hostelworld_soup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1008,
   "id": "a7fed92e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from requests import get\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, median_absolute_error, max_error, mean_squared_log_error\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "import time\n",
    "\n",
    "import re\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "from yellowbrick.regressor import ResidualsPlot, prediction_error, residuals_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "15ab10b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the following South American countries have more than three hostels listed on HostelWorld\n",
    "country_list = ['Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Peru', 'Uruguay']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "c3289c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Website has drop-down menu of cities in each country represented on HostelWorld\n",
    "\n",
    "soup_argentina_html = '''\n",
    "<select name=\"city\" id=\"citySelect\" class=\"needsclick\" style=\"display: inline-block;\">\n",
    "                                    \n",
    "                                <option value=\"\">Select a city</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/bariloche\">Bariloche</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/barreal\">Barreal</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/buenos-aires\">Buenos Aires</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/cachi\">Cachi</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/cafayate\">Cafayate</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/concordia\">Concordia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/cordoba\">Cordoba</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/el-bolson\">El Bolson</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/el-calafate\">El Calafate</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/el-chalten\">El Chalten</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/esquel\">Esquel</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/gualjaina\">Gualjaina</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/humahuaca\">Humahuaca</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/la-plata\">La Plata</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/mar-del-plata\">Mar del Plata</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/mendoza\">Mendoza</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/mina-clavero\">Mina Clavero</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/puerto-iguazu\">Puerto Iguazu</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/puerto-madryn\">Puerto Madryn</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/purmamarca\">Purmamarca</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/rada-tilly\">Rada Tilly</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/rosario\">Rosario</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/salta\">Salta</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/san-ignacio\">San Ignacio</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/san-lorenzo\">San Lorenzo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/san-martin-de-los-andes\">San Martin De Los Andes</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/san-miguel-de-tucuman\">San Miguel de Tucuman</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/san-pedro\">San Pedro</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/san-rafael\">San Rafael</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/san-salvador-de-jujuy\">San Salvador de Jujuy</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/tandil\">Tandil</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/tilcara\">Tilcara</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/ushuaia\">Ushuaia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/villa-carlos-paz\">Villa Carlos Paz</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/villa-gesell\">Villa Gesell</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/villa-la-angostura\">Villa La Angostura</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/argentina/villa-pehuenia\">Villa Pehuenia</option></select>\n",
    "'''\n",
    "\n",
    "soup_bolivia_html = '''\n",
    "<select name=\"city\" id=\"citySelect\" class=\"needsclick\" style=\"display: inline-block;\">\n",
    "                                    \n",
    "                                <option value=\"\">Select a city</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/bolivia/copacabana\">Copacabana</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/bolivia/la-paz\">La Paz</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/bolivia/rurrenabaque\">Rurrenabaque</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/bolivia/samaipata\">Samaipata</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/bolivia/santa-cruz-de-la-sierra\">Santa Cruz de la Sierra</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/bolivia/sucre\">Sucre</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/bolivia/tupiza\">Tupiza</option></select>\n",
    "'''\n",
    "\n",
    "soup_brazil_html = '''\n",
    "<select name=\"city\" id=\"citySelect\" class=\"needsclick\" style=\"display: inline-block;\">\n",
    "                                    \n",
    "                                <option value=\"\">Select a city</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/alcantara\">Alcantara</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/alter-do-chao\">Alter do Chao</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/andarai\">Andarai</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/aparecida\">Aparecida</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/arraial-d-ajuda\">Arraial D'Ajuda</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/arraial-do-cabo\">Arraial do Cabo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/balneario-camboriu\">Balneario Camboriu</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/barreirinhas\">Barreirinhas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/belem\">Belem</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/belo-horizonte\">Belo Horizonte</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/bertioga\">Bertioga</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/boa-vista\">Boa Vista</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/bombinhas\">Bombinhas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/bonito\">Bonito</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/brasilia\">Brasilia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/buzios\">Buzios</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/cabo-frio\">Cabo Frio</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/cajueiro-da-praia\">Cajueiro da Praia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/cambara-do-sul\">Cambara do Sul</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/canoa-quebrada\">Canoa Quebrada</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/caraiva\">Caraiva</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/caxias-do-sul\">Caxias Do Sul</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/corumba\">Corumba</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/curitiba\">Curitiba</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/embu-das-artes\">Embu das Artes</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/fernando-de-noronha\">Fernando de Noronha</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/florianopolis\">Florianopolis</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/fortaleza\">Fortaleza</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/foz-do-iguacu\">Foz do Iguacu</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/gramado\">Gramado</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/guarda-do-embau\">Guarda do Embau</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/guaruja\">Guaruja</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/guarulhos\">Guarulhos</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/ilha-grande\">Ilha Grande</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/ilhabela\">Ilhabela</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/imbassai\">Imbassai</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/itacare\">Itacare</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/itanhaem\">Itanhaem</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/jericoacoara\">Jericoacoara</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/joao-pessoa\">Joao Pessoa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/lencois\">Lencois</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/maceio\">Maceio</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/manaus\">Manaus</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/maragogi\">Maragogi</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/mata-de-sao-joao\">Mata de São João</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/morro-de-sao-paulo\">Morro de Sao Paulo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/natal\">Natal</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/niteroi\">Niteroi</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/ouro-preto\">Ouro Preto</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/palmas\">Palmas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/paraty\">Paraty</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/parnaiba\">Parnaiba</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/penha\">Penha</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/pipa\">Pipa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/piracicaba\">Piracicaba</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/porto-alegre\">Porto Alegre</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/porto-belo\">Porto Belo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/porto-de-galinhas\">Porto de Galinhas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/praia-do-forte\">Praia do Forte</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/praia-do-rosa\">Praia do Rosa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/praia-grande\">Praia Grande</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/presidente-figuereido\">Presidente Figuereido</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/recife\">Recife</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/rio-das-ostras\">Rio das Ostras</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/rio-de-janeiro\">Rio de Janeiro</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/salvador\">Salvador</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/santos\">Santos</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/sao-bento-do-sul\">Sao Bento do Sul</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/sao-joao-del-rei\">Sao Joao Del Rei</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/sao-luis\">Sao Luis</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/sao-paulo\">Sao Paulo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/sao-sebastiao\">Sao Sebastiao</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/saquarema\">Saquarema</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/serra-do-cipo\">Serra do Cipo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/tiradentes\">Tiradentes</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/torres\">Torres</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/trindade\">Trindade</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/ubatuba\">Ubatuba</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/brazil/vitoria\">Vitoria</option></select>\n",
    "'''\n",
    "\n",
    "soup_chile_html = '''\n",
    "<select name=\"city\" id=\"citySelect\" class=\"needsclick\" style=\"display: inline-block;\">\n",
    "                                    \n",
    "                                <option value=\"\">Select a city</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/arica\">Arica</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/chiloe\">Chiloe</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/coyhaique\">Coyhaique</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/elqui-valley\">Elqui Valley</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/iquique\">Iquique</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/la-serena\">La Serena</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/pichilemu\">Pichilemu</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/pucon\">Pucon</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/puerto-natales\">Puerto Natales</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/puerto-varas\">Puerto Varas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/punta-arenas\">Punta Arenas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/san-pedro-de-atacama\">San Pedro de Atacama</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/santiago\">Santiago</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/valdivia\">Valdivia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/valparaiso\">Valparaiso</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/vicuna\">Vicuna</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/chile/vina-del-mar\">Vina del Mar</option></select>\n",
    "'''\n",
    "\n",
    "soup_colombia_html = '''\n",
    "<select name=\"city\" id=\"citySelect\" class=\"needsclick\" style=\"display: inline-block;\">\n",
    "                                    \n",
    "                                <option value=\"\">Select a city</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/abejorral\">Abejorral</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/arcabuco\">Arcabuco</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/armenia\">Armenia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/bahia-solano\">Bahia Solano</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/barbosa\">Barbosa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/barichara\">Barichara</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/barranquilla\">Barranquilla</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/baru-island\">Baru Island</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/bogota\">Bogota</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/bucaramanga\">Bucaramanga</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/buenaventura\">Buenaventura</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/cali\">Cali</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/capurgana\">Capurgana</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/cartagena\">Cartagena</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/choachi\">Choachi</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/cienaga\">Cienaga</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/el-penol\">El Penol</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/filandia\">Filandia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/galerazamba\">Galerazamba</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/guachaca\">Guachaca</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/guadalupe\">Guadalupe</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/guaduas\">Guaduas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/guamal\">Guamal</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/guatape\">Guatape</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/isla-fuerte\">Isla Fuerte</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/isla-palma\">Isla Palma</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/jardin\">Jardin</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/jerico\">Jerico</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/la-mesa\">La Mesa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/leticia\">Leticia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/manizales\">Manizales</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/medellin\">Medellin</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/minca\">Minca</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/mocoa\">Mocoa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/mompos\">Mompos</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/mongui\">Mongui</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/neiva\">Neiva</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/nuqui\">Nuqui</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/palomino\">Palomino</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/pasto\">Pasto</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/pereira\">Pereira</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/popayan\">Popayan</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/pueblo-bello\">Pueblo Bello</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/puerto-colombia\">Puerto Colombia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/puerto-narino\">Puerto Narino</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/rincon-del-mar\">Rincon del Mar</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/riohacha\">Riohacha</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/rosario-island\">Rosario Island</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/salento\">Salento</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/salgar\">Salgar</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/san-agustin\">San Agustin</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/san-andres-island\">San Andres Island</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/san-gil\">San Gil</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/san-rafael\">San Rafael</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/santa-fe-de-antioquia\">Santa Fe de Antioquia</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/santa-marta\">Santa Marta</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/santa-rosa-de-cabal\">Santa Rosa de Cabal</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/sapzurro\">Sapzurro</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/sogamoso\">Sogamoso</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/taganga\">Taganga</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/tintipan-island\">Tintipan Island</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/villa-de-leyva\">Villa de Leyva</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/villavicencio\">Villavicencio</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/villavieja\">Villavieja</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/colombia/zipaquira\">Zipaquira</option></select>\n",
    "'''\n",
    "\n",
    "soup_ecuador_html = '''\n",
    "<select name=\"city\" id=\"citySelect\" class=\"needsclick\" style=\"display: inline-block;\">\n",
    "                                    \n",
    "                                <option value=\"\">Select a city</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/alausi\">Alausi</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/atacames\">Atacames</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/banos\">Banos</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/banos-de-agua-santa\">Banos de Agua Santa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/canoa\">Canoa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/chugchilan\">Chugchilan</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/cuenca\">Cuenca</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/guayaquil\">Guayaquil</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/ibarra\">Ibarra</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/las-tunas\">Las Tunas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/latacunga\">Latacunga</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/machachi\">Machachi</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/manglaralto\">Manglaralto</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/mindo\">Mindo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/misahualli\">Misahualli</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/mompiche\">Mompiche</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/montanita\">Montanita</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/olon\">Olon</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/otavalo\">Otavalo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/puerto-lopez\">Puerto Lopez</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/quito\">Quito</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/riobamba\">Riobamba</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/salinas\">Salinas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/santa-marianita\">Santa Marianita</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/tababela\">Tababela</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/tena\">Tena</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/ecuador/tonsupa\">Tonsupa</option></select>\n",
    "'''\n",
    "\n",
    "soup_peru_html = '''\n",
    "<select name=\"city\" id=\"citySelect\" class=\"needsclick\" style=\"display: inline-block;\">\n",
    "                                    \n",
    "                                <option value=\"\">Select a city</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/aguas-calientes\">Aguas Calientes</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/arequipa\">Arequipa</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/carhuaz\">Carhuaz</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/chachapoyas\">Chachapoyas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/cusco\">Cusco</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/huacachina\">Huacachina</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/huanchaco\">Huanchaco</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/huaraz\">Huaraz</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/ica\">Ica</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/iquitos\">Iquitos</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/lima\">Lima</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/lobitos\">Lobitos</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/machu-picchu\">Machu Picchu</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/mancora\">Mancora</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/nazca\">Nazca</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/ollantaytambo\">Ollantaytambo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/paracas\">Paracas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/pisac\">Pisac</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/puerto-malabrigo-chicama\">Puerto Malabrigo Chicama</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/puerto-maldonado\">Puerto Maldonado</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/puno\">Puno</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/tarapoto\">Tarapoto</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/trujillo\">Trujillo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/peru/urubamba\">Urubamba</option></select>\n",
    "'''\n",
    "\n",
    "soup_uruguay_html = '''\n",
    "<select name=\"city\" id=\"citySelect\" class=\"needsclick\" style=\"display: inline-block;\">\n",
    "                                    \n",
    "                                <option value=\"\">Select a city</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/atlantida\">Atlantida</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/barra-de-valizas\">Barra de Valizas</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/cabo-polonio\">Cabo Polonio</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/carmelo\">Carmelo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/colonia-del-sacramento\">Colonia del Sacramento</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/la-paloma\">La Paloma</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/la-pedrera\">La Pedrera</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/montevideo\">Montevideo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/piriapolis\">Piriapolis</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/punta-del-diablo\">Punta del Diablo</option><option value=\"https://www.hostelworld.com/st/hostels/south-america/uruguay/punta-del-este\">Punta del Este</option></select>\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "edaef462",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract all city names from above html\n",
    "def get_cities(country):\n",
    "    countries = {\n",
    "        \"argentina\": BeautifulSoup(soup_argentina_html, \"lxml\"),\n",
    "        \"brazil\": BeautifulSoup(soup_brazil_html, \"lxml\"),\n",
    "        \"bolivia\": BeautifulSoup(soup_bol_html, \"lxml\"),\n",
    "        \"chile\": BeautifulSoup(soup_chile_html, \"lxml\"),\n",
    "        \"colombia\": BeautifulSoup(soup_col_html, \"lxml\"),\n",
    "        \"ecuador\": BeautifulSoup(soup_ecuador_html, \"lxml\"),\n",
    "        \"peru\": BeautifulSoup(soup_peru_html, \"lxml\"),\n",
    "        \"uruguay\": BeautifulSoup(soup_uruguay_html, \"lxml\"),\n",
    "    }\n",
    "    \n",
    "    country_str = str(countries[country.lower()].prettify())\n",
    "    pattern = \"\\n    (.*?)\\n\"\n",
    "    cities = re.findall(pattern, country_str)[1:]\n",
    "\n",
    "    return cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "id": "7403b41a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bariloche', 'Barreal', 'Buenos Aires', 'Cachi', 'Cafayate', 'Concordia', 'Cordoba', 'El Bolson', 'El Calafate', 'El Chalten', 'Esquel', 'Gualjaina', 'Humahuaca', 'La Plata', 'Mar del Plata', 'Mendoza', 'Mina Clavero', 'Puerto Iguazu', 'Puerto Madryn', 'Purmamarca', 'Rada Tilly', 'Rosario', 'Salta', 'San Ignacio', 'San Lorenzo', 'San Martin De Los Andes', 'San Miguel de Tucuman', 'San Pedro', 'San Rafael', 'San Salvador de Jujuy', 'Tandil', 'Tilcara', 'Ushuaia', 'Villa Carlos Paz', 'Villa Gesell', 'Villa La Angostura', 'Villa Pehuenia', 'Copacabana', 'La Paz', 'Rurrenabaque', 'Samaipata', 'Santa Cruz de la Sierra', 'Sucre', 'Tupiza', 'Alcantara', 'Alter do Chao', 'Andarai', 'Aparecida', \"Arraial D'Ajuda\", 'Arraial do Cabo', 'Balneario Camboriu', 'Barreirinhas', 'Belem', 'Belo Horizonte', 'Bertioga', 'Boa Vista', 'Bombinhas', 'Bonito', 'Brasilia', 'Buzios', 'Cabo Frio', 'Cajueiro da Praia', 'Cambara do Sul', 'Canoa Quebrada', 'Caraiva', 'Caxias Do Sul', 'Corumba', 'Curitiba', 'Embu das Artes', 'Fernando de Noronha', 'Florianopolis', 'Fortaleza', 'Foz do Iguacu', 'Gramado', 'Guarda do Embau', 'Guaruja', 'Guarulhos', 'Ilha Grande', 'Ilhabela', 'Imbassai', 'Itacare', 'Itanhaem', 'Jericoacoara', 'Joao Pessoa', 'Lencois', 'Maceio', 'Manaus', 'Maragogi', 'Mata de Sao Joao', 'Morro de Sao Paulo', 'Natal', 'Niteroi', 'Ouro Preto', 'Palmas', 'Paraty', 'Parnaiba', 'Penha', 'Pipa', 'Piracicaba', 'Porto Alegre', 'Porto Belo', 'Porto de Galinhas', 'Praia do Forte', 'Praia do Rosa', 'Praia Grande', 'Presidente Figuereido', 'Recife', 'Rio das Ostras', 'Rio de Janeiro', 'Salvador', 'Santos', 'Sao Bento do Sul', 'Sao Joao Del Rei', 'Sao Luis', 'Sao Paulo', 'Sao Sebastiao', 'Saquarema', 'Serra do Cipo', 'Tiradentes', 'Torres', 'Trindade', 'Ubatuba', 'Vitoria', 'Arica', 'Chiloe', 'Coyhaique', 'Elqui Valley', 'Iquique', 'La Serena', 'Pichilemu', 'Pucon', 'Puerto Natales', 'Puerto Varas', 'Punta Arenas', 'San Pedro de Atacama', 'Santiago', 'Valdivia', 'Valparaiso', 'Vicuna', 'Vina del Mar', 'Abejorral', 'Arcabuco', 'Armenia', 'Bahia Solano', 'Barbosa', 'Barichara', 'Barranquilla', 'Baru Island', 'Bogota', 'Bucaramanga', 'Buenaventura', 'Cali', 'Capurgana', 'Cartagena', 'Choachi', 'Cienaga', 'El Penol', 'Filandia', 'Galerazamba', 'Guachaca', 'Guadalupe', 'Guaduas', 'Guamal', 'Guatape', 'Isla Fuerte', 'Isla Palma', 'Jardin', 'Jerico', 'La Mesa', 'Leticia', 'Manizales', 'Medellin', 'Minca', 'Mocoa', 'Mompos', 'Mongui', 'Neiva', 'Nuqui', 'Palomino', 'Pasto', 'Pereira', 'Popayan', 'Pueblo Bello', 'Puerto Colombia', 'Puerto Narino', 'Rincon del Mar', 'Riohacha', 'Rosario Island', 'Salento', 'Salgar', 'San Agustin', 'San Andres Island', 'San Gil', 'San Rafael', 'Santa Fe de Antioquia', 'Santa Marta', 'Santa Rosa de Cabal', 'Sapzurro', 'Sogamoso', 'Taganga', 'Tintipan Island', 'Villa de Leyva', 'Villavicencio', 'Villavieja', 'Zipaquira', 'Alausi', 'Atacames', 'Banos', 'Banos de Agua Santa', 'Canoa', 'Chugchilan', 'Cuenca', 'Guayaquil', 'Ibarra', 'Las Tunas', 'Latacunga', 'Machachi', 'Manglaralto', 'Mindo', 'Misahualli', 'Mompiche', 'Montanita', 'Olon', 'Otavalo', 'Puerto Lopez', 'Quito', 'Riobamba', 'Salinas', 'Santa Marianita', 'Tababela', 'Tena', 'Tonsupa', 'Aguas Calientes', 'Arequipa', 'Carhuaz', 'Chachapoyas', 'Cusco', 'Huacachina', 'Huanchaco', 'Huaraz', 'Ica', 'Iquitos', 'Lima', 'Lobitos', 'Machu Picchu', 'Mancora', 'Nazca', 'Ollantaytambo', 'Paracas', 'Pisac', 'Puerto Malabrigo Chicama', 'Puerto Maldonado', 'Puno', 'Tarapoto', 'Trujillo', 'Urubamba', 'Atlantida', 'Barra de Valizas', 'Cabo Polonio', 'Carmelo', 'Colonia del Sacramento', 'La Paloma', 'La Pedrera', 'Montevideo', 'Piriapolis', 'Punta del Diablo', 'Punta del Este']\n"
     ]
    }
   ],
   "source": [
    "#create list of cities and corresponding country\n",
    "cities = []\n",
    "countries = []\n",
    "for country in country_list:\n",
    "    cities.extend(get_cities(country))\n",
    "    countries.extend(country for i in range(len(get_cities(country))))\n",
    "\n",
    "#replace portuguese accents\n",
    "cities = [city.replace('ã','a') for city in cities]\n",
    "print(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "b7e7f6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#each city has a webpage with all its hostels\n",
    "url_base = 'https://www.hostelworld.com/st/hostels/'\n",
    "\n",
    "#replace spaces with dashes for URL to run (for example: Buenos Aires becomes Buenos-Aires)\n",
    "cities_url = [city.replace(' ', '-') for city in cities]\n",
    "\n",
    "#create empty list for each column in data frame\n",
    "country = []\n",
    "location = []\n",
    "name = []\n",
    "link = []\n",
    "distance = []\n",
    "ratings = []\n",
    "reviews = []\n",
    "price = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "5578e90f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k2/jnby64_574b3xrhctstlh30w0000gp/T/ipykernel_19868/1262118445.py:1: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for city in tqdm(cities_url):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f63f6ce7c81422a9227caa8dd240892",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/267 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scraping Bariloche...\n",
      "Scraping Barreal...\n",
      "Scraping Buenos-Aires...\n",
      "Scraping Cachi...\n",
      "Scraping Cafayate...\n",
      "Scraping Concordia...\n",
      "Scraping Cordoba...\n",
      "Scraping El-Bolson...\n",
      "Scraping El-Calafate...\n",
      "Scraping El-Chalten...\n",
      "Scraping Esquel...\n",
      "Scraping Gualjaina...\n",
      "Scraping Humahuaca...\n",
      "Scraping La-Plata...\n",
      "Scraping Mar-del-Plata...\n",
      "Scraping Mendoza...\n",
      "Scraping Mina-Clavero...\n",
      "Scraping Puerto-Iguazu...\n",
      "Scraping Puerto-Madryn...\n",
      "Scraping Purmamarca...\n",
      "Scraping Rada-Tilly...\n",
      "Scraping Rosario...\n",
      "Scraping Salta...\n",
      "Scraping San-Ignacio...\n",
      "Scraping San-Lorenzo...\n",
      "Scraping San-Martin-De-Los-Andes...\n",
      "Scraping San-Miguel-de-Tucuman...\n",
      "Scraping San-Pedro...\n",
      "Scraping San-Rafael...\n",
      "Scraping San-Salvador-de-Jujuy...\n",
      "Scraping Tandil...\n",
      "Scraping Tilcara...\n",
      "Scraping Ushuaia...\n",
      "Scraping Villa-Carlos-Paz...\n",
      "Scraping Villa-Gesell...\n",
      "Scraping Villa-La-Angostura...\n",
      "Scraping Villa-Pehuenia...\n",
      "Scraping Copacabana...\n",
      "Scraping La-Paz...\n",
      "Scraping Rurrenabaque...\n",
      "Scraping Samaipata...\n",
      "Scraping Santa-Cruz-de-la-Sierra...\n",
      "Scraping Sucre...\n",
      "Scraping Tupiza...\n",
      "Scraping Alcantara...\n",
      "Scraping Alter-do-Chao...\n",
      "Scraping Andarai...\n",
      "Scraping Aparecida...\n",
      "Scraping Arraial-D'Ajuda...\n",
      "Scraping Arraial-do-Cabo...\n",
      "Scraping Balneario-Camboriu...\n",
      "Scraping Barreirinhas...\n",
      "Scraping Belem...\n",
      "Scraping Belo-Horizonte...\n",
      "Scraping Bertioga...\n",
      "Scraping Boa-Vista...\n",
      "Scraping Bombinhas...\n",
      "Scraping Bonito...\n",
      "Scraping Brasilia...\n",
      "Scraping Buzios...\n",
      "Scraping Cabo-Frio...\n",
      "Scraping Cajueiro-da-Praia...\n",
      "Scraping Cambara-do-Sul...\n",
      "Scraping Canoa-Quebrada...\n",
      "Scraping Caraiva...\n",
      "Scraping Caxias-Do-Sul...\n",
      "Scraping Corumba...\n",
      "Scraping Curitiba...\n",
      "Scraping Embu-das-Artes...\n",
      "Scraping Fernando-de-Noronha...\n",
      "Scraping Florianopolis...\n",
      "Scraping Fortaleza...\n",
      "Scraping Foz-do-Iguacu...\n",
      "Scraping Gramado...\n",
      "Scraping Guarda-do-Embau...\n",
      "Scraping Guaruja...\n",
      "Scraping Guarulhos...\n",
      "Scraping Ilha-Grande...\n",
      "Scraping Ilhabela...\n",
      "Scraping Imbassai...\n",
      "Scraping Itacare...\n",
      "Scraping Itanhaem...\n",
      "Scraping Jericoacoara...\n",
      "Scraping Joao-Pessoa...\n",
      "Scraping Lencois...\n",
      "Scraping Maceio...\n",
      "Scraping Manaus...\n",
      "Scraping Maragogi...\n",
      "Scraping Mata-de-Sao-Joao...\n",
      "Scraping Morro-de-Sao-Paulo...\n",
      "Scraping Natal...\n",
      "Scraping Niteroi...\n",
      "Scraping Ouro-Preto...\n",
      "Scraping Palmas...\n",
      "Scraping Paraty...\n",
      "Scraping Parnaiba...\n",
      "Scraping Penha...\n",
      "Scraping Pipa...\n",
      "Scraping Piracicaba...\n",
      "Scraping Porto-Alegre...\n",
      "Scraping Porto-Belo...\n",
      "Scraping Porto-de-Galinhas...\n",
      "Scraping Praia-do-Forte...\n",
      "Scraping Praia-do-Rosa...\n",
      "Scraping Praia-Grande...\n",
      "Scraping Presidente-Figuereido...\n",
      "Scraping Recife...\n",
      "Scraping Rio-das-Ostras...\n",
      "Scraping Rio-de-Janeiro...\n",
      "Scraping Salvador...\n",
      "Scraping Santos...\n",
      "Scraping Sao-Bento-do-Sul...\n",
      "Scraping Sao-Joao-Del-Rei...\n",
      "Scraping Sao-Luis...\n",
      "Scraping Sao-Paulo...\n",
      "Scraping Sao-Sebastiao...\n",
      "Scraping Saquarema...\n",
      "Scraping Serra-do-Cipo...\n",
      "Scraping Tiradentes...\n",
      "Scraping Torres...\n",
      "Scraping Trindade...\n",
      "Scraping Ubatuba...\n",
      "Scraping Vitoria...\n",
      "Scraping Arica...\n",
      "Scraping Chiloe...\n",
      "Scraping Coyhaique...\n",
      "Scraping Elqui-Valley...\n",
      "Scraping Iquique...\n",
      "Scraping La-Serena...\n",
      "Scraping Pichilemu...\n",
      "Scraping Pucon...\n",
      "Scraping Puerto-Natales...\n",
      "Scraping Puerto-Varas...\n",
      "Scraping Punta-Arenas...\n",
      "Scraping San-Pedro-de-Atacama...\n",
      "Scraping Santiago...\n",
      "Scraping Valdivia...\n",
      "Scraping Valparaiso...\n",
      "Scraping Vicuna...\n",
      "Scraping Vina-del-Mar...\n",
      "Scraping Abejorral...\n",
      "Scraping Arcabuco...\n",
      "Scraping Armenia...\n",
      "Scraping Bahia-Solano...\n",
      "Scraping Barbosa...\n",
      "Scraping Barichara...\n",
      "Scraping Barranquilla...\n",
      "Scraping Baru-Island...\n",
      "Scraping Bogota...\n",
      "Scraping Bucaramanga...\n",
      "Scraping Buenaventura...\n",
      "Scraping Cali...\n",
      "Scraping Capurgana...\n",
      "Scraping Cartagena...\n",
      "Scraping Choachi...\n",
      "Scraping Cienaga...\n",
      "Scraping El-Penol...\n",
      "Scraping Filandia...\n",
      "Scraping Galerazamba...\n",
      "Scraping Guachaca...\n",
      "Scraping Guadalupe...\n",
      "Scraping Guaduas...\n",
      "Scraping Guamal...\n",
      "Scraping Guatape...\n",
      "Scraping Isla-Fuerte...\n",
      "Scraping Isla-Palma...\n",
      "Scraping Jardin...\n",
      "Scraping Jerico...\n",
      "Scraping La-Mesa...\n",
      "Scraping Leticia...\n",
      "Scraping Manizales...\n",
      "Scraping Medellin...\n",
      "Scraping Minca...\n",
      "Scraping Mocoa...\n",
      "Scraping Mompos...\n",
      "Scraping Mongui...\n",
      "Scraping Neiva...\n",
      "Scraping Nuqui...\n",
      "Scraping Palomino...\n",
      "Scraping Pasto...\n",
      "Scraping Pereira...\n",
      "Scraping Popayan...\n",
      "Scraping Pueblo-Bello...\n",
      "Scraping Puerto-Colombia...\n",
      "Scraping Puerto-Narino...\n",
      "Scraping Rincon-del-Mar...\n",
      "Scraping Riohacha...\n",
      "Scraping Rosario-Island...\n",
      "Scraping Salento...\n",
      "Scraping Salgar...\n",
      "Scraping San-Agustin...\n",
      "Scraping San-Andres-Island...\n",
      "Scraping San-Gil...\n",
      "Scraping San-Rafael...\n",
      "Scraping Santa-Fe-de-Antioquia...\n",
      "Scraping Santa-Marta...\n",
      "Scraping Santa-Rosa-de-Cabal...\n",
      "Scraping Sapzurro...\n",
      "Scraping Sogamoso...\n",
      "Scraping Taganga...\n",
      "Scraping Tintipan-Island...\n",
      "Scraping Villa-de-Leyva...\n",
      "Scraping Villavicencio...\n",
      "Scraping Villavieja...\n",
      "Scraping Zipaquira...\n",
      "Scraping Alausi...\n",
      "Scraping Atacames...\n",
      "Scraping Banos...\n",
      "Scraping Banos-de-Agua-Santa...\n",
      "Scraping Canoa...\n",
      "Scraping Chugchilan...\n",
      "Scraping Cuenca...\n",
      "Scraping Guayaquil...\n",
      "Scraping Ibarra...\n",
      "Scraping Las-Tunas...\n",
      "Scraping Latacunga...\n",
      "Scraping Machachi...\n",
      "Scraping Manglaralto...\n",
      "Scraping Mindo...\n",
      "Scraping Misahualli...\n",
      "Scraping Mompiche...\n",
      "Scraping Montanita...\n",
      "Scraping Olon...\n",
      "Scraping Otavalo...\n",
      "Scraping Puerto-Lopez...\n",
      "Scraping Quito...\n",
      "Scraping Riobamba...\n",
      "Scraping Salinas...\n",
      "Scraping Santa-Marianita...\n",
      "Scraping Tababela...\n",
      "Scraping Tena...\n",
      "Scraping Tonsupa...\n",
      "Scraping Aguas-Calientes...\n",
      "Scraping Arequipa...\n",
      "Scraping Carhuaz...\n",
      "Scraping Chachapoyas...\n",
      "Scraping Cusco...\n",
      "Scraping Huacachina...\n",
      "Scraping Huanchaco...\n",
      "Scraping Huaraz...\n",
      "Scraping Ica...\n",
      "Scraping Iquitos...\n",
      "Scraping Lima...\n",
      "Scraping Lobitos...\n",
      "Scraping Machu-Picchu...\n",
      "Scraping Mancora...\n",
      "Scraping Nazca...\n",
      "Scraping Ollantaytambo...\n",
      "Scraping Paracas...\n",
      "Scraping Pisac...\n",
      "Scraping Puerto-Malabrigo-Chicama...\n",
      "Scraping Puerto-Maldonado...\n",
      "Scraping Puno...\n",
      "Scraping Tarapoto...\n",
      "Scraping Trujillo...\n",
      "Scraping Urubamba...\n",
      "Scraping Atlantida...\n",
      "Scraping Barra-de-Valizas...\n",
      "Scraping Cabo-Polonio...\n",
      "Scraping Carmelo...\n",
      "Scraping Colonia-del-Sacramento...\n",
      "Scraping La-Paloma...\n",
      "Scraping La-Pedrera...\n",
      "Scraping Montevideo...\n",
      "Scraping Piriapolis...\n",
      "Scraping Punta-del-Diablo...\n",
      "Scraping Punta-del-Este...\n"
     ]
    }
   ],
   "source": [
    "#scrape name, link, city, distance from city center, rating, number of reviews, and price from each hostel\n",
    "#tqdm shows a progress bar while the loop is running to determine where an error occurs\n",
    "for city in tqdm(cities_url):\n",
    "    print(f\"Scraping {city}...\")\n",
    "    url = url_base + str(city)\n",
    "    response = get(url)\n",
    "    soup_arg = BeautifulSoup(response.text, 'html.parser')\n",
    "    hostel_info = soup_arg.find_all(class_= 'fabresult rounded clearfix hwta-property')\n",
    "    \n",
    "    for item in hostel_info:\n",
    "        attribute = item.attrs\n",
    "        name.append(attribute['data-name'])\n",
    "        link.append(attribute['url'])\n",
    "        try: location.append(city)\n",
    "        except:\n",
    "            location.append('')\n",
    "        distance.append(item.find(class_= \"addressline\").text[12:18].replace('k','').replace('m','').strip())\n",
    "        ratings.append(item.find(class_='hwta-rating-score').text.replace('\\n', '').strip())\n",
    "        reviews.append(item.find(class_=\"hwta-rating-counter\").text.replace('\\n', '').strip())\n",
    "        price.append(item.find(class_= \"price\").text.replace('\\n', '').strip()[3:])\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "d1c771f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1199 1199 1199\n"
     ]
    }
   ],
   "source": [
    "#check that number of rows match\n",
    "print(len(location), len(name), len(price))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1070,
   "id": "1a82b330",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df to csv to preserve data\n",
    "df = pd.read_csv('hostels.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1071,
   "id": "ac6d2e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "id": "2a639d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make df of all cities in South America with population > 500,000\n",
    "url_wikipedia = 'https://en.wikipedia.org/wiki/List_of_cities_in_South_America#Largest_cities'\n",
    "response = get(url_wikipedia)\n",
    "\n",
    "#create soup\n",
    "soup = BeautifulSoup(response.text, 'html.parser')\n",
    "\n",
    "table = soup.find('table',{'class':\"wikitable\"})\n",
    "\n",
    "df_wikipedia = pd.DataFrame(pd.read_html(str(table))[0])\n",
    "df_wikipedia = df_wikipedia[['Name', 'Country', 'Population']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1073,
   "id": "d799e5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wikipedia = df_wikipedia.rename(columns={'Name':'city'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1074,
   "id": "2c201d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                b'Sao Paulo'\n",
       "1                     b'Lima'\n",
       "2                   b'Bogota'\n",
       "3           b'Rio de Janeiro'\n",
       "4                 b'Santiago'\n",
       "               ...           \n",
       "75    b'Aparecida de Goiania'\n",
       "76             b'Bucaramanga'\n",
       "77              b'Ananindeua'\n",
       "78                  b'Soacha'\n",
       "79             b'Porto Velho'\n",
       "Name: city, Length: 80, dtype: object"
      ]
     },
     "execution_count": 1074,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cities are encoded as bytes, change this to latin letters to remove accents\n",
    "df_wikipedia['city'] = (df_wikipedia['city'].str.normalize('NFKD')\n",
    "       .str.encode('latin-1', errors='ignore'))\n",
    "df_wikipedia.city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "id": "a9a8a866",
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_list = df_wikipedia.city.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1076,
   "id": "856fa2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wikipedia.to_csv('cities.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1077,
   "id": "c959ae74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sa = pd.read_csv('cities.csv', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1078,
   "id": "34fd5b70",
   "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>Unnamed: 0</th>\n",
       "      <th>city</th>\n",
       "      <th>Country</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b'Sao Paulo'</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>12,038,175[1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b'Lima'</td>\n",
       "      <td>Peru</td>\n",
       "      <td>8,894,412[a][2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>b'Bogota'</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>7,862,277[b][4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>b'Rio de Janeiro'</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>6,476,631[1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>b'Santiago'</td>\n",
       "      <td>Chile</td>\n",
       "      <td>5,507,282[c][5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>b'Aparecida de Goiania'</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>521,910[1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>76</td>\n",
       "      <td>b'Bucaramanga'</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>521,446[b][4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>b'Ananindeua'</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>505,404[1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>b'Soacha'</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>505,319[b][4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>b'Porto Velho'</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>502,748[1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                     city   Country       Population\n",
       "0            0             b'Sao Paulo'    Brazil    12,038,175[1]\n",
       "1            1                  b'Lima'      Peru  8,894,412[a][2]\n",
       "2            2                b'Bogota'  Colombia  7,862,277[b][4]\n",
       "3            3        b'Rio de Janeiro'    Brazil     6,476,631[1]\n",
       "4            4              b'Santiago'     Chile  5,507,282[c][5]\n",
       "..         ...                      ...       ...              ...\n",
       "75          75  b'Aparecida de Goiania'    Brazil       521,910[1]\n",
       "76          76           b'Bucaramanga'  Colombia    521,446[b][4]\n",
       "77          77            b'Ananindeua'    Brazil       505,404[1]\n",
       "78          78                b'Soacha'  Colombia    505,319[b][4]\n",
       "79          79           b'Porto Velho'    Brazil       502,748[1]\n",
       "\n",
       "[80 rows x 4 columns]"
      ]
     },
     "execution_count": 1078,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1079,
   "id": "4c1c2507",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove b'' from each city\n",
    "wiki_list = df_sa.city.tolist()\n",
    "wiki_list = [name.strip('b') for name in wiki_list]\n",
    "wiki_list = [name.strip(\"'\") for name in wiki_list]\n",
    "wiki_list = [name.replace(' ','-') for name in wiki_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1082,
   "id": "05d8b1d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wikipedia['pop_over_500k'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1083,
   "id": "b02f2b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wikipedia['city'] = wiki_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1085,
   "id": "a1b79e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, df_wikipedia[['city','pop_over_500k']], how='left', on= 'city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1086,
   "id": "1e3862d6",
   "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>country</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Bariloche</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Barreal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Buenos-Aires</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Cachi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Cafayate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>La-Pedrera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Montevideo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Piriapolis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Punta-del-Diablo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Punta-del-Este</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country              city\n",
       "0    Argentina         Bariloche\n",
       "1    Argentina           Barreal\n",
       "2    Argentina      Buenos-Aires\n",
       "3    Argentina             Cachi\n",
       "4    Argentina          Cafayate\n",
       "..         ...               ...\n",
       "262    Uruguay        La-Pedrera\n",
       "263    Uruguay        Montevideo\n",
       "264    Uruguay        Piriapolis\n",
       "265    Uruguay  Punta-del-Diablo\n",
       "266    Uruguay    Punta-del-Este\n",
       "\n",
       "[267 rows x 2 columns]"
      ]
     },
     "execution_count": 1086,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add dashes to cities so that the names match the original df\n",
    "cities = [city.replace(' ','-') for city in cities]\n",
    "df_countries = pd.DataFrame({'country': countries, 'city': cities})\n",
    "df_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1149,
   "id": "e5fdc53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df as csv\n",
    "hostels_3 = pd.merge(df, df_countries, how='left', on='city')\n",
    "hostels_3.to_csv('hostels_3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1142,
   "id": "bb565e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove duplicate columns generated by concatenating df's more than once\n",
    "df = hostels_3\n",
    "df = df.fillna(0)\n",
    "df = df.loc[:,~df.columns.duplicated()]\n",
    "df = df.drop('country_x', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1146,
   "id": "c0d01be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('country', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1147,
   "id": "6cdafd89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>hostel_name</th>\n",
       "      <th>city</th>\n",
       "      <th>distance_center_km</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>number_reviews</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>pop_over_500k</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Chile</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Ecuador</th>\n",
       "      <th>Peru</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Selina Bariloche</td>\n",
       "      <td>Bariloche</td>\n",
       "      <td>0.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>94</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tangoinn Hostel Downtown</td>\n",
       "      <td>Bariloche</td>\n",
       "      <td>0.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>791</td>\n",
       "      <td>31.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Berkana Hostel y Bar De Playa</td>\n",
       "      <td>Bariloche</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>108</td>\n",
       "      <td>33.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hostel Nix</td>\n",
       "      <td>Bariloche</td>\n",
       "      <td>1.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>16</td>\n",
       "      <td>12.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Patagonia Jazz Hostel 2</td>\n",
       "      <td>Bariloche</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>38.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>La Quinta Hostel &amp; Bar</td>\n",
       "      <td>Punta-del-Este</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>Hostel 32</td>\n",
       "      <td>Punta-del-Este</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>105</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>Selina Punta del Este</td>\n",
       "      <td>Punta-del-Este</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>Candy Hostel</td>\n",
       "      <td>Punta-del-Este</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>Planet Punta del este Hostel</td>\n",
       "      <td>Punta-del-Este</td>\n",
       "      <td>1.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>59</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1019 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        hostel_name            city  distance_center_km  \\\n",
       "0                  Selina Bariloche       Bariloche                 0.9   \n",
       "1          Tangoinn Hostel Downtown       Bariloche                 0.7   \n",
       "2     Berkana Hostel y Bar De Playa       Bariloche                 0.6   \n",
       "3                        Hostel Nix       Bariloche                 1.4   \n",
       "4           Patagonia Jazz Hostel 2       Bariloche                 0.6   \n",
       "...                             ...             ...                 ...   \n",
       "1014         La Quinta Hostel & Bar  Punta-del-Este                 9.9   \n",
       "1015                      Hostel 32  Punta-del-Este                 2.6   \n",
       "1016          Selina Punta del Este  Punta-del-Este                 0.2   \n",
       "1017                   Candy Hostel  Punta-del-Este                10.5   \n",
       "1018   Planet Punta del este Hostel  Punta-del-Este                 1.2   \n",
       "\n",
       "      average_rating  number_reviews  price_usd  pop_over_500k  Argentina  \\\n",
       "0                8.3              94      15.00            0.0          1   \n",
       "1                9.0             791      31.57            0.0          1   \n",
       "2                8.4             108      33.49            0.0          1   \n",
       "3                8.2              16      12.46            0.0          1   \n",
       "4                7.0               7      38.48            0.0          1   \n",
       "...              ...             ...        ...            ...        ...   \n",
       "1014             0.0               0      18.00            0.0          0   \n",
       "1015             8.9             105      17.50            0.0          0   \n",
       "1016             0.0               0      18.40            0.0          0   \n",
       "1017             0.0               3      25.00            0.0          0   \n",
       "1018             8.3              59      11.00            0.0          0   \n",
       "\n",
       "      Bolivia  Brazil  Chile  Colombia  Ecuador  Peru  \n",
       "0           0       0      0         0        0     0  \n",
       "1           0       0      0         0        0     0  \n",
       "2           0       0      0         0        0     0  \n",
       "3           0       0      0         0        0     0  \n",
       "4           0       0      0         0        0     0  \n",
       "...       ...     ...    ...       ...      ...   ...  \n",
       "1014        0       0      0         0        0     0  \n",
       "1015        0       0      0         0        0     0  \n",
       "1016        0       0      0         0        0     0  \n",
       "1017        0       0      0         0        0     0  \n",
       "1018        0       0      0         0        0     0  \n",
       "\n",
       "[1019 rows x 14 columns]"
      ]
     },
     "execution_count": 1147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1148,
   "id": "419ca33b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 919 entries, 968 to 416\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   hostel_name         919 non-null    object \n",
      " 1   city                919 non-null    object \n",
      " 2   distance_center_km  919 non-null    float64\n",
      " 3   average_rating      919 non-null    float64\n",
      " 4   number_reviews      919 non-null    int64  \n",
      " 5   price_usd           919 non-null    float64\n",
      " 6   pop_over_500k       919 non-null    float64\n",
      " 7   Argentina           919 non-null    uint8  \n",
      " 8   Bolivia             919 non-null    uint8  \n",
      " 9   Brazil              919 non-null    uint8  \n",
      " 10  Chile               919 non-null    uint8  \n",
      " 11  Colombia            919 non-null    uint8  \n",
      " 12  Ecuador             919 non-null    uint8  \n",
      " 13  Peru                919 non-null    uint8  \n",
      "dtypes: float64(4), int64(1), object(2), uint8(7)\n",
      "memory usage: 63.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#data cleaning\n",
    "\n",
    "#remove letters from distance values\n",
    "df.distance_center_km = df.distance_center_km.str.strip(' fr')\n",
    "df.distance_center_km.astype('float')\n",
    "\n",
    "#remove hostels with unreasonably low and high prices\n",
    "df = df[df.price_usd.astype('float') > 3]\n",
    "df = df[df.price_usd.astype('float') < 100.00]\n",
    "\n",
    "#remove hostels with 0 reviews and rating\n",
    "df = df.sort_values(['average_rating','number_reviews'])[100:]\n",
    "\n",
    "#convert numerical columns to numerical format\n",
    "list_to_convert = ['distance_center_km', 'average_rating', 'number_reviews', 'price_usd']\n",
    "\n",
    "for column in list_to_convert:\n",
    "  df[column] = pd.to_numeric(df[column], errors= 'coerce')\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1110,
   "id": "ad7cc6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dummy variables to represent country as numeric\n",
    "dummy = pd.get_dummies(df['country'])\n",
    "dummy\n",
    "df = pd.concat([df,dummy], axis=1)\n",
    "df = df.sort_index()\n",
    "df = df.drop('Uruguay',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "id": "71b44e04",
   "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>hostel_name</th>\n",
       "      <th>city</th>\n",
       "      <th>distance_center_km</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>number_reviews</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>pop_over_500k</th>\n",
       "      <th>country_x</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>...</th>\n",
       "      <th>Peru</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Chile</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Ecuador</th>\n",
       "      <th>Peru</th>\n",
       "      <th>country_y</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Gaia Hostel</td>\n",
       "      <td>Rio-de-Janeiro</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>134</td>\n",
       "      <td>8.22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Maracanã Hostel Vila Isabel</td>\n",
       "      <td>Rio-de-Janeiro</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9</td>\n",
       "      <td>6.39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Selina Copacabana</td>\n",
       "      <td>Rio-de-Janeiro</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>68</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Ipanema Beach Hostel</td>\n",
       "      <td>Rio-de-Janeiro</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>518</td>\n",
       "      <td>8.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Selina Lapa Rio de Janeiro</td>\n",
       "      <td>Rio-de-Janeiro</td>\n",
       "      <td>5.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>228</td>\n",
       "      <td>9.94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</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>397</th>\n",
       "      <td>Rural - La Florida</td>\n",
       "      <td>San-Pedro-de-Atacama</td>\n",
       "      <td>0.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>631</td>\n",
       "      <td>8.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Aji Verde Hostel San Pedro de Atacama</td>\n",
       "      <td>San-Pedro-de-Atacama</td>\n",
       "      <td>0.9</td>\n",
       "      <td>9.7</td>\n",
       "      <td>786</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Hostal Chiloe</td>\n",
       "      <td>San-Pedro-de-Atacama</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>7</td>\n",
       "      <td>14.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Sol Atacama Hostel</td>\n",
       "      <td>San-Pedro-de-Atacama</td>\n",
       "      <td>0.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>1047</td>\n",
       "      <td>8.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>Planet Punta del este Hostel</td>\n",
       "      <td>Punta-del-Este</td>\n",
       "      <td>1.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>59</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1019 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                hostel_name                  city  \\\n",
       "308                             Gaia Hostel        Rio-de-Janeiro   \n",
       "314             Maracanã Hostel Vila Isabel        Rio-de-Janeiro   \n",
       "288                       Selina Copacabana        Rio-de-Janeiro   \n",
       "287                    Ipanema Beach Hostel        Rio-de-Janeiro   \n",
       "286              Selina Lapa Rio de Janeiro        Rio-de-Janeiro   \n",
       "...                                     ...                   ...   \n",
       "397                      Rural - La Florida  San-Pedro-de-Atacama   \n",
       "398   Aji Verde Hostel San Pedro de Atacama  San-Pedro-de-Atacama   \n",
       "399                           Hostal Chiloe  San-Pedro-de-Atacama   \n",
       "400                      Sol Atacama Hostel  San-Pedro-de-Atacama   \n",
       "1018           Planet Punta del este Hostel        Punta-del-Este   \n",
       "\n",
       "      distance_center_km  average_rating  number_reviews  price_usd  \\\n",
       "308                  7.7             7.6             134       8.22   \n",
       "314                  1.8             8.4               9       6.39   \n",
       "288                 10.0             8.9              68      16.00   \n",
       "287                  9.6             8.7             518       8.95   \n",
       "286                  5.8             9.0             228       9.94   \n",
       "...                  ...             ...             ...        ...   \n",
       "397                  0.1             9.0             631       8.95   \n",
       "398                  0.9             9.7             786      10.57   \n",
       "399                  0.2             9.4               7      14.92   \n",
       "400                  0.7             9.7            1047       8.44   \n",
       "1018                 1.2             8.3              59      11.00   \n",
       "\n",
       "      pop_over_500k country_x  Argentina  Bolivia  ...  Peru  Argentina  \\\n",
       "308             1.0    Brazil          0        0  ...     0          0   \n",
       "314             1.0    Brazil          0        0  ...     0          0   \n",
       "288             1.0    Brazil          0        0  ...     0          0   \n",
       "287             1.0    Brazil          0        0  ...     0          0   \n",
       "286             1.0    Brazil          0        0  ...     0          0   \n",
       "...             ...       ...        ...      ...  ...   ...        ...   \n",
       "397             0.0     Chile          0        0  ...     0          0   \n",
       "398             0.0     Chile          0        0  ...     0          0   \n",
       "399             0.0     Chile          0        0  ...     0          0   \n",
       "400             0.0     Chile          0        0  ...     0          0   \n",
       "1018            0.0   Uruguay          0        0  ...     0          0   \n",
       "\n",
       "      Bolivia  Brazil  Chile  Colombia  Ecuador  Peru  country_y  country  \n",
       "308         0       1      0         0        0     0     Brazil   Brazil  \n",
       "314         0       1      0         0        0     0     Brazil   Brazil  \n",
       "288         0       1      0         0        0     0     Brazil   Brazil  \n",
       "287         0       1      0         0        0     0     Brazil   Brazil  \n",
       "286         0       1      0         0        0     0     Brazil   Brazil  \n",
       "...       ...     ...    ...       ...      ...   ...        ...      ...  \n",
       "397         0       0      1         0        0     0      Chile    Chile  \n",
       "398         0       0      1         0        0     0      Chile    Chile  \n",
       "399         0       0      1         0        0     0      Chile    Chile  \n",
       "400         0       0      1         0        0     0      Chile    Chile  \n",
       "1018        0       0      0         0        0     0    Uruguay  Uruguay  \n",
       "\n",
       "[1019 rows x 24 columns]"
      ]
     },
     "execution_count": 1126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"pop_over_500k\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1150,
   "id": "3c7619a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_center_km</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>number_reviews</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>pop_over_500k</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Chile</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Ecuador</th>\n",
       "      <th>Peru</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distance_center_km</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025318</td>\n",
       "      <td>-0.022804</td>\n",
       "      <td>0.063391</td>\n",
       "      <td>-0.029318</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>-0.007161</td>\n",
       "      <td>-0.011506</td>\n",
       "      <td>-0.012118</td>\n",
       "      <td>0.049275</td>\n",
       "      <td>-0.014867</td>\n",
       "      <td>-0.017897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_rating</th>\n",
       "      <td>0.025318</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065609</td>\n",
       "      <td>-0.079422</td>\n",
       "      <td>-0.075679</td>\n",
       "      <td>-0.009150</td>\n",
       "      <td>-0.082316</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>-0.030047</td>\n",
       "      <td>0.110100</td>\n",
       "      <td>-0.059677</td>\n",
       "      <td>-0.019466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_reviews</th>\n",
       "      <td>-0.022804</td>\n",
       "      <td>0.065609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109548</td>\n",
       "      <td>0.091636</td>\n",
       "      <td>-0.083578</td>\n",
       "      <td>-0.006004</td>\n",
       "      <td>-0.051065</td>\n",
       "      <td>0.079498</td>\n",
       "      <td>-0.011653</td>\n",
       "      <td>-0.049970</td>\n",
       "      <td>0.122600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_usd</th>\n",
       "      <td>0.063391</td>\n",
       "      <td>-0.079422</td>\n",
       "      <td>-0.109548</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.126113</td>\n",
       "      <td>0.233378</td>\n",
       "      <td>-0.015141</td>\n",
       "      <td>-0.136680</td>\n",
       "      <td>0.142627</td>\n",
       "      <td>-0.113779</td>\n",
       "      <td>0.007589</td>\n",
       "      <td>-0.081932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop_over_500k</th>\n",
       "      <td>-0.029318</td>\n",
       "      <td>-0.075679</td>\n",
       "      <td>0.091636</td>\n",
       "      <td>-0.126113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022671</td>\n",
       "      <td>0.089993</td>\n",
       "      <td>0.043002</td>\n",
       "      <td>-0.055195</td>\n",
       "      <td>-0.060089</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>0.060575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.001169</td>\n",
       "      <td>-0.009150</td>\n",
       "      <td>-0.083578</td>\n",
       "      <td>0.233378</td>\n",
       "      <td>-0.022671</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.054144</td>\n",
       "      <td>-0.203651</td>\n",
       "      <td>-0.109775</td>\n",
       "      <td>-0.213383</td>\n",
       "      <td>-0.128263</td>\n",
       "      <td>-0.140680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>-0.007161</td>\n",
       "      <td>-0.082316</td>\n",
       "      <td>-0.006004</td>\n",
       "      <td>-0.015141</td>\n",
       "      <td>0.089993</td>\n",
       "      <td>-0.054144</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.083677</td>\n",
       "      <td>-0.045105</td>\n",
       "      <td>-0.087676</td>\n",
       "      <td>-0.052702</td>\n",
       "      <td>-0.057803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>-0.011506</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>-0.051065</td>\n",
       "      <td>-0.136680</td>\n",
       "      <td>0.043002</td>\n",
       "      <td>-0.203651</td>\n",
       "      <td>-0.083677</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.169653</td>\n",
       "      <td>-0.329776</td>\n",
       "      <td>-0.198227</td>\n",
       "      <td>-0.217415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>-0.012118</td>\n",
       "      <td>-0.030047</td>\n",
       "      <td>0.079498</td>\n",
       "      <td>0.142627</td>\n",
       "      <td>-0.055195</td>\n",
       "      <td>-0.109775</td>\n",
       "      <td>-0.045105</td>\n",
       "      <td>-0.169653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177760</td>\n",
       "      <td>-0.106851</td>\n",
       "      <td>-0.117194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>0.049275</td>\n",
       "      <td>0.110100</td>\n",
       "      <td>-0.011653</td>\n",
       "      <td>-0.113779</td>\n",
       "      <td>-0.060089</td>\n",
       "      <td>-0.213383</td>\n",
       "      <td>-0.087676</td>\n",
       "      <td>-0.329776</td>\n",
       "      <td>-0.177760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.207699</td>\n",
       "      <td>-0.227805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>-0.014867</td>\n",
       "      <td>-0.059677</td>\n",
       "      <td>-0.049970</td>\n",
       "      <td>0.007589</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>-0.128263</td>\n",
       "      <td>-0.052702</td>\n",
       "      <td>-0.198227</td>\n",
       "      <td>-0.106851</td>\n",
       "      <td>-0.207699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.136932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>-0.017897</td>\n",
       "      <td>-0.019466</td>\n",
       "      <td>0.122600</td>\n",
       "      <td>-0.081932</td>\n",
       "      <td>0.060575</td>\n",
       "      <td>-0.140680</td>\n",
       "      <td>-0.057803</td>\n",
       "      <td>-0.217415</td>\n",
       "      <td>-0.117194</td>\n",
       "      <td>-0.227805</td>\n",
       "      <td>-0.136932</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    distance_center_km  average_rating  number_reviews  \\\n",
       "distance_center_km            1.000000        0.025318       -0.022804   \n",
       "average_rating                0.025318        1.000000        0.065609   \n",
       "number_reviews               -0.022804        0.065609        1.000000   \n",
       "price_usd                     0.063391       -0.079422       -0.109548   \n",
       "pop_over_500k                -0.029318       -0.075679        0.091636   \n",
       "Argentina                     0.001169       -0.009150       -0.083578   \n",
       "Bolivia                      -0.007161       -0.082316       -0.006004   \n",
       "Brazil                       -0.011506        0.028209       -0.051065   \n",
       "Chile                        -0.012118       -0.030047        0.079498   \n",
       "Colombia                      0.049275        0.110100       -0.011653   \n",
       "Ecuador                      -0.014867       -0.059677       -0.049970   \n",
       "Peru                         -0.017897       -0.019466        0.122600   \n",
       "\n",
       "                    price_usd  pop_over_500k  Argentina   Bolivia    Brazil  \\\n",
       "distance_center_km   0.063391      -0.029318   0.001169 -0.007161 -0.011506   \n",
       "average_rating      -0.079422      -0.075679  -0.009150 -0.082316  0.028209   \n",
       "number_reviews      -0.109548       0.091636  -0.083578 -0.006004 -0.051065   \n",
       "price_usd            1.000000      -0.126113   0.233378 -0.015141 -0.136680   \n",
       "pop_over_500k       -0.126113       1.000000  -0.022671  0.089993  0.043002   \n",
       "Argentina            0.233378      -0.022671   1.000000 -0.054144 -0.203651   \n",
       "Bolivia             -0.015141       0.089993  -0.054144  1.000000 -0.083677   \n",
       "Brazil              -0.136680       0.043002  -0.203651 -0.083677  1.000000   \n",
       "Chile                0.142627      -0.055195  -0.109775 -0.045105 -0.169653   \n",
       "Colombia            -0.113779      -0.060089  -0.213383 -0.087676 -0.329776   \n",
       "Ecuador              0.007589       0.011000  -0.128263 -0.052702 -0.198227   \n",
       "Peru                -0.081932       0.060575  -0.140680 -0.057803 -0.217415   \n",
       "\n",
       "                       Chile  Colombia   Ecuador      Peru  \n",
       "distance_center_km -0.012118  0.049275 -0.014867 -0.017897  \n",
       "average_rating     -0.030047  0.110100 -0.059677 -0.019466  \n",
       "number_reviews      0.079498 -0.011653 -0.049970  0.122600  \n",
       "price_usd           0.142627 -0.113779  0.007589 -0.081932  \n",
       "pop_over_500k      -0.055195 -0.060089  0.011000  0.060575  \n",
       "Argentina          -0.109775 -0.213383 -0.128263 -0.140680  \n",
       "Bolivia            -0.045105 -0.087676 -0.052702 -0.057803  \n",
       "Brazil             -0.169653 -0.329776 -0.198227 -0.217415  \n",
       "Chile               1.000000 -0.177760 -0.106851 -0.117194  \n",
       "Colombia           -0.177760  1.000000 -0.207699 -0.227805  \n",
       "Ecuador            -0.106851 -0.207699  1.000000 -0.136932  \n",
       "Peru               -0.117194 -0.227805 -0.136932  1.000000  "
      ]
     },
     "execution_count": 1150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find correlations between variables\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1151,
   "id": "cfd7f24b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 1151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['distance_center_km','average_rating','number_reviews','pop_over_500k','price_usd']].corr(), annot=True, cmap=\"seismic\", vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1152,
   "id": "ef87d81b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a37e6190>"
      ]
     },
     "execution_count": 1152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x2160 with 156 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "id": "6e15bce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'South American Hostel Prices')]"
      ]
     },
     "execution_count": 766,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['price_usd'], color='orange', binrange=(0,50)).set(title='South American Hostel Prices')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1202,
   "id": "2d2e6d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define and split features X and target y\n",
    "X, y = df[['distance_center_km','average_rating','number_reviews','pop_over_500k','Argentina','Bolivia','Brazil','Chile','Colombia','Ecuador','Peru']], df['price_usd']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "#simple linear regression model\n",
    "lm = LinearRegression()\n",
    "model_fit = lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1165,
   "id": "bfe4859c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 1165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1203,
   "id": "e14bbec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13116430954569958"
      ]
     },
     "execution_count": 1203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting predictions\n",
    "y_pred = model_fit.predict(X_test)\n",
    " \n",
    "#R2\n",
    "lr.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1045,
   "id": "86217145",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 on training set: 0.1603\n",
      "R2 on test set: 0.0310\n",
      "Mean Squared Error: 35.5378913222653\n",
      "Mean Absolute Error: 4.2841708403584535\n",
      "Median Absolute Error: 3.4130924792600696\n",
      "Root Mean Square Error (RMSE): 5.961366564997098\n"
     ]
    }
   ],
   "source": [
    "print(f'R2 on training set: {model_fit.score(X_train, y_train):.4f}')\n",
    "print(f'R2 on test set: {model_fit.score(X_test, y_test):.4f}')\n",
    "print('Mean Squared Error: {}'.format(mean_squared_error(y_test, y_pred)))\n",
    "print('Mean Absolute Error: {}'.format(mean_absolute_error(y_test, y_pred)))\n",
    "print('Median Absolute Error: {}'.format(median_absolute_error(y_test, y_pred)))\n",
    "print('Root Mean Square Error (RMSE): {}'.format(np.sqrt(mean_squared_error(y_test, y_pred))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1046,
   "id": "4e19601f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kate/opt/anaconda3/envs/metis/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1a02e7e80>"
      ]
     },
     "execution_count": 1046,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot actual vs predicted values \n",
    "plot = sns.jointplot(y_pred, y_test, kind='reg')\n",
    "plot.set_axis_labels('Predicted Price', 'Actual Price', fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 876,
   "id": "7f2a78fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate variance inflation factor\n",
    "def vif(df):\n",
    "    vif_data = [variance_inflation_factor(df.values, i)\n",
    "                for i in range(df.shape[1])]\n",
    "    \n",
    "    return pd.Series(vif_data, index=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "id": "b6d83efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance_center_km    1.003794\n",
       "average_rating        1.073251\n",
       "number_reviews        1.066515\n",
       "pop_over_500k         1.031097\n",
       "Argentina             1.736025\n",
       "Bolivia               1.139605\n",
       "Brazil                2.805210\n",
       "Chile                 1.548010\n",
       "Colombia              2.804198\n",
       "Ecuador               1.644896\n",
       "Peru                  1.886967\n",
       "Uruguay               1.216078\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1050,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#only analyze the VIF for predictors\n",
    "vif(df[['distance_center_km','average_rating','number_reviews','pop_over_500k','Argentina','Bolivia','Brazil','Chile','Colombia','Ecuador','Peru','Uruguay']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1166,
   "id": "6dad6dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.30312876,  0.22869468,  0.1408792 , -1.00430982, -0.01333877])"
      ]
     },
     "execution_count": 1166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "\n",
    "cross_val_score(lm, X, y, # estimator, features, target\n",
    "                cv=5, # number of folds \n",
    "                scoring='r2') # scoring metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1167,
   "id": "44923537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.12377713, -3.49549925, -3.42186284, -4.49626043, -4.69042196])"
      ]
     },
     "execution_count": 1167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lm, X, y, # estimator, features, target\n",
    "                cv=5, # number of folds \n",
    "                scoring='neg_mean_absolute_error') # scoring metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "id": "42f9a2ec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['accuracy',\n",
       " 'adjusted_mutual_info_score',\n",
       " 'adjusted_rand_score',\n",
       " 'average_precision',\n",
       " 'balanced_accuracy',\n",
       " 'completeness_score',\n",
       " 'explained_variance',\n",
       " 'f1',\n",
       " 'f1_macro',\n",
       " 'f1_micro',\n",
       " 'f1_samples',\n",
       " 'f1_weighted',\n",
       " 'fowlkes_mallows_score',\n",
       " 'homogeneity_score',\n",
       " 'jaccard',\n",
       " 'jaccard_macro',\n",
       " 'jaccard_micro',\n",
       " 'jaccard_samples',\n",
       " 'jaccard_weighted',\n",
       " 'max_error',\n",
       " 'mutual_info_score',\n",
       " 'neg_brier_score',\n",
       " 'neg_log_loss',\n",
       " 'neg_mean_absolute_error',\n",
       " 'neg_mean_absolute_percentage_error',\n",
       " 'neg_mean_gamma_deviance',\n",
       " 'neg_mean_poisson_deviance',\n",
       " 'neg_mean_squared_error',\n",
       " 'neg_mean_squared_log_error',\n",
       " 'neg_median_absolute_error',\n",
       " 'neg_root_mean_squared_error',\n",
       " 'normalized_mutual_info_score',\n",
       " 'precision',\n",
       " 'precision_macro',\n",
       " 'precision_micro',\n",
       " 'precision_samples',\n",
       " 'precision_weighted',\n",
       " 'r2',\n",
       " 'rand_score',\n",
       " 'recall',\n",
       " 'recall_macro',\n",
       " 'recall_micro',\n",
       " 'recall_samples',\n",
       " 'recall_weighted',\n",
       " 'roc_auc',\n",
       " 'roc_auc_ovo',\n",
       " 'roc_auc_ovo_weighted',\n",
       " 'roc_auc_ovr',\n",
       " 'roc_auc_ovr_weighted',\n",
       " 'top_k_accuracy',\n",
       " 'v_measure_score']"
      ]
     },
     "execution_count": 1057,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#types of metrics sklearn accepts\n",
    "sorted(sklearn.metrics.SCORERS.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1168,
   "id": "4995d9f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature engineering\n",
    "def split_and_validate(X, y, metric='rmse'):\n",
    "    \"\"\"For a set of features and target X, y, perform a 80/20 train/val split, \n",
    "    fit and validate a linear regression model, and report results.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Train and validation split\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "    \n",
    "    # Fit linear regression to training data\n",
    "    lr_model = LinearRegression()\n",
    "    lr_model.fit(X_train, y_train)\n",
    "    \n",
    "    # Score fit model on validation data\n",
    "    if metric == \"rmse\":\n",
    "        y_actual = y_val\n",
    "        y_predicted = lr_model.predict(X_val)\n",
    "        val_score = mean_squared_error(y_actual, y_predicted, squared=False)\n",
    "    else:\n",
    "        metric =\"R²\"\n",
    "        val_score = lr_model.score(X_val, y_val)\n",
    "\n",
    "    \n",
    "    # Report results\n",
    "    print(\"\")\n",
    "    print(f\"Validation score for {metric}: {val_score:,.0f}\")\n",
    "    print(\"\")\n",
    "    print('Feature coefficient results:')\n",
    "    for feature, coef in zip(X.columns, lr_model.coef_):\n",
    "        print(f\"{feature:<20}: {coef:>12,.0f}\") \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1169,
   "id": "6c3cdfd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation score for rmse: 6\n",
      "\n",
      "Feature coefficient results:\n",
      "distance_center_km  :            0\n",
      "average_rating      :           -1\n",
      "number_reviews      :           -0\n",
      "pop_over_500k       :           -1\n",
      "Argentina           :            0\n",
      "Bolivia             :           -4\n",
      "Brazil              :           -5\n",
      "Chile               :            1\n",
      "Colombia            :           -5\n",
      "Ecuador             :           -3\n",
      "Peru                :           -5\n",
      "rating2             :            0\n",
      "review2             :            0\n"
     ]
    }
   ],
   "source": [
    "#pair plots of average rating and number of reviews potentially had upward-curved relationships with price\n",
    "#square them and see validation results\n",
    "X2 = X.copy()\n",
    "\n",
    "X2['rating2'] = X2['average_rating'] ** 2\n",
    "X2['review2'] = X2['number_reviews'] ** 2\n",
    "\n",
    "split_and_validate(X2, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1170,
   "id": "808b3045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation score for rmse: 6\n",
      "\n",
      "Feature coefficient results:\n",
      "distance_center_km  :            0\n",
      "average_rating      :           -1\n",
      "number_reviews      :            0\n",
      "pop_over_500k       :           -1\n",
      "Argentina           :            0\n",
      "Bolivia             :           -4\n",
      "Brazil              :           -5\n",
      "Chile               :            1\n",
      "Colombia            :           -5\n",
      "Ecuador             :           -3\n",
      "Peru                :           -5\n",
      "rating2             :            0\n",
      "review2             :            0\n",
      "rating*reviews      :           -0\n"
     ]
    }
   ],
   "source": [
    "X3 = X2.copy()\n",
    "\n",
    "# multiplicative interaction\n",
    "X3['rating*reviews'] = X3['average_rating'] * X3['number_reviews']\n",
    "\n",
    "split_and_validate(X3, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1171,
   "id": "7e733b6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 of Train Set with Poly: 0.2847\n",
      "R^2 of Validation Set with Poly: 0.1592\n",
      "R^2 of Test Set with Poly: 0.0815\n"
     ]
    }
   ],
   "source": [
    "#polynomial features\n",
    "poly = PolynomialFeatures(degree=2)\n",
    "X_train_poly = poly.fit_transform(X_train)\n",
    "\n",
    "# Apply polynomial transformation to val set\n",
    "X_val_poly = poly.transform(X_val)\n",
    "\n",
    "# Fit a model using polynomial features\n",
    "lr_poly = LinearRegression()\n",
    "lr_poly.fit(X_train_poly,y_train)\n",
    "\n",
    "print(\"R^2 of Train Set with Poly:\", round(lr_poly.score(X_train_poly,y_train),4))  \n",
    "print(\"R^2 of Validation Set with Poly:\", round(lr_poly.score(X_val_poly, y_val),4))\n",
    "print(\"R^2 of Test Set with Poly:\", round(lr_poly.score(poly.transform(X_test), y_test),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1172,
   "id": "647a7a61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso R^2 of train set including all features: 0.12673148491566133\n",
      "Lasso R^2 of validation set including all features: 0.17661237740496472\n"
     ]
    }
   ],
   "source": [
    "#scaling X\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_val_scaled = scaler.transform(X_val)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "#fitting Lasso\n",
    "lasso = LassoCV()\n",
    "lasso.fit(X_train_scaled,y_train)\n",
    "\n",
    "print(\"Lasso R^2 of train set including all features:\", lasso.score(X_train_scaled,y_train))\n",
    "print(\"Lasso R^2 of validation set including all features:\", lasso.score(X_val_scaled, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1173,
   "id": "2be24199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.3958288194378501,),\n",
       " (-0.41467312452679644,),\n",
       " (-0.5308898555565595,),\n",
       " (-0.5944309717215411,),\n",
       " (0.6625821575937746,),\n",
       " (-0.15095520637777857,),\n",
       " (-1.2885996724278004,),\n",
       " (0.6262506320811624,),\n",
       " (-1.023711915669202,),\n",
       " (-0.17255966434341966,),\n",
       " (-0.8127267990237802,)]"
      ]
     },
     "execution_count": 1173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(lasso.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1174,
   "id": "d794d2ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing regularization path using the LARS ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import lars_path\n",
    "\n",
    "print(\"Computing regularization path using the LARS ...\")\n",
    "alphas, _, coefs = lars_path(X_train_scaled, y_train, \n",
    "                             method='lasso')\n",
    "\n",
    "xx = np.sum(np.abs(coefs.T), axis=1)\n",
    "xx /= xx[-1]\n",
    "\n",
    "plt.plot(xx, coefs.T)\n",
    "ymin, ymax = plt.ylim()\n",
    "plt.vlines(xx, ymin, ymax, linestyle=\"dashed\")\n",
    "plt.xlabel(\"|coef| / max|coef|\")\n",
    "plt.ylabel(\"Coefficients\")\n",
    "plt.title(\"LASSO Path\")\n",
    "plt.axis(\"tight\")\n",
    "plt.legend(['distance_center_km','average_rating','number_reviews','pop_over_500k','Argentina','Bolivia','Brazil','Chile','Colombia','Ecuador','Peru'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1175,
   "id": "6032a6e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge R^2 of training set: 0.1307919762368237\n",
      "Ridge R^2 of validation set: 0.186218856078314\n"
     ]
    }
   ],
   "source": [
    "#fitting Ridge\n",
    "ridge = RidgeCV()\n",
    "ridge.fit(X_train_scaled,y_train)\n",
    "\n",
    "print(\"Ridge R^2 of training set:\", ridge.score(X_train_scaled,y_train))\n",
    "print(\"Ridge R^2 of validation set:\", ridge.score(X_val_scaled, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1177,
   "id": "4e2cc6fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1178,
   "id": "41d26f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08987001886064394\n"
     ]
    }
   ],
   "source": [
    "#K-fold cross validation\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state = 42)\n",
    "print(np.mean(cross_val_score(lasso, X_scaled, y, cv=kf, scoring='r2')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1179,
   "id": "e11f8b3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0209700539995721\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state = 42)\n",
    "print(np.mean(cross_val_score(lm, X, y, cv=kf, scoring='r2')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1180,
   "id": "e296bd2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression test R^2: 0.169\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "lm_reg = Ridge(alpha=1)\n",
    "lm_reg.fit(X_scaled,y)\n",
    "print(f'Ridge Regression test R^2: {lm_reg.score(X_test_scaled, y_test):.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1181,
   "id": "e7e1e5bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08940140245241894\n",
      "0.08925591811410014\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state = 1000)\n",
    "\n",
    "print(np.mean(cross_val_score(lm, X, y, cv=kf, scoring='r2')))\n",
    "print(np.mean(cross_val_score(lm_reg, X, y, cv=kf, scoring='r2')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "id": "aa6a36d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.23467730718629\n",
      "[ 8.30093201e-04 -4.74645477e-01 -1.08589652e-03 -1.32743927e+00\n",
      "  3.21501785e+00  4.33070556e-01 -2.76025602e+00  2.39055977e+00\n",
      " -1.99907582e+00 -1.06309725e+00 -2.24223959e+00  2.02602050e+00]\n"
     ]
    }
   ],
   "source": [
    "# extract feature coefficients and intercept from fit model\n",
    "print(lr.intercept_)\n",
    "print(lr.coef_)\n",
    "\n",
    "# y(pred) = f(x) = 9.48 + .42x1 -.25x2 - .001x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1190,
   "id": "483a5731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>price_usd</td>    <th>  R-squared:         </th> <td>   0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   12.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 28 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>4.16e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:12:50</td>     <th>  Log-Likelihood:    </th> <td> -2997.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   919</td>      <th>  AIC:               </th> <td>   6019.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   907</td>      <th>  BIC:               </th> <td>   6077.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>              <td>   17.3613</td> <td>    1.349</td> <td>   12.872</td> <td> 0.000</td> <td>   14.714</td> <td>   20.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance_center_km</th> <td>    0.0008</td> <td>    0.000</td> <td>    2.077</td> <td> 0.038</td> <td> 4.56e-05</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>average_rating</th>     <td>   -0.2035</td> <td>    0.106</td> <td>   -1.926</td> <td> 0.054</td> <td>   -0.411</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>number_reviews</th>     <td>   -0.0010</td> <td>    0.000</td> <td>   -2.901</td> <td> 0.004</td> <td>   -0.002</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pop_over_500k</th>      <td>   -1.4848</td> <td>    0.458</td> <td>   -3.244</td> <td> 0.001</td> <td>   -2.383</td> <td>   -0.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Argentina</th>          <td>    0.7133</td> <td>    1.217</td> <td>    0.586</td> <td> 0.558</td> <td>   -1.676</td> <td>    3.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bolivia</th>            <td>   -3.9583</td> <td>    1.773</td> <td>   -2.233</td> <td> 0.026</td> <td>   -7.438</td> <td>   -0.479</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Brazil</th>             <td>   -5.0841</td> <td>    1.135</td> <td>   -4.479</td> <td> 0.000</td> <td>   -7.312</td> <td>   -2.857</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Chile</th>              <td>   -0.2377</td> <td>    1.272</td> <td>   -0.187</td> <td> 0.852</td> <td>   -2.734</td> <td>    2.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Colombia</th>           <td>   -4.8127</td> <td>    1.130</td> <td>   -4.259</td> <td> 0.000</td> <td>   -7.030</td> <td>   -2.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ecuador</th>            <td>   -3.4270</td> <td>    1.223</td> <td>   -2.802</td> <td> 0.005</td> <td>   -5.827</td> <td>   -1.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Peru</th>               <td>   -4.5867</td> <td>    1.199</td> <td>   -3.827</td> <td> 0.000</td> <td>   -6.939</td> <td>   -2.235</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>845.119</td> <th>  Durbin-Watson:     </th> <td>   1.868</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>49839.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.966</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>38.195</td>  <th>  Cond. No.          </th> <td>9.96e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.96e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              price_usd   R-squared:                       0.136\n",
       "Model:                            OLS   Adj. R-squared:                  0.125\n",
       "Method:                 Least Squares   F-statistic:                     12.96\n",
       "Date:                Fri, 28 Jan 2022   Prob (F-statistic):           4.16e-23\n",
       "Time:                        00:12:50   Log-Likelihood:                -2997.5\n",
       "No. Observations:                 919   AIC:                             6019.\n",
       "Df Residuals:                     907   BIC:                             6077.\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "const                 17.3613      1.349     12.872      0.000      14.714      20.008\n",
       "distance_center_km     0.0008      0.000      2.077      0.038    4.56e-05       0.002\n",
       "average_rating        -0.2035      0.106     -1.926      0.054      -0.411       0.004\n",
       "number_reviews        -0.0010      0.000     -2.901      0.004      -0.002      -0.000\n",
       "pop_over_500k         -1.4848      0.458     -3.244      0.001      -2.383      -0.586\n",
       "Argentina              0.7133      1.217      0.586      0.558      -1.676       3.102\n",
       "Bolivia               -3.9583      1.773     -2.233      0.026      -7.438      -0.479\n",
       "Brazil                -5.0841      1.135     -4.479      0.000      -7.312      -2.857\n",
       "Chile                 -0.2377      1.272     -0.187      0.852      -2.734       2.259\n",
       "Colombia              -4.8127      1.130     -4.259      0.000      -7.030      -2.595\n",
       "Ecuador               -3.4270      1.223     -2.802      0.005      -5.827      -1.027\n",
       "Peru                  -4.5867      1.199     -3.827      0.000      -6.939      -2.235\n",
       "==============================================================================\n",
       "Omnibus:                      845.119   Durbin-Watson:                   1.868\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            49839.820\n",
       "Skew:                           3.966   Prob(JB):                         0.00\n",
       "Kurtosis:                      38.195   Cond. No.                     9.96e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 9.96e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 1190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ordinary Least Squares\n",
    "model = sm.OLS(y, sm.add_constant(X)) \n",
    "\n",
    "#Fit\n",
    "fit = model.fit()\n",
    "\n",
    "#Print out summary\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1194,
   "id": "561b9889",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression: 0.13872693411307258 4.185355718050714\n",
      "Lasso Regression: 0.1365030951119811 4.188456666849007\n",
      "Ridge Regression: 0.13871284419526153 4.185404589697174\n"
     ]
    }
   ],
   "source": [
    "#test models\n",
    "lr = LinearRegression().fit(X_train, y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "print(\"Linear Regression:\", r2_score(y_test, y_pred), mean_absolute_error(y_test, y_pred))\n",
    "\n",
    "lasso = Lasso(alpha = 0.006579332246575682).fit(X_train, y_train)\n",
    "y_pred_lasso = lasso.predict(X_test)\n",
    "print(\"Lasso Regression:\", r2_score(y_test, y_pred_lasso),mean_absolute_error(y_test, y_pred_lasso))\n",
    "\n",
    "ridge = Ridge(alpha = 0.007564633275546291).fit(X_train, y_train)\n",
    "y_pred_ridge = ridge.predict(X_test)\n",
    "print(\"Ridge Regression:\", r2_score(y_test, y_pred_ridge),mean_absolute_error(y_test, y_pred_ridge))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1198,
   "id": "7e68001b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1b17ea070>"
      ]
     },
     "execution_count": 1198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot ridge residuals\n",
    "ridge_residuals = y_test - y_pred_ridge\n",
    "plot=sns.jointplot(x=y_pred_ridge,y=ridge_residuals, kind='reg',joint_kws = {'scatter_kws':dict(alpha=0.3)}, color='darkolivegreen')\n",
    "plot.set_axis_labels('Predicted Price', 'Residual', fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1200,
   "id": "0bf75f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1b169cdc0>"
      ]
     },
     "execution_count": 1200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot lr residuals\n",
    "lr_residuals = y_test - y_pred\n",
    "plot=sns.jointplot(x=y_pred,y=lr_residuals, kind='reg',joint_kws = {'scatter_kws':dict(alpha=0.3)}, color='darkolivegreen')\n",
    "plot.set_axis_labels('Predicted Price', 'Residual', fontsize=10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:metis] *",
   "language": "python",
   "name": "conda-env-metis-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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