Boston Airbnb data modeling --- predicting future earnings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Become a data driven Airbnb host 2\n",
    "\n",
    "This is a blog continued from the first one: . In this blog, we try to solve a business problem through machine learning --- predicting future earnings based on the current listing information. Imagine that you are an Airbnb host and you would like to know the expected earning for one of your listings next year. Once you have the model, you can play with the information of your listing to see how you might change your future earning if there's any variations of your current listing. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load data and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import datetime\n",
    "from datetime import date\n",
    "import sklearn\n",
    "from sklearn.preprocessing import Imputer\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "pd.set_option(\"display.max_columns\", 100)\n",
    "pd.set_option('max_colwidth',200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar = pd.read_csv(\"calendar.csv\")\n",
    "listings = pd.read_csv(\"listings.csv\")\n",
    "reviews = pd.read_csv(\"reviews.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Investigate rows with high missing rates\n",
    "\n",
    "First, let's check if there are special groups of listings with a lot of N/A columns ---- maybe they are very new or bad listings which we want to leave out when building our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Missing rates (rows)')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NgSuBI4EJwJclTaj0dszMKqUav5j1AKA1IpYBSLoJmAw8VYVtmVkD2Nh/+Ws1To1HAStKpttSn5lZQ6rGEaG66IsNBknTgGlp8k1Jz1Ro+yOBVyq0rkpzbX3XqHWBa+uvftWmS/q1rd3KGVSNIGwDxpRMjwZWdh4UEbOAWZXeuKSWiJhY6fVWgmvru0atC1xbfzVibdU4Nf49MF7SWElDgROBeVXYjplZRVT8iDAi1kk6FfglsCnwXxHxZKW3Y2ZWKVX5c54RcSdwZzXWXYaKn25XkGvru0atC1xbfzVcbYrY4D6GmVlW/BU7M8veRhOEvX1tT9IwSTen+QskNaf+ZknvSHo0Pa6pQ20HS3pY0jpJx3eaN0XSkvSY0mC1fViy3yp+w6uM2mZIekrS45LukbRbybx677eeaqv3fvumpEVp+w+UfrNL0tlpuWckHd4otdXifdqjiGj4B8VNl6XAOGAo8BgwodOYfwCuSe0TgZtTuxl4os61NQN/BlwHHF/Svz2wLD2PSO0RjVBbmvdmnffbXwFbpva3Sn6mjbDfuqytQfbbtiXtY4G7UntCGj8MGJvWs2mD1FbV92lvj43liPDjr+1FxPtAx9f2Sk0G5qT2XGCSpK4+3F3z2iJieUQ8DnzUadnDgbsj4rWIWA3cDRzRILVVWzm13RcRb6fJhyg+kwqNsd+6q63ayqnt9ZLJrfjkCw2TgZsi4r2IeBZoTetrhNrqamMJwnK+tvfxmIhYB6wFdkjzxkp6RNJ8SZ+tQ23VWLYW699cUoukhyQdV8G6oO+1TQV+0c9la1kbNMB+kzRd0lLgX4Dv9GXZOtUG1X2f9qgqH5+pgnK+ttfdmFXArhHxqqQ/B34maa9O/zNVu7ZqLFuL9e8aESsljQPulbQoIpbWujZJXwUmAof0ddl+Gkht0AD7LSKuBK6UdBJwHjCl3GXrVFu136c92liOCMv52t7HYyQNAbYDXkunAa8CRMRCimsYn6pxbdVYturrj4iV6XkZcD+wb61rk3QYcC5wbES815dl61RbQ+y3EjcBHUelDbHfuqqtBu/TntXr4mRfHhRHrssoLvB2XITdq9OY6ax/s+SW1G4iXRCmuIj7ArB9LWsrGftjNrxZ8izFBf8Rqd0otY0AhqX2SGAJnS581+Bnui/FG2J8p/6677ceamuE/Ta+pH0M0JLae7H+zZJlVPZmyUBqq+r7tNfaa7WhCuzko4A/pH9856a+71H8bwywOXArxQXg3wHjUv8XgSfTD+Vh4Jg61LY/xf+WbwGvAk+WLPuNVHMrcEqj1AZ8GliU9tsiYGodavs18BLwaHrMa6D91mVtDbLfLk//5h8F7isNI4oj2KXAM8CRjVJbLd6nPT38zRIzy97Gco3QzKxqHIRmlj0HoZllz0FoZtlzEJpZ9hyEZpY9B6GZZc9BaGbZ+3+lNgjad7sMCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How much data is missing in each row of the dataset?\n",
    "missing_values_in_rows = listings.isna().mean(axis=1).values\n",
    "plt.rcParams['figure.figsize'] = (5, 3)\n",
    "ax = plt.hist(missing_values_in_rows)\n",
    "plt.title('Missing rates (rows)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bad senario will be that there's a group of rows with significantly higher missing rates. In our case, it's good to see that most of the listings have at least 70% of the fields flled, and the shape of the distribution is kind of ideal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Feature engineering\n",
    "\n",
    "We then create features from the existing columns. Once this is done, we will add some columns in the raw data that we can directly use without much engineering. The features we generated:\n",
    "1. downtown_bos, east_bos: whether the listing is in one of the two neighborhoods. From the exploration, many of the listings are from these areas, maybe they are convenient/go-to areas in Boston.\n",
    "2. host_age: generated from host_since (months)\n",
    "3. reponse_rate, acceptance_rate: convert to numbers\n",
    "4. one_or_two_listings, three_to_ten_listings, more_than_ten_listings: how many listings the host has\n",
    "5. response_time_ordinal： generated from response_time\n",
    "6. real_bed: if the bed is a real bed\n",
    "7. neighborhoods_one_hot: one-hot coding to indicate if the listing is in one of 10 most popular neighborhoods\n",
    "8. amenities_len: length of the amenities field to roughly indicate how many amenities the listing offers\n",
    "9. cancellation_policy_ordinal: the larger the number the more flexible the cancellation policy is\n",
    "10. price_per_bed_compared_to_nbh: the difference between the price per bed for that listing and the average price per bed for that neighborhood (**this is a slightly complicated feature to build! We will do it in the end**)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Generated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lower(s):\n",
    "    try:\n",
    "        return s.lower()\n",
    "    except:\n",
    "        return s\n",
    "\n",
    "def perc_to_numbers(p):\n",
    "    '''\n",
    "    \"30%\" --> 0.3\n",
    "    '''\n",
    "    try:\n",
    "        return float(p.split(\"%\")[0])/100\n",
    "    except:\n",
    "        return float(\"nan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = listings['summary'].apply(lower)\n",
    "downtown_bos = summary.str.contains(\"downtown boston\").astype(\"float32\")\n",
    "east_bos = summary.str.contains(\"east boston\").astype(\"float32\")\n",
    "host_age = listings['host_since'].apply(lambda x: (datetime.datetime.today() - pd.to_datetime(x)).days/30)\n",
    "reponse_rate = listings['host_response_rate'].apply(perc_to_numbers)\n",
    "acceptance_rate = listings['host_acceptance_rate'].apply(perc_to_numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A small investigation on the numbers of listings:"
   ]
  },
  {
   "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>host_total_listings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3585.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>58.902371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>171.119663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>749.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_total_listings_count\n",
       "count                3585.000000\n",
       "mean                   58.902371\n",
       "std                   171.119663\n",
       "min                     0.000000\n",
       "25%                     1.000000\n",
       "50%                     2.000000\n",
       "75%                     7.000000\n",
       "max                   749.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listings[['host_total_listings_count']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59.02% of the hosts have no more than 2 listings, 22.45% of the hosts have more than 10 listings\n"
     ]
    }
   ],
   "source": [
    "print(\"{:.2%} of the hosts have no more than 2 listings, {:.2%} of the hosts have more than 10 listings\"\\\n",
    ".format(sum(listings['host_total_listings_count']<=2)/len(listings), sum(listings['host_total_listings_count']>10)/len(listings)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hosts with one or two listings might just have two rooms for rent in one apartment/house. We will group them together. 40% of the hosts have multiple properties and some of them even have hundred of listings. We thus create the groups:\n",
    "- hosts with 1~2 listings\n",
    "- hosts with 3~10 listings\n",
    "- hosts with more than 10 listings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_or_two_listings = listings['host_total_listings_count'] <= 2 \n",
    "three_to_ten_listings = (listings['host_total_listings_count'] > 2) & (listings['host_total_listings_count'] < 10)\n",
    "more_than_ten_listings = listings['host_total_listings_count'] > 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "response_time_dict = {\n",
    "    'a few days or more':1,\n",
    "    'within a day': 2,\n",
    "    'within a few hours': 3,\n",
    "    'within an hour': 4\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some records have `N/A` response time. Are they very new listings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 63., 100.,  95.,  74.,  41.,  33.,  28.,  18.,   9.,  10.]),\n",
       " array([ 37.46666667,  45.16666667,  52.86666667,  60.56666667,\n",
       "         68.26666667,  75.96666667,  83.66666667,  91.36666667,\n",
       "         99.06666667, 106.76666667, 114.46666667]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(host_age[listings['host_response_time'].isna()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of host_age for these listings is consistent with the distribution for all the listings. So we fill the nan values with `\"within a few hours\"` (the 2nd popular value for this column; We didn't choose to fill with `within an hour` to avoid overestimating the field. We believe that most hosts can reply within a few hours)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "response_time_ordinal = listings['host_response_time']\\\n",
    "                        .fillna(\"within a few hours\").apply(lambda x: response_time_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_bed = listings['bed_type'] == \"Real Bed\"\n",
    "popular_neighborhoods = list(listings\\\n",
    "                        .groupby('neighbourhood_cleansed')\\\n",
    "                        .count()['id'].sort_values(ascending=False)[:10].index)\n",
    "neighborhoods_one_hot = pd.get_dummies(listings[['neighbourhood_cleansed']])\n",
    "amenities_len = listings['amenities'].apply(len)\n",
    "cancellation_policy_dict = {\n",
    "    'flexible': 1,\n",
    "    'moderate': 2,\n",
    "    'strict': 3,\n",
    "    'super_strict_30': 4\n",
    "}\n",
    "cancellation_policy_ordinal = listings['cancellation_policy']\\\n",
    "                              .fillna(\"super_strict_30\").apply(lambda x: cancellation_policy_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.DataFrame({\n",
    "    'id': listings['id'],\n",
    "    'downtown_bos': downtown_bos,\n",
    "    'east_bos': east_bos,\n",
    "    'host_age': host_age,\n",
    "    'reponse_rate': reponse_rate,\n",
    "    'acceptance_rate': acceptance_rate,\n",
    "    'one_or_two_listings': one_or_two_listings,\n",
    "    'three_to_ten_listings': three_to_ten_listings,\n",
    "    'more_than_ten_listings': more_than_ten_listings,\n",
    "    'response_time_ordinal': response_time_ordinal,\n",
    "    'real_bed': real_bed,\n",
    "    'amenities_len': amenities_len,\n",
    "    'cancellation_policy_ordinal': cancellation_policy_ordinal\n",
    "}).join(neighborhoods_one_hot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Binary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_binary(x):\n",
    "    try:\n",
    "        return 1 if x == \"t\" else 0\n",
    "    except:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_pd = listings[['host_is_superhost',\n",
    "              'host_has_profile_pic',\n",
    "              'host_identity_verified',\n",
    "              'is_location_exact',\n",
    "              'requires_license',\n",
    "              'instant_bookable',\n",
    "              'require_guest_profile_picture',\n",
    "              'require_guest_phone_verification']].applymap(convert_binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.join(binary_pd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Convert money value strings to numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_money(s):\n",
    "    '''\n",
    "    \"$250.00\" --> 250\n",
    "    '''\n",
    "    try:\n",
    "        return float(eval(s.split(a\"$\")[1]))\n",
    "    except:\n",
    "        return float(\"nan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "money_pd = listings[['price','extra_people']].applymap(convert_money)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.join(money_pd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Add `price_per_bed_compared_to_nbh`\n",
    "-- The difference between the price per bed for that listing and the average price per bed for that neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "listings['price_num'] = final_df['price']\n",
    "listings['price_per_bed'] = listings['price_num'] / listings['beds'].replace(0,1)\n",
    "avg_price_per_bed = listings.groupby('host_neighbourhood')['price_per_bed'].mean().reset_index()\\\n",
    "                              .rename({'price_per_bed':'price_per_bed_nbh'}, axis=1)\n",
    "listings = listings.merge(avg_price_per_bed, on=\"host_neighbourhood\")\n",
    "listings['price_per_bed_compared_to_nbh'] = listings['price_per_bed'] - listings['price_per_bed_nbh']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Difference between price per bed v.s. neighborhood average')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = plt.hist(listings['price_per_bed_compared_to_nbh'])\n",
    "plt.title('Difference between price per bed v.s. neighborhood average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Add other numeric columns and id column (`id`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.join(listings[[\n",
    "                                   'accommodates',\n",
    "                                   'bathrooms',\n",
    "                                   'bedrooms',\n",
    "                                   'beds',\n",
    "                                   'number_of_reviews',\n",
    "                                   'review_scores_rating',\n",
    "                                   'review_scores_accuracy',\n",
    "                                   'review_scores_cleanliness',\n",
    "                                   'review_scores_checkin',\n",
    "                                   'review_scores_communication',\n",
    "                                   'review_scores_location',\n",
    "                                   'review_scores_value',\n",
    "                                   'price_per_bed_compared_to_nbh'\n",
    "                                   ]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 Deal with missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns with missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "downtown_bos                      143\n",
       "east_bos                          143\n",
       "reponse_rate                      471\n",
       "acceptance_rate                   471\n",
       "price                              12\n",
       "accommodates                      339\n",
       "bathrooms                         351\n",
       "bedrooms                          349\n",
       "beds                              343\n",
       "number_of_reviews                 339\n",
       "review_scores_rating             1058\n",
       "review_scores_accuracy           1067\n",
       "review_scores_cleanliness        1062\n",
       "review_scores_checkin            1064\n",
       "review_scores_communication      1062\n",
       "review_scores_location           1066\n",
       "review_scores_value              1065\n",
       "price_per_bed_compared_to_nbh     355\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = final_df.isna().sum()\n",
    "m[m>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6.1 Columns that we want to set the NaN values as 0s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df[['downtown_bos',\n",
    "            'east_bos',\n",
    "            'price_per_bed_compared_to_nbh',\n",
    "            'number_of_reviews']] = final_df[['downtown_bos',\n",
    "                                                            'east_bos',\n",
    "                                                            'price_per_bed_compared_to_nbh',\n",
    "                                                            'number_of_reviews']].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6.2 Columns that need be filled with the means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tianyiwang/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "final_df_sub1 = final_df[['reponse_rate',\n",
    "                  'review_scores_rating',\n",
    "                  'review_scores_accuracy',\n",
    "                  'review_scores_cleanliness',\n",
    "                  'review_scores_checkin',\n",
    "                  'review_scores_communication',\n",
    "                  'review_scores_location',\n",
    "                  'review_scores_value']].copy()\n",
    "\n",
    "final_df[['reponse_rate',\n",
    "          'review_scores_rating',\n",
    "          'review_scores_accuracy',\n",
    "          'review_scores_cleanliness',\n",
    "          'review_scores_checkin',\n",
    "          'review_scores_communication',\n",
    "          'review_scores_location',\n",
    "          'review_scores_value']] = pd.DataFrame(Imputer(missing_values=float('nan'), \n",
    "                                                         strategy=\"mean\", \n",
    "                                                         axis=0)\\\n",
    "                                                         .fit_transform(final_df_sub1),\n",
    "                                                 columns = final_df_sub1.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6.3 Columns that need be filled with the most common values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tianyiwang/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "final_df_sub2 = final_df[['bathrooms','bedrooms','beds','acceptance_rate']].copy()\n",
    "\n",
    "final_df[['bathrooms','bedrooms','beds','acceptance_rate']] = pd.DataFrame(Imputer(missing_values=float('nan'), \n",
    "                                                         strategy=\"most_frequent\", \n",
    "                                                         axis=0)\\\n",
    "                                                         .fit_transform(final_df_sub2),\n",
    "                                                         columns = final_df_sub2.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6.4 Other missing values filling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['accommodates'] = final_df['accommodates'].fillna(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the table again: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = final_df.isna().sum()\n",
    "m[m>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`price` is a very importance piece of information and we don't want to impute this value here. We will just drop the listings with `NaN` price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df[final_df['price'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.7 Make sure all the columns except the id column are numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.iloc[:,1:] = final_df.iloc[:,1:].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>downtown_bos</th>\n",
       "      <th>east_bos</th>\n",
       "      <th>host_age</th>\n",
       "      <th>reponse_rate</th>\n",
       "      <th>acceptance_rate</th>\n",
       "      <th>one_or_two_listings</th>\n",
       "      <th>three_to_ten_listings</th>\n",
       "      <th>more_than_ten_listings</th>\n",
       "      <th>response_time_ordinal</th>\n",
       "      <th>real_bed</th>\n",
       "      <th>amenities_len</th>\n",
       "      <th>cancellation_policy_ordinal</th>\n",
       "      <th>neighbourhood_cleansed_Allston</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>host_is_superhost</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>is_location_exact</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>price</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>number_of_reviews</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>price_per_bed_compared_to_nbh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.573000e+03</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.0</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.433550e+06</td>\n",
       "      <td>0.102155</td>\n",
       "      <td>0.008116</td>\n",
       "      <td>67.312996</td>\n",
       "      <td>0.949837</td>\n",
       "      <td>0.862107</td>\n",
       "      <td>0.589980</td>\n",
       "      <td>0.180800</td>\n",
       "      <td>0.225301</td>\n",
       "      <td>3.231458</td>\n",
       "      <td>0.963056</td>\n",
       "      <td>228.891129</td>\n",
       "      <td>2.210187</td>\n",
       "      <td>0.072768</td>\n",
       "      <td>0.084243</td>\n",
       "      <td>0.006717</td>\n",
       "      <td>0.053736</td>\n",
       "      <td>0.051777</td>\n",
       "      <td>0.030786</td>\n",
       "      <td>0.019871</td>\n",
       "      <td>0.075287</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>0.041982</td>\n",
       "      <td>0.080325</td>\n",
       "      <td>0.008676</td>\n",
       "      <td>0.095998</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.002519</td>\n",
       "      <td>0.006717</td>\n",
       "      <td>0.034705</td>\n",
       "      <td>0.040022</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>0.040022</td>\n",
       "      <td>0.048419</td>\n",
       "      <td>0.022670</td>\n",
       "      <td>0.090960</td>\n",
       "      <td>0.013714</td>\n",
       "      <td>0.012874</td>\n",
       "      <td>0.113630</td>\n",
       "      <td>0.997761</td>\n",
       "      <td>0.727400</td>\n",
       "      <td>0.859502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.165687</td>\n",
       "      <td>0.018752</td>\n",
       "      <td>0.066331</td>\n",
       "      <td>169.072769</td>\n",
       "      <td>10.908760</td>\n",
       "      <td>2.890568</td>\n",
       "      <td>1.213406</td>\n",
       "      <td>1.248531</td>\n",
       "      <td>1.566751</td>\n",
       "      <td>17.919954</td>\n",
       "      <td>91.860168</td>\n",
       "      <td>9.421767</td>\n",
       "      <td>9.270578</td>\n",
       "      <td>9.635466</td>\n",
       "      <td>9.636694</td>\n",
       "      <td>9.413442</td>\n",
       "      <td>9.154778</td>\n",
       "      <td>-0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.502261e+06</td>\n",
       "      <td>0.302896</td>\n",
       "      <td>0.089740</td>\n",
       "      <td>20.693491</td>\n",
       "      <td>0.116846</td>\n",
       "      <td>0.210115</td>\n",
       "      <td>0.491903</td>\n",
       "      <td>0.384903</td>\n",
       "      <td>0.417840</td>\n",
       "      <td>0.716080</td>\n",
       "      <td>0.188652</td>\n",
       "      <td>74.204720</td>\n",
       "      <td>0.876652</td>\n",
       "      <td>0.259791</td>\n",
       "      <td>0.277804</td>\n",
       "      <td>0.081697</td>\n",
       "      <td>0.225525</td>\n",
       "      <td>0.221610</td>\n",
       "      <td>0.172760</td>\n",
       "      <td>0.139583</td>\n",
       "      <td>0.263888</td>\n",
       "      <td>0.214091</td>\n",
       "      <td>0.200585</td>\n",
       "      <td>0.271826</td>\n",
       "      <td>0.092753</td>\n",
       "      <td>0.294630</td>\n",
       "      <td>0.037387</td>\n",
       "      <td>0.050131</td>\n",
       "      <td>0.081694</td>\n",
       "      <td>0.183059</td>\n",
       "      <td>0.196040</td>\n",
       "      <td>0.124219</td>\n",
       "      <td>0.196040</td>\n",
       "      <td>0.214681</td>\n",
       "      <td>0.148869</td>\n",
       "      <td>0.287589</td>\n",
       "      <td>0.116315</td>\n",
       "      <td>0.112746</td>\n",
       "      <td>0.317401</td>\n",
       "      <td>0.047272</td>\n",
       "      <td>0.445366</td>\n",
       "      <td>0.347555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.371857</td>\n",
       "      <td>0.135664</td>\n",
       "      <td>0.248890</td>\n",
       "      <td>111.677078</td>\n",
       "      <td>19.149181</td>\n",
       "      <td>1.829529</td>\n",
       "      <td>0.495221</td>\n",
       "      <td>0.737968</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>35.479752</td>\n",
       "      <td>7.957876</td>\n",
       "      <td>0.787944</td>\n",
       "      <td>0.964567</td>\n",
       "      <td>0.655241</td>\n",
       "      <td>0.631235</td>\n",
       "      <td>0.752137</td>\n",
       "      <td>0.853925</td>\n",
       "      <td>92.501076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.353000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.466667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-162.673569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.653267e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.266666</td>\n",
       "      <td>0.949891</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.836166</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.635065</td>\n",
       "      <td>9.636544</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>-57.386757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.575476e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>91.836166</td>\n",
       "      <td>9.421366</td>\n",
       "      <td>9.267935</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.411672</td>\n",
       "      <td>9.154366</td>\n",
       "      <td>-9.959467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.278177e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.466667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>97.000000</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>29.533754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.493346e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>132.666672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>404.000000</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>848.148621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  downtown_bos     east_bos     host_age  reponse_rate  \\\n",
       "count  3.573000e+03   3573.000000  3573.000000  3573.000000   3573.000000   \n",
       "mean   8.433550e+06      0.102155     0.008116    67.312996      0.949837   \n",
       "std    4.502261e+06      0.302896     0.089740    20.693491      0.116846   \n",
       "min    3.353000e+03      0.000000     0.000000    37.466667      0.000000   \n",
       "25%    4.653267e+06      0.000000     0.000000    52.266666      0.949891   \n",
       "50%    8.575476e+06      0.000000     0.000000    63.000000      1.000000   \n",
       "75%    1.278177e+07      0.000000     0.000000    78.466667      1.000000   \n",
       "max    1.493346e+07      1.000000     1.000000   132.666672      1.000000   \n",
       "\n",
       "       acceptance_rate  one_or_two_listings  three_to_ten_listings  \\\n",
       "count      3573.000000          3573.000000            3573.000000   \n",
       "mean          0.862107             0.589980               0.180800   \n",
       "std           0.210115             0.491903               0.384903   \n",
       "min           0.000000             0.000000               0.000000   \n",
       "25%           0.780000             0.000000               0.000000   \n",
       "50%           0.980000             1.000000               0.000000   \n",
       "75%           1.000000             1.000000               0.000000   \n",
       "max           1.000000             1.000000               1.000000   \n",
       "\n",
       "       more_than_ten_listings  response_time_ordinal     real_bed  \\\n",
       "count             3573.000000            3573.000000  3573.000000   \n",
       "mean                 0.225301               3.231458     0.963056   \n",
       "std                  0.417840               0.716080     0.188652   \n",
       "min                  0.000000               1.000000     0.000000   \n",
       "25%                  0.000000               3.000000     1.000000   \n",
       "50%                  0.000000               3.000000     1.000000   \n",
       "75%                  0.000000               4.000000     1.000000   \n",
       "max                  1.000000               4.000000     1.000000   \n",
       "\n",
       "       amenities_len  cancellation_policy_ordinal  \\\n",
       "count    3573.000000                  3573.000000   \n",
       "mean      228.891129                     2.210187   \n",
       "std        74.204720                     0.876652   \n",
       "min         2.000000                     1.000000   \n",
       "25%       180.000000                     1.000000   \n",
       "50%       229.000000                     2.000000   \n",
       "75%       281.000000                     3.000000   \n",
       "max       500.000000                     4.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Allston  neighbourhood_cleansed_Back Bay  \\\n",
       "count                     3573.000000                      3573.000000   \n",
       "mean                         0.072768                         0.084243   \n",
       "std                          0.259791                         0.277804   \n",
       "min                          0.000000                         0.000000   \n",
       "25%                          0.000000                         0.000000   \n",
       "50%                          0.000000                         0.000000   \n",
       "75%                          0.000000                         0.000000   \n",
       "max                          1.000000                         1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Bay Village  neighbourhood_cleansed_Beacon Hill  \\\n",
       "count                         3573.000000                         3573.000000   \n",
       "mean                             0.006717                            0.053736   \n",
       "std                              0.081697                            0.225525   \n",
       "min                              0.000000                            0.000000   \n",
       "25%                              0.000000                            0.000000   \n",
       "50%                              0.000000                            0.000000   \n",
       "75%                              0.000000                            0.000000   \n",
       "max                              1.000000                            1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Brighton  neighbourhood_cleansed_Charlestown  \\\n",
       "count                      3573.000000                         3573.000000   \n",
       "mean                          0.051777                            0.030786   \n",
       "std                           0.221610                            0.172760   \n",
       "min                           0.000000                            0.000000   \n",
       "25%                           0.000000                            0.000000   \n",
       "50%                           0.000000                            0.000000   \n",
       "75%                           0.000000                            0.000000   \n",
       "max                           1.000000                            1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Chinatown  neighbourhood_cleansed_Dorchester  \\\n",
       "count                       3573.000000                        3573.000000   \n",
       "mean                           0.019871                           0.075287   \n",
       "std                            0.139583                           0.263888   \n",
       "min                            0.000000                           0.000000   \n",
       "25%                            0.000000                           0.000000   \n",
       "50%                            0.000000                           0.000000   \n",
       "75%                            0.000000                           0.000000   \n",
       "max                            1.000000                           1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Downtown  neighbourhood_cleansed_East Boston  \\\n",
       "count                      3573.000000                         3573.000000   \n",
       "mean                          0.048139                            0.041982   \n",
       "std                           0.214091                            0.200585   \n",
       "min                           0.000000                            0.000000   \n",
       "25%                           0.000000                            0.000000   \n",
       "50%                           0.000000                            0.000000   \n",
       "75%                           0.000000                            0.000000   \n",
       "max                           1.000000                            1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Fenway  neighbourhood_cleansed_Hyde Park  \\\n",
       "count                    3573.000000                       3573.000000   \n",
       "mean                        0.080325                          0.008676   \n",
       "std                         0.271826                          0.092753   \n",
       "min                         0.000000                          0.000000   \n",
       "25%                         0.000000                          0.000000   \n",
       "50%                         0.000000                          0.000000   \n",
       "75%                         0.000000                          0.000000   \n",
       "max                         1.000000                          1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Jamaica Plain  \\\n",
       "count                           3573.000000   \n",
       "mean                               0.095998   \n",
       "std                                0.294630   \n",
       "min                                0.000000   \n",
       "25%                                0.000000   \n",
       "50%                                0.000000   \n",
       "75%                                0.000000   \n",
       "max                                1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Leather District  \\\n",
       "count                              3573.000000   \n",
       "mean                                  0.001399   \n",
       "std                                   0.037387   \n",
       "min                                   0.000000   \n",
       "25%                                   0.000000   \n",
       "50%                                   0.000000   \n",
       "75%                                   0.000000   \n",
       "max                                   1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Longwood Medical Area  \\\n",
       "count                                   3573.000000   \n",
       "mean                                       0.002519   \n",
       "std                                        0.050131   \n",
       "min                                        0.000000   \n",
       "25%                                        0.000000   \n",
       "50%                                        0.000000   \n",
       "75%                                        0.000000   \n",
       "max                                        1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Mattapan  neighbourhood_cleansed_Mission Hill  \\\n",
       "count                      3573.000000                          3573.000000   \n",
       "mean                          0.006717                             0.034705   \n",
       "std                           0.081694                             0.183059   \n",
       "min                           0.000000                             0.000000   \n",
       "25%                           0.000000                             0.000000   \n",
       "50%                           0.000000                             0.000000   \n",
       "75%                           0.000000                             0.000000   \n",
       "max                           1.000000                             1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_North End  neighbourhood_cleansed_Roslindale  \\\n",
       "count                       3573.000000                        3573.000000   \n",
       "mean                           0.040022                           0.015673   \n",
       "std                            0.196040                           0.124219   \n",
       "min                            0.000000                           0.000000   \n",
       "25%                            0.000000                           0.000000   \n",
       "50%                            0.000000                           0.000000   \n",
       "75%                            0.000000                           0.000000   \n",
       "max                            1.000000                           1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_Roxbury  neighbourhood_cleansed_South Boston  \\\n",
       "count                     3573.000000                          3573.000000   \n",
       "mean                         0.040022                             0.048419   \n",
       "std                          0.196040                             0.214681   \n",
       "min                          0.000000                             0.000000   \n",
       "25%                          0.000000                             0.000000   \n",
       "50%                          0.000000                             0.000000   \n",
       "75%                          0.000000                             0.000000   \n",
       "max                          1.000000                             1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_South Boston Waterfront  \\\n",
       "count                                     3573.000000   \n",
       "mean                                         0.022670   \n",
       "std                                          0.148869   \n",
       "min                                          0.000000   \n",
       "25%                                          0.000000   \n",
       "50%                                          0.000000   \n",
       "75%                                          0.000000   \n",
       "max                                          1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_South End  neighbourhood_cleansed_West End  \\\n",
       "count                       3573.000000                      3573.000000   \n",
       "mean                           0.090960                         0.013714   \n",
       "std                            0.287589                         0.116315   \n",
       "min                            0.000000                         0.000000   \n",
       "25%                            0.000000                         0.000000   \n",
       "50%                            0.000000                         0.000000   \n",
       "75%                            0.000000                         0.000000   \n",
       "max                            1.000000                         1.000000   \n",
       "\n",
       "       neighbourhood_cleansed_West Roxbury  host_is_superhost  \\\n",
       "count                          3573.000000        3573.000000   \n",
       "mean                              0.012874           0.113630   \n",
       "std                               0.112746           0.317401   \n",
       "min                               0.000000           0.000000   \n",
       "25%                               0.000000           0.000000   \n",
       "50%                               0.000000           0.000000   \n",
       "75%                               0.000000           0.000000   \n",
       "max                               1.000000           1.000000   \n",
       "\n",
       "       host_has_profile_pic  host_identity_verified  is_location_exact  \\\n",
       "count           3573.000000             3573.000000        3573.000000   \n",
       "mean               0.997761                0.727400           0.859502   \n",
       "std                0.047272                0.445366           0.347555   \n",
       "min                0.000000                0.000000           0.000000   \n",
       "25%                1.000000                0.000000           1.000000   \n",
       "50%                1.000000                1.000000           1.000000   \n",
       "75%                1.000000                1.000000           1.000000   \n",
       "max                1.000000                1.000000           1.000000   \n",
       "\n",
       "       requires_license  instant_bookable  require_guest_profile_picture  \\\n",
       "count            3573.0       3573.000000                    3573.000000   \n",
       "mean                0.0          0.165687                       0.018752   \n",
       "std                 0.0          0.371857                       0.135664   \n",
       "min                 0.0          0.000000                       0.000000   \n",
       "25%                 0.0          0.000000                       0.000000   \n",
       "50%                 0.0          0.000000                       0.000000   \n",
       "75%                 0.0          0.000000                       0.000000   \n",
       "max                 0.0          1.000000                       1.000000   \n",
       "\n",
       "       require_guest_phone_verification        price  extra_people  \\\n",
       "count                       3573.000000  3573.000000   3573.000000   \n",
       "mean                           0.066331   169.072769     10.908760   \n",
       "std                            0.248890   111.677078     19.149181   \n",
       "min                            0.000000    10.000000      0.000000   \n",
       "25%                            0.000000    85.000000      0.000000   \n",
       "50%                            0.000000   150.000000      0.000000   \n",
       "75%                            0.000000   220.000000     20.000000   \n",
       "max                            1.000000   999.000000    200.000000   \n",
       "\n",
       "       accommodates    bathrooms     bedrooms         beds  number_of_reviews  \\\n",
       "count   3573.000000  3573.000000  3573.000000  3573.000000        3573.000000   \n",
       "mean       2.890568     1.213406     1.248531     1.566751          17.919954   \n",
       "std        1.829529     0.495221     0.737968     0.997956          35.479752   \n",
       "min        1.000000     0.000000     0.000000     0.000000           0.000000   \n",
       "25%        2.000000     1.000000     1.000000     1.000000           0.000000   \n",
       "50%        2.000000     1.000000     1.000000     1.000000           4.000000   \n",
       "75%        4.000000     1.000000     1.000000     2.000000          19.000000   \n",
       "max       16.000000     6.000000     5.000000    16.000000         404.000000   \n",
       "\n",
       "       review_scores_rating  review_scores_accuracy  \\\n",
       "count           3573.000000             3573.000000   \n",
       "mean              91.860168                9.421767   \n",
       "std                7.957876                0.787944   \n",
       "min               20.000000                2.000000   \n",
       "25%               91.836166                9.000000   \n",
       "50%               91.836166                9.421366   \n",
       "75%               97.000000               10.000000   \n",
       "max              100.000000               10.000000   \n",
       "\n",
       "       review_scores_cleanliness  review_scores_checkin  \\\n",
       "count                3573.000000            3573.000000   \n",
       "mean                    9.270578               9.635466   \n",
       "std                     0.964567               0.655241   \n",
       "min                     2.000000               2.000000   \n",
       "25%                     9.000000               9.635065   \n",
       "50%                     9.267935              10.000000   \n",
       "75%                    10.000000              10.000000   \n",
       "max                    10.000000              10.000000   \n",
       "\n",
       "       review_scores_communication  review_scores_location  \\\n",
       "count                  3573.000000             3573.000000   \n",
       "mean                      9.636694                9.413442   \n",
       "std                       0.631235                0.752137   \n",
       "min                       4.000000                2.000000   \n",
       "25%                       9.636544                9.000000   \n",
       "50%                      10.000000                9.411672   \n",
       "75%                      10.000000               10.000000   \n",
       "max                      10.000000               10.000000   \n",
       "\n",
       "       review_scores_value  price_per_bed_compared_to_nbh  \n",
       "count          3573.000000                    3573.000000  \n",
       "mean              9.154778                      -0.000006  \n",
       "std               0.853925                      92.501076  \n",
       "min               2.000000                    -162.673569  \n",
       "25%               9.000000                     -57.386757  \n",
       "50%               9.154366                      -9.959467  \n",
       "75%              10.000000                      29.533754  \n",
       "max              10.000000                     848.148621  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Calculate future earnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first check that all listings were scraped on the same day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-09-06\n",
      "2016-09-06\n"
     ]
    }
   ],
   "source": [
    "print(listings['calendar_last_scraped'].min())\n",
    "print(listings['calendar_last_scraped'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another thing that we are concerning is that the availability data are probably much lower than what they actually will be, especially for the days that are in far future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Vacancy rates with months')"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calendar['year_month'] = calendar['date'].apply(lambda x: x[:7])\n",
    "calendar.groupby('year_month')['available'].mean().reset_index().plot()\n",
    "plt.title('Vacancy rates with months')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data was scraped on September 2016, and on that month about 27% of the listings are available, while in the next month 41% of rooms are available. However, for the next year until 2017 December, each month about half of the rooms are booked already. We believe that the future earnings we calculate out of the data are meaningful. Or if we have more historical, we might find out that usually when the month actually comes, the booking rate will rise by 20% (an example). In that way we can adjust our predicted results to approximate the real values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to my understanding, the `calendar` data shows the availablity of the listings for the following one year. When the room is not available, we won't have the price information. We know that the price for a listing is very seasonal and usually the price will increase a lot during busy seasons. Since we don't know the prices for the listings that were already booked, we will just use the price in the `listing` data to approximate that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar = calendar.merge(listings[['id','price']].rename({'id':'listing_id'}, axis=1), on='listing_id')\n",
    "calendar['available'] = calendar['available'].apply(convert_binary)\n",
    "calendar['price_x'] = calendar['price_x'].apply(convert_money)\n",
    "calendar['price_y'] = calendar['price_y'].apply(convert_money)\n",
    "calendar_available = calendar[calendar['available']==1]\n",
    "calendar_booked = calendar[calendar['available']==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that in `calendar` data, if a room is available on a certain date, we can see its price on that day. We also have the price information in `listing` data. Thus we would like to compare the available prices in `calendar` data and those in `listing` data to see if the theoratical prices are very different from the prices in reality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'DIfference between prices in listings and actual prices')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = plt.hist(calendar_available['price_x'] - calendar_available['price_y'], bins=100)\n",
    "plt.title(\"DIfference between prices in listings and actual prices\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Future Earnings')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAADSCAYAAADHXDKcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEhFJREFUeJzt3X+wXGV9x/H3pwmE8kMTTKAhyXgRMy0pozG9hTBaS0cakjBOcAo2mbaklDZUYUanxRqklai1YmfUmYwWjSUCokAcBEIJxjTQ2k4rcLEhhB8hF4zkkjS5EAggDiP67R/nWXruZve5e/fur4TPa+bO7n732fN89+zmk3P27A9FBGZmVtuvdLsBM7Ne5pA0M8twSJqZZTgkzcwyHJJmZhkOSTOzDIekWQMk/Y6k7d3uwzrPIWkjSNop6WeSXi79ndTA7VZJurETPZbmrNXrl9sxV0T8R0T8ejuWbb1tYrcbsJ70/oj4105OKGliRLzWxE3H3es45rY3AG9JWkMknSVpqKq2U9LZkhYCnwD+MG3NPVS+vjT+9a1NSX2SQtLFkp4G7kn1+ZL+S9ILkh6SdFaT/Z4i6R5Jz0l6VtK3JE2u6v3jkrYCP5U0MdUul7RV0gFJt0g6qtb9z41N1/+NpD2Sdkv683Rf356uWyzpUUkvSXpG0uXN3EfrDIekjVtEfA/4B+CWiDg2It45hpv/LnAqcI6kGcBdwN8DxwOXA7dKmtZEWwI+B5yUlj8LWFU1ZhlwLjC5tCX5QWAhcDLwDuBPM3PUHJv+0/gr4Gzg7ek+ll0LXBIRxwGnkf6DsN7kkLRabk9bci9Iur3Nc62KiJ9GxM+APwY2RMSGiPhlRGwCBoDFDfb6gqS/AIiIwYjYFBGvRsQw8EUODqvVEbErzV2u7Y6I/cCdwNzM3PXGfhD4RkQ8EhGvAJ+qut3PgTmS3hQRz0fEjzJzWJc5JK2W8yJicvo7r81z7SqdfytwQTn0gPcA0zO3L/c6OSK+DiDpBEk3p93ZF4EbgamZuSv+t3T+FeDYzNz1xp5Utezqef6AIvh/IunfJZ2ZmcO6zCFpjfopcHTlgqQJQHk3uNbXSY24DfBrNcaUb7cL+GZV6B0TEVc30e/n0rLfERFvothKVWbuVtoDzCxdnjVi0ogHImIJcAJwO7CuTX1YCzgkrVFPAEdJOlfSEcDfApNK1+8F+iSVn1NbgKWSjpDUD5w/yhw3Au+XdI6kCZKOSgdMZo5yu1qOA14GXkivdX6siWU0ax1wkaRTJR0NfLJyhaQjJf2RpDdHxM+BF4FfdLA3GyOHpDUkIg4AHwb+GXiGYiuxfLT7O+n0OUmV19j+DjgFeJ7idblvjzLHLmAJxZHyYYoty4+Rf57eWfU+ydtS/VPAPOAAxcGg7zZyP1shIu4GVgP3AoPAf6erXk2nfwLsTC8D/CXFVq71KPlLd83aS9KpwDZgkt+PeejxlqRZG0j6QNq1ngJ8HrjTAXlockiatcclFC8ZPEnxmuOHutuONcu722ZmGd6SNDPLcEiamWX09LcATZ06Nfr6+rrdhpkdZh588MFnI6Kh7wTo6ZDs6+tjYGCg222Y2WFG0k8aHevdbTOzDIekmVmGQ9LMLMMhaWaW4ZA0M8vo6aPbzehbeVfb59h59bltn8PMeoO3JM3MMkYNSUmzJN0r6TFJj0j6SKofL2mTpB3pdEqqS9JqSYPpl+TmlZa1PI3fIWl5++6WmVlrNLIl+Rrw1xFxKjAfuFTSHGAlsDkiZgOb02WARcDs9LcCuAaKUAWuAs4ATgeuqgSrmVmvGjUkI2JP5dfcIuIl4DFgBsU3SF+fhl0PVH4waglwQxR+CEyWNB04B9gUEfsj4nlgE8XPcZqZ9awxvSYpqQ94F3AfcGJE7IEiSCl+1AiKAC3/OtxQqtWrm5n1rIZDUtKxwK3ARyPixdzQGrXI1KvnWSFpQNLA8PBwo+2ZmbVFQyGZfh3vVuBbEVH5QaW9aTeadLov1YcY+ROaM4HdmfoIEbEmIvojon/atIa+pMPMrG0aObot4FrgsYj4Yumq9UDlCPVy4I5S/cJ0lHs+cCDtjm8EFkiakg7YLEg1M7Oe1cibyd9N8ROYD0vakmqfAK4G1km6GHgauCBdtwFYTPFTmq8AFwFExH5JnwEeSOM+HRH7W3IvzMzaZNSQjIj/pPbriQDvqzE+gEvrLGstsHYsDZqZdZM/cWNmluGQNDPLcEiamWU4JM3MMhySZmYZDkkzswyHpJlZhkPSzCzDIWlmluGQNDPLcEiamWU4JM3MMhySZmYZDkkzswyHpJlZhkPSzCzDIWlmluGQNDPLcEiamWU4JM3MMhySZmYZDkkzswyHpJlZhkPSzCzDIWlmluGQNDPLcEiamWU4JM3MMhySZmYZo4akpLWS9knaVqqtkvSMpC3pb3HpuiskDUraLumcUn1hqg1KWtn6u2Jm1nqNbEleByysUf9SRMxNfxsAJM0BlgK/mW7zT5ImSJoAfAVYBMwBlqWxZmY9beJoAyLiB5L6GlzeEuDmiHgV+LGkQeD0dN1gRDwFIOnmNPbRMXdsZtZB43lN8jJJW9Pu+JRUmwHsKo0ZSrV69YNIWiFpQNLA8PDwONozMxu/ZkPyGuAUYC6wB/hCqqvG2MjUDy5GrImI/ojonzZtWpPtmZm1xqi727VExN7KeUlfB/4lXRwCZpWGzgR2p/P16mZmPaupLUlJ00sXPwBUjnyvB5ZKmiTpZGA2cD/wADBb0smSjqQ4uLO++bbNzDpj1C1JSTcBZwFTJQ0BVwFnSZpLscu8E7gEICIekbSO4oDMa8ClEfGLtJzLgI3ABGBtRDzS8ntjZtZijRzdXlajfG1m/GeBz9aobwA2jKk7M7Mu8yduzMwyHJJmZhkOSTOzDIekmVmGQ9LMLMMhaWaW4ZA0M8twSJqZZTgkzcwyHJJmZhkOSTOzDIekmVmGQ9LMLMMhaWaW4ZA0M8twSJqZZTgkzcwyHJJmZhkOSTOzDIekmVmGQ9LMLMMhaWaW4ZA0M8twSJqZZTgkzcwyHJJmZhkOSTOzDIekmVnGqCEpaa2kfZK2lWrHS9okaUc6nZLqkrRa0qCkrZLmlW6zPI3fIWl5e+6OmVlrNbIleR2wsKq2EtgcEbOBzekywCJgdvpbAVwDRagCVwFnAKcDV1WC1cysl40akhHxA2B/VXkJcH06fz1wXql+QxR+CEyWNB04B9gUEfsj4nlgEwcHr5lZz2n2NckTI2IPQDo9IdVnALtK44ZSrV7dzKyntfrAjWrUIlM/eAHSCkkDkgaGh4db2pyZ2Vg1G5J702406XRfqg8Bs0rjZgK7M/WDRMSaiOiPiP5p06Y12Z6ZWWs0G5LrgcoR6uXAHaX6heko93zgQNod3wgskDQlHbBZkGpmZj1t4mgDJN0EnAVMlTREcZT6amCdpIuBp4EL0vANwGJgEHgFuAggIvZL+gzwQBr36YioPhhkZtZzRg3JiFhW56r31RgbwKV1lrMWWDum7szMusyfuDEzy3BImpllOCTNzDIckmZmGQ5JM7MMh6SZWYZD0swswyFpZpbhkDQzy3BImpllOCTNzDIckmZmGQ5JM7MMh6SZWYZD0swswyFpZpYx6pfu2sH6Vt7VkXl2Xn1uR+Yxs/q8JWlmluGQNDPLcEiamWU4JM3MMhySZmYZDkkzswyHpJlZhkPSzCzDIWlmluGQNDPLcEiamWU4JM3MMsYVkpJ2SnpY0hZJA6l2vKRNknak0ympLkmrJQ1K2ippXivugJlZO7ViS/L3ImJuRPSnyyuBzRExG9icLgMsAmanvxXANS2Y28ysrdqxu70EuD6dvx44r1S/IQo/BCZLmt6G+c3MWma8IRnA9yU9KGlFqp0YEXsA0ukJqT4D2FW67VCqjSBphaQBSQPDw8PjbM/MbHzG+6W7746I3ZJOADZJejwzVjVqcVAhYg2wBqC/v/+g683MOmlcW5IRsTud7gNuA04H9lZ2o9PpvjR8CJhVuvlMYPd45jcza7emQ1LSMZKOq5wHFgDbgPXA8jRsOXBHOr8euDAd5Z4PHKjslpuZ9arx7G6fCNwmqbKcb0fE9yQ9AKyTdDHwNHBBGr8BWAwMAq8AF41jbjOzjmg6JCPiKeCdNerPAe+rUQ/g0mbnMzPrBn/ixswswyFpZpbhkDQzy3BImpllOCTNzDIckmZmGQ5JM7MMh6SZWYZD0swswyFpZpbhkDQzyxjv90laG/WtvKvtc+y8+ty2z2F2KPOWpJlZhkPSzCzDIWlmluGQNDPLcEiamWU4JM3MMhySZmYZDkkzswyHpJlZhkPSzCzDIWlmluGQNDPLcEiamWU4JM3MMhySZmYZ/j7JN7hOfGcl+Hsr7dDV8S1JSQslbZc0KGllp+c3MxuLjoakpAnAV4BFwBxgmaQ5nezBzGwsOr27fTowGBFPAUi6GVgCPNrhPqzD/FMUdqjqdEjOAHaVLg8BZ3S4BztMder11U5w4PeOToekatRixABpBbAiXXxZ0vYxzjEVeLaJ3tqll/rppV7A/dSlz/dOL8nh1s9bGx3Y6ZAcAmaVLs8EdpcHRMQaYE2zE0gaiIj+Zm/far3UTy/1Au4np5d6gTd2P50+uv0AMFvSyZKOBJYC6zvcg5lZwzq6JRkRr0m6DNgITADWRsQjnezBzGwsOv5m8ojYAGxo4xRN76q3SS/100u9gPvJ6aVe4A3cjyJi9FFmZm9Q/uy2mVlORBw2f8BCYDswCKxs8bJ3Ag8DW4CBVDse2ATsSKdTUl3A6tTHVmBeaTnL0/gdwPJS/bfS8gfTbVU1/1pgH7CtVGv7/LXmqNPLKuCZtH62AItL112RlrsdOGe0xws4GbgvzXkLcGSqT0qXB9P1fak+C7gXeAx4BPhIF9fPaXV66cr6AY4C7gceSv18qpl13Ko+M/1cB/y4tH7mduK53NC//W4HWwtDbALwJPC29GA8BMxp4fJ3AlOrav9YeVIAK4HPp/OLgbvTAzwfuK/0ID2VTqek85V/uPcDZ6bb3A0sqprrvcA8RgZT2+evNUedXlYBl9dYb3PSYzEp/aN5Mj1WdR8vYB2wNJ3/KvChdP7DwFfT+aXALen89Mo/HuA44Ik0bzfWz5fr9NKV9ZP6PTbVjqAIrflNrOOW9Jnp5zrg/Brrp63P5Yb+7Xc73FoYYmcCG0uXrwCuaOHyd3JwSG4Hppf+oW5P578GLKseBywDvlaqfy3VpgOPl+ojxpXqfYwMprbPn5mjupdV1A6BEY8DxTsbzqz3eKUn9rPAxOrHtXLbdH5iGqcac94B/H4310+NXrq+foCjgR9RfMptTMtoZZ91+rmO2iHZsceq3t/h9JpkrY88zmjh8gP4vqQH06eCAE6MiD0A6fSEUXrJ1Yea6L0T89ebo5bLJG2VtFbSlCZ7eQvwQkS8VqOX12+Trj+Qxr9OUh/wLootlK6un6peurZ+JE2QtIXiJZJNFFt+Y13HLeuzup+IqKyfz6b18yVJk5pcP616Lr/ucArJUT/yOE7vjoh5FN9gdKmk9zbRy1jrzerG/NcApwBzgT3AF9rQS7ZPSccCtwIfjYgXM722ff3U6KVr6ycifhERcyk+4XY6cOpYl9HKPqv7kXQaxdbnbwC/TbEL/fEW99O0wykkR/3I43hExO50ug+4jeLJtlfSdIB0um+UXnL1mU303on5680xQkTsTU/+XwJfp1g/zfTyLDBZ0sSq+ohlpevfDOxPl4+gCKVvRcR3u7l+avXS7fWTengB+DeK1/bGuoxW9lndz8KI2BOFV4FvjGP9jPu5XO1wCsm2feRR0jGSjqucBxYA29Lyl6dhyylefyLVL1RhPnAgbd5vBBZImpJ2txZQvE6zB3hJ0nxJAi4sLSunE/PXm6N6HU0vXfxAWj+V2y+VNEnSycBsihfWaz5eUbxgdC9wfp37VenlfOCeiIjU87XAYxHxxR5YPwf10q31A0yVNDn18KvA2RRH3se0jlvVp6RpNfp5vBReAs6rWj8dfS4fpJEXLg+VP4ojYU9QvOZyZQuX+zaKo3aVty1cmepvATZTvKVgM3B8qoviy4WfpHgrQn9pWX9G8daEQeCiUr0/PTGepDhCWv0WoJsodtN+TvG/5cWdmL/WHHV6+Waaa2t6Mk4vLfvKtNztlI7a13u80vq+P/X4HWBSqh+VLg+m69+W6u+h2KXaSuktNl1aP4vq9NKV9QO8A/ifNO824JPNrONW9Znp5560frYBN/L/R8Db+lxu5N+/P3FjZpZxOO1um5m1nEPSzCzDIWlmluGQNDPLcEiamWU4JM3MMhySZmYZDkkzs4z/Az0SzIEySWkMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "earnings = calendar_booked.groupby('listing_id')['price_y'].sum().reset_index().rename({'price_y':'future_earnings'}, axis=1)\n",
    "a = plt.hist(earnings['future_earnings'])\n",
    "plt.title(\"Future Earnings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many records have future earnings as 0?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(earnings['future_earnings'] == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's not a lot. The earnings data are very right skewed. We will do a log transformation on it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "earnings['log_future_earnings'] = np.log(earnings['future_earnings']+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def y_to_earnings(y):\n",
    "    '''\n",
    "    The function to convert the log earnings\n",
    "    '''\n",
    "    return np.exp(y)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Log Future Earnings')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = plt.hist(earnings['log_future_earnings'])\n",
    "plt.title(\"Log Future Earnings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Align the features table   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.84% of the listings don't have future earnings\n"
     ]
    }
   ],
   "source": [
    "features = final_df.copy()\n",
    "id_earnings = features[['id']].merge(earnings.rename({'listing_id':'id'}, axis=1).drop('future_earnings', axis=1), on=\"id\", how=\"left\").fillna(0)\n",
    "print(\"{:.2%} of the listings don't have future earnings\".format(sum(id_earnings['log_future_earnings']==0)/len(id_earnings)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Split the dataset to testing and training sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 2858 samples.\n",
      "Testing set has 715 samples.\n"
     ]
    }
   ],
   "source": [
    "X_all = features.drop('id', axis=1)\n",
    "y_all = id_earnings['log_future_earnings']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_all,\n",
    "                                                                  y_all,\n",
    "                                                                  test_size = 0.2,\n",
    "                                                                  random_state = 0)\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Build the gradient boosting regressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regressor(X, y, params, random_state=1):\n",
    "    '''\n",
    "    This function was borrowed from. https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py\n",
    "    It takes the X, y and parameters of the model and plots how deviance change as we have more iterations and the important variables\n",
    "    '''\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=random_state)\n",
    "    print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "    print(\"Testing set has {} samples.\".format(X_test.shape[0]))\n",
    "    \n",
    "    # Fit regression model\n",
    "    clf = GradientBoostingRegressor(**params)\n",
    "    clf.fit(X_train, y_train)\n",
    "    mse = mean_squared_error(y_test, clf.predict(X_test))\n",
    "    print(\"MSE: %.4f\" % mse)\n",
    "    \n",
    "    # Plot training deviance\n",
    "    test_score = np.zeros((params['n_estimators'],), dtype=np.float64)\n",
    "    for i, y_pred in enumerate(clf.staged_predict(X_test)):\n",
    "        test_score[i] = clf.loss_(y_test, y_pred)\n",
    "\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.title('Deviance')\n",
    "    plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',\n",
    "             label='Training Set Deviance')\n",
    "    plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',\n",
    "             label='Test Set Deviance')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.xlabel('Boosting Iterations')\n",
    "    plt.ylabel('Deviance')\n",
    "    \n",
    "    # Plot feature importance\n",
    "    feature_importance = clf.feature_importances_\n",
    "    \n",
    "    # make importances relative to max importance\n",
    "    feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "    sorted_idx = np.argsort(feature_importance)\n",
    "    pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "    im = pd.DataFrame({'feature': features.columns[1:], 'relative importance': feature_importance}).sort_values('relative importance')[-20:]\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.barh(im['feature'], im['relative importance'])\n",
    "    plt.xlabel('Relative Importance')\n",
    "    plt.title('Variable Importance')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.subplots_adjust(wspace=30)\n",
    "    return clf, im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the first trial\n",
    "params = {\n",
    "    'n_estimators': 200,\n",
    "    'max_depth': 6,\n",
    "    'min_samples_split': 2,\n",
    "    'learning_rate': 0.01,\n",
    "    'loss': 'ls',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 2000 samples.\n",
      "Testing set has 858 samples.\n",
      "MSE: 4.9733\n"
     ]
    },
    {
     "data": {
      "image/png": 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v06lTJwDWrFlDo0aNSn1PZmZmZla+ZCV5JOkKYDVQ7G6kEXE3cDdAXl5ebKPQzMyshDZ3hlBZqVmzZsHxBx98wD/+8Q+mTp1KnTp1OO2001ixYsUG5+ywww4Fx1WrVi1IthSWk5OzQZt1SZuSaNWqFa1ateKUU05hv/32Y9iwYQWzeTJj2BwzZsxgv/32IyJo0KDBBkvnAE488USuvPJKrrnmGubMmcNhhx22Xv2yZcs4//zzmT59Orvvvjt//OMf13texd2/pPX6iQhatWpVbBLOzMzMzLZP2/xta5L6kWykfWqUZuRtZmZWQt999x21atVil1124csvv+SFF17Y6tc45JBDGDNmDABz5szhnXfeKTKO1157reD7zJkz2WOPPYBkv6KhQ4euVwdQq1Ytli5dWqIYJk6cyPDhwznrrLOoW7cuDRs2ZOzYsQCsXbuWWbNmAbDLLrvQpk0bBg0axAknnLDBZuLLly+nSpUqNGjQgKVLl/L4449v8tqdO3fmjjvuYM2aNQAsXLiQ5s2b8/nnnzN16lQAVq1axdtvv12iezEzMzOz8mubJo8kHQNcBpwQEcu25bXNzKzyaNu2Lc2bN6dly5acffbZ/OIXv9jq17jgggv4/PPPadWqFf/3f/9Hy5YtqV279nptIoLrr7+eZs2akZuby1/+8heGDx8OwNChQ5k8eTKtWrWiefPmBZted+vWjTFjxtCmTZsiN8weOXIkubm57Lvvvtxwww2MGzeOZs2aATBq1CjuvPPOgmV0zzzzTMF5vXv35qGHHqJ3794b9Fm/fn369etHy5Yt6d69OwceeOAm7/+cc85ht912o1WrVrRu3ZoxY8aQk5PDY489xsUXX0zr1q1p06YNb775ZskfqpmZmZmVSyqryT+SHgEOBxoAXwNXkbxdLQdYkDabEhEDNtVXXl5e5Ofnl0mcZmZWcu+++y777bdftsMoF1avXs3q1aupUaMGH3zwAZ07d+aDDz7YYCNq21BR/x1JmhYReVkKyYrhMZiZmVnFVtIxWFm+be3kIorvLavrmZmZbUvff/89Rx55JKtXryYiuOuuu5w4MjMzM7MKyaNcMzOzzVCnTh2mTZuW7TDMzMzMzMrcNt8w28zMzMzMzMzMth9OHpmZmZmZmZmZWbEqffKojPYLNzMzMzMzMzOrECp18qhfP+jTJ9tRmJmZmZmZmZmVX5V6w+zq1eGpp2D1avALcszMyr8FCxZw5JFHAvDVV19RtWpVdt11VwCmTp3KDjvsUKJ+hg8fzrHHHstuu+22Qd3kyZO5+OKLWblyJStXruSUU07hT3/6U7F9TZ8+nW+++YZjjjlmg7oJEybQo0cP9tprL3744QcaNmzIZZddxrHHHluiOAsbOnQoderU4dRTT92s881Ka87nS2hy+bPZDsPMzKzSmjfkuGyHAFTy5FHnznDvvZCfDx06ZDsaMzPblPr16zNz5kwArr76anbeeWcuueSSUvczfPhw2rZtW2TyqF+/fowbN46WLVuyZs0a3nvvvY32NX36dObOnVtk8gjgiCOOYNy4cQVtu3fvzgMPPMBhhx1W6rjPO++8Up9jZmZmZralKvWytSOPBAnGj892JGZmtqXuv/9+2rdvT25uLr/97W9Zu3Ytq1evpk+fPuy///60bNmSW265hdGjRzNz5kx69+5Nbm4uq1atWq+fb7/9tiCpVLVqVZo3bw7A999/z+mnn0779u1p06YNTz/9NMuXL2fw4MGMHDmS3NxcHnvssY3G2LZtW6644gpuu+02AL7++mtOOukk8vLyaN++PVOmTGHNmjXssccefPfddwBEBHvttRfz58/nj3/8IzfffDMAd955JwcccACtW7emV69eLF++HIDTTjuNgQMHcvDBB7PXXnsxduzYgutfd9117L///rRu3ZorrrgCgA8++ICjjz6adu3a0bFjR95///0t/acwMzMzswqmUs88ql8f2rVLkkdXXpntaMzMtjODBkE6C2iryc2FNDlSGnPnzmXs2LG88cYbVKtWjf79+zNq1CiaNm3K/PnzmTNnDgCLFy+mTp063Hrrrdx2223k5uZu0NegQYPYZ599OOKII+jSpQt9+/YlJyeHwYMHc8wxxzBixAgWLVrEgQceyOzZs7nyyiuZO3duQVJnU9q2bcutt94KwIUXXsill15Khw4dmDdvHscffzxz587l+OOP58knn6RPnz688cYb7LvvvjRo0GC9fnr16sWAAQMAuPzyyxkxYgTnnnsuAN988w2TJ09mzpw5/OpXv6J79+48/fTTPPfcc0ydOpUdd9yRhQsXAtC/f3+GDRtG06ZNmTx5Mueffz7j/auKmZmZmWWo1MkjSJau3XADLFkCtWtnOxozM9scEyZM4K233iIvLw+A5cuX8/Of/5yjjz6a9957j4EDB3LsscfSuXPnTfZ1zTXX0KdPH8aPH88DDzzA6NGjmTBhAuPHj+e5555jyJAhAKxYsYJPPvmk1LFGxms+J0yYsN6yuEWLFrF8+XJ69+7NX//6V/r06cOoUaPo3bv3Bv2sS1wtXryYpUuXcvzxxxfUnXjiiUiiVatWfP755wXXOvPMM9lxxx0BqFevHosXL2bKlCn06NGj4NzVq1eX+p7MzMzMrGJz8qgzXHcdTJwIJ56Y7WjMzLYjmzFDqKxEBGeeeSZ//vOfN6ibPXs2zz33HLfccguPP/44d9999yb723vvvdl77705++yzqV+/PkuWLCEiGDduHE2bNl2v7WuvvVaqWGfMmMF+++1XEHdRG30feuihnH766SxYsICnnnqqyPvq27cvzz33HC1btmTYsGFMmTKloC4nJ6fgeF2yKiKQtF4fEUGDBg0K9pEyMzMzMytKpd7zCOCgg6BmTXjxxWxHYmZmm6tTp06MGTOG+fPnA8lb2T755BO+/fZbIoJevXpxzTXXMH36dABq1arF0qVLi+zr2WefLUi4vP/+++Tk5FCrVi2OPvpobrnlloJ2M2bM2GRfhc2cOZPrrruuYOPrTp06MXTo0PXqASTRrVs3Bg0aROvWralTp84Gff3www/stttu/Pjjjzz88MObvHbnzp259957C/ZGWrhwIXXr1qVhw4YF+yKtXbuWWbNmlehezMzMzKzyqPTJox12gCOO8KbZZmbbs/3335+rrrqKTp060apVKzp37szXX3/Np59+SseOHcnNzeXss8/muuuuA+CMM87gN7/5TZEbZo8YMYJmzZqRm5vL6aefzsMPP0yVKlW46qqrWLZsGfvvvz8tWrTg6quvBuCXv/wls2bNok2bNkVumD1x4kTatGlDs2bNuPDCC7n99tsL3rQ2dOhQJk+eTKtWrWjevDn33HNPwXm9e/fmoYceKnLJGsDgwYNp3749Rx11VMGm3htz/PHHc8wxx5CXl0dubi433XQTAKNGjeLOO++kdevWtGjRgmeeeWbTD9zMzMzMKhVl7r1QXuXl5UV+fn6Z9X/rrXDhhfDhh7DXXmV2GTOz7d67775bsOTKbHMV9d+RpGkRkbepcyW9EREHl+Z6kuYBeRExv1SBFt/f4cCqiHgj/T4AWBYRD2yN/suapNOB8RHxxaba5jTcJxr2Kz9LVM3MzCqbeUOOK9P+SzoGq/QzjyDZ9wi8dM3MzKy8K23iqIwcDhTEERF3bi+Jo9TpwM+yHYSZmZltP5w8AvbdFxo39tI1MzOz8k7S9+lnQ0mvSZopaa6kQ0t4/sVp+7mSBmWU95U0W9IsSQ+mZV0lvSlphqQJkn4qqQkwALgovfahkq6WdEl6Tq6kKWlfYyXVTctfkXSDpKmS3t9YvJKqSrpR0ltpP+ek5d3TOJTe//uSdpPURNIkSdPTv4Mz+rpU0pz0voZI6gnkASPT+Hcs4vr9JeVLyl+zbElJHquZmZlVcJX+bWsAUjL76NFHYfVqqOanYmZWrKLe2mVWUltxufwpwAsRca2kqsBOmzpBUjvgDOBAQMCbkl4FVgFXAL+IiPmS6qWnvA50iIiQ9Bvg0oj4naQ7ge8j4m9pv0dmXOYB4IKIeFXSYOAqYF2SqlpEtJd0bFreqZhQzwKWRMQBknKAyZLGR8RYST2A84BjgKsi4itJOwFHRcQKSfsAjwB5kroAJwIHRsQySfUiYqGk84FLIqLIPQEi4m7gbkiWrW3quZqZmVnF5zRJqnNnGDYM3noreQObmZltqEaNGixYsID69es7gWSlFhEsWLCAGjVqbI3u3gKGS6oOjIuImSU45xBgbET8ACDpCeBQIIDH1u2JFBEL0/aNgNGSGgI7AB9trHNJtYE6EfFqWnQ/8GhGkyfSz2lAk4101Rlolc4SAqgN7JNe/wJgLjAlIh5J66sDt0nKBdYA+6blnYD7ImJZofsyMzMzKxUnj1JHHpnMQHrxRSePzMyK06hRIz777DO+/fbbbIdi26kaNWrQqFGjLe4nIl6T1BE4DnhQ0o0l2HeouIynSBJIhd0K/D0inko3yb56c+NNrUw/17DxMZhIZi+9UETd7sBa4KeSqkTEWuAi4GugNcmWBCsy+vHMITMzM9tiTh6l6tWDAw5I9j268spsR2NmVj5Vr16dPffcM9thmCFpD+DziLhHUk2gLcmSsY15DRghaQhJYqU70Idk2dpYSTdFxIJ1y7tIZvx8np7bL6OfpcAuhTuPiCWSFkk6NCImpX2/WrhdCbwAnCvp5Yj4UdK+aRwrgftIluz1BS4G/pbG+VlErJXUD6ia9jMeuFLSw5nL1tL4a21GXGZmZlZJOXmUoXNnuP56WLIEatfOdjRmZma2EYcD/yvpR+B7kmTKRkXEdEkjgKlp0bCImAEg6VrgVUlrgBkkbyS7GnhU0ufAFGBd5vRp4DFJ3UiWkWXqB9yZ7kP0X5I9lkprGMmytulK1od+S7J30e+ASRExSdJM4C1JzwK3A49L6gVMBH5I7/f5dClbvqRVwD+BPwAj0hiXAwdFxPLNiNHMzMwqEW3FjSvLTF5eXuTnF7mn41Y1aRJ07AhPPAHdu5f55czMzCwlaVpE5GU7DltfTsN9omG/m7MdhpmZWaU1b8hxZdp/ScdgVco0iu1Mhw5QqxY8/3y2IzEzMzMzMzMzKx+8bC1D9erQqVOSPIpINtA2MzOz7YOkN4GcQsV9ImJONuLZFElHAzcUKv4oIjz/2czMzMoVJ48K6dIFxo6Fd9+F5s2zHY2ZmZmVVEQcmO0YSiN9m1pRb1QzMzMzK1ecPCrkmGOSz+eec/LIzMzMKrf9d69NfhnvtWBmZmbln/c8KuTnP4cWLZLkkZmZmZmZmZlZZefkURG6dEnevPb999mOxMzMzMzMzMwsu5w8KkKXLrBqFUycmO1IzMzMzMzMzMyyy8mjIhxyCNSs6aVrZmZmZmZmZmbeMLsIO+wARx6ZJI8iQMp2RGZmZmbb3pzPl9Dk8mezHYYVMs+bmJuZ2TbmmUfF6NIF5s2D99/PdiRmZmZmZmZmZtnj5FExunRJPr10zczMzMzMzMwqMyePirHHHrDffk4emZmZmZmZmVnl5uTRRhxzDLz6Kixblu1IzMzMzMzMzMyyo3Inj+bMSbJDxejSBVauhFde2XYhmZmZmZmZmZmVJ5U7eTRgAFx4YbHVHTvCTjt56ZqZmZmZmZmZVV6VO3n061/D7NnwzjtFVufkwC9/6eSRmZmZmZmZmVVelTt51KsXVKkCo0cX26RLF/jwQ3jvvW0Yl5mZmWWVpMMlHVxGfZ8u6bb0eICkviWNY1PtzczMzMpC5U4e7bYbHH44jBoFEUU2Of745PPpp7ddWGZmZrZtSKpWTNXhQJkkjzJFxJ0R8cBGmqwXRwnam5mZmW11lTt5BMnStfffh1mziqxu3Bhat4anntrGcZmZmVUSf//73wFaSJoraZCkJpLelXSPpLcljZe0I4CkppKelzRN0iRJ/1Ncv5L2kPSSpNnpZ+O0fISkv0uaCNxQxHlNgAHARZJmSjpM0n+VqCNpraSOadtJkvaWVE/SuPRaUyS1Ksm9S7pa0iXp8YWS3kn7GFVEHIcWav+KpBskTZX0vqRD0/KdJI1J+xkt6U1JeZKqpvc+V9IcSRcVE1N/SfmS8tcsW1KS2zAzM7MKzsmjk06CatVtcUNQAAAgAElEQVSS2UfF6NoVJk+GBQu2YVxmZmaVwLRp07jvvvsA3gU6AGcDdYF9gKER0QJYDPRIT7kbuCAi2gGXALdvpPvbgAciohUwErglo25foFNE/K7wSRExD7gTuCkiciPiVeB9oDlwCDANOFRSDtAoIv4DXAPMSK/1B2BzZgddDrRJ+xhQRByTijinWkS0BwYBV6VlvwUWpf38GWiXlucCu0dEy4jYH7ivqCAi4u6IyIuIvKo71d6M2zAzM7OKxsmj+vWhc+eNLl074QRYu9YbZ5uZmW1tr7/+Ot27dwdYGxHfA08AhwIfRcTMtNk0oImknUmWcD0qaSZwF9BwI90fBDycHj9IkvhZ59GIWFOKUCcBHdO/69O+DgDeSusPSa9BRLwM1JdU2szLbGCkpNOA1SU854n0cxrQJCOWUWksc9N+Af4L7CXpVknHAN+VMj4zMzOrpJw8gmTp2scfw5tvFlndrl2yPZL3PTIzM9u6opgfboCVGcdrgGok45bF6SycdX/7leZyGcc/lC5SJpEktdoD/wTqkOxH9Fpar01crySOA4aSzBSatpH9mDKte07rnlFxsRARi4DWwCvAecCwUsZnZmZmlZSTRwDdukFOTrFL16pUSTbOfu45WLVqG8dmZmZWgXXs2JFx48YBVJFUE+hOkqjZQER8B3wkqRdAugdR6410/wbw6/T4VOD1UoS2FKiV8f1NkllPayNiBTATOCcj1tfSayDpcGB+Gm+JSKoC/DwiJgKXkiSndi4ijpJ4HfhV2m9zYP/0uAFQJSIeB/4EtC1lv2ZmZlZJOXkEsMsucOyxMGYMrCl6BvsJJ8DSpfDaa0VWm5mZ2WZo27Ytp59+OsB+JAmaYcCijZxyKnCWpFnA20C3jbS9EDhD0mygDzCwFKE9DXRft1F1RKwEPgWmpPWTSJI6c9LvVwN56bWGAP1KcS2AqsBDkuYAM0j2OVpcOI4S9nU7sGsay2Uky9aWALsDr6RL/kYAvy9ljGZmZlZJaSPTxcuNvLy8yM/PL9uLjBkDvXvDxIlw+OEbVC9blmyP1L8//OMfZRuKmZlZZSNpWkTkZTuOikBSVaB6RKyQ1BR4Cdg3Iko9fzqn4T7RsN/NWz1G2zLzhhyX7RDMzKyCKOkYzDOP1jnuOKhZE0aPLrJ6p52gU6dk36PtIN9mZmZmlddOwOvp7KyxwLmbkzgyMzMzW8fJo3Vq1kzWpj32GPz4Y5FNunaFjz6Ct9/exrGZmZlZsSRdkS7ryvy7ooTnnlHEuUO3Ulxl1vfGRMTSiMiLiNYR0Soi/L5YMzMz2yIleYtH5fHrX8Mjj8BLL8Exx2xQffzxyefTT0PLlts4NjMzMytSRFwLXLuZ594H3Ld1Iyr7vs3MzMy2JSePMh19NNStCw89VGTy6Gc/g7y8JHn0e28xaWZmZhXc/rvXJt/765iZmVV6XraWKScn2TT7iSeSV6sVoWtXmDIFvvlmG8dmZmZmZmZmZpYFTh4V1rcvLF+eJJCK0LVrsmH2s89u47jMzMzMzMzMzLLAyaPCOnSAvfeGBx4osjo3Fxo1SpaumZmZmZmZmZlVdE4eFSZBnz4wcSJ88kmR1V27wvjxsGJFFuIzMzMzMzMzM9uGvGF2UU47Da66CkaOLHJn7K5d4Y47kvxSly5ZiM/MzMxsG5jz+RKaXF4x1+rP80bgZmZmJeaZR0XZay849FB48MFkg6NCjjgCatb00jUzMzMzMzMzq/icPCpO377w7rswbdoGVTVqQOfO8MwzReaWzMzMzMzMzMwqDCePitOzJ+TkFLtxdteu8OmnMGvWNo7LzMzMzMzMzGwbcvKoOHXqQLdu8MgjsGrVBtXHHZdsnv3UU1mIzczMzMzMzMxsG3HyaGP69oX58+H55zeo+slP4MADve+RmZmZmZmZmVVsTh5tTOfOSZbowQeLrD7hBMjPhy++2MZxmZmZmZmZmZltI04ebUz16nDyycnatEWLNqju2jX5fLZivsHWzMzMtiOSBkvqlO04zMzMrOJx8mhT+vZN9jwaM2aDqhYtoEkT73tkZmZm2SWpakRcGRETsh2LmZmZVTxOHm1KmzZJlqiIt65JyeyjCRNg2bIsxGZmZmYVnqQmkv4t6X5JsyU9JmknSfMkXSnpdaCXpBGSeqbnHCDpDUmzJE2VVEtSVUk3Snor7eecLN+amZmZbSecPNoUKZl99MYb8J//bFB9wgmwYgW89FIWYjMzM7PKohlwd0S0Ar4DfpuWr4iIQyJi1LqGknYARgMDI6I10AlYDpwFLImIA4ADgLMl7Vn4QpL6S8qXlL9m2ZKyvSszMzPbLjh5VBKnnJIkkR56aIOqjh1hl1381jUzMzMrU59GxOT0+CHgkPR4dBFtmwFfRsRbABHxXUSsBjoDfSXNBN4E6gP7FD45Iu6OiLyIyKu6U+2tfR9mZma2HXLyqCQaNYIjj0yWrkWsV7XDDnD00UnyaO3aLMVnZmZmFV0U8/2HItqqiPbryi+IiNz0b8+IGL81gzQzM7OKycmjkurbFz76CCZP3qDqhBPgq68gPz8LcZmZmVll0FjSQenxycDrG2n7b+Bnkg4ASPc7qga8AJwrqXpavq+kmmUZtJmZmVUMTh6VVPfusNNORW6cfeyxUK0ajB2bhbjMzMysMngX6CdpNlAPuKO4hhGxCugN3CppFvAiUAMYBrwDTJc0F7gLqFbWgZuZmdn2z8mjktp5Z+jRA8aMSXbIzlCvHvzyl/DYYxusajMzMzPbGtZGxICIaBURPSJiWUQ0iYj56xpExOkR8Vh6/FZEdIiI1unn9xGxNiL+EBH7R0TLiDgiIrwjtpmZmW2Sk0el0bcvLFlS5O7YPXsmL2ObPTsLcZmZmZmZmZmZlREnj0rjiCNg992LXLp24olQpUoy+8jMzMxsa4mIeRHRMttxmJmZWeXl5FFpVK0Kp54Kzz0H33yzXtWuu8Lhh8Ojj3rpmpmZmZmZmZlVHE4elVafPrBmDTz88AZVPXvCe+/BO+9kIS4zMzMzMzMzszLgN2yUVsuW0K4dDB8OAweCVFDVvTucdx48/ji0aJHFGM3MzMy2gv13r03+kOOyHYaZmZllmWcebY6zz4Y5c2DKlPWKd9sNDjnE+x6ZmZmZmZmZWcXh5NHmOOUU2HlnuOuuDap69kzySu+9l4W4zMzMzMzMzMy2MiePNketWkkCafRoWLRovaqTTko+H388C3GZmZmZmZmZmW1lTh5trnPOgRUr4MEH1ytu1AgOOshL18zMzMzMzMysYvCG2ZurbVvIy0uWrl1wwXobZ/fsCb/7HXz4ITRtmsUYzczMzLbAnM+X0OTyZ7MdxhaZ5w2/zczMtphnHm2JAQPgnXdg8uT1ir10zczMzMzMzMwqCiePtsSvfw277LLBxtlNmiSTkpw8MjMzMzMzM7PtnZNHW6JmTTjtNHj0UViwYL2qnj1h6lT4+OMsxWZmZmZmZmZmthU4ebSlzjkHVq6EBx5Yr7hHj+TziSeyEJOZmZmZmZmZ2Vbi5NGWatUKOnRIlq5FFBTvvTfk5vqta2ZmZmZmZma2fXPyaGs45xx47z147bX1inv2hDfegM8/z1JcZmZmtlVJOlFS82zHURKSciUdm+04zMzMbPvn5NHW8KtfQe3aG2yc7aVrZmZmFc6JQLlJHkmqtpHqXMDJIzMzM9tiTh5tDTvtBH37Jq9Xmz+/oPh//gdatPDSNTMzs7IiaZykaZLeltQ/LTtG0nRJsyS9lJbtLOk+SXMkzZbUIy3vLOlfaftHJe2cls+TdIOkqenf3pIOBk4AbpQ0U1JTSWdLeiu91uOSdkrPHyHpFklvSPqvpJ4ZMV+axjFL0pC0rKmk59N7mSTpfzZyzyMk/V3SROAGSe3T68xIP5tJ2gEYDPROY+0tqaak4Wm8MyR1K5N/FDMzM6twnDzaWs45B1atghEj1ivu2RMmTYKvvspOWGZmZhXcmRHRDsgDLpT0U+AeoEdEtAZ6pe3+BCyJiP0johXwsqQGwB+BThHRFsgHLs7o+7uIaA/cBtwcEW8ATwH/GxG5EfEh8EREHJBe613grIzzGwKHAMcD65JEXUhmLx2YnvPXtO3dwAXpvVwC3L6J+943jft3wL+BjhHRBrgSuC4iVqXHo9NYRwNXAC9HxAHAESRJsJqbfMJmZmZW6Tl5tLW0aAG/+AXcfTesXVtQ3KtXso+2Zx+ZmZmViQslzQKmAD8H+gOvRcRHABGxMG3XCRi67qSIWAR0IFmCNlnSTKAfsEdG349kfB5UzPVbpjOF5gCnAi0y6sZFxNqIeAf4aUYc90XEsnXxpbOdDgYeTeO4iyTxtDGPRsSa9Lh2eu5c4KZCMWTqDFyeXuMVoAbQuHAjSf0l5UvKX7NsySbCMDMzs8rAyaOt6Zxz4IMPYOLEgqIWLaBlS3jkkY2cZ2ZmZqUm6XCSZMxB6SyeGcAsIIpqXkS5gBfTmTm5EdE8IjJnDkUxx5lGAOdHxP7ANSQJmXVWFrpWcXFUARZnxJEbEfsVc711fsg4/jMwMSJaAl0LxZBJJDOy1l2jcUS8W7hRRNwdEXkRkVd1p9qbCMPMzMwqAyePtqaePaFu3Q02zj755OStax9/nKW4zMzMKqbawKKIWJbuEdQByAEOk7QngKR6advxwPnrTpRUl2S20i8k7Z2W7SRp34z+e2d8/is9XgrUymhTC/hSUnWSmUebMh44M2NvpHoR8R3wkaReaZkktS7RE0jUBta92/X0jPLCsb4AXCBJ6XXalOIaZmZmVok5ebQ17bgj9OsHY8fC118XFJ98cvI5alSW4jIzM6uYngeqSZpNMvtmCvAtydK1J9LlbKPTtn8B6kqam5YfERHfkiRbHkn7mAJkblSdI+lNYCBwUVo2CvjfdMPppiR7Kb0JvEiy99BGRcTzJPsm5afLxy5Jq04FzkpjexsozWbWfwWulzQZqJpRPhFovm7DbJJnVB2YnS5x+3MprmFmZmaVmCKKm4VdfuTl5UV+fn62wyiZd9+F5s3h+uvh8ssLig86CJYvh5kzsxibmZlZOSVpWkTkZTuOdSTNA/IiYv6m2lZkOQ33iYb9bs52GFtk3pDjsh2CmZlZuVXSMZhnHm1t++0HHTvCPfest3H2ySfDrFlJbsnMzMzMzMzMbHvh5FFZOOcc+O9/YcKEgqJf/QqqVPHG2WZmZtuDiGiS7VlHkq5Il5xl/l2RzZjMzMysciqz5JGk4ZK+SdfUryurJ+lFSR+kn3XL6vpZ1aMH1K+/3sbZu+0GRxyRJI+2g5WCZmZmlmURcW2hN7DlRsS12Y7LzMzMKp+ynHk0AjimUNnlwEsRsQ/wUvq94snJgTPOgCefhM8+Kyg++WT4z39g2rQsxmZmZmZmZmZmVgplljyKiNeAhYWKuwH3p8f3AyeW1fWz7rzzkilGt99eUHTSSVC9upeumZmZmZmZmdn2o0zftiapCfBMRLRMvy+OiDoZ9Ysiosila5L6k7xql8aNG7f7+OOPyyzOMtO9O0yaBJ9+CjvuCEC3bpCfD598AlWrbuJ8MzOzSqK8vW3NEtvVG2/NzMys1Lb7t61FxN0RkRcRebvuumu2w9k8AwfCggXw8MMFRaecAl98keSUzMzMzMzMzMzKu22dPPpaUkOA9PObbXz9beuww6BVK/jHPwp2ye7aFWrW9NI1MzMzMzMzM9s+bOvk0VNAv/S4H/DkNr7+tiXBhRfCnDnw6qsA7LRTsnTtscdg1aosx2dmZmZmZmZmtgllljyS9AjwL6CZpM8knQUMAY6S9AFwVPq9YjvlFKhfP5l9lDr5ZFi4EF58MYtxmZmZmZmZmZmVQLWy6jgiTi6m6siyuma5tOOO0L8/3HADfPQR7LknnTtD3brJ0rXjjst2gGZmZmZFm/P5Eppc/my2w1jPvCEePJmZmW1r5XbD7Arlt79NlrANHQrADjtAz54wbhwsW5bl2MzMzMzMzMzMNsLJo22hUSPo0QOGDYPvvweSpWs//ADPPJPl2MzMzMzMzMzMNsLJo21l4EBYsgQefBCAjh2hYUO/dc3MzMzMzMzMyjcnj7aVgw6CvDy4+WZYu5aqVaF3b/jnP2Hx4mwHZ2ZmZmZmZmZWNCePthUJLr4Y3n8/yRiRvIht1Sp4/PEsx2ZmZmZmZmZmVgwnj7alnj2T/Y9uuglIJiI1a1awks3MzMzMzMzMrNxx8mhbql4dLrgAXn4ZZs5Egj594NVX4eOPsx2cmZmZmZmZmdmGnDza1s4+G2rWLJh9dOqpSfHIkVmMyczMzLYKSU0kzd3CPg6XdPDWisnMzMxsSzl5tK3VrQtnnpm8Zu3LL2nSBA49NFm6FpHt4MzMzKwcOBxw8sjMzMzKDSePsmHgQFi9GoYOBZKla//+N0yfnuW4zMzMbGuoKukeSW9LGi9pR0m5kqZImi1prKS6AJIulPROWj5KUhNgAHCRpJmSDi3qApK6SnpT0gxJEyT9NC3fVdKLkqZLukvSx5IapHWnSZqa9nuXpKrF9N1fUr6k/DXLlpTF8zEzM7PtjJNH2dC0KXTrBnfcAcuW0asX7LCDN842MzOrIPYBhkZEC2Ax0AN4ALgsIloBc4Cr0raXA23S8gERMQ+4E7gpInIjYlIx13gd6BARbYBRwKVp+VXAyxHRFhgLNAaQtB/QG/hFROQCa4BTi+o4Iu6OiLyIyKu6U+3NfghmZmZWcTh5lC0XXwwLF8IDD1CnDnTtmqxkW70624GZmZnZFvooImamx9OApkCdiHg1Lbsf6JgezwZGSjoNKM0ooBHwgqQ5wP8CLdLyQ0iSSUTE88CitPxIoB3wlqSZ6fe9SntjZmZmVjk5eZQthxwCeXnJxtlr19KnD3zzDYwfn+3AzMzMbAutzDheA9TZSNvjgKEkiZ1pkqqV8Bq3ArdFxP7AOUCNtFzFtBdwfzqbKTcimkXE1SW8lpmZmVVyTh5li5TMPnr/fXj6abp0gXr1vHTNzMysAloCLMrYv6gP8KqkKsDPI2IiybKzOsDOwFKg1ib6rA18nh73yyh/HfgVgKTOQN20/CWgp6SfpHX1JO2xRXdlZmZmlYaTR9nUqxfsuSdcdx07VA9694Zx4+C777IdmJmZmW1l/YAbJc0GcoHBQFXgoXTp2QySfY4WA08D3Te2YTZwNfCopEnA/Izya4DOkqYDXYAvgaUR8Q7wR2B8GsOLQMOtfZNmZmZWMZV0arSVhWrV4LLLYMAAeOkl+vTpxB13wBNPwOmnZzs4MzMzK610w+uWGd//llHdoYhTDimij/eBVpu4zpPAk0VULQGOjojVkg4CjoiIlek5o4HRm7oHMzMzs8I88yjbTj8dGjaE666jQwfYe2944IFsB2VmZmbbqcYkm2LPAm4Bzs5yPGZmZlYBOHmUbTk5cMklMHEimvIv+vaFiRNh3rxsB2ZmZmbZJumKdPla5t8VxbWPiA8iok1EtI6IAyLirW0Zr5mZmVVMTh6VB/37Q/36cN119OuX7KV9333ZDsrMzMyyLSKuzXhD2rq/a7Mdl5mZmVUu3vOoPNh5Zxg4EK68ksZ/mcVRR7XmvvvgyiuhatVsB2dmZmaV1f671yZ/yHHZDsPMzMyyzDOPyovzz4dateD66znzTPj0U3j55WwHZWZmZmZmZmaVnZNH5UXduvDb38KYMXRv8T716sHw4dkOyszMzMzMzMwqOyePypOLLoKcHHa46QZOPRXGjoWFC7MdlJmZmZmZmZlVZk4elSc//Sn85jfwwAOc0+UTVq6Ehx/OdlBmZmZmZmZmVpkpIrIdwybl5eVFfn5+tsPYNj75BJo2hXPPpe3rtwAwfXqWYzIzMytjkqZFRF6247D15TTcJxr2u7lMrzHPG3KbmZllTUnHYJ55VN40bgx9+sA993Ber2+YMQNmzMh2UGZmZmZmZmZWWTl5VB5ddhmsXMkpX99ETg7cd1+2AzIzMzMzMzOzysrJo/KoWTPo1Ysdhw/l1OMW89BDsGJFtoMyMzMzMzMzs8rIyaPy6ve/h6VL+X2t21i0CJ58MtsBmZmZmZmZmVll5ORReZWbC8cdR9NnbqZZox8YPjzbAZmZmZmZmZlZZeTkUXn2hz+gBQu4ufndvPhi8iI2MzMzMzMzM7Ntycmj8uzgg+Gww+g0629Uj5Xcf3+2AzIzMzMzMzOzysbJo/Luiiuo9vUXDGk2gvvug7Vrsx2QmZmZlZSkYZKap8d/KFT3xla8ziuS8rZWf2ZmZmaZnDwq7zp1ggMPpP/86/j8o5W8+mq2AzIzM7OSiojfRMQ76dc/FKo7OAshmZmZmZWak0flnQSDB1NzwSecV2M4996b7YDMzMy2b5LGSZom6W1J/dOy7yXdkJZPkNQ+nc3zX0knpG2qSrpR0luSZks6Jy0/PG37mKR/SxopSWndK5LyJA0BdpQ0U9LIddfMiOl/M/q9Ji2rKelZSbMkzZXUu4T311nSvyRNl/SopJ3T8nmSrknL50j6n2LO7y8pX1L+mmVLNvs5m5mZWcXh5NH24Kij4Be/4E9Vr+WZx1awaFG2AzIzM9uunRkR7YA84EJJ9YGawCtp+VLgL8BRQHdgcHreWcCSiDgAOAA4W9KeaV0bYBD8P3t3Hqf1vP9//PGsKJHsTrJk79CGKW0I4Zxjp05IllCIlJNEi7IcRbaEZMu+73zRsYQkNSWV/TjCwc9e2tDy+v3x+UxnjFmumWbmamae99vtus11fa738rzm3G7H1Wve7/eHXYDtgA75J4yIQcDSiGgVEd3zvyfpQGBHoA3QCthD0t7AX4CvIqJlRDQDni/pg0naBBgCdI6I3YFc4Nx8Tb5Pr98EDChsjIgYHxE5EZFTu37DkqY0MzOzGsDFo6ogXX204eIv6fHrLdxzT7YDmZmZVWl9Jb0DTAW2Iinc/Mb/ijNzgFcjYln6vEl6/UDgBEmzgLeAjdO+ANMi4r8RsRKYla9PJg5MH28DM4Gm6bhzgM7piqi9IiKTZUBtSQpYb6Q5TwS2yff+Y+nPGaXMaGZmZjWYi0dVxb77wj77cFGdf3LXzUuJyHYgMzOzqkdSJ6Az0C4iWpIUbOoByyJW/dd1JfArQFoMqpPXHTg7XT3UKiK2jYiJ6Xu/5ptmRb4+GcUCLs837g4RcVtEfATsQVJEulzSsAzH+le+sXaJiFPyvZ+Xs7QZzczMrAZz8aiqkGDECDZZ/v/o8O7NTJuW7UBmZmZVUkPgp4hYkp7507YUfV8AzpC0FoCknSStW4r+y/L6FjJuz3xnEzWWtJmkLYAlEXEPMBrYPYM5pgIdJO2QjlVf0k6lyGhmZmb2By4eVSX77MPyvffjQi7nzhsXZzuNmZlZVfQ8UEfSbOASkmJLpm4F3gNmSpoL3EzpVu+MB2bnHZidJ129dB/wpqQ5wCNAA6A5MC3dfjaY5BymYkXEd8BJwP3pZ5xKsg3OzMzMrMwUVWD/U05OTuTm5mY7xpph8mTYay8uXPtKLvh+AA0aZDuQmZnZ6pM0IyJysp3Dfq9uox2j0YnXVugc80YeXKHjm5mZWdEy/Q7mlUdVTceOzN/zQPr/NoqH71hUcnszMzMzMzMzs9Xg4lEV1PDai9mU7/ll1HXZjmJmZmaVSNLjkmYVeByU7VxmZmZWvfkuG1WQ2u7Jf5odynFzr2T2q2fSYp8Nsx3JzMzMKkFEHJntDGZmZlbzuHhURW18wyU03KcVU88dTYsZl2U7jpmZmVVDzRs3JNdnEpmZmdV43rZWRTXcuyVvbtONjjOvY/Gn32Y7jpmZmZmZmZlVUy4eVWG1LxnBOixlXu/Lsx3FzMzMzMzMzKopF4+qsNbH78yTDU9khxdvIr74b7bjmJmZmZmZmVk15OJRFSbB4n8MQ7GSb8++JNtxzMzMzMzMzKwa8oHZVdyR/Zsw4ZJe9HzqZvhkIGy/fbYjmZmZWTUx58sFNBn0bIWNP8+HcZuZmVUJXnlUxa23HnxxwmB+i7VYOvCibMcxMzMzMzMzs2rGxaNqoMfARoyhL+s8di+8/Xa245iZmZmZmZlZNeLiUTWw004wbb8L+LHWxqwccB5EZDuSmZmZmZmZmVUTLh5VEz37N2T4ymHUevkleP75bMcxMzMzMzMzs2rCxaNq4q9/hYnbns5/620PAwfCihXZjmRmZmZmZmZm1YCLR9VE7dpwWp+16f/L5TB3LkyYkO1IZmZmZmZmZlYNuHhUjZx8MjxbrwufbNYWhg2DxYuzHcnMzMzMzMzMqjgXj6qRjTaC43uI0+aPhq++gmuuyXYkMzNbDcOGDePFF1/MdoxiderUidzc3Kz1X5NJmiCpSzHv95NUvzLmlHSSpLHlOZeZmZnVHC4eVTN9+sArv3Xg42ZHwqhR8M032Y5kZmZlsGLFCi6++GI6d+6c7ShAksdAUp1yHK4fUK7FIzMzM7OK4OJRNdOyJey1F5w+fySxdCmMGJHtSGZmVsC8efNo2rQpJ554Ii1atKBLly4sWbKEJk2acPHFF9OxY0cefvhhTjrpJB555BEApk+fTvv27WnZsiVt2rRh4cKFrFixgvPOO4/WrVvTokULbr755iLnnDRpEnvvvTdHHnkku+yyC6effjorV64EYOLEibRr147dd9+drl27smjRIoA/5CnKPffcQ/v27WnWrBnTpk0DYPHixfTs2ZPWrVuz22678eSTTwKwdOlSjjnmGFq0aEG3bt1YunRpsb8rSX+RNFPSO5JeSq9tJOkJSbMlTZXUIr0+XNKdkiZKmifpKElXSJoj6XlJa6Xt5kkaJWla+tghvX6opLckvS3pRUmb5xt3vKSJwF2Saku6UtL0NEPvtJ0kjZX0nqRngc2K+Vx9gS2AVyS9kl47Ns06V9KoEn4viyRdlv5epuZlTXWW9LqkjyQdku/6Funv4WNJVxQzdi9JuZJyVyxZUFwMMzMzqyFcPCH86t4AACAASURBVKqGzj4bXv7vTnx2UG8YPx4++CDbkczMrIAPP/yQXr16MXv2bNZff31uvPFGAOrVq8fkyZM55phjVrX97bff6NatG9dddx3vvPMOL774Iuussw633XYbDRs2ZPr06UyfPp1bbrmFTz/9tMg5p02bxlVXXcWcOXP45JNPeOyxx/j++++59NJLefHFF5k5cyY5OTlcffXVq/oUlqegxYsXM2XKFG688UZ69uwJwGWXXcZ+++3H9OnTeeWVVzjvvPNYvHgxN910E/Xr12f27NkMHjyYGTNmFDmupE2BW4CjI6Il0DV9awTwdkS0AC4E7srXbXvgYOBw4B7glYhoDixNr+f5OSLaAGOBa9Nrk4G2EbEb8AAwMF/7PYDDI+I44BRgQUS0BloDp0naFjgS2BloDpwGtC/qs0XEGOArYN+I2FfSFsAoYD+gFdBa0hFF/nJgXWBq+nt5LZ0vTxNgn/TzjpNUL73eCuiW5usmaasiso2PiJyIyKldv2ExEczMzKymKM+l17aGOOII2GILOH/JRTxY/2644AJ4/PFsxzIzs3y22morOnToAMDxxx/PmDFjAOjWrdsf2n744Yc0atSI1q1bA7D++usDyYqh2bNnr1qdtGDBAj7++GO23XbbQuds06YN2223HQDHHnsskydPpl69erz33nursvz222+0a9duVZ/C8hR07LHHArD33nvz888/M3/+fCZOnMhTTz3F6NGjAfjll1/4/PPPee211+jbty8ALVq0oEWLFsUN3RZ4LSI+BYiIH9PrHYGj02svS9pYUl6V47mIWCZpDlAbeD69PoekqJLn/nw/8w4J3BJ4UFIjYG0gfyXuqYjIWyZ1INAi39lCDYEdgb2B+yNiBfCVpJeL+3AFtAYmRcR3AJLuTcd7ooj2vwHPpM9nAAfke++hiFgJfCzpP0DT9PpLEbEgHf89YBvgi1JkNDMzsxrKxaNqaK214PTTYdiwzRjb73w2vXYIvP56sp/NzMzWCJIKfb3uuuv+oW1E/KF93vXrr7+egw46qMxzRgQHHHAA999/f6F9CsuT6biPPvooO++8c4ntixsaiCKuF5TX7leAiFgpaVlE5F1fye+/90Qhz68Hro6IpyR1Aobna5P/FqYCzo6IF34XSvpbEXkzkfEvJZX/s62g6M+W//Wv+a4V7GNmZmZWJG9bq6Z69UqKSKN+7Q+NG0O/fuDDTs3M1hiff/45b775JgD3338/HTt2LLJt06ZN+eqrr5g+fToACxcuZPny5Rx00EHcdNNNLFu2DICPPvqIxYsXFznOtGnT+PTTT1m5ciUPPvggHTt2pG3btrzxxhv8+9//BmDJkiV89NFHpfosDz74IACTJ0+mYcOGNGzYkIMOOojrr7+evPrG22+/DSSrk+69914A5s6dy+zZs4sb+k1gn3RLGJI2Sq+/BnRPr3UCvo+In0sVOtm+lffzzfR5Q+DL9PmJxfR9ATgj3xlKO0laN811THomUiNg3xIyLAQapM/fIvmsm0iqDRwLvFqaD5RPV0m1JG0PbAd8WMZxzMzMzAAXj6qtzTeHv/8dbrm3PksvGQ0zZ8K4cdmOZWZmqT//+c/ceeedtGjRgh9//JEzzjijyLZrr702Dz74IGeffTYtW7bkgAMO4JdffuHUU09ll112Yffdd6dZs2b07t2b5cuXFzlOu3btGDRoEM2aNWPbbbflyCOPZNNNN2XChAkce+yxtGjRgrZt2/JBKc/K23DDDWnfvj2nn346t912GwBDhw5l2bJltGjRgmbNmjF06FAAzjjjDBYtWkSLFi244ooraNOmTZHjplu4egGPSXoHeDB9aziQI2k2MJLiCz1FqSvpLeAcoH++cR+W9DrwfTF9bwXeA2ZKmgvcTLKK53HgY5ItcjdRcvFnPPCcpFci4mvgAuAV4B1gZkQ8WYbPBUmx6FXgOeD0iPiljOOYmZmZAaD/rXhec+Xk5ERubm62Y1Q5U6dCu3Zww9jgzCcOhOnTk8Oz//SnbEczM6vR5s2bxyGHHMLcuXMrbc5JkyYxevRonnnmmZIbZ4GkGRGRU0lzzQNyIqK4ApEBdRvtGI1OvLbkhmU0b+TBJTcyMzOzCpPpdzCvPKrG9twTcnJg7A0ixt4AS5fCgAHZjmVmZmZmZmZmVYgPSqzGJDjrLDjpJHjly53Yb+BAuPRSOOMMSO+qY2Zmla9JkyYVtupozpw59OjR43fX6taty1tvvUWnTp3KPG6fPn144403fnftnHPO4eSTTy7zmAU0lTSrwLUeETGnvCbIExFNynvM4kh6HCh4C7zzCx64XUTft4C6BS5XyO/FzMzMrCjetlbN/fILbLUVtG8PT963GHbeGRo1grfeglpeeGZmZmuGyty2ZpnztjUzM7PqLdPvYF55VM3Vqwennw6XXQYff7UuO44cCT16wN13w4llOV/UzMzMaormjRuS6wKPmZlZjeelJzVAnz6w1lpwzTXAccdBmzZwwQWwaFG2o5mZmZmZmZnZGs7FoxrgT3+C44+HCRPgh59qwXXXwddfw/Dh2Y5mZmZmZmZmZms4F49qiHPPTW62dtNNQNu20KtXshRpxoxsRzMzMzMzMzOzNZiLRzXErrvCX/4CY8cmh2gzahRsvjmceiosW5bteGZmZmZmZma2hvKB2TXIgAHQuTPcdx/07LlBUkk6+uhkBdLAgdmOZ2ZmZmuYOV8uoMmgZ8t1TN9hzczMrOrxyqMaZL/9oGVLuPpqiACOOgoOOwwuvhi++irb8czMzMzMzMxsDeTiUQ0iwT/+Ae++Cy+8kF68+upk29r552c1m5mZmZmZmZmtmVw8qmG6dYMttoCrrkovbL99sp/tnntgypSsZjMzMzMzMzOzNY+LRzXM2mtD377w4ovwzjvpxQsuSCpKZ58Ny5dnNZ+ZmZmZmZmZrVlcPKqBeveG9daDK69ML6y3XnJo9syZ+ZYkmZmZmZmZmZm5eFQjbbAB9OoFDzwAn32WXuzaNTlAe9gweO+9rOYzMzOz1SfpCEm7ZDuHmZmZVX0uHtVQ/fsnB2hffXV6QYIbb4QGDeDkk719zczMahwlqtR3I0m1i3n7CMDFIzMzM1ttVeoLkpWfLbeE7t3h1lvhhx/Si5tvDmPHwrRpcN11Wc1nZmZWGSQ1kfS+pBuBmUAPSW9KminpYUnrpe3mSRolaVr62CG9vo2klyTNTn9unV6fIGmMpCmS/iOpS3q9kaTXJM2SNFfSXun1Awubt4jM8yQNkzQZ6CrpNEnTJb0j6VFJ9SW1Bw4Drkzn2j59PC9phqTXJTWt0F+umZmZVRsuHtVg550HS5bADTfku9itGxx6aLJ97dNPs5bNzMysEu0M3AUcAJwCdI6I3YFc4Nx87X6OiDbAWODa9NpY4K6IaAHcC4zJ174R0BE4BBiZXjsOeCEiWgEtgVmSNgGGFDNvYX6JiI4R8QDwWES0joiWwPvAKRExBXgKOC8iWkXEJ8B44OyI2AMYANxYit+RmZmZ1WAuHtVgu+4KhxwC11+fFJGAZPvaDTdArVpwxhkQkdWMZmZmleCziJgKtCXZ5vWGpFnAicA2+drdn+9nu/R5O+C+9PndJMWiPE9ExMqIeA/YPL02HThZ0nCgeUQszGDewjyY73mzdCXRHKA7sGvBxulKpvbAw+kcN5MUt/5AUi9JuZJyVyxZUEIMMzMzqwlcPKrhzj8fvv8e7rgj38WttoJ//hNeeAHuv7/IvmZmZtXE4vSngH+lK3VaRcQuEXFKvnZRxHOKuP5rvucCiIjXgL2BL4G7JZ2QwbzFZQaYAJwVEc2BEUC9QtrXAubnm6NVRPy50A8QMT4iciIip3b9hiXEMDMzs5rAxaMarkMHaNcORo8ucEb2mWfCnntCv375DkUyMzOr1qYCHfKdZ1Rf0k753u+W7+eb6fMpwDHp8+7A5OImkLQN8G1E3ALcBuyewbwlaQB8LWmtNEOehel7RMTPwKeSuqZzSFLLUsxhZmZmNZiLRzWclKw+mjcPHnkk3xu1a8P48fDTTzBgQLbimZmZVZqI+A44Cbhf0mySok7+Q6XrSnoLOAfon17rS7INbTbQI32vOJ1Izjl6GzgauC6DeUsyFHgL+BfwQb7rDwDnSXpb0vYkhaVTJL0DvAscXoo5zMzMrAZTVIEzbXJyciI3NzfbMaqtlSuT84/q1YOZM5OC0ioXXgiXXw4vvgj775+1jGZmVr1JmhEROdnOURRJ84CciPg+21kqU91GO0ajE68tuWEpzBt5cLmOZ2ZmZmWX6XcwrzwyatVK7rw2a1ZSI/qdoUNhhx2gd+98p2qbmZmZmZmZWU3h4pEB0L07bLEFjBpV4I111oFbb4VPPklWIZmZmdVAEdGkslcdSXpc0qwCj4MqM4OZmZkZuHhkqbp1k7OxX3oJZswo8OY++8BZZ8GYMfD661nJZ2ZmVtNExJEF7o7WKiJeyHYuMzMzq3lcPLJVevWC9deHK64o5M3LL4cmTaBnT29fMzMzMzMzM6tBXDyyVRo2hDPOSO669u9/F3hzvfXg9tuTNwYPzko+MzMzMzMzM6t8vtua/c7XX8O228LxxydHHf3BWWfBjTfCq6/CXntVej4zM6ue1vS7rdVU/g5mZmZWvflua1YmjRol29fuvBPmzSukwciR3r5mZmZmZmZmVoO4eGR/MHAg1KqV1In+wNvXzMzMzMzMzGoUF4/sD7bcEk45JakRffFFIQ06dYI+feC662Dy5MqOZ2ZmZmZmZmaVKCvFI0n9Jb0raa6k+yXVy0YOK9qgQcnPUaOKaJC3fe3kk719zczMzMzMzKwaq1PZE0pqDPQFdomIpZIeAo4BJlR2Fiva1lvDSSfBLbfABRdA48YFGqy3Htx2G+y3X7J97ZprshHTzMzMKtCcLxfQZNCzZe4/b+TB5ZjGzMzMsiVb29bqAOtIqgPUB77KUg4rxgUXwIoVcOWVRTTYd99k+9q118LEiZWazczMzMzMzMwqR6UXjyLiS2A08DnwNbAgIv5QeZDUS1KupNzvvvuusmMasO22cMIJcPPN8PXXRTS68krYddek4TffVGo+MzMzMzMzM6t4lV48krQhcDiwLbAFsK6k4wu2i4jxEZETETmbbrppZce01IUXwm+/wejRRTRYZx144AFYsABOPBFWrqzUfGZmZmZmZmZWsbKxba0z8GlEfBcRy4DHgPZZyGEZ2GEH6N4dbroJvv22iEbNmiVnHr3wAtx4Y6XmMzMzMzMzM7OKlY3i0edAW0n1JQnYH3g/CzksQ4MHw6+/wlVXFdOod2/461/h/PPh448rLZuZmZmZmZmZVaxsnHn0FvAIMBOYk2YYX9k5LHM77wzHHAM33ADff19EIym5Ndvaaye3aVuxojIjmpmZmZmZmVkFycrd1iLioohoGhHNIqJHRPyajRyWucGDYcmSZHdakRo3huuvhylTijkkyczMrGqStIGkM7OdI1OSOkl6Jts5zMzMrOrLSvHIqp5ddoGuXZPa0I8/FtOwe3fo0gWGDIGpUystn5mZWSXYACi0eCSpdiVnMTMzM6s0Lh5ZxoYMgYUL4brrimmUt32tcWM49liYP7/S8pmZmZWFpOMlTZM0S9LNkraR9LGkTSTVkvS6pAOBkcD2absr05U9r0i6j2QrPpKekDRD0ruSepUw7yJJV0maKeklSZum17eX9Hw6zuuSmqbXt0nbzU5/bp1enyBpXNr2I0mHFDLXupJulzRd0tuSDi/nX6OZmZlVYy4eWcaaN4ejjkqKR8XWhDbYAB54AL74Anr1gohKy2hmZlYakv4MdAM6REQrYAWwDzAKGAf8A3gvIiYCg4BPIqJVRJyXDtEGGBwRu6Sve0bEHkAO0FfSxsVMvy4wMyJ2B14FLkqvjwfOTscZAOTdynQscFdEtADuBcbkG6tJmvtgYJykegXmGgy8HBGtgX2BKyWtW8TvpJekXEm5K5YsKCa+mZmZ1RQuHlmpDB0KCxYk29eK1bYtXHYZPPxwshLJzMxszbQ/sAcwXdKs9PV2EXEr0AA4naSAU5RpEfFpvtd9Jb0DTAW2AnYspu9K4MH0+T1AR0nrAe2Bh9M8NwON0jbtgPvS53cDHfON9VBErIyIj4H/AE0LzHUgMCgdcxJQD9i6sFARMT4iciIip3b9hsXENzMzs5qiTrYDWNXSqhUcdlhycPY558D66xfT+Lzz4OWXk4bt2iVLl8zMzNYsAu6MiAt+d1GqD2yZvlwPWFhE/8X5+nQCOgPtImKJpEkkRZpMBckf9uanq6AyaV/Y88JeCzg6Ij4sRR4zMzMzwCuPrAyGDoWffspg9VGtWnDXXdCwIXTrBosXl9DBzMys0r0EdJG0GYCkjSRtQ7Jt7V5gGJC3hHYhyWqkojQEfkoLR02BtiXMXQvokj4/DpgcET8Dn0rqmuaRpJZpmynAMenz7sDkfGN1Tc9n2h7YDihYJHoBOFuS0nF3KyGbmZmZ2SouHlmp5eTAIYfA6NFJEalYm28O99wDH3wA/fpVSj4zM7NMRcR7wBBgoqTZwL9Izg9qDYyKiHuB3ySdHBE/AG9ImivpykKGex6ok45zCcnWteIsBnaVNAPYD7g4vd4dOCXd/vYukHe4dV/g5HT8HsA5+cb6kOTcpOeA0yPilwJzXQKsBcyWNDd9bWZmZpYRRRU4zDgnJydyc3OzHcPyeeedZAvbhRcmRxuV6MIL4fLL4f774ZhjSm5vZmY1iqQZEZGT7RyVSdKiiFivHMaZADwTEY+sfqrfq9tox2h04rVl7j9v5MHlmMbMzMzKW6bfwbzyyMqkZctkJ9p118G332bQYcSI5NyjXr3gk08qPJ+ZmZmZmZmZlQ8Xj6zMRoyApUuTBUUlWmutZNVR7dpw9NGwZEmF5zMzM1sTSHpL0qwCj+blseoIICJOqohVR2ZmZmZ5XDyyMtt5ZzjpJLjpJvjiiww6bLMN3HcfzJ4NvXtDFdgyaWZmtroiYs+IaFXgMSfbuczMzMwy5eKRrZZhw2DlSrgk02M3//pXGD48OUT7hhsqMpqZmZmZmZmZlYM62Q5gVds22ySLiG66CQYOhB12yKDTkCEwfTr075+cut2xY4XnNDMzs9Jr3rghuT702szMrMbzyiNbbYMHw9prJwuKMlKrFtx9NzRpAl27wtdfV2A6MzMzMzMzM1sdLh7ZavvTn6Bv3+Q4ozmZnuCwwQbw+OPw88/QpQv89luFZjQzMzMzMzOzsnHxyMrFwIGw/vpw4YWl6NSsGdx+O0yZAuecU2HZzMzMzMzMzKzsXDyycrHRRjBoEDzzDLz2Wik6duuWVJ7GjYPx4yssn5mZmZmZmZmVjaIK3C49JycncnNzsx3DSrBkCey4I2y9dbKYSMqw44oVcPDB8PLLMGkStG9fkTHNzGwNJGlGRORkO4f9Xt1GO0ajE68tdb95PmTbzMysSsj0O1jGK48krSNp59WLZdVZ/fowYgRMnQpPPlmKjrVrw/33J1Wno4+GL7+ssIxmZmZmZmZmVjoZFY8kHQrMAp5PX7eS9FRFBrOq6aSToGlTuOACWL68FB033DCpOC1aBEcdBb/8UlERzczMzMzMzKwUMl15NBxoA8wHiIhZQJOKiWRVWZ068M9/wgcfwJ13lrLzrrvCXXfBtGnQqxdUgS2VZmZmZmZmZtVdpsWj5RGxoEKTWLVxxBHQti1cdFFyDlKpHHkkXHwx3H03DB9eEfHMzMzMzMzMrBQyLR7NlXQcUFvSjpKuB6ZUYC6rwiQYOTI5uuj668swwJAh0LNnUkS67bZyz2dmZmZmZmZmmcu0eHQ2sCvwK3AfsADoV1GhrOrbZ5/kBmqXXw7ff1/KzhKMGwcHHQS9e8Pzz1dIRjMzMzMzMzMrWUbFo4hYEhGDI6J1+hgSET7R2Ip1xRXJ+dcjRpSh81prwcMPQ/Pm0LUrvP12ueczM7OKNWHCBM466ywAhg8fzujRo4ttL+kISbvke32xpM4Vm7LYPE0kzU2f50gak60saYaTJI1Nn58u6YQyjrPqc5mZmZllItO7rf1L0gb5Xm8o6YWKi2XVwS67JOde33RTcoB2qTVoAM8+CxttlCxj+vzzcs9oZmZrlCOAVcWjiBgWES9mMc8qEZEbEX0raz4livyeFhHjIuKuyspjZmZmNVum29Y2iYj5eS8i4idgs4qJZNXJ8OFQvz4MHFjGAbbYAv7v/5KTt//6V/jpp/KMZ2ZWo9x11120aNGCli1b0qNHD55++mn23HNPdtttNzp37sw333wDJKuEevbsSadOndhuu+0YM2ZMkWMAfPfddxx99NG0bt2a1q1b88YbbxSb45ZbbqF169a0bNmSo48+miXJ3RXWBQ4DrpQ0S9L2kiZI6gIgaX9Jb0uaI+l2SXXT6/MkjZA0M32vaVHzShou6W5JL0v6WNJp6XVJulLS3HSMboX07STpmfT5epLuSNvOlnS0pFMkXZOv/WmSri4my7npfHMl9UuvNZH0vqQbgZnAVpJOlvSRpFeBDgU+y4D0+SRJoyRNS9vulW+819PfzUxJ7Yv9H8bMzMysCHUybLdS0tYR8TmApG0A30fdSrTZZjB4MAwaBC+9BPvvX4ZBdt0VHn88OQPpyCPhhRegbt1yz2pmVp29++67XHbZZbzxxhtssskm/Pjjj0hi6tSpSOLWW2/liiuu4KqrrgLggw8+4JVXXmHhwoXsvPPOnHHGGXz00Ud/GAPgnHPOoX///nTs2JHPP/+cgw46iPfff7/ILEcddRSnnXYaAEOGDOG25OYIi4GngGci4hEASaQ/6wETgP0j4iNJdwFnANemQ34fEbtLOhMYAJxazK+iBdCWpFj1tqRngXZAK6AlsAkwXdJrxYwxFFgQEc3TfBsCvwGzJQ2MiGXAyUDvwjpL2iN9f09AwFtpcegnYGfg5Ig4U1IjYASwB8l5k68ARe3jrhMRbST9DbgI6Ax8CxwQEb9I2hG4H8gp5nPl5esF9AKovf6mJTU3MzOzGiDT4tFgYHL6xQZgb9IvFWYlOeecZOvaP/4BM2ZA7dplGGTffWHCBOjeHU44Ae67r4wDmZnVTC+//DJdunRhk002AWCjjTZizpw5dOvWja+//prffvuNbbfddlX7gw8+mLp161K3bl0222wzvvnmm0LHAHjxxRd57733VvX9+eefWbhwYZFZ5s6dy5AhQ5g/fz6LFi3ioIMOKin+zsCnEfFR+vpOoA//Kx49lv6cARxVwlhPRsRSYKmkV4A2QEfg/ohYAXyTft9pDcwuYozOwDF5L9IV2Uh6GThE0vvAWhExp4j+HYHHI2Jx2u8xYC+S4tlnETE1bbcnMCkivkvbPQjsVMSY+X8HTdLnawFjJbUCVhTT93ciYjwwHqBuox39x0IzMzPLrHgUEc9L2p3kL3UC+kdEae+hZTVUvXowahQccwzceSf07FnGgY47Dr7+GgYMgHXXhVtvhVqZ7rw0M6vZImLVSp48Z599Nueeey6HHXYYkyZNYvjw4aveq5tvhWft2rVZvnx5oWMArFy5kjfffJN11lknoywnnXQSTzzxBC1btmTChAlMmjSppC5/nPT3fk1/rqDk7zYFiyGRwfiF5SmsqHIrcCHwAXBHCf2LsrjA60yLN4X9DvoD35CsqKoF+GYnZmZmVial+Zd3XeBHkmXTu0jau2IiWXX0979D27YwZEhyB7Yy+8c/4KKL4I47oF8/CP9B1MwsE/vvvz8PPfQQP/zwAwA//vgjCxYsoHHjxgDceeedZRoD4MADD2Ts2LGr2s2aNavYcRYuXEijRo1YtmwZ99577+/eAhoU0uUDoImkHdLXPYBXC2mXicMl1ZO0MdAJmA68BnSTVFvSpiQrrKcVM8ZE4Ky8F+m2NSLiLWAr4DiSLWJFeQ04QlJ9SesCRwKvF9LuLaCTpI0lrQV0zfAz5mkIfB0RK0l+Z16ya2ZmZmWS0cojSaOAbsC7wMr0cpB8+TErkQTXXAPt2sHIkXDppasx2EUXJRWoq66C9daDf/6z3HKamVVXu+66K4MHD2afffahdu3a7LbbbgwfPpyuXbvSuHFj2rZty6efflrqMSZMmMCYMWPo06cPLVq0YPny5ey9996MGzeuyHEuueQS9txzT7bZZhuaN2+ef4vbA8AtkvoCXfIupmf2nAw8LKkOScGn6AmKNw14FtgauCQivpL0OMm5R++QfL8ZGBH/T1KTIsa4FLghvd39CpJzifK2jT0EtMrbylaYiJgpaQL/K1DdGhFvF5wvIr6WNBx4E/ia5BDt0hSAbgQeldSV5LykgquazMzMzDKiyGDlhqQPgRYR8WuJjStATk5O5ObmZmNqK2fHHw+PPALvvQfbbbcaA0XAmWfCuHFw2WVw4YXlltHMzCqfpBkRUeJhzqs5x3BgUUSMrsA5ngGuiYiXKmqOylS30Y7R6MRrS25YwLyRB1dAGjMzMytvmX4Hy3Tb2n9IDl00Wy1XXAF16kD//qs5kAQ33AA9eiS3c7vuunLJZ2ZmVhaSNpD0EbC0uhSOzMzMzPJkere1JcAsSS/xvwMZiYi+FZLKqq0ttoChQ2HQIHj+efjLX1ZjsFq14PbbYfHi5Pyj9daDU04pt6xmZlY1pVvczilw+Y2I6FNRc0bEfArczSw9V6mwQtL+EfFDRWUxMzMzK2+ZFo+eSh9mq61fP7jtNjjnHJgzB9ZeezUGq1MH7rsPjjgCTjsN6teHY48tt6xmZlb1RMQdFH+3s8rK8QPQKts5zMzMzFZXRsWjiCj5FixmGapbN9ll9re/wbXXwsCB5TDgo48mA/bokRSQDj+8XLKamZmZmZmZ1XSZHpi9I3A5sAtQL+96RKzOkccZ84HZ1dNhh8Err8CHHybb2VbbwoVwwAHw9tvwzDPJczMzqxIq48BsKz1/BzMzM6veyvvA7DuAm4DlwL7Ab1tk0QAAIABJREFUXcDdZY9nBtdcA8uWlcPKozwNGsBzz8Gf/5ysPHr99XIa2MzMzMzMzKzmyrR4tE565xBFxGcRMRzYr+JiWU2w/fYwYADcey9MnlxOg264IUycCFtvDQcfDP5rqZmZmZmZmdlqybR49IukWsDHks6SdCSwWQXmshriggtgq63g7LNhxYpyGnSzzeCll2CTTeCgg2DWrHIa2MzMzMzMzKzmyfRua/2A+kBf4BKSVUcnVlQoqznWXRdGj4Zu3eCWW+D008tp4MaN4cUXoVMn2G8/eOEFaN26nAY3MzOrGeZ8uYAmg54tVZ95Iw+uoDRmZmaWLRmtPIqI6RGxKCL+GxEnR8RRETG1osNZzdC1K+y7LwweDD/8UI4Db7cdvPYabLABdO4MU6aU4+BmZmZmZmZmNUOxxSNJ16Y/n5b0VMFH5US06k6CMWNgwQIYOrScB2/SJCkgbb45HHggvPpqOU9gZmZmZmZmVr2VtG0t745qoys6iNVszZpBnz4wdiz06gWtWpXj4FtumRSNOneGv/4VnnwSDjigHCcwMzMzMzMzq76KXXkUETPSpxsBUyPi1fyPio9nNcmIEbDxxsnh2RHlPHijRvDKK7DjjnDoofBs6c5vMDMzMzMzM6upMr3b2mHAR5LulnSwpEwP2jbL2AYbwOWXw+TJcN99FTDBZpvByy8ny5yOPBIef7wCJjEzMzMzMzOrXjI9MPtkYAfgYeA44BNJt1ZkMKuZTj45uSnagAEwf34FTLDxxsld2PbYIzmp+557KmASMzMzMzMzs+oj05VHRMQy4DngAWAGcHhFhbKaq1YtGDcOvv0WLrywgibZYAOYOBH23ht69EgOWjIzMzMzMzOzQmVUPJL0F0kTgH8DXYBbgUYVmMtqsN13h759kyLS1KkVNEmDBvB//weHH54csnTxxRVw0JKZmWWTpEmScipxvislvSvpygoa/zBJgypibDMzM7PiZHp20UkkK456R8SvFRfHLHHxxfDII9C7N+TmwlprVcAk9eolk5x6Klx0Efz4I1x9dbL8yczMajRJdSJieSm79QY2zeS7UlnGj4ingKdKmcnMzMxstWV65tExwNvAXgCS1pHUoCKDWc3WoAGMGQOzZ8N111XgRHXqwO23Q79+yUQ9e8Ly0v5bwczMVse8efMAdpV0S7pyZ2L6XWPVyiFJm0ialz4/SdITkp6W9KmksySdK+ltSVMlbZRv+OMlTZE0V1KbtP+6km6XND3tc3i+cR+W9DQwsbCsSlyZjjdHUrf0+lPAusBbedcK6TtB0tWSXgFGFZPjLUm75us3SdIeab6x6bVNJT2a9p0uqUN6fY6kDdKcP0g6Ib1+t6TOknaVNE3SLEmzJe1YSM5eknIl5a5YsiDD/xXNzMysOst029ppwCPAzemlLYEnKiqUGcARR8ChhyaLgj77rAInqlUrWXE0YgTceSd07w7LllXghGZmVoh6wA0RsSswHzi6hPbNSG7i0Qa4DFgSEbsBbwIn5Gu3bkS0B84Ebk+vDQZejojWwL7AlZLWTd9rB5wYEfsVMe9RQCugJdA57dsoIg4DlkZEq4h4sJjcOwGdI+IfxeR4APg7gKRGwBYRMaPAONcB16R9jyY5UgDgDaADsCvwH9I//AFtganA6cB1EdEKyAH+WzBgRIyPiJyIyKldv2ExH8XMzMxqikz35/Qh+SLyM0BEfAxsVlGhzACk5CxrCfr0qeAjiSQYNgxGj4aHHoIuXWDp0gqc0MzMCvg1Imalz2cATUpo/0pELIyI74AFwNPp9TkF+t4PEBGvAetL2gA4EBgkaRYwiaRwtXXa/l8R8WMx83YE7o+IFRHxDfAq0Lrkj7fKwxGxIn1eVI6HgK5pm7+T3O22oM7A2LTvU+lnawC8DuydPm4CmktqDPwYEYtIimsXSjof2CYi/B87MzMzK1GmxaNfI+K3vBeS6gA+Xdgq3NZbJwuCnn0WHn20Eib8xz/ghhvg6aeTu7F99VUlTGpmZvz+e8UKknMZl/O/7yr1CrTPf67QynyvV/L7Mx0Lfl8JQMDR6SqhVhGxdUS8n76/uIScKuH9kuQfv9AcEfEl8IOkFkA3kpVIBdUC2uXr2zgiFgKvkaw22oukIPUdyc1OXgeIiPuAw4ClwAuSilphZWZmZrZKpsWjVyVdCKwj6QCSv4A9XUIfs3JxzjnJHdjOOis507rCnXkmPPEEvP8+tG4N06dXwqRmZlaIecAe6fMuZRwj70yijsCCiFgAvACcLUnpe7uVYrzXgG6SakvalGSFz7QyZisuxwPAQKBhRMwppO9E4Ky8F5JaAUTEF8AmwI4R8R9gMjCAtHgkaTvgPxExhmTFUosyZjczM7MaJNPi0SCSv1zNIbmTyP8BQyoqlFl+derAbbfBDz/AuedW0qSHHQZTpiS3edt7b3igsD/6mplZBRsNnCFpCklBpCx+SvuPA05Jr10CrAXMljQ3fZ2px4HZwDvAy8DAiPh/ZcxWXI5HgGNItrAVpi+Qkx56/R7JWUZ53gI+Sp+/DjQmKSJBUkybm253awrcVcbsZmZmVoMoMjxIJv3rGunZApUqJycncnNzK3taW8MMGQKXXQbPPQd/+UslTfrdd3DUUTB5chJgxIjkgG0zMytXkmZERE62c9jv1W20YzQ68dpS9Zk38uAKSmNmZmblLdPvYMX+Kzi9zetwSd8DHwAfSvpO0rDyCmqWqaFDoWnTZFdZpZ1lvemm8NJL0LMnXHopdO0Ki0s6DsPMzMzMzMys+ihpCUU/krustY6IjSNiI2BPoIOk/hWeziyfunXhppvg00/h8ssrceK114Zbb4VrrknOQurQIQlhZmbVlqTmkmYVeLyVYd/BhfQdXNGZzczMzCpKScWjE4BjI2LVv5TTwxePT98zq1SdOkH37jBqFHz8cSVOLEG/fslt3z77DPbYA55/vhIDmJlZZYqIOfnuZJb32DPDvpcV0veyis5sZmZmVlHqlPD+WhHxfcGLEfGdpLUqKJNZsUaPhqefTu6+9vzzSV2n0vzlL5Cbm5yD9Le/JWcgDR7sc5DMzKxaat64Ibk+w8jMzKzGK+lfvL+V8T2zCvOnP8Ell8DEifDoo1kIsP328OabyRKoYcPg8MNh/vwsBDEzMzMzMzOreCUVj1pK+rmQx0KgeWUENCvMmWdCq1bJTrKFC7MQoH59uOsuGDs2Wf6UkwOzZ2chiJmZmZmZmVnFKrZ4FBG1I2L9Qh4NIsLb1ixr6tSBG2+EL7+Eiy/OUggJ+vSBV19Nbv/Wti3cc0+WwpiZmZmZmZlVDB/UYlVWu3Zwyilw7bUwZ04Wg7RvDzNmQOvW0KMHnH02/OZdnWZmZmZmZlY9KCKynaFEOTk5kZubm+0Ytgb6/nv4859h221hypRkRVLWLFsGgwbB1VcnBaWHH4YttshiIDOzqkPSjIjIyXYO+726jXaMRideW2ybeT5Q28zMrMrK9DuYVx5ZlbbJJnD99TB9erICKavWWguuugoeeADeeQd23x1eey3LoczMzMzMzMxWj4tHVuV165bc8GzoUPj442ynIQn01lvQsCHstx9ccw1UgRV+ZmZmZmZmZoVx8ciqPCk5PLtu3eQMpJUrs50I2HVXmDYNDj0Uzj0Xjj0WFi3KdiozMzMzMzOzUnPxyKqFLbZIjhp6/XUYNy7baVING8Jjj8HIkcn5R23bwkcfZTuVmZmZmZmZWam4eGTVxsknwwEHwPnnw2efZTtNSkoCvfACfPMN5OTAE09kO5WZmZmZmZlZxlw8smpDgvHjk+OFevdew44Z6twZZsyApk3hyCOTgtLy5dlOZWZmZmZmZlYiF4+sWmnSJNkl9sILcOed2U5TwNZbJ3df690brrgiOUz7yy+zncrMzMzMzMysWC4eWbVz5pnQsSP07w9ff53tNAXUq5ccynTPPclKpN12gxdfzHYqM7MKNX/+fG688UYAJk2axCGHHFJpc0vqJKl9GfseIWmXcs7TRNLc9HmOpDEltD0u3+ti25uZmZlVFBePrNqpVQtuuw1++SUpJK1R29fydO8O06fDppvCgQfC8OGwbFm2U5mZVYj8xaNMSapdTtN3AspUPAKOAMq1eJRfRORGRN9imjQBVhWPMmhvZmZmViFcPLJqaaedYMSI5Gzqhx/Odpoi7LILTJuWFJJGjIA2bWDmzGynMjMrd4MGDeKTTz6hVatWnHfeeSxatIguXbrQtGlTunfvTqRVfknzJA2TNBnoKml7Sc9LmiHpdUlN03abSnpU0vT00aGweSU1AU4H+kuaJWkvSdtIeknS7PTn1kX0bQ8cBlyZ9t2+mDwTJI2RNEXSfyR1yeT3kq6KeiZ9vk86zyxJb0tqAIwE9kqv9S/Qfrik2yVNSufsm2/coZI+kPQvSfdLGpBe7yvpvfSzP1BMrl6SciXlrliyIJOPYmZmZtVcnWwHMKso554LDz0EZ52VHC+0ySbZTlSIddeFu+9ODtHu0ycpIF10EVx4IdQurz+6m5ll18iRI5k7dy6zZs1i0qRJHH744bz77rtsscUWdOjQgTfeeCN/818ioiOApJeA0yPiY0l7AjcC+wHXAddExOS0+PMC8OeC80bEPEnjgEURMTod82ngroi4U1JPYAzJCqOCfadIegp4JiIeKSEPQCOgI9AUeAp4pJS/pgFAn4h4Q9J6wC/AIGBARBySzt+pQJ+mwL5AA+BDSTcBLYGjgd1IvufNBGak7QcB20bEr5I2KCpIRIwHxgPUbbTjmrh+18zMzCqZVx5ZtVWnDtxxB8yfD/36ZTtNCY46Ct57D7p1g2HDYP/94Ysvsp3KzKxCtGnThi233JJatWrRqlUr5s2bl//tBwHSAkp74GFJs4CbSQo0AJ2Bsen1p4D105U6mWgH3Jc+v5uk4FOiEvIAPBERKyPiPWDzDLPk9wZwdbqCaIOIyOSWnM9GxK8R8T3wbTpvR+DJiFgaEQuBp/O1nw3cK+l4wLf8NDMzs4y5eGTVWvPmySKee++FZ57JdpoSbLhhcpD2nXdCbi40awa33rqGHtpkZlZ2devWXfW8du3aLF/+uzrG4vRnLWB+RLTK9/hzvvfa5bveOC2UlEWm/ydbXB6AX/M9V6lDRIwETgXWAabmbYkrQf45V5CsNCpu7oOBG4A9gBmSvALdzMzMMuLikVV7F16YFJF694affsp2mhJIcMIJMHs27L47nHYaHHQQfPZZtpOZmZVZgwYNWLiwdLWdiPgZ+FRSVwAlWqZvTwTOymsrqVUxQy0k2daVZwpwTPq8OzA5k74l5FltkraPiDkRMQrIJdmSVjB7JiYDh0qql66WOjgdvxawVUS8AgwENgDWK6/8ZmZmVr25eGTV3tprJ9vXvvkG+vfPdpoMbbcdvPQS3HADTJmSrEIaNw5Wrsx2MjOzUtt4443p0KEDzZo147zzzitN1+7AKZLeAd4FDk+v9wVy0oOf3yM5FLsoTwNH5h2YnfY9WdJsoAdwTjF9HwDOSw+w3r6YPOWhn6S56dhLgedItpktl/SOpIz+CxYR00m28r0DPEZSiFoA1AbukTQHeJvkzKj55ZjfzMzMqjFFFdgSk5OTE7m5udmOYVXc0KFw6aXJ9rWDD852mlKYNw9OPTUpJu27L9x2G2y7bbZTmZmVK0kzIiIn2zmqA0nrRcQiSfWB14BeEVGm23nWbbRjNDrx2mLbzBtZlf6jamZmZvll+h3MK4+sxhgyJFnAc+qp8O232U5TCk2awL/+BTffnJyF1Lw5jB3rVUhmZlaU8emh3jOBR8taODIzMzPL4+KR1Rh16yYHZ8+fD8cfX8VqLxL06gVz50LHjnD22ckqpH//O9vJzMzWCJJOTrem5X/ckGHfwYX0HbyaeZoXMuZbqzNmpiLiuPRA76YRcXllzGlmZmbVm4tHVqO0aAFjxiQLeS6vil+nt94annsu2br2zjvJB7r2WlixItvJzMyyKiLuKHAntFYR0SfDvpcV0vey1cwzp5Ax91ydMc3MzMyyxWceWY0TAd27w4MPwmuvQYcO2U5URl9+mdxC7tlnkw8xblyyL8/MrArymUdrJn8HMzMzq9585pFZEaTk+KBttoETToBS3j16zdG4MTz9NNx1F7z/PrRqBQMGVOEPZGZmZmZmZmsiF4+sRmrQAO6+O7mRWb9+2U6zGiTo0QM+/BB69oSrroKmTZNlVVVgVaGZmZmZmZmt+Vw8shqrQwc4/3y4/XZ44olsp1lNm2wC48fD1Knwpz/BMcfAAQfABx9kO5mZmZn9f/buOzyLKu3j+PdOYEE6CioiJboUgcQQQgfpiIIurCArImVXxLZWXHFXBTsuKKyKBcvSlRWx7yKCUpWSQAhFEJTwoqiACoIUBc77x5lAAqkYmJTf57rmeuY5M3Pmnidc+uTOOfcREREp4JQ8kiJt+HBo2BAGDYJvvw07mjzQtCksXQpjx0Jioi+oPXQo/Pxz2JGJiIiIiIhIAaWC2VLkrV0LjRpBhw6+hJBZ2BHlkW3b/NCq8eOhWjUYPRr++MdC9IAiUpioYHb+VKJKLVel/5jj2lNGdA0hGhEREclrKpgtkkP16sHjj/tFy8aNCzuaPHTmmfDvf8OCBVCxIvTsCZdcAhs2hB2ZiIiIiIiIFCBKHokAN98MHTvCHXcUwtxKq1Z+CtuYMfDJJ9CgAdx3H+zdG3ZkIiIiIiIiUgAoeSQCRET42V0lSvjFyw4eDDuiPFasGNx6q1+VrVcvePhhqF8f3n5bq7KJiIiIiIhIlpQ8EglUrQrPPQdLlsCjj4YdzUlSpQpMngxz50Lp0tC9O7RuDfPnhx2ZiIiIiIiI5FNKHomk0bs3XH01PPign+FVaLVpAytW+GzZl1/69126+OltIiIiIiIiImmEkjwyswpmNt3M1pnZZ2bWPIw4RDIydizUqOETSTt2hB3NSVS8OFx/PWzcCCNHwrJlEB/vC2t/9lnY0YmIiIiIiEg+EdbIo38BM51zdYELAf2mKvlG+fLw+ut+pftrroHDh8OO6CQrVQqGDIFNm2DYMJg1yxfVHjAAvvoq7OhEREREREQkZKc8eWRm5YCLgJcBnHO/OOd2nuo4RLISFwf/+hfMnAkjRoQdzSlSrhwMH+6nsd1+O7z2GtSu7RNKP/0UdnQiIgCYWU0z65Pm/QAze+Yk3q+tmbU4Cf3uCV7PMbPpWZxXwcxuTPM+y/NFREREToYwRh6dB2wH/m1mK8zsJTMrfexJZnadmSWYWcL27dtPfZRS5A0eDFdd5Ve1nzs37GhOoUqVYNQoWLcOLrvMF4CqWdO/7lSeV0Ty1sHcL29ZE+iT3Ul5qC2Q58mjVM65rc65nlmcUgG4MRfni4iIiOS5MJJHxYA44DnnXEPgZ2DosSc558Y55+Kdc/GVK1c+1TGKYAYvvAC1avkk0rffhh3RKVazJkybBkuXQqtWfgRSjRpw773w/fdhRyciIUtJSaFu3bpce+21NGjQgKuvvprZs2fTsmVLatWqxdKlS/nhhx/o3r07MTExNGvWjOTkZACGDx/OddddR+fOnenXrx+HDh0CONfMlplZspkNzuLWI4DWZpZkZrcHbeeY2Uwz22Bm/0w90cyeC/4QtcbMHkjTnmJmD5jZcjNbZWZ1M7qRmdUErgduD+7X2swqm9kbQazLzKxlcO5wM3vFzOaa2ZdmdktOPsdgJNXqYL++mS0N7pVsZrWC5z0/aBt5zPkDzGxGJs/+FzP7PIjnxdTRWWbWy8xWm9lKM8twqc20f8A7tHdXTh5DRERECrkwkkdfAV8555YE76fjk0ki+U7Zsr7+0a5d0KcP+N9vipjGjeGdd/zqbJ06wSOP+MTSAw/A7t1hRyciIdq4cSO33norycnJrFu3jqlTp7Jw4UJGjRrFo48+yrBhw2jYsCHJyck8+uij9OvX78i1iYmJvP3220ydOpWXX34Z4JBzrjHQGBhkZlGZ3HYosMA5F+ucGx20xQK9gWigt5lVC9r/4ZyLB2KANmYWk6afHc65OOA5YEhGN3LOpQDPA6OD+y3A120cHcR6BfBSmkvqAhcDTYBhZlY8+08xneuBfznnYoF4/HemocAXwf3vyuCa457dzM4B7gOaAZ2CuFLdD1zsnLsQuDyT5z7yB7zIUuVz+QgiIiJSGJ3y5JFz7ltgi5nVCZo6AGtPdRwiORUd7Vdg+/hjny8psmJjYfp0WL0aLr7Y10c67zxfHOrAgbCjE5EQREVFER0dTUREBPXr16dDhw6YGdHR0aSkpLBw4UKuueYaANq3b8/333/Prl1+JMvll1/OaaedBsCsWbMAzjCzJGAJcAZQKxehzHHO7XLO7cd/p6gRtF9pZsuBFUB9oF6aa2YEr4n4qXA51RF4Joj1HaCcmZUNjr3vnDvgnNsBbAPOykW/AJ8Cfzezu4Eazrl9Obgmo2dvAsxzzv3gnPsVeD3N+YuA8WY2CIjMZXwiIiJSRIW12tpfgSlmloz/i9mjIcUhkiMDB/rFxx5+2C9GVqTVr++TSEuXwoUXwm23+cLa48bBvpz8niMihUWJEiWO7EdERBx5HxERwcGDB3HOHXeNmQFQuvTRcofBef8XjK6Jdc5FOedy81/btBnsQ0CxYOTSEKCDcy4GeB8omcE1h/BT6nMqAmieJtaqzrnUYZjHxZGLfnHOTcWPBtoHfGBm7XNwWUb3tCzucT1wL1ANSDKzM3ITo4iIiBRNoSSPnHNJwXDoGOdcd+fcj2HEIZIbY8f6vMnVV8PmzWFHkw80bgyzZ8OHH8JZZ/kK49Wrwz/+4VdsE5Ei76KLLmLKlCkAzJ07l0qVKlGuXLnjzrv44osBKqdO8zKz2hktphHYDZTN5Fha5fB1FXeZ2VnAJbl/ggzvNwu4OfWNmcWeYL/HMbPzgC+dc0/hRzXFZHD/nFiKn6ZX0cyK4afXpd7jfOfcEufc/cAOfBJJREREJEthjTwSKXBKlfIDbn79FS6/HPbsCTuifKJjR1iyxC9J16IFPPYYnH8+dOgAU6dqNJJIETZ8+HASEhKIiYlh6NChTJgwIcPzrr32WoD9wPKgGPQLZD5qJxk4GBR8vj2Tc3DOrcRPV1sDvIKfrnUi3gV6pBbMBm4B4oOC1mvxdYrySm9gdTAlri4w0Tn3PbAoKHI9MiedOOe+xo/qXgLMxk9nS618PTIoEr4amA+szMP4RUREpJCyjIaU5zfx8fEuISEh7DBEAJg5E7p29QmkN96ACKVg09uyBSZMgFdegU2boEIF+POf/fS2avoDt4hkzMwSg+LWkgfMrIxzbk8w8uhN4BXn3Ju57adElVquSv8xx7WnjOiaB1GKiIhI2HL6HUy/9orkUpcu8OST8NZb8OCDYUeTD1WrBvfeCxs3wpw5/gP71798ce1rroH586EAJK1FRAq44cEIptXAJuCtkOMRERGRAixXhRxFxLvlFli+3CePWrSAzp3DjigfioiA9u39NmKEz7iNHw+TJ/sC27fd5quQB6stiYikZWbRwKRjmg8455qepPsNBG49pnmRc+6m39DnGcCcDA51CKajnTTOuSEns38REREpWjRtTeQE/fwzNG0K330HK1bAueeGHVEBsHevLxw1dqxfre3MM+HWW+GGG6BixbCjE5EQadpa/qRpayIiIoVbTr+DKXkk8husW+cXHYuJ8fWiixcPO6ICwjmYNw8ef9wXkSpTxq/WdvvtULVq2NGJSAiUPMqf9B1MRESkcFPNI5FToG5dePFF+OQTuOeesKMpQMygbVv43/8gKclXHx8zBqKiYOBAmDULfvkl7ChFREREREQEJY9EfrM//QluugmeeMIX0ZZcuvBCmDIFNmzwo4/+8x+4+GKoXBmuu87PCRQREREREZHQKHkkkgeeeMJPXxswANavDzuaAioqCp5+GnbsgHfegR49fHHtuDiIj4dRo+D//i/sKEVERERERIocJY9E8kCJEvD66/C73/kZWD/+GHZEBdhpp8Fll/mV2b7+GkaP9u133eUTTH/6E6xcGWqIIiIiIiIiRYkKZovkoYUL/cr0bdr4cj7FioUdUSGycSO89BI8+yzs3g0NG0LXrtC7NzRoEHZ0IvIbqWB2/qTV1kRERAo3FcwWCUGrVjBuHMye7RcOkzz0+9/DiBF+6trIkVC6NDz6KERH+5FKCxf6VdxEREREREQkTyl5JJLHBgyAO++EZ56B558PO5pCqEIFGDIEFiyA776DBx/0y921bg116sDw4fD552FHKSIiIiIiUmgoeSRyEjz+OFx6Kfz1r/Dxx2FHU4hVqgT33QebN/spbdWq+WRSnTq+gvno0fDNN2FHKSIiIiIiUqApeSRyEkRGwquvQu3acMUVvlyPnERlysBf/gJz5sCWLX75u8OH4Y474NxzoXNneO893yYiIiIiIiK5ouSRyElSrhy8+y5ERPgV2HbtCjuiIqJqVZ80SkyEzz6Df/zDv152GdSt60cmrV6t+kgiIiIiIiI5pOSRyEl03nkwfTps2OBXmD94MOyIipjUZNGXX/qhYGef7WsiRUf7Y0OHwtKlSiSJiIiIiIhkQckjkZOsbVu/uvzMmXDjjcpThKJ4cZ+9mz8ftm71lcxr1vTT25o2herV4ZZbYO5cZfhERERERESOoeSRyCkwaJCfPfXii/Dww2FHU8SdfTYMHgwffADbtsHEiRAf73847dpBlSpw7bXw3//CgQNhRytSKKWkpNCgQYPf1IeZtTWzFtmcM97Mev6mG50kZlbCzGabWZKZ9Tazl8ysXnAsxcwq5eG9HjSzjnnVn4iIiBQ9xcIOQKSoeOgh+OoruP9+X8N54MCwIxIqVoRrrvHbzz/74WEzZsB//gMvvwylS8NFF0GHDtCxo5/uFqGcu0g+0RbYA3wSchyZMrNizrnMhjM2BIo752KD99NOVhzOuftPVt8iIiJSNOj2WxciAAAgAElEQVS3IJFTxMwPbunc2Y9E+t//wo5I0ild2i+NN2UKbN8O778PAwb4eklDhkBsLJx1FvTrB2+9BXv3hh2xSIF26NAhBg0aRP369encuTOAmVmsmS02s2Qze9PMKgYHbjGztUH7a2ZWE7geuD0YudM6i1tdZGafmNmXqaOQzKyMmc0xs+VmtsrM/hC0lzaz981spZmtNrPemXUajA563MyWBtvvg/bxZvakmX0MPG5mp5vZW0Hsi80sxszOBCYDsUH855vZXDOLz+A+fYP+k8zsBTOLzCKmPWb2RPBcc8yscpqYUp+9cfB5rAz6LZtBP9eZWYKZJRzaq9UeRERERMkjkVOqeHFfQDsmBnr1goSEsCOSDJUoAZdeCs88A+vWwZYtMH48XHwxvPce9OgBlSrBH/8IkybBjz+GHbFIgbNhwwZuuukm1qxZQ4UKFQAqAhOBu51zMcAqYFhw+lCgYdB+vXMuBXgeGO2ci3XOLcjiVlWAVkA3YETQth/o4ZyLA9oBT5iZAV2Arc65C51zDYCZ2TzGT865JsAzwJg07bWBjs65O4EHgBVB7H8HJjrntgHXAguC+L/IqHMzuwDoDbQMRigdAq7OIp7SwPLgueZx9PNL7e93+BFOtzrnLgQ6AvuO7cQ5N845F++ci48sVT6bj0BERESKAiWPRE6xsmX9oJZKlaBrVz+wRfK5c8+F/v1h8mT47juYPdvPO1yyxI9EOvNMP6Tsuefgm2/CjlakQIiKiiI21s/YatSoEUAJoIJzbl5wygTgomA/GZhiZn2B3Fa1f8s5d9g5txY4K2gz4FEzSwZmA1WDY6uAjsGIotbOueyG3bya5rV5mvbXnXOHgv1WwCQA59xHwBlmltOMTAegEbDMzJKC9+dlcf5hjk5/mxzcO606wDfOuWVBPD9lMa1ORERE5Aglj0RCUKWKL69z8CB06QI7doQdkeRY8eK+BtLYsX5E0uLFcMcdkJLil9OrWhWaNIG77/ZzE3/6KeyIRfKlEiVKHNmPjIyErOswdgXG4hMpiWaWm5qNaSvfW/B6NVAZaBSM6PkOKOmc+zy4xyrgMTPLrlaQy2T/5wzumdl1WTFgQjA6KdY5V8c5NzyH12Z0H8vFvUVERESOUPJIJCR168I77/j8w2WXwZ49YUckuRYRAU2bwuOPw/r1sHo1PPCAn/Y2erSf+nb66f4cJZNEsnMI+DFN/aJrgHlmFgFUc859DPwNqACUAXYDx9XryaHywDbn3K9m1g6oAWBm5wB7nXOTgVFAXDb99E7z+mkm58wnmGpmZm2BHc65nP6HYA7QM6iRRFA/qUYW50cAqavL9QEWHnN8HXCOmTUO+iuby0SciIiIFFH6wiASopYt4dVXfZ3m7t19OZ2SJcOOSk6IGdSv77f77vMFtT/9FObO9dvo0fDPf0JkJDRqBG3b+q1lSyhXLtzYRfKP/sDzZlYK+BIYCEQCk4OpXoavc7TTzN4FpgfFrv+aTd2jY00B3jWzBCAJn1QBiAZGmtlh4Ffghmz6KWFmS/BJm6syOWc48O9gitze4BlzxDm31szuBWYFSbRfgZuAzZlc8jNQ38wSgV0cTW6l9vdLUAT8aTM7DV/vqCN+1ToRERGRTJlz+X/0cnx8vEtQZWEpxCZO9CV1Lr/cF9QuXjzsiCTPHZtMWrIEfv1VySSRgJklOueOW20svzKzFCDeOZdvJh6b2R7nXJm87LNElVquSv8xx7WnjOial7cRERGRkOT0O5hGHonkA/36we7dcPPNvmzOuHF+IIsUIqVK+VpJHTr491mNTGrY0E91a9IE2rWDatXCjFxERERERIo4JY9E8ombbvILdT3yCERFwd//HnZEclJllExavNgnkhYsgAkTfFFugAsugDZt/Ail+HiIifH1lkQEADP7B9DrmObXnXOP5EHfbwJRxzTf7Zyr+Vv7PlHBVLkSxzRfk9ejjkRERERSKXkkko889BBs2gT/+AdUrw59+4YdkZwypUpB+/Z+Azh0CNauhQ8/hA8+gKlT4fnn/bGKFeGii/yopHbtoEEDJZOkSAuSRL85UZRJ3z1ORr+/hXOuadgxiIiISNGimkci+cyBA3DJJTB/Prz+OvTId7+2SCgOH/aZxU8/hY8/9tumTf5Y2bJ+qltc3NGtTh0opr8PSMFR0GoeFRX6DiYiIlK4qeaRSAFVogS8/TZ07gy9e/v9Sy4JOyoJXUQEnH++31KHpG3e7Ke5LV0KK1bACy/Avn3+WMmScOGF6RNK9ev7f2AiIiIiIiK5oJFHIvnUzp2+HM7atfD++0dnM4lk6uBB+PxzSEz0yaTly/3rTz/548WL+yluaRNKMTF+ypxIyDTyKH/SdzAREZHCTSOPRAq4ChVg1iy/evvll/uyNy1bhh2V5GvFikG9en675hrfdvgwfPnl0UTS8uXw1lvw8sv+eEQE1K2bPqEUGwvly4f3HCIiIiIikq8oeSSSj51xhq+X3KYNXHopzJnjF9sSybGICPj97/125ZW+zTn46iufSErdPvoIJk8+el2NGj4JVb/+0YRUvXq+vpKIiIiIiBQpmrYmUgB89ZVfXGvnTl/iJiYm7IikUPruu6Ojk1av9nMm163zVdxTVat2NJGUNrGkkUryG2naWv5UokotV6X/mOPaU0Z0DSEaERERyWuatiZSiJx7rh911Lo1dOoE8+b5mUYieeqss6BLF7+lOnTIT3tbu/botmaN/0e4f//R8847L/3Ut9//HipW9PMvIyJO/bOIiIiIiEieUfJIpICIivIziy66yBfSnj/fL7wlclJFRkKtWn77wx+Oth865Fd7W7MGVq3yI5ZWrIDp09NfX6KEL9IdG+v/EVetenSrXh3KlDm1zyMiIiIiIrmm5JFIAVK7Nsye7YtopyaQqlcPOyopkiIj/Wij886Dyy472r5zJyQlwf/9H/z4I2zZ4t+/8w5s3358P3XqQKNG/h/3+ecf7fOss8Ds1D2PiIiIiIhkSskjkQKmQQO/Clv79kcTSFWqhB2VSKBCBZ/dzMi+fbB1K3z9td82boTERFiwAF591RfyTlWqlE8iHZtQKl/eV5KvXh0qVVKCSURERETkFFDySKQAiouD//3P1z/q2NEX0a5cOeyoRLJx2mk+GZTRfMv9+yElxddX+uIL//rllz7BNGuWTzxl1F/16ke3GjXS7597Lvzudyf9sURERERECjslj0QKqObN4b334JJLoHNnX1D79NPDjkrkBJUs6avAZ1QJ3jn49lvYsQN++gm2bfPT4tJu773nV4tLywzOPjt9Uil1q1LFHzv7bCWYRERERESyoeSRSAHWti289ZavY9yuna+HpBFIUuiY+WRPdvMz9++Hr77yhbxTk0qp+ytWwNtvw4EDx193+uk+iZSaUEqbWEq7X7GipskJZlYTeM851yCH5w8AZjnntgbvU4B459yOkxSiiIiISJ5T8kikgLv4Ynj3XZ9AatPGj0BSDSQpkkqWhN//3m8Zcc6PWtqyxY9k+uYb/5p2/5NP/P7+/Rn3nzpy6YwzfP2lypXTryBXtSqceSZERJzcZ5WCZACwGtia0wvMrJhz7uBJi0hEREQkl5Q8EikEOnXyNZC6doWLLoKPPoJq1cKOSiSfMfNFt886K+vznPPT445NLn39dfqpcrt2+al0hw6lv75YMZ/BPTaplHY7+2woU0YjmQquYmY2AWgIfA70A4YAlwGnAZ8Ag4ErgHhgipntA5oH1//VzC4DigO9nHPrzGw4cA5QE9hhZn8GnguuPwjc4Zz72MxKZtI+AOgORAINgCeA3wHXAAeAS51zP5jZLcD1wbVrnXN/OjkfkYiIiBQmSh6JFBJt2vi6wpdccjSBFBUVdlQiBZCZH1VUvnzGNZjSOnTI11pKXUHu2G31avjgA9iz5/hrS5Two5QqV/avlSr5/UqVMt6vWBEiI0/OM0tu1QH+4pxbZGavADcCzzjnHgQws0lAN+fcdDO7GRjinEsIjgHscM7FmdmN+KTTtUG/jYBWzrl9ZnYngHMu2szqArPMrDZwUybt4JNGDYGSwEbgbudcQzMbjU9wjQGGAlHOuQNmViGjhzOz64DrACLLaS60iIiIKHkkUqi0aOGnrXXufDSBVKtW2FGJFGKRkXDOOX5r3Djz8376CbZuPZpU+u472L7dT6Pbvt1v69f714wSTeCTWqef7hNJZcv6EU4lS/opdJUrH91Sk01p3xcvfnKev+ja4pxbFOxPBm4BNpnZ34BSwOnAGuDdTK6fEbwmAn9M0/6Ocy51acFWwNMAwcikzUDtLNoBPnbO7QZ2m9muNPdfBcQE+8n4kVBvAW9lFJxzbhwwDqBElVouqw9CREREigYlj0QKmfh4+PhjP5Xtoot8MqlevbCjEiniypXzW3YjmcDXW9qx4+i2fXv61x07YPduOHgQ9u3zo5u2b4cffvBT7jJSocLREU5VqvjXsmX91LnU17T7x7aVKqU6Tukd+0E74Fl8IewtwRS0kllcn1q5/RDpv4v9nGY/szmNWc11TFsR/nCa94fT3KcrcBFwOXCfmdVXfSURERHJjpJHIoXQhRfC3LnQoYOfzvbBBxAXF3ZUIpIjJUvCuef6LTcOHvQJpNREU9otte27744mm3bvhl9+yXn/pUv7YmqffZa7uAqn6mbW3Dn3KXAVsBBoga9VVAboCUwPzt0NlD2Be8wHrgY+CqalVQfWZ9Ge7X/lzSwCqBbUSFoI9AHKADtPID4REREpQpQ8Eimk6tWD+fOhY0do186vyHbRRWFHJSInTbFifkTRmWfm/JpffvHT5FK33bvTvx7bVkxfGwKfAf3N7AVgA76AdUX89LAUYFmac8cDzx9TMDsnng2uW4Uvbj0gqFOUWXtO+owEJptZefwIptHOOSWOREREJFvmMhvino/Ex8e7hISEsMMQKZC2bPE1kFJSYPp0vyKbiEh+Y2aJzrn4sOOQ9EpUqeWq9B9zXHvKCP3PREREpDDI6XcwFTAQKeSqVfMjkOrXh+7dYerUsCMSERERERGRgkTJI5EioHJlv/Jay5bQty+MHRt2RCIiIiIiIlJQKHkkUkSUKwczZ8Jll8HNN8Mjj2S+MJOIiIiIiIhIKiWPRIqQkiV93aO+feHee+Guu5RAEhERERERkaxp2RSRIqZ4cZgwAcqXhyeegJ074YUXIDIy7MhERCS/ia5angQVxxYRESnylDwSKYIiIuDpp6FiRXj4Ydi1C6ZMgd/9LuzIREREREREJL9R8kikiDKDhx7yCaQ774Tvv4cZM6BChbAjExERERERkfxENY9Eirg77oBJk2DhQr8aW0pK2BGJiIiIiIhIfqLkkYjQty/MmgVbt0KzZpCQEHZEIiIiIiIikl9o2pqIANC2LSxaBJdeCm3awGuvwWWXhR2ViIiEadXXu6g59P3j2lNURFtERKRI0cgjETmiXj1YvNi/du8OY8eGHZGIiIiIiIiETckjEUnn7LNh7lzo1g1uvtkX0z58OOyoREREREREJCxKHonIcUqX9iuv/fWv8OST0KsX7NsXdlQiIiIiIiISBiWPRCRDkZHw1FMwejS8+Sa0bw/btoUdlYiIiIiIiJxqSh6JSJZuuw3eeAOSkqBpU1i5MuyIRERERERE5FRS8khEstWjB8yfD7/8Ai1a+GSSiIiIiIiIFA1KHolIjjRuDAkJEBMDPXvC/ferkLaIiIiIiEhRoOSRiORYlSp+JbaBA+Ghh+CKK2D37rCjEhHJG2bW3czqpXn/oJl1DDOmY5nZADN7Jti/3sz6nWA/Nc1sdd5GJyIiIoWVkkcikislSsDLL8O//gXvvgvNm8MXX4QdlYjkF845DhfcYYndgSPJI+fc/c652WEFY16m39Wcc8875yaeyphERESkaFLySERyzQxuuQU++AC2bvVT2maH9uuViIQtJSWFCy64gBtvvJG4uDgmTZpE8+bNiYuLo1evXuzZsweAoUOHUq9ePWJiYhgyZAgAAwYM4PrrrweoY2afm1k3ADMraWb/NrNVZrbCzNoF7QPMbIaZzTSzDWb2z6A90szGm9nq4Jrbg/bzg3MTzWyBmdXN6BnMrAVwOTDSzJKC68abWc/geIqZPWpmn5pZgpnFmdkHZvaFmV2fpp+7zGyZmSWb2QNZfW5mdkcQ72ozuy1oq2lmn5nZs8ByoJqZDQw+m3lAyzTXDzezIcH+XDN73MyWBue2TtPfAjNbHmwtsvt5mtl1wTMmHNq7K7vTRUREpAhQ8khETliHDrBsGZxzDnTp4kcjORd2VCIShvXr19OvXz8+/PBDXn75ZWbPns3y5cuJj4/nySef5IcffuDNN99kzZo1JCcnc++99x65NiUlBWA90BV43sxKAjcBOOeigauACUE7QCzQG4gGeptZtaCtqnOuQXDNv4NzxwF/dc41AoYAz2YUv3PuE+Ad4C7nXKxzLqMxlVucc82BBcB4oCfQDHgQwMw6A7WAJkE8jczsoozuZ2aNgIFA06CPQWbWMDhcB5jonGsI/AI8gE8adSLNyKgMFHPONQFuA4YFbduATs65OPxn9lQW1wPgnBvnnIt3zsVHliqf3ekiIiJSBCh5JCK/yfnnw6efQrducNtt8Je/wIEDYUclIqdajRo1aNasGYsXL2bt2rW0bNmS2NhYJkyYwObNmylXrhwlS5bk2muvZcaMGZQqVerItVdeeSUAzrkNwJdAXaAVMCloXwdsBmoHl8xxzu1yzu0H1gI1guvOM7OnzawL8JOZlQFaAK+bWRLwAlDlNzzmO8HrKmCJc263c247sN/MKgCdg20FftRQXXwyKSOtgDedcz875/YAM4DWwbHNzrnFwX5TYK5zbrtz7hdgWhbxzQheE4GawX5x4EUzWwW8TtbJJxEREZEMFQs7ABEp+MqWhRkz4IEH4MEH4bPP/Psqv+VXNBEpUEqXLg34mkedOnXi1VdfPe6cpUuXMmfOHF577TWeeeYZPvroIwDM7NhTHXBcYxppU9SH8CNufjSzC4GL8aOWrsSPwNnpnIs9safK9L6Hj4nhMP47lQGPOedeyEFfWT3fz8e8z+mYztSYDnH0O97twHfAhfg/Gu7PYV8iIiIiR2jkkYjkiYgInzx6/XVITob4eD+lTUSKlmbNmrFo0SI2btwIwN69e/n888/Zs2cPu3bt4tJLL2XMmDEkJSUdueb1118HfH0i4Dz8FLb5wNVBe22getCeITOrBEQ4594A7gPinHM/AZvMrFdwjgUJpszsBsqe4KMDfAD8ORjxhJlVNbMzMzl3PtDdzEqZWWmgB3463LGWAG3N7AwzKw70ymVM5YFvnHOHgWuAyFxeLyIiIqLkkYjkrZ49/TS23/0OWreGyZPDjkhETqXKlSszfvx4rrrqKmJiYmjWrBnr1q1j9+7ddOvWjZiYGNq0acPo0aOPXFOnTh3wdX7+B1wfTEd7FogMpltNAwY457KaFFsVmBtMTxsP3BO0Xw38xcxWAmuAP2TRx2vAXUGB7vNz++zOuVnAVODTIO7pZJKMcs4tD+Jcik8QveScW5HBed8Aw4FPgdn46XC58SzQ38wW46f9HTuqSURERCRb5gpAddv4+HiXkJAQdhgikgs7dkCvXjB3LgwZAiNGQKT+3i0ixxgwYADdunWjV69eic65+LDjkfRKVKnlqvQfc1x7yoiuIUQjIiIiec3McvQdTCOPROSkqFQJZs2Cm26CUaOga1f48cewoxIREREREZHcUsFsETlpiheHZ56BCy/0SaSmTeHtt+GCC8KOTETyi/Hjx5/ye5rZPzi+dtDrzrlHTsK9zgDmZHCog3Pu+7y+n4iIiMjJoOSRiJx0gwb5hNEVV/gE0tSp0K1b2FGJSFEVJInyPFGUyb2+B/JqtTcRERGRUCh5JCKnRKtWfvW1Hj3g8sth+HC4916/SpuIiORP0VXLk6D6RiIiIkWefm0TkVOmenVYsACuvhqGDYM//AF27gw7KhEREREREcmKkkcickqVKgUTJ/paSDNnQnw8JCeHHZWIiIiIiIhkRskjETnlzHwB7XnzYN8+aNbMJ5ScCzsyEREREREROZaSRyISmhYtIDERmjSB/v2hd2/4XmsPiYiIiIiI5CsqmC0ioTr7bJg9G0aO9HWQFiyAl1+GSy8NOzIREVn19S5qDn0/XVuKCmiLiIgUORp5JCKhK1YM7rkHli6FSpWga1cYPBj27Ak7MhEREREREVHySETyjdhYSEiAv/0NXnwRGjeG1avDjkpERERERKRoU/JIRPKVEiXg8cdhzhz48UdfD2n8eBXTFhERERERCYuSRyKSL7VrBytWQNOmMHAg9OqlYtoiIiIiIiJhUPJIRPKtKlV8Me0RI+CddyA6GmbODDsqERERERGRokXJIxHJ1yIj4e67YckSqFgRLrkEbr4Z9u4NOzIREREREZGiQckjESkQGjaExES4/XYYO9a/X7Ys7KhEREREREQKPyWPRKTAKFkSnnzST2XbuxdatIAHH4SDB8OOTCR8LVq0CDuEHBkzZgx7T3DooJnNNbP4PA7ppDOzB82sY7Df2szWmFmSmVU1s+kn2OcAMzsnzfuXzKxeXsUsIiIikpaSRyJS4HToAMnJvoj2sGHQsiWsXx92VCK545zj8OHDedbfJ598clzboUOH8qz/vPJbkkcFkZlFOufud87NDpquBkY552Kdc18753qeYNcDgCPJI+fctc65tb8xXBEREZEMKXkkIgVSxYowdSpMmwYbN0JsLDz9NOTh7+IieS4lJYULLriAG2+8kbi4OCZNmkTz5s2Ji4ujV69e7NmzB4CZM2dSt25dWrVqxS233EK3bt0AGD58OKNGjTrSX4MGDUhJSQGgTJkyAMydO5d27drRp08foqOjAZg8eTJNmjQhNjaWwYMHZ5lUevnll6lduzZt27Zl0KBB3HzzzQAMGDCA6dOPDpJJvR/AyJEjady4MTExMQwbNgyAn3/+ma5du3LhhRfSoEEDpk2bxlNPPcXWrVtp164d7dq1yzQGM9tjZk+Y2XIzm2NmldMc7mVmS83sczNrHZxf0sz+bWarzGyFmbUL2geY2Qwzm2lmG8zsn2nu0dnMPg3u8bqZlSEDZnaJmf0nzfu2ZvZuVn2YWYqZ3W9mC4N4x5tZTzO7FrgSuN/MpphZTTNbHVwTaWajgmdINrO/Bu33m9kyM1ttZuPM6wnEA1OCEUynpR2VZWZXBf2sNrPHj/lcHzGzlWa22MzOyuSZrzOzBDNLOLR3V6Y/JxERESk6lDwSkQLtyith9Wpo3x5uuQU6d4YtW8KOSiRz69evp1+/fnz44Ye8/PLLzJ49m+XLlxMfH8+TTz7J/v37GTRoEO+++y4LFizg22+/zfU9li5dyiOPPMLatWv57LPPmDZtGosWLSIpKYnIyEimTJmS4XVbt27loYceYvHixXz44YesW7cu23vNmjWLDRs2sHTpUpKSkkhMTGT+/PnMnDmTc845h5UrV7J69Wq6dOnCLbfcwjnnnMPHH3/Mxx9/nFW3pYHlzrk4YB4wLM2xYs65JsBtadpvAnDORQNXARPMrGRwLBboDUQDvc2smplVAu4FOgb3SADuyCSWD4FmZlY6eN8bmJaDPvY751o5515LbXDOvQS8A9zlnLv6mPtcB0QBDZ1zMUDqD+kZ51xj51wD4DSgm3NuenC/q4MRTPtSOwmmsj0OtA+evbGZdU/zuS52zl0IzAcGZfTAzrlxzrl451x8ZKnymXwsIiIiUpQoeSQiBV6VKvDeezBuHCxeDA0awMSJ4FzYkYkcr0aNGjRr1ozFixezdu1aWrZsSWxsLBMmTGDz5s2sW7eOqKgoatWqhZnRt2/fXN+jSZMmREVFATBnzhwSExNp3LgxsbGxzJkzhy+//DLD65YuXUqbNm04/fTTKV68OL169cr2XrNmzWLWrFk0bNiQuLg41q1bx4YNG4iOjmb27NncfffdLFiwgPLlc5WEOAxMC/YnA63SHJsRvCYCNYP9VsAkAOfcOmAzUDs4Nsc5t8s5tx9YC9QAmgH1gEVmlgT0D9qP45w7CMwELjOzYkBX4O0c9DHt2L6y0RF4PrgfzrkfgvZ2ZrbEzFbhE0L1s+mnMTDXObc96GsKcFFw7BfgvWA/7ecnIiIikqViYd3YzCLxfzX72jnXLaw4RKRwMINBg3w9pP79/fbGG/DMM1CtWtjRiRxVurQfwOKco1OnTrz66qvpjiclJWFmGV5brFixdHWS9u/fn+U9Uu/Tv39/HnvssWxjc1lkXNPe2znHL7/8cmT/nnvuYfDgwcddk5iYyH//+1/uueceOnfuzP33359tDJmFlmb/QPB6iKPfYzL+wNKfn/YaAz50zl2Vw/tPw49u+gFY5pzbbf6HlFUfP+ew71RG+uckGD31LBDvnNtiZsOBkhlce2w/mfnVHf0hp/38RERERLIU5sijW4HPQry/iBRC550Hc+fCqFHw4YdQrx489RTkw7rBUsQ1a9aMRYsWsXHjRgD27t3L559/Tt26ddm0aRNffPEFQLrkUs2aNVm+fDkAy5cvZ9OmTdnep0OHDkyfPp1t27YB8MMPP7B58+YMz23SpAnz5s3jxx9/5ODBg7zxxhvp7p2YmAjA22+/za+//grAxRdfzCuvvHKkXtPXX3/Ntm3b2Lp1K6VKlaJv374MGTLkSNxly5Zl9+7d2YUdAaQWku4DLMzm/Pn4QtSYWW2gOpBVGf3FQEsz+31wTanguszMBeLw07xSRxTlto/szAKuD0Y3YWanczRRtCOop5S2uPZuoGwG/SwB2phZpeAPdVfhp/6JiIiInLBQkkdmdi5+2PdLYdxfRAq3yEi4805YswZatYJbb4VmzWDFirAjEzmqcuXKjB8/nquuuoqYmBiaNWvGunXrKFmyJOPGjaNr1660atWKGjWOzoS64lzF+/MAACAASURBVIor+OGHH4iNjeW5556jdu3scxX16tXj4YcfpnPnzsTExNCpUye++eabDM+tWrUqf//732natCkdO3akXr16R6abDRo0iHnz5tGkSROWLFlyZHRT586d6dOnD82bNyc6OpqePXuye/duVq1adaRI9yOPPMK9994LwHXXXccll1ySZcFs/Kid+maWiJ+q9WA2j/ksEBlM7ZoGDHDOHcjsZOfcdvxqZa+aWTI+EVQ3i/MP4ad7XRK85rqPHHgJ+D8g2cxWAn2cczuBF4FVwFvAsjTnjweeTy2YnSbWb4B7gI+BlfjaUW//hrhEREREsKyGqJ+0m5pNBx7D/8VsSEbT1szsOnzxSKpXr94os7+SiohkxTm/Itutt8L338Mdd8Dw4VCqVNiRieTM3LlzGTVqFO+99172J+eBPXv2UKZMGQ4ePEiPHj3485//TI8ePU76fc0s0TmXulrYHudchqufyalVokotV6X/mHRtKSO6hhSNiIiI5LW038GycspHHplZN2Cbcy4xq/PSrvRRuXLlrE4VEcmUGfzpT7BuHQwcCCNHQnQ0zJkTdmQi+dPw4cOJjY2lQYMGREVF0b179+wvEhEREZFCLYxCiS2By83sUvxc/nJmNtk5l/vlZEREcqhiRXjxRejTB667Djp29MmkUaPg9NPDjk4kc23btqVt27Z53m/Tpk05cCD9zK5JkyYxatSoPL9XDmOoF6xadk1Yo47M7E0g6pjmu51zH4QRj4iIiEh+Ecq0tSM3N2tLJtPW0oqPj3cJCQmnJigRKfT27YMHH/SjkM44A5580ieVMlngSkROgZwOmZZTS9PWRERECrecfgfTEq0iUuScdho89hj07g2DB0PfvvDKKzB2LNT9LeVuRUQKmeiq5UlQskhERKTIC2W1tVTOubnZjToSETlZYmPhk0/guecgMRFiYuC++/zIJBEREREREfFCTR6JiIQtMhKuvx7Wr/cjkR5+GBo0gFmzwo5MREREREQkf1DySEQEOOssmDTJr8JWrBhcfLFfpW3z5rAjExERERERCZeSRyIiabRvDytXwvDh8PbbUKcO3H037N4ddmQiIiIiIiLhCHW1tZzSamsiEoYtW+Dee2HiRDj3XHj6aejePeyoRAonrbaWP2m1NRERkcItp9/BNPJIRCQT1arBhAm+qHbFitCjh08ebdkSdmQiIiIiIiKnjpJHIiLZaN7cr8b2z3/6QtoXXABPPAG//BJ2ZCIiIiIiIiefkkciIjlQvDjcdResXQtt2sCQIVCvHkyfDgVg9q+IiIiIiMgJU/JIRCQXataE99+H//0PSpaEXr2gdWtYsiTsyERERERERE4OJY9ERE5Aly6QlATjxsHGjdCsGfzpT7BpU9iRiYiIiIiI5C0lj0RETlCxYjBoEGzYAPfdB++8A3Xrwp13wo4dYUcnIiIiIiKSN5Q8EhH5jcqWhQcf9EmkPn1gzBiIivIJpd27w45ORERERETkt1HySEQkj1StCv/+N6xeDZdeCg8/DHXqwMSJcPhw2NGdPM8//zwTJ07M8pzx48dz8803Z3isTJkyJyMs5s6dS7du3fJNvzVr1mRHPhqSNnz4cEaNGpXp8cWLF9O0aVNiY2O54IILGD58+AndZ+fOnTz77LNH3uf08zOz8Wa2ycySzGydmQ07kfubWXczq3ci12bQ15tm1j3N+/Vmdm+a92+Y2R+zuL6mmfU5wXvfYmafmdmUE7k+g/7+nhf9iIiISNGg5JGISB674AKYNg0WL4Zq1aB/f4iJgcmT4eDBsKPLe9dffz39+vULO4x0DhbGD/oU69+/P+PGjSMpKYnVq1dz5ZVXnlA/xyaPcuku51wsEAv0N7OoE+ijO5AnySPgE6AFgJmdAewBmqc53jw4JzM1gVwlj8wsMti9EbjUOXf1MceL5aa/NJQ8EhERkRxT8khE5CRp2hQ+/RSmTgUzuOYan1h64w1wLuzoMpaSksIFF1zAoEGDqF+/Pp07d2bfvn0AfPHFF3Tp0oVGjRrRunVr1q1bB6QfwbJs2TJiYmJo3rw5d911Fw0aNDjS99atW+nSpQu1atXib3/7W7r73nnnncTFxdGhQwe2b98OQFJSEs2aNSMmJoYePXrw448/AtC2bVsSEhIA2LFjBzVr1gT86KZevXpx2WWX0blzZwD27NlDz549qVu3LldffTUu+ODnzJlDw4YNiY6O5s9//jMHDhzIsn3mzJnUrVuXVq1aMWPGjCw/wz179jBw4ECio6OJiYnhjTfeOO6cyZMn06RJE2JjYxk8eDCHDh0C4IYbbiA+Pp769eszbNjRgTY1a9Zk2LBhxMXFER0dfeSznzdvHrGxscTGxtKwYUN2B/MkR44cSePGjYmJiUnXzyOPPEKdOnXo2LEj69evz/I5tm3bRpUqVQCIjIykXj2ff/nhhx/o3r07MTExNGvWjOTkZOD4kUwNGjQgJSWFoUOH8sUXXxAbG8tdd92V5c8lCyWD158BzKyDma0ws1Vm9oqZlQjaR5jZWjNLNrNRZtYCuBwYGYxgOt/MYs1scXDOm2ZWMbh2rpk9bmZLzexzM2udQRyLCJJHwet7QGXzooB9zrlvgxFGC8xsebClXjMCaB3EcruZRZrZSDNbFsQzOIilrZl9bGZTgVVm9jxwHvBOcN1wMxtnZrOAiWZW0sz+HXweK8ysXdDPADObYWYzzWyDmf0z9XMCTgviOG4kk5ldZ2YJZpZwaO+u7H42IiIiUgQoeSQichJFRMBVV8HKlfDWW1CiBPTsCa1a+cRSfrRhwwZuuukm1qxZQ4UKFY4kP6677jqefvppEhMTGTVqFDfeeONx1w4cOJDnn3+eTz/9lMjIyHTHkpKSmDZtGqtWrWLatGls2bIFgJ9//pm4uDiWL19OmzZteOCBBwDo168fjz/+OMnJyURHRx9pz8qnn37KhAkT+OijjwBYsWIFY8aMYe3atXz55ZcsWrSI/fv3M2DAgCOxHDx4kOeeey7L9kGDBvHuu++yYMECvv322yxjeOihhyhfvjyrVq0iOTmZ9u3bpzv+2WefMW3aNBYtWkRSUhKRkZFMmeJ/f3/kkUdISEggOTmZefPmHUnMAFSqVInly5dzww03HEnSjBo1irFjx5KUlMSCBQs47bTTmDVrFhs2bGDp0qUkJSWRmJjI/PnzSUxM5LXXXmPFihXMmDGDZcuWZfkct99+O3Xq1KFHjx688MIL7N+/H4Bhw4bRsGFDkpOTefTRR7MddTZixAjOP/98kpKSGDlyZKY/l0yMNLMk4CvgNefcNjMrCYwHejvnooFiwA1mdjrQA6jvnIsBHnbOfQK8QzCCyTn3BTARuDs4ZxWQdjpcMedcE+C2Y9pTJQINzOx3+OTRp8B64ILgfeqDbAM6OefigN7AU0H7UGBBEMto4C/ALudcY6AxMCjN6KomwD+cc/Wcc9cDW4F2wXUAjYA/OOf6ADcBBJ/HVcCE4HMCP2qrNxAN9Dazas65ofhEV+yxI5mCfsY55+Kdc/GRpcpn8DGIiIhIUaPkkYjIKRARAX/4AyQlwYsvwqZN0KKFTyR99lnY0aUXFRVFbGwsAI0aNSIlJYU9e/bwySef0KtXryOjZb755pt01+3cuZPdu3fTooUfZNGnT/rZOR06dKB8+fKULFmSevXqsXnzZgAiIiLo3bs3AH379mXhwoXs2rWLnTt30qZNG8BPoZo/f362sXfq1InTTz/9yPsmTZpw7rnnEhERQWxsLCkpKaxfv56oqChq166dru/M2tetW0dUVBS1atXCzOjbt2+WMcyePZubbrrpyPuKFSumOz5nzhwSExNp3LgxsbGxzJkzhy+//BKA//znP8TFxdGwYUPWrFnD2rVrj1z3xz/6UjqpPxOAli1bcscdd/DUU0+xc+dOihUrxqxZs5g1axYNGzYkLi6OdevWsWHDBhYsWECPHj0oVaoU5cqV4/LLL8/yOe6//34SEhLo3LkzU6dOpUuXLgAsXLiQa665BoD27dvz/fffs2tX7kanZPRzyUTqtLWzgQ7BCJ46wCbn3OfBOROAi4CfgP3AS0Hdob3HdmZm5YEKzrl5x1ybKnVYWSJ+ilk6zrkDwBogDmgGLMEnkFoEW+qUteLAi2a2CnidzKfNdQb6BQmyJcAZQK3g2FLn3KZMrgN4xzm3L9hvBUwKYlwHbAZqB8fmOOd2Oef2A2uBGln0KSIiIpKhE50nLyIiJ6BYMbj2Wj8a6Ykn4J//hBkz4I9/hL//HeLiwo4QSpQocWQ/MjKSffv2cfjwYSpUqEBSUlKm12U39ejYfjOrS2RmWfZTrFgxDgcVyFNHw6QqXbp0tvfMLM6s4s8upmP7yep85xz9+/fnscceS9e+adMmRo0axbJly6hYsSIDBgxI93ypz5L2sxs6dChdu3blv//9L82aNWP27Nk457jnnnsYPHhwuv7HjBmTq+cAOP/887nhhhsYNGgQlStX5vvvv8/wczKzdD8XOP5nk1ZO/y2kcs7tMbO5+CTJrEzOOWhmTYAOwJ+Am4H2GZ2bhQPB6yEy/470CT7hVNY596OZLQ7u1RB4PjjnduA74EL8H+oy+zAM+Ktz7oN0jWZtCaboZSHt8ax+sAfS7Gf1XCIiIiKZ0sgjEZEQlC4N998PKSk+aTR7NjRqBJdcAgsXhh3d8cqVK0dUVBSvv/464BMgK1euTHdOxYoVKVu2LIsXLwbgtddey1Hfhw8fZvr06QBMnTqVVq1aUb58eSpWrMiCBQsAmDRp0pFRSDVr1iQxMRHgyHW5UbduXVJSUti4cWO6vrNq3/T/7d15nJVl/f/x15vBDVRwX1Owr1qyDTAuiCBuuGaRGqUpal+Xcku/Lli571Fp5pYmS0qa4pJLKqaguCYgIqREKP4CSVyQIMgUPr8/rmvwQHOGQWbmzJx5Px+P+zHnvs597vtz3ffcc675nOu6zttvM2PGDADuuuuuWvffv39/brjhhmXr1XM1Vdtnn30YNWoUc+fOBdIcQu+88w7//Oc/adu2Le3ateO9997jscceW2ldZsyYQZcuXTjvvPOoqqrizTffZP/992fo0KEsXLgQgNmzZzN37lz69u3LAw88wOLFi1mwYAEPP/xwrft+9NFHlyWKpk+fTkVFBe3bt6dv377LhtmNHTuWjTfemPXXX58OHTowceJEACZOnMjbb6dOM+utt96yuZi+qDwp9K7ADOBNoIOk/8lPHw08I2ldoF1E/JE07KwyP78AWA8gIuYD8wrmMzoaqO6FVFfPAycB1TfAZFIvpG1IvZIA2gFzImJpPkb1GM5lsWRPkIbcrZHruYOk5TOgdfMscFT1PnIstU9qBZ9WH9fMzMxsZZw8MjMroY03hssvh3fegSuvhPHjoU8f2HNPGD26aU2sPXLkSG6//Xa6detGp06d+MMf/vBf29x+++2ceOKJ9OrVi4igXbuVz5fStm1bpk6dSs+ePXn66ae58MILARgxYgTnnHMOXbt2ZdKkScvKzz77bG6++WZ23313Pvjgg1Wux9prr82wYcM44ogj6NKlC61ateLkk0+utfzWW2/l4IMPZo899mDbbWsf9fOTn/yEefPm0blzZ7p168aYMWOWe36nnXbi8ssvp3///nTt2pX99tuPOXPm0K1bN7p3706nTp04/vjj6d2790rrct111y07zjrrrMOBBx5I//79OfLII+nVqxddunTh8MMPZ8GCBfTo0YOBAwdSWVnJYYcdRp8+Nc0H/bk77riDHXfckcrKSo4++mhGjhxJRUUFF198MePHj6dr164MHjyYESNGAHDYYYfx0UcfUVlZyc0337xs+N9GG21E79696dy587IJs1dB9ZxHk0nzE92fh18dB9ybh4UtJfX4WQ94RNJkUkLozLyPu4Fz8kTSXwYG5f1OJiWYLl3FmF4gTV79IqQeT6Q5jsbnZBHATaRvh3uJNHysupfQZOAzSa9JOhP4DWko2URJU4Bf88V6Bt0EVOTz8Xvg2DzErja3ApNrmjDbzMzMbEWqwzeclFxVVVVUf7OOmVk5W7QozYk0ZAjMng0775x6Jh16aJo3qalbuHAh6667LpAmSp4zZw6//OUvSxyVNQeSJkREVanjsOWttcX2scWg65Yrm3n1wSWKxszMzOpbXdtgzeBfETOzlqNNGzjjDJgxA269FT78EAYMgG7d4He/g5VMDVNyjz76KJWVlXTu3Jlx48bxk5/8pNQhmZmZmZnZanLyyMysCVprLTjhBJg2De68E5YuhaOOgh13hGuvhY8/LnWENRs4cCCTJk1iypQpPProo2yyySalDqnBDBs2jMrKyuWWwm9Zay5OOeWU/6rHsGHDSh2WmZmZmTUhHrZmZtYMLF0Kf/hD+oa2559PE24ffTSceip06lTq6MxWn4etNU0etmZmZlbe6toGc/LIzKyZmTgRbrghDWP75BPYe2847TT42tegomLlrzdripw8aprcBjMzMytvnvPIzKxM9egBQ4fCrFlw1VUwfXqaF2m77eCaa9I8SWZmZmZmZvXFySMzs2Zq441h8GB46y24776UPBo8GLbeGr73PZg0qdQRmpmZmZlZOXDyyMysmWvdGr75TRgzBiZPhmOOgbvvhu7doU8fuOce+PTTUkdpZmZmZmbNlZNHZmZlpEsX+PWv05C2n/8c3n0XBg6Ejh3h8sth7txSR2hmZmZmZs2NJ8w2MytjS5bAY4/Br34Fo0fDmmumZNJpp8HOO5c6OrPPecLspsnftmZmZlbePGG2mZlRUQGHHAJPPAFvvAEnnggPPAC77AK77QZ33pm+sc3MzMzMzKwYJ4/MzFqIr3wl9UCaPRuuvx7mzYOjj4Ztt4ULL0xD3MzMzMzMzFbk5JGZWQuz/vpp2Nobb8Djj0NVVZoPadtt4dvfhuefh2YwotnMzMzMzBqJk0dmZi1Uq1aw//7wyCMwfXpKKD3+OOyxB3TuDEOGwJw5pY7SzMzMzMxKzckjMzPjy1+GX/wiDWm79VZo1w7OPRe23hoOPBDuvttzI5mZmZmZtVROHpmZ2TJt28IJJ8ALL8C0aXD++TB1KnznO7DVVvB//wdvvlnqKM3MzMzMrDE5eWRmZjXaYYc0F9LMmTB6NOy1V5po+6tfhT33hJEj4d//LnWUZuVNUgdJU+qwzZGNFZOZmZm1PE4emZlZrVq1gv32g3vvhVmz4Oqr0/C273439UY666w0+baZlUwHwMkjMzMzazBOHpmZWZ1tthmcdx789a/wpz/BvvvCDTfATjtBnz4wfDgsXFjqKM1A0oOSJkiaKunEXHaApImSXpP0VC5bV9IwSa9LmizpsFz+nVw2RdI1BftdKOmavO8/SdpF0lhJb0k6NG9zbD7+w5LelnSqpLMkvSrpJUkb5u0q8/pkSQ9I2iCX98wxvgicUnDsDpLG5TpMlLR7fupqoI+kSZLOlFQhaYikV/K+T8qv30LSs3m7KZL6NPiFMDMzs7Lg5JGZma2yVq1gn33g979PvZF++lN47z047jjYfPP089lnIaLUkVoLdnxE9ASqgNMlbQbcBhwWEd2AI/J2FwDzI6JLRHQFnpa0JXANsDdQCews6Rt5+7bA2LzvBcDlwH7AAODSguN3JvUG2gW4AlgUEd2BF4Fj8ja/Bc7Lx30duCiXDwNOj4heK9RpLrBfRPQABgLX5/LBwLiIqIyIa4Hv5TrtDOwMnCCpY47niYioBLoBk2o6cZJOlDRe0vgli+YXP8NmZmbWYjh5ZGZmq2XTTeGcc9IE2889lybXvu++NC/S//xPmjdp1qxSR2kt0OmSXgNeAr4EnAg8GxFvA0TER3m7fYEbq18UEfNICZexEfF+RHwGjAT65k3+AzyeH78OPBMRn+bHHQqOPyYiFkTE+8B84OGC13SQ1A5oHxHP5PIRQN8ayu8o2OcawG2SXgfuBXYqUvf+wDGSJgEvAxsB2wOvAMdJuhjoEhELanpxRNwaEVURUVXRpl2RQ5iZmVlL4uSRmZnVCwl694bbboN//APuvBM6dIALLoBttoEBA+CZZ9wbyRqepH6kpFCv3MvoVeA1oKbfPtVQrlp2/2nEst/ipcAnABGxFGhdsN0nBY+XFqyvuF1d4ql2JvAeqddQFbBmLfs4LfdEqoyIjhExOiKeJSXBZgN3SDqmyOvNzMzMluPkkZmZ1bs2beCoo+Cpp2DGDBg8GMaNg379oEsXGDIE5swpdZRWxtoB8yJikaSvALsBawF75uFbVM87BIwGTq1+YZ536OW87caSKoDvAM9QjyJiPjCvYN6ho0m9mD4G5kvaI5cftUK95uRE1dFARS5fAKxXsN0TwPclrZHrtIOktpK2BeZGxG3A7UCP+qyTmZmZlS8nj8zMrEFttx1ceSX8/e+pV9L668O558LWW8NBB8E998C//13qKK3MPA60ljQZuIw0dO190tC1+/Nwtt/nbS8HNsgTSL8G7BURc4DzgTGkHksTI+IPDRDnIGBIjrOSz+dMOg64MU+Yvbhg+5uAQZJeAnYA/pXLJwOf5Um2zwR+A/wFmChpCvBrUm+nfsAkSa8ChwG/bIA6mZmZWRlSNIPxA1VVVTF+/PhSh2FmZvVk2jT47W/TMmsWbLBB6ql0zDFQVZWGwFnLImlCRFSVOg5b3lpbbB9bDLpuubKZVx9comjMzMysvtW1DeaeR2Zm1uh23BGuuAJmzoTRo+GAA1KvpF12SZNsn38+TJrk+ZHMzMzMzJoCJ4/MzKxkKipgv/3gd79Lk2wPHQrbb5/mROreHb76VbjkEpg+vdSRmpmZmZm1XE4emZlZk9C+PRx3HDz+eJpM+5ZbYIstUvJohx1g553h2mvh3XdLHamZmZmZWcvi5JGZmTU5m2wCJ50EY8akibZ//vM0hO2ss2CrrWC33eCaa+Cdd0odqZmZmZlZ+fOE2WZm1mxMmwajRsGDD0L120K/fnD00XD44emb3Kx58oTZTZPbYGZmZuXNE2abmVnZ2XFH+PGP4ZVX4K234NJLYfZs+N73YLPN4IgjYMQImDu31JGamZmZmZUPJ4/MzKxZ6tgRLrgg9UZ68UU4/nh4/nk49ljYfPM0tO2yy2DiRH9rm5mZmZnZ6nDyyMzMmjUpJYpuvBFmzYIJE+Dii1PC6KKLoGdP2HprOOGENNxt4cJSR2xmZmZm1rw4eWRmZmWjVSvo0QMuvBBefhn+8Q8YPhx694Z77oEBA2CjjeCQQ2DoUPjgg1JHbGZmZmbW9LUudQBmZmYNZdNNYdCgtHz6KTz3HDz8MDzwADz6aEo29e2bkkoDBsCXvlTqiM2altdnz6fD4EeXrc+8+uASRmNmZmal4p5HZmbWIqyxBuy1F/ziF2my7YkT4Uc/gvffhzPOgG22SUPczj0X/vhHWLy41BGbmZmZmTUNTh6ZmVmLI0H37mlC7SlT0qTbV18NbdrAddfBwQfDJpvAwIFw113w4YeljtjMzMzMrHScPDIzsxZvhx3gvPNg3Dj4+GN4/HH47ndh7Fg48siUSNp11zQB94svwpIlpY7YzMzMzKzxOHlkZmZWoE0b2H9/uOUWePfdNPH2xRdDRQVcfjnsvvvnvZKGD0+TcpuZmZmZlTNPmG1mZlZERQXssktaLrwQPvoI/vSn1DPp8cfTN7hBmivpoIPSsvPO6XVmZmZmZuXCPY/MzMzqaMMN4VvfgqFDYfZsmDQJrrwS1lkHrrgCevWCzTdPQ96GD4cZMyCi1FGbmZmZma0e9zwyMzP7AiTo1i0t55+feiU9+WT6prbHHoORI9N2W22VhsHtvz/su29KQJmZmZmZNSfueWRmZlYPNtwwzYM0YkSaB2nKFLjpptQb6f7703ObbJLWL7gAnn4aFi8uddQNZ+bMmXTu3Hm19iGpn6TdV7LNyZKOqaG8g6Qpq3HsH0pqU7D+R0nt8/KDL7rf1SFpS0mjCtbvkjRZ0pmSLpW07yrsa7XOj5mZmbUs7nlkZmZWz1q1gk6d0vL978Nnn8Err6R5kp54Ig11u/xyWGutlEzaa6+07LorrLlmqaNvUvoBC4EXim0QEbc00LF/CNwJLMrHOQhS0gX4AXBTAx23RpJaR8S7wOF5fXNg94jYtjHjMDMzs5bJPY/MzMwaWOvWKUl0ySXw0kswbx488giccgrMn5++za1vX2jfHvbbD666Km332Weljnz1LFmyhBNOOIFOnTrRv39/Fi9ezKRJk9htt93o2rUrAwYMYN68edWbbyrpL7knzd05SXMycKakSZL61HQMSRdLOjs/7inpNUkvAqcUbFMhaYikV/L+T8rl/SSNlTRK0puSRio5HdgSGCNpTN52pqSNgauBL+eYhki6Q9LXC441UtKhRWJ9WVKngvWxOea2kobm+F6t3p+kYyXdK+lhYPQKvYVG53M2SVIfScMlVSeWekp6RtIESU9I2qK281NDnCdKGi9p/JJF84ttZmZmZi2Ik0dmZmaNbP314eCD4ec/h4kT4YMP4IEH4H//Nw15+9GPUrJpgw3SN7gNGQITJsCSJaWOfNVMnz6dU045halTp9K+fXvuu+8+jjnmGK655homT55Mly5duOSSS6o33xzoHhFdgZMjYiZwC3BtRFRGxLg6HHIYcHpE9Fqh/HvA/IjYGdgZOEFSx/xcd1Ivo52A7YDeEXE98C6wV0TstcK+BgMzckznAL8BjgOQ1A7YHfhjkfjuBr6Vt90C2DIiJgA/Bp7O8e0FDJHUNr+mFzAoIvZeYV+HFsSx7NxIWgP4FXB4RPQEhgJXrOT8LCcibo2IqoioqmjTrrZNzczMrIXwsDUzM7MS23BD+MY30gIwdy488wyMGZPmRnrssVTevn3qobTXXrD33tC5cxoi11R17NiRyspKAHr27MmMGTP4+OOP2XPPPQEYNGgQRxxxRPXmi4GRkh4EHlzVY+XETfuIt1u3qAAAFFpJREFUeCYX3QEcmB/3B7pW98wB2gHbA/8B/hwRs/I+JgEdgOfqetyIeEbSjZI2Bb4J3BcRxfqM3QM8CVxESiLdWxDfodU9qIC1gW3y4ycj4qO6xgPsCHQGnpQEUAHMWcn5MTMzM6uVk0dmZmZNzKabwhFHpAXg3Xdh7NjPk0kPPZTKN9ooJZP69YM994QuXZpWMmmttdZa9riiooKPP/64ts2nAzeSetRcUDi8q44ERC3PnRYRTyxXKPUDPikoWsIXaxvdARwFfBs4vthGETFb0oeSugIDgZMK4jssIqatEN+uwL9WMRYBU1fsXSSpPcXPj5mZmVmtmlAT08zMzGqy5ZZw5JFw220wYwa88w4MHw6HHAKvvgpnnAGVlbDxxvC1r8Fll8Ho0WlupaakXbt2bLDBBowbl0ZZ3XHHHey5554sXboUYM2IGAOcC7QH1gUWAOvVZd8R8TEwX9IeueiogqefAL6fh3QhaYeCYWHFFDt2TeXDSUPfiIipK9nv3aQ6touI1wviO025q5Ck7ivZR22mAZtI6pX3tYakTis5P2ZmZma1cs8jMzOzZmabbWDQoLRASiY980xaXnwxTcZdbYcdYJdd0tKrF3TrBmusUZq4AUaMGMHJJ5/MokWL2G677Rg2bBhL0mROHSW9Tuo5c21EfJwnih6VJ5A+rQ7zHh0HDJW0iJSQqfYb0nC0iTlB8z7wjZXs61bgMUlzCuc9iogPJT2fJ65+LCLOiYj3JL1B3YbbjQJ+CVxWUHYZcB0wOcc3EzikDvv6LxHxnzw87/o8VK113vdUip8fMzMzs1opoun3YK6qqorx48eXOgwzM7NmYf58GD8e/vzntLz8MsyZk55r0wZ22w1694Y99kiP11+/tPECSJoQEVWljuOLkNQGeB3oERFl9fVka22xfWwx6Lpl6zOvPriE0ZiZmVl9q2sbzD2PzMzMyky7drDPPmmpNmsWvPACPP88PPccXHEFLF2a5kjq0iUlknr3TsuXvgRpAJWtjKR9Sd9o9otySxyZmZmZVXPyyMzMrAXYemv41rfSArBgQeqRVJ1MGj4cbrwxPbf55qlH0q67pqWqCtar08xDDUfSj4EjVii+NyKuqGn7xhIRf+Lzb0YDQNL+wDUrbPp2RAxotMDMzMzM6pGTR2ZmZi3QeuvBvvumBeCzz2Dy5NQ76eWX0/JgnsGnVSvYaac0Z9Itt5TmG91ykqikiaK6yt/q5jmFzMzMrGw4eWRmZma0bg09eqTl1FNT2UcfpTmTXnopJZOmTi1N4shKp8tW7RjveY7MzMxaPCePzMzMrEYbbggHHJAWMzMzM2u5/PmhmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkVpYgodQwrJel94J163u3GwAf1vM+myPUsHy2hjuB6lhvXs7w0ZD23jYhNGmjf9gVJWgBMK3UcLVhL+dvSlPkalJ6vQWn5/JdeQ1+DOrXBmkXyqCFIGh8RVaWOo6G5nuWjJdQRXM9y43qWl5ZST/ucr3lp+fyXnq9B6fkalJbPf+k1lWvgYWtmZmZmZmZmZlaUk0dmZmZmZmZmZlZUS04e3VrqABqJ61k+WkIdwfUsN65neWkp9bTP+ZqXls9/6fkalJ6vQWn5/Jdek7gGLXbOIzMzMzMzMzMzW7mW3PPIzMzMzMzMzMxWwskjMzMzMzMzMzMrqsUljyQdIGmapL9JGlzqeOqLpC9JGiPpDUlTJZ2Ryy+WNFvSpLwcVOpYV5ekmZJez/UZn8s2lPSkpOn55waljnN1SNqx4JpNkvRPST8sh+spaaikuZKmFJTVeP2UXJ/v18mSepQu8lVTpJ5DJL2Z6/KApPa5vIOkxQXX9ZbSRb5qitSz6O+ppPPz9Zwmaf/SRL3qitTz9wV1nClpUi5vltezlveRsrs/beXKtb3UlK3qPWgNQ1KFpFclPZLXO0p6OZ//30tas9QxljNJ7SWNyu2lNyT18j3QuCSdmf8GTZF0l6S1fR80nOb0v1GLSh5JqgBuBA4EdgK+I2mn0kZVbz4D/i8ivgrsBpxSULdrI6IyL38sXYj1aq9cn6q8Phh4KiK2B57K681WREyrvmZAT2AR8EB+urlfz+HAASuUFbt+BwLb5+VE4OZGirE+DOe/6/kk0DkiugJ/Bc4veG5GwXU9uZFirA/D+e96Qg2/p/lv0reBTvk1N+W/y83BcFaoZ0QMLLhP7wPuL3i6OV7PYu8j5Xh/Wi3KvL3UlK3qPWgN4wzgjYL1a0jvadsD84DvlSSqluOXwOMR8RWgG+la+B5oJJK2Ak4HqiKiM1BBarv5Pmg4w2km/xu1qOQRsAvwt4h4KyL+A9wNfL3EMdWLiJgTERPz4wWkP7RblTaqRvV1YER+PAL4RgljqW/7kP4RfafUgdSHiHgW+GiF4mLX7+vAbyN5CWgvaYvGiXT11FTPiBgdEZ/l1ZeArRs9sHpW5HoW83Xg7oj4JCLeBv5G+rvc5NVWT0kCvgXc1ahB1bNa3kfK7v60lSrb9lJT9gXuQatnkrYGDgZ+k9cF7A2Mypv4/DcgSesDfYHbASLiPxHxMb4HGltrYB1JrYE2wBx8HzSY5vS/UUtLHm0F/L1gfRZlmGCR1AHoDryci07N3dqGlkk3zwBGS5og6cRctllEzIHU+AI2LVl09e/bLP9PabldTyh+/cr5nj0eeKxgvWPuJv+MpD6lCqoe1fR7Wq7Xsw/wXkRMLyhr1tdzhfeRlnh/tnS+tiVWx3vQ6t91wLnA0ry+EfBxwQc/vhca1nbA+8Cw/B76G0lt8T3QaCJiNvAz4P+RkkbzgQn4PmhsTbLt1dKSR6qhLBo9igYkaV3S8IkfRsQ/SV3ZvgxUkv4A/LyE4dWX3hHRg9Rt7xRJfUsdUEPJ44kPBe7NReV4PWtTlvespB+ThieMzEVzgG0iojtwFvC7/Olbc1Xs97QsryfwHZZP8Dbr61nD+0jRTWsoK4frab62JbUK96DVI0mHAHMjYkJhcQ2b+l5oOK2BHsDN+T30X3iIWqPKH/h9HegIbAm0Jf3PtSLfB6VR0r9JLS15NAv4UsH61sC7JYql3klag9TYGBkR9wNExHsRsSQilgK30UyGiNQmIt7NP+eS5gHaBXivuste/jm3dBHWqwOBiRHxHpTn9cyKXb+yu2clDQIOAY6KiADIw7g+zI8nADOAHUoX5eqp5fe0HK9na+CbwO+ry5rz9azpfYQWdH/aMr62JbKK96DVr97AoZJmkoZq7k3qidQ+/60H3wsNbRYwKyKqR0+MIiWTfA80nn2BtyPi/Yj4lDSf4+74PmhsTbLt1dKSR68A2+fZ4tckDQd6qMQx1Ys8Jvt24I2I+EVBeeEYyAHAlBVf25xIaitpverHQH9SnR4CBuXNBgF/KE2E9W65Hg3ldj0LFLt+DwHH5G8W2A2YX92FszmSdABwHnBoRCwqKN+keuJoSduRJsF7qzRRrr5afk8fAr4taS1JHUn1/HNjx1fP9gXejIhZ1QXN9XoWex+hhdyftpyybS81ZV/gHrR6FBHnR8TWEdGB9Dv/dEQcBYwBDs+b+fw3oIj4B/B3STvmon2Av+B7oDH9P2A3SW3y36Tqa+D7oHE1ybaX8gffLYbSV0ZfR5o5fmhEXFHikOqFpD2AccDrfD5O+0ek5EMlqTvbTOCk5ty4z/+IVX/rWGvgdxFxhaSNgHuAbUh/9I6IiLpO4tskSWpDGtO6XUTMz2V30Myvp6S7gH7AxsB7wEXAg9Rw/fKb1g2kbyBYBBwXEeNLEfeqKlLP84G1gA/zZi9FxMmSDgMuJQ1lWwJcFBEPN3rQX0CRevajyO9pHrJ3PKmuP4yIx/5rp01QTfWMiNslDSddx1sKtm2W17OW95GXKbP701auXNtLTdmq3oMlCbKFkNQPODsiDsltz7uBDYFXge9GxCeljK+cSaokTVi+JumDl+NIHR58DzQSSZcAA0ntmFeB/yXNq+P7oAE0p/+NWlzyyMzMzMzMzMzM6q6lDVszMzMzMzMzM7NV4OSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRmZmZmZmZmZkV5eSRWRmTtETSJEmvSZooafd63v+PVlh/oZ7220/SIwWP6y1uSR0kHVmwXiXp+vrav5mZmbVMBe2uKZIeltS+Dq9ZuJLn20v6QcH6lpJG1UOsHSRNWd39rOIxKyUd1JjHNLP64+SRWXlbHBGVEdENOB+4qp73v1zyKCLqNTmV9QNWab+SWtfydAdgWfIoIsZHxOlfKDIzMzOzz1W3uzoDHwGn1MM+2wPLkkcR8W5EHF4P+21UuW1WCTh5ZNZMOXlk1nKsD8wDUDIkfzL2uqSBKynfQtKzBZ+m9ZF0NbBOLhuZt1uYf/aTNFbSKElvShopSfm5g3LZc5Kur+5hVBNJHYCTgTPzcfpI2kTSfZJeyUvvvO3Fkm6VNBr4bf5EbVzucVXY6+pqoE/e35kr9HLaUNKDkiZLeklS14J9D811ekvS6bm8raRHc8+uKdXny8zMzFq8F4GtqlcknZPbLZMlXbLixpLWlfRUbrO8Lunr+amrgS/ndsuQwh5Dkl6W1KlgH2Ml9cztk6H5eK8W7KtGko7N7Z+HJb0t6VRJZ+XXviRpw4L9Xyfphdzu2SWX19Z+WtY2Ay4FBua6DJS0S97Xq/nnjgXx3C/pcUnTJf20INYD8jl6TdJTuWyV6mtmX0xtn86bWfO3jqRJwNrAFsDeufybpE9/ugEbA69IepbUw6em8iOBJyLiCkkVQJuIGCfp1IioLHLs7kAn4F3geaC3pPHAr4G+EfG2pLtqCz4iZkq6BVgYET8DkPQ74NqIeE7SNsATwFfzS3oCe0TEYkltgP0i4t+StgfuAqqAwcDZEXFI3l+/gkNeArwaEd+QtDepoVNdv68AewHrAdMk3QwcALwbEQfnfbWrrT5mZmZW/nJbaR/g9rzeH9ge2AUQ8JCkvhHxbMHL/g0MiIh/StoYeEnSQ6R2S+fq9lb+YK3a3cC3gIskbQFsGRETJF0JPB0RxysNnfuzpD9FxL9qCbszqe22NvA34LyI6C7pWuAY4Lq8XduI2F1SX2Bofl1t7afCttmxQFVEnJrrsj6pTfiZpH2BK4HD8usqczyfkNpdv8rn6DY+b0dumLf98Reor5mtIiePzMrb4oLGRi9Sj5zOwB7AXRGxBHhP0jPAzrWUvwIMlbQG8GBETKrDsf8cEbPysSeRhostBN6KiLfzNncBJ65infYFdlLqyASwvqT18uOHImJxfrwGcIOkSmAJsEMd9r0HudESEU9L2qggIfRoRHwCfCJpLrAZ8DrwM0nXAI9ExLhVrIuZmZmVj3UK2jwTgCdzef+8vJrX1yUlkwqTRwKuzEmZpaReS5ut5Hj35GNcREoi3VtwvEMlnZ3X1wa2Ad6oZV9jImIBsEDSfODhXP460LVgu7sAIuJZSevnZE1t7afCttmK2gEj8od8QWq7VXsqIuYDSPoLsC2wAfBsdTsyIj5ajfqa2Spy8sishYiIF/MnWZuQGig1qbE8NxD6AgcDd0gaEhG/XckhPyl4vIT096bYcVdFK6DXig2RnEwq/ITpTOA9Ui+qVqRPq1ampvgi//yv+kTEXyX1JI3fv0rS6Ii4tE61MDMzs3KzOCIqc+LkEdKcR9eT2hdXRcSva3ntUaQ2Ws+I+FTSTFISpKiImC3pwzxMbCBwUn5KwGERMW0VYi9s5ywtWF/K8v8zBssLam8/1db75zJS0mpA7lE1tkg8he3IFY8PX6y+ZraKPOeRWQsh6StABfAh6ZOugZIqJG0C9AX+XKxc0rbA3Ii4jdQFu0fe7ae5N1JdvQlsV9Dlui5zBC0gDRWrNho4taBexYbNtQPmRMRS4GhS3WvaX6FnSY236uFsH0TEP4sFJmlLYFFE3An8jM/Pi5mZmbVQucfM6cDZuZ30BHC8pHUBJG0ladMVXtaO1Nb6VNJepJ42UHu7BdLQtXOBdhHxei57AjhNWjbfZPf6qFdWPR/mHsD8XNe6tp9WrEs7YHZ+fGwdjv0isKekjvlY1cPWGrK+Zpa555FZeavuPg3pU5lBEbFE0gNAL+A10ic450bEP2opHwScI+lT0tCzY/I+bwUmS5oYEUetLJg83v0HwOOSPiAlrFbmYWBUnvzwNFJj7EZJk0l/w54lTaq9opuA+yQdAYzh80++JgOfSXoNGM7nXcgBLgaG5X0vAgatJLYuwBBJS4FPge/XoT5mZmZW5iLi1dzW+HZE3CHpq8CLOb+xEPguMLfgJSOBh/P8kJNIH7gRER9Kel5pkuzHgBtXONQo4JekXjzVLiPNUTQ5J1RmAofUU9XmSXqB9EUsx+eyi6lb+2kMMDi3Ta8CfkoatnYW8PTKDhwR70s6EbhfUivS+duPhq2vmWWKqKnnn5lZw5C0bkQszG/uNwLTI+LaUsdlZmZmZsVJGkv60pHxpY7FzBqfh62ZWWM7IX/iNJXUXbm28f9mZmZmZmZWYu55ZGZmZmZmZmZmRbnnkZmZmZmZmZmZFeXkkZmZmZmZmZmZFeXkkZmZmZmZmZmZFeXkkZmZmZmZmZmZFeXkkZmZmZmZmZmZFfX/AU2w0XgXvl1YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model, im = regressor(X_train, \n",
    "                                y_train, \n",
    "                                params, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first model has a MSE of 5 --- which is about 8 dollars. The most important variable is `is_location_exact` --- we don't quite know what it means. The most important 9 features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.356469016601148"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(5**0.5)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>relative importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>price</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>host_age</td>\n",
       "      <td>55.482103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>reponse_rate</td>\n",
       "      <td>26.924585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acceptance_rate</td>\n",
       "      <td>26.206234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one_or_two_listings</td>\n",
       "      <td>23.234339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>extra_people</td>\n",
       "      <td>15.485575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>amenities_len</td>\n",
       "      <td>14.712161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cancellation_policy_ordinal</td>\n",
       "      <td>12.177285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>price_per_bed_compared_to_nbh</td>\n",
       "      <td>8.405304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>response_time_ordinal</td>\n",
       "      <td>7.826637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>number_of_reviews</td>\n",
       "      <td>5.964073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>accommodates</td>\n",
       "      <td>4.218796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>neighbourhood_cleansed_Jamaica Plain</td>\n",
       "      <td>3.585059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>host_identity_verified</td>\n",
       "      <td>3.318434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>review_scores_location</td>\n",
       "      <td>3.001823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>neighbourhood_cleansed_Dorchester</td>\n",
       "      <td>2.715981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>downtown_bos</td>\n",
       "      <td>2.596276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>2.585950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>neighbourhood_cleansed_Hyde Park</td>\n",
       "      <td>2.520654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>more_than_ten_listings</td>\n",
       "      <td>2.508766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 feature  relative importance\n",
       "45                                 price           100.000000\n",
       "2                               host_age            55.482103\n",
       "3                           reponse_rate            26.924585\n",
       "4                        acceptance_rate            26.206234\n",
       "5                    one_or_two_listings            23.234339\n",
       "46                          extra_people            15.485575\n",
       "10                         amenities_len            14.712161\n",
       "11           cancellation_policy_ordinal            12.177285\n",
       "59         price_per_bed_compared_to_nbh             8.405304\n",
       "8                  response_time_ordinal             7.826637\n",
       "51                     number_of_reviews             5.964073\n",
       "47                          accommodates             4.218796\n",
       "24  neighbourhood_cleansed_Jamaica Plain             3.585059\n",
       "39                host_identity_verified             3.318434\n",
       "57                review_scores_location             3.001823\n",
       "19     neighbourhood_cleansed_Dorchester             2.715981\n",
       "0                           downtown_bos             2.596276\n",
       "49                              bedrooms             2.585950\n",
       "23      neighbourhood_cleansed_Hyde Park             2.520654\n",
       "7                 more_than_ten_listings             2.508766"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.sort_values('relative importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uDVHdxKETgyWvQnpk33Y8tLsbMzEDsWTvo9XocVTMxnofJiKJrJAnbtjYUO/Lu//ITBxHB0ZxtD+IixPZ3+vejXXY19uOPTe2oclfec2wl4sBEhEREREREVEJ7T/0Ak5emEp/fveWJjxz4AMAilf6rCTE6WzyY3iqePVMMUNTUTT6XFnbfC4VwysMpoqRUiIUNxGKGTCs1Z+mVsyDOztx8Nh5xAwLXpeCuGHDtCUe3NmZ3mcmZuC5c2M42j+KN66Eso7vaPJhX28Ae3vacX1T/tBpvWOAREREREREFUkI8W0AHwUQlFLektz2dQC/BUAH8C6A/yylnC7fKImKyw2PAODkhSnsP/QCnjnwgaIh0UpCnAO7u3F6cCLvNLae9poFj+9s8iMYjqfHBQAxw0JH0+qvFGZaNkJxE+G4AcsubaVRIbu6m/EotuHwmSGMhGLYkFyF7dbOBvxyIIi+/iDOXJyEmTG+Jr8L9/YEsK+3Hdvba9dNM+zlqtoAqd6rIZTZaj5jOxERERERrQtPA/gmgO9mbPsFgC9LKU0hxNcAfBnAF8swNrrGFKsiKiY3PMrdXiwkWkmIs6cngL/7zE589ukzyKzlafFri2qgfWB3Nx47chZR3Uw3lDYsiQO7uxc8drHihoVQzEBEt0o+RW0xdnU3Y1d3Myxb4leXp9DXH8T/9a9vIZax2pvPpeKD25xm2Hd0NUFVqjs0ylS1aYpby9/RvNB2IiIiIiKqLFLKE0KIzTnbfp7x6SkAH1/LMdG1aaEqopUoFhKtNMR5fXgaQhFwCaRXNJuOW3ii7xwe2be9aIPuPT2B9DS64aloejyr0f8okjAxEzMQNwq1+V57UkqcG51FX/8ofvn2WFbDblUR2LW5Gft6A7h7awu867wZ9nJVbYAU1fP/RSy0nYiIiIiI1p3PAvhBuQdB1W+hKqKVKBYS7ekJ4OPD0+kVyWrcKh66Z8uiQ5ynnr8ARQCa4hRSKAIwbRtPPX8Bt3Y0Ltige09PYNUaZtu2RDhuIhRf+/5GxVyZjuFov9MMeyinZ9Qt19Vjb2879mxvQ4PfVeAM1w4GSEREREREtO4IIf4cgAng+0X2eRjAwwDQ1dW1RiOjSpOqsjk3GsJExMh6rNat4M3HP1zS69d5VIQT81+H1nmcKpZilT7HB4J49pUraKvzoCsZLj37yhXc2tG4qGAnolvInYSjCGf7Wq2yZlg2QjED4bgJuwKmqQHAdFTHL992mmG/dTWc9dimFn+6GfaGBm+ZRliZqjZAIiIiIiKi6iSE+DSc5tp7ZZHGKVLKJwE8CQA7duyojFeutKZSK5zppjUvPAKAWd3GLY/9pKQh0v+z/w489N0zMDOKbjTF2Z5SqNJnpSFPjdsJnTLb9NjS2V7qVdbihoWZmIFIYn5v4nKIGRZeeGc83Qw7s1d3S60b994YwH03tWNrW03VN8NeLgZIRERERES0bggh7ofTNPtDUsrSrSdOFeuJvnPzpnTd2tGYt5fPoROD0E0LwXCi4Plm9YWnU929pSnvdDVNAfY/eWrB3kCNfjdmEyYsW0JVBGo9i3spPjQVhSqAwbFZ6JYNt6qgtda96JDnoXu24G+PnodhzVVAKcLZfnJwsiSrrEUSJqZjBhIV0N/IsiVeujSJo/1BPP/OOOLG3M+6xq1i9/Y27O0N4LaOxmuqGfZyMUAiIiIiIqKKJIR4BsAeAK1CiGEAX4Gz6poHwC+SVQKnpJR/XLZBVqlizZXL6Ym+c/ibvvPpz0NxE3/Tdx4NPg2ttZ55vXzOjYYQiptY6crwzxz4wLxG2m4F2L6hPm/voEyHTgyiwefCxgZfettiq4hq3SreGYtAFQKqEDAtiSvTcdzQVrOocd/a0Ygat4qIbsGWTnhU41Zxa0djugfSaqyyJqVEKG4iFCt/fyMpJQZGwujrD+L420FMRecqz1yqwF1bWrCvN4D3d7dc84tsqYqAz734huBVGyClOszn205ERERERJVPSrk/z+a/W/OBXGNS076KNVcutSf6zuGbx96BnnxR59EU/MmerfhGRniUaSZmoqvZCVVMSyIYjuPA916GaUtIKQu+PsynUHiWWm1t/5Onsip3FppWljlVLBw3MBZOIGFaGJ6K4fhAsOj3ND2VSiT/AwCJRU+xOnRiEIF6b1aVUWqszzz8/hWvsmbZEqGYgVDcgLXSlG6FhiajONofxNGBIK5MZzfDvq2jAXt72/Gh7a2o8167zbAV4QRGXpcKn0tdcoBWtQHSje116B8J591ORERERERE+a1Fc+V7/roPwzNz08o6Gjx4/sv7cHwgiL/4lzcwPB3P2j9h2vhG33ksFFGEYgaGpqKQEgvum1LrnnsRvZjwbKm9gzqb/AiG47Bsifem4xDCeSEvBBYM5sIJE9c3ejE+q6ensG2o92B2kX2FFhrrcldZS5ip/kYWirQhK7nJiI5fvh1E31tBvD2a/fq/u7UGe3sDuLcngPb6a7MZthACHk2Bz6XC51bh0ZQV9Xeq2gCp0Zf/Syu0nYiIiIiIiJYekCzW5w+/giOvj+StVBmeSeDOx38GRVEwNqvnPX4xMcXITGzJ09W++ck7039eTHiWCoQW2zvowO5uPHbkLIKhuPNVSAEJoL3OC00VRYO51LW622rT26K6iUDd4gKRpY51IVHdxEzMQKyMq5tHdRPPn3eaYb9yeSrr5x2o8+DengD29QayvmfXEpeqwOdW4Xer8GoqlFWchlW1acqpPA3Oim0nIiIiIiKi1QsdPn/4Ffzw1auL3n8iakJbwYvdqG5Ct+bSBIHioVONW0W9T8sKcBYTnqUCocX2DtrTE8DjAA5872VIOH14Wmu9qPe5IKUsGswt9VqrfTwA2LZEOFHe/kamZePMxSn09Y/ihXcnkMhY0q7Wo+FD29uw76YAfu36BijX2ApqmqLA605WGblUaGrp+jpVbYBU6BcF1+4kIiIiIiIqbDmhQ26D6eVayXSoQJ0X745F5s61wP6mLTEe1mFYc1OfFhOepQKhpfQO2tMTwB1dTUsO5pZzrdU63rBshGIGwnETdhmmqUkpcfa9ULoZdig+N23PpQrcvbUF+3rasWtL8zXVDFtVBHwuFV63Exi5ShgY5araAImIiIiIiIiWbrGhw+Yv/XjVr20tM6dwqQLPPPx+fPhvT+TthZuPbtqQAKKJuelYxcKzlaxMd3wgiOmojosTUbhUgfY6DzRVWVQ10HL7FC33+LhhIRQzFt1nabVdmoigrz+Io/1BjITmemEJAO/rasS+3nZ8cFsraj3XRpyRbnytqfC6FXi0xa+attquje84ERERERERLVpu6HB8IIgP/+0JDIyEyzaro9iUtK2tzgpsX7y/B595+syizpc6V8yw0quhFQrPACx7ZbrMxtwdjV6MhhIYno5hW1st/vIjN63ZynbFSCkR0Z3G2Alj7fsbjc8mcGwgiL7+IN4JzmY9dkOgFvt6A/iNGwNoq/Os+djWmhACXpczJc2b/K9SMEAiIiIiIiKieW788x8jscZZQqGQSADwuBQIADFjfh+eJr/Tt2hPTwC1HgWzicX36nHlNLLOV7Gz/8lTy16ZLrcxd73PjahuoqnGU/bwyLIlwnEDoZgJ017b/kazCRP/dm4MfQNBvHp5OuvnvqHei729AeztDWBzS82ajmutrfZKaaXEAImIiIiIiIgAONUyX/1JPwZGZxfeuQQ0VcDIM49NwGmWPFFghbaTF6ayptQpcIIomTxWANAUAT25ZJdIJlWpxxdaYW4lK9OValW7ldBNG6G4099oJX2nlnPd0xcm0TcwipPvTmT9rOu9Gvbc6KygdvN19RUboqwGj0uFV1PSU9NWc6W0UqraAKlYck1ERERERETZbnnsJ5jVy7PKVjGaAtzQVosvfbh30dPTbCBZyQFI6VTaSOFs000bkE6I5FIETCkXXGFuJSvTrdaqdqshbjjT1CJr2N/IlhJvXJnB0f4gnjs3hnBGM2y3puDXt7Zgb28AOzc3r2lD6LXkUp2wKDUtTV0ngVGuqg2QiIiIiIiIKL8n+s7hW8ffzVoOvRKkKlIEnNWmGv0u/PeP37asqV6aIqBbNtyqgg31HoyEEmjyuzAR0aFAQAjAkhKaoizYyHo5K9OtxrGrJZIwMRMzEF/D/kYXxiP4xVujODYQRDCcSG9XBHB7VxPu6w3gnm2tWcFatXCpCrzJKWleTYFWJcFYSX9SQohvA/gogKCU8pbktmYAPwCwGcBFAL8vpVz5eo85ChXhlavhGxERzVfO+wQREdG1Zv+hF3Dywvq4pUoAqsjuT3R8IFi0kXau7rba9J+juonu1hpEdAstNW6E4yYSpg1VEfiTPVsXDKgWuzLdah+7ElJKhBMmfvbGCL7/4mVcDcWwsd6HB3d2Yld3c0muGQzFcXTAWUFtcDyS9diN7XXYd5PTDLu5xl2S65eLqginuihZZVStlVSljvqeBvBNAN/N2PYlAEellF8VQnwp+fkXSzwOIiKqTE+D9wkiIqKSyewLtJ4oAoAQGA/rMKwwACeA8WgKdMuGvYgUKbfi5y8/0pM+z3KCnHzNtRdrJcculW1LhJKNsV94ZxwHj52HpgjUezVMRBI4eOw8HsW2VQuRwnEDz50bx9H+Ubw2PJP12HWNXuzracfe3gA6m9d+yl6ppFZK87s0eN0KPFrlrJRWSiUNkKSUJ4QQm3M2fwzAnuSfvwPgOPjCgIjomsT7BBERUWkcHwguul9QJbIl0tPr9OTHoako2us9uDqTgBAoOv2u1q0gUOfNCooAJzwamoqic42qgNaSadmYiTmNse1kY+zDZ4agJatjAKQDtcNnhlYUIOmmjVODE+jrD+LFC9nNsBt9Luy5sQ37etvRu7Guapphe1xOdZHTx6hyV0orpXJMNmyXUl4FACnlVSFE9fyLJSKi1cD7BBER0TKt14qjYmaTTZdTzaiva/RiLJyAjvzT2RQAbz7+4axtxweCeOzIWbhUgUafC8FwHI8dOYvHgXUfIiXMVGNsa96KaldDMdR7s1/2e10KRkKxJV/HsiVeG57G0f4gTpwfQyQx10/Jqym4Z1sr9vYGcGdXU1X0/MnsY+Rbx42vV1NFd6sSQjwM4GEA6OrqKvNoiIio0vA+QURE5FjvFUfFpOqMUs2oXarAltYaTEQSCIZ1tNW60VrrSU9Ve/yBm+ed49CJQbhUkW7Y7HdriOpmur/SehTTneAoqhdeUW1jvQ8TkUS6AgkA4oaNDfW+RV1DSol3xyLo6x/F0YEgJmb19GOKAHZsbsa+3gB+fWsrfO71PY3rWuljtBLlCJBGhRAbk+8qbwQQLLSjlPJJAE8CwI4dO9j/mojo2sD7BBER0SJ8/vAr+JdXr14zCwXlNqPe3FKL/TubcXJwcsGeRkNTUTT6XFnbfC4Vw1PRNRr96rBtpzF2KGbAsBZeQe/BnZ04eOw8YoYFr0tB3LBh2hIP7uwsetzITBxHB0bR1x/EpYns71Hvxjrs623Hnhvb0ORfv82wU32MfMkqo2ulj9FKlCNAOgLg0wC+mvz4ozKMgYiIKhfvE0REREXc/43jGBiNLLxjmagKsIhsY1nyNaN+ZBHHpaa/ZS4ZHzMsdDStj8bOCdNCKGYikpjrb7QYu7qb8Si24fCZIYyEYthQZBW2mZiB586Noe+tUbz5XijrsY4mH/b1BrC3px3XNy2ueqkSsY/RypQ0QBJCPAOnEWqrEGIYwFfgvCD4RyHE5wBcBvCJUo6BiIgqF+8TRERES7Meehxd1+DD6Ewc+mKWSlsjqelvuSuzpZprV6rZZLVR3LAW3rmAXd3NBRtmJwwLJwcn8Iu3gjhzcRJmxs+sye/CvT0B7Ottx/b22nUZtrCP0eoq9Sps+ws8tLeU1yUiovWB9wkiIqKF3fn4zzARLdznptIIIWBV2MS61PS3r/6kH+eDswCA7taa8g6qAMuWCMcNhGImTHv1S7ksW+KVy1M42h/Ev50fRywjnPK5VHww2Qz7jq6mdRe4sI9RaVV0E20iIioskjAxEoqXexhERERUIk/0ncPf9J0v9zCWzKUKeDUVUd0JJlYaJeWGGMcHgjh0YhBDU1F0Ful7lE/UsNHR5EtXIVXSSmy1h2rxAAAgAElEQVSGZWMmZmA2vrRpaoshpcS50Vn09Y/il2+PYTIy1wxbVQR2JZth3721BV7X8nsBnR6cxOEzQ7gaimFjkelyqyWzj5E3+R+VTtUGSC1+LW9K3+Kv2i+ZiK4RkYSJoakonn1pGM++PFzu4RAREVEJ3PPXfRieSZR7GMvic6loq/NgeCqWNSUKAFThBErFZrcJAEIAHk2Fbjoh1D1fO4bOJj/u7m7Gs69cgUsVaPS5EAzHFx0CVepKbDHdQihuIJJY/SqzK9MxHOsPoq9/FENTsazHbrmuHnt727Fnexsa/K4CZ1i804OTOHjsPDRFoN6rYSKSwMFj5/Eotq1qiMQ+RuVTtWlKoN6HiWg473YiovVoNmFiMDiLH7w0hH/51RVE9OXPhSciIqLKdHwgiM88fabcw1iRmGGhzutCR5OzmlfcdKZheTUFGxq8uDIdg7AlrIwQSYETLKX+0wSgmxYsCTT51HRY9K3j76K5xoUGnxfA0kKg88EwogkThi3hVhW01XlQ69HKshLbUldTW4rpqI5fvj2Go/2jeOtq9mviTS3+dDPsDQ3eVb3u4TND0JJTyACkq7wOnxlaUYDEPkaVo2oDpKGp/KsSDBfYTkRUiaSUmE2YeHskjP/x4mX8+I2rSKSehLkUPHDbdfh6mcdIREREq6PSV1dbLMOSiOomaj0arm/y4eJEBB2NPtT7nCXf3WoCBmwoAHo21CMUMzAajkNKoK3WjcmIgYRlA8IJjzqanF5FfrcGy5aYiRporZ0LP3wudV4IlDvN7e7uZoSTU8NURcC0Jd6bjqOl1oXNLbVr9r3RTRuh+OpPU4sZFv79nXH09Qfx0sXJrAqvllo37r0xgPtuasfWtpqSVexcDcVQ782OGLwuBSOhWIEj8mMfo8pVtQFSzMj/jzFaYDsRUSWR0nlX6s3hGXzv1CX8/K3RdAl4nVfD79x+Pf5gRyc6W/wMkIiIiNa57i/9GCVa9b4sHn/gZhw6MYjhqShq3Co8qoor03GMz+porfWkp7dpqoCUEpoqEKjz4vEHbs6qIrrna8fQ6MueWuXRFMTN7CrsmGGho8mf/vz4QBCPHTmbNc3tW8ffhd+tIBS3YBp2ui/T+KyOv/6d0q/EFtVNzMQMxFaxgtyyJV66NImj/UE8/8444sbc36Iat4rd29uwtzeA2zoa16RqZ2O9DxORRLoCCQDiho0NC8wCyuxj5HOr8GjsY1SpqjZAsgtMqi20nYiokvxyYAz/cOoinjs3ln4HqbnGjd/f0YHfu6MD1zf50vP3iYiIaP3a/KUfl3sIq0oRTi+iPT2BdJDT4NcwHtYRNy1cnoymwxvTlnjzvRB8LgX/5UNb501B62zyIxiOZz3nqfNqMKNOhVNqipRhSRzYPRcC5et1ZNkSkbiJzKIfAcC0Svf6MPWG4Ex09aapSSkxMBJGX38Qx98OYipqpB9zqQK7tjRjX2877u5ugVtb28qdB3d24uCx84gZFrwuBXHDhmlLPLizc96+7GO0PlXtqw9FEbDyhEUK50sS0Trw2e/M9T7YUO/FH+zsxO/ecT3a671cXYKIiKgKfP7wK/jhq1fLPYxVl/lqKxXkNPi8sG0gGE7MW5FNU5wpb989dQm3djRmhUgHdnfjsSNns8Iit6biT/Z04eTgJIanoujIswpbvl5HHk1BRLfgUZX0a0JbSojkOFezibZtS4TjTsWRaa9OcDQ0GcXR/iCODgRxZTp7SthtHQ3Y29uOD21vRZ135c2wl2tXdzMexTYcPjOEkVAMGzJWYXNryT5Gyf/4unx9qtoAye9SEE7MLw/0uzh/kojWh00tfnxyVxd+67aNaKn1sJyXiIioSlTblLUUAUDL6FeTGeRYttN7yLIlJOaCJlsCLkVgNjG/EfaengAeB9LT4VJhEQCcHJycF0YBzvS1fL2OajwKIjog4VxfSue/DQ2eVWuibdkSoZiBUNzIW8ywVJMRHccGgjjaH8Tbo9nNsLtba7C3N4B7ewJor1/dZtgrsau7eV5g5GXj66pRtQFSg8+VN0Bq8JUvkSUiSlnoScVffexm/ObNG9Bc42bjQCIioiqyXqasKQJZjZgFgFuubwDg9PMZmohCz3k+IwFIW+L4QBAAsoIcw5KQUmaHPsIJcYRwnhvlC3JS0+FS8vU3euzIWTye3PfQiUE0+V2YiOgwTRs2nK/DiNlorXUjFDOQSPYK8mgKdMtGo8+N/U+eSjfczq1oSnmi7xyeev4CIrqFGreKh+7Zgkf2bYdp2ZiJGemvN5/Tg5M4fGYIV0MxbMyozMkV1U08f95phv3K5amsn0FbrQd7ewPY2xvA1ra1a/y9GPkCo9xG5oW+r7R+VG2ANBJKLGk7EdFaMC0b47MJ/OjV94ru98m7NvGdGiIioiqzXsIjALj5ugZIKTEScnoQuZINr1M9h9yagK7PD0sMW+KxI2fhdynpIEc6C6plhUcScxtM22mkndkIu5B8/Y2i+lz10tBUFK21Hli2xPisnq52UgAkDAu6JdNjSZg2rs4kMBs3k0HS/EAq5Ym+czh47B0owpl2FzMs/O3R84gkTDx41ybIIiuqnR6cxMFj56EpAvVeDRORBA4eO49HsQ27upthWjbOXJxCX/8oXnh3Ir3iLgDUejTs3t6K+3rb8WsdDVAqpFeQS1Xgc6vp0Cj3eetCQR+tT1UbIJkF3t0vtJ2IqJQMy8ZoKI7/+fIwnjk9hJFQvOj+DI+IiIiqy3oKj1yq8zxkIpKAbtoATOimDbcqsK29Hgd2d+MzT5/Je6xMHn9hIoptgVp4NBXjswmYUsC2Zd5pZ6YtoSC7EXYhQ1NRqAIYHJuFbtlwqwpaa93p6qVU4+2obsGd7HdkSwlNEYgb+VdAixtWwUAq5annLyTDIwVSSqgCkELiey9exh/s6io65sNnhmBaFqajFgzLhktVUONW8NTzF/DC4ASOvx1EKG6m93epAndvbcG+nnbs2tJcsBn2YquaVoNLTVYYufMHRrkWCvpofaraAImIqBLopo0r01EcPj2Ef3p5GJMRHcD8snAiIiKqXuslPEoFRy5FYHw2jmBYR1utG621nqzVzhYKAFLLuI/PJhBOVvd4NQVuTSAUt/JO5ddt4PXh6QXPXetW8c5YBKoQUIWAaUlcmY7jhrYaAHONtxOmDVVxnm9JCbTVeXBpwgmZRLIESSSn0Jk5Dal8LnXedLqIbkEVMmuKmhBOJdJCLk1GEI4ZEIqAEE7lU8ywMR6ZxTtjs865ALyvqxH7etvxwW2tqPUUf6m+UFXTSqmKcKajJQOjpbRUOD7gTL+zbBseTUVbnQd1XlfW95XT29YnBkhERCWQMC1cGo/iey9ewg9/dQXh5LtKmiJw303t+KO7N2H///dimUdJREREpbZewiPAWdJeCKdvbCRhoa3WjbY6p0HzUipIYoZz7NVQAopw3jjTLRtxE9AUiUIr2j/1/AU8sm970XML4Uyl06VEan6aktwOzDXefuTwrxDRTXg1JR1gpKIfmTwulQXlxlkxw0pPp7NtiXDChE9TEDctCClhZlRSeRexSFPcsGADkNb8CqwbArXY1xvAb9wYQFudZ8FzpRw+MwQtGfIASK9Sd/jM0LICJCEEvC7FWSXNrS578ZbU1DUhAEXMNTG/rtEJpTqa/CWf3sZwqnQYIBERraK4YeH86Cy+e/Ii/tfr7yGe0aTxI7duxKfevwnb2+tQs8C7SkRERAQIIb4N4KMAglLKW5LbmgH8AMBmABcB/L6UcqpcYyymksOj3J5ESH4u5VyvII8mCk4VK8awJOq8LlhSIhQz08d7NAWTUaPgcRF94WqesdlEerASgEiGQentcEKIJx68PR1S+FwqorqZ/poz+y+lvhdR3UyHMIYl8dA9WzCRrKCypcQn7uzA0ycvITcCUuFUA+WGNrMJE/92fhxH+0cRM+YnZgJAnUfFk5+6c8GvOZ+roRjqvdnPJ70uBSOh2KLP4Un2L3IaXyvpEG4lUlPX2uu8eG8m5vx8IDEyE0eg3osDu7tLOr2NvZdKi69giIhWQVQ3cfa9EL77wkX89OwIDMt5clHjUfHb77sen3r/JmxurYHXtbx3c4iIiK5RTwP4JoDvZmz7EoCjUsqvCiG+lPz8i2UYW1GVEB65FCBPdgEFQIEiIABAOGHBpQBjs3N7mZaF4akYtgWc1b9a/C5M5AmDVAE8/sDN+IsfvYmWGg9aa50KpnDcwJWp4uGGlM4Kbnt6AgWrSHTThqIIuJW5yh/TtpO9muakKpEOnRjE+WAYumnn7b8EAB5NIFDnxfBUFBsbfNi/qxNb2mowE5v7+j71gc3432+8h+CskW7M3eh3odajpat+dNPG6QuT6BsYxcl3J9LPB5HcX1EASGeqYK1HQ0dTTdHvRzEb632YiCTSFUgAEDdsbKj3FTxmqX2MlmNoKopGnwvC7Zx7fDYBPVl59fgDN2NPTwB/8aM30ZizOnq+aYPLwd5LpcUAiYhoBWYTJl65NIXvnryIYwPBdF+jRp8LH7+zA5+8qwsdTf6CzQ+JiIioMCnlCSHE5pzNHwOwJ/nn7wA4jgoLkCohPAIARVHgUwUSySlUKcXCo5Tc4EkCsORclVBbnSdvgLQtUIs9PQF0nnCaWadeyI+FE7CKrFQGONVPB773MtyqgG5JNPldaK31ZFWRuFSBmOFMLRMZ09Dc6vwwJBUYPHbkLBp8AuG4mRUipfITl6rg0B/diVDMmBdEZVEU3BCogcDctWxp4/JkBH/zi3N47txYum0B4Cxt/+tbW9DZ5MfP3xpJBjgK4oYN05Z4cGdn0e9HMQ/u7MTBY+cRM6yC51xJH6PlSjUx97s11PtcqPe5ENVNBOq86Z9H5j4pmdMGVyIVYGVarXCKqjhAKpSq8yUcEa2UlBKzCRMn353Ad05exL+/M5F+LFDnwR/s7MTv7+jEhgbvmtyoiYiIrjHtUsqrACClvCqEqKiygkoJjwDAsiUS9mLiosUbnorh+EAQs7qFtloXJiIGbOmEMS01rnTAlGpmnZoaFtUtSDgVSlaBHEnCaTCtm4CmCkxEdHg0NR1CHDoxiO3t9bgwPptuzu1WFdR5XdjSWpv3nJkVKYoytxKcIgBNCNhwVmgbDyfyHp8ps+onYVoIxU2E4iYsW+JfX78KJM97e1cT7usN4J5tremQ5KaN9Th8ZggjoRg25Fkxbakrqu3qbsaj2JZ9zl2d+NCNbckpaWq68n0tewJl/txNy8ZoOAHDknCrSrq6LPfvRmaD9pUqZThFVRwgFcq2uegRES2XbUuE4gaee3sM3z11CS9fmmu30NHkw/5dXfi9O65Ha60HGoMjIiKishNCPAzgYQDo6iq+1PpquOHLlRMeAYBZgiVfJYAvPPsapqM6DNuZmuVSBBQhMBU1cUOb0wg6cwrZ8FQUiuKUC7k1dcGVyySc5122BC5PRuF3q+n+S3/1sVvwhWdfg2VLSClh2c5/meFDZmAyFk5gQ70zJq+mIGE6lTq2BFRNoN69+Klk99/cjm899y5GQvGs6WkAcGN7Hfb2BnBvTwDNNe55x+7qbi4YCC13RbVd3c34YDIwKtTHaK17AqV+7l/9ST8uTsTgUhR0NHqhW3bWdTP/bnSsYqhVynCKGCARES3IsiWmozp+/tYI/uHkZbx1NZR+bGtbDf7wrk346G3XobnGXZK55ERERJRlVAixMVl9tBFAsNCOUsonATwJADt27CjpS4HPH34F5jXyYmMqaqRXUpMADDvdmjorwNjTE0iHArf+nz9DKG4uatl7ILtKKaJbiEzGUOdR0teESF5LZL/GOz4QxBeefQ3huAnTtmHZTtXU9RJornFjZCYOTXGmrbXWehacShaOG3junNMM+/XhmaxruVUFH9zWik/dvQldzcuvcFnKimouVUn3MPIuoo9ROXoC7ekJ4NCJQWy2ZVYlUOZ1M/9urPa1SxVOURUHSEREK6WbNiYjOn78xnv4/ouXMTgWST9283X1+KO7N+E3b2pHg8/tvKtGREREa+EIgE8D+Gry44/KOxzHD1+9Wu4hrJlilU0XxmezPj8+EMRXf9KPULI3UL7V3xYrnLDxp//4KzTWeLCxYa5ZdGYw8bWfDmAqakBVRLIi3IZpA1dnYtjcWoNGv4bpmAmvpqClxpN3qphu2jg1OIG+/iBevJDdDLvR58Jv9ASwrzeAng11q7JyWbEV1TRFwSuXpvD905fw3nQMXc01SwpEytUTqJy9iEoVThEDJCKieeKGhbFwAv/yqys4fGYIV6bnVgzZubkJn3r/Juze3oYGn2tVnjQQERFRfkKIZ+A0zG4VQgwD+Aqc4OgfhRCfA3AZwCfKN0JHJfU9Wi0LrdRWSDyjDCs1fSoYjqd7Hy0UHmkCRSu5JqImNjZmV/tkBhOD4xEoTmESIJFseC1h2MDFiSg6G3340/t65oVGli3x2vA0jvYHceL8GCKJuUopr6bgnm2t2NsbwJ1dTctqVVCsx1HWimoCUIRA3LCxqaUGg2Oz+Ju+c3CpAk1+95KnoJWrJxB7EVUnBkhERElR3cTV6Tj++VfD+MGZIYzP6unHPritFX/0/k24a2sL6jwag6MKZNoSY+GE86RROE8XlWRpe2qb84RSgD8+IqL1QUq5v8BDe9d0IEWUMzwSyf8tsLjZsqxG6+2v/qQfwXAcccOGQPEG2gDQVutCOGHBzF0CLkfMsPIGE3ayL5KUElI4DbONjGqpDfVexDJWWZNS4p3gLPr6gzj2dhATGc/9AKDOq+Ejt2zEpz6wKT29bDmK9Ti6+4YWfPoDm/B///wcTNuG360hZliwJfBfPrR1xVPQytUTiL2IqhMDJCK6pqVWVBueiuKfXhrG/3zlCmZizpK0igD29bbjD9+/Cbd3NaLO61rgbFROli0Rjs9fTng5Lo5HoIi5oEkk3w0UyQBKEXB6LyC1LfvxzNBKJI9P75txjnTQxSmQRES0DO5kU+hS8GgKpJRQFQWGZS+5IffxgSDOj81CTb6BY8v84ZFLFU61kwS8Lg03BOoxNBnB8HS84LkNS2YFE7pp45O7OnF5MoqORh8uTkRh5Kw+51Lmegt95+RFnB8Lo68/iEsT2VOqXKpAjVtDU40LpiXx3PkxvK+zsWgz64Vk9TgSQI1HQ9yw8MNXr+DBu7qwqaUGTX53um9PjVuFW1XwFz96M6sJeMpSpoKVsyeQ36XgQvL7291ag7/8SA+nlq1zDJCI6JokpUQobuLSeASHz1zGj159L73srEsV+PAtG/Gpu7vQu7EBtR7+qrzW2FLCLsXbuUUUDJtytmeGV3lDKgXJz+dXXKWDsNzrsSSLiGhZ9h96oazXL1V4lDq3AGBLG9YyVnM7dGIQLkVxpqwVuKcKANc1+KCpAoE6L555+P0AgHu+dqzgeX0uBY8/cDMOnRjE5ckINtT78IkdHbj5+gbYUuJD29swePLSvOP8Hg3TMQOhuIGhqRj6R8LpxzqafNjXG8CLg5OYTZiwbImRmTgMy4aiCDx54t0VBUgj4RgavS6oipK+72qKwHsZbRJSfXtyV00bDydwZToOQKA+2VNoqVPB1ronUObXsC1Qi5hhpZ9n0/pWta+KXKqYt7RiajsRXbssW2ImZuCdYBjPnB7Cj9+4Cj355MvnUvHAbRvxybu6cEOgDjUMjtaVd4Kz+O1v/TtURcClKtBUAU1xProUJbndaai50L3gyWS5uKYqcCkCavJj6lgt+WfnY/L8yY9axuOunDGkHlMVMS+0kVImn2Sn/7em8gVYQP4QK1V1lVuFlbsf0hVX2Y85h+ZcI/NzZFR0Mdwiogr1RN85nLwwVe5hlESqYkiieMPsQj78tydwLjgLRSDva7JMo+E4AnXerKlNnU1+jIcT0C0bmZdXBXBbRyPu2NyEr7ffmn4Ol+lXQzNorXEholuIZUyFm4mZAMz0501+F+7tCWBfbzu2t9dCCIGfvDkCVQDBcCL5Ro2AtCUuTkZxenBy0SGSS1Xgdanp1dK2tNTi4sQsQjETumXDrSqo92nY3FI779jcKWsbGrwYnophNBxHnVdbF1PByrHyG62Nqn115NXyB0hejU9Eia5FumljJmbgrashPPPiZfyifzT9blq9V8Pv3nE9HtzZha4Wf9acelo/7GRV2Wo4fGZoVc5TjEsVc2GXMhc0aVkBWOEQK+vYAkHV3L5zYVbWsXn2T10/HbhlPF6OMCcVPDl/XjjcmtsPRSu1MsOtzOvkvUa+czDYIrrmfev4u+UeAoCVrWpWiJRIN6JeIP/J692xWdi2RKGaE00RsGznjRMpgccfuDkrWDiwuxtfePY1TEUNaMl+1ZYt0eB34Xdvvx7j4UTBa783E4XbpUK3JOKGnfW9Sf0OtyUwFTXww19dwUxUx5995CYATjPrN69MJ5t4y+QbGs54D58ZKhggpQIjr0uBz6XOa7J9d3czTg1OwDkrYFoWorqF/Tu75p0rd/WyOq8L1zdKjIQSmIkZy5qCdnwgiEMnBjE0FUXnGkxhK+cKbFRaVfsqKZzIX85ZaDsRVae4YWE6auD14Wl8/8XLOHFuLP1EoqXGjd/f0YGP7+jAdQ1++NzLb45I5Xddow9f+a2bYFgSpm3DTH50Ppcwrext//TycMFz3bmpCVbyHNnnk+k+EGbqoy1hmPaSn7wbyXPHF2gUWkmcoGt+ddW80CkdbGVXaWl5jilUIaYqqTCr0HWUnOqv/OMqZdCTOnfRyqpC1VupAzF3XPoYLC7wyjlF1hgyP0fmebL2K/A1MBwjWtDxgeCqTR9baQBUiprVVLCzXLolUew3iS0l3KoCoQC3dzbNCzP29ATw9Y/fhq/9dADvjs0CADY1+/Hw7q3YsaUZ//DCRfzjy8OIGRZ8LhWfuON63LW1FX39o5iMGPOqplJBUK1bxUzG6mq2BPoGxgC8hT/7yE24vbMBrw5PZ30fLAnUe1SMhGLpldRGQjFc3+jH5+7ZjPtu3gDXAquy/e83rqarulIU4Wx/ZN/2rH3zrV6mqQru6GpKT/FbitwpcUtdxW05uAJb9araAImIrm2zCRMzMQMvX5zE91+8jBcvTKYf29jgxf5dnfjY+65He70X3hWsqkGVo9ajYff2tkXvXyxA+vrHb13y9S1bwkoFTJaEYc8FTYY191gqgMrcN3M/5zEbuuV8TB2rm04PCsPOd0z2Nuejs2++6+iWs32psxJSgRlK2HNjtanp0Gv+VMPcSqus6Ym5FVtFg6pUmDW/6iu3Uiw3SMtX7aVUSICzUEAGZIdOIndbbkVY+qD8jy8mFMs6XmR9mrdaLXO/QudjoEbL8af/+OqqnCc3VKgWC4Vizq3EhpDAudEQ9j95al5VzI4tzXjog1vwDycv42oohnqfGwDwDy9cxHdOXYIinO9fzLDw9KnLePrU5axruFUFDT4NLtXpw/TovdvwZ//yRt7xHHt7DH/2EWf6m0sBUu/tpIKniG6h2e/GN3/5DtyaQGutB9MxHV/96dvwu7UFg5gLE1GoioBbmQuaLNtON5jOtNqrl5VjOhlXYKteDJCIqGrYtkQ4YWImquPk4AS+/+JlvD48k358c4sf+3d14aO3bURLjYfBUZVRhUj3rcp81zTZWQhSzj2ZlQu8raoqIn2OpRyjKgJurfi7kJUkK/RKBVK2hGVlBlU51VdW/hArM+wqdD7DcrY5+8iMMC0ZmKWulxmAZexn5PTCWMrXmDABFJxMUVkUgXl9vLKqr9Lh08LTEzNDr9xKsfSx6WmS2VMa55+n8HRH9RpYSTAzSCoWlmUfk338QsflC9oKmYxkLzeee+70+av/R7Pmnug7h4no6qz6WY3hEeD8His29S0V0rbWetBa68GF8Vkc+N7LqPWoaPa7YUtgMqpjNmGiwauhqcaNiUgCB4+dx2TEmb5m2fNDqk3NfuztDaC1xoOfvzWKkVAMgTovHtzZiV3dzQW/36ntV0MxtNV5MBbW04tZ2Ml7kltTAWGXPIhZ7VXTyjGdrJwrv1FpLTpAEkI8CuDvAYQBPAXgdgBfklL+vERjIyJalFRj7JmYjufOjeF/vHgZ50Zn04/fuKEOf3hXF/bd1I7mGjc8GoOjUij3fUJTBdrrvatyrk0tNQUfSwVJuQFT5mPpz7P2yz5u7pjsx/OdO33NzMfl/HNm7p+1b+r8GfvJ5PG5wVpW+FZgn3Kypcyaiphb9WVlhF1zVV85lV3pQGpuPzMj4EoFWKY9F6DlhmILjSEzZFvq6kW2dFY/Wm+hVzqIygyfCoRemb27CoVY+SrE1NxKsVRl17zrzJ9SmfvYUkOvzL/7svAf1sx0VF94J1RWBVW57xOr5WDf+RWfQ1PEsppTrxcL9U26sb0Whi3hd2sIxQyMzzqhUChqYzoZzinC+Zc1HTPSPYWmowYS5vyTp/6Wf/szO9J/5+//tQ3px08PTuK//uC1guNJ9bm7vtGHyYiO6xq9GJ/VoVs2VEXBDa01CCfMZQcx3a01OB+chZASQjj3V1sC29ryP99YzVXTyjWdbK1XfqO1sZQKpM9KKQ8KIf4DgDYA/xnODWBd/cInouqRaow9HdVxtH8Uz5wewqXJuZv4+zob8Yd3deGD21rRyOBoLVwT94n0FJe8r8kq54XaWsgM03LDq8zwCRmfp/6cPke+86X2SR5jy/nnTeVhmQFZoTAscyx2GQKwVOiVGVSlq68yw6k8IdbcY5nbM4KreQFXRuhVoFIstV9qKmO+gGw5oZdu2lhcpFEZFIGcKYZzVVXpgEvN14sr3/TEPNMUc47NrvqaH3rlq+yqQuv+PvH5w6+sSqxblT9dAKoyF44Uc2Eiig31Hli2RDAcd6bHKoCeEQ5Z0ll1zZbASKhw02zA+d3v0fL3vTs9OImDx85DUwRqXAIRY/7gPvJrG7C5xY9H7t2W7BekYEtrTXrq1Rfv77FciKcAACAASURBVMGhE4PLDmK+eH8PvvDsawjHTZiWDU1R0OR34Yv39yx47EqtdDrZWjfgpsq2lAAp9a/xPwL4eynla6KS3tLI4VYE9Dy/udzVeTMmuqbEDQszMQOTswn89OwIDp8ZwmjGE4v3dzfjP93Vhbu2tKDR715XU4rWuXV1n6CVyx+mrY8feVbAlLkN+cMvZ7+5kErKZA+pAlVadp6gy86qDMsfcKXOXW5SynRwZZg5fbvy9Noyrcyqr+xG9rqZEVxlVGtZduEQK7dhvZEO4LKry7I/X3roZVsShrU+qrxy/fa3/n1ecJWq4MptSl9B1v194kevXV2V8xhVWH3k9CRyKqsCtW4EZwtHygnTxtBkDBsanH/DihCw8vwbztwkAHhditMTMM+3r8nvTv851ez6aiiG2bgJn0tBXa0HHc21eG8qgrDuNDlSFYEHbt2Abzx4B4CFp14tN4hJNQUvx5SulUwnK0cDbqpsSwmQXhZC/BzAFgBfFkLUAajYLpqF1sORBbYTUeWL6iamowYmIwkcefU9/NPLw5jKKHPec2MA+3d14raORgZH5bGu7hN0bRNC5Kkiq5zXsekQaoGKrszQSQKQ9vy+X/kCLVvmHJtzXlsKuISES3WmaKwHUs6FTFkVVrYNwyzQXytvU/rMqYr5m+Hnq/ZKh2d5K8jmV4ql/rxcobi5it+9NbPu7xNVmPusGiXZOVsIFA2PUmwA783EIQBYC7xKc6kCgVoPbDhvJAoAU1EDEs5v7ib/3NSy04OT+NrPBhBJmE4fPOlUCvndGhr8bmxuq4OUTvuDf/vivfOuVWjq1Ur7+pRzStdyr12OBtxU2ZYSIH0OwPsADEopo0KIFjhlpxWp0AIx62i1ZCKC84LAaYztzI//4StX8M+/uoJZpzkINEXgP9y8Aft3dWL7hjo0+hgcldG6uk8QVTIhBJzCkfKGWrYt05VTmVMA54Km+eFVZrVVVtC1jL5bS6nGEiI17QyAa8HdK0Iq3MtbfWVJfPrvzxQ89iu/dVNOz66c3l4Z276+hl/TAtb1feKJvnPlHkJF82gq3JrAVHRp4Wahf+WpZ3M2gFq3irZkM+zDZ4ZwZToCr0uBYdnJKZ/AhnofhBB46vlBzEQN2Jj7DWpLYDScQEOySmm5PYAqta9PqaaZlaMBN1W2pQRI70t+7M6oNJ0RQmhSynX5FggRVS7LlgjFDITiBkZm4nj25WH8r9ffQzyZAns1Bf/x1o14cGcntrTWotHvgktlcFRmvE8QVRlFEVDKGGKlAqZUIGVnfp78mA6xcvbLN30wtV9uAFYuqaBQVZZe5fWh7W2LvkYFBUjr+j7x1PMXyj2Eilbv0xbsVbQQAafvkVAEpJRQFIGOBh+e+szO9D5vj4Tw+pXpZONrJ4CdiNh4cGcLNrf4cWkimhUepSRMG1LKsi0pX6qQp5TTzMrVgJsq11ICpP8XwB0AXofz7/GW5J9bhBB/XGmrJyQrKPNuJ6LKZVhOY+xw3MTwVBQ/ODOEn50dSfe2qPGo+J3br8fv3dGBjiY/g6PKsq7uE0RU+VJTDUsdYqWDJWQ3c3ceS37MXfWwSBVWaltq6mAqtMoNsvKdp8qt6/tERF+f/bLWysSsvqwFCRXh/GWwJNDod6HWo8HrUjAV0TETNzEVM/Bff/AaHtzZibu2tuC1KzNorXFjNmFCt2x4NBV1Xg0vXZqGEAJm6t+RmPuQ2jQTM8qypHwpQ55STjNbaQNuqj5LCZAuAviclPIsAAghbgLwBQB/BeCfscTVE4QQnwfwEJxfM28A/z97dx4lV3ne+/777r1r7FlqtaZWGwkkxGBhJgkSDsE2TjzE2CbEBpF7rpMQyImXSXyXveyVmMSHc+9a9opzfHFuVgIhK4mPEdjGA8QxtoOxjDkBCcSMEQJa0GpNPaiHqq5pD+/9Y1dVV3VXV1d1V3VXdT+ftbDcu2vYraHeqmc/z+/l97XWqWoeo5y5XrtW/LIsRJNK2S6TSZt42uHoyBT3HxzgscND+ayBzkiAGy7t5aMXb2Z9e5iuaCC/patoGG/RROuEEELkFGdiLe/lxsJuqnLWtoagRHdWYW7Wcuw6OI+3qOE6sdRagmazZk/VTeFFe9vV+V3YKs20txSYpoGnNSZ+EHZ7OMDbo3HiGZfOiEVXNMR4MsP/t/8NetpDDMXSRIImUxkXpRSupxmbyjAcP8NN9zyFKug+LHoug5KZR0uhnkWeeo6ZLTb3Saw81RSQduZe7AG01r9SSl2ste6vdvMEpdRm4HbgfK11Uin1beBG4F+qeiAhRNNLZlzGkxmSGZdXT06y78AA//vN0fz3e9pCfOLyLXzwnRvpbg1J4aixyTohhBCLVGnXVUekSYKeitVsnVgOt1y1lbseewNXkrQJmoqMWxx8rZk7hxZKT4i4GpzsnbqiFmOJDO2RAFMZFwO/0BIKmIQCJomMwz/+8ihtIYvXh+KYhkIBqez9Q5bBUCyFaSq8GRUsBWzvaVvcD70I9Szy1HvMrFFzn8TyqKaA9JpS6u+BB7JffwI4opQKAfYCnzuilLKBKHBiAY8hhGhS8bTDeCJD2nZ57tg4+w4M8OzAeP77vV0R9u7u49rz17OmJUhnRApHTUDWCSGEEOXUep1YUrdfuwPws5Bi2U4kwwDPW11TDv5IWOU/sQFYpkIphac9XA/aQxbjKSf/+6aAiYSDUjCZzKDx8zAHziQxjBRhy6C7NcjgWIKWYDYzTJOPOPC/1kSDFq0hc1aQt1LwgQs3VHS+9cgq2tIV5ehInFjKH7sLmgZtYYut3a2LelyQMTOxtKopIH0S+BPgz/D/jT8BfBb/xf7d1Typ1vq4UuqrwACQBH5a65lnyUASovForZlMOUwmbTKOy5P9o+w7MMCvTsbytzlnXSt79/Rx9Y51dEUDdEaDmIb8y20Sn6SJ1gkhhBBL7pPUaJ1YLrdfu4Pbr93BTfc8le/66B+Ok8i4q6KIZACmUb7TCKAtbBFLORiKoqxKrSFgKuKZ4gKPzv5nKcXpyXRRYcjL7jR4fDzFOetaiGdcNneGGYlnSLt+hpKp/N3aANK2hwFEgmZRsebJ/jPcPs/PV6+soiu3reHgW2cwlH++GddjOJ5h7+41C37MHBkzE0up4gKS1joJ/E32v5ni1TypUqoL+AiwFRgHvqOU+j2t9Tdn3O5W4FaAvr6+ap5CMpCEaCCFO6plHI9fHBlm34EB+kem8re5cFM7N1/Rx56ta+mISOGoGTXbOiGEEGJp1XKdWG6FI0kpx1txnzFCpqI9bDE85TeGBQqyjVw9/2eqs7tb6W4N8Nhrw7ieh1K5XC7Y0Bri5ETpSEPH07OqUxry3QFTGT8zM5FxCVkGIcvwb6DAyr5vTLsekaDJtnXT3T1a64rGxeqVVfRk/xl62oJMJqc7kNojlRW1KiFjZmKpVFxAUkr9OvAl4B2F99NaL6Q37lrgqNZ6OPvY3wN+DSj6YKC1vge4B+Cyyy5baa/LQqx4GcffUS2edkjbLv/xq9M88PQxjo8n87fZfVYXe/f08a4tXbRHAnREAlI4alKyTgghhCinxuvEsirMnVlpmUj+jmiartYQKcclnvawC2o6lUyv/c9PXETANDjnqbe478AAUxmX1pDFLVdt5duHBvF0djJEzX7MUr+fClgTDXBiIkVPW5BkxiXjerieRuFnh21oD5HIOFiG33FUqNJMoHplFR0bSxCcEcUQNI2aZCAJsZSqGWH7J+AzwCFgsXtYDgBXKKWi+KMJ7wWeWeRjCiEaRDLjMpG0SWQckrbLj146ybeePsZIPAP4bwL+y/Zu9u7pY+eGdikcrRyyTgghhCinluvEssrlzgzHVt7moBp/zOrYmQRJu/Ss2lxxITlb1kQJmAZf+OD5XLGtO58p9GT/GdpCVr4jqdLWLdNQTKYcAqaiuzVMyDIZjqVJaZegaXDW2hbiaYeetjAfuWgTDz57fEGZQPUKpG4NmrwxPIWpFKZSRSN5K1k98qTE8qqmgDShtX6kFk+qtT6glHoQeBZwgOfIXkEWQjQnrTXxtMNE0h9Ti6ccfvD8cb777HEmkn77s6Hgfeev56bL+ziru0UKRyuPrBNCCCHKqdk6sdxyuTN/su/Z5T6Vusi4gFtcPDKUP4IG89d9/us/HcwXbGZmCk0kbUxD4XkaT+cn0FD4OUb54lKWv9OaX2/s7YwA0BYO0BYOMJnMcGoyTSztFBUodvV2FmUCXbltDXc/3s8XH3q5bCFjvkDqhRZE8rsM5n7Q7G9iM+w+uFD1ypMSy6uaAtLPlVJ/DXwPSOcOaq0X9Kqptf4r4K8Wcl8hRONwPU0sZTOZdHA8j7FEhu8eGuSh508wlfEX+4Cp+OA7N/KJy7awsTNChxSOVipZJ4QQQpRT03WiEaTspm6kKstU0BEJMJVx8LK1JK/C3ddyxYKWoDkrUwggbBkMxzO4niZk+SNnjqfzFx2DSuG4Hq72C0otQYtNHWHsgvG2yaTN8fEUllG6QJErUlRTyCgXSL2Ygkgs7eSDv3MZSBvaQ8TTTtn7NbN65UmJ5VVNAWlP9tfLCo5p4D21Ox0hRLOw3Wy+UcrB05rTkym+/cwg//7SSTLZ8MNIwOQj79rEDZf2srY1RFvYokvCsVcyWSeEEEKUs6LWiS9+/0VWWPwRAJGAgWkoptIu4wnb7wqiuOuo3Ahb1FL5YkH/yBTbe/ww61jKZjiWJu24mIbBBy9cz88ODzOVcTEzLrdctRWAv/35G6QcDwVYBnRGg/z1DRcBFHUHnc6OD27oCKOUmrNAUW0hY65A6sUURHKjcYXB3omMP3K3UtUrT0osr2p2YWuKrTWFEPWVsv3dL3JXTAbOJHjg4DH+49XT+dDD9rDF71zSy0cv3kR7JEhb2KIzEsCaER4oVhZZJ4QQQpSzktaJ/YeHGJxIz3/DJpS0PQw1PVIGs4tF5epmyvDf70UCZvbxXFxPc2I8hVJgKIXWmodfPEVPW5C+NVGStsuDzx7nhks20xEJEE87uJ7GNFT+uWZ2B2kNmzvDtIWnixSlChS1KmQs5nHmG41bieqVJyWW17wFpNy2yUqp/6vU97XW/7P2pyWEaDRT2XyjXKv266dj7Dt4jMePDOcX9rWtQT5+aS+/vWsT0ZAlhaNVQtYJIYQQ5azEdeLux/uX+xTqytN+ccR2PVyt85lEAUPheprCdCTL8OOSLNMPiE45Hv3DcVKOH3A9mbSJpRxAg/YLQgZ+ptJk0qG7NZzv5rn3iaOsawuxsSOSf/zCLp/C7qCb7nmKoRkh5qUKFLUqZCzmccqNxq1Uq7FothpU0oGUi4Zvq+eJCCEaj9aayZTDZNLGzgYpvnx8gm8eGODg0TP5223sCHPT7j5+8/z1BC2D1uyoWkAKR6uFrBNCCCHKWXHrxLGxBCFTkXZX4AxbActUKM8v+KRdnQ+BtpRfNNKQD8K2XY2dvaw4lXFRQEdrAMfTOJ5GKfK7qJ2YSGIoyBQEdUcCJlMZl75s51Lh8VJdPpUWKGpVyFjs48w1GrdSrcai2WowbwFJa3139tf/Xv/TEUI0AjcbYhhL2bieRmvNM2+Pcd+BAV4cnMjfbmt3C3t393HNueswDUVr2KIzEiRoSeFoNZF1QgghRDkrcZ3Y0hXF9TxOrtAxthytIWgatEcszkzZ9HZFeHt0CkMp3GyxaK4cKA14nqYjEiDjeKxrC+W7d0bi6XyYdE7SdmkJ+oWZSrp8cgWKr/z4MK8PxQHYunbu2y22kCEFkeqttqLZalBxBpJSah3wR8BZhffTWv9B7U9r8eYKdpPoXiHmlnGywdhpB601ntY88cYI+w4McOR0PH+7nRvauHlPH1eevRZDKVpDFp1RKRytds22TgghhFhaK2mduO3qbXzuwRfKhkk3O8fzW4zaWwIETJNPXdPHk/1nGBxLolTxTmkzKeUXn0amMvS0hwlaBrar8907bWGL4XiG9oiF1jrfzXPLVVt58NnjVXX5TGVcersi+duX2hmtVoUMKYiI1a6aXdgeAn4JPAo0/H6Vc72Qr9QXeCEWI5lxmUjaJDJ+MLbjejx2eIh9B48xcGa6ZfiSvk727unj4i2dKCkcidmaap0QQgix5FbUOqHx839sb96bNiRDzd09BBAwDWzH48yUzda10+HRbWGLWMoh43poXX5HNk/7HUTbe9q47ept+e6drd2t7N29JluQKu7m2dXbWXGXTyNsFb//8BB3P97PsbEEW6QrSaxw1RSQolrrz9ftTIQQS0prTTwbjJ1x/Hc+advlx6+c4oGnj3F6crol+9fOXsve3X2cv6kdQApHYi6yTgghhChnxawTdz/eT0ckwMaOCEdOx8g4Xr6IYmaDphtduTPsbg0SDVoETEUkYDIST3PXY2/Q0xZkQ3uYgJnOj+/luo1KPbihyHcQlereub3Ec1fT5bPcW8XvPzzEXz78CgFT0RkJMBRLleyAEmKlqKaA9EOl1Ae11j+q29kIIerO9TSxlM1k0vFbk/F3WPu3F07wnUODjCVswF/wrzm3h727t7BtXSsALSGLzmiAkGXO+fhiVZN1QgghRDkrZp14YXCcRKZ0E1XTRGaUGj3Dfw84lXboiATynT2xlFO0a1rhe8FStbLcoU0dYe687oKSxZRadO4s91bxjdABJcRSqqaA9KfAnyul0oBNtltRa91elzMTQtTUzHwjgImkzfeeHeT7z50gnvbH1wKm4rcu2MAnLt/C5k5/C1UpHIkKyTohhBCinJquE0qpzwC34NcrXgJ+X2udKn+vxfvMA8/OWTwCcJqg+wjmyItVsKYlwJmEn0GUk3G9/K5psZTNifHyv83tYYtbrtrK7dfuKPn9WnXuLPdW8cvdASXEUquogKSUUsAFWuuBOp+PEKLGZuYbAQzH0nzn0DF++MJJUtnxtbBl8NsXbeR3L93CurYQIIUjUTlZJ4QQQpRT63VCKbUZfwLqfK11Uin1beBG4F9q8fjl/OD5k/V+iiVnKbBMA61hLOEQMo2i3dCCppHfNW04lkbN0WZlKtje08qPP/MbZZ+v0s6d+bqUlntntOXugBJiqVVUQNJaa6XU94FL63w+QogaKJVvBHB8PMm3nj7GT145hZ3dMqM1ZPGxizdx/cW9dET9KyhSOBLVknVCCCFEOXVaJywgopSygShwooaPXdL+w0MrblMeBZimMT17p/0uJNvVDMdSxFIOyYyLB7SFDCZTNsYcFaTczmvzqaRzp9IupeXcGW25O6CEWGrVjLA9pZS6XGv9dN3OpoYCc+yIEJDMX7GCuZ5mMmkTS03nGwEcHZli34EBfv7aUH5OvSsa4Hcv7eXDF22iJeS/FEjhSCxSU60TQgghllzN1gmt9XGl1FeBASAJ/FRr/dNFn+E87n68v95PseS6WwMkMl6+w2hDewhPww2XbOZvf/5G/qKjqSCWdlAoDENB9rjKbsOmlB8gXnjx8uuPHuHeJ44ylXFpCZr5sbZKOneaIV9ouTughFhq1RSQ3g3cppR6G5hiemZ5V13ObJFMQ2GXmD82jaaJtROiYqXyjQBePTnJvgMD/O83R/PHetpC3Hj5Fj5w4QZC2dl2KRyJGmmqdUIIIcSSq9k6oZTqAj4CbAXGge8opX5Pa/3NGbe7FbgVoK+vb5Gn73fOdIQtJlLO/DduEpZpsG3ddOEmkXHoaQvzyMun0BpClpHfac31NBs7wiilGDyTwGN6BzYz25UUNP1fv/7oEe567A0MBZbhF4jueuwNoLLOnWbJF1rODighllo1BaQP1O0s6qBU91G540I0o5Tt5xtNpaffxGitee7YOPsODPDswHj+eG9XhL27+7j2vB4s02/Fk8KRqLGmWieEEEIsuVquE9cCR7XWwwBKqe8BvwYUFZC01vcA9wBcdtlli5o+cz3Npo4IR22XbNNNwzKASj72KGAo5o+crW0JFRVybvvmIQxFflxNKdBKMxRLc/fvXcrtDzxHLOWgFCilCFkGbWGLrd3+7r33PnE0Wzzy33caChzP494njvLil35r3s6dlZYvVItd54RYbhUXkLTWbwMopXqAcN3OqEa8OXY/mOu4EM1Ca81UNhg7bU/vAOJpzZNvjrLv4ACvnozlj5/T08rNe/q46pzufAeeFI5EPTTbOiGEEGJp1XidGACuUEpF8UfY3gs8s8jHnCVluyQyLomMQ8bxuP7izdzx8MuYBhiGwnZ0QxaSCs+pXLFLKQiZipMTaU5OpDENxXW7Nsxb2LhmZw9fv/HifEZRqS6iqYyLNSM+xFD+8dxjlHueZs0XKlUoAmqy65wQy63iApJS6jrgb4BNwBDwDuBV4IL6nJoQopDnaWIph8mUje1OX1NyPc3+14a5/+AA/SNT+eMXbmrn5iv62H3WGpSSwpGoP1knhBBClFPLdUJrfUAp9SDwLOAAz5HtNFqMjOORzLgkbZeU7eLp4tLL7m1raA1ZJDMOttuYxSMoLhiVOsdcUcnTkCgY0fA8zQ+eP8nW7iNsXRvljeEplKfzI2yehnO6pzuAzsRTxDPT979ya1e+INIS9Is+hQkinvaPV6IZ84XmCv5uCZoNn+ckRCWqGWH7H8AVwKNa64uVUu8GbqrPaS1e7kWu1HEhmonj+vlGsZRT9CYm43j89FeneeDpAU6Mp/LHd5/Vxd49fezq7cwfk8KRWCJNtU4IIYRYcjVdJ7TWfwX81WJOyPM0yWyXUTLjFm1CMpe+rignJ5OMxOffbawRGAoKhzDM6fzrWXKH//4Xb/L3N1/KZx98gXjawfU0pqHoDAX4wgfOY//hIf7bN58h6RQ/0JNHx/jMA8/ykXf10hEJMJlycFwX0/DH3DwNt1y1teJzn6tLaa5w7uU2V/B3/8gU23tai27biHlOQsynmgKSrbUeVUoZSilDa/1zpdRX6nZmi1SqeFTuuBCNJmW7TCZtpjJuUTB20nb54Ysn+fYzxxjNvnFRwH/Z0c3e3X3sWN+Wv21ryKJDCkdi6TTVOiGEEGLJNcQ6kcp2FyUyLmnHK3qfVc7bo1M8+uoQx8YSnEnYdT7L2ggYCkMpPDStIZO0o0lkR8iCpiIzRyUpaXtcs7OHr95wUckOoJvueWpW8SjnB8+f5NDABEHLoKc1yHA8g+NBJKD4b79x9qILPeXCuZe7iDRX8Df457lS8pzE6lVNAWlcKdUK/BK4Tyk1hN8u2pAsU2G7GgX5Hk2dPS5EI5tKO0wkbVIF+UYAsZTND54/wXcPDTKZ3fnDNBTXntfDTZf30bd2egGSjiOxTJpqnRBCCLHklnWdsF3N26NTuFVkoo7E0/z88BD/8eoQbwzF63h21cl9opnvJ8l9/mkLBwhaJl+77gI+++ALJNJOyR2rZ5qrA+hYmc4ZDfkuHMfVRIN+oS5gGkUd8qVUEjRdLpx7rgLSUgVYzxX8vXVtlITtNV2ekxAzVVNA+gh+QN2fATcDHcCd9TipWji7u4XXh+J42u86Uvjtmmd3tyz3qQkxi9aayZTDZLI43wjgzFSGBw8N8vALJ/JXjAKm4oPv3MgnLt/ChvbpDEopHIll1lTrhBBCiCW3rOuEp3VFxaN42uGXr4/ws1dP89zAeFGRZkN7mPee18PDzx8nnnaXLQNJ4++01hYyCQZMRuIZ//NOwajYdbs2cGoyM6t7aHtPW77I8fLxiZI/Qyibfj1X4WVLV5TBseSc53dqIkU0aDKetDFQmAZMZZyi4OjCx24NmsTTDicnUwQMg/XtoTmDpucL555prlyiegRYzxX8fceHzgeaK89JiFKq2YVtSin1DmC71vpfszseNOyn1C984LxZM7utIYsvfOC85T41IfIc12My5RBL2bPe0JyaTPGtp4/xyMunyDh+USkaNLnuok3ccGkva1qC+dtGg37hKBxo2H+SYhVotnVCCCHE0mrkdSLjeBw8eoZHD5/myTdHsQtGu9rDFtec28O15/VwwaZ2lFJ899Dgsgdoe9lz+78/tosXB8fzmUDRQPlMoMIih2GoWe9BDaAtbJUtvNx29Tae6h+d8/cg43ok4i6mAsMy8DSELYOAqbj78X5gelcyU8Ebw1PZz2x+cezkRJpNneH87QsLLS1Bk6m0g8bNx5No/Kzbm+55alZhZq5conoEWM8X/C0FI9HsqtmF7Y+AW4E1wNnAZuAf8LfMbDjlZnaFWG5px2UiaTOVdmfN3Q+MJrj/6QEefXUov6C3hy1+55JePnrxJtrC03PVkaBJVzQohSPREJptnRBCCLG0Gm2d8LTmpeMT/OzVIX5xZJhYanqaLmgZ/PrZa3nveT1cftYaAmZxy4vjzh+2vRROTKb5w288zfZ1rXz9xotnfdaZq4NousiR9Is8SuFqTdA0aI9YnLW2tWThZSSe4vYHnqM9EiASNElmiruwDAVB0y8YaTSuBtf2b5MGBs8kmEjaRY/dPxzHVAoHjev5nfYemuFYmq3dLbOCpt+7cx3ff/7krN+L9qBZsrtorlyiegVYzzX2J8RKUM0I26eA3cABAK3160qphv6XIf94RaOZK98I4MjpGPsODvDLIyP5hXhta5CPX7aF3961MR/AB1I4Eg2r6dYJIYQQS6oh1omjI1P8x69O89jhIYZi6fxxQ8HFfV2877wertreXZRjUyhoGRiGwvT0nLuZLRVPQ9BQvHUmMatwMt/oVm6MLHebmdk8X3zo5aLCSyxlMxLLoIG+NVEc1yPtePS0BfMjdKDY0OHHK7w9migqLikg7Wq8pM3rQ7F8DEPG9TCVKtotTin/eKmg6VOTGbqiFhNJJ397Q4GjS3cXzZVLJAHWQlSvmgJSWmudUSr70qCUxfy5bcuqUbd3FKtLuXwjgBcHx9l3YICDb43lj23sCHPT7i385vkbCBYMecuommhwTbdOCCGEWFLLuk6cmcpwy78+Q//IVNHxc9e3ce35Pbz73J6iD8FMlgAAIABJREFUiIBCSilagibtEf992LbuFt4Ynqr7Fs+FRZW5vm8ofwxt5rhXJaNbpUaurty2hrsf7+fURIrjY0lMQxGyDL8zXkHINFBKsa4tTCxlc3JiugiXa9RqCwcIzNjlLf//tCbjePQPx0nY/ntjO/tdBXieRqMxlSoZNH1sLMHmzii9XYrDpyYxlYJswQlmdxfNlUskAdZCVK+aAtIvlFJ/DkSUUu8D/gT4t/qc1uI18vaOYnUol2+ktebpt8a478DbvHR8Mn98a3cLe3f3cc256zCN6R0DpXAkmkRTrRNCCCGW3LKuEyPxNIFs8WhTZ5hrd67nvef1sGXN3J0opuHvYNYetrAKxthyeasj8Uxdz3m+zG9TKbT2x8ZmFk4qHd0qnNrIdSRlHBdPazTgeBocF8fzC0TdrSEATk8kmcoUXxx1Pc3bowk2dIRws8U1Q2U3NcrujO145HcUnskyQBnguHD2uhY+//6dsyZKCjuKgqaBky1SBbN/PjO7i+bLJRJCVK6aAtIXgD8EXgJuA34E3FuPk6qFhWzvKEQtlMs3cj3NE2+McN+BgaKtYHduaOPmPX1cefZaDCWFI9G0mmqdEEIIseSWdZ0wleKj79rE+85fz84NbaiC91wzhQIm7WGL1pBV8na5vNU//MYzFe3sVg8GfveRhyYatHhjOI7W00HSCxndynUtjcYdAoaBpzWO9rOJDOU/Z3u2KDUyNV08U4qiQOuptMv2da28dSaB9sAwFI7rYc/TsWV7cNlZXWULPIUdRd2tQY6Pp0DDhvYQiYxTsrtIok2EqI1qdmHzgH/M/jeLUuq7WuvfqdWJLVa12zsKsVjl8o0c1+Nnh4fYd2CAYwVbnl7c18nNe/q4eEtn0ZsTKRyJZtRs64QQQoiltdzrxLZ1Ldz+3u1zfl8pf9fm9ohFyJr/Pdg1O3vojFiMJ23qlaltABRmAwHhgEF3a4gTEymUAZ0hi7GEDcDmznA+6+iGSzbz4LPHi0a3JpM2AUNx1VceKwrVzsl1LeVyiSzDwETjeppNHWEGx1P5xys8JyguInVEAvkurfGEjfa038kEWIbK/39K3Pf+W68A5g4An9lRdM66FpRSxNMOPW1h6S4Soo6q6UCaT0MNkbYE/RfJgikgPO0fF6JWPE8TSzlMpkrnG6Vtl0dePsUDTx8rCmn89bPXsndPH+dtbC+6vYRjixWuodYJIYQQDaeu68RcHUcB06A9HKA1bBVFCFRiXWuI0Sm7FqdXkgcoTf4zzfkb25lI2vzy8+/JF1ieHRjDMvzw6txuvYmMw5P9Z7jzugv4yo8P8/pQPNsppbFMxdqWUMkdy3JdS7nRsFxhJ2gaWKbBjp5WOqNBBscSmIafvaShKMnKUNDbFc13aX35kVc5OppAowmZio2dEd4aLb0DWu73v5IAcCkSCbH0allAaqig1Fuu2spdj72B43n58DlP+8eFWCzb9ZhM2sRSDl6JVtyptMPDL5zgwUOD+StChoJ3n9vDTbu3sG1da9HtwwGTNS1SOBIrXkOtE0IIIRrOkq4TLSGLtrA1525rlYinS2f51JJmujtncCzBeRs7gOlCylVfeYzOSKCoQFaYdTSVcentinBqIkXG9RiN24QsE61hKJbij77xDJGgScBUrGsNMZm0aQtbjE5l8DwNGtpbAtiu5o4PTWcSfeaBZ/n+8ydnnW/IVPkRssJCz033PJUfqesImUykp7v2cz/fdbs2AJUFgC/EXF1NQojK1LKA1FBuv3YHR0fiPPziKWxXYxqK63ZtkPwjsSgpO5dvVPrNwkTC5rvPDfL9544zlV0UA6biN8/fwI27t7C5M1J0+3DA7ziKSGecEEIIIUTdWYZBW9gvHBWGYi/E/sNDHJ9I1ejMKjOedLhy25qiY+WyjgoLMbmxNA2cHE/i4W/s4niA7ZK0/W4sDXRFgziuR8bVBC2Ds9a2ziq2nJrM0BW1mEg6+XE2U8FZ3a1Fody5gk1byGIi6V9Y7WwJMpGejnUACFuKj7yrF6g8ALwa83U1CSHmV8sCUnX9nnW2//AQT7wxSsBQKDSWoXjijVH2Hx6SFwhRFa01Uxm/cJQukW8EMBxL8+1njvHvL54k5fijbGHL4MMXbeKGS3tZ1xYqun0oYLJGCkdi9WmodUIIIUTDqes6ETAVW9ZEyoZnVypXjJgnE3pewRlb3VfikZdPFV0UL7dN/RcfejlfiDGAtOPl27wsQ+F6fv6QZRj5aIYNHWE6o0Ee+bOry57HsbEEmzuj9HZN/35qrfNFopkFm6TtooCAoRgcSxMOGKxvC+dDuQs7jBYSAD6fenU1CbGaVFVAUkpFgD6t9Wslvv352pxSbXzlx4cZS9iYhsIyDbSGsYTNV358WF4gREVyi+hE0sbxSicjHh9L8sDTx/jJK6fyYYCtIYuPXbyJ6y/upSNafOUkFDDpigYW1SotRCNrpnVCCCHE0lvOdcJQqibFI/CLERnHzUdlLNTMMOmZAjMCpzVwZChedFG83Db1Wx73CzGOq3F08Yxg7nGD2dwhpSDjehV3+sxX5ClVsAHoagkRz7hlx+7KFcUWqh5dTUKsNhV/ilVKfRj4KhAEtiql3gXcqbW+DkBr/dP6nOLC9I9M+VtNqukXRK00/SNTy3xmotHZrsdE0iY+R74RQP9wnH0Hj7H/taH8m4auaIDfvbSXD1+0iZZQ8T+toGXQFQ3OOi7EStJs64QQQoiltZLWiSOnJ5lMOYtObZqv+GTPuIGp/E6qmV0zc4VK5woxQ7EUpgKVDb4G/9QNNR0ungvLrrTT57art/G5B1/g+FgSx/Py44F3fOh8oHzBZr7iU7mi2ELVo6tJiNWmmk+zXwJ2A/sBtNbPK6XOqvkZCbFM5ss3AvjViUn2HRzgP98czR9b3x7ixsu38P4LNhCaEYIdMA26WoK0SuFIrA5fQtYJIYQQc/sSK2SdsF1/a/vSPeqVK9fBZMCsx3c1rAmV7pqZKyD6TuC2bx7C05qQZbKuLYTWcGIiiedp/z/ln0RbOFBVp48GyBWhVHE9rVzBppIOo1rvtFaPriYhVptqPtU6WuuJWrV91tvWtVGODMXJuNOZNYaCHT0ty3hWohHF007ZfCOtNc8NjHPfwQGeGxjPH9/SFWHvnj7eu7NnVgijFI7EKtVU64QQQjQ6ywCnRIXCWlz283Jq6nWisECTtN1Fja4ZZDuAmF0kyil13ABiaZdzetpnnVu5gOhL+rpmFXPSjksi4xK0DDKOR9BUbO2eHZY9l7sf76cjEmBjx/QmMYWZQuUKNvXoMJrPcjynECtNNZ9uX1ZK7QVMpdR24HbgPxf6xEqpTuBe4EL8188/0Fo/udDHm+m8jW0cPh0vOuZp/7gQuXyjyZSN7ZZetj2tefLNUe47MMDhU7H88e09rdy8p49fP6cb0yh+AxQwDTqjAdrCgZkPJ8Rq0FTrhBBCNLqOsMVoYnZndEe4aS9Q1XSdWEozCzQjsTT2IubXDEPRFQ2QyLg4mdIXMXNy7zZ19r9SXTOl8oaGYyluf+A52iMBWoMmk9lw61wxJ2iZfPn6XQsuoMyXKXTNzh5uGBznH37RT8J2UQo2d4Tzt611h1ElluM5hVhJqrl+8WngAiAN7AMmgD9bxHPfBfxYa70TuAh4dRGPNcuPXzld1XGxOtiux2g8zcCZBKNT6ZLFI9fTPPrqaW7512e446FX8sWjXb0dfPn6d/IPv3cJV+9YV1Q8CpgG3W0hersiUjwSq1lTrRNCCNHoJtOlCwtzHW8CtV4nlkxhgUYpxYaOcL6wY/jTW1VtIed4mtGpDBln/uJRwDTINW1pYEdP66wiyLGxBJGCKIXJpM3oVIapjENnJIDtaTR+xtFE0qanLcyd112w6Eyh5IwO/sJMof2Hh/jGU2+Tcf3upoChODWZ5nMPvsD+w0MLfl4hxPKp6PKFUsoE/rvW+nPAXyz2SZVS7cDVwCcBtNYZILPYxy2UtEt3lcx1XKxsKdtlMmkTL5NvlHE8fvqrU9x/8BgnJ1L547u3ruHm3X28s7dj1n0CpkFHNEBbyKrZrh5CNKNmXCeEEKLROXNs7z7X8UZW63Viqc3stmkLB+huDTAct1H442ZmtoqkvbnH0gp5ev4QbY2/GVDQMnA9jaEUn3//zlm3m5k3NBJPAxC2TJSa7kzqjAZ55M+uzo/jffGhl4vykqoxX6bQ3Y/3E087mEph5HZ60/4UwMwQcCFEc6iogKS1dpVSl9bwebcBw8A/K6UuAg4Bf6q1rtkWaYrSmyLIR/zVZb58I4BkxuWHL57g24cGGY37n08VcPWOdezdvYXt62ePPUrhSIhizbhOCCGEWDp1WCeW1MwCzdBkipG4PxKGUkQDBpahCJqK7tYQR0cTpEsFWFXJNPyLnBr//elH37Wh7G5ruWJO2vFQwLq2UP42ufGy+fKSKjVfptCxsQSupzEL3isrBY7rlQwBF0I0vmoGqJ9TSj0MfAfIv4HXWn9vgc97CfBprfUBpdRdwBeAOwpvpJS6FbgVoK+vr6onCFpGyRftYBOnDorKVJJvBBBL2fzguRN899lBfxtWwDQU7ztvPTfu3kLfmtlbelqGQWeLFI6EmENTrRNCCNHo5mpOab7+o7xarhNLqrBAE0vaDGUvOlqGXxRJOx5//J5zuP3aHew/PMRnH3yBdHxxjbNBU/mFIwNClkFb2OLQwAT7Dw/NKvTMLOZEgyYtIbMoWiE3XlYqL6kw/LqUuXZ4K5cptKUrykg8jfaYHsHT/vvp3JibWNnm+nsjmlc1BaQ1wCjwnoJjGljIC/4gMKi1PpD9+kH8DwZFtNb3APcAXHbZZVWtla0hs2QBqS1klri1WAls12MyaRNLOXh67r8uZ6YyPHhokIeeP5Gf2w5aBh+8cAMfv3wLG9rDs+5jGX7HUXtYCkdClNFU64QQQoglV8t1YkkVFmj6h6dQgGWo/E68judx7xNHuf3aHfndyc5M+QWkhe7W5mnNurYQ3a3T700TGYcvP/LqvMWcXJdRqfGyLz70ctnw65kW2rF029Xb+OyDLzCesNHZ3wRPQ1c0MCsEXKw8tep0E42l4gKS1vr3a/WkWutTSqljSqlztdavAe8FflWrxwdY1xpiPGHjafItn4aC7tbQfHcVTaaSfCOAUxMpvvX0MX708knsbHZANGjykXdt4ncu6WVNS3DWfaRwJETlmm2dEEIIsbRquU4sh1yB5uw//xGWAYaanmwwFExld1PL5SUFzdITEfOxDEUoO00xkbCLCkiO6/HWaJKzPF32Q3m58bItjxeP40Fx+PVMC+lYyp3DV2+4iC8/8ipHR/3i1PZ1LXz+/TulgLAKLPTvjWhsFReQlFL/TImOWa31HyzwuT8N3KeUCgL9QE0XFKUUSvlzyEr57ZKup6UIsIJUkm8E8PboFPcfPMajr57OXwHqiAS4/pLNfOxdm2ktsRWuaSg6I0HaI1I4EqJSzbZOCCGEWFp1WCeWRUvQ7+gp2JAXT/vHwR/dems0ns8uqpbjaaLKH1tL2S79w/HsTmYGtusRMIySH8qBWZ1J9996xazHny/8eqaZAeJQvmOpULkRNxlvWtkW8/dGNK5qRth+WPD/w8DHgBMLfWKt9fPAZQu9/3xiaYfNnWFG4pn8C+6G9tC8XSqisVWabwRw5HSM+w4M8MTrI/nFu7s1yCcu38IH37mxaKvTHNNQdEQCtIcD+d0ihBAVa6p1QgghxJKr6TqxXG65aitfe/R1HNfNTzoA3PLurYBfoPnDbzxd0U5sc5lMu4RMcDVkXA9D+b/arqandfaH8teHYhWPC80Xfj3TzABxKN+xVAkZb1r56vH3Riy/akbYvlv4tVLqfuDRmp9RjeT+wm5b15o/lsg49LTNzrcRja/SfCOAFwbH2XdggKffGssf29QZ5qbL+3jf+etLBqkbyi8cdUSkcCTEQjXbOiGEEGJprZR1YldvJ+0Ri8nk9IXp9ojFrt7O/NfzXOcsKzc9kXahM2Jhuzp/QVzhMZlyWN8xffuk7ZJxPDoilY8LlesMmqnajqVKyHjTylePvzdi+VXTgTTTdqBht7zJhbYdH0/620caitaQxR0fOn+5T01UodJ8I601B986w74DA7x0fDJ/fFt3C3v39PEbO9ZhligMGUrRHgnQKYUjIeqhodcJIYQQy64p14m7H++nuzVE35qW/LHC4kdunKxalqHwtEZrv6tJA71d0XycwmTS5tREkpTj8frpGOvbQ1imge1qAqaa1V2fGxda7KhYtR1LlZDxppWvHn9vxPKrJgMpRvHM8ing8zU/oxpSANovLqAVUh5oHvG0w2TSJjVPvpHraX75+gj7DgzwxnA8f/y8jW3cvKePK7etLZlhlCscdUQCJQtLQojqNeM6IYQQYumslHUiV/yIpWyGY2kyrkfAUEwk7fz3cyHY1QiY013yjufhab+7KBq0mEzanJhIAhA0AAWD4yl29LRyx4d2cvfj/SXHhVqCZk1GxarpWKqEjDetDrX+eyOWXzUjbG31PJFau/vxftojATZ0RPLHpC2ysVWTb2S7Ho++OsT9BwcYHEvmj1/a18lNe/q4eEtnycKRUor2sEVnNCiFIyFqrNnWCSGEEEtrpawTuZDs0biNRuN6GtvVJJ0MX3/0CFu6ojiux6nJdFWP63h+1pGn/f+u27WBQwMTJDIOI3H/sRSKTV0R2sIBEhmHzmgw/9mm1LhQ0DQaclRMxpuEaE7VdCD9TGv93vmONQppi2wejusxUWG+Ucp2+dFLp/j2M8cYik0vyr9+9lr27unjvI3tJe+nlKItbNEZCWCZszOQhBCL12zrhBBCNLqwpUg5s98bha3mvAi2UtaJ267exm3fPISrveKsIw1f+9nrtAZNMq4maECmwiaktdEASdsj7Xq0BE1uuWort1+7Iz9+9tZogpCp6GkP0xb2P+PM/GwTDRgcHfW/3tbdwh0f2skXH3q5IT8TNet409cfPcK9TxxlKuMW/TkJsVrMW0BSSoWBKNCtlOpieqOBdmBTHc9tUaQtsvHl8o2mMq4/ZlhGPO3w8PMnePDQIOPZ9mBDwXt29nDT7j62dreUvJ8UjoSov2ZdJ4QQotFd3LeGFwfHmCqoQrQEDXb1di3jWVWvXuuEUqoTuBe4EH807g+01k8u8nTndc3OHlpDJmMJ/8/FUP4P5Gr/LOJpl4Cp8ICwBRnX7ygqp7MlSIurufO6C4qKKLkRoJvueWrOzzaFO5pt72klabtMZfwYiGo/Ey02L6kazTbe9PVHj3DXY29gKLAM//fxrsfeAJAiklg1KvlEfRtwCNgJPJv9/4eAh4C/q9+pLc5tV2/DdjWJjIPW/q/SFtkYptIOJ8aTnBhPEk87ZYtH44kM//TEUW76x6e494mjjCdtAqbit3dt5Bt/sJs//+B5JYtHfuEowJauCN2tISkeCVFfTblOCCFEo7vt6m3MfJukNc34frZe68RdwI+11juBi4BXF3meFduxvh2lIBwwCFmmXzzK0vijZqDobgnR2xWlNWTO9VCAP1oWMNWcAdzlPtsU7mimlCp6rGo+E+UKUUOxVFFe0v7DQ4v4nWo8X3/0CLu+9BPO/vMfsetLP+Hrjx6p6H73PnE0WzwyMJSR/dU/LsRqMW8Hktb6LuAupdSntdZ/uwTnVBPN2ha5UlWTbwQwHEvzrWeO8e8vnswHEIYDBh/etYnfvayX7tZQyfsp5e+21xkNFAURCiHqp1nXCSGEaHQPPT9Iwi5+35SwPR56frCp3tPWY51QSrUDVwOfzD5HBsjU4rErcdvV23h2YMzf7XnGRKEC7GwI9uBEmo0dIbZ2t/LS8Yk5Hy+WsmkNWXOOlpX7bFNuTK2az0SFhShonLykWlpMF9FUxsWa8fHCUOS7vYRYDSrOQAImlFL/deZBrfU3ang+NdVsbZErUTX5RgDHx5Lc//QAP33lNE6217ctbPGxizfzsYs30zFjccyRwpEQDaHp1gkhhGhkD794as7jX7txiU+mNmq5TmwDhoF/VkpdhN/R9Kda66lFnmNFrtnZw6euOZu/2/8mmRkXR3X+f3wjsQwhy2R9W4jTsdnB2qahGI6lMQ01a7SsdObOFUW3mW9MrdLPRM2aIVtNLlFhFxH4BSDH87j3iaPzFpBagn7Yd+E+PJ72jwuxWlRTQLq84P+Hgffit6DKBwMxSzX5RgBvDsfZd2CAXxwZzs+Ir2kJcsOlvVx30caiBXGm1rBFVzQohSMhlp+sE0IIUUPuHME5cx1vArVcJyzgEuDTWusDSqm7gC8AdxTeSCl1K3ArQF9f30LOeU65gsPf7X8z3zGfk/sTMhS4WjNwJjFrHDHH9TRTGZeJpM0dHzo/f/wzDzzL958/6f8cwFTGKdktM9+OZpXmGjVThuy5f/HvpGc0/oQsNW9H0WK6iG65ait3PfbGrN3ybrlq64J/DiGaTcUFJK31pwu/Vkp1AP+r5mckmtpU2mEiaZOyK2vlfOXEBPcdGOCp/jP5Yxvaw3zi8i184MINBGe+whdoDVt0RoJlbyOEWDqyTgghhCinxuvEIDCotT6Q/fpB/ALSzOe8B7gH4LLLLqt55e3J/jP0dkVwXM3x8WS+gx7AMhTRgMHkzErHHApjHvYfHuIHL2SLRwrQ4HlgGHpWt0y5MbXCgO3CXKM7s/crNF8hajl85oFnefjFU/6ooKG4btcGfvTSyVnFI4C0o4kEzLIdRYvpIso9nuzCJlazajqQZkoA22t1IqJ5aa2JpR0mEpXlG2mteXZgnPsOvM3zx6Znwd+xJspNe/p4z7nryoZeS+FIiKYh64QQQixC2FKknNk1j7ClSty6KS14ndBan1JKHVNKnau1fg2/m+lXNT27CuTGvlRQ5b/2tN8xtLkzwkh8emQt96dW+CeaO2YairTt5fOG7n68v7hjqaCIVKpbpnBMLddx9MWHXmYyadMSMumIhIHyuUbLmSFbagzt6Eg834EFfqdW4ddzKddRtNguotuv3SEFI7GqVVxAUkr9GwXdmMD5wLfrcVKiObieZjJpM5myK2ql9rTmP98Y5b6DA7x2KpY/vmN9K3v39HHVOd0Yau43RH7GkRSOhGhUzbZORAMmiRLdktGAZBkIIRrDWWtbeO10fFbB4ay1s3egbQZ1WCc+DdynlAoC/cDvL+4Mq1c49tUeCdCnogyOJbFMRVvY4vh4EoCg6e/K5nheUWFI5Xb1Mvzs0Fze0LGxRL7AMX1jfxe+ct0yMzuOTk2kSGZcQpZJW9jPNyqXa7RUGbJff/QI/++jrzPz0nPhGJqX/eELPx5UkIxRtqNIuoiEWJx5C0hKqZDWOg18teCwA7yttR6s25mJhpVx/GDseNqpKN/I9TSPHR5i38EB3h6dXqwu6u1g754+LntHF0oKR2IVaguZxEr0YLfNs9Vvo2nWdeK3LugpeSXzty6QzReEEI1BKYVp+P+pbPHA9XTZ902NqF7rhNb6eeCyxZ7fYswc+zINRVc0wNqWIBNJm2jQJGW7uJ7GLfG+2cz+GXtaYxlGPm9oS1eUeMpmPOnMKpqU65aZuZNayDLIuB7DsXS+gLScuUb7Dw/xxR+8xOB4quT3C8fQFrK3mZPd/a7c75F0EQmxcJV0ID2JH1B3i9b6/6jz+YgGlsyG+yUyTkW3zzgeP3nlFA88fYyTE9OLxBXb1rB3dx8Xbu4oe38pHImVLuNqDCi6+mZkjzeZplwnTk1mWBMNMJ608bTf8t4ZCXBqcsl2gRZCiLJiaYfNnWFG4hkyrkfQNNjQHiKeruy9WANpynWiEqXGvj5y0Sae7D9DPJNgU0eY/pEp5lraHU+jlF/06IoG8nlDucIUwGTK8cfiFHz0oo3s6u3kpnueKhmKPXMnte7WECcmkqQcf2Obpco1KjWStqu3k798+BVOTJQuHhUyFlgjjQSko0iIeqqkgBRUSv2fwK8ppa6f+U2t9fdqf1qiUWitiWeDsTPO/PlG4Bea/u3FE3znmUFGp/wPYgr4jR3r2Lunj3N6WsvevyVk0RkNELKaqwtDiAXJZhoUfd18mnKdODaWYFNnhM0FV2G11g2/XbEQYvXY0hXltVOTJG0XT4PrucRSDuduaF/uU6tWU64TlZqZP1Q4Qvb6UKzkhaHc8q/xO822r2vh8+/fmX+cufKIXhwc57ZvHsLxPEKmget5RaHYM3dSa48ESDsuieyF4HrmGuWKRpMpv8BpKAiY0yNpG9uChIIWlWwi6GmwDHC82WNrH3vXxllB2iETXvt/PlTDn0YIUUolBaQ/Bm4GOoEPz/ieBhr2Bb/SLSvFbJ6niaX8wpHjVVY4mkzafP+543z/ueP5hcM0FL95/npuvHwLW9aUb5WVwpFYbdrCFiPx4m4XT/vHm0xTrhNbuqK8NRpnMunkr+y3RyzOWlu+yC2EEEtlQ3uQJ/vt/NeehjMJmw3twWU8qwVpynViIQpHyI6dSZAuEYIOfjeRoWD3WWu5/9YrSt5mZmHqKz8+zKunYij84oqrYTRus7aVfCh2qZ3UgpbJl6/fVZfPQZfe+RNGE6U74vyiJwRMA8fzOD6Z5oKNodnZTjPkxtD+9D3bOToSn7UL29duvISv3VjzH0UIUYF5P6VorZ8AnlBKPaO1/qe5bqeUep/W+j9qenaLUM2WlWKa7XpMJm1iKQevkpQ6YDSe5sFDgzz8wkmS2UDakGXwwXdu5OOX9bK+PVz2/lI4EqvVZMKu6nijatZ14sptazj41hmM7Jv4jOsxFMtw0+VrlvvUhBACgJ+8MlTV8UbVrOvEQhSOkI0n517PPQ0h06holCz3uWZo0h/90oDtQQCNYSgmEjaDht89W8+d1GZenH9pcIx4pvyFZsfTBEx/ndXaz1/qbgkyFJ97XHzmGJoUi4RoHBVf5i73Yp/1FaBhXvDvfrwf23UZjRfRu0OEAAAgAElEQVRfWS61ZaWAlO0ymQ3GrtTJiSTfenqQR14+iZ1tzW0Jmnz04s1cf8lmuqLlr45Fg37hKCw7HolVKjPH5be5jje6Zlsnnuw/g6X0rBb4J/vPcPvynZYQQuSV2imy3PFG12zrxELMHCEr55ItHRV9Lsl1Nbla5wsxGnC1xlSKtOsVhWLXaie1/YeH+PIjr3J0NOHviKZgbUuQ7tYQQ7HUvMWjQp6GaNDEdjVtkQAazXC8uMB25dYu7r/t1xZ93kKI+qnlnERDJXe8PhRjImFjZHeucDzNSCyD7cbmv/Mqksg4jCdsUlW8EXl7dIp9B4/xs1dP59tPOyIBbrh0Mx+5aDOt84zfSOFIiFWrodaJ54+NMXMTvLTrHxdCCLEsGmqdWIjCEbL5HHx7nK8/emRW4PPMTp/Xh2JsaA8TNA3S2svv5uZld+WzjMo6maqx//AQn33wBcYTNoYCO/umfySeIWSZtBcEdc8nN5L2x1dvY1dvJ3c/3k/G8bhyW7tEjAjRZGpZQGqoS+YZxwMFRnabU6XAU7riIOiVTGtNLO0wkbCx3cp/P46cjnHfgQGeeH0k/4e9rjXExy/v5UPv3DhvQUgKR0Kseg21TiTt0q9/cx0XQghRdw21TixUNGBwdDRRNusnt1X9vU8czReQcjlHR4biBEzF+ja/0yeWcgiYada1hTgxngLDLxyB/1nnU9ecXfMizN2P9xNPO5hKYRgK5fpXXFxPMxJPz1tAyoWEK2aPpEnBSIjm1XRJrZXyE//9MGilptP7g2bTX9hYMNfTTCZtJlN2ftGZj9aaF49PcN9TAzzz9vRV+d6uCDddvoVrz19PwDTKPkYkaNIVDUrhSAghhBCiCrldqEodF42nMIN1e08rSdtlYHSKmdclFP77ckPBVMYtuu/QZApTgfbgxESKTR0RuqIBzkzZ9HZZbOwIcXoyjUazfV0rX/jAeXUpyBwbS/jB1QUX43Ojc5kKLkC3ha2iopEQYmWoZQHprRo+1qLtWN/O0ZE4sdR0BlJbOMDW7tW3u07G8ZjI5hvpCoOxtdYcOHqGfQcGePnEZP742etauHlPH/9l+zpMo3wxTgpHQogZ3lruExBCiGayoS3E4ES65PEV6q3lPoHFKNyBDfzue9MwME3//bin/UIM2h/rMg1FS9Asuq+r/d3GFAo8GImn2drdguN69LSFGRxLcHFfV91Hv7Z0RRmJp9Gef86WYeQLR0HTIJFx6FsT5Uy8OAtpbdTi0F/+Vt3OSwixvCouICmlri9xeAJ4SWs9pLUu9f1lk5s/3tBh5bewtF1d8/ngRpbMuEwk7YpmsHNcT/PL14e578AAbw5P5Y+fv7Gd37uijz1b16BU+cJROGCypkUKR0LMx1T+FryljjejZlsn1kYDjJbY8W5ttPJcByGEqKe2SBBzMo0H+XkgI3u8GTXbOlGtwh3YchzPQynF2pYgw/FMfipCa1Aabrlqa9F9g6aBk52gUNkdQpO2y/b17dx/6xULOq+ZmUqVFJ9uu3pbPgNJZycXTAWGoYgEDHrawpJfJMQqVE0H0h8CVwI/z359DfAUsEMpdafW+n/V+NwWpZ5bWDYyrTVT2cJRuopgbNv1ePRXp7n/6WMMjiXzxy97Rxc37+ljV2/HvIWjUMBkTTRIJCiFIyEq0dUSZKTENrZdLc35wYAmWyf+5uPv4pP/8nTJ40II0QhiaYc1LQFGp2w8/OLRmpZAVbvmNpimWieqVWoHNsswQMGGjggAo1MZPA2mofjT95yTH/HK3TeXc+Sh0dlupMVcBP/6o0f4u/1v4nqakGXguB5/+fAr3En5LKJrdvbw1Rsuyu/CBrBjfSuff//OFf95Sggxt2oKSB5wntb6NIBSaj3w98Ae4HGg4V7wa7WFZTPwPE0s5TCRtHG8ygNgU7bLj146ybeeHmQ4Pt0ifdU53ezds4WdG9rnfYygZbCmJVjRdqVCiGlpu/QHgLmON4GmWiceen5wzuOrZe0QQjS21qDJqYkUAcPIZ9CMJRzOWde0I2xNtU5Uq3AHttwERFvYQuPvfLy+PUx7JIDtau687oKitSZ334Cp8jlHjtZsW9Oy4Jyj/YeH+Lv9b+JpjWUoHFczOpVhbUuQux/vn/cxV9NnKSFEZar5xH9W7sU+awjYobU+o5SaPQMgloTtZvONUg5ehflGAPG0w0PPH+e7h44znvT/+AwF7z1vPTdevoWt3S3zPkbQMuiKBmkJSeFIiIWIpUsXe+c63gSaap14+MVTcx7/2o1LfDJCCFFCvvtbMb3BvWbervAG1lTrRLVKTUDc8aHzgfmnImbet9qco1Jjanc/3o/jeQRMA4Xy85c8iKUcBscStf3hhRCrQjWf/H+plPoh8J3s1zcAjyulWoDxmp+ZKCtl+2NqU1W2MI8nMnz32eP84Lnj+V0fAqbiAxdu5BOX97Ix215bTsA06GoJ0iqFIyFEsaZaJ+bajbLSXSqFEKLeYmmHNdEAI9mxJ0NBd0uwmUfYmmqdqFa5rKFKCkEL6fjZf3iIr/z4MEeG4gRMxfq2EEOxVL4TKmQauLnwbvxf045Hb1e06p9PCCGqqQB8CrgeuAr/Gsi/At/V/rZe767DuYkZtNbE0/6YWqbUnq5lDE2m+PahQf79xZOks/cNBww+vGsTH7+sl7Wt87dCB0yDzmiAtrAEzAohSpJ1QgghaqgtZPH6RMrvIMmOsJ1J2GzvadoRthW7Tuw/PJQfQeuMBPJFnDuz3y/MEtrW3VKTLKHccw5NpjAVaA9OTKTY1BEhYCoyjkdHNMBo3MbDD+bO7fK2mjYWEkLUTsUFJK21Vko9AWTw94E4qCvdE14siutpJpM2sZRTVb4RwOBYggcOHuOnvzqNk72q3ha2uP7izXzs4s20R+YvBlmGQUc0QHvYauaWaSFEnTXbOhGyoNRF/LA0VwohGkT+JTT3SqpnHG8yzbZOVOPux/sJmCqfCRoNWiQyDp++/1li6eKNbV4fivO5B1/gr2+4aEEdR7kup8mkTUvIzBeFFAo8GImn2drdQtBUBEyTta0wkbBJux6WYfCpa86WbCMhxIJU/DZZKfVx4K+B/fhXDP5WKfU5rfWDdTq3VS/tuEwmHeJpp+o3Cm8Oxdl3cIBfHBkmN42xpiXI717ay4cv2lhR4LVpKDojQdojUjgSoh5ClpHvCJx5vBk12zpxSd9aXjg2RsKe/jOIBgwu2tK1jGclhBDT4hmXrqjl78KWHWFb2xLIxxA0m2ZbJ6pxbCxB54wLsyOTqVnFI/AvDsdSTkVB1jmlRtUSGZdkxsU0FDo7pqYUZFyPpO2yfX17Pgtp0Fg9u1ILIeqnmuusfwFcrrUeAlBKrQMeBZr+Bb/RJDL+mFpyAW8OXj4+wb6DAzzVfyZ/bEN7mE9cvoUPXLiBYAUfTE1D0REJ0B4OYBhSOBKiXnraQhwbS1L4r0xnjzepplonCne8ye2Ws5itkoUQotbaQpa/C5tZvAvb9p7wcp/aQjXVOlGNLV1RhmKpoou0EyWKR+Cv9Y7nVRxkPdeommmo/CY6WoOHRme7kXLrmeykJoSopWoKSEbuxT5rFGjoy+TlguwajedpYmmHyaSN7VY3pqa15tDbY9x3YIAXBifyx9+xNsre3X28Z2cPZgWFIEP5haOOiBSOhFgKLUETU4Gn/TeTCjCVf7xJNdU6cc3OHm4YHOfeJ44ylXFpCZrcctX/z96dh8d51/fe/3xn025LXpTFC7YJwRAakmCyQEj9QMoJyxNoyaEx0NIWSGjPKRwoYTlt0z7p1eshhz6lpOW0zklboEBYzJa2EEoIxgSwE2clIcZx7RDbiS0vsnZptu/zx32PPJJHoxlpRrPo/bouXZJuzdz6/eae+X3v+3v/lvV1GycALD7NNoRNDRYnypG7KTGaTE/elCgmFokUnch6+lC19sSZQ9UUvg9S2axWd7fp6OCE0u7asKxDH33di4hnACqunATS3Wb2XUl3hr//pqRvV75IlVFsIrt6akzTmawGxlIankiXvfJP1l0/3ndCX9z1jH5xdGhy+wvP6tLbLlurV563XJEShp5FzLQkTByVkmgCUBnDyYxW97Tp+HBSyUxWiWhEKzoTDTs0QQ0YJ7Y9dFgru1q0NjzZ3/bQYV24uruu4gSAxWs4mdGq7tYpceLszhbiRB3avLFXtyiYC+lQfzBc7MDxEc10et/VGpuxx+v065jnBsY0lsooIk0ZqpZxaXlHQqPJjLIuXby2p65vmANofOVMon2Tmb1F0isV3Ci/3d2/UbWSzdNME9mVM9a4msZTGQ2OpTSSzJR9FymdyereXxzTnbue0S9Pnu76etGapXrbpWv1suf1lDRnkZlpSWtM3e0JEkdADazpadeB48NTtk2ks1q/orNGJZqfRowTyXRGJ4bTkxdmXa2xuokTAJAbFrVh5em4MJpMq7erMYewNVqcKNf04WIf+NJD+sYjz53xuK6WaMEJtLds/Yl+eqB/8vf2eETP7+1SayyqZCYrM1N22lC1RCyqj//GhcQtAAuirLVm3P1rkr5WpbJUVKGJ7Nri0ZLHGleDu2skmdHAWEoTs3RrLSSZzuo7jx/Rlx84qCOD45PbL9+wTG+7dK1esmppSfsxM3W1xtTdFlcs2hS9hoGGdMWGZdp14MTk3clUJqOxVEZvu3RtbQs2D40UJ/YeHdTgeFoRmaJmSmdcJ0aSSmcGa100AJAUDIu6adujOtw/pnQ2WEGrqzWmP33Di2tdtDlrpDgxX5+8/hJJD+mux44okw0SPtdeeHa4farpySNJGk1l9Z99Q+pd0hq8B9y1qruVoWoAambWBJKZDen0yOspf1KwGueSipeqAgpNZDeWyhQda1wtwUoLKQ2OpZXOlje/kRTcafrXR5/TVx88pJMjSUnBKhy/ev5Kve3StXp+b2m9FcxMnS0x9bSTOALqwXceP6LpHRDdg+3vu/r82hRqDho1TqQyrkzWlZFPDgmQpGSmYecWAdCEXJIsOI+TFW5s612jxolK+OT1l+iT18/+uOnJo5zRVFZdrXGt6MpoZIKhagBqa9YEkrt3Veufm1lU0m5Jh939jZXcd6GJ7BZ6dZ1kOqvB8ZSGxtNzmuxwcCylrz98WN94+LCGxtOSpFjE9GsvPktbLl1TVjKsszWmnvaE4iSOgLrxVN/QGWfTHm5vJI0aJ9x9ytwU3viT0wJoMlt37NfStrjOWdo2ua2epmQoVTXjRCPbvqdPt969R/uPjxR93GgyrXg0qtuuZ6gagNoqawhbFbxf0pOSKn7XoZar64yFw9RGk+k5Pf/48IS+uvuQ/vWxZzWeCnostcQiesOF5+itL1ut3iWlj3vvbAnmOErESBwB9SbX0SV/yjL309shqYpxwswUCVfBy4nk7vIDQB2oxykZUBnb9/Tppm2Pqn80pdmmIu3taqXHEYC6ULMEkpmtlvQGSX8p6YOV3v9Cr67j7hqeSGtgLKVkuvxhapL07KkxfXn3Qd39+BGlwivIjkRUb754lX7jklXqaU+UvK+Olpi62+NqiTXscuBA04uZKeV+Rqf+GAkMSdWPE1KQPLJpvwNAvainKRlQWVt37NfQeFrRiAWrJmcKz496xfoe3XnD5QtcOgAorJY9kP5G0oclVaVL60Ktwjbf+Y0k6cDxEd15/zO6d0/f5MVLd1tc171sta696Fx1tpR+mNoTMfV0kDgCGsF5vZ3ae3RIWSlIIpkUDbdDUpXjhCRFw/lEcnMg0VcTQD2phykZUDnb9/Rp6479Otg/qmNDE0plspOjBHLHN98V63t0542vqEVRAaCgmiSQzOyNkvrc/UEz21zkcTdIukGS1q4tb1Wianf5TWWyGhhLaXg8rewc58vYc2RQX9j1jH6878Tktt6uFr110xq9/lfOVmu89CRQeyLocVTOcwDU1keu2aibtj2qofH0lNV1PnLNxloXreYWIk7Eo6ZIxBSRySxIImXlSkTpAQagPmze2KtbFNwYPdQ/qtU97QxlalDb9/Tp5rueUDxq6m6L6/jwhLIupTOueBh3WqIRWUS6eA2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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (20,20)\n",
    "for i in range(9):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    sns.regplot(x=X_train[im.sort_values('relative importance', ascending=False)['feature'].values[i]], y=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course the future earnings are not determined by only one feature. From the scatter plot we can have an idea of whether a variable will positively or negatively affect the future earning.\n",
    "\n",
    "We also see that 11% of the listings don't have future earnings. Are they very new listings? Are they of a certain kind?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_earnings = id_earnings[id_earnings['log_future_earnings']==0].merge(features, on='id')\n",
    "have_earnings = id_earnings[id_earnings['log_future_earnings']>0].merge(features, on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'price_have_earnings')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (20,10)\n",
    "\n",
    "plt.subplot(2,4,1)\n",
    "a = plt.hist(no_earnings['host_age'])\n",
    "plt.title(\"host_age_no_earnings\")\n",
    "plt.subplot(2,4,2)\n",
    "a = plt.hist(no_earnings['is_location_exact'])\n",
    "plt.title(\"is_location_exact_no_earnings\")\n",
    "plt.subplot(2,4,3)\n",
    "a = plt.hist(no_earnings['one_or_two_listings'])\n",
    "plt.title(\"one_or_two_listings_no_earnings\")\n",
    "plt.subplot(2,4,4)\n",
    "a = plt.hist(no_earnings['price'])\n",
    "plt.title(\"price_no_earnings\")\n",
    "\n",
    "plt.subplot(2,4,5)\n",
    "a = plt.hist(have_earnings['host_age'])\n",
    "plt.title(\"host_age_have_earnings\")\n",
    "plt.subplot(2,4,6)\n",
    "a = plt.hist(have_earnings['is_location_exact'])\n",
    "plt.title(\"is_location_exact_have_earnings\")\n",
    "plt.subplot(2,4,7)\n",
    "a = plt.hist(have_earnings['one_or_two_listings'])\n",
    "plt.title(\"one_or_two_listings_have_earnings\")\n",
    "plt.subplot(2,4,8)\n",
    "a = plt.hist(have_earnings['price'])\n",
    "plt.title(\"price_have_earnings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that listings with no earnings are mostly the listings with `is_location_exact` of 0. Are the no earning listings with `True` is_location_exact very new listings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'host_age of no earning listings with True is_location_exact')"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (6,4)\n",
    "\n",
    "a = plt.hist(no_earnings[no_earnings['is_location_exact']==1]['host_age'])\n",
    "plt.title('host_age of no earning listings with True is_location_exact')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Didn't see that pattern here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Tune the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "rg = GradientBoostingRegressor()\n",
    "parameters = {'learning_rate': [0.01, 0.05, 0.08, 0.1],\n",
    "                    'max_depth': [4, 6, 8, 10],\n",
    "                    'max_features': [None, 50, 40, 30, 20, 15, 10]}\n",
    "scorer = \"neg_mean_squared_error\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_obj = GridSearchCV(rg, parameters, scoring=scorer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tianyiwang/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "grid_fit = grid_obj.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the best model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.05, loss='ls', max_depth=8, max_features=20,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, n_iter_no_change=None, presort='auto',\n",
       "             random_state=None, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = grid_fit.best_estimator_\n",
    "best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of the tuned model: 5.47\n",
      "14.69% have less than $1000 absolute error\n",
      "37.06% have less than $5000 absolute error\n",
      "50.35% have less than $10000 absolute error\n",
      "69.51% have less than $20000 absolute error\n"
     ]
    }
   ],
   "source": [
    "predicted_log_earnings = best_model.predict(X_test)\n",
    "print(\"MSE of the tuned model: {:.3}\".format(np.mean(np.square(predicted_log_earnings - y_test))))\n",
    "difference = abs(y_to_earnings(predicted_log_earnings) - y_to_earnings(y_test))\n",
    "print(\"{:.2%} have less than $1000 absolute error\".format(sum(difference<1000)/len(y_test)))\n",
    "print(\"{:.2%} have less than $5000 absolute error\".format(sum(difference<5000)/len(y_test)))\n",
    "print(\"{:.2%} have less than $10000 absolute error\".format(sum(difference<10000)/len(y_test)))\n",
    "print(\"{:.2%} have less than $20000 absolute error\".format(sum(difference<20000)/len(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Absolute difference')"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (20,5)\n",
    "a = plt.hist(difference, bins=1000)\n",
    "plt.title('Absolute difference')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Earnings of listings with less than $1000 predicting error')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (20,5)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.hist(y_to_earnings(y_test)[difference<1000], bins=100)\n",
    "plt.title('Earnings of listings with less than $1000 predicting error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'actual earnings')"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rgO0DL9vBkIQvycYkW5Js2bNnz5QDlyRJkqTlZtIJXJLHAB8CXllV3wAuBp4CHAvsAt48WnXIy2uvgqpLqmp9Va0fGRmZcuCSJEmStNxMKoFLcgBd8vbeqvowQFXdU1WPVNV3gXfw/WGSO4A1Ay9fDeycvZAlSZIkaXmazFUoA1wK3FFVbxkoXzlQ7YXArW36auCsJAclORpYB3xq9kKWJEmSpOVpMlehPAl4CfD5JDe3st8DXpzkWLrhkduAlwJU1W1JrgRup7uC5XlegVKSJEmSZm7CBK6qPsHw89qu3cdr3gC8YQZxSZIkSZLGmNJVKCVJkiRJC8cETpIkSZJ6wgROkqRpSnJwkk8l+VyS25K8rpUfneTGJHcluSLJga38oDa/tS1fu5DxS5L6xwROkqTpewh4TlX9ON19UU9JciLwRuDCqloH3Aec2+qfC9xXVU8FLmz1JEmaNBM4SZKmqTrfbLMHtEcBzwE+2Mo3A2e06dPbPG35ye12PZIkTYoJnCRJM5Bkv3abnd3AdcAXgfur6uFWZQewqk2vArYDtOUPAI+f34glSX1mAidJ0gxU1SNVdSywGjgBePqwau152NG2GluQZGOSLUm27NmzZ/aClST1ngmcJEmzoKruBz4OnAisSDJ6r9XVwM42vQNYA9CWPw64d8i6Lqmq9VW1fmRkZK5DlyT1iAmcJEnTlGQkyYo2/SjgucAdwA3Ai1q1DcBVbfrqNk9b/rGq2usInCRJ49l/4iqSJGkcK4HNSfaj+1H0yqq6JsntwOVJXg98Fri01b8UeE+SrXRH3s5aiKAlSf1lAidJ0jRV1S3AcUPKv0R3PtzY8n8FzpyH0CRJS5RDKCVJkiSpJ0zgJEmSJKknJkzgkqxJckOSO5LcluQVrfzwJNcluas9H9bKk+SiJFuT3JLk+LluhCRJkiQtB5M5Avcw8NtV9XS6SyOfl+QYYBNwfVWtA65v8wCnAuvaYyNw8axHLUmSJEnL0IQJXFXtqqrPtOkH6S6PvAo4Hdjcqm0GzmjTpwPvrs4n6e6Fs3LWI5ckSZKkZWZK58AlWUt3ta0bgaOqahd0SR5wZKu2Ctg+8LIdrWzsujYm2ZJky549e6YeuSRJkiQtM5NO4JI8BvgQ8Mqq+sa+qg4p2+smpVV1SVWtr6r1IyMjkw1DkiRJkpatSSVwSQ6gS97eW1UfbsX3jA6NbM+7W/kOYM3Ay1cDO2cnXEmSJElaviZzFcoAlwJ3VNVbBhZdDWxo0xuAqwbKz25XozwReGB0qKUkSZIkafr2n0Sdk4CXAJ9PcnMr+z3gAuDKJOcCdwNntmXXAqcBW4FvA+fMasSSJEmStExNmMBV1ScYfl4bwMlD6hdw3gzj6rW1mz660CF8z7YLXrDQIUiSJEmaJVO6CqUkSZIkaeGYwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk9MmMAluSzJ7iS3DpS9NslXk9zcHqcNLDs/ydYkdyZ5/lwFLknSQkuyJskNSe5IcluSV7Tyw5Ncl+Su9nxYK0+Si1o/eUuS4xe2BZKkvpnMEbh3AacMKb+wqo5tj2sBkhwDnAU8o73mfybZb7aClSRpkXkY+O2qejpwInBe6ws3AddX1Trg+jYPcCqwrj02AhfPf8iSpD6bMIGrqn8A7p3k+k4HLq+qh6rqy8BW4IQZxCdJ0qJVVbuq6jNt+kHgDmAVXX+4uVXbDJzRpk8H3l2dTwIrkqyc57AlST02k3PgXt6Gf1w2OjSErtPaPlBnRyvbS5KNSbYk2bJnz54ZhCFJ0sJLshY4DrgROKqqdkGX5AFHtmqT7iclSRpmugncxcBTgGOBXcCbW3mG1K1hK6iqS6pqfVWtHxkZmWYYkiQtvCSPAT4EvLKqvrGvqkPK9uon/ZFTkjSeaSVwVXVPVT1SVd8F3sH3h0nuANYMVF0N7JxZiJIkLV5JDqBL3t5bVR9uxfeMDo1sz7tb+aT6SX/klCSNZ1oJ3Jjx+i8ERq9QeTVwVpKDkhxNd5L2p2YWoiRJi1OSAJcCd1TVWwYWXQ1saNMbgKsGys9uV6M8EXhgdKilJEmTsf9EFZK8H3g2cESSHcBrgGcnOZZu2Mc24KUAVXVbkiuB2+muzHVeVT0yN6FLkrTgTgJeAnw+yc2t7PeAC4Ark5wL3A2c2ZZdC5xGd5GvbwPnzG+4kqS+mzCBq6oXDym+dB/13wC8YSZBSZLUB1X1CYaf1wZw8pD6BZw3p0FJkpa0mVyFUpIkSZI0j0zgJEmSJKknTOAkSZIkqSdM4CRJkiSpJ0zgJEmSJKknTOAkSZIkqSdM4CRJkiSpJ0zgJEmSJKknTOAkSZIkqSdM4CRJkiSpJ0zgJEmSJKknTOAkSZIkqSdM4CRJkiSpJyZM4JJclmR3klsHyg5Pcl2Su9rzYa08SS5KsjXJLUmOn8vgJUmSJGk52X8Sdd4FvA1490DZJuD6qrogyaY2/2rgVGBdezwTuLg9S5IksXbTRxc6BAC2XfCChQ5BkqZlwiNwVfUPwL1jik8HNrfpzcAZA+Xvrs4ngRVJVs5WsJIkSZK0nE33HLijqmoXQHs+spWvArYP1NvRyvaSZGOSLUm27NmzZ5phSJIkSdLyMdsXMcmQshpWsaouqar1VbV+ZGRklsOQJEmSpKVnugncPaNDI9vz7la+A1gzUG81sHP64UmSJEmSRk03gbsa2NCmNwBXDZSf3a5GeSLwwOhQS0mSJEnSzEx4Fcok7weeDRyRZAfwGuAC4Mok5wJ3A2e26tcCpwFbgW8D58xBzJIkSZK0LE2YwFXVi8dZdPKQugWcN9OgJEmSJEl7m+2LmEiSJEmS5shkbuStHvOGqZIkSdLS4RE4SZKmKcllSXYnuXWg7PAk1yW5qz0f1sqT5KIkW5PckuT4hYtcktRXJnCSJE3fu4BTxpRtAq6vqnXA9W0e4FRgXXtsBC6epxglSUuICZwkSdNUVf8A3Dum+HRgc5veDJwxUP7u6nwSWDF6T1VJkibLBE6SpNl11Og9UNvzka18FbB9oN6OVraXJBuTbEmyZc+ePXMarCSpX0zgJEmaHxlSVsMqVtUlVbW+qtaPjIzMcViSpD4xgZMkaXbdMzo0sj3vbuU7gDUD9VYDO+c5NklSz5nASZI0u64GNrTpDcBVA+Vnt6tRngg8MDrUUpKkyfI+cJIkTVOS9wPPBo5IsgN4DXABcGWSc4G7gTNb9WuB04CtwLeBc+Y9YElS75nASZI0TVX14nEWnTykbgHnzW1EkqSlziGUkiRJktQTMzoCl2Qb8CDwCPBwVa1PcjhwBbAW2Ab8YlXdN7MwJUmSJEmzcQTuP1XVsVW1vs1vAq6vqnXA9W1ekiRJkjRDczGE8nRgc5veDJwxB9uQJEmSpGVnphcxKeDvkhTw9qq6BDhq9LLIVbUryZEzDVKSJGk2rd300YUO4Xu2XfCChQ5BUo/MNIE7qap2tiTtuiRfmOwLk2wENgI88YlPnGEYkiRJkrT0zWgIZVXtbM+7gY8AJwD3JFkJ0J53j/PaS6pqfVWtHxkZmUkYkiRJkrQsTDuBS3JIkkNHp4HnAbcCVwMbWrUNwFUzDVKSJEmSNLMhlEcBH0kyup73VdXfJPk0cGWSc4G7gTNnHqYkSZIkadoJXFV9CfjxIeX/DJw8k6C09HiyuCRJkjRzc3EbAUmSJEnSHDCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ6YyX3gpF5aLLc08HYGkiRJmiqPwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPeBVKaYEslqthglfElKSFtFj6A/sCqR/m7AhcklOS3Jlka5JNc7UdSZL6xP5RkjQTc5LAJdkP+FPgVOAY4MVJjpmLbUmS1Bf2j5KkmZqrIZQnAFur6ksASS4HTgdun6PtSZqBxTJ8ZzFxKJHmiP2jFq3F1Bf4GSyNb64SuFXA9oH5HcAz52hbkjTrFtMXmcXCL1Szwv5RmgQ/g/fmZ/DeFtP7ZD73z1wlcBlSVj9QIdkIbGyz30xy5wy3eQTw9Rmuo89s//Jt/3JuOyzv9s9r2/PGWVvVk2ZtTf0zYf8I9pFTsFTbBUu3bbZrmmbxM3iq3GeTMEv7Z1L941wlcDuANQPzq4GdgxWq6hLgktnaYJItVbV+ttbXN7Z/+bZ/Obcdlnf7l3Pbe2zC/hHsIydrqbYLlm7bbFf/LNW29bldc3UVyk8D65IcneRA4Czg6jnaliRJfWH/KEmakTk5AldVDyd5OfC3wH7AZVV121xsS5KkvrB/lCTN1JzdyLuqrgWunav1DzFrQ016yvYvX8u57bC827+c295bC9A/wtJ9ryzVdsHSbZvt6p+l2rbetitVe507LUmSJElahObqHDhJkiRJ0ixbEglcklOS3Jlka5JNCx3PdCVZk+SGJHckuS3JK1r54UmuS3JXez6slSfJRa3dtyQ5fmBdG1r9u5JsGCj/iSSfb6+5KMmwS1ovmCT7Jflskmva/NFJbmztuKKd9E+Sg9r81rZ87cA6zm/ldyZ5/kD5on6fJFmR5INJvtDeA89aZvv+N9v7/tYk709y8FLe/0kuS7I7ya0DZXO+v8fbhpauxfbeHybJtvZ+vTnJllbWy8+/pfq/PU67Xpvkq22/3ZzktIFlU/osns7n/Sy1a8G+e83DPhuvbb3eb+m+H3wqyedau1433Vhmq73zrqp6/aA7CfyLwJOBA4HPAccsdFzTbMtK4Pg2fSjwT8AxwJuATa18E/DGNn0a8Nd09xU6EbixlR8OfKk9H9amD2vLPgU8q73mr4FTF7rdY/4GvwW8D7imzV8JnNWm/wz49Tb9G8CftemzgCva9DHtPXAQcHR7b+zXh/cJsBn41TZ9ILBiuex7upsbfxl41MB+/+WlvP+BnwKOB24dKJvz/T3eNnwszcdifO+PE+c24IgxZb38/Fuq/9vjtOu1wKuG1J3yZzFT/LyfxXYt2Hevedhn47Wt1/ut/R0f06YPAG5s+2LOvzOMt435fsz7Bme9Ad0/xN8OzJ8PnL/Qcc1S264Cfhq4E1jZylYCd7bptwMvHqh/Z1v+YuDtA+Vvb2UrgS8MlP9AvYV+0N0P6XrgOcA17R/068D+Y/c13RXcntWm92/1Mnb/j9Zb7O8T4LF0CUzGlC+Xfb8K2E7X8e3f9v/zl/r+B9byg1+G5nx/j7cNH0vzsVjf+0Pi3MbeCVxvP/+W6v/2kHa9luGJwJQ+i5lGfz+H+27evnvN9+fxQNuWzH4DHg18BnjmVGOZzfbO92MpDKEc/eI3akcr67V2ePc4ul8VjqqqXQDt+chWbby276t8x5DyxeKtwO8C323zjwfur6qH2/xgvN9rY1v+QKs/1b/JYvFkYA/wznRDSP88ySEsk31fVV8F/gS4G9hFtz9vYvns/1Hzsb/H24aWpr689wv4uyQ3JdnYypbS599S/t9+eRtKeNnAEMCptms6/f2sW4DvXvO2z8a0DXq+39KdcnMzsBu4ju6I2Vx/Z9hXe+fVUkjgho1jr3mPYhYleQzwIeCVVfWNfVUdUlbTKF9wSX4G2F1VNw0WD6laEyzrXdub/emGplxcVccB36IbTjGeJdX+1nmcTjeE4QnAIcCpQ6ou1f0/keXWXs2evrwXTqqq4+n+789L8lP7qLuU/h/63paLgacAx9L9+PbmVj6b7ZqXNi/l715D2tb7/VZVj1TVsXSjt04Anj6NWHq3L0cthQRuB7BmYH41sHOBYpmxJAfQ/ZO9t6o+3IrvSbKyLV9J92sDjN/2fZWvHlK+GJwE/FySbcDldMMo3wqsSDJ6v8LBeL/Xxrb8ccC9TP1vsljsAHZU1egvYx+kS+iWw74HeC7w5araU1XfAT4M/HuWz/4fNR/7e7xtaGnqxXu/qna2593AR+i+kC2lz78l+b9dVfe0L9LfBd5Bt99g6u36OlP/vJ81C/jda8732bC2LZX91tpyP/BxunPg5vo7w77aO6+ERp9VAAACLklEQVSWQgL3aWBduyrMgXQnJ169wDFNS7sq0aXAHVX1loFFVwMb2vQGujHMo+Vnp3Mi8EA7BP+3wPOSHNaObDyPbozuLuDBJCe2bZ09sK4FVVXnV9XqqlpLtw8/VlW/BNwAvKhVG9v20b/Ji1r9auVntSsOHQ2sozt5eFG/T6rqa8D2JD/Sik4GbmcZ7PvmbuDEJI9u8Y22f1ns/wHzsb/H24aWpkX/3k9ySJJDR6fp3se3srQ+/5bk//Zo8tG8kG6/jcYy6c/i9vk91c/72WrDQn73mtN9Nl7b+r7fkowkWdGmH0X3I/Ad04hlNts7vxbixLvZftBdEeif6Ma//v5CxzODdvwHukOxtwA3t8dpdGNurwfuas+Ht/oB/rS1+/PA+oF1/QqwtT3OGShfT/eP+kXgbczhicAz+Ds8m+9fhfLJdP9MW4EPAAe18oPb/Na2/MkDr//91r47GbjS2GJ/n9ANZdjS9v9f0l3Fatnse+B1wBdajO+huyrUkt3/wPvphq58h+7XvnPnY3+Ptw0fS/ex2N77Q+J7Mt1V3j4H3DYaY18//5bq//Y47XpPi/sWui/DKwfqT+mzmGl83s9Suxbsu9c87LPx2tbr/Qb8GPDZFv+twB9ON5bZau98P0bfQJIkSZKkRW4pDKGUJEmSpGXBBE6SJEmSesIETpIkSZJ6wgROkiRJknrCBE6SJEmSesIETpIkSZJ6wgROkiRJknrCBE6SJEmSeuL/A3ZFShk5XHMLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15,3)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "a = plt.hist(y_to_earnings(predicted_log_earnings))\n",
    "plt.title(\"predicted earnings\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "a = plt.hist(y_to_earnings(y_test))\n",
    "plt.title(\"actual earnings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When tested on the test set, our model predicted half of the listings with less than $5000 absolute difference. From the histograms of the predicted earnings and actual earnings, we can see that our model is more conservative and produce smaller numbers for the listings that might have very high earnings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted earnings')"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 8)\n",
    "x = np.linspace(0,300000)\n",
    "sns.regplot(x=y_to_earnings(y_test), y=y_to_earnings(predicted_log_earnings))\n",
    "plt.plot(x, x, linewidth=2)\n",
    "plt.xlabel(\"actual earnings\")\n",
    "plt.ylabel(\"predicted earnings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3e90ccc0>"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=y_to_earnings(y_test), y=y_to_earnings(predicted_log_earnings) - y_to_earnings(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. What if we leave out listings with no future earnings when training the model? \n",
    "\n",
    "11% of the listings don't have future earnings --- they are not booked at all for the next year at the time when the data was scraped. Most of them have `Is_location_exact` as 0. They might be outliers. Let's train the model again only with the listings that have future earnings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_train_test_data(X_all, y_all, test_size=0.2):\n",
    "    '''\n",
    "    Split the data into traning set and testing set\n",
    "    '''\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_all,\n",
    "                                                                      y_all,\n",
    "                                                                      test_size = 0.2,\n",
    "                                                                      random_state = 0)\n",
    "    print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "    print(\"Testing set has {} samples.\".format(X_test.shape[0]))\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "def tune_train_model(rg, parameters, scorer, X_train, y_train):\n",
    "    '''\n",
    "    Grid search on parameters to find the best model\n",
    "    '''\n",
    "    grid_obj = GridSearchCV(rg, parameters, scoring=scorer)\n",
    "    grid_fit = grid_obj.fit(X_train, y_train)\n",
    "    best_model = grid_fit.best_estimator_\n",
    "    return best_model\n",
    "\n",
    "def evaluate_model(best_model, X_test, y_test):\n",
    "    '''\n",
    "    Fit the model on testing set; calculate the MSE; calculate percentages of the records with less than X amount of absolute error\n",
    "    '''\n",
    "    predicted_log_earnings = best_model.predict(X_test)\n",
    "    print(\"MSE of the tuned model: {:.3}\".format(np.mean(np.square(predicted_log_earnings - y_test))))\n",
    "    difference = abs(y_to_earnings(predicted_log_earnings) - y_to_earnings(y_test))\n",
    "    print(\"{:.2%} have less than $1000 absolute error\".format(sum(difference<1000)/len(y_test)))\n",
    "    print(\"{:.2%} have less than $5000 absolute error\".format(sum(difference<5000)/len(y_test)))\n",
    "    print(\"{:.2%} have less than $10000 absolute error\".format(sum(difference<10000)/len(y_test)))\n",
    "    print(\"{:.2%} have less than $20000 absolute error\".format(sum(difference<20000)/len(y_test)))\n",
    "    return predicted_log_earnings, predicted_log_earnings, difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters1 = {'learning_rate': [0.01, 0.05, 0.08, 0.1],\n",
    "                    'max_depth': [4, 6, 8, 10],\n",
    "                    'max_features': [None, 50]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 2520 samples.\n",
      "Testing set has 630 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tianyiwang/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "have_future_earnings_index = id_earnings['log_future_earnings'] > 0\n",
    "X_train_new, X_test_new, y_train_new, y_test_new = prepare_train_test_data(X_all[have_future_earnings_index.values], y_all[have_future_earnings_index.values])\n",
    "best_model = tune_train_model(rg, parameters, scorer, X_train_new, y_train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.05, loss='ls', max_depth=10, max_features=10,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, n_iter_no_change=None, presort='auto',\n",
       "             random_state=None, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of the tuned model: 1.18\n",
      "8.25% have less than $1000 absolute error\n",
      "31.59% have less than $5000 absolute error\n",
      "50.16% have less than $10000 absolute error\n",
      "70.79% have less than $20000 absolute error\n"
     ]
    }
   ],
   "source": [
    "predicted_log_earnings, difference = evaluate_model(best_model, X_test_new, y_test_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MSE is much smaller and apparently, this new model performs much better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Absolute difference')"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (20,5)\n",
    "a = plt.hist(difference, bins=1000)\n",
    "plt.title('Absolute difference')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'actual earnings')"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 8)\n",
    "sns.regplot(x=y_to_earnings(y_test_new), y=difference)\n",
    "plt.xlabel(\"actual earnings\")\n",
    "plt.ylabel(\"actual earnings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are quite a few listings that actually don't have any future earnings but were predicted to have earnings by the model. We are then curious --- what if we only train and test the model on the listings where `Is_location_exact` is 1 and have future earnings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 2379 samples.\n",
      "Testing set has 595 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tianyiwang/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "new_index = (id_earnings['log_future_earnings'] > 0) & (X_all.reset_index().drop(\"index\", axis=1)['is_location_exact'] == 1)\n",
    "X_train_new, X_test_new, y_train_new, y_test_new = prepare_train_test_data(X_all.reset_index().drop(\"index\", axis=1)[new_index.values], y_all[new_index.values])\n",
    "best_model = tune_train_model(rg, parameters, scorer, X_train_new, y_train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.05, loss='ls', max_depth=10, max_features=15,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, n_iter_no_change=None, presort='auto',\n",
       "             random_state=None, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of the tuned model: 1.32\n",
      "10.76% have less than $1000 absolute error\n",
      "32.77% have less than $5000 absolute error\n",
      "50.42% have less than $10000 absolute error\n",
      "72.77% have less than $20000 absolute error\n"
     ]
    }
   ],
   "source": [
    "predicted_log_earnings, difference = evaluate_model(best_model, X_test_new, y_test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'difference')"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CG4UFEZEK62qpZ2AsQX3g9H/J8XSWzpb6Cs5KZlMmm9/saTxZ3ZcbzkWXIUREKmzrxlWks5ZYKr8hUCyVIZ21bN24qtJTk/OUc5oqhSPxmg0KoDMLIiIVt2lNGw+Qr13ojcTo1GqIeWEskSYSTZPJVU+/hJlSWBARqQKb1rQpHMwTtVqX4EZhQUREZBaknbqEaA1fbjgXhQUREZHzkMtZhuNpRuJpbA0sg5wJhQUREZEZGk2kiURTZHPzMyQUKCyIiIhMUzyVZTCaJJWp/eLFqVBYEBERmaJUJkckNj/rEtwoLIiIiEwil7NEYilGE5l5W5fgRmFBRETExUg8zXBs/tcluFFYEBERmUAslWFwPEU6uzDqEtwoLIiIiJRIZfL9EmKphVWX4GbKYcEYcyGw2lr7E2NMHeCz1o6Vb2oiIiJzJ+vUJYwt0LoEN1PaSMoY8++Ax4GHnKFO4HvlmpSIiMhcsdYyEksTHooxOo8bK52PqZ5Z+CJwA/AigLX2sDFGTcxFRKSmqS5haqYaFpLW2pQxBgBjjA9Q9BIRkZqUzGQZiqaIp+bPZk/lNNWw8HNjzJ8CdcaY3wb+EPh++aYlIiIy+7I5y1A0xVgiXemp1JQp1SwAXwFOAq8CW4Gnga+Wa1IiIiKzyVrLcCxFeCimoDADUz2zUAd821r7TQBjjNcZi5VrYiIiIrNhPJkhElVdwvmY6pmFn5IPBwV1wE9mfzoiIiKzI5nJ0j8cZ2A0oaBwnqZ6ZiFkrR0v/MFaO26MqS/TnERERGYsk80xFEsxnlBTpdky1bAQNcZcb63dC2CMWQ/EyzctERGR6bHWOvs4pMmpV8I5xVIZfvjq8WndZqph4Y+Bx4wx/c6f24Et03okERGRMhlPZhgaT5HJ6XLDuZwaT/LdvX18/5V+osnpLRmdUliw1u42xqwBLgcMcMhaq3JSERGpqEQ6y2A0RTKtfgnn8vapKI/19PKTgyfIODtnBn1TLVnMm85GUhuAi5zbrDPGYK39zrQeTUREZBZksvnNnsaTqkuYiLWW/b0jbNsd5sW3h4rji+v83LGug1vXruT6/zL1+5tSWDDG/BNwCfAyUIhvFlBYEBGROZPLOXUJ2sNhQtmc5ReHT7GtJ8wbx0/v9djZUsfd6zv5+JXLCfq9077fqZ5Z6AautHpmRESkQsYSaSLRtOoSJpBIZ3n2teM8tqeXYyOJ4viV7c1s2dDFhy5ZgtdjZnz/Uw0LrwErgGMzfiQREZEZUF3CuQ3HUnxvXz/fe7mPUWepqAE+dMkStmzo4uqORbPyOFMNC0uB140xLwHJwqC19tZZmYWIiMhZ0tkcEdUlTKg3EuOxPb08d+AEqUz+TIvfa/j4lSu4u7uTC1pntxXSVMPCn8/qo4qI1ICdhwZ4aNdRwpEYXS31bN24ik1r2io9rXkvl7MMx9OMqC7hfV7vH2VbT5hfHj5V3Pq5KeTj1utWcse6DlobAmV53Kkunfy5MeZCYLW19idO98bpV0iIiNSInYcGuH/HAfxew+I6PwNjCe7fcYAHQIGhjEYTaYZVl3CGnLW88NYg23aHea1/tDi+ojnEXes7+OTV7dQFyvuSPNXVEP8O+ALQSn5VRAfwv4Cbyjc1EZHKeWjXUfxeQ30g/99kfcBHLJXhoV1HFRbKIJ7KMhhNFk+pC6QyOX70+gke6wkTjpxumry6rZEtG7r4zcuWnVfR4nRM9TLEF4EbgBcBrLWHjTH61yIi81Y4EmNxnf+MsTq/l96INtudTWmnX0JUdQlFo/E0O/b38+S+PiKx0/0Pb7i4lS3dnaztWowxcxMSCqYaFpLW2lRhcsYYH6ALSSIyb3W11DMwliieWQCIp7N0tmgPvdmQy1kisRSjiYzqEhzHRxI8vqeXp187RiKdP8Pi9RhuWtPG5u5OVi1rrNjcphoWfm6M+VOgzhjz28AfAt8v37RERCpr68ZV3L/jALFUhjq/l3g6Szpr2bpxVaWnVvPymz2lyOYUEgDePDHGtt1hfv7mSQq/koaAl09f285nr+9kWVOwshNk6mHhK8DngVeBrcDTwD+Ua1IiIpW2aU0bD5CvXeiNxOjUaojzprqE06y19LwbYdvuMHvfGy6OL20M8NnrO/n0te00BqezI0N5TToTY4wXeMRa+2+Ab5Z/SiIi1UXvf89PKpOvS4ilVJeQyeZ4/o2TbO8Jc/RktDh+8dIGNnd38rE1bfi909vkaS5MGhastVljzDJjTMBam5qLSYmIVJqWTp6/rFOXMKa6BKLJDD989RhP7Onj5HixtyFruxazZUMnN1zUOudFi9Mx1XMc7wD/aozZARSjkLX2b8oxKRGRStPSyZmz1jIazzAcV13CybEkT+7r4/v7+4mm8u2qPQZ+87JlbNnQxWXLmyo8w6mZaljodz48QG38ZCIi50FLJ2cmlsowOJ4inV3YdQlvn4qyvSfMTw8OkHECU8jn4ZZr2rlrfScrFoUqPMPpmWoHx/8HwBjTYK2NTna8iEit09LJ6UllcgxGk8RTC3ezJ2stL4eH2dbTy0tvDxXHW+r93LGug1uvW0nzWQG0Vky1g+MHgW8BjcAFxpjrgK3W2j8s5+RERCpFSyenJpuzDEVTjCXSkx88T2Vzll1vnmRbT5g3T4wXxztb6tjc3cXHr1xOwFd9RYvTMdXLEP8N+ASwA8Bau98Ys7FssxIRqTAtnXRXqEuIxFLkFmjxYjyd5ZlXj/PE3l6OjSSK41evbGbLhi4+eMkSPFVctDgdU17Eaa0Nn1WpuXDPNYnIgrBpTZvCwQSiyQxD0YVblzAUTfG9l/vY8XI/o4n8clADfPjSpWzZ0MlVKxdVdoJlMNWwEDbGfAiwxpgA8B+Ag+WbloiIVJtkJstQNLVg6xLeG4rxWE8vP3r9OOls/myK32u4+aoV3LW+k67W+VvPMtWw8O+BB8nvNtkL/Ij85lIiIjLPLfS6hNf6RtjWE+ZXRwaLDbqaQz5uW7uS29d10FIfqOj85oJrWDDGfN1a+2Xgo9ba35mjOYmISBWw1jr7OKQXXF1Czlp+dWSQbT1hDvSPFsfbF4W4a30nN1+9gjq/t4IznFuTnVm4xRjzVeBPgMfmYD4iIlIFFmpdQiqT40evH2d7Ty+9kXhx/PLlTWzZ0MlvrF6G1zM/ihanY7Kw8CxwCmgwxoySr+Gwhc/W2uYyz09ERObQQq1LGImn2bG/n+/t6yMSO3255QMXt7JlQxfXdS6q6nbM5TZZWPiqtfb/MsY8Za29bU5mJCIic26h1iUcG4nz+J4+nnn1GAlnN0yfx3DTFW1s7u7i4qUNFZ5hdZgsLLwAXA+MTnKciIjUoIXaL+GN42Ns2x1m1+GTFLavaAh4+cx1K7ljXQfLmoKVnWCVmSwsBIwx9wIfMsZ89uxvWmu/W55piYhIuS20ugRrLS++PcT2njAvh0eK48sag9y5voNPXdNOQ3DK7YcWlMl+K/8e+B1gMfCZs75nAYUFEZEas9DqEtLZHD89OMD2njDvDJ7eCGzVsga2dHfx0cuX4fPWdjvmcnMNC9baXwK/NMb0WGu/NUdzEhGRMsjmLJFYitH4wqhLGE9m+MH+fp7Y18fgeKo4vv6CxWze0EX3hS0LumhxOibrs/Axa+3zQESXIUREalOhLmE4niKbm/91CSfHkjyxt5cfvHKMmHP2xGNg0+VtbOnuZPXypgrPsPZMdhliI/A8+UsQxSWTJZ8VFkREqlgslWFwfGHUJbx1cpztPb08f2igGIpCfg+3XNPOXes7WdEcqvAMa9dkYWHMGPMfgdc4HRJwvhYRkSqVyuQYiqaIpTKVnkpZWWvZ994w23rC7H4nUhxvqffz2es7uPW6lTSF/BWc4fwwWVhodD5fDmwAniIfGD4D7HK7oTHm28CngQFr7dXOWCuwDbgIeAfYbK2NmPxFoweBW4AY8HvW2r3Obe4Fvurc7V9Yax9xxtcD/wjUAU8D91lr7bkeY5KfU0RkXijUJYwlMth5vBQym7PsfOMk23rCHBkYL453tdSxubuL375yOQGfihZni5nKXyZjzI+AO621Y86fm4DHrLU3u9xmIzAOfKckLPw1MGSt/StjzFeAFmvtl40xtwB/RD4sfAB40Fr7AeeFvwfoJn82Yw+w3gkYLwH3Ab8mHxa+Ya195lyPMdnP2N3dbXt6eib9XYiIVKv8Pg7zuy4hnsryzGvHeHxPH8dHE8XxazoWsbm7kw9esgSPihan5JK2pj3W2u6pHDvVBaUXAKmSP6fIv3M/J2vtLmPM2cfcBmxyvn4E2Al82Rn/js0nl18bYxYbY9qdY39srR0CMMb8GLjZGLMTaLbWvuCMfwe4HXjG5TFEROaleCrLqfHkvK5LGIqmeHJfHzv29zOWyF9aMcBvrF7Klg1dXNGu3QfKaaph4Z+Al4wxT5J/h38H+Rfi6VpurT0GYK09Zoxpc8Y7gHDJcb3OmNt47wTjbo8hIjKvLIS6hPcGY2zfE+bHr58gnc2fMQn4PNx81QruXt9JR0tdhWe4MEwpLFhr/4sx5hngN5yh37fW7pvFeUx0zsjOYHx6D2rMF4AvAFxwwQXTvbmISEXkCv0S5mldgrWW1/pG2dYT5ldvDRbHm0M+bl/Xwe1rV7K4PlDBGS48U+5r6RQc7j3PxzthjGl33vG3AwPOeC/QVXJcJ9DvjG86a3ynM945wfFuj/E+1tqHgYchX7Mw0x9KRGSujCbSRKLzsy4hm7P865FTbOsJc/DYWHG8fVGIzd2dfOKqFYT83grOcOGa6ybYO4B7gb9yPj9VMv4lY8yj5AscR5wX++eAvzTGtDjHfRz4E2vtkDFmzBhzI/Ai8LvAf5/kMUREalY8lWUwmiSVmX91Ccl0lmcPnODxPb30DceL42tWNLFlQxcfuXQpXo+KFiupbGHBGPMv5M8KLDXG9AJ/Rv4FfLsx5vPAe8DdzuFPk18JcYT80snfB3BCwdeA3c5xDxSKHYE/4PTSyWecD1weo2rtPDTAQ7uOEo7E6GqpZ+vGVWxao1ILEcnvazAUTRFNzr+6hJFYmqf29/G9ff0Ml7SgvnFVK1s2dHFtxyK1Y64SU1o6uRBUaunkzkMD3L/jAH6voc7vJZ7Oks5aHrj1KgUGkQUsl7MMx9OMxNPzri6hfzjOY3t6efa14ySdMyV+r+G3rljO3d2dXLSkocIzXBjKsXRSyuShXUfxew31gfxTUR/wEUtleGjXUYUFkQVqLJEmEk2Tyc2vSw6Hjo/y6O4wvzx8ikLJRUPQy63XreSz6zpY0his7ATlnBQWKiwcibG47sxWpHV+L72R2DluISLzVSKd75cwn+oSctby0ttDPLo7zCu9I8XxtqYgd67v5FPXrCi+WZLqpWeowrpa6hkYS5zxjyWeztLZUl/BWYmcm2psZl86myMSTTE+j+oSUpkcPz00wPaeMO8Onn7zc8myBrZs6GLTZcvwedWOuVYoLFTY1o2ruH/HAWKpzBk1C1s3rqr01ETep7TGZnGdn4GxBPfvOMADoMAwA/OxLmE8kWHH/n6e3NfHYPR049/1F7awpbuT9Re2qGixBiksVNimNW08QL52oTcSo1Pv1KSKqcZm9owm0gzPo7qEE6MJntjbyw9fOU48nQXAY+Bja9rY3N3FpW2Nk9yDVDOFhSqwaU2b/qOVmqAam/M33+oS3hoYZ1tPmJ+9cbLYKKrO7+VT167gzus7Wd4cqvAMZTYoLIjIlKnGZubmU78Eay173o2wraeXPe9GiuOtDQE+u66DW69bSWNILy/VymMM9YHpdcLUsykiU6Yam+mbT/s4ZLI5dr55ku27ezlycrw4fmFrPZu7O7npiuUEfCparEYeY6gPemkI+KgPeKddN6KwICJTphqb6Zkv+zjEUhl++OpxntjTy8BYsjh+XeciNnd38YFVrXhUtFh1vB5DXcBLY9BHnX/6AaGUwoKITItqbCY3X/ZxGBxP8t19fezY3080ebpo8SOrl7Klu4sr2psrPEM5m9eTL0BuDPoI+T2ztvJEYUFEZJakMvm6hFiqtusS3h2Msr2nl58cPEE6mz8rEvR5uPmqFdzV3UnH4roKz1BK+Twe6oNeJyCUZ1dOhQURkfOUzVmGa7yLNPlmAAAgAElEQVQuwVrLK30jbNsd5tdHh4rji+r83L52Jbev7WBRvd/lHmQu+b0e6gNeGsoYEEopLIiIzJC1ltFEhuFY7dYlZHOWXx45xbbdYQ4dHyuOr1wc4u71XXziquVz8mIkk/N7PTQE8wWKc/2cKCyIiMxALJVhcDxFOlubdQmJdJbnDhznsT299A8niuNXtDexpbuLD1+6FK9HRYuV5vd6aAz6qA96CfoqF9oUFkREpiGVyTEYTRJPZSs9lRkZjqX43sv9PPVyPyPxdHH8Q5csYUt3F1d3NKsdc4UFfB4aAj4agr6qWYqqsCAiMgXZnGUommIskZ784CrUF4nz2J5enj1wvLhKw+81/PaVy9m8vosLlqixViUFfM4ZhED1BIRSCgsi84h2hJx91lpG4xkisRS5GixefL1/lO09YX5x+BSF2TeFfNx63UruWNdBa0OgovNbyKo9IJRSWBCZJ7Qj5OyLJjMMRWuvLiFnLS+8Ncj2njCv9o0Wx5c3B7lrfSe3XN1O3TTb/crsCPq9NDirGPw1tEW3woLIPKEdIWdPMpNlKJqqubqEVCbHj18/wWN7enlv6PTmXpe2NbKlu4tNly9T0WIFBP1eGgP5IsVaCgilFBZE5gntCHn+MtkckVi65uoSxhJpvr//GN/d18dQNFUc33BRC1s2dLGua7GKFufYfAgIpRQWROYJ7Qg5c9ZaRuJphmPpmqpLOD6a4Ik9vfzw1WMk0vlLJV6P4WNr2tjc3cklyxorPMOFJeT3OqsYvPjmQUAopbAgMk9oR8iZGUukiUTTZHK1U5dw+MQY23t6+dkbAxR6QdUHvHzqmnbuvL6DtuZQZSe4gIT8+fqDhsD8CwilFBZE5gntCDk9iXSWwWiKZLo26hKstfS8G2H77jB73hsuji9pDHDnug4+fd1KGoNT+y/9paNDPLo7zLHROO3NddyzoYsbVrWWa+rzTl3AS31g/geEUqZW+5jPtu7ubtvT01PpaYhImaWzOSLRFOPJ2tjsKZPN8fwbJ9neE+boyWhx/KIl9Wzu7uKmK9qmdU38paNDPPj8YXweQ8jvIZHOkclZ7vvYagUGF3WBwhkE37wpEjXG7LHWdk/lWJ1ZEJEFIZezRGpos6doMsMPXz3GE3v6ODmeLI6v7VrMlg2d3HBR64yKFh/dHcbnMdQ5ewsULlk9ujussFDCmHyYmm8BYaYUFkRk3ssXL9bGZk+nxpN8d28f33+ln2gyf4nEY+A3L1vG5u4uLl/RdF73f2w0TnPozP/6Q34Px0fj53W/84Ex+RBVH/QqIJxFYUFE5q1a2uzp7VNRtveE+enBATJOqAn5PHzymnbuWt9B+6K6WXmc9uY6BqPJ4pkFgEQ6x4rm2bn/WlMICA3BfB2CAsLEFBZEapjaO0+sVpoqWWvZ3zvCtt1hXnx7qDi+uM7PHes6uHXtShad1TvjfN2zoYsHnz9MPJ09o2bhng1ds/o41aw0IDQEfHgUECalsCBSo9Te+f1qZbOnbM7yi8Mn2ba7lzdOjBXHO1vq2NzdyW9fsZygvzztmG9Y1cp9rObR3WGOj8ZZsUBWQxhjqA94qQ8oIMyEwoJIjVJ759NqpalSPJ3l2deO8/ieXo6NJIrjV61sZkt3Fx+6dAmeOei0eMOq1nkfDuB0QGgI+qj3exUQzoPCgkiNUnvnvPFkhqHxVFU3VYrEUnxvXx9PvdzPaCK/ZNMAH7p0CVu6u7i6Y1FlJziPGGNoCHipV0CYVQoLIjVqobd3roWmSr2RGI/19PLc6ydIZfJhxu81fOKqFdy1vpMLWhfGc1VunsIlBqeTovbBmH0KCyI1aqG2d66FpkoH+kfYtruXfz1yisJFkaaQj9vWruT2tR20NgQqOr/5wFN6iUEBoewUFspAFeoyFxZae+dqb6qUs5YX3hpk2+4wr/WPFsdXNIe4a30Hn7y6nbpAeYoWFwqPMcUeCAoIc0thYZapQl3m0qY1bfP+75W1ltFEpmqbKqUyOX70+gke6wkTjpxubHTZ8ka2dHex8bJlWrt/HgoBoTHoo86vgFApCguzTBXqIrMnmswwFK3Opkqj8TQ79vfz5L4+IrHTSzVvuLiVLd2drO1arBe2GfJ6DHUBBYRqorAwy1ShLnL+qrmp0vGRBI/v6eXp146RSOdDjM9juOmKNjZ3d3Hx0oYKz7A2eT35N1mNQR8hv0cBocooLMyyhV6hLnI+MtkcQ7EU44nqK15888QY23aH+fmbJylcDWkIePn0te189vpOljUFKzvBGuTzeIo1CKrnqG4KC7NsoVaoi5wPay3DsTQj8epqqmStZfc7Ebb1hNn33nBxfGljgDuv7+RT17bTGNR/o9Ph93qKqxhCZepSKbNPf8tn2UKrUBd3WhkzubFEmkg0XVVNldLZHM8fGmB7Ty9vn4oWx1ctbWBzdycfXdOG3+up4Axri9/rbPUc9BL0KSDUIoWFMlgIFeoyOa2McVeNTZXGkxl+8Moxvru3l1PjqeL49RcsZnN3FxsuatG19ClSQJhfFBZEyuTslTGZrGVgLMHWf97D9Re0LNizDNXYVOnkWJIn9vbyg1eOEXOKKj0GNl3exubuTi5b3lThGdYGv9dDY9BHvQLCvKOwIFImpStjRuNp+kfiGPLNexbiWYZczjIcz9clVEtTpaMnx9ne08tPDw0UeziE/B5uuaadu67vZMWiUIVnWP0CPg8NAR8NQR8Bny7NzFcKCyJlUroy5tR4Eg8GDAS9ngXXf2M0kSYSrY6mStZaXg4Ps213mJfeiRTHW+r93LGug1uvW0nzWcuf5UwBn3MGIaCAsFAoLIiUSenKmFQ2hwGwprjELpPNsfe9CB/5+vPztvgxnsoyGE0WN1GqpGzO8vM3T7K9J8ybJ8aL410tddzd3cXHr1yuFz4XCggLm8KCSJmcuTImfwlixaIQTSE/Y4k0fcMJfPO0+DGVyTEUTRFLVb4uIZ7K8sxrx3h8Tx/HRxPF8atXNrNlQxcfvGQJHhUtTijo99LgLHPU6o+FTWFBpIwKK2MKKyO8HoO1luMj+Ret5U0hjDHz5rJE1tnsaawKNnsaiqZ4cl8fO/b3M+Y0eTLAR1YvZXN3J1etXFTR+VUrBQSZiMKCyBw4u/+GBToWh864Nl7LbcGttYzGM0RiqYo3VXpvKMZjPb386PXjpLP5uQR8Hj5x1XLuXt+pbqoTCPq9NAbyqxgUEGQiCgsic6S0/8bnHv41A2OJM75fq23Bq2Wzp9f6Rti2O8yv3hqkEFeaQz5uX9vBbetW0lIfqOj8qk0hIDQEvfgUEGQSCgsiFTAf2oInM1kGx1MkKthUKZuz/OqtQbbtDvP6sdHiePuiEHet7+Tmq1dQp5bCRSG/11nmqIAg06OwIFIBtdwWvBo2e0qms/zo9RM8tqeX3ki8OH758ia2bOjkN1Yvw+tR0SI4ASHooyGggCAzp7AgUiG11hbcWstIPM1wrHKbPY3E0+x4uZ8n9/UxHE8Xx29c1cqW7i6u7VykdsxAXcBLfUABQWaPwoKITGo8mWFoPFWxzZ6OjcR5rKeXZ187TsLp2eDzGH7riuXc3d3JxUsbKjKvalIXKJxB8Omsisw6hQWRc9COkZXf7OmN42Ns2x1m1+GTFJo/NgS9fObalXz2+g6WNgYrMq9qoYAgc0VhQWQCC33HyEw231SpEps95azlpbeH2N4T5uXwSHG8rSnIndd38Klr24ubcy1ECghSCQv3X5yIi7N3jJwvTZMmk8s5dQkV2Owpnc3x04MDbO8J887g6X4Tq5Y1sKW7i49evmzBXn9XQJBKU1gQmUDpjpEFtdw0aSrGEmki0fSc1yWMJzP8YH8/T+zrY3A8VRxff8FiNm/oovvClgVZtKiAINVEYUFkAqU7RhbUatOkycxGXcJLR4d4dHeYY6Nx2pvruGdDFzesanW9zcBogif29vHDV48RS+Uf22Pgo5e3sbm7k9XLm2Y8n1qlgCDVSmFBZALzoWnSZNLZHJFZqEt46egQDz5/GJ/H0BzyMRhN8uDzh7mP1RMGhrdOjrO9p5fnDw0Ut6wO+T186pp27lzfyYrm0HnNp5YYYwj5PQoIUvUUFkQmUMtNkyaTy1mG42lGZqku4dHdYXweU+yUWAhXj+4OF8OCtZZ97w2zrSfM7ncixdu2NgT47LoOPnNdO00h/4T3P98Yk/9d1Qe9CghSMxQWRM6h1pomTcVoIk0kmiq+o58Nx0bjNIfO/K8k5PdwfDRONmfZ+cZJtvWEOTIwXvz+Ba31bO7u5LeuWE7AN/+LFgsBoSGYb5akgCC1RmGhSmhN//xXyec4nsoyGE2Syky9eHGqdQjtzXUMRpNn7MEQS2XxGg//5lsvcmI0WRy/tnMRm7s7uXHVEjzzvGixNCA0BHx4FBCkhplK7zlfLbq7u21PT09FHrt0TX/p9fEHbr1KgWGeqNRznHb6JUSnWZdQWocQ8ntIpHNkcpb7Pvb+OoTSY30eOBVNM57IFHd+9Bj4yKVL2bKhiyvam2fpJ6tOxhjqA17qAwoIUv2MMXustd1TOVZnFqrAQl3Tv5DM9XOcy1kisRSjicyM6hKmUodQcMOqVj431sU//updhmKnlz4GfB4+edUK7urupGNx3fn9QFWsEBAagj7q/V4FBJmXFBaqwEJc07/QzOVznN/s6fzqEtzqEAqstbzaN8K23b28cHSwON4c8nH7ug5uX7uSxfWBGc+hmhljaAh4qVdAkAVCYaFMpnN9eiGt6V+o5uI5jqeynBpPks6ef1OlieoQEukcK5rryOYs/3rkFNt6whw8Nlb8/srFIe5e38UnrlpOqOR284WncInB2e55ITaKkoVLNQuO2axZmO716akeryLI6jSV5+Xs5/jUeJJILE1TyMfqtqbzei5TmXxdQiw1e/s4TFSzkM7muPHiJex+N0Lf8OkzDGtWNHHPhi4+fOnSeVfl7ym9xKCAIPPMdGoWFBYcsxkWPvfwr9/3LjKWytDWFOJfvnDjhLcpvOCca02/iiCr03Sel8JzfPjEKGPJLK0NfpY0BGf8XGaduoSxGdYlTKawGqJ/OIbH42EskSaaOt3l8YOrlrBlQyfXdCyaVy+iHmOKPRAUEGQ+U4Fjhc3k+vRka/pVBFmdpvO8FJ7js8PkdJ7LnYcG+F8/f4v3hmIsbw6xpXvytsoz1dFax4VL6jl4fJSks+TS7zX81hXL2dzdyYVLGsryuJVQCAiNQR91fgUEkbMpLJRBOa5PqwiyOs3keZnpc7nz0ABffeo1PAYagvlLGRO1VZ7JPg2lDh4bZVtPmF8ePkWhRrIx6OPW69q5Y10HSxqDU76vaub1GOoCCggiU6GwUAbl2FdARZDVaSbPy0xuk8rk+MbzhzFAyHfu5YzT3aehIGctLx4dYltPmFd6R4rjbU1B7lrfyS3XrDhjvrXK68mfBWoIehUQRKah9v/1V6Fy7CuwEDY2qkUzeV6mc5tCXcJoPE3f8OTLGafTHwHyIeSnB0+wvaeXd4dOn9m4dFkjWzZ08puXLcPnre12zIWA0Bj0EfJ7FBBEZqAiYcEY8w4wBmSBjLW22xjTCmwDLgLeATZbayMm/y/7QeAWIAb8nrV2r3M/9wJfde72L6y1jzjj64F/BOqAp4H77BxXcs72vgLzeWOjWjaT52Uqt7HWMhrPEImlyDl/dd2WMxZMpT8CwHgiw479/Ty5r4/B6OlGSusvbOGeDV1cf8Hiqn1RncplltKAUBeYf8s4ReZaRVZDOGGh21p7qmTsr4Eha+1fGWO+ArRYa79sjLkF+CPyYeEDwIPW2g844aIH6AYssAdY7wSMl4D7gF+TDwvfsNY+4zanSrZ7lrlRK0tPo8kMQ9HU+/olTKUF83/ctv99gSKezrKkIcjfbLmOE6MJntjbyw9fOU48nV/Z4PUYPnr5MrZ0d3FJW+Pc/aAz4PY7+NClS4urGBQQRCZXq6shbgM2OV8/AuwEvuyMf8c5M/BrY8xiY0y7c+yPrbVDAMaYHwM3G2N2As3W2hec8e8AtwOuYUHmt9Iljovr/AyMJbh/xwEegLIGhukElGQmy1A0RbxkeWKpG1a1ch+reXR3mOOjcVZM8K76ng1dPPj8YeLp7BkvphsvW8pfPn2Q5w8NFIsW6/xePn1tO3de30Fbc2jWf/ZyOPsyS33ARyKT5bv7+rjnAxdUeHYi81elwoIFfmSMscBD1tqHgeXW2mMA1tpjxpjC/6gdQLjktr3OmNt47wTjValW3u3Wurleerrz0ABff/YQbw6M4/caljcFzxlQsjnLUDTFWCI96f3esKrVtVCxNFAcG4nRGPQT8hu+8fyR4jFLGgJ89voOPnPtShpD1fR+YXLHRuMsCvnxegwej8FjDH6v4dhIfPIbi8iMVep/ig9ba/udQPBjY8whl2MnunBqZzD+/js25gvAFwAuuGD23pVMNQBU6t3uQjSXS08Lz+vAaAKvAZuD/pEEKxfV4feaYkCx1jr7OKSLdQmz4foLFzOaTLNtd5i3TkaL4xcuqWdzdxc3rWkj4KutokW/10N9wMtFrQ2ciiYJes+8zKJVQSLlVZGwYK3tdz4PGGOeBG4AThhj2p2zCu3AgHN4L9BVcvNOoN8Z33TW+E5nvHOC4yeax8PAw5CvWTi/nypvOgFAjZbmzlwuPS08r1lr8XoMBgM5ODWe5OKlDfRGYownM0QmqEs4H7FUhh++epwn9vQyMJYsjl/XuYgtG7q44eJWPFVatDgRv9dDQzC/zDHoLBf9g02XaFWQSAXM+dsLY0yDMaap8DXwceA1YAdwr3PYvcBTztc7gN81eTcCI87liueAjxtjWowxLc79POd8b8wYc6OzkuJ3S+6r7EoDQH7rWl/x3eTZwpHYGYVooEZL5bJ14yrSWUsslW+NHEtlyvYiU3heA14PhRMGxkAqmyOayrCsKcjAaGLWgsLgeJJv/uIoWx7+NX+/8y0GxpJ4DPzmZcv4u99Zx99uWcuNq5bURFDwez0srg/Q0VJHV2s9rQ2BYlAAZyXJrVfR1hRiJJ6mrSmklucic6ASZxaWA086y7J8wP9nrX3WGLMb2G6M+TzwHnC3c/zT5FdCHCG/dPL3Aay1Q8aYrwG7neMeKBQ7An/A6aWTzzCHxY3TOd2tRktzZy6Xnhae12VNQfqHE+Sw5HI5PB5DIp1j8/quye9kCt4ZjPJYTy8/OXiCdDafSoI+DzdfvYK713eycnHdJPdQHfxeD41BH/UlZxDczPayZBGZ3JyHBWvtUeC6CcYHgZsmGLfAF89xX98Gvj3BeA9w9XlPdgamEwDUaGluzdWLTOF59XsNi0JeTkbzhYtBD9x85fLz2svBWssrvSNs6wnz66NDxfFFdX7uWLeS267rYFG93+UeqkPA56Eh4KMh6Ku5+gmRhai2SqFrwHQCgBotzU+F5/Uvnz7IYCxNwGdY2hDA6/Hw7OsnuHxF87QDQzZn+cXhU2zvCXPo+FhxvGNxHXd3d/KJK5cT9Fd3b4GAzzmDEFBAEKk1CguzbLoBQKdU55/nXjvGQz8/ytuDUXxew9KGII3B/D81t9bLE0mkszx34DiP7emlfzhRHL+yvYnNG7r48CVL8XqqtxZBAUFkflBYKAMFgOpVzr4W6WyOH+zv56+fewOfx5DLWTwGBsYSQKi4N8HZrZcnMhxL8b2X+3nq5X5G4vnLGAb40CVL2NzdxTWdi2ZlzuUQ9HtpCHhpCPrw1/i+EiKSp7Awx9SEqXLK1dcil7MMx9OMxNM88qt3ix0GAz4PmZzFWIjEUjQGfe/by+FsfZE42/eEee7ACVKZ/GoJv9fw8StXcHd3Jxe0Vmfxa8jvdWoQvDW/8ZSIvJ/CwhxSE6bymiyIlaOvxWgizXA0TSaXf2Ev3ciptSHAwGgSsKSzlng6SyZnuWfD+1dDvN4/yraeML88fKrYQawp5OPW61Zyx7oOWhsCM5pfuRiT35uhIeij3q+AIDLfKSzMITVhKp+zg9g7g+Ns/ec9NAa9XLa8ma0bV81qF8dEOsup8WTx3X9B6c6QDQEfbc1w0mmQtKQheMZeDjlreeGtQbb3hHm1b7R4H8ubg9y9vpNPXt1eVRsiGZM/Y9IQ9FIf8FV1rYSIzC6FhTk0ly2HF5rSIDaWSDM4nsZiSaRzxTM4TUEfg9Eko/EMqWyOgNdDc52Pi5ZMfafFdDZHJJpiPJmZ8Ptnb+TkMYYljcEzdoZMZXL85OAJtvf08t7Q6ef+0rZGtnR3senyZVXzQpxvLOYtnkHwVMm8RGRuKSzMITVhKp9wJIbXwNGT48ScXRt9HkMqmyuewRlLpBkYS+Ex4HE6Kg6MpfjchslXJjx/8AR/t/MtwpEY7RPs9ljgtjPkWCLNjv39fHdvH5HY6U2jbriohc0buljXtRhTBV0WjTE0BLzUKyCIiENhYQ6pCVP5NAV9HB4YP+MdeTpnCTnL9fJncOIsawwwljh9ZiHgM/zDL99m+57eCescrLU8/cox/uLpg/g8huZQ/uzEg88f5j5WnzMwlI4fH03wP352hKdfPUYinb9s4fUYblrTxubuTlYtm/qZjXLxlJ5BCHirIrSISPVQWJgjheK7WCpDKpMj4DWsdq6lq17h/NnCJgxnfiKRyXH05DjNdfm/6ksbgyxrCgEwlkjTF4ljgQta699XcBpNZhiKpvjmL94urnAAikFvsn4Jh0+Msa2nl51vDJBzJlQf8PLpa9u58/pOljUFZ/m3MD0KCCIyVQoLc6C0+G5Fc+iMMwoKCrOznHQ8laVjcYhT4ynSOSjd8blwuaG9OUg8nS1eBjo5lgQDQa+nuOnXqfEEf/Qve2kI+VjRlL+EULrCoSDk9/DuUJT/uG0/x0bjxUsTGy5uYfc7Ebb3hNn73nDx+CWNAe5c18Gnr1tZbNBUCQoIIjITCgtlVHgR3PteBGNgeVMIEzBaBVFitpaTFupBVi1r5OjJcZKZLIVNHQNeD00hH41BH7F0rngZKJHJ4jGGpY35d/ij8RQnR5NYYMWiUPFyQ0Mg3x+hdIfQ4Vi+yHEwmqQ5lA8Z/++zh6jzezk+errT4sVLG9jc3cnH1rRVrEGRxxjqg/nVGQoIIjITCgtlsPPQAF9/9hBvDozj9xrSmRw+r6F/JN+5r7nOr1UQjtlaTrp14yr+8+P76RuOF+sCAILefBhoCvkYiaf52m1XF1txF148m0I+MtkcA86ZhoDXg8EULzdgLZmcLa5wSKRzDMczLAr5CHg9RGJphmNpMjlb7La4tmsxWzZ0csNFrRV5cS4EhMagjzq/AoKInB+FhVlWeKc8MJrAa8Dm8tfPc9biNR5OjSdprvOf1yqIau8COZ35zeZyUkO+m2KpbM7SPxJnSSbAxUsbz2jFvfPQAF/93muMJtIEfR6SmRweAy31pxsghfwexhIZ/vimy85Y4TAST5G18PZglNKHDPk8/Ld71nLZ8qZpz/98KSCISLkoLMyywjvlrLV4PQaDwWsN2ZzF47UkMpbDA2Oks5aA18POQwPTeqGv9i6Q053fbC0nfWjXUZrr/MRSWSw5MlmbD2mAFxiMplhcl+QjX3+erpZ6fu+DF3JV5yK+9NFLiyGgzu+lzp/f+Ciayhc3JjOnLz/8zZbrePtUlO09Yfb3DmPJL9E05LeIrvN7WN5cN6dBwesx1AUUEESkvBQWZlnhnXLA6+wLYPLr/QtvPrM5i99j6FwcIpXNTfuFvtq7QJ49v0zWMjCWYOs/7+H6C1red5ZhtpaTFn7vqWyuGNLS2Rw5C37yyyjTOUtzyEf/cIw//8HrxUZJhRUNLx0d4sHnDxOJJYlE0+D0Ywj5DH/9o0Msawzyxonx4mN6DDQGfSxtDJDO2nO2cp5tCggiMtcUFmZZ4Z3ysqYg/cMJclistfi9BmthRXOguHQPmPYLfbV3gSyd32g8Tf9IPH95wNoJzzKcvaV3Q8BLwOvhq0+9Rteuej64qpUXjg5Nekmj8HsvhLR8YPDg8+bP6vi94Pd6yGQtQZ+XnH3/0sdCQ6Wv/fB1LOD3GOr8PsZTWZKZHEPRfD1CZ0sdm7s7aakL8MTevvc1XyoHrycfwBqCXgUEEZlzCguzrPBO2e81tC8KcmI0ScZaVrU2cHI8Way8L5juC31XSz3vDI6fV8viciq9rHBqPIkHU1yeeK6zIIU6gon2d3jpnSGWNQZY2hiccL+Hwv0Ufu/NdT5OjaXImfy5nMagj5PjKVY0B8+oZzjXVtE3rGp19j7wMhxLE4mf7rTo9xr+709dyYcuXYLHebH+8OqlZfk9wumAUNjaWgFBRCpFYWGWnf1OeV3JqffPPfzr874+/8FVrbz0ztCMWhbPhdLLCqlsjlzOkrOQ8RiOnhxnaWPgnOHooV1HSWezDI7nb5t1LuOMJTKE/N4J93sonKUo/b2ns2OkMjl8Hli5uJ6GgI/0WYWPE20VHYmleHJfH4PRNNmS4xsC+c2TVi6q5yNlDAdQPQGh2otoRWRuKSyUQWnFfanZuD7/wtGh97Usbgr5eOHoEP9hNn+IGdq0po27eof5h1++TTqbf8EtrFKIprJEh+J0Lg4VX4zePDGaL/b0eRiJp8lmLX6fB6/H5G9v810YT44lMQY8nLnfQ+Esxc5DA/zVMwd5ezCGtZYLWuv5Pz+yihtWtRZrEUqXPpbWF4SHYjy2p5fnDhwvzhnyIWFpYyAfdspYj1AaEKphl8lqL6IVkbmnsFAm53pnVnrWofMc79jc3tWFI7EzWhZDvtVxtdQs7Dw0wON7+1jWFCSZzpJ0ViVAPjRY4NhIgv/8+H58HsNoIr97YzyVJZNzVjDkLD6vB4+BnM3/fIXCRZvL90GA05dwvvGTN/nvPztSfKH3AG+fivL15w7x5U+sOefmTg0hL/c/dYB/PXKqOMfmkI9b167kwpZ6fvjq8bLVI3g9hoagj7WElb8AACAASURBVIbA+QeE2T4LUO1FtCIy9xQWymCyd2Zu/+FOdttq37mycClhYDRNMnvmqX9jwGcM6ZxlPJnBawweDB6PIWct1jn1n85ZsjZb7F+QsxDymOKlhKWN+aAUT2dpCHj5HyVBAfLLJbEwnkgXixgLHzlr+dWRQf7pxXc50D9avM2K5hB3d3dy89UrqPN7eenoEM7dzJpynEEox1mAai+iFZG5p7BQBufzzmyy21b7zpWHB8aIRPMNi87m83gwgMGSzVmyWLzONXmTr4MECk2szryt12PIWmip99MU8hU35PL6vWTOPtiRzsG7Q1EAUpkcP3r9ONt7eumNnC5svGx5I1u6u9h42bLijpWFyxZuu0y+dHSIR3eHz9gXYqIzD6UB4cWjg7NeB1COswDVHkhFZO4pLJTBTN+Z7Tw0wN73IuSsxQNg8u+4A14PI7EU8P4CynNdyqiUVCZH1p75wl+QzubbXvs8+RdRrzFksvkiRmsh6POQ+P/bO/coy6rywP++c+6r6t56dlfRTXcD3dimAYWIHQYNIR01CpqBOBKVJCuuJA4kcYbEGR3IUjMZk5kFEyeJrjyEZDIx0SVGEl9JILokHTQBoRsERBq6LRr6Xe/Hrfs85+z5Y59z6767qvpWVVf391urVt0699Q+++w6db9vf08vaDYsJd/wgT2X8ujIJEencmzu6+JnXr+VT3zjBRKuUPCaKwzFss9fP/YyX37qGFO5hcyGa7YP8p7dW/nhbf0NQYT3P3GkbZfJ0ykTzdIcVyoOYCWsAGe7QrqaaKCnolhUWVgBlrMzi4RJFAxYNgC2gFPJtwF5UbXH07kyOkWzIMShTBJjDNmS3/TDM+5awdvKfL8hncAPYxNijjAxX7IpjQY29iQ4MVNs+ntFL+CBJ4/xkbfv4jVb+ymWbfXEzb1dBEFAIVtu+nu5csD/+9fDEF7vzZcN8+7d29i+MQ00txC06jIZpVo2UyYKns8X9x/l5tdtaZrFsFJxAM2etYn5IvNFv1KtcqkC7mxXSFcLDfRUlAVUWVgBlrMzi4TJpr4UL0/YXaEAnjHExWEwHe9IgNlid0rRB2XJ8ytBiPNFj6n5EiLClv5U0w/PV1/Qy77DE0QGAomiGsUWRdq+MVNZh3sfGWG+OEWubAtXzRf9tnMXDPc89AK9qXhFuL9uWx+n5grEnTLl5kYJ0gmXn7pyM//h6q0M9SzUuWhlIWjWZbI61bKiTIjtx+CKkIg5jM4VamIRqtd6bK7Ipt4zq7HRjPpnbWK+yOhciaFM4owE3GoppGczGuipKAuosrACLGdnFpmTRcKAv3D3LQYu7E+RScbOWLAsZacUfVBOZL1KEGLB921XRhHGsyV2DGUaPjxvv34HB0fnmM6VCUMACMJYg9+75aqG6/zWV59j2F3o8BgpJs0o+wFHpvJs6e+qCPeHvn+Kt102zEPfP8XJ2VqrhCNwwxWb+NU9l5JONj7qrdwNzbpMRqmTMcdh20A3RybnyRb9mvTV7RsXCmPVr/V4tsix6QIiQk/Kug06EQdQ/6zNF32GMgtVQlXALR8N9FSUBVRZWCGWujOrNifHHaEYBu1F1uxOCJZWO6W7H3y+wdpQ02tBFlwLYuycSr7dxtd/eO7ZNcwnbrmqUvMAYOdQmjtv2NVSIameT39XjOl8o8KwIR1nPFsi5i4I91TMYWK+xP37jtbEOsQc4cK+Lm67fjtvfFXrIkqt3A31XSY393Xxi2+8hLe+ZhOpuMv1OzfyyYenawpjjWVL/Ow1CwGO9fd2QU+KY9N5Ts4UyCRjHY0DqH7WrrvnYRVwHUIDPRVlAVUWzhIic/J4tkDJC2pqExydyjPQHedj77j8jK7RbKfk+QGHJ/JcEpgaa0MmYXfZibCfQiVbIQxGjGodRB+e9e6Nu2687LTKUrP5bOnvIl+ao+QvxD1k4rZU9FSuzKbeJH5gmMmXmc6XazIhrr6on/f8yDZ2XzywqMqH6bjLy5M5gsAQdx0GuhO4jrCpt4s3vmojP3nFBaSTMVLx2jTHR0cmGe5JNJTcri6MVX9vvV1xwHBytshMvrxicQBrIeDO1SBADfRUlAVUWThLiMzJd9z/FI4jxIVKNoTrCBvSiTP+AG4mSE7NFYk7ToO1QcR2bexJxRjLFvGj+EEDRT/AD+D5E7O4jnDlll4+/MDTzBU8vCBgdLbAf/zrSXpTsYYeDvXzeWk8W6lGGXcE1xEChK0DKfwgYDxbIucFDDvC1r4UU3mPk7PFmtTK/q44d7/rtUtqDf34yCRTYcVIR6zSdGq2QF+olF20obVwPTKVY0M6Wan3AI2FsZqtdcx1uPqiAT5/27WLnudSWW0Bdy4HAWqgp6IsoMrCKrDYndeeXcP0dsW5aLC7ZmdsjN1JnymtBMnW/lTNeV1xl5l8md+5+TXc89CBhlgAAM9AQgyD6QR//+zJqqqLQiksspAt+rw0bps/9aRi7Bzuqbn3qM+FYEJzvsErQ18qFroaXDLJODMF29BpJl+uKAkiNnCxK+HyX9/yQxVFYbH1D+5/4giZZIzuhMvUfMkqK67DBT0p3vqaTW3XsdXuPZ1wufW+xzgylaMnGav8zVZzV7raAu5cDwLUQE9FsaiysAJUKweZhMvEfInernjDzuuZsIfCfMkKmvdft71ht90seG65RILko196hpHxeYyxPvfZQpnerkTlvMhsvWfXMPc+MkIq7mACcByh6PmYUGDHYy4bM6lKumNChKIfVMo6F72AiXlbHyJX9GruHeDPvjWCCQwB4BOlXRpmCh5Fb55MMkauHJArLWRJZJIxepIxfBNwYV93jTKwmGJKYLMyTs0VGOiO4zoOg2mbpbBYpayZ0jWTLyPY6pP9XXHyZR/BtrleSbdDM1ZTwGkQoKKcH6iy0GHqzbKHxrJ4viGdjCEilZ3XR7/8LCdmizgCMccK6E8+fIhrLu5nLFtqGzx3JjxzdJoTcyVijoTmd8NUzgPm2dLf3bADPjKVww8WKi1GioJhIcgxUg6q349wEMSxQrQ6oDJb9Jkv+bguYKy7xasq+1jwDAVvQXAnYw7veO1mDo1mOTVXaFAUoH0xpR/dudH2Yki6JGMul2xIh9YBp/L7S/HtpxMuI+O2OuT2Dd0MZZKVBldA5ftAOslDH1w5t8Nao0GAinJ+4Jz+FGUpVJtlRQQ/sCb2kzMFRsayHDg5y4npPMemC6Gi4OCIE36Hx1+eZrgnQcJ1CMJAwuGeBI+GvQrq2XtglFvve4zr7nmYW+97jL0HRtvO78+//VLNdRMxF1dgtmB3x8M9KT5+0xWVnWkm4eIHhoIXWKtCOI6wEOQYC5+iIPQRVOsLRT+gWF7IqEi4DiPj84hYBYAwLdM3YU+HOgSbdnnr7q08OjLBVK5UYzV4vGpdTszmScUXHmkRIZ1wGc8W2DbYzWA6QTJmFYnbr99B2TfkSh7G2O+LcRNU6k/4ATuHM2wd6CIXtszuqguErN9hL/VvtR5Y7joqirK+UMtCh6k2y84VyviBCf3splICudL0qG4b7oh973TBcxHLCS6bL/kV4R4RcwUvgG/d+aaG8U/MFmoaOtk7AVdgYyZBruTR352gUPYpBwbHWAUJFiwOBlsjYWq+iIhgAD8I8AMTrkVjvUcnzL5wHOHOt+06bQlmsNUcJ3NF0okYjiM4IuRKHtsG0w3jL7cD6HSu1NRHX/ZtXYZWO+xzNRBQgwAV5fxAlYUOE5ll/cBwPLQeVAvZasoBxKo2o4GxPRPaCZ1qlhNclg5TIp2F+EkCY4/Xc89DB5hrUvMAYHNvksDAcE+qktIZCYzxbBE/sIWNjLGKRWRdEcf2gzg+U0SajNudcBnOJEnEHPJlnw3pJNfsGOQPvvliyxLMcdchnYzxgZ+4lN/5h+cp+QFdjnvaXe5yOoAensg1DQhNxJzKDrtZQOO5HAioQYCKcu6jykKHiYLfRmcLVOoct2l07AVBRaEIDNx05Sb2vzLD2FyBuYJHvuRjBMbmitx632M1u7blBJe9edcQX/ruCcq+H+7cAYT3X7e94dyR8fmWM9+2IdOQAlhdGKgvFcMgTOdKTOVKFMq2dkR/wmEmbwMW68d2xfaOiMeswhRVTQRrNZiYL1YsCyJCyQu4eEOabYNWkYq7TkMswcfecfmyBVkzAR93hVOzxYaA0CjTo9UOWwMBFUVZz6iy0GEis+ztn90ftlpurSiAFXBFL6hkQ1y5tZ99L0/XpCu6AvmSz+GJbI3peqnBZXsPjLL/lRn6u2LMFjwCA34AXXHh0ZFJrgwbVUUExrRUFp47PtMwdmSun8mVKJR9BrsTZJIx0gmXlybm8QOYzjf2f0jFhMF0gnw5YEM6ycnZPJvCtEeA//KFp3l5cp75okd/V5yNPUmKXkBg4Fd//NLK9SMrwM7hDPmyT65Vs4hF0kzAX9CT5Oh0vqkFod0OWwMBFUVZz6iysALs2TXM1RcNMDpX4JXJHIHfXOQmXOGqrf2VHXok8MbmiiRcoeybMD7AQQRm8x6b+mIV0/VSC/BEO+Vtg2lm82WOz9guigKMzhX48ANPsyGdqHSUbEhtqGKu4FW6YEbzdh3rziiWbRlmEwS4rsPkfJnqztMitmx0UPnZFoC6eDDN77/nqsp5j49M8qmHD5KIOWzuSzGVKzE5XyYwhp11xZ6qrQCz+TLj2SJFL+CO+5/iU+993bKsC60KK+0cyjCQTi7JR6/VABVFWc+osrBCRMLBdQRjTI2wjNjcl+LgqdlKIZ+J0NdfCqsKRqLaCwISMcf64qtM162Cy4DKmNVFoKp3yuPZYk1aox8YpnJl5ooerxrK2LiLNkYRg7WeXH3RQBi4aIg7bhijYCMoJ3KN8Q4J11annPcWBs+XA/LlANfJ8/jIJK4jfGHfEZ45Oo0jthOn6zhszKToTsQY7kk1uECie4uUIAfBdWC+5NXUdqhvuV1fKKrZ37BewC/HtaGBgIqirGdUWVghIuFw94PPc3AsWxO5INhMgqIXMFf0GZ0rWFdDldm8upyxMQv9GOoj7OsrQwIto+6rd8pRg6ho3LE5W/PBD0ylHkTCdWoaNNXjBwEnZvK8MpljU28SQRidK9YUUYrIJFwKZZ/AGOZbdKKeyBb5xNcPgAh9XfGKG+f4dIEL+6EnFW/p54/uLVKCHEcIjG02FXeFex46wHzJr2m53cy10+xv2CkBr4GAiqKsV8Scxqd+vrB7926zb9++FRl774FR7nnoAC+OZnHDlEDfgBeYhvDH6gyB6uNxR9jYkyDuunz8piuABaWgetfbHXcqBZAiciWPhOtgjOHgWJa441QEsTG2S2OkFAg2I2GoJ4kxhpcn8y3vqytu2zWPTGQJgloFB2z9hZgjeGGjpsF0gsn5Uo1SVH3fBmt5cBxh53API2FBK8SOE7XEbmZZiFwhx6fzuGHQpqlq731wNMvWgS5OzhTwfBMqE4aYYy0XzcZUFEU5lxGR/caY3Ys5Vy0LK0T9rv/OG3bxzNFp/njvD/CCoOKwr1fVqq0PEd1xl+6kyyUbMpWd7Q1/8C+VFM2E67AxkyTuCj8Ynyfu2P4MgbFWgphYV8P2jWm29ndxaq5IqRzYTAhTVfeBhcqMx6cLbMjE2TbQxbHpfIMiADaa/8hUDq+FpSDqE+E6ts5COhEjnYxxZGKefJUbQkJNQbD9IaKQwo2ZJMdn8oixpaPbpUJWN+KaL3mkYg5DPUl6UnFyJa8y3+qW21Grbc1KUBRFaY8qCyvA3gOjfOiBp8kWPfzAMJ4t8qEHnmZjOsHWgS483/DyZHvhFInSrf0pvn3XmxvGPziWxRXBlahuQZ7+rhhl39QIf4whkuV+YOjtStDbZYspvTIxj0+jRSAqlHRypsgH37ITgE89fLAh7mIyt1COOeFa18FY1vaCiDuE1gsgiNwBNlCzP50gH9ZZMFU3G3cj5cIK894wvuLUXAExwnBPijfsGOTeR0b46Fe+19CUa8+uYT713tfVWFwiBWP7hu6GltvNXDuKoihKI6osrAB3P/g806EgjaoUFsolxrMlkq5QbBc5GCJia3H3pOIN7937yEjFlSAidmcewFi2fROkwxM5Lh7sprcrjucHlAMr5EtN5hPt9r+4/yjJmENfV4ypnNegWLgCiHVdzBW8igJgPQ325MBAX3ccEZsuGHddtvanODVbIPJIxBxb2THuim0gFQYVxlyplKCG1vEY1QpDq6DP3/rqc/SkYkzMl2xpagO96bhmJSiKopwGVRZWgJcmcoAh7LNU2cUCi1IUwJ7vA4dGs5UUxYgjUzku6E1yYqZIQLhLDgVzzKFp5kVElC55as7u7EUER2zsQjQzJ4wRcB3BCwKOjTd3Q4CNvRhKx4m7DtMtqj0C9HclarovghXeJc9nruBR9Kx74AN7LuXKrf1Ngwpvve+xRVVBbBVIGCkRnj9LKcyGqHbtKIqiKM1RZWEF8IOgVmCfQQxpOTB8+IGn+b1brqoItCjy/8L+FGNztp5A1I+hnaIA1LhA+pIuec/giOBXBboGxsYOiG8q7adb4QhszKQYGcvWHJeqF0nXob87wYO/cX3NOe0yDZoJ7zOtgria2QjNMlVUIVEUZb2iykKH2Xtg9Ex0g6ZM5crc89CBirCJ8v/jrrAxk+DYdKFSz+F0hovqAMqZYovIxLpz25EKezjUW0xM1YsLepNNBfpShfd6qYLY6aZRqngoirLWaIvqDnPPQwcIllFlOOZQ09ypGmNMpd8BhH75m64g4Tq8Mpm3tREwLV0F9Sz0hDhz+rrjjM01bwoVXavkBx0R6GfSDnk120PXtymPekrc+8jIkseKFI/RuUKN4nEutLdWFGX9oMpChzk0ml2WZcEPqGQBNLwXpg7WC7n5ko/jQCJmW0wv5br+mbVNqBB3XQpeY9vrCMeByflyRwIIIyVpuCfFTL5cCXw83S57tQXukalcpeFVRCt3yemUmE4qHoqiKMtF3RAdxlvs9j7EwZZcMNTWO6gn6t8QmbMjIRJ3HIp+sCRFoVNuku6ELRB1x/1PhVUbG0dOxVy64k7HzObLiTu4+8Hnm9akWKn20It1lyzGXaHdKhVFORtQy0KHaSeIpYnhYCkb/Fcmc4zOFrjnoQMcmcoxmy9R9IJ2/Z5WnKi2wYX9XaTiDglXSMYcEq7DJRu62dSXYucFvWs2v6gmRRCYmpoUnh+0Fbhn4rZYrLtkMVaDbQO2PkQ1Z2OchqIo5zaqLHSYeKvAA2ynxWUj1k0RGMOLo1kwhrFsuaPBlMLSHohcya+kdX78piu4ZLAb39gS1pv7kriOrHkNg6gmha1HYUtJOwin5optW3mfidtise6SxbgrziROQ1EUpVOoG6LDvGo4w/Mn55q/KRATGy+wVCFvDJS8AEeEuCtMzpejIe37y55x1TXqxkm4Dl4QtA2crE7rjNpVR+mQwz2pSuT+WkX0t6pJ4flWEDebV/WOH1rXc2jHYtwli3FXrJdulZqxoSjnNqosdJg7b9jFL/7lE02Fd2DCgkeutI1PaEVgbPOnge4YkzmPpCuUAtOxIARHFko/Rw2vTufiiNI6gRph8Ts3v6YiLOp98y+NZ7n9s/vpScXatojuhACqr0kR9Ya4dCgN0LQstzGGzX1dNePU7/g7MbdWLbDrrQZne7fKTqeKKopy9qFuiBWgnXztScUqBZTaIXWvpepYVNY5nYwhndMVGq4fc6Xt2A5WwTg0mm1rtq/eqc8VPFtu2RhyRa+lib+ZK+BDDzzNjX/4yJLiCCIzvusI2zemuWiwm+HeFHfesKtSltsE2HbdAUznyuRKfts4gU5lVyw3u+NsQzM2FOXcR5WFDnO6D8hm/RUWQ8ytjYXIJBwmc+UlBUiejmorgmdgYyZBqlVOJAvBmZ4xbYVFtW9+PFvEwZaSjlppNxMs9QLI8w3TuTIvjc8vSUC3E8gvTeRwBBynKp5BbEZLuziBTgrHPbuG+fxt1/KtO9/E52+7dt0pCrC0VFFFUdYn6oboMEc69AFp6l7Xuy2ypfbVF8/0mgDHZwq8aqhNDAbWbeEKbYVFtW8+cgNEHR/rz42oTxkczxZxBBtAGQroxcYRLNWM74jw8ZuuaBknoOmMtayXypqKoiwftSx0mG0D3S2rGS6GhGt33afLTOhUUaVm1xBsRcnA2BiM7jpFoJr+7jg7h3vamu2rI/oTroNvbOOqoZ5kw7kR9SmDpfCGHWBkLMuBk7OcnClw8NTs0m86ZMfGdCUOxGAIjK2CuWNjuu2OX9MZa9GMDUU591FlocPcfv2OltUM2yHY1MiSb/AD05CZsGLUaTaOQNx1cMWpmNbfdkXzXfmG7hifuOUq7rxhV0thEQUC5koeY3NFHLGNqzZk4pVW1M0ES70Ach3rivCNbYblilDyA+aK/rIrMd55wy4GuuMI4PkBAgx0x7nzhl1tf696brP5EgdPzXF4Yp6p+eJ5WYb5XIm9UBSlNeqG6DDPHJ0+befHZhhoCHxcDWUhMJBOOMyXAhKu4IQuggDDhT0pjk7lmJq3vR+q5yPAUE+qIhCapfcBlSj5Tb2pSrT/LVdv4R+fPcHBUdupcsfGdMO86lMGLxns5gdhfwxxbHyFIAym48uuxLhn1zC/d8tVS05LjOZ2z0MHODyRI+4KW/u7KAfmvM0CONszNhRFOTPErGX5v7OI3bt3m3379p3RGHsPjHL7Z/dTXI62sEYIkIg5FL2gYmRIxBw29aaIucJwT4onX5kKd/cLJhM/CBARXvjdG1uOfet9jzX4siNXxFi2WElZdB0hk7RWinqBU52ieGq2gBiDESHhOgz1JMkkY8zky3zrzjd1clkWRav7G+5J8fnbrl31+SiKoiwFEdlvjNm9mHPVDdFB7n1kZFFpkWcTBtukKuEKMUeIubZKZNFrnvO/FFpFyR8cnWM6V8bzAvzAUCgHTGRLfPRLz9ScW5+i6IoQIFzY18WOoQw9qfiaxgpoFsDasJodRBVFsaiy0EGOTOVado48m7HlmbvYMtBFwnVsDYSSX/E7R4GAXhBQ9GwNgpJvGA4DFFvRk4xxaNQGI46MZZkrlMmXfXxj2257ZqEIlAGOztT6/OtTFDf1pQA4NVc4KwLpNNBx9dGW3YqyNmjMQgfZNtDN8an8Wk9jyfQkHXq74szmbbEnR6TGlXLnDbv49S88xUzeA8IeEmIFY9Qbop69B0YZyxbxAoMjUPYDjkzmcBwbExGJ2EpzrbC4VHX8QX2KYk8qzpZ+w8nZIjP58qqVPq6v1viGHYM8OjLJi6dmyRZ9BtNxNqSTLSswKp2jE6W4FUVZOqosdJDbr9/BoyMTK3qNqKX1Uog5NhiwXOciSYYuh5IPs/kyx6bzlUwMwdT0fdjcm6Lo5WraPMfatHm+95ER+rriOMD4fGmhEFVgSIYxEkAlatKE86k24TfL34+5DldfNLBqMQH1pYwPT2R5/PAkQ5kEm/u6GM8WmZwvU/ZN29LVSmfQGheKsjaoG6IDeH7AdK7E2Fxxxa+1nNBJP6BBUQDY1NfFhf1dlIOAEzN5vPCcKI2zuu9DtuTzqqEMuzb1smMoQ29XvO2H9JGpnF2XvEfcdYi8M76BvtSC8I9SRF2BwUyixoS/1vn7ew+Mcsf9T3FsOsfJmQLZosds3sMRmCt4iAhDPSm2DnSxc7hn3VZgXE+o60dR1ga1LCwTPzDMlzyyBY9C+OH1ue+8ssazaiTmSEUJqGc8W2RTX4qdQxleOGXTGEWsGyIqUHTg5Bx7D4xWdvl+YGoaMm1vkvYI9kP9qVemKuNFSTciMDlfavo7Xl1thiNTOXqSMYwxTd0OK9npMLIo5Ep+ZQ2PTxcIjMF1FopEge5sV5PFNt9SFKWzqLKwBIJQQZgv2iC/+rTTE7NnX7zClv4uXp5sFGRxx2ZBlH3Dx95xObd/dn94P0K5ShAabK2E11/Ux76XJyn7phKzYATGssWmcQu3X7+DX/6rJ2xp56rjYsBrMk/XETakEwA1Zv9IGFR3sYTmnQ4/9MDTDGWSzBW9M1YeIt94Mubg+QbHEQIMQWAQI5VS1aA729VkvbTsVpRzDVUWTkMQGHJln/miR67UqCBUs6knxanZlXdFLIXernglRiDuCm4YUVgOAtIJt5LxsH1DNwdHs/imMa6h5Pn84/dOEYTvGUJ3QsKhryvOR7/0DLNFn/mSTzrh8v7rtnPHW17NzqEMhyfDOIeYQ9kPFiwM4fhx16l0uJwv+YsOYKs/L2o0lS14vGo4c8ZtkiPf+MZMkuMzeev/EasoBcZ2DzXG6M52DdACUIqy+qiy0ARjDPOlxSkIAIWyz0PfO8mR6bPLsiDAXKFMEFhLgSvCBb1JYq5D2Tc1JXkv29zDgdAVUU1/d4K5gofnB/gmFPJiKyjOFn3MbJ7ZolVEYo7dZX/y4UMA3HXjZZXdf1fcZTxb5GSVMhV3HVxHMMZQ9AK2DnSfNoAtcj08fniSVMwGWvZ2xTk5a10kXmB4aXyeoZ5kpVz1YgRLvUsjk7Am7t5wLuPZIkXPkE7GeP9123l0ZFJ3toqinDdoBceQ3bt3m2/923eYK5bJFf3KLrodU7kSX37qGF/57nFmC82M62cHrsBgOs5c0e6CXz2c4c4bdtX4/m//7H7KXtAQQOkIld10peaz1LazhoWuk54fVGIkrt2xoZJmGAnW6VyJ6XyJiWwZERvD4Ae2X8S9P/967n1kpGVVxMhfHXeFE9P5StBmf1ecsWypMl9HbFCniJ3uYHecV1/Q21KoV7s0Ij/4bL6MAfrCQM7IgqA9DxRFOVdYSgXHc9ayICI3AJ8EXODPjTF3tzu/5NmMgMVwdCrHF/cf5Z+eO0UpTAGMu8JbL9/EPzx74gxnvjKkk3E29XWTK3n0dycaTPplv1FRgIWiScBCc4gmepQfGIwxNVkXo3MFHnjyWI2AjQTzhgzM5MoU/YCYzIYAKgAADhBJREFU4/CBPZdWzmkVwHb3g89zciZP2TeVKTgCE/Olaj2m0psjUmhmCx4vjWdbuiWauT7AttDu7050xIKwksGYiqIoK805qSyIiAv8MfCTwFHgCRH5qjHm+61+ZzH2le8fn+UL+47w7YPjlfN7UjFuuupC3vm6LXx676Ezn3yHsfEADmNzRXpSzdMdj0zlONMq1dVBkYLd1TeLN6gJUHMahXCrADaAF0ezDfOMfk44ECAN5bYTYUzEXMFjU1+sqVuiletjJl/mwd+4/ozWBZoHY56vDacURVmfnJPKAnANcMgYMwIgIvcDNwMtlYVWBMbw6A8m+Jt9R3j22Gzl+KbeFLe8fgs3vmYzXQlrgn/4hbGOTH4pSBOXQDUmPCdK9WsWub9toJujZ1h5st4AMZyxpaCbKSenC1Br9v6t9z0WdpqkEh0Z/ew4wrYN3Xi+4ZWqzI+oXoQxhpIftExxbFb8qZMZDlp1UFGU9c65qixsAY5U/XwU+HdLGaDkBXzj+6f44v6jNQJo53CG9/zINn781UMNfSDWpIfUIq7pB4a4Iy2LGp1p5cnhTKJSpVGA4Z4kw722j0OnhO6RqRyOhGsc+RvCl+mES9k3oUB2KfkBnm+IufYkY6yFodVcVjp3X6sOKoqy3jlXlYVm3ZwaxKqI3AbcBnDh1m2ALXv8tWeO83dPHmMqV66ce832Qd69eyuv29aPSOPwPan4gjBbTSJnfQscbFGk7mSsEiRYv5vds2uYrf0pjk4XWo8T3lt1bIAJj1/Q10VPV9wqJGWbGdHptMJtA92MzxXxjQnjI+wc4q5wxYV93H79Du59ZISZXAkvMAx0x2wWR5gJ0pOKt5zLSufur7TlQlEUZaU5V5WFo8C2qp+3AsfrTzLG3AfcB7DrtT9s/ujhQ/zj905QKFsBE3OEN182zLt3b2tZqTAVdxlMJ0jFXW6+ajNf+u7qBDhK1VdAa33hhzb11GQ+tOJ3f/q1/Nrn9pMrN4Y5phMO3QmXsWy5UprZDy+4IR2vWCw+9o7LgZURurdfv4MPP/A0U7kysdCiExhbRyK6Rn1lx4Ojc5TC9tvbN2bazmUlc/e16qCiKOudczJ1UkRiwIvAm4FjwBPAzxpjnmv1O8nNO83m9/0hAN0Jl5+6cjPvunorQy3aMMcch8FMgkyyVt/64P1PtlQYTmMEAKwlQKJdvEAyrCWQL3mU/IXfTrjCxkwSEWEsW2S+6FHyAgJjCxxdujHNXTdetiQBuPfAKB/98rMcmylgDHTHXd52xTAnZ0scncphgoDJvEfRC0i6DoPpOCKyqt0f73noACPj8wBs39C95HtcKyIFRmszKIpytrCU1MlzUlkAEJG3A3+ITZ38C2PM/2x3fnLzTvPaD/wJ77p6K++4cnODEhDhiNDfHaevK97UHaEoiqIo6wFVFpbBxT/0WvP1f/lX4m7rRpyZVIzB7gSxNucoiqIoynpAizItg96ueEtFIRV32ZBJkIy5qzwrRVEURVl7VFloQ9x1GEg3xiUoiqIoyvmESsEmaFyCoiiKoiygykIdPak4g+lEQ8ElRVEURTlfUWUhxBFhy0CXxiUoiqIoSh0a1h8Sd0UVBUVRFEVpgioLiqIoiqK0RZUFRVEURVHaosqCoiiKoihtUWVBURRFUZS2qLKgKIqiKEpbVFlQFEVRFKUtqiwoiqIoitIWVRYURVEURWmLKguKoiiKorRFlQVFURRFUdqiyoKiKIqiKG1RZUFRFEVRlLaosqAoiqIoSltUWVAURVEUpS2qLCiKoiiK0hZVFhRFURRFaYsqC4qiKIqitEWVBUVRFEVR2qLKgqIoiqIobRFjzFrP4axARMaAlzs45EZgvIPjnavoOi0OXafFo2u1OHSdFse5vE4XG2OGFnOiKgsrhIjsM8bsXut5nO3oOi0OXafFo2u1OHSdFoeuk0XdEIqiKIqitEWVBUVRFEVR2qLKwspx31pPYJ2g67Q4dJ0Wj67V4tB1Why6TmjMgqIoiqIop0EtC4qiKIqitEWVhRVARG4QkRdE5JCI3LXW81kNROSwiDwrIt8VkX3hsUER+YaIHAy/D4THRUQ+Fa7PMyJyddU47wvPPygi76s6/vpw/EPh78rq3+XyEJG/EJFREfle1bEVX5tW1zhbabFOvy0ix8Ln6rsi8vaq934zvOcXRORtVceb/v+JyHYR+U64Hl8QkUR4PBn+fCh8/5LVuePlISLbROSfReR5EXlORH49PK7PVBVt1kmfqeVgjNGvDn4BLvADYAeQAJ4GLl/rea3CfR8GNtYd+9/AXeHru4B7wtdvBx4EBLgW+E54fBAYCb8PhK8HwvceB94Q/s6DwI1rfc9LWJvrgauB763m2rS6xtn61WKdfhv4UJNzLw//t5LA9vB/zm33/wf8DfDe8PWngV8NX/8a8Onw9XuBL6z1WpxmnTYDV4eve4AXw/XQZ2px66TP1DK+1LLQea4BDhljRowxJeB+4OY1ntNacTPwmfD1Z4Cfrjr+V8byGNAvIpuBtwHfMMZMGmOmgG8AN4Tv9RpjHjX2v++vqsY66zHGPAJM1h1ejbVpdY2zkhbr1IqbgfuNMUVjzEvAIez/XtP/v3Bn/CbggfD369c8WqcHgDefzZYrY8wJY8yT4es54HlgC/pM1dBmnVpx3j5Ti0GVhc6zBThS9fNR2j+g5woG+LqI7BeR28JjFxhjToD9xwWGw+Ot1qjd8aNNjq9nVmNtWl1jvfGfQvP5X1SZvZe6ThuAaWOMV3e8Zqzw/Znw/LOe0Lz9OuA76DPVkrp1An2mlowqC52nmfZ4PqSc/Kgx5mrgRuADInJ9m3NbrdFSj5+L6NrU8qfApcAPAyeA/xMe7+Q6rcs1FJEM8LfAbxhjZtud2uTYefNMNVknfaaWgSoLnecosK3q563A8TWay6phjDkefh8FvoQ13Z0KTZqE30fD01utUbvjW5scX8+sxtq0usa6wRhzyhjjG2MC4M+wzxUsfZ3Gseb3WN3xmrHC9/tYvDtkTRCROFYAfs4Y83fhYX2m6mi2TvpMLQ9VFjrPE8DOMEo2gQ1u+eoaz2lFEZG0iPREr4G3At/D3ncUYf0+4Cvh668CvxBGaV8LzIQmzX8C3ioiA6Fp8K3AP4XvzYnItaHf7xeqxlqvrMbatLrGuiESTCHvxD5XYO/tvWHU+XZgJzYor+n/X+h7/2fglvD369c8WqdbgIfD889Kwr/z/wWeN8b8ftVb+kxV0Wqd9JlaJmsdYXkufmGjj1/ERtB+ZK3nswr3uwMbIfw08Fx0z1gf3TeBg+H3wfC4AH8crs+zwO6qsX4JG1h0CPjFquO7sf/UPwD+iLCg2Hr4Aj6PNXeWsTuOX16NtWl1jbP1q8U6/XW4Ds9gP4A3V53/kfCeX6AqO6bV/1/4nD4ert8XgWR4PBX+fCh8f8dar8Vp1uk6rEn7GeC74dfb9Zla9DrpM7WML63gqCiKoihKW9QNoSiKoihKW1RZUBRFURSlLaosKIqiKIrSFlUWFEVRFEVpiyoLiqIoiqK0RZUFRVEaEJE9IvLGMxwj26n5LPJ6u0XkU6t5TUU5X4id/hRFUc5D9gBZ4N/WeB41iIhrjPGbvWeM2QfsW+UpKcp5gVoWFOU8QUS+HDb6eq6q2RcicoOIPCkiT4vIN8OmO78CfFBEvisiPyYifykit1T9Tjb8ngl/50kReVZETtthVUR+XkQeD8e+V0Tc8Pifisi+cH7/o+r8wyLyWyLybeBnRGSviNwTjvGiiPxYeN4eEfn78PVvh02C9orIiIjcUTXex0TkgIh8Q0Q+LyIfCo/fISLfF9tg6P4zWmxFOcdQy4KinD/8kjFmUkS6gCdE5G+xG4Y/A643xrwkIoPhOZ8GssaYTwCIyC+3GLMAvNMYMysiG4HHRCQqhduAiFwGvAfbeKwsIn8C/By2DfJHwmu7wDdF5EpjzDPRdYwx14Vj/AoQM8ZcIyJvB/478JYml9sF/ATQA7wgIn8KXAW8C9uBMAY8CewPz78L2G6MKYpI/+mXU1HOH1RZUJTzhztE5J3h623Y2vdDwCPGmJcAjDFLbXYjwP8S22U0wLbmvQA42eL8NwOvxyorAF0sNCN6d2jxiAGbgcuxJXkBvlA3TtQ8aT9wSYtr/YMxpggURWQ0nNd1wFeMMXkAEfla1fnPAJ8TkS8DX25zz4py3qHKgqKcB4jIHuzu+w3GmJyI7MXWrxcW1zrXI3Rbhg16EuHxn8MqHK8PLQWHw3FbTgX4jDHmN+vmtx34EPAjxpgpEfnLunHm68Ypht99Wn+OFateR+c1ax0c8Q7geuAm4GMicoUxxmtzvqKcN2jMgqKcH/QBU6GisAu4Njz+KPDjobBGRAbD43NY833EYaxFAOBmIF417mioKPwEcPFp5vFN4BYRGY6uJyIXA71YhWBGRC4AblzebZ6WbwP/XkRSIpLBKgiIiANsM8b8M/DfgH4gs0JzUJR1h1oWFOX84CHgV0TkGWxHvccAjDFjoen/70KBOQr8JPA14IEwYPE/Y+MaviIij2MFfrTT/xzwNRHZh+3qd6DdJIwx3xeRjwJfD69XBj5gjHlMRJ7Cdi0dAf61g/deff0nROSr2A6pL2OzJ2YAF/isiPRhrQ9/YIyZXok5KMp6RLtOKopyXiEiGWNMVkS6gUeA24wxT671vBTlbEYtC4qinG/cJyKXY2MiPqOKgqKcHrUsKIqiKIrSFg1wVBRFURSlLaosKIqiKIrSFlUWFEVRFEVpiyoLiqIoiqK0RZUFRVEURVHaosqCoiiKoiht+f+dF2CEvXT4rAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 8)\n",
    "sns.regplot(x=y_to_earnings(y_test_new), y=difference)\n",
    "plt.xlabel(\"actual earnings\")\n",
    "plt.ylabel(\"difference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model performs slightly better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Interesting take-aways "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the model we can see that it's important to have `Is_location_exact` as 1 (although we are not quite sure what it means). Price and `Price_per_bed_compared_to_neighborhood` have positive effect on the future earnings but I think it's mostly because listings with higher price have better amenities, locations, etc. So don't worry if your listing is more expensive than most of the other listings in the neighbordhood. If your room is truly in better condition and provides more exciting stuffs for the guests, you will have better future earnings. The length of the amenity field also has a positive effect. Other important factors are acceptance rate and response rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve the model, I think it will help to get more enrichment on the address data. For example, we can get the house price data using the addresses. We can also have a look how many hotels there are in the neighborhood of the listing. "
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}