ny_borough_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Airbnb Lodgings in New York"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "Using a dataset provided by Airbnb, analysis and predictions will be made to understand what effects the total price of an Airbnb. Diving further, questions like, where do people tend to stay most?, how long do they stay there?, does booking at a certain time of year effect price?, where would be the most affordable place to book a Airbnb?,and  what is the average price for a stay? will be addressed. Regressor tree models will be used to predict price of future Airbnbs as well. In the end some results were to be expected but some also came with a surprice. It was no surprise that people stayed the longest in Manhattan or that service fees affected the total price the most. But what was surprising to me at least was that Bedford-Stuyvesant was the neighborhood that people stayed at the most. Upper East Side, Astoria, Prospect-Lefferts Gardens, and East Village were the locations that deemed the most affordable. Finally, people like to stay on average 1-2 nights at Airbnb."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import necessary libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "import math\n",
    "from datetime import datetime\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from statsmodels.formula.api import ols\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from scipy.stats import zscore\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder, LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import accuracy_score, roc_curve, classification_report\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import mean_squared_log_error\n",
    "from imblearn.over_sampling import SMOTE, ADASYN\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import metrics\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the dataset I got from Kaggle in a variable called open_data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "open_data = pd.read_csv('data/Airbnb_Open_Data.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking into the dataset to get a preview of what I will be working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>NAME</th>\n",
       "      <th>host id</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host name</th>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>country</th>\n",
       "      <th>...</th>\n",
       "      <th>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>last review</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>house_rules</th>\n",
       "      <th>license</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001254</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>80014485718</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Madaline</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$193</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10/19/2021</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>Clean up and treat the home the way you'd like...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002102</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>52335172823</td>\n",
       "      <td>verified</td>\n",
       "      <td>Jenna</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5/21/2022</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>Pet friendly but please confirm with me if the...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002403</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>78829239556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elise</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$124</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>I encourage you to use my kitchen, cooking and...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85098326012</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Garry</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$74</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>7/5/2019</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003689</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>92037596077</td>\n",
       "      <td>verified</td>\n",
       "      <td>Lyndon</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11/19/2018</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>Please no smoking in the house, porch or on th...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                              NAME      host id  \\\n",
       "0  1001254                Clean & quiet apt home by the park  80014485718   \n",
       "1  1002102                             Skylit Midtown Castle  52335172823   \n",
       "2  1002403               THE VILLAGE OF HARLEM....NEW YORK !  78829239556   \n",
       "3  1002755                                               NaN  85098326012   \n",
       "4  1003689  Entire Apt: Spacious Studio/Loft by central park  92037596077   \n",
       "\n",
       "  host_identity_verified host name neighbourhood group neighbourhood  \\\n",
       "0            unconfirmed  Madaline            Brooklyn    Kensington   \n",
       "1               verified     Jenna           Manhattan       Midtown   \n",
       "2                    NaN     Elise           Manhattan        Harlem   \n",
       "3            unconfirmed     Garry            Brooklyn  Clinton Hill   \n",
       "4               verified    Lyndon           Manhattan   East Harlem   \n",
       "\n",
       "        lat      long        country  ... service fee minimum nights  \\\n",
       "0  40.64749 -73.97237  United States  ...       $193            10.0   \n",
       "1  40.75362 -73.98377  United States  ...        $28            30.0   \n",
       "2  40.80902 -73.94190  United States  ...       $124             3.0   \n",
       "3  40.68514 -73.95976  United States  ...        $74            30.0   \n",
       "4  40.79851 -73.94399  United States  ...        $41            10.0   \n",
       "\n",
       "  number of reviews last review  reviews per month review rate number  \\\n",
       "0               9.0  10/19/2021               0.21                4.0   \n",
       "1              45.0   5/21/2022               0.38                4.0   \n",
       "2               0.0         NaN                NaN                5.0   \n",
       "3             270.0    7/5/2019               4.64                4.0   \n",
       "4               9.0  11/19/2018               0.10                3.0   \n",
       "\n",
       "  calculated host listings count  availability 365  \\\n",
       "0                            6.0             286.0   \n",
       "1                            2.0             228.0   \n",
       "2                            1.0             352.0   \n",
       "3                            1.0             322.0   \n",
       "4                            1.0             289.0   \n",
       "\n",
       "                                         house_rules license  \n",
       "0  Clean up and treat the home the way you'd like...     NaN  \n",
       "1  Pet friendly but please confirm with me if the...     NaN  \n",
       "2  I encourage you to use my kitchen, cooking and...     NaN  \n",
       "3                                                NaN     NaN  \n",
       "4  Please no smoking in the house, porch or on th...     NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking here how many initial rows of data there are and what type of data each column is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 102599 entries, 0 to 102598\n",
      "Data columns (total 26 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   id                              102599 non-null  int64  \n",
      " 1   NAME                            102349 non-null  object \n",
      " 2   host id                         102599 non-null  int64  \n",
      " 3   host_identity_verified          102310 non-null  object \n",
      " 4   host name                       102193 non-null  object \n",
      " 5   neighbourhood group             102570 non-null  object \n",
      " 6   neighbourhood                   102583 non-null  object \n",
      " 7   lat                             102591 non-null  float64\n",
      " 8   long                            102591 non-null  float64\n",
      " 9   country                         102067 non-null  object \n",
      " 10  country code                    102468 non-null  object \n",
      " 11  instant_bookable                102494 non-null  object \n",
      " 12  cancellation_policy             102523 non-null  object \n",
      " 13  room type                       102599 non-null  object \n",
      " 14  Construction year               102385 non-null  float64\n",
      " 15  price                           102352 non-null  object \n",
      " 16  service fee                     102326 non-null  object \n",
      " 17  minimum nights                  102190 non-null  float64\n",
      " 18  number of reviews               102416 non-null  float64\n",
      " 19  last review                     86706 non-null   object \n",
      " 20  reviews per month               86720 non-null   float64\n",
      " 21  review rate number              102273 non-null  float64\n",
      " 22  calculated host listings count  102280 non-null  float64\n",
      " 23  availability 365                102151 non-null  float64\n",
      " 24  house_rules                     50468 non-null   object \n",
      " 25  license                         2 non-null       object \n",
      "dtypes: float64(9), int64(2), object(15)\n",
      "memory usage: 20.4+ MB\n"
     ]
    }
   ],
   "source": [
    "open_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Could potentially use ID, neighborhood group, neighborhood, lat, long, service fee, minimum nights, number of reviews, last review, reviews per month, availability, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>NAME</th>\n",
       "      <th>host id</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host name</th>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>country</th>\n",
       "      <th>...</th>\n",
       "      <th>price</th>\n",
       "      <th>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>last review</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>house_rules</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001254</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>80014485718</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Madaline</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
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       "      <td>$193</td>\n",
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       "      <td>10/19/2021</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>Clean up and treat the home the way you'd like...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002102</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>52335172823</td>\n",
       "      <td>verified</td>\n",
       "      <td>Jenna</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$142</td>\n",
       "      <td>$28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5/21/2022</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>Pet friendly but please confirm with me if the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002403</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>78829239556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elise</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$620</td>\n",
       "      <td>$124</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>I encourage you to use my kitchen, cooking and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85098326012</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Garry</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$368</td>\n",
       "      <td>$74</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>7/5/2019</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003689</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>92037596077</td>\n",
       "      <td>verified</td>\n",
       "      <td>Lyndon</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>$204</td>\n",
       "      <td>$41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11/19/2018</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>Please no smoking in the house, porch or on th...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                              NAME      host id  \\\n",
       "0  1001254                Clean & quiet apt home by the park  80014485718   \n",
       "1  1002102                             Skylit Midtown Castle  52335172823   \n",
       "2  1002403               THE VILLAGE OF HARLEM....NEW YORK !  78829239556   \n",
       "3  1002755                                               NaN  85098326012   \n",
       "4  1003689  Entire Apt: Spacious Studio/Loft by central park  92037596077   \n",
       "\n",
       "  host_identity_verified host name neighbourhood group neighbourhood  \\\n",
       "0            unconfirmed  Madaline            Brooklyn    Kensington   \n",
       "1               verified     Jenna           Manhattan       Midtown   \n",
       "2                    NaN     Elise           Manhattan        Harlem   \n",
       "3            unconfirmed     Garry            Brooklyn  Clinton Hill   \n",
       "4               verified    Lyndon           Manhattan   East Harlem   \n",
       "\n",
       "        lat      long        country  ...  price service fee minimum nights  \\\n",
       "0  40.64749 -73.97237  United States  ...  $966        $193            10.0   \n",
       "1  40.75362 -73.98377  United States  ...  $142         $28            30.0   \n",
       "2  40.80902 -73.94190  United States  ...  $620        $124             3.0   \n",
       "3  40.68514 -73.95976  United States  ...  $368         $74            30.0   \n",
       "4  40.79851 -73.94399  United States  ...  $204         $41            10.0   \n",
       "\n",
       "  number of reviews  last review reviews per month review rate number  \\\n",
       "0               9.0   10/19/2021              0.21                4.0   \n",
       "1              45.0    5/21/2022              0.38                4.0   \n",
       "2               0.0          NaN               NaN                5.0   \n",
       "3             270.0     7/5/2019              4.64                4.0   \n",
       "4               9.0   11/19/2018              0.10                3.0   \n",
       "\n",
       "   calculated host listings count  availability 365  \\\n",
       "0                             6.0             286.0   \n",
       "1                             2.0             228.0   \n",
       "2                             1.0             352.0   \n",
       "3                             1.0             322.0   \n",
       "4                             1.0             289.0   \n",
       "\n",
       "                                         house_rules  \n",
       "0  Clean up and treat the home the way you'd like...  \n",
       "1  Pet friendly but please confirm with me if the...  \n",
       "2  I encourage you to use my kitchen, cooking and...  \n",
       "3                                                NaN  \n",
       "4  Please no smoking in the house, porch or on th...  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warning in column 25 due to multiple dtypes so removing for now \n",
    "open_data.drop(columns='license', axis=1, inplace = True)\n",
    "open_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing the shape of the dataset. 102599 rows by 25 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102599, 25)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Could find out neighborhood with most airbnbs, how long people use book for, "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking to see if there are other countries in the dataset other than USA.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United States', nan], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see if this is a constant value\n",
    "open_data.country.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because this value is constant, it can be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing this column since it is constant\n",
    "open_data.drop(columns='country', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing the columns I think I will need going forward and calling them main_column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing host id and host name b/c it's not necessary and last review for now\n",
    "main_columns = ['host_identity_verified','neighbourhood group','last review',\n",
    "               'neighbourhood','price','service fee', 'minimum nights', 'number of reviews',\n",
    "                'reviews per month', 'review rate number', \n",
    "               'calculated host listings count', 'availability 365']\n",
    "# Using these columns for Folium map later on in data analysis\n",
    "#df_foliumn = ['neighbourhood', 'price', 'lat', 'long' ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing one more check to make sure I selected the correct columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 102599 entries, 0 to 102598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   host_identity_verified          102310 non-null  object \n",
      " 1   neighbourhood group             102570 non-null  object \n",
      " 2   last review                     86706 non-null   object \n",
      " 3   neighbourhood                   102583 non-null  object \n",
      " 4   price                           102352 non-null  object \n",
      " 5   service fee                     102326 non-null  object \n",
      " 6   minimum nights                  102190 non-null  float64\n",
      " 7   number of reviews               102416 non-null  float64\n",
      " 8   reviews per month               86720 non-null   float64\n",
      " 9   review rate number              102273 non-null  float64\n",
      " 10  calculated host listings count  102280 non-null  float64\n",
      " 11  availability 365                102151 non-null  float64\n",
      "dtypes: float64(6), object(6)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data = open_data[main_columns]\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is where I go through every column that has NaNs and either drop those rows or replace them with another value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_identity_verified              289\n",
       "neighbourhood group                  29\n",
       "last review                       15893\n",
       "neighbourhood                        16\n",
       "price                               247\n",
       "service fee                         273\n",
       "minimum nights                      409\n",
       "number of reviews                   183\n",
       "reviews per month                 15879\n",
       "review rate number                  326\n",
       "calculated host listings count      319\n",
       "availability 365                    448\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for NaNs\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unconfirmed', 'verified', nan], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['host_identity_verified'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Switching  host_identity_verified's NaNs to unconfirmed \n",
    "data['host_identity_verified'] = data['host_identity_verified'].fillna('unconfirmed')\n",
    "data['host_identity_verified'].isna().sum()\n",
    "#data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing NaNs of host name to Unknown\n",
    "#data['host name'] = data['host name'].fillna('Unknown')\n",
    "#data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These rows get dropped because 29 and 16 rows is insignificant when there is a total of 100k+."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_identity_verified                0\n",
       "neighbourhood group                   0\n",
       "last review                       15889\n",
       "neighbourhood                         0\n",
       "price                               245\n",
       "service fee                         273\n",
       "minimum nights                      407\n",
       "number of reviews                   183\n",
       "reviews per month                 15877\n",
       "review rate number                  324\n",
       "calculated host listings count      319\n",
       "availability 365                    436\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the 29 NaNs from neighborhood group\n",
    "data.dropna(subset= ['neighbourhood group', 'neighbourhood'], inplace=True)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brooklyn', 'Manhattan', 'brookln', 'manhatan', 'Queens',\n",
       "       'Staten Island', 'Bronx'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['neighbourhood group'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I noticed Brooklyn and Manhattan were spelled wrong so those names needed to be replaced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing brooklyn with Brooklyn and manhatan with Manhattan\n",
    "data.loc[:, ['neighbourhood group']] = data['neighbourhood group'].str.replace('brookln', 'Brooklyn')\n",
    "data.loc[:, ['neighbourhood group']] = data['neighbourhood group'].str.replace('manhatan', 'Manhattan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turning NaNs into $ for now\n",
    "#data['price'] = data['price'].fillna('0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows where the price was a NaN got replaced with a 0. Using a string here would over complicate things. Also 245 NaNs were insignificant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:, ['price']] = data['price'].fillna('0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One other thing wrong with the price values were that they were strings with commas and dollar signs in them. These needed to be removed so the value could be converted to an integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing NaNs in Price column with the average price of an Airbnb in NY\n",
    "# First need to convert str to integer\n",
    "data.loc[:, ['price']] = data['price'].str.replace(',', '')\n",
    "data.loc[:, ['price']] = data['price'].str.replace('$', '')\n",
    "data.loc[:, ['price']] = data['price'].astype(int)\n",
    "data['price'].dtype\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now they are all integers we need to turn all the 0's into the price avg\n",
    "#data.loc[:, ['price']] = data['price'].replace(to_replace = 0, value = data['price'].mean(), inplace=True)\n",
    "#data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing the same thing here for service fee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing NaNs of Service fee\n",
    "data.loc[:, ['service fee']] = data['service fee'].fillna('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Doing the same thing with service fee\n",
    "data.loc[:, ['service fee']] = data['service fee'].str.replace(',', '')\n",
    "data.loc[:, ['service fee']] = data['service fee'].str.replace('$', '')\n",
    "data.loc[:, ['service fee']] = data['service fee'].astype(int)\n",
    "data['service fee'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_identity_verified                0\n",
       "neighbourhood group                   0\n",
       "last review                       15889\n",
       "neighbourhood                         0\n",
       "price                                 0\n",
       "service fee                           0\n",
       "minimum nights                      407\n",
       "number of reviews                   183\n",
       "reviews per month                 15877\n",
       "review rate number                  324\n",
       "calculated host listings count      319\n",
       "availability 365                    436\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacing minimum nights NaNs with 0's as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:, ['minimum nights']] = data['minimum nights'].fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert rest of strings into integers\n",
    "data.loc[:, ['minimum nights']] = data['minimum nights'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here, I thought it would be better to use the minimum night's average amount instead of 0. So I replaced the 0's with the min nights average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace temp 0's with minimum night avg\n",
    "data.loc[:, ['minimum nights']]= data.loc[:, ['minimum nights']].replace(to_replace=0, value = data['minimum nights'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_identity_verified                0\n",
       "neighbourhood group                   0\n",
       "last review                       15889\n",
       "neighbourhood                         0\n",
       "price                                 0\n",
       "service fee                           0\n",
       "minimum nights                        0\n",
       "number of reviews                   183\n",
       "reviews per month                 15877\n",
       "review rate number                  324\n",
       "calculated host listings count      319\n",
       "availability 365                    436\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decided to do the same thing with the number of reviews. I was trying to keep as many rows as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace NaNs with # of Review avg\n",
    "data.loc[:, ['number of reviews']] = data.loc[:, ['number of reviews']].replace(to_replace = np.nan, value =data['number of reviews'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_identity_verified                0\n",
       "neighbourhood group                   0\n",
       "last review                       15889\n",
       "neighbourhood                         0\n",
       "price                                 0\n",
       "service fee                           0\n",
       "minimum nights                        0\n",
       "number of reviews                     0\n",
       "reviews per month                 15877\n",
       "review rate number                  324\n",
       "calculated host listings count      319\n",
       "availability 365                    436\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert floats to Intergers\n",
    "data.loc[:, ['number of reviews']] = data.loc[:, ['number of reviews']].astype(int)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There was no simple way to convert the last review dates from NaNs into something meaningful, so unfortunately they needed to be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_identity_verified              0\n",
       "neighbourhood group                 0\n",
       "last review                         0\n",
       "neighbourhood                       0\n",
       "price                               0\n",
       "service fee                         0\n",
       "minimum nights                      0\n",
       "number of reviews                   0\n",
       "reviews per month                  12\n",
       "review rate number                287\n",
       "calculated host listings count    261\n",
       "availability 365                  170\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace NaNs with N/A because there is too much useful data to drop\n",
    "data.dropna(subset=['last review'], inplace=True)\n",
    "# Using N/A in place of the NaNs\n",
    "#data['last review'] = data['last review'].fillna('N/A')\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For future analysis, the remaining values for last review needed to first be converted to a datetime type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting last review column to datatime dtype\n",
    "\n",
    "data['last review'] = pd.to_datetime(data['last review'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2021-10-19\n",
       "1        2022-05-21\n",
       "3        2019-07-05\n",
       "4        2018-11-19\n",
       "5        2019-06-22\n",
       "            ...    \n",
       "102588   2019-06-29\n",
       "102591   2019-01-04\n",
       "102593   2015-09-06\n",
       "102595   2015-07-06\n",
       "102597   2015-10-11\n",
       "Name: last review, Length: 86665, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['last review']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because our models require our values to be either categorical or continuous, year, month and day needed to be in their own column. So that is what this code is doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 3 new columns for year, month, and day\n",
    "#Year\n",
    "data['lr_year'] = pd.DatetimeIndex(data['last review']).year\n",
    "#Month\n",
    "data['lr_month'] = pd.DatetimeIndex(data['last review']).month\n",
    "#Day\n",
    "data['lr_day'] = pd.DatetimeIndex(data['last review']).day\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 86665 entries, 0 to 102597\n",
      "Data columns (total 14 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   host_identity_verified          86665 non-null  object \n",
      " 1   neighbourhood group             86665 non-null  object \n",
      " 2   neighbourhood                   86665 non-null  object \n",
      " 3   price                           86665 non-null  int64  \n",
      " 4   service fee                     86665 non-null  int64  \n",
      " 5   minimum nights                  86665 non-null  float64\n",
      " 6   number of reviews               86665 non-null  int64  \n",
      " 7   reviews per month               86653 non-null  float64\n",
      " 8   review rate number              86378 non-null  float64\n",
      " 9   calculated host listings count  86404 non-null  float64\n",
      " 10  availability 365                86495 non-null  float64\n",
      " 11  lr_year                         86665 non-null  int64  \n",
      " 12  lr_month                        86665 non-null  int64  \n",
      " 13  lr_day                          86665 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(3)\n",
      "memory usage: 9.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# No need for the last review column anymore so dropping it\n",
    "data.drop(columns ='last review', axis=1, inplace=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_identity_verified              0\n",
       "neighbourhood group                 0\n",
       "neighbourhood                       0\n",
       "price                               0\n",
       "service fee                         0\n",
       "minimum nights                      0\n",
       "number of reviews                   0\n",
       "reviews per month                   0\n",
       "review rate number                287\n",
       "calculated host listings count    261\n",
       "availability 365                  170\n",
       "lr_year                             0\n",
       "lr_month                            0\n",
       "lr_day                              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace NaNs with average of reviews per month because there is too much useful data to drop\n",
    "#data['reviews per month'] = data['reviews per month'].fillna('N/A')\n",
    "data.loc[:, ['reviews per month']] = data.loc[:, ['reviews per month']].replace(to_replace = np.nan, value =data['reviews per month'].mean())\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert last review to datetime\n",
    "# data['last review'] = pd.to_datetime(data['last review'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remaining NaNs were too small to care about so they were also dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_identity_verified            0\n",
       "neighbourhood group               0\n",
       "neighbourhood                     0\n",
       "price                             0\n",
       "service fee                       0\n",
       "minimum nights                    0\n",
       "number of reviews                 0\n",
       "reviews per month                 0\n",
       "review rate number                0\n",
       "calculated host listings count    0\n",
       "availability 365                  0\n",
       "lr_year                           0\n",
       "lr_month                          0\n",
       "lr_day                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping NaNs of Review rate number, calculated host listings, and availability \n",
    "data.dropna(subset= ['review rate number', 'calculated host listings count', 'availability 365'], inplace=True)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 86017 entries, 0 to 102597\n",
      "Data columns (total 14 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   host_identity_verified          86017 non-null  object \n",
      " 1   neighbourhood group             86017 non-null  object \n",
      " 2   neighbourhood                   86017 non-null  object \n",
      " 3   price                           86017 non-null  int64  \n",
      " 4   service fee                     86017 non-null  int64  \n",
      " 5   minimum nights                  86017 non-null  float64\n",
      " 6   number of reviews               86017 non-null  int64  \n",
      " 7   reviews per month               86017 non-null  float64\n",
      " 8   review rate number              86017 non-null  float64\n",
      " 9   calculated host listings count  86017 non-null  float64\n",
      " 10  availability 365                86017 non-null  float64\n",
      " 11  lr_year                         86017 non-null  int64  \n",
      " 12  lr_month                        86017 non-null  int64  \n",
      " 13  lr_day                          86017 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(3)\n",
      "memory usage: 9.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally I have a dataset with cleaned data. Here is where I take a look at the final product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>lr_year</th>\n",
       "      <th>lr_month</th>\n",
       "      <th>lr_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>966</td>\n",
       "      <td>193</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>142</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>368</td>\n",
       "      <td>74</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>204</td>\n",
       "      <td>41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>577</td>\n",
       "      <td>115</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  host_identity_verified neighbourhood group neighbourhood  price  \\\n",
       "0            unconfirmed            Brooklyn    Kensington    966   \n",
       "1               verified           Manhattan       Midtown    142   \n",
       "3            unconfirmed            Brooklyn  Clinton Hill    368   \n",
       "4               verified           Manhattan   East Harlem    204   \n",
       "5               verified           Manhattan   Murray Hill    577   \n",
       "\n",
       "   service fee  minimum nights  number of reviews  reviews per month  \\\n",
       "0          193            10.0                  9               0.21   \n",
       "1           28            30.0                 45               0.38   \n",
       "3           74            30.0                270               4.64   \n",
       "4           41            10.0                  9               0.10   \n",
       "5          115             3.0                 74               0.59   \n",
       "\n",
       "   review rate number  calculated host listings count  availability 365  \\\n",
       "0                 4.0                             6.0             286.0   \n",
       "1                 4.0                             2.0             228.0   \n",
       "3                 4.0                             1.0             322.0   \n",
       "4                 3.0                             1.0             289.0   \n",
       "5                 3.0                             1.0             374.0   \n",
       "\n",
       "   lr_year  lr_month  lr_day  \n",
       "0     2021        10      19  \n",
       "1     2022         5      21  \n",
       "3     2019         7       5  \n",
       "4     2018        11      19  \n",
       "5     2019         6      22  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>lr_year</th>\n",
       "      <th>lr_month</th>\n",
       "      <th>lr_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "      <td>86017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>624.511922</td>\n",
       "      <td>124.830336</td>\n",
       "      <td>7.471016</td>\n",
       "      <td>32.253183</td>\n",
       "      <td>1.373066</td>\n",
       "      <td>3.279329</td>\n",
       "      <td>7.030122</td>\n",
       "      <td>141.920225</td>\n",
       "      <td>2018.997059</td>\n",
       "      <td>5.903077</td>\n",
       "      <td>15.849367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>332.793549</td>\n",
       "      <td>66.575394</td>\n",
       "      <td>27.907669</td>\n",
       "      <td>51.883511</td>\n",
       "      <td>1.744690</td>\n",
       "      <td>1.283225</td>\n",
       "      <td>29.380053</td>\n",
       "      <td>133.910756</td>\n",
       "      <td>1.668733</td>\n",
       "      <td>3.026654</td>\n",
       "      <td>9.814242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-365.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>338.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>623.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>913.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1200.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>5645.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>3677.000000</td>\n",
       "      <td>2058.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price   service fee  minimum nights  number of reviews  \\\n",
       "count  86017.000000  86017.000000    86017.000000       86017.000000   \n",
       "mean     624.511922    124.830336        7.471016          32.253183   \n",
       "std      332.793549     66.575394       27.907669          51.883511   \n",
       "min        0.000000      0.000000     -365.000000           1.000000   \n",
       "25%      338.000000     68.000000        2.000000           3.000000   \n",
       "50%      623.000000    125.000000        3.000000          11.000000   \n",
       "75%      913.000000    183.000000        5.000000          38.000000   \n",
       "max     1200.000000    240.000000     5645.000000        1024.000000   \n",
       "\n",
       "       reviews per month  review rate number  calculated host listings count  \\\n",
       "count       86017.000000        86017.000000                    86017.000000   \n",
       "mean            1.373066            3.279329                        7.030122   \n",
       "std             1.744690            1.283225                       29.380053   \n",
       "min             0.010000            1.000000                        1.000000   \n",
       "25%             0.220000            2.000000                        1.000000   \n",
       "50%             0.740000            3.000000                        1.000000   \n",
       "75%             2.000000            4.000000                        2.000000   \n",
       "max            90.000000            5.000000                      332.000000   \n",
       "\n",
       "       availability 365       lr_year      lr_month        lr_day  \n",
       "count      86017.000000  86017.000000  86017.000000  86017.000000  \n",
       "mean         141.920225   2018.997059      5.903077     15.849367  \n",
       "std          133.910756      1.668733      3.026654      9.814242  \n",
       "min          -10.000000   2012.000000      1.000000      1.000000  \n",
       "25%            6.000000   2018.000000      4.000000      6.000000  \n",
       "50%          101.000000   2019.000000      6.000000     17.000000  \n",
       "75%          266.000000   2019.000000      7.000000     24.000000  \n",
       "max         3677.000000   2058.000000     12.000000     31.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Value Counts of Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are over 10,000 columns when data is one hot encoded, so this number needs to be reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unconfirmed    43147\n",
       "verified       42870\n",
       "Name: host_identity_verified, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['host_identity_verified'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This just has 2 different values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['host name'].value_counts(normalize=True).to_frame().style.format('{:.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Has 13131 different hosts. To reduce this amount, hosts with only 5% of the total value counts will be categorized as \"Other\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I later realized the Host name values did not matter in the overall analysis of the models. So they were removed entirely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.loc[:, ['reviews per month']]\n",
    "#data['host name'].value_counts(normalize=True).mul(100).round(1).astype(str) + '%'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking on which was the most popular boroughs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brooklyn         35699\n",
       "Manhattan        35685\n",
       "Queens           11445\n",
       "Bronx             2340\n",
       "Staten Island      848\n",
       "Name: neighbourhood group, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['neighbourhood group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only 5 neighborhood groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bedford-Stuyvesant        6963\n",
       "Williamsburg              6526\n",
       "Harlem                    4712\n",
       "Bushwick                  4102\n",
       "Hell's Kitchen            3275\n",
       "                          ... \n",
       "Gerritsen Beach              3\n",
       "Glen Oaks                    2\n",
       "Woodrow                      1\n",
       "New Dorp                     1\n",
       "Chelsea, Staten Island       1\n",
       "Name: neighbourhood, Length: 223, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['neighbourhood'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is where things became a little difficult. There were too many neighborhoods, which caused our models later on to take too long. 223 neighborhoods to be exact causing 223 extra columns to be created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bedford-Stuyvesant        0.080949\n",
       "Williamsburg              0.075869\n",
       "Harlem                    0.054780\n",
       "Bushwick                  0.047688\n",
       "Hell's Kitchen            0.038074\n",
       "                            ...   \n",
       "Gerritsen Beach           0.000035\n",
       "Glen Oaks                 0.000023\n",
       "Woodrow                   0.000012\n",
       "New Dorp                  0.000012\n",
       "Chelsea, Staten Island    0.000012\n",
       "Name: neighbourhood, Length: 223, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['neighbourhood'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bedford-Stuyvesant        8.1%\n",
       "Williamsburg              7.6%\n",
       "Harlem                    5.5%\n",
       "Bushwick                  4.8%\n",
       "Hell's Kitchen            3.8%\n",
       "                          ... \n",
       "Gerritsen Beach           0.0%\n",
       "Glen Oaks                 0.0%\n",
       "Woodrow                   0.0%\n",
       "New Dorp                  0.0%\n",
       "Chelsea, Staten Island    0.0%\n",
       "Name: neighbourhood, Length: 223, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['neighbourhood'].value_counts(normalize=True).mul(100).round(1).astype(str) + '%' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value counts number of each neighborhood needed to be first converted to a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6963,\n",
       " 6526,\n",
       " 4712,\n",
       " 4102,\n",
       " 3275,\n",
       " 3101,\n",
       " 3031,\n",
       " 2891,\n",
       " 2774,\n",
       " 2496,\n",
       " 2047,\n",
       " 1832,\n",
       " 1721,\n",
       " 1654,\n",
       " 1565,\n",
       " 1470,\n",
       " 1229,\n",
       " 1137,\n",
       " 1087,\n",
       " 1083,\n",
       " 1022,\n",
       " 982,\n",
       " 951,\n",
       " 882,\n",
       " 848,\n",
       " 803,\n",
       " 743,\n",
       " 720,\n",
       " 708,\n",
       " 694,\n",
       " 683,\n",
       " 678,\n",
       " 609,\n",
       " 602,\n",
       " 585,\n",
       " 579,\n",
       " 570,\n",
       " 566,\n",
       " 545,\n",
       " 503,\n",
       " 497,\n",
       " 467,\n",
       " 466,\n",
       " 464,\n",
       " 457,\n",
       " 446,\n",
       " 433,\n",
       " 421,\n",
       " 414,\n",
       " 383,\n",
       " 382,\n",
       " 318,\n",
       " 308,\n",
       " 302,\n",
       " 298,\n",
       " 280,\n",
       " 261,\n",
       " 260,\n",
       " 253,\n",
       " 246,\n",
       " 239,\n",
       " 231,\n",
       " 223,\n",
       " 209,\n",
       " 207,\n",
       " 204,\n",
       " 203,\n",
       " 197,\n",
       " 190,\n",
       " 190,\n",
       " 180,\n",
       " 170,\n",
       " 166,\n",
       " 162,\n",
       " 156,\n",
       " 154,\n",
       " 147,\n",
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       " 143,\n",
       " 137,\n",
       " 135,\n",
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       " 127,\n",
       " 126,\n",
       " 126,\n",
       " 126,\n",
       " 124,\n",
       " 124,\n",
       " 123,\n",
       " 120,\n",
       " 113,\n",
       " 110,\n",
       " 109,\n",
       " 106,\n",
       " 106,\n",
       " 104,\n",
       " 102,\n",
       " 102,\n",
       " 101,\n",
       " 94,\n",
       " 92,\n",
       " 89,\n",
       " 88,\n",
       " 88,\n",
       " 80,\n",
       " 69,\n",
       " 69,\n",
       " 68,\n",
       " 67,\n",
       " 66,\n",
       " 63,\n",
       " 62,\n",
       " 61,\n",
       " 61,\n",
       " 61,\n",
       " 60,\n",
       " 60,\n",
       " 59,\n",
       " 58,\n",
       " 58,\n",
       " 57,\n",
       " 56,\n",
       " 54,\n",
       " 52,\n",
       " 50,\n",
       " 50,\n",
       " 48,\n",
       " 48,\n",
       " 47,\n",
       " 46,\n",
       " 46,\n",
       " 46,\n",
       " 44,\n",
       " 44,\n",
       " 43,\n",
       " 42,\n",
       " 42,\n",
       " 41,\n",
       " 40,\n",
       " 38,\n",
       " 38,\n",
       " 36,\n",
       " 35,\n",
       " 34,\n",
       " 33,\n",
       " 31,\n",
       " 30,\n",
       " 29,\n",
       " 29,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 26,\n",
       " 26,\n",
       " 25,\n",
       " 24,\n",
       " 24,\n",
       " 24,\n",
       " 23,\n",
       " 22,\n",
       " 22,\n",
       " 22,\n",
       " 22,\n",
       " 22,\n",
       " 21,\n",
       " 20,\n",
       " 20,\n",
       " 20,\n",
       " 20,\n",
       " 20,\n",
       " 19,\n",
       " 19,\n",
       " 19,\n",
       " 19,\n",
       " 17,\n",
       " 17,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 15,\n",
       " 15,\n",
       " 15,\n",
       " 14,\n",
       " 14,\n",
       " 14,\n",
       " 14,\n",
       " 13,\n",
       " 13,\n",
       " 10,\n",
       " 10,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 8,\n",
       " 7,\n",
       " 7,\n",
       " 7,\n",
       " 7,\n",
       " 7,\n",
       " 7,\n",
       " 7,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 5,\n",
       " 5,\n",
       " 5,\n",
       " 5,\n",
       " 4,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 1]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Storing value counts in a list\n",
    "#neighborhood_vc = list(data['neighbourhood'].value_counts(normalize=True).mul(100).round(2).values)\n",
    "#data['neighbourhood'].value_counts(normalize=True).mul(100).round(2).values\n",
    "neighborhood_vc = list(data['neighbourhood'].value_counts())\n",
    "neighborhood_vc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 223 different neighborhoods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To reduce the amount of neighborhoods, I need to only include neighborhoods with a value counts of over 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6963, 6526, 4712, 4102, 3275, 3101, 3031, 2891, 2774, 2496, 2047, 1832, 1721, 1654, 1565, 1470, 1229, 1137, 1087, 1083, 1022]\n"
     ]
    }
   ],
   "source": [
    "# Get list of value counts greater than 1000\n",
    "new_neighborhood = [x for x in neighborhood_vc if x > 1000]\n",
    "#for neighborhood in neighborhood_vc:\n",
    "#    if neighborhood_vc < 10:\n",
    "#        other = other + neighborhood_vc[neighborhood]\n",
    "        \n",
    "#print('Sum percentage neighborhoods that are less than 1 percent of the data ', other)\n",
    "print(new_neighborhood)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30299"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the sum of all other neighborhoods that people stayed less than 1000 times\n",
    "other = sum(neighborhood_vc) - sum(new_neighborhood)\n",
    "other"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removing neighborhoods visited less than 1000 times, I was left with 21 neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_neighborhood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bedford-Stuyvesant',\n",
       " 'Williamsburg',\n",
       " 'Harlem',\n",
       " 'Bushwick',\n",
       " \"Hell's Kitchen\",\n",
       " 'Upper West Side',\n",
       " 'Upper East Side',\n",
       " 'East Village',\n",
       " 'Crown Heights',\n",
       " 'Midtown',\n",
       " 'East Harlem',\n",
       " 'Chelsea',\n",
       " 'Greenpoint',\n",
       " 'Lower East Side',\n",
       " 'Astoria',\n",
       " 'Washington Heights',\n",
       " 'West Village',\n",
       " 'Financial District',\n",
       " 'Flatbush',\n",
       " 'East Flatbush',\n",
       " 'Prospect-Lefferts Gardens']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The Top 21 most visited neighborhoods with more than 1000 visits\n",
    "top_21 =data['neighbourhood'].value_counts().index[:21].tolist()\n",
    "top_21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neighborhoods outside of the top 21 needed to be converted to 'Other'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Clinton Hill',\n",
       " 'Long Island City',\n",
       " 'Flushing',\n",
       " 'Park Slope',\n",
       " 'Fort Greene',\n",
       " 'Sunset Park',\n",
       " 'Kips Bay',\n",
       " 'Ridgewood',\n",
       " 'Chinatown',\n",
       " 'Murray Hill',\n",
       " 'Sunnyside',\n",
       " 'Greenwich Village',\n",
       " 'Prospect Heights',\n",
       " 'Ditmars Steinway',\n",
       " 'SoHo',\n",
       " 'Gramercy',\n",
       " 'East New York',\n",
       " 'Jamaica',\n",
       " 'South Slope',\n",
       " 'Woodside',\n",
       " 'Morningside Heights',\n",
       " 'Inwood',\n",
       " 'Elmhurst',\n",
       " 'East Elmhurst',\n",
       " 'Nolita',\n",
       " 'Gowanus',\n",
       " 'Carroll Gardens',\n",
       " 'Jackson Heights',\n",
       " 'Theater District',\n",
       " 'Canarsie',\n",
       " 'Cypress Hills',\n",
       " 'Kensington',\n",
       " 'Boerum Hill',\n",
       " 'Sheepshead Bay',\n",
       " 'Windsor Terrace',\n",
       " 'Bay Ridge',\n",
       " 'Forest Hills',\n",
       " 'Brooklyn Heights',\n",
       " 'Tribeca',\n",
       " 'Springfield Gardens',\n",
       " 'Little Italy',\n",
       " 'Borough Park',\n",
       " 'Maspeth',\n",
       " 'Rego Park',\n",
       " 'Arverne',\n",
       " 'St. Albans',\n",
       " 'Richmond Hill',\n",
       " 'Midwood',\n",
       " 'Flatlands',\n",
       " 'Cobble Hill',\n",
       " 'Rockaway Beach',\n",
       " 'Woodhaven',\n",
       " 'Red Hook',\n",
       " 'Rosedale',\n",
       " 'Corona',\n",
       " 'Brownsville',\n",
       " 'Brighton Beach',\n",
       " 'Wakefield',\n",
       " 'Roosevelt Island',\n",
       " 'Bensonhurst',\n",
       " 'Mott Haven',\n",
       " 'Kingsbridge',\n",
       " 'Fordham',\n",
       " 'Downtown Brooklyn',\n",
       " 'Flatiron District',\n",
       " 'Ozone Park',\n",
       " 'South Ozone Park',\n",
       " 'Glendale',\n",
       " 'Queens Village',\n",
       " 'NoHo',\n",
       " 'Two Bridges',\n",
       " 'Bayside',\n",
       " 'Longwood',\n",
       " 'Concourse',\n",
       " 'Gravesend',\n",
       " 'St. George',\n",
       " 'Fort Hamilton',\n",
       " 'Tompkinsville',\n",
       " 'Briarwood',\n",
       " 'Fresh Meadows',\n",
       " 'Parkchester',\n",
       " 'Port Morris',\n",
       " 'Williamsbridge',\n",
       " 'Allerton',\n",
       " 'Columbia St',\n",
       " 'Pelham Gardens',\n",
       " 'Kew Gardens',\n",
       " 'Civic Center',\n",
       " 'Battery Park City',\n",
       " 'Far Rockaway',\n",
       " 'Laurelton',\n",
       " 'Highbridge',\n",
       " 'Concourse Village',\n",
       " 'Norwood',\n",
       " 'Stuyvesant Town',\n",
       " 'Middle Village',\n",
       " 'Cambria Heights',\n",
       " 'Stapleton',\n",
       " 'Vinegar Hill',\n",
       " 'Concord',\n",
       " 'Kew Gardens Hills',\n",
       " 'West Brighton',\n",
       " 'Throgs Neck',\n",
       " 'Mount Hope',\n",
       " 'DUMBO',\n",
       " 'Arrochar',\n",
       " 'Hunts Point',\n",
       " 'Coney Island',\n",
       " 'Schuylerville',\n",
       " 'Bath Beach',\n",
       " 'University Heights',\n",
       " 'Clason Point',\n",
       " 'Bronxdale',\n",
       " 'Claremont Village',\n",
       " 'Howard Beach',\n",
       " 'Jamaica Estates',\n",
       " 'College Point',\n",
       " 'Morris Park',\n",
       " 'Belmont',\n",
       " 'Pelham Bay',\n",
       " 'Randall Manor',\n",
       " 'City Island',\n",
       " 'Clifton',\n",
       " 'Morris Heights',\n",
       " 'Hollis',\n",
       " 'Soundview',\n",
       " 'Bayswater',\n",
       " 'Tremont',\n",
       " 'Woodlawn',\n",
       " 'Baychester',\n",
       " 'Bergen Beach',\n",
       " 'Edenwald',\n",
       " 'Navy Yard',\n",
       " 'Belle Harbor',\n",
       " 'Edgemere',\n",
       " 'Fieldston',\n",
       " 'Great Kills',\n",
       " 'Marble Hill',\n",
       " 'Dyker Heights',\n",
       " 'Eastchester',\n",
       " 'Van Nest',\n",
       " 'Morrisania',\n",
       " 'Mariners Harbor',\n",
       " 'Douglaston',\n",
       " 'Westchester Square',\n",
       " 'Port Richmond',\n",
       " 'Manhattan Beach',\n",
       " 'Grant City',\n",
       " 'East Morrisania',\n",
       " 'Jamaica Hills',\n",
       " 'Melrose',\n",
       " 'North Riverdale',\n",
       " 'Unionport',\n",
       " 'Whitestone',\n",
       " 'Bellerose',\n",
       " 'Grymes Hill',\n",
       " 'Olinville',\n",
       " 'Mount Eden',\n",
       " 'Shore Acres',\n",
       " 'South Beach',\n",
       " 'New Springville',\n",
       " 'New Brighton',\n",
       " 'Midland Beach',\n",
       " 'Rosebank',\n",
       " 'Oakwood',\n",
       " 'Sea Gate',\n",
       " 'Mill Basin',\n",
       " 'Riverdale',\n",
       " 'Dongan Hills',\n",
       " 'Castleton Corners',\n",
       " 'Tottenville',\n",
       " 'Arden Heights',\n",
       " 'Huguenot',\n",
       " 'Silver Lake',\n",
       " 'Spuyten Duyvil',\n",
       " 'Castle Hill',\n",
       " 'Todt Hill',\n",
       " 'New Dorp Beach',\n",
       " 'Bay Terrace',\n",
       " 'Breezy Point',\n",
       " 'Neponsit',\n",
       " 'Graniteville',\n",
       " 'Emerson Hill',\n",
       " 'West Farms',\n",
       " 'Little Neck',\n",
       " 'Howland Hook',\n",
       " 'Eltingville',\n",
       " 'Holliswood',\n",
       " \"Bull's Head\",\n",
       " 'Westerleigh',\n",
       " 'Richmondtown',\n",
       " 'Co-op City',\n",
       " \"Prince's Bay\",\n",
       " 'Bay Terrace, Staten Island',\n",
       " 'Lighthouse Hill',\n",
       " 'Rossville',\n",
       " 'Willowbrook',\n",
       " 'Gerritsen Beach',\n",
       " 'Glen Oaks',\n",
       " 'Woodrow',\n",
       " 'New Dorp',\n",
       " 'Chelsea, Staten Island']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get list of neighborhoods outside top 21\n",
    "outside_21 = data['neighbourhood'].value_counts().index[21:].tolist()\n",
    "outside_21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a DF with just neighborhoods in the top 21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>host_identity_verified</th>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>lr_year</th>\n",
       "      <th>lr_month</th>\n",
       "      <th>lr_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>142</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>204</td>\n",
       "      <td>41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>1060</td>\n",
       "      <td>212</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>1018</td>\n",
       "      <td>204</td>\n",
       "      <td>2.0</td>\n",
       "      <td>430</td>\n",
       "      <td>3.47</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102585</th>\n",
       "      <td>verified</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>643</td>\n",
       "      <td>129</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.08</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102586</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Upper East Side</td>\n",
       "      <td>208</td>\n",
       "      <td>42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102587</th>\n",
       "      <td>verified</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>388</td>\n",
       "      <td>78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102591</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Upper West Side</td>\n",
       "      <td>1139</td>\n",
       "      <td>228</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102593</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Village</td>\n",
       "      <td>1099</td>\n",
       "      <td>220</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55718 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_identity_verified neighbourhood group       neighbourhood  price  \\\n",
       "1                    verified           Manhattan             Midtown    142   \n",
       "4                    verified           Manhattan         East Harlem    204   \n",
       "6                 unconfirmed            Brooklyn  Bedford-Stuyvesant     71   \n",
       "7                 unconfirmed            Brooklyn  Bedford-Stuyvesant   1060   \n",
       "8                    verified           Manhattan      Hell's Kitchen   1018   \n",
       "...                       ...                 ...                 ...    ...   \n",
       "102585               verified            Brooklyn        Williamsburg    643   \n",
       "102586               verified           Manhattan     Upper East Side    208   \n",
       "102587               verified            Brooklyn        Williamsburg    388   \n",
       "102591               verified           Manhattan     Upper West Side   1139   \n",
       "102593               verified           Manhattan        East Village   1099   \n",
       "\n",
       "        service fee  minimum nights  number of reviews  reviews per month  \\\n",
       "1                28            30.0                 45               0.38   \n",
       "4                41            10.0                  9               0.10   \n",
       "6                14            45.0                 49               0.40   \n",
       "7               212            45.0                 49               0.40   \n",
       "8               204             2.0                430               3.47   \n",
       "...             ...             ...                ...                ...   \n",
       "102585          129             5.0                  4               0.08   \n",
       "102586           42             4.0                  8               0.17   \n",
       "102587           78             1.0                 66               1.35   \n",
       "102591          228             5.0                 17               0.35   \n",
       "102593          220             1.0                  8               0.16   \n",
       "\n",
       "        review rate number  calculated host listings count  availability 365  \\\n",
       "1                      4.0                             2.0             228.0   \n",
       "4                      3.0                             1.0             289.0   \n",
       "6                      5.0                             1.0             224.0   \n",
       "7                      5.0                             1.0             219.0   \n",
       "8                      3.0                             1.0             180.0   \n",
       "...                    ...                             ...               ...   \n",
       "102585                 5.0                             1.0             157.0   \n",
       "102586                 5.0                             1.0             159.0   \n",
       "102587                 2.0                             1.0             352.0   \n",
       "102591                 5.0                             1.0             134.0   \n",
       "102593                 4.0                             2.0             152.0   \n",
       "\n",
       "        lr_year  lr_month  lr_day  \n",
       "1          2022         5      21  \n",
       "4          2018        11      19  \n",
       "6          2017        10       5  \n",
       "7          2017        10       5  \n",
       "8          2019         6      24  \n",
       "...         ...       ...     ...  \n",
       "102585     2016         3      27  \n",
       "102586     2016         8       8  \n",
       "102587     2019         6      16  \n",
       "102591     2019         1       4  \n",
       "102593     2015         9       6  \n",
       "\n",
       "[55718 rows x 14 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping rows that contain neighbors outside of top 21\n",
    "top21_df = data[data['neighbourhood'].isin(top_21)]\n",
    "top21_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is where the neighborhoods outside of top 21 got converted to Other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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_identity_verified</th>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>lr_year</th>\n",
       "      <th>lr_month</th>\n",
       "      <th>lr_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>966</td>\n",
       "      <td>193</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>368</td>\n",
       "      <td>74</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>577</td>\n",
       "      <td>115</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Chinatown</td>\n",
       "      <td>319</td>\n",
       "      <td>64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>160</td>\n",
       "      <td>1.33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>verified</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>South Slope</td>\n",
       "      <td>580</td>\n",
       "      <td>116</td>\n",
       "      <td>4.0</td>\n",
       "      <td>167</td>\n",
       "      <td>1.34</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102580</th>\n",
       "      <td>verified</td>\n",
       "      <td>Queens</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>609</td>\n",
       "      <td>122</td>\n",
       "      <td>1.0</td>\n",
       "      <td>209</td>\n",
       "      <td>4.38</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102584</th>\n",
       "      <td>verified</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Arverne</td>\n",
       "      <td>566</td>\n",
       "      <td>113</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102588</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Flatiron District</td>\n",
       "      <td>618</td>\n",
       "      <td>124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>177</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102595</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Morningside Heights</td>\n",
       "      <td>837</td>\n",
       "      <td>167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102597</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>546</td>\n",
       "      <td>109</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30299 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_identity_verified neighbourhood group        neighbourhood  price  \\\n",
       "0                 unconfirmed            Brooklyn           Kensington    966   \n",
       "3                 unconfirmed            Brooklyn         Clinton Hill    368   \n",
       "5                    verified           Manhattan          Murray Hill    577   \n",
       "10                   verified           Manhattan            Chinatown    319   \n",
       "13                   verified            Brooklyn          South Slope    580   \n",
       "...                       ...                 ...                  ...    ...   \n",
       "102580               verified              Queens        East Elmhurst    609   \n",
       "102584               verified              Queens              Arverne    566   \n",
       "102588            unconfirmed           Manhattan    Flatiron District    618   \n",
       "102595            unconfirmed           Manhattan  Morningside Heights    837   \n",
       "102597            unconfirmed              Queens     Long Island City    546   \n",
       "\n",
       "        service fee  minimum nights  number of reviews  reviews per month  \\\n",
       "0               193            10.0                  9               0.21   \n",
       "3                74            30.0                270               4.64   \n",
       "5               115             3.0                 74               0.59   \n",
       "10               64             1.0                160               1.33   \n",
       "13              116             4.0                167               1.34   \n",
       "...             ...             ...                ...                ...   \n",
       "102580          122             1.0                209               4.38   \n",
       "102584          113             2.0                 89               1.82   \n",
       "102588          124             1.0                177               3.78   \n",
       "102595          167             1.0                  1               0.02   \n",
       "102597          109             2.0                  5               0.10   \n",
       "\n",
       "        review rate number  calculated host listings count  availability 365  \\\n",
       "0                      4.0                             6.0             286.0   \n",
       "3                      4.0                             1.0             322.0   \n",
       "5                      3.0                             1.0             374.0   \n",
       "10                     3.0                             4.0               1.0   \n",
       "13                     4.0                             3.0              47.0   \n",
       "...                    ...                             ...               ...   \n",
       "102580                 3.0                             1.0             270.0   \n",
       "102584                 2.0                             1.0             394.0   \n",
       "102588                 4.0                             1.0             410.0   \n",
       "102595                 2.0                             2.0             395.0   \n",
       "102597                 3.0                             1.0             386.0   \n",
       "\n",
       "        lr_year  lr_month  lr_day  \n",
       "0          2021        10      19  \n",
       "3          2019         7       5  \n",
       "5          2019         6      22  \n",
       "10         2019         6       9  \n",
       "13         2019         6      24  \n",
       "...         ...       ...     ...  \n",
       "102580     2019         6      11  \n",
       "102584     2019         5      26  \n",
       "102588     2019         6      29  \n",
       "102595     2015         7       6  \n",
       "102597     2015        10      11  \n",
       "\n",
       "[30299 rows x 14 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If neighborhood is outside of top 21 change neighborhood name to 'Other'\n",
    "outside21_df = data[data['neighbourhood'].isin(outside_21)]\n",
    "outside21_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>lr_year</th>\n",
       "      <th>lr_month</th>\n",
       "      <th>lr_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Other</td>\n",
       "      <td>966</td>\n",
       "      <td>193</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Other</td>\n",
       "      <td>368</td>\n",
       "      <td>74</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Other</td>\n",
       "      <td>577</td>\n",
       "      <td>115</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Other</td>\n",
       "      <td>319</td>\n",
       "      <td>64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>160</td>\n",
       "      <td>1.33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>verified</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Other</td>\n",
       "      <td>580</td>\n",
       "      <td>116</td>\n",
       "      <td>4.0</td>\n",
       "      <td>167</td>\n",
       "      <td>1.34</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102580</th>\n",
       "      <td>verified</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Other</td>\n",
       "      <td>609</td>\n",
       "      <td>122</td>\n",
       "      <td>1.0</td>\n",
       "      <td>209</td>\n",
       "      <td>4.38</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102584</th>\n",
       "      <td>verified</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Other</td>\n",
       "      <td>566</td>\n",
       "      <td>113</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102588</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Other</td>\n",
       "      <td>618</td>\n",
       "      <td>124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>177</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102595</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Other</td>\n",
       "      <td>837</td>\n",
       "      <td>167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102597</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Other</td>\n",
       "      <td>546</td>\n",
       "      <td>109</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30299 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_identity_verified neighbourhood group neighbourhood  price  \\\n",
       "0                 unconfirmed            Brooklyn         Other    966   \n",
       "3                 unconfirmed            Brooklyn         Other    368   \n",
       "5                    verified           Manhattan         Other    577   \n",
       "10                   verified           Manhattan         Other    319   \n",
       "13                   verified            Brooklyn         Other    580   \n",
       "...                       ...                 ...           ...    ...   \n",
       "102580               verified              Queens         Other    609   \n",
       "102584               verified              Queens         Other    566   \n",
       "102588            unconfirmed           Manhattan         Other    618   \n",
       "102595            unconfirmed           Manhattan         Other    837   \n",
       "102597            unconfirmed              Queens         Other    546   \n",
       "\n",
       "        service fee  minimum nights  number of reviews  reviews per month  \\\n",
       "0               193            10.0                  9               0.21   \n",
       "3                74            30.0                270               4.64   \n",
       "5               115             3.0                 74               0.59   \n",
       "10               64             1.0                160               1.33   \n",
       "13              116             4.0                167               1.34   \n",
       "...             ...             ...                ...                ...   \n",
       "102580          122             1.0                209               4.38   \n",
       "102584          113             2.0                 89               1.82   \n",
       "102588          124             1.0                177               3.78   \n",
       "102595          167             1.0                  1               0.02   \n",
       "102597          109             2.0                  5               0.10   \n",
       "\n",
       "        review rate number  calculated host listings count  availability 365  \\\n",
       "0                      4.0                             6.0             286.0   \n",
       "3                      4.0                             1.0             322.0   \n",
       "5                      3.0                             1.0             374.0   \n",
       "10                     3.0                             4.0               1.0   \n",
       "13                     4.0                             3.0              47.0   \n",
       "...                    ...                             ...               ...   \n",
       "102580                 3.0                             1.0             270.0   \n",
       "102584                 2.0                             1.0             394.0   \n",
       "102588                 4.0                             1.0             410.0   \n",
       "102595                 2.0                             2.0             395.0   \n",
       "102597                 3.0                             1.0             386.0   \n",
       "\n",
       "        lr_year  lr_month  lr_day  \n",
       "0          2021        10      19  \n",
       "3          2019         7       5  \n",
       "5          2019         6      22  \n",
       "10         2019         6       9  \n",
       "13         2019         6      24  \n",
       "...         ...       ...     ...  \n",
       "102580     2019         6      11  \n",
       "102584     2019         5      26  \n",
       "102588     2019         6      29  \n",
       "102595     2015         7       6  \n",
       "102597     2015        10      11  \n",
       "\n",
       "[30299 rows x 14 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the neighborhood groups outside of top 21 to Other\n",
    "outside21_df.loc[:,'neighbourhood'] = 'Other'\n",
    "outside21_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the top 21 DF and the newly converted outside 21 DF get combined back together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>service fee</th>\n",
       "      <th>minimum nights</th>\n",
       "      <th>number of reviews</th>\n",
       "      <th>reviews per month</th>\n",
       "      <th>review rate number</th>\n",
       "      <th>calculated host listings count</th>\n",
       "      <th>availability 365</th>\n",
       "      <th>lr_year</th>\n",
       "      <th>lr_month</th>\n",
       "      <th>lr_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>142</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>204</td>\n",
       "      <td>41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>1060</td>\n",
       "      <td>212</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>verified</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>1018</td>\n",
       "      <td>204</td>\n",
       "      <td>2.0</td>\n",
       "      <td>430</td>\n",
       "      <td>3.47</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102580</th>\n",
       "      <td>verified</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Other</td>\n",
       "      <td>609</td>\n",
       "      <td>122</td>\n",
       "      <td>1.0</td>\n",
       "      <td>209</td>\n",
       "      <td>4.38</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102584</th>\n",
       "      <td>verified</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Other</td>\n",
       "      <td>566</td>\n",
       "      <td>113</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102588</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Other</td>\n",
       "      <td>618</td>\n",
       "      <td>124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>177</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102595</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Other</td>\n",
       "      <td>837</td>\n",
       "      <td>167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102597</th>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Other</td>\n",
       "      <td>546</td>\n",
       "      <td>109</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86017 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_identity_verified neighbourhood group       neighbourhood  price  \\\n",
       "1                    verified           Manhattan             Midtown    142   \n",
       "4                    verified           Manhattan         East Harlem    204   \n",
       "6                 unconfirmed            Brooklyn  Bedford-Stuyvesant     71   \n",
       "7                 unconfirmed            Brooklyn  Bedford-Stuyvesant   1060   \n",
       "8                    verified           Manhattan      Hell's Kitchen   1018   \n",
       "...                       ...                 ...                 ...    ...   \n",
       "102580               verified              Queens               Other    609   \n",
       "102584               verified              Queens               Other    566   \n",
       "102588            unconfirmed           Manhattan               Other    618   \n",
       "102595            unconfirmed           Manhattan               Other    837   \n",
       "102597            unconfirmed              Queens               Other    546   \n",
       "\n",
       "        service fee  minimum nights  number of reviews  reviews per month  \\\n",
       "1                28            30.0                 45               0.38   \n",
       "4                41            10.0                  9               0.10   \n",
       "6                14            45.0                 49               0.40   \n",
       "7               212            45.0                 49               0.40   \n",
       "8               204             2.0                430               3.47   \n",
       "...             ...             ...                ...                ...   \n",
       "102580          122             1.0                209               4.38   \n",
       "102584          113             2.0                 89               1.82   \n",
       "102588          124             1.0                177               3.78   \n",
       "102595          167             1.0                  1               0.02   \n",
       "102597          109             2.0                  5               0.10   \n",
       "\n",
       "        review rate number  calculated host listings count  availability 365  \\\n",
       "1                      4.0                             2.0             228.0   \n",
       "4                      3.0                             1.0             289.0   \n",
       "6                      5.0                             1.0             224.0   \n",
       "7                      5.0                             1.0             219.0   \n",
       "8                      3.0                             1.0             180.0   \n",
       "...                    ...                             ...               ...   \n",
       "102580                 3.0                             1.0             270.0   \n",
       "102584                 2.0                             1.0             394.0   \n",
       "102588                 4.0                             1.0             410.0   \n",
       "102595                 2.0                             2.0             395.0   \n",
       "102597                 3.0                             1.0             386.0   \n",
       "\n",
       "        lr_year  lr_month  lr_day  \n",
       "1          2022         5      21  \n",
       "4          2018        11      19  \n",
       "6          2017        10       5  \n",
       "7          2017        10       5  \n",
       "8          2019         6      24  \n",
       "...         ...       ...     ...  \n",
       "102580     2019         6      11  \n",
       "102584     2019         5      26  \n",
       "102588     2019         6      29  \n",
       "102595     2015         7       6  \n",
       "102597     2015        10      11  \n",
       "\n",
       "[86017 rows x 14 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concat back top 21 df and outside 21 df\n",
    "combined_df = [top21_df, outside21_df]\n",
    "main_df = pd.concat(combined_df)\n",
    "main_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One more check of the value counts on neighborhood and now I have a more manageable amount of neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                        30299\n",
       "Bedford-Stuyvesant            6963\n",
       "Williamsburg                  6526\n",
       "Harlem                        4712\n",
       "Bushwick                      4102\n",
       "Hell's Kitchen                3275\n",
       "Upper West Side               3101\n",
       "Upper East Side               3031\n",
       "East Village                  2891\n",
       "Crown Heights                 2774\n",
       "Midtown                       2496\n",
       "East Harlem                   2047\n",
       "Chelsea                       1832\n",
       "Greenpoint                    1721\n",
       "Lower East Side               1654\n",
       "Astoria                       1565\n",
       "Washington Heights            1470\n",
       "West Village                  1229\n",
       "Financial District            1137\n",
       "Flatbush                      1087\n",
       "East Flatbush                 1083\n",
       "Prospect-Lefferts Gardens     1022\n",
       "Name: neighbourhood, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check back value counts\n",
    "#main_df[main_df['neighbourhood'] == 'Other']\n",
    "main_df['neighbourhood'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking range of years people booked for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019    42558\n",
       "2018    11378\n",
       "2022    10282\n",
       "2021     6705\n",
       "2017     6543\n",
       "2016     4282\n",
       "2020     2056\n",
       "2015     1860\n",
       "2014      244\n",
       "2013       80\n",
       "2012       26\n",
       "2040        1\n",
       "2026        1\n",
       "2058        1\n",
       "Name: lr_year, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df['lr_year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bookings range from 2012 to 2022."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                        30299\n",
       "Bedford-Stuyvesant            6963\n",
       "Williamsburg                  6526\n",
       "Harlem                        4712\n",
       "Bushwick                      4102\n",
       "Hell's Kitchen                3275\n",
       "Upper West Side               3101\n",
       "Upper East Side               3031\n",
       "East Village                  2891\n",
       "Crown Heights                 2774\n",
       "Midtown                       2496\n",
       "East Harlem                   2047\n",
       "Chelsea                       1832\n",
       "Greenpoint                    1721\n",
       "Lower East Side               1654\n",
       "Astoria                       1565\n",
       "Washington Heights            1470\n",
       "West Village                  1229\n",
       "Financial District            1137\n",
       "Flatbush                      1087\n",
       "East Flatbush                 1083\n",
       "Prospect-Lefferts Gardens     1022\n",
       "Name: neighbourhood, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_count = main_df['neighbourhood'].value_counts().head(25)\n",
    "neighborhood_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighborhood_count.plot(kind='bar',figsize=(15,6))\n",
    "plt.ylabel('Number of AirBnBs')\n",
    "plt.xlabel('Neighborhoods')\n",
    "plt.title('AirBnBs by Neighbourhood');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like people frequently stay in Bedford-Stuyvesant and Williamsburg the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>minimum nights</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Flatbush</th>\n",
       "      <td>649.732291</td>\n",
       "      <td>5.634775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Village</th>\n",
       "      <td>639.384052</td>\n",
       "      <td>8.522622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Flatbush</th>\n",
       "      <td>638.896584</td>\n",
       "      <td>7.335646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prospect-Lefferts Gardens</th>\n",
       "      <td>637.556751</td>\n",
       "      <td>5.952252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Astoria</th>\n",
       "      <td>636.775719</td>\n",
       "      <td>6.466647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bushwick</th>\n",
       "      <td>629.920770</td>\n",
       "      <td>7.470310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>627.626126</td>\n",
       "      <td>7.092746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harlem</th>\n",
       "      <td>626.583829</td>\n",
       "      <td>7.300790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower East Side</th>\n",
       "      <td>624.380895</td>\n",
       "      <td>6.172859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Williamsburg</th>\n",
       "      <td>623.298192</td>\n",
       "      <td>6.774705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chelsea</th>\n",
       "      <td>623.123362</td>\n",
       "      <td>9.607268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedford-Stuyvesant</th>\n",
       "      <td>622.865862</td>\n",
       "      <td>6.296154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Heights</th>\n",
       "      <td>622.171233</td>\n",
       "      <td>6.798886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Harlem</th>\n",
       "      <td>621.854421</td>\n",
       "      <td>6.445140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper West Side</th>\n",
       "      <td>618.917768</td>\n",
       "      <td>9.845602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial District</th>\n",
       "      <td>618.442392</td>\n",
       "      <td>7.967902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Village</th>\n",
       "      <td>618.354549</td>\n",
       "      <td>7.200004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hell's Kitchen</th>\n",
       "      <td>618.325802</td>\n",
       "      <td>9.078994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper East Side</th>\n",
       "      <td>617.705048</td>\n",
       "      <td>11.580703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greenpoint</th>\n",
       "      <td>615.894829</td>\n",
       "      <td>7.782978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Midtown</th>\n",
       "      <td>604.850561</td>\n",
       "      <td>9.650204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Heights</th>\n",
       "      <td>597.857823</td>\n",
       "      <td>7.688236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                price  minimum nights\n",
       "neighbourhood                                        \n",
       "Flatbush                   649.732291        5.634775\n",
       "West Village               639.384052        8.522622\n",
       "East Flatbush              638.896584        7.335646\n",
       "Prospect-Lefferts Gardens  637.556751        5.952252\n",
       "Astoria                    636.775719        6.466647\n",
       "Bushwick                   629.920770        7.470310\n",
       "Other                      627.626126        7.092746\n",
       "Harlem                     626.583829        7.300790\n",
       "Lower East Side            624.380895        6.172859\n",
       "Williamsburg               623.298192        6.774705\n",
       "Chelsea                    623.123362        9.607268\n",
       "Bedford-Stuyvesant         622.865862        6.296154\n",
       "Crown Heights              622.171233        6.798886\n",
       "East Harlem                621.854421        6.445140\n",
       "Upper West Side            618.917768        9.845602\n",
       "Financial District         618.442392        7.967902\n",
       "East Village               618.354549        7.200004\n",
       "Hell's Kitchen             618.325802        9.078994\n",
       "Upper East Side            617.705048       11.580703\n",
       "Greenpoint                 615.894829        7.782978\n",
       "Midtown                    604.850561        9.650204\n",
       "Washington Heights         597.857823        7.688236"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Neighborhood Average Price\n",
    "neighborhood_price = main_df[['price','neighbourhood','minimum nights']].groupby('neighbourhood').mean().sort_values(by='price',ascending=False)\n",
    "neighborhood_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting a look at which neighborhoods are most expensive on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighborhood_price.plot(kind='bar',figsize=(18,10))\n",
    "plt.ylabel('Average Price ')\n",
    "plt.title('Average Price of AirBnBs by Neighbourhood');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People spend the most money in Flatbush, West Village, East Flatbush, Prospect-Lefferts Gardens, and Astoria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>minimum nights</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Upper East Side</th>\n",
       "      <td>11.580703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper West Side</th>\n",
       "      <td>9.845602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Midtown</th>\n",
       "      <td>9.650204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chelsea</th>\n",
       "      <td>9.607268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hell's Kitchen</th>\n",
       "      <td>9.078994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Village</th>\n",
       "      <td>8.522622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial District</th>\n",
       "      <td>7.967902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greenpoint</th>\n",
       "      <td>7.782978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Heights</th>\n",
       "      <td>7.688236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bushwick</th>\n",
       "      <td>7.470310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    minimum nights\n",
       "neighbourhood                     \n",
       "Upper East Side          11.580703\n",
       "Upper West Side           9.845602\n",
       "Midtown                   9.650204\n",
       "Chelsea                   9.607268\n",
       "Hell's Kitchen            9.078994\n",
       "West Village              8.522622\n",
       "Financial District        7.967902\n",
       "Greenpoint                7.782978\n",
       "Washington Heights        7.688236\n",
       "Bushwick                  7.470310"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Neighborhood Average Price sorted by minimum nights\n",
    "neighborhood_min_nights = main_df[['neighbourhood','minimum nights']].groupby('neighbourhood').mean().sort_values(by='minimum nights',ascending=False).head(10)\n",
    "neighborhood_min_nights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting neighborhoods by most nights spent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighborhood_min_nights.plot(kind='bar',figsize=(10,9))\n",
    "plt.ylabel('Minimum Nights ')\n",
    "plt.title('Minimum Nights at AirBnB by Neighbourhood');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People stay the longest in Upper East Side, Upper West Side, Midtown, Chelsea, and Hell's Kitchen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>minimum nights</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>629.047969</td>\n",
       "      <td>6.070873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>628.259829</td>\n",
       "      <td>5.362738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>626.066669</td>\n",
       "      <td>6.836214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>621.333417</td>\n",
       "      <td>8.750596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>621.253538</td>\n",
       "      <td>5.062976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          price  minimum nights\n",
       "neighbourhood group                            \n",
       "Queens               629.047969        6.070873\n",
       "Bronx                628.259829        5.362738\n",
       "Brooklyn             626.066669        6.836214\n",
       "Manhattan            621.333417        8.750596\n",
       "Staten Island        621.253538        5.062976"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_group_price = data[['price','neighbourhood group', 'minimum nights']].groupby('neighbourhood group').mean().sort_values(by='price',ascending=False)\n",
    "neighborhood_group_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of NY's boroughs seem to have about the same average price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a DF with just Neighborhood, Latitude, longitude and Price\n",
    "#neighborhood_locations = open_data[df_foliumn]\n",
    "#neighborhood_locations.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting price and removings NaNs\n",
    "#neighborhood_locations.loc[:, ['price']] = neighborhood_locations['price'].fillna('0')\n",
    "\n",
    "# Replacing NaNs in Price column with the average price of an Airbnb in NY\n",
    "# First need to convert str to integer\n",
    "#neighborhood_locations.loc[:, ['price']] = neighborhood_locations['price'].str.replace(',', '')\n",
    "#neighborhood_locations.loc[:, ['price']] = neighborhood_locations['price'].str.replace('$', '')\n",
    "#neighborhood_locations.loc[:, ['price']] = neighborhood_locations['price'].astype(int)\n",
    "#neighborhood_locations['price'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check for NaNs\n",
    "#neighborhood_locations.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#neighborhood_locations.dropna(subset= ['neighbourhood', 'lat', 'long'], inplace=True)\n",
    "# Final Check for NaNs\n",
    "#neighborhood_locations.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1296 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize = (20,18));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking to see if any variable was normally distributed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not much normal distribution going on within these graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between price and service fee\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"Relationship between Price and Service Fee\", size=25, color=\"red\")\n",
    "sns.scatterplot(x=data.price, y=data['service fee'] );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "High room price means the service price will also be high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     21193\n",
       "1.0     20963\n",
       "3.0     14143\n",
       "30.0     9366\n",
       "4.0      5667\n",
       "Name: minimum nights, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_nights = data['minimum nights'].value_counts().head()\n",
    "min_nights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_nights.plot(kind='bar', figsize=(15,6))\n",
    "plt.ylabel('Number of Bookings')\n",
    "plt.xlabel('Minimum Nights Booked')\n",
    "plt.title('Minimum Nights Required for Booking');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing minimum nights required for booking. Most popular is 2 nights. Closely followed is 1 night then 3. Weirdly, 4th is 30 nights and last is 4 nights. \n",
    "The New York City Airbnb law says it's illegal to rent apartments for fewer than 30 days in most buildings, particularly Class A dwellings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['host_identity_verified', 'neighbourhood group', 'neighbourhood',\n",
       "       'price', 'service fee', 'minimum nights', 'number of reviews',\n",
       "       'reviews per month', 'review rate number',\n",
       "       'calculated host listings count', 'availability 365', 'lr_year',\n",
       "       'lr_month', 'lr_day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_corr = data.drop('price', axis=1)\n",
    "corr = check_corr.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr, center=0, annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No columns really show any correlations to one another, so there is no need to remove and similar columns. There is some with reviews per month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separating Continuous and Categorical Data & Creating Target Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepping for the train test split and one hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removed 'price' from continuous and last review because it was a dtype datetime\n",
    "# Removed host id and host name b/c it isn't necessary \n",
    "continuous = ['service fee', 'minimum nights','number of reviews','reviews per month',\n",
    "             'review rate number', 'calculated host listings count', 'availability 365',\n",
    "             'lr_year', 'lr_month','lr_day']\n",
    "categoricals = ['host_identity_verified','neighbourhood group','neighbourhood']\n",
    "# Creating categoricals without neighbourhood\n",
    "categoricals2 = ['host_identity_verified','neighbourhood group']\n",
    "# Creating Continuous without service fee b/c it is so highly correlated with price\n",
    "continuous2 = ['minimum nights','number of reviews','reviews per month',\n",
    "             'review rate number', 'calculated host listings count', 'availability 365',\n",
    "             'lr_year', 'lr_month','lr_day']\n",
    "# Creating Continuous with just top features\n",
    "continuous3 = ['number of reviews','reviews per month',\n",
    "               'calculated host listings count', 'availability 365',\n",
    "             'lr_year', 'lr_month','lr_day']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second continuous variable was created here to see what some models looked like with out service fee. Third continuous was created with just the top features in mind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cont = main_df[continuous]\n",
    "data_cont2= main_df[continuous2]\n",
    "data_cont3= main_df[continuous3]\n",
    "data_cat = main_df[categoricals]\n",
    "data_cat2 = main_df[categoricals2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Target variable\n",
    "target = main_df['price']\n",
    "main_dataframe = main_df.drop('price', axis=1)\n",
    "# Creating main_dataframe2 because service fee is so highly correlated\n",
    "main_dataframe2 = main_df.drop(['price', 'service fee'], axis=1)\n",
    "main_dataframe3 = main_df.drop(['price', 'service fee','neighbourhood','minimum nights',\n",
    "                               'review rate number'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train & Test Split + One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train, data_test, target_train, target_test = train_test_split(main_dataframe, target, \n",
    "                                                                    test_size = 0.25, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating second train test split with service fee removed from dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train2, data_test2, target_train2, target_test2 = train_test_split(main_dataframe2, target, \n",
    "                                                                    test_size = 0.25, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a third train test split with just most important features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train3, data_test3, target_train3, target_test3 = train_test_split(main_dataframe3, target, \n",
    "                                                                    test_size = 0.25, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One hot encode categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64512, 3)"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder(categories=\"auto\", sparse=False, handle_unknown=\"ignore\")\n",
    "ohe_train = ohe.fit_transform(data_train[categoricals])\n",
    "data_train[categoricals].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One Hot encode without neighbourhoods feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64512, 2)"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe2 = OneHotEncoder(categories=\"auto\", sparse=False, handle_unknown=\"ignore\")\n",
    "ohe_train2 = ohe2.fit_transform(data_train3[categoricals2])\n",
    "data_train3[categoricals2].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turning categorical data into numerical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64512, 29)"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create transformed dataframe\n",
    "ohe_train = pd.DataFrame(\n",
    "    ohe_train,\n",
    "    columns=ohe.get_feature_names(), index=data_train.index\n",
    "    \n",
    ")\n",
    "# Replace categorical data with encoded data\n",
    "data_train.drop(categoricals, axis=1, inplace=True)\n",
    "data_train = pd.concat([ohe_train, data_train], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 64512 entries, 61735 to 28036\n",
      "Data columns (total 39 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   x0_unconfirmed                  64512 non-null  float64\n",
      " 1   x0_verified                     64512 non-null  float64\n",
      " 2   x1_Bronx                        64512 non-null  float64\n",
      " 3   x1_Brooklyn                     64512 non-null  float64\n",
      " 4   x1_Manhattan                    64512 non-null  float64\n",
      " 5   x1_Queens                       64512 non-null  float64\n",
      " 6   x1_Staten Island                64512 non-null  float64\n",
      " 7   x2_Astoria                      64512 non-null  float64\n",
      " 8   x2_Bedford-Stuyvesant           64512 non-null  float64\n",
      " 9   x2_Bushwick                     64512 non-null  float64\n",
      " 10  x2_Chelsea                      64512 non-null  float64\n",
      " 11  x2_Crown Heights                64512 non-null  float64\n",
      " 12  x2_East Flatbush                64512 non-null  float64\n",
      " 13  x2_East Harlem                  64512 non-null  float64\n",
      " 14  x2_East Village                 64512 non-null  float64\n",
      " 15  x2_Financial District           64512 non-null  float64\n",
      " 16  x2_Flatbush                     64512 non-null  float64\n",
      " 17  x2_Greenpoint                   64512 non-null  float64\n",
      " 18  x2_Harlem                       64512 non-null  float64\n",
      " 19  x2_Hell's Kitchen               64512 non-null  float64\n",
      " 20  x2_Lower East Side              64512 non-null  float64\n",
      " 21  x2_Midtown                      64512 non-null  float64\n",
      " 22  x2_Other                        64512 non-null  float64\n",
      " 23  x2_Prospect-Lefferts Gardens    64512 non-null  float64\n",
      " 24  x2_Upper East Side              64512 non-null  float64\n",
      " 25  x2_Upper West Side              64512 non-null  float64\n",
      " 26  x2_Washington Heights           64512 non-null  float64\n",
      " 27  x2_West Village                 64512 non-null  float64\n",
      " 28  x2_Williamsburg                 64512 non-null  float64\n",
      " 29  service fee                     64512 non-null  int64  \n",
      " 30  minimum nights                  64512 non-null  float64\n",
      " 31  number of reviews               64512 non-null  int64  \n",
      " 32  reviews per month               64512 non-null  float64\n",
      " 33  review rate number              64512 non-null  float64\n",
      " 34  calculated host listings count  64512 non-null  float64\n",
      " 35  availability 365                64512 non-null  float64\n",
      " 36  lr_year                         64512 non-null  int64  \n",
      " 37  lr_month                        64512 non-null  int64  \n",
      " 38  lr_day                          64512 non-null  int64  \n",
      "dtypes: float64(34), int64(5)\n",
      "memory usage: 19.7 MB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 64512 entries, 61735 to 28036\n",
      "Data columns (total 38 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   x0_unconfirmed                  64512 non-null  float64\n",
      " 1   x0_verified                     64512 non-null  float64\n",
      " 2   x1_Bronx                        64512 non-null  float64\n",
      " 3   x1_Brooklyn                     64512 non-null  float64\n",
      " 4   x1_Manhattan                    64512 non-null  float64\n",
      " 5   x1_Queens                       64512 non-null  float64\n",
      " 6   x1_Staten Island                64512 non-null  float64\n",
      " 7   x2_Astoria                      64512 non-null  float64\n",
      " 8   x2_Bedford-Stuyvesant           64512 non-null  float64\n",
      " 9   x2_Bushwick                     64512 non-null  float64\n",
      " 10  x2_Chelsea                      64512 non-null  float64\n",
      " 11  x2_Crown Heights                64512 non-null  float64\n",
      " 12  x2_East Flatbush                64512 non-null  float64\n",
      " 13  x2_East Harlem                  64512 non-null  float64\n",
      " 14  x2_East Village                 64512 non-null  float64\n",
      " 15  x2_Financial District           64512 non-null  float64\n",
      " 16  x2_Flatbush                     64512 non-null  float64\n",
      " 17  x2_Greenpoint                   64512 non-null  float64\n",
      " 18  x2_Harlem                       64512 non-null  float64\n",
      " 19  x2_Hell's Kitchen               64512 non-null  float64\n",
      " 20  x2_Lower East Side              64512 non-null  float64\n",
      " 21  x2_Midtown                      64512 non-null  float64\n",
      " 22  x2_Other                        64512 non-null  float64\n",
      " 23  x2_Prospect-Lefferts Gardens    64512 non-null  float64\n",
      " 24  x2_Upper East Side              64512 non-null  float64\n",
      " 25  x2_Upper West Side              64512 non-null  float64\n",
      " 26  x2_Washington Heights           64512 non-null  float64\n",
      " 27  x2_West Village                 64512 non-null  float64\n",
      " 28  x2_Williamsburg                 64512 non-null  float64\n",
      " 29  minimum nights                  64512 non-null  float64\n",
      " 30  number of reviews               64512 non-null  int64  \n",
      " 31  reviews per month               64512 non-null  float64\n",
      " 32  review rate number              64512 non-null  float64\n",
      " 33  calculated host listings count  64512 non-null  float64\n",
      " 34  availability 365                64512 non-null  float64\n",
      " 35  lr_year                         64512 non-null  int64  \n",
      " 36  lr_month                        64512 non-null  int64  \n",
      " 37  lr_day                          64512 non-null  int64  \n",
      "dtypes: float64(34), int64(4)\n",
      "memory usage: 19.2 MB\n"
     ]
    }
   ],
   "source": [
    "# Merging second dataset without service fee\n",
    "# Replace categorical data with encoded data\n",
    "data_train2.drop(categoricals, axis=1, inplace=True)\n",
    "data_train2 = pd.concat([ohe_train, data_train2], axis=1)\n",
    "data_train2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create transformed dataframe\n",
    "ohe_train2 = pd.DataFrame(\n",
    "    ohe_train2,\n",
    "    columns=ohe2.get_feature_names(), index=data_train3.index\n",
    "    \n",
    ")\n",
    "# Replace categorical data with encoded data\n",
    "data_train3.drop(categoricals2, axis=1, inplace=True)\n",
    "# Merging just top features\n",
    "data_train3 = pd.concat([ohe_train2, data_train3], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 64512 entries, 61735 to 28036\n",
      "Data columns (total 14 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   x0_unconfirmed                  64512 non-null  float64\n",
      " 1   x0_verified                     64512 non-null  float64\n",
      " 2   x1_Bronx                        64512 non-null  float64\n",
      " 3   x1_Brooklyn                     64512 non-null  float64\n",
      " 4   x1_Manhattan                    64512 non-null  float64\n",
      " 5   x1_Queens                       64512 non-null  float64\n",
      " 6   x1_Staten Island                64512 non-null  float64\n",
      " 7   number of reviews               64512 non-null  int64  \n",
      " 8   reviews per month               64512 non-null  float64\n",
      " 9   calculated host listings count  64512 non-null  float64\n",
      " 10  availability 365                64512 non-null  float64\n",
      " 11  lr_year                         64512 non-null  int64  \n",
      " 12  lr_month                        64512 non-null  int64  \n",
      " 13  lr_day                          64512 non-null  int64  \n",
      "dtypes: float64(10), int64(4)\n",
      "memory usage: 7.4 MB\n"
     ]
    }
   ],
   "source": [
    "# Checking final dataset\n",
    "data_train3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use test \n",
    "\n",
    "# Create transformed dataframe\n",
    "ohe_test = ohe.transform(data_test[categoricals])\n",
    "\n",
    "ohe_test = pd.DataFrame(\n",
    "    ohe_test,\n",
    "    columns=ohe.get_feature_names(), index=data_test.index\n",
    "    \n",
    ")\n",
    "# Replace categorical data with encoded data\n",
    "data_test.drop(categoricals, axis=1, inplace=True)\n",
    "data_test = pd.concat([ohe_test, data_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use test \n",
    "\n",
    "# Create transformed dataframe\n",
    "ohe_test2 = ohe2.transform(data_test3[categoricals2])\n",
    "\n",
    "ohe_test2 = pd.DataFrame(\n",
    "    ohe_test2,\n",
    "    columns=ohe2.get_feature_names(), index=data_test3.index\n",
    "    \n",
    ")\n",
    "# Replace categorical data with encoded data\n",
    "data_test3.drop(categoricals2, axis=1, inplace=True)\n",
    "data_test3 = pd.concat([ohe_test2, data_test3], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Doing same thing with test data without service fee\n",
    "# Replace categorical data with encoded data\n",
    "data_test2.drop(categoricals, axis=1, inplace=True)\n",
    "data_test2 = pd.concat([ohe_test, data_test2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21505 entries, 91561 to 88669\n",
      "Data columns (total 14 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   x0_unconfirmed                  21505 non-null  float64\n",
      " 1   x0_verified                     21505 non-null  float64\n",
      " 2   x1_Bronx                        21505 non-null  float64\n",
      " 3   x1_Brooklyn                     21505 non-null  float64\n",
      " 4   x1_Manhattan                    21505 non-null  float64\n",
      " 5   x1_Queens                       21505 non-null  float64\n",
      " 6   x1_Staten Island                21505 non-null  float64\n",
      " 7   number of reviews               21505 non-null  int64  \n",
      " 8   reviews per month               21505 non-null  float64\n",
      " 9   calculated host listings count  21505 non-null  float64\n",
      " 10  availability 365                21505 non-null  float64\n",
      " 11  lr_year                         21505 non-null  int64  \n",
      " 12  lr_month                        21505 non-null  int64  \n",
      " 13  lr_day                          21505 non-null  int64  \n",
      "dtypes: float64(10), int64(4)\n",
      "memory usage: 2.5 MB\n"
     ]
    }
   ],
   "source": [
    "data_test3.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OLS Regression Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing which variables had any great impact on Airbnb price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_train\n",
    "y = target_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.978</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.978</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>8.093e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 06 Nov 2022</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:02:37</td>     <th>  Log-Likelihood:    </th> <td>-3.4255e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 64512</td>      <th>  AIC:               </th>  <td>6.852e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 64475</td>      <th>  BIC:               </th>  <td>6.855e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    36</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                          <td>  -88.5131</td> <td>  153.530</td> <td>   -0.577</td> <td> 0.564</td> <td> -389.432</td> <td>  212.405</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0_unconfirmed</th>                 <td>  -44.2086</td> <td>   76.765</td> <td>   -0.576</td> <td> 0.565</td> <td> -194.668</td> <td>  106.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0_verified</th>                    <td>  -44.3045</td> <td>   76.765</td> <td>   -0.577</td> <td> 0.564</td> <td> -194.765</td> <td>  106.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_Bronx</th>                       <td>  -16.8736</td> <td>   30.756</td> <td>   -0.549</td> <td> 0.583</td> <td>  -77.154</td> <td>   43.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_Brooklyn</th>                    <td>  -17.1883</td> <td>   30.689</td> <td>   -0.560</td> <td> 0.575</td> <td>  -77.340</td> <td>   42.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_Manhattan</th>                   <td>  -17.6437</td> <td>   30.659</td> <td>   -0.575</td> <td> 0.565</td> <td>  -77.736</td> <td>   42.449</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_Queens</th>                      <td>  -17.0828</td> <td>   30.725</td> <td>   -0.556</td> <td> 0.578</td> <td>  -77.304</td> <td>   43.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1_Staten Island</th>               <td>  -19.7248</td> <td>   30.782</td> <td>   -0.641</td> <td> 0.522</td> <td>  -80.058</td> <td>   40.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Astoria</th>                     <td>   -6.2975</td> <td>    7.112</td> <td>   -0.885</td> <td> 0.376</td> <td>  -20.237</td> <td>    7.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Bedford-Stuyvesant</th>          <td>   -4.0470</td> <td>    7.032</td> <td>   -0.576</td> <td> 0.565</td> <td>  -17.830</td> <td>    9.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Bushwick</th>                    <td>   -2.7252</td> <td>    7.032</td> <td>   -0.388</td> <td> 0.698</td> <td>  -16.507</td> <td>   11.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Chelsea</th>                     <td>   -3.4508</td> <td>    7.122</td> <td>   -0.484</td> <td> 0.628</td> <td>  -17.411</td> <td>   10.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Crown Heights</th>               <td>   -4.8392</td> <td>    7.061</td> <td>   -0.685</td> <td> 0.493</td> <td>  -18.678</td> <td>    9.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_East Flatbush</th>               <td>   -2.0758</td> <td>    7.246</td> <td>   -0.286</td> <td> 0.775</td> <td>  -16.277</td> <td>   12.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_East Harlem</th>                 <td>   -3.6134</td> <td>    7.118</td> <td>   -0.508</td> <td> 0.612</td> <td>  -17.564</td> <td>   10.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_East Village</th>                <td>   -5.0527</td> <td>    7.046</td> <td>   -0.717</td> <td> 0.473</td> <td>  -18.862</td> <td>    8.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Financial District</th>          <td>   -2.9938</td> <td>    7.150</td> <td>   -0.419</td> <td> 0.675</td> <td>  -17.009</td> <td>   11.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Flatbush</th>                    <td>   -2.7533</td> <td>    7.187</td> <td>   -0.383</td> <td> 0.702</td> <td>  -16.839</td> <td>   11.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Greenpoint</th>                  <td>   -4.1062</td> <td>    7.098</td> <td>   -0.578</td> <td> 0.563</td> <td>  -18.019</td> <td>    9.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Harlem</th>                      <td>   -2.3843</td> <td>    7.064</td> <td>   -0.338</td> <td> 0.736</td> <td>  -16.230</td> <td>   11.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Hell's Kitchen</th>              <td>   -4.1698</td> <td>    7.089</td> <td>   -0.588</td> <td> 0.556</td> <td>  -18.064</td> <td>    9.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Lower East Side</th>             <td>   -3.8904</td> <td>    7.133</td> <td>   -0.545</td> <td> 0.586</td> <td>  -17.872</td> <td>   10.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Midtown</th>                     <td>   -4.9726</td> <td>    7.119</td> <td>   -0.699</td> <td> 0.485</td> <td>  -18.925</td> <td>    8.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Other</th>                       <td>   -4.1062</td> <td>    7.000</td> <td>   -0.587</td> <td> 0.557</td> <td>  -17.826</td> <td>    9.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Prospect-Lefferts Gardens</th>   <td>   -5.3995</td> <td>    7.206</td> <td>   -0.749</td> <td> 0.454</td> <td>  -19.523</td> <td>    8.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Upper East Side</th>             <td>   -6.3507</td> <td>    7.075</td> <td>   -0.898</td> <td> 0.369</td> <td>  -20.217</td> <td>    7.515</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Upper West Side</th>             <td>   -4.6743</td> <td>    7.049</td> <td>   -0.663</td> <td> 0.507</td> <td>  -18.491</td> <td>    9.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Washington Heights</th>          <td>   -2.5956</td> <td>    7.136</td> <td>   -0.364</td> <td> 0.716</td> <td>  -16.582</td> <td>   11.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_West Village</th>                <td>   -3.2539</td> <td>    7.171</td> <td>   -0.454</td> <td> 0.650</td> <td>  -17.308</td> <td>   10.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2_Williamsburg</th>                <td>   -4.7611</td> <td>    7.002</td> <td>   -0.680</td> <td> 0.497</td> <td>  -18.485</td> <td>    8.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>service fee</th>                    <td>    4.9429</td> <td>    0.003</td> <td> 1706.296</td> <td> 0.000</td> <td>    4.937</td> <td>    4.949</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>minimum nights</th>                 <td>    0.0083</td> <td>    0.006</td> <td>    1.287</td> <td> 0.198</td> <td>   -0.004</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>number of reviews</th>              <td>   -0.0083</td> <td>    0.005</td> <td>   -1.755</td> <td> 0.079</td> <td>   -0.017</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>reviews per month</th>              <td>    0.2119</td> <td>    0.138</td> <td>    1.533</td> <td> 0.125</td> <td>   -0.059</td> <td>    0.483</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>review rate number</th>             <td>   -0.0007</td> <td>    0.151</td> <td>   -0.004</td> <td> 0.996</td> <td>   -0.298</td> <td>    0.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>calculated host listings count</th> <td>   -0.0120</td> <td>    0.007</td> <td>   -1.635</td> <td> 0.102</td> <td>   -0.026</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>availability 365</th>               <td>    0.0003</td> <td>    0.001</td> <td>    0.198</td> <td> 0.843</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lr_year</th>                        <td>    0.0800</td> <td>    0.133</td> <td>    0.603</td> <td> 0.547</td> <td>   -0.180</td> <td>    0.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lr_month</th>                       <td>   -0.0983</td> <td>    0.069</td> <td>   -1.426</td> <td> 0.154</td> <td>   -0.233</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lr_day</th>                         <td>    0.0553</td> <td>    0.020</td> <td>    2.798</td> <td> 0.005</td> <td>    0.017</td> <td>    0.094</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>65755.042</td> <th>  Durbin-Watson:     </th>   <td>   1.997</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>343304984.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 3.674</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>360.300</td>  <th>  Cond. No.          </th>   <td>1.00e+16</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 2.65e-21. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.978\n",
       "Model:                            OLS   Adj. R-squared:                  0.978\n",
       "Method:                 Least Squares   F-statistic:                 8.093e+04\n",
       "Date:                Sun, 06 Nov 2022   Prob (F-statistic):               0.00\n",
       "Time:                        21:02:37   Log-Likelihood:            -3.4255e+05\n",
       "No. Observations:               64512   AIC:                         6.852e+05\n",
       "Df Residuals:                   64475   BIC:                         6.855e+05\n",
       "Df Model:                          36                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================================\n",
       "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------\n",
       "const                            -88.5131    153.530     -0.577      0.564    -389.432     212.405\n",
       "x0_unconfirmed                   -44.2086     76.765     -0.576      0.565    -194.668     106.250\n",
       "x0_verified                      -44.3045     76.765     -0.577      0.564    -194.765     106.156\n",
       "x1_Bronx                         -16.8736     30.756     -0.549      0.583     -77.154      43.407\n",
       "x1_Brooklyn                      -17.1883     30.689     -0.560      0.575     -77.340      42.963\n",
       "x1_Manhattan                     -17.6437     30.659     -0.575      0.565     -77.736      42.449\n",
       "x1_Queens                        -17.0828     30.725     -0.556      0.578     -77.304      43.139\n",
       "x1_Staten Island                 -19.7248     30.782     -0.641      0.522     -80.058      40.609\n",
       "x2_Astoria                        -6.2975      7.112     -0.885      0.376     -20.237       7.642\n",
       "x2_Bedford-Stuyvesant             -4.0470      7.032     -0.576      0.565     -17.830       9.736\n",
       "x2_Bushwick                       -2.7252      7.032     -0.388      0.698     -16.507      11.057\n",
       "x2_Chelsea                        -3.4508      7.122     -0.484      0.628     -17.411      10.509\n",
       "x2_Crown Heights                  -4.8392      7.061     -0.685      0.493     -18.678       9.000\n",
       "x2_East Flatbush                  -2.0758      7.246     -0.286      0.775     -16.277      12.125\n",
       "x2_East Harlem                    -3.6134      7.118     -0.508      0.612     -17.564      10.337\n",
       "x2_East Village                   -5.0527      7.046     -0.717      0.473     -18.862       8.757\n",
       "x2_Financial District             -2.9938      7.150     -0.419      0.675     -17.009      11.021\n",
       "x2_Flatbush                       -2.7533      7.187     -0.383      0.702     -16.839      11.332\n",
       "x2_Greenpoint                     -4.1062      7.098     -0.578      0.563     -18.019       9.806\n",
       "x2_Harlem                         -2.3843      7.064     -0.338      0.736     -16.230      11.461\n",
       "x2_Hell's Kitchen                 -4.1698      7.089     -0.588      0.556     -18.064       9.724\n",
       "x2_Lower East Side                -3.8904      7.133     -0.545      0.586     -17.872      10.091\n",
       "x2_Midtown                        -4.9726      7.119     -0.699      0.485     -18.925       8.980\n",
       "x2_Other                          -4.1062      7.000     -0.587      0.557     -17.826       9.613\n",
       "x2_Prospect-Lefferts Gardens      -5.3995      7.206     -0.749      0.454     -19.523       8.724\n",
       "x2_Upper East Side                -6.3507      7.075     -0.898      0.369     -20.217       7.515\n",
       "x2_Upper West Side                -4.6743      7.049     -0.663      0.507     -18.491       9.143\n",
       "x2_Washington Heights             -2.5956      7.136     -0.364      0.716     -16.582      11.391\n",
       "x2_West Village                   -3.2539      7.171     -0.454      0.650     -17.308      10.800\n",
       "x2_Williamsburg                   -4.7611      7.002     -0.680      0.497     -18.485       8.963\n",
       "service fee                        4.9429      0.003   1706.296      0.000       4.937       4.949\n",
       "minimum nights                     0.0083      0.006      1.287      0.198      -0.004       0.021\n",
       "number of reviews                 -0.0083      0.005     -1.755      0.079      -0.017       0.001\n",
       "reviews per month                  0.2119      0.138      1.533      0.125      -0.059       0.483\n",
       "review rate number                -0.0007      0.151     -0.004      0.996      -0.298       0.296\n",
       "calculated host listings count    -0.0120      0.007     -1.635      0.102      -0.026       0.002\n",
       "availability 365                   0.0003      0.001      0.198      0.843      -0.003       0.003\n",
       "lr_year                            0.0800      0.133      0.603      0.547      -0.180       0.340\n",
       "lr_month                          -0.0983      0.069     -1.426      0.154      -0.233       0.037\n",
       "lr_day                             0.0553      0.020      2.798      0.005       0.017       0.094\n",
       "==============================================================================\n",
       "Omnibus:                    65755.042   Durbin-Watson:                   1.997\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        343304984.137\n",
       "Skew:                           3.674   Prob(JB):                         0.00\n",
       "Kurtosis:                     360.300   Cond. No.                     1.00e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.65e-21. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_int = sm.add_constant(X)\n",
    "model = sm.OLS(y,X_int).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these regression results, it looks like variables that effect the price the most are unconfirmed, verified, or choosing a location part of the 5 boroughs. There is an average of about 89 dollars per AirBnb. Some neighborhoods that would be cheaper than others would be  Upper East Side, Astoria, Prospect-Lefferts Gardens, and East Village. It seems as though time of the year to get an AirBnB does not affect the price that much. The location you choose seems to affect it more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Regressor Tree Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple models will be created to see which one has the lowest RMSE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to start off with a baseline before using more complex models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiating and fitting a DecisionTreeRegressor\n",
    "# Get predictions for training \n",
    "baseline_tree = DecisionTreeRegressor(random_state=123) \n",
    "baseline_tree.fit(data_train, target_train)\n",
    "predictions = baseline_tree.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.49562469e-04, 2.14342685e-04, 1.45394485e-06, 2.67307389e-04,\n",
       "       1.37657987e-04, 1.34919410e-04, 1.27152516e-05, 1.52206610e-05,\n",
       "       2.50950456e-04, 1.19473540e-04, 4.20819088e-05, 1.11187182e-05,\n",
       "       4.13553821e-08, 9.07184853e-05, 1.46107985e-04, 2.11114211e-07,\n",
       "       6.88450508e-06, 1.37050303e-05, 6.18981071e-06, 8.82118729e-05,\n",
       "       1.31773800e-05, 1.79347159e-04, 1.04258574e-04, 3.04305914e-06,\n",
       "       8.37332607e-05, 4.12346335e-05, 2.03533480e-05, 6.32259640e-07,\n",
       "       1.44733812e-04, 9.88443880e-01, 8.94427513e-04, 1.41545222e-03,\n",
       "       1.59956555e-03, 7.55372163e-04, 6.29478049e-04, 1.25991650e-03,\n",
       "       2.56711372e-04, 5.81344304e-04, 1.86446430e-03])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining how important each feature ends up being\n",
    "# Feature importance\n",
    "baseline_tree.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing feature importance \n",
    "def plot_feature_importances(model):\n",
    "    n_features = data_train.shape[1]\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center') \n",
    "    plt.yticks(np.arange(n_features), data_train.columns.values) \n",
    "    plt.xlabel('Feature importance')\n",
    "    plt.ylabel('Feature')\n",
    "\n",
    "plot_feature_importances(baseline_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Service fee seems to be the only important feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test set predictions\n",
    "baseline_pred = baseline_tree.predict(data_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5.392583120204604\n",
      "Mean Squared Error: 3335.096361311323\n",
      "Root Mean Squared Error: 57.750293170782456\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Algorithm\n",
    "# actual vs predicted \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target_test, baseline_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target_test, baseline_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target_test, baseline_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate and fit RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators = 50, random_state = 123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passing training data to train the RF regressor model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=50, random_state=123)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(data_train, target_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting test set results using RF regressor model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "target_pred = regressor.predict(data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate RF regressor model algorithm using error metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5.099519755095713\n",
      "Mean Squared Error: 1821.9673312665452\n",
      "Root Mean Squared Error: 42.6845092658513\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Algorithm\n",
    "# With n_estimators = 50\n",
    "# actual vs predicted \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target_test, target_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target_test, target_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target_test, target_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(regressor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying n_estimator = 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor2 = RandomForestRegressor(n_estimators = 40, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor2.fit(data_train, target_train)\n",
    "# Predicting the Test set results\n",
    "target_pred2 = regressor2.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5.129389889948073\n",
      "Mean Squared Error: 1845.9050140644972\n",
      "Root Mean Squared Error: 42.96399671893314\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Algorithm\n",
    "# With n_estimators = 40\n",
    "# actual vs predicted \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target_test, target_pred2))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target_test, target_pred2))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target_test, target_pred2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying n_estimator = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor3 = RandomForestRegressor(n_estimators = 30, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor3.fit(data_train, target_train)\n",
    "# Predicting the Test set results\n",
    "target_pred3 = regressor3.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5.146629513992686\n",
      "Mean Squared Error: 1864.4637599824623\n",
      "Root Mean Squared Error: 43.17943677240895\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Algorithm\n",
    "# With n_estimators = 30\n",
    "# actual vs predicted \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target_test, target_pred3))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target_test, target_pred3))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target_test, target_pred3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest with Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'max_depth': [80, 90],\n",
    "    'max_features': [2, 3],\n",
    "    'min_samples_leaf': [3, 4],\n",
    "    'min_samples_split': [10, 12],\n",
    "    'n_estimators': [200, 300]\n",
    "}\n",
    "# Create a based model\n",
    "rf_grid = RandomForestRegressor()\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = rf_grid, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = -1, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 32 candidates, totalling 96 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   55.2s\n",
      "[Parallel(n_jobs=-1)]: Done  96 out of  96 | elapsed:  3.2min finished\n"
     ]
    }
   ],
   "source": [
    "# Fit grid search to data\n",
    "grid_search.fit(data_train, target_train)\n",
    "grid_search.best_params_\n",
    "{'bootstrap': True,\n",
    " 'max_depth': 80,\n",
    " 'max_features': 3,\n",
    " 'min_samples_leaf': 5,\n",
    " 'min_samples_split': 10,\n",
    " 'n_estimators': 300}\n",
    "best_grid = grid_search.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are  {'bootstrap': True, 'max_depth': 80, 'max_features': 3, 'min_samples_leaf': 3, 'min_samples_split': 10, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "print(\"The best hyperparameters are \",grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using best hyperparameters for final calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=80, max_features=3, min_samples_leaf=3,\n",
       "                      min_samples_split=10, n_estimators=200)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid = RandomForestRegressor(bootstrap = grid_search.best_params_[\"bootstrap\"],\n",
    "                           max_depth  = grid_search.best_params_[\"max_depth\"],\n",
    "                           max_features     = grid_search.best_params_[\"max_features\"],\n",
    "                           min_samples_leaf     = grid_search.best_params_[\"min_samples_leaf\"],\n",
    "                           min_samples_split     = grid_search.best_params_[\"min_samples_split\"],\n",
    "                           n_estimators  = grid_search.best_params_[\"n_estimators\"])\n",
    "rf_grid.fit(data_train, target_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_pred = rf_grid.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 75.46257790949845\n",
      "Mean Squared Error: 10230.180315716561\n",
      "Root Mean Squared Error: 101.14435384991374\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Algorithm\n",
    "# actual vs predicted \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target_test, rf_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target_test, rf_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target_test, rf_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(rf_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar the other models above, service fee still seems way higher than the rest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XG Boost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate XGBClassifier\n",
    "xg_regressor= xgb.XGBRegressor(eval_metric='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating second one without service fee\n",
    "xg_regressor2 = xgb.XGBRegressor(eval_metric='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating third one with just main feauures\n",
    "xg_regressor3 = xgb.XGBRegressor(eval_metric='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up search grid\n",
    "param_grid = {\"max_depth\":    [4, 5],\n",
    "              \"n_estimators\": [100, 200, 300],\n",
    "              \"learning_rate\": [0.01, 0.015]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are  {'learning_rate': 0.015, 'max_depth': 4, 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "# Trying out every combination of the above values\n",
    "search = GridSearchCV(xg_regressor, param_grid, cv=5).fit(data_train, target_train)\n",
    "print(\"The best hyperparameters are \",search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using best hyperparameters for final calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.015, max_delta_step=0, max_depth=4,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=300, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_regressor = xgb.XGBRegressor(learning_rate = search.best_params_[\"learning_rate\"],\n",
    "                           n_estimators  = search.best_params_[\"n_estimators\"],\n",
    "                           max_depth     = search.best_params_[\"max_depth\"],)\n",
    "\n",
    "xg_regressor.fit(data_train, target_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using model for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_pred = xg_regressor.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 10.570509811823547\n",
      "Mean Squared Error: 1577.1709730373932\n",
      "Root Mean Squared Error: 39.713611936430475\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Algorithm\n",
    "# actual vs predicted \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target_test, xg_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target_test, xg_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target_test, xg_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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du3bx9ddfo6ury+3bt0lISGDOnDk0bNiQqKgo1q1bh7GxMTo6OvTo0YPGjRszefJktLW10dLSYu7cuVhaWpYY68qVK+Pj40N6ejq5ubmMHz+e1q1b06lTJ3bt2oWenh4hISHY2tpibW3N8uXL0dHR4datW/To0YORI0eybNkyMjIyaNy4MZ07d1bgb5YQQgjx9nilydnGjRuxtrYmNDSUmJgYDh06hK6ubpHV8wuEh4fTqVMnXFxcOHz4cIllIQoUVT1/1qxZNGjQgMDAQG7cuMHOnTtZv349GhoaDBs2jDZt2vDdd98xbtw4HBwcWLZsGVeuXHlq7MzMTBYvXkxubi4dOnRg7NixBAQEEBQURIMGDdizZw9z5sxh7NixJCUlsWrVKu7du8e1a9fo0KGDeg7PWvXatm0ba9euxdLSkujo6Keq+f9TtWrVmDFjBps3b2bTpk1MmDCBFStWsG3bNnR1dXF3dwfg0KFDNGzYED8/P06cOMH9+/cLJWdFxfr8+fN8/PHHDB06lPj4eFxcXNizZ0+x846Li+Pnn38mKyuLtm3bMnr0aEaOHMmVK1eemZhJhwBlSRVwZUl8lSOxVZbEVzlvRIeAK1eu0K5dOwDq1q1L3bp1iY+PL7J6foGrV6+qC7QW7CIMCwsr8TrFVc8vEBMTQ1xcHMOGDQPg/v373Lhxg0uXLqkTnyZNmhSZnNWpU0edWGlr54cvISGBBg0aANC8eXPmz59PnTp1GDx4MN7e3uTk5KjbHZXWggULWLBgAUlJSbRt27bEYwuuXbVqVU6dOsWNGzews7OjYsWKQH6nAIABAwawfPlyPv/8cwwNDZ96fq6oWO/YsUPdPsrS0hIDAwOSk5MLnffPaix169ZFW1sbbW1tKlR4vtpl0iFAObJdXlkSX+VIbJUl8VXOG1NKw87Ojr/++guAmzdv8tVXXxVbPb+oc44fP868efPQ09MjMTERyG+w/aTiqudraGigUqmwtbWldu3arFmzhsjISPr370/dunWxtbXl9OnTAPz9999FfgcNDY2n3qtSpQoXLlxQz/G9997j4sWLPHz4kGXLljFnzhxmzpxZaA4lycrK4tdff2XBggWsXr2arVu3cvv27ULV/EuaU40aNbhy5QoZGRnk5eWpVxv37t1L06ZNWb16NZ988gkrVqwodF5Rsbazs+PEiRMAxMfHk5aWhomJCbq6uiQkJKBSqdTfvbj4aGpqFjlvIYQQQjztla6cDRo0iClTpjBkyBByc3OZMmUKDx8+LLJ6foFRo0YxZcoUfv75ZwBmz54NwIQJEzh+/DiNGjV66jrFVc9v3LgxkyZNIiIigtatW+Pi4kJWVhb29vZYWlry9ddf4+XlxcqVK6lcuTJ6enql+l6zZs1i5syZqFQqtLS0mD17NlWqVOH7779n27Zt6OjoMG7cuKfm8GQV/gK6uroYGxvTp08fjI2NcXBwoFq1aoWq+ZekcuXKjBgxAldXV0xMTMjMzERbW5tGjRrh4+NDWFgYmpqaTJ48udB5RcXa0NCQKVOm8Ntvv5GRkcGMGTPQ1tbm888/Z+TIkVhbW6t7aBanbt26hIeH07BhQ3r27FmqmAohhBDvKukQ8BbKyclh+fLljB49GoDBgwczYcIEmjdv/ppnVjypc6YsuXWhLImvciS2ypL4KudlbmtKEdq3kLa2No8fP6Zfv37o6Ohgb29Ps2bNXve0SvTlwI/k/yCEEEIIZOVMlCPJyf+r/yYraGVLfh0rS+KrHImtsiS+ypGVs3IkOjoaY2PjV1LP6+LFi6Slpb2W25UODg4cPHiwzMabtuwQ8fceAWBpps+EAfaSoAkhhHgnSXJWxvr37//KrvX7779jbm5erp8lK634e4+IS3r4uqchhBBCvHaSnBUjOjqaLVu2kJeXx7hx40hNTWXVqlVoamrStGlTJk6cSP/+/Vm4cCE2Njbs2rWLkydPYmxsrO5eMH/+fI4fP45KpWLYsGG89957fPvttyxdupQdO3awbNkyfv75Z06cOMFPP/2kLrcB+b1FTU1NSUtLIywsjICAgEJdFDp37lyoY0BGRgahoaFoaWlRvXp1ZsyYgY6Ojno8Pz+/IjsJ/HMFzMvLi0GDBnH79m3++OMPMjIySExMxN3dnb1793Lp0iUmTZqEo6MjWVlZeHl5cefOHerVq0dgYCDp6en4+/urd8cGBARQr149OnbsiK2tLba2tvj7+7/aP0ghhBDiDSPJWQmMjIwIDw8nNTUVV1dXtmzZQsWKFfHx8eHgwYMMGDCAbdu2MWbMGLZu3crEiRP57bffANi/fz+3bt1i48aNZGZm4uTkRGRkJHFxcWRmZvLnn3+ioaFBUlIS+/bto0uXLk9dv1evXnTp0oWzZ88W2UWhoGPABx98wCeffML69esxMzPj22+/ZevWrTg5ORUa78lOAjNmzCj2uz98+JCIiAh++eUXVq1axebNmzl69Chr1qzB0dGRjIwMJk6ciLW1NePHj2ffvn2cOnWKVq1a4erqyrVr15g8eTIbNmzgzp07REdHY2pqWrZ/QEIIIcRbSJKzEtSqVQuAGzdukJyczMiRI4H8xOXmzZv07t0bFxcXBg4cSHp6OnXr1lUnZzExMZw9e1bdGSAnJ4e4uDjatGnD0aNHuXPnDr169eLQoUOcOHHiqWr9/7y+ubl5iV0UkpOTSUhIYMKECUB+D1MHB4enxnuyk8CT/rk3pOBYQ0ND7Ozs0NDQwNjYmMzMTCA/0bO2tgbya7ddvXqVmJgYjhw5wq5duwBIS0sDwNTU9LkTM2nlVPakRYuyJL7KkdgqS+KrnDeifdObRlMzv4GCjY0NVlZWREREoKOjQ3R0NA0aNMDAwIBGjRrxzTffPPWsma2tLS1btmTmzJnk5eWxePFibGxscHR05Ntvv6V+/fq0adOGr7/+mpo1axa6BVmgoNp+QRcFV1dXjhw5wv79+9Wf5+XlYWpqStWqVVm8eDGGhobs3bsXfX39Ysf7p5ycHB4+fIiOjg6XL18u8dh/unv3LgkJCVSpUoVTp07x2WefkZiYSO/evenVqxf37t0jKiqqUByfh7RyKluyI0tZEl/lSGyVJfFVjuzWVFjlypUZNmwYbm5u5ObmYm1tTffu3QEYOHAgn3/+ubpzQYFOnTpx7NgxXF1defToEY6OjhgYGNCkSROuXr3K559/Tv369bl9+zaff/55idfv2LFjkV0U/tkxwN/fn5EjR6JSqahUqRJz584t1Xdzd3fH2dkZGxsbqlWrVuqYmJiYMGvWLOLj42ncuDHt27fH3t4ef39/Nm/eTHp6OmPGjCn1eEIIIYTIJ3XORLkgpTSUJb+OlSXxVY7EVlkSX+XIypl4480Y+bEUoRVCCCGA538YSAiFSWImhBDiXSYrZ6JcKLitKbc0hRBCvOtk5UxB0dHR7N2793VP45U5fvw4Fy5cACiylEdJCjoEFDx3JoQQQryrJDlTUP/+/V9Jj83yYsuWLSQkJLzuaQghhBBvNLmtWQqvo5VT3759CQ4ORltbGyMjI0JCQjAw+F9hVj8/P1QqFXfu3OHRo0cEBwdjZ2dHZGQkO3bsQENDgx49euDu7o6fnx+pqamkpqaydOlSjI2NAQgLC+P69eukpKRw//59XF1d+f3337l69SrBwcF89NFH6i4B2traNGvWDB8fH8LCwrh16xb37t0jLi6OyZMnY2pqyp9//snZs2epXbs2WVlZfPXVV8TFxWFiYsLChQuLrOUmhBBCiMIkOSulV93Kac+ePXTp0gVPT0/27dtHWlpaoeQMoHr16gQHB7N//37mzZvHxIkT2blzJ+vXr0dDQ4Nhw4bRpk0bAFq1asWwYcOe+l4VKlRg5cqVLFu2jP3797NkyRK2bNnCL7/8QsWKFdm1axcbN25EW1ubsWPH8scffwCgq6vLihUrOHjwIBEREaxcuZK2bdvSo0cPqlWrxqNHj/Dy8sLGxgY3NzfOnz+Pvb19qWIt3QGUIVXAlSXxVY7EVlkSX+VIhwCFvepWTh9++CFLlixh6NChWFpaFpnYtGrVCshvnzR79mxiYmKIi4tTJ2H379/nxo0bheb/pPfffx/Ib9NUu3ZtAHWbpitXrvDhhx+qV7yaNWvGpUuXgMKtoLKysp4a19jYGBsbGyC//dTjx49LHWvpDlD2pJaRsiS+ypHYKkviq5yXqXMmz5yVUlGtnCIjIxkyZAgffvhhqVo5RUZGsnr1arp3765u5bR8+XLq1atHmzZtWLdunbqV0/bt2+nXrx+RkZHUqVOHzZs3PzWns2fPAnDq1Cnq1KmDra0ttWvXZs2aNURGRtK/f3/q1q0LFN+OqaQ2Tba2tpw5c4acnBxUKhXHjx9XJ3lFnaehoaHuz/ms9k9CCCGEKJqsnD2nV9XK6YMPPsDPzw99fX10dHSYMWPGU3M5cOAAe/fuJS8vj2+++Ybq1avTunVrXFxcyMrKwt7eHktLyxf+rvXq1aN79+64uLiQl5dH06ZNcXR0VO/IfNKHH35ISEiIesXseVia6Rf6pxBCCPGukvZNbyg/Pz969OhBu3btXvdUykxBhwC5pVn25NaFsiS+ypHYKkviqxxp3yTeCpKUCSGEEJKcvbHmzJnzuqcghBBCCAXIhoDXwM3NjdjYWEXGXrduHX369GHnzp0vPda71uFACCGEKA9k5ewts3v3bubOnUu9evVeeqwnd50KIYQQQnmSnJVCdHQ0+/fvJyMjgxs3bjBixAj69++Pm5sbgYGB2NnZsWHDBpKSkujXrx9eXl5YWVlx69YtevbsyaVLlzh37hwdOnTA29sbgIULF5KSkoKuri5z586lcuXKT3UR6N69O25ubpiampKWlsbKlSvR0tIC4NatW/j7+5OTk4OGhgYBAQH897//5e+//8bf35/Q0FCqV6+unn9563AghBBCiKJJclZK6enprFy5kmvXrjFq1KgSV5Vu3rxJREQEGRkZdO7cmQMHDlCxYkU6duyoTs66du1Kz549WbduHUuXLuXjjz9+qotAQfPwXr160aVLl0LXmDt3Lm5ubjg6OnL+/HmmTJlCdHQ0O3bsIDAwUJ2YFSiPHQ6eJFWqlSXxVZbEVzkSW2VJfJUjHQIUVr9+fQCsrKyKrIj/z4ok1atXx9DQEF1dXczNzTExMQEKF2Zt1qwZAE2aNGH//v2Ym5sX2UUAiq7uHxsbS/PmzYH8av13794tcf7lscPBk2Q7t3Jku7yyJL7KkdgqS+KrHOkQ8AoUVfFeV1eXxMREAM6dO1fisU/666+/ADhx4oS6un9RXQSKG8/Ozo4TJ04AcP78eczNzUu8XnnscCCEEEKIp8nK2Utwd3dnxowZWFlZUaVKlec6d8+ePaxevZpKlSoRHByMkZFRkV0EijNp0iSmTp1KREQEOTk5BAUFleq65anDgRBCCCGeJh0CRLkhS+vKkVsXypL4KkdiqyyJr3LktqYQQgghxFtCkjNRLnwf9R+0tJ79rJ4QQgjxtpPk7DUZM2ZMsZ8lJiYSGBj46ibzAnM4evQoXl5eT71/8eJFjh8//tzXu3c/47nPEUIIId5Gkpy9JosWLSr2MwsLi9eenL3oHH7//XcuX75c9hMSQggh3hGyW1MB0dHR/PHHH2RkZJCYmIi7uzt79+7l0qVLTJo0CUdHRxwcHDh48CBubm7Ur1+fS5cukZ6eznfffYdKpcLb25vNmzfTq1cvmjVrRkxMDLVq1cLMzIwTJ06gq6vLsmXLWLJkibqKf2xsLIGBgURGRj7zPB0dHfV8nzWHP/74g4ULF2JgYICxsTH16tWjRYsWXL9+nc8//5zk5GQ6duyIk5MTW7duRUdHh4YNG7J3716OHDlCXl4ePXv2ZNiwYa/vD0UIIYR4Q0hyppCHDx8SERHBL7/8wqpVq9i8eTNHjx5lzZo1ODo6FjrW3t5e3XLpl19+oUePHoXG+fTTT2natCmffPIJkydPxsvLiyFDhpS4QvWs8xo0aFCqOeTm5jJr1iw2bdqEubk5X331lfqczMxMFi9eTG5uLh06dGDs2LH069cPc3Nz7O3tGTt2LGvXrsXS0pLo6OhnxqxyZWnvpCSpAq4sia9yJLbKkvgqRzoElDMFyY+hoSF2dnZoaGhgbGxMZmbmU8e+//77AFStWpWkpKSnPm/YsCGQ34LJzs5O/e9FjfWi5xU3h+TkZAwMDNRFbps1a6b+vE6dOujq6gKgrf30X6UFCxawYMECkpKSaNu2bYlzzb9WOrm5UtlFCbJdXlkSX+VIbJUl8VWOlNIoh0rTJaAsxtLT01N3KTh79myZz8HMzIyHDx+SnJwMwH//+98Sx9fQ0CAvL4+srCx+/fVXFixYwOrVq9m6dSu3b99+6fkIIYQQbztZOXvDde/enQkTJnD8+HEaNWpU5uNramoydepURowYgaGhIXl5edSsWbPY4xs1asTcuXOxs7PD2NiYPn36YGxsjIODA9WqVSv2PDPjCmU+dyGEEOJNJB0CxDMtXbqU4cOHo6ury8SJE2nTpg19+/Yt8+vI0rpy5NaFsiS+ypHYKkviq5yXua0pK2fimSpVqoSTkxMVKlTA2tq60IaFsiRFaJUl8VWWxFc5EltlSXyf7VU/Dy0rZ6JcmLbsEPH3Hr3uaQghhBCFWJrpM2GA/XMnaLJyJp7bgQMHuHPnDs7Ozi90/p49e/j2229xcnLC3d39pecTf+8RcUkPX3ocIYQQ4k0nydk7ql27di91/h9//IG3tzedOnUqoxkJIYQQAiQ5e6NcvXqVyZMno62tjZaWFnPnzsXS0pL58+dz/PhxVCoVw4YNo3v37ri5uWFqakpaWhqVKlVi6NChtGjRgjNnzhAeHk6XLl24cuUKEydOZPHixezZs4fc3FxcXFwYNGgQkZGR7NixAw0NDXr06FFodWzv3r3861//4syZM5iamnL37l1WrVqFpqYmTZs2ZeLEiTx48AB/f39SUlIACAgIoF69eq8rdEIIIcQbQ5KzN8ihQ4do2LAhfn5+nDhxgvv373PhwgVu3brFxo0byczMxMnJCQcHBwB69epFly5d2L9/P1u3bqVFixZs3boVJycnddJ07tw5Dhw4QFRUFFlZWcyfP59Lly6xc+dO1q9fj4aGBsOGDaNNmzbY2toC0LlzZ3bv3k2PHj2oVasW/v7+bNmyhYoVK+Lj48PBgwc5dOgQrVq1wtXVlWvXrjF58mQ2bNjw2mInhBBCvKgX7WAjHQLeAQMGDGD58uV8/vnnGBoa4uXlRUxMDGfPnsXNzQ2AnJwc4uLiAKhVqxYAbdu2Zd68eaSmpnLixAkCAgL46aefgPzVOHt7e7S0tKhYsSIBAQHs3LmTuLg4dS/M+/fvc+PGDXVy9k83btwgOTmZkSNHAvlto27evElMTAxHjhxh165dAKSlpSkaGyGEEEIpL9LBRjYEvCP27t1L06ZNGTNmDDt27GDFihU4OjrSsmVLZs6cSV5eHosXL8bGxgb4XwV/TU1NPvnkEwIDA3F0dERLS0s9pq2tLRs2bCAvL4/c3FxGjhyJr68vtWvXZsWKFWhoaLBq1Srq1q1b5JxsbGywsrIiIiICHR0doqOjadCgAVevXqV379706tWLe/fuERUVpXyAhBBCiLeAJGdvkEaNGuHj40NYWBiamppMnjyZ999/n2PHjuHq6sqjR49wdHTEwODp5dfPPvsMR0dHfvvtt0LvN2jQgLZt2+Li4kJeXh4uLi7Ur1+f1q1b4+LiQlZWFvb29lhaWhY5p8qVKzNs2DDc3NzIzc3F2tqa7t27M2rUKPz9/dm8eTPp6emMGTOmxO9maab/4oERQgghFPI6/vskdc5EuZGcnP66p/DWqlzZQOKrIImvciS2ypL4ls6LFKGV25rirfCqKzC/ayS+ypL4KkdiqyyJb/mj+bonIIQQQggh/kdWzhSQnZ3NlClTuH37NllZWYwePZrOnTsXeeyiRYvQ09NjxIgRAHz++edoaWmxdOlSABYuXIixsTGZmZm0atWKy5cvc+XKFQYNGoS3tzebN2/Gy8uL4OBgdHV1y+w7hIWFYW5ujouLS5mNKYQQQohnk5UzBfz888+YmJiwfv16li9fzsyZM4s9tk2bNpw8eRKAx48fk56eTlxcHBkZGQAcO3aMtm3bMnLkSOzt7YscIzQ0tEwTMyGEEEK8PrJyVgbWrVvHqVOnmD9/Pr6+vjRs2JDx48erP/9n6YonffDBB1y6dAmVSsXhw4dp0aIF6enpHD16lFatWnHv3j1sbW3x8/OjR48eRY7RqVMndu3axfXr15kzZw55eXmkpaUREBBAkyZN6NKlC40bN+b69eu0atWKBw8ecObMGWrVqsW8efP4/fffWb58Odra2lhbWzN37lwgv3/mrl27yMjIICAgAHt7exwcHDh48CAAXl5eDBo0iNu3b7Nlyxby8vIYN24ct27dYt26dRgbG6Ojo0OPHj3o379/GUZcCCGEeHtJclYGBg8ezMGDB/Hz8yM7O1vd6ig9PZ1x48YxYcKEYs/V0tKiQYMGxMTEcODAAT799FPS09M5cOAAenp6tGjRotTzuHz5Mr6+vtSrV4/t27cTHR1NkyZNuH37NqtXr8bCwoIWLVoQFRXF1KlT6dy5M2lpaezYsYNhw4bRs2dPtm3bRnp6/s4da2trZsyYwaVLl5g0aRJbt24t9tpGRkaEh4eTnJxMYGAg27ZtQ1dXt0yaogshhBDvEknOysjIkSNxdnYmOjoagDt37vDll1/i6upKr169Sjz3448/5sSJE/znP/8hICCA7OxslixZgomJCW3bti31HKpUqcLixYupUKECDx8+VNc7MzExoVq1agDo6+tTu3ZtAAwNDcnMzGTy5MksXbqUDRs2YGtri6OjIwDNmzcHoE6dOiQmJj51vX9WYSnoRnDjxg3s7OyoWLEiAI0bNy71/F+0zYUoHYmvsiS+ypHYKkviqxxp3/QaZWVlMXv2bGbMmEFgYCDh4eF4eHgwbdo0Wrdu/czzHRwcmDx5MjVr1kRbWxttbW2MjIw4cuQIw4cPL/U8goKCCAkJwc7OjoULF3L79m3gf50CirNp0ybGjh2LmZkZ06ZNY/fu3QCcOXOGXr16cfHiRXVyl5OTw8OHD9HR0eHy5cvqMTQ18x9frFGjBleuXCEjIwNdXV3OnDlTZNunojyrHox4caWptyNenMRXORJbZUl8lSN1zl6zkJAQOnTogLOzMwkJCcyfP5+0tDQWL17M4sWLAVi+fDkVKlQo8vyaNWsSHx/PgAED1O99/PHH7N27t8hq/8Xp3bs3X3zxBWZmZlStWlXd3PxZ7O3tGT58OCYmJlSqVIkOHTqwdu1abt26hbu7O1lZWcyYMQMAd3d3nJ2dsbGxUSds/1S5cmVGjBiBq6srJiYmZGZmoq0tf82EEEKI0pIOAaJM5eTksHz5ckaPHg3kP483YcIE9S3SksivN+XIr2NlSXyVI7FVlsRXObJy9gbYtGkTO3bseOp9b2/v53ouq7zT1tbm8ePH9OvXDx0dHezt7WnWrNnrnpYQQgjxxpCVM1FuyK835civY2VJfJUjsVWWxFc5L7NyJkVohRBCCCHKEUnOFJadnY2Pjw+urq4MGDCAvXv3FnvsokWLWL58ufr1559/zv/93/+pXy9cuJDVq1c/1/U3bdpEdna2+nVeXh6dO3fmxo0bhY4bPXo0hw4dYsyYMQC4ubkRGxtLWFgYGzZseK5rCiGEEOLFSXKmMCVaOT2PpUuXkpeXp36tqanJZ599xk8//aR+LykpiatXr9K6dWsWLVr0XOMLIYQQomzJhoAy9GQbJ3t7e/r27Uu3bt3Ux5RFK6eLFy8ya9YsIL/A7OzZs8nOzmbChAmoVCqys7OZPn06Z86cITExES8vL3VJD4DPPvsMd3d3xo4dC8C2bdvo378/Ghoahdoz/VNubi7Tpk3j7t27pKSk0K5dOyZMmMD169fx8/NTt366ffs2kZGR7Nq1i1WrVqGpqUnTpk2ZOHFiWYVZCCGEeKtJclaGnmzjNHjwYPVnZdnKaerUqcyePZvatWsTFRXFihUraNy4MYaGhsyfP5/Lly+Tnp7OwIEDCQ8PJzQ0tNB1LC0tqVWrFidPnqRp06Zs376dlStXlvjd7ty5w0cffcTAgQPJzMxUJ2dz585l1KhRtG/fns2bN3P79m1SU1MJCwtjy5YtVKxYER8fHw4ePIiDg0OJ15Aq1cqS+CpL4qscia2yJL7KkQ4B5cSTbZyg7Fs5xcbGMn36dCD/mbZatWrRrl07rl27xhdffIG2tra6zlhxnJyc+Omnn9DS0qJmzZqYm5uXeLyJiQl//fUXR44cwcDAgKysLPVcCkqBFCR6N27cIDk5mZEjRwLw8OFDbt68WeL4ILs1lSQ7spQl8VWOxFZZEl/lSJ2zcuLJNk7r1q0jLS2tzFs51apVi+DgYKpVq8bJkydJTEzk6NGjVKlShYiICE6fPs2CBQuIjIxEQ0Oj0DNnBdq3b09oaCgaGho4Ozs/c17R0dEYGhoyY8YMrl+/zubNm1GpVNStW5fTp0/Tvn17/vvf/wJgY2ODlZUVERER6OjoEB0dTYMGDZ4zmkIIIcS7SZKzMlRUG6fc3Nwyb+UUGBiIr68vubm5QH5PTRMTE7y8vFi9ejWampp8+eWXADRr1oyRI0eyZs2aQj02tbS06Ny5M7/++iuBgYHP/G6tW7fG29ubkydPUrFiRWrWrElCQgITJ05kypQpREREYGhoiLa2NpUrV2bYsGG4ubmRm5uLtbU13bt3f6GYCiGEEO8aKUIrXsrPP//Mhx9+SM2aNYmKiuLUqVN88803LzSWLK0rR25dKEviqxyJrbIkvsqR25pvmLeplZOVlRVeXl5UrFgRTU1NZs+e/bqnJIQQQrzRZOVMlBvy60058utYWRJf5UhslSXxVY60byrC66zMf/ToUby8vJ56PygoiLi4uFKPU2Dt2rXPfc6z5hMSElJoR+mTnjXXTp06kZmZWei9zMxMoqKiXmquQgghxLvurU3OXndl/qL4+/tTrVq15z4vPDz8pa/9vF5kromJiZKcCSGEEC/prXjm7FVV5l+7di2///47OTk5GBoaEhYWxu3bt5k8eTLa2tpoaWkxd+5cAK5fv87nn39OcnIyHTt2ZOzYsbi5uREYGMjOnTu5desW9+7dIy4ujsmTJ9O2bVv++OMPFi5ciIGBAcbGxtSrVw9tbW3u379PYGAg/v7+TJkyhZs3b5Kbm8vw4cPp0aMHbm5u1K9fn0uXLpGens53332HtbV1qeM3f/58jh8/jkqlYtiwYXTv3l09V1NTUyZOnEhWVha1atXiyJEj7N69G8jfNXrr1i0gf/VxyZIlXL58mUWLFtG6dWuCg4PVpUBCQkLUu02FEEIIUby3Ijl7FZX58/LySE1NVbck8vT05K+//uLChQs0bNgQPz8/Tpw4wf3794H8W3yLFy8mNzeXDh06qFslFdDV1WXFihUcPHiQiIgIPv74Y2bNmsWmTZswNzfnq6++AvIbkq9du5bAwEDWrl2Lqakp8+bNIz09nf79+9OqVSsA7O3t8ff3JzQ0lF9++UVdALbAkSNHcHNzU7++efMm48aNY//+/dy6dYuNGzeSmZmJk5NToUr+S5YsoXPnzuoY/7O102effUazZs3w8/Pj4MGDjBo1ipiYGMaMGUNwcDBdunTB09OTffv2kZaW9szkTKpUK0viqyyJr3IktsqS+Crnne8QoHRlfk1NTXR0dPD29kZfX5+7d++Sk5PDgAEDWL58OZ9//jmGhobqZ7vq1KmDrq4uANraT4e5oChr1apVycrKIjk5GQMDA3Wl/mbNmpGUlFTonNjYWD7++GMADAwMsLOzU1fef//999XjPXkeQKtWrQq1cQoJCQEgJiaGs2fPqhO3nJycQs+axcbG0q9fP/Wc/qlRo0YAmJubq28BFxg1ahRLlixh6NChWFpaYm9vX1TYC5GHUpUjD/0qS+KrHImtsiS+ynnnNwQ8WZk/KyuLpKQkPDw88PHxKVTQtTgODg788ssv6sr8FStWVFfmb9WqFRcuXGDPnj18++23TJ06lby8PFQqFXv37qVp06asXr2aTz75hBUrVgAUKvhalCc/NzMz4+HDhyQnJwOoq+0DFGyotbOz48SJE0D+imBMTAw2NjalD1QRbG1tadmyJZGRkaxevZru3bsXGrOgAwDAf/7znxK/g6amprobwfbt2+nXrx+RkZHUqVOHzZs3v9Q8hRBCiHfFW7Fy9ioq89esWZOKFSvSv39/dHV1sbCwICEhgY8++ggfHx/CwsLQ1NRk8uTJpKenP/d30NTUZOrUqYwYMQJDQ0Py8vKoWbMmkJ+UTZw4kdmzZzN16lRcXFzIzMxkzJgxmJmZvUDE/qdTp04cO3YMV1dXHj16hKOjY6HbjyNGjGDSpEns2rWLKlWqFLkKWMDMzIzs7GzmzZtH165d8fPzQ19fHx0dHWbMmPFS8xRCCCHeFVLnrBxZunQpw4cPR1dXl4kTJ9KmTRv69u37Wue0f/9+TE1Nsbe359ChQyxZsoQ1a9Yoci1ZWleO3LpQlsRXORJbZUl8lSMdAkqpvFfmr1SpEk5OTlSoUAFra2t69OjxuqeEjY0NU6ZMQUtLi7y8PPz9/V/3lIQQQoi3mqyciXJDfr0pR34dK0viqxyJrbIkvsp55zcElLXn6S4AFCo9AXDgwAH8/PyUnCLu7u6cOXMGyN8Q0bRpU1auXKn+fMiQIVy4cKHU4xVX3T85OZmxY8fi6emJh4cHAQEBZGRkkJiYSGBg4FPHP6vzgBBCCCFKJslZEZ6nu8Dr0qZNG/XOzZMnT9KmTRv+9a9/AfmJ1p07d6hfv36pxyuuuv+KFSv4+OOPWblyJREREVSsWJGNGzdiYWFRZHImhBBCiJfzzidn69atUxd89fX1Zd26dXzyySeMHz9efUxJ3QWepXPnznh7ezNgwAAmT55MXl4eYWFheHl54e7uTt++fdVJ1q5du3B2dsbFxUVdhywsLAwPDw8GDRpEbGysetyCumyQ/9D+wIEDefDgAQ8ePOD06dO0aNGi2DFPnjyJk5MTrq6ujBo1ivT09ELV/f/J2tqa3377jUOHDpGRkYGvry9ubm7cunULJycnAH777Tf69u2Lh4dHoRIg8+fPZ9CgQTg7O7Nr164XjqEQQgjxLnmnNgQU5WW7CxSnoAZYfHw848ePp2bNmowfP549e/YAUKFCBdasWcOlS5f46quvWLNmDWFhYWzZsoWKFSvi4+OjrsZva2tLQEBAofHff/99rly5gkql4vjx43h7e9O6dWsOHTrExYsXadu2LampqUWO+e9///up6v3/rO7/Ty4uLujp6bFy5UrGjx9P06ZN+frrrwsdM2/ePKKiojAxMVF3Jiiu84CRkVGxMZMq1cqS+CpL4qscia2yJL7Keec7BLyMl+0u8GQx1kePHqGnpweAlZWVul5Z48aNuXr1KoC67VKdOnVISkrixo0bJCcnq5Obhw8fqqv/16pV66lrampqUr9+fQ4cOICFhQW6urq0a9eOf/3rX1y4cAF3d/dixyyqen9WVlaR3+3o0aP07duXAQMGkJWVxfLly5k9eza+vr4AJCUlYWBggKmpqfo7QvGdB0pKzuShVOXIQ7/KkvgqR2KrLImvcmRDwEsoi+4CNjY2HD58WP36zz//5IMPPgDyV84SExMBOHXqFLVr1wbg7NmzQH4SY2lpiY2NDVZWVkRERBAZGcmQIUP48MMPgfxErCgODg4sXbqUtm3bAtC0aVPOnTsHgImJSbFjFlW9/5/V/f9p9erV6qRVV1e3UFuqgus8ePBA3dngr7/+Ap7deUAIIYQQRXvnV87KorvArFmzmD59OqGhoeTl5fHRRx/Rp08fID+hmTlzJnfu3OHDDz+kU6dOnDt3jvPnzzN06FAeP37MzJkzqVy5MsOGDcPNzY3c3Fysra3p3r17iXP/+OOPCQgIYO7cueprGRoaqvtsFjdmVlbWU9X7/1nd38fHR32N6dOnM336dNavX0+FChUwNTUlMDCQ7OxsIL9v6DfffIOnpyfGxsbqDgLP6jwghBBCiKJJnTOFOTg4qJ8dKxAWFoa5uTkuLi6vaVblkyytK0duXShL4qscia2yJL7KkQ4Br0B57y4ghBBCiLeDrJyJckN+vSlHfh0rS+KrHImtsiS+ypENAQp5EzoFHD16lNatW+Pm5qb+37hx4557nN27dxMfH//U+8uWLWPYsGF4eHjg6enJ33//DUBQUBBxcXGFjo2NjVXvzhRCCCHEi5HbmiUo6BQwb948UlJS6NevH507d37d03pKq1atCA0Nfakx1qxZQ2BgIJaWlur3Ll++zL59+9iwYQMaGhqcP38eX19ffv75Z2mALoQQQihEkrP/b926dZw6dYr58+fj6+uLvb09ffv2pVu3bupjXrZTwIcffsiNGzeoU6cOQUFBfP/991y5coV79+6RlpZGQEAAzZo1Y9euXaxatQpNTU2aNm3KxIkTCQsL4/Tp0zx69IigoCDs7Oyeec1jx46pK/5nZGQQHBxMtWrVGD9+POnp6WRkZODj48Pjx4/Vidf69evVpTIqV65MXFwcP/74I+3ataNBgwb8+OOPALi5uREYGIihoSETJ05EpVJhYWFR6NqhoaFoaWlRvXp1ZsyYgY6OzgvHTwghhHhXSHL2/72pnQIAjhw5Uuh2Yvv27fn888+5dOkS8+bNw9LSkiVLlvDrr7/i6OhIUlISq1at4t69e1y7do0OHTrQoEEDAgMDC9Uwq1y5MuHh4axdu5bvv/+eChUq4OXlVShh/eGHH/j0009xcnJi586dbNiwAZVKxdSpU1m/fj1mZmZ8++23bN26Vd3uqThSpVpZEl9lSXyVI7FVlsRXOdIhoAy8iZ0CCsYo6rampaUlQUFB6OvrEx8fT5MmTahTpw6DBw/G29ubnJycEp8Ru379OgYGBnzzzTdAfoHZkSNH0rJlS/Uxly5dUtd0a9KkCRs2bCA5OZmEhAR1MpuRkfHU83hFkYdSlSMP/SpL4qscia2yJL7KkQ0BZeBN7hRQnICAAGbPns2cOXOoUqUKKpWKixcv8vDhQ5YtW8acOXOYOXMmkJ9YPrlx9+LFiwQGBpKZmQnkJ4eGhoaFbu/a2tpy+vRp4H/dAUxNTalatSqLFy8mMjKSUaNGFUrohBBCCFE8WTn7/97kTgFP3tYsmGefPn1wcnLCyMgIc3NzEhISeO+99/j+++/Ztm0bOjo66p2djRs3ZtKkSURERGBiYgJA165diY2NZeDAgejr66NSqZg0aRKGhv/L9MePH4+Xlxc7d+5Ut2fS1NTE39+fkSNHolKpqFSpkrqLgRBCCCFKJnXOXhHpFPBssrSuHLl1oSyJr3IktsqS+CpHOgS8QtIpQAghhBBKkpUzUW7IrzflyK9jZUl8lSOxVZbEVzlvxIaA562236hRI3XFexcXFwICAsjJyXlFs31aZmYmUVFRRX5Wmp2IBUJDQ+nfvz+HDx/G09MTFxcX7t+//8zz4uLi2LdvX6mvs3btWpydnRk8eDCDBw/m+++/L/W5T3JycuLWrVsvfL4QQgghSu+V3dZ83mr7xsbGREZGql9PmDCB/fv3v7YK/YmJiURFRTFw4MCXGmfnzp1s3bqV9PR0UlJSCpXtKMmRI0e4cuUKnTp1euax69ev5/Tp06xZswY9PT2ys7OZOHEi//73v2nTps1LzV8IIYQQylIkOSvravvZ2dk8evQIfX39pyrl79+/n19++QVtbW2aNWuGj48PJ0+eJDg4GG1tbYyMjAgJCeH3339n79696qToyy+/pFu3bkVWss/NzWXy5MnExcWRnZ3N1KlT2bJlC5cvX2bRokWMGTPmmXO+c+cOU6dOJTMzEz09PWbOnMmWLVu4e/cu//d//4empibXrl1j2rRp+Pj44O/vT0pKCpBfAqNevXp07NgRW1tb3nvvPQ4ePEhGRgaNGzfm7t27bNu2DU1NTZo0aYKvr2+ha69fv16dmAHo6Ojw7bffquuwzZ8/n7///puHDx9iZ2fHN99881Rcf/75Z/7880+qVq2qnteDBw+KnGfXrl1p0qQJV69exczMjLCwMG7cuMHkyZPR1tZGS0uLuXPnFmoNJYQQQoiiKZKclUW1/fv376vLQ2hoaNCuXTtat27NiRMn1JXyL168yK5du9i4cSPa2tqMHTuWP/74g2PHjtGlSxc8PT3Zt28faWlpQH5R2B9++IHk5GQGDhxIp06diqxk/+jRI6ytrQkNDSUmJoZDhw4xatQoYmJiSpWYAQQHB+Pm5kb79u05fPgwISEhzJ8/n+joaCIiIkhMTMTb25sZM2Ywb948WrVqhaurK9euXWPy5Mls2LCBO3fuEB0djampKdHR0Vy5coXOnTvz2WefMXXqVD766CPWr19PTk4O2tr/+6NMTU2lcuXKQH5D8zVr1pCRkUGzZs348ssvMTIy4ocffiAvL4+ePXuqG54XxDUmJobjx4/z448/8ujRI7p27QrAkiVLipznzZs3Wb16NVZWVgwaNIi//vqLs2fP0rBhQ/z8/Dhx4gT379+X5EwIIYQoBcVua75stf0nb2v+U0Gl/CtXrvDhhx+qezY2a9aMS5cuMWrUKJYsWcLQoUOxtLTE3t4egObNm6OpqYm5uTlGRkYkJCQUWck+OTmZdu3aAVC3bl3q1q1b6Jkrf39/bty4gampKQsXLixyjjExMSxdupQVK1agUqlK7CsZExPDkSNH2LVrF4A6mTQ1NcXU1PSp47/55hsiIiIICQnho48+eqp4bKVKlUhNTcXExIQuXbrQpUsXDhw4wM6dO9HT0yM5ORlvb2/09fV59OgR2dnZheJ6+fJlGjVqhKamJgYGBtStW/eZ87SysgLyOyFkZmYyYMAAli9fzueff46hoSFeXl7Ffv8C0kJEWRJfZUl8lSOxVZbEVznlqn3Tk9X2161bR1paGh4eHkybNo3WrVu/1PgFlfJtbW354YcfyMnJQUtLi+PHj9O3b1+2b99Ov3798PX1ZenSpWzevJlq1aqpq/EnJSWRnp5O1apV1ZXsDQ0N2bt3L/r6+sTExPDXX3/h6OjIzZs3+fbbb/nqq6/Iy8sDICgo6JlztLW1xcPDgyZNmhAbG8vx48dLPLZ379706tWLe/fuqTce/LMjgKampvr6mzdvZvr06ejp6eHp6cnp06dp0aKF+tjBgwcze/ZsZs2aha6uLrm5uZw8eRINDQ0OHDjAnTt3+Pbbb0lOTmb37t3q5K7gerVq1WLNmjXk5eWRkZHB5cuXS5znk22rAPbu3UvTpk0ZM2YMO3bsYMWKFeo2UMWRHUPKkR1ZypL4KkdiqyyJr3LKXZ2zsqi2Xxr16tWje/fuuLi4kJeXR9OmTXF0dOTMmTP4+fmhr6+Pjo4OM2bM4Pjx4yQlJTF06FAePHjA119/jZaWVpGV7Js0acKUKVMYMmQIubm5TJkyBTMzM7Kzs5k3bx4+Pj6F5pGamkr//v3Vrz08PPD19VW3PsrIyMDf37/Y7zFq1Cj8/f3ZvHkz6enpRd46rVu3LuHh4TRs2JB69eoxYMAATE1NsbS0VLd3KuDu7s6GDRsYPnw4mpqapKen06JFC3x8fMjNzWXx4sU4OTmhq6tL9erVSUhIKHR+gwYN+OSTTxgwYABVqlTBzMys1PMs0KhRI3x8fAgLC0NTU5PJkycX/wcphBBCCLV3ps5ZwTNbEydOfN1TEcWQX2/KkV/HypL4KkdiqyyJr3LK3cpZaUm1fSGEEEKIwt6ZlTNR/smvN+XIr2NlSXyVI7FVlsRXOW9EhwAhhBBCCPFs0vj8LZWdnc2UKVO4ffs2WVlZjB49usTuCg8fPiQ0NJTz58+jqalJpUqV8PX1pVatWmRmZvLzzz8zcOBAwsLCMDc3x8XF5RV+GyGEEOLdIStnb6mCdlnr169n+fLlzJw5s8Tjp06dSs2aNVm3bh2RkZFMmDCBL7/8kgcPHqhbVwkhhBBCeZKcvSXWrVvHV199BYCvry8PHz5k/Pjx6s9LapeVnJxMTEyMuiMDQP369enYsSO///47S5YsUbeugvwaZkOHDqVPnz7qZuy7du3C2dkZFxcXQkJCAAgLC8PDw4NBgwYRGxtb5t9ZCCGEeBvJbc23xJMts9zd3YHStcu6desW1atXf+r96tWrExcXV6h1VVhYGJaWlgQFBXH06FFWrFhBkyZNCAsLY8uWLVSsWBEfHx8OHjwI/K8lVGlIlWplSXyVJfFVjsRWWRJf5ZSrDgHi9XiyZVZp22VVqVKFuLi4p96/fv06dnZ2T73fsGFDAMzNzcnIyODGjRskJyczcuRIIP/5tZs3bwL/awlVGrJjSDmyI0tZEl/lSGyVJfFVjuzWFE+1zEpKSsLDwwMfHx8GDBhQ4rlVq1alRo0arFu3Tv3e2bNn2bdvH127di3UOgqebtdkY2ODlZUVERERREZGMmTIEHXXgn+2oBJCCCHEs8nK2VuiqJZZz9MuKzg4mLlz5zJw4EC0tLQwMjJi8eLFGBkZoaenp25dVdT5lStXZtiwYbi5uZGbm4u1tTXdu3dX9PsKIYQQbyspQivKDVlaV47culCWxFc5EltlSXyV88a2bxKvlrTLEkIIIco/Sc7eIc7Ozjg7O7/uaQghhBCiBPK0thBCCCFEOSLJ2RsiOzsbHx8fXF1dGTBgAHv37i3x+Hr16vH1118Xem/WrFl06tQJgKCgoKfKZ8TGxqoL0R4/fpwLFy6U4TcQQgghRGlIcvaGeN52TCYmJhw/fpycnBwAcnNz+fvvv9Wf+/v7U61atWLP37JlCwkJCWUzeSGEEEKUmjxzVk6tW7eOU6dOMX/+fHx9fWnYsGGp2zEBaGtr06JFCw4ePEj79u3597//TevWrfnpp58AcHNzIzAwEENDQyZOnIhKpcLCwgKAv//+mz///JOzZ89Su3ZtTpw4werVq9HV1eW9995jxowZODk5sWLFCoyMjGjZsiVr167l/fffp1+/fsyfP5/JkydTtWpVbt68yQcffMD06dOf+Z2lSrWyJL7KkvgqR2KrLImvcqRDwFvmZdoxFfj000+Jioqiffv27Nixg9GjR6uTswI//PADn376KU5OTuzcuZMNGzbQqFEj2rZtS48ePahYsSJhYWFs3boVAwMDZs+ezaZNm+jcuTN//vknVatWxcbGhoMHD6qTN11dXa5du8bKlSupWLEijo6OJCYmqpO/4sh2buXIdnllSXyVI7FVlsRXOdIh4C01cuRItm7diqenJ5Dfjsnd3Z0+ffqU2I6pQNOmTTl37hwpKSmkpqZibW391DGXLl3C3t4egCZNmjz1+c2bN6lduzYGBgYANG/enEuXLtG1a1cOHDjAn3/+iZeXF4cPH1Z3FACoUaMGBgYGaGlpYWFhQWZm5gvHQQghhHiXSHJWTr1MO6YCGhoatG/fnsDAQBwdHYs8xtbWltOnTwPw119/FTpXpVJhY2NDbGwsjx49AuDYsWPUqlWLunXrcuvWLc6cOUP79u159OgRe/fupV27durzhRBCCPH8Sp2cnTx5kg0bNpCVlcXx48eVnJOgcDumdu3aFWrH5ObmhpubGxkZGc8cp1evXuzdu5dPPvmkyM/Hjx/PH3/8gZubG/v27VO//+GHHxISEkJKSgpjx47F3d0dJycnUlJScHFxAfJX0SpXroympqb63ytVqlQ2ARBCCCHeUaVq37R69Wr27NlDQkICGzduVJdzKLjdJkRZkOcelCPPlShL4qscia2yJL7KUbx909atW9m8eTNOTk6Ympry448/MnDgQEnOXjNpxySEEEK8fUqVnGlqaqKrq6t+raen98xSDkJ50o5JCCGEePuUKjlr0aIFwcHBPH78mD179rBp0yZatWql9Nxeu+zsbKZMmcLt27fJyspi9OjRdO7cudjjHRwcOHjw4CucYb5GjRo9tVIWEhKCpaVlqce4ePEiaWlpNG/evND7+/fvJyIiAk1NTXJzcxkwYAC9e/cmOjoaY2Pjp+LxumIghBBCvC1KlZxNmjSJzZs3U69ePbZt20b79u0ZNGiQ0nN77Qqq8s+bN4+UlBT69etXYnL2uhgbGxMZGflSY/z++++Ym5s/lZwFBgby008/YWRkRHp6On369MHBwYH+/fu/1PWEEEIIUbRSJWcjRoxg5cqVb3VC9mRFfnt7e/r27Uu3bt3Ux7zIrdxbt27h7+9PTk4OGhoaBAQEcOTIEXJzc/H09GTatGno6uoSEBDA4sWLqV69OnXr1mXWrFlAfhum2bNnc+7cOUJCQtDR0cHJyYm+ffs+89p3794lMDCQzMxMUlNT+fLLL3F0dCQ0NJQjR46Ql5dHz5496d69O1u3bkVHR4eGDRuq654BmJmZsWbNGrp160bt2rXZtWsXurq6hIWFYW5ujpOTE1OnTuXy5ctUr16drKwsIL8m29SpU8nMzERPT4+ZM2diZWVV4nylSrWyJL7KkvgqR2KrLImvchTtEPD48WPu3LnzzP+4vsmerMg/ePBg9WfPU5X/SXPnzsXNzQ1HR0fOnz/PlClTWLRoEVOmTMHT05OrV6+qS2L8+9//ZtmyZXh4eDB79mxq165NVFQUK1as4OOPPyYzM5OoqKinrnH//n11w3KAKlWqMH/+fK5cucLw4cNp2bIlp06dIiwsDEdHR7Zt28batWuxtLQkOjoaS0tL+vXrh7m5eaHEDCA8PJxVq1bh7e1NcnIygwYNYsyYMerPDxw4QGZmJps3byYuLo7ffvsNgODgYNzc3Gjfvj2HDx8mJCSE+fPnlxgr2TGkHNmRpSyJr3IktsqS+CpH8d2aycnJdOrUCTMzM/T09FCpVGhoaLB3797nn205NnLkSJydnYmOjla/d+fOHb788ktcXV1LVZX/SbGxsepbhQ0aNODu3btUq1aNjIwMzpw5g52dHXFxcZw5cwZDQ0MMDAyIjY1V96LMzs6mVq1aAOp/Pqm425oWFhaEh4fz448/oqGhoW6CvmDBAhYsWEBSUhJt27Ytdu73798nLi4OHx8ffHx8iI+PZ+zYsTRs2FB9zD87DFSrVk2dwMfExLB06VJWrFiBSqVCR0fneUMnhBBCvJNKlZytXLlS6Xm8dk9W5F+3bh1paWl4eHgwbdo0Wrdu/ULj2tnZceLECTp37sz58+cxNzcHoH379sybN4+hQ4cSFxfHrFmzGDhwIJCfhAUHB1OtWjVOnjxJYmIikL9r9nl89913DBw4kPbt27Nlyxa2bt1KVlYWv/76KwsWLEClUtGzZ0969uyJhoYGeXl5T8VkwoQJrF+/HisrKywsLDA3Ny+0c9fW1pZffvmFoUOHEh8fT3x8vPp9Dw8PmjRpQmxsrBQuFkIIIUqpVMlZcf9hLapX45vqnxX5ExISmD9/Prm5ueqq/IsXLwZg+fLlVKhQocgxUlNTCz0o7+HhwaRJk5g6dSoRERHk5OQQFBQEQNeuXVm0aBHh4eEkJCQwZ84clixZAuQ/hO/r60tubi4AQUFBJCQkFDv3J29rQn6ts08++YSgoCCWLl2KlZUVKSkp6OrqYmxsTJ8+fTA2NsbBwYFq1arRqFEj5s6di52dnXonroWFBQEBAYwZMwZtbW1yc3Pp0KEDbdq0Ubd8cnR05OTJkwwcOJBq1aphamoKgK+vr/p5t4yMDPz9/Z/7z0QIIYR4F5WqQ8DkyZPV/56dnc3Jkydp1qwZ8+bNU3Ry4t0izz0oR54rUZbEVzkSW2VJfJWj+DNn33zzTaHXqampeHl5lXJ6bxepyi+EEEIIJZUqOXuSvr4+t2/fLuu5vBGkKr8QQgghlFSqJ8zd3Nxwd3fH3d0dNzc3unXr9sIPyL/tsrOz8fHxUTeHf9aOVgcHh0KvDxw4gJ+fX7HHh4WFsWHDBo4ePYqXlxe3bt2iU6dOpZrbP68VGxtLt27dOHz4MNHR0ep5rl27ttjzo6OjCQkJKdW1hBBCCPFiSrVyNnbsWPW/a2hoYGpqSu3atRWb1JvsVXcVMDMzo2rVqs91zqVLlxg7dixz5sx56lZseHg4Q4YMKcspCiGEEOI5lCo5++2335g6dWqh93x9fQkODlZkUm8KpboKFNi1axerVq1CU1OTpk2bMnHixKeOqVixIgsXLgTAz8+PGzdukJmZiaenJz169Hjq+AsXLjB+/Hi+++476tevD6Cu9p+amsr9+/cJDAzEz8+PyZMnExcXR3Z2tvrP/7///S8eHh4kJyfj4uKCs7Mzx44dIzQ0FC0tLapXr86MGTPYvn07+/fvJyMjgxs3bjBixIhntnySKtXKkvgqS+KrHImtsiS+ylGkQ4C/vz83b97k77//5tKlS+r3c3JyePBAdneURVeBJ8tgpKam0rBhQ1JTUwkLC2PLli1UrFgRHx+fYhuKm5ubk56eztGjR9myZQtAkcc+fPgQPz8/tLS0ivzzGz16NGvXriUwMJBVq1ZhbW1NaGgoMTExHDp0CCMjI7S1tVm5ciW3b99m5MiR6vZN69evx8zMjG+//ZatW7eira1Neno6K1eu5Nq1a4waNeqZyZnsGFKO7MhSlsRXORJbZUl8laPYbs3Ro0dz+/ZtgoKCCrXs0dLSws7O7gWm+vZ52a4CT1b3P3DgADt37uTGjRskJyczcuRIID+xunnzZrHjGBgYMHXqVKZOnUp6ejq9e/d+6hgNDQ2+//57UlNTGTt2LFFRUZiZmRU53pUrV2jXrh0AdevWpW7dukRHR/P++++joaGBhYUFGRkZJCcnk5CQoE5CMzIycHBwoEaNGuqVOSsrK3XPTSGEEEKUrMTkzMbGBhsbG37++WdSU1N5/PgxKpWK3Nxczp8//85vClCqqwDkx97KyoqIiAh0dHSIjo6mQYMG7Nmzp8jjExISOHv2LN9//z2ZmZm0b9+ePn36oK39vz9ifX19rK2tsba2ZvDgwUycOPGp7g8FZe/s7Oz466+/cHR05ObNm3z77bc4ODigoaFR6HhTU1OqVq3K4sWLMTQ0ZO/evejr63Pnzp2njhVCCCHEs5XqmbOwsDBWrVpFTk4OJiYmJCQk0KhRoyKbcL9LyqKrQHEqV67MsGHDcHNzIzc3F2tra7p3717s8RYWFiQmJtK3b1/09fXx8PAolJg9ycPDg4MHD6rnWMDOzo6JEycSFBTElClTGDJkCLm5uUyZMqXQre0Cmpqa+Pv7M3LkSFQqFZUqVWLu3LncuXPnub6vEEIIIfKVqkNAp06d+PnnnwkKCmL06NFcuXKF9evXs2zZslcxR/GOkOcelCPPlShL4qscia2yJL7KUbxDQJUqVTAwMKBOnTpcuHCBrl27Mn/+/Oef6TtKugoIIYQQorRKlZwZGBiwbds2GjZsyNq1a6lSpQoZGRlKz+2tIV0FhBBCCFFapeoQEBQURHJyMi1btsTa2ppp06Y9s0SEUJ7S3Qiedb4QQgghyl6pVs4sLS0ZNGgQFy5cYNKkSWRkZKCvr6/03MQzvOpuBEIIIYRQXqmSs8OHDzNt2jRyc3PZtGkTvXr1IiQkhDZt2ig9P/EPT3YkaNiwIePHj1d//jLdCNauXcvvv/9OTk4OhoaGhIWFsWPHDrZs2UJeXh7jxo1TH3vx4kVmzZoFgImJCbNnz+bcuXMsW7YMHR0d7t69y6BBgzhy5AgXLlzA3d0dV1fXF//iQgghxDukVMnZggULWL9+PSNGjMDCwoK1a9fi7e0tydkr9mRHAnd3d+DluxHk5eWRmpqqbhXl6enJX3/9BYCRkRHh4eGFxpk6dSqzZ8+mdu3aREVFsWLFCj7++GPu3r3Ltm3bOHv2LOPHj2f37t3Ex8czZsyYUiVn0kJEWRJfZUl8lSOxVZbEVzmKtG8qkJeXh4WFhfq1ND1/fZ7sSFAW3Qg0NTXR0dHB29sbfX197t69S05ODgC1atV6apzY2FimT58O5D/3VnBMnTp10NHRwdDQkBo1aqCrq4uxsTGZmZml+m6ynVs5sl1eWRJf5UhslSXxVY7ipTSqVq3KH3/8gYaGBmlpaaxbt45q1ao9/0zFS3myI0F4eHiZdCO4cOECe/bsISoqisePH9O/f391pwBNzaf3jNSqVYvg4GCqVavGyZMnSUxMBJCOAEIIIUQZKDE5i4+Px9LSkhkzZhAUFMSdO3fo0qULLVu2ZMaMGa9qjuL/K6ojQVl0I6hZsyYVK1akf//+6OrqYmFhQUJCQrHHBwYG4uvrS25uLpC/m7ek44UQQghReiV2COjXrx9bt24FICIiAg8Pj1c2MfHukaV15citC2VJfJUjsVWWxFc5it3W/Gfetn37dknO3gDSjUAIIYR4s5WYnP3zGaJStOAU5YB0IxBCCCHebKXaEADysHdZy87OZsqUKdy+fZusrCxGjx5dYgHZR48eERoayn/+8x/1M2Xu7u506dLlVU25SOfPn2fv3r2MGTOmyM8zMzP5+eefGThw4CuemRBCCPFmKjE5u3TpkjphiI+PV/+7SqVCQ0Pjme2CRPGet7r/lClTaNKkCf7+/gAkJyfj6elJ8+bNMTExeUWzflqDBg1o0KBBsZ8nJiYSFRUlyZkQQghRSiUmZ7/99turmsdb72Wq+ycmJnL16lW+/fZb9XuVK1cmOjoaDQ0NoqOjC1Xy/2dB2aZNmzJx4kQePHiAv78/KSkpAAQEBFCvXj26du1KkyZNuHr1KmZmZoSFhfHTTz+xd+9e0tPTSUlJ4csvv6Rbt24cPHiQb7/9Fj09PXVngPPnz7Nx40ZCQ0OLHGvJkiVcvnyZRYsWFbu6JoQQQoj/KTE5s7a2flXzeOu9THX/27dvU716dfXrhQsXcvz4ce7fv88XX3wB/K+Sf2pqKq6urmzZsoWKFSvi4+PDwYMHOXToEK1atcLV1ZVr164xefJkNmzYwM2bN1m9ejVWVlYMGjRI3Rng0aNH/PDDDyQnJzNw4EA6derE1KlT2bBhA5aWlqxevZrw8HA6dOignldRY40aNYqYmJhSJWZSpVpZEl9lSXyVI7FVlsRXOYp2CBBl40Wr+1etWpXbt2+rXxf0uQwJCeHRo0fA/yr537hxg+TkZEaOHAnAw4cPuXnzJjExMRw5coRdu3YBkJaWBoCpqSlWVlYAWFlZqav5N2/eHE1NTczNzTEyMiIpKQkDAwMsLS3Vny9YsKBQclbcWKUl27mVI9vllSXxVY7EVlkSX+Uo3iFAvLyXqe5ftWpVbGxsWLduHYMHDwbgwYMHnD9/Hjs7O1QqlbqSv42NDVZWVkRERKCjo0N0dDQNGjTg6tWr9O7dm169enHv3j2ioqKA4jd6nD17FoCkpCTS09OpUqUK6enpJCQkUKVKFY4dO8Z7771X6JyixtLU1CQvL++5YiWEEEK8yyQ5e0Vetrp/cHAwYWFhuLi4oKWlxaNHj+jXrx+ffvop27dvVx9XuXJlhg0bhpubG7m5uVhbW9O9e3dGjRqFv78/mzdvJj09/Zm3GZOSkhg6dCgPHjzg66+/RktLi1mzZjF27Fg0NDQwNjbmm2++4dKlSyWOY2ZmRnZ2NvPmzcPHx+c5oyaEEEK8e0rsECDeTdHR0Vy5coWJEye+0uvK0rpy5NaFsiS+ypHYKkviqxy5rfmWkOr+QgghhJCVM1FuyK835civY2VJfJUjsVWWxFc5L7NypqnEhIQQQgghxIuR5OwNkJ2djY+PD66urgwYMOCZnRk6derE4MGDcXNzw83NTf3wv4ODQ4nnbdq0iezsbI4ePYqXl9dLzflZ1xJCCCFE0eSZszfA87Z6AoiIiEBPT++5rrN06VL69u37EjMVQgghxMuS5KwceplWT6Vx7NgxFi1aBEBGRgbBwcGcOHGCxMREvLy8GDp0KNevX8fT05OUlBRcXFwYOHAgbm5uBAYGYmdnx4YNG0hKSmLkyJGMHz+e9PR0MjIy8PHxoWXLlmRlZfHVV18RFxeHiYkJCxcuREdHp8R5SZVqZUl8lSXxVY7EVlkSX+VIh4C3yMu0eirg4eGhLkzr6elZqJL/pUuXmDdvHpaWlixZsoRff/2V0aNHEx4eTmhoKP/5z3/Izs4mPDycvLw8+vTpU+xK3Y0bN0hKSmLVqlXcu3ePa9euAfntn7y8vLCxscHNzY3z589jb29f4pzloVTlyEO/ypL4KkdiqyyJr3KklMZb6EVbPRUo6bampaUlQUFB6OvrEx8fT5MmTZ465qOPPkJXVxcAOzs7bt26Vejzgk2+derUYfDgwXh7e5OTk4ObmxsAxsbG2NjYAGBubs7jx49L+c2FEEKId5skZ+XQy7R6Ko2AgAD27NmDgYEBvr6+6kRLQ0ND3Wrp3Llz5OTkkJWVRWxsLDVq1EBXV5fExETs7Ow4d+4clpaWXLx4kYcPH7Js2TISEhIYNGgQHTt2LLYtlBBCCCFKJslZOfSyrZ6epU+fPjg5OWFkZIS5uTkJCQkANGvWjJEjR/Lll1+ip6fHiBEjSEtLY+zYsZiYmODu7s6MGTOwsrKiSpUqALz33nt8//33bNu2DR0dHXVTdiGEEEK8GClCK8oNee5BOfJcibIkvsqR2CpL4qsceebsHSStnoQQQoi3kyRnbyhnZ2ecnZ1f9zSEEEIIUcakQ0Axyqoqf8E/leLl5UVWVlaxnxdVqf+fc3VycmL69OlkZmY+c7zU1FS2b9/+1Pvnz59X100ryu7du4mPj3/WVxFCCCEEsnJWrLKqyl9S0lIWQkNDX+i8f861oL6Zn59fieNdvHiRffv2PVXKo0GDBjRo0KDY89asWUNgYCCWlpYvNFchhBDiXSLJGU9X5Le3t6dv375069ZNfcyLVuV3cHDg4MGDuLm5Ub9+fS5dukR6ejrfffcd1tbWzJ8/n7///puHDx9iZ2fHN998Q1hYGLdu3eLevXvExcUxefJk2rZtyx9//KFO9t5//32mT5+Oo6Mju3bt4vr168yZM4e8vDzS0tIICAgosn5ZUYYPH06PHj3w8/OjU6dO7Nq1i/3797N8+XK0tbWxtrZm7ty5LFmyhAsXLrBp0yZOnz5NamoqqampeHp6snPnTkJDQ4mKimLDhg3k5eXRuXNnPvjgA86fP4+vry/r169X104rilSpVpbEV1kSX+VIbJUl8VWOdAh4CU9W5B88eLD6s7Kqyg9gb2+Pv78/oaGh/PLLL7i6umJkZMQPP/xAXl4ePXv2VN/+09XVZcWKFRw8eJCIiAhat27NzJkziYqKwszMjEWLFnH37l312JcvX8bX15d69eqxfft2oqOjS52cVahQQX1bs8COHTsYNmwYPXv2ZNu2baSnpzNq1Cg2btyIs7Mzp0+fplWrVgwbNoyjR48CcO/ePZYvX87PP/+Mrq4uc+bMoXnz5jRo0IDAwMASEzOQ3ZpKkh1ZypL4KkdiqyyJr3Jkt2YZeLIiP5RtVX7IX+0CqFq1KklJSejp6ZGcnIy3tzf6+vo8evSI7OxsAPVtwqpVq5KVlUVKSgpGRkaYmZkBTz/LVqVKFRYvXkyFChV4+PAhBgYGpf7u6enpVKpUqdB7kydPZunSpWzYsAFbW1scHR2fOq9WrVqFXt+8eZM6deqo669NmTKl1HMQQgghRD7ZEMDTFfmzsrJISkrCw8MDHx8fBgwYoMh1Dxw4wJ07d1iwYAHe3t5kZGQUqtb/T2ZmZqSlpZGamgrArFmzOHPmjPrzoKAgxo0bR3BwMHXr1uV5ytctX76c7t27F3pv06ZNjB07lrVr1wL5D/VramqqOwgUNccaNWpw5coV9YaCcePGER8fj4aGxnPNRwghhHiXycoZRVfkz83NLdOq/EWxt7dn8eLFODk5oaurS/Xq1dXV+p+kqanJ119/zf/93/+hqanJ+++/zwcffKD+vHfv3nzxxReYmZlRtWpVUlJSSrx2wS3YvLw8GjRowKRJk56a2/DhwzExMaFSpUp06NCBrKwsYmJiWLVqVZFjVq5cmREjRjBkyBA0NDTo2LEjlpaWNG7cmEmTJhEREYGJiclzxUgIIYR410iHAFFuyHMPypHnSpQl8VWOxFZZEl/lyDNnr4hU5RdCCCGE0iQ5ew4vW5U/OzubKVOmcPv2bbKyshg9enSJtdMaNWr0VNIXEhLyXPXCLl68SFpaGs2bN1e/99133wEwfvx49Xu7d+/mt99+o27durRq1YrLly9z5coVBg0ahLe3N5s3by71NYUQQgjx4iQ5e4Wet7CtsbExkZGRL3XN33//HXNz80LJ2cCBAxk6dCjjxo1TP9S/ZcsWPD091cddvnz5pa4rhBBCiBcjyZlClCxse/fuXQIDA8nMzCQ1NZUvv/wSR0dHQkNDOXLkiLpmWvfu3dm6dSs6Ojo0bNgQe3t7AKpVq0bNmjU5ceIEzZs3JzExkdu3b9O8eXP8/Pzo0aNHkdf99ddfWbdunfr1d999h6mpKdOnT+fvv//G3Nyc27dvEx4ejpaWFlOnTiUzMxM9PT1mzpyJlZXVC31fIYQQ4l0iyZlCyqKw7f3793Fzc1O/rlKlCvPnz+fKlSsMHz6cli1bcurUKcLCwnB0dGTbtm2sXbsWS0tLoqOjsbS0pF+/fpibm6sTswJOTk789NNPNG/enG3btvHZZ5898ztdu3aNZcuWUbFiRaZNm8a///1v9PX1SU1N5ccffyQ5OZmuXbsCEBwcjJubG+3bt+fw4cOEhIQwf/78EseXKtXKkvgqS+KrHImtsiS+ypEOAeXQyxa2Le62poWFBeHh4fz4449oaGiQk5MDwIIFC1iwYAFJSUm0bdu2xLE7duzIggULyMjI4JdffuGHH3545vcxMzPD19eXSpUqceXKFT766CP1PyG/lIatrS0AMTExLF26lBUrVqBSqdDR0Xnm+LJjSDmyI0tZEl/lSGyVJfFVjuzWLIeeLGy7bt060tLS8PDwYNq0abRu3fqFx/7uu+8YOHAg7du3Z8uWLWzdupWsrCx+/fVXFixYgEqlomfPnvTs2RMNDY1ChWML6Ojo4OjoSHh4OHZ2dpiampZ4zQcPHrBw4UL+9a9/Afn9OFUqFXXq1OGnn34C8lf6rl27BoCtrS0eHh40adKE2NhYjh8//sLfVwghhHiXSHKmkLIobPvkbU3IL9vxySefEBQUxNKlS7GysiIlJQVdXV2MjY3p06cPxsbGODg4UK1aNRo1asTcuXOxs7OjVatWhcYaOHAgPXv2JCIi4pnfx8DAgCZNmtCvXz/09fUxMjIiISGB/v37c+DAAQYNGoS5uTkVKlRAR0cHX19f9XNxGRkZ+Pv7v2AkhRBCiHeLFKEVLyU2NpYLFy7Qs2dPUlJS+PTTT/njjz+e2eS8KLK0rhy5daEsia9yJLbKkvgqR25rvsHe9MK2VlZWhISEsHr1anJzc5k4ceILJWZCCCGEyCcrZ6LckF9vypFfx8qS+CpHYqssia9yXmblTFOJCQkhhBBCiBcjtzVfgfLStgnAwcGBgwcPql8fOHCAnTt3MmfOnFKN++T5QgghhChbkpy9AuWlbZMQQgghyj9JzspYeW7b9Cxr167l999/JycnB0NDQ8LCwtixYwdbtmwhLy+PcePGqY+9ePEis2bNAsDExITZs2dz7tw5li1bho6ODnfv3mXQoEEcOXKECxcu4O7ujqura4nXlyrVypL4KkviqxyJrbIkvsqRDgHlRHlv2/Tk2KmpqTRs2JC8vDxSU1NZtWoVmpqaeHp68tdffwFgZGREeHh4oXGmTp3K7NmzqV27NlFRUaxYsYKPP/6Yu3fvsm3bNs6ePcv48ePZvXs38fHxjBkz5pnJmTyUqhx56FdZEl/lSGyVJfFVjpTSKGfKc9umJ8cueOZMU1MTHR0dvL290dfX5+7du+rxa9Wq9dQ4sbGxTJ8+Hch/pq7gmDp16qCjo4OhoSE1atRQF8fNzMwscV5CCCGEyCfJWRkr722binPhwgX27NlDVFQUjx8/pn///hRUWdHUfHpTb61atQgODqZatWqcPHmSxMREADQ0NF74+wkhhBBCkrMy9ya0bSpKzZo1qVixIv3790dXVxcLCwsSEhKKPT4wMBBfX19yc3MBCAoKKvF4IYQQQpSOFKEV5YY896Acea5EWRJf5UhslSXxVY48c/YGetPbNgkhhBBCGZKcvSbOzs44Ozu/7mkIIYQQopyR5KyMlKcuAJ06dcLKykr9IL+xsTGLFi16ZnX/TZs20b9/f06dOsXGjRsJDQ0t9VyeJJ0EhBBCiBcjyVkZKW9dACIiItDT03uu8ZYuXUrfvn1fak5CCCGEeDmSnL2AN7kLAMCxY8dYtGgRABkZGQQHB3PixAkSExPx8vJi6NChXL9+HU9PT1JSUnBxcWHgwIG4ubkRGBiInZ0dGzZsICkpiZEjRzJ+/HjS09PJyMjAx8eHli1bkpWVxVdffUVcXBwmJiYsXLgQHR2dF4qJEEII8S6R5OwFlPcuAAAeHh7q25qenp506NBB/dmlS5eYN28elpaWLFmyhF9//ZXRo0cTHh5OaGgo//nPf8jOziY8PJy8vDz69OlT7CrgjRs3SEpKYtWqVdy7d49r164B8OjRI7y8vLCxscHNzY3z588/M4GUFiLKkvgqS+KrHImtsiS+ypH2Ta9Yee4CACXf1rS0tCQoKAh9fX3i4+Np0qTJU8d89NFH6OrqAmBnZ8etW7cKfV5QgaVOnToMHjwYb29vcnJy1AmnsbExNjY2AJibm/P48eNnzlm2cytHtssrS+KrHImtsiS+ypFSGq/Ym9oFoEBAQAB79uzBwMAAX19fdaL1z/HOnTtHTk4OWVlZxMbGqlsxJSYmYmdnx7lz57C0tOTixYs8fPiQZcuWkZCQwKBBg+jYsaN0ChBCCCFekCRnL+BN7QJQoE+fPjg5OWFkZIS5ubm6sn+zZs0YOXIkX375JXp6eowYMYK0tDTGjh2LiYkJ7u7uzJgxAysrK6pUqQLAe++9x/fff8+2bdvQ0dFh3LhxLxJSIYQQQvx/0iFAlBuytK4cuXWhLImvciS2ypL4Kkdua5ZT0gVACCGEEM9LkjMFSRcAIYQQQjwvzdc9gTdVdnY2Pj4+uLq6MmDAAPbu3Vvi8ffv32fKlCkMHjyYQYMG4eXlxYMHr2Yp+ejRo3h5eRV6LyQkpNBO0ycFBQURFxdX7OedOnUiMzOz0HuZmZlERUW93GSFEEKId5wkZy+ooCPA+vXrWb58OTNnzizxeG9vbzp27Mi6devYuHEjH374IdOmTXtFs31+/v7+VKtW7bnOSUxMlORMCCGEeElyW7MUXrYjwO3bt0lKSqJLly7q99zc3Pjss88A6NixI7a2ttja2jJ06FD8/f3JyclBQ0ODgIAAjhw5Qm5uLp6enkybNg1dXV0CAgJYvHgx1atXZ/PmzdSvX59Lly6Rnp7Od999h7W1dam/3/z58zl+/DgqlYphw4bRvXt3dTcAU1NTJk6cSFZWFrVq1eLIkSPs3r0bgMDAQHX9s0WLFrFkyRIuX77MokWLaN26NcHBwWhra2NkZERISAgGBgbPFXchhBDiXSTJWSm8bEeAhIQEdUHWAlpaWhga5u/SuHPnDtHR0ZiamjJu3Djc3NxwdHTk/PnzTJkyhUWLFjFlyhQ8PT25evUqGRkZAPz73/9m2bJlbN68GXt7e/z9/QkNDeWXX35h5MiRha535MiRQqU7bt68ybhx49i/fz+3bt1i48aNZGZm4uTkhIODg/q4JUuW0LlzZ3UM/tnM/LPPPqNZs2b4+flx8OBBRo0aRUxMDGPGjCE4OJguXbrg6enJvn37SEtLe2ZyJlWqlSXxVZbEVzkSW2VJfJUjHQIU9jIdAapVq8bdu3cLvZednc2vv/5Kr169MDU1xdTUFIDY2Fh1I/MGDRpw9+5dqlWrRkZGBmfOnMHOzo64uDjOnDmDoaGhOuF5//33AahatSpJSUlPzaFVq1aEhoaqX4eEhAAQExPD2bNn1YlbTk5OoWfNYmNj6devH5BfB+2fGjVqBOR3AChIGAuMGjWKJUuWMHToUCwtLUvV+1O2cytHtssrS+KrHImtsiS+ynmZUhryzFkpPNkRICsri6SkJDw8PPDx8WHAgAElnm9paYmpqSl79uxRv7dmzRr164IemJDfKunEiRMAnD9/HnNzcwDat2/PvHnzaNOmDW3atGHWrFk4Ojq+9HeztbWlZcuWREZGsnr1arp3715ola9u3bqcPn0agP/85z+Fzn2yC4Cmpqa6w8D27dvp168fkZGR1KlTh82bN7/0XIUQQoh3gayclUJZdASYO3cuM2bMICIiguzsbGrUqMGsWbOeOm7SpElMnTqViIgIcnJyCAoKAqBr164sWrSI8PBwEhISmDNnDkuWLHnp79apUyeOHTuGq6srjx49wtHRsdDtxxEjRjBp0iR27dpFlSpV0NYu/q+MmZkZ2dnZzJs3j65du+Ln54e+vj46OjrMmDHjpecqhBBCvAukQ4Ao0f79+zE1NcXe3p5Dhw6xZMkS1qxZo8i1ZGldOXLrQlkSX+VIbJUl8VWOdAgoJ97GjgA2NjZMmTIFLS0t8vLy8Pf3V+xaWloa5ObKbwUhhBDvNlk5E+XCtGWHAJgwwF4SNAXIr2NlSXyVI7FVlsRXObJyJt548fceve4pCCGEEOWCJGdvkOzsbKZMmcLt27fJyspi9OjRdO7cudjj79+/T3BwMNevXyc3NxcrKytmzJiBoaEhDg4OhWqWleR5jhVCCCHEy5FSGm+Qt71llBBCCCFk5axce7JtVMOGDRk/frz685dpGZWVlcVXX31FXFwcJiYmLFy4kIyMDPz9/UlJSQEgICCAevXqFZrPtm3b0NTUpEmTJvj6+nLnzh2mTp1KZmYmenp6zJw5EysrK+bPn8/ff//Nw4cPsbOz45tvvinVd65cWVo8KUWqgCtL4qscia2yJL7KkQ4Bb6En20a5u7sDZdMy6tGjR3h5eWFjY4Obmxvnz5/nt99+o1WrVri6unLt2jUmT57Mhg0b1OdHR0czdepUPvroI9avX09OTg7BwcG4ubnRvn17Dh8+TEhICNOnT8fIyIgffviBvLw8evbsSXx8PJaWls/8zsnJ6bIhQAHy0K+yJL7KkdgqS+KrHNkQ8BZ7sm1UWbWMMjY2Vidv5ubmPH78mJiYGI4cOcKuXbsASEtLK3T+N998Q0REBCEhIXz00UeoVCpiYmJYunQpK1asQKVSoaOjg56eHsnJyXh7e6Ovr8+jR4/Izs4uy7AIIYQQby1JzsqxJ9tGhYeH4+HhwbRp02jdunWJ5/6zZVRBm6c1a9Zw5swZevXq9VTrJchv5dS7d2969erFvXv3iIqKKvT55s2bmT59Onp6enh6enL69GlsbW3x8PCgSZMmxMbGcvz4cQ4cOMCdO3f49ttvSU5OZvfu3TyrYoulmf5zRkcIIYR4O0lyVo4V1TZKiZZRBUaNGoW/vz+bN28mPT2dMWPGFPq8Xr16DBgwAFNTUywtLfnwww/x9fUlMDCQzMxM9TNrNjY2LF68GCcnJ3R1dalevToJCQlUr1692GvPGPmx3NIUQgghkCK0ohyR5x6UI8+VKEviqxyJrbIkvsqRZ87eYW9jyyghhBDiXSbJ2RvO2dkZZ2fn1z0NIYQQQpQRKUJbDmVnZ+Pj44OrqysDBgxg7969JR7fqFEj3NzcGDJkCP3792f37t3PfU03NzdiY2Of65wnn0l72fGEEEIIIStn5VJBJ4B58+aRkpJCv379SmzTZGxsTGRkJAAPHjygW7duODo6FrkjsywtWrRI0fGFEEKId5EkZ+XAy3QCeFJ6ejqWlpZoaGgQFhaGubk5Li4uxMbGEhgYSGRkJKGhoRw5ckRdIHbYsGEAfP/99yQlJfH48WMWLFjAN998w+jRo/nggw/o1q0bEydOpEuXLnh4ePDNN9/Qv39/Dh48yH//+1+CgoJQqVRYWloSEhKins++ffv44Ycf+P777zEyMipx7lKlWlkSX2VJfJUjsVWWxFc50iHgDfYynQAgv8G5m5sbeXl5xMTE4OnpWeLx27ZtY+3atVhaWqqL2wK0b9+ePn36EBYWxq+//krXrl05cOAAJiYm6OnpcfDgQVq1akVmZmahav9Tp04lNDQUOzs71q1bp76duXv3bo4fP87SpUvR1392HTPZMaQc2ZGlLImvciS2ypL4Kkd2a74FXrQTABS+rZmens6gQYNo1qxZsccvWLCABQsWkJSURNu2bdXvN2rUCMjvGJCUlETHjh354osvMDU1ZcSIEfzwww8cOHCAjh07Fhrv3r172NnZAfmJZoHDhw+Tnp6Otrb8NRNCCCFKSzYElANPdgJISkrCw8MDHx8fBgwY8FxjVapUCUNDQ7Kzs9HT0yMxMRGAs2fPqq/166+/smDBAlavXs3WrVu5fft2kWMZGxtToUIFdu3aRdu2balWrRqrV6+ma9euhY6rUqUK165dA2DZsmXqDQnTpk2jTZs2LFy48Lm+gxBCCPEukyWNcuBlOwEU3NaE/OTrgw8+oFWrVtjY2DBhwgSOHz+uXhXT1dXF2NiYPn36YGxsjIODA9WqVSt2bp07dyY6OhoTExPatGnD+vXrqVGjRqFjpk+fzpQpU9DU1MTCwoJhw4axZs0aAL788ksGDhxIhw4dSlzNE0IIIUQ+6RAgyo3k5HQAaeGkAHmuRFkSX+VIbJUl8VWOPHP2DnjbOwFMW3aI+HuPsDTTZ8IAe0nQhBBCvLPe6mfOXmUxVy8vL44ePUpubi6enp64uLhw//7955pvbGys+vbkP+Xl5XHlyhW0tbXR1dWlUqVKzJ49m8jISHJycrhw4cJzXedVSk1NZfv27c88Lv7eI+KSHhJ/79ErmJUQQghRfr3VK2evo5hrYmIiKSkphUpUvKw///yThIQEfvjhBwD27NnD7NmzCQ8PZ8uWLfTo0YP69euX2fXK0sWLF9m3b98zd5wKIYQQIt9blZwpVcz1wYMH+Pv7k5KSAkBAQAD16tVj3bp1REVFYWFhwb1794D8ml/Xrl1j2rRpTJw4ER8fH9LT08nNzWX8+PG0bt2aTz/9lPfeew9dXV38/PyYOHEiKpUKCwuLIudStWpV/v77b3bu3EmrVq3o3Lkz7dq14++//+bPP//k7Nmz1K5dm4EDB3Lw4EEgfyVv0KBBbN68mV69etGhQwdiY2MJDg5GV1cXd3d3WrRowZkzZwgPD2fhwoV8/fXXXL9+nby8PCZMmEDLli2LLFh77NgxdXeAjIwMgoOD0dHR4auvvqJq1arcvHmTDz74gOnTp7NkyRIuXLjApk2bpAeoEEIIURqqt8zo0aNVvr6+Ki8vL/V7Dx48UA0ZMkT1888/l3huw4YNVUOGDFG5urqqmjVrpgoPD1epVCrV3LlzVevWrVOpVCrV1atXVYMGDVKlpaWpunbtqsrMzFRlZWWpPv30U9WRI0dUN2/eVA0cOFClUqlUc+bMUa1atUqlUqlUd+/eVXXs2FGVm5ur6tixo+rs2bPqYzZt2qRSqVSqX375RTVkyJAi53b48GGVl5eXqnXr1qp+/fqpjh49qlKpVCpfX1/V/v37VSqVSvXxxx+rj58wYYLqyJEjqsOHD6vGjRunvtZvv/2m+te//qXy8/NTqVQqVWBgoGrfvn2qdevWqebOnatSqVSq5ORkVY8ePVQqlUrVrl071Y0bN1SZmZmqDRs2qFQqlWrt2rWqu3fvqlQqlSo8PFy1ePFi1c2bN1UtWrRQPXjwQJWTk6Pq0KGDKiEhQXXkyBHVhAkTSoy7SqVSjZy9W/Wp9zbVyNm7n3msEEII8TZ7q1bOQJlirjExMRw5coRdu3YBkJaWxpUrV6hduza6uroA2NvbPzVebGys+pqWlpYYGBiQnJwMQK1atQC4dOkSffr0AaBJkyZs2LCB69evExAQAEDv3r354IMPqFWrFgsWLEClUnHw4EEmTJigXiUriur/b8Jt2bIlQUFB3Lt3j4MHD+Lt7Y2Wlhbz5s0jNTWVEydOEBAQwMyZMzl58iRnzpwBICcnh5SUlCIL1lpaWhIUFIS+vj7x8fE0adIEgBo1amBgYACAhYUFmZmZJca7OMnJ6bIhoIzJjixlSXyVI7FVlsRXObJb8/97sphreHg4Hh4eTJs2jdatWz/XWP8s5mpra0vv3r3p1asX9+7dIyoqiurVq3P58mUyMjLQ0dHh/Pnz9O7du9AYdnZ2nDhxgvfff5/4+HjS0tIwMTEBQFMzfy+Gra0tp0+fpn79+vz1118A1KxZU50kAvzwww9cuHCB2bNno6WlRZ06dahYsSIaGhpoaGioE7GcnBwePnyIjo4Oly9fBkBDQ4NevXoRFBSEg4MDOjo6AHzyyScEBgbi6OiIlpYWtra2VK1alVGjRpGRkUF4eDiVKlVSF6xVqVT07NmTnj17EhAQwJ49ezAwMMDX11d9/aKezdPU1CQvL++5Yi+EEEK8y96q5EypYq7169fH39+fzZs3k56ezpgxY6hcuTLjx49n0KBBVK5cmYoVKz413v/93/8xZcoUfvvtNzIyMpgxY8ZTrYzGjx+Pl5cXO3fuxMbGpsh5ubm5ERwcTN++fTEwMEBTU5O5c+cC8OGHHxISEoKNjQ3u7u44OztjY2NTqLBs//796dChAz/99JP6vc8++wxHR0d+++03AAYNGkRAQABDhgwhPT0dV1fXYgvW9unTBycnJ4yMjDA3NychIaHYP5MaNWoQExPDqlWr1A3Wi2Jppl/on0IIIcS7SorQvgPi4+OZNGkSq1evft1TKZEUoVWO3LpQlsRXORJbZUl8lSO3NZ/D217M9Um//fYbixYtIigo6HVP5ZkkKRNCCCFk5UyUI/LrTTny61hZEl/lSGyVJfFVzsusnL3VHQKEEEIIId40kpy94Z63RRXAf//7Xxo1aqQum1GczMxMoqKiSj2X6OjoUl1fCCGEEMWT5OwNV9Ciav369SxfvpyZM2c+85yoqCiGDx/O+vXrSzwuMTHxuZKz/v37l9geSwghhBDP9s5tCHjTvWyLqocPH3LkyBF++eUXevXqRXJyMpUrV+bkyZMEBwejra2NkZERISEhLFmyhMuXL7No0SLc3d2f2YqqVq1amJub4+TkxLRp07h79y4pKSm0a9eOCRMmKBwZIYQQ4u0gGwLeQF988QVGRkZkZWWxYMECIL+jwejRo3FyciqxE0JUVBRXrlzB19eX0NBQKlWqxMiRIwkODqZy5cp4enqyb98+3n//ffLy8vD29mbz5s0EBwdTtWpVhg4dSnx8PC4uLuzZswdHR0cWLVrE+++/T1hYGObm5rRt25bDhw8zcOBAMjMzadeuHUePHn1V4RFCCCHeaLJy9gZ6mRZVUVFRaGlp4enpSUZGBnfv3uXzzz9n1KhRLFmyhKFDh2JpaYm9vT1ZWVnq80rTiqqAiYkJf/31F0eOHMHAwKDQOCWRHUPKkR1ZypL4KkdiqyyJr3Jkt+Y75MkWVUlJSXh4eODj48OAAQNKPPfixYvk5uayYcMGVq5cybp166hRowZ//PEH27dvp1+/fkRGRlKnTh02b95cqPVSQSsqoNhWVAWio6MxNDRk/vz5eHh4kJGRgSzQCiGEEKUjK2dvmJdpURUVFaVusl5g4MCBrFu3jvHjx+Pn54e+vj46OjrMmDEDMzMzsrOzmTdvXqlaURVo3bo13t7enDx5kooVK1KzZk0SEhKwtLQs+4AIIYQQbxl55kyUG7K0rhy5daEsia9yJLbKkvgqR9o3iULetRZVQgghxNtEkrO3kLOzM87Ozq97GkIIIYR4AbIhQEH//e9/cXNze+Zx+/fvZ+jQoQwfPhx3d3d+/vlnAFJTU9m+fXuJ5+7evZv4+PiXmqeDg0Oxn4WFhbFhw4aXGt/Ly0tKaQghhBClJCtnClm+fDk///wzFStWfOaxgYGB/PTTTxgZGZGenk6fPn1wcHDg8uXL7Nu3r8TyGGvWrCEwMFAethdCCCHeEpKcvaQnK/bb29szePBgatSoQVhYGJMmTXrmGGZmZqxZs4Zu3bpRu3Ztdu3aha6uLhMnTuTChQts2rSJxo0bM2fOHPLy8khLSyMgIIC0tDTOnz+Pr68v69evVz9rpqGhQY8ePXB3d8fPzw9dXV1u375NQkICc+bMoWHDhkXO4/fff2f58uVoa2tjbW3N3Llz1Z/l5uYWWfW/uPHXrVtHVFQUFhYW3Lt3r8ziLYQQQrztJDl7SYMHD+bgwYP4+fmRnZ3N4MGDAejWrRu3bt0q1Rjh4eGsWrUKb29vkpOTGTRoEGPGjGHUqFFs3LgRZ2dndu7cia+vL/Xq1WP79u1ER0cza9YsGjRoQGBgIDdu3GDnzp2sX78eDQ0Nhg0bRps2bQCoVq0aM2bMYPPmzWzatIkZM2YUOY8dO3YwbNgwevbsybZt20hPT1d/dufOHT766KNCVf8LWjI9Ob6Pjw9r1qxh+/btaGho0L9//1LFobhdK6JsSHyVJfFVjsRWWRJf5bxobCU5KwNPVux/Hvfv3ycuLg4fHx98fHyIj49n7NixNGzYkEqVKqmPq1KlCosXL6ZChQo8fPgQAwODQuPExMQQFxfHsGHD1OPeuHEDgAYNGgBQtWpVTp06VexcJk+ezNKlS9mwYQO2trY4OjqqPyup6v+T41+5coXatWujq6sLgL29faliIdu5lSPb5ZUl8VWOxFZZEl/lSIeA1+jJiv2lbVX0z/MnTJjAnTt3ALCwsMDc3BxdXd1CFfqDgoIYN24cwcHB1K1bV11xX0NDA5VKha2tLbVr12bNmjVERkbSv39/6tatqz6mNDZt2sTYsWNZu3YtkL/ZoEBJVf+fHL969epcvnyZjIwMcnNzOX/+/HPFRAghhHiXycrZSyqqYv/kyZNLfb6FhQUBAQGMGTMGbW1tcnNz6dChA23atCE+Pp6YmBhWrVpF7969+eKLLzAzM6Nq1aqkpKQA0LhxYyZNmkRERAStW7fGxcWFrKws7O3tn3uTgL29PcOHD8fExIRKlSrRoUMHdaJWXNX/olSuXJnx48czaNAgKleuXKpNEUIIIYTIJx0CRLkhS+vKkVsXypL4KkdiqyyJr3KkQ8AbQKr2CyGEEKI0JDl7RaRqvxBCCCFKQzYECCGEEEKUI5KcvaFK2xrq3r17+Pj44OTkhKurK1999RWJiYmvYIZCCCGEeBFyW/MNVNrWUCqVilGjRjFq1Cg6d+4MwKFDh/i///s/oqKi0NLSehXTFUIIIcRzkOSsnCuqPVRpW0OdPn0aMzMzdWIG8PHHH1OzZk2OHz/O8ePHMTc3x8XFhdjYWAIDA4mMjOTYsWOEhoaipaVF9erV1R0Fvv76a65fv05eXh4TJkygZcuW9OrVixYtWnDx4kU0NDRYvHgx2dnZTJgwAZVKRXZ2NtOnT6devXrP/K5SpVpZEl9lSXyVI7FVlsRXOdIh4C1VXHuo0rSGiouLw8bG5qn3ra2tuX37dpHnqFQqpk6dyvr16zEzM+Pbb79l69at5OTkYGpqyuzZs0lJSWHIkCH88ssvPHz4kJ49ezJ16lS++uorDhw4QKVKldQFay9fvlyoDVRJZDu3cmS7vLIkvsqR2CpL4qscKaXxlnvR9lDVqlUrsnzHtWvXaNWqFXFxcU99lpycTEJCgrpvZkZGBg4ODqSmpnLy5EnOnDkDQE5OjroQ7vvvvw+AlZUVmZmZdO/enWvXrvHFF1+gra3N6NGjn2veQgghxLtMkrNy7sn2UOvWrVP3rHyWxo0bk5SUxN69e+ncuTN+fn6Ymppy/fp1WrZsyblz59SbA86ePQuAqakpVatWZfHixRgaGrJ371709fW5dOkSVatWZdSoUWRkZBAeHo6xsTHwdPumo0ePUqVKFSIiIjh9+jQLFiwgMjKyDKMihBBCvL0kOSvnXqY9lIaGBkuXLmXOnDksW7aM3NxcjI2NsbCw4MqVK3Tv3p0JEyZw/PhxGjVqBICmpib+/v6MHDkSlUpFpUqVmDt3Lk2bNiUgIIAhQ4aQnp6Oq6srmppFb/atX78+Xl5erF69Gk1NTb788ssyi4cQQgjxtpP2Te+gxMREHj9+TI0aNV73VAqR5x6UI8+VKEviqxyJrbIkvsqRZ87ecc/bGsrCwuJVTEsI8f/au/eoqur0j+Nv7qBgOFh4SRwvaZT3W6KZl6SLggyYgkyUDS7LGRsnzcEMEB1wTOwKS5syR2MUw9JC8zJpTk2kkuYlJ0XFS5AFMwkhoBzh7N8f/jx5BQu3HPDzWmvWgrM5+/vsZ87qPH73/n4fEZFfQMVZA6DWUCIiIg2HOgTYgWvd7b9Tp07MnDnzotcSExMZOnTozx4zJSWF9PT0a/rbiooKVq5cCUBxcTFr1qz52eOJiIjItVFxVsfefPNNYmNjqaioqPFvvb29+eKLL6isrASgqqqKffv2mR0i//3vf23FWU5ODh9//LHpY4qIiNysdFvzBqrNbv8Azs7O9O3bl6ysLAYNGsRnn31GQEAAH3zwAQDZ2dmkpqYC5/Yne+GFF3BxcWHq1Kk0b96cvLw8unTpwqxZswDYvHkzGzZsoLi4mMmTJzN06FD+8Y9/8M9//pPKykq8vLxISUnh9ddf5/Dhw6SmprJz504OHDjAO++8Q48ePZg7dy5Wq5WSkhJiY2Pp2bMnDzzwAD179uTo0aP4+PiQkpJyTa2itEu1uZRfcym/5lFuzaX8mkcdAuqB2uz2f15QUBArV65k0KBBrF27lokTJ9qKs0OHDpGcnIyvry+vv/46GzZsIDg4mGPHjvHWW2/h4eHBsGHDbHub+fr6kpSUxPbt21m0aBGDBw+muLiYJUuW4OjoSHR0NF999RVPPfUUBw8eZNKkSWzfvp0VK1YQHh7OunXriImJoVOnTqxZs4ZVq1bRs2dP8vLyWLp0KS1atCAiIoKvvvqK7t2713htWjFkHq3IMpfyax7l1lzKr3m0WrMe+aW7/Z/Xq1cvZs2aRVFREcXFxbRq1cp27Hyx1ahRIwoKCujZsycAfn5+eHp6AudWap6/hXr33XcD0KxZM86cOYOjoyMuLi5MmTKFRo0a8f3339tuoV7JbbfdxoIFC3B3d6esrMw2RtOmTWnRogXwU9cAERERuTYqzm6g2uz2f56DgwODBg0iISGBYcOGXXQsNjaWTZs24enpSUxMDOe3sLt0B/8Lz3WhAwcOsGnTJlauXMnp06cJCwvDMAwcHR2xWq0AF/2clJTE/Pnzad++Pa+99pqtX+fVxhMREZGaaUHADXThbv/33XcfL7744i86T3BwMJs3b+ahhx666PWQkBDGjBlDREQEZWVlFBYW/qzztmnTBg8PD8LCwnjiiSe49dZbKSwsxMfHh7Nnz5KcnIyfnx8HDx5kyZIljBw5kt///vdERkZy7Nixnz2eiIiIXE4dAsRu6LkH8+i5EnMpv+ZRbs2l/JpHz5w1ID93t38RERFpWFSc2Zmr7fZ/fqPatLS0at/fuXNnevTogWEYlJeXM3HiRAIDA2sV09ChQ1m/fj1ubm7VviYiIiK1p+KsHnjzzTfJzMzEw8Ojxr+95ZZbbAXcqVOnePDBBxk2bJge0hcREaknVJzZmdpuVHuh0tJSfH19cXBwICUlhV27dlFeXk5SUhKffPIJH374Ic7OzvTu3Ztp06ZRUlLCtGnTKC0tpaqqismTJxMQEGA7X3p6OllZWbz00ksAWK1WAgMDWblyJd7e3ixfvpzy8nIOHz6Mq6sr3377LYWFhcydO9e2bYeIiIhUT8WZnantRrU//vgjUVFRWK1WDh48SHR0tO1Yu3btiI2NJScnh/Xr17NixQqcnZ15+umn2bJlC9nZ2fTv35/HH3+cgoICxo4dy6ZNmwBIS0tj//79vPrqq7bd/h0dHQkODubDDz/kt7/9LZmZmaSmpjJ//nxatmzJ7NmzycjI4J133mH27Nk1xq5dqs2l/JpL+TWPcmsu5dc86hDQgNRmo9oLb2uWlpYSERFB7969AWjbti0AR44coVu3bri4uADQu3dvDh06RG5uLsHBwcC5DW09PT05efIkAFu3bsXJyemyNkyPPPIIzzzzDH369KFZs2Y0a9YMAH9/fwCaN2/Ol19+eU2xa8WQebQiy1zKr3mUW3Mpv+apzWpN7XNmZy7dqNZisfziczVu3BgvLy/Onj0LnJvpgnMzaHv37qWyshLDMPjiiy9o27Yt7du3Z8eOHQAUFBRQUlKCt7c3AAsWLKBJkyakp6dfNEbLli3x8vLi9ddf55FHHrG9rmfcREREfhnNnNmZCzeqLSws5MUXX+S555675vefv60J5wq9Ll260K9fP1vRBdCpUycefvhhxo4di9VqpVevXgwbNow+ffowY8YMNm7cyJkzZ5g9ezbOzj99RGJjYxk9evRFz6EBjBkzhsTERJKTk2t59SIiIqJNaKXW1q1bx6FDh5g8eXKtzqOpdfPo1oW5lF/zKLfmUn7No01obzL2tFHtSy+9xI4dO1iwYMENHVdERKSh0syZ2A396808+texuZRf8yi35lJ+zaMFASIiIiINhIqzBux8y6eadO7cmaioKB599FHCwsL46KOPbkB0IiIiciV65qyBUssnERGR+kkzZw3AsmXLmDp1KgAxMTEsW7bM1vLp57q05dPvfvc7IiIiyM3NZfHixYwaNYrw8HDbthkpKSnExMQwfvx4hg8fzr///W9KS0sZPnw4OTk5HD58mODgYEpLS6/rNYuIiDRUmjlrAOqy5ROAq6srixYtIisri8WLFzNw4EDmzp1LXFwchmEwb948PD09a4xDLUTMpfyaS/k1j3JrLuXXPGrfdJOrq5ZPcHGrpvMdDbp27YqXlxcuLi624zXRiiHzaEWWuZRf8yi35lJ+zaPVmje5umz5BFdu1bRhwwYaN26Ms7MzGzZs+MXxiIiI3Gw0c9YA1GXLpwMHDlx2vm+//ZZXX32VZcuWYRgGkZGRdOnShVatWtX+YkVERBo4bUIrdkNT6+bRrQtzKb/mUW7NpfyaR+2bpEb21PJJRERErk7F2U0iPDyc8PDwug5DREREaqAFAfWY1WolPj6e8PBwoqKiOH78+HUfY//+/aSmpgLn9lMLCQlh9erVJCQkXNP7KyoqGDp06HWPS0REpKHSzFk9tmnTJiwWC++88w67d+9m7ty5LFy48LqO4e/vb9sK46OPPmLevHl06tSJ0NDQ6zqOiIiInKPirB5ZtmwZX375JS+++CIxMTFs2bKFmTNnAtC9e3f27dt31fe+/fbblJSUMGnSJCwWCyNHjiQzM9P2LJqDgwPDhw/nscceY/r06RQXF1NcXEx0dDTr1q2jX79+7Nu3j+eff56XX36ZqVOnkpGRQXZ2Ni+//DJOTk60bt2a2bNnY7FYePbZZykpKcHPz+9GpUdERKRBUHFWj1zaCSAwMPCinfednJyorKzE2fny/1tDQkKIjIzkD3/4A5s3b2bIkCF88803rFu3juXLl+Pg4MC4ceO49957AejXrx/jxo1j+/btwLln1tauXUtCQoJtXzPDMIiLi2P58uX4+PjwyiuvsHr1aiwWCx07duSZZ55hz549tnPURLtUm0v5NZfyax7l1lzKr3nUIeAmcWEngMzMTMrKymzHrFbrFQszONcFwN/fn507d7J69WpiYmLIycnhxIkTjBs3Dji339k333wD/NQZoDonT56ksLCQP/3pTwCcOXOGAQMGUFRUxMCBAwHo1q3bVWO6lJZzm0fL5c2l/JpHuTWX8msedQi4SVzaCaBbt258+umnAOzevZuOHTtW+/4xY8awdOlSzpw5Q/v27WnXrh0dOnTg7bffJi0tjbCwMNs5rrTr/6WaNm1K8+bNWbBgAWlpaTz11FPcc889tGvXjt27dwPw9ddfU1lZWbsLFxERuYlo5qweubQTwJ49e3B1dSUiIgLDMJgzZ0617+/bty9xcXFMnDgRgDvvvJOAgADGjh2LxWKha9eu+Pr6XnM8jo6OPP/880yYMAHDMGjcuDHz5s2jT58+PPfcc4wdO5Z27drZ+nGKiIhIzdQhQOyGptbNo1sX5lJ+zaPcmkv5NY86BIiNOgGIiIjUbyrOGhh1AhAREanftCBARERExI5o5sxkVquVhIQEcnJycHV1JTExkTZt2tR1WADk5eUxadIk7rzzTpo0acITTzxBy5Ytr/s4AwYMICsr67qfV0REpCFScWayG9Fi6Zf68ssvCQgIYPr06XUdioiIiPw/FWfXWW1aLOXn5zNlyhQyMjKAc/uSvfTSS6xevZr8/Hx++OEHTpw4wXPPPcfAgQPZsmWLrSn5XXfdxaxZs9i6dSuvvPIKbm5ueHt7M2fOHPbv38+bb76Ji4sL+fn5DB8+nJCQEBYuXMiZM2fw8/Nj/fr1JCQksG7dOnbt2kV5eTlJSUlMnz6dFi1akJ+fz4gRIzh06BBff/01gwcPZsqUKeTk5JCYmAhgG69Ro0bExcVx+PBhWrdujcViuabcaZdqcym/5lJ+zaPcmkv5NY86BNiJ2rRYqo6rqyuLFi0iKyuLxYsXExAQwF/+8hdWrlyJj48PqampfPfdd8TFxZGeno6vry9Lly5l4cKFDB48mBMnTpCZmYnFYmHgwIFMnDiRCRMmcOTIESIjI1m/fr1trHbt2hEbG0t+fj55eXksXryYM2fOcP/99/Ppp5/i4eHBkCFDmDJlCnFxccyZM4cOHTqwcuVKFi1aRPfu3amoqCAjI4MTJ06wcePGa7pGLec2j5bLm0v5NY9yay7l1zzqEGBnJkyYwOrVq4mOjsbT0/OaWyxd6sIt6Pz9/QFo3rw5FouFoqIimjRpgo+PDwCTJk3Cw8MDT09P20ayffr04dChQwB07NgRZ2dnGjVqhLu7e7XjXti6qXXr1nh5edGkSROaNWuGt7c3bm5utg4Cubm5zJo1i6ioKN577z0KCws5dOgQXbt2BaBly5a0aNHimq5XREREVJxdd7VpseTm5sYPP/xAVVUVJSUl5Ofn245d2k7Jx8eHkpISiouLAUhMTCQvL4/S0lIKCwsByM7O5te//vUV318dR8efPhY1va9t27a88MILpKWlMW3aNAYNGnRR+6aCggIKCgqueWwREZGbnW5rXme1abF06623MmDAAB555BH8/PyqXdXp6OjIzJkzefLJJ3F0dOSuu+6ia9euJCYm8vTTT+Pg4MAtt9zCX//6V9vsmRkSEhKIiYmhqqoKgKSkJNq2bcvOnTsZPXo0LVu2pGnTpqaNLyIi0tCofZPYDT33YB49V2Iu5dc8yq25lF/zqH1TPaMWSyIiInI1Ks7qgFosiYiIyNVoQYCIiIiIHVFxJiIiImJHVJyJiIiI2BEVZyIiIiJ2RMWZiIiIiB1RcSYiIiJiR1SciYiIiNgRFWciIiIidkTFmYiIiIgdUXEmIiIiYkdUnImIiIjYERVnIiIiInZExZmIiIiIHVFxJiIiImJHVJyJiIiI2BEVZyIiIiJ2xMEwDKOugxARERGRczRzJiIiImJHVJyJiIiI2BEVZyIiIiJ2RMWZiIiIiB1RcSYiIiJiR1SciYiIiNgRFWdyQ1mtVuLj4wkPDycqKorjx49fdPzjjz9m1KhRhIeHk5GRUUdR1k815Xbt2rWMHj2aiIgI4uPjsVqtdRRp/VRTfs+Li4tj/vz5Nzi6+q2m3O7du5fIyEjGjh3LH//4RyoqKuoo0vqppvxmZmYSGhrKqFGjWL58eR1FWb/t2bOHqKioy17/xd9phsgNtHHjRiMmJsYwDMPYtWuX8dRTT9mOWSwWY9iwYUZxcbFRUVFhhIWFGYWFhXUVar1TXW5Pnz5t3H///UZ5eblhGIbxzDPPGJs2baqTOOur6vJ7Xnp6ujFmzBgjOTn5RodXr1WXW6vVaowcOdI4duyYYRiGkZGRYeTm5tZJnPVVTZ/dAQMGGEVFRUZFRYXtv8Fy7d544w0jKCjIGD169EWv1+Y7TTNnckPt3LmTgQMHAtC9e3f27dtnO5abm4ufnx+33HILrq6u9OrVix07dtRVqPVOdbl1dXVlxYoVeHh4AFBZWYmbm1udxFlfVZdfgF27drFnzx7Cw8PrIrx6rbrcHj16FG9vb5YuXcqjjz5KcXEx7dq1q6tQ66WaPrudOnXi1KlTWCwWDMPAwcGhLsKst/z8/EhJSbns9dp8p6k4kxuqtLQUT09P2+9OTk5UVlbajnl5edmONW7cmNLS0hseY31VXW4dHR1p1qwZAGlpaZSXlzNgwIA6ibO+qi6/hYWFpKamEh8fX1fh1WvV5baoqIhdu3YRGRnJ3//+d7Zt28bWrVvrKtR6qbr8Atxxxx2MGjWKESNGMHjwYJo0aVIXYdZbDz74IM7Ozpe9XpvvNBVnckN5enpSVlZm+91qtdo+1JceKysru+iDLdWrLrfnf3/hhRfIysoiJSVF/zr+marL74YNGygqKmLChAm88cYbrF27llWrVtVVqPVOdbn19vamTZs2dOjQARcXFwYOHHjZzI9Ur7r8HjhwgH/9619s3ryZjz/+mJMnT7J+/fq6CrVBqc13moozuaF69uzJp59+CsDu3bvp2LGj7Vj79u05fvw4xcXFWCwWduzYQY8ePeoq1HqnutwCxMfHU1FRwYIFC2y3N+XaVZffxx57jFWrVpGWlsaECRMICgoiLCysrkKtd6rLbevWrSkrK7M9xL5jxw7uuOOOOomzvqouv15eXri7u+Pm5oaTkxO/+tWvKCkpqatQG5TafKddPg8nYqLAwECysrKIiIjAMAzmzJnDmjVrKC8vJzw8nOnTpxMdHY1hGIwaNQpfX9+6DrneqC63nTt35t1336V37948/vjjwLmCIjAwsI6jrj9q+uzKL1dTbpOSkpg6dSqGYdCjRw8GDx5c1yHXKzXlNzw8nMjISFxcXPDz8yM0NLSuQ67Xrsd3moNhGIbJcYqIiIjINdJtTRERERE7ouJMRERExI6oOBMRERGxIyrOREREROyIijMRERERO6LiTESklvLz8+ncuTMhISEX/e+777772efKy8tjxowZ1z3Gr776iueff/66n/dq9u7dS3Jy8g0bT6Qh0T5nIiLXwW233cYHH3xQ6/OcOHGCvLy86xDRxbp06UKXLl2u+3mv5vDhw/zwww83bDyRhkTFmYiIif73v/8RHx/P999/j4ODA1OnTqV///4UFBQwY8YMTp06RWFhIaGhoUyePJnExETy8/OZNWsWDz30EKmpqaSlpQEwffp0+vbtS9++fRk/fjxNmzbF3d2dRYsWMW/ePLKzs6mqqiIsLIxx48ZdFMf27dtt54qKiuKuu+5i586dVFRU8Oyzz/L222+Tm5vLuHHjGDduHCkpKZw4cYLc3FyKiooIDw9n/PjxWK1W5syZw9atW3FwcGDkyJFMmDCB7du3k5ycjNVqxdfXl/3791NeXs7ChQuJiopixowZFBQUUFhYSEBAAElJSWRnZ/O3v/0Nd3d3cnNz6dSpE/Pnz8fV1ZUlS5aQnp6Ok5MTQ4YMYdq0aVfNpUhDo+JMROQ6KCwsJCQkxPZ7cHAw48ePJykpiVGjRnH//fdTWFhIZGQk77//PmvXriUoKIjQ0FBOnTrFoEGDiIqKIjY2ltTUVGbOnMn27duvOt7Ro0dZtGgRt99+O+np6QCsXr0ai8VCdHQ0nTt3pnfv3ld9v2EYvPvuu6SmppKYmEhmZiYnT57kN7/5ja2w27dvHytWrMBqtRIWFkZAQAC7d+/mu+++IzMzE4vFQlRUFB07dsTDw4Njx46xZcsWvLy8WLVqFdnZ2UycOJG1a9fi7+/Pa6+9hsViYcSIEfznP/8BYNeuXaxfv57bbruNMWPG8Nlnn9GsWTOWL1/Oe++9h4eHB+PHj2ffvn289dZbV8zlhU29RRoCFWciItfB1W5rfv755xw5coTXXnsNgMrKSvLy8oiOjmbbtm289dZbHDp0iLNnz3L69OlrHs/Hx4fbb78dgK1bt7J//362bdsGQHl5OTk5OdUWZ/fddx8ALVu2pFu3bnh4eNCqVauL+ioGBQXRuHFjAIYOHcq2bdvYs2cPoaGhODk54eHhQXBwMFu3bmXo0KG0bdv2io2dg4KC2Lt3L0uWLOHIkSMUFxdTXl4OwB133EHz5s2Bc70If/zxR44ePcqQIUNs51qyZEm1ufT397/mvInUByrORERMZLVaWbp0Kd7e3sC5GTYfHx/mzp1LXl4eQUFBDBs2jM8//5xLu+k5ODhc9NrZs2dtP7u7u9t+rqqqYtq0aTzwwAMAnDx50lZUXY2Li4vtZ2fnK38VODk5XXQdTk5OWK3Wi/7GMAyqqqoui+lCaWlpbNy4kTFjxtC/f38OHjxouy43N7fLrtfZ2RkHBwfb6wUFBXh4eFw1lyINjVZrioiYqF+/fixfvhw495B8cHAwp0+fJisri+joaB5++GGOHj1KQUGBrQCqrKwEoGnTpuTl5VFRUUFxcTE7d+686hgZGRmcPXuWsrIyIiMj2b17d61j37RpExaLhR9//JEtW7Zw77330q9fP95//32qqqo4ffo0a9as4Z577rnsvRdeR1ZWFuHh4YwcOZKKigoOHDhwWZF3od69e/PJJ59QVlZGZWUlU6dOZd++fVfNpUhDo5kzERETxcbGEh8fT3BwMADz5s3D09OTJ598kj//+c+4u7vTvHlzOnfuTH5+Pv7+/pw6dYpp06aRnJzMoEGDGDFiBK1ataJXr15XHCMiIoLjx48TGhpKZWUlYWFhVyyYfi43NzciIyMpLS3lySefpEOHDrRp04Zjx44REhLC2bNnCQ4OJjAw8LLn47p27Upqairz58/n8ccfJyEhgTfeeANPT0969OhBfn4+fn5+Vxz37rvv5tFHHyUiIgKr1UpgYCD9+/enffv2V8ylSEPjYFw6jy4iIje9lJQUAJ5++uk6jkTk5qPbmiIiIiJ2RDNnIiIiInZEM2ciIiIidkTFmYiIiIgdUXEmIiIiYkdUnImIiIjYERVnIiIiInZExZmIiIiIHfk/N097ndED/3gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(xg_regressor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of all the models used, XGBoost had the lowest RMSE which was 39.71"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XG Bosst without Service Fee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are  {'learning_rate': 0.015, 'max_depth': 4, 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "# Trying out every combination of param grid\n",
    "search2 = GridSearchCV(xg_regressor2, param_grid, cv=5).fit(data_train2, target_train2)\n",
    "print(\"The best hyperparameters are \",search2.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.015, max_delta_step=0, max_depth=5,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=300, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_regressor2 = xgb.XGBRegressor(learning_rate = search2.best_params_[\"learning_rate\"],\n",
    "                           n_estimators  = search2.best_params_[\"n_estimators\"],\n",
    "                           max_depth     = search2.best_params_[\"max_depth\"],)\n",
    "\n",
    "xg_regressor2.fit(data_train2, target_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_pred2 = xg_regressor2.predict(data_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 287.55282549207305\n",
      "Mean Squared Error: 110496.0780582735\n",
      "Root Mean Squared Error: 332.40950356190706\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Algorithm\n",
    "# actual vs predicted \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target_test2, xg_pred2))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target_test2, xg_pred2))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target_test2, xg_pred2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE increased exponentially from ~39 to ~332. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rewriting plot feature importance function\n",
    "# Visualizing feature importance \n",
    "def plot_feature_importances2(model):\n",
    "    n_features = data_train2.shape[1]\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center') \n",
    "    plt.yticks(np.arange(n_features), data_train2.columns.values) \n",
    "    plt.xlabel('Feature importance')\n",
    "    plt.ylabel('Feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances2(xg_regressor2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Featured importance data is all over the place. Top 6 features according to this chart are West Village, Bedford-Stuyvesant, Upper West Side, East Village, East Flatbush, Prospect-Lefferts Gardens. Among the continuous data the top features seem to be availability_365, lr_year, reviews per month, and calculated host listings count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XG Boost with just main features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are  {'learning_rate': 0.015, 'max_depth': 5, 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "# Trying out every combination of param grid\n",
    "search3 = GridSearchCV(xg_regressor3, param_grid, cv=5).fit(data_train3, target_train3)\n",
    "print(\"The best hyperparameters are \",search3.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.015, max_delta_step=0, max_depth=5,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=300, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_regressor3 = xgb.XGBRegressor(learning_rate = search3.best_params_[\"learning_rate\"],\n",
    "                           n_estimators  = search3.best_params_[\"n_estimators\"],\n",
    "                           max_depth     = search3.best_params_[\"max_depth\"],)\n",
    "\n",
    "xg_regressor3.fit(data_train3, target_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_pred3 = xg_regressor3.predict(data_test3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 287.68327523475193\n",
      "Mean Squared Error: 110623.00374710109\n",
      "Root Mean Squared Error: 332.6003664265887\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Algorithm\n",
    "# actual vs predicted \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(target_test3, xg_pred3))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(target_test3, xg_pred3))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(target_test3, xg_pred3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rewriting plot feature importance function\n",
    "# Visualizing feature importance \n",
    "def plot_feature_importances3(model):\n",
    "    n_features = data_train3.shape[1]\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center') \n",
    "    plt.yticks(np.arange(n_features), data_train3.columns.values) \n",
    "    plt.xlabel('Feature importance')\n",
    "    plt.ylabel('Feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances3(xg_regressor3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this reduced feature importance graph, it shows that an Airbnb being available for 365 days a year is most important to the price. Bookings in Queens seem to also impact the price the most out of all the boroughs. It also seems that booking on a certain day impacts price more slightly than booking on a certain month or year. Reviews per month of a Airbnb equally impacts the price as well. Finally, being an unconfirmed host seems to have more of an impact on the price than a verified host."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After analyzing this dataset from Airbnb, I got a clearer picture of what customers prefer and what effects the total price. To start off with, here is some general information. The most stayed at neighborhood seemed to be Bedford-Stuyvesant (Bed-Stuy) in Brooklyn. Secondly, some of the most expensive neighborhoods people stay at on average are Flatbush, West Willage, East Flatbush, Prospect-Lefferts Gardens, and Astoria. People tend to stay their longest visits in Upper East Side (11.5) days, Upper West Side, Midtown, Chelsea, and Hell's Kitchen. Building off that, the data suggests that people spend the longest stays in Manhattan on average compared to the rest of New York's boroughs. Speaking of booking, the amount of nights people tend to stay at a location is 1-2 nights.\n",
    "Diving further into the data, a OLS Regression chart was created. From this, it was seen that the average price for an Airbnb amongst customers was $89. The neighborhoods that someone could stay at for a cheaper price are Upper East Side, Astoria, Prospect-Lefferts Gardens, and East Village. Finally, it seems that the service fee for these stays predictably affect the total price the most for an Airbnb.\n",
    "Finally the data was applied to several regressor tree models. Those being a baseline one, random forest with different number of estimators, random forest with a grid search applied, and a XG Boost model with Grid Search. Among these variations, the model with the lowest RMSE was XG Boost with a value of 39.71.\n",
    "From all this data collected, one can say people tend to use Airbnbs to stay in Manhattan and their visits are longer than anywhere else. If one wanted to stay at the most affordable areas, they would want to stay in either Upper East Side, Astoria, Prospect-Lefferts Gardens, and East Village. Finally, the host of these Airbnbs should expect the average person to stay 1-2 nights."
   ]
  }
 ],
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