notebook-house_price_predictions-brittany_allen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> \n",
    "    <h1> PACKAGE / DATA IMPORTS</h1> \n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.linear_model import LinearRegression, LassoCV, RidgeCV\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "import statsmodels.api as sm # with this i can do a quick linear regression\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.transforms as mtransforms\n",
    "\n",
    "import random\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#importing all packages i plan to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/train.csv')\n",
    "df_2 = pd.read_csv('./data/test.csv')\n",
    "\n",
    "#reading in my training and test data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> \n",
    "    <h1> DATA CLEANING </h1> \n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>...</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>109</td>\n",
       "      <td>533352170</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13517</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>130500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>544</td>\n",
       "      <td>531379050</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11492</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153</td>\n",
       "      <td>535304180</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7922</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>109000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>318</td>\n",
       "      <td>916386060</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>73.0</td>\n",
       "      <td>9802</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>174000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>255</td>\n",
       "      <td>906425045</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14235</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>138500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "0  109  533352170           60        RL           NaN     13517   Pave   NaN   \n",
       "1  544  531379050           60        RL          43.0     11492   Pave   NaN   \n",
       "2  153  535304180           20        RL          68.0      7922   Pave   NaN   \n",
       "3  318  916386060           60        RL          73.0      9802   Pave   NaN   \n",
       "4  255  906425045           50        RL          82.0     14235   Pave   NaN   \n",
       "\n",
       "  Lot Shape Land Contour    ...     Screen Porch Pool Area Pool QC Fence  \\\n",
       "0       IR1          Lvl    ...                0         0     NaN   NaN   \n",
       "1       IR1          Lvl    ...                0         0     NaN   NaN   \n",
       "2       Reg          Lvl    ...                0         0     NaN   NaN   \n",
       "3       Reg          Lvl    ...                0         0     NaN   NaN   \n",
       "4       IR1          Lvl    ...                0         0     NaN   NaN   \n",
       "\n",
       "  Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  SalePrice  \n",
       "0          NaN        0       3    2010        WD      130500  \n",
       "1          NaN        0       4    2009        WD      220000  \n",
       "2          NaN        0       1    2010        WD      109000  \n",
       "3          NaN        0       4    2010        WD      174000  \n",
       "4          NaN        0       3    2010        WD      138500  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "\n",
    "#displaying the first 5 rows of my training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Alley</th>\n",
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       "      <th>...</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>1587</td>\n",
       "      <td>921126030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>79.0</td>\n",
       "      <td>11449</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>WD</td>\n",
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       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>785</td>\n",
       "      <td>905377130</td>\n",
       "      <td>30</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12342</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>7558</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>Bnk</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>177000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2049</th>\n",
       "      <td>639</td>\n",
       "      <td>535179160</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10400</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>144000</td>\n",
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       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>10</td>\n",
       "      <td>527162130</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7500</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>189000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
       "2046  1587  921126030           20        RL          79.0     11449   Pave   \n",
       "2047   785  905377130           30        RL           NaN     12342   Pave   \n",
       "2048   916  909253010           50        RL          57.0      7558   Pave   \n",
       "2049   639  535179160           20        RL          80.0     10400   Pave   \n",
       "2050    10  527162130           60        RL          60.0      7500   Pave   \n",
       "\n",
       "     Alley Lot Shape Land Contour    ...     Screen Porch Pool Area Pool QC  \\\n",
       "2046   NaN       IR1          HLS    ...                0         0     NaN   \n",
       "2047   NaN       IR1          Lvl    ...                0         0     NaN   \n",
       "2048   NaN       Reg          Bnk    ...                0         0     NaN   \n",
       "2049   NaN       Reg          Lvl    ...                0         0     NaN   \n",
       "2050   NaN       Reg          Lvl    ...                0         0     NaN   \n",
       "\n",
       "     Fence Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  SalePrice  \n",
       "2046   NaN          NaN        0       1    2008        WD      298751  \n",
       "2047   NaN          NaN        0       3    2009        WD       82500  \n",
       "2048   NaN          NaN        0       3    2009        WD      177000  \n",
       "2049   NaN          NaN        0      11    2009        WD      144000  \n",
       "2050   NaN          NaN        0       6    2010        WD      189000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()\n",
    "\n",
    "#displaying the last  5 rows of my training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2051, 81)"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "\n",
    "#displaying the shape of my training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Id</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Overall Cond</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>...</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2.051000e+03</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>1721.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2029.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "      <td>2051.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1474.033642</td>\n",
       "      <td>7.135900e+08</td>\n",
       "      <td>57.008776</td>\n",
       "      <td>69.055200</td>\n",
       "      <td>10065.208191</td>\n",
       "      <td>6.112140</td>\n",
       "      <td>5.562165</td>\n",
       "      <td>1971.708922</td>\n",
       "      <td>1984.190151</td>\n",
       "      <td>99.695909</td>\n",
       "      <td>...</td>\n",
       "      <td>93.833740</td>\n",
       "      <td>47.556802</td>\n",
       "      <td>22.571916</td>\n",
       "      <td>2.591419</td>\n",
       "      <td>16.511458</td>\n",
       "      <td>2.397855</td>\n",
       "      <td>51.574354</td>\n",
       "      <td>6.219893</td>\n",
       "      <td>2007.775719</td>\n",
       "      <td>181469.701609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>843.980841</td>\n",
       "      <td>1.886918e+08</td>\n",
       "      <td>42.824223</td>\n",
       "      <td>23.260653</td>\n",
       "      <td>6742.488909</td>\n",
       "      <td>1.426271</td>\n",
       "      <td>1.104497</td>\n",
       "      <td>30.177889</td>\n",
       "      <td>21.036250</td>\n",
       "      <td>174.963129</td>\n",
       "      <td>...</td>\n",
       "      <td>128.549416</td>\n",
       "      <td>66.747241</td>\n",
       "      <td>59.845110</td>\n",
       "      <td>25.229615</td>\n",
       "      <td>57.374204</td>\n",
       "      <td>37.782570</td>\n",
       "      <td>573.393985</td>\n",
       "      <td>2.744736</td>\n",
       "      <td>1.312014</td>\n",
       "      <td>79258.659352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.263011e+08</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</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>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>12789.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>753.500000</td>\n",
       "      <td>5.284581e+08</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1953.500000</td>\n",
       "      <td>1964.500000</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>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1486.000000</td>\n",
       "      <td>5.354532e+08</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9430.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1974.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</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>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>162500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2198.000000</td>\n",
       "      <td>9.071801e+08</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11513.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>70.000000</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>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2930.000000</td>\n",
       "      <td>9.241520e+08</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>159000.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>432.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>611657.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id           PID  MS SubClass  Lot Frontage       Lot Area  \\\n",
       "count  2051.000000  2.051000e+03  2051.000000   1721.000000    2051.000000   \n",
       "mean   1474.033642  7.135900e+08    57.008776     69.055200   10065.208191   \n",
       "std     843.980841  1.886918e+08    42.824223     23.260653    6742.488909   \n",
       "min       1.000000  5.263011e+08    20.000000     21.000000    1300.000000   \n",
       "25%     753.500000  5.284581e+08    20.000000     58.000000    7500.000000   \n",
       "50%    1486.000000  5.354532e+08    50.000000     68.000000    9430.000000   \n",
       "75%    2198.000000  9.071801e+08    70.000000     80.000000   11513.500000   \n",
       "max    2930.000000  9.241520e+08   190.000000    313.000000  159000.000000   \n",
       "\n",
       "       Overall Qual  Overall Cond   Year Built  Year Remod/Add  Mas Vnr Area  \\\n",
       "count   2051.000000   2051.000000  2051.000000     2051.000000   2029.000000   \n",
       "mean       6.112140      5.562165  1971.708922     1984.190151     99.695909   \n",
       "std        1.426271      1.104497    30.177889       21.036250    174.963129   \n",
       "min        1.000000      1.000000  1872.000000     1950.000000      0.000000   \n",
       "25%        5.000000      5.000000  1953.500000     1964.500000      0.000000   \n",
       "50%        6.000000      5.000000  1974.000000     1993.000000      0.000000   \n",
       "75%        7.000000      6.000000  2001.000000     2004.000000    161.000000   \n",
       "max       10.000000      9.000000  2010.000000     2010.000000   1600.000000   \n",
       "\n",
       "           ...        Wood Deck SF  Open Porch SF  Enclosed Porch  \\\n",
       "count      ...         2051.000000    2051.000000     2051.000000   \n",
       "mean       ...           93.833740      47.556802       22.571916   \n",
       "std        ...          128.549416      66.747241       59.845110   \n",
       "min        ...            0.000000       0.000000        0.000000   \n",
       "25%        ...            0.000000       0.000000        0.000000   \n",
       "50%        ...            0.000000      27.000000        0.000000   \n",
       "75%        ...          168.000000      70.000000        0.000000   \n",
       "max        ...         1424.000000     547.000000      432.000000   \n",
       "\n",
       "        3Ssn Porch  Screen Porch    Pool Area      Misc Val      Mo Sold  \\\n",
       "count  2051.000000   2051.000000  2051.000000   2051.000000  2051.000000   \n",
       "mean      2.591419     16.511458     2.397855     51.574354     6.219893   \n",
       "std      25.229615     57.374204    37.782570    573.393985     2.744736   \n",
       "min       0.000000      0.000000     0.000000      0.000000     1.000000   \n",
       "25%       0.000000      0.000000     0.000000      0.000000     4.000000   \n",
       "50%       0.000000      0.000000     0.000000      0.000000     6.000000   \n",
       "75%       0.000000      0.000000     0.000000      0.000000     8.000000   \n",
       "max     508.000000    490.000000   800.000000  17000.000000    12.000000   \n",
       "\n",
       "           Yr Sold      SalePrice  \n",
       "count  2051.000000    2051.000000  \n",
       "mean   2007.775719  181469.701609  \n",
       "std       1.312014   79258.659352  \n",
       "min    2006.000000   12789.000000  \n",
       "25%    2007.000000  129825.000000  \n",
       "50%    2008.000000  162500.000000  \n",
       "75%    2009.000000  214000.000000  \n",
       "max    2010.000000  611657.000000  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "\n",
    "#getting info on my training data such as shape, data types of each column, and whether or not null values exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_columns(col):\n",
    "    return col.lower().replace(' ', '_').replace('/', '_')\n",
    "\n",
    "#built a function to clean my columns by:\n",
    "#1) making them lowercase and 2) replacing spaces and forward slashes with underscores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>id</th>\n",
       "      <th>pid</th>\n",
       "      <th>ms_subclass</th>\n",
       "      <th>ms_zoning</th>\n",
       "      <th>lot_frontage</th>\n",
       "      <th>lot_area</th>\n",
       "      <th>street</th>\n",
       "      <th>alley</th>\n",
       "      <th>lot_shape</th>\n",
       "      <th>land_contour</th>\n",
       "      <th>...</th>\n",
       "      <th>screen_porch</th>\n",
       "      <th>pool_area</th>\n",
       "      <th>pool_qc</th>\n",
       "      <th>fence</th>\n",
       "      <th>misc_feature</th>\n",
       "      <th>misc_val</th>\n",
       "      <th>mo_sold</th>\n",
       "      <th>yr_sold</th>\n",
       "      <th>sale_type</th>\n",
       "      <th>saleprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>109</td>\n",
       "      <td>533352170</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13517</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>130500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id        pid  ms_subclass ms_zoning  lot_frontage  lot_area street alley  \\\n",
       "0  109  533352170           60        RL           NaN     13517   Pave   NaN   \n",
       "\n",
       "  lot_shape land_contour    ...     screen_porch pool_area pool_qc fence  \\\n",
       "0       IR1          Lvl    ...                0         0     NaN   NaN   \n",
       "\n",
       "  misc_feature misc_val mo_sold yr_sold  sale_type  saleprice  \n",
       "0          NaN        0       3    2010        WD      130500  \n",
       "\n",
       "[1 rows x 81 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = [clean_columns(col) for col in df.columns]\n",
    "df.head(1)\n",
    "\n",
    "#passed my function in a list comprehension to do it all with one line of code rather than many\n",
    "#displayed my head to confirm my desired results were achieved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>pid</th>\n",
       "      <th>ms_subclass</th>\n",
       "      <th>ms_zoning</th>\n",
       "      <th>lot_frontage</th>\n",
       "      <th>lot_area</th>\n",
       "      <th>street</th>\n",
       "      <th>alley</th>\n",
       "      <th>lot_shape</th>\n",
       "      <th>land_contour</th>\n",
       "      <th>...</th>\n",
       "      <th>3ssn_porch</th>\n",
       "      <th>screen_porch</th>\n",
       "      <th>pool_area</th>\n",
       "      <th>pool_qc</th>\n",
       "      <th>fence</th>\n",
       "      <th>misc_feature</th>\n",
       "      <th>misc_val</th>\n",
       "      <th>mo_sold</th>\n",
       "      <th>yr_sold</th>\n",
       "      <th>sale_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2658</td>\n",
       "      <td>902301120</td>\n",
       "      <td>190</td>\n",
       "      <td>RM</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9142</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id        pid  ms_subclass ms_zoning  lot_frontage  lot_area street  \\\n",
       "0  2658  902301120          190        RM          69.0      9142   Pave   \n",
       "\n",
       "  alley lot_shape land_contour    ...     3ssn_porch screen_porch pool_area  \\\n",
       "0  Grvl       Reg          Lvl    ...              0            0         0   \n",
       "\n",
       "  pool_qc fence misc_feature misc_val mo_sold  yr_sold  sale_type  \n",
       "0     NaN   NaN          NaN        0       4     2006        WD   \n",
       "\n",
       "[1 rows x 80 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.columns = [clean_columns(col) for col in df_2.columns]\n",
    "df_2.head(1)\n",
    "\n",
    "#passed my function in a list comprehension to do it all with one line of code rather than many\n",
    "#displayed my head to confirm my desired results were achieved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lot_frontage       330\n",
       "alley             1911\n",
       "mas_vnr_type        22\n",
       "mas_vnr_area        22\n",
       "bsmt_qual           55\n",
       "bsmt_cond           55\n",
       "bsmt_exposure       58\n",
       "bsmtfin_type_1      55\n",
       "bsmtfin_sf_1         1\n",
       "bsmtfin_type_2      56\n",
       "bsmtfin_sf_2         1\n",
       "bsmt_unf_sf          1\n",
       "total_bsmt_sf        1\n",
       "bsmt_full_bath       2\n",
       "bsmt_half_bath       2\n",
       "fireplace_qu      1000\n",
       "garage_type        113\n",
       "garage_yr_blt      114\n",
       "garage_finish      114\n",
       "garage_cars          1\n",
       "garage_area          1\n",
       "garage_qual        114\n",
       "garage_cond        114\n",
       "pool_qc           2042\n",
       "fence             1651\n",
       "misc_feature      1986\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.columns[df.isnull().any()]].isnull().sum()\n",
    "\n",
    "#displaying only the columns with nulls and their sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lot_frontage      160\n",
       "alley             821\n",
       "mas_vnr_type        1\n",
       "mas_vnr_area        1\n",
       "bsmt_qual          25\n",
       "bsmt_cond          25\n",
       "bsmt_exposure      25\n",
       "bsmtfin_type_1     25\n",
       "bsmtfin_type_2     25\n",
       "electrical          1\n",
       "fireplace_qu      422\n",
       "garage_type        44\n",
       "garage_yr_blt      45\n",
       "garage_finish      45\n",
       "garage_qual        45\n",
       "garage_cond        45\n",
       "pool_qc           875\n",
       "fence             707\n",
       "misc_feature      838\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[df_2.columns[df_2.isnull().any()]].isnull().sum()\n",
    "\n",
    "#displaying only the columns with nulls and their sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lot_frontage', 'alley', 'mas_vnr_type', 'mas_vnr_area', 'bsmt_qual',\n",
       "       'bsmt_cond', 'bsmt_exposure', 'bsmtfin_type_1', 'bsmtfin_sf_1',\n",
       "       'bsmtfin_type_2', 'bsmtfin_sf_2', 'bsmt_unf_sf', 'total_bsmt_sf',\n",
       "       'bsmt_full_bath', 'bsmt_half_bath', 'fireplace_qu', 'garage_type',\n",
       "       'garage_yr_blt', 'garage_finish', 'garage_cars', 'garage_area',\n",
       "       'garage_qual', 'garage_cond', 'pool_qc', 'fence', 'misc_feature'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[df.isnull().any()]\n",
    "\n",
    "#displaying a list of columns that have null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_na_with_mean_or_mode(col):\n",
    "    if col.dtype == int or col.dtype == float:\n",
    "        return col.fillna(round(col.mean()), inplace=True)\n",
    "    else:\n",
    "        return col.fillna(col.mode()[0], inplace=True)\n",
    "    \n",
    "#created a function to fill numeric and nonnumeric nulls with either their mean or mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "[fill_na_with_mean_or_mode(df[col]) for col in ['lot_frontage', 'mas_vnr_area','garage_cars', 'garage_area']];\n",
    "\n",
    "#passed my function through a list comprehension so i could fill all null columns with one line of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "[fill_na_with_mean_or_mode(df_2[col]) for col in ['lot_frontage', 'mas_vnr_area', 'electrical']];\n",
    "\n",
    "#passed my function through a list comprehension so i could fill all null columns with one line of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_string_na(col):\n",
    "        return col.fillna('NA', inplace=True)\n",
    "    \n",
    "#created a function to fill np.NaNs with string 'NA' for cases where 'NA' means No (some thing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "[make_string_na(df[col]) for col in ['alley', 'bsmt_qual', 'bsmt_cond', 'bsmt_exposure', 'bsmtfin_type_1', 'bsmtfin_type_2', 'fireplace_qu', 'garage_type', 'garage_finish', 'garage_qual', 'garage_cond', 'pool_qc', 'fence', 'misc_feature', 'mas_vnr_type']];\n",
    "\n",
    "#passed my function through a list comprehension so i could fill all columns with np.NaNs with one line of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "[make_string_na(df_2[col]) for col in ['alley', 'bsmt_qual', 'bsmt_cond', 'bsmt_exposure', 'bsmtfin_type_1', 'bsmtfin_type_2', 'fireplace_qu', 'garage_type', 'garage_finish', 'garage_qual', 'garage_cond', 'pool_qc', 'fence', 'misc_feature', 'mas_vnr_type']];\n",
    "\n",
    "#passed my function through a list comprehension so i could fill all columns with np.NaNs with one line of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "[df[col].fillna(0, inplace=True) for col in ['bsmtfin_sf_1', 'bsmtfin_sf_2', 'bsmt_unf_sf', 'total_bsmt_sf', 'bsmt_full_bath', 'bsmt_half_bath']];\n",
    "\n",
    "#created a list comprehension to fill np.NaNs with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "[df_2[col].fillna(0, inplace=True) for col in ['bsmtfin_sf_1', 'bsmtfin_sf_2', 'bsmt_unf_sf', 'total_bsmt_sf', 'bsmt_full_bath', 'bsmt_half_bath']];\n",
    "\n",
    "#created a list comprehension to fill np.NaNs with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.garage_yr_blt.fillna(df.year_built, inplace=True)\n",
    "df_2.garage_yr_blt.fillna(df_2.year_built, inplace=True)\n",
    "\n",
    "#filling null values in the garage_yr_blt column with the corresponding value of the year_built column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1699    2207.0\n",
       "Name: garage_yr_blt, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['garage_yr_blt'][df['garage_yr_blt'] > 2010]\n",
    "\n",
    "#checking to see if there are any instances where year is greater than 2010\n",
    "#performed this check for all columns indicating year >> ['year_built', 'year_remod_add', 'yr_sold']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>year_built</th>\n",
       "      <th>year_remod_add</th>\n",
       "      <th>roof_style</th>\n",
       "      <th>roof_matl</th>\n",
       "      <th>exterior_1st</th>\n",
       "      <th>exterior_2nd</th>\n",
       "      <th>mas_vnr_type</th>\n",
       "      <th>mas_vnr_area</th>\n",
       "      <th>exter_qual</th>\n",
       "      <th>exter_cond</th>\n",
       "      <th>foundation</th>\n",
       "      <th>bsmt_qual</th>\n",
       "      <th>bsmt_cond</th>\n",
       "      <th>bsmt_exposure</th>\n",
       "      <th>bsmtfin_type_1</th>\n",
       "      <th>bsmtfin_sf_1</th>\n",
       "      <th>bsmtfin_type_2</th>\n",
       "      <th>bsmtfin_sf_2</th>\n",
       "      <th>bsmt_unf_sf</th>\n",
       "      <th>total_bsmt_sf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>2006</td>\n",
       "      <td>2007</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>NA</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>583.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>1546.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year_built  year_remod_add roof_style roof_matl exterior_1st  \\\n",
       "1699        2006            2007        Hip   CompShg      VinylSd   \n",
       "\n",
       "     exterior_2nd mas_vnr_type  mas_vnr_area exter_qual exter_cond foundation  \\\n",
       "1699      VinylSd           NA         100.0         Gd         TA      PConc   \n",
       "\n",
       "     bsmt_qual bsmt_cond bsmt_exposure bsmtfin_type_1  bsmtfin_sf_1  \\\n",
       "1699        Gd        TA            Av            GLQ         583.0   \n",
       "\n",
       "     bsmtfin_type_2  bsmtfin_sf_2  bsmt_unf_sf  total_bsmt_sf  \n",
       "1699            Unf           0.0        963.0         1546.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['garage_yr_blt'] > 2010][df.columns[20:40]]\n",
    "\n",
    "#wanted to look at the entire row where garage_yr_blt was greater than 2010 for more context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.garage_yr_blt[1699] = 2007\n",
    "\n",
    "#changed the value from 2207 to 2007\n",
    "#assuming the 2207 was a data entry error and the year the was garage built was also the year remodeling/additions happened"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.columns[df.isnull().any()]].isnull().sum()\n",
    "\n",
    "#confirming there are no longer any null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2[df_2.columns[df_2.isnull().any()]].isnull().sum()\n",
    "\n",
    "#confirming there are no longer any null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: saleprice, dtype: int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saleprice[df.saleprice < 0]\n",
    "\n",
    "#checking to make sure there were no instances where sale price was less than $0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> \n",
    "    <h1> EXPLORATORY DATA ANALYSIS </h1> \n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (11, 8))\n",
    "sns.distplot(df.saleprice, kde=False, color='#A15EDB', bins=10)\n",
    "\n",
    "plt.xlabel('Sale Price', fontsize=19, labelpad=11)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.ylabel('Count', fontsize=19, labelpad=11)\n",
    "plt.yticks(fontsize=14);\n",
    "\n",
    "#plt.savefig(\"target_variable.png\");\n",
    "\n",
    "#looking at the distribution of my target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      2051.000000\n",
       "mean     181469.701609\n",
       "std       79258.659352\n",
       "min       12789.000000\n",
       "25%      129825.000000\n",
       "50%      162500.000000\n",
       "75%      214000.000000\n",
       "max      611657.000000\n",
       "Name: saleprice, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saleprice.describe()\n",
    "\n",
    "#getting summary stats on my target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (11, 8))\n",
    "sns.distplot(np.log(df.saleprice), kde=False, color='#A15EDB', bins=10)\n",
    "\n",
    "plt.xlabel('Sale Price', fontsize=19, labelpad=11)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.ylabel('Count', fontsize=19, labelpad=11)\n",
    "plt.yticks(fontsize=14);\n",
    "\n",
    "#plt.savefig(\"target_variable_log.png\");\n",
    "\n",
    "#looking at the distribution of my target after taking the log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20, 11))\n",
    "\n",
    "cmap = sns.diverging_palette(150, 275, as_cmap=True)\n",
    "sns.heatmap(df.corr()[['saleprice']][round(df.corr()['saleprice'].sort_values(ascending=False), 1) >= .5], annot=True, linewidths=.4, fmt=\".1f\", cmap=cmap, ax=ax);\n",
    "#sns.set(font_scale=4)\n",
    "\n",
    "#plt.savefig(\"heatmap.png\");\n",
    "\n",
    "#looking at a correlation heatmap of my features in relation to sale price with correlations being greater than or equal to .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d60844c7f0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x792 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 2, ncols = 3, figsize = (18,11), sharey=True)\n",
    "fig.subplots_adjust(wspace=.2, hspace=.35)\n",
    "#sns.set(font_scale=.5)\n",
    "\n",
    "sns.scatterplot(x=df.overall_qual, y=df.saleprice, ax=ax[0, 0], color='#69547C', )\n",
    "sns.scatterplot(x=df.year_built, y=df.saleprice, ax=ax[0, 1], color='#69547C')\n",
    "sns.scatterplot(x=df.year_remod_add, y=df.saleprice, ax=ax[0, 2], color='#69547C')\n",
    "sns.scatterplot(x=df.total_bsmt_sf, y=df.saleprice, ax=ax[1, 0], color='#69547C')\n",
    "sns.scatterplot(x=df.garage_area, y=df.saleprice, ax=ax[1, 1], color='#69547C')\n",
    "sns.scatterplot(x=df.full_bath, y=df.saleprice, ax=ax[1, 2], color='#69547C')\n",
    "\n",
    "#plt.savefig(\"subplots.png\");\n",
    "\n",
    "#plotting some of the features from the correlation heatmap to see the linear relationship beyond numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> \n",
    "    <h1> FEATURE ENGINEERING & MORE EDA </h1> \n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sale_price_bool'] = df.saleprice.map(lambda x: 0 if x <= 162500 else 1)\n",
    "df_2['sale_price_bool'] = df.saleprice.map(lambda x: 0 if x <= 162500 else 1)\n",
    "\n",
    "#engineering a feature that with take into account sale price and return boolean values\n",
    "#(1 if greater than the median and 0 if otherwise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.saleprice = df.saleprice.map(np.log)\n",
    "\n",
    "#applied a permanent log transformation to my target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     42\n",
       "int64      28\n",
       "float64    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.value_counts()\n",
    "\n",
    "#looking at the counts of data types for my training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ms_zoning', 'street', 'alley', 'lot_shape', 'land_contour',\n",
       "       'utilities', 'lot_config', 'land_slope', 'neighborhood', 'condition_1',\n",
       "       'condition_2', 'bldg_type', 'house_style', 'roof_style', 'roof_matl',\n",
       "       'exterior_1st', 'exterior_2nd', 'mas_vnr_type', 'exter_qual',\n",
       "       'exter_cond', 'foundation', 'bsmt_qual', 'bsmt_cond', 'bsmt_exposure',\n",
       "       'bsmtfin_type_1', 'bsmtfin_type_2', 'heating', 'heating_qc',\n",
       "       'central_air', 'electrical', 'kitchen_qual', 'functional',\n",
       "       'fireplace_qu', 'garage_type', 'garage_finish', 'garage_qual',\n",
       "       'garage_cond', 'paved_drive', 'pool_qc', 'fence', 'misc_feature',\n",
       "       'sale_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[df.dtypes == object]\n",
    "\n",
    "#looking at column names for instances where data type is equal to \"object\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_dummy = ['ms_zoning', 'street', 'alley', 'land_contour', 'lot_config', 'land_slope', 'neighborhood', 'condition_1',\n",
    "            'condition_2', 'bldg_type', 'house_style', 'roof_style', 'roof_matl', 'exterior_1st', 'exterior_2nd', \n",
    "            'mas_vnr_type', 'foundation', 'heating', 'central_air', 'electrical', 'functional', 'garage_type', 'utilities',\n",
    "            'paved_drive', 'fence', 'misc_feature', 'sale_type', 'bsmt_exposure', 'lot_shape', 'garage_finish', 'pool_qc']\n",
    "\n",
    "#took features from the list above and added them here to be dummied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_map = ['bsmt_cond', 'bsmt_qual', 'bsmtfin_type_1', 'bsmtfin_type_2', 'exter_cond', 'exter_qual', 'fireplace_qu',\n",
    "          'garage_cond', 'garage_qual', 'heating_qc', 'kitchen_qual']\n",
    "\n",
    "#took features from the list above and added them here to be mapped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>id</th>\n",
       "      <th>pid</th>\n",
       "      <th>ms_subclass</th>\n",
       "      <th>lot_frontage</th>\n",
       "      <th>lot_area</th>\n",
       "      <th>overall_qual</th>\n",
       "      <th>overall_cond</th>\n",
       "      <th>year_built</th>\n",
       "      <th>year_remod_add</th>\n",
       "      <th>mas_vnr_area</th>\n",
       "      <th>...</th>\n",
       "      <th>lot_shape_IR2</th>\n",
       "      <th>lot_shape_IR3</th>\n",
       "      <th>lot_shape_Reg</th>\n",
       "      <th>garage_finish_NA</th>\n",
       "      <th>garage_finish_RFn</th>\n",
       "      <th>garage_finish_Unf</th>\n",
       "      <th>pool_qc_Fa</th>\n",
       "      <th>pool_qc_Gd</th>\n",
       "      <th>pool_qc_NA</th>\n",
       "      <th>pool_qc_TA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>109</td>\n",
       "      <td>533352170</td>\n",
       "      <td>60</td>\n",
       "      <td>69.0</td>\n",
       "      <td>13517</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>2005</td>\n",
       "      <td>289.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 226 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id        pid  ms_subclass  lot_frontage  lot_area  overall_qual  \\\n",
       "0  109  533352170           60          69.0     13517             6   \n",
       "\n",
       "   overall_cond  year_built  year_remod_add  mas_vnr_area     ...      \\\n",
       "0             8        1976            2005         289.0     ...       \n",
       "\n",
       "  lot_shape_IR2 lot_shape_IR3 lot_shape_Reg garage_finish_NA  \\\n",
       "0             0             0             0                0   \n",
       "\n",
       "  garage_finish_RFn  garage_finish_Unf pool_qc_Fa  pool_qc_Gd  pool_qc_NA  \\\n",
       "0                 1                  0          0           0           1   \n",
       "\n",
       "   pool_qc_TA  \n",
       "0           0  \n",
       "\n",
       "[1 rows x 226 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=to_dummy, drop_first=True)\n",
    "df.head(1)\n",
    "\n",
    "#dummied all of the features in my 'to_dummy' list and displayed the head to confirm desired result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>pid</th>\n",
       "      <th>ms_subclass</th>\n",
       "      <th>lot_frontage</th>\n",
       "      <th>lot_area</th>\n",
       "      <th>overall_qual</th>\n",
       "      <th>overall_cond</th>\n",
       "      <th>year_built</th>\n",
       "      <th>year_remod_add</th>\n",
       "      <th>mas_vnr_area</th>\n",
       "      <th>...</th>\n",
       "      <th>bsmt_exposure_NA</th>\n",
       "      <th>bsmt_exposure_No</th>\n",
       "      <th>lot_shape_IR2</th>\n",
       "      <th>lot_shape_IR3</th>\n",
       "      <th>lot_shape_Reg</th>\n",
       "      <th>garage_finish_NA</th>\n",
       "      <th>garage_finish_RFn</th>\n",
       "      <th>garage_finish_Unf</th>\n",
       "      <th>pool_qc_NA</th>\n",
       "      <th>pool_qc_TA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2658</td>\n",
       "      <td>902301120</td>\n",
       "      <td>190</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9142</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1910</td>\n",
       "      <td>1950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 210 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id        pid  ms_subclass  lot_frontage  lot_area  overall_qual  \\\n",
       "0  2658  902301120          190          69.0      9142             6   \n",
       "\n",
       "   overall_cond  year_built  year_remod_add  mas_vnr_area     ...      \\\n",
       "0             8        1910            1950           0.0     ...       \n",
       "\n",
       "  bsmt_exposure_NA bsmt_exposure_No lot_shape_IR2 lot_shape_IR3 lot_shape_Reg  \\\n",
       "0                0                1             0             0             1   \n",
       "\n",
       "   garage_finish_NA garage_finish_RFn  garage_finish_Unf  pool_qc_NA  \\\n",
       "0                 0                 0                  1           1   \n",
       "\n",
       "   pool_qc_TA  \n",
       "0           0  \n",
       "\n",
       "[1 rows x 210 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = pd.get_dummies(df_2, columns=to_dummy, drop_first=True)\n",
    "df_2.head(1)\n",
    "\n",
    "#dummied all of the features in my 'to_dummy' list and displayed the head to confirm desired result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper_1 = {'Ex':5,\n",
    "               'Gd':4,\n",
    "               'TA':3,\n",
    "               'Fa':2,\n",
    "               'Po':1}\n",
    "\n",
    "#created a variable that housed the dictionary that would be passed when mapping my features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3    1247\n",
      "4     697\n",
      "5      81\n",
      "2      26\n",
      "Name: exter_qual_scored, dtype: int64\n",
      "3    1778\n",
      "4     215\n",
      "2      49\n",
      "5       7\n",
      "1       2\n",
      "Name: exter_cond_scored, dtype: int64\n",
      "5    1065\n",
      "3     597\n",
      "4     319\n",
      "2      67\n",
      "1       3\n",
      "Name: heating_qc_scored, dtype: int64\n",
      "3    1047\n",
      "4     806\n",
      "5     151\n",
      "2      47\n",
      "Name: kitchen_qual_scored, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df['exter_qual_scored'] = df.exter_qual.replace(mapper_1)\n",
    "df['exter_cond_scored'] = df.exter_cond.replace(mapper_1)\n",
    "df['heating_qc_scored'] = df.heating_qc.replace(mapper_1)\n",
    "df['kitchen_qual_scored'] = df.kitchen_qual.replace(mapper_1)\n",
    "\n",
    "print(df.exter_qual_scored.value_counts())\n",
    "print(df.exter_cond_scored.value_counts())\n",
    "print(df.heating_qc_scored.value_counts())\n",
    "print(df.kitchen_qual_scored.value_counts())\n",
    "\n",
    "#passed the 'mapper_1' variable in a replace function for all of the columns i wanted to score\n",
    "#simultaneously created a new column with the old column name + '_scored'\n",
    "#printed the new columns to confirm desired results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3    552\n",
      "4    292\n",
      "5     26\n",
      "2      9\n",
      "Name: exter_qual_scored, dtype: int64\n",
      "3    771\n",
      "4     84\n",
      "2     18\n",
      "5      5\n",
      "1      1\n",
      "Name: exter_cond_scored, dtype: int64\n",
      "5    430\n",
      "3    267\n",
      "4    157\n",
      "2     25\n",
      "Name: heating_qc_scored, dtype: int64\n",
      "3    447\n",
      "4    354\n",
      "5     54\n",
      "2     23\n",
      "1      1\n",
      "Name: kitchen_qual_scored, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df_2['exter_qual_scored'] = df_2.exter_qual.replace(mapper_1)\n",
    "df_2['exter_cond_scored'] = df_2.exter_cond.replace(mapper_1)\n",
    "df_2['heating_qc_scored'] = df_2.heating_qc.replace(mapper_1)\n",
    "df_2['kitchen_qual_scored'] = df_2.kitchen_qual.replace(mapper_1)\n",
    "\n",
    "print(df_2.exter_qual_scored.value_counts())\n",
    "print(df_2.exter_cond_scored.value_counts())\n",
    "print(df_2.heating_qc_scored.value_counts())\n",
    "print(df_2.kitchen_qual_scored.value_counts())\n",
    "\n",
    "#passed the 'mapper_1' variable in a replace function for all of the columns i wanted to score\n",
    "#simultaneously created a new column with the old column name + '_scored'\n",
    "#printed the new columns to confirm desired results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper_2 = {'Ex':6,\n",
    "               'Gd':5,\n",
    "               'TA':4,\n",
    "               'Fa':3,\n",
    "               'Po':2,\n",
    "               'NA':1}\n",
    "\n",
    "#created a variable that housed the dictionary that would be passed when mapping my features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4    887\n",
      "5    864\n",
      "6    184\n",
      "3     60\n",
      "1     55\n",
      "2      1\n",
      "Name: bsmt_qual_scored, dtype: int64\n",
      "4    1834\n",
      "5      89\n",
      "3      65\n",
      "1      55\n",
      "2       5\n",
      "6       3\n",
      "Name: bsmt_cond_scored, dtype: int64\n",
      "1    1000\n",
      "5     523\n",
      "4     407\n",
      "3      59\n",
      "6      31\n",
      "2      31\n",
      "Name: fireplace_qu_scored, dtype: int64\n",
      "4    1832\n",
      "1     114\n",
      "3      82\n",
      "5      18\n",
      "6       3\n",
      "2       2\n",
      "Name: garage_qual_scored, dtype: int64\n",
      "4    1868\n",
      "1     114\n",
      "3      47\n",
      "5      12\n",
      "2       8\n",
      "6       2\n",
      "Name: garage_cond_scored, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df['bsmt_qual_scored'] = df.bsmt_qual.replace(mapper_2)\n",
    "df['bsmt_cond_scored'] = df.bsmt_cond.replace(mapper_2)\n",
    "df['fireplace_qu_scored'] = df.fireplace_qu.replace(mapper_2)\n",
    "df['garage_qual_scored'] = df.garage_qual.replace(mapper_2)\n",
    "df['garage_cond_scored'] = df.garage_cond.replace(mapper_2)\n",
    "\n",
    "\n",
    "print(df.bsmt_qual_scored.value_counts())\n",
    "print(df.bsmt_cond_scored.value_counts())\n",
    "print(df.fireplace_qu_scored.value_counts())\n",
    "print(df.garage_qual_scored.value_counts())\n",
    "print(df.garage_cond_scored.value_counts())\n",
    "\n",
    "#passed the 'mapper_2' variable in a replace function for all of the columns i wanted to score\n",
    "#simultaneously created a new column with the old column name + '_scored'\n",
    "#printed the new columns to confirm desired results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4    396\n",
      "5    355\n",
      "6     74\n",
      "3     28\n",
      "1     25\n",
      "2      1\n",
      "Name: bsmt_qual_scored, dtype: int64\n",
      "4    782\n",
      "3     39\n",
      "5     33\n",
      "1     25\n",
      "Name: bsmt_cond_scored, dtype: int64\n",
      "1    422\n",
      "5    221\n",
      "4    193\n",
      "3     16\n",
      "2     15\n",
      "6     12\n",
      "Name: fireplace_qu_scored, dtype: int64\n",
      "4    783\n",
      "1     45\n",
      "3     42\n",
      "5      6\n",
      "2      3\n",
      "Name: garage_qual_scored, dtype: int64\n",
      "4    797\n",
      "1     45\n",
      "3     27\n",
      "2      6\n",
      "5      3\n",
      "6      1\n",
      "Name: garage_cond_scored, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df_2['bsmt_qual_scored'] = df_2.bsmt_qual.replace(mapper_2)\n",
    "df_2['bsmt_cond_scored'] = df_2.bsmt_cond.replace(mapper_2)\n",
    "df_2['fireplace_qu_scored'] = df_2.fireplace_qu.replace(mapper_2)\n",
    "df_2['garage_qual_scored'] = df_2.garage_qual.replace(mapper_2)\n",
    "df_2['garage_cond_scored'] = df_2.garage_cond.replace(mapper_2)\n",
    "\n",
    "\n",
    "print(df_2.bsmt_qual_scored.value_counts())\n",
    "print(df_2.bsmt_cond_scored.value_counts())\n",
    "print(df_2.fireplace_qu_scored.value_counts())\n",
    "print(df_2.garage_qual_scored.value_counts())\n",
    "print(df_2.garage_cond_scored.value_counts())\n",
    "\n",
    "#passed the 'mapper_2' variable in a replace function for all of the columns i wanted to score\n",
    "#simultaneously created a new column with the old column name + '_scored'\n",
    "#printed the new columns to confirm desired results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper_3 = {'GLQ':7,\n",
    "               'ALQ':6,\n",
    "               'BLQ':5,\n",
    "               'Rec':4,\n",
    "               'LwQ':3,\n",
    "               'Unf':2,\n",
    "               'NA':1}\n",
    "\n",
    "#created a variable that housed the dictionary that would be passed when mapping my features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7    615\n",
      "2    603\n",
      "6    293\n",
      "5    200\n",
      "4    183\n",
      "3    102\n",
      "1     55\n",
      "Name: bsmtfin_type_1_scored, dtype: int64\n",
      "2    1749\n",
      "4      80\n",
      "3      60\n",
      "1      56\n",
      "5      48\n",
      "6      35\n",
      "7      23\n",
      "Name: bsmtfin_type_2_scored, dtype: int64\n",
      "2    248\n",
      "7    244\n",
      "6    136\n",
      "4    105\n",
      "5     69\n",
      "3     52\n",
      "1     25\n",
      "Name: bsmtfin_type_1_scored, dtype: int64\n",
      "2    750\n",
      "3     29\n",
      "4     26\n",
      "1     25\n",
      "5     20\n",
      "6     18\n",
      "7     11\n",
      "Name: bsmtfin_type_2_scored, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df['bsmtfin_type_1_scored'] = df.bsmtfin_type_1.replace(mapper_3)\n",
    "df['bsmtfin_type_2_scored'] = df.bsmtfin_type_2.replace(mapper_3)\n",
    "\n",
    "df_2['bsmtfin_type_1_scored'] = df_2.bsmtfin_type_1.replace(mapper_3)\n",
    "df_2['bsmtfin_type_2_scored'] = df_2.bsmtfin_type_2.replace(mapper_3)\n",
    "\n",
    "print(df.bsmtfin_type_1_scored.value_counts())\n",
    "print(df.bsmtfin_type_2_scored.value_counts())\n",
    "\n",
    "print(df_2.bsmtfin_type_1_scored.value_counts())\n",
    "print(df_2.bsmtfin_type_2_scored.value_counts())\n",
    "\n",
    "#passed the 'mapper_3' variable in a replace function for all of the columns i wanted to score\n",
    "#simultaneously created a new column with the old column name + '_scored'\n",
    "#printed the new columns to confirm desired results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=to_map, inplace=True)\n",
    "\n",
    "#dropped the features in the 'to_map' list as they'd been replaced with the scored versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.drop(columns=to_map, inplace=True)\n",
    "\n",
    "#dropped the features in the 'to_map' list as they'd been replaced with the scored versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2051, 226)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "\n",
    "#checking the shape of my training data now that i've engineered features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(879, 210)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.shape\n",
    "\n",
    "#checking the shape of my test data now that i've engineered features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'condition_2_Feedr',\n",
       " 'condition_2_RRAe',\n",
       " 'condition_2_RRAn',\n",
       " 'condition_2_RRNn',\n",
       " 'electrical_Mix',\n",
       " 'exterior_1st_CBlock',\n",
       " 'exterior_1st_ImStucc',\n",
       " 'exterior_1st_Stone',\n",
       " 'exterior_2nd_Stone',\n",
       " 'functional_Sal',\n",
       " 'functional_Sev',\n",
       " 'heating_OthW',\n",
       " 'heating_Wall',\n",
       " 'misc_feature_Gar2',\n",
       " 'misc_feature_TenC',\n",
       " 'ms_zoning_C (all)',\n",
       " 'neighborhood_GrnHill',\n",
       " 'neighborhood_Landmrk',\n",
       " 'pool_qc_Fa',\n",
       " 'pool_qc_Gd',\n",
       " 'roof_matl_CompShg',\n",
       " 'roof_matl_Membran',\n",
       " 'utilities_NoSeWa'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_columns_to_drop = set(df.columns[df.columns != 'saleprice']) - set(df_2.columns)\n",
    "df_columns_to_drop\n",
    "\n",
    "#created a variable that houses the difference of my test data from my training data with the exception of sale price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'exterior_1st_PreCast',\n",
       " 'exterior_2nd_Other',\n",
       " 'exterior_2nd_PreCast',\n",
       " 'heating_GasA',\n",
       " 'mas_vnr_type_CBlock',\n",
       " 'roof_matl_Metal',\n",
       " 'roof_matl_Roll',\n",
       " 'sale_type_VWD'}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2_columns_to_drop = set(df_2.columns) - set(df.columns)\n",
    "df_2_columns_to_drop\n",
    "\n",
    "#created a variable that houses the difference of my training data from my test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=df_columns_to_drop, inplace=True)\n",
    "\n",
    "#dropped the columns from my training data that were not in my test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.drop(columns=df_2_columns_to_drop, inplace=True)\n",
    "\n",
    "#dropped the columns from my test data that were not in my training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2051, 203)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "\n",
    "#confirming shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(879, 202)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.shape\n",
    "\n",
    "#confirming shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x1728 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (4, 24))\n",
    "\n",
    "cmap = sns.diverging_palette(150, 275, as_cmap=True)\n",
    "sns.heatmap(df[df.columns[39:104]].join(df.saleprice).corr()[['saleprice']], annot=True, linewidths=.4, fmt=\".1f\", cmap=cmap, ax=ax);\n",
    "#sns.set(font_scale=4)\n",
    "\n",
    "#plt.savefig(\"heatmap_fe_1.png\");\n",
    "\n",
    "#created at a correlation heatmap of my new features in relation to sale price, looking at the first 65 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (2, 15))\n",
    "\n",
    "cmap = sns.diverging_palette(150, 275, as_cmap=True)\n",
    "sns.heatmap(df[df.columns[104:121]].join(df.saleprice).corr()[['saleprice']], annot=True, linewidths=.4, fmt=\".1f\", cmap=cmap, ax=ax);\n",
    "#sns.set(font_scale=2.5)\n",
    "\n",
    "#plt.savefig(\"heatmap_fe_2.png\");\n",
    "\n",
    "#created at a correlation heatmap of my new features in relation to sale price, looking at the next 17 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qKyzHqRSyThhmNkNSwlC7A+GBfw3BUDvIzLZKOk3BUDtU0mgFG+vrZnZd7AP5MD4g1wHnEX4BZzrfBEmPEQy1EA21sVZSIpI6AjcCs4AJ8Zz3xuazTLxMeJnRVOBT4P0Syp4TyyfzIqFp6lVgaNLoreQa2UpJY4BmFNY6MpEH/D02W20B5hL7HBzHcXJBnXJJ1WQkjQCuNbPxuY4lDe6Scmo97pKqerx92wG2/c/mOI6TkVpfw1AlG2rTHP9loFvK6l+Z2ZuVcfwagj0+5n+5jsFxKsQFA48Hr2FUKbW+hlHZhto0x/9BVR7fcRyntlDrE4bj1CTMjLefepl5U2ZSv2EDTrrkHNqlsdV+/dmX/PeRp9myeTM9+vbi6B/+wGd7OzUet9U6TiUyb8pMVixeypW338DxF57JG/96IW25N554ge9deCZX3n4DKxYvZf7UWdUcqeOUnSpNGNoOrLXR2zRbwZs1Myo3MpUt1TIrKU/B0zUvxjxS0VLr1Hw+nTiNvQbuhyR26dGVjes3sC7FVrsu2mo77toVSew1cD9mu63WqQVUdQ1je7HWnhu9WYOAOyQ1TBNjvSyP9QiwAtjNzPYELgRcZV5LWLdqNc1aFf7OyW/ZgrUrU2y1K1fTrFWhLiS/VfNiScVxaiJlThgqbqytJ7fWJsgjaEu2xmOtk/T7OJLroKR72ETSG5IuS7m3PQiyxZvMrCDGPd/MXovXO0vSI/E+DJN0VJwgOUfRfis31uaUtKMOU/omavnARGc7pkyd3kpvrD3XzJ6QtF1aayPDJH0Xr+1n0QEFsCMwzcxuTrqmPMKM8CfM7ImU4+wJTEraP5VdCe/guBwYB/wwxv994AbglFiuVGOtUmy1jfp0xCkf44ePYtL7HwLBVrsm6d0Wa1euIr9FsyLlm7Vqzpqkd2SsXbGavBZuq3VqPmUdJZVsrAVoQjCiYmaPSDqDYK3dO8P+ydZaCA/P3QgJo1RrLYCkhLX21RL2KYJKsdYCMyQVs9YCiySVZq2FkDTHS9oZGCPpDTP7nFDTeDGl7CvAn81sWBbHTWVBYn6JpOnAcDMzBTFh16Ryr8X3aHwnKWGsXZh8IDN7iCBnBJ+HUSEGHDmYAUcOBmDu5OmMHz6K3gf0Z9H8z2nUpEmxZJDXojmNGjfiq3mf0aF7F6aOGceAIw9Od2jHqVGUNWGkNdbCdm2tLdzJbKmkCYTa0OfAxjS1hdHA9yQ9ZcXbL6YD/WIfTkGaUyTHW5C0XIAba2sEPfr2Zu6UmTzwqz/SoGFDTrzk7G3bHrn5Ti79/XUAHHf+6fznn0+zZdNmeuzVix59e+UqZMfJmrI+SNIaa+Ov6TvYzqy1ac7VlNC5/ucSit0M/Aa4H/h/yRvMbJ6k8cDvJN0caw+7Ab2ByVnG7+QQSRz3o/SD6hLJAqB9t85c/odfVVdYjlMplKnTO75wKGGsnQK8DbSXdCjBWntHbGrZpGCtXQ6Mjp20d5rZW4SH74exGeUFiiaUdOecQFCEjyX0jzxiZhNL2ieBCq21vQnW2kmSSkscLwNzCNbaByjZWptgmMJLpT4BHjOzT0op/zOgsaR0ieVSoB0wN96jh4FFaco5juNUK7XeJeVUCt6H4dR63CVV9XjCcKCcfTaOUwPxhFGF1JnOULm1tkK8NHFErkNwnApxav/Dch1CnafOJAy31jqO41QtdSZhOE5NYPakafz38ecoKChgvyMGc9jJxxXZvmXzZp67byhfLfiCpnk78sOfXkbLNm5+cWoHbqt1nEqioKCAVx99mouuH8LP/+8WJo8ex+KFRQe4jXtvNE3yduS6v/2BwSccxetPvZSjaB2n7FRLwpBba1PLprXWStoa9098upY1Tid3fDl3Aa3btaFV252pX78+/QYOYOb4otNnZo6fzD6HHAhAnwP2Yd70Wen9U45TA6muGoZbawtjLMlau8HM9k76fFYFMTpVxJoVq2jeuuW25WatWrJ6xapiZVq0bgVAvXr1aNykCevXZisscJzcUqGEoeLmWrfWBipkrU25x13jfZwQPwOTtv1S0lRJkxO1Jkk94jE/ifv1LEPcTiWT+hI9SzeC2QeCOrWEcnd6K7259lCCIsStteWz1jaJM8YhiAZ/QJA7Hm1mG6Mm5GlgQKwhnQIcYGbrFTQtELxZV5rZHIUXL90PHJHmPhSx1e603+6lXJpTGs1atWD18pXbltesWEmzlkVbU5u3asmq5Sto3rolW7duZeOGDTTN27G6Q3WcclGRUVJpzbVmdovcWltea+2GRFJNogFwr6S943EST/ajgKFmth7AzFZIygMGAs+r8Kdt6twUYvkitlqfh1FxOvboyrJvlrBiyTKatWrB5DHjOXvIJUXK9Nq3LxNGfkSX3Xsw7eMJ9Nizp7/L26k1VCRhpDXXyq21lWGtTebnwGKgH6EJcWNSnKn77QCsSpN0nGqgXr16fP+is3n0T3/DCgoYcPgg2nbqwNvPvcou3bvQe0A/Bhw+mOfue5Q7f3oTTfN25Jyrs3ViOk7uqUjCSGuuBa7FrbUVstam0BxYaGYFki4AEp3mbwE3x4SzXlKrWMtYIOkMM3te4adrXzNz02010bP/XvTsv1eRdUef+f1t3xs0bMC5P7+iusNynEqh3J3eGcy1XXFrbWVZaxPcD1wg6SNCc9S3AGb2BqE5bnw857Wx/LnAJZImE96vcXIWcTuO45SKywcd8D4Mpw4QXVLeIVSFeMJwwG21Tt3BE0YVUudcUnJrbbkYOvq1XIfgOBXiokEn5DqEOk+dSxhurXUcx6ka6lzCcJxcYma889TLzJs6kwYNG3LCJefQrkvHYuW++exLXvvn02zevJkee/XiqB/+wOdjODUet9U6TiUyf+pMVi5exhW33cBxF5zBm0+8kLbcm/96geMuOJMrbruBlYuXMX/qrGqO1HHKTk4ThrYPi20DSbcrOLamKbi3vhe3fRZdUJPivycn7beunNc0QtKA8uzrVJw5E6fRZ+AAJLFLj658t34D61atKVJm3ao1fLfhO3bZtSuS6DNwAHMmVkoXm+NUKbmuYWwPFttbCa6qPmbWh+CxSp5zcnicmX06cE8ln9upZtauXEN+q8LfMfmtWrB25eqUMqvJb9k8pUzRpOI4NZEqSRhyi23iPjQFLgOGmNl38TyLzey5NMWbEUSKqceQpDvjtU2VdFbStmK22qRtO0h6PM5wd6qN4iOUi3dNuLHWqZ1Ueqe33GKbzK7AF2ZW0s/H96LCoztwZprtpxIkjv2AnQiyx5FxXTpbLYS/6zCCIfePGa63iK22wZ67lBCiUxKfDB/F5JHBfdm+WyfWJr0DY+2KVeS1aF6kfH7LorWOtStWkd+iWfUE6zgVoCpGSbnFtmwcbmbLJPUAhksaYWbJ/ReDk863OCbD/QhJuIitNmmfB4HnMiWLWL6IrdbnYZSffY8czL5HDgZg7uQZTBg+il4H9GfR/M9p1LQxeSnJIK9FMxo2bsRX8z6jQ/cuTBsznn2PGpyL0B2nTFRFwnCLbSFzgc6S8s0s3fUWHtBsnqTFBN/V2AznJWV9pjjGAIdL+j8z25ihjFMF9Ojbi/lTZvLg9X+iQcMGHH/xOdu2Pfrbu7j4d0H5deyPTue1R59my6bNdN+rJ9336pWrkB0na6oiYbjFNhKbiv4J3CPpCjPbJKk9cKSZPZkSRxvCDPLPM5zvcaAVoYZzHbCJNLbauM8/Y7nnJf3AzLZkeW1OBZHEMT86Le22RLKA0HR16a2/rK6wHKdSqPSEYWYzJCUstjsQHvjXEJpRBpnZVkmnKVhsh0oaLWka8LqZXRf7QD6MzVnrgPOIrzrNcL4Jkh6j8Ff5I2Y2MdZKSkSFFttZBIstwL1m9kgJu71MeIPdVOBTSrfY3gT8gdCstZFQ67k5aft7krYSXpR0vZktTnO+g4DJhBrFL83sG+ANhZcqjZe0CfgfcENiJzP7i6TmwL8knRub1hzHccqNywcd8D4Mpw4QXVI+3qwK8YThgNtqnbqDJ4wqZLtxSckttiUy9vOZuQ7BcSrE/l184EBVs90kDLfYOo7jVIxcq0Ecp04xZdwErrv4Kn5x4ZX855kXi22fNWU6N111DRccdypjR47JQYSOU348YThOJVGwdSuP3/sg1/3xZu54+O98OOIDvvr8yyJlWrfZicuvvZqDjjgkR1E6Tvmp1ISh7dw+W1VI+lmc+JhYzmi5TdnvFknXptvmVD7zZs+hbYf2tGnfjvoNGnDgoYP5ZMzHRcrs3K4tnbt39XdfOLWSyq5huH22avgZ0DRlnVtuaxgrl62g1c47bVtutXNrVi5fUcIejlO7yCphyO2ziftQon1W0jpJd0j6RNI7kvZXeD/FfEnfj2XqKdhnE/fkirj+sFj2hVhDGhZjuxroQJjg916asIpYbiXdKGm2pHcIkkSnmrCsTLWOU3spdZSU3D6bTGn22R2BEWb2qzjM9g/A0QQ/1OPxnJcAq81sP0mNgNGS3or79wf2BBYBown3/B5J1xAlhUnnKma5jffu7Hic+oSE+UmG+1PEVrv3sQdnuCQnW1rt1JoVSwv/RCuWLqdFq1Yl7OE4tYtshtW6fTZ7NgFvxO9Tge9ikp0KdI3rjwH6JvWTNCfck03AWDNbGK9hUtxnVIZzFbPcEu7TywmDraRXMwWaaqv1eRgVp/seu/HNV1+z5OvFtNqpFR+9P4qrrr8m12E5TqWRTcJw+2whpdlnN1vh1PmCxPnMrCAmscR5h6RO6JN0WEp8W8ni75NiuS3LtTiVTL169Tj/J5dx5w2/o6BgK4ccexQdu3bmxcefotvuu7LPQfszf/Yc/vq72/l27TomfTSel/71NLc//Pdch+44WZFNwnD7bKQs9tkSeBP4f5LejbWP3YGvStkncV+XpW5QUcvtFgrvXX1Ck9yDqfs4Vcfe+w9g7/2LvlL9tAt+uO179z12456n/lndYTlOpZDNL1i3zxalNPtsaTxCaGqaEPsglhLenFcSDwGvS/razA6P69JZbhdLehaYREggH5QhLsdxnBJx+aAD3ofh1AGiS8rHpVUhnjAc8H4Pp+7gCaMKqbXyQbl9tlJ5aOS/cx2C41SIyw8prWXXqSi1NmG4fdZxHKd6qbUJw3FqImbGe8+8yoKps6nfsAHHXXQmbbvsUqzcqJffYPqHE/hu/QauvvfWHETqOGXHbbWOU4ksmDablUuWcfEfr+PoH53KO8NeTluue99enHvDT6o5OsepGFWWMLR9mGtHSBqQtNw1DikusayytM1WlOin+m9VHNtJz7xJ0+l94L5IokOPLny3fgPrVhU3yXTo0YW8Fs1yEKHjlJ+qrGFsD+bailDptllJ9SrjOE75WbdyDfmtmm9bzm/ZPG3CcJzaSJkThtxcm+19aiLpmXitzxIcXOlItc0Wu964/t8KFtzpCuLAxPp1kn4fR40dJOm4WKsaBZxa3vid8pFufLIba526Qpk6veXm2nQMk7Qhfm9IcEgB/D9gvZn1ldSXkLCSyWSbLXa9ZjYRuNjMVkhqQhBBvmhmywmG3GlmdrOkxsAcwsz1ucCzJdyTIrZaepY7N273THxvDFNHBjFBu24dWbti9bZta1euZsfm3vTk1A3KOkrKzbXFOdfMxsdzdQUSfQaHEJuazGyKpCkp+6WzzWa63onA1ZISQ307Ee7dcoJmJfHy6J7AAjObE/d/kpgUUkm11fo8jPLT//CB9D98IADzp8xk4ntj6Ll/P76e/wWNmjT2vgqnzlDWhOHm2rJR6rFSbLNpr1fBZHsUcFAUII4AGsfNG2OCy/qcTtXRba+ezJ86m3/e+GcaNGzIsReesW3bE7/7K+f/NrQyvv/C/5j18UQ2b9rMg9f9kb0O3p+B3z86V2E7TlaUNWG4uTZ7RgLnEpqe+gB9M8SYyTabfL2dgZUxWfQEDsxwzllAN0k9zGwecE45Y3fKiSSOOjf9jONEsgA49PTjOfT046srLMepFMqUMNxcWyYeAIbGpqhJFF5Dgky22ccofr0zgCvjsWYDaZvizGxj7Jt4TdIywsuX+lTgGhzHcbbh8kEHvA/DqQNEl5SPSatCPGE44P0eTt3BE0YVUidcUnJzbYV5fXqqAz+OAAAgAElEQVSpA84cp0bzvT0zde05lUWdSBhurnUcx6l66kTCcJyawswJU3jp0WFYQQEHHnUoR516YpHtWzZv5sm/PcTC+Z/RND+PC35xFa3b7JyjaB2nbLit1nEqiYKtBbzw8BNccdMvuP5vtzHhg4/45suvipT56J2RNM3bkZvuv5PDTjqW/zzxXI6idZyyUy0JQ9uhuTbLmIYp2GqnSRolKa8i98rJLZ/Pnc9O7duyU7s21G9Qn/6DD2Dq2KJGmKnjJrDf4YMB6HfQfsyZOgMfeOLUFqqrhuHm2uL8FFhsZnuZWR/gEsK8ljLfK6dmsHr5Slq2brVtuUXrVqxesTJjmXr16tG4aRO+XbuuWuN0nPJSoYQhN9dmui/rJN2hYJd9R9L+sQYyX9L3Y7H2wLb2CjObbWbfpd4rpbzTIsZ0Yfy+X4x7cvw75EuqJ+muWHOZImlItnE7FaV4TUHFRnmmKeM6W6eWUO5Ob7m5tiR2BEaY2a/ikNw/AEcTfFGPx3M8SpgxfzpBufJ4lAZen3KvDstwHQ0JNtqzzGycpGbABoJssBvQ38y2KOhb0u1fxFbbaVBac4lTBpq3bsXK5Su2La9avoJmrVqkLdNip1Zs3bqVjes30DRvx+oO1XHKRUVqGMnm2klxuXtUb+QTzLXXZtg32Vw7gfCg3i1uK9Vca2brgITJtaR9iqBSzLVmNoOQHCDJXGtmi4BszLUJNgFvxO9TgffNbHP83hXAzCYR1OZ3Aq0I97FXGc6xB/C1mY2Lx1tjZlsIksJ/xO+Y2Yp0O5vZQ2Y2wMwGXH55WqGtU0Y679qNZV8vZvnipWzZvIWJoz6mz379i5Tps19/xr03CoDJH45jt716eQ3DqTVUZFitm2szs9kKezILEsc3s4KYtIjLicT3kqQC4HgKVeUJtlA0sScstcoQX6b1ThVTr149Trv0R/zj93dSUFDAAUceQvvOHfnf0y/RuUdX+uy/DwceeQhP/u0h/nDVdTTN25Hzr/HuKqf2UJGE4ebaCiBpEKHJbmVsXuoNjKD4vfoc6C2pESFZHEmQCs4COkjaLzZJ5ROapN4iiApHJJqkMtUynMqn97796L1vvyLrjj+n8MWHDRo25KLrflLdYTlOpVDuJqnYfJMw104B3iY0t+wH3GFmw4BNCuba5cDo2Fl9p5m9RXj4fihpKqHPID/tiQrPNwF4jGBy/Zhocs0mVhWaa3sTzLWTJJWWOF4mvL1uKsE8WxFzbTp6AO/H658IjAdeTHOvvgSeA6YQEvFE2NZ5fxbwd0mTCfe/MfAIoS9oSlz/w0qO23GcakLSo5KWKFi/021XHLQzNw5y2Sdp2wVxENIcSRdUSjw+BtwBzF1STm0nuqTqVIdQHAy0DngiDr9P3X48MITQnH0A8DczOyC2+IwHBhCaqD8B9jWzlanHKAuuBnEAF7c5Tk3EzEaq5Pf/nExIJgZ8pDDxtz1wGPB2ojla0tvAcYRBP+WmziUMubm2XNz6n2xHDDtOzeQ3J12c6xBywS7Al0nLC+O6TOsrRJ1LGG6udRwnG26/cG7O2+N//fhuVxDnQ0UeMrOHynCIdE1wVsL6ClHnEobj5Jpj9zyAXdt2YvPWLbw66QO+Wb28yPb69epx+r5H0HLHfMyMT7/5kndnjc9RtE4uicmhLAkilYUUtVt0BBbF9YelrB9RgfMAbqt1nEpl1zYdaZXXnPvefYHXJo/m+L0Gpi334bypPPDeSzz0/it0atWGHm06pi3nOKXwKnB+HC11ILDazL4mTF04RlJLSS0JE6Ur3Gzuttrsj5GNrfYLJU3blfRvSSWa5cpybxLHUvB03ROH3k5VcHKl9qsg6UJJ92ZzbKdy2L1dZ6Z8OReAr1YtpXGDhuQ1alKkzJatW/l8+TcAFFgBX69eTrPGTas91u0dKfef0mPU08CHwB6SFkq6RNKVkq6MRf4HzAfmEua9XQXbDA+3AuPi5/eVMR+rupqkEgbW+7PdIT54ZWbHl2OfgjSb7zKz9+IkueGSvmdmr2d77CxZBQwCRsUk1z6Lfcp8bwjzLzoAfePs8Y5kP9vdqULyGzdlzcbCP8WaDd+S37gp677bkLZ8o/oN2b1tZ8YumFFdITq1CDM7p5TtBvw4w7ZHyd5/lxVuq61cW+0zwNnx+6kE7Ufy/Sp2zan3RuGdGMMlTYi1h5PTnKc9wSNVEK9pYWJ8taSLJH2qIGYclEXMTiVS3E6buadREqfuexhjF0xn1fp0Bh3HqVm4rTZQWbba4cDDkuoREsflwG9KueZUO2194AdmtiYmyo8kvZrkpoIw83uUpIPjOZ80s4lx/PXvCFLI1cB7xJnhae5DEVst7X38Q3kZ0LUX/TvvDsCiVcto1rjQPtusyY6s27g+7X4n9h3EinWrvXaRI9z5WHYq8pRIttUCNAGWmNktks4g2Gr3zrBvsq0WgqRwN0LCKNVWCyApYat9tYR9iqBSbLXADEnFbLXAIknZ2Gq3EjxPZwFNzOyzpC6Nkq65SJjAn2IyKSCMnW4LfJMoYGYLJe0BHBE/w+M9zydo1ZfG630W2D1doCmjM8znYZSf8Z/NZPxnM4HQ6b1ft95MXzSfXVrszMbNm9I2Rx22xz40atCQ/0weVd3hOk65cVttoDJttc8QPFS3pKwv6ZqTORfYmTCNf7Okzyg01BYGFl629DrwuqTFwCmE2kbOx5Zvz8xdspBd23Tix0eczpY4rDbBZYeczMMjXyG/cVMO3n1vlq1dxWWHhBbHcZ/NZNIXn+Yq7O0Tr2GUGbfVlny+8thqPwBuo/gU/EzXnHpvmhNqapslHQ50SXMt+wDfmNkihVfS9iXICT8G/iapNbAGOAOYnOX1OpXEG9M+TLv+4ZGvALB243qfWe/USsqdMMxshqSErXYHwsPvGoKtdpCZbZV0moKtdqik0QrGxdfN7LrYB/JhbLJZB5xHaNLJdL4Jkh4j2Goh2mrT/EIvhgpttbMItlqAe+PLnjLxMqG5ZyrwKVnaamNfw11p1r+V7prNbF7yvSH0Af1H0nhgUow5lTaEvpKEAmVsvJ6Nkm4hDMP7mtC5Xy+buB3HcUrDbbUOeB+GUweILqmsG5ruvHRezh9+1z3So1Y1jPnQGAfYbsVtjuOUgTqXMOS22nLxr4/eKL2Q49RgfnTgcbkOoc5T5xKG22odx8kGn4dRdupcwnCcXGJmvDXsJeZOnkGDhg046bJzad+1mHyArxd8yauPDGPLps3s2q83x5x76rZJqo5TU3FbreNUIvOmzGDFN0u56s83cfxFZ/P648+nLff6489xwkVncdWfb2LFN0uZN2VmNUfq5Fo8WBt/H1R5wtD2Y6qdLWlyHCK7R9L6ARWJvyLIbbXVzuwJ09hr0H5IouOuXdm4fgNrV60uUmbtqtV8t3EjHXfthiT2GrQfsydUSheb41Qp1VHDSNhYsyaK/3Yws+PNbFVZ9smw+S4z6wn0BwZJ+l5Z4smSc82sH/A4cGcVHN+pBaxduYpmrQt/4zRr1Zy1K1MSxsrV5LdMLtOCtSuz+s/ccXJKuROG3FSbiZHArin36hJJdyctXybpL5J+KenquO7uhK9K0pGSnozfz1Gw1k6TdEfSMTKtv0huq61RFDPYppn75P0X1Y92yP2ntlGuTm+5qbYkTiLMDk/mGWCKpF+a2eZ4LVcAOwK/AO4BBgCNJDWI8XwgqQPhnu4br/UtSacQZnanW/8x5bTVNunbOcvLc1IZ/84HTHw/6EDad+vMmuWFtYU1K1aT17JZkfL5KTWKNStWkdeiefUE6zgVoLyjpNxUW5xhkjYAnwFDkjeY2bfxGCdKmgk0MLOpMTnsKymfIECcQEgcBwNXEzQryfbZYTE2y7Aeymmr9XkY5WfAUQcz4KiDAZgzaTrj3/mAPQ/ch6/mfU7jJo3JT0kG+S2a07BxIxbO/YxdenRh6uhx7Hf0wbkIfbvGa3Vlp7wJw021xTnXzMaXsP0R4AaCG2ooQJKN9iJgDEEgeDjQA5hJhoc9Jd+PnOsOtmd27debuVNmcN91t9KgUUNOuvSH27Y9/Js/c9mtvwTgexecyX8eHsbmTZvZtW9vevTtnauQHSdrypsw3FRbRszsY0mdgH0Idtnkc10LXExoyvoL8ImZmcKs9b/FfpyVwDnA3wlNUiWtd1ttjpDE984/I+22RLIA6NCtM1f86ddpyzlOTaVcCcNNteXmOWDvxOtUIx/E+D6MTVcb4zrM7GtJvyb0RQj4n5m9Eq8r0/pbcFut45SOt0iVGbfVViOS/gvcbWbDcx1LCt6H4dR6oksq6zRw91Xzc/7w+/n93WtV2nI1SDUQR2eNBSbXwGQBuLjNcZzSqVMJQzXUVBsnH2bqwK4RPDrqP7kOwXEqxMWDTypTeR8kVXbqVMJwU63jOE7VUacShuPkGjNj+NOvMG/qTBo0bMjxF59Fuy4di5Ub+dLrTBszno3rN3DN/X/KQaSOz8MoO7Vwcrrj1FzmT53FisVLufxP13Ps+afz1r9eTFuuR7/enH/TT6s5OsepGDlPGNo+bLYnSpqoYLOdIemKuP4UST5jqw4xZ9J0+gwcgCR26dGF79ZvZN2qNcXK7dKjC3ktmqU5guPUXHKeMKjjNtuo/3gIOCnabPsDI+LmUwBPGHWIdStX06xV4W+Y/JbNi+nNnZpBrsWDtVE+WGUhy222CfIJfUXL4zm+M7PZkgYC3wfujNfeQ0nvz4j36bP4vZ6kuxTstFMkDYnr94uxTY73Ol9SY0lDY9mJkg4v55/QKQfp5jUVs9U6Ti2lSjq95TbbbZjZCkmvAp9LGg78lyA2HBPX/9fMXki+njRcThjO29/MtkhqJakh8CxwlpmNk9QM2AD8NJ53L0k9CbPxdzezjSnXWsRWW793+9Juj5OBCe+OZvLIjwFo17UTa1YUVnrXrlztTU81FO/zLjtVNUrKbbZJmNmlkvYCjiJ4o44GLiwtpiSOAv5hZlvi8VbE431tZuPiujXxOgYTvFKY2SxJnxPmgExJiamIrdbnYZSffY4YxD5HhFePzJs8g0/eHU2v/fdm0fwvaNS0sScMp85QVQnDbbYpxMmDUyX9C1hA+oSxhcJmwsYp5009X7p1qTE61Uz3vr2YN3UWD/36duo3bMDxF5+1bdvQW/7CRbdcA8B7z/+XGR9PZPOmzdx37a30O3h/Bp98bK7CdpysqKqE4TbbwmPnAQPMbERctTfh+qH4tX9GqJmNBZJHXb0FXClpRKJJiiBT7CBpv9gklU9okhoJnAu8K2l3oDMwO8vrciqIJI4579S02xLJAuDwM07k8DNOrK6wnDR4k1TZqZJObzObASRstlOAt4GuBJvtHWY2DNikYLNdDoyOndV3mtlbhIfvh5KmEvoM8tOeqPB8E4DHCA/aj4k222xiVaHNtjfBZjtJUmmJ42VgDsFm+wAl22wF/FLSbEmTCG/EuzBuewa4LnZO9wDuAv6fpDHATknHeITQJDdF0mTgh7GD/izg73Hd24Rayf1AvXjvngUuNLPkWpLjOE65cFutA96H4dQBoksq63rDvT9bkPOH30/+2q1W1XNcDeIAZRe3OU5tp4RRiU4GtquEoRpqs60JLF24KdchOE6F2Lljw1yHUOfZrhKG22wdx9mGVzDKTC2cnO44NZePxo7inAtO4qwfHc+/ns78FuD33n+LwUfuxazZ06sxOsepGJ4wHKeS2Lp1K3+554/cddv9PPnoK7zz7uss+GxesXLr13/LCy8Po3evvjmI0nHKT7UnDNVxO62kWyTdlrJub0kz4/dyX0OKa+riJLfUNEknpynfVdK08pzLKTszZ02l4y6d2aVDJxo0aMBRh3+PUWPeK1bu4aH38sOzLqZhQ29zzyVS7j+1jVzUMOq0nZagFzkrZd3ZxIl9ZbmGTCTNHRlsZn2BA0lRfzjVz9JlS2izc7ttyzvv3JalyxYXKfPpnJksWfoNgw46tLrDc5wKU2kJQ26nTRx/NrBKUnIH+5mESXokriEp1odjTectSU0UrLUTku7rbpI+STlNG8Is8XXxnOvMbEEsv6+CvfZD4Mel/d2cysPSmFqSh24WFBRwzwN/5idXXludYTlOpVEpCUNF7bR7A1spaqf9BdFOC1wPzDOzvc3sOhW10+4N7Ktgp4VghH3CzPqb2edJ50u20x4IXCapf0n7ZIg7YacdnrQ6Yac9kZDcoKid9jJgYCm35GlCrQJJBwLLzWxOmnK7AfeZ2Z7AKuA0M5sHrJaUkDNeRJjFnsxkYDGwQEFlnjyJYihwtZkdVFKAki6XNF7S+Iceeqikok6WtNmpLUuWfrNteenSxezUuvC3xfr137JgwVyGXHMxp//wWGbMmMKvfjPEO75zRK6bo2pjk1RlDat1O21RngHGSPoFIXE8naHcAjObFL9/QtCnQFCBXCTpGkIi3j95JzPbKuk4gmrlSODumETvBlqYWUJV8i8gbXNbqq3W52FUnJ49+/DlV5+z6OuF7LxTW95573V+e+Md27bn5eXz2ssfbFv+yTUX8ZMrrqXnHnvmIlzHKTOVlTDcTptc0OxLhZcfHQqcBmT6tZ98rq2ERAvwIvBb4F3gk+jbSj2HEdxZYyW9TahZ/LUscTqVS/169blmyA1c86srKSjYygnf+wHdu+7KI0PvpeceezJ4oL/LqibhM73LTmUlDLfTFudpwi/+eWa2MNvYAMxso6Q3CWLDS1K3S+oAtIvSRYgGXDNbJWm1pMFmNopgrXWqkYMOOISDDjikyLpLL/pJ2rL3/mVodYTkOJVGpSQMM5shKWGn3YHwwL+G0GQyKDahnKZgpx0qaXQc7vl67MfoRbDTQujIPY/wizvT+SZIeozwCxuinTbWSkokaYTRLIKdFuBeM8s8yyrYaY8g2Gk/pWQ7bYLnCW/uG5JF2XQMA04lqM1TaQDcFRPHRmApodkPQp/Ho5LWE5Kx4zhOpeC22hqKpGuB5mb2m2o4nfdhOLWe6JLKup3pwV9+nvOH3xV/7lKr2sW2K5dUbUFBYtiDUKupFlzc5jhOadTphKFaaqfNhcTw1y/eX92ndJxK5bbTyiaQyDit18lInU4Ybqd1HMepPOp0wnCcXHBSv8Hs0a4Lm7Zu4YXxw1m0almR7Q3q1eeHBxxLq7xmmBkzv/6MN6eVOnXIcXKOJwzHqUT2aNeZ1nnNuevNYXRq1ZZT+h/K/e+9WKzcB3MmMn/pIuppBy455Pvs3rYzny7+IgcRb7/4NIyyUyWteKr7RtqTJf07afnXkuYmLZ8k6dU0+10o6d4069tK+m90QM2Q9L+yxuTUDHq178bEz2cD8OWKxTRu0JD8xk2LlNm8dQvzly4CYKsVsGjVMpo32bHaY3WcslJV3T513Ug7hqKztw8C1khKiIMGAqPLcLzfA2+bWT8z603wbTm1kOZNdmTVhnXblldv+JZmjTMng8YNGtKrfRfmLv2qOsJznAqRdcJQcRttPW2/RtqlBEHgrnHVLgSdR0JKOJCQVJB0kaRPJb0PDMpwyPbAttngZjaltJjiffqdpAkK78XoGdfvLOntuP5BSZ8n7qdTHRRv58g02H8HibP3P5oxc6ey8ts1VRuWUwxJOf/UNrLqw1BRG+3m+LA+18yekJQw0n5MNNJK+hToE821qKiRVsCrCkbaLwgW2IvM7KpYNnHOZCOtgI/jQ3dl6j6lxJ4w0v4taXXCSNuTICx8gaJG2rbADODREg49BhgoqR4wB/gIOFbSf4G+BBFje+B3BDHjauA9CiWLydwHPCvpJ8A7wFAzW5RFTMvMbB+F5r9rCaqT3wLvmtltCoLCyzPcl8sT2x588EFoXcKVOiVyYPc+7NetNwALVy6hRZM8Eprk5k12ZO3G9HqzH+xzGMvXrWb0XH+ViVM7yLbTO62NFsDMHtH2aaQdTahJ1AM+JGhKbiY0gc2OPqgDgBGxRoKkZ4HdUw9kZm9K6g4cR7DLTpTUJ4uYXor/fkLQiEC4bz+Ix31D0sp0wafaan0eRvn5aP40PpofXmy4R7suHNSjD5MXzqVTq7Zs3LyJtRvXF9vn6N7707hBQ176pPgb+Zxqovb9wM852SaMtDZa2H6NtIQaxhBCwnjYzNZKagwcRtH+i6yOaWYrCELDp2ItJWGwK2n/xHVspfBv6f8b5JDZ33zOHu06c+2x57J56xZeGF+Y44cceSZ/H/4czZrsyBG9BrBkzUp+cuSZAHw4byrjP5uZq7AdJyuyTRhpbbTxBUV3sH0aaWcAHQi1nkTT2CRCTeuXcflj4G+SWgNrgDMILz9KjfUI4CMzWy8pn6AF+aIcMQGMIrzh747YFNiylPJOJfPqpA/Srv/78OcAWLPhW59Z79RKskoYGWy0P441hO3SSGtmpqAeaW5mieT3IaFfYEws87WkW+L6rwmd7/XSHG5f4F5JWwgDER4xs3GSxpclpsjvgKclnRXLf036Wp/jbNfUwj7nnOO22jqGpEbAVjPbIukg4IHE4IMS8D4Mp9YTXVJZp4F/3vRFzh9+l/yhc61KWz7Tu+7RGXgu1gQ3Ed5BXiplFbc5Tm3Haxhlp9YnDNVSI21VYWZzCCO1ysTDH7xSBdE4TvVx2cEn5zqEOk+tTxhupHUcx6kean3CcJyahJnx7tOvsmDqLOo3bMD3Lj6Ttl06Fiv3wUtvMOPDT9i4fgM/ve8POYjU0Q7eJlVW/BUijlOJLJg6i5VLlnHJn37JMeefxttPvpy2XI9+vTj3xvK+7t1xcoPbajMfI+e2WkmHxUl8Ti1h7qQZ7HnQPkiiQ48ufLd+A+tWFfdEdejRhbwWzXIQoeOUH7fVlg+31TppWbdqNfmtCn/v5LdswbpVq3MYkZMJKfef2obbamuwrTaSJ+mFWFsapjgLUW6rrZGkn9ZUC58MjpMGt9XWbFsthBrSnsAiQq1lEEH/AZVoq1WvtumKOVkw8d0xTPngYwDade3E2hWFFeS1K1d501MNpTb+ws81bqut2bZagLFmtjDuPwnoSmHCqDRbrc/DKD/9jxhI/yNC5XLelJlMfHcMPfffm6/nf0GjJk08YTh1BrfV1mxb7fKUWJOttOC22hpH9716smDqLB654Q4aNGzIcRedsW3b47+7mwt++3MA3n/+NWaOncTmTZv5x3V/ZK/B+zHo5GNyFbbjZIXbaouer6bZasvzome31eYQSRx1bvq5nolkAXDoGSdw6BknVFdYTjr8p1WZcVttITXRVntYadeaBrfVOo5TJbitto6hctpqvQ/Dqe1El1TW9YbHf/9lzh9+F9zcqVbVc1wNUvcol63WxW3O9sYOPkyqzNT6hCG31RahvLbatWu91cqp3eTn55deyKkQtT5huK3WcZxy4Sa9MlPrE4bj1CTGjBnDXXfdRUFBAaeccgoXXnhhke0vvPACzz//PPXq1aNJkybceOONdO/ePTfBOk4Z8RzrOJXE1q1bueOOO7jnnnt4/vnnefPNN5k/f36RMscddxzPPvssTz31FOeffz533313jqJ1nLJTpxOGgh22QzWd6xZJX0V/1ixJDyiDGFEZTLiSRkiaHY8xKV0Zp+Yyffp0OnXqRMeOHWnQoAHHHHMM779fdHR2Xl7etu8bNmzYpsJxqp9ciwdr45++TjRJSaoXlR6pXAhMI3iYqoO7zeyumChGAocS/FHbiLqSkjjXzMZXVYBO1bFkyRLati10crVp04Zp06YVK/fcc88xbNgwtmzZwgMPPFCdITpOhSi1hhHtsLMkPaJgjR0m6ag4MW+OpP3jZ4ykifHfPeK+e6rQcDtF0m4ZznGHkt6fEX+t/0LhfRAjlNnWerOkUYQZ1KnHPB0YAAyL5z8hjnhKbD86+qmQtE7S/ykYXodL2jmu7yHpDUmfKJh4e2Z5XxsCjQmixETN4U8K8sSfpsR5a6xxZPxbSPp3jGG6gjQwsf64GPNkScPjuh0lPapgBp4oycfL5pB0NYgzzzyTV155hSFDhvDPf/4zB1E5kPvaRW2sYWTbJLUrwfbal2B4/SFBcnctcANhRvUhZtafIOD7U9zvSuBvceLYAJIU3ik8Q7DhJjgTeD5+7w/8DOgNdKeoInyjmQ02s2dSD2hmLwDjCb/Y9wb+B/RKJAOCCXdo/L4jMMHM9iHMjv5tXP8QMMTM9o3Xen+G+BP8XEEQ+DXwqZlNStrWwswONbP/S6yQ9GeChuSiKEOEwgQ3KSpFAC6OMQwArpbUOl7Hw8BpZtaPwqR5I8FWux9Bb3KnpGKKEUmXSxovafxDDz2UutkpB23atGHx4sXblpcsWcLOO++csfwxxxzDiBEjqiEyx6kcsk0YC8xsanyoTQeGW5giPpVgT20OPB9VIHcTdNwQlBg3SPoV0MXMNqQ7uJlNBNpI6iCpH7DSzL6Im8ea2cJ47oStNcGz2V5ojPdfwHkKyvODgNfj5oKkYz0JDJaUR7DRPh+TwIMELXpJ3B2TUxtgR0lnlxDrbwhJ5AorOt3+XDPbO36Wx3VXS5pMUKh3Iph+DwRGmtmCeH0rYtljgOtjzCMINZ3Oae7HQ2Y2wMwGXH55WgO6U0Z69+7Nl19+yVdffcXmzZt56623OOSQQ4qU+eKLL7Z9HzVqFJ07F/vTOE6NJds+jGRjakHSckE8xq3Ae2b2g+h6GgFgZk/FiXUnAG9KutTMMmnDXwBOB9oRahzpzp1qa83WWptgKPAfYCPwvJltyVDOCMl0VRZajeI7h3eGvEEwziauJTXWccC+klolPeyLEX1SRwEHRTnhCEISEOlNuCLUOmaXNW6nYtSvX5/rrruOIUOGsHXrVr7//e/To0cP/vGPf9CrVy8OPfRQnnvuOcaOHUv9+vXJz8/nlltuyXXY2y0+4KDsVFand3Pgq/j9wsRKhXc8zDeze+L3vkCmhPEMoYllJ0JncWVQxJprZoskLQJuAo5OKrcDIZQVx9cAACAASURBVFk9Q2huG2VmayQtkHSGmT0f+076mlkx22wqsexAQo0oE28QLL6vSTrGzDJNtW5OqHGtj30oB8b1HwL3SepmZguSEs+bwBBJQ6IgsX+swTnVwODBgxk8eHCRdVdeeeW279dee211h+Q4lUZlDav9M3CbpNEUtbGeBUyLzSM9gScyHcDMphMe7l+Z2deVFNdjwD9if0CTuG4Y8KWZzUgq9y2wp6RPCMba38f15wKXxOag6UBpHciJPoxphGRcYp+HmT1PSJKvJsWXyhtAfUlTCDW5j+K+Swlm3JdifIkmr1uBBsCU2ER4aykxO47jZMV2Z6uVdC8w0cz+mbRunZnllbBbXcfcJeXUdqJLKut2pqfv+CrnD79zfrVLrWoXqxPzMLIl1iC+BX6R61hqGi5ucxynNKo1YcRhosPTbDoyaURQeY57H0WH20IYzjs0eUUcmlqMstQuJN1I8Xkfz5vZH7M9Rk3kvU8n5DoEx6kQh+++T5nKe5932anWhBGTQplHHWVx3B9X9jFLONcfgVqdHBzHccrDdtUk5ThVzfRPJvHcw09QUFDAoKMP57gzio6TmDNtJs89/ARfffYFl/zyavYdVKV2fsepVOq0fNBxqpOCrQU8/Y+h/OSWX/Hb++5i3MgxLPqiqNyg5c47ccHPrmS/Q1NbUJ1qRzXgU8vwhFEJqPqtuOsltUlaty6lzA8kWRncV04l8NmcubRp346d27WlfoP67HfIQUz5uKhHcqe2O9OxWxefNObUSjxhlAFJ9TJsuhColoQRWUbJI73OAUYBZ5dQxqlkVi5fScudWm9bbtG6NSuXr8xhRE5JSMr5p7ZRYxKG3IpbFivuo8BZklqliSePMGLsEjxhVC9p5jTVwmeC42SkxiSMiFtxs7PiriMkjZ+m2XYK8IaZfQqskJR2rKHcVlvptNypFSuXFY4OX7V8OS1atcxhRI5TudS0UVILzGwqgKRtVlxJyVbcx2MNwggKDAhepRsldQReMrM56Q5uZhMltYn9DTsTrbgKnquxZrYwnjthxR0Vdy2TFVdSwoo7lGDFPT9uTrXivqSiVtzEYRplcap7gEmS/i9l/TnAX+P3Z+JysUkWZvYQIVEBmM/DqDhdduvBkkXfsOybJbRo3YpxIz/kkmt/kuuwnAx47a/s1LSE4VbcLDGzVZKeApKb2FoTXFh9JBnB62WSfpmiUHeqgHr16nHWlRdyz29vo6CggIFHHUaHLp149cnn6bJbN/odMIDPPp3HP/70F9av+5ap4ybw32HP89v778p16I6TFTUtYZSGW3GL8heCJj3xdzwdeMLMrkgUUHjL32Dgg/JfnpMtew3oz14D+hdZ9/3zCru+uu7eg9sfu6+6w3KcSqGm9WGUhltxkzCzZcDLFDZhnROXk3mRkJwcx0lCO+T+U9vY7my11YVqlxXX+zCcWk90SWXdM/H83Yty/vA74+cdalVPSm1rkqoVqBZaccsqbnOc2k5tnAeRa+pkwpBbccvM4D9eVhWHdZxqY9SND+c6hDpPnUwYbsV1HMepfOpkwnCcXPLTY87moB57sXHzJv7036F8+s0XGcvefsaP6dBiZ85/+JbqC9ABas88DEnHESY01wMeMbPbU7bfDRweF5sCbcysRdy2FZgat31hZt+vSCyeMBynEjmwRx86tWrD2Q/cyJ4dunPtcedy+WO3pS17yB792bDpu7TbHAe2+evuIwzPXwiMk/Rq8uhLM/t5UvkhBGtFgg1lneNVErVwYFfVoxpmn3VqDwfvvjdvTPkIgOmL5pPXuCmt85oXK9ekQSPOPuBoHh/9WnWH6NQu9gfmmtl8M9tEmMNV0rD7c4CnqyqY7TphqPbYZ51awk75LVmyZsW25SVrVrJTfoti5S499GSe+fhtNm7eVJ3hOUlIuf9kwS7Al0nLC+O6NNejLkA3ik5abhydcR9JOqWct2obVZYw3D5bafbZa+L9mybpZ0n3dqakhyVNl/RWYsJgOc7tVCJpHwIpc512bduJjq3aMHL2xOoJyqmxJEtA4+fy1CJpdss0f+Rs4AUz25q0rrOZDSBM3v2rpB4Vibeqaxhun62AfVbSvvF8BwAHApdJSrRP7gbcZ2Z7AquA08pybrmtttI4dd/DGHrpzQy99GaWrV1Fm2aFeb9Ns5YsW7e6SPk+u3Rnj3ZdeP7Ht3H/+b+iU+u2/P28a6s7bCfXb9tTkICa2YCkT+r/jAuBTknLHYFFGa7obFKao+z/s3feYVIV2cN+DzBIzkGQKCACAoogEkTMOWBCl1UxIWbdn+7qmnANa/rMoKBrXFFEUFHXLEEyiGREooAgQWDICDPn+6Oqh56enpnumZ7pvjPnfZ5+6L637q3TDdxzq+rWW6pr/Z/Lce69o3IeFjtFPeht9llHQe2zPYGPVHWn/x6jgeOAMbjfdrYv9yPQLJ66I221bz86I4YQjWiM/nEco38cB0C3lu25sPMJfLtwOu0aHsqOvbv5IyJhfDxrPB/PGg/AwdVr8+Qlt3DLf01AaERlBtBKRJrjPHqXEkX143tnauKunaFtNYFdqrpXROrgbpqfLEwwRZ0wzD4bI9Hss+StOYj8fhULWreROKYsnUe3Fu0ZceOj/rHaN7P2vXHtA1z12r9yP9gwIlDV/SJyM/AV7rHa11V1gYj8C5ipqmN80cuA9yOs1G2AoSKSibs2PB7htoubZD9Wa/bZ7ETaZycAb4rI47jk0Qe4PLeDC1m3kSCe+Wp41O3RksXv6X/YHIwkITGOOicbVf0frms8fNsDEZ8HRTluMtA+kbEk+ykps8+GEWmfVdVZPpbpwDTcpJ38RkoLVLdhGEZ+mK02RiRY9tl4UXNJGUHHu6RibjZ8PPj3pF/8zr/p4GA0czzJ7pIKBBJA+2y8mLjNMIz8CETCELPPFjnbt29PdgiGUSiqVq2afyGjUAQiYZh91jCMRBPEFe+STSAShmEEhcmTJ/P000+TmZnJ+eefT//+/bPt//DDDxk5ciRly5alYsWK3HvvvRx66KHJCdYw4sRyrGEkiIyMDJ544gleeOEFRo4cyVdffcXy5cuzlTn99NMZMWIEw4cP54orruDZZ59NUrSGET+WMMKQ4rXUtva+q9neCzXMbz9SRM4sjhiMxLJgwQIaN25Mo0aNSEtL49RTT2X8+PHZylSpcmDYa/fu3YGZC1ASSbZ4MIh/9aWyS0pEykYIukL0B+aTu6slkbwAPKuqn/iYQhNsQv6s/+V2oJGabNiwgfr162d9rlevHvPnz89R7oMPPuDdd99l//79vPzyy8UZomEUioS3MMQstbGaYhsQJlVU1XkiUh43+a+vj6GviNQSkY/97zFVRDqEfefX/fddLiK3hsX617DfcajkrnE3iphoLYhLLrmETz75hFtuuYX//Oc/UY4yigMRSforaBRVl5RZavO31D4LfC8iX4jIHSJSwy+Q8gAwQlWPVNURwEO4CYMdcL9d+Kz3w4HTcIusPCgiaSLSxv82Pfz3yMDN/s6GmK024dSrV4/169dnfd6wYQN169bNtfypp57KuHHjiiEyw0gMRdUlZZZaR66WWlV9Q0S+Ak7H6TuuF5GOUYr2xKvLVfV7EaktIqEl3D5X1b3AXhHZANQHTgKOxi3lCE5KuCFK/dlstTYPo/C0bduW1atX89tvv1GvXj2+/vprHnnkkWxlVq1aRZMmTQCYOHFi1nvDCAJFlTDMUhsD3lX/OvC6iMwHjohSLK8FVKJ9VwHeUtV7Yo3DSAzlypXjrrvu4pZbbiEjI4Nzzz2XFi1a8Morr9CmTRuOP/54PvjgA6ZPn065cuWoWrUqgwYNSnbYpZYA9gglnWQNepd6S62InI5ree0TkYOB2rjfpFl4DDhjbT/gYRHpDWzydeX2Hb4DPhGRZ1V1g7hV/Kqq6q/x/RRGQejZsyc9e/bMtm3gwIFZ7++80xZKMoJLsh6rNUstnIr7rnNwrvu7VPV3YCzQNjToDQwCOovIXOBx4Mq8voCP8z7ga3/MN7gBdsMwwkj2I7VBbOGYrTYfpGRbakPYGIYReLxLKubL8Oevrk/6xe+s6+oHKm2UynkYsSKlwFIbwsRthmHkR0onDDFLbbFx0WAbIzeCzYc3/Tu+A8xzETcpnTDMUmsYhpE6pHTCMIwgcvVx53BU09b8ue9PXvruQ1Zsymmauffsq6hZuSply5Rh0dqVvDbhEzJtPNFIcSxhGEYCOappaxpUr80t/32aVvUbM6D3+dzzYc4J/898NZzd+9w0mjtP70e3Fu2ZtHRucYdbqgmimiPZJK0XT0RuFWdpfbeIzt/MT4bLr8xfwj53FpEXEhzHwSLyvogsE5GFIvI/ETnM173bPz47R7I7tXqLyGcFrG9HIuM34qNL8zaMW/wTAEvWr6ZS+QrUqJTzgYJQsihbpgzlypRFsdaFkfoks4VxI3CGqq5IYgzNcBPvhgOo6kycTyoh+Ml7H+FmXl/qtx2JU3isBpaFZoaLyPU4V1Se8yyM1KZ25er8sWNr1ufNO9OpXbkaW3flfGz5vnOuomW9xvy0ajFTl+V5b2MUAdbAiJ+ktDBE5BWcGHCMOMtsbjbWO8OOme/vypv5lsmrIrJARL4OTbITkaP93foU4KawY5uJs8fO8q/uftfjwHH+Lv+O8Dt7KYAlNgonAPtU9ZXQBlWdrao/RClbDdgS5bfKLY4qIvKGiMzz+y6MOK6OiEwRkbPyiM8oBnJrPTzy6Rtc9+ZjpJUtxxGHtCjmqAwjfpLSwlDVgV6NcQLO9PqTqp4vIifiZnfn92RUK+AyVb1ORD7Ayfn+i3M/3aKq40XkqbDyG4BTVHWPOOHhezgb7t3Anap6NriuoLBjQpbYaHEd7mOvCiwWkZdVdV+UOI8Afszje7Tws9qrApWArlHK5BbH/UC6qrb3sdcMHSAi9YExwH2q+k20ikVkADAAYOjQoXmEaOTH6Uccy0ntugCwbP0aalepATgTS63K1dm8M/dJkfsy9jNjxSK6NG/L3DVLiyNcwygwqTDonZeNNTdWqOps//5HoJk/poaqhpY4ewc4w79PA17y3UEZwGGFjCuaJTY3FXtehHdJ9cXZY0+PMY6TgUtDhVQ11DpJw81duSnst8hBpK32a5uHUWC+nD+VL+dPBaBT09ac0b4bk5bMoVX9xuz6c0+O7qgKaeWpkHYQW3dtp4yUoVPT1ixat7L4Ay/lWJdU/KRCwsjNxrqf7F1mFcLeR1paK/rz5DZyeAewHujoz7mnEHFFqz+333EBTlIYC2M4sN5GLHHk9n3345Loabi1OoxiZNavi+nUtDUv/fVO9u7fx5DvPsza91TfW7hrxIscVK48d595BWlly1KmTBnmrVnG1/OnJTFqw4iNVEgYudlYVwKhrqJOQPO8TqKqW0UkXUR6qupEsi8aVB1Yo6qZInIlB4SH2ey0McYVz3f7HnhMRK5T1Vf9d+mC636KtMf2BJbFEcfXwM24xaIQkZq+laHA1bh1Oe5W1cfjCdgoPK9NGBN1+10jXgQgffcO7v5wcHGGZBgJIRUSxiDgDW9W3cWBp4RGAVf4Pv4ZwC8xnOsq3NoSu3AG2BBDgFEicjHOBhtaF2MusF+cMfZN4KcY4ooZvwhTH+A5Ebkb17JZib/Ic2AMQ4A/gWujnCa3OB4BBvtHhzNwYx2jfb0ZInIp8KmIbFPVvFb+M4xSiXVJxY/Zag0ANZeUEXS8SyrmNPDVWxuSfvE77cp6gUpbqdDCMFKAuMVthhFw4uxeNrCEkRCkiKy6xcnMb7bmX8gwUpjOp9RIdgglHksYCaCorLqGYRiphCUMw0ggcxZO4Z0PnyEzM5Pe3c/l3FOzPysxfupnvPfxi9SsXheAU4+/mBO657WSr1FkWI9U3FjCMIwEkZmZwZsfPMU9N79IrRr1uP+p/nRqfxyNGhyardyxnU6m/yV3JSlKwyg4KbHmlJQec22G91bNF5GRIlLJb49qtE1k3UbRs2zlQurXaUS9OodQrlwax3Y6hR/nTkh2WEYuiCT/FTRSImHgzLVnqmq/fEsWHc1w5lrAmWtVNS+xYEHYrapHquoRuHkXA8OMtuNUtYWqtsVZa+snuG6jiNmcvoHaNQ/8tdWqWY8t6RtzlJsxeyx3P9aP5167mz+2rC/OEA2jUCQ9YZQic20kPwAtycNoK46n/Ped531TofUyxonIhyLys4i86xMPItJF3Noac0RkuojkNpPdSDRRnuqXiI7yTkccx3MPfczj/3yXI1ofwyvvPFRMwRlG4Ul6wlDVgcBa3IWzGc7M2gF3l/12DKdoBQxW1XbAVryoD+dlulVVu0WUD5lrOwF9gVC3093AD74F8GzEMSFjbLS4Dsd5m44BHhSRtPwCFpFyODHiPPI22l6Ae/qqI042+JSINPD7jsLNGG+LS7g9RKQ8MAK4TVVDx+zOJYYBIjJTRGYOGzYsWhEjTmrVqJetxbB5ywZqVK+TrUzVKtVJSysPwIk9zmPFqp+LNUbjAMnujrIuqcLTE2eZRVW/BxJprg2RBrwqIvOAkbgLbmHi+lxV96rqJlwyyqsrqaJXgcwEVgH/iaHe91Q1Q1XX42SCXfy+6aq6RlUzgdm4ZNsaWKeqM3ys21R1f7QTq+owVe2sqp0HDBiQTxhGLBzatA2/b1zNhk1r2b9/H1NnfcPRHXplK7MlfVPW+x/n/UDDg5sVc5SGUXBS7SmpkmyuBT+Gke3EInkZbfO6B4lWb17f2yhiypYtR/9L7uSJwbeSqZkcf+w5NGpwKB9+NpTmTdpwdIdefDVuBLPm/UDZsmWpXKkaA//6QLLDNoyYSbWEUZLNtbmRl9F2AnC9iLwF1AJ6AXfhusGi8TPQUES6qOoMP36xO7dWhpF4jmzXgyPb9ci27aKzr896f+l5N3HpeTdFHmYkASkTwD6hJJNqXVKDgM7ezPo42c21tXx3zg3Ebq4d7Ae9w/vxhwBXishU3EJKOcy1InJHjHEVGnX2xz7AKf6x2gW+vrW4p6fmAnNwieXvqvp7Huf6Ezcu86I38H5D9taYYRhGgTFbrQGg5pIygo53ScXcbPj+/U1Jv/ideGmdQDVzUq1LykgSJm4zDCM/LGEkmKCaax/85NVkh2AYheKh865LdgglHksYCcbMtYYRDII4DyLZWMIwjARzRvtutKrXmH0Z+/n4p/GsS8/esEwrW5ZLOp9MzcrVUFUWr/+VbxfOSFK0hhE7ljAMI4G0qteY2pWr88J3H9CoZj3O7tiTVyd8kqPcpGVzWblpHWWlDFf2OIuW9RqxdMOaJERcerEV9+In1R6rzZNSZLW917ux5nq3VVe/fZyIdM7n2JUiUievMkbRcXiDpsxevQSANVs2UCGtPFUOqpitzL6MDFZuWgdAhmaybusmqlesXOyxGka8BK2FcSNwhqquSGIMzXBW2+HgrLY41UdCEJFuuEmKnVR1r7/4l0/U+Y2ipWqFymzbvSPr87bdO6lWsTI79kZVelGhXHkOO7gJU5fneZ9iGClBYFoYpchq2wA3k3wvgKpuUtW1UX6Pl708cIGIRCpP7/Km2uki0jK+X9ooDFEdMrk87V9GhIs6n8i05QvYsmt7kcZlREFS4BUwApMwSpHV9mugsYj8IiJDROT4XMrdq6qdgQ7A8aHk5NmmqscALwHPRTvYbLWJ45jmbRnY+wIG9r6A7Xt2Ua1ilax91SpWZvuenVGPO6fjcfyxM91aF0ZgCFqXVIie+Au+qn4vIom02p7h36cBL4nIkTi5Xywr4OUV1+e+1bBXREJW2xyjnKq6Q0SOBo7DJccRInK3qr4ZUfQSERmA+ztsgLPuzvX73gv7MzKpheoZBoQyhdo8jIIzfcVCpq9YCECr+o3p2rwd839bRqOa9diz78+o3VEnHt6ZCmnlGTPbVuQzgkNQE0aJttqqagYwDhjnNexXAm9mVSLSHLgT6KKqW0TkTbJ/V83lvVHELFm/msPqN+a2k/tmPVYbYmDvC3hl3GiqVajM8a2PYuP2LVzf+wIApi9fwKxVi5MVdqnEHpKKn6AmjBJrtRWR1kCmqi7xm44Efo0oVg0nTUwXkfq4VtG4sP19cWMtfYEpMVduJITP506Ouv2VcaMB2LZnp82sNwJJUBPGIOANb4/dRXar7RXeajuD2K22r4vILuCrsO1DgFEicjEwlihWW9xd/08xxBUPVXC22Rq4FtNSINsKR6o6R0R+AhYAy4FJEec4SESm4VpGlxUgBsMo8VgLI37MVmuAjWEYJQDvkoo5DUwY9UfSL369LqwdqLQV1BaGkWBM3GYYRn5YwkgSqWa1fWfql8VdpWEklMuPPT2u8rbiXvxYwkgSZrU1DCNoWMIwjASiqnz97miWzllIWvk0zrmuHw2aNc5Rbt2K1Yx57V32/7mPlh3bcmq/C0yGZ6Q8gZnpbRhBYNnchWz+fSM3PnkfZ151KV+8NTJquS/e+oCzrurLjU/ex+bfN7Js7qJijtQQSf4raKRcwjAjrdwuIpUSWZdRfCyeNZ/2PbogIjRq2Yw9u3azfWt6tjLbt6azd88eGrVsjojQvkcXFs+al6SIDSN2UrFLqrQbaW8H/oubx2EEjO1btlKt9oH10avVqs72LelUrVE9rEw6VWuGl6nB9i1bizVOI5h3+MkmpVoYpd1I649pCIwVkbH+vJeJyDz/PZ8Ii32HiDzqv9dUP+MbEakrIqNEZIZ/9SjUX4pRaCRyakCUuU82fmEEgZRKGKXdSKuqL4S+v6qeICINgSeAE3FPVHURkfP9OSoDU1W1I05JEppI8TzwrKp28d//tWgBmK02ccz89gdevf9JXr3/SarUqM62Pw60FrZtTqdKzWrZyleNaFFs27yVKjXyc2caRvJJxS6pEGakhS7AOFXdCODHdXoBHwN/Ap+FfddT/PuTgbZhd6zVRKSqqmZbcCHSVmvzMApO55OPo/PJxwGwZPYCZn77A+2O7cRvy36lQsUK2bqjAKrWqE75CgexZulKDmnRlHmTZtDllOOSEXqpxhp18ZPKCaNUG2nzqCvEPj3gdQmvqwzQTVWjL/FmFCktO7Zl6dyFDL7rYdIOKs8512Y9O8Gr9z/JdQ//HYAzrryET199l31/7qNlh7a06NA2WSEbRsykcsIorUbaUN2bgGnA835QfAtOJPhiPqf/GrgZeMrXdWRYq8soYkSEM664OOq+ULIAaNi8Cdc/dk9xhWVEwWZ6x08qJ4xBlE4j7TDgCxFZ58cx7vGxCfA/Vf0kn3PfCgz28ZXDJbiBBYjRMAwjG2arNcDGMIwSgHdJxdxsmPLZlqRf/LqdXTNQzZxUbmEYxUi84jbDCDyBulSnBpYwipBUM9LmxahZY5MdgmEUigs7nZDsEEo8ljCKEDPSGoZRkrCEYRgJ5JfZC/js7Q/IzMykywk9OP687F19+/ftY+SQN/ltxSoqVanMZbddS826dZIUbenG5mHET0rN9DaMIJOZmcmYN96j/z9u5vanH2TO5BmsX7M2W5mZYydRsXIl7nzuYXqceRJfDv8oSdEaRvykXMIoDbZaEXlWRG4P+/yViLwW9vn/icjfElDPmyJyUWHPY8TGmqUrqX1wPWrVr0u5cuXo0K0Li2bOzVZm0Y9z6dTLGWqO6NqJZfN/xp5UTA7JVpsHsYWTcgkDZ6s9U1X75Vuy6GiGs9UCzlarqnnJBONlMtAdQETKAHWAdmH7uwOTElifUQykb9lC9do1sz5Xr12DbVu2ZC+zeWtWmbJly1KhUkV2bd+JYQSBlEoYpchWOwmfMHCJYj6wXURqishBQBvgJ3E85b/jPBHp6+vKa/tLIrJQRD4H6hXqL8SIj6gNhVhMtUUSjWEknJRKGKXFVquqa3EzyZvgEscUnAakG9AZmKuqfwIX4J6y6oiTCj4lIg3y2N4HaA20x9lru5MLZqtNPNVr1ST9jwMtivQ/tlItbN0LgOq1D5TJyMhgz67dVKxSuVjjNBwikvRX0EiphBFBT5xZFlX9HkikrTZEGvCqOPnfSCAWA1xecX2uqntVdRMuGdXP4zyhVkYoYUwJ+zw5rK73VDVDVdcD43EG29y29wrbvhb4PrfKVXWYqnZW1c4DBgzIrZgRB4e0aMqm3zewecMm9u/fz9wpM2hzdIdsZQ4/ugOzJkwBYP60WRzarnUgLxxG6SSVH6st0bZaDoxjtMd1Sa0G/g/YBryeR115bQ+PxShmypYty7n9+/LGv19AMzM5und36jduyDcjx9CoeVPadO5I5949GDnkDZ6+/X4qVanEpbdcm+ywSy2Wp+MnlRNGibXVeibhEsRyrzrfLE5G2I4DiyFNAK4XkbeAWrgWxF24v7e8tr+NG784Ab/MrFE8tD6qPa2Pap9t2ykXn5v1Pq18Gn+53Vp0RjBJ5YQxiJJrqwWYh3s6anjEtiq+SwvgI9y4xhxcy+Hvqvq7iOS1/UR/nl9wXVWGYRgJwWy1BoCaS8oIOt4lFXNTf+bXW5N+8et8ao1AdYylcgvDKEZM3GYYRn5YwihCJEC22renfJHsEAyjUFzR7Yxkh1DisYRRhJit1jBSmEB1BqUGljAMI4GoKl+/O5plcxeRVj6Ns6/9Cw2aNc5Rbt3K1Xz62nD2/7mPFh3acGq/C2w+hpHypPLEPcMIHMvmLmLz+o3c8MS9nNm/L1++PTJquS/eGsmZ/S/hhifuZfP6jSybt6iYIzWSPcs7iDcIJSphSAwm2hjP01sOeKUQkYEickVhzxtRRxUReVlElonITyLyo4hcl/+ROc5znoh8HPb5HhFZGvb5HBEZk6i4jbz55ad5dOjRBRHhkJbN2LNrN9u3pmcrs31rOn/u3kOjls0RETr06MIvs+YlKWLDiJ0SlTASSG/CPEyq+oqqxuKyiofXgC1AK1U9CjgdNwkvXibj5mSE6AZsE5GQeNDMt8XIQGq1AgAAIABJREFU9i3pVKt1wFhbrWYNtm9Jz1Gmaq0DjqmqUcoYRipSEhNGWYkw1opICxH50t/F/yAih0PW3fc0f4f/rYjUF5FmwEDgDnG22uMkzJArzkb7hIhMF5FfROQ4v72SiHwgzmI7wp+3c7QARaQFTlB4n6pmAqjqRlV9wu+vIiLfiTPozhOR8/z2yiLyuTjz7nwR6auqG4F0EWnpT38IbnJjKOGFu6mMIibatKYcXQ+xWG2NIkfKJP8VNErioHcr4DJVvU5EPsAZa68CBqrqEhHpipvhfSIwEThWVVVErsXNmP4/cZr1Har6NICInBRRRzlVPUZEzgQexBljbwS2qGoHETkCmE3utAPmhJJFFPYAfbxypA4w1XcrnQ6sVdWzfFwh6eFkoLuIlAWWAFOB08Qp2TvgZsRnQ0QGAAMAhg4dSoX2OQdmjdiY+e0P/DTeCQUbNm/Cts0HjLXbtmylSo1q2cpXrVWd7Zu3Zn3evmUrVWtmL2MYqUhJTBg5jLW4u+yRYXd6B/k/GwEjxKnBywMrYqxjdMT5wRlknwdQ1fleHRITInIvcDFQT1Ub4m43HxORXkAmrtVQH6f8eFpEngA+U9Uf/ClC5tuyOOvtdOAB4ChgsarmkCqq6jAg5DVXm4dRcDqffBydTz4OgCWzFzDzux9o27UTa5f9ykEVK1K1RnbJctUa1Slf8SB+W7qShi2aMnfSDLqc3CsZoRtGXASwUZQvkcbYWsBWv7ZF6NXG738ReElV2wPXk918G0sd4UbaePoUFgIdxa22h6o+qqpHAqHbzH5AXeBov309UEFVfwGOxiWOf4vIA758yHzbHZiiqtv9d+mNjV8UKy07tqVm3ToM+fsjfP7mCE6/4sAKua/e/2TW+9OvuJjP33ifIX9/hJr16tCiQ5topzOKkGQvzxrAh6RKZAsjkm3AChG5WFVHimtmdFDVOThb7W++XLhEcDsHLt6xMhG4BBgrIm1x2vKoqOpSEZkJPCIi96tqhohU4EDSqQ5sUNV9InIC0BRARBoCm1X1vyKyA+jvyy8EGgLH4brGwHWJDQT+Huf3MAqBiGRLEuFc9/CBv4qGzZsw4NG7iyssw0gIJbGFEY1+wDXi7LMLgPP89kG4rqofgE1h5T8F+oQGvWOsYwhQ13dF/QNnvM3r0ZdrgdrAUhH5EfjWHwfwLtDZJ5V+wM9+e3tgujhT773AIwDqDJLTcKr1fb7sFNxytzbgbRhRSHbrIogtDLPVJgg/4Jymqnv8U1DfAYf5pVZTHRvDMAKPd0nFfBmePS496Re/I3tXD1TaKA1dUsVFJVx3VBruH+0NAUkWgInbDMPIH0sYCcIPNOeYdyEi0zjwVFaIy1U1pab2Pv2VLcxnBJs7T/tLXOWDqOZINpYwihhV7ZrsGAzDMBKBJQzDSCCqypRRX7J64RLKlU/j+H7nU6dxgxzlZnz2HUumz2Xvrt1c9fQ/kxCpYQ2M+CktT0kZRrGweuFS0jdu5pL7b6Fn33OY+MHnUcs1adea8//v2mKOzjAKR4lPGKXRYGskj1/n/UyrYzogItRv3og/d+9hV/r2HOXqN29EpepVkxChYRQc65KKnd7ADvy8BlV9pQjqeA1YjjPYZopIXeDqyEIiUlZVM4qgfqOQ7EzfTpUwFUjlGtXYmb7dkkMKYl1S8VPiWxiekmCw7S0iY0VkOE4Ngoh87ONf4GWCiMgNIvJk2Hn7i8iLRfKrGjlJ+pP9hlF0lJaE0QoYrKrtgK04g+0w4BZVPRq4EzdTGw4YbI8C3scZbFcCrwDPehfVD5EV4A22wO04gy2EGWyBh3EeqNzIz2ALLqHcq6pt/eerffydgVtFpDbwIXBB2DF9gRGRJxKRASIyU0RmDhs2LHK3EQcLJkxn1BOvMOqJV6hUvQo7whZM2rl1G5WtdWEUAhE5XUQWi8hSEcnhk/E3hRv9zexsb94O7btSRJb415WRx8ZLaemSKgkGW4Dpqhoez60i0se/b4zrypoqIstF5Fic6rw1UQSEkbZam4dRcNr1OoZ2vY4BYNWCX1gwYQYtOh3BhpW/Ub7CQdYdlaoEoEvKGyQGA6cAa4AZIjJGVRdGFB2hqjdHHFsLd/PaGdf2/dEfu4UCUlpaGCXBYAuwM/RGRHrj1uHopqodgZ/CYh2BEyFeCHyk5n8pNhq3bUW12jUY8a8X+eH9T+lxyVlZ+0Y9cWDYa9on3zD8/mfYv28fw+9/hh//Ny4J0RoB4Bhgqaou9+aI9zngwsuP04BvVHWzTxLf4NbUKTClpYURSRANtpFUx3V37fLjL8eG7RuNkxP+ygGhoVEMiEi2JBHOhf8YmPW+63mn0PW8U4orLCMKUiYATQy3Fs7qsM9rgGiTgS/06+f8AtyhqqtzOfaQwgRTWloY0QiawTaSL4Fy/twP41bZA8DfTSwEmqrq9BhjNQyjmAkfS/SvAZFFohwW2WPwKdDMj5V+C7wVx7HxxWu9FUVHgAy2NoZhBB7vkoq52TB/8vakX/yO6F41z3hFpBswSFVP85/vAVDVf+dSvixuzZzqInIZ0FtVr/f7hgLjVPW9gsZbWrukiovAGGzjFbcZRtAJyDyMGUArEWmO6yq/FMj2n1VEGqjqOv/xXGCRf/8Vbqnnmv7zqcA9hQnGEkYREiSD7W9Lcyz7bRiB4pCWsT6fEhxUdb+I3Iy7+JcFXlfVBSLyL2Cmqo7BPS15LrAf2IxfiVNVN4vIw7ikA/AvVd1cmHisS8oAUEsYRtDxCSPmdsPCqcnvkmp7bN5dUqlGaR70NoyEM33mJK4YcC5/vfZshn/wn1zLjZ/4DSee1ZHFSxYUY3SGUTgsYRhGgsjIyOD5lx/j8YeG8MbLH/H9hC9ZuWpZjnK7du1k9JjhtGmd61PWhpGSWMKIAxGpISI3FvDYlSJSpwDH/U9EasRYdriI3BD2uav3WNlYVTHw8y/zOaRhYxo2aERaWhon9jqdyVPH5Sj3+n8Hc+lF/SlfPnIYyyhORCTpr6BhCSM+auD8UDnwj7MlHFU9U1W3RtQloRnhEdwB3CUidf3+l4AbVXV/UcRmZGfTHxuoV+fgrM916tRj4x/rs5VZsmwRGzf+Trdjji/u8Ayj0JSqhCEiV/g77jki8o6/sI4SkRn+1cOXGyQir4uz0C4XkVv9KR4HWvjJe09JjAbZGGOLelyoZSJuXY9FIjIEmIVzR2VDVdcDTwNP4uy6c1V1YgF/LiNOoj1AImFjsJmZmQx59WluuPb/ijMsw0gYpaarQkTa4XQZPVR1kxdzvYQz0E4UkSa4R9dCTqnDgROAqsBiEXkZuBs4wjueQj6nY/y2kBTwav84W0WcKGyUqv4RQ4ixHNcauEpV8+oWewWnNOlNlEd6w36PAcAAgKFDh3LWiQldC6pUUrdOfTZs+j3r86ZNG6hTu17W5127d7Li16XccbeTiW7eson7/nUbjzzwPK1btSv2eEs7AewRSjqlJmEAJwIfquomyHpG+WSgbVhfYjURCalFP1fVvcBeEdkA1M/lvPkaZIFYEkYsx/2qqlPJA7/w0lCgc16JKtJWa4/VFp7DD2vHb7+tYt3va6hTuz7fT/iSe+86MCG3SuWqfPze+KzPd9x9DQOv+ZslCyMwlKaEIeT0qJTB2V53ZyvoEkik4Ta33yo3g+wuERlHDLbbOI7bGWVbNDL9yyhGypYtxy033MM/7r+BjMxMzjjlfJo3bckb7wzmsFbt6HFs72SHaIQRdRTQyJPSlDC+Az4SkWdV9Q/fJfU1cDPwFICIHBm2bkY0tuO6qHIjL4NsXhT0OCPFOLbLcRzbJbub8qrLb4pa9tnHc5+nYRipSKnJsaq6AHgUGC/OUPsMcCvQ2Q+EL8QNFOd1jj+ASSIyX0SeilIkV4NsPhT0OMMwjGLD1CAG2BiGUQKIVw2y+McdSb/4tT66SqCG3ktTl5SRByVR3GYYRmKxhFGMiEht3FhKJCfF+Oht+LkSarx98JNXC3KYYaQMD513XbJDKPFYwihGfFI4MkHnirZMo2EYMRJENUeysYRhGAnmjPbdaFWvMfsy9vPxT+NZl5698ZhWtiyXdD6ZmpWroaosXv8r3y6ckcvZDCN1sIRhGAmkVb3G1K5cnRe++4BGNetxdseevDrhkxzlJi2by8pN6ygrZbiyx1m0rNeIpRvWJCHi0os1MOIn8I/VFsIC21tEuuex/1wRubsoYhKRDO+jCr2aFaSeXM49SER+Czv344k6t5E/hzdoyuzVSwBYs2UDFdLKU+WgitnK7MvIYOUmt6JmhmaybusmqlesXOyxGka8lOYWRm9gBzA5coeIlPNLH44porp3h3xURcSzqvp0EZ7fyIWqFSqzbfeOrM/bdu+kWsXK7Ni7O2r5CuXKc9jBTZi6fH5xhWgYBSZQLQwR+auITPd3zkMjleK57ReR00VklrfUfufv6AcCd/iyx4nImyLyjIiMBZ4Qkf4i8pI/vr6IfOSPnxNqmRTUTJvLd8uqz3/+zLeCyvrY5ovIPBG5w+9vISJf+vp/8DPEjSQTrZcjt6lOZUS4qPOJTFu+gC27thdpXEZORJL/ChqBaWGISBugL842u89rvvvlt19EvgBeBXqp6goRqeXFg68AO0J34iJyDXAYcLKqZohI/7DqXwDGq2ofn4Sq+O0FNdNWFJGQgmSFqvbJo+yRwCGqeoSPM7SY0jBgoKouEZGuwBCcYBFcIvyrf/8PVf0q8qSRtlrqB/Bfb4pwTPO2dGrq8vXaLRupVrEK4NbBqFaxMtv3RFeAndPxOP7YmW6tCyMwBCZhACcBR+MuzAAVgQ0x7D8WmBAyyqrq5jzqGKmqGVG2nwhc4Y/PANL99oKaaePpkloOHCoiLwKfA1+LSBWgOzAy7NHA8DkZ+XZJRdpqbR5GwZm+YiHTVywEoFX9xnRt3o75vy2jUc167Nn3Z9TuqBMP70yFtPKMmT2huMM1PEG8w082QUoYArylqvdk23igJZDb/nPJaanNjVhtsAU20+bBfrJ3EVYAUNUtItIROA24CbgEuB3YWsTjIEYBWLJ+NYfVb8xtJ/fNeqw2xMDeF/DKuNFUq1CZ41sfxcbtW7i+9wUATF++gFmrFicrbMOIiSAljO+AT7xtdoO3zVaNYf8UYLCINA/vksKZZ6vFUfcNwHO+S6oyiTfMrgRuFLe06iG4hZnwT1v9qaqjRGQZ8KaqbhORFSJysaqOFNfM6KCqcwoZg5EAPp+b4zkKAF4ZNxqAbXt22sx6I5AEJmGo6kIRuQ/XJVMG2Ie7485zv6pO9f31o/32DcApwKfAhyJyHnBLPtXfBgzz4xwZuOTxJTDQG2YXU3jD7CRgBW6p1/m4ZVjBJY835MAa3qEWVD/gZf+d04D3AUsYhhEjNtM7fsxWa4CNYRglAO+SijkLLJ+7K+kXv0M7VApU1rKEYUDsYzyGkepYwihCAtMlFTQSaaYtDp7/dkSyQzCMQnHbyX3jOyBQl+rUwBJGEZFIM61hGEYqYAnDMBKIqjJx5P/4dcESypVP46TL+1C3ScMc5aaO+ZbF02azd9ceBjx7XxIiNcoEynORGthPZhgJZNWCJaRv/IN+g26j91/OZfz7n0Yt16x9ay76+/XFHJ1hFI7AJwyz1eY4d7itdr6fuGgUEyvm/kzrrkciIhzcvDF/7t7DzvScnqiDmzemcvWqUc5gGKlLae6S6k0Jt9V6v9YPIlJPVTOLsD7DszN9G1VqVM/6XLlGNXZu3WbJIRWxQe+4CVQLw2y18dlqVXURTjkSdwvMKBhRH1O3CWJGCSEwLQyz1cZlqw39Jl2BTGBj5Elz2GoPrR5ZxIiReeOnsXDSjwDUa3oIO7amZ+2z1kXqYjO94ycwCQOz1cZjqw3pzbcDfTXKbW+krdbmYRSc9sd3pf3xXQFYOX8x88dPo+XR7Vm/cg3lK1awhGGUGIKUMMxWG7ut1lbcSxJN2x3GqgVLeHfQc5Qrn8aJfz3QeBzx2BD6/vNGACZ/9BVLZs5j/759vHXv07Tp3oljzjoxt9MaRkoQpIRhtlqz1aY8IkKvvmdH3RdKFgDd+5xG9z6nFVdYRhSsRyp+AjPoraoLgZCNdi7wDdAgv/2quhHXVz9aROYAob6XT4E+oUHvfKq/DThBROYBPwLtcLbacr6uh0msrfZpsttqx/kxjzfJbqu9xn+nBcB5hazfMAwjT0w+aICNYRglAO+SirndsOrn3Um/+DU5vGKg2jmWMAwwW61Rcoj5Arx6cfITRuPWwUoYQRrDCBRBs9Vu3bgv2SEYRqGoUTct2SGUeCxhFBFmqzWM1MYGvePHEoZhJJApUyfyzPOPk5mZwblnX8iVl1+bbf/oj0fw4ej3KVOmDBUrVuKevw/i0OYtkhStYcSHjWEYAGpdUoUnIyODiy87ixeffZV69Q6m/7V9eXjQU9kSwo6dO6hS2YkCJkwcy6jR7/P8M0OTFXKJwndJxdxuWPNL8scwGh0WrDGMwDxWmx8Btdbe6z1Uc/3jvV399nEisthvW5SXp6qg39tIPAsXzaNRoyYcckhj0tLSOOXkM5gw8ftsZULJAmD37t2mp0giIpL0V9CwLqkkWWtFpBtwNtBJVff6i375sCL9VHWmn4C4TETeVNU/I86RTb6YSz2Ca0marbaI2bBxA/XrHZz1uV7d+ixYOC9HuZGj3uO9EW+xb/8+Bj//enGGaBiFIpAtDCkZ1toGwCZV3QugqptUdW2UclVwypIMX9cOEfmXiEwDuoV954ri7LXXiUgz3zIZgpsA2DjGmIzCEKV7N9pd5MUXXsboD77k5oF/4423rDvKCA6BSxiS3Up7JO5Cmpu1Nmu/iNTFWWsvVNWOwMWquhJ4BedeOlJVf/CnCVlr/y+i+pC1tiPQCTfDGpy19migM05IWDuGr/I10FhEfhGRISJyfMT+d/0s8sXAw2FSxMrAfFXtqqoT/bYquJnrw1X1Vb+tNfC2qh6lqr9GVi4iA0RkpojMHDZsWORuowDUq1ef9Rt+z/q8YeN66tSpm2v5U04+g/E/fJ/rfqOIkRR4BYwgdkmVCGutqu4QkaOB44ATgBEicreqvumLhLqk6gKTReRLf+HPAEZFnO4T4ElVfTds26+qmquuJNJWa4PehafN4UewevUq1q5dQ9269fnm2y94+MEns5VZtfpXmjRuCsCkyRNo3KhJMkI1jAIRxIRRYqy1PumMw7mi5gFX4nxR4WU2isgsoCvwK7AnSjKbBJwhIsPDVOYxfwcjMZQrV447//ZPbv3b9WRmZnDOWX049NCWDH3tJdoc3o5ePU9g5KjhzJg5lXLlylG1ajUevPexZIddapHA9a8knyAmjBJhrRWR1kCmqi7xm47EJYTIcpWAo4AnI/eF8QBwP24RpRti/C5GEdCjWy96dOuVbdv1196c9f7/br8n8hDDCAyBy7ElyFpbBXhLRBb6Y9sCg8L2vyvOUPsjTmn+Yz7nux2oICJ5JRbDMIwCYxP3DLAxDKMEEO/EvXUr9iT94tegeYVADX0HsUvKKAJM3GYYRn5YwihiJCDWWlsPwwg6fj2MmAniTOtkYwmjiDFrrWEYJQVLGIaRQFSViSP/x68LllCufBonXd6Huk0a5ig3dcy3LJ42m7279jDg2fuSEKlhxE/gnpIyjFRm1YIlpG/8g36DbqP3X85l/PufRi3XrH1rLvr79cUcnRGOSPJfQSNwCUNEbvWepHfzLx3T+ZqJyF/CPncWkRcSce6IevIz16qIvBP2uZyIbBSRz/I5b1a8ItJPnPl2rohMFpGOifsGRiysmPszrbseiYhwcPPG/Ll7DzvTt+cod3DzxlSuXjXKGQwjdQlil9SNwBkhxUcCaAb8BRgOoKozgZkJOnc87ASOEJGKqrobOAX4Lb+DIuJdARyvqltE5Ayc+qNrUQVs5GRn+jaq1Kie9blyjWrs3LrNkoNRIghUC0NEXgEOBcaISLqI3Bm2b75vLYRMra96e+zXIlLRl2kpIt960+wsEWkBPA4c5yfu3SFufYzPfPla3kQ7V0SmikgHv32QiLwubt2K5SJya1gcBTHXhvgCOMu/vwx4L+y8x/hWw0/+z9Z+e1a8qjpZVbf4Q6YCjeKs3ygkUec1BbHvoRSQ7O6oIP6zCFTCUNWBwFqcrO/ZPIq2AgarajtgK3Ch3/6u394R6A6sA+4GfvC22shzPgT8pKodgH8Cb4ftOxw4DTgGeFBEQhMZCmKuDfE+cKmIVAA6ANPC9v0M9FLVo3AqkPwkRNfgElBUzFabOOaNn8aIx4Yw4rEhVK5ejR1b07P2WevCKEkEsUsqFlao6mz//kegmYhUBQ5R1Y8AVHUP5Pssdk98slHV70WktoiE+hs+92tZ7BWRDUB9YA0FMNeGUNW54tbouAz4X8Tu6jiVSCucRDHXmXYicgIuYfTMo65stlqbh1Fw2h/flfbHu56/lfMXM3/8NFoe3Z71K9dQvmIFSxgpipQJ4C1+kglUCyOC/WSPP9wQuzfsfQYuMRbkX0e0Y0J9DjnqiDDXdgR+IkZzbRhjgKcJ647yPAyMVdUjgHNyO6/vNnsNOC+VJgaWFpq2O4xqdWrx7qDnGDf8E3r1PTtr34jHhmS9n/zRV7x179Ps37ePt+59mumf27oYRuoT5BbGStwSp4hIJ6B5XoVVdZuIrBGR81X1YxE5CCiLs9Xmdgs4Abc408M+GWzy58mtmgKZayN4HUhX1Xm+zvBzhwbB+0c7UESaAKOBy1X1lwLUbRQSEcmWJMLp+88bs95373Ma3fucVlxhGUZCCHILYxRQyxtdbwBiuUBejusymotbw/tgYC6w3w+E3xFRfhDQ2Zd/HLdeRV4U1FybhaquUdXno+x6Evi3iEzCJbpsh/k/HwBqA0P8IH4ynvYyjECQ7AHvIA56m6024IjIhcC5qppfMssLG8MwAo93ScV8Gd70259Jv/jVOaR8oNKGJYwAI24VwSdxT2ZNLsSp7B+BUVKIPWGsTYGE0dAShpELKWyu1bcmRz6UZRjB4sruZ4IljCIlyIPegcPMtYZhBBlLGIaRQFSVb4Z/xLK5iyhXPo1zrrmMg5s1zlFu3crVfPbae+zft48WHdpwyl/65DcnyEgw9nPHT5CfkjKMlGPZ3EVsXr+RgY//kzP7X8KX73wYtdyXb3/IGf0vYeDj/2Tz+o0sn/dzMUdqGPETuIRRWm21InKuiNwdQz1fisjW/Cy3RtHwy0/zad+9CyLCIS2asWfX7myqEIAdW9PZu3sPjVo2Q0Ro370Li2fNS1LEhhE7QeySKpW2WlUdg5sFnh9PAZUAW2whCezYmk61WjWyPletWYPtW9KzGWy3b0mnWq0Dn6vWqp4jqRhFj3UBxk+gWhil3FbbX0Re8u/fFJEXvLV2uYhcFCqnqt/hZq8bSSAWW609mGgElUC1MFR1oIicjrPV3pxH0VbAZap6nYh8gBMI/hdnq31cVT/yRtgyOFvtnaoa0oz0DjtPyFZ7voiciLPVhp5yOtzHURVYLCIvq+o+3JyIzT5JzRCRUXE8Mvs+8IBPWB1wmpDjcinbACcXPBzX8ojeWZ4LPpkNABg6dCgHHWEm9IIy87uJzB4/BYCGzZuwbfPWrH3bt2ylao1q2cpXq1WdbZsPtCi2b87eAjGKB2tgxE+gEkYclERbbSQfq2omsFBE6sdy/oi6stlqbR5Gwel8Uk86n+TEwEvnLGDmdxNp2/Uo1i7/lYMqVsyRDKrUqM5BFQ7it2UraXhoU+ZNnkHnk3K7LzCM1CHICSMeW21Fit9Wu0tExlFwW21vnBcqN8Lrt3ulFKFFh7YsnbuIl//xKGnly3P2NZdm7Xvtgae49l93AXD6FRfx6X/eY/+f+2jRvg0tOrRJVsiGETNBThgrKV22WiMAiAinX35R1H2hZAHQoHkTBjzyj+IKy4iCdUnFT6AGvSMobbbamBCRH4CRwEk+QZpD2zCMhGAuKQNsDMMoAcTrktq6YV/SL3416qUFqp1jCcMAs9UaJYfYE8bGFEgYdYOVMII8hhE4UthWy+sTP01m9YZRaK7ueU6yQyjxWMIoRsxWaxipQ9kKe5IdApCW7ADiwhKGYSQQVeW79z5h2bxFpJUvz5lX9+XgpjknRU4Y/QXzJ89kz67d/G3IY0mI1DDiJ8hPSRlGyrF83s9sXr+RAY/dzWlXXMTX74yKWq5Fx7Zccd9txRydYRSOwCUMs9XmWceRIjLFe6zmikjfxH0DIxaWzF7AEd07e1ttU/bu2sOOrdtylDukRVOqRChDDCPVCWKXlNlqc2cXcIWqLhGRhsCPIvKVqm7N5zgjQezYEmmrrc72remWHIwSQaBaGGarzdtWq6q/qOoS/34tsAGoG2cMRiGI9pi6mLnFKCEEKmGo6kBgLc4S+2weRVsBg1W1HbAVLxDE2WoHq2pHoDuwDmer/UFVj1TVyHOGbLUdgH/ibLUhDgdOA44BHhSR0OMOV6vq0UBn3KzyvHxQkbwPXOpNuh2AaXmUDdlqz8YlvWyIyDFAeWBZtINFZICIzBSRmcOGDYtWxIiRWd9P4o1Bz/DGoGeoUqN6hK3WWhdGySGIXVKxUKpttSLSAHgHuNKXiVZXNlutzcMoOJ1O7EGnE3sAsGzOQn78fhJtjjmStctXcVClCpYwjBJDoFoYEcRjqy1H8dtqOwI/UXBb7Xv5lItqqxWRasDnwH2qGrfLyigch3ZoQ426tRl2z+N8+dZITvnrBVn73hj0TNb7sSM/Y/CdD7Pvz30MvvNhJn7yVTLCNYy4CHILYyVmq82GiJQHPgLeVtWRBajbKCQiwqlhSSKcqwb9Lev9CRefzQkXn11cYRlGQghyC8NstTm5BOgF9PeD+LNFxGaWG4aREEw+aICNYRglAO+Sirnrefv27Um/+FWtWjVQj9BZwjDAbLVGycESRhES5DGMwJEykZsTAAAgAElEQVTKttphEz5OZvWGUWgG9Do/2SGUeCxhFCNmqzUMI8hYwjCMBKKqjH1/DCvmLaZc+TROv+oS6jc9JEe5iR99yYIps9i7aze3vvRwEiI1jPgJ8lNShpFyrJi/mC0bNnH1o3dxyuUX8O27H0Utd2iHNvT7583FHJ1hFI5AJ4zSaq5NVD1G4lk2ewFtjz0aEaFhi6bs3bU7qq22odlqjQAS6ISBM9eeqar9EnS+ZjhzLeDMtap6a+7Fi4wsc63/nM1ca6QuO7Zso2qt6lmfq9asHjVhGEYQCWzCKOXm2txiqe2/408iMpSC6VCMQhDtOc28dWWGERwCmzBKubk2t1geBCaq6lE4J1WT3E5uttrE8dPYybz90HO8/dBzVKlRle2b07P2bd+STuXq1vVklAxKw1NSJdFcm1ssvYAL/PbPRWRLHufPZqu1eRgF56gTunPUCd0BWD53ET+Nnczhx3Rk3fJVHFTRbLVGySGwLYwISpu5Nq9Ykj57tTTTvP3hVK9Tm//c+yTfvDOak/r1ydr39kPPZb0f/+H/GHrXo+z7cx9D73qUyWO+SUa4hhEXJaWFsZLSZa7NLZbQ9kdE5AygZgHqNAqBiHByv+gzjq948Pas98dfdCbHX3RmcYVlGAmhpLQwSpu5NrdYHgJ6icgs4FRgVbx1GoZh5IbJBw2wMQyjBOBdUiVOPigipwPP43pBXlPVxyP2/w24Ftc1vxH3sM2vfl8GMM8XXaWq5xYmXksYBti4h1FyiDlhbN6zI+n/7mtVqJJnvCJSFtdjcgruQZoZwGWqujCszAnANN/9fQPQW1X7+n07VLVKouItKWMYgSPVzLVz1y4r7ioNI6F0aNgi2SEUBccAS1V1OYCIvA+cB2QlDFUdG1Z+KvDXogrGEkaSMHOtYRgxcAiwOuzzGqBrHuWvwU36DVFBRGbiuqseV9VC9T1bwjCMBPLT9Jm88dJQMjMyOems0+jzl0uy7V84Zx5vDh7Gr8tWcPsDd9Pt+J5JitRIhe54b4AIt0AM83OksopEOSxq4CLyV9wk4ePDNjdR1bUicijwvYjMU9UCdydYwjCMBJGRkcF/nh/C/U89Sq26dbhn4O107n4sjZsdmHBfp349bvrH3xgzYlQSIzVShYgJtNFYg5v0G6IRznCRDRE5GbgXON5PIg6df63/c7mIjAOOAgqcMErKY7UlzlzrZ2/P9q/fReS3sM/lEx2HUXiW/vwLBzdsSP2GDUhLS6PHib2YOWlKtjL1Dq5P0xbNkTIl5r+eUbTMAFqJSHP///5S3ITeLETkKGAocK6qbgjbXtPPMcNfY3oQNvZREEpSC+NG4AxVXZGg8zXDmWuHgzPXAjMTdO58CR/jEJFBwA5Vfbq46jfiZ/OmP6hd70Dur1W3DksWLU5iREZepEKXVH6o6n4RuRn4CvdY7euqukBE/gXMVNUxwFNAFWCkn0gceny2DTBURDJxjYPHw5+uKgglImFEmGubAA+HLq4iMh8/Cxw3GDQRJxv8DThPVXeLSEvgFaAuTu1xMW5CXBs/GfAtnNrjTlU9W0Rq4WZhHwrsAgZ499MgnPDvUP/nc6r6go/jY1zTsgLwfEQ/Zbzf99/AGlUd7D8/AfyKe/zuXiAdOAwYC9ysQfifURKI8jPn4yczjHxR1f8R4ZNT1QfC3p+cy3GTgfaJjKVEtItLgbk2kteA/pD1nPbFHPBNdQVux/1DaYN7BC8HZqtNPLXq1uGPDZuyPm/euIlatWslMSIjLzJVk/4KGiUiYcRBTOZaVd2Vz3l6Au/48t8DOcy1qroJCJlrwSWJObjnpEPm2gLhn3LYLiLtgTOA6aoaMtNOVdWVqpqBU6RHfQxHVYepamdV7TxgQLxLdRjRaHn4Yaz7bS3r1/3Ovn37mPT9BDp3L4hCzDBSkxLRJRVBPObaihS/uXaXf1ohXnNtJP/BtTKa4Qa8IuPI7bNRRJQtW5Zrbr2BR/9+H5mZmZxwxqk0bt6U919/hxatW9Glx7Es/fkXnrr/YXbu2MGPU6bxwRv/5dk3X0l26IYREyUxYaykZJprIxmFWzCpLPBt2PZj/TjOb8AlwIsJqMuIkU7HdqHTsV2ybbv06suz3rc8/DCGjnwn8jAjCSiZyQ4hcJTELqkSaa6NxC/6NAF4T1XD/+VPBv4fTjj2CxGP4BmGYRQUkw8GFBEpA8wGzg/zzJyMeyoq+oIMuaPmkjKCjndJxdzFvG7H5qRf/BpUqRWox+hKYpdUiccPdo8BRoaSRWEpoeI2wzASiLUwUoAUMNfqyoX5PRhmGKlNs7aVwFoYRYq1MFIAM9caRvFjN8vxUxIHvQ0jacyYNYlrbjqf/jecy4hRr+da7ofJ33Ban6P4ZemCYozOMAqHJQzDSBAZGRkMHvY4j9z/Eq++MIqxE7/k19U5HybYtXsnH3/+HocfllBrg2EUOaU2YYjI7SJSqRjrKycim7wHKtc4ROSfMZzrTRG5KFHljMSweMl8GjZoTIODG5GWlkbvnqcxZfq4HOXeGj6Ei8/vT/k0kw4nE1VN+itopGTCEJHiGFu5HSi2hAGcCiwGLpHsM/wi48g3YRipyR+bN1C3Tv2sz3Vq12fTHxuzlVm6/Gc2bvqdY7v0Ku7wDKPQFGnCEJH7ReRnEflGRN4TkTtF5DoRmeEnxI0K3V37u+FnRGQs8ISIHCMik0XkJ/9na1+ukoh8ICJzRWSEiEwTkc5+36kiMkVEZonISBGJuvi5iNwKNATGishYEblGRJ4N23+dj6WZj/8tX9+HYfEeLSLjReRHEflKRBrk83NcBjwPrMLP9I4Sx+NARb/mxbu+zBW+7jkiEj5FuJf/XZaHWhHieElEForI50C9eP6+jMIR7YYx/NYgMzOToa8/zYCr/q/4gjJyJdniQZMPhuEv4hfiVni6AGdpBRitql28GXYRbg3aEIcBJ6vq/wE/A71U9SjgAeAxX+ZGnGajA27W9NG+vjrAff74Tri1K/4WLTavHF8LnKCqJ+AkfeeGmWWvAt7w71vjlk3sAGwDbvTlXgQu8gba14FH8/gtKgInAZ/hrLKXRYtDVe8GdntDbj8RaYfTlZ/of6/bwk7bACcWPBs32xygj4+3PXAdzrybW0xmq00wdWrXY+Om9VmfN/2xntq16mZ93r17JytXLePv913LFQPOZNEv83jwsdtt4NsIDEXZ9dMT+ERVdwOIyKd++xEi8ghQA7fox1dhx4z0llVw/qW3RKQVTqAXupj3xN2po6rzvW4D3F17W2CS7/EpD2Rf7iwXVHWniHwPnC0ii4A0VZ0nIs2A1ao6yRf9L3ArTvVxBPCNr6ssTomeG2cDY71LahRwv4jcEfZdc+NE4ENvvkVVN4ft+9grQRaKSKgfpBdOFZIBrPXfKbfvHL40pM3DSACtW7Xjt3Wr+H39b9SuVY9xE7/i7jsODFlVrlyVkW+Pzfp8133Xcl3/OzisZbtkhGsYcVOUCSO3CSlv4nQWc0SkP9A7bN/OsPcP4y6yffyFe1w+5xXgG1W9rGDh8hpu/OBnDrQuILr9VYAFqtotxnNfBvQQkZX+c23c2h3f5nqEQ6LUH2JvRLnw+IwkULZsOW667h/886EbyczM5NSTzqNZkxa8NXwIh7VsS7djeic7RCOMIA46J5uiTBgTccsD/tvXcxbwKs4Au8536/TDWVWjUT1sX/+I816C6/dvy4EVpaYCg0Wkpaou9WMNjVQ1N/lgyEYbunufJiKNgU5Ah7ByTUSkm6pOwV34J+IGr+uGtvvvcpiq5uhbEJFquFZRY/WLs4vIVf5c30bGAewTkTRV3Yeb/f2RiDyrqn+ISK2IVkYkE4DrReRt3PjFCfglZo3i4Zijj+OYo4/Ltu3Kv9wYtexTj7xWHCEZRsIosjEMVZ2B8x3NAUbjxhTSgfuBacA3uLv53HgS+LeITMJ1+YQYgrtYzwX+gbPKpqvqRlxiec/vm4pb/S43hgFf+EH2EB8Ak8IWIwI3znKlP2ct4GVV/RO4CDc4PwcnAcxtvOAC4PtQsvB8ghszOShKHMOAuSLyrk9AjwLjfT3P5PF9AD4CluBMtS8D4/MpbxillmQPeAdx0LtIXVIiUkVVd/i7/Qm4ta9nFfKcZXFjDHtEpAXuLvwwfxEvbLyfAc+q6nf+czPgM1U9orDnTnFsDMMIPPG6pFZuWZ/0K3azmvXNJRXGMN9tVAF4q7DJwlMJ1x2VhvvHcUNhk4WI1ACmA3NCyaK04f+zGYZh5EqJt9WKyEfkXHXvH6r6VbTyhaxrMNAjYvPzqvpGtPIphL4+8dP8SxlGCnN1z3MgjhbG8s3rkn7xO7RWA2thpBKq2qcY67qpuOoyDMMobkp8wjCM4kRV+e69T1g2bxFp5ctz5tV9ObhpoxzlJoz+gvmTZ7Jn127+NuSxKGcyjNQjJV1ShhFUls/7mc3rNzLgsbs57YqL+PqdUVHLtejYlivuuy3qPqN4SLZ4MIjDASUqYUgxGmi9+2qF9z79LCIPFke9sSIiR4rImcmOo7SxZPYCjujeGRHhkBZN2btrDzu2bstR7pAWTfn/7J13fFRV2oCf15DQIXRFQIpIlV5EQVHUtWBZO2vDhrqu+ulid9W179q7oqsogh0b9kZHpPfexEoNvQTyfn+cM2EymZRJJrmZyfv4yy8z95577zsTue897TnV0msEEKFhFJ1SSxiSnAbam1S1E261vItFJLJzPUg6AZYwSpmtGzdRo3Z69vvqtWqyJWNTgBEZeRH0HIxEnIcR801cRP6Fm6G9Gjc7eRpuQt4gnL9pKXCh9yYNBTbgBITTReQd4EmgMrADuERVF/lawVDcRLsFQFPgGlWdKiLHA/8GKgLL/DFbo8QVbn5dh/M+tVfVG/z+K4A2wNM4F9RkH9di4CIfb1fc5Lhq/rMNVNX8HFEhKvnf2/y17gJO8Z9zInClqqqP8SpgDzBfVc8TkXtwo7gOwMkXb8R5sU7EzXQ/RVUz84pNREb7z3I0zs91mX9/L8582xt4SFXfKcTnMIpJtGYGKfzAHcMo08RUwzADbS4eEZGZwC/A26q6xm9/1n8f7XFJo7/ffivQ2V/3qrDztMCpU07DJbofVPVQXFI9uRCxVVDVHrga1t1+XspdwDvefJsrWZitNn5M/34Cr93zOK/d8zjV0muyeUNG9r4tGzdZ05ORNMRawzADbU5uUtX3xa278Z2IHK6qE4GjReRmXPNYbWAe8ClOYzJcRD4CPgo7zxe+FjHHX/dLv30OrrbVqoDYRvrf03z5Aom01do8jKLT5Zgj6HKMm36zbNZ8pn0/gTY9OvHb8p+pWKWSJYwySiJ2OgdNrAnDDLRR8PqT0UBvEZmO8111U9XVvskp1GR1Mk5BfipOcR7yWu/y58kSkUzd939yFu5vVFBsIU/VXmyodKA079CGZXMWMuS2h6mQlspJl56bve+1ex7nkntcBfmH90Yxf/IMMndn8tzg++jYpwe9T/tLUGEbRqGI9eZiBtoo+A79nrhmo1ByWOdrHmcB74vIfjhj7Q8iMh74G642VhiKElvouzBKERHh+AvOiLovlCwAjj67P0ef3T9qOcMoq8TUh2EG2lyE+jBm45qPRqpqBi6JzsE1O03xZVOAN32z0wyc5DAjyjlzUcTYfgDa+mG/5xZQ1jDKHUHPwUjEJrGYXVJmoE1KrA/DSHhidUktWrM68Dt2q/qNE2oIXVHau81Am4T4f2yGUW7I0qygQ0g4EtJWawbauKM/Z6wpuJRhlGGapNeHGGoYC/5cFfjNr02Dg5K+hhE4agZawzCMUichE4ZhlFWmTJrM848/RVZWFiee2p/zLr4gx/73R7zNFx+PIqVCCjXT0xl85200OGD/gKIt3yRi60rQJJV80DCCZO/evTzzyOM8+OSjvPL2MH74+ltWLV+Ro8zBhxzCc6+/wpDhr3PkMX15+dkXggnWMIpAuU4YZd1uKyKnisit/vU9IjLYvx4oIg3Dyo322hYjQBbNX0DDRgdywIENSU1Npe9x/Zg4dnyOMp26daFSJTdVp037dqxdY31HQRG0eDAR5YNlNmGY3RZU9RNVfTjKroE40aJRhli3Zi31GtTPfl+3fj3WrV2XZ/kvPvmMHr0OK43QDCMulHjCEJF/+Sfqb0TkLREZLCJXiMgUEZklIh+EnvL9U/jjfuLdf0Skh4hMFJEZ/ncrX66KiLwrIrNF5B0RmRx6whaR40VkkohMF5H3/GzraHGF221/EJHLROSJsP1X+Fia+vhf99d7PyzeriIyRkSmichXInJAIb+WSLvtSnGiRUSkm9eMhGoSz0bEfRZO+jjc11YqR+zfKiIP+O/2RxFpUMiYjGIS7XlR8hgD8+0XX7F4wULOvqCo1hvDKH1KNGGI2W0jyctuW2hU9X3/uc73JtodEUWqAj/673YscEW084jZauNOvfr1WPvnvj/pujVrqVO3bq5y03+ayoihw7j30YdJS0srzRCNMLQM/JdolHSzj9ltc5KX3Tae7AZG+dfTgOOiFYq01do8jOLTqk1rfl39C7//9ht169Vj9Dffcdt9Obuqli5azJMPP8KDTz5Krdq1AorUMIpGSScMs9tGIdxui1tgaQ/7anuV8jqukITbbs1eW4qkVKjAPwbfwG3X/ZOsrCz+csrJNG3ejKEvvcIhbVpz+JG9GfLM8+zYvoP7br8LgPr7N+C+R6N1UxlG2aOkbyZmt42C5LTbAqzENat9gWvCKwgz0ZZReh7Ri55H5HyGGHjl5dmv//vsk6UdkpEHNg8jdkq0D8PstrnIZbf12/8NPCUi43C1goIYCrwYrdPbMAyjpChxl5SY3TYRsD4MI+GJ1SU185clgVcxOjVqaS6pCMxumwD4f2yGYRh5kpC22lgRs9sWhD73wwdBx2AYxeKao88Eq2GUKOViBI3ZbQ3DiCQR1RxBUy4ShmGUFqrK2HdHsXLuIiqkpXHcxWdSv8mBucpN/OhrFk6ewa7tO7j6qXtKP1DDKAJl1iVlGInIqrmLyViznovu/SfHnH86P4z4OGq5Zh1ac+6tV5dydEY4Qc/yTsSZ3uUmYUgpmmn99QZ7B9Vc73W6KMbj7xGRX/3Q2bkicmqMx/cVkYKG+RpxZvns+bQ+rDMiwgHNm7Brx062bdqcq9wBzZtQtWaNACI0jKJTJhKGJJmZVkSuwik5evjhuEcSQ2dc2PfxhLfbng28KiKF+nv54/tS8LwQI85szdhM9Vo1s99XS6/B1ozcCcMwEpG43qhF5F+4mdurcbOnp+Em6g3CeZ2WAheq6nYRGQpswIkJp4vIO8CTQGVgB3CJqi7ytYKhuAl4C4CmwDWqOlVEjsdNeqsILPPHbI0SV7iZdh3OB9VeVW/w+68A2gBP4xxRk31ci4GLfLxdgcdx7qt1wEBVzcsddTtOargZQFU3Aa/7a90FnOI/50TgSlVVrwqZiBth9Un4yVR1gYjsAer6iXqvAvWAtf4z/xzxfW7w59krIhcA16rquDxiNeJJlFYGKfyzglGKWKd37MSthmFm2uzvoTpQXVWX5fFVPeu/j/a4pNE/bF+6qh6lqo9FnLMnkIVLEM8Cb/j4huOSXIjQ93km8CK+hhItWZitNn7MGj2JEfc/w4j7n6Fqzeps2bgpe9/WjM1UTTeLi5EcxLOGYWZahxB9aYQQR4vIzbjmsdrAPCD0Xb0TUfYGX0PYApzrayK9cAkZYBhOnxIi/PvMl0hbrc3DKDod+/aiY1/nj1oxZyGzR//IId068MeK1VSsVMn6KoykIZ4Jw8y0gKpuFpFtItJcVZfnCFikEs6D1U1VV4vIPeS004Z/H+BqCI8WdMl8jjdKmabtW7Fy7iJe/9djpKalcuzF+1ySI+5/hr/deS0A4z/4gkVTZpG5O5P/3fow7Y7oxmGnHBtU2OWS8jBpOd7EM2GYmXYfD/nYzvUJpAZwHk5sCLDOr4lxFvB+HufIi4n+XMNw3+f4PMptAezRtpQREY4ecFrUfaFkAdD7zBPpfeaJpRWWYcSFuPVhmJk2By8APwBTRGQuMAbYrqoZuCQ6B/gImJLPOfLiOuASH9+FwPV5lPsU+KsfltunCNcxjKRGVQP/STTi6pIyM23CYn0YRsITq0tq8sr5gd+xezZtm1BD6OI9/8HMtAmK/8dmGIaRJ0lnqzUzbZHQfv81Z6KR2Hx383MQQw1j0op5gd/8ejVrV65rGIFjZlrDMIySIekShmEEzTX9zqZn83bsytzNf78YxpI/V+dZ9r4zruSAmnW5/LWo80ANo0xRJlxShpEs9Gjejka16nHRy/fw+FcjuP648/Is27tlR3bs3lWK0RnhqGYF/pNolKuEUZrGWhEZKiIrvKl2sYi8ISK5F0YoQozeZDu4EOcqVDkjfhxxcAe+njcZgAW/r6RapcrUrpp7Okyl1Iqc1b0fwyd9WcoRGkbRKTMJI9mMtZ6bvEOrFTADN9orrYBjSjtGI47UrV6TtZszst+v3ZJB3erpucpd0qc/7035jp2ZxR4dbhSRLNXAfxKNuCcMEfmXXwfiGxF5y68LcYWITPFP2x+EnqD9U/jjfjLdf0Skh4hMFJEZ/ncrX66KiLwrIrNF5B0Rmexlh4jI8SIySUSmi8h7fgZ1tLjCjbU/iMhlIvJE2P4rfCxNffyv++u9HxZvVxEZIyLTROQrETmgMN+JOp4A/gBOzCvuyBh9uRN8mVkiEj4EuK2IjBaR5f640Oe4Q0QWici3uERllCq5B71EjkRsUb8RB6bXY8KSWaUVlGHEhXjrzcONtRWA6TjF+UhVfdmXuR9nrH3GHxYyrO71Co0jVXWPiByLM9aeSZixVkTa42ZaRxprt4nILThj7b2Rsanq0yJyI85Yu05EqgKzReRmVc3EGWuv9MVbAZep6gQReRVnrH3Kx3yaqq4VkXNxxtpLY/iKpgOt/Wz2XHGr6r0RMdbDzQw/UlVXiEjtsHO1Bo7G6U4WicgLOMXJeVG+/1yIyCCcdp6XXnopho9gRHJa5yM5qYMbXb3oj1XUq5GeLbmpVz2d9Vs35SjftmEzWu7fmOFX3kvKfvuRXqU6j513Pf98+6nSDt0wYiLezUBmrM2f0ONnYeM+DBirqit8zBvC9n2mqruAXSKyBmgA9AE+VNXtACLySeQJQ0Taat+xeRhF5uMZY/l4xlgAejZvx+ldjuKHBdNoc0BTtu3awYZtORdQ+nTmOD6d6YzzDWrU5oEzr7ZkEQDJNgetNIh3wjBjbf50xqlNCht3fqr08OE1e9n3t7R/BQEyefk8ejZvx7Ar7mHnnt088sWb2fteuvg2rnz9oQCjM4ziEe8+jPHAKSJSyfclnOy3Rxpr86IgYy2S21h7hIgc7PdVEZFD8jl/yFgLOGMt0Bj4G/BWWLkm4tadgCjGWn+tVBFpl8+1shHHdcABuJpKfnGHxzgJOEpEmvlytcmfsTjhYGVxCzmdUpj4jPjy9LfvcuHL93DFaw+y+I+fs7dHSxZ/bt5gczACIkuzAv9JNOKaMMxYm4tHfNnFQHdc38TuAuLOjtGXGwSM9OeJXGApB97d9Y6P7QPAlmU1DCNuxN0lJWasTUTMJWUkPLG6pMYsmRF48+1RLTuXe5eUGWsTEP+PzTDKDYk4DyJo4p4wVPVvJXDOLUC3wpaXQhhr/WJGufo7VHUlbjRUYa+VFMbaC1++J+gQDKNYDLvinqBDSHqSUj5oxlrDMIz4k5QJwzCC5MJeJ9KxcUt27clkyJiPWLU+93Sdm064gPQq1dhvv/1Y9MfPvD7hM5sXUMrY9x07ljAMI450bNySBjVrM/jdp2lRvxGX9D6Zez5+JVe5Z757j52ZbirNdceeQ89m7fhx+dzSDtcwYqLMyAcTGSl9C+6vIlLRv68rIisjytwgIjtFpGZpxGTso8tBrRjvHVHL1vxClbRK1KycW28WShYpsh8V9ktBbb5lqaOqgf8kGkmfMCQ5Lbh7yd9hNQCYApRaX47hqFW1Bhu27lOBbNi2OareHOCmEy/guQtvYkfmbn5aMb+0QjSMIpMwCUPMghvOk8AN0ZKhn6dSDSc3LKoyxSgi0QbV51V7eOSLN7l2+GOkpqTQrmHkoD7DKHskRMKQnBbcM9g3xHakqnb3a04swFlwQ4QsuP/EzS4/UlU7A3fhLLgQZsHFeay6+uuFW3C74Gas3xgtNlV9GvgNN4v7aOBt4FQ/ZwScBTc0xLYVMMRfbzPOgpuKs+CepapdgVdxFtz8+BmnK7kwyr4BOM3JOKCViNSPdgIRGSQiU0Vk6pAhQ6IVMQrJsW27c/8ZV3H/GVexcfsWalfbV6OoXbUGG7dtyfPYzL17mL5qEV0Oyk9QYJQEQa+FkYjzQBKl09ssuLl5EKdh+Sxi+3nAX1U1S0RGAmcDuWblRdpqx9k8jCLz7fwpfDt/CuA6vY9r14Mfl82lRf1GbN+9i007tuYoX7FCGpVS09i0Yyv7yX50bNySRWHOKcMoqyRKwjALbuSBqktFZCZeygggIh2AluxLPmnAcqIkDKNkmLV6CZ0at+TRc69j955MXh7zcfa++8+4ijtHvkjF1FRu/MsAKuxXgf32E+b/toLvF0wNMOrySSJ2OgdNoiSM8cBLIvIQLuaTcQsLRVpwf83j+IIsuD9EseA+JyIH+xtzFaCRqi7O4/whw+w6cBZcEWkMdMEtahSiiYj0UtVJRLHgquok/1kOUdV5BX8tPEDOGsYA4B5VzdaiiltX/CBVXVWI8xlx4PWJn0fdfufIFwHYvGMbd3/0cmmGZBhxISH6MMyCGx2fVMJdXecBH0YU+9BvNwzDKBZxt9WWFGbBLVHUXFJGouNdUoW2v345b3LgN78T2vUs97baksIsuCWIidsMwyiIhEkYZsEFStCC+8q4PJf/NoyE4PI+pwYdQtKTMAmjLGAWXMNIHkzHEjuWMOb8TpUAACAASURBVAwjjqgq37/1McvnLKRCWionXXouDQ5qlKvcuJFfMG/SNHZu38H/PWdrehuJQUKMkjKMRGHFnIVsXLOOyx+8hb9cdBbfvDkyarkWHdtywR3XlXJ0RjhBiwcTZcBROEmRMERkoIg0DHv/iu8gR0RWetUHIjLR/24qIn8LK99NRJ6OYzwqIo+FvR8sIvcUcEwrERktIjNFZIGImK8jAVkycx7tenVFRGjY4iB2bt/J1ozNuco1bHEQ1dKjSwkNo6ySFAkDN2ciO2Go6uWqmkv/qaqh+Q1Ngb+FbZ+qqvF83NsFnBFKVIXkadww3E6q2gbnl4oL4kiWv3WZZmvGZqrXTs9+X71WTbZmbAowIsOIHwl1E/E1g7lh7wf7992A4f7pvLJ/Us81+klEQlKfh4E+vvwNItLXz5tARKqKyKviLLgzROQ0v72diPzkj5ntvVR5sQc3me+GKDEcJCLf+XN8JyJN/K4DgF9C5VR1ji+fIiKP+Hhmi8iVfvvzInKqf/2hiLzqX18mIvf772qBiDyPm9zXuBBfsVFcojYzJNRQ+3JDlmYF/pNoJEOn9/s4h9RgVZ0K4D1K+XGrL9/fl+8btu8O4HtVvTQ0p0JEvgWuwg1rHS4iaeScMR6N54DZIvLfiO3PAm+o6usicimuZnE68ATwvW82+xp4zQ/RvQw3+7y7uEWTJojI17jJi31wM+APxCUccELFt/3rVsAlqvr3yOBEZBAwCOCll15ivzb7F/BxjLyY/v0EZo+bDMABTRuzZUNG9r4tGzdZ05ORNCRDwog3x+P05IP9+0pAE5yt9g4RaYTTqi/J7ySqullE3sAZaXeE7eqFU7QDDMNpS1DV10TkK+AE4DTgShHp6OPpICJn+WNq4gSD44D/830184Fa4tbR6OWvWQdYpao/5hFfDlutzcMoOl2OOYIux7gpM8tmL2DG9xNo3aMTvy//mYqVK1nCMJKGREsYe8jZjFapBK4hwJmquihi+wIRmYwTH34lIper6vcFnOtJXHNQfpPtstswVPU33HoYr/qmtvY+nmvDJwdmBypSC5dgxuLcVOcAW1V1i4jUIaex1ygFmh/amuVzFvDy7Q+TmpbGiZdky4QZ+u/HGXi3W1Zl9HujWPDTTDJ3Z/LCTffToXcPjjjt+KDCLpdkZSXeKKWgSbSE8SdQ398MtwL9cetJhGyxhSW/8l8B14rItaqqItJZVWeISHNguao+7V93APJNGKq6QUTexTUrveo3T8TJAIfhDLvjAUTkBOA7Vc0Ukf1xNYRffTxXi8j3ft8hwK+qug1X6/k/4Bhf/n3/YwSEiHDc+WdE3RdKFgB9z+5P37P7l1ZYhhEXEqrTW1UzgXtxhtpR7DPUDgVeDHV6F+JUs4E94pZ2jeyYvg+3wNJs/5R/n99+LjBX3BoUrYE3Chn2Y0D4aKnrgEu8sfZC4Hq//Xh//lm4JHGTqv6BW1tjPjDdx/MS+xL9OKCCqi7F1WRq+22GYRSAloH/Eo2EsdUaJYr1YRgJj3dJFXpI2sezxgV+8zutY5+EGkKXaE1SRglh4jbDMArCEkYR8f0o0fTl/VR1fWnHU1ye/u7doEMwjGJxXb9zCi4URiLOgwgaSxhFxCeFTkHHYRiGUVpYwjCMOKKqjHvvc1bNW0xqair9LjqDek0a5ir348ffsGjyTHbu2MmVT/wrgEgN67+NnYQaJWUYZZ1V85awac16Lrjn/+h7/mmMfvvTqOWadmjNWbdcVcrRGYmIiJwgIotEZKmI3Bplf0URecfvnyxuOejQvtv89kUi8pfixmIJo4hEeq0KUT6q38rvy7brxim2lSIyxw8b/trP6zBKgRWzF9CqZydEhP2bNWb39h1s27QlV7n9mzWmas1Ypg4Z5RERScFphk4E2gIDotwrLgM2qurBOMXQf/yxbXFzvtrhJvg+789XZCxhBIyIpORl1y0mR6tqR2AqcHucz23kwbaMzVSrVTP7fdVaNdkWRW9uBE/Qa2EUskmsB7BUVZer6m6cJ+60iDKnAa/71+8D/cQJ9U4D3lbVXaq6Aljqz1dkkiph+Kf+hSLyuje7vi8iVUTkLm97nSsiQ8TRRkR+ijh2tn/dVUTGiMg0EfnKO5pC22eJyCQg3yVUxVlz3/ZxvANUDtu3VUTu9aqRXqHah4hcHS4rFLfOxzP+9QWyz5b7UgxPCmOBgwtZ1iguJqs14suBwOqw97/4bVHLqOoeYBPO/FCYY2MiqRKGpxUwRFU7AJuBvwPPqmp3VW2Pu3H3V9UFQJrXfICbyf2uiKTi1qI4S1W74pQeoTU0XwOuU9VehYjjamC7j+MBoGvYvqrAXFXtqarjw7a/zz4xYSimd0SkjX99hKp2AvbitCKFoT8wJ3KjiAwSkakiMnXIEFurqTjMGTOZtx98jrcffI6q6dXZunHf+hfbNm6iak2TDxrRCf936H8GRRaJcljkY0leZQpzbEwk4yip1ao6wb9+E6fiWCEiNwNVcPqMecCnwLs4Yd/DuBvyubiE0x74xtXqSAF+F5GaQLqqjvHnHoZrV8yLI3HqclR1dqj24tkLfBB5gKquFZHlInIYsMTHMgFXm+kKTPExVQbWFPA9/CAie3EalDujXCuHrdbmYRSdQ4/qyaFH9QRg5ZxFzBkzmZbdDuXPlb+QVrmS9VWUUbLKwCipiH+H0fiFnGvZNAJ+y6PMLyJSAWe03lDIY2MiGRNG5P8FCjwPdFPV1eKWSg1Zbt8B3hORkYCq6hIRORSYF1mLELc2Rqz/h+VVfqeq7s1j3zu4JLYQ+NALEAV4XVVvi+HaR6vquhjKG3HgoPaHsGreYt68+wkqpKXS78J9Fca3H3yO8253LZkTR37F4qmz2bM7k6G3P0Lbw7vSo/8xQYVtlF2mAC1FpBlORnoeYauFej4BLsbJSM/CreejIvIJMEJEHsetSNoS+IlikIwJo4mI9FLVScAAnA32cGCdiFTDfaHvA6jqMv8U/i/cjRpgEVAvdA7fRHWIqs4TkU0i0ts3IxXUJDTWl/lBRNrj7LaFYSRuEadVwC1+23fAxyLyhKquEZHaQHVVXVXIcxqlhIhw1HmnRN0XShYAh5/xFw4/o9ijHI1ikAjzMFR1j4j8AyckTQFe9feie4GpqvoJ8D9gmIgsxdUszvPHzhNny56PWxrimnweVAtFMiaMBcDFIvISrlnnBaAWrh1/JS5jh/MO8AjQDEBVd4tbrOhp3wxVAbeuxTzgEtxaFdtxf8D8eAF4zTdFzaSQmV1VN4rIfKCtqv7kt80XkTuBr8WtzZ2Ja6ayhGEYSY6qfg58HrHtrrDXO4Gz8zj2Afb1wRabpLLV+gkro3zntlF4rA/DSHi8S6rQY9Lenfpd4De/c7r1S6gxdMlYwzCKQKziNsNIdMpCp3eikVQJQ1VX4kY4lRp+uv1/IjavUNW/lsK1JwMVIzZfqKq5htEWxK9Ld8YnKMMIiAMPLokVm41wkiphBIFfa7ug/oySunbPIK5rGMlAMjXHlxbJOHHPMALjp6kTuGjQqVxweX9GvPu/PMuNGf8Nx5zckUVL5pVidIZRPCxhGEac2Lt3L0+98CAP//t5XnvhQ74f+yUrf16Wq9z27dsY+ckI2rQ6NIAoDaPoWMIoImXVVisiI0Tk6rD3Pb3PypofS5iFi+dyYMPGNDygEampqRxz5AlM/HF0rnKvvvkc5501kLS0yO4nozQJWjyYiE1iljACpgRstTcAN4lIPT9n41ng715KZpQg69avoX7dfSb5unXrs3b9nznKLFm2gLVr/6BXj6NKOzzDKDZJlTDMVguq+ifwKPBf4CpgdoTg0Cghoj0xSti0gKysLJ5/+VGuvvyfpRmWYcSNpEoYHrPVwou4xVZuAm6OVsBstfGnXt0GrFn3R/b7devWULdO/ez323dsY8Wqpdxw6+UMuORE5i+czZ33Xm8d3wGxV7MC/0k0krFdu9zbalU1y6tRuqnq+jzK5LDV2jyM4tP6kHb8+uvP/P7HL9St04Dvx37JHTc9lL2/WtXqfPTWmOz3N9x6GVdddiOtWrYLIlzDiJlkTBhmq3Vk+R+jlEhJqcC1V9/GLf+6mr1ZWZx43Ok0O+hgXhv2HIe0bMcRh/UNOkQjjETsdA6aZEwYZqs1AuOw7n04rHufHNsuuTB6d9cTD+c9T8MwyiLJmDDMVmsYhlECmK3WAOvDMJIA75IqtP319YmfB37zu/jwk8xWayQeJm4zDKMgkiphmK0WKKKt9olv3o5PUIYREDccd17QISQ9SZUwgsBstYaRmNh6GLFjCcMw4oiqMuH9L/h53hIqpKVy9IWnU69xw1zlJn/yLYt/msWu7Tu5/PE7AojUMGInGWd6G0Zg/Dx/CZvWrmfA3ddx1IBTGPf2qKjlmh7aijNuGlTK0RnhqGYF/pNolLuEISLXicgCERke0PV7eHfUEhGZLiKf+cmC0cpu9b8/FJHTw7Yv8sNsQ+8/EJEzop3DKF1Wzl7IIT06ISI0aNaYXTt2sm3TllzlGjRrTNWa1QOI0DCKTrlLGDi31EmqWtDEu7gjIg1wOpLbVbWlqnYBHgJaFHDoRNzkQ0SkDrAVCJ+J3suXMQJmW8YWqtWqkf2+WnoNtmVsDjAiw4gf5aoPQ0ReBJoDn4jI27gb9aG47+EeVf1YRAYCp+K8Uy1weo6b/fEnAA/i/FLrVLWfiFTFyQpznCePEP6BU3xk39zD5YMi0gwY4c/zZdhxE3D2WXCJYxRwoleGNAV2qOofIlIJN2GwG7AHuFFVf4j5izKKQRRjrSTUUPtyg3V6x065ShiqepW/6R8N3Ah8r6qXek/UTyLyrS/aCegM7AIWecX4TuBl4EhVXeH1HOA0HrnOo6rbooTQDng9nxCfAl5Q1TdEJNwnMQ1oLyJpuIQxBpf42vg4Q7LFa/znPFREWuNmhh+iqrlm5YnIIGAQwEsvvQTNakQWMQrJ3DGTWTBxOgD1DmrI1o37ahRbMzZTxZqejCShXCWMCI4HThWRwf59JaCJf/2dqm4C8JqOg3B6kbGqugJAVTcUcJ4FBQXg51HUAL5W1euBI4Az/e5h+PkdqrpLROYBXYDDcLWN5rjk0Zl9zVG9cbUdVHWhiKwCDgHCTbn4/TlstTYPo+i0P6on7Y9yI5xXzV3M3LGTObhre9as/IW0ypWsr6KMojG7RI3y2IcRQoAzVbWT/2ni18gAV7MIsReXWIXo9tn8zhNJ6KYPZM+j+BdQM6xMXv8XT8Qp06ur6kbgR1zCOJx9NQxr+wiYJu1aUqNOLd7691OMGfEJfc45OXvfew+9kP160kdfM+zOx9iTmcmwOx9jymfWcmiUfcpzDeMr4FoRudYrxDur6ox8yk8CnhORZqEmKV/LiOU8zwGTReSrsH6MKmH7JwDn4dbxiOyUnwA8Boz272fjahsNcIkI9hlyvxeRQ3A1nUX5fgtGXBER+pzbP+q+s2/LXmqdXqcfT6/Tjy+tsAwjLpTnGsZ9QCowW0Tm+vd5oqprcW3+I0VkFvt06IU+j6r+gVuk6SERWSoiE3G69Wd9keuBa0RkCjlrHeBqGM1xiQu/RvcaYKruG9D9PJAiInN8fANVdReGYeRCVQP/STSSylZrFBnrwzASHu+SKnSz7JCxHwV+8xt05OkJ1YxcnpukjDBM3GYYRkFYwigBgjTYFpWHPstvtK9hlH1uO/nimMrbPIzYsYRRAgRpsDUMwygpLGEYRpw5rm0PWtQ/kMy9exg1awJ/bt6Qq8yRrTpz6IEtqJSaxmNfjQggSsP6b2OnPI+SMoy406LegdSqWp0XR3/IF3MmcUL7w6KWW/rnaoZO+KyUozOM4lGuEkaQploR6SsiKiKXhW3r7LcNLsSxm0Rkho//7pKP2CgKLRs0Zu6vywH4LWMdFVPTqFqxcq5yv2WsY9uuHaUdnmEUi/LWJPV34MSQ3iMA5uDmYfzPvz8PmFXIY8epan8vO5wpIqNUdVpop4hU8HMzjACpXqkKm3fs04ht2bmd6pWqWHIog1ind+yUmxpGhKn2DhF5VUSm+Kf203yZgSIyUkS+9OtV/Dfs+BP8+hWzROQ7v61qtPPkw89AJRFp4E2zJwBfhF1jtIj8R0R+EpHFItIn8gReajgNaOHjfU9EPsWJBt8RkZPCzjdURM6MPIdRgkQz09qNyUgSyk0NowyYakO8D5wNzACmk9NbBVBBVXv4G//dwLHhO/16GIfhZpR3x62F0UFVN4jIX3E1mM+92bYfcDVRyGWrPbBiPiEb+dHloFZ0anwIAL9vWkeNylVho9tXvVIVtljtokySlYAr3gVNuUkYEQRpqn0Xp+1oDbyFXxgpjJH+9zTcWhch+ojIDCALeFhV54lId+CbsHi+AJ4WkYq42stYVY16t4q01do8jKIzfdUipq9yyq4W9Q+k60Gtmf/bChqm12XXnkxrjjKShvKaMEKG2RxiPhHpSdFMtYUW/PmFjjKB43DuqMiEEbp+6NohxqlqNKtddm1GVXeKyGjgL7iaxluFjcuID8vW/EqLeo24qu8ZZO7dw2ezJ2Tvu7T3Kbw6/lMAjm7dlbYNm5GaUoFrjjmLWauXMH5JYbuzDCMYymvCCMJUG85dQH1V3VsCq7G9DVyOW3VvYLxPbhTM1/MmR90eShYAPyycxg8Lp0UtZ5QONg8jdspNp3cEpW6qjTjfRFX9qKjBF8DXuHUzvlXV3SV0DcMwyiFmqzXA+jCMJMC7pApdZX/6u3cDv/ld1+8cs9UaiUes4jbDSHTsYTl2LGHEmUQ01QK8MemLggsZRhnmol4nBh1C0mMJI86YqdYwEgOb6R07ljAMI46oKl8PH8my2QtITUul/+V/44CmjXOV+33laj59ZQR7dmfSokMbjj//DEpgxJxhxJXyOkrKMEqEZbMXsOHPtVz9nzs4aeC5fPnGe1HLffH6e5w08Byu/s8dbPhzLcvm5DfX0zDKBuUiYSSwpXaGiHTyryuIyDYRuSBs/zQR6VJy0RuxsnjGHDoc0R0R4cCDm7Jz+w62ZGzKUWZLxiZ279hJo4ObISJ0OKI7i6fPCSji8ouqBv6TaJSLhIGz1J6kqucHdP2QpTZEYS21E9k3E7wjsCj03ltrmxfyPEYpsWXjJmrUrpX9vkatdLZs3JSrTPXa6dnvq0cpYxhlkaRPGAluqZ3AvoRxOPAiTo4I0AOY7meLzxGRdHGsF5GL/HmHiUgOeaFRskR7aMzVNxH1wdL6L4yyT9J3eie4pXYicL/ffzjwb2CAiFT370OiognAEcAqYDnQB3gDZ7UtlK220qG5O2aNwjH123HMGDMJgIbNmrB5w8bsfZs3ZlAtvUaO8tVr12TLhozs91s2ZlC9Vs4yRsljo6RiJ+kTRgQJZalV1ZUikiYi+/vjFgFTgJ7++Gf8MeNwOpBVwAvAIBE5ENigqlujBRNpq7V5GEWn27F96HasqxQumTmPqd+No23PLvy2bBUVK1emenrNHOWrp9ckrXJFfl26koYtDmL2hCl0P/bIIEI3jJgobwkjES21k4CzgN+94PBHXG2iB/CjLzMWuAaXtO4A/uqPGVfY+Iz4cHDHtiybvYDnb76f1Ipp9L9sQPa+l//1X66472YATrjobEa9MoJMP6y2RYc2QYVcblFbDyNmylvCSERL7QTgBmBoWEyPAH+oagaAqq4WkbpAmqouF5HxwGDgH4W9iBEfRIQTLjor6r5QsgDXdDXogVtLKyzDiAtJ3+kdQSJaaifgOu0n+XP8DqTg+jfCmQws9q/HAQcC42O8lmEYRp6YrdYA68MwkgDvkip01f2/XwwL/OZ384kXJtTwuPLWJGXkgYnbDMMoCEsYcSJRLbUhFq1ZHXQIhlEsWtWPbWi4DauNHUsYccIstYZhJDuWMAwjjkyb/BOvPPU8e7OyOL7/iZx1wYAc+z96+32+GfU5+6WkUDM9netuG0z9/RsEFK1hxEZ5GyVlGCXG3r17eenxZ7j70Qd5btj/GPvtD/y8YlWOMs0POZjHX3meZ15/mcP79mHoC0PyOJtR0gQtHkzEAUeWMGKkjJhvTwnbNkpE+oa9rycimSJyZWnHV95ZsmARBxzYkP0bNiQ1NZU+/foyefyEHGU6dOlExUqVAGjVrg3r1qwLIlTDKBKWMGInaPPtL7jZ3HlxNm4G+IB8yhglwPq166hbv372+7r16rF+3fo8y3/z2Zd0Pax7aYRmGHHBEkYMlBHz7Sxgk4gcl8f+AcA/gUbeJ2WUEhrFIpPXIPsfvvqWpQsXccaAc0o2KCNPsjQr8J9EwxJGDKjqVcBvOPNtVZyxtrt//4hfowKc+fZc4FDgXBFpLCL1cObbM1W1I64mAPvMt9HOkxf3A3dGbhSRxsD+qvoTTnZ4bmSZsLKDRGSqiEwdMsTa0eNB3Xr1WLdmTfb7dWvXUrtunVzlZk6dxnvDRnDnw/eRmpZWmiEaRrGwUVJFJzDzraqOExHC1s0IcR4uUQC8DfwPeDyPc+Sw1do8jOLTsnUrfvvlV/747Xfq1KvLuO9GM/ju23OUWbZ4Cc8/8iT3PPoQ6bVq5XEmozSweRixYwmj6ARmvvU8gKud7AnbNgBoICKh/pWGItJSVZfEeG6jCKRUSOHKG67lnn/eSlZWFseefAJNmjVl+CtDObj1IfTsfThDnx/Cjh07+M9dTj9Wr0F97ny4UCoywwgcSxhFJ1Dzrap+LSL3AQ0BRKQVUFVVs/stROTfuFqH3ZFKiW69etKtV88c286/fGD26/uefKSUIzKM+GF9GEUnUPOt5wGgkX89APgwYv8H2Ggpw4hK0HMwEnEehtlqDbA+DCMJ8C6pQttf7/3kf4Hf/O469TKz1RqJR6ziNsNIdKzTO3YsYZRBgjDffjHvx4ILGUYZ5sR2hwUdQtJjCaMMYuZbwzDKIpYwDCOOLJg+m5GvDkezsjjs2KM49oz+OfbvyczkzaeG8MvylVSpXo2L//l36tSvF1C05ZtoM/ON/LFRUoYRJ7L2ZvH+y29w5Z3/5NanHmL6uB/5Y/WvOcr8+O1YqlSryp3PP0LfU/7Cp2+8m8fZDKPskfAJQ0ROFZFb43SuPiIyT0RmikjlIhx/e8Gliod3VWWJSIewbXNFpGnY+87eavuXko7H2Meqpcupe0AD6u5fnwqpFejcuydzfpqeo8ycKdPpfnRvADr26s6SOfMTcnilUT5J+IShqp+o6sNxOt35wKOq2klVdxTh+JgThogUpVmwIGPtAGA8NgejVNm0fiO16tTOfp9epzabNmzMs0xKSgqVqlRm25atpRqn4Qh6DkYiPiiU6YQhIk1FZKGIvOKfooeLyLEiMsGbYHv4J+5nffmzfblZIjLWb0sRkUdFZI6IzBaRa/O41uXAOcBdobUuROQmb5Gd7WdNh8p+JCLTfG1kkN/2MFDZ106G+9jnhh0zWETu8a9Hi8iDIjIGuN6vYfGBv9YUETmigK9mFNDOz+6O/BwCnAUMBI4XkUqF+rKNOBDNVhs5zD5KGUmoofhGOSYROr0PxpldBwFTgL8BvYFTcU/0H4WVvQv4i6r+KiLpftsgoBnQWVX3iEhtoqCqr4hIb2CUqr4vIscDLYEeuMlAn4jIkao6FrhUVTf4ZqspIvKBqt4qIv9Q1U7gkl0BnytdVY/yZUcAT6jqeBFpghsh1SafY7OA//rPf3HEviNwQ3CXicho4CRgZOQJfKIbBPDSSy/R+IgOkUWMGKlZpzYb12/Ifp+xfgM1aqdHLZNetzZ79+5l5/YdVKlWkJzYKAlsHkbslOkahmeFqs5R1SxgHs4Eq8AcoGlE2QnAUBG5Akjx244FXlTVPZDDElsQx/ufGcB0oDUugQBc5/UePwKNw7bHwjthr48FnhWRmcAnQA0RqV7A8SOAw0SkWcT2AThTLf531GYpVR2iqt1UtdugQYNij97IRZODm7Hu9z9Z/+da9mTuYcb4ybTv3jlHmfbdOzPlh/EAzJo0hZaHtrEahpEwJEINI9z8mhX2PouI+FX1Km+LPRmYKSKdyNsSWxACPKSqL+XY6JZDPRboparb/VN8tGafPeRMyJFltoW93s+fr9D9Jr629BhwS1hsKcCZOF36Hf4z1BGR6qq6pbDnNopGSkoKZ15+IS/e+whZWVn07HckBzRpxOdvjaRJi6a079GFw/odyZtPDeH+v99ElWpVuejGvwcdtmEUmkRIGIVGRFqo6mRgsrh1rxsDXwNXicjoUJNUIWsZXwH3ichwVd3qV6/LBGoCG32yaA2ETy/NFJFUVc0E/gTqi0gdYCvQH/gyj2t9DfwDeMR/jk6qOrMQMQ4FbgZCtZFjgVmqmj06SkReB04HhhXifEYxadu1I227dsyx7aQBZ2S/Tk1L45Kb/lHaYRlRSMRO56BJhCapWHjEd27PBcbiljN9BfgZZ4OdhesDKRBV/RrX7DNJROYA7+NuzF8CFURkNs4sG+7UGOKvM9wnjXuBybhO6oX5XO46oJvvXJ8PXFXIGHcDTwOhhaTzMtYW6jMbhmHkh9lqDQA1l5SR6HiXVKE7hO4Y+WLgN78HzrgqoTqwkqpJyig6Jm4zyhtZmhV0CAlHuUwYIvIhbqhtOLd46V+ZQEQuAa6P2DxBVa8pieu99dM3JXFawyg1BvQ4LugQkp5ymTBKUhMeL1T1NeC1oOMwjGTFmuNjp1wmDMMoKVSVL4a9z5JZ80itmMbpgy6kYdPci1P9tuJnPhoyjMzdmbTs2I4TLzzL5mMYZZ5kGyVlGIGyZNZ8Nvy5lusevZtTLh3AZ6+9HbXcqKHvcMqlA7ju0bvZ8Odals6eX8qRGkbslPmEUd5stP46p/shtgv9MOHTw/YNFJGGYe9Xikjd0ojLKJhF02fTsXcPRITGBzdj5/YdbMnYlKPMloxN7Nqxk8YtmyMidOzdg4XTZgcUcfklSzXwn0SjZBuqfAAAIABJREFUzCeM8majFZGOwKPAaaraGufMejRMZz4QaJjH4XGJwSg6mzdmUKN2rez3NWqns3lDRs4yGzJyOKZq1E5n88acZQyjLBJowjAbbVQGAw+q6goA//sh4CYROQvoBgyPqCVdKyLT/XfQ2sdwj4gMEZGvgTdi+sMYRSfKQ2Nh+iZyW22NkiZLswL/STTKwpOn2Whz0g5XwwhnKnCNql4oIv8ABqvqVH9ugHWq2kVE/o5LOJf747oCvaPVpiJttdU7RY4yNgrLT9+MYdroiQAc2PwgNoetgbF5QwbVa9XMUT6y1hGtjGGURcpCwlihqnMARCTbRut1HE0jyoZstO+yT9kdDxstQDVcAhmLs9GGht6GbLTrY/xckTbatmFPmjXyEQJGkyUWJFAMfRfTgDPCtn+SV9Obqg7BqUwA1OZhFJ0exx1Fj+OOAmDxzLn89M1Y2h/WlV+WraRilcpUT8+ZDKqn16RipYqsXrqCRi2aMmv8T9nHG0ZZpiwkDLPR5mQertkpvBe0C5DfMJrQd7aXnN/ZtihljRKkZcd2LJk5j6cH/5vUtFROu+KC7H0v3PEQVz9wGwAnDzyXj4a8yZ7MTA7u0JaWHdsGFXK5xeZhxE5ZSBiFppzYaB8F3hOR71V1pW/6uh23ih7AFvbZaY0yhohw8sBzo+4LJQtwTVfXPJzfKruGUfZIqISBs9G2xNUOvsPZaOcCh+AssZnAy8CzBZ1IVb8WkTY4Gy24m/4FuJv+Vd5Gu4joNtrpqnq+iIRstCso2Eb7nD9nBVyzV1QjrarOFJFbgE9FJBWXxG4OSzBDgRdFZAfQq6DPaRiGES/MVmuA9WEYSYB3SRV6uNmNbz8Z+M3v8fP+L6GGxyVaDcMoIUzcZhhGQSRlwhCz0cbM6xM/D+KyhhE3Lj78pJjKJ+JM66BJyoRhNlrDMIz4k5QJwzCCQlX5ZsSHLJu9gAppqZxy2QD2j2Kr/X3laka98hZ7MjNp0aENx/3tr2arNco8Zd4lZRiJxLLZC9jw51quevh2Thp4Dl8Oez9quS/feJ8TB57DVQ/fzoY/17J8Tn6D7IySQFUD/0k0ymTCkHJmqBWRBiIyyjuy5ovI5357XxEZVYzz5vBdGSXP4hlzOfTw7ogIB7Zoys7tO9gaYavd6m21jQ5uiohw6OHdWTR9TkARG0bhKZNNUqr6CfBJnE4XMtQWtb/gduDBWA4QkQohVUkhuRf4RlWf8sd3KKC8UUbZmrEph4m2eq10tmzcRLUwPciWjZuoUXvf++q1a+ZKKkbJY53esVPqNQwz1EblAOCX0BtVDdeCVBOR9/13Nlx8Q7eIdBWRMT7mr0TkgLDts0RkEhDIiKvyTNRmhoi+CbtPGYlKUDUMM9Tm5DngHXEm2m+B11T1N7+vM85g+xtOvniEiEwGnsGtmbFWRM4FHgAuxY28ulZVx4jII3kFKhG22ortGxXw0Yy8mPrdeGaOmQRAw2ZNcphot2zMoHp6jRzla9SuyeYN+2oUWzbkrIEYRlklqIRhhtowVPUrEWkOnACcCMwQkfZ+90+q+guAiMzEfT8ZQHvgG3/+FOB3EamJS1pj/LHD/PlyEWmrtXkYRadbv95069cbgKWz5jH1u/G07dmZ35avomLlyrmSQTVvq/112UoaNj+IOROn0K1fnyBCL9ckYqdzOP5B+R3cPWElcI6qbowo0wl4AaiBk5M+oKrv+H1DgaOA0NPLwHwcd0BwCcMMtRH4pDcCGOE7uo/EJazw7ypkoxVgnqrmcEn5Glhi/ytIcFp0aMvS2Qt44ZYHSE1Lo/9l52Xve+WuR7j83psAOOGis/j0f2+xZ3cmLQ5tQ4sOeVU+DSNPbsU9bD/sBwndCtwSUWY7cJGqLhG3tPM0EflKVUPV4JtUNfpQviiUyU7vcKQcGGpF5BjgR3/96kAL4Gegah7nXgTUE5FeqjpJnKTwEFWdJyKbRKS3qo7HdfgbpYiIcMKFZ0XdF0oWAAc0a8Kg+yP/bRtGTJwG9PWvXwdGE5EwVHVx2OvfRGQNUA/XShEzZXJYbQSP+M7tubimo1nAK7gb6mwRmYXrAykQVf0a9xQ/yTd/vY9ThX8JVBBnk72P6Iba4T5phAy1oyjYUNvNd67PJw87racrMNVffxLwiqpOyedz7Mbpzv/jP/9M4HC/+xKcGXcSUJR1yw2jXBD0HIw4NIk1UNXf/Wf5HaifX2ER6QGkAcvCNj/g71FPiEjFgi5otloDrA/DSAK8S6rQ0+WvGfZI4De/5y+6+Ur84BPPEN+/CICIfAvsH+XQO4DXVTU9rOxGVa0V7Tp+FOVo4GJV/TFs2x+4JDIEWKaq9+YXb5lvkjJKh1jFbYaR6GSVge6+iMEn0fYfm9c+EflTRA5Q1d/9zX9NHuVqAJ8Bd4aShT/37/7lLhF5DRhcULxJkzDEDLXF4sOZYwouZBhlmL92Knfron8CXAw87H9/HFlARNKAD4E3VPW9iH2hZCPA6bjF6PIlaRKGmqHWMIzyxcPAuyJyGa5P92wAEekGXKWqoYnLRwJ1RGSgPy40fHa4iNTDNePNJP9+ViCJEoZhlAUWzZzLp0PfQbOy6H5Mb/qennMazJ7MTN597jV+Xb6KKtWrMuD6QdSuXzegaMs3WVlZQYdQLFR1PdAvyvapwOX+9ZvAm3kcf0ys10yEUVKGkRBkZWXx8asjuOS267jh8X8zc8IU/vzltxxlpnw/gcpVq3DT0w/Q+6Rj+XLEyDzOZhhlj3KRMERkqIhEHxyf/3ED/WSXUkFELhLnzZonzlo7WEQ6+hneoTIDRGS7n3uBiBzqh+OGfFaL/DC5hSLybJhOxShhVi9dQZ0G9anToB4VKlSg4+HdmT9lVo4y86fOpMtRbr5l+8O6snTugoSfcWyUH8pFwigGA4FSSRgiciLwf8DxqtoO6IKbsj8HOMhP6AM332IhzjEVej8h7FTnq2oHoANulniujjCjZNi8IYOadfZpzWrWSWfzxo25yqT7MikpKVSqUpntW7aWapyGI+g5GIn4oJCwCUNEqorIZ97MOldEzhWRu8SZYeeKyBCR3EuYSR6W1yjlzgK64TqGZorIyX4kVmj/cSIy0r/eKiKPich0EfnOdyQhIi1E5Et/rXF+Fnle3AYMVi8dVNWdqvqyqmbhBI09fbmuOFlhaKLe4cDEyJP5yX03A01EpGM+1zXiRPQbQISpNtpQTltpz0gQEjZh4ER9v6lqR1Vtj5ut/ayqdvfvK+PUHdn4ZpxngLNUtSvwKs7ymgvvV5mKe2LvBHwOtAklA9yM6tCIp6rAdFXtAowB7vbbh+DMsV1xY5yfz+fztAem5bFvInC4iFTF+bZGkzNhTIh2kKruxc2Mz5WoRGSQiEwVkalDhuQ5DNyIgZp1arFp/T5Dzab1GdSolbNFsGbtWmT4Mnv37mXn9h1UqZaXAcYoSbJUA/9JNBI5YcwBjhWR/4hIH1XdBBwtIpO99uMYnBY8nFbss7zOBO4ECuX1Vvf4OAy4wPcL9AK+8Luz2GeqfRPoLSLVcDfz9/y1XsKte1EUJvhz9QCmqOoy4GCfvKqp6vJ8jo36+KqqQ1S1m6p2GzRoULQiRow0atGU9X+sYcOadezZs4dZE6fQtlvOyl3bbh2Z7lXoc3+cRot2rW0tbyNhSNhhtaq6WES6AicBD4nI17gFg7qp6mpxCxtF2mSjWl5j4DXgU2An8J7mvaqe4pJxhq+dFIZ5uOam76Ps+xHojlszZJLf9gtwHlGao0KISApwKLCgkDEYxSAlJYVTLx3Aqw8+SVZWFt36HkGDxg35+t2PadT8INp260S3o3vz7rP/45Hr7qBytaoMuP6KoMM2jEKTsAnDj17aoKpvishWXAc1wDr/dH8WTi4YTp6W1zwuswUnJwSybY+/4Womx4WV289f722cCHG8qm4WkRUicraqvuf7Uzqoas5hM/t4CPiviPRX1T+8COxKVX1aVbeIyGr/Gfv68pNwneRRm7n8Z3sAWK05V/AzSpDWnQ+ldedDc2w7/pzTsl+npqVy/o0Fzo8ySgHXPWjEQsImDNyT8yMikoVTlF+Nm94+B7eYSC7bq6ru9p3ZT4tbbKgC8CTu6T4aQ4EXRWQH+9a2GA7UU9X5YeW2Ae1EZBpuZNO5fvv5wAsicieQiksoUROGqn4uIg2Ab31yUVwfS4gJuBX2Vvv3k3BrjUfWMIaLyC6gIm71vtMwDMOIA2arjRFxa43PUNX/hW3bqqrVAgyruKi5pIxEx7ukCt0hdOmr9wV+83v10n8lVAdWItcwSh1fg9gG/DPoWOJNORS3GYYRI5YwABF5DjgiYvNTXhaYjR8em4tYahcicgdeEhbGe6oadXhvaTFqTtSRuYaRMPQ/NPKfsBFvLGEApakX94kh0ORgGAYJOQ8iaCxhGEYcWThjDh+9NoKsLKVnvz70++vJOfbvycxkxDOv8MvyVVStVpULb7zabLVGwpDIE/cMo0yRtTeLka+8yRV33MDNT9zPjPGT+WP1rznKTP5uHFWqVuX2Zx/myP7HM+rN9/I4m2GUPcptwkhkg21pXduIjZ+XLqfO/vWp06A+FVIr0PmInsybMjNHmblTZtCtr7O6dOjVjSVzzFYbFEGLBxPx715uE0YxGEjwBlujDLJpQwbpdcNttbXYtCGKrbbuPltt5SqV2Wa2WiNBSKqEUV4Mtv48nUTkR3FrX3woIrX89tHer/WTiCwWkT5F+zaNmIn2xBjxv1u0p0pzSQVD0OLBROx0T6qEQfky2L4B3KJu7Ys5YecHqKCqPXC1k7ujHWy22vhTs04tMtaF22o3UjPSVhtWZu/evewwW62RQCRbwigXBluvNUlX1dD07NdxC72HCK37OQ1omkfsZquNM40Pbsa63/9k/Z9r2ZO5hxkTJtOue073ZLtunZg62tlcZk+aSsv2Zqs1EoekGlZbzgy2+bHL/95Lkv2NyzIpKSmccfkFDLn/cTQrix7H9Gb/xgfy5dsf0qhFU9p370zPfkcy4umXefAft1KlWlUuvOHKoMMutyRip3PQJNXNpDwZbEVko69FjQMuxDV7GQHTpksH2nTpkGPbCef9Nft1aloqFw/+e2mHZRhxIakSBuXLYHuxj6MKsBzXf2IYRiGxGkbsmK02DiSBwVbNJWUkOt4lVegOofNfuivwm9/wK+9NqA6sZKthlDrJYrA1cZthGAVhCSMPypvB9oXRIwsuZBhlmKv7nhFT+b224l7MWMLIAzPYGoZh5MQShmHEEVVlzDufsmLuIlLT0jh+4FnUb3JgrnITPvqKBT/OYNf2HVzz9L8DiNQwYifZJu4ZRqCsnLuIjWvWM/C+wfS74K98N/yjqOWad2jDgNtseG2QBC0eTMQBR0mRMMqbeVZE+orIqCjbR4vIIu+XWigiz/oZ6EYpsWzWAtoc1hkR4YDmTdi9YyfbNm3OVe6A5k2oWrNGABEaRtFJioRRDAaSfObZ871fqgNuxvfHJXANIw+2ZWyieu19Obpaek22bsydMIzgCbp2YTWMOGLm2eKZZ1V1N3Az0EREOhb2OKN4RL0HmCvKSBLKbMLAzLMhCjTP5oWq7sXNIs+VyMxWGz9m/TCJN+97mjfve5pq6TXYsiEje9/WjE1US6+ez9GGkTiU5VFSc4BHReQ/wChVHSciZ4rIzUAVoDZO3/Fp2DHh5lmAFOD3wlxMVVVEQubZ13Dm2Yv87kjz7MgI82zoNBVj/ZB5mGfD1+0s0Dxb0CWibVTVIbiEB6A2D6PodDy6Fx2Pdu7KFXMWMvOHSbTq3pE/VqwmrXIl66sooyTiehRBU2YThplnsymyeVZEUnB+rQUxXtMoIk3bt2LFnEUMvfNRKqSlcvzF+8ZivHnf01zwr+sAGPfBFyz6aSaZuzN55ZaHaNe7O71OOTaosA2jUJTZhGHm2eLhP/sDwGpVnV3c8xmFQ0Q45m+nRd0XShYAfc48kT5nnlhaYRlGXCizCQMzzxZEPxH5Jex9SC0yXER24ZrHvgWi370Mo5yTiKOUgsZstREkgXm2KFgfhpHweJdUoYeknfnsrYHf/D74x8MJNYSuLNcwSp1kMc8WhVjFbYaR6GSZfDBmykXCKG/m2aIwcsbooEMwjGJxRue+QYeQ9JSLhGHmWcMwjOJTLhKGYZQWi2bOZdTr75KVlUX3Y3rT97QTcuzfk5nJu8+9xq8rfqZKtar87forqFW/bkDRlm+s/zZ2yvJMb8NIKLKysvjk1be45NZrueGxe5g1YQp//vJbjjJTfphA5WpVuemp++l98rF8McIGGxiJQ1ImDLPXZm8fLSLdih+pURhWL11Bnf3rU7tBPSpUqEDHw7uxYGrOUdYLps6iy5GHAdC+ZxeWzVtoT7oBEbR4MBH/7kmZMIrBQJLPXmuUEps3ZFCzTq3s9zVq12JTmFcqVCa9Tm0AUlJSqFS5Mtu3bCvVOA2jqCRMwjB7bfHstUYwRP4fqUR5qkyokfhGeSZhEgZmrw1RZHttOGarjT81aqezaf3G7PebN2ykRq2c61fVrF2LjPUbANi7dy87d+ygSrWqpRqn4chCA/9JNBIpYcwBjvVP2H1UdRNwtIhMFpE5wDFAu4hjwu21M3GOqEaFuZi6BsaQvTYdZ6/9wu+OtNf2jrDXzgReAqLWZvIjD3vtkWFFimuvBZytVlW7qWq3QYMGFfU0RhiNWjRl3R9r2LBmHXv27GHWxKm06ZpzKZI2XTswfeyPAMydPJ0W7VoTpWJsGGWShBlWa/babIpsrzVKlpSUFE695DxeffApNCuLbkcfQYPGDfnm3U84sPlBtO3WkW5H9+bd517lkevvpEq1qgy47vKgwzaMQpMwNxyz1xqJQOvOh9K686E5th13zqnZr1PTUjn/hitLOywjCok4SiloEiZhYPbagsjLXmsYhhEXzFZbAOXEXqvmkjISHe+SKnSH0EmP/1/gN7/Pb3wyoTqwEqmGUeqUJ3utidsMwyiIcpkwzF6bm9cnfh50CIZRLC4+/KSgQ0h6ymXCMHutYRhZ1hwfM+UyYRhGSaGqfDPiQ5bNXkCFtFROuWwA+zdtnKvc7ytXM+r/27vz+Kjqq4/jn0MSICEhIUBAZQcr+6IBQRFB0IpLrWCl1A2tAlUfrdatilYrtgg+LqhPFXBBKygqVqnViiu7gsiuyC47gSQQIEiW8/xxb5JJMhMmySyZyXn31ZeZu82ZADn53Xt/3zttJvl5ebTv3onzf3e5zccwNV4kTdwzpsbbtOp7MvdmMHbC/Vw06ko+fr3snd6Oj197h6GjrmTshPvJ3JvB5tU/hLhSE+7gwUi84SjiG4Yl05qa5Mfv1tDtrN6ICKe0b8Oxo7kczi6dKXk4+yA/5x6jRYc2iAjdzurN+uWrw1SxMf6L+IZRDaOwZFoTYIezD9IwtSQ/KqlRCjlZpf+oc7IO0jA1uWSb1ORyTcWYmqhGNgxLpq1cMq2I9PGo9zIRyRWRuiJSX0Q2V/zdNoHk9TRDmb+qEXgmIippDfhfpKmRDQNLpi3ibzLtcqCX+/U5wBqgN3Am8LW3HSytNnCWfbaAaQ9NYtpDk0hKSeaQxzMwcrKySUppWGr7hqnJHMosGVHkZB4kMSUZY2q6mnqX1GrgCRF5HPi3qs4XkeEicg+QAKTixHvM8djHM5kWIAbY7c+bqaqKSFEy7Ss4ybTXuqvLJtPOLpNMW3SYepX9kD6Sad/22MSvZFpVzReRjSLSCegDPImTcBsDzPexzxScpgegNg+j6tIH9yd9cH8ANq5cy7LPFtD5zF7s2ryNevHx5ZpBYkoy9erXY+emrZzcrjWrFy0lfbA92sTUfDWyYVgybbHKJNPOB4bi5Gx9ipOLFYMz+jEh0r57Zzau+p5/3PsYcXXrcsnvf1u8btpDk7jxr3cDcOG1VzDnpZnkH8+jfbdOtO/eKVwl11o2D6PyamTDsGTaKpmHc3rrNVXNEJHGQHN8By2aIBARLrzG+017Rc0C4KS2rRg9/t5QlWVMQNTIhoEl056It2Tar4FmOI0DYBWwTyPxZm9jQsD+aVSepdV6qCXJtN7YNQwT8dwsKb+nyw+eeEvYf/h9ds/zETW9v6aOMEKuNiXTemPBbcaYE4n6hmHJtP7JzsgLdwnGVEtK07hKbW8XvSsv6huGJdMaY0xgRH3DMCaUFi9ZwJPPTKCwsIBfXTKc6665sdT62f96i3dmv0mdOnWIj0/gz/c8TLu27cNUrTGVYw3DmAApKChg0pPjefapqaSlNWfUjSM4p/+gUg3hgvMvZtivnRvt5i34gmeencgzT74YrpJrNdXCcJcQcWpqNEhQREKyrYg8LCJHRSTNY9nhULy3qZ5136+mRYtWnHJKS+Li4jh/yFDmLSg9VzOxQcklsdzcXHsGhokoNsLwzyicfKZdIXq//Th3a9nMrgiyL2MfzdKaF79Oa9qMtevKx5a//e5MZr41nbz8PJ5/5uVy601o2JSCyov4EUYUJtuCM6lvhIikeqnnajfBdoWIvCgiMSJypYg86a6/vSih1n3fBf59J021efkB5G0E8ZvhI5k962NuHXsnr0y301EmckR8wyD6km0BDrs13V6m7k44M83PdmspwJlxPg8npRb3vwdE5BSgPz7CBy2tNvDS0pqxd9+e4tf7MvbSpElTn9ufP2QoX82vbLyYMeETDaekojXZdjKwQkT+12PZYJwQw6XuseJx4j/2iEiiiCQBLYEZOGm151CSeFv2c5RKq7V5GNXXqWNXtm//iV27dtC0aTPmfvoRj/5lYqltftq+jVYtWwOwcNE8WrZoFY5SDTYPoyoivmFEYbKts6NqtojMAG72WCzAdFX9s5ddFuOMdtbjjCpuwGlmtXLmejjExsZy1533c9udYygsLODSiy+nXbsOvDjtOTp17MKA/oN4+90ZLF22hNjYWJKSGvKXB/4W7rKN8VvEN4woTLb19CRO0GLRn9NnwPsi8pSq7nOvcSSp6jac01J/df//HTAIyFVVe/ZnCJ3dbwBn9xtQatmYG28t/vpPf/TW60042EXvyov4hkGUJduWqXO/e4H9Dvf1OvcYn4hIHffz3gJswxlVtATmqWqBiGwHfjjRexhjjL8srbaKoizZ1q5hmIjnZkn5PbGl/2M3hf2H34IHpkbURBxrGFXgkWx7vqr+7LE8YhtGuAswJkAi6gdwpLGG4cHfZNsAvVdNSrbV15d8HIa3NSZwrul7IVjDCKpouIYRMJZsa4wxvlnDMCaAVJVP3pjNxpXriKsbx6U3XcVJbVqW2273lu18MO0N8o/n0aFHZy64apjlSpkaLxpmehtTY2xatY7MPRncPHEcF13/Wz6a/rbX7T6aPouLrx/BzRPHkbkng02rvg9xpcZUXq1uGJGQXuu+32gR+cH9/zci0t9j3R9FJMHjtSXbhtH65WvodnZvRIQWHdpw7GguOdmlp8LkZB/k52PHaNGhLSJCt7N7s355+ZBCY2qaWt0wqmEUEKq480uAMUB/Ve0IjAVmiEhRLOofcSJQTA2Qk5VNw8Ypxa8bpiaTk1WmYWQdJKmR5zYp5GRlh6xGY6oq6hpGFKbX3gvcrar7AVR1OTAduEVEbsNpXF+IyBceNTzmfv4lItKsst9DE1hS9sYdP1Ntjalpoq5hEH3ptV2Ab8ssWwZ0UdXJOM/oGKSqgzzec4mq9sCJC7nJ20EtrTZwln06n6kPTmTqgxNJTEnm0IGS0cKhzIMkNmpYavukMiOKQ5nZJKYkh6xeY6oqGu+Sitb0Wk+C78l2x4F/u19/S+msK8+6S6XV2jyMqksfcg7pQ5x0+Q0r1rLs0/l06Xs6Ozdto358fZLKNIOklGTq1q/Hjo1bOaV9a1YvXErv88/xdmhjapSoaxhRmF67DifS3PPBCae7y73J05LZmAVE4Z9xTdahR2c2rlrH83c/Sly9ulx64++K1019cCI3PXoPAEOvu5I5U98g73geHbp3pn33zuEq2Ri/Rd0PkyhMr50IPC4iF6rqARHp6X6mM8vUsr/Cb4wJCRFh6LVlJ/A7ipoFwMltWzHmb5ZcayJL1DUMoiy9VlU/EOfpeYtERHEaxNWqWnTKbArwkYjs9riOYYwxAWdZUgES4em1dg3DRDzLkgo+axgBEAXptfaXwEQLaxhBZA2jArUovVb/8v7UELyNMcHzyGU3gTWMoIrGaxgBY+m1xhhTwhqGMQE2tFs/Tk1rSV5BPv/67it2HzxQan1cTAxXpg+hUYOGqCrr927j03Xl7sUwpsaxhmFMAJ2a1pLGDZKZ/NksWjRK45Ie/Zk67/1y2y3ctIqt+3cTI3W47uyL6ZDWgo37doShYmP8F43RICdU01NqRSRFRA4UZV6JSD8RURFp4b5OFpFMEanjfpYtbnbUjyLymnsbrgmDjie1ZsX2DQDsyNpH/bi6JNaLL7VNXkEBW/c7d0UXaCG7s/eTHN8g5LUaU1m1smFUwyhCkFKrqtnAHqCTu+gs4Dv3vwB9ga9VtdB9fbebHXWau90XIlI32HWa8pLqN+BQbknC/KHcIzSsoBnUj63LL5q3YnPGrlCUZ0y1REXD8JZQ6y6P5JTahZQ0iLOAp8q8XlR2B3U8hdNshlb4TTNB4e0WHV83ItYR4Yr08/h681qyjuYEtS5jAiEqGgbeE2ohslNqF1HSINoBb+M0LNzlCyvYdzlQUTOytNoA6tO2M2MHDmPswGHkHDtKw/iSqTcN4xuQc+yI1/0u7XEOB44cZMnmNaEq1ZhqiZaL3uUSat3lgyI4pXYhcJ+ItAW2quoxcSTihBF+U8G+J7wXvWxarc3DqLpvtqzjmy1OIsypzVpyZtsurNm5iRaN0jiWd5zDP+eW2+e8junUj6vLByvmhbpcY6osKhqGj4TaiTi/wUdkSq2qbhCRRsClwGJ38bc4I5ktqlrRo1h7AZ8+xrFWAAAY00lEQVT58z4msDbs3c4vmrXk9iEjim+rLTJ24DBe+HI2Des34NzTepGRk8WYgcMA+GbzWpb/tD5cZRvjl6hoGD4SaouaQ6Sm1ILTKG6nJHF3MTAe53SYt++DAP8DnETJaTkTYh+uKnd5CYAXvpwNwKFjR7ARnYlEUdEw8JJQq6rZIjKVCE2pdS3EGTUtc18vxrmeUfYn0iQReRDn1NsSnCfwHa/guMYYU2mWJVVNEZ5SW8SuYZiIZ1lSwWcNoxqiIKW2iP0lMNHCGkYQWcPwohal1BbRrL15IXw7YwKvUbM4sIYRVNYwDFjDMFHAGkbwRctFb2NqhMVfL+CpyRMoLCzgVxcP59qrbyy1fvb7b/Hu7DepE1OH+PgE/nz3w7Rt0z5M1RpTOTbCMGAjjIAoKCjgyqsuZvKTU0lr2pzrR4/g0b9MKtUQjhw5TIMGzuWteQu+YPa/3uTpJ14MV8lRxUYYwRfyaBARaSMiAc1CEJH7A3m8mk5EEkXkHyKySUS+c7OpbvKxbZWSeU3lrft+NS1OacUpJ7ckLi6O8wcPZd6Cz0ttU9QsAI4dy4Xy8WbG1FjRckrqfuBv4S6iKkQkRlULKrnbNGAzcKqqFrp5VjcEvjpTGRn795GW1rz4dVrTZqxdt7rcdu/MnsnMWdPJy8vjuadfDmWJxlRLuMIHY0VkuoisEpF3RCRBRCaIyDp32RNQ/NvxP0TkCxHZLCLnisjLIvK9iLzqbjMBiHcTZN/w9YYicrWIfONu96KIxIhIaxHZICJNxHm2xHwRucAdBf1Qtkb3OIPd3+pXu7XUK6rDR/1XeNRw2P3vQPczzcCZWOi1Ph+foz3QBxhXFG+uqhmq+ri7XkTkObeWD4G06vxBGf95Pb3rZQRxxbCRvPvmx9wy9k5efc1OR5nIEa6GcRowRVW7A4eAW4HLgS7usvEe2zYCzgPuwMltegroAnQTkZ6qeh+Qq6o9VfUqb28mIp1wZluf7WY5FeCkzm4DHgdeAP4ErFPVT3zUeLOI1MeZ7T1CVbvhjND+ICKpFdTvSx/gAVXt7Ks+H/t1AVZ6PAujrMvd2rsBN1GSeFv2e2JptQGW1rQZ+/btKX69L2MvTZs09bn9+YOH8lWZU1bG1GThahjbVbUonvufwACcAL9pIjIMOOqx7Rx1fnVbDexV1dXuD8u1QBs/328wTsLrUhFZ4b5uB6Cq03DyocbiRI77qrE/zg/iLar6o7t8ulv7oQrq9+UbVd1yovpOREQecEclRU/gGQDMVNUCVd0FeP2JpKpTVDVdVdNHjx7tz1uZE+jUsSvbd/zErl07yMvLY+5nH3HO2YNKbfPT9m3FXy9cPI+WLVqFukxjqixc1zDKjt3zcH7jHgz8FmfEcZ67rmgGdaHH10Wv/a1fgOmq+udyK5xTTS3cl4k4AYPealR83IGhqvki4q3+fNym7AYDej4Fz/MhCT7r82Id0ENE6qhqoTvB77Gi010+ajchEBsby11/vJ/b7xpDYWEBl1x0Oe3admDKS8/R8bQuDOg/iHdmz2Dpt0uIjY0lKakhD90fkZfeTC0VrobRqighFhgJrACSVfU/IrIE2FjJ4+WJSJyq+ro39DPgfRF5SlX3uaeQkjxOSb0BbAOmUvKQpbI1LgB+ANqISAdV3QhcA3wlThpugpf6t+KMHGYBl+EEDla2vlJUdaOILAPGi8iDqlrgniorambzgDEi8hrO9YtBwIwTfP9MgJzVbwBn9RtQatno399a/PWdt/vzO4ExNVO4Tkl9D1wnIqtwHmw0Dfi3+/ornOsVlTEFWOXrorebJDsO+MR9j7nASSJyLtAbeFxV3wCOi8j1Pmr8h6oew3kexdsishpnlPMCziktb/VPBc4VkW+AMyk9qjhhfRV83huBxsBGcfKsPgXudde9B2zAOYX3D7ceY4ypNpu454WItMF5cl/XMJcSKjZxz0Q8m7gXfNEyD8NUk/uPzRhjfIqqEYaINMb7o0kHq+qBUNdTXSLyNeWf+32NqpafDVY9+tHaJQE+pDGhNbRLX7ARRlBF1QjDbQp+PTM7EqjqmeGuwRhjikRVwzAm3L5fvorZL7+BFhbSd8i5DBl2San1+Xl5/POZKezYvJWEpESu+9PNNE7zPbnPmJokXHdJGRN1CgsKeWfqa4wZ9yfue+bvLJ+/hD3bd5baZsmn80hIbMC4/5vEwEt/yZzXZoWpWmMqL6QNQyypttpEZKuIzC+zbEWgv6+m8rZt3EyTk5rRpHkasXGx9Op/Jqu/WV5qm9VLl9N7UH8AevTrzYbV67xnUBlTA0XDCCNiG4avgEE/JIlIS/cYnQJYkqmGgweyaNQ4tfh1SuNUDmZm+dwmJiaG+gnxHMk5jDGRIBwNw5Jqq5hU62EWTlghOLPQZ3q8zygRmS0iH7ufb2Il/mxMtZQfKUi5m3a8bGPPxDARIhwNw5Jqq55UW+QdYJj79aXu98ZTT/eY3YARRaORMt8XS6sNsOTGqWQdyCx+nX0gk4apKT63KSgo4NjRXBISG4S0TmOqKhwNw5Jqq59UmwlkichvcSJMyr7nZ6p60I0yWQe0LnsAS6sNvFYd2rJ/914O7M0gPy+f7xZ8TdfevUpt07V3L5Z+sQCAlYuXcmq3TjbCMBEjHLfVWlJt1ZNqPb0FPA+M8rLO83tVgN0+HRIxMTEMv/EaXvjrJAoLCzlz8ABOatWC/8ycTav2beja53T6Dh7AP5+Zwvib7yYhsQHX3nlzuMs2xm/h+EFiSbX+11eR93ACCv8LnHyCbU2IdD6jB53P6FFq2UUjhxV/HVe3LtfffWvZ3YyJCOE4JWVJtX7Ud6IPrao5qvq4qh4/0bbGGBMIUZUlFQi1MKkWLEvKRAHLkgo+axhl1NaGEe4CjAkQaxhBFDUNw5Jqq0XvmjU5CIc1JnSeuPI2sIYRVFFz94wl1RpjTHBFTcMwpqa4rNcAOjVvw/GCfN76Zi47szNKrY+LieXafkNpnJhMoSrrdm3hP6sXhalaY/xnDcOYAOrYvDVNE1OY8NFrtEptzvAzBjH5s/KJtF+u/45NGTuIqVOHMedeTsfmrflhz4nupDYmvCytNsK4abXvery+oihby2PZ+yKyOOTFGbqc0o5lW38A4KfMPdSPq0dS/YRS2+QV5LMpYwcABYWF7MzKIDkhMeS1GlNZllYbRtVIq00XkS4+jpkCnA6kiEjbKhdnqiQ5PpHs3Jzi1wdzD5Mc77sZ1I+rS+eT27Jh7/ZQlGdMtVhabWSm1T6B70Y5HCeM8E2cqBITQt5u0VEfdy3XEeHqvheyYMNKMo8cCm5hxgSApdVGZlrtLOB0EengZV1R3PlM92uvLK02cM7q0J07zh/JHeeP5OCxI6TEJxWvS45P5FCu10n+XJF+HhmHs5m/YUWoSjWmWiytNjLTaguASUCpwEIRaQZ0ABa4deaLiNcJiJZWGziLNq7iqbkzeWruTNbu3Ex6m44AtEptzrG8n8k5Vv6vxIVd+1I/rh4ffDcv1OUaU2XhaBi+0mrfBX4NfOyxLpBptT3d/5+mqg+D17RaXzVWmFbro/7KptWWq+8EXsdpWK08lo3AGZVtEZGtOE3VTkuF0Pe7t3LgyCHuu+g6fpN+HrOXf1m87o7znQFfcnwiQzr3oVnDVP7ojkz6tPV6ScqYGsXSaiM0rVZV80TkKeA+4HN38UjgQrdu3Ivec3HCDU2IvOfRJDw9Ndd5MOLB3MPYzHoTiSytNkLTal0v4TZ9NwOrFVCcIuie9jokIjZr3BhTbVGTJRUotTV80H7jNZHOsqSCzxpGGbW1YYS7AGMCxBpGEEVNw7C02mrRMdMnBOGwxoTOi9fdB9YwgipqsqQsrdYYY4IrahqGMTXFiD5D6HpKe47n5/Hqwg/Znrm33Da3DbmShvGJxNQRNuzdwcyvPyFaRvsmelnDMCaAup7SjrSkRjz43ou0bXIyV/X9JRP+81q57aZ89S+O5TmPYx8z8HLOaN2RZVu/D3W5xlRKyG+rtcTa6vMnsdaER4+Wp7Jks/PXe8v+XcTXrUfD+AbltitqFnWkDrF1YrD7DkwkiIa0WrDEWlNDpCQkkXmkJK02+2gOjRKSvG5725AreWLEbRzL+5lvt60PVYnGVFm4GoYl1gYpsVZEUkXkX24dS0Sku99/KqbaxMs9Or6uTUz+dBb3zHqW2JhYOjZvHeTKjKm+cDUMS6wNXmLtI8B3bh33A+VPoGNptYE08LTTGXfp9Yy79Hqyjx4mtUHJiCIlIYns3MM+980vLGDl9g30aHVqKEo1plrC1TAssTZIibVuna+7n+1zoLGIJJfd2dJqA+fL9csZP+cVxs95hRU/baBvO2fOZ9smJ5Ob93O5ePN6sXHF1zXqiNDtlPbsORhxU4VMLRSuu6R8JdYOxklXvRVnVAGBTawt+8PVW2Jt0QnoSiXWioi3+iubWFuuvhN4HadhrC1zrHIlVvK4porW7NxEtxbtGD9sDMfz85i+8D/F68Zdej3j57xC3dg4bjnvCmLrxFCnjrB+90/MW/9dGKs2xj/hahiWWOt/fT75SKydh3M661ERGQjsV1V7nFsIzfx6rtfl4+e8AkDOsaP8/cPpoSzJmIAI1ykpS6z1oz4/P3txYq3rYZw7qFYBE4Dr/DyOMcZUKGqypAKpFgYQWpaUiXiWJRV81jC8qI0NI9wFGBMg1jCCKKoahiXWVpl+s81iKUxk69O6E1jDCKqoypKyxFpjjAmeaIkGMaZGWLV0OXffcDN/GjWWOW++W279D6vWMu7mO7nuwmF8M29RGCo0puqsYRgTIIUFBUx/7kXufuwhHp/6LIu/nM/ObdtLbdM4rQmj77qNfucNCFOVxlRd1DaMotymCta3EZHfVeG4AU/brez7ullUB91Mqx+KsqvcdVe5OVKrRGSRiPQIda211ab1G2h28kmkndSc2Lg4+p7bn28XfV1qm6bNm9GqXRvEW+iUMTVc1DYMP7QBKt0wapD5qtoL6AVcIiJnu8u3AOe6WVKP4sxRMSGQtT+T1KZNil+nNm1M1oHMMFZkTGBFfcMQxyQRWeMmzI5wV00AznHTYb1OFBSRLh4JsqtEpCghLkZEporIWhH5RETi3e1vEpGlIrJSRN71SLh9VURecNNwfxSRS9zlMW5tS93jj6ns51PVXJyZ8qe4rxepapa7egklsScmyNTL3ck2kDDRJOobBjAM586pHsAQYJKInIQTpzHfTbl9yse+Y4Fn3ATZdGCHu/xU4HlV7QJkA8Pd5bNVtbeq9sCZKf57j2O1Ac4FLgZecJNvfw8cVNXeODPObxKRtpX5cCLSyK1nnpfVvwc+8rGfpdUGWGqTxmRm7C9+nZlxgJTU1DBWZExgRdVttT70B2aqagGwV0S+wvnh7E++0mLgARFpgdMMNrjnnreo6gp3m28pSc3tKiLjgRScIMP/ehxrlpuyu0FENgMdgQuA7h7PzEjG+eG/hRM7x43/OA2YoKp7PFeKyCCchtHf286qOoWS01U2DyMA2p12Knt27mbf7r2kNkllyVcLuPm+O8NdljEBUxsaRpVPCqjqDHfy3MXAf0XkRmAzpVNzC4B49+tXgV+r6koRGQUM9Dxc2cO7tf2Pqv6XypuvqpeIyC+ABSLyXlETcx+aNA0YGokTFiNVTEwM1956E5Puf4TCwgIG/HIILdq04t3pM2j7iw6c3q8Pm9dv4OlHJnAk5zArlixj9uszmTD12XCXboxfakPDmAeMEZHpOCGCA4C7cc75e392pktE2gGbVXWy+3V3nIbhSxKwW0TicBJjd3qs+41bQ1ucZ12sxxmB/EFEPneTZ38B7FRVryGF3qjqjyLyd+BeYKSItAJm48wI/7HivU2g9eyTTs8+6aWWDb+u5N6KdqedyuQZL4W6LGMCojY0jPeAfsBKnN/q71HVPSJyAMgXkZXAqz6uY4wArhaRPGAP8FegYQXv9SDwNU5U+mpKN6T1OEm2zYCxqnpMRKbhnM5a7j4vIwP4dRU+4wvAXe71jweAxsD/uafP8lU1vaKdjTHGH1GVJVVTifP88X+r6jvhrsUHu4ZhIp5lSQVfbRhhGD+4/9iMMcYnG2EAIvJLnCfvedqiqpeHoZZuuM/k9vBzkIMI9b0VXwXx8MYE3+U9zwUbYQSVjTAA9y6lqtypFHBudHnUJO4aY6KHNQxjAmj9ijXMefUttLCQ3uf1Z+Cvh5Zan5+Xx6znX2Hn5m0kJDVg5O2jSU1r4uNoxtQstWGmtzEhUVhYyPsvz+D6P9/GHU8+woqFS9m7Y1epbZZ+vpD4BgncPfkx+l80hI9nzA5TtcZUXtQ2jFqcVnuZm0u1wo3+8DrT2wTe9o1baNwsjcbNmhIbG0uPs3qzbunKUtusW7aC08/tB0DXvmewcc332HVEEymitmH4oQ3RmVb7GdDDzb+6AWfGtwmBQ5nZJDcuyY5KbpzCoaysctukuNvExMRQPyGeozkV/m5jTI0R9Q2jFqbVHtaSX1kbUD6SxASJ95FC6Zt2vCXaWqStiRRR3zCohWm1InK5iPwAfIgzyvC2n6XVBlhy40Yc9Hj+xcED2TRslFJ6m9RGZLvbFBQUcOxoLgmJDUJapzFVVRsaRnFararuxYnn6O3nvouB+0XkXqC1+9s8VJxWO19EVuNkSXXxONYsVS1U1Q04eVRFabXXisgKnEiRxjg//P1RlFa7B2cWeXFaraq+p6odcWJGHvW2s6pOUdV0VU0fPXq0n29pKtKifRsO7NlH5r795Ofns3LRUjqnl37gYef0Hiz/ajEAa5Z8S/suHe3peyZi1IbbamtdWq1H/fNEpL2INFHV/T6OYwIkJiaGX90wkpf/9jSFhYWkDzybZi1P5pNZ79OiXWs6p/ckfVB/Zj33EpNue4D4xAaMvP2mcJdtjN9qQ8OobWm1HYBNqqoicjpQF7CI8xDp2KsbHXt1K7XsgisvK/46rm4cV905NtRlGRMQtaFh1La02uE4p7nygFxghMdFcGOMqTLLkgqBSEirtSwpE+ksSyr4asMIw/jB/cdmjDE+2QgDS6sFdMKojUE8vDHBd9+rHcBGGEFlIwwsrdYYY/xhDcOYALnohjTa90zg6KECXhq33es2Q65qQvvuCeQdVz6cto+92372up0xNVFtmLgXVm4syBUBOla6iEwOxLFM4K1ecIhZ/7vb5/p23RNo1CyOF+/9iY9f3ccvr20awuqMqT4bYUQIEYlV1WXAsnDXYrzb/uMxkpv4/id1aq8GrFmYA8CuTT9TL6EODZJjOHKwIFQlGlMtNsKoAhFpICIfuiGDa0RkhIg85IYIrhGRKeIl70FEzhCRr0TkWxH5r5tphYh8KSJPi8gid/8+7vKH3WN9Arzmxpr/212XKCKvuIGKq0RkuLv8AhFZLCLLReRtEUkM4bfGVCCpUSw5mfnFr3Oy8klqZL+zmchhDaNqLgR2qWoPVe0KfAw85wYPdsWJCrnEcwd39vezwBWqegbwMvCYxyYNVPUs4GZ3XZEzgMtUtWwU+4M4wYXdVLU78LmINAHGAUNU9XSc0cidAfrMprq83L9jNymaSGK/3lTNauAJEXkcZ0LefBEZLiL3AAk4ESRrgTke+5wGdAXmuoOPGMDzhPdMKM5/aigiRTGnH3iEHnoaAvy26IWqZrmx6Z2Bhe571MUJUCxHREYDowFefPFF4LxKfHxTFTmZ+SSllvyTS2oUy+Hs/Ar2MKZmsYZRBW5+0xnARcDf3VNGtwDpqrpdRB4G6pfZTYC1qtrP12F9vPaVKyVe9hFgrqqO9OMzTAGKcs11wiKbhxFsG1cc4fTByXz/9WFObl+Pn3ML7fqFiSh2SqoKRORk4Kiq/hN4AjjdXbXfvWbg7a6o9UBTEennHiNORDzjz0e4y/vjnGo6eIIyPgFu9aipEbAEONsNIEREEtxAQxMCvxrbjGvGtSC1eV1ufrIN3Qck0XNQQ3oOcuLHNq08SnZGHmMmtubCUWl88lpGmCs2pnJshFE13XAexFQI5AF/wAkNXA1sBZaW3UFVj7u3104WkWSc7/3TOKeuALJEZBFOuKHXhx6VMR54XpznfBcAj6jqbDdWfaaI1HO3Gwf8WKVPaSrlgxf2nnCbua/vByxp3kQmiwapAUTkS+Au97bZcLBoEBPxLBok+OyUlDHGGL/YCMNA+YvnxkQqG2EEkV3DMGD/yIJOREa7d6YZE7HslJQxoTE63AUYU13WMIwxxvjFGoYxxhi/WMMwJjTs+oWJeHaXlDHGGL/YCMMYY4xfrGEYU0X2NEVT29g8DGPCzJ6maCKFjTCM8WBPUzTGN2sYxpRmT1M0xgc7JWVMaRH/NEVjgsUahjEeouFpisYEi52SMsaDPU3RGN9shGFMafY0RWN8sJnexgRRDXiaojEBY6ekjDHG+MVGGMYYY/xiIwxjjDF+sYZhjDHGL9YwjDHG+MUahjHGGL9YwzDGGOMXaxjGGGP88v8IDvLWdA6HlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x1224 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (4, 17))\n",
    "\n",
    "cmap = sns.diverging_palette(150, 275, as_cmap=True)\n",
    "sns.heatmap(df[df.columns[121:186]].join(df.saleprice).corr()[['saleprice']], annot=True, linewidths=.4, fmt=\".1f\", cmap=cmap, ax=ax);\n",
    "#sns.set(font_scale=4)\n",
    "#plt.savefig(\"heatmap_fe_3.png\");\n",
    "\n",
    "#created at a correlation heatmap of my new features in relation to sale price, looking at the next 65 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (2, 15))\n",
    "\n",
    "cmap = sns.diverging_palette(150, 275, as_cmap=True)\n",
    "sns.heatmap(df[df.columns[186:]].join(df.saleprice).corr()[['saleprice']], annot=True, linewidths=.4, fmt=\".1f\", cmap=cmap, ax=ax);\n",
    "#sns.set(font_scale=4)\n",
    "\n",
    "#plt.savefig(\"heatmap_fe_4.png\");\n",
    "\n",
    "#created at a correlation heatmap of my new features in relation to sale price, looking at the last 17 features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> \n",
    "    <h1> MODELING </h1> \n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_columns = df._get_numeric_data().columns\n",
    "features = [col for col in numerical_columns if col != 'saleprice']\n",
    "X = df[features]\n",
    "y = df.saleprice\n",
    "\n",
    "# created my X and y variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = PolynomialFeatures()\n",
    "\n",
    "#instantiated Polynomial Features and set it equal to p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_poly = p.fit_transform(df[features])\n",
    "\n",
    "#fitting and transforming my features and setting them equal to a variable, features_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_df = pd.DataFrame(features_poly, columns=p.get_feature_names())\n",
    "\n",
    "#creating a data frame of my fit/transformed features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_columns_2 = df_2._get_numeric_data().columns\n",
    "features_2 = [col for col in numerical_columns_2 if col != 'saleprice']\n",
    "\n",
    "#creating features for my test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_poly_df_2 = p.transform(df_2[features_2])\n",
    "\n",
    "#transforming my features and setting them equal to a variable, features_poly_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_df_2 = pd.DataFrame(features_poly_df_2, columns=p.get_feature_names())\n",
    "\n",
    "#creating a data frame of my transformed features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(poly_df, df.saleprice, random_state=28)\n",
    "\n",
    "#instantiating train test split while creating my X and y train and test variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "ss.fit(X_train)\n",
    "X_train_sc = ss.transform(X_train)\n",
    "X_test_sc = ss.transform(X_test)\n",
    "\n",
    "#instantiating the standard scaler and setting it equal to ss\n",
    "#scaling my Xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_test_data = ss.transform(poly_df_2)\n",
    "\n",
    "#scaling my test data and setting it equal to a variable, scaled_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_log = np.log(y_train)\n",
    "y_test_log = np.log(y_test)\n",
    "\n",
    "#taking the log of my ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1538, 20706)\n",
      "(513, 20706)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "\n",
    "#printing the shape of my Xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lasso = LassoCV()\n",
    "ridge = RidgeCV()\n",
    "\n",
    "#instantiating 3 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train_sc, y_train_log)\n",
    "\n",
    "#fitting my training data to the linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_train_sc, y_train_log)\n",
    "\n",
    "#scoring my training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-324151780.462544"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test_sc, y_test_log)\n",
    "\n",
    "#scoring my testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([ 0.1,  1. , 10. ]), cv=None, fit_intercept=True,\n",
       "    gcv_mode=None, normalize=False, scoring=None, store_cv_values=False)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.fit(X_train_sc, y_train_log)\n",
    "\n",
    "#fitting my training data to the ridge model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999760746390101"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.score(X_train_sc, y_train_log)\n",
    "\n",
    "#scoring my training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7903377471339575"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.score(X_test_sc, y_test_log)\n",
    "\n",
    "#scoring my testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=None, copy_X=True, cv='warn', eps=0.001, fit_intercept=True,\n",
       "    max_iter=1000, n_alphas=100, n_jobs=None, normalize=False,\n",
       "    positive=False, precompute='auto', random_state=None,\n",
       "    selection='cyclic', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.fit(X_train_sc, y_train_log)\n",
    "\n",
    "#fitting my training data to the lasso model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9409307241774675"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.score(X_train_sc, y_train_log)\n",
    "\n",
    "#scoring my training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8624788310654063"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.score(X_test_sc, y_test_log)\n",
    "\n",
    "#scoring my testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lasso.predict(scaled_test_data)\n",
    "\n",
    "#creating a variable, y_pred, and setting it equal to the lasso prediction of my scaled test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.exp(y_pred)\n",
    "y_pred_2 = np.exp(y_pred)\n",
    "\n",
    "#reversing the log transform I did earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (11, 8))\n",
    "sns.distplot(np.exp(df.saleprice), kde=False, color='#A15EDB', bins=50, label='y')\n",
    "sns.distplot(y_pred_2, kde=False, color='#69547C', bins=50, label='yhat')\n",
    "\n",
    "plt.xlabel('Sale Price', fontsize=19, labelpad=11)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.ylabel('Count', fontsize=19, labelpad=11)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(loc='upper right');\n",
    "\n",
    "\n",
    "#plt.savefig(\"y_vs_yhat.png\");\n",
    "\n",
    "#looking at the distribution of my target variable versus my predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ids = df_2['id']\n",
    "\n",
    "#creating a variable, my_ids, from my test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.DataFrame()\n",
    "\n",
    "#creating an empty data frame to house my ids and predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df['id'] = my_ids\n",
    "\n",
    "#storing my ids into my data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df['saleprice'] = y_pred_2\n",
    "\n",
    "#storing my predicted sale prices into my data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.set_index('id', inplace=True)\n",
    "\n",
    "#setting the index of my data frame to my ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.to_csv('./datasets/britt_final_submission.csv')\n",
    "\n",
    "#exporting my data frame of predictions to a csv named, \"britt_final_submission.csv\""
   ]
  }
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