Proyecto AIRBNB - FINAL - Francisco Vela Flores.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c9ccd941",
   "metadata": {},
   "source": [
    "<img src=\"https://blog.logomyway.com/wp-content/uploads/2020/03/airbnb-logo.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "### Proyecto elaborado por: Francisco Vela Flores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25357fb",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42893c1",
   "metadata": {},
   "source": [
    "\n",
    "**Airbnb** fué fundada por Brian Chesky, Joe Gebbia y Nathan Blecharczyk en San Francisco, California. Es una compañía que se encuentra activa desde el año 2008 y consiste en una plataforma digital dedicada a la oferta de alojamientos a particulares y turísticos (alquiler vacacional) mediante la cual los anfitriones pueden publicitar y contratar el arriendo de sus propiedades con sus huéspedes; anfitriones y huéspedes pueden valorarse mutuamente, como referencia para futuros usuarios. Airbnb tiene una oferta de unas 2 millones de propiedades en 192 países y 33 mil ciudades alrededor del mundo.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12012a72",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61936fcb",
   "metadata": {},
   "source": [
    "<img src =\"https://www.clickz.com/wp-content/uploads/2016/03/airbnb-bad.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dfeaac9",
   "metadata": {},
   "source": [
    "[Link al website de Airbnb](https://www.airbnb.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a53118ee",
   "metadata": {},
   "source": [
    "# Objetivo del Proyecto"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39087e7c",
   "metadata": {},
   "source": [
    "El presente proyecto representa los servicios de alquiler de Airbnb en los 05 distritos del estado de Nueva York, (Brooklyn, Manhattan, Queens, Bronx y Staten Island) en los Estados Unidos y tiene como objetivo, analizar a AIRBNB como una de las mejores alternativas para rentar habitaciones en dichos Distritos, dependiendo del presupuesto que tienes destinado y los motivos de visita en la ciudad, ya sea por un viaje de negocios, turismo o familiar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82e6e3e3",
   "metadata": {},
   "source": [
    "# Contexto Comercial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba0fea5",
   "metadata": {},
   "source": [
    "Para ponernos en contexto de cuál es el ámbito del estado de Nueva York en los Estados Unidos, revisaremos cómo se encuentran distribuidos sus 05 distritos (Brooklyn, Manhattan, Queens, Bronx y Staten Island) con la finalidad de poder tener una referencia a nivel general y principalmente comercial."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecdb5eeb",
   "metadata": {},
   "source": [
    "**Mapa del Estado de Nueva York y sus Distritos**\n",
    "                                        \n",
    "![Airbnb_logo](https://nuevayork.travel/wp-content/uploads/2013/02/BARRIOS_NY.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a9fd10",
   "metadata": {},
   "source": [
    "### Manhattan\n",
    "\n",
    "Manhattan es el distrito más famoso e importante de la ciudad de Nueva York.\n",
    "Cuenta con poco más de un millón y medio de habitantes y es donde se concentran la mayoría de los rascacielos y de las atracciones turísticas. Es una isla alargada que limita al oeste con el estado de New Jersey, al norte con el Bronx y al este con Queens y Brooklyn.\n",
    "\n",
    "Se divide en cinco zonas, que de norte a sur son: _Washington Heights, Harlem, Upper Manhattan, Midtown y Lower Manhattan._\n",
    "\n",
    "**Atracciones en Manhattan**\n",
    "\n",
    "- En Manhattan se concentran prácticamente todos los lugares de visita obligatoria en Nueva York, sobretodo en el Midtown.\n",
    "\n",
    "- En el Lower Manhattan nos encontramos una gran extensión verde, el Battery Park City, desde donde salen los ferries a Ellis \n",
    "Island y Liberty Island, que alberga la Estatua de la Libertad y Wall Street.\n",
    "\n",
    "- En el Midtown se encuentran algunos lugares tan famosos como el Empire State Building, la concurrida Times Square, el \n",
    "mirador Top of the Rock o museos tan importantes como el MoMA.\n",
    "\n",
    "- En el Upper Manhattan y prácticamente en el centro de la isla se ubica Central Park, uno de los parques públicos más famosos \n",
    "del mundo.\n",
    "\n",
    "![Airbnb_logo](https://nuevayork.travel/wp-content/uploads/2013/03/SKYSCRAPER.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7722f1a3",
   "metadata": {},
   "source": [
    "### Brooklyn\n",
    "\n",
    "Brooklyn es el distrito más poblado de la ciudad, sus barrios están muy de moda y cuenta con una gran variedad de razas y étnias.\n",
    "\n",
    "![Airbnb_logo](https://nuevayork.travel/wp-content/uploads/2013/03/Brooklyn.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc655637",
   "metadata": {},
   "source": [
    "### Queens\n",
    "\n",
    "Queens es el más grande de los condados y con casi de dos millones y medio de habitantes. Es el más diverso del país.\n",
    "En Queens se ubican dos aeropuertos de la ciudad, el aeropuerto de La Guardia y el JFK.\n",
    "\n",
    "![Airbnb_logo](https://nuevayork.travel/wp-content/uploads/2013/03/Queens.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce2d8b3",
   "metadata": {},
   "source": [
    "### Bronx\n",
    "\n",
    "El Bronx es el único distrito de Nueva York que se ubica en el continente. Es también multiétnico, predominando la raza negra y los puertorriqueños. En él nació el rap y el hip hop.\n",
    "\n",
    "![Airbnb_logo](https://nuevayork.travel/wp-content/uploads/2013/03/Bronx.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4b04fd",
   "metadata": {},
   "source": [
    "### Staten Island\n",
    "\n",
    "Staten Island se ubica al sur de la ciudad y conecta con Brooklyn por el puente de Verrazano – Narrows, a Manhattan con el ferry de Staten Island y a New Jersey por tres puentes.\n",
    "\n",
    "![Airbnb_logo](https://nuevayork.travel/wp-content/uploads/2013/03/Staten-Island.jpg)"
   ]
  },
  {
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   "source": [
    "### En base a ésta información podemos deducir lo siguiente:\n",
    "\n",
    "\n",
    "Usualmente para rentar y/o reservar una habitación en la ciudad de Nueva York, una persona común puede realizarlo de las siguientes 3 maneras:\n",
    "\n",
    "#### 1. A través de una búsqueda presencial en la propia ciudad y/o por contactos de residentes.\n",
    "#### 2. A través de una reserva vía web del tipo Booking.com, etc.\n",
    "#### 3. A través de la APP y/o Plataforma de AIRBNB."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec1f0ed",
   "metadata": {},
   "source": [
    "Para fines de este proyecto analizaremos la 3ra opción, **renta de habitaciones bajo la APP y/o Plataforma de AIRBNB.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e83de87",
   "metadata": {},
   "source": [
    "# Problema Comercial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373724fb",
   "metadata": {},
   "source": [
    "Actualmente uno de los mayores problemas que tiene el sector es la confianza de los huéspedes a la hora de reservar: una de las mayores preocupaciones a la hora de alquilar un alojamiento privado a través de internet es el miedo a que se trate de una estafa. En este caso, al utilizar AIRBNB puedes consultar todas las evaluaciones de los anteriores inquilinos y, además, éste actúa como intermediario a la hora de pagar, por lo que si hay algún problema con la reserva, normalmente es la propia plataforma la que responde.\n",
    "\n",
    "Por otra parte, no sólo el anfitrión es evaluado, sino también el huésped. Ambos tienen que crearse una cuenta para utilizar la web y al finalizar la estancia tienen que escribir una evaluación mutua. Si una de las partes falla en escribir su crítica, ninguna de las dos es publicada. De esta forma el anfitrión también tiene una herramienta de referencia a la hora de aceptar o rechazar una reserva."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565cbb55",
   "metadata": {},
   "source": [
    "### ii) Análisis de Hipótesis\n",
    "\n",
    "Realizaremos el siguiente análisis de hipótesis para absolver algunas dudas referente al tema:\n",
    "\n",
    "1. Cuánto más costoso un alquiler, es mayor el pago de servicio.\n",
    "2. Los alquileres en promedio son más costosos en Manhattan que en Brooklyn.\n",
    "3. Los hoteles no son las ofertas más elegidas, sino más bien alojamientos de particulares.\n",
    "4. Las políticas de cancelación son estrictas, ya que los hosts no quieren perder la renta por alquiler de su inmueble.\n",
    "5. Las construcciones son modernas, para que sean más atractivas al público.\n",
    "6. La demanda de cuartos compartidos es mayor en Manhattan y Brooklyn debido a la cercanía al centro del movimiento financiero y turístico de Nueva York.\n",
    "7. Las personas que están más dispuestas a pagar por habitaciones privadas o dentro de casas y/o apartamentos son más críticas al momento de dar un review del servicio ofrecido."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c03529",
   "metadata": {},
   "source": [
    "# Contexto Analítico"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceef761c",
   "metadata": {},
   "source": [
    "Se busca analizar la oferta de lugares para rentar temporalmente en ellos mediante esta plataforma, a través de distintas variables como antigüedad de la construcción, tipos de habitación (privada, hotel, compartida), precios de alquiler y pago de servicios, mínimo de noches, disponibilidad durante el año, política de cancelación, reglas de la casa, entre otros. Además, se considerará la cantidad de reviews que posean ya que esta variable es muy importante para los posibles clientes. Este dataset contiene datos públicos que pueden extraerse directamente desde la web de Airbnb."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c3aee9",
   "metadata": {},
   "source": [
    "<img src=\"https://link.estadao.com.br/blogs/lol/wp-content/uploads/sites/367/2014/07/logo-airbnb.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e622be3",
   "metadata": {},
   "source": [
    "### iii) Análisis Exploratorio de Datos (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83b0e43a",
   "metadata": {},
   "source": [
    "### Extracción de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5590c28f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importamos librerías a utilizar\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import folium\n",
    "from folium.plugins import MiniMap\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76164c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102599, 26)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Abrimos el dataset\n",
    "airbnb = pd.read_csv(\"./Airbnb_Open_Data.csv\", low_memory = False)\n",
    "\n",
    "#se quitan espacios entre nombres de columnas y se agrega un _\n",
    "airbnb.columns = airbnb.columns.str.replace(' ', '_') \n",
    "airbnb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a178d79",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <th>neighbourhood</th>\n",
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       "      <th>...</th>\n",
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       "      <th>house_rules</th>\n",
       "      <th>license</th>\n",
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       "      <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",
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       "    <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",
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       "        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": [
    "#Revisamos los 5 primeros registros\n",
    "airbnb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "725cc9a7",
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   "outputs": [
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     "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": [
    "#Analizamos la información de las columnas y tipo de variables\n",
    "airbnb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95397351",
   "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>id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>Construction_year</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.025990e+05</td>\n",
       "      <td>1.025990e+05</td>\n",
       "      <td>102591.000000</td>\n",
       "      <td>102591.000000</td>\n",
       "      <td>102385.000000</td>\n",
       "      <td>102190.000000</td>\n",
       "      <td>102416.000000</td>\n",
       "      <td>86720.000000</td>\n",
       "      <td>102273.000000</td>\n",
       "      <td>102280.000000</td>\n",
       "      <td>102151.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.914623e+07</td>\n",
       "      <td>4.925411e+10</td>\n",
       "      <td>40.728094</td>\n",
       "      <td>-73.949644</td>\n",
       "      <td>2012.487464</td>\n",
       "      <td>8.135845</td>\n",
       "      <td>27.483743</td>\n",
       "      <td>1.374022</td>\n",
       "      <td>3.279106</td>\n",
       "      <td>7.936605</td>\n",
       "      <td>141.133254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.625751e+07</td>\n",
       "      <td>2.853900e+10</td>\n",
       "      <td>0.055857</td>\n",
       "      <td>0.049521</td>\n",
       "      <td>5.765556</td>\n",
       "      <td>30.553781</td>\n",
       "      <td>49.508954</td>\n",
       "      <td>1.746621</td>\n",
       "      <td>1.284657</td>\n",
       "      <td>32.218780</td>\n",
       "      <td>135.435024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.001254e+06</td>\n",
       "      <td>1.236005e+08</td>\n",
       "      <td>40.499790</td>\n",
       "      <td>-74.249840</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>-1223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.508581e+07</td>\n",
       "      <td>2.458333e+10</td>\n",
       "      <td>40.688740</td>\n",
       "      <td>-73.982580</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.913660e+07</td>\n",
       "      <td>4.911774e+10</td>\n",
       "      <td>40.722290</td>\n",
       "      <td>-73.954440</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.320120e+07</td>\n",
       "      <td>7.399650e+10</td>\n",
       "      <td>40.762760</td>\n",
       "      <td>-73.932350</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>269.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.736742e+07</td>\n",
       "      <td>9.876313e+10</td>\n",
       "      <td>40.916970</td>\n",
       "      <td>-73.705220</td>\n",
       "      <td>2022.000000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id       host_id            lat           long  \\\n",
       "count  1.025990e+05  1.025990e+05  102591.000000  102591.000000   \n",
       "mean   2.914623e+07  4.925411e+10      40.728094     -73.949644   \n",
       "std    1.625751e+07  2.853900e+10       0.055857       0.049521   \n",
       "min    1.001254e+06  1.236005e+08      40.499790     -74.249840   \n",
       "25%    1.508581e+07  2.458333e+10      40.688740     -73.982580   \n",
       "50%    2.913660e+07  4.911774e+10      40.722290     -73.954440   \n",
       "75%    4.320120e+07  7.399650e+10      40.762760     -73.932350   \n",
       "max    5.736742e+07  9.876313e+10      40.916970     -73.705220   \n",
       "\n",
       "       Construction_year  minimum_nights  number_of_reviews  \\\n",
       "count      102385.000000   102190.000000      102416.000000   \n",
       "mean         2012.487464        8.135845          27.483743   \n",
       "std             5.765556       30.553781          49.508954   \n",
       "min          2003.000000    -1223.000000           0.000000   \n",
       "25%          2007.000000        2.000000           1.000000   \n",
       "50%          2012.000000        3.000000           7.000000   \n",
       "75%          2017.000000        5.000000          30.000000   \n",
       "max          2022.000000     5645.000000        1024.000000   \n",
       "\n",
       "       reviews_per_month  review_rate_number  calculated_host_listings_count  \\\n",
       "count       86720.000000       102273.000000                   102280.000000   \n",
       "mean            1.374022            3.279106                        7.936605   \n",
       "std             1.746621            1.284657                       32.218780   \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  \n",
       "count     102151.000000  \n",
       "mean         141.133254  \n",
       "std          135.435024  \n",
       "min          -10.000000  \n",
       "25%            3.000000  \n",
       "50%           96.000000  \n",
       "75%          269.000000  \n",
       "max         3677.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Revisamos estadísticos de las variables numéricas\n",
    "airbnb.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d57204",
   "metadata": {},
   "source": [
    "### Transformación de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eeddb40e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manhattan        43792\n",
       "Brooklyn         41842\n",
       "Queens           13267\n",
       "Bronx             2712\n",
       "Staten Island      955\n",
       "brookln              1\n",
       "manhatan             1\n",
       "Name: neighbourhood_group, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analizamos los distritos de New York y observamos que hay 2 distritos (Manhattan y Brooklyn) que tienen doble registro por estar mal redactados\n",
    "airbnb['neighbourhood_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a1296f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manhattan        43793\n",
       "Brooklyn         41843\n",
       "Queens           13267\n",
       "Bronx             2712\n",
       "Staten Island      955\n",
       "Name: neighbourhood_group, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Corregimos los distritos mal redactados\n",
    "airbnb['neighbourhood_group'].replace(['manhatan','brookln'],['Manhattan','Brooklyn'],inplace=True)\n",
    "airbnb['neighbourhood_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4f59062",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ServiceFee y Price traen símbolo $, se lo quitaremos para poder cambiar el tipo de dato de object a float\n",
    "airbnb['price']=airbnb['price'].apply(lambda x: x.replace('$','') if pd.isna(x) == False else x)\n",
    "airbnb['service_fee']=airbnb['service_fee'].apply(lambda x: x.replace('$','') if pd.isna(x) == False else x)\n",
    "\n",
    "#Cambiamos el tipo de dato de price y ServiceFee\n",
    "airbnb['service_fee']=airbnb['service_fee'].astype(float)\n",
    "airbnb['price']=airbnb['price'].apply(lambda x: x.replace(',','') if pd.isna(x) == False else x)\n",
    "airbnb['price']=airbnb['price'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba73e689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2012-07-11 00:00:00'), Timestamp('2022-05-21 00:00:00'))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Last_Review está en tipo dato object, lo pasaremos a DateTime para poder usarlo como fecha\n",
    "airbnb['last_review'] = pd.to_datetime(airbnb['last_review'])\n",
    "\n",
    "#Last_Review trae dato con año: 2058 -> esto es un dato erróneo, y genera gran dispersión en los datos.\n",
    "#Por lo tanto lo reemplazaremos por la media.\n",
    "airbnb.loc[airbnb[airbnb['last_review'].apply(lambda x: x.year) > 2022].index,'last_review'] = airbnb['last_review'].mean()\n",
    "\n",
    "#Last_Review reemplazaremos los valores null por la media también\n",
    "airbnb.loc[airbnb['last_review'].isnull(), 'last_review'] = airbnb['last_review'].mean()\n",
    "\n",
    "#Revisamos que haya quedado correctamente Last_Review\n",
    "airbnb['last_review'].min(), airbnb['last_review'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63e6325f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cambiamos los valores nulos de la variable 'Construction_year' por la media\n",
    "#Asimismo, cambiamos el tipo de variable 'Construction_year' de Float a Integer\n",
    "airbnb.loc[airbnb['Construction_year'].isnull(), 'Construction_year'] = airbnb['Construction_year'].mean()\n",
    "airbnb['Construction_year'] = airbnb['Construction_year'].round().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a289bce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2020\n",
       "1         2007\n",
       "2         2005\n",
       "3         2005\n",
       "4         2009\n",
       "          ... \n",
       "102594    2003\n",
       "102595    2016\n",
       "102596    2009\n",
       "102597    2015\n",
       "102598    2010\n",
       "Name: Construction_year, Length: 102599, dtype: int32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mostramos el resultado final para la variable 'Construction_year'\n",
    "airbnb['Construction_year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "afc1d330",
   "metadata": {},
   "outputs": [],
   "source": [
    "#minimun_nights trae dato negativo alto: -1223 -> esto es un dato erróneo, y genera gran dispersión en los datos.\n",
    "#Lo reemplazaremos por la media.\n",
    "airbnb.loc[airbnb[airbnb['minimum_nights'].apply(lambda x: x) < 0].index,'minimum_nights'] = airbnb['minimum_nights'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e130763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          966.0\n",
       "1          142.0\n",
       "2          620.0\n",
       "3          368.0\n",
       "4          204.0\n",
       "           ...  \n",
       "102594     844.0\n",
       "102595     837.0\n",
       "102596     988.0\n",
       "102597     546.0\n",
       "102598    1032.0\n",
       "Name: price, Length: 102599, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Llenamos los campos con datos nulos en la columna \"price\" con el número 0\n",
    "airbnb[\"price\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a219164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unconfirmed    51200\n",
      "verified       51110\n",
      "Name: host_identity_verified, dtype: int64\n",
      "False    51474\n",
      "True     51020\n",
      "Name: instant_bookable, dtype: int64\n",
      "Entire home/apt    53701\n",
      "Private room       46556\n",
      "Shared room         2226\n",
      "Hotel room           116\n",
      "Name: room_type, dtype: int64\n",
      "moderate    34343\n",
      "strict      34106\n",
      "flexible    34074\n",
      "Name: cancellation_policy, dtype: int64\n",
      "5.0    23369\n",
      "4.0    23329\n",
      "3.0    23265\n",
      "2.0    23098\n",
      "1.0     9212\n",
      "Name: review_rate_number, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Revisamos datos de las columnas host identity verified, instant_bookable, roomtype, cancellation_policy, review_rate_number y\n",
    "#lo cambiaremos a tipo de dato categoría\n",
    "print(airbnb['host_identity_verified'].value_counts())\n",
    "print(airbnb['instant_bookable'].value_counts())\n",
    "print(airbnb['room_type'].value_counts())\n",
    "print(airbnb['cancellation_policy'].value_counts())\n",
    "print(airbnb['review_rate_number'].value_counts())\n",
    "\n",
    "#Hacemos el cambio de tipo de dato a Categoría\n",
    "airbnb = airbnb.astype({'room_type':'category','cancellation_policy':'category',\n",
    "                        'instant_bookable':'category','host_identity_verified':'category',\n",
    "                        'review_rate_number':'category','neighbourhood_group':'category'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "065b5ea5",
   "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  category      \n",
      " 4   host_name                       102193 non-null  object        \n",
      " 5   neighbourhood_group             102570 non-null  category      \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  category      \n",
      " 12  cancellation_policy             102523 non-null  category      \n",
      " 13  room_type                       102599 non-null  category      \n",
      " 14  Construction_year               102599 non-null  int32         \n",
      " 15  price                           102352 non-null  float64       \n",
      " 16  service_fee                     102326 non-null  float64       \n",
      " 17  minimum_nights                  102190 non-null  float64       \n",
      " 18  number_of_reviews               102416 non-null  float64       \n",
      " 19  last_review                     102599 non-null  datetime64[ns]\n",
      " 20  reviews_per_month               86720 non-null   float64       \n",
      " 21  review_rate_number              102273 non-null  category      \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: category(6), datetime64[ns](1), float64(9), int32(1), int64(2), object(7)\n",
      "memory usage: 15.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#Validamos los cambios de tipo de variable realizados\n",
    "airbnb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0aeadbb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separamos variables categóricas y numéricas para que nos permitan hacer mayor análisis\n",
    "categoricas = ['host_identity_verified','neighbourhood_group','instant_bookable','cancellation_policy','room_type','Construction_year','review_rate_number']\n",
    "numericas = ['price','service_fee','minimum_nights','number_of_reviews','reviews_per_month','availability_365']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20e60d4",
   "metadata": {},
   "source": [
    "### Análisis de Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0eee6bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Agrupamos información por distrito para posibles insights\n",
    "Manhattan = airbnb[airbnb['neighbourhood_group']==\"Manhattan\"]\n",
    "Brooklyn = airbnb[airbnb['neighbourhood_group']==\"Brooklyn\"]\n",
    "Queens = airbnb[airbnb['neighbourhood_group']==\"Queens\"]\n",
    "Bronx = airbnb[airbnb['neighbourhood_group']==\"Bronx\"]\n",
    "Staten_Island = airbnb[airbnb['neighbourhood_group']==\"Staten Island\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee9855cc",
   "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>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>47</th>\n",
       "      <td>1027294</td>\n",
       "      <td>1 Stop fr. Manhattan! Private Suite,Landmark B...</td>\n",
       "      <td>81253735818</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>40.74771</td>\n",
       "      <td>-73.94740</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>183.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>248.0</td>\n",
       "      <td>2019-07-01 00:00:00.000000000</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>The room is strictly non-smoking, however, you...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1080867</td>\n",
       "      <td>Fully Furnished Basement Apartment</td>\n",
       "      <td>57811865410</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Dominik</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Flushing</td>\n",
       "      <td>40.74028</td>\n",
       "      <td>-73.83168</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-06-11 19:47:31.418129408</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>- NO parties or events. -NO smoking inside. Yo...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>1090809</td>\n",
       "      <td>A room w/ a Manhattan view, longer stay</td>\n",
       "      <td>973585651</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Dominik</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Sunnyside</td>\n",
       "      <td>40.74559</td>\n",
       "      <td>-73.92313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2019-04-12 00:00:00.000000000</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>- We have a very friendly cat and two chickens...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>1110139</td>\n",
       "      <td>( F) Excellent/Pvt Rm</td>\n",
       "      <td>39463875808</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>40.67252</td>\n",
       "      <td>-73.76597</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>217.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2019-05-20 00:00:00.000000000</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reasonably quiet after 9.  Having said that, I...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>1112348</td>\n",
       "      <td>Clean and convenient 2BR apartment</td>\n",
       "      <td>51018805199</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Lydia</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Ridgewood</td>\n",
       "      <td>40.70234</td>\n",
       "      <td>-73.89816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2015-10-08 00:00:00.000000000</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>No smoking.  Please keep cats out of bedrooms ...</td>\n",
       "      <td>NaN</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",
       "      <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>102491</th>\n",
       "      <td>6035551</td>\n",
       "      <td>Large bedroom in Astoria/LIC area</td>\n",
       "      <td>68337898917</td>\n",
       "      <td>verified</td>\n",
       "      <td>Felicia</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>40.75328</td>\n",
       "      <td>-73.92244</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2015-07-28 00:00:00.000000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102504</th>\n",
       "      <td>6042730</td>\n",
       "      <td>Private Room in large 2 bedroom apt</td>\n",
       "      <td>69315612330</td>\n",
       "      <td>verified</td>\n",
       "      <td>David</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Forest Hills</td>\n",
       "      <td>40.72400</td>\n",
       "      <td>-73.84962</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>201.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-08-31 00:00:00.000000000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>#NAME?</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102528</th>\n",
       "      <td>6055986</td>\n",
       "      <td>Furnished room in Astoria apartment</td>\n",
       "      <td>19867591692</td>\n",
       "      <td>verified</td>\n",
       "      <td>Kathrine</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>40.76810</td>\n",
       "      <td>-73.91651</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-02-13 00:00:00.000000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>The studio is located in a smoke-free, no pets...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102568</th>\n",
       "      <td>6078078</td>\n",
       "      <td>Furnished Bedroom in 2 Bedroom apt.</td>\n",
       "      <td>43698780331</td>\n",
       "      <td>verified</td>\n",
       "      <td>Etkin</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>40.75600</td>\n",
       "      <td>-73.92050</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>210.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-06-11 19:47:31.418129408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102580</th>\n",
       "      <td>6084705</td>\n",
       "      <td>Near LGA, Citifield, US Open Tennis</td>\n",
       "      <td>97606085736</td>\n",
       "      <td>verified</td>\n",
       "      <td>Carmen</td>\n",
       "      <td>Queens</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>40.76578</td>\n",
       "      <td>-73.86424</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2019-06-11 00:00:00.000000000</td>\n",
       "      <td>4.38</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>House Rules 1. Check-in is 4 pm local time. If...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7751 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                               NAME  \\\n",
       "47      1027294  1 Stop fr. Manhattan! Private Suite,Landmark B...   \n",
       "144     1080867                 Fully Furnished Basement Apartment   \n",
       "162     1090809            A room w/ a Manhattan view, longer stay   \n",
       "197     1110139                              ( F) Excellent/Pvt Rm   \n",
       "201     1112348                 Clean and convenient 2BR apartment   \n",
       "...         ...                                                ...   \n",
       "102491  6035551                  Large bedroom in Astoria/LIC area   \n",
       "102504  6042730                Private Room in large 2 bedroom apt   \n",
       "102528  6055986                Furnished room in Astoria apartment   \n",
       "102568  6078078                Furnished Bedroom in 2 Bedroom apt.   \n",
       "102580  6084705                Near LGA, Citifield, US Open Tennis   \n",
       "\n",
       "            host_id host_identity_verified host_name neighbourhood_group  \\\n",
       "47      81253735818            unconfirmed  Victoria              Queens   \n",
       "144     57811865410            unconfirmed   Dominik              Queens   \n",
       "162       973585651            unconfirmed   Dominik              Queens   \n",
       "197     39463875808            unconfirmed       NaN              Queens   \n",
       "201     51018805199            unconfirmed     Lydia              Queens   \n",
       "...             ...                    ...       ...                 ...   \n",
       "102491  68337898917               verified   Felicia              Queens   \n",
       "102504  69315612330               verified     David              Queens   \n",
       "102528  19867591692               verified  Kathrine              Queens   \n",
       "102568  43698780331               verified     Etkin              Queens   \n",
       "102580  97606085736               verified    Carmen              Queens   \n",
       "\n",
       "           neighbourhood       lat      long        country  ... service_fee  \\\n",
       "47      Long Island City  40.74771 -73.94740  United States  ...       183.0   \n",
       "144             Flushing  40.74028 -73.83168  United States  ...       121.0   \n",
       "162            Sunnyside  40.74559 -73.92313            NaN  ...        71.0   \n",
       "197              Jamaica  40.67252 -73.76597  United States  ...       217.0   \n",
       "201            Ridgewood  40.70234 -73.89816            NaN  ...        81.0   \n",
       "...                  ...       ...       ...            ...  ...         ...   \n",
       "102491  Long Island City  40.75328 -73.92244  United States  ...       227.0   \n",
       "102504      Forest Hills  40.72400 -73.84962  United States  ...       201.0   \n",
       "102528           Astoria  40.76810 -73.91651  United States  ...        42.0   \n",
       "102568  Long Island City  40.75600 -73.92050  United States  ...       210.0   \n",
       "102580     East Elmhurst  40.76578 -73.86424  United States  ...       122.0   \n",
       "\n",
       "       minimum_nights number_of_reviews                   last_review  \\\n",
       "47                NaN             248.0 2019-07-01 00:00:00.000000000   \n",
       "144               NaN               1.0 2019-06-11 19:47:31.418129408   \n",
       "162              30.0              28.0 2019-04-12 00:00:00.000000000   \n",
       "197               2.0              52.0 2019-05-20 00:00:00.000000000   \n",
       "201               7.0               6.0 2015-10-08 00:00:00.000000000   \n",
       "...               ...               ...                           ...   \n",
       "102491            1.0               5.0 2015-07-28 00:00:00.000000000   \n",
       "102504            3.0               1.0 2015-08-31 00:00:00.000000000   \n",
       "102528          100.0               2.0 2016-02-13 00:00:00.000000000   \n",
       "102568            7.0               0.0 2019-06-11 19:47:31.418129408   \n",
       "102580            1.0             209.0 2019-06-11 00:00:00.000000000   \n",
       "\n",
       "        reviews_per_month  review_rate_number  calculated_host_listings_count  \\\n",
       "47                   2.25                 NaN                             1.0   \n",
       "144                  0.01                 4.0                             1.0   \n",
       "162                  0.26                 1.0                             1.0   \n",
       "197                  0.49                 2.0                             2.0   \n",
       "201                  0.06                 3.0                             5.0   \n",
       "...                   ...                 ...                             ...   \n",
       "102491               0.10                 3.0                             1.0   \n",
       "102504               0.02                 1.0                             1.0   \n",
       "102528               0.04                 3.0                             1.0   \n",
       "102568                NaN                 4.0                             1.0   \n",
       "102580               4.38                 3.0                             1.0   \n",
       "\n",
       "        availability_365                                        house_rules  \\\n",
       "47                  26.0  The room is strictly non-smoking, however, you...   \n",
       "144                389.0  - NO parties or events. -NO smoking inside. Yo...   \n",
       "162                395.0  - We have a very friendly cat and two chickens...   \n",
       "197                  NaN  Reasonably quiet after 9.  Having said that, I...   \n",
       "201                420.0  No smoking.  Please keep cats out of bedrooms ...   \n",
       "...                  ...                                                ...   \n",
       "102491             369.0                                                NaN   \n",
       "102504             215.0                                             #NAME?   \n",
       "102528             233.0  The studio is located in a smoke-free, no pets...   \n",
       "102568              34.0                                                NaN   \n",
       "102580             270.0  House Rules 1. Check-in is 4 pm local time. If...   \n",
       "\n",
       "       license  \n",
       "47         NaN  \n",
       "144        NaN  \n",
       "162        NaN  \n",
       "197        NaN  \n",
       "201        NaN  \n",
       "...        ...  \n",
       "102491     NaN  \n",
       "102504     NaN  \n",
       "102528     NaN  \n",
       "102568     NaN  \n",
       "102580     NaN  \n",
       "\n",
       "[7751 rows x 26 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Queens[Queens['room_type']==\"Private room\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "86bf4fd9",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>NAME</th>\n",
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       "      <th>reviews_per_month</th>\n",
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       "  <tbody>\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>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2022-05-21 00:00:00.000000000</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>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>41.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2018-11-19 00:00:00.000000000</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1004098</td>\n",
       "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
       "      <td>45498551794</td>\n",
       "      <td>verified</td>\n",
       "      <td>Michelle</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>40.74767</td>\n",
       "      <td>-73.97500</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2019-06-22 00:00:00.000000000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>No smoking, please, and no drugs.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1006859</td>\n",
       "      <td>Cute &amp; Cozy Lower East Side 1 bdrm</td>\n",
       "      <td>1280143094</td>\n",
       "      <td>verified</td>\n",
       "      <td>Miranda</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Chinatown</td>\n",
       "      <td>40.71344</td>\n",
       "      <td>-73.99037</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2019-06-09 00:00:00.000000000</td>\n",
       "      <td>1.33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1007411</td>\n",
       "      <td>Beautiful 1br on Upper West Side</td>\n",
       "      <td>18824631834</td>\n",
       "      <td>verified</td>\n",
       "      <td>Alan</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Upper West Side</td>\n",
       "      <td>40.80316</td>\n",
       "      <td>-73.96545</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2019-06-22 00:00:00.000000000</td>\n",
       "      <td>0.43</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>My ideal guests would be warm, friendly, and r...</td>\n",
       "      <td>NaN</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",
       "      <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>102586</th>\n",
       "      <td>6088019</td>\n",
       "      <td>Patio Oasis in Upper East Side</td>\n",
       "      <td>41141109074</td>\n",
       "      <td>verified</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Upper East Side</td>\n",
       "      <td>40.77703</td>\n",
       "      <td>-73.94773</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2016-08-08 00:00:00.000000000</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>- NO SMOKING (You'll have to do that outside i...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102588</th>\n",
       "      <td>6089124</td>\n",
       "      <td>Loft Space for Events, Meetings &amp; Shoots</td>\n",
       "      <td>85557432222</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Kalin</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Flatiron District</td>\n",
       "      <td>40.74068</td>\n",
       "      <td>-73.98999</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>2019-06-29 00:00:00.000000000</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>Keep the apartment clean and damage free please.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102591</th>\n",
       "      <td>6090781</td>\n",
       "      <td>Columbus Ave Apt 1 block from Park</td>\n",
       "      <td>50908010324</td>\n",
       "      <td>verified</td>\n",
       "      <td>Lawrence</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Upper West Side</td>\n",
       "      <td>40.77408</td>\n",
       "      <td>-73.98181</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>228.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2019-01-04 00:00:00.000000000</td>\n",
       "      <td>0.35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>#NAME?</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102592</th>\n",
       "      <td>6091333</td>\n",
       "      <td>3BR/1 Ba in TriBeCa w/ outdoor deck</td>\n",
       "      <td>53266862889</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Nick</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Tribeca</td>\n",
       "      <td>40.71845</td>\n",
       "      <td>-74.01183</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>157.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-06-11 19:47:31.418129408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>Guests should treat my home as if it were thei...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102598</th>\n",
       "      <td>6094647</td>\n",
       "      <td>585 sf Luxury Studio</td>\n",
       "      <td>68170633372</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Rebecca</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Upper West Side</td>\n",
       "      <td>40.76807</td>\n",
       "      <td>-73.98342</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-06-11 19:47:31.418129408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26473 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                              NAME  \\\n",
       "1       1002102                             Skylit Midtown Castle   \n",
       "4       1003689  Entire Apt: Spacious Studio/Loft by central park   \n",
       "5       1004098         Large Cozy 1 BR Apartment In Midtown East   \n",
       "10      1006859                Cute & Cozy Lower East Side 1 bdrm   \n",
       "11      1007411                  Beautiful 1br on Upper West Side   \n",
       "...         ...                                               ...   \n",
       "102586  6088019                    Patio Oasis in Upper East Side   \n",
       "102588  6089124          Loft Space for Events, Meetings & Shoots   \n",
       "102591  6090781                Columbus Ave Apt 1 block from Park   \n",
       "102592  6091333               3BR/1 Ba in TriBeCa w/ outdoor deck   \n",
       "102598  6094647                              585 sf Luxury Studio   \n",
       "\n",
       "            host_id host_identity_verified host_name neighbourhood_group  \\\n",
       "1       52335172823               verified     Jenna           Manhattan   \n",
       "4       92037596077               verified    Lyndon           Manhattan   \n",
       "5       45498551794               verified  Michelle           Manhattan   \n",
       "10       1280143094               verified   Miranda           Manhattan   \n",
       "11      18824631834               verified      Alan           Manhattan   \n",
       "...             ...                    ...       ...                 ...   \n",
       "102586  41141109074               verified  Margaret           Manhattan   \n",
       "102588  85557432222            unconfirmed     Kalin           Manhattan   \n",
       "102591  50908010324               verified  Lawrence           Manhattan   \n",
       "102592  53266862889            unconfirmed      Nick           Manhattan   \n",
       "102598  68170633372            unconfirmed   Rebecca           Manhattan   \n",
       "\n",
       "            neighbourhood       lat      long        country  ... service_fee  \\\n",
       "1                 Midtown  40.75362 -73.98377  United States  ...        28.0   \n",
       "4             East Harlem  40.79851 -73.94399  United States  ...        41.0   \n",
       "5             Murray Hill  40.74767 -73.97500  United States  ...       115.0   \n",
       "10              Chinatown  40.71344 -73.99037  United States  ...        64.0   \n",
       "11        Upper West Side  40.80316 -73.96545  United States  ...       121.0   \n",
       "...                   ...       ...       ...            ...  ...         ...   \n",
       "102586    Upper East Side  40.77703 -73.94773  United States  ...        42.0   \n",
       "102588  Flatiron District  40.74068 -73.98999  United States  ...       124.0   \n",
       "102591    Upper West Side  40.77408 -73.98181  United States  ...       228.0   \n",
       "102592            Tribeca  40.71845 -74.01183  United States  ...       157.0   \n",
       "102598    Upper West Side  40.76807 -73.98342  United States  ...       206.0   \n",
       "\n",
       "       minimum_nights number_of_reviews                   last_review  \\\n",
       "1                30.0              45.0 2022-05-21 00:00:00.000000000   \n",
       "4                10.0               9.0 2018-11-19 00:00:00.000000000   \n",
       "5                 3.0              74.0 2019-06-22 00:00:00.000000000   \n",
       "10                1.0             160.0 2019-06-09 00:00:00.000000000   \n",
       "11                5.0              53.0 2019-06-22 00:00:00.000000000   \n",
       "...               ...               ...                           ...   \n",
       "102586            4.0               8.0 2016-08-08 00:00:00.000000000   \n",
       "102588            1.0             177.0 2019-06-29 00:00:00.000000000   \n",
       "102591            5.0              17.0 2019-01-04 00:00:00.000000000   \n",
       "102592            1.0               0.0 2019-06-11 19:47:31.418129408   \n",
       "102598            1.0               0.0 2019-06-11 19:47:31.418129408   \n",
       "\n",
       "        reviews_per_month  review_rate_number  calculated_host_listings_count  \\\n",
       "1                    0.38                 4.0                             2.0   \n",
       "4                    0.10                 3.0                             1.0   \n",
       "5                    0.59                 3.0                             1.0   \n",
       "10                   1.33                 3.0                             4.0   \n",
       "11                   0.43                 4.0                             1.0   \n",
       "...                   ...                 ...                             ...   \n",
       "102586               0.17                 5.0                             1.0   \n",
       "102588               3.78                 4.0                             1.0   \n",
       "102591               0.35                 5.0                             1.0   \n",
       "102592                NaN                 2.0                             1.0   \n",
       "102598                NaN                 3.0                             1.0   \n",
       "\n",
       "        availability_365                                        house_rules  \\\n",
       "1                  228.0  Pet friendly but please confirm with me if the...   \n",
       "4                  289.0  Please no smoking in the house, porch or on th...   \n",
       "5                  374.0                  No smoking, please, and no drugs.   \n",
       "10                   1.0                                                NaN   \n",
       "11                 163.0  My ideal guests would be warm, friendly, and r...   \n",
       "...                  ...                                                ...   \n",
       "102586             159.0  - NO SMOKING (You'll have to do that outside i...   \n",
       "102588             410.0   Keep the apartment clean and damage free please.   \n",
       "102591             134.0                                             #NAME?   \n",
       "102592             177.0  Guests should treat my home as if it were thei...   \n",
       "102598              69.0                                                NaN   \n",
       "\n",
       "       license  \n",
       "1          NaN  \n",
       "4          NaN  \n",
       "5          NaN  \n",
       "10         NaN  \n",
       "11         NaN  \n",
       "...        ...  \n",
       "102586     NaN  \n",
       "102588     NaN  \n",
       "102591     NaN  \n",
       "102592     NaN  \n",
       "102598     NaN  \n",
       "\n",
       "[26473 rows x 26 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Manhattan[Manhattan['room_type']==\"Entire home/apt\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eba5636",
   "metadata": {},
   "source": [
    "### Análisis general de datos con visualizaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3db5a408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uqaoFwCVtnSS7AIuAXVubU5Js0NqcCiwGFrTH2D6PAB6oqp2Bk4ETR3YkkqQJjSxIquo7wP3jygcBS9ryEuDggfq5VfVIVd0GrAD2TrIdsFlVXVFVBZw1rs3Yvs4H9h8brUiSps90XyPZtqruBmjP27T69sCdA9utbLXt2/L4+mptqmoV8CCw1bA3TbI4ybIky+677761dCiSJHjqXGwfNpKoSeqTtXl8seq0qlpYVQvnzp37JLsoSRpmuoPknna6ivZ8b6uvBHYY2G4ecFerzxtSX61NkjnA5jz+VJokacSmO0guAA5vy4cDXxuoL2p3Yu1Ed1H9qnb666Ek+7TrH4eNazO2r0OAS9t1FEnSNJozqh0n+RKwL7B1kpXAh4GPAUuTHAHcARwKUFU3JlkK3ASsAo6uqsfaro6kuwNsE+DC9gA4Azg7yQq6kciiUR2LJGliIwuSqnrLBC/tP8H2JwAnDKkvA3YbUn+YFkSSpJnzVLnYLkmapQwSSVIvBokkqReDRJLUi0EiSerFIJEk9WKQSJJ6MUgkSb0YJJKkXgwSSVIvBokkqReDRJLUi0EiSerFIJEk9WKQSJJ6MUgkSb0YJJKkXgwSSVIvBokkqReDRJLUi0EiSerFIJEk9WKQSJJ6MUgkSb0YJJKkXgwSSVIvBokkqReDRJLUi0EiSerFIJEk9WKQSJJ6MUgkSb3M+iBJcmCSW5OsSHLsTPdHktY3szpIkmwAfAZ4NbAL8JYku8xsryRp/TKrgwTYG1hRVT+uql8C5wIHzXCfJGm9MmemO9DT9sCdA+srgReP3yjJYmBxW/1FklunoW/ri62Bn850J54K8vHDZ7oLWp2/m2M+nLWxl9+e6IXZHiTDfjr1uELVacBpo+/O+ifJsqpaONP9kMbzd3P6zPZTWyuBHQbW5wF3zVBfJGm9NNuD5GpgQZKdkvwWsAi4YIb7JEnrlVl9aquqViV5N/AtYAPgC1V14wx3a33jKUM9Vfm7OU1S9bhLCpIkTdlsP7UlSZphBokkqReDRE9ako2S/I8k1yV5c5LTp2NmgSRnJjlk1O+j2SfJPyZ5Zlt+b5Kbk5yT5PVPdAqlJLcn2XokHV3HzOqL7ZpxewIbVtUebf28YRsl2aCqHpu2Xmm9kyR013xfM1A+Cnh1Vd3W1r2jc0QckawDksxPcsPA+geTfCTJZUlOTHJVkh8meXl7fYMkH0+yPMn1Sd7T6vsn+X6rfyHJRq1+e5Ljk1zbXnt+km2ALwJ7tBHJc9v7LWxtfpHko0muBF7S1k9Mck0bxezdtv9xktcP9OukJFe3fv1ZqyfJp5PclOQbwDbT+fPV9Gm/I0cNrH8kyQeS/PnA78Xx7bX5bcRxCnAtsMPYKCLJZ4HnABckeX+Styf5dGs3N8lX2v6uTvLSVt8qyUXt/4HPMfwDzxrCIFn3zamqvYFjgA+32mJgJ2DPqtodOCfJxsCZwJur6gV0o9UjB/bz06p6EXAq8MGquhf4d8D/rKo9qup/jXvfpwM3VNWLq+q7bf2yqtoLeAj4K+CPgDcAH21tjgAerKrfA34P+NMkO7Vtnge8APhT4PfXws9FT03nAm8eWH8TcB+wgG5uvT2AvZL8QXv9ecBZVbVnVf1krFFVvYvuw8n7VdXJ497jvwInt9+zPwZOb/UPA9+tqj3pRi87rs0DW5d5amvd9/ft+Rpgflt+JfDZqloFUFX3J3khcFtV/bBtswQ4GvjEkP28cQrv+xjwlYH1XwLfbMvLgUeq6tEkywf69Spg94HrH5vT/QH5A+BL7fTYXUkuncL7axaqqu8n2SbJs4G5wAPA7nS/G99vm21K93txB/CTqvreE3ybVwK7dGfDANgsyTPofs/e2PrxjSQP9DqY9YhBsm5Yxeqjy40Hlh9pz4/xm//e4fFzkq1pGD9sP5N5eNx1kUfrNx9a+tXY/qrqV0kG+/WeqvrWah1LXjOkv1p3nQ8cAjyLboQyH/ibqvrc4EZJ5gP/90ns/2nAS6rqX8ftD/w9e1I8tbVuuAfYpp3j3Qh43Rq2vwh419gf8CRbArcA85Ps3LZ5G3D5qDo8gW8BRybZsPXrd5I8HfgOsKhdQ9kO2G+a+6XpdS7ddEeH0IXKt4B3JtkUIMn27Rrdk3UR8O6xlSR7tMXvAH/Saq8GtujxHusVRyTrgHaK6KPAlcBtdKEwmdOB3wGuT/Io8Pmq+nSSdwBfbgFzNfDZUfZ7gn7NB65td+HcBxwMfBV4Bd0psR8y/QGnaVRVN7ZTTf9SVXcDdyf5XeCKNmr4BfBWutHxk/Fe4DNJrqf7G/gd4F3A8cCXklxL9zt2R78jWX84RYokqRdPbUmSejFIJEm9GCSSpF4MEklSLwaJJKkXg0SS1ItBollp/ESVT3If+yaZdN6uJO9Kctjafv8kxyT5NwPr/5jkme1x1GRtRyXJs5OcP7D+pTZJ4vvbBJyvfAL76v3fR7OHH0jU+mxfug+3/fNEG1TVqD6UeQzd7Mn/r73Pa+DX034cBZwyovcdKsmcqrqL7tPkJHkW8PtV9dvT2Q/NTo5INJttkOTzSW5s039vkmSPJN9r/5L+apIt4NdfcnRTq5/b/mC/C3h/umnwXz7sDdo05h9sy3sl+UGSK+gmtBzbZqLp7/dNN1X++UluSfcFS0nyXuDZwLeTfLttO/YlSh8Dntv6dFKSs5McNPBe56RNuz+kr1cm2XVg/bLW56en+1qAq9sU6Qe119+e5MtJvg5cNG4UcRHdtDvXJXl5Br5MrO3z8nRfCfCtNm3NhD8frQeqyoePWfegm0plFbBHW19KN23G9cAfttpHgU+05buAjdryM9vzR+imxJ/sfX69zbh9n0Q3TT500/L/RVveCFhGN03/vsCDwDy6f7RdAbysbXc7sPXA+9wObN2O64aB+h8C/70tb043Bc6cCfr6fuD4trwd8MO2/NfAW8eOnW6amacDbwdWAlsO/ExvGL/c1s+kG61sSDeCm9vqbwa+MNnPx8e6/3BEotnstqq6ri1fAzyXLiTG5uJaQjc1OHR/5M5J8la6AHpCkmw+bt9nD7z8KuCwJNfRzXe2Fd005wBXVdXKqvoVcB2/mTJ/Str77dwmKXwL8JVq0/8PsRQ4tC2/CfjyQP+Obf27jG526LHv2ri4qu5/Al16HrAbcHHb318A89bw89E6zmskms0eGVh+jO5f2xN5LV2ovB74j4OngKZo2NT7g68Nm/5+3yF9fDL/z51NNyvtIuCdE21UVf+S5GdJdqcbKfzZQP/+uKpuHde/F/PEp2EPcGNVvWTcvp6JU7CvtxyRaF3yIPDAwPWOtwGXJ3kasENVfRv4EF3gbEr3TY3PmMqOq+rnwINJXtZKfzLw8kTT309movceVj+T7uI8VXXjGvZ7Lt0xbl5Vywf69560qXOT7LmGfUzmVmBukpe0fW2YZNc1/Hy0jjNItK45HDgp3RThe9BdJ9kA+GK6b2P8Pt3XrP4c+Drwhskuto/zDrrpx68ABr8U6XTgJrrp728APseaRx6nAReOXWwfU1U/A/4pyQ1JTmq1e4Cbgf82hT6eTzdyWTpQ+0u6axvXt/795RT2M1RV/ZLuWsmJSX5Ad7pu7BbqiX4+Wsc5jbz0FJfu8ybLgRdV1YMz3R9pPEck0lNY+xDgLcCnDBE9VTkikYAk/4Hf3PE05stVdcJM9GcySQ4AThxXvq2q3jAT/ZEMEklSL57akiT1YpBIknoxSCRJvRgkkqRe/j89wut70nsnJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Hacemos funcion que grafique las variables categóricas con las numéricas y me de insights\n",
    "for var_cat in categoricas:\n",
    "    frecuencia = airbnb[var_cat].value_counts()\n",
    "    df_frecuencias = pd.DataFrame({var_cat: frecuencia.index.tolist(), 'cantidad':frecuencia.tolist()})\n",
    "    sns.barplot(x=var_cat, y='cantidad', data=df_frecuencias)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "28c27048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3A/ab6TlNaCVJkkZQox7alcIe2mVIcrckX0vyv0kuTHJkki1n6dxPS7LNbJyrP99Lkmwy8Pig2Ty/JElagJauhFsjJrSTSBLgMOC4qtq8qrYB3gHcdZZe4mnApAlnkhWpmr8EuDWhrap/GFxxYxit4PuUJEmawIR2co8Cbh7s56iqs4ETk+yX5Pwk5yXZE25db/i4JIcm+VmSg/ukmCT/3ld4z03yoSQPAZ4K7NevUbx5f+y/9WsVvz7J55M8a+y1k1w3cP+f+tc+pz/3s4BdgIP7863Rn2+Xfv/n9fufn2TfwXMmeX9/nlOSTJqsJ1knyaVJVu0fr5vksiSr9rEf1U96/OMkW/f7PCXJT5OcleQHY+dO8p4kByY5BvjiLHydJEnSCqqls39rxSrZ5LYFzphk+zOAHejmQtuIbv3hE/rndgTuTzeX2knAQ5NcCDwd2LqqKsn6VXVVksOBI6rqUIA+912/qh7RP/78ZEEleQJddfdBVXVDkg2r6k/9aME3V9XpA+ejb0PYl25y4j8DxyR5WlV9G1gLOKWq3pnkg8ArgPeNf82qujbJccCTgG/TTa/xzaq6OcmBwN5V9T9JHkS37vKjgROBB/fv+R+AfwL+sT/lzsDuVfWXyd6jJEnS8rJCu3x2B75aVUuq6nfA8cCu/XOnVtXlVbUUOBvYDLgG+CtwUJJnADdMce5DZvD6jwE+V1U3AFTVn6bZf1e6tok/VNUtwMHAw/vnbgKO6O+f0ce7LAcBL+3vvxT4XJK1gYcA30hyNvBp4O79PvcAjk5yHvAWukR/zOHLSmYHl8v74w2/m+atSZKkO8Qe2gXvArpK4niTLdc25saB+0uAVfok8oHAN+kqq0dNcfz1A/dvof/a9K0Lqw28/vIMSZwq3purauxcS5iiWl9VJwGbJXkEsLifC24RcFVV7TBw+5v+kP2BT1TVA4BXAqsPnO56lqGqDqyqXapqlzuvOVvtypIkaaEzoZ3cj4A7JXnF2IYku9Jdtt8zyeIkG9NVO09d1kn6KuZ6/Xxrb6BrVwC4Flhnite/jNsS6j2AVfv7xwAv61fNIMmG05zvp8AjkmyUZDHwPLqq8or4IvBV4HMAVXUNcGmSZ/exJMn2/b7rAb/u7794BV9PkiStRAuph9aEdhJ95fLpwN/103ZdALwH+ArdqhXn0CW9/1RVv53iVOsARyQ5ly6RfGO//WvAW/pBU5tPctxn6BLRU+mWe7u+j+souuXhTu8v87+53//zwAFjg8IG3sdvgLcDx/Yxn1lV31mez8WAg4EN6JLaMS8AXp7kHLqq9h799vfQtSL8GLhyBV9PkiStTAuo5SC3XXWWlq2fTWGPqvr7uXi97e/2kKH8xlw0ZReHxixdrs6YuXH6+V9uHcIEO2/7gtYhzBvx/96MDOPPqCUts5wpLB3C/Of8350yp1/AKx/3iFn/JGx09PFNvgmd5UDTSrI/8ATgia1jkSRJs6Nli8BsM6HVrZK8E3j2uM3fqKrXtYhHkiRpJkxodauqej/w/tZxSJKklc8KrSRJkua1hZTQOsuBJEmS5jUrtJIkSaOohm9WjBVlhVaSJEnzmhVaSZKkEbSQemhNaCVJkkZQLbXlQJIkSRoKVmglSZJG0EJqObBCK0mSpHnNCq0kSdIIqgU0bZcJrYbSIhbOf7KV7eZa0jqECVbN4tYhTLDzti9oHcIEZ5x/cOsQ5o1h/Pot9iLnjGRIf54vGs6wtIJMaCVJkkbQQuqhNaGVJEkaQU7bJUmSJA0JK7SSJEkjqKp1BLPHCq0kSZLmNSu0kiRJI2gh9dCa0EqSJI2ghZTQ2nIgSZKkec0KrSRJ0ghyUJgkSZI0JKzQSpIkjaCF1ENrQitJkjSCqhZOQmvLgSRJkuY1E1rdYUnem+QxreOQJEkzV0tn/9aKLQe6Q5Isrqp3tY5DkiSNLiu0WqYkmyX5WZIvJDk3yaFJ1kxyWZJ3JTkReHaSzyd5Vn/MrklOTnJOklOTrJNkcZL9kpzWn+eVjd+aJEkjb2ll1m+tmNBqOlsBB1bVdsA1wKv77X+tqt2r6mtjOyZZDTgEeH1VbQ88BvgL8HLg6qraFdgVeEWS+8zlm5AkSQuXCa2m86uqOqm//2Vg9/7+IZPsuxXwm6o6DaCqrqmqW4DHAi9KcjbwU+DOwBbjD06yV5LTk5x+5Q2/neW3IUmSBlVl1m+t2EOr6YxfR2Ts8fWT7JtJ9h/b/rqqOnrKF6o6EDgQYMe7PXQBrV8iSdLwWUjz0Fqh1XTulWS3/v7zgBOn2PdnwCZJdgXo+2dXAY4GXpVk1X77lknWWplBS5Kk0WFCq+lcBLw4ybnAhsCnlrVjVd0E7Ansn+Qc4PvA6sBBwIXAmUnOBz6NVwckSWqqavZvrZhUaDpLq2rvcds2G3xQVS8ZuH8a8OBJzvOO/iZJkjSrTGglSZJG0ELqoTWh1TJV1WXAtq3jkCRJs6/lvLGzzR5aSZIkzWtWaCVJkkZQy3ljZ5sVWkmSJM1rVmglSZJGUMtptmabCa0kSdIIclCYJEmSNCSs0EqSJI0gB4VJkiRJQ8IKrSRJ0ghaSIPCrNBKkiRpXrNCK0mSNIIW0iwHJrSSZt1SFtB1LElaoBwUJkmSJA0JK7SSJEkjaCG1HFihlSRJ0rxmhVaSJGkELaTRDia0kiRJI8iWA0mSJGlIWKGVJEkaQU7bJUmSJK2AJI9P8vMkFyd52xT77ZpkSZJnTXdOK7SSJEkjaGmD10yyGPgk8HfA5cBpSQ6vqgsn2W9f4OiZnNcKrSRJ0ggqMuu3GXggcHFVXVJVNwFfA/aYZL/XAd8Efj+Tk5rQSpIkaa5sCvxq4PHl/bZbJdkUeDpwwExPasuBJEnSCFq6EiaiTbIXsNfApgOr6sDBXSY5bHwkHwXeWlVLkpkNXDOhlSRJ0qzok9cDp9jlcuCeA4/vAVwxbp9dgK/1yexGwBOT3FJV317WSW05GDFJ3pvkMbN4vv2SXJBkv9k6pyRJWvmWklm/zcBpwBZJ7pNkNeC5wOGDO1TVfapqs6raDDgUePVUySxYoV2QkqxSVbdM9lxVvWuWX+6VwMZVdeMsn1eSJC0wVXVLktfSzV6wGPhsVV2QZO/++Rn3zQ6yQjvEkqyV5L+TnJPk/CR7Jtk5yfFJzkhydJK79/sel+TfkhwPvDPJZUkW9c+tmeRXSVZN8vmx+dz6+d1O7s9/apJ1kizuq66nJTk3ySuniO9wYC3gp31sGyf5Zn/saUkeOvA+PttvOyvJZKMZJUnSHGo0ywFVdWRVbVlVm1fV+/ttB0yWzFbVS6rq0OnOaYV2uD0euKKqngSQZD3ge8AeVfWHJHsC7wde1u+/flU9ot93J+ARwLHAU4Cjq+rmsebqvsx/CLBnVZ2WZF3gL8DLgauratckdwJOSnJMVV06PriqemqS66pqh/6cXwE+UlUnJrkX3V9ffwO8E/hRVb0syfrAqUl+UFXXz/LnS5IkzVCLeWhXFhPa4XYe8KEk+wJHAH8GtgW+3yemi4HfDOx/yLj7e9IltM8F/nPcubcCflNVpwFU1TUASR4LbDewKsd6wBbAhIR2Eo8BthkYkbhuknWAxwJPTfLmfvvqwL2AiwYPHhwZeY917stGa95tBi8pSZJGnQntEKuqXyTZGXgi8AHg+8AFVbXbMg4ZrHgeDnwgyYbAzsCPxu0bJk6TMbb9dVU1o5U5xlkE7FZVf7ndCbsM95lV9fOpDh4cGbnj3R66EiYTkSRJY2baIjAf2EM7xJJsAtxQVV8GPgQ8CNg4yW7986smuf9kx1bVdcCpwMeAI6pqybhdfgZskmTX/lzrJFmFrk3gVUlW7bdvmWStGYZ8DPDagfh36O8eDbyuT2xJsuMMzydJkjQtK7TD7QHAfkmWAjcDrwJuAT7e99OuQjf58AXLOP4Q4BvAI8c/UVU39T24+ydZg65/9jHAQcBmwJl9AvoH4GkzjHcf4JNJzu1jOwHYG/jXPs5z+3NeBjx5hueUJEkrwULqoU2VV3Y1fGw5mLmbJxTf21uc4bv4U5N22LR1xvkHtw5h3th52xe0DmGCxV7knJGlQ/h/D4bzZ8K5v/3JnPYAHHnX5876J+GJv/takz4G/zdKkiRpXrPlQNNK8gDgS+M231hVD2oRjyRJuuMW0qAwE1pNq6rOA3ZoHYckSdJkTGglSZJG0NKFU6A1oZUkSRpFSxdQy4GDwiRJkjSvWaGVJEkaQcM3cdmKs0IrSZKkec0KrSRJ0ghaSCuFWaGVJEnSvGaFVpIkaQQtzcKZ5cCEVpIkaQQ5KEySJEkaElZopeWwZAhb6FdftGrrECb4083XtQ5hgnVWWaN1CBPsvO0LWocwb5xx/sGtQ5hg8y33aB3CBFffdEPrECZ42IZbtw5hUn+tW1qH0Nzw/UZbcVZoJUmSNK9ZoZUkSRpBSxfOmDATWkmSpFG0lIWT0dpyIEmSpHnNCq0kSdIIctouSZIkaUhYoZUkSRpBDgqTJEnSvOY8tJIkSdKQsEIrSZI0ghwUJkmSJA0JK7SSJEkjyEFhkiRJmtccFKZpJXlqkrdNs88mSQ6dq5iWV5Jdknx8mn02S3L+Mp57SZJNVk50kiRJHSu0K0lVHQ4cPs0+VwDPmpuIll9VnQ6cfgdO8RLgfOCKWQlIkiTNGiu0I66vSv4syUFJzk9ycJLHJDkpyf8keWBfnfxEv//nk3w8yclJLknyrIHznN/ff0mSbyf5bpJLk7w2yZuSnJXklCQb9vsdl2SX/v5GSS5bnuOX8X6OS7JvklOT/CLJw/rtj0xyRH9/4yTfT3Jmkk8n+WWSjfpTLE7ymSQXJDkmyRr9e9wFODjJ2f22f09yYZJzk3xoZXxtJEnS6DGhXXH3Az4GbAdsDTwf2B14M/COSfa/e//8k4F/X8Y5t+3P80Dg/cANVbUj8BPgRTOI6Y4cv0pVPRB4A/DuSZ5/N/CjqtoJOAy418BzWwCfrKr7A1cBz6yqQ+mquy+oqh2ANYCnA/evqu2A983g/UiSpJWkMvu3VkxoV9ylVXVeVS0FLgB+WFUFnAdsNsn+366qpVV1IXDXZZzz2Kq6tqr+AFwNfLffvqxzzubx3+o/nrGMfXcHvgZQVUcBfx547tKqOnua468B/goclOQZwA3TxCNJkjQjJrQr7saB+0sHHi9l8t7kwf2X9TfMTM55C7d93Va/gzFNduySZew71d9dg6876fFVdQtd5fibwNOAoya8QLJXktOTnH7lDb+dJlxJknRHLF0Jt1ZMaOefy4Cd+/tzOaDsROA5AEkeC2wwg2OuBdbpj1kbWK+qjqRra9hh/M5VdWBV7VJVu2y05t1mKWxJkjQZE1q19CHgVUlOBjaabudZ9C/AY5OcCTwB+A1dwjqVzwMHJDmbLrE9Ism5wPHAG1deqJIkaZSka/uUppbkTsCSqrolyW7Ap/rBXivFjnd76FB+Yy4ZwklOVsvwzb73p5uvax3CBOusskbrECaoBbWS+sp1xvkHtw5hgs233KN1CBNcfdPwDU942IZbtw5hUn+tW1qHMMEPfnX0nA6r2v+eL5z1H0Kv+9WXmwwNG77fhBpW9wK+nmQRcBPwisbxSJIkASa0IyXJJ4GHjtv8sar63HTHVtX/ADuulMAkSdKcW9pwmq3ZZkI7QqrqNa1jkCRJw2H4muhWnIPCJEmSNK9ZoZUkSRpBVmglSZKkIWGFVpIkaQQtpIkDTWglSZJG0EKa5cCWA0mSJM1rVmglSZJGkIPCJEmSpCFhhVaSJGkELaRBYVZoJUmSNK9ZoZUkSRpBSxdQjdaEVkPpqpuvax3CpNZZdc3WIUxwzS1/aR3CBPdb466tQ5jgdzdf0zqECRZ7kWzGNt9yj9YhTPC/v/hO6xAmuO5VL2sdwgRbHnNx6xAmtft6W7YOoTkHhUmSJElDwgqtJEnSCFo4DQdWaCVJkjTPWaGVJEkaQQuph9aEVpIkaQQtTesIZo8tB5IkSZrXrNBKkiSNoIU0D60VWkmSJM1rVmglSZJG0MKpz5rQSpIkjaSFNMuBLQeSJEma10xoJUmSRtBSatZvM5Hk8Ul+nuTiJG+b5PkXJDm3v52cZPvpzmlCK0mSpDmRZDHwSeAJwDbA85JsM263S4FHVNV2wL8CB053XhPalSDJcUl2mcPX2y/JBUn2W0nnf2+Sx6yMc0uSpDZqJdxm4IHAxVV1SVXdBHwN2ON2cVWdXFV/7h+eAtxjupM6KGzIJFmlqm5ZzsNeCWxcVTeujPNX1buWMx5JkjSCkuwF7DWw6cCqGqywbgr8auDx5cCDpjjly4HvTfe6I12hTbJZkouSfKavcB6TZI3BCmuSjZJc1t9/SZJvJ/lukkuTvDbJm5KcleSUJBsOnP6Ffd/H+Uke2B+/VpLPJjmtP2aPgfN+I8l3gWOWEWv6Suz5Sc5Lsme//XBgLeCnY9smOfbzSf4jybHAvkk2T3JUkjOS/DjJ1knWS3JZkkX9MWsm+VWSVfvjn9Vv3znJ8f2xRye5e5K7JDmjf377JJXkXv3j/+3P9ew+9nOSnHDHvnKSJOmOWroSblV1YFXtMnAb3y4w2YK7kxZ3kzyKLqF963TvxQotbAE8r6pekeTrwDOn2X9bYEdgdeBi4K1VtWOSjwAvAj7a77dWVT0kycOBz/bHvRP4UVW9LMn6wKlJftDvvxuwXVX9aRmv+wxgB2B7YCPgtCQnVNVTk1xXVTtME/eWwGOqakmSHwJ7V9X/JHkQ8J9V9egk5wCPAI4FngIcXVU3J933XpJVgf2BParqD30C/f7+/ayeZF3gYcDpwMOSnAj8vqpuSPIu4HFV9ev+vUuSpIYarRR2OXDPgcf3AK4Yv1OS7YCDgCdU1R+nO6kJLVxaVWf3988ANptm/2Or6lrg2iRXA9/tt58HbDew31cBquqEJOv2SdxjgacmeXO/z+rAvfr7358imQXYHfhqVS0BfpfkeGBX4PBp4h3zjT6ZXRt4CPCNsUQVuFP/8RBgT7qE9rnAf447x1Z0ifn3+2MXA7/pnzsZeCjwcODfgMfT/RX24/75k4DP9380fGuyAAcvU9x5zU1ZZ/U7z/CtSZKkeeI0YIsk9wF+TZdvPH9wh/4q77eAv6+qX8zkpCa0MNh3ugRYA7iF29oxVp9i/6UDj5dy+8/n+D97ii7Be2ZV/Xzwib5Kev00cU5Wol8eY+dfBFy1jIru4cAH+taJnYEfTRLDBVW12yTH/piuOntv4Dt0lwcKOAKgqvbu3+eTgLOT7DD+L67+ssSBAPe58/YLaQETSZKGTotftFV1S5LXAkfTFcY+W1UXJNm7f/4A4F3AnYH/7Atot1TVlIPtR7qHdgqX0SV0AM9awXOM9bjuDlxdVVfTffFel/6rk2TH5TjfCcCeSRYn2ZiuEnrq8gZVVdcAlyZ5dh9D0s/vVlXX9ef8GHBEXw0e9HNg4yS79ceumuT+A/G9EPifqloK/Al4Il1lliSbV9VP+wFmV3L7yw2SJGlEVNWRVbVlVW1eVe/vtx3QJ7NU1T9U1QZVtUN/m3bmKCu0k/sQ8PUkf8/EKuVM/TnJycC6wMv6bf9K12N7bp/UXgY8eYbnO4yuz/Ycuj+q/qmqfruCsb0A+FSSfwZWpZsy45z+uUOAbwCPHH9QVd3UDw77eJL16L5/PkpXtb2sz9PHBnydCNxjYNqN/ZJsQVfl/eHA60mSpAYW0tK3qfLKrobPsLYcrLPqmq1DmOCvS25uHcIEm62+UesQJvjdzde0DmGCRXe4k2h0/PGm4fv6/e8vvtM6hAmue9XLpt9pjm15zG+m36mB3dfbsnUIExz2f9+d0x8K+2y256z/rv34ZYc0+cFmy4EkSZLmNVsOhkySBwBfGrf5xqqaatLhsWPfCTx73OZvjPWnSJIkjVlILQcmtEOmqs6jm292RY59P2DyKkmSRooJrSRJ0ghqtLDCSmFCK0mSNIIWTjrroDBJkiTNc1ZoJUmSRtBCajmwQitJkqR5zQqtJEnSCFpI03ZZoZUkSdK8ZoVWkiRpBNUC6qE1oZUkSRpBthxIkiRJQ8IKrYbSJqvfuXUIk7p2yV9ahzDBoqR1CBNssGj11iFM8AeubR2C7oCrb7qhdQgTXPeql7UOYYK1P/XZ1iFMsMHfPKd1CJP6a93SOoTmFlLLgRVaSZIkzWtWaCVJkkbQQuqhNaGVJEkaQUvLlgNJkiRpKFihlSRJGkELpz5rhVaSJEnznBVaSZKkEbR0AdVoTWglSZJGkPPQSpIkSUPCCq0kSdIIWkjz0FqhlSRJ0rxmhVaSJGkELaRBYVZoJUmSNK+Z0K6gJJskObR1HMMoyQ5Jnjjw+D1J3twyJkmSdHu1Ev61YkLbS2fGn4+quqKqnrUyY5otSea6tWQH4InT7SRJktpZuhJurYx0QptksyQXJflP4Ezg/yU5Lcm5Sf6l32ffJK8eOOY9Sf6xP/b8ftviJPsNHPvKfvt/Jnlqf/+wJJ/t7788yfuSrJXkv5Ock+T8JHtOEetlfSyn9rf79ds3TvLN/rVPS/LQgTgPTHIM8MVlnPMlSb6d5LtJLk3y2iRvSnJWklOSbNjvt0P/+Nz+fWzQbz9uIKZfJHlYktWA9wJ7Jjl74D1t0+9/SZJ9VvRrJkmSNN5IJ7S9regSvrcCmwIPpKsw7pzk4cDXgMFE8znAN8ad4+XA1VW1K7Ar8Iok9wFOAB7W77MpsE1/f3fgx8DjgSuqavuq2hY4appYr6mqBwKfAD7ab/sY8JH+tZ8JHDSw/87AHlX1/CnOuS3w/P59vx+4oap2BH4CvKjf54vAW6tqO+A84N0Dx6/Sx/QG4N1VdRPwLuCQqtqhqg7p99saeFz/Ou9Osuo071WSJK1EVTXrt1ZMaOGXVXUK8Nj+dhZdtXZrYIuqOgu4S98zuz3w56r6v3HneCzwoiRnAz8F7gxsQZe0PizJNsCFwO+S3B3YDTiZLjl8TF/lfFhVXT1NrF8d+Lhbf/8xwCf61z4cWDfJOv1zh1fVX6Y557FVdW1V/QG4Gvhuv/08YLMk6wHrV9Xx/fYvAA8fOP5b/cczgM2meJ3/rqobq+pK4PfAXcfvkGSvJKcnOf23118xTdiSJEkdp+2C6/uPAT5QVZ+eZJ9DgWcBd6Or2I4X4HVVdfSEJ7rL84+nq9ZuSFfhva6qrgWuTbIzXb/pB5IcU1XvnSLWmuT+ImC38YlrksH3NpUbB+4vHXi8lJl9f4ztv2Sa/QdfZ9J9q+pA4ECAh2766IUzl4gkSUPIabsWpqOBlyVZGyDJpknu0j/3NeC5dEntZDMbHA28auwyepItk6zVP/cTusvxJ9BVbN/cfyTJJnSX+L8MfAjYaZoY9xz4+JP+/jHAa8d2SLLDDN7rjPVV4z8nGWud+Hvg+CkOAbgWWGeafSRJUkMLaVCYFdpeVR2T5G+An/TVzeuAFwK/r6oL+sv4v66q30xy+EF0l9vPTHfwH4Cn9c/9GHhsVV2c5Jd0Vdof9889ANgvyVLgZuBV04R5pyQ/pftD5Hn9tn2ATyY5l+7reQKw93K9+em9GDggyZrAJcBLp9n/WOBtfRvEB2Y5FkmSpNtJywZezVySy4Bd+h7UBW9YWw6uXTJdS/Lcu2npLa1DmGD7NTZtHcIEv7jxD61D0B1wyXW/bR3CBJc94Z6tQ5hg7U99tnUIE9z/b57TOoRJbb76xq1DmOB7v/pe5vL1nnyvJ83679oj/u+/5/Q9jLHlQJIkSfOaLQdDJslhwH3GbX5rVW12B875OGDfcZsvraqnr+g5JUnS/LaQBoWZ0A6ZlZFk9rMvTJiBQZIkja6F1HZqy4EkSZLmNSu0kiRJI6jlNFuzzQqtJEmS5jUrtJIkSSOoFtCgMCu0kiRJmtes0EqSJI0gp+2SJEnSvOa0XZIkSdKQsEIrSZI0ghZSy4EVWkmSJM1rVmg1lG6uJa1DmFRI6xAm2GDVtVuHMMF//+Gc1iFMcN9179Y6hAmG8ftpWD1sw61bhzDBlsdc3DqECTb4m+e0DmGCCy76eusQJvXaXd7aOoTmFtK0XSa0kiRJI2ipg8IkSZKk4WCFVpIkaQQtnPqsFVpJkiTNc1ZoJUmSRtBCmrbLhFaSJGkELaSE1pYDSZIkzWtWaCVJkkZQOW2XJEmSNBys0EqSJI0ge2glSZKkIWGFVpIkaQTVAqrQmtBKkiSNIAeFLUOSlyT5xAoe+/kkz5rB+TdZzvNuluT8ac65QjEvb2xJjkuyS3//yCTrT7HvG5KsOfB4yv3ni/HvS5Ik6Y6abz20LwGWK6GdQy9hOWKrqidW1VVT7PIG4NbEbwb7zxdvYOB9SZKkNpZSs35rZUYJbZIXJTk3yTlJvpTkKUl+muSsJD9IctdJjrlrksP6Y85J8pDx1dIkb07ynkmOfVeS05Kcn+TAdJ4F7AIcnOTsJGsk2TnJ8UnOSHJ0krv3x+/cv+ZPgNfM4C1ukuSoJP+T5IMDcTwvyXl9HPv22xb31eTz++feOFlsM/icXpZkoyRrJfnvPt7zk+yZZB+65PjYJMeO23+zJBcl+UySC5IcM/Z6SXbtv04/SbLf2Oc6yf2TnNrHdm6SLaaI63Zf637bvZP8sN/+wyT36rffrqqe5Lr+4yP7avShSX6W5OD+azjhfUmSpNGS5PFJfp7k4iRvm+T5JPl4//y5SXaa7pzTJrRJ7g+8E3h0VW0PvB44EXhwVe0IfA34p0kO/ThwfH/MTsAF073WgE9U1a5VtS2wBvDkqjoUOB14QVXtANwC7A88q6p2Bj4LvL8//nPAPlW12wxfbwdgT+ABwJ5J7pmufWBf4NH987smeVp/f9Oq2raqHgB8bnxsVfWX5XivjweuqKrt+/d7VFV9HLgCeFRVPWqSY7YAPllV9weuAp458L737t/3koH99wY+1n/edgEunyyQZXytAT4BfLGqtgMOpvvaTmdHumrsNsB9gYfO4H1JkqQ5UlWzfptOksXAJ4En0OUIz0uyzbjdnkCX62wB7AV8arrzzqRC+2jg0Kq6sn/zfwLuARyd5DzgLcD9l3Hcp/pjllTV1TN4rTGP6ivA5/Xnmez8WwHbAt9Pcjbwz8A9kqwHrF9Vx/f7fWkGr/fDqrq6qv4KXAjcG9gVOK6q/lBVt9Alcg8HLgHum2T/JI8HrlmO9zWZ84DHJNk3ycNm+Hm6tKrO7u+fAWyWrr92nao6ud/+lYH9fwK8I8lbgXtPkXBP9rUG2G3gfF8Cdp9BjKdW1eVVtRQ4G9hsugOS7JXk9CSn//6G38zgJSRJ0opq1HLwQODiqrqkqm6iK4zuMW6fPegKaVVVpwDrj12FX5aZJLSBCRHuT1dFfQDwSmD1mbwDuqrq4GtOOC7J6sB/0lVeHwB8ZhnnD3BBXxHdoaoeUFWPXUa807lx4P4SutkfMtmOVfVnYHvgOLp2hoOW87XGn+8XwM50ie0HkrxrNuPtX+MrwFOBv9D9IfLoZew608/d2D63fj2TBFhtmhinPmnVgVW1S1Xtcpc1p/y+lSRJ89OmwK8GHl/eb1vefW5nJgntD4HnJLkzQJINgfWAX/fPv3iK417VH7M4ybrA74C7JLlzkjsBT57kuLHk9cokawODMx9cC6zT3/85sHGS3frXWDXJ/fuBU1cnGasivmAG73EyPwUe0fetLgaeBxyfZCNgUVV9E/h/dO0U42Obsb614Yaq+jLwoRU9X59oX5vkwf2m5w68xn2BS/pL/ocD2y3jNJN9rQFOHjjfC+haTgAuo0vGoftratUZhLpCnydJkjS7aiX8G7za2t/2GveykxXgxhfTZrLP7cykanZBkvfTJXNLgLOA9wDfSPJr4BTgPpMc+nrgwCQvp6vQvaqqfpLkvXTJ4qXAzyZ5vauSfIauYnkZcNrA058HDkjyF7rL4M8CPt63GawCfJSuV/elwGeT3AAcPd17XMb7/k2StwPH0n1ij6yq7yTZHvhckrE/Bt4+WWzL0Uf7AGC/JEuBm+n/CAAOBL6X5DfL0W/6cuAzSa6nqyCPtS/sCbwwyc3Ab4H3TnbwMr7WLwH2oft8vgX4A93nF7rq+XeSnEqXDF8/gxhX5H1JkqR5oKoOpPtdvyyXA/cceHwPuvE1y7vP7WQhTao76pKsXVVjMw28Dbh7Vb1+msOG0gM3ecRQfmPeuPTm1iFMsObiO7UOYYLz/nxZ6xAmuO+6d2sdwgRZdqeQxrnnahu0DmGCn159cesQJthgteG7AHbBRV9vHcKkXrvLW1uHMMGnL/vGnP5Q2PauD57137Xn/+6UKd9DklWAXwB/S3e1/zTg+VV1wcA+TwJeCzwReBDw8ap64FTndaWwheVJfVV5FeCXdNVVSZKkCVosfVtVtyR5Ld0V9MXAZ/srxHv3zx8AHEmXzF4M3MBtV4aXaWQS2iSPo5uGa9ClVfX0lfBahzGxDeOtVbVC7Q8zVVWHAIdMt1/fI/vDSZ7626r646wHJkmS1KuqI+mS1sFtBwzcL2a2jsCtRiah7ZPJlZpQDrzWrCfJs6lPWndoHYckSWpn6QJqO51vS99KkiRJtzMyFVpJkiTdpkUP7cpihVaSJEnzmhVaSZKkEbSQemhNaCVJkkaQLQeSJEnSkLBCK0mSNIIWUsuBFVpJkiTNa1ZoJUmSRtBC6qE1oZUkSRpBVUtbhzBrUguof0ILxyqrbTqU35jbbHiv1iHMCxuvsk7rECb43c3XtA5hgkVJ6xDmjbusum7rECZYJ6u1DmGCv9YtrUOY4F6L124dwqQ+cfq+rUOYYNWN7junPxTuc+ftZ/137aV/PKfJDzYrtJIkSSNo6QJqOXBQmCRJkuY1K7SSJEkjaCG1nZrQSpIkjSBbDiRJkqQhYYVWkiRpBC2klgMrtJIkSZrXrNBKkiSNoKULqEJrQitJkjSCFtLSt7YcSJIkaV6zQitJkjSCHBQmSZIkDQkrtJIkSSPIhRW0UiQ5Msn6/f3rptl3syTnL+O5g5Js09+/LMlG/f2TB459/grGuEeSc5OcneT0JLsPPLd+kkOT/CzJRUl267e/J8mv+2POTvLEFXltSZKkyVihHSJVNSuJXlX9wzK2P6S/uxnwfOArK3D6HwKHV1Ul2Q74OrB1/9zHgKOq6llJVgPWHDjuI1X1oRV4PUmStBLYQ6sJknw7yRlJLkiyV5JXJfngwPMvSbL/ZPsO7HNrNXVg29pJfpjkzCTnJdlj4OlVknyhr5gemmTN/pjjkuwySYxjVd9/Bx7WV0vfmOTHSXYY2O+kPlmdoKquq9v+B6wF3fWKJOsCDwf+q9/vpqq6akafPEmSNOeWVs36rRUT2tnzsqraGdgF2Af4FvCMgef3BA6ZbN8kd57ivH8Fnl5VOwGPAj6cJP1zWwEHVtV2wDXAq2cY69uAH1fVDlX1EeAg4CUASbYE7lRV5y7r4CRPT/Iz4L+Bl/Wb7wv8AfhckrP6toe1Bg57bZ94fzbJBjOMU5IkaVomtLNnnyTnAKcA9wTuA1yS5MF9wroVcNIy9t1iivMG+Lck5wI/ADYF7to/96uqGjvnl4HdJzl+Jr4BPDnJqnQJ6uen2rmqDquqrYGnAf/ab14F2An4VFXtCFxPlzgDfArYHNgB+A3w4RWMU5IkzZKqmvVbK/bQzoIkjwQeA+xWVTckOQ5Yna4i+xzgZ8Bhfd/psvZdlhcAGwM7V9XNSS4b2H/8d84KfSf1cXwf2KOPd0K7wjKOOyHJ5n2bxOXA5VX10/7pQ+kT2qr63dgxST4DHDHZ+fr2i70Asng9Fi1aa7LdJEmSbscK7exYD/hznxhuDTy43/4tuirm87it3WBZ+0517t/3yeyjgHsPPHevsZkE+tc4cYbxXgusM27bQcDHgdOq6k/LOjDJ/cZaHpLsBKwG/LGqfgv8KslW/a5/C1zY73f3gVM8HZh0doaqOrCqdqmqXUxmJUlauZZSs35rxQrt7DgK2LtvC/g5XSsBVfXnJBcC21TVqVPtO4WDge8mOR04m67aO+Yi4MVJPg38D92l/Zk4F7ilb3v4fFV9pKrOSHIN8Llpjn0m8KIkNwN/AfYcGCT2OuDgfoaDS4CX9ts/2A86K+Ay4JUzjFOSJK0kC2mWgyykN6MVl2QT4Dhg66pa2jgcVllt06H8xtxmw3u1DmFe2HiV8RcA2vvdzde0DmGCRbeO79R07rLquq1DmGCdrNY6hAn+Wre0DmGCey1eu3UIk/rE6fu2DmGCVTe675z+UFh3rfvO+u/aa66/pMkPNiu0IsmLgPcDbxqGZFaSJK18LafZmm0mtKKqvgh8cXBbkpcCrx+360lV9Zo5C0ySJGkGTGg1qar6HNP300qSpHmqGg7imm0mtJIkSSNoIbUcOG2XJEmS5jUrtJIkSSNoIc10ZYVWkiRJ85oVWkmSpBG0kAaFWaGVJEnSvGaFVpIkaQQtpB5aE1pJkqQRtJASWlsOJEmSNK9ZoZUkSRpBC6c+a4VWkiRJ81wWUv+ENJkke1XVga3jGGRMMzeMcRnTzBjTzA1jXMY0M8MY0yiyQqtRsFfrACZhTDM3jHEZ08wY08wNY1zGNDPDGNPIMaGVJEnSvGZCK0mSpHnNhFajYBh7m4xp5oYxLmOaGWOauWGMy5hmZhhjGjkOCpMkSdK8ZoVWkiRJ85oJrSRJkuY1E1pJkiTNaya0WlCSPGOqW+v4hk2SfWeybdQlWZzky63jmEySeyd5TH9/jSTrDEFMayTZqnUc80mSDZJs1zoOuPX7fZMk9xq7NY7nhzPZptG2SusApFn2lP7jXYCHAD/qHz8KOA741lwHlOQ8plgyu6pa/hL7O+Ct47Y9YZJtcyrJd5n4ObsaOB34dFX9dS7jqaolSTZOslpV3TSXrz2VJK+gm9R9Q2Bz4B7AAcDfNozpKcCHgNWA+yTZAXhvVT21VUx9XB8E3gf8BTgK2B54Q1U1+0MlyXHAU+l+F58N/CHJ8VX1poYxvQ54N/A7YGm/uYA5/zmVZHVgTWCjJBsA6Z9aF9hkruPpY3p8VR3V318P+A9gV+B84I1V9bsWccmEVgtMVb0UIMkRwDZV9Zv+8d2BTzYK68n9x9f0H7/Uf3wBcMPchwNJXgW8GrhvknMHnloHOKlFTONcAmwMfLV/vCfdL9gtgc8Af98gpsuAk5IcDlw/trGq/qNBLGNeAzwQ+Gkfy/8kuUvDeADeQxfTcQBVdXaSzRrGM+axVfVPSZ4OXA48GzgWaFl5X6+qrknyD8Dnqurd4/4/tvB6YKuq+mPjOABeCbyBLnk9g9sS2mto9/P83+j+IAL4MPAbukLKM4BPA09rE5ZMaLVQbTaWzPbGkqE5V1W/BEjy0Kp66MBTb0tyEvDeBmF9Bfge8AHgbQPbr62qPzWIZ7wdq+rhA4+/m+SEqnp4kgsaxXRFf1tEl/gPgxur6qak+z2fZBWmuBowR26pqqvHYhoiq/Yfnwh8tar+NAQxrtL/sf0c4J2tg+n9iu5qSHNV9THgY0leV1X7t45nErtU1Q79/Y8keXHLYEadCa0WquOSHE1X4SvguXTVmJbWSrJ7VZ0IkOQhwFotAqmqq+l+aT2vj+UuwOrA2knWrqr/axHXgI2T3Gssjr6Hb6P+uSaX/KvqX1q87jSOT/IOYI0kf0dXdf9u45jOT/J8YHGSLYB9gJMbxwTdH0U/o2s5eHWSjYE5bV2ZxHuBo4ETq+q0JPcF/qdFIEnG2hwuofv5+d/AjWPPt7wSUVX79z8vN2Mgb6mqLzYI5y795yrAuklSt03o77ikhlxYQQtWPwjsYf3DE6rqsMbx7Ax8Fliv33QV8LKqOrNhTE+h6wHbBPg9cG/goqq6f6uY+rieSNcL+r90vzjuQ5esHQe8oqo+2iCmjYF/Au5Pl/wDUFWPnutYBmJaBLwceCzd5+lo4KBq+IM9yZp01cbH9puOBt43133P4yW5E10/5jV9T/RawNotex6TrN768zImybuneLqqqsWVJACSfImuR/xsYMlATPs0iGX85+k/q+oPSe4GfLCqXjTXMaljQivNsSTr0v3fa35ZL8k5wKOBH1TVjkkeBTyvqvZqHNpYArI1XaL2s9a/+JMcAxwCvBnYG3gx8IeqajaArk/K/lpVS/rHi4E7VVWT3uxBSdaqquun33NuJDmzqnaabtscx3QxXTvUj4ETgJNa/1xI8uyq+sZ02+Y4povoxkSYsGiZLI9rQUkydjn/2iTXDNyuTXJNo5he2H98U3+p6h+Alw88bunmfvDHoiSLqupYYIfGMY3Zma4auh3wnCStKx93rqr/ovucHV9VLwMe3DimHwJrDDxeA/hBo1iArpUmyYXARf3j7ZP8Z8N47tZfHVkjyY5Jdupvj6Sr2DZTVfeja/s5j27w6DlJzm4ZE/D2GW6bS+cDd2scAwBJHtQXJcamp/uXJN9Nsm8/64EasYdWC0pV7d5/HJZBO3Bbn+wwxTTmqiRr01WHDk7ye+CWxjEt8xIj0KJnbszN/cffJHkS3QCxezSMB2D1qrpu7EFVXddf8m/pI8DjgMMBquqcJA+f+pCV6nHAS+i+VoN9oNcC72gR0Jgk9wAeStcatT1wAXBio1ieQDdgbtMkHx94al3a/0zYCLgwyancvq+3xVRwn6X7WgF8jG6mmn3ppsr7HN1sB2rAhFZayarq0/3HKQcVJXl7VX1gbqK61R50A2PeSDeN2Hq0mXVhvF0YvkuM7+srMP8I7E/3i/6NbUPi+iQ7jfVh95XIvzSOiar61bgZBJYsa985iOULwBeSPLOqvtkqjmX4P+A04N+qau/GsVxBN8/zU+mmyBpzLe2/z9/T+PUHLaqqsQR/l4GWlROHoLo+0uyhlYZE636+YZLkG8A+46Ze0zhJdgW+RpeMANwd2LOqzlj2USs9pkPpKqGfoGvJ2IfuF/9zW8XUx3Un4JlMHCnfcrDT9sDuwMOBe9HNcHB839rSKqZVq+rm6fccTf3PpiOr6nNJPgd8sqpOT7IlcHBV7do4xJFlQisNiSRnVdWOc/Ra1zL5fKWhGz287lzEsSxJxnp5h+ES41hMWwKfAu5aVdumW6b0qVX1vlYx9XGtCmzFbYPnmiYjSTaiuxT7mD6mY4DXt56oP8lRdFPVncFAxbiqPtwsKKBv+dmdru3ghV1ItVnDeB5KVxG9N13iP/Yz4b4NYxr8ebUa3ZzC17f4OdVfpfkY3dfrSmAnurl7f0X3R/g5cx2TOia00pCwQnubJI+YbHtVHT/XsYxJcjzwFrqld3fst51fVds2iOXRVfWjfmq6CaqqxRLP+1bVW1uPiF+WVl+rqSQ5HbgT3Ty9J9JNL/jLxjH9jK7FYHziPwwrhwGQ5GnAA6uqWQ90knWA+9Il/Ze3nP5NHXtopeHRfNmiYVFVxye5K90a6QCnVtXvW8YErFlVp47rDW01WOYRwI/oltwcr4A5T2iBJyb5Z7oR8UOX0AInJ3lAVZ3XOpABT6iqP7QOYpyrq+p7rYOYSlV9O8nbpt9zpcZwLXAOQJINW8aijgmtNEfSLX170hTbhjEJaCLJc4D96BZSCLB/krdU1aENw7oyyeb0lz6TPItuHfc5V1Xv7hdV+F5Vfb1FDJM4iu4S7Fr9FHmh+1w1bWNJcl4fxyrAS5NcQtfGMhbXdi3i6i1K8l/AJlX1hCTbALu17KEFjk2yH90fRYPtPi0XgBm8ErGIbtBok8vLfUvGQcBS4GXA+4DN+9af51TVT1rEJVsOpDkzjBO7D6t0Cz783VhVNt0qXT+oqu2nPnKlxnRf4EDgIcCfgUuBF7S8RJzkhKpqOSXWBEm+U1V7tI5jTJJ7T/V846/f9+imenpnVW2fZBXgrKp6QMOYJlsivKrtinifG3h4C3AZ8JkWV236qcNeDqxNt8z006rqxCQ7AftX1UPnOiZ1rNBKK1mS3eiSoI3HLaSwLrC4TVRDb9G4X1Z/pP1CML+sqsekW51rUX/JsbXvJ3kz3Qpmt67KVVV/ahXQdMlskp9U1W5zGM8v+9ed7LJw66/hRlX19SRvB6iqW5I0m+Ksj+FRLV9/MlX10tYxDFh1rG0lyR+q6kToKthJ1pj6UK1MJrTSyrca3V/zq3D7xRWuAZ7VJKLhd1SSo4Gv9o/3BI5sGA/Axf2UVJ+tqosaxzLmZXSXXl89bnuzEekzsHqj1z0TuCdddT3A+nSLZPweeEWjqc6uT3JnbmtjeTDdTAzN9KP43003lRjA8cB7Wy7J2y9AsT/dIhRFN4Du9VV1eYNwBv+wHr+C2mpzGYhuz5YDaY4kufdAtWgRsHZVNVmOdz5I8ky6X2ChG/19WON41gGeC7yU7pfaZ4Gvtfwa9hWhV9NN+1TAj4EDqqr54grL0qrNJskBwGFVdXT/+LHA44GvAx+rqgc1iGknukRtW7rlXTcGnlVV5851LAMxfbOP5Qv9pr8Htq+qZitgJfk+8BXgS/2mF9K1+/xdg1ieStf+dMO47ZsDz6yqD851TOqY0EpzJMlXgL3ppsI5g25Vrv+oqv2aBqbl1i/l+lW6Kt+hwL9W1cUN4vg6XaX/4H7T84D1q+o5cx3LTDVMaE+vql0m25bk7KraYa5j6mNYhdvmEf75EMwjPOFz0fLzs6zXbx3TdJLsX1Wvax3HKLHlQJo721TVNUleQHf5/K10ia0JbW+YF3xIshh4El2FdjPgw3SJ5MPovp5bNghrq3ED5Y7tB9QNs1bT0/0pyVvpVlaDro3lz/3XdelcBjLFPMJbJmkyj/CAvyTZfaw3tB/V37rif2WSF3JbC9Lz6Prqh5mDw+aYCa00d1btp3Z5GvCJqro5iZdIBlTVOtPv1cz/AMcC+1XVyQPbD+0rti2cleTBVXUKQJIHASdNc8xK188ssEVV/aBvi1hlYBDd3zcK6/l0vaHfpkuqT+y3LQbmuqI9jPMIj3kV8IW+lzbAn4CXNIwHul7xTwAfofv8nNxvk25ly4E0R5LsQ1eVPYeu0ncv4MtV9bCmgWlGkqxdVddN8fzbq+oDcxzTRXSXq/+v33Qv4CK6imOTOVaTvALYC9iwqjZPsgVdX+/fznUswy7J4qpqOqvBsiRZF8A+/xXjlIxzz4RWaijJKlXVarUpzaIWv8CGcY7VJGcDDwR+OrBE8Hmt5lZN8tGqekOS7zJJO0tVPbVBWAAk+T+6BSkOAX5UQ/ALOcn6wIvo2mpuvYpbVfs0CokkX6Cb1eCq/vEGwIeramirtEnOGvv+19yw5UCaI8uaDofG0/Ro1sx5b2jLRQGmcGNV3TS2RHA/6KllojY2Mv5DDWNYlq3o2g5eA/xXkiPoZs44sWFMRwKnAOcxx73FU9huLJkFqKo/J2maLCbZtqrOn2KXj81ZMAKs0EpzZhinw9Hs8RJjJ8kHgavoqnyvo5tW7MKqemfLuIZdX3X8GN10VM0WXBnG7+N+oOMjq+rP/eMNgeMbr6h2It28s58HvjKYcKsNE1ppjszHqWc0c15i7PRzLL8ceCxd1fpo4KDWl9P70frvAe5Nd3VybOaMpotQJHkE3YwLTwBOAw6pqm82jOeNwHXAEcCNY9tbrj6X5EV0ixgcSlftfw7w/qr60pQHrvy4tqAbnPZs4FTgc1X1/ZYxjTITWmmOJPkJ8JZx0+F8aC6XAdXKk+QdVfVvreNorV8a+K9jg536abHuNH4i+gZx/Qx4I91UebcOxKqqZtM/JbkUOJtucYfDq+r6qY9Y+ZK8Bng/XZV9LEEYhsR/G+DRdH+I/LCqLhx4boOx6m2DuBbTzVzzcbo5oQO8o/HUayPJhFaaI0l2oGs3WK/f9GfgxS1XBdLMJdkYeAUTB8sM7cCUFpKcAjxmbEaIJGsDx1TVQxrH9dMWq4FNJcm6wzaLQJL/BR5UVVe2jmWmGg3I3I5uTuonAd8H/quqzkyyCfCTqppywKZmn4PCpLlzEfBBYHO6FaaupvvL3oR2fvgO3dKyP2CgwqcJVh+c3qyqrkuyZsuAescm2Y9ujtfBS+lntguJuyU5DLhrVW3bJ0lPrar3NYzpAqBpNX0FtFis4xPAZ+iqsbcuPFFVVyT55wbxjDwTWmnufIfuMt6ZwK/bhqIVsGZVvbV1EPPA9Ul2GksUk+xM+5WmAMaqs4PL3xbdZexWPgO8Bfg0QFWd2y+R3TKhXQKcneRYbp/4N5u2awZaXGr+1vge3iSvr6qPte7tHVUmtNLcuUdVPb51EFphRyR5YlUd2TqQIfcG4BtJrugf351u0FNTVfWoqZ5P8uKq+sJU+6wEa1bVqWNTnPVaz0v97f6mqb0I+Oi4bS/B6bqaMaGV5s7JSR5QVee1DkQr5PXAO5LcBNzcb6uqWrdhTEOnqk5LsjXdHKsBflZVN09z2DB4PbdNqTdXrkyyOX2FMcmzgN/McQy3M11Sn+SbVfXMuYpnhuas5SDJ8+iWTL5PksMHnloHaDbAUCa00kqX5Dy6X1irAC9NcgndpbyxaYPmfHlSLb+qWqd1DMMsyaOr6kdJxs+rvEUS5sGo7xZ9mK8BDgS2TvJr4FLgBQ3iWB5zPttBn/RfXlU3JnkksB3wxYG5X+dyWeWT6f7o2Aj48MD2a3E8RFMmtNLK9+TWAWh2JHkqt630dlxVHdEyniHzCOBHdCtfjVd0g7GG2Zz2YfbTPb2qqh7TT3W2qKquncsYVlCLftVvArskuR/wX8DhwFeAJ8LczpHbr873S8DpFoeMCa20kg3p8qRaTkn+HdgVOLjf9Poku1fV2xqGNTSq6t393X8Ym4N2npnTCm1VLekHzDEM888OuaVVdUuSpwMfrar9k5zVIpAkJ1bV7kmu5fbJ/dgVN1uQGjGhlaSZeSKwQ1UtBUjyBeAswIT29i5NchRwCPCj1iuELYeTGrzmWX0f5jeAW5PaIW/PaNGacXPfu/pibrsCsGqDOKiq3fuPtiANGRNaSZq59YGxy5vrTbHfKNuKLul4DfBfSY4Avja2Ql4rSdanG5m+GbdfGGOf/uNrG4S1Id1AosGpw5q1Z/RtEF+oqhdOsVuLqeteCuxNt9ztpUnuA3y5QRwk2XCq51suETzqXClMkmagrxD9O3AsXZXq4cDbq+prTQMbYkk2oJvG6AVVtbhxLCcDpwDnAUvHtjeYqmvGkry9qj4wx695NPCUqrppLl93vuiXKy4mr1Q3XyJ4lJnQStIMJbk7XR9tgJ9W1W8bhzSUkjyCbu7ZJwCnAYdU1TcbxzTny6PeUY2WdP00sBPdwKvBNoj/mMs4xsU0NlPMoKuB04H3VZXTZcmWA0maSpKtq+pnScYSi8v7j5sk2aTx0qlDp69gnQ18HXjLEA14+lKSVwBHcPsVsIb5EnGLftUr+tsiurlVh8H36FYw+0r/+Ll0n5urgc8z+cwaK8UkPw9ux58H7VihlaQpJDmwqvbqlwIdr6qq5dKpQ6XvwXxnVb23dSzjJXkN8H665afHfvEN9SXillXlJGsNyx8jSU6qqodOti3JeVX1gDmMxZ8HQ8qEVpJmIMnqVfXX6baNuiTHTrfMbAtJ/hd4UFVd2TqWmUpyVlXtOMevuRvdXK9rV9W9kmwPvLKqXj2XcYyL6Rxgr6r6af/4gcBnqmr7Fp8jDadFrQOQpHni5BluG3UnJ/lEkocl2Wns1joo4ALghtZBACTZt//47Gl2/cYchDPeR4HH0S/jWlXncNtiIq38A3BQkkuTXAYcBLyiX5BiTgfNjUmyepI3JflWkm8meUOS1VvEoo4VWkmaQpK7AZvSTRP0fG7ra1wXOKCqtm4V2zAa1kuxSQ4D7k83S8VgD+0+DWI5j27g1U+HbaBakp9W1YMGK59Jzqmq7YcgtvXo8parhiCWr9Mtdzs2fdjzgA2qaro/UrSSOChMkqb2OOAlwD3o1m4fS2ivAd7RKKahNYztBr1v97dhcBRwJbBWkmvoV5ka+9h4talfJXkIUElWA/YBLmoYD0nuBDyTfg7hpPsv2LhXe6txSf6xfWuEGrFCK0kzkOSZraeemg+S3BX4N2CTqnpCkm2A3arqvxqHNnSSfKeq9mgdx6AkG9HNHfwYurbEo4HXt5waq1957mrgDLrZDgCoqg83jOnzdFdoTukfPwh4ccte41FnQitJM5Dk34APjl3u7BcN+Meq+uemgQ2ZJN8DPkc328H2SVYBzprLkejLiGtsQvzbaT3LQf8HwK79w59W1R9axjOMkpxfVdu2jgNuNyfuqnSr4v1f//jewIXDEucosuVAkmbmCVV1a4tBVf05yRMBE9rb26iqvp7k7QBVdUuSJdMdNAd2Gbi/OvBsuqVnm+kHhX0IOI6u3WD/JG+pqkMbxnRfugrtg+kStZ8Ab6yqS1rFRDfQ8AFVdV7DGMY8uXUAmpwJrSTNzOIkd6qqGwGSrAHcqXFMw+j6JHemr4YmeTDd5eKmJrlk/tEkJwLvahFP75+BXavq9wBJNgZ+ADRLaOkWL/gk8PT+8XOBrwIPahYR7A68pK+y38htvcbbzXUgVfXLwcdJ7kL3B5IaM6GVpJn5MvDDJJ+jS9ZeBnyhbUhD6U10y6ZunuQkYGPgWW1DgnFThy2iq9i2Xglr0Vgy2/sj7afTTFV9aeDxl5O8tlk0nSc0fv0JkjyVbpDoJsDv6VoOLqKbSUMNmNBK0gxU1Qf7/rm/pasQ/WtVHd04rGG0OV0Cck+6kekPYjh+1wwOILoFuAx4TptQbnVUkqPpKqAAewJHNowHutH6bwO+RveH257AfyfZEOZ2qeAk61bVNXTTYw2bf6Vry/hBVe2Y5FF0U3epEQeFSZJmTZJzq2q7JLvTzXbwYeAdVdXykvXQSvIMukvqAU6oqsMax3PpFE/P6VLBSY6oqicPDOjLwNNNly1OcnpV7dJP1bVjVS1NcmpVPbBVTKPOhFaSZiDJtdw2Sn41ulHO1zeeM3TojE3In+QDwHlV9ZVhWJ40yfrAi+jnMh3b3mJhhZlK8pOq2q11HIOS/F1Vfb91HK0l+QHwNLqVyjaiazvYtaoe0jKuUda6V0eS5oWqWqeq1u1vq9NdTv9E67iG0K+TfJrucv6R/aT4w/C75ki6ZPY8uvlMx27DbBgHG+071y+Y5Icz2TbH9gD+AryRbqGM/wWe0jSiETcMfU2SNO9U1bf7XkPd3nOAxwMfqqqrktwdeEvjmABWr6o3tQ5iOQ3jJdRMv8ssvVCyOrAmsFE/7/PgstObzFUck6mq6wceOjh0CJjQStIM9L2OY8ZGyQ9jwtFUVd0AfGvg8W+A37SL6FZfSvIK4Ai6qZ+AuR3ktEDM5ff8K4E30CWvZ3D7Zac/OYdx3Gpc69HtnqL9ssUjzYRWkmZm8HLi2Cj5oVq2VFO6CdgPeCe3JSQFNF0pbBpzVg0dRlX1MeBjSV5XVfu3jge61qPWMWhyJrSSNI0ki4Fzq+ojrWPRCnsTcL+qurJ1IHDr99TRVfWYKXb7+7mKZzlc1uA1f5tknaq6Nsk/AzsB76uqM+c6kLGpxMamMRvPin87w9CoL0lDraqWAE9tHYfukAuAG1oHMab/nrohyXpT7HP+HIYEdMvxJlmnv//PSb41uChFVT1j2UevNP+vT2Z3Bx5H17P6qQZxQLeSGnQtEKdz+wGGpzeKSVihlaSZOjnJJ4BDgFsHhLSoEmmFLAHOTnIst++hbTlt11+B85J8n9t/T7WM6f9V1TcGkscP0SWPLecRXtJ/fBLwqar6TpL3tAikqp7cf7xPi9fXspnQStLMjM0v+d6BbQU8ukEsWn7f7m/D5L/72zAZmuRxwNhUcI8B9h2WqeD6mRe2YGB6tao6oV1Eo82FFSRpBpLct6oumW6btDySrAHcq6p+3joW6FbnAn5NlzzuTDfX6qlVtX3DmNakmwruvKr6n34quAdU1TENY/oH4PXAPYCz6ZbB/UlV+QduI83/wpGkeeLQSbZ9Y86j0HJJ8vX+43lJzh1/axzbU+iSoaP6xzskObxlTHTzCB8NPL6qrgI2pNE8wknGpsBaHTgO+GM/GOtG2vervh7YFfhlVT0K2BH4Q9uQRpstB5I0hSRbA/cH1hs3F+26DOdKTrq91/cfn9w0ism9B3ggXbJGVZ2dpHVv5tuAHwNXQPN5hL9C93U7g669Z3Aas9ZTrv21qv6ahCR3qqqfJdmqYTwjz4RWkqa2Fd0v1fW5/Vy01wKvaBGQZq5PyKiqX7aOZRK3VNXVye2mm23dB3gZ8Dzg4/0iAj8GTqiq78x1IEM+AOvyJOvT9WV/P8mf6f8IUBv20ErSDCTZrap+0joOrZi+ur4vcBe6Sl/zlZ2S/BfwQ7qq6DOBfYBVq2rvVjGNSXI3uvaDNwMbtFhQYHC6sMkMywwjSR4BrAccVVU3tY5nVJnQStIMJPkg8D66QTJHAdsDb6iqLzcNTDOS5GLgKVV1UetYxvSDnd4JPJYuwT4a+Neq+mvDmA4CtgF+R1edPRE4s6puaRDLsVM8XS0HYCX5GHBIVZ3cKgbdngmtJM1AkrOraockTweeBrwROLbl6G/NXJKTquqhreOYTD/4qarq2iGI5TBgE+BC4Hi6doOhnskjyd9V1ffn+DVfDOwJbAkcRpfcth6oNtJMaCVpBpJcUFX3T/IZ4JtVdVSSc0xo54e+onY3up7HwYUVvtUwpl2BzwJjl/OvBl5WVWe0imlMkr+hW1jhjcDiqrpH45CWKcmZVTVle8JKfO0N6dpFnks3/doWLeKQg8Ikaaa+m+RndC0Hr06yMd1KT5of1qVb+vaxA9sKaJbQAv8FvLqqfgzQr871OWC7VgEleTLwMODhwAbAj+haD4ZZpt9lpbkfsDWwGV1VW41YoZWkGepXBrqmqpb0/Y/rVtVvW8el+WmyNojWrRFJPgmcAPy4qubFqP0WFdok+wLPAP6Xbjnsw/p5e9WIFVpJmrm/ATZLMviz84utgtH0kvxTVX0wyf5MMiVWVe3TIKax5OvUfknXr9LFtif9nLStVNVrktybbmDYFf1KZqsMQ3/vkLmUbjns+wJ3ArZL4tK3DZnQStIMJPkSsDndyk5j690XJrTDbmxWg9NpP8frmA+Pe/zugftNY0zyCmAvuhXCNqdb2vUA4G8bxnSnqrpxim2XzX1ULKFrx7jd0reAS982YsuBJM1AkouAbcofmvNSPwDrHXS9jmPFnKqqZv2qwyjJ2XSrl/20qnbst51XVQ9oGNOEloKWA8H61z+PbunbU/rZT7YG/qWq9mwV06izQitJM3M+3Sj5VsuA6o75MvAW4DxgaeNYAOhXmnoRt0+ym7RBDLixqm4aW72sb69p8kdcv7jDpsAaSXbktsFf6wJrtohpgEvfDhkTWkmamY2AC5Ocyu2nfXpqu5C0HP5QVYe3DmKcI4FTGKIkGzg+yTvoksi/A14NfLdRLI8DXkJ3Wf/D3JbQXktXbW/JpW+HjC0HkjQD/fKWE1TV8XMdi5Zfkr8Fnke31OywzEPb9LL5ZJIsAl7O7VcvO6hlq02SZ1bVN1u9/nRc+nY4mNBK0gwluStd3xzAqVX1+5bxaOaSfJluvtALuK0aWlX1soYxvRG4DjiC2yfZf2oV0zBK8nq6+XmvBT4D7AS8raqOaRqYhooJrSTNQJLnAPvRTasUusnn31JVh7aMSzPTemDTZJK8Bng/cBW39alWVd23QSxfr6rn9IOdJpverOViD+dU1fZJHge8Bvh/wOeGrbqttuyhlaSZeSew61hVtl8p7AeACe38cEqSbapqmFZzehNwv6q6snUgwOv7j09uGsXkxnpnn0iXyJ6TsVFrUs+EVpJmZtG4FoM/AotaBaPltjvw4iSX0l3eD+2n7bqAbjne5qpqbPaOZwBfr6pft4xnnDOSHAPcB3h7knUYnkF0GhImtJI0M0clOZpuVSfoVnU6smE8Wj6Pbx3AJJYAZyc5ltv30Lactmtd4JgkfwK+BhxaVb9rGA90g9R2AC6pqhuS3Bl4aduQNGzsoZWkKSS5H3DXqjopyTPoKn0B/gwcXFX/2zRAzVtJXjzZ9qr6wlzHMl6S7ej+aHsmcHlVPaZxPE8FHt4/PL6qWk0lpiFlQitJU0hyBPCOqjp33PZdgHdX1VPaRCatPP2iBs8Gngus03hQ2L/TzS5ycL/pecDpVfX2VjFp+JjQStIUkpxfVdsu47mhGzmv+aPv551sRoE5n+VgTJJX0VVmN6Yb8HhI64F0Sc4Fdqiqpf3jxcBZLlusQfbQStLUVp/iuTXmLAotRLsM3F+driK6YaNYxtwbeENVnd04jvHWB8bm512vYRwaUo7QlaSpnZbkFeM3Jnk5cEaDeLRAVNUfB26/rqqPAo9uHNPbgLWTvBS66emS3KdlTMAHgLOSfD7JF+j+3/1b45g0ZGw5kKQp9KuDHQbcxG0J7C7AasDTq+q3rWLT/JZkcGGARXTfV6+qqu0bhUSSd/dxbFVVWybZBPhGVT20VUx9XHen66MN8FP/32k8E1pJmoEkjwLGemkvqKoftYxH818/XdfYL+FbgMuAD1XVLxrGdDawI3BmVe3Ybzu3db9qkk3p2iFubZWsqhPaRaRhYw+tJM1AVR0LHNs6Di0oT6CbFmszbvt9/Fzgva0CAm6qqkpSAEnWahgLfQz70g1Uu4DbFlQowIRWtzKhlSSpjW8DVwFnAn9tGgnQLyd7RJJPA+v3veMvAz7TNjKeRtcCceN0O2p0mdBKktTGPapqaFYw6yuzTwPeClwDbAW8q6q+3zQwuARYlYHV1KTxTGglSWrj5CQPqKrzWgcy4CfAVVX1ltaBJNmfrrXgBrolgn/I8CwRrCHjoDBJkhpIciFwP+BSukQtdIXSlqtyXQhsCfwSuH5se4uYlrU08JhhWCJYw8OEVpKkBpLce7LtVfXLuY5lzDDGNJ0k36yqZ7aOQ23ZciBJUgPDmCQOY0wz0GypYA0PVwqTJEnzmZeaZUIrSZKk+c2EVpIkzWdpHYDaM6GVJEnz2VtbB6D2nOVAkiQNnSTnMUV/bMvpzTR8nOVAkiQNoyf3H1/Tf/xS//EFdIstSLeyQitJkoZWkpOq6qHTbdNos4dWkiQNs7WS7D72IMlDgLUaxqMhZMuBJEkaZi8HPptkPbqe2quBl7UNScPGlgNJkjT0kqxLl7dc3ToWDR9bDiRJ0tBKctck/wUcUlVXJ9kmyctbx6XhYkIrSZKG2eeBo4FN+se/AN7QKhgNJxNaSZI0zDaqqq8DSwGq6hZgSduQNGxMaCVJ0jC7Psmd6RdZSPJguoFh0q2c5UCSJA2zNwGHA5snOQnYGHh225A0bJzlQJIkDa0kd6JrMdgKCPBzYFFV3dg0MA0VE1pJkjS0kpxZVTtNt02jzZYDSZI0dJLcDdgUWCPJjnTVWYB1gTWbBaahZEIrSZKG0eOAlwD3AP5jYPu1wDtaBKThZcuBJEkaWkmeWVXfbB2HhpsJrSRJGmpJngTcH1h9bFtVvbddRBo2zkMrSZKGVpIDgD2B19H10T4buHfToDR0rNBKkqShleTcqtpu4OPawLeq6rGtY9PwsEIrSZKG2V/6jzck2QS4GbhPw3g0hJzlQJIkDbMjkqwP7AecSbcE7kFNI9LQseVAkiTNC/2qYatX1dWtY9FwMaGVJElDJ8kzpnq+qr41V7Fo+NlyIEmShtFTpniuABNa3coKrSRJkuY1K7SSJGloJXnXZNtdWEGDTGglSdIwu37g/urAk4GLGsWiIWXLgSRJmjf6mQ4Or6rHtY5Fw8OFFSRJ0nyyJnDf1kFouNhyIEmShlaS8+hmNQBYDGwM2D+r27HlQJIkDa0k9x54eAvwu6q6pVU8Gk62HEiSpGF2d+BPVfXLqvo1sHqSB7UOSsPFCq0kSRpaSc4Cdqo+YUmyCDi9qnZqG5mGiRVaSZI0zFID1beqWopjgDSOCa0kSRpmlyTZJ8mq/e31wCWtg9JwMaGVJEnDbG/gIcCvgcuBBwF7NY1IQ8ceWkmSJM1r9qBIkqShk2R/bpt/doKq2mcOw9GQM6GVJEnD6PTWAWj+sOVAkiRJ85oVWkmSNLSSbAy8FdgGWH1se1U9ullQGjrOciBJkobZwcBFwH2AfwEuA05rGZCGjy0HkiRpaCU5o6p2TnJuVW3Xbzu+qh7ROjYND1sOJEnSMLu5//ibJE8CrgDu0TAeDSETWkmSNMzel2Q94B+B/YF1gTc0jUhDxx5aSZI0zJ5N1yJ5flU9Cvg74OmNY9KQMaGVJEnDbLuqumrsQVX9CdixXTgaRia0kiRpmC1KssHYgyQbYsukxvEbQpIkDbMPAycnOZRuKdznAO9vG5KGjdN2SZKkoZZkG+DRQIAfVtWFjUPSkDGhlSRJ0rxmD60kSZLmNRNaSZIkzWsmtJIkSZrXTGglSZI0r5nQSpIkaV77/zARjRmz6C6EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Buscamos correlaciones entre variables con un mapa de calor (heatmap)\n",
    "f, ax = plt.subplots(figsize=(10,8))\n",
    "corr = airbnb.corr()\n",
    "sns.heatmap(corr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d96903b",
   "metadata": {},
   "source": [
    "**Interpretación:** De acuerdo al Mapa de calor, podemos apreciar que únicamente las variables price(precio) y service_fee(pago de servicios) tienen una alta correlación.\n",
    "En segundo lugar, las variables reviews_per_month (reviews por mes) y number_of_reviews (número de reviews) tienen menos del 0.6 lo cual no es una buena referencia a tomar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eaace06",
   "metadata": {},
   "source": [
    "## Damos respuesta a las Hipotesis planteadas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834b6506",
   "metadata": {},
   "source": [
    "#### 1. Cuánto más costoso un alquiler, es mayor el pago de servicio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a2874e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='service_fee'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Validamos la alta correlación existente entre el precio y el service_fee\n",
    "airbnb.plot(kind = \"scatter\", x= \"price\" , y=\"service_fee\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6d4a1a",
   "metadata": {},
   "source": [
    "**Interpretación:** Este gráfico da respuesta a la hipótesis 1, y la confirma. podemos apreciar la clara correlación existente entre la variable price(precio) y service_fee (pago de servicios), por ende, a mayor costo de alquiler, mayor es el pago del servicio. (Tener en cuenta que correlación no expresa causalidad necesariamente)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95350609",
   "metadata": {},
   "source": [
    "### Hipótesis 2 y 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274cdd9c",
   "metadata": {},
   "source": [
    "#### 2. Los alquileres en promedio son más costosos en Manhattan que en Brooklyn.\n",
    "#### 3. Los hoteles no son las ofertas más elegidas, sino más bien alojamientos de particulares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b0751bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"neighbourhood_group\", y=\"price\", data=airbnb, kind=\"box\", col=\"room_type\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b90fcda",
   "metadata": {},
   "source": [
    "**Interpretación:**\n",
    "- Este gráfico da respuesta a la hipótesis 2, y la rechaza. Ya que en promedio los alquileres en Brooklyn son más costosos que en Manhattan.\n",
    "- Este gráfico da respuesta a la hipótesis 3, y la confirma. Ya que las habitaciones de hotel sólo se encuentran en 3 distritos.\n",
    "\n",
    "Asimismo, podemos apreciar que a excepción de las habitaciones de hotel los otros tipos de habitaciones tienen una mediana de precio que difiere muy poco tanto para aumento o reducción de precio para los 5 distritos, asimismo, podemos verificar que en Brooklyn los costos de las habitaciones de hotel así como tienen un monto mínimo más alto, tienen un monto máximo menor que Manhattan y Queens, por último, en Staten Island el 25% de la renta mínima para habitaciones compartidas es mayor que la de los demás distritos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583728d7",
   "metadata": {},
   "source": [
    "### Hipótesis 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b78b5d",
   "metadata": {},
   "source": [
    "#### 4. Las políticas de cancelación son estrictas, ya que los hosts no quieren perder la renta por alquiler de su inmueble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "16ae52d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "moderate    34343\n",
       "strict      34106\n",
       "flexible    34074\n",
       "Name: cancellation_policy, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_politica= airbnb['cancellation_policy'].value_counts()\n",
    "serie_politica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e6ff3b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Politicas de cancelación')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize =(10, 6))\n",
    "politica= [\"moderate\", \"strict\", \"flexible\"]\n",
    "ax.pie(serie_politica, labels = politica,autopct='%1.1f%%')\n",
    "ax.set_title('Politicas de cancelación')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7da2ac",
   "metadata": {},
   "source": [
    "**Interpretación:** Este gráfico responde a la hipótesis 4, y la rechaza. Ya que las políticas de cancelación son bastante parejas y no hay una marcada rigidez por parte delos host."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258d56a7",
   "metadata": {},
   "source": [
    "### Hipótesis 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23024cb0",
   "metadata": {},
   "source": [
    "#### 5. Las construcciones son modernas, para que sean más atractivas al público."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1475fb22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Desagregamos por Año de construccion, apreciando de primera impresión la no existencia de muchas diferencias por año \n",
    "fig, ax = plt.subplots(figsize = (13, 6))\n",
    "frec_construccion = airbnb['Construction_year'].value_counts()\n",
    "df_frec_construccion = pd.DataFrame({'construccion': frec_construccion.index.tolist(), 'cantidad':frec_construccion.tolist()})\n",
    "sns.barplot(x='construccion', y='cantidad', data = df_frec_construccion)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b86a2aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Desagregamos por Año de construccion a un nivel más amplio a fin de apreciar de mejor manera las diferencias por año\n",
    "frec_construccion = airbnb['Construction_year'].value_counts()\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.pointplot(x=frec_construccion.index, y=frec_construccion.values, color='blue')\n",
    "plt.xlabel(\"Año de Construcción\")\n",
    "plt.ylabel(\"Cantidad\")\n",
    "plt.title(\"Año de Construcción\", size=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc165769",
   "metadata": {},
   "source": [
    "**Interpretación:** Este gráfico responde a la hipótesis 5, y la confirma. Podemos ver que todos los host son relativamente nuevos, ninguno excede los 20 años de antiguedad. Todos son desde 2003 en adelante y de proporciones bastante parejas.Asimismo, podemos apreciar que desde el 2003 al 2022 el número de construcciones por año no bajan de las 5,000, teniendo como años pico el 2006, 2008, 2012, 2014 y 2019."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a28cf1b5",
   "metadata": {},
   "source": [
    "### Hipótesis 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34955c9d",
   "metadata": {},
   "source": [
    "#### 6. La demanda de cuartos compartidos es mayor en Manhattan y Brooklyn debido a la cercanía al centro del movimiento financiero y turístico de Nueva York."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8d9f5d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (13, 6))\n",
    "sns.stripplot(x=\"neighbourhood_group\", y=\"price\", hue=\"room_type\", data=airbnb, jitter=0.4, dodge=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2da4de3a",
   "metadata": {},
   "source": [
    "**Interpretación:** El presente gráfico da respuesta la Hipótesis 6 y la confirma, asimismo, nos permite apreciar como se distribuyen las rentas de los diferentes tipos de habitaciones Airbnb en los 5 distritos de Nueva York, por lo tanto, vemos que:\n",
    "1. **Bronx:** predominan rentas en habitaciones privadas, casa o departamentos, en mucho menor cantidad las habitaciones compartidas.\n",
    "2. **Brooklyn:** predominan rentas en habitaciones privadas, casa o departamentos y habitaciones compartidas, son contadas las habitaciones de hotel\n",
    "3. **Manhattan:** predominan rentas en habitaciones privadas, casa o departamentos y habitaciones compartidas, a comparación de los otros distritos son mayores las habitaciones de hotel que se rentan\n",
    "4. **Queens:** predominan rentas en habitaciones privadas, casa o departamentos, habitaciones compartidas en menor cantidad, son contadas las habitaciones de hotel\n",
    "5. **Staten Island:** predominan rentas en habitaciones privadas, casa o departamentos en menor cantidad que los otros distritos, son contadas las habitaciones compartidad y no se rentan habitaciones de hotel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8fcbc0b",
   "metadata": {},
   "source": [
    "### Hipótesis 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ce5aae",
   "metadata": {},
   "source": [
    "#### 7. Las personas que están más dispuestas a pagar por habitaciones privadas o dentro de casas y/o apartamentos son más críticas al momento de dar un review del servicio ofrecido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d444ed3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"review_rate_number\", data=airbnb, kind=\"count\", col=\"room_type\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e5db4d",
   "metadata": {},
   "source": [
    "**Interpretación:** El presente gráfico da respuesta a la Hipótesis 7 y la confirma ya que podemos apreciar que existen mayores reviews tanto positivos como negativos en los alquileres de habitaciones privadas y habitaciones dentro de una casa y/o apartamento."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284fd273",
   "metadata": {},
   "source": [
    "### iv) Recomendaciones"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39af4640",
   "metadata": {},
   "source": [
    "Como recomendaciones podemos indicar qué, de los 05 distritos de Nueva York (Manhattan, Staten Island, Brooklyn, Bronx y Queens) existe mayor preferencia por la renta de habitaciones bajo AIRBNB en los distritos de Manhattan, Brooklyn y Queens, entendiéndose que son los distritos mas céntricos y de mejor acceso para realizar actividades de negocios o turismo en Nueva York y dentro del tipo de habitaciones que suelen rentar en mayor volúmen podemos ver una indiscutible preferencia por las habitaciones privadas y las que se rentan dentro de casas o departamentos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdefab5a",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72716137",
   "metadata": {},
   "source": [
    "### Posteriormente procedo a realizar Modelos Estadísticos que nos permitan hacer mayor análisis a las variables con alto nivel de correlación y obtener respuestas que complementen las hipótesis respondidas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ffddac",
   "metadata": {},
   "source": [
    "## Modelo de Regresión Lineal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c43177b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importamos las librerías necesarias para los Modelos\n",
    "import sklearn as sk\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0012384c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>Construction_year</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>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001254</td>\n",
       "      <td>80014485718</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>2020</td>\n",
       "      <td>193.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002102</td>\n",
       "      <td>52335172823</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>2007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002403</td>\n",
       "      <td>78829239556</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>2005</td>\n",
       "      <td>124.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002755</td>\n",
       "      <td>85098326012</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>2005</td>\n",
       "      <td>74.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003689</td>\n",
       "      <td>92037596077</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>2009</td>\n",
       "      <td>41.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id      host_id       lat      long  Construction_year  service_fee  \\\n",
       "0  1001254  80014485718  40.64749 -73.97237               2020        193.0   \n",
       "1  1002102  52335172823  40.75362 -73.98377               2007         28.0   \n",
       "2  1002403  78829239556  40.80902 -73.94190               2005        124.0   \n",
       "3  1002755  85098326012  40.68514 -73.95976               2005         74.0   \n",
       "4  1003689  92037596077  40.79851 -73.94399               2009         41.0   \n",
       "\n",
       "   minimum_nights  number_of_reviews  reviews_per_month  \\\n",
       "0            10.0                9.0               0.21   \n",
       "1            30.0               45.0               0.38   \n",
       "2             3.0                0.0                NaN   \n",
       "3            30.0              270.0               4.64   \n",
       "4            10.0                9.0               0.10   \n",
       "\n",
       "   calculated_host_listings_count  availability_365      y  \n",
       "0                             6.0             286.0  966.0  \n",
       "1                             2.0             228.0  142.0  \n",
       "2                             1.0             352.0  620.0  \n",
       "3                             1.0             322.0  368.0  \n",
       "4                             1.0             289.0  204.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Definimos las Features numéricas\n",
    "df_num = airbnb.select_dtypes('number')\n",
    "df_num['y'] = airbnb['price']\n",
    "df_num = df_num.drop('price', axis=1)\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "152883e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102599"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fec4049",
   "metadata": {},
   "source": [
    "***Valores duplicados***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1708867f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Eliminamos valores duplicados\n",
    "df_num.drop_duplicates(inplace=True) #df_num = df_num.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ce0baf0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102058"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f21e121e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'host_id',\n",
       " 'lat',\n",
       " 'long',\n",
       " 'Construction_year',\n",
       " 'service_fee',\n",
       " 'minimum_nights',\n",
       " 'number_of_reviews',\n",
       " 'reviews_per_month',\n",
       " 'calculated_host_listings_count',\n",
       " 'availability_365',\n",
       " 'y']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Nos quedamos únicamente con las columnas:\n",
    "columnas = df_num.columns.tolist()\n",
    "columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3d43ca3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          966.0\n",
       "1          142.0\n",
       "2          620.0\n",
       "3          368.0\n",
       "4          204.0\n",
       "           ...  \n",
       "102053     696.0\n",
       "102054     909.0\n",
       "102055     387.0\n",
       "102056     848.0\n",
       "102057    1128.0\n",
       "Name: y, Length: 102058, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num['y']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f26fc6a",
   "metadata": {},
   "source": [
    "**ii) Elegimos un Algoritmo de Regresión para entrenar con los datos elegidos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6c7e1555",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separamos las caracteristicas/features del target\n",
    "x = df_num[columnas].drop('y', axis=1)\n",
    "x = x.filter(items=['Construction_year',\n",
    " 'service_fee',\n",
    " 'minimum_nights',\n",
    " 'number_of_reviews',\n",
    " 'reviews_per_month',\n",
    " 'calculated_host_listings_count',\n",
    " 'availability_365'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a2d8a54f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reemplazaremos las columnas de x por la media\n",
    "for var_med in x:\n",
    "    x[var_med].value_counts()\n",
    "    x.loc[x[var_med].isnull(), var_med] = x[var_med].mean()\n",
    "    x[var_med]=x[var_med].round().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "770ca72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num['y'].value_counts()\n",
    "df_num.loc[df_num['y'].isnull(), 'y'] = df_num['y'].mean()\n",
    "y = df_num['y'].round().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cf8d1a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 102058 entries, 0 to 102057\n",
      "Data columns (total 7 columns):\n",
      " #   Column                          Non-Null Count   Dtype\n",
      "---  ------                          --------------   -----\n",
      " 0   Construction_year               102058 non-null  int32\n",
      " 1   service_fee                     102058 non-null  int32\n",
      " 2   minimum_nights                  102058 non-null  int32\n",
      " 3   number_of_reviews               102058 non-null  int32\n",
      " 4   reviews_per_month               102058 non-null  int32\n",
      " 5   calculated_host_listings_count  102058 non-null  int32\n",
      " 6   availability_365                102058 non-null  int32\n",
      "dtypes: int32(7)\n",
      "memory usage: 3.5 MB\n"
     ]
    }
   ],
   "source": [
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1a6f0efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 102058 entries, 0 to 102057\n",
      "Series name: y\n",
      "Non-Null Count   Dtype\n",
      "--------------   -----\n",
      "102058 non-null  int32\n",
      "dtypes: int32(1)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "y.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1ab9f086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tamaño del set de entrenamiento: (81646, 7)\n",
      "Tamaño del set de testeo: (20412, 7)\n"
     ]
    }
   ],
   "source": [
    "# train test split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y,\n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=42)\n",
    "\n",
    "print('Tamaño del set de entrenamiento:', x_train.shape)\n",
    "print('Tamaño del set de testeo:', x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7b960e78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Construction_year</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>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11987</th>\n",
       "      <td>2015</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11418</th>\n",
       "      <td>2012</td>\n",
       "      <td>119</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72598</th>\n",
       "      <td>2022</td>\n",
       "      <td>213</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31228</th>\n",
       "      <td>2006</td>\n",
       "      <td>126</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22915</th>\n",
       "      <td>2021</td>\n",
       "      <td>125</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6265</th>\n",
       "      <td>2010</td>\n",
       "      <td>154</td>\n",
       "      <td>3</td>\n",
       "      <td>114</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54886</th>\n",
       "      <td>2021</td>\n",
       "      <td>213</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76820</th>\n",
       "      <td>2013</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>2007</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>2017</td>\n",
       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81646 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Construction_year  service_fee  minimum_nights  number_of_reviews  \\\n",
       "11987               2015           41               1                  1   \n",
       "11418               2012          119               2                 12   \n",
       "72598               2022          213               1                  4   \n",
       "31228               2006          126               1                  3   \n",
       "22915               2021          125               3                  3   \n",
       "...                  ...          ...             ...                ...   \n",
       "6265                2010          154               3                114   \n",
       "54886               2021          213               1                 14   \n",
       "76820               2013           35               2                  0   \n",
       "860                 2007           63               1                  0   \n",
       "15795               2017          138               2                  0   \n",
       "\n",
       "       reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "11987                  0                               1               410  \n",
       "11418                  1                               1               348  \n",
       "72598                  0                               1               363  \n",
       "31228                  0                               1                 0  \n",
       "22915                  0                               3                21  \n",
       "...                  ...                             ...               ...  \n",
       "6265                   2                               1               217  \n",
       "54886                  4                              17               179  \n",
       "76820                  1                               1                 0  \n",
       "860                    1                               1               302  \n",
       "15795                  1                               1               101  \n",
       "\n",
       "[81646 rows x 7 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"x_train\")\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0235afd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "11987     203\n",
       "11418     593\n",
       "72598    1065\n",
       "31228     631\n",
       "22915     627\n",
       "         ... \n",
       "6265      768\n",
       "54886    1065\n",
       "76820     174\n",
       "860       316\n",
       "15795     692\n",
       "Name: y, Length: 81646, dtype: int32"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"y_train\")\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c62544d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([764.86436115, 360.54757797, 824.90534713, ..., 500.25139535,\n",
       "       330.16181422, 345.51126463])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hacemos Regresión\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(x_train,y_train)\n",
    "y_pred = regr.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c7be93e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70586     767\n",
       "21372     359\n",
       "12889     827\n",
       "41824     863\n",
       "34420    1109\n",
       "         ... \n",
       "64208     805\n",
       "37468     444\n",
       "51504     502\n",
       "75393     330\n",
       "21176     346\n",
       "Name: y, Length: 20412, dtype: int32"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dda14460",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score:  0.9960156642273799\n",
      "Test score:  0.994215716736502\n"
     ]
    }
   ],
   "source": [
    "#Imprimimos resultados de Test y Train a analizar\n",
    "print('Training score: ', regr.score(x_train, y_train))\n",
    "print('Test score: ', regr.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "be17169c",
   "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>original_Price</th>\n",
       "      <th>predicted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>764.864361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>359</td>\n",
       "      <td>360.547578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>827</td>\n",
       "      <td>824.905347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>863</td>\n",
       "      <td>864.712817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1109</td>\n",
       "      <td>1109.101141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1087</td>\n",
       "      <td>1084.368130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1014</td>\n",
       "      <td>1014.579040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>263</td>\n",
       "      <td>265.691756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>502</td>\n",
       "      <td>500.226112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>112</td>\n",
       "      <td>111.056614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   original_Price  predicted_Price\n",
       "0             767       764.864361\n",
       "1             359       360.547578\n",
       "2             827       824.905347\n",
       "3             863       864.712817\n",
       "4            1109      1109.101141\n",
       "5            1087      1084.368130\n",
       "6            1014      1014.579040\n",
       "7             263       265.691756\n",
       "8             502       500.226112\n",
       "9             112       111.056614"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Realizamos tabla de predicción del precio sobre el precio original\n",
    "predictions = pd.DataFrame({\"original_Price\": y_test.values, \"predicted_Price\": y_pred})\n",
    "predictions.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9514817c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ATEQE3CxYnyHNjydpp6V0v8jIyC634pFTi5YsRUTALVkmtLBkmZQLRZu6fzwiclpRQCYiYq0reZGYDcAflleyfH+tuy4p19UnExEJIwVkIiLHCsEXCdHx1NRZ7l9bzQNrq911sb2hvhbKir0do4h8rCkgExE5GCh5ASzbX0fvGMO8glqOVltXBqN3fyja7PEgReTjTAGZiEjJiZIXb+bXMD0rglF9/Ly+o8Zdn5jjdlqKiISJAjIRkeI8iM/AWstb+bVM7OtnWpaf5zY3BGRZUKjEfhEJHwVkIiIleZCYxZZD9dRZyE0wTMzws664jv1l9YHisCp9ISLho4BMRKRkKyRm83Z+DRPS/RhjiPIbpmT6eSmvRjstRSTsFJCJdLeyYvj31V6PQhrU18PhXZCQyRv5tUzI8B+/anp2BM/n1UBcGlQcgqpjHg5URD7OFJCJdLcD62DbO1B+0OuRCMDRvRAVT2FNNNsP1zMi9cTL4ohUHwcrLJsPE5glU09LEQkPBWQi3a1h6WvfGm/HIU7JVkjK4b1dtYxP9xPhM8ev8hnDtGw/L2ypcWUxFJCJSJgoIBPpbsV54I+C/Wu9HomAK3mRkMnrO2oZl+5vdvWM7AheyKuhPiFLPS1FJGwUkIl0t+LNkDMJ9qz0eiQCULKVyl5ZLN3nZsia6pfoIzYSltYP105LEQkbBWQi3a1kKww8RzNkPUXxFhbUDGFQko/4KNPiKdOzIniuKBuKtGQpIuGhgEykO9VWub6J2ZPgyF7t2usJDm7njZK+Lc6ONZie7ef1vTFUlhZCbXU3Dk5EThcKyES608HtkNAXIqKh9wC341K8U1dL/eE9vLM/mjP7th6Qpcb6GJjk4/3Is13fSxGREFNAJtKdivNcX0SA3gNhn5YtPVW6i7VR44iLNGTEtf1yODUrgmdrZ6rJuIiEhQIyke5UshUSM933vQfC3lXejud0V7KdtznrpGKwrTkr08+iilwO79naDQMTkdONAjKR7lS0GRICAVnqYNi32tPhnPZKtvJG5QgmBhGQ9Yo0jE2q4LWtZd0wMBE53SggE+lOxVsaLVn2dzllShL3zO49uymqjWNI7+BeCqdnwrP7M8I8KhE5HSkg66k2vQZv/xTqarweiYTSwe2QlO2+j4hxs2WqbeWZd3bVMzGlCp9pudxFU+Nzk9hanUJBiWbJRHqa2rp67nxuLZU1dV4PpVMUkPVUa56A1Y/CQ5fC0f1ej0ZCofwg1NdCTPKJYymD1ULJQ68f7Mv4jMigz4+IimVq1DZeWrIhjKMSkc74cGsxTyzbzbL8U7NPsAKynqpgGVzwU+gzDP4xC/IXeD0i6ariPNeguvFsTO/+sHe1Z0M6nR05VsbamhzGZCV06HbTE4p4dnUh1towjUxEOuPpZbvJSIhm3pYir4fSKQrIeqIje6G63OUajb0epn4Nnv48zP8T6E3g1FWyFRKzTz6WosR+r3ywehMjI/cRExXRodsNSzZUVNeyfu+RMI1MRDqqtLyGD/OKmTN9APM2KyCTUClYBunDT8ykZJ8Jl/wG1jwOT34WKvVGcEoq3gKJfU8+ljoICjdA/amZ83Aqe2PdfsbFHerw7Ux8GtPj9/P8yoIwjEpEOmPu2r2My01ibE4y+0orKTxa6fWQOkwBWU+0e6lbqmwsPh0u+oUL0v4xCw4oh+WUU7S5+QxZVDzE9oYSVX/vTjV19czbU8fE3hUdv3F8GtPNWuau2UtdvWasRXqCp5btYuaQPvh9htHZiSzYWuz1kDpMAVlPtGsx9Dmj+XF/JJz1FRh9NTz0SVj7dPePTTrvYAtLlgApQ9RovJstzz9EekQFKYkdyx8DIC6d7PJNJPeKYuG2U+9FX+TjZmvhUfYdrmRMdjIAIzMTT8llSwVkPU1tNRxY33yGrLFB58EFd7myGK98yzWslp6tvg4O7YKErObX9e7fPRX7962B/9wR/sdpatOrsOje7n/cNry1YT8TIndBr9SO3zgqDoDpubFathTpAZ5ZXsCMwOwYwNicZD7MKz7lNt4oIOtpDnzkWutE9Wr7vJSBcOnv3DLYAxdBqd4YerTS3RCbDJExza9LGdQ9Oy03vAzLH3AbRrrT0n/CB7/uMQVwrbW8ueEAE+s+grg+Hb8DYyA+g2kpR3l7QyEV1cr/E/FKXb3l+ZV7OHvoib/ljMQYoiJ8bD5w1MORdZwCsp6mYHnLy5UtiYqDc+6AzPHwj3Ng27thHZp0QfFWSMpp+brUIS4QD/enue3vuWXvHfPC+ziNVR2D3Utc4LPlP933uG3YVnSMqpo6+tfugOikzt1JXBrJlQUMyYjnrY0HQjtAEQnah3lF9I6LJKf3yZMYo7OTmJ93aqUUKCDraXYtgj5Dgz/fGBj9aTj72/Dcl+H9X0N9ffjGJ51TktfyciW4pH5fhJtFC5fqMihcD6OuckuI3WX7e27H8PBPwfIHu+9x2/DWhgNMzPBj4vqAr5Mvgb1S4fAupg1K5bkVmp0W8crTy3Yzc0jzme7RWUm8t7nQgxF1ngKynqZgGaSN6Pjt+o51S5gbX4LHr4WKwyEfmnRB8Ra3FB1QUtEkaE4dAvvCmNi/axGkDoUBM2HLG90XtG96FbLOhP7TYe9KOBzGoDNIb6w/wISkYxDXifyxBvHpcHgXkweksGLnIUqOKY+zo6y1p97zVl0ORVtUpqaHKC2v4YO8YqYNah6QjcpKZOWuw6dUGyUFZD3JsUKoLD3R67CjeqXCJ37m+l8u+FNoxyZd06jkxcaSOqY9eoxthxu9UPQeEN4WSts/gIzRbgyRMd1TjLa+HvLehNyzICIaBp4DK/8d/sdtQ/GxKrYWHmVkxB6I7WJAVlpATKSfCf2SeWXtvtAN8jQxd81ezv/dPKpqT503TOb9Gv55HvwiG+47122qWvGwywHV5qpu98pHexmbk0R8TPPiznHREfRP6cWKnR2vNegVBWQ9ScEySBsOpgs/Fl8EDLsItr0TunEFa9u7UFbS/Y97KijZdjwgW7inll6RcOe8yhO7gFIGuRmkcNnxPvQd477PmQybuyGfa88KiE6EhEAx3CEXwKpHPJ1deG9TIWNykok8WgC9Ujp/RzFJbhm4+hjTB6fynHZbdkh1bT2/fn0zkX7D+6dKeYK6Gtdf+JJfwzUPwZjrwPhh41x49ib4RS7cMw1e+CosuQ92LXG/IxI2T7WyXNlgVFbiKdVGSQFZT7JriVtW6qq0Ea5vYncuW1oLc78Bm17uvsc8VVSXQcUhiEsDYP6eOj43MopDlZZnNte4c1IGw/6PwvP4laXu9yFtuLucPbl78sg2vwY5k05cThnkAplt74X/sVvxxvoDTMhNhtI9ndth2cD4ID4DSvcwJjuZXQfLyS/Wm2+wHluyk4zEGK6ekHPq5ODlvel+5sn93IaqvmNg5BUw43/gU3+GGx6HSTe73dTb34OXvwG/HgR/ngjP3AgL/uza4klIbC08RsGhCsbmJLd6zujsJD5QQCadsnsJpAW5w7It/kjIGAX587t+X8E6uN0lpe8J4yzPqapkm1uG9vmpq7cs31/LqD5+bhobxS8WV1FcUe9e6KvL4FgYXjx2LoT0Ee73Atz3RwrC/+aw+TU3G9fY4Nmw4oHwPm4rKmvqWLS9mPG5yXB0L/TqQkAGEJ8Gpbvx+wzTBqXywqo9IRnnx93Ryhr+8u5WrpuUw5SBKSzYWkxpRY3Xw2rf8gfcLG9rIqLd6/cZn3T9hz/5W/jMky5gSx4AW16H/3y/u0b7sffsit3HK/O3Zkh6PLsPlVN8iuQqKiDrKepqXbX2YEtetKfvmO4tg7H9PVfWoTsKnJ5qSvJO5I8drCc52pAcYxiY5GNmjp+fLqh0u2X7DIX9Ycgj2z4P0keeuOzzQ/Yk9wYRLod3wdH9zX+fB54DOz4IT+DZjkXbShiQGkeCr9rl+0R3okp/Y736uP8nMGNIH55fWXDKFaL0wn0fbGd0dhL9U+OIi45gbE4S//moh+fgHdnrWtr1n9mx2/kiXM3IIbPhrK+618maU6/HYk9TV295bsWeNpcrASJ8PkZlJp0ybZQUkPUUhetdonB0fGjur+9Y2P5+aO4rGHlvwcgr3W7CHlIAtMco3goJbofl4r21jEj1H7/q6mGRLN1Xxwe7awOJ/WHYabnjfff70Fj2mbDxldA/VoMtb7jZMZ//5ONRvaDfdFj9ePgeuxVvrN9/YnYsLs0FwV0Rlw6HdgIwqE8cFli9+3BXh/mxVnS0iocX5nPNxBMbl6YN7tPzc/BWPup2KLdU2DlYsckuNUH1Irts/tZikntFkpvSTgF1Anlkp0ieogKynmL30tDNjoHL1ykv7p6chbpa2LkAcqe6wKNoY/gf81RSfGKH5YI9dQxPPfFnFxNhuHF0FHd+UEFF4uDQzzCWH3Qtm5rWtsueCLsXh69q/8ZXXNDXkiEXwIoHw18ItxFrLe9sLOTMfr3d30RnWiY1FViyBDDGLVs+v1LLlm3509tbOHtoGmkJJwKb8bnJbD5wlD2HO9HovTvU18PKh2HIhV2/r9wpsOHFrt/Paa612mMtGZOTdMq0UVJA1lPsXtKxgrDtMT7IHOeWq8Jtzwq3ky42GVIHh7d8w6moOA8Ss47njzWeIQMYn+FnQJKPP+4dGfrnLv9D6DvaLZ00FhXv+qWGYxa16igULIWsiS1fnzYcsK42WjdZt+cI0ZE+MpNjXUJ/V3ZYNuiVChUHoc7NCM8Y0odX1u6lpk6FmVuSX1zGy2v3cvn4kwskR/p9nDUwlbmre2gwu2OemxlLHdL1++o33aUK1J0COXM9VGlFDfO2FDFtcHAfqvomxuDzuU0APZ0Csp5i99ITu+BCJWNM95S/2PoO9B3nvu89UIn9jVnrNjwk5bCxpJ7e0Ybk6OZLZZ8bGcVT+TFsLI2EyiOhe/zt89wGj5ZknRme3Zbb3nMbB1rrx2qMmyXrxsr9b27Y73ZXguv72tWEfnDLsbEpcMQFEhmJMWQkxpxy7Vq6pKbS9SoNotDwr9/YxEWj+pIYE9nsuumDU3l2RQ/NwVvxoPt97eoSN7idvYnZLo9SOuXVtfsYk5NEQgu/Ry0xxjAmO4kPToG/SwVk7ak4HP7Gz2UlUF4CSbmhvd/Mce7TXbhf5La97fppgpshC2c9rVPNsUI3OxWdwOJ9zWfHGiTHGK49I4rv132Fun0hLH+xY17z/LEGuVMgLwxV+ze92vpyZYNB57laaN1UmuXNDQeY2K+3u3CkIDRLlhCo2H+i+8C0QGBx2lj5CLx2O3z4uzZP+6iglCXbD3LJ6MwWrx+WkcCxqlo27uthzaDLSmDruzDw3NDdZ+5ZsOGl0N3faeapZbuCXq5sMCoriXmnQBulsAZkxphkY8yzxphNxpiNxphpxpgUY8xbxpi8wNfejc6/0xiz1Riz2RhzUTjHFqyNK+fz3X++TGV1bfgepGCZ2y4dSIC21vLnFZV8+fVy3tlZQ219JwOqhExXuLB4SwgH20TFYSjcCBmBXXwpg11rEU3JOyV5xwPt+QW1nJHa+p/cuf381Ppj+PfC7aF57KMH4NgBN2sJVNVZCo42Cr4SsyCyF+wLYd5afZ2r15Qzpe3zYpIgewKsezZ0j92KPYcr2He4gqEZCYB1uz+7UoOssbg+ULrr+MWpg1KZt6WIY1VhfL3oKWqrYP7v4LwfwJJ725z1+flrG7lyQjYxkS1/IPEZw/TBfXh+VQ8LZtc84T64hGqzFbhly02vfvzbL5Vsc5MZh3eHLFd1W9Exdh+qYFwbtcdaMjorieU7D/X4rhDhniH7E/C6tXY4MA7YCNwBvGOtHQq8E7iMMWYkcAMwCrgYuMcY0/JfbzfadQyerxjPF/85j4rqMP0wdy89XhDWWstdCyt5Ma+WfomG3yytYtqjx/jVkkrySzs4k2FMYLdlGPPI8j90S2L+KHc5MtbNGhRtCt9jnkpKth7PH1txoK7VGTJwb0pf7F/CHzbEs780BFvj8z90P3+fn5KKem6YW86FTx/j7fxGwXLOZNgUwqr9e1a4YKuhOn9bhlzoajuF2bsb3eyY32cCy8HWBaKh0CvNbZoISIyJZGRmIq/19DIOobDy35DcH/pNdbW2nrvZfQho4sO8InYdLOfcM9LavLvpg1N5adVe6jr7AbQDyqpq218etRZWPNR27bHOSMx0+ba7Fof2fnsSa+GhS+HZL8K/zodfDYCfZcDvhrtuBg9dCk/PgVe/A+/9wnU2+OhZl+6wb43L82zh5/Ps8gKmD05ts/ZYS+JjIsjp3fPbKDVvABUixphEYBZwI4C1thqoNsZcAZwbOO1h4H3g+8AVwJPW2ipghzFmKzAF6L7M31acabYQVZ7N5+9fwkNfnEJ8dIiftt2LYcgF1FvL/31YybL9ddw5NZr4KMMFAyIpOFrPvF21XPXCMQb39vNfIyK5ZGAksZFB/FL2HevyyM66JbRjbrD1neZLYqlD3CejhlY9p7OiLZCQyYaSenrHtJw/1lh2eioX7trAD1/M5Z9zJrV5bru2vw/pI9l6qI4bXytncqafq4dF8715lXynvJ7Pjox2lfRX/htm/1/XHqvB5v+cXJ2/LZnjYPE97ncla3xoHr8Fr6/fz+T+gST+o3tdEBWKfCBwHz6abE44e2gfnliyi+smhTgFoS31dbD6MRh7vStQGm61VfDhb+Hs77rLWRNcgP3MjTDnZfC718j6esvdr27k2jNziPC1/fk/p3cvkmIjWbK9hOkdXJLqiH8vyucnL28gOsJH38QYMpNiyO7di9zesWQlx5KZHEN2cix9j64jurbS9YANtdypbtlywIzQ33dPULTZfb3sj+5vzVr3O1N1xP2rPHLi+8M74cB6txmo4VjFIbdZJmO0S4fJmkBdxlieW7mH73yic9UIRmUl8uGWYqYPDt/vVleFLSADBgFFwIPGmHHACuCbQIa1dh+AtXafMSY9cH420PgjQ0HgmOd8Ph9f9r3Ag72+y+f+tYRHbp7SYmJqp9TXwb7V1E25je+/X8n6kjruOCuaXo2CrZwEH58dFcX1IyJZsb+OxzbU8JMFlVw6KJLPjIxiTB8fprU3mMyxsPQfrjSFPww/7m3vwNm3n3ys90CXRzbx86F/vFNN8WbImeLqj6UEMeEbn8Gn6u7nf/dN5Y31+7loVBAzTa3Z8QELzriTr88t5/rhkZyT637+P5weza+XVLHvmOU7Zw7HHNnjPpF2tql9Y5tfhUlfCu5c43MFM1c8BFl/7Ppjt+BoZQ0rdx7m5hmD3IHSvRAXovwxcPXMju0HW+fSA4Dx/ZJ5YMEOthUdY3BaCJe6WmOta9Oz5imX83T2t8L/mKsec0vxjTuLjLkO3rkL3vs5XPAjAF75aB919ZYpA4Pb1Tot0Bc0XAFZZU0df3onj/93xWgyEqMpOVZN8bEqio9Vs73oGMt3HqLkWBXFx6ooOVpBYsRd9H2ujOwEHznxhuwEH+fkRjCkdxcXb/pNg/d+Bpf8KnQfDnqSbe+4QKrh/2aM26kaGeM+xASjstQtex7cBqsfZ+G+J0gs+yT9Fj7sPvSnDnU5y8n9TqzQtGFMdhLPrtjN9y8J8ea5EArnkmUEMBG411o7ASgjsDzZipZ+K5vNWRpjbjHGLDfGLC8q6qZib1G98FUe4otjo8lMiuGGfyzmcHmIip8WbqQ2No1vLYxg88E6vjfl5GCssQif4aysCL47JZqfz4qhzsItb5Rz0TNlPPhRFYcrW5iCj+3t3jTCUYri4A7X7qf3gJOPpw7RTssGJdsgKYf5e2pPqj/WKn8kUQmpfHF0JD96cR1HKzuZi1dawBOlo/j64kS+PiHqeDAG0DfOx49nxPD6jlpu/6CGmqzJoanaf3iXW7LqMyz42wyeDeueC1s9tA/zihmRmUBsVOAN9MgetzMyVCKiICrB5aU1HPL5OHtoGk8t293GDUPEWvjP99zf2yd/Cwv+6DaShFNtNXzwGxh73cnHfX6Y+S3XQD7vrUAD8U1cPzm39Q+MTUwbnMqb6w9QWROe9JBHF+9kcFo8A/vE0SsqgtyUXkzo15sLR2Zw3eR+fOWcwfzg0pH84aphPBT7J+6aFsH1IyIZnuqjqh4W763jc6+Wc6y6i8uqyf1cK7OP6+tk3ltdn/WOSXL1EsdcC7O+y1OpX2Xm8By3Icj4XUu4eb+Cx6+Hl26D+b+HjS+7NJEWDE2PZ2dJOQfLem7h8nAGZAVAgbV2SeDys7gA7YAxJhMg8LWw0fmN5/hzgGZVTa2191lrJ1lrJ6WltZ2TEDoG0kdidi7i81P7MyQ9nuv+sSgk/bGqdy3jtvJb2H20nu9OiSYmIrgXrtRYH1cNi+R358Vw7RmRvLurlpmPH+Vrb5Uzv6CW+sbr733HupYdobb9PbdU0fTFNnWQm7Ku66bE5kX3hP9NqDPqauDIHuri+rJif9v5YydJyGSkfzejshP5zRubO/yw9fWWnz+/lL/UXMEPp8cwsk/zx02KNvxgWjTbS+u4+cCnKVsfgoBs8+vNqvPXW8szm6pPzltrLC7NlcgI066zN9bvZ1xDuQsIlLwI4QwZuE/8pScno88alsZzKwrCX5Ps7Z+6vJvZP3KzBYNnw9s/Ce9jrn7Mzaa2VKYnNhnO/g688BUen7eG9IRoRmUlBX3XvXtFMTg9nrc3Ns9F66ry6lrufX8bV00IYiY4/0N8qQNJSUpgaG8/07IiuGxwJF8cG8XIVB+/XNLFHE9jTixbBuGNdft6fP7TcTWVrq5mQymkEDhSZXl/Vy3Tc2NcK6oBM2DMp2H6f8P5/+f6h0YlwL7V8MYPTiyZNhLh9zEyK7FHt1EKW0Bmrd0P7DbGNMxpzwY2AHOBOYFjc4CG38i5wA3GmGhjzEBgKLA0XOPrsLSRkD8fYww3TM5lTHYS1/19EYVHOv+HWVVbx63v1FNievOtSdFE+Ts+de0zhtFpfr42IZo/nB9Lei/DDz6s4NvvVZxIWg1XX8u8t1ouqRDZy1UxL+54MNFhlaXw5g/gyc/2vJZNh/IhLo0Nh/2kxBqS2skfOy6+L5TkccPkfryydl+H2vFUVNdx66MrmL+7gp+csZvM+Nb/xGMiDN+aFE1EbCLXbTmP4oNdfMHfdHJ1/hX7a7nsuTL+sqqK/7eosvUk6iEXwPL7u/bYLaitq+f9zUWuOn+DI3tCt8OyQVyfk0pfAGQnx5KeGM374WzZMu/XsOEFuOAnrtAvwNhr3WxnuIozN8yOjbmu9XMyRnNs6JX8+Z2tXDeh5TIXbZk2KJXnwlA65N+LdjKsbwL9U+PaP3nza252pgWfGRHFf3bUsmxfFz9w9p/uqva3s7lgW9ExvvX0Gr7/3Frqu2HDQ5ftWuTSVkK4M/XV7TWMSfOTENXCa6g/EpJzod9ZMPIKGHqh66zQgpGZST26jVK4d1n+N/CYMWYtMB74OfBL4EJjTB5wYeAy1tr1wNO4oO114DZrbc/Zo5o6yCUElxVhjOGaM3OZMjCFa/6+iL2daPlRWVPHzQ8tp7KinG+Og8hOBGNNxUcZLhoYyU9nxLC2sJ77PwoEKH1Hu5Y8NSFsTVJXC/kLjtcfq6y1fOn1cpbvD7xINST2h9uuJS7g9Pnd0k1PUrIVknJYuKeWkcHOjoErR1G8lYSYSD4zpR/fe3ZNUDMthUcquebvC6mpreeOiKdISO/f7m0ifIYvjY9lRK8jXHXPQvKLy4IfZ2NVR135lqyJ7DtWz9ffLufWNys4r18Ev5gVg7WwbH8rf845k13x3KLQlmdZueswqfFRpMY3JLkHSl6EeoYsLs0lJjdx9tA0nli6q4UbhMDCv8GKh+GCu9zSToOoeBj3GVcbLBz1B9c84XbQpo9o87R/1lzE6Jgi+uc/1eGHmDwghWX5h0K6tFRWVcvf523jqvFBzI4d3O5ajqW2vPQeH2X4wqhIbn+/ksraLjzHKYNd4nrhhlZPqamr5xtPrOKGybn4fYaX13ZDK7yu2vqO27ATQk9tqmZGdpCvodlnug9e+5vXcxyTk8SHW4t6ZgFiwhyQWWtXB5YXx1prr7TWHrLWllhrZ1trhwa+Hmx0/t3W2sHW2jOstSHcix8CPr97Edp5YkfVFeOzOWdYGtf8fSG7DwafA1NWVcucB5biq6/hNt/zRCRmhHSo0RGG/5kUxT2rqllQUOtmrFIGhXab9d5VbmagVwrWWr77fgUrDtTx6IbAi2jvAd1TIDZ/PqSPghnfcrOAyx8K/2MGqzgPEjJZuLeW4Skd+FNLyHQzLvV1zBicSq+oCP71Ydu1yTbsPcLlf13A6KwkbpkQQ6StCnomyBjDpwfWclHCNj5978LONcje9i6Vfcbwl498XPzsMaJ88JtzY5iZE4HPGM7JjeDxja28wfoiYPD5rX6q7azX1+07UZ0f3M4tf6QrzRJKcekuf66JaYNSWbrjYJdm0Vu0/EFY9Ge48K6WW0ANuRDKikPfM7GuJpA7dn2bpxWV1/Pguho+fWZ/9/e5c0GHHiY2ys+Efsm8GsLg46GF+YzMSgyqGTVb3nBv6m3sCp2SGUHfOMOfV3QhbcUYVzJkfevLln96O4/oCB8XjMjgmok5/O7NLdR2dBl8yT9cOZrusu1tl8oSIjtK69h5xDIuPciAzOd3eWYrH6ZpGnpWUgz1FrYVdfKDZ5ipUn9HpI10tZ0a+eSYTC4e1Zdr/r6Q7UXt98o6WlnD5+5fQly0n1vPKMPfO6fNP/xOD7WXj9smRPHf71Sw60h9oPxFCPPItr17/FPQb5dVkXewnp/MiOadnbXuU2N3Jfbnf+DqoEX1gnP/F975iZs16wmKNlMbn8WK/XUM78gMWWQMxCbCkQKMMdw4fQB/n7edXSUtB/3vbjrAf/1rMddNyuHKCdmY/etcPlFHdm+lDWd22WvcNL0/cx5Yyrubgs/hsdby+sKVzN57Kwv21PHTGTFcOzzqpHzImTkRvJ1fS2lVG8uWa54I2bJzZU0dz6/cw8whjfJMS/e42axQi08PtE86+f8WE+lnysDePLcyhMtva54M7GK8q/Xdaj4/TLrZ5dKEclZ8zZOBnL+RbZ725xVVzMyJID25lwveFv7VrS50wLRBqTwXokbtRytr+NeH27lqfE77J9dVuXIxrSxXNvaF0VE8vrGGDSVdWMjpN63VwHnFzkM8tmQnXzp7EMYYRmUlkhQbwfOrOvC8HD0Ab/7QBWXd4egBl0/ZkY097Xh2cw0zsvxEdKT2WOZ4N8vZ5D3IGMPY7CQ+zOuZy5YKyDqizxA4tKNZu5cLR/blivHZXP+PxeQdaL31R2l5Df/1zyWkx0dz88xB+Io2QWIQLxKdNLKPn8uHRHDz6+WU9xkX2sT+rW9B5jie2VTNc1tq+NbkaNJ7+RiU5OPdXbWBiv0bw5vYX13mugQ0bL1Pyobp34CnPw9HesDUfkkeGxhIakfyxxokZEOJmxXLSIzh0jF9ufP5tc2m2h9csIPbn1nLty4YxrSG+jr7Vh2vzh+0uFSIiOHMuGK+c+EwvvP0Gp4MYrlt0/4j3HDfIn6xfQA3jvLxzUnRZMQ1f1lJjDaMTfczd2sryf2J2ZCUA1tCMzE+d81eBqfF0zcp5sTBI3tDv1wJ7sOAL8K9ATRxzrB0nly6OzRLJBtegjf+1+WMJWa1fW7mWPc7sPCv7d7te5sLmXL32/zwxXUcaG02r67G7WhrurOyiZ2l9czdWsMVQwJlgZJzYfC5LoisCz7YHpOTRH5xWasfQjrioQX5jMlOIrt3EDOjOxe515HY3u2e2jvGcN3wSL77XkXnu6mkDYfyYig+eWdgWVUt//PkKm6cPpDevVxJB2MMn56Yyx/e2kJ1bZCzZAv+5Gb7trzRPd1Ttr/ngiFfBz6AtqGu3vLs5hpmNtolHhRfQ0md5rNko7ISw5vb2QUKyDrCHwl9zmhWCBLgvDPSuW5yDjfct5gNe5s3hz5YVs319y2iX2ov5kwfgM8YlzsQ6v6VTXxiQAS5CYZvr++PLd7W4ptGh1UegQPrWVg3gp8vruS7k6OPBxxnZUXwQl4NRMW5Bs7hbNtUsMzNxEU0etPNmQzDLoYnPuN2+3ipZBuLjmYwPJj6Y00lZJy0ffuSMZnsLa1g7hoXaNbW1fPDF9fx4IJ8fnTZSIZlJATOtC53ImVQxx+zzxmweylDMxL44aUj+ePbefzxrS0tBhOHyqr5vxc+4oZ/LGZkQgV3J7/C6OzkNu/+nNwIHtvQxpvy4NA0HLfW8sD8Hcwe0WQGKdQlLxqLT4fS5mUuhqbHU28ty7u6Q27Lm/DyN+H8H7mSCcE480ZY9Fc40nrXgLlr9vLtp1YzZ9oADpVXc8Hv5/Hjl9Y1X2Zd+5T7e26nSOpvllVy0cBIEht/AMmdCtGJsOTvwY0bVzpk6uBUXuhiK6UjlTXcP38HVwaTOwaBZP52+rA2cm6unwgf3L+2kzO7xudmyTbOPenwXS9vYEh6fLP6bWf0TSAzKTaoD0uUlcCqf8OUW1waRAeXjjultY1enbRobx0JUYb+iZ0IVTJGQV1lsxWTUdlJLN95MPigthspIOuojBEuL6IFM4ek8bmp/fnsvxaztuDw8eOFRyu59u8LGZGZyGen9HM1eWy963OYHN6AzBjDnNFR7CiFv0bd2OrYOyR/PluTZ3Dbu3XcNjGa7IQTv0ZTMv0s3FPLkarAsmW4dnsB7JjfcnLx6GtcovPL3wx/Y/XWVJZCTRkLi6IZEUz9saYSstzvR0CEz8cXZwzkrpc3UHConC8+tIy1e0r58adGkp7YKCAt3e1q9ATxCb+ZtOGuawSQmRzLjz81kpfX7uWO5z46nrdSW1fPwwt3cP7v3qfoWBW/vmYsn4hYjT+t/SWK0X18HKq0rCtqZYmn/3SXm9hCPlZHrNx1iNKKmpPLXYBbSgllUdjG4tKa7bQE9/c3a1hacG+grdnxAbxwi1uSTx0c/O0S+rodZ2//uMWrH12cz10vr+eOS0YwsX9vPntWf3796bEUH6tm9u/n8ZO56yk8Wulmuef9ut3ZsXVFdSzaU8clg5rMZhgDI690P9sOpE3MGJzK8yv3tDy7WF/vKr+344EPdzA+N5nM5CBmx47scb97aW1vWGjMGMMXx0Rxz+qqjre2a9BvKqx/4fjFtzYcYN6WIj43teVNOZ+emM1f3t3afq22RX+FATNdLmnulPA3NK+vP1EKKUQ6lMzflPG5MjArH3bvtwGJMZFkJcWyalfPKyOigKyj+pwBxZuguuWlyamDUvnizIF84YGlrNh5kP2llVz790Wc2b83101qVCCxtMAl24eyaW0rovyGb0yK4uGjk3h32dou31/Rhg+YU3gDN4yIZFSTGldxkYZRffy8kV9zomJ/uOR/2PIndmNg2n+7GbQOfCoPqZKt1Cb2b7d/ZasSs1zZjEbT7UPSE5gyMIXZv5tHdKSf735iGL2imrz57VvrZsc6U/07uZ9LBi9z0/nJvaL4wSdHsuXAUb70yHLe21TIxX/6kGdWFPD9i4dz4/SBJMREus0iae23M2k3uT8iGgbOgpWPdnzsjTwwP58LRmS4WejGjuxxszzh0KtPq4HkzCF9eHP9gc41HN+1BJ7+Asy6vd2djS0ac63b9dYoqdtay1/ezeNv723j/y4dSb9Gie7JvaL43NT+/OrTYzlwpJLZv5vHTx+eS1FUdrut0H6+uJIrhka0XEsxMgbG3uC6hgQZcA9Oi6emvp61BaUNA3cf8N74P/jDSNcX8cD6Vm9fWlHDgwvzuSLY2bG8N10w0cGOJhlxPj41OJLvz6vo3NJ0xhi3S/fwboqOVnHHc2u59ZxBzf+2AwalxTMoLY5/L8pv/T4rDrlSMqM/7S73m+bK0tR3LGicu2Yvb204ENxGggPr3HtaMH1sg3C02vLerlqmZ3ehw0zacMA0y/0elZXIB1t63rKlArKOioh2Mz+7Wi+RNql/Cl+ZNZibH17O1fcuYOaQPlw1oUmuWNHG4JceQiAlxsd/j6rh25uGsS2IzQetqayp4+ZVA5na1zCrlXX9qVl+t2yZOjh8if21VbB/TeufZiNj4Nw73a6wHR+EZwxtKd7K+qgx9Ik1Jy/fBCs63rUDOXZycv11k3L5+nlDuGn6gJZ7A+7tRP5YA5/PBVYFJ363Y6P8fPsTw/AZ+L8X13HZ2Ez+95IRJ2o5HdsPlYeDXnqflevn5W01lNe0kdy/6hHXUqwTCo9UMm9LEecMa5K8b+vdcxmOHDII7LRsXvoCXJAzMiuRV9Z0MK9x72p44gbXuLuzy0CRvWD8f8Fr3wNrsdbys1c38uyKAv7v0pFkNJ5dbaR3ryi+MG0Av7xyFPt3buH8vV/l/y2soLii5Tfm+QW15B+p57x+bbx5JmbCsE+4lkG17W82MMYwfXAfnl+43jWg/stE93yUFcJ5P4DJN8MjV8CBlstG/OvD7ZzZP/nkPMLW1NfC1rc7tFzZ2MUDIzhYaXlyUyeWLn1+yJ2K3TCX7z27hrOH9mF438Q2b/LpiTncO287Za0F+Yv/DrlnQXxgB39SDkTGdWi35ZLtJfxk7np+/9Zmzvr5O/zitY1tb1zb9u7xMkih8Nr2Gkb18Xfu9bOBMe41ZeW/T3pNGZOdxPsKyD4m0kbAzg/bPGVcbjLfPH8oV47P5rKxLSTgHtjoEpm70bCcPlwXtZCbH1jEkU605Kmvt3zzkQUk2SN8enTr1bcnpvtZW1hHUa9BLk+uk2+ubdqzApL7u4Tq1iT0da1cnrkJDrX8Zhk2xVtYVHtGx3ZXNpWY5VovNRIT6WfSgJSWW9HYeti/rnP5Yw3ShsHOk8ujRPh8fPnswfz22nGcNTD15MfevcwFcUHuFE6N9XFGbz+vbW/l9y9lkFtu7uSO4MeW7GLa4FTiopsEBmVFrkZXRPs97zolPq1Ztf7GZnW0JtmBDfDop+Gsr3Q6SDhuyAVQdYTatc9x+zNrmZ9XzP99ciQpce0/FymFi/hC74/45blx7DlmOe/JY9y9qIKSRoFZvbX8fHEl154R2f5OuKwzIT4TFvyVFjrjnVBWBOueY0b+X3l59W5qCzfD1Nvgyn/AhM+7sjoDz3F5co9c4Tb3NHK4vJpHFu3k8nFBvsYWLHf5hcH2WWzC7zPcPDaKXy+p5kBZJ5Yu+03liUV57DpYzqcntr/RKzelF6OyEnlg/o7mV1YecTORDbNjx280pVmuWmtKy2v45pOruXnmQH502SjuvGQEew5XcPW9C7n6ngU8u6KA8uomwWDemyENyJ7cWMPMnBBsDkgd7N4nGm1qG5qRwI7istC1QAwRBWSdkTbCJU63s6V8eGYiZw9tZZt90SYXUHQn4+O8jErO6HWMbzyxqsNVn3/xn03sLjzIl7N3YtrYRRMdYZiY4ee13dHuRa44r9VzOy1/Qbvb7wH3AjHqKvfJuroba88Ub2FhWXbH6o81ldC32e6rNh3a6V54YoNvVdNM6jAo2hDUDAbgdqX1aX+5srFZuX4e29DGB4LBs2HFAx26T4Dq2noeW7KTC0a0UNfvyN7QV+hvLCbJzdq2ksowLjeZgkMVbC1sfRf2cSXb4N9XwqSbXF5dVxkfVRO+yFef20regVLuuGQ48TFBLAPV18Hqx2HQeaTE+pgzOopfzIph1xHLuU8e4+eLKjlYUc+r22qpqYezMoN48zQGRn4KSrbA5jdOvq6y1CXVv/Zd15vwwDr6njGFtORE5ved4wL/ph9EBp4DE78AD18OhZuOH/7HB9uZ1L93qzOAzbRRmT9Y/RN9nNffzw8+bKMrRSvye43lV8XT+cpZfYjwB/eacdX4bO6fv4PS8iZ/S0v/6ZZem37g7zfN5ZG1MzZrLXc8v5YJ/ZKZGOhykd07ls+e1Z+/3DCBc4al88TSXZz183f4/nNrWb37MLbqmEtPyWx7WTtY+aX17CitY3ywtcfaYox7TVn96PGdppF+HyMzE1mwtaTr9x9CCsg6IyrWBVMFyzp3++pjbto9RGvtHZIykM/1WkzR0Sp+91bwOyAfW7yT1z7ax/+kLCQytf0lsalZEbyQVx1I7F/dhQG3YscHwQVkACMudy9OL36t25L8a4u2suJIQufyxxokZMLBDgRk+9d0bXYM3FJvcr/guixUl7n2WH2GdOghJmT42Xmknq2HWpk5HXiO+/l2sD/p6+v3k5kUe1JO1HFH94ZvhyW4F/349BYT+8HNoJw9tA9PLG2n4fjhXfDwZa5+18BzQjK0shrLnOUDORaRwnf6riYmMsjfyR0fuCK6jX6nUmN93Dgmip+fHUP+kXrOffIYP5pfyXXDI4NuII4/yv3/Vjzk8o62vQdv/RCeu9l90MqeBOfc4T5IpQ5hWnYEz21pYyZj0Lkw8fPw8KegaDMHy6p5bPFOrhjfTmmQBmVF7gNyRteDiSuHRLL5YB3/2RF8vmBtveUb79dwRfIOco6sCvp2mcmxTOyfzH2Ni0ZXl8Hiv7mNTU0F0RkA4Jnlu9mw7wg3TG6eUhPh9zFlYArf/cQZ/OKqMWDhK/9ewSf+MI/7I/6Lg7VBBsDteHZzNTOyIzpWe6wtKQMhNtXN4gWMyExkXg9btlRA1lnpI2FnJ3csFm9x9cdCVKulQ1KHEHFgDf99/hCeWb6b/3zU+pb4Bu9vLuS3b27muxcOIaFwhfvDbseYNB87SuvZ3Wt46PPI6mpg7wq3rTkYxsDUr7oX3fl/CO1YWlJfz7oSV5y3S/kPidmujUuw9q7ufP5YY6nDguvqsHeVWzqK6NiLcITPcHaOv/V8m6he0G86rH6iQ/f7YEulLhqEo6l4U3FpLZa+aDBrWBovrGyj4fje1fDgJTD8Mhj6iZAM6VBlPZ95uYy4SPj6tDQiN710fNNGm2wdrHGzYy1tEOnTy8dNY6L42dkxXD8iktEtNLBvU3w6jLjUFS3d8hqkDoVzvg/jroeMkScl1k/LiuC9XbWUtZZ3CG6cEz4HD3+Kf/xnGVMHpZKWEOTvZd7bbsNCCJazI/1u6fJH8ys5XBnch7+/rarCABcN7uV2jnfAleOz+feifEqOBXacLn/AdS5pKT/5eEPzl1u9vx3FZfz8tU3cdu4QoiLaDg9S46O5ckI2v7tuHNelbOX9+rGc/cQxvvpmOR/srqW+0YffemsprbLsKK1jxf5a3s6v4elN1dy3popfLK7ku+9V8MX/lHPlC2Wc++RR7v+outUc5U4bMtsVn65zz9XYHFcgtie1UVJA1lnpI90bUgeKHR5XuNklWXqhVwoYP0lV+/jm7KHc+fxHbNrfvG5ag437jvA/T67mG7OH0re2AKKTgloSi/AZpmRG8HLZqNDvtNy31jXgjk5o/9wG/ij3qXvxPa5WTjgd3csixjAitYsvKDFJLvisCKJ2nK1zn3xTQhCQpY9ws7+2nVyYXYuhz9BOPcQ5uRE8t6WW6ro2kvtXPBj0jOa6PaUUHCpnUv9WZsG6JSDrA4dazxPLTIolMzmWdza2MPO34mF45EoY/1k3oxsCB8rquealcgYk+rh5bBS+XsmuxMLyIJaDd3wY2MDU9oevtF4+zunsG2fmOLjgxy4nLGu8e7wWJEYbhqf4eXNHO3mvg8+neOQXeHzFPi4fFOQHIVsPeW+4WbkQOSPFz6S+fu5a2P6y/5rCOh76qJpbxkXhSxvmlnKrWn89biotIYZpg1O55/1tLoVmwZ9hTAuzYw36nQUbX2zxquraer7++EqumpgdXIupAJ8xjD7yAV8b5+eP58fSN87w04WVTH30GOc9eZQJDx1l6D+PMv2xo3z2lXL+98NK/r6mmte217C2sI7DVZbkGMP4dB9XDIngtgnR/Hl2LP06U3usLUk5rujvptcAyE6Opaaunh2d7d8bBgrIOis63i0pdWb2p3B92AvCtil1COxdw6C0eD47tT9fenh5i8mNB45UctODy/jc1P5u18+eVR2qgzQty88L+1Pd1vQObrduU/784GfHGovr48oHPH9Ls2T5kCrOYyFjGd6Z+mONGeNeQIIZa8k2Fyx3JEhtTa8Utzuvrdw/Wwd7lge/bNxEZryPnHjD2ztbWdpJGw7YFoswt+ShBfnMHpGBv7UljiP7wptDBoEZsrYT95s1HK+pcEvp838PF/0cBpwdkqHkl9Zz9YtlnJXp5zMjo04sJw442+W/Fm1s/ca2DlY/1ursWEiZ4P5GpmX7eXZL+xuR/n54CjP6VJD6wf+5Vlnt2bfGBYLtdT7ooOuGRzJ/Tx0fFrS+dFleY/nGO+V8flQUKbE+N0OXOhR2t76DvyWXj8vm6eW7ObDgUff63FbaQtoI197oYPPNAL97czOxkX4ubCkHsy1lRa57TUIW8VGGTwyM5Gdnx/C9KdF8dXw0P58Vw0OfjOVfF/fid+fF8pMZMXxncjS3jo/ms6OiuHJoJLP7RzA5M4LhqX5yEnzER4Xp927wbPjoGaipwBjDmOwk5m8tDs9jdYICsq7o1LKldUuW3VjyopmUgW7JD1cjaUK/ZL766MqTas2UVdVy44NLOWdYGjOGBN7I9izvUEB2RoqPw1WGLZFnnFR1vsvyT84feyu/xhWiDUb6SFcG4PHr3G6kMKgt2srKqpzOVehvKj4zuIBs39rQzI41SDsDdrfRE7Roswv+YpM7/RCz2qrc37BdPYjK/YfKqnl9/X7OO6OV5cr6OteeJpw5ZOBKX7SxZAlw1sAUVu465FoUHdwB/7rABQ6X/CZkRaI3ltRx7UtlXDIokk81tDBqEBHtntfF97Y+A5o/PxAcdCw3MJzOzPCzprCOwvLWP9gVltfz1KYaPjUuC4acD2/cEegx2oYt/3HJ/CEOPGMjDDeNjuKOeRWtlni5e1El/RN9TGtcZyt9eLOaWe1JiYvi3KGp/OX9Ha7uXFt8flcOY+PJy5YLtxbz7IoCvhzom9khe1e6mfImO62zE3zkJvpIjjGhywXrqoS+7nUyUCR3VFYS723uWK5qOCkg64r0ke7TTH0HCj6W7nE5NzEhmMnorJTBJ5WjuH5SP8pr6vjFf9wupbp6y9cfX0nfxJgTibE15e4NpAM5Sj5jOCvLz0vm/NAl9tfXuec8MEO2rriOr79dwaXPHWNjsE1+h13smt8+96XQztwFrMvfR1pUddfyxxokZJ5Usb9Vnelf2ZZGVftbtHtJUMVg2zIl089HRXUUHG3lZzDoPNj8n2a9Y5t6ctkuJvXvTWJsZMsnHNvvWvd0sOBnh/VKdWOta72CfEykn7MGpvDsG+/Cv2ZD/xlw9ndc8nwIrNhfy3+9Us5nRrpZhxZljXfV91sqLWIDOysHdsPsWAdERxgm9fXzSmu9UIF7V1UxMzvCzTZlT3K/P6+3EZRVlrpZ/77jwzLm8Rl+Bif7+M3S5i3c3ttVw1s7a/nC6CZ5a2kj3IpCTcd6eF4Wv4W51WdSEB1EEN1v6kkNzQ+VVfOtp1Zzy6xBrf8NtaUguLziHmPQbPf/rz7G6Owklu041HpeZzdTQNYVscluGaQj7YGKNoW9XVK7ouNda53AG73fZ/j6uUN47aN9vLCygLteXk/xsWq+OGPgiU9L+9e5cXcw8XV6VgQvHhuJLQhRHtmB9W7sgdZAv1pSyWdGRHLFkEhueLmM5zYHmdM3+cvuhfr9X4RmXI0s2lPDiOQQ/YEnZsLBdmbI6mvd71UoZ8iScl0vvNZ2Ou5a0uFyF01F+Q3TsyN4urXk/pgkN3ux9L5W76Ou3vLIop1cMLKNZZaj3bBcCW6GoFfb9ciwdZxjl/HkqiLsrDtgxKdCFvjM213Lza9XcOu4KKZltRF8Gh+ccYnL0Wtauid/gWuU3sncwHByuy1bDsgOlNXz7Jaak2cEcya5HZiv3+l22Ta17V2XLxkVmmC4JZ8dGcWLW2tZdeDEh/aDFfV87/1Kbh0XRVxkk599ZIzbKNORHfz1tSRuepILsmv54/L220nRd6xbpTm6H2sttz+7himDUhibkxz8Yzawde7DdmrP+31pVXwf94Fz3fMkxUaSkRjN6t2HvR4VoICs69JHdqxpa+EGt8PSaymD3aaEgPiYCL51wTB++NJ63t1UyDdnDz25Hs7elZ0qqTAgyWB8EazeEUQ+RzB2Ljy+XLl0Xy1bDtZzfr8IZuRE8IOpMfx+eRV3zqugqrVk8Qb+SLera+l9HdvJGIQFh1MYkRaiAqS9+kDlUVcqpTXFW1wg0FaR3I5qoWr/cR2szt+Wc3MjeGpTDXWt1cSb+AVY9i9XabsF72w8QGJMJIPT2mhBVron/An9DdoqEFtZCm/8kMGHFuKLS2VJXejexObtruWb71TwrUlRjAumdlPv/i6A/+jpE8dsfaDu2Lk9anasweg+PvaX2RbLpfxtVRWzciLoHdNk3DmTXTuu/9zRJCizsOX1rhfdbUditOGzIyO5/f1Kqutcp4Tvz6tkapafka3tTE0f2bGew9vfh+hEPjkqjbd21rD9cDsrBf5I97xsepXHl+5ie1EZ157Zyb/l4q1u9rkrtQ+9MPg810qqspTR2Ul8sLlnlL9QQNZV6aPcbjMb5HJZ0SZv88capAw8KSADV/35+xcP545LRjSvdL5nRac+BRljmJoVyUv700KzPLhjHqSPxFrLLxZXcdWwSCL97kU4N9HHXTNj2HHEJTS3uhTWILa3e7Fe81TXxxVQU1nOypp+7bY+CZrP5xKO2woa960J7exYg7QzWi5/sWup+4QZZHX+tvRPcqVBPmgt+Tk+Ay74Kbz9E1j3XLOrH1yQ33qpiwalBeHPH2sQl9pyr8aizfDyN6BXCmbSTczKjepcm50WLN57Ihgb1pG8xSEXwqZXT7Tn2rnABWJ92m8U7wWfMUzL8vNi3smzZPuO1fNCXg2XNc2Xa5A7xW1meP1ON1sK7nW4rtrNRoXZ9Cw/ydFwz6oqnt1cQ96hOq49o42lwfQRrgRKG0vfx9k6WPMkDDqXuEjDxQMi+X0ws2Q5U9i68n1+/fpmvnbeECKDLEbbzN6VHa5D2CPE9nYzhR89w+ispB5Tj0wBWVfFpbolwFb6qZ2kptzt9krIDP+42tN7oEu0rz05v2FIenzzliplRS4BPrFz457WL4aXa6dQ15Gq8y2xgV13fUfz3q5aSirrObtJa41ekYZvTIxiQrqfy58vY97udvL7Bp3r6i2FqBbNuk2byPAfJSEmhDXm4vu2ndi/b3V4ArLUoW43XtNlrV0dr87fFtdwvI0ddEk5cMFP4LXbj29ZB9haeJRN+48wdVA7s1+lBd2zZAnQK71Jmy7rPom//WMYdonr5ejzMTMngrfza4PfjNKKlQdq+cqbFdw2sYPBGLiUi37TYNn9bnZs1WNu5qAHzo41mJ4TwfN5NSfVjvrryirOy40gua2czX5nuXy91+9wM7ybA7Nj3fB/NcZw45goHlxXzc8WV/KVCdHHP0S2KCrO/c4Hs4M/f75b5gysXlw0KIIFBbVsPtj2BEFV34nctnMm143vQ3ZyF5ZsC1ZAyikYkIF77c97k2FJNeQVHmve8cADCshCIS3IZcuSPFfGINzJxcGIiHZLpwfWt3/u3lXuU1CQW9Sbyor30TuylsWrP+rU7Y8r3gIRMdT36sOvllRxzbBIfC28oBpjuHRwJF+fGMW336vgj8srTypSeJKGWb/Odl1oYvHmAobHlobkvo5LzGy9BEVdtbsueUBoHxNOVO3f12gmtbrM/RxC+Kl4WpafRXtqKWpjBx29B7iG0i99Dba+A7hSF+edkd7+p/uj+9zSb3eIb7TTsrYC5v3G7eiacqsreBqQGG0Yk+YarXfW+uI6bn69glvGRXW8MGuDgWe7NIpl9wdmx0IXaIfDgERDhIEVB1zAsedoPS9vq+HSwUEko/ebeiIo27XItRfqJqmxPm4cHcV/jYiifzD1tdKHt79saetd8eSB5x4PLGMj3Gvfb5e2PUv2q5WG5GjDeTFdKP9TXQaHdkDKgM7fh5diEiH7TKLWPc2IzEQWbfe+/IUCslDIGOVym9pqlguu15qX9ceaShkYXIucPcu7vIvmrN5HeWFDFwOV/PnQdzQvb6vFApP6tv0mNCLVz/+bGcOb+bXc+Fo5hypbeMM3xrWoWf1418YWMH9XJSMSglgy6IiENpYsiza5GdfI0LQsaaZPk2XLvSsD1flbLuLZGb0iDVMy/a0mbJ8YyzBX3Pe5mzmat4CX1uxldns1k+qqXb5bYBNI2MX1cUuAh3fBy99yuX9n3epm0ps4OyeCJzZ2btky71AdX3i1nDmjIpmQ0YXZWH8UDL3I7TrrjrpjXWSMccn9m93vyl9WVnF+v4jgdzT3m+q6QGSOD03Nvg6YmhURfPX59FHuQ2JdG38TuxYFguiTU0kuHBDBysI6PipqeZZs3u5aXt5ay81DyzA7O1Zi4yT7P4Le/dzv0KlqwNmw/X1Gphre7wF5ZArIQiE+3e1MKmqnN+SBDd5V6G9JyuCTZz9aYutdjasu1iSalhXJm4VJVNUGmWvXkvwPqUkdyW+XVnLtGcH1zusdY7hzajRJ0YZLnytjbUsvUoPOhfUvQG3Xcnpq6upZdSiaEZ2drWhNfLp7k69tvn2evWvCmweTNvzkqv27lkBa6HOMzsl1wUm7bUwyRsHM7/Dc4/cxpo+v+fJ6U0f3u/yxEOS7BcUf6XaHvvYdt8tv9KdbfcMal+6S1NtbXmoqv7Sez75Szg0jIjmrrd2UwcocC+M/1+UyJt1lRraf13a45PXXttfwyWBmxxrrNxVGXBaewYVKTKL7u9+/tpUTrEu1GHhOsyA6ym+4YkgEv26h3EZxRT3fea+CW8dHkZA5zAVVte13FGjRnlN4ubJBdDzkTmHs4Xf5ME8zZB8PxgSx29K6RsxJPSChv0FyrnvDaqtVx8Ftrmp7FwqAAqSm96UfB5i3qZNF+KyFnQt5+tg4escYRqcFH/RE+Az/NTKK686I5AuvlvPYhqqT3/jjM9yus0aNZzvjoz2l9PWXEp8Y4tkYf4Qb40m5SQH7Vne9oXhbeqVAZLxbFm2ozp82IuQPM7S3D2th6b72g5P6zHE8ZK7kgpLHWqw4fpIje1ucnQqrQefCmTe5ZPI2PjT4jOvp2ZFZsj1HXW/KywdHMDMnRKkPxkDfUT1+dqxBWi8fOQk+bn69ggv6R5AQrqruXksb0fqy5e5lbvY3veW/xXNzI9hysJ7l+0/k0Fpr+c57FczM9jOqj9/tyk7O7Xyv4T0relTx4E7rfzY5B96jsqqanSXetlFSQBYqGaMCfzytfMI/us/NovWk7cE+v5td2dfapzAC7ZJC8EcXFcdZUTt4cWkQRU5bcmgHlfV+/rQhtu0dSm04KyuCH06P5r411XznvQoqGlfQHjDLtYvpgsXbShhOvmuhE2ot1SOrrXA5HL37h/7xGmvYbVm02X1y72Jw3hJjTCC5v/3gZMGeOkxkDMNHTYS3fth2dfwje7pvh2WD7DODngk/JzeCl/La6OnZSGFZPTe8XMaF/SOYPaBzfwMfF9OyIjhQVs8lgz7Gz0PGKFecudkO/obZsXNbDaIj/YYrhkbyqyUnPnw+vK6avccsn278+pk2wtWe66ije93rT0Lfjt+2p4mKxQyYzpjIAj7weJZMAVmoJGa5Ap0H81u+vnBzzyh30VTvgW03/w7hp6CzUsqYt/0IZVUd6GzQYOdCHoq4loFJPob07vySYFa8j5/MiKGownLlC2XklwaW4gbMdA2Vy4No5N2KBVv2MdxX4HZJhVp8RvPE/gOBmnbhzuFIO8O9MexaHNaSCDNzInhnZy2l7ew8fGBtFbP7R2Ayx7g2QG/874lyBk2VFnRfQn8nZMT5yEloo6dnwMGKem54pZxpWRFc0tEluo+hc3L9/HhGTPh6HvYEvVJcf9qmO/j3rnK73tPb7uc7K8fP3mP1LNxTx6aSOv6wooqvTYg6uY1R+kg3691WrlpL9q52G6JOkVnVdvWfxqiqNby/NrQ1KTtKAVmoGON2UbW2bFm4ARKzu3dMwUgd0npif22FK40RopIKCcl9GN7rKG9tONDh2x7JW8g/Dk7kmk7OjjUWE2H46vgoZuT4uerFMtYV1bkgKnuiyyXrhJq6elYVHGFEQmV4XqQSW2gyvj/E/Stbk5TrAtVt7waafodHYrRhbLqfuW20x9l9pJ4VB+qYmR0IyrMnusTcN+505VmaOtKNRWE76ezcCB5vracnUFpl+ewr5Yzt4+PKoT1gh3YPEOEz9Atmt+KpLr2FZcvVgdyxdvIi/T7DVcMi+cWSSr7+dgWfGRFF37gmt4lJdDP6reaqtaJg2anVLqk9ETGMHJDFsp2HPB3GafAb3Y3SR7W+5l+0sWfOkCVkuPpox1oIkg6sd0svodpRl5jN1IjNvLCq41X779sUzbg0yEkIza+sMYYLB0Ry0+hI5rxW7qpbDzyn08uWawtKyYypIz4+TDu3Evq62Z7GfVP3ruqegMznc4FYfV3YN6Wc01bDceCR9VXMyo0gOqJR0NtvKmRPdoU/K5q8oB7Z2301yDrprEw/a4rq2Hus+S7gshrLnNfKGJDk47rhwW1kkY+RjFHuQ37Dppr9H7kPHpnjgrr5tCw/R6stGXGGWTmtrCx0tNtMfS0cWHdqFoRtQ1y/sVTVefv3pYAslJJzoarUtWpprLbSHUvM8mZcbTE+N0vWUj/OPStCmzCelM2kykUszz/IobLgE5mL9+7gkYrpXD2ijfY4nTQ5M4Jrzojks6+Usy9pvEsS70QrpcXbixkeezh8szER0S5363AgX6r6mPud6q5NItlnwoAZna5FF6zRfXwcqrRu1rKJihrLM5truKClptkDz4aM0W6mrGGTSm2Fq5UUE6KuCWES5TdMy4rg2SZ9WCtrLV/8TzmpMYbPjVIwdlqKT4eImBPpCqsfC2p2rIHPGO6cGsMt46Ja//1JH9mxbjPFW1xeZjeXDQk7X4h3x3dmCF4P4GPF+AKtlJp82ijOc0nZ/h6a+9FaHtmelaFtMhwVR0yEj3GZ0by2rpWcnxb89fU1zIjbR1pceP5gzu0Xwfn9Ivjsf6o5lHN+p1opLdhawnCzM7yzMUnZJxL7D2xwM67dVWQ4ZYBrMxVmvkByf0uzZHO31TCkt5+MpssuDQafB70HwRs/cAHrkUBT8TAHkaEwK9fPU5tqjhcwrq6z3PpGOZE++OLYqBYLIMtpomHZsmijm/HtYEHbpGhDVFudAeJSXcpG4abg7rBgxcdrubIH6fmvVKeajJGwo8myZdHmnlUQtqk+DTNkjZKpy4vd8k9CiGf1krKZllTKCyuDW7YsOFTO89vg8pzy0I6jicuGRDK6j485BZdRturZDrVSqqmrZ/Xuwwyv2RjeBPL4DJfTB+7n1Q19+LwwK9fPK9trKG+0C9ZaywNrq1ueHWtgDAy7yLWaeuvHbqazh+ePNRiU5CPKb1i8t47aesvX366gohZuHa9g7LSXMRp2zne5YwPODs9MTvrIQHHzIOxZHtoP6nKcArJQ6z3Q9UprnJNVuL5nFYRtKra326l3KP/Esb2r3VJmqAtqJmQyzmxly4Gj7CttvyDhH97awgVR60jOCP/S3A3DI0lLiOXLh79A1c6lQd9ubcFh+ibGEF++O7w1rxKzoKEfaLjrj3koNdbHGb39vLb9RHL/8v11lNVYxqS18/toDIy41BV8XHxP95e86CQTqEn2+MZqvv1eBUXl9Xx9YpMdcXJ6SujrcsgObnebWMLheB5ZOx9Eq464XNbkMJfaOU0pIAs1nz+wJr8ocMC6Nfee/gvcNI9sT5impROziDy4mUkDevPymr1tnrq18Chvb9jPJ80CNzsUZsYYbhoTRV1Mb775zHrq6oObJVu0rYThqT6ISghvCYqELDicD5WlrqBvTw7yu2hWrv+khuMPflTN7P4Rwc0WGR+MvMot7XTHpocQmZkTwRs7atl2qJ5vntlOA2o5fRgD/afD0E+EL+0loS9g28+f3bfG/U31hH7MH0MKyMIhY6SraQVupszWu3YqPVnKQBeEgRvv3tXhmZZOzIaD25k2KJUXV7UdkP369c1cmltNXO/Mbmt94/cZvnZmAnsOV/CD51e338oHlz82Ir4M4sNQELaxqF4QEQt5b7mZ2B6QhBouEzL85JfWs/VQHQfK6vlwT23wfQDB/b6MvOKUaQcEkBBl+O7kaL4zJfrkXaQi/aaFtxl6Q7eZ/HaWLZU/FlYKyMIhZQgc3gkVB13+WHL/nl9AL2UwFG50W5oPbYfI2LBUZCfazSKNjC9n/5FKthcda/G0tQWHWbHzEJ+IWtvtuVJR8cl8K3UZK/L28Js3Nrd5bnVtPasLDjM8Yh/EdkO+UlIWbJx7Ss38dEaE70RboUc3VDM9K4JekT38bygExqb7iVUwJl4Ipv3f3lXKHwsjBWTh4I9wdZt2LXZBTlIPLAjbVFScS4Au3gJ7Vof3U1BSNr5D2zhrYAovrW55luyX/9nEFROyiC7s/oAMIDZ7FN9Nfp+5a/byzw9an8b/aM9hspJiiC/b1T09E+P7QnnJxz4gA1eT7PkttTy+oYYLBmiJRCSsWivb1KC0wJXGCEdrOAEUkIVPemDZsnBDz2oo3paUQW6pcs/y8DaNje8LJVuZPjiVF1ftabYsuHBbMTuKyzivfywcK/SmflvGaBKLVvL983K478PtPLui5X6JC7eVMLxvYqBFTze8UCVmudnLUO9+7YEy411bIfdPL1UiYXW8bFMry5Z7ArNjPX215xSmV7lw6TPMzTYd3n1qzJCBmxXbtdjVTQvnDExSNhRtYXBaPNV19azfe+T4VdZafvnaJq6emENE8UbXONuLXKnIGOgzlD7FS/j+RcO5+9WNLbZ8WrC1mOGZCe5TZXw3VIRPGQyjr+22nDqvfX5UFJ8fFeZenSLitNSqqcGeZZCq/LFwOj1e1b0QEeU+TSRkhL/5c6j0HuB28SVmu4AkXBJcgVNjYOrAFF5cfWKK/K0NBzhSWcP0wamuTYiXu1Mzx8PWt8juHct3PnEG331mDYu3lxy/urq2njUFpYxIjQpUhO+GjRsRUZAxIvyP00PkJvrI1uyYSPdIGQRH9zXvC1tX7VZ7Uj5e7ZJ6Gr3ShVP25PDujAm1iChIHgCpYa5vFZMAvgg4doBpg/swd/Ve6ustdfWWX7+xmWvOzHXlDfZ/5G3x0z5D4ch+OLqXwWnx3HbeEL7y6ArW7SkF3MaD7ORY4qr3nzIV4UVEWuXzn8h/bqxooys9FNXLm3GdJvQOEk5pQ9125VPJyMshd2r4HycpB0q2kZvSi15RfpblH2Tumj1E+AwT+yW7hudHCrytteXzQ98xsPVdAMZkJ3Hj9AHc+OBSdhSXsWh7CcP7BpYruyN/TEQk3NJHuM4Aje1ZqXIX3UABmZwsPr17PgUlZB5vmDt1UCrPrCjgt29s4dpJua4JbuFGF4x53f8zazxse5eGCtZnDUzlqgnZfPZfi3nto30nEvrjTo2K8CIibeozFEq2uQLUDQrCvNFLAAVk4pXELLfpAZg2KJXnVhSQnhjNyMxEd73Xy5UNEgMbMgo3Hj90/vAMzh2Wxub9RwMzZAXh7WEpItJd/FGQOhQKAu3jKg67AufJp0i1gFOYAjLxRqBiP1jSE2O4YGQG109q1IB9/1qXz+Y1YwKzZO+cdPiysVn84brxxEVHBGbIFJCJyMdE+vATuy33rXa7Kz/GnUF6CgVk4o2YRBfsHHO7eb44YyD9U+PcdXVVrtF5T/lEljkedsx3O40CjDGkJ8YAFo7uVQ6ZiHx8pA2HA+tcLm/BcuWPdRMFZOKdxGw4uLX58aJNkJjpdn32BLHJrvnunuXNr6s4BL5IiIrt9mGJiIRFZKxboShYDvtWuSVMCTsFZOKdxEwoymt+fP86SPKw/lhLMsdB3tvNj5cWqJWIiHz8pI+AtU+5EkXd0RZOFJCJhxKyoWRL8+P710LKgG4fTpsyRrtxVR05+bjyx0Tk4yh9BBzaqd2V3UgBmXgnMRtKXGL/cXU1ULy1ZyT0NxYZA33OgPwPTz5eugd6qeSFiHzMRCe45P60kV6P5LShgEy8ExMocVFWfOJYSZ5bAgxn66bOyhoHW5ssW5buUskLEfl4mvB5V+BcuoUCMvGOMa7ReEmjxP7963pG/bGWpAZaKR050XuT0j3KIRORjydjvB7BaSWsAZkxJt8Y85ExZrUxZnngWIox5i1jTF7ga+9G599pjNlqjNlsjLkonGOTHiIh0y1RNti/xtuG4m1paKW07T13ua4GKg5CrJYsRUSka7pjhuw8a+14a+2kwOU7gHestUOBdwKXMcaMBG4ARgEXA/cYY1SJ7uMuMetEYn99HRRt7nkJ/Y01bqV0bD/EJIM/wuNBiYjIqc6LJcsrgIcD3z8MXNno+JPW2ipr7Q5gKzCl+4cn3Sox2/VNw8LBbRDTG6LivB5V6xq3UtJypYiIhEi4AzILvGmMWWGMuSVwLMNauw8g8DU9cDwb2N3otgWBY/JxFpPkZsbKS+DA+p6bP9agcSulI3tUn0dEREIi3GstM6y1e40x6cBbxphNbZzbUvagbXaSC+xuAejXr4e01pHOMwaSclwe2f41kHIK1LzJHA+L/grZkyBWAZmIiHRdWGfIrLV7A18LgRdwS5AHjDGZAIGvhYHTC4BG3aXJAfa2cJ/3WWsnWWsnpaVpuehjITETirfAgQ3Qe6DXo2lfbLLbjLBroZYsRUQkJMIWkBlj4owxCQ3fA58A1gFzgTmB0+YALwW+nwvcYIyJNsYMBIYCS8M1PulBErJh+/sudywmwevRBCdznNtlqSr9IiISAuFcsswAXjCujkkE8Li19nVjzDLgaWPMzcAu4FoAa+16Y8zTwAagFrjNWlsXxvFJT5GUDccOQO4ptIcjYzQcWOeqWYuIiHRR2AIya+12YFwLx0uA2a3c5m7g7nCNSXqomCQ3O9ZT64+1JDIGzrzR61GIiMjHhCr1i/eMgWEXQ9pwr0ciIiLiCVW0lJ4hZ1L754iIiHxMaYZMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGNhD8iMMX5jzCpjzCuByynGmLeMMXmBr70bnXunMWarMWazMeaicI9NREREpCfojhmybwIbG12+A3jHWjsUeCdwGWPMSOAGYBRwMXCPMcbfDeMTERER8VRYAzJjTA5wKfCvRoevAB4OfP8wcGWj409aa6ustTuArcCUcI5PREREpCcI9wzZH4HvAfWNjmVYa/cBBL6mB45nA7sbnVcQOCYiIiLysRZUQGaMmRHMsSbXXwYUWmtXBDkW08Ix28L93mKMWW6MWV5UVBTkXYuIiIj0XMHOkP0lyGONzQAuN8bkA08C5xtjHgUOGGMyAQJfCwPnFwC5jW6fA+xteqfW2vustZOstZPS0tKCHL6IiIhIzxXR1pXGmGnAdCDNGPPtRlclAm0m3Ftr7wTuDNzPucB3rbWfM8b8BpgD/DLw9aXATeYCjxtjfg9kAUOBpR38/4iIiIicctoMyIAoID5wXkKj40eAazr5mL8EnjbG3AzsAq4FsNauN8Y8DWwAaoHbrLV1nXwMERERkVNGmwGZtXYeMM8Y85C1dmdnH8Ra+z7wfuD7EmB2K+fdDdzd2ccRERERORW1N0PWINoYcx8woPFtrLXnh2NQIiIiIqeTYAOyZ4C/4+qJaRlRREREJISCDchqrbX3hnUkIiIiIqepYMtevGyM+ZoxJjPQizLFGJMS1pGJiIiInCaCnSGbE/h6e6NjFhgU2uGIiIiInH6CCsistQPDPRARERGR01VQAZkx5gstHbfWPhLa4YiIiIicfoJdspzc6PsYXB2xlYACMhEREZEuCnbJ8r8bXzbGJAH/DsuIRERERE4zwe6ybKoc12tSRERERLoo2Byyl3G7KsE1FR8BPB2uQYmIiIicToLNIftto+9rgZ3W2oIwjEdERETktBPUkmWgyfgmIAHoDVSHc1AiIiIip5OgAjJjzHXAUuBa4DpgiTHmmnAOTEREROR0EeyS5Q+AydbaQgBjTBrwNvBsuAYmIiIicroIdpelryEYCyjpwG1FREREpA3BzpC9box5A3gicPl64LXwDElERETk9NJmQGaMGQJkWGtvN8ZcDcwEDLAIeKwbxiciIiLysdfesuMfgaMA1trnrbXfttZ+Czc79sfwDk1ERETk9NBeQDbAWru26UFr7XJgQFhGJCIiInKaaS8gi2njuthQDkRERETkdNVeQLbMGPPlpgeNMTcDK8IzJBEREZHTS3u7LP8HeMEY81lOBGCTgCjgqjCOS0REROS00WZAZq09AEw3xpwHjA4cftVa+27YRyYiIiJymgiqDpm19j3gvTCPRUREROS0pGr7IiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLisbAFZMaYGGPMUmPMGmPMemPMTwPHU4wxbxlj8gJfeze6zZ3GmK3GmM3GmIvCNTYRERGRniScM2RVwPnW2nHAeOBiY8xU4A7gHWvtUOCdwGWMMSOBG4BRwMXAPcYYfxjHJyIiItIjhC0gs86xwMXIwD8LXAE8HDj+MHBl4PsrgCettVXW2h3AVmBKuMYnIiIi0lOENYfMGOM3xqwGCoG3rLVLgAxr7T6AwNf0wOnZwO5GNy8IHBMRERH5WAtrQGatrbPWjgdygCnGmNFtnG5auotmJxlzizFmuTFmeVFRUYhGKiIiIuKdbtllaa09DLyPyw07YIzJBAh8LQycVgDkNrpZDrC3hfu6z1o7yVo7KS0tLZzDFhEREekW4dxlmWaMSQ58HwtcAGwC5gJzAqfNAV4KfD8XuMEYE22MGQgMBZaGa3wiIiIiPUVEGO87E3g4sFPSBzxtrX3FGLMIeNoYczOwC7gWwFq73hjzNLABqAVus9bWhXF8IiIiIj1C2AIya+1aYEILx0uA2a3c5m7g7nCNSURERKQnUqV+EREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY8pIBMRERHxmAIyEREREY+FLSAzxuQaY94zxmw0xqw3xnwzcDzFGPOWMSYv8LV3o9vcaYzZaozZbIy5KFxjExEREelJwjlDVgt8x1o7ApgK3GaMGQncAbxjrR0KvBO4TOC6G4BRwMXAPcYYfxjHJyIiItIjhC0gs9bus9auDHx/FNgIZANXAA8HTnsYuDLw/RXAk9baKmvtDmArMCVc4xMRERHpKbolh8wYMwCYACwBMqy1+8AFbUB64LRsYHejmxUEjjW9r1uMMcuNMcuLiorCOm4RERGR7hD2gMwYEw88B/yPtfZIW6e2cMw2O2DtfdbaSdbaSWlpaaEapoiIiIhnwhqQGWMiccHYY9ba5wOHDxhjMgPXZwKFgeMFQG6jm+cAe8M5PhEREZGeIJy7LA1wP7DRWvv7RlfNBeYEvp8DvNTo+A3GmGhjzEBgKLA0XOMTERER6SkiwnjfM4DPAx8ZY1YHjv0v8EvgaWPMzcAu4FoAa+16Y8zTwAbcDs3brLV1YRyfiIiISI8QtoDMWjuflvPCAGa3cpu7gbvDNSYRERGRnkiV+kVEREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGNhC8iMMQ8YYwqNMesaHUsxxrxljMkLfO3d6Lo7jTFbjTGbjTEXhWtcIiIiIj1NOGfIHgIubnLsDuAda+1Q4J3AZYwxI4EbgFGB29xjjPGHcWwiIiIiPUbYAjJr7QfAwSaHrwAeDnz/MHBlo+NPWmurrLU7gK3AlHCNTURERKQn6e4csgxr7T6AwNf0wPFsYHej8woCx0REREQ+9npKUr9p4Zht8URjbjHGLDfGLC8qKgrzsERERETCr7sDsgPGmEyAwNfCwPECILfReTnA3pbuwFp7n7V2krV2UlpaWlgHKyIiItIdujsgmwvMCXw/B3ip0fEbjDHRxpiBwFBgaTePTURERMQTEeG6Y2PME8C5QB9jTAHwY+CXwNPGmJuBXcC1ANba9caYp4ENQC1wm7W2LlxjExEREelJwhaQWWs/08pVs1s5/27g7nCNR0RERKSn6ilJ/SIiIiKnLQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLiMQVkIiIiIh5TQCYiIiLisR4XkBljLjbGbDbGbDXG3OH1eERERETCrUcFZMYYP/A34BJgJPAZY8xIb0clIiIiEl49KiADpgBbrbXbrbXVwJPAFR6PSURERCSsIrweQBPZwO5GlwuAszway3FldX62F+z3ehgiIiISBtW1dUCcp2PoaQGZaeGYPekEY24BbglcPGaM2RzOAfWKjentS0wfMK/lsX0s1VQcIzI23uthnBb0XHcfPdfdR89199FzHTp+W1tnfnX1mlau7gMUh+Bh+rd2hbHWtnZdtzPGTAN+Yq29KHD5TgBr7S88Hdhpxhiz3Fo7yetxnA70XHcfPdfdR89199Fz3T2643nuaTlky4ChxpiBxpgo4AZgrsdjEhEREQmrHrVkaa2tNcZ8HXgD8AMPWGvXezwsERERkbDqUQEZgLX2NeA1r8dxmrvP6wGcRvRcdx89191Hz3X30XPdPcL+PPeoHDIRERGR01FPyyETEREROe0oIDsNGWNyjTHvGWM2GmPWG2O+GTieYox5yxiTF/jau9Ft7gy0s9psjLnIu9GfeowxfmPMKmPMK4HLep7DwBiTbIx51hizKfC7PU3PdXgYY74VeO1YZ4x5whgTo+c6NIwxDxhjCo0x6xod6/Bza4w50xjzUeC6PxtjTpvSTcFq5bn+TeA1ZK0x5gVjTHKj68L6XCsgOz3VAt+x1o4ApgK3BVpU3QG8Y60dCrwTuEzguhuAUcDFwD2BNlcSnG8CGxtd1vMcHn8CXrfWDgfG4Z5zPdchZozJBr4BTLLWjsZtwLoBPdeh8hDueWqsM8/tvbianUMD/5rep7T8XL8FjLbWjgW2AHdC9zzXCshOQ9bafdbalYHvj+LeuLJxbaoeDpz2MHBl4PsrgCettVXW2h3AVlybK2mHMSYHuBT4V6PDep5DzBiTCMwC7gew1lZbaw+j5zpcIoBYY0wE0AvYi57rkLDWfgAcbHK4Q8+tMSYTSLTWLrIuUfyRRreRgJaea2vtm9ba2sDFxUBO4PuwP9cKyE5zxpgBwARgCZBhrd0HLmgD0gOntdTSKrsbh3kq+yPwPaC+0TE9z6E3CCgCHgwsD//LGBOHnuuQs9buAX4L7AL2AaXW2jfRcx1OHX1uswPfNz0uHfNF4D+B78P+XCsgO40ZY+KB54D/sdYeaevUFo5pe247jDGXAYXW2hXB3qSFY3qegxMBTATutdZOAMoILOu0Qs91JwXyl64ABgJZQJwx5nNt3aSFY3quQ6O151bPeRcZY36AS+95rOFQC6eF9LlWQHaaMsZE4oKxx6y1zwcOHwhMvxL4Whg4XgDkNrp5Dm6JQto2A7jcGJMPPAmcb4x5FD3P4VAAFFhrlwQuP4sL0PRch94FwA5rbZG1tgZ4HpiOnutw6uhzW8CJpbbGxyUIxpg5wGXAZ+2J2mBhf64VkJ2GAjtA7gc2Wmt/3+iqucCcwPdzgJcaHb/BGBNtjBmIS1pc2l3jPVVZa++01uZYawfgkkHftdZ+Dj3PIWet3Q/sNsacETg0G9iAnutw2AVMNcb0CryWzMbloeq5Dp8OPbeBZc2jxpipgZ/RFxrdRtpgjLkY+D5wubW2vNFV4X+urbX6d5r9A2biplTXAqsD/z4JpOJ28OQFvqY0us0PgG3AZuASr/8Pp9o/4FzglcD3ep7D8xyPB5YHfq9fBHrruQ7bc/1TYBOwDvg3EK3nOmTP7RO43Lwa3OzLzZ15boFJgZ/PNuCvBArB61+7z/VWXK5Yw3vj37vruValfhERERGPaclSRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ8poBMRERExGMKyEREREQ89v8BpNBLUFWzXwIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficamos la predicción en mención\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "ax = sns.histplot(predictions, element=\"poly\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f62ba66",
   "metadata": {},
   "source": [
    "**iii) Calculo de Métricas para validar el Modelo**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3f98bd7",
   "metadata": {},
   "source": [
    "Evaluación del modelo:\n",
    "\n",
    "El último paso es evaluar el rendimiento del algoritmo. Este paso es particularmente importante para comparar qué tan bien funcionan los diferentes algoritmos en un conjunto de datos en particular. Para los algoritmos de regresión, se utilizan comúnmente tres métricas de evaluación:\n",
    "\n",
    "El error absoluto medio (MAE)\n",
    "El error cuadrático medio (MSE)\n",
    "Root Mean Squared Error (RMSE)\n",
    "\n",
    "Asimismo, añadimos el R2 que nos indica una cifra cercana a 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5eac0207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error is: 2.9551656011297602\n",
      "Mean Squared Error is: 636.9528119543052\n",
      "Root Mean_squared_error is: 25.237924081712926\n",
      "sklearn: R2 score for Linear Regression is: 0.994215716736502\n"
     ]
    }
   ],
   "source": [
    "score = sk.metrics.r2_score(y_test, y_pred)\n",
    "mae = sk.metrics.mean_absolute_error(y_test, y_pred)\n",
    "mse = sk.metrics.mean_squared_error(y_test, y_pred)\n",
    "\n",
    "print('Mean Absolute Error is: {}'.format(mae))\n",
    "print('Mean Squared Error is: {}'.format(mse))\n",
    "print('Root Mean_squared_error is:', np.sqrt(mse))\n",
    "\n",
    "print('sklearn: R2 score for Linear Regression is: {}'.format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fa06e27d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70586   -2.135639\n",
       "21372    1.547578\n",
       "12889   -2.094653\n",
       "41824    1.712817\n",
       "34420    0.101141\n",
       "           ...   \n",
       "64208   -0.287769\n",
       "37468    1.574953\n",
       "51504   -1.748605\n",
       "75393    0.161814\n",
       "21176   -0.488735\n",
       "Name: y, Length: 20412, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adicionales\n",
    "error=y_pred-y_test\n",
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d5e0f773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.31410137869534"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f2f9cec",
   "metadata": {},
   "source": [
    "**iii) Conclusiones con base en los resultados**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505aecd0",
   "metadata": {},
   "source": [
    "Podemos concluir que al contar con un R2 score de 0.9942146476027874 nuestro modelo predictivo nos indica que existe una buena correlación del precio con las variables independientes (service_fee, minimum_nights, number_of_reviews, reviews_per_month, availability_365) por ende, lo podemos apreciar en el cuadro mostrado donde la predicción de precios de y (y_test) nos arroja precios muy cercanos a los que el modelo tenia en el set de entrenamiento, por lo tanto, podemos tomar en cuenta dichos precios proyectados."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0dd00e4",
   "metadata": {},
   "source": [
    "### Realizamos otra prueba con el Modelo de Regresión Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cde676d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([764.675, 359.045, 824.66 , ..., 500.5  , 330.13 , 346.16 ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importamos libreria necesaria\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "Model=RandomForestRegressor(n_estimators=200)\n",
    "Model.fit(x_train,y_train)\n",
    "y_pred=Model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8ffaafbe",
   "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>original_Price</th>\n",
       "      <th>predicted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>764.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>359</td>\n",
       "      <td>359.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>827</td>\n",
       "      <td>824.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>863</td>\n",
       "      <td>863.595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1109</td>\n",
       "      <td>1109.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1087</td>\n",
       "      <td>1086.245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1014</td>\n",
       "      <td>1014.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>263</td>\n",
       "      <td>264.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>502</td>\n",
       "      <td>501.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>112</td>\n",
       "      <td>110.205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   original_Price  predicted_Price\n",
       "0             767          764.675\n",
       "1             359          359.045\n",
       "2             827          824.660\n",
       "3             863          863.595\n",
       "4            1109         1109.960\n",
       "5            1087         1086.245\n",
       "6            1014         1014.330\n",
       "7             263          264.935\n",
       "8             502          501.515\n",
       "9             112          110.205"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Realizamos predicción\n",
    "predictions = pd.DataFrame({\"original_Price\": y_test.values, \"predicted_Price\": y_pred})\n",
    "predictions.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1e083f80",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='original_Price', ylabel='predicted_Price'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficamos las variables Precio Original y Predictivo\n",
    "sns.scatterplot(x=\"original_Price\",y=\"predicted_Price\", data=predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942f5659",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "### COMPLEMENTAMOS EL PROYECTO CONSUMIENDO UN API REFERENTE AL CLIMA, QUE PODRÍA DARNOS ALGUNA INFORMACIÓN ADICIONAL DE EL CLIMA EN LA CIUDAD DE NUEVA YORK"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866058af",
   "metadata": {},
   "source": [
    "### Consumiendo API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "edbb25bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importamos librerías requests y json\n",
    "import requests\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "12863e64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "#Llamamos al API\n",
    "api_key = \"ec1d8e6845f79efab435f727f6077b19\"\n",
    "url = \"http://api.openweathermap.org/data/2.5/weather?\"\n",
    "total_url = url + \"appid=\" + api_key + \"&q=\" + \"New York\"\n",
    "df = requests.get(total_url)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c533fb48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'{\"coord\":{\"lon\":-74.006,\"lat\":40.7143},\"weather\":[{\"id\":800,\"main\":\"Clear\",\"description\":\"clear sky\",\"icon\":\"01n\"}],\"base\":\"stations\",\"main\":{\"temp\":289.98,\"feels_like\":289.39,\"temp_min\":286.57,\"temp_max\":291.6,\"pressure\":1016,\"humidity\":64},\"visibility\":10000,\"wind\":{\"speed\":3.13,\"deg\":360,\"gust\":4.92},\"clouds\":{\"all\":0},\"dt\":1685336353,\"sys\":{\"type\":2,\"id\":2008101,\"country\":\"US\",\"sunrise\":1685352531,\"sunset\":1685405898},\"timezone\":-14400,\"id\":5128581,\"name\":\"New York\",\"cod\":200}'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Imprimimos la longitud y resultado de la información\n",
    "print(len(df.text))\n",
    "df.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "69757f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'coord': {'lon': -74.006, 'lat': 40.7143},\n",
       " 'weather': [{'id': 800,\n",
       "   'main': 'Clear',\n",
       "   'description': 'clear sky',\n",
       "   'icon': '01n'}],\n",
       " 'base': 'stations',\n",
       " 'main': {'temp': 289.98,\n",
       "  'feels_like': 289.39,\n",
       "  'temp_min': 286.57,\n",
       "  'temp_max': 291.6,\n",
       "  'pressure': 1016,\n",
       "  'humidity': 64},\n",
       " 'visibility': 10000,\n",
       " 'wind': {'speed': 3.13, 'deg': 360, 'gust': 4.92},\n",
       " 'clouds': {'all': 0},\n",
       " 'dt': 1685336353,\n",
       " 'sys': {'type': 2,\n",
       "  'id': 2008101,\n",
       "  'country': 'US',\n",
       "  'sunrise': 1685352531,\n",
       "  'sunset': 1685405898},\n",
       " 'timezone': -14400,\n",
       " 'id': 5128581,\n",
       " 'name': 'New York',\n",
       " 'cod': 200}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Trasladamos la información a un json\n",
    "texto=df.text\n",
    "json.loads(texto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d7d4e22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Vamos separando la información que necesitamos del API\n",
    "x = df.json()\n",
    "y = x[\"main\"]\n",
    "current_temperature = y[\"temp\"]\n",
    "temp_max = y[\"temp_max\"]\n",
    "temp_min = y[\"temp_min\"]\n",
    "current_pressure = y[\"pressure\"]\n",
    "current_humidity = y[\"humidity\"]\n",
    "z = x[\"weather\"]\n",
    "weather_description = z[0][\"description\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ed23fc09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Temperatura (en unidades kelvin) = 289.98\n",
      " Temperatura (en grados celsius) = 16.83000000000004\n",
      " Temperatura máxima (en grados celsius) = 18.450000000000045\n",
      " Temperatura mínima (en grados celsius) = 13.420000000000016\n",
      " Presión atmosférica (en unidades hPa) = 1016\n",
      " Humidad (en porcentaje) = 64\n",
      " Descripción = clear sky\n"
     ]
    }
   ],
   "source": [
    "# Imprimimos los valores correspondiente a temperaturas en distintas unidades de medición para la ciudad de Nueva York en USA \n",
    "print(\" Temperatura (en unidades kelvin) = \" + str(round(current_temperature,2)) +\n",
    "\"\\n Temperatura (en grados celsius) = \" + str((current_temperature) - 273.15) +\n",
    "\"\\n Temperatura máxima (en grados celsius) = \" + str((temp_max) - 273.15) +\n",
    "\"\\n Temperatura mínima (en grados celsius) = \" + str((temp_min) - 273.15) +\n",
    "\"\\n Presión atmosférica (en unidades hPa) = \" + str(current_pressure) +\n",
    "\"\\n Humidad (en porcentaje) = \" + str(current_humidity) +\n",
    "\"\\n Descripción = \" + str(weather_description))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006aa348",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197b9d89",
   "metadata": {},
   "source": [
    "**Nota:** Como nota final, podemos indicar que por el momento se ha procedido a realizar una narrativa adecuada de cómo nuestro proyecto **Airbnb** ha identificado los objetivos del proyecto y en base a la información obtenida se ha realizado un análisis que nos permita obtener insights del contexto comercial y poder dar respuestas a las diferentes hipótesis planteadas, que en un mediano plazo serán insumo para realizar mejoras necesarias para el servicio de alquiler en los 5 distritos e identificar los tipos de habitación y lugares más demandados. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2e1735",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "### COMPLEMENTAMOS ACTUALIZANDO EL ANÁLISIS SEGÚN LA CONSIGNA CON EL FEATURE ENGINEERING A FIN DE MEJORAR EL PROCESO DE ENTRENAMIENTO DE ALGORITMOS DE MACHINE LEARNING PARA EL PROYECTO AIRBNB\n",
    "\n",
    "A continuación voy a proceder a hacer un label enconding a las variables categoricas señaladas anteriormente con la finalidad de poder incluirlas en el análisis y modelado estadístico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "866879ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['host_identity_verified',\n",
       " 'neighbourhood_group',\n",
       " 'instant_bookable',\n",
       " 'cancellation_policy',\n",
       " 'room_type',\n",
       " 'Construction_year',\n",
       " 'review_rate_number']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizamos las variables categoricas = ['host_identity_verified','neighbourhood_group','instant_bookable','cancellation_policy','room_type','Construction_year','review_rate_number']\n",
    "categoricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8326675f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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>country_code</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>room_type</th>\n",
       "      <th>Construction_year</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",
       "      <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>US</td>\n",
       "      <td>False</td>\n",
       "      <td>strict</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2020</td>\n",
       "      <td>966.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2021-10-19 00:00:00.000000000</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>US</td>\n",
       "      <td>False</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2007</td>\n",
       "      <td>142.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2022-05-21 00:00:00.000000000</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>US</td>\n",
       "      <td>True</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2005</td>\n",
       "      <td>620.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-06-11 19:47:31.418129408</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>US</td>\n",
       "      <td>True</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2005</td>\n",
       "      <td>368.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2019-07-05 00:00:00.000000000</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>US</td>\n",
       "      <td>False</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2009</td>\n",
       "      <td>204.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2018-11-19 00:00:00.000000000</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1004098</td>\n",
       "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
       "      <td>45498551794</td>\n",
       "      <td>verified</td>\n",
       "      <td>Michelle</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>40.74767</td>\n",
       "      <td>-73.97500</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>True</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2013</td>\n",
       "      <td>577.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2019-06-22 00:00:00.000000000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>No smoking, please, and no drugs.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1004650</td>\n",
       "      <td>BlissArtsSpace!</td>\n",
       "      <td>61300605564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>40.68688</td>\n",
       "      <td>-73.95596</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2015</td>\n",
       "      <td>71.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2017-10-05 00:00:00.000000000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>Please no shoes in the house so bring slippers...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1005202</td>\n",
       "      <td>BlissArtsSpace!</td>\n",
       "      <td>90821839709</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Emma</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>40.68688</td>\n",
       "      <td>-73.95596</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2009</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2017-10-05 00:00:00.000000000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>House Guidelines for our BnB We are delighted ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1005754</td>\n",
       "      <td>Large Furnished Room Near B'way</td>\n",
       "      <td>79384379533</td>\n",
       "      <td>verified</td>\n",
       "      <td>Evelyn</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>40.76489</td>\n",
       "      <td>-73.98493</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>True</td>\n",
       "      <td>strict</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2005</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>2019-06-24 00:00:00.000000000</td>\n",
       "      <td>3.47</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>- Please clean up after yourself when using th...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1006307</td>\n",
       "      <td>Cozy Clean Guest Room - Family Apt</td>\n",
       "      <td>75527839483</td>\n",
       "      <td>unconfirmed</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Upper West Side</td>\n",
       "      <td>40.80178</td>\n",
       "      <td>-73.96723</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>False</td>\n",
       "      <td>strict</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2015</td>\n",
       "      <td>291.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2017-07-21 00:00:00.000000000</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>NO SMOKING OR PETS ANYWHERE ON THE PROPERTY 1....</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "5  1004098         Large Cozy 1 BR Apartment In Midtown East  45498551794   \n",
       "6  1004650                                   BlissArtsSpace!  61300605564   \n",
       "7  1005202                                   BlissArtsSpace!  90821839709   \n",
       "8  1005754                   Large Furnished Room Near B'way  79384379533   \n",
       "9  1006307                Cozy Clean Guest Room - Family Apt  75527839483   \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",
       "5               verified  Michelle           Manhattan         Murray Hill   \n",
       "6                    NaN   Alberta            Brooklyn  Bedford-Stuyvesant   \n",
       "7            unconfirmed      Emma            Brooklyn  Bedford-Stuyvesant   \n",
       "8               verified    Evelyn           Manhattan      Hell's Kitchen   \n",
       "9            unconfirmed      Carl           Manhattan     Upper West Side   \n",
       "\n",
       "        lat      long        country country_code instant_bookable  \\\n",
       "0  40.64749 -73.97237  United States           US            False   \n",
       "1  40.75362 -73.98377  United States           US            False   \n",
       "2  40.80902 -73.94190  United States           US             True   \n",
       "3  40.68514 -73.95976  United States           US             True   \n",
       "4  40.79851 -73.94399  United States           US            False   \n",
       "5  40.74767 -73.97500  United States           US             True   \n",
       "6  40.68688 -73.95596  United States           US            False   \n",
       "7  40.68688 -73.95596  United States           US            False   \n",
       "8  40.76489 -73.98493  United States           US             True   \n",
       "9  40.80178 -73.96723  United States           US            False   \n",
       "\n",
       "  cancellation_policy        room_type  Construction_year   price  \\\n",
       "0              strict     Private room               2020   966.0   \n",
       "1            moderate  Entire home/apt               2007   142.0   \n",
       "2            flexible     Private room               2005   620.0   \n",
       "3            moderate  Entire home/apt               2005   368.0   \n",
       "4            moderate  Entire home/apt               2009   204.0   \n",
       "5            flexible  Entire home/apt               2013   577.0   \n",
       "6            moderate     Private room               2015    71.0   \n",
       "7            moderate     Private room               2009  1060.0   \n",
       "8              strict     Private room               2005  1018.0   \n",
       "9              strict     Private room               2015   291.0   \n",
       "\n",
       "   service_fee  minimum_nights  number_of_reviews  \\\n",
       "0        193.0            10.0                9.0   \n",
       "1         28.0            30.0               45.0   \n",
       "2        124.0             3.0                0.0   \n",
       "3         74.0            30.0              270.0   \n",
       "4         41.0            10.0                9.0   \n",
       "5        115.0             3.0               74.0   \n",
       "6         14.0            45.0               49.0   \n",
       "7        212.0            45.0               49.0   \n",
       "8        204.0             2.0              430.0   \n",
       "9         58.0             2.0              118.0   \n",
       "\n",
       "                    last_review  reviews_per_month review_rate_number  \\\n",
       "0 2021-10-19 00:00:00.000000000               0.21                4.0   \n",
       "1 2022-05-21 00:00:00.000000000               0.38                4.0   \n",
       "2 2019-06-11 19:47:31.418129408                NaN                5.0   \n",
       "3 2019-07-05 00:00:00.000000000               4.64                4.0   \n",
       "4 2018-11-19 00:00:00.000000000               0.10                3.0   \n",
       "5 2019-06-22 00:00:00.000000000               0.59                3.0   \n",
       "6 2017-10-05 00:00:00.000000000               0.40                5.0   \n",
       "7 2017-10-05 00:00:00.000000000               0.40                5.0   \n",
       "8 2019-06-24 00:00:00.000000000               3.47                3.0   \n",
       "9 2017-07-21 00:00:00.000000000               0.99                5.0   \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",
       "5                             1.0             374.0   \n",
       "6                             1.0             224.0   \n",
       "7                             1.0             219.0   \n",
       "8                             1.0             180.0   \n",
       "9                             1.0             375.0   \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",
       "5                  No smoking, please, and no drugs.     NaN  \n",
       "6  Please no shoes in the house so bring slippers...     NaN  \n",
       "7  House Guidelines for our BnB We are delighted ...     NaN  \n",
       "8  - Please clean up after yourself when using th...     NaN  \n",
       "9  NO SMOKING OR PETS ANYWHERE ON THE PROPERTY 1....     NaN  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "airbnb.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b9ce13c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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>country_code</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>room_type</th>\n",
       "      <th>Construction_year</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",
       "      <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>0</td>\n",
       "      <td>Madaline</td>\n",
       "      <td>1</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>966.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2021-10-19 00:00:00.000000000</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>Jenna</td>\n",
       "      <td>2</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>142.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2022-05-21 00:00:00.000000000</td>\n",
       "      <td>0.38</td>\n",
       "      <td>3</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>2</td>\n",
       "      <td>Elise</td>\n",
       "      <td>2</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>620.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-06-11 19:47:31.418129408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>Garry</td>\n",
       "      <td>1</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>368.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2019-07-05 00:00:00.000000000</td>\n",
       "      <td>4.64</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>Lyndon</td>\n",
       "      <td>2</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>204.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2018-11-19 00:00:00.000000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</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",
       "</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                       0  Madaline                    1    Kensington   \n",
       "1                       1     Jenna                    2       Midtown   \n",
       "2                       2     Elise                    2        Harlem   \n",
       "3                       0     Garry                    1  Clinton Hill   \n",
       "4                       1    Lyndon                    2   East Harlem   \n",
       "\n",
       "        lat      long        country country_code  instant_bookable  \\\n",
       "0  40.64749 -73.97237  United States           US                 0   \n",
       "1  40.75362 -73.98377  United States           US                 0   \n",
       "2  40.80902 -73.94190  United States           US                 1   \n",
       "3  40.68514 -73.95976  United States           US                 1   \n",
       "4  40.79851 -73.94399  United States           US                 0   \n",
       "\n",
       "   cancellation_policy  room_type  Construction_year  price  service_fee  \\\n",
       "0                    2          2                 17  966.0        193.0   \n",
       "1                    1          0                  4  142.0         28.0   \n",
       "2                    0          2                  2  620.0        124.0   \n",
       "3                    1          0                  2  368.0         74.0   \n",
       "4                    1          0                  6  204.0         41.0   \n",
       "\n",
       "   minimum_nights  number_of_reviews                   last_review  \\\n",
       "0            10.0                9.0 2021-10-19 00:00:00.000000000   \n",
       "1            30.0               45.0 2022-05-21 00:00:00.000000000   \n",
       "2             3.0                0.0 2019-06-11 19:47:31.418129408   \n",
       "3            30.0              270.0 2019-07-05 00:00:00.000000000   \n",
       "4            10.0                9.0 2018-11-19 00:00:00.000000000   \n",
       "\n",
       "   reviews_per_month  review_rate_number  calculated_host_listings_count  \\\n",
       "0               0.21                   3                             6.0   \n",
       "1               0.38                   3                             2.0   \n",
       "2                NaN                   4                             1.0   \n",
       "3               4.64                   3                             1.0   \n",
       "4               0.10                   2                             1.0   \n",
       "\n",
       "   availability_365                                        house_rules license  \n",
       "0             286.0  Clean up and treat the home the way you'd like...     NaN  \n",
       "1             228.0  Pet friendly but please confirm with me if the...     NaN  \n",
       "2             352.0  I encourage you to use my kitchen, cooking and...     NaN  \n",
       "3             322.0                                                NaN     NaN  \n",
       "4             289.0  Please no smoking in the house, porch or on th...     NaN  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hacemos un label encoding para transformar las variables categóricas en numéricas\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "airbnb[\"host_identity_verified\"] = le.fit_transform(airbnb[\"host_identity_verified\"])\n",
    "airbnb[\"neighbourhood_group\"] = le.fit_transform(airbnb[\"neighbourhood_group\"])\n",
    "airbnb[\"instant_bookable\"] = le.fit_transform(airbnb[\"instant_bookable\"])\n",
    "airbnb[\"cancellation_policy\"] = le.fit_transform(airbnb[\"cancellation_policy\"])\n",
    "airbnb[\"room_type\"] = le.fit_transform(airbnb[\"room_type\"])\n",
    "airbnb[\"Construction_year\"] = le.fit_transform(airbnb[\"Construction_year\"])\n",
    "airbnb[\"review_rate_number\"] = le.fit_transform(airbnb[\"review_rate_number\"])\n",
    "\n",
    "airbnb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5abe6eb2",
   "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          102599 non-null  int32         \n",
      " 4   host_name                       102193 non-null  object        \n",
      " 5   neighbourhood_group             102599 non-null  int32         \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                102599 non-null  int32         \n",
      " 12  cancellation_policy             102599 non-null  int32         \n",
      " 13  room_type                       102599 non-null  int32         \n",
      " 14  Construction_year               102599 non-null  int64         \n",
      " 15  price                           102352 non-null  float64       \n",
      " 16  service_fee                     102326 non-null  float64       \n",
      " 17  minimum_nights                  102190 non-null  float64       \n",
      " 18  number_of_reviews               102416 non-null  float64       \n",
      " 19  last_review                     102599 non-null  datetime64[ns]\n",
      " 20  reviews_per_month               86720 non-null   float64       \n",
      " 21  review_rate_number              102599 non-null  int64         \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: datetime64[ns](1), float64(9), int32(5), int64(4), object(7)\n",
      "memory usage: 18.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#Visualizamos la conversión de las variables\n",
    "airbnb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "66bc3c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102599, 26)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizamos el shape\n",
    "airbnb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3e24bf26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>NAME</th>\n",
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       "      <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>country_code</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>room_type</th>\n",
       "      <th>Construction_year</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",
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       "      <th>availability_365</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11114</th>\n",
       "      <td>7139598</td>\n",
       "      <td>Cozy 1 BR on Bedford Avenue, Wburg</td>\n",
       "      <td>73023181304</td>\n",
       "      <td>1</td>\n",
       "      <td>Christina</td>\n",
       "      <td>1</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>40.71764</td>\n",
       "      <td>-73.95689</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>702.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Dear Guest, Thank you for appreciating that I ...</td>\n",
       "      <td>41662/AL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                                NAME      host_id  \\\n",
       "11114  7139598  Cozy 1 BR on Bedford Avenue, Wburg  73023181304   \n",
       "\n",
       "       host_identity_verified  host_name  neighbourhood_group neighbourhood  \\\n",
       "11114                       1  Christina                    1  Williamsburg   \n",
       "\n",
       "            lat      long        country country_code  instant_bookable  \\\n",
       "11114  40.71764 -73.95689  United States           US                 1   \n",
       "\n",
       "       cancellation_policy  room_type  Construction_year  price  service_fee  \\\n",
       "11114                    2          2                  7  702.0        140.0   \n",
       "\n",
       "       minimum_nights  number_of_reviews last_review  reviews_per_month  \\\n",
       "11114             1.0                1.0  2016-01-03               0.02   \n",
       "\n",
       "       review_rate_number  calculated_host_listings_count  availability_365  \\\n",
       "11114                   0                             1.0             191.0   \n",
       "\n",
       "                                             house_rules   license  \n",
       "11114  Dear Guest, Thank you for appreciating that I ...  41662/AL  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Eliminamos los NaN\n",
    "airbnb.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b0e0981a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creo nuevo df con las variables a utilizar\n",
    "df = airbnb.drop(columns=[\"id\", \"NAME\", \"lat\", \"long\", \"house_rules\", \"license\", \"host_name\", \"neighbourhood\", \"country\", \"country_code\", \"last_review\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "95e5858d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reemplazamos los NaN por 0\n",
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f1a58256",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>966.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52335172823</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>142.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78829239556</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>620.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85098326012</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>368.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>4.64</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92037596077</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>204.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_id  host_identity_verified  neighbourhood_group  instant_bookable  \\\n",
       "0  80014485718                       0                    1                 0   \n",
       "1  52335172823                       1                    2                 0   \n",
       "2  78829239556                       2                    2                 1   \n",
       "3  85098326012                       0                    1                 1   \n",
       "4  92037596077                       1                    2                 0   \n",
       "\n",
       "   cancellation_policy  room_type  Construction_year  price  service_fee  \\\n",
       "0                    2          2                 17  966.0        193.0   \n",
       "1                    1          0                  4  142.0         28.0   \n",
       "2                    0          2                  2  620.0        124.0   \n",
       "3                    1          0                  2  368.0         74.0   \n",
       "4                    1          0                  6  204.0         41.0   \n",
       "\n",
       "   minimum_nights  number_of_reviews  reviews_per_month  review_rate_number  \\\n",
       "0            10.0                9.0               0.21                   3   \n",
       "1            30.0               45.0               0.38                   3   \n",
       "2             3.0                0.0               0.00                   4   \n",
       "3            30.0              270.0               4.64                   3   \n",
       "4            10.0                9.0               0.10                   2   \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  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e43fdaa",
   "metadata": {},
   "source": [
    "### Se va a proceder a mejorar la data realizando un PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "17dc0c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f19cfd7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparo las variables y las separo\n",
    "X = df.drop(columns=\"price\")\n",
    "y = df[\"price\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "62371298",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NORMALIZACION\n",
    "#Aplicamos una transformación de los datos para poder aplicar las propiedades de la distribución normal\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "X_std = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4f885625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy covariance matrix: \n",
      "[[ 1.00000975e+00  1.55016002e-03  1.78687818e-03  3.73227515e-03\n",
      "  -4.11784398e-03 -3.80701950e-03  4.87011426e-03  3.28184269e-03\n",
      "  -1.82996358e-03 -4.49895663e-03 -2.82861892e-03  3.15678237e-03\n",
      "   1.64664335e-03 -2.11716248e-03]\n",
      " [ 1.55016002e-03  1.00000975e+00  1.42824256e-03 -1.03627743e-03\n",
      "  -1.76256030e-03  2.85478951e-03  1.69538333e-03 -3.80327884e-03\n",
      "   9.48650401e-04  8.97358211e-04  7.64953365e-04  5.60722777e-03\n",
      "  -4.05485133e-03 -6.60159265e-03]\n",
      " [ 1.78687818e-03  1.42824256e-03  1.00000975e+00 -1.93306154e-03\n",
      "  -1.49788964e-04 -6.49866541e-03 -5.48157141e-03  5.11875655e-04\n",
      "   9.69278592e-03  9.88517767e-03  4.50668702e-02  1.45856549e-02\n",
      "   8.80894525e-02  6.09951244e-02]\n",
      " [ 3.73227515e-03 -1.03627743e-03 -1.93306154e-03  1.00000975e+00\n",
      "   3.83167793e-04 -2.50865695e-04  2.50890774e-03  4.95862277e-04\n",
      "  -3.24959018e-03  4.61918891e-03  1.20112335e-03  4.35111213e-03\n",
      "  -6.42758886e-04 -3.46978155e-03]\n",
      " [-4.11784398e-03 -1.76256030e-03 -1.49788964e-04  3.83167793e-04\n",
      "   1.00000975e+00  1.90700936e-03  3.74185249e-03 -1.55059136e-03\n",
      "  -8.13549405e-04 -6.18459590e-04 -5.63065446e-03 -9.41549432e-04\n",
      "  -2.53895346e-03  2.28904431e-03]\n",
      " [-3.80701950e-03  2.85478951e-03 -6.49866541e-03 -2.50865695e-04\n",
      "   1.90700936e-03  1.00000975e+00  4.10782461e-03  8.46755291e-04\n",
      "  -5.65220320e-02 -2.24898937e-03  1.94232822e-02  4.02408702e-03\n",
      "  -8.72081079e-02 -8.38605615e-03]\n",
      " [ 4.87011426e-03  1.69538333e-03 -5.48157141e-03  2.50890774e-03\n",
      "   3.74185249e-03  4.10782461e-03  1.00000975e+00 -3.39524252e-03\n",
      "  -1.12232042e-03  1.82496347e-03  3.56345063e-03  4.54031104e-03\n",
      "  -2.69675969e-03 -8.62141894e-03]\n",
      " [ 3.28184269e-03 -3.80327884e-03  5.11875655e-04  4.95862277e-04\n",
      "  -1.55059136e-03  8.46755291e-04 -3.39524252e-03  1.00000975e+00\n",
      "  -3.80724447e-03  5.53500570e-03  5.52513345e-03 -4.89693659e-03\n",
      "   1.27419272e-04 -3.14461925e-03]\n",
      " [-1.82996358e-03  9.48650401e-04  9.69278592e-03 -3.24959018e-03\n",
      "  -8.13549405e-04 -5.65220320e-02 -1.12232042e-03 -3.80724447e-03\n",
      "   1.00000975e+00 -5.04946950e-02 -9.28951747e-02 -2.40354375e-03\n",
      "   8.52853362e-02  5.88534610e-02]\n",
      " [-4.49895663e-03  8.97358211e-04  9.88517767e-03  4.61918891e-03\n",
      "  -6.18459590e-04 -2.24898937e-03  1.82496347e-03  5.53500570e-03\n",
      "  -5.04946950e-02  1.00000975e+00  6.17862816e-01 -1.54787171e-02\n",
      "  -8.06579556e-02  9.76820103e-02]\n",
      " [-2.82861892e-03  7.64953365e-04  4.50668702e-02  1.20112335e-03\n",
      "  -5.63065446e-03  1.94232822e-02  3.56345063e-03  5.52513345e-03\n",
      "  -9.28951747e-02  6.17862816e-01  1.00000975e+00  3.38179072e-02\n",
      "  -3.99243429e-02  7.31641851e-02]\n",
      " [ 3.15678237e-03  5.60722777e-03  1.45856549e-02  4.35111213e-03\n",
      "  -9.41549432e-04  4.02408702e-03  4.54031104e-03 -4.89693659e-03\n",
      "  -2.40354375e-03 -1.54787171e-02  3.38179072e-02  1.00000975e+00\n",
      "   2.36855671e-02 -5.63705388e-03]\n",
      " [ 1.64664335e-03 -4.05485133e-03  8.80894525e-02 -6.42758886e-04\n",
      "  -2.53895346e-03 -8.72081079e-02 -2.69675969e-03  1.27419272e-04\n",
      "   8.52853362e-02 -8.06579556e-02 -3.99243429e-02  2.36855671e-02\n",
      "   1.00000975e+00  1.58913091e-01]\n",
      " [-2.11716248e-03 -6.60159265e-03  6.09951244e-02 -3.46978155e-03\n",
      "   2.28904431e-03 -8.38605615e-03 -8.62141894e-03 -3.14461925e-03\n",
      "   5.88534610e-02  9.76820103e-02  7.31641851e-02 -5.63705388e-03\n",
      "   1.58913091e-01  1.00000975e+00]]\n"
     ]
    }
   ],
   "source": [
    "# Calculo la matriz de covarianza\n",
    "print('NumPy covariance matrix: \\n%s' %np.cov(X_std.T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e0e964e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvectors \n",
      "[[ 7.73161074e-03  1.24709829e-03  2.34520392e-03  2.23310025e-03\n",
      "   1.61797056e-02 -9.34947657e-02 -1.58365061e-01 -4.81094048e-01\n",
      "   4.11437834e-01  6.60579891e-02  2.15233392e-01  5.98461720e-01\n",
      "   3.87803144e-01  7.66568968e-02]\n",
      " [-1.12200062e-03 -8.21283922e-04 -2.04988141e-02 -6.58827224e-03\n",
      "  -6.38707960e-02  3.56820467e-03 -2.36769032e-01 -1.96298029e-01\n",
      "  -2.21814281e-01 -5.68171674e-01 -2.27428682e-01  7.44034465e-02\n",
      "   1.39916834e-01 -6.72304049e-01]\n",
      " [-5.29961118e-02  3.47875007e-02  3.43458956e-01 -1.47370470e-01\n",
      "   4.85867305e-01  5.68642797e-01 -4.09886021e-01  2.76188661e-01\n",
      "   1.40534385e-01  3.94158268e-02 -1.34808627e-01  9.60837790e-02\n",
      "   6.22938353e-02  1.11172135e-03]\n",
      " [-6.02976414e-03 -4.53285989e-03 -1.33320934e-02 -8.28042543e-03\n",
      "   1.56452889e-02 -4.41722891e-03 -1.27291653e-01 -3.65600529e-01\n",
      "   1.38722807e-01  4.12219441e-01 -3.14709240e-01  1.24425319e-01\n",
      "  -6.89004391e-01 -2.66732236e-01]\n",
      " [ 5.23723505e-03 -5.28682574e-03 -5.69975375e-03  1.76129818e-02\n",
      "   8.38579161e-03  1.30080095e-02 -6.25977785e-04  1.90500029e-01\n",
      "  -4.51543565e-01  5.78944569e-01  3.68248183e-01  1.98277411e-01\n",
      "   1.99236917e-01 -4.57588597e-01]\n",
      " [-4.28460983e-02  2.77574597e-02 -2.90275279e-01  2.71251936e-01\n",
      "   1.27774330e-01 -5.20295656e-01 -3.90472860e-01  4.47960414e-01\n",
      "   6.21705793e-02  9.33763886e-02 -3.73484563e-01  1.86743260e-01\n",
      "   9.09337294e-02  6.45593635e-02]\n",
      " [-4.24988311e-03  1.33316085e-03 -3.40047781e-02 -2.68980376e-02\n",
      "  -1.55916687e-02  7.58223046e-02 -1.75870863e-01 -4.35140141e-01\n",
      "  -3.31174787e-01  3.18638641e-01 -4.42657329e-01 -3.45032800e-01\n",
      "   4.36582342e-01  2.34576425e-01]\n",
      " [-1.15343380e-02 -2.36083361e-04 -1.00874980e-02 -1.91117104e-02\n",
      "   2.30612764e-03 -7.32717554e-02  1.34500018e-01  8.13143549e-02\n",
      "   6.36305551e-01  2.12946192e-01 -3.63089866e-02 -4.88351334e-01\n",
      "   2.88400522e-01 -4.42845884e-01]\n",
      " [ 1.52529862e-01 -4.95207340e-02  3.59810879e-01  5.74586104e-02\n",
      "   7.08316583e-01 -4.47354134e-01  2.78164531e-01 -1.85746191e-01\n",
      "  -1.33576761e-01 -6.10917358e-02  1.39230455e-02 -6.43919908e-02\n",
      "  -7.40249870e-03 -4.27994462e-02]\n",
      " [-6.84699752e-01  7.04239688e-01  2.61954380e-02  7.90758103e-02\n",
      "   8.03523765e-02 -3.65987018e-02  1.14431272e-01 -7.13533858e-02\n",
      "  -3.45288712e-02 -1.59291870e-02  2.46906180e-02 -6.43241218e-03\n",
      "  -4.89197416e-03 -1.38137176e-02]\n",
      " [-6.86394985e-01 -7.01116749e-01  3.19440863e-02  1.66079444e-01\n",
      "   5.16660054e-02  5.76455761e-03  4.18215263e-03 -4.64438241e-02\n",
      "  -1.16120527e-02 -1.60518697e-02  4.93637667e-02 -3.03477980e-02\n",
      "  -7.85257873e-03  2.31549671e-03]\n",
      " [-1.58752561e-02  5.15547093e-02  5.41042608e-02 -1.12261305e-01\n",
      "  -2.41905691e-03 -2.56686053e-01 -6.60079576e-01 -1.39988483e-01\n",
      "  -1.71032839e-02 -4.55016358e-02  5.18533359e-01 -3.97438928e-01\n",
      "  -1.65330407e-01  6.19128675e-02]\n",
      " [ 1.08359819e-01  6.03262665e-02  6.13780771e-01  6.98659129e-01\n",
      "  -3.30942948e-01  2.40681544e-02 -8.34742304e-02  1.09159566e-02\n",
      "   2.46731690e-02  2.88138707e-02 -2.21823693e-02 -2.40274070e-02\n",
      "   6.17321020e-03  5.60130064e-03]\n",
      " [-1.40956161e-01 -4.12202487e-02  5.33907850e-01 -6.04420680e-01\n",
      "  -3.49763574e-01 -3.43382997e-01  6.70912892e-04  1.60274769e-01\n",
      "  -1.15765506e-02  5.84974201e-02 -1.99411766e-01  1.27391518e-01\n",
      "   4.65670577e-02  1.99324499e-02]]\n",
      "\n",
      "Eigenvalues \n",
      "[1.66399293 0.37504477 1.2756696  0.79476844 0.91486102 0.95739274\n",
      " 1.0237448  1.01141798 1.00722079 1.00376687 0.98890027 0.99100279\n",
      " 0.99771518 0.99463828]\n"
     ]
    }
   ],
   "source": [
    "#Calculo los autovalores y autovectores de la matriz\n",
    "cov_mat = np.cov(X_std.T)\n",
    "eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n",
    "\n",
    "print('Eigenvectors \\n%s' %eig_vecs)\n",
    "print('\\nEigenvalues \\n%s' %eig_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3a1444a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autovalores en orden descendiente:\n",
      "1.663992925670731\n",
      "1.275669604770611\n",
      "1.0237448044482687\n",
      "1.011417982125152\n",
      "1.0072207887095497\n",
      "1.0037668677766676\n",
      "0.9977151779270556\n",
      "0.9946382843098358\n",
      "0.9910027878656792\n",
      "0.9889002672511997\n",
      "0.957392737358473\n",
      "0.9148610204252856\n",
      "0.7947684371417629\n",
      "0.3750447691213865\n"
     ]
    }
   ],
   "source": [
    "#  Hacemos una lista de parejas (autovector, autovalor) \n",
    "eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\n",
    "\n",
    "# Ordenamos estas parejas den orden descendiente con la función sort\n",
    "eig_pairs.sort(key=lambda x: x[0], reverse=True)\n",
    "\n",
    "# Visualizamos la lista de autovalores en orden desdenciente\n",
    "print('Autovalores en orden descendiente:')\n",
    "for i in eig_pairs:\n",
    "    print(i[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1618f395",
   "metadata": {},
   "source": [
    "#### Se entiende que una vez ordenados los autovalores, que son una medida de la varianza de los datos, la cuestión es decidir, cuál es el menor número de autovectores o componentes principales, con el que podemos expresar “la esencia principal” de la información contenida en ese dataset. Para ello, usaremos un métrica que se conoce como “varianza explicada”, que muestra cuánta varianza se puede atribuir a cada una de estas componentes principales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "64d53b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A partir de los autovalores, calculamos la varianza explicada\n",
    "tot = sum(eig_vals)\n",
    "var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "919c6f4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 11.88554791  20.99738485  28.30977646  35.53412021  42.72848429\n",
      "  49.89817775  57.02464528  64.1291352   71.20765755  78.27116204\n",
      "  85.10961494  91.64427283  97.3211349  100.        ]\n"
     ]
    }
   ],
   "source": [
    "print (cum_var_exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619c6ccc",
   "metadata": {},
   "source": [
    "### Continuamos con el desarrollo del Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "13073ee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Instancio el modelo y lo entreno\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "eb5e932b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercepción: 7.009764631535177\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>host_id</th>\n",
       "      <td>-1.140417e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_identity_verified</th>\n",
       "      <td>-7.027842e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <td>-3.307409e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instant_bookable</th>\n",
       "      <td>-7.707247e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cancellation_policy</th>\n",
       "      <td>-1.065963e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_type</th>\n",
       "      <td>1.749930e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Construction_year</th>\n",
       "      <td>-1.664658e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_fee</th>\n",
       "      <td>4.950057e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minimum_nights</th>\n",
       "      <td>4.632712e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_reviews</th>\n",
       "      <td>-3.196630e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reviews_per_month</th>\n",
       "      <td>1.833012e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_rate_number</th>\n",
       "      <td>4.380031e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <td>-6.462224e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>availability_365</th>\n",
       "      <td>1.640922e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Coefficient\n",
       "host_id                        -1.140417e-12\n",
       "host_identity_verified         -7.027842e-01\n",
       "neighbourhood_group            -3.307409e-01\n",
       "instant_bookable               -7.707247e-02\n",
       "cancellation_policy            -1.065963e-01\n",
       "room_type                       1.749930e-01\n",
       "Construction_year              -1.664658e-02\n",
       "service_fee                     4.950057e+00\n",
       "minimum_nights                  4.632712e-03\n",
       "number_of_reviews              -3.196630e-03\n",
       "reviews_per_month               1.833012e-01\n",
       "review_rate_number              4.380031e-02\n",
       "calculated_host_listings_count -6.462224e-03\n",
       "availability_365                1.640922e-03"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Muestro los valores del modelo\n",
    "print(\"Intercepción:\", regressor.intercept_)\n",
    "coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1c98aa6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98862</th>\n",
       "      <td>1197.0</td>\n",
       "      <td>1189.376313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72510</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>1096.266742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71729</th>\n",
       "      <td>260.0</td>\n",
       "      <td>263.422092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65918</th>\n",
       "      <td>956.0</td>\n",
       "      <td>951.090821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>1144.0</td>\n",
       "      <td>1139.745050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72196</th>\n",
       "      <td>346.0</td>\n",
       "      <td>347.608606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51924</th>\n",
       "      <td>503.0</td>\n",
       "      <td>507.389469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31665</th>\n",
       "      <td>478.0</td>\n",
       "      <td>482.262902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>696.0</td>\n",
       "      <td>694.277025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61413</th>\n",
       "      <td>883.0</td>\n",
       "      <td>882.411438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25650 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual    Predicted\n",
       "98862  1197.0  1189.376313\n",
       "72510  1100.0  1096.266742\n",
       "71729   260.0   263.422092\n",
       "65918   956.0   951.090821\n",
       "2060   1144.0  1139.745050\n",
       "...       ...          ...\n",
       "72196   346.0   347.608606\n",
       "51924   503.0   507.389469\n",
       "31665   478.0   482.262902\n",
       "9194    696.0   694.277025\n",
       "61413   883.0   882.411438\n",
       "\n",
       "[25650 rows x 2 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hago la predicción\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "df_pred = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "df_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b0b89724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='neighbourhood_group'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pruebo la predicción sóbre todo el df, gráfico ilustrativo con una de las variables\n",
    "df[\"pred\"] = regressor.predict(X)\n",
    "\n",
    "charges_by_age = df[[\"neighbourhood_group\", \"price\", \"pred\"]].groupby(\"neighbourhood_group\").agg({\"price\":\"mean\", \"pred\":\"mean\"})\n",
    "charges_by_age.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "397d6d12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5.781009619751096\n",
      "Median Absolute Error: 2.849686861654618\n",
      "Mean Squared Error: 2251.633612486016\n",
      "Root Mean Squared Error: 47.45138156561952\n",
      "r^2 Score: 0.9794900158606676\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Median Absolute Error:', metrics.median_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('r^2 Score:', metrics.r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c49cab",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "### PARA FINALIZAR, COMPLEMENTO EL PROYECTO CON UN MAPA DE NUEVA YORK DONDE HE FILTRADO A LOS 50 PRIMEROS AIRBNB DE LOS 5 DISTRITOS DE NUEVA YORK QUE TIENEN UN REVIEW DE 5.0 (EL MAXIMO PUNTAJE) LO CUAL PERMITIRÁ TENER ESA REFERENCIA DE PRIMERA MANO AL USUARIO."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2531a00f",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "29c43ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraer las 50 primeras filas con review de 5.0\n",
    "review5 = airbnb[\"review_rate_number\"] == 5.0\n",
    "if review5.any():\n",
    "    subset = airbnb[review5].iloc[:50, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "18842f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generar Mapa de New York\n",
    "ny_mapa = folium.Map(location=[40.712776, -74.005974], zoom_start=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "efea34c1",
   "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>country_code</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>room_type</th>\n",
       "      <th>Construction_year</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",
       "      <th>license</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, NAME, host_id, host_identity_verified, host_name, neighbourhood_group, neighbourhood, lat, long, country, country_code, instant_bookable, cancellation_policy, room_type, Construction_year, price, service_fee, minimum_nights, number_of_reviews, last_review, reviews_per_month, review_rate_number, calculated_host_listings_count, availability_365, house_rules, license]\n",
       "Index: []"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Eliminamos los NaN\n",
    "subset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "734f24aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reemplazamos los NaN por 0 ya que nos emite error de contar con NaN aún\n",
    "subset.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "06eb67fd",
   "metadata": {},
   "outputs": [
    {
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       "            \n",
       "                var html_0967f93399291731f6c0754cdc4ba199 = $(`&lt;div id=&quot;html_0967f93399291731f6c0754cdc4ba199&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Magnifique Suite au N de Manhattan - vue Cloitres&lt;/div&gt;`)[0];\n",
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       "\n",
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       "                popup_ebeb1ddabbd83854f800104e4f28dc76.setContent(html_76f5d7c2f667142c5e0b0f5b312a259f);\n",
       "            \n",
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       "        marker_4fde5572ed523dc811bf25c4b492b133.bindPopup(popup_ebeb1ddabbd83854f800104e4f28dc76)\n",
       "        ;\n",
       "\n",
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       "                [40.81175, -73.94478],\n",
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       "        \n",
       "    \n",
       "        var popup_2c3d6c24253ab4b357ab1734eefd73b5 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_100ee2c0db02b23f218c504f623fc9e7 = $(`&lt;div id=&quot;html_100ee2c0db02b23f218c504f623fc9e7&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BEST BET IN HARLEM&lt;/div&gt;`)[0];\n",
       "                popup_2c3d6c24253ab4b357ab1734eefd73b5.setContent(html_100ee2c0db02b23f218c504f623fc9e7);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_30a77a5b8415df306eda7cc09dbf2f47.bindPopup(popup_2c3d6c24253ab4b357ab1734eefd73b5)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_f57e22b4a751ecb1c5ae5ea286ef1076 = L.marker(\n",
       "                [40.65944, -73.96238],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_a0e0bd93b3c1932f78878df4c920d4b4 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_4058dbc7aeec0558a6abca3972f4d792 = $(`&lt;div id=&quot;html_4058dbc7aeec0558a6abca3972f4d792&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Entire apartment in central Brooklyn neighborhood.&lt;/div&gt;`)[0];\n",
       "                popup_a0e0bd93b3c1932f78878df4c920d4b4.setContent(html_4058dbc7aeec0558a6abca3972f4d792);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_f57e22b4a751ecb1c5ae5ea286ef1076.bindPopup(popup_a0e0bd93b3c1932f78878df4c920d4b4)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_c1124a45bf4931c6fde476d8fced2214 = L.marker(\n",
       "                [40.74771, -73.9474],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_e94541eb906057462fc478264e523e0c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_ea4b0815366a1d241e8427164d06ebbf = $(`&lt;div id=&quot;html_ea4b0815366a1d241e8427164d06ebbf&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;1 Stop fr. Manhattan! Private Suite,Landmark Block&lt;/div&gt;`)[0];\n",
       "                popup_e94541eb906057462fc478264e523e0c.setContent(html_ea4b0815366a1d241e8427164d06ebbf);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_c1124a45bf4931c6fde476d8fced2214.bindPopup(popup_e94541eb906057462fc478264e523e0c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_7344c6097eacb870093c1a11d5acc02f = L.marker(\n",
       "                [40.68111, -73.95591],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_4fd077b96a08a77ac043bbe1084b4f8d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d077b5b5a2349db746b00a9af69e621e = $(`&lt;div id=&quot;html_d077b5b5a2349db746b00a9af69e621e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Charming Brownstone 3 - Near PRATT&lt;/div&gt;`)[0];\n",
       "                popup_4fd077b96a08a77ac043bbe1084b4f8d.setContent(html_d077b5b5a2349db746b00a9af69e621e);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_7344c6097eacb870093c1a11d5acc02f.bindPopup(popup_4fd077b96a08a77ac043bbe1084b4f8d)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_7ce59aa9518fd6aaacc5222debd3f8ba = L.marker(\n",
       "                [40.68634, -73.966],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_91e5f723e5664e4455e32bad5d316c44 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_5ebe90896c5d3f07beb9562080955bfa = $(`&lt;div id=&quot;html_5ebe90896c5d3f07beb9562080955bfa&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Large Parlor Room, Landmark Home 1 block to PRATT&lt;/div&gt;`)[0];\n",
       "                popup_91e5f723e5664e4455e32bad5d316c44.setContent(html_5ebe90896c5d3f07beb9562080955bfa);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_7ce59aa9518fd6aaacc5222debd3f8ba.bindPopup(popup_91e5f723e5664e4455e32bad5d316c44)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_fc3d29da91076209c1d6b188e2a39247 = L.marker(\n",
       "                [40.68035, -73.97162],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_1d099e98d4010d2bc59865740d862a09 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_26d97bb2c3bd14d725b45e66f02b6bc5 = $(`&lt;div id=&quot;html_26d97bb2c3bd14d725b45e66f02b6bc5&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;2-bed brownstone duplex + garden&lt;/div&gt;`)[0];\n",
       "                popup_1d099e98d4010d2bc59865740d862a09.setContent(html_26d97bb2c3bd14d725b45e66f02b6bc5);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_fc3d29da91076209c1d6b188e2a39247.bindPopup(popup_1d099e98d4010d2bc59865740d862a09)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_be04fc4e3aedb7e22be8447f9bb950a3 = L.marker(\n",
       "                [40.70984, -73.95775],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_645b4c0f29a62c815fef1e444a30aed3 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_08075d47b20a6c51bd18b7a04782fd6e = $(`&lt;div id=&quot;html_08075d47b20a6c51bd18b7a04782fd6e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Charming 1 bed GR8 WBurg LOCATION!&lt;/div&gt;`)[0];\n",
       "                popup_645b4c0f29a62c815fef1e444a30aed3.setContent(html_08075d47b20a6c51bd18b7a04782fd6e);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_be04fc4e3aedb7e22be8447f9bb950a3.bindPopup(popup_645b4c0f29a62c815fef1e444a30aed3)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_be179d4342d3de7e43aeff9e9befea81 = L.marker(\n",
       "                [40.70093, -73.92609],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_00ce6f51f151b3e30ca6056784e32434 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_4742b9b9b9d1c4f0964c50df5fa26277 = $(`&lt;div id=&quot;html_4742b9b9b9d1c4f0964c50df5fa26277&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;0&lt;/div&gt;`)[0];\n",
       "                popup_00ce6f51f151b3e30ca6056784e32434.setContent(html_4742b9b9b9d1c4f0964c50df5fa26277);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_be179d4342d3de7e43aeff9e9befea81.bindPopup(popup_00ce6f51f151b3e30ca6056784e32434)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e8443f0b793ddee8978f33367454bedc = L.marker(\n",
       "                [40.79764, -73.96177],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_e9eb1e488c095d8f8cd7f486965054ad = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_7edab41b80e48b077e8087bd9aef0fcb = $(`&lt;div id=&quot;html_7edab41b80e48b077e8087bd9aef0fcb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;0&lt;/div&gt;`)[0];\n",
       "                popup_e9eb1e488c095d8f8cd7f486965054ad.setContent(html_7edab41b80e48b077e8087bd9aef0fcb);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e8443f0b793ddee8978f33367454bedc.bindPopup(popup_e9eb1e488c095d8f8cd7f486965054ad)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_dd19622544731264520f8bd1a6f453a5 = L.marker(\n",
       "                [40.82803, -73.94731],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_8add885d3cd29220acf6fde9835f5423 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_52e2865d45f47eca6f5fcb8d5e67cbdf = $(`&lt;div id=&quot;html_52e2865d45f47eca6f5fcb8d5e67cbdf&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;0&lt;/div&gt;`)[0];\n",
       "                popup_8add885d3cd29220acf6fde9835f5423.setContent(html_52e2865d45f47eca6f5fcb8d5e67cbdf);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_dd19622544731264520f8bd1a6f453a5.bindPopup(popup_8add885d3cd29220acf6fde9835f5423)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_80cffb4dfc1590996d11182fc6e13182 = L.marker(\n",
       "                [40.74008, -74.00271],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_7b7b32e0a55611b6a0a825b7d313ea06 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_68c2398b568f851d02e7a685d9bff97d = $(`&lt;div id=&quot;html_68c2398b568f851d02e7a685d9bff97d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;0&lt;/div&gt;`)[0];\n",
       "                popup_7b7b32e0a55611b6a0a825b7d313ea06.setContent(html_68c2398b568f851d02e7a685d9bff97d);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_80cffb4dfc1590996d11182fc6e13182.bindPopup(popup_7b7b32e0a55611b6a0a825b7d313ea06)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_a2cb6a51c9e37f0ed4643897831d47fd = L.marker(\n",
       "                [40.68413, -73.92357],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_282dfd11522a217ef9c5e31a96d3d481 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_aa488af8d5b59ab8983070640f14f1ec = $(`&lt;div id=&quot;html_aa488af8d5b59ab8983070640f14f1ec&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;0&lt;/div&gt;`)[0];\n",
       "                popup_282dfd11522a217ef9c5e31a96d3d481.setContent(html_aa488af8d5b59ab8983070640f14f1ec);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_a2cb6a51c9e37f0ed4643897831d47fd.bindPopup(popup_282dfd11522a217ef9c5e31a96d3d481)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_92331860a7e0f87a5f80868cf584d2e8 = L.marker(\n",
       "                [40.82279, -73.95139],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_f309bda55148fe6119641a723db6d898 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_14647e7ed127ad36d2988b4bea606ba4 = $(`&lt;div id=&quot;html_14647e7ed127ad36d2988b4bea606ba4&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;loft bed - near transportation-15min to times sq&lt;/div&gt;`)[0];\n",
       "                popup_f309bda55148fe6119641a723db6d898.setContent(html_14647e7ed127ad36d2988b4bea606ba4);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_92331860a7e0f87a5f80868cf584d2e8.bindPopup(popup_f309bda55148fe6119641a723db6d898)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_50b89a63e67dea958fa5546e2ba9ba06 = L.marker(\n",
       "                [40.67967, -74.00154],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_f2545e88ccacc74ab2256b4e2a24f25b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_1db0c2c920d160a1c5787ad5e9f9d987 = $(`&lt;div id=&quot;html_1db0c2c920d160a1c5787ad5e9f9d987&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;House On Henry (3rd FLR Suite)&lt;/div&gt;`)[0];\n",
       "                popup_f2545e88ccacc74ab2256b4e2a24f25b.setContent(html_1db0c2c920d160a1c5787ad5e9f9d987);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_50b89a63e67dea958fa5546e2ba9ba06.bindPopup(popup_f2545e88ccacc74ab2256b4e2a24f25b)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_f4aab38daf2c48b68d0409e5b013bc42 = L.marker(\n",
       "                [40.83927, -73.94281],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_57fc4b5fe22ac40590a189cffa0a9f1c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_04de631abdf1d6868cfb97ae53fb54b8 = $(`&lt;div id=&quot;html_04de631abdf1d6868cfb97ae53fb54b8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Monkey Retreat Manhattan&lt;/div&gt;`)[0];\n",
       "                popup_57fc4b5fe22ac40590a189cffa0a9f1c.setContent(html_04de631abdf1d6868cfb97ae53fb54b8);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_f4aab38daf2c48b68d0409e5b013bc42.bindPopup(popup_57fc4b5fe22ac40590a189cffa0a9f1c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_6ff6b9d06168ae2998b835bf137e1201 = L.marker(\n",
       "                [40.71942, -73.95748],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_47c79aabfacd19f8088f4814a6eec883 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_3980a1177ddb2a8c43009803a8fa8797 = $(`&lt;div id=&quot;html_3980a1177ddb2a8c43009803a8fa8797&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;2 story family home in Williamsburg&lt;/div&gt;`)[0];\n",
       "                popup_47c79aabfacd19f8088f4814a6eec883.setContent(html_3980a1177ddb2a8c43009803a8fa8797);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_6ff6b9d06168ae2998b835bf137e1201.bindPopup(popup_47c79aabfacd19f8088f4814a6eec883)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_63dc20838ac2b254e32bff27336ec427 = L.marker(\n",
       "                [40.77823, -73.97637],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_41f3680279426e625729b04d58ca9a1d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_63ae1e79d9d98365d89215ebceff3885 = $(`&lt;div id=&quot;html_63ae1e79d9d98365d89215ebceff3885&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;0&lt;/div&gt;`)[0];\n",
       "                popup_41f3680279426e625729b04d58ca9a1d.setContent(html_63ae1e79d9d98365d89215ebceff3885);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_63dc20838ac2b254e32bff27336ec427.bindPopup(popup_41f3680279426e625729b04d58ca9a1d)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_f70567eb4bca7837fba28a2e812c1234 = L.marker(\n",
       "                [40.72555, -73.97965],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_9fadb3d0854e6aef905ba35288257912 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_c5f09d4c56f45c5d024afac90fb4a28c = $(`&lt;div id=&quot;html_c5f09d4c56f45c5d024afac90fb4a28c&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Great Large 1 BR apt  in East Village!&lt;/div&gt;`)[0];\n",
       "                popup_9fadb3d0854e6aef905ba35288257912.setContent(html_c5f09d4c56f45c5d024afac90fb4a28c);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_f70567eb4bca7837fba28a2e812c1234.bindPopup(popup_9fadb3d0854e6aef905ba35288257912)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_cde3c194601909665d13d17019f26c45 = L.marker(\n",
       "                [40.66831, -73.98604],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_61ffa66e38621810de6f2a6f282208d4 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_ed1f237e8a32014f30a7974cc9a67077 = $(`&lt;div id=&quot;html_ed1f237e8a32014f30a7974cc9a67077&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Eveland Private Bed &amp; Living Room w/ Own Entrance&lt;/div&gt;`)[0];\n",
       "                popup_61ffa66e38621810de6f2a6f282208d4.setContent(html_ed1f237e8a32014f30a7974cc9a67077);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_cde3c194601909665d13d17019f26c45.bindPopup(popup_61ffa66e38621810de6f2a6f282208d4)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e6451ce6fe9bfded33ac7f8a03a313c4 = L.marker(\n",
       "                [40.82754, -73.94919],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_b222f513ed42723bdcf932bac3893f61 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_05f424f201377b619f1ef0c93ff8ed0f = $(`&lt;div id=&quot;html_05f424f201377b619f1ef0c93ff8ed0f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Sugar Hill Rest Stop&lt;/div&gt;`)[0];\n",
       "                popup_b222f513ed42723bdcf932bac3893f61.setContent(html_05f424f201377b619f1ef0c93ff8ed0f);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e6451ce6fe9bfded33ac7f8a03a313c4.bindPopup(popup_b222f513ed42723bdcf932bac3893f61)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_ab4d27ebcc22d0655e2dbe96b8c6dbe2 = L.marker(\n",
       "                [40.66499, -73.97925],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_a370d485346b379b2873e5c3fa24791b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_872abe42bddb291c4da8aef32df6b24a = $(`&lt;div id=&quot;html_872abe42bddb291c4da8aef32df6b24a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;3 Story Town House in Park Slope&lt;/div&gt;`)[0];\n",
       "                popup_a370d485346b379b2873e5c3fa24791b.setContent(html_872abe42bddb291c4da8aef32df6b24a);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_ab4d27ebcc22d0655e2dbe96b8c6dbe2.bindPopup(popup_a370d485346b379b2873e5c3fa24791b)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_174984fd67d2d87574ba2b23d2399479 = L.marker(\n",
       "                [40.77842, -73.97556],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_481e0c80058cfb496fa1426996b26b8d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_05fc85e2dab8c55769f035e3cbd5d072 = $(`&lt;div id=&quot;html_05fc85e2dab8c55769f035e3cbd5d072&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;apartment next to Central park&lt;/div&gt;`)[0];\n",
       "                popup_481e0c80058cfb496fa1426996b26b8d.setContent(html_05fc85e2dab8c55769f035e3cbd5d072);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_174984fd67d2d87574ba2b23d2399479.bindPopup(popup_481e0c80058cfb496fa1426996b26b8d)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_bb858cb90ab91e39e14929737fb6d44b = L.marker(\n",
       "                [40.67992, -73.9475],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_2ac6a88a4394d0740db90026fda50f12 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_dd9e4119516aa249f66e9d773e766ac8 = $(`&lt;div id=&quot;html_dd9e4119516aa249f66e9d773e766ac8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;SAFE  AND BEAUTIFUL ACCOMODATION&lt;/div&gt;`)[0];\n",
       "                popup_2ac6a88a4394d0740db90026fda50f12.setContent(html_dd9e4119516aa249f66e9d773e766ac8);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_bb858cb90ab91e39e14929737fb6d44b.bindPopup(popup_2ac6a88a4394d0740db90026fda50f12)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e382128762dd1759d5d01523e0fb28af = L.marker(\n",
       "                [40.67868, -73.97307],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_186d4df8d41e0dd1304210e9de24bdfe = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_5449de727be97197ce997cf896104240 = $(`&lt;div id=&quot;html_5449de727be97197ce997cf896104240&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Large Room in private Brownstone in Park Slope&lt;/div&gt;`)[0];\n",
       "                popup_186d4df8d41e0dd1304210e9de24bdfe.setContent(html_5449de727be97197ce997cf896104240);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e382128762dd1759d5d01523e0fb28af.bindPopup(popup_186d4df8d41e0dd1304210e9de24bdfe)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_be951bcd4b3c8950bc95bf8dedc4fa22 = L.marker(\n",
       "                [40.76834, -73.95334],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_360a5d489464ba948c4072c0dedb9338 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_22d43696090656f031da5ee3e99e60c8 = $(`&lt;div id=&quot;html_22d43696090656f031da5ee3e99e60c8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;NEW YORK CITY, 1 BDRM.(NEAR CENTRAL PARK &amp; METRO)&lt;/div&gt;`)[0];\n",
       "                popup_360a5d489464ba948c4072c0dedb9338.setContent(html_22d43696090656f031da5ee3e99e60c8);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_be951bcd4b3c8950bc95bf8dedc4fa22.bindPopup(popup_360a5d489464ba948c4072c0dedb9338)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_2b511bb12d46bdfefe4f47e79a8bf980 = L.marker(\n",
       "                [40.68237, -73.9415],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_a3ed889215295b90dff67b91af4af652 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_ac96996a6ae100651668a1d41644a601 = $(`&lt;div id=&quot;html_ac96996a6ae100651668a1d41644a601&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;LARGE, COMFY 1BDR W/CHARACTER!!!&lt;/div&gt;`)[0];\n",
       "                popup_a3ed889215295b90dff67b91af4af652.setContent(html_ac96996a6ae100651668a1d41644a601);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_2b511bb12d46bdfefe4f47e79a8bf980.bindPopup(popup_a3ed889215295b90dff67b91af4af652)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_7aa64a9d2e51351ebadb0cb0abab165b = L.marker(\n",
       "                [40.74031, -73.99999],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_f83b8ff3f0f46c36fd1938fd785f7fb1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_316e7da65aa45d9aa7f61632ea03c682 = $(`&lt;div id=&quot;html_316e7da65aa45d9aa7f61632ea03c682&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Chelsea Studio sublet 1 - 2 months&lt;/div&gt;`)[0];\n",
       "                popup_f83b8ff3f0f46c36fd1938fd785f7fb1.setContent(html_316e7da65aa45d9aa7f61632ea03c682);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_7aa64a9d2e51351ebadb0cb0abab165b.bindPopup(popup_f83b8ff3f0f46c36fd1938fd785f7fb1)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_93a32c8ecb2ef2300296ddcb32671060 = L.marker(\n",
       "                [40.64446, -73.9503],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_a08f7192d76ca7535bba48069867baaa = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_9a2e4104a4ef5a7c7c817c3b51b93e4e = $(`&lt;div id=&quot;html_9a2e4104a4ef5a7c7c817c3b51b93e4e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BK Sweet Suite w/Kitchen&amp;FullBath&lt;/div&gt;`)[0];\n",
       "                popup_a08f7192d76ca7535bba48069867baaa.setContent(html_9a2e4104a4ef5a7c7c817c3b51b93e4e);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_93a32c8ecb2ef2300296ddcb32671060.bindPopup(popup_a08f7192d76ca7535bba48069867baaa)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_2614c3b56aab6508b170562e71b53ea1 = L.marker(\n",
       "                [40.7268, -73.99079],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_edc48216bfca87c87342be0ccfb77973 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e921158fd24cb87c102a3da4a979b6d8 = $(`&lt;div id=&quot;html_e921158fd24cb87c102a3da4a979b6d8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Great East Village Apartment Rental&lt;/div&gt;`)[0];\n",
       "                popup_edc48216bfca87c87342be0ccfb77973.setContent(html_e921158fd24cb87c102a3da4a979b6d8);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_2614c3b56aab6508b170562e71b53ea1.bindPopup(popup_edc48216bfca87c87342be0ccfb77973)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_d737550fb4a0d250d8c9e87e14a60774 = L.marker(\n",
       "                [40.688, -73.9171],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_290443c833dc3e132989e73e930e12d2 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_668fe40bb7c7f25816c213f2fadd3ee2 = $(`&lt;div id=&quot;html_668fe40bb7c7f25816c213f2fadd3ee2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BROWNSTONE SUNDRENCHED BEAUTY&lt;/div&gt;`)[0];\n",
       "                popup_290443c833dc3e132989e73e930e12d2.setContent(html_668fe40bb7c7f25816c213f2fadd3ee2);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d737550fb4a0d250d8c9e87e14a60774.bindPopup(popup_290443c833dc3e132989e73e930e12d2)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_28599e94d46e5b92924db7e6b642b3d4 = L.marker(\n",
       "                [40.63536, -74.08537],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_dc9bf4318c70821377cea8897abfb1ce = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_fda37867ce5ad8b45048646dc0999224 = $(`&lt;div id=&quot;html_fda37867ce5ad8b45048646dc0999224&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;A SpeciaL!! Private Room in NY&lt;/div&gt;`)[0];\n",
       "                popup_dc9bf4318c70821377cea8897abfb1ce.setContent(html_fda37867ce5ad8b45048646dc0999224);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_28599e94d46e5b92924db7e6b642b3d4.bindPopup(popup_dc9bf4318c70821377cea8897abfb1ce)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_695f403339196ba00f2bfdf1bca66e5a = L.marker(\n",
       "                [40.63627, -74.08543],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_81c42784c960fd9152a57ca502a83308 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e9989665f784c5c725eca6e079d08457 = $(`&lt;div id=&quot;html_e9989665f784c5c725eca6e079d08457&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;B NYC Staten Alternative...&lt;/div&gt;`)[0];\n",
       "                popup_81c42784c960fd9152a57ca502a83308.setContent(html_e9989665f784c5c725eca6e079d08457);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_695f403339196ba00f2bfdf1bca66e5a.bindPopup(popup_81c42784c960fd9152a57ca502a83308)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_43e54bdc0eec986ceb408bb1149b5be8 = L.marker(\n",
       "                [40.63518, -74.08546],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_11eb955ba8e14adbdf1cece8ee51f5dc = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_f0b0aa8eb53a7c9ab46b35308973364f = $(`&lt;div id=&quot;html_f0b0aa8eb53a7c9ab46b35308973364f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;C Private Room By The Ferry&lt;/div&gt;`)[0];\n",
       "                popup_11eb955ba8e14adbdf1cece8ee51f5dc.setContent(html_f0b0aa8eb53a7c9ab46b35308973364f);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_43e54bdc0eec986ceb408bb1149b5be8.bindPopup(popup_11eb955ba8e14adbdf1cece8ee51f5dc)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_141db4c93be514ec3281ea0cc2364bce = L.marker(\n",
       "                [40.86648, -73.9263],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_6dfdbb72ca66bcd2dd94c2a397c03320 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_16cb0c00a7fdb8d00229fa785b66980b = $(`&lt;div id=&quot;html_16cb0c00a7fdb8d00229fa785b66980b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;HOSTING YOUR  SUNNY, SPACIOUS NYC ROOM&lt;/div&gt;`)[0];\n",
       "                popup_6dfdbb72ca66bcd2dd94c2a397c03320.setContent(html_16cb0c00a7fdb8d00229fa785b66980b);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_141db4c93be514ec3281ea0cc2364bce.bindPopup(popup_6dfdbb72ca66bcd2dd94c2a397c03320)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_375ae83729b0cdc915f908896fea9491 = L.marker(\n",
       "                [40.7069, -73.95467],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_4b4ae2054429191cbe0a5ef3c1d35bdd = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e0d0f7579c8fbd444f261e42b356ccbc = $(`&lt;div id=&quot;html_e0d0f7579c8fbd444f261e42b356ccbc&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Williamsburg near soho. / loftbed&lt;/div&gt;`)[0];\n",
       "                popup_4b4ae2054429191cbe0a5ef3c1d35bdd.setContent(html_e0d0f7579c8fbd444f261e42b356ccbc);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_375ae83729b0cdc915f908896fea9491.bindPopup(popup_4b4ae2054429191cbe0a5ef3c1d35bdd)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_658d53833bb84466d3f072448da54c7f = L.marker(\n",
       "                [40.72807, -73.98594],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_d84c90956390f23af5d115e3ad78889c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_105a37a5de2a50079ea52f561b4d13ca = $(`&lt;div id=&quot;html_105a37a5de2a50079ea52f561b4d13ca&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Prime East Village 1 Bedroom&lt;/div&gt;`)[0];\n",
       "                popup_d84c90956390f23af5d115e3ad78889c.setContent(html_105a37a5de2a50079ea52f561b4d13ca);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_658d53833bb84466d3f072448da54c7f.bindPopup(popup_d84c90956390f23af5d115e3ad78889c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e2d46a4967447118e667aa159ea5e7cb = L.marker(\n",
       "                [40.6778, -73.94339],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_bebb10df87d3eb4c631a17ca8adc19ca = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_02a4688d776a51d7b00a9b61696bee42 = $(`&lt;div id=&quot;html_02a4688d776a51d7b00a9b61696bee42&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BROOKLYN  STUDIO APARTMENT&lt;/div&gt;`)[0];\n",
       "                popup_bebb10df87d3eb4c631a17ca8adc19ca.setContent(html_02a4688d776a51d7b00a9b61696bee42);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e2d46a4967447118e667aa159ea5e7cb.bindPopup(popup_bebb10df87d3eb4c631a17ca8adc19ca)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_d6ec1972c29c23b310c838ad3b6efe03 = L.marker(\n",
       "                [40.68317, -73.94701],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_20acd44c2f156f37ad581a5b3c0a615c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d557b8c17235870e07a8718a39e476d0 = $(`&lt;div id=&quot;html_d557b8c17235870e07a8718a39e476d0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;0&lt;/div&gt;`)[0];\n",
       "                popup_20acd44c2f156f37ad581a5b3c0a615c.setContent(html_d557b8c17235870e07a8718a39e476d0);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d6ec1972c29c23b310c838ad3b6efe03.bindPopup(popup_20acd44c2f156f37ad581a5b3c0a615c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_0e93a73275418f253c653ba5b39cf2ed = L.marker(\n",
       "                [40.73738, -73.95482],\n",
       "                {}\n",
       "            ).addTo(map_b2ac390df93ddfa5f3f43051491b4bc8);\n",
       "        \n",
       "    \n",
       "        var popup_df67a59a787392c59a8f4db0f04fba31 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_7dcc9ec13519e7003d7361d876922216 = $(`&lt;div id=&quot;html_7dcc9ec13519e7003d7361d876922216&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Loft w/ Terrace @ Box House Hotel&lt;/div&gt;`)[0];\n",
       "                popup_df67a59a787392c59a8f4db0f04fba31.setContent(html_7dcc9ec13519e7003d7361d876922216);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_0e93a73275418f253c653ba5b39cf2ed.bindPopup(popup_df67a59a787392c59a8f4db0f04fba31)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var tile_layer_711cba8861d2836f0c1caeb30602ccab = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            );\n",
       "            var mini_map_3a7d6f834120d594c3059cfe0edc650b = new L.Control.MiniMap(\n",
       "                tile_layer_711cba8861d2836f0c1caeb30602ccab,\n",
       "                {&quot;autoToggleDisplay&quot;: false, &quot;centerFixed&quot;: false, &quot;collapsedHeight&quot;: 25, &quot;collapsedWidth&quot;: 25, &quot;height&quot;: 150, &quot;minimized&quot;: false, &quot;position&quot;: &quot;bottomright&quot;, &quot;toggleDisplay&quot;: false, &quot;width&quot;: 150, &quot;zoomAnimation&quot;: false, &quot;zoomLevelOffset&quot;: -5}\n",
       "            );\n",
       "            map_b2ac390df93ddfa5f3f43051491b4bc8.addControl(mini_map_3a7d6f834120d594c3059cfe0edc650b);\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1e206a04820>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mostramos en un Mapa los 50 primeros AIRBNB con Review 5.0 ubicados en los 5 distritos de Nueva York, en los cuales si se da click en el marcador de ubicación se podrá tener un popup con una referencia del lugar. Complementario a ello se le puso un Minimap de ubicación en la parte inferior derecha.\n",
    "for index, row in subset.iterrows():\n",
    "    folium.Marker(location=[row['lat'], row['long']], popup=row['NAME']).add_to(ny_mapa)\n",
    "\n",
    "minimap = MiniMap()\n",
    "ny_mapa.add_child(minimap)\n",
    "ny_mapa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "070d4d9e",
   "metadata": {},
   "source": [
    "### ------------------------------------------------------------ FIN DEL PROYECTO AIRBNB -------------------------------------------------------"
   ]
  }
 ],
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