AIS-Summer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AIS Summer Comp (Advay Vyas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import preprocessing\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from string import digits\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Unemployment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>Median House Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA42</td>\n",
       "      <td>840562</td>\n",
       "      <td>35.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>455800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TX3</td>\n",
       "      <td>913161</td>\n",
       "      <td>37.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>54.5</td>\n",
       "      <td>365700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA22</td>\n",
       "      <td>768917</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.5</td>\n",
       "      <td>298300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IL6</td>\n",
       "      <td>710626</td>\n",
       "      <td>42.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>53.2</td>\n",
       "      <td>334200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IN5</td>\n",
       "      <td>791257</td>\n",
       "      <td>38.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>47.9</td>\n",
       "      <td>219100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  Population  Median Age  Unemployment Rate  Median Income  \\\n",
       "0  CA42      840562        35.6                5.1            7.8   \n",
       "1   TX3      913161        37.5                4.0            6.2   \n",
       "2  CA22      768917        33.0                6.6           15.2   \n",
       "3   IL6      710626        42.1                3.1            4.6   \n",
       "4   IN5      791257        38.3                3.5            8.1   \n",
       "\n",
       "   Poverty Rate  Median House Price  \n",
       "0          26.5              455800  \n",
       "1          54.5              365700  \n",
       "2          24.5              298300  \n",
       "3          53.2              334200  \n",
       "4          47.9              219100  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "all_data = pd.read_csv('C:\\\\Users\\\\advay\\\\OneDrive\\\\Desktop\\\\Coding\\\\VSCode\\\\Python\\\\AIS Summer Comp 2022\\\\train.csv')\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    CA\n",
       "1    TX\n",
       "2    CA\n",
       "3    IL\n",
       "4    IN\n",
       "Name: ID, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modifying ID column\n",
    "for j in range(all_data.shape[0]):\n",
    "    all_data.iloc[j, 0] = ''.join([i for i in all_data.iloc[j, 0] if not i.isdigit()])\n",
    "\n",
    "all_data['ID'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating dummies\n",
    "dummied_data = pd.get_dummies(all_data, columns=['ID'], prefix=\"state\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    455800\n",
       "1    365700\n",
       "2    298300\n",
       "3    334200\n",
       "4    219100\n",
       "Name: Median House Price, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get target\n",
    "y = dummied_data['Median House Price']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Unemployment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TX35</td>\n",
       "      <td>857654</td>\n",
       "      <td>33.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PR16</td>\n",
       "      <td>678333</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>13.5</td>\n",
       "      <td>28.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NY4</td>\n",
       "      <td>730314</td>\n",
       "      <td>40.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>43.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OR1</td>\n",
       "      <td>858875</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>40.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GA8</td>\n",
       "      <td>706237</td>\n",
       "      <td>37.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>17.3</td>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     ID  Population  Median Age  Unemployment Rate  Median Income  \\\n",
       "0  TX35      857654        33.0                4.1           18.4   \n",
       "1  PR16      678333        43.0                4.6           13.5   \n",
       "2   NY4      730314        40.4                3.6            5.7   \n",
       "3   OR1      858875        38.0                3.8            8.9   \n",
       "4   GA8      706237        37.6                5.3           17.3   \n",
       "\n",
       "   Poverty Rate  \n",
       "0          25.2  \n",
       "1          28.3  \n",
       "2          43.6  \n",
       "3          40.7  \n",
       "4          22.7  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading competition data\n",
    "test_data = pd.read_csv('C:\\\\Users\\\\advay\\\\OneDrive\\\\Desktop\\\\Coding\\VSCode\\\\Python\\\\AIS Summer Comp 2022\\\\evaluation_input.csv')\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    TX\n",
       "1    PR\n",
       "2    NY\n",
       "3    OR\n",
       "4    GA\n",
       "Name: ID, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modifying test ID column\n",
    "for j in range((test_data).shape[0]):\n",
    "    test_data.iloc[j, 0] = ''.join([i for i in test_data.iloc[j, 0] if not i.isdigit()])\n",
    "\n",
    "test_data['ID'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Unemployment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>state_AK</th>\n",
       "      <th>state_AL</th>\n",
       "      <th>state_AR</th>\n",
       "      <th>state_AZ</th>\n",
       "      <th>state_CA</th>\n",
       "      <th>...</th>\n",
       "      <th>state_PR</th>\n",
       "      <th>state_RI</th>\n",
       "      <th>state_SC</th>\n",
       "      <th>state_TN</th>\n",
       "      <th>state_TX</th>\n",
       "      <th>state_UT</th>\n",
       "      <th>state_VA</th>\n",
       "      <th>state_WA</th>\n",
       "      <th>state_WI</th>\n",
       "      <th>state_WV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>750993.207547</td>\n",
       "      <td>38.718396</td>\n",
       "      <td>4.526415</td>\n",
       "      <td>12.453774</td>\n",
       "      <td>32.354717</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>0.018868</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>0.094340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047170</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>0.028302</td>\n",
       "      <td>0.028302</td>\n",
       "      <td>0.066038</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>0.033019</td>\n",
       "      <td>0.033019</td>\n",
       "      <td>0.028302</td>\n",
       "      <td>0.004717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>52634.173803</td>\n",
       "      <td>3.665348</td>\n",
       "      <td>1.285449</td>\n",
       "      <td>4.340292</td>\n",
       "      <td>10.614375</td>\n",
       "      <td>0.068680</td>\n",
       "      <td>0.118393</td>\n",
       "      <td>0.136381</td>\n",
       "      <td>0.118393</td>\n",
       "      <td>0.292992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212504</td>\n",
       "      <td>0.068680</td>\n",
       "      <td>0.166226</td>\n",
       "      <td>0.166226</td>\n",
       "      <td>0.248936</td>\n",
       "      <td>0.096898</td>\n",
       "      <td>0.179109</td>\n",
       "      <td>0.179109</td>\n",
       "      <td>0.166226</td>\n",
       "      <td>0.068680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>530066.000000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>719479.500000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>9.475000</td>\n",
       "      <td>24.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>746262.000000</td>\n",
       "      <td>38.750000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>779470.500000</td>\n",
       "      <td>40.425000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>960957.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
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      ],
      "text/plain": [
       "          Population  Median Age  Unemployment Rate  Median Income  \\\n",
       "count     212.000000  212.000000         212.000000     212.000000   \n",
       "mean   750993.207547   38.718396           4.526415      12.453774   \n",
       "std     52634.173803    3.665348           1.285449       4.340292   \n",
       "min    530066.000000   28.700000           2.200000       3.500000   \n",
       "25%    719479.500000   36.500000           3.600000       9.475000   \n",
       "50%    746262.000000   38.750000           4.300000      11.700000   \n",
       "75%    779470.500000   40.425000           5.200000      14.800000   \n",
       "max    960957.000000   56.000000           9.600000      26.100000   \n",
       "\n",
       "       Poverty Rate    state_AK    state_AL    state_AR    state_AZ  \\\n",
       "count    212.000000  212.000000  212.000000  212.000000  212.000000   \n",
       "mean      32.354717    0.004717    0.014151    0.018868    0.014151   \n",
       "std       10.614375    0.068680    0.118393    0.136381    0.118393   \n",
       "min       10.700000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%       24.625000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%       30.600000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%       38.500000    0.000000    0.000000    0.000000    0.000000   \n",
       "max       72.500000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         state_CA  ...    state_PR    state_RI    state_SC    state_TN  \\\n",
       "count  212.000000  ...  212.000000  212.000000  212.000000  212.000000   \n",
       "mean     0.094340  ...    0.047170    0.004717    0.028302    0.028302   \n",
       "std      0.292992  ...    0.212504    0.068680    0.166226    0.166226   \n",
       "min      0.000000  ...    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  ...    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000  ...    0.000000    0.000000    0.000000    0.000000   \n",
       "75%      0.000000  ...    0.000000    0.000000    0.000000    0.000000   \n",
       "max      1.000000  ...    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         state_TX    state_UT    state_VA    state_WA    state_WI    state_WV  \n",
       "count  212.000000  212.000000  212.000000  212.000000  212.000000  212.000000  \n",
       "mean     0.066038    0.009434    0.033019    0.033019    0.028302    0.004717  \n",
       "std      0.248936    0.096898    0.179109    0.179109    0.166226    0.068680  \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "75%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating test dummies\n",
    "dummied_test_data = pd.get_dummies(test_data, columns=['ID'], prefix=\"state\")\n",
    "dummied_test_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making data sets equivalent\n",
    "dummied_data, dummied_test_data = dummied_data.align(dummied_test_data, join = 'right', axis = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Unemployment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>state_AK</th>\n",
       "      <th>state_AL</th>\n",
       "      <th>state_AR</th>\n",
       "      <th>state_AZ</th>\n",
       "      <th>state_CA</th>\n",
       "      <th>...</th>\n",
       "      <th>state_PR</th>\n",
       "      <th>state_RI</th>\n",
       "      <th>state_SC</th>\n",
       "      <th>state_TN</th>\n",
       "      <th>state_TX</th>\n",
       "      <th>state_UT</th>\n",
       "      <th>state_VA</th>\n",
       "      <th>state_WA</th>\n",
       "      <th>state_WI</th>\n",
       "      <th>state_WV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>750993.207547</td>\n",
       "      <td>38.718396</td>\n",
       "      <td>4.526415</td>\n",
       "      <td>12.453774</td>\n",
       "      <td>32.354717</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>0.018868</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>0.094340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047170</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>0.028302</td>\n",
       "      <td>0.028302</td>\n",
       "      <td>0.066038</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>0.033019</td>\n",
       "      <td>0.033019</td>\n",
       "      <td>0.028302</td>\n",
       "      <td>0.004717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>52634.173803</td>\n",
       "      <td>3.665348</td>\n",
       "      <td>1.285449</td>\n",
       "      <td>4.340292</td>\n",
       "      <td>10.614375</td>\n",
       "      <td>0.068680</td>\n",
       "      <td>0.118393</td>\n",
       "      <td>0.136381</td>\n",
       "      <td>0.118393</td>\n",
       "      <td>0.292992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212504</td>\n",
       "      <td>0.068680</td>\n",
       "      <td>0.166226</td>\n",
       "      <td>0.166226</td>\n",
       "      <td>0.248936</td>\n",
       "      <td>0.096898</td>\n",
       "      <td>0.179109</td>\n",
       "      <td>0.179109</td>\n",
       "      <td>0.166226</td>\n",
       "      <td>0.068680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>530066.000000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>719479.500000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>9.475000</td>\n",
       "      <td>24.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>746262.000000</td>\n",
       "      <td>38.750000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>779470.500000</td>\n",
       "      <td>40.425000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>960957.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Population  Median Age  Unemployment Rate  Median Income  \\\n",
       "count     212.000000  212.000000         212.000000     212.000000   \n",
       "mean   750993.207547   38.718396           4.526415      12.453774   \n",
       "std     52634.173803    3.665348           1.285449       4.340292   \n",
       "min    530066.000000   28.700000           2.200000       3.500000   \n",
       "25%    719479.500000   36.500000           3.600000       9.475000   \n",
       "50%    746262.000000   38.750000           4.300000      11.700000   \n",
       "75%    779470.500000   40.425000           5.200000      14.800000   \n",
       "max    960957.000000   56.000000           9.600000      26.100000   \n",
       "\n",
       "       Poverty Rate    state_AK    state_AL    state_AR    state_AZ  \\\n",
       "count    212.000000  212.000000  212.000000  212.000000  212.000000   \n",
       "mean      32.354717    0.004717    0.014151    0.018868    0.014151   \n",
       "std       10.614375    0.068680    0.118393    0.136381    0.118393   \n",
       "min       10.700000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%       24.625000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%       30.600000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%       38.500000    0.000000    0.000000    0.000000    0.000000   \n",
       "max       72.500000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         state_CA  ...    state_PR    state_RI    state_SC    state_TN  \\\n",
       "count  212.000000  ...  212.000000  212.000000  212.000000  212.000000   \n",
       "mean     0.094340  ...    0.047170    0.004717    0.028302    0.028302   \n",
       "std      0.292992  ...    0.212504    0.068680    0.166226    0.166226   \n",
       "min      0.000000  ...    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  ...    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000  ...    0.000000    0.000000    0.000000    0.000000   \n",
       "75%      0.000000  ...    0.000000    0.000000    0.000000    0.000000   \n",
       "max      1.000000  ...    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         state_TX    state_UT    state_VA    state_WA    state_WI    state_WV  \n",
       "count  212.000000  212.000000  212.000000  212.000000  212.000000  212.000000  \n",
       "mean     0.066038    0.009434    0.033019    0.033019    0.028302    0.004717  \n",
       "std      0.248936    0.096898    0.179109    0.179109    0.166226    0.068680  \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "75%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dummied test data\n",
    "dummied_test_data.dropna(axis = 1)\n",
    "dummied_test_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Population  Median Age  Unemployment Rate  Median Income  Poverty Rate  \\\n",
      "0         False       False              False          False         False   \n",
      "1         False       False              False          False         False   \n",
      "2         False       False              False          False         False   \n",
      "3         False       False              False          False         False   \n",
      "4         False       False              False          False         False   \n",
      "..          ...         ...                ...            ...           ...   \n",
      "195       False       False              False          False         False   \n",
      "196       False       False              False          False         False   \n",
      "197       False       False              False          False         False   \n",
      "198       False       False              False          False         False   \n",
      "199       False       False              False          False         False   \n",
      "\n",
      "     state_AK  state_AL  state_AR  state_AZ  state_CA  ...  state_RI  \\\n",
      "0       False     False     False     False     False  ...     False   \n",
      "1       False     False     False     False     False  ...     False   \n",
      "2       False     False     False     False     False  ...     False   \n",
      "3       False     False     False     False     False  ...     False   \n",
      "4       False     False     False     False     False  ...     False   \n",
      "..        ...       ...       ...       ...       ...  ...       ...   \n",
      "195     False     False     False     False     False  ...     False   \n",
      "196     False     False     False     False     False  ...     False   \n",
      "197     False     False     False     False     False  ...     False   \n",
      "198     False     False     False     False     False  ...     False   \n",
      "199     False     False     False     False     False  ...     False   \n",
      "\n",
      "     state_SC  state_TN  state_TX  state_UT  state_VA  state_WA  state_WI  \\\n",
      "0       False     False     False     False     False     False     False   \n",
      "1       False     False     False     False     False     False     False   \n",
      "2       False     False     False     False     False     False     False   \n",
      "3       False     False     False     False     False     False     False   \n",
      "4       False     False     False     False     False     False     False   \n",
      "..        ...       ...       ...       ...       ...       ...       ...   \n",
      "195     False     False     False     False     False     False     False   \n",
      "196     False     False     False     False     False     False     False   \n",
      "197     False     False     False     False     False     False     False   \n",
      "198     False     False     False     False     False     False     False   \n",
      "199     False     False     False     False     False     False     False   \n",
      "\n",
      "     state_WV  Median House Price  \n",
      "0       False               False  \n",
      "1       False               False  \n",
      "2       False               False  \n",
      "3       False               False  \n",
      "4       False               False  \n",
      "..        ...                 ...  \n",
      "195     False               False  \n",
      "196     False               False  \n",
      "197     False               False  \n",
      "198     False               False  \n",
      "199     False               False  \n",
      "\n",
      "[200 rows x 48 columns]\n"
     ]
    }
   ],
   "source": [
    "# adding back the house prices\n",
    "dummied_data['Median House Price'] = y\n",
    "dummied_data.drop(dummied_data.tail(12).index, inplace = True)\n",
    "dummied_data.fillna(0, inplace = True)\n",
    "print(dummied_data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# customizing features\n",
    "features = dummied_data.columns.values.tolist()\n",
    "features.remove('Median House Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# customizing key features\n",
    "key_features = ['Population', 'Median Age', 'Unemployment Rate', 'Median Income', 'Poverty Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# customizing states\n",
    "states = features.copy()\n",
    "states.remove('Population')\n",
    "states.remove('Median Age')\n",
    "states.remove('Unemployment Rate')\n",
    "states.remove('Median Income')\n",
    "states.remove('Poverty Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Population</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>Unemployment Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>state_AK</th>\n",
       "      <th>state_AL</th>\n",
       "      <th>state_AR</th>\n",
       "      <th>state_AZ</th>\n",
       "      <th>state_CA</th>\n",
       "      <th>...</th>\n",
       "      <th>state_PR</th>\n",
       "      <th>state_RI</th>\n",
       "      <th>state_SC</th>\n",
       "      <th>state_TN</th>\n",
       "      <th>state_TX</th>\n",
       "      <th>state_UT</th>\n",
       "      <th>state_VA</th>\n",
       "      <th>state_WA</th>\n",
       "      <th>state_WI</th>\n",
       "      <th>state_WV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.00000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.519596e+05</td>\n",
       "      <td>38.859500</td>\n",
       "      <td>4.648500</td>\n",
       "      <td>12.195000</td>\n",
       "      <td>33.35100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.095000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.370597e+04</td>\n",
       "      <td>3.683893</td>\n",
       "      <td>1.441732</td>\n",
       "      <td>4.658809</td>\n",
       "      <td>11.40368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.171015</td>\n",
       "      <td>0.372112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.184241</td>\n",
       "      <td>0.070711</td>\n",
       "      <td>0.070711</td>\n",
       "      <td>0.121857</td>\n",
       "      <td>0.293951</td>\n",
       "      <td>0.099748</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.121857</td>\n",
       "      <td>0.099748</td>\n",
       "      <td>0.099748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.292950e+05</td>\n",
       "      <td>30.300000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>10.10000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.130718e+05</td>\n",
       "      <td>36.675000</td>\n",
       "      <td>3.675000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>24.57500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.411770e+05</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>31.90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.867138e+05</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>14.525000</td>\n",
       "      <td>40.60000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.068778e+06</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>65.80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Population  Median Age  Unemployment Rate  Median Income  \\\n",
       "count  2.000000e+02  200.000000         200.000000     200.000000   \n",
       "mean   7.519596e+05   38.859500           4.648500      12.195000   \n",
       "std    6.370597e+04    3.683893           1.441732       4.658809   \n",
       "min    5.292950e+05   30.300000           2.500000       3.800000   \n",
       "25%    7.130718e+05   36.675000           3.675000       8.900000   \n",
       "50%    7.411770e+05   38.900000           4.300000      11.700000   \n",
       "75%    7.867138e+05   41.100000           5.300000      14.525000   \n",
       "max    1.068778e+06   52.000000          10.600000      34.400000   \n",
       "\n",
       "       Poverty Rate  state_AK    state_AL  state_AR    state_AZ    state_CA  \\\n",
       "count     200.00000     200.0  200.000000     200.0  200.000000  200.000000   \n",
       "mean       33.35100       0.0    0.020000       0.0    0.030000    0.165000   \n",
       "std        11.40368       0.0    0.140351       0.0    0.171015    0.372112   \n",
       "min        10.10000       0.0    0.000000       0.0    0.000000    0.000000   \n",
       "25%        24.57500       0.0    0.000000       0.0    0.000000    0.000000   \n",
       "50%        31.90000       0.0    0.000000       0.0    0.000000    0.000000   \n",
       "75%        40.60000       0.0    0.000000       0.0    0.000000    0.000000   \n",
       "max        65.80000       0.0    1.000000       0.0    1.000000    1.000000   \n",
       "\n",
       "       ...    state_PR    state_RI    state_SC    state_TN    state_TX  \\\n",
       "count  ...  200.000000  200.000000  200.000000  200.000000  200.000000   \n",
       "mean   ...    0.035000    0.005000    0.005000    0.015000    0.095000   \n",
       "std    ...    0.184241    0.070711    0.070711    0.121857    0.293951   \n",
       "min    ...    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%    ...    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%    ...    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%    ...    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "max    ...    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         state_UT    state_VA    state_WA    state_WI    state_WV  \n",
       "count  200.000000  200.000000  200.000000  200.000000  200.000000  \n",
       "mean     0.010000    0.020000    0.015000    0.010000    0.010000  \n",
       "std      0.099748    0.140351    0.121857    0.099748    0.099748  \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "50%      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "75%      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating X\n",
    "X = dummied_data[features]\n",
    "X.head()\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YAAA/qqGu5B6l+vjZA/BRY8zFBT8TVL1NPX2xACtFRh/c9OBq1YSU170HnFHoT72xRxt3HyraGselel0A5aXcYsWUtkYtWTArIUYvmtulzz+8Rpt6+rI6ZrldAwDekvO71oY6vbrjoH7/+q5Ale9CxB32zwKQT7nGFK/7bbd8zU9MJF8DAFSaTOrKoPFbL3OPkj/lkgv5mQG4WNLnjDFHJB1TZC1Ra61tLuiZoep4TUHecXBA82dM0LRFc3Lee6BUIzmqYQQJgNyVY6wIhYzeMape15/fKWMka6X7nt6s7QcGslpCohyvAQBv8flde0u9rjtvspY+vj5Q5btQcScUMnnLYQEg15jid8kvPzGRfA0AUInKdXnMTOpl7lHyo5xyobQzAK21TdbakLV2hLW2OfoznX/IO2cKcjxnCrKzbvF5nWPUOXZk1gWpVCM5ynkECYDiKddY0dY4XPc8uVF3Pb5BX//dBm0/MJD1EhLleg0AuIvP7y6fPSnW+ScFp3wXMu7kK4cFACm3mJLqfjuen5hIvgYAqER+68qgybRe5h4ld+WUC/lZAlTGmFZjzDnGmAuc/wp9Yqg+xZiCnGokRyGV6nUBlJdyjRX5jN/leg0AuIuPD8YokOWbuAOgGvjN1/zEROImAKASlevymNTLxVdO1zztEqDGmE8qsgzoJEmrJZ0n6SlJcwt6Zqg6xZiCXKqNTtlgFYAf5Ror8hm/y/UaAHAXHx92Hzqib63cGLjyTdwBUA385mt+YiJxEwBQicp1eUzq5eIrp2vuZwbgYklnS9psrb1Q0lmSdhf0rFDRkjfIPH48HPt5U0+fprQ1FmwKcqlGcpTrCBIAxVXOsSJfS0gU6hqUw+bM5XCOQDac+HD25NFpy3cpykG6uEPZBFAp/ORrfnIxt8csWTBLPX1HPOMksRQAUA7S1ZWFrs+yOX45tyWVq3K65sba1F8iY8yfrLVnG2NWSzrXWnvEGLPaWntmMU4wWXd3t121alUpXhp5kLxB5uS2Ebpxbpdu+dnLsQ0zlyyYpQ+e3q5hw0IJz9vU06edBwc0vjm30RfOsYo9kqNUr4uqlvcvGDE4O5nEMGKFdPx4WGu3H9D2AwNqb6nXjPaWhDohU+WwOXM5nCMyQvz1kCrGlbIceJ1XkMpmPvNhoMLltWBUQvzNNH74yUfjj3ls0OrzD6/R5p7DrnEySLEU2aEOgk/kwMhKucSYQtdnbsdfsmCW3jGqXm2Nw2lLCpgAXnPXF/fTAfhTSR+X9BlFlv3cJ6nWWntxnk/QFwJ/edu4+5AuXroyNj320xdO1T1PDl0Katl13Tp/6pjANbwAZYbkOwCIYZkpxPVKrnukSF3z2KI56hw7Ml+nnpNyOEdkhPibhSCWgzd2HdIH7xx6Tr+4cY7eOa5450RdAmSEDsA4hY4ffmJ3EOM7/KMOQgbIgZGxcooxha7PvI5//fmduufJjYG9LggM1y9G2uH01trLrLX7rbVfkvR5SfdIujSvp4aqkbxBpjFy3TBz1ea92tTTJ0na1NMXqwScv9/04OrY3wEgyIhhmSnE9SqHzZnL4RyBQgtiOdi8t8/1nLbsLW4Mpy4BkK1Cxw8/sTuI8R3+UQcBKKRyijGFrs+8ju+0nwf1uiDYPDsAjTHN0f+Pdv6TtEbSk5IYooWsOBtkxnP7eTCsWPBMFVzZRwBA0PlJEIllJxQiofaqe4K0OXM5nCNQaEEsB411w1zPqaFumKTixW8azwFkq9Dxw0/sznd8J3cuLuogAIXgxPLXd/aWTYwp9P2K1/GdBRyDel0QbKlmAP4g+v/nJK2K/v+5uJ+BjCVvkPnIi9t064dOT9gwc9HcLj360rZY8PQKfmNH1mv52h26eOlKXXP3M7p46UotX7uD5B9AoKRLEJ3lLohlEYVIqMthc+ZyOEeg0IJYDsY3D9fieV0J57R4XpfGNw8vavwOYucogPJQ6PjhJ3bnM76TOxcfdRCAfIuP5S+/fbBsYkyh71fcjr9obpd+8vzW2M9BvC4ItrR7AAYNaz+Xv+QNMie1jNDTm/Zq1ea9GgxLj760TTfPPy22prHXWtDT25s0/w72EQBSYP39AEi3nj17oiQq1Pr/AdyceYhyOEf4RvzNUtDKQThs9fhrO/XS1gMKWylkpFmTWjT31PHa1NNXtPhdTnujAAHAHoBxihE//MTufMV3cufiow5CBsiB4Ut8LG9vqdd1503W0sfXl0WMKfT9inP8nQcHdGzQ6vMPr9HmnsOBvy4IBNcvxjDPRxszO9XRrLXP53pGqE6hkFHn2JEJyfn5U8doUusI7eod0ILZExOCZyhkNH/GBE1bNCchuD7zZo/nFHESfwBB4RXDnBiXakmdaoxl6a5XLsdNrnuCphzOESi0oJWDUMho7qnj1Tlm5JCYVMz4XajYCKDyFSN++Ind+Yrv5M7FRx0EIN/iY/n2AwO67+nNuv78Ts2a2Kyu8U2BjjGFvl+JP344bPWdj51D7EVOPDsAJX01+v96Sd2SXlSkF3GWpGcknV/YU0Mlih/FML75ROByC55uj43/u7MMRfLIP6ZCAygWr5iWLFWCSCwbKmgdAEHn93sIFEulfSe9YlKx43exYmOlfX5AOSlU+auk3MpP7CWO5V8lfYcAlF5yLN9+YED3PLmR2dxJiL3e8lXXV0PO4NkBaK29UJKMMfdLWmitXRP9+XRJf1+c00MlyWTZCD+PddZFTn4M+yUBKIZ8LYVDLEMuWJIJQVNN38lKjN/V9PkBQUP58ydd7OU6AkDwVWIejeLJV11fLTlD2j0AjTGrrbVnpvtdsbD2c/nKZK1+v48N2j4xQMCw/n4B5XP/EWIZssU+OIFVtfG32r6TlRa/q+3zQ8Uqyz0AKX/+pYq9XEegpKo2B0bmKi2PRvHkq66vwJwhsz0A46wzxnxL0vclWUl/JWldHk8MVSKTtfr9Ppap0ABKJZ/7jwQ5llXDcgjljH1wEDRB+U4WK3YFOX5nIyifH1CNSlX+yjHXSxV7iWMAUB5KlUeXY72HRPmq66slZ/DTAfhxSf9L0uLoz09I+q+CnREqVib7pIxrcn/s2JGRxxKsAZRaqffuK0YcrJblEMpZqb+HQLIgfCfDYavHX9upl7YeUNhKNUaaOalFc08dT+xKIwifH1CtSlH+gpzrZZvrEscAAF7i673Whjpd2T1Jp4xr0mntzTp5DG3L5SJfdX215AyhdA+w1g5I+qakf7LWXmat/Vr0d0BGnPWd62sjX7tU6zvXhKTF87oSHrt4XpdqQieC9cVLV+qau5/RxUtXavnaHQqHUy9nCwD5lElMy7dixcFNPX2xBiEpMhLqpgdXa1NPX15fB9kr5fcQcBOE7+SWvX1av/OQlj2xUXc9vkH//cRGrd95SFv2ErvSCcLnB1SrUpS/oOZ6ueS6xDEAgBen3mttqNN1503Wsic26oYfvqAP3knbcjnJV11fLTmDnz0A/1LSVyTVWWtPNsacKelfrLV/WYTzG4K1n8uD12g9v+s7P/XGHt304Iu6fPYkGSNZK/3k+a362tVnaFxTfaWtzwsUCuvvF1ip1qwv1jrlT72xR9fc/cyQ39+/8Fyd1zkm5+Mzmzs/2DshkKo6/pb6O/nMxh799XeeHRIjv/vxc3RuZ1vCORJ/hir15wfkQVnuASgVv/wVOtfzki4G55rrEseAkqnqHBjZK1Zu7tR7n75wqu55ciNty2UsX3V9heUMWe8B+EVJ50j6vSRZa1cbY6bk7bRQcdItI+JnfefxzfXa139UX//dhtjvnCm4qdbnndLWSGMOgKLJdc36bJPcYq1TXsjlEIK85FS5qbQ9yFD+Sv2d7Dt63DVG9h89Lim/8acSOxJL/fkB1azY5S+oy47mmuv6uY6VGL8BoJw4cbin74je3j+gmx96qeBtA069Z4yqYu+3SpavnKka7n3SLgEq6bi19kA2BzfGzDfGvGaM2WCM+SePx7zXGLPaGLPWGPOHbF4HweK1jMibe/wvI5JqCq4TrOM5+wOyNCiAcpHL0kZecTCbxppw2Grj7kN66o092rj7UMLrF3I5hKAuOQWg/E0e3egaIztGR2JXrvEnPm7+vw179PF7nyX3BFCWgrrsqN9cN1UemwrbigBA4aWK0fFx+Pev7Yl1/kmFbRtw6r0ao7y1qQBB52cG4MvGmGsl1RhjuiQtkvTHdE8yxtRI+rqk90vaKulPxpifW2tfiXvMKEnfkDTfWrvFGDMui/eAgPEarbdux0HfG6qGQkbzZ0zQtEVzhkzBdYJ18ojBmpBcbySmMX0bQAB5NX74iVlecTDTxpp0I7BTxeJcFWsWI4Dqc/IY9xh58phIjMwl/rjFzUVzu3Tf05u1/cAAuSeAslLIXM+LnxjsJ9fNZTZ3Lnk4ACC9dDE6Pg4XczaeU+9Nb2/S5LZGfe6na3JqUwHKgZ8OwBsl/W9JRyT9UNKvJN3q43nnSNpgrd0oScaY+yV9SNIrcY+5VtJPrLVbJMlau8v/qSOovJYReX1nr6a3N/sO3l5TcL1uUp55sydwjcksKwLASy4N0KGQ0UWnjdcDC8/T9gMDam+p14z2lozji5/Gj0Ith1CKJacA5EfQ85t0Ddq5xB+3uLn08fW6/vxOff13G0qeewJApuJzvWLEdz8x2E/HZC6deAxEA4DCShejk+NwMdsGQiGjKWNGqmN0o848aVSl7P2GgAjivXLaJUCttf3W2v9trT3bWtsd/feAj2NPlPRW3M9bo7+Ld4qkVmPM740xzxljPup2IGPMQmPMKmPMqt27d/t4aZTSlLZGffmymQnLiCya26UfrdqqXb1+vjrenOnjz7zZI0k6Z0qbOseOVChk8rokXj6wrAgqRbXH4GyXFkonl5gVDlv9et1OXb3saX3q+8/r6mVP69frdmZ8bqkaPwqtFEtOAeUmiPG3XPIbp0H7vM4xscZcJ5ZbK9117VlZxR+vuGmi93QMZAAqQxDjb6EVK777zQGT47jTeObk5q/v7NUn53SqveVEzPWbxwat7QBAomqMwdkqVHtFrtK1NcTH4Yee26pFc7uK3jbgVc8A2QrqvbLnDEBjzM9TPdFa+5dpju1WapLf7TBJ75I0T9IISU8ZY5621r6e9FrLJC2TpO7u7mBEMngKhYxmd4zSwgs6FbaStdJ9T2/Wvv6jOSXU6aaP52tJvHxhWRFUimqOwbksLZROLjErX/GllLPwSrHkFFBughh/yzG/8YrlyxfP0Y6DmcUfr7hpLQMZgEoSxPhbaMWK77nkgOmWYfabxwat7QBAomqMwdkoZHtFrtK1NcTH4e0HBvTAqi1adl23amtMYGZNAZkK6r1yqiVA363IDL4fSnpG7h16qWyVdFLcz5Mkve3ymD3W2j5JfcaYJySdIel1oax1jG7UtAnNeU2o0xWioDUms6wIUP4KWXnnErPyFV9K3fhRqOVFARROOeY3XrH8sUVzdF7nmIyO5RY3lyyYpYmj6rVg9kQaKwCUrWLG92xzwFTLMN/z5EbfeWzQ2g4AIBtB7WyQ0rc1EIdRiYJ6r5yqA3CCpPdLukaRvfp+IemH1tq1Po/9J0ldxpiTJW2T9OHoceI9LOkuY8wwSXWSzpX0Nf+nj6DKJpCnWyPXTyEKUmMy+1sB5S+flbdXjMsmZuUrvpB0A8hUOeY3+YzlyXFzQnO9BsMqytLJAFBI5RDfveL5rInNemzRnIzy2FzaDoK4vw+A6pMuxy1lrPLT1hCkNlwgH4KaS3l2AFprByUtl7TcGDNckY7A3xtj/sVae2e6A1trjxtjbpD0K0k1kr5trV1rjPlU9O/ftNauM8Ysl/SSpLCkb1lrX879bSEIMgnkfqatB7UQeSn1zBoAuctX3Mn30hz5jC8k3QAyUY75Tb5zSCduTmlrDOyySwCQqXKI717xvGt8U9Fy2SAvuQeguqTKcYMQq2hrQLUJai5lrPVeSjna8fdBRTr/pkj6uSIdeduKcnYuuru77apVq0r18iiQjbsP6eKlK4dUWo/FTVsPQuWVKWe0DTNrUCJ5/7JVWwzOV9zxE+OyOTfiCxBYFR1/yy3+FCqHLERsB5AXeQ1IQYq/hRb0+B6ENgFiP5BSRefAQZMqJm7q6SNWASVQ4lzK9YU8ZwAaY74r6XRJv5T0z8zMQyH5Xd6z3JaqY7QLUN7yFXcKsQ448QVAqZRb/ClUDhnUPR4AIFtBj+9BaBMg9gMIilQxkVgFlEYQc6lUewBeJ6lP0imSFhkTS6iMJGutbS7wuaGK+F2aKYiFCEBly0fcKbcljAGg0hQihyS2A0DxlbpNgNgPIEi8YiKxCoAj5PUHa23IWtsU/a857r8mOv+Qb84aufW1ka9kUNbIBYB8IMYBQOUhtgNA9SH2AygHxCoAjlQzAIGiCcJSHgBQKMQ4AKg8xHYAqD7EfgDlgFgFwEEHIAKj1Et5AEAhEeMAoPIQ2wGg+hD7AZQDYhUAKcUSoAAAAAAAAAAAAADKDx2AAAAAAAAAAAAAQAWhAxAAAAAAAAAAAACoIHQAAgAAAAAAAAAAABVkWKlPAJCkcNhqU0+fdh4c0Pjmek1pa1QoZEp9WgnK4RyBSkBZyx3XEEC5IF4FD58JUB4oq/DCdwPVjjIAVCfKvjs6AFFy4bDV8rU7dNODqzVwLKz62pBuv+pMzZ8xwbOQFrtAZ3OOADJHWctdKa4hSRaAbFRCzK+0+FcJnwlQDcj34IU4jmpHGQAKI+h5AGXfG0uAouQ29fTFCqckDRwL66YHV2tTT5/r450CffHSlbrm7md08dKVWr52h8JhG5hzTCccttq4+5CeemOPNu4+VNBzB8pJvstaNcrlGmYTm0oRkwEES7Z5TbnH/EqMf+X+mQDVothlNV284/42OIjjqHaUASD/gn7fEw5brdm2X6/uOKhPzulUe0s9ZT8OHYAouZ0HB2IVs2PgWFi7egdcH1+KyjzTc0wl6EETKKV8lrVqle01zDY2cYMFVLdc8ppyj/mVGP/K/TMBqkWxy2qqeMf9bbAQx1HtKANA/gX5vsfJQ65e9rSWrtigb63cqOvOmxzrBKTs0wGIABjfXK/62sSvYn1tSOOa6l0f71WZ7zxYuAKd6TmmEuSgCZRatmWNUccnZHsNs41N3GAB1S2XvCaf+VUpVGL8K/fPBOWPnM6fYpfVVPGO+9tgIY4jW5USf4tRBirlWgF+Bfm+xy0PWfr4el0+exL1XxQdgCi5KW2Nuv2qM2MVtLNG75S2RtfHe1Xmxwata6Wbj4o503NMJchBEyi1bMoao44TZRuvso1NNDLkDzeSKEe55DX5zK+SFaM8VWL8K+RnAqRDTudfocqqV+xMFe+4vw0W4jiyUUnxt9BloJKuVbXivjtzQb7v8cpDakKi/osaVuoTAEIho/kzJujUG+doy94+NdQN0/jm4Z6Pn9LWqCULZunmh16Kbeq5aG6XPv/wGn3nY+eoc+zI2GMz2QA01WamzjlOWzRHu3oHNK4p+81OnaAZH5yCEjSBUsumrHmNOp62aE5CPKgW2carbGOTc4OVHGdzSbKCvrm0I5/nyYbVKFe55DW55lfJZbCjtUFb9vWrp++I3t4/kJArFqI8FSL+lVo+c14gU5WU0xU6l/Eqq5K0cfehrF43VS6SLt5xfxscxHFko5Lib6HLQCVdq2rEfXd2innfk2kO5XU/Ou/UcZo5aRSfq+gARIC8trM3bQB2gsCI2pC+csUZ2ra/X70Dg7rv6c3afmBAu3oHEipcvxWznwogFDLqHDsy5wq9EhuLgHzKtKylGnVcrQl4NvEq29iU7xusoCXkXslnvs+TG0mUq1zzmmzzK7cyeNulp+vOx9frklkTdc+TGzMqT9k01ldqI2u+cl4gU5WS0+UjR/ATk5LLaq6vmy4X8Yp33N8GD3EcmaqU+OsoZBmotGtVbbjvzk6x7nvccpkvXzZTsztGqWO0++u55SGL53Vpd9+RvJ5bOaMDEIHgJwC7BYFFc7v0k+e3avuBAddRhl4V8+akm6liVgB+g2a5zIABSq0SZtUGobznktDl8wYrk4Ebhb5mqRrS8l1vcCOJcuUndhSivLqVwVt+9rKuP79Txiij8pRLo3m6+BeE+A6Ui0rI6aTcGxeziUnhsNWabft9v65bbEqXi3jFu0odDAFUk0qJv8XAtYoo1xyX++7sZdPuk+n3xC2H+txP12jhBZ2aNqHZNRcKhYymtzdp4QWdClvJWul7T23Wvv6jeoyOXUl0ACIg/ARgr009rz+/U/c8udF1lKFXxfzCW/u1dMWG2M1Ua0NtUSsAP41FQZoBAwRZuY86DlJ5D8JoYT/1QbGuWaoGvHzfOHAjiXKWKnYUqrx6lUETPWQm5alQA8GCFN+BclDuOZ0j1xwh05jkxJpXdxz09bpesenU8U05Lelc6hwSQPYqJf4WA9eqvHNc7ruLJ5vviVcOFbZKmQttPzCgpSs2DPk9HbsRofQPAQrPz2aiXkFg1sRmPbZojmsAcdv8d/G8Lv1o1dbY8296cLUa6oYFajNTr5u+TT19JTkfIMicUcePLZqj+xee6xkPgorynshPfVCsa5aqAS/fm2AXerN6oFQKVV69yqC10kPPbdWiuV2+y1Oqsp4L4juQmXLP6Ry55giZxiQn1oStfL2uV2yqCYlcBKhSlRJ/i4FrVd45LvfdxZPN9yTVPV6qXCjf7TOVhhmACAQ/I2i8Rml0jW/y7M1PXo7EyOgzD6zW9gMnAsbAsbCODQ4GagQPU9KBzJTzqGPKeyI/9UGxrlmq0YH5HvnJ8lmoVIUqr25l0NkDcPuBAT2waouWXdet2hqTdrmZQo0EJr4DmSvnnM6Ra46QaUxyYo0z+GHp4+tTvq5XbNpxcIBcBKhilRB/i6Xar1U557jcdxdPNt8Ttxxq0dwu3ff05pS5EDNzU6MDEIHgJwBnW5jjK+aNuw9pX//RhL/X14Y0unG4ZneMDkwFwJR0oHpQ3hP5qQ+Kdc1S1TuFuHGo9htJVKZClVe3MtjR2qDZHa0Zl8lC3TAS34HqlGuOkGlMcmLN9gMDuu/pzbr+/E7VhKR508Zp5sRRQ143VWwiFwEApFPuOS51XXFk8z1xcqhTb5yjdTsO6vWdvbrv6ch+fqlyITp2UzPW2lKfQ0a6u7vtqlWrSn0aKBFn89BUhTnVBqPlsk51uZwnAi/vXxZicP5R3jOXyzXLdBNqP/UO4CLw8TfTspDL65RDjCtEWS+X9w5UoLwWsFLkv5nEpExjDbHJXbHqRaDCBT4HRu6oR3JTLfVNrt8T2mKy4nqB6ABERUkOLpPbRujWD81MWP4pHLZau/2Ath8YUHvLCM1ob9awYcHbDpNAhzwg+fap1AlYJZT3Yl/DbK4ZNyoookDH32KXhXKLcfmMZ5k24ldDYwBQBGXfAZipTONsIeNyoWNZIY5PjgjkTaBz4KAqxxwwyPl9kK9ntdU3Qf6eVCg6ABE8+Q7KG3cf0sVLV2rgWFjtLfW67rzJQ/ZAqBtmdMMPXijrQBvkygyBQvLtQ7UlYFL+Y0i5XMP4OkKS2lvqdWX3JJ150ihNaWskliKfAh1/k8uCFFmO5bFFcwq+FE6pchi/r1uqeFYucRQoE1XXAZislLG2kKs0FCpWlrJeBCpMoHPgICIHzK9iXc9s69l81Te0C8OD65cgeNOeUDWcoHzx0pW65u5ndPHSlVq+dofC4ew7peM3GL189qRY558U2Wj0pgdX66WtB4b8blNPn+c5btx9SE+9sUcbdx/K6dzypRDXDahmm3r6YsmhlD4uFFIxYk4hYkiQrmEq8XWEM0hk2RMb9Yl7VxFLUVVSbcheSKXKYTJ53VLFs0K+bhDzWQCFU8r7xWxjmd9z9nP8bGJeqepFACh07llteWAxcvlc6tl81De0CyNTdACiZAoRlJ0NRiXJGLkG1eR46BVogxpQy6WhHSgXQbnhL1bMKUQMCco1TCe+jvAaJEIsRTWILwuOdBuy50M5dK6VKp4V6nWDms8CKJxS3i9mG8v8nnO642cb80pVLwJAIXPPaswDi5HL51LP5qO+oV0YmSpoB6AxZr4x5jVjzAZjzD+leNzZxphBY8wVhTwfBEshgvKUtkbdftWZsWDqFlSTZ0R7BdqgBlS/163aRvkA2QrKDX+xYk6qGJJt3AjKNUwnvo7wGiQStE5LoBDc8qXbrzpTU9oaszqe39hRDp1rpYpnhXrdoOazAAqnELmeX9nGMr9xOt3xs415+a4XAcCvQuae1ZgH5vt6utWbudzT5KO+KZcB2AiOYYU6sDGmRtLXJb1f0lZJfzLG/Nxa+4rL45ZI+lWhzgXB5ATl5HWPc6nkQiGj+TMmaNqiOTp4+KjeOaZRn/3pmti6z1+98kzVhBR73VSBNlVALeU+AH6uG2uIA/45CVhyeUmXgOV7zfVixRyvGDJ2ZH3GccO5Bj19R7RkwSzd/NBLGV3DYouvI3YfOqJvrdyY1zoIKBfxZSHXDdkzyTnynfv5jcOZvG62dUKu51yo1w1qPgugcLxi3oTmzHO9TGUby8Y1pY7TfnPObGNePutFAMhEIXPPfOaB5bLnXCbXM9178rrPmd7elPU9TT7qm0K0p6OyFawDUNI5kjZYazdKkjHmfkkfkvRK0uNulPSQpLMLeC4IoHxUcqkaUZav7dV/rnhd15/fqZqQ1D15tKzC+sLDaxN+957Otpwbi7I5x2z5uW5eo3ymsYk5MESqBMyr/Baik71YSZxXDKkJKaO4kXwNJreN0LLrulVbYwJ9QxAKGXWOHZlVHVQuNz2AH05ZyDUv8JNzxJedu6/r1i0Pr9HmnsM5NXBkEoczKe+FbAROd86FeF0aCFAI1IfB5hXzBsP+c71sP+NsYlk4bPVmzyEtntelO1asHxKnM8k5c4l5+aoX/aAMAZUpm7JdyNwzn+2a5TLJwO/19POevO5zfnHjnJzas7Opb+K/W+0t9QUdsIjKY6wtzLKA0eU851trPxn9+TpJ51prb4h7zERJP5A0V9I9kh611v7Y5VgLJS2UpI6Ojndt3ry5IOeM4nMCWDaVnFuwvuvas3Ry20ht3tunF9/arwdXbdX2A5Ep0PW1IS28oFNLV2yIHaO+NqTHfDZuZ1PBFaqSTHfdnnpjj665+5khz7t/4bk6r3NM1q+LspOXTKxaY3Cq8rupp08XL105JJH2iie5vl6+k2q3GPLMmz0ZxY2Nuw/l/RpkIh8NJ5nUQeV004NAqJr4my7ncCs7SxbM0sRR9RrdOFwdrQ3asq8/47KcaQzKJefMl1LETWIX8q1MvlM5n0g5xN9U/OR67S31unz2JM2c2KxTxjcVdKBbKk5sbG2o0+WzJ8kYKWSky8+aqCljRmYUO8vh+1kO5wjkoGpy4GRBLNv5OqdS3/sXgp/3lOo+55wpbUW7t3D7HP/7utkaVV+n7QcH1N4yQjPamzVsWEF3ekN5cP0SFnIGoNsLJvc2/qekm621g8Z4FxJr7TJJyySpu7ubjcwqSC6j7JJHYrQ21Gn9zkO64QcvxALiorlduu/pzdp+IDLtPXmLg1RT351RI9MXz9HOg0fUd/S4Jo/ObDRFoWbipbtujPZGPpU6BpdqhGyq8luIJdWKufSQWwzJNG6Uclm5fN3IZFIHMbMapVDq+OtHutjhVnZufuglPbZoTnTFhuzKcqYxqJgzO7zkGjeDNqoc1ala6sNyiL+ppMv12lvqdd15k7X08fVD4m8xP+Nw2Gp37xF9ck6nJOmh504M4H3PO9s0ZczIjGJnOcS8ailDQC7KMQZnUraL1caRr5hYiUvK+3lPqe5zinlv4db+/crbvUNmzTOQBF4K2TW8VdJJcT9PkvR20mO6Jd1vjNkk6QpJ3zDGXFrAc0IFSQ7Wl8+eFAt+UiRwL318vS6fPUlSJEgnx0Fn36tUG6G/sr1Xf/2dZ/WJe1fpg3eu1PK1O3xvll6qjVnZxByVwunouXjpSl1z9zO6eGlmZTAXqcpvoTbqdpLI8zrHqHPsyKImb5nGjUyugdvG2bkoxWbmbLQNuEsXO1KVnVzKcr7jcL7jlJtczjmX+rCUdQsqD/Vh+YqP15fPnhTr/JNOxN812/YX7TN24tpff+dZ3fX4Bn1r5UZdd95ktbfUJ8TGTGNn0GMeZQioTH7LdrHbOPIREwvV/lFKft5TqvucYtw7OPy0fxe6PQTlrZAzAP8kqcsYc7KkbZI+LOna+AdYa092/m2MuVeRJUB/VsBzQgVpb6nXonlTY7P66mpCrpWtMSeCdN0wExu94fzuzZ7EWYPxoyZyHZ1Xqpl4xRr5yN4FKLRSjpBNVX4LuVF3oSWvHT8YVqxT86LTxusxn3HD7zUoxFIopRiByMxqwF26nCNV2UlXllPlGfmMw8VasimXc2bGCIKC+jAYcp0R/PrOXtf4u+LVXTp78ui0n3E+7gPd4trSx9dr4QWdmjahORYbyznvdkMZAiqT37JdjjldpcVhyd978rrPkZTTvUOmdWjyd8sYVdyMzHi0NedfwToArbXHjTE3SPqVpBpJ37bWrjXGfCr6928W6rVR+cJhq1e292rZExtjwfZrV5/pWtnOmTomsn9ANEjHN26HjDT/jpWeFW+ujcylrCQLPR09iOubo/KUcqmJVOW3HJYXchNfblsb6vTRd092XTbCz7X1ew0yvcHxk+yVouGkEm96gHxJlXOkKzteZdlPnlE3zGjhBZ0K28h+UXXDsovBxWqIyaXuyKU+5CYa+UR9WHq53Ic58Vpyj7+DYemWh9doyYJZuvmhl1w/41xePz4eHT426BrXzjpplP78lHGxY5Vr3u2FMgRUJr9luxyX06y0OCz5f09u9zkbdx/K+t4hmzo0+btVY7zvofwI8r0Bbc2FUcgZgLLWPibpsaTfuXb8WWs/VshzQXnyCkpuDTX/9st1+uJfzNA/P7I2IUicPWV0QpCID9xPvbEnZcWbayNzPirJoAbmchy1hPJTyhGy6cpvEPaTylR8ufVaNiKTMuznGrjd4LQ21Gl375Ehcc1vspdpw0k+4mgl3vQAxZCq7KQqy+nyjE09fbEVHBz1tSE9lkUeUsiGGLf4k03dka4+9Ipz3EQj36gPSy8f92Fu8XfR3C7d9/RmbT8woImj6mMDZ8eOrFdNSHrmzR6Nb66XtXJ9/baPn6OxTcM9vw/J8WjxvKmucW2yzwbYckUZAiqT37JdrrOAgxCHU+W72dzvZ/uecrl3yKYOT/5uTWiu16kTmrMaSBL0ewPamgujoB2AQC5SBSW3YLu557AOHD6q68/vlDGRkeDT25tilcGbe/q0eW+fGuuGaXzzcHWMbkxb8eZjdF4ulaTbNbjr2rN0ctvI2JJ9pbpZKMdRSyg/U9oadde1Z+mlrQcUtlKNkWZOainaCNkgJLn5FF9uvZaN2JznAQfJcba9pV4fffdk/fV3nh0S2/0me5k0nOQzwQ3a9yGoA0SAZF5lx60sd7Q2aFNPn+fydE6ekc88ZHxzvSa3jdAlsybKRIvQIy9uy6ohJr5cjmuqT7nUfCZS5aSp4hw30SiEoNWH1SbX+OfEqbFNdfr+9efq+S37NKFlhN7e368F75qkR17cptGNw9U5dqSmtDUOiS//ccUZrq+/csMefWvlRs84lxyPfvfqLn3+kum69dFXqm4mHGUIqEx+yjazgLPjle9edNp4/XrdzqzaTbO9n86lE9erDt/c05fy9ZO/Wx2jG7MaSBL0ewPamguDDkCUVKpgmyooeQXb3oFBff13G2K/e88729QxujFhybsruyepY3SDtu0/rLM7RqeseEs9Oi/5GrQ21Gn9zvw0JOWqXEctofwcPW4Tlvu9/aozS31KZSu53LqV4Rfe2q+lKzbkLb4k3+Bc2e098zCTZM9vw8mWvX16dcdBfXJOpyTpoee25i3BLWUHXNBH7gGZsjby3+/X79INP3hBn5zT6RqjRtTW6Kk39qihbpgmt43Q5p7DCX/PJg/paG3QjXO7dMvPXo6Vp9suPV0drQ0ZHcetXC6e16XWhjptPzCQ0w12qpw01TJE3EQD5SddfpHLfVhynJrcNkKfvrBL//jjF13jn9s9+fpdvUMGd13ZPUkTW0bok3M6tWT5Ok2b0DQkxsTHo/aWes0/vV3LnnhD15/fqZqQ1D15tN7T2UYeA6CilbqdsVx5tRE/sPC8rNpNc7mfzqYT16nbDx8b1OJ5U/Xgqq3afmBA0ol2mMPHwpo/Y0Ls/aZqZyjF7MVioK25MOgARMkcPx7WL17ePmRvASfYpgpK50xpGxJsF8/r0vee2hx7rBMgnEqitaFO1503WUsfP7Hf1ZIFs/TB09sT9gVMDqylHJ2XfA3ysWRfvjBqCcUQ9NFJ5Sa+3D703FYtnteVsAdgfBzN17VOvsHpP+q+34szOi+fyV44bPX8lv0JHcjO8lav7+yVpKxvtkrdAUfZQCnlq/M7VYfZQ89t1aK5XQl5222Xnq5F97+gzT2HYz/f+fj62M/Z5iFb9vXHOv+kSHm65Wcva3ZHa0blya1c3rFiva4/vzM2QC2XG2yvnDRVzsxNNBBMuSzbm8t9WHKcumTWRH3hYe/45xZfHly1VV++bKY+99M1rntKL5rbpb19R4bEqvh4dPnsSbH47sTHbJdxBoBywyzgzHnlu84gO4ffdtNc7qcz7cT1uuf53lObta//aKyNYl//UZ164xy9trO3YO0MQbk38MqDaGsuDDoAURLhsNUfN/bEOv+kocE2Pii1t9Tr8tmTVBOSRtRGvrbxwXbsyMgSS/v6j0pSQoB45s2eITcZzuvd/NBLmjmxJbAVb3Jg9lqyrxQjNRi1hGII+uikcuO2dvxF0ydo96EBGRl95oHVsVFoUv6udfwNzsbdhzwTznwne5t6+vS5n65JiPtLH1+vhRd0as22g/rMA6uzTqZL3QFH2YAfhZilms/O73QdZvc9vVnXn9+pWRObNaGlPtb55zz2lp+9rAcWnqfDxwZzykPyVZ68jmPiTqkQN9ipbuS5iQaCJ1UcfXNP+vwil/uw5DiV7v7SLb7s6z+q2R2j9NiiOdp+4LCu/+6qIbnWAwvPG/La8fEoSPe1AIDg88p321uyazfNNf/PpBPX657nK1ecoVd39Mb235UiKxgVsp0hX/cGudxnprufpK05/+gAREls6unTqs17UwZbJygtWb5OV3d3xDrvlj1xYl+B+GB78phG15l8TiUR1JuMVEEzOTDXGPcl+0o1iptRSyi0oIxOqiRu5fad4yIdc84gCke6a53NJtwdrQ267dLTXZfay3ey55XUd7Q26Ku/eT2nZLrUHXCUDaRTqFmqm3r6tGT5utiey5I8l3tLJ12H2fYDA7rnyY367sfP0cCxsI4et0Mee/jYoM7rHJP1+5HyV568juNc7kJ1vqW6kecmGgger0FE0xfP0brtB33lF/H5XCaNcF5xyiv+ecWXjtGR19hxwD2O9x8dHPLa8fFo96Ej+tbKjeQxAABfvOqjGe0tWbWb5pr/Z1L3et3zrN/Vm7CNVX1tSA11wwrazhBfF+/tO6LampD6jw5qU5o9COPlep+ZbjA1bc35RwcgSmLnwQGFbeqg7ASliaPqdfWyp9OOfvAKEE4l8dqOg66vZxTZO6UYjSHJFURHa8OQzWrjg2Zyo017S71OGt2Q8x41QLlg5kKiQu45l+m1znYT7oa6Ybr/2c2xzgNrpTsfXx9baiqfyZ5XUr/j4EBshF22yXSpO+AoG0inULNUe/qOJAzMSrXcWzp+OswWz+vSZx5YrX39R/W5D0zTwYHjGjgeefwjL27LS5nLV3nyOs709ia9551tvpYHyibGp+vk4yYaCBavhsCdB48M2V9PSp1fZNoIlxynHnlxW2xgVmtDna7snqRTxjXJ2sixQyGji04brwcWnqftByL3ozPaW2IDvgbD1vV8xze7n68Tj8hjAACZSJXvJq9ydOqE5rT1Sy77+PX0HdHb+wc8t7RK5nXP0z15dOz3zjHGNw8f8tjJbSNi+6Dnox3IWWrz1R3ZLTWa631mqQdTVyM6AFES45vr9fQbu/X5S6br1kdfiQWbJQtmJQTbUMik3C/K77Ts+TMm6LQJTTppdKNu+dma2OvFNyplOio+00Yat5uzZdd1+1riJX7pvDsfX+/ZcA5UGmYunFDoPecyvda5bMLtrHEfv9xopsmenxjsltR77Rebj2MXs+GKsoF08n1j5ZSL/f3HdOT4oFob6mJ7bngt95ZOqg6zs04apRfe2q/vPbU51ujcd3RQd/1uQ94HQeWrPKU6zpQxqa+5nxifKjbRyQeUD6+GwL6jx/XgqqH7n375spme+UWmjXBucaqjtUHdk1v1/Jb9saXTUw3scmLTpp4+3fLwmiHnm3xP76bYeYzbQNwt+/oLMqgOACpRNqv/5JtXvpv8+47RjWnrl1z28bv+/E7d8+RG33Wv1z3PezrbhqxkJynhsZPbRujGuV2xiTH5XNUl2068XO8zSz2YuhrRAYiS6Ght0IfPmRzrzKoJSWedNEp/1jlmSADLR2AIhYxOHjtSk9saNbtjlDb39CU0KknKaLRCNg3xW/b26dUdB/XJOZ2SpIee25p2GdRkOw8OaHPP4YQp4lLmDedAOSlGo2Yxk9ZsFWPPuUyudS6bcC99/MQeX1LmMd1vDE5O6lPtF5vrsUvRAUeDP1LJ542VW7mI78j3Wu4tnVTlaPuBAS1dcSLfcYslt/zs5bwPgrI2/WNSybZcpovxhR4EAqB4vBoCJ49u1L7+o7H9T42RQkaa3TEq42XFUt0fusWpsNWQfZPdBnbFxybn3jT+fK2VJo6q9z17uRh5TDhs9fhrO/XS1gMKW2lkXY0mto7QP/zY38wNAKh2maz+E4R46rd+yXYfv0y3mPIafOM1ECX+sSNqa3ytipepXDrxcr3PLPVg6mpEByBKYsu+/tgylvENwI+5BLB8BgYnuO88mNioJGU2WiHThvhw2Or5Lfu17ImNCQ1noVBme/oxSgLIv3JpVA3aMgm5bsJdEzrxnExjeiYxODmp99ovNh/HBoIkn/mTW7mI78hPtdxbOl7lKDnGFHIv5yDUA+lifDEGgQAoDq/BD9KJUf9ObHX22/OSr/tDvwO7nN/v6h2Ivfb2AwMJ9/QLZk/M6LULbcvePq3feSjhXnzxvK6EmezEUwDw5nf1n0qOp8n1ZKZ1b/LevanuPeIf+9QbewpyD5RL/pDrfWYQBlNXm1CpTwDVKVUjRzInMDy2aI7uX3iuHls0J+cGGSfQxcvkRimT85cilWXyiMqlj6+XtdKSBbNi55IuaDpB1u/jAbgLh6027j6kp97YozXb9rsmrZt6+kp8lolyjVv55hWPnE24nd87m3DHq68Nad60cVnH9ExjcDwnmT6vc0xsz8F8HRsIknzmT17lwpjC5SLJMcYrluQjBno1ahSzHkgX44Mam+Lr0427DykcznEKJVAl3PKRbOJ2vu4PvWKQM7Ar+fdOY1053JvuPHhkyAzyO1as1+WzJ8Ue4xZPiW8Aqo1X3MtmkEilia8nH3ouslx3tvVfJvcehWoHyqUOz8d9Zrp2GeQXMwBREm4jDVJtapo8OtyplLJdrq+jtSG22bkzWiGTfWQyHSnhVVmeNLpB86dP0MyJLb5GPTBKAshd8mirRfOmBmpmnZdiLZPgdznUXDfhnjnRezmrdAo5Gzr52O0t9bqye5L6jw5q4+5DxFyUlXzNUvUqc3OmjtHlZ01UR2tD3vdW8htL8hEDgzDDOl2MD+IqEEGYOQlUmlRx2ytHy8f9oVcMcgZ2Jf/eifutDbV6YOG7dWxwUKMbhwcyT+o7etxzEIsjOZ4S3wBUm1Rxz3v1nxGByE+LsaVLfD25/cCAHli1Rcuu61ZtjfH9ms55vr6z1/e9R6HagXLNH1gNqbxURQdgOeztVG2SA1j35BZ9+sIurXh1l8JWeuTFbbp5/mmuCXY+kvEt+/pj+w86exXc+fh6dU9uVdgq7aa2k1pGaNl13Vq1eW/C+XoFYK/K8rQJzRo2LJTxTR5BFpWqGPE6ebRV2Ga+fEOusnmfxRgAkGl8TTc445wpbQqFjK9NuP2cm3PNxjXV665rz9INP3ghpyTY7XOIr59aG+r00XdPjo0ap/EnEflVecvk8/O68Tx7ymhJGhI3brv0dN35+Hpt7jmccblxOy8nxmQSS5KPk6pTMgida+li/JS2Rt117VmxPaxqjDRzUktWN//5KrssSwoUT7ocLZP7Qz8diROa6zUYlv60ea9OHd+k5YvnaMfBE/sWJe/5tGTBLM16x6iS5AXpYtrk0Y2uMd55iFseSXwDUG1SxT3vQSLNectP04mP9e0tkTpqV2/k369s7824jTjTfDib9pjkNow3ew7phh+8oE/O6fR975HJ62bznmhfrg7G5rrTfZF1d3fbVatW+X48I7eCywlMe/uO6K29h/XZ6BKZzv54D6zaou987JwhgWjj7kO6eOnKIYHSbf9AL0+9sUfX3P1Mwu/aW+r1D//j1NhSnc6NTMfoEdqy97BufiiySfjkthG6cW5XwuzBJQtm6cyTWvT2fvcg6/Y9XLJglj54eruGDQslXI+eviOqqwmp78igBsNWtzy8JqsGNEBS3r8omcbgTBQrXieX//aWel133mQtfbw4nTxBqJe8EsNc4mv8+2ptqNOV3ZN0yrgmndberJPHnIiJ2TQ8e12z6e1Nscao+OP4eY1Un4MUuQHa3XtEf/2dZ3OqbypVEL7HhZKnzpFAx18/n59XB1ryjadX3HD2B3R+zjSOOOf1r5fNVEvDMLU11mtGe3Msb8rkOG65mxNDth9IvCEP6vc50zLn9T3OZ9l1y6cl6f6F5+q8zjFZv1cgD/JacAuZ//qVj3tgyT0+fukvT5esVcfoRp08JtJo65V3bT8woIa6Gl297Okh5/JfH5mtLz2yNnbv+uXLZmp2xyh1jC5cR6CfmHb8eFiPvbxd/xi9n3cGquw9dEQHjwwqZKRZk1o099TxsecQ34CsBToHhrd0cc/JLZP3rk13T52PQSHJbQ3xg3QXzZsa2+PVUV8b0gMLz1P/0UHfbbT5zv3dXuPzl0xX78AxSZKR0dd++3qsLr71QzMTZhNKmV2/Sr4/R0ZcP+yKnwHIyK3gckYaSNJf3fNswme0NDo7b2/fEUmJM/LysUyT20jvK7snDdmn7+aHXtK/X3FGrPNPki6ZNTHWgBT/uIUXdGrpig1DgqxTSY5tqtN3Pna2ntu8T4ePhXX7b15TbU0oVjEuX7tDS5av09XdHQkdEYvmdum+pzdr+4EBvruoaMWK18nl31m+4YGF5+nwscGCL61byPeZa8dXLvHVeV+tDXWeHaqS9w1Cquvtdc0eWzRnSCOM38Q33efQOXZkIJYFDKpKza+q5cYp3eeX6jokf76p9geM/zmTOBJ/Xp/96Rpdf36n7nnyBX3lilk6bUKzdh86kvJGOPk4brnbTQ+uHpK7xc9wCdqM1kzKXKrPL59lNwgzJ4FykssAk3zlJPExoL2lXld3d+h/ff+5hFhx6vgm1zjhxEyv5fNfeGu/Lpk1UV//3QYNHAvrcz9do4UXdGrahOacZoFnkyPG12e/XrdTX/3Na7r+/E7VhKR3d7bpn37ykjb3HI4dJ7kzlfgGoNqki3tus8U27j7kGoNPvXGOXtuZ+aw8L/Gx/vLZkxL2dQ1budZJK17d5dpGm3y8+PPO572s22vc+ugruv78Tj360jZ9/pLp+uqVZ6hheI0OHj6uhfetil2rr155pkY31uqpjT1pV8lL9XqVcH+O/Eg/hLbMpUqUEQxun1FrQ51mtDfp7QMDenj1Nt304Iu6eOlKLV+7Q+NGDs96A9T45enuvq5bk9tGxJ5/yrgm1+/K4SOJewYY4165OHuCO0F2U09frAHm4qUrdeU3n9bH7/2TQsboJ89v1eaew7HHOYH6klkTY43mzrGWPn5ig3K+u6hkxYrXbpsd3zz/NM2cOErnTGmTJD3zZk/Cptf55BXzdvceGbLZdibi4801dz8Ti5nJx0q14XQuG0w77+vy2ZOGxDHn+G/ucX/tN/cM3eza7djxvL4bfjfU9jrmzoMnjlmoDbcrQaXmV5lsyF7O0n1+btdhyfJ1WrNt/5A4Na7JvZzUx83UyzSOJJ+XMZE4uXXfYV1y15MpY5xznNaGOn36wqm6Ye5UdbSO8JW7ha0CuxF9vuJgPsuuW31aiL1py5Vz35FL3Y7K4TdP83puQ12NFs2LxLT2lkg8zSYniY8BXjnb5r19KWOms3x+vPrakAbDShj80dpQp65xTXp1x0Gt2XYg7XvN5hr5rc829xzW13+3QUtXbNBTG3sSOv+SnyPlFt8o+wBSCWqMyCbuecXgLXvze08V/zpubbJedZLXaxfjXtbrNYYPC+nq7g7d8IMX9OkfvKAXtuxPmHQycCysv/vRaj21sUdLV2zQt1Zu1NXdHVqyfF3K61eo95Tq+xrU7zKGqvgZgIzcyo/k5Sm9plGne76ffVfaW+r10XdP1k0/ejE2+sGZBbdk+Tp99gOnafG8roQ9mb582UyFTOR1Uu0F47YM58RR9RrdOFw2eiPT2lCny2dPkjGR9aubG2pdv0PJP8evpus0IvcOHBtS6TmzG52Rkbt6B2TtiQauVKPos/3uVvo+TZX+/qpFseK11xrqUvrZabl815znhoxxjXnOUpNes4jTvWZyg29rQ51e3XFQ9bWh2N52oZBJmRieM6Ut6w2mnc/PK47t6h1Q/9FBzxuEd47zHpXm57sRv6H2J+d06qHntmr7gYGE148f+eZ1zGODNlaXpNtwu5pjj9f1M4osCVmu16JaZn16fX4jamv01Bt7FDJGrQ11sTLkzBJxlnyLX6amqX6Ybnr/Kbr9N6/HysnieV2qSbG3UqbnZa2GjPZNNarViavO4xfPm+ord3M+53RlO3k/j5pQZDZ5usfmEicyqSNTfY/zWdcWY2/aUsv286uW2cTwL9uR+W7fJWe7DLc96FN9ZyMdicNiMcArZ2scPixlzHzoua36/CXTdeujr8TO6fOXTFffwDG1j2qIdVB+9N2T9Q8/jtzTL3tio2sZiD/fhrphWrJ8XcprlPz+nMHBXjHNLR762f/bLb51tDbktNIGZR9AMWNEMfa488orG4cP0/Xnd8baMp1782zvqZJfJ/7fDz23dUgb8Rf/YoZ++Mzm2POT7+fij9feUq/LZ09STUgaUTss1haQaw7vdW2mjGnUP0brRsl7BmP8QEWnHTnV9ctnjh//3o8Pum9NJWW3uhNKo+I7ANM13iE9p4JyW57S78aqqYJC8md0ZffQBh4n2EnSZx6ILDHnVCYhE0ns59+xMuX5uN103fzQS7GlPsJhq7uuPUvrdx5KqDj+98Wn6W/fd0psbeZHXtym2y49PWEfmcXzuvS9p05ULk4j8opXd/nu0IsfZeN2s5Xtd7dYa1uXqhGcm6zKUcx4ncnyFX6WxEv3XTt+PKxfvLxdNz/0klob6hISVLeYF7/Ztt/XjG/gSLWvYarEMJfGXOfzey3a6eh2/N29R1z/1lCXOh3x0xHn1jjmLJ/slvh2tDbo1g+drs8/fCKWf/GSGVq64jVNah0RmwHkdT2qPfa4fSaL53XpMw+s1r7+o2V7Lapl4Jjb53fbpadr0f0vxG7unNxm+4GBhFkiTmdg/DI1n50/TTdcOFUDx8OyVvreU5v15ctO1/0Lz80qjriV5QXvmuS7c3YwrIS4+uCqoQ0DbrnbuKb6tGXb7e/OsZK/+/mME27XZsmCWeqJLpefanBd/PvLd13rVp9Wilw+P5ZhQrJsB5i4fZeWPr5eDyw8TzMnjvK9p5F0YsuJRXO7tPTx9ZLc7z1H1tXoXz50ur4QnyPFNaZuPzCgnz7/lr75V+/Sy9sOqHPsSC1Zvi5Wfyya2yVjlHbQRrr8Lfkaue1f+PcXnTokvsfHNLd4+MiL27RkwazYjAuvOBgf3/K1xDyA6lasGJFtDpNpXueVV+7tO6p7ntw4ZOBKtvdU8a+T3OG3r/+oJrTUa/G8LvUdjezreuz4oBbMnqTdhza6tgc4x0tu5172xEYtWTBLHaNHaMvew0PqiUxyeK/7mrf39w/JB/wMVKwJKeX1y1eOn65udr6vkqjvyoixtrymZ2az+avbRqXl1iBVSs6G45H9V4ZurJpu43E/G5bHf0b9Rwf1iXuHfsY3vf8UTZvQpCPHw+o/clx7+o7o+09v0fYDA7ph7lTd9fiGlOfjZyPvN3Yd0gfvHHquN72vS9PaW2Ibsna0NmjLvn7t6h3Q2JH1erPnkG74wQsJDTK3/+Y1/cUZE/WtlUOvmXMt3W7IkjtZ42cpZvPdzdeG8V5K3Qhe6PdXKnnqVC27DbhLGa/TxYhsv2vhsNWTG/bEGsulSAfdld2TdNZJo2Ql15jnNJz7fc348/v0hVM947XfTsVsvoPhsNWWvX16fsv+2J6q8cffsrdPv3x5x5BG+HOmjFZrY11C51rya0vy/G54fTbJsTb+/DfuPqSP3/usLpk1UU31NXrHqAZt6enT5LZGtbcMV3d0OVgvlRp7MuF8Tpt7+vTCW/v1o1UnZl2W67XIY50W+PgbH29H1NbEOv8c9bWhhP2elq7YIEmu8WVy2wj93UXT9PrOXkmRxtXvfOycrD7/+PI/cGxQX3pkrTb3HNbieVP130+kzkPjZwJ/6vvPJxy3vaVen7v4NL22s1f1w0Jqrh+mL//y1SGf86aevpRlO1W8+frvNvh6bLZlI/7aHBu0+nzSaNzp7U3afmBA7S31emW7974rx4+HtXb7gehjR2hGe7OGDav4nSEylsvn5+e+AwWV1xicj/ib7fcp3XcpeQbdovufd93bTlLs9Z3ZDi31NWptHJ4wGOq2S0/X3kNH9H+f3aJLZk2UMZK10qMvbdOHzpyYsJ/SRaeN19rtB2Kzw+Nf8ytXnKEbf/iC53mnuiZOPE2+Rs7jnRV7OlpH6O0Dh/W7V3dpzinjYoODLz9roqaMGdrJ2NpQpyu7J+mUcU06bUKTamqM731f/X5+lH0g+DlwKRUrRhTzXjW5DSdkpPl3DH3tZdd16/ypY9JOIIlvB3DaXncejOS3g2Fp96EBTWiu18HDx/TbV3dp6rgmffXXr8bqPqetZeKoEWqoG6b/+PWrrvvnhcNWa7btd63D/v2KMxJm6Tm/j59A4qetxC13T24nTl65JHkgZjbXL5f2ND918/0Lz5W1or4LJtcPvOJnAEqVPTK1GJzRiqmWdUt1bVPts+Q8L/4z2rj7kOvohxnvaNLrSbPz/vZ9p+gHz26OjYxIdT5+RvXv6nU/167xTZo4akRs7eTk79TJYxr1WNwMkZ6+I9rcc1gPPbc1NsIyuUNvweyJCcF4/owJmjahSXv7juiBhedlvMyql0IvZ1bqUY6VuFxbqTtVS6mU8TpdjMhl5PaqzXsTnrv9wICWrtgQ6+Tzet1MXjN+xFe6eJ1ull8u38GwlU4aPcI1jnWMblTX+JFaeEGnwjbSSNNYV6MbfvhCbObMRaeN16/X7XR9ba/vhtd1mjWxOdbp6TZjcnPPYf3k+a267rzJsQTfidOzUywpneo1yzn2ZMoprzsPDsQ6hxzlei2qYUlDR3y8feqNPa77IZ110ijdv/BcjagdpmXRzrfk+OLMCIwvQ7ddero6WhtyPq/jx8Na+uGztOPAgCa2jtAp45v1dz9KPxP4k3M6h8TVff1H9eqO3tiNa3tLvb778XNkZRM+53RlO9U+hX4fm23ZcK6NpIQbcyf3cjps62tDuuvas/SLG+do96Ghs5e9Ymwlfs9zkcvnVy2zieFftiPzU32XMplBZ+OWGNt+YCAWC79//dn67sfPUf/R42prrNON97+gvzhjYmzPvHiTRo3QP33gVE0a1SBrrTbv7fdc3r1tpPs2GvFlwKuM1YROPD7+Gjn7uyavcpH8nt/zzrZYB6BTr09fPMdzgJqf2Oc3HlD2AaRSrBhRzHvV5Dacp97Y4/ratTUm49Xjbrv0dN35+Pohy0+GQkZPvbFHS1ds0A1zpyZ0/iXXEUsWzNJFp40f8tqhkPGsww4fPe55/TJZpSl5Jvl3PnaO9vYdUde4kbHZhfv6j6pr/Ej94sbI/WdjXY3e2ndY+/qPSlLsPbynsy1tfZWP9rR09zrx31fqu/LBUE+k5VRQkvvGqukKt7PPQPLzGupqYj/HbxwaMhqy8eyXL5uZ0PknRQLQ1377um6ef5p+8vzWtOfjZ0Pb+Pcaf641oZA+eKf3ZuROkD2vc4w6x45UW2NkL4LtBwZ039Obdf35nVo0b6oeWHie/mLWO9Q9pS22vFzyMbqntOmMk1r17neOGfKYbHi9p3wF5WJsnptKod9fKXh1qma7YTL8SRcjsv2u7Tw4ENtrxO25qV43k9d0GjgeWzRHc7rGuD7P2Z9NUkLMSo4z2XwHnYT94qUrdeU3n9bVy57Wvv5jCZ0noZDR3FPH69IzJ+pdHaMkSf/1h8iyHM5rrN1+IOPX9rpOXeObPOOo85z4pQ2d17v5oZfSlrdKjD3ZqrRrkVynV0OniNdnOLmtUed1jtGM9mbddunprvmgWxm65Wcva8u+/pzPKxQyaqqvVfOIWjXUDdP/mD5ejy2ao/sXnqvHFs1JuNGOj1vOAKz48108ryuWL0qRDsGxTcOHfM7pvs9ef3cGo/l5bK5lwyv3it8r5IYfvCBjNOT9kWP4l8vn5+e+A9UlPk9zi2FeUn2XvJYHvXz2pNjzne+s1/f5HaMadG5nmy6cNl59Rwdjjahuj22qr1VTfa3+/scv6tM/eEEfvHOljg9a9+O2jMj63nvetHGu12h8c2RWR3KdE/+e3cpoKGQUtop1/jnPyyT2+Y0HlH0AqRQrRpTy/szrtcc3p35ttzrtlp+9rEtmTYz9HB+33dqqve7t4+9L4tufvdur3X8/rqk+61w6vs33L2a9IyEfmHvqeJ08plH7+o/pqmVP67ZfrNPCCzpjA/r+YtY7irZaR6p7nfjvK/VdeaEDsEzFB6yNuw8ldEblm1OoH3lx25DGFD+F++jg4JDnff6S6bLWKhy2emPXIT22ZrseXr1NNz34oubfsVJ1w4x+ceOJYHjS6BHq8xiZsXH3odiazqnOx89Nl1sAW7Jglj7/cGY3C/HH2X5gQPc8uVHTJjQP2aehGLIJypl8v0rd8FuJlU6pO1WrVboYke13bXxzvWv8XLJgVqxzzOt13V7zrmvPkrVyLZ9OUnn25NFDnufsz+Y2iCG5zGfzHfSbCDvnWF9bo6UrNsRGazvPcToD3V7bKzZl89k4z6kJDZ0t2dpQp929R1LGwEqMPdniWpS/dJ/hln39ujO6H/PI4TX6/CXTY491K0PJ8SJVXuH1t/hBBc4ArF+v26kp0U7J5M7Z+LgVPwDrv/9qtn5x4xx1jR+ZMJLW6zua7lq4/d3pXPTz2HyUjXSdkJJ3zCbH8C+Xzy/bzh5UtmwGmKT6LmUygy6TwbBegyg29fTp1kdfSWycfXiNliyYNeS4xkhjRtbpzmvO0gMLz9MvF83R9PYmPfNmTyzWe53TzImjXK/RlLZGnTKuyXNmQqoymmvs8xsPglz2i9mGBMCdEyN+ceMcfedj3frux8/R9PamvL9OKe/Psn3tdDPPnJ+duB3fVv237zslMuA5xUpI0tD7i0X3Pz9kkOOiuV26+4k3PNu/k8+zvaVe15/fqVd3HNQbu/zFVrd8IL49xVkx6u9/9GJkeesi1iFe7eIXnjomoU4Lcn2HoapiD8BKU4rlAZ11hPf2HVFtTSij5Sk37j6kf/zxan3i/Hfq1R0HNRiO7CHw2Q+cprCV65Il+/qPJqxNvXH3IT28epvr3i8PLDxPh48N5m2ZrnDY6s09fdqyt08NdcM0LGR0ww9fSGikltKva5xu7eVc9njL9LmZrAOd6fcrCMtVVto+n3lcr5319/Msm3Itndjj85JZE1UTkronj9Z7Ott8jeKKf80Jzan3dXJ7Xrr92dzK8N3Xdetv4vYsTH6Om0z3M/D6nj+w8N26etlTQ37/ixvn6LWd3u89mzgQWff/QMLrua3Bn+4aV0rsyUXinmL1mtHeEruRyXEv03JVdvE31fc5uXw7+0fNmtisCS0jXMtsqhiTvAey29/S7cUXf87O3lfpziOTXMhvrB+0Vq9sO6CDRwYVMtKsSS2ae+qJZYayeV2vMhP/9+ODVrfE7QHotleIW8z22u/6FzfO0TvHldeSvcVAnC9bgdsDsBC8cyn3+2M/sS15v7yucSO1dd9hfe+pzVrwrkm66/ETy4I6dcG7JreorXG4+o9GXnPr/j698nZvQi7ltYyb5L3Hc/x5pYv1933iHNUN826nyMf9VTnHgyDcs6NqlF0OXGz5Lo9e+WMpY1Y2r+2Vo/79Rafotl+8GvvZ7V5gb98R1dWEdOjocX3iXu92DLe6YHLbCC398FnqPzqYsMf25LYRuvVDM1VbYxKua/wx3JYczfazDNIesuVc38E9BtMBWIaKuZlrPoTDVk9u2KOFSY3Ji+ZNje0n46ivPbGx6I8/dZ5GNw7XzoMDeseoer29f0Ab95wYdViopNWtMvbbqJLuuE6l3N7ivxHfz/nl8zpk8/2icsivPH7GJN9FlK6RO74TbzAc2XM0006RbMpnukTSKwm+6f2nxtal9/MdzPTcvK5X/B6ATuPTKeOa1NHWoBt/+HzCPmX5qPuSz8OrbgpqHRsEbp/lXdeepaPHbTU3NFVU/E1VvtPtg5HquZI8/7bz4EDK2JX8vZvcNkI3zu3SLT97OXYeSxbM0jtG1autcXhGA7H8DrTKZ06eru53+7uzr3Rrw3C92XNIN/zghbTlbdOeQ/rlyzsSGuYXz+vSB06fENszC6gAVdEBWIj7wuSBZ7t6j+iJ9XskSSOH1+g/f7s+ZaPnjHc0ac3Wg3p9V6/CVnroua2x1Xqc+3zJPVamGkiXKtbfftWZqhtmUsbAau8AK7c2JJS1isqBC6GY+WO5CIet1m0/oLXbe/WFh19OyFEntNTr35e/pn39R4e0r7h1eqa6HunaRvy0bca/xvXnd+qeJ9O3Hfi5tyBOI09cC/6wYp8FclfMzVzzIRQyqq0xQ845HLcRucOZ3j25bYS27R/QX93zrFob6mKzMVob6rTwgk51jRup0yY0F2RvHrdl7O5YsV4LL+jU0hUbVF8b2ZMwZBSb2p0ukKdrYHaWypvmI7B7LbPn57l+ZPP9ysdGszjBmUo/bdEcOlXLSLqy2Tl2ZEYbRrvJpnym22jc7Zibew5r4qh6/eLGObHZ0OObh6c8t0ktI/RfH5mtF97ar7CVHnlxm26c26WO1gbXx6f6ns+fMUHTF8/R81v2x/ZriZ8l7gzGyEfdl3weXhuBB7WODQK37/5LWw9kXc8heJylYJJjV3yZ9aqzvOLW6zt7VTcs5Fne0sWu5O/d5p7DuvPx9Xpg4XnqPzoYm5m3ftch1RhpZtzMvGxnJSbH6Xzm5OnqELe/3/zQS7FGgZPHNOoxH3nD9gMD+t5TkeVRjZGslb731Gad1TGKDkCgzOTrnsGtYdItZ/3b952iz86fpn9d/qrrPktLlq/T4nmneOZubsu4xc/icIu9p45v8oz1zizHkJHm37EyZc7hdq06WhuqZqWCcmtDAipZMfPHcpA8+3zhBZ3qaG3QjoORnHVf/1F99+PnaGzTcNeBIcm5eqp6Md39hZ+2Tec12j5+jjbt6Uv7WfrtpE11v1UMuaxSh+CjA7AMpQtYQRMOWzXU1WjRvKkJowBrouv0J7+PkJFu/dDM2IzBy2dPio1SdtZBdkZBFCIYeVXGZ500Sndde5Ze39mrr/wqMvrE7wyH5ErZq/PTT4Vf6OS93L5flYpO1fLjp2zmmqBnUz7TJZJex2xtGJ5yyc144bDVL1/ZkTBj8POXTNf9z27W7I7WjAcPhEJGYatYA5JzrZZG9yCLHz2ej9gUfx4bdx8KZAwMckLu9t3PpZ5D8KS7mU5VZ3nFmDXbDmpYaGguOLlthEbU1mjnwQHdfV13wjKX8bHLa/DC4WODam+pd53lNnXsSE0Z496Z5sRiSb7jdD5zpnR1SLq/+80bxjfXa1//0VgczeWcAZRervcMfjveBo6F9bXfvq7F87q07Lpu9R89PiQmXTJromfuds+TGxP2Ka2vDWlCc31s7+mGumFasnzdkNj7jY/M9oz1zpJoT72xx1fOEX+tKmXWjF/c4wPBUcz8sRy47X3nzBp3Bv7u6z+qsU2RAclv7nHP40+NLmefql7MVydbKGQ0tmm4/vjGnrSfpd82oFJOBKi2OrEapd98CIFTys1c00neWPr48bCWr92hq5c9raUrNuhbKzfquvMma3LbCM2c1DLkfXz5spm6/KyJCTMG023imm9OZRyvvjaktsY6/f2PXtTSFRu0/cBAbIaDWyDf1NOX8Hy3StntNfxU+F7nN3ZkfpL3IH+/UBkqdQN6r7IZX65TJeh+ZFM+023O7HXMmpB7I3hyfJMiSfjtv3lN15/fqRvmTtUn53Rq2RNv6NzOsVnHaq9rVRO9xIWKTUGMgcmblV+8dKWWr90RmLLj9t13BvnEo6GpvLltVu+HW5laNLdLP3l+qx5ctVWL53XF/uYs7Xb1sqd1zd3P6G/uW6Wb3n+qfvyp84bErlQxd+fBI7HOP+nEag47Dx6RlDoWZxKn/cQLv3We1/sZUVujp97Yo4a6YZrcNsL1/WailDGuUut/oJx5NUxu3us+q2HahCadP3WMThnfNLTuD7nft9eEpNsuPV2PvrRNkmJLhb+yvTeW21y97Cld3d2h9pb6hOc2Dh+WNp/wk4P7fd9uea4fuca3QsfHYsZ+Yj2qXboykM/ymE38y0QxyrNX7u3MGncGDjr3wG8f6Hd9/Ja93vHbeR/PvNmjU8c3afli97aRTExpa9TMSS0J9zJun2Um9xbZ3m/lKt91IoKnoDMAjTHzJd0hqUbSt6y1/5b0949Iujn64yFJ/8ta+2Ihz6kSBGl5wHT72i1ZMEu3/+a1IaMAH1h4nma0t2jz3n594yOz1Th8mMY3DVfH6MbYzI/4URRuIyqMIvvKpHvvmc6acBsRsnhel97ad1itDXWxESiS/xkOySN8Hnou0uAVPzLdb4XvdX5v9hzSyWNy/x4E6fuFylPJI4tSjSZz4tDhY4NaPG+qHly1NWFPUb8Jerbl02sUnHNerQ21emDhu3VscFCjo3tlPfNmT8r4Fh9bJatrz5msr/329dh7XzS3S8NCyvrmw2tk5Lxp4/Sed7YVLDalusalmoUX9KVd3L77ziCfUi1hguCIL1Ov7+zVmm0HE5by/d5Tm/Xdj58jK6sRtTW6etnTCd/1+GUu47l977565ZkKGamn74g+OacztuqEc6z+o8clpR957TUr8ak39iSU/XQxOZM6z+393Hbp6Vp0/wuxGZC3XXq67nx8veuMyGw+j2LmeeVS/wd5tjVQCF4Nk07HW3KcnBwtE24x6+zJo91zt1PHacY7WjS7ozUWd6yVPnhn4rKdbis9jG8anjKfCIetQkb68mUzE5Yevf2qM9XR2hCbYZhcnvMxa8aJFz19R/T2/oGM9s5OPk6h42OxYn+5xHqgUPyUgXyWx0IuG1nIfWadNuTBsBQyxrXusNF24c/On6beI8f1yTmdem3HQf1511jXxzfUuXdxFCouhUJGc08dr6ljR2p2R6v6jx5Xx+jGIe2ymcz4LFUeWgkzSZFawToAjTE1kr4u6f2Stkr6kzHm59baV+Ie9qakP7fW7jPGfEDSMknnFuqcKkkQlgf0s6/dzQ+9pBsunKrDcaM3Hnpuq44NhvXrdTuHBOCO0ZFKKr4Sc+ssWzyvS595YHXCJrBuQTGbQB8KGU1vb9LCCzoVtif2RtnXfzS2D6DDaxnT5ECeXCnv6z+qrvEj9Ysb52j3ocwq/FTnl6/NYYPw/UJlCnpHRi68Enlp6Br1i+d1xcptpgl6vsqnV3yc3TFaoZBJmaj6if9LH1+vu6/rlrXRxpkME1evm5mZE0cVPAl2u8albNAIekKe6rvPYBJIJ8qUJH3mgdUJ32dnSZ/OsSN9L+PmqBtmYvlQY12NwjYc2wfKGYjgdDbW14Zc80y3xpL4v8XPSnQr+6liciZ1XnI5GlFbo1sfXatLZk2M5dB3Pr5eSz98Vmzfq2zLVCnyvHKo/2m4RjXyyvfSdbxJiTE4ZKSwwu6526RRQ+KOV7xPXumhY3SjOkY3eg7MSt436pTxTTptQrMmj25wbW9wynOuS/DFv7azxGkm8S2+kddr+dN8x8dixP5yiPVAIWWy3GM+ymMhO/ezLc9enVjJMfuj756sO1asV2tD3ZA23y9fNlPNI4Zp8bwuDRwP667fbYj97aTRjfrcB6bpy798NaF9ZXzz8Ly+Dz9CIaMpY0am3EfbbydtKfNQlomufIWcAXiOpA3W2o2SZIy5X9KHJMU6AK21f4x7/NOSJhXwfJAlr+Dtd1+7CS31uuVnLycE5rqaUMoAnFyJTWiu10XTJ2jL3j698NZ+fe+pEyPHUwVur0DvrA3txVl3Opmz1EnyDIcly9fpklkTVROSzp48Wh2tDQnPS1UppzoPr8/i9Z29CfspOoLSGAx4CXpHRq7cEvmNuw8NiUN3rFifsJF1sdZ1j4/l1rov8Tlx4XmaOXFUykTVb/x/ZtNefWvlxoTE1e+otqDNRnarT5YsX6eJo+rVf3SwoCP0yiEh97qJZTBJdfBbrrPdk9Ttu76pp083/OCF2GM/feFU/cevI0sROx1mD6zaostnT9I9T0bi0MljIq+TLr4kd8Qlz0r022iQrs5zu25OmfnTph7NnTZBSx8/0RiyaG6Xjg2GY/telZNyqP9puEY1cuJy8v3spFENnh1vkrRlb59e2npAzmpwP1q1Vcue2Kjli+foMZcBQckz8TJd6cEtn/DaN+qxRXO0ZV9/yvKczayZxE67mthzU21Z4hY73Bp54wespHt+kJVDrAcKqRRlIJ+d+/FxLmTMkFXQ0r0Xt/i2ZMEsvWNUvepqamKDHS6fPSnW4bf9wIC+99RmLbygUzPaWzShZbhmtLdoy75+vbT1wJCBxrf8bI2+9dfdCQNQusaPjA30S+b1mWwu0kw7v+0apcxDCzmTFMFQyA7AiZLeivt5q1LP7rte0i/d/mCMWShpoSR1dHTk6/zgQ6oRCF772iUn8Vv29icEsDtWrNep45vSVopuldiu3qEdcwPHwtrbd2JPFyd4S5EA6vY663YcTLlcptcNyWkTmofc0ITDVscGw2mX/CjEBu3JI9szbQxmmSP4kc8YXA4dGfnmlXBa2aLdCLvFj/+44gzX81rx6i5t2z+g+TMmaP6MCZq+eI52HjyivqPHNTmaVPuN/9YmJq5T2hpTjmpL1Rheasnvub2lXld3d3jOCsonEvLqFaQc2M9o3nRlIRQyuui08Xpg4XnafmBA7S0jNKO9Ofa4jtYG3Xbp6QkDx2679PQhA6ukSJlsbajT5bMnyRjp9Hc0a9zIOv3r8lcT8qTT2kdqwew5Q3KcVHlZ/N8ynZUYL5OZ1MnXra4mFOv8c17TWUa/HJVD/U/DNeIFKf4WkhOXve5nvZaQf37L/lijrBNvl7+8XTsPHlHY2oR78uRYd9e1Z2nK6Eb9xxVnaP2uXj24amtsVQxnpQc/96mpyqxNs1VHpgPN3Fa/SI5nfuObWyOv2/KnQYqPfpVDrEd5KNcYXM5lwC03dVYu8rt9iVt8u/mhl2IzpZ02zOSBE84gjpvef4oWP/CCbr/qTF102nidMs697bjGGM2bNm7I/YRb3eH1mbzw1v7YwJFCz7Tz0x5cyjw0aIOvkX+F7AB0+5a47hZqjLlQkQ7A893+bq1dpsjyoOru7mYH4Tzw2/GTagSCn33tvnzZTH3lV68lHHPgWFiN9e57CqSrFN0Cd/fkFr2197D+6p5nE24qjh63em3Hwdjj21vqdfnsSbFlRbbs7fOcpu3V2Op0GsYH3009fbGbpeRrlM8gnepGwRnZnkljMMscwa98xuBq7MjI5SYgX530W/b26dUdB/XJOZ2SIvF6675+1/NyZmhPWzRHHa0NenHrgSENQqfGzYZ2jpcc/53kXjqRuErusw79dA4W8vr4kfw5Xj570pDG+UKN0CMhr15ByYFT5QyZjFYNh23KJdm27OvX/c9u1r9fcYYOHzmuUY112ravX09s2K0pbY0J3/v2lvrY0kHxjRTOSOX4DrNMy2Ty0mzZxvBMZlInX7f+o4OujQD9Rwczei9BUQ71fzk32iH/ghJ/i2HLvv6M7mc39fTF9ttzHv/Aqi1aeME79dffeTahjE9vb0qIda0NdVq/81BsBrfTZjC7Y5Q6RqceWHLRaeO1ZV9/LO9rb8lsP1ev8mx9fLpuq184x3/oua1aNLcrYcZ2qvjm1cibvPxpkOKjX+UQ61EeyjUGl3MZSI5zrQ11OnxsUJ95X5fe2ndYj7y4TTfPPy3le/GKb06Hn9OGKbnH6KnjRuqUcSP1arQt96TRDa6POzZohwzEnfGOJj23ef+Q/WDfd+o4LVkwK6FNw+nYdM4vCCs+lDoPZSuoylbIDsCtkk6K+3mSpLeTH2SMmSXpW5I+YK3tKeD5IMpvx084bLW790hCg7HToLKrd0DnTGlLu69dyET2dolXXxvSuJHDh4zw/j+XzdS2ff3a3XtE45uHx24A4k1pa9Rd154VW26keXiNpr+jWc9u2pdwns408daGOi2a26UHVm3R1d0dCUn5ly+b6foajlPHN+kbH5mtxuHDNKF5uI4PSs+82VOQDcT98HqdWRMjsxIzbQxmmSOUQjV2ZGR7E+C38SPd9Tt+PKw3e/pjPz/y4jZdd95kNdTVuO6vKp2YWb1132HXBqHli+cMif8nj2nUfZ84R/sPH9OabQcSljGqrw1p7Mj6WJ0ycniNjg9aDRyPHNeZxe0czxmw8eqOg5o4aoRmTmwZ8h5T1WWShnQMuv0uk+9d8udYE8psuadckZAjG/nqJH9zj/eS6rt6E2fiSZFczK0spMs9evqOaO60CfrHH7+YsDeIW746GFbsb86x7liROIMivsMs3bVw/t7Td0Rv7x+Ixb7JbSOG5Kx3XXuWrI3sYZXtUsbp8kfPfbma3RsBgr6qQzHr/2yvRTk32gHZSnfP75Z39PQdSVhu+aHntuqSWRN166OvJMTkJcvX6SsLzkg4dvyyb87jPvfTNXps0ZxYOXWrc5YsX+c6S/Gua8/SrY++oktmTVRLfY1mThqlHQcG9I5R9SnLs582kXDY6s09fdq8t0+1NSF95n1d+u4fI/ltfKff9gMDemDVFi27rlu1NSZt3Ml0+dNSyDaOVuO9HhCvUGUg33me2/Hic9P2lnpdd97khDbU2y6dqentTXpzT5929bqfR3J8a2+p15XdkzSxZYRumDtVDz23VTWhyLLRn79keqzecAYQL1m+TjfPPy1hL+7kPHzJgln6/MNrhtxPLP3wWdrU05dQ59z04Gotu65bt/8msk1ATUg6q6NVX/v1axktbVoM5KEopEJ2AP5JUpcx5mRJ2yR9WNK18Q8wxnRI+omk66y1rxfwXBDHT8dPquUm9/UfjXTu+djXLhy2QwLYkgWzJEl3Rkd+DB8WUte4kfq35eu0uedwrBH65DGNet+08Ro2LJRw/keP29hyI5/7wKnavPfwkOVHQtGG2e0HBnTf05v12YtP0z/++MUhNxpnnjTKdXR68nu/7dLTdefj62PnFz9K0S2Bn9w2QiNqa9I2DCW/bqoK3etGoWt8U1aVFMscoVSK3ZHhVrak3DqDMpHtTYDXnnPxjR/dk1v0dxdN04HDx2JLX8THzHDY6hcvb09oLHEGRfzDRdP0X39YF2vAsVb63lObteBdk1RfG1JtTUirNrsvfbfj4IDne4osi2xjgz+cxPXNnshob6dRP34j765xI3Vs0MY6/+JvNpY9sdF1kIpXh8T0xXP0yvbeIYlz3TCTMNrca3ahVyxO/hxH1A5L2I/Aea/MFEFQ5HOm/+a97kuqb9nbp5PHNLrOxJvg0lGVLveIX/bSrZHY2ae0/+igDh9znyFn4t6a3+U24//urKzgHHtzz2Hd+fh6ff/6c7Xn0BFNah2hzT2H9cE7V/q6rl51XrpRvukaAeJjVXtLvWvcy3Tv1UIrRv2fy/eehmtUGz/3/G7PeXv/QCxOOs8JW5sQz5yl0j8aNyMw/j49XvI9qFudc8msia6D0r73iXP0mXmn6j9XvKaruzv0Tz95KbaX4Z9NbYtbcrpeM9pPDCjzahNx6pj2lnqtfbtXf/ejxKXwPnVBp775xMZYp98DC8/T4WODGjuyXjUhJTQoe/GK787yp27XvJgxPNf8gUFrqHb5LgO5lEmvtpDk4335spk6ZfxIdU9u0bmdYzVtQpP+IakN9ZafrdHXrjpTf5v0vPgZ3PHxzW0w3+J5XZrTNUbveWebjg2GE9oinAHEr+44GHvdo8et+o8cT9jvr742pM09hxPeZ2tDnfb3Hx3SNnzf05u1avNebe45nLDE8sILOvXStoOx5wfhPp48FIVUsA5Aa+1xY8wNkn4lqUbSt621a40xn4r+/ZuSviCpTdI3TORu/bi1trtQ54QIPx0/XstNLrygU9MmNMcqjVQVm1PRjG2q03c+drae27xPh4+FdftvXtM/XDQtFoA/feHUWAXivNYdKyKv1TKiVmdPHh1r0E5exm5ia4P+7kcvDjnP/7jijFijyvYDA3p9Z6/vzi63937Lz17WDRdO1eFow9Lmnj7t6R3QpNGNuui08QkJ/OS2EbpxblfafaHCYaste/sie2sdOa7htSF97qdrYp2Myc/J92iQUk8vBwopPtE9Pmh1y8OJZctvZ1C+JMfKcNhq4+5DsUS8o7VhyKw+t1gd3/gxa2KzFszu0Mfv/VPCYIVLz5gYi5luSxTHlt2IztB2EmEpEgNCRrr9qjPVf3QwYXmj+Mc4g0Cc95QQz44e16njRmr54jnacfDEbPD5d6z0bNS/+aGX9MDCd6u+NuRrac1w2Grd9oOucX3nwSOuDToLL+hMeUznuKlurpLfs9+YHJTGd1SXfM70b/RYBrOhbpjnTLz3dLYNGQiVLveIX/YyeW8Q59grXt2lpSs2aPG8qa7HcoqW08hQE4pci28/+UZsadGG4cP07Sff0MRR9eo/OqiGuprYtXJ73c09h/XE+t1aumKDbp5/qmvH5Kk3zokNgPOjo7VBX7liltbvOqSwlWqMNHXcyNh+h6kaAdz2ooofkBD/WWezvHI5y/V7T8M1qonfe/7k57jllndf1512qfTk+3RH/GCNTT2R2XbJj/FaeeHN3X364iNrdf35nUNW/Vn2xMbYMm91w4xu/dDM2Aw9rzYRp45xi6tOG8Xlsyfpnic36ub5p2nmxFGShjampxsY4reRN9OG/3zknKwUBASLV5mcvniOwlYpV7dIjh//fd1sNdbVDjne5366Rovndemq7sn6ws9f1ifndLrGyHVxnXPO85w6w4lLTnzb3Xsktiy08/g7VqzX+08br9MnjtLG3Yd0z5PPDakPBuNe9vLZk/TlX76a8Bi3e4AruyfpCz9f61qfDSa+DQ0cC+uUuC1NgjTTjjwUhVLIGYCy1j4m6bGk330z7t+flPTJQp4DhvLT8eOVEJ910ijNmTo2bVLpNZrwJ89HlhRZv6s3dg5eDTxhKz21sUe7eo/oL2a9Q5KGbDb+H1ec4fFcq7993yn62m9f18CxsGqMeyO2kdHG3YcSZgR5dRZOaK7XLQ+fmHb++Uuma8nydZo2oSlpVkhNrPPPea5b4/Xjr+3U+p2HEkbD/O37TtG9f9yk7QcGhjwn36NBmF6OSpVqNLNTtvx0BmX6mn5vtpPPzxk0EL+shbNnSqrGj09e8M7YzGZnycwte/v14tb9OmPSKG3Z1+8Zz2pC0o4Dh4fsVxI/gm9TT58eeXHbkMcsWTBLU9oaE95z2Fq9setQLDlPbpx46o0TMwm9Yv6xwUHdftWZCSP+4v+ePEglvh5x1NeG1Hf0uGedkuqYznH9Nnj4jcn53m+VzkT4lc+Z/uObh7suGTy+eXhsqbh4rQ112rb/sF7Z0asaI82c1KK5p45Pm3sk56huZdy5iX9w1dC9R//2facobK1umDs1Nqv5rI5RGj4spAWzO2Ixs742pC9eMkNrtu7XLQ+/okXzpg55Ha/XHd1Q53pdX9t5UMbIdUCHWxndur9f2w8MJOS1N73/FG3d3x/bo9qrEcBtLyqvz1ry3ns1Pi+tlLjCCheAf6nu+f/8lHGuccDrOa/v7E3IGb067EYOr3Ed/DCpZUQsX/rcxdP0xb+YoW/+YUNsNt+5J492H4gyfJhaG+o0bUKTOlqn6u0DhxP2gr1jxXotntcla6WF962KxdvkDkspsorP1HFNumHuVE1sGeGZT04ePULLruvWezrbFApF2hMy7TDz28ib6T67+cg5iaNAcHgt09zaUKfntwzd7y5+9Yc12/bHJlA89NxWjR1Zp7f3H9H2A/tdy/johjp94ecv+8rD4583cdQIvbbjoKa3N8XaEXYeHFDImFg8jn/8qzt71Tl2pOt9wZcvm6k7VpxYINCt7eBBl+VDO0Y3uL6nk8c06j9/m7jgYH1tSKdNiGylxEw7VIuCdgAimPx0/Hh1EnaMbtSv1+1Mu+dSQ90wLVm+LmFPmCPHB/XRd0/WkuWv6cFVW/Xly2bqcz9dEzt28muFjDQYlm5+6CVNaWvQyOG1QzYb37qv3/W5u3oHdO8fN+mGC6dqQnO99vYdSegQdBquPvPAau3rP6q7rj1LR49b3fTgan1yTqfrMetraxIq3VsffUXXn98ZS4Sd/+Ibuh1ujdfOPoXx7+drv309tneNW5Kdz9EgTC9HpfIazRxftvx0BvmV6c128vldMmtirPPPOZebHlytX9w4Z0isPnvyicaPw0eOxzr/kpfM/MoVs7R132FNanXfNHvWpFHasKtXoZB0w4VTdXQwrHnTxiUsPdTR2qBbPzRTr24/oK9ccYY29/Rp1qRRek9nm6ShI50Xz+tKaHCJb5zw06g/unG4ZneM1sRRI9Iurbnz4IAeXLXVtQNz8ujGlLOCvI7pHNetI2N37xHXhnE/s+B397rPSMymwznfnYmobPmc6d8xulFd40cmLL/TNX6kOkY36sDh4wmv095Sr4++e7Ju+tGLCfFh6tjI4+uGmYTjNA4PxfYSaW85sWfTQ8+l7uCTpF+u2a7vfvwcWVmNqK3RovtfSFgSyHm/e/uO6p8fTRwV/M+PrtV/X/cuSUqY7Ry/r5Pzup+dP029R47rhrlT1T7K/bqufbtXNz344pBl450BHdsPJMaQnQeP6PbfvJ5wTrf/5nWdMWlUrAPQi1us8vqs0zXkVlpcyfR7X0mdn0CmvMrL5Cz2r9vbf0yPvrQttg9eQ537Uuljmobr9V2Hhgx+eH33iWWMDx4+rt+/tk0LL3hnrIF3ctsI3fqh0/X5uAG5i+Z2aV//EX303ZNjS9UlD/wbOBbWpNaGoUvZPbxG/3rZTH32p2tiy9S1t4zQG7t69dBzW3VV9yTPfHLz3sP6ws/X6peL5shKen1nb6yB3WnoHjgWju1xnUt8SRXDp7Q1JsSvkEk/4MMPVgoCgiHVwOYruycNaR9NtfrD377vFE0Z06jF97/g2eY5qrEutiRn64ha/fsVs/SPPz6xlcjnL5muZU+8kXCO9bUhbdl7WPc8uVGT2xq1YfehhJWWnFnYTmysrw3p9Z29mt7erM6xIzV/xgSdeuMcbdnbp4a6YZrQMjxhD0C3yRz7+o/q4OFjsXOdM3WMjofDru+p59ARffjsjoT7ibuuPUtSbrEZKDd0AFYhPx0/Xp2ENSH3pNJtz6X/ffFpGlYT0j8/sjb2u39fMFP/9ZHZemP3IU2NLhG359ARnTJupL76m9diI/xOa29Wb/9RfW1FpLF+5fo9mtLWOCT5/e5Tm4dsCHvT+0/Rz1e/rctnT9LYkcP11r5+PbhqqyTp+vM7NXn0CG3dfzihEorvjHNr+PmXvzxdX/n1q7HGHKfSrQlpSCLsd4al12htZ++aYiTZTC9HJfK6UY4vW346g/zKdJmc5PPzmhG3+9DQffY6WhtisblheGRJvuQlllob6rR132HdsWK9WhvqhjSgf/mymdrfN6Dv/jGyv8vieV2a1d4S6/xzlvNMHlG4ZMEsvaezTcOGhVxHOt+xYr1uuHCq/uPXr8d+5zQwx9cpbo36ziCUUMho5sQWX4NU9vUf1X1Pb44l/iGj2OxFt+fXDTNpl/lw27T8o++eHFu6xG/DePzNmtfyKdl0OBdqSSYawCtTPmf6h0JGc08dr84xI4fkjkcHBxPypiu7hy7ze8eK9Zrd0aqwlW599BVdMmuijJGGDwtp855+/c/7nk+4Kf/FjXO0+9CAxjfV68/e2aa3DwxobNNwrdt+ULf9Yl1Co8L45uGaMibSkRXfYBD/fj2Xgj8YaZyNz/2cfZ2cRuym+mH64xs9sX1LJ7eN0Bf/YkZCfuvkhQPHIsvGxw84cWadL12xIfb+Tm4bqZ6+I64Nxv1Hj6f9PJJjlVtc/e/rZqv38LHYQLdlf3gjttdJfJ1XaUu9ZfK99+r8dOuwBSpRNvWE23OWLJilyaNH6KLp49R/dDC2vL3bsfuPDroOfjitvTs2sG1yW6OuPnuytu7rjw0u29xzWHf9br0eWHie9vYd1Qtv7dd9T2/WR87tiMVn53jxA//qa0PqPzJ0dYjNPYe1u3dAi+d1qXlEbcJMkkVzu7T85e2uM98bamv0zSc2qrWhTs9t2a9bfrZmSF2w/cCAJreN0Lb9A/qre07kkMl7Zfnh1bYwobnefQ+vcSMT9rVyyznT5X2sFAQEQ6plmk9/R0vsPlg6MTNwV++ArB3abvu1376ub3xktmub5+S2Efr8JdO1t+9Ywv6uX/yLGbr5f5yqfYePadqEZv3oT5Fllh9YteVE2+2EZv3X7yMx2FkONPkeID4PduLke97ZFotLr+3s1ZLl6xJmfP9y0Rzt7B3QhOZInRDfLrFo7olOxfrakC4/a6I6Whu0ZMGs2BLV8a/1+Uumx+4tJjRH9s32u5c3UCnoAKxS6Tp+vDoJn3mzx7UBZdv+w0MqmN2HjiSM+ossBzUQS6Lv+t2GWKA9c5KVtdLtv410Aq7bflDT25s1dmSd9vUfVefYkdq6t8915Me7OlpjU7fHjqzX1v19Ggy3JyTrTuD/+u826Ia5U3XX4xsS3kN8Z9z2AwOxRuUpbQ0a1zxcX3j45diI8vhKt3vy6CGJsN8Zll7LktroKHSSbCA7XjfK8WXLT2eQX5kuk+N1fm6DBtxitRObDx4+qls/dLre2tef8Nz4Pfa2HxjQL9ds19euOlPrdhyM7tX1uj58doc+dUGnvvnERt2xYr0eveF8berpU0/fEb29f0Bv7D40ZIbyzQ+9pJkTW9Q5dqTne57QXK/2lvpYMu40MCfXKROa63XR9Anafch9EMqMdzTpex8/R3v6jmjiqAbNaG/2bJhwGnduv+rMWIOKW/0lKe0yH8nx260jw0/DePLNWr5GURdiSaZKm/2DE/I9098rd2xrHK4HVm2JNUJ4LZvWf/S4evqUsD+T2+zhG37wgh5bNEfnTGkbss/dw6u3JTR23P+nLbpo+oS077e9ZYRrORxe+/+3d+fxUdX33sA/38niZE9IIMRgQCDsS4yI1CpXoddSS+sKah9vq4/30vs8KtQut9ttva0+bbUurdXWx4praxW3Vn0s2qp16YPaKKAgKBhIDIYthCUbWeZ3/zhzDrOcM3NmMplz5szn/Xr5kkxmMr/fWb7nt/9yAMDo9Ht0xQL0DgyFffajvV1hjdUtHb2465XtuPmiuejuH0TrgV6jwVfPq0h43nN92r6sFYX52LYnfGR0aIOxP09bbSOeyFjV2dOP+upio3GjpsyPf+zsDOtYve4LM4E3W/Dh3q6wZ57XlnpL5Lq36vwMbajiks3kZck8J8w+U1dRaLpK0NnTq6PKXn/7cK9pzOkbGML4ygJcPK/OcjZfS0cvDnT3o9ifi0mji5GfKxhfWRS1NJ4eh/VnzP7uo6bPgJpybc/VyNmBegfig+tacPNFcxFQCvk5Puzo6MZdrzaj/VAfVi6ebHT+RX5u9evNuP7c2caSo/rv9cbxSaOL8flZNcae3bFYtS0MBaIb+L/31Hu4bXkDfvzs+2GzbULLnHbKfVwpiMgdrMpo8ydUoCOis27lono82tSKsaV+7DrYaxoXB4cU/Hm+sDbPMn8OKkv8piuU/eiZzbh1eQPufm0HOnv6ceXpE/HS1t342uIp+G5Eh9y+rn60H+ozXWlpXHmBsTz/Q29og5BDB6LduHZLVP3gxgvn4AtzjofPJxABblvegI/2dWFqdQl27O/ChSePM7YZ0OPTF+Ycj9m1ZcFV6XIwMBTAklljjd9PGlOc1JLNRF7ADkCXcGMF0ayhx6rh2mz/l8gZbqGN0sCxQFu7YoE28u+vH0QF/R8snYHBwQBuXLsFy04+wVg2NLSwqi9Roi+BUZCXG/U9oaMAzTreIl9rP9SH1a8347blDTjcOxi2nJT+N+vHFBvr/kceNzszLGePK4saVXjThXNwfLkfFzbWuuIaIEqFdMe3CZVFuONLJ+HdtkPG3iLTa0pRVZxv3FtA/M4guxJdJieyIv/Mxl1RM5ljdUjqsRkAZh1fjo1tB8MK65EzCs+YMgbXhhRyARgj8S5oHIc7X96OrXuO4JuPbTQaLaxmre053Ge6pKee59bOHlzQOA6rX2/GTcG9AiPTHfpMmTQmeu8Ss/1RzRomzp5ejUdXLED7oT7UlBVgenVJ1HUWWYiP/H6zazM0fvf0DyXVMB5aWTObVZ5sh/NILMnktdk/FC4dM/0nVBaFzbxbtXiy6XVaN6oIXUcHwmYs6yOD9TKa/lqLyfK5Zf68qHLiykX1ONBzFJMQe8+8mTWlUXtOTRpTjAf/f7ORvm8vmR62DLJu75HoMm5LRy+KjsvB6NLjcN3Tm6PyqkIaP/x5Pkyo0u53s7JwaIPxrcsbcGKVdWwIjVlTq0uwdtUZ2H04/Dk2aUwxNn7cGbW09I+e2Yz7Lj8F+Tk+zD6+zMinF5d6s3vdWzWs6Y1Xw4mHHFxBmSKZ50TkZ6waVJ8L3juhf9tqqfYJlUX40Rdn4d9/97ZlPd6f58P6jw/i9he3Y974MqxcPMW0s7Czpx9nTK7C+Q3abJL9XUcxLmJmyKrF9fjJc1uwbN440zggog023rL7iNEu8Mg/WnFB4zjk+IAp1SWmn5tTq+0rFSu+fPuJd1FRmI/TJ1fFjQehbQsHuo8iL8eHnv4htBzoNv37W3YfxrJ548IGMYSWOe2W+6yuCze2WxElw+lr2c73W5XR8nNy8O0nomPlfZefErUyW2hcLMzPidpfdWZtGa58oMmy/r9192GjzSDHB6xcPDVqcENoWdZspaWxZQX44dObw8pDoQPRls6pjaofhA4+HhwCrl2zARWF+bj8tAm47a/h9WqdneeZ1wa+EdnFDkAXyKQKotkItK//8xSU+fPidqpZLXP32rb9GFvmNw361z/7Pq4+azJaOnrRPxRAY125aaO9neXW9FGAs8dFLy83pbokao/Aaz8zBT0Dg2jtMN9ncEZNmeWoPTszLBdNrcbk0cVorKtAT/8g6kYV4cQqFqDJW5yKb/2DKmxvkVuXN6CxblTYd6aqUTzRZXKsRk431lXE7ZA0qyicVFcR9v12Y29AwYiL+vJ4oe81i3uF+TlGniMHZOiVi+8smYarz5qM48v9CZ/j1gPd6OsPxJ11FwioqJHmZvtuxbrOYl2b+rXRvK8rqYbx0MqaPsJyxcKJOOmEcoyvLEq6gjkSSzKxEkTDFdkhP66iAFOqS/GNx8Kv0xOrtJUkQveHBrRO8tAZc/48H7qPDuHt1oNh1+bk6mJ89aHoxo7fXXmqrTT6xBf2XLhlWQNuXtYQ1YEWyarxZVRRPvqHAlHLDV23dCbuenW78b6Vi+rxycEeANbxWG8wjhUbEnmemg3M6xsIoPVAD657ejNuOG8Wzptbi9xcX1Yv9RZrxQBdsvGQgysomyRSljixKjrm3LKsAS0HurG13Xy5Zr28ev25s9DRfRRXL5oMf64P338qegbeqsX1mFZTGlxO+TjUjSrChKpiNAYUZteWoaWjG+s/PmgsHxe6B6zOn6dtFxC6vDOgwvYktBrsUl9dYuTZKr70DQTQ1HIA4yoKbMUDfbDz1t1H4g62GQoA88aX45EVp5o+24ZT7sukdiuiWJy+lu1+v1kZbdXieryx44BF/V5ZLhlaXepHV98AXtjUjqvPqjf2U125eHLM+v9Q4FibweJpY9B91HyAbI4PlistnTax0nLwdXWpHzk+8/KxHpf0wXgXNI4z2mz19yRatvLiwDciO9gB6AKZVEGMbLgWCN5p7cQNz70fNcOhsig/rFPNasnLqdUlGFTKMuj3DWp/b974UcbybpHHxc5ya2dMrsIFJx2b/RPa+O4T4KbntxrLSikFCBS+/9QmVBTmm87eiDVC2+6xnFBVjAlV7jrHRKnkRHxL93cmu3xSZAdkvA7JWBWFJTPHonbFAry4dS/KC/Lw4y/ONEbZWcVePXk/OX82fv78B2G/M5u1tnJRPQaGAkb6G+vKsWLhRAQUwpbz+HBvF1a/3oxzZp+R0HEMBBTeaT2InR3mI5r1CkAgoPDeroNR59hs361Y59zOdZJsw7jZ8nzTxpbin6aMGValciSWZGIliIbLrEM+dB+/8KU4tX01I/dUygnZo3Xlonq0BTvMQq/NfUeOmsaGA939cdO4s6Pb6JDUP/eNx7TZKQsmVsX8rFkcuOG8WVj5yHq0dPRifGWBsV9gYX4Orn92s7HHoVLAo02tOLehFkD04Dg9j6ENxrHyYPfZZrXkaUF+rhEv68cUY+4JFVm91JtVw9qD61qM9yQbDzm4grJJImUJs5jjE2DJL1/Dv54x0fTvTB9bgtuWN+Bna7cYA72uP3eW6T02tsyP/xWcRRjZqK4vY3/7i8e2AzEr8/5g6Qwc7h0I22Pq+LICXLvm2FKha5qs97QGzOOL3qGoN6onEg8inwFrmtrww6Uz8OOIvQsfbWrFhY21ln93OOW+TGq3IorF6Ws5kZm4erwMHbxw4cnjTO/jts5e07hYW1aAX7+yHV9dOAnzJ1YanX8AjEEQVvV/vRx76/IGzKwpw7rmDtPvXjxtDGbXlgMwX2nJqq1jQmURThk/KmZc0uOW1UC6RGJpNg98o+zGDkAXyNQKolJAQX4OckTQ0tFrrCFdV1GAAz39UAp4+C3ttRwf0Di+At88eypufuGDsML1jv1d+P1brfjOkumWjdQ/PX+26XKbOjvLrZ0ywXr2TyCgwpav8uf5cNNFc9A3EAhbH1sEOGNyVdTfIiJzTsQ3J74zHcvsxasozK4tx66Dfdi6+zDuX7fTiFn+vBx867NT8fPnj8XeVYvrUTeqEDOPLwWgLXEEhMdPfdZaXUUhdh/W9sdaMmuskZ66UUWYNrY0qmHj0abWpArROzu68b2n3rNs/BlT4jc6QbfuPmw5Qjz051jn3M51kmzD+Eg2qKf6WmMliIbLLDbp+/hFdq5p+5BGLwF62/IGY2+QR5tasXROLZ58J7I8l2MaG6pLj4ubRqv7XV/WOJbI+7kgL8fo/AO05UBXPNRkzOD7n6dPirqfZtSU4LRJlRhb6jedHWnnfkvk2TazpjRqaenrls7EPa9+ZHxu96E+zD3hWB5H+hnmRpHndnSxHzs6uoxn4nDiIQdXUDZJZjWM0Jiz7qP96BsIWDZA7+rswa1/DX92tHWar9KzY3+3ZVkZiL439T1g77v8FLQe6MGRvgEMDgbCOvZuOG8W+gOBsO9qP9SHB9e14IEr5kNBRZX19Pgy9Zoz8H77IWzb22UMlgvtqLMr8hnQfqgPj7zVit9cdjLWt3ZiKKA9P7+9ZHrMmDWccl+mtlsRRXL6Wk7k+80GL5jFylWL67G/y3y/09bOXrR09OLHz76Pn180N+z3T7zdZkzcMOr/owqxOxgbVyychGnVJTiprgI79nfjP//0XtR333jhnLBl9BMpU/p8gk9NrIxaUcNsQMUHuw8Pu2yVzQPfKLuxA9AF3F5BHBwMYHP7IbQf6sPx5QXYe6QPVz+83gjMq78yDysXTzb2y+js7ceNaz9ATZnfWOJpKAA07+3C6td3YMXCiZhSXYK6igK80dyBw0eH0NLRi9/8bTt+sHSGsayG3kE4sUobERJrk+zhLrdm9hBQIcuBtB/qM/YduOCkWj4ciGxyIr65PaYmK15FQY9j/jwfbn9xu7GfFgDUlPlx3+Wn4HDvIIr9OSjz56F3cAgBBdRVFBoNAXojyD1fmYeDPQP4cM8R3PKXD9HZ0x/VOBAaN6022k4mf2YVmluWNRj7vOpLPUee4/GVBZhSXYKrF00GADyzcZfRaWi2v4Ld6yTZhvFMaVBnJYiGK5FGDLP99PoGtD2L7nhpe9gMrPZDfVi7qR03XzQXPh8wvrIwqlPrhvNmYWZNWdw0Fubnmt7v+rLG8YTez+s+2m+6N7SeX6v7aUKVNoN5+74uY/a0T4D8XHv3WiLPttxcH86bW4v6McVo6+yFUsDdr36Ed3cdNj43tsz6mej0vjiJGk56I2P1iVVFKdkj2C2DKzLtXFJmGm5ZQo9voQNvq4ryMKu2DJ09/Sj256KiMB/th/qMz6xpasP1584yZrLobQd3vLQ97G9HPo/M7s1LTqnD19dsNDrnnt7UZgxinjymBLe8sBW3X9IYFYM7e/oxuuS4mNt+TBpTjPGjClFW0AFAaxd5aetuXH/ubOw53GekKd6xMnsGfLi3CydWFmL8qELsPdJn7HWub5Fidu8P51ylqo7FuEROc7q9wOr7Rxf70byvy/TeiPyMCHDzRXPhz/Oh1J+Hdz7uRI5I1NZG+sxjQIuHvf2DYX9ndHE+Jo8pws0XzUV3/yC6jw6ivDAPnxzU9ua7+9WPcN/l8+HzCVoOdIdN/tBXu6gsyhvWPZyb68MX5hyP2bVlpnFJj1szakowvrIobBuSZMpWmVJPJ0oldgC6gFsqiGb6+4fw9+b9WP/xQQQU8NM/b8Elp9QZBfCKwnxs/uRw2J4qP1w6A+MrC9DS0Ws0QPvzfLjrspPxq0sbMKroOCNvh3oH0dRyAP48H97ddRj7XtpuFLZPn1SFMaXHGct+xpKK5dYiHwKBgHLteSHKFE7ENzfH1OGIV1HRK9MAsGrxZKxpajMaSjp7+rGuuQOPNbVFLb936/IGnD29OqrBEwBm1JTitEmVlo0DqSw8mzX+5PiA6WNLMb2mBD6fWHYSjq8swFVn1eM/Ht8Y1jEwrqzActlUr14nyWAliIYjkUYUq/cunjYGp02qDJuBVVPmx+dm1+CbIff1HV86CWuCew2OLfNjZoz9mEP1Dw3FXNY4lfmNdT/t7Og2BtGFfvY5G0tOJRqzcnN9mHtCBWbWlOGPG3fhw71dxvfF6jhN5b446WjkTfU+PqmKh24YXOH0HkeUXYZz74TGt/ZDfXj23V24ZlE9/uXet8Jmt+iDQwCtbDtvfIVRfi3Iy8GbzR3GDF5d5PPIakk9/e/e/tI2XHn6RKx+vRkrF9Xjp89tQfuhPgwMDSVdbszN9eH0yVUYV1GAA91HMaW6GCseakrovrR6BphtkRLv3k/2XA237BwIKOzY340t7Yexbe8RrGlqMwYZMi5ROjldD7T6/h0dXWGTLULvDf0zN67dgovn1eGXL25DRWF+VN3+++dMx23LG9A/FECOCO5+9SMjvvnzfJg2tgS3LGvANx7bgIrCfFw8vw5XhXznykX1uP7Z97F0Ti1Wv94cdlyKggPq9AkS+t9cWD9/2MckXlzSB9PVjSpCwwnlHLhKlCBRobucZ4B58+appqYmp5ORcnoF2U1BLBBQ+NOGXfhuyOgKfbmKpXNqcefL23HVWZOx+vXmqIaMO7/UiKsefifqc/ddPj8soA8OBvDXrXuwY393VIN0ooXAkTiGbjwvRAlI+cWaTAx24j7y4r0bqzIPIOp3ekNJZ0+/8e8LGseZxmw7jc+JpjXRRt9AQOGPG3aFjejTRyzedvFcLJhYheZ9XTjn9tfQNxAwZpnn+ICF9aNx2eo3o/L16IoFuPjuNyzz68XrhFzDFfE3HRLpZLDzXv2+3HfkKL5y31spiVfN+7pwxf1vhe3N9+y7u6LKpanOb6R1H+3Hpb99M+r1R1acGncvQv27k4lZ+moeu210nIbGWV0yxz1dnU+pSq8XWR2bR1csQE//kNdn3qQ0U26Nv14SGt8K8nJMy28rFk7E7S9ut3x2vPTBHmzb02W7XcEqJt904Wy0HOjFk++0GXsA6ss8D7fcOJyYZfcZMJJx0SwNAOKW+82eCXo5v7OnnzHbWzKiDOx0PTDy+/W9UGPdt4GAwnu7Dhrx0ao9NjRWhrYJhLYdxCprX3n6RMypLUV9dUnYcdm5vwt/3rQ7ai/vz80aiwlV0fcvZ/sSOcL0JuMMwGFIZTBz4+j7nR3dRucfoE0X10fE6fssWW3C2jcwFDYl/KE3tFF1kctB5eb6cPaMsWg90I3Gugr09A+iblQRTqxK/FiOxDF043khyjRO3EdevHdjzSZo3tcVtQfXL1/chgeu0Ebjfe1RbUR1KjbOjifZRl+fT9BYV24sjac/Ozp7+o2R25EjxPVRiQNDAdN8tR+Kv2xqOq4TVn7IyxKZ6WTnvaF7naQqXk2oLIra6znZkd5OLp+WbMzSZwPqe/7FkqrjHm/f2lRJNL3ZFI+tjs2LW/dadqIQOSVyqWWza/ekE8rxyIpTLZ8di6ZWY/LoYtvtClYxeWxZAX749Oao50Uqyo2JxCyzeGXn+0dyfzOzVZPslPvNngl629KdL2/nPoKUdk60F1jd0wAs417k/vQ9/UNhy4CafUbfnim0TWB0yXFhcTNWWTvHB9RXl0Qdm7pRRaivLg5byr6+WpuVZ5ZXrkJA5B7sAExSNgSzWA8DXY7AtNBcXerHNx7bGLeBI/oBOMozx4+InOXFRj6riopVvFZQGFPiD1sOaaT3OxhOo2/dqCJMG1tq2Uhv1fC+Y3+3ab5qypzfDzIbygtEiTSi2H1vKvdnSfVyjLHyEOvZ4/SSU3ak6riPZAN0qOpSP8ZXFhizO4Fje8BGyrZ4bHUu9ZVvR6pTlmi4rK7d8SGN5Wb0JeLMZqKYsYrJp02sTMleoGbsxtjhxKt07m9mt9xv9UwQMd/SYM9h7djn+ID2Q96py1HmstO2EOs98e5pu/dt5PvMPhO60J/eJmAWO62+c974UaZlU32gxcSq4rjxMV0DwYjInvibZpApq2Cm77/kBfrDIJQ/z4eTTijH+Q21uOuyRvjzcnDtZ6YY79OXcvjZn9/HDefNCns9soFDfwCec/truPS3b+Kc21/D2s27EQhk1rK0ROQ+2RZfrOK1Xii/dXkD/Hk+PPF2G1Ytro8Zm4crVqNvPHoj/XMrz8AjK07FcyvPiGro0BveF0ysMioPOzq6TPM1s6bMyHvo6+lsbM+G8gLRSAiNXcDw79/I2DESjYjxnj12YpzTUnXcYz2XUqmuohDXLKrH6tebccdL23HPa824ZlE96ioKo96bbfHY7FyuXFSPJ99pM95j9/lMlE6pjv9WrGJybq5vxJ4XdvM2nHiVruMH2C/3Wz0TfAIjbZHP0M//6jX8edNufH3NRs/X5cjd7LQtxHtPvHva7n0br26/anH4cz5W2cvsO2+8cA5Om1hpGffslqeH0yZARKnHGYBJSteoVieZjYj76fmz8emJVcjPz4GCtqxcRWE+brpoLrbvPYKhwLHlPvd1bcOjKxagd2DIdGQIR4QQ0UjJtvgSa1ZJ5MyXsaV+nD1jLPZ1jcx+B+le4m5nRzeufng9KgrzjaWnfQLMqClBbq4vpbN+kpEN5QWikZDqWXvpYOfZ4/YlqlN13NM127G1swf/+cdNYcf8P/+4CY11FbZny3s1Hkeey4K8HKx8ZD3aDx1rfEv3rHgiO9IZ/9Mdk+3mbTjxKp3Hz2653+yZ8JPzZ6Oxrhx1o2JvaaAvE+rluhy5m53yXbz3xLun7d63ser2o4v92NHRZaz+E6/sNZKxIp0zkYkoPnYAJikbglm8h0FoIe7DPUdwx0vbwz7f0tGL3oEhLJhYZfr3s60STkTpk23xJV68NmvcmDRmZI5Dupe40891+6E+3PnysefQaZMqMaEqffv8WcmG8gLRSHH6/k2UV549qTju6WqATuSYZ2M8Dj2XgYBK2V6YRCMt0+J/Iuzkzak9YxNlt9xv55kQa5lQ/d+Z9jwlb7BT1oj3Hjv3tN37Nlbd/sSqooSWMB6pWJEJy94TZRN2ACYpW4JZrIdBaCFuX9dR3PNac0IF1GyshBNRemRjfHFLQ0m6Z+24/VxnS3mBiNwfj9ItHc+lRI55tsfjTJxVS5StMiVeJRJX4j0TrOK5vp9ZNj9PyVl2yhrx3pOuezpb2wSIKDZRKrPW0J43b55qampyOhkAjm3wymCW3CbVw9nYmohsSfmN5KYYHAvjS/bIhHPN8kJWytr4m80yIR55TaLHnPE4a6T0pDL+khOyLV6ZxfNVi+vx4LoWdPb083maOTxXBrZT1rD7nmy6p4nIEaZBhR2AlDLJPMz4ACQaUZ4rfCeC8SV78FyTC2V1/M1mjEfpx2NOJtgBSJSBQuP56GI/cnzA7sOM7RnGk2VgO2UNlkeIyAVMgw6XAKWUSWaquVumpxOR9zC+ZA+eayJyC8aj9OMxJyLyBrN4PqGKsZ2cZ6eswfIIEbmVz+kEEBEREREREREREREREVHqsAOQiIiIiIiIiIiIiIiIyEPYAUhERERERERERERERETkIewAJCIiIiIiIiIiIiIiIvIQdgASEREREREREREREREReQg7AImIiIiIiIiIiIiIiIg8hB2ARERERERERERERERERB7CDkAiIiIiIiIiIiIiIiIiD2EHIBEREREREREREREREZGHiFLK6TQkRET2AWhxOh0jpArAfqcTMYKYv8zG/GWe/UqpJan8gwnEYC8ez1TgcTHH4xKNx8RcphwXJ+PvSHPjOWCa7HFjmgB3potpsseNaQIAv1JqVqr+WJrjr1uPaaK8kA/mwR28kAfAG/mwkwc3lYG9cMzNeDVfgHfzxnxllkzOl2kMzrgOQC8TkSal1Dyn0zFSmL/MxvxRIng8zfG4mONxicZjYo7HxXluPAdMkz1uTBPgznQxTfa4MU2Ae9NlRyanPZQX8sE8uIMX8gB4Ix+ZlodMS69dXs0X4N28MV+ZxYv54hKgRERERERERERERERERB7CDkAiIiIiIiIiIiIiIiIiD2EHoLvc7XQCRhjzl9mYP0oEj6c5HhdzPC7ReEzM8bg4z43ngGmyx41pAtyZLqbJHjemCXBvuuzI5LSH8kI+mAd38EIeAG/kI9PykGnptcur+QK8mzfmK7N4Ll/cA5CIiIiIiIiIiIiIiIjIQzgDkIiIiIiIiIiIiIiIiMhD2AFIRERERERERERERERE5CHsAEwzEVkiIh+IyHYR+Y7Fe84UkQ0isllEXkl3GocjXv5E5FvBvG0QkU0iMiQio5xIazJs5K9MRJ4RkY3B83eFE+lMlo38VYjIUyLyroi8JSKznEhnskTkXhHZKyKbLH4vInJ7MP/vikhjutOYiUTEH7we9Ov+R8HXR4nIX0RkW/D/FU6nNV1iHJP/EpFdIXHwHKfT6gQRyRGR9SLybPDnrL1WdCbHJOuvFRHZKSLvBfPfFHwt668VJ1jFNDeIvHfcwOzadZqIlIvI4yKyVUS2iMinHE7P1JD4tkFEDovI15xMUzBd1wav8U0i8gcR8TudJgAQkVXBNG126jiZlaOdjskWaVoWPE4BEZmXzvQMlxtjRzxuvC6SYZGPjCqLicgJIvJyMMZvFpFVwdcz5nzEyEPGnIsY9cBMOg8ZV5f1SiyK5IX72owX7pNYxIPtHWZlFI/kK6qO5IV8RWIHYBqJSA6AOwF8DsAMAJeKyIyI95QD+DWALyqlZgJYlu50JstO/pRSP1dKNSilGgB8F8ArSqkDaU9sEuzkD8BVAN5XSs0FcCaAW0QkP60JTZLN/H0PwAal1BwAXwbwy/SmctjuB7Akxu8/B6A++N8KAL9JQ5q84CiARcHrvgHAEhFZAOA7AF5UStUDeDH4c7awOiYAcJseB5VSzzmWQmetArAl5OdsvlZ0kccE4LUCAGcF86834vJacUasmOY0s3vHDSKvXaf9EsBapdQ0AHPh8DFTSn0QUic4GUAPgKecTJOI1AJYCWCeUmoWgBwAlziZJgAQbcDdvwGYD+3cLRWRegeScj+iy9FOx2SzNG0CcAGAV9OcllRxW+yI536477pIxv0wrydmUllsEMA3lFLTASwAcFWwPp9J58MqD0DmnAsv1I0zsS57P7wRiyJ54b4244X7JBavtnd4sW5uVkfyQr7CsAMwveYD2K6UalZK9QN4BMC5Ee/5EoAnlVKtAKCU2pvmNA6HnfyFuhTAH9KSstSwkz8FoEREBEAxgAPQHtiZwE7+ZkALflBKbQUwQUSq05vM5CmlXoV2TqycC+BBpXkDQLmI1KQndZkreLy6gj/mBf9T0I7nA8HXHwBwXvpT54wYxyTricg4AJ8HcE/Iy1l7rQCWx4TMZfW14hS3xjTeO/aISCmAhQBWA4BSql8pddDRRIVbDOAjpVSL0wkBkAugQERyARQC+MTh9ADAdABvKKV6lFKDAF4BcH66E2FRjnY0JpulSSm1RSn1QTrTkc3ceF0kw0Y90fWUUu1KqXeC/z4CrRGzFhl0PmLkIWN4oW7s1nJfLF6JRZG8cF+b8cJ9YiXL2jsyOl8x6kgZnS8z7ABMr1oAH4f83IbowswUABUi8jcReVtEvpy21A2fnfwBAESkENronCfSkK5UsZO/O6BV0j8B8B6AVUqpQHqSN2x28rcR2ohaiMh8AOMBjEtL6tLD9jVM4YJLHGwAsBfAX5RSbwKoVkq1A1rBFcAYB5OYdhbHBACuFm2J2Xu9sJRAEn4B4D8AhMbGrL5WYH5MAF4rCsALwfLQiuBr2X6tOCZGTHPSL2B+7zjN7Np10kQA+wDcF1yO6B4RKXI6USEugQsGBSqldgG4GUArgHYAh5RSLzibKgDajLaFIlIZrEOdA+AEh9OkY0xOLbfFjmR56brIyLKYiEwAcBKAjK2TReQByKBz4YW6sUfqshl1zOPxwn0dygv3iYVfwJvtHV6sm1vVkTI9X1HYAZheYvJa5CiaXGjL4HwewGcB/EBEpox0wlLETv50XwDwd5Uhy38G2cnfZwFsAHA8tGnsdwRHFGQCO/n7GbQO6g0ArgGwHpkzw9GORK5hCqGUGlLaMl7jAMyXDNsfciRYHJPfAJgELT60A7jFsQQ6QESWAtirlHrb6bS4RYxjktXXStCnlVKN0JZnvkpEFjqdoGzmtjjv8njitms3F0AjgN8opU4C0A2XLGUTXCr/iwAec0FaKqCN+D0RWlm+SEQuczZV2ow2ADcC+AuAtdAG5Hmp/E3HuC12ZLuMLIuJSDG0gdZfU0oddjo9yTDJQ0adC7eVmZLBuqy7eOG+juSF+ySSy+snw+XFMopr60ipxg7A9GpD+GjNcYheVqYN2tqz3Uqp/dD2LpibpvQNl5386Vwx0jdBdvJ3BbQlXJVSajuAHQCmpSl9wxU3f0qpw0qpK4IP6S8DGA0tj16RyDVMJoLT5f8GbYbvHn0J1eD/M2lJ45QJPSZKqT3Bgm4AwG+hLb2bTT4N4IsishPaMsOLROR3yO5rxfSY8FoBlFKfBP+/F9reYPOR3deKK0TEeSdZxRPHWVy7TmoD0BYyev9xaJVdN/gcgHeUUnucTgiAzwDYoZTap5QaAPAkgNMcThMAQCm1WinVqJRaCG2Js21OpymIMTmFXBg7kuWJ6yITy2Iikgetk+D3Sqkngy9n1Pkwy0MmngvAG3XjDK/LZuQxj+SF+zoWL9wnITzb3uHRurlVHSnT8xWFHYDp9Q8A9SJyYnC06yUAno54z58AnCEiucElXk5F+MahbmYnfxCRMgD/BC2vmcRO/lqh7WMC0fbGmwqgOa2pTF7c/IlIefB3APCvAF71yuijoKcBfFk0C6At/dTudKLcTkRGi0h58N8F0BrQtkI7nl8Jvu0ryLx7PmlWx0TC95Q8H9qyXllDKfVdpdQ4pdQEaDHmJaXUZcjia8XqmGT7tSIiRSJSov8bwNnQjkHWXitOihHnHRMjnjgqxrXrGKXUbgAfi8jU4EuLAbzvYJJCuWlP8FYAC0SkUEQE2nFyRT1MRMYE/18HbTl+txwzxuQUcWPsGAZPXBeZVhYLxq3VALYopW4N+VXGnA+rPGTSufBC3dhDddmMOeZWvHBfm/HCfWLGq+0dXq2bx6gjZXS+zOQ6nYBsopQaFJGrATwPIAfAvUqpzSLy78Hf36WU2iIiawG8C2294HuUUm5/qAKwl7/gW88H8IJSqtuhpCbFZv6uB3C/iLwHbTnJbwdncrqezfxNB/CgiAxBC4pXOpbgJIjIHwCcCaBKRNoAXAdts2E9f89B21dlO4AeaDM6Kb4aAA+ISA60gSVrlFLPisg6AGtE5EpojWrLnExkmlkdk4dEpAHa0rI7AXzVuSS6ys+QvdeKlZuy/FqpBvCUVudFLoCHlVJrReQf4LXiBNOY5nCa3Mr02nU2SQC0pdt/HxzI1QwXlHGCgx3/GS6Jb0qpN0XkcQDvQFticz2Au51NleEJEakEMADgKqVUZ7oTYFGOdvT5bZGmAwB+BW2lkv8nIhuUUp9NZ7qS5NbYEZMbr4tkWOTjzAwri30awL8AeE+0LTsA4HvIrPNhlYdLM+hceKFunHF1Wa/EIhNeuK/NeOE+SUSmny8v183N6kg+ZH6+wohS3N6KiIiIiIiIiIiIiIiIyCu4BCgRERERERERERERERGRh7ADkIiIiIiIiIiIiIiIiMhD2AFIRERERERERERERERE5CHsACQiIiIiIiIiIiIiIiLyEHYAEhG5jIjcKyJ7RWSTzfcvF5H3RWSziDw80ukjIiIiIiIiIiIiIndjByDRMIlIl9NpIM+5H8ASO28UkXoA3wXwaaXUTABfG7lkEbkL4y8RkXMYg4mInMH4S0TkDMZfykTsACQichml1KsADoS+JiKTRGStiLwtIq+JyLTgr/4NwJ1Kqc7gZ/emOblERERERERERERE5DLsACSKICI3isj/Dvn5v0TkOhF5UUTeEZH3RORck8+dKSLPhvx8h4hcHvz3ySLySrDz5nkRqUlLZshL7gZwjVLqZADfBPDr4OtTAEwRkb+LyBsiYmvmIJEbMf4SETmHMZiIyBmMv0REzmD8pWzADkCiaI8AuDjk5+UA7gNwvlKqEcBZAG4REbHzx0QkD8CvAFwU7Ly5F8D/SW2SyctEpBjAaQAeE5ENAP4vAL0AkQugHsCZAC4FcI+IlKc/lUQpwfhLROQcxmAiImcw/hIROYPxlzwv1+kEELmNUmq9iIwRkeMBjAbQCaAdwG0ishBAAEAtgGoAu238yakAZgH4S/B5kRP8e0R2+QAcVEo1mPyuDcAbSqkBADtE5ANoHYL/SGP6iFKC8ZeIyDmMwUREzmD8JSJyBuMvZQN2ABKZexzARQDGQhsN8j+gPQhOVkoNiMhOAP6IzwwifFat/nsBsFkp9akRTTF5llLqsIjsEJFlSqnHgiOP5iilNgL4I7SZf/eLSBW0JUGbHUwu0XAx/hIROYcxmIjIGYy/RETOYPwlT+MSoETmHgFwCbQHwOMAygDsDQb+swCMN/lMC4AZInKciJQBWBx8/QMAo0XkU4A2HVxEZo54DihjicgfAKwDMFVE2kTkSmgFkCtFZCOAzQD0NcifB9AhIu8DeBnAt5RSHU6kmyhFGH+JiJzDGExE5AzGXyIiZzD+kqdxBiCRCaXUZhEpAbBLKdUuIr8H8IyINAHYAGCryWc+FpE1AN4FsA3A+uDr/SJyEYDbgw+FXAC/gNaJQxRFKXWpxa+WmLxXAfh68D+ijMf4S0TkHMZgIiJnMP4SETmD8Ze8TrS2YyIiIiIiIiIiIiIiIiLyAi4BSkREREREREREREREROQh7AAkIiIiIiIiIiIiIiIi8hB2ABIRERERERERERERERF5CDsAiYiIiIiIiIiIiIiIiDyEHYBEREREREREREREREREHsIOQCIiIiIiIiIiIiIiIiIPYQcgERERERERERERERERkYf8N12xiRz2S+YlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1800x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn plotting key features\n",
    "discrete_features = X.dtypes == int\n",
    "\n",
    "sns.relplot(\n",
    "    x=\"value\", y=\"Median House Price\", col=\"variable\", data=dummied_data.melt(id_vars=\"Median House Price\", \n",
    "        value_vars=key_features), facet_kws=dict(sharex=False));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t3qTNm7RZm7V5WNq8SZs3abM2a/OwtHmTNm/SZm3W5mFp8yZt3qTN2qzNw9LmTdq8SZu1WZuH1WqbW+1yos0ttlmXL65i47R5nTZP02Zt1uZhafM6bZ6mzdqszcPS5nXaPE2btbmVNlftmzav0+Zp2qzN2jwsbV6nzdO0WZu1eYm1eu3mxPWbt5jn979rN7t2s2s3t7NnTuybW9w3t7pnTuybL6Zi47R5kzZP02Zt1ubhaPMmbZ6mzdqszcPR5k3aPE2btVmbl1irjwV6HHBKq48Ftvo4YOKxwHnZN2+yb55m32zfbN88HG3e1EqbW+1yos3avL2KjdPmTdq8SZu1WZuHpc2btHmTNmuzNg9Lmzdp8yZt1mZtHpY2b9LmTdqszdo8LG3epM2btHm52qzLF1exca22udUuJ9qszdr8chUbp83rtHmaNmuzNg9Lm9dp8zRt1uZW2ly1b9q8TpunabM2a/OwtHmdNk/TZm3W5mFp8zptnqbN2qzNw2m1y4k2t9jmVrucaPPFVGycNm/S5mnarM3aPBxt3qTN07RZm7V5ONq8SZunabM2a/NwtHmTNk/TZm3W5ivV972PBX4k+btJvmXjv78lyd/Z5pg9ST6W9TfDujbJh5Pctc1xTyY5NPRMuzV7kgeS/FaSw0PPMsOsl/wZJvkTSX4ySZfkK5P8yuX8/Kt+zDl7l+SHkpwYeo7dnPtlx7wlyU8MPc9Yb78xfsw4++1JPprkjw693t2e/WXH/2CStw+97l36mb8+yYc2jr0hyUeS3D302ndp9v8tyd/Y+O+jSX4/I9rvXGL+Y0k+ssPnlrJxc95eTe6b5507I9ozz/oz3On8uNzfI9U+5pzdvtm+2b7Zvtm+ubdvjn1zicZpszZrszZvOUabizROm7VZm7V5yzHaXKRx2txem1vt8mX8zLW51+YC69Vmbdbm6WO0udfmAuvVZm3W5uljtLnX5gLr1WZt1ubpY7S51+YC69Vmbdbm6WO0udfmAuvVZm3W5uljtLnX5gLrbbLNrXZ51tlfdrw2j7zNrXd5YyZtHsGHNmuzNmvzls+Va5w2a7M2a/PGMdpcqHHarM3arM0bxyxtm8fW5cv4mZVc+y7Mrc29Nmvz8OtfwPzaPJIPbdZmbdbmjc8tXd8WcHs1ee3mRcyeEV2/eZ7f/5f7e6Tax5yzd3Ht5rfEtZub2TNfxuz2zf3y7Jtn/Jkv3Z75Mma3by7SOG3WZm3W5i3HaHORxmmzNmuzNm85RpuLNE6btVmbtXnLMdq8RI2b8/Zq8rHAeefOiB4HnPVnuNP5cbm/Ryp9zDn3aB8HnHf2lx3zlngs0L7Zvnmn438w9s2Dr38XZrdvLtI4bW6vza12+TJ+5trca3OB9WqzNmvz9DHa3GtzgfVqszZr8/Qx2txrc4H1arM2a/P0Mdrca3OB9WqzNmvz9DHa3GtzgfVqszZr8/QxS9fm1ru8MZM2j+Cj1S7POvvLjtdmbR79hzaP40ObtVmbtXnL58o1Tpu1WZu1eeOYpW3z2Lp8GT+zkmvfhbm1uddmbR5+/QuYX5tH8qHN2qzN2rzxuZJ902Zt1mZt3jhGm4v0rdUuX8bs2twvT5tb7fJlzK7NRRqnzdqszdq85RhtLtI4bdZmbdbmLcdoc5HGabM2a7M2bzlGm4s0Tpu1WZu1ecsx2rygxu0Ji/a3kjzadd07knwiyZ9Kkq7rviDJ9/d9/9a+7891XfeuJD+d5JokP9D3/eODrXhx5p39HyS5LsnPdF2XJL/c9/1f2O0hZrHTHF3X/YWNz/+jJB9M8tas/3J+Ick3XexrBxjjiswze5L/NMl/l+Q3u657bOPvvrXv+w/u4ghXZM65m9fy7Tfj7N+W5GCSf7jRv3N933/ZUGtelBlnXzqzzN33/W93XfdTSX4jyVrWf09+ZLhVL8aMP/PvSPKDXdf9ZtY3td/c9/1nBlv0gnRd939m/U0QD3Vd98kk355kb7LcjZtTq/vmZvbMiX2zfXNb+75FaPn2s2+2b459s31z0cZpszZHm7U52pxijdNmbY42a3O0OcUap83ttbnVLifarM3aPAbarM3RZm0u2jht1uZoszZHm1OscdqszdFmbY42p1jjtFmbo83aHG1OscZpszZHm7U52pxijWu1za12OdHmFtvccpcTbR4TbdbmaLM2p27jtFmbo83arM3lGqfN2hxt1uYlbvMYu5xoc7RZm7VZmwv2TZu1Odqszdq8VH1bgL+VNq/dnDR0/eZ5fv+P/ec/597HtZsb1fLtZ9/c3r651T1zYt+cke2btVmbo83aHG1OscZpszZHm7U52pxijdNmbY42a3O0OUvYuDm1+lhgM48DJu0+Ftjq44BJ2/u+RWj59rNvtm+OfbN9c9HGaXN7bW61y4k2a7M2j4E2a3O0WZuLNk6btTnarM3R5hRrnDZrc7RZm6PNKdY4bdbmaLM2R5tTrHHarM3R5qVvc8tdTrR5TFrtcqLN2qzN0eaytFmbo83anLqN02ZtjjZr8xK3eYxdTrQ52qzN2qzNBfumzdocbdZmbS7XN23W5mizNmtzqb612uVEm1tsc6tdTrQ52jwa2qzN0WZtLto4bdbmaLM2R5tTrHHarM3RZm2ONqdY47RZm6PN2hxtzoIb1/V9P+/3AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIALJkMvAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIDlMhl6AQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAsl8nQCwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABguUyGXgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAy2Uy9AIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAWC6ToRcAy6rruueHXgMA07QZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwFq0WWAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoBZdBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerSZZTIZegEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALJfJ0AuAsei67m93XfcXt/z5b3Rd9+1d132o67p/03Xdb3Zd9w3bfN1buq77iS1//gdd1/25jf/+0q7r/mXXdb/Wdd1Pd113664MA7AktBmgHm0GqEebAerRZoB6tBmgHm0GqEebAerRZoB6tBmgHm0GqEebAWrRZYB6tBmgHm0GqEebAerRZoB6tBmgHm0GqEebAerRZoB6tBmgHm0GqEebAerRZoB6tBmgHm0GqEebAerRZoB6tBmgFl0GqEebAerRZoB6tBmgHm0GqEebAerRZoB6tBmgHm0GqEebAerRZoB6tJmWTYZeAIzI+5P8V1v+/GCS/yPJn+z7/o1JvjrJ3+u6rpvlm3VdtzfJdyd5e9/3X5rkB5L8L4tdMsDS02aAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhFlwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAWnQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaAerQZoB5tBqhHmwHq0WaatWfoBcBY9H3/613XHem67guSHE7y2SSfSvL3u657c5K1JLclOZrk0zN8yy9OcneSn9n4/XLNxvcDYEbaDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80AtegyQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCLLgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aTMv2DL0AGJl/muTtSW5J8v4kfybrvzi+tO/7s13XPZlk5WVfcy7JZMufX/p8l+Txvu+/6qquGGD5aTNAPdoMUI82A9SjzQD1aDNAPdoMUI82A9SjzQD1aDNAPdoMUI82A9SiywD1aDNAPdoMUI82A9SjzQD1aDNAPdoMUI82A9SjzQD1aDNAPdoMUI82A9SjzQD1aDNAPdoMUI82A9SjzQD1aDNALboMUI82A9SjzQD1aDNAPdoMUI82A9SjzQD1aDNAPdoMUI82A9SjzQD1aDNNmlz6EGCL9yf5r7P+C+OfJrkxydMbvyi+Oslrtvmajyf5kq7rruu67sYk92/8/b9Ncrjruq9Kkq7r9nZdd9dVnwBg+WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUossA9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQC26DFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzQD3aDFCPNgPUo80A9WgzTdoz9AJgTPq+f7zruv1Jfr/v+091XffDST7Qdd2vJnksye9s8zW/13Xdo0l+I8kTSX594+/PdF339iTftfFLZE+SE0ke35V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      "text/plain": [
       "<Figure size 15120x360 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn plotting states\n",
    "sns.relplot(\n",
    "    x=\"value\", y=\"Median House Price\", col=\"variable\", data=dummied_data.melt(id_vars=\"Median House Price\", \n",
    "        value_vars=states), facet_kws=dict(sharex=False));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# features editing\n",
    "features.remove('Median Age')\n",
    "features.remove('Unemployment Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Population</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>state_AK</th>\n",
       "      <th>state_AL</th>\n",
       "      <th>state_AR</th>\n",
       "      <th>state_AZ</th>\n",
       "      <th>state_CA</th>\n",
       "      <th>state_CO</th>\n",
       "      <th>state_CT</th>\n",
       "      <th>...</th>\n",
       "      <th>state_PR</th>\n",
       "      <th>state_RI</th>\n",
       "      <th>state_SC</th>\n",
       "      <th>state_TN</th>\n",
       "      <th>state_TX</th>\n",
       "      <th>state_UT</th>\n",
       "      <th>state_VA</th>\n",
       "      <th>state_WA</th>\n",
       "      <th>state_WI</th>\n",
       "      <th>state_WV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>840562.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>913161.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>768917.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>710626.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>53.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>791257.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>47.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population  Median Income  Poverty Rate  state_AK  state_AL  state_AR  \\\n",
       "0    840562.0            7.8          26.5       0.0       0.0       0.0   \n",
       "1    913161.0            6.2          54.5       0.0       0.0       0.0   \n",
       "2    768917.0           15.2          24.5       0.0       0.0       0.0   \n",
       "3    710626.0            4.6          53.2       0.0       0.0       0.0   \n",
       "4    791257.0            8.1          47.9       0.0       0.0       0.0   \n",
       "\n",
       "   state_AZ  state_CA  state_CO  state_CT  ...  state_PR  state_RI  state_SC  \\\n",
       "0       0.0       1.0       0.0       0.0  ...       0.0       0.0       0.0   \n",
       "1       0.0       0.0       0.0       0.0  ...       0.0       0.0       0.0   \n",
       "2       0.0       1.0       0.0       0.0  ...       0.0       0.0       0.0   \n",
       "3       0.0       0.0       0.0       0.0  ...       0.0       0.0       0.0   \n",
       "4       0.0       0.0       0.0       0.0  ...       0.0       0.0       0.0   \n",
       "\n",
       "   state_TN  state_TX  state_UT  state_VA  state_WA  state_WI  state_WV  \n",
       "0       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "1       0.0       1.0       0.0       0.0       0.0       0.0       0.0  \n",
       "2       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "3       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "4       0.0       0.0       0.0       0.0       0.0       0.0       0.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# redeclaring X\n",
    "X = dummied_data[features]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Refined RandomForestRegressor with ID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up data distribution\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Refining Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hyperparameter grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameter grid\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# max features\n",
    "max_features = ['sqrt', 'log2', None]\n",
    "\n",
    "# bootstrap\n",
    "bootstrap = ['True', 'False']\n",
    "\n",
    "# tree depth\n",
    "max_depth = [int(x) for x in np.linspace(start = 1, stop = 10, num = 5)]\n",
    "max_depth.append(None)\n",
    "\n",
    "# max leaf nodes\n",
    "max_leaf_nodes = [int(x) for x in np.linspace(start = 2, stop = 20, num = 8)]\n",
    "max_leaf_nodes.append(None)\n",
    "\n",
    "# min samples leaf\n",
    "min_samples_leaf = [int(x) for x in np.linspace(start = 2, stop = 10, num = 5)]\n",
    "min_samples_leaf.append(1)\n",
    "\n",
    "# min samples split\n",
    "min_samples_split = [int(x) for x in np.linspace(start = 3, stop = 10, num = 5)]\n",
    "min_samples_split.append(2)\n",
    "\n",
    "# number of trees\n",
    "n_estimators = [int(x) for x in np.linspace(start = 50, stop = 450, num = 8)]\n",
    "n_estimators.append(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Creating hyperparameter grid to use in tuning with k-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': ['True', 'False'],\n",
      " 'max_depth': [1, 3, 5, 7, 10, None],\n",
      " 'max_features': ['sqrt', 'log2', None],\n",
      " 'max_leaf_nodes': [2, 4, 7, 9, 12, 14, 17, 20, None],\n",
      " 'min_samples_leaf': [2, 4, 6, 8, 10, 1],\n",
      " 'min_samples_split': [3, 4, 6, 8, 10, 2],\n",
      " 'n_estimators': [50, 107, 164, 221, 278, 335, 392, 450, 100]}\n"
     ]
    }
   ],
   "source": [
    "# create hyperparameter grid\n",
    "hyperparameters = {\n",
    " 'bootstrap': bootstrap,\n",
    " 'max_depth': max_depth,\n",
    " 'max_features': max_features,\n",
    " 'max_leaf_nodes': max_leaf_nodes,\n",
    " 'min_samples_leaf': min_samples_leaf,\n",
    " 'min_samples_split': min_samples_split,\n",
    " 'n_estimators': n_estimators}\n",
    "               \n",
    "pprint.pprint(hyperparameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K-fold CV with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create base model\n",
    "cv_model = RandomForestRegressor(random_state = 42, criterion = 'squared_error', max_features = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters\n",
      "{'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_leaf_nodes': None, 'max_features': None, 'max_depth': 7, 'bootstrap': 'False'}\n"
     ]
    }
   ],
   "source": [
    "# fit and tune model\n",
    "rf_clf = RandomizedSearchCV(cv_model, hyperparameters, n_iter = 150, cv = 5)\n",
    "rf_clf.fit(X_train, y_train)      \n",
    "\n",
    "print('Best Parameters')\n",
    "print(rf_clf.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluating function\n",
    "def evaluate(model, X_test, y_test):\n",
    "    predictions = model.predict(X_test)\n",
    "    error = sqrt(mean_squared_error(y_test, predictions))\n",
    "    print(error)\n",
    "\n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (lower is better)\n",
      "117062.00395850056\n"
     ]
    }
   ],
   "source": [
    "# evaluating model\n",
    "model = RandomForestRegressor(random_state = 42, criterion = 'squared_error', max_features = None)\n",
    "model.fit(X_train, y_train)\n",
    "print('RMSE (lower is better)')\n",
    "base_accuracy = evaluate(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (lower is better)\n",
      "115857.3063571198\n"
     ]
    }
   ],
   "source": [
    "print('RMSE (lower is better)')\n",
    "base_accuracy = evaluate(rf_clf, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting competition data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Poverty Rate</th>\n",
       "      <th>state_AK</th>\n",
       "      <th>state_AL</th>\n",
       "      <th>state_AR</th>\n",
       "      <th>state_AZ</th>\n",
       "      <th>state_CA</th>\n",
       "      <th>state_CO</th>\n",
       "      <th>state_CT</th>\n",
       "      <th>...</th>\n",
       "      <th>state_PR</th>\n",
       "      <th>state_RI</th>\n",
       "      <th>state_SC</th>\n",
       "      <th>state_TN</th>\n",
       "      <th>state_TX</th>\n",
       "      <th>state_UT</th>\n",
       "      <th>state_VA</th>\n",
       "      <th>state_WA</th>\n",
       "      <th>state_WI</th>\n",
       "      <th>state_WV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>857654</td>\n",
       "      <td>18.4</td>\n",
       "      <td>25.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>678333</td>\n",
       "      <td>13.5</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>730314</td>\n",
       "      <td>5.7</td>\n",
       "      <td>43.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>858875</td>\n",
       "      <td>8.9</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>706237</td>\n",
       "      <td>17.3</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population  Median Income  Poverty Rate  state_AK  state_AL  state_AR  \\\n",
       "0      857654           18.4          25.2         0         0         0   \n",
       "1      678333           13.5          28.3         0         0         0   \n",
       "2      730314            5.7          43.6         0         0         0   \n",
       "3      858875            8.9          40.7         0         0         0   \n",
       "4      706237           17.3          22.7         0         0         0   \n",
       "\n",
       "   state_AZ  state_CA  state_CO  state_CT  ...  state_PR  state_RI  state_SC  \\\n",
       "0         0         0         0         0  ...         0         0         0   \n",
       "1         0         0         0         0  ...         1         0         0   \n",
       "2         0         0         0         0  ...         0         0         0   \n",
       "3         0         0         0         0  ...         0         0         0   \n",
       "4         0         0         0         0  ...         0         0         0   \n",
       "\n",
       "   state_TN  state_TX  state_UT  state_VA  state_WA  state_WI  state_WV  \n",
       "0         0         1         0         0         0         0         0  \n",
       "1         0         0         0         0         0         0         0  \n",
       "2         0         0         0         0         0         0         0  \n",
       "3         0         0         0         0         0         0         0  \n",
       "4         0         0         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating competition X\n",
    "dummied_competition_X = dummied_test_data[features]\n",
    "dummied_competition_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters\n",
      "{'n_estimators': 50, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_leaf_nodes': 9, 'max_features': None, 'max_depth': 3, 'bootstrap': 'True'}\n"
     ]
    }
   ],
   "source": [
    "# fitting to test data\n",
    "test_rf_clf = RandomizedSearchCV(cv_model, hyperparameters, n_iter = 150, cv = 5)\n",
    "test_rf_clf.fit(X, y)      \n",
    "\n",
    "print('Best Parameters')\n",
    "print(test_rf_clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 178985.29038012  205935.98953901  407235.54812656  342708.92726545\n",
      "  177283.09179856  203459.2444989   182556.20892771  342708.92726545\n",
      "  331208.83236158  177283.09179856  389800.0809816   359416.45492129\n",
      "  407530.43384085  342708.92726545  180599.58062955  177283.09179856\n",
      "  362133.46646132  562751.26410883  207550.27978844  220937.92760907\n",
      "  177283.09179856  177283.09179856  178897.38204799  370354.87868255\n",
      "  351939.58305557  722596.22933153  382735.62945178  331208.83236158\n",
      "  353359.82432541  244006.94982212  177283.09179856  353359.82432541\n",
      "  431719.03308517  182556.20892771  178897.38204799  426725.67411081\n",
      "  180599.58062955  353359.82432541  207638.18812057  226298.95307034\n",
      "  211209.10666816  177283.09179856  220937.92760907  407530.43384085\n",
      "  226707.91823229  412239.82457737  342708.92726545  212911.30524972\n",
      "  177283.09179856  177283.09179856  219323.63735964 1138138.07425382\n",
      "  177283.09179856  178897.38204799  224596.75448878  205935.98953901\n",
      "  342708.92726545  177283.09179856  177283.09179856  220937.92760907\n",
      "  353359.82432541  856914.75438801  342708.92726545  178897.38204799\n",
      "  177283.09179856  420269.46360275  219323.63735964  331208.83236158\n",
      "  389800.0809816   427826.23308517  220937.92760907  180599.58062955\n",
      "  226429.23381209  177283.09179856  182556.20892771  177283.09179856\n",
      "  562751.26410883  431719.03308517  177283.09179856  180599.58062955\n",
      "  177283.09179856  177283.09179856  177283.09179856  342708.92726545\n",
      "  389800.0809816   203459.2444989   182556.20892771  434674.67411081\n",
      "  211209.10666816  228131.43239365  177283.09179856  255559.02819341\n",
      "  353359.82432541  291636.62344039  178897.38204799  177283.09179856\n",
      "  353359.82432541  177283.09179856  221025.8359412   220937.92760907\n",
      "  244006.94982212  370354.87868255  178897.38204799  177283.09179856\n",
      "  224596.75448878  220937.92760907  420269.46360275  182556.20892771\n",
      "  177283.09179856  177283.09179856  342708.92726545  178985.29038012\n",
      "  203459.2444989   207550.27978844  177283.09179856  180599.58062955\n",
      "  177283.09179856  342708.92726545  238733.83269297  219323.63735964\n",
      "  219323.63735964  412239.82457737  187926.99198356  225093.62798286\n",
      "  203459.2444989   389568.43196023  342708.92726545  226429.23381209\n",
      "  412239.82457737  342708.92726545  207550.27978844  342708.92726545\n",
      "  182556.20892771  379149.18392163  224596.75448878  177283.09179856\n",
      "  224596.75448878  224596.75448878  182556.20892771  353359.82432541\n",
      " 1146793.75599605  201502.61620075  366098.85408732  205161.44308046\n",
      "  362133.46646132  226429.23381209  177283.09179856  423770.03308517\n",
      "  353359.82432541  205935.98953901  222770.40693238  554802.26410883\n",
      "  177283.09179856  224596.75448878  199800.41761919  178985.29038012\n",
      "  770594.35959421  331208.83236158  177283.09179856  351939.58305557\n",
      "  342708.92726545  351939.58305557  370354.87868255  177283.09179856\n",
      "  226298.95307034  178985.29038012  178985.29038012  423770.03308517\n",
      "  431719.03308517  201966.40426104  362433.30032757  207638.18812057\n",
      "  554802.26410883  342708.92726545  178897.38204799  203459.2444989\n",
      "  219323.63735964  205935.98953901  781766.08831373  353359.82432541\n",
      "  224596.75448878  212911.30524972  199800.41761919  342708.92726545\n",
      "  177283.09179856  219323.63735964  203459.2444989   203459.2444989\n",
      "  177283.09179856  353359.82432541  180624.80974728  180624.80974728\n",
      "  224596.75448878  562751.26410883  239582.42642336  855248.78156099\n",
      "  177283.09179856  182556.20892771  239582.42642336  342708.92726545\n",
      "  203459.2444989   342708.92726545  353359.82432541  789603.74400979\n",
      "  342708.92726545  224596.75448878  353359.82432541  177283.09179856\n",
      "  196693.2871319   177283.09179856  983563.97634325  224596.75448878]\n"
     ]
    }
   ],
   "source": [
    "# competition predictions\n",
    "competition_preds = test_rf_clf.predict(dummied_competition_X)\n",
    "print(competition_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To hold ID values\n",
    "placeholder = pd.read_csv('C:\\\\Users\\\\advay\\\\OneDrive\\\\Desktop\\\\Coding\\VSCode\\\\Python\\\\AIS Summer Comp 2022\\\\evaluation_input.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating and outputting DataFrame\n",
    "output = pd.DataFrame({'ID': placeholder.ID, 'Median House Price': competition_preds})\n",
    "output.to_csv('Vyas_Advay_answer.csv', index=False)"
   ]
  }
 ],
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