AirBnB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. AirBnB price trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max.columns\", None)\n",
    "pd.set_option('min_rows', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_boston_calendar = pd.read_csv('boston\\calendar.csv', sep=',')\n",
    "df_seattle_calendar = pd.read_csv('seattle\\calendar.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available price\n",
       "0    12147973  2017-09-05         f   NaN\n",
       "1    12147973  2017-09-04         f   NaN\n",
       "2    12147973  2017-09-03         f   NaN\n",
       "3    12147973  2017-09-02         f   NaN\n",
       "4    12147973  2017-09-01         f   NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boston_calendar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Process the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>i</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">boston</td>\n",
       "      <td>0</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                id        date available price\n",
       "city   i                                      \n",
       "boston 0  12147973  2017-09-05         f   NaN\n",
       "       1  12147973  2017-09-04         f   NaN\n",
       "       2  12147973  2017-09-03         f   NaN\n",
       "       3  12147973  2017-09-02         f   NaN\n",
       "       4  12147973  2017-09-01         f   NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine calendars of Boston and Seattle\n",
    "df_calendar = pd.concat([df_boston_calendar, df_seattle_calendar], keys=['boston', 'seattle'], names=['city','i'], sort=False)\n",
    "df_calendar.rename(columns={'listing_id': 'id'}, inplace=True)\n",
    "df_calendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>t</td>\n",
       "      <td>$65.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>t</td>\n",
       "      <td>$65.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>367</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>t</td>\n",
       "      <td>$65.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>t</td>\n",
       "      <td>$75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>t</td>\n",
       "      <td>$75.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city       id        date available   price\n",
       "365  boston  3075044  2017-08-22         t  $65.00\n",
       "366  boston  3075044  2017-08-21         t  $65.00\n",
       "367  boston  3075044  2017-08-20         t  $65.00\n",
       "368  boston  3075044  2017-08-19         t  $75.00\n",
       "369  boston  3075044  2017-08-18         t  $75.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar.reset_index(inplace=True)\n",
    "\n",
    "# drop available='f'\n",
    "df_calendar.dropna(inplace=True)\n",
    "df_calendar.drop(axis=1, columns=['i'], inplace=True)\n",
    "df_calendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1577579, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert string price to numeric\n",
    "df_calendar.replace('[$,]', '', regex=True, inplace=True)\n",
    "df_calendar.price = df_calendar.price.astype(float)\n",
    "\n",
    "# convert string date to actual date\n",
    "df_calendar['date'] = pd.to_datetime(df_calendar['date'])\n",
    "\n",
    "# convert id(listing_id) to string\n",
    "df_calendar.id = df_calendar.id.astype('O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001B72EA39A48>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check price distribution\n",
    "df_calendar.hist(figsize=(15, 4), bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001B73ADA3088>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_calendar[(df_calendar.price < 1000)].hist(figsize=(15, 4), bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1564289, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop records with price >= $700 (most of the records have price within $700/day)\n",
    "df_calendar = df_calendar[df_calendar.price < 700]\n",
    "df_calendar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>month</th>\n",
       "      <th>WEEKDAY</th>\n",
       "      <th>weekday</th>\n",
       "      <th>YYYYMM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>t</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2</td>\n",
       "      <td>201708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>t</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>201708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>367</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>t</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>201708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>t</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>6</td>\n",
       "      <td>201708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>t</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Friday</td>\n",
       "      <td>5</td>\n",
       "      <td>201708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city       id       date available  price  YEAR   MONTH month  \\\n",
       "365  boston  3075044 2017-08-22         t   65.0  2017  August    08   \n",
       "366  boston  3075044 2017-08-21         t   65.0  2017  August    08   \n",
       "367  boston  3075044 2017-08-20         t   65.0  2017  August    08   \n",
       "368  boston  3075044 2017-08-19         t   75.0  2017  August    08   \n",
       "369  boston  3075044 2017-08-18         t   75.0  2017  August    08   \n",
       "\n",
       "      WEEKDAY weekday  YYYYMM  \n",
       "365   Tuesday       2  201708  \n",
       "366    Monday       1  201708  \n",
       "367    Sunday       0  201708  \n",
       "368  Saturday       6  201708  \n",
       "369    Friday       5  201708  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add some date variables for each plotting\n",
    "df_calendar['YEAR'] = df_calendar.date.apply(lambda x: x.strftime('%Y'))\n",
    "df_calendar['MONTH'] = df_calendar.date.apply(lambda x: x.strftime('%B'))\n",
    "df_calendar['month'] = df_calendar.date.apply(lambda x: x.strftime('%m'))\n",
    "df_calendar['WEEKDAY'] = df_calendar.date.apply(lambda x: x.strftime('%A'))\n",
    "df_calendar['weekday'] = df_calendar.date.apply(lambda x: x.strftime('%w'))\n",
    "df_calendar['YYYYMM'] = df_calendar['YEAR'] + df_calendar['month']\n",
    "df_calendar.head()"
   ]
  },
  {
   "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>city</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>month</th>\n",
       "      <th>WEEKDAY</th>\n",
       "      <th>weekday</th>\n",
       "      <th>YYYYMM</th>\n",
       "      <th>YYYY-MM</th>\n",
       "      <th>WeekDay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>t</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2</td>\n",
       "      <td>201708</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>2-Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>t</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>201708</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>1-Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>367</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>t</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>201708</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>0-Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>t</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>6</td>\n",
       "      <td>201708</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>6-Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>boston</td>\n",
       "      <td>3075044</td>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>t</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>08</td>\n",
       "      <td>Friday</td>\n",
       "      <td>5</td>\n",
       "      <td>201708</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>5-Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city       id       date available  price  YEAR   MONTH month  \\\n",
       "365  boston  3075044 2017-08-22         t   65.0  2017  August    08   \n",
       "366  boston  3075044 2017-08-21         t   65.0  2017  August    08   \n",
       "367  boston  3075044 2017-08-20         t   65.0  2017  August    08   \n",
       "368  boston  3075044 2017-08-19         t   75.0  2017  August    08   \n",
       "369  boston  3075044 2017-08-18         t   75.0  2017  August    08   \n",
       "\n",
       "      WEEKDAY weekday  YYYYMM  YYYY-MM     WeekDay  \n",
       "365   Tuesday       2  201708  2017-08   2-Tuesday  \n",
       "366    Monday       1  201708  2017-08    1-Monday  \n",
       "367    Sunday       0  201708  2017-08    0-Sunday  \n",
       "368  Saturday       6  201708  2017-08  6-Saturday  \n",
       "369    Friday       5  201708  2017-08    5-Friday  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar['YYYY-MM'] = df_calendar['YEAR'] + '-' + df_calendar['month']\n",
    "df_calendar['WeekDay'] = df_calendar['weekday'] + '-' + df_calendar['WEEKDAY']\n",
    "df_calendar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3a Calculate daily average rental price by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>boston</td>\n",
       "      <td>2016-09-06</td>\n",
       "      <td>235.348673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>boston</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>246.603896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>boston</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>243.106061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>boston</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>253.857438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>boston</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>256.309322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city       date       price\n",
       "0  boston 2016-09-06  235.348673\n",
       "1  boston 2016-09-07  246.603896\n",
       "2  boston 2016-09-08  243.106061\n",
       "3  boston 2016-09-09  253.857438\n",
       "4  boston 2016-09-10  256.309322"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar_mean_price_city_date = df_calendar.groupby(['city','date'])['price'].mean()\n",
    "df_calendar_mean_price_city_date = df_calendar_mean_price_city_date.reset_index()\n",
    "df_calendar_mean_price_city_date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3b Plot daily average rental price by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'daily average rental price')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "# https://cmdlinetips.com/2019/04/how-to-specify-colors-to-scatter-plots-in-python/\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.xticks(rotation=45)\n",
    "sns.lineplot(x=df_calendar_mean_price_city_date.date, \n",
    "             y=df_calendar_mean_price_city_date.price, \n",
    "             data=df_calendar_mean_price_city_date, hue='city')\n",
    "plt.title('daily average rental price @Airbnb in Boston and Seattle')\n",
    "plt.ylabel('daily average rental price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4a Calculate monthly average rental price by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>city</th>\n",
       "      <th>boston</th>\n",
       "      <th>seattle</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>01</td>\n",
       "      <td>175.495119</td>\n",
       "      <td>120.379709</td>\n",
       "      <td>55.115410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>02</td>\n",
       "      <td>172.960755</td>\n",
       "      <td>120.769052</td>\n",
       "      <td>52.191704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>03</td>\n",
       "      <td>172.315801</td>\n",
       "      <td>124.938345</td>\n",
       "      <td>47.377456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>04</td>\n",
       "      <td>181.030356</td>\n",
       "      <td>131.498440</td>\n",
       "      <td>49.531916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>05</td>\n",
       "      <td>180.740529</td>\n",
       "      <td>135.930507</td>\n",
       "      <td>44.810022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>06</td>\n",
       "      <td>183.987053</td>\n",
       "      <td>144.110780</td>\n",
       "      <td>39.876273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>07</td>\n",
       "      <td>184.421001</td>\n",
       "      <td>147.912624</td>\n",
       "      <td>36.508377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>08</td>\n",
       "      <td>181.883284</td>\n",
       "      <td>146.458517</td>\n",
       "      <td>35.424767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>09</td>\n",
       "      <td>226.238506</td>\n",
       "      <td>139.138093</td>\n",
       "      <td>87.100413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>225.908633</td>\n",
       "      <td>133.009015</td>\n",
       "      <td>92.899618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>195.774070</td>\n",
       "      <td>131.592466</td>\n",
       "      <td>64.181604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>184.668370</td>\n",
       "      <td>133.230860</td>\n",
       "      <td>51.437510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "city       boston     seattle       diff\n",
       "month                                   \n",
       "01     175.495119  120.379709  55.115410\n",
       "02     172.960755  120.769052  52.191704\n",
       "03     172.315801  124.938345  47.377456\n",
       "04     181.030356  131.498440  49.531916\n",
       "05     180.740529  135.930507  44.810022\n",
       "06     183.987053  144.110780  39.876273\n",
       "07     184.421001  147.912624  36.508377\n",
       "08     181.883284  146.458517  35.424767\n",
       "09     226.238506  139.138093  87.100413\n",
       "10     225.908633  133.009015  92.899618\n",
       "11     195.774070  131.592466  64.181604\n",
       "12     184.668370  133.230860  51.437510"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar_mean_price_city_month = df_calendar.groupby(['city','month'])['price'].mean()\n",
    "df_calendar_mean_price_city_month = df_calendar_mean_price_city_month.reset_index()\n",
    "df_calendar_mean_price_city_month_pivot = \\\n",
    "                            df_calendar_mean_price_city_month.pivot(index='month', columns='city', values='price')\n",
    "df_calendar_mean_price_city_month_pivot['diff'] = df_calendar_mean_price_city_month_pivot.boston - \\\n",
    "                                                  df_calendar_mean_price_city_month_pivot.seattle\n",
    "df_calendar_mean_price_city_month_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4b-1 Linechart plot monthly average rental price by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'monthly average price $')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j6datGwAHDhzAYrHQsGFDW883wNdff83ly5fRNO2O9p9f8+bN2bVrl+2CuR9//JFevXrd9qLB/Od1586dDB48mD59+uDv78/u3btt5+JWvvjiCyDvAq1du3bRunXr22Zdv349VqsVk8nE6NGj2bNnDy1btmTVqlWYTCasViuTJ09m7ty5N2xfsWJFjh49StWqVTl+/Djx8fEkJiYyceJEatasSVpaGi+88ALPPvssLi4ubNq0CV9fX3bs2MF3333Hd999x7fffktWVlahZl7YvHkzJpMJo9HI+vXrb9s+yBtnffHiRQA+++yzQm3zT1itVn766Sfq1q2Lp6cn9erVs822kp6ezhdffMGDDz7I999/z5AhQ2jQoAGjRo2iT58+thkorn4GbrV9Ye3Zs8dW3DZt2pTt27ff9jNUo0YNlFL8+OOPQN6sJTfrhTcYDERHR7No0SJyc3OBvML61KlTREZG4unpSf369fnwww+BvG9w/vWvf7F9+/Zb5tLr9bb95T8f+dWvX5+zZ8+yZ88eAI4ePUrnzp2Jj48v9LkRwpFIT7QQxVCLFi0YMmQIgwcPxmq14ufnx9KlS297AVX79u2ZO3fuNf/Y3Y6/vz+1a9emSpUqODk53TTL559/TpcuXdA0jaZNm+Ln58fZs2cJDw+ndu3a6PV6unTpYutxbNmyJU8//TRPPvkkmqbh6enJggULbtoz9+KLLzJy5Ejc3d3x9PSkSZMmnDt3Dl9fX+bOncv48ePR6XTUrl0bg8GAm5vbHe0/v6pVq9p66a72li5evBgPD49bbte6dWvefPNNAEaOHMns2bN59913cXJyomHDhrbhILcSExPDww8/TE5ODpMmTSI8PJzExMQC13/++eeZNWsWvXv3xmKx0K1bNx566CFat27NW2+9Rd++fbFYLERERDBhwoQbtu/evTuDBg2ib9++DBkyhF69elG5cmW6d+/OvHnz+Ne//sWzzz5L69atSU9P59NPP2Xo0KHX9A57e3szaNAgli9fzrhx427ZvtDQUAYMGEBmZiadOnWib9++tz0n1atXZ+LEiSQlJREeHm67iPFeWL58ORs3bkTTNLKzs6lVqxZTp04F4O2332b69OmsW7cOk8lEz549efjhh23Fdo8ePXB3d8fHx4cZM2YA0KlTJ8aOHcu0adMK3P7ChQuFytajRw+++eYbunbtitVqpV27dqSlpdmG1dyMk5MTCxcuZNq0acydO5eIiIgCL2Z99913mTNnDp07d8bNzQ2r1UqnTp1swyzefvttZsyYQc+ePTGZTPTo0YNevXqRlJRUYK6qVavi4uJCv379WLNmzTXn4yo/Pz/++9//Mnv2bIxGI0opZs+eTWhoaKHOixCORlOF+S5MCFFipaSk0K9fP1atWkW5cuXsHccmIyODRYsWMWrUKNzc3Dh8+DDDhw9nx44dty2Wi5v27dvz7rvvUqdOnft63G+//Za5c+cyfPhwOnTocM0MHklJSbaZLxYuXEhwcPB9zSaEEI5OeqKFKMWioqKYO3cuo0aNKlYFNICnpydOTk7069cPg8GAwWBg3rx5DldA21PHjh2JiIhg5cqVfPTRR+Tm5qLT6WzTAnbp0oVPP/30hnH6Qgghbk96ooUQQgghhLhDcmGhEEIIIYQQd0iKaCGEEEIIIe6QFNFCCCGEEELcIYe7sNBqtWKx2GcYt16v2e3YRa0ktw1KdvukbY6rJLdP2ua4SnL7pG2Oy17tc3LSF7jM4Ypoi0WRmppll2P7+rrb7dhFrSS3DUp2+6Rtjqskt0/a5rhKcvukbY7LXu0LDPQqcJkM5xBCCCGEEOIOSREthBBCCCHEHZIiWgghhBBCiDvkcGOihRBCCCHEvWOxmLl0KRGz2WTvKAWKj9coyvsDGgzOlCkTiF5f+NJYimghhBBCiFLs0qVEXF3d8fAIRtM0e8e5Kb1eh8ViLZJ9K6XIzLzMpUuJBASUK/R2MpxDCCGEEKIUM5tNeHh4F9sCuqhpmoaHh/cd98RLES2EEEIIUcqV1gL6qn/SfimihRBCCCFEsfHLL7vZsGEdABs2rMNsNts50c3JmGghhBBCCFFsPPDAg7bHK1Z8SJcu3e2YpmBSRAshhBACpRTfnUzCqtdjzDGh12noNQ29TkN35Wfec2zPDTcs09DpsD2++rpOp2HQrizLt+7VfYvSzWjM4fXXXyMuLg6z2Uzbtu3JyMigQoUKpKQkM23aRCpXDsffP5BHHunP5cuXGTNmBB98sNKuuaWIFkIIIQTbTyTxypdH7XLsvMIaW1Ft0F1fvHPt8+sKfIMOdPkL9psU/nqdRu+GoTQs62mXNoqCffHFWoKDy/Paa29w+vRf7N37GxkZGfTo0Yfly99n2rTXuXQpmcmTX+GRR/qzbdtWHnqoi71jSxEthBBClHZmi5XFu85QJcCdDwY34VJaFlYrWKwKs1JYrQrLlZ9m2+O85RalsFgV1is/zVaFVd1+mVVdeZ5vPYsV23EsBSy/uq+/X/t7/7n5Xrv+2OlGC9uOJ/LeY/WoXc7b3qdc5HPu3FnbEI7w8KocO3aU5OTka9YJCQnF3d2D6OjTbNu2lTffnGuPqNeQIloIIYQo5TYeiuPcpWze6VOL8r5uuFN0N7Wwl9TsXIZ+8gfjNx7h4yca4u/hbO9I4oqKFStz9OgRWrVqy4ULMSxdutA2DlrTdLabrPTq1YePPnqfwMAgfH197RkZkNk5hBBCiFItJ9fCsp/PUa+8N63C/ewdp8j4ujmx4F8NSMsx8+rmo5itJe8PBUfVu/fDXLx4geeff4aZM6fy2GMDbcvq1avPyy+PRilF69bt2Lv3V3r06G3HtH+TnmghhBCiFPt03wWSMk282TOixM8VXKu8NxM6VuW1rSdY8FM0Y9qG2zuSAFxcXJg2bdZNl02a9BqQN4+zxWIhOLg8TZo0u5/xCiQ90UIIIUQplZady8d7ztMq3I96IT72jnNf9KgVTL965Vj1ewzfHEuwdxxRSAcPHuCZZwYzZMgwdLriUb5KT7QQQghRSn3023kyjRZGtKps7yj31UvtqnA8IZOZ35ygSoAHVQI87B1J3EbduvX4+OPV9o5xjeJRygshhBDivopPNxL1x0W61SpL1VJWRDrpdbzVKwI3Jz3jNh4hw1g874gnijcpooUQQohSaNnus1iVYviDFe0dxS4CPV14o2cEF1KzmfrVcaxKLjQUd0aKaCGEEKKUiU7OYtPhOPrVK085b1d7x7GbhqG+vNC2Cj+dSmb5r+ftHUc4GCmihRBCiFJm0c5o3Jz0PNkszN5R7O7xBuXpXDOQJbvOsDs6xd5xhAORIloIIYQoRf68eJkf/krmicah+Lo72TuO3WmaxqsPVadKgAeTtxzjQlq2vSOVOlu2bGLx4vn/ePsNG9ZhNt//ce1FUkTn5uYyduxYBgwYQL9+/di+fTtHjx5lwIABDBo0iGHDhpGUlARAVFQUDz/8MP379+f7778vijhCCCGEAJRSLNgRjZ+7EwMahdo7TrHh5qRndq9IrEoxbsMRcnIt9o4k7sCKFR9isdz/96xIprjbuHEjvr6+zJkzh0uXLtG3b19CQ0OZPHkyERERfPbZZyxbtoynnnqKFStWsHbtWoxGIwMGDKBFixY4O8utOIUQQoh77eczl9gXk8bY9lVxd9bbO06xUqGMG9O71uSlLw7z5rcnmdqlRom/+Uxxcvjwn7zwwnNkZmby5JPP4ORk4L33FuPi4oK3tw+TJk3DaDQxdeorWK1WLBYzL788kaNHD5GSksy0aRN54413mD///zh48A8AOnXqQv/+/2LWrGk4OTkRFxdLcnISEydOo0aNmneduUiK6C5dutC5c2fbc71ez9y5cwkKCgLAYrHg4uLCwYMHadCgAc7Ozjg7OxMWFsaxY8eoW7duUcQSQgghSi3rlV7oEB9X+tYNtnecYqlVFX+ebh7Gsp/PUaucN4/WL2/vSPfd5sPxbDwUd0/32at2MN1rlb3lOq6ursyZ8y6pqZd45pkhACxa9D8CA4OIivqUDz/8Hw0aNMLDw5Np02YSHR1NZmYGPXr0Yfny95k27XV27dpBbOxF3ntvORaLheeeG0ajRk0ACA4ux7hxr7Jx43o2blzH2LET77pdRVJEe3jkzTeZkZHB6NGjGTNmjK2A3rdvHytXrmTVqlXs2LEDLy+va7bLyMi45b71eg1fX/eiiH1ber3ObscuaiW5bVCy2ydtc1wluX3StuJn44GLnEzM5J1+dQn09yxwPUdtX2EUpm0vd4ngRHI2c78/RaNwfxqGlblP6e7O3bxv8fEaen3eCF+dTuNed8DrdH/vv6Dl9eo1wGDQExAQgKtr3owxwcF5f+w1bNiIJUsW8PzzL3DhwnleeeVlDAYDQ4YMs+1Xr9dx7twZ6tfP24/BoKdOnbqcOxeNpmnUrBmBXq8jODiYQ4cO3jSPpt1ZjVlkdyyMjY1l5MiRDBgwgJ49ewKwZcsWFi9ezHvvvYefnx+enp5kZmbatsnMzLymqL4Zi0WRmppVVLFvydfX3W7HLmoluW1QstsnbXNcJbl90rbiJddiZe62E1QP9KBlmM8t8zti+wqrsG2b0qkq/45PZ+Qn+1kxqCEBHsV/mOndvG9KKSwWKwBdI4LoGhF0L6MB2PZ/M1ar4siRw1gsVpKTkzAajSiliI9PICAggH379hIWVpG9e/fg5+fP3LkLOHToIIsXL2D+/KVomg6z2UJYWCW2bNlI//4DMJvNHDx4gM6du6OUwmrNa6PVqq5p7/Xn4fpzGBhYcF1aJEV0UlISTz75JFOmTKF58+YAbNiwgdWrV7NixQp8fX0BqFu3LvPmzcNoNGIymTh16hTVq1cvikhCCCFEqbX+YCwX0nJ49+Ha6GSc7215uzoxp3ckQz/5g4mbjrDo0boYbtGTKu6e0Whk9Ohnyc7OYty4V1FK8eqrY9HpNLy8vJk8+TWsVsWUKROJivoUnU7H0KFPA1CvXn1efnk08+cvZf/+3xk+fCi5ubm0b9/xnox9Loim1L2/Rc/MmTP56quvCA8PB/LGQJ88eZLy5cvj7e0NQJMmTRg9ejRRUVGsXr0apRTDhw+/Ziz1zeTmWqQnugiU5LZByW6ftM1xleT2SduKjyyThb7v/0Zlf3cWP1r3thfLOVr77sSdtu2ro/FM2XKcxxuG8J92VYow2d27m/ctLu4swcHF+86Ver3ulr3Z98LNzsN974meNGkSkyZNKtS6/fv3p3///kURQwghhCj1Pvk9hpSsXN7pU1lmm7hDXSPKcjg2nc/2XaBWsBddimCYg3Bc8t2EEEIIUUJdyjKxcm8Mbav6U7uct73jOKQxbcKpH+LNzG9OcDLx1pMfiNJFimghhBCihPrw1/Nk51oY0bKyvaM4LINexxs9IvByMTBu4xHSc+7/nfFE8SRFtBBCCFECxV7O4fMDF+lZK5jK/iVzyrr7JcDThTd7RhB72ciUr45hvfeXkwkHJEW0EEIIUQIt3XUGnabx9IPF+4IxR1EvxIeX2lZh5+kU3v/5nL3jiGJAimghhBCihPkrKZMtRxLoX788Zb1c7B2nxHi0fjm6RQax7Oez7DydbO84ws6kiBZCCCFKmEU7ovFw0TO4aQV7RylRNE3jlY7VqBrowZQtx4lJzbZ3JAFs2LAOszlvrPratasB2LJlE4sXzy/S40oRLYQQQpQgf8SkseN0Cv9uUgEfNyd7xylxXJ30zO4ViabBuI1HyMm12DtSqbdixYdYLHnvw0cffXDfjltkt/0WQgghxP2llGLBjmgCPJz5V8MQe8cpsUJ93ZjRrSZj1h1i5jcnmNGtpszBfRfOnTvL66+/hsFgQK/XM2nSa6xdG8WBA/uwWhWPPTaQTp0eYv/+3/nww2UA5OTkMGnSaxw8uJ+UlGSmTZtIzZqRXL6cxttvv0lkZC3b/j///DO2bfsaTdPo0OEhHn308XuSW4poIYQQooTYcTqFAxcv80rHqrg66e0dp0R7sLIfw1tUZMmus9Qu583jJeSPFpdjn+N69LN7us+ciMcx1uxX4PI9e36lRo2ajBr1EgcO7OfHH78jNvYCixd/gNFoZPjwoTzwQHOio08zZXN7ibYAACAASURBVMoMAgIC+fjjD/j++28ZPHgYy5e/z7Rpr+Pi4sLatVG8/PIEtmzZBEB09Gm2b9/GokX/Q9M0xowZQbNmDxAWVumu2yVFtBBCCFECWKyKhTuiCSvjRq/awfaOUyoMbRbG4dh05v14mppBntQP9bF3JIfUo0dvVq36iP/8ZxQeHp5Uq1ad48eP8fzzzwBgNpuJi4slMDCQefPm4ObmTmJiAnXq1Lvtvk+fPkV8fBwvvPAcAOnp6cTExEgRLYQQQog8W48mcDo5izd6RGDQyyVP94NO03ita00Gr9rHhC+PsuKJBgR6OvZsKMaa/W7Za1wUdu78kXr1GvDkk8+wbdtW3ntvEY0bN2P8+FexWq0sX/4/ypcPYfToZ4mK2oC7uwczZ061ba9pOtSVubvVdXN4h4VVpFKlcN55579omsbq1asID696T3LLb5kQQgjh4ExmK0t3nyGirCcdqgfYO06p4uVqYHbvWmQazUzYdJRci9XekRxOzZqRvPfeIkaMeIoNG9YxY8ZbuLu7MWLEUwwb9gSapuHh4UHnzt145pkhPPfck2RlZZGUlAhAvXr1efnl0SilqFSpMtOnT7btu1q16jRu3IQRI4YxbNggzp8/T2Bg4D3JranrS/ZiLjfXQmpqll2O7evrbrdjF7WS3DYo2e2Ttjmuktw+adv99cnvMfzfD6dZ2K8OTSuWuat9Fcf23StF2bZvjiXw6uZj9K9fnrEd7k1P5524m7bFxZ0lOLh435RHr9dhKeI/UG52HgIDvQpcX3qihRBCCAeWYTTz4a/naRrme9cFtPjnHqoZxIBGIUT9cZEtR+LtHUfcB1JECyGEEA5s5d4YUrNzeb51ZXtHKfVGtQ6nYagPr287yfGEDHvHEUVMimghhBDCQSVnmvjk9xg6Vg8komzBXzuL+8Og03i9RwQ+rgbGbTxCWnauvSOJIiRFtBBCCOGgPvjlHCaL4rmWlewdRVzh7+HMmz0jSUg3MnnLMSxWx7j0zMEukbvn/kn7pYgWQgghHFBMajbrDsbSp04wYWXc7B1H5FOnvDcvt6/Cz2cusezns/aOc1sGgzOZmZdLbSGtlCIz8zIGg/MdbSfzRAshhBAOaMmuM+h1Gk89EGbvKOImHq5bjsOx6bz/yzkiynrRpqq/vSMVqEyZQC5dSiQjI9XeUQqkaVqRFvkGgzNlytzZ1HdSRAshhBAO5nhCBl8fS2RI0woEOPjNPUoqTdMY16EqJxMzmfrVMT5+omGx/cZArzcQEFDO3jFuqThOvSjDOYQQQggHs3BHNN6uBv7dpIK9o4hbcHXSM7t3JAadxriNh8kyWewdSdxDUkQLIYQQDuT386n8fOYSQ5pWwMtVvlAu7sp5uzKrewTRyVnM/OZEqR13XBJJES2EEEI4CKUU83+KJsjTmf4NQuwdRxRSs0pleLZFJbYdT+TTfRfsHUfcI1JECyGEEA7ih7+SORyXzvAHK+FikH/CHcmQphVoW9Wf//54mt/PF98L+EThyW+gEEII4QDMVsWindFU9nOnW62y9o4j7pCmaUztUoNQXzcmfnmU+HSjvSOJuyRFtBBCCOEANh+O40xKNs+1rIRBp9k7jvgHPF0MzO4dSU6ulVc2HcFktto7krgLUkQLIYQQxVxOroX3dp+lTjkv2hbj+YbF7YX7ezClS3X+jE1n7g+n7B1H3AUpooUQQohibs0fF0nIMDGyVWU0TXqhHV2H6oH8u0koaw/EsvFQnL3jiH9IimghhBCiGEvPMbP8t/M8WLkMjSr42juOuEeea1mZxmG+vPXtSY7Gp9s7jvgHpIgWQgghirGP95wnPcfMyJaV7R1F3EMGncbr3WtSxt2Z8RuPkJqda+9I4g5JES2EEEIUU4kZRj7dd4HOEUFUD/K0dxxxj5Vxd+atXpEkZZqYtPkoFqvciMWRSBEthBBCFFP/+/kcFqti+IMV7R1FFJFawV6Ma1+VX8+msmTXGXvHEXdAimghhBCiGDqbksWGP2N5uG45Qn3d7B1HFKE+dcvRp04wy387z/cnk+wdRxSSFNFCCCFEMbRk1xmcDTqefCDM3lHEfTC2fVUig714betxziRn2TuOKAQpooUQQohi5khcOt+eSGJgo1D8PZztHUfcB84GHW/1jMBJr2PcxiNkmsz2jiRuQ4poIYQQophZuCMaXzcnBjYOtXcUcR8Fe7vyeo+anL2UxYyvT6CUXGhYnEkRLYQQQhQjv569xG/nUnnygTA8XQz2jiPusyZhZXi+VWW2n0hi5d4Ye8cRtyBFtBBCCFFMWJVi4Y5oynm78EjdcvaOI+zkicahdKgewIId0ew5d8necUQBiuRP3NzcXCZOnMiFCxcwmUw899xzVK1alQkTJqBpGtWqVWPq1KnodDoWLFjADz/8gMFgYOLEidStW7coIgkhhBDF3vYTSRyNz2Balxo4G6Sfq7TSNI3JnatzOimLiV8eY8UTDQj2drV3LHGdIvkN3bhxI76+vnzyyScsW7aMGTNm8MYbbzBmzBg++eQTlFJs376dw4cP89tvv7FmzRrmzp3La6+9VhRxhBBCiGLPbLGyeGc0VQLc6RIRZO84ws48nA3M7h1JrsXK+E1HMZqt9o4krlMkRXSXLl144YUXbM/1ej2HDx+madOmALRu3Zrdu3fz+++/07JlSzRNo3z58lgsFlJSUooikhBCCFGsbTgUx/nUHEa2rIxep9k7jigGKvm5M7VLDY7EpfP2d3/ZO464TpEU0R4eHnh6epKRkcHo0aMZM2YMSik0TbMtT09PJyMjA09Pz2u2S09PL4pIQgghRLGVk2th2c/nqB/iTctwP3vHEcVIu2oBDGlagS/+jOOLg7H2jiPyKbLLfmNjYxk5ciQDBgygZ8+ezJkzx7YsMzMTb29vPD09yczMvOZ1Ly+vW+5Xr9fw9XUvqti3ObbObscuaiW5bVCy2ydtc1wluX3Stjuz5MdTJGeaWDSgAWXKeNzTfd8pee+KnwndIzmZnMXs7/6iQbg/9UJ9b1jHUdtWWMWxfUVSRCclJfHkk08yZcoUmjdvDkBkZCS//vorzZo146effuKBBx4gLCyMOXPmMGzYMOLi4rBarfj53fovcItFkZpqnzv5+Pq62+3YRa0ktw1KdvukbY6rJLdP2lZ4adm5LN1xmtZV/An3drH7eZP3rnia1rk6/165j5Gf7GfFEw0o437tTXgcuW2FYa/2BQYW3LlbJMM5lixZwuXLl1m0aBGDBg1i0KBBjBkzhvnz5/PYY4+Rm5tL586dqV27No0bN+axxx5j1KhRTJkypSjiCCGEEMXW8t/Ok2m08FzLSvaOIooxXzcnZveKJDU7l4mbj2G2yo1Y7E1TDnY7nNxci/REF4GS3DYo2e2Ttjmuktw+aVvhxF3O4ZEP9tCpZhDTutS4J/u8W/LeFW+bDsUx/esT/LtJKKNah9teLwltu5VS0xMthBBCiNtb9vNZFDD8wYr2jiIcRM/awTxSrxwf74lh+4lEe8cp1aSIFkIIIewgOjmLLw/H82j98pSTG2mIO/BS2yrUKefF9K0nOJ2cefsNRJGQIloIIYSwg0U7o3Fz0jO0aZi9owgH42zQ8WbPSFyddIzdcIQMo9nekUolKaKFEEKI++zPi5f54a9kBjUJxdfdyd5xhAMK8nLh9R4RXEjN5rWtx3GwS9xKBCmihRBCiPtIKcWCHdH4uTvxr4ah9o4jHFijCr6MbhPOD38ls+jH01JI32dSRAshhBD30e4zl9gXk8awByri7qy3dxzh4P7VMISHagQyb/tJBq/az3cnErHI9Hf3xS2LaIvFwvnz5wEwm82sWbOGdevWYTbL2BshhBDiTlmVYuGOaEJ8XOlbN9jecUQJoGkar3Wtwczetcgwmhm/6Sj9l+9lw5+xmMxWe8cr0W5ZRI8aNYpNmzYB8Nprr7Fv3z5OnjzJ+PHj70s4IYQQoiT55lgiJxMzea5FJZz08mWwuDcMeh2PNa7AmqFNeKNHBO5OemZ+c5I+7//Gyr0xZJqk87MoFPgbfPDgQaKjoylfvjzr16/n+++/p1mzZlSvXp2TJ0/yxRdf3M+cQgghhEPLtVhZsusMNYI86VQz0N5xRAmk12l0rBHIx080YEG/OlTyc+fdH0/T873fWLzrDJeyTPaOWKIUWESHhITg7OxM06ZNMZvNdOzYkaZNm9K4cWPc3Nxo2rTp/cwphBBCOLT1B2O5kJbDyFaV0GmaveOIEkzTNJpVLMOiR+uyfGADGof58uEv5+i57Ddmb/+Li2k59o5YIhgKWuDv70+bNm0YMmQIFouFFStW4OXlxcCBA+nduzfly5e/nzmFEEIIh5VlsvD+L+doVMGHByqWsXccUYrUCvZidq9IzqRksXJPDOsPxrLuwEU61QxicJMKVA30sHdEh6Wp28yHkpGRgYeHB9qVv5ovXrxo1wI6N9dit3vDl+T70pfktkHJbp+0zXGV5PZJ26617OezvLf7LB8OqE/tct5FlOzekPfOMRW2bQnpRj75/QLrDl4kO9dKy3A/BjepQP1Qn/uQ8p+z13sXGOhV4LLbXtXg6elpK6AB6YEWQggh7sClLBMr98TQrlpAsS+gRckX5OXCmLbhbHq6Gc+2qMih2HSeXn2Apz79gx2nkrHKXNOFVuBwDiGEEELcvQ9/PU+O2cKIFpXsHUUIGx83J4Y9UJGBjULZeCiOlXtjeOmLw1QJcOffTSrwUI1ADDKDzC0V+uxkZGRw/PhxsrJK5tcgQgghxL0WezmHzw9cpGftYCr5u9s7jhA3cHXS079BCOuebMJrXWsAMPWr4zz8wR6i9l8gJ9di54TFV6F6ordu3cqSJUuwWCx06dIFTdMYMWJEUWcTQgghHNrSXWfQaRpPN69o7yhC3JJBr6NbZFm6RgSxKzqF5b+eZ853p1j28zkea1CeR+uXx8fNyd4xi5VC9UQvX76cqKgofH19GTFiBN9++21R5xJCCCEc2l+JmWw5kkD/+uUp6+Vi7zhCFIqmabQM9+d//6rPssfqUbucF0t3n6Xnsl/5vx9OEZ9utHfEYqNQPdE6nQ5nZ2c0TUPTNNzc3Io6lxBCCOHQFu6MxtPFwOCmFewdRYh/pH6oD/VDffgrMZOP95xn9b4LRO2/SLfIIAY1rlDqhygVqohu3LgxL730EvHx8UyZMoU6deoUdS4hhBDCYf0Rk8bO0ymMbFlJvgIXDq9qoAfTu9Xk2RaVWLU3hg2H4th0KJ621QIY3LQCtYILngauJCtUEf3SSy/x008/ERkZSZUqVWjXrl1R5xJCCCEcklKK+TuiCfR05vGGIfaOI8Q9U97HlbEdqjKseRir919kzf6LfH8yicZhvgxpUoGmFX2vmRa5pCvUmOjvvvuO/fv389RTT7Fy5Up27txZ1LmEEEIIh7TjdAoHL17mqeYVcXXS2zuOEPecn7szz7WoxKZnmvJCm3DOpmTx/No/GbRyP9uOJ2Kxlo65pgtVRM+fP58nnngCgHnz5rFgwYIiDSWEEEI4IotVsXBHNGFl3OhVO9jecYQoUh7OBp5oHMoXw5oy6aFqZOdamPjlUR79cA/rDsZiNFvtHbFIFaqINhgM+Pv7A+Dl5YVOJ5NvCyGEENf76mg8p5OzeK5FJQy60vO1tijdnA06etcpR9SQxrzVMwJPFwNvbDtJ7//9xse/nSfDaLZ3xCJRqDHRdevW5T//+Q/169fn4MGDREZGFnUuIYQQwqEYzVaW7jpLRFlPOlQPsHccIe47vU6jffVA2lULYM+5VD7ec575O6L58Ldz9KtXnscbhuDv4WzvmPdMoYroSZMmsX37dk6fPk3Xrl1p3759UecSQgghHMraAxeJSzcyuXP1UnVxlRDX0zSNphXL0LRiGY7Gp/Pxb+f56LfzfPJ7DD1rB/NE41BCfR1/uuRbjsv4/vvvAYiKiiI5ORkfHx8SExNZvXr1fQknhBBCOIIMo5kPfjlHs4q+NK1Yxt5xhCg2Isp68UbPSNYMbUy3yLJsPBTHIx/sYdLmo5xIyLB3vLtyy57o1NRUABITE+9LGCGEEMIRrdwbQ1qOmedbVbZ3FCGKpYp+7rz6UHWeebAin/5+gbUHYvn6WCLNK5VhcNMKNAz1cbhvcG5ZRPft2xeA6Oho3nnnnfsSSAghhHAkyZkmPvk9hk41AqlZtnTedEKIwgr0dGF0m3CGNKvA2gOxfPr7BZ6NOkidcl4MblqBVlX80TlIMV2oaTZyc3M5duwYRqMRk8mEyWQq6lxCCCGEQ3j/l3OYLIrnWlSydxQhHIa3qxNDm4Wx8emmjOtQleRMEy9vOMLjy3/ny8Nx5FqK//R4hbqwMDo6mhEjRtiea5rG9u3biyyUEEII4QhiUrNZdzCWPnWCqVDG8S+UEuJ+c3XS82j98vStW45vjyfy0W/neW3rCZbsOsvAxqH0qROMWzG9aVGhiuhNmzZhsVhISUnB399f5okWQgghgCW7zuCk03jqgTB7RxHCoRl0Gl0iguhcM5Dd0Zf4aM955n5/ivd/PstjDUJ4sXMNe0e8QaGK6G3btvHGG2/g4+NDRkYG06ZNo0WLFkWdTQghxBVKKawKzFaF2WrFYlV5jy0Ki8r7eftlCovVanv892tX18+3bf7XbY+tBezzyrJ823i7O1PG1UBZT2cCPV0I9HShrFfe4zLuTg4z5vFWjsdn8PWxRIY2q0CAp4u94whRImiaRotwP1qE+3HgQhof74nhvZ/P0rFOOSp7Fa85pgtVRC9cuJA1a9bg7+9PUlISzz77rBTRQghRCEopLmXncv5SNhfScohJzSYmNYe4yzmYgRyT5cZCVoHZYr2hkL3fDDoNvU7DcOU/22O97oZlhnzLXA0amUYzf8Wnk5xpwqJu3G/gleI6yNOFIC9ngjxdCPR0vvI877GTvnh/67lwZzQ+rgb+3aSCvaMIUSLVC/HhnRAfjGYrZQM8SU3NsnekaxSqiPb19bXd9jsgIABPT88iDSWEEI7EbFXEp+cQk5rDhStFcsyVgvlCag5ZuRbbuhoQ5OVCeR9X/NycUK7qlsWqXtMw6LUbi9brlhlu2P66Qleff/ubLLPtS4dep6HXuKvppnx93UlNzcJiVaRkmUjIMJGQbiQxw3jN4xOJGew8bSTHfONFRH7uTld6sZ0p65WvyPZ0IfBK4e3pUqh/xu6538+n8vOZS7zQJtxuGYQoLVwMxfMP6kL95nt6ejJs2DCaNGnC4cOHycnJYe7cuQC89NJLRRpQCCGKg5xcCzFp+Yrk1Gzb84uXjVjy9RQ76zXK+7gS6utGw1AfQn3dCPV1JdTHjXI+rrZ/EK4WmiWZXqfZhnPUCr759G9KKTKMFuIzrhTZ6XmFdt5jE/HpRv68eJm0HPMN27o76fOKay8Xgq78zOvh/vux3z0ePqKUYv5P0QR5OvNo/fL3bL9CCMdSqCK6Q4cOtsdly5YtsjBCCGEvSinScsz5epLzFcupOSRlXju1p6eLngq+btQI8qJD9cC8ItnXjRAfV4K8XErEmN/7RdM0vFwNeLkaqBrgUeB6RrP1Sk+2kcR0EwkZ+YttI3vPp5GUYbxh+IhepxHo4XzNuOyrRXfgleEkgR4uOBeyt+ubI/Ecjktn8kPVi20PmRCi6BWqiL560xUhhHBkVqVISDfe0JN8tWjOMFquWT/Q05lQH1ceqFTG1pMc6utKiK8bPq4Gh7u7lqNzMeiu9OoXPJWcxaq4lG/4SEJGXrF9dRjJicRMdp5OuenwkTJuTvl6tfOP0b5agLvg6qRn7rcnqeznTrda0qkkRGkmA7mEECWK0WzlYtpNiuTUbC5eziE3XzelQZc37CLEx5U65b3zCuSrhbKPK67FdG5SUTC9TiPA04UATxcibzN8xFZc23q1jSReKb4PxaaTmp17w7YuBh1Gs5U5vSIx6OSPKCFKMymihRAOJz3HfM1wiwv5hl8kpBvJ/22+u5OeEF9XqgR40KaqPyG+boReGa9c1ssFvRRCpU7+4SNVCjF8JPHKkJH49LzH/t6utKnqfx8TCyGKo0IV0fHx8cyZM4dLly7RuXNnatSoQb169W673YEDB3j77bdZsWIFR48eZerUqej1eipVqsSsWbPQ6XRERUXx2WefYTAYeO6552jXrt1dN0qIki4tO5dtxxPZejSB5OxcNKXQaRo6Xd4sC3qdhk7jys+8mRZ0tsc3LtdpGnodtn0YNA3dlef6K6/pNP7e9srsDTrbvv7en167drnO9lq+7a87pm351XWvbOeUmsOxmNRrZry4kJp9wwVmfu5OhPq60aiCD6E+boRcGZ8c6utKGTcnGXYh/pGCho+UhgtChRC3V6gievLkyQwdOpRFixbRuHFjJkyYQFRU1C23WbZsGRs3bsTNLe9/PgsWLGDkyJG0adOG//znP/zwww/UqVOHFStWsHbtWoxGIwMGDKBFixY4OxevybSFKA5yLVZ2R6ew+UgCO08nk2tRVAlwp1FYGXKMuViseWN+rSpvbmGrAotSWK3K9jP3uuVWlTf/sNV65XXFDY8ttvWv7DPf4/tFr0Gwtyuhvq50rBFIiM/fRXKIjxvuzjLsQgghxP1VqCLaaDTSvHlzFi9eTHh4OC4ut78zU1hYGPPnz2fcuHEAREREkJqailKKzMxMDAYDBw8epEGDBjg7O+Ps7ExYWBjHjh2jbt26d9cqIUoIpRRH4zPYfDier48lkJZjxs/diX71ytM9sizVgzwoU8bDLr1i6oaiW2G1XincrxTt5nzFekGFff5i3ZpvHxarooyPG74GjWAvFwzF/MYbQgghSpdCFdHOzs7s2LEDq9XKH3/8Uaie4s6dOxMTE2N7XqlSJaZPn87ixYvx8vKiWbNmbN26FS+vvy/88PDwICMj45b71es1fH3dCxP7ntPrdXY7dlEryW0Dx2tfbFo2Gw/Esv6PC5xKzMTZoKNjzSD61C9Py6oB19zJzdHadif0eh0Wy42zKJQUJf29k7Y5ppLcPmmb4yqO7StUET1jxgzeeustLl26xAcffMC0adPu+ECzZs1i1apVVKtWjVWrVvHmm2/SsmVLMjMzbetkZmZeU1TfjMWi7DYWrSSPgyvJbQPHaF+WycL3J5PYfCSevedSUUD9EG8mdqpGx+qBeLnm/bpmpudcs50jtO2fKsltg5LdPmmb4yrJ7ZO2OS57tS8wsOC6tFBFtNVqZezYsX9vZDCQm5uLk5NToUP4+PjYbhceFBTEvn37qFu3LvPmzcNoNGIymTh16hTVq1cv9D6FcHQWq2Lv+VS2HInnuxNJ5JithPi48nTzinSNDLrlfLhCCCGEsJ9CFdHDhw8nPj6e8PBwoqOjcXNzw2w2M3bsWHr37l2oA82cOZMXX3wRg8GAk5MTM2bMIDAwkEGDBjFgwACUUrz44ouFGm8thKM7nZzJliMJfHUknoQME54uerpEBNE9siz1QrxlNgkhhBCimCtUER0aGspHH32En58faWlpTJo0iRkzZvD000/fsogODQ21zeLRuHFjPvvssxvW6d+/P/379/+H8YVwHJeyTHxzLJHNR+I5Gp+BXoPmlf0Y07YsrcL95MYeQgghhAMpVBGdnJyMn58fkDcsIykpCV9fX3Q6uVpeiFsxma3sPJ3M5iMJ7IpOwWJV1Ajy5MW24XSuGYS/h0znKIQQQjiiQhXRtWrV4qWXXqJ+/fr88ccfREREsGXLFvz95Y5NQlxPKcWh2HQ2H4ln2/FELueYCfBwZkDDELpFlqVqYMF3SBNCCCGEYyhUET116lS2b9/OqVOn6NWrF23btuX06dNyd0Eh8rmYlsNXR+PZciSBc5eycTHoaFvVn+61ytI0rIzcXloIIYQoQQpVRKemppKdnU1QUBCXLl1i6dKlDB8+vKizCVHsZRjNfHcib1q6fTFpADSq4MPgphVoXy0AT5dC/YoJIYQQwsEU6l/40aNHU6lSJU6cOIGLi4vtVt5ClEZmq+K3s5fYciSeH/5Kxmi2ElbGjedaVKJLRBDlfVztHVEIIYQQRazQ3WTTp0/nlVdeYdasWQwcOLAoMxVLM785wS9nLuHv4UyQpzNBni4EejoT5OVCkKfLlZ/OMsNCCfZXYiabj8Sz9WgCSZkmvF0N9KhVlh61ylIr2EumpRNCCCFKkUIX0UajkezsbDRNIyur5N4RpyCtwv0xOOm5kJzFuUvZ7D2fSobRcsN6Xi4GgrycCfR0+bvY9nKh7NWi29MFHzeDFFwOIjnTxNfHEth8OJ4TiZnodRotK/vRrVZZWlb2w9kgM9QIIYQQpVGhiuiBAwfy0Ucf0aJFC9q0aUOjRo2KOlex06aqP70bV7jmlpNZJgsJGUYSM4wkZpiIT8/7mZhhJD7dyF+JmSRnmlDX7ctZr/1dZHu55D2+0pN99fUAD2cMeinQ7CEn18JPp5LZciSBX86kYFEQGezF2PZVeKhGEL7uhb9TpxBCCCFKpkIV0UajkWeeeQaArl272m7fXdq5O+up5OdOJT/3AtcxW6wkZZpIzDCRkGEkIcNEYrrR9vhwXDoJ6UmYLNeW2hrgl2/oSF6xffXx1WLbBXdnGT5yLyilOHDhMpuPxPPtiUQyjBaCPJ0Z1KQC3SLLUtm/4PdYCCGEEKVPoYroqKgoevXqBSAF9B0y6HUEe7sS7F3wxWZKKdJyzCRc6clOyDBe8zgmLZv9F9K4nGO+YVtPF/1Nho78XWQHeTnj6+Ykw0cKEJOazZYj8Ww+ksDFtBzcnHS0rxZAt8iyNKrgK9PSCSGEEOKmClVEm0wm+vTpQ+XKlW13KXznnXeKNFhpomkavm5O+Lo5UT2o4PVyci15PdlXhoskXvc4OvkSSZkmrNeNH3HSawR65Bs6ck1vdt7rrh4uWJVCVwqK7fQcM9tOJLLlcDwHLl5GA5qE+TL8wYq0rRogvftCY4wL1wAAIABJREFUCCGEuK1CFdEvv/xyUecQheDqpCesjBthZQqeYtBsVaRkXimubUNHTLax28fi0/npVN60bDdj0Gk46TWc9Tqc9Dqc9VreT8N1z/W6v9cz5L1+wzb5ll19fvWx0zWv6XAyFLC9Xrsnvei5liu33z6cwE+n8obPVPZz5/lWlekSEURZL5e7PoYQQgghSo9CFdGRkZEsW7aMxMRE2rZtS40aNYo6l/iHDDot7yJFLxdqFbCOUop0o5mE9L+L6xylcTnDiMlixWSxkmtRV35aMZlV3s8rz41mKxlG89/rmW/c5vre8Ltx06L+ahF/i+Le2aDDoNMwWaz8dCqF5EwTvm5O9K1bju61ylIzyFOGuQghhBDiHylUET1x4kRat27Nnj17CAgI4NVXX2XlypVFnU0UEU3T8HZ1wtvViaqBHgD4+rpfM/PI3TJbrxTe5iuFeL4CO9fyd1Fusihyb1KEmyx/b2+yXFvE5192dZsskwWTxZyv8P97GUDLqgF0qhbAg5XL4CSzngghhBDiLhX6tt/9+vVj48aNNGzYEKXuYTejKJEMOg2DTo9bMbn5zL3+I0EI8f/t3Xt0VeWd//H3PtfcLySAQBIIJAiRpgoiIBW8VJkyFUUrIi3+Wi+jdopr0FYdR5SxgrVDsbNkLUcZWzqRKfKrOmDrr1IvBUXBCxam3AzhEoKFYkgg13PZ5/n9cQ4nQAMmgcPOCZ/XWlk5l80+329OgE+ePM+zRUTObR0ekquqqgJg//798cWFIiIiIiLnog6l4UceeYSHH36YLVu2cO+99/LQQw8lui4RERERkW6rQ9M5qqur+fWvf60RaBEREREROjgS/f7773Pdddfx9NNPs3fv3kTXJCIiIiLSrXVoJPrRRx8lGAzy1ltv8fjjjxMKhViyZEmCSxMRERER6Z46PD9j06ZNvPfee9TW1jJu3LhE1iQiIiIi0q11aCR68uTJDBs2jJtuuol58+YluiYRERERkW6tQyF66dKl5Obmxu+HQiG8Xm/CihIRERER6c46FKLfeOMNfvnLXxIOhzHG4PF4WLVqVaJrExERERHpljo0J3r58uVUVFQwYcIEnnzySUpKShJdl4iIiIhIt9WhEJ2bm0ufPn1oampizJgxHD58ONF1iYiIiIh0Wx0K0ZmZmbz55ptYlsWyZcs4dOhQousSEREREem2OhSin3jiCfr378/999/P7t27mTt3boLLEhERERHpvjq0sDAjI4OysjIAHnrooYQWJCIiIiLS3XX4YisiIiIiIhLVoRAdCoUSXYeIiIiISNLoUIi+4YYbmDdvHp999lmi6xERERER6fY6NCd6xYoVvPvuuyxatIi6ujqmTJnC5MmTSU9PT3R9IiIiIiLdTodGol0uFxMmTODGG28kJyeHiooKbr/9dl566aVE1yciIiIi0u10aCT6pz/9KW+99RaXXHIJd955J+Xl5UQiEW644QZuvvnmRNcoIiIiItKtdChEDxo0iFdeeeW46Rsul4tFixYlrDARERERke7qlCH6Zz/7GZZlAfDcc88d99x9991HQUFB4ioTEREREemmThmiBw8e3O7jR4O1iIiIiMi56JQheurUqQA0NDSwdu1aWltbz0pRIiIiIiLdWYd25/jHf/xHVq9eTVVVFVVVVezcubNDJ9+4cSMzZ84EoLa2lnvuuYdvf/vbTJ8+nerqagCWL1/ODTfcwLRp03jnnXe62IaIiIiIyNnToYWFxhiefPLJTp148eLFrFy5ktTUVAD+7d/+jWuvvZbJkyezbt06du7cSWpqKhUVFbz88ssEAgFmzJjB+PHj8fl8ne9EREREROQsOeVIdDAYJBgMUlhYyKeffhq/HwwGv/TERUVFPPPMM/H7GzZs4MCBA3z3u9/ltdde45JLLmHTpk1cdNFF+Hw+MjMzKSoqYtu2bafflYiIiIhIAp1yJPrv/u7vsCwLYwzr1q2LP25ZFm+99dYpTzxp0iRqamri9/ft20dWVhZLlixh0aJFLF68mEGDBpGZmRk/Jj09ncbGxlOe1+22yMlJO+UxieJ2uxx77UTryb1Bz+5PvSWvntyfektePbk/9Za8umN/pwzRb7/9NgCbNm2ivLw8/vj69es7/UI5OTlceeWVAFx55ZU8/fTTjBgxgqampvgxTU1Nx4Xq9ti2ob6+udOvfybk5KQ59tqJ1pN7g57dn3pLXj25P/WWvHpyf+oteTnVX+/eJ8+lpwzRH3/8MVVVVfzyl7/ke9/7HgCRSISlS5fy29/+tlNFjBo1itWrV3P99dfz0UcfUVJSQnl5OT//+c8JBAIEg0GqqqoYOnRop84rIiIiInK2nTJEZ2VlcfDgQYLBIAcPHgSiUzl+9KMfdfqFHnzwQR555BGWLVtGRkYGP/vZz8jOzmbmzJnMmDEDYwyzZ8/G7/d3rRMRERERkbPEMsaYLzvowIED9O3b92zU86VCIVvTORKgJ/cGPbs/9Za8enJ/6i159eT+1FvySrrpHEd98MEHPPfccwSDQYwxHVpYKCIiIiLSU3UoRC9evJj/+I//oF+/fomuR0RERESk2+tQiC4sLGTgwIGJrkVEREREJCl0KESnpKRwxx13MHz4cCzLAuC+++5LaGEiIiIiIt1Vh0L0xIkTE12HiIiIiEjSOOVlv4+69tpraW5uZtOmTRw5coS///u/T3RdIiIiIiLdVodC9KOPPsrevXsZP348+/bt45FHHkl0XSIiIiIi3VaHpnPs2bOHpUuXAvD1r3+d6dOnJ7QoEREREZHurEMj0YFAgJaWFgBaW1uxbTuhRYmIiIiIdGcdGom+9dZbue666ygtLWXHjh3MmjUr0XWJiIiIiHRbHQrRU6ZMYcKECezdu5eCggJyc3MTXZeIiIiISLfVoRD99ttv88orrxAIBOKPLV68OGFFiYiIiIh0Zx0K0U899RSPP/442dnZia5HRERERKTb61CILi0tZcyYMYmuRUREREQkKXQoRF911VXcfPPNDB48OP7Yk08+mbCiRERERES6sw6F6IqKCu644w4yMzMTXY+IiIiISLfXoRCdn5/P5MmTE12LiIiIiEhS6FCITklJ4fbbb6esrAzLsgC47777ElqYiIiIiEh31aEQfcUVVyS6DhERERGRpNGhED116tRE1yEiIiIikjRcThcgIiIiIpJsFKJFRERERDqpQ9M5RERETinUjKduB+66Stx1Vbi8hjSTSsSfhfFlYfzZRHyZGH9W9MOXhfGmQ2yxuohIslGIFhGRDrNa63AfqsRTV4m7bkf8s7uhJn6Msdzg9pIebj3luYzlxvgy2w3YbeE7i0js898858sElzvRLYuItEshWkREjmcMrsa/4K5rC8vR2ztwtdS2HeZJIZwzhNB5o2gdPp1wbgl2bil2TjE5eTnUf3EIK9iAK9iAFTiMFTgSvX/s7WDsduAIrmADrvpdWMEjWIEGXKHGLy014s2IhetYGI+H70wivuz4cxF/9vHHxY7B7U/kV1JEejCFaBGRc1UkjPvwntgUjGNGlet24Ao1tR3mz8bOLSUw6OpoSM4tIdyrlEhmAVinWFrjScF4UrDTene5PivYEAveR6JB/GjADh69f+xzDbga9+MKfha/b5nIKV/CuP0nBOysk4dvXxYRfzZYxUBe13oSkR5DIVpEpKcLt+Cu2xkLyZWxucs7cNfvwooE44fZ6X2jYXnYTYRzS7F7lRLOLcWk5jszd9nlwaTkYlJyOXUUPgljsEJNsVHvo6PdR+L3jw3f8edaD+M5sjf23JHjvj7Hys0dSrD4GgLF1xDue+Gpf5gQkR5JIVpEpIewWutPGFGOTcE4shcLA4CxXNhZRdi5pQQHXhkNy7kl2LklGH+Wwx2cYZaF8WVgfBlA/66dI9z6N+E7s3UPkS2/I/XTZ0nbsAg7rS/BQV8nWHwNwYLx4Ek5o22ISPekEC0ikkyMwdV8APehY0eVK/Ec2oGr5WDbYW4/ds5gQn0uxD7/W9i5pYR7lWBnFyvkdcbRKSnpfeIPRXLSODz0/2C11uHb8zb+XavwV/4PqVuWEvGmEyqaSKD4GoIDr8Kk5DpYvIgkkkK0iEh3FLFxHaluC8nx7eN24Ao2tB3my8TOLSE48Irowr5eQwnnlhDJLNTOFQlmUnIJnH8jgfNvhHArvn3v49v5Br7df8Bf9TrGchPqfwnB4kkEiq8hklXkdMkicgYpRIuIOCncirt+53Eh2VNXGZ2vbAfih9lpfbBzSwgMvaFtF4xeJUTS+mqv5e7Ak0Jw4JUEB14J5kk8B/6Ef9cqfLtWkfHeXDLem0s4bxiB4kkEi68h3Ltc75tIklOIFhE5i6zAEXy73sC/8/d46j8jv35PfAcJg0Ukq4hwbgnBwomxKRil2DlDMCk5DlcuHWa5CJ83kvB5I2ka9xCu+l3xQJ32yTOkf/zv2OnnxRYmTiI0YBy4fU5XLSKdpBAtIpJooRb8u9/Ev2MFvj3vYNkB7IwBmIJRtAyZEgvLQ7FzisGT6nS1coZFcoppueguWi66C6ulFt/ut/DveoOUbf+X1D//FxFfJsGiK6ILEwdegfFnO12yiHSAQrSISCLYQXzVq/FXrsC/axVWuBk7rQ8tF3yHQOkUwn1HkpObTnN9s9OVyllkUvMIDJ9GYPg0CLfg2/te9DcTu98kZcdKjMtDqP+46MLE4muIZA5wumQROQmFaBGRMyUSxrvvg2hw3vn/cAUOE/Hn0Dp0KoHSKYT6j9ViP2njSSVYfDXB4qtpjNh4DnyKf9cb+HatIvPdOfDuHEL5I+LTPuz8Ms2jFulGFKJFRE6HieDZ/wkplSvw7/gtrpYviHjTCQ7+OwIlUwgWXqb5rvLlXG7C/S4m3O9imi79F9x1VdER6l2rSPvoadI/WoidWUBg0NUEiycR6j8G3F6nqxY5pylEi4h0ljF4vvhzdMS58jXcjfswbj/BQV+ntXRKdIcGzW2W02DnDqEl9/u0jPw+VvNB/LvfxLdrFalb/pu0//0lEX92bB71JIIDL8f4Mp0uWeScoxAtItJB7kOV0eC8YyWe+p0Yl4dg4USaxj5AsHhS7Mp4ImeWSetNa9kttJbdAqFmfHtXR3f72P0mKZX/g3H5CBVcGts+72oi6ec5XbLIOSGhIXrjxo0sWLCAioqK+GOvvfYaL774Ii+99BIAy5cvZ9myZXg8Hu655x6uuOKKRJYkItIpriPV+CtXklK5Ek/tFgwWoQGX0nDhXQSGTNYV6eTs8qYRHPwNgoO/EZ2Dv/9jfDtX4d/1Bpmr/xlW/zOhPl+NX+DF7nW+5lGLJEjCQvTixYtZuXIlqaltv9LcunUrv/nNbzDGAHDw4EEqKip4+eWXCQQCzJgxg/Hjx+Pzaf6giDjH1bQf/47f4q9ciffABgBC542i8Wv/SqDkm0TS+zpcoQjg8hDqP5ZQ/7E0jZ+D+9Bnsf2o3yB9/U9JX/9T7KyB8Z0+Qv1Gg0u/gBY5UxL2t6moqIhnnnmGBx54AIC6ujoWLFjAww8/zJw5cwDYtGkTF110ET6fD5/PR1FREdu2baO8vDxRZYmItMtqrcNf9btocN73ARaGUP4FNI77ZwIlU4hkFTpdosjJWRZ23vk0551P88WzcDXtx7frTXy73iD1f39F2sbFRFJyCQ68Khqqiy4Hb5rTVYsktYSF6EmTJlFTUwOAbdv8y7/8Cw8//DB+vz9+TGNjI5mZbYsh0tPTaWxsTFRJIiLHsYIN0R0QKlfi27sGKxImnDOY5tH/RKD0OuzcEqdLFOmSSPp5tI74Dq0jvoMVbMRb/cfYPOo/kLL9N9GFsAVfi26fN+hqTHofp0sWSTpn5fc6mzdvZs+ePcydO5dAIMCOHTuYN28eY8eOpampKX5cU1PTcaG6PW63RU6OMz89u90ux1470Xpyb9Cz+1NvnRRqwdqxCteWV7B2/AEr3IrJKiAy5vtEym6EviPwWRZnY1KZ3rvklHy9pUGfaXDxNGw7RGTvOqzPXsf32ev4//gWGTyEGTAKM3QykaHfwO0almT9dVzyvXcd15N7g+7Zn2WOTlBOgJqaGu677z6WL1/e7mMHDx7ktttu4ze/+Q3BYJCbbrqJFStWHDdafaJQyKbeoSt85eSkOfbaidaTe4Oe3Z966wA7iG/vu/grV+Db9QauUBOR1N60lnyTwNDrCfcd6cjiK713yanH9GYM7tqt8Qu8eA/+b/ThrAEE+l9KcMB4QgWXEsno73ChZ06Pee/a0ZN7A+f669375IO7jq4w6N27NzNnzmTGjBkYY5g9e/YpA7SISIdFbLyfr4tuSVf1Oq5APRF/NoHSKQRKr9fVA0UsCzu/jOb8MppHz8bV8Dm+PW+SfmBddPu8bf8XgHB2MaGCrxEsGE9owDhMap7DhYt0DwkdiU4EjUQnRk/uDXp2f+rtGMbgObAhtpfzb3E3/zV69cDiawiUXt/trh6o9y459eTeINZfXSPu2m34at7DW7MW7+frcIWi0y/DecOjgbrga4T6j0mqC7305PeuJ/cGGokWETnzjMH9xRZSdqzAX7kSd0NNdNHUwCtpLL2O4MCrwKurB4p0iuXCzi+jJb+Mlgv/AewQnoOb8NW8j3ffWlL/XEHaxv/EWG7CfcoJDRgfDdb9LtbVOuWcoRAtIknJXVfVdvXAuh2xqwdOoGnMD2NXD0ye0TGRbs/tJXzeKMLnjYKLZ0G4Fe/+T/Duex9fzVpSP32WtA2LoldP7DcqHqrDfS4Et9fp6kUSQiFaRJKG60gN/h0r8VeuwPvF5tjVA8fR8NU7CAyejEnt5XSJIucGTwqhgvGECsbTPOZH0W30/vJhdOpHzVrSPvwZ6R8uwHjSCPYfEz82nFemtQjSYyhEi0i3ZjX9FX/Vb0mpXIF3/ycAhPqOpPFrc2NXDzzP4QpFxPgyCA68kuDAK4HoxYu8+z7At28t3pr38b//BAARfzahAeNiO398LboXuy5LLklKIVpEug9jcDXtx/PFFjxfbMF94APy9ryHZSKE88poHPsQgdIpRLKKnK5URE7BpOQSHDKZ4JDJALia9uONzaf21azFv/P3ANhpfQgNuJRQQXT6h/5uSzJRiBYRZ0TCuOuq8HyxOfaxBc8Xm3G1HoofYvKH0jzqXgKlU7B7DXWwWBE5HZH08wicfwOB828AwHWkGl/NWrw17+GrWUtK5f8AYGcVEYyF6tCAS4mk93WybJFTUogWkYSzgg24v9h6fGA+tB3LDgBg3H7CecMIDJ5EOP8CwvkXYOcNI7tPX5p78JZNIueqSFYRrWVFtJbdEt1hp64Sb81afPvW4t/5/0jdugyAcG5pfJQ61H8sJiXX4cpF2ihEi8iZYwyuxr+0heXaLXgObsZ9ZE/8kEhKL8K9R9BS/j3CeWXRwJw7BFz650jknGRZ2L2GYvcaSmv59yBi4/liczxUp2x9idT/XYLBItx7BKEBlxIs+BqhfpeAL93p6uUcpv+1RKRr7BDuusr4/OWjwdkVqAfAYGHnFBPq/RVah08nnF9GuPcFRNL6aiGRiJycK7r3dLhPOS0j7wE7iOevG+MXfknd9EvS/vQcxuUh3PeitukffUeCJ8Xp6uUcohAtIl/KChzGU7s1Oqp8NDAf+gwrEgSOTscYTmDI3xPuHZ2OEe41TKNEInL63D7C/UYT7jcaRs+GUAve/R9H51TvW0vaJ89gffzvGLefUL/R0VHqAZcS7lOu33BJQum7S0TaGIOrYd8xc5ej85fdDXvjh0RS8wn3voCWojuiYTmvDDunWP9ZicjZ4U0lVHgZocLLALACR/B+vj6+80fGup8AEPFmEBowNn7hFztvmJNVS2fYQazAYVyt9ViBeqxIGLKvcLqqv6H/9UTOVXYQ96HK4wNz7VZcgcNAbDpG7hBC542kZcR34vOXTXofhwsXEWlj/FkEi68mWHw1TYDVUhvdozq2+4d/95tAdD0GfYaR5UrH+LMwvkwivqz4bePLIuKPfo4/788Cd4qmoHWFMVihRqzWelyBw1ixQOwK1Mceq297LlAfC8yx4Bz+2wXl4azXIbPcgUZOTiFa5BxgtdadMHd5C+66SqxICADjSSWcX0agZEpsd4wywnnDwJvmcOUiIp1jUvMIlnyTYMk3AXA1fB69PPm+tfiaP8fV+Dmu2m1YwSNYwQYsEzn1+VzeeKA2vljg9mcS8WVj/Jmx+9lEfG23owE9Mx7GcfvORuuJYYei4TZwTMgN1MVDbzwQHzNyHA/Nxj7paY3bTyQlB+PPIeLPwc4qin4d/TmYlBwi/uzY5xwi6X3ILBwF3Wy3JoVokZ7EGFxHqo/Zdzkamt2N++KH2Gl9CeeXERx4ZTQw974AO2ugLsUrIj1SJLM/gWHfIjDsW+TkpFF/bBAzBivUFA3UgQas4BFcgVi4Dh7BChzBFWyI3g8cxgo24Ao24KrfhSf2Z1yhxi+twXhS2hn1Pnr7mBB+zEh4JB7YszDejNP7N9oYCDUfPwocH/1tC73R5w8f/1yo6eSnxYrW58+JBeJs7MyCthB8zONHA7HxZxNJyQFPatf76SYUokWSlTG4D23H2r2F9OpPo4G5dguuYEP0acuFnVNCqN9oWvK/G13wl1eGSct3uHARkW7CsjC+DIwvAzK6eI6IHQvdDbHQ3U4IDxyJB3VXLJB7GmqioTxwOL5n/ilfxpvRFsL9WbGR77Zg7kpPJ/1w7QlB+YR5xSdhXD4iKbltYTdjAHZ+2fGjwscE4qOPG1/WOT0AoxAtkkSslkP49q7Bt3c13uo1uJsPAODypmPnlxE4/4b4Yr9w3vk94id9EZFuzeWOBsqUHABOPoHhFOzg8SH8lKPisTDefBBXXVXb85EwKb6s40KvndEfc8zobzQInxiIc6NbA2red6cpRIt0Z3YI74FP8FZHg7Pnr5uwMET8OQQLJ9BUNJHU8ydQb/UFy+V0tSIi0hVuHyY1D5Oax6lnaJ+EMeRkp1B/5MtHtOXMUYgW6WZch/fg27saX/VqvDVrcYUaMZab8HmjaB7zQ4KFEwj3Lo//Ci01J63bLbYQEZGzyLLO6WkVTlGIFnGYFWyMrhzfuxpv9Wo8h3cDYGcWEhh6PcGiiYQGjMf4s5wtVEREROIUokXONhPB88VmvNWr8VX/Ee/+T7AiIYwnjWDBpbSU306oaCJ2drHmqImIiHRTCtEiZ4HV9NfogsDqP+KreRdXSy0AofwLaLnwToKFEwn1uxjcfocrFRERkY5QiBZJBDuA9/OP4nObPbVbgOgls4OFEwkWTSRYOAGT1tvhQkVERKQrFKJFzgRjcNfvjE7P2Lsa374PsMItGJeXUL+LaRz7EKGiywnnl2kXDRERkR5AIVqki6zAYbw17+GLbT/nbqgBIJxdTOvwmwkWXU6w/zjwpTtcqYiIiJxpCtEiHRWx8fx1Y9sUjQOfYhmbiDeDUMF4mkf+I8GiiUSyipyuVERERBJMIVrkFFyNn0f3a967Bt/ed3EF6jFYhPuU0zzqBwQLJxLuexG4vU6XKiIiImeRQrTIscIteD9fj686Ntpc9xkAdnpfgsXXRBcEFlyGSe3lcKEiIiLiJIVoObcZg/vQ9mho3rsa7+frsewAxu0n1H8MjcNvJlg0EbvX+dqzWUREROIUouWcY7XW4du7Bu/RBYFN+wEI5w6lZcSt0SsE9hsD3lSHKxUREZHuSiFaej47hLV3HWlb3sBX/Uc8f92EhSHizyZYcBnNRRMJFk4kktnf6UpFREQkSShES49itdbhqd2Gu3YbntpteA5Fb7tCTbgtF+G+I2m+5L7ogsA+XwWX2+mSRUREJAkpREtysgO4D+3Ac2hrNCzXbsNduxV304H4IRF/NuG8YQSG3YR36OXU547G+LMdLFpERER6CoVo6d5MBNeRvceNKntqt+Gu34ll7Oghbj/h3BJCBV+jpdcwwnnDsPOGEUk/L74YMCcnDVPf7GQnIiIi0oMoREu3YbUcwlMbHVl2H4pNx6jdjhVuC7921sDo6PKQydhHA3NOMbj0rSwiIiJnj5KHnH3hFjyHKv9m3rK7+a/xQyIpvQjnDaOlbDp23jDCvYYR7nW+LqEtIiIi3YJCtCROxMZ9ZM/fhuXDu7FMBIhNxeh1PqGiy2nJi44sh3sNw6T11r7MIiIi0m0pRMsZYTUfPGaBXzQwew5txwq3AmCwsLMHYecNI1B6XWze8nDsrIHaIUNERESSjkK0dE6oGc+h7X+zjZyrpTZ+SCS1d3QqxgUz44v8wrlDdfESERER6TEUoqV9kTDuw7tjQbltGznXkWosDADGk0q41/kEBl0dDcp5wwnnDcOk5jlcvIiIiEhiJTREb9y4kQULFlBRUcHWrVv58Y9/jNvtxufz8dRTT5Gfn8/y5ctZtmwZHo+He+65hyuuuCKRJZ0bTAQiYYjYWCbcdjsSgogNJowVsWOPh7FMGKu2mdTqjW0jzHWVWHYgejrLhZ0zmFDvr2APuyk6bzlvGJGsIrBcDjcrIiIicvYlLEQvXryYlStXkpoa/RX+vHnzmDNnDsOHD2fZsmUsXryYO+64g4qKCl5++WUCgQAzZsxg/Pjx+Hy+RJXVZa6GfVj7t+NrbI6HUSsSBvPlAZXYfcuEwQ4ff4w55s9GwtG9jyPh6Lljzx973zruMRsiodg5wm2vH1u011kZgJ3eFztvGC0F4wnnDY9NxSgBT8qZ/YKKiIiIJLGEheiioiKeeeYZHnjgAQAWLlxInz59ALBtG7/fz6ZNm7jooovw+Xz4fD6KiorYtm0b5eXliSqryzLfvh9PzXt05np3xnKBywuWG+PygMuNcXmjC+ksD8blju5v7PJgrOjzR28blxc8qdFjrBP+rMuDsdzRc7ti57Zij8fOd+z52x5rqwOXN3aO6HPpvfKp9w3EpOQm7GsoIiIi0lMkLERPmjSJmpqSVhELAAALTUlEQVSa+P2jAXrDhg28+OKLLF26lHfffZfMzMz4Menp6TQ2Np7yvG63RU5OWmKKPpXpLxI5shcbVyx4emOf24Lo8R/uLk91sE5yO5FcbhfZdtdGsJOB2+1y5vvmLFBvyasn96fekldP7k+9Ja/u2N9ZXVj4+uuv8+yzz/L888/Tq1cvMjIyaGpqij/f1NR0XKhuj20b6h25fLOHnN4XHP/aBrBjH8c9GIp9JI+cnDSHvq5nR0/uT70lr57cn3pLXj25P/WWvJzqr3fvk+fSs7YqbMWKFbz44otUVFRQWFgIQHl5OZ988gmBQICGhgaqqqoYOnTo2SpJRERERKRLzspItG3bzJs3j379+jFr1iwARo8ezb333svMmTOZMWMGxhhmz56N3+8/GyWJiIiIiHRZQkN0QUEBy5cvB+DDDz9s95hp06Yxbdq0RJYhIiIiInJGaZNfEREREZFOUogWEREREekkhWgRERERkU5SiBYRERER6SSFaBERERGRTlKIFhERERHpJIVoEREREZFOsowxxukiRERERESSiUaiRUREREQ6SSFaRERERKSTFKJFRERERDpJIVpEREREpJMUokVEREREOkkhWkRERESkkzxOF9BdRSIR5s6dy/bt2/H5fDzxxBMMHDgQ27aZPXs23/rWt5gwYYLTZXZJe719/vnn/PznP8fj8ZCXl8dTTz1Famqq06V2Wnu9HTx4kKeeegrLspgwYQI/+MEPnC6zy072fQnw7LPP8tlnn/H00087XGXXtNfb9u3b+elPf0q/fv0AmDVrFpdcconDlXZee70BPPbYY4RCIXw+HwsXLiQ3N9fhSrumvf4eeeSR+PM7d+5k6tSp/PCHP3Swyq5pr7d9+/axYMECPB4P48aNY/bs2U6X2SXt9bZ3714WLFhAamoql112Gd///vedLvO0bNy4kQULFlBRUcGePXt46KGHsCyL0tJSHnvsMVyu5B5LPLa/o+bPn09xcTG33HKLg5WdvmN727p1Kz/+8Y9xu934fD6eeuop8vPznS4RjLTrjTfeMA8++KAxxphPP/3U3H333WbPnj1m+vTp5vLLLzerV692uMKua6+3a665xhw8eNAYY8yCBQvMr371KydL7LL2eps6daqprq42xhjzne98x2zevNnJEk9Le/0ZY8wf//hHM336dPNP//RPTpZ3WtrrbeHCheb3v/+9w5WdvvZ6mzlzpvn000+NMcb8/ve/Nxs2bHCyxNNysu9LY4yprq42U6dONY2NjU6Vd1ra6+26664zlZWVJhKJmOnTp5tt27Y5XGXXnNjbXXfdZSZOnBj/9/L+++83H330kZMlnpbnn3/efPOb3zQ33XSTMcaYu+66y6xbt84YY8ycOXPMqlWrnCzvtJ3YX21trbn99tvNVVddZf77v//b4epOz4m9ffvb3zZbtmwxxhjz61//2syfP9/J8uKS+0ewBPrkk0+47LLLALjwwgv585//THNzM0888QRjxoxxuLrT015vFRUV8Z/qwuEwfr/fyRK7rL3eli9fTmFhIU1NTTQ2NpKTk+NwlV3XXn979uzhpZdeYtasWQ5Xd3ra623z5s28/PLLzJgxg5/85CeEw2GHq+yaE3vbsGEDhw4d4p133mHmzJn86U9/ory83OEqu6699+6oefPm8aMf/Yj09HSnyjst7fU2fPhw6uvrCYVCBAIB3G63w1V2zYm9vf/++2RlZVFYWAjAyJEj2bBhg5MlnpaioiKeeeaZ+P3NmzfHf5M1YcIE3n//fadKOyNO7K+pqYlZs2Zx3XXXOVjVmXFibwsXLmT48OEA2LbdbTKKQvRJNDY2kpGREb/vdrspKSlhyJAhDlZ1ZrTXW69evQD4wx/+wPr167n++uudKu+0tNcbwJ/+9CeuvfZa8vPz470moxP7CwQCPProozz++ONJ+x/5Ue29d2PGjGHOnDksXbqU5uZmli1b5mCFXXdib42NjVRWVjJu3Dj+67/+i8OHD/Pqq686WOHpae+9C4fDbNu2jaamJsaNG+dgdaenvd6GDBnC3XffzeTJk+nXrx+DBw92sMKuO7G33NxcGhsbqaqqwrZt1qxZQ3Nzs4MVnp5Jkybh8bTNWjXGYFkWAOnp6TQ0NDhV2hlxYn+FhYV89atfdbCiM+fE3vr06QPAhg0bePHFF/nud7/rUGXHU4g+iYyMDJqamuL3I5HIcW9oMjtZb0uWLOGFF17gP//zP7vNT3mddbLeLrzwQt5++23Kysp4/vnnHazw9JzYXyAQoK6ujtmzZzN//nzWrVuXtP21997dfPPNFBYWYlkWV111FVu2bHGwwq47sbeMjAzS09MZO3YslmVxxRVXHDd6m2xO9vdu5cqV3HTTTQ5WdvpO7O3w4cO88MIL/O53v+PNN99k4MCB/OIXv3Cwwq47sTdjDAsXLmTu3Lnce++9FBcXJ+08/fYcO/+5qamJrKwsB6uRznr99dd57LHHeP7557vNYJhC9EmMHDmSNWvWANFRzKFDhzpc0ZnTXm/PPvssH3/8MUuWLOk235xd0V5vM2bM4PDhw0B09CGZF5Kc2N/o0aNZuXIlFRUVPPzww4wdO5Z/+Id/cLjKrmnvvZsyZQr79+8H4IMPPuCCCy5wssQuO7G3r3zlKwwaNIiPP/4YgI8++ojS0lInSzwtJ/v3ct26dfHpAsnqxN5GjRpFWloaaWlpQHSE7MiRI06W2GXtvW9r1qzhueeeY9GiRVRXV3PppZc6XOWZU1ZWxvr16wFYs2YNF198scMVSUetWLGCF198kYqKivh0o+6gZwytJsDVV1/N2rVrmT59OsYY5s+f73RJZ8yJvd1///3cdtttlJWVceeddwLwjW98gxkzZjhcaee1977t2rWLO++8E5/PR+/eveM7IySjc+n7cv78+fzlL3/hBz/4ASkpKQwZMoRp06Y5XWaXtNdbKBTiX//1X7Ftm4KCgqTcueKok31fHjx4MOlHMtvrbefOndx22234/X4yMzP5yU9+4nSZXdJeb5988gm33HILKSkpXHvttUn9w92JHnzwQebMmcPChQsZPHgwkyZNcrok6QDbtpk3bx79+vWLr/0ZPXo09957r8OVgWWMMU4XISIiIiKSTJL399oiIiIiIg5RiBYRERER6SSFaBERERGRTlKIFhERERHpJIVoEREREZFOUogWETmHbd++nY8++giAK6+8kkAg4HBFIiLJQSFaROQctmrVKnbs2OF0GSIiSUcXWxERSTKvvPIK77zzDq2trRw8eJBbb72Vt956i8rKSh544AGam5v51a9+hc/nY9CgQTz++OO89tprrF69mtbWVqqrq7nzzjsZP348r776Kl6vN341yLlz51JTUwPAokWLyM7OdrJVEZFuSyFaRCQJNTU18Ytf/ILf/e53LFmyhOXLl7N+/XqWLFlCVVUVr776KhkZGcyfP5+XXnqJtLQ0GhsbeeGFF9i9ezd33303N9xwA1OnTiU/P5/y8nIAbrzxRi6++GIeeugh1q5dy+TJkx3uVESke9J0DhGRJDR8+HAAMjMzGTJkCJZlkZ2dTUtLCyUlJWRkZADRy+NWVlYCMGzYMAD69etHMBhs97wjRowAID8/n9bW1kS3ISKStBSiRUSSkGVZJ328qqqK5uZmAD788EOKi4tP+mcsyyISiXzpeUVE5HgK0SIiPYjb7WbWrFnceuutTJs2jbq6Om655ZaTHj9ixAiWLl3KunXrzmKVIiLJzzLGGKeLEBERERFJJhqJFhERERHpJIVoEREREZFOUogWEREREekkhWgRERERkU5SiBYRERER6SSFaBERERGRTlKIFhERERHpJIVoEREREZFO+v/W8+UVhJZXzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "plt.figure(figsize=(12,5))\n",
    "ax = sns.lineplot(x=df_calendar_mean_price_city_month.month, \n",
    "                  y=df_calendar_mean_price_city_month.price, \n",
    "                  data=df_calendar_mean_price_city_month, hue='city')\n",
    "plt.title('monthly average rental price @Airbnb in Boston and Seattle')\n",
    "plt.ylabel('monthly average price $')\n",
    "# ax.set(title='monthly average rental price @AirBnB in Boston and Seattle', ylabel='monthly average price $')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4b-2 Barchart plot monthly average rental price by city "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'monthly average price $')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "plt.figure(figsize=(12,5))\n",
    "ax = sns.barplot(x=df_calendar_mean_price_city_month.month, \n",
    "                 y=df_calendar_mean_price_city_month.price, \n",
    "                 data=df_calendar_mean_price_city_month, hue='city')\n",
    "plt.title('monthly average rental price @Airbnb in Boston and Seattle')\n",
    "plt.ylabel('monthly average price $')\n",
    "# plt.ylim(120, 240)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5a Calculate weekday average rental price by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>city</th>\n",
       "      <th>boston</th>\n",
       "      <th>seattle</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WeekDay</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0-Sunday</td>\n",
       "      <td>186.678288</td>\n",
       "      <td>132.677756</td>\n",
       "      <td>54.000532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1-Monday</td>\n",
       "      <td>184.874580</td>\n",
       "      <td>131.805800</td>\n",
       "      <td>53.068780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2-Tuesday</td>\n",
       "      <td>184.371507</td>\n",
       "      <td>131.571103</td>\n",
       "      <td>52.800404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3-Wednesday</td>\n",
       "      <td>184.962679</td>\n",
       "      <td>131.681904</td>\n",
       "      <td>53.280775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4-Thursday</td>\n",
       "      <td>186.699843</td>\n",
       "      <td>132.764815</td>\n",
       "      <td>53.935028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5-Friday</td>\n",
       "      <td>190.239257</td>\n",
       "      <td>139.335302</td>\n",
       "      <td>50.903955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6-Saturday</td>\n",
       "      <td>190.523086</td>\n",
       "      <td>139.472621</td>\n",
       "      <td>51.050465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "city             boston     seattle       diff\n",
       "WeekDay                                       \n",
       "0-Sunday     186.678288  132.677756  54.000532\n",
       "1-Monday     184.874580  131.805800  53.068780\n",
       "2-Tuesday    184.371507  131.571103  52.800404\n",
       "3-Wednesday  184.962679  131.681904  53.280775\n",
       "4-Thursday   186.699843  132.764815  53.935028\n",
       "5-Friday     190.239257  139.335302  50.903955\n",
       "6-Saturday   190.523086  139.472621  51.050465"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar_mean_price_city_weekday = df_calendar.groupby(['city', 'WeekDay'])['price'].mean()\n",
    "df_calendar_mean_price_city_weekday = df_calendar_mean_price_city_weekday.reset_index()\n",
    "df_calendar_mean_price_city_weekday\n",
    "df_calendar_mean_price_city_weekday_pivot = \\\n",
    "                        df_calendar_mean_price_city_weekday.pivot(index='WeekDay', columns='city', values='price')\n",
    "df_calendar_mean_price_city_weekday_pivot['diff'] = df_calendar_mean_price_city_weekday_pivot.boston - \\\n",
    "                                                    df_calendar_mean_price_city_weekday_pivot.seattle\n",
    "df_calendar_mean_price_city_weekday_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5b-1 Linechart plot weekday average rental price by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'weekday average price $')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "plt.figure(figsize=(10,5))\n",
    "ax = sns.lineplot(x=df_calendar_mean_price_city_weekday.WeekDay, \n",
    "                  y=df_calendar_mean_price_city_weekday.price, \n",
    "                  data=df_calendar_mean_price_city_weekday, hue='city')\n",
    "# plt.ylim(120, 210)\n",
    "plt.title('weekday average rental price @Airbnb in Boston and Seattle')\n",
    "plt.ylabel('weekday average price $')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5b-2 Barchart plot weekday average rental price by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'weekday average price $')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "plt.figure(figsize=(10,5))\n",
    "ax = sns.barplot(x=df_calendar_mean_price_city_weekday.WeekDay, \n",
    "                 y=df_calendar_mean_price_city_weekday.price, \n",
    "                 data=df_calendar_mean_price_city_weekday, hue='city')\n",
    "# plt.ylim(130, 200)\n",
    "plt.title('weekday average rental price @Airbnb in Boston and Seattle')\n",
    "plt.ylabel('weekday average price $')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Summary\n",
    "* Within the same period (Sep 6, 2016 to Jan 2, 2017), daily average rental price (DAR price) is higher in Boston (\\\\$191) than in Seattle (\\\\$136).\n",
    "* DAR price in both Seattle and Boston shows a strong seasonal pattern. \n",
    " * In both cities, DAR price is relatively low at the beginning of the year and gradually increases as the season enters summer. \n",
    " * The months of peak DAR price varies though for each city. In Seattle, DAR price is relatively high from May through September, with June (\\\\$146), July (\\\\$150), and August (\\\\$148) the obvious top three,  more than \\\\$10 more than the annual average. In Boston, DAR price is relatively high from September through November, with September (\\\\$229) and October (\\\\$228) the obvious top two, close to \\\\$30 more than the annual average.\n",
    "* DAR price in both Seattle and Boston also shows a strong weekday pattern as shown in the above plot by many small peaks. Each peak cycle corresponds to one week. \n",
    " * In both cities, DAR price is relatively high on Friday and Saturday, close to \\\\$6~$7 more than the annual average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Boston Listing Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read boston listing data, ignore 'access','interaction','house_rules' columns, set 'id' as index\n",
    "df_boston = pd.read_csv('boston\\listings.csv', sep=',', index_col='id', \n",
    "                 usecols = lambda column : column not in ['access','interaction','house_rules'],\n",
    "                 parse_dates=['last_scraped', 'host_since'], na_filter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
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       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>notes</th>\n",
       "      <th>transit</th>\n",
       "      <th>thumbnail_url</th>\n",
       "      <th>medium_url</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>xl_picture_url</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_url</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_about</th>\n",
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       "      <th>host_response_rate</th>\n",
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       "      <th>host_verifications</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>street</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>market</th>\n",
       "      <th>smart_location</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>is_location_exact</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>amenities</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>price</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>calendar_updated</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>calendar_last_scraped</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12147973</td>\n",
       "      <td>https://www.airbnb.com/rooms/12147973</td>\n",
       "      <td>20160906204935</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>Sunny Bungalow in the City</td>\n",
       "      <td>Cozy, sunny, family home.  Master bedroom high...</td>\n",
       "      <td>The house has an open and cozy feel at the sam...</td>\n",
       "      <td>Cozy, sunny, family home.  Master bedroom high...</td>\n",
       "      <td>none</td>\n",
       "      <td>Roslindale is quiet, convenient and friendly. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The bus stop is 2 blocks away, and frequent. B...</td>\n",
       "      <td>https://a2.muscache.com/im/pictures/c0842db1-e...</td>\n",
       "      <td>https://a2.muscache.com/im/pictures/c0842db1-e...</td>\n",
       "      <td>https://a2.muscache.com/im/pictures/c0842db1-e...</td>\n",
       "      <td>https://a2.muscache.com/im/pictures/c0842db1-e...</td>\n",
       "      <td>31303940</td>\n",
       "      <td>https://www.airbnb.com/users/show/31303940</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>2015-04-15</td>\n",
       "      <td>Boston, Massachusetts, United States</td>\n",
       "      <td>We are country and city connecting in our deck...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a2.muscache.com/im/pictures/5936fef0-b...</td>\n",
       "      <td>https://a2.muscache.com/im/pictures/5936fef0-b...</td>\n",
       "      <td>Roslindale</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>['email', 'phone', 'facebook', 'reviews']</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>Birch Street, Boston, MA 02131, United States</td>\n",
       "      <td>Roslindale</td>\n",
       "      <td>Roslindale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>02131</td>\n",
       "      <td>Boston</td>\n",
       "      <td>Boston, MA</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>42.282619</td>\n",
       "      <td>-71.133068</td>\n",
       "      <td>t</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,\"Wireless Internet\",Kitchen,\"Free Parking ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$250.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$35.00</td>\n",
       "      <td>1</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>2 weeks ago</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-09-06</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    listing_url       scrape_id last_scraped  \\\n",
       "id                                                                             \n",
       "12147973  https://www.airbnb.com/rooms/12147973  20160906204935   2016-09-07   \n",
       "\n",
       "                                name  \\\n",
       "id                                     \n",
       "12147973  Sunny Bungalow in the City   \n",
       "\n",
       "                                                    summary  \\\n",
       "id                                                            \n",
       "12147973  Cozy, sunny, family home.  Master bedroom high...   \n",
       "\n",
       "                                                      space  \\\n",
       "id                                                            \n",
       "12147973  The house has an open and cozy feel at the sam...   \n",
       "\n",
       "                                                description  \\\n",
       "id                                                            \n",
       "12147973  Cozy, sunny, family home.  Master bedroom high...   \n",
       "\n",
       "         experiences_offered  \\\n",
       "id                             \n",
       "12147973                none   \n",
       "\n",
       "                                      neighborhood_overview notes  \\\n",
       "id                                                                  \n",
       "12147973  Roslindale is quiet, convenient and friendly. ...   NaN   \n",
       "\n",
       "                                                    transit  \\\n",
       "id                                                            \n",
       "12147973  The bus stop is 2 blocks away, and frequent. B...   \n",
       "\n",
       "                                              thumbnail_url  \\\n",
       "id                                                            \n",
       "12147973  https://a2.muscache.com/im/pictures/c0842db1-e...   \n",
       "\n",
       "                                                 medium_url  \\\n",
       "id                                                            \n",
       "12147973  https://a2.muscache.com/im/pictures/c0842db1-e...   \n",
       "\n",
       "                                                picture_url  \\\n",
       "id                                                            \n",
       "12147973  https://a2.muscache.com/im/pictures/c0842db1-e...   \n",
       "\n",
       "                                             xl_picture_url   host_id  \\\n",
       "id                                                                      \n",
       "12147973  https://a2.muscache.com/im/pictures/c0842db1-e...  31303940   \n",
       "\n",
       "                                            host_url host_name host_since  \\\n",
       "id                                                                          \n",
       "12147973  https://www.airbnb.com/users/show/31303940  Virginia 2015-04-15   \n",
       "\n",
       "                                 host_location  \\\n",
       "id                                               \n",
       "12147973  Boston, Massachusetts, United States   \n",
       "\n",
       "                                                 host_about  \\\n",
       "id                                                            \n",
       "12147973  We are country and city connecting in our deck...   \n",
       "\n",
       "         host_response_time host_response_rate host_acceptance_rate  \\\n",
       "id                                                                    \n",
       "12147973                NaN                NaN                  NaN   \n",
       "\n",
       "         host_is_superhost                                 host_thumbnail_url  \\\n",
       "id                                                                              \n",
       "12147973                 f  https://a2.muscache.com/im/pictures/5936fef0-b...   \n",
       "\n",
       "                                           host_picture_url  \\\n",
       "id                                                            \n",
       "12147973  https://a2.muscache.com/im/pictures/5936fef0-b...   \n",
       "\n",
       "         host_neighbourhood  host_listings_count  host_total_listings_count  \\\n",
       "id                                                                            \n",
       "12147973         Roslindale                    1                          1   \n",
       "\n",
       "                                 host_verifications host_has_profile_pic  \\\n",
       "id                                                                         \n",
       "12147973  ['email', 'phone', 'facebook', 'reviews']                    t   \n",
       "\n",
       "         host_identity_verified  \\\n",
       "id                                \n",
       "12147973                      f   \n",
       "\n",
       "                                                 street neighbourhood  \\\n",
       "id                                                                      \n",
       "12147973  Birch Street, Boston, MA 02131, United States    Roslindale   \n",
       "\n",
       "         neighbourhood_cleansed  neighbourhood_group_cleansed    city state  \\\n",
       "id                                                                            \n",
       "12147973             Roslindale                           NaN  Boston    MA   \n",
       "\n",
       "         zipcode  market smart_location country_code        country  \\\n",
       "id                                                                    \n",
       "12147973   02131  Boston     Boston, MA           US  United States   \n",
       "\n",
       "           latitude  longitude is_location_exact property_type  \\\n",
       "id                                                               \n",
       "12147973  42.282619 -71.133068                 t         House   \n",
       "\n",
       "                room_type  accommodates  bathrooms  bedrooms  beds  bed_type  \\\n",
       "id                                                                             \n",
       "12147973  Entire home/apt             4        1.5       2.0   3.0  Real Bed   \n",
       "\n",
       "                                                  amenities  square_feet  \\\n",
       "id                                                                         \n",
       "12147973  {TV,\"Wireless Internet\",Kitchen,\"Free Parking ...          NaN   \n",
       "\n",
       "            price weekly_price monthly_price security_deposit cleaning_fee  \\\n",
       "id                                                                           \n",
       "12147973  $250.00          NaN           NaN              NaN       $35.00   \n",
       "\n",
       "          guests_included extra_people  minimum_nights  maximum_nights  \\\n",
       "id                                                                       \n",
       "12147973                1        $0.00               2            1125   \n",
       "\n",
       "         calendar_updated  has_availability  availability_30  availability_60  \\\n",
       "id                                                                              \n",
       "12147973      2 weeks ago               NaN                0                0   \n",
       "\n",
       "          availability_90  availability_365 calendar_last_scraped  \\\n",
       "id                                                                  \n",
       "12147973                0                 0            2016-09-06   \n",
       "\n",
       "          number_of_reviews first_review last_review  review_scores_rating  \\\n",
       "id                                                                           \n",
       "12147973                  0          NaN         NaN                   NaN   \n",
       "\n",
       "          review_scores_accuracy  review_scores_cleanliness  \\\n",
       "id                                                            \n",
       "12147973                     NaN                        NaN   \n",
       "\n",
       "          review_scores_checkin  review_scores_communication  \\\n",
       "id                                                             \n",
       "12147973                    NaN                          NaN   \n",
       "\n",
       "          review_scores_location  review_scores_value requires_license  \\\n",
       "id                                                                       \n",
       "12147973                     NaN                  NaN                f   \n",
       "\n",
       "          license  jurisdiction_names instant_bookable cancellation_policy  \\\n",
       "id                                                                           \n",
       "12147973      NaN                 NaN                f            moderate   \n",
       "\n",
       "         require_guest_profile_picture require_guest_phone_verification  \\\n",
       "id                                                                        \n",
       "12147973                             f                                f   \n",
       "\n",
       "          calculated_host_listings_count  reviews_per_month  \n",
       "id                                                           \n",
       "12147973                               1                NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boston.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3585, 91)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boston.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Drop unnecessary columns\n",
    "feat_info.csv was manually created and contains info about necessary property/actions to each column, such as id, numeric, categorical, location, drop, convert to numeric and convert to date etc.\n",
    "It is worth mentioning why certain columns were dropped.\n",
    "1. host_total_listings_count/calculated_host_listings_count: high correlation with host_listings_count\n",
    "2. host_verifications: use host_identity_verified\n",
    "3. zipcode: use neighbourhood_cleansed \n",
    "4. weekly_price/monthly_price: somewhat related to dependent variable or target daily 'price'. From an AirBnB home landlord's perspective, he/she is mostly like to set the daily 'price' first, before setting weekly_price/monthly_price charge.\n",
    "5. number_of_reviews: use reviews_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3585, 43)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info = pd.read_csv('feat_info.csv', sep=',')\n",
    "\n",
    "# drop unnecessary columns\n",
    "column_dropped_1 = feat_info[feat_info.action == 'drop']['name'].to_list()\n",
    "df_boston.drop(columns = column_dropped_1, axis=1, inplace = True)\n",
    "df_boston.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Convert data\n",
    "'extra_people' is somewhat related to dependent variable or target 'price'. From a home landlord's perspective, he/she is mostly like to set the price first, before setting 'extra_people' charge. This means 'extra_people' can be ignored. However, 'extra_people' does affect the 'price' to some extend. To keep this effect, 0/1 dummy was used. The same applies to security_deposit' and 'cleaning_fee'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# object to numeric\n",
    "df_boston['host_acceptance_rate'] = df_boston['host_acceptance_rate'].str.replace('%', '').astype(float)/100\n",
    "df_boston['host_response_rate'] = df_boston['host_response_rate'].str.replace('%', '').astype(float)/100\n",
    "df_boston['price'] = df_boston['price'].replace('[$,]', '', regex=True).astype(float)\n",
    "df_boston['extra_people'] = df_boston['extra_people'].replace('[$,]', '', regex=True).astype(float)\n",
    "\n",
    "# convert to 0/1\n",
    "df_boston['extra_people'] = df_boston['extra_people'].apply(lambda x: 1 if x>0 else 0)\n",
    "# https://stackoverflow.com/questions/37543647/how-to-replace-all-non-nan-entries-of-a-dataframe-with-1-and-all-nan-with-0\n",
    "df_boston[['security_deposit', 'cleaning_fee']] = df_boston[['security_deposit', 'cleaning_fee']].notnull().astype('int')\n",
    "\n",
    "# calculate host days\n",
    "df_boston['host_days'] = (df_boston['last_scraped'] - df_boston['host_since']).dt.days\n",
    "\n",
    "# convert calendar_updated to numeric days\n",
    "calendar_updated_dict = {'today': 0, '2 weeks ago': 14, 'a week ago': 7, '3 weeks ago': 21, '2 months ago': 60, 'yesterday': 1, '4 weeks ago': 28, '3 months ago': 90, '4 days ago': 4, '5 weeks ago': 35, '3 days ago': 3, '5 days ago': 5, '4 months ago': 120, '6 weeks ago': 42, '2 days ago': 2, '5 months ago': 150, '7 weeks ago': 49, '7 months ago': 210, '6 days ago': 6, '1 week ago': 7, '6 months ago': 180, '8 months ago': 240, '10 months ago': 300, '9 months ago': 270, 'never': -1, '11 months ago': 330, '13 months ago': 390, '14 months ago': 420, '12 months ago': 360, '17 months ago': 510, '15 months ago': 450, '16 months ago': 480, '22 months ago': 660, '18 months ago': 540, '25 months ago': 750, '30 months ago': 900, '20 months ago': 600, '23 months ago': 690} \n",
    "df_boston.calendar_updated.replace(calendar_updated_dict, inplace=True)\n",
    "df_boston['calendar_updated'] = np.where(df_boston['calendar_updated'] <0, df_boston['host_days'], df_boston['calendar_updated']).astype(int)\n",
    "df_boston.drop(columns=['last_scraped','host_since'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Drop outliers\n",
    "From the histogram of our target variable 'price', it is easy to observe that 'price' has a wide range of values. \n",
    "The extreme values (outliers) may affect the predicting power of the final model. Thus, some rows with extreme values of 'price' will be deleted. \n",
    "From the plot, it seems $700 is a good cutoff point. A total of 21 rows were deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b73739cd08>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_boston.price.hist(figsize=(15, 5), bins=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3560, 42)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boston = df_boston[df_boston.price < 700]\n",
    "df_boston.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Drop rows with >30% data missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '% percentage of missing columns for each row')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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331ReXp4k6YcfftDRo0cVHh6ubdu2yRijJUuWKC4uTh07dtTixYslSWvWrFGbNm3OZXcAAOAk53TknZycrNGjR2vAgAFyOByaNGmSQkJCNGrUKHk8HsXHx+v666/XL37xCy1dulT9+/eXMUaTJk0KdP8BALjknFN4h4WF6amnnjrt/ddff73S65CQED366KPn1jMAAFAlFmkBAMAyhDcAAJYhvAEAsAzhDQCAZQhvAAAsQ3gDAGAZwhsAAMsQ3gAAWIbwBgDAMoQ3AACWIbwBALAM4Q0AgGUIbwAALEN4AwBgGcIbAADLEN4AAFiG8AYAwDL1gt0BAABOFhnVQA3qBy6ejh6vUMnhowHb3sWA8EZQ8I8TgD8N6tdTi8wPAra9LXk9VRKwrV0cCG8EBf84AeDccc0bAADLEN4AAFiG8AYAwDKENwAAliG8AQCwDOENAIBlCG8AACxDeAMAYBnCGwAAyxDeAABYhvAGAMAyhDcAAJbhwSQAag1PjwNqR62Ht9fr1fjx47Vx40aFhYVp4sSJat68eW3vFsBFgKfHAbWj1sP7k08+UVlZmV577TWtWbNGeXl5mjlzZm3vtlYdK/coOtoVsO1xNAHgQgn02RCJ77BgqPXwXr16tbp06SJJ6tChg9atW1fbu6x1l4U6L+qjiZr+4zybX0T4x3lpqMncCeQvsPCvNsI2kN9fEmdEgsFhjDG1uYPs7Gzdcccd+tWvfiVJuvXWW/XJJ5+oXj0utwMAcC5q/W7zyMhIlZaW+l57vV6CGwCA81Dr4d2xY0ctXrxYkrRmzRq1adOmtncJAECdVuunzU/cbb5p0yYZYzRp0iS1bNmyNncJAECdVuvhDQAAAosV1gAAsAzhDQCAZQhvAAAsU2f/ZotlWavXp08fuVw/LrLRtGlTTZ48Ocg9Cr6vv/5aTz75pObOnautW7cqMzNTDodDrVu31rhx4xQScun+rntybQoLCzVs2DC1aNFCkjRgwAD16NEjuB0MgvLycmVlZWnnzp0qKyvT8OHD1apVK+aNqq7NVVddxbz5L4/HozFjxmjz5s1yOp2aPHmyjDE1mjt1Nrzr4rKsgXL8+HFJ0ty5c4Pck4vH7NmzNX/+fDVo0ECSNHnyZKWnp6tz584aO3asFi5cqMTExCD3MjhOrc369es1dOhQpaamBrlnwTV//ny53W7l5+frwIEDuuuuu3TNNdcwb1R1be6//37mzX999tlnkqRXX31VK1as8IV3TeZOnf2VsC4uyxooGzZs0NGjR5WamqrBgwdrzZo1we5S0DVr1kxTp071vS4sLFSnTp0kSQkJCVq2bFmwuhZ0p9Zm3bp1WrRoke69915lZWWppOTSXBizW7dueuihh3yvnU4n8+a/qqoN8+Z/br/9duXm5kqSioqK1Lhx4xrPnTob3iUlJYqMjPS9djqdqqioCGKPLh6XXXaZ0tLS9Pzzz2vChAkaNWrUJV+bpKSkSiv/GWPkcDgkSRERESouLg5W14Lu1Nq0b99ejzzyiF5++WX9/Oc/1/Tp04PYu+CJiIhQZGSkSkpK9OCDDyo9PZ15819V1YZ5U1m9evWUkZGh3NxcJSUl1Xju1NnwZllW/2JiYnTnnXfK4XAoJiZGbrdbe/bsCXa3LionX2sqLS1VVFRUEHtzcUlMTFS7du18/71+/fog9yh4du3apcGDB6t3797q1asX8+Ykp9aGeXO6xx9/XB999JFycnJ8lzOls5s7dTa8WZbVvzfffFN5eXmSpB9++EElJSWKjo4Ocq8uLm3bttWKFSskSYsXL1ZcXFyQe3TxSEtL09q1ayVJy5cv13XXXRfkHgXH3r17lZqaqocffljJycmSmDcnVFUb5s3/vPvuu5o1a5YkqUGDBnI4HGrXrl2N5k6dXWGNZVn9Kysr0+jRo1VUVCSHw6FRo0apY8eOwe5W0O3YsUMjR47U66+/rs2bNysnJ0fl5eWKjY3VxIkT5XQ6g93FoDm5NoWFhcrNzVVoaKgaN26s3NzcSpeoLhUTJ07Uhx9+qNjYWN972dnZmjhx4iU/b6qqTXp6uvLz8y/5eSNJR44c0ejRo7V3715VVFTovvvuU8uWLWv0nVNnwxsAgLqqzp42BwCgriK8AQCwDOENAIBlCG8AACxDeAMAYBnCGxdMaWmpBg8erH79+mnDhg2SpFWrVum5554Lcs9ON2/evAu6v0ceeUT33HOPvvvuu7P+zL///W9NmzatRvt57LHHVFRUVNPunZPbbrut0sITF8qUKVN09913+/5m9kJ5++239eSTT17QfeLSxZJjuGCWLl2q2267TZ06ddKbb76p7OxsvfTSS8rPzw92104zc+ZMDRo06ILtb8mSJTVeB/vaa6/VtddeW6PPZGdn16i9jRYsWKB33nnnkv0bYlwaCG9cMOHh4Tp69KiOHDmi8PBwvffee0pMTFT9+vWrbJ+SkqKYmBht3rxZxhhNmTJF0dHReuqpp/Tll1/KGKMhQ4aoe/fuSklJ0eWXX67Dhw9rxowZys7OVlFRkcrLy5WTk6N27dpp3Lhx2rp1q7xer+/pPb169VKnTp20ceNGORwOzZgxQ/PmzdOhQ4c0fvx4jRo1StnZ2SouLtaBAwfUt29fDRw4UGvXrtWECRMUERGhK664QvXr11deXp7mzp2r999/Xw6HQz169NDgwYMrjWnp0qV6+umnVb9+fbndbk2aNEkFBQU6fPiwhg8fXunJd5mZmapXr56KiopUVlamHj166LPPPtOuXbs0Y8YM7dq1S6+++qqmTJmizMxMbdu2TcePH1daWpp69OihKVOm6IsvvpDX61XPnj01ZMgQpaSkaPz48VqwYIF27Nihffv2qaioSKNHj1aXLl302Wef6ZlnnlFkZKQaNmyoq6++WiNGjPD1ad++fcrMzFRxcbGMMXr88cfVqFEjPfzwwyopKZHH49FDDz2km2++udI4evTooYSEBC1evFgLFixQXl6eEhMT9ctf/lJbt27VTTfdpOLiYq1du1YxMTHKz89XZmamwsLCtHPnTu3evVt5eXm67rrrqhzrCdOmTdP333+vP/zhD3r++ef19NNPa/Xq1ZKk3/zmN/rd736nzMxMHTx4UAcPHtSsWbPUsGFDSVJxcbGys7N14MABSdKYMWN09dVXa968efr4449VUVEhl8ulqVOnyuv1+hY6OjHHpB8fnZqamqr9+/drwIAB6tevn69vO3bs0PDhw+V2u5WQkKBbbrlFubm5cjqdql+/vnJzczVnzhzdcMMN6tatm9LS0tSlSxcNGTJE2dnZ+u1vf8tiSvgfA1wgHo/H5ObmmpEjR5rt27ebESNGmE2bNpmcnBzz3HPPndZ+0KBB5p133jHGGDNv3jyTm5trFi1aZNLT040xxhw7dszceeed5tChQ2bQoEHm448/NsYYM2fOHJOfn2+MMWbjxo1mzpw55uWXXzZPPPGEMcaY/fv3mx49ehhjjOnatatZvXq1McaYkSNHmvfff98YY8z//d//GWOMWbdunfnoo4+MMcZ8//33JjEx0RhjTJ8+fcymTZuMMcYUFBSYjIwM880335j+/fubiooK4/F4TEpKivnuu+984/F6vaZr167m+++/N8YY88ILL5i8vLxK+ztZRkaGmTFjhjHGmJycHPP4448bY4z505/+ZObMmWO++OILk56eboqLi82tt95q9u3bZ/bt22fmz59vjDEmISHBbNu2zRw/ftz89a9/9dX022+/Nc8884wZM2aMMcaYJUuWmNTUVFNRUWFuu+02s2fPHl89nnnmmUp9ys3NNa+88ooxxphly5aZv/3tbyYvL8+88MILvhp17drVeDwe07VrV3Ps2DGTkZFhPv/8c2OMMZ9//rnJyMgwxhhz7bXXmp07d5qysjLToUMH88033/hqdOjQIZORkWFmzpxpjDHmtddeMzk5OX7HerIT+/3000/N/fffb7xerykrKzPJyclmw4YNJiMjw8yZM+e0zz3xxBPm5ZdfNsYYs3nzZtO/f3/j8XjM1KlTjcfjMcYYk5qaalatWlXlHHvrrbfMkCFDjNfrNdu3bzfdu3evtP3t27ebzp07m+PHjxtjjLnrrrvM+vXrjTHG/OMf/zAjRowwK1euNKNHjzZHjx41ycnJ5r777jNer9f06dPHeL3e0/qMSxdH3rhgQkJCNGbMGEnSrFmzNHjwYM2cOVNjxozRtGnTtHnzZsXExFT6zE033STpx7XqP/30U1155ZUqLCxUSkqKJKmiosJ3DffEZ//zn/8oISFBktSmTRu1adNG48eP1+rVq31rK1dUVPiOsNq2bStJ+ulPf3raNdrGjRvrxRdf1Mcff6zIyEjf09d2796t1q1bS5JuuOEGLViwQJs2bVJRUZGGDBkiSTp06JC2bdvmWyLywIEDioyM1JVXXilJuvHGG1VQUFBtzU70LSoqyredqKgolZWV+dpERkYqJydHOTk5Kikp0Z133ilJKigoUEFBgfbu3et7PO7JTpxyv+qqq1RWVqb9+/crMjJSjRs3liTFxcVp7969lT6zefNm31rVJ46u33//ffXq1UuSdOWVVyoyMlL79++vcjzmpAUd3W63mjRpIunHszKtWrWSJLlcLt//h5P7+NVXX/kda1W+++47xcXFyeFwKDQ0VNdff73vnoJT55kkbdq0SV988YU+/PBDSdLhw4cVEhKi0NBQjRw5UuHh4fr+++9VUVFR5Rx7++231bZtWzkcDkVHR+vYsWOn7aNp06YKCwuT9OMcOjG+G2+8UU899ZRuuOEGPfbYY1qxYoXuuOMOffTRR1q1apU6dOjge+IUIHHDGoJg37592rJli+Li4nT06FE5nU45HA4dPXr0tLYnnsP+1VdfqVWrVoqNjVXnzp01d+5cvfjii+revbuaNm0qSb4vt5YtW+pf//qXJGn79u364x//qNjYWPXs2VNz587V7Nmz1a1bN9/p0qq+FE+EzF/+8hd16NBBTz75pLp16+Z7/6qrrtK3334r6cdTpZIUGxurVq1a6aWXXtLcuXN19913V3ogzuWXX66SkhLt3r1bkrRy5Uq1aNGi2lqdzRf27t27VVhYqOnTp+u5555Tfn6+ysrK9Pe//10FBQV68cUX9c4772jnzp3VbvuKK65QaWmpL3hPjOtkJ9f2yy+/VH5+vlq2bKlVq1ZJ+vFBN4cPH5bb7fZ9JiwszPfUupOfJHU2Yzu1TVVj9fc425YtW/pOmZeXl+uf//ynmjdv7nffsbGxGjJkiObOnaunn35avXr10oYNG/TJJ5/o6aefVk5Ojrxer4wxVc6xsxnTyU8d+8lPfuK7cfPLL79UixYtFBISonbt2unPf/6z4uPjdcMNNyg/P1933HHHGWuFSwtH3rjgZs6cqWHDhkmSBg4cqLS0NDVp0kTXXHPNaW3feecdvfDCC2rQoIGeeOIJud1urVy5UgMHDtSRI0d0++23n3ZjUv/+/ZWVlaVBgwbJ4/EoKytLV199tcaMGaNBgwappKREAwcOrPRFeqqWLVtq1KhRSk5O1vjx4/Xee+/J7XbL6XSqrKxM48aNU1ZWlsLDwxUaGqorr7xS11xzjW6++WYNGDBAZWVlat++ve8oW/rxi33ixIkaMWKEHA6HGjZsqMmTJ593PaOjo7Vnzx716dNH4eHhSk1NVVhYmBo2bKjevXurYcOGuuWWW3xHuf6EhIQoJydH9913n1wul7xery/sThg2bJiysrI0f/58SdKkSZPkcrmUlZWljz76SMeOHdOjjz5a6fG7ffv2VVZWlt57770z/rJyLmP196jfrl27auXKlerXr5/Ky8vVrVu3ap9kNWzYMGVnZ+v1119XSUmJHnjgATVv3lwNGjTQ3XffrbCwMEVHR2v37t1VzrFvvvmmRmOZOHGicnNzZYyR0+nUpEmTJP34uMzRo0frmmuuUXx8vN59913deOONNdo26j4eTIKL1ombqy7Gp8G9/PLL6t69uxo1aqQpU6YoNDRUDzzwQLC7dd5mzZqloUOHKiwsTKNGjVJ8fLz69OkT7G4BOAVH3sA5uOKKK5Samqrw8HC5XC7f89FtFxERoXvuuUeXXXaZfvazn1W6kxvAxYMjb1pVMyMAAAAlSURBVAAALMMNawAAWIbwBgDAMoQ3AACWIbwBALAM4Q0AgGX+H9zMfieOC1thAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing data in each row\n",
    "missing_row_ct = df_boston.isnull().sum(axis = 1)\n",
    "missing_row_ct.sort_values(ascending = False, inplace=True)\n",
    "missing_row_pct = missing_row_ct/(df_boston.shape[1]) * 100\n",
    "\n",
    "# percentage of missing data in each row\n",
    "missing_row_pct.hist(figsize=(8, 4), bins=20)\n",
    "plt.xlabel('% percentage of missing columns for each row')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3560, 42)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete rows with 30% more value missing, 0 rows deleted\n",
    "df_boston = df_boston[df_boston.isnull().sum(axis=1) < df_boston.shape[1] * 0.3]\n",
    "df_boston.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Impute nulls in columns\n",
    "The majority of columns have no missing values. For those having missing values, no single column has missing value than 25%. We will impute the nulls with mean or mode values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'missing %')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing data in each column\n",
    "missing_ct = df_boston.isnull().sum()\n",
    "missing_ct.sort_values(ascending = False, inplace=True)\n",
    "missing_pct = missing_ct/(df_boston.shape[0]) * 100\n",
    "\n",
    "# percentage of missing data in each column.\n",
    "missing_pct.plot.bar(figsize=(15, 4))\n",
    "plt.ylabel('missing %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fillna with mode for categorical columns, and the numeric columns that only take integer/fixed values\n",
    "for col in ['host_response_time','property_type','bathrooms','bedrooms', 'beds']:\n",
    "    df_boston[col].fillna(df_boston[col].mode().iloc[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fillna with mean for numeric columns\n",
    "df_boston.fillna(df_boston.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double check there is no null values\n",
    "df_boston.isnull().mean()[df_boston.isnull().mean() > 0].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.7 Convert 'amenities' to individual amenity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_boston['amenities'].replace(to_replace='[\\{\\}\\\"]', value='', regex=True, inplace=True)\n",
    "amenities_dummy = df_boston['amenities'].str.get_dummies(sep=',').add_prefix('amenity_')\n",
    "\n",
    "# concatenate all individual amenities column with the full dataset\n",
    "df_boston = pd.concat([df_boston, amenities_dummy], axis=1, sort=False)\n",
    "df_boston.drop(columns=['amenities'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list of object columns\n",
    "cols_cat = df_boston.select_dtypes(include=[object]).columns.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.8 One-hot encoding of categorical fetures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_boston = pd.get_dummies(data=df_boston, prefix=cols_cat, prefix_sep='_', columns=cols_cat, drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.9 Final review of the processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>calendar_updated</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>host_days</th>\n",
       "      <th>amenity_24-Hour Check-in</th>\n",
       "      <th>amenity_Air Conditioning</th>\n",
       "      <th>amenity_Breakfast</th>\n",
       "      <th>amenity_Buzzer/Wireless Intercom</th>\n",
       "      <th>amenity_Cable TV</th>\n",
       "      <th>amenity_Carbon Monoxide Detector</th>\n",
       "      <th>amenity_Cat(s)</th>\n",
       "      <th>amenity_Dog(s)</th>\n",
       "      <th>amenity_Doorman</th>\n",
       "      <th>amenity_Dryer</th>\n",
       "      <th>amenity_Elevator in Building</th>\n",
       "      <th>amenity_Essentials</th>\n",
       "      <th>amenity_Family/Kid Friendly</th>\n",
       "      <th>amenity_Fire Extinguisher</th>\n",
       "      <th>amenity_First Aid Kit</th>\n",
       "      <th>amenity_Free Parking on Premises</th>\n",
       "      <th>amenity_Free Parking on Street</th>\n",
       "      <th>amenity_Gym</th>\n",
       "      <th>amenity_Hair Dryer</th>\n",
       "      <th>amenity_Hangers</th>\n",
       "      <th>amenity_Heating</th>\n",
       "      <th>amenity_Hot Tub</th>\n",
       "      <th>amenity_Indoor Fireplace</th>\n",
       "      <th>amenity_Internet</th>\n",
       "      <th>amenity_Iron</th>\n",
       "      <th>amenity_Kitchen</th>\n",
       "      <th>amenity_Laptop Friendly Workspace</th>\n",
       "      <th>amenity_Lock on Bedroom Door</th>\n",
       "      <th>amenity_Other pet(s)</th>\n",
       "      <th>amenity_Paid Parking Off Premises</th>\n",
       "      <th>amenity_Pets Allowed</th>\n",
       "      <th>amenity_Pets live on this property</th>\n",
       "      <th>amenity_Pool</th>\n",
       "      <th>amenity_Safety Card</th>\n",
       "      <th>amenity_Shampoo</th>\n",
       "      <th>amenity_Smoke Detector</th>\n",
       "      <th>amenity_Smoking Allowed</th>\n",
       "      <th>amenity_Suitable for Events</th>\n",
       "      <th>amenity_TV</th>\n",
       "      <th>amenity_Washer</th>\n",
       "      <th>amenity_Washer / Dryer</th>\n",
       "      <th>amenity_Wheelchair Accessible</th>\n",
       "      <th>amenity_Wireless Internet</th>\n",
       "      <th>amenity_translation missing: en.hosting_amenity_49</th>\n",
       "      <th>amenity_translation missing: en.hosting_amenity_50</th>\n",
       "      <th>host_response_time_within a day</th>\n",
       "      <th>host_response_time_within a few hours</th>\n",
       "      <th>host_response_time_within an hour</th>\n",
       "      <th>host_is_superhost_t</th>\n",
       "      <th>host_has_profile_pic_t</th>\n",
       "      <th>host_identity_verified_t</th>\n",
       "      <th>neighbourhood_cleansed_Back Bay</th>\n",
       "      <th>neighbourhood_cleansed_Bay Village</th>\n",
       "      <th>neighbourhood_cleansed_Beacon Hill</th>\n",
       "      <th>neighbourhood_cleansed_Brighton</th>\n",
       "      <th>neighbourhood_cleansed_Charlestown</th>\n",
       "      <th>neighbourhood_cleansed_Chinatown</th>\n",
       "      <th>neighbourhood_cleansed_Dorchester</th>\n",
       "      <th>neighbourhood_cleansed_Downtown</th>\n",
       "      <th>neighbourhood_cleansed_East Boston</th>\n",
       "      <th>neighbourhood_cleansed_Fenway</th>\n",
       "      <th>neighbourhood_cleansed_Hyde Park</th>\n",
       "      <th>neighbourhood_cleansed_Jamaica Plain</th>\n",
       "      <th>neighbourhood_cleansed_Leather District</th>\n",
       "      <th>neighbourhood_cleansed_Longwood Medical Area</th>\n",
       "      <th>neighbourhood_cleansed_Mattapan</th>\n",
       "      <th>neighbourhood_cleansed_Mission Hill</th>\n",
       "      <th>neighbourhood_cleansed_North End</th>\n",
       "      <th>neighbourhood_cleansed_Roslindale</th>\n",
       "      <th>neighbourhood_cleansed_Roxbury</th>\n",
       "      <th>neighbourhood_cleansed_South Boston</th>\n",
       "      <th>neighbourhood_cleansed_South Boston Waterfront</th>\n",
       "      <th>neighbourhood_cleansed_South End</th>\n",
       "      <th>neighbourhood_cleansed_West End</th>\n",
       "      <th>neighbourhood_cleansed_West Roxbury</th>\n",
       "      <th>is_location_exact_t</th>\n",
       "      <th>property_type_Bed &amp; Breakfast</th>\n",
       "      <th>property_type_Boat</th>\n",
       "      <th>property_type_Camper/RV</th>\n",
       "      <th>property_type_Condominium</th>\n",
       "      <th>property_type_Dorm</th>\n",
       "      <th>property_type_Entire Floor</th>\n",
       "      <th>property_type_Guesthouse</th>\n",
       "      <th>property_type_House</th>\n",
       "      <th>property_type_Loft</th>\n",
       "      <th>property_type_Other</th>\n",
       "      <th>property_type_Townhouse</th>\n",
       "      <th>property_type_Villa</th>\n",
       "      <th>room_type_Private room</th>\n",
       "      <th>room_type_Shared room</th>\n",
       "      <th>bed_type_Couch</th>\n",
       "      <th>bed_type_Futon</th>\n",
       "      <th>bed_type_Pull-out Sofa</th>\n",
       "      <th>bed_type_Real Bed</th>\n",
       "      <th>instant_bookable_t</th>\n",
       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_strict</th>\n",
       "      <th>cancellation_policy_super_strict_30</th>\n",
       "      <th>require_guest_profile_picture_t</th>\n",
       "      <th>require_guest_phone_verification_t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6976</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>46</td>\n",
       "      <td>61</td>\n",
       "      <td>319</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>2676</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1436513</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>98</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1235</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7651065</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "      <td>59</td>\n",
       "      <td>334</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>850</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          host_response_rate  host_acceptance_rate  host_listings_count  \\\n",
       "id                                                                        \n",
       "12147973            0.950284              0.841404                    1   \n",
       "3075044             1.000000              1.000000                    1   \n",
       "6976                1.000000              0.880000                    1   \n",
       "1436513             1.000000              0.500000                    1   \n",
       "7651065             1.000000              1.000000                    1   \n",
       "\n",
       "          accommodates  bathrooms  bedrooms  beds  price  security_deposit  \\\n",
       "id                                                                           \n",
       "12147973             4        1.5       2.0   3.0  250.0                 0   \n",
       "3075044              2        1.0       1.0   1.0   65.0                 1   \n",
       "6976                 2        1.0       1.0   1.0   65.0                 0   \n",
       "1436513              4        1.0       1.0   2.0   75.0                 1   \n",
       "7651065              2        1.5       1.0   2.0   79.0                 0   \n",
       "\n",
       "          cleaning_fee  guests_included  extra_people  minimum_nights  \\\n",
       "id                                                                      \n",
       "12147973             1                1             0               2   \n",
       "3075044              1                0             0               2   \n",
       "6976                 0                1             1               3   \n",
       "1436513              1                2             1               1   \n",
       "7651065              1                1             0               2   \n",
       "\n",
       "          calendar_updated  availability_30  availability_60  availability_90  \\\n",
       "id                                                                              \n",
       "12147973                14                0                0                0   \n",
       "3075044                  7               26               54               84   \n",
       "6976                     5               19               46               61   \n",
       "1436513                  7                6               16               26   \n",
       "7651065                 14               13               34               59   \n",
       "\n",
       "          availability_365  review_scores_rating  review_scores_accuracy  \\\n",
       "id                                                                         \n",
       "12147973                 0             91.888406                9.429818   \n",
       "3075044                359             94.000000               10.000000   \n",
       "6976                   319             98.000000               10.000000   \n",
       "1436513                 98            100.000000               10.000000   \n",
       "7651065                334             99.000000               10.000000   \n",
       "\n",
       "          review_scores_cleanliness  review_scores_checkin  \\\n",
       "id                                                           \n",
       "12147973                   9.255898               9.645114   \n",
       "3075044                    9.000000              10.000000   \n",
       "6976                       9.000000              10.000000   \n",
       "1436513                   10.000000              10.000000   \n",
       "7651065                   10.000000              10.000000   \n",
       "\n",
       "          review_scores_communication  review_scores_location  \\\n",
       "id                                                              \n",
       "12147973                     9.645372                9.412214   \n",
       "3075044                     10.000000                9.000000   \n",
       "6976                        10.000000                9.000000   \n",
       "1436513                     10.000000               10.000000   \n",
       "7651065                     10.000000                9.000000   \n",
       "\n",
       "          review_scores_value  reviews_per_month  host_days  \\\n",
       "id                                                            \n",
       "12147973             9.166788           1.976816        511   \n",
       "3075044              9.000000           1.300000       1553   \n",
       "6976                10.000000           0.470000       2676   \n",
       "1436513             10.000000           1.000000       1235   \n",
       "7651065             10.000000           2.250000        850   \n",
       "\n",
       "          amenity_24-Hour Check-in  amenity_Air Conditioning  \\\n",
       "id                                                             \n",
       "12147973                         0                         0   \n",
       "3075044                          0                         1   \n",
       "6976                             0                         1   \n",
       "1436513                          0                         1   \n",
       "7651065                          0                         1   \n",
       "\n",
       "          amenity_Breakfast  amenity_Buzzer/Wireless Intercom  \\\n",
       "id                                                              \n",
       "12147973                  0                                 0   \n",
       "3075044                   0                                 0   \n",
       "6976                      0                                 0   \n",
       "1436513                   1                                 0   \n",
       "7651065                   1                                 0   \n",
       "\n",
       "          amenity_Cable TV  amenity_Carbon Monoxide Detector  amenity_Cat(s)  \\\n",
       "id                                                                             \n",
       "12147973                 0                                 0               0   \n",
       "3075044                  0                                 1               0   \n",
       "6976                     1                                 1               0   \n",
       "1436513                  0                                 1               0   \n",
       "7651065                  0                                 1               0   \n",
       "\n",
       "          amenity_Dog(s)  amenity_Doorman  amenity_Dryer  \\\n",
       "id                                                         \n",
       "12147973               1                0              1   \n",
       "3075044                1                0              1   \n",
       "6976                   0                0              1   \n",
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       "id                                                                          \n",
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       "          amenity_Indoor Fireplace  amenity_Internet  amenity_Iron  \\\n",
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       "          amenity_Kitchen  amenity_Laptop Friendly Workspace  \\\n",
       "id                                                             \n",
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       "id                                                             \n",
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       "1436513                                                   0    \n",
       "7651065                                                   0    \n",
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       "          host_response_time_within a day  \\\n",
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       "          host_response_time_within a few hours  \\\n",
       "id                                                \n",
       "12147973                                      0   \n",
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       "1436513                                       1   \n",
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       "          host_response_time_within an hour  host_is_superhost_t  \\\n",
       "id                                                                 \n",
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       "          host_has_profile_pic_t  host_identity_verified_t  \\\n",
       "id                                                           \n",
       "12147973                       1                         0   \n",
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       "          neighbourhood_cleansed_Back Bay  neighbourhood_cleansed_Bay Village  \\\n",
       "id                                                                              \n",
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       "          neighbourhood_cleansed_Beacon Hill  neighbourhood_cleansed_Brighton  \\\n",
       "id                                                                              \n",
       "12147973                                   0                                0   \n",
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       "7651065                                    0                                0   \n",
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       "          neighbourhood_cleansed_Charlestown  \\\n",
       "id                                             \n",
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       "3075044                                    0   \n",
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       "1436513                                    0   \n",
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       "          neighbourhood_cleansed_Chinatown  neighbourhood_cleansed_Dorchester  \\\n",
       "id                                                                              \n",
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       "7651065                                  0                                  0   \n",
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       "          neighbourhood_cleansed_Downtown  neighbourhood_cleansed_East Boston  \\\n",
       "id                                                                              \n",
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       "          neighbourhood_cleansed_Fenway  neighbourhood_cleansed_Hyde Park  \\\n",
       "id                                                                          \n",
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       "id                                               \n",
       "12147973                                     0   \n",
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       "1436513                                      0   \n",
       "7651065                                      0   \n",
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       "id                                                  \n",
       "12147973                                        0   \n",
       "3075044                                         0   \n",
       "6976                                            0   \n",
       "1436513                                         0   \n",
       "7651065                                         0   \n",
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       "id                                                       \n",
       "12147973                                             0   \n",
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       "6976                                                 0   \n",
       "1436513                                              0   \n",
       "7651065                                              0   \n",
       "\n",
       "          neighbourhood_cleansed_Mattapan  \\\n",
       "id                                          \n",
       "12147973                                0   \n",
       "3075044                                 0   \n",
       "6976                                    0   \n",
       "1436513                                 0   \n",
       "7651065                                 0   \n",
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       "          neighbourhood_cleansed_Mission Hill  \\\n",
       "id                                              \n",
       "12147973                                    0   \n",
       "3075044                                     0   \n",
       "6976                                        0   \n",
       "1436513                                     0   \n",
       "7651065                                     0   \n",
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       "id                                                                              \n",
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       "6976                                     0                                  1   \n",
       "1436513                                  0                                  1   \n",
       "7651065                                  0                                  1   \n",
       "\n",
       "          neighbourhood_cleansed_Roxbury  neighbourhood_cleansed_South Boston  \\\n",
       "id                                                                              \n",
       "12147973                               0                                    0   \n",
       "3075044                                0                                    0   \n",
       "6976                                   0                                    0   \n",
       "1436513                                0                                    0   \n",
       "7651065                                0                                    0   \n",
       "\n",
       "          neighbourhood_cleansed_South Boston Waterfront  \\\n",
       "id                                                         \n",
       "12147973                                               0   \n",
       "3075044                                                0   \n",
       "6976                                                   0   \n",
       "1436513                                                0   \n",
       "7651065                                                0   \n",
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       "          neighbourhood_cleansed_South End  neighbourhood_cleansed_West End  \\\n",
       "id                                                                            \n",
       "12147973                                 0                                0   \n",
       "3075044                                  0                                0   \n",
       "6976                                     0                                0   \n",
       "1436513                                  0                                0   \n",
       "7651065                                  0                                0   \n",
       "\n",
       "          neighbourhood_cleansed_West Roxbury  is_location_exact_t  \\\n",
       "id                                                                   \n",
       "12147973                                    0                    1   \n",
       "3075044                                     0                    1   \n",
       "6976                                        0                    1   \n",
       "1436513                                     0                    0   \n",
       "7651065                                     0                    1   \n",
       "\n",
       "          property_type_Bed & Breakfast  property_type_Boat  \\\n",
       "id                                                            \n",
       "12147973                              0                   0   \n",
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       "6976                                  0                   0   \n",
       "1436513                               0                   0   \n",
       "7651065                               0                   0   \n",
       "\n",
       "          property_type_Camper/RV  property_type_Condominium  \\\n",
       "id                                                             \n",
       "12147973                        0                          0   \n",
       "3075044                         0                          0   \n",
       "6976                            0                          0   \n",
       "1436513                         0                          0   \n",
       "7651065                         0                          0   \n",
       "\n",
       "          property_type_Dorm  property_type_Entire Floor  \\\n",
       "id                                                         \n",
       "12147973                   0                           0   \n",
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       "6976                       0                           0   \n",
       "1436513                    0                           0   \n",
       "7651065                    0                           0   \n",
       "\n",
       "          property_type_Guesthouse  property_type_House  property_type_Loft  \\\n",
       "id                                                                            \n",
       "12147973                         0                    1                   0   \n",
       "3075044                          0                    0                   0   \n",
       "6976                             0                    0                   0   \n",
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       "7651065                          0                    1                   0   \n",
       "\n",
       "          property_type_Other  property_type_Townhouse  property_type_Villa  \\\n",
       "id                                                                            \n",
       "12147973                    0                        0                    0   \n",
       "3075044                     0                        0                    0   \n",
       "6976                        0                        0                    0   \n",
       "1436513                     0                        0                    0   \n",
       "7651065                     0                        0                    0   \n",
       "\n",
       "          room_type_Private room  room_type_Shared room  bed_type_Couch  \\\n",
       "id                                                                        \n",
       "12147973                       0                      0               0   \n",
       "3075044                        1                      0               0   \n",
       "6976                           1                      0               0   \n",
       "1436513                        1                      0               0   \n",
       "7651065                        1                      0               0   \n",
       "\n",
       "          bed_type_Futon  bed_type_Pull-out Sofa  bed_type_Real Bed  \\\n",
       "id                                                                    \n",
       "12147973               0                       0                  1   \n",
       "3075044                0                       0                  1   \n",
       "6976                   0                       0                  1   \n",
       "1436513                0                       0                  1   \n",
       "7651065                0                       0                  1   \n",
       "\n",
       "          instant_bookable_t  cancellation_policy_moderate  \\\n",
       "id                                                           \n",
       "12147973                   0                             1   \n",
       "3075044                    1                             1   \n",
       "6976                       0                             1   \n",
       "1436513                    0                             1   \n",
       "7651065                    0                             0   \n",
       "\n",
       "          cancellation_policy_strict  cancellation_policy_super_strict_30  \\\n",
       "id                                                                          \n",
       "12147973                           0                                    0   \n",
       "3075044                            0                                    0   \n",
       "6976                               0                                    0   \n",
       "1436513                            0                                    0   \n",
       "7651065                            0                                    0   \n",
       "\n",
       "          require_guest_profile_picture_t  require_guest_phone_verification_t  \n",
       "id                                                                             \n",
       "12147973                                0                                   0  \n",
       "3075044                                 0                                   0  \n",
       "6976                                    1                                   0  \n",
       "1436513                                 0                                   0  \n",
       "7651065                                 0                                   0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3560, 127)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boston.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Seattle Listing Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read Seattle listing data, ignore 'access','interaction','house_rules' columns, set 'id' as index\n",
    "df_seattle = pd.read_csv('seattle\\listings.csv', sep=',', index_col='id', \n",
    "                 parse_dates=['last_scraped', 'host_since'], na_filter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>notes</th>\n",
       "      <th>transit</th>\n",
       "      <th>thumbnail_url</th>\n",
       "      <th>medium_url</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>xl_picture_url</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_url</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_about</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_thumbnail_url</th>\n",
       "      <th>host_picture_url</th>\n",
       "      <th>host_neighbourhood</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>street</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>market</th>\n",
       "      <th>smart_location</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>is_location_exact</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>amenities</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>price</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>calendar_updated</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>calendar_last_scraped</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>241032</td>\n",
       "      <td>https://www.airbnb.com/rooms/241032</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Stylish Queen Anne Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a1.muscache.com/ac/pictures/67560560/c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>956883</td>\n",
       "      <td>https://www.airbnb.com/users/show/956883</td>\n",
       "      <td>Maija</td>\n",
       "      <td>2011-08-11</td>\n",
       "      <td>Seattle, Washington, United States</td>\n",
       "      <td>I am an artist, interior designer, and run a s...</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>96%</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a0.muscache.com/ac/users/956883/profil...</td>\n",
       "      <td>https://a0.muscache.com/ac/users/956883/profil...</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'kba']</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Gilman Dr W, Seattle, WA 98119, United States</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>98119</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>47.636289</td>\n",
       "      <td>-122.371025</td>\n",
       "      <td>t</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>$5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>4 weeks ago</td>\n",
       "      <td>t</td>\n",
       "      <td>14</td>\n",
       "      <td>41</td>\n",
       "      <td>71</td>\n",
       "      <td>346</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>207</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>95.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>953595</td>\n",
       "      <td>https://www.airbnb.com/rooms/953595</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Bright &amp; Airy Queen Anne Apartment</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>Beautiful, hypoallergenic apartment in an extr...</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>none</td>\n",
       "      <td>Queen Anne is a wonderful, truly functional vi...</td>\n",
       "      <td>What's up with the free pillows?  Our home was...</td>\n",
       "      <td>Convenient bus stops are just down the block, ...</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/14409893/f...</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
       "      <td>5177328</td>\n",
       "      <td>https://www.airbnb.com/users/show/5177328</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>2013-02-21</td>\n",
       "      <td>Seattle, Washington, United States</td>\n",
       "      <td>Living east coast/left coast/overseas.  Time i...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>98%</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>https://a0.muscache.com/ac/users/5177328/profi...</td>\n",
       "      <td>https://a0.muscache.com/ac/users/5177328/profi...</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>['email', 'phone', 'facebook', 'linkedin', 're...</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>7th Avenue West, Seattle, WA 98119, United States</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>98119</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>47.639123</td>\n",
       "      <td>-122.365666</td>\n",
       "      <td>t</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$150.00</td>\n",
       "      <td>$1,000.00</td>\n",
       "      <td>$3,000.00</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>$40.00</td>\n",
       "      <td>1</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>today</td>\n",
       "      <td>t</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>291</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>43</td>\n",
       "      <td>2013-08-19</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>96.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3308979</td>\n",
       "      <td>https://www.airbnb.com/rooms/3308979</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>New Modern House-Amazing water view</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>Our house is modern, light and fresh with a wa...</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>none</td>\n",
       "      <td>Upper Queen Anne is a charming neighborhood fu...</td>\n",
       "      <td>Our house is located just 5 short blocks to To...</td>\n",
       "      <td>A bus stop is just 2 blocks away.   Easy bus a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a2.muscache.com/ac/pictures/b4324e0f-a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16708587</td>\n",
       "      <td>https://www.airbnb.com/users/show/16708587</td>\n",
       "      <td>Jill</td>\n",
       "      <td>2014-06-12</td>\n",
       "      <td>Seattle, Washington, United States</td>\n",
       "      <td>i love living in Seattle.  i grew up in the mi...</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>67%</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a1.muscache.com/ac/users/16708587/prof...</td>\n",
       "      <td>https://a1.muscache.com/ac/users/16708587/prof...</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>['email', 'phone', 'google', 'reviews', 'jumio']</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>West Lee Street, Seattle, WA 98119, United States</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>98119</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>47.629724</td>\n",
       "      <td>-122.369483</td>\n",
       "      <td>t</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>11</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$975.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1,000.00</td>\n",
       "      <td>$300.00</td>\n",
       "      <td>10</td>\n",
       "      <td>$25.00</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>5 weeks ago</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>20</td>\n",
       "      <td>2014-07-30</td>\n",
       "      <td>2015-09-03</td>\n",
       "      <td>97.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  listing_url       scrape_id last_scraped  \\\n",
       "id                                                                           \n",
       "241032    https://www.airbnb.com/rooms/241032  20160104002432   2016-01-04   \n",
       "953595    https://www.airbnb.com/rooms/953595  20160104002432   2016-01-04   \n",
       "3308979  https://www.airbnb.com/rooms/3308979  20160104002432   2016-01-04   \n",
       "\n",
       "                                        name  \\\n",
       "id                                             \n",
       "241032          Stylish Queen Anne Apartment   \n",
       "953595    Bright & Airy Queen Anne Apartment   \n",
       "3308979  New Modern House-Amazing water view   \n",
       "\n",
       "                                                   summary  \\\n",
       "id                                                           \n",
       "241032                                                 NaN   \n",
       "953595   Chemically sensitive? We've removed the irrita...   \n",
       "3308979  New modern house built in 2013.  Spectacular s...   \n",
       "\n",
       "                                                     space  \\\n",
       "id                                                           \n",
       "241032   Make your self at home in this charming one-be...   \n",
       "953595   Beautiful, hypoallergenic apartment in an extr...   \n",
       "3308979  Our house is modern, light and fresh with a wa...   \n",
       "\n",
       "                                               description  \\\n",
       "id                                                           \n",
       "241032   Make your self at home in this charming one-be...   \n",
       "953595   Chemically sensitive? We've removed the irrita...   \n",
       "3308979  New modern house built in 2013.  Spectacular s...   \n",
       "\n",
       "        experiences_offered  \\\n",
       "id                            \n",
       "241032                 none   \n",
       "953595                 none   \n",
       "3308979                none   \n",
       "\n",
       "                                     neighborhood_overview  \\\n",
       "id                                                           \n",
       "241032                                                 NaN   \n",
       "953595   Queen Anne is a wonderful, truly functional vi...   \n",
       "3308979  Upper Queen Anne is a charming neighborhood fu...   \n",
       "\n",
       "                                                     notes  \\\n",
       "id                                                           \n",
       "241032                                                 NaN   \n",
       "953595   What's up with the free pillows?  Our home was...   \n",
       "3308979  Our house is located just 5 short blocks to To...   \n",
       "\n",
       "                                                   transit  \\\n",
       "id                                                           \n",
       "241032                                                 NaN   \n",
       "953595   Convenient bus stops are just down the block, ...   \n",
       "3308979  A bus stop is just 2 blocks away.   Easy bus a...   \n",
       "\n",
       "                                             thumbnail_url  \\\n",
       "id                                                           \n",
       "241032                                                 NaN   \n",
       "953595   https://a0.muscache.com/ac/pictures/14409893/f...   \n",
       "3308979                                                NaN   \n",
       "\n",
       "                                                medium_url  \\\n",
       "id                                                           \n",
       "241032                                                 NaN   \n",
       "953595   https://a0.muscache.com/im/pictures/14409893/f...   \n",
       "3308979                                                NaN   \n",
       "\n",
       "                                               picture_url  \\\n",
       "id                                                           \n",
       "241032   https://a1.muscache.com/ac/pictures/67560560/c...   \n",
       "953595   https://a0.muscache.com/ac/pictures/14409893/f...   \n",
       "3308979  https://a2.muscache.com/ac/pictures/b4324e0f-a...   \n",
       "\n",
       "                                            xl_picture_url   host_id  \\\n",
       "id                                                                     \n",
       "241032                                                 NaN    956883   \n",
       "953595   https://a0.muscache.com/ac/pictures/14409893/f...   5177328   \n",
       "3308979                                                NaN  16708587   \n",
       "\n",
       "                                           host_url host_name host_since  \\\n",
       "id                                                                         \n",
       "241032     https://www.airbnb.com/users/show/956883     Maija 2011-08-11   \n",
       "953595    https://www.airbnb.com/users/show/5177328    Andrea 2013-02-21   \n",
       "3308979  https://www.airbnb.com/users/show/16708587      Jill 2014-06-12   \n",
       "\n",
       "                              host_location  \\\n",
       "id                                            \n",
       "241032   Seattle, Washington, United States   \n",
       "953595   Seattle, Washington, United States   \n",
       "3308979  Seattle, Washington, United States   \n",
       "\n",
       "                                                host_about  \\\n",
       "id                                                           \n",
       "241032   I am an artist, interior designer, and run a s...   \n",
       "953595   Living east coast/left coast/overseas.  Time i...   \n",
       "3308979  i love living in Seattle.  i grew up in the mi...   \n",
       "\n",
       "         host_response_time host_response_rate host_acceptance_rate  \\\n",
       "id                                                                    \n",
       "241032   within a few hours                96%                 100%   \n",
       "953595       within an hour                98%                 100%   \n",
       "3308979  within a few hours                67%                 100%   \n",
       "\n",
       "        host_is_superhost                                 host_thumbnail_url  \\\n",
       "id                                                                             \n",
       "241032                  f  https://a0.muscache.com/ac/users/956883/profil...   \n",
       "953595                  t  https://a0.muscache.com/ac/users/5177328/profi...   \n",
       "3308979                 f  https://a1.muscache.com/ac/users/16708587/prof...   \n",
       "\n",
       "                                          host_picture_url host_neighbourhood  \\\n",
       "id                                                                              \n",
       "241032   https://a0.muscache.com/ac/users/956883/profil...         Queen Anne   \n",
       "953595   https://a0.muscache.com/ac/users/5177328/profi...         Queen Anne   \n",
       "3308979  https://a1.muscache.com/ac/users/16708587/prof...         Queen Anne   \n",
       "\n",
       "         host_listings_count  host_total_listings_count  \\\n",
       "id                                                        \n",
       "241032                   3.0                        3.0   \n",
       "953595                   6.0                        6.0   \n",
       "3308979                  2.0                        2.0   \n",
       "\n",
       "                                        host_verifications  \\\n",
       "id                                                           \n",
       "241032                ['email', 'phone', 'reviews', 'kba']   \n",
       "953595   ['email', 'phone', 'facebook', 'linkedin', 're...   \n",
       "3308979   ['email', 'phone', 'google', 'reviews', 'jumio']   \n",
       "\n",
       "        host_has_profile_pic host_identity_verified  \\\n",
       "id                                                    \n",
       "241032                     t                      t   \n",
       "953595                     t                      t   \n",
       "3308979                    t                      t   \n",
       "\n",
       "                                                    street neighbourhood  \\\n",
       "id                                                                         \n",
       "241032       Gilman Dr W, Seattle, WA 98119, United States    Queen Anne   \n",
       "953595   7th Avenue West, Seattle, WA 98119, United States    Queen Anne   \n",
       "3308979  West Lee Street, Seattle, WA 98119, United States    Queen Anne   \n",
       "\n",
       "        neighbourhood_cleansed neighbourhood_group_cleansed     city state  \\\n",
       "id                                                                           \n",
       "241032         West Queen Anne                   Queen Anne  Seattle    WA   \n",
       "953595         West Queen Anne                   Queen Anne  Seattle    WA   \n",
       "3308979        West Queen Anne                   Queen Anne  Seattle    WA   \n",
       "\n",
       "        zipcode   market smart_location country_code        country  \\\n",
       "id                                                                    \n",
       "241032    98119  Seattle    Seattle, WA           US  United States   \n",
       "953595    98119  Seattle    Seattle, WA           US  United States   \n",
       "3308979   98119  Seattle    Seattle, WA           US  United States   \n",
       "\n",
       "          latitude   longitude is_location_exact property_type  \\\n",
       "id                                                               \n",
       "241032   47.636289 -122.371025                 t     Apartment   \n",
       "953595   47.639123 -122.365666                 t     Apartment   \n",
       "3308979  47.629724 -122.369483                 t         House   \n",
       "\n",
       "               room_type  accommodates  bathrooms  bedrooms  beds  bed_type  \\\n",
       "id                                                                            \n",
       "241032   Entire home/apt             4        1.0       1.0   1.0  Real Bed   \n",
       "953595   Entire home/apt             4        1.0       1.0   1.0  Real Bed   \n",
       "3308979  Entire home/apt            11        4.5       5.0   7.0  Real Bed   \n",
       "\n",
       "                                                 amenities  square_feet  \\\n",
       "id                                                                        \n",
       "241032   {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...          NaN   \n",
       "953595   {TV,Internet,\"Wireless Internet\",Kitchen,\"Free...          NaN   \n",
       "3308979  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...          NaN   \n",
       "\n",
       "           price weekly_price monthly_price security_deposit cleaning_fee  \\\n",
       "id                                                                          \n",
       "241032    $85.00          NaN           NaN              NaN          NaN   \n",
       "953595   $150.00    $1,000.00     $3,000.00          $100.00       $40.00   \n",
       "3308979  $975.00          NaN           NaN        $1,000.00      $300.00   \n",
       "\n",
       "         guests_included extra_people  minimum_nights  maximum_nights  \\\n",
       "id                                                                      \n",
       "241032                 2        $5.00               1             365   \n",
       "953595                 1        $0.00               2              90   \n",
       "3308979               10       $25.00               4              30   \n",
       "\n",
       "        calendar_updated has_availability  availability_30  availability_60  \\\n",
       "id                                                                            \n",
       "241032       4 weeks ago                t               14               41   \n",
       "953595             today                t               13               13   \n",
       "3308979      5 weeks ago                t                1                6   \n",
       "\n",
       "         availability_90  availability_365 calendar_last_scraped  \\\n",
       "id                                                                 \n",
       "241032                71               346            2016-01-04   \n",
       "953595                16               291            2016-01-04   \n",
       "3308979               17               220            2016-01-04   \n",
       "\n",
       "         number_of_reviews first_review last_review  review_scores_rating  \\\n",
       "id                                                                          \n",
       "241032                 207   2011-11-01  2016-01-02                  95.0   \n",
       "953595                  43   2013-08-19  2015-12-29                  96.0   \n",
       "3308979                 20   2014-07-30  2015-09-03                  97.0   \n",
       "\n",
       "         review_scores_accuracy  review_scores_cleanliness  \\\n",
       "id                                                           \n",
       "241032                     10.0                       10.0   \n",
       "953595                     10.0                       10.0   \n",
       "3308979                    10.0                       10.0   \n",
       "\n",
       "         review_scores_checkin  review_scores_communication  \\\n",
       "id                                                            \n",
       "241032                    10.0                         10.0   \n",
       "953595                    10.0                         10.0   \n",
       "3308979                   10.0                         10.0   \n",
       "\n",
       "         review_scores_location  review_scores_value requires_license  \\\n",
       "id                                                                      \n",
       "241032                      9.0                 10.0                f   \n",
       "953595                     10.0                 10.0                f   \n",
       "3308979                    10.0                 10.0                f   \n",
       "\n",
       "         license jurisdiction_names instant_bookable cancellation_policy  \\\n",
       "id                                                                         \n",
       "241032       NaN         WASHINGTON                f            moderate   \n",
       "953595       NaN         WASHINGTON                f              strict   \n",
       "3308979      NaN         WASHINGTON                f              strict   \n",
       "\n",
       "        require_guest_profile_picture require_guest_phone_verification  \\\n",
       "id                                                                       \n",
       "241032                              f                                f   \n",
       "953595                              t                                t   \n",
       "3308979                             f                                f   \n",
       "\n",
       "         calculated_host_listings_count  reviews_per_month  \n",
       "id                                                          \n",
       "241032                                2               4.07  \n",
       "953595                                6               1.48  \n",
       "3308979                               2               1.15  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3818, 91)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Drop unnecessary columns\n",
    "feat_info.csv was manually created and contains info about necessary property/actions to each column, such as id, numeric, categorical, location, drop, convert to numeric and convert to date etc.\n",
    "It is worth mentioning why certain columns were dropped.\n",
    "1. host_total_listings_count/calculated_host_listings_count: high correlation with host_listings_count\n",
    "2. host_verifications: use host_identity_verified\n",
    "3. zipcode: use neighbourhood_cleansed \n",
    "4. weekly_price/monthly_price: somewhat related to dependent variable or target daily 'price'. From an AirBnB home landlord's perspective, he/she is mostly like to set the daily 'price' first, before setting weekly_price/monthly_price charge.\n",
    "5. number_of_reviews: use reviews_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3818, 43)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info = pd.read_csv('feat_info.csv', sep=',')\n",
    "\n",
    "# drop unnecessary columns\n",
    "column_dropped_1 = feat_info[feat_info.action == 'drop']['name'].to_list()\n",
    "df_seattle.drop(columns = column_dropped_1, axis=1, inplace = True)\n",
    "df_seattle.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Convert data\n",
    "'extra_people' is somewhat related to dependent variable or target 'price'. From a home landlord's perspective, he/she is mostly like to set the price first, before setting 'extra_people' charge. This means 'extra_people' can be ignored. However, 'extra_people' does affect the 'price' to some extend. To keep this effect, 0/1 dummy was used. The same applies to security_deposit' and 'cleaning_fee'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# object to numeric\n",
    "df_seattle['host_acceptance_rate'] = df_seattle['host_acceptance_rate'].str.replace('%', '').astype(float)/100\n",
    "df_seattle['host_response_rate'] = df_seattle['host_response_rate'].str.replace('%', '').astype(float)/100\n",
    "df_seattle['price'] = df_seattle['price'].replace('[$,]', '', regex=True).astype(float)\n",
    "df_seattle['extra_people'] = df_seattle['extra_people'].replace('[$,]', '', regex=True).astype(float)\n",
    "\n",
    "# convert to 0/1\n",
    "df_seattle['extra_people'] = df_seattle['extra_people'].apply(lambda x: 1 if x>0 else 0)\n",
    "# https://stackoverflow.com/questions/37543647/how-to-replace-all-non-nan-entries-of-a-dataframe-with-1-and-all-nan-with-0\n",
    "df_seattle[['security_deposit', 'cleaning_fee']] = df_seattle[['security_deposit', 'cleaning_fee']].notnull().astype('int')\n",
    "\n",
    "# calculate host days\n",
    "df_seattle['host_days'] = (df_seattle['last_scraped'] - df_seattle['host_since']).dt.days\n",
    "\n",
    "# convert calendar_updated to numeric days\n",
    "calendar_updated_dict = {'today': 0, '2 weeks ago': 14, 'a week ago': 7, '3 weeks ago': 21, '2 months ago': 60, 'yesterday': 1, '4 weeks ago': 28, '3 months ago': 90, '4 days ago': 4, '5 weeks ago': 35, '3 days ago': 3, '5 days ago': 5, '4 months ago': 120, '6 weeks ago': 42, '2 days ago': 2, '5 months ago': 150, '7 weeks ago': 49, '7 months ago': 210, '6 days ago': 6, '1 week ago': 7, '6 months ago': 180, '8 months ago': 240, '10 months ago': 300, '9 months ago': 270, 'never': -1, '11 months ago': 330, '13 months ago': 390, '14 months ago': 420, '12 months ago': 360, '17 months ago': 510, '15 months ago': 450, '16 months ago': 480, '22 months ago': 660, '18 months ago': 540, '25 months ago': 750, '30 months ago': 900, '20 months ago': 600, '23 months ago': 690} \n",
    "df_seattle.calendar_updated.replace(calendar_updated_dict, inplace=True)\n",
    "df_seattle['calendar_updated'] = np.where(df_seattle['calendar_updated'] <0, df_seattle['host_days'], df_seattle['calendar_updated']).astype(int)\n",
    "df_seattle.drop(columns=['last_scraped','host_since'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Drop outliers\n",
    "From the histogram of our target variable 'price', it is easy to observe that 'price' has a wide range of values. \n",
    "The extreme values (outliers) may affect the predicting power of the final model. Thus, some rows with extreme values of 'price' will be deleted. \n",
    "From the plot, it seems $600 is a good cutoff point. A total of 18 rows were deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b72f477a48>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_seattle.price.hist(figsize=(15, 5), bins=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3800, 42)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle = df_seattle[df_seattle.price < 600]\n",
    "df_seattle.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Drop rows with >30% data missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '% percentage of missing columns for each row')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing data in each row\n",
    "missing_row_ct = df_seattle.isnull().sum(axis = 1)\n",
    "missing_row_ct.sort_values(ascending = False, inplace=True)\n",
    "missing_row_pct = missing_row_ct/(df_seattle.shape[1]) * 100\n",
    "\n",
    "# percentage of missing data in each row\n",
    "missing_row_pct.hist(figsize=(8, 4), bins=20)\n",
    "plt.xlabel('% percentage of missing columns for each row')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3798, 42)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete rows with 30% more value missing, 2 rows deleted\n",
    "df_seattle = df_seattle[df_seattle.isnull().sum(axis=1) < df_seattle.shape[1] * 0.3]\n",
    "df_seattle.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 Impute nulls in columns\n",
    "The majority of columns have no missing values. For those having missing values, no single column has missing value than 25%. We will impute the nulls with mean or mode values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'missing %')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing data in each column\n",
    "missing_ct = df_seattle.isnull().sum()\n",
    "missing_ct.sort_values(ascending = False, inplace=True)\n",
    "missing_pct = missing_ct/(df_seattle.shape[0]) * 100\n",
    "\n",
    "# percentage of missing data in each column.\n",
    "missing_pct.plot.bar(figsize=(15, 4))\n",
    "plt.ylabel('missing %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fillna with mode for categorical columns, and the numeric columns that only take integer/fixed values\n",
    "for col in ['host_response_time','property_type','bathrooms','bedrooms', 'beds']:\n",
    "    df_seattle[col].fillna(df_seattle[col].mode().iloc[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fillna with mean for numeric columns\n",
    "df_seattle.fillna(df_seattle.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double check there is no null values\n",
    "df_seattle.isnull().mean()[df_seattle.isnull().mean() > 0].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.7 Convert 'amenities' to individual amenity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_seattle['amenities'].replace(to_replace='[\\{\\}\\\"]', value='', regex=True, inplace=True)\n",
    "amenities_dummy = df_seattle['amenities'].str.get_dummies(sep=',').add_prefix('amenity_')\n",
    "\n",
    "# concatenate all individual amenities column with the full dataset\n",
    "df_seattle = pd.concat([df_seattle, amenities_dummy], axis=1, sort=False)\n",
    "df_seattle.drop(columns=['amenities'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list of object columns\n",
    "cols_cat = df_seattle.select_dtypes(include=[object]).columns.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.8 One-hot encoding of categorical fetures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_seattle = pd.get_dummies(data=df_seattle, prefix=cols_cat, prefix_sep='_', columns=cols_cat, drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.9 Final review of the processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>calendar_updated</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>host_days</th>\n",
       "      <th>amenity_24-Hour Check-in</th>\n",
       "      <th>amenity_Air Conditioning</th>\n",
       "      <th>amenity_Breakfast</th>\n",
       "      <th>amenity_Buzzer/Wireless Intercom</th>\n",
       "      <th>amenity_Cable TV</th>\n",
       "      <th>amenity_Carbon Monoxide Detector</th>\n",
       "      <th>amenity_Cat(s)</th>\n",
       "      <th>amenity_Dog(s)</th>\n",
       "      <th>amenity_Doorman</th>\n",
       "      <th>amenity_Dryer</th>\n",
       "      <th>amenity_Elevator in Building</th>\n",
       "      <th>amenity_Essentials</th>\n",
       "      <th>amenity_Family/Kid Friendly</th>\n",
       "      <th>amenity_Fire Extinguisher</th>\n",
       "      <th>amenity_First Aid Kit</th>\n",
       "      <th>amenity_Free Parking on Premises</th>\n",
       "      <th>amenity_Gym</th>\n",
       "      <th>amenity_Hair Dryer</th>\n",
       "      <th>amenity_Hangers</th>\n",
       "      <th>amenity_Heating</th>\n",
       "      <th>amenity_Hot Tub</th>\n",
       "      <th>amenity_Indoor Fireplace</th>\n",
       "      <th>amenity_Internet</th>\n",
       "      <th>amenity_Iron</th>\n",
       "      <th>amenity_Kitchen</th>\n",
       "      <th>amenity_Laptop Friendly Workspace</th>\n",
       "      <th>amenity_Lock on Bedroom Door</th>\n",
       "      <th>amenity_Other pet(s)</th>\n",
       "      <th>amenity_Pets Allowed</th>\n",
       "      <th>amenity_Pets live on this property</th>\n",
       "      <th>amenity_Pool</th>\n",
       "      <th>amenity_Safety Card</th>\n",
       "      <th>amenity_Shampoo</th>\n",
       "      <th>amenity_Smoke Detector</th>\n",
       "      <th>amenity_Smoking Allowed</th>\n",
       "      <th>amenity_Suitable for Events</th>\n",
       "      <th>amenity_TV</th>\n",
       "      <th>amenity_Washer</th>\n",
       "      <th>amenity_Washer / Dryer</th>\n",
       "      <th>amenity_Wheelchair Accessible</th>\n",
       "      <th>amenity_Wireless Internet</th>\n",
       "      <th>host_response_time_within a day</th>\n",
       "      <th>host_response_time_within a few hours</th>\n",
       "      <th>host_response_time_within an hour</th>\n",
       "      <th>host_is_superhost_t</th>\n",
       "      <th>host_has_profile_pic_t</th>\n",
       "      <th>host_identity_verified_t</th>\n",
       "      <th>neighbourhood_cleansed_Alki</th>\n",
       "      <th>neighbourhood_cleansed_Arbor Heights</th>\n",
       "      <th>neighbourhood_cleansed_Atlantic</th>\n",
       "      <th>neighbourhood_cleansed_Belltown</th>\n",
       "      <th>neighbourhood_cleansed_Bitter Lake</th>\n",
       "      <th>neighbourhood_cleansed_Briarcliff</th>\n",
       "      <th>neighbourhood_cleansed_Brighton</th>\n",
       "      <th>neighbourhood_cleansed_Broadview</th>\n",
       "      <th>neighbourhood_cleansed_Broadway</th>\n",
       "      <th>neighbourhood_cleansed_Bryant</th>\n",
       "      <th>neighbourhood_cleansed_Cedar Park</th>\n",
       "      <th>neighbourhood_cleansed_Central Business District</th>\n",
       "      <th>neighbourhood_cleansed_Columbia City</th>\n",
       "      <th>neighbourhood_cleansed_Crown Hill</th>\n",
       "      <th>neighbourhood_cleansed_Dunlap</th>\n",
       "      <th>neighbourhood_cleansed_East Queen Anne</th>\n",
       "      <th>neighbourhood_cleansed_Eastlake</th>\n",
       "      <th>neighbourhood_cleansed_Fairmount Park</th>\n",
       "      <th>neighbourhood_cleansed_Fauntleroy</th>\n",
       "      <th>neighbourhood_cleansed_First Hill</th>\n",
       "      <th>neighbourhood_cleansed_Fremont</th>\n",
       "      <th>neighbourhood_cleansed_Gatewood</th>\n",
       "      <th>neighbourhood_cleansed_Genesee</th>\n",
       "      <th>neighbourhood_cleansed_Georgetown</th>\n",
       "      <th>neighbourhood_cleansed_Green Lake</th>\n",
       "      <th>neighbourhood_cleansed_Greenwood</th>\n",
       "      <th>neighbourhood_cleansed_Haller Lake</th>\n",
       "      <th>neighbourhood_cleansed_Harrison/Denny-Blaine</th>\n",
       "      <th>neighbourhood_cleansed_High Point</th>\n",
       "      <th>neighbourhood_cleansed_Highland Park</th>\n",
       "      <th>neighbourhood_cleansed_Holly Park</th>\n",
       "      <th>neighbourhood_cleansed_Industrial District</th>\n",
       "      <th>neighbourhood_cleansed_Interbay</th>\n",
       "      <th>neighbourhood_cleansed_International District</th>\n",
       "      <th>neighbourhood_cleansed_Laurelhurst</th>\n",
       "      <th>neighbourhood_cleansed_Lawton Park</th>\n",
       "      <th>neighbourhood_cleansed_Leschi</th>\n",
       "      <th>neighbourhood_cleansed_Lower Queen Anne</th>\n",
       "      <th>neighbourhood_cleansed_Loyal Heights</th>\n",
       "      <th>neighbourhood_cleansed_Madison Park</th>\n",
       "      <th>neighbourhood_cleansed_Madrona</th>\n",
       "      <th>neighbourhood_cleansed_Mann</th>\n",
       "      <th>neighbourhood_cleansed_Maple Leaf</th>\n",
       "      <th>neighbourhood_cleansed_Matthews Beach</th>\n",
       "      <th>neighbourhood_cleansed_Meadowbrook</th>\n",
       "      <th>neighbourhood_cleansed_Mid-Beacon Hill</th>\n",
       "      <th>neighbourhood_cleansed_Minor</th>\n",
       "      <th>neighbourhood_cleansed_Montlake</th>\n",
       "      <th>neighbourhood_cleansed_Mount Baker</th>\n",
       "      <th>neighbourhood_cleansed_North Admiral</th>\n",
       "      <th>neighbourhood_cleansed_North Beach/Blue Ridge</th>\n",
       "      <th>neighbourhood_cleansed_North Beacon Hill</th>\n",
       "      <th>neighbourhood_cleansed_North College Park</th>\n",
       "      <th>neighbourhood_cleansed_North Delridge</th>\n",
       "      <th>neighbourhood_cleansed_North Queen Anne</th>\n",
       "      <th>neighbourhood_cleansed_Olympic Hills</th>\n",
       "      <th>neighbourhood_cleansed_Phinney Ridge</th>\n",
       "      <th>neighbourhood_cleansed_Pike-Market</th>\n",
       "      <th>neighbourhood_cleansed_Pinehurst</th>\n",
       "      <th>neighbourhood_cleansed_Pioneer Square</th>\n",
       "      <th>neighbourhood_cleansed_Portage Bay</th>\n",
       "      <th>neighbourhood_cleansed_Rainier Beach</th>\n",
       "      <th>neighbourhood_cleansed_Ravenna</th>\n",
       "      <th>neighbourhood_cleansed_Riverview</th>\n",
       "      <th>neighbourhood_cleansed_Roosevelt</th>\n",
       "      <th>neighbourhood_cleansed_Roxhill</th>\n",
       "      <th>neighbourhood_cleansed_Seaview</th>\n",
       "      <th>neighbourhood_cleansed_Seward Park</th>\n",
       "      <th>neighbourhood_cleansed_South Beacon Hill</th>\n",
       "      <th>neighbourhood_cleansed_South Delridge</th>\n",
       "      <th>neighbourhood_cleansed_South Lake Union</th>\n",
       "      <th>neighbourhood_cleansed_South Park</th>\n",
       "      <th>neighbourhood_cleansed_Southeast Magnolia</th>\n",
       "      <th>neighbourhood_cleansed_Stevens</th>\n",
       "      <th>neighbourhood_cleansed_Sunset Hill</th>\n",
       "      <th>neighbourhood_cleansed_University District</th>\n",
       "      <th>neighbourhood_cleansed_Victory Heights</th>\n",
       "      <th>neighbourhood_cleansed_View Ridge</th>\n",
       "      <th>neighbourhood_cleansed_Wallingford</th>\n",
       "      <th>neighbourhood_cleansed_Wedgwood</th>\n",
       "      <th>neighbourhood_cleansed_West Queen Anne</th>\n",
       "      <th>neighbourhood_cleansed_West Woodland</th>\n",
       "      <th>neighbourhood_cleansed_Westlake</th>\n",
       "      <th>neighbourhood_cleansed_Whittier Heights</th>\n",
       "      <th>neighbourhood_cleansed_Windermere</th>\n",
       "      <th>neighbourhood_cleansed_Yesler Terrace</th>\n",
       "      <th>is_location_exact_t</th>\n",
       "      <th>property_type_Bed &amp; Breakfast</th>\n",
       "      <th>property_type_Boat</th>\n",
       "      <th>property_type_Bungalow</th>\n",
       "      <th>property_type_Cabin</th>\n",
       "      <th>property_type_Camper/RV</th>\n",
       "      <th>property_type_Chalet</th>\n",
       "      <th>property_type_Condominium</th>\n",
       "      <th>property_type_Dorm</th>\n",
       "      <th>property_type_House</th>\n",
       "      <th>property_type_Loft</th>\n",
       "      <th>property_type_Other</th>\n",
       "      <th>property_type_Tent</th>\n",
       "      <th>property_type_Townhouse</th>\n",
       "      <th>property_type_Treehouse</th>\n",
       "      <th>property_type_Yurt</th>\n",
       "      <th>room_type_Private room</th>\n",
       "      <th>room_type_Shared room</th>\n",
       "      <th>bed_type_Couch</th>\n",
       "      <th>bed_type_Futon</th>\n",
       "      <th>bed_type_Pull-out Sofa</th>\n",
       "      <th>bed_type_Real Bed</th>\n",
       "      <th>instant_bookable_t</th>\n",
       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_strict</th>\n",
       "      <th>require_guest_profile_picture_t</th>\n",
       "      <th>require_guest_phone_verification_t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>241032</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>41</td>\n",
       "      <td>71</td>\n",
       "      <td>346</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.070000</td>\n",
       "      <td>1607.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>953595</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>291</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7421966</td>\n",
       "      <td>0.948933</td>\n",
       "      <td>0.999671</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>94.535193</td>\n",
       "      <td>9.63538</td>\n",
       "      <td>9.554638</td>\n",
       "      <td>9.785873</td>\n",
       "      <td>9.809841</td>\n",
       "      <td>9.609027</td>\n",
       "      <td>9.453736</td>\n",
       "      <td>2.085161</td>\n",
       "      <td>789.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>278830</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>365</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <td>0</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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5956968</td>\n",
       "      <td>0.948933</td>\n",
       "      <td>0.999671</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>302</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>1836.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         host_response_rate  host_acceptance_rate  host_listings_count  \\\n",
       "id                                                                       \n",
       "241032             0.960000              1.000000                  3.0   \n",
       "953595             0.980000              1.000000                  6.0   \n",
       "7421966            0.948933              0.999671                  1.0   \n",
       "278830             1.000000              0.999671                  2.0   \n",
       "5956968            0.948933              0.999671                  1.0   \n",
       "\n",
       "         accommodates  bathrooms  bedrooms  beds  price  security_deposit  \\\n",
       "id                                                                          \n",
       "241032              4        1.0       1.0   1.0   85.0                 0   \n",
       "953595              4        1.0       1.0   1.0  150.0                 1   \n",
       "7421966             3        1.0       0.0   2.0  100.0                 0   \n",
       "278830              6        2.0       3.0   3.0  450.0                 1   \n",
       "5956968             2        1.0       1.0   1.0  120.0                 0   \n",
       "\n",
       "         cleaning_fee  guests_included  extra_people  minimum_nights  \\\n",
       "id                                                                     \n",
       "241032              0                2             1               1   \n",
       "953595              1                1             0               2   \n",
       "7421966             0                1             0               1   \n",
       "278830              1                6             1               1   \n",
       "5956968             1                1             1               1   \n",
       "\n",
       "         calendar_updated  availability_30  availability_60  availability_90  \\\n",
       "id                                                                             \n",
       "241032                 28               14               41               71   \n",
       "953595                  0               13               13               16   \n",
       "7421966               180                0                0                0   \n",
       "278830                 49               30               60               90   \n",
       "5956968                14                0                0               27   \n",
       "\n",
       "         availability_365  review_scores_rating  review_scores_accuracy  \\\n",
       "id                                                                        \n",
       "241032                346             95.000000                10.00000   \n",
       "953595                291             96.000000                10.00000   \n",
       "7421966               143             94.535193                 9.63538   \n",
       "278830                365             92.000000                 9.00000   \n",
       "5956968               302             95.000000                10.00000   \n",
       "\n",
       "         review_scores_cleanliness  review_scores_checkin  \\\n",
       "id                                                          \n",
       "241032                   10.000000              10.000000   \n",
       "953595                   10.000000              10.000000   \n",
       "7421966                   9.554638               9.785873   \n",
       "278830                    9.000000              10.000000   \n",
       "5956968                  10.000000              10.000000   \n",
       "\n",
       "         review_scores_communication  review_scores_location  \\\n",
       "id                                                             \n",
       "241032                     10.000000                9.000000   \n",
       "953595                     10.000000               10.000000   \n",
       "7421966                     9.809841                9.609027   \n",
       "278830                     10.000000                9.000000   \n",
       "5956968                    10.000000               10.000000   \n",
       "\n",
       "         review_scores_value  reviews_per_month  host_days  \\\n",
       "id                                                           \n",
       "241032             10.000000           4.070000     1607.0   \n",
       "953595             10.000000           1.480000     1047.0   \n",
       "7421966             9.453736           2.085161      789.0   \n",
       "278830              9.000000           0.890000     1497.0   \n",
       "5956968            10.000000           2.450000     1836.0   \n",
       "\n",
       "         amenity_24-Hour Check-in  amenity_Air Conditioning  \\\n",
       "id                                                            \n",
       "241032                          0                         1   \n",
       "953595                          0                         0   \n",
       "7421966                         0                         0   \n",
       "278830                          0                         0   \n",
       "5956968                         0                         0   \n",
       "\n",
       "         amenity_Breakfast  amenity_Buzzer/Wireless Intercom  \\\n",
       "id                                                             \n",
       "241032                   0                                 0   \n",
       "953595                   0                                 1   \n",
       "7421966                  0                                 0   \n",
       "278830                   0                                 0   \n",
       "5956968                  0                                 0   \n",
       "\n",
       "         amenity_Cable TV  amenity_Carbon Monoxide Detector  amenity_Cat(s)  \\\n",
       "id                                                                            \n",
       "241032                  1                                 0               0   \n",
       "953595                  0                                 1               0   \n",
       "7421966                 0                                 1               0   \n",
       "278830                  1                                 1               0   \n",
       "5956968                 0                                 0               0   \n",
       "\n",
       "         amenity_Dog(s)  amenity_Doorman  amenity_Dryer  \\\n",
       "id                                                        \n",
       "241032                0                0              1   \n",
       "953595                0                0              1   \n",
       "7421966               0                0              1   \n",
       "278830                0                0              0   \n",
       "5956968               0                0              0   \n",
       "\n",
       "         amenity_Elevator in Building  amenity_Essentials  \\\n",
       "id                                                          \n",
       "241032                              0                   0   \n",
       "953595                              0                   1   \n",
       "7421966                             0                   1   \n",
       "278830                              0                   1   \n",
       "5956968                             0                   1   \n",
       "\n",
       "         amenity_Family/Kid Friendly  amenity_Fire Extinguisher  \\\n",
       "id                                                                \n",
       "241032                             1                          0   \n",
       "953595                             1                          1   \n",
       "7421966                            1                          1   \n",
       "278830                             1                          1   \n",
       "5956968                            0                          0   \n",
       "\n",
       "         amenity_First Aid Kit  amenity_Free Parking on Premises  amenity_Gym  \\\n",
       "id                                                                              \n",
       "241032                       0                                 0            0   \n",
       "953595                       1                                 1            0   \n",
       "7421966                      0                                 0            0   \n",
       "278830                       1                                 0            0   \n",
       "5956968                      0                                 1            0   \n",
       "\n",
       "         amenity_Hair Dryer  amenity_Hangers  amenity_Heating  \\\n",
       "id                                                              \n",
       "241032                    0                0                1   \n",
       "953595                    0                0                1   \n",
       "7421966                   0                0                1   \n",
       "278830                    0                0                1   \n",
       "5956968                   0                0                1   \n",
       "\n",
       "         amenity_Hot Tub  amenity_Indoor Fireplace  amenity_Internet  \\\n",
       "id                                                                     \n",
       "241032                 0                         0                 1   \n",
       "953595                 0                         0                 1   \n",
       "7421966                0                         1                 1   \n",
       "278830                 0                         0                 1   \n",
       "5956968                0                         0                 0   \n",
       "\n",
       "         amenity_Iron  amenity_Kitchen  amenity_Laptop Friendly Workspace  \\\n",
       "id                                                                          \n",
       "241032              0                1                                  0   \n",
       "953595              0                1                                  0   \n",
       "7421966             0                1                                  0   \n",
       "278830              0                1                                  0   \n",
       "5956968             0                0                                  0   \n",
       "\n",
       "         amenity_Lock on Bedroom Door  amenity_Other pet(s)  \\\n",
       "id                                                            \n",
       "241032                              0                     0   \n",
       "953595                              0                     0   \n",
       "7421966                             0                     0   \n",
       "278830                              0                     0   \n",
       "5956968                             0                     0   \n",
       "\n",
       "         amenity_Pets Allowed  amenity_Pets live on this property  \\\n",
       "id                                                                  \n",
       "241032                      0                                   0   \n",
       "953595                      0                                   0   \n",
       "7421966                     0                                   0   \n",
       "278830                      0                                   0   \n",
       "5956968                     0                                   0   \n",
       "\n",
       "         amenity_Pool  amenity_Safety Card  amenity_Shampoo  \\\n",
       "id                                                            \n",
       "241032              0                    0                0   \n",
       "953595              0                    1                0   \n",
       "7421966             0                    1                1   \n",
       "278830              0                    0                1   \n",
       "5956968             0                    0                1   \n",
       "\n",
       "         amenity_Smoke Detector  amenity_Smoking Allowed  \\\n",
       "id                                                         \n",
       "241032                        0                        0   \n",
       "953595                        1                        0   \n",
       "7421966                       1                        0   \n",
       "278830                        1                        0   \n",
       "5956968                       1                        0   \n",
       "\n",
       "         amenity_Suitable for Events  amenity_TV  amenity_Washer  \\\n",
       "id                                                                 \n",
       "241032                             0           1               1   \n",
       "953595                             0           1               1   \n",
       "7421966                            0           0               1   \n",
       "278830                             0           1               0   \n",
       "5956968                            0           0               0   \n",
       "\n",
       "         amenity_Washer / Dryer  amenity_Wheelchair Accessible  \\\n",
       "id                                                               \n",
       "241032                        0                              0   \n",
       "953595                        0                              0   \n",
       "7421966                       0                              0   \n",
       "278830                        0                              0   \n",
       "5956968                       0                              0   \n",
       "\n",
       "         amenity_Wireless Internet  host_response_time_within a day  \\\n",
       "id                                                                    \n",
       "241032                           1                                0   \n",
       "953595                           1                                0   \n",
       "7421966                          1                                0   \n",
       "278830                           1                                0   \n",
       "5956968                          1                                0   \n",
       "\n",
       "         host_response_time_within a few hours  \\\n",
       "id                                               \n",
       "241032                                       1   \n",
       "953595                                       0   \n",
       "7421966                                      0   \n",
       "278830                                       0   \n",
       "5956968                                      0   \n",
       "\n",
       "         host_response_time_within an hour  host_is_superhost_t  \\\n",
       "id                                                                \n",
       "241032                                   0                    0   \n",
       "953595                                   1                    1   \n",
       "7421966                                  1                    0   \n",
       "278830                                   1                    0   \n",
       "5956968                                  1                    0   \n",
       "\n",
       "         host_has_profile_pic_t  host_identity_verified_t  \\\n",
       "id                                                          \n",
       "241032                        1                         1   \n",
       "953595                        1                         1   \n",
       "7421966                       1                         1   \n",
       "278830                        1                         1   \n",
       "5956968                       1                         1   \n",
       "\n",
       "         neighbourhood_cleansed_Alki  neighbourhood_cleansed_Arbor Heights  \\\n",
       "id                                                                           \n",
       "241032                             0                                     0   \n",
       "953595                             0                                     0   \n",
       "7421966                            0                                     0   \n",
       "278830                             0                                     0   \n",
       "5956968                            0                                     0   \n",
       "\n",
       "         neighbourhood_cleansed_Atlantic  neighbourhood_cleansed_Belltown  \\\n",
       "id                                                                          \n",
       "241032                                 0                                0   \n",
       "953595                                 0                                0   \n",
       "7421966                                0                                0   \n",
       "278830                                 0                                0   \n",
       "5956968                                0                                0   \n",
       "\n",
       "         neighbourhood_cleansed_Bitter Lake  \\\n",
       "id                                            \n",
       "241032                                    0   \n",
       "953595                                    0   \n",
       "7421966                                   0   \n",
       "278830                                    0   \n",
       "5956968                                   0   \n",
       "\n",
       "         neighbourhood_cleansed_Briarcliff  neighbourhood_cleansed_Brighton  \\\n",
       "id                                                                            \n",
       "241032                                   0                                0   \n",
       "953595                                   0                                0   \n",
       "7421966                                  0                                0   \n",
       "278830                                   0                                0   \n",
       "5956968                                  0                                0   \n",
       "\n",
       "         neighbourhood_cleansed_Broadview  neighbourhood_cleansed_Broadway  \\\n",
       "id                                                                           \n",
       "241032                                  0                                0   \n",
       "953595                                  0                                0   \n",
       "7421966                                 0                                0   \n",
       "278830                                  0                                0   \n",
       "5956968                                 0                                0   \n",
       "\n",
       "         neighbourhood_cleansed_Bryant  neighbourhood_cleansed_Cedar Park  \\\n",
       "id                                                                          \n",
       "241032                               0                                  0   \n",
       "953595                               0                                  0   \n",
       "7421966                              0                                  0   \n",
       "278830                               0                                  0   \n",
       "5956968                              0                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Central Business District  \\\n",
       "id                                                          \n",
       "241032                                                  0   \n",
       "953595                                                  0   \n",
       "7421966                                                 0   \n",
       "278830                                                  0   \n",
       "5956968                                                 0   \n",
       "\n",
       "         neighbourhood_cleansed_Columbia City  \\\n",
       "id                                              \n",
       "241032                                      0   \n",
       "953595                                      0   \n",
       "7421966                                     0   \n",
       "278830                                      0   \n",
       "5956968                                     0   \n",
       "\n",
       "         neighbourhood_cleansed_Crown Hill  neighbourhood_cleansed_Dunlap  \\\n",
       "id                                                                          \n",
       "241032                                   0                              0   \n",
       "953595                                   0                              0   \n",
       "7421966                                  0                              0   \n",
       "278830                                   0                              0   \n",
       "5956968                                  0                              0   \n",
       "\n",
       "         neighbourhood_cleansed_East Queen Anne  \\\n",
       "id                                                \n",
       "241032                                        0   \n",
       "953595                                        0   \n",
       "7421966                                       0   \n",
       "278830                                        0   \n",
       "5956968                                       0   \n",
       "\n",
       "         neighbourhood_cleansed_Eastlake  \\\n",
       "id                                         \n",
       "241032                                 0   \n",
       "953595                                 0   \n",
       "7421966                                0   \n",
       "278830                                 0   \n",
       "5956968                                0   \n",
       "\n",
       "         neighbourhood_cleansed_Fairmount Park  \\\n",
       "id                                               \n",
       "241032                                       0   \n",
       "953595                                       0   \n",
       "7421966                                      0   \n",
       "278830                                       0   \n",
       "5956968                                      0   \n",
       "\n",
       "         neighbourhood_cleansed_Fauntleroy  neighbourhood_cleansed_First Hill  \\\n",
       "id                                                                              \n",
       "241032                                   0                                  0   \n",
       "953595                                   0                                  0   \n",
       "7421966                                  0                                  0   \n",
       "278830                                   0                                  0   \n",
       "5956968                                  0                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Fremont  neighbourhood_cleansed_Gatewood  \\\n",
       "id                                                                         \n",
       "241032                                0                                0   \n",
       "953595                                0                                0   \n",
       "7421966                               0                                0   \n",
       "278830                                0                                0   \n",
       "5956968                               0                                0   \n",
       "\n",
       "         neighbourhood_cleansed_Genesee  neighbourhood_cleansed_Georgetown  \\\n",
       "id                                                                           \n",
       "241032                                0                                  0   \n",
       "953595                                0                                  0   \n",
       "7421966                               0                                  0   \n",
       "278830                                0                                  0   \n",
       "5956968                               0                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Green Lake  neighbourhood_cleansed_Greenwood  \\\n",
       "id                                                                             \n",
       "241032                                   0                                 0   \n",
       "953595                                   0                                 0   \n",
       "7421966                                  0                                 0   \n",
       "278830                                   0                                 0   \n",
       "5956968                                  0                                 0   \n",
       "\n",
       "         neighbourhood_cleansed_Haller Lake  \\\n",
       "id                                            \n",
       "241032                                    0   \n",
       "953595                                    0   \n",
       "7421966                                   0   \n",
       "278830                                    0   \n",
       "5956968                                   0   \n",
       "\n",
       "         neighbourhood_cleansed_Harrison/Denny-Blaine  \\\n",
       "id                                                      \n",
       "241032                                              0   \n",
       "953595                                              0   \n",
       "7421966                                             0   \n",
       "278830                                              0   \n",
       "5956968                                             0   \n",
       "\n",
       "         neighbourhood_cleansed_High Point  \\\n",
       "id                                           \n",
       "241032                                   0   \n",
       "953595                                   0   \n",
       "7421966                                  0   \n",
       "278830                                   0   \n",
       "5956968                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Highland Park  \\\n",
       "id                                              \n",
       "241032                                      0   \n",
       "953595                                      0   \n",
       "7421966                                     0   \n",
       "278830                                      0   \n",
       "5956968                                     0   \n",
       "\n",
       "         neighbourhood_cleansed_Holly Park  \\\n",
       "id                                           \n",
       "241032                                   0   \n",
       "953595                                   0   \n",
       "7421966                                  0   \n",
       "278830                                   0   \n",
       "5956968                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Industrial District  \\\n",
       "id                                                    \n",
       "241032                                            0   \n",
       "953595                                            0   \n",
       "7421966                                           0   \n",
       "278830                                            0   \n",
       "5956968                                           0   \n",
       "\n",
       "         neighbourhood_cleansed_Interbay  \\\n",
       "id                                         \n",
       "241032                                 0   \n",
       "953595                                 0   \n",
       "7421966                                0   \n",
       "278830                                 0   \n",
       "5956968                                0   \n",
       "\n",
       "         neighbourhood_cleansed_International District  \\\n",
       "id                                                       \n",
       "241032                                               0   \n",
       "953595                                               0   \n",
       "7421966                                              0   \n",
       "278830                                               0   \n",
       "5956968                                              0   \n",
       "\n",
       "         neighbourhood_cleansed_Laurelhurst  \\\n",
       "id                                            \n",
       "241032                                    0   \n",
       "953595                                    0   \n",
       "7421966                                   0   \n",
       "278830                                    0   \n",
       "5956968                                   0   \n",
       "\n",
       "         neighbourhood_cleansed_Lawton Park  neighbourhood_cleansed_Leschi  \\\n",
       "id                                                                           \n",
       "241032                                    0                              0   \n",
       "953595                                    0                              0   \n",
       "7421966                                   0                              0   \n",
       "278830                                    0                              0   \n",
       "5956968                                   0                              0   \n",
       "\n",
       "         neighbourhood_cleansed_Lower Queen Anne  \\\n",
       "id                                                 \n",
       "241032                                         0   \n",
       "953595                                         0   \n",
       "7421966                                        0   \n",
       "278830                                         0   \n",
       "5956968                                        0   \n",
       "\n",
       "         neighbourhood_cleansed_Loyal Heights  \\\n",
       "id                                              \n",
       "241032                                      0   \n",
       "953595                                      0   \n",
       "7421966                                     0   \n",
       "278830                                      0   \n",
       "5956968                                     0   \n",
       "\n",
       "         neighbourhood_cleansed_Madison Park  neighbourhood_cleansed_Madrona  \\\n",
       "id                                                                             \n",
       "241032                                     0                               0   \n",
       "953595                                     0                               0   \n",
       "7421966                                    0                               0   \n",
       "278830                                     0                               0   \n",
       "5956968                                    0                               0   \n",
       "\n",
       "         neighbourhood_cleansed_Mann  neighbourhood_cleansed_Maple Leaf  \\\n",
       "id                                                                        \n",
       "241032                             0                                  0   \n",
       "953595                             0                                  0   \n",
       "7421966                            0                                  0   \n",
       "278830                             0                                  0   \n",
       "5956968                            0                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Matthews Beach  \\\n",
       "id                                               \n",
       "241032                                       0   \n",
       "953595                                       0   \n",
       "7421966                                      0   \n",
       "278830                                       0   \n",
       "5956968                                      0   \n",
       "\n",
       "         neighbourhood_cleansed_Meadowbrook  \\\n",
       "id                                            \n",
       "241032                                    0   \n",
       "953595                                    0   \n",
       "7421966                                   0   \n",
       "278830                                    0   \n",
       "5956968                                   0   \n",
       "\n",
       "         neighbourhood_cleansed_Mid-Beacon Hill  neighbourhood_cleansed_Minor  \\\n",
       "id                                                                              \n",
       "241032                                        0                             0   \n",
       "953595                                        0                             0   \n",
       "7421966                                       0                             0   \n",
       "278830                                        0                             0   \n",
       "5956968                                       0                             0   \n",
       "\n",
       "         neighbourhood_cleansed_Montlake  neighbourhood_cleansed_Mount Baker  \\\n",
       "id                                                                             \n",
       "241032                                 0                                   0   \n",
       "953595                                 0                                   0   \n",
       "7421966                                0                                   0   \n",
       "278830                                 0                                   0   \n",
       "5956968                                0                                   0   \n",
       "\n",
       "         neighbourhood_cleansed_North Admiral  \\\n",
       "id                                              \n",
       "241032                                      0   \n",
       "953595                                      0   \n",
       "7421966                                     0   \n",
       "278830                                      0   \n",
       "5956968                                     0   \n",
       "\n",
       "         neighbourhood_cleansed_North Beach/Blue Ridge  \\\n",
       "id                                                       \n",
       "241032                                               0   \n",
       "953595                                               0   \n",
       "7421966                                              0   \n",
       "278830                                               0   \n",
       "5956968                                              0   \n",
       "\n",
       "         neighbourhood_cleansed_North Beacon Hill  \\\n",
       "id                                                  \n",
       "241032                                          0   \n",
       "953595                                          0   \n",
       "7421966                                         0   \n",
       "278830                                          0   \n",
       "5956968                                         0   \n",
       "\n",
       "         neighbourhood_cleansed_North College Park  \\\n",
       "id                                                   \n",
       "241032                                           0   \n",
       "953595                                           0   \n",
       "7421966                                          0   \n",
       "278830                                           0   \n",
       "5956968                                          0   \n",
       "\n",
       "         neighbourhood_cleansed_North Delridge  \\\n",
       "id                                               \n",
       "241032                                       0   \n",
       "953595                                       0   \n",
       "7421966                                      0   \n",
       "278830                                       0   \n",
       "5956968                                      0   \n",
       "\n",
       "         neighbourhood_cleansed_North Queen Anne  \\\n",
       "id                                                 \n",
       "241032                                         0   \n",
       "953595                                         0   \n",
       "7421966                                        0   \n",
       "278830                                         0   \n",
       "5956968                                        0   \n",
       "\n",
       "         neighbourhood_cleansed_Olympic Hills  \\\n",
       "id                                              \n",
       "241032                                      0   \n",
       "953595                                      0   \n",
       "7421966                                     0   \n",
       "278830                                      0   \n",
       "5956968                                     0   \n",
       "\n",
       "         neighbourhood_cleansed_Phinney Ridge  \\\n",
       "id                                              \n",
       "241032                                      0   \n",
       "953595                                      0   \n",
       "7421966                                     0   \n",
       "278830                                      0   \n",
       "5956968                                     0   \n",
       "\n",
       "         neighbourhood_cleansed_Pike-Market  neighbourhood_cleansed_Pinehurst  \\\n",
       "id                                                                              \n",
       "241032                                    0                                 0   \n",
       "953595                                    0                                 0   \n",
       "7421966                                   0                                 0   \n",
       "278830                                    0                                 0   \n",
       "5956968                                   0                                 0   \n",
       "\n",
       "         neighbourhood_cleansed_Pioneer Square  \\\n",
       "id                                               \n",
       "241032                                       0   \n",
       "953595                                       0   \n",
       "7421966                                      0   \n",
       "278830                                       0   \n",
       "5956968                                      0   \n",
       "\n",
       "         neighbourhood_cleansed_Portage Bay  \\\n",
       "id                                            \n",
       "241032                                    0   \n",
       "953595                                    0   \n",
       "7421966                                   0   \n",
       "278830                                    0   \n",
       "5956968                                   0   \n",
       "\n",
       "         neighbourhood_cleansed_Rainier Beach  neighbourhood_cleansed_Ravenna  \\\n",
       "id                                                                              \n",
       "241032                                      0                               0   \n",
       "953595                                      0                               0   \n",
       "7421966                                     0                               0   \n",
       "278830                                      0                               0   \n",
       "5956968                                     0                               0   \n",
       "\n",
       "         neighbourhood_cleansed_Riverview  neighbourhood_cleansed_Roosevelt  \\\n",
       "id                                                                            \n",
       "241032                                  0                                 0   \n",
       "953595                                  0                                 0   \n",
       "7421966                                 0                                 0   \n",
       "278830                                  0                                 0   \n",
       "5956968                                 0                                 0   \n",
       "\n",
       "         neighbourhood_cleansed_Roxhill  neighbourhood_cleansed_Seaview  \\\n",
       "id                                                                        \n",
       "241032                                0                               0   \n",
       "953595                                0                               0   \n",
       "7421966                               0                               0   \n",
       "278830                                0                               0   \n",
       "5956968                               0                               0   \n",
       "\n",
       "         neighbourhood_cleansed_Seward Park  \\\n",
       "id                                            \n",
       "241032                                    0   \n",
       "953595                                    0   \n",
       "7421966                                   0   \n",
       "278830                                    0   \n",
       "5956968                                   0   \n",
       "\n",
       "         neighbourhood_cleansed_South Beacon Hill  \\\n",
       "id                                                  \n",
       "241032                                          0   \n",
       "953595                                          0   \n",
       "7421966                                         0   \n",
       "278830                                          0   \n",
       "5956968                                         0   \n",
       "\n",
       "         neighbourhood_cleansed_South Delridge  \\\n",
       "id                                               \n",
       "241032                                       0   \n",
       "953595                                       0   \n",
       "7421966                                      0   \n",
       "278830                                       0   \n",
       "5956968                                      0   \n",
       "\n",
       "         neighbourhood_cleansed_South Lake Union  \\\n",
       "id                                                 \n",
       "241032                                         0   \n",
       "953595                                         0   \n",
       "7421966                                        0   \n",
       "278830                                         0   \n",
       "5956968                                        0   \n",
       "\n",
       "         neighbourhood_cleansed_South Park  \\\n",
       "id                                           \n",
       "241032                                   0   \n",
       "953595                                   0   \n",
       "7421966                                  0   \n",
       "278830                                   0   \n",
       "5956968                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Southeast Magnolia  \\\n",
       "id                                                   \n",
       "241032                                           0   \n",
       "953595                                           0   \n",
       "7421966                                          0   \n",
       "278830                                           0   \n",
       "5956968                                          0   \n",
       "\n",
       "         neighbourhood_cleansed_Stevens  neighbourhood_cleansed_Sunset Hill  \\\n",
       "id                                                                            \n",
       "241032                                0                                   0   \n",
       "953595                                0                                   0   \n",
       "7421966                               0                                   0   \n",
       "278830                                0                                   0   \n",
       "5956968                               0                                   0   \n",
       "\n",
       "         neighbourhood_cleansed_University District  \\\n",
       "id                                                    \n",
       "241032                                            0   \n",
       "953595                                            0   \n",
       "7421966                                           0   \n",
       "278830                                            0   \n",
       "5956968                                           0   \n",
       "\n",
       "         neighbourhood_cleansed_Victory Heights  \\\n",
       "id                                                \n",
       "241032                                        0   \n",
       "953595                                        0   \n",
       "7421966                                       0   \n",
       "278830                                        0   \n",
       "5956968                                       0   \n",
       "\n",
       "         neighbourhood_cleansed_View Ridge  \\\n",
       "id                                           \n",
       "241032                                   0   \n",
       "953595                                   0   \n",
       "7421966                                  0   \n",
       "278830                                   0   \n",
       "5956968                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Wallingford  neighbourhood_cleansed_Wedgwood  \\\n",
       "id                                                                             \n",
       "241032                                    0                                0   \n",
       "953595                                    0                                0   \n",
       "7421966                                   0                                0   \n",
       "278830                                    0                                0   \n",
       "5956968                                   0                                0   \n",
       "\n",
       "         neighbourhood_cleansed_West Queen Anne  \\\n",
       "id                                                \n",
       "241032                                        1   \n",
       "953595                                        1   \n",
       "7421966                                       1   \n",
       "278830                                        1   \n",
       "5956968                                       1   \n",
       "\n",
       "         neighbourhood_cleansed_West Woodland  \\\n",
       "id                                              \n",
       "241032                                      0   \n",
       "953595                                      0   \n",
       "7421966                                     0   \n",
       "278830                                      0   \n",
       "5956968                                     0   \n",
       "\n",
       "         neighbourhood_cleansed_Westlake  \\\n",
       "id                                         \n",
       "241032                                 0   \n",
       "953595                                 0   \n",
       "7421966                                0   \n",
       "278830                                 0   \n",
       "5956968                                0   \n",
       "\n",
       "         neighbourhood_cleansed_Whittier Heights  \\\n",
       "id                                                 \n",
       "241032                                         0   \n",
       "953595                                         0   \n",
       "7421966                                        0   \n",
       "278830                                         0   \n",
       "5956968                                        0   \n",
       "\n",
       "         neighbourhood_cleansed_Windermere  \\\n",
       "id                                           \n",
       "241032                                   0   \n",
       "953595                                   0   \n",
       "7421966                                  0   \n",
       "278830                                   0   \n",
       "5956968                                  0   \n",
       "\n",
       "         neighbourhood_cleansed_Yesler Terrace  is_location_exact_t  \\\n",
       "id                                                                    \n",
       "241032                                       0                    1   \n",
       "953595                                       0                    1   \n",
       "7421966                                      0                    1   \n",
       "278830                                       0                    1   \n",
       "5956968                                      0                    1   \n",
       "\n",
       "         property_type_Bed & Breakfast  property_type_Boat  \\\n",
       "id                                                           \n",
       "241032                               0                   0   \n",
       "953595                               0                   0   \n",
       "7421966                              0                   0   \n",
       "278830                               0                   0   \n",
       "5956968                              0                   0   \n",
       "\n",
       "         property_type_Bungalow  property_type_Cabin  property_type_Camper/RV  \\\n",
       "id                                                                              \n",
       "241032                        0                    0                        0   \n",
       "953595                        0                    0                        0   \n",
       "7421966                       0                    0                        0   \n",
       "278830                        0                    0                        0   \n",
       "5956968                       0                    0                        0   \n",
       "\n",
       "         property_type_Chalet  property_type_Condominium  property_type_Dorm  \\\n",
       "id                                                                             \n",
       "241032                      0                          0                   0   \n",
       "953595                      0                          0                   0   \n",
       "7421966                     0                          0                   0   \n",
       "278830                      0                          0                   0   \n",
       "5956968                     0                          0                   0   \n",
       "\n",
       "         property_type_House  property_type_Loft  property_type_Other  \\\n",
       "id                                                                      \n",
       "241032                     0                   0                    0   \n",
       "953595                     0                   0                    0   \n",
       "7421966                    0                   0                    0   \n",
       "278830                     1                   0                    0   \n",
       "5956968                    1                   0                    0   \n",
       "\n",
       "         property_type_Tent  property_type_Townhouse  property_type_Treehouse  \\\n",
       "id                                                                              \n",
       "241032                    0                        0                        0   \n",
       "953595                    0                        0                        0   \n",
       "7421966                   0                        0                        0   \n",
       "278830                    0                        0                        0   \n",
       "5956968                   0                        0                        0   \n",
       "\n",
       "         property_type_Yurt  room_type_Private room  room_type_Shared room  \\\n",
       "id                                                                           \n",
       "241032                    0                       0                      0   \n",
       "953595                    0                       0                      0   \n",
       "7421966                   0                       0                      0   \n",
       "278830                    0                       0                      0   \n",
       "5956968                   0                       1                      0   \n",
       "\n",
       "         bed_type_Couch  bed_type_Futon  bed_type_Pull-out Sofa  \\\n",
       "id                                                                \n",
       "241032                0               0                       0   \n",
       "953595                0               0                       0   \n",
       "7421966               0               0                       0   \n",
       "278830                0               0                       0   \n",
       "5956968               0               0                       0   \n",
       "\n",
       "         bed_type_Real Bed  instant_bookable_t  cancellation_policy_moderate  \\\n",
       "id                                                                             \n",
       "241032                   1                   0                             1   \n",
       "953595                   1                   0                             0   \n",
       "7421966                  1                   0                             0   \n",
       "278830                   1                   0                             0   \n",
       "5956968                  1                   0                             0   \n",
       "\n",
       "         cancellation_policy_strict  require_guest_profile_picture_t  \\\n",
       "id                                                                     \n",
       "241032                            0                                0   \n",
       "953595                            1                                1   \n",
       "7421966                           0                                0   \n",
       "278830                            1                                0   \n",
       "5956968                           1                                0   \n",
       "\n",
       "         require_guest_phone_verification_t  \n",
       "id                                           \n",
       "241032                                    0  \n",
       "953595                                    1  \n",
       "7421966                                   0  \n",
       "278830                                    0  \n",
       "5956968                                   0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3798, 187)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Compare superhost vs regular_host"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 1 Check shapes of the processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston listing data shape:  (3560, 127)\n",
      "Seattle listing data shape: (3798, 187)\n"
     ]
    }
   ],
   "source": [
    "print(\"Boston listing data shape:  {}\".format(df_boston.shape))\n",
    "print(\"Seattle listing data shape: {}\".format(df_seattle.shape))"
   ]
  },
  {
   "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>host_is_superhost_t</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_is_superhost_t  counts\n",
       "0                    0    3157\n",
       "1                    1     403"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of regular_host vs superhost in Boston\n",
    "df_boston.groupby('host_is_superhost_t').size().reset_index(name='counts')"
   ]
  },
  {
   "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>host_is_superhost_t</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_is_superhost_t  counts\n",
       "0                    0    3024\n",
       "1                    1     774"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts of regular host vs super_host in Seattle\n",
    "df_seattle.groupby('host_is_superhost_t').size().reset_index(name='counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Compare superhost vs regular_host in Boston\n",
    "Boston has 403 superhosts and 3157 regular hosts, while Seattle has 774 superhosts and 3024 regular hosts. Seattle has a higher proportion of superhosts than Boston.\n",
    "We will compare the mean of each data column between superhosts and regular hosts, and find out those that have a percentage difference higher than 30%, ignoring all neighbourhood columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>host_is_superhost_t</th>\n",
       "      <th>column</th>\n",
       "      <th>regular_host</th>\n",
       "      <th>superhost</th>\n",
       "      <th>host_diff_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>reviews_per_month</td>\n",
       "      <td>1.861839</td>\n",
       "      <td>2.877515</td>\n",
       "      <td>54.552314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>amenity_Shampoo</td>\n",
       "      <td>0.652835</td>\n",
       "      <td>0.868486</td>\n",
       "      <td>33.033062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>amenity_Hair Dryer</td>\n",
       "      <td>0.485588</td>\n",
       "      <td>0.665012</td>\n",
       "      <td>36.950044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>amenity_Fire Extinguisher</td>\n",
       "      <td>0.419385</td>\n",
       "      <td>0.607940</td>\n",
       "      <td>44.959818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>extra_people</td>\n",
       "      <td>0.363320</td>\n",
       "      <td>0.555831</td>\n",
       "      <td>52.986862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>amenity_24-Hour Check-in</td>\n",
       "      <td>0.330060</td>\n",
       "      <td>0.491315</td>\n",
       "      <td>48.856227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>amenity_First Aid Kit</td>\n",
       "      <td>0.272411</td>\n",
       "      <td>0.491315</td>\n",
       "      <td>80.358359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>security_deposit</td>\n",
       "      <td>0.361419</td>\n",
       "      <td>0.473945</td>\n",
       "      <td>31.134589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>cancellation_policy_moderate</td>\n",
       "      <td>0.248337</td>\n",
       "      <td>0.334988</td>\n",
       "      <td>34.892325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>amenity_Free Parking on Premises</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.330025</td>\n",
       "      <td>50.344637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>amenity_Buzzer/Wireless Intercom</td>\n",
       "      <td>0.225847</td>\n",
       "      <td>0.295285</td>\n",
       "      <td>30.745565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>property_type_House</td>\n",
       "      <td>0.145708</td>\n",
       "      <td>0.240695</td>\n",
       "      <td>65.189880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>amenity_Safety Card</td>\n",
       "      <td>0.116883</td>\n",
       "      <td>0.218362</td>\n",
       "      <td>86.821064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>amenity_Breakfast</td>\n",
       "      <td>0.079189</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>103.677419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>amenity_translation missing: en.hosting_amenit...</td>\n",
       "      <td>0.120051</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>34.351860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>amenity_Pets live on this property</td>\n",
       "      <td>0.114032</td>\n",
       "      <td>0.158809</td>\n",
       "      <td>39.266612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>amenity_Indoor Fireplace</td>\n",
       "      <td>0.104846</td>\n",
       "      <td>0.138958</td>\n",
       "      <td>32.534691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>amenity_Cat(s)</td>\n",
       "      <td>0.045296</td>\n",
       "      <td>0.089330</td>\n",
       "      <td>97.213209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>amenity_Dog(s)</td>\n",
       "      <td>0.060817</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>34.642525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>require_guest_profile_picture_t</td>\n",
       "      <td>0.013937</td>\n",
       "      <td>0.057072</td>\n",
       "      <td>309.491315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>property_type_Bed &amp; Breakfast</td>\n",
       "      <td>0.009503</td>\n",
       "      <td>0.027295</td>\n",
       "      <td>187.237386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>property_type_Townhouse</td>\n",
       "      <td>0.013621</td>\n",
       "      <td>0.024814</td>\n",
       "      <td>82.180160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>property_type_Loft</td>\n",
       "      <td>0.010453</td>\n",
       "      <td>0.014888</td>\n",
       "      <td>42.431762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>amenity_Other pet(s)</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>0.012407</td>\n",
       "      <td>256.079404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>property_type_Villa</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>0.002481</td>\n",
       "      <td>56.674938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "host_is_superhost_t                                             column  \\\n",
       "25                                                   reviews_per_month   \n",
       "61                                                     amenity_Shampoo   \n",
       "45                                                  amenity_Hair Dryer   \n",
       "40                                           amenity_Fire Extinguisher   \n",
       "11                                                        extra_people   \n",
       "27                                            amenity_24-Hour Check-in   \n",
       "41                                               amenity_First Aid Kit   \n",
       "8                                                     security_deposit   \n",
       "121                                       cancellation_policy_moderate   \n",
       "42                                    amenity_Free Parking on Premises   \n",
       "30                                    amenity_Buzzer/Wireless Intercom   \n",
       "109                                                property_type_House   \n",
       "60                                                 amenity_Safety Card   \n",
       "29                                                   amenity_Breakfast   \n",
       "70                   amenity_translation missing: en.hosting_amenit...   \n",
       "58                                  amenity_Pets live on this property   \n",
       "49                                            amenity_Indoor Fireplace   \n",
       "33                                                      amenity_Cat(s)   \n",
       "34                                                      amenity_Dog(s)   \n",
       "124                                    require_guest_profile_picture_t   \n",
       "102                                      property_type_Bed & Breakfast   \n",
       "112                                            property_type_Townhouse   \n",
       "110                                                 property_type_Loft   \n",
       "55                                                amenity_Other pet(s)   \n",
       "113                                                property_type_Villa   \n",
       "\n",
       "host_is_superhost_t  regular_host  superhost  host_diff_%  \n",
       "25                       1.861839   2.877515    54.552314  \n",
       "61                       0.652835   0.868486    33.033062  \n",
       "45                       0.485588   0.665012    36.950044  \n",
       "40                       0.419385   0.607940    44.959818  \n",
       "11                       0.363320   0.555831    52.986862  \n",
       "27                       0.330060   0.491315    48.856227  \n",
       "41                       0.272411   0.491315    80.358359  \n",
       "8                        0.361419   0.473945    31.134589  \n",
       "121                      0.248337   0.334988    34.892325  \n",
       "42                       0.219512   0.330025    50.344637  \n",
       "30                       0.225847   0.295285    30.745565  \n",
       "109                      0.145708   0.240695    65.189880  \n",
       "60                       0.116883   0.218362    86.821064  \n",
       "29                       0.079189   0.161290   103.677419  \n",
       "70                       0.120051   0.161290    34.351860  \n",
       "58                       0.114032   0.158809    39.266612  \n",
       "49                       0.104846   0.138958    32.534691  \n",
       "33                       0.045296   0.089330    97.213209  \n",
       "34                       0.060817   0.081886    34.642525  \n",
       "124                      0.013937   0.057072   309.491315  \n",
       "102                      0.009503   0.027295   187.237386  \n",
       "112                      0.013621   0.024814    82.180160  \n",
       "110                      0.010453   0.014888    42.431762  \n",
       "55                       0.003484   0.012407   256.079404  \n",
       "113                      0.001584   0.002481    56.674938  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boston regular_host vs Super_host\n",
    "df_Boston_groupby_host = df_boston.groupby(['host_is_superhost_t']).mean().T.reset_index()\n",
    "df_Boston_groupby_host.rename(columns={'index':'column', 0:'regular_host', 1:'superhost'}, inplace=True)\n",
    "\n",
    "df_Boston_groupby_host['host_diff_%'] = (df_Boston_groupby_host.superhost - df_Boston_groupby_host.regular_host)\\\n",
    "                                        /df_Boston_groupby_host.regular_host*100\n",
    "\n",
    "df_Boston_groupby_host[(df_Boston_groupby_host['host_diff_%']>30) & \n",
    "                       (~df_Boston_groupby_host.column.str.startswith('neighbourhood'))]\\\n",
    "                        .sort_values(by='superhost', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "In Boston, compared to regular hosts, superhosts tend to:\n",
    "1. have more reviews_per_month.\n",
    "2. charge extra_people. \n",
    "3. offer better services, such as more chances of having Shampoo, Hair Dryer, Fire_Extinguisher, First Aid Kit, 24-Hour Check-in, cancellation_policy_moderate, Safety Card, Breakfast, Free Parking on Premises, Indoor Fireplace\n",
    "4. be more pet friendly. They tend to have Dogs, Cat(s), Other Pet(s), Pets live on this property.\n",
    "5. require security_deposit, and guest_profile_picture.\n",
    "6. have more chances to offer Houses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Compare superhost vs regular_host in Seattle"
   ]
  },
  {
   "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>host_is_superhost_t</th>\n",
       "      <th>column</th>\n",
       "      <th>regular_host</th>\n",
       "      <th>superhost</th>\n",
       "      <th>host_diff_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>reviews_per_month</td>\n",
       "      <td>1.865071</td>\n",
       "      <td>2.945044</td>\n",
       "      <td>57.905165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>extra_people</td>\n",
       "      <td>0.435847</td>\n",
       "      <td>0.569767</td>\n",
       "      <td>30.726612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>amenity_First Aid Kit</td>\n",
       "      <td>0.409722</td>\n",
       "      <td>0.556848</td>\n",
       "      <td>35.908553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>amenity_Pets live on this property</td>\n",
       "      <td>0.210979</td>\n",
       "      <td>0.310078</td>\n",
       "      <td>46.970912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>amenity_Safety Card</td>\n",
       "      <td>0.169643</td>\n",
       "      <td>0.270026</td>\n",
       "      <td>59.173127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>amenity_Hair Dryer</td>\n",
       "      <td>0.189153</td>\n",
       "      <td>0.254522</td>\n",
       "      <td>34.558465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>instant_bookable_t</td>\n",
       "      <td>0.139550</td>\n",
       "      <td>0.214470</td>\n",
       "      <td>53.686763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>amenity_24-Hour Check-in</td>\n",
       "      <td>0.151124</td>\n",
       "      <td>0.200258</td>\n",
       "      <td>32.512340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>amenity_Dog(s)</td>\n",
       "      <td>0.119048</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>56.279070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>require_guest_phone_verification_t</td>\n",
       "      <td>0.082672</td>\n",
       "      <td>0.157623</td>\n",
       "      <td>90.660465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>require_guest_profile_picture_t</td>\n",
       "      <td>0.070106</td>\n",
       "      <td>0.136951</td>\n",
       "      <td>95.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>amenity_Cat(s)</td>\n",
       "      <td>0.091601</td>\n",
       "      <td>0.133075</td>\n",
       "      <td>45.277475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>amenity_Doorman</td>\n",
       "      <td>0.017196</td>\n",
       "      <td>0.038760</td>\n",
       "      <td>125.402504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>property_type_Cabin</td>\n",
       "      <td>0.002315</td>\n",
       "      <td>0.018088</td>\n",
       "      <td>681.395349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>property_type_Loft</td>\n",
       "      <td>0.009590</td>\n",
       "      <td>0.014212</td>\n",
       "      <td>48.195670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>property_type_Camper/RV</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>73.643411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>property_type_Treehouse</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>0.001292</td>\n",
       "      <td>95.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>property_type_Yurt</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001292</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "host_is_superhost_t                              column  regular_host  \\\n",
       "25                                    reviews_per_month      1.865071   \n",
       "11                                         extra_people      0.435847   \n",
       "41                                amenity_First Aid Kit      0.409722   \n",
       "56                   amenity_Pets live on this property      0.210979   \n",
       "58                                  amenity_Safety Card      0.169643   \n",
       "44                                   amenity_Hair Dryer      0.189153   \n",
       "181                                  instant_bookable_t      0.139550   \n",
       "27                             amenity_24-Hour Check-in      0.151124   \n",
       "34                                       amenity_Dog(s)      0.119048   \n",
       "185                  require_guest_phone_verification_t      0.082672   \n",
       "184                     require_guest_profile_picture_t      0.070106   \n",
       "33                                       amenity_Cat(s)      0.091601   \n",
       "35                                      amenity_Doorman      0.017196   \n",
       "163                                 property_type_Cabin      0.002315   \n",
       "169                                  property_type_Loft      0.009590   \n",
       "164                             property_type_Camper/RV      0.002976   \n",
       "173                             property_type_Treehouse      0.000661   \n",
       "174                                  property_type_Yurt      0.000000   \n",
       "\n",
       "host_is_superhost_t  superhost  host_diff_%  \n",
       "25                    2.945044    57.905165  \n",
       "11                    0.569767    30.726612  \n",
       "41                    0.556848    35.908553  \n",
       "56                    0.310078    46.970912  \n",
       "58                    0.270026    59.173127  \n",
       "44                    0.254522    34.558465  \n",
       "181                   0.214470    53.686763  \n",
       "27                    0.200258    32.512340  \n",
       "34                    0.186047    56.279070  \n",
       "185                   0.157623    90.660465  \n",
       "184                   0.136951    95.348837  \n",
       "33                    0.133075    45.277475  \n",
       "35                    0.038760   125.402504  \n",
       "163                   0.018088   681.395349  \n",
       "169                   0.014212    48.195670  \n",
       "164                   0.005168    73.643411  \n",
       "173                   0.001292    95.348837  \n",
       "174                   0.001292          inf  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle_groupby_host = df_seattle.groupby(['host_is_superhost_t']).mean().T.reset_index()\n",
    "df_seattle_groupby_host.rename(columns={'index':'column', 0:'regular_host', 1:'superhost'}, inplace=True)\n",
    "\n",
    "df_seattle_groupby_host['host_diff_%'] = (df_seattle_groupby_host.superhost - df_seattle_groupby_host.regular_host)\\\n",
    "                                        /df_seattle_groupby_host.regular_host*100\n",
    "\n",
    "df_seattle_groupby_host[(df_seattle_groupby_host['host_diff_%']>30) & \n",
    "                        (~df_seattle_groupby_host.column.str.startswith('neighbourhood'))]\\\n",
    "                        .sort_values(by='superhost', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "In Seattle, compared to regular hosts, superhosts tend to:\n",
    "1. have more reviews_per_month.\n",
    "2. charge extra_people. \n",
    "3. offer better services, such as more chances of having Doorman, Safety Card, First Aid Kit, Hair Dryer, instant_bookable, 24-Hour Check-in etc.. \n",
    "4. be more pet friendly. They tend to have Dogs, Cat(s), Pets live on this property, \n",
    "5. require guest_profile_picture and guest_phone_verification.\n",
    "6. have more chances to offer exotic property types, such as Yurt, Cabin, Treehouse, Camper/RV, Loft etc.. Keep in mind though, the overall number of superhosts offering these kinds of property types are slim though. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. AirBnB price modeling - Boston and Seattle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Check shapes of the processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston listing data shape:  (3560, 127)\n",
      "Seattle listing data shape: (3798, 187)\n"
     ]
    }
   ],
   "source": [
    "print(\"Boston listing data shape:  {}\".format(df_boston.shape))\n",
    "print(\"Seattle listing data shape: {}\".format(df_seattle.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Define Lasso model\n",
    "1. StandardScaler was used to transform the data X. Scaler was performed on X_train and X_test separately. \n",
    "2. LassoCV was used to perform feature selection and to find best model (alpha) automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def lasso_model(df):\n",
    "    y = df['price']\n",
    "    X= df.drop(columns=['price'], axis=1)\n",
    "\n",
    "    #Split into train and test\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "    # scale the data\n",
    "    scaler=StandardScaler()\n",
    "    X_train = scaler.fit_transform(X_train)\n",
    "    X_test = scaler.fit_transform(X_test)\n",
    "\n",
    "    # use LassoCV to find best alpha automatically\n",
    "    lasso_model = LassoCV(cv=4, random_state=0) # Instantiate\n",
    "    lasso_model.fit(X_train, y_train) #Fit\n",
    "\n",
    "    train_score=lasso_model.score(X_train,y_train)\n",
    "    test_score=lasso_model.score(X_test,y_test)\n",
    "    coeff_used = np.sum(lasso_model.coef_!=0)\n",
    "\n",
    "    print(\"best alpha: {}\".format(lasso_model.alpha_))\n",
    "    print(\"training score: {}\".format(train_score))\n",
    "    print(\"test score: {}\".format(test_score))\n",
    "    print(\"number of features used: {}\".format(coeff_used))\n",
    "\n",
    "    # find model's correlation coefficients\n",
    "    coef = pd.Series(lasso_model.coef_, index = X.columns)\n",
    "    coef.sort_values(ascending=False, inplace=True)\n",
    "    print('\\n')\n",
    "    print(coef)\n",
    "    \n",
    "    # plot model's correlation coefficients\n",
    "    imp_coef = pd.concat([coef.sort_values().head(16), coef.sort_values().tail(20)])\n",
    "    plt.figure(figsize=(10, 15))\n",
    "    imp_coef.plot(kind = 'barh')\n",
    "    plt.title('Coefficients in the Lasso Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Boston price model\n",
    "For Boston listing, the scores of both training and test data sets are 0.66, which are reasonably good. The final number of features used is 89. The list of coefficients shows the magnitude of each feature importance. \n",
    "For Boston listing, neighbourhood matters. And the price can go either up or down depending on the neighbourhood.\n",
    "\n",
    "#### Positively correlated factors with correlation coefficient > 3\n",
    "1. bedrooms, bathrooms, beds, accomodates, guest_included\n",
    " * This makes perfect sense. The more these variables are, the higher the price is. \n",
    "2. require_guest_phone_verification, host_is_superhost, calendar_updated, cancellation_policy_super_strict_30\n",
    " * These variables are somewhat related to the hosts' rental policies/characteristics. \n",
    " * If require_guest_phone_verification is True, it is possibly because the host values his/her property. The more the host values his/her property, the more tendency the host will set the price high. \n",
    " * The same logic above explains why cancellation_policy_super_strict_30 is positively correlated with price. \n",
    " * Superhost's price tends to be higher than regular hosts. This is possibly because superhosts offer more values based on  services/aminities. \n",
    " * The positive correlation between calendar_updated and price is somewhat hard to explain. One possible explanation is the longer the host has not updated his/her AirBnB calendar, the more hesitant the host is willing to rent his property, thus the higher rental price. \n",
    "\n",
    "3. amenity - TV, Air Conditioning, Indoor Fireplace, Doorman \n",
    " * This makes perfect sense. Having Air Conditioning in summer, and Indoor Fireplace is desirable in Boston.\n",
    " * The presense of Doorman can also increase perceived value (thus charge higher price) by giving a sense of security to the potential customers.\n",
    "\n",
    "\n",
    "#### Negatively correlated factors with correlation coefficient absolute value > 3\n",
    "1. room_type Shared room, Private room\n",
    " * This makes sense. The whole house/apartment is always a better option if you can afford it. \n",
    "2. extra_people \n",
    " * If there is a charge for extra people, the price is relatively lower. Sounds reasonable.\n",
    "3. reviews_per_month, host_listings_count, require_guest_profile_picture\n",
    " * The more reviews_per_month, the lower the price tends to be. In the old days, consumers trust word of mouth. Nowdays, consumers read reviews. The more (positive) reviews, the better. To some extent, having no review is as bad as having slightly negative reviews, because it is hard for guests to trust your property. So hosts strive for reviews, especially positive reviews. To solicit more reviews, hosts have to attract more guests. One way is by charging slightly lower price. \n",
    " * The more host_listings_counts, the lower the price is. One possible explanation is as the host owns more listings, the economy of scale will lower the average fixed cost per property, which enables the host to gain equal or higher profit while charging a lower price.\n",
    " * require_guest_profile_picture is negatively correlated with price. The host tends to trust guests with profile pictures. The host does not mind charging a slightly lower price knowing the guest(s) will take good care of his/her property. \n",
    "4. amenity - Free Parking on Premises, Kitchen, Hangers\n",
    " * It is possible that free parking is mostly offered at areas further away from blockbuster locations. As a result, the rental price tends to be cheaper. \n",
    " * The same logic applies to Kitchen. \n",
    " * For Hangers?\n",
    "5. minimum_nights\n",
    " * The longer the rental period, the cheaper the price. Sounds reasonable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best alpha: 0.7345744435570088\n",
      "training score: 0.6599006914891123\n",
      "test score: 0.6638489338079512\n",
      "number of features used: 89\n",
      "\n",
      "\n",
      "bedrooms                              24.042260\n",
      "neighbourhood_cleansed_Back Bay       16.983291\n",
      "neighbourhood_cleansed_Beacon Hill    12.602399\n",
      "neighbourhood_cleansed_South End      11.447879\n",
      "accommodates                          11.326117\n",
      "                                        ...    \n",
      "reviews_per_month                     -6.340251\n",
      "extra_people                          -6.723574\n",
      "neighbourhood_cleansed_Dorchester     -7.178877\n",
      "room_type_Shared room                 -9.221237\n",
      "room_type_Private room               -26.435829\n",
      "Length: 126, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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MGjSIxMREoqKisNvtjBkzhqZNmzJ16lSmTJmCYRhYLBamTZt2UXG1bt2ayMhIGjduTGBgIO3bt7/cly4iIiLickzGhdaNiMhVoaqqWstcrgAtJ3I9GhPX4u/voy9qFLnKFc3oS2npqd+0j7qWeGkGRURERC6biqpqimb0re8wRORXOG13XPig35AKFBEREblsLPCbv/Mql0azjK5F43FhekheRERERERchgoUERERERFxGSpQRERERETEZahAERERERERl6ECRUREREREXIYKFBERERERcRkqUERERERExGWoQBEREREREZehAkVERERERFyGChQREREREXEZbvUdgIiIiFw7qgF/f5/6DkN+RmNyrtN2B2UnT9d3GHIeKlBERETksvGwWrjluQ/rOwyRCyqa0Zey+g5CzktLvERERERExGWoQPmN5Ofn88orr9S6Pzc3l/T09HO29+zZE7vdftniiImJobCw8LK1N27cODZs2FCvMfwSgwYNoqSkpNb9MTExREZGOv+dkZFxyX0899xz5OXl/aZ9iIiIiFzrtMTrN9K2bVvatm1b32HIJUhNTcVms2EYBtHR0Wzfvp0//OEPV10fIiIiIlczFSiXIDc3l88//5yKigr27t3LyJEjiYiIYNeuXUydOhUAX19fpk2bxo4dO1i2bBkZGRksX76c7OxsGjVqhNVqpU+fPgBs3bqV4cOHc+zYMaKionjooYcAmDRpEvv27aNp06akpqZisViIi4ujuLiY6upqHnnkEfr06UNMTAxJSUnYbDaWLl3KkSNHGDBgAKNHj8bX15du3boBMGfOHI4cOcLp06d58cUXad68OTNmzGDz5s0A9OvXj4cffpiSkhLi4+NxOByYTCYSEhJo06YN2dnZLF++HH9/f44ePVpnjrZu3UpKSgqGYRAYGHjWLNGpU6eIj4/n+PHjACQkJNC6dWsWL17M6tWrcTgc+Pj4MHv2bD744IPz5jo7O5sVK1ZgNpsJCwsjNjaWAwcOkJiYiN1up0GDBkyZMoWgoCAyMjL44osvuP766519XozKykocDgcBAQFUV1czadIkDh48yPHjx+nWrRtjx46lqKiIhIQEqqqq8PDwOGs2ZOvWrUydOpWXX36ZoKCgX9THk08+yf3338/y5cvx9fVlyZIllJeX8/e//73WuC0WE76+Xhd9nfLLWCxm5dnFaExE5Jeqj3uH7lkXpgLlEpWVlbFgwQKKiooYNWoUERERJCYmMm3aNFq2bMny5cvJzMykc+fOABw7dozMzExWrFiBu7s7Q4cOdbbl5ubGggUL2LdvH48++qizQImKiiI0NJQXXniBnJwczGYzjRs3Ji0tjbKyMiIiIggPD681xtLSUt59913c3d3Jy8uje/fuPPjgg8yePZtVq1bRsmVLSkpKyMnJweFwEB0dTXh4OHPmzCEmJoZ77rmH/Px84uLiWLRoEW+99RYrV67EZDIRERFRZ34SExPJyMjAZrORnZ191tKuefPmER4eTnR0NEVFRUycOJHs7GxOnDjBm2++idlsZsSIEWzfvr3WXOfm5pKYmEhoaChLlizB4XCQmppKTEwM3bt3Z926daSnp/PYY4+xceNG3nnnHcrLy7nvvvsuOLaxsbF4enpSXFxMmzZtaNy4MQcOHCA0NJSBAwdit9udBUpqaiqPPvoo3bp146OPPmLHjh0AbNmyhXXr1jFv3jyaNm36q/ro378/H374IUOGDOH999+vc8kgQHW1wYkT5Re8Tvl1fH29lGcXozFxLfq0KLma1Me9Q/esM+q6V6hAuURt2rQBICgoiMrKSgAKCwtJTk4GoKqqiuDgYOfxe/fuxWaz4enpCUCHDh2c+2677TZMJhP+/v5UVFQAYLVaCQ0NBSAsLIy1a9cCOAseb29vbDYbxcXFZ8VlGIbz52bNmuHu7u78vV27dgD4+flx5MgRCgsL6dixIyaTCavVSvv27SksLKSwsJBOnToBZ5aoHTx4kD179tCyZUtneyEhIXXm5+jRo9hsNgCGDBly1r6CggLWr1/Pxx9/DMDJkycxm81YrVbGjx+Pl5cXBw8exOFw1Jrr6dOns3DhQtLT0wkNDcUwDAoKCnjttdfIzMzEMAysViu7d++mXbt2mM1mvL29adWqVZ1xw/8tv6qpqSEuLo7MzEyGDh3K9u3bWb9+Pd7e3s44vvvuO+dY/ndG7IMPPmDt2rX8+OOPuLmd/6V1KX1ERkYybtw4OnXqhJ+fH35+fhe8BhEREZGrnR6Sv0Qmk+mcbcHBwaSmppKVlcWzzz5L9+7dnftuuukm9uzZQ0VFBTU1NWzbtq3OtqqqqsjPzwdg06ZN3HrrrdhsNjZt2gScmVUoKChwFiGlpaUAznfwAczmuofVZrM5l3dVVVWxZcsWbr755rP6yc/Px8/Pj+bNm7N7924qKiqorq52xlabgIAAioqKAJg/fz6ffPKJc1+LFi0YNmwYWVlZzJo1i/79+7Nz507WrFnDrFmzSExMpKamxllsnS8/OTk5JCcns3jxYvLz89myZQstWrTgmWeeISsri+TkZO6//36Cg4PZtm0bNTU1lJeXs3v37jrj/imz2UxgYCBVVVXk5ubi4+PDzJkzGT58OBUVFRiGgc1mc870vP/++2RlZQEwZswYhg0bRlJS0q/u44YbbsDHx4d58+YRGRl50fGLiIiIXM00g3IZJCUlERsbS3V1NQApKSkcPnwYgCZNmjBy5Eiio6Px9fXFbrfj5ubmnCX4OavVSlZWFt9//z033HADTz/9NIZhkJiYSFRUFHa7nTFjxtC0aVOGDh3K888/T1BQEAEBARcdb48ePfjqq6946KGHqKqqolevXtx+++1MmDCBxMREFi5ciMPhICUlhSZNmvDUU08xePBgmjRp4pwJqk1ycjJxcXGYzWb8/f0ZNmwYb731FgCjRo0iPj6enJwcysrKGDNmDDfffDOenp5ERETg7u6Ov7+/M3fn07p1ayIjI2ncuDGBgYG0b9+e2NhYkpKSsNvtVFRUEB8fT9u2benVqxeRkZEEBAScd7nVz/13+RWAh4cHaWlplJaWMn78eDZv3oynpyc333wzhw8fZsKECUyaNIlXX33Veez//u//AjBw4EBWrVrFypUr6d+//y/uIzAwkEGDBjF16lTS0tIuGL+IiIjItcBk/HRtkFx2DoeD119/ndGjRwNnlj2NHTvWuZRKpC4fffQR3377LU899dQFj62qqtaa1itAa4ddj8bEtfj7++iLGuWqUDSjL6Wlp654v7pnnaFnUOqRm5sbp0+fZsCAAVitVkJCQujYsWN9h/Wr7N+/n9jY2HO2d+rUiSeffLIeIro427ZtO+9MRO/evYmOjq6HiOr24osvsmnTJubOnVvfoYiIXLSKqmqKZvSt7zBELui0/fyrWaT+aQZF5BqhGZQrQ+98uR6NiWvReLgejYlr0XicUdcMih6SFxERERERl6ECRUREREREXIYKFBERERERcRkqUERERERExGWoQBEREREREZehAkVERERERFyGChQREREREXEZKlBERERERMRlqEARERERERGX4VbfAYiIiMi1o5q6vyFa6seVGJPTdgdlJ0//5v3ItU8FioiIiFw2HlYLtzz3YX2HIfWgaEZfyuo7CLkmaImXiIiIiIi4DBUov1B+fj6vvPJKrftzc3NJT08/Z3vPnj2x2+2XLY6YmBgKCwsvW3vjxo1jw4YN9RrDLzFo0CBKSkpq3R8TE0NkZKTz3xkZGVcwuvPr0qXLWb/n5eXx3HPPATBmzBjg/3I7e/Zsli5desVjFBEREbnStMTrF2rbti1t27at7zDkEqSmpmKz2TAMg+joaLZv384f/vCH+g7rvOoqfkVERESuZSpQfiI3N5fPP/+ciooK9u7dy8iRI4mIiGDXrl1MnToVAF9fX6ZNm8aOHTtYtmwZGRkZLF++nOzsbBo1aoTVaqVPnz4AbN26leHDh3Ps2DGioqJ46KGHAJg0aRL79u2jadOmpKamYrFYiIuLo7i4mOrqah555BH69OlDTEwMSUlJ2Gw2li5dypEjRxgwYACjR4/G19eXbt26ATBnzhyOHDnC6dOnefHFF2nevDkzZsxg8+bNAPTr14+HH36YkpIS4uPjcTgcmEwmEhISaNOmDdnZ2Sxfvhx/f3+OHj1aZ462bt1KSkoKhmEQGBh41izRqVOniI+P5/jx4wAkJCTQunVrFi9ezOrVq3E4HPj4+DB79mw++OCD8+Y6OzubFStWYDabCQsLIzY2lgMHDpCYmIjdbqdBgwZMmTKFoKAgMjIy+OKLL7j++uudfV6MyspKHA4HAQEBAMycOZONGzdiGAbDhg2jd+/efPXVV84ioaKigtTUVIKDg5k7dy5r1qyhurqaqKgoBg8ezMKFC/nwww9xc3OjY8eOPPvss8yePZuSkhKOHj3K/v37mThxIl27dr3oGLt06cLatWsv+ngRERGRa4UKlJ8pKytjwYIFFBUVMWrUKCIiIkhMTGTatGm0bNmS5cuXk5mZSefOnQE4duwYmZmZrFixAnd3d4YOHepsy83NjQULFrBv3z4effRRZ4ESFRVFaGgoL7zwAjk5OZjNZho3bkxaWhplZWVEREQQHh5ea4ylpaW8++67uLu7k5eXR/fu3XnwwQeZPXs2q1atomXLlpSUlJCTk4PD4SA6Oprw8HDmzJlDTEwM99xzD/n5+cTFxbFo0SLeeustVq5ciclkIiIios78JCYmkpGRgc1mIzs7+6ylXfPmzSM8PJzo6GiKioqYOHEi2dnZnDhxgjfffBOz2cyIESPYvn17rbnOzc0lMTGR0NBQlixZgsPhIDU1lZiYGLp37866detIT0/nscceY+PGjbzzzjuUl5dz3333XXBsY2Nj8fT0pLi4mDZt2tC4cWM+//xzSkpKWLZsGXa7nUGDBtGlSxe+/fZb0tLSCAwMZN68eaxatYru3buTl5fH8uXLqaysZObMmezatYuPP/6YZcuW4ebmxhNPPME///lPANzd3cnMzGTt2rUsXLjwnALlhx9+ICYmxvn7iRMnuP322y94HbWxWEz4+nr94vPl4lgsZuXZxWhMRFyHXosXpnvWhalA+Zk2bdoAEBQURGVlJQCFhYUkJycDUFVVRXBwsPP4vXv3YrPZ8PT0BKBDhw7Ofbfddhsmkwl/f38qKioAsFqthIaGAhAWFuZ8l/y/BY+3tzc2m43i4uKz4jIMw/lzs2bNcHd3d/7erl07APz8/Dhy5AiFhYV07NgRk8mE1Wqlffv2FBYWUlhYSKdOnYAzS9QOHjzInj17aNmypbO9kJCQOvNz9OhRbDYbAEOGDDlrX0FBAevXr+fjjz8G4OTJk5jNZqxWK+PHj8fLy4uDBw/icDhqzfX06dNZuHAh6enphIaGYhgGBQUFvPbaa2RmZmIYBlarld27d9OuXTvMZjPe3t60atWqzrjh/5Z41dTUEBcXR2ZmJlarlf/93/91FgoOh4P9+/cTGBhISkoKXl5eHDp0iLCwML777jtCQkKwWCx4enqSkJDAxx9/TPv27bFarQB07NiRb7/91pljgOuvv955fT/VqFEjsrKynL/n5eXx0UcfXfA6alNdbXDiRPkvPl8ujq+vl/LsYjQmrkUfMfz7ptfihemedUZd9wo9JP8zJpPpnG3BwcGkpqaSlZXFs88+S/fu3Z37brrpJvbs2UNFRQU1NTVs27atzraqqqrIz88HYNOmTdx6663YbDY2bdoEnJlVKCgocBYhpaWlAOzYscPZhtlc97DZbDbn8q6qqiq2bNnCzTfffFY/+fn5+Pn50bx5c3bv3k1FRQXV1dXO2GoTEBBAUVERAPPnz+eTTz5x7mvRogXDhg0jKyuLWbNm0b9/f3bu3MmaNWuYNWsWiYmJ1NTUOIut8+UnJyeH5ORkFi9eTH5+Plu2bKFFixY888wzZGVlkZyczP33309wcDDbtm2jpqaG8vJydu/eXWfcP2U2mwkMDKSqqooWLVpw5513kpWVxaJFi+jduzfNmjUjISGBadOmMWPGDAICAjAMgxYtWrBjxw5qamqoqqrikUceccbhcDgwDIONGzc6C9jzXZ+IiIiI1CgURf0AACAASURBVE0zKBchKSmJ2NhYqqurAUhJSeHw4cMANGnShJEjRxIdHY2vry92ux03NzfnLMHPWa1WsrKy+P7777nhhht4+umnMQyDxMREoqKisNvtjBkzhqZNmzJ06FCef/55goKCnM9LXIwePXrw1Vdf8dBDD1FVVUWvXr24/fbbmTBhAomJiSxcuBCHw0FKSgpNmjThqaeeYvDgwTRp0sQ5E1Sb5ORk4uLiMJvN+Pv7M2zYMN566y0ARo0aRXx8PDk5OZSVlTFmzBhuvvlmPD09iYiIwN3dHX9/f2fuzqd169ZERkbSuHFjAgMDad++PbGxsSQlJWG326moqCA+Pp62bdvSq1cvIiMjCQgIoGnTphfMy3+XeAF4eHiQlpZGo0aN+Oqrr4iOjqa8vJx77rkHb29vHnzwQQYNGsR1112Hn58fhw8fpm3btnTt2pWoqChqamqIioqiTZs29O7d27ntjjvu4J577mHnzp0XPV4iIiIi8n9Mxk/XDsklczgcvP7664wePRo4s+xp7NixzqVUIldKVVW1poyvAE3Nux6NiWvx9/fRFzX+ThXN6Etp6an6DsPl6Z51Rl1LvDSD8iu5ublx+vRpBgwYgNVqJSQkhI4dO9Z3WL/K/v37iY2NPWd7p06dePLJJ+shoouzbds20tLSztneu3dvoqOj6yEiEREREblUmkERuUZoBuXK0Dtfrkdj4lp8fL3wsFrqOwypB6ftDspOnq7vMFye7llnaAZFRERErggLaJmPi9EfxHK10ad4iYiIiIiIy1CBIiIiIiIiLkMFioiIiIiIuAwVKCIiIiIi4jJUoIiIiIiIiMtQgSIiIiIiIi5DBYqIiIiIiLgMFSgiIiIiIuIyVKCIiIiIiIjL0DfJi4iIyGVTDfj7+9R3GPIzv2RMTtsdlJ08/RtEI1I3FSgiIiJy2XhYLdzy3If1HYZcBkUz+lJW30HI75KWeImIiIiIiMtQgXKZ5Ofn88orr9S6Pzc3l/T09HO29+zZE7vdftniiImJobCw8LK1N27cODZs2FCvMfwSgwYNoqSkpNb933//PY8++igjRozg4YcfJi0tjZqamkvu55NPPuHQoUOUlJQwaNCgOo+NiYkhMjKSmJgY5z//+te/LqqfvLw8nnvuuUuOT0RERORqoyVel0nbtm1p27ZtfYchF+nFF1/kb3/7G926dcMwDMaMGcOnn37Kvffee0ntvPXWWyQlJdGgQYOLOj41NRWbzfZLQhYRERH5XVCBUofc3Fw+//xzKioq2Lt3LyNHjiQiIoJdu3YxdepUAHx9fZk2bRo7duxg2bJlZGRksHz5crKzs2nUqBFWq5U+ffoAsHXrVoYPH86xY8eIiorioYceAmDSpEns27ePpk2bkpqaisViIS4ujuLiYqqrq3nkkUfo06cPMTExJCUlYbPZWLp0KUeOHGHAgAGMHj0aX19funXrBsCcOXM4cuQIp0+f5sUXX6R58+bMmDGDzZs3A9CvXz8efvhhSkpKiI+Px+FwYDKZSEhIoE2bNmRnZ7N8+XL8/f05evRonTnaunUrKSkpGIZBYGDgWbNEp06dIj4+nuPHjwOQkJBA69atWbx4MatXr8bhcODj48Ps2bP54IMPzpvr7OxsVqxYgdlsJiwsjNjYWA4cOEBiYiJ2u50GDRowZcoUgoKCyMjI4IsvvuD666939lmbG264gffee4+GDRsSEhLCrFmzcHM783I4X66ee+45+vTpQ7du3cjLy+Ojjz6iV69e5OfnExsbS1paGseOHePxxx+ntLSU1q1bO/8buZANGzbw+uuvY7VaKSkpoU+fPowePZrCwkLi4uLw9PTE09OTRo0aXVR7IiIiIlczFSgXUFZWxoIFCygqKmLUqFFERESQmJjItGnTaNmyJcuXLyczM5POnTsDcOzYMTIzM1mxYgXu7u4MHTrU2ZabmxsLFixg3759PProo84CJSoqitDQUF544QVycnIwm800btyYtLQ0ysrKiIiIIDw8vNYYS0tLeffdd3F3dycvL4/u3bvz4IMPMnv2bFatWkXLli0pKSkhJycHh8NBdHQ04eHhzJkzh5iYGO655x7y8/OJi4tj0aJFvPXWW6xcuRKTyURERESd+UlMTCQjIwObzUZ2dvZZS7vmzZtHeHg40dHRFBUVMXHiRLKzszlx4gRvvvkmZrOZESNGsH379lpznZubS2JiIqGhoSxZsgSHw0FqaioxMTF0796ddevWkZ6ezmOPPcbGjRt55513KC8v57777qsz7nHjxrFkyRJefPFFCgoK6N69O5MmTWLz5s3nzdX5/PnPf6Zt27YkJSVhtVopKytj+vTp+Pj4cO+993L06FGaNm161jmxsbF4eno6f3/ppZcA2L9/P++//z6VlZV07dqV0aNH89JLL/Hkk0/SpUsX5s+fz549e+q8JovFhK+vV53HyK9nsZiVZxejMRH57ei1dfnpnnVhKlAuoE2bNgAEBQVRWVkJQGFhIcnJyQBUVVURHBzsPH7v3r3YbDbnH6EdOnRw7rvtttswmUz4+/tTUVEBgNVqJTQ0FICwsDDWrl0L4Cx4vL29sdlsFBcXnxWXYRjOn5s1a4a7u7vz93bt2gHg5+fHkSNHKCwspGPHjphMJqxWK+3bt6ewsJDCwkI6deoEnFmidvDgQfbs2UPLli2d7YWEhNSZn6NHjzqXLA0ZMuSsfQUFBaxfv56PP/4YgJMnT2I2m7FarYwfPx4vLy8OHjyIw+GoNdfTp09n4cKFpKenExoaimEYFBQU8Nprr5GZmYlhGFitVnbv3k27du0wm814e3vTqlWrOuNev349w4YNY9iwYfz444+kpqYyd+5c/Pz8zpur2nL/U82bN3fOcjRt2pTTp8/9aMbalni1atUKNzc33Nzc8PDwAODbb7915j8sLOyCBUp1tcGJE+V1HiO/nq+vl/LsYjQmrkUfMXxt0Wvr8tM964y67hV6SP4CTCbTOduCg4NJTU0lKyuLZ599lu7duzv33XTTTezZs4eKigpqamrYtm1bnW1VVVWRn58PwKZNm7j11lux2Wxs2rQJODOrUFBQ4CxCSktLAdixY4ezDbO57mG02WzOJUtVVVVs2bKFm2+++ax+8vPz8fPzo3nz5uzevZuKigqqq6udsdUmICCAoqIiAObPn88nn3zi3NeiRQuGDRtGVlYWs2bNon///uzcuZM1a9Ywa9YsEhMTqampcf7Bf7785OTkkJyczOLFi8nPz2fLli20aNGCZ555hqysLJKTk7n//vsJDg5m27Zt1NTUUF5ezu7du+uMOy0tzVkMNmzYkODgYNzd3WvNVW25N5lMdcZ/sc53bosWLdiyZQsA33zzzS9uW0RERORqohmUXyApKYnY2Fiqq6sBSElJ4fDhwwA0adKEkSNHEh0dja+vL3a7HTc3N+cswc9ZrVaysrL4/vvvueGGG3j66acxDIPExESioqKw2+2MGTOGpk2bMnToUJ5//nmCgoIICAi46Hh79OjBV199xUMPPURVVRW9evXi9ttvZ8KECSQmJrJw4UIcDgcpKSk0adKEp556isGDB9OkSZOzliOdT3JyMnFxcZjNZvz9/Rk2bBhvvfUWAKNGjSI+Pp6cnBzKysoYM2YMN998M56enkRERODu7o6/v78zd+fTunVrIiMjady4MYGBgbRv357Y2FiSkpKw2+1UVFQQHx9P27Zt6dWrF5GRkQQEBJyztOrnZs2axdSpU5k5cybu7u40a9aMpKQkvL29z5urgQMHEhcXx8qVK7nllluc7XTo0IEJEyYwZcqUixqLny/x6t27d60PzU+ePJlx48axYMECmjRpctEP4ouIiIhczUxGbetV5BdxOBy8/vrrjB49Gjiz7Gns2LHOpVQiv5WqqmpNGV8Bmpp3PRoT1+Lv76MvarxGFM3oS2npqfoO45qje9YZdS3x0gzKZebm5sbp06cZMGAAVquVkJAQOnbsWN9h/Sr79+8nNjb2nO2dOnXiySefrIeILs62bdtIS0s7Z3vv3r2Jjo6uh4hERK59FVXVFM3oW99hyGVw2n7+1R8ivzXNoIhcIzSDcmXonS/XozFxLRoP16MxcS0ajzP0kLyIiIiIiFwVVKCIiIiIiIjLUIEiIiIiIiIuQwWKiIiIiIi4DBUoIiIiIiLiMlSgiIiIiIiIy1CBIiIiIiIiLkMFioiIiIiIuAwVKCIiIiIi4jJUoIiIiIiIiMtwq+8ARERE5NpRDfj7+9R3GPIzlzomp+0Oyk6e/o2iEambChQRERG5bDysFm557sP6DkN+paIZfSmr7yDkd0tLvERERERExGWoQBH5DSxdupTZs2fXuv/EiROsXLnyCkYkIiIicnVQgSJSD3bt2sVnn31W32GIiIiIuBw9gyIuraysjPj4eE6dOsXx48cZOHAgt99+OykpKRiGQWBgIOnp6ezateucbXv27GHKlClYLBYaNGjAlClTqKmpYdy4cQQFBVFSUkLfvn359ttv2bFjB3/+858ZP348MTExtG7dmm+//RYvLy86duzIv//9b06ePMnChQvx8vIiLi6O4uJiqqureeSRR+jTpw+bNm1i2rRpNGrUCLPZTGhoKAAzZ87km2++4ccff8RmszF9+nTmzZvHzp07efvtt+nWrRuJiYnY7XZnnE2aNOGpp56irKyMiooKnn32We688846c2WxmPD19boSw/K7ZrGYlWcXozER+W3odfXb0D3rwlSgiEv7/vvv6du3L/fddx+HDh0iJiYGDw8PMjIysNlsZGdnU1hYSGJi4nm3paSk0LZtW9asWcOMGTOYMGECxcXFLFy4kIqKCu6++27y8vLw9PSkR48ejB8/HoCQkBASEhIYMWIEHh4evPHGG8TGxrJx40YOHjxI48aNSUtLo6ysjIiICMLDw5k+fTozZ84kODiYyZMnA2cKrOuuu4433niDmpoa+vbty6FDhxg1ahTLli3joYceYuzYscTExNC9e3fWrVtHeno6o0aN4siRI7z55pscPXqUoqKiC+aqutrgxIny33I4hDP/w1aeXYvGxLXoE7yuHXpd/TZ0zzqjrnuFChRxaX5+fixatIjVq1fj7e2Nw+Hg6NGj2Gw2AIYMGQJw3m2HDx+mbdu2AHTq1ImZM2cC0Lx5c3x8fHB3d8fPzw9fX18ATCaTs9/bb78dgOuuu46WLVs6f7bb7RQWFtK5c2cAvL29sdlsFBcXc+jQIYKDgwEICwtj7969NGjQgGPHjjF+/Hi8vLwoLy+nqqrqrGssKCjgtddeIzMzE8MwsFqt3HrrrQwZMoTx48fjcDiIiYm5zJkVERERcU0qUMSlLVy4kNDQUKKjo1m/fj2ff/45AQEBFBUVccsttzB//nyCg4Nr3bZz507atGnDxo0bueWWW4CzC5FfwmazsWnTJu69917KysooKCigWbNm+Pv7U1hYiM1mY/v27TRq1Ii8vDwOHDjArFmzOHbsGJ988gmGYWA2m6mpqQGgRYsWDB8+nLCwMAoLC9m4cSO7du3ixx9/ZP78+Rw+fJjBgwfTo0ePX5tOEREREZenAkVcWo8ePUhKSmLlypX4+vpisVhISkoiLi4Os9mMv78/w4YNIzAw8JxtN954I1OmTMEwDCwWC9OmTbssMQ0aNIjExESioqKw2+2MGTOGpk2bkpaWRmxsLA0bNqRhw4Y0atSIkJAQ5s6dy6BBg3B3d6d58+YcPnyYm266iYKCAt58801iY2NJSkrCbrdTUVFBfHw8t9xyC3PmzGHFihVYrVaefPLJyxK7iIiIiKszGYZh1HcQIvLrVVVVa03rFaC1w65HY+Ja/P199EWN14CiGX0pLT1V32Fck3TPOkPPoIiIiMgVUVFVTdGMvvUdhvxKp+2O+g5BfsdUoIiIiMhlYwG98+5i9I69XG30RY0iIiIiIuIyVKCIiIiIiIjLUIEiIiIiIiIuQwWKiIiIiIi4DBUoIiIiIiLiMlSgiIiIiIiIy1CBIiIiIiIiLkMFioiIiIiIuAwVKCIiIiIi4jL0TfIiIiJy2VQD/v4+9R2G/Iy/vw+n7Q7KTp6u71BELkgFioiIiFw2HlYLtzz3YX2HIedRNKMvZfUdhMhF0BIvERERERFxGSpQ5HeltLSUpKQkAHr27Indbq/12C5dupyzLTc3l08//ZQNGzYwbtw4AMaMGQPArl272Lhx4yXHlJuby8CBA4mIiGDOnDkAHDt2jOHDhxMdHc3YsWM5fVpT8iIiIvL7oAJFflf8/f2dBcovERERwd13333WtldeeQWA1atXs3v37ktqb+/evSxdupSsrCzeeecdqqqqqKqqYu7cufTr148lS5Zw22238fbbb//imEVERESuJipQ5KpTVlbGU089xfDhwxkwYABz5syhd+/eGIYBQHJyMp988glfffUVQ4cOZejQoQwaNIjvvvuOkpISBg0adFZ7BQUFDB8+nGHDhhEREcHXX38NQGVlJePGjWPw4MFMnjwZwzCYPXs2S5cuPev8Ll26cOjQId577z3efPNNtm3bRmRkpHP/2LFj2bZt23mv5csvv6Rdu3bExsbyt7/9jbCwMKxWK5s3b6Zr164AdOvWjS+//PKy5U9ERETElekhebnqfP/99/Tt25f77ruPQ4cOERMTw2233camTZto3749X331FfHx8bz99tukpaURGBjIvHnzWLVqFf379z+nvd27dxMbG0vr1q1ZuXIlubm5hIWFUVFRwTPPPMONN97IU089xWeffVZrTIGBgQwYMAA/Pz9CQkLw8PBg9+7d+Pn5UVJSQkhIyHnPO378OJs2bWLp0qXY7XaioqJ45513KCsrw8fnzKfgNGzYkFOnTl0wLxaLCV9fr4vMovxSFotZeXYxGhORi6fXSv3TPevCVKDIVcfPz49FixaxevVqvL29cTgcDBo0iPfee4/S0lJ69uyJm5sbgYGBpKSk4OXlxaFDhwgLCztvewEBAcydOxcPDw9+/PFHvL29Abjhhhu48cYbAejQoQPffffdRcc4cOBAcnNzueGGG3jggQdqPc7X15c//vGPeHt74+3tjc1mo6ioCG9vb3788UdnTNddd90F+6yuNjhxovyiY5RfxtfXS3l2MRoT16KPGHZteq3UP92zzqjrXqElXnLVWbhwIaGhoaSnp9OrVy8Mw+Cuu+4iPz+fd99917m8KiEhgWnTpjFjxgwCAgKcS8B+LiUlhSeffJLU1FRatWrlPO7gwYMcPnwYgK+//ppbb721zrhMJhM1NTUA9OrVi7Vr1/LJJ5/UWaCEhYXx1VdfYbfbKS8vp7CwkJtuuomwsDA+//xzAPLy8rjjjjsuLUkiIiIiVynNoMhVp0ePHiQlJbFy5Up8fX2xWCxUVVVx//338+WXX3LzzTcD8OCDDzJo0CCuu+46/Pz8nMXGzz3wwAM8/vjjNG3alOuvv57jx48DZ2Y3pk6dyqFDh+jQoQPdu3ev9VkSgHbt2vHCCy9gs9kIDw+nU6dOHDt2DF9f31rPad26NX/961+JiorCMAwef/xxfH19GT16NLGxseTk5NC4cWNmzpz5KzImIiIicvUwGbW9rSwiv0pSUhL3338/d9111xXpr6qqWlPGV4Cm5l2PxsS1+Pv76IsaXVTRjL6Ull74mUb5bemedUZdS7w0gyLyGxg+fDgBAQHO4uSVV15hw4YN5xw3bdo0mjdvfqXDExEREXFZmkERuUZoBuXK0Dtfrkdj4lp8fL3wsFrqOww5j9N2B2Un9cW/9U33rDM0gyIiIiJXhAW0jMjF6A9iudroU7xERERERMRlqEARERERERGXoQJFRERERERchgoUERERERFxGSpQRERERETEZahAERERERERl6ECRUREREREXIYKFBERERERcRkqUERERERExGXom+RFRETksqkG/P196jsM+Rnv6zwpO3m6vsMQuSgqUEREROSy8bBauOW5D+s7DPmZohl9KavvIEQukpZ4iYiIiIiIy1CBchnk5+fzyiuv1Lo/NzeX9PT0c7b37NkTu91+2eKIiYmhsLDwsrU3btw4NmzYUK8x/BKDBg2ipKSk1v0xMTFERkYSExPD4MGDeeaZZzh+/Phlj2Px4sWXvU0RERGRa50KlMugbdu2jBkzpr7DkEuQmppKVlYWy5Yto1u3bkyaNOmy9/Hqq69e9jZFRERErnV6BqUOubm5fP7551RUVLB3715GjhzJ7bffztSpUwHw9fVl2rRp7Nixg2XLlpGRkcHy5cvJzs6mUaNGWK1W+vTpA8DWrVsZPnw4x44dIyoqioceegiASZMmsW/fPpo2bUpqaioWi4W4uDiKi4uprq7mkUceoU+fPsTExJCUlITNZmPp0qUcOXKEAQMGMHr0aHx9fenWrRsAc+bM4ciRI5w+fZoXX3yR5s2bM2PGDDZv3gxAv379ePjhhykpKSE+Ph6Hw4HJZCIhIYE2bdqQnZ3N8uXL8ff35+jRo3XmZ+vWraSkpGAYBoGBgWfNEp06dYr4+HjnzERCQgKtW7dm8eLFrF69GofDgY+PD7Nnz+aDDz44J88RERFkZ2ezYsUKzGYzYWFhxMbGcuDAARITE7Hb7TRo0IApU6YQFBRERkYGX3zxBddff/0lz4Y88MADzJo1C7vdTmFhIVOmTMFisTjbf+ONN7jjjjvo1asXI0aMoGvXrgwbNoz4+Hj++te/MnnyZP74xz+ya9cuTCYTc+fOZfHixfzwww8kJSURHx9/zpgGBwcza9YsXnvtNT744APmz5/P+++/z6ZNm/jHP/5BQEAAJSUlHD16lP379zNx4kS6du16SdclIiIicjVSgXIBZWVlLFiwgKKiIkaNGsV1113HtGnTaNmyJcuXLyczM5POnTsDcOzYMTIzM1mxYgXu7u4MHTrU2Y6bmxsLFixg3759PProo84CJSoqitDQUF544QVycnIwm800btyYtLQ0ysrKiIiIIDw8vNb4SktLeffdd3F3dycvL4/u3bvz4IMPMnv2bFatWkXLli0pKSkhJycHh8NBdHQ04eHhzJkzh5iYGO655x7y8/OJi4tj0aJFvPXWW6xcuRKTyURERESduUlMTCQjIwObzUZ2dvZZS7vmzZtHeHg40dHRFBUVMXHiRLKzszlx4gRvvvkmZrOZESNGsH379vPmOSIigtzcXBITEwkNDWXJkiU4HA5SU1OJiYmhe/furFu3jvT0dB577DE2btzIO++8Q3l5Offdd98lj/N1113HyZMnSUhIICUlhbZt27JmzRpmzJhBTEwM7733Hn/+8585efIkX375JQ8//DA7duxg6tSp/Pjjj/Tt25fExESefvpp8vLyGD16NIsXLyYpKYnFixefM6bLli1j//792O12vvjiC0wmE0eOHOGzzz7j3nvvZevWrbi7u5OZmcnatWtZuHDhBQsUi8WEr6/XJV+7XBqLxaw8uxiNicjF0evENeiedWEqUC6gTZs2AAQFBVFZWUlhYSHJyckAVFVVERwc7Dx279692Gw2PD09AejQoYNz32233YbJZMLf35+KigoArFYroaGhAISFhbF27VoAZ8Hj7e2NzWajuLj4rJgMw3D+3KxZM9zd3Z2/t2vXDgA/Pz+OHDlCYWEhHTt2xGQyYbVaad++PYWFhRQWFtKpUyfgzBK1gwcPsmfPHlq2bOlsLyQkpM7cHD16FJvNBsCQIUPO2ldQUMD69ev5+OOPATh58iRmsxmr1cr48ePx8vLi4MGDOByO8+YZYPr06SxcuJD09HRCQ0MxDIOCggJee+01MjMz/x979x5WVZn+f/y9gb1VQgXkIKYmYALqF600j6mVOSr5LRlPUBhNo2lfSs0DKlJoahomzlhZqZQieSDJxhynrJnJw2RqOdEknnBQ0AJUTBHYnPbvDy/3LxPwhLLCz+u6upJ1eJ57PfdeXNz7edbe2Gw2zGYzhw8fpn379jg4OODi4kKbNm2qjfvXbDYbJ0+epEmTJuTm5hIUFARA586def3117nvvvuYM2cOX3/9Nf369ePTTz9lz549dOzYEZPJBFzI78X4f/1cUUZGRqU57dmzJ19//TU//vgjgwYN4l//+hd79uxhwoQJfPfdd/Y4mjZtah+T6pSX2zhzpvCarl2unaurs8bZYJQTY9FHDBuX7hNj0O+sC6r7XaFnUK7g4h+gF/n6+tqfX5g8eTK9e/e272vZsiVHjhyhuLiYiooK0tLSqmwHLhQ46enpAOzZs4e7774bf39/9uzZA1yYVTh48KC9CMnLywNg37599jYcHKpPob+/v315V2lpKXv37uWuu+66pJ/09HQ8PDxo0aIFhw8fpri4mPLycntsVfHy8iIzMxOAd999ly1bttj3+fn5ERkZSVJSEosWLWLQoEHs37+fzz//nEWLFhEbG0tFRYW92KpsfNatW8fMmTNZtWoV6enp7N27Fz8/PyZNmkRSUhIzZ87kd7/7Hb6+vqSlpVFRUUFhYSGHDx+uNu5f+/DDD+natSsODg54eXmxf/9+AHbv3k2rVq1wcHCgffv2LFu2jJ49e3LfffcRHx9/yUxNZfFfvLaqctq3b1+WLl1KQEAAPXv2JDk5mbvuuguz2VxlmyIiIiJ1nWZQrlFcXBzR0dGUl5cDMGfOHHJzcwFwd3dn1KhRhIeH4+rqitVqxcnJyT5L8Gtms5mkpCSOHj1Ks2bNmDhxIjabjdjYWMLCwrBarURFRdGkSRNGjhzJrFmz8PHxwcvL66rjffDBB9m1axfDhw+ntLSU/v37065dO6ZMmUJsbCyJiYmUlZUxZ84c3N3dGTduHCNGjMDd3d0+E1SVmTNnMn36dBwcHPD09CQyMpKVK1cCMGbMGGJiYli3bh0FBQVERUVx11130aBBA0JDQ7FYLHh6etrHrjIBAQEMGTIENzc3vL296dChA9HR0cTFxWG1oNwchQAAIABJREFUWikuLiYmJoagoCD69+/PkCFD8PLyokmTJlccl+joaPv1eXt78/LLLwMwe/ZsXnnlFWw2G46OjsydOxeARx55hGnTphEYGEjPnj3ZsGGDfQaqKv7+/kyaNIm5c+dWmlN3d3f++9//8sc//pHAwECOHz/OH//4xyvGLiIiIlKXmWy/XC8kN6SsrIylS5cyduxY4MKyp/Hjx1/xD1mRmlBaWq4p41tAU/PGo5wYi6dnQ31RowFlzgshL+9cbYch6HfWRdUt8dIMSg1ycnKiqKiIwYMHYzabCQ4OplOnTrUd1g05ceIE0dHRl23v3LkzL7zwQi1EdHXS0tKIj4+/bPuAAQMIDw+vhYhERG4PxaXlZM4Lqe0w5FeKrJWv5hAxIs2giNQRmkG5NfTOl/EoJ8aifBiPcmIsyscFekheRERERER+E1SgiIiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGE41XYAIiIiUneUA56eDWs7DAGKrGUUnC2q7TBErpkKFBEREakx9c2OtJq6qbbDECBzXggFtR2EyHXQEi8RERERETEMFSgGkZ6ezhtvvFHbYVTqxIkT/P3vf7/m86ZOncrWrVtvQkQ1a8KECZSUlJCVlcVjjz1GdHQ0c+bM4cSJE9fUzu7du9m/fz8AUVFRNRbfL9sVERERqetUoBhEUFBQjf5RW5N27tzJt99+W9th3DQJCQlYLBa+/fZbunXrxvz584mJiaFZs2bX1M769evJzc0FqNFi85ftioiIiNR1egblBqSmprJ+/XoqKiqIiIhgxYoVODg4cN999zFp0iRyc3OZOHEiAHfffTeHDh0iKSmJhx56iM2bN1OvXj0WLFiAn58fd955J2vWrCEhIYEHH3wQPz8//Pz8+MMf/kBsbCxWq5V69erxyiuv4OPjU2k8p0+fZtKkSZSUlODr68vOnTvZsmVLpf2Fhoby+uuvs3v3bmw2G5GRkQwYMIDk5GQ2bNiAg4MD9957L5MmTeLdd9+luLiYe+65h4cffviyfrOzsxk3bhyenp7k5OTQq1cvJkyYAMDatWtZtmwZBQUFxMXFERwcTGJiIps2bcLJyYlOnToxefJkFi9eTHZ2NqdOneLEiRNMmzaNBx54gF27dpGQkICjoyMtWrRg1qxZmM3mSq/9iSee4K9//Ssmk4mZM2fSvXt3WrZsyezZswFwdXVl7ty57Nu3jwULFmA2mxk2bBh//vOfWbVqFUuWLKG4uJiWLVuyefNm4uLicHV1ZerUqZw7dw6bzcb8+fOpX78+cXFxWK1Wzpw5w//93//RtGlTtm3bxg8//EDr1q0ZOnQoO3bsYN++fbzyyis4Ojra81dRUcHEiRNp2rQpWVlZ/M///A8zZ86sNKf/+c9/Lmm3uqLJ0dGEq6tz9S9auWGOjg4aZ4NRTkSq5urqrHvEYJSPK1OBcoMaNWrEq6++Snh4OOvXr6dBgwZMnjyZHTt2sGXLFgYNGsSwYcPYuHEjhw4duqo2f/zxR1JTU3Fzc2P8+PFERETQu3dvvvrqKxYsWMDrr79e6Xlvv/02Dz/8ME888QQ7duxgx44dVfbx5Zdfkp2dzZo1a7BarQwbNowePXqQmppKbGwsHTt25IMPPsBmszF69GiOHDlSaXFy0fHjx1m+fDkNGzYkPDycH374AYB27drx3HPPkZqaSmpqKvXq1WPz5s2sWbMGJycnnn/+ef7xj38AYLFYWLZsGTt27CAxMZGePXsSGxvLBx98QJMmTVi0aBEfffQRw4YNu6x/d3d3AgIC2LNnDx06dGDXrl3ExMQQHh7O3Llzad26NSkpKSxbtozu3btjtVpJSUkB4M9//jNNmjSxX2d4eDibN28GYMmSJTz00EOEhYXx1VdfkZaWhoeHB08//TRdunTh22+/ZfHixbz33ns88MADDBw48JIiYsaMGcyZM4egoCA+//xz5s2bx5QpU8jMzGT58uU0aNCAvn37kpeXh6en52XX1b59+0rbrUx5uY0zZwqrPUZunKurs8bZYJQTY9EneBnLmTOFukcMRvm4oLrfFSpQbpCvry/Hjh3j9OnTjB49GoDz58+TlZVFdnY2I0aMAKBLly6sW7fusvNtNttl29zc3HBzcwPg4MGDvPPOOyxbtgybzVbp7MFFGRkZDB48GIBOnTpVeszF/g4ePMgPP/xAREQEAGVlZZw4cYJXX32VxMREFixYQMeOHSuNrzKBgYG4uroCEBwczH//+1/gQoEC4OHhQXFxMUeOHKFDhw726+jUqZO9cAsKCgKgadOmlJSUcPr0aXJzcxk/fjwAxcXF9OjRo8oYhg0bxkcffUReXh4PPfQQTk5OZGRk2GcnSktL8fX1BbD//0r++9//MmTIEAC6desGwKFDh1iyZAkffvghJpOJsrKyKs/Pzc21X1fnzp3txWXLli1xcXEBwNPTE6vVelXxiIiIiNR1KlBukIODA82bN8fHx4fExETMZjOpqakEBQWRlZXFN998Q2BgIP/5z3/s51gsFnJzc2nevDn79+/H39//sjYvurjM69577yUjI4Pdu3dXGUubNm3Yu3cvQUFB/Pvf/662Pz8/P7p06WJfcvTWW2/RvHlzFi1axMyZM6lXrx7PPPMMe/fuxcHBgYqKimrHISMjg6KiIiwWC2lpafz+979n+/btmEymS47z8/Pjvffeo6ysDEdHR3bv3s3jjz/O/v37LzvWzc2Npk2b8tZbb9GwYUO++OILnJ2rnhLt1q0b8fHx5OTk8NJLLwEXCpH58+fTrFkzvvnmG/Ly8i4b4+r4+/vz/fffExgYyO7du/nnP//J0aNHGTp0KL1792b9+vV89NFHAJhMpssKOi8vL/bv328/v1WrVvZjr1Zl7YqIiIjUVSpQaoC7uzuRkZFERERQXl7OnXfeyYABAxg9ejRTp07l008/pXHjxvbj//jHPzJ69GjuvPNOGjVqVG3b0dHR9ucdiouLiYmJqfLYUaNGMWXKFDZv3oyXlxdOTk5V9vfQQw+xa9cuwsPDKSwspG/fvri4uBAQEMCQIUNwc3PD29ubDh064OLiwpIlS2jXrh0hISGV9m02mxk3bhwnT56kf//+BAYGVnpcQEAAAwYMICwsjIqKCu677z769u1b6adUOTg4EBMTw+jRo7HZbNxxxx289tprVV6/yWTid7/7Hf/617+46667AIiLiyM6Opry8nIA5syZc00PnI8ZM4bp06fzl7/8BYC5c+fy3XffMWfOHN555x18fHzIz88HoEOHDixYsIDmzZvbz589ezavvPIKNpsNR0dH5s6de9V9X/TLdn9dzIqIiIjUNSab3pq9JTIyMoiLiyMpKemm9fHll1/i5uZGcHAw//rXv3j77bdZuXLlTevvouzsbF588cVKl7DJrVNaWq41rbeA1g4bj3JiLJ6eDfVFjQaROS+EvLxzukcMRvm4QM+g1DFRUVH8/PPPl2xzcXFh0qRJTJ8+HUdHRyoqKqqdbbkea9eu5ZNPPrls+4svvlij/VTnxIkTREdHX7a9c+fOvPDCC7csjpqWlpZGfHz8ZdsHDBhAeHh4LUQkInJ9ikvLyZxX+Wy73FpF1qqfkRQxMs2giNQRmkG5NfTOl/EoJ8aifBiPcmIsyscF1c2g6IsaRURERETEMFSgiIiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDCcajsAERERqTvKqf4boqXmFVnLKDhbVNthiNQYFSgiIiJSY+qbHWk1dVNth3FbyZwXQkFtByFSg7TES0REREREDKPGCpT09HTeeOONKvenpqayYMGCy7Y/9NBDWK3WmgqDiIgIMjIyaqy9CRMm8PXXX9dqDNdj2LBhZGdnV7n/6NGjjB49mmeeeYannnqK+Ph4KioqrrmfLVu2kJOTQ3Z2NsOGDav22IiICIYMGWL/f0JCwjX3d+bMGTZu3HjN511UUlJCjx49KC8vB2Dv3r0EBgby/fffA2C1WnnggQeqHItr7T8tLY2QkBBef/31644ZYNWqVTd0voiIiMhvRY0VKEFBQURFRdVUc3KTLVy4kCeffJLly5fz/vvvk5mZyRdffHHN7axcuZKCgqufWJ4/fz5JSUmkpKSwa9cue2FwtQ4cOMDf//73aw3TzmKxEBgYSHp6OgBffvkl/fv358svvwQuFCydO3fGwaHyW+Na+9++fTsjRoxg4sSJ1x0zwJIlS27ofBEREZHfimqfQUlNTeXLL7+kuLiYY8eOMWrUKEJDQzlw4ACzZ88GwNXVlblz57Jv3z7WrFlDQkICKSkpJCcn07hxY8xmMwMHDgTgu+++4w9/+AOnT58mLCyM4cOHA/DSSy9x/PhxmjRpwvz583F0dGT69OlkZWVRXl7O008/zcCBA4mIiCAuLg5/f39Wr17NyZMnGTx4MGPHjsXV1ZVevXoB8Oabb3Ly5EmKiopYuHAhLVq0YN68eXzzzTcAPProozz11FNkZ2cTExNDWVkZJpOJGTNmEBgYSHJyMikpKXh6enLq1KlqB/C7775jzpw52Gw2vL29L5klOnfuHDExMeTn5wMwY8YMAgICWLVqFZ999hllZWU0bNiQxYsX88knn1Q61snJyWzYsAEHBwfuvfdeoqOj+fHHH4mNjcVqtVKvXj1eeeUVfHx8SEhIYNu2bTRt2tTeZ1WaNWvGRx99xB133EFwcDCLFi3CyenCy6GysZo6dSoDBw6kV69ebN26lb/+9a/079+f9PR0oqOjiY+P5/Tp0zz33HPk5eUREBBgf41UpqSkhLKyMry8vKrs87PPPmPp0qU4OTlx55138tprr/H222+zf/9+1q5dS48ePSrNX79+/bj33nv573//S5MmTVi8eDGOjo72vnv06MGePXto3749O3fuJD4+nokTJxIVFcWuXbt44IEHAHj99df5z3/+w/nz5/H39+fVV1+9pP9evXpdlofy8vJLXo8ffvghZrOZpk2b8qc//YlWrVphsViIi4tj8uTJFBQUUF5ezrhx4+jWrRuDBg3i/vvv58CBA5hMJt566y1WrVrFzz//TFxcHHFxcdXmVUREROS37ooPyRcUFLB8+XIyMzMZM2YMoaGhxMbGMnfuXFq3bk1KSgrLli2je/fuAJw+fZply5axYcMGLBYLI0eO/P+dOTmxfPlyjh8/zujRo+0FSlhYGB07duS1115j3bp1ODg44ObmRnx8PAUFBYSGhtK1a9cqY8zLy2P9+vVYLBa2bt1K7969eeyxx1i8eDF/+9vfaN26NdnZ2axbt46ysjLCw8Pp2rUrb775JhEREfTt25f09HSmT5/OihUrWLlyJRs3bsRkMhEaGlrt+MTGxpKQkIC/vz/JycmXLO16++236dq1K+Hh4WRmZjJt2jSSk5M5c+YM77//Pg4ODjzzzDP2WYTKxjo1NZXY2Fg6duzIBx98QFlZGfPnzyciIoLevXvz1VdfsWDBAp599ll2797Nhx9+SGFhIf369as27gkTJvDBBx+wcOFCDh48SO/evXnppZf45ptvKh2ryvTp04egoCDi4uIwm80UFBTw6quv0rBhQx555BFOnTpFkyZNLjknOjqaBg0akJWVRWBgIG5ubvzjH/+otM9PPvmEyMhIQkJC2LBhAwUFBYwZM4Y1a9YwfPhwXnjhhcvyl5qaSlZWFitWrMDHx4cRI0bw/fff07FjR3sM3bt358033+TRRx+lQYMGtGjRApvNxunTp9m9ezdhYWEUFBTQqFEj3nvvPSoqKggJCSEnJ+eS/sePH39ZHiZMmHDJ67G4uBgPDw8eeeQRXn31VZ577jnatm3L/Pnz6d69O0899RQ5OTmEhYXx+eefc/78eUJCQoiNjWXixIls3bqVsWPHsmrVqisWJ46OJlxdnas9Rm6co6ODxtlglBMRqr0HdI8Yi/JxZVcsUAIDAwHw8fGhpKQEgIyMDGbOnAlAaWkpvr6+9uOPHTuGv78/DRo0AOCee+6x72vbti0mkwlPT0+Ki4sBMJvN9j8e7733Xnbs2AFgL3hcXFzw9/cnKyvrkrhsNpv9382bN8disdh/bt++PQAeHh6cPHmSjIwMOnXqhMlkwmw206FDBzIyMsjIyKBz587AhSVqP/30E0eOHKF169b29oKDg6sdn1OnTuHv7w/AE088ccm+gwcPsnPnTjZv3gzA2bNncXBwwGw28+KLL+Ls7MxPP/1EWVlZlWP96quvkpiYyIIFC+jYsSM2m42DBw/yzjvvsGzZMmw2G2azmcOHD9O+fXscHBxwcXGhTZs21ca9c+dOIiMjiYyM5Pz588yfP5+33noLDw+PSseqqrH/pRYtWtC4cWMAmjRpQlHR5R95OH/+fPz9/amoqGD69OksW7YMi8VSaZ/Tpk3jnXfeYfXq1fj5+dG3b99L2qosfwBubm74+PjYx/LXzzgFBASQmZnJtm3b7LMlPXv25Ouvv6akpARPT09KS0s5ffq0PU+FhYWUlpZe0k5leYDLX4+/dPFeycjIYNCgQQB4e3vj4uLC6dOngQv3SVWxV6e83MaZM4VXfbxcH1dXZ42zwSgnxqKPGK4d1d0DukeMRfm4oLrfFVd8BsVkMl22zdfX1/4sweTJk+ndu7d9X8uWLTly5AjFxcVUVFSQlpZWbVulpaX25wH27NnD3Xffjb+/P3v27AEuzCocPHjQ/kdfXl4eAPv27fv/F1HF8wIX+fv725cPlZaWsnfvXu66665L+klPT8fDw4MWLVpw+PBhiouLKS8vt8dWFS8vLzIzMwF499132bJli32fn58fkZGRJCUlsWjRIgYNGsT+/fv5/PPPWbRoEbGxsVRUVNj/4K9sfNatW8fMmTNZtWoV6enp7N27Fz8/PyZNmkRSUhIzZ87kd7/7Hb6+vqSlpVFRUUFhYSGHDx+uNu74+Hh7MXjHHXfg6+uLxWKpcqyqGnuTyVRt/FVxcHDA29ub0tLSKvtcu3Ytzz//vP0B8S1btuDg4GB/gL2y/F1NHCaTiYCAAFJSUuzLAnv16sXKlSu5//77Adi6dSs//vgjCxcu5MUXX6S4uBibzXZJ/5Xl4eK1VXfdv449JyeHs2fP4urqWmX8VRWFIiIiInXNdX0PSlxcHNHR0fZPQpozZw65ubkAuLu7M2rUKMLDw3F1dcVqteLk5GSfJfg1s9lMUlISR48epVmzZkycOBGbzUZsbCxhYWFYrVaioqJo0qQJI0eOZNasWfj4+NifXbgaDz74ILt27WL48OGUlpbSv39/2rVrx5QpU4iNjSUxMZGysjLmzJmDu7s748aNY8SIEbi7u9tngqoyc+ZMpk+fjoODA56enkRGRrJy5UoAxowZQ0xMDOvWraOgoICoqCjuuusuGjRoQGhoKBaLBU9PT/vYVSYgIIAhQ4bg5uaGt7c3HTp0IDo6mri4OKxWK8XFxcTExBAUFET//v0ZMmQIXl5ely2t+rVFixYxe/ZsXn/9dSwWC82bNycuLg4XF5dKx2ro0KFMnz6djRs30qpVK3s799xzD1OmTOGVV165qlxcXOIFUL9+feLj43F1da20z5ycHJ5++mlcXV2544476NOnDyUlJRw8eJD333+/0vxdrR49erB48WJat24NXJgpO3LkCBMmTLD//NZbbzFs2DAsFgstWrQgNzeXli1b2vuvLA9X69lnn2X69Ol8+umnFBcXM2vWLPszQJXx9/dn0qRJlX4SnoiIiEhdYrLV8FuzZWVlLF26lLFjxwIXlj2NHz/evhRHRG6O0tJyTRnfApqaNx7lxFg8PRvqixpvscx5IeTlnatyv+4RY1E+LqhuiVeNf5O8k5MTRUVFDB48GLPZTHBwMJ06darpbm6pEydOEB0dfdn2zp0788ILL9RCRFcnLS2N+Pj4y7YPGDCA8PDwWohIRERERKR6NT6DIiK1QzMot4be+TIe5cRYGro6U9/seOUDpcYUWcsoOHv5B9NcpHvEWJSPC27pDIqIiIjcvhyh2uVGIiJXUmPfJC8iIiIiInKjVKCIiIiIiIhhqEARERERERHDUIEiIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGCpQRERERETEMPRN8iIiIlJjygFPz4a1HUadVmQto+BsUW2HIXLTqEARERGRGlPf7EirqZtqO4w6LXNeCAW1HYTITaQlXiIiIiIiYhgqUKTOO3PmDBs3brzm8yZMmEBJSck1ndOjR4+rPnbYsGFkZ2dfdSxff/31NcUiIiIi8lukAkXqvAMHDvD3v//9ms9LSEjAYrHchIhEREREpCp6BkUMpbi4mClTppCbm4uPjw+7d+/G19eXuLg4/P39Wb16NSdPnuT5558nKSmJTz75BJPJxMCBAxk5ciSfffYZS5cuxcnJiTvvvJPXXnuNt99+m/3797N27Vrc3Nwu2+/gUHmd/tBDD7F582ZefvllLBYLx48fJzc3l3nz5tGuXTtSUlJYvXo1FRUVPPzwwzz//PP2cyMiIiqNOSEhgW3bttG0aVPy8/MBOHfuHDExMfafZ8yYQUBAAMnJyaSkpODp6cmpU6du/uCLiIiIGIAKFDGUtWvX0rx5c/785z+TkZHBo48+iq+v72XHHT58mL/+9a988MEHmEwmIiMj6dmzJ5988gmRkZGEhISwYcMGCgoKGDNmDGvWrGH48OG88MILl+1v1KjRFeNq1qwZs2bNYt26daxdu5Zx48axdOlS/vKXv2CxWJg3bx7nz5+vto2DBw+ye/duPvzwQwoLC+nXrx8Ab7/9Nl27diU8PJzMzEymTZvGu+++y8qVK9m4cSMmk4nQ0NArxujoaMLV1fmKx8mNcXR00DgbjHIit6Nrec3rHjEW5ePKVKCIoWRkZNCrVy8A/P39cXd3v2S/zWYDLvyxf+LECSIjIwH4+eefOXbsGNOmTeOdd95h9erV+Pn50bdv30vOv9L+qgQFBQHQtGlTvv32W7Kysrj77rupX78+ANOnT6/y3IsxHz58mPbt2+Pg4ICLiwtt2rSxX8vOnTvZvHkzAGfPnuXIkSO0bt3avsQsODj4ijGWl9s4c6bwqq5Hrp+rq7PG2WCUE2PRRwzfGtfymtc9YizKxwXV/a7QMyhiKG3atGHv3r0AHDt2jPz8fCwWC3l5eQDs27cPAD8/P1q3bs3KlStJSkoiNDSUNm3asHbtWp5//nlWrVoFwJYtW3BwcKCiogKg0v1Xw2QyXfJzy5YtOXLkiP0h+hdeeIGcnBz7/spi9vX1JS0tjYqKCgoLCzl8+LD9WiIjI0lKSmLRokUMGjSIFi1acPjwYYqLiykvLyc9Pf0aR1JERETkt0kzKGIoQ4YMYerUqTzxxBM0a9aMevXqMXLkSGbNmoWPjw9eXl4ABAYG0q1bN8LCwigpKSE4OBhvb2+Cg4N5+umncXV15Y477qBPnz6UlJRw8OBB3n///Ur3Xw93d3dGjRrFk08+iclk4sEHH8Tb29u+v7KYg4KC6N+/P0OGDMHLy4smTZoAMGbMGGJiYli3bh0FBQVERUXh7u7OuHHjGDFiBO7u7jRo0ODGBlZERETkN8Jku7j+RMQAvv32WwoLC+nZsyeZmZn88Y9/5PPPP6/tsH4TSkvLNWV8C2hq3niUE2Px9GyoL2q8yTLnhZCXd+6qj9c9YizKxwXVLfHSDIoYSosWLXjxxRd54403KCsr46WXXrqp/aWlpREfH3/Z9gEDBhAeHn5T+xYRqYuKS8vJnBdS22HUaUXWstoOQeSm0gyKSB2hGZRbQ+98GY9yYizKh/EoJ8aifFygh+RFREREROQ3QQWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGCpQRERERETEMFSgiIiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIZTbQcgIiIidUc54OnZsLbDqBOKrGUUnC2q7TBEbjkVKCIiIlJj6psdaTV1U22HUSdkzguhoLaDEKkFWuIlIiIiIiKGoQKlFqSnp/PGG29UuT81NZUFCxZctv2hhx7CarXWWBwRERFkZGTUWHsTJkzg66+/rtUYrsewYcPIzs6ucv/UqVMZNGgQERER9v9OnDhxCyMUERERuX1oiVctCAoKIigoqLbDkGswefJkevXqVdthiIiIiNR5KlBqUGpqKl9++SXFxcUcO3aMUaNG0a5dO2bPng2Aq6src+fOZd++faxZs4aEhARSUlJITk6mcePGmM1mBg4cCMB3333HH/7wB06fPk1YWBjDhw8H4KWXXuL48eM0adKE+fPn4+joyPTp08nKyqK8vJynn36agQMHEhERQVxcHP7+/qxevZqTJ08yePBgxo4di6urq/2P7TfffJOTJ09SVFTEwoULadGiBfPmzeObb74B4NFHH+Wpp54iOzubmJgYysrKMJlMzJgxg8DAQJKTk0lJScHT05NTp05VOz7fffcdc+bMwWaz4e3tfcks0blz54iJiSE/Px+AGTNmEBAQwKpVq/jss88oKyujYcOGLF68mE8++eSycQ4NDSU5OZkNGzbg4ODAvffeS3R0ND/++COxsbFYrVbq1avHK6+8go+PDwkJCWzbto2mTZva+7xWBw4cqDS3S5cuxWw2k52dzcCBAxkxYgSRkZF8/PHH7N27l2effZavvvqKvLw8YmJi+NOf/kRMTAznzp0jPz+foUOHMmjQIAYPHsynn36Ko6Mj8fHxtG/fngEDBlQZj6OjCVdX5+u6Frl6jo4OGmeDUU6kLquJ17buEWNRPq5MBUoNKygoYPny5WRmZjJmzBgNghBSAAAgAElEQVQaNWrE3Llzad26NSkpKSxbtozu3bsDcPr0aZYtW8aGDRuwWCyMHDnS3o6TkxPLly/n+PHjjB492l6ghIWF0bFjR1577TXWrVuHg4MDbm5uxMfHU1BQQGhoKF27dq0yvry8PNavX4/FYmHr1q307t2bxx57jMWLF/O3v/2N1q1bk52dzbp16ygrKyM8PJyuXbvy5ptvEhERQd++fUlPT2f69OmsWLGClStXsnHjRkwmE6GhodWOTWxsLAkJCfj7+5OcnHzJ0q63336brl27Eh4eTmZmJtOmTSM5OZkzZ87w/vvv4+DgwDPPPMP3339f6TiHhoaSmppKbGwsHTt25IMPPqCsrIz58+cTERFB7969+eqrr1iwYAHPPvssu3fv5sMPP6SwsJB+/fpdMa/x8fEsXboUgO7duzN27FhiY2Mrze2JEyf4y1/+QklJCQ888IC9KPzxxx/tRdEPP/zA999/T9++fTl69CghISH069ePnJwcIiIiCA8P57777mP79u307NmTrVu3Mm7cuGpjLC+3ceZM4RWvRW6Mq6uzxtlglBNj0Sd41ayaeG3rHjEW5eOC6n5XqECpYYGBgQD4+PhQUlJCRkYGM2fOBKC0tBRfX1/7sceOHcPf358GDRoAcM8999j3tW3bFpPJhKenJ8XFxQCYzWY6duwIwL333suOHTsA7AWPi4sL/v7+ZGVlXRKTzWaz/7t58+ZYLBb7z+3btwfAw8ODkydPkpGRQadOnTCZTJjNZjp06EBGRgYZGRl07twZuLBE7aeffuLIkSO0bt3a3l5wcHC1Y3Pq1Cn8/f0BeOKJJy7Zd/DgQXbu3MnmzZsBOHv2LA4ODpjNZl588UWcnZ356aefKCsrq3ScAV599VUSExNZsGABHTt2xGazcfDgQd555x2WLVuGzWbDbDZz+PBh2rdvj4ODAy4uLrRp06bauKHyJV5V5bZNmzY4OTnh5ORE/fr1AXjkkUf48ssv2bt3L6NGjWLHjh3s3buXOXPmUFFRwYoVK/jss89wcXGxX+PQoUNJSkqioqKC7t27X5I3ERERkbpKD8nXMJPJdMnPvr6+zJ8/n6SkJCZPnkzv3r3t+1q2bMmRI0coLi6moqKCtLS0KtuBC38Ep6enA7Bnzx7uvvtu/P392bNnD3BhVuHgwYP2IiQvLw+Affv22dtwcKg+5f7+/vblXaWlpezdu5e77rrrkn7S09Px8PCgRYsWHD58mOLiYsrLy+2xVcXLy4vMzEwA3n33XbZs2WLf5+fnR2RkJElJSSxatIhBgwaxf/9+Pv/8cxYtWkRsbCwVFRX2Yquy8Vm3bh0zZ85k1apVpKens3fvXvz8/Jg0aRJJSUnMnDmT3/3ud/j6+pKWlkZFRQWFhYUcPny42rirUlVuK4utb9++fPLJJ7i4uNCrVy8+//xzSkpK8PT0JDExkY4dO7JgwQL69+9vv8ZOnTqRlZXFhx9+yJAhQ64rRhEREZHfGs2g3GRxcXFER0dTXl4OwJw5c8jNzQXA3d2dUaNGER4ejqurK1arFScnJ/s76L9mNptJSkri6NGjNGvWjIkTJ2Kz2YiNjSUsLAyr1UpUVBRNmjRh5MiRzJo1Cx8fH7y8vK463gcffJBdu3YxfPhwSktL6d+/P+3atWPKlCnExsaSmJhIWVkZc+bMwd3dnXHjxjFixAjc3d3tM0FVmTlzJtOnT8fBwQFPT08iIyNZuXIlAGPGjCEmJoZ169ZRUFBAVFQUd911Fw0aNCA0NBSLxYKnp6d97CoTEBDAkCFDcHNzw9vbmw4dOhAdHU1cXBxWq5Xi4mJiYmIICgqif//+DBkyBC8vL5o0aXLV4/NL1eX215o2bYrVaqVr1640btwYJycn+vTpA1wY87i4ODZu3IirqyuOjo6UlJRgsVgYNGgQf/vb37j77ruvK0YRERGR3xqT7Zfrf+SWKisrY+nSpYwdOxa4sOxp/Pjx9qVUIkuXLsXNze2qZlBKS8u1pvUW0Nph41FOjMXTs6G+qLGGZM4LIS/v3A23o3vEWJSPC/QMikE5OTlRVFTE4MGDMZvNBAcH06lTp9oO64acOHGC6Ojoy7Z37tyZF154oRYiujppaWnEx8dftn3AgAGEh4fXQkQXvn8lPz+fxYsX10r/IiLXo7i0nMx5IbUdRp1QZK18RYVIXacZFJE6QjMot4be+TIe5cRYlA/jUU6MRfm4oLoZFD0kLyIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGGoQBEREREREcNQgSIiIiIiIobhVNsBiIiISN1RTvXfEC2XKrKWUXC2qLbDEDEUFSgiIiJSY+qbHWk1dVNth/GbkTkvhILaDkLEYLTES0REREREDEMFitx2UlNTWbBgwVUdu2rVqms+R0RERESunwoUkWosWbKktkMQERERua3oGRS5Lf373//mqaeeoqCggOeff57i4mKSk5Pt+//0pz+xdu1afv75Z+Li4ggODua7777jD3/4A6dPnyYsLIzhw4fz6KOP0qpVKywWC3FxcUyePJmCggLKy8sZN24c3bp1Y8eOHSxatIh69erh6urK3LlzSU9P591338VsNvPTTz8xYsQIdu7cyf79+xk5ciTh4eEkJCSwc+dOKioqCAkJITIysvYGTEREROQWUYEit6UGDRrw7rvvcvr0aYYOHcqwYcN49913adCgAS+99BLbt29n7NixrFq1iri4OFJTU3FycmL58uUcP36c0aNHM3z4cAoLC3nuuedo27Yt8+fPp3v37jz11FPk5OQQFhbGli1biI2NZfXq1Xh7e7NixQqWLFlCnz59+Omnn9iwYQM//PAD48aNY8uWLeTk5BAVFUV4eDgbNmxg1apVeHt7k5qaesVrcnQ04erqfAtG7/bm6OigcTYY5UR+627261f3iLEoH1emAkVuS/fddx8mk4kmTZrQsGFDnJyciI6O5o477uDIkSN07NjxsnPatm2LyWTC09OT4uJi+3ZfX18AMjIyGDRoEADe3t64uLiQn5+Pi4sL3t7eAHTu3JmFCxfSp08f7r77bsxmMw0bNqRly5ZYLBYaN26M1WoFYOHChSxcuJCTJ0/ywAMPXPGaysttnDlTeMNjI9VzdXXWOBuMcmIs+ojha3ezX7+6R4xF+bigut8VKlDktvT9998DkJeXx7lz51ixYgX//Oc/AXj66aex2WwA9v8DmEymSttycLjwKJe/vz979uyhbdu25OTkcPbsWRo3bkxBQQG5ubl4eXmxa9cuWrVqVW17ACUlJfztb39j4cKF2Gw2QkJCCAkJ4c4777zRSxcRERExNBUoclsqLi5m5MiRFBYWMmfOHNasWcPgwYNxdnamUaNG5ObmAheKjkmTJtG9e/crtvnss88yffp0Pv30U4qLi5k1axZms5nZs2fz/PPPYzKZaNy4Ma+++iqHDh2qtq2LsymPPfYYjRs3pkePHjRr1qxGrl1ERETEyEy2X75FLCK/WaWl5ZoyvgU0NW88yomxeHo21Bc1XoPMeSHk5Z27qX3oHjEW5eOC6pZ46WOGRURERETEMLTES0RERGpMcWk5mfNCajuM34wia1lthyBiOCpQREREpMY4wk1fsiQidZuWeImIiIiIiGGoQBEREREREcNQgSIiIiIiIoahAkVERERERAxDBYqIiIiIiBiGChQRERERETEMFSgiIiIiImIYKlBERERERMQwVKCIiIiIiIhh6JvkRUREpMaUA56eDWs7jFpRZC2j4GxRbYch8punAkVERERqTH2zI62mbqrtMGpF5rwQCmo7CJE6QEu8RERERETEMDSDInKd0tPT+eKLL4iKimLLli0EBwfj7e191edPnDiR3Nxcjh8/jtlsxsvLizZt2rB//36ioqLo1q2b/djZs2cTEBDA0KFDb8aliIiIiBiGChSR6xQUFERQUBAAK1euJC4u7poKlNdffx2AxYsX4+HhQVhYGAAff/wxH3/8sb1AKSkp4R//+AcvvvhiDV+BiIiIiPGoQJE6q6CggJiYGM6dO0d+fj5Dhw5l8+bNBAQEcOjQIZydnenUqRPbt2/n7NmzJCYm4uzszMsvv8zRo0epqKhg/PjxdOnShUGDBnH//fdz4MABTCYTb731Fvv27WPNmjU89thjpKenEx0dzdChQ8nMzCQ6Opry8nIef/xx1q9fj8Viueq4+/fvz6JFiygqKqJBgwZ88cUX9OjRA2dn55s4WiIiIiLGoAJF6qyjR48SEhJCv379yMnJISIiAm9vb4KDg5kxYwbPPPMM9evX57333iM6Oprdu3eTm5uLm5sbc+fOJT8/nyeffJJNmzZx/vx5QkJCiI2NZeLEiWzduhUPDw8A+vTpQ1BQkH0GJTQ0lEmTJrFt2za6dOlyTcUJQL169Xj44YfZsmUL//u//0tqairjx4+/4nmOjiZcXVXE3GyOjg4aZ4NRTsRIjPha1D1iLMrHlalAkTrLw8ODFStW8Nlnn+Hi4kJZWRkA7dq1A6BRo0a0bt3a/m+r1crBgwf55ptvSEtLA6CsrIz8/HwA2rZtC4CPjw9Wq7XSPl1cXOjcuTPbt28nNTWV55577rpiHzp0KK+99hpdunTh7Nmz9pirU15u48yZwuvqT66eq6uzxtlglBNjuV0/YvgiI74WdY8Yi/JxQXW/K1SgSJ2VmJhIx44dCQ8PZ+fOnXz55ZdXPMfPz4+mTZsyZswYiouLWbJkCY0bNwbAZDJVeZ7JZMJmswEwbNgwli5dSn5+PoGBgdcVe0BAAOfPn2flypX8/ve/v642RERERH6L9DHDUmc9+OCDrFy5krCwMFasWIGjoyMlJSXVnjNixAiOHDnCk08+yYgRI7jzzjtxcLjybXLPPfcwZcoUzpw5Q4cOHTh69CiDBg26ofh///vfk5KSQkhIyA21IyIiIvJbYrJdfNtXRGpERUUFYWFhLF++HBcXl1vWb2lpuaaMbwFNzRuPcmIsnp4Nb+svaszLO1fbYVxG94ixKB8XaImXyC2SlZVFVFQUw4cPtxcnUVFR/Pzzz5cc5+LiwpIlS2ojRBGRm6q4tJzMebfnzG+Rtay2QxCpEzSDIlJHaAbl1tA7X8ajnBiL8mE8yomxKB8XVDeDomdQRERERETEMFSgiIiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGE41XYAIiIiUneUA56eDWs7jFuqyFpGwdmi2g5DpM5QgSIiIiI1pr7ZkVZTN9V2GLdU5rwQCmo7CJE6REu8pMalp6fzxhtvALBlyxZycnKuq52XX36Zxx9//JJtEyZMoKSkpNrz9uzZw9NPP01ERAS///3vSU5Ovq7+f2nYsGFkZ2eTmprKF198AcCqVasA2Lp1K2vXrr3mNq/mWkRERERuN5pBkRoXFBREUFAQACtXriQuLg5vb+9raqOoqIhvv/2WNm3a8PXXX9OlSxcAEhISqj0vKyuL2bNns2zZMjw8PCguLmbkyJG0aNGCXr16Xd8F/UJoaKj930uWLOHJJ5+87navdC0iIiIityMVKLeZgoICYmJiOHfuHPn5+QwdOpTw8HAiIiIICAjg0KFDODs706lTJ7Zv387Zs2dJTEzE2dmZl19+maNHj1JRUcH48ePp0qULgwYN4v777+fAgQOYTCbeeust9u3bx5o1a3jsscdIT08nOjqaoUOHkpmZSXR0NOXl5Tz++OOsX78ei8VSaZybN2+mW7du9OrVi+TkZHuB8tBDD7F582Zefvllzpw5w5kzZ3jnnXdo3LgxAB9//DGPP/44Hh4eANSvX5/ly5fj7OxMaWkp06dPJysri/Lycp5++mkGDhxIREQEgYGBHDp0iIKCAv70pz9x5513kpCQwLZt22jatCn5+fkALF68GA8PD86cOcPPP/9MXFwcwcHBHDlyhEmTJpGYmMimTZtwcnKiU6dOTJ48mcWLF5Odnc2pU6c4ceIE06ZN44EHHrjkWiwWC8ePHyc3N5d58+bRrl07UlJSSE5OpnHjxpjNZgYOHHhJgSQiIiJSF6lAuc0cPXqUkJAQ+vXrR05ODhEREYSHhwMQHBzMjBkzeOaZZ6hfvz7vvfce0dHR7N69m9zcXNzc3Jg7dy75+fk8+eSTbNq0ifPnzxMSEkJsbCwTJ05k69at9uKgT58+BAUF2WdQQkNDmTRpEtu2baNLly5VFicAKSkpzJo1C39/f+Li4sjJyblsFqZr165ERkZesi03N5fAwMBLtjVseOFhzdWrV+Pm5kZ8fDwFBQWEhobStWtX+7XHxMSQkJDApk2b6NOnD7t37+bDDz+ksLCQfv36XdLm2LFjWbVqFXFxcaSmpgJw4MABNm/ezJo1a3BycuL555/nH//4BwAWi4Vly5axY8cOEhMTeeCBBy5pr1mzZsyaNYt169axdu1axo8fz7Jly9iwYQMWi4WRI0deMbeOjiZcXZ2veJzcGEdHB42zwSgnYgRGfg3qHjEW5ePKVKDcZjw8PFixYgWfffYZLi4ulJWV2fe1a9cOgEaNGtG6dWv7v61WKwcPHuSbb74hLS0NgLKyMvusQtu2bQHw8fHBarVW2q+LiwudO3dm+/btpKam8txzz1UZY0ZGBocOHWLevHkAmEwmVq9ezfjx4y85ztfX97JzmzVrxk8//XTJtv3792Oz2cjIyKB79+72ePz9/cnKyrrkGpo2bcrJkyc5fPgw7du3x8HBARcXF9q0aVNlvBcdOXKEDh06YDabAejUqROHDh0CsC95a9q0aaXPnfxy/7fffsuxY8fw9/enQYMGANxzzz1X7L+83MaZM4VXPE5ujKurs8bZYJQTY7ndPsHrIiO/BnWPGIvycUF1vyv0kPxtJjExkY4dO7JgwQL69++PzWa7qvP8/PwICQkhKSmJpUuX0r9/f/uyKpPJVOV5JpPJ3sewYcNISUnh1KlTl81y/FJKSgoTJkxg+fLlLF++nBUrVrB+/frL/rCvrN9HH32UlJQUTp8+DcD58+d56aWXyM3Nxd/fnz179gAXlrodPHiQ5s2bVxqDr68vaWlpVFRUUFhYyOHDhy875tdj5+fnR1paGmVlZdhsNnbv3m0voqobo8r2t2zZkiNHjlBcXExFRYW9MBQRERGp6zSDcpt58MEHiYuLY+PGjbi6uuLo6HhVnyQ1YsQIZsyYwZNPPklBQQHh4eE4OFy5vr3nnnuYMmUKiYmJdOjQgaNHj/LEE09UeXxJSQmbNm3i448/tm9r1qwZgYGBfPrpp1fsr3nz5kyePJmoqCgcHR05f/48Q4YMoXfv3pSUlBAbG0tYWBhWq5WoqCiaNGlSaTtBQUH079+fIUOG4OXlVelx/v7+TJo0yT4rExAQwIABAwgLC6OiooL77ruPvn37sn///ivG/Wvu7u6MGjWK8PBwXF1dsVqtODnpdhUREZG6z2S72rfQRW5QRUUFYWFhLF++HBcXl9oOx9DKyspYunQpY8eOBeCJJ55g/PjxdO7cucpzSkvLNWV8C2hq3niUE2Px9Gx4W34PSl7eudoOo0q6R4xF+biguiVeektWbomsrCyioqIYPny4vTiJiori559/vuQ4FxcXlixZUhshGoqTkxNFRUUMHjwYs9lMcHAwnTp1qu2wRERERG46zaCI1BGaQbk19M6X8SgnxtLQ1Zn6ZsfaDuOWKrKWUXC2qLbDqJLuEWNRPi7QDIqIiIjcEo5g6OVOImJ8+hQvERERERExDBUoIiIiIiJiGCpQRERERETEMFSgiIiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQx9k7yIiIjUmHLA07NhbYdxUxVZyyg4W1TbYYjUWSpQREREpMbUNzvSauqm2g7jpsqcF0JBbQchUodpiZeIiIiIiBiGChSpUenp6bzxxhsAbNmyhZycnGs6/+uvv2bChAlXfXyPHj2uqf0rad++PREREfb/4uLiyMvLIy4u7obaXbx4MatXr66ZIEVERETqMC3xkhoVFBREUFAQACtXriQuLg5vb+9ajurqNW7cmKSkpMu232iBIiIiIiJXRwXKbaSgoICYmBjOnTtHfn4+Q4cOJTw8nIiICAICAjh06BDOzs506tSJ7du3c/bsWRITE3F2dubll1/m6NGjVFRUMH78eLp06cKgQYO4//77OXDgACaTibfeeot9+/axZs0aHnvsMdLT04mOjmbo0KFkZmYSHR1NeXk5jz/+OOvXr8disVQbb2XtOzs7Exsby+HDh2nRogUlJSUAZGdnExMTQ1lZGSaTiRkzZhAYGMhf/vIXVqxYgcVioVWrVsyaNYuNGzeyfv16KioqeOGFF+jWrVu1cWRnZ/Piiy+ybt06Hn30UVq1aoXFYmHmzJnExMSQn58PwIwZMwgICODhhx+mQ4cOHDt2jLvvvps5c+bY2yovL+ell17ip59+Ij8/n169ejF+/HgyMzOZMWMGpaWl1K9fn4SEBKxWK7GxsVitVurVq8crr7yCj4/PDb4KRERERIxNBcpt5OjRo4SEhNCvXz9ycnKIiIggPDwcgODgYGbMmMEzzzxD/fr1ee+994iOjmb37t3k5ubi5ubG3Llzyc/P58knn2TTpk2cP3+ekJAQYmNjmThxIlu3bsXDwwOAPn36EBQUZJ9BCQ0NZdKkSWzbto0uXbpcsTgBKm3f2dkZq9XKunXrOHHiBJ9++ikAr732GhEREfTt25f09HSmT5/O8uXLWbx4MR999BEuLi7MnTuXtWvX4uzsTKNGjViyZMllff78889ERETYf46OjsbV1dX+c2FhIc899xxt27YlPj6erl27Eh4eTmZmJtOmTWP16tXk5OTw/9i7/7io6rz//4+ZYVAIcxCQ8LfApqiL6Iq5ea2/6mYmuSXrL1DManW1S0vLlRRoUZP8QeKumZXKhkgmFnpb9bJL273K6kpTt9S9JFEMQy1FxXQWGRmY7x9+mm8mIBo5J3zebzdvN2bOOe/367xfc5DXvN9n5umnn6Zt27Y8/fTTvPfee+7jv/76a6Kiohg+fDgOh8NdoCxYsIAJEybQp08f/uu//ouDBw/y9ttvk5CQQN++ffnkk09IT0/npZdeqnG8LBYTNpvvdcdVfhyLxaxxNhjlRDzh5/Sa0zViLMrH9alAuY0EBgaSlZXFtm3b8PPzw+l0urd17twZgDvvvJPw8HD3zw6Hg4KCAvbu3cv+/fsBcDqd7lmDTp06ARASEoLD4ai2Xz8/P6Kjo/noo4/Iy8vjySefrHPMP2z/xIkTREZGAtCiRQv3jEJhYSHR0dHAlWVm33zzDcXFxYSHh+Pn5wfgjqFr1660b9++2v6qW+J1/Pjxqx5/d2xBQQE7d+5k69atAFy4cMEda9u2bQHo1q0bX375pftYm83GgQMH2LlzJ35+fu4ZoC+//JJu3boBMHjwYADS0tJ47bXXWLlyJS6XC6vVWutYVVa6OH++rNZ95Mez2Xw1zgajnBhLQ/+I4e/8nF5zukaMRfm4orbfFSpQbiOZmZlERUURHx/Pzp07+eCDD+p0XGhoKHfddRcTJ06kvLyc5cuX07RpUwBMJlONx5lMJlwuFwAjRoxgxYoVlJaW0rFjxzrH/MP2Q0ND2bJlC48++iinTp1y34QfFhbGnj17uO+++8jPzycwMJBWrVpRWFhIWVkZvr6+fPrpp+7iwmy++c+H+O7Y0NBQfvvb3zJkyBDOnj3L+vXrATh16hQlJSUEBQXxz3/+k4cffpiDBw8CkJeXR5MmTZgzZw7Hjh0jNzcXl8tFWFgYBw4c4N577+Vvf/sb3377LaGhoTz++ON0796dwsJCdu/efdMxi4iIiPxcqEC5jfTv35/U1FQ2bdqEzWbDYrG438GvzahRo0hOTmbMmDHY7Xbi4+Pr9Ad+t27dmDFjBpmZmXTt2pVjx44xevToH3UO999/P3v37mX48OG0aNECf39/AGbMmEFKSgqZmZk4nU7mzZtHs2bNmDJlCmPHjsVsNtOmTRumT5/Oli318/n8EydOJCkpidzcXOx2O5MnTwbA29ubuXPn8vXXX9O1a1cGDBjgLlB+/etf88wzz7B37158fHxo27Ytp0+fZsaMGTz//PMsX76cxo0bs2jRIvr160dqaioOh4Py8nKSkpLqJW4RERERIzO5vnuLW+QnVFVVRVxcHKtWrXIvuWqoevfuzccff3zL+62oqNSU8S2gqXnjUU6MJSioyW3xRY0lJRc9HUad6RoxFuXjCi3xEo8qLi5m8uTJjBw50l2cTJ48mW+//faq/fz8/Kq9cV1ERH4+yisqKZof4+kwflKXHM7r7yQiN00zKCINhGZQbg2982U8yomxKB/Go5wYi/JxRW0zKPomeRERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGGoQBEREREREcNQgSIiIiIiIoahAkVERERERAxDBYqIiIiIiBiGChQRERERETEMFSgiIiIiImIYXp4OQERERBqOSiAoqImnw6gXlxxO7BcueToMkduOChQRERGpN42tFto9t8XTYdSLovkx2D0dhMhtSEu8RERERETEMFSg1KP8/HxefvnlGrfn5eWRnp5+zfMDBgzA4XDUWxwJCQkUFhbWW3vTpk1j165dHo3hZowYMYLjx4/XuD0hIYEXX3zR/djhcDBgwIA6t3/y5En+8Y9/uNuq7Xzz8vLo168fCQkJ7n9z586tc1+9e/eu874iIiIiP2da4lWPIiIiiIiI8HQYcgM2b97MfffdR8+ePW/42J07d3L06NE6FzUPPfQQ06dPv+F+RERERG4nKu8cPJoAACAASURBVFCuIy8vjw8++IDy8nK++uorxo8fT2xsLIcOHeKFF14AwGazkZaWxsGDB3nrrbfIyMhg/fr15OTk0LRpU6xWK4MHDwZg3759PP7445w7d464uDhGjhwJwPPPP8+JEycICAhgwYIFWCwWZs2aRXFxMZWVlTz22GMMHjyYhIQEUlNTCQsLY+3atZw5c4ahQ4cyadIkbDYbffr0AWDZsmWcOXOGS5cusXjxYlq3bs38+fPZu3cvcOWP5UcffZTjx4+TlJSE0+nEZDKRnJxMx44dycnJYf369QQFBXH27Nlax2jfvn3MmzcPl8tFcHDwVbNEFy9eJCkpidLSUgCSk5Pp0KEDa9asYdu2bTidTpo0acLSpUvZvHlztWOdk5PDxo0bMZvNdO/encTERL7++mtSUlJwOBw0atSIuXPnEhISQkZGBh9++CF33XWXu8/aJCUlkZKSQl5eHl5e///lUNO49O/fn9DQUNq1a8fHH39MeXk53bp1q3HM62LIkCH07NmTQ4cOYTKZeOWVV/D19SUlJYUjR47QunVrLl++fN12LBYTNptvnfqUm2exmDXOBqOcyE+pIby2dI0Yi/JxfSpQ6sBut7Nq1SqKioqYOHEisbGxpKSkkJaWRnh4OOvXr2flypXce++9AJw7d46VK1eyceNGvL29GTt2rLstLy8vVq1axYkTJ5gwYYK7QImLiyMqKoqFCxeSm5uL2WzG39+fRYsWYbfbiY2NpVevXjXGWFJSwjvvvIO3tzc7duygb9++PPzwwyxdupR3332X8PBwjh8/Tm5uLk6nk/j4eHr16sWyZctISEjg/vvvJz8/n1mzZpGVlcXq1avZtGkTJpOJ2NjYWscnJSWFjIwMwsLCyMnJuWqp06uvvkqvXr2Ij4+nqKiImTNnkpOTw/nz53njjTcwm8088cQTHDhwoMaxzsvLIyUlhaioKN58802cTicLFiwgISGBvn378sknn5Cens4f/vAHdu/ezdtvv01ZWRkDBw68bm47dOjAI488wvz580lOTnY/v3DhwmvGJS8vj6+//pq8vDz8/f3Jy8vj6NGj3HfffbzxxhvXjPn48eOv6mvz5s3s27fP/fh3v/sdjzzyCP/+97+JiYkhJSWFZ599lh07duDr64vD4SA3N5eTJ0/y3//939c9l8pKF+fPl113P/lxbDZfjbPBKCfG0lA+wes7DeG1pWvEWJSPK2r7XaECpQ46duwIQEhIiPud7MLCQmbPng1ARUUF7du3d+//1VdfERYWho+PD4D7HXaATp06YTKZCAoKory8HACr1UpUVBQA3bt35+OPPwZwFzx+fn6EhYVRXFx8VVwul8v9c6tWrfD29nY/7tKlCwCBgYGcOXOGwsJCevTogclkwmq10rVrVwoLCyksLCQ6Ohq4skTtm2++4ejRo4SHh7vbi4yMrHV8zp49S1hYGACjR4++altBQQE7d+5k69atAFy4cAGz2YzVauWZZ57B19eXb775BqfTWeNYv/jii2RmZpKenk5UVBQul4uCggJee+01Vq5cicvlwmq1cuTIEbp06YLZbMbPz4+777671ri/M2HCBOLi4tixY4f7uerGBcDf3x9/f/9q2/nhmP9QbUu8OnXq5D5vh8PBiRMn3OPeokULQkJC6nQuIiIiIj93ukm+Dkwm0zXPtW/fngULFpCdnc0f//hH+vbt697Wpk0bjh49Snl5OVVVVezfv7/WtioqKsjPzwdgz549/OIXvyAsLIw9e/YAV2YVCgoK3EVISUkJAAcPHnS3YTbXnsqwsDD38q6Kigo+++wz2rZte1U/+fn5BAYG0rp1a44cOUJ5eTmVlZXu2GrSvHlzioqKAHj99dfZvn27e1toaCjjxo0jOzubJUuWMGTIEL744gvee+89lixZQkpKClVVVe5iq7rxyc3NZfbs2axZs4b8/Hw+++wzQkNDmT59OtnZ2cyePZsHHniA9u3bs3//fqqqqigrK+PIkSO1xv0di8XC/Pnzr7phvrpxgavH2Ww2U1VVVac+rueH5x0aGsrnn38OwKlTpzh16lS99CMiIiJidJpBuUmpqakkJiZSWVkJwLx58zh9+jQAzZo1Y/z48cTHx2Oz2XA4HHh5eblnCX7IarWSnZ3NsWPHaNGiBc8++ywul4uUlBTi4uJwOBxMnjyZgIAAxo4dy5w5cwgJCaF58+Z1jrd///58+umnjBw5koqKCgYNGkTnzp2ZMWMGKSkpZGZm4nQ6mTdvHs2aNePpp59m1KhRNGvWzD0TVJPZs2cza9YszGYzQUFBjBs3jtWrVwMwceJEkpKSyM3NxW63M3nyZNq2bYuPjw+xsbF4e3sTFBTkHrvqdOjQgWHDhuHv709wcDBdu3YlMTGR1NRUHA4H5eXlJCUlERERwaBBgxg2bBjNmzcnICCgzuMTGhrKo48+SlZWFkC14/JDd999N8uXL6dz58516uOHS7z8/PxYvnx5tfvef//97N27l+HDh9OiRYsaZ21EREREGhqT6/vrhKReOJ1OVqxYwaRJk4Ary56mTp3qXjIk8lOoqKjUmtZbQGuHjUc5MZagoCYN6osaS0ouejqMH03XiLEoH1foHpRbzMvLi0uXLjF06FCsViuRkZH06NHD02H9KCdPniQxMfGa56Ojo3nqqac8EFHd7N+/n0WLFl3z/IMPPkh8fLwHIhIRadjKKyopmh/j6TDqxSVH9SsfROSnpRkUkQZCMyi3ht75Mh7lxFiUD+NRToxF+biithkU3SQvIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGCpQRERERETEMFSgiIiIiIiIYahAERERERERw1CBIiIiIiIihuHl6QBERESk4aik9m+I/rm45HBiv3DJ02GI3JZUoIiIiEi9aWy10O65LZ4O40crmh+D3dNBiNymtMRLREREREQMQwWKyE8kLy+P9PT0GzrG4XAwYMCAnygiEREREeNTgSIiIiIiIoahe1BEfkKff/45jz76KHa7nSlTpuDr60tGRgYWi4XWrVszZ84cLl++zPTp07lw4QJt2rRxH5uTk8PGjRsxm810796dxMRED56JiIiIyK2hAkXkJ+Tj48Prr7/OuXPnGD58OFarlTfffJOAgACWLFnChg0buHz5MnfffTfTpk1j37597Nq1C7iyRCwlJYWoqCjefPNNnE4nXl41X7IWiwmbzfdWndpty2Ixa5wNRjmRn0pDeV3pGjEW5eP6VKCI/IR+9atfYTKZCAgIoHHjxhw/fpypU6cCUF5eTu/evSktLeU3v/kNAF27dnUXIS+++CKZmZmkp6cTFRWFy+Wqta/KShfnz5f9tCck2Gy+GmeDUU6MpSF8xPB3GsrrSteIsSgfV9T2u0IFishP6MCBAwCUlJTgcDho2bIlr7zyCk2aNOHvf/87vr6+FBQU8Pnnn3P//fdz8OBBnE4nALm5ucyePZtGjRrxxBNP8Nlnn9GzZ09Pno6IiIjIT04FishPqLy8nLFjx1JWVsYLL7xAZWUlEyZMwOVycccdd7Bw4UKio6OZOXMmcXFxhIaGYrVaAejQoQPDhg3D39+f4OBgunbt6uGzEREREfnpmVzXWzciIj8LFRWVmjK+BTQ1bzzKibEEBTVpMF/UWFJy0dNh1AtdI8aifFxR2xIvfcywiIiIiIgYhpZ4iYiISL0pr6ikaH6Mp8P40S45nJ4OQeS2pQJFRERE6o0FGszSKBHxDC3xEhERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGGoQBEREREREcPQN8mLiIhIvakEgoKaeDqMm3bJ4cR+4ZKnwxC5ralAERERkXrT2Gqh3XNbPB3GTSuaH4Pd00GI3Oa0xEtERERERAxDBYo0CPn5+bz88ssAbN++nVOnTt3Q8Xl5efTr14+EhARGjx7NmDFj+OSTT36KUEVERESkFlriJQ1CREQEERERAKxevZrU1FSCg4NvqI2HHnqI6dOnA3DmzBlGjx7NmjVrCAoKqvd4RURERKR6KlDklrDb7SQlJXHx4kVKS0sZPnw4W7dupUOHDhw+fBhfX1969OjBRx99xIULF8jMzMTX15c//elPHDt2jKqqKqZOnco999zDkCFD6NmzJ4cOHcJkMvHKK69w8OBB3nrrLR5++GHy8/NJTExk+PDhFBUVkZiYSGVlJY888gjvvPMO3t7e1403MDCQBx54gPfff59HHnmEWbNmUVxcTGVlJY899hiDBw/m4MGDzJ07F4vFQqNGjZg7dy5VVVVMmjQJm81Gnz592LFjR63naLFYrhmX+Ph4EhIS6NixI4cPH8Zut/PnP/+Zli1b3oJMiYiIiHiWChS5JY4dO0ZMTAwDBw7k1KlTJCQkEBwcTGRkJMnJyTzxxBM0btyYv/71ryQmJrJ7925Onz6Nv78/aWlplJaWMmbMGLZs2cK///1vYmJiSElJ4dlnn2XHjh0EBgYC0K9fPyIiItwzKLGxsUyfPp0PP/yQe+65p07FyXcCAgIoLS1l3bp1+Pv7s2jRIux2O7GxsfTq1Yvk5GTmzZtHREQE7733HvPnz2fGjBmUlJS4C6EdO3bUeo4hISHXjEt8fDwAkZGRJCUlkZGRwZYtW5gwYUKt8VosJmw235tPktSJxWLWOBuMciL1raG9nnSNGIvycX0qUOSWCAwMJCsri23btuHn54fT6QSgc+fOANx5552Eh4e7f3Y4HBQUFLB37172798PgNPppLS0FIBOnToBEBISgsPhqLZPPz8/oqOj+eijj8jLy+PJJ5+8oZhPnjxJp06d+Pzzz7n33nvdbYaFhVFcXMzp06fdy8qio6N56aWXAGjVqtVVhVBt51jTuHz/HO+66y7OnDlz3XgrK12cP192Q+coN85m89U4G4xyYiw/548Y/k5Dez3pGjEW5eOK2n5X6CZ5uSUyMzOJiooiPT2dQYMG4XK5rntMaGgoMTExZGdns2LFCgYNGkTTpk0BMJlMNR5nMpnc7Y8YMYL169dz9uxZOnbsWOd4T58+zd///nf69u1LWFgYe/bsAa4sVSsoKKBVq1Y0b96cL774AoDdu3fTrl07AMzmul9WNzMuIiIiIg2ZZlDklujfvz+pqals2rQJm82GxWLh8uXLtR4zatQokpOTGTNmDHa7nfj4+Dr98d+tWzdmzJhBZmYmXbt25dixY4wePfq6x23evJl9+/ZhNptxuVy8+OKL2Gw2RowYQUpKCnFxcTgcDiZPnkxAQAAvvPACc+fOxeVyYbFYSEtLq/N4fOdmxkVERESkITO59JatNGBVVVXExcWxatUq/Pz8PB3OT6qiolJTxreApuaNRzkxlqCgJj/7L2osKbno6TDqla4RY1E+rqhtiZdmUKTBKi4uZvLkyYwcOdJdnEyePJlvv/32qv38/PxYvny5J0IUEWlwyisqKZof4+kwbtolh/P6O4nIT0ozKCINhGZQbg2982U8yomxKB/Go5wYi/JxhW6SFxERERGRnwUVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGGoQBEREREREcNQgSIiIiIiIoahAkVERERERAxDBYqIiIiIiBiGChQRERERETEMFSgiIiIiImIYXp4OQERERBqOSiAoqImnw7gplxxO7BcueToMkdueChQRERGpN42tFto9t8XTYdyUovkx2D0dhIhoiZeIiIiIiBiHChSpF3l5eaSnp9/08evWraOioqLG7a+//jr79++/6fZ/KkuXLmXt2rU/qo01a9bUuv3QoUPs3r37R/UhIiIi8nOhAkUM4bXXXqOqqqrG7RMmTCAyMvIWRnTrLF++vNbt27Zt48iRI7coGhERERHP0j0oUm/27dvH448/zrlz54iLi6NVq1YsWbKERo0aYbPZSEtLw+l0MnXqVFwuFxUVFcyePZv9+/dTUlLCtGnTeOWVV6pt+7nnnmPw4MG0bt2amTNn4uXlhcViYeHChQQHB1d7zLZt21ixYgVeXl60bNmShQsXsmzZMgIDA4mLi6OwsJDU1FSys7MZPHgwPXr04PDhwzRt2pTFixdjtVr505/+xLFjx6iqqmLq1Kncc889PPTQQ7Rr1w5vb2/at2/P3//+d959913Onz/P008/zYABA/jb3/5GVlYW3t7etGvXjjlz5nD8+PFrYs/Ly+Pbb78lNTWV1NTUa87h1KlTbNiwAavVSufOnWst0iwWEzab703lTurOYjFrnA1GOZH61BBfS7pGjEX5uD4VKFJvvLy8WLVqFSdOnGD8+PE4HA7Wrl1LcHAwWVlZLF++nHvuuYcmTZrw0ksvceTIEex2O8OHD2f58uVkZGRct4///d//pXPnzjz33HPs2bOHb7/9tsYCZfPmzYwbN46YmBg2btyI3V7zrY/l5eUMGTKE6OhoFi5cyLp162jUqBH+/v6kpaVRWlrKmDFj2LJlC2VlZTz55JN06tSJpUuXEhwczLx589i1axcrV66kW7duLF26lA0bNuDn50daWhrr1q3DZDJdE/ukSZNYs2ZNtcUJQHBwMEOHDiUwMPC6M0iVlS7Ony+77hjKj2Oz+WqcDUY5MZaf6yd4fachvpZ0jRiL8nFFbb8rtMRL6k2nTp0wmUwEBQXx9ddf4+fn5y4eoqOjOXz4MH369CE6Oponn3ySv/zlL5jNN/YSHDZsGP7+/vz+978nJycHi8VS474zZ85k9+7djBkzhn/+85+19uXl5UV0dDQA3bt358svv6SgoIAdO3aQkJDAU089hdPppLS0FID27du7j+3cuTMAgYGBlJeXU1xcTHh4OH5+fled+43ELiIiInK7UoEi9cZkMrl/9vf3x263c/r0aQA+/fRT2rVrx65du2jevDmZmZlMmjSJxYsXu4+t7R6U7/z973/nV7/6FVlZWQwaNIiVK1fWuO+6deuYMmWK+yb07du306hRI0pKSgD4v//7P/e+TqeTL774AoC9e/cSHh5OaGgoMTExZGdns2LFCgYNGkTTpk0Brip2vn/eAK1ataKwsJCysjL3uX+3FKy62F0uV63nXNexEREREWkItMRLfhImk4kXXniBKVOmYDKZaNq0KS+++CImk4lp06aRlZWF2WzmP//zPwHo0aMHEyZMYPXq1df8wf99Xbp04Y9//CNLly7FbDYzc+bMGveNjIzksccew2azcccdd9CvXz/sdjtTp05l9+7ddOnS5ar9V6xYwcmTJ2nRogXTpk0DIDk5mTFjxmC324mPj6/TjE+zZs2YMmUKY8eOxWw206ZNG6ZPn86pU6eqjT0sLIzp06fX+CloXbp0YeHChYSFhdGrV6/r9i8iIiLyc2ZyXe/tW5HbwIABA9i6dSuNGjXydCg3raKiUmtabwGtHTYe5cRYgoKa/Ky/qLGk5KKnw6h3ukaMRfm4orZ7UDSDIoZx+fJlnnjiiWueb9++PXPmzKm3Y4zo5MmTJCYmXvN8dHQ0Tz31lAciEhG5OeUVlRTNj/F0GDflksPp6RBEBM2giDQYmkG5NfTOl/EoJ8aifBiPcmIsyscV+hQvERERERH5WVCBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGGoQBEREREREcPw8nQAIiIi0nBUUvs3RHvKJYcT+4VLng5DROpABYqIiIjUm8ZWC+2e2+LpMK5RND8Gu6eDEJE60RIvERERERExDBUoYkgJCQkUFhbe9PEOh4MBAwbUY0Q3Ztq0aezatavG7YcOHWL37t11asvT5yIiIiJyK6lAEfGAbdu2ceTIEU+HISIiImI4ugdFbqny8nJmzpzJyZMnqaio4LnnniMnJ4eLFy9SWlrK8OHDiY+Pd+9/8eJFkpKSKC0tBSA5OZkOHTowcOBAunfvzpdffklAQABLly6lvLyc6dOnc+HCBdq0aeNu49NPP+Xll192979gwQKsViuTJk3CZrPRp08fxo8ff02sx48f55lnniE3NxeAESNGsHjxYjZs2MDRo0c5e/YsFy5cIDk5mR49epCTk8P69esJCgri7NmzANjtdpKSkq46v/vuu48NGzZgtVrp3Lkz5eXlZGRkYLFYaN26NXPmzOHy5cvVnouIiIhIQ6cCRW6pt956i5YtW5KRkUFBQQEff/wxMTExDBw4kFOnTpGQkHBVgfLqq6/Sq1cv4uPjKSoqYubMmaxdu5bi4mKysrIICQlh1KhRHDhwgH/961/cfffdTJs2jX379rmXWB0+fJhFixYRHBzMq6++yrvvvsuQIUMoKSnhnXfewdvb+4bPo3HjxqxevZrDhw/z7LPPkpOTw+rVq9m0aRMmk4nY2FgAjh07Vu35DR06lMDAQH75y18yaNAg3nzzTQICAliyZAkbNmzg8uXL1Z5LbSwWEzab7w2fi9wYi8WscTYY5UTq6nZ9negaMRbl4/pUoMgtdfToUfr06QPA3XffTdOmTXnppZfYtm0bfn5+OJ3Oq/YvKChg586dbN26FYALFy4A4O/vT0hICAAhISE4HA4OHz7Mb37zGwC6du2Kl9eVl3dwcDDz5s3D19eXU6dO0b17dwBatWp1Q8WJy+Vy/9yrVy8AfvGLX3DmzBmOHj1KeHi4u73IyEgAAgMDycrKqvH8zp07x+nTp5k6dSpwZYand+/elJaWVnsutamsdHH+fFmdz0dujs3mq3E2GOXEWIz4EcPfuV1fJ7pGjEX5uKK23xW6B0VuqbCwMA4cOABAcXExc+fOJSoqivT0dAYNGnRVEQAQGhrKuHHjyM7OZsmSJQwZMgQAk8l0TduhoaF8/vnnABw8eNBdDCQnJ5OWlsb8+fNp3ry5uw+zufaXf6NGjTh79iyVlZVcuHCB48ePu7f93//9H3ClgAoODqZ169YcOXKE8vJyKisryc/PByAzM7Pa8zOZTFRVVeHv789dd93FK6+8QnZ2NhMnTuSee+6p8VxEREREGjrNoMgtNWrUKGbNmsWYMWOorKzkvvvucy+NstlsWCwWLl++7N5/4sSJJCUlkZubi91uZ/LkyTW2PXr0aGbOnElcXByhoaFYrVYAHn74YUaMGMGdd95JYGAgp0+frlOsQUFB9O7dm2HDhtGmTRvatm3r3pafn8+jjz7KpUuXmDt3Ls2aNePpp59m1KhRNGvWDB8fHwD69+9PamrqNefXpUsXFi5cSFhYGElJSUyYMAGXy8Udd9zBwoULiY6OrvZcRERERBo6k+uHb1mLSK2WLl1KYGAgcXFxng7lKhUVlZoyvgU0NW88yomxBAU1MewXNZaUXPR0GB6ha8RYlI8ralvipRkUue2tW7eOzZs3X/P8M888Q7du3TwQkYiIiMjtSzMoIg2EZlBuDb3zZTzKibE0sfnS2GrxdBjXuORwYr9wydNheISuEWNRPq7QDIqIiIjcEha4bZdSiUj90Kd4iYiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGHom+RFREQaML87ffBpdOv+uy+vqLxlfYlIw6QCRUREpAHzaeRFu+e23LL+iubHcPGW9SYiDZGWeImIiIiIiGGoQPl/8vPzefnll2vcnpeXR3p6+jXPDxgwAIfDUW9xJCQkUFhYWG/tTZs2jV27dnk0hpsxYsQIjh8/Xu223bt3M378ePfj1157jZ49e+J0OgHYuXMn//mf/3lD/R06dIjdu3df83yXLl1ISEggISGBUaNGMWLECIqLi2+o7ZpeOyIiIiJyLS3x+n8iIiKIiIjwdBhSB1FRURw6dIiqqirMZjMfffQRvXr14p///Cc9e/bk008/5Te/+c0Ntblt2zYCAwOJjo6+6vmmTZuSnZ3tfvzWW2/x17/+leeff75ezkVERERErtagC5S8vDw++OADysvL+eqrrxg/fjyxsbEcOnSIF154AQCbzUZaWhoHDx7krbfeIiMjg/Xr15OTk0PTpk2xWq0MHjwYgH379vH4449z7tw54uLiGDlyJADPP/88J06cICAggAULFmCxWJg1axbFxcVUVlby2GOPMXjwYBISEkhNTSUsLIy1a9dy5swZhg4dyqRJk7DZbPTp0weAZcuWcebMGS5dusTixYtp3bo18+fPZ+/evQA89NBDPProoxw/fpykpCScTicmk4nk5GQ6duxITk4O69evJygoiLNnz9Y6Rvv27WPevHm4XC6Cg4Oveqf/4sWLJCUlUVpaCkBycjIdOnRgzZo1bNu2DafTSZMmTVi6dCmbN2+udqxzcnLYuHEjZrOZ7t27k5iYyNdff01KSgoOh4NGjRoxd+5cQkJCyMjI4MMPP+Suu+5y91kdq9VKp06dOHToEC1btqSqqorBgwfz/vvv07NnT3bv3s38+fOpqKjgT3/6E8eOHaOqqoqpU6dyzz33kJGRwc6dO6mqqiImJoYHH3yQDRs2YLVa6dy5M5GRkTX2ffLkSe68804A/va3v5GVlYW3tzft2rVjzpw55Obm8s9//pOXXnqJxMREIiMj8fHx4fPPP+fRRx/FbrczZcoU+vXrx4ABA9i6dSuNGjUiPT2d0NBQWrZsSXp6OlarlXvvvZf333+ft99+G4CpU6fy+OOP1xqfiIiIyM9dgy5QAOx2O6tWraKoqIiJEycSGxtLSkoKaWlphIeHs379elauXMm9994LwLlz51i5ciUbN27E29ubsWPHutvy8vJi1apVnDhxggkTJrgLlLi4OKKioli4cCG5ubmYzWb8/f1ZtGgRdrud2NhYevXqVWOMJSUlvPPOO3h7e7Njxw769u3Lww8/zNKl/4YU6AAAIABJREFUS3n33XcJDw/n+PHj5Obm4nQ6iY+Pp1evXixbtoyEhATuv/9+8vPzmTVrFllZWaxevZpNmzZhMpmIjY2tdXxSUlLIyMggLCyMnJycq5Z2vfrqq/Tq1Yv4+HiKioqYOXMmOTk5nD9/njfeeAOz2cwTTzzBgQMHahzrvLw8UlJSiIqK4s0338TpdLJgwQISEhLo27cvn3zyCenp6fzhD39g9+7dvP3225SVlTFw4MBa47733nvZs2cPX375Jffeey+9e/fm1VdfxeFwcPHiRVq2bMmbb76Jv78/aWlplJaWMmbMGLZs2cLGjRtZs2YNwcHB5OXlERwczNChQwkMDLzmj/9vv/2WhIQE7HY758+fZ+DAgTz11FOUlpaydOlSNmzYgJ+fH2lpaaxbt44xY8bw8ccf89xzz1FRUcHo0aPJy8vDx8eH119/nXPnzjF8+HB3MVodh8PB+vXrgSvL1Y4cOUJgYCDHjx+vtTixWEzYbL61jpv8eBaLWeNsMMqJ8SgfxqJrxFiUj+tr8AVKx44dAQgJCeHy5csAFBYWMnv2bAAqKipo3769e/+vvvqKsLAwfHx8AOjWrZt7W6dOnTCZTAQFBVFeXg5ceTc/KioKgO7du/Pxxx8DuAsePz8/wsLCrrlvweVyuX9u1aoV3t7e7sddunQBIDAwkDNnzlBYWEiPHj0wmUxYrVa6du1KYWEhhYWF7iVJERERfPPNNxw9epTw8HB3e9d7t/3s2bOEhYUBMHr06Ku2FRQUsHPnTrZu3QrAhQsXMJvNWK1WnnnmGXx9ffnmm2/c935UN9YvvvgimZmZpKenExUVhcvloqCggNdee42VK1ficrmwWq0cOXKELl26YDab8fPz4+6776417t69e/OXv/wFX19fRo8eTZMmTWjSpAkffvghPXv2dMe/d+9e9u/fD4DT6aS0tJTFixezePFizpw5c92lYN8t8aqsrOS5557DarVyxx13UFhYSHh4OH5+fgBER0fz0UcfAbiL17y8PHc7v/rVrzCZTAQEBNCkSRPOnz9/VT/ffz18//U4fPhw8vLyaNGiBb/97W9rjbWy0sX582W17iM/ns3mq3E2GOWkdkFBTW55n8qHsegaMRbl44rafjc1+JvkTSbTNc+1b9+eBQsWkJ2dzR//+Ef69u3r3tamTRuOHj1KeXk5VVVV7j9ua2qroqKC/Px8APbs2cMvfvELwsLC2LNnD3BlVqGgoMBdhJSUlABw8OBBdxtmc+1pCAsLcy/vqqio4LPPPqNt27ZX9ZOfn09gYCCtW7fmyJEjlJeXU1lZ6Y6tJs2bN6eoqAiA119/ne3bt7u3hYaGMm7cOLKzs1myZAlDhgzhiy++4L333mPJkiWkpKRQVVXl/uO6uvHJzc1l9uzZrFmzhvz8fD777DNCQ0OZPn062dnZzJ49mwceeID27duzf/9+qqqqKCsr48iRI9cdk9OnT1NQUEDnzp0B+I//+A9WrVrlLjpCQ0OJiYkhOzubFStWMGjQIO644w7effddFi9eTFZWFhs2bODEiROYTCaqqqpq7M9isTB37ly2b9/O+++/T6tWrSgsLKSs7MovmE8//ZT27dtz+fJl0tLSmDNnDqmpqe5C7btZppKSEsrKyvD398fb25vTp0/jcrn44osv3H19//UwaNAgPv74Y7Zv337dAkVERESkIWjwMyjVSU1NJTExkcrKK18mNW/ePE6fPg1As2bNGD9+PPHx8dhsNhwOB15eXu5Zgh+yWq1kZ2dz7NgxWrRowbPPPovL5SIlJYW4uDgcDgeTJ08mICCAsWPHMmfOHEJCQmjevHmd4+3fvz+ffvopI0eOpKKigkGDBtG5c2dmzJhBSkoKmZmZOJ1O5s2bR7NmzXj66acZNWoUzZo1c88E1WT27NnMmjULs9lMUFAQ48aNY/Xq1QBMnDiRpKQkcnNzsdvtTJ48mbZt2+Lj40NsbCze3t4EBQW5x646HTp0YNiwYfj7+xMcHEzXrl1JTEwkNTUVh8NBeXk5SUlJREREMGjQIIYNG0bz5s0JCAi47ri0a9cOl8vlLoz69OnDsmXL3DMoo0aNIjk5mTFjxmC324mPj8fb25umTZvy8MMP07RpU3r37k2LFi3o0qULCxcuJCwsrMbleI0bN2bevHkkJiayadMmpkyZwtixYzGbzbRp04bp06eTnp5Ov379GDlyJKdPn+all16iQ4cOlJeXM3bsWMrKypgzZw4mk4nf//73TJgwgZYtW7rva/mhRo0aER0dzblz57DZbNcdExEREZGfO5Pr+2tLBKfTyYoVK5g0aRJwZdnT1KlTr/l0J5FbJTU1lQceeIBf//rXte5XUVGpKeNbQFPzxqOc1C4oqMkt/6LGkhJ9VaOR6BoxFuXjitqWeN2WMyi18fLy4tKlSwwdOhSr1UpkZCQ9evTwdFg/ysmTJ0lMTLzm+ejoaJ566ikPRFQ3+/fvZ9GiRdc8/+CDDxIfH++BiG69xx9/nObNm1+3OBERqcklh5Oi+TG3rL/yispb1peINEyaQRFpIDSDcmvonS/jUU6MRfkwHuXEWJSPK27rm+RFREREROTnQwWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGCpQRERERETEMFSgiIiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIaXpwMQERGR+uV3pw8+jTzzX3x5RaVH+hWRhkMFioiISAPj08iLds9t8UjfRfNjuOiRnkWkodASLxERERERMQwVKNIg5efn8/LLLwOwfft2Tp06dUPH5+XlkZ6eftVz06ZNY9euXfUWo4iIiIhcSwWKNEgRERFMnjwZgNWrV2O32z0ckYiIiIjUhe5BEY+w2+0kJSVx8eJFSktLGT58OFu3bqVDhw4cPnwYX19fevTowUcffcSFCxfIzMzE19eXP/3pTxw7doyqqiqmTp3KPffcw5AhQ+jZsyeHDh3CZDLxyiuvcPDgQd566y0efvhh8vPzSUxMZPjw4RQVFZGYmEhlZSWPPPII77zzDt7e3j869vj4eBISEujYsSOHDx/Gbrfz5z//mZYtW7Js2TLee+89mjVrxqVLl3j66afp1KkTSUlJlJaWApCcnEyHDh3o378/oaGhhIaGEh0dzYoVK/Dy8qJly5YsXLgQs7nm9xQsFhM2m++Pyotcn8Vi1jgbjHJiPMqHsegaMRbl4/pUoIhHHDt2jJiYGAYOHMipU6dISEggODiYyMhIkpOTeeKJJ2jcuDF//etfSUxMZPfu3Zw+fRp/f3/S0tIoLS1lzJgxbNmyhX//+9/ExMSQkpLCs88+y44dOwgMDASgX79+REREkJqaSnBwMLGxsUyfPp0PP/yQe+65p9biZPPmzezbt8/9+MiRI4waNara2OPj4wGIjIwkKSmJjIwMtmzZQp8+ffjwww95++23qaioYMiQIQC8+uqr9OrVi/j4eIqKipg5cyZr167l66+/Ji8vD39/f5566inGjRtHTEwMGzduxG63c+edd9YYb2Wli/Pny+ojPVILm81X42wwysm1goKaeLR/5cNYdI0Yi/JxRW2/p1SgiEcEBgaSlZXFtm3b8PPzw+l0AtC5c2cA7rzzTsLDw90/OxwOCgoK2Lt3L/v37wfA6XS6ZyA6deoEQEhICA6Ho9o+/fz8iI6O5qOPPiIvL48nn3yy1hgfeughpk+f7n48bdq0WmP/fhx33XUXZ86cobCwkF/+8pdYLBYsFgtdunQBoKCggJ07d7J161YALly4AIC/vz/+/v4AzJw5k9dee421a9cSGhrK/ffff/2BFREREfmZ0z0o4hGZmZlERUWRnp7OoEGDcLlc1z0mNDSUmJgYsrOzWbFiBYMGDaJp06YAmEymGo8zmUzu9keMGMH69es5e/YsHTt2/MljDw8P58CBA1RVVXH58mUOHjzoPpdx48aRnZ3NkiVL3DMr31/CtW7dOqZMmcKaNWuAKzf7i4iIiDR0mkERj+jfvz+pqals2rQJm82GxWLh8uXLtR4zatQokpOTGTNmDHa7nfj4+FrvyfhOt27dmDFjBpmZmXTt2pVjx44xevToWxJ7hw4d6Nu3LyNGjMDf3x+r1YqXlxcTJ04kKSmJ3Nxc7Ha7+4b+74uMjOSxxx7DZrNxxx130K9fv5uOWUREROTnwuSqy1vXIg1EVVUVcXFxrFq1Cj8/v5+8v7Nnz/Luu+8yevRoLl++TExMDFlZWbRo0aLe+6qoqNSa1ltAa4eNRzm5VlBQE49+UWNJib6q0Uh0jRiL8nGF7kERAYqLi5k8eTIjR450FyeTJ0/m22+/vWo/Pz8/li9fXi99+vv7869//Yvf/e53mEwmhg8f/pMUJyIi33fJ4aRofoxH+i6vqPRIvyLScGgGRaSB0AzKraF3voxHOTEW5cN4lBNjUT6uqG0GRTfJi4iIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGF4eToAERERqT9+d/rg08hz/72XV1R6rG8RaRhUoIiIiDQgPo28aPfcFo/1XzQ/hose611EGgIt8RIREREREcNQgVJH+fn5vPzyyzVuz8vLIz09/ZrnBwwYgMPhqLc4EhISKCwsrLf2pk2bxq5duzwaw80YMWIEx48fr3F7QkICw4YNIyEhwf3v/fffv6E+zp8/z6ZNm2pte9iwYWRkZNxo+DW2LSIiInK70xKvOoqIiCAiIsLTYcgNWLBgAWFhYTd9/KFDh/jHP/7BkCFDamzb5XIRHx/PgQMH+OUvf1kvbYuIiIjczm7rAiUvL48PPviA8vJyvvrqK8aPH09sbCyHDh3ihRdeAMBms5GWlsbBgwd56623yMjIYP369eTk5NC0aVOsViuDBw8GYN++fTz++OOcO3eOuLg4Ro4cCcDzzz/PiRMnCAgIYMGCBVgsFmbNmkVxcTGVlZU89thjDB48mISEBFJTUwkLC2Pt2rWcOXOGoUOHMmnSJGw2G3369AFg2bJlnDlzhkuXLrF48WJat27N/Pnz2bt3LwAPPfQQjz76KMePHycpKQmn04nJZCI5OZmOHTuSk5PD+vXrCQoK4uzZs7WO0b59+5g3bx4ul4vg4OCrZokuXrxIUlISpaWlACQnJ9OhQwfWrFnDtm3bcDqdNGnShKVLl7J58+ZqxzonJ4eNGzdiNpvp3r07iYmJfP3116SkpOBwOGjUqBFz584lJCSEjIwMPvzwQ+666y53nzeqsrKS559/nm+++YbS0lL69OnD1KlT2bZtGytWrMDLy4uWLVuycOFCXn31Vb744gvWrVvnzuUPXb58GafTSfPmzQGqzcP12u7du3e1eRo4cCDdu3fnyy+/JCAggKVLl2KxWG7qvEVERER+Lm7rAgXAbrezatUqioqKmDhxIrGxsaSkpJCWlkZ4eDjr169n5cqV3HvvvQCcO3eOlStXsnHjRry9vRk7dqy7LS8vL1atWsWJEyeYMGGC+4/auLg4oqKiWLhwIbm5uZjNZvz9/Vm0aBF2u53Y2Fh69epVY4wlJSW88847eHt7s2PHDvr27cvDDz/M0qVLeffddwkPD+f48ePk5ubidDqJj4+nV69eLFu2jISEBO6//37y8/OZNWsWWVlZrF69mk2bNmEymYiNja11fFJSUsjIyCAsLIycnJyrlna9+uqr9OrVi/j4eIqKipg5cyY5OTmcP3+eN954A7PZzBNPPMGBAwdqHOu8vDxSUlKIiorizTffxOl0smDBAhISEujbty+ffPIJ6enp/OEPf2D37t28/fbblJWVMXDgwOvmNjExER8fH/fjP//5z5SVlREVFcXw4cNxOBzuAmXz5s2MGzeOmJgYNm7ciN1uZ+LEibz11lvVFifftV1cXEzHjh3x9/fnf/7nf6rNw/Xafuqpp67JU15eHsXFxWRlZRESEsKoUaM4cOAAUVFRNZ6vxWLCZvO97rjIj2OxmDXOBqOcGI/yYSy6RoxF+bi+275A6dixIwAhISFcvnwZgMLCQmbPng1ARUUF7du3d+//1VdfERYW5v7Dt1u3bu5tnTp1wmQyERQURHl5OQBWq9X9R2X37t35+OOPAdwFj5+fH2FhYRQXF18Vl8vlcv/cqlUrvL293Y+7dOkCQGBgIGfOnKGwsJAePXpgMpmwWq107dqVwsJCCgsLiY6OBq4sUfvmm284evQo4eHh7vYiIyNrHZ+zZ8+6l0mNHj36qm0FBQXs3LmTrVu3AnDhwgXMZjNWq5VnnnkGX19fvvnmG5xOZ41j/eKLL5KZmUl6ejpRUVG4XC4KCgp47bXXWLlyJS6XC6vVypEjR+jSpQtmsxk/Pz/uvvvuWuOG6pd4eXt7c+DAAXbu3Imfn587jpkzZ/Laa6+xdu1aQkNDuf/+++vUdlVVFbNmzWLlypV4e3tXm4frtV1dngD8/f0JCQlxj9n17mWqrHRx/nzZdcdFfhybzVfjbDDKydWCgpp4OgTlw2B0jRiL8nFFbb+rbvub5E0m0zXPtW/fngULFpCdnc0f//hH+vbt697Wpk0bjh49Snl5OVVVVezfv7/WtioqKsjPzwdgz549/OIXvyAsLIw9e/YAV2YVCgoK3EVISUkJAAcPHnS3YTbXnqawsDD3sqKKigo+++wz2rZte1U/+fn5BAYG0rp1a44cOUJ5eTmVlZXu2GrSvHlzioqKAHj99dfZvn27e1toaCjjxo0jOzubJUuWMGTIEL744gvee+89lixZQkpKClVVVe5iq7rxyc3NZfbs2axZs4b8/Hw+++wzQkNDmT59OtnZ2cyePZsHHniA9u3bs3//fqqqqigrK+PIkSO1xl2TvLw8mjRpwksvvcTjjz9OeXk5LpeLdevWMWXKFNasWQPA9u3bMZvNVFVV1dqe2WwmODiYioqKGvNwvbary1NN4yUiIiLS0N32MyjVSU1NJTExkcrKK182NW/ePE6fPg1As2bNGD9+PPHx8dhsNhwOB15eXu5Zgh+yWq1kZ2dz7NgxWrRowbPPPovL5SIlJYW4uDgcDgeTJ08mICCAsWPHMmfOHEJCQtz3NNRF//79+fTTTxk5ciQVFRUMGjSIzp07M2PGDFJSUsjMzMTpdDJv3jyaNWvG008/zahRo2jWrNlVS6CqM3v2bGbNmoXZbCYoKIhx48axevVqACZOnEhSUhK5ubnY7XYmT55M27Zt8fHxITY2Fm9vb4KCgtxjV50OHTowbNgw/P39CQ4OpmvXriQmJpKamorD4aC8vJykpCQiIiIYNGgQw4YNo3nz5gQEBFx3XH64xOvBBx/k17/+Nc888wx79+7Fx8eHtm3bcvr0aSIjI3nsscew2Wzccccd9OvXj8uXL1NQUMAbb7zBuHHjamy7cePGLFq0CJvNVm0eTp06VWvb1eVJRERE5HZlcn1/LZFcl9PpZMWKFUyaNAm4suxp6tSp7iU6Ip5SUVGpKeNbQFPzxqOcXC0oqInHv6ixpERf1WgkukaMRfm4orYlXppBuUFeXl5cunSJoUOHYrVaiYyMpEePHp4O60c5efIkiYmJ1zwfHR3NU0895YGI6mb//v0sWrTomucffPBB4uPjPRCRiIiIiPxYmkERaSA0g3Jr6J0v41FOruZ3pw8+jTz3/mN5RSUXlQ9D0TViLMrHFZpBERERuU3YL1zC7sH+9fGpIvJj3faf4iUiIiIiIsahAkVERERERAxDBYqIiIiIiBiGChQRERERETEMFSgiIiIiImIYKlBERERERMQwVKCIiIiIiIhhqEARERERERHDUIEiIiIiIiKGoW+SFxERaUD87vTBp5Hn/nsvr6j0WN8i0jCoQBEREWlAfBp50e65LR7rv2h+DBc91ruINARa4iUiIiIiIoahAqWe5Ofn8/LLL9e4PS8vj/T09GueHzBgAA6Ho97iSEhIoLCwsN7amzZtGrt27fJoDDdjxIgRHD9+vMbtP4zR4XAwYMCAOrW9du1ali5dWqd9ExISGDZsGAkJCSQkJDBu3DhOnTpVp2N37drFtGnT6rSviIiISEOhJV71JCIigoiICE+HIQa0YMECwsLCAHjzzTfJzMxk5syZHo5KRERExJhUoNQiLy+PDz74gPL/j717D6uqzPs//t4bNgQBgoBKaibgAfVCNE1rZrTTNB5SRy5PoDikj472UHlGRQoyT4lhqWkeGJUog+TyutJsspmeLCdNHR9tkkRwUFFTUEhR95YN+/cHP/cTI6CmxVI/r78263Cv77q/rM3+7vteLKuVY8eOMWbMGCIjIzl06BCvvfYaAL6+vsydO5eDBw+yYcMGUlNTycrKIiMjgwYNGmCxWOjTpw8A+/fvZ9SoUZw7d46oqCiGDh0KwMsvv8yJEyfw9/dnwYIFuLi4MHPmTI4fP05FRQXPPfccffr0ISYmhqSkJEJCQnj//fcpLi5m4MCBjB8/Hl9fX3r06AHAsmXLKC4u5vLly7zxxhs0b96c+fPns3fvXgCeffZZ/vSnP1FYWEhCQgJ2ux2TycSsWbNo27YtGRkZZGVlERgYyNmzZ+vso/379zNnzhwcDgeNGzeuNkp04cIFEhISKCkpAWDWrFm0adOGd999l08//RS73Y63tzdLlixh8+bNNfZ1RkYGmzZtwmw207lzZ+Lj4zl16hSJiYnYbDbc3d2ZPXs2QUFBpKam8uWXX9KkSRPnMW/WhQsXGDhwIH/9619xcXFh4cKFdOjQgcDAQObOnUuDBg0wm81EREQAkJ6ezubNmzGZTPTp04eRI0fW2f6PP/6Ip6cnFRUVvPzyy/zwww+UlJTQo0cPJkyYwPTp0yktLaW0tJTRo0cDcPnyZeLi4hgwYAD9+/f/WeclIiIicqdQgXIdZWVlrFmzhoKCAsaNG0dkZCSJiYnMnTuX0NBQsrKyWL16NY899hgA586dY/Xq1WzatAk3N7dqH1hdXV1Zs2YNJ06cYOzYsc4CJSoqioiICF5//XUyMzMxm834+fmxcOFCysrKiIyMpHv37rXGWFRUxMaNG3Fzc2P79u307NmTAQMGsGTJEj755BNCQ0MpLCwkMzMTu91OdHQ03bt3Z9myZcTExPD000+Tk5PDzJkzWbduHevXr+ejjz7CZDIRGRlZZ/8kJiaSmppKSEgIGRkZ1aZNrVixgu7duxMdHU1BQQEzZswgIyOD0tJS1q5di9lsZvTo0Xz77be19nV2djaJiYlERETw3nvvYbfbWbBgATExMfTs2ZOvv/6alJQU/vznP7N7924+/PBDLl26xDPPPHPd3MbHx+Ph4QFAZWUlAN7e3jz88MN89dVX/Pa3v2X79u289NJLREVFsWjRIlq2bMkrr7wCQF5eHh9//DHvvfceJpOJ2NhYfvvb3xIcHFzjcUwmEy1btmTq1KmcOnWKiIgIBg8ejM1mcxYoAN27dyc2NpZdu3Zx6dIlxo0bx8iRI3nqqafqPB8XFxO+vp7XPW+5NS4uZvWzwSgnxqN8GIuuEWNRPq5PBcp1tG3bFoCgoCCuXLkCQH5+PsnJyQCUl5fTsmVL5/bHjh0jJCTE+cG3U6dOznXt2rXDZDIRGBiI1WoFwGKxOL+N79y5Mzt27ABwFjxeXl6EhIRw/PjxanE5HA7n62bNmuHm5ub8uUOHDgAEBARQXFxMfn4+Xbp0wWQyYbFY6NixI/n5+eTn59O1a1egaoraDz/8wJEjRwgNDXW2Fx4eXmf/nD171jl9afjw4dXW5ebmsnPnTrZu3QrA+fPnMZvNWCwWJk2ahKenJz/88AN2u73Wvp43bx5paWmkpKQQERGBw+EgNzeXd955h9WrV+NwOLBYLOTl5dGhQwfMZjNeXl60bt26zrih+tQrm81G7969ARg8eDDp6elUVlby2GOP4ebmxunTp5157ty5M8eOHSM3N5eTJ08SGxsLVI2OHDt27JoC5afH+alvv/2WnTt34uXl5TxfoNrv0zfffEObNm2qra9NRYWD0tJL191Obo2vr6f62WCUk+oCA73rOwTlw2B0jRiL8lGlrvcq3SR/HSaT6ZplLVu2ZMGCBaSnpzN16lR69uzpXPfggw9y5MgRrFYrlZWVHDhwoM62ysvLycnJAWDPnj20atWKkJAQ9uzZA1SNKuTm5jqLkKKiIgAOHjzobMNsrjuNISEhzuld5eXl7Nu3jxYtWlQ7Tk5ODgEBATRv3py8vDysVisVFRXO2GrTqFEjCgoKAFi5ciXbtm1zrgsODiY2Npb09HQWL15Mv379+P777/nss89YvHgxiYmJVFZWOoutmvonMzOT5ORk3n33XXJycti3bx/BwcFMmTKF9PR0kpOT+cMf/kDLli05cOAAlZWVXLp0iby8vDrjrkuXLl04fvw4H374IYMGDQIgMDDQOTp0dcQnODiY0NBQ1q9fT3p6OpGRkTdUGEHV9EFvb28WLVrEqFGjsFqtNfbD448/ztKlS1m8ePEN31wvIiIicifTCMrPkJSURHx8PBUVVQ+jmjNnDmfOnAGgYcOGjBkzhujoaHx9fbHZbLi6ujpHCf6TxWIhPT2do0eP8sADDzB58mQcDgeJiYlERUVhs9mIi4vD39+fkSNH8uqrrxIUFESjRo1uON4nnniCb775hqFDh1JeXk6vXr1o374906ZNIzExkbS0NOx2O3PmzKFhw4a89NJLDBs2jIYNGzpHgmqTnJzMzJkzMZvNBAYGEhsby/r16wEYN24cCQkJZGZmUlZWRlxcHC1atMDDw4PIyEjc3NwIDAx09l1N2rRpw6BBg/Dz86Nx48Z07NiR+Ph4kpKSsNlsWK1WEhISCAsLo1evXgwaNIhGjRrh7+9/w/1Tk379+vHJJ5/QqlUrABYuXEh8fDz3338/999/Pw18AuczAAAgAElEQVQaNKBt27Y8+uijREVFceXKFcLDw2ncuPENtf/oo48yadIk9u7di4eHBy1atKi1HwICAnjhhReYOXMmq1evrrGQExEREblbmBw/nSskt8xut7Nq1SrGjx8PVE17mjBhgnMqldwZVq1ahZ+fn3ME5U5QXl6hIeNfgYbmjUc5qS4w0LveH9RYVKRHNRqJrhFjUT6q1DXFSyMot5mrqyuXL19m4MCBWCwWwsPD6dKlS32HdUtOnjxJfHz8Ncu7du3Kiy++WA8R3ZgDBw6wcOHCa5b37t2b6OjoWvebPn06JSUlN/ysExERI7lss1Mwv2+9Hd9aXlFvxxaRu4NGUETuEhpB+XXomy/jUU6MRfkwHuXEWJSPKrpJXkRERERE7ggqUERERERExDBUoIiIiIiIiGGoQBEREREREcNQgSIiIiIiIoahAkVERERERAxDBYqIiIiIiBiGChQRERERETEMFSgiIiIiImIYKlBERERERMQwXOs7ABEREbl1Xj4eeLjX/591a3lFfYcgIne4+n8nExERkVvm4e7KQ9O31HcYFMzvy4X6DkJE7mia4iUiIiIiIoahAkXuCTk5OSxduhSAbdu2cfr06ZvaPzs7m5SUFOfP69atY9iwYZw/f56JEydy5coVTp48yd///vda24iJiSE/P//nnYCIiIjIPUIFitwTwsLCiIuLA2D9+vWUlZX97LZWr17N559/TlpaGj4+PqSmpuLm5sbOnTv55z//ebtCFhEREbkn6R4UMYSysjISEhK4cOECJSUlDB48mK1bt9KmTRsOHz6Mp6cnXbp04auvvuL8+fOkpaXh6enJK6+8wtGjR6msrGTChAl069aNfv368cgjj3Do0CFMJhNvv/02Bw8eZMOGDQwYMICcnBzi4+MZPHgwBQUFxMfHU1FRwR//+Ec2btyIm5tbrXGuWLGCPXv2sHLlSud2Tz75JJs3b2blypVYrVY6depEQEAAc+bMweFw0LhxY+foy7JlyyguLuby5cu88cYbNG/enEWLFrF7924cDgexsbH07t2bmJgY2rZty+HDhykrK+PNN9+kadOmdfahi4sJX1/P25cUqZGLi1n9bDDKifEoH8aia8RYlI/rU4EihnD06FH69u3LM888w+nTp4mJiaFx48aEh4cza9YsRo8ezX333cdf/vIX4uPj2b17N2fOnMHPz4+5c+dSUlLCiBEj2LJlCxcvXqRv374kJiYyefJktm/fTkBAAACPP/44YWFhJCUl0bhxYyIjI5kyZQpffvkl3bp1q7M4+eijj2jRogXnz5/H4XBUW+fi4sLYsWM5cuQITz31FP379yc1NZWQkBAyMjKcU7t69uzJgAEDWLJkCZ988gmtW7emsLCQDRs2YLPZGDJkCL/5zW8ACA8PJyEhgdTUVLZs2cLYsWPr7MOKCgelpZduJQ1yA3x9PdXPBqOcVAkM9K7vEJyUD2PRNWIsykeVut6zVKCIIQQEBLBu3To+/fRTvLy8sNvtALRv3x4AHx8fQkNDna9tNhu5ubns3buXAwcOAGC32ykpKQGgXbt2AAQFBWGz2Wo8ppeXF127duWrr74iOzub559/vs4Yw8LCePvtt1m4cCGvvvoqc+bMqXXbs2fPEhISAsDw4cOdyzt06OA83+LiYnJzc/nuu++IiYlxnsPJkyernUOTJk0oLi6uMzYRERGRu4XuQRFDSEtLIyIigpSUFHr16nXNCEVNgoOD6du3L+np6axatYpevXrRoEEDAEwmU637mUwmZ/tDhgwhKyuLs2fP0rZt2zqPFxoaitlsZuLEieTk5LBp06Zq681mM5WVlQA0atSIgoICAFauXMm2bdtqPYdu3bqRnp7OunXr6N27N82aNbvuuYuIiIjcrVSgiCE88cQTrF+/nqioKNatW4eLiwtXrlypc59hw4Zx5MgRRowYwbBhw2jatClm8/V/pTt16sS0adMoLS2lY8eOHD16lH79+t1wrG5ubqSkpPD666+Tl5fnXN66dWv+9re/sWXLFpKTk5k5cyYjRowgJyeHnj171tjWk08+iaenJ9HR0URGRgJVIzsiIiIi9yqT40a+qha5S1VWVhIVFcWaNWvu+MKgvLxCc1p/BZo7bDzKSZXAQG/DPKixqEiPajQSXSPGonxU0T0oIjU4fvw4cXFxDB061FmcxMXF8eOPP1bbzsvLi+XLl9dHiCIiN+yyzU7B/L71HQbW8or6DkFE7nAaQRG5S2gE5dehb76MRzkxFuXDeJQTY1E+qtQ1gqJ7UERERERExDBUoIiIiIiIiGGoQBEREREREcNQgSIiIiIiIoahAkVERERERAxDBYqIiIiIiBiGChQRERERETEMFSgiIiIiImIYKlBERERERMQwXOs7ABEREbl1Xj4eeLjX/591a3lFfYcgIne4+n8nExERkVvm4e7KQ9O31HcYFMzvy4X6DkJE7mia4iUiIiIiIoahAkXuGHFxcbWuKyoqIikp6dcL5jpycnJYunRpreuzs7NJSUm5Zvnu3bv5/vvvf8nQRERERAxNBYrcMer6wB8YGGioAiUsLKzOgqo2Gzdu5MyZM79ARCIiIiJ3Bt2DIoaRnZ3N559/jtVqpaioiJEjR/K3v/2Nw4cPM23aNF555RV27NhBTEwMbdu25fDhw5SVlfHmm2/icDiYNGkSmZmZ9OvXjy5dupCbm0vLli3x9/dnz549uLm5sXLlSlasWEFAQABRUVHk5+eTlJREenr6dfezWCw1xt2vXz8eeeQRDh06hMlk4u233+bgwYNs2LCB1NRUsrKyyMjIoEGDBlgsFvr06QPA/v37GTVqFOfOnSMqKor27dvz5Zdf8t133xEaGspbb73FsWPHsNlsjB492rmfiIiIyN1MBYoYysWLF0lLS2PLli2sXbuWzMxMdu3axfr166ttFx4eTkJCAqmpqWzZsqXah/eLFy/y7LPP8vDDD9OrVy9mzJjBxIkTGTFiBHl5eXUeu679wsLCat2vb9++JCYmMnnyZLZv305AQAAA586dY/Xq1WzatAk3NzdGjhzp3M/V1ZU1a9Zw4sQJxo4dy8cff8zvfvc7+vTpg4+PD7t27WLjxo0A7Nix47p95+JiwtfX87rbya1xcTGrnw1GOTEe5cNYdI0Yi/JxfSpQxFCuFgHe3t6EhIRgMplo0KABNput2nbt2rUDoEmTJhQXF1/TTvv27QHw8fEhJCTE+fo/27ld+12NJygoqNq2x44dIyQkBA8PDwA6depUbR+TyURgYCBWq7Vae15eXiQmJpKYmEhZWRn9+/ev8/gAFRUOSksvXXc7uTW+vp7qZ4NRTqoEBnrXdwhOyoex6BoxFuWjSl3vWSpQxFBMJtMv3o67uztFRUUAfPfdd7fl+LXt9+CDD3LkyBGsVitubm4cOHCA4ODgWvcxmUw4HA7OnDnDd999x7Jly7DZbPTs2ZMBAwbg6qpLVkRERO5u+rQj95zevXszYcIEdu/eTYcOHX7RYzVs2JAxY8YQHR2Nr68vNpsNV1dX7HZ7jdt37NiRlJQUFi9eTFFREX/84x/x9PRk1KhRKk5ERETknmByOByO+g5C5G5lt9tZtWoV48ePB2D48OFMmDCBrl273vZjlZdXaMj4V6CheeNRTqoEBnob5kGNRUV6VKOR6BoxFuWjiqZ4idyiAwcOsHDhwmuW9+7dm+jo6Fr3c3V15fLlywwcOBCLxUJ4eDhdunT5JUMVERERuaNpBEXkLqERlF+HvvkyHuWkipePBx7u9f+9o7W8ggvKh6HoGjEW5aOKRlBERETucmXnL1NW30GgfzEsIrdOT5IXERERERHDUIEiIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGCpQRERERETEMFSgiIiIiIiIYahAERERERERw9CT5EVERO5gXj4eeLgb58+5tbyivkMQkTuccd7RRERE5KZ5uLvy0PQt9R2GU8H8vlyo7yBE5I6mKV4iIiIiImIYKlDuQjk5OSxdurS+w6jRyZMn+fvf//6LtH38+HEGDBhAfHw8c+bM4eTJkyxZsoT333//trS/cuVKDhw4UOM6m81GVlbWbTnOr9m2iIiIiNFoitddKCwsjLCwsPoOo0Y7d+7kyJEjPPnkk7e97X/+8588+uijTJ8+/ba3DTB27Nha1xUVFZGVlcXgwYNv+3F/ybZFREREjEYFikFkZ2ezceNGKisriYmJYd26dZjNZh5++GGmTJnCmTNnmDx5MgCtWrXi8OHDpKen8+STT7J161bc3d1JSUkhODiYpk2bsmHDBlJTU3niiScIDg4mODiYUaNGkZiYiM1mw93dndmzZxMUFFRjPOfOnWPKlClcuXKFli1bsnPnTrZt21bj8SIjI1m0aBG7d+/G4XAQGxtL7969ycjIYNOmTZjNZjp37syUKVNYuXIlVquVTp068dRTT11z3MLCQl566SUCAwM5ffo0PXr0YOLEiUyfPp3S0lJKS0t55513WL58OXv37gXg2Wef5fe//z3Lly/HarXy4IMPsnXrVpKSkqq1XVOMNakrhj59+vDII48wY8YMTp48SXl5OYmJiWzcuJG8vDyWLl2Kw+EgICCAqKgo8vPzSUpKIj09nWeffZaHHnoINzc3kpOTSUhIoKSkBIBZs2bRpk2bGuNZsWKFs+24uLgb+n0SERERuVOpQDEQHx8f5s2bR3R0NBs3bsTDw4OpU6eyY8cOtm3bRr9+/RgyZAgfffQRhw8fvqE2T506RXZ2Nn5+fkyYMIGYmBh69uzJ119/TUpKCosWLapxvxUrVvDUU08xfPhwduzYwY4dO2o9xhdffEFhYSEbNmzAZrMxZMgQfvOb35CdnU1iYiIRERG89957OBwOxo4dy5EjR2osTq46ceIEa9aswdvbm+joaL777jsAunfvTmxsLJ9//jmFhYVkZmZit9uJjo6me/fuzrajo6PZunXrDcXo4+NzUzEAbNiwgaZNm5Kamkpubi7/+Mc/GDduHLm5ucTFxbFkyZIa27x06RLPP/887dq1Y+HChXTv3p3o6GgKCgqYMWNGrVPRftp2XVxcTPj6eta5jdw6Fxez+tlglBPjUT6MRdeIsSgf16cCxUBatmzJsWPHOHfunHM60cWLFzl+/DiFhYUMGzYMgG7dupGZmXnN/g6H45plfn5++Pn5AZCbm8s777zD6tWrcTgcWCyWWmPJz89n4MCBAHTp0qXGba4eLzc3l++++46YmBgA7HY7J0+eZN68eaSlpZGSkkJERESN8dWkbdu2+Pr6AhAeHs6///1voKp/rsbWpUsXTCYTFouFjh07kp+fX2ebtcVYW4FSWwwAR44coUePHgC0bt2a1q1bU1hYeEPndvUccnNz2blzp7OQOn/+/A3tX5eKCgelpZduuR2pm6+vp/rZYO71nAQGetd3CNe4l/NhRPf6NWI0ykeVut67dJO8gZjNZpo1a0ZQUBBpaWmkp6czYsQIOnbsSJs2bZxTmv71r38593Fzc+PMmTM4HA6+//77Gtu8Kjg4mClTppCenk5ycjJ/+MMfao2ldevW7Nu3D4D//d//rfN4wcHBdOvWjfT0dNatW0fv3r1p1qwZmZmZJCcn8+6775KTk8O+ffswm81UVlbW2Q/5+flcvnyZiooKDhw4QGhoKAAmkwmAkJAQZ1+Ul5ezb98+WrRoUWebtcV4szFcPf63334LVN2YP3ny5Grn5e7uTlFREUC1kRf4v3wEBwcTGxtLeno6ixcvpl+/frXGciN9JiIiInK30AiKwTRs2JDY2FhiYmKoqKigadOm9O7dm7FjxzJ9+nT++te/0qBBA+f2//Vf/8XYsWNp2rRpraMBV8XHx5OUlITNZsNqtZKQkFDrtmPGjGHatGls3bqVRo0a4erqWuvxnnzySb755huio6O5dOkSTz/9NF5eXrRp04ZBgwbh5+dH48aN6dixI15eXixfvpz27dvTt2/fGo9tsVh46aWXKC4uplevXrRt27ba+ieeeIJvvvmGoUOHUl5eTq9evWjfvj2HDh2q9Xxqi7E2dcUwbNgwZs6cyYgRI6ioqGDmzJn4+/tTXl7OwoULGTZsGBMmTGD37t106NChxvbHjRtHQkICmZmZlJWV1Tl966dtT506tdbtRERERO4GJseNzrsRw/jpjde/lC+++AI/Pz/Cw8P5xz/+wYoVK1i/fv0vdryrCgsLmTRpUo1T2H4tRojh5ygvr9CQ8a9AQ/PGc6/nJDDQ23APaiwq0qMajeRev0aMRvmoUtcUL42g3OPi4uL48ccfqy3z8vJiypQpzJw5ExcXFyorK+scbfk5PvjgAzZv3nzN8kmTJt3W4xg9hp9KSkqq8V6aVatWcd9999VDRCJyJ7hss1Mwv+YR6fpgLa+o7xBE5A6nERSRu4RGUH4d+ubLeJQTY1E+jEc5MRblo4pukhcRERERkTuCChQRERERETEMFSgiIiIiImIYKlBERERERMQwVKCIiIiIiIhhqEARERERERHDUIEiIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExDNf6DkBERERujpePBx7uxvwTbi2vqO8QROQOZ8x3NxEREamVh7srD03fUt9h1Khgfl8u1HcQInJH0xQvERERERExDBUot0FOTg5Lly6tdX12djYpKSnXLH/yySex2Wy3LY6YmBjy8/NvW3sTJ05k165d9RrDzzFkyBAKCwtrXR8TE8OgQYOIiYlh+PDh9OvXjy+++OKmj/Ob3/wGgDlz5nDy5Mkb2iclJYXs7Oxa10+fPp3t27ffdCwiIiIidwtN8boNwsLCCAsLq+8w5CYsWLCAkJAQAI4cOcKLL75Iz549f1ZbCQkJtzM0ERERkXuaCpQ6ZGdn88UXX2C1Wjl27Bhjxoyhffv2vPbaawD4+voyd+5cDh48yIYNG0hNTSUrK4uMjAwaNGiAxWKhT58+AOzfv59Ro0Zx7tw5oqKiGDp0KAAvv/wyJ06cwN/fnwULFuDi4sLMmTM5fvw4FRUVPPfcc/Tp04eYmBiSkpIICQnh/fffp7i4mIEDBzJ+/Hh8fX3p0aMHAMuWLaO4uJjLly/zxhtv0Lx5c+bPn8/evXsBePbZZ/nTn/5EYWEhCQkJ2O12TCYTs2bNom3btmRkZJCVlUVgYCBnz56ts3/279/PnDlzcDgcNG7cuNoo0YULF0hISKCkpASAWbNm0aZNG959910+/fRT7HY73t7eLFmyhM2bN1/Tz5GRkWRkZLBp0ybMZjOdO3cmPj6eU6dOkZiYiM1mw93dndmzZxMUFERqaipffvklTZo0cR7zRp08eRIfHx8ADh48yOzZs3FxcXG27+/vz0svvURZWRlWq5WpU6fSrVs35/5Xc/Pxxx9TWFjI2bNnOXnyJDNmzOB3v/sdf/3rX1m+fDkNGzakvLyc4OBgKioqePnll/nhhx8oKSmhR48eTJgwwdlmeXk5r7zyCkePHqWyspIJEyZUO2ZNXFxM+Pp63tS5y81zcTGrnw1GOTEe5cNYdI0Yi/JxfSpQrqOsrIw1a9ZQUFDAuHHj8PHxYe7cuYSGhpKVlcXq1at57LHHADh37hyrV69m06ZNuLm5MXLkSGc7rq6urFmzhhMnTjB27FhngRIVFUVERASvv/46mZmZmM1m/Pz8WLhwIWVlZURGRtK9e/da4ysqKmLjxo24ubmxfft2evbsyYABA1iyZAmffPIJoaGhFBYWkpmZid1uJzo6mu7du7Ns2TJiYmJ4+umnycnJYebMmaxbt47169fz0UcfYTKZiIyMrLNvEhMTSU1NJSQkhIyMjGpTu1asWEH37t2Jjo6moKCAGTNmkJGRQWlpKWvXrsVsNjN69Gi+/fbbGvs5MjKS7OxsEhMTiYiI4L333sNut7NgwQJiYmLo2bMnX3/9NSkpKfz5z39m9+7dfPjhh1y6dIlnnnnmunmNj4/H1dWVkydPEhERwbx584CqQmrOnDmEhYXx2WefMX/+fF544QWKi4tZu3YtZ8+epaCgoNZ23dzcWL16NTt27CAtLY3f/e53LFy4kKysLHx9fRk7diwAp06dIiIigsGDB2Oz2a4pULKysvDz82Pu3LmUlJQwYsQItmyp+4bYigoHpaWXrnvucmt8fT3VzwZzL+YkMNC7vkOo072WD6O7F68RI1M+qtT1PqYC5Tratm0LQFBQEFeuXCE/P5/k5GSg6lvuli1bOrc9duwYISEheHh4ANCpUyfnunbt2mEymQgMDMRqtQJgsViIiIgAoHPnzuzYsQPAWfB4eXkREhLC8ePHq8XkcDicr5s1a4abm5vz5w4dOgAQEBBAcXEx+fn5dOnSBZPJhMVioWPHjuTn55Ofn0/Xrl2BqilqP/zwA0eOHCE0NNTZXnh4eJ19c/bsWec0qeHDh1dbl5uby86dO9m6dSsA58+fx2w2Y7FYmDRpEp6envzwww/Y7fYa+xlg3rx5pKWlkZKSQkREBA6Hg9zcXN555x1Wr16Nw+HAYrGQl5dHhw4dMJvNeHl50bp16zrjhv+b4rVhwwY2b95MUFAQAGfOnHFO1+vatSuLFi2iVatWDB8+nEmTJmG324mJiam13av7NmnShCtXrlBcXIyXlxd+fn7A//1O+Pr68u2337Jz5068vLyc5/zT/tu7dy8HDhwAwG63U1JS4mxHRERE5G6lm+Svw2QyVfu5ZcuWLFiwgPT0dKZOnVrtvoUHH3yQI0eOYLVaqaysdH64rKkdqCpwcnJyANizZw+tWrUiJCSEPXv2AFWjCrm5uc4ipKioCKiahnSV2Vx3CkNCQpzTu8rLy9m3bx8tWrSodpycnBwCAgJo3rw5eXl5WK1WKioqnLHVplGjRs7RhJUrV7Jt2zbnuuDgYGJjY0lPT2fx4sX069eP77//ns8++4zFixeTmJhIZWWls9iqqX8yMzNJTk7m3XffJScnh3379hEcHMyUKVNIT08nOTmZP/zhD7Rs2ZIDBw5QWVnJpUuXyMvLqzPunxo2bJhzitjVc/r+++8B2L17Nw899BCHDh3i4sWLrFy5kvnz5zN79uxa2/vP8/D19eXChQucO3cOwDlilJ2djbe3N4sWLWLUqFFYrdZqhWdwcDB9+/YlPT2dVatW0atXLxo0aHDD5yUiIiJyp9IIyk1KSkoiPj6eioqqB1HNmTOHM2fOANCwYUPGjBlDdHQ0vr6+2Gw2XF1dnaME/8lisZCens7Ro0d54IEHmDx5Mg6Hg8TERKKiorDZbMTFxeHv78/IkSN59dVXCQoKolGjRjcc7xNPPME333zD0KFDKS8vp1evXrRv355p06aRmJhIWloadrudOXPm0LBhQ1566SWGDRtGw4YNnSNBtUlOTmbmzJmYzWYCAwOJjY1l/fr1AIwbN46EhAQyMzMpKysjLi6OFi1a4OHhQWRkJG5ubgQGBjr7riZt2rRh0KBB+Pn50bhxYzp27Eh8fDxJSUnYbDasVisJCQmEhYXRq1cvBg0aRKNGjfD397/h/oGqm9z79+/PgAEDeO2115g9ezYOhwMXFxfmzp1Lo0aNWLZsGZs2bcJisfDiiy/ecNuurq7MmzeP0aNH06BBA1xdqy65Rx99lEmTJrF37148PDxo0aJFtb4YNmwYs2bNYsSIEZSVlREdHX3dYlRERETkbmBy/PRrW7kldrudVatWMX78eKBq2tOECROcU6lEfknl5RWa0/or0Nxh47kXcxIY6G3oBzUWFelRjUZyL14jRqZ8VNE9KL8SV1dXLl++zMCBA7FYLISHh9OlS5f6DuuWnDx5kvj4+GuWd+3a9aZGEn5tBw4cYOHChdcs7927N9HR0fUQkYjI7XPZZqdgft/6DqNG1vKK+g5BRO5wGkERuUtoBOXXoW++jEc5MRblw3iUE2NRPqrUNYKiSe0iIiIiImIYKlBERERERMQwVKCIiIiIiIhhqEARERERERHDUIEiIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGK71HYCIiIjcOC8fDzzcjfvn21peUd8hiMgdzrjvcCIiInIND3dXHpq+pb7DqFXB/L5cqO8gROSOpileIiIiIiJiGCpQ/r+cnByWLl1a6/rs7GxSUlKuWf7kk09is9luWxwxMTHk5+fftvYmTpzIrl276jWGn2PIkCEUFhZed7tdu3YxceLE23787du388EHH/zs/adPn06/fv2IiYkhJiaG6OhoDh8+TGFhIUOGDKl1v+v9HoqIiIjc7TTF6/8LCwsjLCysvsMQg+jRo8cttzF16lRnO1988QVvvvkm06dPr3Mf/R6KiIjIve6uLlCys7P54osvsFqtHDt2jDFjxhAZGcmhQ4d47bXXAPD19WXu3LkcPHiQDRs2kJqaSlZWFhkZGTRo0ACLxUKfPn0A2L9/P6NGjeLcuXNERUUxdOhQAF5++WVOnDiBv78/CxYswMXFhZkzZ3L8+HEqKip47rnn6NOnDzExMSQlJRESEsL7779PcXExAwcOZPz48fj6+jo/zC5btozi4mIuX77MG2+8QfPmzZk/fz579+4F4Nlnn+VPf/oThYWFJCQkYLfbMZlMzJo1i7Zt25KRkUFWVhaBgYGcPXu2zj7av38/c+bMweFw0Lhx42qjRBcuXCAhIYGSkhIAZs2aRZs2bXj33Xf59NNPsdvteHt7s2TJEjZv3lxjX2dkZLBp0ybMZjOdO3cmPj6eU6dOkZiYiM1mw93dndmzZxMUFERqaipffvklTZo0cR7zZtQW1+eff47VaqWoqIiRI0fyt7/9jcOHDzNt2jSefvrpWvc7cuQIU6ZM4e233+azzz6joqKCqKgohg0bxqJFi/jXv/7FxYsXCQkJYd68eXXG9uOPP+Lp6Vlt2SeffEJGRplCiY0AACAASURBVIbz5zfffJPDhw87fw+feeYZOnfuzL///W/8/f1ZsmQJLi4uN90vIiIiIneSu7pAASgrK2PNmjUUFBQwbtw4IiMjSUxMZO7cuYSGhpKVlcXq1at57LHHADh37hyrV69m06ZNuLm5MXLkSGdbrq6urFmzhhMnTjB27FhngRIVFUVERASvv/46mZmZmM1m/Pz8WLhwIWVlZURGRtK9e/daYywqKmLjxo24ubmxfft2evbsyYABA1iyZAmffPIJoaGhFBYWkpmZid1uJzo6mu7du7Ns2TJiYmJ4+umnycnJYebMmaxbt47169fz0UcfYTKZiIyMrLN/EhMTSU1NJSQkhIyMjGpTu1asWEH37t2Jjo6moKCAGTNmkJGRQWlpKWvXrsVsNjN69Gi+/fbbWvs6OzubxMREIiIieO+997Db7SxYsICYmBh69uzJ119/TUpKCn/+85/ZvXs3H374IZcuXeKZZ565qTxXVlbWGtfFixdJS0tjy5YtrF27lszMTHbt2sX69et58skna90P4ODBg2zfvp2srCyuXLnCokWLuHDhAj4+PvzlL3+hsrKSvn37cvr0aRo3blwtpoULF7Jq1SrMZjONGjVi6tSpXLlyxbm+oKCAlStX4uHhwcsvv8xXX31VrY3jx4+zbt06goKCGDZsGN9++y0RERG19oGLiwlfX89a18vt4eJiVj8bjHJiPMqHsegaMRbl4/ru+gKlbdu2AAQFBTk/HObn55OcnAxAeXk5LVu2dG5/7NgxQkJC8PDwAKBTp07Ode3atcNkMhEYGIjVagXAYrE4PzR27tyZHTt2ADgLHi8vL0JCQjh+/Hi1uBwOh/N1s2bNcHNzc/7coUMHAAICAiguLiY/P58uXbpgMpmwWCx07NiR/Px88vPz6dq1K1A1NeiHH37gyJEjhIaGOtsLDw+vs3/Onj1LSEgIAMOHD6+2Ljc3l507d7J161YAzp8/j9lsxmKxMGnSJDw9Pfnhhx+w2+219vW8efNIS0sjJSWFiIgIHA4Hubm5vPPOO6xevRqHw4HFYiEvL48OHTpgNpvx8vKidevWtcZcWVnJxYsX8fb2BsBkMtUZ19UpU97e3oSEhGAymWjQoAE2m63O/QD+/e9/Ex4ejouLCx4eHsyaNYvy8nLOnTvn3OfSpUuUl5dfE+dPp3hd9dP7avz9/YmPj+f+++/nyJEj1xQffn5+BAUFOfv0evc6VVQ4KC29VOc2cut8fT3VzwZzr+UkMNC7vkO4rnspH3eCe+0aMTrlo0pd72V3fYFiMpmuWdayZUsWLFjAAw88wN69eykqKnKue/DBBzly5AhWqxU3NzcOHDhAcHBwrW2Vl5eTk5NDWFgYe/bsoVWrVlRWVrJnzx5+//vfU1ZWRm5urrMIKSoqIiQkhIMHDzq/LTeb6/5fBSEhIWRnZxMbG0t5eTn79u1j4MCBhISEsGfPHp566ilycnIICAigefPm5OXlYbVasVgs5OTk0L9//1rbbtSoEQUFBTz00EOsXLmyWrEWHBxM//796devH2fPniUrK4vvv/+ezz77jKysLC5fvkxkZKSz2KqpfzIzM0lOTsbd3Z3Ro0ezb98+goODGTVqFJ07dyY/P5/du3fTsmVL1q9fT2VlJVarlby8vFpjPnz4MLNnz+bdd9/lzJkzNGzY8Kbjuqqu/a72wfvvv09lZSUVFRWMHTuW4cOHc+rUKRYvXsy5c+fYtm1btX1uxIULF3jrrbf4n//5HwCee+65a9qoK24RERGRu9VdX6DUJCkpifj4eCoqqh4mNWfOHM6cOQNAw4YNGTNmDNHR0fj6+mKz2XB1da32rfpPWSwW0tPTOXr0KA888ACTJ0/G4XCQmJhIVFQUNpuNuLg4/P39GTlyJK+++ipBQUE0atTohuN94okn+Oabbxg6dCjl5eX06tWL9u3bM23aNBITE0lLS8NutzNnzhwaNmzISy+9xLBhw2jYsKFzJKg2ycnJzJw5E7PZTGBgILGxsaxfvx6AcePGkZCQQGZmJmVlZcTFxdGiRQs8PDyIjIzEzc2NwMBAZ9/VpE2bNgwaNAg/Pz8aN25Mx44diY+PJykpCZvNhtVqJSEhgbCwMHr16sWgQYNo1KgR/v7+dbbZrFkzhg0bhru7O/Pnz8fX1/em4rrqeucTFhbG7373O6KioqisrCQqKoqOHTuyfPlyhgwZgpubG82bN+fMmTM0b978use7ysvLi86dOzNw4EA8PT3x8fHhzJkzNGvW7IbbEBEREbkbmRw3+9XvXc5ut7Nq1SrGjx8PVE17mjBhgnMqlYhRlZdXaMj4V6CheeO513ISGOht+Ac1FhXpUY1Gcq9dI0anfFS5p6d43SxXV1cuX77MwIEDsVgshIeH06VLl/oO65acPHmS+Pj4a5Z37dqVF198sR4iujEHDhxg4cKF1yzv3bs30dHR9RCRiIiIiPzSNIIicpfQCMqvQ998Gc+9lhMvHw883I37/aK1vIIL91A+7gT32jVidMpHFY2giIiI3CXKzl+mrL6DqIP+faqI3Kq6/32UiIiIiIjIr0gFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGGoQBEREREREcNQgSIiIiIiIoahAkVERERERAxDT5IXERGpB14+Hni4331/hq3lFfUdgojc4e6+d0YREZE7gIe7Kw9N31LfYdx2BfP7cqG+gxCRO5qmeImIiIiIiGFoBMXgcnJy+Nvf/kZcXBzbtm0jPDycxo0b3/D+u3btYsKECYSGhjqXPfvsswwdOvSWYyssLKR///60b98egCtXrtCtWzcmTZp0Q/tPnz6dPn360KNHD+eylStX0r17d8LDw285vpvxn/1ks9no168fMTExt6X97OxsGjRowFNPPXVb2hMRERG5W6lAMbiwsDDCwsIAWL9+PUlJSTdVoAB0796d1NTUXyI8QkNDSU9PB6CyspKoqCi+//572rZt+7PaGzt27O0M76b8tJ+uXLlCr169GDBgAD4+PrfcdmRk5C23ISIiInIvUIFyC8rKykhISODChQuUlJQwePBgoqOjiYmJoU2bNhw+fBhPT0+6dOnCV199xfnz50lLS8PT05NXXnmFo0ePUllZyYQJE+jWrRv9+vXjkUce4dChQ5hMJt5++20OHjzIhg0bGDBgADk5OcTHxzN48GAKCgqIj4+noqKCP/7xj2zcuBE3N7cbjj07O5uNGzdSWVnJiy++SGlpKWvXrsVsNvPwww8zZcoULly4QEJCAiUlJQDMmjWLNm3a1Nqm1WrlypUreHh4kJuby/z586msrOT8+fPMmjWLzp0788QTTxAcHExwcLBzv/379/Paa6/x1ltv8eabb9KnTx+Ki4v54osvsFqtHDt2jDFjxhAZGcmBAwdITk7m/vvvx9/fH3d3d+bPn+9sq7y8nJkzZ3L8+HEqKip47rnn6NOnDzExMbRt25bDhw9TVlbGm2++SdOmTevMrdlsxsXFhZiYGPz8/Dh//jwrV64kKSmpxtx16dKF3NxcWrZsib+/P3v27MHNzY2VK1eyYsUKAgIC+MMf/sCECRNwOByUl5eTnJxMmzZtSE9PZ/PmzZhMJvr06cPIkSP59NNPWbVqFa6urjRt2pTXX38ds1mzMkVEROTupgLlFhw9epS+ffvyzDPPcPr0aWJiYoiOjgYgPDycWbNmMXr0aO677z7+8pe/EB8fz+7duzlz5gx+fn7MnTuXkpISRowYwZYtW7h48SJ9+/YlMTGRyZMns337dgICAgB4/PHHCQsLc46gREZGMmXKFL788ku6detWZ3Gyc+fOalOV1q5dC4CPjw/Lly+ntLSU6OhoNm7ciIeHB1OnTmXHjh384x//oHv37kRHR1NQUMCMGTN4//33q7Wdl5fnbNvFxYWRI0fSokULPv74Y+Lj42nTpg0fffQR2dnZdO7cmVOnTpGdnY2fnx/Tp09n3759fP3116xYsQJ/f/9qbZeVlbFmzRoKCgoYN24ckZGRvPLKK7z++uu0atWK1NRUTp8+XW2fDz74AD8/PxYuXEhZWRmRkZF0797dmZOEhARSU1PZsmXLNaM1V/vJZDJhsVhITEzk/vvvB6Bfv378/ve/57333qs1d88++ywPP/wwvXr1YsaMGUycOJERI0aQl5fnPMaBAwfw9vZm0aJF5OXlUVZWRl5eHh9//DHvvfceJpOJ2NhYfvvb37J582ZiY2Pp27cvmzZtoqysrM7RHBcXE76+nrWul9vDxcWsfjYY5cR4lA9j0TViLMrH9alAuQUBAQGsW7eOTz/9FC8vL+x2u3Pd1fsyfHx8nPc1+Pj4YLPZyM3NZe/evRw4cAAAu93uHKVo164dAEFBQdhsthqP6+XlRdeuXfnqq6/Izs7m+eefrzPO2qZ4tWzZEoBjx45x7tw55wf2ixcvcvz4cXJzc9m5cydbt24F4Pz589e08dMpXj/VqFEj3n77be677z4uXryIl5cXAH5+fvj5+Tm327FjBxcvXsTV9dpfxavTxIKCgrhy5QoAZ86coVWrVgA8/PDDfPzxx9X2yc/P57HHHgOq+ikkJITjx48D/9e3TZo0obi4+Ib76ad9VVfufprzkJAQ5+uf5rFHjx4UFBTw/PPP4+rqyvjx48nNzeXkyZPExsYC8OOPP3Ls2DFmzJjBO++8w/vvv09wcDBPP/10jbFdVVHhoLT0Up3byK3z9fVUPxvMnZqTwEDv+g7hF3Mn5uNudqdeI3cr5aNKXe+BKlBuQVpaGhEREURHR7Nz506++OKLG9ovODiYJk2aMG7cOKxWK8uXL6dBgwYAmEymWvczmUw4HA4AhgwZwqpVqygpKfnZ93tcnS7UrFkzgoKCSEtLw2KxkJ2dTVhYGP/+97/p378//fr14+zZs2RlZd1w23PmzCElJYWQkBDeeustTpw4Ue2YV8XFxXH69GmSkpKuKQ5q6osmTZqQl5dHaGgo+/fvv2Z9SEgIe/bs4fe//z1lZWXk5ubSrFmzG467Nldj+bm5u2rXrl00atSItLQ09u3bxxtvvEFCQgKhoaGsXr0ak8nE2rVrad26NR988AEvvPAC/v7+vPzyy2zbto2BAwfe8rmIiIiIGJkKlFvwxBNPkJSUxEcffYSvry8uLi7Ob/rrMmzYMGbNmsWIESMoKysjOjr6hu4t6NSpE9OmTSMtLY2OHTty9OhRhg8ffsvn0bBhQ2JjY4mJiaGiooKmTZvSu3dvxo0bR0JCApmZmZSVlREXF3fDbfbv35/nn38ef39/mjRp4hxlqMngwYP55JNP+Oijj67b7iuvvMLMmTPx9PTEYrFc8w8DhgwZQmJiIlFRUdhsNuLi4q6ZOnYrfm7urmrbti0TJ05k3bp1mM1m/vu//5u2bdvy6KOPEhUVxZUrV5z/qS08PJznnnsOX19f7r//fh5//PHbdh4iIiIiRmVyXP1KXu4oV/9j1po1a5zTp+4FGRkZ9O7dm4YNG5KamorFYrmpwuluVl5eoSHjX4GG5o3nTs1JYKD3XfugxqIiParRSO7Ua+RupXxU0RSvu8zx48eJi4tj6NChzuIkLi6OH3/8sdp2Xl5eLF++vD5C/MX4+/szatQoPD098fb2rvYfvERE7iSXbXYK5vet7zBuO2t5RX2HICJ3OI2giNwlNILy69A3X8ajnBiL8mE8yomxKB9V6hpB0UMVRERERETEMFSgiIiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGG41ncAIiIi9wovHw883O/uP73W8or6DkFE7nB397ukiIiIgXi4u/LQ9C31HcYvqmB+Xy7UdxAickfTFC8RERERETEMFSjyi8nOziYlJeVn7//BBx9QXl5e6/rp06ezfft2tm/fzgcffFDrdtu2beP06dMUFRWRlJT0s+OpT1fPQURERORupwJFDOudd96hsrLyutv16NGDoUOH1rp+/fr1lJWVERgYeMcWKFfPQURERORup3tQ5Be1f/9+Ro0axblz54iKiqJZs2YsXrwYd3d3fH19mTt3Lna7nQkTJuBwOCgvLyc5OZkDBw5QVFTExIkTefvtt+s8RnZ2NkeOHOGFF17gpZdeoqysDKvVytSpU7l8+TI5OTnEx8ezcOFC4uPjyczMpF+/fjzyyCMcOnQIk8nE22+/jZeXF8nJyfzrX/8iICCAEydOsHz5cg4ePMiqVatwdXWladOmvP7665jNNdf2n3/+OUuXLgWgXbt2JCcn8/XXX19zzjk5OWzYsIHU1FQAfvOb37Bjxw6mT5+Om5sbJ06c4MyZM8yfP5+ioiLnObz33nu4ubnVeGwXFxO+vp63kC25ES4uZvWzwSgnxqN8GIuuEWNRPq5PBYr8olxdXVmzZg0nTpxgzJgx2Gw23n//fRo3bsy6detYvnw53bp1w9vbm0WLFpGXl0dZWRmDBw9m+fLlzg/wN+LYsWMUFxezdu1azp49S0FBAY8//jhhYWEkJSVhsVic2168eJG+ffuSmJjI5MmT2b59O+7u7pSWlvLhhx9y7tw5nnnmGQA2b95MbGwsffv2ZdOmTZSVleHj43PN8e12O7NnzyYrKwt/f3+WLl3KqVOnSExMvOacH3/88VrP44EHHuDVV18lMzOTDz74gFdffdV5DrUVJwAVFQ5KSy/dcH/Jz+Pr66l+Npg7KSeBgd71HcKv4k7Jx73iTrpG7gXKR5W63g81xUt+Ue3atcNkMhEYGMipU6fw8vKicePGAHTt2pXDhw/To0cPunbtyvPPP89bb71V6+jE9bRq1Yrhw4czadIkkpOTrzs9rF27dgAEBQVhs9k4cuQIERERADRs2JDg4GAAZsyYwe7duxkxYgT//Oc/a42vpKQEHx8f/P39AYiLi8PDw6PGc/5PDofD+TosLAyAJk2acOXKlZvpAhEREZE7ngoU+UWZTCbnaz8/P8rKyjhz5gwA33zzDQ899BC7du2iUaNGpKWlMX78eN544w3nvjdyD8pVhw4d4uLFi6xcuZL58+cze/ZsZzs/LQBqig2qCpz//d//BeDHH3+koKAAqLpZ/4UXXuDdd98Fqm5Yr4m/vz/nz5+ntLQUgNdee43jx4/XeM7u7u4UFRUBcOLECX788cda46rrHERERETuNpriJb8ak8nEa6+9xgsvvIDJZKJBgwbMmzcPk8nExIkTWbduHWazmf/+7/8GoEuXLowdO5b169fX+KH9Pz300EMsW7aMTZs2YbFYePHFFwHo1KkT06ZNcxYstXn88cfZvn07w4YNIyAggPvuuw+LxUJ4eDjPPfccvr6+3H///bVOzzKbzbzyyiv8+c9/xmw2065dO8LDw2s8Zx8fH7y9vRk8eDAhISE0a9asztiunkNaWhq+vr7X7QsRERGRO5XJoa9lRf5fe3cfFlWd/3/8OQMDQiOCgKihCUOp2WIRFKVtmzer4s0ml6iwjqKWX3fXTM3CVVAzRRLvNl2tLM1cMyWxb3fut8x2TStF1nJzNRRTISwxMR2N+/n9wa/ZTEAtdY7yelwX1zW35/M+n/ecw3nP53PmAJCfn8++ffvo3bs3JSUl9OnThw8++KDe8z6MpKKiSnNarwLNHTaeayknwcGNG8SFGouLdalGI7mWtpGGQPmoUd85KBpBEUMrLy9n5MiR5z0eFhbGjBkzLmtbLVq0YO7cuaxcuZKqqiomTpxYa3Gye/duMjMzz3u8V69eJCUlXdaYROT68n1ZJYcyers7jCuqtKLK3SGIyDVOIygi1wmNoFwd+ubLeJQTY1E+jEc5MRblo4Z+xUtERERERK4JKlBERERERMQwVKCIiIiIiIhhqEARERERERHDUIEiIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGJ7uDkBEROR6Y/Xzwce7Yf6LLa2ocncIInKNa5h7TxERkSvIx9uTNpPedncYbnEoozen3R2EiFzTNMVLREREREQMQwWKGEJ2djbvv/++u8Nwu7Vr11JRUcH27dsZP368u8MRERERuepUoIghxMfH07VrV3eH4XbPPfcc1dXV7g5DRERExG10DopcdtnZ2axfv57q6mrsdjsrV67EbDZz5513MnHiROLj43nmmWcIDQ1l48aN5Obm0qRJE4KCgkhMTGTevHnk5OTgdDpJTk6mTZs2LFy4kOeee4633nqL559/njfeeIOdO3fyv//7vzz44IM8/fTTeHp64ufnx9y5c7FarbXGZrfbCQsL48svv8TpdLJgwQKCg4PPa7NXr17Y7XYCAgI4deoUL774Ih4eHrUur23btuzfvx9fX1+io6PZunUrp06dYvny5fj6+jJ58mQKCgqoqqpi+PDhxMXFYbfbadeuHfv378fhcPCXv/yFjz76iOLiYsaPH8+wYcM4fPgwDz30ECdOnOCBBx7gkUceudKpExEREXE7FShyRfj5+TF79mySkpJYv349Pj4+PP7442zbto0BAwbw+uuvM2bMGDZs2MDEiRP5v//7PwD++c9/UlhYyKuvvkpZWRkDBw5k1apVFBUVUVZWxocffojJZOL48eNs3ryZ7t27s2nTJrp3787IkSPZvHkzp06dqrNAAYiKimLGjBmsXr2a5557jvvuu++8Njt16gRA37596d69e73rGhkZSWpqKiNHjqRRo0asWLGClJQUcnJy+PrrrwkICCAzMxOHw0F8fDyxsbGu902ZMoUFCxbw9ttvM2rUKJYuXcqCBQv49NNPKSsrY8mSJVRVVfGb3/zmggWKh4cJf3/fS0mT/AweHmb1s8EoJ8ajfBiLthFjUT4uTAWKXBFhYWEcOXKEEydOMGrUKADOnDlDQUEB/fr1IzExkYSEBBwOB7fccourQMnLy2PPnj3Y7XYAKisrKSoqonPnzmzfvp2jR4/St29fPvroI3bu3Mn48ePp2LEjzz77LMOGDSMkJITIyMh6Y/uhQIiKimLz5s2EhITU2uYP63EhHTp0AGqKsoiICNftsrIy8vPzuffeewGwWq3YbDYKCgoAuPXWWwFo3rw5x48fP2+5N998M15eXgB4el54U62qcnLy5NkLvk5+GX9/X/WzwRgxJ8HBjd0dglsZLR8NnRG3kYZM+ahR335S56DIFWE2mwkNDaVFixYsX76cVatWMWTIEDp27IjVauW2225j9uzZxMfHn/O+8PBw7r77blatWsXKlSvp1asXoaGhdOvWjWXLltG2bVs6d+7M6tWruemmm7BYLLz55pv079+fVatWcfPNN7Nu3bp6Y/v8888B+Ne//kVERESdbQKYTKZf1A82m42dO3cC4HA4yMvLcy27NiaTyXUOyi9tW0RERORapAJFrpimTZuSnJyM3W4nISGBLVu20KZNGwDX/bi4uHPe06VLF3x9fUlKSnIVL1arlaioKL788ks6d+5Mu3bt+Oqrr/jtb38LwK9+9SsmTZrEkCFD+OSTT/jd735Xb1wbNmxgyJAh/OMf/2D06NF1tnk5DBw4kJMnT5KYmMjQoUMZM2YMgYGBdb4+OjqaUaNG4XQ6L0v7IiIiItcak1NHQtKA2O12pk+fjs1mc3col11FRZWGjK8CDc0bjxFzEhzcuEFfqLG4WJdqNBIjbiMNmfJRo74pXjoHRa47RUVFpKSknPd4TEzMZV/e2LFjf9YyRURERKR2GkERuU5oBOXq0DdfxmPEnFj9fPDxbpjfAZZWVHHaYPlo6Iy4jTRkykcNjaCIiIhcRY5T3+NwdxBuop9PFZFfSifJi4iIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhgqUERERERExDBUoIiIiIiIiGHoSvIiIiK/kNXPBx9v/UsFKK2ocncIInKN095URETkF/Lx9qTNpLfdHYYhHMrozWl3ByEi1zRN8RIREREREcNQgSINTk5ODvv27XN3GLXavn0748ePd3cYIiIiIm6jAkUanPXr13Ps2DF3hyEiIiIitdA5KHLdqKioYNq0aRw+fJjq6moeeugh5s2bx4IFC/Dw8GD8+PGkpaXx4YcfsmfPHiIiIvj9739PeHg44eHhJCQkkJGRQXV1NadOnSI1NZWoqKha28rOzub999/H4XBQUlLCn/70J3r06MGOHTtc7bVq1YoZM2YAMHnyZAoKCqiqqmL48OHExcVht9sJCwvjyy+/xOl0smDBgnPa2LhxIy+99BJms5k777yTiRMnXvE+FBEREXE3FShy3cjKyiIgIID09HRKSkoYMmQIGRkZpKWl4XQ6mTNnDu3bt+e+++4jLi6Oli1bcvToUbKzswkICOCdd94hJSWFtm3b8uabb5KdnV1ngQJw9uxZVqxYwYkTJ0hISKBLly6kpaXxyiuvEBgYyMKFC9mwYQPl5eUEBASQmZmJw+EgPj6e2NhYAKKiopgxYwarV6/mueeeo3v37gCcPHmSRYsWsX79enx8fHj88cfZtm0bnTp1qjMeDw8T/v6+l7dT5TweHmb1s8EoJ8ajfBiLthFjUT4uTAWKXDfy8vLIzc1l9+7dAFRWVtKqVSsaN26MxWKhffv2570nICCAgIAAAJo1a8aSJUto1KgRZ86cwWq11tteTEwMZrOZoKAg/Pz8OHbsGMeOHWPcuHEAlJaW0qlTJ7777jvuvfdeAKxWKzabjYKCAoBzCpXNmze7ln3kyBFOnDjBqFGjADhz5ozrPXWpqnJy8uTZC/aT/DL+/r7qZ4MxQk6Cgxu7tX2jcXc+5FxG2Ebkv5SPGvXtN1WgyHUjPDyc5s2bM3r0aEpLS1m6dCkff/wxN9xwA9XV1fz973+nzpoFDwAAHitJREFUZ8+emEwmnE4nAGbzf0/DmjVrFnPnzsVms/HMM8/w1Vdf1dvenj17ADh+/DgOh4PmzZvTvHlzlixZQuPGjXn//ffx9fUlPz+fnTt30r17dxwOB3l5eYSGhgLw+eef07x5c/71r38RERHhWnZoaCgtWrRg+fLlWCwWsrOzay2wRERERK43KlDkujF48GBSU1MZMmQIDoeDbt26sWjRIlavXo3T6SQpKYlf/epXdOzYkblz57qKhB/069ePP/7xjwQGBtK8eXNKSkrqbe/48eMMGzaM06dPM23aNDw8PJgyZQqjRo3C6XRyww03MGfOHO68807S0tJITEykrKyMMWPGEBgYCMCGDRt46aWX8PHxYc6cOeTl5QHQtGlTkpOTsdvtVFVVceONN9KrV68r03EiIiIiBmJy/vBVsohctOzsbA4ePPiLTly32+1Mnz4dm812WWKqqKjSkPFVoKF54zFCToKDG+tCjf/foYzeFBfrUo1GYoRtRP5L+aihKV4iP9P06dPJz88/73GNZojIj31fVsmhjN7uDsMQSiuq3B2CiFzjNIIicp3QCMrVoW++jEc5MRblw3iUE2NRPmrUN4KiCzWKiIiIiIhhqEARERERERHDUIEiIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGCpQRERERETEMFSgiIiIiIiIYahAERERERERw/B0dwAiIiLXEqufDz7e+vdZl9KKKneHICLXOO1hRURELoGPtydtJr3t7jAM61BGb067OwgRuaZpitcvsHfvXhYvXlzn89nZ2cydO/e8x7t06UJZWdlli8Nut5Ofn3/Zljd+/Hi2b9/u1hh+joEDB1JYWFjn83a7nQEDBmC32xk8eDATJ06kpKTkF7X5S9e7qKiIzZs3/6IYRERERK4nGkH5Bdq3b0/79u3dHYZcgqeffhqbzQbAG2+8wdSpU1m0aJHb4vnkk084ePAgXbp0cVsMIiIiIkaiAqUW2dnZ/POf/6S0tJQjR47w8MMP06FDB2bOnAmAv78/6enp/Oc//+HVV19lwYIFZGVlsXr1apo0aYLFYiEuLg6Azz77jBEjRnDixAkSExMZNGgQAFOnTuWrr74iMDCQp59+Gg8PDyZPnkxBQQFVVVUMHz6cuLg47HY706dPx2azsWbNGo4fP07//v35wx/+gL+/P7/+9a8B+Otf/8rx48f5/vvvmT9/Pq1atSIjI4Pc3FwA+vTpw7BhwygsLGTKlClUVlZiMplITU2lXbt2rF69mqysLIKDg/n222/r7Z/PPvuMWbNm4XQ6CQkJOWeU6PTp00yZMsU1MpGamkrbtm3529/+xrvvvktlZSWNGzdm0aJFvPXWW+f1c3x8PKtXr+b111/HbDYTFRVFSkoKR48eJS0tjbKyMry9vXnqqado0aIFCxYs4MMPP6R58+aXPBrSr18/Fi5cSFlZGfn5+Tz11FN4eHi4ll9dXX1OP9911121rndtfT9v3jxycnJwOp0kJyfTq1ev89Zr4sSJPP/885SWlnLHHXcQGhpa62ds7ty5WCwWBg4cyIMPPnhJ6ygiIiJyrVGBUgeHw8GLL77IoUOHGD16NH5+fqSnpxMREUFWVhYvvPAC9957LwAnTpzghRde4PXXX8fLy4uhQ4e6luPp6cmLL77IV199xahRo1wFSmJiIrfffjtz5sxh3bp1mM1mAgICyMzMxOFwEB8fT2xsbJ3xFRcXs379ery8vNiyZQv3338/v/vd71i0aBF///vfiYiIoLCwkHXr1lFZWUlSUhKxsbH89a9/xW63061bN/bu3cvkyZNZuXIlL7/8Mm+++SYmk4n4+Ph6+yYtLY0FCxZgs9lYvXr1OVOcnn32WWJjY0lKSuLQoUP8+c9/ZvXq1Zw8eZKXXnoJs9nMyJEj+fe//11rP8fHx5OdnU1aWhq33347r7zyCpWVlTz99NPY7Xbuv/9+Pv74Y+bOncv//M//kJOTw2uvvcbZs2f57W9/e8l59vPz49SpU6SmpjJr1izat2/Ppk2byMjI4Iknnjinn/v161frev+072+55RYKCwt59dVXKSsrY+DAgXTq1Om89XI6nYwaNYqDBw/StWtXBg4cWOtnrKysjKysrAuui4eHCX9/30vuA7k0Hh5m9bPBKCfGo3wYi7YRY1E+LkwFSh3atWsHQIsWLSgvLyc/P58nn3wSgIqKCsLCwlyvPXLkCDabDR8fHwDuuOMO13O33norJpOJ4OBgSktLAbBYLNx+++0AREVFsW3bNgBXwWO1WrHZbBQUFJwTk9PpdN0ODQ3Fy8vLdf+2224DICgoiOPHj5Ofn090dDQmkwmLxULHjh3Jz88nPz+fmJgYoGaK2tdff83BgweJiIhwLS8yMrLevvn2229d06R+//vfn/NcXl4en3zyCRs3bgTg1KlTmM1mLBYLEyZMwNfXl6+//prKyspa+xlg9uzZLF++nLlz53L77bfjdDrJy8vjueee44UXXsDpdGKxWDhw4AC33XYbZrMZq9XKLbfcUm/cP+V0Ojl+/DiBgYEcO3bMNV0vJiaGefPmndfPda33T/s+Ly+PPXv2YLfbAaisrKSoqKjW9fqxuj5jP/6s1aeqysnJk2cvqQ/k0vn7+6qfDeZq5yQ4uPFVa+tapW3EWLTfMhblo0Z9+1IVKHUwmUzn3A8LC+Ppp5+mZcuW5ObmUlxc7HqudevWHDx4kNLSUry8vNi9ezfh4eG1LgdqDj737t1L+/bt2blzJzfffDPV1dXs3LmT7t2743A4yMvLcx0cFxcXY7PZ+M9//kNISAgAZnP9v29gs9nIzs4mOTmZiooKdu3aRf/+/bHZbOzcuZOuXbuyd+9egoKCaNWqFQcOHKC0tBSLxcLevXvp169fnctu1qwZhw4dok2bNjz//PPnHECHh4fTr18/+vbty7fffktWVhb79u1j06ZNZGVl8f333xMfH+86OK+tf9atW8eTTz6Jt7c3I0eOZNeuXYSHhzNixAiioqLIz88nJyeHsLAwXn75ZaqrqyktLeXAgQP19slPvfbaa8TGxmI2m2nWrBn79u2jXbt25OTk0KZNm/P6ub71/rHw8HDuvvtu1zSxJUuWEBoaysKFC89bL7PZTHV1NVD3Z+xCuRYRERG5nqhAuUjTp08nJSWFqqqa33efNWsWx44dA6Bp06Y8/PDDJCUl4e/vT1lZGZ6enq5Rgp+yWCysWrWKw4cP07JlSx577DGcTidpaWkkJiZSVlbGmDFjCAwMZOjQocyYMYMWLVrQrFmzi473gQceYMeOHQwaNIiKigp69uxJhw4deOKJJ0hLS2P58uVUVlYya9YsmjZtyqOPPsrgwYNp2rSpaySoLk8++SSTJ0/GbDYTHBxMcnIyL7/8MgCjR49mypQprFu3DofDwZgxY7jpppvw8fEhPj4eLy8vgoODXX1Xm7Zt2zJgwAACAgIICQmhY8eOpKSkMH36dMrKyigtLWXKlCm0b9+enj17MmDAAJo1a0ZgYOAF+yUlJcW1fiEhIUybNg2AmTNn8tRTT+F0OvHw8CA9Pf2S1vvHunTpwo4dO0hKSuLs2bN069YNq9Va63pZrVaWLl1Khw4d6v2MiYiIiDQUJudP55nIJausrGTZsmX84Q9/AGqm/4wbN841lUrkaqioqNKQ8VWgoXnjcccUL10HpW6HMnpTXKwroRiJ9lvGonzU0BSvK8zT05Pvv/+e/v37Y7FYiIyMJDo62t1h/SJFRUWkpKSc93hMTAxjx451Q0QXZ/fu3WRmZp73eK9evUhKSnJDRCIiIiJyKTSCInKd0AjK1aFvvoznaufE6ueDj7e+36tLaUUVp7WNGIr2W8aifNTQCIqIiMhl4jj1PQ53B2Fg+vlUEfml9PNAIiIiIiJiGCpQRERERETEMFSgiIiIiIiIYahAERERERERw1CBIiIiIiIihqECRUREREREDEMFioiIiIiIGIYKFBERERERMQwVKCIiIiIiYhi6kryIiFzXrH4++Hjr393VUlpR5e4QROQapz22iIhc13y8PWkz6W13h9FgHMrozWl3ByEi1zRN8RIREREREcNQgSJXxRdffEFOTs4VbWPDhg0MHTqU4cOHk5yczNatWwGYNGkSW7ZsuSJtbtmyhUmTJl2RZYuIiIg0RJriJVfFu+++S1BQEDExMVdk+adPn2bJkiW8/fbbeHl58c0335CQkMA//vGPK9KeiIiIiFwZKlAaoOzsbNavX091dTVjx46luLiYlStX4uXlRZs2bZgxYwYAkydPpqCggKqqKoYPH05cXBx2u522bduyf/9+fH19iY6OZuvWrZw6dYrly5fTpEmT89r75ptv2LBhAxaLhQ4dOjBjxgxee+01AMaNG8eIESOYNGkS0dHR7N+/nyZNmjB//nwsFgvTpk3j8OHDVFdXM27cOO6+++5a18nX15eqqirWrFnDAw88QOvWrdm0aRNmc80g4dq1a3nhhRdwOBxMnz6dyMhI5s2bx+eff86ZM2ew2WzMnj2bRYsWsWvXLs6ePcusWbP46KOPeOuttzCZTMTFxTF06FDy8/OZPHkyPj4++Pj4nLfO27dvZ+7cuVgsFgYOHEhwcDALFy7E29sbf39/0tPT8fPzIyMjg9zcXAD69OnDsGHDmDRpEp6enhQVFVFeXk5cXBwffPABR48eZcmSJbRu3fqyfQ5EREREjEgFSgPl5+fH0qVLKSkpYerUqWzYsAGr1Up6ejpr164FICAggMzMTBwOB/Hx8cTGxgIQGRlJamoqI0eOpFGjRqxYsYKUlBRycnLo1q3beW2FhITQv39/goKCiIyMpFGjRhw4cICgoCAKCwuJjIyktLSUvn37EhMTw5w5c1i7di3e3t4EBASQnp5OSUkJQ4YM4e23az/R1cPDgxUrVrBy5UoeeughKioqePjhh0lKSgKgQ4cO/PGPfyQ7O5vs7GzCw8Px8/NjxYoVVFdX07t3b7755hsAwsPDSU1N5cCBA7zzzju88sormEwmkpOT6dy5M3/5y18YO3YsnTp14vnnn+fgwYPnxVNWVkZWVhZOp5OuXbuyZs0aQkJCWLlyJUuXLuWuu+6isLCQdevWUVlZSVJSkqt/b7zxRmbOnMnUqVMpLCxk2bJlPPPMM2zevJnk5OQ6c+rhYcLf3/fiPwTys3h4mNXPBqOcGI/yYSzaRoxF+bgwFSgNVFhYGAAFBQVERERgtVoBiImJYevWrZjNZu69914ArFYrNpuNgoICoOZgH2qKnIiICNftsrKyi2o7ISGB7OxsWrZsSb9+/QDw9PR0Tf+Kiopiy5YtmM1mcnNz2b17NwCVlZWUlJQQEBBw3jK/+eYbSktLmTp1KgBffvklDz30EHfeeec5MQcFBVFaWoq3tzcnTpxgwoQJ+Pr6cvbsWSoqKs7pm7y8PIqKilxFwXfffceRI0fYv38/kZGRrlhrK1B+WEZJSQlWq5WQkBBX/86fP5/AwECio6MxmUxYLBY6duxIfn4+ALfeequrT8PDw123y8vL6+3XqionJ0+erfc18sv5+/uqnw3mQjkJDm58FaMRQNuIwWi/ZSzKR4369s06Sb6B+mHqU2hoKPn5+Zw9W7Oh7Nixg7CwMGw2Gzt37gTA4XCQl5dHaGjoz27PZDJRXV0NQM+ePdm2bRvvvfeeq0CprKxk3759AOTm5hIREUF4eDi9e/dm1apVLFu2jJ49e9Y6hQzg+PHjTJw4ke+++w6oGYUICAjAYrG42v+xLVu2cPToUebPn8+ECRMoLS3F6XSe0zfh4eFERETw8ssvs2rVKuLj47nlllsIDw9n165dAHz++ee1xvPDMgICAnA4HBw7dgyo6d82bdpgs9lc07sqKirYtWsXN910U62xioiIiDQkGkFp4Jo2bcojjzzC0KFDMZvNtG7dmokTJ2IymUhLSyMxMZGysjLGjBlDYGDgz27ntttuY86cOdhsNmJjY4mJieHEiRP4+/u7XrNs2TKKiopo2bIl48ePByA1NZUhQ4bgcDhISkpyHfj/VIcOHRg6dCjDhg2jUaNGVFVVkZCQ4BqB+KnIyEiWLFnCwIED8fLyolWrVq4i4gft2rXjnnvuITExkfLyciIjIwkJCWHatGmMHz+eF198kaZNm+Lt7V3neptMJmbOnMkjjzyCyWSiSZMmzJ49m6ZNm7Jjxw4GDRpERUUFPXv2dI3yiIiIiDRkJucPXxuLXEXTp0+nR48e3HPPPQB06dKFjRs31nuwL/WrqKjSkPFVoKF547mYKV66UOPVcyijN8XFulSjkWi/ZSzKR436pnhpBEUum6KiIlJSUs57PCYmhrFjx7rujxgxgmbNmrmKk0uxePFitm/fft7j6enptGrV6pKXJyLXv+/LKjmU0dvdYTQYpRVV7g5BRK5xGkERuU5oBOXq0DdfxqOcGIvyYTzKibEoHzV0kryIiIiIiFwTVKCIiIiIiIhhqEARERERERHDUIEiIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExDBUoIiIiIiJiGCpQRERERETEMFSgiIiIiIiIYXi6OwARubZY/Xzw8W7Yu47g4MbuDkF+QjkxjtKKKneHICLXuIZ9lCEil8zH25M2k952dxgiYlCHMnpz2t1BiMg1TVO8RERERETEMFSgyFXzxRdfkJOTc8WWX1hYSFRUFHa7HbvdzqBBg5g/f/55r9u7dy+LFy++pGWXlZWRlZV1uUIVERERkTpoipdcNe+++y5BQUHExMRcsTYiIiJYtWoVANXV1SQmJrJv3z7atWvnek379u1p3779JS23uLiYrKwsEhISLmu8IiIiInIuFSgNVHZ2NuvXr6e6upqxY8dSXFzMypUr8fLyok2bNsyYMQOAyZMnU1BQQFVVFcOHDycuLg673U7btm3Zv38/vr6+REdHs3XrVk6dOsXy5ctp0qTJee198803bNiwAYvFQocOHZgxYwavvfYaAOPGjWPEiBFMmjSJ6Oho9u/fT5MmTZg/fz4Wi4Vp06Zx+PBhqqurGTduHHffffdFrWNpaSnl5eX4+PgwadIkTp48ycmTJxk5ciTvvPMOffr0YdOmTcyePRuABx98kBdffJGNGzfy7rvvUllZSePGjVm0aBHPPvssBw4cYPHixQwbNowpU6ZQUlICQGpqKm3btnW1u337dubOnYvFYmHgwIEEBwezcOFCvL298ff3Jz09HT8/PzIyMsjNzQWgT58+DBs2jEmTJuHp6UlRURHl5eXExcXxwQcfcPToUZYsWULr1q3rXF8PDxP+/r4X1TciIleS9kXG4uFhVk4MRPm4MBUoDZifnx9Lly6lpKSEqVOnsmHDBqxWK+np6axduxaAgIAAMjMzcTgcxMfHExsbC0BkZCSpqamMHDmSRo0asWLFClJSUsjJyaFbt27ntRUSEkL//v0JCgoiMjKSRo0aceDAAYKCgigsLCQyMpLS0lL69u1LTEwMc+bMYe3atXh7exMQEEB6ejolJSUMGTKEt9+u+wTtAwcOYLfbAfDw8GDo0KHcdNNNAMTGxpKcnMz27dsB+M1vfkNmZiZnz57lwIEDtG7dmoCAAE6ePMlLL72E2Wxm5MiR/Pvf/2b06NHk5eUxZswYMjMziY2NJSkpiUOHDvHnP/+ZNWvWnBPHD1PCnE4nXbt2Zc2aNYSEhLBy5UqWLl3KXXfdRWFhIevWraOyspKkpCRX3954443MnDmTqVOnUlhYyLJly3jmmWfYvHkzycnJda57VZWTkyfPXmT2fz79WpKIXMjV2BfJxfP391VODET5qFHf8YQKlAYsLCwMgIKCAiIiIrBarQDExMSwdetWzGYz9957LwBWqxWbzUZBQQEAHTp0AGqKnIiICNftsrKyi2o7ISGB7OxsWrZsSb9+/QDw9PR0Tf+Kiopiy5YtmM1mcnNz2b17NwCVlZWUlJQQEBBQ63J/PMWrrvX9gYeHBz169ODdd9/l008/JSEhAbPZjMViYcKECfj6+vL1119TWVl5zvvy8vL45JNP2LhxIwCnTp2qs62SkhKsVishISFATd/Onz+fwMBAoqOjMZlMWCwWOnbsSH5+PgC33norUNOf4eHhrtvl5eX1damIiIjIdUEnyTdgZnNN+kNDQ8nPz+fs2ZpqfseOHYSFhWGz2di5cycADoeDvLw8QkNDf3Z7JpOJ6upqAHr27Mm2bdt47733XAVKZWUl+/btAyA3N5eIiAjCw8Pp3bs3q1atYtmyZfTs2bPWKWQX2/5PDRgwgDfeeIPPPvuMTp06sW/fPjZt2sTChQtJS0ujuroap9OJ2Wx2xR4eHk5ycjKrVq1i4cKF9O3b97zl/tC3AQEBOBwOjh07BtT0bZs2bbDZbK7pXRUVFezatcs10lNbnCIiIiINhUZQhKZNm/LII48wdOhQzGYzrVu3ZuLEiZhMJtLS0khMTKSsrIwxY8YQGBj4s9u57bbbmDNnDjabjdjYWGJiYjhx4gT+/v6u1yxbtoyioiJatmzJ+PHjgZpzPIYMGYLD4SApKcl18H85tGrVCoCuXbtiNpu56aab8PHxIT4+Hi8vL4KDgzl27Bh33HEHFRUVZGZmMnr0aKZMmcK6detwOByMGTOmzuWbTCZmzpzJI488gslkokmTJsyePZumTZuyY8cOBg0aREVFBT179nSNSomIiIg0ZCan0+l0dxDSME2fPp0ePXpwzz33ANClSxc2btyIt7e3myO7NlVUVF21c1B0oUYRqcuhjN4UF+tSjUaicx6MRfmooXNQ5KopKioiJSXlvMdjYmIYO3as6/6IESNo1qyZqzi5FIsXL3ad6P5j6enprhERuXK+L6vkUEZvd4chIgZVWlHl7hBE5BqnERSR68TVGkFp6PTNl/EoJ8aifBiPcmIsykeN+kZQdJK8iIiIiIgYhgoUERERERExDBUoIiIiIiJiGCpQRERERETEMHSSvIiIiIiIGIZGUERERERExDBUoIiIiIiIiGGoQBEREREREcNQgSIiIiIiIoahAkVERERERAxDBYqIiIiIiBiGChQRERERETEMFSgiIhdw+vRpRo8ezZAhQxg0aBC7du0C4NNPPyUhIYHBgwezePFiN0fZML333ns89thjrvvKiftUV1czdepUBg0ahN1u5/Dhw+4OqcH67LPPsNvtABw+fJjExESSkpKYNm0a1dXVbo6uYamoqODxxx8nKSmJAQMG8P777ysnF0EFiojIBaxYsYLY2Fj+9re/MXv2bGbMmAHAtGnTmDdvHmvWrOGzzz5jz549bo60YZk5cybz5s0755+7cuI+mzZtory8nLVr1/LYY4+RkZHh7pAapGXLlpGamkpZWRkAs2fPZty4cbzyyis4nU7ef/99N0fYsLzxxhv4+/vzyiuvsGzZMp566inl5CKoQBERuYDk5GQGDx4MQFVVFd7e3jgcDsrLy2ndujUmk4nOnTvz8ccfuznShiUqKorp06e77isn7pWbm8t9990HwO23387nn3/u5ogaptatW7No0SLX/T179nDXXXcB8Otf/5qPPvrIXaE1SD179uTRRx913ffw8FBOLoIKFBGRH8nKyqJPnz7n/B06dIhGjRpRXFzM448/zoQJE3A4HFitVtf7brjhBk6fPu3GyK9fteVk9+7dxMXFYTKZXK9TTtzrp/3v4eFBZWWlGyNqmHr06IGnp6frvtPpdG0n2iauvhtuuAGr1YrD4WDs2LGMGzdOObkInhd+iYhIw5GQkEBCQsJ5j3/xxRdMmDCBJ554grvuuguHw8GZM2dcz585cwY/P7+rGWqDUVdOfspqtSonbvTT/q+urj7nQFncw2z+73fR2ibc4+jRo/zpT38iKSmJvn37kpmZ6XpOOamdRlBERC7gwIEDPProo8ybN4/7778fqDkYs1gsHDlyBKfTydatW4mOjnZzpA2bcuJeUVFRbNmyBaj5sYJbbrnFzREJwK233sr27dsB2LJli7aJq+z48eOMGDGCxx9/nAEDBgDKycXQVxsiIhcwb948ysvLmTVrFlBzILx06VKefPJJJk6cSFVVFZ07d6Zjx45ujlSUE/fp3r0727ZtY/DgwTidTtLT090dkgApKSmkpaUxf/58wsPD6dGjh7tDalCeffZZTp06xZIlS1iyZAkAU6ZMYebMmcpJPUxOp9Pp7iBERERERERAU7xERERERMRAVKCIiIiIiIhhqEARERERERHDUIEiIiIiIiKGoQJFREREREQMQwWKiIiIiIgYhgoUERERERExjP8H3lWqhx1x3OMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso_model(df_boston)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Seattle price model\n",
    "For Seattle listing, the training score is 0.68, while the test score is 0.63, which are reasonably good. The final number of features used is 149. The list of coefficients shows the magnitude of each feature importance. \n",
    "Just like Boston listing, neighbourhood matters in Seattle too. And the price can go either up or down depending on the neighbourhood.\n",
    "\n",
    "#### Positively correlated factors with correlation coefficient > 3\n",
    "1. bedrooms, accomodates, bathrooms, guest_included, beds\n",
    " * This makes perfect sense. The more these variables are, the higher the price is. \n",
    "2. review_scores_rating, host_is_superhost, calendar_updated\n",
    " * These variables are somewhat related to the hosts' rental policies/characteristics.\n",
    " * review_scores_rating - the higher review score rating, the better services/values provided to the guests, the more guests interested, the higher demand, the higher price.\n",
    " * Superhost - same as the explanation for Boston\n",
    " * calendar_updated - same as the explanation for Boston\n",
    "3. Amenity - Doorman, Elevator in Building\n",
    " * Doorman - same as the explanation for Boston\n",
    " * Elevator in Building - valuable to some guests. More value, higher price. \n",
    "\n",
    "#### Negatively correlated factors  with correlation coefficient absolute value > 2\n",
    "1. room_type Private room， Shared room, Dorm\n",
    " * This makes sense. The whole house/apartment is always a better option if you can afford it. \n",
    "2. host_acceptance_rate， extra_people, reviews_per_month, review_scores_value\n",
    " * host_acceptance_rate - more possibly because of correlation only, not causal relationship. For some properties that are hard to get booked, such as remote areas, the hosts have no choice but to accept any reservation while charging a low price.\n",
    " * extra_people - same as the explanation for Boston\n",
    " * reviews_per_month - same as the explanation for Boston\n",
    " * review_scores_value: Value shopping guests tend to maximize utility/price. By doing so, they tend to choose properties that ask for lower prices. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best alpha: 0.3009697813983334\n",
      "training score: 0.6809131398220695\n",
      "test score: 0.6345227147681203\n",
      "number of features used: 149\n",
      "\n",
      "\n",
      "bedrooms                                 24.812787\n",
      "accommodates                             12.848408\n",
      "bathrooms                                 9.754120\n",
      "neighbourhood_cleansed_Broadway           6.573381\n",
      "neighbourhood_cleansed_Pioneer Square     5.873957\n",
      "                                           ...    \n",
      "reviews_per_month                        -4.409133\n",
      "extra_people                             -5.546166\n",
      "host_acceptance_rate                     -9.000192\n",
      "room_type_Shared room                   -12.926596\n",
      "room_type_Private room                  -16.952159\n",
      "Length: 186, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso_model(df_seattle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}