Airbnb_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Airbnb Listing Nightly Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['name', 'description', 'neighborhood_overview', 'host_name',\n",
      "       'host_location', 'host_about', 'host_response_rate',\n",
      "       'host_acceptance_rate', 'host_is_superhost', 'host_listings_count',\n",
      "       'host_total_listings_count', 'host_has_profile_pic',\n",
      "       'host_identity_verified', 'neighbourhood_group_cleansed', 'room_type',\n",
      "       'accommodates', 'bathrooms', 'bedrooms', 'beds', 'amenities', 'price',\n",
      "       'minimum_nights', 'maximum_nights', 'minimum_minimum_nights',\n",
      "       'maximum_minimum_nights', 'minimum_maximum_nights',\n",
      "       'maximum_maximum_nights', 'minimum_nights_avg_ntm',\n",
      "       'maximum_nights_avg_ntm', 'has_availability', 'availability_30',\n",
      "       'availability_60', 'availability_90', 'availability_365',\n",
      "       'number_of_reviews', 'number_of_reviews_ltm', 'number_of_reviews_l30d',\n",
      "       'review_scores_rating', 'review_scores_cleanliness',\n",
      "       'review_scores_checkin', 'review_scores_communication',\n",
      "       'review_scores_location', 'review_scores_value', 'instant_bookable',\n",
      "       'calculated_host_listings_count',\n",
      "       'calculated_host_listings_count_entire_homes',\n",
      "       'calculated_host_listings_count_private_rooms',\n",
      "       'calculated_host_listings_count_shared_rooms', 'reviews_per_month',\n",
      "       'n_host_verifications'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>description</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_about</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>n_host_verifications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>Beautiful, spacious skylit studio in the heart...</td>\n",
       "      <td>Centrally located in the heart of Manhattan ju...</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>A New Yorker since 2000! My passion is creatin...</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.17</td>\n",
       "      <td>True</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.79</td>\n",
       "      <td>4.86</td>\n",
       "      <td>4.41</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Whole flr w/private bdrm, bath &amp; kitchen(pls r...</td>\n",
       "      <td>Enjoy 500 s.f. top floor in 1899 brownstone, w...</td>\n",
       "      <td>Just the right mix of urban center and local n...</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>Laid-back Native New Yorker (formerly bi-coast...</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.69</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.71</td>\n",
       "      <td>4.64</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.86</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spacious Brooklyn Duplex, Patio + Garden</td>\n",
       "      <td>We welcome you to stay in our lovely 2 br dupl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rebecca</td>\n",
       "      <td>Brooklyn, New York, United States</td>\n",
       "      <td>Rebecca is an artist/designer, and Henoch is i...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Large Furnished Room Near B'way</td>\n",
       "      <td>Please don’t expect the luxury here just a bas...</td>\n",
       "      <td>Theater district, many restaurants around here.</td>\n",
       "      <td>Shunichi</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>I used to work for a financial industry but no...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.42</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.36</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cozy Clean Guest Room - Family Apt</td>\n",
       "      <td>Our best guests are seeking a safe, clean, spa...</td>\n",
       "      <td>Our neighborhood is full of restaurants and ca...</td>\n",
       "      <td>MaryEllen</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>Welcome to family life with my oldest two away...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.95</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.92</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  \\\n",
       "0                              Skylit Midtown Castle   \n",
       "1  Whole flr w/private bdrm, bath & kitchen(pls r...   \n",
       "2           Spacious Brooklyn Duplex, Patio + Garden   \n",
       "3                   Large Furnished Room Near B'way　   \n",
       "4                 Cozy Clean Guest Room - Family Apt   \n",
       "\n",
       "                                         description  \\\n",
       "0  Beautiful, spacious skylit studio in the heart...   \n",
       "1  Enjoy 500 s.f. top floor in 1899 brownstone, w...   \n",
       "2  We welcome you to stay in our lovely 2 br dupl...   \n",
       "3  Please don’t expect the luxury here just a bas...   \n",
       "4  Our best guests are seeking a safe, clean, spa...   \n",
       "\n",
       "                               neighborhood_overview    host_name  \\\n",
       "0  Centrally located in the heart of Manhattan ju...     Jennifer   \n",
       "1  Just the right mix of urban center and local n...  LisaRoxanne   \n",
       "2                                                NaN      Rebecca   \n",
       "3    Theater district, many restaurants around here.     Shunichi   \n",
       "4  Our neighborhood is full of restaurants and ca...    MaryEllen   \n",
       "\n",
       "                       host_location  \\\n",
       "0  New York, New York, United States   \n",
       "1  New York, New York, United States   \n",
       "2  Brooklyn, New York, United States   \n",
       "3  New York, New York, United States   \n",
       "4  New York, New York, United States   \n",
       "\n",
       "                                          host_about  host_response_rate  \\\n",
       "0  A New Yorker since 2000! My passion is creatin...                0.80   \n",
       "1  Laid-back Native New Yorker (formerly bi-coast...                0.09   \n",
       "2  Rebecca is an artist/designer, and Henoch is i...                1.00   \n",
       "3  I used to work for a financial industry but no...                1.00   \n",
       "4  Welcome to family life with my oldest two away...                 NaN   \n",
       "\n",
       "   host_acceptance_rate  host_is_superhost  host_listings_count  ...  \\\n",
       "0                  0.17               True                  8.0  ...   \n",
       "1                  0.69               True                  1.0  ...   \n",
       "2                  0.25               True                  1.0  ...   \n",
       "3                  1.00               True                  1.0  ...   \n",
       "4                   NaN               True                  1.0  ...   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \\\n",
       "0                         4.79                    4.86                 4.41   \n",
       "1                         4.80                    4.71                 4.64   \n",
       "2                         5.00                    4.50                 5.00   \n",
       "3                         4.42                    4.87                 4.36   \n",
       "4                         4.95                    4.94                 4.92   \n",
       "\n",
       "  instant_bookable calculated_host_listings_count  \\\n",
       "0            False                              3   \n",
       "1            False                              1   \n",
       "2            False                              1   \n",
       "3            False                              1   \n",
       "4            False                              1   \n",
       "\n",
       "   calculated_host_listings_count_entire_homes  \\\n",
       "0                                            3   \n",
       "1                                            1   \n",
       "2                                            1   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "\n",
       "   calculated_host_listings_count_private_rooms  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             1   \n",
       "4                                             1   \n",
       "\n",
       "   calculated_host_listings_count_shared_rooms  reviews_per_month  \\\n",
       "0                                            0               0.33   \n",
       "1                                            0               4.86   \n",
       "2                                            0               0.02   \n",
       "3                                            0               3.68   \n",
       "4                                            0               0.87   \n",
       "\n",
       "  n_host_verifications  \n",
       "0                    9  \n",
       "1                    6  \n",
       "2                    3  \n",
       "3                    4  \n",
       "4                    7  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnbDataSet_filename = os.path.join(os.getcwd(), \"data\", \"airbnbListingsData.csv\")\n",
    "\n",
    "df = pd.read_csv(airbnbDataSet_filename)\n",
    "\n",
    "print(df.columns)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the ML Problem\n",
    "\n",
    "This project uses the Airbnb dataset to predict the price per night of listings. This is a supervised learning regression problem that I'll be implementing a feedforward neural network to solve. The feature label is `price`.\n",
    "\n",
    "Price prediction could enable a feature that suggests nightly prices to a host during the listing process. This helps hosts set a baseline for what other similar properties are going for in similar areas. If this system is continually trained on new data, it could also serve as a way to update hosts on opportunities to change the price. This model assumes the second use case as it includes data collected from listings that would not exist at creation (like review scores)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Data\n",
    "This section includes the exploratory data analysis I'll use to inform what data preparation needs to be done and what features should be included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                                             object\n",
       "description                                      object\n",
       "neighborhood_overview                            object\n",
       "host_name                                        object\n",
       "host_location                                    object\n",
       "host_about                                       object\n",
       "host_response_rate                              float64\n",
       "host_acceptance_rate                            float64\n",
       "host_is_superhost                                  bool\n",
       "host_listings_count                             float64\n",
       "host_total_listings_count                       float64\n",
       "host_has_profile_pic                               bool\n",
       "host_identity_verified                             bool\n",
       "neighbourhood_group_cleansed                     object\n",
       "room_type                                        object\n",
       "accommodates                                      int64\n",
       "bathrooms                                       float64\n",
       "bedrooms                                        float64\n",
       "beds                                            float64\n",
       "amenities                                        object\n",
       "price                                           float64\n",
       "minimum_nights                                    int64\n",
       "maximum_nights                                    int64\n",
       "minimum_minimum_nights                          float64\n",
       "maximum_minimum_nights                          float64\n",
       "minimum_maximum_nights                          float64\n",
       "maximum_maximum_nights                          float64\n",
       "minimum_nights_avg_ntm                          float64\n",
       "maximum_nights_avg_ntm                          float64\n",
       "has_availability                                   bool\n",
       "availability_30                                   int64\n",
       "availability_60                                   int64\n",
       "availability_90                                   int64\n",
       "availability_365                                  int64\n",
       "number_of_reviews                                 int64\n",
       "number_of_reviews_ltm                             int64\n",
       "number_of_reviews_l30d                            int64\n",
       "review_scores_rating                            float64\n",
       "review_scores_cleanliness                       float64\n",
       "review_scores_checkin                           float64\n",
       "review_scores_communication                     float64\n",
       "review_scores_location                          float64\n",
       "review_scores_value                             float64\n",
       "instant_bookable                                   bool\n",
       "calculated_host_listings_count                    int64\n",
       "calculated_host_listings_count_entire_homes       int64\n",
       "calculated_host_listings_count_private_rooms      int64\n",
       "calculated_host_listings_count_shared_rooms       int64\n",
       "reviews_per_month                               float64\n",
       "n_host_verifications                              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data types for each column\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>...</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>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>n_host_verifications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16179.000000</td>\n",
       "      <td>16909.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>25104.000000</td>\n",
       "      <td>26668.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "      <td>28022.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.906901</td>\n",
       "      <td>0.791953</td>\n",
       "      <td>14.554778</td>\n",
       "      <td>14.554778</td>\n",
       "      <td>2.874491</td>\n",
       "      <td>1.142174</td>\n",
       "      <td>1.329708</td>\n",
       "      <td>1.629556</td>\n",
       "      <td>154.228749</td>\n",
       "      <td>18.689387</td>\n",
       "      <td>...</td>\n",
       "      <td>4.814300</td>\n",
       "      <td>4.808041</td>\n",
       "      <td>4.750393</td>\n",
       "      <td>4.647670</td>\n",
       "      <td>9.581900</td>\n",
       "      <td>5.562986</td>\n",
       "      <td>3.902077</td>\n",
       "      <td>0.048283</td>\n",
       "      <td>1.758325</td>\n",
       "      <td>5.169510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.227282</td>\n",
       "      <td>0.276732</td>\n",
       "      <td>120.721287</td>\n",
       "      <td>120.721287</td>\n",
       "      <td>1.860251</td>\n",
       "      <td>0.421132</td>\n",
       "      <td>0.700726</td>\n",
       "      <td>1.097104</td>\n",
       "      <td>140.816605</td>\n",
       "      <td>25.569151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438603</td>\n",
       "      <td>0.464585</td>\n",
       "      <td>0.415717</td>\n",
       "      <td>0.518023</td>\n",
       "      <td>32.227523</td>\n",
       "      <td>26.121426</td>\n",
       "      <td>17.972386</td>\n",
       "      <td>0.442459</td>\n",
       "      <td>4.446143</td>\n",
       "      <td>2.028497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.810000</td>\n",
       "      <td>4.810000</td>\n",
       "      <td>4.670000</td>\n",
       "      <td>4.550000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.960000</td>\n",
       "      <td>4.970000</td>\n",
       "      <td>4.880000</td>\n",
       "      <td>4.780000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.830000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3387.000000</td>\n",
       "      <td>3387.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_response_rate  host_acceptance_rate  host_listings_count  \\\n",
       "count        16179.000000          16909.000000         28022.000000   \n",
       "mean             0.906901              0.791953            14.554778   \n",
       "std              0.227282              0.276732           120.721287   \n",
       "min              0.000000              0.000000             0.000000   \n",
       "25%              0.940000              0.680000             1.000000   \n",
       "50%              1.000000              0.910000             1.000000   \n",
       "75%              1.000000              1.000000             3.000000   \n",
       "max              1.000000              1.000000          3387.000000   \n",
       "\n",
       "       host_total_listings_count  accommodates     bathrooms      bedrooms  \\\n",
       "count               28022.000000  28022.000000  28022.000000  25104.000000   \n",
       "mean                   14.554778      2.874491      1.142174      1.329708   \n",
       "std                   120.721287      1.860251      0.421132      0.700726   \n",
       "min                     0.000000      1.000000      0.000000      1.000000   \n",
       "25%                     1.000000      2.000000      1.000000      1.000000   \n",
       "50%                     1.000000      2.000000      1.000000      1.000000   \n",
       "75%                     3.000000      4.000000      1.000000      1.000000   \n",
       "max                  3387.000000     16.000000      8.000000     12.000000   \n",
       "\n",
       "               beds         price  minimum_nights  ...  review_scores_checkin  \\\n",
       "count  26668.000000  28022.000000    28022.000000  ...           28022.000000   \n",
       "mean       1.629556    154.228749       18.689387  ...               4.814300   \n",
       "std        1.097104    140.816605       25.569151  ...               0.438603   \n",
       "min        1.000000     29.000000        1.000000  ...               0.000000   \n",
       "25%        1.000000     70.000000        2.000000  ...               4.810000   \n",
       "50%        1.000000    115.000000       30.000000  ...               4.960000   \n",
       "75%        2.000000    180.000000       30.000000  ...               5.000000   \n",
       "max       21.000000   1000.000000     1250.000000  ...               5.000000   \n",
       "\n",
       "       review_scores_communication  review_scores_location  \\\n",
       "count                 28022.000000            28022.000000   \n",
       "mean                      4.808041                4.750393   \n",
       "std                       0.464585                0.415717   \n",
       "min                       0.000000                0.000000   \n",
       "25%                       4.810000                4.670000   \n",
       "50%                       4.970000                4.880000   \n",
       "75%                       5.000000                5.000000   \n",
       "max                       5.000000                5.000000   \n",
       "\n",
       "       review_scores_value  calculated_host_listings_count  \\\n",
       "count         28022.000000                    28022.000000   \n",
       "mean              4.647670                        9.581900   \n",
       "std               0.518023                       32.227523   \n",
       "min               0.000000                        1.000000   \n",
       "25%               4.550000                        1.000000   \n",
       "50%               4.780000                        1.000000   \n",
       "75%               5.000000                        3.000000   \n",
       "max               5.000000                      421.000000   \n",
       "\n",
       "       calculated_host_listings_count_entire_homes  \\\n",
       "count                                 28022.000000   \n",
       "mean                                      5.562986   \n",
       "std                                      26.121426   \n",
       "min                                       0.000000   \n",
       "25%                                       0.000000   \n",
       "50%                                       1.000000   \n",
       "75%                                       1.000000   \n",
       "max                                     308.000000   \n",
       "\n",
       "       calculated_host_listings_count_private_rooms  \\\n",
       "count                                  28022.000000   \n",
       "mean                                       3.902077   \n",
       "std                                       17.972386   \n",
       "min                                        0.000000   \n",
       "25%                                        0.000000   \n",
       "50%                                        0.000000   \n",
       "75%                                        1.000000   \n",
       "max                                      359.000000   \n",
       "\n",
       "       calculated_host_listings_count_shared_rooms  reviews_per_month  \\\n",
       "count                                 28022.000000       28022.000000   \n",
       "mean                                      0.048283           1.758325   \n",
       "std                                       0.442459           4.446143   \n",
       "min                                       0.000000           0.010000   \n",
       "25%                                       0.000000           0.130000   \n",
       "50%                                       0.000000           0.510000   \n",
       "75%                                       0.000000           1.830000   \n",
       "max                                       8.000000         141.000000   \n",
       "\n",
       "       n_host_verifications  \n",
       "count          28022.000000  \n",
       "mean               5.169510  \n",
       "std                2.028497  \n",
       "min                1.000000  \n",
       "25%                4.000000  \n",
       "50%                5.000000  \n",
       "75%                7.000000  \n",
       "max               13.000000  \n",
       "\n",
       "[8 rows x 36 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look the df stats\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It may not make sense for this algorithm to accomodate expensive outliers right now since these are often special cases that aren't very related to the data provided. For example, a room in a very special specific location would be much more expensive but the data doesn't reflect that beyond the neighborhood. Let's do some further investigation to decide where the cutoff should be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{100: 54.94968239240596,\n",
       " 150: 33.288130754407256,\n",
       " 200: 19.62743558632503,\n",
       " 250: 12.971950610234813,\n",
       " 300: 8.711012775676254,\n",
       " 350: 6.12019127828135,\n",
       " 400: 4.557133680679466,\n",
       " 450: 3.6792520162729283,\n",
       " 500: 2.96195846120905}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds = [100, 150, 200, 250, 300, 350, 400, 450, 500]\n",
    "\n",
    "# Calculate the percentage of values greater than each threshold\n",
    "percentages = {threshold: (df['price'] > threshold).sum() / len(df) * 100 for threshold in thresholds}\n",
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                                                5\n",
       "description                                       570\n",
       "neighborhood_overview                            9816\n",
       "host_name                                           0\n",
       "host_location                                      60\n",
       "host_about                                      10945\n",
       "host_response_rate                              11843\n",
       "host_acceptance_rate                            11113\n",
       "host_is_superhost                                   0\n",
       "host_listings_count                                 0\n",
       "host_total_listings_count                           0\n",
       "host_has_profile_pic                                0\n",
       "host_identity_verified                              0\n",
       "neighbourhood_group_cleansed                        0\n",
       "room_type                                           0\n",
       "accommodates                                        0\n",
       "bathrooms                                           0\n",
       "bedrooms                                         2918\n",
       "beds                                             1354\n",
       "amenities                                           0\n",
       "price                                               0\n",
       "minimum_nights                                      0\n",
       "maximum_nights                                      0\n",
       "minimum_minimum_nights                              0\n",
       "maximum_minimum_nights                              0\n",
       "minimum_maximum_nights                              0\n",
       "maximum_maximum_nights                              0\n",
       "minimum_nights_avg_ntm                              0\n",
       "maximum_nights_avg_ntm                              0\n",
       "has_availability                                    0\n",
       "availability_30                                     0\n",
       "availability_60                                     0\n",
       "availability_90                                     0\n",
       "availability_365                                    0\n",
       "number_of_reviews                                   0\n",
       "number_of_reviews_ltm                               0\n",
       "number_of_reviews_l30d                              0\n",
       "review_scores_rating                                0\n",
       "review_scores_cleanliness                           0\n",
       "review_scores_checkin                               0\n",
       "review_scores_communication                         0\n",
       "review_scores_location                              0\n",
       "review_scores_value                                 0\n",
       "instant_bookable                                    0\n",
       "calculated_host_listings_count                      0\n",
       "calculated_host_listings_count_entire_homes         0\n",
       "calculated_host_listings_count_private_rooms        0\n",
       "calculated_host_listings_count_shared_rooms         0\n",
       "reviews_per_month                                   0\n",
       "n_host_verifications                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values in each col\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Room Types: ['Entire home/apt' 'Private room' 'Hotel room' 'Shared room']\n",
      "neighbourhood group cleansed: ['Manhattan' 'Brooklyn' 'Queens' 'Staten Island' 'Bronx']\n",
      "Number of unique amenities: 2719\n"
     ]
    }
   ],
   "source": [
    "# inspect features that could be encoded\n",
    "print(\"Room Types: \" + str(df['room_type'].unique()))\n",
    "\n",
    "# neighbourhood_group_cleansed\n",
    "print(\"neighbourhood group cleansed: \" + str(df['neighbourhood_group_cleansed'].unique()))\n",
    "\n",
    "# unique amenities\n",
    "amenities_list = df['amenities'].apply(lambda x: x.split(', '))\n",
    "all_amenities = [item for sublist in amenities_list for item in sublist]\n",
    "unique_amenities = set(all_amenities)\n",
    "num_unique_amenities = len(unique_amenities)\n",
    "print(f'Number of unique amenities: {num_unique_amenities}')\n",
    "\n",
    "# print(unique_amenities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some data prep needs to be included at this stage to properly analyze encoded features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_about</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>...</th>\n",
       "      <th>n_host_verifications</th>\n",
       "      <th>room_Entire home/apt</th>\n",
       "      <th>room_Hotel room</th>\n",
       "      <th>room_Private room</th>\n",
       "      <th>room_Shared room</th>\n",
       "      <th>borough_Bronx</th>\n",
       "      <th>borough_Brooklyn</th>\n",
       "      <th>borough_Manhattan</th>\n",
       "      <th>borough_Queens</th>\n",
       "      <th>borough_Staten Island</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>Beautiful, spacious skylit studio in the heart...</td>\n",
       "      <td>Centrally located in the heart of Manhattan ju...</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>A New Yorker since 2000! My passion is creatin...</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.17</td>\n",
       "      <td>True</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Whole flr w/private bdrm, bath &amp; kitchen(pls r...</td>\n",
       "      <td>Enjoy 500 s.f. top floor in 1899 brownstone, w...</td>\n",
       "      <td>Just the right mix of urban center and local n...</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>Laid-back Native New Yorker (formerly bi-coast...</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.69</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spacious Brooklyn Duplex, Patio + Garden</td>\n",
       "      <td>We welcome you to stay in our lovely 2 br dupl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rebecca</td>\n",
       "      <td>Brooklyn, New York, United States</td>\n",
       "      <td>Rebecca is an artist/designer, and Henoch is i...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Large Furnished Room Near B'way</td>\n",
       "      <td>Please don’t expect the luxury here just a bas...</td>\n",
       "      <td>Theater district, many restaurants around here.</td>\n",
       "      <td>Shunichi</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>I used to work for a financial industry but no...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cozy Clean Guest Room - Family Apt</td>\n",
       "      <td>Our best guests are seeking a safe, clean, spa...</td>\n",
       "      <td>Our neighborhood is full of restaurants and ca...</td>\n",
       "      <td>MaryEllen</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>Welcome to family life with my oldest two away...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  \\\n",
       "0                              Skylit Midtown Castle   \n",
       "1  Whole flr w/private bdrm, bath & kitchen(pls r...   \n",
       "2           Spacious Brooklyn Duplex, Patio + Garden   \n",
       "3                   Large Furnished Room Near B'way　   \n",
       "4                 Cozy Clean Guest Room - Family Apt   \n",
       "\n",
       "                                         description  \\\n",
       "0  Beautiful, spacious skylit studio in the heart...   \n",
       "1  Enjoy 500 s.f. top floor in 1899 brownstone, w...   \n",
       "2  We welcome you to stay in our lovely 2 br dupl...   \n",
       "3  Please don’t expect the luxury here just a bas...   \n",
       "4  Our best guests are seeking a safe, clean, spa...   \n",
       "\n",
       "                               neighborhood_overview    host_name  \\\n",
       "0  Centrally located in the heart of Manhattan ju...     Jennifer   \n",
       "1  Just the right mix of urban center and local n...  LisaRoxanne   \n",
       "2                                                NaN      Rebecca   \n",
       "3    Theater district, many restaurants around here.     Shunichi   \n",
       "4  Our neighborhood is full of restaurants and ca...    MaryEllen   \n",
       "\n",
       "                       host_location  \\\n",
       "0  New York, New York, United States   \n",
       "1  New York, New York, United States   \n",
       "2  Brooklyn, New York, United States   \n",
       "3  New York, New York, United States   \n",
       "4  New York, New York, United States   \n",
       "\n",
       "                                          host_about  host_response_rate  \\\n",
       "0  A New Yorker since 2000! My passion is creatin...                0.80   \n",
       "1  Laid-back Native New Yorker (formerly bi-coast...                0.09   \n",
       "2  Rebecca is an artist/designer, and Henoch is i...                1.00   \n",
       "3  I used to work for a financial industry but no...                1.00   \n",
       "4  Welcome to family life with my oldest two away...                 NaN   \n",
       "\n",
       "   host_acceptance_rate  host_is_superhost  host_listings_count  ...  \\\n",
       "0                  0.17               True                  8.0  ...   \n",
       "1                  0.69               True                  1.0  ...   \n",
       "2                  0.25               True                  1.0  ...   \n",
       "3                  1.00               True                  1.0  ...   \n",
       "4                   NaN               True                  1.0  ...   \n",
       "\n",
       "   n_host_verifications  room_Entire home/apt  room_Hotel room  \\\n",
       "0                     9                     1                0   \n",
       "1                     6                     1                0   \n",
       "2                     3                     1                0   \n",
       "3                     4                     0                0   \n",
       "4                     7                     0                0   \n",
       "\n",
       "   room_Private room  room_Shared room  borough_Bronx  borough_Brooklyn  \\\n",
       "0                  0                 0              0                 0   \n",
       "1                  0                 0              0                 1   \n",
       "2                  0                 0              0                 1   \n",
       "3                  1                 0              0                 0   \n",
       "4                  1                 0              0                 0   \n",
       "\n",
       "  borough_Manhattan  borough_Queens  borough_Staten Island  \n",
       "0                 1               0                      0  \n",
       "1                 0               0                      0  \n",
       "2                 0               0                      0  \n",
       "3                 1               0                      0  \n",
       "4                 1               0                      0  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One Hot encoding for room type and neighbourhood group\n",
    "df = pd.get_dummies(df, columns=['room_type'], prefix='room')\n",
    "df = pd.get_dummies(df, columns=['neighbourhood_group_cleansed'], prefix='borough')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify Relationships Between Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price                                           1.00000\n",
      "accommodates                                    0.51906\n",
      "bedrooms                                        0.47551\n",
      "beds                                            0.40924\n",
      "room_Entire home/apt                            0.34690\n",
      "bathrooms                                       0.33130\n",
      "borough_Manhattan                               0.23764\n",
      "availability_60                                 0.15275\n",
      "availability_90                                 0.14823\n",
      "availability_30                                 0.14689\n",
      "room_Hotel room                                 0.12791\n",
      "availability_365                                0.12398\n",
      "maximum_maximum_nights                          0.10790\n",
      "review_scores_location                          0.09661\n",
      "maximum_nights_avg_ntm                          0.08159\n",
      "review_scores_cleanliness                       0.08140\n",
      "host_total_listings_count                       0.08099\n",
      "host_listings_count                             0.08099\n",
      "minimum_maximum_nights                          0.06401\n",
      "has_availability                                0.06218\n",
      "number_of_reviews_ltm                           0.05394\n",
      "calculated_host_listings_count_entire_homes     0.05369\n",
      "number_of_reviews_l30d                          0.04547\n",
      "review_scores_rating                            0.04507\n",
      "instant_bookable                                0.03919\n",
      "host_acceptance_rate                            0.03755\n",
      "reviews_per_month                               0.03082\n",
      "calculated_host_listings_count                  0.02027\n",
      "host_response_rate                              0.00648\n",
      "review_scores_communication                     0.00059\n",
      "maximum_nights                                 -0.00102\n",
      "review_scores_checkin                          -0.00405\n",
      "review_scores_value                            -0.00476\n",
      "maximum_minimum_nights                         -0.00769\n",
      "minimum_nights_avg_ntm                         -0.01127\n",
      "n_host_verifications                           -0.02460\n",
      "number_of_reviews                              -0.03349\n",
      "borough_Staten Island                          -0.03698\n",
      "calculated_host_listings_count_shared_rooms    -0.04439\n",
      "calculated_host_listings_count_private_rooms   -0.04494\n",
      "room_Shared room                               -0.04794\n",
      "minimum_minimum_nights                         -0.07126\n",
      "borough_Bronx                                  -0.07212\n",
      "minimum_nights                                 -0.07995\n",
      "borough_Brooklyn                               -0.11197\n",
      "borough_Queens                                 -0.13210\n",
      "room_Private room                              -0.35546\n",
      "host_is_superhost                                   NaN\n",
      "host_has_profile_pic                                NaN\n",
      "host_identity_verified                              NaN\n",
      "Name: price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Identify relationship between features\n",
    "corr_matrix = round(df.corr(),5)\n",
    "corrs = corr_matrix['price'].sort_values(ascending=False)\n",
    "print(corrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is commented out since it takes a while to run and sometimes it crashes the VM I'm using. It renders a KDE chart to help visualize the relationships calculated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to run since this takes a bit to render\n",
    "\n",
    "# top_corrs = corrs.iloc[0:6]\n",
    "# plot_corrs = df[top_corrs.index]\n",
    "# print(plot_corrs)\n",
    "# sns.pairplot(plot_corrs, kind = 'kde', corner = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance\n",
    "Feature weights can be extracted from decision trees. Since there are a lot of potential features here I'm going to use this to help with my feature selection process. The best $R^2$ I can seem to get is .19 which isn't particually high ($R^2$ is a measure of the proportion of variabiliy so a value closer to 1 is most accurate; I'll explain these metrics more later) but hopefully that's a sign of a complex relationship in the data that a neural net will be able to solve rather than price being mathematically difficult to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 16624.254433391005\n",
      "Mean Absolute Error (MAE): 62.475021626297575\n",
      "Root Mean Squared Error (RMSE): 128.93507836656013\n",
      "R-squared (R²): 0.19619376095493257\n"
     ]
    }
   ],
   "source": [
    "DT_model = DecisionTreeClassifier(max_depth=5, min_samples_leaf=9, min_samples_split=4)\n",
    "\n",
    "string_columns = df.select_dtypes(include='object').columns\n",
    "\n",
    "# creating a separate training and test sets\n",
    "X = df.drop(columns=['price'] + list(string_columns))\n",
    "y = df['price']\n",
    "dt_X_train, dt_X_test, dt_y_train, dt_y_test = train_test_split(X, y, test_size=0.33, random_state=17)\n",
    "\n",
    "DT_model.fit(dt_X_train, dt_y_train) \n",
    "prediction = DT_model.predict(dt_X_test) \n",
    "\n",
    "# Calculate metrics\n",
    "mse = mean_squared_error(dt_y_test, prediction)\n",
    "print(f'Mean Squared Error (MSE): {mse}')\n",
    "mae = mean_absolute_error(dt_y_test, prediction)\n",
    "print(f'Mean Absolute Error (MAE): {mae}')\n",
    "rmse = np.sqrt(mse)\n",
    "print(f'Root Mean Squared Error (RMSE): {rmse}')\n",
    "r2 = r2_score(dt_y_test, prediction)\n",
    "print(f'R-squared (R²): {r2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine the hyperparameters for the above DT I used a grid search optimized for $R^2$. This is commented out since it takes a while to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to run Grid Search for hyperparameters\n",
    "\n",
    "# DT_model = DecisionTreeClassifier()\n",
    "\n",
    "# param_grid = {\n",
    "#     'min_samples_leaf': [2, 4, 6, 9, 10, 11, 14],\n",
    "#     'max_depth': [2, 3, 4, 5, 6, 7],\n",
    "#     'min_samples_split': [1, 2, 3, 4]\n",
    "# }\n",
    "\n",
    "# grid = GridSearchCV(DT_model, param_grid, cv=5, scoring='r2') \n",
    "# # Fit the model on the training data\n",
    "# grid_search = grid.fit(dt_X_train, dt_y_train)\n",
    "\n",
    "# # Get the best parameters and the best score\n",
    "# print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature importance from DT')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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KCjJtYezhNUTevHl14MAB28g6/E/Lli0lSZ07d7Zts1gsTP0X1xB/p0GDBho0aJBOnjyZaFHVrDO5/m0UxoBkvPnmm1q2bJlGjBhhdBSHc+XKFZ09e9au4bPZ/+Bv27ZN0oMm2NOnTzd1I/CkBAUFqWPHjrp06ZLi4+P1+eef6+eff9bixYv1xRdfGB3PcP369dOsWbM0bdo0o6M4JE6mk/bRRx/p448/VuvWrRUaGqp3331X/v7+CgoKMm2j3ixZsui3335Tnjx5dOXKlQSjVQsWLKhLly4ZlM5xLF68WB988IFOnz4t6cFxGTRokKlHy6dJk0Y3btyQ9GBUXb169exur1evHgVVMeIwORybpHENkbyHPXhHjRqV4DaOz7+HwpjJ3bp1Szt27Eh02oVZ+0o86vbt2/r444+1ZcsWlSxZMsFFmJkbYXfu3FkBAQFavnw5vX4eQxPspDVu3FgbN27UqFGjlCFDBgUFBalMmTLauHGjXn31VaPjGW7gwIFq0KCB8uXLp6JFiyZ4zfn8888NSuYYOJlOWkREhG2Ec/r06W0X9e3bt1elSpU0c+ZMI+MZomnTpho7dqzWrVunxo0b24qHD/9effjhh6af3j5lyhSNGDFCPXv2tI1W3blzp7p166arV6+qX79+Bic0RrVq1bR8+XKVLFlSAQEB2r59u13hfdu2bcqVK5eBCR0Hi1Ql7sKFC6pSpYrSpLG/3L5//752796dYOS8mXANkTxmdRnDYk2qSzSee0eOHFH9+vUVGxurW7duKUuWLLp69arc3Nzk5eVl6iWWH6pevXqSt1ksFm3duvUZpnEsGTJk0LFjxxLtF2V2t27d0oQJE5I8UeR3C0np2bOn5s2bp+rVqyd6smj2omvRokVVpEgRvfvuu4keHzNfaPj7+2vNmjUKCAhQuXLl1LVrV7399tv65ptv1KpVK1OOGrt+/bpq1aql6OhoVa5cWZ999pm8vb1VsGBBnTlzRn/88Ye+/vprVaxY0eiohsmbN69CQkLUoUMHu+2LFi3SyJEjTTvq5aefftJLL72kBg0aqECBAnr//ffVpEkTW3+6lStXas6cOabvB/l3i1StXbvWoGTGc3Z2TnSF8mvXrsnLy8vUb+RwDQFHxIgxE+vXr58aNmyoOXPmyMPDQ3v37lXatGnVrl079enTx+h4DuHh1DgkVKNGDf6oJeHNN9/Ujh071L59e1YzTcbNmzcTFA3NPv100aJFWrNmjRo0aGB0FId04cIFbdiwgdedRNSoUUMbNmxQQECAAgMD1a9fP61evVoHDx5Us2bNjI5nCA8PD+3evVvz58/Xxo0b5efnp/j4eN29e1etW7dW9+7dlTt3bqNjGur3339PtJdqlSpV9PvvvxuQyDEUKVJE+/bt0/DhwzVx4kTdunVLn376qdKkSaPy5ctrxYoVtgb0ZsYiVUl7OJL5cdeuXVOGDBkMSOQ4uIZIXmJTKB8VFBT0jJKYCyPGTCxz5szat2+fChUqpMyZM2vPnj22E4GOHTvq1KlTRkd0KL/++qskmf4k+qGPP/5YY8aMUefOnWkM+ZjMmTPryy+/pIl6IsLDw9WzZ09t375dt2/ftm1nKtwDvr6++vrrr1W4cGGjozikhg0bqlOnTnr99deNjuJw4uPjFR8fb5u2s2LFCu3evVsFChTQ22+/bbcYCBK3a9culStXTq6urkZHeWaKFy+uNm3aaOjQoXbbx4wZo5UrV+rEiRMGJXMcVqvVNvo7a9asCc53zOyFF17Q/v37lS9fPqOjOIyHb0SsX79edevWtXs9iYuL0/Hjx1WoUCFt3rzZqIiG4xoieY8vRnXv3j2Fh4crTZo0ypcvnylXmX4WGDFmYmnTppWTk5MkycvLSxERESpSpIg8PDx08eJFg9M5hvj4eI0ZM0aTJ0/WzZs3JUmZMmXSgAEDNGzYMNvxM6Nu3bpJojFkYjw9PZUlSxajYzikdu3ayWq1asGCBfSVSMTIkSMVHByshQsXys3Nzeg4Dqdhw4bq16+fTpw4wcn0Y3799Vf5+PjYvm7VqpVatWolq9WqixcvKk+ePAam+2+oV6+ejh49Kn9/f6OjPDMhISFq2bKlvvvuO9ubObt27VJYWJhWrVplcDrHYLFY5O3tbXQMh8QiVQl5eHhIelBQzZQpk9KnT2+7zcXFRZUqVbI1VzcrriGS93CV+0fFxMSoU6dOatq0qQGJzIERYyZWu3ZtderUSW3atFHXrl11/Phx9e7dW0uWLNGff/6pffv2GR3RcO+9957mz5+vkJAQu6a0I0eOVNeuXTV27FiDE8IRLV26VOvXr9eiRYsobjwmY8aMOnTokAoVKmR0FIcUEBCgs2fPymq1ys/PL0Hhx+zvEib3ZoTZT6bpZ/PPZcqUSceOHTNVYUySDh06pKlTp+qnn36S9GAa4YABAxKMWjCbb7/9Vjt37lS1atVUo0YNfffddxo/frzu3Lmj9u3b2y0CYib9+/e3fR4fH69FixapZMmSLFL1mJCQEA0cOND00ybx9Jw4cUINGzbU+fPnjY7yXGLEmImNGzfOtmrV2LFj1aFDB3Xv3l0FChQw9Soyj1q0aJHmzZtnNwqhZMmSypUrl9555x0KY7AJCAiwG/105swZeXt7U9x4TPny5XXx4kUKY0mgZ03yWKkpaUn1s7l586bSpUtnQCL8V5QtW1ZLly41OoZDWbp0qQIDA1WyZElNmTJFH374ofr166fmzZsrPj5e3bp1U6ZMmdS8eXOjoz5zj49mebiy6w8//GC33ewjwoODg42OgOfM9evXdf36daNjPLcojJlQbGys3NzcVK5cOds2Ly8vU891T8off/yRaK+fwoULm3KFr8ft2LFDkyZNsr3LXLRoUQ0aNEgvvfSSwcmePQoaKTNv3jx169ZNly5dUvHixRMUDUuWLGlQMsfAiTRS6+HoDYvFohEjRtiNUo2Li9O+fftsF65AYuLi4rRu3Trb3/JixYqpUaNGcnZ2NjiZcSZPnqzJkyerd+/eCgsLU8OGDTV27Fj169dP0oPznWnTppmyMMbCVCkTFRWlgQMH2lYof3ySltlH8XINkbQZM2bYfW21WvX7779ryZIlqlevnkGpnn9MpTQhNzc31ahRQ40aNVKjRo2UPXt2oyM5rIoVK6pixYoJXqB69eqlAwcOaO/evQYlM97Dd1ObNWtm15dk7dq1Cg0NVZs2bQxOCEe0d+9etWnTxm4YuMViofn+Yw4dOmR3kWr2KU2P4mTaXvXq1SU9OC6VK1e2a7Lv4uIiPz8/DRw4UAUKFDAq4n+GGadSnjlzRg0aNNCvv/5qG8n7888/y8fHR19++aVpm6pnzJhRJ06cUN68eSU9+F06ePCg7c2bU6dO6cUXX9TVq1eNjAkHVq9ePUVERKhnz56JrlDeuHFjg5IZj2uI5D183XnIyclJ2bJlU40aNfTee+8pU6ZMBiV7vlEYM6GIiAitX79e69ev186dO1WqVClbkaxEiRJGx3MoO3bsUIMGDZQnTx5VrlxZkrRnzx5dvHhRmzZtMu2FmPSgB8lbb71le/f0oSlTpuiTTz6xXbQCjypatKiKFCmid999N9Hm+76+vgYlcwyXL19Wq1attH37dmXOnFmSFB0drerVq2vFihXKli2bsQENxsl00gIDAzV9+nS5u7sbHeU/y93d3XTN9+vXry+r1apPP/3UtmjMtWvX1K5dOzk5OenLL780OKExPD09tXfvXlux8PGiaXh4uIoXL65bt24ZGdNwTZs2TXTKpMViUbp06ZQ/f361adPGlO0TMmXKpO+//54Ru4ngGgKOiMKYyV2/fl2bNm3S+vXrtXnzZmXJksVWJKtWrZqph9E/9Ntvv2nWrFk6deqUpAcv5u+8845y5sxpcDJjubq66scff1T+/Pnttp85c0bFixfX7du3DUpmPE9Pz789UezUqZMpG/dmyJBBx44dS/C8wQMtW7bUuXPntHjxYhUpUkSSdPLkSXXs2FH58+fX8uXLDU5oLE6mk9a5c2dNnz49wTvJt27dUq9evegdmgJmHDGWIUMG7d27N8Ebo8eOHVPVqlVtK3KbTfny5TV8+HDbqJ6YmBhlypTJ9rd9y5Yt6tGjh37++WcjYxquU6dOWrdunTJnzqyyZctKetBHNTo6WrVr19axY8d0/vx5hYWF2d7MMIuiRYvq008/ZcR3IriGSLlff/1VkpQ7d26Dkzz/kl7eCabg4eGh1q1ba8WKFbpy5Yrmzp2ruLg4BQYGKlu2bPr000+Njmi4nDlzauzYsVqzZo3WrFmjMWPGmL4oJkk+Pj4KCwtLsH3Lli3y8fExIJHjCAoKkpOTkxo0aKCQkBCFhISoQYMGcnJyUo8ePVSwYEF1795dn3zyidFRn7kaNWro2LFjRsdwWJs3b9ZHH31kK4pJD06uZ82apa+++srAZI7h3LlzatiwYYLtjRo1Unh4uAGJHMeiRYv0119/Jdj+119/afHixQYkchzBwcG6cOHC3+5348YNUxXFpAcXqA8XYnrUzZs37ablms3QoUPl6elp+9rd3d3uDa+DBw/qjTfeMCKaQ8mePbvatGmjc+fO2c6Tz549q3bt2ilfvnz66aef1LFjRw0ePNjoqM/ctGnTNGTIEFYQTATXEMmLj4/XqFGj5OHhIV9fX/n6+ipz5swaPXo0ixD9i2i+D5u0adPq1Vdf1auvvqoPP/xQR44c0f37942O9cwdP35cxYsXl5OTk44fP57svmZuFD5gwAD17t1bR48eVZUqVSQ9mNIUGhqq6dOnG5zOWDt37tSYMWPUrVs3u+1z587VN998ozVr1qhkyZKaMWOGunbtalBKYzRs2FD9+vXTiRMnVKJEiQTN9x9dAdaM4uPjExwT6cHrMydD/zuZfvxdZjOfTMfExMhqtcpqterGjRt2K1DGxcVp06ZN8vLyMjCh8davX6+xY8eqWrVq6tKli15//XW5uroaHcshvPbaa3rrrbc0f/58VahQQZK0b98+devWzdSvx02bNk329iFDhth9vWvXLpUrV850z6v58+dr165dcnL631gLJycn9erVS1WqVNG4cePUs2dPU7YeadmypWJjY5UvXz65ubkl+Ntu5kW8uIZI3rBhwzR//nxNmDDBNtJy586dGjlypG7fvq2xY8canPD5xFRKk/v+++81d+5cnT17VqtXr1auXLm0ZMkS5c2bVy+++KLR8Qzh5OSkyMhIeXl5ycnJydYY/HE0CpfWrl2ryZMn26YvFSlSRIMGDTJ1Q1HpQdPeo0ePJjpEvHTp0rp586bOnj2rkiVLmq4/yaMnz4/jd+pBM97o6GgtX77cNjL10qVLatu2rTw9PbV27VqDExpr9uzZ6tu3rzp37pzoyfTbb79tcMJn7+HfqaRYLBaFhIRo2LBhzzCV4zly5IgWLlyo5cuX6/79+2rVqpU6d+6s8uXLGx3NUNHR0erYsaM2btxou3C/f/++GjVqpNDQUHl4eBic8L/BjP3ppAetIxYtWpSgiLphwwZ17NhRf/75p06fPq0KFSrozz//NCilMRYtWpTs7R07dnxGSRwT1xBJy5kzp+bMmZPg92r9+vV65513dOnSJYOSPd8YMWZia9asUfv27dW2bVsdOXJEd+7ckfSg79i4ceO0adMmgxMaIzw83Nbg2uxTc/5O06ZN//ZdVTPKkiWLNm7cmKAP0saNG23NjW/dumXKVWUY9ZS8mTNnqlGjRvLz87ONgLp48aKKFy+upUuXGpzOeN27d1f27Nk1efJkrVq1StKDk+mVK1ea9mR627ZtslqtqlGjhtasWWN7jZEerKTn6+vL9H9JAQEBCggI0OTJk7Vx40YtXLhQVatWVeHChdWlSxd16tTJdEUgq9WqmJgYrVixQpcuXbK7QKUPZOqYdZxB+/bt1aVLFw0dOtRWZD5w4IDGjRunDh06SHqwkFWxYsWMjGmIlBa+JkyYoG7dutkW3DELriGS9scff6hw4cIJthcuXNjUIw3/bYwYM7GAgAD169dPHTp0sGs4e+TIEdWrV0+RkZFGRzTcd999pypVqihNGvsa8v3797V79269/PLLBiVzHIcOHbKdTBcrVowmo5I++eQTde/eXfXr17dNTTlw4IA2bdqkOXPmqEuXLpo8ebL279+vlStXGpwWjsZqtWrLli12C37UqlXL4FRwdBcuXJCPj0+yozIh3b17V2vXrtWCBQu0detWValSRb/99puioqL0ySefqGXLlkZHfGbi4+OVLl06/fjjjypQoIDRcf7TzLhwg/RguvaECRM0c+ZMRUVFSZK8vb3Vq1cvDR48WM7OzoqIiJCTkxPNw5Ng1tGGEtcQSalYsaIqVqyoGTNm2G3v1auXDhw4oL179xqU7PlGYczE3NzcdPLkSfn5+dn9QT937pyKFi3KiiCSnJ2d9fvvvyfoz3Lt2jV5eXmZetrX5cuX1apVK23fvt32Lld0dLSqV6+uFStW2EbdmdWuXbs0c+ZM24pVhQoVsvXcMLsdO3Zo0qRJtpOhokWLatCgQabsQYInw8l00mJjYxUREaG7d+/abTdzT0zpwXPm4VRKV1dXdejQQW+++aZtZNSHH36oMWPG2C7uzaJYsWKaP3++KlWqZHSU/zSzFsYeFRMTI+lBoQcpZ8bnDtcQyduxY4caNGigPHnyqHLlypKkPXv26OLFi9q0aRPny/8S3lY0sezZs+vMmTMJtu/cudNUL87JsVqtifZuuXbtmjJkyGBAIsfRq1cv3bhxQz/++KP++OMP/fHHH/rhhx8UExOj3r17Gx3PcFWrVtXy5ct1+PBhHT58WMuXL6coJmnp0qWqVauW3Nzc1Lt3b/Xu3Vvp06dXzZo1tWzZMqPjOYQdO3aoYcOGyp8/v/Lnz69GjRrp+++/NzqWQ7h8+bJq1Kih8uXL254/ZcuWVc2aNXXlyhWj4xnqypUreu2115QpUyZbsfDRDzMrUaKEKlWqpPDwcM2fP18XL17UhAkT7KYLtm7d2pTPoQkTJmjQoEH64YcfjI6C/zh3d3eKYkgRriGSV61aNf38889q2rSpoqOjFR0drWbNmunnn3+mKPYvYsSYiY0fP15Lly7VggUL9Oqrr2rTpk26cOGC+vXrpxEjRqhXr15GRzRMs2bNJD1ocli3bl27VYbi4uJ0/PhxFSpUSJs3bzYqouE8PDy0ZcuWBI2L9+/fr9q1ays6OtqYYAaJiYmxnRA+fNc0KWY+cSxSpIjeeuutBP3XpkyZok8++cQ2Csisli5dqsDAQDVr1sxuJaJ169YpNDRUbdq0MTihsVq2bKlz585p8eLFKlKkiCTp5MmT6tixo/Lnz6/ly5cbnNA4bdu21YULFzRt2jS98sorWrt2raKiojRmzBhNnjxZDRo0MDqiYUaPHq3OnTsrV65cRkdxOJ6enoqNjdX9+/fl4uKi9OnT291OP5uUMdN0uDJlyigsLEyenp4KCAhIdvGPw4cPP8Nk/01mHDHGNQQcEc33TWzIkCGKj49XzZo1FRsbq5dfflmurq4aOHCgqYtikmwNeK1WqzJlymR3ouji4qJKlSqpa9euRsVzCPHx8QmWnpaktGnTmrLBuqenp23abebMmRM9UXw4AtHMU3DPnTunhg0bJtjeqFEjDR061IBEjmXs2LGaOHGiXeGwd+/emjJlikaPHm36wtjmzZu1ZcsWW1FMejAVd9asWapdu7aByYy3detWrV+/XuXKlZOTk5N8fX316quvyt3dXePHjzd1YcxqtcrT0zPB9r/++ksffPCBgoKCDEjlGKZNm2Z0hOeCmcYZNG7c2PaGcZMmTYwNg/8kriGSt3DhQmXMmFEtWrSw2/7ZZ58pNjbW9Cua/lsYMQbdvXtXZ86c0c2bN1W0aFFlzJjR6EgOIyQkRAMHDjT9tMnENG7cWNHR0Vq+fLltxbNLly6pbdu28vT01Nq1aw1O+Gzt2LFDVatWVZo0abRjx45k961WrdozSuV48ufPr0GDBuntt9+22z5nzhxNnjxZp0+fNiiZY3B1ddWPP/6YYEW4M2fOqHjx4qbv/ZgpUyZ9//33Kl26tN32I0eOqFq1an87WvN55u7uruPHj8vPz0++vr5atmyZqlatqvDwcBUrVkyxsbFGRzQM/ULxpIKDg9W5c2f5+voaHQXPKTOOGOMaInkFCxbU3LlzVb16dbvtO3bs0FtvvWXrX4ynixFjkIuLi4oWLWp0DIcUHBxsdASHNXPmTDVq1Eh+fn7y8fGRJF28eFHFixfX0qVLDU737D1a7DJz4evvDBgwQL1799bRo0dtPdd27dql0NBQTZ8+3eB0xvPx8VFYWFiCwtiWLVtsv2dmVqNGDfXp0yfByXS/fv1Us2ZNg9MZq1ChQvr555/l5+enUqVKae7cufLz89OcOXOUI0cOo+MZKql+oceOHVOWLFkMSIT/ivXr12vs2LGqVq2aunTpotdff92uvQYeuHv3ri5fvpxgtE+ePHkMSvTf8dJLLyWYwvy84xoieREREcqbN2+C7b6+voqIiDAgkTkwYszEbt++rQ8//FDbtm1L9I8ZfQEeWL16tVatWpXoKl9mP0ZWq1VbtmzRqVOnJD3oH1WrVi2DUzmG6Oho7d+/P9HfrQ4dOhiUyjGsXbtWkydPtvUTK1KkiAYNGqTGjRsbnMx4s2fPVt++fdW5c+dEC4ePj7Qzm4sXL6pRo0b68ccfE5xMb9iwQblz5zY4oXGWLl2q+/fvq1OnTjp06JDq1q2rP/74Qy4uLgoNDVXLli2NjvjMeXp6ymKx6Pr163J3d7crjsXFxenmzZvq1q2bZs2aZWBKOLojR47YVjS9f/++WrVqpc6dOyfoj2RGv/zyi7p06aLdu3fbbad1RNL9Zi0Wi1xdXeXi4vKMEzkWriGSlidPHlvx8FHr169Xjx499OuvvxqU7PlGYczE2rZtq2+++UbNmzeXt7d3gndTGS0lzZgxQ8OGDVOnTp308ccfKzAwUGfPntWBAwfUo0cPjR071uiIhrh3757Sp0+vo0ePqnjx4kbHcTgbN25U27ZtdfPmzQQXYxaLhWbGSBaFw+RxMp0ysbGxOnXqlPLkyaOsWbMaHccQixYtktVqVefOnTVt2jRb/1DpwWh5Pz8/Va5c2cCE+C+5d++eNm7cqIULF+rrr79W4cKF1aVLF3Xq1MnuuWUmD1tIDBkyRDly5EhwLVGqVCmDkhnPyckp2YUJcufOrU6dOik4OFhOTk7PMJmxuIb4e4MHD9bKlSu1cOFCvfzyy5IeTKPs3LmzmjdvrkmTJhmc8PlEYczEPDw8tGnTJtvKZ0iocOHCCg4OVuvWre16AAQFBemPP/7QzJkzjY5oGH9/f61du9bUJz1JKViwoOrXr69x48bJzc3N6DgOiWkXCd2/f1/jxo1T586dTT3yKSmcTONJ7dixQ1WqVEm02TOQUnfv3tXatWu1YMECbd26VVWqVNFvv/2mqKgoffLJJ6YclZkhQwYdOnRIhQsXNjqKw1m8eLHtzfUKFSpIerDq4qJFizR8+HBduXJFkyZN0qBBg0y3+BDXEMm7e/eu2rdvr88++0xp0jzofBUfH68OHTpozpw5ph9t+G+hMGZiRYsW1YoVK1SyZEmjozgsNzc3/fTTT/L19ZWXl5e+/fZblSpVSqdPn1alSpV07do1oyMaZv78+fr888+1ZMkSerQ8JkOGDDpx4oSpGqmm1OnTp9W5c2emXSQhY8aM+uGHH+Tn52d0FIfEyXTS4uLiFBoaqrCwsESLzlu3bjUomTFiYmLk7u5u+zw5D/cDEnPo0CHbVEpXV1d16NBBb775pq0X5IcffqgxY8YoKirK4KTPXvny5TV16lS9+OKLRkdxODVr1tTbb7+tN954w277qlWrNHfuXIWFhWnJkiUaO3asbQS0WXANkTKnT5/W0aNHlT59epUoUYJFQP5lNN83scmTJ2vw4MGaM2cOv2hJyJ49u/744w/5+voqT5482rt3r0qVKqXw8HBTLc2dmJkzZ+rMmTPKmTOnfH19E6zcaeb+a3Xq1NHBgwcpjCWiU6dOSpMmjb744otEp12YXc2aNbVjxw4KY0kYNmyYhg4dysl0Ivr06aPQ0FA1aNBAxYsXN/3vlqenp20lysyZMyd6PMxakG/WrFmK9/3888//xSSOr0SJEjp16pRq166t+fPnq2HDhnJ2drbbp3Xr1urTp49BCY31/vvv691339W4ceNUokSJBKMyzVx03r17t+bMmZNge0BAgPbs2SNJevHFF03ZTJ1riJQpUKCAChQokOTt7u7uOnr0KNcbTwmFMRMrV66cbt++LX9/f7m5uSX4Y0YfpAcroG3YsEEBAQEKDAxUv379tHr1ah08eDBVJ5bPoyZNmhgdwWE1aNBAgwYN0smTJxM9UXy8maaZHD16lGkXyahXr56GDBmiEydOqGzZsglOFs383JE4mU7OihUrtGrVKtWvX9/oKA5h69attuLptm3bDE7jWB7th2W1WrV27Vp5eHioXLlykh6MkIqOjjb9eY4kvfHGG+rcubNy5cqV5D5Zs2ZNMELTLB72d3x8VWCzFp0f5ePjo/nz52vChAl22+fPn29bPObatWvy9PQ0Ip6huIZ4Osw+SONpYyqlidWqVUsRERHq0qVLos33O3bsaFAyxxEfH6/4+Hjb/O4VK1Zo9+7dKlCggN5++23meKfA8uXL1ahRowQXsM+z5Jqomv1EkWkXyeO5k7yQkJBkbzfzojE5c+bU9u3bVbBgQaOj4D9k8ODB+uOPPzRnzhzbSKi4uDi98847cnd31wcffGBwQmONGjVKAwcOTNAv9K+//tIHH3ygoKAgg5I5hh07diR7e7Vq1Z5REsezYcMGtWjRQoULF7atYHrw4EGdOnVKq1ev1muvvabZs2fr9OnTmjJlisFpHZMZryFS49H+1/jnKIyZmJubm/bs2UOvFvyrGOaLR/v7HDx4UMOHD2faBfCUTZ48WefOndPMmTNNP41Sko4fP57ifc3cazVbtmzauXOnChUqZLf9559/VpUqVUzdS1WSnJ2dbVNyH3Xt2jV5eXmZ/s2KlHrnnXc0atQo062QGx4ero8//lg///yzJKlQoUJ6++23aZeQQlxDJI/C2NPFVEoTK1y4sP766y+jYzi0hQsXKmPGjGrRooXd9s8++0yxsbGMqksBau9JK1GihDZt2mQbUv+8ery/j9VqZdoF8BQ8PtVt69at+uqrr1SsWLEERWez9YoqXbq0LBbL3/4NMvvrzv3793Xq1KkEhbFTp06Zdnrgox7+bXrcsWPH6HOYCkuXLtXAgQNNVxjLmzevxo8fb3SM/yyuIfAsURgzsQkTJmjAgAEaO3YsIzeSMH78eM2dOzfBdi8vL7311lsUxvCPnD9/Xvfu3TM6xr+O/j6pExYWluTKggsWLDAolXE8PT1TPALKbL0xH+0VJUlNmzY1KInjCQ8PNzrCf0JgYKC6dOmis2fPqkKFCpKkffv2acKECQoMDDQ4nXEevu5YLBYVLFjQ7jUoLi5ON2/eVLdu3QxM+N9ixgJH/vz51a5dO7Vt2zbZBurAk2J0+NNFYczE6tatK4mGmcmJiIhQ3rx5E2z39fU15SoywJN4kh4jZp12ERISolGjRqlcuXKs2vn/pk2bZvv82rVrGjNmjOrUqaPKlStLkvbs2aOvv/5aI0aMMCihcRYuXGh0BIfFatspM2nSJGXPnl2TJ0/W77//LknKkSOHBg0apAEDBhiczjjTpk2T1WpV586dFRISYleEdnFxkZ+fn+01CEhMjx49tGzZMo0aNUply5ZVu3bt1LJlS2XPnt3oaHhOmLHg/G+ix5iJ0TDz7+XJk0czZ85MsBLc+vXr1aNHD/36668GJfvvYP570jg2STNrX4kcOXJo4sSJat++vdFRHNLrr7+u6tWrq2fPnnbbZ86cqS1btmjdunXGBIPD2bBhg+rVq6e0adNqw4YNye5r9tVeH3rYD5IZA/+zY8cOValSJcGsCqSOmc93fvnlF3366adavny5wsPDVb16dbVr104dOnQwOprDM/Pz5lFxcXE6ceKEfH197VYx3blzp8qXLy9XV1cD0z0/KIwByRg8eLBWrlyphQsX6uWXX5b04CSpc+fOat68uSZNmmRwQsfHH7WkcWySZtZj88ILL2j//v3Kly+f0VEcUsaMGXX06FHlz5/fbvuZM2dUunRp3bx506BkxouKitLAgQNt03AfP70z2yhwJycnRUZGysvLi9Ve/8b9+/e1fft2nT17Vm3atFGmTJn022+/yd3dXRkzZjQ63jMXExNjKw4+unhMYigipoxZ/6Y/bu/everevbuOHz9u+tedlDDr86Zv374qUaKEunTpori4OFWrVk27d++Wm5ubvvjiC73yyitGR3wuMZXS5KKjozV//nz99NNPkqRixYqpc+fOCfqWmNXo0aN1/vx51axZU2nSPPh1iY+PV4cOHTRu3DiD0/03+Pr68k4rkEJvvvmmli1bZsppgSnxwgsvaP369QmmeK1fv14vvPCCQakcQ6dOnRQREaERI0YwDVey689HE/mkXbhwQXXr1lVERITu3LmjV199VZkyZdL777+vO3fuaM6cOUZHfOY8PT1tK1E+vnjMQ7QdQWrs379fy5Yt08qVKxUTE5NgUS8kzqzXEKtXr1a7du0kSRs3blR4eLhOnTqlJUuWaNiwYdq1a5fBCZ9PjBgzsYMHD6pOnTpKnz69reHqgQMH9Ndff+mbb75RmTJlDE7oOH755RcdO3ZM6dOnV4kSJehd8v+io6O1evVqnT17VoMGDVKWLFl0+PBheXt7K1euXEbHc3hmfScsJcx0bPr372/7PD4+XosWLVLJkiVVsmTJBCeEU6ZMedbxHEpoaKjefPNN1atXTxUrVpT0oFH45s2b9cknn6hTp07GBjRQpkyZ9P3336t06dJGR8F/SJMmTZQpUybNnz9fL7zwgu11d/v27eratatOnz5tdMRnbseOHapatarSpElD25GnpHv37ho9erSp+oY+PoWyRo0aatu2rZo1a2bKkZhIuXTp0unMmTPKnTu33nrrLbm5uWnatGkKDw9XqVKl/nYkK54MI8ZMrF+/fmrUqJE++eQT22io+/fv680331Tfvn313XffGZzQcRQsWFAFCxY0OoZDOX78uGrVqiUPDw+dP39eXbt2VZYsWfT5558rIiJCixcvNjqiw5s7d668vb2NjgGDHTlyxO7rh4WNH374wYA0jq1Tp04qUqSIZsyYoc8//1ySVKRIEe3cudNWKDMrHx8fGvEmIywsTFOnTrWNkC9SpIj69u2rWrVqGZzMWN9//712794tFxcXu+1+fn66dOmSQamM9Wixi8LX37t9+7aOHz+e6ErKD/v3zZ4924hohipcuLDKly+vHj16qFWrVqY/32OF6ZTz9vbWyZMnlSNHDm3evNn2+xMbGytnZ2eD0z2/KIyZ2MGDB+2KYpKUJk0avfvuuypXrpyByYzVv39/jR49WhkyZLAbyZEYM4/e6N+/vzp16qSJEycqU6ZMtu3169dXmzZtDEzmGA4cOKBt27YleqL48HnDcYIkbdu2zegI/ykVK1bUp59+anQMhzNt2jQNGTJEc+fOlZ+fn9FxHMpHH32kPn36qHnz5urTp4+kB71+6tevr6lTp6pHjx4GJzROfHx8otMBf/31V7u/7WZy/PjxFO9bsmTJfzGJ49u8ebM6dOigq1evJrjN7FNNf/75ZxUoUMDoGA6DFaZTLjAwUG+88YatLcLDN3D27dunwoULG5zu+cVUShPz9vbWkiVLVLt2bbvtX3/9tTp06KCoqCiDkhmrevXqWrt2rTJnzqxXXnklyXc3LBaLtm7d+ozTOQ4PDw8dPnxY+fLls5v2duHCBRUqVEi3b982OqJhxo0bp+HDh6tQoULy9va2ew6Z/XmTUmacdiFJnTt31vTp0xNckN66dUu9evXSggULDErmOM6ePauFCxfq3LlzmjZtmry8vPTVV18pT548KlasmNHxDOPp6anY2Fjdv39fbm5uCabhmvkd+Ny5c2vIkCEJVjOdNWuWxo0bZ9qRUZLUsmVLeXh46OOPP1amTJl0/PhxZcuWTY0bN1aePHm0cOFCoyM+c05OTrJYLH87AtPshR9JKlCggGrXrq2goCDTj4hCyrHC9N9bvXq1Ll68qBYtWih37tySpEWLFilz5sxq3LixwemeTxTGTKx3795au3atJk2apCpVqkiSdu3apUGDBun111+3q+ybyfHjx1W8ePFkV7GC5OXlpa+//loBAQF2hbFvv/1WnTt31sWLF42OaBhvb2+9//77pu53lJzvv/9ec+fO1dmzZ7V69WrlypVLS5YsUd68efXiiy8aHc9Qzs7OtqbPj7p69aqyZ8+u+/fvG5TMMezYsUP16tVT1apV9d133+mnn36Sv7+/JkyYoIMHD2r16tVGRzTMokWLkr29Y8eOzyiJ40lqNdPTp08rICDA1KuZXrx4UXXr1pXVatXp06dVrlw5nT59WlmzZtV3332X4LXIDC5cuJDifc3ec9bd3V1HjhxhJeVExMXFaerUqVq1apUiIiJ09+5du9vN/GYFK0zDETGV0sQmTZoki8WiDh062C620qZNq+7du2vChAkGpzNOQECA7cLU399fBw4cMP1qZ4lp1KiRRo0apVWrVkl68M5pRESEBg8erNdff93gdMZycnJS1apVjY7hkNasWaP27durbdu2OnLkiO7cuSNJun79usaNG6dNmzYZnNAYMTExslqtslqtunHjhtKlS2e7LS4uTps2bTLlBerjhgwZojFjxqh///52o+pq1KihmTNnGpjMeGYufP2dRo0aae3atRo0aJDd9vXr1+u1114zKJVj8PHx0bFjx7Ry5UodO3ZMN2/eVJcuXdS2bVulT5/e6HiGMHuxKzWaN2+u7du3UxhLREhIiObNm6cBAwZo+PDhGjZsmM6fP69169YpKCjI6HiGYoXp5M2YMSPR7RaLRenSpVP+/Pn18ssv02/sKWPEGBQbG6uzZ89KkvLlyyc3NzeDExnrhRde0KZNm1SxYkU5OTkpKipK2bJlMzqWw7l+/bqaN2+ugwcP6saNG8qZM6ciIyNVuXJlbdq0SRkyZDA6omEmTpyo3377zbSjLpMTEBCgfv36qUOHDnYjDY8cOaJ69eopMjLS6IiGeDh1JykWi0UhISEaNmzYM0zleDJmzKgTJ04ob968ds+f8+fPq3Dhwqaewv2o27dvJxid4O7ublAaYzx6YRETE6NJkyapatWqtn42e/fu1a5du2wXrWZ07949FS5cWF988YWKFClidByHsWHDBtWrV09p06bVhg0bkt33YXN5s4qNjVWLFi2ULVs2lShRIsEU7t69exuUzHj58uXTjBkz1KBBA2XKlElHjx61bdu7d6+WLVtmdETDsMJ08vLmzasrV64oNjZWnp6ekqQ///xTbm5uypgxoy5fvix/f39t27ZNPj4+Bqd9flAYAx7z1ltvafHixcqRI4ciIiKUO3fuJCvy586de8bpHM+uXbts7zKXKVPG9Ct8SQ+aGTdo0EC//PKLihYtmuBE8eFqembk5uamkydPys/Pz66wce7cORUtWtS0hY0dO3bIarWqRo0aWrNmjbJkyWK7zcXFRb6+vsqZM6eBCR1D7ty5tWrVKlWpUsXu+bN27VoNHDjQ9iaPGd26dUuDBw/WqlWrdO3atQS3m60XUt68eVO0n8ViMfXf8ly5cmnLli0Uxh7h5OSkyMhIeXl5JdtWgx5j0vz589WtWzelS5dOL7zwQoKeqmb+3cqQIYN++ukn5cmTRzly5NCXX36pMmXK6Ny5cwoICND169eNjmioffv2acaMGXYrBffu3dv0K0xL0vLly/Xxxx9r3rx5ttGYZ86c0dtvv6233npLVatWVatWrZQ9e3ZTt5B42phKaWK3bt3ShAkTFBYWlujKeWb9Y/bxxx+rWbNmOnPmjHr37q2uXbuadmWm5CxevFgtW7ZU1apV7aYN3r17VytWrFCHDh0MTGes3r17a9u2bapevXqCE0Wzy549u86cOZNg1bydO3fK39/fmFAOoFq1apKk8PBw+fj40OMwCa1atdLgwYP12WefyWKxKD4+Xrt27dLAgQNN/ZojSe+++662bdum2bNnq3379po1a5YuXbqkuXPnmrI9Qnh4uNER/hN69Oih999/X/PmzbNbpdzMHj0ffvzcGPaGDRumkJAQDRkyhL9bj8mdO7d+//135cmTR/ny5dM333yjMmXK6MCBA3J1dTU6nuFYYTppw4cP15o1a+ymKOfPn1+TJk3S66+/rnPnzmnixImmb13ztDFizMRat26tHTt2qH379rblYB/1cElzMwsMDNSMGTMojCUiqSbh165dk5eXl6nfRc2UKZNWrFihBg0aGB3F4YwfP15Lly7VggUL9Oqrr2rTpk26cOGC+vXrpxEjRqhXr15GR3QIsbGxiTbrLVmypEGJHMPdu3fVo0cPhYaGKi4uTmnSpNH9+/fVtm1bhYaGmrrfRp48ebR48WK98sorcnd31+HDh5U/f34tWbJEy5cvN23/PiSvadOmCgsLU8aMGVWiRIkEbRDMPMIZfy9Lliw6cOAAPcYSMWTIELm7u2vo0KFauXKl2rVrJz8/P0VERKhfv36mfMPiUawwnTQ3Nzd99913KleunN32AwcOqFq1aoqNjdX58+dVvHhxFip4iiiMmVjmzJn15Zdf0iQcTySp/mvHjh1T9erVTb3ajq+vr77++msVLlzY6CgOx2q1aty4cRo/frxiY2MlSa6urho4cKBGjx5tcDrjXblyRYGBgfrqq68Svd3MBedHXbx4USdOnNCtW7cUEBCQYGUrM8qYMaNOnjypPHnyKHfu3Pr8889VoUIFhYeHq0SJEqY/ef7111+1YcOGRAvOU6ZMMSiV8QIDA5O9feHChc8oieMKCwvT1KlT7aZ89e3bl9YRkvr166ds2bJp6NChRkdxeHv37tXu3btVoEABNWzY0Og4hmKF6eQ1aNBAkZGRmjdvngICAiRJR44cUdeuXZU9e3Z98cUX2rhxo4YOHaoTJ04YnPb5wZhpE/P09LTrY4MHmjVrptDQULm7u6tZs2bJ7mvGd1IDAgJksVhksVhUs2ZNu6kXcXFxCg8PV926dQ1MaLyRI0cqODhYCxcuNP1iFo+Ki4vTrl271KNHDw0aNEhnzpzRzZs3VbRoUWXMmNHoeA6hb9++io6O1r59+/TKK69o7dq1ioqK0pgxYzR58mSj4zmE+fPna+rUqTp9+rQkqUCBAurbt6/efPNNg5MZy9/fX+Hh4cqTJ48KFy6sVatWqUKFCtq4caMyZ85sdDxDhYWFqVGjRvL399epU6dUvHhxnT9/XlarVWXKlDE6nqEofCXvo48+Up8+fdS8eXPbTIq9e/eqfv36mjp1qnr06GFwQmPFxcVp4sSJ+vrrr1WyZMkEPVXNXHR+XKVKlVSpUqUE2xs0aKB58+YpR44cBqQyBitMJ2/+/Plq3769ypYta/udun//vmrWrKn58+dLevBmGOeFTxeFMRMbPXq0goKCtGjRIi7eH+Hh4WGbVurh4WFwGsfTpEkTSdLRo0dVp04du4KGi4uL/Pz8TD/nfcaMGTp79qy8vb3l5+eX4ETx8OHDBiUzlrOzs2rXrq2ffvpJmTNnVtGiRY2O5HC2bt2q9evXq1y5cnJycpKvr69effVVubu7a/z48aafnhsUFKQpU6aoV69ettUF9+zZo379+ikiIkKjRo0yOKFxAgMDdezYMVWrVk1DhgxRw4YNNXPmTN27d8/0F6fvvfeeBg4cqJCQEGXKlElr1qyRl5eX2rZta/o3cpC8cePGaerUqerZs6dtW+/evVW1alWNGzfO9IWxEydO2Ea0/PDDD3a30V81Zb777jv99ddfRsd4pk6cOJHoqpxeXl66evWqAYkcS/bs2fXtt9/q1KlT+uWXXyRJhQoVUqFChWz7VK9e3ah4zy2mUprMw9E+D505c0ZWq5WL90RYrVZdvHhR2bJlU/r06Y2O43AWLVqkli1bKl26dEZHcTghISHJ3h4cHPyMkjiecuXK6f3331fNmjWNjuKQ3N3ddfz4cfn5+cnX11fLli1T1apVFR4ermLFitmmn5pVtmzZNGPGDLVu3dpu+/Lly9WrVy9OqB9x4cIFHTp0SPnz5zd9b7pMmTLp6NGjypcvnzw9PbVz504VK1ZMx44dU+PGjXX+/HmjIxpq9erVWrVqVaLTTM1+LpgxY0YdPXo0wXTt06dPKyAgwPRTlPHPPbrCslmwwjQcESPGTObhaB/8PavVqvz58+vHH39UgQIFjI7jcDp27Gh0BIdl5sLX3xkzZoytn1jZsmUTNHp2d3c3KJljKFSokH7++Wf5+fmpVKlSmjt3rvz8/DRnzhxTTbNIyr179xI0o5WksmXL6v79+wYkcly+vr7y9fU1OoZDyJAhg63gkyNHDp09e9bW3NnsxdQZM2Zo2LBh6tSpk9avX6/AwECdPXtWBw4cMP1oKElq1KiR1q5dq0GDBtltX79+vV577TWDUgH/bawwnby4uDiFhoYqLCxMly9fTrA67tatWw1K9nyjMGYyXLCnnJOTkwoUKKBr165RGEtEXFycpk6dmuS7zGZuvo+k1a9fX9KDi41HR69arVZZLBbTN5fv06ePfv/9d0kPXq/r1q2rpUuXysXFRYsWLTI4nfHat2+v2bNnJ5ga+PHHH6tt27YGpTLW4sWLU7SfmS82KlWqpJ07d6pIkSKqX7++BgwYoBMnTujzzz9PtOePmXz00Uf6+OOP1bp1a4WGhurdd9+Vv7+/goKCTPt3fMaMGbbPixYtqrFjx2r79u226dt79+7Vrl27NGDAAKMiOozq1asnO2WSC3gk5uE0ZB8fH8XFxalo0aKKi4tTmzZtNHz4cKPjGa5Pnz4KDQ1VgwYNVLx4caYlPyNMpQSSsXHjRk2cOFGzZ89W8eLFjY7jUIKCgjRv3jwNGDBAw4cP17Bhw3T+/HmtW7dOQUFB6t27t9ERDePk5JTsHzEzF3927NiR7O3VqlV7Rkkcn9Vq1V9//aVTp04pT548ypo1q9GRDNG/f3/b5/fv31doaKjy5MljK2js27dPERER6tChgz788EOjYhrGyclJGTNmVJo0aZTUKZ3FYjFtkUOSzp07p5s3b6pkyZK6deuWBgwYYFsdbsqUKaYeWefm5qaffvpJvr6+8vLy0rfffqtSpUrp9OnTqlSpkq5du2Z0xGcub968KdrPYrHo3Llz/3Iax9avXz+7r+/du6ejR4/qhx9+UMeOHTV9+nSDkv13mG0q5aOtaq5evaoTJ07o5s2bCggIYCDC/8uaNasWL15sezMZzwaFMSAZnp6eio2N1f379+Xi4pKg15iZLzTy5cunGTNmqEGDBnb9W2bMmKG9e/cm2lTTLNavX2/39b1793TkyBEtWrRIISEh6tKli0HJ8F/Aqov2Utpg1mKxmHJ0QrFixRQVFaV27dqpc+fOpu8nhtTx9/fXmjVrFBAQoHLlyqlr1656++239c0336hVq1amPs/Bkxs5cqRu3rypSZMmGR3F4ZmtMBYfH6906dLRqiYZOXPm1Pbt21WwYEGjo5gKUymBZEybNs3oCA4rMjJSJUqUkPSgOe3169clSa+99ppGjBhhZDTDNW7cOMG25s2bq1ixYlq5cqXpCmPHjx9X8eLF5eTkpOPHjye7r9kv6ll1MaFt27YZHcGh/fjjj9q3b58WLFigl19+Wfnz51eXLl3Utm1b0/fsw9+rUaOGNmzYoICAAAUGBqpfv35avXq1Dh48qGbNmhkdD/9R7dq1U4UKFSiMpcDQoUOVJUsWo2M8M7Sq+XsDBgzQ9OnTNXPmTKZRPkOMGAPwRAoVKqTFixerYsWKevHFF/Xaa69pyJAhWrlypXr16qXLly8bHdHhnDt3TiVLljTdKlZOTk6KjIyUl5eXbZppYn966DHGqov4Z/766y999tlnWrhwofbv368mTZpowYIFcnV1NTraM+fp6ZniCwozj4qKj49XfHy80qR58F75ihUrbNNM3377bbm4uBic0Hi//vqrNmzYkGg/1cf7HeKBJUuWaPDgwfrtt9+MjmKYRYsWKWvWrGrQoIEk6d1339XHH3+sokWLavny5aaewk2rmuQ1bdpU27ZtU5YsWVSsWDGlTZvW7vbPP//coGTPN0aMQXfv3lV4eLjy5ctnOzHC/5w9e1YLFy7U2bNnNX36dHl5eemrr75Snjx5bKtamVHTpk0VFhamihUrqlevXmrXrp3mz5+viIiIBD0n8OCCdcaMGcqVK5fRUZ658PBwZcuWzfY5ksaqi/gn0qdPrw4dOsjPz0/BwcFasWKFZs6cacrCGCO+U8bJyUlOTk62r1u1aqVWrVoZmMixhIWFqVGjRvL399epU6dUvHhxnT9/XlarVWXKlDE6nuEeH1VotVr1+++/6+DBg6afPTBu3DjNnj1b0oOR37NmzdLUqVP1xRdfqF+/fqYubnTo0EGxsbEqVaoUrWoSkTlzZjVt2tToGKbDiDETi42NVa9evWwrnf3yyy/y9/dXr169lCtXLg0ZMsTghMbbsWOH6tWrp6pVq+q7777TTz/9JH9/f02YMEEHDx7U6tWrjY7oMPbu3Wt7l7lhw4ZGxzHU4yMVrFarbty4ITc3Ny1dulSNGjUyMJ2xvvvuO1WpUiVBEf7+/fvavXu3Xn75ZYOSOYZevXopbdq0CUYhDBw4UH/99ZdmzZplUDI4ukuXLmnRokVauHChbt26Zes5VrhwYaOjwcFFR0dr//79unz5suLj4+1uM/NqppJUoUIF1atXTyEhIbZeUF5eXmrbtq3q1q2r7t27Gx3RUIGBgXZfOzk5KVu2bKpRo4Zq165tUCrH4ObmZls8Z/Dgwfr999+1ePFi/fjjj3rllVd05coVoyMa5u9W2e7YseMzSgL8D4UxE+vTp4927dqladOmqW7dujp+/Lj8/f21fv16jRw5UkeOHDE6ouEqV66sFi1aqH///nbNMffv369mzZrp119/NTqiYW7fvq106dIZHcMhPf4H/+GJYsWKFeXp6WlQKsfg7Oys33//XV5eXnbbr127Ji8vL1NOpWTVRfwTq1at0sKFC7Vjxw7VqVNHgYGBatCggZydnY2OZqiYmBhbj7WYmJhk9zVzL7aNGzeqbdu2unnzptzd3e3e1DH7aqaS7BYX8vT01M6dO1WsWDEdO3ZMjRs31vnz542OCAfl5eWlr7/+WgEBAQoICFD//v3Vvn17nT17VqVKlTJdWw3A0TFvzsTWrVunlStXqlKlSnYnQsWKFdPZs2cNTOY4Tpw4kejqil5eXqbv9ePl5aWmTZuqXbt2qlmzpt1UDLPjna6kWa3WRPv+XLt2TRkyZDAgkfEefxOibNmykmR7Hc6aNauyZs2qH3/88Zlng+Nr1aqV8uTJo379+snb21vnz59PdGRh7969DUhnHE9PT1sRPnPmzIm+7jx8PTJjQf6hAQMGqHPnzho3bpzc3NyMjuNwMmTIYOsrliNHDp09e9bWRsPs54GPu3nzZoIRh2YuOr/66qt68803FRAQoF9++UX169eX9GDBFD8/P2PDOZDbt28n6N1nxudNmTJlFBYWJk9PTwUEBCTbI/Pw4cPPMJl5UBgzsStXriQYtSFJt27dYgWM/5c5c2b9/vvvyps3r932I0eOmLJX1KMWLVqkZcuWqXHjxvLw8FDLli3Vrl27RPsjmdHt27d1/PjxRKemmHEq5cM+JBaLRZ06dbLreRQXF6fjx4+rSpUqRsUzFKsu4p/IkyePLBZLom/iPGSxWExXGNu6dattpTd+x5J26dIl9e7dm6JYEipVqqSdO3eqSJEiql+/vgYMGKATJ07o888/t43qNbPw8HD17NlT27dv1+3bt23bKTpLs2bN0vDhw3Xx4kWtWbNGL7zwgiTp0KFDCRbYMZtbt25p8ODBWrVqla5du5bgdjM+bxo3bmw7N27cuDHX4gagMGZi5cqV05dffqlevXpJku0XcN68eapcubKR0RxGq1atNHjwYH322WeyWCyKj4/Xrl27NHDgQNP33WjatKmaNm2qGzduaPXq1Vq+fLkqVaokf39/tWvXTkFBQUZHNMzmzZvVvn37RP/Ym/VE0cPDQ9KDk+VMmTLZNVp1cXFRpUqV1LVrV6PiAf9ZTOVKXLVq1RL9HPbq1KmjgwcPyt/f3+goDmnKlCm2KW8hISG6efOmVq5cqQIFCrAipaR27drJarVqwYIF8vb25mL+ETExMZoxY0aCGRUjR47UxYsXDUrlGN59911t27ZNs2fPVvv27TVr1ixdunRJc+fO1YQJE4yOZ4jg4GDb5yNHjjQuiInRY8zEdu7cqXr16qldu3YKDQ3V22+/rZMnT2r37t3asWOHbTqPmd29e1c9evRQaGio4uLilCZNGsXFxalNmzYKDQ01fQ+Xx508eVJt27bV8ePHTVn8eahAgQKqXbu2goKC5O3tbXQchxISEqKBAweadtokYLQSJUpo06ZN8vHxMTrKMxcbG6uIiIgE03ZKlixpUCJjbNiwwfb5lStXNGrUKAUGBqpEiRJKmzat3b5mHOGMlMuYMaMOHTqkQoUKGR3F4dBTNWl58uTR4sWL9corr8jd3V2HDx9W/vz5tWTJEi1fvlybNm0yOqKh/P39deDAAdsow4eio6NVpkwZnTt3zqBkzzcKYyZ37tw5jR8/XseOHdPNmzdVpkwZDR48WCVKlDA6mkOJiIjQDz/8oJs3byogIEAFChQwOpLDuH37tjZs2KBly5Zp8+bN8vb2VuvWrU37jo/0oDfCkSNHlC9fPqOjAICdRxeSMYsrV64oMDBQX331VaK3m+0CNaU9Qc06whkpV716dQ0bNky1atUyOorDcXJyUmRkZILC2IULF1S0aFHdunXLoGTGy5gxo06ePKk8efIod+7c+vzzz1WhQgWFh4erRIkSpl+YIKnnTlRUlHx8fBK8uYOng6mUJnXv3j29/fbbGjFihD755BOj4zisnTt36sUXX1SePHmUJ08eo+M4lK+//lrLli3TunXrlCZNGjVv3lzffPONXn75ZaOjGa558+bavn07hbFEREVFaeDAgQoLC9Ply5f1+HszXIQBeNr69u2r6Oho7du3T6+88orWrl2rqKgojRkzRpMnTzY63jP3eN9L2PP09EzxlECzr9o5b948devWTZcuXVLx4sUTjDg022hM6X8rTVssFgUFBdn174uLi9O+fftUunRpg9I5Bn9/f4WHhytPnjwqXLiwVq1apQoVKmjjxo3KnDmz0fEM8+ho3q+//trWhkR68NwJCwtL0PcaTw8jxkzMw8NDR48e5RcsGS4uLsqVK5dat26tdu3aqWjRokZHchhubm567bXX1LZtW9WvXz/ByZCZxcbGqkWLFsqWLVuiU1PM1gT7UfXq1VNERIR69uypHDlyJLj4aNy4sUHJAHMw44ixHDlyaP369apQoYLc3d118OBBFSxYUBs2bNDEiRO1c+dOoyPCgSxatCjF+5p9Feq9e/eqTZs2dr0OLRaLqZvvV69eXZK0Y8cOVa5cWS4uLrbbXFxc5Ofnp4EDB5p69snUqVPl7Oys3r17a8uWLWrYsKGsVqvu3bunKVOmqE+fPkZHNMTD0bwPf4celTZtWvn5+Wny5Ml67bXXjIj33KMwZmIdO3ZU6dKl1a9fP6OjOKyrV69qxYoVWr58ufbs2aOSJUuqbdu2at26tXLnzm10PEPduHFDmTJlMjqGQ5o/f766deumdOnS6YUXXrAr/lgsFlP3BsiUKZO+//57079bChjFjIUxd3d3HT9+XH5+fvL19dWyZctUtWpVhYeHq1ixYoqNjTU64jO3Z88eXbt2ze4Ca/HixQoODtatW7fUpEkTffjhh3YrCAOPK1q0qIoUKaJ333030eb7vr6+BiUzXmBgoKZPny53d3ejozi8Cxcu6NChQ8qfP78pRxk+Lm/evDpw4ICyZs1qdBRToTBmYg+nENSsWVNly5ZN0AzbzKNaEhMeHq5ly5Zp+fLlOnXqlF5++WVt3brV6FjPVExMjO0PfExMTLL7mvlEIHv27Ordu7eGDBmS4l4uZlG0aFF9+umnCggIMDoKYEpmLIyVL19eY8aMUZ06ddSoUSNlzpxZ48eP14wZM7R69WqdPXvW6IjPXL169fTKK69o8ODBkqQTJ06oTJky6tSpk4oUKaIPPvhAb7/9tilXR+NcJ+UyZMigY8eOKX/+/EZHAZ5r0dHRpp5m+ixQGDOx5KZQmn1US1Li4uL01VdfacSIEaZcefHRFXacnJwS7cFh5uHzD2XJkkUHDhygx1givvnmG02ePFlz586Vn5+f0XEA0zFjYWzp0qW6f/++OnXqpEOHDqlu3br6448/5OLiotDQULVs2dLoiM9cjhw5tHHjRpUrV06SNGzYMO3YscM2rfSzzz5TcHCwTp48aWRMQ3Cuk3INGzZUp06d9PrrrxsdxeHcunVLEyZMsPVUfby3n9mvs8LCwpI8NgsWLDAolWN4//335efnZ/vb1KJFC61Zs0Y5cuTQpk2bVKpUKYMTPp9ovm9i4eHhRkf4z9i1a5c+/fRTrV69Wrdv31bjxo01fvx4o2M9c1u3blWWLFkkSdu2bTM4jePq2LGjVq5cqaFDhxodxeG0bNlSsbGxypcvn9zc3BL0XzN7I2Pg3zZ37lz9X3t3H59z/f///34Ma0425mRCmJMhzElEiEJRVk5DzotOqEzm9J2TrNBSiKiR8+9HTt5IKmnORQlZTnIy50MjpqVhYfb7w2/H2+HYZg6257G9btfLpUs7Xsf+uF92wV7H4/l83Z9FixY1HSNTde3a1f51rVq1dOLECR04cEClSpWy7KMqf/31l8Ofg40bN+rZZ5+1v3700Ud18uRJE9GM414n/Z5//nn1799fe/bsSbFTtWXLloaSmffKK69o48aN6tatW4qdqlYWGhqq9957T7Vr1+Znk4Lw8HDNnz9fkrR69WqtWbNGq1at0uLFizVo0CBFREQYTpg9sWMMSMN//vMfLVy4UH/88YeefvppdenSRa1atXI4YcaKrl+/rrFjx6pnz56W71pLSXBwsObNm6fq1aurWrVqTjeKEyZMMJTMvDuVGlu9yBi4F6zApyz5hGn8T+nSpfX//t//U6NGjXT16lUVKFBA33zzjZo2bSrp5qOVTzzxBIsVSFNadRFW31FXoEABfffdd2rQoIHpKG6nWLFiGjdunLp162Y6ilvKnTu3oqKiVLJkSfXr108JCQmaNm2aoqKiVLduXf3111+mI2ZL7BizmJCQEL3//vvKmzev/Tjh1Fj5w3uyTZs2adCgQerQoYNlV5VTkjNnTn300Ufq3r276Shuac+ePfYOrb179zq8Z/VVMQZfQMZgBT51TZo04YTp27Ro0UJDhw7Vhx9+qOXLlytPnjxq2LCh/f3du3dTB3CLy5cvKzo6WlevXnW4bvWi8NsH8PgfX19f+85DOLp69arq169vOobb8vX11cmTJ1WyZEmtWrVKo0ePlnTzEW4rD5szGoMxi4mMjNS1a9fsXyNtW7ZsSdf3BQUFacaMGSpWrFgGJ3IfTZo00caNG+mJSgGPXqTtyJEjmj17to4cOaJJkybJz89P33//vUqVKqUqVaqYjgdkSeHh4ZozZw4r8Cn4448/7CdMh4WFccK0pPfff19t27bVE088oXz58mnu3Lny9PS0vz9r1iw1a9bMYEL3cO7cOb388sv6/vvvU3yfD6lIzfvvv6+RI0dq7ty5ln/S5HavvPKKvvzyS40YMcJ0FLfUtm1bde7cWQEBAYqNjbU/5h4ZGclBFxmIRymB+8CKZcbh4eEKDQ1Vly5dUjzV1Mq9Ekhdco9NgwYNtGnTJu3fv19ly5ZVWFiYduzYoSVLlpiOCGRJhQoV0rZt29jlcwecMO3o77//Vr58+ZQjRw6H6xcuXFC+fPnsw7JTp06pePHiljtpuUuXLjpx4oQ++eQTPfnkk/rqq6909uxZ+8nuQUFBpiNmusmTJ+u1116Tl5eXJk+enOb3Wu2E+5o1azrs1j18+LCSkpLk7+/vVKuxc+fOzI5n1K1PKt24cUNz585VtWrVqBxJwbVr1zRp0iSdPHlSL730kv0plIkTJ8rb21uvvPKK4YTZE4MxC+vZs6cmTZokb29vh+uXLl1S3759Ld1HcresOBijV8JR27ZtNWfOHPn4+Kht27Zpfu+yZcsyKZX7qVevntq3b6+QkBCHvzfbtm1T27ZtderUKdMRgSxpyJAhypcvHyvw6WD1E6Zd4ePjo99++81S9znSzS6kr7/+WnXq1JGPj4927NihChUqaMWKFRo3bpz9FE8rKVOmjHbs2KFChQpxwv1tQkND0/297777bgYmcT+NGzdO1/fZbDZLL1bAHB6ltLC5c+cqLCzMaTB25coVzZs3j8EY0kSvhKP8+fPbVwnz589vOI372rNnj7788kun635+fjp//ryBREDWdfsK/PTp07VmzRpW4FPBCdOus+o6+qVLl+Tn5yfpZu/PuXPnVKFCBQUGBlpux0+yW0+154R7R1Ybdt0NakbStmLFCj377LPKlSuXVqxYkeb38lROxmAwZkEXL15UUlKSkpKS9M8//8jLy8v+XmJiolauXGm/CQCQPrNnz07xazgqUKCAYmJinFaZIyMjVaJECUOpgKzp9q7QGjVqSOLQj9slnzB9+vRpNWvWTJMmTeKEaaRLxYoVdfDgQfn7+6t69eqaNm2a/P39FR4ebqleWSAjXbx4UevWrVOlSpVUqVIl03GMaN26tc6cOSM/Pz+1bt061e+z4lM5mYXBmAUVKFBANptNNptNFSpUcHrfZrPd1VZgWNfatWs1ceJE7d+/X5L08MMP6+2339ZTTz1lOBnc1YsvvqghQ4bov//9r2w2m27cuKEtW7Zo4MCBnHIK3CVW4NOHE6bhqn79+ikmJkbSzd1AzzzzjObPny9PT0/NmTPHbDg3kJiYqDlz5mjt2rX6888/nZ4msPIjcb6+vikuSthsNnl5eal8+fJ66aWX9PLLLxtIZ1aHDh3UqFEjvfXWW7py5Ypq166t48ePKykpSQsXLlS7du1MR8x0t/7d4akcMxiMWdD69euVlJSkJk2aaOnSpQ5HCXt6eqp06dIqXry4wYTICj777DP169dPL7zwgvr16ydJ2rp1q1q0aKGJEyfqzTffNJzQnLNnz2rgwIH2G8XbH0Gx8krP2LFj9eabb6pkyZJKTExU5cqVlZiYqM6dO2v48OGm4wHIhpJPmN63b5927Nihq1evOrzPYylITdeuXe1f16pVSydOnNCBAwdUqlQphqy6OTicM2eOgoKCVLVqVcvvTr3VyJEjNWbMGD377LOqU6eOJGnbtm1atWqV3nzzTR07dkx9+vTR9evX9eqrrxpOm7k2bdqkYcOGSZK++uorJSUlKS4uTnPnztXo0aMtORiDeZTvW9iJEydUsmRJy50wdDc2bdqk+vXrK2dOxxny9evX9dNPP6lRo0aSpA8++EB9+vRRgQIFDKQ046GHHtLQoUP11ltvOVyfOnWqxo4dq9OnTxtKZt6zzz6r6OhovfXWWypWrJjTjWKrVq0MJXMfJ0+e1J49exQfH6+aNWsqICDAdCQgS2vTps0ddyd07txZFStWNJDOrGPHjqlNmzbavXu3bDabfbEi+edl5cWK9LJq+f7mzZv1+OOPm47htgoXLqx58+apRYsWpqO4nXbt2unpp59W7969Ha5PmzZNERERWrp0qT799FNNnz5de/bsMZTSjNy5cysqKkolS5ZU9+7dVbx4cYWFhSk6OlqVK1dWfHy86YiZ7k4nvN7Kaqe9ZhYGYxYXFxenbdu2pbj9mceapBw5cigmJsapcy02NlZ+fn6WvpnOly+ffvvtN5UvX97h+qFDh1SzZk1L/lJL5u3trR9//NHe94O7Z9UPYYCrXnrpJS1fvlwFChRQrVq1JEk7d+5UXFycmjVrpl27dun48eNau3atGjRoYDht5nr++eeVI0cOzZgxQ2XKlNG2bdsUGxurAQMG6OOPP1bDhg1NR3R7Vjx9W7r5JEWJEiXUqVMnde3aVZUrVzYdya0UL15cGzZsSLGaxepSu08+fPiwatSoofj4eB05ckTVqlXTpUuXDKU0o0KFCho9erSCgoJUpkwZLVy4UE2aNNGuXbvUtGlTSx7GlNYJr7ey4mmvmYVHKS3sm2++UZcuXRQfHy8fHx+HlWabzcZgTDdPYUppBT42NlZ58+Y1kMh9tGzZUl999ZUGDRrkcP3rr7/Wc889ZyiVeyhZsqRlT/C6X/j5AXfnwQcfVOfOnTVlyhT7TvAbN26oX79+8vb21sKFC9W7d28NGTJEmzdvNpw2c/38889at26dChcuLA8PD3l4eOjxxx/XBx98oODgYKdDDOBs3759lqzZ+OOPP7Rw4UItWLBAYWFhqlatmrp06aJOnTrpoYceMh3PuAEDBmjSpEmaMmUKj1HepmDBgvrmm2/Uv39/h+vffPONvcbm0qVL8vb2NhHPqLfffltdunRRvnz5VLp0aT355JOSbj6pExgYaDacIZzwah47xiysQoUKatGihcaOHcvJTLdp27atpJtDnmeeeUYPPPCA/b3ExETt3r1bFStW1KpVq0xFNG706NH6+OOP1aBBA9WrV0/SzY6xLVu2aMCAAfLx8bF/r9W2/EZERGj8+PH206tw96y6OwFwVZEiRbRlyxannRtRUVGqX7++zp8/rz179qhhw4aKi4szE9IQX19f7dy5U2XKlFG5cuU0Y8YMNW7cWEeOHFFgYKAuX75sOqIxCQkJ+vTTT7V+/foUnx7YuXOnoWTu59ixY/ryyy+1YMECHThwQI0aNbJ0ubx08xHu9evXq2DBgqpSpYpy5crl8P6yZcsMJTPviy++UJ8+fdSiRQt7x9j27du1cuVKhYeHq1evXho/fry2bdumRYsWGU6b+X799VdFR0fr6aefVr58+SRJ3333nQoUKGC5Xc2u4umK+4sdYxZ2+vRpBQcHMxRLQf78+SXd3LXi7e2t3Llz29/z9PTUY489ZrmizNvNnDlTvr6+2rdvn/bt22e/XqBAAc2cOdP+2mazWW4w1rFjR12+fFnlypVTnjx5nG4UL1y4YCgZgOzq+vXrOnDggNNg7MCBA/bH/r28vCy5q6Nq1aratWuXypQpo7p162rcuHHy9PTU9OnTLf+BolevXoqIiNALL7ygOnXqWPLPR3qVKVNGQ4cOVfXq1TVixAht3LjRdCTjChQooDZt2piO4ZZeffVVVa5cWVOmTLEPCCtWrKiNGzeqfv36km7uuLOqWrVq2R/7TxYUFOTwmsFP2tjfdH8xGLOw5s2ba8eOHfxjk4LZs2dLkvz9/TVw4EDLPzaZErb8pu6TTz4xHQGAxXTr1k29evXSO++8o0cffVTSzd0JY8eOtVcjbNy4UVWqVDEZ04jhw4fbO3zee+89Pffcc2rYsKEKFSpkyZ0at/r222+1cuVKdmjcwZYtWzR//nwtWbJECQkJatWqlT744APTsYxLvl9Gyho0aMDfrXvA4AeZicGYhQUFBWnQoEHat2+fAgMDnXa1cHy5NHjwYId/lE+cOKGvvvpKlStXVrNmzQwmyzqsuNrTo0cP0xGyPHYtAHdn4sSJKlq0qMaNG6ezZ89KkooWLar+/ftryJAhkqRmzZrpmWeeMRnTiObNm9u/Ll++vA4cOKALFy7I19fX8v/WlChRwpIdR+n1n//8RwsXLtTp06fVrFkzTZo0Sa1ateJpC6To4sWL9iqRixcvpvm9t1aOADCPjjELSy7nTYnNZrP0iYvJmjVrprZt26p3796Ki4tTxYoV5enpqfPnz2vChAnq06eP6YhuzypdUdwM3V9W+XMDZITkf4P4twZ38v3332vy5MkKDw9X6dKlTcdxOw0aNFCXLl3UoUMHFS5c2HQct/DII49o7dq18vX1Vc2aNdMcLluto+7W0+w9PDxS/NkkH+zF56w7414wbfx87i92jFnY7QWrcLZz505NnDhRkrRkyRI9+OCDioyM1NKlSzVy5EgGY7Dz9fW13wwVKFCAm6FUvPfeexo4cKDTavuVK1f00UcfaeTIkZJuflgrUaKEiYhAlsdADOlVu3ZtJSQkqGzZsnRipmDLli2Sbp7KuWPHDl29etXhfSs+XdGqVSv7oVStW7c2G8bNrFu3zn7i5Pr16w2nQXZn9R3P9xs7xoA05MmTRwcOHFCpUqXUoUMHValSRe+++65OnjypihUrWvokq/SyymrGxo0b1aBBA+XMmfOOhbxPPPFEJqVyP7eupt4qNjZWfn5+lh4aAveiTJkyad4kHz16NBPTIKt46qmnFB0drV69eqlo0aJOf4asXg1w7NgxtWnTRrt375bNZrPXayT/nPidBWQcK9ax3A2rfMbKLOwYs6AWLVpowYIF9pMXw8LC1Lt3bxUoUEDSzQ+oDRs2dDhp0KrKly+v5cuXq02bNvrhhx/Uv39/SdKff/7Jijwc3DrssvLg606Sd83dbteuXfZVVgB37+2333Z4fe3aNUVGRmrVqlUaNGiQmVBwez/99JN+/vlnVa9e3XQUtxQcHCx/f3+tWbNGZcqU0bZt2xQbG6sBAwbo448/Nh3PrcTHxzs9jWL1e+W4uDht27ZNf/75p9PPJvlQFKSO/Ttp4+mK+4vBmAX98MMP+vfff+2vx44dqw4dOtgHY9evX9fBgwcNpXMvI0eOVOfOndW/f381adJE9erVkyRFRESoZs2ahtNlDVbd5puQkKDdu3eneDNkxUcvkkuubTabKlSo4PDnIjExUfHx8erdu7fBhEDW1q9fvxSvT506VTt27MjkNMgqKlWqpCtXrpiO4bZ+/vlnrVu3ToULF5aHh4c8PDz0+OOP64MPPlBwcLAiIyNNRzTq2LFjeuutt7RhwwYlJCTYr1MdIX3zzTfq0qWL4uPj5ePj43DfY7PZLD0Yo1bDWUhISLq/d8KECZKkxx9/PKPiWBKPUlqQh4eHzpw5Y3+U6fZtmGfPnlXx4sUt/cvsVmfOnFFMTIyqV69uP7Bg27Zt8vHxUaVKlQync39W3Oa7atUqde/eXefPn3d6z6o3inPnzlVSUpJ69uypTz75xL5jVZI8PT3l7+9vHzwDuH+OHj2qGjVq3PFQEFhTRESEQkNDNWbMmBRPKLf6jh9fX1/t3LlTZcqUUbly5TRjxgw1btxYR44cUWBgoOUrNRo0aKCkpCT169cvxUdxrbyDvkKFCmrRooXGjh3LKaa3oVbDWePGjdP1fTabTevWrcvgNNbEjjHgDh588EE9+OCDOnXqlCTpoYceUp06dQynMo/VntT17dtX7du318iRI1W0aFHTcdxCck9NmTJlVL9+facPXwAyxpIlS3hMGal65plnJElNmzZ1uM6On5uqVq2qXbt2qUyZMqpbt67GjRsnT09PTZ8+3VILfqnZtWuXfv31V1WsWNF0FLdz+vRpBQcHMxRLAbUazjiswTwGYxaU/DjT7dfg7MaNGxo9erTGjx+v+Ph4STd3QA0YMEDDhg2z7yCzotDQUPXu3dvpF/7ly5cVGhpqH4xZcZvv2bNnFRISwlAsBU888YRu3LihqKioFB8zbdSokaFkQNZWs2ZNh9/lSUlJOnPmjM6dO6fPPvvMYDK4Mz6MpW348OG6dOmSpJsLgs8995waNmyoQoUKadGiRYbTmffoo4/aD6SCo+bNm2vHjh0MUG9BrQbcGYMxC0pKStJLL71kP2o5ISFBvXv3Vt68eSXJoX/M6oYNG6aZM2cqLCxMDRo0kCRt3rxZo0aNUkJCgsaMGWM4oTms9qTuhRde0IYNG1SuXDnTUdzO1q1b1blzZ504ccKpVJXdCYDrWrdu7fDaw8NDRYoU0ZNPPslj/0iVlR91S4/mzZvbvy5fvrwOHDigCxcu2D/gW92MGTPUu3dvnT59WlWrVnXaDV6tWjVDycwLCgrSoEGDtG/fvhQfU7Zi3+wnn3xir9UIDQ2lViMNO3bs0OLFixUdHa2rV686vLds2TJDqbI3OsYs6OWXX07X982ePTuDk7i/4sWLKzw83OmX19dff6033nhDp0+fNpTMnOSbwb///tupTPTW1Z6pU6caTGnW5cuX1b59exUpUiTFm6Hg4GBDycyrUaOGKlSooNDQUBUrVszpg8WtN0kAgIwXFxenmTNnav/+/ZKkKlWqqGfPnvx7jDtKXuw6fvy4/ZrNZuNRXCnNp0qs/rPZuHEjtRppWLhwobp3767mzZsrIiJCzZo1U1RUlM6ePas2bdrwGT2DMBjDHZ06dUrFixe35GODXl5e2r17typUqOBw/eDBg6pRo4YlT3KiRP3OZs6cqd69e8vLy0uFChVyOono6NGjBtOZlTdvXu3atUvly5c3HQXIdhITE7V8+XKHAUfLli2VI0cOw8ngrnbs2KHmzZsrd+7c9v7U7du368qVK4qIiNAjjzxiOCHcWeXKlfXwww9r8ODBKZbvly5d2lAyuLsbN27o8OHD1GqkoFq1anr99df15ptv2g8xK1OmjF5//XUVK1ZMoaGhpiNmSwzGcEc+Pj767bffLPmMfN26dVW3bl1NnjzZ4Xrfvn21fft2bd261VAy81jtSd2DDz6o4OBgDR061JID5bQ0adJEgwcPthc+A7g/Dh8+rBYtWuj06dP2vp+DBw+qZMmS+u6773i0Gylq2LChypcvry+++EI5c95sWLl+/bpeeeUVHT16VJs2bTKcEO6Mxa57FxgYqJUrV6pkyZKmo2QaajXSljdvXv3+++/y9/dXoUKFtGHDBgUGBmr//v1q0qSJYmJiTEfMlugYwx1ZeXY6btw4BQUFac2aNfZdUD///LNOnjyplStXGk5nFiXqqbt69ao6duzIUCwFffv21YABA3TmzJkUHzO1ch8JcC+Cg4NVrlw5bd261d7zGBsbq65duyo4OFjfffed4YRwRzt27HAYiklSzpw5NXjwYNWuXdtgMmQFTZo0YTB2j44fP65r166ZjpGpevfurdq1a+u7775LsVbD6nx9ffXPP/9IkkqUKKG9e/cqMDBQcXFxunz5suF02ReDMSANTzzxhKKiojR16lQdOHBAktS2bVu98cYbKl68uOF0ZrHak7oePXpo0aJFeuedd0xHcTvt2rWTJPXs2dN+jT4S4N5t3LjRYSgmSYUKFXI4PAa4nY+Pj6Kjo50OaDh58qS8vb0NpUJW8fzzz6t///7as2cPBfNIt0OHDmnJkiUMVFPRqFEjrV69WoGBgWrfvr369eundevWafXq1WratKnpeNkWgzHgDooXL27p0ydTw2pP6hITEzVu3Dj98MMPqlatmtON4oQJEwwlM+/YsWOmIwDZ0gMPPGBfYb5VfHy8PD09DSRCVtCxY0f16tVLH3/8serXry9J2rJliwYNGqROnToZTgd317t3b0nSe++95/Qei11ITd26dXX48GEGY6mYMmWKEhISJEnDhg1Trly59NNPP6ldu3YaPny44XTZF4Mx4A7i4uK0bdu2FB8X7N69u6FU5rHak7o9e/aoZs2akqS9e/c6vGf1ASJFvEDGeO655/Taa69p5syZ9hL1X375Rb1792bXBlL18ccfy2azqXv37rp+/bokKVeuXOrTp4/CwsIMp4O7u/2+GEgPajXSduvObw8PDw0dOtRgGuugfB93ZOXy/W+++UZdunRRfHy8fHx8nE4XvHDhgsF0ZlGifu+seOLrvHnz0nzfysNm4F7ExcWpR48e+uabb+wfMq5fv66WLVtqzpw5DicIA7e7fPmyjhw5IkkqV66c8uTJYzgRshMrFsynV/Kpg1b6nJXSfS+1Gv+TI0cOxcTEyM/Pz+F6bGys/Pz8LP/zySjsGMMdWXl2OmDAAPXs2VNjx47lJvE2rPbcu8qVK1tu6NyvXz+H19euXdPly5fl6empPHnyMBgDXJCUlKSLFy9q4cKFOn36tPbv3y9Jevjhh9nVi3TJkyePfH197V8D95MVC+aROmo10pbaZ+9///2XaoQMxGAMd7Rv3z7LFs2fPn1awcHB3CSmgBL1e2fFofNff/3ldO3QoUPq06ePBg0aZCARkPUlJSWpfPny+v333xUQEMAwDOl248YNjR49WuPHj1d8fLykmztYBgwYoGHDhllqRzNgwrRp01S0aFHTMTIVtRopmzx5sqSbn6dmzJihfPny2d9LTEzUpk2bnA5Kwf3DYMzCEhIS9Omnn2r9+vUp9mft3LlTkiy97bl58+basWOHpXb0pBerPbhfAgICFBYWpq5du9pPfwWQfh4eHgoICFBsbKwCAgJMx0EWMmzYMM2cOdPh9NLNmzdr1KhRSkhI4PAh4B5s37491c9ZyQcxde7c2UQ0o6jVSNnEiRMl3VzsCg8PV44cOezveXp6yt/fX+Hh4abiZXsMxiysV69eioiI0AsvvKA6depYvhQ82YoVK+xfBwUFadCgQdq3bx/HUN+G1R7cTzlz5tQff/xhOgaQZYWFhWnQoEH6/PPPVbVqVdNxkEXMnTtXM2bMcLifqVatmkqUKKE33niDwRjgorFjx2r48OGqWLGiihYt6tRTbGXUaqQsedNB48aNtWzZMvvj7cgclO9bWP78+bVy5Ur7CiFuSu9jA1Z/XJDVnntnxcLVWwfP0s1VsZiYGE2ZMkUlS5bU999/bygZkLX5+vrq8uXLun79ujw9PZU7d26H9618WAxS5+Xlpd27d6tChQoO1w8ePKgaNWroypUrhpIhO7Hi/U7RokX14Ycf6qWXXjIdJUu4tVajefPmpuO4jeRRjdWHqZmBHWMWVqJECXl7e5uO4XY4ejp9WO25d1b8Jde6dWuH1zabTUWKFFGTJk00fvx4M6GAbOCTTz4xHQFZUPXq1TVlyhR7t02yKVOmqHr16oZSAVmfh4cHmw/uArUajubNm6ePPvpIhw4dkiRVqFBBgwYNUrdu3Qwny74YjFnY+PHjNWTIEIWHh/NY3D2y4jHUlKjfOytu2GXwDGSMHj16mI6ALGjcuHEKCgrSmjVrVK9ePUnSzz//rJMnT2rlypWG0yG7sGLBfP/+/TV16lQWLe4CtRo3TZgwQSNGjNBbb73l0P3Yu3dvnT9/Xv379zecMHviUUoLO3funDp06KBNmzYpT548Tv1ZPHaRflbcIp6aHTt2sNqTTidPnlTx4sUdyjWthO3hwP2VmJio5cuXa//+/ZKkKlWqqGXLlpb9Nwbp88cff2jq1Kn239sPP/yw3njjDcueSI67k56CeSu6ceOGgoKCFBUVpcqVKzt9zlq2bJmhZOZRq5G2MmXKKDQ01Onpm7lz52rUqFEcgJZB2DFmYZ06ddLp06c1duxYp1JIwFWs9nDi652wPRy4/w4fPqwWLVro9OnTqlixoiTpgw8+UMmSJfXdd9+pXLlyhhPC3Vy7dk3PPPOMwsPDKdmHSyiYT11wcLDWr1+vxo0bq1ChQpb/edyKWo20xcTEqH79+k7X69evr5iYGAOJrIHBmIX99NNP+vnnn+mQgEvSWu2xeqcCJ76mju3hQMYIDg5WuXLltHXrVhUsWFCSFBsbq65duyo4OFjfffed4YRwN7ly5dLu3btNx0AWNmnSJM2aNYuC+RTMnTtXS5cuVVBQkOkobodajbSVL19eixcv1jvvvONwfdGiRQoICDCUKvtjMGZhlSpV4rQhuIzVntR9++23nPiaik8//VSff/65w/bwli1bqkqVKho1ahSDMcBFGzdudBiKSVKhQoUUFhbGv0VIVdeuXTVz5kyFhYWZjoIsiIL51BUsWJCduulArYaz0NBQdezYUZs2bbL//dqyZYvWrl2rxYsXG06XfTEYs7CwsDANGDBAY8aMUWBgoNOz7z4+PoaSIStgtSd1nPiaOraHAxnjgQce0D///ON0PT4+Xp6engYSISu4fv26Zs2apTVr1qhWrVrKmzevw/tW7ojCnVEwn7pRo0bp3Xff1ezZs5UnTx7TcdwOtRqpa9eunX755RdNnDhRy5cvl3Sz+3Hbtm2qWbOm2XDZGOX7Fubh4SHJeUKflJQkm82mxMREE7GyJKuX77Pa4+j777/X5MmTOfE1BVWrVlXnzp2dtoePHj1aixYt0p49ewwlA7K27t27a+fOnZo5c6bq1KkjSfrll1/06quvqlatWpozZ47ZgHBLjRs3TvU9m82mdevWZWIaZDUUzKeuZs2aOnLkiJKSkuTv7+/0s0num7Wi1Go1pk6dqtGjR/P0AIxgx5iFrV+/3nSELGHt2rVau3ZtiiXqs2bNkmTNY6glVntSU7t2bSUkJKhs2bKc+HobtocDGWPy5Mnq0aOH6tWrZ/8359q1a2rVqpUmTZpkOB3cFfeCuBcUzKfu9soR/A+1GmnLkSOHYmJi5Ofn53A9NjZWfn5+bF7JIOwYA9IQGhqq9957T7Vr11axYsWcfuF/9dVXhpKZx2pP6p566ilFR0erV69eKZ742qNHD0PJ3MOvv/6qiRMnav/+/ZJubg8fMGAA28OB++Dw4cPat2+fJKly5coqX7684UQAsitvb28tXLiQgnncFS8vL+3du9fp99OhQ4cUGBiohIQEQ8ncg4eHh86cOeM0GPvjjz9Urlw5OsIzCDvGLC4uLk4zZ860f0CtUqWKevbsqfz58xtO5h7Cw8M1Z84cy++ASgmrPanjxNe01apVS//3f/9nOgaQ7cycOVMTJ0607+INCAjQ22+/rVdeecVwMgDZEQXzcAWnLqZs8uTJkm4+xj5jxgzly5fP/l5iYqI2bdqkSpUqmYqX7TEYs7AdO3aoefPmyp07t72PZMKECRozZowiIiL0yCOPGE5o3tWrV1MsCgcl6mnhxNfUrVy5Ujly5FDz5s0drv/www+6ceOGnn32WUPJgKxt5MiRmjBhgvr27at69epJkn7++Wf1799f0dHReu+99wwnBJDdUDCfOg8PjzQfLbXy43DUaqRs4sSJkm52N4eHhytHjhz29zw9PeXv76/w8HBT8bI9HqW0sIYNG6p8+fL64osvlDPnzRnp9evX9corr+jo0aPatGmT4YTmDRkyRPny5dOIESNMR3E7lKinLiIiQqGhoZz4moJq1aopLCxMLVq0cLi+atUqDRkyRLt27TKUDMjaihQposmTJ6tTp04O1xcsWKC+ffvq/PnzhpIByK4omE/d119/7fD62rVrioyM1Ny5cxUaGqpevXoZSuYeqNVIXePGjbVs2TL5+vqajmIpDMYsLHfu3IqMjHTakrlv3z7Vrl1bly9fNpTMffTr10/z5s1TtWrVVK1aNadf+FY+xnzp0qXq2LGjnnrqqRRXe9q0aWM4oTmc+Jq63Llza//+/fL393e4fvz4cVWpUkWXLl0yEwzI4goUKKDt27c7PYYSFRWlOnXqKC4uzkwwANlWaGhomu+/++67mZQk6/jyyy+1aNEip8EZcLd8fHz022+/qWzZsqajZAs8SmlhPj4+io6OdhqMnTx5Ut7e3oZSuZfdu3erRo0akqS9e/c6vGf1k3fatWunX375RRMnTtTy5csl3Vzt2bZtm+VXezjlK3X58+fX0aNHnQZjhw8fVt68ec2EArKBbt266fPPP3dasJk+fbq6dOliKBWA7IzB19177LHH9Nprr5mOYRS1GvcH+5vuLwZjFtaxY0f16tVLH3/8sb0rasuWLRo0aJDToxhWxYAjbZSop+yJJ54wHcFttWrVSm+//ba++uore2Hv4cOHNWDAALVs2dJwOiBrCQkJsX+dXNYbERGhxx57TJL0yy+/KDo62uGQFACAGVeuXNHkyZNVokQJ01GMGjp0qMLCwpyuJyUlaejQoQzGYASDMQv7+OOPZbPZ1L17d12/fl2SlCtXLvXp0yfFf6yAW7Hak7o79fM1atQok5K4n3HjxumZZ55RpUqV9NBDD0mSTp06pYYNG+rjjz82nA7IWiIjIx1e16pVS5J05MgRSVLhwoVVuHBh/f7775meDUD2R8F86nx9fR1+NklJSfrnn3+UJ08eyy8qHzp0SJUrV3a6XqlSJR0+fNhAIoDBmKV5enpq0qRJ+uCDD+w30eXKleNUGaQLqz2pe/LJJ52u3XpzZOUbxfz58+unn37S6tWrtWvXLuXOnVvVqlWz9LAQcBW7mgGY9NVXXzm8vr1g3so++eQTh9ceHh4qUqSI6tata/lSdWo14I4o34ekmzs2JNl3cAB3Qol66v7++2+H18k3iiNGjNCYMWPUtGlTQ8myjsDAQK1cuVIlS5Y0HQUAANwFCuaRltdff10///yzU61Gu3bt9Oijj2rGjBmGE2YNlO/fX+wYs7AbN25o9OjRGj9+vOLj4yVJ3t7eGjBggIYNG2Y/WQ9ICas9qcufP7/Ttaefflqenp4KCQnRr7/+aiBV1nL8+HFdu3bNdAwAAHCXKJi/KSEhQbt379aff/6pGzduOLxn5V5VajXuD/Y33V8Mxixs2LBhmjlzpsLCwtSgQQNJ0ubNmzVq1CglJCRozJgxhhPCnVGifveKFi2qgwcPmo4BAACQISiYv2nVqlXq1q2bYmNjnd6z2WzUalCrkar33ntPAwcOdKo3unLlij766CONHDlSkvT9999b/u/Z/cSjlBZWvHhxhYeHOw0xvv76a73xxhs6ffq0oWTICv7++28988wz2rFjh9Nqz7Jly1SgQAGzAQ3avXu3w+ukpCTFxMQoLCxM169f1+bNmw0lyzq8vb21a9cutocDAOCm7lQwb+WF0oCAADVr1kwjR45U0aJFTcfJkqxaq5EjRw7FxMTIz8/P4XpsbKz8/PwsPVTNSOwYs7ALFy6oUqVKTtcrVaqkCxcuGEiErITVntTVqFFDNpvNaYvzY489plmzZhlKBQAAcP9QMJ+6s2fPKiQkhKHYPbBqrUZSUlKKp73u2rVLBQsWNJDIGhiMWVj16tU1ZcoUTZ482eH6lClTVL16dUOpkJXYbDY1a9ZMzZo1S/V7rLjac+zYMYfXyTeKXl5ehhIBAADcXz169DAdwW298MIL2rBhg71uBLiT5B2YNptNFSpUcDrRPj4+Xr179zaYMHtjMGZh48aNU1BQkNasWaN69epJkn7++WedPHlSK1euNJwO2YUVV3tKly5tOgIAAECGo2A+ZVOmTFH79u31448/KjAwULly5XJ4Pzg42FAyuKtPPvlESUlJ6tmzp0JDQx0O8/L09JS/v7/9MzvuPzrGLO6PP/7Q1KlTdeDAAUnSww8/rDfeeEPFixc3nAzZhVW7orZv367169eneKM4YcIEQ6ncU1xcnFMn3ZdffqlWrVpZ/oRTAADcFQXzqZs5c6Z69+4tLy8vFSpUyGH3j81m09GjRw2myxqs+hli48aNatCggXLmZA9TZmIwZlHXrl3TM888o/DwcAUEBJiOg2zMir/Uxo4dq+HDh6tixYoqWrSo083QunXrDKYz68MPP5S/v786duwoSerQoYOWLl2qBx98UCtXruQxbgAAsggK5lP34IMPKjg4WEOHDpWHh4fpOFmSFT9DSNLOnTuVK1cuBQYGSrp5MN7s2bNVuXJljRo1Sp6enoYTZk/8LbWoXLlyOZ2cB+D+mDRpkmbNmqX9+/drw4YNWr9+vf0/Kw/FJCk8PNzeN7d69WqtXr1a33//vZ599lkNGjTIcDoAAJBeFMyn7urVq+rYsSNDMdy1119/XVFRUZKko0ePqmPHjsqTJ4/++9//avDgwYbTZV/8TbWwrl27aubMmaZjANmOh4eHGjRoYDqGWzpz5ox9MPbtt9+qQ4cOatasmQYPHqzt27cbTgcAANIruWAeznr06KFFixaZjpFlxMXFOV2bNm2aJYeuUVFRqlGjhiTpv//9r5544gl9+eWXmjNnjpYuXWo2XDbGg6sWdv36dc2aNUtr1qxRrVq1nLp86EECXNO/f39NnTrV6Rhz3Dxx5+TJkypZsqRWrVql0aNHS7p5NLWVu0gAAMhqKJhPXWJiosaNG6cffvhB1apVc/rZWPlzVnprNTp37mwypjFJSUn2fuI1a9boueeekySVLFlS58+fNxktW2MwZmF79+7VI488Ikn27ZrJbu1EAtIrpRJ1K672DBw4UEFBQSpXrpwqV67sdDO0bNkyQ8nMa9u2rTp37qyAgADFxsbq2WeflSRFRkaqfPnyhtMBAID0WrBggSIiIuTl5aUNGzY4dapaeTC2Z88e1axZU9LNz1y3svrnrPDwcM2fP1+SY63G4sWLNWjQIEVERBhOaFbt2rU1evRoPfXUU9q4caM+//xzSdKxY8cs95kqM1G+D8AllKin7q233tKMGTPUuHFjp/J9SZo9e7ahZOZdu3ZNkyZN0smTJ/XSSy/ZbxonTpwob29vvfLKK4YTAgCA9KBgHq7InTu3oqKiVLJkSfXr108JCQmaNm2aoqKiVLduXf3111+mIxq1e/dudenSRdHR0QoJCdG7774rSerbt69iY2P15ZdfGk6YPTEYA+CSMmXKaP78+apfv75Wr16tDh06aNGiRVq8eLGio6Mtvdrj7e2thQsXKigoyHQUAACADFGwYEFt375d5cqVMx0FWUjx4sW1ZMkS1a9fXxUrVtTo0aPVvn17HTx4UI8++qguXrxoOqJbSkhIUI4cOZyeRMH9waOUAFySWom6v7+/6tatazidWQULFuQmMRUrVqxI8brNZpOXl5fKly+vMmXKZHIqAABwt5IL5t955x3TUdxC27ZtNWfOHPn4+Kht27Zpfi+1GtRq3Mmvv/6q/fv3S5IqV65sr0BCxmAwBsAllKinbtSoUXr33Xc1e/Zs5cmTx3Qct9K6dWvZbDbdvlk5+ZrNZtPjjz+u5cuXy9fX11BKAABwJxTMO8qfP7+9PiN//vyG07iviRMnyt/fXydPntS4ceOUL18+SVJMTIzeeOMNw+nM+/PPP9WxY0dt3LjR3t0cFxenxo0ba+HChSpSpIjZgNkUj1ICcMlbb72lb7/9VgEBAYqMjNTx48eVL18+LVy4UOPGjdPOnTtNRzSmZs2aOnLkiJKSkuTv7+90o2jln83atWs1bNgwjRkzRnXq1JEkbdu2TSNGjNDw4cOVP39+vf7666pbt65mzpxpOC0AAEhN48aNU33PZrNp3bp1mZgGyB46duyoo0ePat68eXr44YclSfv27VOPHj1Uvnx5LViwwHDC7InBGACXUKKeutDQ0DTfTy7RtKKqVatq+vTpql+/vsP1LVu26LXXXtPvv/+uNWvWqGfPnoqOjjaUEgAAABmBWo205c+fX2vWrNGjjz7qcH3btm1q1qyZ4uLizATL5hiMAQAyTe7cubV9+3ZVrVrV4fqePXtUp04dXblyRSdOnNDDDz+sy5cvG0oJAADgurNnz2rgwIFau3at/vzzT6cKCSvXjnh4eFCrkQZvb2/9+OOPqlGjhsP1yMhIPfHEExxOkEEYjAFwCas9cMXjjz8ub29vzZs3z96RcO7cOXXv3l2XLl3Spk2btGbNGr355ps6ePCg4bQAAOBWFMynz7PPPqvo6Gi99dZbKlasmL17LFmrVq0MJTOPWo20tWrVSnFxcVqwYIGKFy8uSTp9+rS6dOkiX19fffXVV4YTZk+U7wNwCSXqjgoWLKioqCgVLlxYvr6+TjdAt7pw4UImJnMvM2fOVKtWrfTQQw/ZTzU9efKkypYtq6+//lqSFB8fr+HDh5uMCQAAUkDBfPps3rw5xV0/kPr16+dUq9G0aVN5eXnZazU++eQT9ezZ02BKc6ZMmaKWLVvK39/ffq8cHR2twMBA/d///Z/hdNkXO8YAuITVHkdz587Viy++qAceeEBz5sxJczDWo0ePTEzmfm7cuKGIiAhFRUVJkipWrKinn35aHh4ehpMBAADcu8qVK2v+/Pn2Dl78D7Uad5aUlKS1a9dq//79kqSHH35YTz31lOFU2RuDMQAuoUQdAAAAcBYREaHx48dr2rRp8vf3Nx3HrVCrcWdr166199PduHHD4b1Zs2YZSpW98SglAJccOXJEPj4+Ttd9fHx09OhRSVJAQIDOnz+f2dGMmzNnjl566SWn69evX9eIESP0wQcfZH4oN8IvewAAsj4K5lPXsWNHXb58WeXKlVOePHmUK1cuh/ep1aBWIzWhoaF67733VLt27RT76ZAx2DEGwCWs9qTOx8dHzZs31/Tp0+39agcPHlTnzp0VGxur48ePmw1o0J1+2VMoCgBA1kDBfOrmzp2b5vvUalCrkZpixYpp3Lhx6tatm+kolsJgDIBLDh48qFatWunYsWMprvZUqFBBy5cv1z///GO5f9iPHDmirl276uTJk5o9e7aioqI0ePBgtW7dWp999pmly2r5ZQ8AQPbg7e1NwTxwnxUqVEjbtm1TuXLlTEexFAZjAFzGak/qbty4obfffltTp05Vjhw5NHfuXHXq1Ml0LOP4ZQ8AQPZAwbyjixcv2mtGLl68mOb3plRHYiXUaqRuyJAhypcvn0aMGGE6iqUwGAOADPDNN9+oV69eqlChgqKiolStWjXNmzdPxYsXNx3NKH7ZAwCQPVAw7yhHjhyKiYmRn5+fPDw8UuyGSkpKks1ms3T/GrUazkJCQuxf37hxQ3PnzlW1atVUrVo1p366CRMmZHY8S6B8H4DLWO1J2euvv665c+dqzJgxCgkJ0dmzZ9WzZ08FBgbq888/V4cOHUxHNCYhIUHTp0/XmjVr+GUPAEAWRsG8o3Xr1qlgwYKSpPXr1xtO477Cw8M1Z84cajVuERkZ6fA6+fHkvXv3OlyniD/jsGMMgEtY7Uld1apVNX/+fFWvXt3h+tSpUzVkyBDFx8cbSmZe48aNU33PZrNp3bp1mZgGAAC4ioL5tCUkJGj37t0pLiC3bNnSUCrzqNWAO2IwBsAllKin7t9//9UDDzyQ4nsHDx5UxYoVMzkRAAAAMsuqVavUvXt3nT9/3uk9qz9KSa0G3BGDMQAuYbUHAADAWiiYT5+AgAA1a9ZMI0eOVNGiRU3HcSv9+vXTvHnz6NCCW2EwBsAlrPakbcmSJVq8eLGio6N19epVh/d27txpKJUZbdu21Zw5c+Tj46O2bdum+b3Lli3LpFQAAOBuUTCfPj4+PoqMjGQBOQXUasAdUb4PwCWUqKdu8uTJGjZsmF566SV9/fXXevnll3XkyBFt375db775pul4mS5//vz2G2cfHx+KQwEAyKIomE+fF154QRs2bGAwlgL+3MAdsWMMgEtY7UldpUqV9O6776pTp07y9vbWrl27VLZsWY0cOVIXLlzQlClTTEcEAAC4ZxTMp+zy5ctq3769ihQposDAQKcF5ODgYEPJAKSEwRgA3Gd58uTR/v37Vbp0afn5+Wn16tWqXr26Dh06pMcee0yxsbGmIxrTpEkTLVu2TAUKFHC4fvHiRbVu3drSA1UAALISCuZTN3PmTPXu3VteXl4qVKiQw255m82mo0ePGkyX+ajVgLvzMB0AALKbBx98UBcuXJAklSpVSlu3bpUkHTt2TFZfi9iwYYNT55p0c8X5xx9/NJAIAAC4om/fvmrfvr1iYmJ048YNh/+sPBSTpGHDhik0NFR///23jh8/rmPHjtn/s9pQTHKu1cifP3+q/wEm0DEGIN1Y7UmfJk2aaMWKFapZs6Zefvll9e/fX0uWLNGOHTvu+HPLrnbv3m3/et++fTpz5oz9dWJiolatWqUSJUqYiAYAAFxw9uxZhYSEcOpiCq5evaqOHTvKw4N9KJI0e/Zs+9dz5swxFwRIBYMxAOlGiXr6TJ8+3d6z8eabb6pw4cLasmWLWrZsqd69extOZ0aNGjVks9lks9nUpEkTp/dz586tTz/91EAyAADgCgrmU9ejRw8tWrRI77zzjukobodaDbgjOsYAIAOkVkZrs9n0/PPPG0xmxokTJ5SUlKSyZctq27ZtKlKkiP09T09P+fn5KUeOHAYTAgCAu0HBfOqCg4M1b948Va9endPbb+Ph4aEzZ87Iz8/P4fqff/6pEiVK6Nq1a4aSwcrYMQbAJaz2pG7VqlXq1q1biiX7Vi2jLV26tCQ5nVgFAACypgULFigiIkJeXl7asGGDU8G8lQdje/bsUc2aNSVJe/fudXjPqk9cUKsBd8aOMQAuYbUndQEBAWrWrJlGjhxJ78Zt5s6dq8KFCysoKEiSNHjwYE2fPl2VK1fWggUL7AM0AADg3h588EEFBwdr6NChdGnhjjw8POxDwZRGEMm1Gj179szsaACDMQB3J3m1p0aNGlq3bp0KFixofy95tWfatGk6fvy4oYTm+fj4KDIyks6NFFSsWFGff/65mjRpop9//llNmzbVJ598om+//VY5c+a09KENAABkJQULFtT27du530G6UKsBd8ZgDMBdYbXnznr27KkGDRqoV69epqO4nTx58ujAgQMqVaqUhgwZopiYGM2bN0+///67nnzySZ07d850RAAAkA79+/dXkSJFKJgHkOXRMQbgrhw7dozVnjuYMmWK2rdvrx9//JEy2tvky5dPsbGxKlWqlCIiIhQSEiJJ8vLy0pUrVwynAwAA6ZWYmKhx48bphx9+oGAe6UatBtwRO8YA4D6bOXOmevfuLS8vLxUqVMipjPbo0aMG05nVpUsXHThwQDVr1tSCBQsUHR2tQoUKacWKFXrnnXecCmoBAIB7aty4carv2Ww2Sx/EhNRRqwF3xGAMgEtY7UkdZbSpi4uL0/Dhw3Xy5En16dNHzzzzjCTp3Xfflaenp4YNG2Y4IQAAADIKtRpwRwzGALiE1Z7UUUYLAAAAOPPz89MPP/ygmjVrqmbNmgoJCVG3bt105MgRVa9eXfHx8aYjwoLoGAPgkpMnT6p8+fKSpOXLl+uFF17Qa6+9pgYNGujJJ580G86wHj16aNGiRZTR/v92796tqlWrysPDw36qaWqqVauWSakAAACQ2Z5++mm98sorqlmzpqKiotSiRQtJ0u+//y5/f3+z4WBZDMYAuIQS9dRRRuuoRo0aOnPmjPz8/FSjRg3ZbDaHE02TX9tsNiUmJhpMCgAAgIw0depUe63G0qVLVahQIUnSr7/+qk6dOhlOB6viUUoALqFEPXWU0To6ceKESpUqJZvNphMnTqT5vVbupgMAAACQ+RiMAXAJJeoAAAAA7oRaDbg7BmMAgAy1YsWKdH9vy5YtMzAJAAAAMpuHh4e9VsPDw4NaDbgdBmMA0o3VHrjCw8MjXd/HzRAAAED2Q60G3B2DMQDpxmoPAAAAACA74VRKAOl27NgxFSlSxP41kFECAwO1cuVKlSxZ0nQUAAAA3ANqNeDu2DEGAHA73t7e2rVrl8qWLWs6CgAAAO4BtRpwd+wYA5BurPYAAAAAuBs3btwwHQFIE4MxAOnWunXrdH0fqz0AAAAAXEWtBjITgzEA6cZqDwAAAICMdvz4cV27ds10DFhE+h72BQAXBQYG6uTJk6ZjAAAAAADghMEYgAzFag8AAAAAwF0xGAMAZJp58+bp33//dbp+9epVzZs3z/562rRpKlq0aGZGAwAAAGBBtqSkpCTTIQBkX97e3tq1a5fKli1rOgrcQI4cORQTEyM/Pz+H67GxsfLz8+PQBgAAAPAZApmKHWMAgEyTlJQkm83mdP3UqVPKnz+/gUQAAAAArIxTKQEAGa5mzZqy2Wyy2Wxq2rSpcub836+fxMREHTt2TM8884zBhAAAAMho8+bNU8eOHfXAAw84XL969aoWLlyo7t27S6JWA5mLRykBZCi2QUOSQkND7f8fMGCA8uXLZ3/P09NT/v7+ateunTw9PU1FBAAAQAajVgPuiB1jAFzCag/uxrvvvitJ8vf314svvuj05wYAAADZH7UacEfsGAPgElZ74IqTJ0/KZrPpoYcekiRt27ZNX375pSpXrqzXXnvNcDoAAABkhORajV27dqlKlSqp1mosXrzYYEpYFTvGALiE1R64onPnznrttdfUrVs3nTlzRk899ZSqVq2q+fPn68yZMxo5cqTpiAAAALjPWrduLUn67bff1Lx581RrNQATGIwBuCuUqONe7N27V3Xq1JEkLV68WIGBgdqyZYsiIiLUu3dvBmMAAADZELUacGcMxgDcFVZ7cC+uXbtmvxFas2aNWrZsKUmqVKmSYmJiTEYDAABABmvSpInOnTtHrQbcCh1jAFwyd+5cVntw1+rWravGjRsrKChIzZo109atW1W9enVt3bpVL7zwgk6dOmU6IgAAADJIw4YNHWo1KlSooKpVq+rQoUPq27cvTw/ACA/TAQBkTcmrPcm2bdumt99+W9OnTzeYCu7uww8/1LRp0/Tkk0+qU6dOql69uiRpxYoV9kcsAQAAkD2lVKvx008/af78+ZozZ47ZcLAsHqUE4BJK1OGKJ598UufPn9fFixfl6+trv/7aa68pT548BpMBAAAgo1GrAXfEjjEALmG1B67KkSOHrl+/rs2bN2vz5s06d+6c/P395efnZzoaAAAAMlCVKlUUHh6uH3/8UatXr7Yf2vXHH3+oUKFChtPBqhiMAXAJqz1wxaVLl9SzZ08VK1ZMjRo1UqNGjVS8eHH16tVLly9fNh0PAAAAGYhaDbgjyvcBuIQSdbji9ddf15o1azRlyhQ1aNBAkrR582YFBwfr6aef1ueff244IQAAADJSYmKiU63G8ePHlSdPHp4ggBEMxgC4ZMOGDWrTpo0uXryoHj16aNasWZKkd955RwcOHNCyZcsMJ4Q7Kly4sJYsWaInn3zS4fr69evVoUMHhwMdAAAAkD2dO3dOBw8elCRVrFhRRYoUMZwIVkb5PgCXUKIOV1y+fFlFixZ1uu7n58ejlAAAANncpUuX1LdvX82bN083btyQdLN/tnv37vr000/5HAEj6BgD4DJK1HG36tWrp3fffVcJCQn2a1euXFFoaKjq1atnMBkAAAAyWkhIiDZu3KhvvvlGcXFxiouL09dff62NGzdqwIABpuPBoniUEoBLWO2BK/bu3avmzZvr33//tZet7tq1S15eXvrhhx9UpUoVwwkBAACQUajVgDtixxgAl7DaA1dUrVpVhw4d0gcffKAaNWqoRo0aCgsL06FDhxiKAQAAZHPUasAdsWMMgEtY7QEAAABwN5o2bapChQpp3rx58vLyknSzVqNHjx66cOGC1qxZYzghrIjyfQAuYbUHrjp06JDWr1+vP//80/4YbrKRI0caSgUAAICMNmnSJDVv3lwPPfRQirUagAnsGAPgElZ74IovvvhCffr0UeHChfXggw/KZrPZ37PZbNq5c6fBdAAAAMholy9f1vz583XgwAFJ0sMPP6wuXbood+7chpPBqhiMAXAJJepwRenSpfXGG29oyJAhpqMAAAAAAIMxAK5jtQd3y8fHR7/99pvKli1rOgoAAAAMoFYD7obBGAAg0/Tq1UuPPvqoevfubToKAAAAMhm1GnBHDMYAuIzVHqTH5MmT7V9funRJEyZMUFBQkAIDA5UrVy6H7w0ODs7seAAAAMgk1GrAHTEYA+ASVnuQXmXKlEnX99lsNh09ejSD0wAAAMAUajXgjhiMAXAJqz0AAAAA7ga1GnBHDMYAuITVHmQk/nwBAABkD9RqwN0xGAPgElZ7kJG8vb21a9cuBmMAAABZHLUacHc5TQcAkHXcutpTvnx5jRgxQlu3bmW1BwAAAECKjh07ZjoCkCZ2jAFIN1Z7kFnYMQYAAGBd1GogM7FjDEC6sdoDAAAAIKOxfweZycN0AADZm4+PD7vHcNdsNpvpCAAAAAAsgMEYgAzFag9cwZ8bAAAAAJmBwRgAwO18//33KlGihOkYAAAAALI5OsYAABkqJCQk3d87YcIESdLjjz+eUXEAAADg5qjVQGZiMAYAyFCRkZEOr3fu3Knr16+rYsWKkqSoqCjlyJFDtWrVMhEPAAAAboZaDWQmBmMAMhSrPVi/fr396wkTJsjb21tz586Vr6+vJOmvv/7Syy+/rIYNG5qKCAAAADdCrQYyky2JUSyADOTt7a1du3apbNmypqPADZQoUUIRERGqUqWKw/W9e/eqWbNm+uOPPwwlAwAAQEZwpVYDyEzsGAOQoVjtwa0uXryoc+fOOV0/d+6c/vnnHwOJAAAAkJGo1YC7YzAGIN0oUce9atOmjV5++WWNHz9ederUkST98ssvGjRokNq2bWs4HQAAAO43ajXg7niUEkC6NW7c2OF1Wqs969atMxERbu7y5csaOHCgZs2apWvXrkmScubMqV69eumjjz5S3rx5DScEAABARqFWA+6IHWMA0o3VHtyrPHny6LPPPtNHH32kI0eOSJLKlSvHQAwAAMACqNWAO2LHGACXsNqDe3H48GEdOXJEjRo1Uu7cuZWUlMQJpgAAANlc9+7d9eOPP6ZYq9GwYUPNnTvXcEJYETvGALiE1R64IjY2Vh06dND69etls9l06NAhlS1bVr169ZKvr6/Gjx9vOiIAAAAySHh4uAYOHKjOnTunWKsBmOBhOgCArCm5RH3ZsmU6deqUTp06paVLl6pXr16UqCNV/fv3V65cuRQdHa08efLYr3fs2FGrVq0ymAwAAAAZLblWIzY2VpGRkYqMjNSFCxf02WefUa0BY9gxBsAlrPbAFREREfrhhx/00EMPOVwPCAjQiRMnDKUCAABAZoqJiVFMTAy1GnALDMYAuIQSdbji0qVLDjvFkl24cEEPPPCAgUQAAADILNRqwB3xKCWAe5K82hMQEKC8efOK8zyQloYNG2revHn21zabTTdu3NC4cePUuHFjg8kAAACQ0ajVgDtixxgAl7DaA1eMGzdOTZs21Y4dO3T16lUNHjxYv//+uy5cuKAtW7aYjgcAAIAMRK0G3BE7xgC4hNUeuKJq1aqKiorS448/rlatWunSpUtq27atIiMjVa5cOdPxAAAAkIGo1YA7YscYAJew2gNXREdHq2TJkho2bFiK75UqVcpAKgAAAGSG5FqN999/XxK1GnAPDMYAuITVHriiTJkyiomJkZ+fn8P12NhYlSlTRomJiYaSAQAAIKNRqwF3xKOUAFxCiTpckdpR3PHx8fLy8jKQCAAAAJmFWg24I1sSR8gBcMHevXvVtGlTPfLII1q3bp1atmzpsNrDLzbcKiQkRJI0adIkvfrqqw67DRMTE/XLL78oR44crBQCAABkY8m1GiktlFKrAVN4lBKAS5JXe6ZMmSJvb2/Fx8erbdu2evPNN1WsWDHT8eBmIiMjJd3cMbZnzx55enra3/P09FT16tU1cOBAU/EAAACQCajVgDtixxgAl7DaA1e8/PLLmjRpknx8fExHAQAAQCbz8PDQ2bNnVaRIEYfrJ06cUOXKlXXp0iVDyWBlDMYAuCRHjhyprvb4+fmx2gMAAABAErUacG88SgnAJZSow1U7duzQ4sWLFR0dratXrzq8t2zZMkOpAAAAkFGo1YA7YzAG4K4kr/bYbDaNGDEixdWeGjVqGEoHd7dw4UJ1795dzZs3V0REhJo1a6aoqCidPXtWbdq0MR0PAAAAGWD9+vWSqNWAe+JRSgB3pXHjxpKkjRs3ql69ek6rPf7+/ho4cKACAgJMRYQbq1atml5//XW9+eab8vb21q5du1SmTBm9/vrrKlasmEJDQ01HBAAAAGAhDMYAuITVHrgib968+v333+Xv769ChQppw4YNCgwM1P79+9WkSRPFxMSYjggAAIAMRK0G3I2H6QAAsqbZs2czFMNd8/X11T///CNJKlGihPbu3StJiouL0+XLl01GAwAAQAZbuHCh6tevr/379+urr77StWvX9Pvvv2vdunXKnz+/6XiwKDrGALiM1R7crUaNGmn16tUKDAxU+/bt1a9fP61bt06rV69W06ZNTccDAABABho7dqwmTpxor9WYNGmSQ60GYAI7xgC4hNUeuGLKlCl68cUXJUnDhg1TSEiIzp49q3bt2mnmzJmG0wEAACAjHTlyREFBQZJu9hNfunRJNptN/fv31/Tp0w2ng1WxYwyAS1jtgSsKFixo/9rDw0NDhw41mAYAAACZKaVajcDAQGo1YBSDMQAuSWu1p0mTJpwuCLuLFy+m+3vprQMAAMi+qNWAO2IwBsAlrPYgvQoUKCCbzZbm9yQlJclmsykxMTGTUgEAACCzTZkyRQkJCZJu1mrkypVLP/30k9q1a6fhw4cbTgerYjAGwCWs9iC91q9fbzoCAAAA3AC1GnBHtqSkpCTTIQBkPRcuXFBCQoKKFy+uGzduaNy4cfrpp58UEBCg4cOHy9fX13REAAAAAIZRqwF3x2AMAJBpNm3alOb7jRo1yqQkAAAAyAweHh7UasCt8SglgHRjtQf36sknn3S6duuNEjdDAAAA2Qu1GnB37BgDkG6s9uBe/f333w6vr127psjISI0YMUJjxoyhnw4AAABApmLHGIB0Y7UH9yp//vxO155++ml5enoqJCREv/76q4FUAAAAyAzUasAdsWMMAGDcgQMHVLt2bcXHx5uOAgAAgAzi4eHhdI1aDZjGjjEALmG1B67YvXu3w+ukpCTFxMQoLCxMNWrUMBMKAAAAmeKvv/5yeH17rQZgAjvGALiE1R64Irmn7vZfPY899phmzZqlSpUqGUoGAAAAUzZu3EitBoxhxxgAl7DaA1ccO3bM4bWHh4eKFCkiLy8vQ4kAAABgWtGiRXXw4EHTMWBR7BgDcF+x2gMAAAAgJWnValy/fl2bN282lAxWxo4xAPcVqz1IS3BwsMqXL6/g4GCH61OmTNHhw4f1ySefmAkGAACADFejRo00azUAE9gxBsAlrPbAFSVKlNCKFStUq1Yth+s7d+5Uy5YtderUKUPJAAAAkNFOnDjh8JpaDbgDdowBcAmrPXBFbGys8ufP73Tdx8dH58+fN5AIAAAAmaV06dKmIwBOGIwBcAkl6nBF+fLltWrVKr311lsO17///nuVLVvWUCoAAABkBmo14I4YjAFwCas9cEVISIjeeustnTt3Tk2aNJEkrV27VuPHj+dGCAAAIJtbunSpVqxY4XS9fv36CgsL434QRjAYA+ASVnvgip49e+rff//VmDFj9P7770uS/P399fnnn6t79+6G0wEAACAjUasBd+RhOgCArGnp0qVq0KCB0/X69etryZIlBhIhq+jTp49OnTqls2fP6uLFizp69ChDMQAAAAtIrtW4HbUaMIkdYwBcwmoP7lWRIkVMRwAAAEAmolYD7ojBGACXUKKO9KpZs6ZsNlu6vnfnzp0ZnAYAAACmUKsBd8RgDIBLWO1BerVu3dp0BAAAALiJPn36qE+fPjp37pxy586tfPnymY4Ei7MlJSUlmQ4BIGv6/PPPNWbMGP3xxx+Sbq72jBo1itUeAAAAAECWwGAMwD1jtQcAAABASqjVgLvjUUoA94wSdaRXYmKiJk6cqMWLFys6OlpXr151eP/ChQuGkgEAACAjUKsBd8eOMQDpxmoP7tXIkSM1Y8YMDRgwQMOHD9ewYcN0/PhxLV++XCNHjlRwcLDpiAAAAAAshB1jANKN1R7cq/nz5+uLL75QUFCQRo0apU6dOqlcuXKqVq2atm7dymAMAAAAQKZixxgAINPkzZtX+/fvV6lSpVSsWDF99913euSRR3T06FHVrFlTf//9t+mIAAAAyCDUasAdeZgOAACwjoceekgxMTGSpHLlyikiIkKStH37dj3wwAMmowEAACCDhYaGasKECerYsaP+/vtvhYSEqG3btvLw8NCoUaNMx4NFsWMMgEtY7YErhg4dKh8fH73zzjtatGiRunbtKn9/f0VHR6t///4KCwszHREAAAAZpFy5cpo8ebKCgoLk7e2t3377zX5t69at+vLLL01HhAUxGAPgEkrUcT9s3bpVP/30kwICAvT888+bjgMAAIAMRK0G3BGPUgJwSXKJ+oABA5QzZ0516tRJM2bM0MiRI7V161bT8eCmPvjgA82aNcv++rHHHlNISIjOnTunDz/80GAyAAAAZDRqNeCOGIwBcMmZM2cUGBgoScqXL599dee5557Td999ZzIa3Ni0adNUqVIlp+tVqlRReHi4gUQAAADILG3atNHatWslSX379tWIESMUEBCg7t27q2fPnobTwapymg4AIGtKXu0pVaqUfbXnkUceYbUHaTpz5oyKFSvmdL1IkSL21UMAAABkT7f2yXbs2FGlS5emVgPGsWMMgEtY7YErSpYsqS1btjhd37Jli4oXL24gEQAAADILtRpwR5TvA7gvKFFHeowbN07jxo3TRx99pCZNmkiS1q5dq8GDB2vAgAH6z3/+YzghAAAAMoq/v7++/PJL1a9f3+H6L7/8ohdffFHHjh0zlAxWxmAMgEs++OADFS1a1Gl32KxZs3Tu3DkNGTLEUDK4s6SkJA0dOlSTJ0/W1atXJUleXl4aMmSIRo4caTgdAAAAMpKXl5f279+vMmXKOFw/evSoKleurISEBEPJYGU8SgnAJZSowxU2m00ffvihzp07p61bt2rXrl26cOECQzEAAAALoFYD7ojyfQAuoUQd9yJfvnx69NFHTccAAABAJnr11Vf19ttv69q1aynWagAmMBgD4JLk1Z7bt0Gz2gMAAAAgJYMGDVJsbKzeeOMNp1oNumZhCoMxAC5htQcAAADA3Uiu1RgxYoT279+v3LlzKyAgQA888IDpaLAwyvcBuIQSdQAAAABAVsdgDMA9iY+PZ7UHAAAAAJAlMRgDAAAAAACAJXmYDgAAAAAAAACYwGAMAAAAAAAAlsRgDAAAAAAAAJbEYAwAAAAAAACWxGAMAAAAAAAAlsRgDAAAAAAAAJbEYAwAAAAAAACWxGAMAAAAAAAAlvT/AWHgmnz8H5YNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get most important features from trained DT\n",
    "fi = DT_model.feature_importances_\n",
    "\n",
    "# get most important features by name\n",
    "names_sorted = [x for _,x in sorted(zip(fi, dt_X_train.columns.values), reverse = True)]\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(15,4))\n",
    "\n",
    "ax.bar(np.arange(15), sorted(DT_model.feature_importances_, reverse=True)[:15])\n",
    "ax.set_xticks(np.arange(15))\n",
    "ax.set_xticklabels(names_sorted[:15], rotation = 90)\n",
    "plt.title('Feature importance from DT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top features on this chart are worth including in the neural net features list. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Plan\n",
    "After inspecting the data and considering a few approaches, I decided to remove the amenities feature as there were over two thousand unique values (within each list) and many were uniquely user-entered. I thought about picking specific amenities to one hot encode but ultimately decided against it since that would involve a lot of manual intervention (for example someone might put \"wifi\" as an amenity while someone else puts \"fast internet\" and so on for each example). I decided against TF-IDF for similar reasons. TF-IDF actually makes little sense with this data because it might score \"dove soap\" over \"soap\" for its uniqueness.\n",
    "\n",
    "The rest of the features I'm going to select based on the correlation matrix/ KDE plot and feature importance chart. Most features are already floats or ints which means they're already prepared for training. I've already encoded room types and boroughs so those are all set. All other object types (strings) will not be selected going forward.\n",
    "\n",
    "I’ve decided to drop examples with a price over \\$500 since that’s a really high nightly price which is probably caused by special circumstances like amenities and specific location which are not reflected in my training data. This seems reasonable given only 2.96% of examples are above 500 dollars.\n",
    "\n",
    "I’m also going to need to scale some features since most of my feature values are low, especially for one-hot encoded and/or binary features.\n",
    "\n",
    "I'll settle on the learning rate, loss function, optimization function, and number/type of hidden layers through iterative investigation.\n",
    "\n",
    "I plan to train my model by splitting my data into training and test sets. I'll tune the model with the training data and confirm the accuracy of the final model with the test set. The test set is only used at the very end.\n",
    "\n",
    "Since this is a regression model I’m using $R^2$, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) as my metrics:\n",
    "* $R^2$ is a measure of the proportion of variability in the prediction that the model was able to make using the test data. It represents the proportion of the variance for a dependent variable that’s explained by an independent variable. This is normalized so that a value of 1 indicates that all variance in price is explained by the model and a value of 0 indicates no variance is explained.\n",
    "* **MAE** is the average absolute difference between actual values and predicted values. Here, that’s how many dollars off the model predictions are on average. That makes this a particularly helpful metric for this project.\n",
    "* **RMSE** is the average magnitude of the errors between predicted values and actual values. Lower values represent better model performance since the average error is less.\n",
    "\n",
    "The feature list is below, sorted by the source:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_feature_list = [\n",
    "\t\n",
    "\t# From DT & corr matrix\n",
    "\t'room_Entire home/apt',\n",
    "\t'bedrooms',\n",
    "\t'borough_Manhattan',\n",
    "\t'availability_60',\n",
    "\t\n",
    "\t# From DT feature importance\n",
    "\t'calculated_host_listings_count_private_rooms',\n",
    "\t'bathrooms',\n",
    "\t'room_Shared room',\n",
    "\t'maximum_minimum_nights',\n",
    "\t\n",
    "\t# From corr matrix\n",
    "\t'accommodates',\n",
    "\t'beds', # note that is different from bedrooms\n",
    "\t'room_Hotel room',\n",
    "\t'review_scores_location',\n",
    "\t'review_scores_cleanliness',\n",
    "\t'borough_Queens',\n",
    "\t'minimum_nights',\n",
    "\t'borough_Brooklyn',\n",
    "\t'borough_Queens',\n",
    "\t'host_is_superhost',\n",
    "\t\n",
    "\t# My intuition\n",
    "\t'borough_Bronx',\n",
    "    'borough_Staten Island',\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-03 01:26:49.369540: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2024-08-03 01:26:49.369566: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.regularizers import l2\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that boroughs and rooms have already been one-hot encoded.\n",
    "\n",
    "I'm removing examples with missing data for `bedrooms` and `beds` because these are important, high correlation features. I don't want to set the missing values as the mean of the column since that would be training the model on potentially inaccurate information and I think there's enough data remaining to just remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>price</th>\n",
       "      <th>room_Entire home/apt</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>borough_Manhattan</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>room_Shared room</th>\n",
       "      <th>maximum_minimum_nights</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>...</th>\n",
       "      <th>room_Hotel room</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>borough_Queens</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>borough_Brooklyn</th>\n",
       "      <th>borough_Queens</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>borough_Bronx</th>\n",
       "      <th>borough_Staten Island</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4.71</td>\n",
       "      <td>4.49</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>275.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4.87</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.96</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  room_Entire home/apt  bedrooms  borough_Manhattan  availability_60  \\\n",
       "1   75.0                     1       1.0                  0                6   \n",
       "2  275.0                     1       2.0                  0                3   \n",
       "3   68.0                     0       1.0                  1               16   \n",
       "4   75.0                     0       1.0                  1                0   \n",
       "7   62.0                     0       1.0                  1                4   \n",
       "\n",
       "   calculated_host_listings_count_private_rooms  bathrooms  room_Shared room  \\\n",
       "1                                             0        1.0                 0   \n",
       "2                                             0        1.5                 0   \n",
       "3                                             1        1.0                 0   \n",
       "4                                             1        1.0                 0   \n",
       "7                                             1        1.0                 0   \n",
       "\n",
       "   maximum_minimum_nights  accommodates  ...  room_Hotel room  \\\n",
       "1                     1.0             3  ...                0   \n",
       "2                     5.0             4  ...                0   \n",
       "3                     2.0             2  ...                0   \n",
       "4                     2.0             1  ...                0   \n",
       "7                    30.0             1  ...                0   \n",
       "\n",
       "   review_scores_location  review_scores_cleanliness  borough_Queens  \\\n",
       "1                    4.71                       4.49               0   \n",
       "2                    4.50                       5.00               0   \n",
       "3                    4.87                       3.73               0   \n",
       "4                    4.94                       4.82               0   \n",
       "7                    4.84                       4.96               0   \n",
       "\n",
       "   minimum_nights  borough_Brooklyn  borough_Queens  host_is_superhost  \\\n",
       "1               1                 1               0               True   \n",
       "2               5                 1               0               True   \n",
       "3               2                 0               0               True   \n",
       "4               2                 0               0               True   \n",
       "7              30                 0               0               True   \n",
       "\n",
       "   borough_Bronx  borough_Staten Island  \n",
       "1              0                      0  \n",
       "2              0                      0  \n",
       "3              0                      0  \n",
       "4              0                      0  \n",
       "7              0                      0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(subset=['bedrooms', 'beds'])\n",
    "df = df[['price']+new_feature_list]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23212, 21)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove examples where price is really higher per as these are uncommon outliers that aren't reflected well in the data provided. \n",
    "df = df[df['price'] < 500]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale minimum and max nights because the values can be huge compared to the rest of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "to_scale = ['maximum_minimum_nights', 'minimum_nights', 'review_scores_location', 'review_scores_cleanliness', 'availability_60']\n",
    "df[to_scale] = scaler.fit_transform(df[to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>price</th>\n",
       "      <th>room_Entire home/apt</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>borough_Manhattan</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>room_Shared room</th>\n",
       "      <th>maximum_minimum_nights</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>...</th>\n",
       "      <th>room_Hotel room</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>borough_Queens</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>borough_Brooklyn</th>\n",
       "      <th>borough_Queens</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>borough_Bronx</th>\n",
       "      <th>borough_Staten Island</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>275.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023219</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  room_Entire home/apt  bedrooms  borough_Manhattan  availability_60  \\\n",
       "1   75.0                   1.0       1.0                0.0         0.100000   \n",
       "2  275.0                   1.0       2.0                0.0         0.050000   \n",
       "3   68.0                   0.0       1.0                1.0         0.266667   \n",
       "4   75.0                   0.0       1.0                1.0         0.000000   \n",
       "7   62.0                   0.0       1.0                1.0         0.066667   \n",
       "\n",
       "   calculated_host_listings_count_private_rooms  bathrooms  room_Shared room  \\\n",
       "1                                           0.0        1.0               0.0   \n",
       "2                                           0.0        1.5               0.0   \n",
       "3                                           1.0        1.0               0.0   \n",
       "4                                           1.0        1.0               0.0   \n",
       "7                                           1.0        1.0               0.0   \n",
       "\n",
       "   maximum_minimum_nights  accommodates  ...  room_Hotel room  \\\n",
       "1                0.000000           3.0  ...              0.0   \n",
       "2                0.003203           4.0  ...              0.0   \n",
       "3                0.000801           2.0  ...              0.0   \n",
       "4                0.000801           1.0  ...              0.0   \n",
       "7                0.023219           1.0  ...              0.0   \n",
       "\n",
       "   review_scores_location  review_scores_cleanliness  borough_Queens  \\\n",
       "1                   0.942                      0.898             0.0   \n",
       "2                   0.900                      1.000             0.0   \n",
       "3                   0.974                      0.746             0.0   \n",
       "4                   0.988                      0.964             0.0   \n",
       "7                   0.968                      0.992             0.0   \n",
       "\n",
       "   minimum_nights  borough_Brooklyn  borough_Queens  host_is_superhost  \\\n",
       "1        0.000000               1.0             0.0                1.0   \n",
       "2        0.003203               1.0             0.0                1.0   \n",
       "3        0.000801               0.0             0.0                1.0   \n",
       "4        0.000801               0.0             0.0                1.0   \n",
       "7        0.023219               0.0             0.0                1.0   \n",
       "\n",
       "   borough_Bronx  borough_Staten Island  \n",
       "1            0.0                    0.0  \n",
       "2            0.0                    0.0  \n",
       "3            0.0                    0.0  \n",
       "4            0.0                    0.0  \n",
       "7            0.0                    0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the df to floats for Keras\n",
    "df = df.astype('float64')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data for training and testing\n",
    "X = df.drop(columns='price')\n",
    "y = df['price']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.50, random_state=42)\n",
    "\n",
    "# Convert to numpy arrays for Keras\n",
    "X_train = X_train.to_numpy()\n",
    "y_train = y_train.to_numpy()\n",
    "X_test = X_test.to_numpy()\n",
    "y_test = y_test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11606, 20)\n",
      "(11606,)\n",
      "(11606, 20)\n",
      "(11606,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building and Training\n",
    "Create the model, define the optimization algorithm, define the loss function, and compile the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input layer's input shape is equal to the number of features of each training example.\n",
    "\n",
    "Since this is a regression problem, our output layer should be linear so that our prediction is a real number. Classification functions, like sigmoid, which makes a prediction between 0 and 1, won't work here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hl_2 (Dense)                 (None, 32)                672       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "hl_3 (Dense)                 (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 1,217\n",
      "Trainable params: 1,217\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-03 01:26:50.479817: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2024-08-03 01:26:50.479840: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2024-08-03 01:26:50.479857: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (i-016e883cadaad8f94): /proc/driver/nvidia/version does not exist\n",
      "2024-08-03 01:26:50.480039: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Create the model\n",
    "model = keras.Sequential()\n",
    "\n",
    "# Input layer\n",
    "model.add(keras.layers.InputLayer(input_shape=(X_train.shape[1]), name='input'))\n",
    "\n",
    "# Hidden Layer 1 - removed because of overfitting\n",
    "# model.add(keras.layers.Dense(units=64, activation='relu', name='hl_1', kernel_regularizer=l2(0.01)))\n",
    "# model.add(keras.layers.Dropout(0.2))  # Add dropout for regularization\n",
    "\n",
    "# Hidden Layer 2\n",
    "model.add(keras.layers.Dense(units=32, activation='relu', name='hl_2', kernel_regularizer=l2(0.01)))\n",
    "model.add(keras.layers.Dropout(0.2))  # Add dropout for regularization\n",
    "\n",
    "# Hidden Layer 3\n",
    "model.add(keras.layers.Dense(units=16, activation='relu', name='hl_3', kernel_regularizer=l2(0.01)))\n",
    "\n",
    "# Output Layer\n",
    "output_layer = keras.layers.Dense(units=1, activation='linear', name='output')\n",
    "model.add(output_layer)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I was back and forth between mean squared error and MAE as my loss function but I think mean squared error is slightly better here based on the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss function\n",
    "# loss_function = keras.losses.MeanAbsoluteError()\n",
    "loss_function = keras.losses.MeanSquaredError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I started with SGD as my optimizer but switched to [Adam](https://www.geeksforgeeks.org/adam-optimizer/), a similar gradient descent method, because it seemed to work better with large datasets. I immediately got better results so I'm sticking with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimization Algorithm\n",
    "opt_alg = keras.optimizers.Adam(learning_rate=.01) # learning rate set here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm using R^2 as the metric to measure the proportion of variability. This is built into keras with the method `R2Score()` but I don't have access to that in this VM so I'm essentially redefining it here as given in the docs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# R2 metric\n",
    "def r2(y_true, y_pred):\n",
    "    sum_square_res = keras.backend.sum(keras.backend.square(y_true - y_pred))\n",
    "    total = keras.backend.sum(keras.backend.square(y_true - keras.backend.mean(y_true)))\n",
    "    return 1 - sum_square_res / (total + keras.backend.epsilon())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile Model\n",
    "model.compile(optimizer=opt_alg, loss=loss_function, metrics=[keras.metrics.RootMeanSquaredError(name='rmse'), r2, 'mae']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit Model to Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The logger function from BreakThoughTech AI course materials to output every 50 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ProgBarLoggerNEpochs(keras.callbacks.Callback):\n",
    "\n",
    "    def __init__(self, num_epochs: int, every_n: int = 50):\n",
    "        self.num_epochs = num_epochs\n",
    "        self.every_n = every_n\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        if (epoch + 1) % self.every_n == 0:\n",
    "            s = 'Epoch [{}/ {}]'.format(epoch + 1, self.num_epochs)\n",
    "            logs_s = ['{}: {:.4f}'.format(k.capitalize(), v)\n",
    "                for k, v in logs.items()]\n",
    "            s_list = [s] + logs_s\n",
    "            print(', '.join(s_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fit()` method learns model parameters from the training data. It returns a history object with info for each epoch. The goal is to lower the RMSE and raise the R2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-03 01:26:50.568360: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2024-08-03 01:26:50.568731: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2649995000 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/ 50], Loss: 4292.0664, Rmse: 65.4429, R2: 0.4187, Mae: 45.3646\n",
      "Epoch [10/ 50], Loss: 4178.8511, Rmse: 64.5068, R2: 0.4411, Mae: 44.6876\n",
      "Epoch [15/ 50], Loss: 3963.3369, Rmse: 62.7522, R2: 0.4695, Mae: 43.4379\n",
      "Epoch [20/ 50], Loss: 3956.0317, Rmse: 62.6527, R2: 0.4697, Mae: 43.1164\n",
      "Epoch [25/ 50], Loss: 3891.5337, Rmse: 62.1023, R2: 0.4784, Mae: 42.6725\n",
      "Epoch [30/ 50], Loss: 3928.4497, Rmse: 62.3673, R2: 0.4790, Mae: 42.7180\n",
      "Epoch [35/ 50], Loss: 3861.1660, Rmse: 61.8027, R2: 0.4782, Mae: 42.5891\n",
      "Epoch [40/ 50], Loss: 3839.3782, Rmse: 61.6045, R2: 0.4824, Mae: 42.3854\n",
      "Epoch [45/ 50], Loss: 3840.3708, Rmse: 61.5928, R2: 0.4930, Mae: 42.3004\n",
      "Epoch [50/ 50], Loss: 3784.7981, Rmse: 61.1296, R2: 0.4939, Mae: 42.0593\n",
      "Elapsed time: 9.57s\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 50 # number of epochs\n",
    "\n",
    "t0 = time.time()\n",
    "\n",
    "history = model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    epochs=num_epochs,\n",
    "    verbose=0,\n",
    "    callbacks=[ProgBarLoggerNEpochs(num_epochs, every_n=5)]\n",
    ")\n",
    "\n",
    "t1 = time.time()\n",
    "print(f'Elapsed time: {t1-t0:.2f}s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize training loss and accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, num_epochs + 1), history.history['loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss (mae)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, num_epochs + 1), history.history['rmse'], color='orange')\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('RMSE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, num_epochs + 1), history.history['r2'], color='green')\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('R2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking with Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 124.69999694824219 | Actual: 95.0 | Difference: 29.7\n",
      "Predicted: 252.10000610351562 | Actual: 275.0 | Difference: -22.9\n",
      "Predicted: 77.0999984741211 | Actual: 48.0 | Difference: 29.1\n",
      "Predicted: 78.5 | Actual: 89.0 | Difference: -10.5\n",
      "Predicted: 26.899999618530273 | Actual: 38.0 | Difference: -11.1\n",
      "Predicted: 86.19999694824219 | Actual: 90.0 | Difference: -3.8\n",
      "Predicted: 74.5999984741211 | Actual: 58.0 | Difference: 16.6\n",
      "Predicted: 66.0 | Actual: 41.0 | Difference: 25.0\n",
      "Predicted: 194.5 | Actual: 149.0 | Difference: 45.5\n",
      "Predicted: 79.19999694824219 | Actual: 65.0 | Difference: 14.2\n",
      "Predicted: 103.9000015258789 | Actual: 92.0 | Difference: 11.9\n",
      "Predicted: 174.10000610351562 | Actual: 300.0 | Difference: -125.9\n",
      "Predicted: 149.8000030517578 | Actual: 175.0 | Difference: -25.2\n",
      "Predicted: 213.10000610351562 | Actual: 180.0 | Difference: 33.1\n",
      "Predicted: 79.19999694824219 | Actual: 96.0 | Difference: -16.8\n",
      "Predicted: 128.39999389648438 | Actual: 160.0 | Difference: -31.6\n",
      "Predicted: 284.1000061035156 | Actual: 193.0 | Difference: 91.1\n",
      "Predicted: 250.39999389648438 | Actual: 164.0 | Difference: 86.4\n",
      "Predicted: 152.1999969482422 | Actual: 125.0 | Difference: 27.2\n",
      "Predicted: 84.5999984741211 | Actual: 86.0 | Difference: -1.4\n"
     ]
    }
   ],
   "source": [
    "# Make predictions\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Print predictions and actual values\n",
    "for pred, actual in zip([round(pred[0], 1) for pred in predictions[:20]], y_test[:20]):\n",
    "    difference = round(pred - actual, 1)\n",
    "    print(f\"Predicted: {pred} | Actual: {actual} | Difference: {difference}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 40.42060232022785 (number of dollars the prediction is off by on average)\n",
      "RMSE: 60.432572076350986 (a lower value is better here)\n",
      "R2: 0.5336964784165437 (a higher value close to 1 here is good)\n"
     ]
    }
   ],
   "source": [
    "# Evaluation metrics with the test data\n",
    "\n",
    "# Mean absolute error\n",
    "mae_eval = mean_absolute_error(y_test, predictions)\n",
    "print(f'MAE: {mae_eval} (number of dollars the prediction is off by on average)')\n",
    "\n",
    "# Root Mean Squared Error\n",
    "mse_eval = mean_squared_error(y_test, predictions)\n",
    "rmse_eval= np.sqrt(mse_eval)\n",
    "print(f'RMSE: {rmse_eval} (a lower value is better here)')\n",
    "\n",
    "# R^2\n",
    "r2 = r2_score(y_test, predictions)\n",
    "print(f'R2: {r2} (a higher value close to 1 here is good)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "I got an MAE of 40.42, RMSE of 60.43, and $R^2$ of 0.53 on the test data. These are not particularly high values but it’s a big improvement from the decision tree trained earlier which had an $R^2$ of 0.196. It’s also an improvement from earlier iterations of the neural net. Overfitting was tricky because it could start happening quickly, but the charts helped set the epochs to a reasonable value before the lines flattened out.\n",
    "\n",
    "My goal for this project was to get an MAE under $50 since Airbnb prices are set by hosts and can be very subjective. Although an MAE of 40 is high, it’s a pretty good result for an application like this, especially without additional information about exact locations or amenities. The model doesn’t know if it’s looking at a luxury apartment or specific view, things that absolutely impact the price.\n"
   ]
  }
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