HDB Resale Price ML Project 7. Using town and random forest with MRT distance (Best Result).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "resaleflat1 = pd.read_csv('resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv')\n",
    "#resaleflat2 = pd.read_csv('resale-flat-prices-based-on-approval-date-2000-feb-2012.csv')\n",
    "resaleflat3 = pd.read_csv('resale-flat-prices-based-on-registration-date-from-mar-2012-to-dec-2014.csv')\n",
    "resaleflat4 = pd.read_csv('resale-flat-prices-based-on-registration-date-from-jan-2015-to-dec-2016.csv')\n",
    "resaleflat = pd.concat((resaleflat1,resaleflat3,resaleflat4), axis=0, sort=True)\n",
    "\n",
    "resaleflat_l = resaleflat.copy()\n",
    "resaleflatview = resaleflat1.copy()\n",
    "\n",
    "locations = pd.read_csv('street_to_MRT.csv')\n",
    "locations = locations.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>block</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>street_name</th>\n",
       "      <th>town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>406</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1979</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>61 years 04 months</td>\n",
       "      <td>232000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>60 years 07 months</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>602</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 05 months</td>\n",
       "      <td>262000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>465</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 01 month</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>601</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 05 months</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  block      flat_model flat_type  floor_area_sqm  lease_commence_date  \\\n",
       "0   406        Improved    2 ROOM            44.0                 1979   \n",
       "1   108  New Generation    3 ROOM            67.0                 1978   \n",
       "2   602  New Generation    3 ROOM            67.0                 1980   \n",
       "3   465  New Generation    3 ROOM            68.0                 1980   \n",
       "4   601  New Generation    3 ROOM            67.0                 1980   \n",
       "\n",
       "     month     remaining_lease  resale_price storey_range        street_name  \\\n",
       "0  2017-01  61 years 04 months      232000.0     10 TO 12  ANG MO KIO AVE 10   \n",
       "1  2017-01  60 years 07 months      250000.0     01 TO 03   ANG MO KIO AVE 4   \n",
       "2  2017-01  62 years 05 months      262000.0     01 TO 03   ANG MO KIO AVE 5   \n",
       "3  2017-01   62 years 01 month      265000.0     04 TO 06  ANG MO KIO AVE 10   \n",
       "4  2017-01  62 years 05 months      265000.0     01 TO 03   ANG MO KIO AVE 5   \n",
       "\n",
       "         town  \n",
       "0  ANG MO KIO  \n",
       "1  ANG MO KIO  \n",
       "2  ANG MO KIO  \n",
       "3  ANG MO KIO  \n",
       "4  ANG MO KIO  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>street_name</th>\n",
       "      <th>dist_nearestMRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CIRCUIT RD</td>\n",
       "      <td>0.435332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>0.567413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JURONG EAST ST 24</td>\n",
       "      <td>0.738477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>0.884302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>0.884302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         street_name  dist_nearestMRT\n",
       "0         CIRCUIT RD         0.435332\n",
       "1  JURONG EAST ST 21         0.567413\n",
       "2  JURONG EAST ST 24         0.738477\n",
       "3  JURONG EAST ST 21         0.884302\n",
       "4  JURONG EAST ST 21         0.884302"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = locations[-locations.duplicated(keep='first',subset='street_name')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "resaleflat_l = pd.merge(resaleflat,locations,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 139788 entries, 0 to 139787\n",
      "Data columns (total 12 columns):\n",
      "block                  139788 non-null object\n",
      "flat_model             139788 non-null object\n",
      "flat_type              139788 non-null object\n",
      "floor_area_sqm         139788 non-null float64\n",
      "lease_commence_date    139788 non-null int64\n",
      "month                  139788 non-null object\n",
      "remaining_lease        87585 non-null object\n",
      "resale_price           139788 non-null float64\n",
      "storey_range           139788 non-null object\n",
      "street_name            139788 non-null object\n",
      "town                   139788 non-null object\n",
      "dist_nearestMRT        138819 non-null float64\n",
      "dtypes: float64(3), int64(1), object(8)\n",
      "memory usage: 13.9+ MB\n"
     ]
    }
   ],
   "source": [
    "resaleflat_l.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "block                      0\n",
       "flat_model                 0\n",
       "flat_type                  0\n",
       "floor_area_sqm             0\n",
       "lease_commence_date        0\n",
       "month                      0\n",
       "remaining_lease        52203\n",
       "resale_price               0\n",
       "storey_range               0\n",
       "street_name                0\n",
       "town                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat.isnull().sum()\n",
    "\n",
    "#Remaining lease has a lot of null, need to fix"
   ]
  },
  {
   "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>floor_area_sqm</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>dist_nearestMRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>139788.000000</td>\n",
       "      <td>139788.000000</td>\n",
       "      <td>1.397880e+05</td>\n",
       "      <td>138819.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.048536</td>\n",
       "      <td>1991.237145</td>\n",
       "      <td>4.473022e+05</td>\n",
       "      <td>0.790981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.518557</td>\n",
       "      <td>11.209949</td>\n",
       "      <td>1.385259e+05</td>\n",
       "      <td>0.440534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>1.600000e+05</td>\n",
       "      <td>0.035610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>3.480000e+05</td>\n",
       "      <td>0.456600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>4.200000e+05</td>\n",
       "      <td>0.660821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>5.150000e+05</td>\n",
       "      <td>1.077458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>280.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.200000e+06</td>\n",
       "      <td>3.533475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       floor_area_sqm  lease_commence_date  resale_price  dist_nearestMRT\n",
       "count   139788.000000        139788.000000  1.397880e+05    138819.000000\n",
       "mean        97.048536          1991.237145  4.473022e+05         0.790981\n",
       "std         24.518557            11.209949  1.385259e+05         0.440534\n",
       "min         31.000000          1966.000000  1.600000e+05         0.035610\n",
       "25%         74.000000          1984.000000  3.480000e+05         0.456600\n",
       "50%         96.000000          1989.000000  4.200000e+05         0.660821\n",
       "75%        112.000000          2000.000000  5.150000e+05         1.077458\n",
       "max        280.000000          2016.000000  1.200000e+06         3.533475"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat_l.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input 75% percentile for dist_nearestMRT for missing values, assume that these streets are far away from MRT\n",
    "resaleflat_l['dist_nearestMRT'] = resaleflat_l['dist_nearestMRT'].fillna(1.077)\n",
    "\n",
    "#Distance to nearest MRT is alr normalized (0-3.53), dont need to normalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We had to convert categorical to numerical first to see correlations, now we redo the table using onehotencoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>block</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>street_name</th>\n",
       "      <th>town</th>\n",
       "      <th>dist_nearestMRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>406</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1979</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>61 years 04 months</td>\n",
       "      <td>232000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>0.497407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>60 years 07 months</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>1.697526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>602</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 05 months</td>\n",
       "      <td>262000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>0.752168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>465</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 01 month</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>0.497407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>601</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 05 months</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>0.752168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  block      flat_model flat_type  floor_area_sqm  lease_commence_date  \\\n",
       "0   406        Improved    2 ROOM            44.0                 1979   \n",
       "1   108  New Generation    3 ROOM            67.0                 1978   \n",
       "2   602  New Generation    3 ROOM            67.0                 1980   \n",
       "3   465  New Generation    3 ROOM            68.0                 1980   \n",
       "4   601  New Generation    3 ROOM            67.0                 1980   \n",
       "\n",
       "     month     remaining_lease  resale_price storey_range        street_name  \\\n",
       "0  2017-01  61 years 04 months      232000.0     10 TO 12  ANG MO KIO AVE 10   \n",
       "1  2017-01  60 years 07 months      250000.0     01 TO 03   ANG MO KIO AVE 4   \n",
       "2  2017-01  62 years 05 months      262000.0     01 TO 03   ANG MO KIO AVE 5   \n",
       "3  2017-01   62 years 01 month      265000.0     04 TO 06  ANG MO KIO AVE 10   \n",
       "4  2017-01  62 years 05 months      265000.0     01 TO 03   ANG MO KIO AVE 5   \n",
       "\n",
       "         town  dist_nearestMRT  \n",
       "0  ANG MO KIO         0.497407  \n",
       "1  ANG MO KIO         1.697526  \n",
       "2  ANG MO KIO         0.752168  \n",
       "3  ANG MO KIO         0.497407  \n",
       "4  ANG MO KIO         0.752168  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat_l.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#Convert first two char into integer for years left\n",
    "resaleflat_l['remaining_lease_year'] = resaleflat_l['remaining_lease'].str.slice(stop=2).astype('float')\n",
    "#Substring/slice the month out\n",
    "resaleflat_l['remaining_lease_month'] = resaleflat_l['remaining_lease'].str.slice(start=9,stop=12)\n",
    "\n",
    "replacemonth = {}\n",
    "replacemonth[''] = 0\n",
    "replacemonth['0 m'] = 0\n",
    "resaleflat_l['remaining_lease_month'] = resaleflat_l['remaining_lease_month'].replace(replacemonth)\n",
    "\n",
    "resaleflat_l['remaining_lease_month'] = resaleflat_l['remaining_lease_month'].astype('float') / 12\n",
    "resaleflat_l['remaining_lease'] = resaleflat_l['remaining_lease_year'] + resaleflat_l['remaining_lease_month']\n",
    "resaleflat_l = resaleflat_l.drop(['remaining_lease_year','remaining_lease_month'], axis=1)\n",
    "\n",
    "resaleflat_l.month = resaleflat_l.month.apply(lambda x: float(x[0:4])+float(x[5:7])/12)\n",
    "#Inpute missing values with remaining lease (take current year-month minus the lease start year)\n",
    "resaleflat_l.remaining_lease[resaleflat_l.remaining_lease.isnull()] = resaleflat_l['month'] - resaleflat_l['lease_commence_date']\n",
    "\n",
    "stats = resaleflat_l.describe()\n",
    "\n",
    "#Normalize the month, floor area and remaining lease (timeframe)\n",
    "resaleflat_l.month = (resaleflat_l.month - resaleflat_l.month.mean()) / resaleflat_l.month.std()\n",
    "resaleflat_l.floor_area_sqm = (resaleflat_l.floor_area_sqm - resaleflat_l.floor_area_sqm.mean()) / resaleflat_l.floor_area_sqm.std()\n",
    "resaleflat_l.remaining_lease = (resaleflat_l.remaining_lease - resaleflat_l.remaining_lease.mean()) / resaleflat_l.remaining_lease.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>floor_area_sqm</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>dist_nearestMRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>139788.000000</td>\n",
       "      <td>139788.000000</td>\n",
       "      <td>139788.000000</td>\n",
       "      <td>139788.000000</td>\n",
       "      <td>1.397880e+05</td>\n",
       "      <td>139788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.048536</td>\n",
       "      <td>1991.237145</td>\n",
       "      <td>2015.904144</td>\n",
       "      <td>42.522339</td>\n",
       "      <td>4.473022e+05</td>\n",
       "      <td>0.792964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.518557</td>\n",
       "      <td>11.209949</td>\n",
       "      <td>2.179811</td>\n",
       "      <td>26.483713</td>\n",
       "      <td>1.385259e+05</td>\n",
       "      <td>0.439645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>2012.250000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.600000e+05</td>\n",
       "      <td>0.035610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>2013.916667</td>\n",
       "      <td>20.416667</td>\n",
       "      <td>3.480000e+05</td>\n",
       "      <td>0.459176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>4.200000e+05</td>\n",
       "      <td>0.668514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2017.833333</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>5.150000e+05</td>\n",
       "      <td>1.077000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>280.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2019.416667</td>\n",
       "      <td>96.666667</td>\n",
       "      <td>1.200000e+06</td>\n",
       "      <td>3.533475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       floor_area_sqm  lease_commence_date          month  remaining_lease  \\\n",
       "count   139788.000000        139788.000000  139788.000000    139788.000000   \n",
       "mean        97.048536          1991.237145    2015.904144        42.522339   \n",
       "std         24.518557            11.209949       2.179811        26.483713   \n",
       "min         31.000000          1966.000000    2012.250000         1.250000   \n",
       "25%         74.000000          1984.000000    2013.916667        20.416667   \n",
       "50%         96.000000          1989.000000    2016.000000        33.250000   \n",
       "75%        112.000000          2000.000000    2017.833333        66.500000   \n",
       "max        280.000000          2016.000000    2019.416667        96.666667   \n",
       "\n",
       "       resale_price  dist_nearestMRT  \n",
       "count  1.397880e+05    139788.000000  \n",
       "mean   4.473022e+05         0.792964  \n",
       "std    1.385259e+05         0.439645  \n",
       "min    1.600000e+05         0.035610  \n",
       "25%    3.480000e+05         0.459176  \n",
       "50%    4.200000e+05         0.668514  \n",
       "75%    5.150000e+05         1.077000  \n",
       "max    1.200000e+06         3.533475  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Blocks in Singapore is rather irregular and does not impact on the pricing\n",
    "#Lease commence date is highly correlated with remaining lease as remaining lease = current date - lease commence date\n",
    "\n",
    "#Use town instead of street_name for random forest, both are highly correlated so just use one\n",
    "\n",
    "resaleflat_l = resaleflat_l.drop(columns=['block','lease_commence_date','street_name','flat_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>flat_model</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>town</th>\n",
       "      <th>dist_nearestMRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Improved</td>\n",
       "      <td>-2.163608</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.710285</td>\n",
       "      <td>232000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>0.497407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New Generation</td>\n",
       "      <td>-1.225543</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.681966</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>1.697526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New Generation</td>\n",
       "      <td>-1.225543</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.751191</td>\n",
       "      <td>262000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>0.752168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Generation</td>\n",
       "      <td>-1.184757</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.738605</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>0.497407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New Generation</td>\n",
       "      <td>-1.225543</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.751191</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "      <td>0.752168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       flat_model  floor_area_sqm     month  remaining_lease  resale_price  \\\n",
       "0        Improved       -2.163608  0.540959         0.710285      232000.0   \n",
       "1  New Generation       -1.225543  0.540959         0.681966      250000.0   \n",
       "2  New Generation       -1.225543  0.540959         0.751191      262000.0   \n",
       "3  New Generation       -1.184757  0.540959         0.738605      265000.0   \n",
       "4  New Generation       -1.225543  0.540959         0.751191      265000.0   \n",
       "\n",
       "  storey_range        town  dist_nearestMRT  \n",
       "0     10 TO 12  ANG MO KIO         0.497407  \n",
       "1     01 TO 03  ANG MO KIO         1.697526  \n",
       "2     01 TO 03  ANG MO KIO         0.752168  \n",
       "3     04 TO 06  ANG MO KIO         0.497407  \n",
       "4     01 TO 03  ANG MO KIO         0.752168  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat_l.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Improved', 'New Generation', 'Standard', 'DBSS', 'Apartment',\n",
       "       'Simplified', 'Model A', 'Premium Apartment', 'Adjoined flat',\n",
       "       'Model A-Maisonette', 'Maisonette', 'Type S1', 'Type S2',\n",
       "       'Model A2', 'Terrace', 'Improved-Maisonette', 'Premium Maisonette',\n",
       "       'Multi Generation', 'Premium Apartment Loft', 'Premium Apartment.',\n",
       "       '2-room'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check that no repeats for flat model\n",
    "resaleflat_l.flat_model.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_flat_model = pd.get_dummies(resaleflat_l['flat_model'], prefix='flt')\n",
    "all_towns = pd.get_dummies(resaleflat_l['town'], prefix='st')\n",
    "\n",
    "resaleflat_l = resaleflat_l.drop(columns=['flat_model','town'])\n",
    "resaleflat_l = pd.concat((resaleflat_l,all_flat_model,all_towns), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10 TO 12', '01 TO 03', '04 TO 06', '07 TO 09', '13 TO 15',\n",
       "       '19 TO 21', '22 TO 24', '16 TO 18', '34 TO 36', '28 TO 30',\n",
       "       '37 TO 39', '49 TO 51', '25 TO 27', '40 TO 42', '31 TO 33',\n",
       "       '46 TO 48', '43 TO 45', '06 TO 10', '01 TO 05', '11 TO 15',\n",
       "       '16 TO 20', '21 TO 25', '26 TO 30', '36 TO 40', '31 TO 35'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat_l.storey_range.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get average of storey range e.g. 01 to 03 would be 2\n",
    "resaleflat_l.storey_range = resaleflat_l.storey_range.apply(lambda x: (float(x[0:2]) + float(x[7:9]) ) /2 )\n",
    "\n",
    "#Summary stat for normalization of Serangoon flat later\n",
    "flat_storey = resaleflat_l.storey_range.describe()\n",
    "\n",
    "resaleflat_l.storey_range = (resaleflat_l.storey_range - resaleflat_l.storey_range.mean()) / resaleflat_l.storey_range.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>storey_range</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>storey_range</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.303071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resale_price</th>\n",
       "      <td>0.303071</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              storey_range  resale_price\n",
       "storey_range      1.000000      0.303071\n",
       "resale_price      0.303071      1.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storey_resales = resaleflat_l[['storey_range','resale_price']]\n",
    "storey_resales.corr()\n",
    "\n",
    "#Storey range has correlation of 0.30 with resale price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>remaining_lease</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.0671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resale_price</th>\n",
       "      <td>-0.0671</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 remaining_lease  resale_price\n",
       "remaining_lease           1.0000       -0.0671\n",
       "resale_price             -0.0671        1.0000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lease_resales = resaleflat_l[['remaining_lease','resale_price']]\n",
    "lease_resales.corr()\n",
    "\n",
    "#Remaining lease only has -0.067 correlation with resale price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>month</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resale_price</th>\n",
       "      <td>-0.073186</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 month  resale_price\n",
       "month         1.000000     -0.073186\n",
       "resale_price -0.073186      1.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_resales = resaleflat_l[['month','resale_price']]\n",
    "month_resales.corr()\n",
    "\n",
    "#Year/month bought (month) has -0.07 correlation with resale price"
   ]
  },
  {
   "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>dist_nearestMRT</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dist_nearestMRT</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.085624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resale_price</th>\n",
       "      <td>-0.085624</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dist_nearestMRT  resale_price\n",
       "dist_nearestMRT         1.000000     -0.085624\n",
       "resale_price           -0.085624      1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MRT_resales = resaleflat_l[['dist_nearestMRT','resale_price']]\n",
    "MRT_resales.corr()\n",
    "\n",
    "#MRT has -0.0856 correlation with resale price, very high!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>floor_area_sqm</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.667404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resale_price</th>\n",
       "      <td>0.667404</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                floor_area_sqm  resale_price\n",
       "floor_area_sqm        1.000000      0.667404\n",
       "resale_price          0.667404      1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floor_resales = resaleflat_l[['floor_area_sqm','resale_price']]\n",
    "floor_resales.corr()\n",
    "\n",
    "#Floor area SQM has 0.667 correlation with resale price, very high!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>floor_area_sqm</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>dist_nearestMRT</th>\n",
       "      <th>flt_2-room</th>\n",
       "      <th>flt_Adjoined flat</th>\n",
       "      <th>flt_Apartment</th>\n",
       "      <th>flt_DBSS</th>\n",
       "      <th>...</th>\n",
       "      <th>st_PASIR RIS</th>\n",
       "      <th>st_PUNGGOL</th>\n",
       "      <th>st_QUEENSTOWN</th>\n",
       "      <th>st_SEMBAWANG</th>\n",
       "      <th>st_SENGKANG</th>\n",
       "      <th>st_SERANGOON</th>\n",
       "      <th>st_TAMPINES</th>\n",
       "      <th>st_TOA PAYOH</th>\n",
       "      <th>st_WOODLANDS</th>\n",
       "      <th>st_YISHUN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.163608</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.710285</td>\n",
       "      <td>232000.0</td>\n",
       "      <td>-0.030180</td>\n",
       "      <td>0.497407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.225543</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.681966</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>-1.370113</td>\n",
       "      <td>1.697526</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.225543</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.751191</td>\n",
       "      <td>262000.0</td>\n",
       "      <td>-1.370113</td>\n",
       "      <td>0.752168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.184757</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.738605</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>-0.365163</td>\n",
       "      <td>0.497407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.225543</td>\n",
       "      <td>0.540959</td>\n",
       "      <td>0.751191</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>-1.370113</td>\n",
       "      <td>0.752168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   floor_area_sqm     month  remaining_lease  resale_price  storey_range  \\\n",
       "0       -2.163608  0.540959         0.710285      232000.0     -0.030180   \n",
       "1       -1.225543  0.540959         0.681966      250000.0     -1.370113   \n",
       "2       -1.225543  0.540959         0.751191      262000.0     -1.370113   \n",
       "3       -1.184757  0.540959         0.738605      265000.0     -0.365163   \n",
       "4       -1.225543  0.540959         0.751191      265000.0     -1.370113   \n",
       "\n",
       "   dist_nearestMRT  flt_2-room  flt_Adjoined flat  flt_Apartment  flt_DBSS  \\\n",
       "0         0.497407           0                  0              0         0   \n",
       "1         1.697526           0                  0              0         0   \n",
       "2         0.752168           0                  0              0         0   \n",
       "3         0.497407           0                  0              0         0   \n",
       "4         0.752168           0                  0              0         0   \n",
       "\n",
       "     ...      st_PASIR RIS  st_PUNGGOL  st_QUEENSTOWN  st_SEMBAWANG  \\\n",
       "0    ...                 0           0              0             0   \n",
       "1    ...                 0           0              0             0   \n",
       "2    ...                 0           0              0             0   \n",
       "3    ...                 0           0              0             0   \n",
       "4    ...                 0           0              0             0   \n",
       "\n",
       "   st_SENGKANG  st_SERANGOON  st_TAMPINES  st_TOA PAYOH  st_WOODLANDS  \\\n",
       "0            0             0            0             0             0   \n",
       "1            0             0            0             0             0   \n",
       "2            0             0            0             0             0   \n",
       "3            0             0            0             0             0   \n",
       "4            0             0            0             0             0   \n",
       "\n",
       "   st_YISHUN  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat_l.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 139788 entries, 0 to 139787\n",
      "Data columns (total 53 columns):\n",
      "floor_area_sqm                139788 non-null float64\n",
      "month                         139788 non-null float64\n",
      "remaining_lease               139788 non-null float64\n",
      "resale_price                  139788 non-null float64\n",
      "storey_range                  139788 non-null float64\n",
      "dist_nearestMRT               139788 non-null float64\n",
      "flt_2-room                    139788 non-null uint8\n",
      "flt_Adjoined flat             139788 non-null uint8\n",
      "flt_Apartment                 139788 non-null uint8\n",
      "flt_DBSS                      139788 non-null uint8\n",
      "flt_Improved                  139788 non-null uint8\n",
      "flt_Improved-Maisonette       139788 non-null uint8\n",
      "flt_Maisonette                139788 non-null uint8\n",
      "flt_Model A                   139788 non-null uint8\n",
      "flt_Model A-Maisonette        139788 non-null uint8\n",
      "flt_Model A2                  139788 non-null uint8\n",
      "flt_Multi Generation          139788 non-null uint8\n",
      "flt_New Generation            139788 non-null uint8\n",
      "flt_Premium Apartment         139788 non-null uint8\n",
      "flt_Premium Apartment Loft    139788 non-null uint8\n",
      "flt_Premium Apartment.        139788 non-null uint8\n",
      "flt_Premium Maisonette        139788 non-null uint8\n",
      "flt_Simplified                139788 non-null uint8\n",
      "flt_Standard                  139788 non-null uint8\n",
      "flt_Terrace                   139788 non-null uint8\n",
      "flt_Type S1                   139788 non-null uint8\n",
      "flt_Type S2                   139788 non-null uint8\n",
      "st_ANG MO KIO                 139788 non-null uint8\n",
      "st_BEDOK                      139788 non-null uint8\n",
      "st_BISHAN                     139788 non-null uint8\n",
      "st_BUKIT BATOK                139788 non-null uint8\n",
      "st_BUKIT MERAH                139788 non-null uint8\n",
      "st_BUKIT PANJANG              139788 non-null uint8\n",
      "st_BUKIT TIMAH                139788 non-null uint8\n",
      "st_CENTRAL AREA               139788 non-null uint8\n",
      "st_CHOA CHU KANG              139788 non-null uint8\n",
      "st_CLEMENTI                   139788 non-null uint8\n",
      "st_GEYLANG                    139788 non-null uint8\n",
      "st_HOUGANG                    139788 non-null uint8\n",
      "st_JURONG EAST                139788 non-null uint8\n",
      "st_JURONG WEST                139788 non-null uint8\n",
      "st_KALLANG/WHAMPOA            139788 non-null uint8\n",
      "st_MARINE PARADE              139788 non-null uint8\n",
      "st_PASIR RIS                  139788 non-null uint8\n",
      "st_PUNGGOL                    139788 non-null uint8\n",
      "st_QUEENSTOWN                 139788 non-null uint8\n",
      "st_SEMBAWANG                  139788 non-null uint8\n",
      "st_SENGKANG                   139788 non-null uint8\n",
      "st_SERANGOON                  139788 non-null uint8\n",
      "st_TAMPINES                   139788 non-null uint8\n",
      "st_TOA PAYOH                  139788 non-null uint8\n",
      "st_WOODLANDS                  139788 non-null uint8\n",
      "st_YISHUN                     139788 non-null uint8\n",
      "dtypes: float64(6), uint8(47)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "resaleflat_l.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['floor_area_sqm', 'month', 'remaining_lease', 'resale_price',\n",
       "       'storey_range', 'dist_nearestMRT', 'flt_2-room',\n",
       "       'flt_Adjoined flat', 'flt_Apartment', 'flt_DBSS', 'flt_Improved',\n",
       "       'flt_Improved-Maisonette', 'flt_Maisonette', 'flt_Model A',\n",
       "       'flt_Model A-Maisonette', 'flt_Model A2', 'flt_Multi Generation',\n",
       "       'flt_New Generation', 'flt_Premium Apartment',\n",
       "       'flt_Premium Apartment Loft', 'flt_Premium Apartment.',\n",
       "       'flt_Premium Maisonette', 'flt_Simplified', 'flt_Standard',\n",
       "       'flt_Terrace', 'flt_Type S1', 'flt_Type S2', 'st_ANG MO KIO',\n",
       "       'st_BEDOK', 'st_BISHAN', 'st_BUKIT BATOK', 'st_BUKIT MERAH',\n",
       "       'st_BUKIT PANJANG', 'st_BUKIT TIMAH', 'st_CENTRAL AREA',\n",
       "       'st_CHOA CHU KANG', 'st_CLEMENTI', 'st_GEYLANG', 'st_HOUGANG',\n",
       "       'st_JURONG EAST', 'st_JURONG WEST', 'st_KALLANG/WHAMPOA',\n",
       "       'st_MARINE PARADE', 'st_PASIR RIS', 'st_PUNGGOL', 'st_QUEENSTOWN',\n",
       "       'st_SEMBAWANG', 'st_SENGKANG', 'st_SERANGOON', 'st_TAMPINES',\n",
       "       'st_TOA PAYOH', 'st_WOODLANDS', 'st_YISHUN'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat_l.sum().index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(139788, 52)\n",
      "(139788,)\n"
     ]
    }
   ],
   "source": [
    "#Seperate datasets\n",
    "\n",
    "X = resaleflat_l.drop('resale_price', axis=1)\n",
    "Y = resaleflat_l['resale_price']\n",
    "\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since we use town names instead of street names, features not that numerous to be selected, just use all\n",
    "#We also know that each town has its own house pricing so its good to keep all of them\n",
    "\n",
    "#Choose top 300 features for model out of 561\n",
    "\n",
    "# from sklearn.feature_selection import SelectKBest\n",
    "# from sklearn.feature_selection import f_classif\n",
    "\n",
    "# X = SelectKBest(f_classif, k=300).fit_transform(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(139788, 52)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "RFR = RandomForestRegressor(max_depth=40, random_state=42, n_estimators=200, criterion='mae')\n",
    "\n",
    "RFR.fit(x_train,y_train)\n",
    "pred_RFR = RFR.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([305959.00297619, 467835.07      , 382706.85714286, ...,\n",
       "       294628.88      , 328101.08      , 398992.85475034])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_RFR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51472     308000.0\n",
       "103548    460000.0\n",
       "63445     381000.0\n",
       "40489     750000.0\n",
       "60647     480000.0\n",
       "Name: resale_price, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51472      2040.997024\n",
       "103548    -7835.070000\n",
       "63445     -1706.857143\n",
       "40489    -13038.280000\n",
       "60647     37031.500000\n",
       "Name: resale_price, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_test - pred_RFR).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25610.809886576168"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see how our model performed\n",
    "\n",
    "import math\n",
    "\n",
    "math.sqrt(mean_squared_error(y_test, pred_RFR, multioutput='uniform_average'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138415.3257061527"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find RMSE for baseline model - i.e. take average house price\n",
    "\n",
    "base = math.sqrt(((y_test - y_test.mean()) ** 2).mean())\n",
    "\n",
    "base\n",
    "\n",
    "#RMSE of $138415 for prediction of house price using average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25610.80988657625"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFR_diff = math.sqrt(((y_test - pred_RFR) ** 2).mean())\n",
    "RFR_diff\n",
    "\n",
    "#RMSE of $138415 for prediction of house price using average\n",
    "#RMSE of $51868 using linear regression\n",
    "#RMSE of $32233 using random forest 100 trees, 40 variables\n",
    "#RMSE of $25662 using random forest 100 trees, 40 variables, with MRT distance\n",
    "#RMSE of $25610 using random forest 200 trees, 40 variables, with MRT distance\n",
    "#RMSE of $25622 using random forest 200 trees, 45 variables, with MRT distance\n",
    "#RMSE of $25621 using random forest 200 trees, 35 variables, with MRT distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18502871525185324"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def RMSE(actual,predict):\n",
    "    base = math.sqrt(((actual - actual.mean()) ** 2).mean())\n",
    "    x = math.sqrt(((actual - predict) ** 2).mean())\n",
    "    return x/base\n",
    "\n",
    "RMSE(y_test, pred_RFR)\n",
    "\n",
    "#RMSE of 0.2328 using random forest, 100 trees, 40 variables\n",
    "#RMSE of 0.1854 using random forest, 100 trees, 40 variables, with MRT\n",
    "#RMSE of 0.18503 using random forest - 200 trees, 40 variables, with MRT\n",
    "#RMSE of 0.18511 using random forest - 200 trees, 45 variables, with MRT\n",
    "#RMSE of 0.18510 using random forest - 200 trees, 35 variables, with MRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.023723365167781126"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Root mean square log error\n",
    "#It's like measuring relative/percentage difference\n",
    "RFR_RMSLE = math.sqrt(((np.log10(y_test + 1) - np.log10(pred_RFR + 1)) ** 2).mean())\n",
    "RFR_RMSLE\n",
    "\n",
    "#Results of a skilled programmer, Sie Huai Gan\n",
    "#https://towardsdatascience.com/singapore-flat-price-predictor-6f74ed8da311\n",
    "# loss_score via RMSLE\n",
    "# elasticnet 0.064\n",
    "# extra_tree 0.049\n",
    "# gboost 0.072\n",
    "# lasso 0.092\n",
    "# model_xgb 0.058\n",
    "# random_forest 0.051\n",
    "# stack_clf 0.049\n",
    "# svm_loss 0.053\n",
    "\n",
    "#Loss score of my random forest - 0.024 or 0.02372"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9657643745322483"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get R^2 of prediction in linear regression, 0.860\n",
    "#Get R^2 for random forest, 0.790\n",
    "#Get R^2 for random forest, 40 variables, 0.94577\n",
    "#Get R^2 for random forest, 100 trees, 40 variables, MRT, 0.9656\n",
    "#Get R^2 for random forest, 200 trees, 40 variables, MRT, 0.96576\n",
    "#Get R^2 for random forest, 200 trees, 45 variables, MRT, 0.96573\n",
    "#Get R^2 for random forest, 200 trees, 35 variables, MRT, 0.96573\n",
    "\n",
    "RFR.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18160.658933833525"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "mean_absolute_error(y_test, pred_RFR)\n",
    "\n",
    "#Negative mean absolute error of $18191\n",
    "\n",
    "#Compared to the other project that incorporates GDP growth n demand of flat, MAE for Random Forest is: $15860"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([308000., 460000., 381000., ..., 290000., 310000., 420000.])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([305959.00297619, 467835.07      , 382706.85714286, ...,\n",
       "       294628.88      , 328101.08      , 398992.85475034])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_RFR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result1 = np.concatenate(([y_test.values], [pred_RFR],[y_test.values - pred_RFR])).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "result1_df = pd.DataFrame({'actual': y_test.values.flatten(), 'predict': pred_RFR.flatten(), 'difference':(y_test.values - pred_RFR).flatten()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result1_df = pd.DataFrame(result1, columns=['actual','predict','difference'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>actual</th>\n",
       "      <th>predict</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>308000.0</td>\n",
       "      <td>305959.002976</td>\n",
       "      <td>2040.997024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>460000.0</td>\n",
       "      <td>467835.070000</td>\n",
       "      <td>-7835.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>381000.0</td>\n",
       "      <td>382706.857143</td>\n",
       "      <td>-1706.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>750000.0</td>\n",
       "      <td>763038.280000</td>\n",
       "      <td>-13038.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>480000.0</td>\n",
       "      <td>442968.500000</td>\n",
       "      <td>37031.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>325000.0</td>\n",
       "      <td>316938.586349</td>\n",
       "      <td>8061.413651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>385000.0</td>\n",
       "      <td>359189.700000</td>\n",
       "      <td>25810.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>360000.0</td>\n",
       "      <td>381967.773333</td>\n",
       "      <td>-21967.773333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>350000.0</td>\n",
       "      <td>363435.520000</td>\n",
       "      <td>-13435.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>337000.0</td>\n",
       "      <td>343570.395729</td>\n",
       "      <td>-6570.395729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>388000.0</td>\n",
       "      <td>401792.416175</td>\n",
       "      <td>-13792.416175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>350000.0</td>\n",
       "      <td>344195.780000</td>\n",
       "      <td>5804.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>280000.0</td>\n",
       "      <td>271186.333333</td>\n",
       "      <td>8813.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>360000.0</td>\n",
       "      <td>358974.440000</td>\n",
       "      <td>1025.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>330277.690000</td>\n",
       "      <td>-30277.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>365000.0</td>\n",
       "      <td>351798.809524</td>\n",
       "      <td>13201.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>320000.0</td>\n",
       "      <td>361836.788259</td>\n",
       "      <td>-41836.788259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>358000.0</td>\n",
       "      <td>346486.160000</td>\n",
       "      <td>11513.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>423000.0</td>\n",
       "      <td>437621.873220</td>\n",
       "      <td>-14621.873220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>345000.0</td>\n",
       "      <td>321415.910000</td>\n",
       "      <td>23584.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>365000.0</td>\n",
       "      <td>362275.000000</td>\n",
       "      <td>2725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>475000.0</td>\n",
       "      <td>466188.810497</td>\n",
       "      <td>8811.189503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>415500.0</td>\n",
       "      <td>411652.500000</td>\n",
       "      <td>3847.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>326300.0</td>\n",
       "      <td>316358.000000</td>\n",
       "      <td>9942.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>451000.0</td>\n",
       "      <td>455164.200000</td>\n",
       "      <td>-4164.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>720000.0</td>\n",
       "      <td>739527.500000</td>\n",
       "      <td>-19527.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>688888.0</td>\n",
       "      <td>607476.592051</td>\n",
       "      <td>81411.407949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>318000.0</td>\n",
       "      <td>319582.500000</td>\n",
       "      <td>-1582.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>332000.0</td>\n",
       "      <td>330686.199444</td>\n",
       "      <td>1313.800556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>640000.0</td>\n",
       "      <td>592978.720000</td>\n",
       "      <td>47021.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27928</th>\n",
       "      <td>278000.0</td>\n",
       "      <td>265546.875000</td>\n",
       "      <td>12453.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27929</th>\n",
       "      <td>220000.0</td>\n",
       "      <td>209077.360000</td>\n",
       "      <td>10922.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27930</th>\n",
       "      <td>290000.0</td>\n",
       "      <td>315592.500000</td>\n",
       "      <td>-25592.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27931</th>\n",
       "      <td>350000.0</td>\n",
       "      <td>352892.166667</td>\n",
       "      <td>-2892.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27932</th>\n",
       "      <td>550000.0</td>\n",
       "      <td>582790.339835</td>\n",
       "      <td>-32790.339835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27933</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>467827.940000</td>\n",
       "      <td>32172.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27934</th>\n",
       "      <td>588000.0</td>\n",
       "      <td>619740.400000</td>\n",
       "      <td>-31740.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27935</th>\n",
       "      <td>446000.0</td>\n",
       "      <td>424160.000000</td>\n",
       "      <td>21840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27936</th>\n",
       "      <td>278000.0</td>\n",
       "      <td>290570.000000</td>\n",
       "      <td>-12570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27937</th>\n",
       "      <td>403000.0</td>\n",
       "      <td>395917.000000</td>\n",
       "      <td>7083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27938</th>\n",
       "      <td>325000.0</td>\n",
       "      <td>361379.333333</td>\n",
       "      <td>-36379.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27939</th>\n",
       "      <td>602000.0</td>\n",
       "      <td>631956.380000</td>\n",
       "      <td>-29956.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27940</th>\n",
       "      <td>348888.0</td>\n",
       "      <td>354331.410256</td>\n",
       "      <td>-5443.410256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27941</th>\n",
       "      <td>558000.0</td>\n",
       "      <td>557281.500000</td>\n",
       "      <td>718.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27942</th>\n",
       "      <td>230000.0</td>\n",
       "      <td>211293.056000</td>\n",
       "      <td>18706.944000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27943</th>\n",
       "      <td>595000.0</td>\n",
       "      <td>637937.500000</td>\n",
       "      <td>-42937.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27944</th>\n",
       "      <td>350000.0</td>\n",
       "      <td>374374.440000</td>\n",
       "      <td>-24374.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27945</th>\n",
       "      <td>438000.0</td>\n",
       "      <td>440327.704495</td>\n",
       "      <td>-2327.704495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27946</th>\n",
       "      <td>375000.0</td>\n",
       "      <td>372508.941595</td>\n",
       "      <td>2491.058405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27947</th>\n",
       "      <td>570000.0</td>\n",
       "      <td>550528.880000</td>\n",
       "      <td>19471.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27948</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>438582.680000</td>\n",
       "      <td>11417.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27949</th>\n",
       "      <td>535000.0</td>\n",
       "      <td>569181.940000</td>\n",
       "      <td>-34181.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27950</th>\n",
       "      <td>280000.0</td>\n",
       "      <td>275536.880000</td>\n",
       "      <td>4463.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27951</th>\n",
       "      <td>350000.0</td>\n",
       "      <td>381327.486667</td>\n",
       "      <td>-31327.486667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27952</th>\n",
       "      <td>505000.0</td>\n",
       "      <td>500219.699261</td>\n",
       "      <td>4780.300739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27953</th>\n",
       "      <td>380000.0</td>\n",
       "      <td>399955.250000</td>\n",
       "      <td>-19955.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27954</th>\n",
       "      <td>365000.0</td>\n",
       "      <td>363187.354167</td>\n",
       "      <td>1812.645833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27955</th>\n",
       "      <td>290000.0</td>\n",
       "      <td>294628.880000</td>\n",
       "      <td>-4628.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27956</th>\n",
       "      <td>310000.0</td>\n",
       "      <td>328101.080000</td>\n",
       "      <td>-18101.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27957</th>\n",
       "      <td>420000.0</td>\n",
       "      <td>398992.854750</td>\n",
       "      <td>21007.145250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27958 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         actual        predict    difference\n",
       "0      308000.0  305959.002976   2040.997024\n",
       "1      460000.0  467835.070000  -7835.070000\n",
       "2      381000.0  382706.857143  -1706.857143\n",
       "3      750000.0  763038.280000 -13038.280000\n",
       "4      480000.0  442968.500000  37031.500000\n",
       "5      325000.0  316938.586349   8061.413651\n",
       "6      385000.0  359189.700000  25810.300000\n",
       "7      360000.0  381967.773333 -21967.773333\n",
       "8      350000.0  363435.520000 -13435.520000\n",
       "9      337000.0  343570.395729  -6570.395729\n",
       "10     388000.0  401792.416175 -13792.416175\n",
       "11     350000.0  344195.780000   5804.220000\n",
       "12     280000.0  271186.333333   8813.666667\n",
       "13     360000.0  358974.440000   1025.560000\n",
       "14     300000.0  330277.690000 -30277.690000\n",
       "15     365000.0  351798.809524  13201.190476\n",
       "16     320000.0  361836.788259 -41836.788259\n",
       "17     358000.0  346486.160000  11513.840000\n",
       "18     423000.0  437621.873220 -14621.873220\n",
       "19     345000.0  321415.910000  23584.090000\n",
       "20     365000.0  362275.000000   2725.000000\n",
       "21     475000.0  466188.810497   8811.189503\n",
       "22     415500.0  411652.500000   3847.500000\n",
       "23     326300.0  316358.000000   9942.000000\n",
       "24     451000.0  455164.200000  -4164.200000\n",
       "25     720000.0  739527.500000 -19527.500000\n",
       "26     688888.0  607476.592051  81411.407949\n",
       "27     318000.0  319582.500000  -1582.500000\n",
       "28     332000.0  330686.199444   1313.800556\n",
       "29     640000.0  592978.720000  47021.280000\n",
       "...         ...            ...           ...\n",
       "27928  278000.0  265546.875000  12453.125000\n",
       "27929  220000.0  209077.360000  10922.640000\n",
       "27930  290000.0  315592.500000 -25592.500000\n",
       "27931  350000.0  352892.166667  -2892.166667\n",
       "27932  550000.0  582790.339835 -32790.339835\n",
       "27933  500000.0  467827.940000  32172.060000\n",
       "27934  588000.0  619740.400000 -31740.400000\n",
       "27935  446000.0  424160.000000  21840.000000\n",
       "27936  278000.0  290570.000000 -12570.000000\n",
       "27937  403000.0  395917.000000   7083.000000\n",
       "27938  325000.0  361379.333333 -36379.333333\n",
       "27939  602000.0  631956.380000 -29956.380000\n",
       "27940  348888.0  354331.410256  -5443.410256\n",
       "27941  558000.0  557281.500000    718.500000\n",
       "27942  230000.0  211293.056000  18706.944000\n",
       "27943  595000.0  637937.500000 -42937.500000\n",
       "27944  350000.0  374374.440000 -24374.440000\n",
       "27945  438000.0  440327.704495  -2327.704495\n",
       "27946  375000.0  372508.941595   2491.058405\n",
       "27947  570000.0  550528.880000  19471.120000\n",
       "27948  450000.0  438582.680000  11417.320000\n",
       "27949  535000.0  569181.940000 -34181.940000\n",
       "27950  280000.0  275536.880000   4463.120000\n",
       "27951  350000.0  381327.486667 -31327.486667\n",
       "27952  505000.0  500219.699261   4780.300739\n",
       "27953  380000.0  399955.250000 -19955.250000\n",
       "27954  365000.0  363187.354167   1812.645833\n",
       "27955  290000.0  294628.880000  -4628.880000\n",
       "27956  310000.0  328101.080000 -18101.080000\n",
       "27957  420000.0  398992.854750  21007.145250\n",
       "\n",
       "[27958 rows x 3 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "326.4830900830091\n",
      "318485.66000000003\n",
      "-222589.0\n"
     ]
    }
   ],
   "source": [
    "print(result1_df['difference'].mean())\n",
    "print(result1_df['difference'].max())\n",
    "print(result1_df['difference'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>actual</th>\n",
       "      <th>predict</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>920000.0</td>\n",
       "      <td>7.542745e+05</td>\n",
       "      <td>165725.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>1038000.0</td>\n",
       "      <td>9.212770e+05</td>\n",
       "      <td>116723.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>660000.0</td>\n",
       "      <td>5.547538e+05</td>\n",
       "      <td>105246.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>810000.0</td>\n",
       "      <td>6.963720e+05</td>\n",
       "      <td>113628.031667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>475000.0</td>\n",
       "      <td>3.582336e+05</td>\n",
       "      <td>116766.408076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>600000.0</td>\n",
       "      <td>4.894045e+05</td>\n",
       "      <td>110595.480822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>1088000.0</td>\n",
       "      <td>9.527530e+05</td>\n",
       "      <td>135247.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2481</th>\n",
       "      <td>718000.0</td>\n",
       "      <td>5.936769e+05</td>\n",
       "      <td>124323.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2728</th>\n",
       "      <td>865000.0</td>\n",
       "      <td>7.157328e+05</td>\n",
       "      <td>149267.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>1028000.0</td>\n",
       "      <td>8.928464e+05</td>\n",
       "      <td>135153.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>710000.0</td>\n",
       "      <td>5.798780e+05</td>\n",
       "      <td>130122.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3185</th>\n",
       "      <td>820000.0</td>\n",
       "      <td>7.008048e+05</td>\n",
       "      <td>119195.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3401</th>\n",
       "      <td>925000.0</td>\n",
       "      <td>6.687189e+05</td>\n",
       "      <td>256281.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4306</th>\n",
       "      <td>880000.0</td>\n",
       "      <td>7.269155e+05</td>\n",
       "      <td>153084.475600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>888000.0</td>\n",
       "      <td>6.265730e+05</td>\n",
       "      <td>261427.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5062</th>\n",
       "      <td>620000.0</td>\n",
       "      <td>4.992357e+05</td>\n",
       "      <td>120764.251154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5690</th>\n",
       "      <td>620000.0</td>\n",
       "      <td>4.949131e+05</td>\n",
       "      <td>125086.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5887</th>\n",
       "      <td>760000.0</td>\n",
       "      <td>6.518736e+05</td>\n",
       "      <td>108126.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6020</th>\n",
       "      <td>798000.0</td>\n",
       "      <td>6.732689e+05</td>\n",
       "      <td>124731.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6653</th>\n",
       "      <td>880000.0</td>\n",
       "      <td>7.755761e+05</td>\n",
       "      <td>104423.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6782</th>\n",
       "      <td>955000.0</td>\n",
       "      <td>8.468489e+05</td>\n",
       "      <td>108151.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>660000.0</td>\n",
       "      <td>5.352700e+05</td>\n",
       "      <td>124730.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7401</th>\n",
       "      <td>830000.0</td>\n",
       "      <td>6.030674e+05</td>\n",
       "      <td>226932.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7946</th>\n",
       "      <td>820000.0</td>\n",
       "      <td>6.984322e+05</td>\n",
       "      <td>121567.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8140</th>\n",
       "      <td>710000.0</td>\n",
       "      <td>5.888350e+05</td>\n",
       "      <td>121165.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8199</th>\n",
       "      <td>1170000.0</td>\n",
       "      <td>1.045189e+06</td>\n",
       "      <td>124811.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>725000.0</td>\n",
       "      <td>5.398981e+05</td>\n",
       "      <td>185101.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8859</th>\n",
       "      <td>700000.0</td>\n",
       "      <td>5.842890e+05</td>\n",
       "      <td>115711.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9059</th>\n",
       "      <td>916000.0</td>\n",
       "      <td>8.039100e+05</td>\n",
       "      <td>112090.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9350</th>\n",
       "      <td>870000.0</td>\n",
       "      <td>7.628917e+05</td>\n",
       "      <td>107108.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19815</th>\n",
       "      <td>660000.0</td>\n",
       "      <td>5.432050e+05</td>\n",
       "      <td>116795.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20529</th>\n",
       "      <td>635000.0</td>\n",
       "      <td>5.345519e+05</td>\n",
       "      <td>100448.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20566</th>\n",
       "      <td>800000.0</td>\n",
       "      <td>6.667129e+05</td>\n",
       "      <td>133287.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20745</th>\n",
       "      <td>845000.0</td>\n",
       "      <td>7.282670e+05</td>\n",
       "      <td>116733.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20827</th>\n",
       "      <td>590000.0</td>\n",
       "      <td>4.538228e+05</td>\n",
       "      <td>136177.204762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21607</th>\n",
       "      <td>845000.0</td>\n",
       "      <td>7.382744e+05</td>\n",
       "      <td>106725.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21718</th>\n",
       "      <td>640000.0</td>\n",
       "      <td>5.318706e+05</td>\n",
       "      <td>108129.381891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21804</th>\n",
       "      <td>820000.0</td>\n",
       "      <td>7.193789e+05</td>\n",
       "      <td>100621.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22179</th>\n",
       "      <td>545000.0</td>\n",
       "      <td>4.178700e+05</td>\n",
       "      <td>127130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22189</th>\n",
       "      <td>838000.0</td>\n",
       "      <td>7.199379e+05</td>\n",
       "      <td>118062.115600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22235</th>\n",
       "      <td>772000.0</td>\n",
       "      <td>6.576468e+05</td>\n",
       "      <td>114353.179349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22292</th>\n",
       "      <td>850000.0</td>\n",
       "      <td>6.867194e+05</td>\n",
       "      <td>163280.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22450</th>\n",
       "      <td>825000.0</td>\n",
       "      <td>7.113431e+05</td>\n",
       "      <td>113656.862302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22502</th>\n",
       "      <td>670888.0</td>\n",
       "      <td>5.523572e+05</td>\n",
       "      <td>118530.791111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22597</th>\n",
       "      <td>780000.0</td>\n",
       "      <td>6.458864e+05</td>\n",
       "      <td>134113.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22760</th>\n",
       "      <td>750000.0</td>\n",
       "      <td>6.254750e+05</td>\n",
       "      <td>124525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23774</th>\n",
       "      <td>733000.0</td>\n",
       "      <td>5.668446e+05</td>\n",
       "      <td>166155.365000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23891</th>\n",
       "      <td>713000.0</td>\n",
       "      <td>6.116127e+05</td>\n",
       "      <td>101387.346667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24739</th>\n",
       "      <td>980000.0</td>\n",
       "      <td>6.784860e+05</td>\n",
       "      <td>301514.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>995000.0</td>\n",
       "      <td>8.803527e+05</td>\n",
       "      <td>114647.338333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25025</th>\n",
       "      <td>550000.0</td>\n",
       "      <td>4.380756e+05</td>\n",
       "      <td>111924.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25194</th>\n",
       "      <td>833000.0</td>\n",
       "      <td>7.320227e+05</td>\n",
       "      <td>100977.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25336</th>\n",
       "      <td>840000.0</td>\n",
       "      <td>7.288024e+05</td>\n",
       "      <td>111197.565273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25378</th>\n",
       "      <td>1150000.0</td>\n",
       "      <td>9.922591e+05</td>\n",
       "      <td>157740.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25880</th>\n",
       "      <td>972000.0</td>\n",
       "      <td>8.390465e+05</td>\n",
       "      <td>132953.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26213</th>\n",
       "      <td>760000.0</td>\n",
       "      <td>6.404478e+05</td>\n",
       "      <td>119552.226667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26627</th>\n",
       "      <td>845000.0</td>\n",
       "      <td>7.289988e+05</td>\n",
       "      <td>116001.236667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26862</th>\n",
       "      <td>888000.0</td>\n",
       "      <td>7.801433e+05</td>\n",
       "      <td>107856.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26867</th>\n",
       "      <td>740000.0</td>\n",
       "      <td>6.033018e+05</td>\n",
       "      <td>136698.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27018</th>\n",
       "      <td>720000.0</td>\n",
       "      <td>5.811201e+05</td>\n",
       "      <td>138879.883810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          actual       predict     difference\n",
       "182     920000.0  7.542745e+05  165725.500000\n",
       "310    1038000.0  9.212770e+05  116723.000000\n",
       "406     660000.0  5.547538e+05  105246.240000\n",
       "627     810000.0  6.963720e+05  113628.031667\n",
       "973     475000.0  3.582336e+05  116766.408076\n",
       "1451    600000.0  4.894045e+05  110595.480822\n",
       "2031   1088000.0  9.527530e+05  135247.020000\n",
       "2481    718000.0  5.936769e+05  124323.060000\n",
       "2728    865000.0  7.157328e+05  149267.240000\n",
       "2853   1028000.0  8.928464e+05  135153.600000\n",
       "3081    710000.0  5.798780e+05  130122.000000\n",
       "3185    820000.0  7.008048e+05  119195.160000\n",
       "3401    925000.0  6.687189e+05  256281.120000\n",
       "4306    880000.0  7.269155e+05  153084.475600\n",
       "4996    888000.0  6.265730e+05  261427.000000\n",
       "5062    620000.0  4.992357e+05  120764.251154\n",
       "5690    620000.0  4.949131e+05  125086.920000\n",
       "5887    760000.0  6.518736e+05  108126.433333\n",
       "6020    798000.0  6.732689e+05  124731.120000\n",
       "6653    880000.0  7.755761e+05  104423.870000\n",
       "6782    955000.0  8.468489e+05  108151.120000\n",
       "7122    660000.0  5.352700e+05  124730.000000\n",
       "7401    830000.0  6.030674e+05  226932.560000\n",
       "7946    820000.0  6.984322e+05  121567.800000\n",
       "8140    710000.0  5.888350e+05  121165.005000\n",
       "8199   1170000.0  1.045189e+06  124811.480000\n",
       "8758    725000.0  5.398981e+05  185101.920000\n",
       "8859    700000.0  5.842890e+05  115711.000000\n",
       "9059    916000.0  8.039100e+05  112090.000000\n",
       "9350    870000.0  7.628917e+05  107108.333333\n",
       "...          ...           ...            ...\n",
       "19815   660000.0  5.432050e+05  116795.000000\n",
       "20529   635000.0  5.345519e+05  100448.080000\n",
       "20566   800000.0  6.667129e+05  133287.060000\n",
       "20745   845000.0  7.282670e+05  116733.040000\n",
       "20827   590000.0  4.538228e+05  136177.204762\n",
       "21607   845000.0  7.382744e+05  106725.560000\n",
       "21718   640000.0  5.318706e+05  108129.381891\n",
       "21804   820000.0  7.193789e+05  100621.120000\n",
       "22179   545000.0  4.178700e+05  127130.000000\n",
       "22189   838000.0  7.199379e+05  118062.115600\n",
       "22235   772000.0  6.576468e+05  114353.179349\n",
       "22292   850000.0  6.867194e+05  163280.560000\n",
       "22450   825000.0  7.113431e+05  113656.862302\n",
       "22502   670888.0  5.523572e+05  118530.791111\n",
       "22597   780000.0  6.458864e+05  134113.633333\n",
       "22760   750000.0  6.254750e+05  124525.000000\n",
       "23774   733000.0  5.668446e+05  166155.365000\n",
       "23891   713000.0  6.116127e+05  101387.346667\n",
       "24739   980000.0  6.784860e+05  301514.000000\n",
       "24996   995000.0  8.803527e+05  114647.338333\n",
       "25025   550000.0  4.380756e+05  111924.360000\n",
       "25194   833000.0  7.320227e+05  100977.280000\n",
       "25336   840000.0  7.288024e+05  111197.565273\n",
       "25378  1150000.0  9.922591e+05  157740.880000\n",
       "25880   972000.0  8.390465e+05  132953.520000\n",
       "26213   760000.0  6.404478e+05  119552.226667\n",
       "26627   845000.0  7.289988e+05  116001.236667\n",
       "26862   888000.0  7.801433e+05  107856.680000\n",
       "26867   740000.0  6.033018e+05  136698.240000\n",
       "27018   720000.0  5.811201e+05  138879.883810\n",
       "\n",
       "[88 rows x 3 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1_df[result1_df['difference'] > 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>predict</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>620000.0</td>\n",
       "      <td>7.358900e+05</td>\n",
       "      <td>-115890.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>283000.0</td>\n",
       "      <td>5.055890e+05</td>\n",
       "      <td>-222589.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>760000.0</td>\n",
       "      <td>8.770572e+05</td>\n",
       "      <td>-117057.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>760000.0</td>\n",
       "      <td>8.708476e+05</td>\n",
       "      <td>-110847.604000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>520000.0</td>\n",
       "      <td>6.400649e+05</td>\n",
       "      <td>-120064.865000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>695000.0</td>\n",
       "      <td>8.145583e+05</td>\n",
       "      <td>-119558.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2377</th>\n",
       "      <td>380000.0</td>\n",
       "      <td>4.950850e+05</td>\n",
       "      <td>-115085.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2736</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>6.452667e+05</td>\n",
       "      <td>-145266.653333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2859</th>\n",
       "      <td>590000.0</td>\n",
       "      <td>7.381293e+05</td>\n",
       "      <td>-148129.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3554</th>\n",
       "      <td>720000.0</td>\n",
       "      <td>8.239100e+05</td>\n",
       "      <td>-103910.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3812</th>\n",
       "      <td>810000.0</td>\n",
       "      <td>9.144881e+05</td>\n",
       "      <td>-104488.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4320</th>\n",
       "      <td>434000.0</td>\n",
       "      <td>5.363055e+05</td>\n",
       "      <td>-102305.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4706</th>\n",
       "      <td>508000.0</td>\n",
       "      <td>6.584830e+05</td>\n",
       "      <td>-150483.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6612</th>\n",
       "      <td>660000.0</td>\n",
       "      <td>7.662375e+05</td>\n",
       "      <td>-106237.475200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7227</th>\n",
       "      <td>520000.0</td>\n",
       "      <td>6.245850e+05</td>\n",
       "      <td>-104584.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7296</th>\n",
       "      <td>670000.0</td>\n",
       "      <td>8.101444e+05</td>\n",
       "      <td>-140144.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7640</th>\n",
       "      <td>660000.0</td>\n",
       "      <td>7.667050e+05</td>\n",
       "      <td>-106705.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8761</th>\n",
       "      <td>605000.0</td>\n",
       "      <td>7.152150e+05</td>\n",
       "      <td>-110215.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9249</th>\n",
       "      <td>495000.0</td>\n",
       "      <td>6.012331e+05</td>\n",
       "      <td>-106233.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9440</th>\n",
       "      <td>480000.0</td>\n",
       "      <td>5.900430e+05</td>\n",
       "      <td>-110043.047885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9817</th>\n",
       "      <td>608000.0</td>\n",
       "      <td>7.751944e+05</td>\n",
       "      <td>-167194.413333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9823</th>\n",
       "      <td>525000.0</td>\n",
       "      <td>6.259050e+05</td>\n",
       "      <td>-100905.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9899</th>\n",
       "      <td>346000.0</td>\n",
       "      <td>4.822500e+05</td>\n",
       "      <td>-136250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10675</th>\n",
       "      <td>413000.0</td>\n",
       "      <td>5.522838e+05</td>\n",
       "      <td>-139283.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11180</th>\n",
       "      <td>638000.0</td>\n",
       "      <td>7.399633e+05</td>\n",
       "      <td>-101963.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11815</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>6.239744e+05</td>\n",
       "      <td>-113974.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12587</th>\n",
       "      <td>633000.0</td>\n",
       "      <td>7.599800e+05</td>\n",
       "      <td>-126980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12636</th>\n",
       "      <td>720000.0</td>\n",
       "      <td>8.399200e+05</td>\n",
       "      <td>-119920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12909</th>\n",
       "      <td>635000.0</td>\n",
       "      <td>7.647137e+05</td>\n",
       "      <td>-129713.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13077</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>6.380601e+05</td>\n",
       "      <td>-138060.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15220</th>\n",
       "      <td>570000.0</td>\n",
       "      <td>7.291120e+05</td>\n",
       "      <td>-159112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15239</th>\n",
       "      <td>542000.0</td>\n",
       "      <td>6.464863e+05</td>\n",
       "      <td>-104486.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15306</th>\n",
       "      <td>740000.0</td>\n",
       "      <td>8.508733e+05</td>\n",
       "      <td>-110873.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15497</th>\n",
       "      <td>280000.0</td>\n",
       "      <td>3.892650e+05</td>\n",
       "      <td>-109265.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16113</th>\n",
       "      <td>518000.0</td>\n",
       "      <td>6.817422e+05</td>\n",
       "      <td>-163742.157778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16196</th>\n",
       "      <td>380000.0</td>\n",
       "      <td>5.095955e+05</td>\n",
       "      <td>-129595.483801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17210</th>\n",
       "      <td>520000.0</td>\n",
       "      <td>6.248451e+05</td>\n",
       "      <td>-104845.106667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19344</th>\n",
       "      <td>665000.0</td>\n",
       "      <td>7.728561e+05</td>\n",
       "      <td>-107856.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19625</th>\n",
       "      <td>460000.0</td>\n",
       "      <td>5.745694e+05</td>\n",
       "      <td>-114569.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19744</th>\n",
       "      <td>650000.0</td>\n",
       "      <td>7.503122e+05</td>\n",
       "      <td>-100312.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20236</th>\n",
       "      <td>508000.0</td>\n",
       "      <td>6.081850e+05</td>\n",
       "      <td>-100185.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>4.054175e+05</td>\n",
       "      <td>-105417.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21961</th>\n",
       "      <td>382000.0</td>\n",
       "      <td>4.846055e+05</td>\n",
       "      <td>-102605.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22562</th>\n",
       "      <td>580000.0</td>\n",
       "      <td>7.210339e+05</td>\n",
       "      <td>-141033.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22750</th>\n",
       "      <td>930000.0</td>\n",
       "      <td>1.047710e+06</td>\n",
       "      <td>-117710.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22754</th>\n",
       "      <td>445000.0</td>\n",
       "      <td>5.684952e+05</td>\n",
       "      <td>-123495.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22900</th>\n",
       "      <td>470000.0</td>\n",
       "      <td>5.972750e+05</td>\n",
       "      <td>-127275.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22903</th>\n",
       "      <td>750000.0</td>\n",
       "      <td>8.601673e+05</td>\n",
       "      <td>-110167.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22934</th>\n",
       "      <td>550000.0</td>\n",
       "      <td>6.539966e+05</td>\n",
       "      <td>-103996.573333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23060</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>5.595300e+05</td>\n",
       "      <td>-109530.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23820</th>\n",
       "      <td>405000.0</td>\n",
       "      <td>5.423744e+05</td>\n",
       "      <td>-137374.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24119</th>\n",
       "      <td>360000.0</td>\n",
       "      <td>4.755138e+05</td>\n",
       "      <td>-115513.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24768</th>\n",
       "      <td>820000.0</td>\n",
       "      <td>9.512550e+05</td>\n",
       "      <td>-131255.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24773</th>\n",
       "      <td>545000.0</td>\n",
       "      <td>6.530816e+05</td>\n",
       "      <td>-108081.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25205</th>\n",
       "      <td>640000.0</td>\n",
       "      <td>7.508011e+05</td>\n",
       "      <td>-110801.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26130</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>6.591950e+05</td>\n",
       "      <td>-209195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26264</th>\n",
       "      <td>271000.0</td>\n",
       "      <td>4.331964e+05</td>\n",
       "      <td>-162196.428095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26334</th>\n",
       "      <td>525000.0</td>\n",
       "      <td>6.263194e+05</td>\n",
       "      <td>-101319.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27411</th>\n",
       "      <td>395000.0</td>\n",
       "      <td>5.279247e+05</td>\n",
       "      <td>-132924.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27852</th>\n",
       "      <td>375000.0</td>\n",
       "      <td>4.807557e+05</td>\n",
       "      <td>-105755.749259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         actual       predict     difference\n",
       "180    620000.0  7.358900e+05 -115890.000000\n",
       "210    283000.0  5.055890e+05 -222589.000000\n",
       "219    760000.0  8.770572e+05 -117057.160000\n",
       "1507   760000.0  8.708476e+05 -110847.604000\n",
       "1685   520000.0  6.400649e+05 -120064.865000\n",
       "2315   695000.0  8.145583e+05 -119558.320000\n",
       "2377   380000.0  4.950850e+05 -115085.000000\n",
       "2736   500000.0  6.452667e+05 -145266.653333\n",
       "2859   590000.0  7.381293e+05 -148129.320000\n",
       "3554   720000.0  8.239100e+05 -103910.000000\n",
       "3812   810000.0  9.144881e+05 -104488.120000\n",
       "4320   434000.0  5.363055e+05 -102305.466667\n",
       "4706   508000.0  6.584830e+05 -150483.000000\n",
       "6612   660000.0  7.662375e+05 -106237.475200\n",
       "7227   520000.0  6.245850e+05 -104584.960000\n",
       "7296   670000.0  8.101444e+05 -140144.440000\n",
       "7640   660000.0  7.667050e+05 -106705.000000\n",
       "8761   605000.0  7.152150e+05 -110215.000000\n",
       "9249   495000.0  6.012331e+05 -106233.100000\n",
       "9440   480000.0  5.900430e+05 -110043.047885\n",
       "9817   608000.0  7.751944e+05 -167194.413333\n",
       "9823   525000.0  6.259050e+05 -100905.000000\n",
       "9899   346000.0  4.822500e+05 -136250.000000\n",
       "10675  413000.0  5.522838e+05 -139283.840000\n",
       "11180  638000.0  7.399633e+05 -101963.330000\n",
       "11815  510000.0  6.239744e+05 -113974.440000\n",
       "12587  633000.0  7.599800e+05 -126980.000000\n",
       "12636  720000.0  8.399200e+05 -119920.000000\n",
       "12909  635000.0  7.647137e+05 -129713.680000\n",
       "13077  500000.0  6.380601e+05 -138060.083333\n",
       "...         ...           ...            ...\n",
       "15220  570000.0  7.291120e+05 -159112.000000\n",
       "15239  542000.0  6.464863e+05 -104486.320000\n",
       "15306  740000.0  8.508733e+05 -110873.320000\n",
       "15497  280000.0  3.892650e+05 -109265.000000\n",
       "16113  518000.0  6.817422e+05 -163742.157778\n",
       "16196  380000.0  5.095955e+05 -129595.483801\n",
       "17210  520000.0  6.248451e+05 -104845.106667\n",
       "19344  665000.0  7.728561e+05 -107856.080000\n",
       "19625  460000.0  5.745694e+05 -114569.440000\n",
       "19744  650000.0  7.503122e+05 -100312.180000\n",
       "20236  508000.0  6.081850e+05 -100185.000000\n",
       "21251  300000.0  4.054175e+05 -105417.500000\n",
       "21961  382000.0  4.846055e+05 -102605.520000\n",
       "22562  580000.0  7.210339e+05 -141033.880000\n",
       "22750  930000.0  1.047710e+06 -117710.000000\n",
       "22754  445000.0  5.684952e+05 -123495.200000\n",
       "22900  470000.0  5.972750e+05 -127275.000000\n",
       "22903  750000.0  8.601673e+05 -110167.320000\n",
       "22934  550000.0  6.539966e+05 -103996.573333\n",
       "23060  450000.0  5.595300e+05 -109530.000000\n",
       "23820  405000.0  5.423744e+05 -137374.440000\n",
       "24119  360000.0  4.755138e+05 -115513.800000\n",
       "24768  820000.0  9.512550e+05 -131255.000000\n",
       "24773  545000.0  6.530816e+05 -108081.640000\n",
       "25205  640000.0  7.508011e+05 -110801.080000\n",
       "26130  450000.0  6.591950e+05 -209195.000000\n",
       "26264  271000.0  4.331964e+05 -162196.428095\n",
       "26334  525000.0  6.263194e+05 -101319.400000\n",
       "27411  395000.0  5.279247e+05 -132924.666667\n",
       "27852  375000.0  4.807557e+05 -105755.749259\n",
       "\n",
       "[65 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1_df[result1_df['difference'] < -100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.4961242717791389: 'floor_area_sqm', 0.030666806727931588: 'month', 0.050609121842867644: 'remaining_lease', 0.09510601587168355: 'storey_range', 0.055816139196098666: 'dist_nearestMRT', 5.264040587198275e-09: 'flt_2-room', 0.00021687234073549877: 'flt_Adjoined flat', 0.0005838715364643133: 'flt_Apartment', 0.020409991819295037: 'flt_DBSS', 0.0056024623235870355: 'flt_Improved', 8.739705182976694e-05: 'flt_Improved-Maisonette', 0.0002051238822659426: 'flt_Maisonette', 0.0055039108418142154: 'flt_Model A', 7.857303319457772e-05: 'flt_Model A-Maisonette', 0.0001180390254750205: 'flt_Model A2', 0.0001022326645632774: 'flt_Multi Generation', 0.002648776229137709: 'flt_New Generation', 0.0014560872374435312: 'flt_Premium Apartment', 6.0845325537104995e-06: 'flt_Premium Apartment Loft', 0.00019212484003731687: 'flt_Premium Apartment.', 7.311041226185775e-05: 'flt_Premium Maisonette', 0.0001949943768794285: 'flt_Simplified', 0.0007419972502091502: 'flt_Standard', 0.0023263258694585153: 'flt_Terrace', 0.00020588379027917014: 'flt_Type S1', 0.0006734139888183508: 'flt_Type S2', 0.006617557921411274: 'st_ANG MO KIO', 0.004274195128107958: 'st_BEDOK', 0.022135274294171902: 'st_BISHAN', 0.002823228975264568: 'st_BUKIT BATOK', 0.042496003798904176: 'st_BUKIT MERAH', 0.007408065487485725: 'st_BUKIT PANJANG', 0.004843844917367431: 'st_BUKIT TIMAH', 0.008637220180051244: 'st_CENTRAL AREA', 0.011001605186345534: 'st_CHOA CHU KANG', 0.01097023757410812: 'st_CLEMENTI', 0.006248488737845681: 'st_GEYLANG', 0.0010859586961634877: 'st_HOUGANG', 0.0010071565977134516: 'st_JURONG EAST', 0.0060822219930228676: 'st_JURONG WEST', 0.010528857092624775: 'st_KALLANG/WHAMPOA', 0.007365889177977023: 'st_MARINE PARADE', 0.001109707335841283: 'st_PASIR RIS', 0.0011738703413469761: 'st_PUNGGOL', 0.02696046956914239: 'st_QUEENSTOWN', 0.004756272871850597: 'st_SEMBAWANG', 0.0025980708342867305: 'st_SENGKANG', 0.0046089244935578755: 'st_SERANGOON', 0.002859823589683803: 'st_TAMPINES', 0.00958637756340267: 'st_TOA PAYOH', 0.018426878309074916: 'st_WOODLANDS', 0.004644165605183265: 'st_YISHUN'}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Quick and dirty way to get features & importances in a dictionary\n",
    "#See below for better method (sklearn + dataframe) and new library rfpimp\n",
    "\n",
    "varimpt = dict(zip(RFR.feature_importances_, x_train.columns))\n",
    "print(varimpt)\n",
    "print('\\n')\n",
    "\n",
    "\n",
    "#from io import StringIO\n",
    "#out = StringIO()\n",
    "#out = export_graphviz(RFR, out_file='tree.dot')\n",
    "\n",
    "# import os\n",
    "# os.environ[\"PATH\"] += os.pathsep + r'D://Anaconda3//Lib//site-packages//graphviz'\n",
    "\n",
    "# import pydot\n",
    "# (graph,) = pydot.graph_from_dot_file(r'C://Users//jx/Desktop//Python Tutorial//TUTORIALS BASICS OF PYTHON//Machine Learning//tree.dot')\n",
    "# graph.write_png('tree.png')\n",
    "\n",
    "#from subprocess import check_call\n",
    "#check_call(['dot','-Tpng','tree.dot','-o','OutputFile.png'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 Training Score: 0.99 \n",
      "OOB Score: 0.00 \n",
      "R^2 Validation Score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print('R^2 Training Score: {:.2f} \\nOOB Score: {:.2f} \\nR^2 Validation Score: {:.2f}'.format(RFR.score(x_train, y_train), RFR.oob_score, RFR.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from rfpimp import permutation_importances\n",
    "\n",
    "def r2(RFR, x_train, y_train):\n",
    "    return r2_score(y_train, RFR.predict(x_train))\n",
    "\n",
    "perm_imp_rfpimp = permutation_importances(RFR, x_train, y_train, r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+IAAAJhCAYAAADFSQqJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XdUFNf7+PE3SBEEsYCCLWJZVBQQ7F3E2LtBRBZ7lOjXWCBgScSo2CUGE+wlYDQiaKyJwRg1RiF2TaxYERUURQFByv7+8Md+XOk2JD6vc3JO9s7MnWfuHfb47L0zV0ulUqkQQgghhBBCCCHEO6Fd1AEIIYQQQgghhBAfEknEhRBCCCGEEEKId0gScSGEEEIIIYQQ4h2SRFwIIYQQQgghhHiHJBEXQgghhBBCCCHeIUnEhRBCCCGEEEKId0gScSGEELkKCAjAysoq23/W1tY0bdoUpVLJzz//XNRhvjFnzpzhzz//fOXjMzIyCA4OJjk5WV2W1Ybh4eFvIsRXcv36dQYPHkzDhg2xt7dn2bJlb/V8sbGxhIaGvtVzvKr3oT/elPe5nd+ER48e0bJlS/bu3Vug/cPCwvj444+pX78+LVq04Pr164U6n6OjI40aNXqFSN+MkJAQOnTooPH9IYT47yrh6+vrW9RBCCGEeD9FRkYSGRlJhw4d6NatG02aNKFJkybY2dlRqVIljhw5wq5du0hPT6d58+ZFHe5r+eOPPxgyZAj16tXDzs7uleqYOHEiq1evZsSIEejr66vLK1euTJMmTShXrtybCrdQRo0axbFjx2jXrh1t27alefPmVKpU6a2c68GDB3Tq1Im0tDS6dev2Vs7xuoq6P96E4tDOr2v69OmoVCq8vb3z3TcqKooRI0aQmZmJs7MzderU4eOPP6ZEiRIFPp9KpcLOzg4HB4fXCfuV1a1bl9DQUGJiYmjdunWRxCCEeHd0ijoAIYQQ7z8nJyf69u2brXz48OH06dOHlStX4uzsTOXKlYsgujcjPj6ezMzM16rjwYMH2cqaNm1K06ZNX6ve1/XPP/9gbm7O0qVL3/q5nj59SlJS0ls/z6t6H/rjTXjf2/l1RUZGsm3bNtavX1+g/c+fP09mZiaurq5MmDDhlc45ZMiQVzruTdHW1ubzzz/n//7v/+jTpw916tQp0niEEG+XTE0XQgjxyqpXr06HDh3IyMh4rSnd4u1KS0ujTJkyRR2GEAW2fPlyFAoFzZo1K9D+z549A6Bs2bJvM6y3ztHREXNzc1auXFnUoQgh3jJJxIUQQryWihUrAs+f53zRnj17cHFxUT+XPHjwYI4ePaqxT0REBFZWVvz4449MnDgRGxsbWrVqxfHjx9XP8l6/fp358+fTqlUrbG1tcXFx4ezZs2RmZrJy5UocHR2xs7Ojf//+REREaNSf2zOfWeedPXs2AD4+PkyePBmAOXPmYGVlRXR0NPA8iV2/fj3Ozs44ODhQv3592rdvz1dffUV8fLy6TisrKyIjIwFo3LgxSqUSyP2Z5MOHDzN06FDs7e2xsbGhT58+bNiwIduovKOjI0qlkqioKEaPHo2DgwMNGzZk5MiRXLhwIc++yTo3wIULF7CyssLR0VG9PTExkYULF+Lk5ET9+vVp3bo106dPz3Fk//bt20yfPh0nJycaNGhAw4YN6du3Lxs3blTvExYWRocOHQDYt28fVlZWhIWFqdunV69e2eoNCwvDysqKdevWZbvm0NBQWrRoQcOGDZk7d656+5EjRxg6dCgODg7Y2dkxYMAAfvnllzzb4uU2ebE/rKysmDp1KpGRkbi6umJra0urVq1YvHgxGRkZXLlyheHDh9OwYUNat27NzJkzefr0qfr4rPspJCSEDRs24OTkhK2tLT179lRf/8t2796Ni4sLdnZ2NGzYEBcXF3bt2qWxT3R0NFZWVixZsoRZs2ZhZ2dH06ZNWbVqVa7tDLB//35GjBhBs2bNsLa2plmzZnz22WecP39eo36lUomjoyN3795l0qRJNG3aFFtbWwYNGpTtbwmezxrx8/PD0dERGxsbOnXqhL+/f7aR+cLcVzm5fPkyf/75Jz169NAoz7pX9uzZw/Dhw2nQoAHt27enXbt22f5+AwICgOd96+npydGjR+nfvz82NjY4Ojri7+9PamqqRv0vf19knS8yMpIVK1aor7t3794cOnQIgC1bttClSxdsbW3p0aNHtvvQx8cHKysrHjx4gJeXF40aNaJJkyZ89tlnXL58Odu1a2tr07VrV3755RdiYmIK1F5CiOJJEnEhhBCv5ebNm8D/EnKAJUuWMH78eGJjY+nTpw99+vThypUrDB06NMeXu3333XecPXsWNzc36tWrR7169dTbxo8fz549e+jWrRtt27bl5MmTjBgxgqlTp7JmzRratGlDly5dOH/+PKNHj+bevXuFvgYnJyd1YtOqVSvGjh1L6dKlAZg0aRJ+fn7o6Ojg7OzMgAED0NPT46effmLkyJHqOsaOHauemj9y5Ej69OmT6/mCgoIYNmwYZ8+epWPHjvTr148nT57w9ddfM2nSJFQqlcb+d+7cYeDAgTx48ABnZ2eaNm3KwYMHcXd3JzExMdfzNGnShLFjxwJgamrK2LFjcXd3B+DJkycMHDiQlStXUqVKFdzd3WnYsCGbN2/mk08+ITY2Vl1PdHQ0/fr1Y9u2bdjZ2TFkyBA6duxIVFQUvr6+BAcHA8+fcc2q39LSkrFjx1K3bt2CdcJLLl++zNdff42TkxOdO3dWP7cfEhLC0KFDuXjxIl27dmXAgAE8ePCAzz///LVeQnf69GmGDRtGuXLlGDhwIHp6eixfvpyvvvqKgQMHkpmZycCBAzExMSE4OBh/f/9sdWzcuJHZs2dja2tLv379ePToEZMnT1YnhVnmzZvHhAkTiI6Opnv37nTr1o3o6GgmTpzIggULstW7efNm9uzZw8CBA7Gzs8POzi7Xdg4ODmb06NHcuHGD7t274+7uTq1atdi3bx+DBg3S6FeApKQkXF1duXDhAr1798bJyYkTJ04wfPhw9d82QFxcHP3792f9+vVUqVKFQYMGYW5uzrJlyxgzZgzp6elA4e6r3GT9IJHbc9KzZs0iPj4epVJJgwYNGDJkSLa/3yZNmqj3v3jxIiNGjMDAwIBBgwZhYmLCsmXL+PTTTwv0OIqfnx9r1qyhffv2dOvWjUuXLuHh4cGsWbOYPXs29vb29O3bl+joaCZMmMC///6brY6RI0dy9OhR+vXrR/Pmzfnjjz8YOHBgjj+mtWrVivT0dH799dd8YxNCFGMqIYQQIhfffvutSqFQqEJDQ3PcfubMGVW9evVUNjY2qgcPHqhUKpXq9OnTKisrK5Wbm5sqOTlZvW98fLyqY8eOKltbW/W+R48eVSkUCpWtra0qNjY2x3O3b99elZCQoC6fOHGiSqFQqOzt7VV3795VlwcEBKgUCoVqw4YN6rL27durHBwcssWddd5Zs2apy0JDQ1UKhUK1du1addnJkydVCoVCNWnSJI3j09LSVN27d1cpFArV1atX1eVubm4qhUKhEW/Wdfz2228qlUqlunnzpqpevXqqdu3aqW7evKneLykpSeXu7q5SKBSqrVu3alyDQqFQzZgxQ5WZmakunzZtmkqhUKhCQkKyXd/LFAqFqmfPnhplvr6+KoVCoQoODtYoDw8PVykUCtW4cePUZV9++aVKoVCoDh8+rLHv6dOnVQqFQjVgwAB12a1bt1QKhULl4eGRbwwqVc7tnnXNP/zwg8a+d+7cUdWvX1/VpUsXVXx8vLr86dOnqgEDBqjq1KmjunjxYp5t8XJ/ZMX2cgxRUVHq8rlz56rLnzx5orK3t1c1b95cXZZ1PykUCtWePXvU5ffv31d16NBBVa9ePdW1a9dUKpVK9ffff6sUCoWqd+/e6r8DlUqlevDggfqeioyMVKlU/2tLKysr1fnz5zWuI6d2Tk1NVdnb26s+/vhjVVJSksb+06dPVykUCtWmTZvUZVn3q4eHh+rZs2fq8sDAQJVCoVB988036jIvL69sbaRS/e/e+PXXX1UqVeHuq9wMHDhQVb9+fVV6erpGeda90qZNG43vlhe3vRxfVr/MmDFDXZaWlqYaM2ZMtu+2l78vsuq0t7dX3b59W12+aNEilUKhUNWtW1ejX8LCwlQKhUI1f/58dZm3t7f6e+zF/v7ll19UCoVC5ebmlu36nzx5olIoFKpRo0bl11RCiGJMRsSFEELkKzw8nICAAPV//v7+jBs3jkGDBpGens4XX3yhfgP1li1bUKlUfPHFFxgYGKjrKFu2LCNHjuTp06fs2bNHo34HBwfMzMxyPHffvn3Vo9MA9vb2AHTr1k1jFN7GxgZ4PoX6TTE3N2fu3Ll8/vnnGuU6OjrqNysXdLptlu3bt5Oens6YMWOoWrWqutzQ0JBp06YB5Lgk1ciRI9HS0lJ/btu2LUChl2gCSE9PZ9u2bdSuXZtBgwZpbOvQoQP29vb89ttv6tH2nj17Mnv2bFq0aKGxr42NDSVLlix0GxRUp06dND5v376dZ8+eMW7cOI1ngUuWLMm4cePIzMxk69atr3QuPT09XF1d1Z9r1KihPsewYcPU5UZGRtSsWZMHDx6QkpKiUUfDhg3p3Lmz+nP58uUZNWoU6enp6ns+awr5i38zAOXKlWPSpElA9v7/6KOPCvTiroyMDGbOnMns2bMxNDTU2JY1QpxTXw0bNgxdXV3155fvrWfPnvHbb79RvXr1bC80GzVqFKNHj8bMzKzQ91Vu/v33X6pXr57rG8/btm2r8d2SH0NDQ42/YR0dHb744gsAduzYke/xH3/8scYqA1nfQc2bN9fol7y+gzw8PDT6u1OnTjg4OBAZGcndu3c19jUyMsLc3Jx//vmnIJcnhCim5K3pQggh8rVv3z727dun/qyrq0uZMmVo2bIlgwYNolWrVuptWf943Lt3L3/88YdGPVn/4Hz5WdW83rZerVo1jc9Z/wCvUqWKRnnWcmFZL216E8zNzenTpw/p6en8888/XLt2jZs3b3L+/Hn++usvgEK/aT1rKmrjxo2zbatduzalS5fONl1VX18fCwsLjTIjIyPg1a732rVrJCcnk5GRkW3aNEBqaioZGRlcvHgRBwcHGjVqRKNGjXj06BHnz5/n5s2bXLt2jVOnTqn3fdN0dXWpUKGCRtm5c+eA58+Iv/x8bdbay/k9N58bCwsL9PT0NMoMDQ1JTk7O9iPRi/dayZIl1eU5vY09KznLiuvChQtoa2vnuERWVtnL1/DyvZ4bAwMDunbtCjzv46ioKG7evMnly5c5cuQIkPP9Wr16dY3PL99bN2/eJDk5Ocdl/SpXrqx+S/nly5cLdV/lJDk5madPn+b50rXCrs5gZWWFiYmJRlm1atUoU6ZMge6XN/EdlNPfu42NDcePH+fixYuYm5trbCtbtixXrlzJNzYhRPElibgQQoh8zZkzJ8fly3Ly5MkTAFasWJHrPgkJCRqfX1xz+2Uvj+xleTlpels2bdrEd999p362tXTp0tja2lKzZk1Onz6d7Xnu/GSNBhobG+e4vUKFCty4cUOjLKdrzRodL+z5AR4/fgzA1atX81zSLKufEhISmDNnDjt37iQtLQ0tLS0qV65Ms2bNcnwe9k14McHNknVvbdq0Kd+YCyu3EdbC3Gcv/3AAqJP4rH5PTExEX18/x3qNjY0xMDDQeBEc5P338bK///6bOXPmqH8Q09fXp06dOlhbW3Pnzp0c75eXY3n53spq06wEPTeFva9yktXHeY14F6Y9QPP9FS8yNTXN9reWkzdxb+QUg6mpKfC/a375nGlpaSQnJ+f6HSiEKN4kERdCCPFGGRoaUqJECU6fPq0x3bWo5JR4vDylODd79uxh+vTpWFlZMX36dKytrdUj09OnT+f06dOFjqdUqVIAxMbGakxVzZKQkPDWlxrLiqFXr17Mnz8/3/29vLw4cOAALi4u9OrVC4VCoU7KCjK1N0tOffFy0pmXrIQkPDxcY1r/++Llt3DD/5LTrD4tVaoUT58+5cmTJ9l+jElNTSUlJeWVl+C6ffs2I0eORF9fn5kzZ+Lg4KCe4r179+5sb+4vqKz7Jbd1y7OSxcLeVznJaqecktNXlVO/wPO+eVfLnaWkpGRL6LOuMacYsrYV9kcHIUTxIc+ICyGEeKOsrKzIyMjINv0c4OTJkyxcuJBjx469k1h0dXVJSUnJlgC++DboLC8+f51l586dACxatAgnJyeN6eFXr14FCj8infVMaU5tcOPGDeLi4qhdu3ah6iwsS0tL9PT0+Oeff3KMf926dXz//fc8fPiQx48fc+DAAerXr8+MGTOwt7dXJ+HR0dGkpqZq1JFTO8LzvsiaPv6iW7duFTjurKXYzp49m23b9evXmTdvHr///nuB63vTcorr1KlTANja2gJ59//x48dRqVTUqlUr33Pl1M7h4eE8ffqUcePG4ezsTM2aNdXPWUdFRQGvNoPC0tISXV1dzpw5k23bvXv3aNiwIV9++WWh7qvc6OvrU7p06Tz3Kaxz585lm5J/+/ZtYmNj1f3ytuV0b5w8eRIdHR2sra2zbXv48CEVKlTI9Tl5IUTxJ4m4EEKINypr2S4/Pz+NlzIlJibi6+vLypUr38ozxTmpUaMG6enpHDx4UF326NEjNmzYkG1fHZ3nk8TS0tLUZVmjUffv39fYd9u2beo1w7OWbQLUMwBerONlvXr1QkdHh2XLlmkkocnJyXz99dfqfd4mfX19unbtypUrV1i7dq3GtoiICObPn09oaCgmJibo6uqira3N48ePNZ59TUlJYebMmYDm9ebUjvC8L6KjozWe7b59+zbbtm0rcNw9e/akRIkSfPPNN8TFxanL09PTmTlzJmvWrMm2nv279Ntvv2kk2HFxcQQGBmJoaEiXLl0A1I94LF68WGMd+vj4ePUockH6vzD364ULF/jhhx8Azfu1oPT19enUqRNRUVGEhIRobMtaMq558+aFuq/yUrt2bW7dupXrSHZhxcXFsWrVKvXntLQ09br0/fr1eyPnyE9AQIDG9+Gvv/5KREQEHTp0yDYDJj4+nvv37xfoBX1CiOJLpqYLIYR4o5o1a4ZSqSQoKEi99reenh7h4eHcuXMHFxeXHF9q9TY4Ozvz+++/M378eHr06IGenh6//PILH330EdeuXdPYN+sZzo0bN5KQkIBSqaRnz57s2rWLsWPH0q1bN4yMjDh79iyRkZGUL1+eBw8eaCR+WXVMmTKFli1bqtd6flHVqlXx9vZm9uzZ9OnTBycnJwwNDTl48CC3bt2iW7du9O7d+y22ynPe3t6cPHmSefPmsW/fPmxsbLh37x579+5FR0cHPz8/tLW1MTAwoGPHjvz666988skntGzZkuTkZPbv38/9+/cxMTHhyZMnZGZmoq2tTdmyZdHT0yMiIoI5c+bQsWNHGjVqhLOzMzNnzkSpVNK9e3eePXvGnj17UCgUBZ4hUb16dby8vJg7dy7du3fH0dERExMTDh48SFRUFO3bt6dnz55vueVyV6pUKYYMGULnzp0xMjIiPDyc+/fvM3PmTPWz4o0bN2bo0KGsXbuWnj170r59ewD2799PXFwcI0eOzPHFXi/LqZ3bt2/PokWLWL58OVevXqVatWrcuHGD/fv3q6fBv+oPFV988QXHjx9n2rRp/Prrr9SuXZuzZ8/y999/4+TkpH5JXEHvq7y0a9eO48ePc+bMmQK1RX5KlSrFt99+S0REBDVr1uTIkSNcunSJXr16qdv/bbt58ya9e/emXbt23Lt3j/DwcCpWrIiPj0+2fU+cOAFAy5Yt30lsQoiiISPiQggh3rhp06Yxf/58LCws2L59O1u3bsXU1BQ/Pz+mT5/+zuJo3749CxcupFq1amzdupXffvuNPn368M0332Tbt3HjxgwaNIiEhAQ2bNhAVFQU7dq1w9/fn2rVqrFjxw62bt1KamoqX331lXqE7cCBA+o6Ro8eja2tLYcPH85x1D2Lu7s7K1euxNramr1797J161bKlCnDrFmzWLRo0ZtviByUK1eOzZs3M2zYMO7du0dQUBDHjh3D0dGRzZs3a/xY4ufnx+DBg3ny5AnBwcEcOnSIBg0asHHjRnr37k1KSgoRERHA8xdYffXVV5iYmPDjjz9y9OhRANzc3JgyZQomJiZs2rSJI0eOMGrUKKZMmVKouIcOHcqKFSuoU6cOe/fu5aeffkJHRwcfHx++/fZb9UhxUejZsyfe3t4cO3aMbdu2UbVqVZYvX84nn3yisZ+Pjw8LFiygcuXK7Nixgz179mBpaUlAQACenp4FOldO7VyxYkXWrl1Ls2bNOHr0KD/++CPXrl1DqVSyZ88eypQpw6FDh15penrFihUJCQlhwIABXLx4kR9++IGYmBg8PDzw9/dX71eY+yo3HTp0AODPP/8sdJw5qVq1KqtWrSIuLo5NmzaRkZHBlClTmDdv3hupvyCWLFlC/fr1CQ0N5fjx4/Tu3ZuQkBCNZdGyHD58mBIlSqhnUQgh/pu0VK/ybSyEEEIIIYDn067d3d1xd3dn6tSpRR3Of8Lw4cOJiori999/z3cEPS9WVlbUqVOHn3/++Q1GV3A+Pj5s3bqVbdu2Ubdu3Xz3f/bsGa1ataJt27YsWLDgHUQohCgqMiIuhBBCCCHeKx4eHty5c4dDhw4VdSjv1C+//MLjx48ZNWpUUYcihHjLJBEXQgghhBDvlUaNGtG1a1e+/fbbV5pKXxylp6fz3XffMXDgwAK9OV8IUbxJIi6EEEIIId47X375JXfu3GHPnj1FHco7sWXLFtLS0gr8ngAhRPEmz4gLIYQQQgghhBDvkIyICyGEEEIIIYQQ75CsIy7emri4J0UdQo7KljXk4cPkog5DvCLpv+JP+rB4k/4r3qT/ij/pw+JN+q94e5X+MzMzzrFcEnHxwXH1OV3UIQghhBBCCCHeoO+8a7/1c+jolHhzdb2xmv7jMjIy+PTTT0lOTqZdu3ayrIQQQgghhBBCiFciz4gXUFxcHA8fPqRFixaULl26qMMRQgghhBBCCFFMyYh4AX355Zdcv36duLg4TE1NAZg7dy7Hjx8HoHv37gwePJjo6GimTp1Keno6WlpaTJs2jTp16tC+fXtq1KhBjRo1mDp1ao7nCA4OZu/evaSnp2NsbExAQAA7d+4kNDSUzMxMxo0bx6NHj1i3bh3a2to4ODjg6enJ3bt38fX1JTU1lUePHjFmzBicnJxyPEd8fDzjx49HpVKRlpbGjBkzsLKyYvHixfz5559UrFiR+Ph4Fi1axNatW7lx4wYPHz4kISEBV1dX9u7dy7Vr15g3bx52dnZvp7GFEEIIIYQQ4j9MEvECmj59OhMnTsTMzAyA/fv3Ex0dzebNm0lPT8fV1ZVmzZrx3XffoVQqcXJy4vz580yZMoWwsDDu3LlDWFgYZcuWzbH+zMxMjSR7+PDhnD17FoDSpUsTGBjIo0ePcHV1JTQ0FAMDA7y8vDh8+DBaWloMHTqUpk2bcuLECQICAnJNxM+cOYOxsTGLFi3iypUrJCYmcubMGY4dO8aWLVtITEykc+fO6v1LlizJ6tWrWbFiBQcOHGDZsmWEhoaya9cuScSFEEIIIYQQ4hVIIv6KoqKiaNSoEVpaWujq6mJra0tUVBRRUVE0btwYgLp163L37l0AypYtm2sSDqCtrY2uri4TJ07E0NCQu3fvkp6eDoClpSUAN2/eJD4+nk8//RSApKQkbt26hYODA4GBgWzZsgUtLS31cTlp06YN169f57PPPkNHRwcPDw+io6OpX78+2tralC5dmrp166r3r1evHgDGxsbUqlULABMTE1JTU1+16YQQQgghhBDigybPiL+imjVrqqelp6WlcfLkST766CNq1qzJsWPHADh//rx6Gru2dt5NfeHCBcLDw/nmm2/48ssvyczMRKVSaRxbpUoVLCwsWLNmDUFBQbi5uWFra8uSJUvo1asXCxYsoGnTpurjchIREUGFChVYs2YNHh4eLF68GIVCwZkzZ8jIyODp06dcuXJFvb+WltarN5IQQgghhBBCiGxkRPwVtW/fnsjISAYMGEBaWhqdO3fG2tqaL774gi+//JI1a9aQnp7O7NmzC1TfRx99hIGBAX379kVPTw8zMzNiY2M19ilXrhxDhgxBqVSSkZFB5cqV6dKlC507d2b27NksX74cCwsLHj58mOt56tSpw4QJE1i/fj3a2tqMGTOGWrVq0alTJwYMGICpqSk6OnJbCCGEEEIIIcTboqXKa/hUfJCcnZ1ZvHgxVapUea164uKevKGI3qwx8y4XdQhCCCGEEEKIN+hdrCNuZmZc6BzHzMw4x3IZ+nzH9u3bx7p167KVu7u707Fjxzd2nqVLlxIREZGt3M/Pj6pVq76x8xRHmxfav7c/Eoj8vcoXoHi/SB8Wb9J/xZv0X/EnfVi8Sf+JLDIiLt6a9/VLRr4Aizfpv+JP+rB4k/4r3qT/ij/pw+JN+q94kxFxIV6Ds+eJog7hg/UupgwJIYQQQgjxvpNEPBepqal06dKFDh06MHToUCpVqpTjPtu3b+eTTz4pggjz99NPP9G3b1/u3btHhw4dmDRpknrpM4DRo0eTlJREUFAQSqWSp0+fYmBgQGZmJo8fP8bT05MGDRrw+eefA8/fAl+9enUMDAzo2bPne3vdQgghhBBCCPE+k0Q8H1OnTs11W1xcHCEhIe9tQrp8+XJ69+4NQLVq1fj111/VifijR4+4ceOGenk1gHnz5lGzZk0Arl69yrhx49i5cydBQUEAKJVKfH191fsIIYQQQgghhCg8ScRfkJSUhKenJ48fP6ZatWrA/5LPR48eMW/ePHR0dChdujQLFy5k2bJlXLlyhaVLlzJ27Ngc6+zRowdNmjTh4sWLaGlp8f3332NsbMyiRYv4+++/UalUDBkyhC5duhAZGcnSpUsBSElJYd68eejq6uLh4UGZMmVo06YNbdq0YdasWQCUKVMGPz8/0tLSGD9+PCqVirS0NGbMmMGZM2eIi4tjwoQJTJkyhbJly1KmTBmioqKoWbMmu3fvpnPnzuo1z18WExND6dKl30IrCyGEEELx/o+uAAAgAElEQVQIIcSHTRLxF2zduhWFQsGECRM4ffq0xlvHw8PD6dixI8OHD+f333/n8ePHjB49mkuXLuWahMPz5L5bt258+eWXTJo0iYMHD2JkZER0dDSbNm0iNTUVZ2dnWrZsyeXLl1mwYAEVK1Zk2bJl/PLLL/To0YO4uDhCQ0PR09PD2dkZPz8/atWqRUhICKtWraJhw4bq5P7KlSskJibyySefEBgYiL+/P3FxcQB069aNXbt2MW7cOPbt28fEiRM1EnFvb290dHSIiYnBzs6OOXPmvL3GFkIIIYQQQogPlCTiL7h8+TKtW7cGwNbWFh2d/zXP6NGjWbZsGYMHD6ZixYrY2Njw7NmzAtVbr149ACwsLEhNTSUmJoZ//vkHpVIJQHp6OjExMVSsWJHZs2djaGjIvXv3sLe3B6BKlSro6ekBEBUVxYwZMwBIS0vD0tKSNm3acP36dT777DN0dHTw8PDIMQ4nJycGDRpE3759MTMzo2TJkhrbs6amb9q0iZ07d2JhYVHQphNCCCGEEEIIUUDaRR3A+6RGjRqcOnUKgH///Zf09HT1th07dtCnTx+CgoKoXbs2mzdvRltbm8zMzHzr1dLSynaepk2bEhQUxPr16+nSpQtVqlRh2rRp+Pn5MXfuXCpUqEDWynLa2v/rJktLS+bNm0dQUBBeXl60bduWiIgIKlSowJo1a/Dw8GDx4sXq874YX6lSpbC0tGTBggV0794913hdXFywsLDA39+/AK0mhBBCCCGEEKIwJBF/waBBg7h37x4DBw5kw4YN6Orqqrc1aNAAHx8f3NzcOHr0KL169aJ8+fKkpaWxYMGCQp3H0dERQ0NDXF1d6du3LwBGRkb06tULZ2dnXFxcSEpKIjY2Ntuxvr6+eHt74+rqyqJFi7CysqJOnTps3ryZAQMGMH/+fEaNGgVAo0aN+PTTT3lxqfgePXpw/PhxmjdvnmeMU6dOZdeuXVy4cKFQ1yaEEEIIIYQQIm9aqhezNCHeoMIudv+umJkZv7exifxJ/xV/0ofFm/Rf8Sb9V/xJHxZv0n/F26v0n5mZcY7l8oz4G3DmzJkcR8W7dOmCq6trEUQkhBBCCCGEEOJ9JYn4G2BjY6Nea1u8/5w9TxR1CO/cd961izoEIYQQQgghxP8nz4iLXF28eJG///4beP5ce2pqahFHJIQQQgghhBDFnyTiIld79+7lypUrRR2GEEIIIYQQQvynyNT0/5iwsDD2799PSkoKcXFxuLu7s2/fPi5fvswXX3xBcnIy69evR09Pj+rVq/P111+zY8cODhw4QEpKCjdv3mTkyJG0bNmSrVu3oquri7W1NfD8je3R0dEALF26FBMTk6K8VCGEEEIIIYQoliQR/w9KSkpizZo17Nq1i3Xr1rF582YiIiJYt24dUVFRbN26FSMjI/z8/Pjpp58wNDQkMTGR1atXc/36dUaPHk3fvn3p06cPpqam2NjYANCvXz8aNWqEj48Phw8fpmvXrkV8pUIIIYQQQghR/MjU9P+gunXrAmBsbEzNmjXR0tLCxMSEp0+fUqtWLYyMjABo3Lgxly9fBqBOnToAWFhY8OzZsxzrrV+/PgCmpqakpKS87csQQgghhBBCiP8kScT/g7S0tHItj4qKIjk5GYDIyEgsLS1zPUZLS4vMzMx86xVCCCGEEEIIUXCSiH9ASpQowf/93//h7u6Os7MzDx8+ZODAgbnuX79+fTZs2MDRo0ffYZRCCCGEEEII8d+mpVKpVEUdhPhviot7UtQh5MjMzPi9jU3kT/qv+JM+LN6k/4o36b/iT/qweJP+K95epf/MzIxzLJcRcSGEEEIIIYQQ4h2SRFwIIYQQQgghhHiHZPmydywsLAwTExM6dOiQ4/YVK1bQrFkz9ZJhryMgIABTU9M8nwP/EDl7nijqEN6J77xrF3UIQgghhBBCiBxIIv6O9e3bN8/tn3766TuKRAghhBBCCCFEUZBEPBdhYWGEhoaSmZmJUqlk/fr1aGtr4+DggKenJwEBAdy4cYOHDx+SkJCAq6sre/fu5dq1a8ybNw87OzsWLVrEuXPnSEpKombNmsyZM0c9Sl2jRg1WrlyJrq4u0dHRdO3aFQ8PD3x8fOjatSv379/nwIEDpKSkcPPmTUaOHEnfvn05c+YMM2bMoFSpUpQvXx59fX3mzp2b7/UsWrSIv//+G5VKxZAhQ+jSpQuRkZEsXboUgJSUFObNm0elSpX4/PPPSUxMJCUlBS8vL5o2bcqePXtYt26dRhsIIYQQQgghhCg8ScTzULp0aebMmYOrqyuhoaEYGBjg5eXF4cOHAShZsiSrV69mxYoVHDhwgGXLlhEaGsquXbuoVasWpUuXZu3atWRmZtKtWzfu3bunUX9MTAzbt2/n2bNntG7dGg8PD43tiYmJrF69muvXrzN69Gj69u3L9OnTmT9/PrVr18bf3z9bnTk5cOAA0dHRbNq0idTUVJydnWnZsiWXL19mwYIFVKxYkWXLlvHLL7/g5OTE/fv3WbduHQ8ePOD69es8evSIgICAbG3QsmXLN9fYQgghhBBCCPGBkEQ8D5aWlty8eZP4+Hj1lPGkpCRu3boFQL169QAwNjamVq1aAJiYmJCamoq+vj7x8fFMnDgRQ0NDkpOTSUtL06hfoVCgo6ODjo4OJUuWzHb+OnXqAGBhYcGzZ88AiI2NpXbt58/+Ojg4sHv37nyv49KlS/zzzz8olUoA0tPTiYmJoWLFisyePRtDQ0Pu3buHvb09tWvXZtCgQUycOJH09HSUSmWebSCEEEIIIYQQonAkEc+DtrY2VapUwcLCgjVr1qCrq0tYWBh169YlPDwcLS2tXI89ePAgd+7c4ZtvviE+Pp7ffvuNl5dsz+v43Labm5tz5coVatWqxenTpwt0HTVq1KBp06bMnDmTzMxMvv/+e6pUqcKQIUMIDw/HyMgIb29vVCoVFy9eJCkpiRUrVhAbG4uLiwtbtmzJsQ2EEEIIIYQQQhSeJOL5KFeuHEOGDEGpVJKRkUHlypXp0qVLvsfZ2Njw/fff4+zsjJ6eHlWrViU2Nva145k+fTpTpkzB0NAQXV1dKlasmO8xjo6OREZG4urqSnJyMk5OThgZGdGrVy+cnZ0pXbo0pqamxMbGUr16db777ju2bduGrq4u48aNe+U2EEIIIYQQQgiRnZbq5WFa8V7bsGEDXbp0oVy5cvj7+6Orq8vYsWOLOqwcxcU9KeoQcjRm3uWiDuGd+K8uX2ZmZvze3luiYKQPizfpv+JN+q/4kz4s3qT/irdX6T8zM+Mcy2VEvJgpX748w4YNw9DQEGNjY+bOncvYsWNJSEjQ2M/IyIjAwMAiivL9tnmhvXwBCiGEEEIIIYqMJOLFTOfOnencubNGWdYSZEIIIYQQQggh3n+SiIsPjrPniaIO4Z34r05NF0IIIYQQorjTLuoAhBBCCCGEEEKID4kk4i8JDg7Oc7uPjw89evRAqVQyYMAApk2bpl4fvGXLlhr7Hjx4EB8fH+D5m8tTU1MBiIuLo0ePHvz8889ER0fj7OxMfHw8SqUSpVJJo0aN6N+/P0qlkpCQEI06lUpltjeW7927FysrK6KjowkLC6Ndu3bqupRKJTNnzlQfm1XvoEGD6NGjBwcOHNCoq1evXsyYMUOjLK/rEkIIIYQQQghRODI1/SWBgYG4ubnluY+Xlxdt2rQBYNKkSezbty/bc9u5uXfvHiNHjuTzzz/HycmJ6Oho4PkyaUFBQcDzhNnX15eaNWvmWs/58+fVa3nv2rWLypUrq7d1794dT0/PHI+bN2+eut6rV68ybtw42rZtC8Dx48dRKBQcPXqUxMREjIyMCnRNQgghhBBCCCEK7oMdEb927RouLi64ubkxePBg7t27R2BgIAkJCfj6+haojoyMDJKSkqhUqVKB9o+JiWHo0KH4+Pjg5OT0yrF369aNnTt3AvD48WNSU1MxNTUtdD0xMTGULl1a/TkkJIROnTrRsWNHtm3b9srxCSGEEEIIIYTI3Qc7Iv7XX39hbW2Nj48Px44dIyEhAQ8PD4KDg/NNxBcsWMDKlSuJjY3F2NgYS0vLXPfV0tJS//+4ceMoWbIkDx48eK3YHR0d8fb2xtPTk19//ZXOnTvz448/qrfv3LmT06dPqz/369eP3r17A+Dt7Y2Ojg4xMTHY2dkxZ84cABITEzl+/DizZs2idu3afPbZZ+qZAQkJCSiVSnV9jx49wtra+rWuQQghhBBCCCE+VB9sIt6/f39WrlzJiBEjMDY2ZsKECQU+9sWp6UuWLGHu3LnMnj1bI+kGSE5ORl9fX/3Zz88PU1NTBg4cSL169fKcep4XfX196taty8mTJ/ntt9/w9/fXSMQLMjV906ZN7Ny5EwsLCwC2b99OZmYmo0aNAp4/x37kyBGaN2+OiYmJeto8PH9GfPfu3a8UuxBCCCGEEEJ86D7Yqen79u3DwcGB9evX07lzZ1atWgWASqUqVD0WFhbql7VVqVKFI0eOqLcdOnSIBg0aqD8rFAosLCzw8fFh/PjxpKSkvHL83bt3Z926dZiYmFCqVKlCH+/i4oKFhQX+/v4AbNmyhWXLlrF69WpWr17NtGnT2LBhwyvHJ4QQQgghhBAiZx/siHj9+vXx8vIiICAAbW1tJk+eDEDNmjXx9PRk4cKFuR6bNTVdW1ubzMxM/Pz8AJg1axYzZszA39+fzMxM7Ozs6NWrV7bjO3fuzKFDh5gxYwZjxox5pfhbtmyJj4+Pemr5i16emm5kZERgYGC2/aZOnUrPnj1p3749KpWK2rX/t+50p06dmDNnDnfu3Hml+N5nmxfaExf3pKjDEEIIIYQQQnygtFSFHQIWooDe12TXzMz4vY1N5E/6r/iTPizepP+KN+m/4k/6sHiT/iveXqX/zMyMcyz/YEfE8xITE4O3t3e28saNGzNu3LgiiEi8Sc6eJ4o6hDfmO+/a+e8khBBCCCGEeK9IIp6DSpUqabyc7E0IDg7Od31yIYQQQgghhBD/fR/sy9retZye0RZCCCGEEEII8eGREfG34Nq1a0yePBkdHR1KlChBs2bNSEhIwNfXl6lTpzJlyhRu3bpFRkYGQ4cOpWvXriiVSsqWLcvjx49ZsWIFvr6+3Lhxg8zMTMaPH0+FChXw8vJiy5YtAIwfP55hw4ZhY2OT7fwREREsXLgQXV1dnJ2dKVmypMYb0JcsWcLly5dZuXIlurq6REdH07VrVzw8PLhx4wY+Pj7o6OhQuXJlbt++TVBQEHv27GHdunVoa2vj4OCQ6/JoQgghhBBCCCHyJon4W/DXX39hbW2Nj48Px44do3z58gQHB+Pr60twcDBly5ZlwYIFJCYm0rdvX5o1awZAjx496NixIz/++CNly5bFz8+Phw8f4ubmxq5duyhZsiRXrlzB1NSU6OjoHJPwLKmpqYSEhACwbNkyVqxYgYGBAV999RV//vknFStWJCYmhu3bt/Ps2TNat26Nh4cH8+fPZ/To0bRt25bNmzdz+/ZtHj16REBAAKGhoRgYGODl5cXhw4dp2bLlO2lPIYQQQgghhPgvkUT8Lejfvz8rV65kxIgRGBsbM2HCBPW2qKgoWrRoATxfVqxmzZrcunULAEtLSwAuXbrE8ePHOXPmDADp6ek8fPiQTz75hLCwMCpVqkTPnj3zjCGrLoDy5cvj7e1NqVKluHr1KnZ2dsDzdc11dHTQ0dGhZMmS6vgaNmwIgIODAzt27ODmzZvEx8fz6aefApCUlKSOWQghhBBCCCFE4Ugi/hbs27cPBwcHxo4dy86dO1m1ahVZq8TVrFmTY8eO0bFjRxITE7l06RJVqlQBQEtLC4AaNWpgbm7O6NGjSUlJITAwEBMTEzp37syaNWsoU6YMS5YsyTMGbe3nj/8/efKEb7/9lj/++AOAoUOHqmPJOt+LFAoFJ0+epG3btuq1yKtUqYKFhQVr1qxBV1eXsLAw6tat+/oNJYQQQgghhBAfIEnE34L69evj5eVFQEAA2traTJ48mejoaDw9PfHz8+PLL79k4MCBpKamMnbsWMqXL69xvIuLC9OmTcPNzY3ExERcXV3R1tZGX1+fxo0bEx8fT5kyZQoUi5GREfb29vTp0wdDQ0NKly5NbGysOvl/maenJ1OmTGHNmjUYGxujo6NDuXLlGDJkCEqlkoyMDCpXrkyXLl1eu52EEEIIIYQQ4kOkpcoaHhXFgq+vL506daJ58+Zvpf7t27dja2vLRx99REhICCdOnGDOnDmvVFdhF7t/V8bMu1zUIbwxH+I64mZmxu/tvSUKRvqweJP+K96k/4o/6cPiTfqveHuV/jMzM86xXEbEi5Fhw4ZRoUIFdRK+dOlSIiIisu3n5+dH1apVX+kcFhYWTJgwAQMDA7S1tfHz83utmN9HmxfayxegEEIIIYQQosjIiLh4a97XZFd+iSzepP+KP+nD4k36r3iT/iv+pA+LN+m/4k1GxIV4Dc6eJ4o6hFx9iFPNhRBCCCGE+NBoF3UAQgghhBBCCCHEh+SDHhEPDg7Gzc0tz31++OEHduzYgb6+PlpaWowYMYK2bdsC0LJlSw4fPqze9+DBg+zevZu5c+fi6OiIhYWFehkxAG9vb5KSkhg/fjy1atVSl5ctW5Zvv/0WHx8fEhMTWbp0qXpb1jkyMzOZN28ely5dQltbG11dXaZOnUpycjKzZs0C4NSpU9jY2KCtrc3w4cNp164de/bsITg4GG1tbdLT0xkwYAC9e/dm27ZtnDp1Cl9fXwC++uorTp48yY4dOwAIDQ3l4sWL1KlTh6VLl7J9+3aMjIwAmDBhAi4uLjRt2vQ1Wl8IIYQQQgghPkwfdCIeGBiYZyIeFBTE6dOn2bBhA3p6ejx8+JBPP/0UExMT7Ozs8q1/zZo16Ovra5RFRETQrFkz/P39czzm+PHjbNu2jd69e2uUHzp0iNjYWNauXQtAeHg4fn5+BAYGEhQUBICjo6PGOf/88082bdrEsmXLMDY2JiUlhXHjxqGvr0+LFi1YvXq1uv6zZ89Srlw5oqOjqVKlCpGRkXTv3p24uDiePn2Kn5/ff/LFbUIIIYQQQgjxrn0wU9OvXbuGi4sLbm5uDB48mMDAQBISEtQjwjkJDg5m6tSp6OnpAc9Hrv/v//6PjRs3vrU4J02aREBAAHfv3tUoNzc359y5c+zevZv4+Hg6dOjAkiVL8qwrKCgIT09PjI2fvyCgZMmSeHt7s2HDBipUqICWlhaPHj3i4sWL1KhRgzZt2nDgwAEAzpw5Q5MmTQDo3bs3V69eZf/+/W/hioUQQgghhBDiw/LBjIj/9ddfWFtb4+Pjw7FjxyhfvjzBwcF5JuKPHz+mXLlyGmWVK1fm9u3buR6jpaWl/v9hw4app6Zra2uzfv16AI4ePYpSqVTv17ZtW0aMGAFAhQoV+Pzzz5k6darGiLWVlRUzZ85k8+bNzJo1C3Nzc3x8fNTJck5u3bpFtWrVNMqqVq1KTEwMAM2bN+fEiRNERUXRunVrrK2tWbRoEW3atKFy5crqkfUSJUowd+5cRo4cWaCZAEIIIYQQQgghcvfBJOL9+/dn5cqVjBgxAmNjYyZMmJDvMaVLlyY+Pl4jGb9+/ToVK1YENJNugOTkZI2p6DlNTQfynJoO0LNnT8LDw/nxxx/VZRcuXMDS0pLFixejUqk4fPgw48eP5/Dhw9niyFKxYkVu376NiYmJRvwWFhYAtGjRgoiICP755x/8/f0pV64cd+/eJTIyktatW2vUVb16ddzd3ZkxY0au5xNCCCGEEEIIkb8PZmr6vn37cHBwYP369XTu3JlVq1aR3xLqgwYNws/Pj2fPnrF7924mT55MQEAAAwcOBKBKlSocOXJEvf+hQ4do0KDBG4nX19eXNWvWkJSUBMCRI0dYvHgxGRkZaGlpUbt2bQwMDPJMipVKJfPnzycxMRGApKQk5s+fz6BBgwBo0qQJp06dIi0tTf1jg42NDVu2bMmWiAO4ubnx6NEjjh49+kauUQghhBBCCCE+RB/MiHj9+vXx8vIiICAAbW1tJk+eTHR0NJ6enixcuDDHY9zd3fnhhx9wc3NDW1ubjIwMjIyMuHr1Ko0aNWLWrFnMmDEDf39/MjMzsbOzo1evXurjX5yanlVf6dKls01NB1i5cqXG53LlyuHj48OYMWOA50n1vHnz6N27N0ZGRmhrazN//vw8r9nR0ZHExERGjBiBlpYWmZmZ9O/fn65duwJgYGCAjo4OjRs3Vh/Tpk0b/vzzT2rUqJGtPi0tLfz8/OjRo0ee533fbV5oT1zck6IOQwghhBBCCPGB0lLlNywsNGRkZHDu3DlsbW2LOpT33vua7JqZGb+3sYn8Sf8Vf9KHxZv0X/Em/Vf8SR8Wb9J/xdur9J+ZmXGO5R/MiHhuYmJi8Pb2zlbeuHFjxo0bl628RIkSkoQXc86eJ4rkvN951y6S8wohhBBCCCHeLx98Il6pUiX1OtxCCCGEEEIIIcTb9sG8rK2wgoODc90WExNDt27d1J937txJvXr1ePDgAQDR0dH07t0bgPj4eLy9vVEqlbi6ujJp0iTi4uLUx164cIERI0agVCpxcXHB39+fZ8+eAeDj40OPHj1QKpUMHDiQzz77jFu3bgEQEBCQ63rmp0+fpn79+pw5c0ZdFhYWpn5mPMuECROIiIggOjoae3t7lEolbm5uODs7a1z/gQMHGDx4MEOHDsXd3Z3t27cXuB2FEEIIIYQQQmiSRDwXgYGBuW6rVKkSmZmZxMfHA88T1Y8//piDBw8CEBERQevWrVGpVIwdO5aOHTsSFBTEjz/+SL9+/Rg1ahQZGRncv3+fiRMnMnXqVIKCgti4cSO6urrMmTNHfS4vLy/1tmHDhjF+/Ph8Yw8JCWHo0KEay58BPH36FD8/vxyPqVWrFkFBQQQHB7NhwwYOHjzI77//Djx/g3tAQABr167l+++/Z8mSJeofHYQQQgghhBBCFI4k4sC1a9dwcXHBzc2NwYMHExgYSEJCAr6+vrke06JFC06cOEFmZiaXLl1i2LBh/PHHHwDqdbjPnTuHsbExTk5OGsdVq1aNv//+m59//pl+/fphaWkJPH8r+ZgxYzhw4AApKSnZztmoUSN0dXW5ceNGrnElJSVx9OhRxo4dy4kTJ9Q/FgD07t2bq1evsn///jzbQ1dXF3d3d3bv3g1A+fLl+eGHH7h8+TKlSpViz549lC9fPs86hBBCCCGEEELkTBJx4K+//sLa2pq1a9cyevRoOnTogImJSb6J+LFjxzh37hzW1tY0aNCACxcukJmZyb///kvDhg25desWVatWzXZs1apViYmJ4datW1SrVk1jm5aWFmZmZty/fz/H85YvX56HDx/mGtfu3bvp2LEj+vr6dOnShS1btqi3lShRgrlz5+Ln55dnHQCmpqbqfQIDA3n69CkTJ06kVatWLF++PN812IUQQgghhBBC5EwScaB///6ULVuWESNGsGHDBkqUKJHvMU2bNuXUqVMcPHiQtm3boqWlha2tLfv376datWro6upSsWJFbt++ne3YGzduYGFhkeP2jIwMYmNjMTU1zfG8MTExmJub5xpXSEgIp06dYvjw4Rw7doyffvqJzMxM9fbq1avj7u7OjBkz8ry+27dvY25uTkJCAjExMXh5ebFjxw7CwsI4dOhQvqPqQgghhBBCCCFyJok4sG/fPhwcHFi/fj2dO3dm1apV+Y74GhkZoaenx+HDh2nRogUAbdq0YdWqVbRu3RoAe3t77t+/r37WGuDgwYPcuHGDJk2a0KdPH3766SeuX78OgEqlYunSpbRp04aSJUtmO+fhw4cpWbJkron4xYsXycjIYOPGjaxevZoNGzZQrVq1bEmzm5sbjx494ujRoznW8+zZM3744Qe6devGs2fPGD9+PHfu3AHAzMwMU1NT9PT08mwfIYQQQgghhBA5++CXLwOoX78+Xl5eBAQEoK2tzeTJk4mOjsbT05OFCxfmelyTJk2IiIjA2Pj5Iu0tW7bEy8tLfYyWlhbLli3Dz8+P5cuXA2Bubs6KFSsoUaIE5ubmzJ8/nxkzZpCSkkJaWhpNmjRh6tSp6nMsWLCAlStXoq2tTalSpfjmm2/U21asWEFISAgApUqVwsrKil69emnE+Mknn7Bhwwa6d++uLtPS0sLPz48ePXqoy65cuYJSqURLS4v09HR69Oih/oFh2rRpjB07Fh0dHTIyMmjXrh2tWrV6pbYWQgghhBBCiA+dlkoe9hVvSVzck6IOIUdmZsbvbWwif9J/xZ/0YfEm/Ve8Sf8Vf9KHxZv0X/H2Kv1nZmacY7mMiOchJiYGb2/vbOWNGzdm3LhxRRCREEIIIYQQQojiThLxPFSqVImgoKCiDkO8Yc6eJ97Jeb7zrv1OziOEEEIIIYQoXuRlbe+R4ODgPLf7+PjQo0cPlEolAwYMYNq0aaSlpQHg6OhIamoqmZmZzJkzh6FDhzJ8+HBGjx7NrVu3AFAqlURFRanrS01NxdHRUeMcvXr1yvZGdSsrK8LDw9WfDx48iI+Pz2tdqxBCCCGEEEJ8qCQRf48EBgbmu4+XlxdBQUH89NNPPH36lH379mlsP3ToELGxsaxdu5bVq1fTv39//Pz8CnT+48ePo1AoOHr0KImJiepyAwMD5s6dS3x8fOEuSAghhBBCCCFENpKIF5Fr167h4uKCm5sbgwcPJjAwkISEBHx9fQt0fEZGBklJSVSqVEmj3NzcnHPnzrF7927i4+Pp0KEDS5YsKVCdISEhdOrUiY4dO7Jt2zZ1ealSpRg6dGiBYxNCCCGEEEIIkTtJxIvIX3/9hbW1NWvXrmX06NF06NABExOTfJPdBQsWoFQq6dq1K/fv38fS0lJju5WVFTNnziQ8PJzu3bvTr18/Tp06pd7u7e2NUqlEqVQybNgwdXliYiLHjx+nXbt29OvXj40bN7GHvgAAACAASURBVGrU6+rqSmJiIjt27Hj9ixdCCCGEEEKID5i8rK2I9O/fn5UrVzJixAiMjY2ZMGFCgY7z8vKiTZs2ACxZsoS5c+cye/Zs9fYLFy5gaWnJ4sWLUalUHP5/7N15dM3X/v/x5wkJSowNEjdaUqVN9CpFGy1K1VwqERE5htu0hpaKRnNcWuEagrhc0RtTNHESQXxVTff2p+mQEkNRHWhLVXulcSOKVAyR4fz+cH3qNCPVxqnXYy1rOfuz9/68P5/d1bXe9v7svXMn48ePZ+fOnQDMmTMHLy8v4Oo34j179gRg06ZNFBYWMnLkSACysrLYtWsXjz32GPDz2eNDhgxh9OjRt+YliIiIiIiI3IE0I15BUlJSaNOmDfHx8fTo0YMVK1Zwo0e6u7u7G5u1XbNr1y7+/ve/U1BQgMlkolmzZlSrVg2TyVRqX+vXr2fJkiXExsYSGxvLlClTSExMtKvTsGFDxo4dy/z5828oThEREREREfmZZsQriI+PDxMnTiQ6OhonJycmTZpEeno6YWFhREVFldhu3rx5LF++HCcnJwoLC4tsxGY2m5kzZw79+/enRo0aODk5MXfu3FJjOXz4MDabjWbNfj5uq3v37syePZuTJ0/a1e3fvz/bt2+/iScWERERERERAJPtRqdhRcopK+t8RYdQLDc319s2Nimbxs/xaQwdm8bPsWn8HJ/G0LFp/BzbzYyfm5trseWaEb/NZGRkEB4eXqS8bdu2jBs3rgIiEhERERERkVtJifhtxsPDA6vVWtFh/KEFhB24Jf28Ed6s7EoiIiIiIiK/oM3aRERERERERH5HmhG/jRUUFPDCCy9w8eJFOnfuzMiRI0lISCA4OLjENhaLhUOHDlG7dm3y8/OpU6cOkyZNwtPTk+joaLZs2UL9+vXJz8+nXr16REZGUqNGDT777DMWLlyIzWajsLCQTp06GeeML1u2jLS0NJycnDCZTISGhuLj4/N7vQYREREREZE/FCXit7GsrCzOnj3Lk08+Sc2aNQGIiYkpNREH+7PG9+3bx/jx4/m///s/AIYPH87gwYMB+Pvf/87atWt57rnnmD59unHGeF5eHoGBgTz66KO4uLjw3nvvkZSUhMlk4ssvvyQ8PJxNmzb9hk8uIiIiIiLyx6Wl6bex1157je+++46srCzgahKenZ1NREREuft45JFHcHZ25vvvvy9yLTs7m0aNGgFXv01PTEzkiy++wMnJiaSkJB588EHq1q1LRkYG69evJzMzkwceeID169ffkucTERERERG5EykRv41NnTqV++67Dzc3NwBGjx5NrVq1bigRB6hXrx5nz54FIC4uDrPZTN++fdmxYwcPP/wwALNmzaJevXpERETg6+vLnDlzuHLlCnXr1iUmJoYDBw4waNAgevTowfvvv39Ln1NEREREROROoqXpd4CMjAwaNmwI2C9NT05OJjw8nKVLl3Lo0CFefPFFXnzxRc6ePctf//pX1q5dS8eOHalRowazZ88G4PPPP+eFF16gffv21K5du8KeSURERERExFFpRtzB2Gy2G6q/c+dOqlataiTi1/Pw8CAvLw+TycTEiRM5cuQIAHXq1KFRo0a4uLjw9ddfExERQW5uLgBNmjTB1dWVSpUq/fqHERERERERuQNpRtzBeHl5ERYWRlRUVIl15s2bx/Lly3FycqJ69eosXLjQuBYXF8e2bduoVKkSly9f5q9//SsuLi4sXLiQ119/nYKCAkwmEy1btsTPz4/KlStz7NgxBg4cyF133YXNZuPVV1/F1dX193hcERERERGRPxyT7UanWEXKKSvrfEWHUCw3N9fbNjYpm8bP8WkMHZvGz7Fp/ByfxtCxafwc282Mn5tb8ROYmhF3QBkZGYSHhxcpb9u2LePGjauAiERERERERKS8lIg7IA8PD6xWa0WH4bACwg786j7eCG92CyIREREREZE70R9us7aEhIQy66xdu5YhQ4ZgNpsJDAxkz549AERHR5OUlGRXNyAggPT0dAC++uorQkJCjHYLFizgypUrdvX79evHtGnTSrz30aNHeeGFFzCbzfj5+bFo0SJsNhvp6ekEBATY1U1KSiI6OhqADh062F1LTU3FYrHYlf2yj3379vH000/z1VdfGWWjRo1i1KhRdu26dOnCqlWrjN/Hjh3DbDYbv7du3UpQUBBBQUGYzWZmzpxZ5LlFRERERESkfP5wiXhMTEyp17du3crOnTuJi4vDarUyb948Xn31Vc6cOVNqu9OnTzNhwgQmT56M1WolKSkJZ2dn41gvgP3793P//feze/ducnJyivTx008/MWHCBP76179itVpZt24dR44cYc2aNTf3sKXYs2cPr7/+OkuXLqVFixYAnDx5kosXL5Kdnc2JEyfs6sfFxfHtt98W6efDDz9k3bp1LFmyhNWrV7Nq1SpMJhMbN2685TGLiIiIiIjcCRw2ET9+/DiBgYEEBwczbNgwMjMziYmJITs7m4iIiBLbrVmzhlGjRuHs7AyAp6cnGzdupG7duqXe7+2338bPz48mTZoAYDKZePHFF/nwww+5fPkycPVc7u7du9OtW7diE9WUlBTat2/PvffeC0ClSpWYM2cOfn5+N/EGSpaWlsa0adNYsWKFES/A+vXr6dq1K/3792f16tV2bSwWCxaLhYKCArtyq9XKq6++Ss2aNYGrzz1p0qQis/ciIiIiIiJSPg77jXhaWhre3t5YLBb27dtHdnY2o0ePJiEhodRE/NSpU3h6etqV1alTx/j7teO9rvnmm28AOHHiRJHl4SaTCTc3N06fPk3t2rXZv38/M2bMoFmzZowZM4bg4OAy7129enW7e12/JPzUqVP06dOnxGcxmUxFyv7zn/+wYMECcnNzjX8gACgsLGTLli2sXbuWypUr07t3b15++WWqVq0KQKdOnUhNTWX58uV069bNaJeens4999wDwCeffMLf//538vLycHd3Z8GCBSXGJiIiIiIiIsVz2ETc39+f5cuXExISgqurK6GhoeVq16hRI06ePGl3DvaOHTto3rw5AMOHD2fw4MHGtWszvw0aNOCHH36w66ugoIBTp05x9913s2HDBgoLCxk5ciQAWVlZ7Nq1i8cee8yo7+HhweHDh+36OHHiBP/9739xd3fnvvvus9uELSkpidOnTwNFk+6LFy9SpUqVIs9XtWpVli9fzieffML48eNZt24dVatW5aOPPuLChQu88sorwNXEfPPmzQwcONBoa7FY8PPzo3HjxkaZu7s76enptGjRgocffhir1cqxY8dK/ccOERERERERKZnDLk1PSUmhTZs2xMfH06NHD1asWAFAWcei+/n58c9//pP8/Hzg6hL3yZMn4+RU+qt49tlnWbt2Ld99951xn8WLF9OxY0eqVq3K+vXrWbJkCbGxscTGxjJlyhQSExPt+njyySf56KOP+M9//gNAXl4ekZGRHDlypMzn/dOf/sSuXbuM3x999BEtW7YsUq9+/frUrl2bJ598kkceeYTp06cDV5elz5gxw4hv4cKFRZan16hRg+nTpzNz5kyjLDg4mLlz53L+/M/n5e3du7fMeEVERERERKR4Djsj7uPjw8SJE4mOjsbJyYlJkyYB4OXlRVhYGFFRUcW26927N1lZWQQFBeHs7ExBQQHz5s2jXr16pd6vYcOGzJ07l2nTpnH58mXy8vJo164dkydP5vDhw9hsNpo1+/lIq+7duzN79mxOnjyJu7s7cDXRjYyMZMqUKdhsNi5cuMCTTz5JUFBQkdn2X5oxYwbTpk1jwYIFFBYW0qpVK/r161dqm/DwcPz9/Xnrrbf49NNP7ZaSt2nThtzcXA4csD/Kq3379vTu3Zsvv/wSgK5du5Kfn8+YMWMAuHDhAi1atGDOnDml3ltERERERESKZ7KVNYUscpOyss6XXakCuLm53raxSdk0fo5PY+jYNH6OTePn+DSGjk3j59huZvzc3FyLLXfYGfHSZGRkEB4eXqS8bdu2jBs3rgIiEhEREREREbnqD5mIe3h42G16JnK9gLADZVcqwRvhzcquJCIiIiIiUgqH3axNRERERERExBEpEb9JCQkJJV47c+YMZrMZs9nMI488gr+/P2azmeTkZAB2797N0KFDMZvNBAYG8uabb9rt9p6bm0uHDh2MneB/acOGDXTu3Nm4x6BBg+zOPi+ufWRkJK+//rrxu6CggIEDB/Lhhx+WGZPZbObYsWN2/Xfp0uVGXpeIiIiIiIj8jxLxmxQTE1Pitbp162K1WrFarTzwwAPMmTMHq9XKwIEDOXr0KHPmzCEqKgqr1UpCQgLHjh0jNjbWaP/OO+/Qq1cv3nrrLQoLC4u9R58+fYx7LF26lMjISCNxLq79hAkTOHDgAGlpaQDExsby0EMP0alTp3LFJCIiIiIiIreGEvEyHD9+nMDAQIKDgxk2bBiZmZnExMSQnZ1NRETEDfeXlJTEyJEjqV+/PgCVK1fGYrGwdu1ao05ycjJ+fn60aNHCmLEuzfnz56latSomk6nE9i4uLsyZM4dp06bx1Vdf8e9//5tXX3213DGJiIiIiIjIrfGH3KztVkpLS8Pb2xuLxcK+ffvIzs5m9OjRJCQk3FQifuLECfz9/e3KatSowaVLlygsLOQ///kPly5dokWLFvj5+bFy5UqefPLJIv1s2bKFTz/9FJPJRLVq1Zg7dy4A3333XYntvb296du3L8OHDycuLo4qVaqUKya4eiZ5tWrVAEqcpRcREREREZGyKREvg7+/P8uXLyckJARXV1dCQ0N/VX8NGjTghx9+4MEHHzTKcnJycHFxwcnJieTkZC5dusRzzz0HwIEDB/j++++555577Prp06cPYWFhRfovq33//v1JTU2lRYsW5Y4JYM6cOXh5eQFXvxHv2bPnr3oPIiIiIiIidyotTS9DSkoKbdq0IT4+nh49ehgboF2/udqNGDx4MDExMWRlZQGQl5fHzJkzCQwMJD8/n23btpGYmEhsbCyxsbG88MILrF69ulx932z70mISERERERGRW0uJeBl8fHxYuHAhQUFBrFmzhuDgYAC8vLyKnZEui7e3N6GhoYSGhjJ48GCCgoJo0qQJISEhvPfee3h7e1O7dm2j/oABA3j77be5dOlSmX3fbPvSYhIREREREZFby2S72aldkTJkZZ2v6BCK5ebmetvGJmXT+Dk+jaFj0/g5No2f49MYOjaNn2O7mfFzc3MttlzfiP8KGRkZhIeHFylv27Yt48aNq4CIRERERERE5HanRPxX8PDwwGq1VnQYcoMCwg7cdNs3wpvdwkhEREREROROdNt8I56QkFDqdYvFQmpqKnB1U7KXX36ZiIgIY9O0Tz/9FB8fHz777DOjzYYNG4iKiirSV5cuXcjNzS32PlOnTqV///52ZWazmdmzZxu/c3Nz6dKli/E7LS2NYcOGMXjwYMxmMxaLhfPnf16yMGPGDBYvXszy5cuNspCQEEaOHGn8XrRoEfHx8URHR5OUlGR3/4CAANLT08uM8Zc7mf+///f/aN68Oenp6WzYsIHOnTtjNpsxm80MGjSIbdu2AVc3nktMTDTiN5vNRc4vL+79ioiIiIiIyI27bRLxmJiYctXLy8tj/PjxeHp6EhERgclkAq4e2zVixIhy7zBenEuXLnHgwAG8vLzYs2eP3bUtW7awd+/eIm2++uor5s2bx9y5c0lKSsJqtdKiRQtjd3WA9PR0Hn/8cfbv32/cJycnh4yMDC5fvgzA3r17eeKJJ35VjABffvml8fetW7fSqFEj43efPn2wWq1YrVaWLl1KZGQkNpuNtWvXcuDAAeLi4rBarSxatIjFixdz8OBBo+2teL8iIiIiIiJSQYn48ePHCQwMJDg4mGHDhhETE0N2djYRERGltrty5Qpjx46lRYsWdjuWX7hwgd27d/PSSy9x4MABzpw5c1Nx/etf/+Kxxx7j2WefJTEx0e7a5MmTee2117hw4YJdeVJSEqNHj6ZBgwZG2fDhw43zxo8ePYqXlxctW7bk6NGj2Gw2du3aRbt27Wjbti179uwhNzeXH3/8kaZNm/6qGHv37s2WLVsA+Omnn8jNzeXuu+8utp/z589TtWpVTCYTCQkJTJ48mSpVqgBQp04dXnrpJWNm/la9XxEREREREamgRDwtLQ1vb2/efPNNRo0aRdeuXalVq1aZifjMmTO5ePEimZmZduXbtm2jW7duVKlShZ49e7J+/fqbiis5OZmBAwfi6+vL4cOH7e7TvHlz+vfvT2RkpF2b9PR0GjduDMCJEycwm80EBwczePBgAN5//32efPJJKlWqxAMPPMCRI0dITU2lY8eOdOzYkdTUVD755BPatWtn9BkXF2csETebzXzzzTflirFLly6kpqZis9l455136NGjh12sW7ZswWw2M3ToUGbMmMHcuXMBOHv2LHXr1rWr6+npSUZGxi19vyIiIiIiIlJBibi/vz916tQhJCSExMREKlWqVK52wcHBrFy5kiNHjvD2228b5cnJyRw8eJDnnnuOffv2sXbtWgoLC28opmPHjnH06FEiIyN5/vnnMZlMRb7VfuGFF/j666+Nb9UB3N3dje+3PT09sVqtxMbGGgnywYMHefjhhwHw9fVl3759HDx4kFatWtG+fXsOHTrExx9/bLcsffjw4cYScqvVyn333VeuGKtUqcIDDzzAJ598wvbt2+nWrZtd/NeWpq9atYqlS5fSqlUrAGrUqMG5c+fs6n7//fe4u7vfsvcrIiIiIiIiV1XIrukpKSm0adOGl156iS1btrBixQrKc5x5s2bNqFy5MlFRUQwePBgfHx/y8/MpKChg3bp1Rr0RI0bw/vvv31BMycnJhIaGMmTIEODq0WSDBg1izJgxRp1KlSoRGRlJSEiIURYYGMjUqVN56KGHqF+/PgC7d+8G4Ny5c7i6uhr/0NChQwcmTZrEPffcQ+XKlalcuTI1a9Zk9+7djBgx4pbE2KdPH+Li4qhVqxbVq1cv17MHBwczY8YMZs2ahYuLCz/++COLFy9m8uTJfP311yW+365du5arfxEREREREflZhSTiPj4+TJw4kejoaJycnJg0aRLp6emEhYUVu8v5L3l6ejJx4kRefvllHn74Yfr162d3feDAgSQmJtKnTx82btxIWlqace3acWPXlo4D9OzZk61bt9rNsnt4eNCiRQveeecdu76bNm3KsGHDiI+PN57l1VdfxWKxkJeXx6VLl/Dw8GDZsmV89NFHdjPd99xzD5mZmfj7+xtlvr6+pKSkUKNGjVKfOS8vr1wxdujQAYvFYrfLe1nMZjMFBQUMGTKEypUrYzKZGDNmDK1bt2bGjBklvl8l4iIiIiIiIjfOZCvPVLTITcjKOl92pQrg5uZ628YmZdP4OT6NoWPT+Dk2jZ/j0xg6No2fY7uZ8XNzcy22vEJmxEuSkZFBeHh4kfK2bdsybty4CohIRERERERE5Na6rRJxDw8PY+m4yG8lIOzADdV/I7zZbxSJiIiIiIjciSpk13QRERERERGRO9VtNSP+R5GQkEBwcHCpddauXcumTZtwcnIiLy+P0NBQ2rdvT3R0NHfffbfdZnJwdVO4a8egXRMVFcXOnTuZNGkS69at489//jNwdWO3xx9/nODgYMaOHVtq28WLF7Np0yZjs7jQ0FACAwP5/PPP+fDDD/npp584deqUcYRaXFwc3bp141//+hdVqlT5Ve9JRERERETkTqRE/DcQExNTaiK+detWdu7cSVxcHM7Ozpw4cYLg4GDeeuutEtvUqlWrxGX7TZs2ZcuWLUYi/tFHH+Hq6lqutpcuXWLWrFnMmjXLrjwkJISQkBD27NnDmjVrWLBgQYmxiYiIiIiISPlpafqvdPz4cQIDAwkODmbYsGHExMSQnZ1NREREiW3WrFnDqFGjcHZ2Bq4ex7Zx40bq1q17UzF07NiRtLQ0CgsLgauJfu/evcvVtn///nz77bc3fO66iIiIiIiI3BzNiP9KaWlpeHt7Y7FY2LdvH/Xq1SMhIaHURPzUqVN4enraldWpU6fU+2RnZ2M2m43f9evXZ/78+QA4OzvTqlUr9u7di4+PDzk5OTRs2JDTp0+X2bZSpUpERkby/PPP06pVqxt6dhEREREREblxSsR/JX9/f5YvX05ISAiurq6EhoaW2aZRo0acPHnSbvn4jh07aN68eYltSlteDtCnTx+2bt3KyZMn6datG3l5eeVue++99zJ06FCmTZuGyWQqM34RERERERG5eVqa/iulpKTQpk0b4uPj6dGjBytWrMBms5Xaxs/Pj3/+85/k5+cDV5e3T548GSenmx+O9u3bc/DgQf7973/To0ePG24fHBzMuXPn2L17903HICIiIiIiImXTjPiv5OPjw8SJE4mOjsbJyYlJkyaRnp5OWFgYUVFRxbbp3bs3WVlZBAUF4ezsTEFBAfPmzaNevXoALFu2jOTkZACqV6+O1WotsrwcYMKECcbfnZyc6NChAydPnjR2QL+mrLYAJpOJWbNm0bdv35t7EQ5kXVRrsrLOV3QYIiIiIiJyhzLZypq+FblJt2uy6+bmetvGJmXT+Dk+jaFj0/g5No2f49MYOjaNn2O7mfFzc3Mttlwz4r+RjIwMwsPDi5S3bduWcePGVUBEIiIiIiIicjtQIv4b8fDwKHWDNKk4AWEHbqj+G+HNfqNIRERERETkTqTN2m5QQkJCuer169ePadOm2ZU1b96cd9991/idmpqKxWKx63vQoEEMGTKEIUOG8MYbbxTpd8+ePTRv3pxt27bZlfft29foKy8vj8WLFxMUFITZbGbEiBF8+umnRfrasGGD3Xfs8fHxBAYG8tNPP2GxWEhNTQXgzJkzhIeHYzabCQoK4pVXXiErK6tc70FERERERETsKRG/QTExMWXW2b9/P/fffz+7d+8mJyfHKK9WrRqRkZGcOXOmSJvVq1fzySefsGrVKhITE4mLi+PIkSPs2LGjSN2mTZuyZcsW4/fXX3/NpUuXjN+LFi3i4sWLJCQkYLVamTFjBpMnT+bEiRMlxrxixQref/99Vq5cSc2aNY1ym83GSy+9RLdu3bBaraxevRo/Pz9GjhxJQUFBme9CRERERERE7CkRL8Hx48cJDAwkODiYYcOGkZmZSUxMDNnZ2URERJTaNjk5me7du9OtWzc2btxolFevXp0RI0YU23716tVMnjyZKlWqAODs7MzChQt5/PHHi9Rt0aIFJ0+e5KeffgJg06ZNdrudb9q0iQkTJhjHoTVq1IigoCDeeuutYuNdsmQJu3fvZtmyZdx1111217744gtcXV156qmnjDJfX18aN27Mxx9/XOp7EBERERERkaKUiJcgLS0Nb29v3nzzTUaNGkV2djajR4+mVq1apSbiOTk57N+/n86dO+Pn50dSUpLd9aCgIHJycti8ebNd+blz56hbty4A27dvx2w2ExAQwJw5c4q9T7du3di+fTs2m43PPvuMhx9+GIAff/yRWrVqUbmy/ef/np6eZGRkFOln8+bNpKWlcfr06WLPPz9x4gSenp5FykvqT0REREREREqnRLwE/v7+1KlTh5CQEBITE6lUqVK52m3atInCwkJGjhzJ9OnTycrKYteuXcb1a+d1L1y4kFOnThnl1atX59y5cwDGMvCxY8dy9uzZYu/Tt29ftm3bxscff8wjjzxilLu6upKdnU1+fr5d/e+//x53d/ci/TzwwAPExcXx2GOPMX369CLXGzRowA8//FCkvKT+REREREREpHRKxEuQkpJCmzZtiI+Pp0ePHqxYsQKg2Fnj661fv54lS5YQGxtLbGwsU6ZMITEx0a5Ow4YNGTt2LPPnzzfKhgwZwqxZs7hy5QoABQUF7N+/H5PJVOx9PD09uXjxIlarlWeeecYod3FxoWfPnixYsIDCwkLg6qz26tWrGTBgQJF+7rvvPpycnAgNDeXLL7+0W0oP0Lp1a06fPs17771nlKWmpvL999/Trl27Ut+FiIiIiIiIFKXjy0rg4+PDxIkTiY6OxsnJiUmTJgHg5eVFWFiY3W7j1xw+fBibzUazZj8fd9W9e3dmz57NyZMn7er279+f7du3G7+HDh1KUlISI0aMwMnJiZycHNq1a8fEiRNLjLFXr168/fbbNGnSxG4jtrCwMKKjowkICMDZ2RkXFxdmzJhR7BLza1xcXIiKiiI4OBgfHx+j3GQysWTJEmbNmsXSpUuBq/+QsGzZsnKvEhAREREREZGfmWxlTfGK3KSsrPMVHUKx3Nxcb9vYpGwaP8enMXRsGj/HpvFzfBpDx6bxc2w3M35ubq7FlmtG/CZkZGQQHh5epLxt27aMGzeuAiISERERERERR6FE/CZ4eHhgtVorOgy5SQFhB8qs80Z4szLriIiIiIiI3Axt1iYiIiIiIiLyO7qjZ8QTEhIIDg4us156ejoTJkxg3bp1mM1mIiIi8PLyAiA3N5eePXvy3nvvYbFYOHToELVr18Zms3Hu3DlGjBiBn58fALt37+af//wnNpuNvLw8unfvzvDhwzGZTJjNZh588EFjU7jr+4Wr55ovXbqUK1euULlyZRo1asTkyZNxdf35m4PFixdTpUoVnn/+eQBCQkKoVKmSscnaokWLqFWrFj/99BNbtmyhfv36RltfX19Gjx7NZ599xsKFC7HZbBQWFtKpUyf+8pe/EBkZyaFDh8jKyuLy5ct4enpSp04dFi1adAtGQkRERERE5M5xRyfiMTEx5UrEb8TEiRPp2LEjAOfOnaNPnz4MGDCAb775hjlz5rB06VLq169Pfn4+ERERxMbGEhISAsCWLVvo2rVrkWPBvvrqK+bNm8eSJUto0KABAHFxcaxYsYLQ0FCj3uOPP86SJUt4/vnnuXTpEjk5OVy4cIHLly9TtWpV9u7dy/Tp09m6dSvDhw9n8ODBReKfPn06c+bMwcvLi7y88Mr1VQAAIABJREFUPAIDA3n00UexWCwAbNiwgW+//ZawsLBb+t5ERERERETuFHfE0vTjx48TGBhIcHAww4YNIzMzk5iYGLKzs4mIiPjN7nv69GlcXFwwmUwkJSUxcuRIYxa6cuXKWCwW1q5da9SfPHkyr732GhcuXLDrJykpidGjRxtJOMDw4cPtknCAli1bcvToUWw2G7t27aJdu3a0bduWPXv2kJuby48//kjTpk1LjdnDw4PExES++OILnJycSEpK4sEHH/y1r0JERERERET+545IxNPS0vD29ubNN99k1KhRZGdnM3r0aGrVqnXLE/F58+YRFBRE586dmT17Nv/4xz8AOHHiBI0bN7arW6NGDS5dukRhYSEAzZs3p3///kRGRtrVS09PN9qeOHECs9lMcHBwkRntSpUq8cADD3DkyBFSU1Pp2LEjHTt2JDU1lU8++cRupj0uLg6z2Wz82blzJwCzZs2iXr16RERE4Ovry5w5c7hy5cotfUciIiIiIiJ3sjsiEff396dOnTqEhISQmJhIpUqVytUuOzvb+LvJZAKgSpUq5OXlGeUXLlygatWqxu+JEyeyevVqpk2bxqlTp4wEukGDBvzwww92/efk5ODi4oKT08/D8MILL/D111+TmppqlLm7u5Oeng6Ap6cnVquV2NhYMjMzi8Ts6+vLvn37OHjwIK1ataJ9+/YcOnSIjz/+mCeeeMKoN3z4cKxWq/GnQ4cO5ObmcujQIV588UXWr1/Pv//9bzIyMuxm7UVEREREROTXuSMS8ZSUFNq0aUN8fDw9evRgxYoVANhsthLb5OTk8Oyzz2Kz2Th16hR169YFwNvbm3feeceol5qaSsuWLYu079SpE127duW1114DYPDgwcTExJCVlQVAXl4eM2fOJDAw0K5dpUqViIyMZPbs2UZZYGAgMTExnDp1yijbvXt3sXF36NCBrVu3cs8991C5cmWqVatGzZo12b17N48++mip78lkMjFx4kSOHDkCQJ06dWjUqBEuLi6lthMREREREZHyuyM2a/Px8WHixIlER0fj5ORk7Ezu5eVFWFgYUVFRRdrUqFGDvn37EhAQgLOzM6+//joAzz//PK+//joDBgzAxcWF2rVr87e//a3Y+44ZM4YBAwbwwQcf0LlzZ0JDQwkNDaWgoID8/Hy6detmbNR2vaZNmzJs2DDi4+ON+F999VUsFgt5eXlcunQJDw8Pli1bVqTtPffcQ2ZmJv7+/kaZr68vKSkp1KhRwyiLi4tj27Ztxu8mTZowffp0Fi5cyOuvv05BQQEmk4mWLVsau77/UayLak1W1vmKDkNERERERO5QJltp08Iiv8Ltmuy6ubnetrFJ2TR+jk9j6Ng0fo5N4+f4NIaOTePn2G5m/NzcXIstvyNmxEuTkZFBeHh4kfK2bdsybty4CohIfmsBYQfKrPNGeLPfIRIREREREbkT3fGJuIeHB1ar9Te9R0FBAS+88AIXL16kc+fOjBw5koSEhFLPMI+Ojubuu+8u9qzvipaUlMTp06cZO3ZsRYciIiIiIiLicO6IzdoqWlZWFmfPnsXX15eaNWsCEBMTU8FRiYiIiIiISEW442fEfw+vvfYa3333HVlZWdx9993ExMSQnZ1NREREmeeYp6enExoaahxh1rt3b44ePcrhw4fp3LkzEyZMwGw206RJE44fP47NZmPBggV8++23REVF4ezsTEBAAG5ubixcuJAqVapQu3ZtZs2axRtvvEGLFi149tlnycrKYuTIkWzYsIH58+fz8ccfY7PZGD58OD179mTfvn3MmjWLWrVq4eTkRKtWrX6flyciIiIiIvIHo0T8dzB16lQmTJiAm5sbAKNHjyYhIaHMJPyaEydOsHLlSi5fvkzXrl1JTU2lWrVqPPnkk0yYMAGA1q1bM336dBITE1m6dCndunUjNzeX5ORkbDYbXbt2JSkpiQYNGhAfH09MTAwBAQFMmzaNZ599lrfffpsBAwbw4Ycfkp6ezpo1a8jNzSUgIIAOHTowe/Zs5s+fT5MmTZg6depv9apERERERET+8LQ03QF4enri6upKzZo1ufvuu6lduzZVqlTBZDIZda6dEd66dWuOHz8OXD2SDODs2bPUqFGDBg0aAFc3ojt69CheXl4UFBTwww8/sG3bNp555hmOHDnCoUOHMJvNhISEkJ+fT0ZGBpmZmUZ/rVu3/j0fX0RERERE5A9FiXgFuZFT465PuEvyxRdfAHDgwAHuu+8+AJycrg5vnTp1yMnJ4dSpUwDs3buXe++9FwB/f3/mzZvHfffdR82aNWnatCnt27fHarUSHx9Pz549+dOf/oSbmxvHjh0D4PPPPy937CIiIiIiImJPiXgF8fLyIiws7Jb199ZbbxEcHMwHH3zAqFGj7K6ZTCZmzJjB2LFjCQwMZNeuXYwZMwaAHj16sGPHDgYOHAhAly5duOuuuwgKCmLAgAEA1KhRg3nz5hEeHs6wYcPIyMi4ZXGLiIiIiIjcaUy2G5malduS2WwmIiICLy+vig7Fzo0edv97eXHO0TLr6Bzx25ebm+tt+9+WlI/G0LFp/Bybxs/xaQwdm8bPsd3M+Lm5uRZbrs3aKlBGRgbh4eFFytu2bcu4ceMqIKI7w7qo1vofoIiIiIiIVBgl4hXIw8MDq9X6q/u5FX2IiIiIiIjI70OJuNxxAsIOlHpdy9JFREREROS3pM3aRERERERERH5HSsR/QwkJCWXW2bp1K0FBQQQFBWE2m5k5cyZXrlwBru5gPmTIEMxms/Hniy++YNy4cSxbtszo48KFC3Tv3p2vvvoKi8VCampqsffatm0brVq1IjMz0yiLjo7G39+f/Px8oywgIID09HQATpw4wbhx4wgICGDo0KG88MILHD1a9mZnIiIiIiIiUjwl4r+hmJiYUq9/+OGHrFu3jiVLlrB69WpWrVqFyWRi48aNRp2VK1ditVqNPz4+PkRERJCUlMQ333wDwJw5cxg0aBAtWrQo9X7JyckEBwezbt06u/IffviBpUuXFql/6dIlRo8ezYgRI1i3bh2rVq3ipZdeYvr06eV9BSIiIiIiIvILSsRvkePHjxMYGEhwcDDDhg0jJiaG7OxsIiIiSmxjtVp59dVXqVmzJnD1vO9JkyYREBBQ6r3q1q3La6+9xpQpU9i7dy8nTpxgxIgRpbY5ceIE2dnZjBw5krfffpu8vDzjWkhICJs3b+bw4cN2bd5//30effRRHn74YaPsoYceYtWqVaXeS0REREREREqmzdpukbS0NLy9vbFYLOzbt4969eqRkJBQaiKenp7OPffcA8Ann3zC3//+d/Ly8nB3d2fBggUA/OUvf8HJ6eq/lzg5OREfHw9cXba+fft2LBYLSUlJmEymUuNbv349fn5+uLq60qpVK7Zv306vXr0AuOuuu5gxYwYWi4X169fbxde4cWPj9+jRo8nJyeHUqVPEx8fTsGHDG39RIiIiIiIidzgl4reIv78/y5cvJyQkBFdXV0JDQ8ts4+7uTnp6Oi1atODhhx/GarVy7Ngxu+R95cqVVKlSpdj2/fv35/LlyzRo0KDU+xQUFLB582YaNWrEe++9R3Z2NgkJCUYiDvDII4/g6+vLP/7xD6OsYcOGfPHFF8bva0vtAwIC7L4pFxERERERkfLT0vRbJCUlhTZt2hAfH0+PHj1YsWIFNput1DbBwcHMnTuX8+fPG2V79+695bF9+OGH+Pj4YLVaiY2NZf369fz444989dVXdvVCQ0NJTU3l+++/B6Br167s2rWLgwcPGnW+//57/vvf/5Y5Ay8iIiIiIiLF04z4LeLj48PEiROJjo7GycmJSZMmkZ6eTlhYGFFRUcW26dq1K/n5+YwZMwa4uvt5ixYtmDNnjlHn+qXpAEOHDqVbt26lxjJz5kwWLlwIQJMmTbhw4QIDBw60q+Pv709iYiL169c3yqpUqcKsWbMIDAwEoHr16sTExDB//nyioqLIz8+ncuXK/O1vf6NRo0Y38HZuL+uiWpOVdb7siiIiIiIiIr8Bk62saVuRm3S7Jrtubq63bWxSNo2f49MYOjaNn2PT+Dk+jaFj0/g5tpsZPzc312LLNSP+G8vIyCA8PLxIedu2bRk3blwFRCQBYQdKvf5GeLPfKRIREREREbkTKRH/jXl4eGC1Wis6DBEREREREblN/GET8YSEBIKDg0u8brFYePfdd0lLS8PFxQWAQ4cOMWDAAFatWkX79u0BWLZsGatWrSIlJcXYvdxisXDo0CFq166NzWbj3LlzjBgxAj8/PzZs2MC3335LWFgYXbp0Yfjw4QwdOhTA2BHdarXa9XHNM888Y/ct9549exg/fjz33XcfALm5ufTt2xez2WzU6devH61bt2bq1KlGmY+Pj3H2d15eHoWFhcyfPx9PT08AMjMzefrpp4mMjKRnz55F7mWz2cjPz2fo0KH06tWL9PR0nnnmGby9ve3eYVxcHJUqVbqRYREREREREbnj/WET8ZiYmFITcQA3NzdSU1N56qmnANi8ebORrF6zefNmevXqxdatWxkwYIBRPnHiRDp27AjAuXPn6NOnj931a+Li4nj88cdp2rRpkWvX91GSRx991DhT/MqVK/To0YN+/fpRs2ZN9u/fz/3338/u3bvJycmhRo0aANSqVctuFn7NmjW8+eabvP766wBs2LCBoUOHsnr1aiMR/+W9Lly4gNlspkmTJri6unLfffdpZl9EREREROQWcPjjy44fP05gYCDBwcEMGzaMzMxMYmJiyM7OtjuPuzi9e/dmy5YtABQWFnLo0CFatmxpXN+zZw+NGzcmMDCQxMTEEvs5ffo0Li4uxR7pZbFYsFgsFBQU3NwDXicnJwcnJydjFjo5OZnu3bvTrVs3Nm7cWGK7jIwMatasCYDNZuPtt99mxIgR5OXlceTIkWLbVK9enUGDBvHvf//7V8ctIiIiIiIiP3P4GfG0tDS8vb2xWCzs27eP7OxsRo8eTUJCQpmJ+EMPPcT27du5ePEiBw8epH379hw7dsy4npyczMCBA2natCkuLi58+umn/PnPfwZg3rx5LFmyhIyMDLy8vPjHP/5R7D06depEamoqy5cvL3Ls2Lx581i+fLnxe8qUKTRv3tyuzu7duzGbzZhMJpydnXnttdeoXr06OTk57N+/nxkzZtCsWTPGjBljrADIzs7GbDaTk5PDuXPnePrpp42N4Xbt2sX9999P3bp18fPzIzExkWnTphUbe7169Th06BAA33zzjd2S+GvvXERERERERG6Mwyfi/v7+LF++nJCQEFxdXQkNDb2h9l26dCElJYW0tDRGjx5tLM3Ozs4mNTWVM2fOYLVaycnJISEhwUjEry0r//DDD4mKiqJx48Yl3sNiseDn51ekzo0uTb/epk2bKCwsZOTIkQBkZWWxa9cuHnvsMWNpekFBARaLBWdnZ6pXrw7AunXrSE9P57nnniMvL4+vvvqKsLCwYu+dkZFBw4YNAbQ0XURERERE5BZx+EQ8JSWFNm3a8NJLL7FlyxZWrFjB7NmzKe/x6H379mXmzJmYTCa7RHnTpk34+fkZR49dunSJrl27cubMGbv2nTp14pNPPuG1115j0aJFxd6jRo0aTJ8+nQkTJhT7rfjNWL9+PUuWLKFZs2ZGvImJiTz22GNGnUqVKvG3v/2Nfv368cgjj/DQQw/x6aef8u677xrL26dMmcJbb71VZCY+JyeH5OTkEmf6RURERERE5OY4fCLu4+PDxIkTiY6OxsnJiUmTJgHg5eVFWFgYUVFRpbZv2rQpZ8+exc/Pz648OTmZuXPnGr+rVavG008/zbp164r0MWbMGAYMGMAHH3xQ4n3at29P7969+fLLL42yXy5NL+/Z4ocPH8ZmsxlJOED37t2ZPXs2J0+etKtbtWpVZs6cSXh4OP7+/jz99NN2O50HBATw6quvEhERYSyDd3JyoqCggLFjx9K0aVPS09OLLE0HmDVrVpHN7URERERERKR0Jlt5p45FblBW1vmKDqFYbm6ut21sUjaNn+PTGDo2jZ9j0/g5Po2hY9P4ObabGT83N9diyx1+Rrw0GRkZxtLy65V35llERERERETkVvtDJ+IeHh7aYEyKCAg7UOr1N8KblXpdRERERETk13D4c8R/DwkJCaVet1gs9O3bF7PZzKBBg5gyZQp5eXkAdOjQwa5uamqqcexXly5dyM3NBa7uet63b1/efvtt0tPTCQgI4MyZM5jNZsxmM4888gj+/v6YzWaSk5ON/r7++mujTsuWLRkyZAhms5kPPviA6OhokpKSAGjevDlTp061i2XGjBl06dLFrqxfv35FjjMr7RlERERERETkxvyhZ8RvlZiYGOOM7pJcfxTZK6+8QkpKCj169ChX/5mZmTz//PO8/PLLPPXUU6SnpwNQt25dY0bfbDYTERGBl5eXXdvmzZsbdbp06cLKlSupUqUKAJ9//rlRr3bt2nz88cfk5+dTuXJlCgoK+OKLL+z62r9/P/fffz+7d+8mJyeHGjVqlCt+ERERERERKT/NiF/n+PHjBAYGEhwczLBhw8jMzCQmJobs7GwiIiLK1UdBQQEXLlzAw8OjXPUzMjIYMWIEFouFp5566ldEX7rKlSvTrl07du7cCcCOHTvsjjqDqzvFd+/enW7durFx48bfLBYREREREZE7mRLx66SlpeHt7c2bb77JqFGjyM7OZvTo0dSqVavMRHzevHmYzWZ69erF6dOnadKkSYl1TSaT8fdx48ZRtWpVfvzxx1v1GCXq06cP27ZtA2DLli307dvXuJaTk8P+/fvp3Lkzfn5+xpJ2gOzsbGP5u9lsZt68eb95rCIiIiIiIn9UWpp+HX9/f5YvX05ISAiurq6EhoaWu+31S9P/8Y9/EBkZycyZM+2SboCLFy8aS8fh6lncd999N4MHD+bBBx8ssvT8VmrTpg3Tpk3j7NmznDt3jkaNGhnXNm3aRGFhISNHjgSufrO+a9cuHnvsMWrVqmW36V1qaqqR0IuIiIiIiMiN0Yz4dVJSUmjTpg3x8fH06NGDFStWAHCjR627u7sbm7X96U9/YteuXca1jz76iJYtWxq/77//ftzd3bFYLIwfP57Lly/fgicpnslkolOnTkRERBRZBr9+/XqWLFlCbGwssbGxTJkyhcTExN8sFhERERERkTuVEvHr+Pj4sHDhQoKCglizZo2xQZuXlxdhYWGltr22NH3YsGFs3ryZsWPHAld3Jv/nP/9JQEAA/v7+VKtWjX79+hVp36NHDx566KEiO5bfan379i2ykdzhw4ex2Ww0a/bzsV3du3dn//79nDx58jeNR0RERERE5E5jst3odK9IOWVlna/oEIrl5uZ628YmZdP4OT6NoWPT+Dk2jZ/j0xg6No2fY7uZ8XNzcy22XN+Il1NGRgbh4eFFytu2bcu4ceMqICIRERERERFxRErEy8nDw8NuwzJxXAFhB0q89kZ4sxKviYiIiIiI3Ar6RlxERERERETkd6QZ8VIkJCQYG7YV58yZM0ydOpWLFy9is9nw8PBgypQpVK1aFR8fHx5++GG7+lFRUezcuZNFixbh6ekJwE8//UTr1q2ZOnWqUW/ZsmWsWrWKlJQU46gzi8VCTk4OixcvNup16NCBnTt3Alc3XFuwYAHnz5/HxcWFWrVqMWXKFBo0aADAv/71LxISEnByciI/P59BgwbRv39/APLy8li6dClpaWlUqlSJypUrM378eP785z+Tnp5O9+7dWbt2LT4+PgAkJSVx+vRpY0M6ERERERERKT8l4qWIiYkpNRFfsWIFvr6+DB48GICZM2eyZs0ahg8fXuTs7ev16dPH2IW9sLCQoKAgPv/8c+NYs82bN9OrVy+2bt3KgAEDjHb79+9n48aNRgJ9zalTpwgLC2Px4sU0bdoUgHfffZe5c+cyf/58duzYwZo1a1iyZAmurq5cvnyZcePGUaVKFXr27MmiRYsoKCgwEvUffviBkSNHEhMTg8lkokaNGkyaNIn/+7//w8XF5eZfqIiIiIiIiGhp+jXHjx8nMDCQ4OBghg0bRkxMDNnZ2URERJTYplGjRrzzzjukpaVx+fJlwsPDMZvNN3TfCxcucP78eVxdr+6mt2fPHho3bkxgYGCRc7xfeeUVoqOj+e9//2tXvnHjRgYOHGgk4QBPPfUUUVFRAFitVsLCwox7VK1alfDwcKP/TZs2MWHCBJycnIznCgoK4q233gLgnnvu4YknnmDBggU39GwiIiIiIiJSlGbE/yctLQ1vb28sFgv79u2jXr16JCQklJqIDx48mCpVqhAbG8vLL79MmzZtmDp1Ku7u7mRnZ9sl5fXr12f+/PkAbNmyhYMHD5KVlUX16tUZNWoU9957LwDJyclGUu3i4sKnn37Kn//8Z6OPl19+mcmTJxMbG2v0nZ6eTqdOnQC4fPkyzz//PAAnT57k3Xff5cSJEzRu3Ngudk9PTzIyMvjxxx+pVasWlStXLnL9s88+M36PHz8ef39/9u3bd4NvVkRERERERK6nGfH/8ff3p06dOoSEhJCYmEilSpXKbLNnzx769+9PbGwsO3fupGXLlsyaNQvAWJp+7c+1JByuLk1PSEhgxYoVXLhwwUjCs7OzSU1NZdWqVTz33HPk5OSQkJBgd89nnnmG6tWrs3r1aqPM3d2d9PR04Ops97V7Xrp0CYAGDRrwww8/2PXz3Xff4e7ujqurK9nZ2eTn59td//7773F3dzd+u7i4MHv2bKZMmWL0KyIiIiIiIjdOifj/pKSk0KZNG+Lj4+nRowcrVqzAZrOV2iY+Pp4NGzYAVxPVZs2a3dA31J6enkydOpWXX36ZS5cusWnTJvz8/Fi5ciWxsbGsW7eOnTt3cubMGbt2ERERrFy5kgsXLgDQv39/kpOTOX78uFHniy++4OLFiwCYzWbmzp1LTk4OcHU5/Ny5cxkyZAguLi707NmTBQsWUFhYCMCJEydYvXq13ffpAN7e3vTp04fly5eX+xlFRERERETEnpam/4+Pjw8TJ04kOjoaJycnJk2aRHp6OmFhYca31r80bdo0pk2bxurVq6latSp16tQxlrL/cmk6wIQJE4r04evri6+vL4sWLWLnzp3MnTvXuFatWjWefvpp1q1bZ9embt26WCwWXnzxReDqjHhUVBRz5szhwoUL5ObmUrNmTVauXAlAly5dyMnJISQkBJPJRGFhIf7+/vTq1QuAsLAwoqOjCQgIwNnZGRcXF2bMmIGnp6cx037NqFGjeP/992/gzYqIiIiIiMj1TLaypn1FblJW1vmKDqFYbm6ut21sUjaNn+PTGDo2jZ9j0/g5Po2hY9P4ObabGT83N9diyzUjXoaMjAzCw8OLlLdt25Zx48ZVQEQiIiIiIiLiyJSIl8HDw6PE88DFMQWEHSi2/I3wZr9zJCIiIiIicifSZm3F+OVO5b905swZxo4dy3PPPcdf/vIXpkyZwuXLl4Gr35qbzWa7P5mZmWzYsIHmzZvz6aefGv3k5eXRvn17oqOj7doGBwczYMAAtm/fbnffqVOn0r9/f+P3xx9/bBxVBrB06VLatWtn7IC+e/du4ztygGXLlvH444+Tm5trlFksFl566SW7+3To0MH4++HDh3n++ecJDAxk6NChjB07lszMzNJfoIiIiIiIiJRIM+LFiImJITg4uMTrK1aswNfXl8GDBwMwc+ZM1qxZw/Dhw41jy4rTtGlTtmzZYpwL/tFHH+Hq+vM3A9e3PX/+PN27d+epp57CZDJx6dIlDhw4wP3338+ePXto3749rVq14uuvv6awsBAnJyd27NjBo48+yoEDB2jXrh179+7liSeeMPrfvHkzvXr1YuvWrXY7ou/fv5+NGzfaJfkAp06dIiwsjMWLF9O0aVMA3n33XebOnWt3HJuIiIiIiIiU3x0/I378+HECAwMJDg5m2LBhxMTEkJ2dbex+XpxGjRrxzjvvkJaWxuXLlwkPDy+yQ3pxOnbsSFpamnFM2NatW+ndu3exdXNycmjQoAEmkwmAf/3rXzz22GM8++yzJCYmAuDs7MyDDz7I119/zU8//URhYSG9evXigw8+AK7OmF9LxPfs2UPjxo0JDAw02l/zyiuvEB0dzX//+1+78o0bNzJw4EAjCQd46qmnStxFXkRERERERMp2xyfiaWlpeHt78+abbzJq1Ci6du1KrVq1Sk3EBw8eTJ8+fYiNjeWJJ57gpZde4tSpU8DPx5Zd+/PKK68Y7ZydnWnVqhV79+4lJyeHnJwcGjZsaFy/1nbIkCE888wzdO/e3biWnJzMwIED8fX15fDhw8bycF9fX/bt28eOHTvw9fWlQ4cOpKWlkZuby/nz52nUqJFd+6ZNm+Li4mK3RL5+/fq8/PLLTJ482e4509PTady4MQCXL182nqlbt243+bZFRERERETkjl+a7u/vz/LlywkJCcHV1ZXQ0NAy2+zZs4f+/fvj7+/PlStXWL58ObNmzSI6OrrUpekAffr0YevWrZw8eZJu3bqRl5dnXLu+bU5ODoGBgTzyyCPUqVOHo0ePEhkZCYDJZCIpKYnx48fToUMHFi1axF133cWQIUNwdXXF1fX/s3fnYVUe9///nwfcFXEJLkRMFLcGTE2IcUttotYS4y4aMByXSKNmQVEIBzURFReUuJQYtIJCDouKNVoN1iakX/Uj0dbamsRqTY0mEozgdhJR2X9/8PNuTkFRNCLl9biuc12cuWfmft9n/OftzD3jxL59+3j66aeB0gR/7969XLx4EavVypUrV0hMTDSWyAMMHTqUjz/+mOTkZKOsdevWxjni9erVM2L78TvkIiIiIiIicmdq/Ix4eno6Xl5eJCQk4O3tTWxsLBUdrZ6QkMDWrVsBqFOnDh07dqROnTq3db8ePXrwj3/8gz/+8Y94e3vftF7Dhg1xcnKioKCA1NRUgoKCiIuLIy5O0EKEAAAgAElEQVQujoSEBH7/+9+Tn5+Pu7s72dnZnDhxAg8PDwCeeeYZY7Ye4A9/+AOjRo1i/fr1xMXFsXnzZvbv38/Fixft7hkeHs769evJzc0FYPjw4aSmpnLq1CmjzhdffMHVq1dv61lFRERERESkrBo/I+7p6UlISAjR0dE4ODgQFhZGZmYmwcHBN30Xet68ecybN4/k5GTq1atH06ZNjaXsN5aX/9iMGTOMvx0cHOjTpw9nz56lUaNGdvV+3DY/P5+uXbvi5eXFm2++yfbt2416rq6udOnShd27dzNkyBAeffRRSkpKjPfJ+/bty+rVq40Z8dTUVJYuXWq0r1+/PgMHDmTz5s1292/WrBkWi8XYab1169ZERUURGRlJbm4ueXl5NG7cmPXr19/27ysiIiIiIiL2TCUVTf+KVFJOzg9VHUK5XFycHtjYpGIav+pPY1i9afyqN41f9acxrN40ftVbZcbPxcWp3PIaPyN+M1lZWYSGhpYp7969O4GBgVUQkYiIiIiIiPwvUCJ+E66urrfcdE2qrzHBh8stXx3a8T5HIiIiIiIiNVGN36xNRERERERE5H7SjPgDKDExEX9//5tet1gsHD16lCZNmpCfn0/Hjh2ZO3cutWvXxtPTkyeeeMKuflRUFPv37+e3v/0tbm5uFBcXYzKZeO211+jVqxcAZ86cYenSpVy+fJmCggK6dOlCcHAwjRo1Ijo6moceegg/Pz8AFi9ezJkzZ1i5cuVt7xYvIiIiIiIipZSIP4BiYmJumYgDhISE0LdvXwBmzpxJeno63t7etzzHfPDgwQQHBwNw/vx5XnrpJRITE3FycuLVV18lIiLCOFv8gw8+YObMmaxdu9ZoX1JSQkREBDabjd/+9rfUqqV/PiIiIiIiIndKmVQVO3XqFGFhYdSqVQtHR0d69uyJzWYjPDzcOBLtVoqKisjNzcXV1fWO7vvQQw/x61//mv/3//4fTk5OdO/e3UjCAUaMGEFKSgpnzpwBSpPwuXPnUlhYyNKlS3Fw0FsNIiIiIiIilaFEvIplZGTg4eGBxWLh0KFDNG/enMTExAqT8GXLlrFu3Tqys7NxcnKiXbt2QNlzzFu0aME777xTbh/Nmzfn0qVLXL58mbZt25a53qZNG7KysgBYu3Yt7dq1w9HR0TivXERERERERO6cpjWrmI+PD02bNiUgIICkpCQcHR1vq11ISAhWq5Xdu3fzi1/8giVLlgAYS9NvfG6WhEPpEW0tW7akZcuWZGZmlrl++vRpY6a9f//+xMfH07BhQ2JiYirxpCIiIiIiIgJKxKtceno6Xl5eJCQk4O3tTWxsLCUlJXfUR+vWrSkoKLijNtnZ2aSnp/PLX/6S/v37k5GRwWeffWZcT01NpVmzZri5uQHQsWPp0V4LFixgy5YtHDx48I7uJyIiIiIiIqW0NL2KeXp6EhISQnR0NA4ODoSFhZGZmUlwcDBRUVE3bXdjabqDgwPFxcUsWrQIKLs0HWDGjBkA7Ny5kyNHjuDg4EBJSQmLFy+mSZMmAKxZs4ZFixZx+fJlioqK6Ny5M8uXLy9zX2dnZyIjI5k5cyZbt27loYceulc/hYiIiIiISI1gKrnT6VeR25ST80NVh1AuFxenBzY2qZjGr/rTGFZvGr/qTeNX/WkMqzeNX/VWmfFzcXEqt1wz4g+orKwsQkNDy5R3796dwMDAKohIRERERERE7gUl4g8oV1fXm54HLndnTPDhcstXh3a8z5GIiIiIiEhNpM3aHhCJiYk3vfbdd9/xi1/8gm+++cYo++STT/D19aWoqIh+/fqRl5dHcXExixcvZuLEiUyaNIkpU6YY54CbzWZOnjxptM/Ly6Nfv34AWCwW9u7da3fPPn36ABAdHY2Pjw+FhYXGtTFjxpS7y7qIiIiIiIhUTIn4A+JWR4K1atWKmTNnMmvWLEpKSrDZbCxdupRly5bZHXe2b98+srOz2bBhA3Fxcfj4+BibuN2Nb7/9lrVr1951PyIiIiIiIqKl6VXi1KlThIWFUatWLRwdHenZsyc2m43w8HDCw8PLbTN8+HDS09PZtGkTn332GVOmTDGOFruhVatWfPHFF6SlpdGzZ0/69+9P37597zregIAAUlNTee6553jsscfuuj8REREREZGaTIl4FcjIyMDDwwOLxcKhQ4do3rw5iYmJN03Cb5g3bx4vvvgiXbt2Zfjw4WWud+7cmQULFrB582YiIiJo1aoVFouFp59+GoDQ0FDq168PQHFx8S3vZTKZjL8bNGhAREQEFouFLVu23OHTioiIiIiIyI8pEa8CPj4+rFu3joCAAJycnAgKCrqtds2aNcPLy4tBgwaVe/348eO0a9eO5cuXU1JSwv79+5k+fTr79+8HIDIyEnd3d6D0HfHnn38egLp165Kfn2/X14/fCQd46qmn6N27N6tWrbqjZxURERERERF7eke8CqSnp+Pl5UVCQgLe3t7ExsZyL45z//TTT1m+fDlFRUWYTCY6duxI/fr17Wa3y+Ph4cFHH31kfD906BAdOnQoUy8oKIi9e/fy9ddf33WsIiIiIiIiNZVmxKuAp6cnISEhREdH4+DgQFhYGJmZmQQHBxMVFVXpfs1mM5GRkQwfPpxGjRrh4ODA0qVLK2w3YsQIjh07xrBhw2jYsCG1a9dm/vz5ZerVrVuXRYsW4evrW+kYRUREREREajpTyb2YihUpR07OD1UdQrlcXJwe2NikYhq/6k9jWL1p/Ko3jV/1pzGs3jR+1Vtlxs/Fxanccs2IP0CysrIIDQ0tU969e3cCAwOrICIRERERERG515SIP0BcXV2xWq1VHcb/vDHBh8stXx3a8T5HIiIiIiIiNZE2axMRERERERG5j2rcjHhiYiL+/v63rLNp0yb+8Ic/4ODgQEFBAUFBQfTo0YPo6Gh27txJixYtjLq9e/dm6tSp9OvXjwkTJjBu3DgATp48SXh4OKtWrWLatGkAHDt2jEcffZT69eszdOhQvvvuO7v+Ll++zKBBg5g6darR/9y5czly5Ajbtm0zysxmM+Hh4cZRZDczZcoUANasWWOU9evXj9atW+Pg4EBRURFXr15lwYIFdO3aFbPZzLVr14yzxgEmTZrEs88+C0BaWhqzZs1i9+7dtGzZ8pb3FhERERERkfLVuEQ8Jibmlon4hx9+yP79+4mPj6d27dqcOXMGf39/PvjgAwAmTJiAn59fuW3j4+N55plnaN++vVHWrFkzY7n5fyfQ0dHRdv3l5+czaNAgxowZQ/Pmzbl27RqHDx+mU6dOHDx4kB49etz2c549e5arV69SUFDAmTNncHNzM66tX7+eunXrArBv3z7effdd1q5dC9ifNf7fUlNT8ff3Z/Pmzbzxxhu3HYuIiIiIiIj8x//s0vRTp07h6+uLv78/48eP59y5c8TExGCz2QgPD79pu40bNzJlyhRq164NgJubG9u2baNZs2YV3tNisWCxWCgqKqpUzJcuXaKwsNBIknft2kWvXr0YMWIESUlJd9TXli1b6N+/P8OHDyc5Ofmm9bKysmjcuHGF/Z05cwabzcbkyZPZvn07BQUFdxSPiIiIiIiIlKowEf/222+ZOHEiAwcOJDs7m3HjxpGZmXk/YrsrGRkZeHh4sGHDBqZMmYLNZmPq1Kk4OzvfMhHPzs62mz0GaNq0qfF3fHw8ZrPZ+Ozfv9+49stf/pJOnTqxbt26244zPj4ef39/+vfvT1BQEBERETRq1AgonYEePXo0vXv35p///Cfnzp27rT6Li4vZuXMnw4YN44UXXiAtLY3r168b119++WV8fHzo27cvn332md1O7aGhoXbPd/HiRaA0sR81ahROTk5069aNjz766LafUURERERERP6jwqXpb7/9NpMmTeKdd97BxcWFwYMHExoaescztPebj48P69atIyAgACcnJ4KCgm6r3cMPP8zZs2dxcvrPeW//93//R+fOnYFbL02H0lnxUaNG0bZt29u6343+vvjiC2bMmMGjjz4KlL5j/uWXX7JkyRIATCYTKSkpTJ8+vcI+9+3bR25uLjNnzgRKE/MdO3YwevRo4D9L05cvX05mZibNmzc32pa3NL2oqIgdO3bw8MMP88knn2Cz2UhMTGTQoEG39YwiIiIiIiLyHxXOiF+6dIlnnnmGkpISTCYTY8aM4cqVK/cjtruSnp6Ol5cXCQkJeHt7ExsbC0BJSckt240aNYr33nuPwsJCoHSJ++zZs3FwuL1V/I0aNWL+/PksXLjwjuL19PTkN7/5DTNmzKC4uJjU1FSCgoKIi4sjLi6OhIQEfv/735Ofn19hX1u2bCEiIsJou3LlynKXp0+fPp3s7OxbLl0H2LNnD56enlitVuLi4tiyZQsXLlzg+PHjd/SMIiIiIiIichsz4vXq1eO7777DZDIBcOjQIerUqfOTB3a3PD09CQkJITo6GgcHB8LCwgBwd3cnODiYqKioctu98MIL5OTkMHbsWGrXrk1RURHLli0zZo3j4+NJS0sz6rdr14758+fb9dGjRw9eeOEFjh07dkcxjx49ml27dmG1Wvnwww/Zvn27cc3V1ZUuXbqwe/duAKZNm2aMQ48ePYzl5RcuXODIkSOsWLHCaOvl5UVeXh6HD9ufn+3g4MDChQt56aWXGDBgAFC6NP3Hu6Y///zz7N2715hNv8HHx4ekpCQWLFhwR8/4INgc9SQ5OT9UdRgiIiIiIlJDmUoqmCL+/PPPmTNnDt988w1t27bFZrOxatUqfv7zn9+vGKWaelCTXRcXpwc2NqmYxq/60xhWbxq/6k3jV/1pDKs3jV/1Vpnxc3FxKre8whnxCxcusGXLFk6fPk1RURHt27evFjPit5KVlWW3QdkN3bt3JzAwsAoiEhERERERkZqiwkR82bJlPPvss3Ts2PF+xHNfuLq6Gmd7S80zJvhwmbLVof87/75FREREROTBVuEOZG5uboSFhbFx40a2bdtmfKqTxMTEW163WCwMGTIEs9nMiy++yJw5c4xzsvv06WNXd+/evVgsFgD69etHXl4eADk5OQwZMoTt27eTmZnJmDFjuHjxonEM2FNPPYWPjw9ms5nU1FS7Ps1ms3HNbDYzYcIEu6PK0tLS6Natm11ZdHQ0Pj4+xqZyAGPGjClztJzZbObkyZPG97lz5zJ8+PAydRYvXmx8z8vLo1+/fsb3jIwMxo8fj5+fH2azGYvFwg8/aEmNiIiIiIhIZVSYiN84Q/vIkSMcPHjQ+FQnMTExFdYJCQnBarWyadMmrl27Rnp6+m33f+7cOSZNmsS0adMYNmyYUd6sWTOsVitWq5Wf/exnREZGYrVay2x8BhjXrFYrAwcOZP369ca11NRU/P392bx5s12bb7/9lrVr1952nNeuXePw4cO4u7uXGcOdO3fyl7/8pUyb48ePs2zZMpYuXUpKSgpWq5UuXboYu9CLiIiIiIjInalwafqPZ0ofdKdOnSIsLIxatWrh6OjI0qVL2bp1KzabjfDwcMLDwyvso6ioiNzcXFxdXW/rnllZWbz22mvMmTOH3r173+UTlLLZbDRo0ACAM2fOYLPZmDx5MiNGjGDKlCnUrl0bgICAAFJTU3nuued47LHHKux3165d9OrVi759+5KUlESPHj2Ma7Nnz+att95i69at1Kr1n38WKSkpTJ06lZYtWxplEyZMuCfPKSIiIiIiUhNVmIj369fPOLrsx+5kxvh+ycjIwMPDA4vFwqFDh7DZbEydOpXExMQKk/Bly5axbt06srOzcXJyol27djet++PfIzAwkHr16nHhwoW7iv3GsWEmk4l27doREhIClJ4JPmrUKJycnOjWrRsfffQRgwYNAqBBgwZERERgsVjYsmVLhfdITU1l/vz5uLu7Ex4ezrlz54wEu3PnzgwfPpwlS5YwZ84co01mZiZt27YFSv9TYNasWZSUlFBUVERKSspdPbOIiIiIiEhNVGEi/uNNzQoLC/noo4/Iz8//SYOqLB8fH9atW0dAQABOTk4EBQXddtuQkBD69u0LwKpVq1iyZAkLFy4s858QV69epW7dusb3RYsW8dBDD+Hn58djjz2Gu7t7pWKPjIws07aoqIgdO3bw8MMP88knn2Cz2UhMTDQScYCnnnqK3r17s2rVKqMsNzeXOnXqGDPnJpOJkydP8uWXX7JkyRKjLCUlhenTpxvtXnnlFfz8/Ni7d69R1rp1azIzM+nSpQtubm5YrVby8vJ4/vnnK/WcIiIiIiIiNV2F74g//PDDxueRRx4hICCAjz/++H7EdsfS09Px8vIiISEBb29v4z3mCo5KL6N169bGZm1t2rTh008/Na7t27ePrl27Gt87depE69atsVgsTJ8+nevXr9+DJym1Z88ePD09sVqtxMXFsWXLFi5cuMDx48ft6gUFBbF3716+/vproHTzub/97W8UFxdz4cIFmjVrRmpqKkFBQcTFxREXF0dCQgK///3v7f5TxdHRkSVLlti9juDr60tMTAzZ2dlG2YEDB+7ZM4qIiIiIiNQ0Fc6I//WvfzX+Likp4csvvzR2Cn/QeHp6EhISQnR0NA4ODoSFhQHg7u5OcHAwUVFRN217Y2m6g4MDxcXFLFq0CICIiAjmzZvHihUrKC4uplu3bnYbst3g7e3Nvn37mDdvHq+99to9eZ7NmzeX2djNx8eHpKQkWrRoYZTVrVuXRYsW4evrC8DEiROJiIigbt26jBgxggYNGvDhhx+yfft2o42rqytdunRh9+7ddv23b9+e8ePHk5CQAJT+pm+++SYWi4WCggKuXbuGq6srv/vd7+7JM4qIiIiIiNQ0ppIKpovNZvN/KptMNG3alICAALtZYZHy5OQ8mEecubg4PbCxScU0ftWfxrB60/hVbxq/6k9jWL1p/Kq3yoyfi4tTueUVzoi/9dZbdOrUya7sH//4xx3d/EGQlZVFaGhomfLu3bsTGBhYBRGJiIiIiIhITXTTRPzGO8Zz5sxh4cKFxnvWhYWFhIeHl1nS/KBzdXW123hOaq4xwYfLlK0O7VgFkYiIiIiISE1000Q8IyODv/zlL2RnZ9vtyF2rVi1efPHF+xKciIiIiIiIyP+amybib7zxBgDbtm1j+PDh9y2gmqCoqIhXXnmFq1ev4uHhgbu7O35+fiQmJuLv73/TdhaLhY8//piMjAzq1KkDwNGjRxk5ciTvv/8+PXr0qPDeKSkpnD9/3hjf8u4xaNAg4yi3H5syZQoAa9asuZ3HFBERERERkXJU+I54t27diIiI4OrVq5SUlFBcXExmZiZJSUn3I77/STk5OVy6dImtW7cSHR1tlMfExNwyEQdwcXFh7969DBgwAIAdO3bg5ub2k8YLcPbsWa5evUpBQQFnzpy5L/cUERERERH5X1ThOeIzZsygcePGHDt2jJ/97GdkZWXRsaPep70bb731FqdPn+btt982ymJiYrDZbISHh9+y7QsvvMDOnTsBKC4u5ujRo8YO9gUFBYSEhODr68vo0aNJS0sD4NChQ4wcOZKJEyfanQFvtVp58cUX8fX15f3337/lfbds2UL//v0ZPnw4ycnJlXlsERERERER4TYS8YKCAgIDA/nFL37BY489xrp16+zOFpc7N3fuXDp06MD8+fONsqlTp+Ls7FxhIv74449z6tQprl69yoEDB+yWo2/atImmTZuyceNGNmzYwMqVK7l48SKLFy/mnXfeYcOGDbRp0waAf//736SlpZGcnExycjIff/wxX331Vbn3LC4uZufOnQwbNowXXniBtLQ0rl+/fvc/hIiIiIiISA1UYSJev3598vPzefTRRzl69Cj16tW7H3HJLfTr14/09HR27NjB0KFDjfKTJ0/SvXt3ABo1aoS7uztnzpzh3LlztGvXDoAnn3wSgBMnTpCVlcWECRMYP348ly9f5ptvvin3fvv27SM3N5eZM2cybdo0iouL2bFjx0/8lCIiIiIiIv+bKkzEhw4dypQpU3j22WdJTEwkICCAli1b3o/YapwbR8RVZMiQIWzbto2cnBzatm1rlLu7u3Po0CEArly5wokTJ2jTpg0uLi6cPHkSgM8//xyA9u3b06FDB95//32sVisjR44sc178DVu2bCEiIoK4uDji4uJYuXKllqeLiIiIiIhUUoWbtfn7+zN8+HAaNWqE1Wrl888/p0+fPvcjthrH3d2d4OBgoqKiblmvffv2XLp0iVGjRtmVjxkzhrfeegs/Pz/y8vJ4/fXXad68OcuWLSM0NJSGDRvSsGFDnJ2d6dKlC7169cLPz4/8/Hwef/zxcv+D5cKFCxw5coQVK1YYZV5eXuTl5XH48GFjhr062Rz1JDk5P1R1GCIiIiIiUkOZSiqYhs3Pz2f9+vV89dVXvP3228THx/PKK68Yx2eJ3MyDmuy6uDg9sLFJxTR+1Z/GsHrT+FVvGr/qT2NYvWn8qrfKjJ+Li1O55RXOiM+fP59mzZrxz3/+E0dHR7755htmzZpV4aytVE5WVhahoaFlyrt3705gYGAVRPS/Z0zwYbvvq0N1CoCIiIiIiNw/FSbiR48e5YMPPmDv3r3Ur1+fyMhIhgwZcj9iq5FcXV2xWq1VHYaIiIiIiIj8RCrcrM1kMpGfn4/JZALg0qVLxt81XWJi4i2vX7x4kTfeeINJkybx8ssvM2fOHOPYL09PT8xms93n3LlzbN26lX79+nHlyhWjn6CgIA4ePGj0abFYGD16NGazmYkTJxobtG3dutVupUJCQgK+vr58//33AOTl5dGnTx9iY2ONOpmZmXh4ePDFF18YZSkpKURHRwNQWFjIu+++y+jRo/H398ff359Nmzbdzc8mIiIiIiJSo900EU9LSwNg3LhxTJw4kZycHBYuXMjIkSMZN27cfQvwQRYTE3PL67GxsfTu3Zu4uDjWr19P/fr12bhxIwDOzs5YrVa7z43N0q5du8aiRYvK7fPVV1+lf//+pKamYrVamT9/PhERERQUFJS595///GfWr19P48aNAdi9ezeDBg3igw8+oLi42KjbqFEjwsLCyM/PL3O/FStWkJuby8aNG0lMTGTt2rXs2LHD2IVdRERERERE7sxNl6avWLGCgQMHkpCQQFRUFAcOHKC4uJi1a9fSuXPn+xnjA+HUqVOEhYVRq1YtHB0d6dmzJzabjfDwcMLDw8tt8/DDD7N7924eeeQRnnzySUJDQ29rNcHw4cP5+9//zp///Geee+45o/zIkSM0adKEX/3qV0aZm5sbH3zwgV2/a9as4dChQ/zud7+z21QvNTWV2bNnc/HiRfbs2WP0/cgjj/DUU0+xYsUKu/fTCwsL2bVrF3/6059wdHQEoGHDhlitVq2KEBERERERqaSbJuJPPfUUXbt2paSkhMGDB9udcW0ymTh27Nh9CfBBkZGRgYeHBxaLhUOHDtG8eXMSExNvmoQD+Pn5UbduXeLi4pg2bRpeXl7MnTuX1q1bY7PZMJvNRt0WLVrwzjvvAODo6MiSJUv4zW9+Q7du3Yw6mZmZPPLII8b3t99+m1OnTnHx4kUWLlwIwI4dO3jkkUf4/vvv7cbs9OnTXLt2jS5dujBq1CjWr19vl+RPnz4dHx8fY5k7lL6G4OzsTK1apf9MkpOT2bVrF7m5uQwdOpQJEyZU7scUERERERGpwW66NH3x4sUcO3aM5557jmPHjnH8+HHjU9OScAAfHx+aNm1KQEAASUlJxgzxrRw8eJDhw4cTFxfH/v376dq1q7Hk/L+Xpt9Iwm949NFHGTduHPPmzTPKWrVqRWZmpvF9/vz5WK1WOnXqRF5eHgA/+9nPiI+Pp1evXsyfP9+om5qayrVr15g0aRJxcXH87W9/4+uvvzau16lTh8WLFzNnzhyuXbsGQJMmTbh8+TJFRUUAjB07FqvVyujRo/nhBx27ICIiIiIiUhkVbtZW0XvQNUV6ejpeXl4kJCTg7e1NbGwsFRzBTkJCAlu3bgVKE92OHTve0fnr/v7+XL58mQMHDgDw5JNPcv78edLT0406OTk5fPXVV8ZS8Q4dOuDg4EBQUBDHjh1j27ZtFBYWkpaWRlJSEnFxccTFxfHKK6+QnJxsdz8PDw8GDx7MunXrAKhduzYDBw5k5cqVxjvleXl5HDlyREvTRUREREREKqnC48uklKenJyEhIURHR+Pg4EBYWBiZmZkEBwff9Ez1efPmMW/ePJKTk6lXrx5NmzY1lrL/99J0gBkzZth9N5lMLFq0yDguzmQyERMTw/Lly4mLiwNK3+N++eWX6d69u91seZ06dYiKisLf35/vvvsODw8PmjRpYlwfOXIkw4YNY/To0Xb3nDJlCn/+85+N7yEhIcTGxvLSSy9Rq1Ytrly5woABA5g4ceId/oIiIiIiIiICYCqpaFpXpJJych7M5esuLk4PbGxSMY1f9acxrN40ftWbxq/60xhWbxq/6q0y4+fi4lRuuWbE71JWVpbdTuM3dO/encDAwCqISERERERERB5kSsTvkqurK1artarDkDswJviw8ffq0I5VGImIiIiIiNREFW7WJiIiIiIiIiL3jmbEfwKJiYn4+/uXe+3ixYtMmzYNgGPHjvHoo49Sv359hg4damycNmXKFADWrFljtOvXrx/t27cnNjbWKNuwYQNLlizhX//6F9HR0ezcuZMWLVoAUFBQQFBQED169MBsNhMeHs758+d57bXX2LFjB61btwYgKiqK9u3bM3LkSDw9PXniiSfs4o2KisLFxYXIyEhOnDiBg4MDtWvXZvbs2bi5ud2jX0xERERERKTmUCL+E4iJiblpIt6sWTNjKfuNBNnd3d24fvbsWa5evUpBQQFnzpyxS3bPnTvHxYsXadasGQB79uzB2dnZuD5hwgT8/PwAOHnyJMHBwXzwwQd2969duzZhYWFs2LChzBFkN842/2979uwhOzubDRs2APDxxx+zaNEiHW0nIiIiIiJSCVqafupMYnsAACAASURBVJdOnTqFr68v/v7+jB8/npiYGGw2m3FM2Z3asmUL/fv3Z/jw4WXO+f71r3/NH//4R6A00W7bti21a9cut5/Lly/ToEGDMuU9e/bE2dmZpKSk246pVatWfPHFF6SlpXHx4kX69+/PqlWr7uCpRERERERE5AYl4ncpIyMDDw8PNmzYwJQpU+jfvz/Ozs6VSsSLi4vZuXMnw4YN44UXXiAtLY3r168b1wcPHsyuXbsA+MMf/mCcL35DfHw8ZrOZ8ePHEx8fz4IFC8q9T3h4OPHx8Zw+fdqu/MbZ5jc+M2fOBKBz584sWLCAjz/+mMGDBzNq1Cj+8Y9/3PHziYiIiIiIiJam3zUfHx/WrVtHQEAATk5OBAUFVbqvffv2kZubayTAxcXF7Nixw3h3/MZ73WfPnuXw4cNMnz7drv2Pl6bfStOmTZk1axYWi4Unn3zSKL/Z0vTjx4/Trl07li9fTklJCfv372f69Ons37+/zPJ2ERERERERuTXNiN+l9PR0vLy8SEhIwNvbm9jYWEpKSirV15YtW4iIiCAuLo64uDhWrlxZZnn6oEGDWLJkCU888cRdJcH9+vWjXbt2Zd4hL8+nn37K8uXLKSoqwmQy0bFjR+rXr68kXEREREREpBI0I36XPD09CQkJITo6GgcHB8LCwsjMzCQ4OJioqKjb7ufChQscOXKEFStWGGVeXl7k5eVx+PB/zr329vZm4cKFbNu27a5jnz17NgcOHDC+31ia/mMzZszAbDYTGRnJ8OHDadSoEQ4ODixduvSu719VNkc9SU7OD1UdhoiIiIiI1FCmkspO34pU4EFNdl1cnB7Y2KRiGr/qT2NYvWn8qjeNX/WnMazeNH7VW2XGz8XFqdxyzYj/RLKysggNDS1T3r17dwIDA6sgIrlhTPB/VhisDu1YhZGIiIiIiEhNpET8J+Lq6lruxmciIiIiIiJSsykRv4nExET8/f1vet1isXD06FGaNGlCfn4+HTt2ZO7cudSuXZs+ffqwf/9+o+7evXtJS0tjyZIl9OvXj127dlG3bl1ycnJ4+eWXCQgIwMvLixkzZrBmzRqmTZsGwLFjx3j00UepX78+Q4cONXZPBzCbzVy7do369etz7do1+vTpY7dje1paGrNmzWL37t20bNkSgPHjx1NcXMxXX31Fs2bNaNKkCb1792bq1KkcP36cqKgo8vLyKCgooEePHrz22mvUqVMHi8XCoEGD6Nu3L4WFhcycOZOmTZsyd+5cbdgmIiIiIiJyh5SI30RMTMwtE3GAkJAQ+vbtC8DMmTNJT0/H29v7tvo/d+4cv/nNb5g2bRoDBgwgMzMTgGbNmhkz6WazmfDwcNzd3cvtIzIyEnd3d0pKShg7diyff/45Xbt2BSA1NRV/f382b97MG2+8AUBCQgKAXWINcP78eWbMmMHq1atp164dJSUlrF69msWLFzN37lzjfgUFBQQFBfHoo48SHBx8W88pIiIiIiIi9mr88WWnTp3C19cXf39/xo8fz7lz54iJicFmsxEeHn5bfRQVFZGbm4urq+tt1c/KymLixIlYLBYGDBhwF9GXys/Pp7CwkBYtWgBw5swZbDYbkydPZvv27RQUFNyy/fbt2xk1ahTt2rUDwGQy8dprr7Fnzx6uX79u3OONN96gS5cuSsJFRERERETuQo1PxDMyMvDw8GDDhg1MmTIFm83G1KlTcXZ2rjARX7ZsGWazmUGDBnH+/HkjkS3Pj5dwBwYGUq9ePS5cuHBXsYeGhmI2m/n1r39N8+bNadq0KVB6HvmoUaNwcnKiW7dufPTRR7fs58yZM7Rt27ZMvC4uLpw/fx6AhQsXcvXqVc6dO3dXMYuIiIiIiNR0NT4R9/HxoWnTpgQEBJCUlISjo+Nttw0JCcFqtbJ7925+8YtfsGTJEoAy701fvXqVunXrGt8XLVrE6tWreeeddzh58mSlY4+MjMRqtfLJJ5/QpEkTYmNjKSoqYseOHfzxj39k0qRJnD59msTExFv207JlS7799lu7sqKiIrKzs3nooYcA8Pf3Z/369Zw4cYLt27dXOmYREREREZGarsYn4unp6Xh5eZGQkIC3tzexsbEA3Onx6q1btzaWgLdp04ZPP/3UuLZv3z7j3W2ATp060bp1aywWC9OnTzeWf1eWg4MDLVu2pKCggD179uDp6YnVaiUuLo4tW7Zw4cIFjh8/ftP2I0aMYNOmTZw+fRooffZ3332Xvn37Uq9ePQA6duxIrVq1iIqKYtmyZXf1HwgiIiIiIiI1WY1PxD09PVm5ciVjx45l48aNxgZt7u7uFb4LfWNp+vjx49mxY4exKVpERATvvfceY8aMwcfHh/r16zNs2LAy7b29vXn88ceZN29epWK/sTTdbDbzz3/+k/Hjx7N58+Yy9/Lx8SEpKemm/bRq1YqlS5cyb948/Pz8GD16NHl5ecyePbtMXTc3N0JCQpg2bRrXrl2rVNwiIiIiIiI1mankTqd+RW5TTs4PVR1CuVxcnB7Y2KRiGr/qT2NYvWn8qjeNX/WnMazeNH7VW2XGz8XFqdxyHV92C1lZWYSGhpYp7969O4GBgVUQkYiIiIiIiFR3SsRvwdXV1TjTW/53jAk+DMDq0I5VHImIiIiIiNRESsR/QomJicY75+WxWCwcPXqUJk2aAKU7lc+bN4+OHUsTxCNHjvDSSy+RnJzM448/DkBxcTGRkZGcOHECBwcHateuzezZs3Fzc8NisTBo0CDat2/P0KFD8fDwACAvL48GDRqwatUqnJ2djfsfPHiQ6dOn06FDBwByc3Np06YNUVFR1KlThz59+rB//36uX79OeHg42dnZmEwmGjVqRHh4uHFcmoiIiIiIiNy+Gr9Z208pJiamwjo3jkCzWq1MnjyZVatWGddSU1OZOHEiycnJRtm+ffvIzs5mw4YNxMXF4ePjw6JFi8r026FDB6PfzZs307VrV7Zs2VKmXs+ePY16W7dupXbt2nzyySd2dX7/+9/z0EMPsX79euLi4njiiSdYvXr1nfwUIiIiIiIi8v9TIn4PnDp1Cl9fX/z9/Rk/fjznzp0jJiYGm81GeHj4bfdjs9lo0KABUDo7feDAAV5//XUOHz7MxYsXgdIdzr/44gvS0tK4ePEi/fv3t0vey1NSUsLZs2dp3LjxLevl5+eTnZ1tN2sO8PDDD7N//34++eQTrly5gtlsxmKx3PZziYiIiIiIyH9oafo9kJGRgYeHBxaLhUOHDmGz2Zg6dSqJiYkVJuLLli1j3bp1ODg40KJFC0JCQgBIS0vjV7/6FXXr1uX5559ny5YtvPLKK3Tu3JkFCxawefNmIiIiaNWqFRaLhaefftqu33//+9+YzWYuX75MXl4eQ4YMYcSIEWXuf+DAAcxmMxcuXMDBwYExY8bQq1cvuzrPPvss+fn5bNmyhbCwMDp16sScOXPo3Lnz3f1wIiIiIiIiNZAS8XvAx8eHdevWERAQgJOTE0FBQbfdNiQkhL59+5YpT01NxdHRkUmTJnH9+nW+++47AgICOHHiBO3atWP58uWUlJSwf/9+pk+fzv79++3a31iafv36daZMmULz5s2pVavscPfs2ZMVK1Zw6dIlXn75Zdq0aVOmzt///nd69erFwIEDKSoqYvv27YSFhbF169bbfk4REREREREppaXp90B6ejpeXl4kJCTg7e1NbGwsULokvDL+9a9/UVRUREpKCnFxcSQlJdG2bVv+/Oc/8+mnn7J8+XKKioowmUx07NiR+vXrYzKZyu2rXr16REVF8d5773H8+PGb3rNp06YsW7aMOXPmkJ2dbXftww8/NJ7J0dGRzp07U6dOnUo9m4iIiIiISE2nRPwe8PT0ZOXKlYwdO5aNGzcaO6W7u7sTHBx8x/2lpqYybNgwu7LRo0eTlJSE2WymcePGDB8+HD8/P2bMmMHSpUtv2d9DDz3Em2++ydtvv01xcfFN63Xo0AGz2UxERIRd+fTp0/nuu+8YNmwYvr6+vPPOOyxcuPCOn0tERERERETAVFLZaVuRCuTk/FDVIZTLxcXpgY1NKqbxq/40htWbxq960/hVfxrD6k3jV71VZvxcXJzKLdc74j+xrKwsQkNDy5R3796dwMDAKohIREREREREqpIS8Z+Yq6srVqu1qsOQHxkTfJjVoR2rOgwREREREamh9I64iIiIiIiIyH2kGfGfSGJiorFpW3ksFgtHjx6lSZMmABQVFTFv3jw6duxInz597I4j27t3L2lpaSxZsoR+/foxYcIExo0bB8DJkycJDw83Zt0//PBDkpKSgNIdzrt06UJISAh16tShsLCQNWvWsGfPHurWrQvAkCFDePHFFwG4ePEikZGRZGVlUVRUROvWrbFYLLi4uLB161a++uqrSm0+JyIiIiIiIv+hRPwnEhMTc8tEHOzPEN+zZw+rVq3i3XffrbDv+Ph4nnnmGdq3b29XvmfPHjZv3syaNWto3LgxJSUlLF68mG3btjFmzBhWrFhBcXExGzduxNHRkdzcXCZPnsxTTz1F+/btef3113n55ZcZMGAAABkZGUyePJnU1NRK/goiIiIiIiLy35SI3wOnTp0iLCyMWrVq4ejoSM+ePbHZbISHhxMeHn5bfdhsNho0aHBbdS0WCxaLhZSUFLtyq9XKm2++SePGjQEwmUyEhYVhMpkoLCxk165d/OlPf8LR0RGAhg0bYrVaMZlMfP755zg5ORlJOEDv3r1p27Ytf/3rX28rLhEREREREamYEvF7ICMjAw8PDywWC4cOHaJ58+YkJiZWmIQvW7aMdevW4eDgQIsWLQgJCblpXZPJZPz9y1/+kr1797Ju3Tp+9atfGeWZmZk88sgjAPz9739n+fLlFBQU0Lp1a2bNmoWzszO1apUOeXJyMrt27SI3N5ehQ4fSokUL3NzcytzXzc2NrKysO/k5RERERERE5BaUiN8DPj4+rFu3joCAAJycnAgKCrqtdj9emv5jP066Aa5evWq8032DxWJh1KhRtG3b1ihr3bo1mZmZdOnShSeeeAKr1Wq8Q96kSRMuX75MUVERjo6OjB07lrFjx5KSksL58+fp2rUr3377bZlYvv76a3r37s3Zs2dv65lERERERETk1rRr+j2Qnp6Ol5cXCQkJeHt7ExsbS0lJSaX7a9OmDZ9++qnxfd++fXTt2tWuTqNGjZg/fz4LFy40yvz9/Vm6dCk//PCfQ+b/8pe/AFC7dm0GDhzIypUrKS4uBiAvL48jR45gMpl48sknOX/+PJ988onRdu/evXz99dc8/fTTlX4WERERERERsacZ8XvA09OTkJAQoqOjcXBwICwsjMzMTIKDg4mKirrj/iIiIpg3b56xuVq3bt0YNmxYmXo9evTghRde4NixYwD079+fwsJCXn31VQByc3Pp0qULkZGRQOkMfGxsLC+99BK1atXiypUrDBgwgIkTJ2IymVizZg2LFi1i7dq1ALRq1Yrf/e53xjvl27ZtIyMjw7i/1WqlYcOGd/x8IiIiIiIiNZmp5G6mbkVuISfnh4orVQEXF6cHNjapmMav+tMYVm8av+pN41f9aQyrN41f9VaZ8XNxcSq3XDPiP6GsrCxCQ0PLlHfv3p3AwMAqiEhERERERESqmhLxn5CrqytWq7Wqw5D/Mib4MKtDO1Z1GCIiIiIiUkPVmM3aioqKmDRpEn5+fkRERBhncCcmJt6yncVi4fXXX7cr69Onzz2JqbCwkHfffZfRo0fj7++Pv78/mzZtuid9366srCxjg7aFCxfqqDIREREREZGfWI1JxHNycrh06RIpKSk4Ozsb5TExMRW2/dvf/sa2bdvueUwrVqwgNzeXjRs3kpiYyNq1a9mxYwcnT5685/e6mQMHDnD48GEAZs+ejaur6327t4iIiIiISE1UY5amv/XWW5w+fZq3334bFxcXoDQJt9lshIeHEx4eftO2M2fOJDo6mp49e9KqVSuj/IcffmD27NlcunQJgDlz5vDpp58as+9vv/02derUYc6cObz33nu4ubkxZMgQoHQ2fNeuXfzpT38ydiVv2LAhVqvVOEf8nXfe4a9//SslJSVMmDCB559/HrPZTJcuXfjyyy+5cuUKq1at4uGHH8ZqtbJz505MJhODBg1i3LhxWCwWLl++zOXLl4mJiSEqKorvvvuOS5cu0bdvX9544w1+97vfcf36dZ544gni4+MJDw/HxcWFkJAQrly5QlFREdOmTaNXr14MGTKEp59+mn/961+YTCbee+89nJzK33xAREREREREyldjZsTnzp1Lhw4dmD9/vlE2depUnJ2db5mEA7Ro0YJp06Yxe/Zsu/I1a9bQs2dPrFYrCxYsIDw8nIEDB7Jv3z4ATp06xZEjRwD4v//7P5577jmj7aVLl3B2dqZWrdL/C0lOTsZsNjNq1Cji4+PZs2cPmZmZbNy4kffff581a9bw/fffA/D4448THx9Pnz59+PDDD/n3v/9NWloaycnJJCcn8/HHH/PVV18B0LNnTzZu3Ehubi7dunUjLi6OlJQUUlJScHR05JVXXmHw4MH079/fiC0mJobevXuTlJTEqlWrmD17NsXFxeTm5vLCCy+QmJhIixYt2Lt3byVHQ0REREREpOaqMTPid2vo0KF8/PHHJCcnG2UnTpzgwIED7Nq1C4Dvv/8eV1dXrl+/zmeffYa7uztZWVl89tlnODk50ahRI6NtkyZNuHz5MkVFRTg6OjJ27FjGjh1LSkoK58+f58SJExw9ehSz2QyUzqDfeH/7scceA0rP+b5RNysriwkTJgBgs9n45ptvAGjXrp1xv88//5wDBw7QqFEj8vPzb/qsJ0+eNGbuW7ZsSaNGjbh48aLdvVu3bk1eXt7d/agiIiIiIiI1UI2ZEb+ZOzlGPTw8nPXr15ObmwtA+/btmTBhAlarlZUrVxrJ6y9/+UuWLVvGM888wzPPPENERAQDBgyw66t27doMHDiQlStXUlxcDEBeXh5HjhzBZDLRvn17evTogdVqJSEhgeeff542bdqUG1f79u3p0KED77//PlarlZEjR9KpUycAY5n71q1bcXJy4p133uHll1/m+vXrlJSU4ODgYNz/Bnd3dw4dOgTAuXPn+P7772nSpIldfyIiIiIiIlI5NT4Rd3d3Jzg4+LbqNmvWDIvFwrVr1wCYMmUKu3btwmw2ExAQQMeOpUdiDRw4kMOHD9OzZ0+eeeYZvvjiC7ul3zeEhITQsGFDXnrpJcxmM76+vri5ufHyyy/Tr18/GjRowNixYxk5ciSA3Yz6j3Xp0oVevXrh5+fHyJEjOX36NC1btrSr06tXL/bu3Yuvry/h4eE88sgjZGdn06lTJ9LT0/nwww+NupMnT+bAgQO89NJLvPrqq8yfP99YQi8iIiIiIiJ3x1RyJ1PCIncgJ+eHqg6hXC4uTg9sbFIxjV/1pzGs3jR+1ZvGr/rTGFZvGr/qrTLj5+JS/ubWmuak9Czt0NDQMuXdu3cnMDCwCiISERERERGR/1VKxAFXV1esVmtVhyH3yZjgw6wO7VjVYYiIiIiISA1V498RFxEREREREbmflIjfY4mJibe8brFYypy/3adPH+PvAwcOMG7cOGPztg0bNhg7u5vNZk6ePGnUzcvLo1+/fsb3Dz/80DgGzWw2s3DhQrtjys6dO8fPf/5z47g1gIMHD/LUU09x9uxZoywqKoqtW7cCcPXqVRYuXMjo0aMxm82YzWY++uijO/lJRERERERE5EeUiN9jMTExlW775ZdfEhkZSVRUFFarlcTERE6ePElcXFyFbffs2cPmzZtZs2YNycnJvP/++5hMJrZt22bU2bp1K+PGjbM7Cx1Kj1ILCwsr9yi3WbNm4ebmRmpqKlarlVWrVvHee+9x+fLlSj+niIiIiIhITaZE/C6cOnUKX19f/P39GT9+PDExMdhsNsLDwyvVX0pKCpMnT6ZFixYA1KpVC4vFwqZNmypsa7VaefPNN2ncuDFQet53WFgYY8aMAUrPS9++fTsTJ06koKCAEydOGG179uyJs7MzSUlJdn3m5ORw6tQpxo0bZ5Q1a9aMrVu3GueKi4iIiIiIyJ3RZm13ISMjAw8PDywWC4cOHaJ58+YkJiZWmIgvW7aMdevWGd9tNhsAZ86cwcfHx65uo0aNuHbtGsXFxbfsMzMzk0ceeQSAv//97yxfvpyCggJat27NihUr+PTTT+nUqRPNmjVj1KhRJCUlMW/ePKN9eHg4o0eP5plnnjHKvv32W9zc3Izvv/3tb/nrX/+KzWbj1Vdfxdvb+9Y/kIiIiIiIiJShGfG74OPjQ9OmTQkICCApKQlHR8fbahcSEoLVajU+zs7OALRs2ZJvv/3Wru6VK1eoU6cODg4O1K1bl4KCAuNabm4u9erVA6B169ZkZmYC8MQTT2C1Wlm4cCHnz58HYPPmzWRmZjJp0iR27NjBrl27+OGH/5yB17RpU2bNmoXFYjGS/latWtnFExgYiNVqpW/fvly9evVOfy4RERERERFBifhdSU9Px8vLi4SEBLy9vYmNjS33Pevb5efnR0xMDDk5OQAUFBSwcOFCfH19AfDw8GD37t1G/b1799K1a1cA/P39Wbp0qV1y/Ze//AWAixcvcuTIEVJTU4mLi+P9999n4MCBfPDBB3b379evH+3atTPKW7VqRZs2beyWrP/www8cO3YMk8lU6ecUERERERGpybQ0/S54enoSEhJCdHQ0Dg4OhIWFkZmZSXBwMFFRUXfcn4eHB0FBQQQFBVFUVERhYSG/+tWvCAgIAOA3v/kNb7/9NiNHjqROnTo0adKEBQsWANC/f38KCwt59dVXgdLZ8i5duhAZGcn27dsZOHCg3Yz9mDFjePPNN8sso589ezYHDhwwvkdGRhIdHY2fnx+Ojo5cvXqVESNGMHjw4Dt+PhEREREREQFTyd1M4YrcQk7ODxVXqgIuLk4PbGxSMY1f9acxrN40ftWbxq/60xhWbxq/6q0y4+fi4lRuuWbEfwJZWVmEhoaWKe/evTuBgYFVEJGIiIiIiIg8KJSI/wRcXV2xWq1VHYbcxJjgw6wO7VjVYYiIiIiISA2lRBxITEzE39+/wnqZmZnMmDGDzZs3YzabCQ8Px93dHYC8vDyef/55PvnkEywWC0ePHqVJkyaUlJRw+fJlJk6cyKhRowA4cOAA7733HiUlJRQUFPDrX/+aCRMmYDKZMJvNPPbYY4SFhZXpF0qPTFu7di35+fnUqlWLhx9+mNmzZ+PkZL/kwdPTkyeeeMKuLCoqipYtWwIwd+5cjhw5wrZt24zrX3/9NQsXLjTeT/f09GTmzJmsX7+ePXv28P3335OdnU2HDh0AiI+Pv+2d4kVERERERKSUEnEgJibmthLxOxESEkLfvn0BuHz5MoMHD2bkyJH8+9//JjIykrVr19KiRQsKCwsJDw8nLi7O2JRt586d9O/fn6efftquz+PHj7Ns2TLWrFljJNTx8fHExsYSFBRkV9fZ2fmms/LXrl3j8OHDdOrUiYMHD9KjRw8Ali9fjr+/P3379qWkpITXX3+d9PR0AgICCAgI4ODBg2zcuJEVK1bc099KRERERESkJqlRx5edOnUKX19f/P39GT9+POfOnSMmJgabzVZm9/B76fz589SpUweTyURKSgqTJ0+mRYsWANSqVQuLxcKmTZuM+rNnz+att94iNzfXrp+UlBSmTp1qJOEAEyZMKJOEV2TXrl306tWLESNG2B1N5urqygcffMDf/vY3CgsLWblyJQMGDKjMI4uIiIiIiMhN1KhEPCMjAw8PDzZs2MCUKVOw2WxMnToVZ2fne56IL1u2jLFjx/Lss8+yePFiVq1aBcCZM2do27atXd1GjRpx7do1iouLAejcuTPDhw9nyZIldvUyMzONtmfOnMFsNuPv74+fn1+Z+9tsNsxms/GZOXOmcS01NZXRo0fTu3dv/vnPf3Lu3DkAgoKC+PnPf87y5cvp3bs3YWFhdueSi4iIiIiIyN2rUYm4j48PTZs2JSAggKSkpNt+v9lmsxl/m0wmAOrWrUtBQYFRnpubS7169YzvISEhJCcnM2/ePP4/9u4/rKoy3///c6NQoggoO5TEBjYqDUppOJqeOKk5OSiVqAgmSjGRzDFL0wGHEHTUNDRt6CNqqCgIGo5aElaj/XBOHSxxzrELSwRFJQr3JKKo/BD4/uF39gwjKiqKO1+P6+oK77Xu+36v9eafN/da6z558qSlgHZ1deX7779vNH5lZSV2dnbY2PwzHZGRkRw6dIg9e/ZY2rp27UpJSQkA7u7upKWlsWbNGksh/a/+8Wj6P/5bunQpAEVFRRw+fJhFixbxwgsvWFbp4dK76+Hh4WzcuJHPPvsMe3t7VqxY0ax7JCIiIiIiIs1zVxXiu3fv5pFHHmH9+vWMGDGClJQUAK62lXplZSWjR4+moaGBkydP0qlTJwB8fHz46KOPLOft2bOHPn36XNb/P//zPxk2bBhxcXEAhIaGkpycjNlsBqC2tpYFCxYQEhLSqF+bNm1YtGgRr7/+uqUtJCSE5ORkTp48aWnLzc29rnuQlZXF9OnTWbNmDWvWrGH9+vX8+c9/pqamhsTERL744gsA2rdvj4eHB3Z2dtc1voiIiIiIiFzdXfWxtt69ezNr1iySkpKwsbGxfJncZDIxc+ZMlixZclmfDh06EBgYSHBwMLa2tsyZMweAF154gTlz5hAUFISdnR1OTk788Y9/bHLe3/3udwQFBfHZZ5/x+OOPM336dKZPn275Ovnw4cMtH2r7V56enkyePJn169db4v/9739PTEwMtbW1XLhwATc3N1avXn1Z3388mv6vXnrpJT744APee+89S5ubmxve3t589NFHLF++nPnz57N06VLs7Ozo1q3bLX13XkRERERE5G5kaLjacrDITTCb78z3y41Ghzs2Nrk25c/6KYfWTfmzbsqfPOQOLgAAIABJREFU9VMOrZvyZ91uJH9Go0OT7XfVivjVlJaWEh0dfVl7//79mTZtWitEJCIiIiIiIj9HKsT/f25ublfcd1tERERERESkpVjFx9rq6uqIiIggNDSU+fPnW77ynZ6eftV+MTExBAYGWrbwmjBhAocPH26xuKZPn05NTU2LjVddXc3gwYMtH5G7WYcOHeLrr79ukbH+VXV1NVlZWS0+roiIiIiIyN3AKgpxs9lMeXk5mZmZODo6WtqTk5Ov2XfWrFmWLbxefPFFy37eLWHZsmUt+lXxjz76iICAALZt22bZU/xmfPzxxxQWFrZAZI2ZzWYV4iIiIiIiIjfIKh5Nj4uLo7i4mDlz5mA0GoFLRXhFRQUJCQnN/rJ3RUUF9vb2lJSUEBUVhZOTE/7+/vj7+zN//nwAnJycWLhwIQcPHmT16tXY2try448/EhISQm5uLt999x2TJk1iwoQJDB06lJ07dxIfH09AQAD+/v7s2bOHnJwcFi1axPDhw+nbty/Hjh1j4MCBnD17lgMHDuDh4UFiYuJl8WVlZREbG8upU6f4/PPPGTJkCHv37mXlypXY2NhgNpsZP348zz77LF999RVvv/02AFVVVSxevBhbW1vLdQ0YMIBt27Zha2uLj48PsbGx+Pn5UVBQgIeHB507d2bfvn3Y2dmxevVqqqqqiI2Npby8HIDXXnuNXr168etf/5p+/fpx9OhROnfuTFJSEitXrqSwsJC3336bqVOntkCGRURERERE7h5WUYjHx8czY8YM5s2bR1JSEgBRUVGkp6dfswhPTEzknXfewcbGhvvuu49Zs2ZRU1OD2Wzmz3/+M3Z2dgQHB7Nw4UK8vLzIysoiJSWFQYMG8eOPP7J9+3by8/N5+eWX+ctf/kJZWRlTp05lwoQJ14z7+++/Z/369RiNRn71q1+RlZVFXFwcw4YN48yZM3Ts2NFybnFxMRcuXMDb25sxY8awdu1ahgwZAkBZWRnbt2+nvr6ewMBARowYweHDh0lMTMTV1ZWVK1fy4YcfEhgY2Oi6GhoacHFxwdfXl3PnzjFq1CgeeeQRRowYwezZs5k+fToTJ06ksLCQ7OxsBg4cyIQJEyguLmb27NlkZmZy4sQJ1q9fT9euXQkJCeGbb75hypQpFBQUqAgXERERERG5AVZRiN+MWbNm4e/v36itpKSEbt26WR4rLyoqYu7cuQDU1tbi4eEBQI8ePbC1tcXBwYHu3btjZ2eHo6Mj1dXVV5zvX3eDc3Jyws3NDQB7e3u8vLwAcHBwuGyMrKwsLly4QEREBAD79+/n2LFjAPTt29cSa48ePTh+/Diurq4sWLAAe3t7ysrK6NevH0Cj6/p3Pj4+AHTs2BGTyWT5ubq6moKCAnJzc9m5cycAZ86cAcDZ2ZmuXbsC0LVr16teu4iIiIiIiFybVRfiN7MFuo3NP1+P9/DwYPHixbi5uZGXl4fZbAbAYDA0ayw7OztLn4MHD1ram9v/4sWL5OTksG3bNpycnIBLj95nZGQwdOhQvv32W+rq6qipqaGwsJAHHniAqKgodu3aRYcOHYiOjrbci3+9LoPB0Ohd86vF4+npyVNPPUVgYCA//fST5R3wpvrY2Ni0yDvsIiIiIiIidyOr+FjblZhMJmbOnHnT4yQkJBAdHc2ECRNYunQpvXr1uq7+48aNIzU1lfDwcMrKyq57/k8++QQfHx9LEQ4QFBTEe++9R1VVFRcvXuSFF17g2WefJSoqik6dOvH0008THBxMSEgI586d4+TJk5eN27t3bzZu3Ehubu41Y5gyZQo7d+4kLCyM3/72t/To0eOK53bu3Jna2tom33MXERERERGRqzM03Myystxye/fuZdOmTSxbtqy1Q7luZvPZ1g6hSUajwx0bm1yb8mf9lEPrpvxZN+XP+imH1k35s243kj+j0aHJdqt+NB2gtLSU6Ojoy9r79+/PtGnTWiEiERERERERkSuz+kLczc2NtLS01g7jlhkwYAADBgxo7TBERERERESkhVj1O+IiIiIiIiIi1kaF+G1QV1dHREQEoaGhrFq1CoD09PSr9jl16hQvvfQSERERPP/887z22mtUVVUBsHnzZmpra28qpunTp7N3797r7lddXc3QoUNvam4REREREZG7mQrx28BsNlNeXs6gQYPo2LEjcGl7sqtJSUlh0KBBrFmzhrVr19KuXTs2bdoEwKpVq7R9mIiIiIiIiJWy+nfErUFcXBzFxcWYzWZcXFxITk6moqKChIQEEhISmuxz//3389FHH/HAAw/Qr18/oqOjMRgMZGVlYTabmT59OklJScyZM4cff/yR8vJy/P39eeWVV4iJicHOzo7vv/+ekydPsmjRInx8fNi4cSNZWVkYjUZ++uknACorK4mNjeXs2bOUl5czbtw4JkyYQFhYGM7Ozpw5c4akpCR+//vfc+bMGbp3734b75yIiIiIiMjPj1bEb4P4+Hi8vLwwGo0AREVF4ejoeMUiHCA0NJRRo0axZs0aHnvsMaZOncrJkycZN24cRqORZcuW8cMPP/Dwww+zZs0aMjMzyczMtPR3c3NjzZo1hIWFsXnzZs6ePcuGDRt49913WbFiheXR9mPHjjFy5EjWrl3LypUrSU1NtYwRGBhIamoq7733Hj179mTjxo2EhITcknskIiIiIiJyt9CK+B1q7969PPPMM4wdO5aamhreeecdFi5cSFJSkuUcJycnvvnmG3Jzc+nQoQM1NTWWYw8++CAAXbp0Yf/+/Rw5cgQvLy/s7OwA8PX1BcDFxYX169fz8ccf06FDBy5evGgZw8PDA4DDhw/z2GOPAfDQQw/Rtq1+bURERERERG6UVsRbSUNDw1WPr1+/nq1btwJgZ2dHjx49LEW0wWCgvr6erVu34uDgwNKlS3n++eepqqqyjGswGBqN5+7uTmFhIVVVVdTV1fHtt98CsHbtWh5++GGWLFnCiBEjGsX1jzE8PT353//9XwAOHjzYqFgXERERERGR66OlzVZiMpmYOXMmS5YsafL43LlzmTt3LhkZGdx77704OztbHmX38/MjMjKSOXPmMGPGDPLy8mjXrh0PPPAAJ0+ebHK8Tp068fLLLxMSEkKnTp1o164dAEOGDCEhIYEdO3bg5OREmzZtGq2sAzz77LPMnj2b0NBQPD09sbW1bbkbISIiIiIicpcxNFxraVbkBpnNZ1s7hCYZjQ53bGxybcqf9VMOrZvyZ92UP+unHFo35c+63Uj+jEaHJtu1It6KSktLiY6Ovqy9f//+TJs2rRUiEhERERERkVtNhXgrcnNzIy0trbXDEBERERERkdtIH2trYXV1dURERBAaGsqqVasASE9Pb1bfp59+mrlz57ZIHKWlpXzyySctMta/a+71iIiIiIiIyOVUiLcws9lMeXk5gwYNomPHjgAkJydfs19eXh49e/YkNzeXysrKm44jNzeX/fv33/Q4TWnO9YiIiIiIiEjT9Gh6C4uLi6O4uBiz2YyLiwvJyclUVFSQkJBg+ep5U7KysnjyySfp2rUr27dvZ+LEiZSUlPDyyy9jNBopKyvD39+f6dOnU1BQwKJFi6ivr+fMmTO89tpr9OvXjyFDhuDp6ckvfvELvvjiC6qqqujbty+pqan06tWLw4cPY29vj5+fH//93//NmTNnWLt2Lfb29sTHx3Ps2DHq6+t55ZVXGDBgAIGBgfzqV7/i0KFDGAwGVqxYQXp6erOuR0RERERERJqmFfEWFh8fj5eXF0ajEYCoqCgcHR2vWrRWVlaSl5fH448/zpgxY8jMzLQc+/7771m0aBFbtmwhNzeX/Px8CgsLiY6OJjU1leeee86y3/gPP/zAkiVLiIuLIzIyklGjRjFs2DAAfH19Wb9+PTU1Ndx7772sW7cOLy8vvv76a7KysnB2dmbjxo2sWLGCefPmAXDu3DlGjhxJeno69913H3v27GnW9YiIiIiIiMiVaUX8DvD+++9TX1/Piy++CFx6vP1//ud/cHd3x9vbGycnJ+BSMX306FG6dOnCihUruPfeezl37hwdOnQAwNnZGWdn5ybn8PHxAaBjx454eXlZfq6urqagoIC8vDwOHDgAwMWLFykvLwfgl7/8JQBdu3alurr6Ft0BERERERGRu4cK8dvgWlu1b9myhZUrV9KjRw/gUmG+ceNGYmJiKCoq4sKFC9jZ2XHgwAHGjBnD7NmzWbJkCSaTiT/96U98//33ANjY/PMBBxsbG+rr65sVn6enJ126dGHKlClUVVWRnJyMo6MjAAaD4bqvR0RERERERK5Mj6bfBiaTiZkzZzZ57ODBgzQ0NFiKcIAnn3ySvLw8fvzxR2xtbXn55ZcZN24cw4YNw9vbm6eeeorf/e53TJgwgeLiYk6ePHnZuD179mT37t188MEH14wvJCSEI0eOMHHiREJCQrj//vsbFfXXcz0iIiIiIiJydYYGLW/esUpKSpgxYwbvvvtua4dyQ8zms60dQpOMRoc7Nja5NuXP+imH1k35s27Kn/VTDq2b8mfdbiR/RqNDk+16NP02KS0tJTo6+rL2/v37M23atFaISERERERERFqDCvHbxM3NjbS0tOvq061bN6tdDRcREREREZGmqRBvBXV1dURGRnL+/Hl8fHwwmUyEhoaSnp7OxIkTm+xz6tQpXn75ZQC+/fZbfvGLX9CuXTueeuopxo0b12KxHTt2jAULFlBXV8fFixfp3bs3r776KjY2NixevJj9+/dz8eJFxo8fT3BwcIvNKyIiIiIicrdQId4KzGYz5eXlbN26laSkJEt7cnLyFQvxTp06WVbUw8LCSEhIwGQytXhsb775JhMnTsTf35+GhgamTp3K7t27cXBw4Pjx42zevJmamhpGjhzJk08+afm6uoiIiIiIiDSPCvFWEBcXR3FxMXPmzMFoNAKXivCKigoSEhJISEho9lj19fU8+eSTZGVl4eTkREZGBufPn6ewsJCGhgZ++OEHzp8/z+LFizGZTKSlpZGdnY3BYCAgIIBJkyY1Gs/NzY1t27bRvn17fH19Wb58OW3btqWmpoYHH3zQcl5dXR1t2+rXR0RERERE5Hpp+7JWEB8fj5eXF/PmzbO0RUVF4ejoeF1FOFzaLzwwMNCyTdn777/PM888A4C7uzsbNmzgpZdeIjExkcLCQnJycsjIyCAjI4Ndu3Zx5MiRRuNNnz6dhx56iDfffJNBgwYxe/Zszp49yz333IOjoyO1tbXExMQwfvx42rdvf3M3QkRERERE5C6kQvxnYOzYsbz//vsUFBTg4uKCi4sLAAMHDgSgb9++HD16lIKCAkpLSwkPD2fy5MmcPn2a48ePNxorNzeX8PBwNm7cyGeffYa9vT0rVqwAoKKigt/+9reYTCZefPHF23uRIiIiIiIiPxN6tvgOcqNburu5ueHg4MDKlSsZO3aspT0/Px8/Pz/2799Pjx498PT0xMvLi5SUFAwGA6mpqfTs2bPRWImJibRp04bBgwfTvn17PDw8KC8vp6qqivDwcJ577jmeeuqpm7pOERERERGRu5lWxO8gJpOJmTNn3lDf4OBg9u3bx2OPPWZp27NnD5MmTSIlJYXo6Gi8vb159NFHCQ0NJSgoiOLiYlxdXRuNs3z5clJSUggKCiIkJIT8/HwiIyPZtGkTJ06cICsri7CwMMLCwjhx4sRNXa+IiIiIiMjdyNBwo8uwckfJycnh8OHDli3OYmJiCAgIwN/fv9ViMpvPttrcV2M0Otyxscm1KX/WTzm0bsqfdVP+rJ9yaN2UP+t2I/kzGh2abNej6XeY0tJSoqOjL2vv378/06ZNa7LPm2++yb59+yzvcouIiIiIiMidSyvicsvcqX/t018irZvyZ/2UQ+um/Fk35c/6KYfWTfmzbi25Iq53xEVERERERERuozu2EK+rqyMiIoLQ0FDmz59PZmYmAOnp6VftFxMTQ2BgoOWDYhMmTODw4cMtFtf06dOpqalpsfGqq6sZPHgwKSkpLTLeoUOH+Prrr1tkrH9VXV1NVlZWi48rIiIiIiJyt7ljC3Gz2Ux5eTmZmZk4Ojpa2pOTk6/Zd9asWaSlpZGWlsaLL77IW2+91WJxLVu2DDs7uxYb76OPPiIgIIBt27ZRX19/0+N9/PHHFBYWtkBkjZnNZhXiIiIiIiIiLeCO/VhbXFwcxcXFzJkzB6PRCFwqwisqKkhISCAhIaFZ41RUVGBvb09JSQlRUVE4OTnh7++Pv78/8+fPB8DJyYmFCxdy8OBBVq9eja2tLT/++CMhISHk5uby3XffMWnSJCZMmMDQoUPZuXMn8fHxlq+S79mzh5ycHBYtWsTw4cPp27cvx44dY+DAgZw9e5YDBw7g4eFBYmLiZfFlZWURGxvLqVOn+PzzzxkyZAh79+5l5cqV2NjYYDabGT9+PM8++yxfffUVb7/9NgBVVVUsXrwYW1tby3UNGDCAbdu2YWtri4+PD7Gxsfj5+VFQUICHhwedO3dm37592NnZsXr1aqqqqoiNjaW8vByA1157jV69evHrX/+afv36cfToUTp37kxSUhIrV66ksLCQt99+m6lTp7ZAhkVERERERO5Od2whHh8fz4wZM5g3bx5JSUkAREVFkZ6efs0iPDExkXfeeQcbGxvuu+8+Zs2aRU1NDWazmT//+c/Y2dkRHBzMwoUL8fLyIisri5SUFAYNGsSPP/7I9u3byc/P5+WXX+Yvf/kLZWVlTJ06lQkTJlwz7u+//57169djNBr51a9+RVZWFnFxcQwbNowzZ87QsWNHy7nFxcVcuHABb29vxowZw9q1axkyZAgAZWVlbN++nfr6egIDAxkxYgSHDx8mMTERV1dXVq5cyYcffkhgYGCj62poaMDFxQVfX1/OnTvHqFGjeOSRRxgxYgSzZ89m+vTpTJw4kcLCQrKzsxk4cCATJkyguLiY2bNnk5mZyYkTJ1i/fj1du3YlJCSEb775hilTplBQUKAiXERERERE5CbdsYX4zZg1a9Zl+2eXlJTQrVs3y2PlRUVFzJ07F4Da2lo8PDwA6NGjB7a2tjg4ONC9e3fs7OxwdHSkurr6ivP964fnnZyccHNzA8De3h4vLy8AHBwcLhsjKyuLCxcuEBERAcD+/fs5duwYAH379rXE2qNHD44fP46rqysLFizA3t6esrIy+vXrB9Douv6dj48PAB07dsRkMll+rq6upqCggNzcXHbu3AnAmTNnAHB2dqZr164AdO3a9arXLiIiIiIiItfH6grxm9ltzcbmn6/Ee3h4sHjxYtzc3MjLy8NsNgNgMBiaNZadnZ2lz8GDBy3tze1/8eJFcnJy2LZtG05OTsClR+8zMjIYOnQo3377LXV1ddTU1FBYWMgDDzxAVFQUu3btokOHDkRHR1vuxb9el8FgaPSu+dXi8fT05KmnniIwMJCffvrJ8g54U31sbGxa5B12ERERERGRu90d+7G2KzGZTMycOfOmx0lISCA6OpoJEyawdOlSevXqdV39x40bR2pqKuHh4ZSVlV33/J988gk+Pj6WIhwgKCiI9957j6qqKi5evMgLL7zAs88+S1RUFJ06deLpp58mODiYkJAQzp07x8mTJy8bt3fv3mzcuJHc3NxrxjBlyhR27txJWFgYv/3tb+nRo8cVz+3cuTO1tbUkJiZSWFjY7Hf0RUREREREpDFDw80sMcstsXfvXjZt2sSyZctaO5Sbcr2b3d8uRqPDHRubXJvyZ/2UQ+um/Fk35c/6KYfWTfmzbjeSP6PRocl2q3s0HaC0tJTo6OjL2vv378+0adNaISIRERERERGR5rHKQtzNzY20tLTWDuOWGTBgAAMGDGjtMEREREREROQWsLp3xK1JXV0dERERhIaGsmrVKgDS09Ov2ufUqVO89NJLRERE8Pzzz/Paa69RVVWF2Wy+6feyBw8eDMCCBQsoLS3lzJkzjB8/nueff57Vq1dz4MCBZo2zZMkStm7delOxiIiIiIiI3K1UiN9CZrOZ8vJyBg0aZNk/PDk5+ap9/rGf+Zo1a1i7di3t2rVj06ZNGI3GFvtAWmxsLG5ubhQUFHDfffexdu1aIiMj8fX1bZHxRURERERE5Mqs8tF0axEXF0dxcTFmsxkXFxeSk5OpqKggISHhikX1/fffz0cffcQDDzxAv379iI6OxmAwUFJSwowZM3j33XcJDAzEz8+PgoICPDw86Ny5M/v27cPOzo7Vq1ezcuVKjhw5wk8//cSZM2d47bXX8PPzs8wRFhZGbGwsf/zjHzl58iR/+tOfKC0tJSAggEcffZT4+HiOHTtGfX09r7zyCgMGDOCjjz4iOTmZTp06UVtbi6en5226iyIiIiIiIj8vWhG/heLj4/Hy8sJoNAIQFRWFo6PjVVe2Q0NDGTVqFGvWrOGxxx5j6tSpl21Tdu7cOUaNGsXGjRvZt28f/fr1Y+PGjdTW1lJYWAjAvffey4YNG0hMTGTevHmXzWNra8sf/vAHBg4c2OgDd1lZWTg7O7Nx40ZWrFhh6ZuYmMi6detYs2YN9957783eGhERERERkbuWVsTvMHv37uWZZ55h7Nix1NTU8M4777Bw4cLLvhLv4+MDQMeOHTGZTJafq6urARg4cCAAPXr04O9//3uz5y8oKCAvL8/yvvjFixf5+9//TocOHXB2dgagb9++N3eRIiIiIiIidzGtiN9m19q2ff369ZYPodnZ2dGjRw/s7OwuO89gMFx1nPz8fOBSYe3q6trs+Dw9PRk5ciRpaWm88847jBgxgo4dO3L27FlOnToFwDfffNPs8URERERERKQxrYjfZiaTiZkzZ7JkyZImj8+dO5e5c+eSkZHBvffei7OzMwkJCdTW1l7XPN9++y2TJ0/mwoUL/PGPf2x2v5CQEF577TUmTpxIZWUlEyZMwM7Ojtdff52IiAgcHR1p21a/NiIiIiIiIjfK0HCtJVqxOklJSbi4uBAaGtqqcZjNZ1t1/isxGh3u2Njk2pQ/66ccWjflz7opf9ZPObRuyp91u5H8GY0OTbZrabMVlJaWXvbON0D//v0bfThNREREREREfn5UiLcCNzc30tLSbtn4L7300i0bW0RERERERG6OPtYmIiIiIiIichupEG8BdXV1REREEBoayqpVqwBIT0+/ap+YmBj8/PyoqamxtOXn59OrVy/27t17xX4LFiygtLS0ZQJvQmlpKZ988gkAhw4d4uuvv75lc4mIiIiIiNyNVIi3ALPZTHl5OYMGDaJjx44AJCcnX7Of0Whkz549ln/v2LEDd3f3q/aJjY3Fzc3t5gK+itzcXPbv3w/Axx9/TGFh4S2bS0RERERE5G6kd8RbQFxcHMXFxZjNZlxcXEhOTqaiooKEhAQSEhKu2G/kyJFkZ2fzxBNPUF9fT35+Pn369AGgsrKS2NhYzp49S3l5OePGjWPChAmEhYWRkJDA6dOnWbx4MW3btqVjx44sWbKEe+65hz/84Q+cOHGCuro6nnvuOQICAggLC8Pb25vDhw9TWVnJW2+9xf33309aWhrZ2dkYDAYCAgJ49tlnWb16NVVVVZhMJrZt24atrS0+Pj5UVVWxbNky2rRpg7u7O/PmzcPW1vY23WEREREREZGfD62It4D4+Hi8vLwwGo0AREVF4ejoeNUiHMDX15ejR49y/vx5cnNzGTBggOXYsWPHGDlyJGvXrmXlypWkpqY26rtr1y6GDx9Oeno6Y8eO5cyZM2zevBlnZ2c2bdrEunXrWL58OadOnbLMlZqayuDBg/nggw8oLCwkJyeHjIwMMjIy2LVrF8eOHSMyMpJRo0YxevRoRo8eTXh4OH369CEuLo63336b9PR0XF1d2bZtW4veQxERERERkbuFVsRb2dChQ9m9ezdffvklUVFRLFu2DAAXFxfWr1/Pxx9/TIcOHbh48WKjflOmTGHlypVMnjwZV1dXfH19KSoqYtCgQQB06NABk8nEiRMnAPjlL38JQJcuXfj73/9OQUEBpaWlhIeHA1BRUcHx48ebjPHUqVOcPHmSV155BYCqqioGDx7c4vdCRERERETkbqAV8VukoaGhWecFBgayfft2zGYz3bt3t7SvXbuWhx9+mCVLljBixIjLxtuxYwejR48mLS2NHj168O6772Iymdi3bx9w6dH2goICunXr1uS8np6eeHl5sWHDBtLS0ggKCqJnz57Y2NhQX18PgMFgoL6+HmdnZ7p06cKKFStIS0tjypQpjVbvRUREREREpPm0In6LmEwmZs6cyZIlS656nqenJ+Xl5YwZM6ZR+5AhQ0hISGDHjh04OTnRpk2bRl9Y79OnDzExMdjb22Nra8u8efNwdXUlLi6O0NBQqqurmTp1Kp07d25yXm9vbx599FFCQ0OpqanB19cXV1dXevbsSXJyMj4+PvTu3Zs33ngDk8lEbGwskZGRNDQ00L59e954442bv0kiIiIiIiJ3IUNDc5duRa6T2Xy2tUNoktHocMfGJtem/Fk/5dC6KX/WTfmzfsqhdVP+rNuN5M9odGiyXSvit1BpaSnR0dGXtffv359p06a1QkQiIiIiIiLS2lSI30Jubm6kpaW1dhgiIiIiIiJyB9HH2q5DXV0dERERhIaGMn/+fDIzMwFIT09vVv+nn36auXPnXvF4cHAwJSUlbN26ld27d1/xvNWrV3PgwIHrC/7fNPXV8127djFq1Cg2bNhwza+ib968mdra2puKQURERERE5G6kQvw6mM1mysvLyczMxNHR0dKenJx8zb55eXn07NmT3NxcKisrr3puUFAQw4YNu+LxyMhIfH19mx94M3366afMmDGDSZMmXfPcVatWWb6uLiIiIiIiIs2nR9OvQ1xcHMXFxcyZMwej0QhcKsIrKipISEggISHhin2zsrJ48skn6dq1K9u3b2fixIkALFu2jL/+9a906dKF8vJyAJKSknBxcSE0NJRFixaRl5cHwKhRo5gzmddHAAAgAElEQVQ8eTIxMTEEBATw97//nc8//5yqqiqOHz/OCy+8QFBQEIcOHWL+/PkAODk5sXDhQuzt7YmLi6OwsBB3d/dGX2AH2L17N5999hkHDhzA2dnZ0v7VV1/x9ttvA5f2D1+8eDH79u3DbDYzffp0VqxY0TI3V0RERERE5C6hFfHrEB8fj5eXF/PmzbO0RUVF4ejoeNUivLKykry8PB5//HHGjBljeaS9oKCAr7/+mi1btvDGG29w7ty5Rv0+/fRTSkpKePfdd8nIyCA7O5tDhw5dNvaqVatITk5m9erVwKU/GMTHx5OWloa/vz8pKSns2bOH6upq3n33XV599VUuXLjQaJxhw4bx2GOPMWvWLPr27WtpP3z4MImJiWzYsIGhQ4fy4YcfMm7cOIxGI8uWLbuh+ygiIiIiInI304r4bfD+++9TX1/Piy++CFx6xP1//ud/KC8vp3fv3tjY2NChQwd69uzZqF9RURF+fn4YDAZsbW156KGHKCoqanSOt7c3AF27drWschcVFVneRa+trcXDw4PDhw9bHmd3c3Oja9euzYrd1dWVBQsWYG9vT1lZGf369bvxGyEiIiIiIiIqxFvCtbZi37JlCytXrqRHjx7ApcJ848aN/Nd//RcbNmygvr6eqqoqCgsLG/UzmUxs3bqV8PBwamtr+dvf/sbo0aMbnWMwGC6bz8PDg8WLF+Pm5kZeXh5ms5m2bdvywQcfMHnyZMrKyigrK2vWtb322mvs2rWLDh06EB0dbblWg8Ggd8RFRERERERugArxFmAymZg5cyZLliy57NjBgwdpaGiwFOEATz75JK+//jpOTk6MGDGCsWPHct9999G5c+dGfYcMGcJXX33F+PHjqa2tZcSIEfj4+FwznoSEBKKjo6mrqwNgwYIFeHh4kJeXx7hx43Bzc2v0HvjVPP300wQHB9OxY0dcXFw4efIkAH5+fkRGRrJhw4Ym/xggIiIiIiIiTTM0XGs5V267ZcuW4e7uztixY1s7lJtiNp9t7RCaZDQ63LGxybUpf9ZPObRuyp91U/6sn3Jo3ZQ/63Yj+TMaHZps14p4CyktLSU6Ovqy9v79+zNt2rRmj/PnP/+Z3bt3W75ULiIiIiIiIj8vKsRbiJubG2lpaTc9zpgxYxgzZkwLRCQiIiIiIiJ3Im1fJiIiIiIiInIbqRC/xerq6oiIiCA0NJT58+db9hBPT0+/ar+YmBj8/PwsW5IB5Ofn06tXL/bu3dusuTMzM0lKSrrqHHv27Gny2JQpU5gyZUqjtuzsbMaNG0dISAhz5szRV9NFRERERERugArxW8xsNlNeXk5mZiaOjo6W9uTk5Gv2NRqNjQrlHTt24O7ufkvi/Fc//PAD58+fp6KighMnTgBQVVXF8uXL2bBhA5s2baKyspJPP/30lsciIiIiIiLyc6NC/BaLi4ujuLiYOXPmWNqSk5OpqKggISHhqn1HjhxJdnY2APX19eTn59OnTx8AamtrmTVrFiEhIYwbN46cnBwA9u3bR1BQEM899xy7du2yjJWWlsb48eMJCQlhw4YNV513y5YtDBs2jGeeeYaMjAwA7Ozs2LRpE+3atQPg4sWL3HPPPdd3M0RERERERESF+K0WHx+Pl5cX8+bNs7RFRUXh6Oh4zULc19eXo0ePcv78eXJzcxkwYIDl2ObNm3F2dmbTpk2sW7eO5cuXc+rUKV5//XWWLl3KunXr6NatGwCFhYXk5OSQkZFBRkYGu3bt4siRI03OWV9fT3Z2Nk8//TQjR44kJyeHqqoqbGxscHFxAS4V9efPn2fw4ME3eXdERERERETuPirE73BDhw5l9+7d7Nixg6eeesrSXlRURP/+/QHo0KEDJpOJEydOUFZWhoeHBwD9+vUDoKCggNLSUsLDw5k8eTKnT5/m+PHjTc7317/+lXPnzvHqq6/y8ssvU19fz44dO4BLRfrixYv54osvSEpKwmAw3MpLFxERERER+VlSId5KGhoamnVeYGAg27dvx2w20717d0u7yWRi3759AFRWVlJQUEC3bt0wGo0UFRUB8M033wDg6emJl5cXGzZsIC0tjaCgIHr27NnkfFu2bGH+/PmsWbOGNWvWsHz5csvj6XPmzKG6upoVK1ZYHlEXERERERGR66NCvJWYTCZmzpx5zfM8PT0pLy9nyJAhjdqDg4M5ffo0oaGhTJo0ialTp9K5c2cSExOJjo5m8uTJlJaWAuDt7c2jjz5KaGgoQUFBFBcX4+rqetlcP/30E//3f//Hf/zHf1jaHnnkEaqrq9m/fz9btmyhoKCAyZMnExYWxl/+8pebvAsiIiIiIiJ3H0NDc5dmRa6T2Xy2tUNoktHocMfGJtem/Fk/5dC6KX/WTfmzfsqhdVP+rNuN5M9odGiyvW1LBCQ3prS0lOjo6Mva+/fvz7Rp01ohIhEREREREbnVVIi3Ijc3N9LS0lo7DBEREREREbmNVIi3grq6OiIjIzl//jw+Pj6YTCZCQ0NJT09n4sSJTfY5deoUL7/8MgDffvstv/jFL2jXrh1PPfUU48aNa7HYjh07xoIFC6irq+PixYv07t2bV199FRsbG8vx//qv/7Lsby4iIiIiIiLXR4V4KzCbzZSXl7N161aSkpIs7cnJyVcsxDt16mRZPQ8LCyMhIQGTydTisb355ptMnDgRf39/GhoamDp1Krt372b48OFs376dDRs2UF5e3uLzioiIiIiI3C1UiLeCuLg4iouLmTNnDkajEbhUhFdUVJCQkEBCQkKzx6qvr+fJJ58kKysLJycnMjIyOH/+PIWFhTQ0NPDDDz9w/vx5Fi9ejMlkIi0tjezsbAwGAwEBAUyaNKnReG5ubmzbto327dvj6+vL8uXLadv20q+Jo6Mj6enpDB8+vMXuhYiIiIiIyN1G25e1gvj4eLy8vJg3b56lLSoqCkdHx+sqwgFsbGwIDAzkgw8+AOD999/nmWeeAcDd3Z0NGzbw0ksvkZiYSGFhITk5OWRkZJCRkcGuXbs4cuRIo/GmT5/OQw89xJtvvsmgQYOYPXs2Z89e+jLgkCFDsLe3v4krFxERERERERXiPwNjx47l/fffp6CgABcXF1xcXAAYOHAgAH379uXo0aMUFBRQWlpKeHg4kydP5vTp0xw/frzRWLm5uYSHh7Nx40Y+++wz7O3tWbFixW2/JhERERERkZ8rPZp+B7nRLd3d3NxwcHBg5cqVjB071tKen5+Pn58f+/fvp0ePHnh6euLl5UVKSgoGg4HU1FR69uzZaKzExETatGnD4MGDad++PR4eHnonXEREREREpAVpRfwOYjKZmDlz5g31DQ4OZt++fTz22GOWtj179jBp0iRSUlKIjo7G29ubRx99lNDQUIKCgiguLsbV1bXROMuXLyclJYWgoCBCQkLIz88nMjLypq5LRERERERE/snQcKPLsHJHycnJ4fDhw5YtzmJiYggICMDf37/VYjKbz7ba3FdjNDrcsbHJtSl/1k85tG7Kn3VT/qyfcmjdlD/rdiP5MxodmmzXo+l3mNLSUqKjoy9r79+/P9OmTWuyz5tvvsm+ffv0LreIiIiIiIgV0Iq43DJ36l/79JdI66b8WT/l0Lopf9ZN+bN+yqF1U/6sW0uuiOsdcREREREREZHbyCoK8bq6OiIiIggNDWX+/PlkZmYCkJ6eftV+MTEx+Pn5UVNTY2nLz8+nV69e7N27t1lzZ2ZmkpSUdNU59uzZ0+SxKVOmMGXKlCv2TUpK4sEHH6SsrMzS9tNPP+Hj48PWrVuv2G/16tUcOHCgGdHfmNOnT7Njxw7g0qPyn3zyyS2bS0RERERE5G5jFYW42WymvLyczMxMHB0dLe3JycnX7Gs0GhsVyjt27MDd3f2WxPmvfvjhB86fP09FRQUnTpy44nm/+MUv2Llzp+XfOTk5dO3a9apjR0ZG4uvr22Kx/rtDhw5Ziu/c3Fz2799/y+YSERERERG521jFx9ri4uIoLi5mzpw5GI1G4FIRXlFRQUJCAgkJCVfsO3LkSLKzs3niiSeor68nPz+fPn36AFBbW8sf/vAHTpw4QV1dHc899xwBAQHs27ePhQsX4ujoiI2NDQ8//DAAaWlpZGdnYzAYCAgIYNKkSVecd8uWLQwbNox7772XjIyMJj/ABhAQEMCHH35IeHg4AJ9++ilDhgwBLj0JMGfOHH788UfKy8vx9/fnlVdesXwR3d3dndmzZ9O2bVvatGnDG2+8gaurK4sWLSIvLw+AUaNGMXnyZGJiYrCzs+P777/n5MmTLFq0CB8fH3bu3Elqaio2NjY88sgjzJw5k5UrV/Ldd9+xadMmUlNTqaqqom/fvnTr1o358+cD4OTkxMKFC3FwaPqdBxEREREREWmaVayIx8fH4+Xlxbx58yxtUVFRODo6XrUIB/D19eXo0aOcP3+e3NxcBgwYYDm2efNmnJ2d2bRpE+vWrWP58uWcOnWK119/naVLl7Ju3Tq6desGQGFhITk5OWRkZJCRkcGuXbs4cuRIk3PW19eTnZ3N008/zciRI8nJyaGqqqrJc11cXGjXrh0nTpzg2LFjdOnShXvuuQe4tKr+8MMPs2bNGjIzMy2P5P/Dl19+iY+PD+vWrWPKlClUVFTw6aefUlJSwrvvvktGRgbZ2dkcOnQIADc3N9asWUNYWBibN2/m9OnTJCUlkZqaSmZmJmVlZXzxxRdMmTKFgQMHEhISQmRkJKNGjWLYsGHExcURHx9PWloa/v7+pKSkXD1xIiIiIiIichmrWBG/WUOHDmX37t18+eWXREVFsWzZMgCKiooYNGgQAB06dMBkMnHixAnKysrw8PAAoF+/fhw/fpyCggJKS0stK9cVFRUcP368yfn++te/cu7cOV599VXgUmG+Y8cOPDw8eOuttwCIiIiwnD9y5Eg++OADLl68SGBgIF988QVwadX5m2++ITc3lw4dOjR61x1g7NixvPPOO/z2t7/FwcGB6dOnU1RUhJ+fHwaDAVtbWx566CGKiooAePDBBwHo0qUL+/fv5/jx45w6dYrIyEgAzp07x4kTJyzX/u+KioqYO3cucOlpgiudJyIiIiIiIldm1YV4c3deCwwMZMGCBRgMBrp3725pN5lM7Nu3j+HDh1NZWUlBQQHdunXDaDRSVFSEyWTim2++wdHREU9PT7y8vEhJScFgMJCamkrPnj358MMPL5tvy5YtzJ8/n8cffxyAvLw85s+fz7Zt20hLS7Oc98033wDw5JNP8vzzz9O+fXt+97vfWQrxrVu34uDgwLx58zh27Bjvvvtuo2vevXs3jzzyCFOnTiU7O5uUlBR+/etfs3XrVsLDw6mtreVvf/sbo0ePBsBgMDSKs1u3bnTt2pW1a9dia2vL1q1befDBB6msrKS+vh4AGxsby88eHh4sXrwYNzc38vLyMJvNzbr/IiIiIiIi8k9WXYibTCZmzpzJkiVLrnqep6cn5eXljBkzplF7cHAwcXFxhIaGUl1dzdSpU+ncuTOJiYlER0fTvn172rdvj6OjI97e3jz66KOEhoZSU1ODr68vrq6ul831008/8X//93+WVXeARx55hOrqavbv30+/fv0u6+Pg4ECXLl1wd3fHxuafbws8+uijzJgxg7y8PNq1a8cDDzzAyZMnLcd79+7NrFmzSEpKwsbGhtmzZ+Pj48NXX33F+PHjqa2tZcSIEfj4+DR5Xzp16kR4eDhhYWHU1dVx//3385vf/IYzZ85QUFBAamoqv/rVr0hOTsbHx4eEhASio6Opq6sDYMGCBVe97yIiIiIiInI5Q0Nzl5VFrtP1bnZ/uxiNDndsbHJtyp/1Uw6tm/Jn3ZQ/66ccWjflz7rdSP6MxqY/bm3VK+JwaZ/rpr5I3r9/f6ZNm9YKEYmIiIiIiIhcmdUX4m5ubo3euxYRERERERG5k1nF9mUiIiIiIiIiPxc/+0K8rq6OiIgIQkNDmT9/vmUv7vT09Kv2i4mJwc/Pr9GWYfn5+fTq1Yu9e/desV9YWBhFRUWcPn2aHTt2ALB69WoOHDhw2blffvklkydPJjQ0lLCwMGJiYjh79va+M/KP+7Bnzx42b958W+cWERERERG5G/3sC3Gz2Ux5eTmZmZk4Ojpa2pOTk6/Z12g0smfPHsu/d+zYgbu7e7PmPXToEJ988gkAkZGR+Pr6Njr+3XffkZiYyBtvvEFmZiZpaWl4e3uTkpLSrPFbyj/ug7+/P+PHj7+tc4uIiIiIiNyNrP4d8WuJi4ujuLiYOXPmYDQagUvFZ0VFBQkJCSQkJFyx78iRI8nOzuaJJ56gvr6e/Px8+vTpA1za4/vIkSPMnDmT6upqfvOb31gKb4CVK1fy3XffsXnzZv72t78REBCAv7+/5XhmZiZRUVGNtkALDw+3/PzVV1+xbNky2rRpg7u7O/PmzWPHjh18/vnnVFVVcfz4cV544QWCgoI4dOgQ8+fPB8DJyYmFCxdy8OBBlixZgq2tLcHBwdx7771s3LjRMv5bb73F5s2bLffB19fXcj1r167lgw8+oG3btvj5+Vm2SCspKeGnn36itLSU2bNn89hjj91UbkRERERERO5GP/sV8fj4eLy8vJg3b56lLSoqCkdHx6sW4QC+vr4cPXqU8+fPk5uby4ABA5o975QpUxg4cOAVV5lLSkro3r07ACdOnCAsLIyJEycSGhpKQ0MDcXFxvP3226Snp+Pq6sq2bdsAqKysZNWqVSQnJ7N69Wrg0h8b4uPjSUtLw9/f37KqXl1dTUZGBs888wzFxcWsXr2atLQ0PDw8+O///u8m78OhQ4fYuXMnmzZtYtOmTRw7doxPP/0UADs7O1JSUoiNjSU1NbXZ90JERERERET+6We/In6zhg4dyu7du/nyyy+Jiopi2bJll51zI1uxd+3alZKSEry9vXF3dyctLc2ysn7q1ClOnjzJK6+8AkBVVRWDBw+me/fueHt7W/r/4/31oqIi5s6dC0BtbS0eHh4Alv8DdO7cmejoaNq3b8+RI0d4+OGHm4zryJEjPPTQQ9ja2gLg5+fH4cOHAXjwwQcB6NKlS6N350VERERERKT57tpCvLnFc2BgIAsWLMBgMFhWsAHuuecezGYzcOkjbv/OxsaG+vr6K44bEhJCfHw8vr6+3HfffQDk5uYC4OzsTJcuXVixYgUODg7s3r0be3t7fvjhBwwGw2VjeXh4sHjxYtzc3MjLy7PEZWNz6YGHs2fP8qc//YnPPvsMgOeee85y/f9+Hzw9PVm3bh0XL16kTZs2fP311zzzzDN89913Tc4tIiIiIiIi1+euLcRNJhMzZ85kyZIlVz3P09OT8vJyxowZ06j9scceIzMzk9DQUHx8fGjfvn2j4927d6egoOCKj3D37t2b3//+98TExFBbW8uFCxdwc3Nj9erV2NjYEBsbS2RkJA0NDbRv35433niDH374ocmxEhISiI6Opq6uDoAFCxZw8uRJy/EOHTrQr18/Ro8ejb29PR07drQc/8d9GDRoEAC9evXiN7/5DaGhodTX1/PII4/wxBNP8N133131PomIiIiIiEjzGBpu5LlqkWYwm2/vVmzNZTQ63LGxybUpf9ZPObRuyp91U/6sn3Jo3ZQ/63Yj+TMaHZpsv2tXxAFKS0uJjo6+rL1///5MmzatFSISERERERGRn7s77qvpdXV1REREEBoayqpVqwBIT0+/ap+kpCQyMzOvey43NzfS0tIu+68li/DMzEySkpIua4+JicHPz6/RR8/y8/Pp1asXe/fuveJ4CxYsoLS0tMXi+3elpaWWbdgOHTrE119/fcvmEhERERERuRvdcYW42WymvLycQYMG0bFjR+DSvt8/R0ajkT179lj+vWPHDtzd3a/aJzY2Fjc3t1sWU25uLvv37wfg448/prCw8JbNJSIiIiIicje64x5Nj4uLo7i4GLPZjIuLC8nJyVRUVJCQkHDNfb9LSkqYPn26ZWuwkSNHcvjwYQ4ePMjjjz/OjBkzCAsLw8PDg6NHj9LQ0MCyZcs4cuQIS5YswdbWluDgYIxGI8uXL+eee+7BycmJhQsX8v/+3//D29ub0aNHYzabefHFF9m6dStLly7l66+/pqGhgfDwcH7zm9+wb98+Fi5ciKOjIzY2NlfcKmzkyJFkZ2fzxBNPUF9fT35+Pn369AEu7RceGxvL2bNnKS8vZ9y4cUyYMIGwsDASEhI4ffo0ixcvpm3btnTs2JElS5Zwzz338Ic//IETJ05QV1fHc889R0BAAGFhYXh7e3P48GEqKyt56623uP/++0lLSyM7OxuDwUBAQADPPvssq1evpqqqCpPJxLZt27C1tcXHx4eqqiqWLVtGmzZtcHd3Z968eZYtzkRERERERKT57rhCPD4+nhkzZmA0GgGIiooiPT39mkX4P5w4cYK1a9dSVVXFsGHD2LNnD+3atWPIkCHMmDEDgH79+jFv3jw2btzIqlWrGD58ONXV1WRlZdHQ0MCwYcPIzMzE1dWV9evXk5ycTHBwMHPnzmX06NG89957BAUF8fnnn1NSUsKmTZuorq4mODiYwYMH8/rrr7N06VI8PDyIj4+/Yqy+vr785S9/4fz58/zv//4vAwYMoKioCIBjx44xcuRIfv3rX1NWVkZYWBgTJkyw9N21axfDhw8nIiKCTz75hDNnzvDJJ5/g7OxMYmIilZWVBAUFMXDgQMtcsbGxLFu2jA8++IChQ4eSk5NDRkYGBoOB8PBw/uM//oPIyEiOHDnC6NGjKSkpwcXFhT59+jBixAgyMjLo3Lkzy5cvZ9u2bQQHB99IikVERERERO5qd1whfrPc3d1xcHDAzs4OFxcXnJycABrtgf2P4rRfv36W96E9PDwAKC8vp0OHDri6ugKXPtz25ptvYjKZqKur4/vvvycnJ4fU1FQ2b95Mfn4+YWFhAFy8eJHS0lLKysos4/Xr14/jx4/z4YcfsnHjRoBGH4gbOnQou3fv5ssvvyQqKor/r717D6uqyv84/j4omAgCCqJITly8oVGapmYxiTnDeGl+6ohKoZhlUaZZOuAoFxVIRTNHR0jTtIMhmqG/LE0tH+lXj3lNKy8oiYGOehoRQgWEw+8PH89IcvHKRT+vv2Dtvdb+rvN9eODLWnvvefPmAeDs7MyKFSvYvHkzdnZ2FBcXl5nnK6+8QmJiIiNHjsTV1RVfX18yMjIsryGzs7PDy8uLrKwsAHx8fABo3rw5v/76K+np6Zw6dYqQkBAAcnNz+eWXX8r9TM+dO8fZs2d54403ACgoKKBnz543kRURERERERG5qk4U4jfzhrVrC+6K/PjjjzRv3py9e/fi7e0NgJXVldvlnZycyM/P5+zZszRr1oydO3fy0EMPAfC3v/2N+Ph4vL29ady4MZ6ennTr1o0ZM2ZgNptZtGgR7u7uuLi4kJGRgZeXFz/88AMODg4EBAQQEBBwXSwDBgwgNjYWg8FAq1atLO3Lli3j0UcfJSgoiB07drB9+/Yy/T799FMGDhxIWFgY7733HqtXr8bLy4vdu3fTp08f8vPzSU9Px93dvdzPwNPTE29vb95//30MBgPLly+nTZs27Ny5E7PZbPkszWYzTk5ONG/enEWLFmFvb8+XX36Jra1t1ckQERERERGR69SJQtzLy4uJEycyZ86cOzJeamoqy5cvp2HDhsyePZv09HTLMYPBQExMDK+//joGgwEHBwfefvttAAICAoiNjbU8PM7f35+dO3cSFBTExYsXeeaZZ7CzsyM+Pp6wsDAaNWpEo0aNcHBwqDAWT09PcnJyGDx4cJn2Xr16ER0dzaeffoqjoyP16tUr84T1hx9+mPDwcGxtbbG2tmb69Om4uroSERHB8OHDKSwsZOzYsTRt2rTc67Zr144ePXowfPhwioqK8PX1xdXVlTZt2pCQkECHDh3o2LEjs2fPxsvLiylTpjBmzBhKS0tp1KgRs2fPvuXPX0RERERE5H5mKL2Z5eZ7wNWHnXl5edV0KPe8m33ZfXVxcbGvtbFJ1ZS/uk85rNuUv7pN+av7lMO6Tfmr224lfy4u9uW214kVcbjyfutr762+qmvXrnf0vd8iIiIiIiIid1OdKcTd3NwwGo23Pc6dGENERERERETkVlnVdAAiIiIiIiIi9xMV4iIiIiIiIiLVqM5sTb9XlZSUMGbMGC5evMjTTz/Nyy+/TFJSEs8//3y55587d47x48cDcOjQIR566CEaNmzIs88+y5AhQ6ozdBEREREREbkFKsRrmMlkIicnh169etG4cWMAEhISKizEmzRpYrnPXU+AFxERERERqXtUiNewiIgIMjMzMZlMODs7k5CQQG5uLtHR0URHR9/UWCdPniQyMpKioiIeeOABYmJiKCgoYOzYsTg6OtKrVy+2bNmCq6sreXl5vPPOO0RGRpKfn09OTg7Dhw8nMDCQffv28fbbb1NaWkqLFi2Ij4/n+PHjxMbGAuDk5ERcXBx2dnZ34RMRERERERG5t+ke8RoWFRWFt7c3Li4uAISGhuLg4HDTRTjAzJkzeeGFFzAajYwYMYJ33nkHuLKdfdmyZbzwwgsAPPvssyxbtoysrCzL1//617/44IMPAIiMjGTmzJmsWbOGrl27cvz4cf7xj38wffp0jEYjTzzxBMuWLbszH4CIiIiIiMh9Rivi95D09HQWLVpEYmIiZrOZBx54AIAHH3wQa2try3keHh4AODs7YzQa+eKLL7C1taW4uBiAnJwcPD09gSvb3wGOHz9OZGQkAJcvX9Z2eBERERERkVukQrwWKi0tvaV+Hh4evPrqq/j6+nL06FH27dsHgMFgKHOeldWVjRBLly6lS5cuBAYG8s033/DNN98AV+5Dz8rK4sEHHyQxMZHWrVvj4eFBfHw8zZs3Z9euXeTk5NzGDEVERERERO5fKsRrIS8vLyZOnMicOXNuqt/kyZOZNm0ahYWFFBUVERERUen5/vfiqtYAABv5SURBVP7+TJs2jdTUVJo0aYLBYKCoqIjp06cTFhaGlZUVrq6uvPDCC7i4uDBx4kRKSkqwsrIiLi7udqYoIiIiIiJy3zKU3uryq0gVTKbfajqEcrm42Nfa2KRqyl/dpxzWbcpf3ab81X3KYd2m/NVtt5I/Fxf7ctu1Il5LnTp1irCwsOvau3btyrhx42ogIhEREREREbkTVIjXUm5ubpb3hYuIiIiIiMi9Q68vqyVKSkoYPXo0w4cP57333gMgKSmp0j6HDh0iKCiI4OBgRo8eza+//lodoYqIiIiIiMhtUCFeS5hMJnJycnjiiSdo3LgxAAkJCZX2iY2NJSIiAqPRSJ8+fViyZEl1hCoiIiIiIiK3QVvTa4mIiAgyMzMxmUw4OzuTkJBAbm4u0dHRREdHl9vnnXfeoVmzZsCVFfUGDRpcd05wcDBOTk7k5eWxePFipkyZQlZWFiUlJYwaNYq+ffty8OBBZsyYQb169WjQoAEzZszAbDYzYcIEWrRoQXZ2Nv369ePo0aMcPHiQp59+mjfffPNufhwiIiIiIiL3LBXitURUVBRvvvkmLi4uAISGhpKUlFRhEQ5YivC9e/eSlJTEypUryz1vwIAB9OnTh6SkJJycnIiPjyc/P59BgwbRvXt3pk6dSmxsLO3bt2fr1q3MnDmTv//972RlZbFs2TIKCgro3bs3aWlpNGzYkF69eqkQFxERERERuUXaml7Hff7550RFRbF48WKaNGnCpk2bCA4OJjg4mB9//BEADw8PADIyMujatSsAdnZ2eHl5kZWVxdmzZ2nfvj1w5ansR48eBeDBBx/E3t6exo0b4+zsjKOjIw0aNMBgMNTATEVERERERO4NWhGvxap6xfv69etJSUnBaDTi6OgIQEBAAAEBAWXOu1o4e3l5sXv3bvr06UN+fj7p6em4u7vTrFkzDh8+TLt27di1axcPPfRQmX4iIiIiIiJy56gQr8W8vLyYOHEic+bMue5YSUkJsbGxtGjRgtdffx2o+h3jgYGBREREMHz4cAoLCxk7dixNmzYlJiaGGTNmUFpaSr169YiLi7trcxIREREREbnfGUqrWnYVuUUm0281HUK5XFzsa21sUjXlr+5TDus25a9uU/7qPuWwblP+6rZbyZ+Li3257VoRr+VOnTpFWFjYde1VrX6LiIiIiIhI7aRCvJZzc3PDaDTWdBgiIiIiIiJyh9Tap6aXlJQwevRohg8fTkxMDMnJyQAkJSVV2i88PJwBAwZYnhweFBRkeQr4nTBhwgSKioruyFjBwcH85S9/KdO2efNm2rZtS3Z2drXEUJ4jR46wa9cuAHbt2sXhw4fv2rVERERERETuN7W2EDeZTOTk5JCcnIyDg4OlPSEhocq+kyZNwmg0YjQaefnll5k/f/4di2vevHnY2NjcsfEADh06ZPn6s88+o2XLltUew7U2b97MsWPHAFi7di1nz569a9cSERERERG539TarekRERFkZmYSGRmJi4sLcKUIz83NJTo6mujo6BsaJzc3F1tbW7KzswkNDcXR0RE/Pz/8/PyIiYkBwNHRkbi4OA4ePMjixYuxtrbm9OnTDBs2jB07dnD48GFGjBhBUFAQ/v7+bNy4kaioKPr27Yufnx9paWl8/vnnzJw5kz59+tCpUydOnDhB9+7d+e233zhw4AAeHh7Ex8dfF1+/fv3YsGED7du3Jy8vj8LCQpydnQE4ffo00dHRFBYWcv78eV577TWeeeYZSwzbt29nyZIl1K9fn5YtWzJ79mzy8/OZNGkS+fn5lJSUMH78eHr06MGAAQN4/PHHOXLkCAaDgUWLFmFvb8/cuXPZtWsXpaWlhISE0LlzZ1JTU7G2tsbHx4evv/6an376CW9vb/bv38/y5cuxsrLiscceY+LEiXcm2SIiIiIiIveRWrsiHhUVhbe3N9OnT7e0hYaG4uDgUGURHh8fT3BwMCNHjuTrr7+2FIwmk4mlS5fy0ksvERERQVRUFEajET8/P95//33gSvG7YMECoqOjSUhIYPbs2SxZsoSUlJQbivvkyZO88cYbJCUl8eGHHxIUFMSaNWvYs2cPeXl5153v7+9PWloapaWlfPHFF2XeAf7zzz8zatQoPvjgAyIiIli5cmWZvhs2bCAkJITk5GSefPJJ8vPzSUhI4IknnmDlypXMnz+fKVOmYDabuXDhAv369SMpKYlmzZqRlpbG9u3byc7OZtWqVXz44YckJibSsGFDBg4cSEhICI888ghPPfUUkyZNwtbWlgULFrB8+XKSk5M5c+YM33zzzQ19JiIiIiIiIvJftXZF/HZMmjQJPz+/Mm3Z2dm4u7tbtnRnZGQwbdo0AC5fvoyHhwcArVu3xtraGnt7e1q1aoWNjQ0ODg4UFhZWeL1r3wDn6OiIm5sbALa2tnh7ewNgb29f7hgNGjSgffv27Nu3jy1btjBv3jw++ugjAFxcXEhISODjjz/GYDBQXFxcpu/kyZN57733SE5OxtPTk2eeeYaMjAwGDBgAgKurK3Z2dpw7dw4AHx8fAFq0aEFhYSGnTp3ip59+Ijg4GIDi4mJOnTpV7hx/+eUXzp07x5gxYwC4cOECWVlZFX4mIiIiIiIiUr46V4jfzmvPraz+uwHAw8ODWbNm4ebmxp49ezCZTAAYDIYbGsvGxsbS5+DBg5b2G+1/rf79+7N8+XIcHBxo1KiRpX3+/PkMGTKEP/7xj6xdu5bU1NQy/VJSUnj99ddp2rQpkZGRbNmyBS8vL3bv3o2Pjw9nzpwhLy8PR0fHcmPz9PSkW7duzJgxA7PZzKJFi3B3d8dgMGA2my19SktLcXd3p0WLFixbtgxra2s++eQT2rdvf9NzFRERERERud/V2q3pFfHy8roj9yZHR0cTFhZGUFAQc+fOpW3btjfVf8iQISxfvpyQkBDOnDlzW7H07NmT3bt3079//zLtAQEBxMbGEhQUxLfffktOTk6Z476+vowaNYoRI0ZgMpl4+umnefnll9mxYwfPPfccr776KtOnT6d+/fL/3+Lv74+trS1BQUEMGjQIADs7Ozp27MjKlSvZsWMHjzzyCHPmzCEnJ4eQkBCCg4MZMmQIaWlpPPTQQ7c1bxERERERkfuRofR2lphFKmEy/VbTIZTLxcW+1sYmVVP+6j7lsG5T/uo25a/uUw7rNuWvbruV/Lm42JfbXue2pgOcOnWKsLCw69q7du3KuHHjaiAiERERERERkRtTJwtxNzc3jEZjTYchIiIiIiIictPuyj3iJSUljB49muHDhxMTE0NycjIASUlJlfYLDw9nwIABBAcHExwcTFBQEEePHr1jcU2YMIGioqI7Nl5hYSE9e/a0vPrsdh05coRdu3bdkbGuVVhYyJo1a65rDw8PJy0t7YbGyMrK4q9//SthYWF3LU4REREREZH7wV0pxE0mEzk5OSQnJ+Pg4GBpT0hIqLLvpEmTMBqNGI1GXn75ZebPn3/H4po3b57l9WV3whdffEHfvn1JTU21PGX8dmzevJljx47dgcjKMplM5RbiN2Pv3r306NGDWbNm3bU4RURERERE7gd3ZWt6REQEmZmZREZG4uLiAlwpwnNzc4mOjiY6OvqGxsnNzcXW1pbs7GxCQ0NxdHTEz88PPz8/YmJigCvv7Y6Li+PgwYMsXrwYa2trTp8+zbBhw9ixYweHDx9mxIgRBAUF4e/vz8aNG4mKiqJv3774+fmRlpbG559/zsyZM+nTpw+dOnXixIkTdO/end9++40DBw7g4eFBfHz8dfGtWbOGKVOmcO7cObZv306vXr347rvvSExMxMrKCpPJxNChQ3nuuefYuXMnCxcuBKCgoIBZs2ZhbW1tmVe3bt1ITU3F2tqaDh06MGXKFLp06UJ6ejoeHh40bdqU3bt3Y2Njw+LFiykoKGDKlCmWJ6lPnTqVtm3b8qc//YnOnTtz/PhxmjZtyoIFC0hMTOTYsWMsXLiQsWPHVvm5z5w5kz179gBXXq3Wp08fEhISKCgowMHBoUycvr6+N5RLERERERERueKuFOJRUVG8+eabTJ8+nQULFgAQGhpKUlJSlUV4fHw8S5YswcrKimbNmjFp0iSKioowmUysXbsWGxsbAgMDiYuLw9vbmzVr1vD+++/zxBNPcPr0adatW8dPP/3E+PHj2bJlC2fOnGHs2LEEBQVVGffJkydZsWIFLi4uPP7446xZs4aIiAh69+5NXl4ejRs3tpybmZnJpUuXaNeuHYMHD2bZsmX06tULgDNnzrBu3TrMZjMDBgwgICCAo0ePEh8fj6urK4mJiWzatIkBAwaUmVdpaSnOzs74+vpy4cIF+vfvz2OPPUZAQACTJ09mwoQJPP/88xw7dowNGzbQvXt3goKCyMzMZPLkySQnJ5OVlcWKFSto0aIFw4YN44cffuCVV14hPT39horwbdu2kZ2dzerVqykuLiYoKIju3bszZswYfv75Z0JDQykuLrbEKSIiIiIiIjen1j2sbdKkSfj5+ZVpy87Oxt3d3bKtPCMjg2nTpgFw+fJlPDw8AGjdujXW1tbY29vTqlUrbGxscHBwoLCwsMLrXfv2NkdHR9zc3ACwtbXF29sbAHt7++vGWLNmDZcuXWL06NHAla3bJ06cAKBTp06WWFu3bs0vv/yCq6srsbGx2NracubMGTp37gxQZl6/16FDBwAaN26Ml5eX5evCwkLS09PZsWMHGzduBCAvLw8AJycnWrRoAUCLFi0qnXt5MjIy6NKlCwaDAWtrax555BEyMjJuagwRERERERGpWLUW4rfzynIrq//ezu7h4cGsWbNwc3Njz549mEwmAAwGww2NZWNjY+lz8OBBS/uN9i8uLubzzz8nNTUVR0dH4MrW+48++gh/f38OHTpESUkJRUVFHDt2jD/84Q+EhoaydetW7OzsCAsLs3wW187LYDCUude8sng8PT159tlnGTBgAP/5z38s94CX18fKyuqG72H38vLik08+ISQkhMuXL7Nv3z4GDhzIkSNHKoxTREREREREbtxdeVhbRby8vJg4ceJtjxMdHU1YWBhBQUHMnTuXtm3b3lT/IUOGsHz5ckJCQjhz5sxNX/+rr76iQ4cOliIcYNCgQaxfv56CggKKi4t56aWXeO655wgNDaVJkyb89a9/JTAwkGHDhnHhwgXOnj173bgdO3Zk5cqV7Nixo8oYXnnlFTZu3EhwcDAvvvgirVu3rvDcpk2bcvny5XLvc4+NjWXQoEEMGjSIt956i169euHu7s7QoUMZOnQof/7zny0r87cSp4iIiIiIiJRlKL2dZWq5znfffceqVauYN29eTYdS40ym32o6hHK5uNjX2tikaspf3acc1m3KX92m/NV9ymHdpvzVbbeSPxcX+3Lbq/0e8VOnThEWFnZde9euXRk3blx1hyMiIiIiIiJSrbQiLiIiIiIiIlKNqvUecREREREREZH7nQpxERERERERkWqkQlxERERERESkGqkQFxEREREREalGKsRFREREREREqpEKcREREREREZFqpEJc7klms5nIyEiGDh1KcHAwJ06cKHN89erVDBo0iMDAQLZt21ZDUUplqsrh8uXLGTJkCEOGDGHhwoU1FKVUpKr8XT3nxRdfJDk5uQYilMpUlb/t27cTGBhIYGAg0dHR6E2otU9VOVy6dCmDBg1i8ODBbNmypYailKrs37+f4ODg69q/+uorBg8ezNChQ1m9enUNRCY3oqL8bdiwgSFDhjBs2DAiIyMxm801EJ3ciIpyeFVERARz5sy5pbFViMs9aevWrRQVFZGSksJbb73FzJkzLcdMJhNGo5FVq1axdOlS3nnnHYqKimowWilPZTnMysrif//3f1m1ahUpKSn83//9H4cPH67BaOX3KsvfVe+++y65ubk1EJ1UpbL85efnEx8fT2JiIqtXr6Zly5bk5OTUYLRSnspymJeXZ/k9uGzZMuLi4mowUqnIkiVLmDp1KoWFhWXaL1++zNtvv82yZcswGo2kpKRgMplqKEqpSEX5Kygo4N133+XDDz9k1apV5Ofna1Golqooh1etWrWK9PT0Wx5fhbjck/bs2cNTTz0FwKOPPsqPP/5oOXbgwAE6deqEjY0N9vb2tGrVSkVcLVRZDps3b877779PvXr1sLKyori4mAYNGtRUqFKOyvIHsGnTJgwGA35+fjURnlShsvzt27ePNm3aMGvWLIKCgnB2dqZJkyY1FapUoLIcNmzYEDc3Ny5dusSlS5cwGAw1FaZUolWrVixYsOC69oyMDFq1aoWDgwM2NjY89thj7N69uwYilMpUlD8bGxtWrVpFw4YNAfQ3TC1WUQ7hyu/C/fv3M3To0FseX4W43JPy8/Oxs7OzfF+vXj2Ki4stx+zt7S3HGjVqRH5+frXHKJWrLIfW1tY0adKE0tJSZs2ahY+PDx4eHjUVqpSjsvylp6ezYcMGxo8fX1PhSRUqy19OTg7fffcdEydOZMmSJaxYsYLjx4/XVKhSgcpyCNCiRQv69evHwIEDGTFiRE2EKFX485//TP369a9r198xdUNF+bOyssLZ2RkAo9HIxYsX6dmzZ3WHJzegohyePXuWhQsXEhkZeVvjXz+yyD3Azs6OCxcuWL43m82WH6TfH7tw4UKZX2hSO1SWQ4DCwkL+8Y9/0KhRI6KiomoiRKlEZflbt24dZ86cYeTIkZw8eRJra2tatmyp1fFapLL8OTo68vDDD+Pi4gJAly5dOHTokP4ZVstUlsO0tDTOnj3Ll19+CcDo0aPp3Lkzvr6+NRKr3Bz9HVP3mc1m4uPjOX78OAsWLNCulDpm06ZN5OTkMGbMGEwmEwUFBXh6ejJo0KCbGkcr4nJP6ty5M2lpaQB8//33tGnTxnLM19eXPXv2UFhYyG+//UZGRkaZ41I7VJbD0tJSXn31Vdq2bcv06dOpV69eTYUpFagsf3//+99Zs2YNRqORgQMHEhISoiK8lqksfx07diQ9PZ1z585RXFzM/v378fb2rqlQpQKV5dDBwYEHHngAGxsbGjRogL29PXl5eTUVqtwkLy8vTpw4wfnz5ykqKmL37t106tSppsOSmxAZGUlhYSGLFi2ybFGXumPEiBF88sknGI1GxowZQ//+/W+6CAetiMs9qk+fPnzzzTcMGzaM0tJS4uLi+OCDD2jVqhW9e/cmODiYoKAgSktLmTBhgu7NqYUqy6HZbGbnzp0UFRXx9ddfA/Dmm2/qD5FapKqfQandqsrfW2+9xYsvvghAQECA/plZC1WVw2+//ZbAwECsrKzo3LmztsbWAZ9++ikXL15k6NChhIeHM3r0aEpLSxk8eDCurq41HZ5U4Wr+OnbsyMcff0yXLl0YOXIkcKWw69OnTw1HKFW59mfwTjCU6p0jIiIiIiIiItVGW9NFREREREREqpEKcREREREREZFqpEJcREREREREpBqpEBcRERERERGpRirERURERERERKqRCnERERGp1OTJk+nduzcbNmy4qX7//Oc/2b17912K6or58+fz5Zdf3tVrXKs65iQiIvc+vUdcREREKpWamsqBAwewsbG5qX67du2iW7dudymqK8aPH39Xx/+96piTiIjc+/QecREREanQK6+8wrZt22jXrh2jRo1ixYoVmM1mOnToQFRUFA0aNCApKYn169dz6dIlrK2tmTt3LgcOHGDatGk4OzuzcOFCYmJiGDt2LN26dSM7O5sRI0bw1VdfER4ezvnz5zlx4gSTJk3C2dmZt99+m4KCApycnJg2bRoPPvhghfGFh4fz+OOP8/jjj/Paa6/h6enJsWPH8PHxoVOnTqSmppKbm8u//vUvvLy88Pf3JyAggG+//RaAuLg4fHx8OH78OJGRkZw/fx5bW1umTJmCr69vmfjGjBnD9OnTLXPKzc1l3rx5FBQUkJeXx+TJk3nmmWcIDw/Hzs6On376iTNnzvDaa68xePBgzp8/z5QpU/j555+xsbEhPDycHj16kJaWxj//+U+Ki4txd3dnxowZODk5VVeKRUSkBmhruoiIiFQoMTERgDlz5rB69WpWrVrF+vXradq0KUuXLiU/P5+tW7diNBrZsGEDTz/9NCtXruR//ud/6NixIzExMbRt27bSazg6OrJx40aefPJJpk6dyty5c0lNTWXUqFFERETccKxHjhzhpZdeYv369ezdu5eTJ0+SkpJC//79SUlJsZxna2vLunXrGDduHGFhYQBMmjSJ4OBgPv30UyZPnsz48eMpKioqE9/AgQPLzCkpKYmYmBhSU1OJiYlh/vz5lmucPn2ajz76iISEBGbPng1c2UbfqlUrNm7cyOzZs3n33Xc5d+4cc+fOZenSpaxbt44nn3ySOXPm3PCcRUSkbtLWdBEREanSd999x4kTJwgMDATg8uXL+Pj4YGdnx9y5c/nss8/IzMzk66+/pn379jc1tq+vLwCZmZlkZWURGhpqOZafn3/D4zg7O+Pj4wNA8+bN6dGjBwBubm5kZ2dbzrs6B39/f8LDwzl9+jS//PILf/rTnwB49NFHcXBw4Oeffy4T3+/Fx8ezbds2Nm3axP79+7lw4YLlWM+ePTEYDLRp04bz588DV7a1Xy2y27ZtS0pKCtu2bePf//43I0aMAMBsNuPg4HDDcxYRkbpJhbiIiIhUqaSkhL/85S9MnToVgAsXLlBSUsK///1vgoODef755/Hz88PZ2ZlDhw6VO8bVu+GKi4vLtD/wwAPAlSLU3d2d9evXW67566+/3nCMv7+HvV69euWeV7/+f//8MZvNlJSUlBvr1far8f1eUFAQ3bp1o1u3bvTo0YOJEydajjVo0AAAg8FQ5rrXfp+RkUFJSQmdO3e27DwoLCwsU9CLiMi9SVvTRUREpErdunVjy5Yt/Oc//6G0tJTo6GhWrFjBDz/8wB/+8AdCQkJ4+OGH2bp1q6WArVevnuVrJycnjh07BsDWrVvLvYanpye5ubmWp5KvXbu2THF7p3z22WcAbNmyBS8vL1q2bIm7uzubN28G4Pvvv+fXX3+ldevW1/W9Oqfz58+TmZnJ+PHj8fPz48svvyy3oL9Wly5dLNfOyMjgpZdewtfXl++//57jx48DsGjRIstWdhERuXdpRVxERESq1K5dO8aOHcvIkSMxm820b9+eMWPGUFxcTHJyMn379qW0tJSuXbty9OhRAJ566imioqKYNWsWL774IuHh4axdu5bevXuXew0bGxvmz59PbGwshYWF2NnZMWvWrDs+l7179/Lxxx/TsGFDZs6cCVzZZh4dHc2CBQuwtrZmwYIF5T4l/to5/e1vf6Nfv37Ur1+f7t27U1BQwMWLFyu87rhx45g6dSrPPvss9evXZ/bs2TRr1oy4uDjeeOMNzGYzrq6uxMfH3/E5i4hI7aKnpouIiMh9w9/fnw8//BB3d/eaDkVERO5jWhEXERGRWm3WrFmW141dq2PHjsTGxtZARCIiIrdHK+IiIiIiIiIi1UgPaxMRERERERGpRirERURERERERKqRCnERERERERGRaqRCXERERERERKQaqRAXERERERERqUYqxEVERERERESq0f8D1o17Zd09hHkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualisations\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set(rc = {'figure.figsize':(15, 10)})\n",
    "\n",
    "# plotting a feature importance dataframe (horizontal barchart)\n",
    "\n",
    "def var_imp_plot(imp_df, title):\n",
    "    imp_df = imp_df.reset_index()\n",
    "    imp_df.columns = ['feature', 'feature_importance']\n",
    "    sns.barplot(x = 'feature_importance', y = 'feature', data = imp_df, orient = 'h', color = 'royalblue').set_title(title, fontsize = 20)\n",
    "\n",
    "var_imp_plot(perm_imp_rfpimp, 'Permutation feature importance (rfpimp)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <td>1.354200e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist_nearestMRT</th>\n",
       "      <td>1.690148e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>1.098183e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remaining_lease</th>\n",
       "      <td>1.043538e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_BUKIT MERAH</th>\n",
       "      <td>1.028499e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>storey_range</th>\n",
       "      <td>9.188504e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_QUEENSTOWN</th>\n",
       "      <td>7.048882e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_WOODLANDS</th>\n",
       "      <td>5.462071e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_BISHAN</th>\n",
       "      <td>4.592868e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_DBSS</th>\n",
       "      <td>3.313140e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_CHOA CHU KANG</th>\n",
       "      <td>2.996951e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_TOA PAYOH</th>\n",
       "      <td>2.762253e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_KALLANG/WHAMPOA</th>\n",
       "      <td>2.716650e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_CLEMENTI</th>\n",
       "      <td>2.269556e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_ANG MO KIO</th>\n",
       "      <td>2.243124e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_JURONG WEST</th>\n",
       "      <td>1.580207e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Improved</th>\n",
       "      <td>1.560032e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_GEYLANG</th>\n",
       "      <td>1.557824e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_MARINE PARADE</th>\n",
       "      <td>1.456659e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_BUKIT TIMAH</th>\n",
       "      <td>1.447945e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_SERANGOON</th>\n",
       "      <td>1.281602e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_SEMBAWANG</th>\n",
       "      <td>1.146587e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_BEDOK</th>\n",
       "      <td>1.128531e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_YISHUN</th>\n",
       "      <td>1.104795e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_CENTRAL AREA</th>\n",
       "      <td>1.080872e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_BUKIT PANJANG</th>\n",
       "      <td>1.001486e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Model A</th>\n",
       "      <td>9.160745e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_SENGKANG</th>\n",
       "      <td>6.824145e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_TAMPINES</th>\n",
       "      <td>5.467730e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_BUKIT BATOK</th>\n",
       "      <td>5.096388e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_PASIR RIS</th>\n",
       "      <td>3.729638e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_PUNGGOL</th>\n",
       "      <td>3.084722e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_New Generation</th>\n",
       "      <td>2.955759e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_HOUGANG</th>\n",
       "      <td>2.220557e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st_JURONG EAST</th>\n",
       "      <td>2.132685e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Premium Apartment</th>\n",
       "      <td>1.332766e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Standard</th>\n",
       "      <td>7.468069e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Apartment</th>\n",
       "      <td>5.592924e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Type S2</th>\n",
       "      <td>4.752321e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Premium Apartment.</th>\n",
       "      <td>3.934467e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Simplified</th>\n",
       "      <td>2.553801e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Maisonette</th>\n",
       "      <td>2.346420e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Adjoined flat</th>\n",
       "      <td>9.568537e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Model A2</th>\n",
       "      <td>7.236635e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Type S1</th>\n",
       "      <td>1.864465e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Model A-Maisonette</th>\n",
       "      <td>1.095557e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Multi Generation</th>\n",
       "      <td>7.172641e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Improved-Maisonette</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Terrace</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_2-room</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Premium Maisonette</th>\n",
       "      <td>-3.007802e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt_Premium Apartment Loft</th>\n",
       "      <td>-6.859500e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Importance\n",
       "Feature                                 \n",
       "floor_area_sqm              1.354200e+00\n",
       "dist_nearestMRT             1.690148e-01\n",
       "month                       1.098183e-01\n",
       "remaining_lease             1.043538e-01\n",
       "st_BUKIT MERAH              1.028499e-01\n",
       "storey_range                9.188504e-02\n",
       "st_QUEENSTOWN               7.048882e-02\n",
       "st_WOODLANDS                5.462071e-02\n",
       "st_BISHAN                   4.592868e-02\n",
       "flt_DBSS                    3.313140e-02\n",
       "st_CHOA CHU KANG            2.996951e-02\n",
       "st_TOA PAYOH                2.762253e-02\n",
       "st_KALLANG/WHAMPOA          2.716650e-02\n",
       "st_CLEMENTI                 2.269556e-02\n",
       "st_ANG MO KIO               2.243124e-02\n",
       "st_JURONG WEST              1.580207e-02\n",
       "flt_Improved                1.560032e-02\n",
       "st_GEYLANG                  1.557824e-02\n",
       "st_MARINE PARADE            1.456659e-02\n",
       "st_BUKIT TIMAH              1.447945e-02\n",
       "st_SERANGOON                1.281602e-02\n",
       "st_SEMBAWANG                1.146587e-02\n",
       "st_BEDOK                    1.128531e-02\n",
       "st_YISHUN                   1.104795e-02\n",
       "st_CENTRAL AREA             1.080872e-02\n",
       "st_BUKIT PANJANG            1.001486e-02\n",
       "flt_Model A                 9.160745e-03\n",
       "st_SENGKANG                 6.824145e-03\n",
       "st_TAMPINES                 5.467730e-03\n",
       "st_BUKIT BATOK              5.096388e-03\n",
       "st_PASIR RIS                3.729638e-03\n",
       "st_PUNGGOL                  3.084722e-03\n",
       "flt_New Generation          2.955759e-03\n",
       "st_HOUGANG                  2.220557e-03\n",
       "st_JURONG EAST              2.132685e-03\n",
       "flt_Premium Apartment       1.332766e-03\n",
       "flt_Standard                7.468069e-04\n",
       "flt_Apartment               5.592924e-04\n",
       "flt_Type S2                 4.752321e-04\n",
       "flt_Premium Apartment.      3.934467e-04\n",
       "flt_Simplified              2.553801e-04\n",
       "flt_Maisonette              2.346420e-04\n",
       "flt_Adjoined flat           9.568537e-05\n",
       "flt_Model A2                7.236635e-05\n",
       "flt_Type S1                 1.864465e-05\n",
       "flt_Model A-Maisonette      1.095557e-05\n",
       "flt_Multi Generation        7.172641e-06\n",
       "flt_Improved-Maisonette     0.000000e+00\n",
       "flt_Terrace                 0.000000e+00\n",
       "flt_2-room                  0.000000e+00\n",
       "flt_Premium Maisonette     -3.007802e-07\n",
       "flt_Premium Apartment Loft -6.859500e-07"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm_imp_rfpimp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>feature_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>floor_area_sqm</td>\n",
       "      <td>4.961243e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>storey_range</td>\n",
       "      <td>9.510602e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dist_nearestMRT</td>\n",
       "      <td>5.581614e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>remaining_lease</td>\n",
       "      <td>5.060912e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>st_BUKIT MERAH</td>\n",
       "      <td>4.249600e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>month</td>\n",
       "      <td>3.066681e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>st_QUEENSTOWN</td>\n",
       "      <td>2.696047e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>st_BISHAN</td>\n",
       "      <td>2.213527e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>flt_DBSS</td>\n",
       "      <td>2.040999e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>st_WOODLANDS</td>\n",
       "      <td>1.842688e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>st_CHOA CHU KANG</td>\n",
       "      <td>1.100161e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>st_CLEMENTI</td>\n",
       "      <td>1.097024e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>st_KALLANG/WHAMPOA</td>\n",
       "      <td>1.052886e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>st_TOA PAYOH</td>\n",
       "      <td>9.586378e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>st_CENTRAL AREA</td>\n",
       "      <td>8.637220e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>st_BUKIT PANJANG</td>\n",
       "      <td>7.408065e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>st_MARINE PARADE</td>\n",
       "      <td>7.365889e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>st_ANG MO KIO</td>\n",
       "      <td>6.617558e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>st_GEYLANG</td>\n",
       "      <td>6.248489e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>st_JURONG WEST</td>\n",
       "      <td>6.082222e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>flt_Improved</td>\n",
       "      <td>5.602462e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>flt_Model A</td>\n",
       "      <td>5.503911e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>st_BUKIT TIMAH</td>\n",
       "      <td>4.843845e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>st_SEMBAWANG</td>\n",
       "      <td>4.756273e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>st_YISHUN</td>\n",
       "      <td>4.644166e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>st_SERANGOON</td>\n",
       "      <td>4.608924e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>st_BEDOK</td>\n",
       "      <td>4.274195e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>st_TAMPINES</td>\n",
       "      <td>2.859824e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>st_BUKIT BATOK</td>\n",
       "      <td>2.823229e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>flt_New Generation</td>\n",
       "      <td>2.648776e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>st_SENGKANG</td>\n",
       "      <td>2.598071e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>flt_Terrace</td>\n",
       "      <td>2.326326e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>flt_Premium Apartment</td>\n",
       "      <td>1.456087e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>st_PUNGGOL</td>\n",
       "      <td>1.173870e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>st_PASIR RIS</td>\n",
       "      <td>1.109707e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>st_HOUGANG</td>\n",
       "      <td>1.085959e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>st_JURONG EAST</td>\n",
       "      <td>1.007157e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>flt_Standard</td>\n",
       "      <td>7.419973e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>flt_Type S2</td>\n",
       "      <td>6.734140e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>flt_Apartment</td>\n",
       "      <td>5.838715e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>flt_Adjoined flat</td>\n",
       "      <td>2.168723e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>flt_Type S1</td>\n",
       "      <td>2.058838e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>flt_Maisonette</td>\n",
       "      <td>2.051239e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>flt_Simplified</td>\n",
       "      <td>1.949944e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>flt_Premium Apartment.</td>\n",
       "      <td>1.921248e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>flt_Model A2</td>\n",
       "      <td>1.180390e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>flt_Multi Generation</td>\n",
       "      <td>1.022327e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>flt_Improved-Maisonette</td>\n",
       "      <td>8.739705e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>flt_Model A-Maisonette</td>\n",
       "      <td>7.857303e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>flt_Premium Maisonette</td>\n",
       "      <td>7.311041e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>flt_Premium Apartment Loft</td>\n",
       "      <td>6.084533e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>flt_2-room</td>\n",
       "      <td>5.264041e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       feature  feature_importance\n",
       "0               floor_area_sqm        4.961243e-01\n",
       "1                 storey_range        9.510602e-02\n",
       "2              dist_nearestMRT        5.581614e-02\n",
       "3              remaining_lease        5.060912e-02\n",
       "4               st_BUKIT MERAH        4.249600e-02\n",
       "5                        month        3.066681e-02\n",
       "6                st_QUEENSTOWN        2.696047e-02\n",
       "7                    st_BISHAN        2.213527e-02\n",
       "8                     flt_DBSS        2.040999e-02\n",
       "9                 st_WOODLANDS        1.842688e-02\n",
       "10            st_CHOA CHU KANG        1.100161e-02\n",
       "11                 st_CLEMENTI        1.097024e-02\n",
       "12          st_KALLANG/WHAMPOA        1.052886e-02\n",
       "13                st_TOA PAYOH        9.586378e-03\n",
       "14             st_CENTRAL AREA        8.637220e-03\n",
       "15            st_BUKIT PANJANG        7.408065e-03\n",
       "16            st_MARINE PARADE        7.365889e-03\n",
       "17               st_ANG MO KIO        6.617558e-03\n",
       "18                  st_GEYLANG        6.248489e-03\n",
       "19              st_JURONG WEST        6.082222e-03\n",
       "20                flt_Improved        5.602462e-03\n",
       "21                 flt_Model A        5.503911e-03\n",
       "22              st_BUKIT TIMAH        4.843845e-03\n",
       "23                st_SEMBAWANG        4.756273e-03\n",
       "24                   st_YISHUN        4.644166e-03\n",
       "25                st_SERANGOON        4.608924e-03\n",
       "26                    st_BEDOK        4.274195e-03\n",
       "27                 st_TAMPINES        2.859824e-03\n",
       "28              st_BUKIT BATOK        2.823229e-03\n",
       "29          flt_New Generation        2.648776e-03\n",
       "30                 st_SENGKANG        2.598071e-03\n",
       "31                 flt_Terrace        2.326326e-03\n",
       "32       flt_Premium Apartment        1.456087e-03\n",
       "33                  st_PUNGGOL        1.173870e-03\n",
       "34                st_PASIR RIS        1.109707e-03\n",
       "35                  st_HOUGANG        1.085959e-03\n",
       "36              st_JURONG EAST        1.007157e-03\n",
       "37                flt_Standard        7.419973e-04\n",
       "38                 flt_Type S2        6.734140e-04\n",
       "39               flt_Apartment        5.838715e-04\n",
       "40           flt_Adjoined flat        2.168723e-04\n",
       "41                 flt_Type S1        2.058838e-04\n",
       "42              flt_Maisonette        2.051239e-04\n",
       "43              flt_Simplified        1.949944e-04\n",
       "44      flt_Premium Apartment.        1.921248e-04\n",
       "45                flt_Model A2        1.180390e-04\n",
       "46        flt_Multi Generation        1.022327e-04\n",
       "47     flt_Improved-Maisonette        8.739705e-05\n",
       "48      flt_Model A-Maisonette        7.857303e-05\n",
       "49      flt_Premium Maisonette        7.311041e-05\n",
       "50  flt_Premium Apartment Loft        6.084533e-06\n",
       "51                  flt_2-room        5.264041e-09"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SKLEARN VAR IMPORTANCE\n",
    "\n",
    "# function for creating a feature importance dataframe\n",
    "def imp_df(column_names, importances):\n",
    "    df = pd.DataFrame({'feature': column_names,\n",
    "                       'feature_importance': importances}) \\\n",
    "           .sort_values('feature_importance', ascending = False) \\\n",
    "           .reset_index(drop = True)\n",
    "    return df\n",
    "\n",
    "base_imp = imp_df(x_train.columns, RFR.feature_importances_)\n",
    "base_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>floor_area_sqm</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>dist_nearestMRT</th>\n",
       "      <th>flt_2-room</th>\n",
       "      <th>flt_Adjoined flat</th>\n",
       "      <th>flt_Apartment</th>\n",
       "      <th>flt_DBSS</th>\n",
       "      <th>flt_Improved</th>\n",
       "      <th>...</th>\n",
       "      <th>st_PASIR RIS</th>\n",
       "      <th>st_PUNGGOL</th>\n",
       "      <th>st_QUEENSTOWN</th>\n",
       "      <th>st_SEMBAWANG</th>\n",
       "      <th>st_SENGKANG</th>\n",
       "      <th>st_SERANGOON</th>\n",
       "      <th>st_TAMPINES</th>\n",
       "      <th>st_TOA PAYOH</th>\n",
       "      <th>st_WOODLANDS</th>\n",
       "      <th>st_YISHUN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51472</th>\n",
       "      <td>-1.307113</td>\n",
       "      <td>-1.676358</td>\n",
       "      <td>-0.236838</td>\n",
       "      <td>0.639787</td>\n",
       "      <td>0.977207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103548</th>\n",
       "      <td>0.650587</td>\n",
       "      <td>-0.376551</td>\n",
       "      <td>-1.073830</td>\n",
       "      <td>-0.030180</td>\n",
       "      <td>0.643589</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63445</th>\n",
       "      <td>0.038806</td>\n",
       "      <td>-1.485210</td>\n",
       "      <td>-0.825250</td>\n",
       "      <td>-1.370113</td>\n",
       "      <td>0.860349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40489</th>\n",
       "      <td>2.730645</td>\n",
       "      <td>1.382011</td>\n",
       "      <td>0.965159</td>\n",
       "      <td>-0.365163</td>\n",
       "      <td>0.803612</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60647</th>\n",
       "      <td>-1.307113</td>\n",
       "      <td>-1.523440</td>\n",
       "      <td>-0.148733</td>\n",
       "      <td>0.974770</td>\n",
       "      <td>0.229922</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        floor_area_sqm     month  remaining_lease  storey_range  \\\n",
       "51472        -1.307113 -1.676358        -0.236838      0.639787   \n",
       "103548        0.650587 -0.376551        -1.073830     -0.030180   \n",
       "63445         0.038806 -1.485210        -0.825250     -1.370113   \n",
       "40489         2.730645  1.382011         0.965159     -0.365163   \n",
       "60647        -1.307113 -1.523440        -0.148733      0.974770   \n",
       "\n",
       "        dist_nearestMRT  flt_2-room  flt_Adjoined flat  flt_Apartment  \\\n",
       "51472          0.977207           0                  0              0   \n",
       "103548         0.643589           0                  0              0   \n",
       "63445          0.860349           0                  0              0   \n",
       "40489          0.803612           0                  0              0   \n",
       "60647          0.229922           0                  0              0   \n",
       "\n",
       "        flt_DBSS  flt_Improved    ...      st_PASIR RIS  st_PUNGGOL  \\\n",
       "51472          0             1    ...                 0           0   \n",
       "103548         0             0    ...                 0           0   \n",
       "63445          0             0    ...                 0           0   \n",
       "40489          0             0    ...                 0           0   \n",
       "60647          0             1    ...                 0           0   \n",
       "\n",
       "        st_QUEENSTOWN  st_SEMBAWANG  st_SENGKANG  st_SERANGOON  st_TAMPINES  \\\n",
       "51472               0             0            0             0            0   \n",
       "103548              0             0            1             0            0   \n",
       "63445               0             0            0             0            0   \n",
       "40489               0             0            0             0            0   \n",
       "60647               1             0            0             0            0   \n",
       "\n",
       "        st_TOA PAYOH  st_WOODLANDS  st_YISHUN  \n",
       "51472              0             0          0  \n",
       "103548             0             0          0  \n",
       "63445              0             0          1  \n",
       "40489              0             0          1  \n",
       "60647              0             0          0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>block</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>street_name</th>\n",
       "      <th>town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>406</td>\n",
       "      <td>Improved</td>\n",
       "      <td>2 ROOM</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1979</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>61 years 04 months</td>\n",
       "      <td>232000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>60 years 07 months</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 4</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>602</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 05 months</td>\n",
       "      <td>262000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>465</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 01 month</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>ANG MO KIO AVE 10</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>601</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>62 years 05 months</td>\n",
       "      <td>265000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>ANG MO KIO AVE 5</td>\n",
       "      <td>ANG MO KIO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  block      flat_model flat_type  floor_area_sqm  lease_commence_date  \\\n",
       "0   406        Improved    2 ROOM            44.0                 1979   \n",
       "1   108  New Generation    3 ROOM            67.0                 1978   \n",
       "2   602  New Generation    3 ROOM            67.0                 1980   \n",
       "3   465  New Generation    3 ROOM            68.0                 1980   \n",
       "4   601  New Generation    3 ROOM            67.0                 1980   \n",
       "\n",
       "     month     remaining_lease  resale_price storey_range        street_name  \\\n",
       "0  2017-01  61 years 04 months      232000.0     10 TO 12  ANG MO KIO AVE 10   \n",
       "1  2017-01  60 years 07 months      250000.0     01 TO 03   ANG MO KIO AVE 4   \n",
       "2  2017-01  62 years 05 months      262000.0     01 TO 03   ANG MO KIO AVE 5   \n",
       "3  2017-01   62 years 01 month      265000.0     04 TO 06  ANG MO KIO AVE 10   \n",
       "4  2017-01  62 years 05 months      265000.0     01 TO 03   ANG MO KIO AVE 5   \n",
       "\n",
       "         town  \n",
       "0  ANG MO KIO  \n",
       "1  ANG MO KIO  \n",
       "2  ANG MO KIO  \n",
       "3  ANG MO KIO  \n",
       "4  ANG MO KIO  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['floor_area_sqm', 'month', 'remaining_lease', 'storey_range',\n",
       "       'dist_nearestMRT', 'flt_2-room', 'flt_Adjoined flat',\n",
       "       'flt_Apartment', 'flt_DBSS', 'flt_Improved',\n",
       "       'flt_Improved-Maisonette', 'flt_Maisonette', 'flt_Model A',\n",
       "       'flt_Model A-Maisonette', 'flt_Model A2', 'flt_Multi Generation',\n",
       "       'flt_New Generation', 'flt_Premium Apartment',\n",
       "       'flt_Premium Apartment Loft', 'flt_Premium Apartment.',\n",
       "       'flt_Premium Maisonette', 'flt_Simplified', 'flt_Standard',\n",
       "       'flt_Terrace', 'flt_Type S1', 'flt_Type S2', 'st_ANG MO KIO',\n",
       "       'st_BEDOK', 'st_BISHAN', 'st_BUKIT BATOK', 'st_BUKIT MERAH',\n",
       "       'st_BUKIT PANJANG', 'st_BUKIT TIMAH', 'st_CENTRAL AREA',\n",
       "       'st_CHOA CHU KANG', 'st_CLEMENTI', 'st_GEYLANG', 'st_HOUGANG',\n",
       "       'st_JURONG EAST', 'st_JURONG WEST', 'st_KALLANG/WHAMPOA',\n",
       "       'st_MARINE PARADE', 'st_PASIR RIS', 'st_PUNGGOL', 'st_QUEENSTOWN',\n",
       "       'st_SEMBAWANG', 'st_SENGKANG', 'st_SERANGOON', 'st_TAMPINES',\n",
       "       'st_TOA PAYOH', 'st_WOODLANDS', 'st_YISHUN'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>floor_area_sqm</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>resale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>139788.000000</td>\n",
       "      <td>139788.000000</td>\n",
       "      <td>1.397880e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.048536</td>\n",
       "      <td>1991.237145</td>\n",
       "      <td>4.473022e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.518557</td>\n",
       "      <td>11.209949</td>\n",
       "      <td>1.385259e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>1.600000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>3.480000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>4.200000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>5.150000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>280.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.200000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       floor_area_sqm  lease_commence_date  resale_price\n",
       "count   139788.000000        139788.000000  1.397880e+05\n",
       "mean        97.048536          1991.237145  4.473022e+05\n",
       "std         24.518557            11.209949  1.385259e+05\n",
       "min         31.000000          1966.000000  1.600000e+05\n",
       "25%         74.000000          1984.000000  3.480000e+05\n",
       "50%         96.000000          1989.000000  4.200000e+05\n",
       "75%        112.000000          2000.000000  5.150000e+05\n",
       "max        280.000000          2016.000000  1.200000e+06"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    139788.000000\n",
       "mean          6.090094\n",
       "std           2.985223\n",
       "min           2.000000\n",
       "25%           5.000000\n",
       "50%           6.000000\n",
       "75%           8.000000\n",
       "max          27.000000\n",
       "Name: storey_range, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean, std of flat storeys for normalization\n",
    "flat_storey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    139788.000000\n",
       "mean         97.048536\n",
       "std          24.518557\n",
       "min          31.000000\n",
       "25%          74.000000\n",
       "50%          96.000000\n",
       "75%         112.000000\n",
       "max         280.000000\n",
       "Name: floor_area_sqm, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean, std of house sizes\n",
    "stats.floor_area_sqm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "serangoonpredict = pd.DataFrame(np.zeros((1,52), dtype=float, order='C'),columns=x_test.columns)\n",
    "#Serangoon 5 room flat, 1302 sqft to 121 sqm\n",
    "serangoonpredict['floor_area_sqm'] = 121\n",
    "#serangoonpredict['lease_commence_date'] = 1989\n",
    "serangoonpredict['remaining_lease'] = 99 - (2019-1989)\n",
    "serangoonpredict['storey_range'] = 5\n",
    "serangoonpredict['flt_Improved'] = 1\n",
    "serangoonpredict['st_SERANGOON'] = 1\n",
    "serangoonpredict['month'] = 2019\n",
    "serangoonpredict['dist_nearestMRT'] = 0.4\n",
    "\n",
    "#Normalize the month, floor area and remaining lease (timeframe)\n",
    "serangoonpredict.month = (serangoonpredict.month - stats.month[1]) / stats.month[2]\n",
    "serangoonpredict.floor_area_sqm = (serangoonpredict.floor_area_sqm - stats.floor_area_sqm[1]) / stats.floor_area_sqm[2]\n",
    "serangoonpredict.remaining_lease = (serangoonpredict.remaining_lease - stats.remaining_lease[1]) / stats.remaining_lease[2]\n",
    "serangoonpredict.storey_range = (serangoonpredict.storey_range - flat_storey[1]) / flat_storey[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>dist_nearestMRT</th>\n",
       "      <th>flt_2-room</th>\n",
       "      <th>flt_Adjoined flat</th>\n",
       "      <th>flt_Apartment</th>\n",
       "      <th>flt_DBSS</th>\n",
       "      <th>flt_Improved</th>\n",
       "      <th>...</th>\n",
       "      <th>st_PASIR RIS</th>\n",
       "      <th>st_PUNGGOL</th>\n",
       "      <th>st_QUEENSTOWN</th>\n",
       "      <th>st_SEMBAWANG</th>\n",
       "      <th>st_SENGKANG</th>\n",
       "      <th>st_SERANGOON</th>\n",
       "      <th>st_TAMPINES</th>\n",
       "      <th>st_TOA PAYOH</th>\n",
       "      <th>st_WOODLANDS</th>\n",
       "      <th>st_YISHUN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.976871</td>\n",
       "      <td>1.420241</td>\n",
       "      <td>0.999771</td>\n",
       "      <td>-0.365163</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   floor_area_sqm     month  remaining_lease  storey_range  dist_nearestMRT  \\\n",
       "0        0.976871  1.420241         0.999771     -0.365163              0.4   \n",
       "\n",
       "   flt_2-room  flt_Adjoined flat  flt_Apartment  flt_DBSS  flt_Improved  \\\n",
       "0         0.0                0.0            0.0       0.0             1   \n",
       "\n",
       "     ...      st_PASIR RIS  st_PUNGGOL  st_QUEENSTOWN  st_SEMBAWANG  \\\n",
       "0    ...               0.0         0.0            0.0           0.0   \n",
       "\n",
       "   st_SENGKANG  st_SERANGOON  st_TAMPINES  st_TOA PAYOH  st_WOODLANDS  \\\n",
       "0          0.0             1          0.0           0.0           0.0   \n",
       "\n",
       "   st_YISHUN  \n",
       "0        0.0  \n",
       "\n",
       "[1 rows x 52 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serangoonpredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([706760.])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFR.predict(serangoonpredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>block</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>street_name</th>\n",
       "      <th>town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3713</th>\n",
       "      <td>257</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-03</td>\n",
       "      <td>81 years 03 months</td>\n",
       "      <td>735000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5542</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-04</td>\n",
       "      <td>81 years 01 month</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7441</th>\n",
       "      <td>420</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>71 years 06 months</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7442</th>\n",
       "      <td>416</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>71 years</td>\n",
       "      <td>768000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7443</th>\n",
       "      <td>411</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>70 years 11 months</td>\n",
       "      <td>662500.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10998</th>\n",
       "      <td>256</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-07</td>\n",
       "      <td>80 years 10 months</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12885</th>\n",
       "      <td>208</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>67 years 03 months</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14644</th>\n",
       "      <td>422</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2017-09</td>\n",
       "      <td>70 years 07 months</td>\n",
       "      <td>740000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14645</th>\n",
       "      <td>257</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-09</td>\n",
       "      <td>80 years 08 months</td>\n",
       "      <td>760000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16383</th>\n",
       "      <td>202</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>67 years 02 months</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18270</th>\n",
       "      <td>419</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>70 years 06 months</td>\n",
       "      <td>695000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21157</th>\n",
       "      <td>202</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>2018-01</td>\n",
       "      <td>66 years 11 months</td>\n",
       "      <td>638000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24069</th>\n",
       "      <td>411</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2018-03</td>\n",
       "      <td>70 years 01 month</td>\n",
       "      <td>770000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25897</th>\n",
       "      <td>255</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2018-04</td>\n",
       "      <td>80 years 02 months</td>\n",
       "      <td>780000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25898</th>\n",
       "      <td>256</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2018-04</td>\n",
       "      <td>80 years 04 months</td>\n",
       "      <td>620000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29583</th>\n",
       "      <td>205</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1986</td>\n",
       "      <td>2018-06</td>\n",
       "      <td>67 years 05 months</td>\n",
       "      <td>715000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38100</th>\n",
       "      <td>411</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>69 years 06 months</td>\n",
       "      <td>633000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38101</th>\n",
       "      <td>255</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>79 years 07 months</td>\n",
       "      <td>788000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40008</th>\n",
       "      <td>202</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>66 years 01 month</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40009</th>\n",
       "      <td>255</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>79 years 06 months</td>\n",
       "      <td>748000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41570</th>\n",
       "      <td>205</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1986</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>67 years</td>\n",
       "      <td>715000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41571</th>\n",
       "      <td>256</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>79 years 06 months</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41572</th>\n",
       "      <td>256</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>79 years 06 months</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43075</th>\n",
       "      <td>256</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>79 years 04 months</td>\n",
       "      <td>640000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>256</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>79 years 04 months</td>\n",
       "      <td>770000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46016</th>\n",
       "      <td>208</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>65 years 09 months</td>\n",
       "      <td>710000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47861</th>\n",
       "      <td>419</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>69 years 01 month</td>\n",
       "      <td>680000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49925</th>\n",
       "      <td>427</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>68 years 08 months</td>\n",
       "      <td>630000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49926</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>79 years 04 months</td>\n",
       "      <td>685000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6234</th>\n",
       "      <td>416</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2012-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>01 TO 05</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36926</th>\n",
       "      <td>422</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2014-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36928</th>\n",
       "      <td>203</td>\n",
       "      <td>Adjoined flat</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1986</td>\n",
       "      <td>2014-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>763000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39232</th>\n",
       "      <td>427</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2014-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>640000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40776</th>\n",
       "      <td>257</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2014-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>710000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46154</th>\n",
       "      <td>205</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1986</td>\n",
       "      <td>2014-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49129</th>\n",
       "      <td>413</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2014-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>708000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49130</th>\n",
       "      <td>205</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1986</td>\n",
       "      <td>2014-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>730000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49131</th>\n",
       "      <td>421</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2014-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>738000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50531</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51880</th>\n",
       "      <td>257</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>710000.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3438</th>\n",
       "      <td>410</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>72</td>\n",
       "      <td>620000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6565</th>\n",
       "      <td>411</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2015-05</td>\n",
       "      <td>72</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8282</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2015-06</td>\n",
       "      <td>82</td>\n",
       "      <td>738000.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9829</th>\n",
       "      <td>255</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2015-07</td>\n",
       "      <td>82</td>\n",
       "      <td>730000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12778</th>\n",
       "      <td>421</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2015-09</td>\n",
       "      <td>72</td>\n",
       "      <td>730000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16060</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2015-11</td>\n",
       "      <td>82</td>\n",
       "      <td>738000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16061</th>\n",
       "      <td>257</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2015-11</td>\n",
       "      <td>82</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19955</th>\n",
       "      <td>257</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-02</td>\n",
       "      <td>82</td>\n",
       "      <td>800000.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21532</th>\n",
       "      <td>255</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-03</td>\n",
       "      <td>82</td>\n",
       "      <td>715000.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21533</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-03</td>\n",
       "      <td>82</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23295</th>\n",
       "      <td>257</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-04</td>\n",
       "      <td>82</td>\n",
       "      <td>745000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25157</th>\n",
       "      <td>414</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2016-05</td>\n",
       "      <td>71</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25160</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-05</td>\n",
       "      <td>82</td>\n",
       "      <td>725000.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26957</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-06</td>\n",
       "      <td>81</td>\n",
       "      <td>755000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30466</th>\n",
       "      <td>257</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-08</td>\n",
       "      <td>81</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33815</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>81</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33816</th>\n",
       "      <td>258</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>81</td>\n",
       "      <td>752500.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35393</th>\n",
       "      <td>416</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>71</td>\n",
       "      <td>688000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35395</th>\n",
       "      <td>256</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>81</td>\n",
       "      <td>755000.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>SERANGOON CTRL DR</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36843</th>\n",
       "      <td>413</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>71</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>SERANGOON CTRL</td>\n",
       "      <td>SERANGOON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      block     flat_model flat_type  floor_area_sqm  lease_commence_date  \\\n",
       "3713    257       Improved    5 ROOM           115.0                 1999   \n",
       "5542    258       Improved    5 ROOM           115.0                 1999   \n",
       "7441    420       Improved    5 ROOM           121.0                 1989   \n",
       "7442    416       Improved    5 ROOM           121.0                 1989   \n",
       "7443    411       Improved    5 ROOM           132.0                 1989   \n",
       "10998   256       Improved    5 ROOM           115.0                 1999   \n",
       "12885   208       Improved    5 ROOM           122.0                 1985   \n",
       "14644   422       Improved    5 ROOM           121.0                 1989   \n",
       "14645   257       Improved    5 ROOM           115.0                 1999   \n",
       "16383   202       Improved    5 ROOM           122.0                 1985   \n",
       "18270   419       Improved    5 ROOM           123.0                 1989   \n",
       "21157   202       Improved    5 ROOM           122.0                 1985   \n",
       "24069   411       Improved    5 ROOM           121.0                 1989   \n",
       "25897   255       Improved    5 ROOM           115.0                 1999   \n",
       "25898   256       Improved    5 ROOM           115.0                 1999   \n",
       "29583   205       Improved    5 ROOM           122.0                 1986   \n",
       "38100   411       Improved    5 ROOM           121.0                 1989   \n",
       "38101   255       Improved    5 ROOM           115.0                 1999   \n",
       "40008   202       Improved    5 ROOM           122.0                 1985   \n",
       "40009   255       Improved    5 ROOM           115.0                 1999   \n",
       "41570   205       Improved    5 ROOM           122.0                 1986   \n",
       "41571   256       Improved    5 ROOM           115.0                 1999   \n",
       "41572   256       Improved    5 ROOM           115.0                 1999   \n",
       "43075   256       Improved    5 ROOM           115.0                 1999   \n",
       "43076   256       Improved    5 ROOM           115.0                 1999   \n",
       "46016   208       Improved    5 ROOM           122.0                 1985   \n",
       "47861   419       Improved    5 ROOM           123.0                 1989   \n",
       "49925   427       Improved    5 ROOM           121.0                 1989   \n",
       "49926   258       Improved    5 ROOM           115.0                 1999   \n",
       "6234    416       Improved    5 ROOM           121.0                 1989   \n",
       "...     ...            ...       ...             ...                  ...   \n",
       "36926   422       Improved    5 ROOM           121.0                 1989   \n",
       "36928   203  Adjoined flat    5 ROOM           128.0                 1986   \n",
       "39232   427       Improved    5 ROOM           121.0                 1989   \n",
       "40776   257       Improved    5 ROOM           115.0                 1999   \n",
       "46154   205       Improved    5 ROOM           122.0                 1986   \n",
       "49129   413       Improved    5 ROOM           121.0                 1989   \n",
       "49130   205       Improved    5 ROOM           122.0                 1986   \n",
       "49131   421       Improved    5 ROOM           121.0                 1989   \n",
       "50531   258       Improved    5 ROOM           115.0                 1999   \n",
       "51880   257       Improved    5 ROOM           115.0                 1999   \n",
       "3438    410       Improved    5 ROOM           121.0                 1989   \n",
       "6565    411       Improved    5 ROOM           132.0                 1989   \n",
       "8282    258       Improved    5 ROOM           115.0                 1999   \n",
       "9829    255       Improved    5 ROOM           115.0                 1999   \n",
       "12778   421       Improved    5 ROOM           121.0                 1989   \n",
       "16060   258       Improved    5 ROOM           115.0                 1999   \n",
       "16061   257       Improved    5 ROOM           115.0                 1999   \n",
       "19955   257       Improved    5 ROOM           115.0                 1999   \n",
       "21532   255       Improved    5 ROOM           115.0                 1999   \n",
       "21533   258       Improved    5 ROOM           115.0                 1999   \n",
       "23295   257       Improved    5 ROOM           115.0                 1999   \n",
       "25157   414       Improved    5 ROOM           123.0                 1989   \n",
       "25160   258       Improved    5 ROOM           115.0                 1999   \n",
       "26957   258       Improved    5 ROOM           115.0                 1999   \n",
       "30466   257       Improved    5 ROOM           115.0                 1999   \n",
       "33815   258       Improved    5 ROOM           115.0                 1999   \n",
       "33816   258       Improved    5 ROOM           115.0                 1999   \n",
       "35393   416       Improved    5 ROOM           121.0                 1989   \n",
       "35395   256       Improved    5 ROOM           115.0                 1999   \n",
       "36843   413       Improved    5 ROOM           123.0                 1989   \n",
       "\n",
       "         month     remaining_lease  resale_price storey_range  \\\n",
       "3713   2017-03  81 years 03 months      735000.0     04 TO 06   \n",
       "5542   2017-04   81 years 01 month      750000.0     13 TO 15   \n",
       "7441   2017-05  71 years 06 months      750000.0     10 TO 12   \n",
       "7442   2017-05            71 years      768000.0     07 TO 09   \n",
       "7443   2017-05  70 years 11 months      662500.0     07 TO 09   \n",
       "10998  2017-07  80 years 10 months      750000.0     07 TO 09   \n",
       "12885  2017-08  67 years 03 months      670000.0     04 TO 06   \n",
       "14644  2017-09  70 years 07 months      740000.0     10 TO 12   \n",
       "14645  2017-09  80 years 08 months      760000.0     10 TO 12   \n",
       "16383  2017-10  67 years 02 months      700000.0     04 TO 06   \n",
       "18270  2017-11  70 years 06 months      695000.0     01 TO 03   \n",
       "21157  2018-01  66 years 11 months      638000.0     01 TO 03   \n",
       "24069  2018-03   70 years 01 month      770000.0     10 TO 12   \n",
       "25897  2018-04  80 years 02 months      780000.0     07 TO 09   \n",
       "25898  2018-04  80 years 04 months      620000.0     04 TO 06   \n",
       "29583  2018-06  67 years 05 months      715000.0     04 TO 06   \n",
       "38100  2018-10  69 years 06 months      633000.0     01 TO 03   \n",
       "38101  2018-10  79 years 07 months      788000.0     10 TO 12   \n",
       "40008  2018-11   66 years 01 month      675000.0     01 TO 03   \n",
       "40009  2018-11  79 years 06 months      748000.0     07 TO 09   \n",
       "41570  2018-12            67 years      715000.0     04 TO 06   \n",
       "41571  2018-12  79 years 06 months      700000.0     10 TO 12   \n",
       "41572  2018-12  79 years 06 months      750000.0     04 TO 06   \n",
       "43075  2019-01  79 years 04 months      640000.0     01 TO 03   \n",
       "43076  2019-01  79 years 04 months      770000.0     10 TO 12   \n",
       "46016  2019-03  65 years 09 months      710000.0     04 TO 06   \n",
       "47861  2019-04   69 years 01 month      680000.0     10 TO 12   \n",
       "49925  2019-05  68 years 08 months      630000.0     01 TO 03   \n",
       "49926  2019-05  79 years 04 months      685000.0     04 TO 06   \n",
       "6234   2012-05                 NaN      670000.0     01 TO 05   \n",
       "...        ...                 ...           ...          ...   \n",
       "36926  2014-01                 NaN      720000.0     01 TO 03   \n",
       "36928  2014-01                 NaN      763000.0     01 TO 03   \n",
       "39232  2014-03                 NaN      640000.0     01 TO 03   \n",
       "40776  2014-04                 NaN      710000.0     07 TO 09   \n",
       "46154  2014-08                 NaN      700000.0     07 TO 09   \n",
       "49129  2014-10                 NaN      708000.0     04 TO 06   \n",
       "49130  2014-10                 NaN      730000.0     04 TO 06   \n",
       "49131  2014-10                 NaN      738000.0     10 TO 12   \n",
       "50531  2014-11                 NaN      700000.0     10 TO 12   \n",
       "51880  2014-12                 NaN      710000.0     13 TO 15   \n",
       "3438   2015-03                  72      620000.0     01 TO 03   \n",
       "6565   2015-05                  72      670000.0     10 TO 12   \n",
       "8282   2015-06                  82      738000.0     13 TO 15   \n",
       "9829   2015-07                  82      730000.0     01 TO 03   \n",
       "12778  2015-09                  72      730000.0     07 TO 09   \n",
       "16060  2015-11                  82      738000.0     07 TO 09   \n",
       "16061  2015-11                  82      750000.0     10 TO 12   \n",
       "19955  2016-02                  82      800000.0     13 TO 15   \n",
       "21532  2016-03                  82      715000.0     13 TO 15   \n",
       "21533  2016-03                  82      750000.0     10 TO 12   \n",
       "23295  2016-04                  82      745000.0     10 TO 12   \n",
       "25157  2016-05                  71      660000.0     10 TO 12   \n",
       "25160  2016-05                  82      725000.0     13 TO 15   \n",
       "26957  2016-06                  81      755000.0     07 TO 09   \n",
       "30466  2016-08                  81      750000.0     07 TO 09   \n",
       "33815  2016-10                  81      750000.0     07 TO 09   \n",
       "33816  2016-10                  81      752500.0     07 TO 09   \n",
       "35393  2016-11                  71      688000.0     04 TO 06   \n",
       "35395  2016-11                  81      755000.0     13 TO 15   \n",
       "36843  2016-12                  71      675000.0     04 TO 06   \n",
       "\n",
       "             street_name       town  \n",
       "3713   SERANGOON CTRL DR  SERANGOON  \n",
       "5542   SERANGOON CTRL DR  SERANGOON  \n",
       "7441      SERANGOON CTRL  SERANGOON  \n",
       "7442      SERANGOON CTRL  SERANGOON  \n",
       "7443      SERANGOON CTRL  SERANGOON  \n",
       "10998  SERANGOON CTRL DR  SERANGOON  \n",
       "12885     SERANGOON CTRL  SERANGOON  \n",
       "14644     SERANGOON CTRL  SERANGOON  \n",
       "14645  SERANGOON CTRL DR  SERANGOON  \n",
       "16383     SERANGOON CTRL  SERANGOON  \n",
       "18270     SERANGOON CTRL  SERANGOON  \n",
       "21157     SERANGOON CTRL  SERANGOON  \n",
       "24069     SERANGOON CTRL  SERANGOON  \n",
       "25897  SERANGOON CTRL DR  SERANGOON  \n",
       "25898  SERANGOON CTRL DR  SERANGOON  \n",
       "29583     SERANGOON CTRL  SERANGOON  \n",
       "38100     SERANGOON CTRL  SERANGOON  \n",
       "38101  SERANGOON CTRL DR  SERANGOON  \n",
       "40008     SERANGOON CTRL  SERANGOON  \n",
       "40009  SERANGOON CTRL DR  SERANGOON  \n",
       "41570     SERANGOON CTRL  SERANGOON  \n",
       "41571  SERANGOON CTRL DR  SERANGOON  \n",
       "41572  SERANGOON CTRL DR  SERANGOON  \n",
       "43075  SERANGOON CTRL DR  SERANGOON  \n",
       "43076  SERANGOON CTRL DR  SERANGOON  \n",
       "46016     SERANGOON CTRL  SERANGOON  \n",
       "47861     SERANGOON CTRL  SERANGOON  \n",
       "49925     SERANGOON CTRL  SERANGOON  \n",
       "49926  SERANGOON CTRL DR  SERANGOON  \n",
       "6234      SERANGOON CTRL  SERANGOON  \n",
       "...                  ...        ...  \n",
       "36926     SERANGOON CTRL  SERANGOON  \n",
       "36928     SERANGOON CTRL  SERANGOON  \n",
       "39232     SERANGOON CTRL  SERANGOON  \n",
       "40776  SERANGOON CTRL DR  SERANGOON  \n",
       "46154     SERANGOON CTRL  SERANGOON  \n",
       "49129     SERANGOON CTRL  SERANGOON  \n",
       "49130     SERANGOON CTRL  SERANGOON  \n",
       "49131     SERANGOON CTRL  SERANGOON  \n",
       "50531  SERANGOON CTRL DR  SERANGOON  \n",
       "51880  SERANGOON CTRL DR  SERANGOON  \n",
       "3438      SERANGOON CTRL  SERANGOON  \n",
       "6565      SERANGOON CTRL  SERANGOON  \n",
       "8282   SERANGOON CTRL DR  SERANGOON  \n",
       "9829   SERANGOON CTRL DR  SERANGOON  \n",
       "12778     SERANGOON CTRL  SERANGOON  \n",
       "16060  SERANGOON CTRL DR  SERANGOON  \n",
       "16061  SERANGOON CTRL DR  SERANGOON  \n",
       "19955  SERANGOON CTRL DR  SERANGOON  \n",
       "21532  SERANGOON CTRL DR  SERANGOON  \n",
       "21533  SERANGOON CTRL DR  SERANGOON  \n",
       "23295  SERANGOON CTRL DR  SERANGOON  \n",
       "25157     SERANGOON CTRL  SERANGOON  \n",
       "25160  SERANGOON CTRL DR  SERANGOON  \n",
       "26957  SERANGOON CTRL DR  SERANGOON  \n",
       "30466  SERANGOON CTRL DR  SERANGOON  \n",
       "33815  SERANGOON CTRL DR  SERANGOON  \n",
       "33816  SERANGOON CTRL DR  SERANGOON  \n",
       "35393     SERANGOON CTRL  SERANGOON  \n",
       "35395  SERANGOON CTRL DR  SERANGOON  \n",
       "36843     SERANGOON CTRL  SERANGOON  \n",
       "\n",
       "[74 rows x 11 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat[((resaleflat['street_name'] == 'SERANGOON CTRL') | (resaleflat['street_name'] == 'SERANGOON CTRL DR') | (resaleflat['street_name'] == 'LOR LEW LIAN')) & (resaleflat['flat_type'] == '5 ROOM')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>block</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>street_name</th>\n",
       "      <th>town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>631</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>80 years 07 months</td>\n",
       "      <td>430000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>644</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>80 years 06 months</td>\n",
       "      <td>440000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>632</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-02</td>\n",
       "      <td>81 years 07 months</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4012</th>\n",
       "      <td>657</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-03</td>\n",
       "      <td>80 years</td>\n",
       "      <td>431000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5793</th>\n",
       "      <td>628A</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-04</td>\n",
       "      <td>80 years 06 months</td>\n",
       "      <td>395000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5800</th>\n",
       "      <td>628A</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-04</td>\n",
       "      <td>80 years 06 months</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5807</th>\n",
       "      <td>638</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-04</td>\n",
       "      <td>81 years 03 months</td>\n",
       "      <td>410000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5823</th>\n",
       "      <td>660</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-04</td>\n",
       "      <td>79 years 10 months</td>\n",
       "      <td>458000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7776</th>\n",
       "      <td>607</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>79 years 11 months</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7777</th>\n",
       "      <td>659</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>79 years 09 months</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7778</th>\n",
       "      <td>655</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>79 years 10 months</td>\n",
       "      <td>418000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9548</th>\n",
       "      <td>644</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>80 years 01 month</td>\n",
       "      <td>408000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11310</th>\n",
       "      <td>640</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-07</td>\n",
       "      <td>81 years</td>\n",
       "      <td>425000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13221</th>\n",
       "      <td>607</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>79 years 08 months</td>\n",
       "      <td>412500.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13222</th>\n",
       "      <td>659</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>79 years 06 months</td>\n",
       "      <td>405000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13223</th>\n",
       "      <td>632</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>81 years 01 month</td>\n",
       "      <td>385000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13224</th>\n",
       "      <td>657</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>79 years 07 months</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14916</th>\n",
       "      <td>653</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-09</td>\n",
       "      <td>79 years 06 months</td>\n",
       "      <td>438000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14917</th>\n",
       "      <td>663</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-09</td>\n",
       "      <td>79 years 05 months</td>\n",
       "      <td>399000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14918</th>\n",
       "      <td>634</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-09</td>\n",
       "      <td>80 years 11 months</td>\n",
       "      <td>428000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14919</th>\n",
       "      <td>629</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-09</td>\n",
       "      <td>79 years 11 months</td>\n",
       "      <td>426800.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16683</th>\n",
       "      <td>661</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>79 years 04 months</td>\n",
       "      <td>379000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16684</th>\n",
       "      <td>609</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>79 years 09 months</td>\n",
       "      <td>430000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16685</th>\n",
       "      <td>609</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>79 years 09 months</td>\n",
       "      <td>398000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16686</th>\n",
       "      <td>655</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>79 years 05 months</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16687</th>\n",
       "      <td>638</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>80 years 09 months</td>\n",
       "      <td>418000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18644</th>\n",
       "      <td>659</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>79 years 03 months</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18645</th>\n",
       "      <td>607</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>79 years 05 months</td>\n",
       "      <td>385000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18646</th>\n",
       "      <td>640</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>80 years 08 months</td>\n",
       "      <td>430000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22516</th>\n",
       "      <td>644</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2018-02</td>\n",
       "      <td>79 years 05 months</td>\n",
       "      <td>418000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21798</th>\n",
       "      <td>663</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-03</td>\n",
       "      <td>81</td>\n",
       "      <td>455000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23586</th>\n",
       "      <td>662</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-04</td>\n",
       "      <td>80</td>\n",
       "      <td>410000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23589</th>\n",
       "      <td>653</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-04</td>\n",
       "      <td>80</td>\n",
       "      <td>415000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23618</th>\n",
       "      <td>644</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-04</td>\n",
       "      <td>81</td>\n",
       "      <td>472000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25402</th>\n",
       "      <td>607</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-05</td>\n",
       "      <td>80</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25414</th>\n",
       "      <td>662</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-05</td>\n",
       "      <td>80</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25432</th>\n",
       "      <td>644</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-05</td>\n",
       "      <td>81</td>\n",
       "      <td>459000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25433</th>\n",
       "      <td>643</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-05</td>\n",
       "      <td>81</td>\n",
       "      <td>463000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25434</th>\n",
       "      <td>663</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-05</td>\n",
       "      <td>80</td>\n",
       "      <td>465000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27244</th>\n",
       "      <td>649</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-06</td>\n",
       "      <td>81</td>\n",
       "      <td>410000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27261</th>\n",
       "      <td>653</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-06</td>\n",
       "      <td>80</td>\n",
       "      <td>430000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28859</th>\n",
       "      <td>631</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-07</td>\n",
       "      <td>81</td>\n",
       "      <td>455000.0</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28860</th>\n",
       "      <td>643</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-07</td>\n",
       "      <td>81</td>\n",
       "      <td>455000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32381</th>\n",
       "      <td>638</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-09</td>\n",
       "      <td>81</td>\n",
       "      <td>370000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32387</th>\n",
       "      <td>608</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-09</td>\n",
       "      <td>80</td>\n",
       "      <td>397000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32388</th>\n",
       "      <td>630</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-09</td>\n",
       "      <td>81</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32399</th>\n",
       "      <td>630</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-09</td>\n",
       "      <td>81</td>\n",
       "      <td>428888.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32404</th>\n",
       "      <td>661</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-09</td>\n",
       "      <td>80</td>\n",
       "      <td>442888.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34035</th>\n",
       "      <td>628A</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>81</td>\n",
       "      <td>394000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34053</th>\n",
       "      <td>639</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>81</td>\n",
       "      <td>430000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34061</th>\n",
       "      <td>638</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>81</td>\n",
       "      <td>440000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34065</th>\n",
       "      <td>664</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>80</td>\n",
       "      <td>459000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35634</th>\n",
       "      <td>608</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>80</td>\n",
       "      <td>417000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35649</th>\n",
       "      <td>632</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>81</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37055</th>\n",
       "      <td>644</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>80</td>\n",
       "      <td>435000.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37056</th>\n",
       "      <td>638</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>81</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37057</th>\n",
       "      <td>663</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>80</td>\n",
       "      <td>410000.0</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37058</th>\n",
       "      <td>629</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>80</td>\n",
       "      <td>435000.0</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37059</th>\n",
       "      <td>662</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>80</td>\n",
       "      <td>448888.0</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37060</th>\n",
       "      <td>661</td>\n",
       "      <td>Improved</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>80</td>\n",
       "      <td>413000.0</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>WOODLANDS RING RD</td>\n",
       "      <td>WOODLANDS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      block flat_model flat_type  floor_area_sqm  lease_commence_date  \\\n",
       "1087    631   Improved    5 ROOM           122.0                 1998   \n",
       "1091    644   Improved    5 ROOM           121.0                 1998   \n",
       "2157    632   Improved    5 ROOM           121.0                 1999   \n",
       "4012    657   Improved    5 ROOM           122.0                 1998   \n",
       "5793   628A   Improved    5 ROOM           122.0                 1998   \n",
       "5800   628A   Improved    5 ROOM           122.0                 1998   \n",
       "5807    638   Improved    5 ROOM           120.0                 1999   \n",
       "5823    660   Improved    5 ROOM           122.0                 1998   \n",
       "7776    607   Improved    5 ROOM           122.0                 1998   \n",
       "7777    659   Improved    5 ROOM           121.0                 1998   \n",
       "7778    655   Improved    5 ROOM           121.0                 1998   \n",
       "9548    644   Improved    5 ROOM           121.0                 1998   \n",
       "11310   640   Improved    5 ROOM           121.0                 1999   \n",
       "13221   607   Improved    5 ROOM           122.0                 1998   \n",
       "13222   659   Improved    5 ROOM           122.0                 1998   \n",
       "13223   632   Improved    5 ROOM           121.0                 1999   \n",
       "13224   657   Improved    5 ROOM           122.0                 1998   \n",
       "14916   653   Improved    5 ROOM           122.0                 1998   \n",
       "14917   663   Improved    5 ROOM           122.0                 1998   \n",
       "14918   634   Improved    5 ROOM           121.0                 1999   \n",
       "14919   629   Improved    5 ROOM           122.0                 1998   \n",
       "16683   661   Improved    5 ROOM           122.0                 1998   \n",
       "16684   609   Improved    5 ROOM           122.0                 1998   \n",
       "16685   609   Improved    5 ROOM           122.0                 1998   \n",
       "16686   655   Improved    5 ROOM           121.0                 1998   \n",
       "16687   638   Improved    5 ROOM           121.0                 1999   \n",
       "18644   659   Improved    5 ROOM           123.0                 1998   \n",
       "18645   607   Improved    5 ROOM           120.0                 1998   \n",
       "18646   640   Improved    5 ROOM           120.0                 1999   \n",
       "22516   644   Improved    5 ROOM           121.0                 1998   \n",
       "...     ...        ...       ...             ...                  ...   \n",
       "21798   663   Improved    5 ROOM           122.0                 1998   \n",
       "23586   662   Improved    5 ROOM           123.0                 1998   \n",
       "23589   653   Improved    5 ROOM           122.0                 1998   \n",
       "23618   644   Improved    5 ROOM           121.0                 1998   \n",
       "25402   607   Improved    5 ROOM           121.0                 1998   \n",
       "25414   662   Improved    5 ROOM           123.0                 1998   \n",
       "25432   644   Improved    5 ROOM           121.0                 1998   \n",
       "25433   643   Improved    5 ROOM           121.0                 1998   \n",
       "25434   663   Improved    5 ROOM           122.0                 1998   \n",
       "27244   649   Improved    5 ROOM           121.0                 1998   \n",
       "27261   653   Improved    5 ROOM           122.0                 1998   \n",
       "28859   631   Improved    5 ROOM           122.0                 1998   \n",
       "28860   643   Improved    5 ROOM           121.0                 1998   \n",
       "32381   638   Improved    5 ROOM           121.0                 1999   \n",
       "32387   608   Improved    5 ROOM           121.0                 1998   \n",
       "32388   630   Improved    5 ROOM           124.0                 1998   \n",
       "32399   630   Improved    5 ROOM           122.0                 1998   \n",
       "32404   661   Improved    5 ROOM           122.0                 1998   \n",
       "34035  628A   Improved    5 ROOM           122.0                 1998   \n",
       "34053   639   Improved    5 ROOM           121.0                 1999   \n",
       "34061   638   Improved    5 ROOM           120.0                 1999   \n",
       "34065   664   Improved    5 ROOM           121.0                 1998   \n",
       "35634   608   Improved    5 ROOM           122.0                 1998   \n",
       "35649   632   Improved    5 ROOM           121.0                 1999   \n",
       "37055   644   Improved    5 ROOM           121.0                 1998   \n",
       "37056   638   Improved    5 ROOM           121.0                 1999   \n",
       "37057   663   Improved    5 ROOM           122.0                 1998   \n",
       "37058   629   Improved    5 ROOM           122.0                 1998   \n",
       "37059   662   Improved    5 ROOM           123.0                 1998   \n",
       "37060   661   Improved    5 ROOM           122.0                 1998   \n",
       "\n",
       "         month     remaining_lease  resale_price storey_range  \\\n",
       "1087   2017-01  80 years 07 months      430000.0     10 TO 12   \n",
       "1091   2017-01  80 years 06 months      440000.0     04 TO 06   \n",
       "2157   2017-02  81 years 07 months      420000.0     10 TO 12   \n",
       "4012   2017-03            80 years      431000.0     04 TO 06   \n",
       "5793   2017-04  80 years 06 months      395000.0     01 TO 03   \n",
       "5800   2017-04  80 years 06 months      400000.0     10 TO 12   \n",
       "5807   2017-04  81 years 03 months      410000.0     04 TO 06   \n",
       "5823   2017-04  79 years 10 months      458000.0     07 TO 09   \n",
       "7776   2017-05  79 years 11 months      400000.0     04 TO 06   \n",
       "7777   2017-05  79 years 09 months      420000.0     07 TO 09   \n",
       "7778   2017-05  79 years 10 months      418000.0     07 TO 09   \n",
       "9548   2017-06   80 years 01 month      408000.0     04 TO 06   \n",
       "11310  2017-07            81 years      425000.0     07 TO 09   \n",
       "13221  2017-08  79 years 08 months      412500.0     07 TO 09   \n",
       "13222  2017-08  79 years 06 months      405000.0     04 TO 06   \n",
       "13223  2017-08   81 years 01 month      385000.0     10 TO 12   \n",
       "13224  2017-08  79 years 07 months      400000.0     01 TO 03   \n",
       "14916  2017-09  79 years 06 months      438000.0     04 TO 06   \n",
       "14917  2017-09  79 years 05 months      399000.0     04 TO 06   \n",
       "14918  2017-09  80 years 11 months      428000.0     10 TO 12   \n",
       "14919  2017-09  79 years 11 months      426800.0     13 TO 15   \n",
       "16683  2017-10  79 years 04 months      379000.0     01 TO 03   \n",
       "16684  2017-10  79 years 09 months      430000.0     07 TO 09   \n",
       "16685  2017-10  79 years 09 months      398000.0     07 TO 09   \n",
       "16686  2017-10  79 years 05 months      390000.0     07 TO 09   \n",
       "16687  2017-10  80 years 09 months      418000.0     10 TO 12   \n",
       "18644  2017-11  79 years 03 months      390000.0     01 TO 03   \n",
       "18645  2017-11  79 years 05 months      385000.0     04 TO 06   \n",
       "18646  2017-11  80 years 08 months      430000.0     01 TO 03   \n",
       "22516  2018-02  79 years 05 months      418000.0     07 TO 09   \n",
       "...        ...                 ...           ...          ...   \n",
       "21798  2016-03                  81      455000.0     07 TO 09   \n",
       "23586  2016-04                  80      410000.0     07 TO 09   \n",
       "23589  2016-04                  80      415000.0     04 TO 06   \n",
       "23618  2016-04                  81      472000.0     10 TO 12   \n",
       "25402  2016-05                  80      390000.0     04 TO 06   \n",
       "25414  2016-05                  80      420000.0     04 TO 06   \n",
       "25432  2016-05                  81      459000.0     10 TO 12   \n",
       "25433  2016-05                  81      463000.0     10 TO 12   \n",
       "25434  2016-05                  80      465000.0     07 TO 09   \n",
       "27244  2016-06                  81      410000.0     01 TO 03   \n",
       "27261  2016-06                  80      430000.0     01 TO 03   \n",
       "28859  2016-07                  81      455000.0     13 TO 15   \n",
       "28860  2016-07                  81      455000.0     10 TO 12   \n",
       "32381  2016-09                  81      370000.0     04 TO 06   \n",
       "32387  2016-09                  80      397000.0     01 TO 03   \n",
       "32388  2016-09                  81      400000.0     10 TO 12   \n",
       "32399  2016-09                  81      428888.0     07 TO 09   \n",
       "32404  2016-09                  80      442888.0     07 TO 09   \n",
       "34035  2016-10                  81      394000.0     04 TO 06   \n",
       "34053  2016-10                  81      430000.0     04 TO 06   \n",
       "34061  2016-10                  81      440000.0     07 TO 09   \n",
       "34065  2016-10                  80      459000.0     10 TO 12   \n",
       "35634  2016-11                  80      417000.0     07 TO 09   \n",
       "35649  2016-11                  81      450000.0     07 TO 09   \n",
       "37055  2016-12                  80      435000.0     10 TO 12   \n",
       "37056  2016-12                  81      390000.0     01 TO 03   \n",
       "37057  2016-12                  80      410000.0     01 TO 03   \n",
       "37058  2016-12                  80      435000.0     07 TO 09   \n",
       "37059  2016-12                  80      448888.0     10 TO 12   \n",
       "37060  2016-12                  80      413000.0     04 TO 06   \n",
       "\n",
       "             street_name       town  \n",
       "1087   WOODLANDS RING RD  WOODLANDS  \n",
       "1091   WOODLANDS RING RD  WOODLANDS  \n",
       "2157   WOODLANDS RING RD  WOODLANDS  \n",
       "4012   WOODLANDS RING RD  WOODLANDS  \n",
       "5793   WOODLANDS RING RD  WOODLANDS  \n",
       "5800   WOODLANDS RING RD  WOODLANDS  \n",
       "5807   WOODLANDS RING RD  WOODLANDS  \n",
       "5823   WOODLANDS RING RD  WOODLANDS  \n",
       "7776   WOODLANDS RING RD  WOODLANDS  \n",
       "7777   WOODLANDS RING RD  WOODLANDS  \n",
       "7778   WOODLANDS RING RD  WOODLANDS  \n",
       "9548   WOODLANDS RING RD  WOODLANDS  \n",
       "11310  WOODLANDS RING RD  WOODLANDS  \n",
       "13221  WOODLANDS RING RD  WOODLANDS  \n",
       "13222  WOODLANDS RING RD  WOODLANDS  \n",
       "13223  WOODLANDS RING RD  WOODLANDS  \n",
       "13224  WOODLANDS RING RD  WOODLANDS  \n",
       "14916  WOODLANDS RING RD  WOODLANDS  \n",
       "14917  WOODLANDS RING RD  WOODLANDS  \n",
       "14918  WOODLANDS RING RD  WOODLANDS  \n",
       "14919  WOODLANDS RING RD  WOODLANDS  \n",
       "16683  WOODLANDS RING RD  WOODLANDS  \n",
       "16684  WOODLANDS RING RD  WOODLANDS  \n",
       "16685  WOODLANDS RING RD  WOODLANDS  \n",
       "16686  WOODLANDS RING RD  WOODLANDS  \n",
       "16687  WOODLANDS RING RD  WOODLANDS  \n",
       "18644  WOODLANDS RING RD  WOODLANDS  \n",
       "18645  WOODLANDS RING RD  WOODLANDS  \n",
       "18646  WOODLANDS RING RD  WOODLANDS  \n",
       "22516  WOODLANDS RING RD  WOODLANDS  \n",
       "...                  ...        ...  \n",
       "21798  WOODLANDS RING RD  WOODLANDS  \n",
       "23586  WOODLANDS RING RD  WOODLANDS  \n",
       "23589  WOODLANDS RING RD  WOODLANDS  \n",
       "23618  WOODLANDS RING RD  WOODLANDS  \n",
       "25402  WOODLANDS RING RD  WOODLANDS  \n",
       "25414  WOODLANDS RING RD  WOODLANDS  \n",
       "25432  WOODLANDS RING RD  WOODLANDS  \n",
       "25433  WOODLANDS RING RD  WOODLANDS  \n",
       "25434  WOODLANDS RING RD  WOODLANDS  \n",
       "27244  WOODLANDS RING RD  WOODLANDS  \n",
       "27261  WOODLANDS RING RD  WOODLANDS  \n",
       "28859  WOODLANDS RING RD  WOODLANDS  \n",
       "28860  WOODLANDS RING RD  WOODLANDS  \n",
       "32381  WOODLANDS RING RD  WOODLANDS  \n",
       "32387  WOODLANDS RING RD  WOODLANDS  \n",
       "32388  WOODLANDS RING RD  WOODLANDS  \n",
       "32399  WOODLANDS RING RD  WOODLANDS  \n",
       "32404  WOODLANDS RING RD  WOODLANDS  \n",
       "34035  WOODLANDS RING RD  WOODLANDS  \n",
       "34053  WOODLANDS RING RD  WOODLANDS  \n",
       "34061  WOODLANDS RING RD  WOODLANDS  \n",
       "34065  WOODLANDS RING RD  WOODLANDS  \n",
       "35634  WOODLANDS RING RD  WOODLANDS  \n",
       "35649  WOODLANDS RING RD  WOODLANDS  \n",
       "37055  WOODLANDS RING RD  WOODLANDS  \n",
       "37056  WOODLANDS RING RD  WOODLANDS  \n",
       "37057  WOODLANDS RING RD  WOODLANDS  \n",
       "37058  WOODLANDS RING RD  WOODLANDS  \n",
       "37059  WOODLANDS RING RD  WOODLANDS  \n",
       "37060  WOODLANDS RING RD  WOODLANDS  \n",
       "\n",
       "[225 rows x 11 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resaleflat[((resaleflat['street_name'] == 'WOODLANDS RING RD')) & (resaleflat['flat_type'] == '5 ROOM')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "woodlandspredict = pd.DataFrame(np.zeros((1,52), dtype=float, order='C'),columns=x_test.columns)\n",
    "#woodlands 5 room flat, 1302 sqft to 121 sqm\n",
    "woodlandspredict['floor_area_sqm'] = 122\n",
    "#woodlandspredict['lease_commence_date'] = 2007\n",
    "woodlandspredict['remaining_lease'] = 99 - (2019-1998)\n",
    "woodlandspredict['storey_range'] = 8\n",
    "woodlandspredict['flt_Improved'] = 1\n",
    "woodlandspredict['st_WOODLANDS'] = 1\n",
    "woodlandspredict['month'] = 2019\n",
    "woodlandspredict['dist_nearestMRT'] = 1\n",
    "\n",
    "#Normalize the month, floor area and remaining lease (timeframe)\n",
    "woodlandspredict.month = (woodlandspredict.month - stats.month[1]) / stats.month[2]\n",
    "woodlandspredict.floor_area_sqm = (woodlandspredict.floor_area_sqm - stats.floor_area_sqm[1]) / stats.floor_area_sqm[2]\n",
    "woodlandspredict.remaining_lease = (woodlandspredict.remaining_lease - stats.remaining_lease[1]) / stats.remaining_lease[2]\n",
    "woodlandspredict.storey_range = (woodlandspredict.storey_range - flat_storey[1]) / flat_storey[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([403586.32])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFR.predict(woodlandspredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}