       "\n",
       "[76 rows x 19 columns]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_harris['Harris Releases Binned'].fillna(1, inplace=True)\n",
    "df_harris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     4\n",
       "2     4\n",
       "3     4\n",
       "4     4\n",
       "     ..\n",
       "71    1\n",
       "72    1\n",
       "73    1\n",
       "74    1\n",
       "75    1\n",
       "Name: Harris Releases Binned, Length: 76, dtype: category\n",
       "Categories (4, int64): [1 < 2 < 3 < 4]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_logreg = df_harris['Harris Releases Binned']\n",
    "y_logreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features in the green area: ['Percent_Asian']\n",
      "features in the blue area: []\n"
     ]
    }
   ],
   "source": [
    "### fit Boruta (it accepts np.array, not pd.DataFrame)\n",
    "boruta.fit(np.array(X_harris_norm), np.array(y_logreg))\n",
    "### print results\n",
    "green_area = X_harris_norm.columns[boruta.support_].to_list()\n",
    "blue_area = X_harris_norm.columns[boruta.support_weak_].to_list()\n",
    "print('features in the green area:', green_area)\n",
    "print('features in the blue area:', blue_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_logreg = X_harris_norm[['Percent_Asian', 'Total Population', 'Percent_125_PL', 'Percent_Below_Poverty_Level']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "XH_train, XH_test, yH_train, yH_test = train_test_split(X_harris_norm, y_logreg, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidcuervo/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000000000000.0, class_weight=None, dual=False,\n",
       "                   fit_intercept=False, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "logregH = LogisticRegression(fit_intercept=False, C=1e12, solver='liblinear')\n",
    "\n",
    "# Fit the model\n",
    "logregH.fit(XH_train, yH_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21052631578947367"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the score\n",
    "logreg4.score(XH_test, yH_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "learn-env",
   "language": "python",
   "name": "learn-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}